esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Verification and Physical Experiment
### Method Validity Verification
#### 4.2.1 Experimental Results of Single-Switch Fault  
Figure 5 illustrates the diagnostic results when an OC fault occurs in \(T_{a1}\). As shown in the figure, when the inverter is operating normally, it generates a three-phase sinusoidal current with an amplitude of 15 A and a frequency of 50 Hz. The fault detection variable \(R_{a}\) is consistently lower than the adaptive threshold \(T_{a}\), with the initial location variable satisfying \(W_{a}=w_{a}\), while \(\eta_{a}\) maintains a value of 1.11. An OC fault occurs in \(T_{a1}\) at \(t=0.163\) s. Concurrent with the pronounced phase-a positive current attenuation, all three-phase outputs manifest heterogeneous harmonic distortion characteristics. The detection variable \(R_{a}\) exceeds the threshold \(T_{a}\) within 22% of the fundamental current period following the fault. Meanwhile, \(W_{a}<w_{a}\) suggests a fault in positive-level switches (\(T_{a1}\) and \(T_{a3}\)). Combined with \(\eta_{a}\geq 1.165\), an OC fault in \(T_{a1}\) is conclusively identified.  
Figure 6 presents the diagnostic results when \(T_{a2}\) has an OC fault. After the fault occurs, the negative phase current of phase a exhibits a slight decay. The fault detection variable \(R_{a}\) gradually increases until exceeding the threshold \(T_{a}\), thereby identifying phase a as the faulty phase. Additionally, the positioning variable \(W_{a}>w_{a}\) indicates a fault in the negative-level switches (\(T_{a2}\) and \(T_{a1}\)). Since \(\eta_{a}\) varied yet consistently remained within the range of \([1.12,1.165)\), it is ultimately determined that \(T_{a2}\) is faulty.  
Figure 5: Simulation results of the \(T_{a1}\) OC fault. (**a**) Three-phase currents. (**b**) Fault detection. (**c**) Initial location. (**d**) Precise location.
## 4 Simulation results  
### Assumptions, Cable Properties  
The simulations with transverse damper are performed for the following damper types, assumptions, vibration modes, cable properties and damper positions:  
* Damper types:
* Passive linear viscous damper being optimized to one targeted mode, i.e., the excited mode, which represents a theoretical benchmark as passive dampers always need to mitigate several targeted modes.
* Passive linear viscous damper being optimized to the targeted modes 1 to 4 describing the realistic benchmark.
* Semi-active damper controlled by clipped VDNS including the control force constraints due to the minimum and maximum controllable viscous coefficients.
* The vibration mitigation of the considered transverse dampers are computed for the first four cable modes.
* Single mode vibrations are considered as resonant vibrations result in greatest cable displacement amplitudes.
* The excitation force due to vortex-shedding is modelled as harmonic excitation.
* The simulations are performed assuming typical cable properties and typical damper positions:
* Cable length \(L\) = 300 m.
* Cable tension force \(T\) = 6700 kN.
* Mass per unit length \(m\) = 100 kg/m.
* First eigenfrequency \(f_{1}\) = 0.431 Hz (determined by \(L\), \(T\), \(m\)).
* Inherent cable damping ratio \(\zeta_{\text{cable}}\)= 0.4%.
* Relative damper positions \(a\)/\(L\) = 1%, 1.67%, 2%, 2.5% and 3%.
# Adaptive Allocation Algorithm for Multi-Radio Multi-Channel Wireless Mesh Networks
## 3 Mathematical Formulation of Adaptive Channel Allocation Problem  
The objectives of this problem aim to maximize the network performance and to minimize the channel re-allocation. In the following section, we introduce the assumptions and mathematical model of the problem under study.
## 2 Multi-Fidelity Data Learning Methods
### Comprehensive Correction
For the given Equation (10), it can be treated as a quadratic function with respect to \(\rho\). To find the value of \(\rho\) that minimizes the function, you can calculate its derivative and set it equal to zero for solving. MF data learning methods have their own advantages and disadvantages. For example, when calculating \(\rho(x)\) in the multiplicative correction, if \(y_{l}(x)\) as the denominator is zero, then \(\rho(x)\) has no solution. Additive correction can make up for the deficiency of multiplicative correction, but it is not unexpected that additive correction is not always better than multiplicative correction, which is proven in reference [104; 105]. The comprehensive correction method makes up for the shortcomings of additive correction and multiplicative correction to a certain extent, but a major drawback of this method is that it can only deal with two kinds of linearly related fidelity data, while in some cases [8; 106], the relationship between LF and HF data follows a nonlinear relationship. For this case, reference [21] decomposed the relationship between HF and LF data into linear and nonlinear parts and solved it using a neural network. Next, we evaluate the performance of these three correction methods based on GPR.
