esa-sceva/satcom-chunk-collection huggingface dataset:
# Situationally Sensitive Path Planning
## 4 Methodology
### Global Environment Layer
#### 4.1.4 Situational Preference-Based Path Planning: Cost and Shortest Path Calculations
Conversely, as this weight approaches 0, the more likely it becomes that \(A\) will ignore collision avoidance. Designers can thus use this parameter to adjust the overall balance of the crowd flow, e.g., introducing selfcentered agents who do not adjust their velocities, versus selfless agents who bear all the responsibility for reconciling the encounter. In practice, multiple candidate velocities can be viable at the same time (e.g., moving backwards can be just as viable as making a hard right to an incoming agent on the left). In the RVO paradigm, a penalty value is assigned to each possible candidate velocity \(v^{\prime}_{A}\in V^{\prime}_{A}\). A penalty minimization process (Algorithms 4 and 5) is required to identify the candidate velocity with the smallest penalty cost \(p^{min}\). In [PERSON]'s RVO implementation [112; 197], this penalty cost is the additive combination of two sub-heuristics: (1) time to collision (TTC) and (2) the Euclidean distance between a candidate velocity and the agent's preferred velocity (Algorithm 6). We deploy a penalty function, with a \"safety\" weight \(\omega_{A}\) adjusting the TTC penalty:

\[penalty\big{(}v^{\prime}_{A}\big{)}=\omega_{A}\frac{1}{TTC\big{(}v^{\prime}_{A }\big{)}}+\left\|v^{pref}_{A}-v^{\prime}_{A}\right\| \tag{3}\]
```
procedure\(RVO\Big{(}p_{A},\,\alpha_{A},\,v^{pref}_{A},\,\alpha^{A}_{B},\,\omega_{A}\Big{)}\) \(v^{opt}\gets v^{pref}_{A}\) \(p^{min}\gets PENALTY\Big{(}v^{pref}_{A},v^{pref}_{A},p_{A},v_{A},\,\alpha^{A} _{B},\,\omega_{A}\Big{)}\) for\(v^{\prime}_{A}\in V^{\prime}_{A}\)do \(p\gets PENALTY\Big{(}v^{\prime}_{A},v^{pref}_{A},p_{A},v_{A},\,\alpha^{A} _{B},\omega_{A}\Big{)}\) if\(p<p^{min}\)then \(v^{opt}\gets v^{\prime}_{A}\) \(p^{min}\gets p\) endif endfor return\(v^{opt}\) endprocedure
```
**Algorithm 4** Local Agent Layer: Penalty-Minimizing RVO
```
procedure\(PENALTY\Big{(}v^{\prime}_{A},v^{pref}_{A},p_{A},v_{A},\,\alpha^{A}_{B},\,\omega_{A} \Big{)}\) \(p^{time}\leftarrow\infty\) for neighbor\(B\in N_{A}\)do \(v^{A}_{B}\leftarrow\frac{1}{\alpha^{A}_{B}}v^{\prime}_{A}+\left(1-\frac{1}{ \alpha^{A}_{B}}\right)v_{A}-v_{B}\) \(p\gets TTC\big{(}p_{A},p_{B},v^{A}_{B},r_{A}+r_{B}\big{)}\) if\(p<p^{time}\)then \(p^{time}\gets p\) endif endfor return\(\omega_{A}\frac{1}{p^{time}}+\left\|v^{pref}_{A}-v^{\prime}_{A}\right\|\) endprocedure
```
**Algorithm 5** Local Agent Layer: Calculation
``` procedure\(PENALTY\Big{(}v^{\prime}_{A},v^{pref}_{A},p_{A},v_{A},\,\alpha^{A}_{B},\,\omega_{A} \Big{)}\) for\(v^{\prime}_{A}\in V^{\prime}_{A}\)do \(p\gets PENALTY\Big{(}v^{\prime}_{A},v^{pref}_{A},p_{A},v_{A},\,\alpha^{A}_{B}, \,\omega_{A}\Big{)}\) \(p
## 3 Data Fusion
### State-of-the-Art Research on Data Fusion
#### 3.1.2 Feature Extraction  
In general, features can be defined as the abstraction of raw data. The aim of feature extraction is to get the feature vectors in a tidy form, representing the characteristics of the original data accurately. Most activity recognition methods use windowing techniques to divide sensor signals into smaller time segments, and features will be extracted from training data [24]. Those features often characterize windows of body-fixed sensor data, and are used as inputs of classifiers.  
