esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 4 Proposed DRL-Based Method
### Design of State, Action and Reward
#### 4.3.3 Reward  
In practice, multiple factors significantly impact the deployment efficiency of emergency communication networks and the channel quality of heterogeneous communication methods. To address the deployment problem of emergency communication network elements using reinforcement learning, it is essential to first model the environment and design a rational reward function.  
In this problem, environmental rewards for different elements can originate from four key aspects: geographic environment, disaster directionality, communication distances between elements, and spatial separation distances. Detailed calculation methods for each reward component are revisited in Section 3.2. This design actively suppresses the occurrence of invalid or harmful behaviors by pre-limiting invalid decisions in the action space, thereby eliminating the reliance on negative rewards. This approach avoids manual design of penalty terms and significantly reduces redundant computational overhead during decision-making.  
For elements, a dynamic weighting mechanism is required to allocate weights to individual reward components. The total reward function can be expressed as  
\[R_{t}=\sum_{i=5}^{n}\beta_{i}R_{i}, \tag{7}\]where \(\beta_{i}\) is the correction coefficient of each reward sub-item in the total reward function, and \(R_{i}\) is each reward sub-item. The correction coefficients of each reward sub-item in this total reward function are adjusted according to the type of element.  
* ATM switch, satellite communication vehicle: \(\beta_{1}=\beta_{2}=0.5\), \(\beta_{3}=\beta_{4}\)=\(\beta_{5}=0\).
* Optical terminal equipment: \(\beta_{1}=\beta_{2}=0.3\), \(\beta_{3}=0.4\), \(\beta_{4}=\beta_{5}=0\).
* Microwave relay vehicle: \(\beta_{1}=\beta_{2}=0.3\), \(\beta_{4}=0.4\), \(\beta_{3}=\beta_{5}=0\).
* 5G and trunked base stations: \(\beta_{1}=\beta_{2}=0.3\), \(\beta_{5}=0.4\), \(\beta_{3}=\beta_{4}=0\).
# C Band 360\({}^{\circ}\) Triangular Phase Shift Detector for Precise Vertical Landing RF System
## 2 \(\pm\)90\({}^{\circ}\) Versus \(\pm\)180\({}^{\circ}\) Landing System
### Triangular Phase Shift Landing System with Analog Multipliers  
Phase detectors based on analog multipliers (Figure 3) have an unambiguous phase detection range of \(\pm\)90\({}^{\circ}\) (bijective function between input phase shifts and output voltage) when the branches are well balanced (\(\beta\) = 0\({}^{\circ}\) in Figure 3a). Otherwise, there is a reduction in the detection range directly proportional to the unbalance between branches (Figure 3b); for example, the unambiguous symmetrical range is reduced to \(\pm\)(90\(-\beta\))\({}^{\circ}\), \(\pm\)60\({}^{\circ}\) in Figure 3b. That reduction in the unambiguous range results in a decrease in the tracking area in which the drone can be guided to the landing point. Figure 4a shows the tracking area for \(\pm\)90\({}^{\circ}\) and \(\pm\)60\({}^{\circ}\) of unambiguous phase when the drone is at 2 m height, 7 cm between antennas (\(D\) = 7 cm), and a working frequency of 2.5 GHz. The maximum distance goes from 113.5 cm to 69.7 cm, while the minimum distance goes from 94.8 cm to 59.6 cm.
