esa-sceva/satcom-chunk-collection huggingface dataset:
# The Virial Effect--Applications for SF\({}_{6}\) and CH\({}_{4}\) Thermal Plasmas
## 3 Virial Coefficients
### The Lennard-Jones Potential
#### 3.1.1 Expression of the Potential  
The Lennard-Jones (LJ) potential derives from the Mie potential, assuming that \(n=12\) and \(m=6\). The number 12 corresponds to the repulsive index at short distances, and 6 to the attractive index, based on Van der Waals interactions. This shape of the potential is very commonly used by other authors [13; 18; 43; 44] for describing particle interactions in pure gas and mixtures. This potential is given by  
\[u(r)=4.\varepsilon.\left[\left(\frac{\sigma}{r}\right)^{12}-\left(\frac{ \sigma}{r}\right)^{6}\right] \tag{27}\]  
where \(r\) is the distance between two atoms.  
With the LJ potential, [PERSON] [18] proposed the use of reduced amounts of data expressed with hard-sphere potential \(b_{0}\) (Equations (28) and (29)). This approach has been validated by this author for the second and third virial coefficients for several species:  
\[B(T)=b_{0}.B^{*}(T^{*}) \tag{28}\]  
\[C(T)=b_{0}^{2}.C^{*}(T^{*}) \tag{29}\]  
where \(B^{*}(T^{*})\) and \(C^{*}(T^{*})\) are reduced values of, respectively, the second and third virial tabulated coefficients versus the reduced temperature \(T^{*}\) given in Equation (30):  
\[T^{*}=\frac{k_{B}.T}{\varepsilon}. \tag{30}\]  
Equation (31) expresses the hard-sphere potential:  
\[b_{0}=\frac{2}{3}.\pi\sigma^{3}. \tag{31}\]  
The significance of \(\sigma\) and \(\varepsilon\) is given in the next paragraph.  
For a gas mixture, the terms \(B_{ij}(T)\) and \(C_{ijk}(T)\), in Equations (25) and (26), respectively, can be obtained using the LJ potential. Their respective expressions are given in Equations (32) and (33):  
\[B_{ij}(T)=b_{0_{ij}}.B^{*}\left(T_{ij}^{*}\right) \tag{32}\]  
\[C_{ijk}(T)=\left[C_{ijk}(T)\right]_{S,W}\left[A(T_{i}^{*}).A\left(T_{j}^{*} \right).A(T_{k}^{*})\right] \tag{33}\]  
where\(b_{ij}=\frac{1}{8}\left[b_{0}^{\frac{1}{4}}+b_{0j}^{\frac{1}{4}}\right]^{3}\) is the interaction parameter of the mixture;
* \(T_{ij}^{*}=\frac{k_{B}T}{\varepsilon_{ij}}\) is the reduced temperature of the mixture with \(\varepsilon_{ij}=\left(\varepsilon_{i}.\varepsilon_{j}\right)^{\frac{1}{2}}\);
* \(\left[C_{ijk}(T)\right]_{S,W}\) is the third virial coefficient obtained from the square-shaped potential [18];
* \(A\left(T_{i}^{*}\right),A\left(T_{j}^{*}\right),\text{ and }A\left(T_{k}^{*}\right)\) are the tabulated values under the reduced temperature [18].  
When \(i=j=k\), \(C_{iii}(T)=\left[C_{iii}(T)\right]_{S,W}\).\(\left[A\left(T_{i}^{*}\right)\right]^{3}=C_{i}(T)=b_{0}^{2}C^{*}(T^{*})\).  
From these expressions, it appears that the virial coefficients obtained using the LJ potential use parameters of other kinds of potentials (hard-sphere and square-shaped), in order to better describe real fluid behavior at high pressure.
# 5G-MEC Testbeds for V2X Applications
## 5 Summary of Available Tools
### VI and VIM  
The VI runs the MEP and applications by providing computation, memory, and storage capabilities. The VI enhances the MEC system deployment, and as a result, all open source tools use virtualization. The VI manager is responsible for allocating, managing, and releasing the virtualized storage, memory, and computation based on real-time needs.  
**Docker**[93] is a lightweight container-based virtualization tool that is mainly used for hosting MEC applications. However, some MEC tools use Docker to deploy and run an MEP. Docker accelerates MEC application deployment and management with the help of Kubernetes (K8s). Early versions of the K8s cluster were based on Docker.  
