esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Privacy-Preserving Collection of Health Data from a User's Wearable Device
## 3 Problem Definition, Background and Naive Solution
### Problem Definition  
Generally, wearable devices periodically measure a user's health status within a predefined fixed time interval. Let \(U=\{U_{1},U_{2},\cdots,U_{w}\}\) be the set of users in which \(w\) represents the total number of users. In this study, we model the health data of the \(i\)-th user \(U_{i}\) (which are measured by a wearable device) as a sequence \(S_{i}=\left((t_{1},x_{1}^{i}),(t_{2},x_{2}^{i}),\cdots,(t_{n},x_{n}^{i})\right)\) of length \(n\). Note that \((t_{d},x_{d}^{i})\) in a sequence denotes the value, \(x_{d}^{i}\), measured by the wearable device at timestamp, \(t_{d}\). Let us further assume that \(x_{d}^{i}\) is within the predefined range \([x_{min},x_{max}]\). Especially, in this paper, we focus on a scenario that generates health data with a monotonically increasing property (i.e., \(x_{1}^{i}\leq x_{2}^{i}\leq x_{3}^{i},\cdots,\leq x_{n}^{i}\)), such as the daily cumulative step-count (Figure 1) and daily cumulative calorie consumption, which are among the most common forms of health data. We also consider a scenario in which each user reports the daily health data measured by their wearable device to an aggregator. In this case, the user's health data are sent to the aggregator every 24 h.  
Let \(S=\{S_{1},S_{2},\cdots,S_{w}\}\) be a set of sequences the aggregator receives from \(w\) users. Then, the problem addressed in this paper can be stated as follows:Given  
* the set of users \(U=\{U_{1},U_{2},\cdots,U_{w}\}\), and
* the set of health data \(S=\{S_{1},S_{2},\cdots,S_{w}\}\) received from users,  
compute the aggregate statistics \(AS=\left((t_{1},ax_{1}),(t_{2},ax_{2}),\cdots,(t_{n},ax_{n})\right)\). Here, the value of \(ax_{d}\) at timestamp \(t_{d}\) is computed as follows:  
\[ax_{d}=\frac{1}{w}\times\sum_{h=1}^{w}x_{d}^{h}.\]  
In other words, once receiving \(S=\{S_{1},S_{2},\cdots,S_{w}\}\) from \(w\) users, the aggregator wants to compute the aggregate statistics \(AS=\left((t_{1},ax_{1}),(t_{2},ax_{2}),\cdots,(t_{n},ax_{n})\right)\) where the value of \(ax_{d}\) at timestamp \(t_{d}\) is computed by averaging all values of \(x_{d}^{h}\) where \(1\leq h\leq w\).
## 3 System Model Based on Time Switching and NOMA
### Stage 2  
In this stage, the \(IoT_{R}\) node transmits a superimposed composite signal \(\hat{Z}_{I_{C1}}\) which consists of source information \(x_{s}\) and \(IoT_{R}\) information \(x_{IoT_{R}}\) to the respective destination of source and IoT relay node using NOMA protocol. The superimposed composite signal \(\hat{Z}_{I_{C1}}\) following NOMA protocol can be given as:  
\[\hat{Z}_{I_{C1}}=\sqrt{\phi_{1}\hat{P}_{IoT_{R}}}x_{s}+\sqrt{\phi_{2}\hat{P}_{IoT _{R}}}x_{IoT_{R}} \tag{4}\]  
where \(\phi_{1}+\phi_{2}=1\) and \(\phi_{2}=1-\phi_{1}\) is the power allocation factor for the NOMA protocol.  
