esa-sceva/satcom-chunk-collection huggingface dataset:
# An Overview of Cube-Satellite Propulsion Technologies and Trends
## 4 Solid Rocket Propulsion (SRP) Systems
### Design Considerations and Technologies
None of the mentioned systems have a heritage of flying on a CubeSat mission, however, as already indicated, one of these, the DSSP CAPS-3 propulsion system has flown on the SPINSAT mission.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 4 Discussion
### Damage Assessment  
Undoubtedly, the retrieval of spatio-temporal patterns is extremely important when assessing the spatial and temporal extent of the transformations suffered by an area. In this experiment, we aimed to identify the area affected by the floods that occurred in 2000 in the Dobrogea region, Romania, and to identify their harmful effects over several months. As shown in Figure 5, the first image of the series was captured immediately after the Danube river swelled, whereas the rest of the images were acquired after the river began to retreat. The result of the query-by-example retrieval proposed method is shown Figure 10, along with the corresponding ground truth. The numerical evaluation of the performance achieved by the proposed algorithm is presented in Table 3 and confirms the effectiveness of the method for delimiting specific areas, e.g., flooded areas in this particular case. An interesting aspect is the fact that DTW managed to bypass the problem of distorted data--the region of interest is partially occluded by clouds in the last image from the series, but the proposed algorithm was still able to correctly retrieve the spatio-temporal evolutions in the flooded area.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Query**} & **Overall** & **Missed** & **False** \\  & **Accuracy** & **Alarm Rate** & **Alarm Rate** \\ \hline Flooded area & 99.96\% & 2.36\% & 0.13\% \\ \hline \end{tabular}
\end{table}
Table 3: Performance evaluation for delimitation of flooded areas in Dobrogea.  
Figure 9: Ground truth for short Landsat SITS. White pixels delimit (**a**) Morii & Dridu accumulation lakes and (**b**) Mihailesti accumulation lake.
## 4 Problem Formulation
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(\mathcal{T}\) & Time period \\ \(\mathcal{P}_{b}\) & UAV has available battery power \\ \(\mathcal{H}\) & UAV flying at a height in meters (m) \\ \(L_{w}\) & Transmission windows \\ \(\mathbf{V}\) & Number of vehicles in a cluster \(\mathcal{C}\) \\ \(s_{i,t}\) & Data rate in bits/Hz transmitted by each vehicle over UAV flight time \\ \(\psi_{t}\) & Binary variable indicating if a vehicle transmits to the UAV in a transmission window \\ \(\mathcal{D}_{t}\) & Delay which is the sum of the queuing delay and processing delay \\ \(\mathcal{B}_{j}\) & Bandwidth \\ \(p_{l}^{l}\) & Power consumed by the UAV while communicating with the \(i\)th vehicle \\ \(\sigma^{2}\) & Noise power density at the receiver of the vehicle \\ \(h_{ij}[t]\) & Channel gains of UAV at time slot \(t\) \\ \(s_{j}^{i}(b_{j^{\prime}}^{i},x_{i},y_{i})\) & Instantaneous transmission rate for the \(i\)th vehicle in a time slot \\ \(b_{j}^{i}\) & Instantaneous bandwidth utilized by \(i\)th vehicle in \(j\)th transmission window \\ \(\Gamma\) & SNR \\ \(\lambda_{t}\) & Inter-arrival time between successive packets \\ \(\Psi_{j}\) & Local models considered for federated averaging \\ \(P_{total}(t)\) & Total power consumption during time period \(t\) \\ \(P_{\mathrm{L}\mathrm{L}\mathrm{V}}^{(V)}\) & Power required for operating the UAV during time period \(t\) \\ \(P_{l}(t)\) & Power consumption in the \(i\)th time slot \\ \(\psi_{j}^{l}\) & Local models arrive at the queue which accumulates at a rate \\ \(\tau_{u}\) & Uplink transmission window \\ \(u_{i}(t)\) & Binary variable; 1 indicates UAV is processing data from a vehicle; 0 indicates UAV is idle \\ \(\psi_{i,k}(t)\) & Size of the \(k\)th type of vehicular data served by the UAV in the \(i\)th time slot \\ \(\gamma_{k}\psi_{i,k}(t)\) & Coefficient of power consumption during uplink and downlink of \(k\)th type of vehicular data \\ \(\mathbf{q}_{(x,y)}\) & UAV trajectory \\ \(p_{max}\) & Maximum speed of UAV in meter/second (m/s) \\ \((\delta_{i})^{2}\) & Limiting parameter for UAV distance traveled in a TTI \\ \(\widehat{h}_{i}^{l}\) & Channel-gain experienced by the \(i\)th vehicle in \(j\)th window considering Rician K-factor \\ \(w_{1}-w_{4}\) & Weight values scaling factor \\ \(d_{cell}^{law}\) & Distance between the UAV and the \(i\)th vehicle \\ \(D_{max}\) & Upper bound on delay \\ \(\psi\) & Size of packets \\ \(\varsigma\) & Assignment coefficient; 1 denotes vehicle scheduled for transmission, 0 denotes waiting \\ \(\varrho_{i}\) & Control parameter for the data rates to reduce the computational complexity of the solution \\ \(Q^{T}(\mathbf{s}_{i},\tau(\mathbf{s}_{i}|\boldsymbol{\psi})\) & Policy function \\ \(V(\boldsymbol{\psi})\) & Value functions \\ \(\boldsymbol{\mu}_{i}(\mathcal{H}_{t})\) & Mean vector \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Definition of some key symbols and parameters used in this paper.
