esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Water Absorption by Immersion  
The water absorption results for the different mortar formulations incorporating washing fines are presented in Table 5.  
The results indicate an increase in water absorption with a higher incorporation of wash fines. The reference mortar (Ref) showed a water absorption rate of 4.6%. Formulations L10 and L20 showed slightly higher absorption rates of 5.9% and 5.3%, respectively. However, formulation L30, which contained the highest amount of washing fines, showed a significant increase in water absorption to 10.3%. This increase in water absorption with the addition of washing fines can be attributed to several factors. Firstly, higher porosity: as observed in the porosity results, mortars with wash fines have higher porosity. This increased porosity creates more capillary pores capable of absorbing and retaining water, resulting in higher water absorption rates. Secondly, particle compaction density: the less efficient compaction of washing fines compared to cement particles results in a more open microstructure, facilitating water entry because of their irregular shape [29; 36]. Thirdly, surface characteristics: washing fines may have different surface characteristics to cement, possibly with higher surface roughness or specific surface, which enhances water absorption. Finally, hydration products: the incorporation of washing fines can alter the type and quantity of hydration products used. This may be due in part to the fact that washing fines contain far fewer active ingredients than cement, as previous studies on this topic have pointed out [29].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Formulation** & **Porosity [\%]** & **Density [kg/m\({}^{3}\)]** \\ \hline Ref & 19.3 & 2008 \\ L10 & 23.2 & 1937 \\ L20 & 24.1 & 1898 \\ L30 & 25.8 & 1835 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical properties of mortar incorporating washing fine.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Formulation** & **Water Absorption [\%]** \\ \hline Ref & 4.6 \\ L10 & 5.9 \\ L20 & 5.3 \\ L30 & 10.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Water absorption of mortars incorporating washing fines.
# Sound Event Detection Using Derivative Features in Deep Neural Networks
## 2 Feature Extraction  
### Preprocessing  
For the training and testing of the CRNN, the log-mel filterbank (LMFB) was extracted to provide acoustic features to the network, as shown in Figure 1. The audio signal was sampled at 16 kHz, and the short-time Fourier transform (STFT) was computed using a hamming window of length 1024(64 ms) with an overlap of 360 (41.5 ms) [19]. Sixty-four bands of the mel-scale filterbank outputs from 0 to 16 kHz were obtained using the STFT and then were log-transformed to produce the same dimensional LMFB for each 41.5 ms frame. The feature extraction process generated 240 frames with 64 dimensions for the 10 s clips used for training and testing. After the LMFB was computed, it was normalized by subtracting its mean and dividing by its standard deviation over the entire training data. Subsequently, it was used as input to the CRNN.
## 4 Experiments
### Evaluation of Segmentation and Counting Performance  
To prove the satisfactory segmentation performance of the proposed PID-Net for dense tiny object counting, we compare different down-sampling methods to show the advancement of our proposed method. Furthermore, several state-of-the-art approaches are applied for comparative experiments. All of the experimental setting and evaluation indices are same for comparative experiment. Furthermore, the same dataset is applied for all comparative experiments, which is proposed in Section 4.1.1. The models are trained from scratch without pre-training and fine-tuning.
