esa-sceva/satcom-chunk-collection huggingface dataset:
# NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
## 7 Analysis of NTA Research (NTARC Database)
### Claimed Contribution  
Checking algorithm families in the backdrop of authors' proposals is relevant since the improvement of detection ratios is by far the main claimed contribution (Figure 5(b)). Beyond such achievement, other repeated claims are generally related to enhancing method feasibility, namely: reducing complexity, allowing fast processing, transportability, or addressing big data. However, low attention is given to two important requirements that NTA applications currently demand and prioritize, which are: analyzing encrypted communications and analyzing stream data. Data encryption is a strong limitation for modern NTA and makes most previous proposals almost unpractical. Encryption is progressively gaining attention in recent works, e.g., [102]. Furthermore, considering that research is mainly focused on the actual detection and not that much on knowledge discovery, obviating the temporal implications of real setups and facing analysis only from static perspectives are disturbing. Online detection and prompt reactions are principal demands in the NTA application, whereas forensic analysis plays a secondary role. Related research often proposes methods that do not necessarily distinguish between online and offline applications, and therefore, the derived limitations and peculiarities are omitted. Furthermore, as Figure 6(a) shows, implementations of the detection proposals in real systems are seldom undertaken.
## 4 Numerical Results
### Results and Discussion
#### 4.3.5 Comparison Considering Single and Multiple Secondary Users  
In source camera identification, traditional methods typically rely on a single secondary user to perform the identification task. While single-user identification can produce reasonable results, it is limited by the information and computational resources available to that one user, which can result in reduced accuracy, especially when handling complex datasets or images impacted by noise, compression, and other post-processing effects. To address these limitations, we propose a distributed source camera identification scheme that utilizes multiple secondary users working collaboratively. By combining the resources and perspectives of multiple secondary users, the distributed approach enables us to capture the unique characteristics of camera fingerprints more effectively, even in challenging environments. Additionally, the distributed method enhances the overall reliability of the identification process, as the consensus drawn from multiple users helps to mitigate the impact of noise and other distortions, reducing the likelihood of errors.  
The accuracy results for each secondary user and the central classifier in the distributed source camera identification scheme are detailed in Table 3 across six datasets: Dresden, ALASKA, SOCRatES, Forchheim, SIHDR, and VISION. Note that bold text in the table indicates the best result among all methods for each indicator in this paper. The results indicate that the central classifier consistently achieved an accuracy equal to or higher than that of individual secondary users across all datasets, demonstrating superior performance and stability. Notably, the central classifier matched the accuracy of the secondary users on the Forchheim and VISION datasets, achieving scores of 0.9925 and 0.9999, respectively. However, the central classifier surpassed some secondary users on the Dresden, ALASKA, SOCRatES, and SIHDR datasets, achieving accuracies of 0.9822, 0.9793, 0.9886, and 0.9750, respectively. In fact, in the digital forensics field, even subtle improvements in identification accuracy are critical for ensuring fair and reliable outcomes in decision-making processes. Therefore, the results in Table 3 highlight the robustness and efficacy of our proposed distributed approach in maintaining a high identification accuracy across diverse datasets.  
Figure 10: ROC curves of Sony XperiaE5 on different platforms.
# A Study of LoRa: Long Range & Low Power Networks for the Internet of Things
## 1 Introduction
### Related Work  
Different communication technologies aimed at low power, wireless IoT communication have been proposed and deployed. As indicated above, these grossly fall within two categories:  
* Low power local area networks with a less than 1000-m range. This category includes IEEE 802.15.4, IEEE P802.1 ah, Bluetooth/LE, etc., which are applicable directly in short-range personal area networks, in body area networks or, if organized in a mesh topology, also in larger areas.
* Low-power wide area networks, with a greater than 1000-m range, essentially low-power versions of cellular networks, with each \"cell\" covering thousands of end-devices. This category includes LoRaWAN, but also protocols, such as Sigfox, DASH7, etc.  
