esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Model Test Uncertainty Analysis  
In order to analyze the uncertainty of the model test system, the sensors' measurement accuracy was discussed first. Then, repeated tests of pipes with buoyancy modules and strikes were carried out to illustrate the stability of the test device system.  
(1) Sensor measurement accuracy  
In this experiment, fiber grating strain sensors and a force transducer were used to measure the vortex-induced vibration strain and forces at the end of pipe models. Among them, the measurement resolution of the fiber grating strain sensor is \(+/-\)\(1u\epsilon\) (microstrain), which is about 0.5% of VIV strains; the resolution of the force transducer is \(+/-\) 0.5% of the measuring range. Both of the two sensors have satisfactory measurement accuracy.  
(2) Repeated test result  
To illustrate the stability of the test device system, we carried out repeated tests for randomly selected test cases. Figure 14 presents the RMS value of VIV displacements for the F01 pipe model under a flow velocity of 2.0 m/s. As can be seen from the figure, the three test results are very close, within 5% differences. This shows that the repeatability of the test results is very high and the test system has very good stability.  
In general, the uncertainty of this test system is small and within the acceptable range.
# Control of Automotive Semi-Active MR Suspensions for In-Wheel Electric Vehicles
## 2 Methods
### Controllers Performance Indexes for the QoV
#### 2.8.2 Frequency Domain  
In the frequency domain, the PSD (power spectral density) and the pseudo-bode diagram are considered and described.  
The performance of the semi-active suspension can be quantitatively analyzed employing PSD. [PERSON] [22], determines a performance index for each control objective in terms of improving, that is to say, the semi-active system PSD is compared versus the performance of a passive suspension, employing the next equation:  
\[IPSD=\frac{PSD_{passive}-PSD_{semiauctive}}{PSD_{passive}}. \tag{22}\]  
[PERSON] and [PERSON] [41], proposed an algorithm for evaluating controllers performance in the frequency domain known as pseudo-Bode (PSB) algorithm. A pseudo-bode plot illustrates the evaluated frequencies versus the measured gains in the transfer function. With this algorithm, it is possible to visualize the gains of the interest variables in certain frequencies in order to evaluate comfort and road holding improvement. The PSB algorithm is given by:  
1. Excite the system employing a sinusoidal signal as an input at the \(\varphi\) frequency for \(M\) periods.
2. Measure the output signal \(z_{output}\) (signal of interest).
3. Compute the discrete Fourier transform of the input and output signal.
4. Compute the magnitudes \(|Z_{r}|\) and \(Z_{output}\).
5. Compute the expression: \[|G|_{\varphi}=\frac{|Z_{output}|}{|Z_{r}|},\] (23) where \(|G|_{\varphi}\) is the gain to the transfer function \(\frac{|Z_{output}|}{|Z_{r}|}\) for the given frequency \(\varphi\).
6. The sequence must be repeated \(M\) times, each time considering a specific frequency until the desired PSB is achieved.  
Four bandwidths of interest are considered as in References [42; 43] for visualizing performance improvement.  
_BW1_: [0-2] Hz range. The main goal is comfort. The resonance frequency of the sprung mass is included.  
_BW2_: [2-9] Hz range. The main goal is also the comfort. High gains of vertical accelerations in this bandwidth generate overall discomfort by shaking [44].  
_BW3_: [9-16] Hz range. The main goal is road holding. This bandwidth is known as _tirehop BW_ because it contains the unsprung mass frequency resonance.  
_BW4_: [16-20] Hz range. This will be named the _head injury BW_. The main goal is road holding. The dangerous vibration can generate internal damage to the body.
# Rain Streak Removal for Single Images Using Conditional Generative Adversarial Networks
## 4 Experimental Details
### Evaluation Matrix and Results  
The peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) [48] were used to evaluate and compare the performance of the model. PSNR measures how far the de-rained image is distorted from its real ground truth image by using the mean squared error at the pixel level. As shown in Table 1, the proposed CGANet model obtained the best PSNR value compared to the other two methods. The structural similarity index (SSIM) is a perception-based index that evaluates image degradation as the perceived difference in structural information while also incorporating both luminance masking and contrast masking terms. Table 3 shows the SSIM value comparison between the proposed CGANet model and the other two state-of-the art methods. By referring to this comparison, we could verify that the proposed method performed well compared to other de-raining mechanisms, and this is also visually verifiable in Figures 2 and 3.  