**KVM hypervisor**[94] is a kernel-based virtual machine solution for Linux systems that runs on top x86 hardware. The KVM hypervisor allows the kernel to function as a hypervisor. The 5 GCarmen testbed uses a KVM hypervisor to host the MEH and run the MEC applications.  
**LXC**[95] is an acronym for Linux container. The LXC runs and hosts a single Linux kernel system. LXC has been experimented with and tested for having a low-level container runtime.  
**Kubernetes (K8s)**[96] is an open source tool used to deploy, scale, and manage containerized applications. K8s helps to automate processes by creating a cluster to host multiple MEHs and allowing the initiation of containerized applications. It performs the role of a manager (VIM) for the container-based solution. K8s is used by many MEP tools and in the 5G-MEC testbed. Some of the 5G-MEC testbed solutions extend K8s usage for orchestration.  
**OpenStack**[97] offers an IaaS cloud infrastructure solution to deploy and manage a VM. It is mainly used for larger-scale deployment models and controls large computing, storage, and networking resources. OpenStack allows the configuration, creation, and deployment of VMs based on specific requirements. However, in some cases, OpenStack is used to manage containers. Many MEC solutions use K8s rather than OpenStack. The ETSI refers to OpenStack for the VIM in the MEC system.
## 4 Method  
### Overview  
Figure 2 shows the framework of ADNet. We can divide the structure of our method into three stages: (1) feature extraction, (2) the attention layer, and (3) parametric fusion. In the feature extraction stage, the model extracts both temporal and periodic features from the input. In the attention layer, it computes attention scores based on the extracted features and applies these scores as weights. Finally, in the parametric fusion stage, the model combines the temporal and periodic features by weighting them with learnable parameters and summing them, allowing it to prioritize either the temporal or periodic components as needed.
# Cyber-Storms Cone from Clouds: Security of Cloud Computing in the IoT Era
## 5 Cloud-Specific Security Issues
### Confidentiality
#### 5.1.1 Virtualization Level Issues  
Virtualization technology is one of the key enablers of Cloud computing. However, this additional abstraction layer has severe security repercussions. In the following paragraphs, we report key security issues caused by this layer and capable of compromising data confidentiality.  
Multi-Tenancy Issues  
Virtualization technology allows to develop a multi-tenant environment in which virtual machines operate sharing communal hardware resources. The placement of different users on the same platform is what enables new types of attacks on data confidentiality. In [39], the authors describe how they were able to exploit several characteristics of Amazon Elastic Compute Cloud (EC2) in such a way to have their own virtual machine co-resident (i.e. on the same physical machine) with that of a victim. Once co-residence is reached, an attacker has the unprecedented possibility of performing several types of side-channel attacks in such a way to extract confidential information from users who are sharing the same machine with the attacker. Consequently, through attacks struck to the Cloud, a malicious user could be capable to disrupt the confidentiality of IoT devices data, stored on the Cloud infrastructure. In [39], it is shown that, by means of cache measurements, an attacker can perform: keystroke timing attack, traffic rates estimation of victim's web servers and even co-residence detection.  
Moreover, side-channel attacks affecting cryptographic implementations have been reported in [40; 41; 42; 43]. The work in [44] shows the possibility to exploit memory deduplication issues for performing another type of cross-VM side channel attack. Furthermore, the recent vulnerabilities Meltdown [45] and Spectre [46] have demonstrated that not only memory-based side-channel attacks are possible, but that even processor vulnerabilities can be exploited to perform attacks capable of breaking any security assumption and allowing other co-resident VMs to access confidential information belonging to other users.  
From the IoT devices' point of view, virtualization issues have a critical implication: security cannot be solely evaluated by looking at the characteristics of a single product. Even if we assume that an IoT device is bug-less on every layer (from the hardware layer to the Cloud APIs one), its data could be accessed by an attacker capable of trespassing isolation limits. Indeed, other services hostedon the same physical machine might exhibit exploitable vulnerabilities, which might allow the attacker to access to sections meant to be off-limits.
# Area of Operation Planning for Free-Floating Car Sharing Systems
## 5 Conclusions and Future Work  
In this article, we proposed an analytical, data-driven approach for planning the area of operation (AoO) for a free-floating car sharing system with respect to a certain expected mobility demand. Given a limited budget, our algorithm finds a selection of subdivisions of the planning area that allows the operator to serve the most profitable regions, where profitability is calculated based on the mobility demand forecast between pairs of these subdivisions.  
