esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanotechnology Applications in Ground Heat Exchanger Pipes: A Review
## 3 Discussion  
Nanotechnology is a technological innovation whose use has grown in recent years in different energy sectors (solar, fuel cells, hydrogen, nuclear, photovoltaic, and wind). This technology is currently applied in various areas of geothermal energy. One of these technologies, Geothermal Heat Pumps (GSHP), is known as the best available technology for energy-efficient air conditioning systems to condition residential or commercial spaces to provide space heating and cooling. Currently, GSHPs are used in 54 countries, making up 59% of total energy use in the world, with China, the US, Sweden, Germany and Finland leading with 84% of total use [64]. Although GSHPs are highly efficient, their installation costs are very high, mainly due to the Ground Heat Exchanger (GHE), which is the most expensive component of all the systems, which is the main obstacle to the widespread use of GSHPs. The horizontal system configuration is generally easier and less expensive to install than a vertical setup, because vertical hole drilling is more expensive than horizontal loop trenching. However, horizontal loops require a large floor space due to the longer length of pipes. New research using nanotechnology in GSHPs points to the use of new nanomaterials as alternatives to traditional fluids to increase heat in the GHE. Some of these traditional fluids and materials (water pipes and HDPE) are mentioned in [65; 66; 67; 68; 69]. It is clear that improvements in the heat exchanger are relevant using nanotechnology. Therefore, the analysis of recent advances in nanotechnology for GHSP becomes of paramount importance. The objective of this review was to show different methods, types, applications, results and conclusions that have been made with the use of nanotechnology applied to GHE to improve the absorption/rejection of heat from the ground and from the space to be conditioned. Theoretical, numerical and experimental developments in the use of nanofluids and nanomaterials applied to GHE pipes to improve heat transfer efficiency were presented. Reviews indicate that early studies of nanotechnology applications to GSHP have been critical in optimizing and improving sink energy capture by splitting it into two main approaches; the first is the use of nanofluids moving through HDPE pipes, and the second one is developing new pipes with nanomaterials. The use of nanofluids in the circulation circuit or the development of new nanomaterials has the objective of increasing heat transfer, reducing the installation cost and reducing the operating cost of GSHPs.
## Related Methodology
### Kruskal-Wallis (KW) Test  
The Kruskal-Wallis (KW) method is presented as a nonparametric technique to detect whether different samples originate from the same probability distribution [35; 36; 37; 38]. Since no normality assumption is made, the KW test is based on an analysis of medians instead of means.  
Assume a set of \(p\) random variables, \(X_{k}\) (\(1\leq k\leq p\)), are selected from different populations. Define \(\eta_{k}\) as the median of \(X_{k}\). The null hypothesis \(H_{0}\) and the alternative hypothesis \(H_{1}\) of the KW test can be expressed as follows [35]:  
\[\left\{\begin{array}{l}H_{0}\text{: }\eta_{1}\text{= }\eta_{2}=\cdots=\eta_{n}\\ H_{1}\text{: }\eta_{k_{i}}\
eq\eta_{k_{j}}\text{ for at least one }k_{i}\
eq k_{j} \end{array}\right.. \tag{1}\]  
If the null hypothesis is rejected, then the \(p\) random variables are assumed to be drawn from more than a single population. For detailed information on the KW test, please refer to Reference [38].
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 3 Blockchain vs. Federated Learning
### Reasons Why Blockchain Enables Federated Learning
#### 3.3.4 System Security  
The blockchain has the characteristics of a stable FL model, such as digital signatures, hash algorithms, etc., and can further resist poison attacks. In [51], a consensus algorithm is used to propose a blockchain-based federated learning scenario, which can effectively avoid the problem of network overload. In terms of defense systems, [PERSON] et al. [59] proposed a decentralized blockchain federated learning (BFL) framework with a simpler consensus mechanism, avoiding the risk of network paralysis caused by central equipment failure. In [60], authors utilized the characteristics of decentralization and anti-tampering of the blockchain to store data records and other important information on the blockchain, while the complete data are encrypted and stored in the distributed database to realize the safe storage of model data and prevent user privacy data leakage to ensure system security.
