esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Compliant 2-DOF Ejector Pin Mechanism for the Mass Transfer of Robotic Mini-LED Chips
## 2 Mechanical Design and Modeling
### Mechanical Design  
In the traditional chip ejection transfer process of the mechanical ejector pin, the epitaxial film lined with mini-LED chips is placed from the ejector pin to the substrate. The ejector pin pushes the epitaxial film downward to make the chip contact with the substrate. As shown in Figure 1, the chip transfer process is divided into the following four steps:1. The stage is moving and then the target chip moves below the ejector pin.
2. When the target chip is aligned with the ejector pin, the stage stops moving, and then the ejector pin pushes the epitaxial film downward.
3. The stage remains stationary, and the ejector pin penetrates the epitaxial film. The target chip is separated from the epitaxial film, then it is contacted and fixed on the substrate.
4. The ejector pin returns to the starting position, then the stage moves and the next chip is transferred.  
As shown in Figure 1, the size of the chip is 75-120 \(\upmu\)m, the thickness is about 80 \(\upmu\)m and the die-placement of the eject pin is at least 100 \(\upmu\)m.  
As mentioned above, in the conventional chip ejection transfer process, the motion steps of the stage can be summarized as moving-stationary-moving. The ejector pin can only be pushed out when the stage is completely stationary. The stage starts and stops repeatedly, which not only affects the chip transfer's efficiency, but also reduces the transfer's accuracy. To solve this problem, a novel compliant 2-DOF ejector pin mechanism for the mass transfer of robotic mini-LED chips is proposed in this paper. As shown in Figure 2a, the piezoelectric actuators in an orthogonal configuration can output the displacement in both horizontal and vertical directions. Limited by the designed compliant mechanism, the ejector pin is driven to move in two directions. As shown in Figure 2c, in the process of mini-LED chip transfer, when the target chip is aligned with the ejector pin, the vertically configured piezoelectric actuator drives the ejector pin to push the chip. At the same time, another piezoelectric actuator drives the ejector pin to move at the same speed in the horizontal direction of the stage. In this way, the motion stage moves continuously in the process of mini-LED transfer, which is different from the traditional transfer methods that is discontinuous. It is necessary to avoid the repeated starting and stopping of the platform, so as to improve the efficiency of mini-LED transfer. The three-dimensional diagram of the designed device is shown in Figure 2b.  
Figure 1: Conventional chip ejection transfer process with a mechanical ejector pin. (**a**) The target chip is moved below the ejector pin. (**b**) The ejector pin pushes the epitaxial film downward. (**c**) The target chip is contacted and fixed on the substrate. (**d**) The stage moves and the next chip is transferred.
## 4 Discussion
Additionally, the lack of software tools that integrate the HJ-Biplot withartificial intelligence techniques limits its applicability in emerging fields such as data mining and process optimization. A key aspect in the future development of the HJ-Biplot is the complementarity between its different extensions, such as the Sparse HJ-Biplot and the Cenet HJ-Biplot. The Sparse HJ-Biplot excels in applications requiring dimensionality reduction, such as in genomic data analysis, where its ability to identify key variables is invaluable. On the other hand, the Cenet HJ-Biplot offers robustness and flexibility in handling high- and low-dimensionality matrices, being particularly useful in the evaluation of public policies, where it allows for a precise representation of relationships between variables. These complementary characteristics open the door to future developments that integrate both techniques, as well as the exploration of innovative approaches, such as the use of functional data or disjoint techniques, which could further expand the capabilities of the HJ-Biplot to address complex problems in various fields. The combination of these extensions and the incorporation of new methodologies promise to enrich multivariate analysis, offering more versatile and adaptable tools to the current needs of research. To overcome these limitations and fully leverage the potential of the HJ-Biplot, several directions for future research are proposed. First, it is crucial to explore the integration of the HJ-Biplot with machine learning techniques, such as penalized principal component analysis (Sparse PCA) and neural networks, to improve its scalability and applicability in large datasets. Second, complementary studies between the different extensions of the HJ-Biplot, such as the Sparse HJ-Biplot and the Cenet HJ-Biplot, are recommended to identify their advantages and limitations in different contexts. Finally, the development of software tools that integrate the HJ-Biplot with artificial intelligence techniques would facilitate its use in interdisciplinary and big data applications, thereby expanding its reach and relevance in current research. The HJ-Biplot is an invaluable tool for multivariate data analysis with demonstrated applications in multiple disciplines. However, its full potential has yet to be realized, and it is necessary to address challenges such as the integration of heterogeneous data, scalability in large datasets, and the lack of advanced software tools. By overcoming these limitations, the HJ-Biplot could establish itself as an indispensable tool in data analysis, offering innovative solutions to complex problems in research and professional practice.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 3 Joint Optimization Algorithm
### Subproblem 3: User Computation and Communication Resources  
When \(\mathbf{q}_{m}\), \(h\), \(\eta\) are determined, Subproblem 3 can be expressed as the P7 problem.  
