esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Combination of Copper Film and COOH-APTMS SAM  
To investigate the combination of copper film and a COOH-APTMS SAM, the phase components of the samples were afterward analysed via XPS and XRD (Figure 4). The oxide layer was peeled on the sample surface before the XPS test. In Figure 4a, the characteristic peak of the Cu-O bonds at 933.4 eV meant that there was a strong interaction between the copper atoms and the -COOH groups. In a word, the Cu-O bonds enabled one to fix the copper atoms, which could improve the barrier performance of the COOH-APTMS  
Figure 3: AFM images of COOH-APTMS/SiO\({}_{2}\)/Si with different modification times: (**a**) 1 h; (**b**) 2 h; (**c**) 3 h; (**d**) 4 h.  
SAM. The analysis results of the C1s and N1s of the Cu/COOH-APTMS/SiO\({}_{2}\)/Si samples showed that neither C nor N did not bond with Cu (Figure 4b,c), which was consistent with the literature report [38]. In Figure 4d, the XRD profile exhibited the presence of the Cu phase, while no diffraction peaks of copper oxides were found.  
FPB is a reliable method to measure the interfacial adhesion in thin film structures [30,39]. In this study, the interfacial toughness between the copper film and the COOH-APTMS SAM was evaluated before and after annealing at 400 \({}^{\circ}\)C to verify the applicability of the semiconductor devices under consideration. Figure 5 depicts the FPB results for different samples. In particular, the interfacial toughness of the modified samples increased significantly compared to that of the Cu/APTMS/SiO\({}_{2}\)/Si structure, which could be attributed to the good fixation of the Cu film on the APTMS by the forming of Cu-O bonds. After annealing at 400 \({}^{\circ}\)C, the dehydration condensation of COOH-APTMS and hydroxyl on the surface of SiO\({}_{2}\) was strengthened. Therefore, the interfacial toughness of the Cu/COOH-APTMS/SiO\({}_{2}\)/Si system increased from 6.58 J/m\({}^{2}\) to 7.88 J/m\({}^{2}\) (Table 1), which was 19.7% higher than that of the Cu/APTMS/SiO\({}_{2}\)/Si structure. Thus, the bonding improvement enabled one to better fix the Cu atoms and improve the barrier performance of the diffusion barrier layer.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Samples** & **As-Deposited** & **Annealed at 400 \({}^{\circ}\)C** \\ \hline Cu/APTMS/SiO\({}_{2}\)/Si & 1.58 \(\pm\) 0.29 J/m\({}^{2}\) & 6.58 \(\pm\) 0.28 J/m\({}^{2}\) \\ Cu/COOH-APTMS/SiO\({}_{2}\)/Si & 1.72 \(\pm\) 0.25 J/m\({}^{2}\) & 7.88 \(\pm\) 0.34 J/m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Interface toughness of samples.  
Figure 4: XPS and XRD patterns of Cu/COOH-APTMS/SiO\({}_{2}\)/Si: (**a**) XPS analysis of Cu; (**b**) XPS analysis of C; (**c**) XPS analysis of N; (**d**) XRD pattern.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 3 Proposed Method
### Data Augment  
Generally, when the numbers of the input data are insufficient for network training, sample diversity needs to be improved to describe the complete sample distribution. The data augmentation technique is introduced to enhance sample diversity, contributing to the generalization ability and robustness of the model [53]. Based on the existing study [11], the three most effective data augmentation techniques are selected, and corresponding improvements are made based on the characteristics of the studied dataset. In this paper, the number of UAV images significantly outweighs that of satellite images, with a ratio of 54:1. This imbalanced proportion of positive and negative samples makes it difficult to learn useful information. Therefore, only the satellite images are augmented to conquer the data disproportion. Random rotation, inversion, and cropping are implemented to ensure the invariance property of geometric structure in the images, such as the building and roads. After data augmentation, the ratio of drone and satellite images reaches 1:1, achieving balance in the number of two types of images. In Section 4, the effectiveness of the data augmentation is verified, and the comparison is presented in the experiment section.