## 7 Security Considerations and Proposed Zero-Trust Security Model
### Security Considerations to Support Zero-Trust Model
#### 7.1.5 Privilege Remote Access  
Remote access to the OT environment is not direct but through a Privilege Remote Access (PRA) jump server. All connections from the external network will be directed to the jump server via a VPN tunnel. This jump server will act as a proxy to the OT environment and all the connections are filtered through a set of firewall rules and sessions are automatically recorded for audit purposes. Each user connecting to the OT network from the internet must go through a series of authentication and authorization beginning with connecting to the VPN tunnel with MFA and then on the jump server, they must authenticate again to get connected to the OT environment.  
Role-based and least privilege access is provided for remote logins and separate accounts must be maintained for IT and OT environments. Even if a user's IT account credentials get compromised, the malicious party could only connect through the VPN tunnel to gain access to the jump server but cannot go beyond that. Figure 13 demonstrates the flow of a remote connection.  
Figure 12: Endpoint compliance management overview.  
Figure 13: Privilege Remote Access management.
## 3 Results
### Tumor Volume
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patient Number** & **PTV\({}_{\text{3D}}\) (cm\({}^{3}\))** & **PTV\({}_{\text{4D}}\) (cm\({}^{3}\))** & **PTV\({}_{\text{MF}}\) (cm\({}^{3}\))** \\ \hline
1 & 54.4 & 25.5 & 14.1 \\
2 & 548.6 & 417.5 & 394.1 \\
3 & 361.8 & 252.3 & 214.8 \\
4 & 344.2 & 267.4 & 260.3 \\
5 & 732.9 & 509.7 & 499.4 \\
6 & 55.2 & 24.7 & 24 \\
7 & 64.8 & 30.1 & 22.8 \\
8 & 60.6 & 37.4 & 32.1 \\
9 & 100.5 & 80.1 & 75.6 \\
10 & 76.2 & 35.3 & 33.5 \\
11 & 59.1 & 37.1 & 32.9 \\
12 & 129.2 & 97.1 & 88.1 \\
13 & 113.1 & 90.9 & 92.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Planning target volumes (PTVs) delineated in different treatment plans. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Patient Number** & **3D-PET\({}_{\text{MIP}}\)** & **4D-PET\({}_{\text{MIP}}\)** & **MF-PET\({}_{\text{MIP}}\)** \\ \hline
1 & 5.91 & 6.65 & 6.65 \\
2 & 24.03 & 22.39 & 22.42 \\
3 & 15.76 & 17.91 & 17.91 \\
4 & 20.57 & 21.58 & 21.58 \\
5 & 14.16 & 13.25 & 13.25 \\
6 & 10.87 & 9.86 & 9.86 \\
7 & 0.54 & 1.36 & 1.36 \\
8 & 10.05 & 9.41 & 9.45 \\
9 & 2.94 & 2.81 & 2.81 \\
10 & 4.20 & 5.10 & 5.10 \\
11 & 1.11 & 1.06 & 1.06 \\
12 & 7.86 & 9.73 & 9.73 \\
13 & 9.73 & 7.81 & 7.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Standardized uptake value (SUV)\({}_{\text{max}}\) value in different PET\({}_{\text{MIP}}\) images. MIP-maximum intensity projection.
# Influence of Cavity Geometry on the Fracture Strength of Dental Restorations: Finite Element Study
## 3 Results
### Ceramic Restorations  
The patterns of stress distribution on both ceramic restorations were similar and are shown in Figure 4a. The inlay model presented the highest values of effective stress. Nevertheless, the highest values appeared in the areas that were in direct contact with the loading body in the occlusal surfaces of both models. The mean von Mises stress was 17 \(\pm\) 12 MPa, ranging from 1.4 to 181.5 MPa. Statistical descriptive results of the von Mises and maximum shear stresses are presented in Table 4.  
