esa-sceva/satcom-chunk-collection huggingface dataset:
# Privacy-Preserving Data Aggregation with Dynamic Billing in Fog-Based Smart Grid
## 3 Problem Formalization
### Security Requirements  
Based on the proposed threat model, we defined the following security requirements. When the proposed PPDB satisfies the security requirements, it indicates that PPDB is able to resist the attacks mentioned in the threat model.  
* **Confidentiality:** Reports transmitted through public communication channels and stored in databases may contain users' privacy information. Thus, all reports should be sent and stored in ciphertext format. Neither external nor internal adversaries can reveal the contents of the ciphertext even if they can obtain it; thus, the confidentiality of the data is preserved.
* **Authentication and Integrity:**\(\mathcal{A}\) may impersonate legal entities to send false reports or tamper with them during transmission. Hence, all reports sent by legitimate entities should be authenticated, and any tampering with them should be detected.
## 2 Materials and Methods
### SAR-SL Production and Quality Control Indicators
#### 2.4.1 Shoreline Extraction  
During the first stage, erroneous features that might appear in the SAR image are eliminated, and a shoreline vector is obtained. The task consists of four sub-tasks: enhancement, segmentation, healing, and vectorization. We employ two algorithms in the enhancement process, wavelet decomposition denoising [29] and anisotropic diffusion [30]. In both methods, the speckle in SAR images is further reduced, while the structural features and textural information of the scene are maintained. During segmentation, a binary raster is generated as an initial estimate of the land-sea boundary. Based on the calculation of an appropriate intensity threshold, pixels belonging to the land and the sea can be separated. In this study, the Kittler method [31] is employed to determine this threshold. The binary raster is then improved using binary morphology operations [32]. Binary opening removes foreground features caused by bright reflections from waves or vessels. Next, a minimum size filter is applied, which removes, from the raster, contiguous foreground areas that do not reach a specific number of pixels defined previously by the user. The last step is to fill in the holes within the foreground features. During the last sub-task (vectorization), a shoreline vector is generated from the healed binary raster. This is accomplished by using the marching squares algorithm [33]. A blurring option is implemented prior to applying the latter, which allows the vector to be smoothed to sub-pixel precision and reduces aliasing. Upon completing the extraction stage, a 1D vector line (SL) is produced from each input SAR image, consisting of a sequence of points identified by their geographic coordinates (latitude-longitude).
# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 2 Related Work
### LMMs Applications  
LMMs have a very wide range of application capabilities. LMMs are the most cutting-edge technology and have been widely employed in diverse domains. In the medical field, [PERSON] et al. found that the current multimodal models, GPT-4 and Bard, could handle visual assignments. For example, GPT-4 successfully solved 96.7% of the visual problems, facing minimal difficulty only with Parsons problems [15]. Parsons problems require precise logical ordering of code snippets, which requires a deep understanding of programming languages. Solving Parsons problems also requires a detailed understanding of the context and a prediction of the results of code execution. GPT-4 can generate and understand natural language, but for this task, which requires rigorous logical reasoning and programming knowledge, this is beyond the main design scope of GPT-4. [PERSON] et al. found that LMMs can be applied to enhance various aspects of healthcare. Particularly, they highlighted the crucial role of LMMs, investigating their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic accuracy [14]. [PERSON] et al. proposed a novel zero-shot species classification framework that leverages multimodal foundation models. This framework involves instruction-tuning vision-language models to generate detailed visual descriptions of camera-trap images, using terminology similar to that of experts [19]. [PERSON] et al. evaluated GPT-4V, a vision language model, in engineering design tasks, demonstrating its capabilities and limitations. Their study provides foundational insights for the application of vision language models in engineering [20]. [PERSON] et al. explored the shift in medical AI systems towards deep learning models, focusing on LMM's impact on medical image analysis and clinical decision support systems [21]. [PERSON] et al. introduced an LMM for radiation therapy, integrating clinical text with images, demonstrating enhanced performance in breast cancer treatment, a first in such clinical text integration for oncology [22]. Microsoft delved into the capabilities of GPT-4 Vision, highlighting its proficiency in video understanding, visual reasoning, and other areas. They underscored the substantial potential applications of this technology in various sectors, including industry, medical fields, auto insurance, and image generation. In summary, LMMs have showcased their strong and diverse application capabilities in solving visual problems in the aforementioned domains. Following these existing studies, we take LMMs as the baseline model to further develop our location-integrated model, GeoLocator.
