esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.6 Mesh-Type Configuration Network  
Mesh-type configuration network refers to a network topology in which each device in a network is connected to multiple other devices, forming a mesh-like structure. This type of network is often used in self-healing because it provides redundancy and resilience against network failures. In a mesh-type configuration network, if one device fails, the network can automatically reroute traffic through an alternate path, maintaining connectivity and availability. This approach allows the network to operate seamlessly, even when specific devices or links are unavailable. A proposal by [33] uses a mesh-type configuration to achieve the full potential of a power restoration scheme. The proposal's aim is achieved by deploying the autonomous self-healing technique. However, despite the concerns of the critics of the proposal, who argue that a mesh-type configuration network introduces complications, is costly, and has a higher probability of bi-directional fault current flow,recent studies have shown that a mesh-type configuration network provides flexibility and security and performs independently. Mesh-type configuration network, by its design, establishes multiple points of failure, making the system resilient and making coupling or decoupling processes easily accessible. Using a mesh-type configuration network ensures the decentralisation of systems management and paves the way for the independent control of multi-level architecture. Mesh-type configuration was demonstrated in [30] implementation of wireless mesh networks (WMNs), which are multi-hop wireless networks with instant deployment, self-healing, self-organisation, and self-configuration features. WMNs offer multiple redundant communication paths throughout the network, and the network automatically reroutes packets through alternative pathways in situations where failure occurs in one section of the network or any threat that interferes with a particular path.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
## 6 Challenges in FNDN Security
### Efficient Key and Identity Management  
Existing works discussed only how efficient currently used signature algorithms (such as SHA256 with RSA, ECDSA, or HMAC) are? However, very few works addressed the problem of certificate distribution, revocation techniques, and pseudonyms changing to preserve producers and their data privacy. Moreover, in FNDN the conventionally trusted authorities may not be reachable sometimes. So, how will the data authenticity be checked? More in depth investigation is needed to check the feasibility of currently available schemes in FNDN.
# An Analysis of Cloud Security Frameworks, Problems and Proposed Solutions
## 3 Cloud Security Frameworks
### Cloud Security Alliance (STAR)
#### 3.4.2 Limitations  
1. Reliance on Self-Assessment: The CSA STAR methodology is based on self-assessment by cloud service providers. While CSPs are urged to submit accurate and full information, the framework makes no guarantees regarding the quality or completeness of the information given. Customers must use caution and check the promises provided by CSPs.
2. Developing Technology Coverage Is Limited: The CSA STAR framework may not adequately address the security problems associated with developing technologies or niche cloud services. As technology advances, new security issues that are not expressly addressed in the framework may emerge.
3. Lack of Enforcement: CSPs' compliance with the CSA STAR framework is entirely voluntary. Although the framework promotes openness and best, it lacks legal or regulatory enforcement measures. Organizations must evaluate the credibility of a CSP's promises and adopt extra security measures depending on their unique needs.
## 4 Simulation Results and Analysis
### Current Distribution  
The current distribution shown in Figure 9 shows a strong surface current density (up to 103 A/m) concentrated near the feed and radiating edges of the patch elements, indicating effective excitation and radiation. Uniform excitation across elements implies balanced power division. The phase progression visible across elements implies support for beam steering. High-density areas correspond to patch edges, typical of microstrip radiation. The visualization confirms that the array is operating at the desired resonant mode and supports directional radiation through constructive interference. Current distribution analysis indicates a progressive phase shift across the patches, supporting the steering behavior.  
Figure 8: Simulated 3D far-field radiation pattern of 1 \(\times\) 6 antenna array.  
Figure 9 below shows that the feedline delivers maximum power at this point, exciting the structure. The visible phase progression (approx. 15\({}^{\circ}\) per element) confirms progressive wave excitation, causing beam tilting in the H-plane--a hallmark of passive beam steering. This is where the 2.4 mm coaxial connector injects energy into the microstrip line. Power is split evenly into two branches. The symmetric distribution confirms good matching and power balancing. The transition is smooth, indicating low reflection and good impedance matching, which confirms the power divider is effectively matched and balanced. In Figure 9, these patches are excited by the current from the microstrip branches. Standing  
Figure 9: Surface current distribution for (**a**) 1 \(\times\) 6 antenna array with power divider and (**b**) 1 \(\times\) 6 antenna array.  
wave patterns are visible--this is common in resonant structures. The tapering currents indicate energy radiated outward into free space. The uniformity across patches suggests consistent excitation, crucial for beam forming and pattern shaping, which confirms effective energy transfer from the feed to the patches.