This section provides a perspective on LoRaWAN by giving a brief overview of these related IoT communication technologies.
# Relation-Aware Graph Transformer for SQL-to-Text Generation
## 3 Model
### Relation-Aware Graph Transformer  
We utilize Transformer [41] as the backbone of our model, which can be viewed as an instance of graph attention network ([PERSON], [PERSON] et al. [34], 2017) where the receptive field for each node is the entire node set. We view SQL tree as a special graph. Assume the input graph is \(G=(V,R)\), \(V=\{v_{i}\}_{i=1}^{|V|}\), \(R=[r_{ij}]_{|V|\times|V|}\), where \(V\) is the vertex set and \(R\) is the relation matrix. Each node \(v_{i}\in V\) has a randomly initialized embedding \(\mathbf{x}_{i}\in\mathbb{R}^{d_{x}}\). [PERSON] et al. [37] proposes to incorporate the _relative position_ between nodes \(v_{i}\) and \(v_{j}\) into relevance score calculation and context aggregation step. Similarly, we adapt this technique to our framework by introducing additional relational vectors. Mathematically, given the relation matrix \(R\), we construct a relation embedding function \(E_{rel}\) to retrieve the feature vector \(\mathbf{e}_{ij}=E_{rel}(r_{ij})\in\mathbb{R}^{d_{x}/H}\) for relation \(r_{ij}\). Then, the output embedding \(\mathbf{y}_{i}\) of node \(v_{i}\) after one iteration layer is calculated via  
\[\hat{a}_{ij}^{h} =\frac{\mathbf{x}_{i}W_{Q}^{h}(\mathbf{x}_{i}W_{K}^{h}+\mathbf{e }_{ij})^{T}}{\sqrt{d_{x}/H}},\quad a_{ij}^{h}=\text{softmax}_{j}\{\hat{a}_{ij}^ {h}\},\] \[\mathbf{z}_{i}^{h} =\sum_{j=1}^{n}a_{ij}^{h}(\mathbf{x}_{j}W_{V}^{h}),\quad\mathbf{z }_{i}=[\mathbf{z}_{i}^{1};\cdot\cdot\cdot;\mathbf{z}_{i}^{H}]W_{O},\] \[\hat{\mathbf{y}}_{i} =\text{LayerNorm}\{\mathbf{x}_{i}+\mathbf{z}_{i}\},\] \[\mathbf{y}_{i} =\text{LayerNorm}\{\hat{\mathbf{y}}_{i}+\text{FC}(\text{ReLU}( \text{FC}(\hat{\mathbf{y}}_{i})))\},\]  
where \(\text{FC}(\cdot)\) denotes a fully-connected layer, \(\text{LayerNorm}\{\cdot\}\) is layer normalization trick [42], \([\ ;\ ]\) represents vector concatenation, parameters \(W_{Q}^{h},W_{K}^{h},W_{V}^{h}\in\mathbb{R}^{d_{x}\times(d_{x}/H)},W_{O}^{h} \in\mathbb{R}^{d_{x}\times d_{x}}\), and \(1\leq h\leq H\) is the multi-head index. The relation embedding function \(E_{rel}\) is shared across different heads and multiple layers unless otherwise specified. For the convenience of discussion, we simplify the notation of our RGT encoding module into  
\[\mathbf{X}_{out}=\text{RGT}(\mathbf{X}_{in},E_{rel},R),\]  
Figure 4: Architecture of the encoder. DRD: _directional relative depth_. DBS: _database schema_. LCA: _lowest common ancestor_. RPR: _relative position relation_. AOD: _attention over descendants_. AOA: _attention over ancestors_. The dataflow is ordered by the number.  
