esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Uncertainty Analysis  
For experimental studies, uncertainty analysis is critical to provide reliability of the measured results. While the mean values of the measurements provide an estimation of system performance, the uncertainty quantifies the potential error margins associated with these measurements. Considering uncertainties enables researchers to distinguish  
Figure 6: Factors.  
between actual effects caused by experimental factors and random measurement variations. Furthermore, uncertainty analysis supports better decision-making by identifying the confidence intervals of the results and ensuring the robustness of the conclusions drawn from the experiments. First, the arithmetic mean of the measured values is calculated according to Equation (13). Then, the standard deviation, which reflects the spread of the measurements around the mean, is obtained using Equation (14). Type A uncertainty, originating from statistical variations in repeated measurements, is determined by Equation (15). Type B uncertainty, arising from equipment inaccuracies and systematic effects, is calculated by combining independent sources, as shown in Equation (16). Finally, the combined uncertainty is evaluated according to Equation (17), and the Z-score Equation (18) is used to assess the agreement between experimental and simulation results within the uncertainty bounds.  
\[\overline{x}=\frac{\sum_{i=1}^{n}x_{i}}{n} \tag{13}\]  
\[s=\sqrt{\frac{\sum\left(x_{i}-\overline{x}\right)^{2}}{n-1}} \tag{14}\]  
\[u_{a}=\frac{s}{\sqrt{n}} \tag{15}\]  
\[u_{b}=\sqrt{u_{1}^{2}+u_{2}^{2}+u_{3}^{2}} \tag{16}\]  
\[u_{total}=\sqrt{u_{a}^{2}+u_{b}^{2}} \tag{17}\]  
\[Z=\frac{x_{CFD}-\overline{x}}{u_{total}} \tag{18}\]  
In the equations above, \(\overline{x}\) is the mean of the measurements, \(n\) is the number of experiments, \(s\) is the standard deviation of the measurements, \(u_{a}\) is uncertainty Type A, \(u_{b}\) is uncertainty Type B, \(u_{total}\) is the combination of uncertainty Type A and Type B, \(x_{CFD}\) is the CFD results, and \(Z\) is the control score of the harmony between experimental results and CFD results.
## 2 Preliminaries on the 3 GPP NR Physical Layer Specification
### Code Block Group-Based Transmission  
CBG-based transmission is another important feature required for the implementation of preemption-based DM. In general, an entire transport block (TB) must be re-transmitted if a single code block (CB) is in error. Therefore, resource utilization can be seriously degraded when the TB size increases. Instead, the retransmission unit can be reduced, for example, in a unit of CB, to handle the inefficiency of retransmissions in a unit of TB. However, CB-by-CB retransmission may suffer from a serious control overhead of ACK/NACK reporting for individual CBs. To reduce such an overhead, 3 GPP defines a CBG, which is a group of multiple CBs, as a basic retransmission unit.  
Figure 1 illustrates how each TB is constructed by grouping multiple CBGs, i.e., \(M\) CBGs with each CBG of C CBs. When the maximum number of CBGs that one TB can contain is \(N\), the number of CBGs in the TB is determined as \(M=\min(N,C)\). The \(M\) CBGs are divided as follows: \(M_{1}\) CBGs having the same size and the remining \(M_{2}\) CBGs. That is, \(M_{1}=\mod(C,M)\) and \(M_{2}=M-M_{1}\). The number of CBs in \(M_{1}\) is given as \(K_{1}=\left\lceil C/M\right\rceil\), and the CBs in \(M_{2}\) is \(K_{2}=\left\lfloor C/M\right\rfloor\). When the transmission fails, the gNB does not retransmit an entire TB consisting of M CBGs; instead, it transmits only the failed CBGs. For this reason, gNB carries 0, 2, 4, 6, or 8 bits of code block group transmission information (CBGTI), which has an in-order one-to-one mapping with M CBGs of the TB, with the MSB mapped to the first CBG [14]. A value of 0 or 1 in the CBGTI field indicates that the corresponding CBG is not transmitted or is to be transmitted, respectively.