# Data Cleaning Model of Mine Wind Speed Sensor Based on LOF-GMM and SGAIN
## 2 LOF-GMM-SGAIN Model
### Real-Time Anomaly Data Detection
Since the parameters of individual Gaussian components in the GMM are unknown, the expectation-maximization (EM) algorithm is used for estimation. The EM algorithm consists of two steps: the expectation step (E-step) and the maximization step (M-step). In the E-step, for each data point \(x_{i}\), the posterior probability \(P(z_{i}=k|x_{i})\) of it belonging to each cluster is calculated, which is the probability that the data point \(x_{i}\) originates from each cluster. The formula is as follows:  
\[P(z_{i}=k|x_{i})=\frac{P(x_{i}|z_{i}=k)P(z_{i}=k)}{\sum\limits_{j=1}^{K}P(x_{i} |z_{i}=j)P(z_{i}=j)} \tag{6}\]  
In the formula, \(z_{i}\) represents the latent variable, \(P(x_{i}|z_{i}=k)\) denotes the likelihood function conditioned on class \(z_{i}=k\); \(P(z_{i}=k)\) is the prior probability of class \(z_{i}=k\); and \(\sum\limits_{j=1}^{K}P(x_{i}|z_{i}=j)P(z_{i}=j)\) is the normalization factor. In the M-step, the mean vector, covariance matrix, and weight of each cluster are re-estimated. Specifically, for cluster \(k\), the mean vector \(\mu_{k}\), covariance matrix \(\sum\limits_{k}\) and weight \(\omega_{k}\) are calculated. The formulas are as follows:  
\[\mu_{k}=\frac{\sum\limits_{i=1}^{N}P(z_{i}=k|x_{i})x_{i}}{\sum\limits_{i=1}^{ N}P(z_{i}=k|x_{i})} \tag{7}\]  
\[\sum\limits_{k}=\frac{\sum\limits_{i=1}^{N}P(z_{i}=k|x_{i})(x_{i}-\mu_{k})(x_ {i}-\mu_{k})^{\text{T}}}{\sum\limits_{i=1}^{N}P(z_{i}=k|x_{i})} \tag{8}\]  
\[\omega_{k}=\frac{\sum\limits_{i=1}^{N}P(z_{i}=k|x_{i})}{N} \tag{9}\]  
In this context, \(N\) represents the total number of data points. The E-step and M-step are repeated until convergence. The convergence criteria for the algorithm can be when the increment of the log-likelihood function is smaller than a specified threshold, or when the changes in the mean vector, covariance matrix, and weight of each cluster are smaller than a specified threshold. The final GMM model is obtained after multiple iterations. The GMM model is then used to cluster the LOF thresholds, enabling the automatic identification of outliers by the LOF algorithm.
# BLE RSS Measurements Dataset for Research on Accurate Indoor Positioning
###### Abstract  
RSS-based indoor positioning is a consolidated research field for which several techniques have been proposed. Among them, Bluetooth Low Energy (BLE) beacons are a popular option for practical applications. This paper presents a new BLE RSS database that was created to aid in the development of new BLE RSS-based positioning methods and to encourage their reproducibility and comparability. The measurements were collected in two university zones: an area among bookshelves in a library and an area of an office space. Each zone had its own batch of deployed iBKS 105 beacons, configured to broadcast advertisements every 200 ms. The collection in the library zone was performed using three Android smartphones of different brands and models, with beacons broadcasting at \(-12\) dBm transmission power, while in the other zone the collection was performed using of one those smartphones with beacons configured to advertise at the \(-4\) dBm, \(-12\) dBm and \(-20\) dBm transmission powers. Supporting materials and scripts are provided along with the database, which annotate the BLE readings, provide details on the collection, the environment, and the BLE beacon deployments, ease the database usage, and introduce the reader to BLE RSS-based positioning and its challenges. The BLE RSS database and its supporting materials are available at the Zenodo repository under the open-source MIT license.  
B 2019; 4 12; doi:10.3390/data4010012
## 3 Simulation Experiments and Analysis
### Performance Comparison with Different Values of K  
In this section, we explore how the classification performance is affected by the number of samples in the support set (K) under the 5-way K-shot condition. To mitigate the influence of random factors, we consider four different values of K: 1, 5, 10, and 20. For each value of K, multiple experiments are conducted, and the average recognition rate is calculated. The experimental results are shown in Figure 6.  
