esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Phytochemical Characterisation
#### 2.2.2 Physicochemical Characterisation by UHPLC
The bibliography reports the presence of mainly alkamides [69; 77; 85; 86], principally based on isobutylamide, the main ones being pellitorine and anacycline [37; 38; 39; 40; 41; 42; 70; 81; 87; 88], which have a wide range of biological activities, such as antimicrobial, antiviral, diuretic, antioxidant, and analgesic [89; 90; 91; 92], including pellitorine, the main constituent, isolated in 1895 by [PERSON] and [PERSON] [85; 93]. Other studies have demonstrated that the plant roots contain hydrocaroline, inulin, sesamin, palmitic acid, hexadecenoic acid, octadecanoic acid, eugenol, and also traces of volatile oil [31; 32; 42; 71; 72; 73; 74; 76; 77]. Almost all the identified components have been studied for their pharmacological effects, such as gallic acid, known for its anti-tumoral, pro-apoptotic, anti-inflammatory, and antioxidant properties [94; 95; 96; 97; 98; 99]. Caffeic acid has been shown to have antibacterial, antiviral, antioxidant, anti-inflammatory, anti-atherosclerotic, immunostimulant, antidiabetic, cardioprotective, antiproliferative, hep-atoprotective, anticancer, and hepatocellular carcinoma activities [100; 101; 102; 103; 104; 105; 106]. Ferulic acid has been reported to have numerous therapeutic effects, including antioxidant, antimicrobial, anti-inflammatory, antithrombotic, and anticancer activities [107; 108]. Chlorogenic acid has been reported to have antioxidant, anti-inflammatory, analgesic, antipyretic, antiviral, anticancer activities [109; 110; 111; 112; 113; 114]. Catechin has anti-obesity, anti-diabetic, cardiovascular, anti-infectious, hepatoprotective, and neuroprotective properties [115]. Quercetin has great therapeutic potential in the prevention and treatment of various cardiovascular and neurodegenerative diseases, as well as cancer [116; 117; 118; 119; 120]. Hesperetin has been reported to have antioxidant, anticarcinogenic, antidiabetic, and many other properties [93; 121; 122; 123]. Furthermore, the results obtained revealed a phytochemical difference between the two varieties _A.P_ var. _pyrethrum_ and _A.P_ var. _depressus_. The richness of the studied extracts in these bioactive compounds could justify the therapeutic use of the different parts of the two varieties [49; 58].
###### Abstract  
Bakery products are one of the main sources of dietary sodium intake of the world's population. During the last decade, sodium intake has increased worldwide and nowadays the World Health Organization recommends reducing sodium intake by up to 2 g Na/day. KCl is the leading substitute for reducing sodium in bakery products. Therefore, the main purpose of our study was to investigate the impact of sodium reduction on dough's rheological properties by reformulating the dough recipe using two types of salts, namely NaCl and KCl, with different amounts added to wheat flour. In order to establish their combination for obtaining the optimum rheological properties of dough, the response surface methodology (RSM) by the Design Expert software was used. The effect of combined NaCl and KCl salts were made on mixing, viscometric and fermentation process by using Farinograph, Extensograph, Amylograph and Rheofermtometer devices. On dough's rheological properties, KCl and NaCl presented a significant effect (\(p<0.01\)) on water absorption, stability, energy, dough resistance to extension, falling number and all Rheofermtometer-analyzed values. Mathematical models were achieved between independent variables, the KCl and NaCl amounts, and the dependent ones, dough rheological values. The optimal values obtained through RSM for the KCl and NaCl salts were of 0.37 g KCl/100 g and 1.31 g NaCl/100 g wheat flour, which leads to a 22% replacement of NaCl in the dough recipe.  
Keywords:KCl; NaCl; rheological properties; multiple criteria optimization; desirability functions +
Footnote †: journal: Applied Sciences
###### Abstract  
At COP26, the Glasgow Leaders Declaration committed to ending deforestation by 2030. Implementing deforestation-free supply chains is of growing importance to importers and exporters but challenging due to the complexity of supply chains for agricultural commodities which are driving tropical deforestation. Monitoring tools are needed that alert companies of forest losses around their source farms. ForestMind has developed compliance monitoring tools for deforestation-free supply chains. The system delivers reports to companies based on automated satellite image analysis of forest loss around farms. We describe an algorithm based on the Python for Earth Observation (PyEO) package to deliver near-real-time forest alerts from Sentinel-2 imagery and machine learning. A Forest Analyst interprets the multi-layer raster analyst report and creates company reports for monitoring supply chains. We conclude that the ForestMind extension of PyEO with its hybrid change detection from a random forest model and NDVI differencing produces actionable farm-scale reports in support of the EU Deforestation Regulation. The user accuracy of the random forest model was 96.5% in Guatema and 93.5% in Brazil. The system provides operational insights into forest loss around source farms in countries from which commodities are imported.  
