esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion  
### Breakdown Voltage Characteristics and Potential Distributions  
Breakdown voltages for the conventional FFR and proposed trenched edge-termination structures are shown in Figure 2. It has been reported that the breakdown voltage is affected by an expansion ratio of the space or width and a slightly increasing expansion ratio is beneficial for a higher breakdown voltage [3]. If the problem includes a structural variation, the space/width ratio, and the expansion ratio, it becomes too complex. In this study, the simulation was done with a fixed periodic length of the ring structure, which enables a clearer analysis of the effects of termination structures.  
Most breakdown voltages of trenched structures are higher than that of the conventional FFR structure. This means that the trenched structure is driving the potential distribution more efficiently because the junction curvature is increased.  
As expected, for the conventional FFR structure shown in Figure 2, the breakdown voltage increases and then decreases again with increasing space (decreasing width). For a shorter space region, the punch-through does not occur, or the effect of the punch-through on the enhancing curvature of the equipotential line is insufficient to obtain an optimal condition. On the other hand, for a longer space region, the breakdown voltage decreases due to the increase in the equipotential region provided by the P+ doped ring structure. The effects of enlarging the radius of the junction curvature, by introducing a combined BPW and ring-doping structure, are clearly shown in Figure 3d. Because the depletion region can be formed between the BPW and the first P+ ring, the highest breakdown voltage is already obtained with the short space. For the polysilicon trench structure, the breakdown voltage is almost independent of the space/width variation. We can easily expect this trend because this structure provides an equipotential state to the entire trench region. There is nearly no potential difference along the ring structure. The reverse bias voltage is sustained by the last BPW and the P+ ring alone, as shown in Figure 3b. The breakdown voltage of the combined the FFR and trenched structure is quite higher than that of the FFR or the poly trench structure as shown in Figure 2. Because this structure combines the effect of the FFR and the poly-trench. It should be noted that no additional process step is necessary for the poly-trench with the FFR. The oxide trench shows the highest breakdown  
Figure 2: Breakdown voltages of various edge-termination structures versus different spaces.  
voltage within our simulation cases. The results probably come from the combined effect of the voltage difference within the oxide region and the increasing junction curvature.
## 2 Methods
### Detrended Fluctuation Analysis (DFA)  
In order to investigate the correlation structures inherent in PM\({}_{2.5}\) series, the authors applied three kinds of fluctuation analysis methods, i.e., DFA (detrended fluctuation analysis) [15], MFDFA (multifractal DFA) [23], and DCCA (detrended cross-correlation analysis) [24], which are briefly illustrated below. In the application of these fluctuation analyses, the authors took the detrending approach with overlapping sliding boxes, which is a widely used method to obtain better statistics  
Figure 3: The seasonal trend is weak in G1 (stations #15 and #18), while G2 (stations #5, #11, and #16) shows a strong seasonality. There is a surprising difference in PM\({}_{2.5}\) concentration between stations #16 and #18, although they are located in the same city. This is a clue that PM\({}_{2.5}\) concentration could be strongly affected by an endogenous factor rather than an exogenous one.  
Figure 2: PM\({}_{2.5}\) charts with different concentration levels for seasons. G2 (stations #5, #11, and #16) has days with a relatively severe concentration in the spring and winter, while G1 (stations #15 and #18) shows a mild status over all seasons.  
when the data points are finite [25]. The authors firstly considered a noisy PM2.5 series, \(x(i)\) (\(i=1,\ 2,\ \ldots,\ N_{\text{max}}\)). Then, the authors integrated the time series \(x(i)\) to construct the following profile:  
\[X(k)=\sum_{i=1}^{k}[x(i)-\langle x\rangle], \tag{1}\]  
where  
\[\langle x\rangle=\frac{1}{N_{\text{max}}}\sum_{i=1}^{N_{\text{max}}}x(i). \tag{2}\]  
The integrated series was divided into \(N_{n}\) (\(=N_{\text{max}}-n+1\)) overlapping boxes of equal size \(n\), and the authors fit it using a polynomial function, \(\widetilde{X}_{v}(k)\), which was the local trend for the \(v\)-th box. Herein, the authors used the first-order polynomial. To detrend the integrated time series \(X(k)\), the authors subtracted the local trend and calculated the local detrended fluctuation function for the \(v\)-th box such that  
\[F_{n}^{2}(v)=\frac{1}{n}\sum_{i=1}^{n}\left\{X[(v-1)n+i]-\widetilde{X}_{v}(i) \right\}^{2}. \tag{3}\]  
Then, by summing over all overlapping \(N_{n}\) boxes of size \(n\), the authors computed the detrended variance function as follows:  
\[F_{DFA}(n)=\left\{\frac{1}{N_{n}}\sum_{v=1}^{N_{n}}F_{n}^{2}(v)\right\}^{1/2}. \tag{4}\]  
The above computation provides a relationship between \(F_{DFA}(n)\) and \(n\), such as  
\[F_{DFA}(n)\sim n^{\alpha}, \tag{5}\]  
for scale-invariant signals with power-law correlations. The scaling exponent \(\alpha\) represents the degree of correlation in the signal. If \(\alpha=0.5\), there is no correlation, that is, the signal is uncorrelated. On the other hand, if \(\alpha<0.5\), the signal is negatively correlated. Lastly, if \(\alpha>0.5\), there is a positive correlation in the signal.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 3 Bounded-Error Sensor Data Compression (BESDC)
### EB-RLE, EB-HC and Spatial Correlation Compression  
RLE shortens a representation of a long sequence of a symbol by the sequence length and the symbol itself. For example, \"rttttrr\" can be encoded as \"8,r\". Notably, real sensor data usually have no long running lengths of the same value. Traditional RLE seems unsuitable for sensor data compression. However, in our EB-RLE, if a long sequence of data is within a certain bounded error range, it can be encoded by RLE. In this paper, we proposed a greedy algorithm to find the \"longest sequence\" of sensor data which can be represented as the same value within the given bounded error.  
