esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Wireless sensor networks (WSNs) are among the most popular wireless technologies for sensor communication purposes nowadays. Usually, WSNs are developed for specific applications, either monitoring purposes or tracking purposes, for indoor or outdoor environments, where limited battery power is a main challenge. To overcome this problem, many routing protocols have been proposed through the last few years. Nevertheless, the extension of the network lifetime in consideration of the sensors capacities remains an open issue. In this paper, to achieve more efficient and reliable protocols according to current application scenarios, two well-known energy efficient protocols, i.e., Low-Energy Adaptive Clustering hierarchy (LEACH) and Energy-Efficient Sensor Routing (EESR), are redesigned considering neural networks. Specifically, to improve results in terms of energy efficiency, a Levenberg-Marquardt neural network (LMNN) is integrated. Furthermore, in order to improve the performance, a sub-cluster LEACH-derived protocol is also proposed. Simulation results show that the Sub-LEACH with LMNN outperformed its competitors in energy efficiency. In addition, the end-to-end delay was evaluated, and Sub-LEACH protocol proved to be the best among existing strategies. Moreover, an intrusion detection system (IDS) has been proposed for anomaly detection based on the support vector machine (SVM) approach for optimal feature selection. Results showed a 96.15% accuracy--again outperforming existing IDS models. Therefore, satisfactory results in terms of energy efficiency, end-to-end delay and anomaly detection analysis were attained.  
LEACH protocol; EESR protocol; neural networks; support vector machine; energy efficiency; end-to-end delay; intrusion detection system +
Footnote †: journal: Applied Soft Matter  
1  
MMDI 14 2021  
MMDI 14 3125
# Analysis of the Effect of Capillary Water Absorption on the Resistivity of Cementitious Materials
###### Abstract  
In a humid environment, the stray current generated by subway operation will corrode the underground pipe network and the internal steel structure of the track plate, which will seriously affect the service life of the subway track plate and increase the maintenance cost later. Groundwater in subway projects mainly enters the concrete interior through capillary action and affects the concrete resistivity, and there is a lack of research on the mechanism of groundwater effect on concrete resistance. In this study, the variation of cement mortar resistivity with capillary water absorption time for cement mortar with different amounts of mineral admixtures (fly ash, ground-granulated blast furnace slag, and silica fume) was measured by the four-electrode method, and the mechanism of the influence of the electrical properties of cementitious materials under the effect of capillary water absorption was analyzed based on the mercury-pressure method (MIP) and thermogravimetric method (TG-DTG). The results show that with the increase in capillary water absorption time, the change curve of cement mortar resistivity can be divided into two stages; in the first stage, capillary water absorption leads to gel pores and transition pores quickly connecting to capillary pores and other large pores to form a water-saturated conductive pathway, resulting in a rapid decrease in resistivity, when the gel pores and excessive pores have a greater impact on resistivity. The second stage is that of capillary water absorption, to a certain extent, after the specimen's internal water upward development rate slows down; at this time, the formation speed of the conductive pathway decreases, resulting in the rate of change in resistivity decreasing and gradually stabilizing. The incorporation of silica fume can effectively improve the resistivity of cementitious materials under the action of capillary water absorption, in which the resistivity of specimens incorporated with 15% silica fume after 36 days of capillary water absorption is 10.39 times that of the reference group, which is mainly due to its lower porosity and a higher percentage of gel pores.  
electro-chemical corrosion; capillary water absorption; resistivity; mineral admixtures; cement mortar; pore structure +
Footnote †: journal: Applied Sciences
## 3 Dataset and Feature Description
### Feature Description
#### 3.2.3 Gabor Filter  
In this work, we have also used the Gabor filter, a well-known frequency-based feature descriptor [48; 49]. It was a linear filter that was mainly applied for texture study. The Gabor filter mainly analyzes the presence of an explicit frequency content in a specific direction within a localized region around the point or region of interest in the image. It is also invariant to rotation, translation and scale. Besides, it is also robust towards any kind of photometric disorders, mainly occur as illumination changes and image noise [37]. In the spatial domain, two dimensional Gabor filter includes the sinusoidal plane modulated Gaussian kernel function [50]. Equations of the kernel function for calculating Gabor filter-based features in the spatial domain are given in Equations (2)-(4).  
