esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Data Collection  
A trained researcher monitored the patients during each robotic-technological rehabilitation session (RS). The data collected during each session for each patient was called \"observation\". During the study, for each patient, several observations were performed, according to the individual rehabilitation program. In order to limit bias due to researcher's presence, neither physiotherapists nor patients knew when data collection would take place.  
To investigate whether and in which patients this new organizational model was applicable, we compared the different rehabilitation settings (R2, R3, and R4) in terms of the rehabilitation dose and clinical characteristics of the patients. For each session, the number of patients supervised by one physiotherapist was recorded, together with the clinical status of the patients, using validated clinical scales administered by trained operators. Specifically, we evaluated disability (according to the Barthel Index, BI [14]), upper limb impairment (using the Motricity Index, MI [15]), mobility (according to the Deambulation Index, DI [16]), comorbidities and severity (described according to the Cumulative Illness Rating Scale-Comorbidity Index, CIRS-CI, and the Cumulative Illness Rating Scale-Severity Index, CIRS-SI [17; 18; 19]). Complete demographic and clinical details were collected at the first observation for each patient.  
Moreover, to evaluate the feasibility of the model, the following measures were recorded: (a) tSetup (minutes): time spent for the correct patient placement and for planning of the session exercises; (b) tTraining (minutes): time spent by patient to perform the training; (c) tSupervision (minutes): time spent by the physiotherapist in the individual interaction with the patient; and (d) tClosure (minutes): time spent by the physiotherapist to close the exercise session and remove the patient from the device. These measures were obtained in a subsample of observations. In addition, the Visual Analogue Scale for Satisfaction [20], a self-assessment scale ranging from 0 (no satisfaction) to 10 (extreme satisfaction), was used to measure patients' satisfaction. The exact question was \"Are you satisfied with your rehabilitation session?\"
## 3 Improved Q-Learning Algorithm for Better Management of Channel Utilization in UWCNs
### Explanation of Our Proposed Q-Learning Technique  
UWCNs need to learn the optimum transmission methods over time for sensor nodes, which may be accomplished by developing a reinforcement learning framework and using Q-learning techniques for an energy-efficient MAC protocol. Every sensor node in this configuration participates in the Q-learning process as an agent. Parameters such as battery level, current transmission power, number of surrounding nodes, channel condition, and recent collision history may be included in the state space of the Q-learning model. The action space would include various transmission power levels and perhaps different packet timing techniques. The incentive functions that help to facilitate the learning process (energy efficiency, collision avoidance, and high PDR) must be balanced. Penalties may be applied for unsuccessful transmissions or excessive energy usage, while significant rewards can be given for successful transmissions with little energy use and few collisions. The nodes learn to anticipate the expected utility of various actions given their present state by continually updating their Q-values based on the results of their actions while they are in operation. A larger learning rate might be used at the beginning of the procedure to promote exploration, and it could be progressively lowered to stabilize the learning process. To ensure that the nodes are able to respond to changing network circumstances and avoid becoming trapped in less-than-ideal methods, the protocol may also have a mechanism to adaptively alter the exploration rate. Sensor nodes are able to develop an effective transmission technique over time that minimizes collisions, maximizes energy usage, and enhances network performance in general.
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 2 Antenna Design and Performance  
### Single Slot Radiator Design and Performance  
Figure 1(a),b shows the geometry of a single slot radiator without and with a director, respectively. Enhancement of the beamwidth along the \(\pm\)\(\pm\) direction of the array antenna can be achieved by utilizing radiators which radiate strongly in the \(\pm\)\(\pm\) direction such as a slot antenna. In addition, the magnetic-type antenna, such as a slot antenna, is less sensitive to the human body effect as compared to other antennas [12; 13]. However, since the main beam of the slot antenna is formed perpendicular to the slotted aperture (\(\pm\)\(x\) direction in Figure 1(a)), a director is added to increase the gain in the \(+\)\(z\) direction. Without a director, the gain in the \(+\)\(z\) direction of the array antenna will be greatly  
Figure 1: Fan-beam radiation pattern and hemispheric beam coverage: (**a**) fan-beam radiation pattern, and (**b**) hemispheric beam coverage required in 5G mobile terminals.  
