esa-sceva/satcom-chunk-collection huggingface dataset:
# New Isolated Autochthonous Strains of _S. cerevisiae_ for
## 2 Materials and Methods
### Chromatographic Analyses  
#### 2.5.1 Method of Analytical Determination of Selected Polyphenolic Compounds  
Selected phenolic acids (gallic, chlorogenic, caffeic, and vanillic; all were purchased from Sigma-Aldrich, Steinheim, Germany) were determined using the HPLC technique with a UV detector. The samples of white wines were filtered through a cellulose syringe filter (0.45 \(\upmu\)m) prior to chromatographic analysis. The red wine samples were purified by solid-phase extraction (SPE) prior to chromatographic analysis. Of the sample, 3 mL was applied to a preconditioned with methanol and water SPE C18 column. The obtained eluates were subjected to direct chromatographic analysis. Separation of phenolic compounds was performed with the Gilson system (Meddleton, WI, USA) equipped with an autosampler (GX-271), a multi-solvent pump (322), a column oven, and UV/Vis detector based on a method described by [PERSON] et al. [44], with some modifications. Briefly, the working conditions of the analytical system were set as follows: chromatographic column:Aquasil-C18 (250 \(\times\) 4.6 mm, 5 \(\upmu\)m; Thermo Fisher Scientific); mobile phase A: 1% formic acid (Chem-Lab NV, Zedlegem, Belgium) in water; B: 1% solution of formic acid in acetonitrile (Sigma-Aldrich, Steinheim, Germany); gradient run: 0-5 min. 100% A; 5-20 min. 70% A; 20-30 min 40% A; 30-35 min. 100% A; mobile phase flow rate: 1 mL/min; analytical wavelength of the UV detector: 280 nm. The content of the compounds was calculated based on the calibration curve, which was prepared with the use of methanol solutions of analytical standards. The calibration curves of the analyzed phenolics were made in triplicate for each individual standard and were plotted separately for each standard at three different concentrations (5-100 \(\upmu\)g/mL). The determination coefficients for the calibration curves were higher than 0.997.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 2 Related Works  
### PDR Improvements with Visual Information  
Recently, many works have been done to improve PDR with visual information. These visual signals include 2D images, depth images, binocular cameras, etc. Some use a camera and PDR combined for localization and tracking, designing a new visual detection and tracking system [19]. For wearable devices, [PERSON] et al. [20] used optical flow and PDR to help get the trajectory of movement. They also used the previous vision to build a visual circuit, which can be used on maps without priors. They focused on an algorithm to combine two localization result to get the right result, which increases the confidence in positioning results [21]. In addition, [PERSON] et al. [22] used target detection to get the 3D positions of pedestrians, which assists PDR to achieve good positioning. Some works [23] use the matching between 3D and 2D for position correcting, where visibility and co-visibility information is adopted. The visual position corrects the PDR positioning error, which achieves good results. In [24], the authors proposed helping indoor positioning by combining sign recognition and DR positioning They focused on correcting PDR movements by visual information, thereby reducing cumulative errors. Meanwhile, we directly use visual position recognition to obtain the exact positioning results to assist the correction of PDR.
# Automatic Definition of Traffic Analysis Zones Based on Big Data
## 7 Discussion and Conclusions  
Based on the findings of our study in district XI of Budapest, we can confidently state that automatically gathered traffic flow information, such as the data obtained from WAAE in our study, can be utilized as an input to generate TAZs for transportation modeling needs, without the need for specific local expertise.  
Hence, we have indicated that it is feasible to generate a zonal system for macroscopic transport models without manual intervention in the computation procedure. This automated process can create a zonal system using the transportation network (road network) and traffic flow information.  
Although the algorithm demonstrates promising results, more research is needed to address the challenges related to data quality, computational complexity, and validation. With continued advancements in data analytics and transportation technology, automated traffic management systems such as the one proposed here have the potential to evolve traffic models.  