\[Gabor(x,y)=\frac{f^{2}}{\pi\gamma\eta}\textit{exp}\bigg{(}-\frac{x^{\prime 2 }+\gamma^{2}y^{\prime 2}}{2\sigma^{2}}\bigg{)}\times\textit{exp}(i.\ (2\pi fx^{\prime}+\omega)) \tag{2}\]  
\[x^{\prime}=x\textit{cos}\theta+\textit{ysin}\theta \tag{3}\]  
\[y^{\prime}=-\textit{xsin}\theta+\textit{ycos}\theta \tag{4}\]  
Here, the standard deviation of the Gaussian envelope is expressed as \(\sigma\). \(\gamma\) is the spatial aspect ratio and the ellipticity of the support of the Gabor function. \(i\) denotes the imaginary number. Phase offset is specified as \(\omega\). \(f\) stands for sinusoid frequency and \(\theta\) symbolizes the alignment of normal to parallel stripes of the Gabor function. Eight different orientations and five distinct scales were taken in the Gabor model that results in 40 diverse Gabor filters.  
Multiple spatial resolutions and orientations were measured from the set of 2D Gabor filter bank. Then, these were used for convolution of each facial image sample. Let us consider a sample facial image, \(img(x,y)\) and the corresponding Gabor filter kernel is \(\Delta_{u,v}(x,y)\). The characterization of the output image, \(Out_{u,v}(x,y)\) is given in Equation (5) [39] as follows:  
\[Out_{u,v}(x,y)=img(x,y).\ \Delta_{u,v}(x,y) \tag{5}\]Finally, a down sample by a factor of 8 was performed on the obtained Gabor features. The size of the feature vector varies with the image dimension. In the present work, image dimensions which were taken into account are \(32\times 32,\ 48\times 48\) and \(64\times 64\). Table 1 describes the feature dimension corresponds to the previously mentioned image size.
## 3 The Proposed Framework for Anomalous Event Recognition
### Extraction of Deep Motion and Appearance Features  
Prior to extracting the deep motion and appearance features in Figure 1, we divide all training videos (both normal and anomalous, along with their motion flow maps) into a matching number of non-overlapping temporal segments. These video segments (\(N\) segments) and their respective motion flow segments (\(N\) segments) are classified as either positive (anomalous) or negative (normal) samples. In this work, we exploited the information from video-level annotations as follows. We group all segments from anomalous event videos as the positive samples group, \(G_{a}\), and all segments from normal event videos are grouped as the negative samples group, \(G_{n}\).  
After dividing them into groups, each video sample, along with its motion flow sample, is individually sent through the pre-trained 3D ResNet [21]. It extracts spatial-time-dependent features (from the video sample) and deep motion flow features (from the motion flow samples), such as relative motion information of objects and local variation of motion flow. Though 3D ResNet captures spatial-time-dependent features from visual data, we still separately extract deep motion flow using 3D ResNet. The reason for extracting deep motion flow is to extract and utilize the video's maximum motion information, like relative motion and local flow information, which can not be extracted only using 3D ResNet for visual data. Consequently, it further helps our framework, DMRMs, in learning context-dependency more accurately (shown in Section 4.4.2). Previous work [12] has required training three separate networks in order to extract all of these features (appearance, motion, and their correlation features). However, in our framework, DMRMs, we use only a single pre-trained network to extract them all. We use 3D ResNet-34, which was pre-trained on the Kinetics dataset [51], a vast and challenging action-recognition dataset. As demonstrated by authors [PERSON] et al. [52], pre-trained convolutional neural networks (CNNs) are considered effective in extracting features for the task of anomalous event detection. Additionally, [PERSON] et al. [21; 22] demonstrates that a 3D ResNet outperforms any other 3D CNNs in the action recognition task. Motivated by these studies [21; 22; 52], our work utilizes a pre-trained 3D ResNet for its efficacy as a spatial-time-dependent feature extractor for an anomalous event detection task. Its effectiveness is shown in Section 4.4.3.  