\begin{table}
\begin{tabular}{c} \hline \hline
**Discriminator Architecture** \\ \hline Input Image (256 \(\times\) 256 \(\times\) 3) + Target Image (256 \(\times\) 256 \(\times\) 3) \\ \hline Concatenation: Input (256 \(\times\) 256 \(\times\) 3), (256 \(\times\) 256 \(\times\) 3), Output (2 \(\times\) 2 \(\times\) 1024) \\ \hline Downsample 4 \(\times\) 4 Convolution + BN + ReLu, Output: 128 \(\times\) 128, Num\_c: 64 \\ \hline Downsample 4 \(\times\) 4 Convolution + BN + ReLu, Output: 64 \(\times\) 64, Num\_c: 128 \\ \hline Downsample 4 \(\times\) 4 Convolution + BN + ReLu, Output: 32 \(\times\) 32, Num\_c: 256 \\ \hline Zero Padding 2D: Output: 34 \(\times\) 34, Num\_c: 256 \\ \hline Downsample 4 \(\times\) 4 Convolution + BN + ReLu, Output: 31 \(\times\) 31, Num\_c: 512 \\ \hline Zero Padding 2D: Output: 33\(\times\)33, Num\_c: 512 \\ \hline Downsample 4 \(\times\) 4 Convolution + BN + ReLu, Output: 30 \(\times\) 30, Num\_c: 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Discriminator architecture of the CGANet model.  
Figure 3: CGANet on real-world dataset ((**Left**) Input image; (**Right**) de-rained output).  
Figure 2: Qualitative comparison between GMM, pyramid networks, and proposed CGANet methods.
# Immunomodulatory Properties of Probiotics and Their Derived Bioactive Compounds
## 5 Probiotic Derived Bioactive Compounds and Immunity
### SCFAs
#### 5.2.2 SCFAs as Immunomodulatory Molecules
Lactate plays a critical role in abrogating TLR and IL-1\(\upbeta\) dependent IECs activation [162], and it downregulates the expression of pro-inflammatory cytokines in IECs [133; 136]. Interestingly, lactate has been found to induce NETs formation when administered to neutrophils [139; 163]. Activation of GPR81-mediated signaling by lactate in colonic DCs and macrophages contributes to the suppression of colonic inflammation [137; 164]. However, lactate can also enhance GPR81-independent metabolic changes in LPS-activated macrophages, thus inducing a reduction in pro-inflammatory cytokine levels [133; 165]. Furthermore, lactate promotes M2 polarization of macrophages producing IL-10 and inhibits IL-12 production [166]. An in vitro study has also indicated that lactate is involved in the suppression of TNF-\(\alpha\) secretion in LPS-stimulated monocytes in the tumor microenvironment (TME) [167]. The significant immunomodulatory role of lactate in TME has been extensively reviewed [168; 169]. Lactate has been shown to inhibit the motility of CD4+ and CD8+ T cells [170]. In the case of CD4+ T cells it induces Th17 differentiation [168] and Tregs proliferation [171], whereas it promotes the loss of cytolytic function in CD8+ T cells [170].
## 3 Results and Discussions  
### Morphology of Metal-Coated Fibers  
Figure 4 presents SEM images that illustrate the uniform and compact deposition of nickel on a carbon fiber surface, highlighting the tight packing of nickel particles to form a continuous, dense layer across the fiber. Figure 4 depicts the extensive coverage of nickel particles, essential for creating a conductive path that enhances electromagnetic shielding properties. Figure 4, derived from the nickel elemental distribution map associated with Figure 4, confirms the uniform distribution of nickel across the fiber, indicating a consistent shielding effectiveness essential for electromagnetic protection. Figure 4, a high-magnification SEM image, offers a detailed look at the tightly interconnected nickel  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Chassis Number**} & **Arrangement of Holes in the Outer Shield** & **Arrangement of Holes in the Inner Shield** \\ \hline chassis 1 & Horizontal–Horizontal & Horizontal–Horizontal \\ chassis 2 & Horizontal–Horizontal & Horizontal–Vertical \\ chassis 3 & Horizontal–Horizontal & Vertical–Vertica \\ \hline \hline \end{tabular}
\end{table}
Table 1: Arrangement of hole array and corresponding number.  
Figure 3: Three arrangements of hole arrays: (**a**) Horizontal–Vertical; (**b**) Horizontal–Horizontal; (**c**) Vertical–Vertical.  
particles, showing no visible gaps and particle diameters ranging from 0.5 to 1 \(\upmu\)m. This compact layer is crucial for improving the material's effectiveness in blocking electromagnetic interference, showcasing the effectiveness of the electroless deposition process in achieving a nickel-coated carbon fiber with superior electromagnetic shielding capabilities and structural integrity.
