esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Study on the Phase Sensitivity Variation in Low Frequency Primary Microphone Calibrations
## 3 Independent Microphone Simulation
### Results  
#### 3.2.1 Results in Frequency Domain  
The phase difference between the associated sound pressure and the diaphragm deformation is shown in Figure 9. Due to the smaller chamber, the pressure leakage and heat conduction effects of the microphone are more severe than for the pistonphone. In other words, the phase lead will be larger than that of the pistonphone and will happen at higher frequencies. Therefore, the frequency is set from 0.1 Hz to 1000 Hz and 1/3 octave for the simulation. Note that the constant 90\({}^{\circ}\) phase compensation has already been done.  
Similar to the trend of the pistonphone, in the ideal sealing and adiabatic conditions the sound pressure is synchronized with the diaphragm excitation. In actual cases, the effect of pressure leakage acting on the sound pressure is more obvious than the heat conduction effect, and will cause the sound pressure to be 90\({}^{\circ}\) ahead of the diaphragm deformation at low frequencies. Meanwhile, the heat conduction effect acts only at middle frequencies.
## 6 Results and Discussion
### Analysis of Alive Nodes  
The study of the BEA-SSA model concerning alive nodes is shown in Figure 6. The improvement of BEA-SSA over GWO [26], MOFPL [22], PRO, SSA [36], BES [37], ROA, HGS, SSO, RCSO [21], and FCR [20] was established. Here, assessment is carried out for 100 and 200 nodes. The alive node count has to be higher for superior performance. Here, the count of an alive node is lowered with a rise in the count of rounds. In Figure 6a, the alive nodes for BEA-SSA at the 0 th round is 100, while at the 2000 th round the alive node count for BEA-SSA is 40. Likewise, in Figure 6b, the alive nodes for BEA-SSA at the 0 th round is 200, while at the 2000 th round the alive node count for BEA-SSA is 65. Thus, for both 100 and 200 nodes, the alive nodes are high, up to 700 rounds; after that, the count of alive nodes starts lessening with the rise in rounds.
# Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration
## 3 The Architecture of Software-Defined Vehicular Cloud
### The Components of SDVC
#### 3.1.1 Multi-Access Edge Cloud in SDVC  
The MEC computing micro-data center includes the control layer and the forwarding layer. The control layer is abstracted in the computing hardware, which includes the host operating system, a hypervisor, and other software entities based on VMs, namely active VM controller(s), SDN communication infrastructure controller(s), and SDN OpenFlow controller. The active VM controller controls the next replication and migration of VMs. The SDN communication infrastructure controller has the role to select the wireless access communication radio to connect the central cloud or MEC network resources during VM migration and resume procedures. The SDN OpenFlow Controller is hereafter considered as the main SDN controller. This SDN OpenFlow controller controls the global  
Figure 5: Software-defined vehicular cloud network architecture.  
management of the SDVC network as well as the active VM and SDN communication infrastructure controllers. SDN OpenFlow controller enforces the management of the SDVC, as shown in Figure 6.  
The forwarding layer includes forwarding devices such as an OpenFlow Wi-Fi access point and switch, local gateway, and local server service enablers. The VC supports V2X communications over C-V2X and Dedicated Short Range Communication (DSRC). The control plane decisions are not entirely made by a single SDN controller centralized element; adjacent SDN controllers in the MEC cloud could collaborate to make decisions. In fact, this collaborative decision is provided through mobility management, and Quality of Service (QoS) support provided at the SDN OpenFlow controller. The SDN OpenFlow controller should achieve a fast recovery after failures between adjacent controllers in the SDVC. A logically distributed control plane assisted by mobility management achieves optimum performance and robustness against failures [36].
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas  
In total, we identify 114 key publications. Out of these, 52 papers address damage assessment and 21, 40, and seven articles focus on recovery, vulnerability, and resilience assessments, respectively. Papers with a focus on two DRM areas, e.g., both damage and recovery assessments, are counted on both of them separately. A chronological overview of the publications shows an increasing trend in studies for RS-based proxy literature for urban DRM (Figure 1). Continuous progress in RS technology and sensors, which supplies ever more diverse and detailed image data to a growing number of communities, is one of the reasons behind this increase. Furthermore, an understanding that DRM assessments have to go beyond capturing the physical impact, calls for a need for indirect measurements, which also contributed to this increase.  
