esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Research and Results
### Model Training  
We train the DSPEB models using the Adam optimizer [46], with mean squared error (MSE) loss and a batch size of 256. We optimize the learning process of W-DSPEB and C-DSPEB separately, using a simple grid-search procedure over a predefined set of values for the learning rate (\(lr\)) and Adam's hyperparameters (\(\epsilon\), \(\beta_{1}\) and \(\beta_{2}\)) as described in our previous work [42]. The best-performing configurations were used to train the models used in this work. The \(lr\), \(\epsilon\), \(\beta_{1}\), and \(\beta_{2}\) were respectively set to \(1\times 10^{-4}\), \(1\times 10^{-8}\), 0.99, and 0.999 for W-DSPEB, and to \(1\times 10^{-3}\), \(1\times 10^{-6}\), 0.5, and 0.9 for C-DSPEB. To stop the training, we make use of an early stopping mechanism that stops training if no improvement in performance on the validation set is achieved for 10 consecutive epochs, known as a  
Figure 6: The full workflow of dataset creation (**a**), single date estimation using DSPEB (**b**), and composite estimate creation in (**c**).  
patience value of 10. The learning curves of the trained models are presented in Figure 7, showing the models' errors on the training and validation sets at each training step.  
Figure 7 shows the MSE training and validation losses of W-DSPEB and C-DSPEB on both study sites and demonstrates the difference in convergence speed between the two models due to the higher learning rate used for C-DSPEB. We note that W-DSPEB was unable to converge using larger learning rates. The training of W-DSPEB stops at 53 and 29 epochs in French Guiana and Gironde and achieves 4.2 and 5.9 m RMSE on the test set of each site respectively. The training of the C-DSPEB model halted at 30 and 13 epochs and achieves 5.8 and 7.8 m RMSE on the test sets in French Guiana and Gironde.
## 2 Materials and Methods
### Development and Design Process of the Control System Hardware
#### 2.2.1 EMG Sensors  
The sensors support a low-noise, high-fidelity sensing circuit for detecting EMG biofeedback signals. The sensor's bandwidth range is 20-450 Hz, and the input range is 11 mV. The EMG sensor incorporates a differential amplifier with a gain of 1000 \(v/v\) and a Butterworth bandpass filter. The common mode rejection ratio (CMRR) is greater than 80 dB. Each EMG sensor has a distance transmission range of up to 40 m with a sampling rate of up to 4000 samples/s. The output signal has a fixed delay of 48 ms from the time the sensor detects the event to the time the analogue signal is reproduced (TRIGNOTM Wireless System User's Guide, 2014) [16].
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 7 Conclusions and Discussion  
The software data plane based on the spin-polling mechanism considerably improves maximum throughput rate and forwarding latency compared with the traditional software data plane. However, there is still a performance waste problem. Meanwhile, the batch processing mechanism of the software data plane does not cope well with different input traffic rates. We propose a dynamic adjustment method for output batch size, which aims to utilize the wasted CPU core cycles to optimize the forwarding latency of the software data plane. Experimental results show that in a single-queue scenario, compared with the original data plane, our scheme can reduce the forwarding latency by up to 45% and this optimization effect can reach up to 55.54% in an evaluation for the multiple queue scenario.  
The CPU cores of the software data plane based on the spin-polling are always running full-tilt, but the CPU utilization is not high at all times. Our work optimizes the forwarding latency by dynamically adjusting the output batch size, which improves the CPU utilization. Nevertheless, the CPU utilization is still low at low input traffic rates, which means that data plane may have some room for performance improvement. Making full use of CPU to improve the performance of data plane and solving the problem of energy consumption are two aspects of our future work.  
Conceptualization, X.T. and L.S.; methodology, X.T., X.Z. and L.S.; software, X.T. and L.S.; validation, X.T., X.Z. and L.S.; formal analysis, X.T.; data curation, X.T. and L.S.; writing--original draft preparation, X.T.; writing--review and editing, X.Z. and L.S.; visualization, X.T.; supervision, X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Strategic Priority Research Program of Chinese Academy of Sciences: Standardization research and system development of SEANET technology (Grant No. XDC02070100).  