From Figure 6, we observe that when K = 1, the model achieves a recognition accuracy of 75.88% at 18 dB. This indicates that the proposed algorithm performs well even with only one training sample. When K = 5, the model achieves a recognition accuracy of 83.95%, which is an improvement of 8.07% compared to the case when K = 1. As the value of K increases, the average recognition rate of the model continues to improve. At 18 dB, with K = 10 and K = 20, the model achieves recognition accuracies of 86.16% and 88.30%, respectively. By increasing the value of K, we allow the model to learn the differences between classes from a larger sample pool, thus enhancing its generalization capability and performance on new tasks. However, we also observe that the rate of improvement in recognition accuracy slows down, indicating diminishing returns from increasing the number of samples in the support set.
# Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings
## 3 Parametric Investigation
### Evaluation of Variable Sensitivity  
Table 2 shows the results of the grey relational evaluation between hysteretic performance and parameters. From Table 2, it can be seen that:  
(i) for the maximum lateral load \(P_{\text{u}}\), the influencing sequence was: \(L/h>\rho_{\text{s}}=f_{y}=n>r\); (ii) for the displacement ductility \(\mu\), the influencing sequence was: \(L/h>\rho_{\text{s}}=f_{y}>n>r\).  
Observations from Table 2 are summarized as:  
1. The steel strength (i.e., \(f_{y}\)), the geometric ratio (i.e., \(L/h\)), the reinforcement area ratio (i.e., \(\rho_{s}\)), and the axial load ratio (\(n\)) had the most significant influences on the seismic performance of RRC columns, with \(\gamma\) ranging from 0.80 to 0.87;
2. The sensitivity of RCA percentage on the seismic performance of RRC columns was much less remarkable than that of the steel strength and the other three structural factors (i.e., \(L/h\), \(\rho_{s}\), and \(n\)); however, considering that concrete constituted a large portion of the overall resistance, the influence of RCA percentage should be considered in the seismic design and evaluation of RRC columns.
## 1 Introduction
### Particle Swarm Optimization  
The main idea of PSO is based on the food searching behavior (foraging) of birds or fish that move towards food at certain speeds and positions. Their movement depends on their own experience and experience from other 'friends' in the group (\(P_{best}\) and \(G_{best}\)). The new velocity, \(v_{i}+1\) and the new position, \(x_{i}+1\) for the fishes or birds are obtained using Equations (14) and (15) after the current \(G_{best}\) and \(P_{best}\) have been calculated [13].  
\[v_{i+1}=\omega V_{i}+c_{1}r_{1}(P_{best}-x_{i})+c_{2}r_{2}(G_{best}-X_{i}), \tag{14}\]  
\[x_{i+1}=v_{i+1}+X_{i}, \tag{15}\]  
where \(r_{1}\) and \(r_{2}\) are two random variables in the range zero to one, \(c_{1}\) and \(c_{2}\) are positive constants, \(\omega\) is the inertia weight. \(X_{i}=\left(x_{i}^{1},x_{i}^{2},\ldots,x_{i}^{n}\right)\) and \(V_{i}=\left(v_{i}^{1},v_{i}^{2},\ldots,v_{i}^{n}\right)\) represent the position and velocity of the \(i\)th particle, respectively. \(P_{best}\) represents the personal best location of \(i\)th particle. \(G_{best}\) represents the global best location of the whole swarm which can be expressed as follows,  
\[X^{Pbest}=\left\{\begin{array}{ll}X^{Pbest(j)}&if~{}~{}FV^{j+1}\geq FV^{j}\\ X_{i}^{t}&if~{}~{}FV^{j+1}\leq FV^{j}\end{array}\right., \tag{16}\]  
\[X^{Gbest}=\left\{\begin{array}{ll}X^{Gbest(j)}&if~{}~{}FV^{j+1}\geq FV^{j}\\ X^{Pbest(j+1)}&if~{}~{}FV^{j+1}\leq FV^{j}\end{array}\right., \tag{17}\]  
where \(FV\) is fitness value.  
The optimization process of PSO algorithm can be listed in points as follows  
**Step 1:** Initialization: This stage provides the configuration of the distribution network and candidate DG sizing and location. By establishing the random initial population and the number of iterations, the functional model is generated. Other elements drawn on are the random initial selection of position and velocity in the context of the search space.  
**Step 2:** Calculate fitness (\(f(x)\)): The network solution program is set up with the initial data whereby the fitness will calculate the summation of each particle.  
**Step 3:** Find \(P_{best}\) and \(G_{best}\) for all populations:  
* Finding \(P_{best}\): The parameters of fitness values associated with the placement of each particle are considered in relation to the matching value in previously held positions. Following this, the lower fitness value is listed as \(P_{best}\) in the current iteration.