Figure 4b shows the histogram of the material level loading distribution for both restorations, i.e., on the inlay and onlay geometries. The geometry that had a greater percentage of material loaded within the range of 5-20 MPa was the onlay, whereas for loading levels higher than 20 MPa, the inlay had a greater percentage of material.  
Figure 3: Band plot of the displacement magnitude (m) over the ceramic restorations for the two models, inlay (**left**) and onlay (**right**), with non-matching color scales.  
Figure 4: Smoothed stress distributions of the restoration models. (**a**) Inlay (left) and onlay (right). (**b**) Histograms of the distributions for both models.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Aver.** & **SD** & **Min.** & **1 st Quart.** & **Median** & **3 rd Quart.** & **Max.** \\ \hline \multirow{2}{*}{Von Mises (MPa)} & \(\mathit{Inlay}\) & \(17.73\) & \(12.80\) & \(1.39\) & \(6.84\) & \(16.62\) & \(25.30\) & \(181.47\) \\  & \(\mathit{Onlay}\) & \(17.00\) & \(12.20\) & \(1.43\) & \(7.14\) & \(14.86\) & \(24.14\) & \(171.02\) \\ \hline \(\left(\mathit{Inlay}-\mathit{Onlay}\right)/\mathit{Onlay}\) (\%) & \(4.42\) & \(4.94\) & \(-2.77\) & \(-4.25\) & \(11.86\) & \(4.82\) & \(6.11\) \\ \hline Maximum Shear & \(\mathit{Inlay}\) & \(9.75\) & \(6.96\) & \(0.76\) & \(3.79\) & \(9.23\) & \(13.77\) & \(102.23\) \\ (MPa) & \(\mathit{Onlay}\) & \(9.314\) & \(6.63\) & \(0.81\) & \(3.95\) & \(8.23\) & \(13.08\) & \(94.28\) \\ \hline \(\left(\mathit{Inlay}-\mathit{Onlay}\right)/\mathit{Onlay}\) (\%) & \(4.68\) & \(4.91\) & \(-5.28\) & \(-3.94\) & \(12.08\) & \(5.24\) & \(8.34\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical description of the stress distributions for the ceramic restorations.
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 6 Analytical Work
### Fracture Damage of Perfobond Ribs  
When the strength of the perfobond rib is lower than the anchoring effect provided by the concrete and the perforating rebar on the flange, the perfobond rib typically experiences stress concentration at the hole's maximum length first. This concentration of stress initiates at the point where the rib is most likely to fracture. The fracture damage of the perfobond rib can be assessed using the calculation detailed in Equation (6).  
\[T_{s}=f_{s}A_{s}=f_{s}(l_{p}-d_{i})t_{p} \tag{6}\]  
where \(f_{s}\) represents the tensile strength of the perforated steel plate, a measure of its capacity to withstand pulling forces before breaking. Meanwhile, \(A_{s}\) denotes the cross-sectional area at the point where the perforated steel plate fractures, which is considered to be the largest area involved in the breakage.
## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.4. \(k\)Nndvi-Pt-Former Model  
The vegetation change dataset is put into the \(k\)_NDVI_-PT-Former model for training and testing. This process inputs all the data in the dataset, including two-phase remote sensing image data, \(k\)_NDVI_ data and label data in the dataset. The results of monitoring vegetation changes using the \(k\)_NDVI_-PT-Former model are as follows:  
CPA: 0.7295. It shows that the accuracy of classification and recognition of vegetation by this model is improved compared with the model before improvement.  
IoU: 0.7228. It shows that the model has improved the classification boundary performance of vegetation and non-vegetation (sandy land) compared with the model before improvement. The output of the model is shown in Figure 13.  
