esa-sceva/satcom-chunk-collection huggingface dataset:
# Affordable Broadband with Software Defined IPv6 Network for Developing Rural Communities
## 3 Affordable Broadband for Rural Communities
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Bandwidth (Mbps)**} & \multicolumn{2}{c}{**Annual Price (USD) (Assuming 1 USD = 110 NRS)**} \\ \cline{2-4}  & **World Link** & **Vianet** & **Subishu** \\ \hline
15 & - & - & 104.45 \\
20 & - & 130.90 & 126.27 \\
25 & 136.36 & - & - \\
30 & - & 152.72 & 159 \\
40 & 159.09 & - & - \\
60 & 236.36 & 187.63 & 202.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fix broadband prices of major ISPs of Nepal (As of March 2019, www.worldlink.con.np (accessed on 22 June 2019), www.vianet.com.np (accessed on 22 June 2019), [[https://subisu.net.np/](https://subisu.net.np/)]([https://subisu.net.np/](https://subisu.net.np/)) (accessed on 22 June 2019)). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Country**} & \multirow{2}{*}{**Service Provider**} & \multicolumn{2}{c}{**Price of Plan (Local**} & \multicolumn{1}{c}{**Validity**} & \multirow{2}{*}{**Tax \%**} & \multicolumn{1}{c}{**Data Cap**} & \multicolumn{1}{c}{**Basket in Local**} & \multicolumn{1}{c}{**Price**} \\  & & **Currency, incl. tax)** & **(days)** & **Tax \%** & **(GB)** & **Currency** & **(USD)** \\ \hline Afghanistan & Roshan & 350 & 30 & 0 & 2 & 350 & 4.65 \\ Bangladesh & Grameen Phone & 229 & 30 & 21 & 1.5 & 229 & 2.77 \\ Bhutan & Bhutan Telecom & 209.00 & 30.00 & 5 & 1.7 & 209 & 2.95 \\ India & Reliance Jio & 149 & 28 & 18 & 42 & 149 & 2.10 \\ Maldives & Diriagua & 230 & 30 & 6 & 2.5 & 230 & 15.92 \\ Nepal & Neel & 113.64 & **7** & **26** & 1 & **154.56** & **4.03** \\ \hline Pakistan & Jazz & 110 & 7 & 0 & 1.9 & 440 & 3.17 \\ Sri Lanka & Dialog & 349.00 & 30 & 19.74 & 2 & 349 & 1.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mobile broadband prices in South Asian countries [11]. Figure 1: Focus areas for affordable and sustainable broadband services. DLS, digital subscriber line.
# Road Pavement Damage Detection Based on Local Minimum of Grayscale and Feature Fusion
## 2 Methods
### SVM Classification  
Considering that these image blocks are usually relevant, even belonging to the identical crack or cave as shown in Figure 3, it would be more efficient to combine the features of these image blocks. All the feature vectors of each feature point in the image window are combined to form a composite feature vector, which is able to describe the whole image window. Analyses based on these associated blocks, which is a kind of multi-feature fusion, would increase the accuracy of pavement detection [28,29].  
These image blocks, proceeded by convolution and subsampling, are transformed to feature vectors by fully connected layer. The feature vectors are further combined to form a composite feature vector of the image window. As a descriptor of the image window, the composite feature vector can be classified by a machine learning algorithm, such as SVM, Adaboost, neural network and random forest. Among them, SVM is often used to replace the softmax classifier of CNN in many recognition and classification cases. SVM has advantages in nonlinear high-dimensional cases, where SVM could transfer low-dimensional linearly inseparable problems to high-dimensional separable ones, with kernel function used to avoid the dimension catastrophe. The composite feature vector, which is of high dimension, can be predicted whether there is pavement damage by SVM-constructed classifier. Therefore, the detection model in our work is of high accuracy and generalization, which could be very efficient even on a small dataset.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 2 Materials and Methods
### Equipment
#### 2.1.2 Updated Equipment (TelosB Motes and Custom Sensor Board)  
Starting in 2015, the wireless motes have gradually been updated to the CM5000-SMA (TelosB) by Advancticsys (Madrid, Spain) with our specially designed sensor board for data acquisition, forming a heterogeneous WSN consisting of MICAz, IRIS and TelosB motes. TelosB motes incorporate the 2.4 GHz CC2420 transceiver and the MSP430 microcontroller with 10 KB of RAM and provide a 16-pin expansion interface to connect external sensors. For the sensors described above, an excitation voltage is required before a reading. In the standard method, general-purpose input/output (GPIO) pins are used as excitation pins and ADC pins are used to gather sensor readings; however, when powering sensors directly using GPIO pins, the excitation voltage is unstable and can change under different workloads and battery levels. Since analog sensor readings are proportional to the excitation voltage, the readings might be inconsistent even in the same environment.  
