esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Throughflow Model
### Empirical Models  
To reconstruct a physically reasonable entropy field in the S2 stream surface for the centrifugal compressor off-design performance analysis, the complete empirical correlations available in the open literature were adapted to the present throughflow method. Viscous effects from both the impeller and vaneless diffuser were considered. Moreover, the slip models were employed in the present throughflow model to simulate the deviation between the real flow angle and the blade angle. Referring to [PERSON]'s work [40] and the authors' previous work [41], a list of the empirical models already implemented in the present throughflow solver is given in Table 1. These selected empirical models were validated in the aforementioned works and showed satisfying performance in terms of compatibility and thus the accuracy of the analysis results.  
In general, most of these empirical correlations were established by correlating the losses to the flow parameters at the blade leading and trailing edges. Therefore, special treatment is required when they are applied to the throughflow simulation, which means a redistribution of the evaluated losses along the spanwise and streamwise directions is necessary.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Categories of Models** & **Correlations** \\ \hline Slip factor & [PERSON] et al. [42] \\ Incidence loss & [PERSON] [43] \\ Blade loading loss & [PERSON] and [PERSON] [44] \\ Skin friction loss & [PERSON] [45] \\ Clearance loss & [PERSON] [45] \\ Mixing loss & [PERSON]\&[PERSON] [46] \\ Disc friction loss & [PERSON] and [PERSON] [47] \\ Recirculation loss & [PERSON] et al. [48], [PERSON] et al. [40] \\ Vaneless diffuser loss & [PERSON] and [PERSON] [44] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Empirical correlations list in centrifugal compressor simulation.
## 5 Critical Seismic Acceleration Based on the Upper Bound Theory
### Calculation of Critical Seismic Acceleration  
Combining the Equations (9)-(27), the critical seismic acceleration coefficient could be obtained by equating the energy dissipation rate to the total rate of work of external forces based on upper-bound limit analysis theory as follows:  
\[k_{hc}=\min_{\theta_{0},\theta_{h},\frac{r_{0}^{2}}{r_{0}^{2}},\frac{h}{H}, \varphi_{t}}\overline{2\omega\gamma\left[\begin{array}{c}D_{3D}+D_{insert} -W_{\gamma-3D}-W_{\gamma-insert}\\ \int_{\theta_{0}}^{\theta_{h}}\int_{0}^{x_{1}^{\star}}\int_{a}^{y^{\star}}(r_ {m}+y)^{2}\sin\theta dxdyd\theta+\\ \int_{\theta_{0}}^{\theta_{h}}\int_{0}^{x_{2}^{\star}}\int_{d}^{y^{\star}}(r_ {m}+y)^{2}\sin\theta dxdyd\theta\end{array}\right]+\omega\gamma\tau_{0}^{4} \frac{b}{H}(f_{4}-f_{5}-f_{6})\big{[}\sin\theta_{h}e^{(\theta_{h}-\theta_{0}) \tan\varphi_{t}}-\sin\theta_{0}\big{]}\]  
It should be noted that the critical seismic acceleration coefficient \(k_{hc}\) obtained in this work was the minimum of Equation (28) with respect to \(\theta_{0},\theta_{h},\frac{r_{0}^{2}}{r_{0}^{2}},\frac{b}{H},\varphi_{t}\), with given constraint MHB parameters (\(\sigma_{ci}\), \(GSI\), \(m_{i}\), \(D_{i}\), \(\gamma\)) and slope geometry (\(H\), \(\beta\)), with the aid of the optimization tool (particles warm) in Matlab.  
From Equation (28), when the values of material parameters (\(\sigma_{ci}\), \(GSI\), \(m_{i}\), \(D_{i}\)) are given, one could easily find that the critical seismic acceleration coefficient \(k_{hc}\) was uniquely related to the slope geometry (\(H\)), which could be presented in Equation (29).  
\[k_{hc}=f(\sigma_{ci},GSI,m_{i},D,\gamma,H,\beta), \tag{29}\]A dimensionless parameter \(SR\) was defined in this study as follows:  
\[SR=\frac{\sigma_{ci}}{\gamma H}, \tag{30}\]  
Then Equation (29) can be substituted by Equation (31),  
\[FoS=f(GSI,m_{i},D,SR,\beta). \tag{31}\]  
Four different groups of critical seismic acceleration coefficient \(k_{hc}\) with different parameters \((\sigma_{ci},\gamma,H)\) but the same \(SR\) are presented in Table 1. From Table 1, the values of the critical seismic acceleration coefficient \(k_{hc}\) kept constant for the cases of the same \(SR\), which indicated that the \(k_{hc}\) was uniquely related to the dimensionless parameter \(SR\), regardless of the magnitude of individual parameters \(\sigma_{ci},\gamma,H\).
