esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Routing in 3D Sensor Network with Internal Holes
### 3D Sensor Networks with Multiple Internal Holes  
For a network with more than one inner hole, a clustering algorithm is adopted to segment the network into clusters. Each cluster centers at an inner hole, and contains one hole only. The same mapping algorithm discussed in Section 5.1 is then deployed for each cluster to generate virtual coordinates. Note here that the virtual coordinates of each clusters are independent of each other. \"Aligning\" the \"cluster spheres\" according to their positions in the original network is neither practical nor correct for global end-to-end greedy routing. For example, after the mapping, the network with two holes consists of two \"cluster spheres\". Even we can align them according to their sharing nodes, the virtual coordinatesobtained after the alignment do not guarantee the greedy end-to-end route across the two spheres (see Figure 7 for an example). Since global alignment is infeasible, routing across the clusters often relies on the \"gateways\" between adjacent clusters. However, it remains challenging to design such gateway-based routing under the constraints of constant-bounded storage and computation at individual nodes. To this end, we propose a _tunnel_-based routing scheme. It does not require global alignment and, at the same time, keeps the storage and computation cost on each node \"nearly\" a small constant. Moreover, it guarantees the global greedy end-to-end routing across the clusters and does not overload the gateway nodes like the traditional gateway scheme does. The proposed algorithm is outlined below.
## 4 Experimental Results
### Evaluation of the Prediction of Gasoline Orders Using Regression Models  
Our proposed regression models used 935 augmented datasets. The datasets were grouped into 11 clusters using the K-means clustering algorithm, and then 85 datasets within each cluster were obtained using VAE. Table 4 shows the performance of the Lasso, Ridge, and Elastic-Net regressions, which add regularization terms to the linear regression model. We analyzed the Lasso, Ridge, and Elastic-Net regressions by varying the weight of the regularization terms to 0.01, 0.1, 1, and 10. In Elastic-Net, we set the ratios of the two regularization terms L1 and L2 to 30%:70%, 50%:50%, and 70%:30%. The models were evaluated by using R-Squared, Root Mean Squared Errors (RMSE), and accuracy. Regardless of the size and ratio of the regularization terms, all models showed lower performance than linear regression. Notably, the Lasso and Elastic-Net regressions failed to predict when the weight of regularization was set to 1 and 10. This shows that, when modeling using data samples with a small number of independent variables, the regularization terms are biased.  
Table 5 describes the performance of the Random Forest, Extra Trees, AdaBoost, and XGBoost regressions, which are ensemble models. The models were evaluated by using R-Squared, Root Mean Squared Errors (RMSE), and accuracy. Random Forest, Extra Trees, AdaBoost, and XGBoost are common ensemble models used for classification. Specifically, Random Forest and XGBoost provide high performance in classification tasks. Our gasoline order prediction model is a regression task that predicts numerical values, so it is not suitable for classification-based models. However, in order to apply the strength of an ensemble model to a regression model and make up for regression weakness, we validated it using the regression modules provided by each model.  
All ensemble models showed lower performance than linear regression. This shows that using linear data samples can lead to similar predictions from individual trees, thus limiting the performance of ensemble models. This can also occur when using fewer independent variables. Through our evaluation, we confirmed that linear regression is the most suitable method for predicting gasoline orders at gas stations in South Korea.  
Figure 4: Visualization of the change in accuracy based on the variation in the number of augmented datasets per cluster.
