esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Tuberculosis (TB) remains one of the top 10 causes of death worldwide, with 10 million new cases and 1.4 million deaths in 2018. The problem of antimicrobial drug resistance (AMR) is rising, with drug resistance associated with 3.4% of new TB cases globally and up to 50% of previously treated cases in some areas of the world [1]. The discovery of new drugs to treat _Mycobacterium tuberculosis_ (_M.tb_) is challenging, with only pretomandi, delamamid and bedaquiline marketed for use in the last 40 years despite sustained international efforts [2]. Multiple logistical and physiological factors contribute to the difficulty of this task (reviewed eloquently elsewhere [3; 4; 5]). They include biosafety constraints of working with a slow-growing pathogenic bacterium, heterogeneity of clinical disease and bacterial phenotypes in vivo, intracellular and extracellular _M.tb_ sites, drug penetration into lung pathology, the lipid-rich _M.tb_ cell wall as a barrier to drug uptake and intrinsic drug resistance, limited number of validated drug targets, the requirement for combination drug therapy, and the length and cost of clinical trials. Omics technologies aim to measure and evaluate together the ensemble of molecular entities by biological class to understand the contribution of each component. Whatever the category of molecule under investigation, the key advantage of omics approaches is that they are unsupervised, and thus less biased by dogma, which is valuable for overcoming drug development bottlenecks [6; 7]. Omics in a hypothesis-generating discovery biology setting, is an excellent means of identifying new targets for drug discovery. In a compound-first (discovery chemistry) approach, liberated from reductionist assays, omics technologies are useful tools to reveal or confirm drug mode of action. Mycobacterial omics are applicable throughout the drug development process from initial drug discovery to preclinical and clinical stages, at each step describing the action of compounds, derivatives, and formulations on _M.tb_. For example, identifying target drift or off-target effects during lead optimization, or characterizing drug resistance conferring mutations in clinical trials.  
This review summarizes the application of _M.tb_ omics strategies in the early stages of the discovery of new drugs for TB, incorporating genomics (DNA), transcriptomics (mRNA), proteomics (proteins), metabolomics (metabolites) and lipidomics (lipids). The review is not intended to be comprehensive--omics are now fully established in most drug discovery settings--but aims to highlight landmark and interesting approaches to the TB drug development problem. The review centers on omics applied directly to _M.tb_, using examples from other mycobacterial models only to illustrate groundbreaking discovery tools. We focus on (a) target identification, in this context the recognition of potentially druggable pathways worthy of drug discovery efforts in a target-based approach; (b) mode of action studies, often aimed at progressing hits from whole cell compound screening strategies on the long road to the TB clinic (Figure 1).
# Arsenic (V) Removal by an Adsorbent Material Derived from Acid Mine Drainage Sludge
## 3 Results and Discussion
### Adsorbent Characterization
Notably, MIRESORBTM materials have approximately 24% smaller surface areas than GFH materials, but the nearly 72 and 66% larger pore volume and size of these materials, respectively, might lead to high As(V) adsorption efficacy. The data of arsenic removal from solution were used to estimate arsenic adsorption kinetics by fitting of the pseudo-first-order (PFO, Equation (3)) kinetic model and the pseudo-second-order (PSO, Equation (4)) kinetic model. Rate constants from this fitting analysis are tabulated in Table 3. The \(q_{e}\) values for the MIRESORBTM and GFH were similar, but the kinetic constants differed substantially. The kinetic constants for GFH were 0.076 h\({}^{-1}\) and 0.005 g/mg.h based on the PFO and PSO models. The kinetic constants for MIRESORBTM were approximately three times higher; namely, the constant for the PFO was 0.227 h\({}^{-1}\) and for the PSO was 0.015 g/mg.h. Similar trends were observed for adsorption capacity, which is described in the following isotherm section. The kinetic parameters positively correlated with the pore volumes of the adsorbent (Table 1). The rapid As(V) adsorption kinetics of the MIRESORBTM adsorbent were attributed to the large pore volumes versus GFH.
