esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Precipitation, as an important component of the Earth's water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000-2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products.  
Gridded Precipitation Products (GPPs); evaluation; TMPA; CHIRPS; PERSIANN; GSMaP; IMERG; ERA5; Iran +
Footnote †: journal: Remote Sensing
###### Abstract  
This paper presents the study of the properties of objects that were fabricated with fused deposition modeling technology while using Poly (lactic acid)/Walnut shell powder (PLA/WSP) biocomposite filaments. The WSP was treated while using NaOH followed by silane. The infrared spectrum of treated and untreated WSP was characterized. The result was that thermal and mechanical properties could be improved by adjusting the concentration of silane. The experimental results showed: the surface compatibility between WSP and PLA was dramatically improved through treatment with KH550. The crystalline, thermal gravity, and thermal degradation temperatures of biocomposite with untreated WSP were improved from 1.46%, 60.3 \({}^{\circ}\)C, and 239.87 \({}^{\circ}\)C to 2.84%, 61.3 \({}^{\circ}\)C, and 276.37 \({}^{\circ}\)C for the biocomposites with treated WSP, respectively. The tensile, flexural, and compressive strengths of biocomposites were raised each by 8.07%, 14.66%, and 23.32%. With the determined silane concentration, PLA/10-15 wt.% treated WSP biocomposites were processed and tested. The results showed that the tensile strength was improved to 56.2 MPa, which is very near to that of pure PLA. Finally, the porous scaffolds with controllable porosity and pore size were manufactured.  
fused deposition modeling; biocomposite; poly (lactic acid)-matrix; walnut shell; surface treatment +
Footnote †: journal: Applied Sciences
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 4 Experimental Results and Analysis
### Simulation Results and Analysis of The Rolling Bearing Outer Ring Faults
#### 4.1.2 Performance Comparison  
This section compared the experimental results of ADKPCA, Deep DPCA, Deep DKPCA, and Deep ADKPCA by analyzing the fault detection rate and detection delay of the incipient failure experiment results of the rolling bearing outer ring. The detection delay refers to the interval from failure to detection. The fault detection rate refers to the ratio of the number of detected failures to the total number of failures. In the experiments, the thresholds of ADKPCA, Deep DPCA, Deep DKPCA, and Deep ADKPCA method were determined by the same confidence level, and the false detection rate of all methods was similar, so it was not used as the evaluation index.  
Figure 8a shows the fault detection rate of rolling bearing outer ring fault by ADKPCA, Deep KPCA, Deep DKPCA, and Deep ADKPCA method. The fault detection rate of the ADKPCA fault detection method was 62.86%. The detection effect by ADKPCA was low, this method basically did not have incipient fault detection capability. The fault detection rate of Deep KPCA and Deep DKPCA was about 80%, compared with ADKPCA method the detection rate had increased, but the detection  
Figure 7: Experimental results of the Deep DKPCA fault detection method for the rolling bearing outer ring fault. (**a**) The result of \(T_{21}^{2}\), (**b**) The result of \(Q_{21}\), (**c**) The result of \(T_{22}^{2}\),(**d**) The result of \(Q_{22}\), (**e**) The result of \(T_{23}^{2}\), (**f**) The result of \(Q_{23}\), (**g**) The result of \(T_{24}^{2}\) and (**h**) The result of \(Q_{24}\).  
capability still needs to be improved. The fault detection rate of Deep ADKPCA was 92.43%, which was significantly higher than other methods, and had a certain incipient fault detection capability.  
Figure 8b shows the detection delay of the rolling bearing outer ring fault by ADKPCA, Deep KPCA, Deep DKPCA, and Deep ADKPCA method. The ADKPCA method detected a failure at the fifth augmented data point, that is, at approximately the 15 th sampling point. We could obtain that the Deep KPCA and Deep DKPCA methods were the 7 th sampling points. The Deep ADKPCA method detected a failure at the second augmented data point, that is, at approximately the 6 th sampling point. It can be seen from the failure delay that the Deep ADKPCA method could detect the incipient failure of the rolling bearing outer ring earlier.  
Therefore, the validity and accuracy of proposed methods were better than that of other methods.
## 4 Case Study: Analysis of a Device
### Application of the Methodology
#### 4.2.4 Phase 4: Checking the Device's Memory Type  
The device used in the case study had eMMC type memory and was therefore compatible with ISP. If the device was equipped with another type of memory, other techniques could be employed, such as Chip-Off.
