esa-sceva/satcom-chunk-collection huggingface dataset:
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN
### Computational Resource Management Based on RRH Clustering Techniques
#### 3.2.2 Load-aware RRH Clustering
proposed a deep learning-based framework in C-RAN optimization to maximize the capacity utilization and minimize the deployment cost to achieve statistical multiplexing gain. The framework is two-phased: (1) dynamic RRH profiling phase consisting of RRH traffic forecasting and RRH complementarity measurement and (2) complementary RRH clustering phase consisting of weighted-graph-based RRH modeling and distance-constrained RRH clustering. In the first phase, a multivariate long short-term memory (MuLSTM) is utilized for traffic pattern forecasting of RRHs in a future period based on historical traffic data. Furthermore, the complementarity of RRHs is measured based on the traffic snapshot forecast. In the second phase, a weighted graph model is constructed to represent the relationship of RRHs. In addition, a distance-constrained complementarity-aware (DCCA) algorithm is proposed to cluster RRHs iteratively such that the complementarity among RRHs is increased within each cluster and reduced across different clusters. In [56], [PERSON] et al. considered the selection of active RRUs and the allocation of multiple computational resources in BBUs. The objective was to minimize the number of active BBUs required to serve all the users in the network. They formulated the multi-resource allocation problem as a multi-dimensional bin packing BP problem by first considering that the virtual machines (VMs) in BBUs have diverse resource requirements, and then considering each BBU as a bin and each VM as an item. In this problem, the number and sizes of items can be adjusted for packing by varying the associations between the RRU and UEs. The size of the BBU indicates its resource capacities. They proposed an iterative resource allocating (IRA) algorithm to solve this problem. It assigns a VM to the BBU with the required resource allocation by following the largest-dot-product-first strategy. This strategy prevents over allocation of computational resources and achieves a higher packing efficiency than the first-fit strategy. However, while assigning VMs to BBUs, the VM's requirements of all the resource types need to be considered.
## 3 Results  
### Captures  
From November 1984 to 30 September 2020, 926 individual tiger sharks (73.1% \(n\) = 677 females, 26.9% \(n\) = 249 male) for which accurate size, sex, and tag information was available were sampled near Bimini. All size classes were represented in captures for both sexes (overall mean FL = 172.8 cm \(\pm\) 61.4). Females ranged from 67 to 334 cm FL (mean = 177.6 \(\pm\) 60.8) and males from 55 to 319 cm FL (mean = 160.1 \(\pm\) 61.3). Small juveniles (\(<\)180 cm FL) were the predominant life stage, representing 57.0% (\(n\) = 528) of the tiger sharks caught, and 18.1% (\(n\) = 168) of the tiger sharks were less than 110 cm FL, considered to be YOY [36]. Catches of all tiger shark age classes were distributed throughout the year, but there was an increase in the frequency of YOY individuals during the summer and early fall months (Figure 2). From 2017 to 2019, ultrasounds were conducted for nine mature females, resulting in seven confirmed pregnant, one recently postpartum (within a few weeks post pupping; personal communication Dr. [PERSON] DVM Disney's Animals, Science, and Environment), and one not pregnant. Ultrasound of all mature females were not possible due to technical issues with the unit.  
Figure 2: Relationship between day of year and fork length for tiger sharks, _Galeocardo curier_, captured near Bimini, The Bahamas. Red circles represent female sharks, and blue triangles represent male sharks. The horizontal black line is the designation of fork length for ’young-of-year’ size class.
## 4 Simulation Results
The sliding mode controller, with its robustness against uncertainties and its ability to handle nonlinearities, proves to be a viable solution for maintaining the desired trajectory, even with the presence of model uncertainties and external disturbances, providing a balance between performance and computational efficiency. It is important to note that our control law in Equation (12) is formulated as a function of the vehicle parameters, which underlines its robustness in the face of parameter variations. In the context of truck trailer systems, it is a valid assumption to consider the parameters as fixed, given the prevalent local regulations and standards that govern real articulated vehicles. This further enhances the practical applicability and reliability of our proposed control approach.
