esa-sceva/satcom-chunk-collection huggingface dataset:
# Crowdsourcing Based Performance Analysis of Mobile User Heterogeneous Services
## 4 Data Analysis
### Device Types  
The impact of the characteristics of the user terminal is detected in our dataset. In fact, we have displayed the statistics in terms of bitrate for the two phones used (Redmi Note 9 Pro and Samsung A10). Table 5 presents a summary of these results.  
From Table 5, we can clearly see that using different terminals implies different performances. Indeed, we observe that the average bitrate using the \"Redmi\" phone is 24 MB/s against 15 MB/s for the Samsung A10. This implies an increase of 60%, justified by the hardware characteristics that are better in the \"Redmi\".  
Figure 5: Distribution of data according to speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Statistics (Bitrate)** & **Redmi Phone** & **Samsung Phone** \\ \hline min & 0.00 & 0.00 \\
25\% & 1696.54 & 1402.76 \\
50\% & 9445.40 & 5190.91 \\
75\% & 30,425.03 & 22,657.07 \\ max & 237,068.80 & 113,644.17 \\ mean & 24,165.38 & 15,110.18 \\ count & 97,047.00 & 67,344.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistics of collected bitrate with used smartphonesIn addition, we also note that the maximum bitrate value reaches 237 MB/s and 113 MB/s for the Redmi and the Samsung A10, respectively. Thus, the maximum measured bitrate with Redmi is twice that of Samsung's bitrate. This can be justified by the compatibility of the Redmi with \(LTE_{A}\) (\(4G\)) technology that includes the new \(LTE\) system with \(4\times 4\)\(MIMO\) (\(4T4R\)) while the A10 Samsung phone supports only simple \(LTE\) access technology. In addition, we will see later that the antenna gain in Redmi is almost much better than the gain in A10.
## 2 Coupling Coordination Framework  
### Basic Framework  
TPUs play a crucial role in providing flexibilities for RESs like WT and PV in regions with insufficient flexible regulatory resources. Traditionally, in the power grid's operational framework, TPUs, WT farms, and PV plants operate as standalone entities [12]. The main power grid's operator sends dispatch instructions to these various energy sources independently. As depicted in Figure 1a, the dispatch instructions to TPUs are given based on forecasted outputs from the RES and load demand. Under this scenario, TPUs lack the capability to monitor and accommodate the real-time energy production from RESs; thus, the flexibility potential of TPUs cannot be fully utilized. The complex network structure and operational limitations within the large power grid mean that only a fraction of the RES fluctuations can be mitigated by the grid's flexibility. Consequently, any RES generation exceeding this capacity will be discarded, leading to inefficient utilization.  
To address the above issues, a CGS that aggregates the RESs and TPUs into an integrated control object and operating entity connected to the main power grid at the same point of common public (PCC) is a feasible and effective solution. In this scenario, both the physical distance and electrical connections of RESs and TPUs are close. Hence, the REs and TPUs can be dispatched by the power grid as an integrated singular unit. As depicted in Figure 1b, the CGS coordinates the output of the internal power sources based on the dispatching instructions received from the main power grid operator. The instruction specifies the power required to be generated by the CGS during a time period; it is typically related to the load demand and RES output. To effectively manage the fluctuations associated with  
Figure 1: Before vs. after coupling integration of thermal power and renewable energy generation. (**a**) Operate independently; (**b**) Coupling integration.  
a high penetration of RESs within the CGS, TPUs typically undergo technological upgrades. These enhancements are aimed at increasing their ramping capabilities and expanding the output range.
