esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 6 Experimental Results
### A-OP Algorithm Simulation Experiment  
The fusion algorithm designed in this paper enables the mobile robot not only to complete global path planning, but also to achieve local planning to avoid obstacles. The fusion algorithm was simulated in 20 \(\times\) 20 and 40 \(\times\) 40 maps. Static and dynamic obstacles were added to the map, and the simulation results of the fusion algorithm are shown in Figure 10. Figure 10a,b shows the experiments in different environments. The black dashed line is the planned path of the improved A* algorithm, and the blue solid line is the planned path of the fusion algorithm.  
The simulation results of the fusion algorithm in Figure 10a,b show that the fusion algorithm starts from the starting point and there are no obstacles on the path until the first critical point. Figure 11 shows the state of the simulated vehicle. It can be seen that the path planned by the fusion algorithm is close to the path planned by the improved A* algorithm and conforms to global path planning. When there are obstacles between two critical points, the locally planned route will avoid the obstacles until it reaches the target point. The experimental results in Table 1 show that in the 20 \(\times\) 20 map, the fusion algorithm uses the feature points in the A* algorithm as intermediate target points when planning the path, compared to the improved A* algorithm which completed regularized path planning from the start point to the target point. The experimental data in Table 1 shows that the fusion algorithm improves the planning efficiency by about 61.09% compared to the traditional A* algorithm, the fusion algorithm avoids the influence of dynamic and static obstacles on robot motion, and the planned path can avoid the obstacles in time to achieve safety. Likewise, the fusion algorithm achieves ideal obstacle avoidance in complex environments with many obstacles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Map** & **Path Length** & **Time/s** \\ \hline Traditional A* & Simple map & 31.38 & 10.97 \\ Fusion algorithms & Simple map & 30.88 & 7.88 \\ Traditional A* & Complex map & 95.43 & 20.33 \\ Fusion algorithms & Complex map & 94.65 & 15.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental results of the two algorithms.  
Figure 9: Path-smoothing experiments. (**a**) traditional algorithms, (**b**) smooth processing path.  
Figure 11: Simulated vehicle status diagram.  
Figure 10: Simulation experiments of A-OP fusion algorithm. (**a**) simple environment, (**b**) complex environments.
## 2 Proposed Scheme
### On Third-Party Side
#### 2.3.1 Data Extraction  
If the third-party has the data hiding key \(K_{h}\), the embedded additional data can be extracted. As the embedded additional data are located behind the recovery information, the length of the recovery information should be extracted from the first usable vertex to determine the breakpoint between the recovery information and additional data. Next, except for the recovery information, all bits of the usable vertices' coordinates can be extracted. Then, the additional data can be obtained by decrypting these bits using the data hiding key.  
D Mesh Model Recovery  
If the third-party has the model encryption key \(K_{m}\) and the recovery information encryption key \(K_{r}\), the original 3D mesh model can be recovered. The detailed steps are as follows.  
**Step 1:** Extract the length of the recovery information from the first usable vertex to determine the recovery information part.  
**Step 2:** Decrypt the reference vertices using the model encryption key \(K_{m}\), and convert the coordinates into decimal.  
**Step 3:** Extract all bits of the usable vertices' coordinates from the recovery information, and decrypt them using the recovery information encryption key \(K_{r}\).  
**Step 4:** Recover each usable vertex using the following steps.  
**Step 4.1:** According to the number \(n_{r}\) of the reference vertices which are connected with the current usable vertex, extract \(\left\lceil log_{2}^{n_{r}}\right\rceil\) bits to confirm its closest reference vertex.  
**Step 4.2:** Extract \(\theta\) bits to obtain the value of \(\beta\).  
**Step 4.3:** Extract \(3\beta\) bits to obtain the binary three-dimensional distance between the current vertex and its closest reference vertex, and convert it into decimal to obtain \(d^{c}_{(x,y,z)}:\left(d^{c}_{x},d^{c}_{y},d^{c}_{z}\right)\). According to the coordinate of the closest reference vertex, the current original usable vertex can be recovered into the integer by Equation (8), and can be further converted into a floating coordinate by Equation (4).  