# A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection
## 4 Experiments
### Ablation Studies
#### 4.5.3 Effectiveness of Iterative Strategy  
As shown in Table 3, we directly feed the outputs of MSCSub that operate on the features from backbone to EGA to evaluate the effectiveness of our iterative strategy (#7). Comparatively, the results obtained with the iterative approach (#OUR) show a markedly improvement in performance (\(E_{\phi}\): \(+2.5\%\) on COD10K-Test, \(+1.2\%\) on NC4K; \(F_{\beta}^{av}\): \(+3.1\%\) on COD10K-Test, \(+1.6\%\) on NC4K). This demonstrates that our difference complementary features are refined effectively after a sequential iterative strategy.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**EGA**} & \multirow{2}{*}{**LossNet**} & \multirow{2}{*}{**Iteration**} & \multicolumn{5}{c}{**COD10K-Test**} & \multicolumn{5}{c}{**NC4K**} \\ \cline{5-11}  & & & & \(S_{\mathbf{x}}\uparrow\) & \(E_{\phi}\uparrow\) & \(F_{\beta}^{\mathbf{av}}\uparrow\) & M\(\downarrow\) & \(S_{\mathbf{x}}\uparrow\) & \(E_{\phi}\uparrow\) & \(F_{\beta}^{\mathbf{av}}\uparrow\) & M\(\downarrow\) \\ \hline \#6 & & & ✓ & ✓ & 0.873 & 0.884 & 0.781 & 0.023 & 0.878 & 0.914 & 0.821 & 0.037 \\ \#7 & ✓ & ✓ & & 0.871 & 0.911 & 0.787 & 0.023 & 0.878 & 0.919 & 0.821 & 0.037 \\ \#8 & ✓ & & ✓ & 0.876 & 0.932 & 0.807 & 0.022 & **0.880** & 0.929 & 0.832 & 0.036 \\ \#OUR & ✓ & ✓ & ✓ & **0.879** & **0.938** & **0.812** & **0.021** & **0.880** & **0.931** & **0.837** & **0.034** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation study on the effectiveness of different modules on COD10K and NC4K.  
Figure 6: Visual comparison of the results generated by applying different convolution methods and integration strategies to the features from the backbone. (**a**), (**b**), (**c**), and (**d**), respectively, represent the results of using ordinary convolutions and feeding the adjacent level features as input, using strip convolutions and feeding the adjacent level features as input, using ordinary convolutions and feeding the cross level features as input, and using strip convolutions and feeding the cross level features as input.
## 5 Conclusions  
In this study, we propose a novel TLM that synergizes the TEM and the LSM to enhance the suppression of numerical dispersion across the full wavenumber range. This integrated approach capitalizes on the strengths of the TEM in controlling dispersion at the small wavenumber range, while the LSM effectively addresses the challenges posed at medium and wide wavenumber ranges. To elucidate the benefits of the TLM, we conducted a comprehensive comparison with the traditional TEM and LSM, analyzing numerical simulations and model tests across the full wavenumber range, rather than limiting our study to a specific wavenumber range. Our analysis revealed that the TLM, under unitary conditions (\(L=1\)), exhibited markedly reduced numerical errors compared to its performance under non-unitary conditions (\(L\
eq 1\)), where it demonstrated optimum accuracy throughout the small, medium, and high wavenumber ranges. These findings were consistent across both homogeneous and complex medium models, underscoring the TLM(\(L=1\))'s enhanced capability to minimize numerical artifacts and its superior precision across the entire wavenumber range. In addition, this method can also be applied to high-precision numerical simulations of more complex media and regular grid methods. Furthermore, while this study applies the TLM(\(L=1\)) only to 2D acoustic wave SGFD modeling, the method's potential extends to 3D acoustic wave equations and FDM  
Figure 7: Waveforms recorded at receivers positioned at \(x=5000\) m and \(x=10,000\) m from 2D acoustic SGFD modeling of a heterogeneous model associated with TEM-, LSM-, and TLM(\(L=1\))-based schemes for different values of (\(\mu\), \(\epsilon\)): the small wavenumber range in panel (**a**,**b**), the medium wavenumber range in panels (**c**,**d**), and the large wavenumber range in panels (**e**,**f**).  
calculations of partial differential equations in other research directions, suggesting broad applicability in mathematical and physical research.  
Conceptualization, M.Z. and L.Z.; Methodology, M.Z.; Software, H.W.; Validation, L.Z.; Formal analysis, M.Z. and D.F.; Investigation, M.Z.; Resources, Z.C.; Data curation, D.F.; Writing--original draft, M.Z.; Writing--review & editing, M.Z.; Supervision, L.Z. and H.W.; Funding acquisition, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Institutes of China (Grant number: CKSF2023307/YT, CKSF2023316/YT).  
The data associated with this research are available and can be obtained by contacting the corresponding author.  
The authors declare no conflicts of interest.