# Non-Orthogonal eMBB-URLLC Radio Access for Cloud Radio Access Networks with Analog Fronthauling
## 1 Introduction
### Notation  
Bold upper- and lower-case letters describe matrices and column vectors, respectively. Letters \(\mathbb{R}\), and \(\mathbb{C}\) refer to real and complex numbers, respectively. We denote matrix inversion, transposition and conjugate transposition as \(\left(\cdot\right)^{-1},\left(\cdot\right)^{T},\left(\cdot\right)^{H}\). Matrix \(\mathbf{I}_{n}\) is an identity matrix of size \(n\) and \(\mathbf{1}_{n}\) is a column vector made by \(n\) \"\(1\)s\". Symbol \(\otimes\) denotes the Kronecker operator, \(\text{vec}(\cdot)\) is the vectorization operator, and \(\mathbb{E}[\cdot]\) is the statistical expectation. Notation \(\text{diag}(A_{1},A_{2},...,A_{n})\) denotes a diagonal matrix with elements \(A_{1},A_{2},...,A_{n}\) on the main diagonal. The Q-function \(\mathcal{Q}(\cdot)\) is the complementary cumulative distribution function of the standardized normal random variable, and \(\mathcal{Q}^{-1}(\cdot)\) is its inverse.
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 4 Healthcare Data Protection Legislation and Framework
### IoHT Governance Challenges
#### 4.3.1 Conflicts in Laws  
After the implementation of the General Data Protection Regulation (GDPR) for the enforcement of data security and privacy, standards have been widened. For the protection of European citizens' data outside of Europe, their data is allowed to be used under strict conditions: If any data privacy legislation enacted by a non-EU country accepts Standard Contractual Clauses (SCC) and Binding Corporate Rules (BCR). These terms and conditions cause challenges for developing countries because BCR's and SCC are time-consuming and costly [79].  
A country may or may not have a law for user data protection, but the healthcare system should follow the laws of the country from which the user belongs. The same thing applies to the social and cultural norms, which should be applied according to the laws of the state to which the user belongs. In the case of data residing on cloud servers, again, the privacy laws will be applied according to the region of the patients.
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 4 Results
### Results for the Change of Network Parameters  
In Scenario 2, comparison was performed on the dynamic characteristics of four routing algorithms in different network environments. In different scenarios, we have recorded message  
Figure 14: Results of Scenario 1 of Spray and Wait. **(a)** Delivery ratio, delivery time with different number of copies in Scenario 1.1–1.3; **(b)** delivery ratio, deliver time with different number of copies in Scenario 1.4–1.5.  
Figure 12: Results of Scenario 1 of DTMA. **(a)** Delivery ratio, network life time and hop count with different alpha in Scenario 1.1–1.3; **(b)** delivery ratio, network life time and hop count with different alpha in Scenario 1.4–1.5.  
Figure 13: Results of Scenario 1 of Prophet V2. **(a)** Delivery ratio, delivery time with different beta in Scenario 1.1–1.3; **(b)** delivery ratio, deliver time with different beta in Scenario 1.4–1.5.  
consumption acts as an increasing function of all parameters for all protocols in Scenario 2. DTMA shows the minor energy consumption as the parameters increase, followed by Prophet V2 and Spray and Wait (Figures 17b and 20b).  
We can also see from the results of Scenario 2 that the delay shows different trends with the increase in all parameters of four protocols. Firstly, the delay is positively related to the buffer size (Figure 18b); secondly, it is negatively related to the transmission range of nodes (Figure 15b). In most cases, Prophet V2 shows the minor delay, followed by DTMA and Spray and Wait and Epidemic.  
Figure 19: Results of Scenario 2.2. (**a**) Overheats with different buffer sizes; (**b**) hop count with different buffer sizes.  
Figure 17: Results of Scenario 2.1. (**a**) Network life time with different transmission ranges of nodes; (**b**) energy consumption with different transmission ranges of nodes.  
For Scenario 2, hop count performance is presented in Figures 16b and 19b. DTMA and Epidemic try to deliver more messages through more hops, while Prophet V2 and Spray and Wait cannot change the number of hops when the parameters change.  
