esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of pH  
Calibration studies were performed using the proposed biosensors in conjunction with constant potential amperometry in stirred solution; studies were carried out with both fatty acids over the concentration range 2 to 20 uM, between pH values of pH 6.0 to pH 9.0. The resulting calibration plots were then used to deduce the sensitivities and linear ranges for both linoleic acid and \(\alpha\)-linolenic acid using these conditions (Table 1).  
For linoleic acid, pH 8 appears to offer the best sensitivity (0.014 uA mM\({}^{-1}\)), whereas the linear range was the same for pH values between 7 to 9 (2 to 10 uM). For \(\alpha\)-linolenic acid, it would appear that pH 8 and 9 offer the best sensitivity (0.009 uA mM\({}^{-1}\) for both) and linear range (2 to 10 uM for both). This study led us to choose a phosphate buffer of pH 8 containing 0.1 M sodium chloride for all further studies.
## 2 Theoretical Background
### Parameterized Policies  
The most common way to generate a parametric policy in a continuous action space is to use the Gaussian density function:  
\[\pi(a|s,\theta)=\frac{1}{\sigma(s,\theta)\sqrt{2\pi}}\exp\left(-\frac{(a-\mu( s,\theta))^{2}}{2\sigma(s,\theta)^{2}}\right). \tag{6}\]  
where \(\mu(s,\theta)\) and \(\sigma(s,\theta)\) are both state dependent parametric feature extractors. We use neural network feature extractors for both \(\mu\) and \(\sigma\) in this work. In this way, \(\mu=nn_{\mu}(s,\theta)\) is a multilayer perceptron with three hidden layers with 100, 50, and 25 hidden neurons, respectively, where \(\theta\) are the weights of the neural network, mapping the state space into the mean of the Gaussian function. We decided to use this neural network architecture following [43], where a feedforward neural network policy with the same number of layers and hidden neurons is used to test and evaluate several tasks with continuous action spaces. \(\sigma\) can either be a fixed vector, \(\sigma=r\in\mathbb{R}^{d}\), where \(d\) is the dimension of the state space, or the output of a different neural network, \(\sigma=nn_{\sigma}(s,\theta)\). In this case, the multilayer perception used for \(\sigma\) consists of two hidden layers, each with 32 hidden neurons. It is common to take the exponential of \(\sigma\) to ensure a positive standard deviation [29; 45]. In the multivariate case, a diagonal covariance matrix is used. For both neural networks, \(\mu\) and \(\sigma\), we used a non-linear _tanh_ intermediate-layer activation functions, while linear activation functions are used in the output layers. Thus, the action is a sample from \(\mathcal{N}(\mu,\sigma^{2})\). An illustration is shown in Figure 2.
# Long-Range Navigation in Complex and Dynamic Environments with Full-Stack S-DOVS +
## 2 Background
### Dynamic Object Velocity Space (DOVS)  
The Dynamic Object Velocity Space (DOVS) [1] is a model used in robotic autonomous navigation to compute safe motion commands within a time horizon. It is based on the velocity space (VS), which is defined as the set of velocities a robot can reach while respecting the constraints of the maximum and minimum velocities. DOVS abstracts the dynamic environment by representing safe and unsafe velocities that the robot can choose in every sampling period. The model defines the Dynamic Object Velocity (DOV) as the set of velocities that can lead to a collision with moving obstacles within a time horizon. The DOVS uses this information to compute safe motion commands for the robot.  
