esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation
### Parameter Settings  
For the UKF model, the formula for determining the first-order weight coefficient \(W_{i}^{m}\) and the second-order weight coefficient \(W_{i}^{c}\) in Equation (15) is:  
\[\left\{\begin{array}{l}W_{0}^{m}=\frac{\lambda}{\mu+\lambda}\\ W_{0}^{c}=\frac{\lambda}{\mu+\lambda}+1-{\alpha_{0}}^{2}+\beta_{0}\\ W_{i}^{m}=W_{i}^{c}=\frac{1}{2(n+\lambda)},i=1,2,\ldots\ldots,2n\end{array}\right. \tag{26}\]  
where \(\lambda={\alpha_{0}}^{2}(n+\kappa)-n\). The parameters \(\alpha_{0}\) and \(\kappa\) are ratio parameters that determine the range of the sigma point distribution around the mean. To ensure the distribution state of sigma points and the positive definiteness, \(1\times 10^{-4}\leq\alpha_{0}\leq 1\) and \(\kappa\geq 0\). The parameter \(\beta_{0}\) is used to introduce higher-order information of the state variable probability distribution. In the case of a Gaussian distribution, \(\beta_{0}\) is set to 2. According to relative  
Figure 8: Sliding window strategy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fault** & **Fault Sensor** & **Label** & **Fault Severity** & \
\begin{tabular}{c} **Training** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Testing** \\ **Samples** \\ \end{tabular} \\ \hline Normal & - & 0 & - & 500 & 100 \\ \hline \multirow{3}{*}{Step Fault} & Angle of attack & 1 & 0.04–0.4 (\({}^{\circ}\)) & 500 & 100 \\  & Sideslip angle & 2 & 0.02–0.2 (\({}^{\circ}\)) & 500 & 100 \\  & Airspeed & 3 & 3–10 (m/s) & 500 & 100 \\ \hline \multirow{3}{*}{Ramp Fault} & Angle of attack & 4 & 0.001–0.01 (\({}^{\circ}\)/s) & 500 & 100 \\  & Sideslip angle & 5 & 0.001–0.01 (\({}^{\circ}\)/s) & 500 & 100 \\ \cline{1-1}  & Airspeed & 6 & 0.05–0.5 (m/s\({}^{2}\)) & 500 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fault setting.  
experience and testing, set \(\kappa=0\), \(\alpha_{0}=0.8\), \(\beta_{0}=2\). The covariance matrix of process noise in Equation (15) \(Q=diag\big{(}\big{[}1\times 10^{-4},1\times 10^{-4},1\times 10^{-4},3\times 10^{-8}, 3\times 10^{-8},3\times 10^{-8}\big{]}\big{)}\) and the covariance matrix of measurement noise in Equation (15) \(R=diag\big{(}\big{[}1\times 10^{-4},3\times 10^{-6},3\times 10^{-6},3\times 10^{-8},3\times 10^{-8},3\times 10^{-8}\big{]}\big{)}\)_diag_ represent the diagonal matrix.  
For the Inception network, the input dimension is 20 \(\times\) 12, where 20 represents the number of sample points. Since the platform samples a point every 10 ms, it corresponds to a time window of 200 ms. The number 12 represents the data dimension, including 6 dimensions for innovations and those for residuals. The structural configuration can be seen in detail in Table 1. The model is built on the PyTorch framework, with a training/validation set ratio of 4:1. The loss function is cross-entropy, and the optimizer is Adam with a learning rate of 0.0005. The batch size and training epoch are 64 and 50, respectively.
## 5 Analysis of the Numerical Results  
Considering that the tunnel excavation will lead to a redistribution of surrounding rock stress field, the displacement field of the surrounding rocks will also exhibit a corresponding distribution pattern with the adjustment of secondary stress [9]. In practical engineering, the variation course of the surrounding rock displacement field is generally studied more because the displacement of surrounding rocks is easier to measure than the stress state.  