## 2 Data Processing and Network Description
### Relation Network  
After transforming the signal samples into two-dimensional images, this paper employs a meta-learning framework based on a relational network to classify these preprocessed signal images.
## 2 Materials and Methods
### Method Validation  
The method was validated for parameters such as specificity, linearity, accuracy, precision, the LOD/LOQ, stability of solutions and robustness. Linearity was performed by injecting prepared sample solutions of OATD-02 of different concentrations in the range of 0.8-1.2 mg/mL to the HPLC system. The peak area was plotted against the concentration and the generated regression line equation along with the R\({}^{2}\) was reported. To evaluate the linearity and range of the method, five different concentrations were prepared: 0.8, 0.9, 1.0, 1.1 and 1.2 mg/mL. Three separate injections of each concentration were analyzed under the same conditions and the average result was reported. Specificity of the method was evaluated by injecting the blank solution and sample solution to ensure the peak of OATD-02 can be well separated from each other and free of interference from the blank solution. Accuracy and precision (repeatability) analyses were performed by assaying triplicate preparations for accuracy solutions (80%, 100%, 120%) against the assay solution of the well-characterized standard material and calculation of the recovery results. Instrumental precision was established by five replicate injections of the sample solution (1 mg/mL). The RSD was then calculated for the generated peak areas. The same analytical conditions were used by different analysts and on different columns. The intermediate precision of the method was established by testing six test solutions against the assay solution of the well-characterized standard material and calculation of the recovery results. The sensitivity of the method was established by measuring the detection limit (LOD) and the quantification limit (LOQ). Serial dilutions of OATD-02 solution were made and then each dilution was injected to the HPLC system. The LOD is expressed as a concentration that gives a signal-to-noise ratio over 3:1, while the LOQ of the sample can be determined with a signal-to-noise ratio over 10:1. The stability of the sample solution was evaluated after 3 days by assaying the sample solution against the fresh prepared sample solution and calculated the % assay of OATD-02. Robustness of the developed analytical method was assessed by small changes in the experimental conditions. The chromatographic changes include mobile phase composition, flow rates, temperature of the column, emission and excitation wavelengths.
## 3 Characterization of Anchor Cable in FDEM
### Results
#### 4.2.3 Analysis of Entire Dynamic Evolution Process of the Landslide-Anchorage Structure System
The landslide-anchorage structural system does not fail under seismic intensities VII, VIII, and IX, and does not enter the overall damage stage after this stage; instead, it is further fragmented under seismic loads, but the landslide-anchorage structural system is still tightly integrated owing to the prestressed anchor cables (Figure 15f,g). The final crack numbers are 199, 440, and 1492 for intensities VII, VIII, and IX, respectively (Figure 13), and the law is apparent: the damage degree of the landslide-anchorage structure system increases with the increase in seismic intensity. 4. Overall failure stage. The landslide-anchorage structure system occurs overall failure under seismic intensity X. It can be seen from Figure 11d that the anchor cable N0 at the toe of the slope fails first at \(t=10.06\) s, followed by anchor cable N2 at \(t=10.08\) s and NI at \(t=10.32\) s, and the failure sequence is consistent with the distribution of PGA amplification factor in Figure 8d. After the second step anchor cables N0-N2 failure, the first step anchor cables N3-N5 fail sequentially under the dual effects of gravity and seismic load (\(t=10.36\) s, \(t=10.48\) s, \(t=10.6\) s). Before the anchor cable N0 fails, as shown in Figure 15h, the slope becomes fragmented, and the crack number is 655, which is much smaller than the crack number of 1431 in the unreinforced slope at the same time, indicating that the prestressed anchor cable can connect the slope and the anchorage system as a whole, thus significantly reducing the probability of landslides. When all anchor cables fail at \(t=11\) s, overall failure occurs in the landslide-anchorage structure system (Figure 15i,j). 5. Energy dissipation stage. The system fails in stages, so the kinetic energy of the model in Figure 14 has several peaks, and finally, the sliding body comes to rest under the energy dissipation mechanism (Figure 15k); the final crack number is 9271. Figure 15: \(Cont.\)  
Figure 15: Schematic diagram of the entire dynamic evolution process of the landslide–anchorage structure system at different seismic intensities (similar process is omitted).
