esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper proposes a new method to get explicit expressions of some quantities associated with performance analysis of the maximum likelihood DOA algorithm in the presence of an additive Gaussian noise on the antenna elements. The motivation of the paper is to make a quantitative analysis of the ML DOA algorithm in the case of multiple incident signals. We present a simple method to derive a closed-form expression of the MSE of the DOA estimate based on the Taylor series expansion. Based on the Taylor series expansion and approximation, we get explicit expressions of the MSEs of estimates of azimuth angles of all incident signals. The validity of the derived expressions is shown by comparing the analytic results with the simulation results.  
maximum likelihood (ML); angle-of-arrival (AOA); uniform linear array (ULA); approximation Article
## 4 Experiments
### Results and Analysis
#### 4.2.7 Rare Attack Types and Adaptability Analysis  
While our primary dataset provides comprehensive coverage of common attack types, we specifically evaluated ARCS's performance on rare attack scenarios using the UNSW-NB15 dataset [33], which includes uncommon attack categories such as Worms (0.13% of total attacks), Shellcode (0.18%), and Analysis attacks (0.35%). As shown in Table 5, for these rare attack types, ARCS achieved detection rates of 76.3%, 72.8%, and 79.1%, respectively, showing lower but still acceptable performance compared to common attack types. To evaluate adaptation capabilities, we conducted experiments where certain attack types were entirely excluded from training data and only introduced during testing. ARCS showed varying degrees of generalization: for attacks sharing similar patterns with known types, the detection rate averaged 71.4%; meanwhile, for attacks with wholly unique signatures, performance dropped to 58.6%. These results indicate that, while ARCS can leverage learned patterns to handle some novel attacks, its effectiveness is limited when encountering completely new attack methodologies.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Attack Type** & **Ratio** & **Detection Rate** & **False Positive Rate** & **Average Response Time** \\ \hline Worms & 0.13\% & 76.3\% & 5.2\% & 27.8 s \\ Shellcode & 0.18\% & 72.8\% & 5.8\% & 29.3 s \\ Analysis & 0.35\% & 79.1\% & 4.9\% & 26.1 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance analysis on rare attack types.
## 4 Cases Studies  
### Test Rig  
The acoustic signal was collected using a microphone sensor from a rolling bearing fault simulation test rig with an acceleration process. The test rig was driven by an electric motor, and the power was transmitted to the rotating shaft through flexible coupling. The fault bearing was installed at the farthest end of the motor. The locations of the fault bearing and the microphone sensor are shown in Figure 5.  
The faulty bearing is depicted in Figure 6. Use wire cutting to preset faults on bearings. The shape of the fault was a through-slot with a width and depth of 0.1 mm. The signal sampling frequency was 20 kHz, and the rotational speed was an acceleration process from 0 to 1328.2 r/min.
## 4 Dataset and Experimental Setup
### Experimental Environment Summary  
The experimental testbed setup is summarized in Table 5, covering the hardware, software, networking tools, and protocols used throughout all the trials.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Metric** & **Definition** & **Purpose** \\ \hline Precision (P) & \(\text{P}\,=\dfrac{\text{TP}}{\text{TP}+\text{FP}}\) & False alert minimization \\ Recall (R) & \(\text{R}\,=\text{TP}\,=\dfrac{\text{TP}}{\text{TP}+\text{FN}}\) & Anomaly sensitivity \\ False Positive Rate (FPR) & \(\text{FPR}\,=\dfrac{\text{FP}}{\text{FP}+\text{TN}}\) & Measures false alarms on normal data \\ F1-Score & \(\text{F1}\,-\text{Score}\,=\dfrac{2\times\text{P}\times\text{R}}{\text{P}+\text {R}}\) & Balance of P and R \\ AUC-ROC & \(\text{AUC}\,=\,\int_{0}^{1}\text{r}\text{T}PR(\text{FPR})\,d(\text{FPR})\) & Threshold-independent accuracy \\ Inference Latency & Avg. time per sample (ms) & Real-time viability \\ Update Time & Local training + communication (s) & Training efficiency \\ Memory Usage & RAM consumption on edge (MB) & Resource feasibility \\ Gradient Norm Clipping Ratio & \% updates above L2 threshold & Privacy leakage indicator \\ Noise-to-Signal Ratio (NSR) & \(\text{NSR}\,=\,\dfrac{\parallel\text{c}\parallel_{2}}{\parallel\text{Avg} \parallel_{2}}\) & DP strength vs. accuracy \\ Privacy Budget (\(\epsilon\)) & Cumulative \(\epsilon\) tracked over rounds & Differential privacy accounting \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of metrics used to assess detection accuracy, efficiency, adversarial robustness, and privacy protection. Each metric is described alongside its mathematical definition and intended purpose.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Attack Type** & **Source** & **Description** \\ \hline DDoS & CICIDS, Synthetic & High-volume packet floods targeting edge nodes \\ Spoofing & Synthetic & IP/MAC spoofing in temporal bursts \\ Unauthorized Access & TON\_IoT & Credential stuffing and privilege escalation \\ Model Poisoning & Synthetic & Malicious updates or label flipping during FL rounds \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of attack scenarios evaluated during experimentation. Each scenario represents a realistic or adversarial threat vector relevant to 5G-enabled IoT environments.
