esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The first used a specialized program based on the finite difference method, where partial differential equations (PDEs) and ordinary differential equations (ODEs) were employed to find the solution. This method involves approximating the derivatives of a differential equation by finite differences, resulting in a discretization of the problem domain. The result is a system of algebraic equations that can be solved using suitable numerical algorithms. This method was used to determine the bearing load capacity, lubrication gap height, and aerodynamic pressure distribution. The characteristics of the same gas bearing were then determined using a second method, based on the use of a commercially available universal CFD program, Ansys CFX. The program uses the finite volume method (FVM) to discretize partial differential equations describing fluid flow, such as the Navier-Stokes equations, the continuity equation, and the energy equations. The second approach has been used previously by other researchers, and in this case, it was employed mainly for comparative purposes. A direct comparison of the characteristics of gas bearings obtained using the two different methods is presented in the final section of this article, which allows the differences between the two methods to be captured. The bearing load capacity obtained by two methods, the aerodynamic pressure distribution at nominal speed, and the static equilibrium semicircles determined by different methods were compared. The research presented herein is part of current global trends aimed at finding increasingly accurate and computationally efficient methods for modeling gas bearings. Sharing the results obtained may help other researchers and engineers select appropriate methods and tools for specific computational tasks.
# Corrosion Assessment of Zr2.5 Nb Alloy in Ringer's Solution by Electrochemical Methods
## 1 Introduction  
The average human body contains about one microgram of zirconium, but the chemical element itself does not serve to any known vital biological function. In the chemical industry, it is found in commercial cosmetics, such as antiperspirants, or in water purification systems. In addition to the aforementioned benefits, some research shows that, in the short term, zirconium powder can cause irritation on the skin and granulomas when inhaling zirconium compounds, or other pathologies, such as hemoglobin and low hematocrit with lethal effects in rats and guinea pigs, but only after persistent exposure to zirconium tetrachloride [1, 2].  
The major use of zirconium alloys by the nuclear community as a material for radioactive fuel plating boxes in the water-cooling reactor is indispensable, as it provides a low neutron uptake rate of zirconium in the cross section, a behavior suitable for mechanical wear, as well as corrosion resistant in specific environments [3]. In addition, an experimental study found that alloying zirconium with a small amount of niobium (less than 5%) improves the corrosion resistance of the material without further increasing the hydrogenation process, thus limiting the formation of the brittle hydride phase [3, 4]. It is also necessary to specify the high temperature resistance of zirconium alloys, with interest for the aerospace industry in the manufacture of components in jet engines or stationary gas turbines [5].  
The clinical applicability of the original zirconium alloys as an implant material has been known since 1969, being part of the hip and knee joint device [6]. The first reference to the use of 3Y-TZP zirconium in dentistry was in the 1970s; then, in 1993, zirconium was integrated into dentistry as a basis for the manufacture of crowns, dental bridges and other surgical or prosthetic devices. It should be noted that zirconium type 3Y-TZP is a single-phase material made of tetragonal polycrystals, made by alloying zirconium with 2-3 mol % of yttrium oxide (Y\({}_{2}\)O\({}_{3}\)); so in this form, the first bone dental implant was inserted in 2008 [7].  
According to the data available in the literature, a zirconium alloy with niobium is a bio-inert material, which does not affect the growth of bone and tissue cells and also doesnot cause visible morphological changes in internal organs and has a minimal adhesion of bacteria [7].  
Another study illustrates the favorable applicability of Zr2.5 Nb zirconium alloy as a base material in biomedical applications [8].  
The purpose of this study is to evaluate the electrochemical corrosion resistance of Zr2.5 Nb alloy in Ringer's saline solution. According to existing data in the literature, this alloy, in terms of applicability in the biomedical field, presents a number of advantages, such as low cost, higher corrosion resistance and is not allergenic [9; 10; 11].