Commonly used features are as follows [25]: (1) Time-Domain Features: such as Variance and Root Mean Square (RMS); (2) Frequency-Domain Features: such as Spectral Energy, Spectral Entropy; (3) Time-Frequency Domain Features: such as Wavelet Coefficients; (4) Heuristic Features: such as Signal Magnitude Area (SMA), Signal Vector Magnitude, and Inter-axis Correlation;(5) Domain-Specific Features: such as features used in Time-Domain Gait Detection. In ECG detection, the QRS waveform analysis features must be extracted due to the importance of QRS [68].  
Techniques commonly used in feature extraction include SVM-Based Feature Selection, K-Means Clustering, and Forward Backward Sequential Search. Besides, Feature Transform Methods include Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Local Discriminant Analysis (LDA). In practical applications, researchers explore many methods to extract more accurate features in addition to the techniques introduced above. Among these feature extraction methods, wavelet analysis is particularly useful in recognizing human activities, for it can identify activity transition points and generate time-frequency characteristics while enhancing the signal. Reference [70] proposes a feature extraction algorithm based on time series approximation which converts the sensor-based time series data into a density map. It realizes fast, accurate and efficient classification of activity, while it can easily be deployed in a BSN at the same time. In Reference [71], the authors propose a systematic approach of feature detection and extraction for measuring human postures based on sensing and feedback. They demonstrate that the construction of sensing and feedback system for dynamic postures is much more complicated than that for static postures. In particular, they consider that dynamic postures can be described by a set of blueprints or human body movement trajectories.
## 2 Experimental Investigations
### Test Rig, Procedure, and Measurements  
The test rig was developed at the Institute of Structural Concrete at RWTH Aachen University (Figure 5) and consists of a fan-shaped steel frame that allows five loading angles to be applied. In previous research work [19], uncracked test specimens have already been tested with this test rig for the five discussed load directions. In order to investigate the reduction of the load-bearing capacity caused by cracks for different load directions, the test rig was adapted. With the new test rig, cracks can now be introduced in the concrete blocks and adjusted according to the predefined crack widths for all five loading directions prior to loading, while maintaining the initial crack state during the tests' duration.  
The required force for the crack initiation was introduced by means of eight small-format test cylinders, which were inserted between the massive steel plates positioned in the concrete recesses. The crack width could be roughly adjusted by means of the hand  
Figure 5: Test setup of tensile-shear tests with a load application angle of \(\alpha=45^{\circ}\) with crack initiation and adjustment device.  
pump-controlled small-format test cylinders and then fine-adjusted and \"locked\" using threaded rods (Figure 5, left). A similar method was used in [17] to initiate and adjust the concrete cracks in a controlled manner.  
The dowel force was introduced directly into the steel dowel in the cases of 90\({}^{\circ}\) and 67.5\({}^{\circ}\) and via an additional L-shaped steel component for load angles between 0\({}^{\circ}\) and 45\({}^{\circ}\) as shown in Figure 5, right. The additional steel component is necessary so that the line of action of the introduced load crosses the centre of the steel dowel ensuring a moment-free dowel loading. The concrete blocks were supported by the steel fan structure both horizontally and vertically. The same applies to the test cylinder, thus creating a \"closed\" force system.  
The crack widths at both predefined crack locations were continuously measured via eight inductive displacement transducers (IDT) as shown in Figure 6 both during the initiation and adjustment of the cracks and throughout the test procedure. For each of the two cracks, IDTs were positioned to the left and right of the dowel bar both at the top and bottom of the specimen. In addition, IDTs were used to measure both the horizontal and vertical slip of the steel dowel. Finally, a load cell was used to record the applied force.
## 3 Optimization
### Optimized Workflow Characteristics
#### 3.2.3 Number of Camera Positions and Number of Training Images per Part  
The number of training images per part influences the part recognition accuracy. In this evaluation, a VGG-16 network was trained on different numbers of images per part to identify the optimal number images that must be rendered to achieve high classification accuracy on the test set. As the number of images per part is directly related to the number of simulated camera positions, the influence of using one, four and eight camera positions was also evaluated. Here, it was found that reducing the number of training images per part reduced the computational costs of the rendering.  