environmental and social responsibility; forestry; climate mitigation 2020 15 617 10.390/115040617
## 4 Future Prospects for China's Weather Routing Services
### Enhancing China's Global Satellite Communication Capabilities  
To strengthen China's self-reliance in satellite communications, efforts should prioritize accelerating the construction of indigenous communication satellite constellations to improve global coverage. Concurrently, the development of satellite internet infrastructure and the collaborative establishment of satellite relay transmission and reception stations with partner nations will reduce dependency on foreign satellite communication systems. Additionally, the BeiDou-3 Navigation Satellite System should be further integrated into global meteorological data transmission and weather routing services [53]. Enhancing data transfer capabilities between Fengyun geostationary and polar-orbiting meteorological satellites will provide innovative solutions for transmitting data from deep-sea buoys, ocean-going vessels, and other remote maritime assets. The intended benefits of these enhancements include significantly increased data transmission bandwidth, enabling higher resolution forecasts and more frequent updates onboard, reduced latency for real-time decision support, improved coverage, particularly in polar regions and remote oceanic areas currently underserved by some commercial systems, enhanced system resilience and autonomy, and potentially lower communication costs for Chinese-flagged vessels in the long term.  
To optimize technical performance, advancements in data transmission technologies are critical. Research on lossless data compression methods tailored for weather routing services will improve data accuracy and real-time processing efficiency, thereby elevating the precision and timeliness of weather routing guidance. Significant investment in satellite development and ground infrastructure, international coordination for spectrum allocation, and robust cybersecurity measures represent key challenges to achieving these goals.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.9. BodyLAN  
BodyLAN is an ultra-low power, low-cost and reliable BAN platform created as a proprietary technology by FitLinxx [79]. BodyLAN is designed to be used in a vast variety of applications such as consumer electronics, activity and wellness devices, medical devices and fitness equipment. In terms of power usage, BodyLAN provides much lower power consumption rate compared to Bluetooth devices. This wireless technology uses a single radio channel, short burst duration and extremely low duty cycle. BodyLAN utilizes GFSK modulation technique which prevents BodyLAN packets from colliding with 802.11 g/Orthogonal Frequency-Division Multiplexing (OFDM)/ DSSS packets. BodyLAN operates in the 2.4 GHz ISM band and supports data rates of 250 kb/s and 1 Mb/s. It also utilizes a peer-to-peer network topology without centralized timing. Devices in a BodyLAN network are categorized into two groups of transmit-only and transmit/receive devices. In terms of security, BodyLAN encrypts frame payloads and dynamically changes algorithms based on device addressesand timing plans. In addition, following the collection of data, the ActiHealth network utilizes a secure VPN connection between the ActiHealth data server and application servers in order to guarantee the security of the collected information.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 1 Introduction
### Motivation  
Recently, many researchers have proposed secure authentication protocols for the IoD environment. However, existing authentication protocols for the IoD environment are vulnerable to various cybersecurity attacks, such as impersonation attacks, ephemeral secret leakage attacks, and physical drone capture attacks. Furthermore, devices with low computation and storage capabilities, such as drones, are unsuitable for use with heavy cryptographic techniques such as elliptic curve cryptography (ECC) [12]. In 2024, [PERSON] [13] proposed a key agreement protocol employing ECC to secure the IoD environment. However, critical vulnerabilities exist in [PERSON]'s protocol, including its high computational and communication costs due to using ECC and its susceptibility to impersonation and session key disclosure attacks by eavesdropping on public messages. Therefore, this work proposes a security-enhanced and lightweight key agreement scheme that resolves the security performance limitations of [PERSON]'s protocol using physical unclonabe function (PUF) technology, a cryptographic one-way hash function, and XOR operations.
## 3 Framework of Forecasting AOG Models in Aviation
### Interpretation of Case Studies  
An example of a successful case in AOG management is Lufthansa Technik, which devised a comprehensive AOG response plan. They found that the utilization of a proactive approach to maintenance helped them reduce their AOG bills. With predictive analytics, they could predict potential failures and be prepared before they occur [17]. In the same dimension, Delta Air Lines demonstrated how they resolved an AOG event through a creative logistics solution. By establishing a chain of warehouses in key locations, Delta reduced AOG times by ensuring that necessary parts were promptly available through a short supply chain [18].
