esa-sceva/satcom-chunk-collection huggingface dataset:
# Temporal Stability and Practical Relevance of Velocity and Velocity-Loss Perception in Back Squat
## 2 Materials and Methods
### Familiarization Session and One-Repetition Maximum Test  
During the whole session, a linear position transducer \"Vitruve\" (SPEED4 LIITS S.L., Madrid, Spain) was utilized to measure mean propulsive velocity (MPV). The first three familiarization sessions were designed to help participants become acquainted with PV and PVL. During the initial testing session, participants provided informed consent and allowed for data processing prior to the beginning of data collection. Information regarding their height, body weight, age, and experience in RT was subsequently gathered. During the first three sessions, after a standardized warm-up (i.e., 5 min of self-selected intensity cycling followed by 5 min of joint mobility exercises), participants performed six or seven sets of squats per session, with a rest period of 3 to 5 min between sets. On each occasion, the first two sets (three on the first day) were designated as warm-up sets, with participants selecting their own loads. They were instructed to progressively increase the load with each successive warm-up set. After the warm-up, four sets of squats were completed during every familiarization session. On the first day, participants performed four sets of back squats using heavy loads, characterized by a MPV < 0.50 m\(\cdot\)s\({}^{-1}\). On the second day, the four sets of back squat were executed with moderate loads (MPV < 0.80 m\(\cdot\)s\({}^{-1}\) > 0.60 m\(\cdot\)s\({}^{-1}\)). The third day included two sets with moderate loads (MPV \(\sim\) 0.70 m\(\cdot\)s\({}^{-1}\)) and two more with heavy loads (MPV < 0.50 m\(\cdot\)s\({}^{-1}\)). Each of the loads with a specific target velocity was performed at both 20% and 40% VL. Finally, on the fourth day, participants performed a maximal incremental back squat test to determine their 1 RM [16]. During every familiarization session, participants received auditory feedback regarding the velocity achieved during each repetition and another auditory signal was given when the velocity loss threshold required was reached, both signals were given from the encoder. After each set, participants were presented with the validated PV squat scale [19], along with the velocity attained during that set. Participants were instructed to focus on the perceived effort associated with 20%VL or 40%VL rather than the number of repetitions performed.
## 5 Experimental Simulation Analysis
### Effect of Spread Spectrum Factor on System Performance  
Simulation parameters include second-order Chebyshev mapping with an initial value of 0.4, and spreading factors are chosen as 32, 64, 128, and 256. The impact of spreading factors on the RSST-SR-DCSK system is illustrated in Figure 12. In graph (a), second-order Chebyshev mapping with an initial value of 0.4 and \(P=2\) is employed, while graph (b) uses the same mapping with \(P=4\). From graph (a), it can be observed that the system's bit error rate performance is optimal when \(P=2\) and \(R=32\). At this point, the system performs approximately 1 dB better than the system with \(R=64\) and about 2 dB better than the system with \(R=128\). This indicates that as the spreading factor increases, the system's error performance relatively weakens.  
Based on the observations from Figure 13a, it can be noted that setting the spreading factor \(R\) to 100 results in similar bit error rate performance between the RSST-SR-DCSK system and the SR-DCSK system. However, the DCSK system demonstrates better error performance, exhibiting approximately 1 dB higher performance compared to the other two systems at the same BER value. In Figure 13b, with a spreading factor \(R\) of 150, the DCSK system continues to exhibit better error performance, again showing approximately 1 dB higher performance compared to the other two systems at the same BER value. Overall, increasing the value of R leads to varying degrees of increase  
Figure 12: BER simulation of RSST-SR-DCSK system with different semi spread spectrum factors. (**a**) \(P=2\); (**b**) \(P=4\).  
Figure 13: Comparison of the BER of four different systems. (**a**) R = 100; (**b**) R = 150.  
in the bit error rates of all systems. Consequently, further investigation is required to assess the impact of different \(R\) values on the systems and determine the most suitable \(R\) value for the system.  
