esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Framework and Results
### Case Study Analysis of CARAG
#### 5.1.1. Thematic Embedding Generation for Claim 59
This selectivity highlights how this method prioritizes the most pertinent evidence and connections for Claim 59. This visualization underscores the rich thematic interconnections that the SOI provides, enhancing contextual understanding and facilitating more targeted evidence retrieval for the claim under investigation, as discussed in the subsequent text. Following this preprocessing step of SOI identification, we introduced one of the core contributions of this work: constructing a thematic embedding for Claim 59 from the SOI, which serves as a key component of the query for evidence retrieval in the proposed CARAG framework. Specifically, we selected three key components from the SOI: annotated evidence, related claims, and thematic cluster evidence. Each of these components was then encoded using _all-mpnet-base-v2_. The individual embeddings were then aggregated through averaging, as outlined in Equation (1), to create a unified thematic embedding via CEA that encapsulates the wider context of Claim 59 while intentionally excluding the claim itself. This thematic embedding supports CARAG's context-aware approach by integrating both local and global perspectives, ensuring the influence of direct and contextual insights from the underlying corpus to inform evidence retrieval. This foundation not only enhances subsequent claim verification and post-hoc explanations beyond instance-level local explainability but also advances the capabilities of traditional RAG methods.
## 5 Performance Analysis of MSD Algorithm Based on Hybrid SC
### BER Performance of MSD Algorithm  
In the MSD algorithm, the output result is determined based on the maximum correlation value derived from comparisons between the received signal and a set of local signals, distinguished by their symbols being either zero or one. The performance of the MSD cannot be adequately represented solely according to the computational error. Therefore, this study evaluates the BER performance under three-symbol, five-symbol, and seven-symbol MSD algorithms based on hybrid SC.  
Figure 15 illustrates the BER performance of the three-symbol MSD algorithm under different computational schemes and sequence lengths. For a sequence length of 500 in hybrid SC, the BER performance slightly surpassed that with 8-bit binary computing. As the sequence length increased to 1000, the BER performance of hybrid SC was comparable to that of 9-bit binary computing. Additionally, when compared to hybrid SC, the MSD algorithm experienced a more pronounced performance decrease under pure SC.  
Figure 16 illustrates the BER performance of the five-symbol MSD algorithm under different conditions. Similar to the three-symbol MSD, when the sequence length was set to 500, hybrid SC demonstrated a marginally superior BER performance compared to 8-bit binary computing. However, to attain BER performance equivalent to 9-bit binary computing, the sequence length for hybrid SC must be extended to 1500. This is primarily due to the fact that, as the number of symbols in the MSD algorithm increases, the performance loss of binary computing under the same bit quantization becomes smaller. The loss in binary computing mainly stems from data truncation. As the number of symbols in the MSD algorithm increases, the number of potential outcomes of the symbol decision process expands, thereby mitigating the adverse effects of quantization loss. Conversely,  
Figure 14: The RMSE of MSD algorithm variants.  
the primary error source in hybrid SC arises from the fluctuation of its computation results, rendering it relatively insensitive to the number of symbols in the MSD algorithm.  
Figure 17 illustrates the BER performance of the seven-symbol MSD algorithm under different conditions. Compared to those of the three- or five-symbol MSD algorithms, the hybrid SC in the seven-symbol MSD necessitated a longer sequence length to sustain its BER performance at a comparable level to binary computing. When the sequence length reached 1000, the BER performance of hybrid SC marginally exceeded that of 8-bit binary computing. However, even when the sequence length extended to 1500, the performance of hybrid SC remained slightly inferior to that of 9-bit binary computing.  
Figure 16: The BER performance of the 5-symbol MSD algorithm.  
Figure 15: The BER performance of the 3-symbol MSD algorithm.
