esa-sceva/satcom-chunk-collection huggingface dataset:
# Airfoil Optimization Design Based on the Pivot Element Weighting Iterative Method
## 3 Aerodynamic Optimization Design of Airfoil
### Optimization Process  
The design process is represented in Figure 17. It is divided into 4 parts, namely initialization and normalization of the design parameters, DOE, RBF approximation model and CFD simulation of airfoil, and GA optimization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Number of generations & 25 \\ Number of sub-populations & 10 \\ Number of individuals on each sub-population & 20 \\ Interval generations of migration & 4 \\ Crossover rate & 0.9 \\ Mutation rate & 0.01 \\ Migration rate & 0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of MIGA.  
As we all know, the stratosphere low speed aircrafts include the stratosphere airship and long-endurance solar unmanned aerial vehicle. At the height of 20 km, the air density is 0.088 kg/m\({}^{3}\), the viscosity is 1.4216 \(\times\) 10\({}^{-5}\) kg/(m\(\cdot\)s). The speed of these aircrafts ranges from 20 m/s to 80 m/s and the range of Reynolds number fall between 10\({}^{5}\) and 10\({}^{6}\). The S1223 airfoil is the laminar and high lift airfoil, which has the good aerodynamic performance under the condition of the low Reynolds number [30; 31]. So it can be used in the design of the high altitude propeller and the solar wing. The aerodynamic characteristics of the S1223 airfoil are analyzed in this paper. The optimization goal is to raise the lift-drag ratio of S1223. The aerodynamic constraints are the lift coefficient and drag coefficient. The geometric constraint is the area of the S1223 airfoil. The design conditions are that \(Ma=0.1\), \(\alpha=4^{\circ}\), \(Re=1.86\)\(\times\) 10\({}^{5}\). The objective function is  
\[\begin{array}{llll}\max&K=C_{L}/C_{D}\\ s.t.&Ma=0.1\quad Re\ =1.86\times 10^{5}\quad\ \ \alpha=4^{\circ}\\ &C_{L}\geq 0.95C_{L0}\\ &C_{D}\leq C_{D0}\\ &S\geq 0.95S_{0}\end{array}\]  
where \(C_{L0}\) is the lift coefficient of the intial airfoil, \(C_{D0}\) is the drag coefficient of the intial airfoil, \(K\) is the lift-drag ratio and \(S_{0}\) is the initial area.
## 3 Methodology
### Lightweight CNN Models for Machining Feature Recognition
#### 3.2.2 MsvNetLite  
Another well-known machining feature recognizer is MsvNet [20], which is a multi-view based approach. Multi-view is a method to represent a 3D object through multiple 2D images from different viewpoints. Similar to FeatureNet, MsvNet also has a large number of parameters (128.86 M) and FLOPs (7.46 G). Therefore, we present a lightweight CNN called MsvNetLite, whose architecture is similar to MsvNet. MsvNetLite can be  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operator** & **SE** & **Stride** & **Input (\(C\times H\times W\times D\))** & **Output (\(C\times H\times W\times D\))** \\ \hline Stem Conv3D, k = 3 & - & 2 & \(1\times 64^{3}\) & \(16\times 32^{3}\) \\ DSConv3D, k = 3 & - & 1 & \(16\times 32^{3}\) & \(32\times 32^{3}\) \\ DSConv3D, k = 3 & - & 2 & \(32\times 32^{3}\) & \(64\times 16^{3}\) \\ DSConv3D, k = 3 & - & 1 & \(64\times 16^{3}\) & \(64\times 16^{3}\) \\ DSConv3D, k = 3 & - & 2 & \(64\times 16^{3}\) & \(128\times 8^{3}\) \\ DSConv3D, k = 3 & - & 1 & \(128\times 8^{3}\) & \(128\times 8^{3}\) \\ DSConv3D, k = 3 & - & 2 & \(128\times 8^{3}\) & \(256\times 4^{3}\) \\ \(5\times\) DSConv3D, k = 5 & - & 1 & \(256\times 4^{3}\) & \(256\times 4^{3}\) \\ DSConv3D, k = 5 & ✓ & 2 & \(256\times 4^{3}\) & \(512\times 2^{3}\) \\ DSConv3D, k = 5 & ✓ & 1 & \(512\times 2^{3}\) & \(512\times 2^{3}\) \\ GAP3D & - & - & \(512\times 2^{3}\) & \(512\times 1^{3}\) \\ Conv3D, k = 1 & - & 1 & \(512\times 1^{3}\) & \(1280\times 1^{3}\) \\ Flatten & - & - & \(1280\times 1^{3}\) & \(1280\) \\ Linear & - & - & \(1280\times 1^{3}\) & \(1280\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration details of FeatureNetLite; \(k=3\) denotes that convolution kernel size is set to 3, and \(SE\) denotes whether to use the Squeeze-and-Excite module. And if \(SE\) is set to ✓, it means Squeeze-and-Excite module is enabled.  