## 3 Results
### Contour Extraction and ISME Tip Localization  
A contour extraction result is shown in Figure 15. Each color represents a separate contour, and the experiment showed that there were many contours in the image. The results of contours filtration and ISME tip location are shown in Figure 15b, where the blue point P is the position of the ISME.  
To test the performance of the ISME-location algorithm, 145 images were used as a test set. Almost all the ISME tip could be correctly located (Figures 16 and 17). The mean error was 2.86 \(\upmu\)m, and the root mean square error was 2.89 \(\upmu\)m. The RE\({}_{T}\) was 0.000665%.  
Figure 14: The results of edge detection of various image edge detection techniques. (**a**) Input images; (**b**) Canny edge detection results; (**c**) Sobel edge detection results; (**d**) Laplace edge detection results; and (**e**) Scharr edge detection results.  
Figure 15: Contour extraction and ISME tip localization. Note: (**a**) is the output image of contour extraction, and (**b**) is the output image of the outermost contour selection and the ISME tip location. P is the ISME tip.
# Detection of Household Furniture Storage Space in Depth Images
## 5 Results
### Synthetic Dataset  
Since this is the first method proposed for generating training data for storage volume detection, it is not possible to compare its success to that of other methods which solve the same problem. Nevertheless, as some form of comparison, we can use the results obtained by the VoteNet trained for houshold object detection on a similar data type (indoor depth images) using the SUN RGB-D dataset [33], which achieved average precision (mAP) of 57.7% and 78.7% recall, with the 3D IoU threshold of 0.25.  
However, to test the performance of our dataset, we trained two adapted neural networks, VoteNet and FCAF3D. Overall, the trained VoteNet achieved storage volume detection precision of 43.66% and recall 62.29%; and FCAF3D achieved precision of 47.72% and recall 71.41% on synthetic dataset with the IoU threshold of 0.25.
## Appendix A Multiple Linear Regression Analysis of IEQ Parameter Correlations
### Discussion of Findings  
The multiple regression analysis revealed several important insights:  
1. Reduced correlation magnitudes: when controlling for building type and study period, most correlation coefficients decreased in magnitude, suggesting that some of the apparent relationships were partially influenced by these contextual factors.
2. Building type significance: the educational facility type emerged as the most influential covariate, with stronger correlations generally observed in higher education settings compared to primary and secondary schools. This may reflect differences in building characteristics, ventilation systems, occupancy patterns, and monitoring technologies.
3. Temporal trends: more recent studies (2023-2024) typically showed stronger correlations between parameters than earlier studies (2020-2022), possibly reflecting improvements in measurement methodologies or changes in ventilation practices following the COVID-19 pandemic.
4. Parameter relationship strength: despite the adjustments for covariates, the strong positive correlation between indoor air quality and relative humidity remained robust across all building types, confirming the interrelationship between these important IEQ factors.
## 5 Security Analysis
### Resistance Against Offline Attacks  
In the proposed management system, the key used for communication is transmitted and stored in the database in an encrypted form. In each session, the original key, which is employed for encrypting messages between the parties, is deleted from the database. Therefore, even if an attacker gains complete access to the device and the database, they will not be able to obtain the key in its entirety. Consequently, this strategic approach safeguards the scheme against offline attacks.