# Line-of-Sight Initial Pointing Model of Space Dynamic Optical Network and Its Verification
## 5 Discussion  
Whether an LOS can be correctly rotated in relation to the FOU determines whether an optical link can be established, and the size of the FOU determines the length of the optical link's establishment time. This is not only the key to successful capture, but is also key to the premise and guarantee of optical link networking. This paper establishes a multi-link LOS initial pointing model, determines the factors that affect the size of the FOU, and proposes an FOU calculation method based on the pointing azimuth angle variance and elevation angle variance. In the \"one-to-two\" simultaneous LOS pointing demonstration test, the size of the multi-link FOU was tested, the correctness of the pointing model was verified, and these results laid the foundation for subsequent multi-link fast beam scanning and capture. Consequently, through structural optimization design, signal filtering and prediction, high-precision position and attitude sensors and other methods can be used to improve the LOS pointing accuracy, reduce the size of the FOU, and reduce the overall capture time. The space multi-link LOS pointing method being studied is universal. Based on the corresponding space optical network application scenarios, it can be applied to inter-satellite laser network communications, time-frequency transmission, laser ranging, gravitational wave detection and other related fields in order to achieve optical network LOS pointing and correction, determine the size of the FOU, provide time analysis, etc. It provides a corresponding reference for the establishment of space optical networking links.  
Conceptualization, S.C. and X.D.; methodology, S.C.; software, S.C.; validation, S.C., X.W. and D.L.; formal analysis, S.C.; investigation, S.C.; resources, S.C.; data curation, S.C.; writing--original draft preparation, S.C.; writing--review and editing, S.C.; visualization, S.C.; supervision, S.C.; project administration, X.Z. and S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China, Ministry of Science and Technology of the People's Republic of China, grant number No. 2022 YFC2203700.  
Not applicable.  
**Data Availability Statement:** All data in support of the findings of this paper are available within the article.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Data Selection  
The selection of data plays a significant role in the retrieval of patterns among the databases. Hence, if the data is not accurately processed then it will manifest with irregular results, which can hamper the prediction-based system. To overcome the above flaws, we designed LADS, which can preempt and configure the data process of the information for future prediction modelling. The fundamental objective of data preprocessing is to remove missing and noisy readings before feeding the data into the algorithm. As a result, if the data contains multiple blank and noisy values, it will almost certainly find certain patterns that are nonsensical. This will bias the analyzed results of the research prediction modeling. Typically, the size of the dataset has a significant effect on the efficiency of any deep learning system. In general, training with a sparse dataset tends to overfit, and a transfer learning technique is employed to address this issue. The repository of the dataset for the transfer learning model was configured and the features suggested were edges, corners (interest points), blobs (region of interest points), and ridges, where each feature was correlated to measure the considerable values. The trash categorization data consisted of about 25,077 waste photos split into two sorts: organic and recyclable. Table 2 shows the number of photographs in each class, whereas Figure 4 shows the same in a pie chart. Table 3 depicts various photos from the input dataset.  
In Figure 4, we clearly show that the distribution of data in waste management was categorized into two categories. Organic waste was classified in category 1, while recyclable waste was classified in category 2. Further, in Table 2 the representation of the database was in the format of text.img files. The images included 25,000 features, such as edges, corners (interest points), blobs (regions of interest points), and ridges, each of which measured substantial values.  
Additionally, using Table 4, we can compare the performance of the three pretrained models, AlexNet, VGG16 and ResNet34, to our proposed model, and, as a result, we can see that our proposed model has performed very well in categorizing the waste images into their respective categories. In Table 5, the number of neurons and the activation layer function of each layer are presented with flattening and filtering of each image.  
Additionally, the study utilized real image databases derived from varied sources, containing real-world complex images. The images included specific features, such as rotten chapattis, electronic waste, used tea bags and other resources, each of which has measured substantial values [
## 2 Materials and Methods
### Image Segmentation Techniques
#### 2.2.2 K-Means Segmentation  
The K-Means algorithm [50] is a machine learning technique that is used to cluster a dataset into K classes. The algorithm divides the dataset into classes such that there is maximum distance between classes and minimum distance within the class [28]. The algorithm is as follows:  