Figure 14 is a comparative graph depicting the relationship between the number of sub-spreading factors and the BER for the RSST-SR-DCSK, the MC-ODBR-DCSK system mentioned in [8], and the SR-ODBR-DCSK system mentioned in [14]. The graphs consider a Gaussian white-noise channel with a signal-to-noise ratio of 13 db. As the number of sub-spreading factors increases, both systems show an initial improvement in error performance followed by a decrease. The error performance trends are similar for all three systems, with performance troughs occurring at minimum values of around 14, 12, and 20, respectively. It is clear that when the expansion factor is below these troughs, the error performance of the systems is better, while when these values are exceeded, the error performance gradually deteriorates.
## 2 Regional Geological Setting  
A series of fault depressions has developed on the southwestern margin of the Songliao Basin, bordering Jilin Province to the east and Liaoning Province to the south. The region has a triangular shape, extending approximately 300 km from north to south and about 370 km from east to west, with a total area of 57,469.76 km\({}^{2}\). Its geographical coordinates range from 119\({}^{\circ}\)04\({}^{\prime}\) E to 123\({}^{\circ}\)43\({}^{\prime}\) E and from 42\({}^{\circ}\)29\({}^{\prime}\) N to 45\({}^{\circ}\)00\({}^{\prime}\) N. The Naiman Sag is a secondary negative tectonic unit on the southwestern side within the faulted depression group. It is mainly located on the southeastern boundary of Inner Mongolia Province (Figure 1a). Its formation is primarily controlled by the Hongshan-Balihan and Xilamulun River faults. It is adjacent to the Zhangsanyuanzhi-Xinmiao and Luijapu depressions to the northwest and the Baxiantong depression to the east, forming a narrow NNE-trending belt [8; 9; 12; 13]. In terms of structural and sedimentary evolution, it shares similarities with small Meso-Cenozoic sedimentary basins in eastern China, characterized by three main features: small area, strong segmentation, and significant fault-controlled subsidence. Controlled by boundary faults and two major internal faults, the Naiman Sag is divided from west to east into three secondary structural units: the western steep slope belt, the central depression belt, and the eastern gentle slope belt [12; 13; 14] (Figure 1b). The Lower Cretaceous in the sag hosts the main hydrocarbon-bearing strata, and the following formations developed in ascending order: the Yixian Formation (K1y), Jiufotang Formation (K1 jf), Shahai Formation (K1 sh), and Fuxin Formation (K1f) (Figure 1c). In the subsidence center in the northern part of the sag, the Yixian Formation developed extensive lacustrine fine-grained sedimentary strata with favorable hydrocarbon generation and expulsion conditions, serving as the primary hydrocarbon source system within the sag.  
Figure 1: Regional overview of the study area.
# A Novel Network Protocol Syntax Extracting Method for Grammar-Based Fuzzing
## 4 Design and Implementation  
Based on the method mentioned above, we implemented a protocol syntax information extraction system. The main function of this system is to automatically extract the syntax information of a specified protocol and its related protocols through one process and give the results in a structured format. The system consists of a pre-processing module and a main parsing module.  
The pre-processing module is responsible for the preliminary processing of all Wireshark protocol dissector files, including extracting all protocol names and protocol dissector information, and generating protocol tables and parser tables for subsequent retrieval of target protocols and related dissectors. At the same time, the pre-processing module can extract the hierarchical relationship of multi-layer protocols and support the generation of multi-layer protocols for complete network data packets. The main-parsing module conducts an in-depth analysis of the protocol to be processed and extracts all packet types of that protocol, all field information that makes up each packet type, and the dependencies between fields. Figure 3 describes the overall architecture of the system, and each step in the figure is described in detail below.
# Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
## 5 Methodology
### Convolutional Neural Networks (CNNs)
#### 5.2.3 Batch Normalization  
Training a network with modifications to parameters and weights will change the real data distribution of the total inputs of the whole layers in the DNN. This matter makes them either too small or too large and in turn, makes them incomprehensible for training the network, particularly with activation functions that apply nonlinear saturations like tanh and sigmoid. In 2015, the concept of batch normalization was proposed by [PERSON] and [PERSON] [58]. It enhances the accuracy of the DNN, as well as the training time. For each mini-batch, batch normalization updates the inputs to have both zero-mean and unit variance.
