esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 4 Simulations and Results
### Comparison with Current PLS  
We next compare our proposed GLS with current PLS. From a conceptual perspective, the idea of our GLS is to use the underlying networked dynamics to encrypt the transmitted information between [PERSON] and [PERSON] in the IoT scenarios. This is totally different with the current PLS, which relies on the communication channels (e.g., superiority or common feature). As such, we only provide conceptual comparisons other than experimental results, and three aspects are given in the following:  
(1) PLS uses the communication channel itself for encryption/security, but our GLS leverages the networked dynamics that are totally irrelevant with the communication aspects (e.g., channels, RSS). The networked dynamics can be the electricity flows in a smart grid system (as what we used in simulations), or the water pressures in a water distribution network.  
(2) As described, the usage of our GLS requires an IoT with underlying networked dynamics (e.g., communications of two nodes in one smart grid network, or communications of two water junctions in one water distribution network). This is different from PLS, whereby any two legitimate users can have encrypted communications if the small-scale fading (randomness) of their channel is enough.  
(3) From attacker aspects, our GLS can be eavesdropped only if an eavesdropper can reconstruct the networked dynamics under all IoT nodes, or any attackers can destroy the GLS encryption by changing the dynamic pattern (e.g., the correlations of the dynamic signals on different nodes). This is different from the attackers considered in PLS, which try to either reconstruct the common channel feature for SKG, or weaken the superiority of legitimate channels.
## 3 Multiobjective Optimization Approach
### Mathematical Background  
We consider the following multiobjective MINLP problem of the form  
\[\min_{\mathbf{x}\in X}\quad\mathbf{f}(\mathbf{x})=\{f_{1}(\mathbf{x}),\ldots,f_{k}(\mathbf{x})\}, \tag{31}\]  
where \(\mathbf{x}\in X=\{\mathbf{x}=(\mathbf{y},\mathbf{z})\mid\mathbf{y}\in\mathbb{R}^{n},\mathbf{z}\in \mathbb{Z}^{m}\}\cap C\) is a decision variable, \(C\) is the set of constraints, and \(X\) is a nonempty and compact set of feasible solutions. The objectives \(f_{i}:X\rightarrow\mathbb{R}\) for all \(i\in I=\{1,\ldots,k\}\) are assumed to be lower semicontinuous with respect to \(\mathbf{y}\) and at least partially conflicting. Therefore, we cannot find a minimal solution for every objective simultaneously and the minimization of only one objective may lead to an arbitrary bad solution with respect to other objectives. In order to compare the objectives, for \(\mathbf{x},\mathbf{y}\in\mathbb{R}^{k}\) we denote by \(\mathbf{x}<\mathbf{y}\) if \(x_{i}<y_{i}\) for all \(i\in I\) and \(\mathbf{x}\leq\mathbf{y}\) if \(x_{i}\leq y_{i}\) for all \(i\in I\)In multiobjective optimization, we say that a solution is Pareto optimal if we cannot improve any objective without causing a deterioration for some other objective at the same time. Mathematically speaking, a solution \(\mathbf{x}^{*}\in X\) is Pareto optimal if there does not exist any solution \(\mathbf{x}\in X\) such that \(\mathbf{f}(\mathbf{x})\leq\mathbf{f}(\mathbf{x}^{*})\) and \(f_{i}(\mathbf{x})<f_{j}(\mathbf{x}^{*})\) for at least one index \(j\in I\). It is noteworthy that usually we do not have a unique Pareto optimum but a set of Pareto optimal solutions, called the Pareto set. All these Pareto optimal solutions belong also to a larger class of weakly Pareto optimal solutions. The solution \(\mathbf{x}^{\prime}\in X\) is an element of this class if there does not exist another solution \(\mathbf{x}\in X\) such that \(\mathbf{f}(\mathbf{x})<f(\mathbf{x}^{\prime})\).  
In order to obtain some information about the Pareto set, we can define an ideal and a nadir vector, \(\mathbf{f}^{id}\in\mathbb{R}^{k}\) and \(\mathbf{f}^{nad}\in\mathbb{R}^{k}\), to give the lower and the upper bound for a Pareto optimal solution, respectively. The ideal vector consists of individual minima of the objectives. This means that the component \(f_{i}^{id}\) is calculated as a solution of the problem \(\min_{\mathbf{x}\in X}f_{i}(\mathbf{x})\). Due to the conflicting objectives, the ideal vector is not feasible. The nadir vector, in its turn, represents the worst objective values in the Pareto set. Unfortunately, the exact calculation of the nadir vector needs the maximization of objectives over the set of Pareto optimal solutions being a hard task. Thus, the nadir vector needs to be approximated, for example, with a pay-off table (see e.g., [31; 32]).