Now, the received signals at the receiver of Source user and IoT user can be respectively given as:  
\[\hat{y}_{src}=\sqrt{\hat{P}_{IoT_{R}}}h_{src_{R}}\hat{Z}_{I_{C1}}+n_{src_{ rec}}, \tag{5}\]  
\[\hat{y}_{IoT_{rec}}=\sqrt{\hat{P}_{IoT_{R}}}h_{IoT_{rec}}\hat{Z}_{I_{C1}}+n_{IoT _{rec}}, \tag{6}\]  
Figure 3: System model based on time switching and NOMA.  
where \(n_{src}\) and \(n_{IoT_{nc}}\) is the additive white Gaussian noise at the receiver of source and IoT user node respectively with mean zero and variance \(\sigma_{src}^{2}\) and \(\sigma_{IoT_{nc}}^{2}\). Also, \(h_{src}\sim CN(0,\lambda_{g})\) is the channel coefficient between \(IoT_{R}\) node and receiving source user with zero mean and variance \(\lambda_{g}\) and \(h_{IoT_{nc}}\sim CN(0,\lambda_{z})\) is the channel coefficient between \(IoT_{R}\) node and receiving IoT user with zero mean and variance \(\lambda_{z}\). We have also assumed that \(h_{src}>h_{IoT_{nc}}\). Therefore, \(\lambda_{g}>\lambda_{z}\) and \(\phi_{1}<\phi_{2}\).
# Panoramic Digital Image Correlation for 360-Deg Full-Field Displacement Measurement
## 2 Materials and Methods
### Epipolar Geometry-Based Extrinsic Parameters Determination
#### 2.3.5. Point Registration Based Scale Information Determination
According to the corresponding relationship, the objective function is formulated here based on least square criterion as follows:  
\[\mathcal{F}(\mathbf{R},\mathbf{t},s)=\sum_{i=1}^{k}\|p_{i}-s(\mathbf{R}q_{i}+ \mathbf{t})\|_{2}^{2}. \tag{14}\]  
Equation (14) contains the scale factor \(s\), the rotation matrix \(\mathbf{R}\), and the translation vector \(\mathbf{t}\), which are substituted into the ICP algorithm to calculate the alignment error and compared with the set threshold value of alignment error, scale factor, rotation matrix, and translation vector. These are refined when the termination condition is not satisfied, and the iterative calculation is carried out to finally obtain the scale factor at the minimum alignment error. The relevant algorithm steps are presented in Algorithm 3. It should be pointed out that the rigid transformation matrices \(\mathbf{R}\) and \(\mathbf{t}\) between the reconstructed local three-dimensional points and the laser point cloud are also synchronously optimized in the process of the iterative optimization of scale factors, which are used for registration between the reconstructed local three-dimensional points and the laser point cloud. This registration process is necessary because the registration results are to be used for subsequent overlapping three-dimensional point mergers. ```
Input: original-scale point cloud: P, normalized-scale point cloud: Q, alignment error threshold: \(\delta\). Output: scale factor: \(s\), rotation matrix: R, translation vector: t. Initially scale factor estimation
1. Calculate center of gravity and the centroid of P and Q, respectively. 2. Calculate the initial value of s according to Equation (13). Refine scale factor, rotation matrix and translation vector
1. Enlarge the normalized-scale point cloud Q according to the initial value of s.
2. Refine scale factor, rotation matrix and translation vector by minimizing Equation (14). ```  
**Algorithm 3** Determining scale factor from point cloud registration.
## 2 Calculation of the Length Correction from the Projection and Deformation
Figure 1.6 Solving the second geodesic problem by transitioning from an ellipsoid to a sphere \(R\)[32]. To determine the length correction from the projection and the deformation, i.e., the global length correction, the resulting lengths \(s_{AB}\) calculated on the ellipsoid using the second geodetic problem were compared with the lengths in the Krovak projection \(d_{AB}\). The desired length correction from the S-JTSK projection and deformation in meters per 1 km of length is calculated from the following relation:  
Figure 1.7  
The calculation is performed for the entire territory of the Czech Republic in a regular network. Since the deviation table is defined in the S-JTSK coordinate grid with a step of 2 km, it is convenient to choose this S-JTSK coordinate grid as the regular grid. This decision avoids the error in the interpolation of deviations between the S-JTSK and S-JTSK/05. At each node of the grid, we define four points on parallel lines with S-JTSK coordinate axes at a distance of 2 km; see Figure 1.8  
The calculations for the length correction from the S-JTSK projection and deformation at each \(O\) node for all four links \(O\)-\(1\), \(O\)-\(2\), \(O\)-\(3\), and \(O\)-\(4\) were performed. For the nodes on the edge of the Czech Republic, only three links were defined. The resulting length correction from the S-JTSK projection and deformation at a given node \(O\) is obtained by averaging all of the defined links (three or four) calculated according to relation (13). In parallel, the standard deviation of the corrections is calculated, which characterizes the variability of the length correction depending on the direction, i.e., the effect of the non-conformity given by the S-JTSK deformation at node \(O\). It is possible to study the change in the length correction in north-south vs. east-west directions, but we can't perform that study.