\[\begin{array}{ll}\text{P7}:&\min\limits_{\begin{subarray}{c}\text{\emph{f}},\text{\emph{t}},\text{\emph{p}},\text{\emph{b}}\end{subarray}}\frac{\text{ d}}{(1-\eta)}\sum\limits_{i\in\mathcal{N}_{m}}^{|\mathcal{N}_{m}|}\Big{(}v\log \left(\frac{1}{\eta}\right)C_{i}D_{i}f_{i}^{2}+p_{i}t_{i}\Big{)}\\ &\text{s.t.\,\,C1}:0\leq f_{i}\leq f_{i}^{\max},\forall i\in\mathcal{N}_{m}\\ &\text{C2}:0\leq p_{i}\leq p_{i}^{\max},\forall i\in\mathcal{N}_{m}\\ &\text{C3}:I_{g}\Big{(}t_{i}+l_{i}\frac{C_{i}D_{i}}{f_{i}}\Big{)}\leq T,\forall i \in\mathcal{N}_{m}\\ &\text{C4}:\text{\emph{t}}_{i}b_{i}\log_{2}\Big{(}1+\frac{p_{i}g_{i}}{b_{i}N_{ 0}}\Big{)}\geq s,\forall i\in\mathcal{N}_{m}\\ &\text{C5}:\sum\limits_{i\in\mathcal{N}_{m}}^{|\mathcal{N}_{m}|}b_{i}\leq B,b_{ i}\geq 0,\forall i\in\mathcal{N}_{m}\end{array} \tag{23}\]  
Since P7 is a non-convex problem, it is difficult to solve it directly. So this paper further divides subproblem 3 into two optimization subproblems: subproblem 3-1 and subproblem 3-2.
## 3 Results  
### Initial Statistical Analysis  
The normal distribution hypothesis was verified using the Ryan-Joiner test, similar to the popular Shapiro-Wilk test. At a significance level of \(\alpha=5\%\), the collected brightness L*, b* colour coordinate and gloss G data were normally distributed for the raw data. The null hypothesis that the error variance of the dependent variable was equal in all groups was assumed at the fixed confidence level (>0.05). The exception was the parameter a*, its data did not present a normal distribution and the null hypothesis was rejected [51].  
ANOVA analysis was performed. The data were evaluated based on six variables: density of high-density fibreboard (HDF), sand belt used during sanding, amount of basecoat and topcoat applied, number of coats, different UV mercury lamps and different UV LED lamp powers used to cure the surface of coating (Table 4). It was found that the mean gloss level values differed at a significance level of \(\alpha=0.05\) for UV mercury lamp power W/cm, amount of topcoat, number of applicators and sandpaper. In the case of brightness L* and colour b*, the parameters related to the varnish application process (amount of topcoat and basecoat, number of applicators) were statistically different at \(\alpha=0.05\). The other factors examined had no statistically significant effect on the coating's optical parameters considered in the paper.
## 4 Analysis of Old Hanger Test Detection
### Anchor Head Detection  
The method used to measure the hardness of the anchor is to press the diamond indenter vertically into the material's surface to produce a dent for testing under the specified external load. According to the depth of the dent, after the load is released, the Rockwell hardness can be calculated using the Rockwell hardness calculation formula, as shown in Equation (1). The Rockwell hardness value is displayed on the dial of the hardness tester, as follows:  
\[HR=(K-H)/C \tag{1}\]  
where \(HR\) is Rockwell hardness value; \(K\) is constant, \(K\) = 0.2 mm when testing with diamond cone indenter, \(K\) = 0.26 mm when testing with steel ball indenter; \(H\) is indentation depth after load release; \(C\) is residual indentation depth increment of 0.002 mm.  
