esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Screening and Selection Process  
All articles identified through a search across various databases were imported into reference management software (Mendeley Desktop, version 1.19.8), where duplicates were eliminated. Following this, the titles and abstracts of the remaining studies were independently reviewed by two reviewers (FN and AN) to determine their relevance to the research question. Case reports, case series, reviews, and meta-analyses were excluded. Studies containing relevant search terms in the title and/or abstract were selected for full-text review. The two reviewers (FN and AN) independently evaluated the full-text articles of potentially relevant studies based on predefined eligibility criteria. Studies that satisfied all criteria were included for data extraction, while those that did not were excluded, with reasons for exclusion documented. Any disagreements between the two reviewers at this stage were discussed and resolved by consensus. If disagreements persisted, a third independent reviewer (FN) was consulted.
## 3 Results and Discussion
### Changes of Active Components of R. roxburghii Fruit at Different Stages of Maturity
However, excessive levels of ROS can cause oxidative damage to biomolecules like DNA, proteins, and lipids, ultimately resulting in cell death [35]. In order to prevent toxic concentrations of ROS, taller plants produce superoxide dismutase (SOD) to efficiently convert ROS into H\({}_{2}\)O\({}_{2}\) and O\({}_{2}\)[36]. As _R. roxburgh_ fruit matures, the consumption of SOD increases to suppress the production of ROS. Soluble tannin (ST) is a crucial component contributing to the astringency of _R. roxburgh_ fruit [37]. Research indicates that the higher the tannin content, the more pronounced the astringent taste [12]. Throughout the ripening stages of _R. roxburgh_ fruit, there was a significant decrease (_p_ < 0.05) in ST content from stage I (3674.12 mg/100 g) to stage V  
Figure 1: **(A)** Pictures of _R. roxburgh_ fruit at different stages of maturity. The numerals I, II, III, IV, and V represent 45, 65, 75, 90 and 105 days after anthesis, respectively. **(B)** The content of active components of _R. roxburgh_ fruit at different stages of maturity. TA, total acid; TF, total flavonoid; Vc, vitamin C; SOD, superoxide dismutase; TP, total phenolic; ST, soluble tannin; SS, soluble sugar. The different lowercase letters above the same color bars represent significant differences (_p_ < 0.05). (2759.63 mg/100 g). This reduction could be attributed to tannin polymerization, leading to the formation of insoluble tannins as the fruit matures [38]. Previous studies have shown that tannin and soluble pectin formed during fruit ripening tended to form complexes, resulting in the production of high molecular weight insoluble tannins that bound closely to the cell wall [39; 40]. Consequently, the ST content decreases progressively during the ripening process. The total flavonoid (TF) and total phenol (TP) levels are key contributors to the antioxidant properties of _R. roxburghii_ fruit, with their levels being influenced by factors such as growth environmental conditions and maturity stages [2; 26]. During the ripening process of _R. roxburghii_ fruit, the TF content significantly decreased (\(p<0.05\)) from stage I (829.65 mg/100 g) to stage V (533.37 mg/100 g). In contrast, the TP content exhibited a fluctuating pattern, initially decreasing significantly (\(p<0.05\)) from stage I (3678.71 mg/100 g) to stage III, reaching its lowest value of 2220.89 mg/100 g, before gradually increasing to 2729.40 mg/100 g at stage V.
# On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment
## 4 On-Demand Maintenance Method
### Development of On-Demand Maintenance Content  
According to the requirements of safety specifications, elevator maintenance is divided into four categories: semi-monthly, quarterly, semi-annual, and annual. The on-demand maintenance method proposed in this paper retains the quarterly, semi-annual, and yearly maintenance items while rationalizing the semi-monthly ones. This adjustment is based on the monthly entrapment risk index report generated by the entrapment prediction model, and different maintenance measures are taken for elevators marked with red and green codes. In the specific implementation, reference is made to the requirements and inspection rules for elevator maintenance in TSG-T5002-2017 \"Lift Maintenance Regulation\" and TSG-T7001-2023 \"Regulation for Lift Supervisory Inspection and Periodical Inspection\" [27; 28]. Combined with the historical fault analysis results in Section 1, the door system fails frequently. The number of faults accounts for the highest proportion; therefore, when formulating preventive maintenance measures for the red code elevator, the door system is the key maintenance object, and the \"w\" mark in the table is the key maintenance and inspection items of the door system. The rest are the basic maintenance and inspection items, as shown in Table 10.  
