esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Adsorption Isotherms  
The adsorption isotherms of the MMIPs were investigated by dispersing the adsorbents in OFL solutions (20-1000 mg\(\cdot\)L\({}^{-1}\)) and shaking for 30 min. After separation with a magnet, the supernatants were analyzed by HPLC. Figure 6 shows the adsorption isotherms of OFL on the MMIPs. The results indicated that the adsorption capacity of the MMIPs for \(S\)-(\(-\))-OFL and \(R\)-(\(+\))-OFL increased linearly with increasing initial concentration of OFL, with high adsorption capacities for OFL exhibited. A \(t\)-test of the q values of \(S\)-(\(-\))-OFL and \(R\)-(\(+\))-OFL indicated that there was no significant difference between the adsorption of \(S\)-(\(-\))-OFL and \(R\)-(\(+\))-OFL on the MMIPs that used _rac_-OFL as the template (\(p>0.05\)). Moreover, when using MMIPs as solid adsorbents, the abundant surface recognition sites resulted in increased binding capacity, which should allow the enrichment of trace OFL enantiomers from complex systems. To further estimate the adsorption properties of the MMIPs, two classical isotherm models, Langmuir and Freundlich, were selected to fit the experimental data. The Langmuir equation can be used to describe monolayer adsorption, whereas the Freundlich equation can be used to describe monolayer adsorption, as well as multilayer adsorption. Table 1 lists the parameters obtained using the Freundlich and Langmuir isotherm models, as well as the correlation coefficients (R\({}^{2}\)) for the adsorption data. For both \(S\)-(\(-\))-OFL and \(R\)-(\(+\))-OFL, the R\({}^{2}\) value for the Freundlich model was somewhat higher than that for the Langmuir model. Therefore, the adsorption amounts of OFL on the MMIPs were fitted to the [PERSON] isotherm model. Thus, multilayer OFL coverage on the surface of the MMIPs was verified.
# Object Detection Related to Irregular Behaviors of Substation Personnel Based on Improved YOLOv4
## 4 Experimental Results and Analysis
### Experimental Process and Parameter Configuration  
The case implementation process is shown in Figure 7. The images of substation personnel irregular behaviors were preprocessed by gamma correction and the corresponding annotations were divided into a train_val set and a test set with a certain ratio. Then, the K-means clustering method was used to cluster the ground-truth boxes in the train_val set to obtain the anchor sizes (11:11; 17:37; 25:17; 32:64; 41:146; 43:27; 73:48; 82:177; 161:260).  
During the model training process, the preprocessed images were augmented by geometric and optical transformation to expand the samples, then the weight basis in the pre-training model was used to accelerate model training, which was obtained by using ImageNet public dataset to train the improved YOLOv4 model. Next, the train_val set was divided into a training set and a validation set at a ratio of 9:1 and were input to the improved YOLOv4 model for training in combination with Mosaic data augmentation, cosine annealing, and label smoothing to enhance the effectiveness and generalization of model training. At the same time, the stage-wise training method was adopted in order to save training resources. Finally, the optimal improved YOLOv4 model was used to  
In order to facilitate the comparison of detection performance between different algorithm models, three evaluation indexes commonly used in the field of object detection were introduced. The first evaluation index was average precision (AP), which measures the recognition accuracy for each class. The second evaluation index was mean average precision (mAP), which is the mean value of the AP of each class. The third evaluation index was frames per second (FPS), which represents the number of images detected per second and reflects the detection speed of the model. The fourth evaluation index was F1-score, which represents the harmonic mean of precision and recall and reflects the detection accuracy of the model on each class.
