esa-sceva/satcom-chunk-collection huggingface dataset:
# Telepresence Social Robotics towards Co-Presence: A Review
## 2 Co-Presence Taxonomy
### Contextual and Social Properties  
Early studies on predictors of social presence focused on immersive qualities; however, the research began to address contextual and individual properties. Given the subjectivity associated to social presence experience, and aside from the physical distance and the medium's technological qualities, analyses started to consider a psychological distance between the interactants [29; 82; 83]. These include factors such as Personality/traits of Virtual Human, Agency [17], Physical Proximity [57; 59], Task Type [48; 84; 85], Social Cues [27; 48; 86], or Identity Cues [27].  
In [30], a contextual responsiveness predictor is explored that assesses the capability of a virtual human (VH) to detect and respond to events and cues that happen in the shared space of the VH and the user (e.g., to a broom that falls in the user's physical environment or that falls into the virtual VH space). They showed that when the VH detects and directs gazing at the event or orients itself in that direction, the user presents higher levels of co-presence. Studies suggest that users' perception of the physical space affects their co-presence in mixed reality. Ignoring events in the background, such as objects moving or a person walking [87], or the inability to shift attention to an external event does not contribute to co-presence. In [62], the robot that plays a game with the user uses a \"cheat\" function to trick the user, which affects the user's trust, contributing positively to co-presence.  
In [27], users reported a higher level of social presence when communicating simultaneously with several remote interlocutors through a telepresence robot than with a single remote person. In [27], a second study showed that users felt the presence of the remote interlocutor more when the telepresence robot had a low identity than a higher identity (e.g., robot's head LCD with or without a face drawing).
# A TAM-Based Study of the Attitude towards Use
## 3 Research Method
### Data Handling
#### 3.5.4 Regression Analysis  
Regression analysis is used to understand which of the independent variables are related to the dependent variable. In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. In this study, we examine the relationships among perceived usefulness, perceived ease of use, attitude to use, and intention to use. We also explored the forms of these relationships. There were three relationships we examined to explore the coefficients and difference among these variables. They were:  
The relationship between perceived ease of use and perceived usefulness.  
The relationship among perceived ease of use, perceived usefulness, and attitude to use.  
The relationship among perceived usefulness, attitude to use, and intention to use.  
On the analysis approach, once a regression model has been created, confirmation of the goodness of fit of the model is the following step. The statistical significance of the estimated parameters, such as \(R^{2}\), residuals and hypothesis testing, \(F\)-test of the overall fit, followed by \(t\)-tests of individual parameters, have to be analyzed. Interpretations of these tests rest heavily on the model assumptions. In this study, we applied \(R^{2}\), \(F\)-test, and \(t\)-tests to identify the coefficient and the relationships among variables.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Factor** & **Item No.** & **CR** & \begin{tabular}{c} **Correlation** \\ **Coefficient** \\ \end{tabular} & \begin{tabular}{c} **Cronbach’s \(\mathbf{\alpha}\) if** \\ **Item Deleted** \\ \end{tabular} & **Cronbach’s \(\mathbf{\alpha}\)** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Perceived \\ usefulness \\ \end{tabular} } & U1 & 12.803 & 0.880 & 0.795 & \\  & U2 & 10.942 & 0.859 & 0.813 & \\  & U3 & 10.967 & 0.863 & 0.808 & \\  & U4 & 6.824 & 0.775 & 0.879 & \\ \hline \multirow{4}{*}{\begin{tabular}{c} Perceived \\ ease of use \\ \end{tabular} } & E1 & 9.865 & 0.909 & 0.826 & \\  & E2 & 10.237 & 0.878 & 0.848 & \\  & E3 & 11.402 & 0.887 & 0.844 & \\  & E4 & 8.337 & 0.783 & 0.898 & \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Attitude \\ toward use \\ \end{tabular} } & A1 & 9.829 & 0.903 & 0.838 & \\  & A2 & 11.076 & 0.845 & 0.874 & \\  & A3 & 9.436 & 0.859 & 0.863 & \\  & A4 & 9.411 & 0.866 & 0.862 & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Intention to \\ use \\ \end{tabular} } & I1 & 14.099 & 0.878 & 0.885 & \\  & I2 & 9.084 & 0.811 & 0.910 & \\  & I3 & 12.936 & 0.862 & 0.889 & \\ \cline{1-1}  & I4 & 12.751 & 0.857 & 0.892 & \\ \cline{1-1}  & I5 & 15.635 & 0.901 & 0.878 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Item and reliability analysis.
