esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Techniques to Predict a Patient's No-Show in the Healthcare Sector
## 2 Materials and Methods
### Research Approach  
The present work brings a new contribution to elucidate the reasons for the no-show of patients and build a ML model according to the following steps (see Figure 1):  
1. Collect the patient dataset consisting of data from both appointments and patients;
2. Apply data cleaning techniques to prepare the dataset;
3. Include peripheral databases to add more value to the initial dataset;
4. Analyse potential correlations between attributes in the dataset;
5. Start a descriptive data analysis to detect key factors and trends that contribute to patients no-show;
6. Adapt the dataset for the training and testing phase, and try several classification algorithms to process the data;
7. Compare different performance metrics from the ML models, and select the model that provides the most accurate results for the problem at hand.  
Figure 1 summarizes the building steps of the ML model, aggregating preprocessing, model building and model evaluation stages.
## 4 Method Application and Resulting Discussion
### Method Application in Three Cases
The main parameters of the wind turbines are detailed in Table 5. It shows that the Vestas v80-2000 has lower speed, lower power and the same rotor radius, and the SWT-2.3-93 has lower speed and higher rotor radius compared to the optimal turbine. Besides, from the Figure 8b, the minimum COE of the two actual wind turbines was approximately the same. Figure 7: The results of the objective function (COE) and optimal layout: (**a**) results of objective function, (**b**) optimal layout of GW109/2500, and (**c**) optimal layout of W2000/93. In order to prove the effectiveness of the proposed COE method, an analytical comparison of the minimum COE of the optimal turbines, SWT-2.3-93 and Vestas v80-2.0 MW was proposed. This considered the optimal wind turbine layout obtained by IPSO algorithm. According to the planned-installed capacity of the offshore wind farm, 27 wind turbines were expected to be installed, \(N=27\). Analogously, the planned-installed number of Vestas v80 was 30. The optimization results and the optimal layouts obtained by the IPSO algorithm are shown in Figure 9. It can be seen from Figure 9 that when the Vestas v80-2.0 MW had a COE slightly smaller than the SWT-2.3-93, both of their COE were higher than the optimization result. Figure 9b,c shows the optimal layouts of the wind farm with the two actual wind turbines. Similar to Figure 7b,c, there are three rows of the layout of the wind turbines, among which two rows are located in the outermost zone of the grid, while one row is located in the medial grid. The biggest spacing was maintained between two wind turbines in the wind direction and, thus, the influence of the wake-effect on the energy production was most diminished.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Asymmetry (\%)** & **PT Mean \(\pm\) SD** & **RTD Mean \(\pm\) SD** & \(p\)**-Value** & **Effect Size** \\ \hline Hip abduction & 3.68 \(\pm\) 3.15 & 11.23 \(\pm\) 9.67 & 0.000 & 0.56 \\ Hip adduction & 5.59 \(\pm\) 4.77 & 10.72 \(\pm\) 10.34 & 0.002 & 0.23 \\ Hip external rotation & 7.90 \(\pm\) 5.82 & 13.18 \(\pm\) 8.83 & 0.001 & 0.27 \\ Hip internal rotation & 11.52 \(\pm\) 7.17 & 13.17 \(\pm\) 9.55 & 0.514 & 0.01 \\ Hip extension & 11.01 \(\pm\) 8.92 & 30.56 \(\pm\) 21.79 & 0.000 & 0.38 \\ Hip flexion & 9.86 \(\pm\) 8.36 & 40.35 \(\pm\) 20.42 & 0.000 & 1.00 \\ \hline \hline \end{tabular} PT = peak torque, RTD = rate of torque development; SD = standard deviation.  
\end{table} 
Table 3: Peak torque and Rate of torque development differences (Mann–Whitney U test). A six-variable regression model explained 48% (R\({}^{2}\) = 0.48; \(p\) < 0.01) of the variation in the T-test performance (Table 5). T-test time was predicted by left hip internal/external rotation strength ratio (\(\beta=-0.58;p\) < 0.01) and inter-limb asymmetries in hip abduction RTD (\(\beta=-0.38;p\) = 0.01), hip flexion ROM (\(\beta=0.32;p\) = 0.03), functional leg length (\(\beta=0.31;p\) = 0.02), SLTJ distance (\(\beta=0.29;p\) = 0.04), and peak torque during trunk lateral flexion (\(\beta=0.27;p\) = 0.05).
