esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Residual Dense Convolutional Graph-U-Net-Based GAN for Target Speech Enhancement
### Architecture of Residual Dense-U-Net with Embedded GCRNN  
In prior research works involving graph convolutional blocks embedded in U-Net's bottleneck layer [27; 28; 29], residual dense blocks were not explicitly applied in encoder and decoder parts of the architecture. In this article, a residual dense-U-Net architecture with a GCRNN embedded within the bottleneck layer (i.e., the GCRNN acts as the core embedding layer) is used as the neural framework of choice, and processes the spectro-spatial features to predict the target source-specific T-F mask. In addition, the skip connections also include GCRNN blocks. As mentioned earlier, neural architecture is applied in a GAN setting. In the generator, the embedded GCRNN's nodes are set equal to the number of channels that are also referred to as kernels of the last RDN layer in the encoder part of the U-Net architecture. The generator and discriminator network's operations are based on mini-max principles.  
The encoder is composed of a stack of residual, densely connected convolutional blocks, whereas the decoder is composed of a stack of residual, densely connected deconvolutional blocks. Each densely connected block is a stack of four convolutional layers, with dense connections between layers. The output of each densely connected block in the encoder is passed through a GCRNN block-based skip pathway and then concatenated with the features of the corresponding residual densely connected de-convolutional block in the decoder.  
A residual dense block (RDB) is a set of neural architecture, which can not only obtain the state from the preceding RDB via a contiguous memory (CM) mechanism but can also fully utilize all of the layers via local dense connections [41; 42; 43]. It contains densely connected layers, local feature fusion (LFF) and local residual learning together to lead to the CM mechanism. While residual learning modules, as in Res-Net architecture, can lead to better generalization capability of the model, they lack the dense concatenation of previous convolutional outputs to successive feature maps. Due to the presence of dense connectivity, a dense convolutional network, such as DenseNet [44], is able to utilize information from all previous convolutional operations, which allows the gradients to flow through several paths and thus enables richer information to be combined for feature extraction in consecutive layers. While the combination of residual and dense modules has been proposed in contemporary research works, their application in the domain of target speech enhancement for sources in motion seems limited. In the following subsections, the operation of the residual dense Graph-U-Net in a GAN setting is explained.
# Impacts of Lightning-Induced Overvoltage on a Hybrid Solar PV-Battery Energy Storage System
## 5 Simulation Results and Discussion
### Effects of Different Cable Lengths  
Lastly, in this part, the effects of lightning-induced overvoltage with lightning current amplitude I0 = 19 kA at d = 50 m on different cable lengths (5, 10, and 20 m) are discussed accordingly. The flexible tinned copper cable was designed according to standard IEC 60228 class 5, as shown in Table 3[72].  
It can be observed from Table 9 and Figure 17 that the lightning-induced overvoltage was the same for all three cable lengths (5, 10, and 20 m). This was because the cable lengths were considered short and the voltage drops for all three cable lengths were too small. Figure 18 also presents the lightning-induced overvoltage profile, whereby the lightning overvoltage was induced at an observation point of 10 m and the induced overvoltage was measured every 2 m thereafter across a total cable length of 20 m. This resulted in the same induced overvoltage values. However, if the cable lengths were longer than 20 m, the impacts of lightning-induced overvoltage could have been noticed, since the cable resistance would be large enough for the high-voltage drop along the line [24; 73].  
Figure 16: Lightning-induced overvoltage peak versus lightning current peak value (x = 0, d = 50 m, v = 1.2 \(\times\) 10\({}^{-8}\) m/s).  
Figure 17: Lightning-induced overvoltage versus cable length (5, 10, and 20 m; I0 = 19 kA, x = 0, d = 50 m, v = 1.2 \(\times\) 10\({}^{-8}\) m/s).
## 4 Results
### Statistical Analysis
#### 4.1.1 Theory and Calculation  
To check the study's hypotheses, a statistical analysis was developed from the data collected to identify correlations or associations between different metrics of mobile usage and users' ages (e.g., comparing the number of characters deleted by each user, the frequency of typing, the amount of time spent using the mobile phone, etc.). These metrics were also linked to the physical activity performed at each moment.  
