esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
## 4 Hardware/Software Partitioning Based on IBSO Algorithm
### Analysis of BSO Algorithm
#### 4.1.1 Clustering Method  
The original BSO algorithm uses a K-means algorithm as the clustering method and the numbers of individuals in each cluster may be different. When the clustering process is completed, the optimal individual in each cluster will be taken as the new cluster center and it will be utilized to guide the updating of the individuals. But this clustering method may lead individuals to gather in the same area and cause the algorithm to fall into a local optimum. Figure 1 is an example.  
As shown in Figure 1a, it is assumed that all feasible solutions are distributed in three regions: region 1, region 2 and region 3. Suppose there are nine randomly generated individuals, where \(x_{1},x_{2},x_{3}\) are distributed in region 1, \(x_{4},x_{5},x_{6}\) are distributed in region 2, \(x_{7},x_{8},x_{9}\) are distributed in region 3, and \(x_{7},x_{8},x_{9}\) are the three best individuals in the population. When clustering begins, based on the k-means algorithm, the clustering result is shown in Figure 1b where the cluster 1 includes \(x_{1},x_{2},x_{3},x_{7}\), the cluster 2 includes \(x_{4},x_{5},x_{6},x_{8}\) and the cluster 3 includes \(x_{9}\). Since \(x_{7},x_{8},x_{9}\) are the best solutions in their own clusters, they will be set as the centers after clustering. Then the individuals begin to update. According to the updating rules, the search direction of individuals will be guided by the cluster centers. In this case, as shown in Figure 1b, the existing individuals will move towards the position of \(x_{7},x_{8},x_{9}\) which will cause most of the individuals gather to region 3 and the global searching cannot be performed. So the algorithm is easy to fall into local optimum.
## 2 SPH-FEM Coupling Analysis Method
### SPH and FEM Contact Setup  
The connection between particles in the SPH system is generated in the calculation. The N-S equation is the control equation for each particle in the system. The equation calculation process is reflected in the connection between the particles changing with time. The contact between the SPH particles is shown in Figure 2.  
Figure 1: SPH-FEM near-field and far-field simulation.  
In the formula, \(i\) and \(j\) represent the particle number, \(kh_{i}\) and \(kh_{j}\) represent the support domain of \(i\) and \(j\) particles, \(r_{ij}\) represents the distance between \(i\) and \(j\) particles and \(f_{ij}\) represents the contact force between \(i\) and \(j\) particles.  
The key to the coupling of SPH and FEM is that the interface can effectively transfer the information of SPH particles to the finite element mesh. The common method is to make the SPH particles bond with the finite element mesh by setting the interface and realize the displacement coordination of the two methods by means of point-surface bonding contact. Contact parameters are adjusted by setting the keyword *CONTROL_CONTACT. The schematic diagram of the contact is shown in Figure 3.
# Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion
## 2 Methods
### Feature Extraction and Pre-Processing
Then the probability of the observed sequence \(P\big{(}y_{1}^{T}\big{)}\) is calculated as:  
\[P\Big{(}y_{1}^{T}\Big{)}=\sum_{q_{t}}P\Big{(}q_{t},y_{1}^{t},y_{t+1}^{T}\Big{)} =\sum_{q_{t}}\beta_{q_{t}}(t)\alpha_{q_{t}}(t) \tag{16}\]  
The most likely state sequence \(q_{1}^{T}\) corresponding to a given observation sequence \(y_{1}^{T}\) is defined by the probability, \(P\big{(}q_{t}|y_{1}^{T}\big{)}\), which is the product of forward-backward variables and normalized by the joint distribution of the observation sequences as the follows:  
\[P\Big{(}q_{t}\Big{|}y_{1}^{T}\Big{)}=\frac{P\big{(}q_{t},y_{1}^{T}\big{)}}{P \big{(}y_{1}^{T}\big{)}}=\frac{P\big{(}y_{1}^{t}|q_{t}\big{)}P(q_{t})P\big{(} y_{t+1}^{T}|q_{t}\big{)}}{P\big{(}y_{1}^{T}\big{)}}=\frac{P\big{(}y_{1}^{t},q_{t} \big{)}P\big{(}y_{t+1}^{T},|q_{t}\big{)}}{P\big{(}y_{1}^{T}\big{)}} \tag{17}\]  
The most likely state is measured by maximizing \(P\big{(}q_{t}\big{|}y_{1}^{T}\big{)}\) for \(q_{t}\). After the sequence of observations is calculated, the HMM training is performed by applying the Baum-Welch algorithm [44] to calculate the values of the transition matrix \(A\) and the emission matrix \(B\). Following the HMM training, the gesture \(Q\) with best likelihood corresponding to the feature vector sequence \(P\big{(}y_{i}^{T},q_{i}^{T}\big{)}\) is found using the Viterbi algorithm [44].
