esa-sceva/satcom-chunk-collection huggingface dataset:
# A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting
## 3 Methods
### Operating Environment  
Figure 1 shows the operating environment of the SCAP. This platform is built on a distributed network where hospitals store CDM data on their server, and these servers are connected to the network. In this figure, the central server group consists of the AS, CS, TGS, and RS. The hospital network consists of an SS and a database of CDM data. The CA is placed on the Internet and provides trust for all SCAP components with public key certificates. The SCAP uses the observational medical outcomes partnership CDM (OMOP-CDM) [7] as a medical CDM. OMOP-CDM is a database scheme defined by observational health data sciences and informatics (OHDSI). In general, researchers using OMOP-CDM analyze data using R language and software provided by OHDSI [31; 32]. All hospital networks are connected to a server group through the Internet.  
Each component of the SCAP and the process of analyzing the medical CDM data are as follows: (1) The users prove their identity to the platform. The AS is responsible for user authentication and issues a token to authenticated users. (2) The users obtain a digital signature for the analysis codes written to analyze medical CDM data held by hospitals through the CS. (3) The users receive a ticket from the TGS proving their identity for the SS of each hospital. (4) The users distribute the analysis codes to all hospitals connected to the platform through the RS and receive the analysis results. As a preliminary task for the safe operation these processes, all platform servers and users create their private/public keys and receive public key certificates from the CA. This certificate conforms to the X.509 version 3 [33] standard, which is currently the most widely applied. Platform participants can obtain certificates, such as generic electronic financial transactions.  
In this section, we describe the network protocol and information transmitted between the components of the SCAP. In the results section, we present the detailed implementation of each component along with the algorithms used.
## 2 Theoretical Background
### Classical and Fuzzy Hough Transforms
We decided to use this property and omit the edges with too high and too low gradient magnitudes in order to both reduce the number of pixels to be Hough transformed and to sort out those pixels that certainly do not belong to the polyp edges. Thus, in the next calculations, beside the full Hough transforms for all the edges, we performed restricted Hough transform for the edges with normalized gradient values within a wide threshold interval [0.06, 0.3] and a thin threshold interval [0.08, 0.2]. The full Hough transform results can be interpreted as a reference, to analyze how the restricted transforms influence the results. Figure 1.9 The total histograms (linear scale) of the normalized gradient-weighted edges pixels resulted from Prewitt edge detection method. The 1 st column is for the edge points in the full images, and the 2 nd column is for the edge points in the corresponding ring masks, for the databases CVC-Clinic [9], CVC-Colon [42], and ETIS-Larib [34]. The horizontal axes are the normalized gradient magnitudes intervals and the picture number in the given database. ### Gradient-Weighted Voting Approach for Classical and Fuzzy Hough Transforms

In addition to excluding the edge points with too high or too low contrast (i.e., gradient thresholding), the gradient values can be used for another purpose, namely, to modify the voting process. Even after the thresholding, polyp contours usually have segments with higher gradient magnitudes, mixed with lower gradients, whereas some of the background patterns have very low gradients. To decrease the influence of these lower gradient background edges on the Hough transform results, the following weighted voting approach was introduced. During the original Hough transform, all edge points receive the same vote, no matter how strong these edges are. In our method, instead of 1, each point uses its normalized gradient magnitude as a vote. In the case of the fuzzy Hough transforms, the whole voting membership function is multiplied by the gradient magnitude of the given pixel. As a

Figure 2.0 The top view of the logarithmic scale plots for the total histograms of the normalized gradient-weighted edges pixels resulted from Prewitt edge detection method. The 1 st column is for the edge points in the full images, and the 2 nd column is for the edge points in the corresponding ring masks, for the databases CVC-Clinic [9], CVC-Colon [42], and ETIS-Larib [34]. The horizontal axis is the picture number in the given database and the vertical axis is the normalized gradient magnitudes intervals. result, the smaller the step in the intensity at the edge is, the smaller the weight in the voting becomes for the edge point, no matter if it is a classical or a fuzzy point. To clarify better, the pseudocode of the fuzzy circular Hough transform with gradient-weighted voting approach is given in Algorithm 3.
