esa-sceva/satcom-chunk-collection huggingface dataset:
# Risk Assessment of Drilling and Blasting Method Based on Nonlinear FAHP and Combination Weighting
## 4 Case Study
### Calculation of Risk Indicator Weights
#### 4.2.3 Calculation of Combined Weights  
Based on the previously calculated subjective and objective weights, the basic weight set \(M_{2}=\{\mathbf{m}_{21},\mathbf{m}_{22}\}\) is obtained as follows:  
\[\begin{pmatrix}1.6039&0.7450\\ 0.7450&1.0410\end{pmatrix}\cdot\begin{pmatrix}\alpha_{1}\\ \alpha_{2}\end{pmatrix}=\begin{pmatrix}1.6039\\ 1.0410\end{pmatrix} \tag{25}\]  
where \(\mathbf{m}_{21}\cdot\mathbf{m}_{21}^{T}=1.6039\), \(\mathbf{m}_{22}\cdot\mathbf{m}_{22}^{T}=1.0410\). Solving Equation (25) yields \(\alpha_{1}=0.8022\), \(\alpha_{2}=0.4259\).  
From Equation (21), \(\beta_{1}=0.6532\), \(\beta_{2}=0.3468\).  
Finally, based on Equation (22), the combined weights of the secondary factors in the risk assessment of the Daliangshan No. 1 Tunnel are calculated. The specific combined weights are shown in Table 7.  
Analyzing the weights of the primary and secondary factors, it is concluded that among the primary risk factors, equipment risk and blasting risk have the greatest impact on risk in drilling and blasting construction scenarios. Among the secondary risk factors, poor blasting effect and borehole spacing have a significant impact on blasting risk, while blasting network failure and poor ventilation have a significant impact on equipment risk.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**PC**} & \multirow{2}{*}{**Eigenvalue**} & \multicolumn{2}{c}{**Variance**} & \multicolumn{2}{c}{**Cumulative**} & \multirow{2}{*}{**PC**} & \multirow{2}{*}{**Eigenvalue**} & \multicolumn{2}{c}{**Variance**} & \multicolumn{2}{c}{**Cumulative**} \\  & & **Contribution** & & & **Ratio** & & **Ratio** & & **Ratio** \\ \hline
1 & 434.493 & 32.953 & 32.953 & 12 & 22.023 & 1.67 & 92.266 \\ \hline
2 & 156.07 & 11.837 & 44.789 & 13 & 20.833 & 1.58 & 93.847 \\ \hline
3 & 118.968 & 9.023 & 53.812 & 14 & 18.606 & 1.411 & 95.258 \\ \hline
4 & 102.347 & 7.762 & 61.574 & 15 & 14.473 & 1.098 & 96.355 \\ \hline
5 & 92.1688 & 6.99 & 68.565 & 16 & 12.448 & 0.944 & 97.299 \\ \hline
6 & 64.385 & 4.883 & 73.448 & 17 & 10.545 & 0.888 & 98.099 \\ \hline
7 & 61.541 & 4.667 & 78.115 & 18 & 9.474 & 0.719 & 98.818 \\ \hline
8 & 60.042 & 4.554 & 82.669 & 19 & 7.961 & 0.604 & 99.422 \\ \hline
9 & 45.549 & 3.455 & 86.124 & 20 & 5.032 & 0.382 & 99.803 \\ \hline
10 & 31.628 & 2.399 & 88.522 & 21 & 2.595 & 0.197 & 100 \\ \hline
11 & 27.347 & 2.074 & 90.596 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Principal component variance contribution rates.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\(\alpha_{P/N}\), \(\alpha^{\prime}_{P/N}\) Frequency dependent non-ideal current gains  
\(\alpha_{Vt}\) Threshold voltage temperature coefficient  
\(\beta_{P/N}\), Frequency dependent non-ideal voltage gains  
\(\varepsilon_{g_{n}}\), \(\varepsilon_{g_{m}}\) Transconductance errors  
\(\varepsilon_{ip},\varepsilon_{iN}\) Current tracking errors  
\(\varepsilon_{v}(p,N)\) Voltage tracking errors  
\(\gamma_{r}\), \(\gamma^{\prime}\) Frequency dependent non-ideal transconductance transfer gains  
\(\mu\) Carrier mobility  
ABB Active building blocks  
AP All pass  
BP Band pass  
BR Band reject  
\(C_{ox}\) Gate oxide capacitance per unit area  
CCII Second-generation current conveyor  
CFOA Current feedback operational amplifier  
CM Current-mode  
DDCC Differential difference current conveyor  
DPCCII Digitally programmable second-generation current conveyor  
DVCC Differential voltage current conveyor  
HP High pass  
ICCII Inverting second-generation current conveyor  
\(L\) Effective length of the channel  
LP Low pass  
MISO Multi input single output  
MOCCII Multi output current controlled current conveyor  
MOCCII Multi output second-generation current conveyor  
OTA Operational transconductance amplifier  
Q Quality factor  
SIMO Single input multi output  