To represent moving obstacles, the robot's size is reduced to a point and the obstacles are enlarged by the robot radius. The collision band is then defined as the area swept by the obstacle while it moves. The intersection of the robot's trajectory with the collision band defines the possible collision points, as seen in Figure 2. From these points, the maximum and minimum velocities that lead to collision are computed for every obstacle, then a dynamic window is used to select safe velocities within the robot's kinodynamic constraints for the next step. The DOVS is graphically represented in Figure 3a, with safe velocities shown in white, unsafe velocities in dark, the dynamic window represented with a green rhombus, velocities that lead to the goal following a circular path with a magenta line, and safe velocities that lead to the goal with a green line. The robot can choose from a set of only eight possible velocities, which include: the current velocity of the robot (1); the velocities of the extremes of the rhombus, which are the velocities reachable in the next control period at maximum linear or angular acceleration from the current velocity (2, 3, 4, 5); the minimum and maximum velocities that lead to the goal (6, 7); and the velocity that leads to the goal when using the current linear velocity (8). These actions are represented in Figure 3b, with the numbers having the meanings noted above. In our original work, a planner (S-DOVS) was developed by which the robot selects a velocity based on predefined strategies designed to balance safety and maximum robot velocity.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 4 Algorithm
### Static Planning and Scheduling Algorithm
#### 4.1.1 Construct the Initial Greedy Solution  
In the multi-UAV-vehicle cooperative data collection system, regardless of speed fluctuations, the central control unit plans and assigns routes for the UAVs and vehicles before they depart to execute tasks. This is completed based on the initially received static tasks and the computed initial static solution. The process of generating an initial greedy solution generally involves four steps: anchor point selection and the partitioning of anchor point task sets, the partitioning of routes within anchor points, UAV route assignment, and vehicle route planning. The Initial Greedy Solution Construction Algorithm (IGSCA) generates an initial solution that may not be optimal, but is certainly feasible. The initial parent population generated by this algorithm is optimized by the subsequent Hybrid Genetic Algorithm (HGA), continually producing more effective offspring solutions, eventually resulting in a near-optimal solution.  
In Algorithm 1, first, the K-means algorithm is used to select \(K\) centroid anchor points based on K-means clustering and assign each data collection task point to the nearest anchor point, outputting the customer set corresponding to each anchor point. Second, for each anchor point, the task point set within that anchor point is processed by using Algorithm 2 to partition and obtain a set of UAV flight routes. Algorithm 3 is then used to assign each route in the UAV flight route set to various UAVs without repetition. Finally, Algorithm 4 is used to determine the vehicle driving route based on the UAV solution obtained from the previous steps, ultimately resulting in an initial greedy global solution \(S\).  
```
0: Initial static task point set \(P_{0}=\{p_{1},p_{2},\ldots,p_{n}\}\), the set of anchor points to be selected \(A=\{A_{start},a_{1},a_{2},\ldots,a_{1}\}\), the anchor point location \(a_{q}\) where the vehicle is currently located, the UAV state set U;
0: Global solution \(S\);
1: Set of task sets \(H\leftarrow\) Call K-means algorithm and select the docking anchors and the set of clients corresponding to each anchor point
2:for\(a_{i}\in A\)do
3: UAV flight route set \(routes\leftarrow\) Call Algorithm 2
4: Solution \(S\) within a single anchor \(\leftarrow\) Call Algorithm 3
5:endfor
6: Global Solution \(S\leftarrow\) Call Algorithm 4
7: Return \(S\);
```  
**Algorithm 1** IGSCA Algorithm
## 3 Proposed Methodology
### Optimize the Design Scheme
#### 3.3.3 Determination of Optimal Scheme  
The determination of the optimal scheme is divided into two steps:  
1. The alternatives are arranged into descending order by the values of \(S_{i}\), \(R_{i}\) and \(Q_{i}\);
2. Ranked by \(Q\) from smallest to largest, option \(a_{1}\) at position 1 is the best option if it satisfies the following two conditions:  
* Condition 1: \(Q(a_{2})-Q(a_{1})\geq 1/(m-1)\), \(a_{2}\) is the second scheme by \(Q_{i}\) value and m represents the number of alternative schemes.
* Condition 2: Acceptable decision stability, option a\({}_{1}\) must be ranked first in \(S_{i}\) or \(R_{i}\) values and remain stable in decision making.  
If both conditions 1 and 2 are satisfied, a\({}_{1}\) is the best scheme; if only condition 1 is satisfied, there is a compromise solution set \(\{a_{1}\), \(a_{2}\)}; if only condition 2 is satisfied, there is a compromise solution set \(\{a_{1}\), \(a_{2}\),..., \(a_{m}\)} and the maximum value of \(m\) is determined by \(Q(a_{2})-Q(a_{1})\geq 1/(m-1)\) to determine the compromise solution set.
