esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Comparison
### Scalability
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **uPort** & **Sovrin** & **EverID** & **Blockstack** & **ShoCard** & **Hyperledger Indy** \\ \hline \multirow{3}{*}{**Blockchain Type**} & Ethereum (Public) [70; 72] & Hyperledger Indy (Consortium) [61; 65] & Proprietary & Bitcoin & Bitcoin & Hyperledger Indy (Consortium) [66; 65] \\  & & (Convortium) [61; 65] & (EverChain/ Private) [71; 73] & & Blockchain & Blockchain (Public) [71; 74] & \\ \hline \multirow{3}{*}{**Identity Management**} & \multirow{3}{*}{SSI [70]} & \multirow{3}{*}{SSI [66]} & Centralised and decentralised components [71] & Decentralised & SSI [71] & Self-sovereign identity (SSI) [61] \\ \hline \multirow{3}{*}{**Interoperability**} & Supports & Uses open standards like & Limited, mostly within EverID & Limited to Blockstack & Limited, mostly within ShoCard ecosystem [74] & Supports \\  & & through DIDs [70] & DKMS [66] & & & \\ \hline \multirow{3}{*}{**Scalability**} & \multirow{3}{*}{-} & Centralised control with some decentralised features [71] & Full control over identity and data [68] & Full control over identity data [74] & Full control over identity data [61] \\ \hline \multirow{3}{*}{**User Control**} & Full control over identity data [70] & Full control over identity data [66] & - & Full control over identity and data [68] & Full control over identity data [74] & Full control over identity data [61] \\ \hline \multirow{3}{*}{**Cost**} & \multirow{3}{*}{-} & Off-chain, with DID on-chain [66] & Hybrid (on-chain and off-chain) [71] & Off-chain, with data [68] & Off-chain [74] & Off-chain [61] \\ \hline \multirow{3}{*}{**Data Storage**} & \multirow{3}{*}{Off-chain [70]} & Off-chain, with DID on-chain [66] & Hybrid (on-chain and off-chain) [71] & Hybrid (on-chain, with minimal on-chain data [68] & Off-chain [74] & Off-chain [61] \\ \hline \multirow{3}{*}{**Use Cases**} & General-purpose identity management, DApps [70] & Identity management, equipers [66] & Recruitment, healthcare [71] & Decentralised apps, digital assets [68] & Enterprise & Identity \\  & & & & & & \\ \hline \multirow{3}{*}{**Cryptographic Methods**} & Elliptic Curve Cryptography (ECC) [70] & Zero-Knowledge Proofs, ECC [66] & ECC, AES encryption [71] & ECC, Proof of Work [68] & ECC, RSA encryption [74] & Zero-Knowledge Proofs, ECC [71] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of decentralised identity solutions. sponse times under 200 milliseconds for up to 10,000 queries per second in well-optimised environments [62].
## 2 The Smart Rainfall System (SRS)
### The RF Front-End  
Summarizing, the system is composed of a network of microwave sensors, each of which measures the signal intensity received from a satellite thanks to a parabolic dish antenna and an Low Noise Block (LNB) converter [25].  
In particular, the RF part of a sensor can be schematized according to the block diagram shown in Figure 2. Each sensor receives the signal from a commercial antenna, already converted to \(L\) band by the LNB, and operates through four fundamental devices:  
1. directional coupler,
2. \(L\)-band Low Noise Amplifier (LNA),
3. \(L\)-band band pass filter, and
4. logarithmic power detector.  
The directional coupler is used to spill a calibrated portion of the received power for the subsequent processing, while most of the signal can still be used in a TV decoder, so that the system can also be used at home as an opportunistic measurement station. The signal portion obtained by the coupler is then amplified by a low-noise amplifier (in the present implementation, an Avago MGA-86563), duly filtered and the resulting power is transformed in a DC signal by a detector having a logarithmic characteristic (Analog Devices AD8314). The DC signal is then converted into digital representation and processed \"on-board\" by means of a low-cost low-power micro-controller unit (Microchip PIC18F14K22 (MCU)), which generates raw data and provide local input-output as well as access to the internet, through a proper network interface.  