## 3 Results and Discussion
To further understand the classification ability of the SERS spectra, receiver operating characteristic (ROC) curves were built using the \"pROC\" package [66] to generate ROC curves using the probability estimates for classifying spectra to a particular class. A ROC curve plots the sensitivity (true positive rate) versus the specificity (true negative rate). The points on the curve represent different thresholds for determining the class assignment of a spectrum and allows the user to understand the tradeoff between sensitivity and specificity for all possible thresholds, rather than just the one that was used in the ANN models. Using this method, it was possible to assess the performance of the model using a variety of thresholds. The results of the ROC analysis can be seen in Figure 4. The AUC value indicates the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Binary Model**} \\ \hline  & Sensitivity (\(\pm\)STD) & Specificity (\(\pm\)STD) & Accuracy (\(\pm\)STD) \\ AD vs. HC & 98.00 (0.447) & 93.40 (1.497) & 96.47 (0.688) \\ \hline  & \multicolumn{3}{c}{**Tertiary Model–AD**} \\ \hline  & Sensitivity (\(\pm\)STD) & Specificity (\(\pm\)STD) & Accuracy (\(\pm\)STD) \\ Mild AD & 88.80 (0.047) & 94.50 (0.025) & 93.13 (0.010) \\ Moderate AD & 90.80 (0.025) & 96.80 (0.007) & 94.79 (0.010) \\ HC & 97.20 (0.010) & 96.30 (0.009) & 96.60 (0.006) \\ \hline  & \multicolumn{3}{c}{**Tertiary Model–OD**} \\ \hline  & Sensitivity (\(\pm\)STD) & Specificity (\(\pm\)STD) & Accuracy (\(\pm\)STD) \\ AD & 96.40 (0.015) & 98.21 (0.003) & 97.46 (0.006) \\ HC & 95.00 (0.007) & 98.79 (0.007) & 98.00 (0.006) \\ OD & 100.00 (0.000) & 99.13 (0.002) & 99.46 (0.001) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Final network results of binary and tertiary ANN models for differentiating between classes of blood serum samples. probability of correct classification. These results further support the strong classification ability of the SERS spectra to correctly determine to which class a donor belongs for diagnostic purposes.
# Adaptive Beamforming Damage Imaging of Lamb Wave Based on CNN
## 4 Discussion and Analysis of Results
### Experimental Results and Analysis
Through the analysis of both simulation and experimental imaging results, it is evident that the proposed CNN-based adaptive beamforming method effectively improves Lamb wave damage imaging. Compared to the DAS imaging method, the CNN method produces images with a narrower main lobe and lower side lobe levels, thereby enhancing imaging resolution and significantly reducing background noise. This demonstrates the effectiveness of the CNN method in achieving higher-quality damage imaging. The proposed framework not only improves the SNR and computational efficiency but also exhibits strong potential for real-time SHM applications, owing to its rapid weight prediction capability. However, certain limitations warrant further investigation. Specifically, the method's performance on complex structures and scenarios involving multiple damage types requires additional validation. Furthermore, optimizing the model's generalizability will necessitate the incorporation of more diverse experimental datasets to address potential domain shifts in practical implementations. Figure 16: FCNN-based damage imaging (experimental data): (**a**) single damage, (**b**) double damage. Figure 15: Transverse damage index profiles at y = 180 mm for different imaging methods based on experimental data: (**a**) full view, (**b**) zoom-in view.
## 2 Establishment and Verification of an FE Model for HVB-LA-DLTL Systems
### Verification of the FE Modeling  
Because the high-voltage casing lead structure system studied in this paper is complex, there is a lack of reliable test results to verify the correctness of the model; thus, results from the literature [19] are chosen as the basis for validating this work. For an accurate comparison with the existing loading method, the same loading method is chosen to compare the stress values at the same location and the results are shown in Table 2. The area where the UHV substation is located belongs to Class B terrain, the basic wind pressure (determined by the 100-year recurrence period) is 0.35 kN/m\({}^{2}\), and the design wind speed at a height of 10 m above the ground is obtained by a logarithmic law as 25.3 m/s, which corresponds to the equivalent static wind loads at different heights of the \"\(\lambda\)\"-shaped lead wire model.  
According to the above comparison results, the maximum error of 7.98% between the simulation results and the test results of the model in this paper occurs at the high-voltage bashing side wire clamp. The error is caused by the bending and the distortion of the finite element mesh. The remaining stresses are in agreement with the experimental and simulation results from the literature, and the model can be considered to have high accuracy, which provides the basis for further analysis.
