esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The One-Shot Projection Moire: Model and Experimental Setup
### The Proposed Moire Model for Inflated Membranes
Displacement derivatives are given in input to vector and tensor expressions of local slopes/curvatures of the deformed surface to satisfy continuity conditions. The use of cross-gratings allows surface slopes and curvatures to be computed regardless of the fact that surface elements are arbitrarily located in the space and no simplifying assumption is made on displacement distributions. Since the system of orthogonal lines gives information on the first- and second-order derivatives of the function defining the deformed surface, slope information is embedded in curvature information. In this study, moire patterns were differentiated as indicated in [31; 35; 36], which illustrate relationships between differential geometry of surfaces and projection moire measurements. The iterative process described below was implemented here to properly account for variations of local slopes/curvatures over the deformed surface:  
1. The inverse of curvature radius \(R_{\rm c}\)(k) determined with Equations (6) and (8)-(10) for the \(k\)th element of the deformed surface is computed. The \(1/R_{\rm c}\)(k) term represents the average curvature of the \(k\)th surface element. 2. Local curvatures of deformed surface (\(\kappa_{\rm i}\))(k) are computed at each point \((P_{i}^{\prime})\)(k) of the \(k\)th element by differentiating moire fringes. Since fringes do not necessarily pass through \((P_{i}^{\prime})^{(k)}\) points, the phase map relative to \(w\)-displacements must be differentiated with respect to \(x\) and \(y\) coordinates. 3. The average of \((\kappa)^{(k)}\) curvatures--indicated as \(<\kappa>^{(k)}\) in the rest of article--is computed. 4. The \(<\kappa>^{(k)}\) curvature is compared to \(1/R_{\mathrm{c}}\)\({}^{(k)}\), and the % curvature difference \(\kappa\) is defined as \(\Delta\kappa=100\cdot|<\kappa>^{(k)}-1/R_{\mathrm{c}}\)\({}^{(k)}|\)\(|\) / Min\(\{<\kappa>^{(k)};1/R_{\mathrm{c}}\)\({}^{(k)}\}\). The minimum curvature in the denominator of the \(\Delta\kappa\) expression makes it harder to verify the validity of the assumption that the deformed membrane segment is spherical. 5. If \(\Delta\kappa\leq 0.01\%\), then the solution provided by Equations (6) and (8)-(10) is retained. A new surface element is considered and steps 1-4 are repeated. 6. If \(\Delta\kappa>0.01\%\), then in Equations (6) and (10), \(R_{\mathrm{c}}\)\({}^{(k)}\) is replaced by the \((R_{\mathrm{c}}\)\({}^{(k)}+1/<\kappa>^{(k)})/2\) average. Equations (6) and (8)-(10) are solved again and steps 1-5 are reiterated for the \(k\)th surface element. The above-described correction process was found to satisfy the \(\Delta\kappa\leq 0.01\%\) convergence condition for the whole surface within 3-5 iterations. Hence, the model for reconstructing the 3D deformed shape of the inflated membrane developed in this study is fully consistent with projection moire data. This confirmed that curvature information is the basic start point to be considered in the 3D contouring of objects.
# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 3 Methodology
### Proposed Architecture: Integrated Spatio-Temporal Graph Neural Network
Figure 2 presents the ISTGCN architecture Graph convolution is applied as self representation \(X_{self}^{l}\), which is combined with the spatio-temporal aggregated vector \(X_{ST}^{l}\) using weighted fusion. This result is represented as \(X_{self}^{l+1}\), which serves as input for the next layer \(l+1\) or is passed into the regression task. Figure 3, illustrates the process of traffic prediction using the ISTGCN model. Traffic data are collected across multiple days of the week (Monday to Sunday) for two specific time intervals each day: 9 AM to 10 AM and 5 PM to 6 PM, marked by green and blue icons. These data, including last week's Monday traffic data, are passed through an intermediate step called \"Intermediate Time Stamp\", where traffic information from different days and times is aggregated into a feature matrix. The feature matrix represents traffic speeds or conditions at various nodes in the network. This matrix is then fed into the ISTGCN model, which processes the spatio-temporal data using graph convolutional networks. The model uses these historical data to predict future traffic, for example, using last week's Monday data to predict traffic for the next Monday. Finally, the model predicts traffic intensities specifically for the two peak time intervals, 9 AM to 10 AM and 5 PM to 6 PM. Figure 2: Integrated spatio-temporal graph convolutional network architecture.