Figure 4 shows an example with a sequence of 10 sensor data (22.2, 22.6, 22.8, 23.1, 23.5, 23.7, 24.2, 23.6, 23.3, 22.8) and the given bounded error 0.5. By using EB-RLE, we at first get the longest running sequence (22.2, 22.6, 22.8, 23.1) in the left gray-shaded area on Figure 4 within the given bounded error 0.5. They can be encoded as \"4,22.7\". Then, we continue to get the longest running sequence (23.5, 23.7, 24.2, 23.6, 23.3) within the given bounded error 0.5. They can be encoded as \"5,23.8\". The final EB-RLE result is an RLE sequence (\"4,22.7\", \"5,23.8\", \"1,23.3\").
# Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals
## 4 Research on and Application of Multimodal Physiological Signals
### Multiple Physiological Signals
The results showed that the ECG and EDA signals were best in detecting negative emotions, with 88% and 90% accuracy, respectively. [PERSON] and [PERSON] [90] studied the application of ECG, EDA, EMG and respiration signals for evaluating positive and negative emotions using data-driven feature extraction. They put forward two methods of multimodal data fusion with deep convolutional neural networks and evaluated positive and negative emotions using the proposed architecture. The results indicated that the presented model had better performance than traditional machine learning, i.e., using only two modalities, the presented model evaluated positive emotions with a correlation of 0.69 (\(p<0.05\)) vs. 0.59 (\(p<0.05\)) with traditional machine learning, and negative emotions with a correlation of 0.79 (\(p<0.05\)) vs. 0.73 (\(p<0.05\)). [PERSON] et al. [91] proposed a new system to detect driving stress based on the features extracted from time and spectral and wavelet domains of physiological signals including ECG, EDA and respiration. They employed sparse Bayesian learning and principal component analysis to seek optimal feature sets and used kernel-based classifiers to increase accuracy. The results showed that driver stress could be characterized by peculiar signal sets that could be utilized by in-vehicle intelligent systems in various ways to assist drivers in controlling their negative emotions while driving. [PERSON] et al. [92] presented an experimental setup for stimulating spontaneous emotions with a face-to-face conversation and a dataset of physiological signals from EEG, EDA and photoplethysmography, and pointed out new directions for future research in conversational emotion recognition. [PERSON] et al. [93] studied how sex differences affected recognition under three different sleep conditions using EEG and eye movements. The results showed that sleep deprivation impaired the stimulation of happy emotions and also notably weakened the ability to discriminate sad emotions in males, while females maintained the same emotions as under common sleep. [PERSON] et al. [94] identified and recognized emotions of happy, sad, neutral, fear, and anger using facial expressions, ECG and heart rate variability, and the accuracy obtained was 68.42% based on the results from ECG. [PERSON] et al. [95] studied recognizing context over time using physiological signals, and indicated that the fusion of EMG signals was more accurate compared to the fusion of non-EMG signals. Although the fusion of non-EMG signals yielded comparatively higher accuracy, ECG data resulted in the highest unimodal accuracy.
# Semantic Attention Guided Hierarchical Decision Network for Age Estimation
## 3 Hierarchical Age Estimation with Semantic Attention
### Local Feature Learning and Semantic Attention Mechanism
#### 3.2.2 Local Feature Learning  
Local age estimation is essentially a fine-grained visual classification task due to the subtle differences in facial appearance between neighboring ages. In this section, we propose a local feature learning strategy that utilizes multi-scale feature extraction and cross-layer interaction to improve the accuracy of age estimation.  