**Step 5:** Process all usable vertices according to **Step 4** to recover the original 3D mesh model.
# A Long-Distance WiFi Network as a Tool to Promote Social Inclusion in Southern Vercarruz, Mexico
## 3 Methodology
### Strategic Management Matrices
#### 3.3.1 Internal Factors Evaluation Matrix  
Internally, Mecayapan has several positive internal factors (strengths) that it can take advantage of to overcome and improve weaknesses by using strategies that help generate a solid internal position, as shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Material** & **Amount** & **Unit Value (USD)** & **Total (USD)** \\ \hline Router & 1 & 369.60 & 369.60 \\ \hline Cabinet for wall mounting of 9 rack units & 1 & 155.40 & 155.40 \\ \hline
305 m coil cable, black shielded for outdoor Cat5e & 2 & 173.88 & 347.76 \\ \hline Container with 100 RJ45 Cat connectors & 1 & 38.64 & 38.64 \\ \hline Cloud controller for centralized equipment management & 1 & 115.92 & 115.92 \\ \hline
1000 VA/600 W UPS 120 input, 1 RU rack & 1 & 463.68 & 463.68 \\ \hline Dual-band AC IP67 radio & 1 & 171.36 & 171.36 \\ \hline Omnidirectional antenna 5 GHz frequency of 13 dBi & 1 & 193.20 & 193.20 \\ \hline IP 66 steel enclosure for outdoor use & 6 & 96.60 & 579.60 \\ \hline Kit with 6 m telescopic pole with installation accessories & 6 & 173.88 & 1043.28 \\ \hline
2 \(\times\) 2 MIMO subscriber antenna with integrated 23 dBi antenna & 6 & 96.60 & 579.60 \\ \hline Outdoor UnifFi access point detachable MIMO 2 \(\times\) 2 antennas & 6 & 135.24 & 811.44 \\ \hline TOTALS (USD) & & 2184.00 & 4869.48 \\ \hline \end{tabular}
\end{table}
Table 6: Cost of the project to install the long-distance link in the municipality of Mecayapan.  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Description** & **Units** & **Unit Price** & **Total Price** & **Benefited Homes** & **Total Cost** \\ \hline USB WIFI adapter & 1 & 4.50 & 4.5 & 100 & 450.00 \\ \hline RG58 coaxial cable & 6 & 0.50 & 3 & 100 & 300.00 \\ \hline SMA connector & 1 & 1.50 & 1.5 & 100 & 150.00 \\ \hline Transport & 2 & 0.30 & 0.6 & 100 & 60.00 \\ \hline TOTAL (USD) & & & 9.60 & & 960.00 \\ \hline \end{tabular}
\end{table}
Table 7: Budget for home internet distribution amounts (USD).  
The total score of the IFEM is a little below the average of 2.5, which means that, although the municipality of Mecayapan has the support of the public entity and has access to public and private transportation, the high rates of poverty, unemployment, and marginalization keep the territory in a weak position, which is why telecommunications companies and Internet Service Provider (ISPs) do not bet on investing in it. Therefore, strategies must be generated that promote investment in the territory and allow directing resources to improve the quality of life of the inhabitants.
# Study of Electrical Neural Stimulation Effects Using Extraneural and Intraneural Electrodes
## 4 Discussion
### Limitations of the Study  
The findings of this study have to be seen in light of minor limitations. In building the computational FEM models, the shape of the nerve and inner fascicles were considered constant along the z-axis (i.e., the longitudinal direction of the nerve); however, from experimental data about the anatomy of peripheral nerves, the shape of the fascicles section varies along the longitudinal axis. Therefore, the nerve models considered in this study assume the approximation of topographic stability along the z-axis, which is valid if the hypothesis that the implant is performed in a nerve section of topographical stability is assumed. Some studies showed that to have fascicles branching in the median nerve is not common in the range of length of a few millimeters [18]. According to these considerations and to previous works found in the literature [4], the assumption considered in this paper could be considered valid.
