esa-sceva/satcom-chunk-collection huggingface dataset:
# An Information Theoretic Approach to Privacy-Preserving Interpretable and Transferable Learning
### Methodology
#### 1.4.3 Variational Approximation of Information Theoretic Measures  
The variational membership-mapping Bayesian models are used to determine the lower bounds on the defined information theoretic measures for privacy leakage, interpretability, and transferability. The lower bounds are then maximized using variational optimization methodology to derive analytically the expressions that approximate the privacy leakage, interpretability, and transferability measures. The analytically derived expressions form the basis of an algorithm that practically computes the measures using available data samples, where expectations over unknown distributions are approximated by sample averages.
# Selection Criterion of Reanalysis Methods for Plane Truss Optimization
## 4 Construction of Selection Criterion  
### Method Comparison Study  
Based on the information from Table 2, the flop ratio (\(Fr\)) and Ms ratio (\(Mr\)) were defined to compare the computational efficiency of different methods, as shown in Equations (16) and (17). The numerators of the two indicators represent the flops and MS values of the first reanalysis method, while the denominators represent these of the second method.  
\[Fr=\frac{flop_{1}}{flop_{2}}, \tag{16}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Flops** & **MS** \\ \hline CA & \(2s^{3}/3+2s^{2}+12 nsb+8 ns+2 ns^{2}-4 nb-2n\) & \(s^{2}+(n+2)s+(3b+5)n\) \\ ICA & \(2 nb+n+\left[2s^{3}/3+2s^{2}+12 nsb+8 ns+2 ns^{2}-4 nb-2n\right]k\) & \((3b+5)n+\left(s^{2}+ns+2s\right)k\) \\ PCG & \(\left[8 nb+14n+3\right]k+2n\) & \(2 nb+9n\) \\ Epsilon & \(8 nsb+11 ns/2+5 ns^{2}/2-4 nb-2n\) & \(\left[s^{2}/2+5s/(2+2b+3)\right]n\) \\ MPCG & \(\left(4 mb+4 nb+14m+2n\right)k\) & \(\left(m+n\right)b+n+9m\) \\ Guyan + CA & \(n^{2}+4 nt^{2}+2n^{2}t+7t^{2}/3+4 nt-n+t+2s^{3}/3+2s^{2}+t^{2}+2 ns^{2}+4 nb\) & \(t^{2}+s^{2}+3 bn+2 nm+2n+2m+s\) \\ Guyan + Epsilon & \(n^{2}+4 nt^{2}+2n^{2}t+7t^{2}/3+4 nt-n+t+11 ns/2+5 ns^{2}/2+\) & \(ns^{2}/2+5 ns/2+2 nb+2 nm+3 tn+m\) \\ Guyan + PCG & \(4 tb+2t+b^{2}+b+2n+(2+8 nb+14n+4 tb+2t)k\) & \(t^{2}+2 nb+2 nm+8n+2n\) \\ Cholesky & \(nb^{2}+7 nb+2n\) & \(2 nb+4n\) \\ \hline \hline \end{tabular} \(k\): number of iterations; \(n\): number of DOFs of \(K_{0}\); \(m\): number of DOFs of \(K_{i}\) \(t=m-n\).  
\end{table}
Table 2: Summary of index results.  
\[Mr=\frac{MS_{1}}{MS_{2}}. \tag{17}\]  
If the \(Fr\) value is greater than 1, it indicates that the flops of the first method are higher than that of the second, and it can be considered that the second method has relatively higher computational efficiency. Similarly, if the \(Mr\) value is greater than 1, then the second method has a smaller storage space and shorter read and write time, which contributes to the improvement of the method's computational efficiency to a certain extent. The impact of the \(Mr\) value is limited and is only used as an auxiliary judgment factor.
# DLMFCOS: Efficient Dual-Path Lightweight Module for Fully Convolutional Object Detection
## 3 Proposed Method
### Proposed Feature Pyramid Structure
#### 3.1.1 Dual Path Lightweight Module  
A CNN extracts features and classifies objects using various convolution techniques. In other words, this is to extract foreground information and background information of the input image and use them as feature information. Therefore, kernels of various sizes are billed to convolutions that are needed for accurate object detection. However, this method's computational cost increases. Therefore, in this paper, we proposed the dual-path lightweight module (DLM) that minimizes feature loss with a few computations. Figure 5 illustrates its structure. First, the input feature map is compressed to half the size of the input channel using convolution to minimize the increased computation. Second, the LSM  
Figure 4: Proposed dual-path lightweight module (DLM) feature pyramid structure. Conv: convolution.  