## Materials and Methods
### Global Land Data Assimilation System (GLDAS)  
The GLDAS (Global Land Data Assimilation System) database was created and developed by NASA to assemble satellite and ground-based observational data products, using advanced land surface modelling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes [42]. It is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC).  
The GLDAS archive, which is always in evolution, drives currently different land surface models: NOAH, Mosaic, Catchment, the Community Land Model (CLM) and the Variable Infiltration Capacity (VIC). Two different datasets compose the archive: GLDAS-1 and GLDAS-2. The first one consists of data with temporal coverage from 1979 to present (spatial resolution of 1.0\({}^{\circ}\)) and from 2000 to present (spatial resolution of 0.25\({}^{\circ}\)). Recently, GLDAS-2 was restructured into two components: GLDAS-2.0, forced entirely with the Princeton meteorological forcing data and currently extending from 1948 through 2010, and GLDAS-2.1, forced with a combination of models and observations based forcing datasets and extending from 2000 to present [43; 44; 45], with a monthly update. Regarding LSMs included in GLDAS, the GLDAS-2.0 simulations were initialized on 1 January 1948, using soil moisture and other state fields from the LSMs climatology for that day of the year, and were forced by the global meteorological forcing dataset from Princeton University [46]. The GLDAS-2.1 simulations were initialized using the conditions from the GLDAS-2.0 on 1 January 2000, and were forced with national oceanic and Atmospheric Administration (NOAA)/Global Data Assimilation System (GDAS) atmospheric analysis fields [47], the disaggregated Global precipitation Climatology Project (GPCP) precipitations fields [48], and the Air Force Weather Agency AGRicultural METeorological modelling system (AGRMET) radiation fields. Basic characteristics of the GLDAS-2 database are listed in Table 1.  
GLDAS represents an important data source for global water cycle research, and its products were evaluated by many studies. A systematic evaluation of the monthly scale precipitation and temperature forcing inputs of GLDAS-2.0-2.1 over China was performed in Reference [49]: in this study, the authors demonstrated that air temperature and humidity from the GLDAS-1/NOAH model had a high accuracy, while downward solar radiation and wind speed data results were overestimated [50]. Another study related to the four LSMs included in GLDAS-1 showed different estimates of river discharge and distinct geographic patterns in the accuracy of each model, when compared with gauged discharge data [51]. In addition, the simulation of moisture of first soil layer (0-5 cm) resulted underestimated. Other authors [52], instead, discovered that the four models included in GLDAS simulated well the soil moisture of the 20-40 cm layer over the Tibetan Plateau.
## 2 APD Framework  
The parameter determination framework is implemented in Python (version 3.8) and structured as a sequence of interconnected modules that transform raw field measurements into constitutive model parameters. For parameter determination from cone penetration test (CPT) results, the framework consists of five modules. The first module imports the field measurements, which are then processed in the second module for CPT-based stratification. The stratified layers are subsequently analysed in the third module to determine their state, including the overconsolidation ratio (\(OCR\)) and the coefficient of earth pressure at rest (\(K_{0}\)). In the fourth module, soil parameters are computed for each layer, while the fifth module derives the corresponding constitutive model parameters. A comprehensive description of the framework and its modules is provided in [3], with additional details available in [4; 5; 6].  
APD employs a graph-based approach, linking source parameters (CPT data) to destination parameters (soil or constitutive model parameters) through intermediate parameters. These connections, defined by a set of validated correlations, form a flexible and traceable network. The graph is constructed from user-defined CSV files, enabling customisable workflows and transparent parameter computation [2; 3].  
Graphs are generated from two CSV files specifying methods (correlations) and parameters. The system allows filtering by soil type, applicability ranges, and user-defined constraints. An example is shown in Figure 1, where a correlation for the relative density (\(D_{r}\)) is implemented.  
The generated graph contains two distinct types of nodes: parameter nodes and method nodes. Parameter nodes are listed in the parameters CSV file, whereas method nodes are described in the methods CSV file. The edges--representing connections between nodes--are determined by the parameters_in and parameters_out fields in the methods CSV. The parameters_in field specifies which parameters serve as inputs to a method node (i.e., incoming edges), while the parameters_out field identifies the parameter produced by the method (i.e., the outgoing edge to a parameter node).  
