esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Methodology for Acquiring Coseismic InSAR Data Based on LiCSAR and GCMT  
This study proposes a methodology for acquiring coseismic InSAR data based on LiCSAR and GCMT, aiming to meet the increasing demand for large-scale, high-quality datasets in deep learning research. In this approach, the global earthquake catalog is automatically retrieved from the GCMT website ([[https://www.globalcmt.org/CMTsearch.html](https://www.globalcmt.org/CMTsearch.html)]([https://www.globalcmt.org/CMTsearch.html](https://www.globalcmt.org/CMTsearch.html)), accessed on 11 February 2025). Researchers define filtering criteria, including time range (start and end dates), magnitude range, geographic bounds (latitude and longitude), and focal depth. Based on these parameters, the system accesses the GCMT database and employs Python-based web scraping techniques to efficiently extract relevant earthquake information. The retrieved catalog contains key attributes, including event time, epicenter coordinates, and focal depth. The structured data are then stored locally to support subsequent InSAR interferogram retrieval.  
Building on this, the system automatically scrapes the LiCSAR website to retrieve the geographic extent (latitude and longitude range) associated with each Sentinel-1 satellite FrameID. By parsing the LiCSAR product catalog, the system maps each FrameID to its geographic coverage, providing an accurate foundation for subsequent data acquisition. This process generates a reference table containing FrameIDs and their associated geographic bounds, ensuring that future InSAR interferogram downloads can be accurately matched to relevant FrameIDs based on earthquake epicenter locations. This significantly improves both the efficiency and accuracy of the data retrieval process.  
After retrieving the earthquake catalog and FrameID reference files, the system uses the epicenter coordinates of each earthquake to accurately match the corresponding FrameID. It then automatically downloads the preprocessed Sentinel-1 coseismic interferograms from the LiCSAR website. All downloaded data are stored in a local database, along with detailed metadata, including FrameID, acquisition time, and data source, to ensure full traceability. Furthermore, the system incorporates an automated validation mechanism to verify data integrity and prevent redundant downloads. In the event of a download failure or missing data, an automatic retry mechanism is triggered to ensure maximum data quality, completeness, and usability.
# Intelligent Packaging for Real-Time Monitoring of Food-Quality: Current and Future Developments
## 3 Methods for Real-Time Food Monitoring  
The European Commission has defined intelligent food packages as \"materials and articles that monitor the condition of packaged food or the environment surrounding the food\" [60]. Generally speaking, intelligent packaging can be explained as a combination of science and technology able to enhance the shelf-life, improve safety, guarantee the quality, and warn about any harmful changes occurring in the packages [18; 21; 49; 61; 62; 63; 64]. Specifically, intelligent packaging makes use of the internal or external package environment to monitor the food quality status [13; 19; 65]. A summary of the most common methods currently employed for the real-time monitoring of food samples based on the indicator nature is discussed in the following.
## 3 Results and Discussion  
### Method Validation  
The comparison of snapshots is shown in Figure 3. It can be seen that 3D simulations gave more accurate results for they described the extreme thin liquid film on the top of the particle at 20 ms and 25 ms better. This is due to the fact that the 3D region allowed liquid to flow along the particle surface in various directions. These results proved that the simulation framework was capable of describing the behavior of liquid on solid particles. However, it could be found that some differences exist between the reported experiment and 3D simulation results.  
Table 3 shows the thickness of liquid film on particle surface of different positions. Dry surface and unmeasurable thin film are ignored in the table. It can be seen there are still several significant difference between reported experiment and 3D simulation. Most of thicknesses in the experiment are larger than those in 3D simulations, and the wetting of particle surface is faster in the experiment. All these differences could be concluded from two reasons. First the droplet in simulation is perfect sphere while in experiment is nearly ellipsoidal, which is larger in volume. Therefore the thickness of liquid film and flowing velocity are larger in experiment. Second, it can be seen that in the experiment there is a droplet hanging on the bottom of the particle at t = 0 ms, which means the particle was wetted. However, the particle surface in the simulation was totally dry. This could lead to the differences between the experiment and simulation.  