# Scaling of FAPAR from the Field to the Satellite
## 3 Methodology
### Multi-Scale FAPAR Truth Data  
As the support scale of _in-situ_ measurement is limited, the study used fine-resolution ASTER data to expand the \"point\" scale of field measurement to coarse-resolution \"pixel\" scale. As the homogeneities of the areas surrounding the _in-situ_ sites were carefully controlled, the pixel at 15 m resolution could be representative of each site. The derived ASTER FAPAR at fine resolution and the aggregated ASTER FAPAR at coarse resolution were thus taken as the truth reference data at two scales.
## 3 Discussion
### Empirical Case Study and Analysis
#### 3.1.3 Wind Turbine Performance Evaluation  
1. Performance Evaluation Criteria  
The experimental unit's performance is extensively assessed using the criteria detailed in Section 2.2. As depicted in Figure 7, the turbine's actual power output shows a proportional relationship with wind speed, maintaining a consistent trend.  
As depicted in Figure 8, the wind turbine's actual power trajectory aligns closely with theoretical expectations. Notably, the power performance coefficient, as computed in Section 2.1.3, achieves an approximate value of 0.95 across various wind speed ranges Cp, with the exception of the mid-range segment.  
Figure 9 illustrates how summertime's propensity for thunderstorms, heatwaves, and gusty conditions can lead to marked fluctuations in turbine rotational speeds, thereby elevating the standard deviation indicative of rotational stability.  
Figure 8: Wind turbine power deviation.  
Figure 6: Healthy area for wind turbine performance evaluation.  
Figure 7: Wind turbine operating status.  
The monthly power generation efficiency graph in Figure 10 indicates an average efficiency rate of approximately 0.90608 for the unit, with peak performance observed during spring and autumn, and notably in winter.  
Evaluating Wind Turbine Performance  
The actual power curve of the wind turbine, depicted by the red line in Figure 11, reveals that, particularly in mid-range wind speeds, the curve approaches but does not surpass the upper boundary of the designated health zone. An in-depth analysis, incorporating the previously discussed performance metrics, uncovers a notable discrepancy between actual and theoretical power outputs in this wind speed range. This deviation, compounded by delays in the turbine control system's response at higher wind speeds, can push the unit into an overburdened operational state. While such conditions may temporarily boost power output, they pose a risk of long-term damage to critical turbine components, including the generator, rotor system, and blades, potentially compromising the unit's overall performance. Consequently, while the turbine's current state is assessed as healthy, there is a discernible risk of degradation toward the anomaly zone.  
Figure 10: Wind turbine power generation efficiency.  
Figure 9: Wind turbine speed stability.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 4 Integration of Quantum Money with Quantum Networks
### Large-Scale Quantum Money Operations via Distributed Quantum Computing  
To enable large-scale operations with quantum money, such as interbank transactions, quantum teleportation can be extended through distributed quantum computing. In this scenario, entanglement is distributed between quantum computers located in different banks, allowing quantum money transfers to be performed securely and efficiently.  
A promising approach involves the use of satellite-based entanglement distribution [34]. A satellite can generate and distribute pairs of entangled photons directly between two banking institutions, as illustrated in Figure 2. Alternatively, this distribution can be facilitated by a third party, known as a quantum repeater, where entanglement between the two institutions is established through entanglement swapping [37]. Each institution stores its part of the entangled pair in a quantum memory. When a transfer is requested,  
Figure 2: Schematic representation of satellite entanglement distribution to two ground stations.  
quantum teleportation can be performed using the pre-shared entanglement, allowing secure movement of quantum money across distant locations.  