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 4 Results  
This section evaluates the privacy-preserving performance of the proposed image inpainting method. The evaluations include the effectiveness of the sensitive object detection algorithm, along with the re-detection rate of privacy-sensitive objects after image restoration. Additionally, the point cloud encryption algorithm is assessed for its resilience against inversion attacks. The impact of privacy protection methods on the pose estimation accuracy of ORB-SLAM3 is also assessed. Finally, the running time of ORB-SLAM3 before and after incorporating privacy-preserving methods is compared. Experiments are conducted on the Oxford Robocar, KITTI, and KITTI SLAM Evaluation 2012 datasets. These are taken in real urban environments that include moving pedestrians or vehicles, as well as changes in lighting, to effectively simulate autonomous driving scenarios.
## 2 Lab-Built System to Make TPOFs  
We have developed a novel automatic system to realize TPOFs. Figure 1 shows a schematic view of the realized system. In particular, it includes two motorized linear positioning stages (M150.11, Physik Instrumente (PI), Karlsruhe, Germany) connected to a computer and a heating plate (up to 100 \({}^{\circ}\)C). The OPF is kept at a fixed height of about 1.5 cm from the heating plate (see Figure 1). The plastic optical fiber used presents a Poly(methyl methacrylate) (PMMA) core of 980 \(\upmu\)m and a fluorinated cladding of 10 \(\upmu\)m.  
First, the fiber was kept steady on both sides upon the stages by special holders; then, the POF was heated at 100 \({}^{\circ}\)C along about 2 cm and was stretched by varying the movement of the two micropositioners.  
The linear movement of the stages of the two micropositioners was handled by a card produced by Physik Instrumente and installed on the computer. This board is programmable through LabVIEW software (National Instruments, Austin, Texas, USA) by two \"sub-virtual instruments\" (sub-VIs) made available by Physik Instrumente. Starting from these two sub-VIs, we created a \"virtual instrument\" (VI) in LabVIEW 5.1, a custom interface where the user can select the stages' movements in microns from two different panels and, by changing these two parameters, it is possible to obtain different taper ratios. The taper ratio was defined as the ratio between \(\mathrm{r_{i}}\) and \(\mathrm{r_{0}}\), where \(2\mathrm{r_{0}}\) is the diameter of the tapered region and \(2\mathrm{r_{i}}\) is the total diameter of the used POF (1 mm). Figure 2 shows the top view of the realized TPOF (with \(\mathrm{r_{i}}\) and \(\mathrm{r_{0}}\)).  
When the length setting in the micropositioners increases, the taper ratio increases too. In this work, the taper ratio ranges from 1.3 to 1.8, depending on the chosen parameters.
# Load-Bearing Performance of a Reinforced Fill Structure with Pile Penetration
## 5 Analysis of Results
### The Distribution Law of Pile-Bending Moments
#### 5.2.2 Effect of Different Pile Lengths  
Figure 10 illustrates the variation in pile-bending moments for pile lengths of 50 cm, 55 cm, 60 cm, and 65 cm in the structure. Table 12 presents the maximum pile bending moments for different pile lengths in the structure.  
Based on Figure 10,b, it can be observed that the trend in pile-bending moments is quite similar for shorter pile lengths. Initially, between depths of 140 and 300 mm, the pile-bending moment gradually increases with depth. However, between depths of 300 and 380 mm, there is a decrease in the bending moment, followed by an increase between depths of 380 and 460 mm. Therefore, the largest bending moments occur at depths of 300 mm and 460 mm. In the mid and later stages of loading, as the load increases, the bending moment at a depth of 140 mm experiences a noticeable and significant increase.  
Figure 9: Moment distribution diagram of pile.  
Figure 10: Change diagram of the bending moment of reinforced structures with different long piles.  
Upon analyzing Figure 10c, it can be observed that for a pile length of 60 cm, the distribution of the pile-bending moments exhibits a parabolic trend with the depth. The maximum moment is found at a depth of 300 mm.  
Based on Figure 10d, it can be observed that for a pile length of 65 cm, the pile-bending moment shows different trends at different depths. On the other hand, between depths of 300 and 460 mm, the distribution follows a parabolic trend with depth. When the vertical load is relatively low, the maximum pile-bending moment is found at a depth of 300 mm and decreases as the load increases. However, with higher vertical loads, the maximum bending moment occurs at a depth of 140 mm and increases with the load.  