We used our algorithm to compute various expansion strategies for the Vienna-based electric car sharing company ELOOP. In the case study, we used two different mobility demand forecasts, one based on a mobility survey, and one based on urban taxi traffic data. We observe that although the basic structure of the optimized AoO are similar, the extension of the AoO can be very different. With the help of our analytical approach, the operator can obtain valuable decision support in which area he wants to expand and which type of potential customers he wants to attract.  
As future work, we want to apply our model to other car sharing operators and/or other cities, as we believe that it is both simple and flexible. From the methodological point of view, the aspect of demand uncertainty could be addressed with stochastic programming. This is a different way of modeling that accounts for the uncertainty of the input data in a more sophisticated way.  
All authors contributed equally to the problem definition, the design of the ILP formulation and the experimental setup. L.F.K. implemented the described algorithm, conducted the computational experiments, analyzed their results and wrote an initial draft of the corresponding article sections. All authors then contributed equally to the creation of this final article version. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the Klima- und Energiefonds under grant number 872110 (\"ShareMob\") and by the European Commission H2020 TWINNING Networking for Excellence in Electric Mobility Operations (NEEMO) Project under Grant 857484.  
Not applicable.  
Data Availability StatementData on the movement of individuals (which was used for generating OD matrices) was provided by ELOOP and cannot be shared publicly for privacy reasons. All other data is available from the authors upon request.  
The authors declare no conflict of interest.  
\begin{tabular}{l l} \(r_{i}\) & Variable for the root vertex in the \(f\)-flow \\ \(S\) & Area of operation \\ \(x_{ij}\) & Variable for including vertices \(i\) and \(j\) in the solution \\ \(y_{i}\) & Variable for including \(i\) in the solution \\ \end{tabular}
# Closed-Form Expressions of Upper Bound for Polarization-MDCSK System
###### Abstract  
The performance analysis of polarization \(M\)-ary differential chaos shift keying (P-MDCSK) has been expressed using a tight upper bound with the Q-function. However, evaluating the Q-function directly is not a closed expression and there has been less work on closed expression for the upper bound. In order to solve the problem, this paper presents approximate closed-form expressions on the error probability of P-MDCSK. This expression is derived by employing a polynomial approximation of the Q-function. These closed-form expressions are verified through simulations conducted under both additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. The simulation results reveal that there exists only a negligible gap between the simulations and the derived closed-form expressions. For example, it is observed that the theoretical approximate closed-form expressions exhibit a marginal deviation of approximately 0.4 dB from the simulations when the bit error rate (BER) reaches \(10^{-4}\). Although the proposed method can only give approximate closed-form expressions of the upper bound, it provides an effective method for other communication schemes where the exact BER closed-form formula cannot be obtained.  
polarization modulation; \(M\)-ary differential chaos shift keying; closed expression  
closed form of the BER expressions, a lot of work has proved that the closed form of BER can be courted [16; 17; 18] by representing the Q function approximately [19]. In this paper, we utilize polynomial approximation of the Q-function to obtain a simple closed-form BER formula for the P-MDCSK scheme.  
The contributions of the paper are summarized as follows:  
(1) The polarization \(M\)-ary differential chaos shift keying (P-MDCSK) is introduced, which uses the upper bound instead of the exact BER as a theoretical verification. Since the Q-function in the tight upper bound is not a closed form, the paper derives a close expression. It makes use of polynomial approximation instead of the Q-function.  
(2) To achieve the closed expression, the bounds of the Q-function are used. It provides a generalized analytical expression of the closed-form on the upper bound, and calculates each of the three subsections in the upper bound for their different cases. The results show that there is only a very small gap between the simulations and the closed-form expressions  
The study is organized as follows. Section 2 presents the system model of the P-MDCSK system briefly. Section 3 provides the closed-form expressions. Next, in Section 4, we show by numerical examples that the closed-form expression has a tight gap with simulations. Finally, Section 5 concludes the study.