# Modeling Hourly Soil Temperature Using Deep BiLSTM Neural Network
## 4 Results
### Compared with the Other Literatures  
[PERSON] [15] uses four machine learning methods to predict the half hourly ST of a maize field in northern China. The results show that the four methods can provide ideal ST prediction at all depths, and the extreme learning machine (ELM) performs best. We compared the prediction results of our proposed algorithm at 5 cm, 10 cm, and 20 cm (only these three depths are involved in both studies) with [PERSON]'s optimal prediction results. The results are shown in Table 9. Our proposed method is less accurate than [PERSON]'s when the soil depth is 5 cm, but when the soil depth is 10 cm and 20 cm, it is more accurate than the optimal method mentioned in his article.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **BSk** & **BWh** & **Cfa** & **Csa** & **Dfa** \\ \hline Integrated BiLSTM & 1.56 & 1.82 & 1.34 & 1.63 & 1.25 \\ LSTM & 1.86 & 2.09 & 1.51 & 1.92 & 1.44 \\ BiLSTM & 1.92 & 1.75 & 1.57 & 2.07 & 1.54 \\ DNN & 2.13 & 2.16 & 1.60 & 2.35 & 1.56 \\ RF & 2.03 & 2.17 & 1.67 & 2.08 & 1.64 \\ SVR & 2.25 & 2.46 & 1.82 & 2.64 & 1.90 \\ LR & 3.56 & 3.81 & 2.88 & 4.16 & 3.04 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average RMSE of each model for different climate types (°C).  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **BSk** & **BWh** & **Cfa** & **Csa** & **Dfa** \\ \hline integrated BiLSTM & 1.27 & 1.44 & 1.07 & 1.30 & 1.00 \\ LSTM & 1.47 & 1.67 & 1.20 & 1.50 & 1.11 \\ BiLSTM & 1.50 & 1.74 & 1.22 & 1.57 & 1.19 \\ DNN & 1.68 & 1.72 & 1.26 & 1.84 & 1.22 \\ RF & 1.58 & 1.69 & 1.30 & 1.61 & 1.26 \\ SVR & 1.82 & 2.02 & 1.44 & 2.11 & 1.51 \\ LR & 2.86 & 3.09 & 2.29 & 3.30 & 2.48 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Average MAE of each model for different climate types (°C).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method** & **BSk** & **BWh** & **Cfa** & **Csa** & **Dfa** \\ \hline Integrated BiLSTM & 0.94 & 0.94 & 0.94 & 0.89 & 0.87 \\ LSTM & 0.91 & 0.92 & 0.92 & 0.83 & 0.83 \\ BiLSTM & 0.91 & 0.91 & 0.91 & 0.81 & 0.80 \\ DNN & 0.89 & 0.92 & 0.90 & 0.78 & 0.77 \\ RF & 0.89 & 0.92 & 0.89 & 0.80 & 0.78 \\ SVR & 0.86 & 0.88 & 0.87 & 0.56 & 0.67 \\ LR & 0.65 & 0.69 & 0.67 & \(-\)0.02 & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average determination coefficient (R\({}^{2}\)) of each model for different climate types.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Soil Depth** & \multicolumn{2}{c}{**Feng’s Research**} & \multicolumn{2}{c}{**Our’ Research**} \\ \cline{2-5}  & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline
5 cm & 1.85 & 1.44 & 2.04 & 1.63 \\
10 cm & 2.05 & 1.6 & 1.69 & 1.34 \\
20 cm & 2.47 & 1.91 & 1.55 & 1.44 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of the results of the two studies.
# Flying Real-Time Network to Coordinate Disaster Relief Activities in Urban Areas +
## 3 Proposed Network System  
We argue that the use of UAVs has several advantages for the implementation of an alternative network system that provides communication in a disaster environment. For instance, it can help reduce the risks assumed by rescue teams and enhance the teams' effectiveness, while providing viewing angles that are impossible to obtain with other types of mules. The cost of these vehicles is falling, and their technological capabilities are rapidly increasing, including services such as automatedobstacle avoidance, route planning, point-to-point navigation using way-points, and tracking points of interest. All these features facilitate the autonomous deployment of the network and thus the rapid provision of the connectivity required by the participants of the first response process.  
UAVs may become the primary form of information capture and transportation in disaster scenarios. Mostly used for video surveillance, UAVs equipped with data communication and software capabilities can share data through a collaborative architecture, thus allowing an efficient FANET to be built. These vehicles hold a great potential when their collected data is integrated with geographic information systems and crisis management tools. Considering that drones may have different sizes and flight autonomy, a proper selection and distribution of these vehicles is necessary to provide the best network coverage in the disaster area, and also an efficient communication support to the search-and-rescue activities.  
