esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Vegetation Risk Modeling Framework  
The proposed VRM [19] is a tree-related outage probability assessment tool that comprises roadside tree characteristics, vegetation management information, utility infrastructure, and power outage data across the EE service territory in CT (Table 1). The predictor variables for the VRM were selected by considering the existing literature on various machine learning distribution network outage prediction models [5, 11, 18, 19]. All of the input variables were extracted, aggregated, and averaged at the DEZ level. The following subsections explain each component of the ML model comparison process (Figure 2).  
Figure 1: Distribution grid of Eversource Energy in Connecticut.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable Category** & **Variable** & **Variable Description** & **Variable Type** \\ \hline \multirow{8}{*}{Vegetation} & medianH & The percentage of pPix median heights & Continuous \\ \cline{2-4}  & pTreeCov & The percentage of canopy cover & Continuous \\ \cline{2-4}  & H15\_t & The percentage of pPix with heights exceeding 15 m & Continuous \\ \cline{2-4}  & Exp4\_5\_t & The percentage of pPix that are 4.5 m taller than & Continuous \\ \cline{2-4}  & Clsr\_tot & The percentage of pPix area within DEZ with tree & Continuous \\ \cline{2-4}  & Soils & The combined percentage of wetland and rocky and & Continuous \\ \cline{2-4}  & stp\_tot & The percentage of steep slopes (-50 pct) & Continuous \\ \cline{2-4}  & avgGrdAz & Average ground azimuth between two DEZ points & Continuous \\ \cline{2-4}  & gSlp\_mean & The percentage of steep slopes (-50 pct) & Continuous \\ \cline{2-4}  & TPI150\_mn & TPI for window approximately 150 m in radius to & Continuous \\ \cline{2-4}  & TPI450\_mn & TPI for a window approximately 450 m in radius to & Continuous \\ \hline \multirow{8}{*}{Utility Infrastructure} & coverWire & Wire properties—covered or bare & Binary \\ \cline{2-4}  & UG\_ratio & The ratio between overhead and underground & Continuous \\ \cline{2-4}  & length\_km & Length of primary overhead in km & Continuous \\ \cline{2-4}  & Class & Presence of outages (Yes/No) & Binary \\ \hline \multirow{2}{*}{Vegetation Management} & pctETT & The percentage of ETT performed within a DEZ & Continuous \\ \cline{2-4}  & SUM\_ETTLen & Total ETT length in km & Continuous \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of predictor and response variables used for five machine learning models (CT DEEP: Connecticut Department of Energy and Environmental Protection, NLCD: National Land Cover Database, pPix: proximity pixels, TPI: Topographic Positioning Index, ETT: Enhanced Tree-Trimming, nn: Nearest Neighbors, DEZ: device exposure zone).  
Figure 2: Non-parametric machine learning model comparison workflow. (DEZ: device exposure zone, VRM: vegetation risk model, DT: decision tree, RF: random forest, k-NN: k-Nearest Neighbor, SVM: support vector machine, XGBoost: extreme gradient boosting).
## 2 Results and Discussion
### Sorption Properties
#### 2.2.6 Metal Desorption and Sorbent Recycling  
Strontium desorption was tested using a 0.2 M HCl solution. This eluent was enriched with CaCl\({}_{2}\) (0.5 M) in order to facilitate the ion-exchange mechanism and reinforce the stability of the sorbent; calcium ions contribute to the ionotropic gelation of alginate (carboxylic groups). Figure S10 shows the kinetic profile: the complete desorption of Sr(II) is achieved within 150 min. The desorption step is a little faster than the sorption process. The pseudo-first-order rate and pseudo-second-order rate equations were used for fitting the desorption profile. While the PSORE fits well the initial section of the curve (0-15 min time range), the second section of the curve (15-50 min) obeys the PFORE. Table S9 reports the relevant parameters. The determination coefficients are of the same order (0.983-0.986). Actually, the overall desorption kinetics obeys a variable order rate equation.  