## 4 Design and Implementation
### BFSOOK Modulation
#### 4.2.2 Decoding in BFSOOK Modulation  
BFSOOK modulation uses distinct OOK frequencies to transmit logic ones and logic zeros, respectively. Thus, the thresholding scheme used for BFSOOK has to distinguish between these two frequencies. This can be achieved in two ways, either by finding the number of stripes in the image or by finding the width of stripes. Equation (3), shows implementation of function to count the number of stripes from the given frame. Here, \(N_{Stripes}\) represent the number of Stripes, \(C\) is the set of values obtained after performing column-wise sum, normalization and binarization. Thus \(C\) consists only of binary values, and m is the length of the vector \(C\).  
\[N_{Stripes}=\frac{\sum_{1}^{m-1}{(C_{i}-C_{i+1})^{2}}}{2} \tag{3}\]  
Consider for a sample binary bit stream of \(b_{1},b_{2},b_{3},b_{4},b_{5},b_{6},b_{7}\) represented by 0, 0, 1, 1, 1, 0, 0. Here, consecutive 1 s represent a white stripe. After performing \((C_{i}-C_{i+1})^{2}\) from \(b_{1}\) to \(b_{7}\) the output generated is 0, 1, 0, 0, 0, 1, 0. After performing summation over it we get 2 which represent edges of the stripe and by dividing it by 2 we get 1 i.e., the 1 white stripe is detected.  
For detecting the width of stripes, Equations (4) and (5) is used. Here, \(count_{i}\) maintains the sum of consecutive non-zero values. Thus, bit values from \(b_{1}-b_{7}\) are represented as 0, 0, 1, 2, 3, 0, 0 respectively. By finding the maximum of all the values, the maximum width of the stripe in the frame is estimated.  
\[count_{i}=\begin{cases}count_{i-1}+1,&ifmax>count_{i}\\ 0,&\text{otherwise}\end{cases} \tag{4}\]  
\[max=\begin{cases}maxifmax>count_{i}\\ count_{i}otherwise\end{cases} \tag{5}\]  
Algorithm 5 shows algorithmic implementation of BFSOOK receiver based on discussed equations. Post thresholding the value obtained is added to a buffer. Once the size of this buffer is full, data normalization is performed. Each value from the buffer is then compared with the obtained threshold from the previous function, and the respective binary value is decoded.  
```
1:Initialize a Buffer(size)
2:functionCountEdges(CroppedImage)
3: Perform Edge detection
4: Return number of edges detected
5:endfunction
6:functionCountWidth(CroppedImage)
7: Convert to a Row Vector using Columnwise Sum
8: Normalize the Row
9: Binarize the row
10: Find length non-zero sub-sequences
11: Return average width
12:endfunction
13:functionMain(video)
14:for each frame in the video do
15:Preprocessing(image) \(\triangleright\) As shown in Algorithm 1
16:CountEdges(CroppedImage)/CountWidth(CroppedImage)