## 5 Testing and Navigation
### Collision Avoidance
Anti-collision systems remain in the realm of research, even though an important effort has been seen in recent years with increased interest in autonomous ships. There are two main factors that make the problem all the more difficult in the Microtransat Challenge. The first is that it is difficult for a ship to avoid another ship that is typically going 10 times faster. It may be feasible to avoid merchant ships that have regular routes, but for fishing vessels whose trajectories are much less predictable, it becomes very difficult [25]. The difficulty is compounded when the other vessel makes an intentional maneuver. The second is the energy cost of such a system. Power is required for the additional sensors as well as for the actuators that are needed for the additional maneuvers. Specifically, additional computational resources are required to run the avoidance algorithms, and the route travelled is extended to avoid obstacles.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 3 High-Efficiency Mobile Positioning Method
### Pre-Deployment Stage  
In this stage, a mobile positioning algorithm is proposed to retrieve the location information and cellular network signals (_i.e._, cell ID and RSSI) of historical data from GPS-equipped commercial vehicles and to estimate location for each cell-RSSI pair. For the computation requirements of the voluminous CVO data, the MapReduce programming model [31] and HIVE [32] are assumed to be built into cloud computing environments for quick location determination.
## 3 Node Clustering for WMSNs  
Multimedia sensors, such as cameras, are multidimensional sensors that can capture a directional view. We assume wireless sensor nodes with fixed lenses providing a \(\emptyset\) angle FoV, densely deployed in a random manner. The assumption of fixed lenses is based on the current WMSN platforms. Almost all of them (SensEye, MicrelEye, CITRIC, Panoptes, Meerkats [15-19]) have fixed lenses and only high powered PTZ cameras have movement capabilities. We assume that sensors are aware of their position. We consider a monitor area with N wireless multimedia sensors, represented by the set S = {S\({}_{1}\),S\({}_{2}\),...,S\({}_{\rm N}\)} randomly deployed. Each sensor node is equipped to learn its location coordinatesand orientation information via any lightweight localization technique for wireless sensor networks. It is not the purpose of this paper to define mechanisms to find this location. Without loss of generality, let us assume that nodes in the set S belong to a single-tier network or the same tier of a multitier architecture. The following definitions will be used throughout the paper:  
* _Field of View_ (_FoV_): refers to the directional view (see Figure 1) of a multimedia sensor and it is assumed to be an isosceles triangle (two-dimensional approximation) with vertex angle \(\theta\), length of congruent sides \(\mathrm{R_{s}}\) (sensing range) of the sensor and orientation \(\alpha\). The sensor is located at point A (\(\mathrm{x_{A}}\),\(\mathrm{y_{A}}\)).
* _Cluster_ (_\(C_{j}\)_,_ \(j=1\)_,...,_M_): consists of a subset of multimedia nodes with high overlapping FoV areas. The size of the overlapping area between FoVs of two nodes determines whether they can be in the same cluster.
* _Overlapping scale_ (\(\gamma\))_: defines the minimum percentage of node's FoV area that is required to be overlapped for membership in a cluster.
# Transformative Technology for FLASH Radiation Therapy
## 3 High-Gradient Ion Linacs for FLASH-RT Developed by Argonne National Laboratory and RadiaBeam
### Linacs for Ion Beam Therapy  
Being a single-pass machine, a pulsed linear accelerator (linac) is capable of adjusting the pulse repetition rate and the beam energy hundreds of times per second (\(\sim\)200 Hz). This much-desired flexibility in beam tuning enables fast and efficient beam scanning to allow 3D dose painting, as well as the real-time image-guided range calculation and targeting of moving targets. By changing the pulse repetition rate, the beam intensity could be adjusted up to \(10^{9}\) ions per second (\(10^{10}\) for protons), typically needed for therapy. For carbon ions, the energy could be changed continuously up to the full energy of 430 MeV/u required to penetrate the depth of a human body, which is equivalent to 30 cm of water. In addition, the beam quality from a single-pass full-energy linac is generally better than other systems that may require energy degraders or multi-turn acceleration.  