# Software Integration of Power System Measurement Devices with AI Capabilities
## 5 Real-Time Application: Machine Learning-Based Load Identification
### Online Experimental Results  
To validate the performance of the classifiers and to achieve more accurate comparisons, the PQM's capability to record information from three phases simultaneously was taken advantage of. Consequently, one lamp was connected to each of the three phases. This allowed for the three classifiers (previously trained) to be evaluated simultaneously on the three lamps with the same on and off times. To accomplish this, starting from a state with lamps off, they were turned on and off multiple times, with varying durations as illustrated in Figure 22, for a period of approximately 55 min.  
Figure 21: Random Forest composed of 4 DTs.  
Figure 22: Experimental results: Lamp connected at each phase vs. predictions.  
The data were sampled and evaluated every second by applying the three classifiers to each of the phases. As can be seen in Figure 22, both the NN and the RF predicted satisfactorily most of the cases. Only in two instances during lamp 3 ignition was it estimated as lamp 2, possibly due to a phenomenon of Inrush Current [60]. In contrast, the DT exhibited additional errors in the initial switch-on and switch-off of lamp 1, estimating it as lamp 2, possibly due to its lower robustness to Inrush Current or temperature phenomena [61].  
Figure 23 shows the confusion matrix of each algorithm on the experimental results. Table 5 shows in detail the performance results obtained during the experiment with each of the models. The standard metrics were calculated based on the number of True Positives (TPs), False Positives (FPs), False Negatives (FNs) and True Negatives (TNs), which are defined as  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[F1\text{-}Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{7}\]  
\[MCC=\frac{(TP\times TN)-(FP\times FN)}{\sqrt{(TP+FP)\times(TP+FN)\times(TN+ FP)\times(TN+FN)}} \tag{8}\]  
Additionally, to understand the results of the DT, it is useful to compare the harmonics measured during the real-time test with those available in the learning phase. As shown in Figure 24, there are differences between the harmonics used in the learning phase (see Figure 20) and those measured during the experimental phase. The DT proved to be more sensitive to these differences, while both the NN and the RF demonstrated their capacity for generalization. Furthermore, the voltage harmonics of lamp 3 corresponding to the only two instances in which the NN and RT failed can be clearly seen as outliers in Figure 24.  
As can be seen, both the NN and the RF obtained the same result, with practically no failures. However, the NN required a computation time of 5.5 times that of the DT, while the RF required only 1.67 times that time.  
Figure 23: Confusion matrix of the experimental results.
# IRS, LIS, and Radio Stripes-Aided Wireless Communications: A Tutorial
## 2 Several Promising Techniques: IRS, LIS, and Radio Stripes
### Categorizing Recent Studies on Radio Stripes
#### 2.3.5 Radio Stripe Network Energy Efficiency  
In a cell-free massive MIMO system, with Random AP Switch Off (RAPSO), you can save power consumption and make the cell-free mMIMO technology that relies on radio stripes (CFMMSBRS) more energy efficient [39].  
Table 3 lists the findings of recent studies on radio stripes.