The vegetation changes in the whole study area are shown in Figure 14.  
Figure 12: PT-Former model output results of the study area.  
Figure 13: \(k\)_NDVI_-PT-Former model output diagram.  
The stability of the model was evaluated by using 5-fold cross-validation. The dataset was randomly divided into 5 subsets. Each time, 4 subsets were used for training and 1 subset for validation. The average value was taken after repeating the process 5 times. The results showed that the mean CPA of _kNDVI_-PT-Former was 0.732 \(\pm\) 0.016 and the IoU was 0.725 \(\pm\) 0.021. This result indicates that the datapoints are closely clustered around the mean, demonstrating the stable performance of the model.
## 2 Historical Data
### Aerial Photography in the Polar Regions
#### 2.1.2 The Antarctic Peninsula
Whilst the negatives are still available, some of the metadata about individual sorties have also been lost. Nevertheless, over 330,000 photos were scanned by the United States Geological Survey and are available with flight lines through the Polar Geospatial Center ([[http://www.pgc.umn.edu/imagery/aerial/antarctica](http://www.pgc.umn.edu/imagery/aerial/antarctica)]([http://www.pgc.umn.edu/imagery/aerial/antarctica](http://www.pgc.umn.edu/imagery/aerial/antarctica))). Since the TMA campaigns in the 1960s, vertical aerial photography on the Antarctic Peninsula has been carried out by both the Royal Navy (RN) and the British Antarctic Survey (BAS). The RN photography was acquired to support hydrographic charting and is usually single strips of photographs following the coastline. The BAS photography has always been flown to support specific projects, rather than for systematic regional coverage, and the coverage is patchy. The majority of the RN and BAS coverage was acquired using high quality Zeiss or Leica metric cameras, but there is also some BAS photography using a Vinten medium format reconnaissance camera. In 1989, the German Institut fur Angewandte Geodasie (IfAG), in collaboration with BAS, acquired systematic coverage of the Marguerite Bay area, on the west side of the Antarctic Peninsula, at 1:70,000 scale. This photography has been used for rigorous photogrammetric mapping of selected areas by BAS, as well as the University of Darmstadt, Germany [25].
## 2 Methods
### Damage Identification Approach  
In this study, the proposed damage identification approach was investigated experimentally and numerically. In Figure 1, a schematic of the approach, \"\(e\)\" and \"\(m\)\" denote the expanded strain and measured strain. When the structure is intact, the calculated expanded strain is equal to the measured strain at the same location. However, when the structure is damaged, the expanded strain differs from the measured strain at the same position. This study used these differences to identify damage and employed the degree of difference to determine damage severity.
# The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review
## 2 Methodology
### Research Protocol and Research Questions  
Research studies were found by performing a search using keywords on major digital libraries focusing on smart grid and Digital Twin applications. The search method was designed to capture both theoretical frameworks and practical implementations of Digital Twin technology in power systems.  
The key search terms were combined using Boolean operators:  
(\"Digital Twin\" OR \"Digital Replica\") AND (\"Smart Grid\" OR \"Power System\" OR \"Energy Grid\") AND (\"Implementation\" OR \"Application\" OR \"Integration\")  
For technology-specific aspects, the following additional terms were included:  
* AI/ML integration: AND (\"Artificial Intelligence\" OR \"Machine Learning\").
* Security aspects: AND (\"Security\" OR \"Privacy\" OR \"Cybersecurity\").
* Implementation: AND (\"Case Study\" OR \"Deployment\" OR \"Framework\").  
Table 1 summarizes initial search results from five databases. After filtering, the initial set of papers was refined, with major sources including IEEE Xplore, ScienceDirect, Web of Science, Google Scholar, and Semantic Scholar, resulting in a more focused collection of relevant studies.  