* Finding \(G_{best}\): Here, the lowest fitness for \(P_{best}\) is associated with every particle in the current iteration. It is then compared to the previous iteration and the lower one is recorded as the \(G_{best}\).  
**Step 4:** Calculate the new velocity for each population. The position and velocity of particles can be calculated for next iteration.  
**Step 5:** Adjust the position (new position, \(x_{new}\)). Here, the value is updated.  
**Step 6:** If the specification is not satisfied, meaning the most minimum value, then, back to Step 2.  
**Step 7:** The final output is the value of \(G_{best}\).
###### Abstract  
This paper presents a mixed-mode universal filter using differential difference current conveyor transconductance amplifiers (DDCCTA). Despite using a minimum number of MOS differential pairs, the proposed DDCCTA is a multiple-input, multiple-output device, that was achieved using the multiple-input bulk-driven MOS transistor (MIBD-MOST) technique, multiple-output current followers and transconductance gains. A subthreshold technique is used to achieve minimum power consumption of the DDCCTA. Thanks to the multiple-input and multiple-output of DDCCTA, the mixed-mode universal filter based on the proposed element can realize five standard filter responses, i.e., low-pass, high-pass, band-pass, band-stop, and all-pass responses, of four modes, i.e., voltage-mode, current-mode, transadmittance-mode, and transimpedance-mode, thus providing 194 filter responses from a single circuit. The natural frequency and quality factor of the filter response can be controlled electronically and orthogonally. The proposed DDCCTA and mixed-mode universal filter are simulated and designed using 0.18 \(\upmu\)m CMOS technology to confirm the functionality of the new circuit. The mixed-mode universal filter uses \(\pm\)0.5 V of supply voltage and consumes 0.374 mW of power when operating at a natural frequency of 10 kHz.  
differential difference current conveyor transconductance amplifier; active filter; analog integrated circuit; bulk-driven MOS transistor; low-voltage low-power +
Footnote †: journal: Applied Sciences  
1
# Panoptic Segmentation Method Based on Feature Fusion and Edge Guidance
## 3 Method
### Dynamic Multi-Scale Feature Fusion Module
#### 3.2.2 Dynamic Feature Fusion Module  
The DFF module facilitates adaptive aggregation of cross-level features (Figure 3), with its core technical components comprising:  
Geometric Alignment: Implementing deformable upsampling on high-level feature \(E_{high}\) to ensure precise spatial alignment with low-level feature \(E_{low}\).  
\[E_{up}=DeformConv(E_{high},\Delta p) \tag{7}\]  
where \(\Delta p\) represents the output generated by the offset prediction network.  
Dynamic Weight Generation: Producing fusion weights through comprehensive global context modeling.  
\[W=Softmax(Conv_{1\times 1}(E_{up}\oplus E_{low})) \tag{8}\]  
Figure 2: Detailed structure of the Dynamic Large Kernel (DLK) module. It employs parallel depthwise separable convolutions (5 \(\times\) 5 and 7 \(\times\) 7 with dilation) cascaded to achieve a large effective receptive field (23 \(\times\) 23). Dual-dimensional attention (spatial ‘As’ via Conv7 \(\times\) 7 on pooled features, channel ‘Ac’ via MLPs on GAP/GMP) adaptively modulates features before residual addition (‘Z’).  
Multi-modal Fusion: Employing a gating mechanism for effective feature integration using dynamically generated weights:  
\[E_{fusion}=W\cdot E_{up}+(1-W)\cdot E_{low} \tag{9}\]  
where \(W\) is the fusion weight matrix, and \(\cdot\) denotes element-wise multiplication. The output \(E_{fusion}\) is the fused feature map.  
Crucially, the fusion weights \(W\) (Equation (8)) are computed based on the content of both feature levels (\(E_{up}\) and \(E_{low}\)) through convolutions and softmax normalization applied to their combination. This allows the DFF module to dynamically adjust the contribution of high-level context versus low-level detail for each spatial location based on the input features, rather than using fixed fusion rules. This adaptive weighting enhances the representation of features across varying scales.  
The dynamic characteristics of both DLK and DFF modules are derived from the incorporation of attention mechanisms [13]. Through comprehensive utilization of global information (spatial and channel dimensions) from input feature maps, the model adaptively modulates feature weights, effectively circumventing the constraints inherent in conventional fixed fusion approaches. This adaptive mechanism enables the model to dynamically adjust its processing strategy based on the specific characteristics of input data, thereby facilitating more efficient extraction of salient features.