## 2 Modeling of The Compliant Buoy-Ballast Sensing System  
### Neutral Buoyancy of The Buoy-Ballast System  
Given a neutrally buoyant tether, the static equilibrium of the buoy-ballast system, namely the V-system, depends on the apparent weights of the buoys and the ballast. The ballast is placed between the two identical buoys on the cable (Figure 1). In order to ensure a neutral balance, the apparent weight of the ballast must be compensated by the apparent weights of the buoys:  
\[W_{buoy} = |\rho_{buoy}-\rho_{w}|.V_{buoy}.\mathcal{S}=(\rho_{w}-\rho_{buoy}).V _{buoy}.\mathcal{S} \tag{1}\] \[W_{ballast} = (\rho_{ballast}-\rho_{w}).V_{ballast}.\mathcal{S}=2.W_{buoy} \tag{2}\]  
where \(W_{buoy}\) and \(W_{ballast}\) stand for the gravity forces minus the buoyancy of the buoy and ballast, respectively. \(\rho_{buoy}\), \(\rho_{ballast}\), and \(\rho_{w}\) are the density of the buoy, ballast, and water, respectively. \(V_{buoy}\) and \(V_{ballast}\) designate the volumes of the buoy and the ballast, respectively. \(g\) is the gravity constant.  
The ballast is chosen from among off-the-shelf spherical weights, and the volume of the buoy is adjusted by machining it into a cuboid shape with rounded edges, since it is made of foam (LAST-A-FOAM(r) R-3318), taking into account the following equation obtained from the above equations:  
\[V_{buoy} = \frac{1}{2}\frac{\rho_{ballast}-\rho_{w}}{\rho_{w}-\rho_{buoy}}. V_{ballast} \tag{3}\]
## 5 Analysis of Influence Parameters
### Analysis of the Influence of Upper Soil Thickness \(H\)  
In the calculation of the ultimate bearing capacity of the two-layered foundation, the thickness of upper soil \(H\) has a significant influence on the results. The soil parameters of the two-layered foundation in Example 1 are adopted. The fixed foundation width \(B\) is 2 m, and the thickness \(H\) of the upper layer soil is 1 m, 1.5 m, 2 m, 3 m, and 4 m, respectively. To determine the ultimate bearing capacity of a two-layered foundation, the IRMO algorithm is employed. The comparison of the ultimate bearing capacity of the foundation under different \(H/B\) ratios is shown in Figure 19. And Figure 20 shows the corresponding search result of the critical sliding surface of the two-layered foundation.  
It can be seen that for the two-layered foundation with a hard upper layer and soft lower layer, when the value of \(H/B\) ranges from 0.5 to 1.5, both upper and lower layers of soil participate in the foundation failure, so the ultimate bearing capacity of the foundation increases gradually, and the critical sliding surface of the foundation also gradually expands. When the value of \(H/B\) changes from 1.5 to 2, the ultimate bearing capacity of the foundation almost does not change, and the critical sliding surface is close to coincidence. This is because when the value of \(H/B\) is 1.5, only the upper layer of the foundation soil is involved in the foundation damage, and further increasing the thickness of the upper layer of the foundation will not have a great impact.  
Figure 18: Comparison of ultimate bearing capacity of foundation under different cohesion ratios.  
On the contrary, if the upper and lower soil layers in Example 1 are interchanged, that is, the upper soft and lower hard two-layered foundation is formed. The fixed foundation width \(B\) is 2 m, and the thickness \(H\) of the upper soil is taken as 1 m, 1.5 m, 2 m, 3 m, 4 m, and 5 m, respectively. The ultimate bearing capacity of the foundation under different \(H/B\) ratios is solved by IRMO algorithm, as shown in Figure 21.  
Figure 19: Comparison of ultimate bearing capacity of foundation under different \(H/B\) ratios (the upper soft and lower hard two–layered foundation).  
Figure 20: Search results of the critical sliding surface of the foundation under different \(H/B\) ratios.  