considered a lightweight and modernized version of MsvNet (only 1.70 M parameters and 0.16 G FLOPs). Compared to MsvNet, the most significant change in MsvNetLite is the replacement of the 2D CNN backbone network from VGGNet-11 [55] to PP-LCNet [50]. Furthermore, a smaller MLP takes the place of the original large MLP as the final classifier. After the aforementioned adjustments, the recognition performance also shows strong competitiveness while using fewer parameters and FLOPs. As shown in Figure 7, this network takes multiple sectional views (MSV) representation as input. In MSV, many 2D images are taken from a variety of viewpoints to represent a 3D voxel model. The detailed configuration of MsvNetLite is shown in Table 2. The Stem Conv2D block contains Conv2D, BatchNorm2D, and HardSwish layers. DSConv2D denotes a depth-wise separable convolution block [50]. The view pooling layer uses an element-wise max operator to combine all embeddings into one embedding [56]. The dimension order of convolution output is _view \(\times\) channel \(\times\) height \(\times\) width (\(V\times C\times H\times W\))_.
## 3 Learned Semantic Index Structure
### SPARQL Join Query Algorithms for Learned Semantic Index
In line 2, we judge whether a query (e.g., a complex query type Q = (?s, p\({}_{1}\), o\({}_{1}\)) \(\bowtie\) (?s, p\({}_{2}\), o\({}_{2}\)) \(\bowtie\) (?s, p\({}_{3}\),?o) \(\bowtie\) (s\({}_{4}\), p\({}_{4}\),?o)) is one of the possible SPARQL triple patterns. In line 4, the nearest points are calculated from the NN algorithm between each subquery pattern and centroids in \(k\)-d tree, if the Euclidean distance between them is shortest, the corresponding R*-tree will be searched by the mapping list. In line 6, each subquery's search results will be added to the result list. From line 10, a hybrid join search algorithm is processed. The hybrid join search algorithm consists of two parts: result segmentation and join operation. If there are two or more search results in the ResultList, the complex query statement will be split and combined into multiple result pairs. For instance, the complex query type Q can be split and combined into two result pairs, i.e., star((?s, p\({}_{1}\), o\({}_{1}\)) \(\bowtie\) (?s, p\({}_{2}\), o\({}_{2}\))) and chain((?s, p\({}_{3}\),?o) \(\bowtie\) (s\({}_{4}\), p\({}_{4}\),?o)). In line 13, we initialize ResultList before the join operation to ensure that its query result is up-to-date. From lines 14 to 19, we perform the join operation for each result pair; if it is star type, then the hash join algorithm is used, otherwise the nested loop join algorithm is applied. This looping operation is done until the final result is obtained. After obtaining the final results, a recursive uniform resource identification (URI) lookup process is performed using link traversal techniques. ```
1:# Example: a complex query type Q = (7s, p\({}_{1}\), o\({}_{1}\)) \(\bowtie\) (7s, p\({}_{2}\), o\({}_{2}\)) \(\bowtie\) (7s, p\({}_{3}\),?o) \(\bowtie\) (5s\({}_{4}\), p\({}_{4}\),?o)
2:If Q is a standard SPARQL triple pattern
3:For each subquery in Q
4:If distance between subquery and centroids in \(k\)-d tree is shortest Then
5:Search the corresponding R*-tree by mapping list
6:Adding search results to ResultList
7:Else break
8:End For
9:End If
10:# Hybrid join search algorithm
11:While (two or more search results in ResultList)
12:ResultList is split and combined into multiple result pairs
13:Initialize ResultList
14:For each result pair
15:If result pair is star type Then
16:ResultList \(\leftarrow\) Procedure HASH_JOIN(result pair)
17:Else:
18:ResultList \(\leftarrow\) Procedure NESTED_LOOP_JOIN(result pair)
