esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Satellite Attitude on Altimetry Calibration with Microwave Transponders
## 2 Ground Cal/Val Infrastructure  
The ground infrastructure of the PFAC is located in west Crete, Greece (Figure 1). It is dedicated to satellite altimetry Cal/Val activities by providing both indirect (sea-surface) and direct (transponder) absolute calibrations of all overlying altimeters. Established in 2004, the PFAC has been delivering bias results for every international altimetry mission, such as Jason-1/2/3, SARAL/AltiKa, HaiYang-2A & B, CryoSat-2, Sentinel-3A & B and Sentinel-6A MF [33]. It consists of (a) four permanent sea-surface calibration sites, namely the GVD8 Cal/Val site on Gavdos island, the CRS1, SUG1 and RDK1 Cal/Val sites in southwest Crete, and (b) two Ku-band (central frequency 13.575 GHz) transponder sites for absolute calibration of satellite altimeters [29].  
The first transponder site, named CDN1, is located in a mountainous region at an elevation of about 1050 m in the western part of Crete and has been continuously operational since September 2015. The second site, named GVD1, was established at an elevation of 98 m on Gavdos island in October 2021 and has also been continuously operational since then.  
With regard to the reference orbits of Jason-3 and Sentinel-6A MF, the CDN1 transponder site has been used for the calibration of their descending pass D018. The GVD1 transponder site, located on a crossover point, provides calibrations for D018 about 11 s after the CDN1 calibration of the same pass and calibrations for the ascending pass A109 about 5 days after the calibration of D018. Apart from the principal instrument of a transponder, each Cal/Val site is equipped with additional instrumentation required for calibration, such as permanent Global Navigation Satellite System (GNSS) stations, meteorological sensors, microwave radiometer, etc. A detailed description of the infrastructure and all geophysical corrections necessary for calibrations can be found in [29].  
Both transponder Cal/Val sites are compliant with the strategy of Fiducial Reference Measurements (FRM), established by ESA for satellite calibrations [34; 35]. This FRM strategy entails, among other things, reporting of every constituent of uncertainty associated to the calibration process before calculating the final uncertainty of the altimeter bias. Moreover, FRM asks for redundancy of scientific instrumentation (different manufacturers, diverse measuring principles, various setups, etc.) and diversity in the techniques and methods to estimate the bias. Following these guidelines, every key parameter for altimetry calibration is estimated using different approaches and methodologies. For example, transponder coordinates are determined by relative GNSS positioning and precise point positioning, while tropospheric delays are estimated using GNSS observations, radiometers and satellite sensors (e.g., Ocean and Land Color Instrument on board Sentinel-3A & B) [36].  
Figure 1: A map of the PFAC infrastructure with Jason-3 orbits forming a crossover at Gavdos island.
## 3 RPL Overview  
The Routing over Low Power and Lossy network (ROLL) is an IETF working group to analyze the routing requirements of applications including industrial, home, and building automation [27]. The main objective of ROLL was to develop and design the routing solutions for IP based Low Power and Lossy Networks (LLN) that have the support of a variety of link layers. LLN is composed of constrained resources embedded devices that have limited memory, low battery power, and low processing capability. RPL is considered as 6 lowpan IPv6 routing protocol proposed to choose the best path to destination with the minimum cost.  