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Methodology  
This work adopts as its main methodology the development and running of experimental simulations based on the DVR, AODV, and DSR routing protocols using the Cupcarbon simulator. Initially, these protocols were simulated while disregarding eventual errors that could occur in the network nodes or mobility situations; thus, several tests were carried out for each protocol with the purpose of applying the routing steps and their operation in a mobility scenario with both fixed and mobile sensors. Then, our new proposed radio power adjustment (RPA) routing protocol for energy saving was compared with different alternatives.
## 3 Results and Discussion
### Sugars and Organic Acids  
Three sugars were detected in all samples: glucose, fructose, and sorbitol, showing significant differences among cultivars (Table 1). In all cases, the most abundant sugar was glucose, whose concentration was similar between round and pear cultivars [3; 17].  
In pear cultivars, Dolcetini showed the highest concentration of sugars with significant differences with respect to the other cultivars; therefore, it would not be the optimal cultivar to replace Angelle. Glucose content showed significant differences among pear cherry tomatoes with values ranging from 34.9 g kg\({}^{-1}\) dw to 39.0 g kg\({}^{-1}\) dw for Seychelles and Dolcetini, respectively. Regarding fructose, Angelle obtained the lowest value (13.5 g kg\({}^{-1}\) dw), Santylplum and Seychelles being the closest to this concentration with 15.3 g kg\({}^{-1}\) dw and 16.1 13.5 g kg\({}^{-1}\) dw, respectively. Again, Dolcetini cultivar showed the highest content of fructose (17.2 g kg\({}^{-1}\) dw) as in the case of glucose. The sorbitol concentration in pear cultivars showed very similar trend to fructose, the lowest value being for Angelle (7.5 g kg\({}^{-1}\) dw), Santylplum and Seychelles showed intermediate content (8.7 g kg\({}^{-1}\) dw, both), Dolcetini being the cultivar with the highest content of sorbitol.  
Santylpum was probably the cultivar in terms of sugar which could be placed into a similar place than Angelle.  
Regarding round cultivars, Katalina, Sweet star, and Karelya had very similar concentrations of glucose (38.0 g kg\({}^{-1}\) dw, 37.5 g kg\({}^{-1}\) dw, and 38.7 g kg\({}^{-1}\) dw, respectively), while the content of fructose of Sweet star had the highest concentration (17.1 g kg\({}^{-1}\) dw); in this sugar, the most similar variety for Katalina was C-95 (14.3 g kg\({}^{-1}\) dw and 14.4 g kg\({}^{-1}\) dw, respectively). Sorbitol was very similar to fructose, Sweet star being the cultivar with the highest concentration (9.6 g kg\({}^{-1}\) dw). Katalina and C-95 obtained very close results with 8.1 g kg\({}^{-1}\) dw and 8.4 g kg\({}^{-1}\) dw, respectively. C-95 was probably the cultivar closest to Katalina in sugar terms. On the other hand, only citric acid was found in all cultivars and in both pear and round types, there were significant differences between cultivars.  
