esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background Knowledge
### Machine Learning
#### 2.2.4 Support Vector Machines  
SVM is a commonly utilized machine learning algorithm that was developed in the 1990s. It is a supervised algorithm [63] and is used in both classification and regression problems. SVM separates data in either a linear or nonlinear manner, making it a versatile tool [64]. SVM can yield favorable outcomes despite the presence of noisy data. The benefits of the SVM algorithm consist of high accuracy, strong generalization properties, a limited number of hyperparameters, and proficiency in processing multidimensional data. Nevertheless, processing large datasets can be time-intensive, and selecting an appropriate kernel function is crucial.  
The SVM algorithm is used to solve classification problems. It classifies data using a hyperplane that best separates the dataset into two classes. The chosen hyperplane provides the best margin to separate the classes of data. Margin refers to the distance between the data classes and the hyperplane. SVM can be used to classify both linearly separable and nonlinearly separable data. SVM works by selecting a hyperplane that maximizes the margin between two classes in linearly separable data. In cases where the data cannot be linearly separated, SVM transforms the data into a high-dimensional feature space using the kernel trick [63; 64]. SVM uses various parameters, including the complexity parameterthat controls the flexibility of the line used to separate classes. A value of 0 enforces a strict margin, while the default value is 1. Another crucial parameter is the kernel type. The linear kernel is the simplest and separates data with a straight line or hyperplane. A polynomial kernel separates data into classes using a curve or curved line. The strength of the separation decreases as the polynomial exponent value increases. The radial basis function (RBF) kernel, which is the most popular and powerful kernel, uses closed polygons and complex shapes to separate classes. This study employs linear, polynomial, and radial basis function kernels with default parameters. The input vectors are mapped nonlinearly on a high-dimensional plane, and a linear decision surface is formed on this plane to ensure the generalizability of the learning machine.
## 2 UVB LED General Lighting System
### IoT UV Measuring Device  
An IoT UV measuring device was constructed with the BLE module for wireless communication with the UVI sensor to measure the UV and microcontroller unit (MCU) and to carry out the measurement and data sending of UVI. The implemented UVI sensor was from Tocon E2 (Sglux, Germany) which discharges output voltage according to the strength of the UV index. In addition, ATmega328 (Atmel, San Jose, USA) was implemented for the MCU. The packet for communication was then constructed while sending functions through the BLE module (HM-10). Figure 3 shows the construction and process flow of the IoT UV measuring device.  
UVI is expressed as Equation (1) by quantifying the relative intensity of UV as an arithmetic indicator by the World Health Organization (WHO) [20]. Additionally, the UVI sensor (Tocon E2) returns UVI, which is the integrated value for each wavelength in Equation (1). Therefore, EUV, which is essential for the calculation of the amount of vitamin D synthesis, was calculated by applying the UVI value collected through the sensor to Equation (1).  
\[UVI=40\int_{280}^{400}E(\lambda)\cdot W_{e}(\lambda)\;d\lambda=40\;[\mathrm{m} ^{2}/\mathrm{W}]\cdot EUV\;[\mathrm{W}/\mathrm{m}^{2}] \tag{1}\]  
In Equation (1), \(\lambda\) is the wavelength, \(E(\lambda)\) is the spectral irradiance of the wavelength \(\lambda\), and \(W_{e}(\lambda)\) is the action spectrum for erythema for wavelength \(\lambda\). The communication function was implemented to send real-time UVI measurement results to the smartphone.