reduced. This will be discussed in the next paragraph in detail. As a result, a quasi-Yagi slot antenna was adopted as a radiator for the proposed array antenna. As shown in Figure 2, a slotted aperture is inserted on the ground plane which is made of an Arlon/AD430 substrate (\(\varepsilon_{\text{r}}\) = 4.3, tan \(\delta\) = 0.003) with a thickness of 0.2 mm, and a microstrip feeding line is printed on the opposite side of the substrate. The length and width of the slotted aperture are optimized for operating at the 28 GHz band (27.5-28.35 GHz) [14]. As shown in Figure 2b, a director is added with the optimized distance to the slot antenna to reinforce the radiated field toward the director. The detailed optimization procedure of the antenna with the director will be discussed in the next subsection. Figure 2d shows the simulated reflection coefficient (S\({}_{11}\)) results of the antenna with and without the director. The simulated -10 dB S\({}_{11}\) bandwidths of the antenna with and without the director are 2.89 GHz (26.66-29.55 GHz) and 2.84 GHz (26.54-29.38 GHz), respectively. Despite the fact that the resonance frequency of the antenna with the director is shifted slightly towards the higher frequency side, the simulated \(-\)10 dB S\({}_{11}\) fully covers the target band. Figure 2e shows the simulated radiation patterns of the antenna with and without the director. The gains in the \(+z\) direction with and without the director are 1.08 and \(-\)1.02 dBi, respectively. However, the gains in the \(-z\) direction in both cases remain almost the same.
## 4 Using LoRaWAN in Linear Monitoring Systems: KSA Oil Pipeline Case Study
### The Design of the Architecture of the Multi-Hop LoRaWAN  
From the previous application requirements, the objectives that the network architecture needs to fulfill are as follows. First, to provide a linear network with good coverage to transmit packets with higher throughput. The reached throughput needs to be higher towards the GW while avoiding increased network congestion. The second objective is to preserve the energy of the sensors.  
Our solution for the oil pipeline monitoring system is based on multi-hop LoRaWAN. The network architecture consists of sensors that are distributed along the pipelines. Considering the leak-detection systems [42; 43; 44] and our review in [9], to detect leaks in oil pipelines we monitor pressure and temperature. Two types of sensors were used: temperature and pressure sensors.  
Our proposed network architecture has two types of nodes: sensor nodes and gateway nodes (GWs). The sensors are hybrid nodes; they can only be end devices or relays that aggregate the received packet(s) and send it (them) to the next relay. That means that the sensors have both sensing and relay functionalities.  
The study of the network capacity, energy consumption, and throughput was conducted over a distance of 88.6 km, considering 443 sensors distributed among the pipelines. Figure 2 shows the architecture of the study area. The temperature sensor takes samples every 3 h, so we have eight packets per day, while the pressure sensor sends the measurements every 1 h.
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
###### Abstract  
Validating RF applications is traditionally time consuming, even for relatively simple systems. We developed the WISCA Software-Defined Radio Network (WISCANet) to accelerate the implementation and validation of radio applications over-the-air (OTA). WISCANet is a hardware-agnostic control software that automatically configures and controls a software-defined radio (SDR) network. By abstracting the hardware controls away from the user, WISCANet allows a non-expert user to deploy an OTA application by simply defining a baseband processing chain in a high level language. This technology reduces transition time between system design and OTA deployment, accelerates debugging and validation processes, and makes OTA experimentation more accessible to users that are not radio hardware experts. WISCANet emulates real-time RF operations, enabling users to perform real-time experiments without the typical restrictions on processing speed and hardware capabilities. WISCANet also supports multiple RF front-ends (RFFEs) per compute node, allowing sub-6 and mmWave systems to coexist on the same node. This coexistence enables simultaneous baseband processing that simplifies and enhances advanced algorithms and beyond-5G applications. In this study, we highlight the capabilities of WISCANet in several sub-6 and mmWave over-the-air demonstrations. The open source release of this software may be found on the WISCA GitHub page.  
