esa-sceva/satcom-chunk-collection huggingface dataset:
# Unmanned Vehicles' Placement Optimisation for Internet of Things and Internet of Unmanned Vehicles
###### Abstract
* We propose a new method for reducing the searching space for optimum position problems, namely the great circle path, and we implement an algorithm that determines the closest UV nodes to specific points on this path. * We propose and implement an algorithm for determining the optimal positions of the UVs such that the communication between the gateway and a target WSN is assured by higher throughput, lower range communication technology. Based on those positions, the Cloud Center can adaptively assign tasks to the UVs. * We mention the potential use cases of the proposed architecture and algorithm when LoRa/LoRaWAN communication technology is employed and, respectively, when LoRa/LoRaWAN together with Wi-Fi cellular communications is used. * We highlight the future directions and the open challenges. The remainder of the paper is as follows: Section 2 presents the state of the art on UAV and USV networks and on the advancements in improving the communication between them in comparison with the approach proposed in this paper. In Section 3, the components of the proposed UAV-USV-WSN network are described, and the proposed Internet of Unmanned Vehicles architecture is depicted. Section 4 briefly presents the context in which the approach is necessary. In Section 5, we introduce the theoretical background and the propagation concept and relevant parameters. In addition, we present the air-related channels and water-related channels that model the path losses in different scenarios. Section 6 describes the great circle path method and the algorithm that derives from it and presents the integration of the practical communication optimisation measures in the proposed position optimisation algorithm. In Section 7, the testing and the performance of the algorithm are emphasised. Section 8 presents the particular use cases of the proposed architecture and algorithm when LoRa/LoRaWAN/Wi-Fi communication technologies are used. Finally, Section 9 concludes the paper, highlights the future perspectives and underlines the open challenges.
## 3 Results and Discussion
### Effect of Experimental Parameters
#### 3.1.3 Effect of Stirring Speed  
The effect of stirring speed on the removal of methylparaben by adsorption on activated carbon and activated olive stones is shown in Figure 1c. As can be seen, by increasing the stirring speed from 80 rpm to 240 rpm, the adsorption capacity of methylparaben increases from 17.26 mg/g to 77.18 mg/g (4.47 times) in the case of activated carbon and from 2.75 to 15.95 (5.80 times) in the case of activated olive stones. This increase is a consequence of the increased diffusion of methylparaben from inside the solution to the surface and inside the pores of the adsorbent.
# A Morphing Deployable Mechanism for Re-Entry Capsule Aeroshell
## 4 Multibody Simulations  
In order to fully understand and predict the deployment behaviour of the assembly, a rigid multi-body simulation was set up in MSC/Adams. Each morphing rib was modelled as two rigid bodies connected by a revolute joint and a rigid link connected to the main body. The stowed configuration was optimized by envisaging a relative rotation between the two segments of the ribs. The system is shown in Figure 7.  
Figure 7: Deployment of the morphing aeroshell as a function of the central actuation ring position.  
Figure 8 outlines the variation in the main hinge angle of the ribs as the moveable ring deploys, where 0% corresponds to a stowed configuration and 100% corresponds to a fully deployed configuration. The system deploys symmetrically and a sphere-cone angle of about 60 degrees is reached by every rib. The system is then locked through a mechanical hard stop.  
The deployment is fully controlled by the actuated ring, provided that each rib deploys symmetrically. It follows that the relative rotations between the rib segments with respect to the ring displacement will always remain the same for a given geometric case. Figure 9 shows how the two segments of a single morphing rib move relatively depending on the rate of deployment, up to the fully deployed configuration. The relative rotation is defined to be null (hinge angle = 0\({}^{\circ}\)) when the two rib segments are aligned. Then, it is considered to be negative when toward the inner part of the capsule, moving to the stowed position.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 3 6G-IoT: New Technologies and Emerging Services
### Digital Twin Technology  
With the use of digital twin technology, engineers can create virtual replicas of physical objects or even systems to test and validate concepts before production. A digital twin can also be used in real time and, by regularly synchronizing and exchanging data with its real counterpart, it could greatly facilitate both its maintenance as well as its integration with other systems [76]. In addition, digital twins can be utilized in diagnostic or prognostic procedures employing historical data and leveraging big data and artificial intelligence technology [84]. The necessity to evaluate novel concepts, designs, and services is what motivates the deployment of digital twins in sixth generation IoT networks. As a result, the technology of digital twins is now included in 6G-IoT research and standardization processes.  
