esa-sceva/satcom-chunk-collection huggingface dataset:
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 5 TTN Server Configuration and Application Server Design  
As mentioned before, to implement our solution, the TTN platform will be used as a network server. This section shows the main steps to perform the correct configuration of platform.  
To configure the server network, the TTN website will be accessed. The next step requires to create as many identifiers as nodes comprise the network. TTN will automatically assign the AppEUI key which will be used to connect the nodes to the network.  
Once the application is created, the nodes will be registered as devices, and an identifier and a DevEUI of 8 bytes will be assigned. TTN will generate the AppKey later. Once it is created, the activation method of the device must be indicated. In this case, Activation By Personalization (ABP) is selected since it allows a faster link to the gateway. Finally, TTN will provide the session keys and the DevAddr that must be copied in the program code of each node. It is also possible to define the payload formats tab to visualize a decoder of the data sent by our nodes. With this, the user will be able to numerically see the data in the console, otherwise, it would display them in hexadecimal format.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Channels & 8 \\ Working Frequency & 868 MHz (EU version) \\ Wireless Connectivity & LoRa and Wifi \\ Power & USB-C adapter with 900 mA \\ Antenna & Omnidirectional for indoor use \\ Manufacturer of the radio module & Semtech \\ \hline \hline \end{tabular}
\end{table}
Table 2: Features of TTN gateway.  
Figure 11: Calibration of anemometer.
## 5 Numerical Results
### Energy Consumed by RN2 Due to PF Transmissions with Success and Failure, and Due to Overhearing  
In this subsection, we present the average energy consumed by RN1 and RN2 per cycle, along the _data_ period of a cycle. We represent the variation of the different components of the energy consumed with the offered load to C2 SNs (\(\lambda_{2}\)), i.e., the energy consumed due to a PF transmitted with success, \(E_{tx,s}^{d,2}\), the one when a PF collides, \(E_{tx,f}^{d,2}\), and the one due to RN\(c\) overhearing the transmission of PFs by other SNs of the same class that win the contention to access the medium, \(E_{oh}^{d,2}\). We also represent the total average energy consumed per cycle during the _data_ period of a cycle, \(E_{d,2}=E_{tx,s}^{d,2}+E_{tx,f}^{d,2}+E_{oh}^{d,2}\).  
We evaluate a scenario where C\(c\) SNs deploy the APT transmission scheme with \(F_{1}=F_{2}=10\) and have a queue capacity of \(Q_{1}=Q_{2}=10\)_DATA_ packets. The results are shown in Figure 11. Recall that \(E_{oh}^{d,2}\) defines the average energy consumed by an active RN2 that looses the contention to access the channel. At the beginning of the _data_ period of a cycle, active SNs start sensing the medium until their backoff timers expire or sense a busy medium. In the latter case, SNs move to the _sleep_ mode until the end of the cycle. Then, \(E_{oh}^{d,c}\) measures the average energy wasted by an active RN\(c\) per cycle due to having the receiver active to perform channel sensing along _data_ periods where it loses the contention.  
Observe that the average energy wasted per cycle due to PF collisions, \(E_{tx,f}^{d,2}\), is almost negligible. That is, when C2 SNs deploy the APT scheme with \(F_{2}=10\), the packet collision rate is substantially reduced. As mentioned before, as \(F\) increases, fewer SNs per cycle are active, and a higher rate of successfully transmitted PFs is achieved.  
To reinforce this perspective, we show the variation of the packet average access delay with the offered load to C2 SNs (\(\lambda_{2}\)), for different values of \(F\) in Figure 12. This figure is taken from [24]. Observe that the average _DATA_ packet delay expressed in cycles for C1 SNs, \(D_{1}\), is insensitive to the value of \(F_{1}\). However, \(D_{2}\) decreases substantially as \(F_{2}\) increases, confirming the benefits that can be obtained by deploying the APT scheme.  
Figure 11: Average energy consumed by RN2 per cycle due to PF transmissions with success and failure, and to overhearing, when deploying APT (\(F_{2}=10\)).
