esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Optimization and Evaluation on CBHF  
As mentioned in Section 3.1, the counting Bloom filter is adopted in the CBHF structure, to support subtrie deletions. The basic update operation of the original PR-Trie has been provided in Reference [31], and in this paper, updates in the imaginary root table of the proposed architecture are quite easy, since there are not many imaginary roots in real-life routing tables (in quantity) and the update procedure is not necessarily performed in real-time (in terms of speed).  
In this section, we detail the counting Bloom filter structure, including the size configuration of its counters. The behavioral simulation of the CBHF structure is implemented in C++ language. Predicting the size of a satellite routing table is challenging, as vendors are resistant to allowing access and the rapid growth of global subscribers can be continued. Therefore, we refer to the current routing tables of backbone routers in terrestrial networks,  
Figure 8: Block diagram of PR-Trie Lookup module.  
Figure 9: Block diagram of NHI Lookup module.  
which are downloaded from the Route Views project of the University of Oregon [33]. We create three routing prefix sets: 1 k (contains 1078 prefixes), 5 k (contains 5096 prefixes), and 25 k (contains 25,707 prefixes), which are used to evaluate the performance of filters in the CBHF. Since the number of current satellite internet users is still tiny compared to the number of wired cable or fiber subscribers, we use the above sets with a small number of prefixes instead of the present large-scale IPv4 backbone routing tables (containing nearly 1 M prefixes) in this paper.
## 2 Materials and Methods
### Characterization  
Optical transmittance spectra of CNT and graphene films were collected by a UV-2450 UV/vis optical spectrometer (Shimadzu, Kyoto, Japan). The sheet resistance of these films were measured using a four-probe resistivity test system. Raman spectra were obtained with a Renishaw 2000 Raman system (Renishaw, London, UK) with a 514 nm laser. Optical photographs were obtained by an optical microscope (Axio Scope A1, Zeiss, Oberkochen, Germany). The surface flatness of CNT and graphene films were investigated using an atomic force microscope (AFM, Agilent 5100, Agilent, Palo Alto, CA, USA). The morphologies of CNT and graphene samples were characterized using a transmission electron microscope (TEM, JEM-2010, JEOL, Tokyo, Japan).
## 3 Active Disaster Response System (ADRS)
### Active Disaster Response System
#### 3.3.3 Emergency Report System  
The emergency report system is designed to send collected disaster information to the responsible emergency response agency for further use. Figure 9 shows how ADRS reports emergency events to the platform Ushahidi\({}^{+}\)[26], which is an extension of Ushahidi. As Figure 9 shows, when a disaster happens, the disaster information is first collected by official responders, ADRS, witnesses or victims (Step 1). The responsibility of ADRS is to collect victims' information through sensors deployed in buildings and to forward the information to Ushahidi\({}^{+}\). The collected disaster information is stored in the database of Ushahidi\({}^{+}\) for the threat level or threatened areas analysis (Step 2). The original Ushahidi classified events into eight types: emergency, vital lines, public health, security threats, infrastructure damage, natural hazard, services available and others. In the implementation, system administration should assign a score to each type of event to indicate its emergency level. If the threat level or the threatened area is very serious, the official agency will post volunteer recruitment messages on a social network, such as Facebook or Twitter, in order to attract more volunteers to improve rescue actions (Step 3). People who want to volunteer for rescue actions can report their personal information, such as name, location and professional specialty, to the official agency by an emergency app or Ushahidi\({}^{+}\) web interface. After recruiting enough volunteers, the official agency then triggers the participant selection process in order to supplement surveillance sensor coverage (Step 4). The allocation results are sent to volunteers (Step 5). Different volunteers may be assigned to different regions to collect different disaster information. After volunteers arrive at the target region, the path planner then calculates a precise route to the destination for each volunteer (Step 6). Finally, all information collected by the volunteers is sent back to the official agency for further analysis. For readers who are interested in Ushahidi\({}^{+}\), please refer to our previous work [12,13].