Figure 11 shows the influence of different numbers of simulated camera positions on the classification accuracy with various numbers of training images per part. Here, the accuracy distributions for one, four and eight camera positions are shown in blue, yellow, and red, respectively. It was found that the accuracy obtained with eight camera positions increased continuously after exceeding 128 training images per part. In contrast, the classification accuracy was reduced with 400 images for one and four camera positions. Using multiple camera positions resulted in higher accuracy compared to using only one camera. For the proposed workflow, the number of training images per part was set to 256 with four camera positions because the increase in accuracy for 288, 324 and 392 images was low and increased the rendering costs. We observed that the maximum average accuracy increases by 6.18% for a 53.12% increase in the number of training images.  
Figure 10: Influence of the gravity simulation in the proposed workflow on the accuracy when classifying the test set.
# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 2 Materials and Methods
### Field Experiment  
The system was evaluated for its performance and operation in real field conditions, in a trial setup in a 4-ha commercial walnut orchard, located in central Greece. Field surface elevation was mapped using a high precision GNSS (Real time kinematic; Spectra Geospatial, Trimble Inc., Sunnyvale, CA, USA) achieving an accuracy up to 2 cm. The same device was also used to georeference the soil apparent electrical conductivity (ECa) measurements acquired using EM38 MK2 (Geonics Ltd., Mississauga, ON, Canada) electromagnetic sensor. The sensor probe was setup at vertical dipole mode, mounted on a customized sledge, pulled in the orchard between the tree rows to map the whole surface of the field. With the specific setup, the system performed soil ECa mapping at two depths; namely at 0.65 m and 1.4 m, since it regards tree crops [17], where the active rootzone is located deeper than arable crops.  
The georeferenced elevation and ECa datasets were analyzed using fuzzy clustering algorithms to delineate management zones for the allocation of the sensor nodes. Two performance indices were used to define the optimum number of management zones; the Fuziness Performance Index (FPI), and the Normalized Classification Entropy (NCE) [18]. The categorization is optimal when both indices are minimized.  
Since the trial field was a walnut orchard, the walnut trees were expected to obstruct the field of view between the End-nodes and the Coordinator and, as result, the communication range could become significantly deteriorated. Agricultural environments change dynamically with time. Particularly in deciduous trees, the changes in canopy shapes are seasonal and therefore maximum signal obstruction is expected in the summer when the tree canopies are fully developed. On the other hand, decreased signal obstruction is expected during winter, after all leaves fall off, with a minimum after pruning operations.  
Therefore, the topology of the orchard's environment was mapped by creating a 3D point cloud, in order to efficiently position the nodes. A quadcopter Unmanned Aerial System (UAS) (Phantom 4, DJI Technology Co., Ltd., Shenzhen, China) equipped with a high resolution RGB camera, and high accuracy (RTK) GNSS, was used to acquire a high quality orthomosaic of the orchard during summer (for maximum canopy coverage), following a detailed flight mission. The orthomosaic was further analyzed resulting the 3D point cloud of the orchard. Based on the 3D point cloud, the orchard's topology was studied, and the nodes and Coordinator of the wireless network were positioned accordingly. Four nodes were installed in the field in order to test the network's performance and limitations in communications and operations.
## 5 Experimental Results  
In this section, we analyze the experimental results in different environments, obtained with a test-bed composed by four LG Optimus One (P-500) smartphones and a Samsung Galaxy Tab tablet, all equipped with release 2.3.3 of the Android OS. The experimental campaign is conducted in three different phases. In the first phase, we preliminarily investigate the relationship between the _Packet Error Rate_ (PER) and a couple of physical parameters, namely, the IEEE 802.11 transmission data rate (in Subsection 5.2.1) and the inter-node distance (in Subsection 5.2.2). In the second phase, described in Subsection 5.3, we evaluate the performance of the X-NETAD application in a couple of ideal static scenarios, where the nodes are fixed. In the third phase, described in Subsection 5.4, we consider a more realistic scenario, where the devices are positioned within distinct vehicles moving along a predefined path. In the second and third phases, the behavior of the X-NETAD application is characterized according to the performance metrics introduced in Subsection 5.1. Finally, in Subsection 5.5, the obtained results are discussed.  
Figure 6: System architecture of an X-NETAD node.