Figure 3: Comparisons of snapshots between experiment [19] and VOF simulations of a droplet falling.
## 3 Results and Discussion  
### Projected Climatic Variables  
Among the main climatic variables, which bring about the change in climate of an area are temperature and rainfall. They have greatest effect on the estimation of irrigation water demand [50]. Temperature is projected to increase due to climate change effects by an average of 1.18, 1.14, and 1.97 \({}^{\circ}\)C, respectively under RCPs 4.5, 6.0, and 8.5 compared to the baseline period. Similarly, the projected minimum temperature increase by 1.27, 1.21, and 2.08 \({}^{\circ}\)C for the same future periods. This culminated in the expectation that the basin would be warmer in the future, especially during the dry season months.  
On the other hand, rainfall may slightly increase in the wet season (July-December) and decrease in the dry season (January-June) during the future period. The wet season average changes are projected to be 1.0%, 0.8%, and 2.4% under RCPs 4.5, 6.0, and 8.5 scenarios, respectively with a range of 0.2% for RCPs 4.5 and 6.0 in the 2050s to 2.7% for the RCP 8.5 in the 2080s. Whereas, the average changes for the dry season are \(-\)2.4%, \(-\)3.2%, and \(-\)3.7% under RCPs 4.5, 6.0, and 8.5 scenarios, respectively. Based on these findings, flood increases are likely to occur during the wet (main rice growing) season and so are some intermittent dry spells during the dry season. This difference in rainfall in both seasons needs to be taken into account in the scheme's water resources planning.
## 3 Results
### Application Domains
#### 3.2.7 Data Analytics  
Concerning the application of data analytics, three articles [74; 90; 92] were identified. The studies aimed to process data to determine activities and behaviors of older adults in the urban space.  
The study presented in [74] adopts a smartphone-based mobile application as a tool to better understand the daily activity of older adults. Using the smartphone sensor information collected through the mobile application, the authors intended to prove that it is possible to identify the points of interest of older adults in Singapore.  
In turn, in the study reported by [90], a large volume of smart card data was used to determine the public transport travel behavior of older adults in Beijing, China, that were classified as long, medium, and short bus-travelers considering their objective mobility characteristics. According to the authors, the findings might be used to tailored mobility policies of the cities.  
The research reported in [92] aimed to understand the older adults' means of traveling considering contextual data and data coming from the smartphones. The correct classification of transportation can be also used for providing suggestions in the context of public or private transportation.
# Special Issue on Optical Communications and Networking: Prospects in Industrial Applications
###### Abstract  
In the past two decades, Internet traffic has increased by over 10,000 times by taking advantage of both efficient information processing technology in the electronic domain and efficient transmission technology in the optical domain, which are the foundation of today's Internet infrastructure [1; 2]. The advancement of electronics processing circuits has followed [PERSON]'s law, and perhaps will continue this exponential growth for years to come. This may make the electrical systems significantly outpace the advancement of optical systems in information and communications technologies. To support the ever-growing Internet traffic, optical communication systems face a great challenge in transporting information processed by electronic systems for sustained exponential growth. The industry has explored multiple degrees of freedom of the photon (time, wavelength, amplitude, phase, polarization, and space) to significantly reduce the cost/bit for data transmission by increasing the capacity/fiber through multiplexing and reducing the size and power through integration.  
This Special Issue aims to explore the latest advancements in the optical communication industry. The applications range from short-reach chip-to-chip interconnections to the long-haul backbone communications at the trans-oceanic distance, including the technologies used in devices, systems, and networks levels. It focuses on state-of-the-art advances and future perspectives in commercially deployed systems.