## 4 Results and Analysis
### Theoretical Insights into the COSGO Algorithm  
The COBL method improves the suggested hybrid GWO-SCSO algorithm, which provides unique theoretical contributions beyond the simple combination of two metaheuristics. The hybrid structure achieves a more balanced search process by combining the local exploitation strength of SCSO with the global exploration capabilities of GWO. The algorithm can converge more quickly while being resilient against local optima due to this combination. Additionally, the population diversity is enhanced by the chaotic opposition-based learning (COBL) method, which results in improved initialization and more consistent convergence behavior across runs. Our approach combines a complementary dynamics hierarchical leadership-based search in GWO with adaptive motion in SCSO, which improves the optimization performance in hybrid metaheuristic algorithms that frequently mix similar search behaviors. The COSGO hybridization process collectively  
Figure 9: FSIM analysis across different threshold levels (levels 2–5) using Otsu and Kapur methods with various algorithms. COSGO consistently outperforms others in feature similarity, especially when combined with Otsu, highlighting its robustness in preserving perceptual image quality.  
offers advantages in both convergence speed and stability, which is established by our experimental findings.
# A Review of AlGaN-Based Deep-Ultraviolet Light-Emitting Diodes on Sapphire
## 1 Introduction
### Outlines and Objective of This Review  
One of the objectives of this review is to compare various techniques to fabricate DUV-LEDs and clarify the reasons for our choices, including the use of high-miscut sapphire. The reports on DUV-LEDs from different groups mainly evaluated the LED performances using their own techniques. The discussion cross-linking papers from different groups or from the same group including our group is considered to be lacking, similar to the situation at the beginning of InGaN research. We aim to link the studies of various groups related to AlGaN-based DUV-LEDs despite their limited disclosure due to business and patent strategies. We also comment on how our independent results helped to determine our current device structure.  
Following this section, the main approaches to obtaining AlN for the growth of AlGaN device layers are categorized in Section 2. The factors determining crystal quality of AlGaN on AlN are explained in Section 3. In the past, a conventional AlN template grown on sapphire within a reasonable growth time [23] had a threading dislocation density (TDD) on the order of \(10^{9}/\text{cm}^{2}\), which was not sufficiently low. The improvement of TDD was a priority issue to be solved. In Section 4, the techniques for growing AlN with an improved TDD on the order of \(10^{8}/\text{cm}^{2}\) on sapphire are compared. The reason for using high-miscut sapphire is partially clarified by discussion of the TDD, growth time, and growth temperature. In addition to epitaxial growth, the constitution and techniques necessary to fabricate our current LEDs are explained in Sections 5-7. Section 8 describes the packaging required for fabrication of DUV-LEDs. In Section 9, a factor to be considered when measuring the DUV output is briefly given. Following Section 10, where the device layers are clarified, Sections 11-14 describe the performance of the DUV-LEDs with novel QWs. The expected performance of our dies in the near future upon improving the LEE is written in Section 15. From the viewpoint of the long-term future, the frontiers of DUV-LEDs are introduced showing the challenges by the various groups to improve DUV-LEDs in Section 16. Then, the application and the demonstration of irradiation modules are outlined in Section 17.
## 5 Conclusions  
RFF is a relatively new voice measure that has been described in the literature since 1998. At present, the clinical adoption of RFF is limited by two primary factors, including the availability of automated processing methods, and the interpretation of RFF values. Our review showed that automated algorithms have been incorporated into nearly 55% of all research studies since their availability in 2016. The algorithms have undergone several iterations and have been extended into ambulatory monitoring of voice disorders, a key clinical advancement for daily monitoring and biofeedback for patients. We found evidence for RFF as a potential treatment outcome measure and clinical tracking tool. However, more intervention studies with RFF as a clinical outcome measure are needed in order to complete a formalized meta-analysis and identify changes in RFF that are clinically meaningful and applicable. Finally, investigations into the underlying physiology of RFF are still needed to fully understand how this measure corresponds to the identified factors of tension, aerodynamics, and kinematics.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12618121/s1](https://www.mdpi.com/article/10.3390/app12618121/s1)]([https://www.mdpi.com/article/10.3390/app12618121/s1](https://www.mdpi.com/article/10.3390/app12618121/s1)), Table S1: Studies focused on automated algorithmic processing of RFF; Table S2: Summary of studies (\(n=32\)) examining RFF in people with and without voice disorders.  