software-defined radio; software-defined radio network; cognitive radio; multiple-input multiple-output; integrated sensing and communications; spectral convergence; sub-6; mmWave 2022 3 682-707. [[https://doi.org/10.3390/signals3040041](https://doi.org/10.3390/signals3040041)]([https://doi.org/10.3390/signals3040041](https://doi.org/10.3390/signals3040041))  
1
## 2 Materials and Methods  
### Studied Area and Soil Sampling  
The examined sites are situated in mountain region of the High Tatras, in northern Slovakia (Central Europe), where a bora--katabatic windstorm--swept down 12,500 ha of forest canopy in 2004. Sampled soils were Typic and Humic Dystrudepts [16] developed on a quaternary moraine gravel layer with igneous character. Soil water and temperature regimes were udic and frigid, respectively. Vegetation in the area is characterized by dominance of _Picea abies_ in a tree layer, which is sporadically accompanied by _Larix decidua_. Main understory plants include _Vaccinium (myrtillus, vitis-idaea)_, _Calamagrostis villosa_, _Avenella flexuosa_, and _Chamazion angustifolium_.  
Soil sampling was carried out during the summer period since lower soil water contents are favorable for WR development. The samples (n = 45) were taken from three stands with diverse toposoil conditions (Figure 1). These comprised one reference and two windblown sites which differed in applied management practices. Site T1 (49\({}^{\circ}\)07\({}^{\prime}\)12.0\({}^{\prime}\) N, 20\({}^{\circ}\)09\({}^{\prime}\)47.5\({}^{\prime\prime}\) E, altitude: 1043 m) was situated within an area where wood mass of fallen and broken trees was harvested. Reference site T2 (49\({}^{\circ}\)07\({}^{\prime}\)17.5\({}^{\prime\prime}\) N, 20\({}^{\circ}\)06\({}^{\prime}\)16.4\({}^{\prime\prime}\) E, altitude: 1221 m) was located under intact forest. Experimental site T3 (49\({}^{\circ}\)09\({}^{\prime}\)60.5\({}^{\prime\prime}\) N, 20\({}^{\circ}\)15\({}^{\prime}\)14.8\({}^{\prime\prime}\) E,altitude: 1067 m) was situated within area where uprooted and broken trees were left without further (human) intervention. Apart from the differences in altitude, all three sites were south-orientated, with relatively similar character of local topographic conditions. In the case of each experimental site, 15 samples of topsoil (depth interval 0-5 cm) were taken from a 400 m\({}^{2}\) area at random.
# Unlocking the Potential: How Flying Taxis Will Shape the Future of Transportation
## 3 Customer Need to Use Flying Taxis
### Flying Taxis Save Passengers Time Compared to Traditional Ground Transportation  
Flying taxis could save passengers time in several ways compared to traditional ground transportation. First, they can travel faster because they are not restricted by traffic on the ground, which can significantly reduce travel time, particularly in congested urban areas. Second, flying taxis can offer more direct routes to the destination, avoiding detours and other obstacles that prolong ground travel time. Finally, flying taxis can potentially reduce the time spent waiting for transportation, as they can be available on-demand and are not subject to traffic congestion or other factors that can delay ground transportation. These benefits can result in significant time savings for passengers who use flying taxis [149].
## 2 Materials and Methods
### Determination of Carboxylic Acids Liquid Chromatography-Mass Spectrometry (LC-MS)  
One millilitre aliquot of the CWK sample was collected from each treatment, as shown in Table 1, at 0, 24, 48, 72, and 96 h, and added to ice-cold 50% MeOH containing 10 mg/L 4-chlorobenzoic acid (CBA) as an internal standard. The mixture was centrifuged at 10,000 \(\times\)\(g\) for 5 min at 4 \({}^{\circ}\)C (Z216 MK, HERMLE Labortechnik GmbH, Wehingen, Germany). The 2-hydrazinoquinoline (HQ) derivatization reaction was conducted by mixing 5 \(\upmu\)L of the sample with 100 \(\upmu\)L of acetonitrile solution containing 1 mM 2,2\({}^{\prime}\)-dipyridyl dipyridyl disulfide (DPDS), 1 mM triphenylphosphine (TPP), and 1 mM HQ. The reaction mixture was incubated at 60 \({}^{\circ}\)C for 60 min. The HQ derivatives in the reaction mixture were stored in a \(-\)80 \({}^{\circ}\)C freezer (ULT freezer, Haier Biomedical ULT freezers, Auckland, New Zealand) until further analysis using LC-MS. The carboxylic acids standards used for this assay were lactic acid, succinic acid, acetic acid, glutamic acid, pyruvate acid, and malic acid prepared with 50% methanol and 20 mM sodium hydroxide.  