## 2 Materials and Methods
### Design of ADRC
#### 2.4.3 Nonlinear State Error Feedback
 \tag{21}\]  
The Lyapunov function _V(e)_ is chosen, as shown in Equation (22):  
\[\mathrm{V(e)}=\frac{1}{2}\mathrm{e}_{1}^{2}+\frac{1}{2}\mathrm{e}_{2}^{2} \tag{22}\]  
The time derivative of _V(e)_ is shown in Equation (23):  
\[\dot{\mathrm{V(e)}}=\mathrm{e}_{1}\dot{\mathrm{e}}_{1}+\mathrm{e}_{2}\dot{ \mathrm{e}}_{2} \tag{23}\]  
Substituting Equation (21) into Equation (23) yields Equation (24)  
\[\dot{\mathrm{V(e)}}=\mathrm{e}_{1}\Big{(}\mathrm{v}_{1}-\dot{\mathrm{z}}_{1} \Big{)}+\mathrm{e}_{2}\Big{(}\mathrm{v}_{2}-\dot{\mathrm{z}}_{2}\Big{)} \tag{24}\]  
The error dynamics are expressed in terms of a nonlinear feedback term, and the result is shown in Equation (25):  
\[ \begin{array}{l}\dot{\mathrm{z}}_{1}=\beta_{1}fal(\mathrm{e}_{1},\mathrm{a }_{1},\delta)\\ \dot{\mathrm{z}}_{2}=\beta_{2}fal(\mathrm{e}_{2},\mathrm{a}_{2},\delta)\end{array}  \tag{25}\]  
Substituting Equation (25) into Equation (24) yields Equation (26):  
\[\dot{\mathrm{V(e)}}=\mathrm{e}_{1}(\mathrm{v}_{1}-\beta_{1}fal(\mathrm{e}_{1 },\mathrm{a}_{1},\delta))+\mathrm{e}_{2}(\mathrm{v}_{2}-\beta_{2}fal(\mathrm{ e}_{2},\mathrm{a}_{2},\delta)) \tag{26}\]  
By adjusting \(\alpha_{1}\), \(\delta\), \(\beta_{1}\), \(\beta_{2}\), \(\alpha_{2}\), and \(\delta\) appropriately to ensure that the time derivative of the Lyapunov function is negative, Equation (27) is satisfied as follows:  
\[\dot{\mathrm{V(e)}}<0,\forall\mathrm{e}\
eq 0 \tag{27}\]  
When the error magnitude is significant, the function \(fal(\mathrm{e}_{1},\mathrm{a}_{1},\delta)\) introduces a smaller gain to mitigate excessive system responses, thus enhancing stability. Conversely, in scenarios where the error is minimal, \(fal(\mathrm{e}_{1},\mathrm{a}_{1},\delta)\) offers a higher gain to expedite the rapid convergence of the error towards zero, further enhancing system stability. This approach ensures that the system remains robust and stable across a diverse range of operating conditions, with the error asymptotically converging to zero, thereby affirming the stability of the overall system.
## 4 Discussion  
The increase in the droplet stopping distance is the effect of the Magnus force acting. Subsequently, the growth in the thickness of the BL implies a reduction in the number of small droplets present in the core flow. The results show that the phenomena of the \(M\) and \(S\) lift forces acting on a droplet, characterized by high dynamics, is also difficult to predict. Nevertheless, the data gained has confirmed that \(S\) and \(M\) lift forces influence essentially the quantity of small (\(\leq\)10 \(\upmu\)m), medium (\(\leq\)20 \(\upmu\)m), and larger droplets in the core of the flow, respectively. The research analysis carried out proves that \(M\) and \(S\) forces ought to be involved in the droplet equation of motion when the droplet trajectory is being investigated, and much attention is paid to droplet deposition and re-entrainment. When BL thickness was large, the small droplet content in the main flow was the lowest. This demonstration confirms that the BL effects and, consequently, the \(M\) and \(S\) forces influence are of prime importance, especially when a low range of \(d_{d}\) is desired. Higher local velocity gradients in BL constitute favorable conditions under which the larger droplets (\(d_{d}\ \geq\) 20 \(\upmu\)m) movement is affected significantly by the Magnus force. The significance of the Magnus force effect, which results from the increase in the value of the local velocity gradient, is followed by the increase in droplet diameter, which is dominant. The coefficient of diffusion obtained for medium and larger droplets (\(d_{d}\ \geq\) 16.45 \(\upmu\)m) is a few orders of magnitude higher than the one of smaller droplets (\(d_{d}\) = 5.04 \(\upmu\)m). The work on the current article was financed from the statutory funds of the Institute of Fluid-Flow Machinery, Polish Academy of Sciences. Not applicable. The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. I want to express my gratitude to my Colleague, [PERSON], who assisted me with my laboratory work. The author declares no conflicts of interest. The following abbreviations are used in this manuscript:  