Figure 2 shows the number of RS-based proxies that have been developed for each component of DRM. The built-up environment is the most frequently studied one with 35 RS-based proxies developed. Natural environment accounts for the lowest number of developed RS-based proxies, offering a choice of 13. Furthermore, the Economic and Social environments both have 21 developed RS-based proxies. Some proxies that are used interchangeably in more than one area, e.g., damage and recovery assessments, are counted on all of the used categories separately.
# Ontology with Deep Learning for Forest Image Classification
## 6 Proposed Model  
In this section, the proposed image classification model and its components are explicitly described. As presented in Figure 5, the image classification approach is made up of 3 components, i.e., (1) feature extraction, (2) domain ontology construction, and (3) forest-type image classification. In the proposed model, an ensemble stack of ResNet50, Xception, and VGG16 is used to generate a feature vector (top of Figure 5) required to perform the image classification process. Image categories from the dataset are set to be used for building the ontology, which forms the basis of establishing semantic relationships between concepts of the forest domain (bottom right of Figure 5). A linear SVM multi-classifier is selected to classify images into their respective categories (bottom left of Figure 5).
# Collaborative Caching for Implementing a Location-Privacy Aware LBS on a MANET
## 6 Performance Evaluation
### Effect of Obsolescence  
Data obsolescence in cache-based systems significantly impacts both performance and privacy protection [11,29]. We analyze how expiration time affects query distribution between LBSs and MANET nodes.  
Our analysis of Figure 6 reveals several critical patterns:  
1. _LBS Query Reduction:_ As the expiration time increases, LBS queries decrease across all systems: * Baseline: 2.97 to \(1.74\times 10^{4}\); * M-LBSv1: 2.09 to \(1.04\times 10^{4}\); * M-LBSv2: 2.09 to \(1.11\times 10^{4}\). This pattern aligns with cache efficiency studies in distributed environments [11], demonstrating that longer data validity periods effectively reduce external query requirements.
2. _MANET Response Evolution:_ Local query resolution improves with longer expiration times:  
Figure 5: Privacy loss for different k-anonymity values across three approaches.  
* Baseline: 5.15 to 5.29 \(\times\) 10\({}^{4}\);
* M-LBSv1: 6.78 to 7.23 \(\times\) 10\({}^{4}\);
* M-LBSv2: 6.65 to 6.98 \(\times\) 10\({}^{4}\).  
This trend matches the expectations of collaborative caching research [13], showing improved local resolution capacity over time.
* _Collaborative Advantage:_ Both M-LBS variants consistently outperform the baseline to reduce LBS queries, the performance gap widening at longer expiration times. This demonstrates the robustness of our collaborative approach to data aging, which is a critical factor in mobile environments [14].
* _Privacy Implications:_ The reduced frequency of LBS queries with longer expiration times suggests enhanced privacy protection through decreased exposure to centralized servers. However, this benefit must be balanced against the requirements for data freshness, as noted in recent LBSs research preserving privacy [7].  
The relationship between expiration time and query distribution demonstrates that careful cache management can significantly enhance privacy protection while maintaining service quality. Our results suggest that adaptive expiration policies, potentially based on data type and usage patterns [29], could further optimize this trade-off between data freshness and privacy protection.
# Structural Augmentation in Seismic Data for Fault Prediction
###### Abstract  
Fault interpretation tasks become more and more difficult as the complexity of seismic exploration increases, especially for ultra-deep seismic data. Recently, numerous researchers have utilized automatic interpretation techniques based on deep learning to improve the efficiency and accuracy of fault prediction. Although deep learning methods have powerful data information processing capabilities, the applicability of deep neural networks may still be limited by the range of learned information. Therefore, we develop a new technique called structural data augmentation to enhance the diversity of the datasets. Concretely, we utilize different geological structure theories to incorporate virtual folds and faults in the field seismic data to improve the diversity and generalization ability of the training datasets. To cope with the multi-stage and multi-scale complex structures developed in ultra-deep strata, the proposed augmentation workflow increases data diversity by generating various virtual structures containing multi-scale folds, listic faults, oblique-slip displacement fields, and multi-directional fault drags. Tests on the field seismic data show that our method not only outperforms conventional seismic attributes but also has advantages over other machine learning methods.  