# Building Geometry Simplification for Improving Mesh Quality of Numerical Analysis Model
## 4 Automatic Geometry Simplification Method for FVM
### Face Classification
```
Input:

\(S_{target}\): Set of geometry solids of the target model

\(S_{preserve}\): Set of geometry solids to preserve

\(\phi_{threshold}\): Angle threshold of simplification

\(d_{threshold}\): Distance threshold of simplification

Output:

\(F_{major}\): Set of major faces of target geometry \(S_{target}\)

Algorithm:
``` (Note that \(a_{i}\) is area, \(\overrightarrow{c}_{i}\) is geometric center, \(\overrightarrow{n}_{i}\) is unit normal of face \(f_{i}\))  
\(F\) Sequence of faces on \(S_{target}\)  
\(F_{preserve}\) Set of faces on \(S_{preserve}\)  
\(A(a_{i}|f_{i}\in F)\)  
\(P(\frac{\sum_{j=1}^{n}a_{j}|\overrightarrow{n}_{i}\overrightarrow{n}_{j}}{ \sum_{j=1}^{n}a_{j}}|f_{i}\in F)\)  
Sort \(F\) by \(A\) and \(P\) in descending order  
\(F_{minor}\varnothing\)  
FOR EACH \(f_{1}\) in \(F\)  
IF (\(f_{1}\
otin F_{minor}\)) and (\(f_{1}\
otin F_{preserve}\))  
\(F^{\prime}|f|(f\in F)\) and (\(f\
eq f_{1}\)) and (\(f\
otin F_{preserve}\)) and (\(f\
otin F_{major}\))  
FOR EACH \(f_{2}\) in \(F^{\prime}\)  
\(d|\overrightarrow{n}_{1}\cdot(\overrightarrow{c}_{1}-\overrightarrow{c}_{2})|\)  
IF (\(a_{2}\leq a_{1}\)) and (\(d\leq d_{threshold}\)) and (\(|\overrightarrow{n}_{1}\cdot\overrightarrow{n}_{2}|\leq\cos\left(\phi_{threshold}\right)\))  
\(F_{minor}\gets F_{minor}\cup\{f_{2}\}\)  
\(F_{major}|f|(f\in F)\) and (\(f\
otin F_{minor}\))  
RETURN \(F_{major}\)
## 2 Materials and Methods
### Methods
#### 2.3.3 Mann-Kendall Test for Trend Analysis  
The Mann-Kendall (MK) test is used to analyze \(ET_{0}\) and climate factors trends. This method does not need to consider the distribution form of the detection sequence, and can exclude the influence of individual extreme values on the sequence test, which is suitable for testing the trend change of time series of meteorological, hydrological and other types of variables [32; 33]. It is highly recommended for general use by the World Meteorological Organization [34]. Its principle is as follows: the original assumption is that \(H_{0}\) is \(n\) (\(x_{1}\), \(x_{2}\),..., \(x_{n}\)) independent time series samples with the same distribution; alternative hypothesis \(H_{I}\) is a bilateral test, the statistical value \(S\) is calculated as follows [17]:  
\[S=\sum_{i=2}^{n}\sum_{j-1}^{i-1}sign(X_{i}-X_{j}) \tag{4}\]  
\[sign\big{(}X_{i}-X_{j}\big{)}=\left\{\begin{array}{cc}1&X_{i}-X_{j}>0\\ 0&X_{i}-X_{j}=0\\ -1&X_{i}-X_{j}<0\end{array}\right. \tag{5}\]  
Figure 2: The relationship between P-M \(ET_{0}\) and the 20 cm pan evaporation.  
where: any \(i\
eq j\), \(i\leq n\), \(j\leq n\), when \(S\) approximately obeys normal distribution, and the trend test statistical value \(Z\) is obtained by means of \(S\) standardization processing:  
\[Z=\left\{\begin{array}{ll}\frac{S-1}{\sqrt{Var(S)}}&S>0\\ 0&S=0\\ \frac{S+1}{\sqrt{Var(S)}}&S<0\end{array}\right. \tag{6}\]  
If \(|Z|>Z_{1-\alpha/2}\), the null hypothesis is rejected with a given confidence level \(\alpha\); namely, there is a significant trend in the time series data. When \(\alpha\) is equal to 0.05, \(Z_{1-\alpha}\) is equal to 1.96, respectively. The \(Z\) value is used to assess the statistics trend; a positive \(Z\) value expresses an upward trend, a negative \(Z\) value expresses a downward trend.  
The annual change rate of data is evaluated by the least square method. The principle is to look for the best function matching of data by minimizing the square sum of errors [35].
# Energy Sources of Mobile Robot Power Systems: A Systematic Review and Comparison of Efficiency
## Energy Sources of Mobile Robots
### Battery Management  
The battery management system (BMS) is the first circuitry directly connected to a battery. The role of the BMS is protection, monitoring, and control of the battery pack. The BMS controls the battery condition, monitors the temperature and charge level, and provides over-current, over-voltage, and under-voltage protection. In addition, the BMS can provide safety features, allowing disconnection of the whole battery pack or individual battery cells if it detects a fault. In addition, diagnostics, acquisition of the charge-discharge history, and protection of the battery from short circuits can be achieved through the BMS. Battery management systems can in certain applications be omitted, i.e., in low-power, low-voltage devices, where condition monitoring is part of the user's responsibility. While such an arrangement can save some cost and space on the BMS implementation, it is a bad practice and can lead to premature battery failure due to human error or misuse.  
