esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Compression of Convolutional Neural Networks Using Tensor Decomposition Methods  
The general pipeline for compressing convolutional neural networks using tensor decomposition methods is illustrated in Figure 3.  
In this work, we focus on compressing convolutional layers by decomposing their weight tensors using the CP, SVD, and Tucker decomposition methods. The compression process begins by selecting a predefined set of layers to compress. For each selected layer, a specific decomposition method--CP, SVD, or Tucker--is assigned based on the requirements of the experiment.  
To streamline the compression process, we introduce a universal compression parameter \(\alpha\), which allows for determining a decomposition rank for layers of different dimensions. We discuss this parameter in detail in Section 3.2.5.  
After the decomposition of layers with identified ranks, the model is fine-tuned for \(e\) epochs. After that, we reconstruct the tensors to full form, choose \(\alpha\), which allows for achieving a higher compression ratio, again identify the decomposition ranks, re-decompose the matrices with new ranks, and fine-tune the model again for \(e\) epochs. This process is repeated multiple times, until the desired compression ratio and model accuracy is achieved.  
Using this approach allows for achieving high compression ratios without a significant drop in model accuracy: the model weights are smoothly adapted to the decomposed representation due to the iterative nature of the algorithm.  
Figure 3: Pipeline of compressing pre-trained model using our proposed method.  
Figure 2: Tensor train (TT) and grid search (GS): expected runtime in maximum objective function evaluations vs. growth of problem dimension \(d\).
## 3 DSPOT-Enhanced Self-Evolving Neural Networks
### Bayesian Optimization-Based Parameter Search  
The performance of NNs depends on the quality of datasets \(X_{up}^{class}\), \(X_{down}^{class}\), and \(X_{non}^{class}\) obtained by the DSPOT-based ramp classifier in Section 3.1. As the performance of the DSPOT-based ramp classifier relies on the parameters \(\mathbf{b}=(l,d,q^{up},q^{down})\), we develop a Bayesian optimization-based approach that can efficiently find the best parameters \(\mathbf{b}^{*}\). The idea is to model the unknown function between the parameters and the training errors as a multivariate Gaussian distribution, and then use a computationally cheap acquisition function to guide the search for the best parameters.  
Specifically, we introduce an acquisition function \(\zeta(\cdot)\) as the optimization objective, which characterizes the expected training error improvement under \(\mathbf{b}\),  
\[\zeta(\mathbf{b})=\mathbb{E}\Bigg{[}\sum_{class}\sum_{i}(F_{i}^{class}(\mathbf{ b}^{*})-F_{i}^{class}(\mathbf{b}))^{+}\Bigg{]}, \tag{14}\]  
where \(F_{i}^{class}(\mathbf{b})\) denotes the training error of the NN trained under \(\mathbf{b}\) using \(X_{i}^{class}\) described in Section 3.2, and \(F_{i}^{class}(\mathbf{b}^{*})\) is the lowest error that has been obtained so far. It is assumed that the training errors \(\{F_{i}^{class}(\mathbf{b})|i\in\{up,down,non\},class\in\{GE,Mitsubishi\}\}\) are random variables following the multivariate Gaussian distribution \(\mathcal{G}\sim\mathcal{N}(m(\mathbf{b}),\Sigma(\mathbf{b}))\) with mean \(m(\mathbf{b})\) and covariance \(\Sigma(\mathbf{b})\). In each attempt, we find \(\mathbf{b}\) that maximizes the acquisition function \(\zeta(\mathbf{b})\). Then, we use this \(\mathbf{b}\) as the input of Algorithm 1 to determine \(X_{up}^{class}\), \(X_{down}^{class}\), and \(X_{non}^{class}\), based on which we evolve the NNs. Then, \(\{F_{i}^{class}(\mathbf{b})|i\in\{up,down,non\},class\in\{GE,Mitsubishi\}\}\) will be added into a sample set \(\mathcal{S}\), and the mean \(m(\mathbf{b})\) and covariance \(\Sigma(\mathbf{b})\) of \(\mathcal{G}\) will be updated based on Bayesian optimization [48]. The details of the Bayesian optimization-based parameter search are given in Algorithm 2.  
Figure 8: Mutation by appending a node, where Node 6 is inserted between Node 1 and Node 5.  