## 5 The INS-Aided Acquisition Experiments with Different Grade INS
### Parameters of Different Grade INS  
The INS plays an important role in the progress of signal acquisition and tracking. Considering that the INS can offer the relative position and velocity of the receiver with respect to the satellite, which are the core parameters used to calculate the Doppler shift and the code phase, in the INS-aided acquisition experiments, we use the gyro and accelerometer data generator to calculate the INS information and the generator is developed based on the mathematical simulation model given in [22].  
In the gyro and accelerometer data generator, the true angular rate and acceleration are listed in Table 2. Then, in order to simulate the outputs, the gyro and accelerometer errors which include constant biases, random drifts and noises are added into the true value. The gyro and accelerometer errors can lead to the reflecting constant bias, bias stability and random walk.  
In Equation (29), different grades of INS bring different velocity and position errors. This would then affect the acquisition results due to the different Doppler shift and code phase ranges. Thus, for the purpose to analysis the performance changes caused by different INS grades, a variety of INS are simulated in the designed scenario. The parameters of the selected INS in the experiments are listed in Table 3. In the above analysis, the precision of INS varies by 10 times, which covers the typical precision used in the current application.
## 1 Introduction  
For clastic reservoirs, one of the most challenging issues is to accurately assess the quality of the reservoir in the presence of clay minerals. Various clay minerals can be found in reservoirs, both as primary or diagenetic products, in different forms and compositions. Depending on the shale type, they can take different forms, including detrital shale, clasts, or as dispersed matrix between quartz grains. Based on their composition clay minerals could have a specific site in the pores; for instance, kaolinite can fill pores, chlorite can line pores, and illite can bridge pores [1]. Clay mineral content affects the petrophysicaland hydraulic properties of clastic reservoirs. It has been documented that the porosity and permeability of sandstone reservoirs are directly affected by the development of clay minerals [2]. The diagenetic parameters are responsible for most of the heterogeneity in sandstone reservoirs [3], which is considered being one of the main problems facing the oil and gas industry. Onshore outcrop datasets provide a good analogue for similar geologic settings and depositional environments of offshore basins for reservoir modeling and characterization [4]. Therefore, it is necessary to understand the processes at a microscopic level to predict the quality of a clastic reservoir. Depending on how micropores are distributed and developed, the reservoir quality can either be enhanced or reduced, which may affect how much hydrocarbon can be recovered. It is essential to make field observations, describe outcrops, sample the reservoir, and conduct petrophysical investigations to assess reservoir properties; hence the clastic reservoirs in the Sabah Basin are no exception. Several changes affecting the lithofacies could influence the evolution of the pore system in oil and gas reservoirs [5], which are required to understand the role of diagenesis in porosity development. Labuan Island represents a connection between onshore and offshore Sabah. It is considered to be in a geologically strategic location between offshore Sabah and Brunei [6]. Labuan is situated within the Brunei-Sabah Basin [7]. This paper aims to provide a better understanding of the role of clay minerals in clastic reservoirs and how those minerals can improve or reduce the quality of the reservoir, through (a) studying some sandstone intervals and samples collected from the Labuan Island outcrops to evaluate the porosity and permeability, (b) understanding the sedimentological and diagenetic parameters from thin section analysis and grain size analysis, (c) evaluating the clay minerals' distribution using the Th and K concentrations cross-plot, (d) determining the effect of the clay minerals on the porosity using image analysis and differentiating the micro from macropores, (e) evaluating the permeability in the presence of clay minerals, and (f) correlating the different relations among the porosity, permeability, and distribution of grain sizes.