## 4 Trajectory Planning Model
### Trajectory Optimization Model  
(1) Optimization objective function  
The objective function is to minimize the time and energy consumption of the robot human system.  
\[\min\left\{\begin{aligned} T=\sum\limits_{i=1}^{n}T_{i}\\ E=\int_{0}^{T}\frac{1}{2}\hat{q}^{T}D(q)\hat{q}dt\end{aligned}\right. \tag{28}\]  
where, \(\hat{\mathbf{q}}\) is the angular velocity of joint, \(\mathbf{D}(\mathbf{q})\) is the inertia matrix of robot, \(\mathbf{D}(\mathbf{q})=\sum_{i=1}^{n}(\mathbf{I}_{i}(\mathbf{q}))^{T}\mathbf{M}_{i}\mathbf{J}_{i}(\bm {q}),\mathbf{J}_{i}(\mathbf{q})\) is object Jacobian matrix, and \(T\) is the whole motion period.  
(2) Optimization constraints  
The margin to control the flip stability of the robot has the following constraint during the whole motion period.  
\[\psi=min\{\psi_{12},\;\psi_{23},\;\cdots,\;\psi_{n1}\}>const \tag{29}\]  
where \(const\) is constant, in this paper, \(const>0\).  
The maximum joint angle, maximum acceleration, maximum speed, and maximum torque of each joint are also considered. The joint space constraints are defined as follows.  
\[\left\{\begin{aligned} \left|\mathbf{q}_{i}(t)\right|& \leq\mathbf{q}_{i}^{max}\\ \left|\hat{\mathbf{q}}_{i}(t)\right|&\leq\hat{q}_{i}^{ max}\\ \left|\hat{\mathbf{q}}_{i}(t)\right|&\leq\mathbf{q}_{i}^{ max}\\ \end{aligned}\right.\left|\tilde{\mathbf{q}}_{i}(t)\right|& \leq\mathbf{q}_{i}^{max}\\ \end{aligned}\right. \tag{30}\]  
The initial and end positions, speed, and acceleration of the robot also have some constraints.  
Positional boundary constraints:  
\[\left\{\begin{aligned} \Omega(t=0)&=\Omega^{start}\\ \Omega(t=T)&=\Omega^{goal}\end{aligned}\right. \tag{31}\]And the speed /acceleration boundary constraint:  
\[\begin{cases}\dot{\mathbf{q}}(t=0)=0,\ddot{\mathbf{q}}(t=0)=0\\ \dot{\mathbf{q}}(t=T)=0,\ddot{\mathbf{q}}(t=T)=0\end{cases} \tag{32}\]
# Fiber-Based Sensors and Energy Systems for Wearable Electronics
## 2 Fabrication Techniques for Fiber-Based Electronics  
Fibers for sensors and energy-storage systems are synthesized through various manufacturing processes. Materials and fabrication methods should be carefully selected, considering not only the performance of the fibers, but also the economic aspect. Numerous manufacturing methods have been utilized, such as dip-coating, spinning, deposition, and thermal drawing. The advantages and disadvantages of the most commonly used fiber manufacturing processes are compared in Table 1. Dip-coating is a method of coating fibers by dipping them into a solution containing active materials. The coating can be done relatively quickly without special equipment, making it suitable for mass production. However, it consumes excessive solution and active materials, and the resulting fibers are associated with poor performance. Wet-spinning is a method of extruding a solution of polymers and active materials. Although the manufacturing process is complicated and time-consuming, it boasts tunability of the fiber diameter and excellent dispersion of the filler materials [26]. A similar method, dry-spinning, does not require purification, so it is more productive than wet-spinning [27]. Melt-spinning extrudes materials by applying heat and making them viscous. It can be simple and cost-effective. However, it is compatible with only a limited concentration of active filler materials. Electrospinning is a method of manufacturing nanofibers by pulling a polymer solution using electric force. A deposition is the process of growing a thin film on a substrate. Chemical vapor deposition is a method of chemically depositing a gas reactant using plasma or thermal energy. Compared to other methods, it has the advantage of having high bonding properties with active materials. Electrodeposition is a method of depositing colloidal particles in a solution onto a conductive material using an electric field [28]. It is possible to manufacture fibers with high conductivity at a low cost, but its process takes a long time. Lastly, thermal drawing is a scale-down method to fabricate fibers by applying temperature to a preform that can incorporate various materials. The resulting fiber's structure follows that of the preform, and this makes complicated fiber designs possible. Furthermore, the fiber's thickness can be easily adjusted by varying the temperature and external force of the capstan. It is also characterized to be very simple and expandable [29].