Figure 1: Measurement segment for the rain estimation by evaluation of the DVB-S/S2 signal’s attenuation.
# Vehicular Visible Light Communication for Intersection Management +
## 5 Conclusions and Future Trends  
We propose a way to optimize urban traffic network operations by integrating traffic signal control and driving behavior with V-VLC-ready connected cars. To command the passage of vehicles crossing the intersection, request/response mechanisms and temporal/space relative pose concepts were used. A communication scenario is established and a \"mesh/cellular\" hybrid network configuration proposed. The comparison between a four-legged intersection and a split intersection is presented. A phasing of traffic flows is proposed as a proof of concept. According to results, vehicles are controlled and scheduled to cross intersections in order to minimize traffic delays. Taking into account the pose analysis, the delays between left turns and forward movements are also allocated. Split intersections reduce the number of phases and increase the pedestrian phase, resulting in fewer stops at intersections. Additionally, through synchronization of the two signalized intersections, the corridor travel times is improved on both the major and side streets. The simulated/experimental results confirmed that the proposed cooperative VLC architecture is suitable for the intended applications. By using VLC between connected vehicles and their environment, critical points such as queue formation and dissipation, relative speed thresholds, and inter-vehicle spacing can be directly monitored.  
For further development, the research team plans to finalize the embedded application, which will be used to experiment with either static or moving vehicles on several road configurations. This paper focuses on the concept rather than on the practical implementations. Implementing the system in a real environment requires considering several constraints, including the dimensions, orientation, and motion of the target vehicles; the precise motion and position of the vehicle; the strategic locations of landmarks; and the unambiguously covered distances and speeds, as well as the field of view. The practical implementation of many tasks such as steering/velocity control, perception of the environment, and performance under all conditions of the roadway and environment will be challenging.  
Conceptualisation, M.A.V., Methodology, M.V., Software, P.L. and P.V., A.F. and M.V. All authors have read and agreed to the published version of the manuscript.  
This work was sponsored by FCT-Fundacao para a Ciencia e a Tecnologia, within the Research Unit CTS--Center of Technology and systems, reference UIDB/00066/2020 and IPL/202/POSEIDON_ISEL.  
The authors declare no conflict of interest.
# A Study on Dropout Prediction for University Students Using Machine Learning
## 4 Experimental Results  
### Performance Measure  
Dropout prediction is a two-class classification problem, where dropout records are classified into the P (Positive) class, while non-dropout records are classified into the N (Negative) class. When evaluating the prediction result, T (True) means that the prediction is correct, and F (False) means that it is not. From this, there are four cases for determining the prediction performance in the two-class classification problem, including TP (True Positive), FP (False Positive), FN (False Negative), and TN (True Negative). In the prediction of student dropout, TP refers to a case where the model correctly predicts the P class, i.e., student dropout, while FP refers to a case where the model incorrectly predicts the dropout. TN and FN can be interpreted in the same way.  
The most common measure to evaluate the prediction performance is _accuracy_, which is defined as follows.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}} \tag{2}\]  
Note that when the data are skewed toward one class, accuracy cannot be used properly. For example, the dropout rate of four-year universities in South Korea is about 5% [6; 7], where the data are highly skewed to the N class. In this case, simply predicting that no one will drop out would yield 95% accuracy. However, if the opposite prediction is made, the accuracy is significantly lowered to 5%.  
The above problem indicates that FP and FN should be used together when measuring the prediction performance. Precision can be used to measure the performance from the perspective of FP and is defined as  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{3}\]  
Similarly, recall can be used to measure the performance from the perspective of FN, which is defined as  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{4}\]  
A simple way to measure the performance considering both FP and FN is to use the average of precision and recall. The F1-score is defined as the harmonic mean of precision and recall as follows.  
\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision} +\text{Recall}} \tag{5}\]  
In this paper, the F1-score was used for evaluating the performance of prediction models since it can properly reflect the data imbalance in the performance evaluation.
