esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of Results with Other Methods
From Figure 14d, it is evident that the method proposed in this paper effectively identifies various types of faults with remarkably high classification accuracy, nearly reaching 100%. However, it should be noted that the recognition rate for C4 faults is slightly lower at 91%. This discrepancy suggests potential misestimation related to cage failure and its coupling fault. The other three methods exhibit inferior accuracy compared to our proposed approach. The training process for each method is depicted in Figure 15. It can be observed that the proposed method demonstrates the most rapid convergence rate and ultimately stabilizes at approximately 99%. However, despite the relatively fast convergence speed of ResNet and DenseNet methods, their training processes are characterized by instability primarily due to significant interference from background noise. Meanwhile, the LeNet-5 network model exhibits lower training accuracy and unstable convergence accuracy. These results further validate the robustness of our proposed method. Figure 14: Comparison of the confusion matrix of different algorithms: (**a**) LeNet-5, (**b**) ResNet, (**c**) DenseNet, (**d**) The proposed method.
## 1 Introduction
### Novelty and Contribution  
The geographical distribution of edge devices increases the chances of security risk as well as physical interference or damage. In addition, remote accessibility of edge devices presents opportunities for data theft and sabotages corporate operations. There are numerous research works available in the literature that address the above-discussed issues. Some of the research publications are survey conclusions related to the security aspects of IoT networks without any specific consideration of EC-assisted network deployments. The contributions made in this survey are listed below:  
* We present a summary as well as detailed scrutiny and analysis of security and privacy-related issues about EC-assisted IoT services. Also, security objectives and functions on EC-based IoT applications are discussed.
* A classification of data security threats and attacks due to poor design approaches, misconfigurations, and implementation flaws is discussed. Also, appropriate mitigation techniques for the detection and prevention of attacks are covered.
* Detailed taxonomy of PUF classification based on silicon and non-silicon-based fabrication is presented, and significant performance and quality matrices are discussed.
* A comprehensive summary of AI/ML-based cryptography techniques for the mitigation of data security and privacy threats is presented. Also, the significance of reliable datasets and training data for the development of accurate ML algorithms is discussed in this survey.
* A discussion about future security research goals, privacy-related open challenges, and deeper insights into future research directions in the context of the EC-based IoT ecosystem is offered.
## 3 Methodology
### Multi-scale Decomposition Steps  
WMCF can be used for image decomposition because it can preserve edge information while smoothing the image. In addition, Gaussian filter (GF) is a widely used image smoothing operator. In this paper, the multi-scale decomposition is based on WMCF and GF. The multi-scale decomposition of the PAN image using WMCF and GF obtains a large-scale image \(LS\), a small-scale image \(SS\) and a basic image \(Basic\). By this decomposition method, an input image can be decomposed into three sub-images with different scales. The specific decomposition process is as follows:  
Firstly, the input image \(M\) is processed using WMCF and GF to obtain the filtered images \(I_{w}\) and \(I_{g}\), respectively.  
Secondly, \(SS\) is obtained by the difference of \(M\) and \(I_{w}\). \(LS\) is obtained by the difference of \(I_{w}\) and \(I_{g}\).  
Finally, \(I_{g}\) is used as the basic image, i.e., \(Basic\). Formulas (30)-(34) show the specific computational procedure. The flow chart of the image decomposition method based on WMCF and GF is shown in Figure 7.  
\[I_{g}=\text{GF}(M,\delta,\rho) \tag{30}\]  
\[I_{w}=\text{WMCF}(M) \tag{31}\]  
\[SS=M-I_{w} \tag{32}\]  
\[LS=I_{w}-I_{g} \tag{33}\]\[Basic=I_{g} \tag{34}\]  
where \(\rho\) and \(\delta\) represent the size of the radius and the variance of the GF. \(M\) represents a PAN image. WMCF\((\cdot)\) and GF\((\cdot)\) represent the GF and WMCF operation, respectively.
# Investigation of CuTi Alloy for Applications as Non-Sparking Material
## 4 Conclusions  
Two experimental Cu-Ti alloys with excellent chemical and structural homogeneity were obtained by conventional casting.  