## 1 Introduction
Therefore, it is required to investigate the feasibility of EIS as an online monitoring tool in crystallization processes [29]. In global food processing industries, sucrose (C\({}_{12}\)H\({}_{22}\)O\({}_{11}\)) production has a significant importance, and crystallization plays a fundamental role in its manufacturing process. Applications of process monitoring techniques in sucrose crystallization and manufacturing have been seldom reported in the literature and works have been limited to cooling and evaporative crystallization and concentration measurements [30]. For instance, applications of EIS [31] and image acquisition by high-speed camera [32] have been successfully demonstrated during cooling crystallization of sucrose. Moreover, ultrasound computed tomography [7] systems have been used to evaluate concentration change in different sucrose solutions. Therefore, advancement in innovative monitoring methods and new crystallization techniques are critical to ensuring the improvement in final particle quality as well as intensifying the overall process in terms of energy and sustainability. Hence, given the status quo of research and development both in antisolvent-based crystallization processes and PAT, applications of two different process monitoring techniques, namely, 1-D spectroscopy and 2-D electrical resistance tomography, have been proposed and investigated in this study. The aim of the work was to investigate the feasibility of both methods for crystallization process monitoring and control. Given the low conductivity of the sucrose solutions in the range of a few uS/cm [33], electrical resistance tomography (ERT) reconstructions become challenging due to the small changes in the resistances [34]. However, in the recent years, it has been demonstrated that the ERT can be successfully implemented with materials with low conductivity such as composite structures [35], graphene-based nano-composite films [36] and bone cement [37]. Herein, electrical properties of antisolvent crystallization in the ternary-sucrose-ethanol-water system were analysed by utilizing 1-D thin electrode impedance spectroscopy. The acquired experimental data was used to build a frequency response model in the form of a transfer function. Additionally, this study is further supplemented by 2-D visualization of the crystal distribution in low conductivity standard solutions of saturated sucrose via an in-house ERT unit. The results of EIS can be utilized in the further development of electrical resistance tomography to design a multispectral acquisition methodology [38; 39], specifically for crystallization processes and for process control purposes. This work has been done within the European Union Horizon 2020 TOMOCON project (Smart tomographic sensors for advanced industrial process control) [40]. The aim within the project is to create a multi-sensor network to monitor, visualize, and control the different industry-relevant cases, including batch crystallization processes.
## 5 Testing and Evaluation  
The presented solution was tested both subjectively and objectively. Subjective tests included a pre-development user study to define the gesture set used for drone control and a number of field trials where users had a chance to try the system in action. Objective measurements concerned the responsiveness time between gesture execution and drone reaction, IMU-based tracking error, and the latency in video transmission. Each of the above is presented in the following subsections.
# Comparative Study Between Active AMD and ABS Devices by Using \(\mu\)-Synthesis Robust Control
## 2 Materials and Methods
### Active Vibration Control Devices  
To mitigate the structural response, we strategically implemented structural control devices to attenuate the dynamic effects, particularly displacements and internal forces. Because the internal forces and structural deformations were naturally linked, these devices were mostly used to reduce the sideways movements caused by earthquakes and wind loads.  
These control devices, distinguished by their unique operational mechanisms, fall into four primary categories: active, semi-active, passive, and hybrid controllers. We can group active structural control systems into three categories: the active tuned mass damper (ATMD), the active bracing system (ABS), and the active tendons system (ATS). In our work, we specifically focused on two main pieces of equipment used in the active vibration control of structures (AVC): the active mass damper (AMD) and the active bracing system (ABS). These active devices, also called actuators, produced the required forces via a power supply.
# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 6 In-Depth Discussion and Comparative Analysis of Experimental Results
### Comprehensive Evaluation of Model Performance  
Based on the complex Chinese MRC dataset, the previous MRC model R-Net was further extended, and two methods were proposed: text purification based on key sentence extraction and the encoding of problem type information. Text purification technology is the process of extracting key sentences from complex text MRC datasets before predicting answers, in order to achieve the goal of purification. The specific implementation is as follows: firstly, select the appropriate key sentence extraction algorithm; secondly, conduct information statistics on the dataset used in the experiment, including the number of questions, the number of questions included in each article, the number of sentences in the text segment, the length of questions, and the length of answers; once again, filter out the optimal number of extracted sentences for each dataset; finally, conduct experiments on a complex text MRC dataset. Problem classification is the addition of the encoding of problem type information in the MRC model, predicting the category of problems through problems and articles, and providing the model with more problem information. The specific implementation is as follows: firstly, classify the problems in the dataset, analyze the proportion of each problem type in the dataset, and conduct experiments based on different problem types to obtain the advantageous problem types; secondly, integrate the problem classification module into the model; finally, compare the performance of the baseline model and the model integrated with the problem classification module. Both methods have shown some improvement in the original accuracy.  
In regard to BLEU-4 and ROUGE-L metrics, the model in this study demonstrates substantial improvement compared to traditional models like match-LSTM and BiDAF. This implies that the incorporation of the hybrid attention mechanism does, indeed, enhance the model's capacity to handle intricate semantic relationships. However, compared to other advanced models in the BERT series, like Baseline_MacBert and Baseline_RoBERTa, the competitive advantage of this model is not very significant. This indicates a need for further research into the deeper integration of the hybrid attention mechanism with the BERT structure.