To address this problem, a novel custom sensor board was designed for TelosB motes (Figure 2) using voltage regulators to provide a stable excitation voltage. In the literature, TelosB acquisition boards are usually designed for specific sensors, such as motion sensors [44; 45; 46], physiological sensors [46], and SM sensors [47]. In contrast, our design is a generic sensor board for the TelosB mote. Our sensor board has two distinguishing features. First, it has ADC channels for analog sensor output and a UART channel for digital sensor output. Second, it provides two levels of stable excitation voltage that enables different combinations of external sensors to be attached based on the application configuration, which is essential in heterogeneous networks, such as the ASWP network.  
Figure 1: Schematics of the environmental sensors deployed at the ASWP network, including the (**a**) Decagon Devices MPS-1/MPS-2 water potential (WP) sensor; (**b**) Decagon Devices EC-5 soil moisture (SM) sensor; (**c**) thermometric sap flow sensor probes; and (**d**) sap flow sensor circuit.  
The sensor board is attached to the TelosB mote's 16-pin expansion, providing screw wire connectors, ADC channels, UART0 serial port, and two excitation voltages. Analog sensors, such as EC-5 and MPS-1, can be attached to ADC channels and powered through a 2.5 V excitation voltage, obtained by using a TLV70025 voltage regulator (Texas Instrument, Dallas, TX, USA). Digital sensors can be connected to the UART0 serial port, such as the MPS-2, which generates a byte stream representing ASCII characters as its sensor readings. The MPS-2 requires an excitation voltage between 3.6 V and 15.0 V. A 5 V voltage booster (U1V11F5, Pololu, Las Vegas, NV, USA) is included in the custom sensor board to provide a 5.0 V excitation voltage from the 3.6 V nominal battery supply to power the MPS-2 sensors.
# Deep Q-Learning for Two-Hop Communications of Drone Base Stations
###### Abstract  
In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone's trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users' movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.  
d rone base station; deep Q-learning; Q-learning; autonomous navigation; UAV +
Footnote †: journal: _Sensors_  
1
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Communication Time between the Satellite and GST(\(\tau\)(i))
#### 3.1.2 Receiver Sensitivity
For instance, decreasing data rate from 46 bps to 11 bps, the transmission time increases from 3 s to 14 s. As a result, data transmission for longer periods would result in higher power consumption for the GSTs, and the data packets could be affected by doppler shift rate and the monopole antenna rotation with tumbling satellite.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Low Latency Applications
#### 12.1.3 High-Speed Trading  
Electronic stock and mercantile exchanges attracted a special category of traders who built automatic \"High Speed Trading\" systems with lower latency 10 Gb/s connections to data centers of stock exchange operators than their competitors. The two-way latency advantage of less than 10 \(\upmu\)s, corresponding to 1 km of SMF, is enough for a systematic winning of transactions [119; 120; 121]. Data centers serving main exchanges in the U.S., located in the Chicago and New Jersey/New York City areas, are separated by distances exceeding 1200 km, and reducing latency by 0.1% is enough.  
The distances between exchanges in Chicago, NYC, London and Frankfurt, measured along geodesic lines, are about 1330 km, 5570 km and 640 km, respectively. Two-way latencies in an ordinary fiber optic network, NYC (data centers in New Jersey)--Chicago and NYC--London, are about 16 ms and 73 ms, respectively. The latencies in dedicated fiber links with straightened routes, and without aerial cables and fiber-based dispersion compensators, dropped to 13 ms and 59 ms, respectively. Several such links were built. Hibernia Express built a low-latency link from NYC to London, with a 4600 km long submarine segment and supporting 100 Gb/s services, which was activated in October 2015.  