Linacs have already been proposed for proton [31; 32] and carbon beam therapy [33], but no all-linac-based facilities exist. This is due to the length and space required for the linac, which has limited its deployment in a hospital or other clinical setting. Using traditional accelerator technology, a linac would be hundreds of meters long, and this is the main reason why synchrotrons are currently dominating the field of ion beam therapy. The beam delivery from a linac will be similar to synchrotron beam delivery through fixed beamlines or gantry systems. However, the superior beam quality of the linac enables much smaller magnets and, therefore, more compact gantries.
## 4 V2H and V2G
### Technological Developments
#### 4.3.5 Hong Kong  
According to the Japanese EVPOSSA V2H standard, the Hong Kong APAS has designed a multifunctional integrated product called the Smart V2H System as shown in Figure 6d. Under the control of the EV Power System (EVPS), the electricity stored in EV and household batteries, the power from the grid, and the renewal power source could be converted and transmitted to some others following the EVPOSSA V2H/V2L standard. The DC/DC converter in EVPS realizes power transmission between the household and EV batteries. The DC/AC inverter enables the energy supply to household appliances from household batteries and EV batteries. The Household Battery Power Storage System obtains energy from the solar power system as well as the EV battery for powering the household according to the need. The Solar Power System consists of panels and a DC/DC converter that store green energy into the household battery. Specifically, the DC/DC converter is assembled with a Maximum Power Point Tracking (MPPT) capability to maximize solar energy capture. APAS has implemented mature V2L/V2H functionality through the Smart V2H System based on the EVPOSSA protocol. In the future, APAS plans to develop V2G technology based on V2L/V2H projects and further adapt the system with more comprehensive V2H/V2G standards according to market demand.
# Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application
## 5 Results and Discussions
### Mining Reliability and Resilience  
Mining operations are often subject to unexpected changes, disturbances, unplanned downtimes, equipment failures, and challenging conditions, making reliability a critical factor. To ensure the effective implementation of swarm models in such environments, it is essential that they continue functioning even in the presence of errors within the swarm. This study evaluates the reliability of the honeybee, ant, and firefly models by simulating error conditions with rates varying from 20% to 80%, as depicted in Figure 13.  
Figure 12: Swarm mining scalability and adaptability performance from the considered four models for various ore block deposits. (Note: graphs for honeybee model for the cases of 1, 5 and 10 piles are overlapping in the figure).  
Figure 13: Swarm models’ operational efficiency drop at different error rates.  
The honeybee model, which relies on a master robot for data interpretation and decision-making within a hybrid control system, has demonstrated significant vulnerabilities. As error rates increased, its efficiency sharply declined, with a 65% drop at the 80% error rate, highlighting the risks associated with its centralized control system. A failure in the master robot can lead to the collapse of the entire operation, making the honeybee model less reliable in environments prone to errors, such as space or hazardous mining sites. In contrast, the ant and firefly models employing decentralized control systems, where each agent operates independently, have exhibited a robust performance across all tested error rates. The ant model showed only a 5% efficiency decrease at the 20% error rate, maintaining 65% efficiency even at the 80% error rate. Similarly, the firefly model has demonstrated resilience, with a performance decrease of 6% at the 20% error rate, and of 60% at the 80% error rate. These models' decentralized nature ensures that the failure of individual robots does not critically impact the overall operations, making them highly adaptable and reliable, particularly in uncertain and challenging environments such as remote, underground, or space mining. The results indicate that while the honeybee model offers efficiency advantages under controlled conditions due to its centralized control, it is highly susceptible to single-point failures at higher error rates. In contrast, the ant and firefly models maintain stable performance despite increasing errors, proving their effectiveness in handling faults and making them more suitable for environments where system robustness is essential. The decentralized control in these models allows for continued operation despite individual failures, offering superior reliability and adaptability compared to the honeybee model.
# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## 3 Materials and Methods  
### Robotic System and Processing  
A quadrupedal robot equipped with hardware-software instrumentation was employed to develop the mission indoors and outdoors. This instrumentation enables both data collection and onboard processing using the ROS framework. The utilized instruments are outlined in Table 2, providing details on the robotic system's characteristics and specifications for the cameras employed in the process. Specifically, the spectral ranges of light acquisition for these cameras are described.  