# Joint Optimization of Data Freshness and Fidelity for Selection Combining-Based Transmissions
## 4 Problem Formulation
### Tradeoff between Data Freshness and Fidelity
#### 4.3.1 Monotonicity of Average AoI  
As is derived from Lemma 1, the monotonicity of \(\Delta(N)\) can be figured out by calculating the difference between \(\Delta(N)\) and \(\Delta(N-1)\):  
\[\overline{\Delta}(N)-\overline{\Delta}(N-1)=\frac{3N+H_{N-1}-NG_{N-1}}{2 vN^{2}}. \tag{20}\]  
It can be easily verified that Equation (20) is positive, which indicates \(\overline{\Delta}(N)\) monotonically increases w.r.t. the number of sensor nodes \(N\).
## 6 Isolated Empirical Studies on ML-based xApp Vulnerabilities
### Classification of Vulnerabilities  
We classify vulnerabilities in the O-RAN ecosystem into three main categories [5]. The first category, **system-level vulnerabilities**, encompasses attacks that target the structural components of the O-RAN architecture. These include _interface exploits_, which focus on vulnerabilities in open interfaces such as A1 and E2, potentially allowing adversaries to inject malicious commands or disrupt communications. Additionally, _compromised xApps/rApps_ represent malicious or tampered third-party applications deployed within the RIC, which can manipulate decision-making processes or compromise system integrity. Another critical threat under this category is _resource manipulation_, where unauthorized access to network resources can lead to service degradation or denial-of-service scenarios [5].  
The second category involves **ML-specific vulnerabilities** that target the ML components embedded in the O-RAN ecosystem. These threats include _data poisoning_, where adversaries corrupt training data to mislead FL models, resulting in degraded model performance or biased predictions. _Model inference attacks_ seek to extract sensitive information from trained models, thereby threatening user privacy and exposing confidential data. Moreover, _evasion attacks_ involve crafting malicious inputs that allow adversaries to bypass ML-based detection mechanisms and security filters [5].  
Finally, the third category pertains to **cross-layer attack chains**, where adversaries exploit multiple vulnerabilities across different layers of the system in a synergistic manner to amplify the overall impact. Such _synergistic attacks_ demonstrate how combinations of distinct vulnerabilities can interact to produce compounded security risks, often exceeding the sum of their individual impacts [5].
## 3 Criteria of Vital Pulp Therapy  
### Tooth Structure and Caries Invasion  
Understanding the tooth structure is essential for evaluating dental pulp capping methods, as each layer, enamel, dentin, cementum, pulp, and alveolar bone, plays a specific role in tooth health and response to injury. Enamel is the outermost layer, which protects the dentin and also aids in mastication. Dentin is the second layer, which supports the enamel and also protects the pulp with growth factors as a defense. The cementum covers the dentin and also connects the tooth to the alveolar bone [18]. The pulp, the innermost layer, houses blood vessels, nerves, and connective tissue [19]. The alveolar bone supports teeth with compact and cancellous bone and a cribribiform plate [20].  
Dental caries is a chronic and multifaceted condition that is characterized by bacterial invasion, traumatic injuries, and loss of enamel and dentin structure. It is a pathological process that is spread through an inflammatory response, leading to exposure of enamel, dentin, and the pulp chamber, resulting in pulpitis, periapical abscess, and periodontitis with bone loss. Caries lesions are initiated by undisturbed dental plaque accumulation and biochemical bacterial biofilm metabolism, which utilizes dietary sugars to grow and ferment the lactic acid process [21]. In the early stages, the pulpal reaction is reversible; however, as caries progress, the formation of tertiary dentin is accompanied by increased nociceptive sensitivity due to the activation of pulpal nerve fibers and inflammatory mediators. Tertiary dentin can be observed as two dentin structures. One is reactionary dentin (formed due to a mild stimulus by existing odontoblasts) [22], and the other is reparative dentin (formed due to deep caries close to the pulp by odontoblast-like cells without dentinal tubules) [23]. The current biomedical approach aims to replace damaged dental tissue using innovative biomaterials such as calcium silicate-based cement, bioactive glass, and hydrogels by controlled ion release and enhanced mechanical properties to optimize pulp tissue regeneration and dentin bridge formation. These innovations aim to improve the material's sealing ability, antibacterial effects, and long-term clinical outcomes.