Not applicable.  
The authors would like to thank the reviewers for their valuable feedback.  
The authors declare no conflict of interest.
# Monitoring of Training Load and Body Composition in Elite Male Kayakers
## 1 Introduction  
According to the International Canoe Federation (ICF) rules ([[https://www.canoeicf.com/rules](https://www.canoeicf.com/rules)]([https://www.canoeicf.com/rules](https://www.canoeicf.com/rules)), accessed on 28 August 2024), in kayaking, athletes sit in a kayak with a closed deck and use a double-bladed paddle to propel themselves forward. Concerning race distances, men and women compete in 200 m, 500 m, and 1000 m events at the Olympic Games in individual (K1), pairs (K2), or teams of four (K4). These distances are similar during World Championships but may also include additional distances such as 5000 m.  
Optimizing athletic performance for kayaking competitions requires adjusting training stimuli throughout the season, which reflect changes in performance-related indicators(e.g., energetics, kinematics, etc.). The keyaking paddling technique is highly specific, and out-of-water simulations using land ergometers differ significantly from the actual effort. This highlights the importance of conducting experiments under real practice conditions to gain deeper insights into keyakers' performance-related variables. Likewise, integrated assessments in keyaking could better guide researchers and coaches in improving planning strategies and training methods. However, kayak ergometers, which are commonly used in training routines (especially during winter), have also been employed in laboratory settings to simulate water effort and assess keyaking performance throughout the season while avoiding climate-related fluctuations [1]. Despite this, selecting the appropriate evaluation device and protocol remains important. For instance, treadmill incremental tests could not distinguish between elite and non-elite sprint and slalom keyakers in terms of aerobic power (i.e., maximal or peak oxygen uptake: \(\mathrm{VO_{2 max}}\); \(\mathrm{VO_{2 peak}}\)) [2; 3].  
Another issue is the challenge of recruiting volunteers with a high-performance caliber [4]. Indeed, elite keyakers exhibit performance-related data (e.g., bioenergetics, biomechanics, and body composition) considerably superior to those of non-elite keyakers, underscoring the need to distinguish and specifically evaluate this elite subgroup to better understand their unique demands and capabilities. Thus, besides the necessity to assess specific training-induced changes in keyakers, the availability of performance indicators within a cohort of elite athletes is essential, not only for the broader scientific sports community but particularly for those engaged in this branch of canoeing. Therefore, this study aimed to evaluate the physiological, body composition, and performance changes in elite male keyakers, both indoors (laboratory setting) and outdoors (on-field, i.e., keyaking/rowing track) throughout preparatory and transition training periods.