## 2 Materials and Methods
### Phenotypic Screening and In Vitro Assays
#### 2.4.3 Immunofluorescence-Based Antiviral Assays  
A549-hACE2 and HeLa-hACE2 cells were infected with SARS-CoV-2 at an MOI of 0.1 in 1536-well Aurora plates (Aurora Microplates, Whitefish, MT, USA) at 3000 cells/well and 384-well collagen-coated plates (Cell Carrier Ultra, Perkin Elmer, Waltham, MA, USA) at 4000 cells/well, respectively. For the A549-hACE2 cells, plates were pre-spotted with compound before adding the cells and virus, whereas for the HeLa-hACE2, compound was added to 24-h pre-seeded cells prior to adding the inoculum. For SARS-CoV-1 antiviral testing, A549-hACE2 cells (10,000 cells/well) were seeded and inoculated simultaneously in pre-spotted 384-well compound plates (Cell Carrier Ultra) at MOI 0.1. Antiviral activity against HCoV-229E in HeLa-hACE2 cells was evaluated in pre-spotted 384-well assay plates (Cell Carrier Ultra). Eight thousand cells per well and virus were also added at the same time, but the MOI was 0.8 in this assay design. Plates were kept in a humidified incubator for 24 h at 37 \({}^{\circ}\)C (SARS-CoV-2 and SARS-CoV-1) or at 35 \({}^{\circ}\)C (HCOv-229E). After incubation, infected cells were fixed with methanol-free formaldehyde (Polysciences, Warrington, PA, USA) at a final concentration of 3-4% (\(v/v\)) for 20 min. Subsequently, the assay plates were treated with permeabilizing and staining solutions, to ultimately image the viral S protein, and/or dsRNA viral marker with nucleus and cell outline stains by high-throughput confocal microscopy (Cell Voyager 8000, Yokogawa, Musashino, Tokyo, Japan). The generated high-content imaging (HCI) data were exported to the Phaedra HCI analysis software [40] for further analysis in order to calculate the percentage of infected cells.  
4.4 Immunofluorescence-Based Antiviral Assay Using LysoTracker Dye to Detect Accumulation of Acidic Organelles  
Two thousand HeLa-hACE2 cells per well were seeded and infected with SARS-CoV-2 at MOI 0.1 in 1536-well plates (Aurora Microplates) pre-spotted with compound. After incubation at 37 \({}^{\circ}\)C for 24 h, LysoTracker reagent (LysoTracker DND-99, Invitrogen, Waltham, MA, USA) was added to the plates at a final concentration of 5 \(\upmu\)M. The plates were then fixed and subjected to subsequent permeabilization, blocking, and staining for dsRNA, cell outline, and nucleus. Plates were imaged on the Cell Voyager 8000 Yokogawa confocal microscope. The LysoTracker signal and the percentage of dsRNA-positive cells were analyzed using Phaedra HCI analysis software. The LysoTracker signal was plotted relative to the blank signal and was defined as positive for acidic organelle accumulation if a significant increase in signal was observed relative to the baseline (virus control).