Figure 3: Proposed method architecture. The proposed method used ResNet-50 as the backbone input to the DLM-reconstructed FPN, which is designed to minimize feature map loss. Subsequently, to minimize the overall computational burden, the feature map extracted from the backbone is adjusted to 192 channels using a \(1\ \times\ 1\) convolution. DLM: dual-path lightweight module.  
consists of a dilated depth-wise convolution that efficiently extracts spatial information by taking a wide receptive field with a few computational costs. Simultaneously, the channel information of the input feature map is extracted by the CA. The dual lightweight module combines features by concatenating the spatial and channel information to minimize feature loss. Third, the combined feature maps are compressed with a \(3\ \times\ 3\) convolution and the feature loss is minimized by concatenating the initially compressed features. Finally, the feature maps are extracted by a convolution to minimize feature loss. Equation (1) shows the proposed dual-path lightweight module.  
\[DLM(f)=\omega_{3}(\ \omega_{1}(f)\bigotimes\omega_{1}((LSM(\omega_{1}(f)) \bigotimes CA(\omega_{1}(f)). \tag{1}\]  
where \(\omega\) refers to the convolution, LSM is the lightweight spatial modulus, CA is the channel attention, and \(\bigotimes\) implies concatenation.
# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 4 Methodology
### Blockchain Model  
We considered the blockchain as a solution for storing the collected data because of its transparency, security and accessibility. To demonstrate these properties, we look deeper at how this architecture works, what the key components are and how trust between peers is guaranteed. It is known that the blockchain architecture brings multiple benefits, but it must be noted whether its integration into the proposed solution improves performance or not.  
Hyperledger is one of the newest solutions in the blockchain world and came with a new vision in the way of developing blockchain solutions [114]. Each Hyperledger project/solution respects a predefined architecture and is composed by the following components: Consensus Layer, Smart Contract Layer, Communication Layer, Data Store Abstraction, Crypto Abstraction, Identity Services, Policy Services, APIs and Interoperation [115].  
To reach consensus, Hyperledger Fabric uses the Raft algorithm [116] as shown in Figure 7. It resembles a voting system, in which the nodes play various roles (followers, candidates, leaders), depending on their state. Specifically, each node plays the role of the candidate and tries to convince the other members to vote for him. He becomes a leader if more than half of the voters/nodes support him. Therefore, any node in the algorithm can be a candidate and can send requests to other followers. Moreover, if more than half of the followers send an answer, then the elections are successful, and the candidate with the most votes becomes the leader.  
In addition, a log containing the entire election history is copied to the followers. If the new leader goes down, a new leader will be chosen, according to the steps saved in the log.  
Another feature of the nodes is the measurement of the heartbeat through a timer. Normally, leaders have priority in measuring their heartbeat. If they no longer respond properly, then the leader is considered lost because he is no longer able to provide services. Therefore, he becomes a candidate and participates in the election of a new leader for a new term.  
The advantage of using the Raft algorithm is the possibility of losing nodes, including leader nodes, without affecting the system as long as there is a majority of nodes that can take command. Therefore, it can be stated that the Raft algorithm is crash-fault-tolerant (CFT). For example, if a network contains three nodes, then one node can be lost, leaving another two nodes functional; if the network has five nodes, then two nodes can be lost and the other three can function without problems. Therefore, as there are more nodes in the network, the number of lost nodes does not influence the proper functioning of the system. In other words, this feature offered by Raft ensures the availability of services and offers a high degree of security [117].
## 4 Materials and Methods  
### Experimental Design and Sample Processing  
Experiments were performed in 11-year-old \"Arbequina\" olive trees located at Carmona (37.49\({}^{\circ}\) N, \(-\)5.67\({}^{\circ}\) W, Seville, Spain). The orchard has a super-high density (4.0 m \(\times\) 1.5 m), is 360 m\({}^{2}\), and has 60 trees organized in 3 lines (30 m). The design was done with randomized blocks with 4 repetitions per treatment. Harvesting was done with a mechanical harvester, like at super-intensive farming. The trees from the inside row (20) of each orchard were harvested for olive oil production. Harvest was carried out when olives had 1.9 maturity index [37]. Each block was collected in one day, and the average yield was 7117 kg ha\({}^{-1}\). Afterward, olive oil was elaborated in an olive mill model Frantoino Bio (Toscana Enologica Mori, Florence, Italy) at 40-50 kg h\({}^{-1}\), with oil extraction 2 phases technique. Each sample milled was 100 kg of olives per plot (4 per irrigation treatment). Firstly, the olives were cleaned and washed, then they were transferred to the milling, which was held in a mill mixer (<28 \({}^{\circ}\)C, 20 min), with 1% (_w:w_) talc and 2% (_w:w_) water, for the extraction of water flow meter 5 L h\({}^{-1}\).  