## 3 Effects of Vitamin D\({}_{3}\) on Dental MSCs
### Biological Activity of 24R,25(OH)\({}_{2}\)D\({}_{3}\) Vitamin D\({}_{3}\) Metabolite  
The biological role of 1,25(OH)\({}_{2}\)D\({}_{3}\) has been intensively investigated and sufficiently clarified over the last 30-40 years [20; 21; 25]. However, there are other potential biologically active vitamin D\({}_{3}\) metabolites, and their physiological role remains obscure. One of these metabolites is 24R,25(OH)\({}_{2}\)D\({}_{3}\), which is formed by 24-hydroxylation of 25(OH)D\({}_{3}\). Its serum levels are comparable to those of 25(OH)D\({}_{3}\) and are about 1000 times higher than those of 1,25(OH)\({}_{2}\)D\({}_{3}\)[95], but the mechanisms of its biological effects are not entirely clear. Earlier studies on chicken egg formation, racitic chicks, and chick models of fracture healing showed that these processes depend on 24R,25(OH)\({}_{2}\)D\({}_{3}\) rather than on 1,25(OH)\({}_{2}\)D\({}_{3}\)[96; 97; 98], challenging the prevailing dogma about the central role of 1,25(OH)\({}_{2}\)D\({}_{3}\) in the vitamin D\({}_{3}\) activity. Further confirmation about the importance of 24R,25(OH)\({}_{2}\)D\({}_{3}\) in fracture healing comes from a recent study on CYP24A1-mice [99]. Here, impairment of fracture repair in CYP24A1-/- mice, which can be corrected by exogenous administration of 24R25(OH)\({}_{2}\)D\({}_{3}\) and not by 1,25(OH)\({}_{2}\)D\({}_{3}\), was reported [99].  
Biological effects of 24R,25(OH)\({}_{2}\)D\({}_{3}\) were observed in MSCs and other cells [82; 95; 100; 101]. In bone marrow-derived MSCs, 24R,25(OH)\({}_{2}\)D\({}_{3}\) induced osteogenic differentiation even in the absence of dexamethasone, which was not observed for 1,25(OH)\({}_{2}\)D\({}_{3}\)[100]. Furthermore, 24R,25(OH)\({}_{2}\)D\({}_{3}\) also inhibits CYP27B1 expression and reactive oxygen species' production in MSCs [100]. A tight regulatory relationship between the effects of 1,25(OH)\({}_{2}\)D\({}_{3}\) and 24R,25(OH)\({}_{2}\)D\({}_{3}\) was hypothesized [100]. There is some evidence that 24R,25(OH)\({}_{2}\)D\({}_{3}\) might also influence the inflammatory response. A recent study showed that 24R,25(OH)\({}_{2}\)D\({}_{3}\), but not 1,25(OH)\({}_{2}\)D\({}_{3}\), blocks the effects of IL-1\(\beta\) in rat articular chondrocytes [82]. The mechanisms of 24,25(OH)\({}_{2}\)D\({}_{3}\) bioactivity remain unclear. An early study on human osteoblasts shows that 24-hydroxylated vitamin D\({}_{3}\) metabolites enhance osteogenesis through the VDR-dependent mechanism, similarly to 1,25(OH)\({}_{2}\)D\({}_{3}\)[101]. In contrast, the cellular effects of 24,25(OH)\({}_{2}\)D\({}_{3}\) in corneal epithelial cells seem to be independent of VDR [102]. A recent study identified FAM57B2 as a mediator of 24R,25(OH)\({}_{2}\)D\({}_{3}\) effects [41]. One report showed that 25(OH)D\({}_{3}\) also has some biological effects in cells originating from CYP27B1-deficient mice, which suggests that 25(OH)D\({}_{3}\) may act independently of 1\(\alpha\)-hydroxylation [103].
## 3 Principals of Routing Protocols and Their Breakdowns
### Energy Aware Multi-Path Routing Protocols
#### 3.1.10 EEEDBR [33]  
In [33], the authors put forward an upgraded version of the EEDBR routing protocol, namely enhanced energy-efficient DBR (EEEDBR). The main aim of the proposed protocol is to increase the lifetime of the medium layer of depth-based routing protocols. EEEDBR is a reactive routing protocol, unlike EEDBR, a proactive protocol. Like EEDBR, the nodes use a priority value to decide the forwarding node. The main difference is that in EEEDBR the middle layer of the network switches off when not in use; increasing the whole network's lifetime. EEEDBR performs better than EEDBR in terms of lifetime and throughput, according to the data provided. The simulation environment was missing in the paper.