# Sensor Topology Optimization in Dense IoT Environments by Applying Neural Network Configuration
## 2 Related Works and Motivation
Recent studies in [25] focus on solving the deployment problem by applying heuristic and metaheuristic optimization algorithms. In approximately 35% of these studies, the authors apply an improved version of swarm optimization algorithms to solve the sensor deployment problem under constraints. However, network scalability and total energy consumption are not always addressed. The topology optimization problem in large IoT and WSN deployments is a combinatorial and NP-hard problem to solve in polynomial time. The majority of existing algorithms apply heuristic or nature-inspired rules to reduce the search number of potential problem solutions within a solution space, so as to obtain a suboptimal solution in polynomial time. Recently, researchers have started investigating the application of neural network methods to solve the sensor deployment problem (SDP). Therefore, to the best of our knowledge, these works are mostly a work in progress. In [26], the authors propose a deep-reinforcement-learning-based topology optimization problem for energy-efficient and self-organizing WSNs. According to simulations, they show that the proposed algorithm achieves better performance as compared to other heuristic solutions. Furthermore, the energy-efficient topology control problem in large WSNs is investigated in [27], with a non-cooperative game theoretic approach. The extensive simulation results verify the validity of a utility function, which effectively balances the transmit power, residual energy, and network connectivity. In our work, we follow the direction of investigating and applying several neural network configurations as a solver for our proposed optimization problem under constraints. The difference from [26] is related to the type of neural network solver and the network optimization problem with constraints and scalable network deployments. Compared with [25], this work focuses on solving a certain WSN deployment problem with energy and communication constraints. The proposed solution is based on several neural network configurations and not on a heuristic algorithm. In the following section, we provide details about our system model and optimization framework. We also discuss our neural network solver parameters, which propose which sensor nodes need to be relocated so that the objective function stays within acceptable limits.
## 3 Methodology  
### General Speech Emotion Recognition System  
To automatically recognize emotions from a speech signal, there must be an SER system. The traditional SER system consists of three major parts, including pre-processing, feature extraction and classification, as shown in Figure 2.  
In the pre-processing part, a signal processing technique, such as filtering, is performed to reduce the noise in a speech signal. It helps to enhance the overall accuracy of the SER system. Moreover, the speech signal is segregated into voiced and unvoiced regions in this part. The reason for this is to decrease the computational cost of feature extraction, as well as to obtain the meaningful part of a speech signal [31].  
The objective of the feature extraction stage is to obtain a feature vector that can characterize the speech signal. This part is crucial in building the SER system. There is still no prevailing agreement regarding the features that are the most essential in discriminating emotions.  
The final part of the SER system is classification. Generally, there are two primary classification approaches. The first includes static classification models, support vector machines (SVM) [32], and neural networks (NN) [33]. The second is the dynamic classification model, which is the hidden Markov model (HMM) [34]. Currently, static classification approaches predominate. The classification model is first trained with extracted features of an emotional speech signal on training data to optimize the model parameters, and then it is tested with the testing data to predict the emotion.
# Flexible Thermo-Optic Variable Attenuator based on Long-Range Surface Plasmon-Polariton Waveguides
## 4 Results and Discussion
As shown in Figure 9, the eye is well open at a different transmission speed. The BER at 12.5 Gb/s was measured about 1.13 \(\times\)\(10^{-10}\). The results indicate that the flexible LRSPP waveguide is suitable for high speed optical interconnections. Evidently, the fabricated devices still suffer from too high insertion loss to meet practical applications. In comparison with the other low power VOAs [39; 40; 41], the power consumption is a little larger as well. It is mainly due to the thick polymer cladding that extends the distance of thermal diffusion. However, a good performance of the optical and microwave attenuation has been acquired. Furthermore, the transmission of a light signal modulated by high frequency microwave  
Figure 8: The total microwave transmission characteristics of the microwave photonics link at different applied voltage to the heater. signals through the flexible LRSPP waveguide-based VOA has been tested in spite of obstacles of large insertion loss, wave vector difference, and dispersion of the LRSPP mode. It is a potential solution to realize the flexible chip-scale interconnections and power equalization in the optical systems. The problem of large insertion loss can be solved by further optimizing the coupling loss between fiber and waveguide or using gain media doped polymer to reduce the propagation loss in the future.