Figure 6: On-demand maintenance implementation process.  
Elevators with a green code entrapment risk index have a relatively low likelihood of entrapment incidents. When elevators break down and become entrapped, IoT sensors detect the problem. Rescue teams can then respond quickly.
# Privacy-Preserving Transfer Learning Framework for Kidney Disease Detection
## 1 Introduction
Insufficient measures may result in people being subjected to discrimination, stigmatization, or financial loss if their health information is disclosed. Furthermore, implementing privacy measures can support the establishment of trust between patients and healthcare professionals, thereby promoting early diagnosis without the concern of their confidential health information being disclosed. It is essential to use privacy-preserving methods for extracting important information from such data [8; 9]. Current kidney disease detection studies face several key challenges, such as the following:  
1. Lack of high detection accuracy: since kidney disease is highly likely to result in death, high detection accuracy is very important for early diagnosis. The studies in the literature still do not present higher accuracy;
2. Privacy concerns and inference attack issue: kidney disease detection studies employ sensitive data, such as CT scans, which may disclose the privacy of the patients. In order to overcome these challenges, the proposed framework introduces these solutions:  
1. Creation of optimal classifier: to obtain high detection accuracy, the proposed framework employs an optimal classifier, including appropriate dense layers, which gives better results;
2. Implementation of differential privacy for optimizer: the proposed framework utilizes differential privacy for noisy gradient computing. Hence, we applied differential privacy in an Adam optimizer in order to provide privacy. The main contributions of this paper are listed below:  
1. We introduce a novel framework that includes an optimal neural network classifier including appropriate dense layers. The proposed framework provides the best results in the literature. 2. Motivated by the privacy issue in kidney disease detection, we present the use of a differentially private Adam optimizer in transfer learning to detect kidney diseases by respecting privacy for the first time. This study introduces a novel framework that aims to facilitate the detection of chronic kidney disorders, including tumors, cysts, stones, and normal tissue, while respecting privacy. This framework facilitates the deployment of differential privacy with CNN-basedarchitectures for the analysis of CT images of the kidney. The objective of the study is to employ CNN-based architectures to identify anomalies in the kidney by acquiring knowledge of particular patterns and characteristics embedded in the images, while ensuring the privacy of the data is preserved. The rest of this paper is organized as follows. Section 2 includes a summary of prior studies conducted in the field. Section 3 outlines the materials and methods employed in the study. Section 4 introduces the proposed framework that addresses the handled problem. Section 5 presents the experimental studies conducted in this paper. Finally, Section 6 concludes the research by summarizing the essential findings and their implications.
# Green Hydrogen Storage in an Underground Cavern: A Case Study in Salt Diapir of Spain
## 4 Discussion
The brine extracted from the cavity can have three final destinations:  
* Discharge to the sea;
* Geological injection and reuse; and
* Treatment or sale of salt  
Due to the great distance to the sea, the difficulty in finding a geological structure capable of storing the brine, and the high costs of its treatment in the project itself, the option of discharging it in a raft, built for this purpose, on the surface for subsequent sale to the chemical industry is proposed. The smaller extent and more defined geometry of salt caverns means that they require less cushion gas than depleted hydrocarbon reservoirs and aquifers having wide, elongated structures. The cushion gas requirement in an aquifer could be as much as 80% of the total reservoir capacity compared to 50-60% in depleted gas reservoirs [33; 34; 35]. The cushion gas remains in the reservoir as a precautionary measure to maintain an operating pressure, limit the risks of induced seismic activity, and minimize hydrogen losses and groundwater intrusion. It seems clear that part of the stored H\({}_{2}\) is used as a cushion gas in storage in saline structures, since it avoids harmful effects of mixing, dilution, and reactivity with other gases [36; 37]. In the case of underground storage in depleted hydrocarbon reservoirs and aquifers, other authors suggest the possibility of using CH\({}_{4}\) as a cushion gas because of its residual presence, and CO\({}_{2}\) and N\({}_{2}\) because of their higher wettability, density, and viscosity than H\({}_{2}\), to facilitate both their separation during injection/extraction cycles and, if necessary, the displacement of water [29; 31]. The use of mines in Mesozoic and Tertiary salt deposits [38], such as those located in northeastern Spain, presents a flexible and efficient option for green hydrogen storage. The use of wells, galleries, chimneys, and ramps would drastically reduce the costs and execution times of underground storage. The mine infrastructure would allow the development of lining, ventilation, and geotechnical control of the storage cavern, increasing its tightness and safety conditions.
