esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Architectures of 5G and 6G Networks
### ML Algorithms for Detection
#### 3.1.2 Optimized WIDAT-Based Algorithm
```
**Wireless Intrusion Detection Algorithm**
```
1:Setup Environment
2:Import libraries:  
Numpy for numerical computations  
Pandas for data handling  
Standard Scaler for feature normalization  
Isolation Forest for anomaly detection  
Classification report for evaluating model
3:Data Ingestion & Generation
4:Set random seed for reproducibility
5:Generate normal data with mean = 0, std = 1
6:Generate anomalous data with mean = 3, std = 1
7:Create labels:  
1 for normal samples  
\(-1\) for anomalous samples
8:Combine normal and anomalous data into a single dataset
9:Shuffle combined dataset for randomness
10:Create a Data Frame to store features and labels
11:Data Preprocessing
12:Remove missing or corrupted rows from dataset
13:Split data into:  
X:Features  
Y:Labels
14:Normalize features using StandardScaler
15:Train-Test Split
16:Split data into training (80%) and test (20%) sets
17:Model Training
18:Initialize IsolationForest with:  
n_estimators = 100  
contamination = 0.1
19:Train model using training data
20:Model Evaluation
21:Predict labels for the test dataset
22:Convert predictions to match label format (1 for normal, \(-1\) for anomalies)
23:Display classification report to evaluate:  
Precision  
Recall  
F1-score
24:Real-Time Detection Simulation (Optional)
25:Generate new incoming data to simulate real-time detection
26:Normalize new data using fitted Standard Scaler
27:Predict anomaly scores and labels for new data
28:for each new sample
29:if prediction = 1 then
30:Print \"Normal\" with anomaly score
31:elseif prediction = \(-1\) then
32:Print \"Anomalous\" with anomaly score
33:endforFrom the above results, we observe the following:  
\(\bullet\)**Isolation Forest (Scenario A):** Achieves a balanced recall of 0.80, but with a moderate precision of 0.57, indicating good detection but with many false positives. This suits situations where missing anomalies are costlier than false alarms. \(\bullet\)**Autoencoder (Scenario B):** Delivers slightly precision (0.62) and good recall (0.77), though the overall accuracy is lower. This approach is appropriate for detecting unknown threats with minimal training data. \(\bullet\)**Random Forest (Scenario C):** Performs the highest precision (0.85) and accuracy (0.94), though with slightly lower recall (0.72). It is ideal for precision-critical environments but may miss some attacks. While the data is synthetic, these findings underscore WIDA's adaptability in diverse threat landscapes, with model selection and tuning influencing trade-offs between false positives and detection rates.
# Wormhole Whispers: Reflecting User Privacy Data Boundaries Through Algorithm Visualization
## 4 Findings and Reflection
### Strengths and Limitations  
This study offers several advantages. First, our research focuses on the perspectives and concerns of the general public, rather than experts, regarding data collection and processing. Although ongoing discussions in fields like computer science and programming address data and privacy security, which are of significant theoretical and practical value, our work shifts the focus to enhancing user awareness. By exploring the presentation of data privacy-related algorithms, we aim to empower users with the knowledge necessary for informed privacy decisions.  
Second, the study's novelty lies in integrating poetic metaphors with the complex processes of data flow. We designed a series of simple, visually appealing visualizations that reflect the characteristics and goals of data flow. These visualizations create a symbolic scenario that helps users better understand data security and privacy through interactive, metaphor-driven design.  
Third, our empirical research highlights users' experiences and potential shifts in their decision-making. The results show that introducing new visual symbols to represent real-world data privacy challenges can effectively help participants visualize and interact with their understanding of algorithms and mechanisms in a more engaging and immersive way.  
Our study has several limitations. First, we conducted open-ended interviews instead of in-depth ones, which may have led us to overlook complex relationships from the participants' perspectives. Additionally, we did not track long-term changes in privacy-related decision-making behaviors. Second, the current interactive system is still in its early prototype phase, requiring multiple iterations to optimize the system's interaction design. Future research will aim to include more participants to gather comprehensive feedback and refine the visualization design. Furthermore, we plan to expand the study to a wider range of regions and apply the analytical framework to major global social platforms. This will allow us to explore the adaptability and effectiveness of privacy algorithm visualizations in different cultural contexts. Through these efforts, we hope to better understand and address the limitations identified in the current study, providing valuable insights for future research and design in related fields.