It can be found that the ultimate bearing capacity of the two-layered strip foundation tends to decrease with the increasing \(H/B\) value. After the \(H/B\) value is greater than 2, only the upper soil will participate in the foundation damage, so the variation of the foundation bearing capacity in this example is relatively small. Therefore, in the actual project, the \(H/B\) value can be appropriately increased to improve the ultimate bearing capacity of the two-layered foundation.
## 4 Experiment and Result Analysis
### Comparison of Different Target Detection Models  
To verify the proposed algorithm in blueberry maturity detection, the same dataset and experimental conditions were used to compare it with mainstream models such as YOLOv3-tiny, YOLOv5n, YOLOv6, YOLOv7-tiny, and YOLOv9c. The experimental results are shown in Table 7.  
According to Table 6, the proposed algorithm outperformed other models in multiple key performance indicators, especially in precision, recall, and average precision ([EMAIL_ADDRESS]). Among them, the [EMAIL_ADDRESS] is improved by 13.8%, 5.3%, 7.5%, 2.3%, 6.0%, and 0.5% compared with YOLOv3-tiny, YOLOv5n, YOLOv6n, YOLOv6s, YOLOv7-tiny, and YOLOv9c, respectively. In addition, although the number of parameters and the amount of calculation have increased compared to the older models YOLOv5n and YOLOv6n, the proposed model has excellent performance in terms of performance indicators such as precision, recall, and [EMAIL_ADDRESS]. By improving the performance and resource efficiency of the proposed model, an ideal balance is achieved, which significantly improves the accuracy and reliability of the blueberry maturity detection task.
# Lossless Compression of Sensor Signals Using an Untrained Multi-Channel Recurrent Neural Predictor
## 5 Conclusions  
In this work, we present a context-based lossless compression technique using a novel recurrent neural network architecture, namely the MCRU, which is specially designed for compressing sensor signals. Experiments have proven that the BPC performance of the proposed MCRU on sensor datasets with more complex patterns outperforms classic recurrent units. Furthermore, MCRU is more robust to a sensor dataset with different ADC resolutions due to its flexible predictor resolution setting. Based on MCRU, we propose MCDRC, whose compression ratios on several datasets exceed the current state-of-the-art compressor CMIX. Regarding the running time of compression and decompression, although our work gives priority to the compression ratio, MCDRC achieves an obvious speed advantage over CMIX through a large batch size (256). The proposed MCDRC is five times faster than CMIX and can be further improved.  
Many further ideas may be explored based on our work. For example, the highway operator \(\mathcal{H}\) can be optimized to achieve stronger memory capability for the sensor signals. On the other hand, the channel number of MCRU can be set adaptively according to signal characteristics rather than pre-set to obtain better compression performance.  
Although the proposed method has achieved state-of-the-art performance regarding the compression ratio on signal data, it is more suitable for offline compression due to its relatively slow compression speed. Therefore, another direction of future work can be implementing the proposed approach on hardware for real-time compression with low energy consumption.  
Conceptualization, Q.C. and W.L.; methodology, Q.C. and W.L.; software, Q.C. and W.L.; validation, Q.C. and W.W.; formal analysis, Q.C., W.W. and W.L.; investigation, Q.C.; data curation, Q.C.; writing--original draft preparation, Q.C. and W.L.; writing--review and editing, Q.C., W.W. and W.L.; visualization, Q.C.; supervision, W.L.; project administration, W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Mesh Independence Verification  
Considering the cavitation model's high demands on the mesh number and quality, taking the seven-teeth gear pump as an example, six computational models with different numbers of meshes are generated by adjusting the mesh division scale.  
Figure 4 shows the effect of different mesh numbers on the outlet flow Q. The calculation results fluctuate greatly when the number of cells is less than 1.73 \(\times\) 10\({}^{5}\) (E point). Compared with point E, the average outlet flow calculation error of points A, B, C, and D is 5.18%, 1.92%, 0.87%, and 0.3%, respectively. As the number of cells gradually increases from point E to point G, the calculation results of the outlet flow are relatively stable. Compared with point E, the average outlet flow calculation errors of point F and point G are 0.029% and 0.04%, respectively.  