This eluent was used for testing the recycling of the sorbent. Table 4 summarizes the efficiencies of sorption and desorption for five cycles (under similar experimental conditions). The performances remain remarkably stable: the sorption efficiency decreases from 60.7% to 56.3% at the fifth cycle. The decrease in desorption efficiency does not exceed 5% (from 96.5% to 91.7%). This good stability is consistent with the characterization of the materials (using FTIR, SEM, and XPS); though some (weak) changes were observed, the physical structure and chemical characteristics were globally maintained (Figure S3, Tables S1 and S2).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Sorption Efficiency**} & \multicolumn{2}{c}{**Desorption Efficiency**} \\  & \multicolumn{2}{c}{**(\%)**} & \multicolumn{2}{c}{**(\%)**} \\ \hline
**Cycle \#** & **Aver.** & **St. Dev.** & **Aver.** & **St. Dev.** \\ \hline
1 & 60.70 & 0.14 & 96.49 & 0.79 \\
2 & 60.10 & 0.76 & 99.16 & 1.27 \\
3 & 59.05 & 0.56 & 95.09 & 2.29 \\
4 & 57.66 & 0.27 & 91.85 & 4.49 \\
5 & 56.29 & 0.26 & 91.66 & 1.16 \\ \hline \end{tabular}
\end{table}
Table 4: Metal desorption and sorbent recycling. Comparison of sorption and desorption performances for five successive cycles.  
Figure 6: Effect of flow rate on the breakthrough curves for the sorption of Sr(II) using AO-APEI in fixed-bed reactor – Modeling with the Thomas equation [87] (sorbent mass: 70 mg d.w.; bed volume: 5 mL; column depth: 4.4 cm; column diameter: 5 mm; C\({}_{0}\): 54.3 mg Sr L\({}^{-1}\); pH\({}_{\text{inlet}}\): 6.0; room temperature: 25 \(\pm\) 1 \({}^{\circ}\)C).
# A Survey on Robust Video Watermarking Algorithms for Copyright Protection
## 4 Robust Watermarking Algorithms Based on Original Videos
### Video Watermarking in Transform Domain
#### 4.2.1 DCT-Based Watermarking Algorithms
[43] applied a spatiotemporal saliency-modulated just noticeable distortion (JND) profile to a video watermarking algorithm, which adopted the JND method to control the watermark strength and make the distortion of the video under the sensitivity threshold. The algorithm combines the saliency-modulated JND profile and QIM model to achieve embedding of watermark. In addition, it takes full advantage of the spatiotemporal characteristics of video sequence to minimize its perceptual redundancies and reduce the computational complexity. Compared with watermarking algorithms in spatial domain, DCT-based watermarking algorithms are more robust. However, for strong geometric attacks, the robustness of the DCT-based watermarking algorithm is poor. The summary comparison of several watermarking algorithms in DCT domain is shown in Table 3.
## 1 Introduction
However, their approach includes also clinical metadata, which is incorporated into the model by using a specially designed metadata fusion block placed after the encoder branch of the network. In practice, there can be some limitations to maintaining this kind of approach, like lack of imaging and clinical data. As hemorrhagic stroke is less frequent than ischemic stroke and HE occurs in less than 40% of cases, it can be hard for researchers to collect reliable images for developing their algorithms. Moreover, despite clinical information being very helpful in HE prediction [15], some of it might not be available to the clinician right away together with the image. Recently, synthetic images have been actively used for data augmentation, with many works showing that they can aid different medical image analysis approaches, especially in limited-data settings [16; 17]. For instance, there are proposals to use synthetic images for cardiovascular region segmentation [18], liver tumor segmentation [19], brain lesion segmentation [20; 21], brain atrophy quantification [22], and many other applications. In this work, inspired by the main contributions of the seminal work proposed in [14], we develop a deep learning approach to predict hematoma growth in a limited-data setting and without requiring the inclusion of clinical data. We propose a 3D patch-based U-Net model which learns the deformation field from longitudinal CT images of hemorrhagic stroke lesions to then perform the prediction of the HE area by using only the initial patient CT image. To address the scarcity of data, we adapted the recent work of [23] to augment the initial training set with different synthetic variations in the baseline hematoma regions from the available cases and studied the impact of using these synthetic images to improve the prediction of the follow-up HE mask.