# The Interface of Privacy and Data Security in Automated City Shuttles: The GDPR Analysis
## 4 The Interface of Privacy and Data Security in ACSs
### Privacy-Preserving Pitfalls  
The aforementioned discussion highlights the discrepancy between regulator's efforts and scientific works. From the legal perspective, we summarize below our key findings:  
* Pseudonymization is indicated as an appropriate \"technical and organizational measure\" for data protection (Article 25(1)) [7] without proposing the mixed use of pseudonymization and anonymization schemes to make the privacy-preserving level even higher.
* The GDPR considers the pseudonymization to be a reversible process and anonymization to be permanent without highlighting the de-anonymization risk over the time.
* The opinion 05/2014 WP29 introduced the main risks and mitigation solutions against the re-identification risk of anonymization; though the recommendations do not cope  
Figure 4: Classification of privacy-preserving techniques.  
with the rapid evolving technologies as more risks and countermeasures are worthy to be extended by the WP29, which is currently represented by EDPB.
* There is no precision from legal provisions about which anonymization technique should be applied to each context, even if the likelihood of re-identifying personal information might vary from one technique to another [35; 46]. Nonetheless, from the technical perspective:
* The re-identification likelihood can never be zero.
* Anonymization is not everlasting, as it can be reverted in the future. Instead of a one-time operation, it should be assessed continuously.
* The choice of privacy-preserving scheme depends on the nature of the attribute of the private data itself. To illustrate, techniques applied to anonymize a data subject's name or data entry identifier might not be suitable for location data.
* There is a common misunderstanding, as pointed out in Section 4.1.2, in defining data masking as a subcategory of anonymization techniques. However, this technique is broad enough to embed both pseudonymization and anonymization, which would apply to different data protection obligations.
* Some researchers wrongly discussed encryption as an anonymization scheme, though it should be considered as a powerful pseudonymization technique.
* There is no unique solution that fits all processing, but the privacy-preserving technique should be selected on a case-by-case basis and depending on technologies involved within the ACS.  
To that end, the implementation of the GDPR principles may fall short even without the compliance violation. Pseudonymization and anonymization help to comply with the data protection obligations. However, they just assist in reducing the risks and not to completely preserve privacy within the automated vehicle ecosystem. Hence, the GDPR-ordinated implementation requires further guidance from DPAs and the EDPB to keep pace with the rapidly evolving technologies embedded within the ACS environment.
## 2 Results and Discussion  
### Growth of Halomicronema sp. and Biofilm Formation  
For imaging biofilm formation, cultures of _Halomicronema_ sp. were grown on slides. ESEM images in low vacuum mode (Figure 1A,B) showed that _Halomicronema_ sp. forms long tangled filaments, a characteristic that was also observed with classical light microscopy (Figure 1C). Using fluorescence staining with SYTO-9, which stains DNA, it was possible to visualize individual cells within these filaments (Figure 1D).  
Next, cyanobacteria were grown in a liquid BA+ medium in 24-well plates. Here, cells of _Halomicronema_ sp. grew predominantly in a biofilm attached to the bottom of the wells with few planktonic cells in the supernatant (Figure 2 condition A). This cyanobacterium is relatively insensitive to the highest concentration of salt tested; even with 1 M NaCl, it displays almost half the biomass with respect to the control condition (Figure 2, condition D). In some cyanobacteria, salt stress increases EPS production, a phenomenon that was suggested to be linked to a higher salt tolerance [24]. Higher tolerance to salt is a phenotype not present in cyanobacteria with a thick and firmly attached capsule [17]. The presence of any of the other tested chemicals disturbed its growth, except for MgSO\({}_{4}\), which induces biofilm formation (Figure 2, condition E). The effect could be the result of both the presence of sulfate and Mg\({}^{2+}\). Indeed, sulfur is an essential nutrient and is needed to support different functions such as photosynthesis, respiration, and redox metabolism [25], while Mg\({}^{2+}\), as well as Ca\({}^{2+}\), forms ion bridging that strengthens the EPS matrix [26; 27]. Not surprisingly, the chelating agent EDTA at a 250 uM concentration did not have a strong influence, but at 2.5 mM, it completely inhibited growth (Figure 2, conditions G and L). EDTA destabilizes biofilms by sequestering calcium, iron, magnesium, and zinc [28].  
