esa-sceva/satcom-chunk-collection huggingface dataset:
# Considered Factors of Online News Based on Respondents' Eye Activity Using Eye-Tracker Analysis
## 4 Results
### Result of the Content Interest Measurement Experiment  
The next step was to calculate the user's interest using the following metrics. Results from content interest measurements are in Table 6.  
**D. Duration of fixation on AOI images in each news article**  
B1 = 1586 s, B2 = 1925 s, and B3 = 1304 s. B2 had the most interaction time in the image content category in this study, while B3 had the shortest time.  
**E. Fixation duration in AOI text in each news article**  
B1 = 4133 s, B2 = 1925, B3 = 3100. B1 had the most interaction time for the text content category in this study, while B2 had the shortest.  
**F. Percentage of time on AOI images and news**  
Comparison of fixation duration on text and images in each news article (F. Picture; F. Text). Results were as follows: B1 = 27.73%: 72.27%, B2 = 39.42%: 60.58%, and B3 = 29.61%: 70.39%.  
From Table 6, the most substantial image fixation ratio occurred in N2, while text content occurred in N1. When compared, the rate of text fixation was higher than the rate of image fixation in all three news types, with a significant difference between the data. With this result, it can be concluded that respondents were more likely to try to get news information from reading and observing text rather than images. Table 7 shows the duration of the respondents' fixation on images (D) and text (E). For image content, N2 had the longest duration, which was 1925 s, and the shortest was N3 with 1304 s. For text content, N1 had the longest duration of 4133 s, and N2 had the shortest with 2959 s. When the durations of fixation were compared, the text fixation duration was greater than the duration of fixation on images. With this result, it can be concluded that respondents used more time to read the news compared to looking at images. After getting the data results from the measurement analysis tracker, in the next section we will discuss and process the respondents' anxiety data based on the BAI questionnaire. Anxiety was not directly affected by nor resulted from eye-tracker measurement data.  
Description:  
N1, N2, and N3 = News 1, News 2, and News 3;  
IF = image fixation;  
TF= text fixation;  
A = total fixation on AOI images;  
B = total fixation on AOI text;  
C = fixation ratio of images and text;  
D = gaze duration on AOI images (seconds);  
E = gaze duration on AOI text (seconds);  
F = percentage of time used to look at images and text.
# Co-Clustering under the Maximum Norm+
## 2 Formal Definitions and Preliminaries  
We use standard terminology for matrices. A matrix \(\mathcal{A}=(a_{ij})\in\mathbb{R}^{m\times n}\) consists of \(m\) rows and \(n\) columns where \(a_{ij}\) denotes the entry in row \(i\) and column \(j\). We define \([n]:=\{1,2,\ldots,n\}\) and \([i,j]:=\{i,i+1,\ldots,j\}\) for \(n,i,j\in\mathbb{N}\). For simplicity, we neglect the running times of arithmetical operations throughout this paper. Since we can assume that the input values of \(\mathcal{A}\) are upper-bounded polynomially in the size \(mn\) of \(\mathcal{A}\) (Observation 2), the blow-up in the running times is at most polynomial.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(m\) & \(|\Sigma|\) & \(k\) & \(\ell\) & \(c\) & **Complexity** \\ \hline - & - & - & - & 0 & P [Observation 1] \\ - & 2 & - & - & - & P [Observation 1] \\ - & - & 1 & - & - & P [Theorem 4] \\ - & - & 2 & 2 & - & P [Theorem 5] \\ - & 3 & 2 & - & - & P [Theorem 6] \\ - & - & 2 & \(\copyright\) & 1 & FPT [Corollary 2] \\ - & \(\copyright\) & 2 & - & 1 & FPT [Corollary 2] \\ \(\copyright\) & - & \(\copyright\) & \(\copyright\) & \(\copyright\) & FPT [Lemma 2] \\ - & 3 & 3 & 3 & 1 & NP-hard [Theorem 1] \\ 2 & - & 2 & - & 2 & NP-hard [Theorem 2] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of results for \((k,\ell)\)-Co-Clustering\({}_{\copyright\copyright\) with respect to various parameter constellations (\(m\): number of rows; \(|\Sigma|\): alphabet size; \(k/\ell\): size of row/column partition; \(c\): cost). A \(\copyright\) indicates that the corresponding value is considered as a parameter, where FPT (fixed-parameter tractable (FPT)) means that there is an algorithm solving the problem where the super-polynomial part in the running time is a function depending solely on the parameter. Multiple \(\copyright\)’s indicate a combined parameterization. Other non-constant values may be unbounded.
