esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Performance Evaluation
### Effect of New Metrics in Skyline Computation  
The main problem of skyline computation is that either it may give a very small result set or a very large result set. It will create a great problem for a user if this condition occurs. Growing the size of social media matrices can limit the chance of experiencing such an issue. In [12], they used 5 matrices. As we use 10 metrics, we obtained more precise results, which are shown graphically in Figure 5.
# Structural Design and Study of an Integrated Cutter System Based on Machine Operation
## 3 Evaluation and Decision Making for the Integrated Cutter System
### Analysis of the AHP Evaluation System  
#### 3.2.1 Static Strength Analysis  
In this paper, the standard loads commonly used in cutter testing [18] were selected to carry out a static analysis of three cutter systems. The standard loads are shown in Figure 9a, where the normal load, lateral load and rolling load are 315 kN, 47.25 kN and 31.5 kN. In this paper, the finite element method (FEM) was used in the static analysis module of the Ansys Workbench to carry out the static analysis of the three cutter systems. In the static analysis of this paper, the material of the part is selected as Q345. The constraints are set on both sides of the cutter box. The load conditions are shown in Figure 9a. The static analysis of the three cutter systems is shown in Figure 9. It can be observed that the maximum equivalent stress of the EICS in Figure 9b occurs in the L-block, with a maximum value of about 78.9 MPa. The maximum equivalent stress of the TCS in Figure 9c occurs in the fixed block, with a maximum value of about 88.8 MPa. The maximum equivalent stress of the CICS in Figure 9d occurs at the connection between the connecting rod and the screw bushing, with a maximum value of about 50.6 MPa. The results show that all three cutter systems have good mechanical properties.  
Figure 9: Static analysis results: (**a**) standard load; (**b**) EICS equivalent stress diagram; (**c**) TCS equivalent stress diagram; (**d**) CICS equivalent stress diagram.
# Gamifying Massive Online Courses: Effects on the Social Networks and Course Completion Rates
## 4 Results  
### Social Networks and Descriptive Statistics  
The final structure of the social network of participants in each group is represented as a directed graph in which nodes are participants and edges are connections (followers). In massive online courses, many participants do not do any work and do not follow other participants. Two hundred and eighty-seven participants in the experimental group and 269 in the control group did not make any connection. Only nodes with edges are included to improve the readability of the graphs. A connected component is a subgraph in which all nodes are connected. Both instances of the course present only one central connected component. Figure 2 presents the central connected component of the experimental group (313 nodes), and Figure 3 shows the central connected component of the control group (167 nodes).  
For the experimental group, we see that there is a central cluster with many nodes and many connections between them. There are also several hubs (nodes with many connections) and only a few nodes in the periphery with one or two connections. The density of connections makes it difficult to see if two particular nodes are connected. For the control group, we see only a few hubs with many connections and many nodes with only one or two connections. We observe three nodes with more than 100 connections; those correspond to the teachers. Contrastingly, in the experimental group, central nodes are both students and teachers. The five nodes with more than 130 connections comprise three students and two teachers, and the node with the most connections in the experimental group is a student. Although the differences in Figures 2 and 3 could be attributed to the differences in the size of both groups (591 vs. 427), we can observe that the number of connections is 4.48 times larger for the treatment group, suggesting that something besides the size of the group shapes the final structure of the social network.  
Figure 2: The connected components of the social network for the experimental group. Nodes represent participants, and edges represent connections (followers). The size of each node is proportional to the degree (number of connections).  
Table 2 presents descriptive statistics and network metrics--the average degree increases (5.33 for the experimental group and 1.64 for the control group). Graph density represents the ratio between the number of edges and the number of possible edges. A graph is complete (density = 1) if all possible edges between each pair of nodes are present. The experimental group graph has twice as much density as the control group. The number of participants with connections, the number of participants who passed, and the minimum number of connections to pass are also higher for the experimental group. The average path length (average shortest distance from one node to all others) decreases for the experimental group, suggesting that additional connections reduce the distance between participants.