# Intrusion Detection for in-Vehicle Communication Networks: An Unsupervised Kohonen SOM Approach
## 4 Theoretical Background
### K-Means Clustering Algorithm  
The k-means is one of the most used clustering algorithms for large datasets. It is an unsupervised Machine Learning algorithm which allows one to partition data into \(K\) groups, minimizing the variance within the clusters. After determining the number of \(K\) clusters, \(K\) input data vectors are randomly selected as initial cluster centroids. The Euclidean distance is computed to assign input data vectors to the closest centroids. The \(K\) centroids are updated and the input data vectors are reassigned at each iteration. These steps are iteratively repeated until input data assignments stop changing and convergence is achieved.  
Formally, we defined the input data vectors \(X_{i}\), with \(i=1,\ldots,n\), the number of clusters \(K\) and the centroids \(\Phi_{k}\) with \(k=1,\ldots,K\). The pseudo-code is detailed in Algorithm 2.  
```
Input: Input vectors \(X_{i}\) with \(l=1,...,n\) // Number of desired clusters \(K\) Output: Final centroids \(\Phi_{k}\) whit \(k=1,...,K\) Results: Assign cluster membership to all input vectors \(X_{i}\) \(\Phi_{k}\leftarrow\emptyset\) set(\(K\)) // set number of clusters for\(k=1,...,K\)do \(\Phi_{k}\leftarrow\) random(\(X_{i}\)) // random centroids initialization endfor repeat for\(i=1:n\)do for\(k=1:K\)do \(d_{k}=\|X_{i}-\ \Phi_{k}\|\) // compute Euclidean distances endfor \(l=\operatorname*{argmin}\limits d_{k}\) // compute minimum distance \(\Phi_{l}=\ \Phi_{l}\cup\{X_{i}\}\) // assign input vector to the closest cluster centroid endfor \(\Phi_{k}\)\(\leftarrow\)\(\Phi_{l}\) // update centroid \(\Phi_{k}\) based on current partition until the class assignment converges return\(\{\Phi_{1},...,\Phi_{K}\}\)
```  
**Algorithm 2**K-means algorithm.
###### Abstract  
This study addresses the growing concern of water contamination by pharmaceutical residues, focusing on the simultaneous removal of paracetamol (PAR) and metronidazoleazole (MTZ). Batch and fixed-bed column adsorption processes were evaluated using activated carbon. In the batch experiments, the effects of pH (3, 7, and 11), adsorbent mass (0.5, 1.25, and 2 g), and contact time (10, 30, and 60 min) were evaluated, while the fixed-bed column was optimized considering initial pollutants concentration (30, 40, and 50 mg/L), adsorbent mass (0.5, 0.75, and 1 g), and flow rate (5, 10, and 15 mL/min) to improve the maximum adsorption capacity of the bed for both pollutants (qmaxPAR and qmaxMTZ). Parameter estimation and model selection were performed using a Bayesian Monte Carlo approach. Optimal conditions in the batch system (pH = 7, W = 2 g, and time = 60 min) led to high removal efficiencies for both compounds (\(\geq\)98%), while in the column system, the initial pollutant concentration was the most significant parameter to improve the maximum adsorption capacity of the bed, resulting in values equal to 49.5 and 43.6 mg/g for PAR and MTZ, respectively. The multicomponent Gompertz model showed the best performance for representing the breakthrough curves and is suitable for scale-up (R\({}^{2}\geq\) 0.75). These findings highlight the complexity of multicomponent adsorption and provide insights, contributing to the development of more efficient and sustainable water treatment technologies for pharmaceutical residues.  
water contamination; pharmaceutical residues; adsorption; batch process; fixed-bed column; Bayesian technique Article
## 2 Quantum Key Distribution  
The genesis of QKD traces back to the late 1960s, when [PERSON] first proposed the idea of encoding information on polarized photons using two conjugate bases [12]. In 1984, [PERSON] and [PERSON] extended this idea by introducing the first QKD protocol, known as \"BB84\", to generate shared secret keying material between two parties [13]. Today, QKD is gaining attention as an important development in the cybersecurity solution space because of its ability to generate unlimited amounts of symmetric keying material for use with the One-Time-Pad (OTP)--the only known encryption algorithm to achieve perfect secrecy [14; 15]. In this way, QKD enables unbreakable communications and has inspired research efforts across Asia, Europe, and North America [16]. While there are many competing QKD protocols, BB84 is primarily considered in this work because it remains a popular implementation choice and is relatively easy to understand [1].