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
###### Abstract
Regular scalp checks to monitor changes and take preventive measures will be possible, allowing for the service to be used without the inconvenience of appointment scheduling and waiting times. This approach will make scalp health management much more efficient compared to traditional methods. Additionally, the web platform will provide customized solutions, suitable for users' scalp conditions. By presenting the prevention and treatment methods according to the scalp condition and its various stages, the platform will facilitate the accurate and personalized management of scalp health. The web platform that was developed in this study combines efficient deep learning technology with a user-friendly interface, providing an innovative solution in the field of scalp diagnosis and contributing to improving users' scalp health. Ultimately, this study focused on developing a platform that will help to maintain healthy scalp conditions through early detection and accurate diagnosis. Another aim of this study was to provide everyone with the opportunity to easily receive scalp diagnoses, allowing them to check and address scalp health. It also aimed to raise people's awareness of scalp health and to provide innovative solutions in the field of scalp diagnosis, contributing to improving scalp health worldwide. The contributions of this study can be summarized as follows:  
* A pre-trained EfficientNet-B0 model was fine-tuned on a scalp image dataset to achieve high performance even in a limited data environment. * By utilizing the EfficientNet-B0 model, a diagnostic model for fine dandruff and perifollicular erythema was developed, achieving high diagnostic accuracy for each condition. This demonstrates that machine learning can be a practical and reliable tool in the field of medical diagnostics. * A user-friendly web platform was developed to allow users to upload images of their scalp and instantly receive diagnostic results and personalized solutions.
# Analysis of the Accessibility of the Swinoujscie-Szczecin Fairway for Deep-Draught Ships
## 2 Study Area
### Meteorological Conditions  
Meteorological conditions such as wind direction and speed [20] play an important role in shaping water levels in the Swinoujscie-Szczecin fairway. Winds from a north-westerly and northerly direction favour an increase in water levels through inflow from the Baltic Sea, which increases the depth of the fairway. On the other hand, winds from the south and south-west may lower the water level, which poses a challenge to ships with greater draught, which may be forced to reduce their cargo or delay their voyage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Szczecin—Most Dlugi**} \\ \hline \multicolumn{3}{c}{**Water Level Drops Below the Mean Level in 2008–2022**} \\ \hline \multirow{2}{*}{**Delta \"D\" [cm]**} & **Average Annual Number** & **Average Duration of Water** \\  & **of Occurrences** & **Level Drops [h]** \\ \hline
0 & 91.53 & 46.56 \\ \hline
10 & 68 & 34.81 \\ \hline
20 & 37 & 28.61 \\ \hline
30 & 17.33 & 23.62 \\ \hline
40 & 7.13 & 19.98 \\ \hline
50 & 2.07 & 15.9 \\ \hline
60 & 0.6 & 16.78 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Frequency and duration of lower than average water levels.  
Figure 14: Cumulative frequency of water levels in Szczecin.  
Cyclical changes in water levels due to weather conditions have a direct impact on vessel traffic management, route planning, and navigational safety. Therefore, port authorities regularly monitor weather and water level forecasts in order to respond appropriately to changing hydrological conditions.  
Recent technological advancements also offer new tools for real-time monitoring of vessel position and water levels. For instance, [PERSON] et al. [29] proposed a 3D point cloud-based system that enables simultaneous ship detection and high-accuracy water level measurement regardless of lighting or weather conditions. Their improved PointRCNN model demonstrated increased precision in detecting distant vessels and robust riverbank extraction for water level estimation, suggesting promising applications in navigational safety and traffic management on inland waterways.
## 3 Results and Discussion
This is a consequence of the manual editing done in each image at different levels. A significant improvement is obtained after superimposing layers. The areas classified as paved, unpaved, or rivers are visually consistent with the images, since the user had control over superimposing them. Furthermore, reclassifying shadows and clouds support the reduction in the uncertainty of the LULC in those areas. This suggests the imperative need to edit such layers when working with satellite images, since clouds and shadows are a common features of the images. Employing these results contribute to more precise interpretations of spatial and temporal variations in the study areas, although a more detailed analysis could be done by considering more images. To summarize, both the HRC and HRC_e satisfactorily address the LULC classification. For the case of HRC, undesired features, such as clouds or shadows, may influence a more detailed interpretation of the results. In contrast, the edition which was subject to the HRC_e allows the identification of said misclassifications, and improves the results by a user-control process. In any case, the level of accuracy desired in the results depends on the purpose of the information. The following sections explain the main issues we identified during the procedure and how they were overcome. In addition, the potential applications of this methodology and the results obtained are addressed.