Regarding overheads, Scenario 2 shows that the proposed DTMA and Epidemic has the worst performance by replicating more messages than it is delivering. Prophet V2 has the best performance as the number of robots increases, followed by Spray and Wait. For all sub-scenarios, all protocols have a similar overhead performance except Prophet V2's smaller overhead in general.  
Finally, for Scenario 2, the network life time performance can be seen in Figures 17a and 20a, where Prophet V2 and DTMA have the best performance for this metric, with similar figures, while the others have a less life time in general.
## 4 Experimental Design and Results Analysis
### Scenario Design  
In the Systems Tool Kits-designed simulation scenario, we selected four ground stations (Hainan, Kashgar, Yunnan, and Xi'an) and various numbers of satellites for data transmission experiments. We designed ten sun-synchronous orbits, each with nine satellites evenly distributed. This section shows 24 scenarios. Each scenario varies in the number of ground stations (one to four) and satellites (10 to 90), with a planning period of 24 h, from 1 March 2024 00:00:00 to 2 March 2024 00:00:00. The parameters are detailed in Table 3.  
The essential attributes for the transmission tasks are as follows:  
1. Each satellite can only transmit data to one ground station;
2. The transmission priority of each satellite is randomly assigned using a 1-10 scale, where 10 denotes the highest priority;
3. The priorities for the ground stations are Hainan (seven), Kashgar (five), Xi'an (ten), and Yunnan (three);
4. The transmission time window must fall within the planning period, and if the window is shorter than 7 min, it must equal the task's minimum required transmission time;
5. All tasks use a standard communication protocol between satellites and ground stations;
6. The transmission frequency band of all tasks is set to be S-band.  
This section evaluates MAPSO by comparing it with NSGA-II [24], MOEA/D [25], MOGA [26], DRSC-MOAGDE [27], DSC-MOPSO [28], and MO-Ring-PSO-SCD [29]. NSGA-II is Pareto-based, MOGA is indicator-based, and MOEA/D is decomposition-based.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Experiment ID** & \begin{tabular}{c} **Number of** \\ **Satellites** \\ \end{tabular} & \begin{tabular}{c} **Number of** \\ **Ground Stations** \\ \end{tabular} & **Experiment ID** & \
\begin{tabular}{c} **Number of** \\ **Satellites** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **Ground Stations** \\ \end{tabular} \\ \hline c1 & 10 & 1 & c13 & 40 & 3 \\ c2 & 20 & 1 & c14 & 50 & 3 \\ c3 & 30 & 1 & c15 & 60 & 3 \\ c4 & 40 & 1 & c16 & 10 & 4 \\ c5 & 10 & 2 & c17 & 20 & 4 \\ c6 & 20 & 2 & c18 & 30 & 4 \\ c7 & 30 & 2 & c19 & 40 & 4 \\ c8 & 40 & 2 & c20 & 50 & 4 \\ c9 & 50 & 2 & c21 & 60 & 4 \\ c10 & 10 & 3 & c22 & 70 & 4 \\ c11 & 20 & 3 & c23 & 80 & 4 \\ c12 & 30 & 3 & c24 & 90 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental scenarios and parameters.  
DRSC-MOAGDE has a competitive performance on practical MOP, DSC-MOPSO is a multi-objective PSO algorithm with dynamic-switching crowding, MO-Ring-PSO-SCD is a powerful PSO algorithm for solving multi-objective problems. We generated 24 test instances, as described in Section 4.1. Each test instance was run 30 times, and the average value was taken as the final result. Our algorithm was implemented using MATLAB R2022b and tested on a personal computer equipped with an AMD Ryzen 7 4800H CPU with Radeon Graphics (2.90 GHz).
## 4 Case Study
### Result Analysis
#### 4.2.2 Extraction of Soil and Water Conservation Measures  
This study uses a relatively small SDA in the GD section of GS-HSR (Figure 7) to demonstrate the process of collecting information on water conservation measures through UAV imagery. Based on UAV imagery, through manual visual interpretation and manual verification, the soil and water conservation measures in SDAs were extracted, and then, through spatial overlap analysis, the changes in water conservation measures in different periods were analyzed.  
Figure 5: SDAs determined by manual verification.  