## 3 Materials and Methods
### GC-MS and GC-FID Analysis  
GC-MS and GC-FID analyses were carried out for qualitative and quantitative purposes, respectively.  
GC-MS analyses were carried out on a GC-QP2020 system (Shimadzu, Kyoto, Japan). For the separation, an SLB-5 ms fused-silica capillary column (30 m \(\times\) 0.25 mm i.d. \(\times\) 0.25 \(\upmu\)m df) (29804-U) (Merck KGaA, Darmstadt, Germany) was applied. Helium was used as carrier gas at a constant linear velocity of 30.0 cm/s, which corresponded to an inlet pressure of 24.2 kPa. An inlet liner, direct SPME type, straight design unpacked (2633501) (Merck KGaA, Darmstadt, Germany) was used. The injector was equipped with a Thermogreen LB-2 Septa, plug (20608) (Merck KGaA, Darmstadt, Germany), and the temperature was set at 260 \({}^{\circ}\)C. The temperature program was the following: 40 \({}^{\circ}\)C, held for 1 min, to 350 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, held for 5 min. The interface and ion source temperatures were 250 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, respectively. The acquisition was made in full scan mode in the mass range of 40-500 m/z, with a scanning rate interval of 0.2 s. Data handling was supported by GCMS solution ver. 4.30 software (Shimadzu, Kyoto, Japan). For the characterization, the following databases were used: W11N17 (Wiley11-Nist17, Wiley, Hoboken, NJ, USA; and FFNSC 4.0 (Shimadzu, Kyoto, Japan). The identification was performed applying two filters, namely, spectral similarity match over 85% and linear retention index (LRI) match calculated using a C7-C40 saturated \(n\)-alkane homolog series with a filter window of \(\pm\)10 LRI units.  
The LRIs were calculated applying the equation proposed by [PERSON] and [PERSON] (Equation (1)) [23], developed for programmed-temperature retention index calculation.  
\[LRI=100\times\Big{[}z+(t_{Ri}-t_{Rz})/\Big{(}t_{R(Z+1)}\ -t_{Rz}\Big{)}\Big{]} \tag{1}\]  
GC-FID analyses were carried out on a GC2010 system (Shimadzu, Kyoto, Japan). Column, oven temperature program, and injection parameters were the same as for MS applications. Helium was used as carrier gas at a constant linear velocity of 30.0 cm/s, which corresponded to an inlet pressure of 97.4 kPa. The FID temperature was set at 280 \({}^{\circ}\)C (sampling rate 200 ms), and hydrogen and air flows were 40 mL/min and 400 mL/min, respectively. Data were collected by LabSolution software ver. 5.92 (Shimadzu, Kyoto, Japan). Quantitative results were determined as peak area percentage without any correction. Samples were analyzed in triplicates.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
## 1 Introduction
Results of the conventional methods are presented in Section 4. Results using the proposed CNN method are presented in Section 5. A study of model transferability between different models is covered in Section 6. Conclusions are drawn in Section 7.
## 6 Conclusions
### Innovation and Implications  
By integrating CV thresholds with multi-sensor synergies, this work bridges terrestrial scale-correction principles and aquatic dynamics, offering three advancements:  
1. Operational standardization: The CV-based zoning framework provides the first quantitative criteria for scale-aware validation in coastal waters, adaptable to parameters like chlorophyll and CDOM.
2. Methodological replicability: The workflow, leveraging nearly simultaneous data to isolate spatial scale effects, is transferable to other estuaries globally, such as the Mississippi Delta or Pearl River Estuary.
3. Theoretical validation: The empirical confirmation of the 0.10 CV threshold reinforces the need for resolution-heterogeneity alignment in sensor design, echoing foundational remote sensing theories.