## 4 Case Studies
### Process Monitoring/Control and Policies Integration  
With manufacturing process monitoring and control becoming a driver for the growth and sustainability of manufacturing industries today [108], making use of certification schemes [109] in process monitoring can be highly complex. To illustrate the applicability of the current classification under the scope of certification for policies integration, the following problem is discussed: \"Non-data-intensive certification & optimization of proper monitoring for process control of laser welding under the prism of Zero-defect manufacturing and upfront investment\".  
The pseudo-mathematical description in this case would be _optimize (Certify (Optimize (monitoring (Laser Welding), with respect to cost and efficiency)),with respect to data)_. The corresponding traditional workflow would then be as per the following image (Figure 6). The first loop implies the sequential choice of alternatives for the equipment that is available and the evaluation. An immediate conclusion of this workflow would be the rejection of thermal cameras and a focus on pyrometers, due to the cost and data rate. Then, once the equipment is fixed, the certification mechanism has to be finalized. Under the requirement of a non-data-intensive approach [85], a mechanism for data abstraction is required. Then, it is time for the choice of implementation. In the case where Unified Architecture of Open Platform Communications (OPC-UA) communication [110] is required, then there may be restrictions for the sampling rate. Thus, this implies that a controller has to be designed before the implementation design. For instance, a bang-bang controller, with a low on-off rate, may have to be used in this case instead of a traditional one (i.e., PID [111]).  
The difficulties in this workflow are evident: the implementation specifications should be used in advance so that all related technologies are able to be eliminated beforehand. The implemented schema, along with the communications, as well as messages passing towards certification and data logging, can be seen in Figure 7. Moreover, two aspects of modelling have been superimposed to it; vertically, the cyber-physical-social aspects are considered, while horizontally the spatial distribution of functionalities is shown. The virtual manufacturing devices, handling the message passing, are also shown in this schematic, with the communication protocols, rendering it as a holistic scenario depiction.  
Figure 6: Workflow for process monitoring/control.  
Figure 7: 7C Protocol, adopted from [112] & Metamodelling of process control scenario including certification framework and transactions.