## 3 Security and Privacy-Preserving Solutions for IoE
### Edge AI and Secure Computing in IoE
#### 3.2.2 Privacy-Preserving Data Processing in IoE with Edge Computing and AI  
Edge computing eliminates the need for centralized data processing in IoE systems by enabling data to be processed close to the source [15; 16]. This approach not only reduces latency but also allows local data processing before sending data to the cloud, thereby enhancing privacy [103; 106].  
Edge-based data processing is especially critical in scenarios requiring high levels of privacy, such as the management of personal health data or sensitive information from smart home devices [36; 42; 87; 88].  
Key techniques for privacy-preserving data processing include:  
* _Federated Learning (FL)_: Model weights are shared, but data is not. User data remains on the edge device [23; 25; 65].
* _Differential Privacy (DP)_: At the end of the modeling process, data is statistically obfuscated to prevent the re-identification of individual users [109; 110].
* _Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE)_: Multiple devices collaborate in joint analysis without accessing each other's data [46; 56; 104].  
Through these approaches, AI processing on the edge becomes both secure and efficient [107; 108]. Especially in IoE systems, distributed AI algorithms with a privacy-preserving focus offer great potential for applications in healthcare, transportation, and smart cities [26; 31; 87].
## 3 Materials and Methods  
The provided data for this work contains datasets on internet connectivity for the years 2015 to 2023, indicating structured data organized in spreadsheets with columns for variables and rows for data entries. Each file likely includes tables with indicators on internet connectivity, categorized by region, household characteristics, and potentially the type of technology used, such as fiber optic, DSL, or mobile broadband. The variables may include connectivity rates, types of internet connections, regional differences, and possibly demographic factors like household size, income level, or urban/rural classification. Specific indicators could address the percentage of the population with access to different types of internet, along with average speed and quality metrics. Since these files span three different years, they suggest a time-series component, allowing for trend analysis in internet access across time, which could reveal significant shifts or improvements in digital infrastructure. Given that the data likely cover multiple regions or states in Mexico, the dataset is expected to be extensive in terms of rows, especially if each file contains detailed data for each state or major municipality. These datasets are ideal for longitudinal analysis to observe internet adoption trends, examine regional disparities, and assess the impact of infrastructure developments over time. They also support studies in digital inclusion and access to technology, making them valuable for shaping policy initiatives aimed at reducing the digital divide. This article emphasizes the significance of using logistic regression within telecommunications infrastructure analysis, primarily because of its ability to predict the likelihood of an outcome based on multiple influencing factors. Specifically, the choice of logistic regression in the article is justified by its suitability for binary outcomes, such as classifying states into high or low fiber optic adoption categories. This predictive capacity is valuable for policy innovation, as it can identify regions with a higher probability of adopting advanced telecommunications infrastructure. Logistic regression is particularly fitting for this study due to its efficiency in handling categorical target variables and its interpretability, which enables policymakers to easily assess the effect of predictors (e.g., broadband access, computer equipment availability, and bundled services) on fiber optic adoption. Unlike linear regression, logistic regression accommodates the bounded nature of probabilities, ensuring that outputs remain within a plausible range (0 to 1), which is essential for making
# CUDA-Optimized GPU Acceleration of 3 GPP 3D Channel Model Simulations for 5G Network Planning
## 4 GPU-Based Acceleration Using NVIDIA CUDA
### Thread Synchronization  
Explicit designer-driven thread group synchronization via barriers is the most commonly used synchronization mechanism between otherwise independent threads. It allows, for example, kernel code to transfer data between (1) large and slow off-chip memory and (2) smaller and faster on-chip memory, ensuring that:  
* All threads involved in a concurrent set of memory transfers, where each thread copies one or a few words of a large off-chip memory buffer to an on-chip memory one, are finished when computations using the transferred data begin,  
Figure 2: GPU architecture and CUDA programming model.  
* All threads performing parallel computations are finished when the results begin to be transferred back from on-chip memory to off-chip memory.  
Implicit automatic thread synchronization occurs in programs with divergent control flows, i.e., where conditional branches in the code may have different outcomes for different threads in a warp. Programmers must carefully consider using conditionals (if-then-else and switch statements) in kernel code, because it may cause significant performance losses in a GPU architecture. If a thread has two nested if-then-elses, and the conditions are independent, then typically only 25% of each GPU processor can be exploited, because all four combinations of the condition values must be executed _in sequence_, rather than in parallel.  