As shown in Figure 14, the hardness of the anchor cup and anchor ring was measured in the lab using two measurement zones and three tests per zone. The anchor cup and anchor ring hardnesses at each test point are less than the standard value [18], suggesting low hardness due to long-term corrosion and alternating loads. The hanger's load-carrying capacity is not significantly weakened by the overall static load test, but as the anchor end corrodes, its anchorage performance will be threatened, which could lead to bridge structure safety issues like deformation, cracks, or destabilization and collapse.  
In engineering applications, galvanization, chromium plating, epoxy resin-spraying, etc., may improve anchoring corrosion resistance. At the same time, one should avoid installing anchorages in dry and wet environments and pay attention to drainage system settings to limit environmental corrosion. Furthermore, it is crucial to constantly inspect the anchorage's surface and detect early corrosion, before removing it using an antirust agent, and replacing damaged pieces.  
Figure 13: Static load test of the hanger: (**a**) tensioning table, (**b**) anchor end, (**c**) oil pressure gauge, and (**d**) tension display.
# Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures
## 2 Methods
### Formal Analysis
#### 2.3.2 Logistic Regression  
Logistic regression (LR) analysis establishes a functional connection between a binary response variable and one or more explanatory variables [72]. This statistical technique enables the modeling and understanding the relationship between these variables in a binary classification context [73]. The outcomes that emerged mostly referred to two categories, \"1\" or \"0\" [74]. A logistic regression aims to capture the probability of an outcome based on individual characteristics. In logistic regression, the equation among the input and the output variables is not linear. Rather than, the logarithmic transformation of the probability, denoted as \(\pi\), is considered as [75]  
\[\pi=\frac{\mathrm{e}^{\mathrm{B}_{0}+\mathrm{B}_{1}\mathrm{x}_{1}+\ldots\mathrm{ B}_{n}\mathrm{x}_{n}}}{1+\mathrm{e}^{\mathrm{B}_{0}+\mathrm{B}_{1}\mathrm{x}_{1}+ \ldots\mathrm{B}_{n}\mathrm{x}_{n}}} \tag{19}\]  
Therefore, the equation of the model is employed [76]:  
\[\ln\left(\frac{\pi}{1-\pi}\right)=\mathrm{B}_{0}+\mathrm{B}_{1}\mathrm{X}_{1} +\mathrm{B}_{2}\mathrm{X}_{2}+\ldots+\mathrm{B}_{n}\mathrm{X}_{n} \tag{20}\]  
in which \(\pi\) represents the probability of an event occurring, \(\mathrm{B}_{0}\) are the coefficients of the analysis, and \(\mathrm{X}_{n}\) are the explanatory variables. The significance of each input is evaluated by conducting statistical tests to determine the importance of the coefficients. In logistic regression, the output is determined by applying a function that classifies the input data. The most commonly used function for determining the probability of the logistic regression method is the sigmoid function, in which if the probability is greater than 0.5, the outcome of the function tends to approach 1, so it belongs to class 1, and if the probability is less than 0.5, the outcome of the function tends to approach 0, and as a result, it belongs to class 0 [77].  
Logistic regression is an accurate predictive approach for modeling binary outputs. Many researchers were interested in combining LR with fuzzy logic and claimed that this approach led to a more effective predictive system. For example, [PERSON] et al. [78] used logistic regression and fuzzy inference system (FIS) to manage traffic on different routes. Also, logistic regression analysis and fuzzy logic were integrated to identify gully erosion susceptibility [79]. This task was accomplished by evaluating the fuzzy membership values of every coefficient. Another model for evaluating binary data was proposed by [PERSON] [80], who used fuzzy logistic regression, in which the output belonged to a fuzzy set. In our study, we applied a hybrid model in three structures in which LR analysis was implemented for evaluating the probability \(\pi\) of structural collapse. Then, the probability values of every set were subjected to the fuzzy linear regression method to determine the similarity ratio of the structures.
