esa-sceva/satcom-chunk-collection huggingface dataset:
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 1 Introduction
However, our aim here is to categories V2P users based on their vulnerability by using a distance-based classification, and then allocate them resources, accordingly. Unlike traditional network scenarios, our scenario focuses only on the safety communication of VRUs. A user in the vicinity of the D2D range should receive the information regarding the approaching vehicle. The feasible range for D2D Communication is studied in [6]. In this paper, we have taken the range to be 50 meters, which means that any user lying in 50m radius of another user can involve in D2D communication. In this paper, we have proposed a distance-based resource allocation scheme in a D2D-based V2P scenario. Therefore, our major contribution is:  
* Optimum power allocation using Q-learning based reinforcement learning with different discount factors. * A one-to-many weighted bipartite matching scheme (with maximum flow) to create connections between users for frequency assignment. The rest of the paper is organized as follows: Section 2 is related to current research trends in this area. Section 3 describes the network and system model. Section 4 is the classification of the VRUs. Section 5 is regarding optimal power assignment based on the classification of users in Section 4. Section 6 introduces a one-to-many bipartite matching scheme. Section 7 will present the simulation setup and results. Finally, Sections 8 and 9 discusses future directions and conclusions, respectively.
# Using Multimodal Foundation Models for Detecting Fake Images on the Internet with Explanations
### Related Work  
For various reasons, misinformation containment is largely an unsolved problem today [8]. While large language models (LLMs) have been used for detecting textual misinformation [9], there is hardly any evidence of using foundation multimodal models for fake image detection. A prior superficial study [10] concluded that GPT-4, Bard, and Bing were unreliable for detecting fake images, but no metrics were provided to support this conclusion. Another study [11] demonstrated that using large language models (LLMs) and that Vision Language Models (VLMs) can significantly improve object detection accuracy. In a different study [12], GPT 3.5, an LLM, was used to extract features to detect out-of-context (OOC) media. The study focused on the automated detection of the misuse of real photographs with conflicting captions. The authors proposed a method that enhances the COSMOS structure, which assesses the coherence between an image and its captions. By employing prompt engineering, the authors developed a robust feature extraction method that captures the correlation between captions.  
Explainability techniques, LIME, and SHapley Additive exPlanations (SHAP) have been used to explain the classification of pneumonia from chest X-rays [13] and a rare and aggressive form of childhood eye cancer called retinoblastoma from fundus images [14], as well as the diagnosis of different abnormalities in human kidneys from computer tomography images [15], and more such image processing applications. SHAP has also been used in the context of land cover and land use classification in remote sensing [16]. However, there does not seem to be any evidence of these techniques being used in detecting fake images, which this study attempted.  
Existing techniques for fake image detection include deepfake detection. Deepfake detection relies on traditional convolutional neural network-based architectures to make predictions with a focus on classification techniques. These techniques largely focus on maximizing accuracy but provide limited explainability features. Using foundational multimodal models can help provide transparency in detections to understand the strengths and weaknesses of each model. Some of the deepfake detection approaches are discussed below.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 4 OFDM Radar Sensing Method
### Velocity Estimation  
The maximum observable Doppler frequency shift \(f_{max}^{(D)}\) is given by the following formula:  
\[f_{max}^{(D)}=\frac{f_{s}^{(p)}}{2}-\frac{f_{s}^{(p)}}{N_{pilots}^{sc}}=\frac{ f_{s}^{(p)}}{2}-\frac{1}{T}, \tag{17}\]  
where \(f_{s}^{(p)}\) denotes the sampling frequency of the channel frequency response by PRS pilots in time/symbol axis, which is equal to \(\frac{N_{pilots}^{sc}}{T}\), while \(T\) denotes the time duration of the transmitted/received signal fragment. Therefore, the Doppler frequency shift resolution \(f_{res}^{(D)}\) can be expressed as follows:  
\[f_{res}^{(D)}=\frac{f_{s}^{(p)}}{N_{pilots}^{sc}}=\frac{1}{T}. \tag{18}\]  
Figure 8: Exemplary range\(-\)velocity matrix (RVM) with one target. The warmer the color, the greater strength of the reflection.  