fault detection; deep learning; training sets; data augmentation; geological structure +
Footnote †: journal: Applied
## 2 Materials and Methods
### Correction Factor Accounting for Soil Reflectance  
The NIRv/FAPAR model, proposed by [PERSON] et al. [28] (Equation (2)), is currently a popular method for downscaling SIF in the near-infrared band. However, this method is limited by the fact that it does not explicitly consider the impact of soil reflectance, which can result in errors in \(\mathrm{f_{esc}}\) estimation, particularly for sparse vegetation. Therefore, a correction factor is necessary to optimize the estimation of \(\mathrm{f_{esc}}\) in [PERSON] et al. [28] to reduce  
Figure 3: Photographs of canopies at (**a**) XTS in December (winter wheat), (**b**) NBF (sweet potato), and (**c**) SYS (gold coin grass). Only one representative photo is shown at each site here.  
the impact of soil reflectance. The contribution of soil background to \(\mathrm{f_{esc}}\) estimation is not only related to its reflectance but also to vegetation coverage, which can be represented by the vegetation index. Hence, the correction factor can be expressed as a function of soil reflectance in the near-infrared band and vegetation index--_f_ (_Ref\({}_{soil}\), VI_). In this study, four vegetation indices (NDVI, simple ratio (SR), two-band enhanced vegetation index without the blue band (EVI2), and perpendicular vegetation index (PVI)) were selected to characterize vegetation structure (Table 4). The correction factor was added to the widely used NIRv/FAPAR model by multiplying and adding functions, resulting in two improved models:  
\[\mathrm{f_{esc}}=\frac{\mathrm{NIRv}}{\mathrm{FAPAR}}\times f_{1}(\mathit{ Ref}_{soil},\mathit{VI}) \tag{6}\]  
\[\mathrm{f_{esc}}=\frac{\mathrm{NIRv}}{\mathrm{FAPAR}}+f_{2}(\mathit{Ref}_{soil}, \mathit{VI}) \tag{7}\]  
where \(f_{1}\) (_Ref\({}_{soil}\), VI_) and \(f_{2}\) (_Ref\({}_{soil}\), VI_) are the two correction factors for multiplicative correction and additive correction, respectively.  
To improve the robustness of \(\mathrm{f_{esc}}\) estimation, a machine learning method was employed to estimate the correction factor \(f\) (_Ref\({}_{soil}\), VI_). A number of machine learning algorithms were tested for model training, including decision tree, random forest, support vector machine, and Gaussian process regression (GPR), and it was found that the exponential Gaussian process regression (GPR) was the best training model in this study (Figure S3), which can quantitatively determine the prediction uncertainty in a systematic manner. The model was trained using the 5-fold cross-validation method. The detailed GPR model parameters can be found in Table S1. According to Equations (6) and (7), the input parameters of the two machine learning models were _Ref\({}_{soil}\)_ and _VI_, and the outputs were \(\mathrm{f_{esc}}/\frac{\mathrm{NIRv}}{\mathrm{FAPAR}}\) and \(\mathrm{f_{esc}}-\frac{\mathrm{NIRv}}{\mathrm{FAPAR}}\), respectively. The Gaussian regression algorithm was used to train two different correction factors, \(f_{1}\) (_Ref\({}_{soil}\), VI_) and \(f_{2}\) (_Ref\({}_{soil}\), VI_), in the models. All input and output parameters can be obtained from the SCOPE simulations.