## Materials and Methods
### Methods
#### 2.2.3 Electrical Conductivity  
In order to evaluate the electrical conductivity of samples the reverse value--surface resistance was measured according to EN 1149-1 [40] standard, with Terra-Ohm-Meter 6206 (produced by Eltex-Electrostatik-GmbH, Weil am Rhein, Germany) applying voltage of 10 V. Five test specimens were cut to a size between the overall dimensions of the electrodes and of the base plate from the material designed. Specimens were pressed with the load of about 10 N between an assembly of cylindrical and annular electrode arranged concentrically with each other and base plate, on which the specimen was placed. The diameter of electrode used--100 mm. The range of the ohmmeter used 10\({}^{3}\)\(\Omega\) to 10\({}^{14}\)\(\Omega\). The conditioning for 24 h and tests were carried out in dry conditions--air temperature (\(23\pm 1\)) \({}^{\circ}\)C, relative humidity (\(25\pm 5\)) %, as indicated in the standard used. The measuring circuit of electrodes during measurements of resistance is presented in Figure 1.  
The surface resistivity (\(\rho\)) was calculated according to equation:  
\[\rho=k\cdot R, \tag{1}\]  
where \(R\) is the measured surface resistance, \(k\) is the geometrical factor of the electrode. The geometrical factor of the electrode is calculated according to equation:  
\[k=\frac{2\cdot\pi}{\log_{e}\left(\frac{r_{2}}{r_{1}}\right)}=\frac{2\cdot 3.14}{ \log_{e}\left(\frac{34.6}{25.2}\right)}=19.8, \tag{2}\]  
Figure 1: Measuring circuit of surface resistance measurement.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{2}{c}{**Properties**} \\ \hline  & Propylene glycol \(\leq\) 66.5 \\  & 2,2\({}^{\prime}\)-oxydiethanol \(\leq\) 15 \\ Composition/information on ingredients & Benzensulphonic Acid, Ethenyl-, Homopolymer, \\ (component name and \% by weight) & Compl.with \\  & 2,3-dihydropythienol[3,4-b]-1,4-dioxin \\  & Homopolymer \(\leq\) 15 \\ \hline Surface resistivity & 700 \(\Omega\)/sq \\ (test print) & Aqueous dispersion \\ \hline \hline \end{tabular}
\end{table}
Table 3: Properties of conductive paste applied (product supplier information)where \(r_{1}\) is the radius of the inner electrode and \(r_{2}\) is the inner radius of the outer electrode.
# Hybrid Positioning for Smart Spaces: Proposal and Evaluation
## 4 Hybrid Positioning Approach
### Modular Design  
The presented framework is designed in a modular fashion, so the supported technologies and algorithms are considered as positioning sources. More technologies and algorithms can be easily incorporated to the system. For example, PDR algorithms offer good results and could complement the presented implementation based on proximity and fingerprinting.  
As some positioning algorithms need the previous estimated position in order to apply optimization measures or calculate the next one, the rest of positioning sources allow to retrieve it properly from the system. This would be the case if PDR-based methods were developed. In our case, the fingerprinting algorithm uses the previous position to consider only a reduced subset of potentially matching fingerprints, reducing computational effort and saving energy, as it is shown in Figure 1.
# A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter
## 2 Background and Related Work
### Machine Learning in Cyberbullying Detection
#### 2.2.5 AdaBoost  
Adaptive boosting (AdaBoost) is an ensemble learning method, and it is a prevalent boosting technique that was initially developed to make binary classifiers more efficacious [100; 101]. It uses an iterative approach to learn from weak classifiers' errors, and transform them into strong ones. Therefore, each training observation is initially assigned equal weights. It uses several weak models and attributes higher weights to experimental misclassification observations. As the results of the definitive boundaries obtained during several iterations are combined using several low models, the accuracy of the erroneously classified observations is improved. Thus, the accuracy of the overall iteration is enhanced [102]. An example of AdaBoost classifier implantation is shown in Figure 1, where it showed a similar dataset that has two features and two classes in which week learner #2 improve by mistake made by weak leaner #1 and the accuracy of the misclassified observations is further improved when the two-week classifier are combined (strong leaner).  
Figure 1: Implementation of Adaboost classifier [101].  
Moreover, AdaBoost has been used in cyberbullying detection by some researchers like [103] and [63], as well as, the work in [104] who used it for cyberbullying detection, where they obtained an accuracy of 76.39% with AdaBoost, utilizing unigrams, comments, profile, and media information as features.