Given the constraints considered in this proposal for message delivery, we have called these particular FANETs \"Flying Real-Time Networks\" (FRTNs). Next, we describe the types of flying witness unit (FWU) that can be present in a FRTN, and the way in which these units should be deployed in the work area to try maximizing the communication support and minimizing the number of intermediary participants.
## 4 Discussion and Conclusions
### Research Limitations and Future Developments  
The main research boundary is represented by the limited number of cases that have been included in the study. In fact, the case studies sample is not meant to represent a significant percentage of the overall number of FSED nor HBED cases worldwide, but it is the basis for the explorative study to collect important key features. Each case has indeed been analyzed in detail in terms of floor plan, functional area, construction parameters and verified with the support of the architectural office and/or the hospital managers. As such, we are confident that the results can inform future developments or studies on a wider and more comprehensive sample. Additionally, the analysis is concentrated into two specific geographical contexts, and subsequently focused on the areas where data collection was more feasible (i.e., Texas for USA and Italy for Europe); therefore it is still too early to generalize from those results, but they represent a concrete first step in filling the knowledge gap with regard to FSED and supporting future research in this direction, with both practical and academic implications. On one hand, decision makers and practitioners can find relevant insights to design and plan future facilities, while from the academic perspective, the study fills a knowledge gap and allows the future comprehensive analysis on possible FSED model interpretation or application.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12105099/s1](https://www.mdpi.com/article/10.3390/app12105099/s1)]([https://www.mdpi.com/article/10.3390/app12105099/s1](https://www.mdpi.com/article/10.3390/app12105099/s1)), Table S1: Full list of papers collected; Table S2: Full analysis of case studies.  
Conceptualization, A.B., M.D. and S.L.; methodology, A.B.; validation, S.C.; formal analysis, M.D., S.L. and S.M.; investigation, M.D. and S.L.; data curation, M.D. and S.M.; writing--original draft preparation, M.D. and S.M. writing--review and editing, A.B. and S.C.; visualization, S.L.; supervision, S.C.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Acknowledgments:** The authors would like to acknowledge the architectural firms and hospital organizations that provided detailed materials for the analysis.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Neural Network Model Performance
#### 5.3.4 Prediction Accuracy  
As seen in Figure 2, the model converges around epoch 50, with both training and validation loss stabilizing. The final training loss was 0.231, and the validation loss was 0.258, indicating good generalization without overfitting.  
Our neural network model was able to achieve an overall accuracy of 85.7% (SD = 1.2%) on the test set in predicting shot outcomes (hit/miss) across the five training runs. The model demonstrated high precision (88.3%, SD = 0.9%) and recall (83.1%, SD = 1.4%), with an average F1 score of 0.856 (SD = 0.011).  
Table 6 summarizes the model's performance metrics.
# New and Emerging Treatments for Generalized Pustular Psoriasis: Focus on IL-36 Receptor Inhibitors
## 5 New Treatments for GPP
### Insidolimab (ANB019)  
More recently, another therapeutic agent has been designed to treat GPP by targeting the IL-36 pathway. Insidolimab is a humanized IgG4 monoclonal antibody with high affinity to IL-36R. By binding to this receptor, it inhibits IL-36 activity [11; 45; 46].  
To assess insidolimab's clinical efficacy, tolerability, and safety, the GALLOP trial was designed (Table 2). This single-arm, open-label, multiple-dose study enrolled a total of eight patients with relevant GPP. Participants received a single 750 mg IV dose of insidolimab on the first day of the trial, followed by three subcutaneous doses of 100 mg of the same drug, on Days 29, 57, and 85 [46].  
The primary endpoint of the trial was to evaluate the number of patients clinically responding to the treatment at Weeks 4 and 16 after the first administration, using the Clinical Global Impression (CGI) scale. A clinical response was considered positive if the scores were \"very much improved\", \"much improved\", or \"minimally improved\" [46].  
By Weeks 4 and 16, it was evident that 75% of the patients had achieved clinical response, with 50% even achieving the \"very much improved\" score. This indicated that imsidolimab acted effectively, rapidly improving the patients' GPP status [46].  
As a secondary outcome, it was also noted that the patients' quality of life improved [46].  
Regarding its safety and tolerability, 75% of the patients reported at least one adverse effect related to imsidolimab, with 2 patients experiencing severe ones. Overall, the doses administered appeared to be adequate and tolerable [46].  