## 5 Experiments and Results
### Enhancing Model Explainability and Transparency with SHAP and LIME  
This section examines the model's decision-making process, utilizing SHAP and LIME to improve explainability, which is especially crucial in the context of 6G networks. As 6G networks are expected to be more autonomous and complex, ensuring transparency in model predictions will be vital for maintaining security and trust. These approaches deconstruct the model's predictions, providing a comprehensive understanding of how different features influence the final classification. By increasing transparency, we not only build confidence in the model's predictions but also enable system administrators to make better-informed decisions in dynamic 6G environments. To explore these explanations through SHAP and LIME, we analyzed two representative test samples, referred to as Sample 1 and Sample 2, to understand the impact of specific features on the model's classification as \"Benign\" or \"Attack\".
# Tilted Beam Fabry-Perot Antenna with Enhanced Gain and Broadband Low Backscattering
## 2 Proposed Antenna System and Its Operation
### Unit Cell Configuration
The frequency responses of \(\mid\)S\({}_{11}\)\(\mid\), \(\mid\)S\({}_{21}\)\(\mid\), and \(\phi\)(S\({}_{22}\)) against varying \(g\) are displayed in Figure 10a-c, respectively. From Figure 10a,b, it is observed that the intervening air gap between the absorptive FSS and PRS plays a significant role in eliminating the backscattered wave. This is evident because the effect on wave refection is more, while it is meagre for the wave transmission. Thus, the bandwidth, as well as the levels of absorption, are highly dependent on the air gap value. The impact of \(g\) on the phase of reflectivity (\(\phi\)(S\({}_{22}\))) is insignificant (Figure 10c). Finally, the frequency response of \(\mid\)S\({}_{11}\)\(\mid\) with respect to \(R\) is shown in Figure 2:4 It manifests that the value of resistor also has a significant effect on the magnitude of incident wave reflection, thereby impacting the absorption phenomenon. Once the impedance of the top surface matches closely to the impedance of the impinging wave, most of the wave is dissipated as heat on the surface of the absorber, and thus the radar backscatter is reduced. The optimized dimensions of unit cell and the F-P cavity are given in Figure 2:5
## 3 Preliminaries
### Polynomial Key Establishment Scheme  
Using Equation (1), KDC generates a symmetric bivariate \(t\)-degree polynomial over a finite field \(F_{p}\), where \(p\) is a large prime number. Device IDs are substituted in this generated polynomial to generate polynomial shares, i.e., \(f(ID_{i}\), \(y)\). The polynomial share corresponding to the device is loaded in the device during the key distribution phase. With \(f(x\),\(y)\), \(p\) unique polynomial shares can be generated. Each device needs to store \(t+1\) coefficients of the polynomial. The scheme [33] is  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Schemes**} & \multicolumn{2}{c}{**Crypto System**} & \multirow{2}{*}{\begin{tabular}{c} **Centralized (C)/** \\ **Decentralized (D)** \\ \end{tabular} } & \multirow{2}{*}{**Mechanism Used**} & \multicolumn{2}{c}{\begin{tabular}{c} **Security** \\ **Requirements** \\ \end{tabular} } \\ \cline{4-7}  & & & & **RR** & **BC** & **MC** \\ \hline SKE [20] & Both (S) and (P) & (C) & LTK and Hash & & & \\ \hline SKMA [21] & (S) & (C) & ISO 11770-2 & ✓ & & \\ \hline ASKMA [22] & (S) & (C) & LKH & ✓ & ✓ & \\ \hline ASKMA+ [23] & (S) & (C) & LKH and Iolus & ✓ & ✓ & ✓ \\ \hline BUMP IN WIRE [12] & (S) & (D) & Sec-KeyD & ✓ & & \\ \hline SecFB [24] & (S) & (C) & DES & ✓ & & \\ \hline SECURE POWER SCADA [27] & (P) & (D) & X.509 certificates & ✓ & & \\ \hline Versatile KMS [25] & (S) & (C) & LTK and Hash & ✓ & ✓ & ✓ \\ \hline Layered Defense Principle [28] & (S) & (D) & Hash chaining & ✓ & & \\ \hline Advanced metering & (S) & (C) & Key graph & ✓ & ✓ & ✓ \\ infrastructure [26] & (S) & (C) & \begin{tabular}{c} Identity-Based \\ Cryptography \\ \end{tabular} & ✓ & ✓ & ✓ \\ \hline \multirow{2}{*}{Fault tolerance KMI [31]} & \multirow{2}{*}{Both (S) and (P)} & \multicolumn{2}{c}{\
\begin{tabular}{c} Needham-Schroeder \\ authentication protocol \\ and elliptic curve \\ \end{tabular} } \\ \cline{2-5}  & KMI for SCADA and & & & & \\ WirelessHART [17; 32] & (S) & (D) &
\begin{tabular}{c} Polynomial key \\ establishment scheme \\ \end{tabular} & ✓ & ✓ & ✓ \\ \hline KMI with Join/leave [8] & (S) & (D) & Symmetric Matrix & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of existing key management schemes for SCADA networks.  