## 2 Equations of Motion
\(\eta_{3}=\frac{8s_{0}}{l_{0}}\cos(\alpha)k_{5}\), \(\eta_{4}=\frac{8s_{0}}{l_{0}}\cos(\alpha)k_{6}\), \(\eta_{5}=\frac{\delta_{1}}{\omega_{\pi}^{2}}\), \(\eta_{6}=\frac{\delta_{2}}{\omega_{\pi}^{2}}\), \(\eta_{7}=\frac{\delta_{1}}{\omega_{\pi}}\), \(\eta_{8}=\frac{\eta_{4}}{\omega_{\pi}}\), \(f=\frac{\varepsilon}{s_{0}}\), \(\Omega=\frac{\
u}{\omega_{n}}\), we have from Equations (1), (2) and (7)-(10) the following dimensionless equations of motion:  
\[ \begin{array}{l}\ddot{y}_{1}+2\mu\dot{y}_{1}+\omega^{2}y_{1}-\left(\alpha_{ 1}y_{1}^{3}+\alpha_{2}y_{1}y_{2}^{2}+\alpha_{3}y_{1}^{2}\dot{y}_{1}+\alpha_{4 }\dot{y}_{1}y_{2}^{2}+\alpha_{5}y_{1}\dot{y}_{2}^{2}+\alpha_{6}y_{1}\dot{y}_{1 }^{2}\right.\\ \left.+\alpha_{7}y_{1}y_{2}\dot{y}_{2}+\beta_{1}y_{1}^{2}y_{3}+\beta_{2}y_{1} y_{3}+\beta_{3}y_{1}y_{3}^{2}+\beta_{4}y_{1}y_{2}y_{4}+\beta_{5}y_{1}y_{2}^{2}+ \beta_{6}y_{1}\dot{y}_{2}y_{4}\right.\\ \left.+\beta_{7}y_{2}^{2}y_{3}+\beta_{8}y_{1}y_{3}y_{5}+\beta_{9}y_{1}y_{1}y_{5 }+\beta_{10}y_{1}y_{4}y_{6}+\beta_{11}y_{1}^{2}y_{5}+\beta_{12}y_{1}^{2}y_{6} ^{2}+\beta_{13}y_{1}y_{2}y_{6}\right.\\ \left.+\beta_{14}y_{1}\dot{y}_{2}y_{6}+\beta_{15}y_{1}y_{5}^{2}+\beta_{16}y_{2} ^{2}y_{5}\right)=f\Omega^{2}\cos(\Omega\tau)+\eta_{1}y_{3}+\eta_{2}y_{5}\\ \ddot{y}_{2}+2\mu\dot{y}_{2}+\omega^{2}y_{2}-(\alpha_{1}y_{2}^{2}+\alpha_{2}y_ {2}y_{1}^{2}+\alpha_{3}y_{2}^{2}y_{2}+\alpha_{4}y_{2}y_{1}^{2}+\alpha_{5}y_{2} y_{1}^{2}+\alpha_{6}y_{2}\dot{y}_{2}^{2}\\ \left.+\alpha_{7}y_{2}y_{1}\dot{y}_{1}+\gamma_{1}y_{2}^{2}y_{4}+\tau_{2}y_{2}y_ {2}y_{4}+\tau_{3}y_{2}y_{4}^{2}+\tau_{4}y_{2}y_{1}y_{3}+\gamma_{5}y_{2}y_{3}^ {2}+\gamma_{6}y_{2}\dot{y}_{1}y_{3}\\ +\tau_{7}y_{4}y_{1}^{2}+\tau_{8}y_{2}y_{4}y_{6}+\tau_{9}y_{2}\dot{y}_{2}y_{6}+ \tau_{10}y_{2}y_{3}y_{5}+\tau_{11}y_{2}^{2}y_{6}+\tau_{12}y_{2}y_{5}^{2}+ \tau_{13}y_{2}y_{1}y_{5}\\ +\tau_{14}y_{2}y_{1}y_{5}+\tau_{15}y_{2}y_{6}^{2}+\tau_{16}y_{1}^{2}y_{6})=f \Omega^{2}\sin(\Omega\tau)+\eta_{3}y_{4}+\eta_{4}y_{6}\\ \ddot{y}_{3}+2\mu\dot{y}_{3}+\omega_{1}^{2}y_{3}=\eta_{5}y_{1}\\ \ddot{y}_{4}+2\mu\dot{y}_{4}+\omega_{2}^{2}y_{4}=\eta_{6}y_{2}\\ \dot{y}_{5}+\rho_{1}y_{5}=\eta_{7}y_{1}\\ \dot{y}_{6}+\rho_{2}y_{6}=\eta_{8}y_{2}\end{array}  \tag{22}\]  
where Equations (21) and (22) represent the equations of motion of the controller rotor system, while Equations (23)-(26) are the equations of the coupled LIPPF-controller. The coefficients of Equations (21)-(26) are given in the Appendix A.
## 3 Results
### Step 2--Habitat Suitability and Connectivity Analysis
#### 3.1.2 Small Blue (SB) Butterfly  
Figure 6 presents the suitable habitats for the SB butterfly, based on two environment variables: slope degree (Maps 1 and 2) and land-cover classes (Map 3). In addition, it overlays the records of SB butterfly from 2015 to 2019 which can indirectly confirm the presence of the Kidney Vetch plant (as this is only source of food for the caterpillars). It can be observed in both approaches (Maps 1 and Map 3) that most of the SB butterfly occurrence has been recorded across areas of chalk grassland (with a high slope degree), heather, and improved grassland.  