## 3 Results and Discussion
### Thermal Stability and Crystallinity of P3 HB Nanocomites
Isothermal Crystallization of P3 HB/C20 Nanocomposites: Lamellar Thickness and Equilibrium Melting Temperature  
The relationship between crystallization temperature and melting behavior was examined for a representative P3 HB/C20 nanocomposite containing 5 wt% clay. Isothermal crystallizations were conducted at temperatures between 125 \({}^{\circ}\)C and 135 \({}^{\circ}\)C, within a range that ensured practical crystallization times. Detailed crystallization curves are provided in Figure S3, while the corresponding heating curves are shown in Figure 11a. The results revealed that higher crystallization temperatures led to an increase in the melting peak temperature, indicating the formation of thicker lamellae at elevated temperatures. Specifically, a 10 \({}^{\circ}\)C increase in crystallization temperature delayed the exothermic peak by 12 min and raised the melting peak temperature by approximately 6 \({}^{\circ}\)C, shifting from 160 \({}^{\circ}\)C to 166 \({}^{\circ}\)C. This trend highlighted the influence of crystallization conditions on the thermal properties and stability of the nanocomposites, as thicker lamellae are generally associated with improved thermal stability. The Hoffmann-Weeks representation (Figure 11b) allowed for the calculation of the equilibrium melting temperature, which corresponds to the melting point for a theoretically infinite thickness. This approach is widely recognized for evaluating the relationship between the melting temperature (\(T_{m}\)) and crystallization temperature (\(T_{c}\)). The calculated  
Figure 10: DSC heating (**a**,**b**) and cooling (**c**,**d**) runs of micromolded P3 HB/C20 (**a**,**c**) and P3 HB/C116 (**b**,**d**) specimens obtained by USM technology under optimal conditions. Nanocomposites with 3, 5, and 8 wt% are represented by black, red, and blue curves, respectively. equilibrium melting temperature in this study is consistent with previously reported values for P3 HB, as reported in earlier literature [32]. The addition of nanoclay does not appear to affect the thermodynamic equilibrium conditions. The observed variations in melting peak temperatures can be attributed to the increased lamellar thickness resulting from higher crystallization temperatures.
## 3 Results
### Operation Results  
The results obtained in the water balance and weight difference are shown in Table 3 and Figure 7, respectively.  
The weight variations aided to detect the variables involved in the irrigation process of the plant-soil system. This water balance showed accurate measurements of LP1 prototype water losses during a whole season as they were recovered precisely (Table 3).  
The weight of the cultivation tank increased due to irrigation and rainfall, as well as it decreased as expected (Figure 7). Initially, the weight of the cultivation tank decreased rapidly due to the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Water Film (mm)** & **Mass (g)** & **Difference In-O (g)** \\ \hline \multirow{4}{*}{Inputs (In)} & Rainfall (R) & 92.4 & 49,630 & \\  & Irrigation (I) & 399.1 & 214,333 & \\  & Condensation (C) & 37.8 & 20,284 & \\ \hline \multirow{2}{*}{Outputs (O)} & ETc & 496.4 & 266,562 & \\  & Drainage (D) & 32.9 & 17,682 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of the water balance obtained with the prototype LP1 weighing lysimeter.  
Figure 6: Installation process of the removable weighing lysimeter. (**a**) Base on top of undisturbed soil over. (**b**) Main structure and cultivation tank support. (**c**) Cultivation tank placed inside the main structure. (**d**) The cultivation tank filled with the excavated soil.  
Figure 7: Weight variation of the cultivation tank during a complete culture cycle (minute results).  
drainage of the water through the soil, followed by a slower decline because of crop water consumption. Some precipitation events increased the water content beyond field capacity. The drainage tank then reached its maximum weight and had a frequent cycle of recharge with subsequent water drains, which resulted into ups and downs of weight in the drainage tank (Figure 7). These weight oscillations were also observed in the cultivation tank as its weight slightly increased during the night due to the condensation and diminished throughout the day.
