esa-sceva/satcom-chunk-collection huggingface dataset:
# Designing a Softwarized Network Deployed on a Fleet of Drones for Rural Zone Monitoring
## 2 Video Monitoring Platform Description
### Platform Elements
Video transmitter drones are drones equipped with high-resolution cameras that dynamically connect to the closest BND to access the video monitoring platform. Fixed wireless IP cameras are video cameras installed on the ground, for example on poles, and likewise video transmitter drones, access the platform through the closest BND. Receivers of the video monitoring service provided by the above platform are people that, for some reason, need to monitor one or more portions of the rural area. To this purpose, they have a receiver device, which can be either a smartphone, a tablet or a personal computer with a client application installed. From this client application, a user is able to select the rural area portions he is  
Figure 2: Backbone Mesh Network Node Architecture. VNF: virtual network function; VM: virtual machines; NFVI: network functions virtualization infrastructure; VAF: virtual application function. interested to, by choosing them from a map of the area. Moreover, it can be decided to watch a live video stream of the selected areas, recoding it thanks to the storage service provided by the platform, or play out some video stream video stream recorded in the past. Thanks to the SDN paradigm used by the backbone mesh network, each video flow generated by a video transmitter and entering the backbone network is automatically re-routed to the devices of all the users interested in it. In addition, thanks to the NFV facilities of the backbone, each flow can be elaborated to receive some network and application functions implemented as pieces of software in some drone node of the network, as for example information encryption or event-analysis for alarm triggering setup by the clients. Let us note that the application of the joint SDN/NFV paradigm allows service continuity even in the case that video transmitters and users change their access point to the backbone network, due for example to movements of users, video transmitter drones and backbone node drones.
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 5 Performance Assessment
### Scenario  
As shown in Figure 4, an indoor layout of 120 m \(\times\) 50 m where two Operators A and B with four base stations and a total of 20 static users per operator randomly placed all over the layout are coexisting.With this setting established, a hotspot use case where the users have very low or quasi-static mobility is considered. The base station/access points are aligned with 30 m separation between them and at 5 m distance between operators. The results are evaluated by averaging 50 different random user distribution scenarios with 500 s runs each.  
Thus, in each run, a randomly selected user who makes use of the FTP/Real Time Video Streaming (RTVS) services is evaluated (\"Random User traffic\" in Figure 4) while the rest of the user devices (\"Background traffic\" in Figure 4) generate a download FTP Model I traffic with intensity that varies according to the lambda parameter (from 0.5 to 3 files/s). In this manner, the QoE performance (i.e., KQI) of the user is evaluated in the presence of a background traffic load from low to high.  
The interference between WiFi access points and base stations is managed by contention mechanisms (CSMA/CA in WiFi and 3 GPP LBT); no further Distributed Control Function is enabled. Unlike the synchronized and collision-free access in which 3 GPP technologies operate, the necessary medium access layer given by the CSMA and LBT protocols has some drawbacks associated with it, such as the hidden node or the exposed node. When a terminal has started transmitting for the limited time (in the form of TxOP or MCOT), it is possible that interference may occur in nodes that are not able to detect such transmission (because it is below its energy detection threshold). Thus, in short transmissions, there is more chance of collisions by nodes that cannot detect it. Therefore, when an interference is produced during the transmission of a WiFi Access Point and, given the half-duplex nature of the protocol, the retransmission mechanism is activated trying to resend the interfered frame and causing greater congestion of the air environment and making further collisions of part of the hidden nodes of the co-existing technology more likely.  
All the aforementioned performance indicators in Section 4 are analyzed following a top-down approach in order to correctly analyze the interaction phenomena produced in coexistence. The background FTP Model I (UDP) traffic from the application layer is analyzed first, including the total number of files sent and which of these have arrived complete. The average performance figures among all background users are also shown. Then, the KQI obtained by the user taking into account these conditions are shown and compared with the KPI obtained by the network. Next, to explain the results, details of some lower layer parameters are shown to support the explanations. Finally, the fairness of both coexisting mechanisms is evaluated as described in the previous section.  