Let us define \(idx_{f}^{(D)}\) as the index of the CIR column with the maximum value. The estimated Doppler frequency shift \(f_{est}^{(D)}\) is given as follows:  
\[f_{est}^{(D)}=\left[ind_{f}^{(D)}-\left(\frac{N_{pilots}^{sc}}{2}+1\right) \right]\cdot f_{res}^{(D)}. \tag{19}\]  
Then, the estimated velocity \(v_{est}\) can be expressed as follows:  
\[v_{est}=\frac{f_{est}^{(D)}\cdot c}{2f_{c}}. \tag{20}\]
# A Modeling Application for GHG Fluxes Estimates in Betel Nuts Plantations in Taiwan
## 2 Materials and Methods  
### Study Area  
A member of the Palm family, the betel nut or area nut, known scientifically as _Areca catechlu_, is a relative of the coconut (_Cocos nucifera_). Betel nut palms thrive in tropical and subtropical areas and are less tolerant of the cooler climatic conditions that may be sustained by other palms. The betel nut palm is widely cultivated in India, Bangladesh, Sri Lanka, Indonesia, and on numerous islands in the Western Pacific. Betel nut palms produce flowers which transform into nuts. The flowers are bunched in a cluster called an inflorescence. A palm can start producing inflorescences between the fourth and seventh year of age. The inflorescence appears after the dropping of a front (leaf). A new inflorescence may appear after each front dropping or at irregular intervals, for an average of three to four inflorescences per year. Two to three inflorescences, at different stages of development, can be found on a single palm. This plant is sensitive to moisture deficit and usually grows where the rainfall reaches annual values from 750 to 4500 mm [23]. A betel nuts plantation area of 533.23 ha was detected in the Chiayi County in Taiwan (Figure 1) and a shapefile was created (extension: 197801.65, 2595425.94: 217659.41, 2605386.36; EPSG:3826-TWD97/TM2 zone 121). The examined period was from 1 January to 28 February 2018; this choice was mostly driven by the kind of model used and by the unavailability of clear Landsat 8 Satellite images. Meteorological data, such as temperature and solar radiation, were taken from the Taiwan Central Weather Bureau website [[https://www.cwb.gov.tw/eng/](https://www.cwb.gov.tw/eng/)]([https://www.cwb.gov.tw/eng/](https://www.cwb.gov.tw/eng/))(accessed on 17 May 2021). Eleven stations in Taiwan had available data for radiation in the chosen period, while twenty stations were considered for temperature; the meteorological data were interpolated and made continuous on the interested area by the triangular irregular net (TIN) algorithm. The meteorological parameters are summarized in Table 1.  
Calculations were developed in Python environment using GDAL library and QGIS. All meteorological values were spatialized to arrays with 30 m resolution, covering the whole studied area.
## 4 Experimental Research
### Computation Time  
The computation time can actually reflect the complexity of a method, and it is closely related to the application in a BCI system. Figure 12 presents the test time of feature extraction in a trial by using the proposed IMFE method and the conventional feature extraction methods (AR, WT, CSP, CSSP, FBCSP, ApEn, SampEn, FE, PE, WPE, MSE, and MPE). Less time is consumed in application of AR, WT, CSP, CSSP, and ApEn, but their effect of feature extraction is not ideal, as we know from the above analysis. The time consumption of SampEn, PE, WPE, MSE, and MPE is at a medium level. FBCSP, FE, and IMFE need more time, especially IMFE, which means that IMFE has a relatively higher computational complexity compared to the other methods. This is mainly because of the exponential membership function in IMFE. However, it could basically satisfy the requirements of a BCI system.