## 2 Key Theories and Technical Points of Ground Source Heat Pump
### Physical Configuration and Working Mechanism  
A schematic illustration of the refrigerant circuit of the GSHP is presented in Figure 2a. The system comprises four primary components, including a compressor, evaporator, throttling valve, and condenser [19]. The compressor plays a crucial role in facilitating the efficient transfer of latent heat and ensuring that the compressor shell temperature is maintained at the same level as the condensing temperature. The refrigerant leaves the compressor and enters the condenser, where the surrounding medium extracts heat from the refrigerant, thereby condensing it; at the outlet of the throttle valve, a two-phase flow exists and the refrigerant flows to the evaporator, which is typically a wrapped tube. In the evaporator, the refrigerant extracts heat from the surrounding medium in the form of latent heat. The superheated vapors of the refrigerant then return to the condenser, completing a one-stage refrigeration cycle.  
Figure 1: Climate classification according to the feasibility of geothermal heat pumps for heating in China [18].
# Wettability of Carbonate Reservoir Rocks:
## 1 Introduction
They stated that carbonate reservoir rocks analyzed in their study were generally ranged from slightly water-wet to slightly oil-wet. [PERSON] et al. [57] found that the results of the Amott and USBM methods were similar indicating that the Arab-D carbonate reservoir was slightly oil-wet. In another study, [PERSON] et al. [58] evaluated the Bu Hasa reservoir by measuring the contact angle by image analysis and showed the reservoir had slightly water-wet responses. [PERSON] and [PERSON] [59] assessed the wettability of 17 core plug samples from three wells of an Iranian carbonate reservoir via the Amott-Harvey method. Their results showed that the Amott-Harvey index varied from \(-0.8\) to \(0.01\) indicating oil-wet to slightly water-wet conditions. [PERSON] et al. [60] studied wettability characteristics of a carbonate reservoir in the Middle East via optical-based contact angle measurements. They found that most of samples ranged between neutral-wet and slightly oil-wet. Recently, [PERSON] and [PERSON] [61] analyzed the wettability of 25 core plug samples from an Iranian carbonate reservoir via [PERSON]'s triple rules of thumb and the Amott-Harvey index and reported that samples were oil-wet. Using the Lak index, [PERSON] [15] evaluated the wettability of ten plug samples from six carbonate reservoirs. His results showed that the Lak index for their samples varied from \(-0.64\) to \(-0.19\), which indicated oil-wet conditions. [PERSON] et al. [13] also reported the wetness of 20 core plug samples from two carbonate reservoirs of Asmari and Fahlian from the Middle East. Those authors used the Amott-Harvey, USBM, Lak, and modified Lak indices and found slightly water- to oil-wet conditions. In the literature, various studies evaluated and compared the performance of wettability indices [62; 63; 64]. [PERSON] [32] stated that the USBM method performed better than the Amott method in differentiating near-neutral-wet systems. However, some studies argued that both the USBM and Amott methods may not accurately detect the type of wetness in strongly water-wet systems [40; 62; 63]. [PERSON] et al. [65] investigated the relationship between the Amott and USBM indices and found that these two methods provided similar results only in weakly-wetted media with randomly distributed water-wet and oil-wet pores. The main objective of this study is to evaluate and compare different quantitative and qualitative wettability assessment methods including Amott, Amott-Harvey, and USBM as well as relative permeability-based techniques, such as [PERSON]'s triple rules of thumb, the modified Craig's second rule, the normalized water fractional flow curve, Lak, and modified Lak. For this purpose, a set of capillary pressure curves and relative permeability measurements was conducted on 19 core plug samples from a carbonate reservoir. The relationship between wetting indices and rock quality indicators is also discussed.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 2 Support Vector Machine (SVM)
### Nonlinear Support Vector Machine  
In most cases, the samples are not separable, so it is necessary to construct an optimal classification surface in this high-dimensional space. When an appropriate mapping function is chosen, the formerly linearly inseparable situation can be transformed into a linearly separable one. For nonlinear problems, SVM can reduce the difficulty of high-dimensional space operation by defining kernel function, thus avoiding dimension disaster.  