## 4 Results and Discussion
### Performance Analysis
#### 4.3.3 Analysis with CICIDS2017 Dataset  
The proposed system has been validated with four standard metrics in relation to the CICIDS2017 dataset, and the corresponding outcomes are presented in Table 7.  
Table 7 shows that the proposed system reached a 0.98 Accuracy rate, a 0.97 F1-Score, a 0.97 Precision rate and 0.97 Recall rate. Then, the accuracy values for diverse weights in the CICIDS2017 dataset were assessed and the analytical outcomes obtained are shown in Table 8.  
From Table 8, it is evident that the accuracy value varied based on weights. Similarly, when weight = 1, the accuracy value was 83.5. This rate progressively increased and decreased with the increase in weights, and it reached 83.5 when weight = 9 and 97 when weight = 10. Following this, the outcomes of k-fold cross-validation for the CICIDS2017 dataset are shown in Table 9.  
From Table 9, it can be seen that the accuracy value changed for each of the folds. In accordance with this, the accuracy value was 0.9375 for fold 1, 0.9563 for fold 2, 0.9688 for fold 3, 0.975 for fold 4 and 0.98 for fold 5. Hence, the accuracy value differed based on the folds. Besides this, a confusion matrix for the CICIDS2017 dataset has been built in order to explore the correct and misclassification rates of the proposed system. The results are shown in Figure 6.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Accuracy** & **Recall** & **Recall** & **Precision** \\ \hline
0.98 & 0.97 & 0.97 & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Analysis with the CICIDS2017 dataset.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt}} \hline \hline
**Weight** & **Accuracy** \\ \hline
1 & 83.5 \\ \hline
2 & 97 \\ \hline
3 & 83.5 \\ \hline
4 & 83.5 \\ \hline
5 & 83.5 \\ \hline
6 & 83.5 \\ \hline
7 & 97 \\ \hline
8 & 97 \\ \hline
9 & 83.5 \\ \hline
10 & 97 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Accuracy for varied weights in the CICIDS2017 dataset.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt}} \hline \hline
**Folds** & **Accuracy** \\ \hline Fold 1 & 0.9375 \\ \hline Fold 2 & 0.9563 \\ \hline Fold 3 & 0.9688 \\ \hline Fold 4 & 0.975 \\ \hline Fold 5 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Results of k-fold cross-validation for CICIDS2017.  
Figure 6 shows that the number of correct classifications of attack classes was 541,412, while the number of correct classifications of normal classes was 114,047. Concurrently, 68 normal classes were misinterpreted as attack classes. Contrarily, one attack class was misinterpreted as normal. In this case, as the correct classification rate seemed to be higher than the misclassification rates, the proposed system has been inferred to be efficient. The performance analysis showed that the proposed system reached a 99.92 accuracy rate with the NSL-KDD dataset, 0.98 accuracy with the CICIDS2017 dataset and 99.783 accuracy with the UNSW-Bot dataset. In this case, the proposed work has shown high accuracy in relation to the NSL-KDD dataset.
# Roasting Extraction of Lithium from Fly Ash: A Study of Influential Parameters and Mechanisms
## 3 Results and Discussion
### Acid Leaching Effects
#### Acid Leaching Process Characterization Analysis  
The SEM results of the residues after acidic leaching are shown in Figure 12a,b. The residues exhibited more extensive particle destruction, and almost no spherical residues were found within the large particles. Additionally, the background of the residues appeared to be fine and smooth, and little large size residue existed. Herein, the residues implied the finely textured material enveloping disrupted roasted fly ash particles.  
The XRD pattern of the residues (see Figure 12c) shows a broad amorphous diffraction peak. XRF analysis (see Table 2) further confirms that the finely textured residues consisted of a mixture of silica and sulfites. This suggests that the reaction produced a significant amount of silicic acid, showing the effectiveness of acid leaching experiment in our study. Based on the lithium extraction results and the characterization analysis, the following reaction equations were derived:  
\[2\text{NaAlSiO}_{4}+4\text{H}_{2}\text{SO}_{4}=\text{Na}_{2}\text{SO}_{4}+ \text{Al}_{2}(\text{SO}_{4})_{3}+2\text{H}_{2}\text{SiO}_{3}+2\text{H}_{2} \text{O} \tag{4}\]  
\[\text{H}_{2}\text{SiO}_{3}=\text{H}_{2}\text{O}+\text{SiO}_{2} \tag{5}\]  
Figure 11: Effect of sulfuric acid concentration on the leaching rate of lithium.