Currently, APD supports three primary workflows, enabling parameter assessment based on cone penetration testing (CPT), dilatometer testing (DMT), and shear wave velocity measurements. To accommodate these workflows, the system includes three validated databases of methods and parameters, collectively containing over 200 correlations. Notably, both soil parameters and constitutive model parameters are stored within the same database.
# Analyzing Zone-Based Registration Using a Three Zone System: A Semi-Markov Process Approach
## 3 New Mathematical Model
### Paging Cost  
As described in Section 3.2, it is assumed that three-step sequential paging to the three zones was performed. The most recently registered zone is paged first. If there is no response, the center zone is paged next. If there is no response again, the remaining edge zone is paged.  
Let us consider the paging cost of a 3Z system when the above three-step sequential paging is applied. Assuming \(V\) to be the paging cost for one cell, the paging cost between two incoming calls, \(C_{p}\), can be obtained as follows:\[\begin{array}{ll}C_{P}&=V\cdot(n\cdot P[1\text{st}\text{ paging success}]+2n\cdot P[2\text{nd}\text{ paging success}]+3\cdot P[3\text{rd paging success}])\\ &=V\cdot[n(\overline{\pi}_{E21}+\overline{\pi}_{I21}+\overline{\pi}_{I32}+ \overline{\pi}_{I12}+\overline{\pi}_{C12}+\overline{\pi}_{C32})+2n(\overline {\pi}_{E12}+\overline{\pi}_{E32}+\overline{\pi}_{C21})\\ &+3n(\overline{\pi}_{E23}+\overline{\pi}_{C23})]\\ &=V\cdot[2n-n\cdot(\overline{\pi}_{E21}+\overline{\pi}_{C21}+\overline{\pi}_{ I21}+\overline{\pi}_{I32}+\overline{\pi}_{I12})+n\cdot(\overline{\pi}_{E23}+ \overline{\pi}_{C23})]\end{array} \tag{10}\]
## 2 Methods
### Simulation Model  
The simulation model aims to simulate an internal combustion engine vehicle in a car-following scenario. Its goal is to evaluate the potential fuel consumption benefits of full-range ACC and the effectiveness of MPC through two different formulations of the cost function. The vehicle model was developed in MATLAB, considering only longitudinal dynamics to simplify the analysis while retaining the essential dynamics for ACC.  
As previously discussed, the primary vehicle model employed to describe vehicle dynamics and simulate the real vehicle, for the purpose of evaluating controller performance, incorporates a detailed representation of the longitudinal dynamics and the powertrain. The vehicle's position, speed, and acceleration were computed using a fixed reference system. The ICE provides the necessary torque to overcome road load and achieve the desired acceleration, as commanded by the controller. This setup mimics real-world driving conditions where the following vehicle adjusts its acceleration in order to proceed at the speed required to maintain a safe distance from the leading vehicle while optimizing fuel consumption.  
The equation of motion [34] for the ego vehicle is given as follows:  
\[m_{\text{tot}}a=\eta_{\text{gb}}\cdot\tau_{\text{gb}}\cdot\frac{T_{\text{wh}} }{r_{\text{wh}}}-\left[mg\sin(\alpha)+mgf_{0}\cos(\alpha)+mgf_{2}\cos(\alpha)v ^{2}\right] \tag{1}\]  
where:  
* \(m_{\text{tot}}\) is the equivalent translating mass of the vehicle, considering both translating and rotating components;
* \(a\) and \(v\) are the vehicle's acceleration and speed;
* \(T_{\text{wh}}\) is the torque at the wheels;
* \(f_{0},f_{2}\) are the rolling resistance coefficients;
* \(r_{\text{wh}}\) is the wheel radius;
* \(\tau_{\text{gb}}\) and \(\eta_{\text{gb}}\) are the transmission's gear ratio and efficiency, respectively;
* \(\alpha\) is the road slope.  
The wheel speed is given as follows:  
\[\omega_{\text{wh}}=\frac{v}{r_{\text{wh}}}. \tag{2}\]  
The powertrain model includes a final drive, a six-speed gear transmission, and the engine; its goal is to link the wheel speed and torque to the engine fuel consumption. In particular, a backward vehicle model was considered, meaning that the required engine torque and speed are computed in order to match the given acceleration. The model is also influenced by the engaged gear, as this changes the transmission's gear ratio (\(\tau_{\text{gb}}\)). In this work, we assume that the gear shift logic is not part of the ACC controller but rather is implemented with a traditional gear shift schedule. Therefore, the gear number was not included as a control variable for the DP and MPC algorithms.  
