esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration and Field Evaluation of an IoV System for Enhancing Road Safety
## 2 System Architecture
### Roadside Unit to Vehicle Communication Unit  
Depending on whether the passing vehicle is equipped or non-equipped, the information sent used the corresponding communication channel. Equipped vehicles communicate with the mediator through the ITS-G5 network, while non-equipped vehicles--through the MQTT Broker and via the LTE network. To meet the communication demands, the mediator is equipped with both ITS-G5 and LTE communication modules.  
For the C-ITS solution of the equipped vehicles, a transceiver for direct short-range communication (DSRC) with the mediator is necessary to establish direct communication with the passing vehicle. For this purpose, the THEO-P1 by u-blox was selected based on its technical specifications (see Table 2), which cover the requirements for V2X communication. From the side of the roadside unit, a Cohda MK5 is used for ITS-G5 communication.  
The information on environmental conditions and road events is contained in DENM messages, transmitted to the equipped vehicles through the ITS-G5 protocol [27] along with a CAM message [28], sent every 100 ms, to inform the passing vehicle of its presence. In return, the mediator informs the passing vehicles about the topology of the road, every second, with a MAP message. MAP messages comprise a common ITS PDU header and mapem [29] data.  
For the non-equipped passing vehicles, the mediator has to communicate with the MQTT broker, which in turn, communicates with the application (see Section 3.4) installed on the terminal device of the vehicle. This communication between the MQTT broker and the mediator is established through the LTE network. To this end, a u-blox TOBY-L210 module is chosen, since the technical characteristics comply fully with the requirements set.
# Microwave Liquid Crystal Technology
## 6 Dielectric Waveguides
Its permittivity is around 1.05 with a dissipation factor of 0.01 at 27 GHz. As LC, GT5-26001 was used. For characterization, the same WR10-to-subwavelength fiber transition were used as introduced before. The measured S-parameters are shown in Figure 21:4 It can be noticed, that the phase shifter is well matched over the whole W-band. Figure 21:5 Cross-section of the phase shifter designed as subwavelength fiber as presented in [43]. Figure 21:6 Measured (solid) and simulated (dashed) results of the step-index phase shifter. For simplification, S-parameter plots show perpendicular orientation only. The achieved FoM is in a range of 60\({}^{\circ}\)/dB, as the phase shifter reaches a differential phase shift between 90\({}^{\circ}\) to 160\({}^{\circ}\) accompanied with an insertion loss of 1.5 dB to 2.9 dB. Again, the results for magnetic and electric biasing are in a good agreement which again highlights the advantage of this technology. Another application could be the integration of the subwavelength fiber phase shifter in a fully dielectric phased array. In [44], a first fully dielectric antenna array was presented, see Figure 21:7 It consists of four dielectric rod antennas, which is also fed by a special multimode interference (MMI) power divider. Within this divider, multimode propagation is used to provide power splitting in one single step. Multiple maxima are formed due to the superposition and the different propagation constants of all propagable modes inside the waveguide. This interference phenomenon is visualized in Figure 21:8  
Figure 21:9 Measurement setup of the subwavelength fiber phase shifter. \[\cos\theta_{\rm C}=\frac{\varepsilon_{2}}{\varepsilon_{1}}. \tag{17}\]0 Measured (solid) and simulated (dashed) results of the subwavelength fiber phase shifter. For simplification, S-parameter plots show perpendicular orientation only. With the input position on the left, the interference phenomenon occurs in the broad waveguide section. It can be seen, that multiple field maxima are formed, with each single maximum feeding a rod antenna. This power divider allows a very compact design, where power splitting is achieved in one single step. This reduces the dielectric loss, and further, parasitic radiation. The next step will be the integration of LC-based subwavelength fiber phase shifters in the presented antenna array design. For this, LC can be directly inserted in the waveguide section before the rod antennas, see \[\cos\theta_{\rm C}=\frac{\varepsilon_{2}}{\varepsilon_{1}}. \tag{17}\]1 Although the LC integration can be realized quite easily, the main challenge will be the integration of the required biasing electrode as they can disturb the propagating electromagnetic wave. Furthermore, high permittivity plastics are in the focus of research for minimization of the antenna.
