esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this study, we propose an adaptive least-squares algorithm considering the distance scale factor (ADLSC), and successfully apply it to the fitting and estimation of the GPS crustal horizontal velocity field. The new algorithm considers distance scale factor to establish the Gaussian covariance function, the data utilization rate is improved, and the influence of the negative value of the cross-covariance on the construction of the covariance model is also avoided. Furthermore, the new algorithm introduces adaptive factors to adjust the balance between the observation and prior information during the solving process of the model parameters. In particular, it should be noted that the coordination of the signal covariance matrix and the observed covariance matrix established is further improved after the distance scale factor is taken into account.  
The new fusion algorithm can more provide more accurate fitting and estimation of regional GPS crustal movement characteristics and provide basic data of a higher spatial distribution density to solve the crustal strain field. We only studied the GPS crustal velocity field fitting and estimation, but the global navigation satellite system (GNSS) is becoming increasingly diverse due to the technology and algorithms now available. However, we believe that our new algorithm is also applicable to the crustal velocity field measured from other satellite constellations (such as Beidou, GLONASS, or Galileo). This is because the crustal velocity calculated from these satellites can be also used as the basic data for the new algorithm. More importantly, the latter is not limited to using GPS data; it can also use other observation data such as level and gravity data. Our future work will focus on fusing robust estimation and anisotropic covariance functions into the ADLSC algorithm.  
All the authors participated in editing and reviewing the manuscript. [PERSON], and [PERSON], conceived and designed the experiments; [PERSON], [PERSON], and [PERSON], performed the experiments; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] analyzed and interpreted the results.  
This study was supported by the National Key Research and Development Program of China (No. 2018 YFC1503604), the Nature Science Fund of China (NSFC) (Nos: 41674001, 41731066, 41604001, and 41202189), Natural Science Basic Research Plan in Shaanxi Province of China (No. 2019 JM-202, 2018 JQ4031), and Special Fund for Basic Scientific Research of Central Universities, CHD (No: 300102268204, 300102269207).  
Figure 9: Comparison of the iteration times and fitting RMS in the iterative process of the VCEM calculation: (**a**) east direction; (**b**) north direction.  
**Acknowledgments:** Some figures were prepared using the public domain Generic Mapping Tools GMT. Thanks [PERSON] and [PERSON], from Wuhan University, provide help. We would like to thank Editage (www.editage.cn) for English language editing. Constructive comments from the Editor and three anonymous reviewers improved the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Towards Automatic Depression Detection:
## 3 Proposed Approach and Models
### One-Dimensional Convolutional Neural Network  
A convolutional layer in CNN convolves over the width and the height in two dimensions. However, it is not applicable when the inputs are Mel spectrograms, of which the width and the height represent the time and the frequency, respectively. Compared with two-dimensional convolution, one-dimensional convolution is more suitable when dealing with Mel spectrograms in the depression detection problem. In the proposed model, convolution over the frequency axis is used. This allows the model to generate features that capture a short-term temporal correlation. In other words, 1D convolution over the frequency axis aims to acquire the persistence of characteristics in Mel spectrograms.  
Specifically, 1D convolution sets the filter size to one along the axis other than the convolution axis. For instance, if 1D convolution is along the frequency axis of Mel spectrograms, the filter size should be set to \((1,k)\), where \(k\) is the kernel size along the frequency axis.  
The detailed configuration of the 1D CNN model used in the experiments is shown in Table 1. The \"Out features\" of FC1, FC2, and FC3 in Table 1 represents the number of nodes in the fully connected layers FC1, FC2, and FC3.  
The proposed 1D CNN model consists of two 1D convolutional layers, two pooling layers, and a three-layer FC network. The final activation function and the kernel stride in the second convolutional layer vary depending on different evaluation tasks. For the depression severity assessment task (regression), the linear activation function and a kernel stride of two are used. For the depression prediction task (classification), softmax and a kernel stride of one are used.
# A Payload Based Malicious HTTP Traffic Detection Method Using Transfer Semi-Supervised Learning
## 2 Related work
### Small Data Learning  
Small data learning is referred to the techniques designed to carry out learning on the condition of lacking sufficient training samples [23], and the main strategies of small data learning include data augmentation and knowledge transfer.  