# Intersection-Based Routing with Fuzzy Multi-Factor Decision for VANETs
## 3 Protocol Design
### Road Segment Selection
#### 3.2.2 Direction  
In order for the data to be forwarded to the destination faster, the shorter the road passed, the fewer the number of hops in the link, and the less time it takes. The straight line between two points is the shortest; however, the urban environment determines that the data cannot be forwarded to the destination along the straight line. Make a straight line so that it passes through the current intersection and the destination, the smaller the angle between the road segment and the straight line, the closer the next intersection is to the destination, and the shorter the road through which the data passes. Calculate the counterclockwise angle between each road and the straight line. In Figure 2, the red dotted line is the line between the current intersection S and the destination D. \(\alpha\)1, \(\alpha\)2, \(\alpha\)3, and \(\alpha\)4 are respectively the counterclockwise angles of roads r1, r2, r3, and r4 with the dotted line. The direction attribute of candidate roads r1, r2, r3, and r4 could be evaluated by the angles \(\alpha\)1, \(\alpha\)2, \(\alpha\)3, and \(\alpha\)4 separately. \(360^{\circ}\) will be divided into eight portions with seven threshold degrees generated including \(45^{\circ}\), \(90^{\circ}\), \(135^{\circ}\), \(180^{\circ}\), \(225^{\circ}\), \(270^{\circ}\), and \(315^{\circ}\). According to the angle between the direction to a candidate and the direction to the destination of messages, certain scores can be assigned to each candidate. The smaller the angle is, the higher score the candidate is assigned, and the Dir to each candidate is calculated following the formula:  
\[Dir=\left\{\begin{array}{ll}\widetilde{\delta},\ \ \text{if}\ 0\leq\ angle\leq 45^{ \circ}\ \Big{|}\ \Big{|}\ 315^{\circ}\leq\ angle\leq 360^{\circ}\\ \widetilde{\delta},\ \ \text{if}\ 45^{\circ}<distance\leq 90^{\circ}\ \Big{|}\ 270^{\circ}\leq\ angle<315^{\circ}\\ \widetilde{4},\ \ \text{if}\ 90^{\circ}<distance\leq 135^{\circ}\ \Big{|}\ \Big{|}\ 215^{\circ}\leq\ angle<270^{\circ}\\ \widetilde{2},\ \ \text{if}\ 135^{\circ}<d\ angle<215^{\circ}\\ \end{array}\right.. \tag{8}\]
# Complex-Valued Phase Transmittance RBF Neural Networks for Massive MIMO-OFDM Receivers
## 1 Introduction
In Section 4, simulation results of the MIMO-PTRBF are compared with the results obtained with the OSTBC under maximum likelihood decoding in 5G channel models. Computational complexities are presented in Section 5 and conclusions are discussed in Section 6.
## 3 Detection Approaches Review
### Multi-Modal Fusion Sentiment Analysis  
Regarding multi-modal fusion sentiment analysis, there are three main approaches in the literature, which are categorized by type of fusion (feature, decision, and hybrid) [9]. Feature level fusion fuses information creating features that have information of different data sources; Decision level fusion fuses different modalities in the semantic space; Hybrid level fusion combines both feature and decision level fusion.  