# OFF-The-Hook: A Tool to Detect Zero-Font and Traditional Phishing Attacks in Real Time
## 2 Background
### Phishing Techniques and Attack Vectors
#### 2.2.2. Business Email Compromise (BEC)  
Business Email Compromise is a targeted phishing technique that uses social engineering to defraud organizations engaged in routine financial transactions. In BEC attacks, cybercriminals impersonate trusted figures, often senior executives or business partners, to manipulate employees into revealing sensitive information or transferring funds to fraudulent accounts [9; 10]. One common variant is CEO fraud, where attackers pose as an executive requesting urgent or confidential actions.  
BEC incidents have grown rapidly in recent years, especially during the COVID-19 pandemic, leading to significant financial losses. These emails are particularly difficult to detect because they often lack obvious malicious payloads and instead rely on psychological manipulation. As a result, conventional defenses, such as email filters and static content analysis, are frequently ineffective.  
Combating BEC requires a multifaceted strategy. Employee and leadership training programs are essential to raise awareness and improve recognition of these threats. Preventive measures such as multifactor authentication, robust access controls, anti-malware systems, and digital email signing have proven effective in reducing organizational risk. Together, these defenses improve overall resilience and reduce vulnerability to BEC attacks.
# Signal Preprocessing for Enhanced IoT Device Identification Using Support Vector Machine
## 3 Results  
This section evaluates the performance of an SVM-based classifier under four different RF signal preprocessing strategies applied to two publicly available datasets: dataset B from [PERSON] et al. (2017), which contains smartphone RF recordings, and the wearable-device dataset by [PERSON] et al. (2016), which captures Bluetooth communications from four distinct devices. The evaluation focuses on assessing the effectiveness of the classifier using two different performance metrics. The first metric, referred to as _diagnostical accuracy_, is defined as the mean value of the diagonal elements of the confusion matrix. This metric quantifies the overall effectiveness of the SVM-based classifier by encompassing all classes and representing the percentage of correct classifications over the entire dataset when the evaluator has previously confirmed which Bluetooth device is responsible for the RF signal being analyzed. It provides a comprehensive measure of the overall performance of the SVM-based classifier in identifying devices. In contrast, the second metric, _effective accuracy_, delves into the mean effectiveness of the SVM-based classifier considering the class assignment criterion determined by the maximum values in each column of the confusion matrix. Since each column in the confusion matrix sums to one, the SVM-based classifier assigns each instance to the class based on the maximum value in its respective column. To compute this metric, the frequency with which the classifier correctly assigns instances to their actual classes is determined by identifying the maximum values in each column and verifying their correspondence to the correct class. Each correct assignment is equally weighted in the final effectiveness of the classifier, reflecting the total number of hits divided by the number of classes.
# On the Fairness of Internet Congestion Control over WiFi with Deep Reinforcement Learning
### Proposed Solution and Contributions  
To address these challenges, we propose a CCA switching mechanism that uses Deep Reinforcement Learning (DRL) to adjust congestion control strategies based on network conditions. This method is designed to work with existing TCP and WiFi standards, allowing the system to adapt to different network environments. Our DRL models are trained on features like noncongestive delay variations, helping to reduce the risk of unfairness and starvation in data transmission.  
Current TCP CCAs, which rely on loss or delay, struggle to maintain fairness and efficiency under various network conditions. To improve this, we developed a DRL-based CCA switching mechanism that aims to make data transmission more equitable and efficient. Our key contributions are:  
* **CCA Switching Mechanism:** We propose a DRL-based solution that adjusts congestion control strategies based on real-time network conditions, compatible with existing TCP and WiFi standards.
* **Delay Variation Analysis:** Our method uses DRL models trained on non-congestive delay variations to help avoid extreme unfairness and starvation.
* **Testing with Real Data:** We tested our approach offline over simulated environment using real data to ensure it aligns with the goals of fairness and efficiency in TCP. Online training and evaluation is beyond the scope of this paper.  
In this paper, we first discuss prevalent TCP issues and the role of DRL in optimizing flow fairness, along with a review of relevant prior works in Section 2. Next, we present a comprehensive analysis of four widely used CCAs, mathematically demonstrating how unfairness and starvation occur in Section 3. In Section 4, we propose a DRL model to address these issues, detailing the model's design, training, and validation processes. Following this, Section 5 outlines our experimental setup, results, DRL model performance, and relevant graphs. Finally, we conclude by summarize our findings, discuss the limitations of our work, and provide directions for future work in Section 6.