TIM Trans-impedance-mode  
\(V_{t}\) Threshold voltage  
VDBA Voltage difference buffered amplifier  
VDCC Voltage differencing current conveyor  
VDEXCCII Voltage Differencing Extra X Current Conveyor  
VDTA Voltage differencing transconductance amplifier  
VM Voltage-mode  
W Effective channel width
## 3 Materials and Methods
### Data Processing and Sources  
The data of economic indicators came from the statistical yearbook of Wuhan from 2014 to 2020 and the statistical data of various districts in Wuhan. The ecological index data were calculated from Landsat-8 and MODIS remote sensing satellite data, and the remote sensing images were obtained from the Google Earth Engine (GEE). The MODIS satellite provides annual net primary productivity (NPP) data with a spatial resolution of 500 m \(\times\) 500 m. In addition, this paper also used the vector map of the Wuhan administrative division. The vector data came from the national geographic information resources directory service system. Some data of Hannan in 2016 and 2017 were missing; thus, the average annual growth rate method was used to make up the difference.
# Hybrid Cooperative Cache Based on Temporal Convolutional Networks in Vehicular Edge Network
## 4 Cooperative Caching Decisions Based on Temporal Convolutional Networks and Hybrid Content Values
### Content Popularity Prediction Based on Temporal Convolutional Networks
#### 4.2.2 Future Content Popularity Assessment  
In order to achieve a balance between short-term and long-term memory in the popularity data \(\bar{P}_{m,i,t+1}\) output at the next moment, we will perform a weighting operation between the historical popularity \(P_{m,i,t}\) and the predicted short-term future popularity \(\bar{P}_{m,i,T+1}\). The specific operations are as follows:  
\[\bar{P}_{m,i,T+1}=(1-\lambda)\bar{P}_{m,i,T+1}+\sum_{t=T-n+1}^{T}\lambda^{T-t+ 1}P_{m,i,t} \tag{16}\]  
where \(\lambda(0<\lambda<1)\) is the weighted ratio between the new and historical data, and the larger \(\lambda\) represents the more important historical popularity; \(n\) represents the number of historical data we consider.
# Breast Cancer Detection in the Equivocal Mammograms by AMAN Method
## 1 Introduction
### Cnn  
Convolutional neural networks (CNN) are a form of the artificial neural network (ANN) model. It enables the extraction of more complex representations for picture material despite traditional image recognition, which requires the user to specify the characteristics of the target picture. The CNN accepts images; raw pixel data trains the model and automatically extracts features for improved categorization. However, CNN is prone to overfitting if not managed appropriately, i.e., the model may get over-trained to the point that it is unable to generalize to new data [18].  
The rest of the article is structured as follows: Section 2 describes background work, which includes binary and multi-level classifications of breast cancer. Section 3 elaborates on the structure and design of the proposed \"AMAN\" system. Results and discussions are carried out in Section 4. Finally, the conclusion and future work are highlighted in Section 5.
# Globally Optimizing QAOA Circuit Depth for Constrained Optimization Problems
## 2 Materials and Methods
### Qaoa  
In order to use QAOA to solve a CO problem, we apply two operators, \(U(C,\gamma)=e^{-iC\gamma}\) and \(U(B,\beta)=e^{-iB\beta}\), in succession on an initial state. The initial state is the uniform superposition, \(\ket{s}=\frac{1}{\sqrt{2^{n}}}\sum_{z}\ket{z}\), where the sum is over the computation basis \(\ket{z}\). The outcome of one iteration of QAOA is  
\[\ket{\gamma,\beta}=U(B,\beta)U(C,\gamma)\ket{s}.\]  
Here \(C\) encodes the problem to be solved and \(B\) is a mixing operator. Often, \(C\) is the sum over a collection of clauses, \(C=\sum_{a}C_{a}\), and \(B\) is typically \(B=\sum_{o\in V(G)}B_{o}\), where \(B_{o}=\sigma_{o}^{x}\) is the Pauli-X operator acting on the \(v^{th}\) qubit. For more detail about QAOA, see [1; 2; 20].  