# Thrust and Pressure Control in a Solid Propulsion System via Reinforcement Learning
## 1 Introduction
The development of an RL-based mission planning strategy for a partial space elevator (PSE) addresses the challenge of maintaining the main satellite's orbital stability during climber operations, demonstrating that using a deep Q-network to optimize the waiting intervals between transfer missions can minimize orbital radius changes effectively [21]. [PERSON]'s [22] review explores the application of machine learning (ML) and RL techniques in enhancing the navigation and obstacle avoidance capabilities of unmanned aerial vehicles (UAVs), revealing the effectiveness of various RL algorithms in enabling autonomous navigation through real-time environmental interactions. Additionally, [PERSON]'s [23] research introduces an RL framework for robotic manipulators to adapt to joint malfunctions during tasks, achieving a high success rate of 93.6% in diverse failure conditions. Furthermore, a proposed trajectory tracking control algorithm for unmanned surface vehicles (USVs) employs an improved deep deterministic policy gradient (DDPG) method [24], demonstrating its effectiveness in dynamic ocean environments through experimental validation. In aerospace vehicle control, RL has demonstrated potential in addressing complex and uncertain environments, enhancing autonomous decision-making capabilities, and handling nonlinear control problems. However, there is a lack of literature on the application of RL in aerospace propulsion systems. Leveraging RL, we developed an intelligent control method and architecture for the solid attitude and divert propulsion system (SADPS). Considering the system's strong nonlinearity, multiple inputs and outputs, disturbances, and coupling characteristics, we established a multi-agent architecture for pressure, thrust, and nozzle opening control. We devised an adaptive reward function and trained an expert neural network, demonstrating superior control effects compared to PID. Simulation tests validated the effectiveness of the intelligent control method.
# LaRaCog: A Protocol for Cognitive Radio-Based LoRa Network
## 4 LoRaWAN Protocol
### LoRaWAN Key Features
#### 4.2.2 Bidirectional Communication  
LoRaWAN grants bidirectional communication, where the system can communicate with EDs through receive windows. The NS can communicate with the EDs for better network performance and connectivity (such as time synchronization frame, channel parameters, etc.). The bidirectional connectivity makes the LoRaCog implementation simpler, since it allows the NS to send the channel parameterization to the EDs once the channel selection is done.
# Interference Avoidance through Periodic UAV Scheduling in RIS-Aided UAV Cluster Communications
## 4 Simulation Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**(a) \(M=4\), \(L=4\), Schedule Periods (\(4,8,12,16\)), (\(8,8,14,16\)).**} \\ \hline interference probability & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline sequential schedule & 0.132 & 0.237 & 0.351 & 0.491 & 0.604 \\ \hline LIM & 0.022 & 0.047 & 0.075 & 0.149 & 0.225 \\ \hline RIM & 0 & 0 & 0 & 0 & 0 \\ \hline \multicolumn{5}{c}{**(b) \(M=6\), \(L=4\), Schedule Periods (\(4,8,8,12,16,20\)), (\(8,8,14,16,18,20\)).**} \\ \hline interference probability & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline sequential schedule & 0.276 & 0.458 & 0.754 & 0.881 & 1.200 \\ \hline LIM & 0.033 & 0.089 & 0.126 & 0.205 & 0.298 \\ \hline RIM & 0 & 0.001 & 0.003 & 0.009 & 0.016 \\ \hline \multicolumn{5}{c}{**(c) \(M=8\), \(L=4\), Schedule Periods (\(4,6,8,8,12,14,16,20\)), (\(4,6,8,8,14,16,18,20\)).