Next came microwave systems: the first Chicago--NYC radio link, activated in March 2011, had a two-way latency reduced to 8.5 ms. In April 2020, there were nine such systems. The fastest one, working in the 11 GHz band, had an estimated two-way latency of 3961.71 \(\upmu\)s, the second one of 3962.09 \(\upmu\)s (0.38 \(\upmu\)s more, corresponding to a difference in route length of just 114 m, or 0.0085%) [122]. Cables with hollow-core fibers followed, although only in short feeder links to data centers of the exchanges in New Jersey and London: first with OFS AccuCore HC-PBGF in 2020 [123], and then with Lumenisity NANF in 2022.
## 8 Conclusions  
This research has delved into applying an innovative Pre-Signature scheme for V2V communications. We provide recommendations for changing standards, formats, and specifications to ensure that our approach is usable in the real-world.  
The approach is particularly suitable for rural landscapes where RSU availability is often limited or irregular. Through detailed simulations that closely emulate real-world rural scenarios, our study has provided an in-depth evaluation of this scheme efficiency under the typical infrastructural constraints of rural settings. The findings underscore the scheme's adaptability in varying RSU conditions, demonstrating its efficacy in maintaining communication integrity even in sparse RSU networks. This contributes significantly to our understanding of strategic RSU deployment, highlighting its vital role in enhancing rural V2V communication systems.  
Future research should focus on integrating emerging technologies like 5G and satellite communications to strengthen connectivity in remote areas. Additionally, exploring the synergy between this scheme and connected vehicle technologies could offer significant advancements in rural smart transportation systems. Further studies could also involve real-world trials to validate and fine-tune the applicability of our findings.  
**Author Contributions:** conceptualisation, D.A., T.M. and S.F.; Methodology, D.A. T.M., S.F and X.C.; Validation, D.A., T.M. and S.F.; Formal analysis, D.A.; Investigation, D.A.; Data curation, D.A.; writing--original draft preparation, D.A.; Writing--review and editing, D.A., T.M., S.F., X.C. and T.Y.; Visualization, D.A., T.M., S.F., X.C. and T.Y.; Supervision, T.M. and S.F.; Project administration, D.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work of [PERSON] was partially supported by CyberSecurity Research Flanders with reference number VR20192203.  
**Data Availability Statement:** The data can be shared up on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Results
Execution time measurements (Table 7), averaged over ten runs on an Intel Core i7-9750H with 16 GB RAM (ASUS VivoBook K571, NVIDIA GeForceGTX, Timisoara, Romania), indicate that random forest required longer training and prediction (\(\approx\)13.8 s) compared to SVM (\(\approx\)6.0 s) and KNN (\(\approx\)6.0 s), reflecting its ensemble of 200 trees versus the single-model structures of SVM and KNN.
## 2 Materials and Methods
### Clinical Assessment  
Demographic data were obtained including age, weight, height, body mass index (BMI), gender, symptom duration, and pathological side. Severity of average pain in the anterior knee was evaluated using the numeric rating scale (NRS) (0, no pain; 10, worst pain). Knee flexion ROM was measured by universal goniometer [22]. To measure the angle of knee flexion, the patient was placed in a supine position with the knee and hip of the symptomatic limb flexed at 90\({}^{\circ}\), with the contralateral leg extended. The knee was then passively flexed by the examiner until the participant reported the onset of pain in the anterior region of the knee. The examiner was blinded after the assignment of interventions. The axis of the universal goniometer remained on the lateral epicondyle of the femur, with the movable arm on the midline of the lateral face of the tibia and with the fixed arm on the midline of the lateral face of the thigh, aligned with the greater trochanter [23]. The intraclass correlation coefficient (ICC) was used to determine the reliability of the measurements and it was established based on the 10 volunteered patients as sufficient for clinical measurement (ICC = 0.95). Pain and functional ability over the previous week were evaluated using the Victorian Institute of Sport Assessment-Patella (VISA-P) scale [24; 25] and the Kujala questionnaire [17; 26]. VISA-P is the only condition-specific scale for patellar tendonopathy. This self-administered questionnaire consists of 8 items: 6 are used to evaluate pain levels or disability in daily activities and specific functional tests and 2 provide information regarding ability to play sport (asymptomatic patient, 100 points; disability, maximum 0 points) [15; 24]. The [PERSON] questionnaire is a specific, time-saving, and easily understandable tool for patellofemoral pain (asymptomatic patient, 100 points; disability, maximum 0 points) [26; 27].  