Figure 7: Mutation by appending a connection, where the link from Node 1 to Node 4 is inserted.
## 4 Performance Evaluation
### Reliability Analysis  
From (4), we have \(P_{et}\leq P_{e}+P_{AB}^{\text{key}}-2P_{e}P_{AB}^{\text{key}}\), where \(P_{eN}=P_{e}+P_{AB}^{\text{key}}-2P_{e}P_{AB}^{\text{key}}\) represents the BER of the existing NRK-SC scheme [15; 16; 17; 18]. Note that the proposed scheme has a lower BER compared to the NRK-SC scheme. This is due to the fact that the errors are corrected via an XOR operation when the encryption channel \([S_{A},p(e_{A}|s_{A}),E_{A}]\) and the transmission channel \([E_{A},p(e_{B}|e_{A}),E_{B}]\) are correlated. From (4) and (10), the comparison of the secure transmission rate can be expressed as  
\[R_{I}^{UB}\geq R_{N}^{UB} \tag{14}\]  
where \(R_{N}^{UB}\) and \(R_{I}^{UB}\) represent the upper bounds of the secure transmission rate of the NRK-SC scheme and our proposed scheme, respectively. It is worth noting that our proposed scheme has superior performance compared to the NRK-SC scheme with respect to the secure transmission rate.  
Moreover, we analyze the reliability of the proposed scheme. When \(X_{A}\) obeys an equal probability distribution, i.e., \(\Pr[x_{A}=1]=\Pr[x_{A}=0]=0.5\), the channel capacity can be derived as  
\[\begin{split} C&=1-H(p(x_{B}|x_{A}))\\ &\geq 1-H(P_{e}+p_{AB}^{\text{key}}-2P_{e}p_{AB}^{\text{key}}).\end{split} \tag{15}\]  
That is to say, compared with the NRK-SC scheme, our proposed scheme has a higher channel capacity. In addition, [PERSON]'s second theorem shows that when the information transfer rate is lower than the channel capacity, reliable communication can be guaranteed by channel coding. This means that [PERSON] can recover the same information as [PERSON].
## 4 Numerical Simulation Analysis of Slope Instability
### Analysis of Finite Element Simulation Results  
#### 4.4.1 Initial Ground Stress  
Through the initial ground stress analysis, the initial ground stress field of the high slope on the left bank is formed, as shown in Figure 10. The displacement distribution characteristics before and after balance under the action of self-weight are shown in Figure 11.  
It can be seen from Figures 10 and 11 that under the action of self-weight, the initial ground stress of the left bank slope is larger below the slope, and smaller at the river valley and the deeper Daihen ditch, which conforms to the characteristics of ground stress distribution. The river valley and initial displacement are mainly vertical, and the area with relatively large displacement is concentrated primarily on the higher ridge on the upstream side and the block surrounded by faults FZ8 and F15.
Additionally, these interactions can have different significant effects on RoF performance depending on the type of high-power laser source (HPLS) used for the PoF system. The main objective of this paper is to present an optical and RF experimental analysis of a hybrid combined PoF-RoF architecture using Raman amplification within the context of two types of PoF systems. To achieve high power (up to 5 W) and stimulate the Raman scattering, we use two common high-power sources: an Erbium-Doped Fiber Amplifier (EDFA) associated with a 1550 nm white source laser and a Distributed Raman Laser. The proposed comparative analyses of the experimental and theoretical results focused on the RF signal degradation caused by the noise generated due to Raman optical amplification. A comparative study is also performed for different lengths of the SMF link (5 km, 10 km, and 15 km) to consider the influence of the link length.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation Through Anomaly Detection Algorithms
#### 5.2.4 Local Outlier Factor  
Local Outlier Factor (LOF) [31] is a density-based anomaly detection technique that identifies outliers by comparing the local density of data points to that of their neighbors. LOF is especially useful for detecting localized anomalies that may be missed by global models like Isolation Forest. In water quality monitoring, LOF can identify unusual patterns in specific regions of the data, such as localized contamination or sudden changes in sensor readings, that deviate from the typical water quality trends in a given area or time. This makes LOF an ideal choice for detecting subtle anomalies in real-world monitoring systems where the data may have varying densities across different locations or times. Figure 22 presents the Local Outlier Factor AUC.  