# Topology Optimization Based on SA-BESO
## 2 The Joint Topology Optimization Method Based on SA-BESO
### SA-BESO Mathematical Model  
The BESO algorithm is usually divided into a soft killing method and hard killing [28] method [29]. The latter directly removes inefficient elements, while the former introduces artificial material interpolation models with penalty factors such as the SIMP model, to replace the deleted element density values with smaller values. In this paper, the SIMP interpolation model will be used to seek the optimal topological structure that satisfies a certain target volume with the maximum stiffness or minimum compliance. Its mathematical model can be stated as follows:  
\[\begin{array}{l}\text{Find}:\ \ X=\{x_{1},x_{2},\ \ldots,x_{n}\}\\ \text{min}\ \ C=\frac{1}{2}u^{T}Ku\overset{\text{SIMP}}{=}\frac{1}{2}\sum\limits_{ i=1}^{n}x_{i}^{p}u_{i}^{T}k_{0}u_{i}\\ \text{s.t.}\ \ v^{*}-\sum\limits_{i=1}^{N}v_{i}x_{i}=0\\ \ F=KU\\ \ x_{i}=x_{\text{min}}\ or\ 1\end{array} \tag{1}\]  
\(x_{i}\) refers to the first structural element, whose value represents the cell density. \(x_{i}\) is equal to 1, which indicates that the element is a solid element. \(x_{i}\) is equal to \(x_{\text{min}}\), which indicatesthat the element is a solid element. In this article, \(x_{\text{min}}=0.001.C\) is the average compliance value of the structure, \(K\) is the structural stiffness matrix, \(k_{0}\) is the solid element stiffness, \(u_{0}\) is the element displacement vector, and \(n\) is the total number of divided elements.  
In the SA-BESO algorithm, structure element sensitivity represents the degree of contribution to which an element contributes to the structure, which has an essential effect on the addition and deletion of structural elements. The sensitivity \(\alpha_{i}\) of the \(i\) th element is defined as the partial derivative of the objective function with respect to the density of the \(i\) th element. The addition and deletion of structural elements is determined by the magnitude of sensitivity, which is independent of the magnitude of sensitivity. Therefore, sensitivity can be defined as follows:  
\[\alpha_{i}=-\frac{1}{p}\frac{\partial C}{\partial x_{i}}=\frac{1}{2}u_{i}^{T}k _{0}u_{i}, \tag{2}\]  
In order to resolve the checkerboard and grid dependence of the algorithm, the sensitivity values need to be filtered. The stopping of the SA-BESO algorithm should satisfy the volume constraint, the convergence criterion (3) [30] and the minimum temperature condition.  
\[\frac{\sum_{j=1}^{N}\left(C_{k-j+1}-C_{k-N-j+1}\right)}{\sum_{j=1}^{N}C_{k-j+1 }}\leq\tau, \tag{3}\]  
where \(k\) is the current iteration step, which is usually 5, and \(a\) and \(b\) are the structural compliance of the \(k-N-j+1,k-j+1\) iteration steps in the next five iteration steps, respectively. \(N\) is the total number of iterations, \(\tau\) is the allowable error of convergence, and \(\tau=0.001\) in this paper.
## 1 Introduction
Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO) [37], Whale Optimization Algorithm (WOA) [38], Artificial Bee Colony (ABC) [39], Firefly Algorithm (FA) [40], Differential Evolution (DE) [41], Gravitational Search Algorithm (GSA) [42], Bat Algorithm (BA), Pattern Search Algorithm (PSA), and Genetic Algorithm (GA) are examples of heuristic optimization methods that can be employed to tune the PID controller parameters [43; 44]. The PSO technique has been used for tuning the PID controller in several applications [45; 46; 47; 48; 49; 50; 51]. For instance, the PSO technique has been used for tuning the PID controller of an SRG employed in wind energy applications [45]. Using the PSO approach, the optimal firing angle for SRG in the current-controlled mode has been examined, balancing the energy efficiency maximization and torque ripple minimization. The major goal of [46] was to improve the SRG's efficiency, hence, a tracking algorithm was created and integrated with the PID controller. This algorithm aims to follow the SRG's highest efficiency point, regardless of the setpoint or the generator's rotor speed. Combining the PID controller with the MEPT algorithm increases SRG efficiency, making the machine more appealing for wind generation systems. In [49], PSO has been employed for digitally tuning a PID controller for a CAN-based dc motor. In [50], PID controller tuning using PSO has been addressed considering the system robustness. In [51], PSO has been explored for tuning a PID controller using AVR systems. This paper presents two modes of operation, namely Constant Current (CC) and Constant Voltage (CV) modes, for SRG as an ISG in aerospace applications. A Particle Swarm Optimization (PSO)-based tuning approach is employed to optimize the PID controller of an SRG. The proposed controller optimally adjusts both the turn-on and turn-off angles under possible load types (CV and CC loads). The paper structure is as follows. Section 1 addresses the operation and mathematical modeling of a 6/4 SRG employed in this study. Section 2 provides the PSO-based algorithm for fine-tuning the PID. In Section 3, the employed Simulink/MATLAB model with the suggested control system is given. Section 4 provides the proposed controller under different schemes, namely, controlled turn-off angle, controlled turn-on angle, and controlled both turn-on and off angles. Furthermore, the controller generates the needed turn-off angle while the turn-on is fixed and generates the required turn-on angle while the turn-off is fixed. The OPAL-RT platform is used for experimental validation given in Section 5. Finally, the main conclusions are summarized in Section 6.