Based on the paper's main goal mentioned earlier in this section, three primary research questions were formulated:  
* RQ1: What are the current applications and implementation approaches, with case studies, of Digital Twins in smart grid systems?
* RQ2: How are emerging technologies like AI, IoT, and blockchain being integrated with Digital Twins in smart grid applications?
* RQ3: What are the key challenges, limitations, and future directions in implementing Digital Twins for smart grid applications?  
The initial search results provided a lot of studies across different aspects of Digital Twin applications, including asset management frameworks [16; 17; 18], security implementations [19; 20; 21], and system operation solutions [22; 23; 24]. Moreover, recent papers (2022-2024) showed increased focus on AI integration and security aspects compared to earlier works.  
A timeline visualization in Figure 3 shows the distribution of final selected papers across the years 2019-2024.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **Papers Found** & **After Filtering** \\ \hline IEEE Xplore & 180 & 165 \\ ScienceDirect & 145 & 132 \\ Web of Science & 95 & 88 \\ Google Scholar & 65 & 58 \\ Semantic Scholar & 35 & 32 \\ Total & 520 & 475 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial search result distribution.  
Figure 3: Paper distribution timeline.  
* 2019: References [25; 26; 27; 28].
* 2020: References [24; 29; 30].
* 2021: References [18; 20; 31; 32; 33; 34; 35].
* 2022: References [22; 36; 37].
* 2023: References [17; 19; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54].
* 2024: References [55; 56; 57; 58; 59; 60; 61; 62; 63; 64].
## 3 Results and Discussion
### Validation  
The described procedure was thoroughly validated in accordance with the International Conference for Harmonization (ICH) guidelines to verify its fit for the intended purpose [47]. Several industry committees, regulatory agencies, and individual researchers have published reviews about validation strategies, quality assurance, and regulatory purposes [48], wherein most are related to the pharma and chemical industry. Over the years, method validation guidelines have been offered by numerous international organizations such as the American Society for Testing and Material (ASTM), the European Analytical Chemistry Group (EURACHEM), and the European Committee for Normalization (CEN) [49]. Consequently, several validation guidelines with different scopes have been issued describing the validation parameters to be studied, the way to determine each one, and their acceptance criteria. The ICH guideline is our preference, especially as this guideline has been developed for harmonization purposes. Our method was validated regarding linearity and slope, method LODs and LOQs, repeatability and accuracy and precision.
## 5 Experimental Validation
### Experimental Results
#### 5.2.3 Results
In addition, because the amplitude of Source C is 20 dB lower than the other two sources, the OMP and off-grid-\(GL_{1}\) methods fail to successfully identify Source C under certain working conditions, which shows that the methods have failed. (3) Amplitude error in identification of sound sources  
Next, the amplitude identification of the three sources is further analyzed. The four test conditions of 0 m/s@500 Hz, 0 m/s@300 Hz, 40 m/s@500 Hz, and 40 m/s@300 Hz are named Conditions 1, 2, 3, and 4, respectively. The amplitude errors of the three sources by different methods are depicted in histograms for comparison, as shown in Figure 15. When a source is not successfully identified, \"Failure\" is displayed. Figure 14: The schematic distribution of the identified locations of the three sound sources for the different methods. Figure 15: The amplitude identification errors of three sound sources by different methods: (**a**) Source A; (**b**) Source B; (**c**) Source C.  