Aerosol Particle Size Spectrometer LAP 332  
boundary layer  
droplet diameter, m  
mean diameter of droplet, m
# Subspace Detours Meet Gromov-Wasserstein
## 2 Background
### Subspace Detours and Disintegration
#### 2.2.2 Coupling on the Whole Space  
Let us note \(\mu_{E^{\perp}|E}\) and \(\
u_{E^{\perp}|E}\) as the disintegrated measures on the orthogonal spaces (i.e., such that \(\mu=\mu_{E}\otimes\mu_{E^{\perp}|E}\) and \(\
u=\
u_{E}\otimes\
u_{E^{\perp}|E}\) (if we have densities, \(p(x_{E},x_{E^{\perp}})=p_{E}(x_{E})p_{E^{\perp}|E}(x_{E^{\perp}}|x_{E})\).)). Then, to obtain a transport plan between the two originals measures on the whole space, we can look for another coupling between disintegrated measures \(\mu_{E^{\perp}|E}\) and \(\
u_{E^{\perp}|E}\). In particular, two such couplings are proposed in [14], the Monge-Independent (MI) plan:  
\[\pi_{\text{MI}}=\gamma_{E}^{*}\otimes(\mu_{E^{\perp}|E}\otimes\
u_{E^{\perp}|E})\]  
where we take the independent coupling between \(\mu_{E^{\perp}|E}(x_{E},\cdot)\) and \(\
u_{E^{\perp}|E}(y_{E},\cdot)\) for \(\gamma_{E}^{*}\) almost every \((x_{E},y_{E})\), and the Monge-Knothe (MK) plan:  
\[\pi_{\text{MK}}=\gamma_{E}^{*}\otimes\gamma_{E^{\perp}|E}^{*}\]  
where \(\gamma_{E^{\perp}|E}^{*}\big{(}(x_{E},y_{E}),\cdot\big{)}\) is an optimal plan between \(\mu_{E^{\perp}|E}(x_{E},\cdot)\) and \(\
u_{E^{\perp}|E}(y_{E},\cdot)\) for \(\gamma_{E}^{*}\) almost every \((x_{E},y_{E})\). [PERSON] and [PERSON] [14] observed that MI is more adapted to noisy environments since it only computes the OT plan of the subspace. MK is more suited for applications where we want to prioritize some subspace but where all the directions still contain relevant information [14].  
This subspace detour approach can be of much interest following the popular assumption that two distributions on \(\mathbb{R}^{d}\) differ only in a low-dimensional subspace as in the Spiked transport model [24]. However, it is still required to find the adequate subspace. [PERSON] and [PERSON] [14] propose to either rely on a priori knowledge to select the subspace (by using, e.g., a reference dataset and a principal component analysis) or to optimize over the Stiefel manifold.
## 5 Results and Discussion
### Real-Time Wear Prediction and Hole Depth Control
However, more advanced techniques for data processing could be implemented in order to obtain more information from the data collected during the process, for example by using pattern recognition techniques [36; 37]. This could further increase the prediction accuracy of the regression models, thus reducing the drilling depth error.
## 3 Overview of LoRa and Zigbee
### LoRa  
LoRa is Long Range, low power wireless, a low-data-rate communication protocol for building an IoT network developed by Cycleo of Grenoble, France. LoRa uses license-free subgigahertz radio frequency bands [49] It provides long-range transmissions (1-15 km) with low power consumption. It enables the communication between remote sensors and gateways connected to the network Server and Application Servers. The LoRa network is based on a star topology in which end nodes send messages to multiple gateways, which further communicate with the network server [50]. Multiple gateways can receive the message sent by the end node as there is no dedicated linkage between the end node and gateway. Gateways and network servers are connected through proper internet protocol (IP), as shown in the LoRa basic architecture in Figure 1.  
Figure 1: Architecture of LoRa.  
End devices can send their small amount of data over a long range with low frequencies. The end nodes consist of sensors, controllers, converters, etc., and these nodes are devices embedded with LoRa chips. _LoRa Gateway:_ The gateway receives the communications from the LoRa endpoints and then transfers them onto the backhaul system. Typically, the gateways and network servers are connected via some backhaul (cellular, Wi-Fi, Ethernet, or satellite). The LoRa gateway receives data from the end nodes and sends them to the network server. A LoRaWAN contains a multitude of gateways with each possibly receiving and sending the same data.  