A non-parametric statistical test was performed as the sample was small and did not follow a normal distribution. Due to the nature of the data as continuous quantitative variables, [PERSON]'s correlation coefficient [27] was applied in order to measure the ordinal association between two measured quantities. [PERSON]'s correlation coefficient ranges from \(-1\) to \(+1\), indicating in both cases the degree of association between variables but in opposite directions. According to the suggestions given in [28], the interpretation of the magnitude of [PERSON]'s correlation coefficient is shown in Table 1. It represents the relationship between X and Y in absolute values but applies to any pair of variables.  
The first step was to make certain assumptions about the parameters by establishing two opposite hypotheses: a null hypothesis and an alternative hypothesis. Then, depending on the coefficient obtained, a decision was made to accept or reject the hypothesis. This measure of inferential statistics made it possible to draw conclusions or general patterns for the whole population from the study of the sample and the degree of significance of the results obtained.
## 4 A Fusion Link Prediction Method Based on Limit Theorem  
### The Algorithm  
The limit theorem of combination method shows that when selecting transformation function as \(l(\mathbf{x})=f(\mathbf{x})/g(\mathbf{x})\) or its monotone increasing transformation, the AUC and Precision of synthetic score reaches the maximum. In the real network, because \(f(\mathbf{x})\) and \(g(\mathbf{x})\) are unknown, the pdfs need to be estimated from multidimensional data. Let the estimated pdfs be \(\hat{f}(\mathbf{x})\) and \(\hat{g}(\mathbf{x})\). On the basis of limit theorem, we define the transformation function as the ratio of estimated pdfs, i.e.,  
\[\hat{l}(\mathbf{x})=\hat{f}(\mathbf{x})/\hat{g}(\mathbf{x}) \tag{14}\]  
then we obtained the synthetic score, \(s=\hat{l}(\mathbf{x})\), and used for link prediction. This method is called theoretical limit fusion (TLF) method.  
Before evaluating \(f(\mathbf{x})\) and \(g(\mathbf{x})\), the input link prediction scores need to be normalized,  
\[s_{k}^{*}(i,j)=\frac{0.5N^{2}.s_{k}(i,j)}{\sum_{i=1}^{N}\sum_{j=1}^{N}s_{k}(i,j)}, \ k=1,\ 2,\ \cdots,\ d \tag{15}\]  
\(s_{k}(i,j)\) represents the \(k\)-th similarity score for node pair \(i,j\). \(N\) is the dimension of adjacent matrix, and \(d\) is the number of similarity indices.  
The limit theorem of combination method transformed the link prediction indices fusion problem into the estimation of pdfs. Statistical methods for estimating density functions can be applied to this problem, directly. The Parzen kernel method [16] of destiny estimation is used in this paper. The multivariate kernel density estimate defined as:  
\[\hat{f}(\mathbf{x})=\frac{1}{n_{s}h^{d}}\sum_{i=1}^{n_{s}}K\bigg{[}\frac{1}{h}(\bm {x}-\mathbf{x}_{i})\bigg{]} \tag{16}\]  
where \(h\) is the window width, \(n_{s}\) is the sample size, and \(K(\mathbf{x})\) is a multivariate kernel defined for \(d\)-dimensional \(\mathbf{x}\), such that  
\[\int_{\mathbb{R}^{d}}K(\mathbf{x})d\mathbf{x}=1 \tag{17}\]  
A form of the pdf estimate commonly used is Gauss kernel,  
\[K(\mathbf{x})=\frac{1}{(2\pi)^{d/2}}\exp\bigg{(}\frac{-\mathbf{x}^{T}\mathbf{x}}{2}\bigg{)} \tag{18}\]  
In summary, the steps of TLF are listed as Table 1.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 6 Simulation Evaluation
### Queue Length and Queue Dynamics  
In Figures 7 and 8, we show the relationship between time-averaged queue length and weights \(V\). It can be noted that the maximum storage buffer and battery capacity increases linearly with the value of \(V\), which is derived in Theorems 2 and 3. As expected, the time-averaged length of \(E_{i}(t)\) and \(\mathcal{D}_{i}(t)\) keeps linearly increasing with \(V\), which matches with the theoretical analysis in Equations (25) and (A4).  