Figure 4: Simulated office indoor coexistence scenario.
## 2 Materials and Methods
### Wine Analyses
#### 2.3.4 Volatile Compounds Analysis  
The volatile compounds were extracted by the solid-phase microextraction (SPME) technique, according to [PERSON] et al. (2017). The samples were weighed (1 \(\pm\) 0.05 g) into 20 mL vials containing 0.2 g/mL of NaCl (to increase the ionic strength), closed by a silicone/PTFE septum and an aluminum cap. All samples were added with internal standard (2-octanol) to perform a semi-quantitation. A mother solution obtained from the pure standard (Sigma Aldrich, Milan, Italy), with a concentration of 820 mg L\({}^{-1}\), was diluted to reach a final concentration of 8.2 ug L\({}^{-1}\), then 10 \(\mu\)L of this final dilution was added to the sample. Samples were loaded into an autosampler Triplus RSH (ThermoFisher Scientific, Rodano, Italy). Before extraction, stabilization of the headspace in the vial was obtained by equilibration for 10 min at 37 \({}^{\circ}\)C. The extraction was carried out using a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) 50/30 mm SPME fiber assembly (Supelco, Bellefonte, PA, USA) at 37 \({}^{\circ}\)C for 15 min. The fiber was desorbed at 220 \({}^{\circ}\)C for 2 min in the injection port of the gas chromatograph, operating in split-less mode. The GC-MS analyses were performed using a Trace1300 gas chromatograph equipped with a mass spectrometer ISQ Series 3.2 SP1. The compounds were separated on a Thermo capillary column VF-WAX MS (60 m, 0.25 mm, 0.25 mm), under the following conditions: injection port temperature, 220 \({}^{\circ}\)C; oven temperatures, 40 \({}^{\circ}\)C for 0.1 min then 4 \({}^{\circ}\)C min\({}^{-1}\) to 140 \({}^{\circ}\)C, 10 \({}^{\circ}\)C min\({}^{-1}\) to 220 \({}^{\circ}\)C final isothermal for 7.5 min. Mass detector was set at the following conditions: detector voltage, 1700 V; source temperature, 250 \({}^{\circ}\)C; ionization energy, 70 eV; scan range, 33-200 amu. Tentative identification of the peaks was done by means of Xcalibur v2.0 software, in particular, Qual Browse, by matching their spectra with the reference mass spectra of NIST library. Semi-quantitation of the compounds was done by the internal standard method, and the amounts were expressed as micrograms of 2-octanol equivalents Liter\({}^{-1}\).
## 3 Materials and Methods
### Classification Models
#### 3.3.3 Linear Discriminant Analysis  
Linear Discriminant Analysis (LDA) [30] is a supervised learning method that aims to find a linear combination of features to create a decision boundary that effectively separates two or more classes in a dataset. The method consists of the following two steps: dimensionality reduction and linear classification. By minimizing within-class variance and maximizing between-class variance, it becomes easier to distinguish between groups in the dataset. LDA is widely used for both dimensionality reduction and as a classifier. It assumes that the features in each class fit a multivariate Gaussian distribution, which is described by the following expression:  
\[P(x|y=k)=\frac{1}{(2\pi)^{d/2}|\Sigma_{k}|^{1/2}}exp\bigg{(}-\frac{1}{2}(x-\mu _{k})^{t}\Sigma_{k}^{-1}(x-\mu_{k})\bigg{)} \tag{2}\]  
Given the new input data \(x\), LDA calculates the posterior probability of each class based on the observed features. The posterior probability is calculated using [PERSON] theorem as follows:  
\[P(y=k|x)=\frac{P(x|y=k)P(y=k)}{P(x)}=\frac{P(x|y=k)P(y=k)}{\sum\limits_{l}P(x| y=l).P(y=l)} \tag{3}\]  
LDA evaluates the likelihood that a new input aligns with each class, and then assigns the input to the class with the highest posterior probability.
# Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus
###### Abstract  
Spatial big data about human mobility have been employed intensively in understanding human spatial activity patterns, which is a central topic in many applications. Available research on spatial clustering patterns of human activities has been investigated mainly based on similarities of locations and temporal attributes of spatial trajectories. These methods are not effective in revealing human groups who move among spaces at different locations but with the same functions. Function, as one semantic attribute of spaces, is a major driver of most human movements. This work investigates human clustering based on space functions of trajectory stay points in human mobility data using graph embedding. Firstly, typical functions of spaces are categorized into 35 types in our research area, which is a university campus. Human trajectories based on Wi-Fi networks were collected as test data. Then, human networks are built among human individuals. Each individual is taken as a node in the network, and an edge is built between two nodes if the corresponding individuals stay in spaces of the same type of function longer than a specific time duration. The graph embedding algorithm is used to calculate feature vector representations of nodes in the network, which can capture complex relationships among nodes through biased random walks. K-means clustering is applied to classify the feature vectors, which reveals potential behavioral pattern similarities of individuals concerning the functions of their staying spaces. The elbow method and silhouette score of clusters are used to determine an appropriate number of clusters. Three scenarios were designed based on three specific time durations, and random walk-biased parameters were fine-tuned to improve the clustering performance. Results reveal typical clusters and correlation between clusters and typical space functions.  
Keywords:human clustering; Wi-Fi network; space function; graph embedding
## 3 Methodology
### GPT-Driven Detection Pipeline
**Input:** The minified.txt file of the project; **Output:** A CSV file with the sensitive elements from the projects and their sensitivity levels; **Prompt Appendix:** Appendix A.1; **Supplementary Material:** S2; **Description:** The analysis first identifies the sensitive data, its sensitivity level, explains how it appears in the application, how it is used, and then an aggregation script uses this information to select the services that were involved in read or write operations on sensitive data in the project. 3. **JSON Mapping of Project Files**. **Input:** The minified.txt file of the project; **Output:** An array of JSON objects following the template of Appendix A.2; **Prompt Appendix:** Appendix A.3; **Supplementary Material:** S3 contains the script, S3_1 contains the prompts; **Description:** A JSON file is created for each project, in which the components of the application are reduced to JSON objects, containing only the most necessary information. 4. **Protection Level Discovery**. **Input:** The array of JSON objects mapped from the project; **Output:** The extended JSON list, containing the protection levels; **Prompt Appendix:** Appendix A.4; **Supplementary Material:** S3 contains the script, S3_1 contains the prompts; **Description:** The JSONs and the relevant routing and guard configurations are passed one by one to the GPT API, which adds the protection level corresponding to our scale. 5. **Vulnerability Detection**. **Input:** The CSV file from Step 2. and the expanded JSON file from Step 4.; **Output:** An Excel file containing the sensitivity levels, protection levels, and detected vulnerabilities for each project; **Prompt Appendix: -; Supplementary Material:** S4_1-S_2 contain the scripts for the various Excel file generations, S4_4 is the vulnerability detector, but it needs to have the results of the previous S4_X scripts; **Description:** By aggregating the results, the vulnerabilities from the tested projects are detected. In every instance, the results of the experiments were evaluated manually. Members of our research team compared the output generated by each phase to the original, unadulterated program code from which it was generated. During the evaluation, we distinguished between two critical error categories from a methodological point of view: missing information, where, for example, a program component was not detected or recognized; and misidentification, where detection was successful but the information was incorrect or incomplete. More detailed explanations and outputs of the steps are presented in the following subsections.