In LLNs, links are lossy and may become unstable for a short time period due to a number of reasons; for example, interference. LLNs include a wide range of link layer technologies, including Bluetooth, IEEE 802.15.4, Power Line Communication (PLC), and low power Wi-Fi. RPL is an IPv6 routing protocol for low power and lossy networks designed by the IETF (ROLL) working group as a proposed standard, its used originally to satisfy the routing requirements in WSN applications. RPL is considered as distance vector routing protocol while link state routing protocols can't satisfy the limited requirements of LLNs because it consumes a lot of power and memory to save the link states. RPL is a proactive routing protocol and starts finding the routes as soon as the RPL network is initialized. RPL forms a tree called DODAG (Destination Oriented Directed Acyclic Graph) with two types of DODAG (Grounded or Floating): Grounded DODAG in which nodes send their traffic to the gateway node and the gateway will forward them to the destination on behalf of them, while Floating DODAG has no gateway node and each node is responsible to forward its traffic. Each node has a rank value that is calculated with respect to the gateway node according to a predefined cost metric like hop count, bandwidth, reliability or number of transmissions. Each node in an RPL network has a preferred parent which works as the gateway of this node to the destination. If an RPL node didn't find any path in its routing table for a packet, the node forwards the packet to its preferred parent and so on until it either reaches the destination or a common parent which forwards it down the tree towards the destination. Nodes in an RPL network must have routes for all the nodes down the tree. It means that the nodes which are near to the root node must have large routing table entries. Route aggregation is not recommended because of several problems in LLN like the mobility of nodes and the losses due to the radio medium.
## 2 Materials and Methods
### Monitoring Reference Points  
To better observe the oscillation of the LNG liquid under seismic activity, several nodes on the surface of the LNG were selected to monitor vertical wave height. The liquid level in the full tank reached a depth of 41.6 m. The monitoring reference points on the liquid surface are illustrated in Figure 7(1), with thirteen points all located on the x and y axes. The coordinates of reference point 4 (RP4) were (0, 0), while RP 0 was located at (\(-\)44, 0), RP 8 was located at (44, 0), and RP 12 was located at (0, 44). The remaining nodes were uniformly distributed along the coordinate axes. Additionally, two measuring points on the storage tank's structure, located at the top and bottom of the tank wall, were selected to primarily monitor the tank acceleration and the relative displacement of the tank along the \(x\)-axis.
## 4 Evaluation
### Pulse Width Adjustments  
Blooming and exposure time affect the width ratio of the stripes in the resulting stripe pattern in a captured image. Even if the modulation signal has a pulse width ratio of exactly 50%, the bright stripes are slightly wider than the dark stripes. In those areas, the modulated signal cannot be decoded, because if the modulation signal is inverted to transmit a logic 1, the state of the light source will be ON in both captured images. This causes errors in the communication, and hence needs to be optimized. As this effect occurs when a transition of the stripe pattern passes through the area of the modulated taillight, for this evaluation  
for an exposure time of 1/4000 s and 1/8000 s is equal. It can be concluded that with such a short exposure time, blooming has a more relevant effect on the resulting width of bright stripes in the image, as the blooming effect is independent from the exposure time. The results also show that a balanced pulse width ratio of 50% is not optimal, but the results of previous research [1] was confirmed with a BER between 3.5% and 4%.  
Figure 16 shows close-up images of a modulated light source to compare them, regarding the resulting stripe pattern. Figure 16 compares two images captured using an exposure time of 1/4000 of a second. On the left, the resulting stripe pattern with a pulse with ratio of 50% is depicted, on the right the improved version with an adapted pulse width ratio of 47.6% is shown. The green lines indicate a perfectly balanced distribution between bright and dark stripes, i.e., the edges of the modulation signal with a pulse width ratio of 50%. In the center of the image those green lines are interrupted for better visibility of the differences between the left and the right part of the image. However, with a modulation signal with a pulse width ratio of 50%, the resulting bright stripes are wider than the dark stripes, due to the blooming effect and exposure. With a pulse width ratio of 47.6%, those effects are neutralized to get an optimal distribution of stripe widths for the best possible BER. The effect of this approach can be seen even better, if an exposure time of 1/1000 of a second is used for capturing such close-up images as shown in Figure 16. Here again a pulse width ratio of 50% was used to capture the left part of the image and the green lines indicate an equal distribution. It is clearly visible that the bright stripes in the left part of the image are wider than the dark stripes. For the right part of the image the pulse width ratio with the best results regarding the BER for data transmission was used, i.e., 40.4%. The effects of blooming and longer exposure time are counteracted using this lower pulse width ratio and the resulting bright and dark stripes have equal widths.  