## 2 Materials and Methods
### Optimal Yield Forecast Period Determination  
The \"clear pixel S2 image collection\" was then used as an input for the determination of the optimal NDVI reference period for the yield forecast. To find the MVC temporal combination that best correlated with the field yield, firstly, the corresponding NDVI collection and clear pixel monthly MVC mosaic collection were generated. Then, for each clear pixel mMVC mosaic from January to May (2018, 2020, and 2022), we calculated the spatial mean, obtaining the relative FmMVC values. After that, we calculated the 3 mean FmMVC values1 (1 per year) in each of the 15 variable-sized periods (for a total of 45 FmMVC values), as shown in Figure 5.  
Figure 4: Implementation scheme of the NDVI-CPP.  
Then, to obtain a synthetic value capable of measuring the performance of each time interval, we cyclically calculated and classified the 15 Pearson correlation coefficients (R) between the three seasonal yields and the three mean FmMVC values for each variable-sized period. The steps of the method are shown in Figure 6.  
Finally, in order to obtain indicative time references for the crop phenological phases, we downloaded the daily temperature data from the meteorological station of Conselice (Ravenna, Italy) [75] starting from the season 2008/2009. After the download, the data were averaged by month in order to compare the three seasons under investigation with the monthly average of the 10 previous seasons.
# Load Balancing Oriented Predictive Routing Algorithm for Data Center Networks
## 3 Algorithm Design
### Framework of Traffic Scheduling  
This section introduces the traffic scheduling framework according to the LLBR algorithm (Figure 1). This framework describes the prediction of the usage frequency of all links in the next time period and then calculates the network path on the basis of the prediction result. The link load balancing scheduling strategy is therefore optimized. The framework model of the traffic scheduling consists of two modules: the module for the dynamic routing decision layer and for the routing using the dynamic routing decision layer.  
The module of the dynamic routing decision layer predicts the next cycle usage of the link. It predicts the link occupation state, selects the transmission path, and updates the flow table. When transmitting data flow in a DCN, the route is selected according to the flow table of the switches. Meanwhile, the link occupation state is collected by the controller to train the network state prediction model which is constructed according to the  
Figure 1: Traffic scheduling architecture diagram.  
ST-ResNet. Finally, the next cycle usage of the link is predicted according to the prediction model, and the path is calculated by the LLBR algorithm to form the dynamic routing decision layer.  
In the route distribution using the dynamic routing decision layer, the optimal path is directly calculated through the decision layer when data transmission requests are made in the network, and then the path is issued for data transmission.
## 4 Experiment Results and Analysis
### Comparison of other Proposed Methods
With 200 training samples in each category, the overall accuracy of the proposed method is 96.41%. The overall accuracy resulting from the method proposed is 6.69%, higher than the lowest one. We can see from the overall accuracy of the Pavia University dataset with 50 training samples in each category, the overall accuracy of the proposed method is 94.34%. The overall accuracy resulting from the method proposed is 7.95%, higher than the lowest one. With 100 training samples in each category, the overall accuracy of the proposed method is 96.25%. The overall accuracy resulting from the method proposed is 7.72%, higher than the lowest one. With 150 training samples in each category, the overall accuracy of the proposed method is 97.64%. The overall accuracy resulting from the method proposed is 6.75%, higher than the lowest one. With 200 training samples in each category, the overall accuracy of the proposed method is 97.89%. The overall accuracy resulting from the method proposed is 5.62%, higher than the lowest one. The proposed method shows higher classification accuracy on Pavia University dataset as well as Salinas datasets.
# Heated Metal Mark Attribute Recognition Based on Compressed CNNs Model
## 4 Methodology
### Top Compressed CNNs Models
#### 4.2.1 MobileNet  
_MobileNets_ was first designed for mobile and embedded vision applications. It was built primarily from depthwise separable convolutional operations [50]. It factorizes a standard convolution into a depthwise convolution and a pointwise convolution. _MobileNets_ applies a single filter to each input channel, and then pointwise convolution combines the outputs with linear combination.  