# An Adaptive Symmetrical Load Balancing Scheme for Next Generation Wireless Networks
###### Abstract  
In dense Wi-Fi networks, achieving load balancing is critical to optimize network utilization and provide equitable network consumption among the users. Traditional Wi-Fi networks have issues in attaining effective load balancing. Software-Defined Networking (SDN) has presented a viable solution by isolating the data plane and control plane, enabling more agile and cost-effective networks. In this paper we put forward an Adaptive Symmetrical Load Balancing (ASLB) scheme to ensure fairness of load symmetry in Software Defined Wi-Fi Networks (SD-Wi-Fi), while also optimizing the flows transition process using an Analytical Hierarchical Process (AHP). User activity is monitored by access points (APs), which operate under OpenFlow standards. Three essential features, packet volume, packet category and delay hindrance, are used for flow assignment to various controllers. The controllers are arranged in two tiers, universal and regional controllers. The universal controller (UC) handles the workload statistics of regional controllers (RC) in the form of clusters. Extensive simulations using OMNeT++ simulator are performed. The performance parameters taken into consideration are throughput, delay, packet loss rate, network transition count and workload distribution. Our findings demonstrate that the ASLB technique effectively optimizes the network utilization and ensures equitable network consumption among the end users. The proposed scheme outperforms the Mean Probe Delay scheme (MPD), Channel Measurement-based Access Selection scheme (CMAS), Received Signal Strength Indicator-based scheme (RSSI) and Distributed Antenna Selection scheme (DASA), being 40% higher in throughput and 25% lower in delay.  
adaptive; load balancing; SDN; symmetry; Wi-Fi; simulations 2023
## 3 Results
### Optical Properties  
The light transmittance was analyzed by a spectrophotometer measuring from the 250 to 2000 nm wavelength range. The comparison between glass substrate, ASD film, and ITO-nanorod treated with oxygen plasma of varying gas content at 20, 40, 60, 80, and 100 sccm is displayed in Figure 10. From the spectral lines, it was found that the light transmittance of the plasma-treated ITO-nanorod film was in a good range and was close to each other in the visible wavelength range of 380-750 nm, which is the appropriate wavelength. The overall transmittance values are approximately 85%.  
Furthermore, the average transmittance in the visible range (400-700 nm) of ITO-nanorod films treated with oxygen plasma under different gas flow rates of 20, 40, 60, 80, and 100 sccm was calculated and compared with glass, ASD film, and ITO commercial, as shown in Figure 11. As can be seen in the graph, the average transmittance in the visible range of the plasma-treated ITO-nanorod film increases with the increase in the oxygen gas flow rate. Due to the increased self-collision of oxygen ions at higher concentrations, a correspondingly higher average transmittance change is achieved. Then, the omnidirectional characteristics were analyzed at 500, 600, and 700 nm wavelengths of the glass substrate, as-deposited film, and treated with oxygen plasma of varying gas 20, 40, 60, 80, and 100 sccm. It was measured at the incident angle between 0\({}^{\circ}\) and 80\({}^{\circ}\). The analysis results are illustrated in Figures 12-14. From the spectrum, the results found that a percentage of light-transmittance at incident angles of ITO-nanorod treated with oxygen plasma was close in overall cases of gas content. Compared with glass, ITO-nanorod films treated with oxygen plasma have a higher light transmittance. Therefore, optimizing light transmittance value is suitable as a thin transparent conductive oxide film in solar cells.  
Figure 10: Transmittance spectra of the ITO-nanorod films compared with clear glass, as-deposited film, and treated with oxygen plasma content of 20, 40, 60, 80, and 100 sccm prepared via bias-magnetron RF sputtering process.  
Figure 11: Average transmittance in the visible range of the ITO-nanorod films compared with clear glass, ASD film, and treated with oxygen plasma content of 20, 40, 60, 80, and 100 sccm prepared via bias-magnetron RF sputtering process.  
Figure 12: Omnidirectional characteristics on transmittance properties of ITO-nanorod treated with oxygen plasma under gas content 20, 40, 60, 80, and 100 sccm compared to glass substrate and ASD film at 500 nm-wavelength.  
Figure 14: Omnidirectional characteristics on transmittance properties of ITO-nanorod treated with oxygen plasma under gas content 20, 40, 60, 80, and 100 sccm compared to glass substrate and ASD film at 700 nm-wavelength.  
Figure 13: Omnidirectional characteristics on transmittance properties of ITO-nanorod treated with oxygen plasma under gas content 20, 40, 60, 80, and 100 sccm compared to glass substrate and ASD film at 600 nm-wavelength.