For the Cu\({}_{2}\)Ti alloy, good solubilization of Ti in the Cu matrix and the formation of a eutectic based on solid solution and \(\beta\)-TiCu\({}_{4}\)-Ti phase were observed. By increasing the Ti content from 2 to 3 wt%, a TiCu\({}_{4}\) intermetallic compound was formed in the alloy, in addition to the eutectic structure observed in the Cu\({}_{2}\)Ti alloy.  
Despite the detection of the Ti-Cu\({}_{3}\) compound by XRD, it was not confirmed by EDS tests, and its metastable state caused it to transform into the intermetallic TiCu\({}_{4}\) compound. Titanium has been identified as solubilized in the Cu matrix and in intermetallic compounds (mainly TiCu\({}_{4}\)) near the boundaries.  
Cu-Ti alloys are a suitable solution for applications in potentially explosive environments because they have passed wear tests and 16,000 cycles in two types of explosive atmospheres without producing hot sparks that could cause explosions.  
When the Ti content was increased from 2% to 3% by weight, an increase in hardness was observed, particularly in the Young's modulus, mainly as a result of the formation of titanium-based compounds, particularly TiCu\({}_{4}\).  
The hot rolling of the alloy has hardened the matrix by producing a greater number of grain boundaries and by generating a greater number of intermetallic compounds, smaller in size than in the cast state, homogeneously distributed in the copper matrix with solubilized titanium.  
Conceptualization, A.-M.C., R.G.C., A.M.R. and G.B.; Methodology, G.B.; Software, B.G.-B.; Validation, M.A.B.; Formal analysis, G.D.V.; Investigation, A.-M.C., R.C., M.B., A.M.J., A.M.R., B.G.-B. and F.C.L.; Resources, R.G.C.; Data curation, R.C., M.A.B., M.B., A.M.J., G.D.V., B.G.-B., F.C.L. and C.B.; Writing--original draft, A.-M.C., R.C., M.A.B., M.B., A.M.R., F.C.L. and G.B.; Writing--review & editing, G.D.V. and C.B.; Visualization, R.G.C. and A.M.J.; Supervision, C.B.; Project administration, A.-M.C. and C.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 13: Main element distribution of CuTi alloy worn surface.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Routing Protocols Performance on 6 LoWPAN IoT Networks
## 2 Related Work  
6 LoWPAN, an Internet Engineering Task Force (IETF) working group, specifies header compression and encapsulation techniques that allow IPv6 packets to communicate with IEEE 802.15.4 standard [10]. 6 LoWPAN covers applications that need lower data rates for wireless internet connectivity. It runs in two frequency bands: 2400-2483.5 MHz internationally and 902-929 MHz in North America. With AES-128 security features, it achieves 250 kbps downlink/uplink with a range of 10-200 m [11].  
The 6 LoWPAN network is extremely resilient; it can self-heal and auto-configure to adjust to changing circumstances. With its mesh network, which eliminates single points of failure and supports numerous border routers for internet connectivity. Mesh network can increase overall energy consumption but marginally less than that of binary tree topology [12]. Scalability is attained by supporting up to sixteen thousand devices and thirty-two routers, which makes it appropriate for both small and big networks. In addition, 6 LoWPAN has lower latency than Bluetooth and Zigbee, it is also more affordable and uses less power [13]. In the following sections, the characteristics of the three types of routing protocols which are assessed in this study are described.