17: Append the obtained vector to a buffer
18:if buffer size is full then
19: Normalize Values in buffer
20:for each value in the buffer do
21:if value > Sliding Window Average then
22: decode bit as 1
23:elseif value < Sliding Window Average then
24: decode bit as 0
25:endif
26:endfor
27:endif
28:endfor
29:endfunction
```  
**Algorithm 5** BFSOOK Demodulation for VLC-OCC Receiver.
# DDL R-CNN: Dynamic Direction Learning R-CNN for Rotated Object Detection
## 4 Experiment
### Comparison with Other New Technologies
RetinaNet also shows insufficient performance in identifying targets that are similar in color to the background, as revealed by the red dashed box in the third image of the first row. Moreover, the misjudgment phenomenon of RetinaNet in the recognition of similar target objects, as shown by the red dashed box in the last image of the first row, is avoided by our method. Compared with R2 CNN, although the overall detection performance is similar, specific cases reveal its limitations, such as the misidentification of a pier as a ship in the second image of the first row, and the failure to accurately identify ships, as shown by the red dashed boxes in the third and fourth images of the first row, indicating R2 CNN's deficiency in anchor box convergence. Compared with O-RCNN, the advantage of our method in small-target recognition is evident, as shown in the first image of the first row, where O-RCNN failed to effectively recognize small-sized targets and also exhibited misjudgment in similar-target recognition, as demonstrated in the third image of the first row. Overall, our method outperforms these three networks in terms of overall anchor box convergence effects and the accuracy of rotation direction, demonstrating the superiority of our method in object detection tasks. Table 6 provides a complexity analysis of existing methods trained on the UCAS-AOD dataset, including FLOPs, Params, and mAP. Compared to two-stage algorithms such as RoI Transformer, our algorithm achieves a precision increase of 1.87%. Moreover, compared to advanced single-stage algorithms like R3 Det-KLD, our algorithm reduces FLOPs by 124.03G and enhances mAP by 6.9%. Compared to single-stage detection methods, DDL R-CNN demonstrates significant advantages in accuracy and feature fusion, effectively enhancing the recognition and localization of targets in complex scenes through  
Figure 7: Comparison of detection results between DDL R-CNN and existing methods on HRSC2016 dataset. two-stage refined processing. At the same time, when compared with dual-stage detection methods, DDL R-CNN maintains high accuracy while optimizing parameters and computational complexity, achieving faster inference speeds and making it more advantageous in applications requiring real-time performance. Furthermore, DDL R-CNN effectively mitigates overfitting through data augmentation and regularization techniques, enhancing the model's generalization and robustness. Finally, these results further validate the accuracy of our method in extracting multi-directional features in high-resolution RS image detection tasks with multiple interferences.
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
## 6 Proposed Algorithm: Communication Optimisation Integration
### Algorithm Evaluation for LB = 106 dB and LB = 115 dB
#### 7.2.3 Algorithm Evaluation for \(LB=115\) dB, \(d=9.3362\) km  
We repeated the simulations for the scenario in Section 7.2.2, this time for the technical solution in [59] having \(LB=115\) dB. The simulation parameters are the same (\(n_{UAV}=7\), \(n_{USV}=2\), \(n_{vn}=7\)). We observed that no placement solution was identified.  
Further, we increased the number of virtual nodes (\(n_{vn}=8\)) and the same behaviour was noticed. Finally, for \(n_{vn}=9\) a solution, as seen in Table 15, was determined.  
From Table 15, we can state that there are very low values of \(\sigma\). Because \(\sum_{i=0}^{n_{vn}}c_{i}=n_{vn}+1\) (communication link was ensured), more close UVs act as relays than needed.  
Given all the above, the proposed algorithm is less suitable for networks equipped with high \(LB\) communication equipment. Even though apparently this represents a drawback, we recall that the algorithm was intended for determining the optimum position of the UVs nodes such that one allows a fast data transfer between a served WSN and a GW when a shorter-range, high-throughput communication technology is employed, a scenario in which the algorithm has very good performance.
# Efficient Node Deployment of Large-Scale Heterogeneous Wireless Sensor Networks
## 3 Problem Description  
Consider a field \(F(A)\) to be monitored for a time horizon \(T\) using a set of sensor nodes, \(TN\). This monitored field is divided into a number of zones \(A\). Each zone \(i\in A\) is associated with a time-varying weight function \(\omega_{i}^{t}\), where \(t\in T\). This weight function quantifies the significance of observations (surveillance requirement) in this zone above horizon \(T\). The sensor nodes are assumed to be stationary or mobile, where the stationary node is assumed to remain in its zone for the entire lifetime. On the contrary, the mobile node can cover multiple zones over time horizon \(T\). The paper's primary goal is to find the optimum location of the mobile nodes to optimize field coverage. Maximum data coverage is obtained when the highest importance values are collected. The coverage is also maximized by serving a zone with only one node at any given time and keeping the sensor nodes active as much as possible. More importantly, mobile nodes should be used just for as long as they are necessary for sensor node energy conservation. The Movement of the node must occur while minimizing the distance between the node's position and the new location.
# Automatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
## 3 Detection Module
### CASAE Architecture
In Figure 4, the convolutions in the left are regular 3 \(\times\) 3 convolutions. The atrous convolution by a factor of two is on the right. Atrous convolutions space out the pixels that are summed over in the convolution, but the summation pixels are the same as regular convolutions. The weights of the atrous convolutions in the blank are zero, and do not participate in the convolutional operation. So, their effective receptive field is 7 \(\times\) 7. The regular convolutions in the encoder section of the AE network are replaced by atrous convolutions with padding 1 and stride 1. The detailed parameters of the atrous convolutions in the AE network are shown in Table 1. There are four convolutional layers replaced by atrous convolutions in the encoder section. Figure 4: Illustration of atrous convolution. Figure 3: The architecture of the autoencoder (AE) network. To train the AE network, an improved pixel-wise cross-entropy loss with weight \(w_{k}\) is designed. In general, a captured image of the metallic surface has more background pixels than defective pixels. To re-weight the imbalanced classes, \(w_{defects}\) = 0.8 and \(w_{background}\) = 0.2 are set in the loss function, which is defined as:  
\[L_{seg}=\sum_{i=1}^{M}\sum_{j=1}^{N}\sum_{k=1}^{K}-w_{k}1(y_{i}^{j}=k)\log p_{ k}(x_{i}^{j}) \tag{1}\]  
where \(w_{k}\) is the weight, \(K\) = 2 represents the number of classes (background and defects), \(M\) represents the mini-batch size of the training samples, \(N\) is the number of pixels in each image patch, \(1(y=k)\) is an indicator function, which takes 1 when \(y=k\), and 0 otherwise, \(\mathrm{x_{i}}^{j}\) is the \(j\)-th pixel in the \(i\)-th image patch, \(y_{i}^{j}\) is the ground-truth label of \(\mathrm{x_{i}}^{j}\), and \(p_{k}(x_{i}^{j})\) is the probability of pixel \(x_{i}^{j}\) being the \(k\)-th class, which is the output of the softmax layer.
## 4 Biosensors in the Field of Healthcare
### Wireless Communication and Data Transfer
In contrast, the load-shift keying (LSK) technique is particularly used for data transmission from implants to external units (uplink). Each of the aforesaid modulations is hindered by constraints. For instance, the ASK technique can be affected by nearby noise, increasing the bit error rate, while LSK suffers from a significant low transfer of power and data due to mistuning between the on/off states [143; 159].
# Studies regarding the Use of Pneumatic Muscles in Precise Positioning Systems
## 4 Conclusions  
The conducted experiments highlighted the occurrence of the phenomenon of hysteresis during the inflation/deflation cycles of a pneumatic artificial muscle under different loads applied to the free end. The variation of the axial contraction depending on the pressure was described by several polynomials, developed based on experimental results. The relationship between axial contraction and the internal pressure of the muscle at inflation differs from the relationship determined at deflation. As seen in the experiments, the maximum contraction can be observed when the muscle is not loaded.  
The obtained results revealed the hysteretic behavior of the studied pneumatic muscle. Hysteresis represents a map between the input and output variables which affect the control performance of the muscle. Therefore, hysteresis should be compensated by using an inverse model, such as an inverse block with the desired value of control variable at its input.  
The obtained equations that describe the relationship between the input pressure and the axial contraction are significant for reaching a high-performance position control. In this regard, the article proposed a solution to increase positioning accuracy based on pressure control using a proportional pressure regulator and a PLC. The experimental results obtained confirm the possibility of accurately positioning the driven systems with the help of pneumatic muscles. The presented work methodology is specific to a certain working situation (range of applied pressures and external loads), it must be repeated for each application. Further research is to find a path for generalizing the outcomes obtained for other applications.  
Figure 15: The motion curve of an object with a mass of 2.55 kg.