where \(\mathbf{X}_{in}=[\mathbf{x}_{1};\cdots;\mathbf{x}_{|V|}]\) represent the matrix of input embeddings for all nodes.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 2 System Model
### Scenario Description  
The performance of the proposed protocol will be analyzed in a scenario described below. Without loss of generality, we consider a single cell with one base station (BS) and multiple users. The BS is connected to a content server, which stores a large set of contents. The number of users in the cell is denoted as \(U\); the number of content files stored in the server is denoted as \(F\). Let \(f(f=1,2,\ldots,F)\) and \(u(u=1,2,\ldots,U)\) be the indexes of the content file and user, respectively. The interest of the \(u\)-th user in the \(f\)-th file is represented by a real value parameter \(r_{uf}(0\leq r_{uf})\). The user interest matrix \(\mathbf{R}\) is a \(U\times F\) matrix, whose entries are taken from \(r_{uf}\). We assume that by applying existing recommendation technologies [23; 24; 25; 26], the user interest matrix \(\mathbf{R}\) is known by the server in advance. The goal of the content retrieval system is to maximize the aggregated interests of recommended content files that can be delivered to users in time.  
To proceed with our analysis, the following assumptions are made. (1) We assume that the size of a recommendation list file is much smaller than the size of the content files, so that transmission of the recommendation list (in Step 2 of the protocol) costs negligible time. (2) It is assumed that after browsing the recommended list, each user will click one and only one content file to view at a time instance. (3) We assume that the (slow fading) channel gains remain consistent in a recommendation cycle, i.e., the channel gains do not change during a single round of the four steps of the proposed protocol. (4) We consider an extreme case where user behaviors are synchronous, so that all users request a piece of content at the same time. This extreme case represents the worst case scenario because it is the most demanding for system capacity. Our subsequent analysis will focus on such a worst case scenario.
###### Abstract  
As the demand for electrical energy increases worldwide, the amounts of harmful gases in the atmosphere, such as carbon dioxide released by burning fossil fuel, are continuously increasing. As a result, the interest in renewable energy resources has been growing. However, renewable energies have fluctuating output characteristics according to local conditions such as the natural environment and geographical characteristics, which is a major factor deteriorating output quality. Recently, energy storage systems (ESSs) have been actively studied as a solution to this problem. A redox flow battery (RFB) is a system in which an active material dissolved in an electrolyte is oxidized/reduced to charge/discharge. A RFB mainly consists of an electrolyte tank, which determines the capacity, and a cell stack, which determines the output. As these components can be independently controlled, a RFB provides the advantages of a large capacity and a long lifespan. In this study, a new flow channel was designed to maximize the reaction area and reduce the pump loss to improve RFB performance. Computational fluid dynamics (CFD) and visualization experiments were used to analyze the internal flow characteristics of vanadium redox flow battery (VRFB). Additionally, we used the variability range coefficient and maximum velocity deviation to check if the flow discharged to the electrode was uniform. In the conventional flow frame, the flow discharged to the electrode has a non-uniformity distribution in the left and right, due to the S-shaped path of the inlet channel. In addition, it was confirmed that the outlet area into the electrode was reduced to 50%, resulting in a high pressure drop. To address this problem, we proposed a design that simplified the flow channel, which significantly improved flow uniformity parameters. The maximum velocity deviations for the existing and new flow channels were 11.89% and 54.16%, respectively. In addition, in the entire flow frame for the new flow channel, the pressure drop decreased by 44% as compared with the existing flow channel.  
caputational fluid dynamics (CFD); vanadium redox flow battery (VRFB); flow frame; flow channel characteristics; uniformity; electrode; pressure drop +
Footnote †: journal: Applied Physics Letters
###### Abstract  
At the core of augmented reality audio (ARA) technology lies the ARA mix, a process responsible for the assignment of a virtual environment to a real one. Legacy ARA mix models have focused on the natural reproduction of the real environment, whereas the virtual environment is simply mixed through fixed gain methods. This study presents a novel approach of a dynamic ARA mix that facilitates a smooth adaptation of the virtual environment to the real one, as well as dynamic control of the virtual audio engine, by taking into account the inherent characteristics of both ARA technology and binaural auditory perception. A prototype feature extraction technique of auditory perception characteristics through a real-time binaural loudness prediction method was used to upgrade the legacy ARA mix model into a dynamic model, which was evaluated through benchmarks and subjective tests and showed encouraging results in terms of functionality and acceptance.  