###### Abstract  
The accelerated growth of 5G technology has facilitated substantial progress in the realm of vehicle-to-everything (V2X) communications. Consequently, achieving optimal network performance and addressing congestion-related challenges have become paramount. This research proposes a unique hybrid power and rate control management strategy for distributed congestion control (HPR-DCC) focusing on 5G-NR-V2X sidelink communications. The primary objective of this strategy is to enhance network performance while simultaneously preventing congestion. By implementing the HPR-DCC strategy, a more fine-grained and adaptive control over the transmit power and transmission rate can be achieved. This enables efficient control by dynamically adjusting transmission parameters based on the network conditions. This study outlines the system model and methodology used to develop the HPR-DCC algorithm and investigates its characteristics of stability and convergence. Simulation results indicate that the proposed method effectively controls the maximum CBR value at 64% during high congestion scenarios, which leads to a 6% performance improvement over the conventional DCC approach. Furthermore, this approach enhances the signal reception range by 20 m, while maintaining the 90% packet reception ratio (PRR). The proposed HPR-DCC contributes to optimizing the quality and reliability of 5G-NR-V2X sidelink communication and holds great promise for advancing V2X applications in intelligent transportation systems.  
5G-NR-V2X; distributed congestion control; power-rate management; vehicle-to-vehicle communication +
Footnote †: journal: Sensors
# Implicit-Explicit Coupling Enhancement for UAV Scene 3D Reconstruction
## 3 Experiments
### Implicit Modeling for Image Generation  
We tested the performance of implicit model generation, with the results shown in Figure 3 and Table 1. PSNR (peak signal-to-noise ratio), SSIM (structural similarity index), and LPIPS (learned perceptual image patch similarity) are metrics used for assessing image quality. Significant improvements were achieved in both the visual quality and evaluation metrics. The neural radiance fields supervised by depth information outperformed methods relying solely on visual images, exhibiting fewer artifacts and achieving a photo-realistic quality. This indicates that the new viewpoint images generated by our method are sufficiently realistic to be included in the co-visibility clusters to compensate for the missing views required for reconstruction.
# An Analysis of the Wind Parameters in the Western Side of the Black Sea
## 3 Results
### Maximum Wind Values Recorded per Year  
From the amount of analyzed data, the maximum values that were recorded should be highlighted. In 2015 the maximum wind speed value was registered by the EuxRO 1 on the 1 st of December at 20.00. The wind had a speed of 17.1 m/s and a direction of 340\({}^{\circ}\). In the next year, on the 18 January 2016 at 20.00 a value of 18 m/s and a direction of 336\({}^{\circ}\) were indicated by the EuxRO 3. Further, in the next years, all of the maximum values were stored by the EuxRO 3. In 2017, a value of 17.3 m/s and a direction of 272\({}^{\circ}\) were present on the 30 October. In 2018 and 2019 values over 20 m/s were observed. On 18 January 2018, wind speed of 22.5 m/s and direction of 327\({}^{\circ}\) were seen at 19.00, while on the 8 January 2019 at 05.00 the wind speed reached 25.5 m/s and direction 344\({}^{\circ}\). The last year that was analyzed shows a maximum wind speed value of 17.2 m/s and a direction of 299\({}^{\circ}\) on 30 April at 24.00.
###### Abstract  
Non-intrusive load monitoring (NILM), monitoring single-appliance consumption level by decomposing the aggregated energy consumption, is a novel and economic technology that is beneficial to energy utilities and energy demand management strategies development. Hardware costs of high-frequency sampling and algorithm's computational complexity hampered NILM large-scale application. However, low sampling data shows poor performance in event detection when multiple appliances are simultaneously turned on. In this paper, we contribute an iterative disaggregation approach that is based on appliance consumption pattern (ILDACP). Our approach combined Fuzzy C-means clustering algorithm, which provide an initial appliance operating status, and sub-sequence searching Dynamic Time Warping, which retrieves single energy consumption based on the typical power consumption pattern. Results show that the proposed approach is effective to accurately disaggregate power consumption, and is suitable for the situation where different appliances are simultaneously operated. Also, the approach has lower computational complexity than Hidden Markov Model method and it is easy to implement in the household without installing special equipment.  