## 5 Results and Discussion
### Comparative Analysis Between 2D and 3D Models  
A critical aspect of this study is the validation of 2D models against 3D models. Table 6 presents the relative percentage errors between the 2D and 3D models' \(\rm R_{AC}/R_{DC}\) ratios across different segmentations and frequencies.  
These relative percentage errors are influenced by the discretization errors inherent in the FE models, as shown in Table 4. Notably, the five-segment 3D model, despite having a higher number of elements and nodes, exhibits a larger discretization error (2.39%) compared to the three-segment model (1.24%) due to its extended length of 4 m. Conversely, both the single-segment and three-segment 3D models are 1 m in length, but the three  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Segment** & **50 Hz** & **150 Hz** & **250 Hz** \\ \hline Single-Segment & 0.54\% & 0.99\% & 1.29\% \\ Three-Segment & 0.00\% & 0.34\% & 1.08\% \\ Five-Segment & 0.57\% & 1.04\% & 1.64\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relative percentage error between 2D and 3D models.  
Figure 7: Impact of cable segmentation on \(\rm R_{AC}/R_{DC}\) at 50 Hz.  
segment model benefits from a denser mesh structure, resulting in lower discretization errors. Key observations include the following:  
* **Model Size and Segmentation:** The five-segment 3D model spans 4 m with 5,265,862 conductor elements and 7,958,575 nodes, leading to higher discretization errors. In contrast, the single and three-segment models are each 1 m long, with the three-segment model having a denser mesh (2,102,958 conductor elements and 3,055,853 nodes) compared to the single-segment model (1,109,053 conductor elements and 1,581,938 nodes), resulting in lower discretization errors.
* **Discretization Errors' Impact:** Higher discretization errors in 3D models, especially in larger and more segmented configurations, contribute to increased relative percentage errors between 2D and 3D models. For example, the five-segment model's higher discretization error (2.39%) aligns with its highest relative percentage error (1.64%) at 250 Hz.
* **Frequency Influence:** As frequency increases, skin and proximity effects become more pronounced, necessitating finer mesh resolutions. This requirement exacerbates discretization errors in 3D models, leading to higher relative percentage errors at elevated frequencies across all segmentations. Figure 8 visualizes the trend of increasing relative error with frequency across different segmentations.
## 2 Study Area and Data
### Data Source  
The daily global solar radiation data for the six selected cities and the meteorological information data of Harbin in this paper were all obtained from the daily data set of basic elements from the China Meteorological Radiation International Exchange Station, issued by National Meteorological Information Center of China ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 21 April 2021). All air quality data in Harbin were collected from the China Air Quality Online Monitoring and Analysis Platform ([[https://www.aqistudy.cn/](https://www.aqistudy.cn/)]([https://www.aqistudy.cn/](https://www.aqistudy.cn/)), accessed on 2 February 2022).
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 4 DR Model Considering Real-Time Tariff
### Real-Time Tariff Model  
The real-time tariff of moment \(i\) is [28]:  
\[P_{i}=\frac{L_{i}}{\sum\limits_{i=1}^{24}{{\rm{L}}_{i}/24}}\times P_{0} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Time** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** & **Scenario 5** & **Scenario 6** & **Scenario 7** & **Scenario 8** & **Scenario 9** & **Scenario 10** \\ \hline
1:00 & 0.100 & 0.100 & 0.100 & 0.100 & 0.100 & 0.100 & 0.100 & 0.100 & 0.100 & 0.100 \\  
where \(L_{i}\) is the load of moment \(i\) and \(P_{0}\) is the sales price without real-time price.  
Using partial load to implementing real-time tariff requires setting a Customer Baseline Load (CBL), which is delimited according to customer's historical load. If customer's load does not exceed CBL, it follows the original tariff policy. The real-time tariff is only used on the exceeding CBL parts.
# Spectrum Sharing Opportunities for 6G Terrestrial and Non-Terrestrial Networks +
## 3 Coexistence Analysis Results
### Separation Distances Between FS CS and FSS vs. FSS Earth Station Elevation Angle
Figure 3: Nominal separation distance between FSS ES transmitter (1.8 m) and FS P-MP CS receiver for suburban deployment: (**a**) FSS ES antenna height is at 2 m; (**b**) FSS ES antenna height is at 10 m.  