# Generative Adversarial Networks for Zero-Shot Remote Sensing Scene Classification
## 3 Results
### Ablation Studies  
Our approach is based on generative adversarial networks. Following the characteristics of remote sensing images, we used classification loss, class-prototype loss, and semantic regression loss to constrain the generator, to ensure that we can obtain a high-performance generator. We constructed ablation experiments to understand our model further and to evaluate their effects. We set \(\lambda=0\) in (12), our model without classification loss term can be referred to as CSPWGAN-\(\lambda\); we set \(\beta=0\) in (12), our model without semantic regression term can be referred to as CSPWGAN-\(\beta\); we set \(\gamma=0\) in (12), our model without class-prototype loss term can be referred to as CSPWGAN-\(\gamma\). We executed ablation experiments on the UCM21, AID30, and NWPU45 datasets. Table 5 shows the result of ablation studies on the UCM21, AID30, and NWPU45 datasets. On the UCM21 dataset, when we removed the classification loss item, the model accuracy decreased by 2.82%; when we removed the semantic regression term, the model accuracy decreased by 2.23%; when we removed the semantic regression term, the model accuracy decreased by 2.99%. The results show the  
Figure 6: Confusion matrix of the two methods on the NWPU45 dataset. Where “AI”, “BA”, “CF”, “CL”, “DE”, “DS”, “HB”, “IT”, “MR”, and “SR” denotes “airport”, “basketball court”, “circular farmland”, “cloud”, “dense residential”, “desert”,“harbor”, “intersection”, “medium residential” and “sparse residential”, respectively.  
Figure 7: t-SNE visualizations of image features for five unseen classes on the AID30 dataset. The pentagram denotes the real features of the unseen class. The solid circle denotes the synthetic features of the unseen class. The real and synthetic features of the same unseen class have the same color.  
effectiveness of the three items for zero-shot RSSC. We can find the same conclusion on the AID30 and NWPU45 datasets.
## 4 Experiments
### Description  
The experiments were performed by using MicaZ nodes with the TinyOS [23] operating system. The MicaZ platform has the following main features: microprocessor ATMEGA128L, 4 KB of RAM memory, 128 KB of ROM memory and a single radio frequency transceiver CC2420. The CC2420 radio uses the IEEE 802.15.4 standard and operates in an unlicensed frequency band of 2.4 GHz with a transmission rate of 250 Kbps [28].  
Figure 11 illustrates the scenario used for the experiments. The target network is composed of **21** sensor nodes and **01** sink node. The sensor nodes run an application that, every minute, measures the ambient temperature and sends the information to the sink node. The payload of the packets sent by the sensor nodes carries the measured temperature and a counter incremented at each temperature measurement.  
The monitoring network is composed of **N** sniffers and **01** base station. Sniffers capture the packets sent by the nodes of the target network and send these packets to the base station by using multihop routing. The base station sends the packets received from sniffers, through a USB cable, to a computer. In this scenario, the computer performs the functions of the local monitor (generation of the trace) and of the server, as shown in Figure 1. Experiments were performed with 3, 5, 7, 9 and 11 sniffers distributed in the monitored area, as shown in Figure 12.  
Figure 10: Operation of the “_SNMP MIB Browser Android Tool_”: (**a**) Viewing the MIB of the EPMOSt. (**b**) Viewing the target nodes. (**c**) Viewing information about the node **0**.  
For each scenario shown in Figure 12, three types of experiments were performed: \"_election without aggregation_\", \"_election with aggregation_\" and \"_without election_\". In the experiment \"_election without aggregation_\" sniffers run the application described in Section 3.1, in which the election mechanism proposed in Section 3.1.1 is implemented, but do not use the aggregation of headers proposed in Section 3.1.2. In the experiment \"_elections with aggregation_\", sniffers run the election mechanism and the aggregation of headers.  
In the experiment \"_without election_\" sniffers do not run any election mechanism and they capture all packets sent by the nodes of the target network that are within the coverage area of their radios. Thus, sniffers send all the bytes of captured packets to the local monitor, which then inserts these packets in the database. In this case, several sniffers can capture a single packet sent by a given node of the target network. It is noteworthy that this is the strategy used by all the monitoring systems described in Section 2 (SNTS, SNIF, Pimoto, LiveNet and PMSW).