(1) Manual visual interpretation and verification of UAV imagery  
As depicted in Figure 7, UAV imagery provides high-resolution visual data that clearly delineate critical infrastructural elements such as roads, construction vehicles, vegetation, and the soil and water conservation measures established around the SDA during the construction stage. Utilizing distinct features such as color, texture, structure, and size present in the UAV imagery, soil and water conservation measures were meticulously extracted through a manual digitization process using ArcGIS software. This process involved identifying and outlining specific conservation structures, such as drainage systems, slope protection barriers, and vegetative buffers, based on their unique spectral and spatial characteristics.  
Figure 6: Disturbance boundaries of SDAs.  
Figure 7: UAV imagery of the selected SDA.  
2. Spatial overlay analysis of UAV imagery from multiple periods  
After the extraction of soil and water conservation measures, conducting a spatial overlay analysis on UAV imagery from multiple periods is essential for assessing the temporal dynamics and current status of these conservation measures within the SDA. This analysis involves the integration of multi-temporal UAV datasets to identify and quantify changes in the spatial distribution and effectiveness of conservation practices over time.  
The spatial overlay analysis was performed using ArcGIS, where UAV imagery captured at different stages of the construction process was superimposed to facilitate a comparative evaluation. By aligning and overlaying these images, this study was able to detect variations in the extent, condition, and implementation of soil and water conservation measures. Key changes, such as the expansion or reduction of drainage systems, alterations in slope protection structures, and variations in vegetative cover, were identified and quantified through this comparative approach.
# A 12-Bit 1-GS/s Pipelined ADC with a Novel Timing Strategy in 40-nm CMOS Process
## 2 Proposed ADC Architecture and Pre-Quantization Timing
### Working Timing Strategy  
#### 2.2.1 Traditional Working Timing  
As shown in Figure 2, the traditional timing includes a sampling phase and an amplification phase. \(\phi\) is the master clock of this ADC. According to the \"two-step\" working timing, the odd-numbered Stage1, Stage3, and Stage5 work under the control of clocks \(\phi_{1}\), \(\phi_{1e}\), and \(\phi_{2}\). The even-numbered Stage2, Stage4, and Stage6 work under the control of clocks \(\phi_{2}\), \(\phi_{2e}\), and \(\phi_{1}\). It can be concluded that the conventional timing fails to specifically allocate enough time for quantization, therefore increasing the pressure on the operational amplifier's effective output settling time.
## 2 Materials and Methods
### Data Comparison Methodology
#### 2.4.3 Rainfall Event Definition and Properties  
In addition to evaluation at daily and hourly time scales, the most performing GPP was evaluated at the event time scale. There are numerous methods for identifying individual rainfall events [58]. In this study, based on a study conducted by [59] over a tropical area (Brazil), a minimum inter-event time interval of 6 h and a minimum rainfall depth threshold of 2.5 mm were chosen for the evaluation. In other words, a cumulative rainfall depth > 2.5 mm is required to be considered as a rainfall event. The temporal resolution used to define rainfall events is one hour for both rain gauge and GPP data.
# Real-Time UAV Trash Monitoring System
## 3 Message Queuing System
In the UAV trash detection task, the UAV continuously consumed the messages from the Kafka topic to check if there were any commands from the control station. Each UAV consumed one partition in the topic. The database supported large volumes of data to store and query. The SQL (Structured Query Language) database, such as MySQL [35], stored the data in a predefined scheme with the table. The table was created by rows and columns, which meant that the data were stored in fixed columns that were not changeable. The addition of a new column for the table in the SQL database is time-consuming. The advantage of SQL is that it is more suitable for complex queries. The NoSQL (Non-Structured Query Language) database has a more flexible data storage format than the SQL database. In an NoSQL database such as Mongo [36], the JSON (JavaScript Object Notation)-like data are used to store in the database. The JSON data are key-value pairs and have no predefined structure to follow. We could add a new key to the data and store it in the database with a high level of freedom. In [37], the authors compared the performance of seven databases with three SQL (Oracle, MySQL, and MsSQL) and four NoSQL (Mongo, Redis, GraphQL, and Cassandra) databases. The NoSQL databases were faster than the SQL databases, and Mongo had the best performance in the experiment, as shown in Tables 3 and 4. We chose Mongo as our database due to the good performance and schema-free nature of the data storage. In Mongo, the collection is created to store the documents. The trash document that is sent by the UAV includes _id_, _time_, _lat_, _lon_, and _trash_, which are the ID of the UAV, the time that the trash is detected, the latitude and the longitude of the trash position, and the polluted area of the trash, respectively. In the UAV trash monitoring system, Kafka is used as the data streaming platform; the data need to be accessed and stored in the database. We built the Kafka connector that consumed the data with batch processing from Kafka and transferred the data into the database.