The separation distances shown in Table 4 and in Table 5 that refer to the coexistence between the FSS and FS CSs decrease more slowly than the ones in Table 3 that have been obtained considering an FS TS victim receiver. Starting from the case where the FS and the FSS are aligned in the azimuth to an FS azimuth of 90 degrees, the separation distances decrease from 44.3 km to 0.3 km, from 34.3 km to 19 km and from 38.3 km to 16 km in Table 3, Table 4 and Table 5, respectively.
###### Abstract  
Non-orthogonal multiple access (NOMA) has been widely recognized as a promising technology to improve the transmission capacity of wireless optical communication systems. NOMA considers the principle of successive interference cancellation (SIC) to separate a user's signal at the receiver side. To improve the ability of optical signal detection, we developed a quantum dot (QD) fluorescent concentrator incorporated with multiple-input and single-output (MISO) to realize an uplink NOMA-based optical wireless system. However, inaccurate interference assessment of multiple users using the SIC detection algorithm at the receiver side may lead to more prominent error propagation problems and affect the bit error rate (BER) performance of the system. This research aims to propose a novel recurrent neural network-based guided frequency interference coefficient estimation algorithm in a NOMA visible light communication (VLC) system. This algorithm can improve the accuracy of interference estimation compared with the traditional SIC detection algorithm by introducing interference coefficients. It provides a more accurate reconstruction possibility for level-by-level interference cancellation and weakens the influence of error propagation. In addition, we designed uplink and downlink NOMA-VLC communication systems for experimental validation. When the power allocation ratio was in the range of 0.8 to 0.97, the experimental results of the downlink validated that the BER performance of both users satisfied the forward error correction (FEC) limit with the least squares (LS)-SIC and the long short-term memory recurrent neural networks (LSTM)-SIC detection strategy. Moreover, the BER performance of the LSTM-SIC algorithm was better than that of the LS-SIC algorithm for all users when the power allocation ratio was in the range of 0.92 to 0.93. In particular, our proposed system offered a large detection area of 2 cm\({}^{2}\) and corresponding aggregate data rate up to 40 Mbps over 1.5 m of free space by using QDs, and we successfully achieved a mean bit error rate (BER) of 2.3 \(\times\) 10\({}^{-3}\) for the two users.  
non-orthogonal multiple access; successive interference cancellation; interference coefficient estimation; quantum dot; visible light communication; power allocation +
Footnote †: journal: _Photonics_  
_2023_, 10, 569. [[https://doi.org/10.3390/photonics10050569](https://doi.org/10.3390/photonics10050569)]([https://doi.org/10.3390/photonics10050569](https://doi.org/10.3390/photonics10050569))
## 3 Results  
The overall results are reported in Figure 1, which shows the number of total samples associated with the different concentration ranges. These ranges were set taking into account both the compliance limits m and M (as reported in the Regulation (EC) No. 2073/2005) and the different levels of fish freshness, as follows: C \(<\) 10 mg kg\({}^{-1}\) = fish of good quality; 10 \(<\) C \(<\) 50 mg kg\({}^{-1}\) = significant deterioration; C \(>\) 50 mg kg\({}^{-1}\) = evidence of definitive decomposition [35, 36]. HIM level \(>\) 2.5 mg kg\({}^{-1}\) was detected in 242 of the 474 analyzed samples (51%), with 12 non-compliant responses (2.5%). HIM concentration less than 10 mg kg\({}^{-1}\) was detected in 410 samples (87% of the total). Figure 2 shows HIM concentration ranges in fish categories (fresh/frozen and defrosted products, canned products, ripened products). For fresh/frozen and defrosted category, 260 samples out of 294 analyzed showed an HIM concentration below 10 mg kg\({}^{-1}\) and 11 non-compliant samples out of the 294 analyzed were detected. On a total of 119 batches of canned samples, 117 contained HIM levels  