## 2 Materials and Methods
### Histopathology  
Equal sections from the left lobe of each lung were trimmed crosswise, with one part taken from each lung sample. All trimmed sections of the left lung lobe were then re-fixed in 10% neutral buffered formalin for a minimum of 24 h. Paraffin blocks were prepared by a tissue processor (Shandon Citadel 2000; Thermo Scientific, Waltham, MA, USA) and an embedding center (Shandon Histostar; Thermo Scientific, Waltham, MA, USA). Sections were sliced to a thickness of 3-4 um with a microtome (RM2255; Leica Biosystems, Nussloch, Germany), yielding four serial sections per paraffin block. Representative sections were hematoxylin and eosin (H&E) stained for general histopathology and with Periodic Acid-Schiff (PAS) for identifying mucus-producing goblet cells, following established protocols [17; 18; 19; 37]. Two additional serial sections were used for avidin-biotin-peroxidase complex (ABC)-based immunohistochemical staining targeting cleaved caspase-3 and cyclooxygenase-2 (COX-2). Histological profiles were examined using a light microscope (Model Eclipse 80i; Nikon, Tokyo, Japan) coupled with a camera system (ProgRes(tm C5; Entoptic Optical Systems GmbH, Jena, Germany) and an image analyzer. The analysis was conducted without knowledge of group distribution.  
For detailed observations, mean alveolar surface area (ASA, %/mm\({}^{2}\)) reflecting pulmonary function and gas exchange capacities [17; 18; 19; 24; 37], mean alveolar septal thickness (um), and numbers of PAS+ mucus-producing inflammatory cells in alveolar regions (cells/mm\({}^{2}\)), on the secondary bronchus, were calculated by an image analysis program [17; 18; 19; 24; 37; 38]. Histological fields were chosen from the upper areas of the secondary bronchus, with at least one field per left lung tissue, resulting in a minimum of 10 histological lung areas per group.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 3 Experiments and Results
### Experimental Settings
#### 3.2.1 Hyperparameter Settings
In contrast, the Dice loss can deliver a consistent gradient at this stage, allowing for better optimization of the model. This complementary nature aids in optimizing the model both globally and locally.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 3 BGP Anomalies and Attacks
### Collusion Attacks  
Unlike other attacks, which may originate from a single AS, collusion attacks involve the cooperative malicious behavior of two or more non-neighboring ASs [6,45]. These ASs create a virtual tunnel between them, establishing a BGP session through which they can exchange and propagate forged routing information without causing conspicuous routing conflicts.  
In a typical collusion attack scenario, the malicious ASs agree to accept and propagate each other's illegitimate BGP announcements [6,45]. This cooperative malicious activity can facilitate a range of other attacks, such as prefix hijacking or AS path spoofing, by providing a mechanism through which malicious BGP announcements can be injected into the global BGP system and propagated to other, non-malicious ASs. The colluding ASs can, for instance, agree to propagate BGP announcements that contain IP prefixes that neither AS is authorized to advertise, or that contain forged AS path attributes, thereby manipulating the path of Internet traffic on a global scale.  
The threat posed by collusion attacks is due to the inherent trust that underpins the BGP. ASs generally trust the BGP announcements received from their peers, and this trust can be exploited by colluding ASs to inject malicious routing updates into the global BGP system [6,45]. This can facilitate various malicious activities, such as traffic interceptions, traffic analyses, or denials of service, by manipulating the path of Internet traffic to traverse malicious ASs or to avoid legitimate ones.