The data augmentation strategy attempts to compensate the training data by adding slightly modified copies of the existing data or newly created synthetic data from the existing data, with the purpose of reducing overfitting when training a learner [24; 25; 26]. On the other hand, knowledge transfer strategy tries to train a learner for the target domain by leveraging knowledge from other domains, and could be divided into three categories according to the types of transferred knowledge, i.e., representation transfer, model transfer, and cognition knowledge transfer. Representation transfer attempts to transfer the knowledge of representing the same object from different domains [27]. Model transfer uses the learner trained from other domains to fit the small training data in the target domain through fine-tuning its parameters [28; 29]. Cognition knowledge transfer leverages high-level knowledge such as common sense, domain knowledge, and other concepts [30; 31].  
Small data learning is widely used in research areas such as computer vision and natural language processing. In contrast, the application of small data learning in malicious network traffic detection has been much more limited, especially when combined with deep learning. In our work, we propose a novel data augmentation scheme for payload data, and design a small data learning framework based on the data augmentation scheme.
# Edge-Guided Hierarchical Network for Building Change Detection in Remote Sensing Images
## 3 Results
### Comparison Experiments on the LEVIR-CD Dataset
**(a)** Prechange image; **(b)** post-change image; **(c)** label; **(d–j)** are the detection results of FC-EF [31], FC-Siam-Conc [31], FCN-PP [33], UNet++MSOF [20], DSFN [34], BIT-CD [32], and the proposed EGHNet, respectively. In the color classification, the true positive is white, the true negative is black, the false positive is red, and the false negative is green.
###### Abstract  
The inherent impairments of visible light communication (VLC) in terms of nonlinearity of light-emitting diode (LED) and the optical multipath restrict bit error rate (BER) performance. In this paper, a model-driven deep learning (DL) equalization scheme is proposed to deal with the severe channel impairments. By imitating the block-by-block signal processing block in orthogonal frequency division multiplexing (OFDM) communication, the proposed scheme employs two subnets to replace the signal demodulation module in traditional system for learning the channel nonlinearity and the symbol de-mapping relationship from the training data. In addition, the conventional solution and algorithm are also incorporated into the system architecture to accelerate the convergence speed. After an efficient training, the distorted symbols can be implicitly equalized into the binary bits directly. The results demonstrate that the proposed scheme can address the overall channel impairments efficiently and can recover the original symbols with better BER performance. Moreover, it can still work robustly when the system is complicated by serious distortions and interference, which demonstrates the superiority and validity of the proposed scheme in channel equalization.  
deep learning; channel impairments; nonlinear equalization; visible light communication; deep neural network +
Footnote †: journal: _Photonics_  
**2021**, \(8\), 453. [[https://doi.org/10.3390/photonics8100453](https://doi.org/10.3390/photonics8100453)]([https://doi.org/10.3390/photonics8100453](https://doi.org/10.3390/photonics8100453))
## 2 Materials and Methods  
### Study Sample  
A sample of 360 stainless steel pre-adjusted orthodontic brackets from 12 different providers was evaluated. All brackets had a declared slot size of 0.022 by 0.028 in., belonged to the right upper central incisor, and were pre-adjusted according to the MBT prescription values. All bracket bodies were produced by metal injection molding (MIM) and bear torque information in the base. The sample belonged to the following bracket systems: Synthesis(r) (Ormco, Orange, CA, USA); Nu-Edge(r) (TP Orthodontics, La Porte, IN, USA); Mini Twin(r) (Lancer Orthodontics, Carlsbad, CA, USA); Fli Twin(r) (RMO, Denver, CO, USA); Mini Prevail(r) (G&H Orthodontics, Franklin, IN, USA); Omniarch(r) (Dentsply GAC, Bohemia, NY, USA); Jazz(r) (Modern Orthodontics, Ludhiana, India); Scape(r) (Ah Kim Pech Corporation, Ciudad de Mexico, Mexico); Mini Master(r) (American Orthodontics, Sheboygan, WI, USA); Mini Sprint(r) (Forestadent, Phorzheim, Germany); Discovery Smart(r) (Dentaurum, Ispringen, Germany); and Victory(r) (3M, Monrovia, CA, USA). For each manufacturer, 30 brackets belonging to three different batches were evaluated, with 10 brackets per batch. Before the study, the required sample size was estimated using Raosoft(r) Version 5.0 software (Raosoft, Inc., Seattle, WA, USA). A sample size of 9 was considered enough to detect a difference of 0.03 degrees, assuming a standard deviation of 0.02 degrees with a power of 90% and a significance level of \(p<0.05\). The testing protocol reported in the ISO 27020:2019 norm suggests including six brackets for every batch in the procedure. Upon reception of the brackets, a member of the research group (MA) randomly coded each bracket using a single-blind design. The brackets were arranged in such a way that the team members, who performed the dimensional tests (LB and DG), were not aware of which system or batch number they were assessing.