Recent advancements have shown that distributed quantum computing can be successfully performed over an optical link [38]. By combining quantum teleportation with distributed quantum computing, financial systems can ensure high security and efficiency in quantum money transactions, paving the way for future quantum-based financial networks. In this scenario, operations involving quantum money could be performed within the Quantum Internet--a space-based network interconnecting quantum devices to enable secure communication and high-capacity computing. Satellite-based implementations for various use cases of the Quantum Internet have already been proposed; see, for example, Ref. [39].
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Denoising  
The data cleaning process is important to remove the environment noise, effects of involuntary movements, and other artifacts, to improve the results of the recognition of human activities. According to the type of sensors used, the implemented method was the low-pass filter [28], which allows extracting features more clearly and is reliable for the implementation of classification methods.
## 3 Mathematical Model of the System
### Kinetic Energy Storage  
Kinetic energy storage systems use an indirect method of electrical energy storing in the form of flywheel rotational kinetic energy. Its shape, material and maximum rotational speed determine the storage dimensions, capacity, and weight, and are simultaneously the basic criteria of their division [9; 24; 26; 27; 28]. Figure 3 presents a general construction diagram of a kinetic energy storage system using a three-phase AC machine.  
Figure 3: Structure of kinetic energy storage using a three-phase AC machine (\(A_{\text{F}}\): flywheel mechanical energy, G: generator, \(\omega_{\text{F}}\): rotational speed of the system, \(f_{\text{F}}\): moment of inertia of the system (mainly the flywheel), AC/DC, DC/AC: power electronics converters.  
Analyses carried out to identify the reliability of power supply to the power grid do not require the consideration of all aspects of kinetic storage modelling and, especially, of dynamic states. A change in the storage charging level as a function of time (subsequent wind speed measurement samples) is important for the issue in question. Therefore, storage modelling was limited to the following equation of the rotational movement dynamics:  
\[J_{\mathrm{F}}\frac{d\omega_{\mathrm{F}}}{dt}=\frac{P_{2}}{\omega_{F}}+\omega_ {\mathrm{F}}K_{1}+K_{2} \tag{6}\]  
where: \(J_{\mathrm{F}}\) is the moment of inertia of the system, \(P_{2}\) is the storage charging/discharge power, \(\omega_{\mathrm{F}}\) is the rotational speed of the rotor, K\({}_{1}\) is the coefficient of friction, \(K_{2}\) is the Coulomb's friction.  
Solving Equation (6), when the storage capacity, dimensions of the rotational mass and scope of changes in the working speed and efficiency of electrical systems, which are part of the storage, are known, one can identify the course of changes in the power stored in the storage system when it is connected to a wind turbine and power grid, and execute the algorithm given in Section 2.1.
# Inertial Parameter Identification in Robotics: A Survey
## 8 Introducing the BIRDy Matlab Toolbox: A Benchmark for Identification of Robot Dynamics
### Symbolic Model Generation  
Model generation is the very first step toward identification: not only should the robot be simulated in a realistic manner, but its dynamics should also be expressed in the form of a system of linear equations (c.f. Equation (17)) with a well-conditioned identification regressor. BIRDy features a symbolic model generation engine, capable of computing the complete kinematics, dynamics and identification models of any fixed-base serial robot. The cases of parallel robots--such as Stewart platforms--or floating-base robots--such as Humanoids--are not yet implemented in this approach and are here considered to be future works. Similar to the robotic-system-toolbox proposed by [96], the simulated robots are described using dedicated parameter files, containing a Denavit-Hartenberg table as well as the corresponding reference dynamic, friction and control parameters of each link. Both classic (distal) and modified (proximal) Denavit-Hartenberg conventions are supported. The dynamic model is generated using the Euler-Lagrange equations of motion (c.f. [36]). It is worth noting that in case the robot is gear-driven, the inertia of the actuators and gearboxes can be precisely taken into account within (1) following the approach of [97]. A simpler alternative--used in BIRDy--consists of modeling the drive-chain inertia as an additional diagonal term \(\mathbf{I}_{a}\in\mathbb{R}^{n\times n}\) within the inertia tensor \(\mathbf{M}(\mathbf{\chi},\mathbf{q})\), following the approach of [44]. The symbolic expression of the regressor matrix \(\mathbf{Y}_{\mathbf{\chi}}(\bar{\mathbf{q}},\mathbf{q},\mathbf{q})\) is computed following the approach detailed in [98]. Finally, the vector \(\mathbf{\beta}\) of base dynamic parameters and the corresponding identification regressor \(\mathbf{Y}_{\mathbf{\beta}}(\bar{\mathbf{q}},\mathbf{\dot{q}},\mathbf{q})\) are generated numerically, following the QR decomposition method of [39] described in Section 2.2.