Direct quantitative evaluation of the feasibility, pros, and cons of the entire construction plan is possible via analyzing the effects of the tunnel excavation on the slope and road since the new tunnel is not only adjacent to the slope, but also buried shallowly under the road. As shown in Figure 7, the focus was on a comprehensive comparative analysis of the simulation results at five key locations: the road surface centerline (line A), the outer slope line of left tunnel sidewall center (line B), the separation boundary between existing tunnel and tunnel sidewall (line 1), the short-distance separation site between the existing tunnel and tunnel sidewall (line 2), and the long-distance separation site between the existing tunnel and tunnel sidewall (line 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Stratum** & **Parameter** & **Range** & **Proposed Selection** \\ \hline \multirow{5}{*}{Limestone} & E (GPa) & 1.1–2.6 & 1.1 \\  & \(\mu\) & 0.30–0.33 & 0.33 \\ \cline{1-1}  & \(\rho\) (kg\(\cdot\)m\({}^{-3}\)) & 2645 & 2645 \\ \cline{1-1}  & \(\varphi\) (\({}^{\circ}\)) & 30.2–33.5 & 30.2 \\ \cline{1-1}  & c (MPa) & 0.45–0.55 & 0.45 \\ \cline{1-1}  & \(\sigma_{\text{t}}\) (MPa) & 1.35–1.82 & 1.35 \\ \hline \end{tabular}
\end{table}
Table 4: Parameter selections of the rock mass in the finite element model.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Medium** & **E (GPa)** & \(\rho\) **(kg\(\cdot\)m\({}^{-3}\))** & \(\mu\) \\ \hline Rebar & 210 & 7800 & 0.30 \\ C25 concrete for backfill & 28 & 2400 & 0.20 \\ and lining & & & \\ \hline \end{tabular}
\end{table}
Table 5: The medium parameters in the finite element physical and mechanical models.  
Figure 7: _Cont._
# Tissue Engineering Strategies for Retina Regeneration
Currently, there are no established treatment strategies to completely halt the degenerative process or reinstate regular retinal function to restore vision. Although electronic retinal interface devices [5] and gene therapies [6] are under clinical trials, the extent of achievable results is likely a long way from permanent visual recovery. In many instances of retinal degeneration, even after RPE and PR loss, the inner layers of the retina with its intricate neural connectivity maintain their architecture for an extended period. If a population of healthy RPE/PR are delivered to the subretinal space, they can survive and integrate with the host retina to restore vision. Based on this, a cell replacement strategy is a promising approach for the treatment of AMD and RP. Reports from initial clinical trials involving transplantation of human embryonic stem cell-derived RPE (hESC-RPE) as suspension [7; 8] are encouraging and found to be safe for the treatment of AMD and SD. Simple bolus injection of stem/progenitor cell suspension into subretinal space may result in injection reflex and poor cell localization. The compromised cell survival will lead to ineffective cell integration into the damaged retina. Even though cells appear to be well tolerated in relatively short-term animal studies, non-integrated cells will lead to potential complications such as subretinal gliosis [9; 10]. To ensure that the cells are in correct orientation and proper interface with the photoreceptor cells, it is desirable to transplant cells as a preformed monolayer along with a supporting substrate. A recent implantation study used, stem cell-derived RPE grown on a bioengineered scaffold (RPE patch), that helps to maintain the polarity and laminar structure of the transplanted RPE cells [9; 10; 11; 12]. Results of the on-going Phase1/2a clinical trials indicate good safety and tolerability for surgical implantation of RPE grown on parylene scaffolds [13]. In addition to RPE transplantation, recent advances in the development of pluripotent stem cell (PSC)-derived 3D neural retina in the culture dish and construction of cellular, three-dimensional structures using robotics and 3D bioprinting have provided new insights in the field of tissue engineering of the retina. In this review, we discuss various tissue engineering strategies for retinal repair using stem cell-derived grafts.
# LightSeg: Local Spatial Perception Convolution for Real-Time Semantic Segmentation
## 3 Methods  
### Overall Architecture  
The overall architecture of LightSeg is depicted in Figure 1, which follows a standard segmentation network design consisting of an encoder and a decoder. The encoder module is responsible for extracting meaningful semantic features from the input image, capturing essential information about the image's content and context. These extracted features are then forwarded to the decoder module, which utilizes them to make a pixel-wise classification for the entire image by effectively leveraging the rich semantic information encoded in the extracted features.  