Keywords:non-intrusive load monitoring; energy disaggregation; smart meter signal; consumption pattern +
Footnote †: journal: Applied Soft. 2018
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 6 Conclusions and Future Work  
This study proposed multi-layer LSTM networks for speech inpainting in the time domain. We compared the inpainting performance of the proposed models on different datasets and with different numbers of LSTM layers. We then evaluated the performance under different gap lengths using the MOS and frequency analysis method. Our tests demonstrated that the proposed multi-layer LSTM model can restore up to 1 s of gaps with fair and good perceived quality using features captured only from the time domain. Specifically, for gaps below 500 ms, the recorded MOS can reach 3 to 4 or above, and for gaps between 500 ms and 1s, the MOS can reach 2 to 3 or above. This study confirmed that the multi-layer LSTM model can be effectively used in speech inpainting to restore up to 1 s of gaps while providing sufficiently accurate results.  
Our future work will focus on performing speech inpainting for high-quality speech coding in information communication settings.  
Conceptualization, H.S., X.S. and S.D.; methodology, H.S., X.S. and S.D.; software, H.S.; validation, H.S., X.S. and S.D.; formal analysis, H.S., X.S. and S.D.; investigation, H.S., X.S. and S.D.; resources, H.S.; data curation, H.S.; writing--original draft preparation, H.S.; writing--review and editing, H.S., X.S. and S.D.; visualization, H.S.; supervision, X.S. and S.D.; project administration, H.S., X.S. and S.D.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Loughborough University (Grant No. GS1016) and the China Scholarship Council (Grant No. 202208060237).  
All experiment data and results are available for public access at [[https://haohan-shi.github.io/](https://haohan-shi.github.io/)]([https://haohan-shi.github.io/](https://haohan-shi.github.io/)) (accessed on 9 February 2024). All raw datasets used in the experiment are publicly available for downloading.  
[PERSON] would like to express his thanks to Loughborough University and the China Scholarship Council for their support and funding and for all the helpful suggestions during the review and revision of this paper.  
The authors declare no conflicts of interest.
## 3 Incremental Pyraformer-DCCA
### Process Monitoring  
Based on the previously introduced objective function for CCA, as defined in Equation (1), the solution is obtained using Singular Value Decomposition (SVD). The next step is to construct the monitoring statistic. Once the statistic is defined, the control limits for fault detection are calculated [24].  
Considering the two outputs of the model, we combine the Pyraformer structure with incremental learning. These outputs are represented as \(f(\cdot)\) and \(g(\cdot)\). After obtaining the canonical vectors \(J\) and \(L\), the residual vector for the training data is defined as:  
\[r(k)=Lg(k)-\Lambda_{\kappa}J^{T}f(k) \tag{26}\]  
where \(r(k)\) represents the residual of the \(k\) sample. Based on this residual vector, the monitoring statistic \(Q_{cca}\) is constructed as:  
\[Q_{cca}(k)=r(k)^{T}\Big{(}I-\Lambda_{k}^{2}\Big{)}^{-1}r(k) \tag{27}\]  
The \(Q_{cca}\) statistic quantifies the discrepancy between the actual output and the expected output, indicating potential faults when it deviates significantly.  
To determine whether a fault exists, a control limit for the statistic is calculated. A chi-square distribution-based control limit is employed to determine if the test data are abnormal. The control limit is given by:  
\[J_{th,Q_{cca}}=g\chi_{1-\alpha}^{2}(k) \tag{28}\]  
where \(g=\frac{S}{2\mu}\), \(H=\frac{2\mu^{2}}{S}\), \(\mu=\frac{1}{N}\sum\limits_{k=1}^{N}Q_{cca,train}(k)\), and \(S=\frac{1}{N-1}\sum\limits_{k=1}^{N}\left(Q_{cca,train}(k)-\mu\right)^{2}\).  