## 3 Methodology
### Models
#### 3.3.3 Random Forest  
Random Forest is an ensemble learning method that builds multiple decision trees using random subsets of data and features. It reduces overfitting by averaging the predictions of these trees, which smooths out noise and variance, resulting in more stable and reliable outcomes [51]. Its randomness improves generalizability and robustness, making it suitable for handling complex interactions within the data.  
For this study, the optimal Random Forest model was achieved with 200 decision trees (n_estimators), each limited to a depth of 10 (max_depth), and a minimum of 5 samples required before further splitting (min_samples_split).
# Comparative Study of Indoor Propagation Model Below and Above 6 GHz for 5G Wireless Networks
## 6 Results and Discussion
### Path Loss Results and Analysis
It can be shown that the ABG model has good fitting for measured data in both bands. The \(\alpha_{ABC}\) and \(\gamma\) values are 2.6 and 2.1,  
Figure 5: Path loss CI and FI models fitting versus Tx-Rx separation distance at 3.5 GHz and 28 GHz for NLOS scenario. Figure 6: ABG path loss model versus Tx-Rx separation distance for LOS scenario. Figure 7: CIF path loss model versus Tx-Rx separation distance for LOS scenario. respectively with optimization parameter \(\beta_{ABG}\) of 36.7 dB. The CIF path loss model for the NLOS scenario is shown in Figure 9. The \(n_{CIF}\) is 3.1 dB and \(b\) is 0.2. As can be seen from Figure 3 showing how these measurements were obtained, the wall edge blocked the directed paths, hence, these measurements presented a NLOS scenario. The signal degradation in a NLOS scenario represents the diffraction loss (DL). Figure 10 shows the DL and FD values. It presents the power of the received signal (path gain) with Tx-Rx separation distance for LOS and NLOS scenarios at all measured frequencies. The loss due to the shadowing of wall edge (diffraction loss) and the loss due to using a high band above 6 GHz (frequency drop FD) compared to the lower band below 6 GHz are also depicted in Figure 10.  
Figure 5.0 ABG path loss model versus Tx-Rx separation distance for NLOS scenario. Figure 5.1 CIF path loss model versus Tx-Rx separation distance for NLOS scenario. Figure 10 shows that at 3.5 GHz the path gain at 7 m Tx-Rx separation distance for LOS scenario is \(-\)57.14 dB, while, at the NLOS scenario in the same band the path gain is \(-\)70.4 dB. This means that for NLOS, the signal degrades by 13.26 dB due to the wall edge shadow that represented the DL proposed parameter. At the same Tx-Rx separation distance of 7 m for 28 GHz, it can be shown that the signal in the NLOS scenario degrades by 27 dB, which is about double of the DL at 3.5 GHz. The means of DL values are 11.11 dB and 23.37 dB for 3.5 GHz and 28 GHz, respectively. It can be concluded that the DL for 28 GHz is high compared to the band below 6 GHz; it is about twice the DL of 3.5 GHz. Another important observation from Figure 10 is the frequency drop, for the LOS scenario at one particular Tx-Rx separation distance of 17 m, where the path gain values are \(-\)59.22 and \(-\)81 dB. 57 dB for 3.5 GHz and 28 GHz, respectively. This implies that the loss due to the higher frequency (FD of signal) is 22.4 dB. In another observation at 13 m of Tx-Rx separation distance for the NLOS scenario the FD loss is 33.14 dB. The mean FD values are 19.73 dB and 32 dB for LOS and NLOS scenarios respectively. The FD loss of the NLOS scenario is greater than that of the LOS scenario, as a mean value, by 12.27 dB, which can represent the DL in the frequency drop estimation.
## 2 Proposed Method  
As shown in Figure 1, the proposed network model based on an end-to-end encoding and decoding structure includes four modules: the REI module, the asymmetric convolution enhancement (ACE) module, and the feature fusion module. First, two convolution layers are used to extract features from the input RGB image. Second, the feature maps are passed through the ACE module to extract comprehensive crack texture information. Third, the REI module focuses on the semantic features and reduces the complexity of the model calculation, which is beneficial in terms of the optimization of the network. Finally, at the encoding stage, a U-shaped structure with long concatenation is used to fuse the shallow detail information with the deep semantic information to improve the accuracy of the segmentation mask. In addition, according to the channel design rule of [40], the number of linearly increased channels is used in this paper.