# VIS-HAPT: A Methodology Proposal to Develop Visuo-Haptic Environments in Education 4.0
## 4 The VIS-HAPT Methodology
### The Definition of the Technological Model
#### 4.2.1 Visuo-Haptic Environment Design  
Once the model is conceptualized, application developers should design the visuo-haptic environment. In this process, the developers visualize the type of aid that would enhance students' interactions (a variety of views in the simulation, visual panels where interactions parameters will be displayed, guiding lines to help students locate in the virtual space, etc.). Developers should consider user experience practices to motivate students and engage them in understanding a complex concept or activity.  
The identified drawbacks in this process include: (a) user manual guides are not provided as to take full advantage of the simulator; (b) some of the visualization spaces are either too small or too big; (c) lack of auxiliary elements that can enrich the phenomena; and (d) the presence of too many significant figures in the outcome variables blurring the physical meaning of the solution.  
The authors strongly recommend the careful consideration of which simulation parameters can be controlled by the students (size, mass, gravity, density) and where to deploy them on the screen. Allowing students to modify environmental variables can increase their interactivity. Consequently, student engagement will last much longer, leading to the growth of learning outcomes. Figure 3 shows an example of the design of a developing interface for a visuo-haptic simulator designed to teach the buoyancy phenomenon.  
Figure 3: Example of an interface design in a visuo-haptic environment focused on teaching the buoyancy concept.
## 5 Discussion
### Does the Intelligence and Confidence in AI Influence Human Decision-Making?  
The concordance rate in Part 2 of the experiment suggests that the accuracy and confidence level of AI judgments influence human decision-making, and that the influence of confidence level varies depending on the accuracy level. When the judgment accuracy of AI was high, changes in the confidence level had no significant effect on the concordance rate, but when the judgment accuracy of AI was low, changes in the confidence level caused significant differences in the concordance rate. The results showed that when the AI judgment accuracy was high, the effect of AI confidence tended to be small, and when the AI accuracy was low, the effect of AI confidence tended to be significant. As explained in Section 5.1, because participants tend to perceive AI accuracy as high when it is high, there is probably minimal requirement to employ AI confidence to indicate whether to trust the AI. Therefore, it can be inferred that the effect of AI confidence became smaller when the AI accuracy was high. Conversely, we believe that the effect of AI confidence was more significant when the AI accuracy was low. Participants might use AI confidence to indicate  
Figure 6: (**A**) Concordance rate (Part 2). The values are means \(\pm\) SD (\(n=30\)). The \(p\) values of two-way repeated-measures ANOVA are shown in the top left corner. Pairwise comparisons with Bonferroni corrections showed significant differences between Unintelligent and Hesitant AI, and others (\({}^{*}p<0.0001\)). (**B**) Improvement in accuracy (Part 2). The values are means \(\pm\) SD (\(n=30\)). The \(p\) value of two-way repeated-measures ANOVA is shown in the top left corner.  
whether to trust the AI because they could not infer the correct answer, due to the low AI accuracy. This result also suggests that participants may perceive the smartness of AI with higher judgment accuracy than themselves but may have difficulty perceiving the stupidity of AI with lower judgment accuracy. There is still room for debate as to whether people can determine how smart AI is, as described in Section 5.1. In future studies, we would like to verify whether the concordance rate changes when participants are not informed of the AI accuracy, as in this experiment, or when participants are informed of the AI accuracy and to investigate in more detail the conditions under which people can perceive the AI accuracy.  
However, when we examined the concordance rate focusing on AI confidence, it was higher when the AI was confident, regardless of AI accuracy, suggesting that the opinion of a confident AI was more likely to be accepted. It has been shown that when people interact to make decisions, more confident opinions are more likely to be accepted by the other person [20], and we found similarities between human-human interactions and human-AI interactions. People may feel a certain humanity toward AI.