# A Dense Neural Network Approach for Detecting Clone ID Attacks on the RPL Protocol of the IoT
## 4 Proposed Framework
### Data Pre-Processing
* _Scaling_: Numerical features were standardized to guarantee equal weights during the learning process [50]. Specifically, standard scaling was used on each numerical feature, \(x\in X\), to center its mean, \(\mu=0\), and scale it with respect to the standard deviation \(\sigma\), as shown in Equation (1). \[x_{standardized}=\frac{x-\mu}{\sigma}.\] (1)  
```
Result: Processed data set Input :\(X,A,N,L,C,U\) while\(i<=m\)do Data set balancing: \(n=\) no. of samples in \(N\) \(k=round\left(\frac{n}{|X|}\right)\) \(A\to A^{\prime}=o_{1},o_{2},...,o_{k}\), where \(o_{i}\) for each\(o_{i}\)do \(D_{i}=\{o_{i},N\}\), \(i=1,2,...,k\) end Value transformation: \(L^{\prime}\leftarrow\textbf{LabelEncoder}(L)\) \(C^{\prime}\leftarrow\textbf{OneHotEncoder}(C)\) Scaling:  Return the final data sets \(D\leftarrow\textbf{StandardScaler}(C^{\prime}+U^{\prime})\) end
```  
**Algorithm 1**Preprocessing tasks over each data set. To interpret the steps involved in the processing algorithm, the following definitions are denoted:  
* \(x_{i=1}^{m}\in X\) is the set of original samples, where \(x\) is a sample and \(X\) is the data set;
* \(A\subset X,N\subset X;\forall|A|<|N|\) are the subsets belonging to minority and majority classes, respectively;
* \(L=\{\textit{ipv6\_src,ipv6\_dst}\}\) represents the set of categorical features to encode to discrete values;
* \(C=L\cup\{\textit{icmpv6\_code,vpan\_dst\_addr\_mode,vpan\_fcf,\textit{ olovpan\_pattern}}\}\) represents the set of categorical features to transform to real values;
* \(U=\{\textit{frame\_time\_delta,frame\_time\_epoch,frame\_time\_relative,frame\_cap\_len,frame\_.len,frame\_.number,wpan\_fcs,vpan\_frame\_length,wpan\_seq\_no,ipv6\_plen,icmpv6\_checksum\}\) represents the set of numerical features;
* \(k\) is the number of minority samples \(A\) contained in \(X\); and
* \(\{\textit{cloneid\_20n,cloneid\_50n,cloneid\_100n}\in D\text{ is the final balanced set, resulting from Label Encoding, OHE, and random selection and sampling without replacement. In Table 7, the data sets after the pre-processing steps are detailed. It is worth mentioning that, for the _cloneid_50_ and _cloneid_100_ data sets, the number of features increased as a result of the encoding steps. While more nodes were transformed, the sequences of IP addresses encoded by OHE required more characterization.
## 4 Results and Discussion  
### Experimental Results of the Individual Components  
#### 4.1.1 Experimental Results of CTR  
Initially, tensile strength tests were conducted at a reference temperature of 20 \({}^{\circ}\)C. All test specimens failed at the 200 mm free testing length. On average, the CTR-EP-Sand exhibited nearly 1.3 times higher tensile strength values compared to CTR-P, cf. Figure 8a. Therefore, this analysis highlights the impact of impregnation material and geometry on tensile strength. Additionally, as acknowledged in [1], the composite within the fiber strand consisting of impregnation and carbon fiber, as well as manufacturing quality, are decisive factors for strength reduction.  
For the data analysis, the reduction factor \(\mathrm{K_{T}}\) at each investigated temperature level was determined relative to the tensile strength at a testing temperature of 20 \({}^{\circ}\)C, as depicted in Figure 8b. As expected from literature, the results of the fiber strand tensile tests indicated a decrease in tensile strength up to 20% with increasing test temperature [1,19]. In comparison to the reference experiments at 20 \({}^{\circ}\)C, the materials CTR-EP-Sand and CTR-P, heated with EH, decreased in average tensile strength by up to 10%. The polystyrene-impregnated material CTR-P with AH showed a decrease of up to 23%, cf. Figure 8b. Exceeding the glass transition temperature \(\mathrm{T_{g}}\) of polystyrene of approx. \(\mathrm{T_{g}}=\) 100 \({}^{\circ}\)C [35,36] can result in a rapid stiffness decrease, leading to softening of the coating and impregnation material and, thus, reduced tensile strength [1]. It should be noted that softening occurs within a temperature range; hence, the process can initiate as early as 80 \({}^{\circ}\)C. The decrease in the strength of CTR-P (AH) can thus be attributed to softening of the impregnation. Furthermore, due to the testing methodology, the energy input was prolonged with AH, allowing the softening and thus the chemical reaction to persist for an extended duration prior to testing. To ensure the core temperature, the test specimens were tempered for 20 min. In contrast, the heating of CTR-P with EH was notably accelerated, as the current flow directly adjusts the core fiber strand temperature. Within 3 min, the surface temperature could be reached, verified using the thermal imaging camera.  
Figure 7: Test setup for the tensile strength tests on rectangular CTRC specimens: (**a**) schematic drawing; (**b**) overview of EH test setup; (**c**) overview of AH test setup.  
In comparison with the CTR, the strain of CTR-EP-Sand was 1.2 times higher than CTR-P at the reference temperature of 20 \({}^{\circ}\)C, cf. Figure 9. As the testing temperature increased, no significant deviations from the reference results were observed for the examined materials. However, comparing AH and EH, an increase in mean ultimate strain of up to 13% was examined for EH. The authors assume that direct heat generation in the filament caused it to expand more.