Figure 1: Imaging _Halomicronema_ sp. with ESEM in low vacuum mode (**A,B**), light microscope (**C**), and with fluorescence microscopy after staining with SYTO-9 (**D**); for image (**C**), the magnification used was 40\(\times\), and for image (**D**), the magnification was 100\(\times\). For each image, the scale bar is reported at the bottom right.
## 4 Proposed Methodology
### Hosting Capacity Evaluation (Optimal Location and Sizing) by PSO Algorithm
\[P_{DG(j)}=\frac{\beta_{jj}\left(P_{dj+b_{j}Q_{dj}}\right)-X_{j}-b_{j}Y_{j}}{ \beta_{jj}\left(b_{j}^{2}+1\right)}. \tag{4}\]  
\[Q_{DG(j)}=b_{j}P_{DG(j)}. \tag{5}\]  
\(\mathbf{P}_{DG(j)}\) and \(\mathbf{Q}_{DG(j)}\) represent the active and reactive power of the DG unit, respectively; \(\mathbf{P}_{dj}\) and \(\mathbf{Q}_{dj}\) represent active and reactive power for the system as a whole. \(\mathbf{\beta}_{jj}\) stands for the loss coefficients; \(\mathbf{X}_{j}\) indicates the branch reactance; and \(\mathbf{Y}_{j}\) stands for the branch admittance. One of the important determinants that must be paid attention to and taken into consideration when adding capacity to the system is the operating cost, as this cost must be within certain limits, and the operating cost can be described in Equation (6). \[Operation\ cost= electricity\ price*[sum(P_{G}\ )-sum(\ P_{PV}\ )]. \tag{6}\]  
The above equation shows that the power generated from PV will be subtracted from the total load in the grid, and it is multiplied by the electricity price. In doing so, the electricity cost associated with power imported from the upstream grid is determined. As mentioned before, the operating cost is assumed to be equal to the MG electricity cost, and it should be maintained within certain limits. We will also create a multi-objective function problem, whose goal (**OF**) is to maximize the total capacity of PV, and simultaneously minimize total energy losses in the MG, as in (7), while keeping the operating indices of the MG within the allowable range. These limits include the node voltages and MG operating cost. Note that the two introduced objective terms are weighted by the weight factor w, which can be set based on the relative preferences of the decision maker. It should be noted that since PV capacity and energy losses are, respectively, measured in MW and MWh, they should be normalized based on some base values so that they can be aggregated. \[Min\;OF=-w\times PV\;Capacity+(1-w)\times Total\;Energy\;Losses \tag{7}\]
# Tethered Balloon Cluster Deployments and Optimization for Emergency Communication Networks
## 3 System Model and Problem Formulation
### Channel Model
#### 3.3.2 Air-to-Ground Channel Model
\begin{array}{ll}1,&\text{if}\quad d_{n,m}\leq\sqrt{h_{n}^{2} +r_{n}^{2}}\text{ and }\gamma_{n,m}\geq\Lambda,\\ 0,&\text{otherwise},\end{array} \right. \tag{15}\]  
where \(P_{n,m}\) and \(g_{n,m}\) are the signal transmit power and channel gain between base station \(n\) and user \(m\), respectively. \(\eta\) is the power spectral density of Gaussian white noise [24]. \(B_{n}\) is the bandwidth used by base station \(n\). \(r_{n}\) and \(h_{n}\) represent the maximum coverage radius and maximum vertical height of base station \(n\). \(C_{n,m}\) represents the coverage of user \(m\) by balloon \(n\). Therefore, by [PERSON]'s theorems, the maximum reception rate of user \(m\) can be calculated as  
\[R_{m}=B_{n}log_{2}(1+\gamma_{n,m}). \tag{16}\]
# Deep Learning Applications in Geosciences: Insights into Technological Analysis
## 2 Materials and Methods  
### Data Set  
The images used in our experiments were collected from a variety of subsurface cores and outcrop exposures representing siliciclastic sedimentary facies from several Cretaceous-aged stratigraphic formations in the Western Canada Sedimentary Basin, Alberta Canada.  
