esa-sceva/satcom-chunk-collection huggingface dataset:
# A Location Privacy Preservation Method Based on Dummy Locations in Internet of Vehicles
## 6 Performance Evaluation and Discussion
### Computation Overhead  
Figure 6 shows the impact of privacy parameter \(k\) on computation overhead in terms of execution time. From Figure 6, we observe that the computation overhead of proposed RR-DLS method is concentrated on the vehicle user side, and the execution time increases rapidly along with the increase of privacy parameter \(k\). The computation overhead at RSU and the LBS server side is small. The execution time of RSU is independent of privacy parameter \(k\), and the execution time of the LBS server increases linearly along with the increase of privacy parameter \(k\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Entity** & **Computation Overhead** & **Communication Overhead** \\ \hline Vehicle user & \(O(k^{2}+I|U)\) & \(O(k)\) \\ \hline RSU & \(O(1)\) & \(O(I|U+kn+k)\) \\ \hline LBS Server & \(O(kn)\) & \(O(I|U+kn)\) \\ \hline \end{tabular}
\end{table}
Table 1: Performance of proposed RR-DLS method.  
Figure 5: The simulation area and the corresponding service request probability distribution.
# A Study of Landfast Ice with Sentinel-1 Repeat-Pass Interferometry over the Baltic Sea
## 2 Fundamentals of InSAR Theory
Other variables are defined in Equations (3)-(5). Terms of Equation (6) are further explained here. The flat earth phase (\(\Phi_{flat-earth}\)) is the phase contribution due to the Earth curvature. After interferogram flattening, the interferometric phase includes both altitude and displacement contributions. Topography phase (\(\frac{4\pi B_{T}}{\lambda Rsin(\theta)}\times\Delta h\)) is the topographic or altitude contribution to the interferometric phase that is used to determine elevation. If an accurate DEM is available, \(\Phi\) topography can be computed and subtracted from the interferometric  
Figure 1: The interferometric phase difference \(\Delta\varphi\) is proportional to distance difference \(\Delta R\) divided by the transmitted wavelength \(\lambda\). The B is the baseline and \(\theta\) is the incidence angle. phase. This term is less relevant in this study. The displacement phase (\(\frac{4\pi}{\lambda}\Delta\eta\)) is the surface deformation or displacement contribution which we want to measure in this study. The fourth source is atmospheric phase (\(\Phi_{path,\text{ }Atm}\)). Atmospheric condition changes between acquisitions. The radiation travel path for each of SAR images is affected differently by the atmosphere. These effects are produced by the atmospheric humidity, temperature and pressure change between the two acquisitions. It is hypothesized that a stable scatterer is present in each resolution cell but, in reality, it is different and many scatterers are present in each resolution cell that may change between acquisitions. Three main contributions to the phase noise (\(\Phi_{noise}\)) are temporal change of the scatterers, phase noise due to different look angle and phase noise due to volume scattering. The last term of Equation (6) reflects the fact that the calculated phase is wrapped (has interferometric fringes) as the InSAR phase (or its difference) varies between 0 and 2\(\pi\). The correct integer multiple of 2\(\pi\) to the InSAR phase is added during phase unwrapping [9; 20]. Single-pass and repeat-pass interferometry are two major types of the InSAR methodology [20]. Note that the factor \(\Delta\eta\) disappears in single-pass InSAR systems, so displacement measurement is impossible by these systems while both topography and small displacements measurements are possible with a repeat-pass system. The sensitivity of InSAR technique to the surface topography depends on satellite baseline in a repeat-pass system while the sensitivity to surface movements does not. Single-pass systems are useful for creating a topographic map while repeat-pass systems with a small baseline are preferable for making displacement maps [11; 20].