# IoT-Enabled Gas Sensors: Technologies, Applications, and Opportunities
## 6 IoT-Based Wireless Gas Sensors
### Gas-Sensing Solutions for IoT and Wireless Sensors and Actuators Networks (WSANs)
A system to detect and withhold CH\({}_{4}\) leakages in industrial environments was proposed by [PERSON] et al. [111], based on catalytic gas sensors and with the ZigBee stack to perform the system's communication. Focusing on indoor environmental quality control, a photoacoustic-based CO\({}_{2}\) sensor was proposed by [106], employing a Z-Wave transceiver to make the communication with a gateway, responsible to transfer data to an online dashboard with periodic data collection and transmission. [PERSON] et al. [107] presented a portable dual gas detector for H\({}_{2}\)S and CO based on Wi-Fi and Bluetooth,operating with MOS transducers and communicating with smartphones; the sensed data being transferred to an online spreadsheet. To avoid accidents related to gas leakages, a CO and LPG gas sensor was proposed based on the MOS technology connected through the 6 LoWPAN over the IEEE 802.15.4 [146]. Sensed data is transferred to a local MQTT broker and stored on a local database to validate the proposed wireless communication. To improve the life quality of incontinence patients, [PERSON] et al. [147] proposed a MOS sensor to detect NH\({}_{3}\), methylmercapqtan (C\({}_{3}\)SH), and dimethylsulfide ((CH\({}_{3}\))\({}_{2}\)S). These gases are present in urine and feces, which allows caretakers to be aware of these patients need through smartphone alerts. The proposed system is connected to a smartphone through BLE, allowing alerts on these devices. It is also small and hence able to be used by these patients as a gadget. [PERSON] et al. [93], [PERSON] et al. [110] and [PERSON] et al. [105] have proposed multi-gas wireless sensors based on ZigBee. The first proposed a sensor to detect and VOC with MOS transducers deploying a solution that is capable for detecting people on the environment to perform the gas sensing, which helps to decrease energy consumption. The second presented a SO\({}_{2}\), NO\({}_{2}\), CO, CO\({}_{2}\), and O\({}_{2}\) sensor with electrochemical transducers with periodic calibration, to monitor greenhouse gases. The last developed a CO, CO\({}_{2}\), NO\({}_{2}\), and CH\({}_{4}\) sensor operating with MOS, electrochemical and optical transducers, to monitor air pollution in diverse environments.
# Efficient and Effective Unsupervised Entity Alignment in Large Knowledge Graphs
## 4 Methodology
### Textual Embedding  
First, we prepare the input for training _MPGT-Align_, which is derived from the textual features of entities. Specifically, we incorporate two types of textual features: entity name and context-dependent information associated with entities in the KG structure.  
**Entity name embedding.** Entity names encapsulate rich semantic information and are instrumental in identifying aligned entities. Consequently, numerous studies utilize pre-trained word embeddings to derive entity name embeddings. While some research [20; 46] focus on translating non-English entity names into English using Google Translate, translation errors and limitations of pre-trained word models in single languages present significant out-of-vocabulary (OOV) challenges [47]. Therefore, we opt for LaBSE [48] to generate name representations that can also be computed offline. LaBSE is a multilingual BERT model developed by Google to capture the context-dependent semantics of entities within  
Figure 2: The overall architecture of _MPGT-Align_.  
KGs. Without loss of generality, let \(W(e_{i})=(w_{1},w_{2},\ldots,w_{d})\) denote the entity name of \(e_{i}\in E\), consisting of \(d\) words or characters; we construct its name embedding as follows:  
\[\mathbf{e}_{i}^{n}=f_{LABSE}(W(e_{i})), \tag{1}\]  
where \(f_{LABSE}\) denotes the LaBSE encoder employed to initialize the embeddings without additional fine-tuning.  
**Entity-context embedding.** In accordance with the methodology outlined in [47; 49], we extract the contextual semantics of an entity within KGs, which further aids in identifying aligned entities. Specifically, for any entity \(e_{i}\), a \(k\)-step random walk will generate a walking path \(P_{e_{i}}^{k}=(e_{i},r_{1},e_{1},r_{2},\ldots,r_{t},e_{k})\). We then concatenate the entities and relations along this walking path into a single extended text. Subsequently, let \(T\) indicate the number of random walk times; we employ the LaBSE model to derive semantic information from this long text in order to obtain entity-context embedding,  
\[\mathbf{e}_{i}^{c}=\sum_{t\in[1,T]}f_{LABSE}(P_{e_{i}}^{k}). \tag{2}\]  
Ultimately, the concatenation of these two embeddings, \(\mathbf{e}_{i}^{nc}=\mathbf{e}_{i}^{n}||\mathbf{e}_{i}^{c}\), constitutes the textual embedding for entity \(e_{i}\). Thus, the textual embeddings for all entities across two KGs can be represented as \(\mathbf{E}^{nc}\).