collision resistant, meaning that, as long as \(t\) number of devices are compromised (captured), the communication between the non-compromised devices of the network is secure.  
\[f(x,y)=\sum_{i,j=0}^{t}a_{ij,i}x^{i}y^{j}\bmod p \tag{1}\]  
Two devices, e.g., \(X\) and \(Y\), with _IDs ID1_ and _ID2_, respectively, can compute pairwise key (\(PK\)) as follows: Both devices exchange their IDs between them. Device \(X\) computes \(f_{X}(ID1, ID2)\). Device \(Y\) computes \(f_{Y}(ID2, ID1)\). The symmetric property ensures \(f_{X}(ID1, ID2)=f_{Y}(ID2, ID1)\) and, hence, the same key is generated between them.
# Enhancements and Challenges in CoAP--A Survey
## 6 Open Challenges
### Security of CoAP  
The biggest challenge is to provide secure communication with high performance in constrained environments [51]. DTLS is considered to provide security in CoAP as it is based on the UDP. DTLS itself has limitations such as large message and handshake compression [52] and is not suitable for CoAP proxy modes [53]. The use of DTLS in constrained environments can be hindered as mentioned in [54], for instance, for message transmission, the defined timers in a protocol which may need large-sized buffers for holding data for retransmission on the receiver side, and the required size of code in order to support DTLS. Some solutions for identifying the cyber-attacks have been proposed by making the TLS/DTLS faster and flexible [55; 56], nevertheless, there are still some issues that need to be tackled. In addition to providing secure communication among resource constrained devices, another challenging task is to secure the data privacy and protection. Numerous cryptography solutions are available to tackle this problem. In this regard, [46] discusses and evaluates several symmetric key algorithms for lightweight devices and provides their comparison with the proposed work. Moreover, the authors measured the several metrics such as execution times, battery drainage and throughput for all the tested ciphers and highlighted the old ciphers which are susceptible to more attacks compared to modern and optimized ciphers.  
Furthermore, in the context of open research challenges in CoAP security, [57] mentions that the support of public keys and certificates can be explored further. Existent methods of Online Certification Status Protocol (OCSP) [58] or the OCSP stapling--TLS Certification Status Request extension mentioned in [59] can be applied by simplifying and modifying these mechanisms accordingly. For supporting multicast communication securely, the deficit of appropriate key management mechanisms is another important aspect to investigate. The authors in [60] mention two ways to design Group key management; One way to design group key management is integration with DTLS handshake in order to support key negotiation and another way is designing it externally to CoAP. Concerning the header compression mechanisms of DTLS [61], either existing implementations may be used to provide some support or new mechanisms need to be designed to map between DTLS and compressed DTLS. Security gateways can be used to support such mechanisms for providing secure end-to-end communications by mapping between TLS and DTLS. Besides, security gateways may also benefit from working against intrusion detection and attack tolerance mechanisms. For developing adequate methods for CoAP-based IoT communication, existing implementations on intrusion detection for sensor networks may be valuable [62; 63; 64].
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Numerical Gravity Performance Simulations
#### 3.1.1 Instrument Noise Impact
This finding is of particular interest for a GRACE-type mission, as this behavior can contribute to the reduction of the typical striping pattern observed in Figure 13a-c, which, aside from temporal aliasing, occurs due to the low retrieval quality of (near-)sectoralcoefficients, which in turn stems from a lack of observations in the east-west direction. In the case of a Bender-type mission (cf. Figure 12), the accelerometer's noise impact on the retrieval of the (near-)sectoral coefficient groups is far more limited in comparison to a single polar pair, because the overall observation geometry is already significantly improved through the additional observation components in east-west directions provided by the inclined pair.