## 3 Results and Discussion
### Electrical Resistivity  
The evolutions of the bulk electrical resistivity (\(\rho\)) values of saturated specimens of both concretes (C and C*) are shown in Figure 2. Mean values along with the corresponding error bars are shown. Electrical resistivity is an indicator of reinforced concrete durability [41], especially regarding rebar corrosion susceptibility [42]: the higher the resistivity, the lower the rebar corrosion susceptibility. Figure 2 shows that resistivity values are similar for both concretes up to 14 days. From 28 days onward, concrete C* shows values about 10% higher than concrete C. This shows, in qualitative agreement with results in Section 3.2, the ability of the admixture to create a more closed porous network, as compared to concrete C, which most probably will reduce the permeability of the concrete C*. However, quantitatively, the effect seems to be more pronounced in Figure 2 than what can be expected from the different values of permeable porosity shown in Table 4. The explanation can be traced to the different conditions in the experimental procedures (see Sections 2.3 and 2.4). While the specimens used for the porosity measurement were cured in a humid chamber (no permanent contact with liquid water) and immediately tested, the specimens for the \(\rho\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Concrete C**} & \multicolumn{2}{c}{**Concrete C*} \\ \cline{2-5}  & **Mean Value** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **Mean Value** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Absorption after immersion & & & & \\ and saturation (\%) & 8.33 & 0.26 & 8.05 & 0.33 \\ Dry bulk density (kg/m\({}^{3}\)) & 2200 & 10 & 2210 & 20 \\ Bulk density after immersion & & & & \\ and saturation (kg/m\({}^{3}\)) & 2380 & 10 & 2390 & 10 \\ Apparent density (kg/m\({}^{3}\)) & 2690 & 0 & 2700 & 0 \\ Permeable pore volume (\%) & 18.31 & 0.46 & 17.83 & 0.61 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results from the ASTM-C642 test.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Concrete C** & **Concrete C* \\ \hline Mean value (MPa) & 30.00 & 31.71 \\ Standard Deviation (MPa) & 0.17 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Compressive strength at 28 days age of the tested concretes.  
measurements were water saturated in vacuum after the same curing step, and later kept immersed in tap water. This points to a possible important effect of the curing and service conditions of concrete, being most probably the permanent contact with liquid water the more favored condition for developing the permeability properties of concrete incorporating an admixture of type CCADM. A careful examination of Figure 2 shows that the differences in \(\rho\) of both concretes are apparent only after 14 days of water immersion.
# Development and Field Testing of a Wireless Data Relay System for Amphibious Drones
## 2 Materials and Methods
### Legal Procedures for Field Tests  
Possible legal registrations for drone flights might be a salient concern for this type of application. We tried to minimize legal procedures by optimizing the system design for water quality monitoring. Therefore, our system did not include requirements for any of the following: (a) throwing objects from drones, (b) hanging objects in flight, (c) high-power radio wave outputs for communication, (d) exceeding 25 kg of weight in flight, or (e) payload including toxic substances. We registered the drones through the Japanese Drone/UAS Information Platform System (DIPS) as a type of drone with 0.100-25 kg flight weight. The rules set by the manager of Akita Prefectural Koizumigata Park were followed. Notification of the flight was made before the flight tests.
## 2 Research Methodology
### Evaluating the Proposed Methodology  
To examine the effective estimation of the actual process capability index, the proposed method will be applied for theoretical distributions with a known actual process capability index. During the evaluation of the proposed methodology process, it is essential to provide multivariate nonnormal data with an arbitrary distribution and determined correlation. An algorithm has been developed that produces nonnormal multivariate data with the required correlation. Initially, random vectors of the multivariate normal distribution are generated, using the zero-mean vector and the covariance matrix that includes the desired correlation coefficients. Then, data are generated utilizing the inverse of the cumulative distribution function (CDF) for each nonnormal variable. The parameters of the desired nonnormal variable are used in the inverse CDF at the values of the normal distribution's cumulative distribution function. The multivariate nonnormal data produced by these steps would thus have a specific distribution and correlation coefficients. The output correlation between the variables is, however, quite close to the required correlation and appears to vary somewhat across runs. As a result, the algorithm will go through multiple iterations until the tolerance between the intended and output correlation is reached.  
Table 2 presents four different samples' properties from three distributions. Also, the distribution type, the number of variables, specification limits, and parameters for each variable, correlation matrix, and actual PCI for each sample are presented. For example, the first sample is generated using gamma distribution, and it has two variables. The first variable has an upper specification limit equal to 13 and is generated using shape and scale parameters of 1 and 2, respectively. The second variable of this sample is generated using a shape parameter of 2 and scale parameter of 3, and its upper specification limit is 26. Also, the correlation coefficient of the first sample is 0.49, indicating a moderately positive correlation. Finally, this sample has an actual PCI of 0.89. The proposed method will be applied to the four samples in Table 2. Consequently, the proposed method's performance is evaluated with different samples from different distributions.