For calculation accuracy and reduction in computer resource consumption, it is determined that the optimal number of cells for the seven-teeth gear pump calculation model is 1.73 \(\times\) 10\({}^{5}\). As shown in Figure 3, this model is divided into 480 circumferential mesh layers, 15 axial mesh layers, 10 radial mesh layers, and 20 inter-tooth clearance mesh layers. Using a similar meshing strategy, we obtain the meshing numbers of the six-teeth, eight-teeth, and nine-teeth gear pump models, which are 1.72 \(\times\) 10\({}^{5}\), 1.74 \(\times\) 10\({}^{5}\), and 1.94 \(\times\) 10\({}^{5}\), respectively.  
Figure 4: Impact of the number of cells on export flows.
## 3 Methodology  
### Surrogate Safety Measure  
Among all traffic conflict indicators, the time-to-collision (TTC) has been commonly used to estimate individual vehicle collision risk [30; 31; 32]. [PERSON] [33] defined TTC as \"the time that remains until a collision between two vehicles would have occurred if the collision course and speed difference are maintained\". It could both identify the safety conditions and assess the severity of vehicle conflicts [34].  
The TTC of vehicle j at time step t with a leading vehicle i can be expressed as:  
\[\text{TTC}_{\text{j}}(\text{t})=\begin{cases}\frac{\text{X}_{\text{i}}(\text{ t})-\text{X}_{\text{j}}(\text{t})-\text{L}_{\text{i}}}{\text{V}_{\text{i}}(\text{t})- \text{V}_{\text{i}}(\text{t})},\text{if V}_{\text{j}}(\text{t})>\text{V}_{ \text{i}}(\text{t})\\ \infty,\text{if V}_{\text{j}}(\text{t})\leq\text{V}_{\text{i}}(\text{t})\end{cases} \tag{1}\]  
where \(\text{X}(\text{t})\) denotes the vehicle position at time t, V(t) is the vehicle speed at time t, and L\({}_{\text{i}}\) is the length of vehicle i. According to the definition in Equation (1), the larger the TTC is, the more time drivers could use to avoid a crash and thus be safer [35; 36].  
However, in most studies, the calculation of conventional TTC assumes that the two vehicles are in the same lane [37; 38; 39]. This assumption would result in errors in the vehicle collision risk calculation when two vehicles approach each other at other angles. To overcome this limitation, the extended TTC (ETTC) is applied for evaluating the vehicles' collision risk in the diverging area. The ETTC can capture the two-dimensional conflict which is more available for traffic areas without lane markings [10; 40]. The ETTC can be expressed as follows:  
\[\text{ETTC}_{\text{j}}=-\frac{\text{D}_{\text{ij}}-0.5\text{L}_{\text{i}}-0. 5\text{L}_{\text{j}}}{\frac{\text{L}}{\text{d}_{\text{ij}}}\big{(}\mathbf{O }_{\text{i}}-\mathbf{O}_{\text{j}}\big{)}^{\text{T}}\big{(}\mathbf{V}_{\text{ i}}-\mathbf{V}_{\text{j}}\big{)}} \tag{2}\]  
where L\({}_{\text{i}}\) and L\({}_{\text{j}}\) are the length of vehicle i and j, respectively; D\({}_{\text{ij}}\) is the distance between two vehicles' centroids; d\({}_{\text{ij}}\) is the distance between two closest points of vehicles; O and V are two-dimensional coordinates and speed vectors of vehicle's centroid, as shown in Figure 2. More detailed information about ETTC can be referred in our previous studies [7; 10].  
Figure 2: The position of two approaching vehicles in coordinate system.
## 3 Results
### Assessment of Skin Tissue Injury  
The microscopic images, depicting the degree of skin injury caused by various fatty acid soaps, are presented in Figure 3. The microscopic image of the normal skin tissue, without any treatment, is shown in Figure 3a. In normal tissue, the outermost keratin of the stratum corneum (stained with eosin) and healthy nuclei in the epidermal cell layer (stained with hematoxylin) were observed. Cells in the dermis layer were maintained in normal shape, as observed by eosin-stained collagen, and contained normal nuclei. The skin tissue section obtained after water treatment was similar to the untreated sample (Figure 3b). Results of the microscopic examination of the skin samples treated with each C18 fatty acid soap are shown in Figure 3c-g. The tissue morphologies of the skin treated with sodium stearate, sodium isostearate, sodium oleate, and sodium linoleate were similar to those of the untreated skin. However, the tissue sample treated with sodium laurate showed significant damage (Figure 3g). A part of the stratum corneum was separated from the epidermis layer and a gap was formed between the epidermis and the dermis. Chromatin was not observed in the epidermal cells. Chromatin loss was also found deep in the dermis, suggesting that the cell damage extended deep into the skin tissue.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Fatty Acid Soap** & **Transmission Index \(\pm\) SE** \\ \hline Sodium stearate & \(1.10\pm 0.08\) \\ Sodium isostearate & \(1.56\pm 0.10\) \\ Sodium oleate & \(2.92\pm 0.11\) \\ Sodium linoleate & \(3.40\pm 0.07\) \\ Sodium laurate & \(3.99\pm 0.10\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Transmission index (TI) value of each surfactant.  