## 1 Introduction
The study utilized thermal oils as HTFs, which were evaluated and assessed for selection in PTC applications. The system generated 1140 kW of electrical energy with a thermal efficiency of 32%. Furthermore, the authors highlighted that selecting the HTF is crucial in optimizing these systems. Additionally, modifications of HTFs using nanoparticles have been investigated to enhance the fluids' properties. For instance, [PERSON] et al. [26] utilized ZnO nanoparticles to improve thermal conductivity and reduce the viscosity of the HTF. Similarly, [PERSON] et al. [27] employed strontium oxide, barium oxide, and strontium oxide-barium oxide nanoparticles for the same purpose in Therminol-brand commercial thermal oils. Other studies have explored the use of aluminium oxide and copper oxide nanoparticles [28], demonstrating enhanced thermal conductivity, albeit with increased viscosity. These results highlight the potential of hybrid nanofluids in solar thermal systems, heat exchangers, and industrial cooling applications. This study presents a comprehensive thermodynamic analysis of an ORC system enhanced by a TES unit operating with three industrial thermal oils. Two TES configurations were evaluated to analyze the impact of temperature profiles and pinch-point temperatures on energy delivery quality and overall system performance. An integrated simulation model is developed to assess thermal (\(\eta_{I}\)) and exergetic (\(\eta_{II}\)) efficiencies, net power output, mass and volumetric flow rates, and key design parameters of the heat exchangers. The primary objective of this work is to identify optimal conditions and configurations that maximize system efficiency while minimizing irreversibilities, providing clear guidelines for the sustainable design of ORC-TES systems in industrial applications, leveraging low-temperature waste heat sources.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### Meta-Heuristic Algorithms
#### 3.4.2 Particle Swarm Optimization (PSO)  
PSO [38], introduced in 1995, is a population-based algorithm and inspired by the movement of individuals in a swarm, such as a bird flock. The movement of particles is based on their current locations and speeds. The neighboring particles, known as informants, share their information about the surrounding environment. Each particle is represented as a vector of position and velocity. In UAV path planning, the particle represents a complete path from the starting location to the end location, i.e., a sequence of waypoints.  
The velocity of the particle is updated based on the current position \(x(t)\) and current velocity \(v(t)\). In specific, the updated velocity \(v(t+1)\) and the updated location \(x(t+1)\) are computed as follows:  
\[v_{t+1}=\omega v_{t}+c_{1}r_{1}(pBest_{t}-x_{t})+c_{2}r_{2}(gBest_{t}-x_{t}) \tag{16}\]  
\[x(t+1)=x(t)+v(t+1) \tag{17}\]  
where \(\omega\) is the inertia coefficient, \((c_{1},c_{2})\) are the acceleration coefficients, and \((r_{1},r_{2})\) are uniform random numbers drawn from \([0,1]\). The update in Equation (16) utilizes the optimal local solution of the swarm denoted by \(pBest_{t}\), and the optimal global solution of the swarm, denoted by \(gBest_{t}\). Subsequently, the generated solution is assessed and \(pBest_{t+1}\) and \(gBest_{t+1}\) are computed for the next iteration. The parameters for PSO are explained thoroughly in [32]. The process is repeated until the maximum iteration or maximum run time is reached. The pseudo-code of PSO for UAV path planning is illustrated in Algorithm 1.  
Figure 2 shows the graphical representation of the evolution of particles in PSO. The figure depicts the positions and velocities of the \(i^{th}\) particle, denoted by \(X_{i}\) and \(V_{i}\), respectively, at times \(t-1\), \(t\), and \(t+1\). The figure illustrates how the particle interacts within the swarm and showcases its behavior. It is important to note that each particle in the PSO algorithm possesses its own information as well as neighbor information and swarm information. This knowledge affects the decision-making process regarding the particle's movement direction and speed. By considering (1) its personal best position, (2) the best position found by its neighbors, and (3) the global best position found by the entire swarm, a particle determines its next movement. The combination of these three key pieces of knowledge guides the particle's behavior, allowing it to explore and exploit the search space effectively.