## 4 Modeling Disturbances of the Geoelectric Field
The formation of the highlighted frequency band in the G  
Figure 6: Combined magnetic and telluric field variations at station BEV on 6 January 2019 in the following order (from **top** to **bottom**): magnetic \(H\) and \(E\) components, magnetic field variability \(\mathrm{d}H/\mathrm{d}t\) and \(\mathrm{d}E/\mathrm{d}t\), and electrotelluric \(E_{\mathrm{H}}\) and \(E_{\mathrm{E}}\) components. to geomagnetic variations can be understood from the following simple consideration, illustrated in Figure 8. GICs can be imagined as currents induced in the electric contour formed by a power transmission line, grounding, and conductive crust. Very approximately, the induced current in this contour according to [PERSON]'s law is proportional to temporal variations in the geomagnetic field, \(J\sim\mathrm{d}B/\mathrm{dt}\). That is why the magnetic field variability \(\mathrm{d}B/\mathrm{dt}\) is a commonly used measure of possible GIC, because information on telluric field \(E\) is not easily available. On the other hand, GIC can be imagined as a \"discharge current\" via a grounded technological system between distant points with different telluric field potentials. These approaches do not contradict each other, because in both cases, \(J(f)\propto f^{\alpha}B(f)\), where \(\alpha=1\) for the \(\mathrm{d}B/\mathrm{dt}\) scenario and \(\alpha=1/2\) for the telluric field in homogeneous Earth. The spectral power of magnetic variations \(B(f)\) typically decays with frequency \(f\), whereas the power of both \(\mathrm{d}B/\mathrm{dt}\) variability and \(E\)-field fluctuations grows with frequency. The resulting GIC is a convolution of those two factors and must have a maximum at some intermediate frequencies. Dedicated studies have shown that this frequency band falls into the range of 3-15 min, that is, the Pi3/Ps6 band. Figure 7: The amplitude spectra of horizontal magnetic (\(H\) and \(E\) components) and telluric (\(E_{\mathrm{H}}\) and \(E_{\mathrm{E}}\) components) field variations at station BEY during the 6 January 2019 event. Bottom panel shows the spectra of the magnetic field variability \(\mathrm{d}H/\mathrm{dt}\) and \(\mathrm{d}E/\mathrm{dt}\).
## 3 Results and Discussion  
A sequence of 1 G noise samples was acquired for each of the randomness sources considered and used in the noise characterization presented in Section 3.1. As previously mentioned, each of these datasets was then considered in two different scenarios: directly in its biased form or after mapping them into a uniform distribution. By taking them in their binary representation, four distinct sequences of random bits were obtained. For each case, only the first 1 GB was submitted to the prime-searching algorithm. Consequently, in all cases, the segment of binary data used in prime number generation directly results from the first 500 M acquired samples from the respective noise source. All the described postprocessing was implemented on an Intel i9-10900k Central Processing Unit (CPU) for increasingly large prime numbers. To assess the performance variation of the scheme, the algorithm was implemented for prime lengths of 32, 64, 128, 256, and 512 bits. Given the fixed size of its binary input, the total amount of prime numbers yielded by the algorithm will decrease with the prime length. Consequently, when directly comparing the figures of merit obtained for different prime lengths, only the first 10 M prime numbers of each case were considered.
## 4 Experiment
### Sentiment Analysis  
After conducting user testing, sentiment analysis was performed on the collected user feedback to gain insights into users' sentiments and opinions regarding their interaction with the chatbot. The sentiment analysis process involved the following steps:  
1. Data collection: The user feedback data, including survey responses and interview transcripts, were collected and preprocessed for analysis. The relevant feedback questions and responses were extracted and organized in a structured format.
2. Data preprocessing: The feedback data underwent preprocessing to prepare for sentiment analysis. This involved cleaning the text, removing any irrelevant characters or information, and handling Arabic-specific challenges such as discretization and morphological variations.
3. Sentiment lexicon: A sentiment lexicon specific to Arabic was utilized to assign sentiment scores to words and phrases. The lexicon contained a list of words along with their associated sentiment scores, ranging from positive to negative.
4. Sentiment scoring: Each feedback response was tokenized, and the sentiment score for each word was calculated using the sentiment lexicon. The overall sentiment scorefor each response was determined by aggregating the sentiment scores of individual words.
* Sentiment classification: Based on the sentiment scores, each feedback response was classified as positive, negative, or neutral. Responses with scores above a certain threshold were considered positive, those below a threshold were considered harmful, and those within a specific range were considered neutral.
* Analysis and visualization: The sentiment analysis results were analyzed to identify patterns, trends, and insights regarding users' sentiments towards the chatbot. The distribution of positive, negative, and neutral sentiments was visualized using charts and graphs to provide a clear overview of user opinions.  
By conducting sentiment analysis on the user feedback, valuable insights were gained into the users' perceptions and experiences with the chatbot. The sentiment analysis results complemented the user testing findings, providing a comprehensive understanding of user satisfaction and areas for improvement.
## 3 Positioning Based on DVB-T
### Range Measurement  
In order to obtain a position fix, several ranges are needed in our system. In the case of additive Gaussian noise (AWGN), they can be obtained by the Maximum Likelihood (ML) estimation through correlation. Since the pilot subcarriers are modulated by PRBS, the incoming signals can be correlated with the local generated replicas, similar to the mechanism used by GNSS receivers [16]. However, some differences between GNSS and DVB-T positioning have to be taken into account in the procedure of range estimation, due to the different signal structures.  