**} \\ \hline interference probability & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline sequential schedule & 0.399 & 0.767 & 1.312 & 1.766 & 2.145 \\ \hline LIM & 0.056 & 0.162 & 0.262 & 0.405 & 0.548 \\ \hline RIM & 0.006 & 0.027 & 0.035 & 0.064 & 0.095 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average interference level per time slot with two UAV clusters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**(a) \(M=4\), \(L=4\), Schedule Periods (\(4,8,12,16\)), (\(8,8,14,16\)), (\(8,12,16,20\)). **} \\ \hline interference probability & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline sequential schedule & 0.424 & 0.867 & 1.274 & 1.736 & 2.135 \\ \hline LIM & 0.089 & 0.216 & 0.353 & 0.496 & 0.595 \\ \hline RIM & 0.004 & 0.013 & 0.026 & 0.037 & 0.047 \\ \hline \multicolumn{5}{c}{**(b) \(M=6\), \(L=4\), Schedule Periods (\(4,8,8,12,16,20\)), (\(8,8,14,16,18,20\)), (\(6,8,14,16,18,20\)). **} \\ \hline interference probability & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline sequential schedule & 0.639 & 1.240 & 1.756 & 2.534 & 3.121 \\ \hline LIM & 0.122 & 0.223 & 0.456 & 0.821 & 1.343 \\ \hline RIM & 0.007 & 0.029 & 0.063 & 0.106 & 0.129 \\ \hline \multicolumn{5}{c}{**(c) \(M=8\), \(L=4\), Schedule Periods (\(4,6,8,8,12,14,16,20\)), (\(4,6,8,8,14,16,18,20\)), (\(4,6,6,8,14,16,18,20\)). **} \\ \hline interference probability & 0.2 & 0.4 & 0.6 & 0.8 & 1 \\ \hline sequential schedule & 1.034 & 2.227 & 3.405 & 4.783 & 6.114 \\ \hline LIM & 0.208 & 0.626 & 1.212 & 0.405 & 0.548
## 1 Introduction
All achieved remarkable retrieval outcomes [29]. However, deep learning for spaceborne GNSS-R ocean altimetry as still in its preliminary stage. [PERSON] et al. developed a TDS-1 spaceborne GNSS-R SSH retrieval algorithm with delay waveforms, metadata, atmospheric delay, and wind speed as inputs and the corresponding DTU18 SSH as outputs [30]. However, the SSH retrieval method for spaceborne GNSS-R is primarily performed using delayed waveforms. Different from the previous studies, this study develops the Modified Multimodal Deep Learning (MRMDL) method and employs the full Delay-Doppler Map (DDM) to retrieve SSH measured by CYGNSS. First, the new modified Residual Net model (MResNet) for DDM data structures is employed to adaptively capture productive features of the full DDM and to convert the two-dimensional DDM data into one-dimensional numerical data. Second, the extracted features and auxiliary parameters are fused as the input data for MHL-NN to retrieve the SSH. Third, the performance of the multimodal deep learning method is evaluated by the tide-corrected Danmarks Tekniske Universitet 18 Mean Sea Surface (DTU18 MSS) and SSHs from radar altimeters such as Jason3, HY-2C, and HY-2B. Finally, this study also implements the HALF retracking algorithm to derive the SSH, and the results are compared with those computed by the multimodal deep learning method.
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 2 Security and Intrusion Detection in the IoT
### Intrusion Detection Approaches on the Internet  
Before delving into the problem of detecting attacks in IoT CoAP communication environments, we find it useful to observe how intrusion detection is approached in the current Internet communications infrastructure. According to [12], IDS solutions currently employed in the Internet are classified in one of three major categories: signature-based detection (SD), anomaly-based detection (AD) and stateful protocol analysis (SPA). SD intrusion detection [13] (also known as knowledge-based detection or misuse detection) employs patterns or strings (signatures) of known attacks. As for AD intrusion detection [14] (also known as behavior-based intrusion detection), an anomaly is characterized as a deviation from the normal (expected) behavior of the network, with such behavior being characterized via a profile built from monitoring regular activities of the network. Profiles can be either static or dynamic, and are defined by a set of attributes which, in the case of a system, may include the frequency of key strokes by a user in a given system, the number of files accessed on a given time period or the number of incorrect attempts on the login screen, among others. Finally, with SPA intrusion detection [15] (also known as specification-based systems), suspicious activities are detected using profiles defined for specific protocols or applications.  