# Topology and Control Strategy of PV MVDC Grid-Connected Converter with LVRT Capability
## 2 Principle of the Converter
### LC Filter in the Converter  
A LC filter is designed in the converter. It has two functions: (1) Filter out high-frequency components of output current to obtain a lower output current ripple. (2) Limit the output surge current during the grid voltage drops or dc fault occurs. During the process of LVRT, a surge current will occur due to discharge of the output capacitor. An output inductor is introduced in the converter to limit the surge current. The output inductor \(L_{o}\) and output capacitor \(C_{o}\) form a LC filter, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Topology**} & **DAB** & **Isolated Boost** & [18] & [20] & **Proposed Topology** \\ \hline High-voltage & Switches & 4 & 0 & 2 & 2 & 0 \\ side & Diodes & 0 & 4 & 4 & 2 & 4 \\ Low-voltage & Switches & 4 & 5 & 4 & 6 & 5 \\ side & Diodes & 0 & 0 & 2 & 2 & 1 \\ Number of capacitors & 2 & 3 & 6 & 4 & 3 \\ Number of inductors & 0 & 1 & 0 & 0 & 1 \\ Output voltage & Low & High & Medium & Medium & High \\ Boosting capability & Low & Strong & Medium & Medium & Strong \\ Output voltage range & Narrow & Medium & Wide & Wide & Wide \\ Efficiency & Medium & High & Medium & High & High \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between different isolated topologies.  
Figure 5: The relationship between voltage gain and duty cycle.
# Performance Characterization of GNSS/IMU/DVL Integration under Real Maritime Jamming Conditions
## 1 Introduction
For maritime application, the so-called R-Mode (R-Ranging) is currently being developed as a terrestrial backup system. Here existing signals of opportunity of globally available maritime infrastructure such as MF radio beacons and ashore AIS stations will be used as possible ranging sources [14]. A first experimental testbed for R-Mode will be established in the R-Mode Baltic project (2017-2020) in the western part of the Baltic Sea [15]. Finally, within the third group, the positioning information from the GNSS receiver is combined within a multi-sensor fusion scheme with independent onboard sensors such as inertial sensors, a speed log, or a gyrocompass. Such hybrid navigation systems enable PNT determination even in cases where GNSS information are either not available or not sufficient for a complete solution. The present paper focuses on an example from the third group, where a classical inertial measurement unit (IMU)/GNSS sensor fusion scheme is augmented with a Doppler velocity log (DVL). Although the benefits of integrated IMU/GNSS navigation systems have already been demonstrated for marine applications [16], a performance test of such a multi-sensor system under real jamming conditions on a vessel is still missing. Typically, the GNSS outage is only emulated by switching the GNSS signals off instantaneously, whereas within a real jamming scenario a complete GNSS outage is only the final stage after a slow degradation of GNSS signal reception. The crucial point is the correct description of the GNSS measurement error statistics under jamming conditions. Whenever the GNSS noise model assumptions are violated, the Kalman filter (KF) could behave suboptimally and therefore could result in inferior performance compared to that which is often demonstrated using pure simulated data. Typically, it is expected that the jamming leads to an increased range noise and might even result in failures in signal tracking loops. Therefore, the characterization of GNSSpseudorange errors statistics under jamming conditions is a prerequisite of using an IMU/DVL/GNSS integration in a real jamming scenario. The rest of the paper is organized as follows. In Section 2, a brief overview of existing work on jamming experiments and the application of hybrid navigation systems for maritime applications is given. The details of the applied GNSS/IMU/DVL unscented KF are given in Section 3 and are followed by the description of the experimental setup of the measurement campaign in Section 4. The results are presented and discussed in Section 5, and a summary and outlook for future work are provided in Section 6.