## 3 System Model and QoT Estimation
### Link Weight Calculation Method  
#### 3.2.1 Primary Path Link Selection Method  
The dynamic arrival and departure of requests as well as the three constraints that need to be satisfied for the resource allocation process lead to spectrum fragmentation. The high susceptibility of links to spectrum fragmentation significantly impacts the blocking rate of the network. Therefore, the spectrum fragmentation on different links should be taken into account when selecting links for requests. The fragmentation of the spectrum on the primary path can be determined by Equations (1) and (2).  
\[F_{P,primary}=\sum_{l_{primary}\in P_{primary}}F_{l,primary}, \tag{1}\]  
\[F_{l,primary}=(\sum_{i=1}^{n}f_{i})\cdot\sqrt{\sum_{i=1}^{n}f_{i}}\cdot\frac {\sum\limits_{i=1}^{n}f_{i}\cdot\sqrt{f_{i}}}{n}, \tag{2}\]where \(n\) denotes all of the available blocks of the current request, \(f_{i}\) denotes the free FSs in the \(i\)th available blocks, \(l_{primary}\) represents the link in primary path, \(F_{l,primary}\) indicates the level of fragmentation on the primary path link \(l\), and \(F_{P,primary}\) represents the degree of fragmentation of the chosen primary path. The smaller n is, the more closely the free spectrum blocks on the link are distributed, and the larger \(f_{i}\) is, the more available FSs the free frequency blocks on the link contain. Therefore, a higher value of \(F_{l,primary}\) means that in this link, there are fewer blocks of free spectrum within the link and a greater concentration of available spectrum resources, and the degree of fragmentation is smaller for the current service. It can better meet the needs of the arriving service requests.
## 4 Simulations and Results
### Simulation Analysis
#### 4.2.2 Simulation of Direct Spread-Spectrum Coding Systems  
The simulation results for the direct spreading coding system based on a Gaussian channel and multipath channels are shown in Figures 7 and 8. The sampling frequency \(f_{s}=16\) kHz, the carrier frequency \(f_{c}=2\) kHz, the bandwidth is 2 kHz, the spreading code is a 5 th-order m-sequence and the spreading gain is 31. The modulation method is binary phase-shift keying (BPSK), the encoding code is CCSDS code, the decoding algorithm is LLR-SPA and the maximum number of iterations is 100. The simulations compare the performance of the direct spreading non-coding system with that of the direct spreading coding system. The direct spreading sequence system has the best performance under Gaussian channels and the performance decreases under multipath channels. With a BER of \(10^{-3}\), a coding gain of about 7.2 dB can be obtained using CCSDS(512,256) codes. The simulation results also compare the performance of the spread-spectrum coding system using CCSDS codes with that of (2,1,7) convolutional codes, Bose-Chaudhuri-Hocquenghem (BCH) codes and Turbo codes. Under \(1\times 10^{-4}\) BER conditions, the code length is the same as 256 and the code rate is 1/2 for both Turbo and LDPC codes. Compared to Turbo codes, the direct spreading sequence system with CCSDS codes has a gain of about 0.4 dB.  
Figure 6: Channel impulse response of the simulation.
## 4 Application of Methodology for Satellite Images
### Selection of the Dataset  
After analyzing the visual characteristics of the satellite image content most important for segmentation, selecting an appropriate dataset becomes the next crucial step.  
Usually, a large sample of images can enhance the precision while evaluating various machine-learning methods. However, a large dataset is not needed for our proposed methodology. The goal is to select the test images with the most prominent visual features (described in Section 3.2).  
The Aerial Image Dataset (AID) [39] is a large-scale aerial scene classification dataset that was selected for this experiment. The AID dataset is larger than the popular UC Merced Land Use dataset. It contains 220-420 images per class (10,000 images in total) in RGB color space, a size of 600 \(\times\) 600 px, 24-bit JPG image format with a multi-pixel resolution that varies from approximately eight meters to half a meter. Files are named using the following format: <CLASSNAME_ID>.jpg, where ID is a numeric value that varies depending on the class of aerial scene.  
AID is divided into 30 classes: (1) airport, (2) bare land, (3) baseball field, (4) beach, (5) bridge, (6) center, (7) church, (8) commercial, (9) dense residential, (10) desert, (11) farmland, (12) forest, (13) industrial, (14) meadow, (15) medium residential, (16) mountain, (17) park, (18) parking, (19) playground, (20) pond, (21) port, (22) railway station, (23) resort, (24) river, (25) school, (26) sparse residential, (27) square, (28) stadium, (29) storage tanks, and (30) viaduct.  