## 3 Results and Discussion
### The Grating Constant \(\Lambda\)
#### 3.1.1 Impact of the Recording Intensity: \(\Lambda(I)\)  
Figure 1 shows lateral progressions of the grating constant, plotted against the recording intensity, for three different exposure durations. Measuring errors (not depicted in the figures) are below 0.3%.  
In regards to Figure 1, the influence of the recording intensity becomes apparent in terms of quality as well as quantity. On the one hand, the grating period always shows a decrease towards the centre of the grating, _i.e._, \(\Lambda(I)\) exhibits a negative slope:  
\[\frac{\partial\Lambda}{\partial I}<0 \tag{3}\]  
On the other hand, the impact of the exposure duration is demonstrated by the fact that the effect of a laterally decreasing periodicity is enhanced with increasing duration of exposure:  
\[\frac{\partial^{2}\Lambda}{\partial I\;\partial t_{\text{exp}}}<0 \tag{4}\]  
In the case of shorter exposure (21 s, blue curve in Figure 1), the grating constant decreases laterally by only 0.6%, which is only slightly above the error limits. In contrast to this, a long exposure duration (38 s, orange curve in Figure 1) results in a significant lateral decrease of 5%. This correlation finds  
Figure 1: Lateral progressions of the grating constant \(\Lambda(I)\), derived from lateral scans. The longer the exposure duration, the more \(\Lambda\) decreases towards the centre of the grating. The period is almost constant for \(t_{\text{exp}}=21\) s (blue squares), while in case of \(t_{\text{exp}}=27\) s (green circles) and \(t_{\text{exp}}=38\) s (orange diamonds) a more and more intensity-dependent behavior arises.  
expression in Equation (4) and is in line with results from studies of the shrinkage caused by holographic grating formation in acrylamide based photopolymer film, where higher shrinkage was measured for recording with lower intensity and longer time of exposure [28]. Furthermore, the dependency of the material response on the recording intensity is in compliance with results from time resolved investigations [9].
## 4 Discussion
### Breakthrough Value of Novel Germplasm and Mechanisms  
The seven PSR materials identified hold significant breeding value: NR21/KL01 (SRI = 1.0), exhibiting complete pod integrity, serves as a foundational parent for elite cultivar development. Yuyou 342/KL01 (SRI = 0.97) and W10/KL01 (SRI = 0.97) combine landrace adaptability with superior resistance traits. Non-introgressed landrace 'Yuyou 342' (SRI = 0.86) reveals a novel mechanical reinforcement mechanism**, independent of the S12.68 locus (Section 3.2).  
Particularly noteworthy is material 187308 (SRI = 0.78). The contradiction between its homozygous susceptibility at S12.68 and moderate PSR phenotype suggests the involvement of uncharacterized QTLs (potentially on chromosomes A10 or C08), redirecting future gene discovery efforts. To further elucidate these novel pathways, future studies will prioritize the following: (i) Transcriptome profiling of the pod dehiscence zone in Yuyou 342 across developmental stages to identify candidate genes involved in vascular bundle strengthening; (ii) Validation of the causal link between BnIND1 suppression and pod shatter resistance in 187,308 via transgenic complementation or CRISPR-based gene editing; (iii) Linkage mapping/QTL analysis using segregating populations derived from 187308 to pinpoint the genomic region(s) responsible for the atypical BnIND1 regulation and its phenotypic effect.  