## 1 Introduction
However, when we extend our experimentation to locally available grounded red chili, the results varied sharply. The system was able to recognize red chili but predicting very high adulterant concentration. We obtained different samples from the local market but the results were the same, while the grounded red chili of multinational companies like National Foods, Shan Foods, Habib Food, and Mehran Foods was predicted accurately by our algorithm. Therefore, a proposition was made that there was something different in the process which we are neglecting while grinding by ourselves. Initially, we visited a local company to observe their process of grinding and found out that we were using a household mixer grinder that utilizes blades for crushing, while in local industry stone-based grinders are used. Therefore, a stone-based grinder was acquired and multiple chili samples were ground. We trained our model on the newly prepared samples but the results were not different from the previous ones. Our algorithm predicted the correct proportion of adulterants in stone grounded red chili. While observing the mean spectrum of samples acquired from the local market and grounded by ourselves, we noticed a groove in the locally grind red chili from 650 to 680 nm (Figure 2). This dip was not found in any of our grind samples, either in household mixer grinder or stone-based grinders. In further investigation, we found out that the local industry is adding edible oil while grounding red chili to increase the shine without knowing its a type of adulteration. Furthermore, humidity increasesin the air as the rainy season began. Red chili without proper storage facility starts losing its color swiftly in this season. Therefore, vendors mix Sudan dyes in oil to make red chili color artificially. In this work, we have analyzed a new adulterant, i.e., edible oil, which is not studied before but only reported in news [33]. We have also discussed market practices for the addition of Sudan dye in red chili and presented a novel method for the detection of oil and Sudan dye adulterated red chili by using an HSI system in the range of 395 to 1000 nm with multi-class SVM. The proposed methodology works in the following way. First, important wavelengths were identified for oil and Sudan dye adulteration. Second, Savitzky-Golay differentiation was utilized to remove the baseline of spectra and preserve important spectral features. Third, to make the model robust, Principal Component Analysis (PCA) was used to remove redundant information, and finally a multi-class SVM model is trained on pure chili, oil-adulterated chili, and Sudan dye-adulterated chili. The trained model can differentiate pure chili from adulterated with an accuracy of 97%, which is further increased up to 100% by eliminating PCA in our process.
## 5 Results and Discussion
### Kendall's Coefficient of Concordance (Kendall's W)  
The value of Kendall's coefficient of concordance (Kendall's W) of overall respondents, contractors' perspectives, consultants' perspectives, and clients' perspectives were 0.183, 0.175, 0.169, and 0.145, respectively. The null hypothesis was rejected because all significance levels were 0.000, which was less than the threshold level of 5%. As a consequence, a substantial level was found among the respondents.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 5 Analysis of Escalator
### Conflict Definition  
Wireless communications of nodes using a single wireless interface may cause conflicts. Wireless conflicts are distinguished by primary (explicit) and secondary (implicit) conflicts [14; 16; 29]. A node with a single wireless interface cannot transmit and receive at the same time and cannot simultaneously receive from two or more nodes. We call a conflict caused by the limitation of the single wireless interface a primary conflict.  
Definition 1: A primary conflict occurs in the following cases: 1. If a node simultaneously transmits and receives, 2. When a node simultaneously receives from two or more nodes.  
Figure 7: Timeslot section where collisions should be avoided. **(a)** A case where the baseline slotframe size (17) is greater than the convergecast slotframe size (12); **(b)** a case where the baseline slotframe size (11) is smaller than the convergecast slotframe size (12).  
Communications among nodes in a wireless environment have an interference area that can affect other communications. When some nodes are communicating, conflicts may occur if other nodes simultaneously communicate using the same channel in the interference area. A conflict resulting from interference between communications using the same channel and the same timeslot is referred to as secondary conflict. Figure (a)a,b show primary and secondary conflicts, respectively.  
Definition 2: A secondary conflict means a conflict that may occur among communications using the same channel and the same timeslot.
## 5 Analysing Feature Fusion Performance According to Kappa-Error Diagrams
### Theory of Kappa-Error Diagram Evaluation  
Suppose that \(F_{1}\) and \(F_{2}\) are a pair of features that are under-estimated. For each AU from one dataset, each feature is extracted respectively. We employ several typical classifiers--KNN (K nearest neighbour), SMO (sequential minimal optimization), MLP (multilayer perception)--in AU recognition. To use various classifiers is to ensure the general and authority of AU judgment. The corresponding pairwise contingency table is counted with each classifier as in Table 3.  