According to the functional theory, as long as the Mercer condition is satisfied for \(K(x_{i},x_{j})\), \(K(x_{i},x_{j})\) would satisfy the inner product of a certain spatial transformation. When \(0<\zeta_{i}<1\), the sample point \(x_{i}\) can still be correctly classified; when \(\zeta_{i}\geq 1\), the sample point \(x_{i}\) would be misclassified. At this point, the problem of optimal classification surface can be transformed into a quadratic programming problem as follows:  
\[\min\psi(w,\zeta)=\frac{1}{2}w^{T}w+C\sum_{i=1}^{n}\zeta_{i} \tag{1}\]  
\[y_{i}(w^{T}x_{i}+b)\geq 1+\zeta_{i},i=1,2,\ldots,n. \tag{2}\]  
When the two categories of  
By solving Equation (6), the coefficient \(\alpha_{i}\) can be obtained. Thus, the classification function can be expressed as:  
\[f(x)=\text{sgn}\Big{[}(w^{T}x)+b\Big{]}=\text{sgn}\Bigg{(}\sum_{i=1}^{n}\alpha_{i }y_{i}K(x,x_{i})+b^{*}\Bigg{)} \tag{6}\]
## 3 Methodology
### Evaluation Metrics  
The statistical evaluation parameters commonly used in this analysis, namely the coefficient of determination (\(R^{2}\)), mean absolute error (\(MAE\)), and root mean square error (\(RMSE\)), are used to assess the accuracy of the computational results produced by the intelligent method, as shown below.  
\[RMSE=\sqrt{\frac{1}{m}{\sum}_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}} \tag{3}\]  
\[MAE=\frac{1}{m}{\sum}_{i=1}^{m}{\left|{y_{i}-\hat{y}_{i}}\right|} \tag{4}\]  
\[R^{2}=1-\frac{{\sum}_{i=1}^{m}\left({\hat{y}_{i}-y_{i}}\right)^{2}}{{\sum}_{i= 1}^{m}\left({\overline{y}-y_{i}}\right)^{2}} \tag{5}\]  
where \(y_{i}-\hat{y}_{i}\) is the actual value-predicted value in the test set, \(\overline{y}\) denotes the mean of all predicted values; \(i\) represents the test deformation data serial number; and \(m\) is the total number of generated test deformation data.
## 3 Proposed Maturity Framework
### Mapping of Regulations  
It is worth stating that we incorporated the regulation requirements of GDPR, PCI DSS, and DSPT into our General Security Requirements. The mapping of the different regulations into the HCYMAF is shown in the upcoming Figures 3-5. This was done by focusing on each individual regulation and mapping it into one of our requirements. For example, in terms of GDPR, we focused on the 7 principles of GDPR--as shown in Figure 3--and mapped each of the principles into one of our requirements. For example, the first principle of GDPR is lawfulness, fairness, and transparency. This was mapped into Sub-Requirement P5.1: GDPR Compliance. The second principle, which is purpose limitation, was mapped into Requirement P5: Policies, Processes, and Procedures for the protection of the services, and so on.  
In terms of incorporating PCI DSS, we focused on the 6 principles of PCI DSS, as each of these principles had its requirements (See Figure 4). In terms of incorporating DSPT, we also focused on the 10 principles of the regulation and likewise each of those principles was mapped into one of our requirements (See Figure 5). Overall, all the aforementioned regulations were incorporated into our model and merged to form a solid maturity model, as illustrated in Figure 6.  
Figure 1: Holistic Cybersecurity Maturity Assessment Framework (HCYMAF) requirements are divided into three groups.  
Figure 3: General Data Protection Regulation (GDPR) Mapping.  
Figure 2: The proposed HCYMAF model in detail.  
Figure 4: Payment Card Industry Data Security Standard (PCI DSS) Mapping.
# Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian PageRank Updates
## 4 Proposed Method
### Adaptive-DecayRank Algorithm with Bayesian Updating
#### 4.2.1 Bayesian Inference and Updating  
The Adaptive-DecayRank algorithm utilizes Bayesian inference [40] to dynamically adjust decay factors, enabling responsive updates to evolving graph structures. Unlike frequency-based approaches, which rely on fixed parameters and repeated sampling, Bayesian inference treats probability as a measure of belief, updating prior distributions in light of new data and observed changes. This makes it well-suited for dynamic, evolving systems such as graphs.  