# Non-Interactive Decision Trees and Applications with Multi-Bit TFHE
###### Abstract
Even though the comparison operation proposed by [PERSON] et al. [7] can make the comparison for arbitrary length, it is hard to accelerate the comparison operation, as it is not using a \(2^{n}\)-cyclotomic polynomial. It also does not support the bootstrapping needed for our decision tree evaluation protocol. Therefore, we propose the first multi-bit comparison method using the TFHE scheme to compare integers. Figure 1: Private machine learning as a service. In summary, we make the following improvements to the existing protocols:  
* Our private comparison method operates on two encrypted inputs with 128-bit security, facilitating the use of third-party evaluators, such as the public cloud. The performance of our method scales linearly with the input bit length and the depth of the decision tree. * Our decision tree evaluation protocol is non-interactive, where the client sends encrypted input and receives the encrypted output. There is no other interaction between the client and the server. * Experimentally evaluated the effect of bit lengths to assess the scalability of our method compared to other methods. * In our implementation, we conducted experiments with decision trees trained with widely used real datasets from the UCI machine learning repository [5] and compared them with the existing protocols. * The final output of the protocol consists of a ciphertext that decrypts the classification result. This can be extended to the decision tree returning categorical values, using one-hot encoding. The results from multiple decision trees can be combined to evaluate random forest classification. The rest of the paper is organized as follows. The next section discusses related work on secure decision tree evaluation and integer comparison. Section 3 introduces the main ideas in fully homomorphic encryption and decision tree evaluation. Using an enhanced comparison protocol, Sections 4 and 5 propose a new method for evaluating decision trees in a privacy-preserving manner. The computational complexity and performance are discussed in Section 6. Finally, Section 7 concludes the paper.
## 5 Discussion
### Limitations and Challenges  
Our research has several limitations that should be acknowledged. First, all experiments were conducted in simulation using the CARLA environment, which may not fully capture the complexity and unpredictability of real-world driving scenarios. While CARLA provides realistic physics and sensor models, real-world implementation would face additional challenges such as sensor noise, environmental variability, and hardware constraints. To address the inherent limitations of simulation-based studies and bridge the gap towards real-world validation, we have procured an AWS DeepRacer agent and iniVation Dynamic Vision Sensors (DVS), detailed specifications of which are provided in Appendix C.  
Second, our attack models represent a subset of possible attack vectors. More sophisticated attacks, such as adversarial machine learning techniques that target specific neural network vulnerabilities, were not fully explored in this study [48]. Such attacks could potentially bypass our anomaly detection systems by generating perturbations that appear normal while causing misclassification.  
Third, our defense mechanisms showed limited effectiveness against certain attack types, particularly for RGB and depth sensors. This highlights the challenge of developing robust defenses that can maintain perception quality while mitigating attacks [62]. The trade-off between security and performance remains a significant challenge for autonomous vehicle designers [60].  
Finally, the computational requirements of our anomaly detection system may present challenges for real-time implementation on resource-constrained vehicle platforms [52]. While our experiments used high-performance GPUs, production vehicles may have more limited computational capabilities, necessitating the optimization of detection algorithms.
# Shapley's Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM
## 4 Discussion
Additionally, the development of algorithms is proposed that reduce both the computational and temporal complexity of the techniques suggested for resource optimization, allowing their implementation in a more agile and efficient way in practical environments. Finally, the use of genetic algorithms could become a promising strategy for resource optimization in IBOC FM digital broadcasting, offering a dynamic and adaptive approach, and with the possibility of significantly improving efficiency, latency, and quality.