Figure 21: Logistic Regression AUC.  
Figure 22: Local Outlier Factor AUC.
# A Systematic Review of Cyber Range Taxonomies: Trends, Gaps, and a Proposed Taxonomy
## 1 Connection Technologies
[92] outlined four connectivity models: (1) Layer 1 physical interconnection, (2) Layer 2 datalink interconnection, (3) Software-Defined WAN (SD-WAN), and (4) Layer 3 logical interconnection, with the ECHO H2020 project identifying the latter--specifically a client-to-server VPN model--as the most scalable and practical solution for federated cyber ranges. * **Monolithic:** This design consolidates all services into a single deployable unit. While this simplifies maintenance for small-scale deployments, it becomes less efficient at scale [95]. * **Microservice:** A more modular approach, where individual functions are developed and deployed independently. Microservice architectures support elasticity and scalability but require more complex orchestration and monitoring [101; 102]. * **4. Artificial Intelligence** AI technologies are gradually being integrated into cyber ranges, particularly in the domains of automation, threat detection, and adaptive learning. * **Conventional Machine Learning (ML) and Deep Learning:** These techniques are used for tasks such as digital twin generation and vulnerability detection [62; 96]. * **Large Language Models (LLMs):** As a part of the now rapidly evolving field, LLMs are beginning to be used for log summarization and scenario narration. * **5. Security Tools** These include both offensive and defensive tools, such as intrusion detection systems (e.g., Snort), deep packet inspection modules, and honeypots. These tools are typically made available to participants as part of exercise scenarios [97]. * **6. Monitoring Tools** This characteristic was moved to _Technology_ from _Monitoring_ as the technologies track both the technical health of the cyber range and user activity during exercises. Tools such as Zeek and ELK provide visibility into network traffic, system logs, and user behavior, supporting real-time scoring and after-action analysis [94]. * **7. Database** This characteristic encompasses the storage solutions used for scenario data, telemetry, vulnerability records, and user performance logs. Depending on the use case, relational, document-based, or vector databases may be employed [99]. * **8. Orchestration Tools** Orchestration tools automate the deployment, configuration, and tear-down of cyber-range scenarios. TOSCA and CRACK support declarative infrastructure-as-code approaches, while Kubernetes orchestrates container-based environments. These tools are crucial for enabling repeatability and scalability in complex training scenarios [96; 98]. In sum, the Technology dimension comprised a diverse set of implementation-oriented characteristics, ranging from low-level connectivity and virtualization to advanced orchestration and artificial intelligence. By distinguishing between capabilities (what a cyber range can do) and the technologies (how those capabilities are implemented), our taxonomy offers a more complete picture of the architectural and technical foundations of cyber range systems.
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 5 Experimental Evaluation
### Evaluation Metrics  
To evaluate the performance of C-SHAP, we implemented SHAP and LIME using various machine learning algorithms, including Random Forest (RF), SVC, XGBoost, Logistic Regression (LR), and K-Nearest Neighbors (K-NNs). These models were applied to the different datasets mentioned in Table 2. The experiments were conducted using the free version of Google Colab. To conduct the experiments in this study, we used the sklearn, [[https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)]([https://scikit-learn.org/stable/](https://scikit-learn.org/stable/)) (accessed on 14 November 2024); Pandas, [[https://pandas.pydata.org/](https://pandas.pydata.org/)]([https://pandas.pydata.org/](https://pandas.pydata.org/)) (accessed on 14 November 2024); SHAP, [[https://pypi.org/project/sharp/](https://pypi.org/project/sharp/)]([https://pypi.org/project/sharp/](https://pypi.org/project/sharp/)) (accessed on 14 November 2024); and matplotlib_venn, [[https://python-graph-gallery.com/venn-diagram/](https://python-graph-gallery.com/venn-diagram/)]([https://python-graph-gallery.com/venn-diagram/](https://python-graph-gallery.com/venn-diagram/)) (accessed on 14 November 2024) libraries. We reported execution time, accuracy, and visualized feature selection similarities using Venn diagrams [55; 56].  