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 3 Eliminating Deterministic FPGA Routing Delays to Avoid EoA-PUF Bias
### Arbiter Routing Bias in FPGA-Based EoA-PUF
#### 3.2.4 Tuning Algorithm  
Based on the previous findings, a fast tuning algorithm based on a binary search for edge detection was developed to determine the optimum combination for a sensitive arbiter (see Figure 7). The algorithm is provided in Algorithm 1. Each pulse is evaluated after a fixed time of 10 ns \(\widehat{=}\) 1 clock-cycle. Depending on the value of the optimum, this algorithm needs 14 to 140 evaluations for finding this tuning vector combination. With two clock-cycles per evaluation, this results in a total runtime of \(\leq\)2.8 \(\upmu\)s for a 100 MHz clock. Therefore, it is possible to re-run the algorithm at every start-up of the system, which eliminates the need to permanently store helper data, in contrast to SotA implementations. This enables obtaining an individual unbiased routing to the arbiter, which creates the same basic conditions for all PUF instances. As the tuning architecture is separated from the source of entropy, these numbers stay true for a varying amount of challenge bits.  
```
\((L_{\text{a}},L_{\text{b}}^{*})\leftarrow(20,-20)\) \((L_{\text{a}}^{*},L_{\text{b}})\leftarrow(-20,20)\) running\(\leftarrow\) True whilerunningdo while\(r(L_{\text{a}},L_{\text{b}}^{*})\
eq<1,1>\)do \(L_{\text{b}}^{*}\leftarrow L_{\text{b}}^{*}+1\) \(\triangleright\) search horizontal endwhile while\(r(L_{\text{a}}^{*},L_{\text{b}})\
eq<1,1>\)do \(L_{\text{a}}^{*}\leftarrow L_{\text{a}}^{*}+1\) \(\triangleright\) search vertical endwhile \((\Delta L_{\text{a}},\Delta L_{\text{b}})\leftarrow((L_{\text{a}}-L_{\text{a} }^{*})/2,(L_{\text{b}}-L_{\text{b}}^{*})/2)\) if\((\Delta L_{\text{a}},\Delta L_{\text{b}})\
eq(0,0)\)then\(\triangleright\) set next starting point \((L_{\text{a}},L_{\text{b}}^{*})\leftarrow(L_{\text{a}}-\Delta L_{\text{a}},-20)\) \((L_{\text{a}}^{*},L_{\text{b}})\leftarrow(-20,L_{\text{b}}-\Delta L_{\text{b}})\) else \(running\gets False\) endif endwhile \(optTuning\leftarrow(L_{\text{a}},L_{\text{b}})\)
```  
**Algorithm 1** Algorithm for finding the arbiter tuning.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 3 Results and Discussion
### Limitations and Areas for Improvement  
Despite the demonstrated advantages of HPC and parallel computing in our water demand forecasting pipeline, certain limitations warrant attention:  
Single-Node Setup: Our experiments used a single physical machine with limited GPU memory (4 GB per GTX 1650). A multi-node or more powerful multi-GPU setup would likely yield greater throughput but introduce added complexity.  
Script-Based Workflow: Dividing the pipeline into six scripts, each reading and writing to HDFS, may introduce overhead. A more tightly coupled solution (e.g., end-to-end pipelines in Spark or direct GPU memory sharing) could reduce latency.  
Hyperparameter Tuning Overhead: The Bi-LSTM Optimization step remains computationally heavy. Techniques like transfer learning, multi-fidelity optimization, or specialized hardware could mitigate this.  
Addressing these issues could further enhance the pipeline's suitability for real-world deployments, especially if real-time adaptation or multi-location scaling is required.
## 2 Materials and Methods  
### Working Principle  
The flowchart of the proposed method is presented in Figure 1. In general, it consists of three steps: percussion signal acquisition, signal processing, and automatic pattern recognition based on the CNN. In the first step, the acoustic signal generated by the percussion on the pipeline with different ponding volumes was recorded by a microphone, where six ponding volumes were considered. The signal processing step included three consecutive processing stages: preprocessing, STFT method, and Mel filtering. Pre-processing was applied to the percussion signal to delete any low-frequency interference components in the sound signal, and to increase the proportion of high-frequency components. Then, using both overlap and a Hamming window, the STFT was used to obtain the time-frequency plane of the current signal. Finally, the Mel filtering was applied to the frequency axis after the STFT to obtain the Mel spectrogram with lower dimensions by compressing the frequency range, which made the CNN less computationally intensive. In the pattern recognition step, a CNN is proposed to classify the ponding volume case in an automatic way. It is worth noting that the time-frequency plane obtained through the Mel spectrogram was treated as an image in order to implement a conventional two-dimensional (2D) CNN. In the 2D CNN design, learning rates, batch sizes, and dataset split ratios were analyzed.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 3 Blockchain vs. Federated Learning
### The Possibility of Combining Blockchain with Federated Learning  
Blockchain is the core technology that underpins most digital cryptocurrencies since Bitcoin's launch in 2008. The combination of blockchain technology can effectively solve the challenges faced by FL. As a new decentralized and distributed computing paradigm, blockchain provides a suitable model aggregation framework for federated learning [61; 62]. The P2P network in the blockchain structure will improve the fault tolerance of the FL system. Customized smart contracts not only motivate more users to participate in training but also automate the management and control of the federated learning process [63; 64].  