Conceptualization, V.N. and B.H.; methodology, V.N.; software, V.N.; writing--original draft preparation, V.N. and B.H.; writing--review and editing, B.H.; visualization, V.N. All authors have read and agreed to the published version of the manuscript.  
This research received external funding from the project \"Digitalisan ossexekaposclot adatforrasokra alapozott, dinamikus, hangolt es adaptiv varosi forgalomiranyitasi rendszer zsolgattaosok es beavatkozasi, ertekleesi kozlekedespolitikai eszkoztar kifejlesztese\", financed by the NKFIH (National Research, Development and Innovation Authority, Hungary).  
Data Availability Statement: Data is contained within the article.  
Authors thank the kind help of FOMTERV Inc. and especially [PERSON] by providing the necessary data set.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:
# A Bidirectional Trust Model for Service Delegation in Social Internet of Things
###### Abstract  
As an emerging paradigm of service infrastructure, social internet of things (SIoT) applies the social networking aspects to the internet of things (IoT). Each object in SIoT can establish the social relationship without human intervention, which will enhance the efficiency of interaction among objects, thus boosting the service efficiency. The issue of trust is regarded as an important issue in the development of SIoT. It will influence the object to make decisions about the service delegation. In the current literature, the solutions for the trust issue are always unidirectional, that is, only consider the needs of the service requester to evaluate the trust of service providers. Moreover, the relationship between the service delegation and trust model is still ambiguous. In this paper, we present a bidirectional trust model and construct an explicit approach to address the issue of service delegation based on the trust model. We comprehensively consider the context of the SIoT services or tasks for enhancing the feasibility of our model. The subjective logic is used for trust quantification and we design two optimized operators for opinion convergence. Finally, the proposed trust model and trust-based service delegation method are validated through a series of numerical tests.  
trust model; social internet of things; service delegation Article
## 3 Proposed Method of Extracting Elemental Image Array  
The proposed method consists of an existing method and two postprocessing techniques for extracting an EIA. Moreover, the method can utilize any existing method to improve its accuracy. Normally, existing methods detect the size of an elemental image and the lattice structure or locations of the lens boundaries in EIA effectively [24; 25; 26; 27; 28]. However, the accuracy is sometimes deteriorated by the lens distortion and the inaccurate edge detection at the boundary of each lens. Even a small misalignment such as 1 or 2 pixels negatively affects the image quality in computational reconstruction. Thus, the size and location of each elemental image can be contaminated by errors from those problems. To minimize the errors, postprocessing is very useful to improve an existing method for EIA extraction since any state-of-the-art method can be integrated with postprocessing.  
Figure 2: The existing method of EIA extraction.  
Here, we propose two postprocessing techniques that consist of re-calibration and lens array area detection, as shown in Figure 3. Our re-calibration technique is carried out independently using the size and locations of elemental images from an existing EIA extraction method. An existing method provides the size and location of each elemental image, which should be real numbers to be accurate. Some problems mentioned above can cause the size of each elemental image to be inaccurate. Moreover, the locations of elemental images can take the same problems. However, the number of elemental images is quite robust since it should be an integer value. In our re-calibration, the number is calculated by quantizing its intermediate value. Here, the quantizing process can eliminate the error in the intermediate value. Thus, the number is more accurate than the size and the locations. This property is therefore utilized in our recalibration technique. In our lens array area (LAA) detection, the intermediate edge map is reused in our method. Usually, some existing methods employ markers to determine the lens array area [29; 30]. The existing methods without markers use a cropped raw image so that the lens array area is the whole area of the input raw image. This may be a limitation to set up the optical configuration of the pickup process. On the other hand, our LAA detection technique can provide the lens array area without markers, and it can offer an efficient optical configuration.
# Securing Infrastructure-as-a-Service Public Clouds Using Security Onion
## 2 Background
### Cloud Computing
#### 2.2.4. Classification of Attacks  
* **Network Based Attacks:** One entry point for attackers into a Cloud system can be through the network that leads to weakening the quality of Cloud services and further putting Cloud confidentiality and privacy at risks. The most common attacks on network infrastructure in the Cloud are port scanning, Botnets, man-in-the-middle (MITM) [15], distributed denial of service (DDoS) [16], and spoofing attacks.