## 3 Results and Discussion
### Purification of APs from KPH  
#### 3.2.1 Ultrafiltration  
KPH was divided by ultrafiltration with MWCO membranes of 3, 5, and 10 kDa, and four fractions, namely, UF-1 (MW \(<\) 3 kDa), UF-2 (3-5 kDa), UF-3 (5-10 kDa), and UF-4 (MW \(>\) 10 kDa), were prepared, and their scavenging activity on the DPPH radical is shown in Figure 1. The EC\({}_{50}\) value of UF-1 was 3.24 mg protein/mL, which was significantly stronger than those of KPH (5.36 mg protein/mL), and other three fractions (EC\({}_{50}\) value of 4.68, 8.74, and 11.20 mg protein/mL for UF-2, UF-3, and UF-4, respectively) (\(p<0.05\)). KPH consisted of a wide variety of APs with different molecular sizes. [PERSON] et al. reported that hydrolysate fractions with a lower MW were more accessible to free radicals and showed high radical scavenging activity [PERSON] et al. (2018). Therefore, UF-1 was rich in low MW APs and selected for the subsequent chromatographic separation.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
###### Abstract  
Magnetometers provide compass information, and are widely used for navigation, orientation and alignment of objects. As magnetometers are affected by sensor biases and eventually by systematic distortions of the Earth magnetic field, a calibration is needed. In this paper, a method for calibration of magnetometers with three Global Navigation Satellite System (GNSS) receivers is presented. We perform a least-squares estimation of the magnetic flux and sensor biases using GNSS-based attitude information. The attitude is obtained from the relative positions between the GNSS receivers in the North-East-Down coordinate frame and prior knowledge of these relative positions in the platform's coordinate frame. The relative positions and integer ambiguities of the periodic carrier phase measurements are determined with an integer least-squares estimation using an integer decorrelation and sequential tree search. Prior knowledge on the relative positions is used to increase the success rate of ambiguity fixing. We have validated the proposed method with low-cost magnetometers and GNSS receivers on a vehicle in a test drive. The calibration enabled a consistent heading determination with an accuracy of five degrees. This precise magnetometer-based attitude information allows an instantaneous GNSS integer ambiguity fixing.  
magnetometer; calibration; satellite navigation; attitude determination; carrier phase +
Footnote †: journal: Sensors
# Shallow Velocity Estimation by Multiples for Monochannel Boomer Surveys
## 6 Conclusions  
We presented an algorithm for estimating the velocity and thickness of a layer below the seafloor by a monochannel acquisition system, by inverting the travel time of direct arrivals, as well as primary and multiple reflections. A nonlinear set of equations was obtained that can be solved numerically with high precision. However, when measurement errors occur in the input data, due to travel-time selection errors or just ambient noise in the survey, severe instabilities emerged. We showed that a very short offset (2.5 m) can estimate only the layer thickness fairly, while at least a 10 m offset is needed to predict the layer velocity within an acceptable error range.  
Real data processing provides additional challenges. A high-frequency source such as a Boomer emits a signal that is rapidly absorbed by the shallow marine sediments. This absorption modifies the signal waveform during its propagation so that direct arrivals, primaries, and multiples have different delays between their first onset and their peak. We showed that a shift may be needed to improve inversion accuracy if the time break of a waveform can be hardly distinguished from the background noise.  
Our method for estimating velocity and thickness in a layer below the seafloor using a peg-leg multiple was validated by a real dataset, i.e., a short-offset Boomer survey acquired at the Gulf of Trieste (Northern Adriatic Sea). The obtained values were in good agreement with studies in a nearby area, where borehole ties are also available.  
[PERSON] contributed with the key ideas, the data acquisition, and its processing; [PERSON] developed the mathematical algorithm, its software implementation, and the basic text. Both authors contributed to the discussion, the conclusions, and the final text revision. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** The experimental data presented in this paper was acquired under confidentiality conditions. The synthetic example data and the developed MATLAB code can be provided by the corresponding author ([EMAIL_ADDRESS]) upon request.  
**Acknowledgments:** We thank our colleagues at OGS that contributed to the Boomer data acquisition.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 The Proposed Scheme
### System Model  
Figure 1 shows the system model of the proposed scheme. There are five main parties in the proposed system: trusted authority (TA), proxy server (PS), medical cloud server (CS), data owner (DO) and the data user (DU).  
Figure 1: The architecture and data flow of the proposed system.  
**Trusted Authority (TA):** This would be responsible for the system initialization and generating public parameters, distributing attribute keys (ABE-Keys) and keyword search keys (KS-Keys) associated with DU's attributes. Moreover, the TA also creates the key pair of proxy servers.  
**Data Owner (DO):** The DO is a patient who defines an access policy for the PHR data or medical records and then encrypts the corresponding keywords into indexes. At last, the DO uploads the ciphertext along with indexes to the cloud.  
**Cloud Server (CS):** The CS provides the services of storage and data retrieval. It will store encrypted data and indexes outsourced by the DO. After receiving requests from data users, it executes search procedures and transmits the matched ciphertext back.  
**Data User (DU):** The DU is a doctor who can access patients' encrypted data from CS. Specifically, the DU owns the KS-Key and the ABE-key; the former is used to generate trapdoor information while the latter is used to decrypt ciphertexts.  