Stem water potential at midday (\(\psi\)) was determined using a pressure chamber (PMS Instrument Company, Albany, OR, USA) in 4 trees per irrigation treatment, weekly during the experiment (March 24 to October 20, 2017). Water stress integral (SI) was calculated (Equation (1)) [38] to describe the accumulative effect of deficit irrigation strategies, from the beginning of pit hardening (9 June 2017) to harvest (30 October 2017) (143 days):  
\[\mathrm{SI}=|\Sigma(\psi-(-0.2))\times\mathrm{n}| \tag{1}\]  
where SI is the stress integral, \(\psi\) is the average midday stem water potential for any interval, n is the number of the days in the interval.  
Table 1 shows the average of minimum stem water potential (min \(\psi_{\mathrm{stem}}\)) and SI values, besides the applied water in each treatment, yield and oil yield.  
Following the pressure chamber technique and the threshold values of midday stem water potential before and after the pit hardening period, 4 irrigation treatments were carried out:  
* Control (T0): trees were watered to supply the 100% crop evapotranspiration (ETc).
* Optimal RDI (T1): trees were under non-limited water conditions during stage I and III while regulated deficit irrigation was applied during stage II (58% of reduction of total water irrigation amount).
* Confederation RDI (T2): the same way was followed as in T1 but with the limitation of water dotation of Guadalquivir hydrographic confederation (66% of reduction of total water irrigation amount).
* Confederation SDI (T3): sustained deficit irrigation with the water amount allowed by the Guadalquivir hydrographic confederation (66% of reduction of total water irrigation amount).
# Numerical Investigation of the Collapse of a Steel Truss Roof and a Probable Reason of Failure
## 2 Theory and Case Study
### Structural Members  
Members with steel tube sections of different diameters and thicknesses are used in the truss roof system. The pipe elements with screwed cone ends are connected to the hot forged steel spherical joints. These joints ensure that no eccentricity occurs and the only axial forces are developed in the bars of the truss system. The yield strength of the material (S235 JR) of the bars and the cone parts at the ends of them (DIN 2458) is 235 MPa, ultimate tensile strength is 510 MPa, and the allowable stress is 144 MPa. The bolts used for mounting the bars to the spherical joints are made of 10.9 material quality. The tensile strength of the bolts is 1000 MPa, yield strength 900 MPa, and the allowable strength is 360 MPa, in which the safety factor is 2.5. The spherical joints used to connect the rods to each other and the supports are made of hot forged steel with a yield stress of 330 MPa and a tensile strength of 590 MPa. The strength values and the chemical decompositions of the materials used in the truss roof system were validated by [PERSON] et al. [8].  
The support spheres are fixed onto the concrete columns by using square or circular plates bolted to the columns. The supports are made of EN C45 steel, and Teflon plates are placed under the sliding supports to reduce friction. The details of the joints and supports are given in Figure 5.  
Figure 4: Damage to the roof’s space truss system.
# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 2 The Proposed Methodology
### Dynamic Convolution Module  
We select a sequence of multiple frames of echo data \(D\in R^{rec}\) as the input data for the network model, where \(r\) represents the number of consecutive echo samples and \(c\) is the number of sampling points in a single echo. In this paper, both \(r\) and \(c\) are set to 224. We employ a dynamic convolution module to perform adaptive feature encoding on the input data \(D\). Depending on the input, different attention mechanisms generate varying filter weights within the same network layer, calculated as follows:  
\[\begin{split} P&=AvgPool(D)\\ C&=Conv2(U(Conv1(P)))\end{split} \tag{1}\]  
where \(AvgPool\) represents the average pooling operation, \(Conv1\) and \(Conv2\) represent the convolution operations, and \(U\) represents the Rectified Linear Unit (ReLU) activation function, defined as \(ReLU=max(0,x)\). Subsequently, Softmax is applied to \(C\) for normalization, yielding the filter weight coefficients \(w_{1},w_{2},\cdots,w_{k}\), calculated as follows:  
\[\begin{split} w_{1},w_{2},\cdots,w_{k}=softmax(C)\\ W_{c}&=\sum_{i=1}^{k}w_{i}W_{i}\quad b_{c}=\sum_{i =1}^{k}w_{i}b_{i}\end{split} \tag{2}\]  
where \(W_{i}\) and \(b_{i}\) represent the learnable parameters in the convolution kernels, \(W_{c}\) and \(b_{c}\) are the parameters after weighted fusion, and \(k\) is the number of convolution kernels. We  
Figure 4: Two comparison examples before and after augmentation based on radar echo data. (**a**) Sample 1—Unfilled. (**b**) Sample 1—Filled. (**c**) Sample 2—Unfilled. (**d**) Sample 2—Filled.  