_Network Server:_ The network server has all the intelligence. It filters the duplicate packets from different gateways, completes security checks, and sends ACKs to the gateways. It is responsible for rejecting and scanning redundant data and security checks. _Application Server:_ It is an end server where data sent by the end device are processed and necessary action taken. The Application can typically be built over IoT platforms such as AWS IoT using Lambda, DynamoDb, or S3 services.  
The frame structure of three different layers of LoRa architecture namely the physical layer, MAC layer, and application layer are as follows, and the frame structure is shown in Figure 2.  
_The physical layer_ defines the signal that is transmitted by LoRa modules and devices. On the physical layer, a frame starts with a preamble. The preamble defines the packet modulation scheme. The preamble is preceded by a 20-bit long header and header CRC, payload, and payload CRC. The _MAC layer_ consists of a MAC Header, a MAC Payload, and a Message Integrity Code (MIC). The complete MAC Header and MAC Payload portion is utilized to calculate the MIC value with a network session key (Nwk_SKey). The MIC value avoids the falsification of messages and validates the end node. _Application Layer and its Packet:_ The application layer handles the MAC layer, and it consists of a Frame Header, Frame Payload, and Frame Port. The Frame Port value is defined as reliant on the application type. The Frame Payload value is encrypted with an application session key (App_SKey) based on the AES 128 algorithm.
# Autonomous Wireless Sensor Networks in
## 4 Discussion
Although we cannot test the simulated population dynamics it exhibits a realistic trend in which the population density decreases up to the diapausing threshold defined by critical values for temperature and photoperiod [50]. Higher temperatures indicated in Figure 7 lead to a shift of diapause date to a later time thus prolonging the seasonal activity of the disease vector. Climate change projections indicate that by the end of the century Belgium is expected to undergo a significant rise in temperature, 1.7-4.9 \({}^{\circ}\)C for winter and 2.4-6.6 \({}^{\circ}\)C for summer. However, climate change will also lead to a possible increase in extreme events such as heat stress and cooling degreedays as well as extreme values for maximum and minimum temperatures [51; 52] making it essential to register extreme events on a local scale with high temporal resolution to inform timely action. The PentaSense network is currently being tested in field settings and the next step will be testing the software for the dual-uplink as well as the robustness of system hardware in remote areas and harsh environmental conditions. Considerations need to be taken so that the WSN is deployed in ways that capture land use heterogeneity. Suitable biotopes for sensor placement and sensor density can be identified using EO. Moreover, on-site tests should always be conducted to test validity and reliability. WSN in combination with cloud services and satellite technologies have the potential to have great impact on the way IPM is conducted worldwide. New algorithms that allow for the combination of ground measured and modeled data at multiple scales will provide information on when and how to conduct pest control with minimal impact on the environment. Conceptualization, M.P., E.D. and A.T.; Data curation, M.P. and A.T.; Formal analysis, M.P., J.V. and A.T.; Funding acquisition, M.P., C.M., E.D. and A.T.; Investigation, M.P., J.V., C.M. and A.T.; Methodology, M.P., C.M., E.D. and A.T.; Project administration, M.P., E.D. and A.T.; Resources, M.P., E.D. and A.T.; Software, M.P., J.V., T.M. and A.T.; Supervision, E.D. and A.T.; Validation, M.P. and A.T.; Visualization, M.P. ; Writing--original draft, M.P., C.M. and A.T.; Writing--review & editing, M.P., J.V., C.M., T.M., E.D. and A.T. The work described in this paper was realized as a part of the feasibility study 'SenZitall\" co-funded by ESA, under the Integrated Applications Program. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} API & Application Programming Interface \\ CRC & Cycle Redundancy Check \\ EO & Earth Observations \\ IPM & Integrated Pest Management \\ MCDA & Multi Criteria Decision Analysis \\ NRT & Near Real Time \\ NRZ & Non Return To Zero \\ PCB & Printed Circuit Board \\ RF & Radio Frequency \\ SBD & Short Burst Data \\ SMT & Surface Mount Technology \\ SPI & Serial Peripheral Inerface \\ USART & Universal Synchronous/Asynchronous Receiver/Transmitter \\ WAN & Wide Area Network \\ WSN & Wireless Sensor Network \\ \end{tabular}
## 3 Results and Discussion
### Comparison of Simulation and Experimental Results  
We observed significant agreement between the modeled and experimental results for \(K/K_{0}\) (Figure 5). The coefficients of determination (\(R^{2}\)) between modeled and experimental values for \(K/K_{0}\) were all greater than 0.900. The physical clogging model predicted that \(K_{i}/K_{0}\) would more rapidly decrease as the influent turbidity increased, especially in the first layer. When the influent turbidity was 0.160 NTU, the simulated \(K_{1}/K_{0}\) remained almost unchanged. The model predictions agreed very well with the experimental results: when the influent turbidity was 1.84, 1.09, and 0.540 NTU, the \(K_{1}/K_{0}\) changed dramatically over time and was controlled mainly by the number of particles retained in the first layer. The model and experiments both indicated that 0.160 NTU was the threshold for preventing clogging during AGR. A further simulation of AGR with an influent turbidity of 0.160 NTU verified that this threshold does not cause clogging even after continuous infiltration for 100 days (Figure S3).  