Figure 16: Comparison of resulting stripe pattern using different pulse width ratios.  
Figure 15: BER depending on pulse width ratio for different exposure settings.
# Applying Vision-Based Pose Estimation in a Telerehabilitation Application
## 2 Materials and Methods
### ExerWeb Module
#### 2.3.3 Game Management  
Game management consists only of prescribing games to patients, as it currently does not allow the creation of new games. In the prescription of games, the therapist will select one of the games and a patient, indicate a deadline to complete the game and customize the game to the needs of the patient. A game can be customized indicating the duration, repetitions, difficulty, number of targets, score needed to overcome the task and adding a file with the positions in which the targets should appear.  
Once exercises are performed by patients, ExerCam provides actionable information to therapists and patients through the detailed results. The result of each game prescribed to a patient should be consulted, as with the tasks, from the Patient Management section (see Figure 9a).  
On this page of results of a patient, a list of the tasks prescribed to that patient is shown first, showing an icon of whether the task has been exceeded or not (it is assumed that the task has been successfully completed if the joint ROM exceeds the minimum set for that task). Following the list are two graphs (maximum) with the historical results of the last two tasks. In the case of not having different associated tasks, a graph is shown, none if there are no patient data. Following the task Results section, a section with the results of the games is shown, showing in this case if the game has been exceeded based on the score obtained.
## 4 Toward Higher Storage Density  
The interval between each structure was reduced, and the original encoding (shown in Figure 2) was kept unchanged to achieve high-density storage of this particular structure. As shown in Figure 9a,b, we changed the interval of \(\alpha\) from 3.75\({}^{\circ}\) to 2.5\({}^{\circ}\) and the interval of \(\psi\) from 10\({}^{\circ}\) to 2\({}^{\circ}\) in \"\(\alpha\)\(\psi\).\" In addition, \(\alpha\) was increased from 25\({}^{\circ}\) to 66.25\({}^{\circ}\), and \(\psi\) was increased from 0\({}^{\circ}\) to 10\({}^{\circ}\). In this study, we reduced the number of overall samples to investigate the possibility of improving storage accuracy under the circumstances of smaller steps. The \"\(\alpha\)\(\psi\)\" is coded as \"1 0,\" which means that the slope angle is 25\({}^{\circ}\) and the azimuth angle is 0\({}^{\circ}\). If the slope angle is 27.5\({}^{\circ}\) and the azimuth angle is 4\({}^{\circ}\), the \"\(\alpha\)\(\psi\)\" is coded as \"2 2.\" We considered a different approach from the original one and used the dual channel mode _TE + TM_ to read out the encoded data more accurately. This approach was used because the step of the structure corresponding to the encoded data becomes smaller than the original one, and the dual channel can read it out better. Figure 9c shows the readout accuracy of the test set, and the accuracy of the training set readout reached 97.25% at 200 epochs. We set the test for every 20 epochs and found that the test set readout accuracy reached 94.3% at 200 epochs, suggesting that the proposed storage strategy can achieve the data-information storage. In this condition, per 2 \(\times\) 1 \(\times\) 1.3 microns can reach 6480 (36 \(\times\) 180) data-information storage, approximately 213 bit per 2 \(\times\) 1 \(\times\) 1.3 microns.  