Accuracy is reported in the results to demonstrate the performance of the selected algorithm on the dataset. While accuracy measures performance rather than feature interpretability, it highlights the importance of feature selection in achieving optimal results and improving both classification and regression processes.  
In addition, Venn diagrams are a graphical tool used to show the overlap between sets. In this context, they illustrate how the important features identified by C-SHAP and SHAP overlap, highlighting mutual features between the two methods.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset Name** & **Data Points** & **Number of Features** & **Type** \\ \hline Indian Liver Disease & 583 & 10 & Classification \\ \hline Heart Disease & 303 & 13 & Classification \\ \hline Wine & 178 & 13 & Classification \\ \hline Vertebral Column & 310 & 6 & Classification \\ \hline Breast Cancer & 569 & 30 & Classification \\ \hline Diabetes & 442 & 10 & Regression \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets used in the experiments.
## 2 Intelligent Inversion Method of the In Situ Stress Field
### Optimization Solution Based on PSO-BP
#### 2.2.3 PSO-BP Inversion Method  
To enhance the accuracy of in situ stress inversion results, a nonlinear numerical analysis approach was proposed that combines the model boundary conditions with the PSO-BP algorithm. In this method, the complex geological movements are resolved into six basic boundary conditions, and then, through FLAC\({}^{3\text{D}}\) software, the stress state of each boundary is calculated to form the network samples. The PSO-BP neural network is used to establish a nonlinear relationship between the boundary conditions and the stress state at specific testing locations. Finally, the optimal boundary conditions that coincide with the measurement results are obtained through iterative searching. The primary implementation steps are shown in Figure 2:
# Semantic Decomposition and Anomaly Detection of Tympan Membrane Endoscopic Images
## 1 Introduction
We applied AHE and Canny edge detection on endoscopic data to construct images incorporating tympanic membrane transparency and depth information and images containing the perforation width and inflammatory distribution to assist the model in understanding the data. Both types of images were converted into grayscale and learned from the model alongside the original RGB image. Anomaly detection is a significant topic in healthcare that has been extensively studied, for which many approaches have been presented. Anomaly detection is the identification of data samples that do not conform to the distribution of all data (normal data) [15]. The VAE used in this study for anomaly detection is a generative model based on unsupervised learning [16]. Test data were fed into the VAE and it learned only normal data distribution. The difference between the learned image and the test image was calculated into an abnormality score, which was then applied by the machine learning classifier. The abnormality score was computed using the VAE reconstruction error and Kullback-Leibler diversity values. In summary, this study proposes a method for overcoming the limitations of supervised-learning-based studies by detecting anomalies using a VAE after the semantic decomposition of high-complexity data through image preprocessing. Using this solution, we detected abnormalities in tympanic membrane endoscopic data with an accuracy of 94.5%. Our first contribution is the use of the semantic decomposition of highly complex data based on clinical evidence to facilitate the models' interpretation of images and improve anomaly detection performance. Our second contribution is that we can identify the abnormal findings of the tympanic membrane, including perforation, retraction, cholesteatoma, and certainly abnormal data, that are difficult to diagnose for a specific disease, analyzing only normal data. The remainder of this paper is organized as follows. Section 2 briefly introduces the datasets and images used in this study, semantic decomposition, and anomaly detection. Sections 3 and 4 describe the image processing and abnormality score extraction procedures. We present the experiment results in Section 5 and discuss them in Section 6; Section 7 concludes the paper.
# LaRaWAN Metering Infrastructure Planning in Smart Cities
## 3 Materials and Methods
### LoRaWAN Technology Overview
As per the standard documentation [28], the end device initially attempts to optimize connectivity by increasing its transmit power. If this proves insufficient, the device proceeds to lower the data rate as a further adjustment. When two nodes utilize different spreading factors (SFs), they can transmit their data simultaneously, provided that neither transmission is received at significantly higher power. In the case of different SFs, each packet can be demodulated if the difference in received power exceeds the SINR (Signal-to-Interference-Plus-Noise Ratio) threshold for each SF, as indicated in Table 1. For instance, a transmission using SF 7 can be successfully received as long as another transmission using SF 8 does not exceed a 16 dB power difference.