In Map 3, the land-cover classes are classified based on our scoring system and the species habitat requirements. Therefore, at this Step 2 level, it is possible to observe several areas of potentially highly suitable habitat (score 4 and 5) that may offer the best opportunities for additional habitat creation for the SB butterfly, e.g., by seeding of Kidney Vetch in autumn or winter. However, further detailed examination of these broad potential habitat locations to find fully optimal sites (e.g., scrub or sparse eroding vegetation with bare ground, calcareous sites such as old quarries, gravel pits, and disused railways) would be advantageous and could be accomplished by site visits or, potentially, via a higher spatial resolution of EO imagery. Likewise, Map 3 illustrates the existence of many scattered areas of suitable habitat which can lead to the isolation of SB populations. Hence, the map from this Step 2 level can be useful for designing the introduction or restoration of connections between colonies (e.g., by even quite small habitat creation interventions) as a vital aspect  
Figure 5: Silver-washed fruitillary butterfly—Step 2 habitat suitability assessment in the SH-AONB and the three intensive study sites (red boxes).  
of the long-term conservation of this species. Due to its good dispersal movement potential (efficient and light wings), maintaining the existing and new habitats and continuity among them depends on the farming activities and agricultural practices such as avoiding overgrazing by the livestock and fertilizer spray drift (see Table S3[98; 102; 103]).
# Automated Pixel-Level Deep Crack Segmentation on Historical Surfaces Using U-Net Models
## 4 Experimental Results
### Results and Discussion
On the other hand, it is noticed that using contrast stretching as a preprocessing step during the testing phase helps in improving the performance of ResU-Net++ when dealing with blurred images, where it increases the Dice score from 37.98% to 61.42%. Although contrast stretching enabled ResU-Net++ to detect a higher percentage of cracks on historical surfaces, it also mistakes shadows for cracks. Conclusively, ResU-Net++, both with and without contrast stretching, lacks the ability to handle images with shadows. The main advantage of using the \(U^{2}\)-Net model for crack segmentation is its capability of detecting tiny cracks and unlabeled cracks, as in Figure 15b,c. Also, the \(U^{2}\)-Net model shows outperformance for detecting cracks on edges, as in Figure 15b and cracks in blurred images, as shown in Figures 12a and 13a,c,d. Moreover, the model is capable of detecting cracks in images with varying lighting conditions that result in shadow, as in Figure 12d. Conversely, the main limitation of crack segmentation based on the \(U^{2}\)-Net model is dealing with deep circle patterns with shadows, as shown in Figure 12b, and patterns with multi-level depth, as in Figure 15b. This is due to the nature of the \(U^{2}\)-Net model for detecting salient objects, as, when moving between two or more different depths, a part of the boundary between depths is mistakenly marked as a crack.
# Design of PAM-8 VLC Transceiver System Employing Neural Network-Based FFE and Post-Equalization
## 3 Analysis of Equalization Methods
### FFE  
An FFE is an equalization technology that is commonly adopted in high-speed optical communication transmitter systems since it is easy to facilitate through digital or analog methods and owns a superior performance at eliminating ISI [31]. To generate the FFE signal, the first step was delaying the original signal with 1 or 0.5 UI to generate the required number of taps. Secondly, these delayed signals were inverted and then amplified with the corresponding tap weights to form the pre- or post-tap signals. Finally, all of these taps were added together with the main tap signal, which was the original one. After combination, the desired FFE signal was generated. The expression of the FFE signal was:  
\[y(n)=\sum_{k=0}^{L}\omega_{k}x(n+k), \tag{9}\]  
where \(\omega_{k}\) is the weight of each tap in the FFE, and \(x(n+k)\) is the signal for each tap. The FFE contributed to removing the ISI and extending the limited bandwidth since the combined waveforms reshaped the rising and falling edges of the main tap signal and attenuated the low-frequency components in the signal, thus both decreasing the low-frequency gain and increasing the high-frequency gain of the signal frequency response.  