A method for estimating spontaneously expressed emotions in audio-visual data was developed in Reference [23]. Support Vector Regression and decision level fusion achieved an average performance gain of 17.6% and 12.7% over the individual audio and visual emotion recognition methods respectively. In Reference [24], a feature-level fusion approach for recognizing emotion from video and physiological data was developed. The authors perform emotion recognition on the valence-arousal emotional space using Hidden Markov models (HMMs). The best recognition accuracies reported are of 85.63% for arousal and 83.98% for valence. A comparison with the proposed feature-level fusion approach against a decision-level fusion and non-fusion approaches was performed on the same DEAP database, showing that significant improvements in accuracy obtained by the feature-level fusion approach were observed. A hybrid output-associative fusion method for emotion prediction in the valence and arousal space was proposed in Reference [25]. The authors used facial expression, shoulder gesture, and audio cues, and used Bidirectional Long Short-Term Memory Neural Networks (BLSTM-NNs) and Support Vector Regression models for performing sentiment classification. The authors claim that their hybrid method outperforms results from predicting either valence or arousal alone (both for feature-level and model level fusion). A model that combines emotion aware big data and cloud technology with 5G is proposed in Reference [26]. The authors claim that the proposed approach achieves 83.10% emotion recognition accuracy. The different alternatives of multi-modal sentiment analysis, which are presented in Reference [9] are used in the works presented in this section, achieving different results. The decision-level fusion method presented in Reference [23] reported a higher overall correlation between the estimates and reference values of emotion than the hybrid output-associative fusion method presented in Reference [25], and the feature-level fusion method presented in Reference [24] not only achieved higher accuracy than the unimodal and decision-level fusion approaches compared in the same work, but also achieved higher accuracy than the big data approach presented in Reference [26]. These works were selected to give insight on the different alternatives of multi-modal sentiment analysis according to the inclusion/exclusion criteria, since each one uses one alternative, except the work in Reference [26] which follows a different aim, that is applying emotion recognition combined with big data technology.
# Spatial Shape-Aware Network for Elongated Target Detection
## 8 Conclusions  
This study proposed a novel Spatial Shape-Aware Detection Network (SSAD-Net) to address the challenges of detecting elongated and multi-directionally rotated objects in remote sensing imagery. Traditional methods struggle with imprecise feature extraction, anchor misalignment, and inconsistent evaluation of positive samples. To overcome these limitations, SSAD-Net introduces three key components: a Boundary-Guided Spatial Feature Perception Module for shape-aligned feature extraction, a Shape-Sensing Module for shape-adaptive anchor selection, and a Potential Evaluation Module to prioritize high-potential samples during training.  
On UCAS-AOD and HRSC2016 datasets, extensive experiments demonstrated that SSAD-Net outperforms state-of-the-art methods, achieving superior accuracy for elongated and complex targets. The results validated the effectiveness and synergy of the proposed modules in enhancing feature alignment, sample selection, and detection precision.  
However, there are several limitations in this study. Although SSAD-Net excels at detecting elongated and rotated targets, the introduction of dynamic modules increases computational complexity, which may hinder real-time performance, especially when detecting simple, non-elongated, or non-rotated targets. This can limit its scalability when applied to large-scale datasets. Additionally, the model's performance relies on the tuning of empirical parameters, which makes the detection process dependent on the quality and diversity of the dataset. When applied to targets with insufficient representation or occlusion, this could result in suboptimal outcomes.  
In conclusion, SSAD-Net offered a robust and generalizable solution for remote sensing detection, particularly for elongated objects. Future work will focus on improving real-time efficiency and extending the approach to more challenging scenarios, such as cluttered or occluded environments.  
Conceptualization, S.X.; software, S.X.; validation, S.X.; investigation, S.X.; methodology, S.X.; formal analysis, S.X.; resources, D.-H.L. All authors have read and agreed to the published version of the manuscript.  
National Natural Science Foundation of China, Grant No. 72350710798.  
**Data Availability Statement:**[[https://github.com/Lbx2020/UCAS-AOD-dataset](https://github.com/Lbx2020/UCAS-AOD-dataset)]([https://github.com/Lbx2020/UCAS-AOD-dataset](https://github.com/Lbx2020/UCAS-AOD-dataset)) (5 February 2025).  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 7: Partial detection results in scenarios with feature bias.
## 4 Antenna Array
### Normal Array  
The first configuration to be analyzed corresponds to the standard case of the linear array arrangement, where all the antennas are identically aligned. This standard configuration will be identified as a normal array, and it is considered as a reference case for later comparisons. The geometrical arrangement of this configuration is depicted in Figure 7. Each antenna is interconnected by means of intermediate aluminum blocks that provide an interelement spacing of 0.6\(\lambda\). The element numeration starts from the left. All the connectors that feed each polarization (\(H_{x}\),\(V_{x}\)) are placed along the same side of the array.  
Figure 6: Picture of the antenna array in the DLR’s Compact Test Range.