There is a direct correlation between the circuit depth and level of noise in a quantum circuit [21; 22; 23], so it is important to consider the circuit depth when developing a circuit that implements QAOA. Throughout this paper, we use unitary circuit depth to mean the depth of the unitary operators needed in a circuit. The actual circuit depth would be the unitary circuit depth times the number of gates needed to implement each operator. In the next subsection, we review dualization and the related previous work that determines the unitary circuit depth of one iteration of QAOA.
# Intelligent Detection and Segmentation of Space-Borne SAR Radio Frequency Interference
## 3 Materials
### Semantic Segmentation Network
#### 3.3.1 Fcn  
In November 2014, [PERSON] et al. designed an end-to-end, pixel-to-pixel semantic segmentation method using a fully convolution network (FCN) [43] (see Figure 7). The authors of the paper proposed to improve an existing and well-researched image classification model (such as AlexNet [44] or VGG). They applied their method to the encoder module of the semantic segmentation network and added a decoder module with a transposed convolution layer to realize coarse feature maps that were up-sampled to obtain full-resolution segmentation maps.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Observation Area** & **Time Span** & **Train Sample Quantity** & **Test Sample Quantity** & **Image Size** \\ \hline PAIS dataset & Korea & 1 May 2021–30 May 2022 & 2000 & 100 & 572 \(\times\) 572 \\ \hline \hline \end{tabular}
\end{table}
Table 2: General information of PAIS dataset.  
The FCN model structures include FCN32s, FCN16s, FCN8s [36], and other structures according to the fine-grained segmentation. FCN32s restores the feature map from a 32-time down-sampling size to the input size, and FCN16s and FCN8s, respectively, restore it from a 16-time and 8-time down-sampling size to the input size. The smaller the number, the more up-sampling operation deconvolution layers used, the more complex the corresponding model structure is, and the finer the segmentation effect is in theory. The schematic diagram of an FCN network is shown as follows:
## 5 The Use of Thermovision to Assess the Effects of Chosen Physiotherapy Treatments
### Diathermy  
Of the 32 papers found (PubMed \(n=23\), Web of Science \(n=9\)), a total of six works were analyzed after applying the inclusion and exclusion criteria and the exclusion of copies. A total of 186 people (including 96 women and 90 men) took part in the study. The larger sample was composed of 60 subjects [68], and the smallest sample was 10 [70]. In the found works, the diathermy methods included extracorporeal shock waves (ESW) [68], short- [69,72,73] and microwave diathermy [69], and Tecan therapy (TT) [70,71].  
[PERSON] et al. [68] used thermography to detect thermal conditions in the region of the carpal flexor muscles to assess the effects of the radial extracorporeal shock wave stimulation. In case of short- and microwave diathermy, thermography was used to evaluate the temperature behavior and arterial blood flow after lower limb administration [69] and to evaluate local and general temperature variations in the area of the right knee joint [72]. Furthermore, [PERSON] [73], using the three capacitive techniques of shortwave diathermy treatment (contra-, coplanar, and longitudinal arrangement), compared the effectiveness in changing skin temperature. [PERSON] [70], using thermography, verified whether TT administered in two modes (resistive and capacitive) floats on intramuscular blood flow, perfusion of skin microcirculation, and skin temperature. In turn, [PERSON] [71], in their research among professional basketball players, analyzed the effect of TT on latent myofascal trigger points on skin temperature, range of ankle motion, and pain.  
Diathermy is a thermotherapeutic method that uses high-frequency electromagnetic currents to induce heat [85]. Diathermy includes ultrasound, shortwave, and microwave [86]. Both shortwave diathermy (SWD) and microwave diathermy (MWD) have been used in the treatment of musculoskeletal conditions [69]. In SWD, heat is generatedat a deep tissue level by generating electromagnetic vibrations, which in turn lead to ion movement, deformation of molecules, and the formation of electric vortices. The treatment causes vasodilation, increases metabolism in the body, and normalizes muscle tension [72]. In addition, they can reduce pain and promote wound healing [87].
# Advances in Fermentation Technology: A Focus on Health and Safety
## 2 Microbial Control of the Pathogens' Growth--Biocontrol Strategies
### Potential Health Hazards in Traditional Fermentation
#### 2.1.2 Biogenic Amine Formation
Traditional microbiological strategies for controlling BA production primarily focus on the suppression of BA-producing microorganisms through rigorous hygienic practices, such as pasteurization, and the use of starter cultures that are devoid of decarboxylase genes [37]. In this regard, acidification (pH < 4.5) and salt (NaCl > 10%) have proven effective in inhibiting decarboxylase activity, especially in fermented meats, fish, and cereal-based products [38]. Moreover, promising advanced technologies have introduced innovative solutions, including bacteriophage that specifically targets BA-producing pathogens without affecting beneficial LAB; for example, bacteriophage 156 reduced tyramine by over 95% and putrescine by 77.85% in cheese [39]. Non-thermal technologies, including high-pressure processing (HPP) and pulsed electric fields (PEF), have been shown to inactivate decarboxylase enzymes and spoilage microbes while preserving food quality [40].