A standard convolution operation has the following computational cost:  
\[D_{K}\times D_{K}\times M\times N\times D_{F}\times D_{F} \tag{3}\]  
where \(M\) and \(N\) are number of input and output channels, \(D_{K}\times D_{K}\) is the size of filters, and \(D_{F}\times D_{F}\) represents the size of feature map. _MobileNets_ splits into two separate operations, one for filtering and one for combining. Batch normalization and ReLU nonlinearity are used in each layer. The depthwise convolution operation has the following computational cost:\[D_{K}\times D_{K}\times M\times D_{F}\times D_{F} \tag{4}\]  
A linear combination of the output of depthwise convolution via \(1\times 1\) convolution is needed to generate new features. Thus, the total computation cost of depthwise separable convolution is as follows:  
\[D_{K}\times D_{K}\times M\times D_{F}\times D_{F}+M\times N\times D_{F}\times D _{F} \tag{5}\]  
The ratio of computational cost decrease can be shown as follows:  
\[\frac{D_{K}\times D_{K}\times M\times D_{F}\times D_{F}+M\times N\times D_{F} \times D_{F}}{D_{K}\times D_{K}\times M\times N\times D_{F}\times D_{F}}=\frac{1 }{N}+\frac{1}{D_{K}^{2}} \tag{6}\]  
Moreover, to make the model smaller and faster, two hyper-parameters are proposed, width multiplier \(\alpha\) and resolution multiplier \(\rho\), which represent the ratio of reduced channels and the size of reduced feature maps, respectively. Finally, The computational cost of depthwise convolution operation with parameters \(\alpha\) and \(\rho\) can be further expressed as follows:  
\[D_{K}\times D_{K}\times\alpha M\times\rho D_{F}\times\rho D_{F}+\alpha M\times \alpha N\times\rho D_{F}\times\rho D_{F} \tag{7}\]  
_MobileNet_ V2 was proposed for further improvement. It was constructed with inverted residuals and linear bottlenecks techniques, which can reduce number of parameters and the loss in activation operation. Combined with single shot detector lite (SSDLite) for object detection, _MobileNet_ V2 is reported to be 35% faster than _MobileNet_ V1, and have 20\(\times\) less computation and 10\(\times\) fewer parameters than _YOLO_ V2.
# An Experimental Analysis of Gas Reduction in Multiphase Flow with a KMS Helical Static Mixer
## 1 Introduction
Inlet devices, then, are critical to be designed in order to control that momentum absorption, and they might be selected depending on the application. Splash and diverter plates, for example, manage foam generation and could be useful for mixtures with a high gas concentration. The pipe distribution must deal with the amount of liquid and velocities, and helical devices must manage the pressure drop. However, having established the essential dynamics factors from a given application, the latter is regarded to be designed in gas separation in this study. Therefore, motivated by a real application in the Gulf of Mexico by Nuvoil corporation for oil extraction, using the jet-pump ALS, the main contributions of this work are summarized as follows:  
* The study of a commercial KMS static mixer for the same effect as a helical separator to be physically implemented as the inlet device for gas separation in scenarios with non-fixed fluid properties and dynamics parameter variations as incoming velocities and different phase distributions in the mixture. * Commonly, numerical and theoretical studies are reported in the literature with minimal experimental implementation. In this work, the inlet device is designed according to the fluid properties, and dynamics conditions are evaluated for the gas reduction in a scaled laboratory prototype to respond to a real application in oil fields. * The multiphase mixture flowing through a KMS is experimentally studied to verify the gas reduction, contributing both defoaming in a scaled separation reservoir and an improvement of the injection pump efficiency into an ALS process. * The effect of gas on the sedimentation time is also analyzed and, instead, is a specific design of the device. In this work, numerical results allow for the physical configuration of the KMS to be determined under different situations, compared to the case where it is not implemented, producing scenarios for decision-making in the oil industry. The work is structured as follows. Preliminary considerations of a KMS static mixer are stated in Section 2 to describe the main problem in Section 3 as an improvement in the reduction of gas from a hydrocarbon mixture coming from an oil well. The strategy of separation in the process is proposed from basic experimental results; numerical studies are developed in order to verify the basic performance of the proposal, allowing some configurations for both single (Section 4) and multiphase (Section 5) cases to define the physical implementation, which, as a complement to the experimental analysis in Section 6, is developed with a focus on the effect of the KMS on the reservoir according to the effect of the gas separation reduction. Finally, some general remarks are stated in Section 7, and final conclusions and future work are presented in Section 8.