* **Virtual Machine Based Attack:** Several security risks occur as a result of many VM instances being hosted on a single system. The major characteristic feature of VM based attacks is the violation of data and hampering of cloud services as a result of flaws in VMs being manipulated.
* **Application Based Attack:** Applications running on Cloud platforms are typically delivered through a web browser. However, flaws in these web applications make the Cloud vulnerable to attacks [17]. Examples of application-based attacks are injections of codes into applications that run on Cloud platforms and attacks on protocols that are implemented to provide services in the Cloud.
* **Storage Based Attacks:** The Cloud storage is a high-value target for attackers because data from various users and business organizations are kept together in a Cloud environment [18]. Hence, breaching into the Cloud storage will potentially give an attacker access to a large pool of sensitive information. Examples of storage-based attacks are data scavenging and data recovery attacks.
# Service for Deploying Digital Twins of QKD Networks
###### Abstract  
Quantum technologies promise major advances in different areas. From computation to sensing or telecommunications, quantum implementations could bring significant improvements to these fields, arousing the interest of researchers, companies, and governments. In particular, the deployment of Quantum Key Distribution (QKD) networks, which enable the secure dissemination of cryptographic keys to remote application entities following Quantum Mechanics Principles, appears to be one of the most attractive and relevant use cases. Quantum devices and equipment are still in a development phase, making their availability low and their price high, hindering the deployment of physical QKD networks and, therefore, the research and experimentation activities related to this field. In this context, this paper focuses on providing research stakeholders with an open-access testbed where it is feasible to emulate the deployment of QKD networks, thus enabling the execution of experiments and trials, where even potential network attacks can be analyzed, without the quantum physical equipment requirement, nor compromising the integrity of an already built QKD network. The designed solution allows users to automatically deploy, configure, and run a digital twin environment of a QKD network, offering cost-effectiveness and great flexibility in the study of the integration of quantum communications in the current network infrastructures. This solution is aligned with the European Telecommunications Standard Institute (ETSI) standardized application interface for QKD, and is built upon open-source technologies. The feasibility of this solution has been validated throughout several functional trials carried out in the 5G Telefonica Open Network Innovation Centre (5 TONIC), verifying the service performance in terms of speed and discarded qubits when generating the quantum keys.  
quantum key distribution (QKD) networks; digital twin; quantum network; service orchestration +
Footnote †: journal: Applied  
0000-0002-4307-2274][PERSON]  
1 Telematic Engineering Department, Universidad Carlos III de Madrid, Avda. Universidad, 30, 28911 Leganes, Madrid, Spain; [EMAIL_ADDRESS] (F.V.); [EMAIL_ADDRESS] (B.N.)
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 4 A Novel Battery SoC Estimation Using a Data-Driven PHM Approach
### Automated Hyperparameters Optimization  
Hyperparameter optimization (HPO) refers to the process of identifying the set of hyperparameters that maximizes a model's performance, playing a critical role in enhancing the accuracy of machine learning algorithms [29].  
Three main HPO algorithms are found in the literature [30]: grid search, random search, and Bayesian optimization. Grid search performs an exhaustive search on the hyperparameter set with limits defined by the end-user, which requires some previous knowledge on all possible candidates. Random search looks at a randomized search over hyperparameters until the targeted accuracy is obtained. Finally, Bayesian optimization (BO) aims at finding the global optimum with the minimum number of trials. Random search presents superior results when compared with grid or manual searches [31].  