In many applications, the BMS is a standalone component that does not communicate with anything. It only protects the battery and does not accumulate any data. In more advanced implementations it provides an interface for the user to monitor, in real time, battery status and usage history. In large battery packs, the BMS can also include active components such as fans for cooling the battery, heaters for maintaining temperature in winter conditions, or balancing circuitry that enables equalization of the charge level between individual battery cells. The BMS can also be integrated in the charging system; in such an arrangement, the charging system should be part of the battery pack, so that the BMS can also protect and monitor the battery during normal use.  
In any application, where multiple battery cells are combined into a battery pack, cell balancing starts to be important. Differences in cell manufacturing lead to variable resistance and capacity of a cell, which influences the work temperature and current passing through individual cells in the battery pack, leading to a shorter lifetime of individual cells and thereby shortening the whole battery pack's lifetime. When cells are combined only in series to form the battery pack, the initial balancing can often be sufficient for the battery lifetime. In parallel configuration, cell resistance differences lead to variable use of battery cells during charge and discharge cycles, leading to uneven aging of the cells in the battery pack. Cell-charge imbalance can lead to problems when a full charge level is expected from the battery pack. As the charger tries to reach the total voltage of the battery pack, if individual cells are undercharged compared to the others, then the other cells will become overcharged and can be damaged.
## 3 Categorization of Visual Inspection (Tasks)
### Overview on How to Solve Automated Visual Inspection with Deep-Learning Models
#### 3.3.2 Visual Inspection via Multi-Class Classification  
Table 3 presents an overview of 42 publications that solve various use cases of AVI through multi-class classification and the models that are used to solve them. The models used to solve these use cases include popular DL architectures such as AlexNet, CNN, DenseNet, EfficientNet, GAN, MLP, MobileNet, ResNet, single-shot detector (SSD), and VGG. Twenty-one publications describe approaches for damage detection, of which six approaches are based on custom CNNs. The other four authors used ResNet-based architec  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**VI Use Case** & **Model** & **Count** & **References** \\ \hline \multirow{4}{*}{Crack Detection} & AlexNet & 1 & [71] \\  & CNN & 4 & [72; 73; 74; 75] \\  & VGG & 1 & [76] \\  & ViT & 1 & [70] \\ \hline \multirow{4}{*}{D Damage Detection} & AlexNet & 1 & [77] \\  & CNN & 1 & [78] \\  & DenseNet & 3 & [38; 69; 79] \\ \cline{1-1}  & Ensemble & 1 & [80] \\ \cline{1-1}  & MLP & 1 & [81] \\ \cline{1-1}  & ResNet & 1 & [50] \\ \cline{1-1}  & SVM & 1 & [82] \\ \cline{1-1}  & Xception & 1 & [46] \\ \hline \multirow{2}{*}{Quality Inspection} & AlexNet & 1 & [83] \\  & MLP & 1 & [84] \\ \hline Other & MLP & 1 & [64] \\ \hline Completeness Check & CNN & 1 & [63] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of VI use cases and models that solve these problems via binary classification.  
tures. [PERSON] et al. [85] proposed an MLP architecture to perform damage detection. Also, an EfficientNet and a single-shot detector (SSD) were employed for multi-class damage detection. Five publications cover crack detection use cases. For example, [PERSON] [86] used a CNN, and [PERSON] et al. [87] as well as [PERSON] et al. [88] used a VGG. Also, DL models like ResNet, DenseNet, and an ensemble architecture are proposed by some authors. Completeness checks were performed with the help of a ResNet by [PERSON] et al. [51] or an SSD, as shown by [PERSON] et al. [89]. In seven publications, the authors used custom CNNs, DenseNet, ResNet, or VGG in quality inspection use cases. Also, other use cases can be addressed by different DL-based CV models or MLPs.
# Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal
## 6 Conclusions  
This paper has described and tested a proposed model of an adaptive traffic signal controller based on fuzzy logic. The aim of the proposed model is to achieve a traffic signal with a balanced distribution of the signalization, without requiring expensive or complex requirements. The model was designed to compute the whole cycle duration instead of specific phase lengths or extensions as occurs with most traditional approaches. That is, the cycle duration is adjusted according to traffic demand determined by the arrival flow rate, which is retrieved from simple vehicle counters. Through a type-1 fuzzy inference system, the cycle length is computed following the reasoning: the higher the traffic flow, the longer the cycle length. Subsequently, the computed cycle is proportionally split into different phases based on the effective green time estimation derived from [PERSON]'s method for signalization.  