The traditional methods of realizing an FFE are either generating modulated signals with an FFE in a digital LUT or combining different taps together through analog circuits. However, these two methods face the problem of low weight precision due to the resolution of DAC or analog circuit mismatch in fabrication. Therefore, a NN-based FFE implementation method was proposed in this work. As shown in Figure 7, this NN-based FFE consisted of two layers of neurons. Input signals with calculated delay times were sent to the input layer towards the corresponding neurons. To train this network, the generated FFE signal was utilized as a training signal, and the weights in this network were adjusted to be the same as the weights of each FFE tap. After the training process, the number of neurons in the input layer with corresponding weights was fixed. There was only one neuron in the output layer to linearly combine the previous data from the input layer and provide a signal modulated with FFE, which was the same as the expression in Equation (9). With this trained network, the test signal could be modulated into an FFE equalization signal. In the proposed VLC transceiver system, a three-tap FFE with one-third UI delay was adopted.  
Figure 6: Schematic of the adopted passive equalizer.
###### Abstract  
This study established a numerical model for a foundation pit at the Zhongyilu Station of the Wuhan Metro Line 12, using Plaxis3D version 2021 finite element software to examine the horizontal displacement of the diaphragm wall, ground surface settlement, and differential settlement between the diaphragm wall and the lattice columns across various construction stages. A comparison with the cut-and-cover method prompted the adoption of a strategy that integrates segmental pouring of the main structure and the installation of internal supports to optimize the original scheme. The results indicated that as the foundation pit was excavated, both the horizontal displacement of diaphragm wall and the ground surface settlement gradually increased, while the differential settlement between the diaphragm wall and the lattice columns shows exhibited an initial decrease followed by an increase. In comparison to the cut-and-cover method, the cover-and-cut method demonstrated greater efficacy in controlling foundation pit deformation and minimizing disturbances to surrounding environment. As the number of segmental pouring layers and support levels increased, the overall deformation of the foundation pit showed a gradual decreasing trend, and the differential settlement between the diaphragm wall and the lattice columns continued to fluctuate. When each floor slab was poured in three layers with two supports placed in the middle, the maximum horizontal displacement of the diaphragm wall could be reduced by 22.47%, and the maximum ground surface settlement could be decreased by 19.01%. The findings in this research can provide valuable basis and reference for the design and construction of similar projects.  
keywords: foundation pit engineering; cover-and-cut method; numerical simulation; deformation optimization analysis +
Footnote †: journal: Applied sciences
# Decision Tree-Based Adaptive Reconfigurable Cache Scheme
## 2 Related Work  
A reconfigurable cache is an optimization route for caches, and the optimization can be conducted from multiple perspectives.  
Regarding the architecture of reconfigurable cache, it can be reconfigured by tuning the cache capacity, number of associative ways, and cache line size utilizing techniques such as way-shutdown, way-concatenation, and line-resizing [5; 6; 7; 8; 9; 10; 11; 12]. [PERSON] et al. [5] proposed a reconfigurable cache with which the cache way can be configured to be instruction dedicated, data dedicated, and used in combinations to optimize system performance. [PERSON] et al. [6] tuned the associativity of the L2 cache dynamically when the miss rate was beyond the system threshold. [PERSON] et al. [7] optimized multi-thread application performance in a multi-core system by tuning cache capacity and cache line size. The authors of [8] concentrated on reducing the vulnerability due to soft errors and saving energy by reconfiguring cache parameters. The authors of [9; 10] adopted cache reconfiguration for performance, energy, and vulnerability tradeoffs in a multi-core system. [PERSON] et al. [11] co-optimized the cache and NoC (network on chip) energy using a machine learning algorithm.  
For cache replacement policies, DLPR [13] improves NUCA system performance by monitoring the dynamic link latency and dynamically adjusting the cache replacement policy. [PERSON] et al. [14] realized adaptive replacement policy and cache partition utilizing a replacement policy adaptable miss curve estimation. ACR policy [15] dynamically tracks the maximum lifetime of each application in the shared last-level-cache (LLC) and adjusts the replacement policy to make full use of the limited cache capacity.  