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 1 Introduction
### Ac-Dc Rectifiers with Voltage Multiplication Function and a Single Input Signal  
The first SC circuits were ac-dc converters whose driving signal was the ac input voltage, changing from positive to negative open and closing certain diodes. Figure 2a shows an ac-to-dc switched-capacitor voltage doubler. The input voltage is an ac voltage source, and it has a cycle in which it changes from positive to negative periodically. During their negative half cycle, a current flowed through the diode \(s_{1}\) charging capacitor \(C_{1}\) to the maximum voltage of the ac-source and, during this time, the circuit behaves in a similar way to the equivalent circuit shown in Figure 2b. In the other half cycle, when the input voltage is positive (see Figure 2c), the voltage from the source adds to the voltage in capacitor \(C_{1}\), charging capacitor \(C_{2}\) with twice the input voltage. This kind of converter was studied mainly at the beginning of the past century [13; 14].  
The circuit in Figure 2 can be extended in several ways. Some of the most common configurations are shown in Figure 3, where 6\(\times\) rectifiers are shown, which means their output is six times their maximum input voltage. All of them have the voltage doubler as their core circuit, Figure 3a shows a topology in which series-connected capacitors make both sides of the converter, and the topology in Figure 3b shows a combination in which capacitors connected to the same point (that we can call grounded) made the left side of  
Figure 1: The main classification of SC converters.  
the multiplier, while series-connected capacitors made the right side of the multiplier. The other two combinations can also be implemented, Figure 3c shows a combination in which series-connected capacitors made the left side of the multiplier while grounded capacitors made the right side, and finally, Figure 3d shows the topology with both sides (left and right) made by grounded-capacitors.
## 1 Introduction  
With the continuous development of underground engineering, the number of tunnels at a deep-buried depth and with large diameters is increasing [1, 2, 3]. The emergence and use of tunnel boring machines (TBMs) have greatly improved the efficiency of excavating and supporting deep tunnels. A TBM not only guarantees effective tunnel excavation progress with an average monthly footage of up to 1600 m [4], but also ensures the safety of construction personnel and equipment [5]. However, deeply buried hard rock TBM-constructed tunnels are still inevitably threatened by high-stress rockburst hazards [6, 7], as shown in Table 1. Deep rockburst hazards may cause large volumetric collapses, rock cracking, ejection, or extreme bulking of hard rocks [8, 9, 10] and delay the construction process [11]. Therefore, effectively controlling the occurrence of rockbursts is one of the key problems to be urgently solved in current deeply buried tunnel projects [12]. As an effective means of rockburst prevention and control, advance stress release boreholes (ASRBs) have the advantages of simple construction, low cost, and low workload during construction [21], so they are widely used in deep tunnels under the condition of a high rockburst risk [22]. By strategically designing the layout parameters of ASRBs, it is possible to effectively release stress and energy from the surrounding rock mass, thereby altering the rockburst incubation process and preventing or controlling such incidents [23]. Advance stress release technology has been successfully applied to tunnels constructed by drilling and blasting. For example, [PERSON] [24], in the Bayu Tunnel of the Sichuan-Tibet Railway, effectively reduced the safety risk of rockbursts in the tunnel by taking measures such as advance geological prediction, rockburst microseism monitoring, construction of ASRBs in the face, injection of high-pressure water into the release boreholes to release stress, weak blasting at the bottom of the boreholes to loosen the rock mass, and \"shotcrete + hanging net + anchor\" support. The construction technology of deep-hole stress release blasting was adopted in the strong rockburst sections of the No. 3 and No. 4 headache tunnels within the Jinping II Hydropower Station to reduce the possibility of a rockburst disaster during construction [25]. [PERSON], [PERSON] [26] used a suitable stress relief blasting design scheme to pre-fracture the surrounding rock in advance in the Lazy Colliery in the Ostrava-Karvina Coal Field of the Upper Silesian Coal Basin, and the 300-m-long wall panels could be mined without further rockbursts. [PERSON], [PERSON] [27] analyzed the tremor generated by long-hole stress release blasting during the mining of the 507 seam in a hard coal mine in the Polish part of the Upper Silesian Coal Basin, evaluated the stress release effect, and analyzed the dynamical mechanisms and impacts on the surrounding rock. [PERSON], [PERSON] [28] introduced the research progress of stress release blast technology in many domestic and foreign coal mines; discussed its main advantages from the perspectives of geology, rock properties, mining conditions,
## 4 Results of the Statistical Analysis of DARP  
The first step to perform the statistical study is to determine the different levels that the main factors can have. We must keep in mind in our experiments, that we have quantitative variables, and not qualitative ones. Therefore, certain intervals for each of the analyzed factor should be defined (what we call levels). In order to have a first approximation of the statistical relevance of the selected factors, a division into three levels or intervals have be made for each of these factors. Table 2 presents the three intervals (without intersection between them) in which the quantitative variables were characterized (the main factors) in order to carry out the ANOVA statistical study.  