a +
Footnote †: journal: Applied  
Academic Editor  
[lineslines=true,backgroundcolor=gray!20,linewidth=0.5 em,linewidth=0.5 em,linewidth=0.  
components, rendering their dynamic integration impossible in terms of audible clarity or a target-fixed audible difference. To address this restriction, one has to continuously adjust the gain of the virtual audio engine or redefine the mixing gains dynamically. Thus, human auditory properties (such as loudness) should be considered through a strategy that applies targeted features on the ARA components according to specific, predefined rules. To that end, mathematical models that estimate the human auditory perception, for example, binaural loudness [19; 20], should be employed.  
The development of a dynamic ARA mix technique is impeded by two practical limitations: the large amount of multi-channel audio data and the real-time signal processing latency. The same restrictions also apply to the field of intelligent multi-channel audio systems [21; 22]. To address these challenges, this study aims to develop an ARA mix model using rigorous criteria based on psycho-acoustic parameters that model loudness sensation. As a result, ARA technology is \"reintroduced\" from a perceptual perspective.  
This paper is organized as follows: Section 2 provides a brief but focused theoretical overview of ARA mixing. Section 3 examines the proposed model's technical design and implementation. In Section 4, the methodology of the conducted subjective evaluation is presented. The results obtained are described in Section 5, and further discussed in Section 6. Finally, Section 7 concludes the work and provides some considerations for future work.
## 3 Results
### The Physical Experiments
#### 3.2.1 The Design of the Physical Experiment
Altogether, the tracking receivers system consists of two parts, the dual receiving coils, and the Receiver PCB. The coils detect the radiated magnetic field, while the Receiver PCB performs signal preprocessing. Finally, the results are displays by an oscilloscope (Tek MSO64-6-BW-2500). The tracking receivers system is powered by a DC power supply. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Shape** & **Inner Diameter** & **Outer Diameter** & **Length** \\ \hline Coil & Hollow Cylinder & 35 mm & 38 mm & 180 mm \\ Core & Cylinder & 0 mm & 35 mm & 200 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The geometric dimensions of the tracking receivers. Figure 9: Experimental configuration for the dual receivers tracking system and the magnetic field sign-integration (MFSI) algorithm. According to the relationship between the measured magnetic field and the original magnetic field shown in (13) and (16), the magnetic field distortion caused by the pitch angle and the lateral offset can be compensated in sequence. The compensated magnetic field \(\textit{TrackingReceiver1}_{\text{compensation}}\) is shown as Figure 11. It can be found that the zero magnetic field point of the compensated magnetic field has been compensated from 80.07 cm to 10.23 cm, which proves that the positioning accuracy has been greatly improved compared to the measured magnetic field \(\textit{TrackingReceiver1}\) and \(\textit{TrackingReceiver2}\).
## 4 Facade Noise Control Strategies Enabling NV
### Active Strategies  
In addition to passive strategies, active noise control (ANC) solutions can be implemented in window openings to control the sound power transmitted through the aperture, thus, reducing the noise level in the indoor space (global control) or in part of it (local control). Transducers (e.g., loudspeakers) are required in the window opening to generate the so-called \"anti-noise\" that interferes destructively with the sound entering from the outside. Loudspeakers can be located on the boundary of the window opening or distributed over the entire aperture (see Figure 3a,b for a schematic representation). A summary of performances derived from recent studies through boundary or distributed methods is provided by [PERSON] et al. [98]. The minimum number of required sources is a function of the frequency upper bound and the incident angle [99]. When wavelengths are large in comparison to the opening size, a smaller number of sources is required, and the boundary layout can be effective [98]. However, diffraction effects occur at large wavelengths, degrading the performance of boundary solutions [99]. Whether the boundary layout is only effective at low frequencies, for small apertures, or for local control [100], the distributed layout can outperform at a cost of opening obstruction. As regards source positioning, better performances are reported with loudspeakers located in the middle of the wall width and distanced from the opening edge [99].  