# Underwater Vehicle Path Planning Based on Bidirectional Path and Cached Random Tree Star Algorithm
## 3 Bidirectional Cached Rapidly Exploring Random Tree Star
### Algorithm  
The preceding sections delineate the procedural schema of the refined algorithm. The ensuing script depicts the pseudocode for devising a trajectory for a subaqueous vehicle predicated on the BCRRT* algorithm (Algorithm 1):  
```
\(\text{T}\leftarrow\text{initializeTree()}\) for k = 1 to k do  If \(p<p_{goal}\) \(X_{rand}=X_{goal}\) else if \(p_{goal}<p<p_{goal+}p_{path}\) \(X_{rand}=X_{path,i}\) else \(X_{rand}=X_{free}\) \(X_{near}\leftarrow\text{Nearest\_Neighbor(}X_{rand,T}\text{)}\); \(X_{new}\leftarrow\text{Steer(}X_{rand},X_{near}\text{)}\) \(\text{u}\leftarrow\text{Select\_Input(}X_{rand},X_{near}\text{)}\); \(X_{new}\leftarrow\text{New\_State(}X_{near},u,\Delta\text{)}\); if obstacle free(\(X_{new}\)) then return \(X_{new}\); \(X_{near\_neighbor}\gets find\_near\_neighbor(\text{T},X_{new},\text{r})\); if obstacle free(\(X_{new}\), T, r) then \(\text{T}\leftarrow\text{Choose\_parent(}X_{new},X_{near\_neighbor},\text{T}\text{)}\); for each \(X_{near\_neighbor}\) calculate(dist(\(X_{new}\), \(X_{near\_neighbor}\)) + cost (\(X_{near\_neighbor}\), \(X_{init}\))) \(X_{new\_parent}\gets X_{near\_neighbor}\), mincost;  return \(X_{new\_parent}\); T \(\leftarrow\) rewire(T, \(X_{new}\), \(X_{near\_neighbor}\)); for each \(X_{near\_neighbor}\) calculate(dist(\(X_{near\_neighbor}\), \(X_{new}\)) + cost(\(X_{new}\), \(X_{init}\))) \(X_{new}\) mincost \(\leftarrow\) min(dist(\(X_{near\_neighbor}\), \(X_{new}\)) + cost(\(X_{new}\), \(X_{init}\))); \(X_{near\_neighbor}\), newparent \(\gets X_{new\_mincost}\); return \(X_{near\_neighbor}\), newparent;  end
```  
**Algorithm 1**Bidirectional Path Planning and Cache RRT* Algorithm
# Residual Level of Chlorpyrifos in Lettuces Grown on Chlorpyrifos-Treated Soils
## 1 Introduction
Similarly, results of empirical correlations between pollutants and plants and leaves have also been reported [25; 26; 27; 28]. A recent report exhibited that the amount of chlorpyrifos absorbed by cucumber roots ranged from 1.0 to 1.3% of the spiked concentration in soil, and that it was not transferred to the fruits [11]. Some experiments have analyzed the levels of pesticides absorbed from soil contaminated with boscalid, chlorfenapyr, fluquinonazole, and tetraconazole. These studies showed that the levels of these pesticides in Korean cabbage or onion were less than 2.5% in comparison to the spiked concentration in the soil [29; 30]. Even if the translocated pesticides in plants depends on the degree of the compounds in the soil, other parameters affecting the absorption of pesticides are the physicochemical properties of the pesticide, the type of plant, and the soil type that sustains the plant [31; 32; 33]. On the other hand, the Korean government agency has required the performing of an efficacy test and a phytotoxicity test on the crops in order to assess the effect of pesticides. However, the results on residual levels of pesticides on succeeding crops have not yet been requested. Therefore, if residual pesticides are transferred into the crop after use, it is likely to remain above 0.01 ppm. There have been no studies on the absorption or translocation of chlorpyrifos from soil contaminated with chlorpyrifos to lettuce, even though [PERSON] et al. [11] reported similar studies using the same insecticide on cucumbers. In our study, we attempted to compare the uptake rates of chlorpyrifos from chlorpyrifos-contaminated soils to lettuce, which possessed a short-growth cycle as well as significant potential to uptake soil pesticide residues due to the proximity between edible leaf parts and roots, which could be significantly influenced by soil contamination. In this paper, we reported the decline in residue in the soil after treatment with chlorpyrifos at different application rates, as well as the level of chlorpyrifos in lettuce grown in the treated soil to ensure the safety of lettuce products.