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 5 Experimental Application Results
### Evaluation of Power Consumption  
Figure 13 shows the evolution of the current required by the IoT device during the activation of one button of the elevator. This current is drawn from a 5V battery. In this case, the IoT device never enters sleep mode thanks to the large capacity of the internal battery. This implementation optimizes the latency (response time) of the IoT device because there is no internal wake-up delay. The different internal states of the IoT device have been illustrated. In (i), the device remains in an idle state, waiting for elevator commands. In this state the base current consumption is 150 mA. In (ii), the device answers a \"press button\" command, one servomotor starts rotating and the intensity reaches a peak of 600 mA. The intensity value decreases to 200 mA when the servomotor reaches a constant velocity and increases when the servomotor starts pushing the physical button of the elevator. In (iii), the servomotor has reached the target angular orientation and the button of the elevator control panel is being pressed. In this state the servomotor is kept stationary and its internal closed loop control provides pulses (up to 600 mA) in order to maintain the angular orientation of the servomotor. The average current drawn in the state (iii) is approximately 250 mA. The servomotor is automatically released approximately 0.5 s after having received the \"press button\" command. In (iv), the servomotor is accelerated to return to its resting position. Figure 13 shows that the internal closed loop control of the servomotor provides current pulses (up to 800 mA) to stop its free rotation. After that, the IoT device returns to the idle state (i) and waits until the reception of the next command message.  
Figure 12: Front view of the right-side elevator (or elevator 1) with the IoT device placed over the lower button panel of the car (yellow circle).  
Figure 13: Instantaneous current consumption by the IoT device during a button press. Servomotor cyclic states: (i) idle, (ii) activation, (iii) button pressed, and (iv) deactivation.
# Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification
## 3 The Proposed Approach
### Accompanying Evolution  
To address the issue of fitness stagnation of the best individual during the iterative process of the GP algorithm, this paper introduces the accompanying evolution strategy. The accompanying evolution strategy consists of two sub-strategies: storage strategy and replacement strategy. The storage strategy is employed to identify and preserve individuals showing potential, referred to as accompanying individuals. These individuals are retained and not immediately reintroduced into the population. When the algorithm detects fitness stagnation of the best individual, the replacement strategy reintroduces these accompanying individuals into the population, thereby effectively enhancing the probability of finding the best individual.
# Go Source Code Vulnerability Detection Method Based on Graph Neural Network
## 3 Preliminaries
### Preprocessing  
Go source code contains extensive semantic information, which cannot be fully captured by simply using graph structures. Additionally, large projects often consist of thousands of lines of code, whereas vulnerabilities are usually concentrated within just a few lines. Therefore, we first perform preprocessing operations on Go source code to reduce the amount of code and thus reduce the training overhead.  
(1) Filter Go files and remove redundant code: We retain only files with the.go extension from the project since large projects often contain multiple programming languages. Our model focuses solely on detecting vulnerabilities in Go code. We also remove comments, test files, and import statements, as they are unrelated to actual vulnerability detection and only increase the code volume.  
(2) Extract each API function and construct an AST: Most security vulnerabilities are related to function calls, so extracting each function is essential for comprehensive vulnerability analysis. The Go language provides the \(go/ast\) and \(go/parser\) packages, which allow us to parse Go source code into an abstract syntax tree (AST). By traversing the AST, we can accurately locate each function call.