The LC-MS was an Agilent 1260 Series liquid chromatograph that comprised a G1311B quaternary pump, G1329B thermostatted autosampler, and a G1330B thermostatted column compartment (Agilent Technologies, Santa Clara, CA, USA). Mobile phase A involved 0.1% formic acid in ultrapure water and mobile phase B involved 0.1% acetic acid in acetonitrile. The column was a Phenomenex Kinetex EVO C18 column, measuring 2.1 \(\times\) 150 mm with a 1.7 \(\upmu\)m diameter of packing material, and was maintained at 25 \({}^{\circ}\)C. The chromatographic gradient was held for 0.1 min at 3% B and then ramp to 6 min at 15% B; it was then increased to 9 min at 90% B before decreasing to 3% B at 11 min. The flow rate was 200 \(\upmu\)L min\({}^{-1}\), total run time was 16 min, and injection volume was 5 \(\upmu\)L. For detection, an Agilent 6420 triple quadrupole mass spectrometer fitted with an Agilent Multimode Ionisation source was operated in positive electrospray mode. The peak areas of derivatized carboxylic acids were normalized and quantified using a series of diluted external standards. The external standards, which included succinic acid, lactic acid, acetic acid, malic acid, and pyruvate acid, were prepared for this purpose. Data were collected and processed using Agilent Mass Hunter Version B06.00 software.
## 5 Conclusions  
This study proposed an integrated data-driven resilience framework for predicting traffic resilience during a disaster while considering multiple sources of uncertainty: data, model, and response uncertainty. The proposed framework adopts the augmented LSTM with the Kalman Filter framework to forecast traffic conditions. A real-time traffic dataset collected during Hurricane Irma is employed to demonstrate the effectiveness of the proposed framework. Before predicting traffic performance, uncertainty factors that account for data uncertainty are introduced in measuring the current traffic condition. A four-stage traffic evacuation scenario is considered, which includes the pre-storm evacuation, during the hurricane, the post-storm evacuee return, and the recovered normal traffic condition period. For the post-storm and recovered periods, the traffic conditions were predicted based on earlier evacuation traffic data. The prediction results revealed that a modified roadway traffic resilience quantification approach was capable of quantifying various levels of traffic resilience. From the prediction results, the daily, segment, and finally, the overall route resilience were quantified by considering the response uncertainty from the travelers. The behavior obtained for the route resilience mimics the general resilience curve. The proposed framework overcomes the challenges of incorporating uncertainty into evaluating traffic conditions and resilience. By implementing the proposed framework, the traffic resilience of a roadway can be predicted, which can further help in decision-making for more disaster-resilient transportation infrastructures. The broader impact of this study is essential for effective emergency management and can lead to better outcomes for people and communities affected by these adverse and often inevitable events.  
Conceptualization, T.A. and N.Y.; methodology, T.A., L.G.A., Z.L. and N.Y.; software, T.A.; validation, T.A., L.G.A., Z.L. and N.Y.; formal analysis, T.A. and N.Y.; investigation, T.A and L.G.A.; resources, N.Y.; data curation, T.A.; writing--original draft preparation, T.A. and N.Y.; writing--review and editing, Z.L. and L.G.A.; visualization, T.A. and N.Y.; supervision, L.G.A., Z.L. and N.Y.; project administration, N.Y.; funding acquisition, N.Y. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the National Science Foundation (NSF) EPSCoR RII Track-2 Program under the NSF award # OIA-2119691.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is available upon request.  
**Acknowledgments:** This research was made possible through a grant from the National Science Foundation (NSF) and data from the Florida Department of Transportation (FDOT). The findings and opinions presented in this manuscript are those of the authors only and do not necessarily reflect the perspective of the sponsors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Indicator Extraction  
#### 4.4.1 NASA-TLX  
The NASA task load index (NASA-TLX) exhibits low inter-subject variability and is one of the most widely used subjective workload assessment tools [41]. It consists of six subscales: mental demand, physical demand, temporal demand, performance, effort, and frustration. The total workload score for each participant is determined by the weighted average of the scores across these dimensions, with higher scores indicating greater workload.  