# Three Diverse Applications of General-Purpose Parameter Optimization Algorithm
## 4 Application to Fingerprint-Based Indoor Localization System
### Signal Fluctuation Problem  
Because of the low transmission power and the narrow channel bandwidth, the _signal fluctuation problem_ can often happen at _IEEE802.15.4_, where the LQI of the received signal is fluctuated. This problem may appear when a person is moved around, a door is opened or closed, and another wireless signal at the 2.4 GHz is activated in the room, and will decrease the detection accuracy of _FILS15.4_.  
In our previous experiments, we fixed the transmitter in _D307_, and collected LQI data for 30 min at the six receivers located on the second and third floors of No. 2 Engineering Building at Okayama University as shown in Figure 1. Figure 2 suggests the signal fluctuation problem. Here, \(LQI=5\) indicates that the receiver cannot receive any data from the transmitter where the _connection loss_ happened.  
To accomplish high detection accuracy by solving the signal fluctuation problem on _IEEE802.15.4_ devices, we limit the detection granularity of _FILS15.4_ to one room in the field. Furthermore, we make multiple fingerprints with distinct values for each room. As a result, the optimization of the number of fingerprints and their values for each room becomes an important issue in determining the detection accuracy of _FILS15.4_, which will be very difficult to achieve manually.
## 3 Materials and Methods
### Support Vector Machine
#### 3.3.3 Model Evaluation  
The theoretical basis of using an algorithm to determine continuous values covers several aspects that reveal possible connections between the data, dependent and independent. It is crucial to select the right metric to evaluate the model as it helps to explain the relationship and primary objective of the phenomenon.  
In this study, four different metrics are used to evaluate the internal temperature forecast of the greenhouse; R\({}^{2}\), RMSE, MAE, and MAPE [56]:  
\[\text{R}^{2}=1-\frac{RSS}{TSS}=1-\frac{\sum(y_{i}-\hat{y}_{1})^{2}}{\sum(y_{i} \hat{y}_{1})^{2}} \tag{23}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-x_{i})^{2}} \tag{24}\]  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\mid y_{i}-x_{i}\mid \tag{25}\]  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{\mid y_{i}-x_{i}\mid}{y_{i}} \tag{26}\]  
where \(n\) is the number of observations, and \(y_{i}-x_{i}\) is the error between the forecasted value and actual value.  
R\({}^{2}\) quantifies to what extent the independent variables determine the dependent variable in terms of variance proportion as expressed in Equation (23), where \(RSS\) is the residual sum of squares and \(TSS\) is the total sum of squares [56].  
The RMSE is a derivation of the MSE used to standardize their units of measurement. The MSE evaluates how well a model fits the training data by measuring the variance. The RMSE is useful because it assigns more weight to certain data points, resulting in a greater impact on the overall error if a prediction is incorrect. This is expressed in Equation (24).  
MAE evaluates the result regarding distances from the regressor to the real points. MAE does not heavily penalize outliers due to its norm that smooths out all errors, providing a generic and bounded performance measure for the model. This is expressed in Equation (25).  
MAPE is used when variations impact the estimate more than the absolute values. This metric is heavily biased toward low forecasts, so it is not suitable for evaluating tasks where errors of large magnitudes are expected. This is expressed in Equation (26).
## 3 Main Technical Research
### Research on Obstacle Risk Detection and Localization Based on Security Boundary Model  
To robustly and precisely evaluate potential collision risks, this paper proposes an obstacle risk identification and positioning method based on a safety boundary model, as depicted in Figure 5. Initially, a two-dimensional contour model is constructed, incorporating the mechanical arm, as well as the attached GPRs and sensors. When using sensor data, the motion path of the mechanical arm relative to the tunnel is predicted in real time. Subsequently, a safety margin of 3 cm is set, an inflation transformation is performed on the two-dimensional contour, and spatial envelope segmentation is carried out along the predicted motion direction to determine the safety boundary area and risk monitoring area. Finally, spatial filtering and clustering computations are conducted to eliminate accumulated point cloud data within the safety boundary area, and the pose and size information of potential invading objects within the risk monitoring area is fed back to the mechanical arm control system to evaluate collision risks. This method significantly reduces the amount of invalid point cloud data, lowers the frequency of missed and false reports, and enhances the efficiency and reliability of obstacle avoidance warnings.  