###### Abstract
[PERSON] and [PERSON] [10] proposed that helical piles behave similarly to [PERSON] and [PERSON] [12], who recommended a shallow failure mechanism H/D value less than five and a deep failure mechanism afterwards. Additionally, [PERSON] [5] theorized that the transition zone occurs at a shallower embedment depth in fine-grained soils than it does in coarse-grained soils. Several investigations on the lateral behavior of helical piles have been conducted before, including theoretical and experimental studies [13; 14; 15], field studies [16; 17; 18], and numerical studies [19]. [PERSON] et al. [20] established a theoretical model to predict the anchor-soil system's lateral load deformation when the lateral load and moments are applied to the helical anchor piles. The model is verified by comparing it to field study results. They included the modified Matlock and Reese [21] model into their theoretical model to get lateral deflections at ultimate lateral loads calculated from test data in sands and clays. The lateral capacity of helical screw anchor piles is found to be entirely controlled by the extension shaft at depths larger than three to five times the crucial relative stiffness parameters, R or T. The laboratory test results on helical piles shows that the lateral capacity of the helical piles increased 1.2 to 1.4 times that of conventional straight shaftted piles [22]. They also devised an analytical model to determine the lateral load capacity of short stiff piles buried in clay by combining various resistive forces operating on the pile and using a static force equilibrium. The findings of the experiment conducted by [PERSON] [16] revealed that increasing the shaft diameter of the helical piles improved their lateral resistance, also increasing the top section length and their lateral resistance. In some situations, the structure supported by helical anchors may be subjected to lateral loads in addition to axial loads due to wind, waves, and currents, etc. Therefore, it is necessary to know the behavior of a helical pile under combined axial and lateral loads. But scarce number of literatures have been found to study the behavior of helical piles under combined loads for the piles embedded in cohesionless or sandy soil [19]. In addition, the conventional straight shaftted pile behaviors are well established under combined loading conditions. Somehow the behavior of the conventional pile will change contrarily with the helical piles. Thus, a detailed investigation is necessary to avail the importance of the combined loading behavior of helical piles in cohesive soils. This paper elucidates the detailed behavioral investigation of a mono helical pile embedded in soft clay under combined uplift and lateral loading using laboratory as well as three-dimensional finite element analysis using Plaxis 3D software.
# A Disruption Tolerant Distributed Routing Algorithm in LEO Satellite Networks
## 5 Performance Analysis
### Simulation Result
#### 5.2.1 Performance Comparison under Different Link Failure Probabilities  
In this part, we fixed the average load of the entire network link to 24% and set the link failure probability from 0.3% to 8% for simulation. Figure 7 shows the simulation results. In terms of packet loss rate, DTDR is always better than other algorithms, while OPSPF has the worst performance. This is because the route convergence in OPSPF is slow, and the response to short-term link failure is slow, so it is easy to generate the wrong paths which leads to routing loops, so packet loss occurs. When the link failure probability is low, the packet loss rate of the DRA algorithm is lower than that of the DODR algorithm, and when the link failure rate is high, its packet loss rate is higher than that of the DODR algorithm. It is more efficient to find a reliable backup path for DRA, but when the network environment is poor, a real-time path discovery strategy is required to deal with link failures. In terms oflatency, DODR is the best, DTDR and OPSPF are about the same, and DRA is the worst. The high delay of OPSPF is mainly caused by routing loops, while the high delay of DRA is due to the lack of advance awareness of failed links in the routing strategy, resulting in unnecessary duplicate paths and congestion. The delay of DTDR is also slightly higher than that of DODR when the link failure rate is high. The main reason is that the link failure information of DTDR is only transmitted within a limited number of hops, so unnecessary detours may occur at the boundary of the route convergence area. This phenomenon is inevitable. DTDR has formulated a loop avoidance strategy to reduce the possibility of packet loss at the boundary of the convergence area to a detour. In general, although the delay of DTDR is slightly higher than that of DODR when the link failure rate is high, DTDR is still optimal compared to its obvious advantage in packet loss rate.
## 3 Proposed Dynamic Fuzzy Logic for Broadcasting
### Adaptive Profile Selection  
As large-scale changes occur in the network (e.g., transitioning from sparse to dense topologies, or encountering severe energy depletion), simply adjusting fuzzy ranges (as in our previous work) can become insufficient. Hence, at each adaptation interval \(\Delta t\), we compute a performance measure. Because the choice of profile \(\mathcal{P}_{j}\) can drastically alter the fuzzy logic decisions, our scheme continuously monitors network-wide metrics (e.g., node density, Residual Energy distribution, average queuing delay) to determine whether the current profile remains suitable. Specifically, at each adaptation interval \(\Delta t\), the system computes a performance measure  
\[M(\mathcal{P}_{j})=f\big{(}\text{coverage, redundancy, delay, energy},\ldots\big{)},\]  
and chooses  
\[\mathcal{P}^{*}\;=\;\arg\max_{j\in\{1,\ldots,k\}}\;M(\mathcal{P}_{j}). \tag{3}\]  
This ensures that, for example, when the network becomes highly congested, a profile emphasizing fewer rebroadcasts and tighter (\(NN\)) thresholds might be selected, whereas under energy-scarce conditions, a profile that factors in \((E_{\text{res}})\) may be favored to prolong the network lifetime.