# Thermal Energy Storage Performance of Tetrabutylammonium Acrylate Hydrate as Phase Change Materials
## 3 Results and Discussion
### The Evaluation of the Heat Exchanger  
The thermal energy storage rate by the heat exchanger can be calculated as in Equation (3), where U is the heat transfer coefficient, A is the surface area of the heat exchange coil, and V\({}_{\text{PCM}}\) is the volume of the thermal energy storage medium. The logarithmic mean temperature difference, \(\Delta\text{T}_{\text{lm}}\), is calculated by Equation (4). As can be seen from Equation (4), the logarithmic mean temperature difference, \(\Delta\text{T}_{\text{lm}}\), is the average of the temperature difference between the thermal energy storage medium and the cooling water using the logarithm. This logarithmic mean temperature difference was substituted into Equation (3) to calculate the heat transfer coefficient for the experiments under each condition. The heat transfer coefficient was calculated to be about 10\({}^{4}\)-10\({}^{5}\) W m\({}^{-2}\) K. The A/V\({}_{\text{PCM}}\) in this experiment was 15 m\({}^{2}\)/m\({}^{3}\), and the UA/V\({}_{\text{PCM}}\) value was calculated to be around 10\({}^{3}\)-10\({}^{4}\) W m\({}^{-3}\) K. For instance, a plate heat exchanger has a UA/V\({}_{\text{PCM}}\) that is similar to this value [40]. As the UA/V increases, the amount of heat exchange will also increase.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Target** & **Static** & & **Agitated** & & **Ultrasonic Vibrated** \\  & **System** & & **System** & & **System** \\ \hline  & & 100 rpm & 300 rpm & 600 rpm & 28 kHz & 56 kHz \\ \hline
100 MJ/m\({}^{3}\) & - & 5.1 & 2.4 & 1.5 & 5.8 & 5.8 \\ Achieved time/h & & & & & & \\ \hline
140 MJ/m\({}^{3}\) & - & - & 5.9 & 2.9 & - & - \\ Achieved time/h & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The time to achieve the target thermal energy storage density.  
It realizes a higher thermal energy storage density. In this study, the UA/V of the heat exchanger was comparable to that of other common heat exchangers [40]. This result demonstrates that the comparable thermal energy storage density in this study can be obtained using other common heat exchangers.  
For the practical use of thermal energy storage using TBAAc hydrate on a larger scale, it is also expected that the comparable thermal energy storage density in this study would be available by using a heat exchanger which has the same level UA/V.  
\[\dot{q} = UA\Delta T_{\text{lm}}/\text{V}_{PCM} \tag{3}\] \[\Delta T_{\text{lm}}\frac{T_{out}-T_{in}}{ln\frac{T_{\text{PCM}} -T_{in}}{T_{\text{PCM}}-T_{out}}} \tag{4}\]
###### Abstract  
In order to improve the accuracy of signal recovery after transmitting over atmospheric turbulence channel, a deep-learning-based signal detection method is proposed for a faster-than-Nyquist (FTN) hybrid modulated optical wireless communication (OWC) system. It takes advantage of the long short-term memory (LSTM) network in the recurrent neural network (RNN) to alleviate the interdependence problem of adjacent symbols. Moreover, an LSTM attention decoder is constructed by employing the attention mechanism, which can alleviate the shortcomings in conventional LSTM. The simulation results show that the bit error rate (BER) performance of the proposed LSTM attention neural network is 1 dB better than that of the back propagation (BP) neural network and outperforms by 2.5 dB when compared with the maximum likelihood sequence estimation (MLSE) detection method.  