The use of digital twins to improve urban aquaponics farming is detailed in [85]. Aquaponics, which employs hydroponic farming principles, aims to create closed nutrient cycle systems that permit the concurrent cultivation of plants and fish. In this project, digital twin technology was used to provide an aquaponics system with the capacity to adapt to and foresee potential problems. Additionally, the study presented in [86] that uses digital twins, IoT, and big data may have accurately predicted future greenhouse conditions, which can assist in securing the planet's sustainability in the future. Even though using digital twin technology has many advantages, there are still a number of issues that need to be addressed as part of the 6G-IoT research and standardization process, particularly with regard to security issues and the accuracy with which digital and real-world systems can be matched [87].
# HERMES CubeSat Payload Thermal Balance Test and Comparison with Finite Volume Thermal Model
## 4 Test Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**TC**} & \multicolumn{3}{c}{**0 °C**} & \multicolumn{3}{c}{**-25 °C**} & \multicolumn{3}{c}{**30 °C**} \\ \cline{3-10}  & & **Off** & **On** & \(\Delta\)**T** & **Off** & **On** & \(\Delta\)**T** & **Off** & **On** & \(\Delta\)**T** \\ \hline Crystal Box & tc1 & 2.65 & 22.91 & 20.26 & \(-19.86\) & 0.83 & 20.69 & 29.31 & 46.99 & 17.68 \\ PSU & tc3 & 3.24 & 46.02 & 42.78 & \(-20.37\) & 26.42 & 46.79 & 29.61 & 66.93 & 37.32 \\ FEE & tc6 & 3.42 & 28.71 & 25.29 & \(-19.58\) & 8.19 & 27.76 & 29.81 & 50.81 & 21.01 \\ BEE FPGA & tc7 & 3.41 & 39.25 & 35.84 & \(-19.84\) & 17.41 & 37.25 & 29.82 & 57.67 & 27.85 \\ PDHU & tc9 & 4.10 & 22.02 & 17.92 & \(-19.58\) & \(-0.36\) & 19.22 & 29.69 & 45.12 & 15.43 \\ TVAC Interface & tc5 & 4.32 & 3.36 & \(-0.95\) & \(-23.98\) & \(-24.32\) & \(-0.34\) & 30.46 & 31.28 & 0.82 \\ Rib +z \(-\)y & tc8 & 4.86 & 14.09 & 9.23 & \(-18.42\) & \(-8.6\) & 9.82 & 29.3 & 37.18 & 7.88 \\ +x Panel & tc10 & 4.54 & 13.06 & 8.52 & \(-19.85\) & \(-10.85\) & 9.00 & 29.41 & 36.86 & 7.45 \\ +y Panel & tc11 & 4.82 & 12.83 & 8.01 & \(-18.91\) & \(-10.43\) & 8.48 & 29.11 & 36.24 & 7.13 \\ \(-\)x Panel & tc13 & 4.79 & 13.56 & 8.77 & \(-18.87\) & \(-9.49\) & 9.38 & 29.18 & 36.7 & 7.52 \\ Rail +y \(-\)x & tc14 & 5.47 & 11.07 & 5.6 & \(-17.67\) & \(-11.75\) & 5.92 & 28.89 & 34.17 & 5.28 \\ Rail +y \(-\)x & tc15 & 5.12 & 10.93 & 5.81 & \(-18.68\) & \(-12.38\) & 6.31 & 28.94 & 34.32 & 5.37 \\ +y Shroul panel 1 & tc12 & 15.93 & 15.95 & 0.01 & 10.99 & 12.36 & 1.37 & 26.61 & 26.68 & 0.08 \\ TVAC Base Plate 1 & tc16 & 4.52 & 2.68 & \(-1.84\) & \(-23.03\) & \(-23.83\) & \(-0.8\) & 29.67 & 30.3 & 0.62 \\ +y Shroud panel 2 & tc17 & 8.92 & 12.91 & 4.00 & \(-5.58\) & \(-1.93\) & 3.65 & 26.32 & 30.24 & 3.92 \\ TVAC Base Plate 2 & tc18 & 4.43 & 3.03 & \(-1.39\) & \(-22.86\) & \(-23.52\) & \(-0.66\) & 29.52 & 30.14 & 0.62 \\ Top shroud 1 & tc19 & 9.54 & 13.40 & 3.86 & \(-3.9\) & \(-0.38\) & 3.52 & 25.83 & 29.79 & 3.95 \\ Top shroud 2 & tc20 & 9.32 & 13.25 & 3.93 & \(-4.24\) & \(-0.65\) & 3.59 & 25.75 & 29.75 & 4.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Steady-state temperatures [°C] heaters off-on. Figure 16: Broken TC12 behaviour compared to its redundant TC17.