**Author Contributions:** Conceptualization, V.S.M.; methodology, V.S.M. and M.P.; software, M.P.; validation, V.S.M. and C.L.K.; formal analysis, V.S.M.; investigation, V.S.M., M.P., C.L.K. and K.E.C.; resources, M.P.; data curation, V.S.M., C.L.K. and K.E.C.; writing--original draft preparation, V.S.M. and J.M.V.; writing--review and editing, M.P., C.L.K. and K.E.C.; visualization, V.S.M., M.P. and J.M.V.; supervision, V.S.M.; project Administration, V.S.M.; funding acquisition: n/a. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Acknowledgments:** Thank you to [PERSON] for sharing gray literature for this review.  
[PERSON] is employed by Delsys, a private company that manufactures, markets, and sells electromyographic sensors and other physiological measurement systems.
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Machine Learning Methods for Yield Prediction  
The commonly used statistical model methods for yield prediction include linear regression, deep learning, and machine learning. Linear regression typically involves simple weights and coefficients but fails to capture nonlinear relationships within the data. Deep learning models are often more complex, requiring training on large datasets to achieve outstanding performance. In contrast, machine learning models stand out by bridging the shortcomings of both approaches, showcasing unique advantages. Machine learning has the ability to explore linear or nonlinear relationships between data features and target variables. It is well-suited for small-scale datasets, exhibiting relatively low model complexity, and is less prone to overfitting effects.
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 4 The U-CLX Model Proposal
### Conceptual Basis of the U-CLX Model
#### 4.1.6 Technological Component B  
* The technological component of the U-CLX Model is technological support, consisting of technical and technological elements that allow ubiquitous learning processes and experiences to take place. These elements are the basis for the development of ubiquitous learning using the four dimensions of the model. This component includes Learning Experiences, Analytics, Technologies, Devices, Management, and Functionalities.
* Learning Experiences are ubiquitous learning processes in which people have the central role. The activities in the different levels of training include the four dimensions of the model (time, place, medium, and context). On the technical and technological side, learning experiences capture all data generated in the U-Learning processes of people; these data are managed, through analysis, to generate information, knowledge, wisdom, and intelligence.
* Learning Analytics: data are collected, measured, analyzed, presented, and reused in order to obtain information about the data generated by people in the different contexts and interactions of ubiquitous learning processes and experiences.
* Technology: all information and communication technologies by which data can be sent and received can produce ubiquitous learning processes and experiences. This element refers to the hardware and software involved in ubiquitous learning, for example: the identification of QR labels, RFID, GPS global positioning, NFC, Bluetooth, WIFI, SMS, satellite, sensors, beacons, and the software to develop these learning processes, as well as widely used technology such as ubiquitous computing, cloud computing, big data, data analytics, etc.
* Devices: all intelligent technological devices with computing and communication capabilities that can capture and measure data generated in ubiquitous learning processes and experiences, such as smartphones, tablets, laptops, cameras, microphones, televisions, watches, and wearable devices.
* Functionalities: this element refers to the design and development of environments for U-Learning, specifying how the processes and experiences should work in ubiquitous learning, and the interaction between academic staff and students. It provides a description and explanation of ubiquitous learning processes. The functionalities are educational support, delivery of content, time, place, medium, and context, correctly applied to ensure and facilitate the U-Learning process.