In the table, parameter \(a\) represents the number of samples for both extraction methods doing the right classification with the same classifier; \(b\) and \(c\) are the number of samples for which one extraction method is right and another is wrong; and \(d\) is the number of samples for which both extraction methods are wrong. Then, error \"\(e\)\" and kappa \"\(kappa\)\" values are computed as follows ([PERSON], 2015):  
\[e=\frac{1}{2}\bigg{(}\frac{c+d}{N}+\frac{b+d}{N}\bigg{)} \tag{1}\]  
\[kappa=\frac{OA-AC}{1-AC} \tag{2}\]  
where \(N\) is the number of samples in the dataset: here \(N=a+b+c+d\). \(OA\) and \(AC\) are computed as follows:  
\[OA=\frac{a+d}{N} \tag{3}\]  
\[AC=\frac{(a+d)(a+c)+(b+d)(c+d)}{N^{2}} \tag{4}\]  
where \(OA\) represents the average sample number for which both extraction methods have the same results with one classifier. Thus, it describes the coherence of two extraction methods. The higher the coherence, the closer the functions of two extraction methods will be. It means no improvement of performance with fusion. On the contrary, it indicates that the functions of two extraction methods are various. Fusing of these two features will help to provide complementary contribution and improve the whole ensemble's results. To provide more objective criteria than accuracy directly, kappa is used, which removes the chance factor by minus \(AC\). The diversity of two technologies being evaluated is based on the rule that the lower the kappa is, the higher the diversity is.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predict Result** & \(F_{1}\) **Correct** & \(F_{1}\) **Wrong** \\ \hline \(F_{2}\) Correct & \(a\) & \(b\) \\ \(F_{2}\) Wrong & \(c\) & \(d\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contingency table with two classification results.  
Error in Equation (1) is the average of the sample number for each feature doing the wrong classification with the same classifier. It is easy to understand that the higher error caused by each extraction method in the pair will reduce the entire function of the ensemble. High diversity and high accuracy are the target in determining the distinctive feature in AU fusion of the stacking ensemble system.  
Further, [PERSON] examines the bound on the region for the dichotomous case where feasible kappa-error trade-offs are found. The paper derives bounds \(k_{\min}\) on kappa in terms of the error \(e\), as in Equation (5). The pairwise closing of the bound has good performance benefit for fusion [25].  
\[k_{\min}=1-\frac{1}{(1-e)},\ 0<e<0.5 \tag{5}\]
# The Carbon Sequestration Potential of Degraded Agricultural Land in the Amhara Region of Ethiopia
## 4 Discussion
We provide examples (see Table 5) for the expected gain in biomass by different rotation lengths and land use forms. It is clear that the rotation length will affect both the gross and the net aboveground carbon stock (AboveC\({}_{\text{NF}}\) in Table 5), and therefore the total sequestration potential of reforestation in Amhara (see Table 6). The current CO\({}_{2}\) emissions from forestry are estimated to be 55 Mt CO\({}_{2\text{e}}\) year\({}^{-1}\)[26], which is an equivalent of 15 Tg C year\({}^{-1}\). Our proposed reforestations in Amhara have the potential to sequester between 159 Tg C (thinning 2) and 177 Tg C (no thinning) within 100 years (100 year rotation Normal-forest system, compare Table 6). This would balance more than 10% of the annual CO\({}_{2}\) emissions from forestry or around 4% of the total emissions in Ethiopia. Prolonging the rotation period to 150 years increases the carbon sequestration by 5-6%, depending on the thinning intensity. Clear cutting and replanting the forest stands after 50 years would reduce the sequestration potential to less than 2% of the emissions from the forestry sector. It is important that if the potential reforestation area is reforested according to the 'Normal-forest' approach (step wise annual reforestation of an area calculated as the total potential area divided by the chosen rotation length), any further net carbon sequestration from that forest area can only be attributed to the soil (compare with Figure 3 and Table 3), because the areas where trees are cut and replanted is equal. Beside the replanting activities, a cascading use of wood should be promoted to build up an anthropogenic carbon storage pool with a considerable additional storage potential, where even a substitution for fossil fuel burning may be considered [87]. Thus, the fate of the cut timber decides the potential for additional carbon storage, whether the timber is burnt directly or whether the timber is stored in wood products. Higher above ground carbon stocks resulting from an increase in rotation length also lead to thicker trees and thus a wider potential use. While short rotation forestry, as is typical in private small-scale plantations of Ethiopia [88], mainly produces fuelwood, with increasing rotation length the production of saw timber will increase and more carbon will be sequestered.