The phase 3 clinical trial GEMINI-1 revealed that 53.3% of patients receiving a single 750 mg IV dose of imsidolimab achieved clear or almost clear skin by Week 4, compared to the 13.3% response rate in the placebo group (\(p\) = 0.0131) (Table 2). Importantly, the trial demonstrated a strong safety profile for imsidolimab, with no severe adverse events, a low incidence of infections comparable to placebo, and no cases of DRESS or Guillain-Barre syndrome. Furthermore, only one patient developed non-neutralizing anti-drug antibodies [47].
## 4 Autonomous Trajectory Planning and Optimization
### Coverage Sample Observations
The path planning is simplified to optimize the close-loop curve parameters (such as the center position of the curve, area covered by the curve, _etc._), the number and location position of the vehicles. Ocean currents affect the vehicles' motion and dynamics, especially AUGs which operate at a low velocity compared to AUVs and USVs. Current data is transformed into a three dimensional grid data and compensated using the vehicles' velocity. 3. Cooperative observation control: During observation processes, marine vehicles should operate collaboratively and maintain their relative positions in a formation. The cooperative control includes two types named \"loose collaboration\" and \"close collaboration\". For the \"loose collaboration\", we mainly plan each single vehicle's path and control the vehicle's dynamic model to maintain its observation path. For example, multi-profile continuous observation and virtual mooring observations are \"loose collaboration\" observations. In the \"close collaboration\" mode, multiple vehicles are required to have strict distribution to maintain their relative positions. For example, multiple vehicles cooperatively sampling on a closed curve is a \"close collaboration\". In this situation, we design the control law to maintain the desired interval by measuring the curve arc length between adjacent vehicles. The vehicles' dynamic control and the cooperative trajectory control law are designed separately. The continuous trajectory is discretized into waypoints, and the vehicles' dynamic control law drives each vehicle to reach the next waypoint in the shortest time. A particle agent model is used to simplify the cooperative trajectory control design. In deep sea, two adjacent AUG exit points are about 5-7 km apart, and time interval is about 5 \(\sim\) 7 hours, so the cooperative control method for AUGs has to magnify many times in the spatial and temporal aspect, compared to land robots. It is possible that sampling data from different AUGs is not synchronized, since AUGs may not float to the sea surface at the same time. In this situation, the gliding velocity and position are pre-planned from data assimilation results.
## 2 Literature Review
### Wind Energy
#### 2.1.3 Offshore Wind Energy Market  
It is important to note that one of the most relevant aspects for the development of offshore wind energy is the technology with which it is associated. Today, the offshore wind energy market comprises a large range of investors: utility companies, equipment manufacturers (OEMs), contractors (engineering, construction, and installation), independent energy producers, infrastructure funds, and institutional investors [29].  
The construction of offshore wind farms can be organized in several industrial segments and is dominated by some companies in each sector. Noteworthy are the companies that produce offshore wind turbines, foundations, wind towers, cables (between arrays and grid connection), construction companies, and installation companies.  
Over the past few years, there has been an increase in the technological market for components for offshore wind energy production, leading the industry in this sector to become increasingly competitive [25]. Increased competitiveness in the sector and, consequently, increased competition, have contributed to reducing manufacturers' profitable margins, even in some of the largest production companies in the world. This aspect has resulted in a reduction of the sale prices of several products, with repercussions on the movement of offshore wind energy to new markets, increasing the sales of its technologies.  
Currently, ten main offshore wind turbine manufacturers stand out, which in 2018 won an 85% share of the entire market.  
The main companies involved in the wind turbines supply chain are described in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components Produced** & **Company** & **Country** \\ \hline \hline  & Vestas & Denmark \\  & Goldwind & China \\ Main manufacturers [8] & Siemens Gamesa & Spain \\  & GE Renewable Energy & United States \\  & Envision & China \\  & Enceron & Germany \\  & A2 Sea & Denmark \\ Wind turbine installers [30] & Van Oord/MPI & Netherlands \\  & Fred Olsen Wind Carrier & Norway \\  & Seajacks & United Kingdom \\  & GeoSea & Belgium \\  & Orsted & Denmark \\ Offshore wind developers [30] & Vattenfall & Sweden \\  & E-on & Germany \\  & Iberdrola & Spain \\  & Imnogia & Germany \\  & EBV & Germany \\  & SiF Tecade & Netherlands \\  & Bladt Industries & Denmark \\  & Smulders JV & Belgium \\  & Windar Renovables & Spain \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main companies in the offshore wind turbine market.