* Management administers the technological and pedagogical elements used in ubiquitous learning, through management of the devices, technologies, learning analysis, learning experiences, and functionalities used by people in U-Learning.
## 2 Patients and Methods  
### Patients  
The protocol of the study was approved by the Pomeranian Medical University Ethics Committee, with formal informed consent signed by all the participants.  
We defined case group (CS) as patients with angiographically documented CAD admitted to the Cardiology Clinic due to CA stenosis. The program enrolled 117 consecutive patients aged 56-90 (76 men and 41 women). This population was further divided into a subgroup with 70-89% stenosis (CS70), including 61 patients (40 men and 21 women), and a second subgroup with critical (\(\geq\)90%) stenosis (CS90), including 56 patients (36 men and 20 women). All patients enrolled in the program underwent percutaneous carotid angioplasty with stent implantation (CAS).  
The control group (CG) consisted of 88 people aged 40-81 (35 men and 53 women) admitted to the hospital with non-specific chest pain and underwent coronary angiography to exclude CAD. The control group also had an ultrasound examination of the CAs, which ruled out the presence of CA stenosis.  
HT was defined as either systolic blood pressure exceeding 140 mmHg, or diastolic blood pressure greater than 90 mmHg, or antihypertensive treatment. Subjects whose fasting plasma glucose was higher than 125 mg% (6.9 mmol/L) or those using antidiabetic medication were recognized as patients with diabetes mellitus (DM). Dyslipidemia was diagnosed when at least one of the following criteria was met:  
* Low-density lipoprotein cholesterol (LDL-c) 115 mg% or higher;
* Triacylglycerlects (TG) 150 mg% or higher;
* High-density lipoprotein cholesterol (HDL-c) below 40 mg% (men) or below 45 mg% (women).  
LDL-c, TG, and HDL-c were measured using enzymatic methods (Roche Diagnostics, Poland). BMI was calculated as weight/height2 (kg/m2). Patients were classified as current smokers if they reported a daily rate of more than five cigarettes. Patients who reported quitting smoking for five years or who did not smoke at all were classified as nonsmokers.  
CA ultrasonography (USG) Doppler was performed according to standard procedures using GE VIVID E9 device with linear transducer 11L-D. The procedure was conducted according to standard procedures recommended by the American Society of Echocardiography (ASE) and Mannheim carotid intima-media thickness consensus [22]. They define atherosclerotic plaque as a focal thickening of the arterial wall \(>\) 50% in comparison to surrounding Intima-Media complex or Intima-Media complex thickness \(>\) 1.5 mm [23].
## 3 Critical and Rare Metals in Greece
### Cobalt (Co)  
Cobalt has been used for a long time as a pigment in glass and porcelain industry. Recently it was classified as one of the critical metals due to its unique applications in lithium ion batteries, in resistant superalloys, in green-energy systems and in magnets [19]. Globally, cobalt is mined as a by-product of copper and nickel deposits in the Republic of the Congo-DRC (40%), in Zambia (20%), in Canada, in Australia, in Cuba, _etc_. China imports large amounts of cobalt (almost all the DRC's production) in order to produce refined cobalt which is exported to the USA and the EU.  
Greece is the major nickel supplier in EU and produces 2%-3% of the world total Ni. Nickel is mined from the laterite deposits in Lokris, Evia and Kastoria and is recovered by pyrometallurgical methods. These Fe-Ni laterites, which also contain cobalt, are mainly allochthonous and are associated with Mesozoic ophiolites. They are intercalated in Mesozoic limestone and show multistage transportation and re-deposition, responsible for a post-sedimentary redistribution of metals, resulting in Mn, Co, Ni-rich zones [20,21]. Chemical composition of different laterite occurrences in Greece shows that cobalt is mainly concentrated in the Fe-Ni-rich zones, demonstrating elevated contents which reach 0.10% in Paleochori, Grevena, 0.14% in Vermio, 0.16% in Kastoria and 0.22% in Lokris [20, 21, 22]. Co in these deposits is concentrated mainly in the mineral asbolane [22]. The metallurgical process of the Greek laterites in Laryma by LARCO, which produces Fe-Ni alloy, does not allow any Co recovering,which can be separated by hydrometallurgical treatment [23]. Consequently, Greece remains a potential region to supply the EU with significant amounts of cobalt in the future.  