Figure 4: System utility comparison under different \(V\).  
Figure 5: System utility comparison under different \(K\).  
Figure 6: System utility comparison under different \(\phi_{\text{max}}\).  
Figure 7: Time-averaged data queue length versus \(V\).  
In order to demonstrate the stability of the RSN under the DTEM scheme, we evaluate the dynamic processes for \(\mathcal{D}_{i}(t)\) over 1440 time slots with different \(V\). As shown in Figure 9, \(\mathcal{D}_{i}(t)\) rises sharply at the initial stage, then decreases slowly and fluctuates around a certain value. The reason lies in that no data is stored in the queue, which allows satellites to collect spatial data as much as possible. Subsequently, the collected data is transmitted to relay satellites through ISL. During the operation of RSN, our proposed online DTEM algorithm controls data acquisition and transmission in a dynamic balance state. In addition, a larger weight \(V\) brings a longer data queue. This implies that a larger \(V\) requires a larger storage buffer to guarantee network stability.  
Figure 10 presents the dynamic processes for \(E_{i}(t)\) over 1440 time slots with different \(V\). We can observe that \(E_{i}(t)\) reaches a large value at \(t=0\). With the operation of the system, the length of the energy queue decreases and quickly converges to a relatively stable state. This is because the energy queue length is initialized to the size of maximum battery capacity according to Equation (26). In addition, the battery management model of DTEM is effective for energy consumption and harvesting, which makes the energy queue achieve a dynamic balance. Although satellites harvest solar energy as much as possible when they move to the sunny side, there is only energy consumption when satellites are eclipsed by the earth. As a result, \(E_{i}(t)\) cannot always be reached the full battery capacity.  
Figure 8: Time-averaged energy queue length versus \(V\).  
Figure 9: The dynamics of data queue with different \(V\).  
Similar to the reasons shown in Figure 9, a larger \(V\) requires the RSN to carry a larger battery capacity to ensure the sustainability of the network.
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
## 3 Results
### Pre-Processing
#### 3.2.1 RGB Segmentation Accuracy  
Experiment one's segmentation results yielded an F1-score of 0.976, with an accuracy of 0.971. Experiment two resulted in a lower F1-score of 0.842, with an accuracy of 0.781. Lastly, experiment three's F1-score and accuracy were 0.833 and 0.932, respectively. Solar illumination could contribute to the lowered results presented in the in-situ measurements.
## 3 Indoor AR Navigation Framework Based on Geofencing and Image-Tracking
### Overview of the Elemental Sessions in the Framework  
Figure 1 presents an overview of the four elemental sessions of the proposed indoor AR navigation framework, which are described as follows:  
* **(A) Session of initialization of the current position and direction**: When starting the AR navigation, the position and orientation of the smartphone are initialized using image-tracking of the location reference markers (A1) with a database of wide-area location reference markers (A4) that have been prepared in advance. This process uses the image-tracking functions of the ARWorldTrackingConfiguration Class in the ARKit library provided by Apple Inc. (Cupertino, CA, USA) [34] to implement (A2), (A3), and (A5).
* **(B) Session of indoor AR navigation**: After initializing the position and orientation of the smartphone, sequential updates of the current position and orientation are realized by VIO, which is based on the data fusion of motion sensors and spatial tracking derived from a scene analysis of camera images (B2), as explained in [34]. This process also uses the ARWorldTrackingConfiguration Class in the ARKit library [35]. Next, the current position and movement trajectory of the user is displayed on a two-dimensional (2D) map, and a three-dimensional (3D) arrow sign pointing in thedirection of the next destination is displayed with a real-time image from the camera for AR (B1). At waypoints and points of interest (POIs) in the service area, LBS, such as voice narration and updates regarding the next destination, are realized by geofencing (B3) (B4).
* **(C) Session of setting the destination and route**: Destinations are inputted from the smartphone screen (C1), and a route (C2), which is a list of waypoints along the way from the current position to the destination, is generated from the database of the POIs and routes (C3).
* **(D) Session for correcting accumulated positioning errors**: In indoor AR navigation, the longer the distance moved after the initialization of the smartphone's position and orientation, the larger the position error accumulates. To reduce the accumulated error before it becomes too large, correction is periodically performed using location reference markers placed in the service area.