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 4 Platooning in Cellular V2X
### System Model  
As a reference scenario for our study, we consider a road where up to \(N\) platoons are under the coverage of one serving eNodeB. Platoons contain \(L\) vehicles (\(L\) being the _platoon size_). Vehicles' length is \(l\), and \(d\) is the inter-vehicle distance or intra-platoon gap (including the length of one vehicle), which represents the length of the link to be covered through V2V communication. The physical road length occupied by a platoon is \((L-1)\cdot d+l\) (Figure 1). The platoon is assumed to be of fixed size, i.e., joining/leaving procedures are not considered here.  
A _predecessor-leader_ controller strategy is implemented, which guarantees better stability compared to other strategies. Information exchanged in the platoon is conveyed in CAMs and needs to be periodically updated by the PL (every update cycle of \(T\) seconds) to satisfy the demands of the controller algorithm in each vehicle. \(T\) must be kept in the order of 10 ms-100 ms; i.e., the update frequency must be at least 10 Hz. Ensuring shorter update cycles (more frequent CAM updates) allows a platoon to travel faster without incurring the risk of collisions.  
We assume that resources are allocated according to Mode 3. Thanks to the eNodeB-assisted resource allocation, the reliability and latency of the platooning application can be more easily controlled. The eNodeB is responsible for allocating subchannels for V2X communications on each TTI. V2X communications occur on dedicated resources. This way, transmissions from non-V2X devices do not interfere with V2X ones. In order to transmit, a VUE (either a PL or a PM) needs to be allocated a number of RBs that depends on the CAM size and the MCS selected for the transmission. We will discuss later how the eNodeB should select the MCS.
# Designed Circularly Polarized Two-Port Microstrip MIMO Antenna for WLAN Applications
## 4 Parametric Study
### Changing Length and Width (ls) of the Patch Corner Cut  
The MIMO-CP antenna, simulated for different widths and lengths of the corner cut, to effect the S-parameters and axial ratio, are presented in Figure 7. The length and width of the corner cut varies from 2.1 to 3.6 mm. When the _ls_ increased from 3.1 mm, the resonance frequency shifted from a lower to a higher frequency, and obtained poor impedance matching between S11 resonance frequency and S21, as shown in Figure 7a; while, axial ratio decreased. By decreasing the _ls_ from 3.1 mm, the reflection coefficient decreased, the isolation decreased, and there was poor axial ratio. Hence, _ls_ are fixed to \(c=3.1\) to improve the impedance bandwidth of the S11 resonance frequency at 5.58 GHz; S21 at the same frequency is \(-27\) dB, and improves axial ratio.
## 6 Results and Discussion
### Clustering of Segmental Floor
#### 6.1.2 Clustering Based on Coplanarity  
Figure 10 shows the variation of the dendrogram and validity measure index during clustering based on coplanarity. Similar to the morphology-based clustering, the neighbor floor series were agglomerated gradually into the same cluster with the evolution of the clustering process. During the agglomerative clustering process, the DBI increased to the maximum when the number of clusters decreased to 99. Then it began to decrease with the agglomerative clustering process. The DI decreased with the agglomerating of clusters until the number of clusters decreased to 5. Then it increased rapidly when the number of clusters further decreased. The CS remained almost constant until the number of clusters decreased to about 15. Then it increased sharply. Considering the dendrogram and validity measure index, the number of clusters was selected as 6, as shown in Figure 10a.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Clusters** & **Position of Cluster Center (X/m)** & **Zone of Cluster (X/m)** \\ \hline
1 & 24.8 & 0–45.6 \\
2 & 65.6 & 46.4–80.8 \\
3 & 101.6 & 81.6–119.2 \\
4 & 136.8 & 120–153.6 \\
5 & 197.6 & 154.4–223.2 \\
6 & 238.4 & 224–247.2 \\
7 & 267.2 & 248–290.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The position and cover zone of the cluster centers based on morphology similarity.  
Figure 9: The morphology of cluster centers based on morphology similarity for segmental floor.  
Figure 11 presents the gradient surface of cluster centers based on coplanarity for the segmental floor. The position, cover zone of each cluster center surface, and the gradient in the face direction determined by the center surfaces are listed in Table 2. The gradient in the face direction of the cluster center represents the floor gradient in the cover zone of the cluster. It can be regarded as the stretching angle of the generating line determined by clustering based on morphology similarity.