Figure 9: Description of the encoding method and readout accuracy: (**a**) The azimuth angle \(\psi\) is fixed (0\({}^{\circ}\)), and the slope angle \(\alpha\) increases gradually in 2.5\({}^{\circ}\) intervals from 25\({}^{\circ}\) to 62.5\({}^{\circ}\). (**b**) The slope angle \(\alpha\) is fixed (25\({}^{\circ}\)), and the azimuth angle \(\psi\) increases gradually from 0\({}^{\circ}\) to 10\({}^{\circ}\) at 2\({}^{\circ}\) intervals (**c**) _TE + TM_ polarization for cross-entropy loss and training for validation accuracy; 96 data sets of reduced wavelength spectra for network testing for readout accuracy.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
###### Abstract  
A hybrid technique combining the multi-level fast multipole algorithm (MLFMA) and the modified adaptive division beam tracing (MADBT) is presented to analyze the radiation patterns of the antennas mounted on large-scale complex platforms. In this technique, the MLFMA is used to characterize the antenna and the transition region that cannot be analyzed accurately by high-frequency asymptotic methods. The MADBT method is used to analyze the contribution of the platforms to the entire radiation pattern by tracing all beams effectively. By applying the beam-based MADBT method instead of the conventional current-based physical optics (PO) method to the platforms, the multi-bounce effects inside the platforms are considered, which enhances the accuracy of the radiation patterns, especially for the complex platforms with corner reflector. An iteration method is proposed to model the interaction between the antennas and the platforms strictly. The proposed iterative MLFMA-MADBT method is mesh-independent and can avoid the matrix-vector production (MVP) of the iterative MLFMA-PO method in each iteration. These characters significantly reduce the memory and time consumption in computation while keeping high accuracy. Numerical results are presented to demonstrate the accuracy and efficiency of the proposed hybrid technique.  
Iterative MLFMA-MADBT; antenna radiation analysis; multi-bounce effects; mesh-independent +
Footnote †: journal: Applied Sciences  
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Footnote 0: Copyright**: © 2020 by the authors. Licrosse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/)]([https://creativecommons.org/licenses/by/40/](https://creativecommons.org/licenses/by/40/))).
## 2 Materials and Methods
### Gas Monitoring  
A stationary gas measurement system (Figure 1) is installed on the TBM and gantries to measure gas concentrations at the height of 1.5 m, which is recommended for environmental and occupational measurements [22; 23].  
The gas-monitoring system (GMS) sensors are divided into two groups, one located in the TBM next to the man locks and the other in the main control box at gantry two. The incoming data are continuously evaluated and stored in the data logging system, and displayed on the operator control cabin display to indicate gas levels. A horn signal is given when a limit value is exceeded to warn all personnel working with the TBM.  
The gas monitoring system includes electrochemical sensors for NO\({}_{2}\) gas detection with specific linearity, range, and resolution information. The TBM stores the average gas concentration during the excavation cycle, and every time the TBM operator presses the ring-built button, the average gas concentration per ring is recorded. The gas monitoring equipment specifications are as follows:  
NO\({}_{2}\) Sensor:  
Range: 0 to 20 ppm;  
Linearity: 2%;  
Time Response: <20 s;  
Resolution: 1 ppm.  
Figure 1: Trolex fixed gas monitoring station.  
The measurement method used in this study has been found to be entirely valid for the current established limits, at that time. However, it should be noted that in the event of a substantial reduction in occupational exposure limit (OELV) levels, the current measuring equipment may no longer be the most suitable.  
Understanding the repercussions of a significant decrease in OELV levels, especially concerning nitrogen dioxide (NO\({}_{2}\)) concentrations, is crucial. The measurement equipment utilized in this study is considered valid and reliable for this purpose. It will allow for an accurate assessment of any changes in NO\({}_{2}\) levels, enabling informed decisions to protect workers' health and maintain air quality in the work environment.
# The Correlation between Users' Cognitive Characteristics and Visualization Literacy
## 1 Introduction
The goal of the study is to find the relationship between visualization literacy and the following three cognitive characteristics: _numeracy_ as a cognitive ability, _need for cognition_ as cognitive motivation, and _visualizer-verbalizer style_ as a cognitive style. Our general hypothesis is that an individual user's visualization literacy is highly correlated with the three cognitive characteristics because graph literacy/comprehension has been shown to have a correlation with them. We test whether visualization literacy correlates with numeracy, which is an individual's ability to understand and process numerical information, because data visualizations are graphical representations of numerical data in most cases. Of course, not all data visualizations are based on numerical data, but we believe that the ability to work with numerical data can help users understand basic mapping processes from data to visual representations as well. We also test whether visualization literacy correlates with need for cognition, an individual's tendency to engage in effortful cognitive activities, because we believe that users need to be willingly inclined toward conducting cognitive activities to discover meaningful information and identify interesting patterns from data visualizations. Finally, we test whether visualization literacy correlates with verbalizer-visualizer information processing style because we believe that visualizers, who prefer to process visual information, may feel more comfortable with data visualizations than verbalizers. In Section 2, we begin with a brief review of literature on visualization literacy and user characteristics with data visualization use, as well as the three user cognitive characteristics investigated in this study. Then, we provide our hypotheses in Section 3 based on the review. In Section 4, we describe our experiment to test the hypotheses, including the participants, measures, and procedure. In Section 5, we present the results of the experiment and analysis. In Section 6, we discuss our findings, implications for education and training approaches for data visualizations to improve individual users' visualization literacy, and suggestions for future visualization research. Finally, we offer concluding remarks in Section 7.