## 3 Procedure to Synthesize the IKM Using Groebner Basis
### Fourth Step: Monomial Order Selection  
The polynomials that integrate the equation system shown in Equation (20) would constitute the generators of an Ideal that will be the starting point for the Groebner Basis calculation [18]. It is important to highlight that any solution of the calculated Groebner Basis is also a solution of the Ideal, which translates in a solution of the original polynomial equation system [26]. Therefore, the obtention of this basis simplifies greatly the calculation of the IKM.  
A fundamental step in the calculation of the Groebner Basis is the selection of the monomial order because this order will establish the way in which the variables are calculated while solving the obtained basis. Different monomial orders will produce different bases, but it is important to remember that the solution of all these bases is the same solution of the initial Ideal [26].  
The main three types of monomial orderings used in Groebner Bases calculations are: Lexicographic Order (lex), Graded Lexicographic Order (grlex) and Graded Reverse Lexicographic Order (grevlex) [26]. Between these three types of monomial orderings, the one that assures the existence of a simple analytical solution for the calculated basis is the lexicographic order (lex) because this order arranges the monomials following a strict ordering in which a variable always precedes those of lesser value in the order. This strict order guarantees that the obtained basis is a triangular equation system, whose variables can be solved one at a time. In this project, we are interested in finding analytical solutions for the Inverse Kinematic Problem, and therefore we selected the lex order as the type of monomial ordering used for the calculated Groebner Basis.  
Continuing with our example of the PUMA 560, the selected lex order for this robot is the one shown in Equation (21). This selected lex order represents the same order in which the variables were solved while applying the geometric method presented in Section 2.2:  
\[c_{2}>s_{2}>s_{3}>c_{3}>c_{1}>s_{1} \tag{21}\]
## 5 Experiments
### Generalization Experiments with Different Backbone Networks Using the Proposed Method  
To validate the generalization ability of the proposed FAS, DDH, and ECS across different backbone networks, we conducted comparative experiments on the DOTA1.0, DOTA1.5, and HRSC2016 datasets, using the mAP and FPS as performance evaluation metrics. Specifically, mAP measures detection accuracy, while FPS reflects the model's inference speed, which is critical for its practicality in edge device deployment. As the objective of this study is to achieve high-precision, low-latency oriented object detection, we prioritized optimizing inference speed during the training phase to lay a solid foundation for subsequent deployment.  
From Table 6, it is evident that the proposed methods, when paired with different backbone networks, consistently achieve high detection accuracy. Notably, using ConvNext-XL as the backbone network yields the highest mAP across all three datasets (83.9% on DOTA1.0, 72.9% on DOTA1.5, and 89.3% on HRSC2016), demonstrating its strong representational capacity in significantly enhancing model performance. However, its inference speed of only 17.5 FPS falls considerably below the requirements for real-time detection, making it less suitable for scenarios with high real-time demands.  
In contrast, Inception-ResNet101 achieves a favorable balance between accuracy and speed. It attains mAP values of 82.6%, 79.5%, and 89.1% on DOTA1.0, DOTA1.5, and HRSC2016, respectively, which are only slightly lower than ConvNext-XL, by 0.8%, 6.6%, and 0.2%. However, its inference speed reaches 24.4 FPS, the fastest among all the backbone networks. This indicates a well-balanced trade-off between feature extraction capability and network complexity, making it particularly suitable for edge device deployment. Compared to ResNet-152, Inception-ResNet101 not only improves inference speed by 7.3 FPS but also enhances mAP (by 14.8% on DOTA1.0 and 30.9% on HRSC2016), demonstrating its superior architectural design, which provides significant advantages in both accuracy and real-time performance.  