The proposed adaptive FL controller was evaluated through a microsimulation model of a real intersection, using SUMO as a platform. Using the microsimulation model as a test bench, the proposed controller was compared against five different controllers found in the literature. Simulation results showed that the proposed adaptive FL controller closely matches the performance of the most sophisticated approaches and even overcomes other approaches based on fuzzy logic, despite having fewer requirements. In this regard, the proposed controller only requires knowing the minimum and maximum values for flow and cycle lengths for setup. Moreover, the algorithmic design has a low-constant computational overhead, since it depends on the number of fuzzy sets and incoming traffic streams, whose values remain immutable during the execution. This also allows implementations over the cheapest/simple microcontrollers. In summary, due to the lower requirements in hardware and setups, the proposed model becomes an affordable solution for traffic signal timing for most cities, especially those in developing countries.  
The current approach has been focused exclusively on regulating traffic operation in isolated intersections. It is hypothesized that the controller could also be scalable for multi-intersection scenarios in a self-organizing scheme, as this is assumed to be independent and decentralized. Therefore, further work will be oriented to extend the capacities of the controller to achieve adaptive traffic signal corridors.  
Conceptualization, V.M.M.A., J.R.P.C., A.H.-B. and J.T.; Data curation, V.M.M.A., A.H.-B. and J.T.; Formal analysis, V.M.M.A., J.R.P.C. and J.T.; Investigation, V.M.M.A., J.R.P.C., A.H.-B. and J.T.; Methodology, V.M.M.A., J.R.P.C., A.H.-B. and J.T.; Resources, V.M.M.A., J.R.P.C. and J.T.; Software, V.M.M.A., J.R.P.C., A.H.-B. and J.T.; Validation, J.R.P.C. and A.H.-B.; Visualization, A.H.-B.; Writing--original draft, V.M.M.A., J.R.P.C. and A.H.-B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### The Algorithm for Trajectory Planning
#### 2.3.2 Joint Adjustment for Equal Bone Distraction  
It is presumed that \(\Gamma_{1}\)(\(X_{G11}\), \(Y_{G11}\), \(Z_{G11}\), \(\theta_{11}\), \(\theta_{21}\), \(\theta_{31}\)) and \(\Gamma_{n}\)(0, 0, \(Z_{G1n}\), 0, 0, 0) are the position and orientation of distal bone segment in the initial deformed position and the final desired position, respectively. When the clinician is performing an osteotomy for distraction osteogenesis, the desired trajectory is considered to be a uniformly spaced (1 mm) linear trajectory, and the desired bone shape is considered as close as possible to be a straight bone [6]. Thus, a strategy that involves the orientation of the bone segment and the shortest growth path, through the method of Cartesian coordinate path control, is proposed to operate the position and orientation to generate a uniformly spaced linear trajectory.  
To equally plan the position of the distal bone end, the difference of the position is equally split into \(n\) parts, which is indicated as Equation (7):  
\[\Delta x_{G_{1}}\ =\ \frac{x_{G_{1n}}-x_{G_{11}}}{n},\ \ \ \Delta y_{G_{1}}\ =\ \frac{y_{G_{1n}}-y_{G_{11}}}{n},\ \ \ \Delta z_{G_{1}}\ =\ \frac{z_{G_{1n}}-z_{G_{11}}}{n} \tag{7}\]  
where \(G_{11}\)(\(x_{G11}\), \(y_{G11}\), \(z_{G11}\)) and \(G_{1n}\)(\(x_{G1n}\), \(y_{G1n}\), \(z_{G1n}\)) are position vectors of \(G_{1}\) at step 1 and step \(n\) in \(O\)-\(xyz\), respectively. Then, \(G_{1h}\)(\(x_{G1h}\), \(y_{G1h}\), \(z_{G1h}\)) (\(h=1,\,2,\ldots,n\)) is the coordinate of \(G_{1}\) at step \(h\), which is indicated as Equation (8):  
\[G_{1h}\ =\ \Big{(}\ x_{G_{11}}+h\Delta x_{G_{1}},\ \ \ y_{G_{11}}+h\Delta y_{G_{1}},\ \ \ z_{G_{11}}+h\Delta z_{G_{1}}\ \Big{)} \tag{8}\]To equally plan the orientation of the distal bone, the difference of the orientation is equally split into \(n\) parts, which is indicated as Equation (9):  
\[\Delta\theta_{1}\ =\ \frac{\theta_{1u}-\theta_{11}}{n},\ \ \ \Delta\theta_{2}\ =\ \frac{\theta_{2u}-\theta_{31}}{n},\ \ \ \Delta\theta_{3}\ =\ \frac{\theta_{3u}-\theta_{31}}{n} \tag{9}\]  
Then, the value of orientation \(\theta_{h}(\theta_{1h},\theta_{2h},\theta_{3h})\) (\(h=1,2,\ldots,n\)) at step \(h\) is indicated as Equation (10):  
\[\theta_{1h}\ =\ \theta_{11}+h\Delta\theta_{1},\ \ \ \theta_{2h}\ =\ \theta_{21}+h\Delta\theta_{2}\ \ \ \theta_{3h}\ =\ \theta_{31}+h\Delta\theta_{3} \tag{10}\]  
where \(\theta_{1h}\), \(\theta_{2h}\), \(\theta_{3h}\)(\(h=1,\,2,\ldots,\,n\)) are the angulation deformity of the bone around three directions at step \(h\), respectively.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 2 Establishment of the Electric-Power-Traffic Network Model
### Traffic Network Topological Structure  
Establishing an appropriate traffic road network model is the foundation for studying EV travel behavior and path selection. The basic road network topology consists of the connectivity between nodes and different roads, as shown in Figure 2.  