Moreover, we trained the 3D ResNet-34 on the UCF-Crime dataset [14] to recognize the type of anomalous events such as road accidents, robbery, etc. The results of activity recognition appear in Section 4.4.4. Simultaneously, Section 4.4.4 also shows the network's effectiveness in this task on the UCF-Crime dataset.
## 4 Result and Discussion
### Energy Consumption Data CEEMDAN Signal Decomposition  
In the signal decomposition section, this study applies the CEEMDAN method to decompose hourly energy consumption data collected from large public buildings. The signal-to-noise ratio in the CEEMDAN method is set to 0.2, the number of noise additions is set to 100, and the iteration count is set to 100. The energy consumption data are decomposed into 10 IMF components and one residual, as shown in Figure 8. The IMF components exhibit a continuous decrease in frequency from low to high, and Figure 9 displays the frequency spectrum of the decomposition results. IMF1's frequency band is mainly distributed between 0.1 and 0.3, IMF2 decreases to the range of 0.1 to 0.15, and the frequency bands of other IMFs sequentially decrease. From IMF5 to IMF10, their frequency bands primarily exist between 0 and 0.05. It is evident that the early IMF components exhibit high irregularity, highlighting the effectiveness of the CEEMDAN method in decomposition.  
Figure 8: CEEMDAN decomposition of energy consumption.  
The different frequency bands of IMF components typically imply varying correlations with features during the regression prediction process. The high-frequency components, IMF1 to IMF4, often exhibit strong correlations with input features of similar higher frequencies. On the other hand, IMF5 to IMF10, along with the residual component, usually demonstrate stronger correlations with long-term input features such as dates. Hence, it is necessary to perform separate regression feature importance screening for different IMF components.
## 10 Future Research Directions  
This paper gives valuable insights into corporate preparedness for deepfake-driven social engineering, but it has limitations. The small sample size, of cybersecurity professionals from five organizations, limits the _generalizability_ of our findings. Including more diverse industries, like finance or healthcare, could provide other perspectives. Also, our sample may show some bias, as most of the interviewees were from tech or consultancy organizations, where the awareness of digital threats is more commonly higher. Non-tech sectors may have different readiness levels and policies, which were not captured in these data. Despite these limitations, we think the analysis provides meaningful insights into corporate perspectives on deepfake threats. Future research should definitely broaden the scope of the sectors included.  
More extensive empirical work on the relationship between organizational policy and technological adoption is warranted. While this paper highlights the role of governance and incident response, future research could go deeper into how policies, both internal and broader regulatory policies, impact the selection, handling, and effectiveness of deepfake detection tools. As legal and ethical considerations around AI-generated media continue to evolve, organizations must navigate the increasing complexities in policy implementation, stakeholder communication, and data protection.  
Further investigations into human factors and company culture are also recommended. Understanding how employees interpret or trust suspicious content could help improve training programs focused on deepfake awareness. Performing case studies might deepen the understanding of how individual bias or social dynamics shape the response to potential AI-generated content. Integrating technical, organizational, and human perspectives would allow future research to more comprehensively address how best to defend companies from increasingly sophisticated deepfake threats.
# Improved Multi-GNSS PPP Software for Upgrading the DEMETRA Project Time Monitoring Service
## 2 Updated Atomium PPP Algorithms
### Atomium Constrained PPP  
The Atomium constrained PPP is based on a 2-step process. The first step is a classic PPP. The instantaneous frequency of the PPP clock solution \"receiver clock--IGS/MGEX time\" is then computed at each epoch as  
\[frq_{i}=\frac{clock_{i+1}-clock_{i}}{t_{i+1}-t_{i}}, \tag{1}\]  
where \(clock_{i}\) is the PPP clock solution at epoch \(t_{i}\). Then, from these instantaneous frequencies, we estimate the parameters \(y0\) and \(d\) of a linear model.  