# Navigating the Maps: Euclidean vs. Road Network Distances in Spatial Queries
## 1 Introduction
Therefore, the primary objective of this study is to assess the accuracy of the Euclidean distance method for retrieving points of interest (POIs) by comparing it with the road network distance method, which utilizes the OSRM to extract distances. The evaluation will be conducted in various scenarios to observe the accuracy of the Euclidean distance method and to analyze factors that may influence its accuracy rate. This analysis provides valuable insights regarding the reliability and effectiveness of the Euclidean distance method in spatial query processing. Figure 1: Search for the nearest hospital. (**a**) Result from road network distance. (**b**) Result from Euclidean distance.
## 2 Features Extraction  
Wavelet transforms are divided into continuous wavelet transform (CWT) and DWT. The CWT of the signal is expressed as follows [22]:  
\[CWT(a,b)=\frac{1}{\sqrt{a}}\psi\left(\frac{t-b}{a}\right)dt \tag{1}\]  
where \(\psi(t)\) is the mother wavelet that represents a contentious function in the time domain and frequency domain, \(\psi(t)\) is the complex conjugates of the mother wavelet, \(a\) is the scale value, and \(b\) is the translation value. The normalized wavelet function is defined more compactly as:  
\[\psi_{a,b}=\frac{1}{\sqrt{a}}\psi\left(\frac{t-b}{a}\right) \tag{2}\]  
The integral equation is rewritten as:  
\[CWT(a,b)=\int_{-\infty}^{\infty}x(t)\psi^{*}{}_{a,b}(t)dt \tag{3}\]  
DWT is presented to address the weaknesses of CWT. The mother wavelet is dilated discretely in DWT by changed \(a=a_{0}^{m}\), \(b=nb_{0}a_{0}^{m}\), where parameters \(a_{0,\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;  
The DWT can be defined as:  
\[DWT(m,n)=\int_{-\infty}^{\infty}x(t)\psi^{*}{}_{m,n}(t)dt \tag{5}\]  
The DWT analyses the signal by decomposing the signal into an approximation and detail coefficients. Figure 1 shows the first level decomposition filter process. The original signal \(x[n]\) is passed through a digital low-pass filter \(g[n]\) and a digital high-pass filter \(h[n]\).  
Approximation coefficients of the signal are the low-pass filter outputs. Detail coefficients of the signal are the high-pass filter outputs. The first level decomposition can be defined as [23]:  
\[a^{1}[n]=\sum_{k=0}^{N-1}g[k]x[n-k] \tag{6}\]  
\[d^{1}[n]=\sum_{k=0}^{N-1}h[k]x[n-k] \tag{7}\]  
In this study, the \(gd\) signals are decomposed by DWT for feature extraction. The Haar mother wavelet and maximum decomposition level are obtained in DWT. The \(gd\) signals are passed in two DWT high-pass and low-pass filters to analyze the signal frequency. The high-pass filter is used to examine the high frequency, and the low-pass filter is used to examine the low frequency. Then, the approximation component (low frequency) is used as input data in the ANFIS model.
Ensemble Model Based on Stacked Long Short-Term Memory Model for Cycle Life Prediction of Lithium-Ion Batteries  
[PERSON]  
1 Department of Industrial Management, National Taiwan University of Science and Technology, Taipei 10607, Taiwan; [EMAIL_ADDRESS] (F-K.  
show great potential for better prediction accuracy than model-based and data-driven prediction methods [8]. Ensemble learning technology is one of the most popular hybrid methods, which improves prediction accuracy by combining multiple learning algorithms [9]. The ensemble learning method is a learning algorithm that can aggregate predictions generated by multiple learning algorithms to enhance prediction performance.  
Previous research has shown that ensemble learning algorithms generally outperform any individual learning algorithm [9; 10; 11; 12; 13]. Most ensemble methods use the average value of multiple models as the final prediction result; however, the prediction of ensemble learning in regression problems does not necessarily guarantee that its prediction results will be better than a single model. Therefore, a new method needs to be developed to test whether an ensemble model combining multiple learning algorithms can provide better prediction performance than a single model. The long short-term memory (LSTM) recurrent neural network (RNN) was used to analyze the capacity degradation of lithium-ion batteries [14]. The LSTM model can be extended and added to other mechanisms such as bidirectional, stacked, and attention mechanisms.  