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 4 Experimental Setup and Results
### Evaluation Criteria  
BFDM returns a list of putative descriptor matches, which we store in a matrix from as:  
\[M(w^{\prime},z^{\prime})=\begin{cases}1&descriptor\ match\\ 0&not\ a\ match\end{cases} \tag{24}\]  
where \(w^{\prime}\) and \(z^{\prime}\) are two descriptors of VS/RGB and LWIR/NIR images, respectively. If BFDM identifies them as a putative match then we use logic 1 otherwise 0 to store the matching result. To identify correct and false matches present in matrix \(M\), we compute a ground truth \(\bar{H}\) matrix with the help of projection error [18] as follows:  
\[\bar{H}(w^{\prime},z^{\prime})=\begin{cases}1&\epsilon\leq 2\\ 0&otherwise\end{cases} \tag{25}\]  
If the feature points of \(w^{\prime}\) and \(z^{\prime}\) descriptors are at \(\epsilon\leq 2\), they are assigned a logic 1 otherwise logic 0. Then the total number of correct (\(N_{c}\)) and false (\(N_{f}\)) matches are computed as follows:  
\[N_{c} = \sum_{w^{\prime}}\sum_{z^{\prime}}M(w^{\prime},z^{\prime})\times \bar{H}(w^{\prime},z^{\prime}) \tag{26}\] \[N_{f} = -N_{c}+\sum_{w^{\prime}}\sum_{z^{\prime}}M(w^{\prime},z^{\prime}) \tag{27}\]  
Matching and precision scores are computed as performance metrics [18; 34]. Matching score is a ratio between number of correct descriptor matches (\(N_{c}\)) and smaller of the number of descriptors inthe pair of images ( \(min\{w,z\}\) ). Precision is defined as a ratio between number of correct descriptor matches (\(N_{c}\)) and total number of descriptor matches (\(N_{c}+N_{f}\)). Matching and precision scores are computed on image pair basis of the test set as illustrated in Figure 4 and then average matching and precision scores are presented.
# A Method for Processing Static Analysis Alarms Based on Deep Learning
## 5 Experiments and Results
### Experimental Setup
#### 5.1.2 Performance Criteria  
To quantitatively evaluate the performance of our approach, we employed several commonly used evaluation metrics, including accuracy, precision, recall, and F1-score. These metrics provided a holistic assessment of the model's performance in accurately classifying actionable warnings and false alarms. Accuracy is a commonly used metric for evaluating the performance of classification models, and it measures the proportion of correctly classified samples with respect to the total number of samples (all generated warnings). A higher accuracy score indicates better performance of the model in correctly classifying the samples. Precision and recall are two widely used measures for evaluating the completeness and exactness of a classifier, while the F1-score is a balanced metric that takes the harmonic mean of the precision and recall, providing a more comprehensive evaluation of how well the classifier performed. Furthermore, we utilized the receiver operating characteristic (ROC) curves, which graphically illustrate the performance of classification models at diverse threshold settings. While varying the threshold from 0 to the maximum based on the predicted results of the learner, the ROC curves visually depict the trade-off between the true positive rate (TPR) and the false positive rate (FPR) for different classification thresholds. The ROC curves help to compare the performance of different classification models, while the area under curve (AUC) summarizes the overall performance of the model across all possible threshold settings. The AUC score is a comprehensive metric for evaluating the overall performance of classification models across various thresholds, and it ranges from 0 to 1, with higher values indicating better performance. It provides a measure of the trade-off between model classification accuracy and the true positivity rate. A model with a higher AUC generally indicates better performance in distinguishing between positive and negative instances. By analyzing these evaluation metrics, we can assess the effectiveness and reliability of our DL-based approach in accurately identifying actionable alarms from static analysis warnings.
## 4 Grid Planning and Design
### The Creation of Grid Development Models
#### 4.2.2 Energy Storage  
Continuing with the energy storage, small-scale storage is also interesting, therefore, [PERSON] et al. [256] evaluate the results of condenser banks, flywheels and compressed air (as storage).  
Storage can be hybridized with wind systems, in this sense [PERSON] et al. [257] dimensions an optimal system using adiabatic compressed air and flywheel, and employs a spectrum analysis method.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Summary of Maintenance Based**} & \multicolumn{2}{c}{**Grid Planning and Design: Alternative Development of the Grid**} \\ \cline{2-3}
**on Asset Lifecycle Management** & **Artificial Intelligence** & **Other Techniques** \\ \hline
**Cables and HTS** & - & [230; 231; 232; 233] \\ \hline
**FACTS** & [237; 238; 239] & [234; 235; 236] \\ \hline
**Lines and Substations** & [240; 242; 247; 248; 249] & [241; 243; 244; 246] \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of Alternative Development of the Grid.  