All variables were measured at baseline, immediately after, after 24 h, and after 1 week following the intervention.  
Figure 1: CONSORT flow diagram of patient recruitment and retention.
## 2 Literature Review and Hypotheses Development
### The Moderating Effect of Nutrition Disclosure and the Heuristic Effect  
Nutrition disclosure is defined as the provision of information to consumers regarding the nutritional content of a product, with food labeling being the most common method of dissemination [19; 20]. Scholars further define nutrition disclosure as a practice that offers clear nutritional information to help consumers make more informed decisions [50; 51; 52]. [PERSON] et al. [32] argued that nutrition disclosure on labeling plays a crucial role in minimizing consumer biases when choosing organic food. Previous research has shown that nutrition disclosure helps reduce the risks associated with purchasing by mitigating the effects of marketing illusions and providing transparency that prevents adverse selection [50; 51; 53].  
Heuristic, as defined by [PERSON] et al. [21] and [PERSON] and [PERSON] [54], refers to the process of evaluating products based on rough judgments or estimates. Previous studies suggest that consumers often rely on heuristics in their consumption decisions and product evaluations, as deep reflection can be mentally taxing [23; 54]. The literature indicates that sellers often capitalize on heuristics, emphasizing specific messages or appealing to consumers based on the vendor's reputation [21; 22]. As a result, consumers may evaluate products with insufficient information, relying on these simplified decision-making shortcuts. To reduce the biases introduced by heuristics, providing more comprehensive product information becomes essential. In the food industry, nutrition disclosure is a key strategy for mitigating heuristic biases. [PERSON] and [PERSON] [23] highlighted the heuristic effects in the marketing of dietary food products, while [PERSON] et al. [55] used heuristics as a theoretical framework to explore consumer behavior toward novel foods. Additionally, [PERSON] et al. [24] demonstrated the role of heuristics in consumer decision making for organic foods. By providing clear and detailed information, nutrition disclosure can help consumers refine their product evaluations in the context of food decision making. This rationale can be extended to consumer behavior regarding propolisis products. Based on these insights, the following research hypotheses are proposed:  
**Hypothesis 4a.**_Nutrition disclosure significantly moderates the relationship between the anti-oxidant effect and healthiness._  
**Hypothesis 4b.**_Nutrition disclosure significantly moderates the relationship between brand trust and healthiness._
# Towards Feasible Capsule Network for Vision Tasks
## 4 Experiments
### Results  
#### 4.3.1 Linear Evaluation of the Classification Task  
In the context of linear evaluation experiments, the primary objective is to provide evidence supporting the superiority of the capsule head as a classifier over the conventional fully connected (FC) layer. This advantage can be attributed to the unique \"Routing Mechanism\" employed in CapsNet. To initiate the evaluation process, we first extract feature maps from a pre-trained ResNet18 model. These feature maps are obtained from the output of the fourth block, situated just before the average pooling layer. It is worth noting that this specific feature map is instrumental in encapsulating all relevant information from preceding receptive fields, making it a valuable tool for model interpretation. Consequently, our evaluation framework comprises two key inputs for our model: before average pooling, dimensions of \(512\times 7\times 7\) because the input size is \(224\times 224\), and feature vector after average pooling with dimensions of 512.  
For the CIFAR10 dataset, the performance of CapsHeads with dynamic routing and settings (3), using the max-in normalization technique, achieved an accuracy of 89.18%. This outperformed the baseline classifiers, such as support vector machine (SVM), with an accuracy of 89.2%, and fully connected (FC) networks with two hidden layers and one layer, which achieved accuracies of 89.1% and 87.97%, respectively. Similarly, for the CIFAR100 dataset, the CapsHeads with dynamic routing and settings (3) attained an accuracy of 69.47%, surpassing the SVM classifier with an accuracy of 68.02%, and the FC networks with two hidden layers and one layer, achieving accuracies of 66.82% and 66.43%, respectively. The results in Table 1 demonstrate that CapsHeads with setting 3 perform competitively against traditional classifiers, such as SVM and FC networks, on both CIFAR10 and CIFAR100 datasets. The CapsHeads approach leverages the benefits of capsule vector in capturing intricate spatial relationships, and the utilization of routing with specific settings further enhances their classification performance.