It is also useful to consider how the technologies related with intrusion detection are employed in practical systems and, in this case, we find host-based IDS (HIDS), network-based IDS (NIDS) and wireless-based IDS (WIDS) [12]. HIDS implementations are mostly focused on monitoring and detecting suspicious activities in hosts, and usually are capable of monitoring all or parts of the dynamic behavior and state of the computer system, when compared with a baseline built during its initial setup. As for NIDS, such systems monitor a network or systems for malicious activities or policy violations, by analyzing the network traffic captured at strategic locations of the communications infrastructure. WIDS are similar to NIDS, while in this case being more focused on the monitoring of wireless communications.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Regression Model**} & \multicolumn{4}{c}{**R2**} & \multicolumn{2}{c}{**Adjusted R2**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**Std. Error**} \\ \cline{2-10} January & 55.16 & 28.08 & 12.19 & \(-\)1.99 & 1.24 & \(-\)17.70 & 0.8985 & 0.8966 & 40.66 & 41.11 \\ \hline February & 94.33 & 14.92 & 21.20 & \(-\)1.83 & 0.93 & \(-\)4.07 & 0.8697 & 0.8671 & 53.97 & 54.63 \\ \hline March & 104.80 & 9.59 & 21.92 & \(-\)0.65 & \(-\)0.32 & 0.54 & 0.8916 & 0.8896 & 51.59 & 52.15 \\ \hline April & 138.86 & 9.63 & 15.91 & \(-\)0.54 & \(-\)0.87 & \(-\)3.39 & 0.8767 & 0.8744 & 59.90 & 60.58 \\ \hline May & 104.78 & 9.52 & 12.57 & 0.03 & \(-\)0.97 & \(-\)1.95 & 0.8142 & 0.8108 & 63.66 & 64.35 \\ \hline June & 128.07 & 9.91 & 5.65 & 0.86 & \(-\)2.28 & 1.33 & 0.8300 & 0.8268 & 54.93 & 55.55 \\ \hline July & \(-\)102.3 & 14.76 & 2.32 & 2.76 & \(-\)1.61 & \(-\)4.70 & 0.7767 & 0.7726 & 60.55 & 61.21 \\ \hline August & \(-\)75.50 & 16.51 & \(-\)3.60 & 1.96 & \(-\)1.23 & 1.98 & 0.7769 & 0.7728 & 55.06 & 55.66 \\ \hline September & \(-\)101.8 & 13.42 & 9.88 & 1.61 & \(-\)0.25 & \(-\)0.32 & 0.7842 & 0.7801 & 62.42 & 63.12 \\ \hline October & 92.74 & 11.01 & 15.23 & \(-\)0.40 & \(-\)0.70 & \(-\)2.49 & 0.7952 & 0.7914 & 61.57 & 62.43 \\ \hline November & 18.84 & 28.62 & 3.35 & \(-\)0.12 & \(-\)0.35 & \(-\)21.75 & 0.74565 & 0.74084 & 64.94 & 65.67 \\ \hline December & \(-\)103.5 & 33.67 & 6.04 & 0.32 & 0.38 & \(-\)1.28 & 0.7228 & 0.7178 & 68.39 & 69.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Statistical test results of the revised regression model for the yearly data (2011–2019). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Coefficient**} & \multicolumn{2}{c}{**Std. Error**} & \multicolumn{1}{c}{**\(p\)-Value**} & \multicolumn{1}{c}{**VIF**} \\ \hline Constant & 201.91 & 8.16 & \(2.2\times 10^{-16}\) & - \\ \hline Insolation intensity during daylight hours (MJ/m\({}^{2}\)) (X\({}_{1}\)) & 10.92 & 0.37 & \(2.2\times 10^{-16}\) & 4.48 \\ \hline Daylight time (h) (X\({}_{2}\)) & 12.28 & 0.67 & \(2.2\times 10^{-16}\) & 4.67 \\ \hline Average relative humidity (\%) (X\({}_{3}\)) & \(-\)1.08 & 0.16 & \(1.1\times 10^{-11}\) & 4.13 \\ \hline Minimum relative humidity (\%) (X\({}_{4}\)) & \(-\)1.09 & 0.15 & \(7.5\times 10^{-14}\) & 5.70 \\ \hline Quantity of evaporation (mm) (X\({}_{5}\)) & \(-\)9.83 & 0.88 & \(2.2\times 10^{-16}\) & 2.22 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Revised multiple linear regression model for the yearly data collected over the past 9 years.
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
## 4 Discussion
### Singleplex Versus Multiplex Strategy  
CRISPRR systems can be designed as a singleplex strategy, using only one gRNA to target a specific site, or as a multiplex strategy with multiple gRNAs targeting multiple sites within one gene or multiple genes simultaneously. Most studies [42; 45; 47; 49; 56; 60; 67] using both strategies observed a higher efficiency in reducing viral replication using the multiplex strategy compared to the singleplex strategy. This could be explained by the hypothesis that more sites are targeted and edited, resulting in more genome instability and subsequent disturbance of viral processes. Moreover, a multiplex strategy could have a higher chance of escaping resistance by the targeted virus, which implies the formation of mutations at the targeted cleavage site and subsequent circumvention of CRISPR-Cas9 editing [45]. More specifically, if one of the targeted sites manages to escape the binding of the gRNA by generating mutations in the target cleavage site, the remaining gRNAs can still direct Cas9 activity, ensuring continued, though potentially reduced, targeting efficiency of the viral genome. On the contrary, viral resistance development against the gRNA of the singleplex strategy could completely inactivate the antiviral activity of the CRISPR system. However, one disadvantage of the multiplex strategy is the higher risk for off-target editing since the higher number of gRNAs could target more sites with a similar sequence.