In pear cherry cultivars, Angelle and Santylpum obtained the same concentration in citric acid (1.2 g kg\({}^{-1}\) dw), being lower than Dolcetini cultivar which was the one that obtained the highest concentration (1.4 g kg\({}^{-1}\) dw). In round cherry types, Katalina had an intermediate concentration (1.7 g kg\({}^{-1}\) dw) between Sweet star and Karelya (1.8 and 1.5 g kg\({}^{-1}\) dw, respectively). These compounds were detected as most concentrated compounds in other cultivars of tomato [18; 19].
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 2 Preliminaries and Proposed Method
### Loss Functions
#### 2.5.2 Watermarking Loss  
To watermark the carrier image \(C\), it is necessary to add the frequency perturbation \(f\) into the frequency domain of \(C\). Following this, we transform the frequency domain back to the spatial domain, yielding the marked image \(C^{\prime}\). The \(C^{\prime}\) is  
\[C^{\prime}=IFFT(f\cdot\alpha+FFT(C)), \tag{3}\]  
where \(\alpha\) denotes the tunable hyperparameter. \(FFT(\cdot)\) denotes the Fast Fourier Transform and \(IFFT(\cdot)\) denotes the Inverse Fast Fourier Transform.  
More importantly, To be able to extract a watermark, we use the watermarking loss  
\[\mathcal{L}_{2}=\frac{1}{|N|}\sum_{x\in C^{\prime}}||E(x)-wm||_{1}. \tag{4}\]  
where \(x\) denotes the marked images that belong to \(C^{\prime}\). \(N\) denotes the pixel numbers. \(E(x)\) represents the watermark recovered from the marked image by the watermark extraction network \(E\).
## 4 Heat Transport Pipeline Damage Probability Model
### Results of Models for Predicting Heat Transport Pipeline Damage Probability  
The Scikit-learn library, which includes Python 3.8 and machine learning packages, was used to develop models predicting heat transport pipeline damage probability. The algorithms used were RF, XGB, and LGBM. Models based on other algorithms (e.g., CatBoost and AdaBoost) were developed but showed issues such as overfitting and performance degradation. Therefore, only results from the selected algorithms are presented.  
The dataset was divided into training, validation, and test sets at a ratio of 64:16:20. Only the training set was used for model training, with overfitting evaluated by comparing performance with the validation set. The test set was used to calculate evaluation indicators [42]. In addition, oversampling techniques were applied to the training set to augment the relatively scarce instances of pipeline failure.  
To improve model development, the output data (heat transport pipeline damage history) were reinforced. It is known that damage risk sharply increases after a certain period of use. Thus, pipes used for over 20, 25, and 30 years were labeled as damaged in addition to existing damage history. Three datasets were created based on this and applied to the ML algorithms. The results are shown in Table 6. Datasets A, B, and C correspond to data with pipes used over 20, 25, and 30 years added to the damage history, respectively.  
The model using dataset A with the LGBM algorithm achieved the highest F2-score (accuracy: 0.737, F2-score: 0.804, AUC: 0.837). The ROC curve for this model is shown in Figure 7, displaying an upward-sloping pattern with a 0.01 difference between training and validation. Thus, the model's generalization ability is stable, indicating significant predictive performance.  
Figure 6: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**AUC**} & \multicolumn{1}{c}{**Evaluation**} \\ \hline AUC \(\geq\) 0.9 & Excellent \\
0.8 \(\leq\) AUC \(<\) 0.9 & Good \\
0.7 \(\leq\) AUC \(<\) 0.8 & Fair \\ AUC \(<\) 0.7 & Poor \\ \hline \hline \multicolumn{2}{c}{} \\ \hline Confusion & \multicolumn{2}{c}{Prediction} \\ \hline Matrix & Negative & Positive \\ \hline \multirow{4}{*}{Reference} & \multirow{2}{*}{Negative} & True Negative \\  & & (TN) \\ \cline{1-1}  & & False \\ \cline{1-1}  & & Negative \\ \cline{1-1}  & & (FN) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Model evaluation according to AUC.