The detection of victims relies on convolutional neural network models (Thermal [20; 21], RGB [19], Multi-spectral [22]) from the authors' previous developments. These models have been integrated into the proposed system to generate a centralized and robust detection by fusing three inferences and a subsequent comparative analysis.  
Figure 3 illustrates the developed structure, which includes a Command Station for monitoring the robot's navigation process. This station sends various user-defined points of interest for navigation, and the robot explores these points reactively. Reactive navigation involves reaching the designated point, capturing three types of data, processing the images through the three convolutional neural network models to generate predictions, establishing a redundant filtering mechanism across the three systems, determining valid detections, and subsequently placing indicators on the map generated by the robot during the mission's progression.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Component** & **Description** \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{ARTU-R} & (A1 rescue Task UPM Robot) \\  & & Quadrupedal Robot with \\  & & instrumentation and \\  & & embedded systems (Nvidia \\  & & Jetson Xavier). \\ \hline \multirow{4}{*}{2} & \multirow{4}{*}{RealSense D435i} & **RGB-D Camera** \\  & & B = [450–495 nm] \\  & & G = [495–570 nm] \\ \cline{1-1}  & & R = [620–750 nm] \\ \hline \multirow{4}{*}{3} & \multirow{4}{*}{Optris Pi640i} & **Thermal Camera** \\  & & [8 \(\mu\)m–14 \(\mu\)m] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Materials for the proposed system implementation.  
Figure 3: Layout of subsystems connections. Source: authors.
# Feature Detection of Focused Plenoptic Camera Based on Central Projection Stereo Focal Stack
## 2 Refocusing Model Conforms to Central Projection
### Problems of the Traditional Refocusing Algorithm
#### 2.1.1 Limited Theoretical Imaging Range  
For any point \(F\) on a given refocus plane, the traditional method restricts \((F_{u},F_{v})\) within the orthographic range of the sensor in order to keep the size of refocused images remains the same. Therefore, the imaging range of the refocus plane is limited. For example, although the raw image contains multiple imaging point of \(Q^{\prime}\), not all refocused images can image \(Q^{\prime}\), as shown in Figure 2a.
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 3 Methodologies
### Evaluation Index  
The confusion matrix is a commonly used performance evaluation method in machine learning, which is mainly used to measure the performance of classification models. It compares the real label of the sample with the predicted label of the classifier by statistically analyzing the classification results of the classifier and then calculating the classification precision, recall, accuracy, _F1 Score_, and other indicators of the classifier, of which the calculation formula of the index is shown in Equations (4)-(6).  
Precision refers to the proportion of samples predicted to be positive that are actually positive.  
Recall refers to the proportion of samples that are predicted to be positive among the samples that are actually positive.  
Accuracy refers to the proportion of correctly classified positive samples among all samples classified as positive samples.  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{6}\]  
where _TP_ (True Positive) represents the number of samples that are actually positive and predicted to be positive, _FP_ (False Positive) stands for the number of samples that are actually negative and predicted to be positive, _FN_ (False negative) is the number of samples that are actually positive and predicted to be negative, and _TN_ (True negative) is the number of samples that are actually negative examples and predicted to be negative.  
In practical problems, we often consider the precision (_P_) and the recall (_R_) comprehensively, that is, the _F1 Score_, which is defined as the harmonic mean of the precision rate and the recall rate.  
\[F1 Score=\frac{2\times P\times R}{P+R}=\frac{2 TP}{2 TP+FN+FP} \tag{7}\]  
To better display the classification results of the EOR technique, the confusion matrix is used as a visual solution.  
The schematic diagram of the confusion matrix is shown in Figure 7, which means that the samples whose instances are positive examples are all correctly predicted as positive examples, and the samples whose instances are negative examples are all correctly predicted as negative examples. This is the ideal confusion matrix.