\[frq_{i}=y0+d*t_{i} \tag{2}\]  
with \(y0\) the frequency offset and \(d\) the frequency drift. The constraint between the clock solutions at adjacent epochs can then be expressed as  
\[\begin{split}& delt_{i}=clock_{i+1}-clock_{i}\\ &=(y0+d*t_{i})*(t_{i+1}-t_{i}).\end{split} \tag{3}\]  
The estimation variance of the constraint can be predicted as  
\[{\sigma_{i}}^{2}=\ (ADev(1s))^{2}*(t_{i+1}-t_{i}), \tag{4}\]  
where \(ADev(1s)\) is the user input Allan Deviation of the receiver clock at 1 s, which can be found in the clock specification or can be estimated from the clock solution \(clock_{i}\). In this paper, we use the \(ADev(1s)\)provided in the specification document of the clocks, e.g., \(2\times 10^{-13}\) for a hydrogen maser, \(5\times 10^{-12}\) for a cesium clock. The weight of the clock constraint \(\mathit{delt}_{i}\) can be expressed as  
\[W_{i}=\ \frac{\sigma^{2}}{\sigma_{i}^{2}}, \tag{5}\]  
where \(\sigma^{2}\) is the a posteriori estimation variance on the clock solution at epoch \(t_{i}\) from the least square in the classic PPP.  
The second step of the approach is then to compute a new PPP, with the additional constraints on the clock solution inserted in the least square computation of PPP. This constraint model is built based on the assumption that the receiver clock is mainly affected by white frequency noise during the analysis period.  
In addition, constrained PPP offers the option for outlier recovery. If the recovery mode is on, it assumes that the clock solution is very noisy, the software will remove the frequency outliers before estimating the linear frequency model. With this refined clock constraint, the clock outliers will be pulled back to a normal noise level in PPP computation. If the recovery mode is off, it assumes there are some intentional changes causing a clock jump. In that case, the clock will not be constrained around those jumps, allowing the true clock jump to appear in the clock solution. This recovery mode is on by default, and if it is known there are some physical changes during the day of computation, from user information, it is then switched off.
## 4 Pairwise Clock Synchronization under Exponential Delays
### Confidence Interval for Clock Offset  
From a statistical point of view, a confidence interval (CI) is used to describe the amount of uncertainty associated with a sample estimate of a parameter. More specifically, if a CI is constructed among a series of experiments, the proportion of such an interval that contains the true value of the parameter is given by the confidence level. Mathematically, suppose \(X\) to be a random sample from a probability distribution with parameter \(\theta\), a CI is an interval with endpoints \(\{s(X),b(X)\}\), such that the following property holds:  
\[\text{Pr}(s(X)<\theta<b(X))=\gamma \tag{29}\]  
where \(\gamma\) stands for the confidence level.  
In terms of the confidence interval for clock offset under exponential delays, [PERSON] _et al._[35] derived satisfactory CIs using an approximate pivotal quantity (APQ). By assuming asymmetric exponential delays, the authors in [35] derived the relative likelihood function for \(\theta\), say \(\Lambda(\theta)\), based on the likelihood function from model Equation (4). It was then shown that \(\Lambda(\theta)\) is an asymptotic pivot in the sense that its limiting distribution does not depend on any unknown parameter. Thus, the asymptotic pivot property of \(\Lambda(\theta)\), as well as its easily invertible form lead to a way to obtain an asymptotic \(100(1-\alpha)\%\) CI for \(\theta\) as follows:  
\[\begin{split} s(U_{i},V_{i})=\max\left\{\frac{U_{(1)}-V_{(1)}}{2 }-\frac{\overline{U}-U_{(1)}}{2}\left(\frac{1}{\alpha^{\frac{1}{N}}}-1\right),-V_{(1)}\right\}\\ b(U_{i},V_{i})=\min\left\{\frac{U_{(1)}-V_{(1)}}{2}+\frac{ \overline{V}-V_{(1)}}{2}\left(\frac{1}{\alpha^{\frac{1}{N}}}-1\right),-U_{(1) }\right\}\end{split} \tag{30}\]  
A generalized pivotal quantity (GPQ) was also employed in [35] to propose a generalized confidence interval for \(\theta\). Even though generalized confidence intervals generally do not have frequentist coverage probabilities that are exactly equal to the nominal value, the proposed GPQ actually leads to frequentist coverage probabilities that are very close to the nominal value. Furthermore, APQ and GPQ CIs under exponential delays were also extended in [35] to any distribution within the one-parameter scale family.  