###### Abstract  
To investigate the influencing factors of the shear failure behavior of rock joints, especially the shear instability characteristics, direct shear tests were performed on marble joints with various grain sizes under different constant normal loads (\(CNL\)s). The experimental results show that the grain size and \(CNL\) have significant effects on the shear mechanical properties of rock joints. The peak shear strength (\(\tau_{p}\)), peak shear displacement (\(u_{p}\)), post-peak modulus (\(S\)), and stress drop (\(\Delta\tau\)) of rock joints all increase first and then decrease with the increase in grain size, but they increase with the increase in CNL. The mineral composition and microstructure also have a certain influence on the shear mechanical properties of rock joints. In addition, the post-peak soften modulus (\(S_{p}\)) was proposed to describe the shear instability characteristics of rock joints, and its relationship with grain size and \(CNL\) was established. The mechanical model of the shear instability of rock joints was established, and the shear instability criterion of rock joints was proposed based on the stiffness criterion and the proposed post-peak soften modulus (\(S_{p}\)). This paper further reveals the shear instability mechanism of rock joints, which can provide a reference for the stability analysis of jointed rockmass.  
rock joint; shear instability; \(IRC\); mineral composition; shear instability criterion Article
## 3 Methodology for Dynamic Risk Evaluation and Early Warning of Crest Cracking
### Bayesian Estimation of Probability Distribution for \(\lambda\)  
The duration \(T_{AS}\) of abnormal state before crest cracking is uncertain as observed for the studied dam and is assumed to be an exponentially distributed random variable in this study. Since the parameter \(\lambda\) (which is the expected value of \(T_{AS}\)) in the PDF of \(T_{AS}\) is epistemic uncertain, the Bayesian approach described in Section 3.3 is applied to estimate the probability distribution for \(\lambda\) given the observed values of \(T_{AS}\). The values of \(T_{AS}\) corresponding to the observed crest cracking events are provided in Table 1. Given the prior distribution of \(\lambda\) without any observed information and the successively observed values of \(T_{AS}\), the probability distribution for \(\lambda\) is progressively updated by recursively applying Equations (5) and (6). The prior distribution of \(\lambda\) without any observed information is presumed to be uniform distribution and is formulated with Equation (8) where \(\lambda_{\min}\) and \(\lambda_{\max}\) are equal to 0 d and 300 d respectively. The updated distributions of \(\lambda\) are shown in Figure 12 and the corresponding statistical characteristics of \(\lambda\) are listed in Table 5.  
Figure 11: The time series of crest settlement inclination around dam axis.  
Figure 12: The progressively updated distributions of \(\lambda\) given the observed values of \(T_{AS}\).  
It is observed in Figure 12 that the posterior distributions of \(\lambda\) after the first, second, third and fourth crest cracking are of apparent heavy-tail features for limited observed information. Along with the accumulation of information related to the duration of abnormal state before crest cracking, the dispersion of the updated distributions of \(\lambda\) gradually decreases, which reflects that the cognition of \(\lambda\) is increasingly clear. After seven updating, the dispersion of distribution of \(\lambda\) has been greatly reduced, and the values of \(\lambda\) mainly cluster in the range between 70 d and 250 d. It can be seen in Table 5 that the standard deviation of \(\lambda\) declines from 72 d to 41.4 d with increasing number of observations on \(T_{AS}\). Therefore, the observed information and evidence can significantly diminish the uncertainty in \(\lambda\), which is consistent with the principle of statistics. In addition, the mean value of \(\lambda\) decreases with accumulation of the observed data on \(T_{AS}\), as shown in Table 5.
## 5 Evaluation
### Vehicle Privacy-Preserving Evaluation  
Each experimental scenario underwent five trials with different 5000 vehicles and the average value is taken as the result to eliminate randomness.  
Figure 7 illustrates the average success rate of attackers under different pseudonym-changing schemes as the vehicle's arrival interval varies.  