But storage is also associated with photovoltaic systems, and in this sense, [PERSON] and [PERSON] [258] optimize the capacity of the battery associated with a photovoltaic plant, and use mixed integer programming model.  
Following the photovoltaic systems, [PERSON] et al. [259] employ a Kalman filter as hybrid battery management in a residential environment. The authors show that storage management is more efficient and reliable through advanced techniques. The results obtained demonstrate optimal storage management.  
ANN have been used in multiple applications in SG, from the forecast of solar or wind resources (photovoltaic and wind) to electrical demand and management of electrical storage, as shown by [PERSON] et al. [260]. [PERSON] et al. [261] present a semi-Markov process model to model photovoltaic power and [PERSON] et al. [262] present sizing and control methodologies for a zinc-bromine flow battery-based energy based on ANN.  
To summarize, Table 11 shows all the work described in this section. In the rows are shown different areas (tools for new plants and energy storage) while in the columns is shown the application rate on the creation of grid development models (AI and other techniques). As can be seen in the previous table, new models for the development of electricity grids are being implemented. These developments will be applied to improve the location of new generation plants and the deployment of storage. Again, the developments are based on AI and other simpler techniques.
# RF-Access: Barrier-Free Access Control Systems with UHF RFID
## 4 Evaluation
### Detection of Motion Direction
#### 4.2.4 Impact of Different Users  
In this study, we investigate the impact of different users on the accuracy of RF-Access. Eight volunteers (five males and three females) were invited to participate in the experiments. Their heights ranged from 155 cm to 185 cm and their weights varied from 45 kg to 80 kg. Each volunteer randomly placed the tag at one of the five positions and passed through the passageway. As shown in Figure 17a, the system can maintain high detection accuracy of active moving directions for different volunteers. Similarly, we can verify that the detection accuracy remains stable when multiple people pass in parallel, as shown in Figure 17b. The results demonstrate that RF-Access can obtain accurate and stable access direction detection results for different visitors.
## Appendix C Proof of Feasibility Region  
For no outage, user 1 has to satisfy the condition in (36). Substituting (5) in (36) gives  
\[log_{2}\bigg{(}1+\frac{\beta_{1}\alpha_{1}\varrho_{s}}{\beta_{1}\alpha_{2} \varrho_{s}+1}\bigg{)}\ \geq\ \dot{\mathrm{D}}_{\mathrm{W}U} \tag{108}\]  
Taking antilogarithm of both the sides  
\[1+\frac{\beta_{1}\alpha_{1}\varrho_{s}}{\beta_{1}\alpha_{2}\varrho_{s}+1}\geq 2 \dot{\mathrm{D}}_{\mathrm{W}U} \tag{109}\]  
Replacing \(\alpha_{1}=1-\alpha_{2}\) and simplifying for \(\alpha_{2}\)  
\[\alpha_{2}\leq \frac{\beta_{1}\varrho_{s}-D_{\mathrm{W}U}}{\beta_{1}\varrho_{s}(1 +D_{\mathrm{W}U})} \tag{110}\]  
For no outage, user 2 has to satisfy the condition in (37). By following the same procedure like user 1, the feasibility region for user 2 is estimated which is given by  
\[\alpha_{2}\geq\ \frac{D_{SU}}{\beta_{2}\varrho_{s}} \tag{111}\]
## 3 Results and Discussion
### NDVI Trends
#### 3.1.2 NDVI Trends in Each Month  
Monthly trends of NDVI were also analyzed (Table 1). Over the past three decades, monthly mean NDVI in China increased in most growing months. The overall trends in NDVI were statistically significant for April, May, July, August and September, peaking in April and May, suggesting NDVI increased the most in spring [35, 37]. Significant TPs of monthly mean NDVI were detected for June 1984, July 1988 and August 2007. The TPs vary because monthly NDVI values are independent of each other and can have different trends. The monthly mean overall NDVI of June and July increased rapidly before the TP, that of June decreased after the TP, and that of July stabilized after the TP. By contrast, annual mean NDVI in August stabilized from 1982 through 2007 (the TP year), and increased at a high rate (0.011 NDVI yr\({}^{-1}\) or 1.71% yr\({}^{-1}\)) after the TP.