## 4 Evaluation of the Logarithmic Transimpedance Circuit
### Experimental Evaluation in Long Range Outdoor Conditions
We conclude this section by specifying that the proposed VLC system was developed mostly focusing on improving the hardware aspects in VLC design. Nevertheless, its performance can be further improved with the integration of an optical system. For example, the optical system integrated in the VLC design presented in [14] is able to provide a gain of about 15 dB. Moreover, the usage of low cost optical filters is able to reject up to 30% of the total optical noise, whereas the usage of narrow band optical filters further improves the SNR. Regarding the BER performances, the proposed system is able to achieve a row BER of \(10^{-6}\) simply by integrating a proper light sensing solution and a decent signal processing plan. However, wireless communication systems generally use additional error correction algorithms. In VLC, Reed Solomon and Convolutional codes are generally used to improve the BER performances [5]. Thus, BERs lower than \(10^{-7}-10^{-9}\) can be expected. These low BER performances could be a major advantage recommending the usage of VLC technology in automotive applications.
## 3 Challenges in FSO Communication
### Atmospheric Channel Effects Phenomena
#### 3.1.1 Atmospheric Interference  
Atmospheric interference poses a major challenge to FSO communication. Various atmospheric conditions, such as weather, aerosols, and particles, can impact the propagation of optical signals. For instance, FSO communication is heavily influenced by weather conditions such as snow, rain, sleet, fog, and haze. These atmospheric phenomena can attenuate, scatter, or absorb optical signals, resulting in significant signal attenuation and weakened signal strength, which degrade performance. Additionally, aerosols and particles in the atmosphere greatly affect FSO signal propagation. These tiny solid or liquid  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Non-Coherent** & **Coherent** & **Self-Coherent** \\ \hline Modulation Techniques & Intensity modulation & Amplitude, frequency, or phase modulation & Phase modulation \\ \hline DSP Complexity & Low & High & Moderate \\ \hline Receiver Complexity & Low & High & Moderate \\ \hline Detection Mechanism & Direct detection of light intensity & Optical mixing with a locally generated optical field & Optical mixing with a delayed version of the received signal \\ \hline Need for polarization control or diversity & No & Yes & Yes \\ \hline Need for Local Oscillator & No & Yes & No \\ \hline Noise Rejection Performance & Moderate & Superior & High \\ \hline Sensitivity to Atmospheric Turbulence & Higher & Lower & Lower \\ \hline Receiver Sensitivity & Moderate & High & High \\ \hline Spectral efficiency & Lower & Higher & Moderate \\ \hline Reach & Lower & Higher & Moderate \\ \hline Power consumption & Lower & Higher & Moderate \\ \hline Footprint & Lower & Higher & Moderate \\ \hline Cost & Lower & Higher & Moderate \\ \hline Common Usage & Terrestrial FSO links & Advanced applications requiring high performance & Applications needing a balance of performance and complexity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fundamental differences between non-coherent (IM/DD), coherent, and self-coherent FSO systems.  
particles can scatter and absorb the optical beams used in FSO communication, causing signal degradation [1; 7; 20]. The transmit power can be increased to compensate for these losses, but the maximum permissible power is constrained by eye-safety standards [2; 20]. To counteract the negative impacts of weather on FSO communication, several strategies such as AO, wavelength selection, hybrid FSO systems, air quality monitoring, redundancy, and diversity techniques can be employed. Further details on atmospheric attenuation models are presented in Section 3.2.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 3 Cellular-V2X Technologies
### Nr-V2x  
The NR-V2X is built to handle V2X applications with a range of latency, reliability, and throughput requirements. Rel-16 includes two modes for selecting sub-channels in NR-V2X sidelink communications using the NR-V2X PC5 interface (Mode 1 and Mode 2). These two modes replace Mode 3 and Mode 4 of LTE-V2X, respectively. Nevertheless, LTE-V2X only enables broadcast communications in the sidelink, while NR-V2X supports broadcast, groupcast, and unicast sidelink communications.  