## 3 State-of-the-Art Review
### Related Projects, Systems and Applications
\begin{table}
\begin{tabular}{|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|p{42.7 pt}|} \hline [PERSON] _et al._[44] & Hong Kong University of Sci. and Tech. & China & 2010 & Ocean sensing \& de monitoring & Sea depth and temperature & ZigBee floating & Sensor & Batteries & Deployed in HKUST campus and Tsigeta & A Perpendicular Intersection (PI) mobile-assisted localization scheme \\ \hline [PERSON] _et al._[45] & Universidad Politecnica de Valencia, & Spain & 2011 & Marine fish farms monitoring & The amount of pollution &? & Buoy &? & Tested on OPNET & A group-based underwater WSN for monitoring fecal waste and uneaten fed \\ \hline [PERSON] _et al._[46] & Universidad de Las Palmas de Gran Canaria & Spain & 2011 & Ocean sensing \& monitoring & Visible-field, sound and temperature & ZigBee and acoustic &? &? & Tested on module of NS-architecture, transmitting video streaming data \\ \hline [PERSON] _et al._[47] & University of Kent _et al._[47] & UK & 2004 & Ocean sensing \& monitoring & Temperature, conductivity, water depth, turbidity &? & Single buoy & Batteries & Buoy & A multi-layered scalable deployed off Scroby sands & A multi-layered scalable and adaptive approach of data management \\ \hline [PERSON] _et al._[48] & Universitat de Barcelona & Spain & 2010 & Fish farm monitoring & Temperature and pH & ZigBee &? & One & Tested in a rechargeb 1e battery & A sub-layer-based power consumption algorithm \\ \[PERSON] _et al._[49] & Dublin City University & Ireland & 2012 & Water quality monitoring & Temperature, conductivity and depth &? & Buoys &? & Tested in River Lee, Poohge & A multi-modal environment monitoring network based on WSN and visual image \\ \[PERSON] _et al._[50] & University of Queensland & Australia & 2009 & Ocean sensing \& monitoring & Temperature and illuminance & ZigBee & Cylinder waterproof & Two solar panels & Deployed in the Morton underwater wireless communication \\ \[PERSON] _et al._[51] & Cyprus University of Technology & Cyprus & 2009 & Water quality monitoring & Temperature, pressure salinity and turbidity & GPRS & Cylinder waterproof & Battery & Deployed in a municipal back & Integrating two technologies of satellite remote sensing and WSN \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._
## 3 Results and Discussion
### Characterization of the Liquid Product
#### 3.3.2 GPC Analysis of the Raw KL and Depolymerized Liquid Product  
A GPC analysis was conducted to determine the molecular weights of the raw KL and liquid fractions. Table 2 details the weight-averaged molecular weight (M\({}_{w}\)), number-averaged molecular weight (M\({}_{n}\)), and polydispersity index (PDI) of the KL, non-DES-treated liquid fraction, and liquid fraction obtained from DES treatments under various conditions. Notably, the molecular weight distributions of the KL and liquid fractions exhibit substantial differences. For instance, the raw KL exhibits an M\({}_{w}\) value of 2450 g/mol and a M\({}_{n}\) value of 1493 g/mol. After depolymerization, the resulting liquid fraction exhibited considerably lower molecular weights, indicating effective lignin disintegration. This  
Figure 7: Yields of lignin depolymerization products in the liquid phase after treating KL with DES and without DES at 190 \({}^{\circ}\)C for 8 h.  
reduction in molecular weight is attributed to the cleavage of \(\alpha\)-O-4 and \(\beta\)-O-4 linkages during depolymerization [42]. Specifically, the liquid fraction obtained from the DES treatment of KL at 190 \({}^{\circ}\)C for 8 h exhibited the lowest molecular weight, with an M\({}_{\text{w}}\) of 1498 g/mol and a M\({}_{\text{n}}\) of 1061 g/mol. This result suggests that DES significantly enhances the depolymerization process, yielding a more favorable molecular weight distribution. Furthermore, the PDI of the DES-treated liquid fraction under optimal conditions was 1.41, indicating that this liquid fraction exhibited a more uniform molecular weight distribution compared to the untreated KL sample, along with improved homogeneity [43]. In contrast, at the same reaction temperature and duration, the non-DES-treated liquid fraction exhibited an M\({}_{\text{w}}\) of 1872 g/mol and a M\({}_{\text{n}}\) of 1259 g/mol, highlighting the substantial improvement in the quality of the liquid fraction subjected to DES treatment. These findings underscore the effectiveness of the DES in reducing the molecular weight of lignin and enhancing the quality of the liquid fraction through improved depolymerization.
## 3 Enhanced User Interaction Components
### 6-DoF SMC: 6 Degrees of Freedom Smartphone Motion Capture  
A-Frame did not include components to translate smartphone motion into movement in virtual environments. A similar limitation was also present in the whole web-based VR. The situation changed in 2017, when Google introduced its ARCore library, which brings 6-DoF motion tracking to smartphones. In web browsers, ARCore is available via the WebXR Device API, which has certain safety restrictions and is still considered experimental in some browsers (A web page using it should be provided via the HTTPS protocol. For browser support and special settings required, one can search for \"WebXR Device API\" at [[https://caniuse.com/](https://caniuse.com/)]([https://caniuse.com/](https://caniuse.com/)), accessed on 22 April 2021). A-Frame integrated the ARCore 6-DoF motion tracking functionality to its version 1.0.3, released in December 2019. While it is possible to turn on the 6-DoF motion tracking in A-Frame 1.0.3 or later, it is not available in the stereoscopic rendering mode, which is essential for the smartphone-based VR headsets, such as Google Cardboard ([[https://arvr.google.com/cardboard/](https://arvr.google.com/cardboard/)]([https://arvr.google.com/cardboard/](https://arvr.google.com/cardboard/)), accessed on 22 April 2021). This issue was solved by the 6-DoF SMC component.  