# Material Characterization for Sustainable Concrete Paving Blocks
## 3 Results and Discussion
### Water Absorption  
According to Figure 10, it is clear to observe that, for the blocks mixed with RCCA, RCFA, and crumb rubber, the increase in the replacement levels of those materials increases the specimens' water absorption. Compared with samples without any replacement (with 3.89% water absorption), the specimens mixed with RCCA and RCFA maintained similar water absorption of 3.91% and 3.90%, at the replacement levels of 20% and 10%, respectively. The value reached the peak of 7.60% and 4.87%, respectively, while the replacement levels were 100% and 30%. The water absorption of blocks sharply rose with the increase of crumb rubber content in the blocks. With only 3% crumb rubber mixed in the blocks, the water absorption reached 6.02%. Crushed glass is one of the materials that can reduce the water absorption when it was mixed into blocks. The values of water absorption ranged from 3.50%, 3.24%, 3.65% and 3.72% while the replacement levels were 10%, 20%, 30%, and 40%, respectively. All the results were lower than that of the control group at 3.89%. Addition of GGBS also helped reduce the block's water absorption. The water absorptions of samples that contained 30% and 50% percentage of GGBS were 3.58% and 3.77%, respectively. When the replacement level of GGBS peaked to 70%, the water absorption value increased to 4.26%, which is smaller than the standard requirement at 6.0% [15].
## 2 Proposed Macrodiversity Reception Architecture for Maritime Wireless Sensor Networks  
The maritime wireless sensor networks macrodiversity reception platform designed in this paper is mainly composed of sensor node and shore-based sink node. The architecture of maritime wireless sensor networks is shown in Figure 1. The sensor node floating on the sea collects the seawater information in real time and transmits it to the shore-based sink node in a fixed time interval. The shore-based sink node is based on macrodiversity reception scheme and consists of multiple local convergence modules and convergence center. A plurality of local convergence modules are placed at different locations on the shore to receive package. The convergence center connects with multiple distributed reception nodes and receives packages from each distributed reception node for combining to against multipath fading effectively.  
The proposed macrodiversity reception scheme indicates that the sink node is mainly composed of mutiple local convergence modules and a convergence center. Each local convergence module equiped with four antennas is based on the diversity reception scheme to against fast fading. The convergence center connects with multiple local convergence modules via fiber or ethernet and receives packages from every local convergence module. Each local convergence module should be placed at a different location to realize macrodiversity reception scheme. The architecture of the proposed macrodiversity reception system is shown in Figure 2. The local convergence module consists of four RF modules, four lower-power microcontroller units (MCU) and a data processing unit. The RF module configured by MCU receives packages from sensor node. The MCU connects with RF module via serial peripheral interface (SPI) and reads packages received by RF module. The data processing unit should be a low-power microprocessor and connects with MCU through serial port. It buffers and processes the packages received by RF module and sends the data combined to convergence center via optical fiber or ethernet. In the convergence center, a router or switchboard is necessary, which connects with multiple local convergence modules and achieves transmission from multiple local convergence modules to the convergence center. The data processing module connnects with the router or switchboard via ethernet to receive and processes packages from different local convergence modules.  
Figure 1: Proposed maritime wireless sensor networks architecture.
## 2 Related Work  
Previous implementation of Machine Learning in this field [4; 5] was mainly related to problems of system improvement (beamforming, multi-user detection, coding/decoding schemes, RF characterization...), network anomaly detection and traffic prediction, or resource management, spectrum allocation and handover prediction. When it comes to capacity planning problems, and the implementation of deep learning techniques, the majority of published studies deal with problems of traffic forecasting. Some of the most recent very relevant examples are [6; 7; 8; 9; 10], where various techniques were tested with a better or worse accuracy achieved, depending on the ability to capture different phenomena, like seasonal changes, abnormal events, or changes in the network configuration. However, in the capacity planning process, traffic forecasting can be further mapped only to the first component of user experience discussed in the introduction chapter, which is the number of available resource blocks per user. The second important factor of user experience, which is spectral efficiency, to the authors' knowledge, was not addressed so far. At the same time, in a situation when pretty much all mobile operators are dealing with a growing traffic load and data tsunami, the evolution of spectral efficiency and its accurate prediction seem very relevant problems to address. Novelty introduced with this paper is Machine Learning application, with the aim to capture network signatures and improve spectral efficiency estimation accuracy in increasing network load conditions.
## 3 Prediction Hybrid Method
### Technical State Prediction Hybrid Model  
To increase the efficiency of predicting the technical state of a technical object (TO), a hybrid model was created. The presented methodology enables the use of two different AI methods; in the model, two high-performance methods should be used for prediction.  