Our network architecture starts with a stem module, which consists of a \(3\times 3\) convolution. This module performs downsampling and reduces the size of the input feature map. Extensive experiments and analysis have shown that this downsampling process has little impact on the overall accuracy of the network [25] because the network effectively captures and retains essential information for accurate predictions. Moreover, the resulting lower spatial resolution improves inference speed. Subsequently, multi-level features are extracted from the original resolution of \(1/4\), \(1/8\), and \(1/16\). These features undergo a fusion process facilitated by the UpHead module, which combines them into a unified representation. Finally, a \(3\times 3\) convolutional layer is employed to predict the segmentation mask at a \(1/4\) resolution. It is important to note that the Decoder consists of \(C\) segmentation classes.  
Figure 1: Network structure diagram of LightSeg, illustrating the encoder-decoder architecture. Each stage is stacked with an LSPBlock module for robust feature extraction. The UpHead module serves as the multi-scale feature fusion module.
# Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach
## 3 Fuzzy Logic Definitions
### Fuzzy Sets and Membership Functions  
**Definition 1**.: _A fuzzy set \(\overset{\sim}{A}\) in the universe of discourse \(X\) can be defined as a set of ordered pairs._  
\[\overset{\sim}{A}=\{(x,\ \mu_{\overset{\sim}{A}}(x)\ )|x\in X\}\]  
_where\(\underset{A}{\overset{\sim}{A}}(x)\) is called membership function of \(\overset{\sim}{A}\). The membership function \(\mu_{\overset{\sim}{A}}(x)\) defines all the information contained in a fuzzy set._  
**Definition 2**.: _The fuzzy number \(A\) is an extension of a regular number in the sense that it does not refer to one single value but rather to a connected set of possible values, where each possible value
## 5 Proposed Channel Model
### Path Loss  
The path loss represented by Equation (6) is  
\[L=L_{FSPL}+L_{A}+L_{ex}\]  
By substituting values from Equations (9), (11)-(14), the path loss can be represented by  
\[L=20 log\frac{(h_{max}-\ h_{r})}{sin\theta}+A-20 log\frac{\theta}{\theta_{3}}+Ls +20\text{log}(1-P_{LoS}) \tag{15}\]  
where  
\[A=20 logf-51.55-2G_{0} \tag{16}\]
# Multigap Resistive Plate Chambers for Time of Flight Applications
## 1 Introduction
A detector based on MCP-PMTs (microchannel plate PMTs) [17] is a promising solution for the fast timing of single photon signals. Coupled with a quartz Cherenkov radiator, it can provide ~10 ps time resolution [18; 19]. The LQbar detector based on MCP-PMT [20] is intended for the LHC pp-diffraction scattering experiment. However, issues like high costs and crosstalk between anodes need to be further studied to significantly progress this ToF technique. Multigap resistive plate chamber (MRPC) is a relatively new and standard technology for the ToF system. It is known for very high detection efficiency (>95%), excellent time resolution (<100 ps) and relatively low cost. This detector is easy to build and possible to cover a large area. It has been extensively employed as a ToF system in many nuclear physics experiments, such as BESIII [21], ALICE [22; 23; 24], CBM [25; 26], STAR [27; 28; 29] and PHENIX [30]. This review will focus on the multigap resistive plate chamber (MRPC) and its applications as a ToF detector in large experiments. The description and detector physics of MRPC are introduced in Section 2. Its applications in current large experiments and developments for future ToF systems are discussed in Section 3.
## 7 Conclusions  
In e-commerce, transaction security is very important, and online transaction security is a key task when it comes to deciding whether to buy a service or product online to safeguard \(\mathcal{M}\)s' information, as transaction information is not impervious to online fraud. In response to these challenges, we introduced a protocol that ensures robust security for e-commerce transactions through a hybrid encryption approach. Our protocol combines the use of asymmetric keys like ELG keys and symmetric encryption using the FER algorithm, ensuring robust data protection. Additionally, we integrated DLD technology to safeguard information from potential leaks and unauthorized access. To assess the efficacy of our protocol, we subjected it to various e-commerce threats and conducted hands-on testing using the Scyther tool.  