## 2 GEO Grid ASTER EVI Products
### Vegetation Index Module
#### 2.2.1 MODIS Backup EVI (\(Evi_{b}\))  
The MODIS VI products appear to be significantly affected by the presence of snow such that EVI values increase largely while NDVI values are reduced [8]. When this is detected, the MODIS VI algorithm switches to a backup EVI equation that is a Soil-Adjusted Vegetation Index (SAVI)-like equation, but its coefficients adjusted for this backup index to have nearly the same dynamic range as the EVI [15]. Since this backup EVI only requires red and NIR reflectances as the inputs, it can directly be computed with ASTER spectral bands. The equation takes the form [15]:  
\[EVI_{B}=2.5\cdot\frac{\rho_{ASTER,NIR}-\rho_{ASTER,red}}{\rho_{ASTER,NIR}+ \rho_{ASTER,red}+1} \tag{6}\]
## Results
### Effects of Water Vapor
the all-sky than in clear-sky conditions, considering the direct and indirect effects of water vapor on solar radiation. To quantify the contribution of the variations of water vapor amount vertically, we calculate the PW at a different range of altitude (Figure 8). Water vapor mainly concentrates on the low-level altitude (<2 km) and rapidly decreases with altitude. The low-level PW decreased by 1.3 and 1.5 mm, with a high significance level in the all-sky (\(p\) = 0.04) and clear-sky conditions (\(p\) = 0.05). These changes dominated the total changes of water vapor amount during the monsoon season in South Asia from 2006 to 2015. Moreover, while the PW at a different range of altitude consistently decreased, the significance levels of trends gradually weakened with the increase in altitude, which indicates the large uncertainties in high-level water vapor variations. Overall, the water vapor importantly decreased during the monsoon season in South Asia from 2006 to 2015, which dominantly contributed by the low-level water vapor variations (below 2 km) with a high significance level. Consequentially, the decreased water vapor enhanced the incident solar radiation due to the reduction in atmospheric absorption from water vapor.
# Fault Diagnosis of a Solenoid Valve Based on Multi-Feature Fusion
## 2 Analysis of Solenoid Valve Current Characteristics
### Circuit Equation  
The equivalent circuit diagram of the solenoid valve is illustrated in Figure 2.  
According to the circuit principle, in general, the circuit equation of the solenoid valve is:  
\[\begin{array}{rl}u&=i(R+R_{L})+\frac{\mathrm{d}\Psi}{\mathrm{d}t}=i(R+R_{L})+ \frac{\mathrm{d}(Li)}{\mathrm{d}t}\\ &\qquad\qquad=i(R+R_{L})+L\frac{\mathrm{d}i}{\mathrm{d}t}+i\frac{\mathrm{d}L}{ \mathrm{d}t}\end{array} \tag{4}\]  
where \(u\) is the coil excitation voltage, \(\Psi\) is the flux linkage, \(t\) represents time, \(R\) is the coil resistance and \(R_{\mathrm{L}}\) is the additional resistance of the coil loop.
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 2 Related Works
### MANET-Based Approaches  
MANET research has extensively explored challenges similar to those we address, particularly in vehicular networks (VANETs). [PERSON] et al. [23], [PERSON] et al. [24], and [PERSON] et al. [25] tackled connectivity and mobility challenges in VANET environments. However, our scenario presents different challenges as we consider human-carried mobile devices with inherent energy constraints, while VANET nodes have consistent vehicle power supply.  
Several studies have advanced MANET caching techniques that we adapt for privacy preservation. [PERSON] et al. [26] presented collaborative caching strategies in wireless ad hoc networks (WANETs), where nodes share information stored in their cache. However, unlike in our study, information related to geographical location was not sought or maintained around a specific geographical location. Following this line, [PERSON] et al. [27] developed a context-aware caching scheme focused on reducing server load, a goal we share, but we extend it to include privacy considerations. [PERSON] et al. [28] optimized transmission distances in MANET caching, which is a concept that we incorporate into our geographic data distribution strategy. [PERSON] et al. [29] proposed query classification to reduce cache overhead, which we adapt to balance privacy protection with system efficiency.
## 1 Introduction
This capability is crucial for real-time monitoring, predictive maintenance, and optimizing production processes, which are fundamental components of Industry 4.0. Our key contributions are as follows:  
* **Unsupervised 3D Reconstruction:** We develop an unsupervised learning framework that obviates the need for manual annotations, facilitating cost-effective 3D model generation. * **Modified Neural Radiance Fields:** By enhancing the NeRF architecture, we achieve accurate and robust 3D reconstructions suitable for diverse industrial applications. * **High-Resolution Model Generation:** Our approach efficiently reconstructs high-resolution 3D digital twin models from 2D images, enabling real-time monitoring and advanced analytics in manufacturing environments.