19:End For
20:End While
```  
**Algorithm 1** SPARQL join query algorithm for semantic search.
# Climatic Indices over the Mediterranean Sea: A Review
## 2 Geographical Frame  
The Mediterranean Sea (Figure 1a) is a semi-enclosed basin situated along the border that separates the European and North African climatic regions. It has a total surface of 2.5 million of km\({}^{2}\), extending for 3000 km in longitude and 1500 in latitude and connecting with the Atlantic Ocean through the Strait of Gibraltar and with the Black Sea through the Dardanelles Strait. The Strait of Sicily separates the basin into two main sub-basins, the Western and Eastern Mediterranean.  
Figure 1: (**a**) Map of the Mediterranean Sea with the main basins and sub-basins. Symbols indicate the locations selected for measuring pressure differences in the station-based Mediterranean Oscillation (MO) indices (a triangle for Algiers–Cairo, a square for Gibraltar–Israel, a circle for France–Levantine and a diamond for Marseille–Jerusalem); (**b**) Sketch of the Mediterranean thermohaline circulation.  
The Mediterranean circulation (Figure 1b) is driven, as the leading mechanism, by atmosphere-ocean interactions. This forcing depends on the heat and freshwater exchange and, hence, on the meteorological and oceanic conditions [19]. Since the average precipitation (P) and river runoff (R) do not balance the average evaporation (E) over the basin, a net inflow of Atlantic waters (AW) through the Strait of Gibraltar is necessary to compensate for the deficit. This compensation mechanism generates thermohaline circulation constituted by several cells [20; 21]. The main one transports the AW entering the basin in the surface layer eastward, across the western and eastern basins, gradually increasing its salinity and density as a consequence of the evaporative losses. This modified Atlantic water (MAW) reaches the Levantine basin, where winter cooling caused by intense dry winds forces its sinking to intermediate depths (300-500 m), producing the Levantine intermediate water (LIW) [22].  
The LIW recirculates westward, crossing the Strait of Sicily and reaching Gibraltar, constituting the main core of the Mediterranean waters leaving the basin [23]. Secondary cells appear during winter in very specific areas where the combination of strong evaporative losses and the preconditioning of the surface and intermediate waters (i.e., waters with increased salinity along their paths to the formation spots) trigger deep convection. In the Eastern Mediterranean, these spots are the Adriatic and Aegean Seas, where the Eastern Mediterranean deep water (EMDW) is produced. In the Western Mediterranean, deep convection takes place in the Gulf of Lions, generating the Western Mediterranean deep water (WMDW), which is incorporated into the Mediterranean outflow through Gibraltar [23; 24; 25; 26; 27].
## 6 Implementation and Evaluation
### Theoretical Calculation
#### 6.3.5 Response Time  
Another key indicator of the performance of any group communication solution is its response time. Figure 21 shows that the average response time when using our group communication solution increases with the increase of packet loss in the network. When there is no loss, both group communication methods have similar response times (0.7 s for multicasts and 0.8 s for our solution). When the packet loss increases, the response times for multicasts remain constant, since either the packet is delivered on time or it is just dropped. In the case of our solution, when a packet is dropped CoAP attempts to retransmit it, leading to an increased overall response time. At 15% packet loss, the average response time for the 1-hop network is about 3.5 s and increases to about 10 s at 30% packet loss.  
The CoAP retransmission behavior can be clearly seen in Figure 22, which shows the same results of Figure 21 in the form of a scattergram. It shows how the response times of the unicasts are grouped along the time axis. Replies with a response time of around 1 s were sent without any retransmissions. Replies with a response time around 7, 15 and 30 s occur when one, two or three retransmissions take  
Figure 20: The number of packets sent in the LLN for each group member at different packet loss levels. For multicasts the number of packets decreases as the loss increases. For unicasts the number increases due to CoAP retransmissions.  
place respectively. The last group between 60 and 100 s is for replies that were received after four retransmissions, or those that timed out without a reply.  
An interesting aspect to consider is the impact of long delays in network on the client. In our case both the communication between the client and EM from one side and the communication between the  
Figure 21: Average group response time.  
Figure 22: Scattergram of the response times for 1- and 2-hop networks using multicast and unicast group communication for different packet loss values.  