## 5 Numerical Example  
The procedural steps and performance of the proposed framework are elucidated through numerical examples in this study. Specifically, one example highlights a scenario characterized by a small failure probability and high nonlinearity, where the nonlinearity is represented by a large curvature. A second numerical example is presented to showcase the method's effectiveness in addressing challenges associated with multiple failure regions within the context of reliability analysis. In both instances, the initial DoE size, denoted as \(n_{0}\), is set to 10, and the stopping criterion for the hybrid learning process is defined by the stability of the reliability index. The accuracy and efficiency of various methods are compared using the parameter \(N_{call}\), which represents the total number of simulations for MCS or the number of actual function evaluations required to estimate the failure probability for FORM and SORM. For HAK and AK-MCS, \(N_{call}\) encompasses the sum of the initial DoE points and the number of simulations during the active learning process. This comprehensive comparison aims to assess and contrast the performance of the different methods under consideration.
# Millimetre-Wave Backhaul for 5G Networks: Challenges and Solutions
## 7 Conclusions
\begin{table}
\begin{tabular}{c|c|c|c|c|c|c} \hline \multirow{2}{*}{**Scheme**} & \multicolumn{3}{c|}{**5-SBS Backhaul Network**} & \multicolumn{3}{c}{**6-SBS Backhaul Network**} \\ \cline{2-7}  & **1 Blockage** & **2 Blockages** & **3 Blockages** & **1 Blockage** & **2 Blockages** & **3 Blockages** \\ \hline \multicolumn{7}{c}{**Backhaul Network Throughput**} \\ \hline HBF and FDP & 14.48 Gbps & 12.43 Gbps & 9.92 Gbps & 15.34 Gbps & 13.54 Gbps & 11.86 Gbps \\ HBF and HDP & 6.50 Gbps & 6.21 Gbps & 3.39 Gbps & 13.25 Gbps & 9.91 Gbps & 6.11 Gbps \\ BF and FDP & 10.36 Gbps & 6.72 Gbps & 3.92 Gbps & 14.83 Gbps & 12.32 Gbps & 6.86 Gbps \\ BF and HDP & 2.43 Gbps & 1.27 Gbps & 0.66 Gbps & 3.74 Gbps & 2.15 Gbps & 0.90 Gbps \\ None-STDMA & 0.98 Gbps & 0.50 Gbps & 0.37 Gbps & 1.71 Gbps & 0.75 Gbps & 0.43 Gbps \\ \hline \multicolumn{7}{c}{**Average Delay**} \\ \hline HBF and FDP & 1.56 ms & 1.80 ms & 3.15 ms & 1.05 ms & 1.14 ms & 1.52 ms \\ HBF and HDP & 23.07 ms & 23.55 ms & 25.23 ms & 15.59 ms & 20.46 ms & 24.23 ms \\ BF and FDP & 20.67 ms & 23.52 ms & 25.64 ms & 3.35 ms & 10.57 ms & 23.05 ms \\ BF and HDP & 41.09 ms & 41.98 ms & 43.48 ms & 30.64 ms & 30.55 ms & 31.14 ms \\ None-STDMA & 43.25 ms & 45.56 ms & 43.80 ms & 31.12 ms & 30.91 ms & 32.49 ms \\ \hline \multicolumn{7}{c}{**Packet Loss Rate**} \\ \hline HBF and FDP & 0\% & 0\% & 0\% & 0\% & 0\%s & 0\% \\ HBF and HDP & 25.61\% & 23.28\% & 33.10\% & 0.16\% & 8.09\% & 21.98\% \\ BF and FDP & 8.69\% & 19.72\% & 31.28\% & 0\% & 0\% & 19.54\% \\ BF and HDP & 56.46\% & 63.20\% & 71.25\% & 45.61\% & 57.92\% & 67.89\% \\ None-STDMA & 68.51\% & 77.40\% & 73.90\% & 53.22\% & 72.97\% & 76.38\% \\ \hline \end{tabular}
\end{table} 
Table 2: System performance in scenarios of different numbers of small cell BSs (SBSs) and blockages.