**Proxy Server (PS):** The PS is an important role between the DO/DU and the CS. The PS is responsible for performing the proxy re-encryption process. After receiving the ciphertext uploaded by the DO, it will execute the re-encryption operation and then transmit the re-encrypted ciphertext to the CS. Similarly, after receiving the ciphertext downloaded by the DU, it will execute pre-decryption process and then return the ciphertext to the DU. Furthermore, when receiving trapdoors of keyword search requests, the PS also modifies it before transmitting it to the CS.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 3 Proposed Methodology
### Preprocessing
\begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **No. of Family Kinds** & **Description** \\ \hline Benign & 6,099,469 & Ordinary safe data packets. \\ Backdoor & 16,809 & Remotely accesses computers by exploiting vulnerable client apps. \\ DoS & 712,609 & Effort to overload a system to make it unavailable. \\ DDoS & 2,026,234 & Similar to DoS, but from multiple sources. \\ Injection & 684,465 & Changes how code executes by injecting insecure inputs (e.g., SQL). \\ MITM & 7723 & Man-in-the-middle attack injecting a malicious user between communications. \\ Password & 1,153,323 & Obtains passwords via sniffing or brute force attacks. \\ Ransomware & 3425 & Encrypts data, decrypts only upon ransom payment. \\ Scanning & 3,781,419 & Uses various methods to gather info about hosts/networks. \\ XSS & 2,455,020 & Injects harmful scripts to alter or steal information. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categories, Number of Family Kinds, and Brief Descriptions in NF-ToN-IoT-v2. binary classification reflects the critical nature of XSS attacks, which can compromise IoT systems by exploiting vulnerabilities in web-based interfaces [33]. ```
Input: Dataset \(D\) with minority class samples \(X_{\text{min}}\) Output: Balanced dataset with synthetic minority samples
1 fore each sample \(x_{i}\) in \(X_{\text{min}}\)
2 Identify \(k\)-nearest neighbors of \(x_{i}\) within \(X_{\text{min}}\)
3 fore each neighbor \(x_{k}\) (up to required synthetic sample count)
4 Generate synthetic sample: \(x_{\text{new}}=x_{i}+\delta\times(x_{k}-x_{i})\), where \(\delta\in[0,1]\)
5 Add \(x_{\text{new}}\) to \(D\)
6 return Updated dataset \(D\) with additional synthetic samples
```  
**Algorithm 1**SMOTE Algorithm for Class Imbalance  
This comprehensive preprocessing phase ensures that the machine learning model is trained on a consistent, normalized, and balanced dataset, which is essential for accurate and efficient threat detection in IoT security applications. The handling of class imbalance, in particular, enhances the model's robustness by ensuring equal consideration of both benign and attack instances, making the model more effective in detecting real-world IoT attacks.
## 4 Mathematical Model
### Numerical Implementation  
The partial differential equations involved in the mathematical model are approximated using the central difference (CDS) semi-implicit scheme. The nonlinear terms on the right-hand side of transport equations \(Q(t,x,T,\varphi)\), \(\dot{m}_{gs}\), \(\dot{m}_{ls}\) and \(\dot{m}_{gl}\) are treated explicitly.
## 1 Introduction
To achieve this, first, we analyzed the locations and accessibility of key facilities, such as hospitals and welfare centers. Second, we identified and visualized densely populated areas of vulnerable groups. Finally, we synthesized these data to propose optimal locations for installing PM alert systems. The core premise of this research is that improving the living environment of vulnerable populations requires providing PM information centered on their primary activity spaces and residences. This study aimed to establish practical and concrete measures to protect vulnerable populations from the health hazards of PM. Unlike previous studies that focused on measuring and forecasting PM concentrations, this study proposes a customized information provision plan tailored to the actual living environments of vulnerable groups. Through this study, we obtained detailed information on the distribution of residences and facilities frequented by vulnerable populations in Gangeo District. The optimal locations for PM alert systems identified can make a practical contribution to protecting the health of these vulnerable groups. Additionally, these findings serve as valuable data for local policymakers in formulating environmental policies to protect vulnerable populations. In conclusion, this study presents practical alternatives for protecting vulnerable populations from PM, thereby contributing to a reduction in social inequality. The remainder of this paper is structured as follows: Section 2 reviews the relevant literature on air pollution and its health impacts. Section 3 details the methodology used for spatial analysis and data collection. In Section 4, we present the results of the analysis, identifying the optimal locations for PM alert systems in Gangeo District. Finally, Section 5 discusses the findings and their implications, and provides the conclusion, including recommendations for future research and policy implementation.