## 4 Discussion
### Strength of Knee Extensors and Flexors  
Our study revealed no significant difference between the groups and the strength levels of the knee extensors (ACL to CON: 1.5%, \(p=0.806\); HAM to CON: 3%, \(p=0.510\)) and knee flexors (ACL and CON: 2.6%, \(p=0.098\); HAM to CON: 3%, \(p=0.098\)). Our results here confirm the findings of [PERSON] et al. (2018) and [PERSON] (2018), who also did not find a significant relationship between the isokinetic strength peak torque and hamstring injuries in soccer players. [PERSON] et al. (2018) did not support that identification of hamstring strain injury risk factors by isokinetic concentric and eccentric strength deficits is a suitable predictor and found small absolute strength differences among 190 injured and 424 non-injured soccer players over 4 seasons. They reported that isokinetic strength measurements as a predictor of hamstring damage are inconsistent (2018; 2018; 2019; 2020; 2021). On the other hand, some authors have proven that a strength deficit has been considered as a risk factor for both primary (2018) and recurrent (2018) hamstring injuries. One reason for the different findings may be that the strong leg compensated for the weak leg's muscle deficit by lateral body movement to distribute the load in proportion to the muscular strength of each leg (2018). Soccer players usually perform specific movements at higher speeds (e.g., kicking, jumping, and sprinting) and also in excentric mode (e.g., braking phase when changing direction). The hamstring's contribution to horizontal strength production during the support phase is determined to generate a high level of eccentric strength and be activated at the end of the swing phase (2018; 2018). This muscle group plays a central role in any sprint demands, mostly between the middle swing phase and a blow or early stance (2018). [PERSON] et al. (2018) presented a discussion evaluating the mechanism leading to HSI in high-speed running. The conclusions suggest that the scientific community is inclined to argue that hamstrings are more prone to tearing during the late phase of the swing (2018) than in the early stance (2018). [PERSON] et al. (2018) provided evidence of an increased incidence of HSI related to the subsequent digging phase, which supports the thesis of [PERSON] et al. (2018) because these maneuvers are much more biomechanically similar to a late swing than an early stance. Activated muscle complexes are able to absorb greater eccentric forces on impact, and high-intensity sprinting involves intricate neuromuscular coordination in the hamstrings (2018). In this context, it is clear that protection against excessive muscle load is provided not only by strong muscles but also by the appropriate timing and size of the nerve control.
# Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks
## 4 Results and Discussion
### Introducing Noise  
After having shown the results for ideal cases in which the datasets are generated by processes with no noise (or equivalently in which the noise has been filtered out), in this section, one of the benchmark cases is analysed, introducing disturbances in the data generation process.  
The chosen model is the nonlinear system defined by Equation (5). The disturbance is introduced in the form of a noisy output variable, resembling a real case in which the reading of the swinging mass angle \(\theta\) is provided by a non-ideal sensor. The signal-to-noise ratio was chosen as 25 dB, ensuring a noise power greater than the signal power. A comparison between the testing datasets is reported in Figure 11, with the output of the Simulink model and the DNN drawn in blue and red, respectively.  
It is possible to notice that the DNN is able to identify the model dynamics, and it additionally shows intrinsic filtering capabilities, both in the steady-state and in the transient sections of the responses.  
The poles' locations were then evaluated. The slow poles of both Simulink and the DNN were estimated with a maximum relative error bounded below 50%, showing comparable performances with respect to the ideal case. The fast pole is on the contrary identified showing a loss of accuracy performance (several orders of magnitude greater, but still identifying that the system is stable).  
It is important to bear in mind that in a real-case scenario involving a noisy signal, before the training step, an intermediate filtering stage would be performed.  
The analysis reported in this section shows how the designed framework can deal with system noise: if the aim of the system identification is only to assess the stability of a process, the system does not require additional steps, whereas if the purpose is to accurately locate the position of the poles, a filtering stage is suggested. If the noise properties are constant (e.g., its variance), it is possible to change the numerical search method, for instance by  
Figure 10: Unstable model—poles’ locations.  
setting _tfestOptions.SearchMethod_ equal to _fmincon lm_, or _gn_, with the advantage of avoiding the filtering stage.
## 3 Motivation for the SSI Wallet Survey
### Methodology for Evaluating SSI Wallets  
To evaluate real-world SSI wallets, we used the wallets listed by the European Blockchain Association [18]. Wallets found in the Google Play Store are installed and tested on a smartphone Pixel 6 with the current Android operating system (OS) version 13 at the time of the study. Thereby, we obtained Lissi Wallet [19], Verimi [20], Data Wallet by iGrant.io [21], esatus Wallet [22], VIDwallet [23], SmartWallet by Jolocom [24], and Gataca Identity [25] (see Table 1). These are also available in Apple's App Store. We then evaluate the wallets in a four-step process.  