# UKF-Based State Estimation for Electrolytic Oxygen Generation System of Space Station
###### Abstract  
Electrolytic oxygen generation system (EOGS) is the only system that can provide oxygen for astronauts in a physicochemical regenerative way in a long-term manned spacecraft. In order to ensure that the astronauts in the cabin can obtain a continuous and enough oxygen supply, it is necessary to carry out real-time condition monitoring and fault diagnosis of the EOGS. This paper deals with condition monitoring and fault diagnosis of the EOGS. Firstly, the dynamic model of the system is established based on the principle electrolysis for actual oxygen production system and the state observer of the system has been designed by using unscented Kalman filter (UKF). The total pressure in the cabin and the partial pressure of oxygen in the electrolytic cell can be observed. Then, considered the actual conditions of the manned space mission with one more astronaut, i.e., 3 astronauts, the simulation experiment is carried out. The simulation results show that the method can effectively estimate the system state, and it is of great significance to ensure the normal operation of the electrolytic EOGS system in the space station.  
environmental control and life support system; electrolytic oxygen generation system; state observer; unscented kalman filter
###### Abstract  
Channel state information in millimeter wave (mmWave) and terahertz (THz) communications systems is vital for various tasks ranging from planning the optimal locations of BSs to efficient beam tracking mechanisms to handover design. Due to the use of large-scale phased antenna arrays and high sensitivity to environmental geometry and materials, precise propagation models for these bands are obtained via ray-tracing modeling. However, the propagation conditions in mmWave/THz systems may theoretically change at very small distances, that is, 1 mm-1 \(\upmu\)m, which requires extreme computational effort for modeling. In this paper, we first will assess the effective correlation distances in mmWave/THz systems for different outdoor scenarios, user mobility patterns, and line-of-sight (LoS) and non-LoS (nLoS) conditions. As the metrics of interest, we utilize the angle of arrival/departure (AoA/AoD) and path loss of the first few strongest rays. Then, to reduce the computational efforts required for the ray-tracing procedure, we propose a methodology for the extrapolation and interpolation of these metrics based on the convolutional long short-term memory (ConvLSTM) model. The proposed methodology is based on a special representation of the channel state information in a form suitable for state-of-the-art video enhancement machine learning (ML) techniques, which allows for the use of their powerful prediction capabilities. To assess the prediction performance of the ConvLSTM model, we utilize _precision_ and _recall_ as the main metrics of interest. Our numerical results demonstrate that the channel state correlation in AoA/AoD parameters is preserved up until approximately 0.3-0.6 m, which is 300-600 times larger than the wavelength at 300 GHz. The use of a ConvLSTM model allows us to accurately predict AoA and AoD angles up to the 0.6 m distance with AoA being characterized by a higher mean squared error (MSE). Our results can be utilized to speed up ray-tracing simulations by selecting the grid step size, resulting in the desired trade-off between modeling accuracy and computational time. Additionally, it can also be utilized to improve beam tracking in mmWave/THz systems via a selection of the time step between beam realignment procedures.  