The specific calculation formula is as follows:  
\[F=\sum_{i=1}^{6}M_{i}\times\frac{P_{i}}{15},\]  
where \(F\) indicates the total score for workload assessment; \(M_{i}\) is the score selected by the participant of the \(i\)th dimension; and \(P_{i}\) is the number of times the \(i\)th item was selected in 15 pairs.
# Ontology-Based Spatial Data Quality Assessment Framework
## 2 Materials and Methods
### Framework
#### 2.3.1 Data Conversion with R2 RML  
Currently, significant portion of spatial data is stored in formats such as ESRI shapefiles. Therefore, to conduct quality assessments in the context of this study, it is necessary to employ tools or software that enable ontology-based data access or conversion. The system will do the following:  
1. Support ESRI shapefiles and a range of spatial data formats as input.
2. Have associated Java library access.
3. Have GeoSPARQL compatibility, given its role as the primary vocabulary for geographic data within the framework.
4. Have the ability to convert attribute data associated with spatial data.  
Several alternatives have been explored within the scope of this study, including GeoTriples, TripleGeo, DataMaster, and Ontop-spatial [34; 35; 36; 37].  
DataMaster (Protege plug-in) is outdated. TripleGeo provides limited compatibility with multiple vocabularies, which constrains the translation of TripleGeo, a tool for integrating features from geospatial databases into RDF triples [35]. It does not support a range of attributes Ontop-spatial creates virtual RDF graphs for database access without materialization [36]. Ontop-spatial is only for limited types of geospatial databases (PostGIS, SpatiaLite, and Oracle spatial). GeoTriples was selected for data conversion as it meets all the requirements specified by the framework. GeoTriples is a tool designed to convert spatial data from various sources (including relational databases, shapefiles, and KML) into RDF graphs [34; 38]. It utilizes R2 RML (RDB to RDF Mapping Language), for data conversion. R2 RML is used for creating custom mappings from relational databases to RDF graphs. This approach simplifies the process of attribute transfer.  
As represented in Figure 2, R2 RML, the W3C recommendation for a relational database to RDF conversion, uses RDF triples to define the mappings to RDF data [39].  
Figure 1: Data Quality Assessment Framework, components, and interactions.
# An Overview of Key Technologies in Physical Layer Security
## 2 Background
### Channel State Information (CSI)  
CSI defines the properties of a channel in a wireless communication link. CSI is used to describe the propagation of the transmitted signal in relation to the corresponding effects such as scattering, fading, and power decay with distance. [PERSON] et al. in [PERSON] et al. (2019) show that the availability of CSI is one of the most important aspects to consider when choosing an appropriate secrecy performance metric in order to design optimal transmission strategies in PLS. CSI can be categorized into two classes, and they are referred to as perfect and imperfect CSI. The former involves the complete knowledge of the channel properties of a communication link. The latter is concerned with characterization of the statistical information only. Such information includes the average channel gain, the type of fading distribution, the line-of-sight (LOS) component, and the spatial correlation.  
In practice, CSI is initially not available at the transmitter and receiver terminals; it is gathered through some channel estimation methods, such as those found in [PERSON] et al. (2019); [PERSON] et al. (2019). The two channel estimation mechanisms commonly employed to acquire CSI are the pilot-based channel training and channel state feedback. In pilot-based channel training, the transmitter distributes the total transmission time and energy such that some of the energy and time are allocated for the transmission of pilot symbols while the remaining portion is used for data transmission. In the channel state feedback method, the receiver is required to share its estimated channel knowledge with the transmitter prior to transmission. It is shown in [PERSON] et al. (2019) that the greatest level of security can be achieved if the transmitter has full knowledge of both the wireless channels to the intended user and the unintended user. This can be achieved by designing MIMO transmit precoders that minimize the information leaked to the eavesdropper's channels or to accurately direct jamming signals towards the eavesdroppers. A detailed discussion on the use of CSI in PLS is given in Section 3.