1. Set the number of classes (=K).
2. Initialize K cluster centers from the dataset randomly.
3. Calculate the distance of each pixel to each cluster center using a distance function.
4. Classify each pixel to the nearest cluster, i.e., the closest distance.
5. Recalculate the cluster center using the pixels belonging to the cluster.
6. Repeat Steps 2 to 5 until there is no change in the cluster center.
## 4 Conclusions  
In this study, the effective value of low-frequency and broadband electric fields that people are exposed to was measured and interpreted for an apartment in four different environments where people spend most of their time in 24 h. Statistical (min, max, average, and std.) values of 1000 electric field effective values measured for all four environments were obtained. The changes in the electric field values obtained from both low and broadband measurements and the most suitable curves fitted to these values were plotted with respect to time using Matlab. The most appropriate curves are fitted to the density and CP functions of the same electric field values, and their changes with respect to the electric field are plotted on the same figure. Density and CP functions were expressed mathematically. To find the SAR value created by the electric field values recorded in broadband measurement on the heads and brains of people in the measurement environment, first, six sub-bands where these measurement values were concentrated were identified, the middle frequencies of these sub-bands were determined, and the statistical values of field changes within this sub-band were extracted. For the head model, assuming that the human head consists of six layers, starting from the skin to the brain, for the electrical modeling of each layer, \(\varepsilon_{r}\) and \(\sigma\) values corresponding to the middle frequencies of the subbands were determined, SAR values occurring in the head and brain were calculated, and their changes over time were plotted. It was observed that the values obtained from the 50 Hz measurement were far below the limit values determined by ICNIRP. In broadband measurements, it was observed that the values recorded, especially in the entrance and living room areas near the wireless modem, were quite high, and the highest value recorded in these environments was 293.12% of the FCC's limit value and 234.5% of the CE limit value. It has been observed that these high SAR values will cause serious health problems for people living in environments where they are recorded, and it has been determined how serious a source of electromagnetic exposure the wireless modem is.  
This research received no external funding.  
Not applicable.  
All the data required are available within the manuscript.  
The author declares no conflicts of interest.
## 3 Optimization Methods
### Differential Evolution Algorithm
#### 3.2.3 Selection  
Selection operation determines whether the target vector or trial vector survives the next generation by comparing the corresponding objective function value (fitness value), retaining the better one. If the trial vector exhibits a better fitness value than the target vector, the trial vector will replace the target vector and enter the next generation. Otherwise, the target vector will be retained in the next generation. The selection method often used in the DE algorithm is the competition method, which can be expressed as follows:  
\[\mathbf{X}_{i}^{t+1}=\left\{\begin{array}{ll}\mathbf{U}_{i}^{t}&\text{if }f(\mathbf{U}_{i}^{t})\leq f(\mathbf{X}_{i}^{t})\\ \mathbf{X}_{i}^{t}&\text{otherwise}\end{array}\right. \tag{26}\]  
where \(\mathbf{X}_{i}^{t+1}\) is the vector retained to the next generation, \(\mathbf{U}_{i}^{t}\) is the trial vector and \(\mathbf{X}_{i}^{t}\) is the target vector. The aforementioned three operations are repeated generation after generation by continuously leaving better vectors, through generation to generation (iterations) and gradually converging, until a termination criterion (i.e., reaching the maximum iteration number of objective function evaluations) is satisfied. The DE algorithm can rapidly determine the best solution. The flow chart of the DE algorithm is shown in Figure 6.
## 5 Discussion
### Limitations and Future Work
#### 5.3.3 Future Work  
The above limitations restrict the efficiency and generality of our proposed GMANet. In future work, we will simplify the network structure to accelerate the computing speed and improve the generality of the deep model. Furthermore, we will verify whether other shallow features, such as edge, phase, and contrast, used in traditional NR-IQA methods, can be introduced as an MA map.  
In our previous work, we fused clear image patches in different locations and the corresponding experimental results are described in [33]. In order to verify the performance of GMANet on assessing the clarity of objects, we used the image fusion method to stitch the clearest image patches together. Details are described in the Supplementary Materials. Using a deep learning method to fuse the microscopic images captured in the autofocus process into one clear microscopic image is our next research direction.
###### Abstract  
Reliable meteorological forecasts of temperature and relative humidity are critically important to take necessary measures to avoid potential damage and losses. An operational meteorological forecast model based on the Weather Research and Forecast (WRF) model has been built in Xinjiang. Numerical forecasts usually have significant uncertainties and errors due to imperfections in models themselves. In this study, a straightforward automated machine learning (AutoML) approach has been developed to post-process the raw forecasts of the WRF model. The method was implemented and evaluated to post-process forecasts from 13 stations in northern Xinjiang. The post-processed temperature forecasts were significantly improved from the raw forecasts, with average RMSE values in the 13 stations decreasing from 3.24 \({}^{\circ}\)C to 2.34 \({}^{\circ}\)C by a large margin of 28%. As for relative humidity, the mean RMSE at 13 stations decreased from 19.54% to 11.54%, or it showed a percentage decrease of 41%. Meanwhile, biases were also significantly decreased, with average ME values being reduced from around 2 \({}^{\circ}\)C to \(\sim\)0.33 \({}^{\circ}\)C for temperature and improved from \(-\)15.6% to \(-\)0% for relative humidity. Moreover, forecast performance values after post-correction became much closer to each other than raw forecast performance values, improving forecast applicability at regional scales.  
post-correction; AutoML; WRF Article