Data collection can be achieved through user equipment (UE), radio access network (RAN) and Internet Service Providers (ISP). Events occurring in the UE are collected either through user programs or through a control signal. In a radio access network RAN with eNodeB (eNB), instantaneous data measurement reports of the QoS requirements from different users, a more detailed principle of collection, are given in the paper [21]. MNOs have a huge amount of data related to media/user services in a heterogeneous network. In addition, a large storage infrastructure must have scalable capacity as well as scalable performance. Thus, latency management must be simple and efficient in order to easily store and sort big data. The second stage is to analyze the collected data. After collecting and storing the data, another major challenge for MNOs is processing such huge amounts of data. The collected data are reusable, heterogeneous, in real-time and voluminous. For this reason, data analysis and information extraction technology are needed to process the data and transform them to optimize the network. So, this information can be used to develop adaptive resource management schemes. Data analysis allows MNOs to systematically manage different access networks and provide services to customers. BD network optimization functions are able to analyze big data to identify problems and decide what and how to optimize at the appropriate level of the heterogeneous network. Improvement measures based on optimization results are then implemented, using control functions in the RAN. In addition, optimization at the user level can be performed. In particular, for users who are in the same cell, optimization can be configured for each user based on the class of service (priority but not priority users). In addition, the BD network optimization function is able to predict traffic fluctuations, both in the local area and in the global coverage area, ultimately helping to improve network performance and quality of service for users. The third stage is the management of the radio access network operator's resources. MNOs need to be informed about their long-term network deployment goals in terms of bandwidth, coverage, number and location of base stations, etc. They also require new resource allocation strategies to meet different traffic requirements across the coverage area. Thus, the use of big data analytics can be a new way to solve these problems. Network analytics involve monitoring and analyzing user statistics that enable the real-time prediction of critical points and the state of mobile networks. Based on the obtained MNOs data, intelligent decisions are made to serve users by balancing the load and prioritizing traffic to improve the efficiency of operation and provide the necessary quality of service in a heterogeneous network. At the fourth stage, the problem of heterogeneous network optimization is solved by applying a comprehensive method, which includes the procedure of vertical handover initiation, redistribution of flows and rejection of non-priority user sessions.
## 5 Simulation Results
### Complexity Comparison  
Finally, we compare the processing complexity of the IFO detectors. For a fair complexity comparison, the mathematical operations were converted to the number of real floating point operations (flops). In doing so, we counted one complex addition as two real flops, whereas one complex multiplication is equivalent to six real flops [26]. It can be readily seen that the flops of the algorithms (6), (9), (10) and (14) are \(4N-1\), \(13N_{u}-1\), \(20N_{p}-2\), and \(8N_{t}-2\), respectively. Considering the number of IFO hypotheses in (7) and (18), the total number of flops is \((2M+1)\) times. It is worth mentioning that the complexity of the proposed detection method (14) is about \(2/5\) times that of the conventional scheme (10), if \(N_{p}=N_{t}\) is applied in (10) and (14).  
When \(M=5\), the computational load of the proposed blind IFO estimator is reduced by about 60% compared to that of the pilot-aided IFO estimator (10), while up to 99% complexity reduction over the blind IFO estimation scheme (9) is obtained. It is concluded that the use of the TMCC signal ensures significant savings of mathematical operations, achieving a good estimation performance in the presence of typical values of residual FFO.
###### Abstract  
Integrating systems are frequently encountered in power plants, paper-production plants, storage tanks, distillation columns, chemical reactors, and the oil industry. Due to the open-loop instability that leads to an unbounded output from a bounded input, the efficient control of integrating systems remains a challenging task. Many researchers have addressed the control of integrating processes: Some solutions are based on a single closed-loop controller, while others employ more complex control structures. However, it is difficult to find one solution requiring only a simple tuning procedure for the process. This is the advantage of the magnitude optimum multiple integration (MOMI) tuning method. In this paper, we propose an extension of the MOMI tuning method for integrating processes, controlled with a two-degrees-of-freedom (2-DOF) proportional-integral-derivative (PID) controller. This extension allows for calculations of the controller parameters from either time domain measurements or from a process transfer function of an arbitrary order with a time-delay, when both approaches are exactly equivalent. The user has the option to emphasise disturbance-rejection or tracking with the reference weighting factor \(b\) or apply two different reference filters for the best overall response. The proposed extension was also compared to other tuning methods for the control of integrating processes and tested on a charge-amplifier drift-compensation system. All closed-loop responses were relatively fast and stable, all in accordance with the magnitude optimum criteria.  