It should be also mentioned here that the non-economic sulfide ores hosted in the ophiolite complexes in Greece, including Cyprus-type and Fe-Cu-Ni-Co type, at Pindos and Othrys contain up to 2300 ppm Co [24].
# Dynamic Monitoring of the Water Flowing Fractured Zone during the Mining Process under a River
## 3 Materials and Methods  
### Determination Methods of the Height of the WFFZ  
Many scholars have calculated the height of the WFFZ. [PERSON] et al. [18] performed a large amount of work on the mining destruction of the overlying strata in the U.S. and concluded that the height of the WFFZ can develop 30-50 times the mining thickness. [PERSON] et al. [20] noted that the height of the WFFZ can develop to 20-100 times the mining thickness. [PERSON] et al. [21] performed many theoretical studies and field surveys on overburden rock failure in coal mines in China, concluding that the height  
Figure 3: Three zones of strata movement above the coal seam (modified from [PERSON]’s work [19]).  
of the WFFZ is dependent on the rock lithology and mining thickness. A harder lithology and a larger mining thickness lead to a higher WFZZ. In general, the height of the WFFZ is 9-12 times the mining thickness for weak strata, 12-18 times the mining thickness for medium-hard strata and 18-28 times the mining thickness for hard strata. Through theoretical analyses and field tests in China, an empirical formula was developed by the National Bureau of Coal Industry of China to calculate the height of the WFFZ [22]. The empirical formula satisfied the design of mining beneath water for most coal mines in China at that time and was widely used to calculate the height of the WFFZ in China. However, the empirical formula is applicable only when the mining thickness ranges from 1.0 to 3.0 m due to the limited technical level of coal mines at that time. The empirical formula is as follows:  
\[\mathrm{H}=\frac{100\sum\limits_{1}M}{p_{1}\sum\limits_{1}M+p_{2}}\pm q \tag{2}\]  
where H is the height of WFFZ, m; \(M\) is the mining height, m; \(p_{1}\) and \(p_{2}\) are coefficients determined by the overlying rock strata lithology; \(\pm q\) is the root mean square error.  
In addition, there are borehole water injection (discharge) fracture measuring systems, borehole teleview logging systems [3,4], radon detection technology [23] and microseismic monitoring technology [24] to determine the height of the WFFZ. These methods are intuitive, precise and effective and widely used in coal mines. [PERSON] et al. [25] combined various detection methods with physical and numerical simulations to determine the height of the WFFZ after mining, and the results were reliable. However, the methods above have limitations to varying degrees. For the longwall face with a mining height larger than 3 m, the empirical formula is not applicable. The latter can only detect fractures that have developed in the boreholes after mining, and the data from several boreholes are not enough to obtain the fracture development for the entire longwall face.
# Modular Bus Unit Scheduling for an Autonomous Transit System under Range and Charging Constraints
## 1 Introduction
Our contributions are as follows:  
* The optimization model for determining the optimal formation and trip sequences of MB units is developed. In particular, given that the vehicles are electrically powered, battery range limits and charging plans are considered in the system scheduling process. * A column generation-based heuristic algorithm is designed to efficiently solve this model. The constraints of trip demand and charging station capacity are included in the main problem, and the problem of the mileage of modular units under a limited range is solved by subproblems. * Taking real data from transit operations for numerical examples, the proposed model performs well in terms of both algorithmic performance and practical applications, enabling strategic support for the promotion of modular bus technology in transit systems. The rest of this paper is organized as follows: Section 2 presents the scheduling model for modular units in detail. Next, Section 3 elucidates a column generation-based heuristic algorithm. Section 4 introduces the case analysis and computational results in actual bus networks. The main conclusions are highlighted in Section 5.