# Valorization of Date Fruit (_Phoenix dactylifera_ L.) Processing Waste and By-Products: A Review
## 4. Valorization Approaches of Date Processing Waste
### Composting  
The decline in soil fertility causes a serious environmental challenge, leading to various harmful consequences [98]. Firstly, it reduces crop yields and quality, impacting the sustainability of agricultural production. Secondly, declining soil fertility can trigger soil erosion, leading to land infertility and disrupting the ecological balance. Additionally, fertility decline may result in soil acidification, alkalization, or salinization, threatening plant growth. Most importantly, reduced fertility puts additional pressure on agriculture, prompting farmers to use excessive chemical fertilizers and pesticides, causing soil and water pollution [99].  
Therefore, composting as an effective soil improvement method was introduced to solve this problem. Composing transforms organic waste into organic fertilizers, enhancing soil organic matter content, which also improves soil structure, enhances soil aeration, water retention, and provides essential nutrients for plant growth. Moreover, the organic matter generated during composting enhances soil resilience, reducing reliance on chemicalfertilizers and promoting soil fertility restoration, thus increasing crop yields [100]. And date processing waste is an excellent source of organic matter, as it is rich in carbohydrates, fiber, and other nutrients, being a suitable candidate to be composed. The composting of date processing waste can be achieved through various methods, such as windowing, vermicomposting, and in-vessel composting. Window composting involves piling the date processing waste into long rows and periodically turning it to promote aeration and decomposition [101]. Vermicomposting involves using earthworms to break down waste, producing a high-quality compost rich in beneficial microorganisms and nutrients [102]. In-vessel composting involves using enclosed containers to control the temperature, moisture, and aeration of the composting process, producing faster and more efficient decomposition of the waste [103].  
The compost produced from date processing waste can be used as a soil amendment or fertilizer, providing numerous benefits for plant growth and soil health. For example, a study by [PERSON] et al. [104] showed that the application of date processing waste compost significantly increased the growth of tomato plants and improved the soil structure and fertility. Similarly, [PERSON] et al. [74] found that the application of date processing waste compost improved the growth and yield of forage alfalfa and increased the soil's nutrient content.  
Overall, composting is a valuable valorization approach for date processing waste, providing a sustainable and nutrient-rich organic fertilizer for plant growth and soil health. With further research and development, this approach could become a cost-effective and environmentally friendly solution for the date industry.
## 3 (DRSM-CNNFIS) Framework
### The Framework Design  
Combining rules with feature extraction results from convolutional neural network showed high accuracy in [9]. The combined model was reused in this framework by changing the training data and improving and changing the fuzzy rules following the application domain, which was diabetic retinopathy stage identification. Convolutional layers and pooling layers extracted the most relevant features that were used by rules in the next step; the addition of rules provided a robust stage for identification because it was medical expert-driven and it was domain-specific. Meanwhile, it reduced the training time and provided high accuracy based on the experts' rules.  
Figure 3 shows the first part the framework (DRSM-CNNFIS), which is the feature extraction part, with the relevant features obtained by multiple convolutional layers (Layers 1, 2, 3, 5, 6, and 7), and two max pooling layers added (Layers 4 and 8) for dimensionality reduction. The filter size and number were changed and optimized experimentally.  
The output vector provided the extracted features of the system, which were numeric values used in the diabetic retinopathy stage measurement part, with the fuzzy inference system (FIS) using the constructed rules based on medical experts that explain the direct relation between the extracted vector and the severity stage. The rules were built based on human expert knowledge. The key inputs for the fuzzy inference were features such as microaneurysms, intraretinal hemorrhage, exudates, and macular edema [25]. Linear membership functions were used for the output features: vector mean, standard deviation, max, and min.  
Figure 4 shows the second part the framework (DRSM-CNNFIS), which builds the rule-based fuzzy inference system starting with the linear membership functions for the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Label** & **Class** & **Number of Samples** & **Augmented Samples** \\ \hline
0 & Normal & 25,810 & 25,810 \\
1 & Mild NPDR & 2443 & 24,410 \\
2 & Moderate NPDR & 5292 & 25,330 \\
3 & Sever NPDR & 873 & 26,470 \\
4 & Proliferative DR & 708 & 25,480 \\ \hline Total & & 35,126 & 127,500 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Distribution for multiclass classification before and after data augmentation.  