## 2 The CSES Satellite and Its Primary Application in EQ Prediction Practice  
### CSES Satellite  
The China Seismo-Electromagnetic Satellite (CSES, also called ZH-1) was launched successfully on 2 February 2018. It is the first satellite dedicated to geophysics and is also the first component of the space-borne EQ stereoscopic monitoring system in China [21]. It aims to preliminarily explore the characteristics and mechanisms of ionospheric responses before and after seismic activities, based on real-time monitoring of changes in the state of the space electromagnetic environment [22]. The CSES is a sun-synchronous satellite orbiting at a height of 500 km with a descending node of 14:00 local time (LT), and its revisiting period is 5 days. Eight scientific payloads are onboard the CSES, an electric field detector (EFD), a search-coil magnetometer (SCM), a high precision magnetometer (HPM), a plasma analyzer package (PAP), a Langmuir probe (LAP), an energetic particle detector (HEPP), a GNSS occupation receiver (GNSS), and a three-band beacon (TBB). More details on these payloads can be found in their corresponding literature [23].  
The LAP and the PAP are the space plasma in situ detection payloads. The operational modes of the LAP include survey mode and burst mode. The survey mode is used mainly to detect global electron density (Ne) and electron temperature with a sweeping period of 3 s (second), while the burst mode primarily allows detection of key areas over China and within the global main seismic belts with sweeping period of 1.5 s. The PAP measures ion density (Ni), composition, temperature, and flow velocity. PAP has also the same operational modes as that of LAP but with a higher resolution of 1 s for survey mode and 0.5 s for burst [23; 24].  
Since the CSES satellite was launched, an increasing amount of literature has been published on the achievement of data availability [25; 26; 27], EQ case studies [23; 28], seismo-ionospheric statistics [24; 26; 29], a global geomagnetic field model [30], ionospheric events, such as magnetic storms [31; 32] and so on. It has been reported that the ion density measures reduced absolute values due to the contaminated sensors on PAP payload one month after the CSES launch [33]. However, the relative variations of the O\({}^{+}\) data have been investigated, and positive results have been obtained [24; 26; 30].
# Dynamic Analysis and Trajectory Tracking Control for a Parallel Manipulator with Joint Friction
## 3 Inverse Kinematics
### Acceleration Analysis  
The translational acceleration of slider \(S_{ji}\) is obtained by finding the second order derivative with respect to time for Equation (1).  
\[\ddot{L}_{j}=\mathbf{J}_{j}\cdot\dot{\mathbf{\omega}}_{j}+\mathbf{\Phi}_{j} \tag{5}\]  
where \(\ddot{L}_{j}=\left[\ddot{L}_{j1}\), \(\ddot{L}_{j2}\), \(\ddot{L}_{j3}\right]^{T}\),\(\ddot{L}_{ji}=\frac{\mathbf{e}_{ji}\cdot\left[\dot{\mathbf{\omega}}_{j}\times\mathbf{r}_{ji}+\mathbf{ \omega}_{j}\times\left(\mathbf{\omega}_{j}\times\mathbf{r}_{ji}\right)\right]+l_{ji} \mathbf{\omega}_{lji}\cdot\mathbf{\omega}_{lji}}{\mathbf{e}_{ji}^{T}\cdot\mathbf{e}_{i}}\), \(\mathbf{\Phi}_{j}=\left[\mathbf{\Phi}_{j1}\), \(\mathbf{\Phi}_{j2}\), \(\mathbf{\Phi}_{j3}\right]^{T}\), \(\mathbf{\Phi}_{ji}=\frac{\left[\mathbf{\omega}_{j}\times\left(\mathbf{\omega}_{j}\times\mathbf{ r}_{ji}\right)\right]\mathbf{e}_{ji}+l_{ji}\mathbf{\omega}_{lji}\cdot\mathbf{\omega}_{lji}}{\mathbf{e}_{ji}^{T} \cdot\mathbf{e}_{i}}\).  