integrating processes; magnitude optimum; PID control; controller tuning; set-point weighting; reference filter; 2-DOF +
Footnote †: journal: Applied Sciences
# Partitioning of Net Ecosystem Exchange Using Dynamic Mode Decomposition and Time Delay Embedding +
## 2 Methods  
The Koopman operator [6] enables the transformation of finite-dimensional nonlinear system dynamics to an infinite-dimensional linear dynamical system. Finding the eigenfunctions of the Koopman operator, however, remains a major obstacle to its implementation. DMD is a simple numerical algorithm that approximates the Koopman operator with a best-fit linear model that advances measurements from one time step to the next ([7], [8]). It is an equation-free system identification method where the underlying dynamics of the system are learned from snap-shot in time of measurement data. DMD decomposes system dynamics into temporal modes whereby each mode represents a fixed oscillation frequency and decay/growth rate. It has been extended to deal with dynamical systems with exogenous control input (DMDc) [9].  
In this paper, we propose a new data-driven yet physics-aware method for the dynamical modeling of Reco, which can serve as an NEE partitioning method. The proposed approach is based on using DMDc in a sliding temporal window approach. The system state Reco is represented as a linear dynamical model with an autonomous component in addition to an exogenous component which is a function of control input. The control input to the system can be soil or air temperature (Tair) in accordance with the thermodynamics law or any other observed drivers such as soil water contents (SWC). Such modeling of Reco dynamics allows to disentangle the exogenous effect of the control input, e.g., Tair, from the autonomous dynamics of Reco, and hence allows to intervene on the control input to study its effect on the system. To deal with unobserved drivers of Reco other than temperature or any user control input, we make use of time-delay embedding (TDE) of the history of the system's state and control input. According to the [PERSON] theory [10], such an embedding guarantees, under certain conditions, that the system will learn the trajectories of the original system. The TDE in DMDc has been shown to facilitate the treatment of nonlinear systems with linear models [11]. Another advantage of using TDE in the proposed method is that it allows for learning Reco dynamics from short data as it compensates for using advanced measurement in time. This is relevant as it enables ecosystem forecast taking into account short-term variations in the system dynamics.
# Metabolism and Regulation of Ascorbic Acid in Fruits
## 3 Transcriptional Regulation of Ascorbic Acid Metabolic Genes in Fruit
Overexpression of _GalUR_ from strawberries results in a 1.2- to 2.5-fold increase in AsA levels of tomato fruits [97]. Pepper fruits are rich in ascorbic acid, and the ascorbic acid content increases with fruit ripening [98]. Although the L-galactose pathway is the main AsA synthesis pathway in pepper fruits and leaves, the transcript levels of genes encoding key enzymes in the AsA biosynthesis pathway were not positively correlated with AsA concentrations in pepper pericarp [99]. In addition to changing the expression of genes related to the AsA synthesis pathway by targeting, the AsA content in fruits can also be regulated by changing the genes associated with AsA degradation and regeneration. For example, AsA content is increased 1.4- to 2.2-fold in the fruits of _APX_-downregulated tomato lines [100]. Downregulation of _AO_ gene expression in tomatoes can also increase AsA content in fruit [101]. There are two SIDHARs (_SIDHAR1_ and _SIDHAR2_) in tomato. Overexpression of _SIDHAR1_ significantly increases the AsA content in tomato leaves and red rip fruits. In contrast, overexpression of _SIDHAR2_ only increases the AsA content in leaves, indicating that the regeneration of AsA in fruits is mainly regulated by _SIDHAR1_[102]. The upregulated [101]_SIMDHAR_ expression leads to a 0.7-fold decrease in AsA content in tomato fruits but has no effect on AsA content in tomato leaves [56]. These results suggest that _SIDHAR1_ and _SIMDHAR_ play regulatory functions in different processes of tomato fruit ripening but are not the main limiting factor in leaves. During citrus fruit ripening, the changes in ascorbic acid content were not completely consistent with the expression of ascorbic acid metabolism-related genes, but were regulated by complex and specific interactions of synthesis and recycling-related genes [103; 104].