# Modeling GPR Wave Propagation in Complex Underground Structures Using Conformal ADI-FDTD Algorithm
## 1 Introduction
A field experiment proved the correctness and effectiveness of the proposed algorithm in actual detection. The numerical simulation results and field experiment indicate that the conformal ADI-FDTD method requires less computation time and can greatly reduce the pseudowave effect compared with the traditional FDTD.
## 3 Results and Discussion
### Total Phenolic and Flavonoid Content  
In our study, the highest value of the total phenolic content was obtained by the ethyl acetate extract (37.47 \(\pm\) 1.73 mg GAE/g extract). Significant differences between the amount of total phenolics in this extract and the other tested extracts were observed. The lowest value was calculated at the aqueous extract (18.19 \(\pm\) 0.17 mg GAE/g extract) (Table 1).  
Quantification of total flavonoid content showed significant differences between the aqueous and the organic extracts. The lowest value was calculated at the aqueous extract (4.27 \(\pm\) 0.42 mg QE/g extract) while the methanol extract showed the highest flavonoid content (14.76 \(\pm\) 0.21 mg QE/g extract) (Table 1).  
Results of the percentage yield and the quantification of flavonoid and phenolic compounds, indicated that the solvent used, is a critical parameter to consider, for the estimation of TFC and TPC. Ethyl acetate, methanol and water are among the solvents used for the extraction of secondary metabolites and especially for polyphenic compounds [33; 34]. However, the different capacity of each individual compound presented in an extract to dissolve in a solvent as well as the polarity of the extraction solvent and the extraction technique, influence the results. Thus, differences between the total phenolic and flavonoid content are expected.
# Adversarial Validation in Image Classification Datasets by Means of Cumulative Spectral Gradient
###### Abstract
To accomplish this, it is necessary to create and train a binary classifier and relate its performance as a function of the separability of the classes. If the classes are easily separable, the training and test data have different characteristics, thus presenting a distribution shift. If, on the other hand, the performance of the classifier is very low, the data have similar behavior or similar characteristics that do not guarantee the presence of distribution shift. When reviewing the applications of this approach, it is found that, although its application for structured data is usual, its application for other types of data, such as image classification, is not common. Moreover, the application of this solution involves the definition, creation and training of a classification model. Again, the question is whether it is possible to determine the distribution shift without the need to create and train a model. Therefore, the aim of this article is to answer this question. Accordingly, this paper presents a proposal to determine the degree of shift in the distribution of two datasets, which can be applied without the need to train multiple models and multiple times, reducing the ecological footprint givens its low computational cost, and as a complement in the analysis process of generalization of classification models. To this end, a metric for evaluating complexity in datasets is used, which can be applied in multi-class problems, comparing each pair of classes of the two sets. The contributions of this work are summarized below:  
* We propose a methodology using class-level adversarial validation to determine the degree of distribution shift in two image datasets through the evaluation of their complexity, allowing us to assess those modifications that have a strong impact on the generalization of the models. * The proposed methodology was tested on three well-known datasets: MNIST, CIFAR-10, and CIFAR-100, along with corrupted versions of the same, proving that this methodology can be applied without the need to train models. * It is proposed to use the Cumulative Spectral Gradient (CSG) metric as the basis for class-level adversarial validation to compare data from the same class in the face of possible changes in their distribution.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## 3 Results and Discussion
### The Yaw Angle Differences and the Duration of Eclipse
#### 3.2.4 BDS Satellites  
According to the empirical threshold for the attitude switch, as shown in Figure 9, C10 starts switching from yaw steering mode to the orbit normal mode at about 20:18:30 (7 July 2017). When the sun angle is very close to 4\({}^{\circ}\), C10 does not switch its attitude mode immediately until its yaw angle approaches to 5\({}^{\circ}\). At this time, the sun angle is about \(-3.6^{\circ}\). It is obvious that the yaw angle differences between these two attitude modes can be up to 180\({}^{\circ}\). Due to the slow variation of the sun angle, the duration of the orbit normal mode can last for several days. Because of the few BDS MEO satellites and the large satellite PCO values, the non-nominal yaw attitude will have a great impact on the positioning of the BDS-only PPP.