The rotational angular acceleration and acceleration of the joint lever center of \(b_{ji}a_{ji}\) can be written as follows:  
\[\left\{\begin{array}{l}\dot{\mathbf{\omega}}_{lji}=\frac{\mathbf{e}_{ji}\times\left[ \dot{\mathbf{\omega}}_{j}\times\mathbf{r}_{ji}+\mathbf{\omega}_{j}\times\left(\mathbf{\omega}_ {j}\times\mathbf{r}_{ji}\right)\right]-\mathbf{e}_{ji}\times\ddot{\mathbf{L}}_{j}\mathbf{e}_{i}} \\ \mathbf{a}_{lji}=\ddot{\mathbf{L}}_{ji}\mathbf{e}_{i}+\frac{l_{ij}}{2}\left[\dot{\mathbf{\omega} }_{lji}\times\mathbf{e}_{ji}+\mathbf{\omega}_{lji}\times\left(\mathbf{\omega}_{lji}\times \mathbf{e}_{ji}\right)\right]\end{array}\right. \tag{6}\]  
Singularities occur when the Jacobian matrices are \(\det(\mathbf{J}_{aj})=0\) and \(\det(\mathbf{J}_{bj})=0\). The first occurs when the joint levers are located in the horizontal plane, and the second occurs when all projections of the joint lever on the horizontal plane cross the center of the platform.
## 5 Conclusions  
A machine learning model for damage assessment of porcelain insulators using the FRF is proposed with multiple SVMs and the Adaboost ensemble method. The machine learning model for damage assessment was developed by analyzing the correlation between the extracted features, materials, and defects using the frequency response data of 117 porcelain insulators collected from 154 kV transmission towers in four regions.  
* A nonlinear classification prediction SVM model was more accurate than a linear classification one, and a combination of three nonlinear SVM models resulted in the development of the most reliable model.
* The ensemble model could obtain more accurate prediction results than the SVM model by combining multiple single classifiers, weighting the classification result errors, and increasing the number of learners. Moreover, when seven learners were used, the prediction accuracy was the highest and the data distribution area could be finely divided.
* The classification of porcelain damage specimens was correct; however, it could be difficult to set the division area because certain data of some internal damage specimens are close to the distribution range of normal data. Therefore, it is necessary to establish a dataset by measuring several types of defects that may occur, in order to more accurately set a damage distribution area and develop a predictive model.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON], [PERSON], and [PERSON] contributed device/analysis tools; [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Electric Power Corporation (KEPCO). This work was supported by Research Assistance Program (2019) in the Incheon National University.  
The authors declare that there is no conflict of interest.  
Figure 11: Results of ensemble classification according to the increase in the number of learners.
# Chaotic Image Encryption: State-of-the-Art, Ecosystem, and Future Roadmap
## 4 Ecosystem
### Challenges
#### 4.1.1 Encrypted Image Processing  
Processing encrypted images is a highly challenging job. The related challenges are discussed in the following.  
Data Hiding Data hiding refers to the technique of hiding information in an encrypted image without knowledge of the original contents [146]. One common method of Reversible DataHiding in Encrypted Images (RDHEIs) is Block Permutation and Co-Modulation (BPCM); however, [PERSON] et al. [147], through cryptanalysis, determined that the method cannot protect against KPAs.  
Another big challenge with current data hiding methods is capacity limits. A high-capacity alternative utilizing a blockwise multi-predictor and Huffman coding was proposed by [PERSON] et al. [146]. Other capacities improving techniques based on pixel correlation preservation [148] have also been proposed.  
Other novel contributions to data hiding have been proposed. [PERSON] et al. [149] proposed improvements to common techniques to increase the resistance against common attacks such as JPEG compression and noise addition. [PERSON] et al. [150] proposed a novel method of decryption using separate keys for both the decryption of the image and the reversion of the data hiding.  
The image retrieval problem refers to the issue of maintaining privacy when outsourcing image search services in the cloud. Work by [PERSON] et al. [151] aimed to fix issues with low accuracy and high involvement from image owners using the process of extracting image features using neural networks and encrypting using the k-Nearest Neighbors (kNN) algorithm. Other progress in the field includes work involving multi-owner access by [PERSON] et al. [152].  
Techniques of mixing image encryption with lossy compression have been proposed. One such proposal was made by [PERSON] [153], where an iterative reconstruction technique was utilized for decompression. However, the encryption technique used was relatively weak compared to exiting algorithms.  
Another algorithm was proposed by [PERSON] et al. [154], where a selective compression technique is paired with inpainting, a method of restoring missing pixels, to reconstruct the image.  
A novel procedure known as encrypted image folding was proposed by [PERSON] and [PERSON] [155]. This technique not only encrypts the image in question, but also decreases its dimensionality, allowing for more efficient storage.