As a result, our protocol demonstrates robust resistance against these attacks, effectively ensuring high-performance security against malicious threats. Notably, it achieved the most efficient execution time for encryption (0.00396 ms) and decryption (0.00101 ms) compared to previous studies' algorithms (as shown in Table 2). For future work, we aim to enhance security by incorporating a symmetric random function generator (SRFG) within the FER algorithm to introduce more encryption randomness. Additionally, we plan to subject the protocol to testing against various attacks, including vampire repeat registered attacks. Finally, we intend to implement public key encryption with an equality test (PKEET) in the ELG algorithm, enabling verification without decryption to determine if two encryptions produced from different public keys contain the same e-commerce request.  
All authors provided contributions to the work. Conceptualization, M.A.-Z. and G.S.S.; methodology, M.A.-Z. and G.S.S.; software, M.A.-Z. and G.S.S.; validation, M.A.-Z. and G.S.S.; formal analysis, M.A.-Z. and G.S.S.; investigation, M.A.-Z. and G.S.S.; writing--original draft preparation, M.A.-Z. and G.S.S.; writing--review and editing, M.A.-Z.; supervision, M.A.-Z.; project administration, M.A.-Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Cryptosystem** & **Encryption Execution Time in ms** & **Decryption Execution Time in ms** \\ \hline AES [42] & 0.1190 & 0.1481 \\ Eigaml + AES [15] & 0.08 & 1.786 \\ DES [43] & 0.062 & 0.024 \\ RSA + AES [44] & 1393 & 1393 \\ RSA + Blowfish [45] & 76.923 & 84.7826 \\ Proposed & 0.00396 & 0.00101 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between the proposed cryptosystem, AES, Eigaml+ AES, DES, RSA+AES and RSA+ Blowfish in terms of execution time for encryption and decryption procedures.
## 2 Data  
Figure 1 shows the study area within the Beaufort Sea, located in the Arctic. Blue areas represent the Sentinel-1 images and the red lines the CryoSat-2 altimeter ground tracks. The Beaufort Sea was chosen for this study because first-year and multiyear sea ice are present during wintertime in this area. Furthermore, a belt of larger multiyear ice floes embedded in first-year ice can usually be found in this area and is thus ideal for studying the ability to detect small-scale changes in sea ice. We collected near-coincidental Sentinel-1 SAR imagery and CryoSat-2 altimeter data of three winter seasons (Feb/Mar 2016, Nov/Dec/Jan 2016/17 and Jan/Feb/Mar 2018) with an average time lag from 34 min ranging from a few to about 80 min. For drift speeds up to about 6 cm/s the motion will be below the along-track resolution of the altimeter (about 300 m) for all time lags. Higher drift speeds are possible in this area [21] and therefore sea ice drift can exceed the altimeter footprint size for larger time lags.
## 2 Materials and Methods
### Procedures
#### 2.4.5 Maximal Heart Rate (HR Max)  
The relationship between maximal heart rate (HR max) and boxing is integral to understanding how cardiovascular and aerobic fitness influence performance in this high-intensity sport. HR max is defined as the highest heart rate an individual can achieve during maximal physical exertion, and it is often used as a benchmark to assess cardiovascular capacity and physical endurance [50]. In boxing, which involves short bursts of intense activity followed by brief rest periods, understanding HR max is crucial for evaluating the ability of boxers to perform under pressure and sustain high-intensity efforts during matches. Boxing relies heavily on anaerobic power, speed, and strength, but the cardiovascular system plays a pivotal role in sustaining performance across multiple rounds. The HR max provides insight into an athlete's ability to recover quickly between rounds and continue to perform at a high level [51]. Studies have shown that boxing, as an intermittent exercise involving rapid bursts of movement, places significant demands on both aerobic and anaerobic systems. Athletes with a higher HR max recover faster between these bursts, leading to greater endurance during prolonged bouts [52]. Furthermore, boxers often train to improve their HR max in a conditioning program that includes aerobic and anaerobic exercises. Research has highlighted that improved HR max correlates with increased endurance, better stamina, and more efficient energy utilization during high-intensity efforts such as punches, footwork, and defensive movements [53].  