EM and the members on the other side are using the same CoAP time out mechanism with default values. Thus it is expected that the client might time out and start retransmitting the request before the EM requests to the members are answered or have timed out. To avoid these unnecessary retransmissions from the client, the EM responds to the client with an empty acknowledgment message as soon as it receives the request, indicating that the EM is processing the request and will send a separate reply once an answer is ready. As a result the client does not send any further retransmissions of the request.
# A Review of Failures and Malfunctions in Hydraulic Sandblasting Perforation Guns
## 3 High-Pressure Leakage in Perforation Guns
### Causes of High-Pressure Leakage
In addition, poorly designed internal structures--such as uneven inner cavities or chaotic nozzle arrangements--generate vortices and turbulence that worsen localized erosion [46; 47]. High downhole temperatures compound these issues: elevated temperatures weaken the strength of materials and sealing elements, while uneven thermal expansion induces thermal stress and fatigue, potentially accelerating crack growth [48]. Recent studies have adopted a variety of approaches to investigate these issues, including focusing on seal failure mechanisms, developing high-strength alloys and corrosion-resistant coatings to enhance pressure resistance [49; 50], and conducting fluid-structure interaction simulations to elucidate how high-pressure loads affect deformation patterns and stress distributions [51]. Fracture mechanics research shows that crack propagation rates closely correlate with the frequency and amplitude of high-pressure pulses, with cracks typically spreading along stress concentration zones, eventually resulting in through-thickness fractures [52]. Material degradation and failure also contribute significantly to high-pressure leakage. Abrasive particles and chemical species in high-pressure fluids erode the gun's interior, diminishing its resistance to wear. In chemically active environments, materials may undergo hydrogen embrittlement or stress corrosion cracking, further reducing their strength [53; 54]. Experimental evidence suggests that stainless and alloy steels can experience intergranular corrosion under high-pressure, high-temperature conditions, accelerating leakage occurrence [55]. In summary, research on the mechanisms of high-pressure leakage reveals certain recurring patterns of structural deformation and material failure under stress. Below the yield strength, elastic deformation predominantly occurs in stress-concentrated regions; surpassing yield strength leads to localized plastic deformation and the subsequent development of fatigue cracks. Moreover, fluctuations in fluid pressure and cyclic loading exacerbate fatigue damage, especially when corrosion and high temperatures are also present. High-pressure jets eroding the gun's inner walls create complex leakage pathways. Overall, high-pressure leakage emerges from the combined effects of seal failure, structural deformation, and material degradation. Future studies should emphasize improving seal materials, optimizing fluid-structure interaction simulations, developing high-performancepressure-resistant materials, refining structural designs, and using numerical modeling and experimental analyses to uncover the micro-mechanisms of leakage formation. Such research will comprehensively enhance the reliability and service life of perforation guns.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 3 Proposed Method
### Feature Fusion Module
#### 3.3.1 Fusion at the Feature Level  
Feature-level fusion is essential in person Re-ID, merging distinct feature sets like gait and appearance into a unified representation. Studies such as that conducted by [PERSON] et al. [36] have shown the effectiveness of feature vector concatenation. For a person identity (\(i\)) in our dataset, we obtain two feature vectors, namely \(\mathbf{z}_{\text{appearance}}^{i}\) and \(\mathbf{z}_{\text{gait}}^{i}\). These are combined using weighted concatenation, as described by the following equation:  
\[\mathbf{z}^{i}=\phi_{\text{concat}}\Big{(}\mathbf{z}_{\text{appearance}}^{i},\mathbf{z}_{\text{gait}}^{i}\Big{)}=\Big{[}\lambda\cdot\mathbf{z}_{\text{ appearance}}^{i},(1-\lambda)\cdot\mathbf{z}_{\text{gait}}^{i}\Big{]}, \tag{7}\]  
where \(\lambda\) is a scalar weighting factor within the range of [0,1] used to balance the contributions of the gait and appearance features, with \(\lambda\) specifically adjusting the influence of the gait features.  