Figure 3: Feature extraction part of the DRSM-CNNFIS framework.  
inputs, and manually added rules based on expert knowledge until the final diabetic retinopathy stage grading.
# Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools
## 5 Tooling in MLOps
### Tool Comparison
#### 5.2.8 Flyte  
The focus of this tool is on ML and data processing workflow orchestration. The Flyte [66] platform is modular and flexible by design. It is directly built on Kubernetes and therefore gets all the benefits that containerization provides but is also difficult to configure. It uses \"workflow\" as a core concept and a \"task\" as the most basic unit of computation. It has grafana [82] and prometheus [80] integration for monitoring. It can generate cross-cloud portable pipelines. There are SDKs for Python, Java, and scala.
## 3 Results
### Microbiological Evaluation
#### 3.2.1 Antimicrobial Properties of the Biomaterial
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Strains** & **2 h** & **4 h** & **8 h** & **24 h** \\ \cline{3-6}  & & \multicolumn{4}{c}{**CFU/mL**} \\ \hline \multirow{4}{*}{K} & _C. albicans_ average & 2.44 \(\times\) 10\({}^{6}\) & 7.67 \(\times\) 10\({}^{6}\) & 4.37 \(\times\) 10\({}^{6}\) & 4.2 \(\times\) 10\({}^{7}\) \\ \cline{2-6}  & _S. mutans_ average & 5.4 \(\times\) 10\({}^{7}\) & 4.52 \(\times\) 10\({}^{7}\) & 1.93 \(\times\) 10\({}^{10}\) & 9.26 \(\times\) 10\({}^{8}\) \\ \cline{2-6}  & _L. rhamns_ average & 5.2 \(\times\) 10\({}^{6}\) & 6.4 \(\times\) 10\({}^{6}\) & 2.97 \(\times\) 10\({}^{7}\) & 9.7 \(\times\)10\({}^{7}\) \\ \hline \multirow{4}{*}{nHAp} & _C. albicans_ average & 3.683 \(\times\) 10\({}^{6}\) & 5.17 \(\times\) 10\({}^{6}\) & 1.24 \(\times\) 10\({}^{7}\) & 1.63 \(\times\) 10\({}^{7}\) \\ \cline{2-6}  & _S. mutans_ average & 7 \(\times\) 10\({}^{7}\) & 9.1 \(\times\) 10\({}^{9}\) & 3.77 \(\times\) 10\({}^{10}\) & 1.05 \(\times\) 10\({}^{9}\) \\ \cline{2-6}  & _L. rhamns_ average & 6.2 \(\times\) 10\({}^{6}\) & 3.95 \(\times\) 10\({}^{7}\) & 3.5 \(\times\) 10\({}^{7}\) & 2.22 \(\times\) 10\({}^{8}\) \\ \hline \multirow{4}{*}{nFAp} & _C. albicans_ average & 2.31 \(\times\) 10\({}^{6}\) & 8.6 \(\times\) 10\({}^{6}\) & 8.47 \(\times\) 10\({}^{6}\) & 1.26 \(\times\) 10\({}^{7}\) \\ \cline{2-6}  & _S. mutans_ average & 4 \(\times\) 10\({}^{8}\) & 7.59 \(\times\) 10\({}^{9}\) & 1.32 \(\times\) 10\({}^{10}\) & 4.37 \(\times\) 10\({}^{7}\) \\ \cline{2-6}  & _L. rhamns_ average & 2.96 \(\times\) 10\({}^{7}\) & 6.37 \(\times\) 10\({}^{6}\) & 1.24 \(\times\) 10\({}^{8}\) & 5.1 \(\times\) 10\({}^{8}\) \\ \hline \end{tabular}
\end{table} 
Table 2: Effects of nHAp and nFAp on C. _albicans_, _S. mutans_ and _L. rhamns_ strains. eventually after 24 h of cultivation presents a lower microbial count when compared to nHAp.