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 1 Introduction  
Mobile Edge Computing (MEC) has emerged as a vital solution to offer computing resources at the edge of the network and in close vicinity to the mobile end-users. The end-users are able to offload their computation tasks to the MEC servers, which can further process the subscribers' offloaded tasks. The concept of MEC was motivated by the unprecedented growth of mobile traffic, especially by the smart phones, and the emergence of enhanced multimedia services, which are characterized by high computing demands. The main benefits of the MEC technology are: its potential to reduce the latency, provide location-awareness, improve the performance of the mobile applications, reduce the energy consumption of the mobile devices by alleviating the burden of executing their computing tasks locally, and provide accurate computing outcomes in a time-wise manner. However, the adoption of the MEC technology in the overall networking architecture has created the need of devising controlmechanisms to route the mobile end-users offloading tasks to the MEC servers, while accounting for network's congestion, MEC servers computation capabilities and end-users Quality of Service (QoS) prerequisites. Towards this direction, Software Defined Networking (SDN) is another 5G enabling technology complementing the MEC advancement towards designing dynamic, manageable, adaptable, and cost-effective networks. Specifically, the SDN paradigm transforms the communications networks into a programmable world, where a centralized entity, i.e., SDN controller, has a global view of the communication links and manages the network traffic more efficiently and dynamically. The MEC environment can substantially benefit from the SDN technology, as the decision making with respect to end-users' selection of the specific MEC server to perform their data offloading, the routing of the end-users' offloading traffic and the guarantee of the end-users' QoS constraints can be performed in the control plane, which is implemented within the SDN controller, in a dynamic manner.
## 4 Proposed Method
### Basic Idea  
The previous methods are vulnerable to PDoS attack because each sensor can arbitrarily select distant sensors, and sends the authentication tag to the selected sensors. To deal with this problem, our approach is straightforward but effective, _i.e._, we restrict that the sensor to which the authentication tag is sent  
Figure 3: Expected number \(\
u(n,t)\) of sensors involved in the authentication of the sensed data of one sensor.  
cannot be too far away from the sensor sending the authentication tag. Clearly, the effect of PDoS attack can be mitigated because the energy waste due to the message relaying on the intermediate sensors can be reduced. Afterwards, the problem turns to be the design of a proper mechanism that can support the query of proximity information. Here, with the observation that the mobile sink in UWSNs is able to harvest the sensed data because it knows the position of each sensor, different Bloom filters containing different proximity information are stored in different sensors for the query purpose.  
On the other hand, the reason that the prior methods suffer from the FEDoS attacks is due to the fact that the compromised sensor can always provide an MAC claimed to be legitimate to the sink. All the sink can do is to verify the data authenticity according to the received MACs. Nevertheless, the received authentication tag could be a random string generated by the compromised sensor, and therefore, the sink declines to accepting the authenticated data. In this respect, our idea is that the MAC used in the authentication procedure should be made in a specific fashion so that the legitimacy of the authentication tag can be verified by the sink before the sink verifies the data authenticity. It should be noted that our defense against FEDoS attacks can also enhance the resilience against sensor compromises. This reveals a particular merit of our method over ExCo.
## 3 Methodology
### Kernel Logistic Regression (KLR)  
Kernel logistic regression is considered as a powerful discriminative method, described as the kernel version of logistic regression capable of transferring into a high-dimensional feature space the original input feature space by using kernel functions [51]. The following kernel function (Equation (3)) is the basic function in which \(\varphi\) is assumed to be unknown:  
\[K(x,x^{\prime})=\varphi(x)^{T}\varphi(x^{\prime}) \tag{3}\]  
where \(T\) is the inner product in the \(Z\) space.  
The training dataset has \(n\)  
The goal of KLR is to estimate a discrimination function that distinguishes the two classes perfectly, in our case, spring from non-spring areas:  
\[\log(p)=\log\left(\frac{p}{1-p}\right)=\sum_{i=1}^{n}a_{i}K\left(x_{i},x_{j} \right)+b \tag{6}\]  
where \(p\) is the logistic function with values ranging between \(-1\) and \(1\), \(K(x_{i},x_{j})\) is the kernel function that takes into consideration the Mercer's condition [52], \(\alpha_{i}\) is a vector of dual parameters and \(b\) is the intercept.  
Several kernel functions can be used, such as the linear kernel, the polynomial, and normalized polynomial [53]. However, in our case, the radial basis function (RDF) kernel was considered to be carried out:  
\[K\left(x_{i},x_{j}\right)=exp\frac{-x_{i}-x_{j}^{2}}{2\sigma^{2}} \tag{7}\]  
where \(\sigma\) is a tuning parameter.