## 2 Background
### 5G Support for Industrial Networks
An integrated 5 GS-TSN architecture is defined by covering the industrial backbone network and the machine/production line segments, considering the user and the control plane integration aspects. Then, an integrated 5 GS-TSN network deployment is presented in different factory communication scenarios, such as 'controller-to-controller', 'controller-to-device', and 'device-to-compute'. The main finding is that valuable benefits can be achieved for industrial use cases with the integration of the 5G and TSN communication networks in the factory environment.
# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 3 Methodology  
The proposed methodology, as shown in Figure 1, is divided into three main steps. We initiated with the construction of the Moroccan Arabic speech Command Dataset (MASCD), which involved gathering audio samples from a diverse group of participants. Subsequently, we undertook the pre-processing of the collected data e.g., excluding any misrecorded audio. Following this, the feature-extraction process was initiated. Finally, utilizing the constructed dataset, we trained and tested the performances of machine-learning and deep-learning models. In the following section, we will provide a detailed description of each step in our methodology.
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 3 Results and Discussion
### Effect of Ag Nanowires on the Driving Voltage of PSLC Devices
Therefore, the threshold voltage is the lowest and the response is the fastest. As a result, small amount of Ag nanowire doping can reduce the amount of ionic impurities and device conductivity effectively, making the device have a better electro-optical response. This result is consistent with the V-T curves and threshold voltage variation tendency in Figure 5a,b. Since the length of the silver nanowires used in this study is about 20 \(\upmu\)m, the density of Ag nanowire was too high when the doping concentration of the Ag nanowires increased to 0.05 wt.% and 0.1 wt.%. and the Ag nanowires became short-circuited with each other and increased the conductivity, causing the actual electric field to decrease and the driving voltage to increase. Figure 5: Voltage–Transmittance (V–T) curves, threshold voltages and leakage current of the Ag nanowire doped PSLC according to the Ag nanowire concentration: (**a**) V–T curve; (**b**) variations in voltages for 10% transmission (V\({}_{10}\)) and voltages for 90% transmission (V\({}_{90}\)); and (**c**) leakage current according to the Ag nanowire concentration.
# Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser
## 3 Proposed Fog-Based CDN Framework
### Fog-Based CDN Framework Components
#### 3.1.4 Fog Worker (\(Fw\))  
The Fog Worker (\(FW\)) is the process that works on the fog node computing processes. The \(FW\) is responsible for registering itself in the \(FR\), joining nearby fog nodes and edge device networks, getting offloaded resources for different services, and acting as a CDN node in the fog stratum.
## 3 Results
### Effect of Flow Rate  
Mixing efficiencies were calculated for the 12 probe points along the length of the serpentine micromixer and for the flow rates in the range of 80-200 uL/min (where stable jetting is observed). Figure 6 shows that based on the numerical results, there is a rapid increase in mixing efficiency for mixer lengths between \(x=~{}100\) and \(x=~{}600\) um due to the high concentration gradient in the first half of the serpentine mixer. Within the second half of the serpentine mixer (\(x>600\) um), the mixing efficiency continues to increase, albeit with a gentler slope, until uniform mixing is achieved at the outlet.  
Figure 5: (a) Comparison of the simulated and experimental results of the mixing efficiency versus the mixing length along the serpentine mixing channel for two different flow rates: 100 and 200 uL/min; (b) the deviation (error) in % between simulated and experimental results.  
Even at the lowest flow rate of 80 \(\upmu\)L/min, the serpentine micromixer reaches a maximum mixing efficiency of 93% at the outlet. Whilst for flow rates higher than 100 \(\upmu\)L/min, the mixing efficiency at the liquid jet outlet is between 95-100%. An increase in the flow rate increases the average velocity magnitude in the mixer microchannel. The increase in the average velocity translates to the dominance of advective mass transport over the diffusive one. For example, for an increase from 100 \(\upmu\)L/min to 180 \(\upmu\)L/min, the average velocity increases from 2.39 to 4.25 m/s, which leads to an increase in the average value for Pe from 15,770 to 29,269. Hence, the mixing efficiency is improved at higher flow rates due to an enhanced chaotic advection effect caused by the serpentine geometry. We should note, however, that whilst the higher flow rates enable faster, more homogeneous mixing resulting in a more conformationally uniform mixture, this comes at the cost of higher sample consumption, which can be an important consideration when dealing with expensive or scarce protein solutions.