By combining Figure 10 and Table 12, it can be inferred that with the increase in the pile length, the corresponding position of the maximum bending moment of the pile is shifted downward from the upper part to the middle part, and the corresponding position of the zero point of the bending moment is shifted downward. This shows that when the pile length is short, the embedment depth of the pile is small, and the bending moment of the pile is mainly caused by the rotational deformation of the pile under the action of a large vertical load. When the pile length is longer, the embedment depth of the pile is larger, which provides sufficient resistance to the rotational deformation of the pile, and the bending moment of the pile is mainly caused by the flexural deformation of the pile under the action of a larger vertical load [31].
# An Intelligent Ensemble-Based Detection of In-Vehicle Network Intrusion
## 5 Results and Discussion
Stacking employs two stages to discover and learn how to effectively combine the individual models in the base learners with the meta learners, ultimately enhancing the performance of supervised classifiers. \begin{table}
\begin{tabular}{l l l} \hline
**Model** & **Hyperparameter** & **Value** \\ \hline \multirow{4}{*}{Random Forest} & max\_features & ‘auto’ \\  & n\_estimators & 100 \\  & max\_depth & 20 \\  & min\_samples\_leaf & 2 \\  & min\_samples\_split & 5 \\ \hline \multirow{4}{*}{XGBoost} & n\_jobs & \(-1\) \\  & max\_depth & 3 \\  & n\_estimators & 300 \\  & objective & ‘multi:softprob’ \\ \hline \multirow{2}{*}{Decision Tree} & max\_depth & 20 \\  & max\_features & ‘log2’ \\ \hline \end{tabular}
\end{table} 
Table 8: Hyperparameter configurations for supervised learning models. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Citation** & **Attacks** & **Model** & **Precision** & **Recall** & **F1-Score** & **ROC** \\ \hline \multirow{4}{*}{Flood} & \multirow{4}{*}{Flood} & KNN & 0.99 & 0.99 & 0.99 & - \\ \cline{3-6}  & & Accuracy & & & 0.96 & \\ \cline{2-6}  & SVM & 0.99 & 0.99 & 0.99 & - \\ \cline{2-6}  & & Accuracy & & & 0.97 & \\ \cline{2-6}  & & SVM & 1.00 & 0.96 & 0.97 & - \\ \cline{2-6}  & & Accuracy & & & 0.97 & \\ \cline{2-6}  & & KNN & 0.99 & 0.96 & 0.97 & - \\ \cline{2-6}  & & Accuracy & & & 0.96 & \\ \hline \multirow{4}{*}{Fuzzing} & \multirow{4}{*}{Sweening} & KNN-based RQA, & & & 0.959 \\  & & K = 8 & & & 0.914 & \\ \cline{2-6}  & & Accuracy & & & 0.85 \\ \cline{1-1} \cline{2-6}  & & Accuracy & 0.853 & & \\ \cline{1-1} \cline{2-6}  & & KNN-based RQA, & & & 0.950 \\ \cline{1-1} \cline{2-6}  & & K = 7 & & & 0.927 & \\ \hline \end{tabular}
\end{table} 
Table 9: Our method compared to promising current approaches. Compared to deep learning-based IDS solutions (e.g., CNN-LSTM, GRU, etc. ), our ensemble learning framework offers several advantages. First, it requires significantly less training data and computational resources, making it suitable for deployment in resource-constrained in-vehicle environments. Second, tree-based models are more interpretable, allowing developers to trace decisions back to input features. While deep learning methods often show strong generalization, they are prone to overfitting and require large labeled datasets, which are scarce in the automotive domain. A limitation of ensemble methods is that they may require longer training time when stacking is used, as seen in our timing analysis (Figure 9), but this tradeoff results in higher accuracy and robustness.