# A Personalized Ontology Recommendation System to Effectively Support Ontology Development by Reuse
## 6 Evaluation  
We conducted a user-based evaluation experiment, where users were asked to develop a new ontology and interact with the system to assess its effectiveness in generating relevant recommendation lists, according to the users' preferences, during the ontology development session (i.e., which consists of many iterations). However, this evaluation was challenging for the following reasons. First, each user has a specific understanding of what makes an ontology relevant and thus worth recommending. Our system considers this by recommending ontologies to each user according to his or her specific preferences and selections which might differ from those of other users. Second, the system generates a different recommendation list for each user in each interaction/iteration during the development session, which ranges from two to seven iterations for each user in the experiment we conducted. Consequently, there is no single reference list (ground truth recommendation) that is valid for every potential user. Moreover, for each user, we would have to consider all potential interactions with the system and create a reference list for each of those interactions. That is not feasible. For these reasons, we came up with a more viable approach. Each user/evaluator re-sorted the ranking of the ontologies in the recommendation lists according to his preferences. The re-ranked list expressed the user's personal and user-specific assessment of relevance. We assessed the rankings' quality using information retrieval evaluation metrics.  
Figure 6: The user selects subclass(es) to extend the input class.  
Figure 7: The input ontology has been extended after adding the selected sub-classes as new sub-classes of the input class.
## 7 Results
### Experiment Environment  
Different platforms help evaluate DLTs. Two of them are simulation and emulation. Simulators use virtual systems to mimic the interactions of various components to provide a reproducible and controlled environment for experimentation. The network is scalable because no hardware or real network is involved. Moreover, it is possible to confine the evaluation to a single machine, as was the case in this analysis, to facilitate debugging, time manipulation, and workload control. Emulators are hybrid platforms with both real and virtual network components. Their output is real and accurate but less reproducible, and the platforms require additional overhead.  
In the prototype, we have as candidates the patient, doctors, lab techs, nurses and health staff, and administrative users. The implementation was carried out on ORACLE VM (VirtualBox Graphical User Interface, version 6.1.26 r 145957 (Qt50602)) with an Ubuntu virtual machine, version 20.4.4 (Linux), on a 64-bit operating system. The virtual machine was deployed on a single computer for simulation. The hardware specifications included 3005 MB RAM, an Intel(R) Core(TM) i7-4510U CPU @ 2.00 GHz 2.00 GHz processor, an Intel Corporation 82540 EM Gigabit Ethernet controller (rev 02) network card, and a hard disk capacity of 85.9 GB. Additionally, two CPUs and four cores of x86-64 architecture were used. The evaluation framework included Hyperledger Fabric v2.3.0, a CouchDB database, a channel (named mychannel), a network organization (0.example.com), two organizations (org0 and org1), four peers, and three orderers.  
To measure the impacts on the performance metrics, we considered five incoming transaction rates (create a patient record, read a patient record, update a patient record, delete a patient record, and set access policy), ranging from one transaction per second (tps) to 1000 tps. Figure 17 shows the process flow of the patient record scenario. Figure 18 shows the number of transactions.  
Figure 17: Process flow for patient record scenario.