Similar to LTE-V2X Mode 4, UEs can autonomously select their sidelink resources (one, or several sub-channels) from a resource pool when using NR-V2X Mode 2. In both cases, the resource pool can be preconfigured by the infrastructure with a list of approved RRIs. There can be a maximum of 16 separate RRIs on the list. LTE-V2X Mode 2 and NR-V2X Mode 4 have some minor differences in the scheduling scheme, in particular, when related to retransmissions. Mode 4 operates following a semi-persistent scheduling scheme. On theother hand, Mode 2 can operate using both a dynamic and a semi-persistent scheduling scheme. The dynamic scheme selects new resources for each TB and can only reserve resources for the retransmissions of that TB. The RRI determines the interval between the resources chosen for the transmission of consecutive TBs for NR-V2X. NR-V2X Mode 4 has more flexibility in the selection of the RRI compared to LTE-V2X, by allowing any integer RRI between 1 and 99 ms. Finally, NR-V2X defines a lower constraint of 10 for the selection window value to guarantee a radio link layer latency of 10 ms [33]. Table 1 summarizes the key differences between the features/mechanisms of LTE-V2X and NR-V2X.  
For Release 17 (Rel-17) [34], a new work item on the NR sidelink upgrade has been approved. This Rel-17 work item aims at improving Rel-16 NR-V2X resource allocation in Mode 2 in terms of power saving, dependability, and latency. Any improvements must be capable of coexisting with Rel-16 NR-V2X in the same resource pool (i.e., co-channel coexistence). Rel-16, NR-V2X Mode 1 and Mode 2 were created for UEs such as cars or remote sensor units (RSUs), which do not experience severe power constraints. Other UE kinds that are employed by pedestrians, and smartphones, also have the same constraints. A UE needs lengthy sensing intervals under the current Rel-16 Mode 2 requirements, which harms the battery life. A modification of Mode 2 that would use partial sensing to cut down on power consumption has been considered for investigation under Rel-17 [34].
# Miniaturized Compact Reconfigurable Half-Mode SIW Phase Shifter with PIN Diodes
###### Abstract
In addition to the required phase shift and simple one-layer design, it is necessary to reduce the total surface of a compact microwave device. As a result, we propose a half-mode SIW to realize a novel practical electronic device. The half-mode SIW is smaller and performs similarly to a typical SIW phase shifter. The proposed structure can be applied to miniaturized RF circuits to generate desirable phase shifts. A similar dielectric constant must exist to compare the width and size of a phase shifter component to determine the amount of miniaturization. Using the concept of magnetic wall, this paper proposes the steps for designing a new half-mode substrate integrated waveguide (HM-SIW), which presents all the advantages of a SIW but with a size reduced by half [37; 38; 39]. Thus, three microstrip lines are connected to three PIN diodes, which are connected at each end to a radial stub. This circuit generates an electrically controllable phase shift between the two ports of a HM-SIW thanks to the eight active states of the diodes associated with the radial stubs. To validate the proposed design, we used several simulation techniques and a concrete measurement step. The length and width of the microstrip lines and their location on the HM-SIW are optimized to have an approximately constant phase shift over the considered frequency range. The rest of the paper is organized as follows: Section 2 presents the design of the HM-SIW and the proposed HM-SIW phase shifter component. Simulation results and fabricated circuit tests for optimized structures are presented in Section 3. Moreover, concluding remarks are given in Section 4. In addition, two useful Appendix A and Appendix B that investigate a high phase shift feature and some parametric studies with the same proposed technology from simulation software have been prepared.
## 6 Conclusions  
The paper has focused on the prediction of the RUL for lithium ion batteries. The most common failure modes identified were capacity and power fade and battery shelf discharge. The various measurable parameters from a battery were defined before suggesting the prediction framework. A prognostic framework that involves End of Discharge (EOD) and End of Life (EOL) derived in this paper has been based on the discharge and capacity fade models developed by [PERSON] et al. [14] Modifications were made and the effects of battery aging and different loading profiles were added. The particle filter was introduced and all the equations used for its implementation were devised. The EOL prediction model was then developed to describe how the reduction in capacity defines the cell cycle life. The EOD prediction model was modified to account for different loading profiles. Finally, the EOL and EOD prediction models were combined to study the effect of capacity fade in the reduction of the EOD time with cycling, thus giving a greater accuracy to the estimation. The proposed framework has been tested using MATLAB and real cell data available from NASA. Both the prediction models provided satisfactory results. The EOL cycle was predicted with a maximum error of 20% of  
Figure 16: EOD prediction error in last discharge cycle of cell B0038.  
the total cycle life and this error was reduced with the availability of more information. The EOD time prediction was more accurate with an error less than 10% once the algorithm converges.  