Further discussions about CIs of clock offset were carried out in [36; 37]. Specifically, under exponential network delays, [36] explored sequential intervals with a preset width and proved that these CIs provide a satisfactory solution to minimize the number of samples. In this way, sequential CIs can be used to obtain the smallest sample size required to achieve a specified precision for the clock offset estimation. On the other hand, by considering the correlation between the uplink and downlink random delays, [37] extended the CI procedure to bivariate models where the random delays are formulated as Freund and Marshall-Olkin bivariate exponential delays. In addition, the MLEs for clock offset under Freund and Marshall-Olkin models were also derived.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 2 Image Watermarking Background
### Embedding Phase
#### 2.1.2 Substitutive Method  
In this case, the mark to be inserted is substituted for components of the original image. There are several substitution methods, such as the substitution of the least significant bits (LSB) [37], which consists of replacing the least significant bits of the pixels of an image by the bits of the mark. Another technique widely used in image watermarking is quantization by index modulation (QIM), which is proposed in [38]. The basic idea behind this technique is to quantize the image using the quantizer that corresponds to the mark to be inserted [39]. Thus, each element of the mark is associated with a different quantifier.
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 3 RF-GPR Prediction Model of Cable Temperature
### Characteristic Variable Importance Score Calculation
#### 3.2.2 Characteristic Variable Importance Calculation Results  
In order to calculate the importance of each characteristic variable, the random forest model should be built first. The optimal parameters are set by the grid search method in the random forest model. The parameters include Max_features, Max_depth, Min_samples_split, Min_samples_leaf, Min_weight_fraction_leaf and Max_leaf_nodes [24]. The specific parameter settings of the random forest model are shown in Table 2.  
After setting the specific parameters of the random forest, the random forest algorithm is used for model training, and the importance score of the 10-dimensional input feature variables is obtained through the generated prediction model. The importance degree of each feature variable in the model training is shown in Table 3.
## 3 Results and Interpretations
### Dynamic Stress-Strain Behavior  
Figure 3a-c show the dynamic stress-strain curves of the limestone specimen under varying temperatures and impact velocities \(V\) of 6-7, 7-8, and 8-10 m/s. This curve can be classified into three stages: the elastic stage, the plastic stage, and the fracture stage. However, as the temperature increases, the curve does not show an obvious compaction stage, which is different from the dynamic compression characteristics of limestone after high temperature. The abovementioned is in good agreement with the existing literature. For instance, [PERSON] et al. reported that the limestone under high temperatures (>300 \({}^{\circ}\)C) appears to have an obvious compaction stage, and it is mainly caused by the closure of existing micro-cracks in the limestone specimen [49], and these micro-cracks are induced by the thermal impact of high temperature and cooling. However, under real-time high-temperature conditions, no thermal damage occurs in the specimen, and thermal expansion occurs in the internal mineral crystals; thus, there are no obvious compaction characteristics. With the increasing temperature, the slope of the stress-strain curve in the elastic stage decreases significantly, and that in the plastic stage increases, while that in the fracture stage with cliff drop features slows down. This indicates that the specimen undergoes a transition from brittle failure to brittle-ductile failure as the temperature increases. Additionally, the peak stress initially rises and then declines in response to an initial increase in real-time temperature. When the temperature is between 25 \({}^{\circ}\)C and 200 \({}^{\circ}\)C, with the increase in temperature, the thermal stress causes the expansion of mineral particles inside the limestone, and the volume increases, resulting in the closure of the original cracks inside the sample, and the integrity of the sample is enhanced. In addition, the evaporation of water within the sample leads to increased friction between mineral particles, resulting in increased peak strength of the limestone. However, when the temperature exceeds 200 \({}^{\circ}\)C, the increase in thermal stress leads to the initiation and gradual expansion of cracks in the sample, the internal damage of the sample is intensified, and the strength of the limestone is gradually reduced.  
Figure 3d-e show the influence of the impact velocity on the dynamic stress-strain curve of the limestone specimen at different real-time temperatures T of 25, 400, and 800 \({}^{\circ}\)C. With the increase in the impact velocity, the slope of this curve increases during the elastic stage and plastic stage. Therefore, an increase in the impact velocity enhances the deformation resistance of the specimen in the elastic stage and increases the plastic deformation. The average strain rate can be calculated by averaging the entire process of impact loading in this study.