A higher HR max indicates a more robust cardiovascular system, essential for maintaining performance under the stress of continuous and intense movements in the ring. The relationship between HR max and boxing performance can be summarized as follows: boxers with higher HR max values tend to exhibit better overall physical conditioning, which is critical for sustaining punching power, movement speed, and reaction time during competitive bouts. Thus, HR max is a key physiological marker for boxers to track and improve upon to enhance their performance in the ring. Heart rate data for athletes was recorded using the Polar heart rate monitor (Polar RS800 multi CX, Kempele, Finland), which was worn on the athletes' arms.  
Reliability: HRmax was measured using the Polar RS800 multi CX heart rate monitor (Polar, Finland), known for high measurement accuracy. HRmax is a key physiological indicator of cardiovascular capacity and recovery ability. Previous research supports its reliability in sports settings (ICC > 0.90) [54].  
Summary of Test-Retest Reliability  
All physical performance tests employed in this study are supported by high reliability metrics based on previous validation in elite or similarly trained athletic populations. Reliability coefficients (ICC and CV) were reported directly from the present data (e.g., PVmax) where feasible. These methodological refinements address concerns regarding replicability and internal validity raised by the reviewer and enhance the robustness of the study outcomes.
# Software-Defined Visible Light Communication for Internet of Things: A Low-Complexity Approach
## 3 Modeling of BBP Unit in SD-VLC System
### Performance Analysis of SDLMS Equalization
Although SDLMS achieves lower power consumption and improved robustness against LED nonlinearities, it exhibits slower convergence and a slightly higher residual error compared to LMS. The quantitative comparison in Table 2 indicates that LMS achieves faster BER and MSE convergence under comparable equalization performance, whereas SDLMS offers significantly lower computational complexity, making it more suitable for hardware-constrained VLC applications such as IoT devices. These findings highlight the trade-off between computational efficiency and equalization performance, emphasizing the importance of selecting an appropriate algorithm based on the system's power constraints and accuracy requirements.
## 2 Data and Methods
### Model Setup  
The deep residual U-Net architecture consists of five down-sample blocks, each with decreasing filter sizes of 256, 128, 64, 32, and 16. The convolution blocks consist of a sequence of convolution, batch normalization, activation, and zero padding layers repeated two times for each block. The convolution layers use a \(3\times 3\) kernel size and the ReLU activation function. There is a \(4\times 4\) transpose convolution with stride \(2\times 2\) in the upsampling stage, which is consistent with the size of the filter. In this stage, the dropout layer was added to each convolution block flow.  
The model was trained with 80% of the dataset, randomly split from the entire optical images, LiDAR, and corresponding mask images. The training data were further divided into 75% training and 25% validation data. The optuna framework [42] was used to optimize the hyperparameters, such as the optimizer, learning rate, and dropout rate. Adam was selected as an optimizer and Binary cross entropy was used as a loss function in the model. The learning rate was tested between \(10^{-1}\) and \(10^{-5}\). The Dropout rate was tested from 10% to 50%. The training process was stopped after 10 epochs if no improvement was observed in the validation accuracy.
# TF-YOLO: An Improved Incremental Network for Real-Time Object Detection
## 4 Experimental Verification and Result Analysis  
In this section, the performance of the proposed TF-YOLO network is evaluated. Specifically, aerial remote sensing images from NWPU VHR-10 [32; 33; 34] are used for training and testing in the experiment. The NWPU VHR-10 dataset contains a total of 800 very-high-resolution (VHR) remote sensing images cropped from Google Earth and the Vaihingen dataset, and manually annotated by experts. The differences between remote sensing and conventional natural images can be briefly described as follows. First of all, there are numerous small targets with a little visual information in the remote sensing images. Assuming that the CNN's pooling layer further reduces the amount of information to the small targets. After four pooling layers, a \(24\times 24\) target has only approximately 1 pixel, leading to the dimension too diminutive to distinguish. Secondly, there are various scale and perspective of remote sensing images. The perspectives of aerial remote sensing images are basically high-altitude, and the targets on the ground may have different size and mode. Some detectors well trained on conventional datasets may fail to perform well in remotely sensed images. Thirdly, the background of remote sensing images is complex for the large field of view. What's more, a variety of backgrounds will extend a certain amount of interference on testing targets.