faster-than-Nyquist; neural network; hybrid modulation; attention mechanism; optical wireless communication +
Footnote †: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 4 Verification Experiments
### Estimation and Analysis of the Experimental Data  
To verify the feasibility and measurement accuracy of the positioning method, two schemes are adopted to verify the model. The first method is to self-verify the measurement results under different attitude of the same target point, and the second is to use total station to verify the measured data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Point ID** & \(x\) & \(y\) & \(z\) \\ \hline P\({}_{0}\) & 0.00 & 0.00 & –943.99 \\ P\({}_{1}\) & 356.30 & 0.00 & –771.45 \\ P\({}_{2}\) & –221.69 & –277.46 & –771.68 \\ P\({}_{3}\) & 193.48 & –9.92 & –442.30 \\ P\({}_{4}\) & –112.68 & –157.93 & –441.85 \\ P\({}_{5}\) & 0.41 & –0.05 & –371.92 \\ Probe & 0.00 & 0.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calibration results of the receiver array in Figure 5 (unit: mm). Here, the probe point is defined as the origin of the o-xyz frame. The direction from the probe to P0 is defined as the z axis. a line perpendicular to the z axis and passing through P1 is defined as the x axis. The direction of the y axis is defined according to the right-hand rule.  
Figure 5: Multipoint (N = 6) receiver array employed for verification testing of the proposed calibration method, where the probe is docked with the target point to be measured.
## 3 Results
### Site-Specific Parameter Variability
#### 3.1.3 Temperature Dependency  
Model parameters used to describe the temperature dependency vary substantially across sites (Table 3). The highest CV values for all tree species are found for the parameters \"minimum temperature sum for foliage activity onset\" (GDDFOLSTART), \"wood activity offset\" (GDDWODEND) and \"wood activity onset\" (GDDWODSTART). All tree species consistently show lowest CV values (<4%) for the parameters \"maximum temperature for photosynthesis\" (PSNTMAX), \"optimum temperature for photosynthesis\" (PSNTOPT) and \"minimum temperature for photosynthesis\" (PSNTMIN).
# Hardware Countermeasures Benchmarking against Fault Attacks
###### Abstract  
The development of differential fault analysis (DFA) techniques and mechanisms to inject faults into cryptographic circuits brings with it the need to use protection mechanisms that guarantee the expected level of security. The AES cipher, as a standard, has been the target of numerous DFA techniques, where its security has been compromised through different formulations and types of fault injections. These attacks have shown vulnerabilities of different AES implementations and building blocks. Consequently, several solutions have been proposed that provide additional protection to cover the identified vulnerabilities. In this paper, an extensive analysis has been carried out covering the existing fault injection techniques, the types of faults, and the requirements needed to apply DFA. Additionally, an analysis of the countermeasures reported in the literature is also presented, considering the protection provided, the type of faults considered, and the coverage against fault attacks. The eight different types of fault that allow us to perform DFAs on the AES cipher have been differentiated, as well as the vulnerabilities of the cipher. On the other hand, two comparisons have been made considering frequency penalty vs. area and fault coverage vs. area and frequency overhead. A metric has been proposed to compare the fault coverage of all the proposed solutions. To conclude, a final analysis is presented discussing the key aspects when choosing a particular solution and the possible development of new countermeasures to provide further protection against DFA.  
AES; hardware countermeasure; benchmarking; trade-off; fault attack; DFA 2022
## 3 Results and Discussion
### Strength Characteristics of Coal Samples
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Coal Sample**} & \multicolumn{2}{c}{**Sample Size**} & \multirow{2}{*}{**Sample Forming**} & \multirow{2}{*}{**UCS \(\sigma_{c}\) (MPa)**} & \multirow{2}{*}{**Elastic**} & \multirow{2}{*}{**Moolus \(E\)**} & \multirow{2}{*}{**Poisson’s Ratio \(\
u\)**} \\  & **Diameter** & & & & & **(MPa)** \\ \cline{1-1}  & **(mm)** & & & & & **(MPa)** \\ \hline BS-1 & 50.80 & 99.70 & 30 & 0.19 & 12.14 & 0.297 \\ BS-2 & 50.80 & 99.80 & 50 & 0.31 & 16.32 & 0.329 \\ BS-3 & 50.80 & 100.00 & 70 & 0.33 & 17.18 & 0.348 \\ BS-4 & 50.80 & 98.60 & 100 & 0.62 & 28.78 & 0.353 \\ BS-5 & 50.80 & 97.86 & 130 & 0.78 & 32.03 & 0.388 \\ \hline \end{tabular}
\end{table} 
Table 4: Results of the uniaxial compression tests for briquette coal samples with different sample forming pressure. Coal, as a dual-porosity medium comprising pores and fractures, possesses a well-developed pore network that serves as critical pathways for gas migration. The gas transport capacity is governed by the abundance, complexity, connectivity, and spatial distribution of these pores. The overmentioned experimental results demonstrated that in raw coal, the initiation and propagation of microcracks, the formation of interconnected weak planes, and the coalescence of macrocracks significantly enhanced pore network connectivity, thereby improving gas migration efficiency. In contrast, briquette coal exhibited inherently higher porosity and lower compressive resistance. Under identical stress conditions, this structural heterogeneity facilitated the development of more extensive gas migration channels, resulting in superior gas permeability compared to raw coal.