Figure 5: Schematic Diagram of Obstacle Risk Detection.
## 4 Results and Analysis  
The performance of both the ad hoc on-demand distance vector (AODV) and the DDBG protocols was evaluated in the presence of malicious nodes. We have compared our simulation results with ADOV, local intrusion detection (LID-AODV), and hybridization of particle swarm optimization-genetic algorithm HPSO-GA [42; 43; 44]. The reason for choosing these techniques to compare with our protocol is that these are the latest approaches in research literature using IDS and that they are similar to our technique, i.e., of gathering information from nodes. Moreover, results are based on performances metrics such as detection rate, packet delivery ratio, throughput, routing overhead and average delay. Result analysis is further discussed for each of the performance metrics separately.
## 3 FPGA-Based Optimization of Deployment
### Int16/Int8/Mixed Quantization  
Model quantization aims to reduce the consumption of storage space and the computing resources of the model. It is a process of converting the network floating parameters into low-bit-width data. Quantification can significantly reduce model size and resource consumption, speed up computing, and make models easier to deploy on resource-constrained edge devices. The commonly used quantization bit widths are int16, int8, and binary quantization/ternary quantization with lower bit widths. However, due to the inevitable rounding and truncation errors in the quantization process, the accuracy of the neural network may be affected. For neural networks with high accuracy requirements, the selection of quantization bit width is a trade-off between network accuracy and network complexity. The text is quantified by the Icraft component, and the main process is shown in Figure 11.  
Quantization mainly includes the following steps:  
* Loading the floating network into the memory to facilitate the subsequent processing of the network;
* Forward calibration: inferring on the floating-point network based on the calibration dataset to obtain the feature map. The parameter forward_mode determines the forward inference mode. Forward_dir and forward_list indicate the position and list of the calibration set;
* Feature map measurement: determining the saturation point of each feature map according to the method specified by the parameter saturation;
* Calculate the normalization ratio: calculating the normalization coefficient of each characteristic map according to the saturation point;
* Normalization: normalizing the network parameters to ensure that the dynamic range of all feature maps is suitable for quantification. After normalization, the network and the previous network are completely equivalent;* Quantization: Quantizing the floating-point number to the fixed-point number after completing the preparation work;
* Test and analysis: verifying and analyzing the quantified network progress through simulation tools.  
For the infrared dim small target detection network, the quantization bit width int8, int16, and mixed are usually used. The mixed precision quantization method uses int16 to calculate the operators that have a greater impact on the quantization precision; meanwhile, other operators are calculated by int8 to reduce the complexity of the network. In the next chapter, the experiment results are compared and analyzed in detail.
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm
### Compression with Confusion-Diffusion through Kd-Tree, Wavelet Tree and Huffman Coding
#### 4.2.3 Huffman Coding  
Huffman encoding is the most widely used encoding technique and is the best known among the most popular techniques. In this technique, first, we calculate the frequency of each symbol with a probability distribution. Then, we assign code words based on the occurrence probability of each symbol. Huffman encoding is similar to a tree in which each node has either 0 or 2 children [41].
## 5 Quality Attribute Outcomes
### Data Integrity, Trustworthiness, Transparency, and Accountability
#### 5.2.2 Transparency and Accountability  
In addition to data integrity, blockchains' decentralized ledger system promotes transparency and accountability. The shared nature of a blockchain allows authorized parties to access a consistent version of the data, which enhances confidence in its reliability. Since all transactions are recorded immutably and can be verified by authorized users, blockchains ensure that data remains transparent and auditable. For example, the vINCI project by [PERSON] et al. (2012) uses a blockchain to record time-stamped activity data from wearable devices, enabling caregivers and patients to verify activity records. This transparency ensures that both providers and users can view and track activity data accurately over time, supporting accountability in healthcare monitoring and fostering a reliable system for long-term health management.