# Statistical Analysis and Modeling for Optical Networks
## 5 Statistical Analysis and Modeling for Optical Networks
### Characterization of Optical Networks
#### 5.1.1 Random Network  
Nodal degrees (the number of links connected with a node) of these networks follow Poisson distribution. When the average nodal degree of these networks becomes high, their distribution takes a bell shape (commonly known as the bell curve). The typical characteristics of random networks, often modeled by the Erdos-Renyi model, means that most nodes have a similar number of connections. This Erdos-Renyi model describes random networks where N nodes connect with probability \(p\) [G(N, p)] or through M randomly chosen edges [G(N, M)]. It helps analyze network properties like connectivity, clustering, and phase transitions in random networks. They exhibit a short average path length, where any two nodes are likely connected by a relatively small number of steps. In addition to that, they tend to have a low clustering coefficient, indicating that neighboring nodes are unlikely to be connected themselves. These networks lack the hubs or central nodes found in many real-world networks, and their structure arises purely from random connections. The nodes in random networks are typically not very well connected. The optical networks which are similar to the random networks are called 'random-like' optical networks. They exhibit a Poisson distribution-like pattern for the nodal degrees though they are not completely random. These topologies follow these 'random-like' patterns. In Figure 2, we show the nodal degree distribution of vBNS network (a Pan-USA core optical network), which is a random-like optical network.  
A large fraction of optical networks, like many complex networks, exhibit a scale-free nature, meaning their degree distribution follows a power law, where a few nodes have many connections, while most nodes have few. This characteristic impacts network robustness and vulnerability. Random networks exhibit key characteristics, such as degree distribution, clustering, and path length. In Erdos-Renyi networks, the degree distribution follows a Poisson distribution, while in scale-free networks (e.g., Barabasi-Albert), the degree distribution follows a power law, with a few highly connected hubs. Clustering coefficients in random networks are typically low, indicating weak local connectivity. The average path length tends to be short, following the \"small-world\" effect in some models. Connectivity and robustness depend on the probability of link formation; above a critical threshold, the network forms a giant connected component, while below it, fragmentation occurs. Percolation properties influence resilience--random failures affect networks differently than targeted attacks. In addition to that, information flow and diffusion dynamics in random networks depend on topology, impacting fields from epidemiology to social media spread.
# A New Framework for Visual Classification of Multi-Channel Malware Based on Transfer Learning
## 4 Experimental Evaluation
### Evaluation Indicators  
First, we needed to understand the concepts of true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FNs). A TP a predicted malware sample that is also a malware sample in fact. An FP is a predicted malware sample, but it is a benign software sample. An FN is a predicted benign software sample, but is actually a malware sample. A TN is a predicted benign software sample that is also a benign software sample in fact. To evaluate the performance of the classifier, the accuracy, precision, recall  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class ID** & **Family** & **Samples** & **Percentage** \\ \hline
1 & Ramnit & 1541 & 0.1418 \\
2 & Lollipop & 2478 & 0.2280 \\
3 & Kelihos\_Ver3 & 2942 & 0.2707 \\
4 & Vundo & 475 & 0.0437 \\
5 & Simda & 42 & 0.0039 \\
6 & Tracur & 751 & 0.0691 \\
7 & Kelihos\_Ver1 & 398 & 0.0366 \\
8 & Obfuscator.ACY & 1228 & 0.1130 \\
9 & Gatak & 1013 & 0.0932 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of samples for each malware family in the Microsoft dataset.  
and F-score were used as the evaluation indicators. At the same time, the macro average and micro average were also used as indicators to detect malware categories in order to evaluate the performance comprehensively.  
The accuracy is the proportion of all predictions that are correct to the total, and the formula is as follows:  
\[Accuracy(A)=\frac{TP+TN}{TP+TN+FP+FN} \tag{10}\]  
The precision is the proportion of samples correctly predicted as malware to all samples predicted as malware, and the formula is as follows:  
\[Precision(P)=\frac{TP}{TP+FP} \tag{11}\]  
The recall is the percentage of correctly predicted malware samples to all actual malware samples, and the formula is as follows:  
\[Recall(R)=\frac{TP}{TP+FN} \tag{12}\]  
The F-score is the harmonic average of precision and recall, and the formula is as follows:  
\[F-score(F)=\frac{P\times R}{P+R}\times 2 \tag{13}\]