## 3 Results and Discussion
### Thermal Degradation of EVA/MH/PCS Composites  
The thermal degradation behavior of the EVA composites is given in Figure 12. There are two stages in the weight loss of EVA composites [39]. The first weight loss stage, at 300-380 \({}^{\circ}\)C, is primarily due to the removal of ester groups in the EVA matrix and the release of partial crystalline water in MH. The second stage, at 380-500 \({}^{\circ}\)C, is due to the degradation of polyolefins formed in the first stage and the further dehydration of MH.  
In comparison to EVA/MH, the initial degradation temperatures (T\({}_{5}\)) of EVA/MH/PCS, EVA/MH/PCS/DVB, and EVA/MH/PCS+DVB were all increased. In addition, both T\({}_{\rm max1}\) and T\({}_{\rm max2}\) rose more than 10 \({}^{\circ}\)C, indicating the thermal stability of the EVA composites was improved by PCS. Interestingly, there were few differences in the first weight loss stage among the three EVA materials containing PCS, but great differences were generated for EVA/MH/PCS/DVB in the second weight loss stage. Compared with EVA/MH/PCS, the T\({}_{\rm max2}\) of EVA/MH/PCS/DVB was 6 \({}^{\circ}\)C higher and the R\({}_{\rm max2}\) decreased by 55.5%, which illustrates the better effect of cross-linked PCS on the thermal stability of EVA composites.  
Based on the thermal degradation parameters of the flame retardant (Table 4) and the zero residues of the EVA resin, the theoretical residues of the EVA composites were calculated. The theoretical residues of EVA/MH, EVA/MH/PCS, and EVA/MH/PCS/DVB were 35.0%, 35.9%, and 36.2%, which were consistent with the experimental values in Table 6, indicating that MH, MH/PCS, and MH/PCS/DVB have no catalytic charring effect on the EVA resin. It is worth noting that the residue of EVA/MH/PCS/DVB was a little  
Figure 12: TG (**a**) and DTG (**b**) curves of the composites.  
higher than that of EVA/MH/PCS, indicating that the cross-linked PCS helped to increase the residual char of the EVA composites.
# A Survey on Network Optimization Techniques for Blockchain Systems
## 5 Network Optimization Techniques Used in Blockchain Networks
### Network Layer
#### 5.1.1 Gossip Optimization
The results presented indicate that this approach did not yield any appreciable increase in transactional throughput. However, their approach yielded about a 15% reduction in the number of messages transmitted by a peer. When compared to the default gossip in bitcoin, their approach realized about a 0.88% reduction in duplicates. Furthermore, probability calculations are not discarded but kept for future transmissions, as well as network size.
## 6 Discussion
### Specifics of Production Industry  
We used six projects as case studies for this paper. All of them were dealing with real processes in companies. Moreover, since we based the research on problems found in production, our proposal was also focused on this area.  
One of the most important features of production process is, obviously, its practical orientation. Most tasks focus either on improving the production, diminishing errors, or on more general predictive maintenance. Getting knowledge just for knowledge is not demanded, every result should have its appliance in praxis.  
A customer, and not the data scientist's team, is the one to decide. Therefore, a more logical solution (from the data scientist's point of view) may be dropped in favor of the one picked by the client (whose reasons may be rooted in company policy, long-term goal, etc.). Here, the team's ability to negotiate and to honestly present pros and cons of every solution and to provide the customer with enough information proved to be crucial for a successful communication with the client. It is very useful when there is not only management of the company present to the meetings but also experts from praxis.  
The level of deployment of Industry 4.0 in a company can make a huge difference in difficulty of acquiring and processing data. Ideally, all the processes are time-synchronized, and the storing of the data is unified for the whole company. In our best practices we address both this ideal case and the opposite (sadly, frequent) case. However, still, most of the data are recorded and often even stored, thus decreasing the need for the team to set up their own measuring. It may be necessary only in the cases where the project explicitly states the need for measuring either completely different features of the process, or already measured features in an unprecedented way.  