Bayesian inference involves three key steps: **(1) The prior distribution, \(\pi(\theta)\), which represents initial beliefs about the parameter \(\theta\) (in this case, the _decay factor_\(\delta_{\mathbf{u}}\)) before observing any data. **(2) The likelihood function, \(L(\theta)\), expressed as \(p(X|\theta)\), captures the probability of observing data, \(X\), given a specific value of \(\theta\). This function reflects how well \(\theta\) explains the observed data. Finally, **(3) the posterior distribution, \(\pi(\theta|X)\), which combines the prior and likelihood to update our beliefs about \(\theta\) after observing data \(X\).**  
Using [PERSON] theorem, the posterior is defined as  
\[\pi(\theta|X)=\frac{L(\theta)\pi(\theta)}{\int L(\theta)\pi(\theta)d\theta}, \tag{5}\]where \(\pi(\theta|X)\) is the posterior distribution (updated belief about \(\theta\)), \(L(\theta)\) is the likelihood function defined as \(L(\theta)=\prod_{i=1}^{n}p(X_{i}|\theta)\), and \(\int L(\theta)\pi(\theta)d\theta\) is the normalizing constant ensuring the posterior is a valid probability distribution.
## 5 Evaluation
### Results and Discussion
#### 5.2.4 Transmission Modes  
Figure 8 shows a topology of our network (\(k=100\)). In this figure, CCS lies at the center of square grid. The four different colored links show communication modes between clusters. Figure 8(a)  
Figure 6: Comparison of network lifetime.  
Figure 7: Network lifetime for various intercluster ranges.  
shows various communication modes for routing in CMIMO. The diamonds, stars, etc., in the same neighborhood show nodes belonging to same cluster in Figure 8(a). In Figure 8(b), packets are routed via HMIMO. There are less number of hops in hierarchical cooperative communication as compared to the traditional CMIMO approach. The figure shows that MIMO mode of communication is dominant in the network.  
Figure 8: Comparison of Routing Nodes in CMIMO and HMIMO. **(a)** Routing in CMIMO; **(b)** Routing in HMIMO via backbone Clusterheads.
# Evaluating Explainable Artificial Intelligence for X-ray Image Analysis
## 3 Results and Discussion
### Quality Assessment  
Using the previously defined QA questions, we obtained the results of each study's quality. The distribution of the punctuation can be seen in Figures 4 and 5: 2 articles were rated with 6 points (1.41%), 3 with 5.5 points (2.13%), 3 with 5 points (2.13%), 16 with 4.5 points (11.4%), 25 with 4 points (17.73%), 21 with 3.5 points (14.89%), 20 with 3 points (14.18%), 18 with 2.5 points (12.77%), 20 with 2 points (14.184%), 12 with 1.5 points (8.51%), and 1 with 1 point (8.51%).  
QA1 and QA4 were the most fully answered questions, with 100 studies with 1 point on the first one and 78 with 1 point on the second one. These two questions tried to identify whether the studies provide suitable justification, taking into account the state of the art and the explanation of the topic. The importance of these questions is shown by the fact that we did not find any study that scored 0 points on both of these questions. Of the articles that did not have a full score for both questions, the majority did fail QA4, without explaining the state of the art, whereas only 10 did not indicate the aims and objectives of the research.  
Sixty-nine studies had 0 points on QA2. These articles did not justify the selection of the explainable algorithm. Furthermore, they did not take into consideration any other technique apart from the one used.  
One hundred and fourteen articles failed QA3. These papers were characterized as not using any kind of metric for the explainability results. From the rest of the methods,  
Figure 4: Total quality punctuation of each article. Vertical axis indicates the percentage of articles for a category.  
Figure 5: Total quality punctuation of each article by question. Vertical axis indicates the percentage of articles for a category.  
we assessed whether they tried to overcome potential biases. Only 16 articles (9.76%) performed a rigorous data analysis without biases. On QA5, the majority of studies, 51.06% of them, had full punctuation. This score means that all of them made clear statements of the findings. Moreover, 36.17% of the articles only scored 0.5 points, penalizing the lack of clarity in the exposition of their findings. Finally, 12.76% of the articles failed this question due to the non-existence of any statement of the findings.  
Finally, on QA6, 27 articles had full punctuation. Most of the studies (90 articles) had either open data or open code, with only 19 studies having neither. We observed that 81.56% of the articles made the data publicly available, while only 20.57% of the articles published their respective code.  
We did not find any study with a complete failure to answer the QA questions (0 points in total). The articles with less punctuation scored 1 point. Furthermore, most of the reviewed articles (76.59%) had at least 2.5 points. This punctuation, half of the maximum score, can be seen as a quality threshold. Taking this into consideration, we can conclude that most articles had sufficient quality.