In our training data set, there was a large variation in individual trace fossils present in the facies, including _Asterosoma_, _Chondrites_, _Conichnus_, _Cylindrichnus_, _Diplocraterion_, _Macaronichnus_, _Ophiomorpha_, _Palaeophycus_, _Planolites_, _Phycoughton_, _Pisciachus_, _Rhizocorallium_, _Rossellia_, _Schaubcylindrichnus_, _Sibonichnus_, _Skoithos_, _Teichtmus_, and _Thalassinoides_, as well as bio-deformation structures and escape structures.  
The majority of the images were derived from sandstone and siltstone facies with some mudstone intervals, as well as rare conglomerate beds and mud-clast breccia units. All images were derived from facies recording relatively shallow-water settings, such as estuaries, bays, shorefaces, offshore-shelf, delta fronts, and prodellas. A wide range of physical sedimentary structures was observed, including low-angle to horizontal parallel lamination, high-angle cross-stratification, trough cross-stratification, hummocky cross-stratification, current ripple lamination, oscillation ripple lamination, normally graded bedding, fluid mud drapes, gravel lags, isolated clasts and rip-up clasts, loading and flame structures, convolute bedding and other soft-sediment deformation structures, syneresis cracks, and various types of concretions.
## 4 Results and Discussion
### Energy Consumption  
Energy consumption of the nodes is defined as the amount of energy utilized while broadcasting and receiving the data packets. Table 3 shows the performance analysis of energy consumption.  
Figure 4 shows the comparison of energy consumption for CEELBRP [24], EECRP-PSO [25], HFF-GSO [28], and the proposed ECTSA. The energy consumption of the ECTSA is varied from 5.4 J to 7.1 J, which is less when compared to the CEELBRP [24], EECRP-PSO [25], and HFF-GSO [28]. The developed ECTSA achieves lower energy consumption due to its cross-layer routing, shortest path generation according to the fitness metrics, and CW adjustment. However, the CEELBRP [24] and EECRP-PSO [25] do not considered distance; therefore, the energy consumption is increased because the transmission path has a higher distance.
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 4 Data Source and Preprocessing
### Data Preprocessing  
The collected data were split into training and testing datasets at an 8:2 ratio. The 8:2 ratio for training and validation was chosen based on the available data and the need to ensure sufficient data points for both processes. Specifically, for each measured variable, such as unit flow rate, guide vane opening, and maximum water guide tile temperature, we selected training data from five key vibration events (6-11 November 2020), while the validation data were sourced from two additional events (22 December 2020). In total, the training dataset consisted of 4078 data points, and the validation set had 1368 data points, yielding a total of 5446 data points. This closely aligns with the 80% training and 20% validation split, hence the 8:2 ratio. It is important to note that this ratio is based on the total number of data points, not the number of vibration events. When considering event distribution, five vibration events were used for training, and two were used for validation. This approach ensures that various scenarios are well represented in the training set while maintaining an independent validation set for accurate model evaluation.  
The sensors used during data collection employed adaptive sampling, where no entries were made to the historical database if the measured values remained unchanged, leading to discontinuities in the recorded timestamps, which indicate that the values were constant in the intervening periods. Consequently, during preprocessing, we filled the data on a per-second basis and then resampled it. Given the operational characteristics of hydropower units in practice, we chose a resampling interval of \(1\) min.  
Preprocessing also involved normalizing the data to scale features to a uniform range, which aids in enhancing the convergence speed during model training. The normalization process employed the min-max scaling technique, as detailed in Equation (17):  
\[X_{norm}=\left(X-X_{min}\right)/\left(X_{max}-X_{min}\right) \tag{17}\]  
where \(X_{norm}\) is the normalized value, \(X\) is the raw data point, and \(X_{max}\) and \(X_{min}\) are the maximum and minimum values in the data series, respectively.  
After prediction, the outputs were denormalized using the same min-max values to ensure accuracy in the interpretation of the results. The denormalization calculation is formulated as shown in Equation (18):  
\[\widehat{y}_{i}=y_{i}\cdot\left(X_{max}-X_{min}\right)+X_{min} \tag{18}\]  
where \(y_{i}\) represents the \(i\)-th predicted value in the sequence, and \(\widehat{y}_{i}\) is the value obtained after denormalizing the \(i\)-th predicted value.