## 3 Geopolymer Concrete (GPC)
### Mechanical Properties of Hardened Geopolymer Concrete  
#### 3.2.1 Compressive Strength  
Compressive strength has usually expressed the behaviour of concrete in compression, and the initial elastic modulus represents the strength development with the shape and age of the stress-strain relationship. [PERSON] and [PERSON] [33] reported that the wet-mixing time, curing time, curing temperature, and particle size influence geopolymer concrete's compressive strength (ASTM C 39). [PERSON] et al. [54] found that geopolymer concrete's compressive strength decreases dramatically when the added water content exceeds 12% of FA mass. After 1 d, heat-cured LCFA-based GPC reaches its maximal compressive strength with no additional increases in compressive strength over time [14]. When cured at 80-90 \({}^{\circ}\)C, about 91% of the final strength is formed in just a few hours. Nevertheless, as in PC concrete, GPC cured in an ambient environment setting becomes stronger over time. Figure 16: slag variation during the initial and final setting times [16]. All curing regimens (ambient temperature) achieve similar long-term strength findings. The amount of time needed to reach the mixture's maximal compressive strength depends on the curing temperature [14; 63]. When employing GPC in the construction of concrete applications subjected to harsh environments, highly high compressive strengths and enhanced durability can be attained [64]. Similar to the relationship between the w/c ratio and the strength of PC concrete, it was found that the ratio of geopolymer particles to water was inversely correlated with the concrete's compressive strength. The entire mass of geopolymer solids [25] is comprised of binder, sodium hydroxide solids, and sodium silicate solids [27]. [PERSON] et al. [65] provided that the ideal mass ratio for compressive strength in a geopolymer combination of fly ash and slag is 35:65. However, this ratio relies on the reactivity of the particular FA and GGBFS utilised. [PERSON] et al. [49] investigated the impact of FA/sand ratio, SiO\({}_{2}\)/Na\({}_{2}\)O ratio, and water/FA ratio on compressive strength, and according to the findings, the Na\({}_{2}\)SiO\({}_{3}\)/NaOH ratio has an entirely different impact on GPC compressive strength than the SiO\({}_{2}\)/Na\({}_{2}\)O ratio. When the Na\({}_{2}\)SiO\({}_{3}\)/NaOH molar ratio is set at 2.5, there is no significant increase in the compressive strength of GPC. The compressive strength of a structure significantly impacts its stability and safety. The curing conditions and the raw materials impact the compressive strength of FA/GGBS-based geopolymer concrete [66]. [PERSON] et al. [45] reported that the zeolite/slag ratio influenced the compressive strength of GPC, as shown in Figure 17. After 7 and 28 days, the GPCs had 30% and 25% higher compressive strengths than the PC concrete. As a result, the mechanical characteristics of the GPC samples were outstanding. Table 3 illustrates the effect of various additives on the behaviour of geopolymer concrete.
## 7 Security Analysis
### Formal Security Analysis Using AVISPA
#### 7.5.2. Results of AVISPA Analysis  
We utilized CL-based Attack Searcher (CL-AtSe) and On-the-fly-Model-Checker (OFMC) back-ends to the verify security of our protocol. The HLPSL code was translated into intermediate format, and then converted to output format using the back-ends. Figure 13 shows the results of simulation using two back-ends. The result of CL-AtSe back-end shows that two states were analyzed and the translation time was 0.10 s. The result of OFMC back-end shows it visited node 1040 nodes with nine plies depth. According to the results of simulation, the proposed protocol is secure against replay and MITTM attacks.  
Figure 12: Role specification of trusted authority.