define the above operations as \(DyConv\). Next, we use the weighted fusion filter for feature encoding, calculated as follows:  
\[C^{\prime}=MaxPool(U(F_{norm}(DyConv(D)))) \tag{3}\]  
where \(MaxPool\) represents the max pooling operation and \(F_{norm}\) represents the batch normalization operation.
## 2 Materials and Methods
### Lipid Extraction and Lipidomics Analysis Using MALDI-TOF Ms  
Total lipids were extracted following a slightly modified version of the Folch method [32], Briefly, _P. citri_ was homogenized using a pencil-type homogenizer with 500 \(\upmu\)L methanol-chloroform (1:2, _v/v_) solution. The homogenate was shaken at room temperature for 20 min and centrifuged at 2000 rpm for 10 min. The supernatant was transferred; then, 200 \(\upmu\)L of 0.9% sodium chloride was added, and the mixture was vortexed for 10 s. The mixture was incubated for 5 min and centrifuged at 2000 rpm for 10 min. The lower chloroform phase containing lipids was used for lipidomic analysis using a matrix-assisted laser desorption ionization time-of-flight mass spectrometer (MALDI-TOF MS; Voyager-DETM STR Workstation, Applied Biosystems, Foster City, CA). The results were statistically analyzed with the Kruskal-Wallis test and the cut-off value set as _p_-value < 0.001 using IBM SPSS Statistics for Windows version 23.0 (IBM Corp.). A heatmap was constructed using the Log2 fold change between the control and treated groups. The mass results of lipids were matched using negative ion mode ([M-H]\({}^{\prime}\)) in the LIPID MAPS (accessed on 18 July 2021 at www.lipidmaps.org). The whole scheme of the methodology used in this study is summarized in Figure 1.
# Research on Robust Day-Ahead Dispatch Considering Primary Frequency Response of Wind Turbine
## 2 Frequency Analysis of Wind Integrated Power System
In this case, the combined system of the wind turbine and the energy storage device could provide an inertial response, and the specific inertial response time constant could be worked out from the dynamic characteristics of the system. The VSG-based wind power units share a similar operating mechanism and external characteristics to the conventional synchronous generators, which is of great significance to the frequency support in the synchronous grid. Taking the primary frequency response as an example, the VSG of the wind turbine can provide a primary reserve through the control of the pitch angle [7]. In a normal operation, the pitch angle is controlled at 0 to ensure the maximum power, and the pitch angle for the primary reserve will be above 0. When the system frequency changes, the VSG adjusts the pitch angle to control the storage or release of the reserve, providing power support. Therefore, in a short time, Equation (2) can be formulated as:  
\[H_{s}=\frac{\sum\limits_{i=1}^{m+n}H_{i}S_{i}}{S_{N,s}} \tag{3}\]  
where \(H_{i}\) and \(S_{i}\) represent the inertia time constant and capacity of unit \(i\), including the synchronous generators and wind power units participating in the PFR.
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 6 Typical Fault Diagnosis of EHA
### The Results of MSPSO Optimizes FCNN  
The standard PSO algorithm and the MSPSO algorithm proposed in this paper are used to optimize the FCNN. The number of populations is set to 30, and the number of iterations is set to 50 in the experiments. We take the fault diagnosis accuracy of EHA as the fitness of particles. From Figure 14, we can see that the PSO and MSPSO fitness values increase as the number of iterations increases. The PSO converges at the 15 th iteration with a global optimal fitness value of 0.8948. The MSPSO converges at the 40 th iteration with a global optimal fitness value of 0.9756. It is not difficult to find that PSO obtains better fitness values than MSPSO when initializing the population due to the randomness of initialization. However, the standard PSO lacks the mechanism to jump from the local optimum and falls into the local optimum at the beginning of the iteration. Although the MSPSO algorithm also has stalled phases, it effectively escapes from the local optimal and achieves better results.  