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
###### Abstract  
A new method is established to construct the 2-D fault diagnosis representation of multiple bearing defects from 1-D acoustic emission signals. This technique starts by applying envelope analysis to extract the envelope signal. A novel strategy is propounded for the deployment of the continuous wavelet transform with damage frequency band information to generate the defect signature wavelet image (DSWI), which describes the acoustic emission signal in time-frequency-domain, reduces the nonstationary effect in the signal, shows discriminate pattern visualization for different types of faults, and associates with the defect signature of bearing faults. Using the resultant DSWI, the deep convolution neural network (DCNN) architecture is designed to identify the fault in the bearing. To evaluate the proposed algorithm, the performance of this technique is scrutinized by a series of experimental tests acquired from a self-designed testbed and corresponding to different bearing conditions. The performance from the experimental dataset demonstrates that the suggested methodology outperforms conventional approaches in terms of classification accuracy. The result of combining the DCNN with DSWI input yields an accuracy of 98.79% for classifying multiple bearing defects.  
defect signature wavelet image; deep convolution neural network; bearing fault diagnosis; acoustic emission signal +
Footnote †: journal: Applied Sciences  
Article
# Detection of Software Security Weakness Using Cross-Language Source Code Representation (CLaSCoRe)
## 3 The Proposed Architecture for the Cross-Language Source Code Representation
### Source Code Tokenization
#### 3.4.1 The Control Flow Vector  
For the tokenization process, we use the vocabulary that was developed based on the method defined in Section 3.1. The vocabulary consists of the 920 keywords specific to C, C++, Java, and C# programming languages, which were identified as having at least one appearance in the NIST dataset for these languages. Source code tokens are checked against the vocabulary and, when identified, they are replaced with their respective cluster identification number, resulting from the clustering step defined in Section 3.3. For example, the token \"else\", which we can identify in source code written in C/C++, Java, or C#, is replaced with the integer value 17, according to Table 3.
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Indoor Localization Applications and Methodologies in Healthcare
#### 3.1.1 Wi-Fi Based Healthcare Facilities System
Data transmitted in clear text can be intercepted. To mitigate this risk, the system uses encryption protocols and strong passwords to prevent brute-force attacks. Edge nodes are susceptible to theft or tampering due to their portability. The use of tamper-resistant cases and secure mounting helps protect the hardware. Default SSH credentials could allow unauthorized remote access. The system mitigates this by implementing SSH key authentication and configuring firewalls (e.g., IPtables) to restrict access. Nodes can be overwhelmed with traffic, making them inoperable. To mitigate this, ICMP requests are disabled, and routers are configured to block unauthorized UDP packets. Exploiting hardware vulnerabilities to extract data poses a risk. To address this, potential hardware upgrades or architectural changes are considered when vulnerabilities are identified [14]. The LoCATE system employs the existing Wi-Fi infrastructure to provide a cost-effective, scalable, and portable solution for real-time tracking in healthcare settings. By collecting and processing RSSI data from edge nodes, the system accurately determines the location of patients, staff, and assets. The cloud-based architecture ensures seamless data transmission, storage, and visualization. Additionally, the system incorporates robust security measures to mitigate potential risks, ensuring data privacy and reliability. This combination of real-time tracking and security makes LoCATE an effective solution for improving operational efficiency and patient safety in healthcare environments [14].
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 2 Related Work
This simulation was effective; nevertheless, the controller needs to be implemented in a gripper for real applicability. Another study [35] implemented a fuzzy controller to reduce the impacts of forces during the object manipulation. Moreover, the authors developed a fuzzy controller of conformity, in which a mass-spring-damper system determines a desired level of conformity. A simulated robotic hand was used to test the performance of the fuzzy conformance controller. As can be seen, previous works have had at least one of the following shortcomings: most of them used parallel grippers, not requiring specifically centering the object but limiting the dexterity possible while grasping, and most of the works were tested only in simulated environments or had high variations while testing with real grippers. Consequently, in this work is presented an approach to control the position and force of each individual motor over a two-independent finger gripper in order to increase dexterity; in other words, a robot can adjust grasping by only adjusting the forces and positions of its fingers. Three controllers have been implemented and tested, a PID controller, a fuzzy type-I controller, and a fuzzy type-II controller. In the following sections, implementations and tests are described.