## 1 Introduction  
### Background  
Past research into the subjective perception of concert hall acoustics provides insightful knowledge that can be applied in multichannel audio and virtual acoustics. Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR), embraced under the term Extended Reality (XR), are rapidly growing areas of research and development, which are being increasingly used in various applications. For example, XR is being adopted in the entertainment industry as a new content delivery format in which the user can interact with virtual beings or objects in virtual or/and physical environments. Using virtual acoustic simulations and a system that supports sensorimotor contingencies (e.g., six-degrees-of-freedom (6 DOF) head/motion tracking), a musical performance could be experienced in such a way that the listener can freely move around in a virtual or physical space. In such a scenario, it is necessary to dynamically render various acoustic parameters of the environment (e.g., early reflections and reverberation characteristics as well as source direction) according to the listener's position and head orientation. Highly accurate virtual acoustic rendering using conventional simulation tools tends to be computationally expensive and is not suitable for real-time audio rendering. Efficient and yet perceptually optimised virtual acoustics rendering methods would allow for the real-time rendering of plausible spatial characteristics of different environments. This however requires investigationsinto fundamental psychoacoustical factors associated with various auditory attributes perceived depending on the listener's position and head orientation. Concert hall and room acoustics research conducted to date mainly focused on comparing different spaces in terms of only a few high-level auditory attributes. However, it is not yet clear what kind of differences are perceived depending on the listener's position and head orientation in a given space, which is what the present study aims to investigate.
# Boosting Model Interpretability for Transparent ML in TBM Tunneling
## 2 Interpretability with SHapley Additive exPlanations (SHAP)
* Feature importance: Tree Explainer ranks feature importance for XGBoost, while Deep Explainer highlights impactful features for the ANN. * Result validation: comparing results across explainers cross-validates findings, boosting confidence in model robustness. Task-specific insights: each model excels in different tasks; comparisons highlight their context-specific strengths and weaknesses. * Decision support: understanding model decision-making is vital for applications like safety and risk assessment, where interpretability is critical. SHAP plots visually explain how individual features impact model predictions (Figure 2). By breaking down each prediction into feature contributions, they clarify variable influences, making complex models transparent and trustworthy. These plots are essential for explaining models, building trust, and improving confidence in an algorithm's performance.
# Outage Probability Analysis in Relaying Cooperative Systems with NOMA Considering Power Splitting
## 1 Introduction
This is because users with different channel conditions can be served timely which meets the rising demands for ultra-low latency and ultra-high connectivity in 5G future communications. In EH-based NOMA systems, there have been a number of investigations conducted on different sorts of systems, i.e., cooperative relaying networks (CRNs), wireless sensor networks (WSNs), and cognitive relaying (CNs) [22; 23; 24; 25; 26; 27]. In [22], EH CRN NOMA system was studied, in which three different relaying protocols AF, DF, and quantize-map-forward (QF) were taken into comparisons. Besides that, the outage probability (_OP_) of the EH NOMA relaying network was examined in [23], where the transmitting antenna selection is applied at BS and maximal ratio combining is applied at the multiple users. The literature in [24] focused on three different cognitive NOMA architectures, including underlay NOMA networks, overlay NOMA networks, and CR-inspired NOMA networks. In addition, the authors in [25] considered the interference from different sources at the sensors in WSNs. After achieving the expressions for _OP_, they examined the average link throughput and energy consumption efficiency of NOMA against conventional OMA technique. Likewise, [26] considered a WSN, an energy efficiency (EE) optimization problem was proposed which challenging difficult to solve for global optimality due to the lack of convexity. In [27], the authors examined the bit error rate (BER) and the performance capacity of a typical NOMA system. Motivated from those aforementioned works, we try to derive approximate and exact closed-form expressions for _OP_. Besides, we study the average bit error probability (ABEP) together with the delay-limited throughput to fully understand the