The cache partitioning technique partitions the shared LLC for each concurrent application. The reconfiguration of the cache partitioning scheme can also improve cache performance. KPart [3] clusters applications by identifying the similarity of the memory access behavior of applications and allocates cache capacity among clusters. Applications within a cluster can share this part of the cache, reducing data conflicts. The LLC-PC controller [16] utilizes the Palloc page coloring framework to partition the cache at runtime by evaluating the performance improvement of increasing the size of the cache partition. Jenga [17] defined cache hierarchies by software. It treats 3D-stacked DRAM and SRAM as a cache resource pool and generates adaptive cache hierarchies for each application.  
Decision trees have low overhead and simple implementations that can be used for adaptive control algorithms. The authors of [18] utilized a decision tree to perform dynamic voltage and frequency scaling on a heterogeneous multicore system to optimize energy and performance. LESSON [19] uses the decision tree model to reduce both the static and dynamic power of NoCs by shutting down network components with low utilization. To the best of our knowledge, there are no existing studies regarding the adaptive reconfigurable cache based on the decision tree.
## 3 Analytical Method for Spatial-Temporal Prediction of the Extreme Pressure
### Spatial-Temporal Distribution of Pressure State Model
#### 3.2.2 Distribution of Pressure State Influenced by Pressure Pulses  
As shown in Figure 5, \(x_{p}\) represents the position where the PP reaches the tunnel at time \(t\). The train nose and its induced PP move from \(t=0\) to \(t=L_{tu}/v_{tr}\) (the moment when train nose reaches the exit). The train-tail-induced PP moves from \(t=L_{tr}/v_{tr}\) (when the tail enters the tunnel, where \(L_{tr}\) is train length) to \(t=(L_{tu}+L_{tr})/v_{tr}\) (when the tail reaches the exit). After the passage of the train nose, the pressure decreases due to the induced PP, \(p_{s}=-1\). After the passage of the train tail, the pressure increases, and the corresponding \(p_{s}\) is 1. Subscripts 1 and 2 correspond to the PP caused by the train nose and tail, respectively.  
The distribution of the pressure state influenced by PP should also be described using a piecewise function. Then, \(f(x)=H\big{(}x_{p}-x\big{)}\) is used, and \(f(x)=\delta\big{(}x-x_{p}\big{)}\) supplements the condition when \(x=x_{p}\). For example, the distribution influenced by nose-induced PP is as follows:  
\[\left\{\begin{array}{l}p_{s_{p_{1}}}(x,t)=-H\big{(}x_{p_{1}}(t)-x\big{)}- \delta\big{(}x-x_{p_{1}}(t)\big{)}\\ x_{p_{1}}(t)=v_{tr}t\quad\quad\left(0\leq t\leq\frac{L_{tu}}{v_{tr}}\right) \end{array}\right. \tag{9}\]  
where \(x_{p_{1}}(t)\) shows the path of the nose-induced PP. \(p_{s_{p_{1}}}(x,t)\) returns \(-1\) when \(x\leq x_{p_{1}}\), indicating a pressure drop after the passage of the train nose.  
Figure 5c shows that the superposition of the two PPs is a relatively low-pressure region around the train and moving with the train speed; it is consistent with the field tests [14] and numerical simulations [45]. Figure 5 shows that for any point in the tunnel, its pressure decreases after the nose passage and increases after the tail passage, which is consistent with the tests [46] and simulations [47].  
Figure 5: Schematic of \(p_{s}\) distribution caused by pressure pulses: (**a**) \(p_{s}\) distribution influenced by nose-induced pressure pulses, (**b**) \(p_{s}\) distribution influenced by tail-induced pressure pulses, (**c**) \(p_{s}\) distribution influenced by 2 pressure pulses (\(n_{w}\): the reflected times of the wave at the tunnel port; \(p_{s}\): pressure state; \(x_{p1}\): the location of pressure pulse induced by train nose; \(x_{p2}\): the location of pressure pulse induced by train tail; \(L_{tu}\): tunnel length; \(v_{tr}\): train speed).