From Figure 7, the following observations can be made: First, the MZ scheme exhibits the highest attacking success rate, highlighting the vulnerability of vehicles to pseudonym-linking attacks. Then, vehicle privacy can be adequately maintained with an arrival interval \(\lambda=2\), even without additional fake traces. However, as vehicle density decreases, the success rate of attackers gradually increases, reaching nearly 100%. Thus, incorporating fake traces becomes crucial in low-vehicle-density scenarios to enhance privacy protection. Thirdly, CPCROK demonstrates a significant decrease in the adversary's success rate, reducing it by over 90% across varying vehicle density scenarios compared to the mix-zone scheme. Additionally, CPCROK performs better compared to the other two fake beacon schemes, by 60% in low vehicle density and over 50% in normal vehicle density. Lastly, and most importantly, when comparing the FB and AFB schemes, despite the latter generating one additional fake trajectory, there is hardly any improvement in the success rate. This is because the fake trajectories generated by traditional methods do not behave like real vehicles, causing the extra fake trajectory to be identified by attack algorithms as inconsistent with real vehicle patterns. As a result, this trajectory is not matched with the real vehicle.  
Simulations were conducted to assess attacks on vehicles as they traverse multiple junctions. Figure 8 shows the success rates for passing through one to four mix zones, considering different vehicle density scenarios with \(\lambda\) values of 2, 6, and 10. The results indicate that the attacker's success rate decreases exponentially with each additional mix zone. This is due to the increased frequency of pseudonym changes as vehicles pass through multiple mix zones, making it challenging for attackers to analyze the beacons.  
Figure 6: Visualization of fake beacon trajectories generated in CPCROK across various types of junctions. (**a**) T-junction 1, (**b**) T-junction 2, (**c**) crossroad 1, (**d**) crossroad 2, (**e**) multi-way.  
Additionally, while the FB and AFB schemes offer some privacy protection compared to the MZ scheme, they require vehicles to pass through more mix zones. In contrast, the attacking success rate when CPCROK is implemented drops to be lower than 10% with only a few pseudonym changes, even in scenarios with limited mixing opportunities.
# Analysis of Volatiles in Food Products
## 4 Sensory Characterization  
### Olfactory Study  
It is well-known that food staples usually contain several hundreds of molecules responsible for their aroma. However, \(\sim\)20-30 of these compounds are important to generate its aroma. Therefore, to perform an important analysis of aroma, it is important to recognize these compounds with a significant impact on the aroma. In this case, gas chromatography and olfactory (GC-O) are the best selection [115]. The GC-O technique uses the human nose as a detector that elutes the aroma from the chromatography column. Further, when an odor is sensed, the time and characteristic aroma quality, and from time to time the intensity are written down. There are different methods to evaluate the relative odor potential of the individual aroma-active molecule, as aroma extraction dilution assay (AEDA), CharmAnalysis, Osme, and some others [116; 117; 118]. Interestingly, [PERSON] et al. [119] combine AEDA with the SPME technique and achieved satisfactory results that allowed to determine the aroma with sensory impact in Japanese soy sauce. Afterward, detection and the quantification of the odor-active molecules by analysis of GC-O, the odor activity value (OAV) (ratio between the concentration of one compound in the food and its threshold concentration) could be determined. Commonly, if one of the compounds shows an OAV value higher than 1, then this molecule might have an aroma impact on the overall odor of a food product. The presented procedure of analysis is called the molecular sensory concept. According to this procedure, [PERSON] and [PERSON] [28] pointed out the off-flavor formation with a significant amount of dimethyl sulphide and 2-methoxy-4-vinylphenol in stored orange juice. Moreover, using this methodology allowed to differentiate the quality of market-available food products [96]. To proper identification of aroma, deuterated standards and/or molecules with isotopic labelled carbon could be used [120].  
Mostly, results realized during the methodology for GC-O are associated with sensorial analysis. The recombination or omission analysis is the last part of the molecular sensory concept, during that test the aroma overall is judged by a professional sensory panel. This methodology allowed [PERSON] and [PERSON] [93] to differentiate three hazelnut cultivars, whereas [PERSON] and [PERSON] [121] established the basic composition of volatiles in edible insects, and compared it with their sensorial properties, and their possible acceptance by consumers. The chemical and sensory connection was also helpful in the elevation of new products [122]. In these cases, sensory panelists are the main \"analytical tool\", who need to be specially trained.