## 3 Applications of the Tactile Internet
### Serious Gaming  
Serious gaming is an activity intended for a reason other than entertainment. For example, these activities incorporate fun with critical thinking challenges and target-oriented activities. In other terms, serious games also influence various application fields imperative to the general public, including health, education, and training.  
What restricts its advancement is the RTT of the communication frameworks, as the recognisable RTTs legitimately impact the apparent authenticity of applications. Thus, the task could be made conceivable, e.g., designing a game application for a learner to simulate the dental care of a patient. The intended purpose will be to promote knowledge about oral care to interact with haptic devices.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Types of Experience** & **Influential Constituent Elements of AR** & **References and VR** \\ \hline Behavioural Experience & Interactivity, Context-aware, Portability and \\  & Wearability, Information Embedding \\ Emotional Experience & Visual Enhancement (Input), Information Embedding and Context-aware \\ Social Experience & Context-aware, Information Embedding and \\  & Visual Enhancement (Input) \\ Cognitive Experience & Information Embedding, Content Creation \\  & and Visual Enhancement (Input) \\ Sensory Experience & Portability and Wearability, Geolocation and \\  & Visual Enhancement (Input and control) \\ Basic Experience & User and Technical Guidance, Context-aware, \\  & Information Embedding, Visual Enhancement \\  & and Interactivity \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mapping of types of AR and VR experience with the constituent components that influence the 5G User Experience (UX).
## 6 Conclusions  
In this work, a thorough analysis for satellite QKD downlinks was conducted, where the aim was to specify the system requirements for an operational satellite-to-ground QKD link under given atmospheric and system assumptions. In this study, an analysis for satellite DS-BB84 QKD downlinks was presented for a telecom C-band wavelength of 1550 nm. The specific requirements for implementing DS-BB84 QKD satellite-to-ground links with acceptable SKRs was discussed for various satellite orbits, QKD payload, and ground terminals specifications. System design parameters, including finite key size effects, atmospheric channel modeling, and quantum layer implementation parameters were considered. An evaluation of downlink scenarios at 1550 nm, focusing on link availability, on link budget, and secure key generation over time, was carried out. Emphasis was placed on the feasibility of distributing space photons through terrestrial fibers extension without the use of a trusted node in the OGS site, thus opening the door for converged space/terrestrial QCIs.  
Conceptualization, A.S., A.N., N.K.L. and G.G.; methodology, A.S., A.N., N.K.L. and G.G.; software, A.S., A.N. and N.K.L.; validation, A.S., A.N., N.K.L., G.G. and A.D.P.; formal analysis, A.S. and A.N.; investigation, A.S., A.N., N.K.L. and G.G; resources, N.K.L., G.G. and H.A.; data curation, A.S. and A.N.; writing--original draft preparation, A.S., A.N., N.K.L. and G.G.; writing--review and editing, A.S., A.N., N.K.L., G.G. and A.D.P.; visualization, A.S. and A.N.; supervision, G.G., A.D.P. and H.A.; project administration, H.A.; funding acquisition, H.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European's Union Horizon Europe Research and Innovation Programme under project \"Leap in **A**davancing of c**r**t**lical **Q**uantum key distribution-sp**A**ce components\" (LaiQa, grant agreement no. 101135245).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon reasonable request.  
This work was also supported in part by the European Commission through the project HellasQCI (under grant agreement no. 101091504) and by the European Union's Horizon Europe Research and Innovation Programme under project \"Quantum Security Networks Partnership\" (QSNP, grant agreement no. 101114043). The views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union. The European Union cannot be held responsible for them.  
The authors declare no conflicts of interest.