The integration of blockchain into the FL framework is widely regarded as a new basic technology for data updating and sharing, which is an inevitable trend in the future.  
Technically, FL and blockchain are mutually beneficial and can therefore evolve together to form a complete solution that mines common value from distributed data owners and guarantees security and privacy controls. The mutual benefit is threefold [32]. First, the cooperation model is similar. Blockchain is a novel multi-party cooperative network architecture for distributed systems, while FL requires the participation of multiple entities and cooperative training models. Therefore, blockchain can be used as the basic architecture for FL. Secondly, they all have good security and privacy protection features. On the one hand, FL is designed to protect data privacy during distributed and collaborative data training. On the other hand, blockchain adopts a consensus mechanism and data verification mechanism in its accounting process, so that recorded transactions cannot be tampered with and denied. Thirdly, their applications can complement each other. The purpose of FL is to \"create value\", facilitate the complementarity of participant data and improve the validity of the model. Blockchain aims to \"deliver value\" by honestly documenting and rewarding the contributions of all participants [65].
# Heart Rate Variability in Basketball: The Golden Nugget of Holistic Adaptation?
## 3 Impact of HRV on Fatigue and Recovery  
Recovery is an essential component of any training program, facilitating the body's adaptation and enhancement. An athlete's ability to recover is influenced by various factors, the foremost among them being fatigue [48; 49]. Fatigue is defined as a suboptimal psychophysiological state, encompassing mental and physical mechanisms, concurrent to physical exertion [15].  
Multiple factors contribute to fatigue, including glycogen depletion, muscle damage, action potential interruption, and excitation-contraction coupling failure [50; 51; 52]. These factors, resulting from basketball activity, can impede a player's capacity to maintain high-intensity activity levels during games [51]. Additionally, since exercise performance is centrally regulated by the central nervous system (CNS), fatigue should no longer be perceived solely as a physical event. Instead, it should be seen as a sensation or emotion, distinct from any overt physical manifestation, such as the reduction in the force output of active muscles [53]. Physical activity is controlled by a central governor in the brain, with the human body operating as a complex system during exercise. Using feed-forward control in response to afferent feedback from various physiological systems, the extent of skeletal muscle recruitment is modulated as part of a pacing strategy. The sensation of fatigue represents the conscious interpretation of these homoeostatic central governor control mechanisms [52]. Given that studying muscle fatigue in isolation, without CNS control [52], is unlikely to advance the understanding of this phenomenon, HRV emerges as a measure that can effectively monitor an athlete's recovery and fatigue levels [50].  
The autonomic nervous system regulates numerous physiological processes, including heart rate, respiration, and blood pressure [6]. HRV serves as an indirect gauge of autonomic nervous system activity, with higher HRV indicating a healthier and more adaptable nervous system. Therefore, HRV is invaluable in monitoring an athlete's fatigue level [17]. A decrease in HRV could be a sign of fatigue in the athlete, also signifying reduced activity in the parasympathetic nervous system, which is responsible for rest and recovery [50; 54]. Conversely, an increase in HRV suggests that the parasympathetic nervous system is more active when an athlete is well rested. A low HRV in an athlete may signal the need for a training break or adjustments to the training program to allow adequate recovery time [55]. Consequently, HRV serves as a valuable tool in monitoring an athlete's recovery and fatigue levels. By tracking HRV, athletes and their coaches can make well-informed decisions regarding training and recovery strategies [10; 50; 56]. Future studies on recovery should consider these variables in other basketball populations.
## 2 Related Work
### Integrated Methods and Cross-Domain Collaboration  
Individual advancements in resource scheduling optimization, collaborative computing, task offloading, integrated methods, and cross-domain collaboration are emerging as promising avenues to enhance overall satellite network performance. These approaches typically address multiple optimization goals simultaneously, such as reducing delays, improving resource utilization, and enhancing system robustness by merging various computing resources, network topologies, and task characteristics.  