Herein, the random search method is selected to find the optimal hyperparameters for the bidirectional LSTM model.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction  
Telemedicine, as the automated process of medical analysis, is becoming one of the most rapidly developing areas of modern technology. We are seeing a global digital transformation of healthcare, which is redefining the approach to patient diagnosis and treatment. Clinical decision support systems (CDSS) play a key role in this process [1, 2], enabling better and more effective diagnosis and increasing the efficiency of the treatment process. The Medical Assistant project was developed in response to the growing demand for advanced tools to support diagnostic processes, drawing inspiration from existing developments in this field, such as MYCIN [3], Watson for Health, and the decision support modules offered by Epic Systems. There are several telemedicine systems in the world with similar functionalities, which provide a reference point for our project. One of the pioneering systems is MYCIN, developed at Stanford University in the 1970s, which was used to diagnose and treat bacterial infections and recommend antibiotic therapy. Watson for Health [4], developed by IBM, uses advanced machine learning and artificial intelligence algorithms to analyse medical data, allowing for more precise support of the diagnostic process. Epic Systems, on the other hand, provides a decision support module as part of their healthcare IT system, which supports doctors in their daily practice.  
In this paper, we present the results of developing an IT system designed to function as a 'digital doctor' capable of conducting an initial medical history interview with patients. This work details the stages of creating a system that simulates doctor-patient communication, enabling both voice and manual interaction on devices such as smartphones or tablets. The system was engineered to refine its questions dynamically, based on the patient's responses, which culminated in a list of probable diagnoses ranked by likelihood. This project underscores the growing role of digital solutions in telemedicine and their potential in augmenting healthcare in Poland.  
The primary motivation for writing this publication is to describe our experience with a project we have been carrying out, the essence of which is a prototype general-purpose medical diagnosis support system. Together with the project physicians and ML researchers, we created a working prototype system.  
The rest of the paper will be presented in the following sections. In Section 1.1, we discuss the problem of a variety of possible inputs into the system. In the next Section 1.2, we provide an introduction to the necessary methodology. In Section 1.3, we present the process of creating disease vectors from the medical interviews, together with appropriate data preparation. In Section 2, we will introduce the experimental part, including model settings, and in Section 3, a summary of the results. This will be followed in Section 5 by conclusions. Let us move on to discuss the research methodology.
## 3 Proposed Algorithm
### SVM Based Classification for Predicting the Response  
The SVM [23] has been widely used for solving both linear and nonlinear classification problems. It uses a binary decision that maps given samples (training data) to a discrete-valued output class \(z\in\{-1,1\}\). Since the eigenvalues of the brake system have two states, i.e., either stable or unstable, the SVM can be directly applied. In this paper, the classes \(z=-1\) and \(z=1\) are defined as to the region where stable and unstable response occur. Given \(N\) samples and their obtained output class using the stability criteria function, a hyperplane equation which separates these two classes can be constructed as follows:  
\[h(\zeta)=b+\sum_{i=1}^{N}\alpha_{i}z^{i}K\big{(}\zeta^{i},\zeta\big{)} \tag{19}\]  
where \(b\), \(K\big{(}\zeta^{i},\zeta\big{)}\), \(z^{i}\) and \(\alpha_{i}\) are the intercept with the origin, kernel function of the SVM, output class of \(i\)-th sample, and Lagrange multipliers, respectively. In applying the kernel function for the SVM, a Gaussian-type function is adopted in this paper. The Lagrange multipliers are unknown and can be obtained by maximizing the margin between support hyperplanes. After Lagrange multipliers are computed, the hyperplane is given by \(h(\zeta)=0\), and output classes of \(-1\) and \(1\) are predicted by \(h(\zeta)<0\) and \(h(\zeta)>0\), respectively. Utilizing the SVM classification, \(k\)-th length scale for the given unsampled input point is estimated as  
\[\overline{l}_{k}(\zeta)=\begin{cases}\theta_{k}^{1}&h(\zeta)<0\\ \theta_{k}^{2}&h(\zeta)>0\end{cases} \tag{20}\]  
Since the length-scale for unsampled input is now defined, the response prediction for nonstationary kriging in Equation (17) can be computed. A flowchart of the proposed combined nonstationary kriging and SVM method is presented in Figure 6.  