# Decoding _Scn2a_ Variants: Bridging Genetics and Phenotypes in Autism Spectrum Disorder
## 3 Results
### Phenotypic Data  
Seizures and epilepsy are among the most common features of _SCN2A_-associated cases. In the study by [PERSON] et al. [59], seizures were present in the single reported patient, alongside late-onset generalized epilepsy. Similarly, [PERSON] et al. [65] documented seizures in 16 out of 22 patients (72.7%), with epilepsy noted in multiple individuals, further highlighting the strong association between _SCN2A_ mutations and seizure disorders. However, in some studies, such as that by [PERSON] et al. [62], seizures were notably absent in the cohort, illustrating phenotypic variability across cases.  
Psychiatric manifestations, including anxiety, hyperactivity, and behavioral disturbances, were documented in several cases. [PERSON] et al. [65] described a subset of patients exhibiting behavioral issues such as aggression and obsessive behaviors. Meanwhile, [PERSON] et al. [57] reported repetitive actions such as stereotypic rocking and echolalia in their cohort. These findings suggest that psychiatric and behavioral features may represent an important subset of _SCN2A_-related phenotypes.  
Physical abnormalities were also a recurring theme in the analyzed data. For example, [PERSON] et al. [60] described facial dysmorphism in their single patient, while [PERSON] et al. [57] identified microcephaly. Additional abnormalities, such as limb anomalies, were also noted in the cohort analyzed by [PERSON] et al. [65], emphasizing the structural and developmental impact of _SCN2A_ mutations.  
Developmental delays (DDs) and communication impairments were consistently documented across studies. In the report by [PERSON] et al. [49], moderate-to-severe developmental delays were observed in all cases, with speech delays highlighted in a significant proportion. Similarly, [PERSON] et al. [57] identified communication impairments in a majority of their cohort, including 12 patients with significant verbal deficits and 27 patients categorized as nonverbal. These findings align with the broader understanding of _SCN2A_ mutations as a significant contributor to neurodevelopmental impairments.  
The phenotypic data (Table 2) reveal consistent trends in _SCN2A_-associated conditions, including a high prevalence of seizures, epilepsy, psychiatric and behavioral features, physical abnormalities, and developmental delays. The variability observed across cases--both in severity and specific manifestations--likely reflects the diverse genetic and environmental factors influencing clinical outcomes. These findings highlight the importance of _SCN2A_ as a critical gene implicated in neurodevelopmental disorders and provide a foundation for further research into targeted therapeutic strategies.
# Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
## 2 State of the Art
### First-Out Contracts  
First-Out Contracts stand out because the ESCO finances investments and retains all energy savings during a given contractual period [18]. Although the contractual period is defined at the time of the conclusion of the contract, if the EnPC covers the costs of the project and its profits in full before the end of the contract, it ends on that date, and the energy savings are retained thereafter by the customer [28]. Therefore, this type of contract has as its main characteristic the fact that its duration is influenced by the amount of savings achieved, and the greater the energy savings, the shorter the duration of the contract [18].  
A variation of the First-Out Contract is the so-called \"Chauffage\", which is an energy management contract in which ese provides the customer with an agreed set of energy services (such as space heating, lighting, driving energy, etc.) [32]. This variant includes energy performance operations, which consist of operating systems to ensure a certain level of comfort (such as balanced humidity and temperature levels) without, however, explicitly committing to making investments in energy efficiency [32].
## 4 Discussion
### Summary of Findings  
Null hypotheses were retained (see Section 1.4). We observed expected significant target effects for ERPs N200 and P300 in all test conditions. The N200 was localized more posterior than the parietal maximal P300, and P300 target effect was more consistently related to classifier performance than the N200. However, we found no effect of SOI jitter on the size of these target effects. The overall variability of ERP averages was decreased in the jittered conditions, but there was no effect of condition on the average variance of the attentional ERPs. Alpha attenuation effects were not quite significant in the test conditions and were unaffected by jitter. Machine learning classification was more performant for ERPs than for the alpha data, but neither showed obvious improvement with increasing jitter range. Intrusion of SSVEP artifact in the electrophysiological measures was visibly reduced in the jittered conditions. However, typing performance was similar between the test conditions and, subjectively, participants were not consistently aware of or concerned with SOI jitter.