The vehicle model also incorporates a fuel consumption map, which provides the fuel flow rate (in grams per second) as a function of the engine's torque \(T_{\text{eng}}\) and speed \(\omega_{\text{eng}}\) via linear interpolation on experimental data points:  
\[\dot{n}_{\text{fuel}}=\max(0,f(\omega_{\text{eng}},T_{\text{eng}})). \tag{3}\]
# Experimental Demonstration of Secure Relay in Quantum Secure Direct Communication Network
## 1 Introduction  
In traditional communication networks, secret messages are encrypted with classical cryptosystems such as the advanced encryption standard (AES) or the RSA algorithm. As an important part of the communication network, the relays receive ciphertexts from the previous sender and send the reproduced signals to the next node without decrypting the message. Only the intended receiver at the end node recovers the messages with their secret key. Hence, relays are indispensable for the expansion of the communication network.  
Recently, the fast development of quantum technology has increased concerns about security and privacy in our communication networks [1, 2]. To avoid compromising the security of information, efforts have been devoted to two scientific fields, namely, quantum cryptography [3] and post-quantum cryptography (PQC) [4]. Quantum cryptography exploits the quantum principles to design new protocols, such as quantum key distribution (QKD) [3, 5, 6], quantum secret sharing (QSS) [7], and quantum secure direct communication (QSDC) [8, 9]. QKD enables a secure private key establishment between legitimate parties over a quantum channel. QSDC securely transmits secret messages with quantum states [10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. Post-quantum cryptography [4] ensures the security of public-key cryptographic algorithms by some carefully chosen problems which are computationally hard even with quantum computers. Recently, the National Institute of Standards and Technology (NIST) selected CRYSTALS-KYBER [20, 21] as the public-key encryption and key-establishment algorithm to be standardized.  
Due to the lack of quantum relays, quantum networks are presently realized in restricted areas, such as the star-type quantum network [22; 23]. At present, trusted relays are utilized to extend the quantum network into a large area. However, these requires means other than cryptography to guarantee their security. They cannot be used directly in existing networks. In Ref. [24], the combination of quantum cryptography with the post-quantum algorithm was proposed, offering a practical way to realize a quantum network with end-to-end computational security.  
In this paper, we demonstrate a real-time QSDC network with computationally secure relay by applying CRYSTALS-KYBER to encrypt the message for transmission of QSDC. The intermediate nodes can only receive the ciphertext of the message. The intended receiver uncovers the secret message with the private key after a series of QSDC communications. The time delay and communication rate, two important performance indicators, are measured. The results demonstrate the feasibility of the computationally secure relay, which offers a practical way to build a large-scale quantum network.
## 2 Six-Hardpoint Positioning Mechanism and Its Mathematical Model
### Kinematic Model of Six-Hardpoint Positioning Mechanisms
#### 2.1.2 Forward Kinematic Model
 \tag{9}\]  
\[\mathbf{r}_{k}=\mathbf{f}(\mathbf{q})=[f_{1}(\mathbf{q}),f_{2}(\mathbf{q}),f_{3}(\mathbf{q}),f_{4}(\bm {q}),f_{5}(\mathbf{q}),f_{6}(\mathbf{q})]^{T} \tag{10}\]  
Since the Jacobian matrix \(\mathbf{J}_{k}\) is a reversible matrix, Equation (8) can be simplified to:  
\[\mathbf{q}_{k+1}=\mathbf{q}_{k}-\mathbf{J}_{k}^{-1}\mathbf{r}_{k} \tag{11}\]  
The steps of the iterative method of the forward kinematic model are as follows:  
1. Given an initial value \(\mathbf{q}_{1}\), and iteration accuracy \(\varepsilon>0\), let k = 1. 2. Substitute \(\mathbf{q}_{k}\) and the lengths \(h_{i}\) of hardpoints into \(\mathbf{J}_{k}\), \(\mathbf{J}_{k}^{-1}\), and \(\mathbf{r}_{k}\), and then calculate \(\mathbf{q}_{k+1}\). 3. If \(\mathbf{l}_{k+1}-\mathbf{q}_{k}\mid<\varepsilon\) is satisfied, then terminate the iterations. Otherwise set k = k + 1, and repeat step 2. 4. When the iterations are terminated, \(\mathbf{q}_{k+1}\) represents the pose of the primary mirror obtained from the forward kinematic model.