1. We first install the apps, instantiate individual wallets, and summarize the first impression.
2. Each of the organizations offers at least one demo workflow, which we used to play with the wallets. Here, we focus on usability and functionality.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Wallet** & **Organization** & **Version** & **SSI Flavor** \\ \hline DataWallet & iGrant.io & 3.4.1 & W3C, X.509 \\ esatus Wallet & esatus & 1.13 & W3C \\ Gataca Identity & Gataca & 1.14.1 & W3C \\ Lissi Wallet & Lissi & 1.8.1. & Lissi \\ SmartWallet & Jolocom & 2.6.0 & W3C \\ Verimi & Verimi & 2.7.0 & Verimi \\ VIDwallet & ValidatedID & 1.7.19 & W3C \\ \hline \end{tabular}
\end{table}
Table 1: Android SSI wallets and versions.  
3. Based on [PERSON]'s [11] principle of transparency, the algorithm should be open source. We assume that this is also true for the wallet software. Hence, we search for code repositories, which are likely located on GitHub, and take a look at the source code.
4. Additionally, we analyze the Android packages (APKs) with (1) Android Studio [26], (2) APKHunt [27], and (3) manually with the criteria provided by [PERSON] et al. [28]. If the wallet is not working properly on the smartphone, we additionally evaluate it with a virtual device in Android Studio [29]. If the app requires secure elements, this step may fail.  
BackgroundAndroid applications are distributed as APK files, which are basically ZIP files similar to the Java archive (JAR) files used to package Java libraries. An APK file contains the app code in Dalvic executable format (DEX), native libraries, resources, assets, and an Android manifest in binary extensible markup language (XML). The APKs are typically signed.  
LimitationsWe note suspicious behavior while using the wallets and analyze the wallets statically with the help of tools and specific terms. Hence, we present the early results of the security analysis of the wallets. A more thorough analysis could include more terms and dynamic analysis, among others. In addition, our analysis was conducted with the versions noted. We did not verify that the issues exist in versions published after the analysis. Lastly, we did not consider apps in the Apple App Store, such as Apple Wallet or Verimi ID-Wallet for iOS.
## 4 Results and Discussion
### Effect of Vibration on the Pressure Drop
#### 4.1.2 0.3 g Vibration at Different Frequency  
Figure 5 shows the comparison of pressure drop between DMMFB and FEA. The amplitude of the applied vibration is 0.3 g and the frequency is, separately, 100, 200, 300, 400 and 500 Hz. From the comparison, it can be seen that the results predicted by DMMFB agree well with those obtained by FEA, no matter without or with vibration applied.  
The effective value of the pressure drop for each condition is detailed in Table 4. The results show the same trend as above, that is, the pressure drop is decreased with the application of vibration, and it is further decreased with the increase of applied vibration frequency. The reason for this phenomenon is the same as discussed above.
## 5 Experimental Results
### Experimental Setups for Models
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**The Dataset** & \begin{tabular}{c} **The Dimensions of** \\ **User Latent** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **The Dimensions of** \\ **Item Latent** \\ **Features** \\ \end{tabular} & \begin{tabular}{c} **The Dimensions of** \\ **Genre/Category** \\ **Features** \\ \end{tabular} & \
\begin{tabular}{c} **The Dimensions of** \\ **User Review** \\ **Features** \\ \end{tabular} &
\begin{tabular}{c} **The Dimension of** \\ **Item Description** \\ **Features** \\ \end{tabular} \\ \hline MovieLens & 50 & 50 & 20 & & \\ \hline Amazon-Toys and Games & 50 & 50 & 321 & 768 & 768 \\ \hline Amazon-Electronic & 50 & 50 & 944 & 768 & 768 \\ \hline Amazon-Video and Games & 50 & 50 & 16,978 & 768 & 768 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Setting up the parameters for the multiview transformer RS. In addition, we have tested a strong baseline model, the Singular Value Decomposition (SVD) on the utility matrix, which is known to be one of the most effective models for the recommender system (RS) problem. We trained our model for 50 epochs and conducted the experiment 3 times for each model. As there was only a negligible difference between the experimental runs, we chose to include the best result from the experiment in Table 4.  