## 4 Photon Diagnostics Systems
### Online Photon Diagnostic Systems
#### 4.1.1 X-ray Gas Monitors  
The X-ray gas monitors (XGM) are pulse energy (photon number and flux) and position monitors that resolve individual photon pulses at MHz rates (temporal resolution better than 100 ns) [51]. Due to a gain of up to \(10^{6}\), individual X-ray pulses with femtosecond durations containing \(10^{7}\) up to \(10^{15}\) photons can be measured with better than 10% absolute accuracy, and with better than 1% relative (pulse-to-pulse) accuracy for pulses with more than \(10^{10}\) photons. The beam position is monitored in both transverse directions with an accuracy on the order of \(\pm 10\) um within a range of \(\pm 1\) mm. There is an XGM installed in the direct beam of each FEL, upstream of the double mirror systems, monitoring the source properties. Three more XGMs are placed closely upstream of the scientific instruments SPB/SFX, SCS, and HED, to monitor the pulse properties actually delivered to the experiments after passing several X-ray optics elements in the tunnels.
## 3 Materials and Methods
### Machine Learning
#### 3.2.3. LSTM  
LSTM is another type of RNN with the same recurrent model as Figure 2. Figure 4 presents the common structure of an LSTM unit. There are three types of gates in the unit, which are the input gate, forget gate, and output gate. The calculation expressions of the parameters of LSTM are shown in Equations (14)-(19) [53].  
\[f_{t}=\sigma_{f}(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}) \tag{14}\]  
\[i_{t}=\sigma_{g}(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}) \tag{15}\]  
\[o_{t}=\sigma_{g}(W_{o}x_{t}+U_{o}h_{t-1}+b_{o}) \tag{16}\]  
\[\hat{C}_{t}=\phi_{c}(W_{c}x_{t}+U_{c}h_{t-1}+b_{c}) \tag{17}\]  
\[C_{t}=f_{t}\bigodot C_{t-1}+i_{t}\bigodot\hat{C}_{t} \tag{18}\]  
Figure 3: Basic structure of GRU.  
Figure 2: Basic structure of RNN.  
\[h_{t}=o_{t}\bigodot\phi_{h}(C_{t})\,, \tag{19}\]  
where \(x_{t}\) is the input vector, \(f_{t}\) is the forget gate's activation vector, \(i_{t}\) is the update gate's activation vector, \(o_{t}\) is the output gate's activation vector, \(h_{t}\) is the output vector, \(\hat{C}_{t}\) is the cell input activation vector, \(C_{t}\) is the cell state vector, \(W\), \(U\) and \(b\) are parameter matrices and vectors, \(\sigma_{g}\), \(\phi_{c}\) and \(\phi_{h}\) are activation functions.