Another important thing about the production process data is that most of them are artificial in their nature, produced by machines programmed by humans, or connected to the scheduled processes (meteorological data captured to uncover influences of weather on the production process represent an exception from the rule). The artificialness of the data means we are often able to guess the possible structure of the process flow, which will in many cases consist of repeating cycle or cycles (their number guessable due to how many different product types and tasks are operated on the machine). Most of the attributes will have either ideal values or boundaries set up, allowing us to mark those parts of the process where these values are incorrect.
## 2 Background Knowledge  
### Ontology  
Ontology is an effective method to enable information sharing and recycling. Ontologies provide a structured and formalized representation of knowledge that can facilitate the sharing and reuse of information across different systems and applications. There is a significant body of literature and research that supports the use of ontologies for information sharing and recycling in various domains, including but not limited to biomedical informatics, e-commerce, and the semantic web. In fact, ontologies have become a fundamental component of many knowledge-based systems, such as expert systems and decision support systems. Ontologies implement concepts and groups of connections within a specific domain (see Figure 2). Protege is generally utilized as an ontology editor to implement a process for scheming and executing domain ontology. Many domains create their ontology for various goals. For example, the e-COGNOS project developed an ontology based on IFC and civil engineering standards in the civil engineering area. Ontology engineering is advantageously applied to information sharing and knowledge encoding [6]. Ontologies can be a potential tool for solving interoperability problems. Ontology has an excellent conceptual hierarchy and supports logical reasoning. The exact meaning of the concept is obtained using strict definitions and the relationship between them [15,16]. Ontology demonstrates common identifiable and shareable knowledge and provides knowledge sharing and reuse across knowledge layers. There are four basics of ontology modeling. Four fundamentals are required for all ontology models. First, the concept of representing what a class is described by its name. Second, it is a relationship that represents the connection between existing concepts and instances. Third, axioms present the constraints of concepts and cases. Fourth, an instance representing a specific individual in the class. The most important part of the domain ontology is the class level, which is an essential glossary that supports a reasoning base for accurate thinking. A reasonable class hierarchy is essential to improve the efficiency and accuracy of information retrieval [9]. In conclusion, ontology assists in storing knowledge concepts through descriptions and relations and exchanges and shares knowledge between systems. Besides, domain ontology allows practitioners or soft agents to share their understanding of information structures [6,17].  
Figure 1: Basic formal ontology based on the structure of BIM.
# Study of Root Canal Length Estimations by 3D Spatial Reproduction with Stereoscopic Vision
## 4 Discussion
This is believed to be due to the segmentation of the STL data that were extracted from the DICOM data. Since it was technically challenging to directly display DICOM data on the SRD in this study, segmentation and OBJ conversion with the use of Mimics were necessary. To improve image quality, it is expected that improvements will be made to enable the direct input of DICOM data into the SRD for measurement, enhancing convenience by improving image quality and eliminating the need for measurement preparation. On the other hand, concerns about VR sickness have been raised in the application of VR technology. The main symptoms of VR sickness include eye strain and simulator sickness [29], and the most commonly used method to subjectively assess VR sickness is the Simulator Sickness Questionnaire (SSQ) [30]. The SRD used in this study is an SR technology classified as XR technology, but there was concern that symptoms similar to VR sickness might occur. Therefore, the ratings related to eye strain and simulator sickness in the SSQ questionnaire were examined. In the feedback questionnaire, 21% of respondents rated simulator sickness as 5 or lower, and 26% reported experiencing simulator sickness, suggesting the presence of symptoms similar to VR sickness in SR technology. In the evaluations related to future applications in education and clinical practice, the satisfaction rate (rating of 6 or higher) was 100% on the Likert scale, indicating the potential usefulness of the SRD. Furthermore, in the open-ended responses, in addition to root canal length measurements, responses were obtained regarding the treatment explanations that were based on root canal morphology, which were obtained through preoperative 3 DCG, and the desire to use it for student education. On the other hand, the topic modeling analysis of negative feedback identified three issues: usability, eye strain, and simulator sickness. Regarding usability, direct suggestions for improvement were made, such as adding a user interface (UI) to allow for the modification of the tooth models and for improving the size of the reference point display during measurements.