Here, \(g\) and \(H\) are parameters that depend on the mean \(\mu\) and variance \(S\) of the training data statistics, which ensure the appropriate scaling of the control limit. The significance level \(\alpha\) is used to set the threshold for detecting anomalies.  
In fault detection processes, a clear set of decision rules based on the monitoring statistic should be established to ensure system stability and reliability. The core of the monitoring logic lies in determining whether the current state of the system deviates from the normal operating range. When the monitoring statistic of the system significantly exceeds the preset control limit, it can be considered that there may be a potential fault in the system. Therefore, based on the value of the monitoring statistic, the system state can be classified into a normal state and fault state.  
The specific decision rules can be formulated as follows:  
\[\left\{\begin{array}{l}Q_{cca}>J_{th,Q_{cca}}\Rightarrow\text{ Fault}\\ Q_{cca}\leq J_{th,Q_{cca}}\Rightarrow\text{ Normal}\end{array}\right. \tag{29}\]  
where \(Q_{cca}\) represents the monitoring statistic, and \(J_{th,Q_{cca}}\) is the control limit. If \(Q_{cca}\) exceeds \(J_{th,Q_{cca}}\), the system is considered to be in a faulty state. Conversely, if \(Q_{cca}\) is within or equal to the control limit, the system is classified as being in a normal state, indicating normal operation.  
To sum up, the fault detection strategy is presented in Figure 7.
# MANET Routing Protocols' Performance Assessment Under Dynamic Network Conditions
## 2 Literature Review
Protocols were compared in terms of throughput, delay, and network overhead; simulation findings indicate that TORA has the most delay while DSR has the best delay out of the four evaluated protocols. When it comes to network overhead, the same findings are noted: DSR has the lowest throughput and AODV the highest. [PERSON] et al. [10] surveyed energy-efficient routing protocols for MANETs, highlighting the significance of energy consumption in protocol performance. [PERSON] et al. [11] analyzed the performance of MANET routing protocols under different mobility models, demonstrating the variability in protocol efficiency based on network dynamics. [PERSON] et al. [12] evaluated MANET routing protocols under realistic environments, emphasizing the importance of practical testing conditions. Their findings showed variations in metrics such as PDR, end-to-end delay, and network lifetime under different real-world scenarios. This study contributes to the literature by providing a detailed evaluation of eight MANET routing protocols. By incorporating metrics such as energy consumption and jitter,it offers valuable insights into selecting the most suitable protocols for various network applications. The focus on hybrid protocols further highlights their potential for balancing efficiency and adaptability in diverse scenarios.
## 2 Fundamentals
### Multi-Level Thresholding Image Segmentation
#### 2.3.2. Kapur Method  
An additional popular technique for multi-level thresholding is [PERSON]'s method, which is founded on the idea of Shannon entropy. Unlike [PERSON]'s method, which relies on variance-based class separation, [PERSON]'s method seeks to maximize the sum of entropies of the segmented regions, thus ensuring that each region contains the most information possible. The method utilizes the normalized histogram of the greyscale image to calculate the probability distribution of pixel intensities. Given a combination of thresholds \(T_{1},\ T_{2},\ \ldots,\ T_{K-1}\), the total entropy H for K segmented regions is calculated as:  
\[H=H_{1}+H_{2}+\cdots+H_{K} \tag{20}\]The entropy of the final region is:  
\[H_{k}=-\sum_{i=T_{k-1}+1}^{L-1}\frac{p_{(i)}}{W_{K}}log(\frac{p_{(i)}}{W_{K}}) \tag{21}\]  
Based on the above equation, \(pi\) is the probability of the \(i\)th grey level, and L is the total number of grey levels. The probability sums (weights) for each segmented region are calculated as follows:  
\[W_{K}=\sum_{i=T_{k-1}+1}^{L-1}p_{(i)} \tag{22}\]  
The optimal set of thresholds is the one that maximizes the total entropy H, thereby achieving the most informative segmentation of the image. [PERSON]'s method is particularly effective for images with complex intensity distributions and is often used in applications requiring the precise segmentation of textures or objects with varying brightness.