Within the graph-based recommendation model, we have constructed a graph that encompasses two distinct node types: user and item. * For the Amazon dataset, we incorporated category and description as attributes for the item nodes, while the user nodes were enriched with the review attribute;
* For the MovieLens dataset, the item nodes were augmented with the genre attribute;
* We established connections between the user and item nodes in the graph to model the interactions between them. To represent the strength of these interactions, we assigned ratings as the weight property of the edges. This allowed us to reflect the degree of preference that each user showed for each item, which is an important factor in our recommendation system.
# Local Community Detection in Dynamic Graphs Using Personalized Centrality +
## 1 Introduction
### Contributions  
This paper's contributions along with main results and the sections in which they appear are presented in Table 1.  
We extend our previous work in [1; 2; 3] by applying it to the problem of local community detection, defined further in Section 3.1. Our work on updating PageRank and Katz Centrality are necessary steps towards tracking \"relevant\" subgraphs around seed vertices using personalized centrality metrics. Specifically, the main contribution of this paper is to tie together the two fields of community detection and centrality by studying how personalized centrality metrics can be used for local community detection in not only static but also dynamic graphs.  
The remainder of the article is organized as follows. Section 2 provides background and definitions. We review relevant literature regarding community detection and centrality metrics in Section 3. Section 4's preliminary results include initial validation of our method on static graphs. The algorithms for application to dynamic graphs as well as a thorough discussion of experiments and results appear in Section 5. Section 6 presents the theoretical guarantees relating an approximation to the exact  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Contribution** & **Main Results** & **Section** \\ \hline New method of identifying local communities using personalized centrality metrics & \(\bullet\) Comparisons to a modified version of greedy seed set expansion & 4 \\  & \(\bullet\) High recall values comparing our method to ground truth on stochastic block model graphs & \\  & \(\bullet\) Several orders of magnitude of speedup obtained using our method & \\ \hline Dynamic algorithm to identify local communities in evolving networks & \(\bullet\) Recalls of over 0.80 for synthetic networks showing community evolution & 5 \\  & \(\bullet\) Speedups of over 60\(\times\) execution time improvement compared to static recomputation for real graphs & 5 \\  & \(\bullet\) Good quality of communities returned by our dynamic method w.r.t. ratios of conductance and normalized edge cut & 6 \\  & \(\bullet\) Quality of communities is preserved over time for real graphs & 7 \\  & \(\bullet\) Comparisons using multiple seeds for our algorithm show our method is robust to using many seeds & \\ \hline Numerical theory to guarantee the accuracy of an approximate solution to a centrality metric & \(\bullet\) Development of a new stopping criterion for iterative solvers to terminate when we can guarantee rankings given desired precision & 6 \\  & \(\bullet\) Speedups obtained compared to running to preset tolerance versus using our new stopping criterion & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contributions and main results presented in this paper.  
centrality metric with experimental validation. Finally, we conclude and discuss future research directions in Section 7.
## 1 Introduction
### Aim of the Present Study  
As outlined above, the investigation of the neural correlates of human gait is challenged by a number of technical restrictions. Therefore, the aim of the present study was to explore possible technical solutions for the time-sensitive analysis of human gERPs during spontaneous and instruction-guided treadmill walking. To realize these solutions, several hardware and software components are necessary for time-sensitive synchronous gait and ERP data interfacing and recording. In the present study, the technical solution chosen was tested and piloted in three healthy volunteers from whom continuous EEG data was recorded with an electrically shielded high-density 32-channel EEG system with active electrodes during uninstructed and instructed treadmill walking. Gait was recorded via a pressure plate implemented in the treadmill. Next, the gait cycle and corresponding gait markers were determined according to guidelines for the standardized classifications of human gait [23]. Two key questions were explored:  
1. Can gait-event-related brain potentials (gERPs) be analyzed based on human gait markers decoded by force plates?
2. Can the analysis of gERPs be automated by applying machine learning approaches based on artificial neural networks (ANNs), especially long short-term memory cells (LSTM) [24]?