## 4 Improved Islanding Detection Method Based on the Proposed Solution
The four-PV scenario has the similar results, the islanding condition can be detected after 48 ms when the islanding occurs by using the improved islanding detection method, but it cannot be detected by using the original one. The results are shown in Figures 11 and 12. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Cases** & **PV1(kW)** & **PV2(kW)** & **PV3(kW)** & **PV4(kW)** & **Total PV(kW)** & **Load(kW)** & **Vdc(V)** \\ \hline
2-PV & 50 & 50 & 0 & 0 & 100 & 100 & 500 \\
3-PV & 50 & 50 & 50 & 0 & 150 & 150 & 500 \\
4-PV & 50 & 50 & 50 & 50 & 200 & 200 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multi-PV operation scenario. Figure 8: Solution result in two-PV scenario. Figure 11: Cancellation result in four-PV scenario. Figure 10: Solution result in three-PV scenario. Figure 9: Cancellation result in three-PV scenario. The eight-PV scenario result is shown in Figure 13. The improved islanding detection method by applying the proposed solution can detect the islanding condition after 48 ms.  
Based on the simulation results and mathematical explanations, the proposed solution eliminates the injected signal cancellation by using the improved islanding detection method based on the proposed solution in two-PV, three-PV, four-PV, and eight-PV scenarios. Moreover, the injected signal cancellation is eliminated not only in the specified cases but also in the general case by using the improved islanding detection method. This result shows the achievement and efficiency of the proposed solution.
# EpiInfer: A Non-Markovian Method and System to Forecast Infection Rates in Epidemics
## 9 Multiple Locales  
### Multilocale Inference  
It is plausible that using data from multiple locales might help to infer \(p_{1}\) and \(p_{2}\) more accurately, assuming that these probabilities are independent of the locale. The goal of our experiments on this topic is to find out which is best: using one locale, similar (in terms of meeting distribution) locales, or all locales.  
For the three locale choices, the search for \(p_{1}\) and \(p_{2}\) is carried out basically as described in Section 3.2 (apply _Epinfer-core_ to a systematic search among \(p_{1}\) and \(p_{2}\) values). The only difference is the way the RMSE is calculated. We are using relative RMSE from multiple locales instead of the RMSE from a single locale. Algorithm 3 shows the pseudocode of this approach.  
```
1:functionsearch(locales)
2:for each \(p_{2}\) value between 0.1 and 1.0 in increments of 0.1 do
3:while doing binary search on \(p_{1}\)do
4:for each locale \(L\) in locales do
5: run Algorithm 1 (_Epinfer-core_) based on current \(p_{1},p_{2}\) pair
6: calculate relative RMSE for the last t training days
7:endfor
8: Calculate relative RMSE over all locales for the last t training days
9: Update best \(p_{1},p_{2}\) if lowest relative RMSE seen so far
10: Update interval for \(p_{1}\) as described in Section 3.2
11:endwhile
12:endfor
13:return best \(p_{1},p_{2}\) pair
14:endfunction
```  
**Algorithm 3** Transition Parameter Inference Algorithm Using Multiple Locales: For each subsequent training day, the goal is to find \(p_{1}\) and \(p_{2}\) values that minimize the relative Root Mean Squared Error (RMSE) of the number of infected people (in state \(I\)). Here the optimization is done over all locales.  
The algorithm for multi-locales, Algorithm 3, searches for the best transition parameters \(p_{1}\) and \(p_{2}\) that minimize the _relative RMSE_ of infected individuals across multiple locales.  
Given a set of \(n\) true values (coming from n different locales) \(y_{1},y_{2},\ldots,y_{n}\) and corresponding predicted values \(\hat{y}_{1},\hat{y}_{2},\ldots,\hat{y}_{n}\), the relative RMSE is defined as:  
\[\text{Relative RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(\frac{\hat{y}_{i}- y_{i}}{y_{i}}\right)^{2}} \tag{6}\]  
Algorithm 3 loops through different \(p_{2}\) values (0.1 to 1.0) and performs a binary search on \(p_{1}\) for each \(p_{2}\). For each pair of \(p_{1}\) and \(p_{2}\), it runs the _Epinfer-core_ (Algorithm 1) on each locale, calculates the relative RMSE over the last \(t\) training days, and updates the best parameter pair if a lower relative RMSE is found. For the following experiment, the value of \(t\) is one. Finally, it returns the \(p_{1}\) and \(p_{2}\) pair with the lowest relative RMSE.