# Bridging User Perception and Stickiness in Business Microblog Contacts: A Moderated Mediation Model
## 3 Research Method
### Data Collection and Measures  
The data used in this research were collected through an online sampling survey. The respondents who had experience using a microblog webpage of a corporation were selected. On the cover page of the questionnaires, this study ensured respondents that the collected information is only for academic use, and declared the voluntary nature of this survey and the confidentiality to every participant. To encourage participation, the respondents were offered the opportunity to win a lottery-based prize (e.g., 7-11 coupons, familiar coupons). The respondents were asked to evaluate the items in a questionnaire based on their usage behavior on the microblog. All items were measured on a seven-point Likert scale ranging from 1 (\"strongly disagree\") to 7 (\"strongly agree\"). After discarding the invalid responses, this study conducted the statistical analysis.  
To examine the hypothesis, the paper specifies the definition and measurement of each variable. This study revised the popular work [10], which defined perceived usefulness as the extent to which a person believes that using a particular system will enhance performance toward the goals, and perceived ease of use as the degree to which a person believes that using a particular system would be free of effort. This study measured perceived usefulness with five items and perceived ease of use with three items. Based on the work of prior study [4], this study defined emotional connection as bonds developed over time through positive interactions with other members, and used five items to measure the construct. Furthermore, based on the study [16], adaptivity was defined as the ability of systems to adapt rules, structures, and information contents of a virtual community to meet user needs, and was measured using three items. In line with the study [53], customer stickiness was defined as the ability of a website to attract and retain visitors, and was measured using four items.  
In addition, this study controlled for sex, age, and spending time on the internet of respondents in order to reduce their potentially confounding effects on emotional connection and stickiness. Research indicates that younger people tend to adopt the online community faster (e.g., [42]). Females may be more likely to be involved in a virtual community and stick to a website [27]. Spending time referred to the amount of time users spend on the Internet every day. Scholars suggest that as individuals stay longer on the Internet, they are more likely to be familiar with the firms' websites and easily stay [54].
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 2 Related Works
### Time-Sensitive Network Routing  
TSNs are composed of series of IEEE technical standards, including precise time synchronization, network traffic shaping, network configuration, and other aspects. The IEEE TSN Standardization Working Group issued a standard amendment [20] to address the problem of path controlling and proposed shortest path bridging (SPB) as the basis for establishing network bridging in Chapter 12. Based on this, a management information base (MIB) was specified in Chapter 17, which formed the IEEE 8021-SPB-MIB standardization framework. Furthermore, the amendment proposed a path control and reservation (PCR) method in Chapter 45, which was based on the use of SPB and spanning tree protocols to achieve the path control and traffic reservation of TSN flows. Following on from IEEE standards, [PERSON] et al. and [PERSON] et al. [21; 22] proposed solutions for the joint traffic scheduling and route planning problem, both of which were based on integer linear programming (ILP). [PERSON] et al. [21] used two performance metrics (i.e., end-to-end delay and scheduling capability) to evaluate their experimental results for different traffic patterns and network topologies. [PERSON] et al. [22] adopted an ILP solver for instances with large parameter variations and evaluated the performance of their algorithm based on the solution time. [PERSON] et al. [23] proposed an iterative algorithm for joint routing and scheduling based on SMT, but the performance of their algorithm was too sensitive to the degree of transmission path conflicts between flows, which led to an unsatisfactory success rate. [PERSON] et al. [24; 25] proposed the concept of a time-sensitive software-defined network (TSSDN), which forms a logically centralized control plane of SDN to compute global routing schemes. The above studies provided feasible routing mechanisms for TT traffic, but it is difficult to meet the requirements of the solution time for large-scale routing scenarios and no relevant studies have been found that route for non-TT traffic. Therefore, the existing methods can hardly cope with the routing challenges brought by future large-scale TSN communication systems.