For example, [PERSON] et al. [32] developed a delay-based deep reinforcement learning (DRL) framework for computation offloading decisions, effectively reducing energy consumption. Similarly, [PERSON] et al. [33] introduced an asynchronous adaptive collaborative aggregation algorithm (AFLS) for satellite-ground architectures, enabling federated members to update model parameters during dynamic aggregation promptly. Additionally, [PERSON] et al. [34] proposed a deep supervised learning-based computational offloading (DSLO) algorithm tailored for dynamic tasks in MEC networks.  
Moreover, the collaborative optimization of task scheduling further enhances system efficiency [35; 36; 37]. For instance, [PERSON] et al. [35] designed a three-layer network architecture integrated with a software-defined networking (SDN) model to manage inter-satellite link (ISL) connections and ECS resource scheduling. Similarly, [PERSON] et al. [36] developed a hybrid cloud and satellite multi-layer multi-access edge computing (MEC) network architecture that provides heterogeneous computing resources to terrestrial users.  
In conclusion, existing research often falls short in addressing the combined challenges of dynamic network environments and multi-objective optimization within satellite networks and STIN. Specifically, there is a lack of comprehensive frameworks that ensure both fairness and efficiency in resource allocation while managing high temporal variability and complex constraints. To bridge these gaps, this paper proposes a novel collaborative optimization framework grounded in cooperative game theory and dynamic programming, enhanced by multi-objective optimization models and Gradient Surgery techniques. This approach aims to optimize resource utilization and minimize system latency, enhancing the performance and robustness of satellite network systems.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 3 The Novel Anti-Jamming Technique
### Prior Distribution of MNCM  
The proofs in Section 2.3 show that the time-varying noise is closer to the Gaussian distribution. Therefore, we assume the prior distribution of the MNCM as Gaussian distribution. The prior distribution can be described as  
\[p(\mathbf{R}_{k}|\mathbf{Z}_{1:k-1})=\mathrm{N}\Big{(}\mathbf{R}_{k};\hat{\mathbf{\mu}}_{k|k-1}, \hat{\mathbf{\sigma}}_{k|k-1}\Big{)} \tag{14}\]  
where \(\mathbf{\mu}\) and \(\mathbf{\sigma}\) are the expectation and variance of the \(\mathbf{R}\), respectively.  
After the kth observation, we replace \(p(\mathbf{R}_{k}|\mathbf{Z}_{1:k-1})\) with \(q(\mathbf{R}_{k}|\mathbf{Z}_{1:k})\). According to general Formula (6), and taking the log of both sides of (6), the expression can be written as  
\[\begin{array}{ll}\ln q(\mathbf{R}_{k})&=\mathrm{E}[\ln p(\mathbf{R}_{k}|\mathbf{Z}_{1:k-1 })]+\mathbf{C}\\ &=-\frac{1}{2}\Big{[}\Big{(}\mathbf{R}_{k}-\hat{\mathbf{\mu}}_{k|k-1}\Big{)}^{\mathsf{ T}}\mathbf{\sigma}^{-1}\left(\mathbf{R}_{k}-\hat{\mathbf{\mu}}_{k|k-1}\right)\Big{]}-\\ &\frac{1}{2}\mathbf{R}_{k}{}^{-1}\mathrm{E}_{\chi_{k}}\Big{[}\big{(}(\mathbf{Z}_{1:k- 1}-\mathbf{H}_{k}\mathbf{X}_{k})_{i}\big{)}^{2}\Big{]}-\frac{1}{2}\ln\Big{(}\mathbf{R}_{k} \hat{\mathbf{\sigma}}_{k|k-1}\Big{)}+\mathbf{C}\end{array} \tag{15}\]  
By comparing the logarithmic expression of the Gaussian PDF, from (15) it can be seen that the posterior distribution of MNCM no longer obeys the Gaussian distribution. That is, the prior distribution and the likelihood distribution are non-conjugate. Therefore, the conditions for VBAKF are not satisfied. The ELB cannot be calculated by the VI method, and the MNCM cannot be estimated.  
Fortunately, [PERSON] et al. proposed the BBVI method, which aims to optimize the ELB by stochastic optimization. The condition for the approximate distribution and likelihood distribution to be conjugate is not required. More specifically, first, we form the derivative of the objective as an expectation with respect to the variational approximation; second, sampling from the variational approximation is used to get noisy but unbiased gradients; and last, we use the gradients to update the parameters of the approximate distribution [23].