The basic road network model consists of two elements: nodes and connecting lines. Nodes represent the road intersections in the actual road network, while line segments  
Figure 1: Coupled network of the distribution grid, EVs, and the transportation network.  
between nodes represent the various roads connecting the network. The connectivity of the road network is described by the adjacency matrix D, as shown in Equations (1) and (2):  
\[d_{ij}=d_{ji}=\left\{\begin{array}{l}l_{ij},\ (i,j)\in E\\ 0,\ i=j\\ inf,\ (i,j)\
otin E\end{array}\right.,\ \ \ \ i,j\in N \tag{1}\]  
\[D=\left(d_{ij}\right)_{n\times n}=\left[\begin{array}{ccccc}0&l_{12}&inf&inf& inf\\ l_{21}&0&l_{23}&inf&inf\\ inf&l_{32}&0&l_{34}&inf\\ inf&inf&l_{43}&0&l_{45}\\ inf&inf&inf&inf&l_{54}&0\end{array}\right] \tag{2}\]  
In the matrix D, If node i is connected to node j, the elements at positions (i, j) and (j, i) in the matrix correspond to the length of that road segment. Otherwise, they are represented as \(\infty\).
## 2 Materials and Methods
### Sample Size and Randomization  
Sample size in the present study was calculated using G*Power software (Dusseldorf, Germany) to measure a variation of 15\({}^{\circ}\) in shoulder range of motion between groups, measured using an electrogoniometer with an estimated standard deviation of 15\({}^{\circ}\), 80% power analysis, and 5% significance level [7]. The number of patients needed in each group was calculated as 21; to account for patient attrition during the study, this number was increased to be 30 subjects. Ten subjects did not meet the inclusion criteria, while two diabetic frozen shoulder patients were excluded from the study before allocation to each group; one of them had fasting blood sugar greater than 11.1 mmol/L, and the other one had a history of prior shoulder surgery. The remaining 48 diabetic frozen shoulder patients were assigned randomly to receive intra-articular corticosteroid injection accompanied by traditional physical therapy sessions (Group A = 24) or received 12 sessions of a shock wave in addition to the traditional physiotherapy sessions (Group B = 24) as shown in Figure 1. Participants were allocated using a random numbers table generated by computer and sealed in opaque envelopes for concealed allocation. The patients and practitioners and the outcome assessors were blinded to the patient groupings.
# A Comparative Study of On-Body Radio-Frequency Links in the 420 MHz-2.4 GHz Range
###### Abstract  
While there exists a wide variety of radio frequency (RF) technologies amenable for usage in Wireless Body Area Networks (WBANs), which have been studied separately before, it is currently still unclear how their performance compares in true on-body scenarios. In this paper, a single reference on-body scenario--that is, propagation along the arm--is used to experimentally compare six distinct RF technologies (between 420 MHz and 2.4 GHz) in terms of path loss. To further quantify on-body path loss, measurements for five different on-body scenarios are presented as well. To compensate for the effect of often large path losses, two mitigation strategies to (dynamically) improve on-body links are introduced and experimentally verified: beam steering using a phased array, and usage of on-body RF repeaters. The results of this study can serve as a tool for WBAN designers to aid in the selection of the right RF frequency and technology for their application.  
Keywords:body area networks; on-body communication; body-coupled communication; UHF RFID; Bluetooth; RF beam steering; RF repeaters; path loss measurements +
Footnote †: journal: Accepted: 10 November 2018; Published: 27 November 2018