The second step is to determine the output variable (which characterizes the behavior of the DARP system [15]). In this sense and analyzing the bibliography [11; 21], the dependent variables selected. To study the behavior of the DARP system has been: (a) Number of control messages sent during the set-up time; (b) Set-Up Energy and (c) Convergence Time (s). It is relevant to remark that the main task is to determine how the output variables (in our case these dependent variables are focused in energy consumption of the net and time required for convergence) are influenced when there are variations in the choice of the different levels of the main factors.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Levels of the Factors**} \\ \hline
**Factors** & **Level 1** & **Level 2** & **Level 3** \\ \hline Baselevel Threshold(TH\_B) & TH\_B1/1–10 (5) & TH\_B2/11–50 (30) & TH\_B3/51–150 (100) \\ \hline Role Threshold (TH\_R) & TH\_RI/75-125 (100) & TH\_R2/126–175 (150) & TH\_R3/176–225 (200) \\ \hline ACK Timer (T\_ACK) & T\_ACK1/0.1–1.0 (0.5) & T\_ACK2/1.1–2.0 (1.5) & T\_ACK3/2.1–10 (6.0) \\ \hline Reconnection Timer (T\_REC) & T\_REC1/0.1–4 (2) & T\_REC2/4.1–10 (6) & T\_REC3/11–50 (30) \\ \hline Down Timer (T\_DOWN), & T\_DOWN1/ 0.1–10 (5) & T\_DOWN2/11–100 (50) & T\_DOWN3/101–200 (150) \\ \hline Link Timer (T\_LINK) & T\_LINK1/0.1–2 (1) & T\_LINK2/2.1–10 (6) & T\_LINK3/11–20 (15) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selection of the different intervals or levels in which the different numerical values of the main factors were categorized to analyze the behavior of the DARP system.  
In order to have greater robustness in the statistical conclusions of the present study, all experiments were repeated 10 times. That is, for each of the possible configurations of the different levels of the main factors, a total of 10 executions were made, measuring the dependent variables.
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 3 Method
### Method Flow  
This paper presents an anomaly recognition method. The overall framework of the method is shown in Figure 1, which mainly includes three modules: abnormal KPI screening, alarm clustering, and abnormal detection. Firstly, we propose a 1D-CNN-TCN prediction model. The model is used to learn the characteristics of past data to predict future trends. The difference between a predicted sequence and an actual observation sequence at the corresponding time point is calculated to obtain the residual sequence. The 3-sigma method [26] is used to identify all KPI anomalies at a known time. Based on the residual sequences, a sliding window is added to the correlation analysis of abnormal KPIs to obtain correlation values, shift values, and the order of influences. Through comprehensive analysis of these three factors, final correlations are obtained, and the correlation matrix is constructed to further realize alarm clustering. All parameters of the HMM are determined, including the correlation matrix that has been converted to be suitable as an HMM parameter, and the HMM is trained. Then, other KPIs are found that may cause a certain KPI anomaly using the trained HMM model.