Figure 1: Distribution of HIM content in overall fish products. C = HIM concentration (mg kg\({}^{-1}\)). below 50 mg kg\({}^{-1}\), with 110 samples below 10 mg kg\({}^{-1}\). Finally, as concerns ripened products samples (61 on a total of 474), HIM levels less than 50 mg kg\({}^{-1}\) were detected in 55 samples. In Table 1 is reported the HIM content in mg kg\({}^{-1}\) (mean, minimum and maximum) for each kind of product. From a statistical analysis, a significant difference was verified among different seafood types, using one-way ANOVA (\(p<0.05\)). The highest HIM levels were quantified in fresh/frozen/defrosted tuna. The results of these levels were statistically higher than in those detected in canned tuna and other fresh fish and were comparable to the concentrations quantified in ripened anchovies, fresh/frozen mackerel, canned mackerel, and fresh/frozen sardines and herrings (\(t\)-test, \(p<0.05\)). Table 1 also reports the number and the concentration levels of non-compliant batches, ranging from 328 to 5542 mg kg\({}^{-1}\). Finally, the exposure assessment from seafood consumption, under an average exposure scenario (Table 2) and under the most likely exposure scenario (Table 3), are reported. Figure 2: Distribution of HIM content in fish categories (fresh/frozen/defrosted fish, canned products, ripened products). C = HIM concentration (mg kg\({}^{-1}\)).
# Ride Comfort Prediction on Urban Road Using Discrete
## 1 Introduction
It performs well in long road sections (over 160 m) and high vehicle speeds [21], which determine a scenario of highways or freeways rather than urban roads whose road section is shorter and vehicle speed is non-uniform (ranges from approximately 20 to 100 km/h). However, the IRI can hardly provide sufficient details to identify localized features, while such local features may strongly diminish ride comfort. For instance, a single large event in a section of a smooth road can result in the same IRI as a road which has moderate events scattered throughout the road section, but the former road section would result in more discomfort. To break the limitations, [PERSON] et al. [22] suggested the discrete roughness index (DRI). The DRI is a discrete indicator and calculated for each discretely measured location along with a road profile, which is more suitable to identify and characterize local features. The DRI also has an important property that the average value of converges to the IRI as the distance between sampled points becomes smaller. This indicates that the DRI characterizes pavement roughness without taking the distance of roads into account. Therefore, the DRI has great potential in ride comfort prediction, especially in road sections with unequal lengths. The main objective of this paper is to propose a more reliable ride comfort prediction method on urban roads, and to prove that the novel roughness index, the DRI, is more appropriate for ride comfort evaluation. The comfort driving speed control strategies are also studied based on the prediction results. The remainder of this paper is organized as follows: the definitions of the two involved pavement roughness indexes (IRI and DRI) and the ISO ride comfort standard are presented in Section 2. Section 3 presents numerical analysis, including simulation model and data processing of the measured road profiles. In Section 4, we developed the correlations between the DRI and the MTVV by considering the speed, the time period, and the combined effect of the left and right wheel paths. The speed-related DRI thresholds and speed control strategy are also presented in this section. The final section summarizes the conclusions and future work.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 3 System Model  
A satellite network service function chain orchestration architecture based on NFV technology comprises two primary layers: physical and virtual. The physical layer encompasses the network infrastructure, consisting of satellite and ground nodes that provide network access services to geographically distributed users. This network facilitates traffic transmission between terminals when users initiate service requests. The virtual layer represents an aggregation of user requests, where each request is abstracted as an SFC--a sequence of interconnected VNFs arranged in a specific order.  
When network traffic originates from user terminals at the network entry point, it triggers an SFC deployment request to the orchestrator. The SFC orchestrator performs two concurrent operations: analyzing the traffic characteristics and required network functions of the request while assessing the resource availability of the underlying physical network. Following a predetermined strategy, it deploys VNFs onto physical nodes (satellites) and maps virtual links to physical inter-node connections. This process culminates in establishing end-to-end network services for service terminals.