The 6-DoF SMC component (Figure 3) has been designed for mobile devices with the operating system Android 8.0 or later. We intended to utilize the Google ARCore library only, but for web-based purposes it was necessary to employ WebXR, too. In general, the ARCore obtains IMU and camera data from the mobile device to detect its position and rotation, relatively to the surrounding space. The motion tracking is based on features detection from the image stream, captured by a camera. This mechanism detects significant  
Figure 2: Part of the ECA dashboard during the performance evaluation.  
points that contrast in the image stream. The ARCore is able to track how those points are moving over time, and, through this, it can process the device motion. After that, the ARCore data are directed to the WebXR module, which controls the whole web-based backend. To achieve cross-compatibility between ARCore and WebXR, the web browser Chrome Canary version 70 was used. In our solution, the WebXR controls A-Frame camera position and rotation, relatively to the virtual scene. When the user moves in the real world, the motion is detected and translated into the virtual environment. 6-DoF SMC has been tested with A-Frame version 1.0.4; however, significant performance issues have been detected. This is most probably caused by the 6-DoF SMC component running in parallel with the 6-DoF motion capture functionality of A-Frame 1.0.4, which, unfortunately, lacks the stereoscopy support. Therefore, A-Frame version 0.9.2 was used on a smartphone during the experiments presented in this article.
## 3 Remote Sensing of Ecosystem Health
### Remote Sensing of Vigor
#### 3.1.1 NPP or GPP  
Changes in NPP are often used to evaluate environmental degradation in the context of desertification, impacts of pollution, climate change, and deforestation [33]. NPP can be estimated and monitored from optical remote sensing images since the 1970s [26], yet remote sensing derived daily global NPP products were not operationally produced until mid-2000s [34]. The modeling approach for predicting NPP is based on the light use efficiency (LUE) concept proposed by [PERSON] [35] and modified by [PERSON] [36]. Based on the concept, the GPP of one ecosystem can be a function of the absorbed Photosynthetically Active Radiation (PAR) namely absorbed solar radiation at 400 to 700 nm wavelength and the photosynthetic efficiency which is specific for an individual plant type. NPP is the product of GPP by subtracting respiration. This LUE based modeling approach has been applied to produce Moderate Resolution Imaging Spectroradiometer (MODIS) global 8-day GPP and annual NPP at 1 km spatial resolution products (MOD17) [34], which have been ready for monitoring ecological conditions and environmental changes [37] since the mid-2000s.  
For studies at regional or even smaller scales, statistical empirical model of GPP or NPP and a vegetation index, such as Normalized Difference Vegetation Index (NDVI), is more operational, considering the large number of parameters of the LUE model which highly affect the accuracy of GPP (or NPP) [26]. Good NDVI-GPP (or NPP) relationships have been observed in low biomass vegetated areas, such as the Arctic tundra [38] and the steppe [39]. However, NDVI becomes saturated at high biomass vegetated areas [40] including dense grass areas, forests, and croplands _etc._, and thus result in significant difference in spatial distribution of NDVI and NPP [41]. The enhanced vegetation index (EVI) thus was developed for MODIS, and showed a good performance to overcome the saturation limitation of NDVI [42]. In addition, the accuracy of GPP estimation from the empirical relationship with vegetation indices is influenced by the spectral resolution of remote sensing data. For example, NDVI derived from EO-1 Hyperion and MODIS with higher spectral resolution yielded more accurate GPP estimation than Landsat \(\mathrm{ETM}+\) with lower spectral resolution in a mountainous meadow ecosystem [43].