## 8 Discussion
### The Influence of Sampling Window Length
In Figure 13, we finally show the effects of different sampling windows on the in-channel detection accuracy for MWO at Location C.  
From the plot, we observe that the classification accuracy is monotonically increasing for all of the channels, meaning that longer sampling windows are always beneficial for MWO detection. It can also be noted that the classification accuracy dip on Channel 14 and on Channel 24 experiences a significant improvement when the sampling window approaches 350 ms, giving a hint about the bursty time distribution of the RSSI samples on the side spectrum of MWO leakage. Figure 11: Average full-spectrum detection rate for interference-free scenarios for different sampling window lengths. Figure 12: Average full-spectrum detection rate for interference from MWO for different sampling window lengths.
###### Abstract  
This study demonstrates the significant advantages of integrating computational fluid dynamics (CFD) with artificial intelligence (AI)-based machine learning (ML) to optimize the pre-reforming process for reversible solid oxide cell (r-SOC) technologies. It places a distinct focus on the relationship between process variables, aiming to enhance the preparation of quality r-SOC-ready fuel, which is an indispensable element for successful operation. Evaluating the intricate thermochemistry of syngas-containing preforming processes involves employing an experimentally validated CFD model. The model serves as the foundation for gathering essential data, crucial for the development and training of AI-based machine learning models. The developed model forecasts and optimizes reforming processes across diverse fuel compositions, encompassing oxygen-containing syngas blends and controlled feedstock outlet process conditions. Impressively, the model's predictions align closely with CFD outcomes with an error margin as low as 0.34%, underscoring its accuracy and reliability. This research significantly contributes to a deeper understanding and the qualitative enhancement of preparing high-quality syngas for SOC under improved process conditions. Enabling the early availability of valuable information drives forward sustainable research and ensures the safe, consistent operation assessment of r-SOC. Additionally, this strategic approach substantially reduces the need for resource-intensive experiments.  
hydrogen; CFD; syngas; r-SOC; pre-reforming; machine learning +
Footnote †: journal: Applied Sciences  
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Footnote 1: email: {[[https://doi.org/10.3390/app142210181](https://doi.org/10.3390/app142210181)]([https://doi.org/10.3390/app142210181](https://doi.org/10.3390/app142210181))}
###### Abstract
The use of satellite data with high spatial (3-5 m) and temporal (daily observations) resolutions, such as those from the Planet's satellite constellation, may now contribute to closing part of these knowledge gaps, especially with the recently open data access from the NICFI program [36; 37]. The overall aim of this study Is to comprehensively assess and compare the effectiveness of Landsat-8 and PlanetScope NICFI satellite data in observing potential blowdown damage and recovery, and in investigating the timing and variability of forest spectral recovery. Our main research question is: how does post-disturbance recovery age affect ANPV and, therefore, its usefulness for predicting tree mortality in blowdowns? To achieve this aim, we conducted a detailed evaluation and comparison of the pre- and post-blowdown spectral trajectories over several sites affected by blowdowns in the Amazon Forest, using time series data with high spatial resolution (4.77 m) from the PlanetScope NICFI constellation and medium spatial resolution (30 m) from the Landsat-8 satellite. Through this analysis, we aim to provide precise and accurate estimates of blowdown damage and vegetation recovery at a finer spatial and temporal scale, thereby contributing to improved monitoring and management strategies for post-blowdown forest regeneration.