## 3 Experimental Results  
The silicon photonic 3-dB power splitters were fabricated in an 8-inch wafer-scale silicon process facility using deep-UV (DUV) lithography. The designed power splitters were implemented on an 8-inch silicon-on-insulator (SOI) wafer with a 220-nm-thick device layer and a 2-um-thick buried oxide (BOX) layer. First, we deposited a 70-nm-thick layer of Tetraethyl Orthosilicate (TEOS) by low-pressure chemical vapor deposition (LPCVD) as a hard mask layer. Then, we deposited patterned rib waveguides with DUV lithography, and a 60-nm-deep shallow etch on the silicon device layer. Strip waveguides were also implemented by subsequent DUV lithography and silicon full etch. Finally, the remaining oxide hard mask layer was removed by wet etching.  
In order to characterize the fabricated power splitter, we used cascaded six stages of the 1 \(\times\) 2 3-dB power splitter, as shown in Figure 5a. We employed vertical grating couplers to couple light into the fabricated device. The coupling loss of the grating couplers was measured to be \(\sim\)6.0 dB from a back-to-back grating coupler pair on the same chip. The input power is P, and the cascaded output powers were then P/2, P/4, P/8, P/16, P/32, and P/64, respectively, as indicated in the Figure 5a. Figure 5b shows the scanning electron microscope (SEM) images of the fabricated power splitter, which confirm a good agreement with the device design. The nominal waveguide width and gap were observed to be 740 nm and 340 nm. The fabrication deviation from the design was below 50 nm. For the experimental characterization, we used a linear array of polarization-maintaining (PM) fibers with 127-um pitch to guarantee consistent coupling losses from optical ports of the cascaded power splitters. TE-polarized light was coupled to the fabricated device through grating coupler array. The transmission of each stage was measured at the wavelength of 1550 nm and plotted as a function of the number of splitters, as shown in Figure 6a. The transmission of the single power splitter was measured to be \(-\)3.06 dB from the linear regression. The excess loss was estimated to be 0.06 dB (= 3.06 dB - 3 dB). The spectral response of each stage is shown in Figure 6b, exhibiting the broad operating bandwidth over the wavelength range of 100 nm from 1470 nm to 1570 nm. The transmission of each stage (one splitter, two splitters, etc.) was normalized to the transmission of a back-to-back grating coupler pair.  
Figure 5: (**a**) Cascaded six-stage structure for excess loss measurement. (**b**) Scanning electron microscope (SEM) images of fabricated optical power splitter.
## 4 Test Results and Discussion
### Remarks on the Results  
Based on the results of both experimental and numerical investigations, the governing failure mechanisms can be categorized into two main types: buckling or crippling. First of all, local and  
Figure 21: The ultimate normalized moment for the minor axis test by reinforcement type.  
Figure 20: The normalized minor axis average curves.  
distortional bucklings happened in almost every unreinforced specimen's major axis test. These failure mechanisms were located near the weakest zone of the beam, which was typically the mid-span of the specimens. Local bucklings were mostly observed in 200 mm type reinforced specimens, but the presence of condense reinforcing was acting as the constraining factor. As the reinforcing pitch was enlarged, the distortional buckling became the dominant failure mode as observed in the 300 mm reinforcing types. The change in the section's half-wavelength resulted in the change of buckling mode and failure load. Reducing the half-wavelength by decreasing the reinforcement pitch increases the distortional buckling capacity as well as changing the mode from distortional to local-distortional in some cases.  
Secondly, in the case of crippling as another factor of failure, the unreinforced specimens failed due to this factor, especially in the major axis tests. The 200 mm and 300 mm reinforcing types also failed through the crippling mechanism; however, the ultimate load of crippling was different from the unreinforced specimens. The crippling failure mechanism was initiated by a yielding notch which was exhibited on the flange. Then, due to the represented notch, the section became weak at that specific zone. Finally, this shortcoming led to the crippling and the frame collapsed.  
Regarding the presented results, the reinforced frames and single-uprights showed a much better flexural performance in both the major and minor axes monotonic tests, especially the ones with 200 mm reinforcements. Furthermore, the specific reinforcements increased the section stiffness, and strengthened the uprights, especially where the uprights tend to deform or initiate buckling failure. For a better understanding, the observed buckling failure modes are demonstrated in the following sections using FE models.