Supplementary Materials: Supplementary Materials are available online at [[http://www.mdpi.com/2076-3417/7/11/1172/s1](http://www.mdpi.com/2076-3417/7/11/1172/s1)]([http://www.mdpi.com/2076-3417/7/11/1172/s1](http://www.mdpi.com/2076-3417/7/11/1172/s1)).  
**Author Contributions:** [PERSON] identified the gap is research and designed the simulation model and wrote the paper. [PERSON] provided research expertise and assisted to analyse the results. [PERSON] analysed the results and provided discussions for the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Nomenclature**  
\(\eta_{C}\): Coulombic efficiency factor  
\(C0\): Initial capacitance  
EOL: End of Life  
EOD: End of Discharge  
SOC: State of Charge  
SVM: Support Vector Machine  
PF: Particle Filter  
\(C_{0}\): Initial battery capacity  
\(R_{E}\): Electrolyte resistance  
\(R_{CT}\): Charge transfer resistance  
\(Z_{w}\): Warburg impedance  
\(\Delta E_{sd}\): Voltage loss due to battery shelf discharge  
\(\Delta E_{rd}\): Voltage drop due to cell reactant depletion  
\(\Delta E_{mt}\): Voltage drop due to internal resistance to mass transfer  
\(\Delta E_{init}\): Voltage drop at start of discharge cycle  
\(f_{s}\): Sampling frequency
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
###### Abstract  
Semantic segmentation of assembly images is to recognize the assembled parts and find wrong assembly operations. However, the training of supervised semantic segmentation requires a large amount of labeled data, which is time-consuming and laborious. Moreover, the sizes of mechanical assemblies are not uniform, leading to low segmentation accuracy of small-target objects. This study proposes an adversarial learning network for semi-supervised semantic segmentation of mechanical assembly images (Adv-SemiSeg-MA). A fusion method of AFF multiscale output is proposed, which combines the outputs of different dimensions of AFF into one output. This fusion method can make full use of the high-level semantic features and low-level fine-grained features, which helps to improve the segmentation accuracy of the model for small targets. Meanwhile, the multibranch structure RFASPP module is proposed, which enlarges the receptive field and ensures the target object is close to the center of the receptive field. The CoordConv module is introduced to allow the convolution to perceive spatial position information, thus enabling the semantic segmentation network to be position-sensitive. In the discriminator network, spectral normalization is introduced. The proposed method obtains state-of-art results on the synthesized assembly depth image dataset and performs well on actual assembly RGB image datasets.  
assembly monitoring; semantic segmentation; semi-supervised; adversarial learning; multiscale feature fusion +
Footnote †: journal: Medical and Computer Engineering  
1
# Hybrid VLC-RF Channel Estimation for GFDM Wireless Sensor Networks Using Tree-Based Regressor
## 5 Performance Metrics
### Accuracy  
Accuracy measures the proportion of correctly predicted bits (both (\(-1\) s) and (\(1\) s) in BPSK modulation) out of the total number of bits across all test data samples. It is defined as  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}, \tag{19}\]where \(TP\) is True Positive and means a bit is actually \(1\), and the model correctly predicted it as \(1\); \(TN\) is True Negative and it represents a bit is actually \(-1\), and the model correctly predicted it as \(-1\); \(FP\) is False Positive and means a bit is actually \(-1\), but the model incorrectly predicted it as \(1\); and \(FN\) is False Negative which means a bit is actually \(1\), but the model incorrectly predicted it as \(-1\).