Figure 14: Fitness curve of PSO & MSPSO.  
After the optimization of MSPSO, the optimal combination of FCNN hyperparameters are shown in Table 3.  
After optimization, the model parameters of 1 DCNN and 2 DCNN in the FCNN network are shown in Tables 4 and 5. 1 DCNN has two convolutional layers, the first convolutional layer has 18 convolutional kernels and the second convolutional layer has 26 convolutional kernels; Moreover, the three convolutional layers of 2 DCNN have 22, 28, and 40 convolutional kernels, respectively. Two sets of 1D feature vectors from 1 DCNN and 2 DCNN are stitched into a 5574 \(\times\) 1 1D vector in the convergence layer, and a 482 \(\times\) 1 feature vector is obtained after the Dropout layer and fully connected layer; finally, the Softmax layer classifies these feature vectors.
# The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
## 4 Discussion  
### How Pitch Errors Reshape Transmission-Error Behaviour  
The results confirm that indexing deviation is the key low-order exciter, even at modest magnitudes (Table 3). Activating the calibrated pitch table increased the mean TE  
Figure 3: FFT of the TE signals. A shaft order peak appears at 16.66 Hz only when pitch error is active; side-bands flank the mesh frequency pair at 383.3 Hz.  
by approximately 80% and introduced a clear shaft order tone at 16.66 Hz, as shown in Figure 2. Earlier spur gear studies reported comparable effects at 25-30 Hz for 1500 rpm drives [5], indicating that the mechanism scales predictably with speed. Adding measured microgeometry did not cancel the shaft order tone; instead, it deepened the negative TE offset and doubled peak-to-peak excursion. This non-linear coupling supports the analytical work of [PERSON], [PERSON], and [PERSON] (2024), who predicted that long-wave pitch patterns modulate mesh stiffness in a manner amplified by flank form irregularities [17].  
The doubling of peak-to-peak TE observed when pitch error and measured microgeometry act together can be explained by a phase-addition mechanism. Pitch error adds a quasi-static circumferential phase error to each tooth pair; under constant speed, this appears as a periodic envelope with the shaft order tone. Measured microgeometry, on the other hand, introduces local stiffness fluctuations and micro-peak contact shifts that modulate the time-varying mesh stiffness. When the two error families are super-posed, the phase error aligns the micro-stiffness peaks on certain teeth, raising the effective mesh stiffness gradient. Thin-slice theory predicts that TE amplitude is proportional to this gradient; thus, the constructive overlap explains the non-linear amplification in Figure 2. A similar reinforcement was predicted analytically by Velex's phase-modulated model and experimentally observed by [PERSON] et al. for hybrid-spur gears, but it had not yet been demonstrated for helical pairs with a fully measured surface scan. Our results, therefore, confirm that indexing tolerance and microgeometry optimization cannot be decoupled; each influences the other through stiffness-phase interaction, especially in the low-load regime relevant to e-powertrains [7].
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.4.2 Minimum Moving Distance  
In the process of redeploying nodes, we choose to schedule the nearest mobile node to repair the coverage holes, thereby minimizing the total moving distance. Its objective function is as follows:  
\[f_{4}=\min(D_{T}) \tag{15}\]  
\[s.t.:D_{T}=\sum_{i=1}^{k}d_{i} \tag{16}\]  
\[g(m_{i})=\left\{\begin{array}{l}0,d_{i}=0\\ 1,otherwise\end{array}\right. \tag{17}\]  
\[\sum_{i=1}^{k}g(m_{i})=n_{m},i=1,2,3,\ldots k \tag{18}\]  
\[E_{R}^{i}=e_{m}\times d_{i} \tag{19}\]  
\[E_{R}^{i}\geq 0.5E \tag{20}\]  
The constraints of the objective function are shown in Formulas (16) to (20). The mobile nodes are restricted and labeled using Formula (17). In addition, an energy threshold is set to ensure energy balance in the network.  
In the above equations, \(m_{i}\) represents the mobile node, \(d_{i}\) represents the moving distance of node \(m_{i}\), and \(D_{T}\) represents the total moving distance by all nodes. \(g(m_{i})\) is the marking function. When \(m_{i}\) moves, \(g(m_{i})=1\); otherwise, \(g(m_{i})=0\). \(m_{m}\) represents the total number of mobile nodes, while \(E_{R}^{i}\) represents the remaining energy after moving.