NR21/KL01 represents the highest-documented shattering resistance to date. Its zero-shattering phenotype provides an unprecedented genetic resource for cultivar breakthroughs. 'Yuyou 342' and 187,308 hold strategic value: Their S12.68-independent resistance mechanisms effectively broaden the genetic diversity of PSR resources, mitigating vulnerabilities from single-gene dependency. The atypical BnIND1 downregulation in 187308 further offers a novel entry point for deconstructing pod shattering regulatory networks.  
Critically, the discovery of S12.68-independent resistance mechanisms in Yuyou 342 and 187308 addresses a major concern in breeding--over-reliance on a single locus. These novel pathways broaden the genetic base of shatter resistance, mitigating the risk of resistance breakdown and offering complementary targets for pyramiding strategies to achieve more durable resistance.
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 4 Case Study: Mold Assembly
### Validation and Discussion  
The case study of the mold assembly, modeled and implemented in the previous section, serves as a practical experiment to validate the core assertions of the FFOM methodology. This detailed application provides a basic validation of the effectiveness of the framework in key areas essential for dynamic industrial environments. First, the scalability and modularity of FFOM are validated through its unique fractal design. This integration is a local add-on, so there is no need to reconfigure the broader ontology or modify existing relationships between the MoldAssembly and CoreAssembly. This demonstrates that the fractal approach allows the ontology to be efficiently and predictably extended through non-disruptive updates, a significant advantage over rigid models that require complex maintenance. Second, the practical utility of the fuzzy semantic hierarchy is validated through the support of data-driven queries that demonstrate subtle differences. Engineers who need to identify the most reliable components for critical tasks can run a SPARQL query to retrieve all the BaseAssembly instances with an assembly confidence greater than 0.9. This ability to filter and reason based on confidence rather than simple binary states enables more intelligent and realistic decision support, such as prioritizing preventive maintenance or selecting premium quality assemblies for a given order. Finally, FFOM's adaptability to real-time changes is verified by its ability to update relationship strengths at the instance level. If real-time sensor data indicates a gradual degradation in assembly alignment, the assemblyConfidence value of a particular linked entity and its lastChecked timestamp can be automatically updated. This is a simple data-level modification that keeps the core ontology schema intact. This demonstrates the framework's ability to reflect changing operational realities, allowing the ontology to function as a living, dynamic model of the factory floor, rather than a static snapshot.  
To further contextualize these validated contributions, Table 5 compares FFOM with other contemporary data management approaches. As the table illustrates, traditional OWL ontologies often lack native uncertainty handling, and domain-specific manufacturing ontologies do not prioritize structural scalability as a core feature. FFOM is unique in its synergistic integration of fractal structures for scalability and formalized fuzzy logic for uncertainty management. The 'High (Modular)' rating for FFOM's modularity is specifically attributed to its fractal-based design; new components or entire process layers can be integrated by simply instantiating the established fractal pattern, a localized change that does not require the restructuring of the existing ontology. This dual capability, designed from the ground up, positions FFOM as a robust and novel solution for dynamic industrial environments, where both challenges are paramount.
## 4 Result and Analysis
### Results of Machine Learning Models  
The initial phase of this study involved evaluating different machine learning models on the given dataset and comparing their accuracy, average F1 score, and precision-recall performance. The logistic regression model achieved an accuracy of 96.91% and an average F1 score of 73.96%. While the model performed well for classes 0-3, it failed to classify instances from class 4, resulting in an F1 score of 0%. The SVM model yielded results  
Figure 3: Correlation heatmap showing the relationships between features after feature engineering, highlighting significant correlations among variables.  
similar to those of logistic regression, while the naive Bayes model achieved an accuracy of 64.59% and an average F1 score of 48.70%. Although it showed some improvement in classifying instances from class 4 compared to logistic regression, its overall performance for the other classes was lower.  