First of all, the PD has to create a local replica of the SPSs, but the locations of the SPSs of two successive OFDM symbols are different, as shown in Figure 2. Therefore, it is necessary to identify the SPS location of the current symbol, before creating the local replica. A possible identification method is the one proposed in [17] and summarized in Appendix A. This phase can be skipped by adding four successive OFDM symbols together [2], as shown in Figure 5. In this case, the correlation can be  
Figure 4: Associating the signals to corresponding receivers.  
performed every four OFDM symbols, and the local replica never changes. This can simplify the receiver structure and speed up the range measurement in the coarse delay estimate. However, after the coarse delay estimate, the correlation should be performed on each OFDM symbol in order to obtain a precise delay estimate.
# Augmented Reality, Mixed Reality, and Hybrid Approach in Healthcare Simulation: A Systematic Review
## 3 Results
### General Features of Simulation Systems (GQ7-9, Fq7-10)
#### 3.8.1 Performance Evaluation Metrics (GQ7, Fq7-8)  
The assessment of clinical competence is one of the most difficult and important tasks in medical education because it provides feedback to trainees about their clinical skills, supports self-paced learning, assures the public, and provides evidence toward the certification of achievement of clinical competencies.  
There are several methods of assessment, each with its own strengths and limitations. As reported by [PERSON] in [51], the main strategies include written examinations, direct observation or video review, assessments by supervising clinician, clinical simulation, and multisource (\"360-degree\") assessments and portfolios. The appropriateness of each method depends on the goal that is addressed (i.e., measuring performance or skill acquisition, etc.) and on the level of the learner's education.  
[PERSON] et al. [52] present a systematic review of the simulation as a clinical assessment tool. They suggest that the simulation can be a valid and reliable method to assess the technical skills and to determine the skill level and the capability to practice safely. Indeed, the simulation can both differentiate performance between experts and novices on given tasks and also identify poor performers.  
With regard to question GQ7 (\"Is a clinical performance evaluation performed?\"), a total of eight studies assess clinical performance. In answering question FQ8 (\"Which metric is used for performance evaluation?\"), two methods of performance evaluation are used [53]: the first technique is based on the human rater's score performance using checklists on scoring rubrics, and it is used in [30]; instead, the second method is based on the automated scoring of measurements integrated into the simulator itself, and it is used in the remaining studies [25,33,34,37,40,41,43].  
The results in answering question FQ7 (\"What kind of artificial intelligence technique is implemented?\") are that one of six studies used an automated scoring based on artificial neural networks [40].
# Literature Review of Digital Twins Applications in Construction Workforce Safety
## 6 Virtual Construction Simulation and Visualization Technology
### Safety Planning  
In the pre-construction stage, there is no physical twin, so there is no DT. However, information and communication technology (ICT) is used throughout the whole life cycle of buildings, so the BIM being created in the pre-construction stage can continue to be used during the construction stage, providing an information basis for DT. Complete safety planning provides various emergency plans, which also provide a knowledge base for computers to understand and solve construction safety issues. Therefore, the study of DT cannot ignore construction safety planning. Through science mapping, BIM has been widely used in visualisation to solve construction safety issues, including hazard recognition and prevention [73]. Through the rendering of 3D buildings, visualisation technology provides stakeholders with a more intuitive construction safety plan. A 4-dimensional (4D) BIM model contains construction details, including geometry models, schedules, and construction activities, which helps safety officers to intuitively identify potential safety problems compared with traditional 2D drawings and schedules. Risk levels can be visually presented in different colours on 3D models for hazardous area identification and risk assessment [74; 75]. With the aid of BIM, on-site hazard areas and hazard types can be effectively determined, and a foundation can be laid for safety management in the construction stage [20].  
Based on abundant safety information, researchers established logic-based mapping from human language to machine-readable form, which makes safety regulations and safety knowledge understandable for BIM software. By mapping engineering information and safety rules in BIM, hazards can be automatically identified and handled during both the pre-construction and construction stages. A BIM-based safety rule checking system was developed to automatically identify locations of hazardous areas and recommend the corresponding protection measures [76]. By comparing the BIM with safety rules, safety issues can be summarised. The countermeasures from the rules can also be presented, which helps safety officers make decisions. [PERSON] et al. [77] further strengthen the system by adding temporary structures and heavy equipment such as scaffolding and a tower crane to the system, increasing its robustness. These rule-checking systems can not only be used to refine safety plans but also to check construction activities in real-time, providing an innovative approach for hazard identification and resolution in DT.