Furthermore, under the YOLOv3 framework, using Inception-ResNet101 as the backbone network results in inference speeds comparable to ResNet-152. However, the former  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FAS** & **DDH** & **ECS** & **mAP/\%** & **FPS/Frame** \\ \hline - & - & - & 73.2 & 26.2 \\ \(\surd\) & - & - & 78.6 & 25.8 \\ \(\surd\) & \(\surd\) & - & 80.1 & 24.5 \\ \(\surd\) & - & \(\surd\) & 79.8 & 25.5 \\ \(\surd\) & \(\surd\) & \(\surd\) & 82.6 & 24.4 \\ \hline \hline \end{tabular} Note: \(\surd\) indicates that the component is used, - indicates that the component is not used.  
\end{table}
Table 5: Ablation experiments of the proposed method on DOTA dataset.  
achieves significantly higher mAP across all three datasets, with a particularly notable improvement of over 30% on HRSC2016. This further demonstrates the superior adaptability of our proposed method in high-density, small-target detection scenarios.
## 2 Single-Stage LGG Overview
### LGG Layout  
The investigation of gas dynamic aspects for a new single-stage LGG for the MCAST Hypervelocity Impacts Facility is the first step for the definition of the layout. One of the most important parameters is the projectile's velocity when it hits the target [43], with a focus on the effect of initial loading parameters on the projectile's velocity as it exits the launch tube [44]. The projectile's velocity is influenced by initial factors such as gas pressure and compressibility. According to the theory, there is a nonlinear relationship between the initial pressure and the velocity as the gas leaves the barrel. Consequently, a dedicated model in MatlabTM SimulinkTM was developed to accurately simulate the performance of the LGG. The data obtained from the model serve as driver inputs for designing the first-stage reservoir, determining the pump tube length, and selecting the appropriate launch package.  
Figure 1 represents the key parts of the single-stage LGG. The launch package, which consists of the protective sabot and the projectile, is accommodated in the pump tube downward and is accelerated with the high-pressure gas from the first stage. The first-stage reservoir contains an inert gas, such as helium (He), under high pressure. After the acceleration phase, a sabot-stopping system enables the projectile to be detached from the sabot; the mechanism of this system is described in Section 3. Two laser blades in the launch tube monitor the projectile's trajectory toward the target while measuring its velocity; the projectile finally impacts the target in the impact chamber.  
A range of pressure values have been considered for the reservoir. The launch package of 40 g at 150 bar allows for velocities up to \(\approx\)500 m/s to be achieved using a 3 m long pump tube, as shown in Figure 2.  
Figure 1: Schematic of a single-stage LGG.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 1 Introduction
Aiming at a clear and comprehensive overview, the mechanisms, materials, performance improvements and applications of piezoresistive strain sensors are all considered, which can help researchers find the proper ways to achieve accurate and reliable monitoring of structural deformation. First, the basic piezoresistive sensing principles are explained, followed by an elaboration on the sensing materials, substrates and electrodes. Second, improvement approaches to sensor performance are introduced in detail by optimizing the sensing parameters of sensitivity, stretchability, linearity, hysteresis and durability. Third, applications of flexible piezoresistive strain sensors in terms of SHM are presented. The existing challenges and potential prospects are indicated. The strategies for selecting proper piezoresistive strain sensors for civil SHM are also presented.
# A Survey: Network Feature Measurement Based on Machine Learning
## 2 Machine Learning Method in Network Measurement
### Supervised Learning in Network Measurement
#### 2.1.9 Discussion on Supervised Learning in Network Measurement  
The performance of frequently used supervised learning techniques is listed in Table 2 based on the research in this paper. Practically speaking, classification problems have more application opportunities than regression problems, and different parts of network assessment have been addressed through the use of random forests, SVM, ANN, and FNN. Though several feature extraction techniques and datasets have been utilized by researchers, more relevant techniques have maintained a high accuracy of more than 90%. Additionally, the best answers varied depending on the various conditions. The complexity of the dataset, feature extraction, user needs, and other aspects should be taken into account while choosing an acceptable approach.