The random forest model exhibited the best performance, with an accuracy of 99.88% and an average F1 score of 97.46%. It successfully classified instances from all classes, including class 4, with high precision and recall. The K-nearest neighbors (KNNs) model recorded an accuracy of 99.36% and an average F1 score of 97.62%. This model also performed well across all classes, although there was a slight reduction in performance for class 3. The decision tree model obtained an accuracy of 99.83% and an average F1 score of 97.76%. Its performance was consistently high across all classes, effectively classifying instances from the underrepresented class 4. Overall, the random forest model was the best performer in terms of accuracy, average F1 score, and precision-recall, achieving an accuracy of 99.88% and an average F1 score of 97.46%. It effectively classified instances from all classes, including the underrepresented class 4. Table 1 summarizes the results for each machine learning model.  
Figure 4 displays the confusion matrices for all the models we evaluated. The following figures illustrate the confusion matrices for each individual model. Upon comparing these matrices, it is clear that the random forest and decision tree models exhibit strong performance in accurate classifications, with high values along the diagonal elements, suggesting they correctly predict most instances across all classes. In contrast, the logistic regression model shows poor performance in classifying the 5 th class (web attacks), as reflected by the low value in the corresponding diagonal element. Similarly, the naive Bayes model shows weaker performance overall, particularly in classifying the majority class (Class 0).
# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Fibromyalgia  
Fibromyalgia is a disorder characterized by widespread musculoskeletal pain accompanied by fatigue, bowel abnormalities, sleep irregularities, anxiety, mood swings and memory issues [89; 90]. There seem to be no specific diagnostic tests to confirm fibromyalgia. Since the causes of fibromyalgia are yet to be precisely determined, it is difficult to process a specific treatment for the condition [91]. Naltrexone is one of the most tried and effective medications against fibromyalgia. This drug (in low dosages) acts in a neuroprotective capacity and is a tolerable and cost-effective solution [92; 93; 94]. Moreover, researchers have explored improvised techniques to deal with such chronic relapsing conditions [95]. In particular, adaptive interventions have been found to exhibit profound efficacy in these treatments. In adaptive interventions, the treatment dosage is modified in accordance with the current condition of the patient. In this regard, control system engineering helps to obtain optimal dosage solutions to maximise the efficacy of such strategies [96; 97; 98; 99]. This approach also minimizes waste and overcomes the limitations of conventional therapies. Conventional treatments tend to be based on standard protocols devised for standard responses, which may not account for individual patient characteristics.  
Hence, investigators implemented the MPC-led adaptive intervention-based treatment of fibromyalgia using naltrexone dosages [93; 100]. Their studies included daily patient reports consisting of self-assessment of treatment outcomes. This self-assessment served as the framework for applying the MPC architecture to naltrexone dosage control. It consisted of overall feedback of the current level of fibromyalgia symptoms, followed by specific questions related to fatigue, stress, sadness, mood, pain, sleep and gastric  
Figure 11: Closed-loop scheme for anesthesia automation in clinical practice [86].  
conditions. The study modeled these symptoms as system outputs. The drug dosage and the placebo were considered as the system inputs. In this way, the problem was identified from a systems and controls perspective, and system identification techniques were used to develop models from regular self-assessment reports of the participants. These dynamical systems models were used as the basis for applying model predictive algorithms to determine the dosage, taking the external disturbances into consideration. Hybrid model predictive control (HMPC) techniques were also explored due to the discrete nature of the dosage. Additionally, a multiple-degrees-of-freedom technique was used to tune the controller instead of conventional weight matrix tuning. This methodology enabled the independent alteration of set-point tracking as well as measured/unmeasured disturbance rejections in the closed-loop system [101; 102].