###### Abstract  
Visible Light Communication (VLC) uses light-emitting diodes to provide wireless connectivity in public environments. Transmission security in this emerging channel is not trivial. Chaotic modulation techniques can provide encryption directly in the physical layer based on the random-alike evolution and strong synchronization prospect given by deterministic chaos. In secure chaotic inclusion or embedding methods, continuous-time chaos oscillator models need to be synchronized via a coupling carrier. Here we present a first numerical simulation study for the impact of the variable delays induced by line-of-sight and non-line-of-sight multipath fading in complete chaotic synchronization. More precisely, we analyze a chaotic Colpitts oscillator that is simultaneously transmitting the carrier to several mobile receivers via nine spotlights. Such induced delays depend on both the receiver position and the carrier frequency, influencing the complete synchronization required in modulation via chaotic inclusion. Correlation values for several receiver positions and carrier frequencies are presented, examining the progressive emergence of the multipath effect and its impact on chaotic synchronization. We show that, for the chaotic oscillator and coupling applied in the defined room settings, complete chaotic synchronization can be achieved and that it is robust up to the tens of MHz region.  
Keywords:visible light communication; chaotic communication; multipath channels; nonlinear systems; network security; chaos synchronization +
Footnote †: journal:  
_Article_
# _photonics_
## 6 Conclusions  
We presented a thorough design study and a feasibility analysis on QKD satellite deployment devoted for Greek QCI. Using the installation parameters of OGSs hosted in Greek observatories, a decoy-state BB84 QKD link between a satellite constellation consisting of 10 LEO satellites and three OGSs was discussed. Realistic protocol implementation parameters have been considered for the QKD layer, and the QKD link performance was studied, considering the key atmospheric processes affecting the wireless optical link. Based on the set of the reported numerical results, the establishment of the QKD downlink is hard to accomplish in daylight conditions, but a sufficient amount of quantum keys can be distilled during nighttime. During the time instances when the satellites are visible to the OGSs at nighttime, the normalized SKR up to \(3.9\times 10^{-4}\) (bps/pulse) can be distilled. Over a period of one year, an amount of up to \(1.435\) Gbits of secret keys can be generated per ground station, and by using the trusted node architecture, these bits can be shared between the OGSs. It has been shown that this value of distilled bits is sufficient to support the AES-256 refresh times that keep an ultra-low attack success probability, ensuring high levels of security in data encryption/decryption. The reported results contribute towards the deployment discussions on Greek QCI based on the telescopes hosted in the observatories placed in South, Central, and North sectors of Greek territory. The successful quantum key delivery in the three OGSs via LEO satellite QKD links can be combined with terrestrial fiber segments to distribute securely optical data flows via terrestrial QKD systems, thereby allowing for end-to-end quantum-secured connectivity scenarios.  
Conceptualization, A.N., N.K.L., D.Z. and G.G.; data curation, A.N., N.K.L., D.Z. and G.G.; investigation, A.N., N.K.L., D.Z. and G.G.; methodology, A.N., N.K.L., D.Z. and G.G.; supervision, A.D.P. and H.A.; writing--the original draft, A.N., N.K.L., D.Z. and G.G.; writing--review and editing, A.D.P. and H.A. All authors have read and agreed to the published version of the manuscript.  
This work was funded by H2020-funded Flagship project UNIQORN (820474).  
Not applicable.  
Part of the research leading to this work was also supported by the ESA SKY-LIGHT QUARTZ, Contract 4000123878/18/NL/MM.  
The authors declare no conflict of interest.
# Fractional Order Processing of Satellite Images
## 1 Introduction
### Paper Organisation  
This paper is organised as follows. Section 2 explains the theoretical formulations for the edge detectors tested. Section 3 presents performance metrics and results. Conclusions are drawn in Section 4.
# A Practical and Adaptive Approach to Predicting Indoor CO2
## 5 Simulation Setup
* **Adam optimizer**[22] was used as the optimization algorithm. * **RMSE** was used as the loss function during the training. * **The validation split** was set to 0.3, as per common practice in studies on CO2 prediction. Before the split, we shuffled the samples so that both training and validation sets contained samples which were spread across the whole time window. Please note that we did not shuffle the time series within a single sample, as we wanted to preserve the chronological order of the consecutive quarters of hour. * **The maximum number of epochs** was set to 5000, alongside an early stopping patience parameter set to 25 epochs. Figure 4: Behavior of the model with different numbers of quarters of an hour per sample, with a focus on the pool size value (**top**), kernel size value (**center**) and number of convolutional filters (**bottom**).