# A Method for Extracting Acoustic Water Surface Waves Based on Phase Compensation
## 3 Method
### Second-Order Differential Joint Outlier Removal  
In the fields of vital sign monitoring, fault detection, etc., differential processing is performed on the time series to achieve static clutter filtering [19]. The first-order difference is the simplest moving target indicator (MTI) filter. Its principle is to subtract two adjacent echo signals, namely a single delay line canceller, and its impulse response is as follows:  
\[h(t)=\delta(t)-\delta(t-T) \tag{14}\]  
The frequency response is as follows:  
\[H_{1}(\omega)=1-e^{-j\omega T} \tag{15}\]  
Figure 4: The flowchart of the proposed algorithm.  
When the clutter is not stable, the double delay line canceller has a better filtering effect on the clutter. Its impulse response is as follows:  
\[h(n)=\delta(n)-2\delta(n-1)+\delta(n-2) \tag{16}\]  
Its power gain is the product of the power gains of two single delay cancellers.  
\[\left|H_{2}(\omega)\right|^{2}=\left|H_{1}(\omega)\right|^{2}\left|H_{1}(\omega )\right|^{2}=16(sin(\omega T/2))^{4} \tag{17}\]  
Figures 5 and 6 show the structure and frequency response diagrams of single, double, triple, and quadruple delay cancellers.  
In Figure 6, it can be seen that the differential operation filters out the low-frequency components corresponding to clutter, and the double delay line canceller has a wider stopband depression compared to the single-delay line canceller, which has a better effect on clutter filtering. The higher the order of the delay line canceller, the narrower the passband and the wider the stopband, which limits the communication bandwidth. Figure 7 gives the comparation using different delay line cancellers on the phase history. It can be seen that for better communication, at least a double delay line canceller is necessary after phase compensation. In this paper, a double delay line canceller is chosen.  
After performing the second-order differential step on the phase history, oscillation points with up and down jumps are separated at discontinuous points. Points that are larger than ten times the temporal mean square deviation are identified as outliers. Removing outliers can reduce the impact of phase discontinuity on the time-frequency analysis and  
Figure 5: The structure of delay cancellers.  
Figure 6: The frequency response of delay cancellers.  
reduce communication bit error rates. Second-order differential joint outlier removal is used to filter out low-frequency sea surface waves and phase discontinuity terms \(\Delta\phi_{2}\).
## 4 Experiments and Analysis of Results
### Experimental Settings
#### 4.3.2 Evaluation  
The model was evaluated using precision, recall, and F1 score. Precision (P) represents the proportion of correctly identified samples, while recall (R) represents the number of identified samples out of all available samples. The F1 score (F1) is the harmonic mean of precision and recall. For all three metrics, higher values indicate better performance. The relevant calculation formulas are presented in Equations (8)-(10):  
Figure 5: The type and quantity distribution of entities and relations.  
Figure 6: Entity lengths.  
\[\text{P} =\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{8}\] \[\text{R} =\frac{\text{TP}}{\text{TP}+\text{FN}}\] (9) \[\text{F1} =\frac{2\text{PR}}{\text{P}+\text{R}} \tag{10}\]  
In the above equations, TP represents the number of true positive samples, FP represents the number of false positive samples, and FN represents the number of false negative samples.
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 2 Geodetic and Satellites Input Data
### ICESat Data Profiles
HDF5 files were downloaded from the official NASA site ([[https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)]([https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)), accessed on 15 December 2021), considering data from the SPI's northern region, including the Upsala, Viedma, O'Higgings, and Chico glaciers. We estimated ice elevation changes by comparing the data profiles to the SRTM elevation model from 2000 to 2018/19.