## 1 Introduction and Literature Survey
### Wing Design Methodologies  
The high level of interaction between the rigid-body dynamics and the structural dynamics poses a significant challenge to a successful actuation of wing system design. Aircraft structures must sustain significant loading variations when flying through turbulence and must suppress catastrophic aeroelastic phenomena such as flutter. One of the earliest works in the field of wing design was performed by [PERSON] [5], who minimized wing structural weight by designing the structure and the control system simultaneously. An aeroevoelastic design framework was also presented by [PERSON] et al. [6], who considered the interactions between the aircraft structure and the control system. The structural and the control optimization were performed separately. The same authors also considered maneuver load inaccuracies and their effects on optimum design.  
In a traditional design process, the aircraft configuration is first designed, followed by the control system design; this prevents the flight control system from affecting the aircraft configuration [7]. It has been shown that by designing the aircraft configuration and the actuation control system (for the adaptive wing) concurrently these two design optimizations can improve aircraft performance [8].  
A variety of modeling and simulation methods to implement and solve the wing optimization problems are available in the literature. These methods can be classified according to their level of fidelity or the degree to which a model or a simulation reproduces the state and behavior of a real-world object [9]. For aerodynamic analysis, the fidelity levels result from different simplification degrees applied to the Navier-Stokes (NS) equations, from the Reynolds-averaged Navier-Stokes (RANS) down to the potential flow theory methods, such as the vortex lattice method (VLM) and the lifting line theory (LLT) [10].
## 4 Results and discussion
### Critical value of van der Waals force parameter  
Figure 2 shows the variation of the critical van der Waals force parameter \(\mu_{\rm cr}\) with respect to the aspect ratio \(\varphi\) for two values of \(\alpha=g_{0}^{2}/h^{2}\) and for \(\lambda=0\) and \(\beta=0\). Numerical data in Figure 2 are interpolated with  
\[\mu_{\rm cr}=f(\alpha)\left(1+\sum_{k=2}^{4}\varphi^{-k}\right), \tag{40}\]  
where the function \(f(\alpha)\), plotted in Figure 3, is given by  
\[f(\alpha)=3.91\alpha+36.7. \tag{41}\]  
We note that the critical parameter \(\mu_{\rm cr}\) increases rapidly as the plate aspect ratio \(\varphi\) decreases. We further remark that if two opposite edges are clamped and the remaining two edges are left traction free, the MEMS can be modeled as a plate undergoing cylindrical deformations [52]. In the case of slender electrodes, effect of fringing fields that has been discarded in this work becomes relevant as discussed in [53; 54; 55]. It is highly unlikely that clamped rectangular plates with \(\varphi\ll 1\) will be used in practice due to their relatively high stiffness. For \(\varphi\ll 1\) and the longer edges traction free, the MEMS can be modeled as a beam for which the analysis in [53; 54] applies.  
For the Casimir force, the function \(f(\alpha)\) in equation (41) equals \(2.31\alpha+35.8\); see [20]. As pointed out in [20], equation (40) gives approximate values of \(\mu_{\rm cr}\).
## 4 LoRaWAN-Based Outdoor Positioning in Application-Specific IoT Domains
### Smart Manufacture (Industry 4.0)
The authors in [93] mentioned that localization of the sensor nodes in agriculture is also an important factor for identifying a node located within the large area of an agricultural field and discusses the role of localization for deploying the sensor nodes in agricultural fields. They proposed an IoT-based WSN architecture for real-time monitoring of agricultural fields and they implemented an energy harvesting mechanism using solar energy and a wind turbine. There are several proposed methods on LoRaWAN parameters performance in smart agriculture. The authors in [94] proposed a system-level analysis that confirms that the use of several gateways improves the network capacity and guarantees a higher success rate. The authors in [95] present an in-depth study of the performance of the LoRaWAN communication network to specify the adequate compromise between the number of nodes and the duration of the transmission interval. They propose a mathematical model that precisely predicts the successful packet delivery rate considering the number of nodes and the transmission interval duration. The proposed method predicts the behavior and performance of the LoRaWAN network and chooses the parameters during the design phase. For effective and appropriate use of robots in industry, a robust and long-range wireless connection technology is required. The authors in [96] present a developed algorithm and approach to solving the problem of local navigation and localization of mobile robotic platforms based on LoRaWAN. Providing maritime communications for ship/boat monitoring, maintenance of navigation marks, goods tracking, data extraction from sensing platforms, unmanned vehicle management, safety operations, voice communications, etc. is essential but it is a challenges task because of the lack of proper communication infrastructure and harsh environment of the harbor. In [97], authors present the different parts and typical operations in a harbor. Then, they investigate the LoRaWAN technology to locate and track assets in harbors. Their result shows that setting up a LoRaWAN infrastructure in a harbor environment for tracking purposes and geolocation is applicable and they mentioned that the user must derive the appropriate LoRaWAN parameters (like SF value, number of nodes, update rate, and coverage area) according to application and regulations. The authors in [98] present a real deployment to monitor Optimist Class sailing races for boat tracking and monitoring based on LoRaWAN. The proposed methods permit tracking not just each boat position but also key environmental and boat parameters. In the evaluation section, the authors discuss the good levels of coverage and link reliability with limited power consumption. Implemented IoT systems in industries are faced with harsh and dynamic weather conditions. Their environments lead to increase location estimation error. Therefore, an acceptable localization method must properly identify environment error sources to improve the accuracy.