# Towards Efficient Wireless Body Area Network Using Two-Way Relay Cooperation
## 5 Hybrid Cooperative Scheme for Energy Efficient WBAN  
A scheme is presented to adapt a suitable approach, either cooperative or direct, to enhance the energy efficiency of the overall system. The proposed technique takes into account the packet size and source-destination hop-length and determines the type of communication which would maximize the EE of the system. The system comprises of three in/on body sensor nodes, which lay within each other's transmission range. The transmission is half-duplex and links are symmetric. The pseudocode for the proposed hybrid scheme is described in Algorithm 1. The outcome of the scheme is presented for various hop-lengths over a range of packet sizes, keeping in view the results of PER and EE. First, the EE is shown against variable hop length for different packet sizes. The outcome for in-body communication is depicted in Figure 8. The system performs efficiently over the direct link for hop-length of 17 cm or less, for a range of permissible packet sizes. After 17 cm, the direct transmission deteriorates and the link starts to perform better for two-way relay communication until its performance worsens at 35 cm for a packet size of 500 bits, 34 cm for a packet size of 1000 bits, and 33 cm for packet sizes of 1500 and 2000 bits. The one-way relay link performs better afterwards. Figure 9a illustrates the results of on-body LOS communication. The direct link should be used for a link length of 150 cm or less for 500 bits packet, for all the other packet sizes, the length reduces to 140 cm. The two-way relay link gives better results in terms of EE from 150-290 cm for 500 bits packet. As the size of packet increases to 1000 bits, and 1500 bits, the efficient link length for two-way relay communication reduces to 280 cm and 270 cm, respectively, and remains at 270 cm for larger packets. For a further increase in link lengths, a one-way relay link would outperform both. In the case of on-body NLOS communication, as depicted in Figure 9b, the direct link should be used for a link length of less than or equal to 25 cm, for a packet size of 500 bits or 1000 bits. For source-destination distance between 26 cm and 51 cm, a two-way relay link would provide better EE. For the link lengths of 51 cm and onwards, a one-way relay link would outperform both. If the packet size is increased to 1500 or 2000 bits, the direct link should be used for a link length of less than or equal to 25 cm, like in the previous case. The link length for two-way relay case reduces from 26 cm to 50 cm, after which one-way relay performs better. ```
0:\(L=100:100:500,\quad d_{in}=0:1:40\) \(d_{\text{LOS}}=0:10:350,\quad d_{NL}=0:1:60\)  Where; \(L\) is payload size and \(d_{in}\), \(d_{\text{LOS}}\) and \(d_{LN}\) are the hop-lengths for in-body comm., on-body LOS comm. and on-body NLOS comm. resp.
# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 4 Results
### Machine Learning Results  
Figure 2(a)-d demonstrate the comparative performance metrics across different machine learning models. The bar charts clearly show that models trained on the NEW dataset consistently outperform those trained on FakeRom, with SVM achieving the highest accuracy of 94.6% when trained on NEW and tested on FakeRom + NEW. The precision, recall, and F1 score graphs follow similar patterns, indicating robust performance across all metrics.  
The obtained values for the performance show notable differences between the tested models. The Support Vector Machine (SVM) achieved the highest accuracy of 94.6% for the model trained on NEW and tested on FakeRom + NEW, along with the highest precision, recall, and F1 score. This indicates it has the best performance among the machine learning models tested. The Logistic Regression (LR) also performed well, with the best accuracy of 90.90% for the model trained on NEW and tested on FakeRom + NEW and high precision and recall values. Multinomial Naive Bayes (MNB) showed the lowest performance, with an accuracy of 82.90% for the model trained on NEW and tested on FakeRom + NEW, and reasonable precision and recall. Also, for other cases of datasets used for training and for different test sets, the best-performing model was the SVM. The results show that models trained on the NEW dataset outperformed models trained on the FakeRom dataset for all cases. On the whole, SVM performed the best, followed by LR, with MNB having the lowest accuracy.  
Figure 4 contrasts the accuracy against log loss for machine learning models trained on the NEW versus the FakeRom dataset. The inverse relationship between accuracy and log loss is evident, with models trained on the NEW dataset showing higher accuracy and lower log loss, indicating more confident and correct predictions.