In comparison with stationary kriging, the proposed algorithm increases the number of hyperparameters in optimization problems and requires SVM training. However, if the complex eigenvalue problem requires a large amount of computation, these additional operations can be  
Figure 6: Flowchart of the combined nonstationary kriging and SVM method.  
ignored compared to solving the eigenvalue problem. Issues regarding the accuracy and computational efficiency of the proposed method will be discussed in the next section.
## 5 Analysis and Discussion
### Overall Performance and Mean Value Comparison  
As shown in Table 9, the mean velocity \(\overline{v}\) under low-speed conditions was highest in the basic configuration (i.e., without a suspension). This outcome aligns with the earlier observation that, at low speeds, the wheel tends to momentarily stop when its spoke contacts the next step edge, because the suspension fails to release energy in time. These brief stops ultimately lower the overall average velocity.  
Under medium-speed conditions, except for the low-k case, the mean velocities of the basic and suspension-equipped platforms were fairly similar. In contrast, at high speeds, all suspensions delivered mean velocities about 10% higher than those of basic; notably, the high-k suspension exceeded basic by 11.6%. This result suggests that, at sufficiently high speeds, a high-stiffness spiral suspension can both increase climbing speed and improve dynamic stability compared to basic.  
Next, the standard deviation of velocity s indicates how tightly the CoR velocity is clustered around the mean. A smaller s reflects less fluctuation and thus higher dynamic stability. According to Table 9, basic had the smallest standard deviation at low and medium speeds, but under high-speed conditions, the high-k suspension showed a 17.5% reduction in s compared to basic. This finding suggests that, at higher speeds, increasing suspension stiffness yields a more stable velocity distribution. In summary, a high-stiffness suspension at high speeds can achieve both a higher average velocity and a lower velocity fluctuation, leading to improved dynamic stability overall.
## 3 Experimental Results  
The results obtained from the experimental four-point bending tests that were carried out are presented in Figure 6. In case 1, the cross-section remains constant for all sections; in case 2, the cross-section is constant for two of the three sections. In case 3, the cross-section is entirely different for each section. Thus, the results of the three tests are listed next: for case 1, the reached stress value was closed to 251.6496 MPa with a deflection of 0.843 mm at approximately \(-20\) mm from the implant center, while in case 2, the stress values were 254.6313 MPa with a deflection of 1.7180 mm at approximately \(-26.66\) mm from the center of the implant, and for case 3, the maximum stress value was 248.6541 MPa with a total deflection of 4.929 mm. Figure 6 shows the complete results reported by the software of the universal testing machine. Figure 6C presents a close view of linear  
Figure 4: Intramedullary telescopic nail testbed under 4-point flexion test.  
Figure 5: (**A**) Loads applied to the telescopic intramedullary implant (**B**) Case study 1. (**C**) Case study 2. (**D**) Case study 3.  
behavior. This approach makes it possible to visualize that the first two cases support a greater load capacity within the elastic limit of the material around 250 MPa. The third case suffers a total deformation of the element, even before it reaches the elastic limit. The loads recorded at the limit of the linear behavior are approximately 79.058 N for case 1 (red line with circles), 60 N for case 2 (blue line with circles), and 46.875 N for case 3 (green line with dashes).  
All the tests were filmed to analyze the data after the run by using the GOM-Inspect software. Figure 7A shows the deformations reached on the male sample for case 1; the maximum deflection obtained was 0.80 mm (Figure 7B). The computer program indicates a maximum deflection of 1.72 mm (Figure 7C). Finally, for the third case, a maximum deflection of 4.92 mm was obtained according to the analysis (Figure 7D).  
Figure 6: (**A**) Cross-section comparative for the four bending tests. (**B**) Total behavior of the experimental test. (**C**) Data acquired from the experimental test.  
Figure 7: Results of experimental tests (**A**) Samples after deformation. (**B**) Correlation of digital images for case 1. (**C**) Case 2. (**D**) Case 3.