# Short-Text Semantic Similarity (STSS): Techniques, Challenges and Future Perspectives
## 4 What Are the Existing Deep Learning Techniques Used in Short-Text Semantic Similarity (STSS)?
### BERT Model for Contextual Word Embeddings (CWE)
For experimentation, the authors used a five-class conversational corpus. The model achieved a 91.5% score in precision, recall, and F1 measures. Many studies used the BERT pre-trained model. The model uses a vast amount of unlabeled data for the general domain. However, multiple types of domains can be utilized for pre-trained models. The more diverse benchmark creates higher complexity for MI algorithms. Thus, for every new specific domain, pre-trained versions are worth utilizing [71; 72; 73; 74]. [PERSON] et al. [75] proposed the SEMBERT model to understand the contextual semantics. The model further used reading comprehension and language inference tasks. The GLUE benchmark was utilized for experimentation. The model showed an 83.6% score in the F1 measure and presented an accuracy of 91.42%. Moreover, the BERT model used input embeddings; these embeddings are the combinations of three other embeddings, such as:  
Token Embeddings: These embeddings use word-piece vocabulary to split the sentences into words. Sentence Embeddings: The BERT model uses next-sentence prediction (NSP), which determines the next sentence and predicts if the words belong to sentence A or sentence B [76]. Position Embeddings: These embeddings encode and identify the position of words inside the given sentence. In the base form, the BERT model includes 12 layers, which are often known as transformer encoders. These encoders are further composed of two layers, the multi-head layer and the self-attention layer, which divide the sentence to learn contextual embeddings. These embeddings create subwords to arrange the sentence in a more retained form. However, the model uses 12 attention heads and presents 110 M trainable parameters to understand the embeddings.
## 2 Proposed Method
### Model-Agnostic Meta-Learning (MAML) Framework  
Building on the foundational concepts of meta-learning, the model-agnostic meta-learning (MAML) algorithm sets out to optimize the initialization parameters within the outer layer of the model. These parameters are crafted to be sufficiently broad-ranging, enabling the model to rapidly achieve an optimal performance on new tasks with limited data, following a minimal number of gradient updates. This process is pivotal from the standpoint of representational learning, as MAML seeks out internal representations that are universally applicable and easily transferable across a variety of tasks. The MAML algorithm undertakes the explicit training of parameters, representing the model with a function \(f_{\theta}\), contingent upon the parameter \(\theta\). The architecture of the model is delineated into two iterative loops: the inner loop focuses on specific task adaptation, and the outer loop targets generalization across tasks, with the parameter \(\theta\) serving as the shared nucleus between them. Within the inner loop, the loss function for the subtask is calculated, precipitating an update of the parameters tailored to the new task. Post gradient descent, the model's parameter \(\theta\) is updated to \(\theta^{\prime}_{i}\), as delineated by the equation:  
\[\theta^{\prime}_{i}=\theta-\alpha\
abla_{\theta}\mathcal{L}_{T}(f_{\theta}) \tag{3}\]  
where \(\alpha\) signifies the inner learning rate.  