## 6 Analysis of Results  
The optimal range of the main acoustic parameters is considered in relation to prose and classical music as the main artistic performance types, which means that both speech intelligibility and music are the subjects of evaluation. The main acoustic parameters are defined based on the ISO 3382-1 standard [32], namely the early decay time (EDT), reverberation time (T\({}_{20}\)), clarity indexes (C\({}_{50}\), C\({}_{80}\)), definition (D\({}_{50}\)), and strength (G). These acoustic parameters are presented in the form of graphs, as shown in Figure 5, and, where appropriate, in the form of spatial distributions, as indicated in Figures 6-8, when the acoustic parameters depend strongly on the position in the seating area. The analysis was performed for the frequency range between 125 Hz and 4 kHz. The results were averaged for all sources and receivers placed in the digital model [33].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Materials** & **125 Hz** & **250 Hz** & **500 Hz** & **1 kHz** & **2 kHz** & **4 kHz** \\ \hline \multirow{2}{*}{Tuff stone [28]} & _0.01_ & _0.06_ & _0.05_ & _0.02_ & _0.08_ & _0.05_ \\  & **0.10** & **0.11** & **0.11** & **0.12** & **0.12** & **0.12** \\  & _0.08_ & _0.20_ & _0.10_ & _0.05_ & _0.03_ & _0.02_ \\ \multirow{2}{*}{Tiber wood [28]} & **0.15** & **0.12** & **0.10** & **0.08** & **0.04** & **0.04** \\  & _1.00_ & _1.00_ & _0.54_ & _0.16_ & _0.04_ & _0.01_ \\ (m = 0.5 kg/m\({}^{2}\)) [29] & **0.05** & **0.05** & **0.05** & **0.05** & **0.05** & **0.05** \\ \multirow{2}{*}{Audience [30]} & _0.51_ & _0.64_ & _0.75_ & _0.80_ & _0.82_ & _0.83_ \\  & **0.20** & **0.25** & **0.37** & **0.40** & **0.38** & **0.31** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Absorption (in _italic_) and scattering (in **bold**) coefficients for all the materials considered for the acoustic simulations of the original shape.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Materials** & **125 Hz** & **250 Hz** & **500 Hz** & **1 kHz** & **2 kHz** & **4 kHz** \\ \hline \multirow{2}{*}{Terrain/soil [27]} & _0.06_ & _0.20_ & _0.32_ & _0.55_ & _0.60_ & _0.55_ \\  & **0.10** & **0.11** & **0.11** & **0.12** & **0.12** & **0.12** \\ \multirow{2}{*}{Brick masonry [28]} & _0.02_ & _0.01_ & _0.02_ & _0.01_ & _0.01_ & _0.01_ \\  & **0.18** & **0.18** & **0.19** & **0.20** & **0.20** & **0.18** \\ \multirow{2}{*}{Tuff stone (\(a1\)) [28]} & _0.01_ & _0.06_ & _0.05_ & _0.02_ & _0.08_ & _0.05_ \\  & **0.10** & **0.15** & **0.30** & **0.31** & **0.24** & **0.22** \\ \multirow{2}{*}{Tuff stone (\(a2\)) [28]} & _0.01_ & _0.06_ & _0.05_ & _0.02_ & _0.08_ & _0.05_ \\  & **0.15** & **0.25** & **0.50** & **0.51** & **0.44** & **0.30** \\ \multirow{2}{*}{Audience [30]} & _0.51_ & _0.64_ & _0.75_ & _0.80_ & _0.82_ & _0.83_ \\  & **0.20** & **0.25** & **0.37** & **0.40** & **0.38** & **0.31** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absorption (in _italic_) and scattering (in **bold**) coefficients for all the materials considered for the acoustic simulations of the existing conditions.