Our results showed that the SE did not cause clogging when the turbidity was 0.160 NTU. However, this threshold is close to the current standards for drinking water, and maintaining this level for AGR is quite strict and costly. Therefore, in combination with drinking water standards in various regions/countries, an appropriate critical threshold for preventing clogging caused by SEs was obtained. Worldwide, SS, and NTU thresholds for potable water vary depending on local regulations. For example, Washington state stipulates that water used in AGR should be pretreated using oxidation, flocculation, filtration, and disinfection to achieve suitable quality; drinking water turbidity should be less than 0.500 NTU, and SS should be less than 5.00 mg L\({}^{-1}\)[20]. In Spain, the turbidity threshold for drinking water is 1.00 NTU [33]. In China, turbidity thresholds for drinking water standards (GB 5749-2006) are 1.00 NTU. Overall, the lowest turbidity threshold for drinking water did not exceed 0.500 NTU. Therefore, to prevent clogging during AGR, we recommend that the turbidity threshold of SE from WWTPs not exceed 0.500 NTU.
## 4 Measurement Uncertainty  
From this device, we can evaluate the uncertainty in measurement in accordance with the GUM (ISO, \"Guide to the Expression of Uncertainty in Measurement,\" Switzerland, 1993, corrected and reprinted 1995). The calibration value is obtained by Equation (9) [8,18].  
\[L_{stepage}=L_{measured}-L_{measured}\alpha_{stepage}\Delta t_{stepage}+\delta_{ Astepage}+\delta_{Alaser}+\delta_{probing}+\delta_{sphere}, \tag{9}\]  
where:  
\(L_{stepage}\): the measured distance of the step gauge at the reference temperature of 20 \({}^{\circ}\)C.  
\(L_{measured}\): the distance of the step gauge from the calculated measurement value of SMII.  
\(\alpha_{stepage}\): the thermal expansion coefficient of the step gauge.  
\(\Delta t_{stepage}\): the difference between the step gauge temperature and the reference temperature of  
20 \({}^{\circ}\)C.  
\(\delta_{Astepage}\): the uncertainty of the step gauge alignment.  
\(\delta_{Alaser}\): the uncertainty of the laser alignment.  
\(\delta_{Probing}\): the reproducibility of the probing.  
\(\delta_{sphere}\): the uncertainty of the standard sphere calibration.  
The uncertainty of the step gauge calibration is calculated by Equation (10).  
\[\begin{array}{ll}u(L_{stepage})^{2}=&u(L_{measured})^{2}+L^{2}_{Nom.}\times u (\alpha_{stepage})^{2}\times\Delta t^{2}_{stepage}\\ &+L^{2}_{Nom.}\times\alpha^{2}_{stepage}\times u(\Delta t_{stepage})^{2}+u( \delta_{Astepage})^{2}+u(\delta_{Alaser})^{2}\\ &+u(\delta_{Probing})^{2}+u(\delta_{sphere})^{2}.\end{array} \tag{10}\]  
Table 2 shows the uncertainty components in the step gauge calibration.  
Figure 7: The trend chart of deviation of measurement result of this device from nominal value.  
In Table 2, 1-3 are the independent sources of uncertainty, and 4-13 are dependent on thermal expansion uncertainties and have been converted to the length L.  
Then, the uncertainty in measurement of the step gauge calibration using our instrument is as follows.  
\[U=\left(k\times\sqrt{0.054^{2}+\left(0.50L\right)^{2}}\right)\upmu;\ L\text{:m; }k\text{:coverage factor}.\]