In particular, we designed a sub-network that first projects the four-stage multi-scale features of the Swin Transformer to a unified channel width with individual \(1\times 1\) MLP layers; then, the enhanced local feature is obtained by semantic attention mechanism. Subsequently, the Transformer bilinearly upsamples the lower-resolution maps so that every feature attains the common spatial size \(\frac{H}{4}\times\frac{W}{4}\) Finally, the attention-enhanced feature set is fed to the hierarchical bilinear pooling module. As has been proved by the HBP work in [22], inter-layer part feature interaction and fine-grained feature learning are mutually correlated and can reinforce each other. Therefore, we employed hierarchical bilinear pooling (HBP), which computes pairwise bilinear interactions across layers. Let \(x\),\(y\),\(z\),\(o\in R^{C}\) be the channel-wise descriptors obtained by global average-pooling the local feature maps \(F_{1}\),\(F_{2}\),\(F_{3}\),\(F_{4}\) and \(U\),\(V\),\(S\),\(R\in R^{c\times d}\) be learnable projection matrices that map each descriptor into a common d-dimensional sub-space. Thus, the fused feature is calculated as:  
\[f=\left[U^{T}x\odot V^{T}y,U^{T}x\odot S^{T}z,U^{T}x\odot R^{T}o,V^{T}y\odot S ^{T}z,V^{T}y\odot R^{T}o,S^{T}z\odot R^{T}o\right] \tag{6}\]  
This operation captures complementary information across layers and enhances feature discriminability.
## 4 4-Chain Based Routing  
Subject to the improvement in the energy efficiency of UWSNs, we propose the 4-Chain based routing protocol. The proposed protocol works on the basic principle of divide-and-conquer. We first divide the whole network into cylinders and the divide the entire cylindrical network area into sub-regions. In each sub-region, the algorithm is independently implemented such that the overall methodology uses shorter parallel routes that is converse to the lengthy rout of PEGASIS and C-PEGASIS. Thus, the energy consumption is reduced leading to improvement in terms of network lifetime, which is, of course, one of the most wanted parameters of tiny battery operated networks. Data routing is performed in a cylindrical network by using four interconnected chains of sensor nodes. Firstly, we create chains and then interconnect them to find global optimum paths instead of the local optimum paths for data routing. Detailed description of the proposed protocols is in the following subsections.
## 5 Discussion
### Limitations  
As in all research, this study comes with a set of limitations. Although several digital libraries were used and five researchers were involved in searching for relevant literature, there is a chance that not all relevant studies were identified and included in the review after applying inclusion and exclusion criteria and quality assessment. The keywords used in searching for relevant literature provided studies that included users with various disability types. It is even possible that with the specified keywords, we missed some studies that included users with some specific disability type which was not included with the search string.  
The time scope of research was set between 2012 and 2022. Although not much relevant research was conducted before the specified period (based on our research), there could exist papers with significant insight into the beginnings of user-based testing with users with disabilities. The articles in the reviewing or publishing process not available when writing this manuscript were also not included in the systematic literature review. There is a possibility that after the conclusion of this paper, new important articles will emerge in 2023. Classification of studies by type of disability was limited by a pre-defined list of disabilities in the form we used to collect data during the articles review. Based on the results, it would be necessary to redefine the list of types of disabilities because, according to our list, we could not classify 30 studies into 1 of the types of obstacles. In future studies, it will be necessary to prepare a better classification scheme to more effectively understand the representation of different user disabilities in the existing literature.  
Within the framework of the identified challenges and best practices of involving users with disabilities in user-based testing, it is necessary to consider the limitations associated with the scope of studies published in the existing literature. The list of challenges and good practices in this study was constructed based on the data extracted from the reviewed papers only. The good practices identified in this study are not aligned with the challenges. At the time of writing this manuscript, the list of challenges is more significant, which means that further research will have to provide additional good practices to help overcome the challenges not well addressed in the existing literature yet.
## 2 Materials and Methods
### Method Development  
The main objective of this work was to develop a selective HPLC-FLD method for the separation and accurate quantification of OATD-02 and its related substances in an active pharmaceutical ingredient. Method development was quite challenging mainly due to the presence of many impurities and strong polarity of the compounds The reversed-phase chromatographic mode with an appropriate analytical column was chosen for separation of OATD-02 and its impurities. Various stationary phases in different dimensions with various particle sizes, with expected differences in their selectivity, were tested. Among all columns, the HPLC Waters XSelect CSH C18 column (2.5 \(\upmu\)m, 100 mm \(\times\) 3 mm) was chosen as the optimal column for the retention and separation of the OATD-02. Due to its Charged Surface Hybrid (CSH) technology and hydrophobic interactions with the C18 ligand, it provides ionic interactions with the analytes [29], which was found to be useful for the retention of OATD-02. The next step in the method development process was the mobile phase selection, which was initially an aqueous solution of ammonium salts with methanol but was later replaced with 10 mM sodium bicarbonate with acetonitrile, mostly due to better retention, peak shapes and resolution between the polar compounds. The flow injection analysis was performed in order to determine emission and absorption maxima of the fluorescent complex. The optimum wavelength of excitation and emission for the fluorescent complex of alizarin-OATD-02 were, respectively, 470 nm and 580 nm. Alizarin is a fluorescent compound, which dissolves well in organic solvents and forms a fluorescent complex with boronic acid moiety present in OATD-02.