In this study, we propose an ensemble model based on a stacked long short-term memory (SLSTM) model to the cycle life estimation of lithium-ion batteries. The final prediction output was obtained by stacking LSTM models, rather than taking the average of the prediction values of each model. Besides, the selected features and optimal hyperparameter values may affect the accuracy of cycle life prediction. Few studies consider multiple features as input to the model. For example, [PERSON] et al. [15] and [PERSON] and [PERSON] [16] used support vector machines with multiple features such as cycle and temperature to predict the SOH of lithium-ion batteries. The best model hyperparameters are obtained by using the differential evolution (DE) algorithm, and several features were also considered in this study. LSTM with attention and gradient boosted regression (GBR) models were used as two single models. Sections 2 and 3 describe the experimental data and a detailed description of the proposed model, respectively. Section 4 describes model verification. Finally, we summarize the conclusions and future research directions.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture  
### Principles of Microalgae-Assisted Aquaculture  
#### 3.1.1 Principles  
The concept of microalgae-assisted aquaculture is to convert organics in eutrophic effluents to biomass by microalgae growth and exploit value-added biomass to partly replace aquaculture feed and enhance aquatic animals' immunity. The construction of microalgae system also could accelerate the carbon dioxide fixation and promote oxygen release, acting like a bio-pump and creating a good environment for aquatic animals.  
The specific scheme of microalgae-assisted aquaculture is shown in Figure 1. First, microalgae, which are inoculated into the fish rearing tank or pond, could enhance the self-purification capacity of aquaculture system by digesting some wastes secreted by aquatic animals and also act like a bio-pump to maintain the content of dissolved oxygen in water body. Second, microalgae cultivation system is constructed to assimilate nutrients in aquaculture effluent. Third, harvesting technology, which is suitable for the aquaculture system, is employed to obtain the microalgae biomass in an environmentally friendly and cost-saving way. Fourth, harvested fresh biomass is used as value-added aquaculture feed to reduce the fish rearing cost and treated effluent is recycled to the aquaculture system.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
###### Abstract  
Wireless communication at sea is an essential way to establish a smart ocean. In the communication system, however, signals are affected by the carrier frequency offset (CFO), which results from the Doppler effect and crystal frequency offset. The offset deteriorates the demodulation performance of the communication system. The conventional Gardner bit-synchronization algorithm performs timing synchronization on the baseband, but it fails to solve the problem of carrier frequency offset. In this paper, a carrier-based timing-synchronization Gardner algorithm was proposed. The algorithm performed error detection in the carrier signal to estimate the synchronization error in real time and the time-domain expansion in the passband signal. Based on the estimated expansion factor, the algorithm located the sampling points of the ideal signal and re-interpolated all the sampling points on the passband to recover the passband signal without Doppler. This algorithm can solve both the frequency shift and time-domain expansion caused by the Doppler effect without further CFO estimation and compensation, thus reducing the overall computational complexity of the system. The simulations showed that the proposed algorithm greatly improves the ability of the communication system to combat Doppler compared to the conventional Gardner algorithm. The algorithm is primarily designed for binary phase-shift keying (BPSK)-modulated signals under the additive white Gaussian noise (AWGN) channel and can be applied to various maritime communication scenarios.  
Doppler; Gardner algorithm; carrier; maritime wireless communication; medium wave +
Footnote †: journal: Marine Science and Engineering  
0000-0002-113-1422-3381][PERSON]  
1 The Engineering Research Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhejiang University, Zhuoshan 316021, China 1  
[PERSON]  
1 The Engineering Research Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhejiang University, Zhuoshan 316021, China 1  
[PERSON]  
1 The Engineering Research Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhejiang University, Zhuoshan 316021, China 12 Dongbai Laboratory, Zhuoshan 316021, China 2  
[PERSON]  
1 The Engineering Research Center of Oceanic Sensing Technology and Equipment, Ministry of Education, Zhejiang University, Zhuoshan 316021, China 12 Dongbai Laboratory, Zhuoshan 316021, China 2