## 2 Proposed Theory
### Multiscale Guided Filter Enhancement  
#### 2.2.1 Multiscale Guided Filter Decomposition  
[PERSON] et al. [12] presented the guided image filter (GF), which not only benefits in edge-preserving but also be computationally efficient. Consequently, it is widely applied in the domain of image processing currently. We adopt the guided filter to decompose the image, in which the guide is identical to the filtering input \(I\). The local linear model between the guide image \(I\) and the filter output \(Q\) is the critical assumption of the guided filter. \(Q\) is a linear transformation of \(I\) in a window \(\omega_{k}\) centered at the pixel \(k\).  
\[Q_{i}=a_{k}I_{i}+b_{k}\qquad\forall i\in\omega_{k} \tag{5}\]  
where  
\[a_{k}=\frac{\sigma_{k}^{2}}{\sigma_{k}^{2}+\varepsilon} \tag{6}\]\[b_{k}=\overline{I_{k}}-a_{k}\mu_{k} \tag{7}\]  
Here, \(|\omega|\) is the number of pixels of \(\omega_{k}\), \(\mu_{k}\) is the mean of I in \(\omega_{k}\), \(\sigma_{k}^{2}\) is the variance of I in the \(\omega_{k}\), and \(\overline{I_{k}}\) is the mean value of \(I\) in \(\omega_{k}\).  
When the area has rich details, the \(\sigma_{k}^{2}\) is relatively large, \(a_{k}\) approaches 1, and \(b_{k}\) tends to 0; the guided filter can keep the details in the local area. When the area has rich details, the \(\sigma_{k}^{2}\) is relatively small, \(a_{k}\) approaches 0, and \(b_{k}\) tends to \(\overline{p_{k}}\); the guided filter behaves as a weighted mean filter. \(\varepsilon\) is a parameter depended on the image information, which determines whether it is an edge should be preserved.  
Therefore, the guided filter behaves as an edge-preserving smoothing operator. For simplicity, we refer to it as \(Q=GF(I)\). \(Q\) can be regarded as the base layer of the input image \(I\), which contains the low-frequency information of the input image \(I\), reflects the intensity change of the image on a large scale, while \((I-Q)\) can be regarded as the detail layer, which contains the high-frequency information of input image \(I\), reflecting the details of the image on a small scale.  
As introduced above, we can obtain a smoothed base layer and a detail layer by the guided filter. In order to obtain more complete details, we could utilize the guided filter iteratively to obtain the multiscale smoothed images. Meanwhile, the multiscale detail images can be generated. The specific procedure can be described as follows.  
\[\left\{\begin{array}{ll}B_{1}=GF(I)\\ D_{1}=I-B_{1}\\ B_{k}=GF(B_{k-1})&k=2,3,\ldots\\ D_{k}=B_{k-1}-B_{k}&k=2,3,\ldots\end{array}\right. \tag{8}\]  
The \(B_{i}\) is the \(i\)th base layer while the \(D_{i}\) is the \(i\)th detail layer. Then, we can decompose the original image as follows.  
\[I=B_{n}+D_{1}+D_{2}+\ldots+D_{n} \tag{9}\]  
Specifically, in our study and experiment, as is shown in the Figure 1, we decompose the image into three layers: one base layer and two detail layers. Therefore, the multiscale decomposition progress can be described as  
\[I=B_{2}+D_{1}+D_{2} \tag{10}\]
# Magnetic Field Penetration Depth in Various Materials and Applications
###### Abstract  
The magnetic field penetration depth poses challenges in communications, geophysical exploration, biomedical therapies, and superconducting device design. The objective of this systematic review was to identify, analyze, and understand the depth of magnetic field penetration in conductors, semiconductors, superconductors, insulators and natural materials, modelling applications in medicine, engineering, and basic sciences. An exhaustive search was carried out in databases such as Scopus, IEEE Xplore, ScienceDirect, and IOPSCIENCE using keywords such as depth and penetration and magnetic and field and materials. PRISMA was used as a method to define inclusion and exclusion criteria, ensuring quality, transparency, and methodological rigor, followed by a detailed statistical analysis. The results showed that the penetration depth varies according to the material, frequency, and application conditions. In human tissues, infrastructure, and agricultural applications, the penetration range is between 0.01 and 0.2 m, with the possibility of a range greater than one meter in specific applications. The higher the frequency, the lower the penetration depth is confirmed. In conclusion, the penetration of the magnetic field depends on material properties such as conductivity and magnetic permeability, frequency, and temperature. The developed models offer relevant information for future research and technological applications.  
magnetic field range; conductive/insulating materials; frequency; conductivity; temperature