The three-valued method of assessing the technical condition of a technical object consists of six stages. In this work, the concept of a stage means a set of interconnected activities aimed at fulfilling a specific task (presented in the heading for each stage). Steps can be repeated in a loop in a way that results from the connections between them.  
In the developed method, we can distinguish the following stages:  
1. Training a predictive model based on data collected during reviews in Level 2.
2. Prediction of the technical state of a technical object using the developed hybrid model for reviews at Level 2.
3. Recommended maintenance activities during technical review at Level 2.
4. Training the predictive model based on data collected during reviews at Level 1.
5. Prediction of the technical state using the developed prediction model for review at Level 1.
6. Recommended maintenance activities during review at Level 1.  
The hybrid model that has been developed combines the advantages of two different prediction methods presented in Section 2.3. For hybrid method testing, the decision tree was used as AI Method 1 and the artificial neural network as AI Method 2. The decision tree is used for data analysis and decision making based on rules, while the artificial neural network is an effective tool for learning from a training set and making predictions based on previously acquired knowledge. Combining these two methods allows for synergy and increased accuracy in predicting the technical state of the TO and clearly illustrates the operation and advantages of using a hybrid model.
## 4 Fabrication and Measurement  
To verify the computed expectations from the full-wave EM simulations, the Tx coil, Rx coil, and AMC prototypes were built. The scattering parameters (S-parameters) and magnetic fields were measured to verify the performance of the WPTS with and without AMCs.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Drone Revocation Phase
#### 7.1.8 AVISPA Simulation  
In this section, we prove that the proposed protocol is resistant to replay and MITM attacks using AVISPA [14]. AVISPA is an automated formal security verification tool that helps ensure the robustness and security of authentication protocols. Furthermore, AVISPA simulates authentication protocols using the \"High-Level Protocol Specification Language (HLPSL)\". Moreover, AVISPA employs the following four back-end models: \"on-the-fly model-checker (OFMC)\", \"constraint logic-based attack searcher (CL-AtSe)\", \"SAT-based model-checker (SATMC)\", and \"tree automata based on automatic approximations for the analysis of security protocols (TA4 SP)\". Since the \"SATMC\" and \"TA4 SP\" back-ends do not support XOR operations, we used the \"CL-AtSe\" and \"OFMC\" back-ends. As shown in Figure 5, the simulation results demonstrate that the proposed protocol is safe. Therefore, the proposed protocol resists replay and MITM attacks.
# Evaluation of Short-Term Aging Protocol for Asphalt Mixtures
## 1 Introduction
### Objectives  
Researchers [3; 5; 25] have indicated a need of further evaluation of the aging protocol, including a range of variables such as material types, binders, factors affecting mixture production, and environmental conditions. The recent NCHRP 09-52 project by [PERSON] et al. [9] demonstrated that conditioning an asphalt mixture at 135 \({}^{\circ}\)C for 2 h simulates the short-term aging level in the field. The new NCHRP 09-61 project [26] will focus on developing practical laboratory aging methods to accurately simulate short- and long-term aging of asphalt binders.  
Clearly, there is a dissimilarity in short-term aging conditioning protocols due to changes in asphalt mixture characteristics and plant operations as well as the lack of studies specifically relating to aging of asphalt mixtures in Qatar. Furthermore, in mechanistic and performance-based designs, dynamic modulus, rut, and fatigue testing is conducted on compacted specimens. Short-term conditioning of an asphalt mixture to simulate plant aging is a prerequisite before the sample is compacted. Therefore, it is of utmost importance that the short-term conditioning is an appropriate protocol that is representative of typical asphalt mixtures in the country. Hot mix asphalt pavements are exposed to very high temperatures in the Arabian Gulf, including Qatar. The study herein is part of a research investigation of aging of asphalt mixtures in Qatar. The objective of this research is to evaluate the short-term aging of asphalt mixtures to construct pavements with a long service life. In particular, this study was conducted to validate the existing short-term aging protocol and propose an appropriate one that is representative of asphalt mixtures and plant operations, if required. Evaluation of short-term aging protocols is one of the first attempts in this region of such extreme weather conditions and involves a range of variables including both unmodified and modified binder, base, and wearing mixture as well as a mixture from a plant and construction site. The long-term aging evaluation was presented in a separate paper by [PERSON] et al. [27].