Figure 3: Micrograph of the skin tissues stimulated with 1% fatty acid soap after the hematoxylin–eosin (HE) staining. (**a**) represents the intact skin tissue, magnified with a 20\(\times\) objective. Sample treated with water for 1 h is represented in (**b**). The skin tissue samples treated for 1 h with 1% (**c**) sodium stearate, (**d**) sodium isostearate, (**e**) sodium oleate, (**f**) sodium linoleate, and (**g**) sodium laurate. (**b–f**) are magnified with a 20\(\times\) objective, and (**g**) with a 10\(\times\) objective.
# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
## 1 Introduction
Therefore, we represent a mobile cellular network in the form of a graph and use graph theory and Social Network Analysis (SNA) techniques to improve QoE and reduce the backhaul traffic load. This study considers social networks aware D2D caching by focusing on influential users, their communities, and content. Initially, we use a preferential attachment model [17] for representing the mobile cellular network. We then perform clustering using a CC-GA algorithm [18], and find influential users using an eigenvector centrality measure [16]. Finally, based on the users' previous history, we proactively cache the content of the influential users with respect to the available cache size. We perform extensive system level experiments to evaluate the gain of the proposed approach. The results reveal that 48% of backhaul traffic load can be reduced (48% satisfied requests) when users generating requests are 100% using the proposed approach. The rest of the paper is organized as follows. Section 2 presents a brief background to caching methodologies in future cellular networks. Section 3 gives the most important related work. Section 4 discusses the methodology of the proposed approach. Section 5 presents the experimental setup and numerical results. Finally, Section 6 summarizes and concludes the paper.
## 5 Validation of Results  
The accuracy of evacuation models depends on empirical data validation to ensure accurate predictions. However, quantitative validations are rare and typically limited to small-scale studies [56]. The validation of this model relied on the total evacuation time. Since the mine layout was simple, the distance of selected miners with the longest distance to the exit, divided by a speed of 1.48 m/s, was used to estimate the best-case scenario evacuation time. The results from these manual calculations were comparable to the results obtained from the minimum-cost network flow algorithm developed by [PERSON] et al. [41]. The average evacuation time obtained from the manual calculation was 2.702 min. After multiple simulation runs, the average evacuation time was 2.7022 min, as shown in Figure 16. The slight variance arose from the dynamics of the evacuation model and the location accuracy of miners in the evacuation model within the AnyLogic simulation platform.
# Out-of-Plane Experimental Study of Strengthening Slender Non-Structural Masonry Walls
###### Abstract  
Non-structural masonry partition walls, which are mainly designed to functionally separate spaces in the buildings and provide physical barriers between rooms, were traditionally built from either solid or hollow clay units or autoclaved aerated concrete blocks. Recent earthquakes have revealed the high vulnerability of these elements, even in the case of low to moderate seismic events. Public buildings (e.g., hospitals and schools) are particularly vulnerable. Due to their greater floor-to-floor heights and the response spectra of floors, the dynamic response of primary structure may provoke significantly higher seismic loads on partition walls. The main goal of the presented experimental study was to investigate the behavior of slender partition walls loaded out-of-plane with a simple and cost-effective approach that may be applied through routine refurbishment works. Eleven full-scale slender non-structural masonry partition walls were built with brickwork and cement-time mortar. Eight of them were additionally strengthened with different techniques, including glass fiber-reinforcing fabric and low-cost glass fiber-rendering mesh. To evaluate the efficiency of the applied strengthening solutions, out-of-plane quasi-static cyclic experiments were conducted. By applying meshes over the entire surfaces, the resistance was significantly improved with the low-cost approach reaching half of the resistance of the commercially available strengthening system preserving the same displacement capacity.  
non-structural masonry partition walls; strengthening: glass fiber; out-of-plane behavior: cyclic loading; seismic performance