As mentioned above, the CUDA programming model employs three types of thread parallelism:  
* Parallelism between thread blocks, where synchronization is impossible;
* Parallelism within a thread block, where synchronization can be requested by the designer;
* Parallelism within thread warp, where synchronization is automatically ensured by the GPU hardware.  
From a hardware perspective, there are three execution hierarchies: cooperative-thread-array (CTA) (also known as streaming multiprocessor (SM)), warp, and SIMD lanes. At kernel startup, each thread block is assigned to a CTA and each thread is assigned to a SIMD lane. If the block-level explicit synchronization barriers are used, then the CTA hardware will wait for all threads in a block to reach the barrier before any thread is allowed to continue beyond it. Using the warp-level synchronization feature of the CUDA cooperative thread array, threads are synchronized only at the warp level, and other warps can continue to execute. This is especially important in our case, because we can map elements in a cluster to threads in a warp and partitioned block into tiles of size equal to warp size. Because each cluster is modeled independently, we can synchronize threads at the warp level and avoid frequent block-level synchronizations.
# Image Analysis Applications for Building Inter-Story Drift Monitoring
## 3 Verification by Reduced Scale Experiments  
The proposed method to estimate the inter-story responses was verified by a set of reduced scale experiments. Considering the high cost of a full-scale structural test and the infeasibility of deforming a real building, the verification was performed by carrying out a series of one-tenth scaled structural experiments. Two sets of experiments were carried out: static and dynamic experiments. The static experiments are used to investigate the accuracy of the proposed method for slow deformations, where the applications of the proposed system are for monitoring the residual deformation and slow deformation induced by creep, subsidence, or slow responses. The dynamic experiments are used to represent the dynamic inter-story responses induced by earthquakes. The dimensions of the specimens and the accessories are displayed in the structural design plots shown in Figure 5. The camera resolution was set to 640 by 480 pixels, not higher than most of the surveillance cameras nowadays. Figure 5d displays the overall specimen, which was taken by another camera.  
Figure 5: Structural specimens for the reduced scale static and dynamic experiments. (**a**) Design of the static experiments. (**b**) Actual configuration of the static experiments. (**c**) Design of the dynamic experiments. (**d**) Actual configuration of the dynamic experiments.  
In the static experiments, a cyclic uniaxial displacement history was applied at the top of the specimen along the \(u\) direction through an accurately controlled step motor and a screw bar. In the dynamic experiments, the reduced scale building specimen was placed on a shaker table providing a \(\pm\)3-mm amplitude sinusoidal uniaxial table motion, equivalent to an inter-story drift ratio of \(\pm\)0.75%. Two tests were performed: one with 1-Hz of table motion applied and the other with 3-Hz of applied table motion. Both table motions were only applied for the first three seconds, allowing the specimen to gradually obtain a free vibration after the table motion. While the table motion is uniaxial, the building specimen responses include all of the inter-story degrees of freedom including the bilateral displacements \(u\), and \(v\), and the torsion \(\theta\) due to a certain level of structural eccentricity. The eccentricity could be induced by the asymmetry of the building geometry or the structural design. In the experiment, the eccentricity was mainly induced by the additional frame for camera installation. Camera rotation induced by the vibration is also possible and is considered in the analysis procedure of the proposed method. Considering the multiaxial responses of the specimen, we employ a high precision optical system equipped with relatively expensive industrial-level high speed image analysis system [61], as it is relatively complicated to measure dynamic multiaxial responses by conventional devices such as LVDTs.
# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
## 2 Material and Methods
### Hyperspectral Imaging System (HSI)
#### 2.2.1 Image Correction  
The HSI system used in this study formulates 224 spectral bands (\(\lambda\)) with the spatial size of 1024 \(\times\) 512 [\(X,Y\)]. The raw information of minced meat contains spectral radiance, which further needs correction to generate accurate reflectance. The whole process is performed due to the involvement of light effect, pixel size, atmospheric changes. Therefore, an imperial line method [30] is used to calculate accurate reflectance by using the following Equation (1):  
\[Ref=\frac{I_{R}-I_{D}}{I_{W}-I_{D}} \tag{1}\]  
where \(Ref\) is the accurate reflectance of minced meat as shown in Figure 2 and represents the \(Ref\) values of chicken, beef, and mutton over the entire wavelength range of the HSI sensor. \(I_{R}\) is radiance spectral information, and \(I_{D}\) is a dark current cube (generally, 0% reflectance), and \(I_{W}\) represents white reference (generally, 99% reflectance). The acquired sample of HSI sensor formulates a total size of \(789\times 512\times 224\), which further needs spatial-spectral pre-processing.
## 2 System Model and Problem Formulation
### Problem Formulation  
The main objective involves creating a secure and effective FL framework for IoV networks, which depends on allowing only trustworthy vehicles to participate. Multiple elements form the trust score of each individual vehicle, as the framework combines external factors from network quality, traffic regulations, and user feedback with internal vehicle metrics for health and efficiency and IDS system alerts. The multiple-factor assessment approach delivers an extensive evaluation method that considers conditions for assessing vehicle reliability.
# High-Reliability Wireless Pressure Measurement System Based on FEC Algorithm
## 3 Key Hardware Circuit Design
### Analog-to-Digital Conversion Acquisition Circuit Design  
The signal collected from the sensor is passed through a voltage follower circuit and then filtered to remove interference signals and kept within the analog input range before being transmitted to the analog-to-digital conversion chip AD9220 for analog-to-digital conversion. The circuit schematic is shown in Figure 4. It features 12-bit analog-to-digital conversion accuracy [23], with the byte output order following a high-to-low transmission method, and has an adjustable sampling rate below 10 MHz to accommodate different application needs. It operates on a single 5 V power supply, with small errors, and can adapt to various input ranges and interface options, including single-ended and differential inputs [24]. An internal clock is used, with an 80 MHz clock generated internally by the FPGA providing the clock for the analog-to-digital chip, eliminating the need for an external crystal oscillator. The CAPT and CAPB pins are connected to an external coupling  
Figure 3: Schematic diagram of the trigger receiving module circuit design.  
capacitor circuit to reduce reference voltage noise. The FPGA is connected to the AD9220's interface, reset, control, status indicator, and other pins to complete the control of the analog-to-digital conversion chip and the configuration of the registers.
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 4 Results
### Analysis and Verification of Results
#### 4.5.2 Verification of Simulation Results  
The effectiveness and reliability of RDTSN are verified by the field experiment and the results are shown in Figure 33. The field experiment is performed on an experimental transmission line with multi loops. In order to construct the experiment scenario, one WCN and two SNs are configured along the transmission line. There are two inspection robots walking on the experimental transmission line. A Mobile Monitoring Station (MMS) is set up as the CMP at 1000 m outside the experimental site.  
DTMA is tested in a field experiment lasting 5 h. During the experiment, two robots are running on two ground wires respectively. The parameters of the test are shown in Table 9. In the process of experiment, MMS, robots and other nodes can establish effective communication with each other. The results of the experiment and the simulation are shown in Figure 34. The results of the field experiment are highly similar to those of simulation, which proves that the simulation can well reflect the performance of network and can act as a reference for comparison.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Default Value** \\ \hline Network area size (m \(\times\) m) & 1000 \(\times\) 15,000 \\ Test time (h) & 5 \\ Number of Robots & 2 nodes \\ Number of SNs & 2 nodes \\ Number of WCNs & 1 nodes \\ R\({}_{\text{C}}\) (m) & 3000 \\ R\({}_{\text{S}}\) (m) & 1000 \\ Movement rate of Robot (m/s) & 3–5 \\ T\({}_{\text{pause}}\) (s) & 10–30 \\ Buffer size (M) & 500 \\ Message size (Kb) & 2000 \\ Robot message interval (s) & 60, 120 \\ SNs message interval (s) & 1800, 3600 \\ Source nodes & Robot, SNs \\ Destination nodes & MMS \\ Movement model & BPRW \\ Alpha and Beta of DTMA & 0.71, 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Default parameters of field experiment.  
Figure 33: The field experiments. (a) Robot and static node; (b) MMS, communication device and antenna.