The outer loop encapsulates the meta-optimization process. It reassesses the inner loop's optimized parameters \(\theta^{\prime}_{i}\) against the novel task. This step facilitates the computation and subsequent update of the initial parameter \(\theta\)'s gradient:  
\[\theta\leftarrow\theta-\beta\
abla_{\theta}\sum_{\tau\sim p(T)}\mathcal{L}_{ \tau}(f_{\theta^{\prime}}) \tag{4}\]Here, \(\beta\) denotes the learning rate for the outer loop. The meta-learning model's optimal parameters, tailored to the task distribution \(p(T)\), emerge from the alternating optimization of the inner and outer loops. The meta-optimization's overarching goal is to minimize the loss function across tasks:  
\[\min_{\theta}\sum_{\tau\sim p(T)}\mathcal{L}_{\tau}(f_{\theta})=\sum_{\tau\sim p (T)}\mathcal{L}_{\tau}(f_{\theta-\alpha\
abla_{\theta}\mathcal{L}_{T}(f_{ \theta})}) \tag{5}\]  
This iterative process, oscillating between the inner loop's task-specific focus and the outer loop's generalization-centric perspective, ensures the adaptability of the learned parameters to new, related challenges, embodying the quintessence of meta-learning: enabling a model to learn from the process of learning.  
The MAML framework epitomizes the adaptability and flexibility required in rapidly evolving learning environments, demonstrating the profound potential of meta-learning algorithms to revolutionize the landscape of machine learning.
## 3 Results
### Representative Spectral Reflectance  
As Figure 2 displays, the canopy spectral reflectance of WOR had obvious differences at different growth stages, the canopy spectral reflectance was mainly affected by leaf chlorophyll and growth status [48]. At seeding, bolting, and pod stages, the canopy reflectance presented the spectral curves of typical green vegetation. At the flower stage, the canopy reflectance was greatly affected by flower, the reflectance increased significantly at visible wavelengths (500-730 nm), while it decreased at NIR wavelengths (800-1300 nm). Generally, the canopy spectral reflectance sensibly responded to the wide variation of WOR growth status and gradually got higher in the visible region and lower in the NIR region with the process of the growth period.
## 4 Application of NDT in Identification of Local Damage
### Parameter Calibration in Laboratory Testing  
The experiment in the laboratory was designed according to the field conditions. An experimental model, made of concrete (C60), was set up to represent a concrete segment, as shown in Figure 6. The size of this model was 100 cm \(\times\) 100 cm \(\times\) 80 cm. Some holes were prefabricated in this model through PVC pipes to represent grouting holes, and the pipes were demolded after molding. The holes were numbered as No. 1, No. 2, and No. 3. To simulate the different conditions of grouting holes, we maintained a clean and dry condition for the No. 1 hole, to represent an empty status as would be found in the field, and injected some water and grout into No. 2 and No. 3, respectively. Then, the holes were covered with a fire-proof plate to simulate the field conditions. The parameters of the GPR and infrared camera are shown in Tables 1 and 2.  
The GPR was placed on the top of every hole to invert conditions in the holes, as displayed in Figure 7. Based on our designed experiment, three different patterns of electromagnetic waves in the grouting holes were obtained, as shown in Figure 8. The inversion results show that the electromagnetic wave was distorted due to effects of the filling medium in the holes. The wave band tends to bulge upward, and it decreases as the detection depth increases. In addition, the difference in the filling medium has a significant influence on the strength and range of detected signals. There was an obvious abnormal signal response in the empty hole, exceeding 50 cm, which was greater than that of the other mediums. The boundary of the hole bottom was clear in the empty hole. In contrast, there was a minimal abnormal signal response in the grout-filled hole, with a value less than 25 cm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sensitivity (\(\mu\varepsilon\))** & **Detection Depth (m)** & **Electric Voltage (V)** & **Size (cm)** & **Frequency (MHz)** \\ \hline
50 & 0.6 & 200 & \(30\times 12\times 11\) & 900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the ground penetrating radar (GPR).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Value** \\ \hline Equipment type & Inf-Rec R500 \\ Precision (\({}^{\circ}\)C) & \(\pm 1\) \\ Visible pixel & 5,000,000 \\ Resolution ratio & 1280 \(\times\) 960 \\ Measurement range (\({}^{\circ}\)C) & \(-\)40\(-\)2000 \\ Infrared image element & 1,200,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the thermal infrared camera.  
Figure 6: Schematic of the physical model for the test.  
The detected signals were gentle, and it was difficult to identify the hole boundary. The characteristics of the detected signals in the water hole were different to the other two mediums. This wave band bulged upward at first and then exhibited a gentle trend. The boundary of hole bottom was also not clear. The three wave phase patterns calibrated by the laboratory experiment provided an essential reference for geological field inversion.