ANC is most effective in attenuating steady-state noises but feedback or feedforward controls can be implemented, able to respond to varying input stimuli [19; 101]. It must be noticed that if the system is not perfectly tuned, ANC could become a source of additional unwanted sounds [19].
# Plant Foliar Geometry as a Biomimetic Template for Antenna Design
First, we generated a database of the foliar geometry of 100 species, which was posteriorly refined to include the potentially best designs, that in our case, included seven different plant species to study and analyze in terms of simulated coefficient of reflection, and patterns of radiation: _Tetrapterys macrocarpa_ (Malpighiaceae), _Sarcorhabis maranjoana_ (Piperaceae), _Cissampelos ovareiensis_ (Mensipermacaeae), _Paramomus spectrum_ (Proteaceae), _Cesearia illicitia_ (Salicaceae), _Liquidambar stractifua_ (Hamamelidaceae), and _Quercus alba_ (Fagaceae), a set of species that encompassed several clades of flowering plants across several biomes, including tropical and temperate forests and drylands. Therefore, the effect of the leaf geometric characteristics (elliptic, obovate, ovate, oblong, toothed, lobed leaves, pinnately lobed) was studied by analyzing these seven different types of leaves across species. In conjunction with this, our main expectation is that some of the analyzed geometries would equal or even outperform the current structural designs of conventional antenna, and that some species might even find ground as templates for current technological applications [31]. Therefore, the main differences in our work with respect to existing work are: the development of a methodology to generate antenna designs considering the natural geometry of plant leaves and secondly, a demonstration via performance analyses, that new antenna geometries can be achieved using foliar geometries as templates that can be quantified in terms of the reflection coefficient and the radiation pattern of each antenna structure. We further show, via simulation results, that the performance of each plant species' leaf design using a CST electromagnetic solver, indicates possible new applications for developing specific uses for different types of antennas through a wide set of specific wavelengths.
# A Lab-Made E-Nose-MOS Device for Assessing the Bacterial Growth in a Solid Culture Medium
## 2 Materials and Methods
### Statistical Analysis  
Linear discriminant analysis (LDA) in combination with the simulated annealing (SA) algorithm was implemented as a supervised classification multivariate procedure to assess the E-nose predictive performance towards the discrimination of the four bacteria evaluated, based on the recorded resistance signals generated by the interaction between the sensors and the VOCs emitted by each bacterium, grown in solid medium. The classification potential was evaluated by determining the sensitivities (i.e., the correct classification percentages) and the specificities for the leave-one-out cross-validation (LOO-CV) variant. This CV variant was implemented since it is recommended for evaluating chemometric models in small sample sizes [35]. In total 40 independent samples (10 per type of bacterium) were used for training, and then for LOO-CV, being at each validation run, one sample removed for validation purposes and the other 39 to establish the model. Furthermore, for the training set, the classification was also checked graphically, by plotting the 2D graphs for the two most significant discriminant functions (DFs). Multiple linear regression (MLR) models, based on feature extraction parameters selected using the SA algorithm, were also developed. For this, the E-nose signals used were those generated when analyzing vials with solid medium, inoculated with pre-established known amounts (in CFU) of each of the four studied bacteria. The accuracy of the E-nose-MLRM was discussed based on the determination coefficients (\(R^{2}\)) and the root-mean-square errors (RMSEs) for the training (10, 9, 6, and 7 samples for _E. faecalis_, _E. coli_, _S. aureus_, and _P. aeruginosa_, respectively) and LOO-CV procedures. The accuracy of the developed models was verified against the analytical conventional counting plate technique, being evaluated if the slope and intercept values of the regression line between the data for both approaches could be statistically assumed equal to those of a perfect line (i.e., 1 and 0, respectively), following the methodology previously described by the research team [29]. All statistical analyses were performed using the packages of the open-source statistical program R (RStudio 2021.09.0 Build 351) at a 5% significance level.