## 2 Conventional SHM Techniques  
Direct visual inspection is a traditional SHM technique for structural assessment, damage detection, and mapping. It involves examining crack patterns and anomalies to identify potential damage mechanisms. However, visual inspections are limited to surface inspection and rely on subjective expert judgment [26]. Destructive testing (DT) techniques can provide important structural behaviour information. However, they are invasive, time-consuming, and expensive, especially when dealing with CHS. Non-destructive testing (NDT) techniques are a better alternative for in situ applications.  
Long-term monitoring systems offer a solution to the limitations of direct visual inspections by gathering detailed information and monitoring the evolution of static and dynamic properties over time. Environmental factors such as temperature and humidity can affect monitoring, and researchers have investigated their impact. To control the environmental effects, statistical models such as multiple data regressions (see, e.g., [27; 28; 29]) and principal component analysis (see, e.g., [30; 31; 32]) have been proposed to remove them from or minimize their effects on structural behaviour analysis.  
The combination of static and dynamic monitoring systems has been applied in CHS, including sites such as churches [23; 33; 34; 35; 36; 37; 38], towers [39; 40; 41], palaces [42; 43] (Figure 1), and other types of complex monumental buildings, such as the Roman Arena of Verona [44]. In the Appendix A, Table 11 shows typical examples from the international literature of static and/or dynamic SHM systems applied in CHS, indicating the purpose of these systems to monitor repairs/strengthening. In the following sections, an overview of both conventional static and dynamic methods for CHS is provided.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 2 Existing Surrogate Safety Measures (SSMs)
### SSM Considering Longitudinal Car-Following Behavior  
WS\({}^{2}\)DR\({}_{n}(t)\) (Weighted Combination of Spacing and Speed Difference Rates) [21] is a new traffic safety assessment metric that combines vehicle spacing and speed differences to identify potential risks in car-following behaviors and show better adaptability in mixed-traffic environments. The calculation of WS\({}^{2}\)DR\({}_{n}(t)\) is given by Equation (5).  
\[\text{WS}^{2}\text{DR}_{n}(t)=1-\beta\frac{\max(S_{n}(t)-\Delta x_{n}(t),0)}{ S_{n}(t)}-(1-\beta)\frac{\max(v_{n}(t)-v_{n-1}(t),0)}{v_{n}(t)} \tag{5}\]  
where \(S_{n}(t)\) represents the equilibrium spacing of vehicle \(n\), and \(\beta\) is a weighting factor between \(0\) and \(1\) that balances the emphasis on spacing versus speed. The terms \(\frac{\max(S_{n}(t)-\Delta x_{n}(t),0)}{S_{n}(t)}\) and \(\frac{\max(v_{n}(t)-v_{n-1}(t),0)}{v_{n}(t)}\) represent the degree of deviation in spacing from the equilibrium spacing and the speed difference rate between two vehicles, respectively. Both terms have a magnitude range of \([0,1]\). Therefore, the WS\({}^{2}\)DR metric, also ranging from 0 to 1, is derived by normalizing these terms to the same range. This normalization balances the spatial and speed components, mitigating the issue of differing magnitudes. Values close to 1 indicate safer conditions.
# Enhanced Epileptic Seizure Detection through Wavelet-Based Analysis of EEG Signal Processing
## 4 Results and Discussion  
### Analysis of EEG Signals  
The EEG signal analysis experiments conducted in this study has been executed using MATLAB software, version 2023b. The experiments were executed on a Lenovo ThinkPad T430 laptop (Lenovo, Beijing, China), equipped with an Intel Core i5 processor and 16 GB of RAM, OS Windows 10, 64 bit. Twenty EEG files were selected from each of the Helsinki Hospital and Boston Hospital databases. The signals presented periods labeled with and without epileptic seizures. The EEG montage configurations used in recording each dataset differed, leading to the creation of a custom SVM feature classification model for each database. Due to the spatial nature inherent in EEG signals, certain channels demonstrated superior performance relative to others in the experiments. The preprocessing of signals, signal decomposition, feature extraction, and feature classification were performed on EEG signal intervals demarcated as seizure or non-seizure.