# On-the-Fly Sleep Scoring Algorithm with Heart Rate, RR Intervals and Accelerometer as Input
## 3 Results  
In this section, the results are reported to assess the performance of the developed algorithms. Two data sets were considered: one composed of 6 subjects recruited for overnight sleep monitoring specifically for the sake of this paper, and 20 subjects belonging to a public data set, the DREAMS Subject Database. The six subjects recruited for this study were monitored in their homes overnight with a PSG while wearing a smartwatch. The PSG data were read by a doctor who produced a scoring, which was used as ground truth for the study. Data acquired from the smartwatch, on the other hand, were sent as input to the algorithm and were used to generate a scoring to compare with the doctor's scoring.
# Research on Wind Speed Inversion Method for X-Band Networked SAR Satellite
## 6 Conclusions  
The time resolution of a single spaceborne SAR to detect the sea surface is low. The wind speed information extracted from the SAR data through the geophysical model function depends on the wind direction information. The networked satellite can improve the time resolution of the SAR observation sea surface, and obtain various types of SAR data. Aiming at the problems in the co-polarization SAR wind speed retrieval, this paper designed a networked SAR satellite working mode. Based on the networked SAR satellite data provided by this model, wind speed information can be extracted without relying on wind direction information.  
The data in this experiment was obtained by three SARs simultaneously observing the same sea surface at different incident angles. Since the current networked satellites do not operate in this mode, this paper studies the simulation data of a single pixel of the SAR image. The X-band networked SAR data under different conditions were simulated by the geophysical model function. The cost function was established based on the networked SAR data. The final wind speed information was determined by minimizing the cost function. If there is little noise in the backscatter coefficient, the accuracy of wind speed can be improved by introducing accurate wind direction information. However, with the increase of noise, the introduced wind direction information will gradually reduce the accuracy of wind speed results.  
Experimental results show that the proposed method is suitable for wind speed extraction of X-band networked SAR data. This method can extract the wind speed information of networked SAR data in pixels without relying on the wind direction information. This research can provide a certain reference for future networked SAR satellite working mode and wind speed inversion.  
Conceptualization: Y.W. and Y.D.; methodology: Y.W.; software: X.S.; validation: X.S., X.Z., and Y.W.; formal analysis: L.L.; investigation: X.Q.; resources: Y.W.; data curation: Y.W.; writing--original draft preparation: X.S.; writing--review and editing: [PERSON] and X.Z.; visualization: X.S.; supervision: [PERSON]; project administration: [PERSON]; funding acquisition: Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number No. 2017 YFC1405600.  
The authors declare no conflict of interest.
# Beat the Heat: Syscall Attack Detection via Thermal Side Channel
## 4 Discussion
### Anomaly Detection Using Machine Learning  
To increase the effectiveness of our technique, we further implement a Machine Learning (ML) algorithm to help us decide whether we encounter an anomaly or not. The chosen algorithm is K-Nearest Neighbors (KNN), which helps us to classify whether a thermal trace comes from an authentic command or an augmented one. Therefore, the two classes used in the algorithm are called _original_ and _augmented_, and both contain, as features, the vectors of temperatures collected in the above experiments.  
Figure 18: Results with mathematical computations as a noise, without selecting CPU affinity.  
We used the algorithm with two sets of data. The first one contains the \(ls\) temperature vectors, and the other one contains the same data for the \(chmod\) command, both having 70 thermal traces for the training part and 24 and 25 traces for the testing part. The datasets are composed of the traces of both commands with a moving file as a noise, created by concatenating the traces for the original and modified commands. The set for testing was made by repeatedly counting the fourth trace in the original dataset and extracting it for a test, the other traces remaining for the training part.  
For both experiments, we used the number of nearest neighbors, with \(k\) having a value of 15, together with the Euclidean distance.  
In Figure 19, there are the confusion matrices for \(ls\) (left) and \(chmod\) (right) commands. Based on these matrices, we calculated some metrics, as can be seen in Table 1.  
The ML algorithm has improved our experimental research by increasing the efficiency and accuracy of the technique. Moreover, it classifies the traces in an automated way and it helps validate our hypothesis.  
The ML algorithm, together with the correlation thermal analysis, helps us to detect an anomaly more effectively.