All of the images were acquired at multiple locations, varying times and seasons, and under diverse imaging conditions, resulting in increased intra-class diversity within the data [39]. An image pair (a, b) in Figure 8 provides an example of the same class/object but at a different scale and lighting conditions. The smaller inter-class dissimilarity feature of the dataset is illustrated by image pair (c, d). Rough or uneven textures composed of grain and similar colors characterize both bare land and desert classes. The only difference is that the bare land class typically contains clear roads and smaller solitary objects such as buildings.  
The higher complexity of the dataset provides more meaningful results for investigating the sensitivity of the segmentation algorithms to visual features of images. More complex content can better highlight the shortcomings and advantages of different methods since most methods will usually perform well with simple image content or a low number of clusters [42].  
Figure 8: \(Cont.\)
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.2 Replay Attack  
Our protocol can resist replay attacks utilizing random nonce that is changed every session. If a \(MU_{a}\) may try to impersonate a mobile user by resending messages that were exchanged in a previous session, \(MU_{a}\) cannot obtain the previous messages because the \(HA\) checks whether \(R_{MU}^{*}\stackrel{{?}}{{=}}R_{MU}\) and \(R_{FA}^{*}\stackrel{{?}}{{=}}R_{FA}\). Consequently, the proposed protocol can withstand replay attacks because \(MU_{a}\) does not know \(R_{MU}\) and \(R_{FA}\).
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 5 Results and Data Analysis
### Analysis of the Measurement Model  
Before analyzing and measuring the proposed conceptual model, the first step was to test the survey instrument. This step was completed by testing the validity and reliability of research instruments. In this section, statistical values are compared to the calculation of the questionnaire results, especially related to the convergent validity of each construct. The first step was testing the reliability and validity. The Composite Reliability (CR) in PLS was used to test reliability. Table 1 is the results of the test where it can be seen that the CR index value on all constructs >0.7 and the Cronbach's Alpha meets the criteria. The determination of convergent validity values follows the criteria [109] where all items are statistically significant and must have values >0.60 or ideally 0.70. In addition, the Average Variance Extracted (AVE) value must be >0.50 for construction. The discriminant validity was measured using the criteria where the AVE square root for a construct must be greater than its correlation with other constructs. In line with these criteria, all question items appear to have values of more than 0.60, and all construction items meet the criteria. Table 1 shows the resulting convergent reliability and validity. These indices simultaneously show a high degree of convergent reliability and validity. The diagonal line in Table 2 clearly shows that the value on the diagonal line had a greater discriminant value than the discriminant value below it, so it was declared valid. This means that the indicator was able to construct the variables formed in this e-health laboratory research. The results show a high level of discriminant validity. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Construct** & **At** & **AI** & **DE** & **IQ** & **PE** & **PU** & **PI** & **TC** & **TTF** & **TCC** \\ \hline Accessibility (AC) & 0.85 & & & & & & & \\ Adoption (AI) & 0.396 & 0.901 & & & & & & \\ Design (DE) & 0.448 & 0.377 & 0.895 & & & & & \\ Information Quality (IQ) & 0.4 & 0.495 & 0.696 & 0.874 & & & & \\ Perceived Ease of Use (PE) & 0.546 & 0.449 & 0.763 & 0.681 & 0.85 & & & \\ Perceived Usefulness (PU) & 0.508 & 0.633 & 0.476 & 0.529 & 0.538 & 0.824 & & \\ Personal Innovativeness & 0.353 & 0.612 & 0.46 & 0.435 & 0.469 & 0.539 & 0.8 & & \\ (PI) & 0.392 & 0.337 & 0.562 & 0.631 & 0.541 & 0.412 & 0.4 & 0.942 & \\ Task Characteristics (TC) & 0.251 & 0.246 & 0.292 & 0.259 & 0.233 & 0.401 & 0.3 & 0.403 & 0.857 & \\ Task Technology Characteristics (TCC) & 0.218 & 0.234 & 0.152 & 0.088 & 0.169 & 0.357 & 0.3 & 0.257 & 0.703 & 0.843 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Discriminant validity.