# ScientIST: Biomedical Engineering Experiments Supported by Mobile Devices, Cloud and IoT
## 4 Results  
### Technical Evaluation  
For our envisioned experiments, determining the technical capabilities of the SCB when used in GSJ, is of utmost importance. In particular, guaranteeing that the sampling period meets the Nyquist rate is a requirement to enable rigorous experimentation. As Figure 5 suggests, there is a great discrepancy in the sampling period \(\Delta_{t}\). To assess the average sampling rate (\(s_{r}\)), the following approximation was calculated:  
\[s_{r}=\frac{1\times 1000}{\frac{\sum_{i=1}^{n-1}\Delta_{t_{i}}}{n-1}}=10.104 \;\mathrm{Hz} \tag{1}\]  
Figure 5: \(\Delta_{t}\) between sample times \(t_{n}\) and \(t_{n-1}\) throughout the duration of a recording (**left**) and as a histogram (**right**).  
It is not a constant sampling rate nor is it sufficiently high for all biomedical signals; such performance limitations appear to be a direct consequence of the default GSJ Arduino firmware. Specifically, there is a delay of approximately 100 ms in every sampling iteration and every acquisition is an average of 30 samples. After removing these two singularities from the firmware, the sampling rate increased ten fold (to 136 Hz) but it remained inconsistent, allowing us to conclude that the main bottleneck may be the Arduino library that handles the Bluetooth communication (ArduinoBLE).  
We also assessed the performance of GSJ when acquiring data from the internal sensors of the mobile device. A total of 17 recordings were performed with different smartphone models, in which participants recorded signals from four channels, including microphone and accelerometer data (from the three axis). The time reference created by GSJ was associated with the recorded data points and used to plot them against time. There was evidence of missing data in around 75% of the recordings, for the four sensor channels collected (Figure 6). Although this can be partially addressed in post-processing using interpolation techniques to reconstruct the signals, it may be a limitation for some use cases.  