# Investigating Models for the Transcription of Mathematical Formulas in Images
## 3 Related Work
### Vision-Language Models  
VL models, i.e., the combination of a computer vision model and language model, receive more popularity since they improve pure computer vision tasks and help to deal with tasks that require information from both vision data and language data [29]. As shown in Figure 6, we investigated the potential of VL models for our task of creating transcriptions from formula images.  
To the best of our knowledge, there is no publication that describes the direct transcription of formulas in images with VL models. However, VL models are used to analyze images and generate text that describes the images [30; 31; 32]. This specific task is referred to as _image captioning_. There are also vision encoder-decoder models like _SWIN-GPT-2_ ([[https://huggingface.co/docs/transformers/main/en/model_doc/vision-encoder-decoder](https://huggingface.co/docs/transformers/main/en/model_doc/vision-encoder-decoder)]([https://huggingface.co/docs/transformers/main/en/model_doc/vision-encoder-decoder](https://huggingface.co/docs/transformers/main/en/model_doc/vision-encoder-decoder)), accessed on 21 January 2024), which consists of a vision transformer _SWIN_[33] as encoder and _GPT-2_[23] as decoder, but, in contrast to the VL models mentioned before, both  
Figure 4: Mathematical expression recognition for LaTeX generation.  
Figure 5: Transcription of LaTeX code.  
Figure 6: Generation of transcriptions.  
parts work independently of each other. VL models that demonstrate the highest performances in image captioning are _BLIP_[34] and _GIT_[35]. _TrOCR_[36] outperforms the best state-of-the-art models in recognizing printed and handwritten text and supports image captioning too. Consequently, we analyzed the VL models _BLIP_[34], _GIT_[35], _TrOCR_[36], and _SWIN-GTP-2_[33; 37] for the task of creating transcriptions from LaTeX code.
## 3 Pseudo-TCT and Verification Experiment
### Pseudo-TCT
#### 3.1.2 Linear Stretching and Percentage Truncation Stretching  
After TCT and the pseudo-TCT, there were negative grey values, and the grey values at the two ends of the grey histogram were very small, which reduced the contrast of the image, and the traditional solution is to add a constant offset, r. Since the pseudo-TCT is an orthogonal linear transformation of the GF-2 image, the correlation between the bands is removed, and the stretching can not only remove the negative grey values but also enhance the contrast of the image [18]. Therefore, the linear stretching of the pseudo-TCT image was performed, and the linear stretching was calculated as follows:  
\[\text{result}=\frac{\left(\text{grey }-\text{ min}_{\text{in}}\right)}{\text{max}_{\text{in}}-\text{ min}_{\text{in}}}\times\left(\text{max}_{\text{out}}-\text{min}_{\text{out}} \right)+\text{min}_{\text{out}} \tag{3}\]  
where the result is the grey value after stretching; grey is the grey value of the input image; \(\text{min}_{\text{in}}\) and \(\text{max}_{\text{in}}\) are the minimum and maximum grey values of the input image; and \(\text{min}_{\text{out}}\) and \(\text{max}_{\text{out}}\) are the minimum and maximum grey values of the output image.  
Although linear stretching was performed on the pseudo-TCT image, the contrast of the image was not strong enough and visually dark due to too few values at both ends of the original image grey histogram, so percentage truncation stretching was used to further enhance the image contrast. Percentage truncation stretching is similar to linear stretching in that the grey values are first truncated at the desired scale and then linearly stretched.
## 2 Related Work
### System Environment  
In LDB, an AUV with a directional transceiver moves at a fixed depth in water and broadcasts its location, which called a beacon, and the angle of its transceiver's beam (\(a\)) at regular intervals, called beacon distance (\(d\)), as shown in Figure 2(a). When the AUV sends a beacon, the sensor nodes that fall in the conical beam receive the beacon; e.g., in Figure 2(a), the sensor node in red can receive the beacon \(B_{2}\) and the sensor node in blue can receive the beacons \(B_{1}\), \(B_{2}\), and \(B_{3}\). The conical beam forms circles with different radii according to the depth of the sensor nodes, as shown in Figure 2(a). When the coordinates of the AUV and a sensor node are \(z_{A}\) and \(z_{s}\) and the angle of the transceiver's beam is \(a\), the radius of the circle formed by the beam for the sensor node is given as follows:  
\[r_{b}=\tan(a/2)\times|z_{A}-z_{s}| \tag{1}\]
## 5 Limitations and Suggestions for Further Research  
This study has some limitations with implications for future research. The first limitation is due to the used datasets (the variables measured in each dataset, including the age of retroreflected sheeting, the region included, and the type of retroreflective sheeting). The present study had a focus on linear and logarithmic models, which limits the performance of the models in terms of \(\mathbb{R}^{2}\) values of 0.50. Additionally, the study had a national focus on road signs mounted in Sweden.  