We also explore an alternative approach to feature-level fusion by first applying Principal Component Analysis (PCA) to enhance the accuracy and reliability of our person re-identification(Re-ID) system. Specifically, PCA is used to reduce the dimensionality of the appearance features to 128, aligning with the gait features' dimensionality. This approach helps preserve the unique characteristics of each modality while ensuring harmonized integration within the fused feature vector. As a result, we achieve a more comprehensive representation of the person's identity, which is critical for improving the performance of the Re-ID system. After dimensionality reduction, we concatenate the appearance and gait features to maintain the full spectrum of information, enhancing the classification process. We posit that using PCA for feature fusion offers a significant advantage in person Re-ID by facilitating more effective integration of multiple modalities. The details of the concatenated fusion are represented in the following equation:  
\[\mathbf{z}^{i}=\phi_{\text{PCA-concat}}\Big{(}\mathbf{z}_{\text{PCA-appearance}}^{i}, \mathbf{z}_{\text{gait}}^{i}\Big{)}=\Big{[}\lambda\cdot\mathbf{z}_{\text{PCA- appearance}}^{i},(1-\lambda)\cdot\mathbf{z}_{\text{gait}}^{i}\Big{]}. \tag{8}\]  
Here, the PCA-modified appearance features are combined with the gait features using a weighted sum, where \(\lambda\) is a term used to balance the contributions after PCA reduction. This method ensures the harmonized integration of dimensionality-normalized appearance features with gait features.
# Analysis of a Preliminary Design Approach for Conformal Lattice Structures
## 3 Results and Discussions
### Structural Analysis Validation
Therefore, a limitation of the application of the beam structural analysis inside FEM software is the assumption that the elements have a minimum slenderness ratio; the results of these analyses show that for slenderness ratios lower than 2.4, the solutions between the various structural analysis tools begin to diverge. Otherwise, mechanical validation methods for lattice structures with thick beams require experimental analyses or FEM simulations involving solid elements. As a drawback, those alternatives are expected to require high costs for manufacturing the specimens, difficulties to test components with complex shapes by standard testing protocols, high computational power, and difficulties in the meshing process. Furthermore, the homogenization method cannot be performed on conformal lattice structures since they are anisotropic and have different mechanical properties along with different directions. Moreover, the actual beam joints' stiffness due to the beams overlapping was not evaluated; this lack could lead to the underestimation of the stiffness of the model, giving an under-evaluation of the stiffness if compared to the experimental results and FE analysis based on solid elements [34; 51; 80]. Both the mono-dimensional beam approaches and the solid element approaches do not include manufacturing irregularities of the beam size, which negatively affect the mechanical proprieties of the entire printed lattice structure [43; 48]. To prevent manufacturing issues, the beam inclination angle relative to the printing direction, also referred to as the overhang angle, and the minimum and maximum diameter of the beams are factors that must be considered. The overhang angle is suggested to be greater than 30\({}^{\circ}\) for avoiding loss of powder, which could lead to a partially melted zone  
Figure 11: Direct stress results of the traction and the compression load cases, according to the FEM software. compared to a vertical beam causing different thermal conditions that may create unique microstructures and highly irregular surface morphology [11; 81]. To avoid the need for supports for the lattice structure, which would result difficult to be removed, the lattice structures could be angled for production, but it is not possible to define an ideal printing orientation due to the variable orientation of the conformal lattice. Nevertheless, recent studies have shown that small sizes of the beam may not require the use of supports [82; 83], so considering the size of the beams of the studied lattice structures, it might be possible to produce them vertically by SLM technologies.
## 4 Results
### Description of External Environment and Practical Scenarios  
According to the literature and elements presented in previous chapters, the future of road safety systems is taking shape and is based heavily on the analysis of unpredictability in and out of traffic, namely pedestrians, infrastructure, cyclists, and objects that can distract and lead to the production of a negative event.  
The predictability of these traffic elements is increasingly difficult due to the flow of cars and the very unpredictable and extremely hurried human character of certain reactions. Based on the structure of the system presented in Section 2, a system dedicated to external data processing and its transmission in a mini-traffic management system is developed in that direction, more precisely an intelligent traffic light that can intercept information promptly and provide a viable and motivating alternative. The proposal under development has the same characteristics as the other modules developed, but it addresses the external environment and gathers different information.  
Therefore, the use of data exchange via VLC or V2X can provide traffic information at a relevant time without disturbing the proper functioning of the devices. When bidirectional communications are used there are advantages not only in informing the system driver, but also other receivers in the next area that benefit from VLC receivers. The existing infrastructure allows the interconnection of these devices without major changes affecting their functionality.