Figure 7 summarizes the findings in terms of percentage of samples recorded (\(\mu\pm\sigma\)), and Table 2 details the sampling percentage obtained in each acquisition from the dataset. The recorded percentage considers the ratio between the samples acquired for a given channel and all the expected sample points, considering the time reference. These results, ranging within 54.7% and 56.9%, are fairly low comparing to what would be desirable. Nevertheless, each average value is associated with a fairly high standard deviation, which highlights that this is not a systematic problem, as confirmed by the fact that four acquisitions recorded 100% of the data. When analysing these recordings, one can observe that for some participants (3, 6, 10 and 14), all samples were obtained for all channels, while in the other cases a high sample loss was observed. It implies that the specifications of the smartphones can greatly influence the reliability of the acquisition process, in particular the existence of missing data.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 1 Introduction
### Motivation  
Privacy is a crucial requirement in e-voting systems which ensures that the connection between voters and their vote remains hidden. Many e-voting systems achieve privacy by making use of several cryptographic mechanisms. The aim is to remove the linkability feature between a vote and the caster of the vote during all election phases. Preserving privacy during the voter-identification (ID) phase is a challenging task to achieve. This is because additional cryptographic arrangements must be established to prevent linking a voter's identity to the contents of their vote. Blind signatures and mixed-nets are among the techniques that can be applied to remove the connection between the voter and their ballot [4]. These arrangements may clash with voter-usability requirements, which can negatively affect voter turn-out. Meeting the verifiable-e-voting requirement is important in reducing the need to trust electronic systems. Systems meeting this requirement afford voters and observers the ability to independently check whether the votes were recorded, counted, and tallied correctly. However, Internet-based voting systems in use today are not sufficiently robust to satisfy the verifiable-election requirement as they do not provide proof, by means of corroborating evidence, that would allow clear, individual and universal determination of the election's verifiability [5]. To ensure verifiability, many E2E methods necessitate voter involvement, such as executing the cut-and-choose action during the vote-casting phase. Cut-and-choose protocols require complex interaction with the voter (e.g., interactive proofs) to reduce the probability of corruption [6]. However, cut-and-choose protocols make the voting process more complicated for voters, negatively affecting their ease of use. Replacing existing systems with a brand new E2E verifiability scheme that supports voters usability presents a risk to businesses.
## 3 Results and Discussion
### Evaluation of the Resistance of Concrete to Continuous Sulfate Attack  
The results of assessing the resistance of vibrated, centrifuged, and vibrocentrifuged concrete to sulfate attack after holding for 180 days in a 5% sulfuric acid solution are presented in Table 11. Indicators of sulfate attack.  
According to the results of experimental studies, concretes made using vibrocentrifugation technology have the best indicators of sulfate attack. The sulfate attack rate of vibrocentrifuged concrete turned out to be 13% less than that of centrifuged concrete and 27% less than that of vibrated concrete. The depth of destruction of vibrocentrifuged concrete is 46% less than that of centrifuged concrete and 65% less than that of vibrated concrete.
## 3 Results and Discussion
Their comparative analysis of the research and simulation results showed a level of convergence ranging from 3.1% to 14.7%, depending on the measurement distance. Referring to the obtained results of the velocity profile characteristics on the surface of the door opening and the achieved volumetric flow rate, it is also important to mention certain limitations of the given configuration. Specifically, the experiment was conducted in free flow (environment with constant pressure) on a mock-up simulating a door opening, without the volume of the structure directly behind the opening. The lack of counterpressure that would be exerted by the air exchange path of the ventilated building affects the flow rate values. Therefore, conclusions drawn from this type of research should be cautiously applied to cases involving ventilation of volumes that impose significant resistance to airflow (particularly when the air exchange path is long and/or has many constrictions). Considering the above, it should be noted that the method used does not allow for the evaluation of  
Figure 5: Visualization of CFD simulation—tests of profile characteristics of air jet flow velocity in free flow generated by the ventilator for positioning distances from 1 m to 7 m.  
the volumetric flow rate under counterpressure conditions that occur in a building, and whose value will change depending on its construction and internal conditions generating resistance. However, for qualitative assessment of the coverage of the door opening by the ventilator's airflow or determining optimal distances of the ventilator from the opening, this is a method that requires significantly fewer resources than replicating a series of variable flow characteristics for buildings. Figure 6: Characteristics of the airflow velocity profile of the positive pressure ventilator positioned 1 m from the door opening (measuring plane), where (**a**) real-scale experiment, (**b**) CFD simulation (FDS program), (**c**) difference between the experiment and CFD simulation. Figure 7: Characteristics of the airflow velocity profile of the positive pressure ventilator positioned 3 m from the door opening (measuring plane), where (**a**) real-scale experiment, (**b**) CFD simulation (FDS program), (**c**) difference between the experiment and CFD simulation. Figure 8: Characteristics of the airflow velocity profile of the positive pressure ventilator positioned 4 m from the door opening (measuring plane), where (**a**) real-scale experiment, (**b**) CFD simulation (FDS program), (**c**) difference between the experiment and CFD simulation. Figure 9: Characteristics of the airflow velocity profile of the positive pressure ventilator positioned 5 m from the door opening (measuring plane), where (**a**) real-scale experiment, (**b**) CFD simulation (FDS program), (**c**) difference between the experiment and CFD simulation.