m 2023 10 August 2023 23 August 2023 2023 Vol. 1, 3634. [[https://doi.org/10.3390/math11173634](https://doi.org/10.3390/math11173634)]([https://doi.org/10.3390/math11173634](https://doi.org/10.3390/math11173634))
## 3 Mapping Performances of the Four Models over the Arctic and Antarctic
### Establishment of Four Ionospheric TEC Models
The expression is the following:  
\[VTEC(\varphi_{c},\lambda_{c})=\sum_{n=0}^{n_{\max}}\sum_{m=0}^{m_{\max}} \widetilde{P}_{nm}(\cos\varphi_{c})\cdot(\widetilde{C}_{nm}\cos(m\lambda_{c}) +\widetilde{S}_{nm}\sin(m\lambda_{c})), \tag{5}\]  
where VTEC\((\varphi_{c},\lambda_{c})\) represents the VTEC at the spherical cap coordinate \((\varphi_{c},\lambda_{c})\) of the IPP; \(n_{\max}\) and \(m_{\max}\) are the maximum degree and order of the series, respectively; \(\widetilde{P}(\cos\varphi)\) is the normalized associated Legendre function; \(\widetilde{C}_{nm}\) and \(\widetilde{S}_{nm}\) are the SCH coefficients to be estimated. Since the ionospheric TEC is directly connected to the LT, 24 h of one calendar day was evenly divided into 12 sessions. Each session was fitted using four ionospheric models, respectively. According to the characteristic of each model, different parameter configurations were used in the study. For the POLY model, the mapping was based on the Earth-fixed geographical coordinate system with degree of 5 and order of 6. Thus, the total number of model parameters was 30 in each session. The GTSF model can map the diurnal TEC variation through a set of parameters [11]. We adopted a 19-parameter GTSF model to map the polar diurnal TEC. The model was based on the Earth-fixed geomagnetic coordinate system. For the SH model, the mapping was based on the solar geomagnetic coordinate system. The degree and order were 5, and the number of model parameters was 36 in each session. For the SCH model, the simulation was based on the Earth-fixed geomagnetic coordinate system. The maximum degree and order were 6, and there were 49 model parameters to be estimated in each session. The half angle of the spherical cap was 25\({}^{\circ}\).
# Full-Scale Experimental Investigation of the Interaction between Trains and Tunnels
## 2 Trackside Tests
### Track Side Influence  
Since in the standards there is no indication (for double track tunnels case) on the side of the track where to place the sensors, the pressure was measured on both sides of the tunnel: on the side of the train (at about 1 m from the lateral surface of the train) and on the opposite side (at about 6 m from the lateral surface of the train). An example of the comparison is shown in Figure 7. As could be expected, there are no differences in the pressure changes due to the pressure waves traveling at sonic speed: the pressure signature and also the evolution after the train passage are not influenced by the position of the sensor. On the other hand, the measure taken closer to the train (the TNS at 4-7 s) is highly affected by the pressure field running with the train. In detail, during the passage of the nose, the pressure is characterized by the typical positive and negative fluctuation, while along the train the oscillations due to the carriages are more evident and, above all, the wake behind the train modify the pressure evolution for about three seconds.  
Although the differences due to the head and the cars are quite small, they are deterministic, since they depend on the geometry both of the nose and of the inter-car gap (they are clearly visible even in CFD studies [38]) and they could be implemented in 1-D computational codes. On the contrary, the wake region is characterized by high fluctuations and the pressure can vary by thousands of Pascal, as is clearly visible in Figure 8. Furthermore, the described effect cannot be easily introduced into 1-D software and it would also require statistical analysis. Although this is not an issue from the point of view of the pressure comfort on board, since the high frequency oscillations are filtered by the car body, it may generate problems with the T  
Figure 7: Comparison between the pressures acquired within the tunnel on the train side and on the opposite side.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results
### DCCA Coefficients on Each Scale and Their Relationship  
The window size was divided into three different scales. \(0<n<1000\) denotes a small scale, \(1000<n<10,000\) a medium scale, and \(n>10,000\) a long scale, for examination and analysis.  
Figure 3 depicts the number of DCCA inter-relationships between the AQI index and meteorological variables (temperature, humidity, barometric pressure, and wind speed) for various time frames. The horizontal coordinate represents the time window's size, with a length of 19,432 h (April 2019 to July 2021). The vertical coordinates represent the DCCA inter-relationship values between \(-1\) and \(1\), where [\(-0.2\), \(0.2\)] imply no correlation. The yellow line represents the relationship between the AQI and the temperature, the green line represents the relationship between the AQI and the relative humidity, the red line represents the relationship between the AQI and the barometric pressure, and the blue line represents the relationship between the AQI and the wind speed.