benefits of NOMA, and we realize that the system performance is better than traditional relaying systems in the case of deploying NOMA. We divide this work into five sections. In particular, Section 2 provides the system model. The system performance analysis is done in Section 3, where expressions for _OP_, ABEP, and the delay-limited throughput are obtained. We prove the system performance by giving some numerical results in Section 4. Finally, a brief conclusion is given in Section 5. **Notation:** We present the cumulative distribution function (CDF) and probability density function (PDF) of the random variable (RV) as \(F_{X}\left(a\right)=1-\frac{1}{\Gamma_{\Omega_{a}}}e^{-\frac{a}{\Omega_{n}}}\) and \(f_{X}\left(a\right)=\frac{1}{\Gamma_{\Omega_{a}}}e^{-\frac{a}{\Omega_{n}}}\), where \(\Omega_{a}\) is the average power. \(Pr(. )\) stands for the probability distribution. \(\mathbb{E}\left\{\left|.\right|\right\}\) is the expectation operator. Besides that, \(K_{1}(. )\) stands for the modified Bessel function of the second kind with order 1. The Whittaker function is \(W_{\mu,\
u}(. )\)
## 3 Computation and Analysis  
### Employed Data  
The observations from the short baselines MATE_MAT1 and KERG_KRGG, provided by the International GNSS Service, are utilized to analyze and validate methods for mitigating multipath effects. The lengths of these baselines are 10.7 m and 8.6 m, respectively, with a sampling interval of 30 s. The antenna photographs of the four stations are shown in Figure 3. Figure 4 illustrates the multipath distribution for both baselines, revealing that the errors are predominantly concentrated in low-elevation areas. The presence of a large metallic structure near station MAT1 leads to significant multipath errors in the MATE_MAT1 baseline, particularly around an elevation angle of 20\({}^{\circ}\) toward the southeast. Additionally, the KERG_KRGG baseline shows higher-frequency multipath errors in low-elevation regions, primarily due to the influence of ancillary equipment, such as antenna mounts [33].  
Figure 3: Antenna photographs of MATE_MAT1 (**a**,**b**) and KERG_KRGG (**c**,**d**). Photographs are from www.sonel.org (accessed on 14 October 2024).  
Figure 2: Flowchart of multipath analysis and mitigation.  
Based on the satellite orbit repeat period of GPS and Galileo [13; 34; 35], we utilize 10 days of observation data from Days of the Year (DOY) 029-038 in 2023 for analysis and modeling, which are then applied to mitigate the multipath effects on DOY 039 and subsequent days. During this period, the GPS and Galileo frequencies available for each baseline are listed in Table 1, with specific frequency values. During data processing, a satellite elevation cutoff angle of \(7^{\circ}\) is applied, and the jigs20_2309.atx antenna file is used to correct the phase center offsets (PCOs) and phase center variations (PCVs) for both satellites and receivers. For the KERG_KRGG baseline, due to the lack of Galileo PCO/PCV correction values, GPS PCO/PCV correction values are used as substitutes.
# Dynamic Rating of Three-Core XLPE Submarine Cables for Offshore Wind Farms
## 2 Dynamic Cable Rating  
Dynamic rating of cables, as the main focus of this paper, involves rating cables considering variable cable loading. It is an iterative method that comprises calculating electrical and thermal parameters on the basis of the IEC 60287 series [4] and conducting a thermal cable analysis according to IEC 60853-2 [8]. The method can be used for AC cables with a voltage above 36 kV, as defined by IEC 60853-2 [8]. In order to find an analytical solution for heat transfer from the centre of the conductor to the surface of the cable and take into account the impact of the surroundings, a thermal ladder network is built up to represent the electrical parameters of the cable and accordingly evaluate the transient temperature response by a step function.  
Figure 4 is a simplified overview of the iterative method. The first stage is to determine an initial cable to design, and the second stage, which is detailed in Section 2.2, involves the determination of cable loading and electrical and thermal parameters. The third process, explained in Section 2.3, evaluates the transient temperature behaviour in the centre of the conductor on the basis of the electrical and thermal parameters determined in Section 2.2. The first decision box represents the iterative process  
Figure 3: Example of a current profile to illustrate rating issues, where \(I_{a}\) is the actual current and \(I_{r}\) is the current used for static rating.  
of assessing the transient temperature response on the basis of the applied step function. With the usage of XLPE as insulation, the upper boundary is at 90 \({}^{\circ}\)C. If the cable temperature is not within the temperature tolerance, a new cable is selected. The fifth stage represents the output of a suitable cable according to dynamic cable rating.