## 4 Distributed Multi-Hop Localization Based on Regularized Extreme Learning  
The classical large-scale multi-hop localization methods assume that the network is isotropic and evenly distributed, so the hop-counts can correspond to the physical distance. Unfortunately, in practice, networks often present anisotropy. If a network is anisotropic, the hop-counts between nodes may not match the physical distances well. Using fixed coefficients (such as the DV-hop algorithm) to match the hop-counts and the physical distances will produce a greater error. Therefore, this match should be related to the direction and position. In order to solve this problem, we use regularized extreme learning to construct the model between the hop-counts and the physical distances, representing the anisotropy of the network. With the help of regularized extreme learning, the model describes the optimal linear transformation between hops and physical distances. With the help of the proposed algorithm, an unknown node can get a more accurate distance conversion, resulting in better position estimation.  
We aim to design a learning machine which transforms the hop-counts to the physical distances. Therefore, we utilize the supervised information (hop-counts and physical distances between anchors) to train a model, including hop-counts and physical distances of the anchor nodes. Then the learned model can be used to predict the physical distances via given hop-counts (from the unknown node to the anchor node). The pipe line of our algorithm is summarized in Figure 4.  
Figure 4: The framework of proposed algorithm. The mapping is firstly trained by REML, using supervised data consist of the known hop-counts and physical distances. After that, in the testing phase, the physical distances of the unknown node are predicted by the learned mapping.  
The localization of nodes based on ML-RELM method can be divided into three stages: data acquisition, modeling and location estimation. In the data acquisition stage, the program collects the hop-counts and physical distance information between nodes and provides them to the modeling stage. In the modeling stage, the mapping relation between the hop-counts and the physical distances are trained by the hop-counts and physical distances information collected in the data acquisition stage. In the location estimation stage, the distance from the unknown node to the known node is estimated by using the mapping model obtained in the training stage, and the location of the unknown node can be calculated. Details are as follows.
# Void Avoidance Opportunistic Routing Protocol for Underwater Wireless Sensor Networks
## 3 EEDOR Void Avoidance with Hop Count Discovery Mechanism
### Description of the Proposed EEDOR-VA Protocol
#### 3.3.3 Data Packet Forwarding Phase
Rank is the index of the node's ID ordered based on their hop count as a first metric and depth as a second one to break the tie when two or more candidates have the same hop count and depth. The candidate node with the smallest hop count will be on the top of the forwarding set list and have a rank (_Rank_) of 1. This node will start its transmission immediately because its holding time will be 0, while the other candidates will wait for a different period of time before transmitting the packet. Using Rank guarantees that all forwarding set nodes, including those with equal hop counts and depths, have different holding times so that their transmissions will not collide. Using node depth difference, DD, gives a larger holding time to nodes closer to the source, making short hops less likely. As in EEDOR, the most appropriate node starts its transmission while the other nodes will suppress their transmission while their holding time is not expired. They will drop the packet if they hear the best forwarder node transmission.
## 4 Experimental Setup
### Experimental Tests and Operating Conditions  
During the experimental tests, the load point of the stack and the air mass flow, which is controlled according to the electric current and to a constant air excess ratio of 1.5, were varied. In order to obtain measurement data under different operating states as well as operating conditions of the FC system, the load point was adjusted arbitrarily either by setting the stack voltage or the stack current level in a range, in which a stable operation of the FC is still guaranteed. All other operating parameters of the FC system (target temperatures, anode pressure setpoint, hydrogen recirculation pump speed, coolant pump speed) were kept constant under all load and operating conditions. The standard values of the constant operating parameters can be seen in Table 1. For more details regarding the experimental tests, please refer to Section 5, in which simulated and measured data are presented and discussed.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Operating Parameter** & **Value** \\ \hline Standard stack voltage range & 60–120 VDC \\ Continuous stack current & 120–400 A \\ Air compressor pressure ratio at 400 A & 1.64 (closed throttle valve) \\ Standard excess air ratio (\(\lambda_{\text{Air}}\)) & 1.5 \\ Air inlet temperature at cathode & 40 \({}^{\circ}\)C \\ Anode pressure & 1700 mbar \\ H2 pump speed & 4000 RPM \\ Stack coolant inlet temperature & 55 \({}^{\circ}\)C \\ Ambient temperature & 23 \({}^{\circ}\)C \\ Ambient pressure & 1000 mbar \\ Relative humidity of ambient air & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Constant operating parameters and standard values of the FC system