# A Deep Multi-Task Learning Model for OD Traffic Flow Prediction Between Highway Stations
## 2 Related Studies
Through sophisticated algorithms such as graph convolution and graph attention, traffic flow data between nodes is disseminated, culminating in the computation of the OD target prediction value. [PERSON] et al. [26] introduced a Time Convolutional Network (T-GCN) model that has emerged as a prototype for addressing OD matrix prediction challenges. This innovative model synergistically integrates Graph Convolutional Networks (GCNs) and Gated Recurrent Units (GRUs). The GCN component is adept at learning intricate node topologies, thereby capturing spatial dependencies, while the GRU segment is proficient in discerning dynamic trends within OD data sequences, ensuring the capture of temporal dependencies. [PERSON] [27] proposed a multi-task learning model based on grid embedding, which embeds GCN into each grid to simulate the flow transfer relationship between different grids. [PERSON] [28] proposed a spatiotemporal residual encoding decoding multi graph convolution model, which takes OD pairs as nodes, models the non-Euclidean spatial and semantic correlations between different OD pairs, forms a multi graph adjacency matrix, and inputs it into the residual multi graph convolution model, achieving good prediction results. [PERSON] [29] proposed a hybrid spatiotemporal network (HSTN) based on Graph Convolutional Network (GCN), attention mechanism, and Seq2 Seq model to solve the problem of spatiotemporal correlation in predicting the OD matrix of ride hailing services. HSTN consists of two components, including Hybrid Space Module (HSM) and Hybrid Time Module (HTM). [PERSON] et al. [30] introduced a dynamic node edge attention network (DNEAT) to address the origin-destination (OD) matrix prediction issue by leveraging dynamic graph learning, considering demand generation and attraction. They incorporated a novel k-hop temporal node edge attention layer in DNEAT to capture the temporal evolution of node topology within dynamic OD graphs. Notably, the topology and corresponding weights between nodes are not predetermined as in previous studies; instead, they are learned through attention mechanisms based on distance relationships. This approach imbues the model with enhanced robustness, especially with highly sparse OD matrix data.
## 5 Potential Challenges
### Imperfect Successive Interference Cancellation  
To separate user data or decode overlaid data for multiuser identification, SIC operations are often carried out on the receiver side of NOMA. No-interference multiple-access (NOMA) systems can greatly benefit from this procedure since it helps eliminate intra-beam interference. However, throughout the SIC method, information from users with high channel gains is taken on behalf of users with low channel gains. Users with low channel gains may be more vulnerable to threats like session hijacking, eavesdropping, and identity theft. Additionally, poor SIC might raise significant problems when implementing NOMA in real-world 5G networks. Therefore, better SIC schemes can address the imperfect SIC difficulties and significantly boost the performance of the NOMA system.
## 3 Review of Limbasiya et al.'s Protocol
### Key Generation Phase  
The vehicle \(v_{i}\) begins a key agreement process in \(TPD_{i}\) for message signature. \(v_{i}\) generates a session key \(SK_{RID_{i}}\) and transmits it to a concerned \(RSU\). The detailed equations are illustrated in Figure 3 and the steps are as following.  
**Step 1:**\(v_{i}\) inserts \(RID_{i}\) and \(PWD_{TPD_{i}}\) into \(TPD_{i}\).  
**Step 2:** then \(TPD_{i}\) computes \(s_{i}=P\oplus RID_{i}\oplus P_{pri_{i}}\) and \(X_{i}^{\prime}=(PWD_{TPD_{i}}||s_{i})\oplus RID_{i}\). Then \(TPD_{i}\) compares \(X_{i}^{\prime}\) with \(X_{i}\) stored in itself.  
**Step 3:** if they are same, \(TPD_{i}\) selects random number \(r_{i}\) and computes \(ID_{1}=r_{i}\cdot P\), \(ID_{2}=RID_{i}\oplus h(r_{i}\cdot P_{pri_{i}})\) and \(ID_{i+2}=h(ID_{1}||ID_{2})\). Then \(TPD_{i}\) generates the session key \(SK_{RID_{i}}=s_{i}\oplus h(ID_{i+2}||T_{1})\oplus ID_{RSU_{i}}\) and transmits the session key to a concerned \(RSU\).  
Figure 3: Key generation phase of [PERSON] et al.’s protocol.  
Figure 2: Formation phase of [PERSON] et al.’s protocol.