The NMF data has some limitations because the age of signs was only up to five years old and the fading rate of retroreflectivity may deteriorate quicker after these first five years. The Nordic group plans to continue collecting data by measuring the retroreflectivity, daylight chromaticity, and luminance factor once a year. A follow-up is suggested on the data that will be collected by NMF to obtain a more accurate prediction. Future research must consider data collected from other regions and with higher ages to accomplish more accurate predictions. Nonlinear models such as neural networks, support vector machines, regression trees, generalized additive models, etc., need to be tested in future work for better prediction. Given the short time series on the retroreflectivity of road traffic signs, it is not currently feasible to develop a more complex prediction, and we leave this issue for future work when we acquire more data. Finally, the significance of other factors needs to be investigated, including the distance from the sea, weather zones, surrounding environment, and weather. The road traffic signs mounted in high-speed or crowded roads need to be studied separately to other road signs to obtain more accurate models for prediction.
## 5 Existing Security Mechanisms
### Robustness to Communication Denial-of-Services  
An attacker attempts to disrupt the network's operation by broadcasting high-energy signals. If the broadcasting is powerful enough, then the entire network communication might be jammed. Other attacks are also possible, such as an adversary may delay communication by violating the medium access control protocol. Moreover, an adversary can transmit packets while a neighbor node is also transmitting. [PERSON] [67] has discussed the details of DoS attacks and their countermeasures at different layers of WSN routing, as shown in Table 2. Most of the DoS countermeasures are suitable for static wireless sensor networks, as shown in Table 3. Since, the WMSN healthcare applications are mobile in nature, as a result, secure DoS attack countermeasures still need further investigation for real-time healthcare application using WMSNs.  
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Network Layer** & **Attacks** & **Countermeasures** \\ \hline \multirow{2}{*}{**Physical layer**} & Jamming & Detect and sleep, route around jammed areas \\ \cline{2-3}  & Node tampering & Temper-proof boxing \\ \hline \multirow{2}{*}{**Link layer/medium access control**} & Collision, unfairness and & Authentication and anti-replay protection \\ \cline{2-3}  & Denial of sleep & Authentication and anti-replay, detect and sleep, broadcast attack protection \\ \hline \multirow{3}{*}{**Network and routing layer**} & Neglect and greed, misdirection, spoofing, replaying, routing-control traffic or clustering & Authentication and anti-replay protection, Secure cluster formation \\ \cline{2-3}  & Homing & Header encryption and dummy packets \\ \cline{2-3}  & Hello floods & Pair-wise authentication, geographic routing \\ \hline \multirow{2}{*}{**Transport layer**} & Flooding & SYN cookies \\ \cline{2-3}  & De-synchronization & Packet authentication \\ \hline \multirow{4}{*}{**Application layer**} & Overwhelming sensors & Sensor tuning, data aggregation \\ \cline{2-3}  & Reprogramming attack & Authentication and anti-replay protection \\ \cline{1-1} \cline{2-3}  & Path-based DoS & Authentication and anti-replay protection \\ \hline \end{tabular}
\end{table}
Table 3: Denial-of-Service attacks and countermeasures at each network layer.  
As we have seen in the above section there are tremendous robust security mechanisms but these mechanisms are not directly applicable to healthcare applications where resource constrained devices are used. Consequently, the security gap between the above securities measures are still needs to explore for healthcare applications.