As seen from Figure 15, due to severe energy dissipation, the amplitude errors of FISTA and off-grid-GL\({}_{1}\) are significantly higher than other methods, while the amplitude error of VG-SBL is the smallest. Overall, the amplitude error of Source C is smaller than the other two sources. This is due to the close spatial proximity of Source A and Source B, which can lead to difficulties in accurately assigning the amplitudes of Source A and Source B when the energy is concentrated and gathered near that region. (4) RMSEL and RMSEM  
Finally, the overall errors of different methods are given. The RMSEL and RMSEM values under the four working conditions are plotted in Figure 16 as box plots, and the mean values are marked above the boxes. The results are calculated based on Equations (28) and (29) and are the combined error of considering the three sound sources simultaneously. It should be noted that RMSEL and RMSEM cannot be calculated for cases where the source is not successfully identified, but for ease of comparison, RMSEL and RMSEM are set to 1 m and 50 dB, respectively. It can be seen that the average RMSEL of the traditional methods is above 0.1 m and the RMSEM is above 5 dB. The average RMSEL and RMSEM of the best-performing RPCA-SBL are 0.1 m and 5.37 dB, respectively. Compared with the conventional method, VG-SBL can effectively reduce the source identification error, with the mean values of RMSEL and RMSEM reduced to 0.07 m and 4.76 dB, respectively. This can be attributed to the result of learned inference based on probabilistic statistical rules, which allows the mesh to be dynamically approximated to the actual sound source and the robust robustness enhancement due to matrix factorization.
# Two-Dimensional Permutation Vectors' (PV) Code for Optical Code Division Multiple Access Systems
###### Abstract  
In this paper, we present a new algorithm to generate two-dimensional (2D) permutation vectors' (PV) code for incoherent optical code division multiple access (OCDMA) system to suppress multiple access interference (MAI) and system complexity. The proposed code design approach is based on wavelength-hopping time-spreading (WHTS) technique for code generation. All possible combinations of PV code sets were attained by employing all permutations of the vectors with repetition of each vector weight (\(W\)) times. Further, 2D-PV code set was constructed by combining two code sequences of the 1D-PV code. The transmitter-receiver architecture of 2D-PV code-based WHTS OCDMA system is presented. Results indicated that the 2D-PV code provides increased cardinality by eliminating phase-induced intensity noise (PIIN) effects and multiple user data can be transmitted with minimum likelihood of interference. Simulation results validated the proposed system for an agreeable bit error rate (BER) of \(10^{-9}\).  
OCDMA; vector permutation; phase induced intensity noise; multiple access interference; cross-correlation +
Footnote †: journal:  
Article
# Logistics Distribution Vehicle Routing Problem with Time Window under Pallet 3D Loading Constraint
## 3 Modeling
### Definition of Symbols  
Table 2 presents the names and meanings of the relevant variables involved in the study.  
\begin{table}
\begin{tabular}{c c} \hline
**Variable Name** & **Meaning of Variables** \\ \hline \(l\) & A collection of all customer nodes. \(J=\{i,j|0,1,2,3,\ldots,P\}\) \\ \(K\) & Distribution vehicle or distribution route collection. \(K=\{k|1,2,3,\ldots,S\}\) \\ \(M\) & A collection of all pallets. \(M=\{m|1,2,3,\ldots,Q\}\) \\ \(N\) & A collection of all goods. \(N=\{n|1,2,3,\ldots,R\}\) \\ \(d_{ij}\) & The distance from customer point \(i\) to \(j\). \(i,j\in J\) \\ \(T\) & The collection of service cycles. \(t\in T\) \\ \(c^{d}\) & Distribution costs per unit distance during vehicle distribution. \\ \(c^{u}\) & The unit cost of vehicle use. \\ \(c^{t}_{i}\) & The time cost of premature arrival. \\ \(c^{t}_{i}\) & The time cost of arriving late. \\ \(OT^{i}_{kj}\) & The unloading time of the vehicle \(k\) without using the pallet at the customer point \(j\) during the service cycle \(t\). \(k\in K,j\in J,t\in T\) \\ \(PT^{t}_{kj}\) & The unloading time of the vehicle \(k\) using the pallet at the customer point \(j\) during the service cycle \(t\). \(k\in K,j\in J,t\in T\) \\ \([E^{t}_{j},L^{t}_{j}]\) & The service time window for each point. \(j\in J,t\in T\) \\ \hline \end{tabular}
\end{table}
Table 2: Definition of symbols.