# A Novel Nonlinear Magnetic Equivalent Circuit Model for Magnetic Flux Leakage System
## 1 Introduction
Furthermore, the fringing effect caused by the air gap in the transition region of the main flux to the tested material is also considered. Finally, the performance of the MEC model is tested based on various MFL systems created in a 3D FEA environment. The rest of this article is organized as follows. Section 2 presents the general principles of the MFL testing, MFL system flux density distributions, and the flux types taken into account in the MEC design. Section 3 presents the designed MEC model and dimensional parameters of the MFL system. Furthermore, the leakage flux permeances calculation is shown in Section 3. Section 4 explains the iterative algorithm used to calculate flux-dependent magnetic permeances. Section 5 includes the performance evaluation of the MEC model based on results obtained from FEA studies. In addition, the calculation times of the MEC model and FEA studies are compared in Section 5. Ultimately, in Section 6, the evaluation of the findings achieved in this study is presented.
# NR Sidelink Performance Evaluation for Enhanced 5G-V2X Services
## 2 Background  
### Cellular Vehicle to Everything (C-V2X)  
C-V2X follows the 3 GPP Release 16 specification and ensures better performance than IEEE 802.11p [12]. The widespread cellular infrastructure that already exists can benefit C-V2X users. As it is not always possible to rely on the presence of a cellular infrastructure, transmission modes provided by C-V2X allow direct V2X communications via the sidelink channel using the PC5 interface [11]. C-V2X has a basic time-frequency resource framework, with a sub-frame (1 msec) consisting of 14 OFDM symbols as the smallest time allocation unit and 12 sub-carriers at 15 kHz each (a total of 180 kHz) as the smallest frequency precision. C-V2X devices can transmit utilizing 16-Quadrature Amplitude Modulation (QAM) or Quadrature Phase Shift Keying (QPSK) schemes with the turbo coding in each Orthogonal Frequency-Division Multiplexing (OFDM) subcarrier [7,13].  
Additionally, C-V2X users transmit signals for reference as well as control information to data symbols. One of these signals, the Demodulation Reference Signal (DMRS), is utilized for each and every channel estimation. A total of 2 out of 14 OFDM symbols used in Long-Term Evolution (LTE) have DMRS symbols integrated into them, and those four DMRS symbols can be placed in a C-V2X sub-frame because C-V2X is built for high-mobility settings [14]. Both the sidelink interface and the traditional LTE interface can be handled by C-V2X to function with Base Stations (BSs) present in the network or without any BS.
## 3 Current State of Object Detection Algorithms
### Region-Based CNN
#### 3.1.4 Cascade R-CNN  
Cascade R-CNN is an object detection model that improves the performance of the Faster R-CNN model. The model was introduced by [PERSON] and [PERSON] [37] in 2018 and addresses the issue of missed detections and false positives in Faster R-CNN by adding a series of detection heads with increasing Intersection Over Union (IoU) thresholds to the Faster R-CNN model. In Cascade R-CNN, each detection head is trained with a different IoU threshold, and the output of one detection head is used as the input for the next head. This cascading approach helps to reduce the number of false positives and improve the accuracy of object detection.  
However, one of the main limitations of Cascade R-CNN is its increased complexity, as it requires multiple detection heads to be trained and deployed, which increases the computational cost and reduces the inference speed. In addition, the model may not perform well on smaller objects, as it relies on the output of previous heads, which may not detect small objects accurately.