## 4 Discussion and Conclusions  
Overall, the results of the smart pipe demonstrator unit burial, and associated tests, are considered to have provided a unique insight into the potential issues associated with incorporating sensors and electronics into buried pipelines. This is the first prototype of its kind which actually attaches a combination of different sensors to a pipe and buries it underground. All these sensors recorded data thus showing that they can survive in a harsh environment although improvements with regards to the ruggedness and miniaturisation of the sensors are required in the future. The following sections give more detailed comments on different aspects of the work.
## 4 Arsenic Removal Processes
### Adsorptive Processes
#### 4.4.1 Iron Oxides/Oxyhydroxides Based Adsorbents  
The effectiveness of various iron compounds for metal ion removal has been demonstrated in a number of studies [35,130]. Those found to be effective sorbents for arsenic removal from aqueous solutions include \(\beta\)-FeOOH, FeOOH, \(\alpha\)-FeOOH and poorly crystalline hydrous ferric oxide [80,90,111,112]. Other iron oxides/oxyhydroxides-based sorbents that have been considered include Cerium (IV)-doped iron oxide adsorbent [115], GFH [113,114], iron-hydroxide coated alumina [91], iron oxide-coated polymeric minerals [100,131], iron oxide-coated sand [132], magnetic iron oxide/activated carbon composite [132], iron oxide-coated cement [96], magnetically modified zeolite [90], natural iron ones [115], silica-containing iron(III) oxide [133] and iron containing waste materials such as fly ash and red mud [120].  
The adsorption of arsenic by hydrous ferric oxide (FeOOH), particularly akaganite, ferrihydrite, goethite and GFH, has been demonstrated to be very strong and effective [114]. The breakthrough behavior of a GFH fixed bed filter was investigated for arsenic removal and maximum uptakes of 28.9 mg/g and 42.7 mg/g at pH 7 were achieved with initial low and high concentrations of 0-1 mg/L and 1-8 mg/L, respectively [114]. The use of goethite for arsenic removal also achieved adsorption capacity of 25 mg/g [112]. The ability to use goethite to achieve a high arsenic removal rate was suggested in another study [134]. The achievement of higher arsenic uptakes of 65 mg/g at pH 3.5 and 22 \({}^{\circ}\)C [90] and of 120 mg/g at a pH range of 4.5-7 and 25 \({}^{\circ}\)C [111] has been successfully demonstrated with the use of a synthetic \(\beta\)-FeOOH.  
The iron oxyhydroxides used in these studies have particle sizes in the nanometer range. However, when used in sorption columns, nanoparticles can be problematic due to the difficulty in achieving solid/liquid separation [111]. It is therefore a major challenge to find a natural adsorbent that contains nanoparticles of iron oxides that can remove arsenic effectively and can also be used in column filtration technology.  
The use of iron oxide coated sand (IOCS) as an adsorbent for removal of arsenic and iron has been investigated by UNESCO-IHE (The Netherlands) for 20 or more years. It is obtained as a by-product of the iron removal process within the Dutch groundwater treatment plants. The investigation of the use of IOCS obtained from different groundwater treatment plants in the Netherlands demonstrates that high to very high arsenic removal efficiencies were achieved for As(III) and As(V) depending on the iron and manganese content in their coatings. Arsenic removal efficiency of 100 percent was achieved with IOCS which contained 353.8 mg Fe/g IOCS and 17.2 mg Mn/g [135].
# Error Dynamics Based Dual Heuristic Dynamic Programming for Self-Learning Flight Control
## 1 Introduction
To further optimize the identification ability of RLS in iDHP and make full use of the iDHP's advantage in learning efficiency, a novel approach called error dynamics-based dual heuristic dynamic programming is proposed for online nonlinear flight control. One merit of ED-DHP is that complete knowledge of the vehicle dynamics and reference trajectory dynamics is unnecessary. Unlike iDHP, RLS is introduced to identify error dynamics containing the input distribution matrix and the total disturbance term. What's more, the state of ED-DHP is augmented by tracking error and reference trajectory, so the actor NN can learn the feedforward and feedback control terms. The contributions of this paper are summarized below:  
1. An ED-DHP controller for the fast loop is designed to control the morphing air vehicle attitude system with different work points without offline learning;
2. The error dynamics of the fast loop are derived. Based on these, the control effectiveness matrix and the total disturbance term are identifiable by RLS. This enhances learning efficiency effectively. The ADP system is augmented by tracking error and reference trajectory, so the feedforward and feedback control terms can be learned by the actor NN, on the basis that the complete knowledge of system dynamics and the reference trajectory dynamics are unknown;
3. The outputs of ED-DHP include the rough trim surface, feedforward and feedback terms, and compensation. The robustness of the method can be enhanced. The remainder of this paper is organized as follows: Section 2 describes the attitude tracking problem of the air vehicle; Section 3 presents the control structure of the system, including design and analysis of the ED-DHP framework; In Section 4, some simulation re sults are given to demonstrate the effectiveness of ED-DHP; Section 5 states the conclusion of this article.