esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-LEO Satellite Stereo Winds
## 2 Materials and Methods
### Stereo Wind Retrievals
VIIRS on the Suomi National Polar-orbiting Partnership (SNPP) satellite and NOAA-20 (N20) plus MODIS-Aqua is an example of the three-satellite tandem arrangement shown in Figure 2, while any two of these three with MODIS-Terra creates a crossing case, as shown in Figure 3.  
The retrieval outputs from fitting the disparities are an estimated state vector with three variables (height and horizontal wind velocity) and a 3 \(\times\) 3 covariance matrix. The covariance matrix provides an uncertainty for the state vector, based on statistics for the observational uncertainties of the disparities. It is useful to identify places where there is poor stereo acuity because of the geometrical arrangement of the lines-of-sight from the satellites to the feature being tracked. In such cases, the diagonal element (i.e., variance) associated with the height state begins to diverge. Figure 4 shows that this happens in both configurations at high latitudes, which is highly undesirable. The crossing case is relatively better than the tandem one but with less area under triple-overlapping coverage and still a small stereo blind spot. Figure 1: The retrieved wind 3D location is anchored to the apparent horizontal coordinates of the tracked feature or pattern (\(lat_{0}\), \(lon_{0}\)) but free to translate along the Line-of-Sight (LOS) of the reference satellite. The retrieved height and horizontal wind vector are jointly retrieved to minimize the sum of the squared differences between observed and modeled disparities after including a third observation of the feature. The knowledge of pixel times is important for modeling the disparities as the correct combination of feature parallax and velocity. Figure 3: A crossing configuration is formed with MODIS on Aqua and Terra and VIIRS on N20. Panel (**a**) shows the overlapping Long-Wave IR (LWIR) imagery compositing MODIS band 31 with VIIRS band M15. Granule times are 1 October 202 with Terra starting 6:45Z, Aqua starting 7:00Z, and N20 starting 7:12Z. Panel (**b**) shows the satellite ground tracks and panel (**c**) shows the pixel times relative to 1 October 2020 7:00Z plotted by site number covering the full triple overlap. Figure 2: A tandem configuration is formed with MODIS-Aqua and the VIIRS on SNPP and N20. Panel (**a**) shows the overlapping Long-Wave IR (LWIR) imagery compositing MODIS band 31 with VIIRS band M15. Granule times are 1 October 2020 with SNPP starting 6:26Z, Aqua starting 7:05Z, and N20 starting 7:12Z. Panel (**b**) shows the satellite ground tracks and panel (**c**) shows the pixel times relative to 1 October 2020 7:05Z plotted by site number covering the full triple overlap.
# A Ku-Band GaAs Multifunction Transmitter and Receiver Chipset
## 2 Ku-Band Multifunction Chip Architecture  
The Ku-band multifunction chipset was designed using a commercial 0.25-\(\upmu\)m GaAs pseudomorphic high-electron mobility transistor (pHEMT) technology. The process offers a 0.25-\(\upmu\)m E-mode HEMT with fT = 75 GHz and a 0.5-\(\upmu\)m D-mode HEMT with fT = 33 GHz. It also provides two metal layers, two types of capacitor (metal-insulator-metal capacitor and stack capacitor), three types of resistor (thin-film resistor, mesa resistor and epi resistor) and two types of inductor (spiral inductor and square inductor).  
Figure 1 shows block diagrams of the Ku-band multifunction transmitter (a) and receiver (b). The multifunction transmitter consists of a 5-bit attenuator, 4-bit digital phase shifter, MPA and 9-bit SPC. The attenuator offers a total of 15.5 dB attenuation with a step of 0.5 dB. The phase shifter fulfills a 360\({}^{\circ}\) phase shift with a step of 22.5\({}^{\circ}\). Both attenuator and phase shifter are digitally controlled by the SPC. The SPC converts a 9-bit serial digital code into a parallel code, which is loaded onto the attenuator and phase shifter for setting the desired attenuation and phase shift. This reduces the number of bonding pads for digital control. The attenuator is placed ahead of the phase shifter for broadband input matching. The MPA is placed in the final stage to enhance the output power and linearity of the transmitter.  
The multifunction receiver is composed of an LNA, 4-bit digital phase shifter, 5-bit digital attenuator and 9-bit SPC. Similar to the transmitter, 15.5-dB attenuation and 360\({}^{\circ}\) phase shift are implemented in the receiver and are controlled by the SPC. The LNA is placed at the input stage for providing high gain and low noise figures.
## 6 Evaluation
### Results and Analysis
#### 6.2.1 Achieving Functional Goals Independently of Deployment  
Our goal is to demonstrate that pulverization allows for reusing the same business logic on diverse deployments, thus making deployment no longer a design constraint with respect to the realization of the functionality of the system. Figure 10 shows correctness by depicting the system functionality in a typical configuration. The system, whose overall performance changes in response to different network delays, retains its application-level behavior across the table, regardless of the actual deployment shape of its sub-components; namely the core business logic of the application is preserved regardless of the underlying deployment configuration (whose alternatives are summarized in Table 2). The example thus shows that pulverization achieves separation between the behavior of the system from its deployment detail, thus providing flexibility to engineers that will be able to select (and change) the deployment scheme based on desired performance and projected operating cost.  
Figure 10: Evolution of \(E[T]\pm\sigma[T]\) (aquamarine), \(T_{max}\) (purple), and \(T_{min}\) (yellow) in the following conditions: \(B\) = cloud (MQTT broker hosted on the cloud) in the top charts; \(B\) = edge (MQTT broker hosted on the edge), in the bottom charts; \(P_{l}=0\) (all thermostat computation hosted on the cloud), in the left-hand column; \(P_{l}=1\) (all thermostat computation hosted on the local device), right-hand column. Every delay is set to the second lowest value in the set (usually resembling common operating conditions). Vertical dashed gray lines depict, from left to right: rainfall arrival on the northern part of the city, rain falling on the total city area, and rainfall conclusion. The functional behavior of the system is consistent regardless of the deployment strategy: results are very similar across the board. Very small differences come from different network performance (mostly due to diverse network latency) and from the randomness inherent to the simulation. The system reacts to changes in the detected \(PM_{10}\) level by adjusting the temperature and allowing higher temperatures to be set when air pollution levels are sufficiently low. \(\sigma[T]\) increases when rainfall hits only part of the city. This is because thermostats located in the area subject to the rainfall allow higher temperatures to be set, while the remainder of devices remains on the pre-rain levels, thus raising variance.  
We stress that no change to the application level software was required to tackle different deployment targets for any component, thus demonstrating the feasibility of a deployment-agnostic approach to self-organizing CPSs design.
# Reimagining Historical Exploration: Multi-User Mixed Reality Systems for Cultural Heritage Sites
## 2 Materials and Methods
### Features
Capture from Quest 3 on the left and capture from the host PC on the right. feedback. The collision plane is designed to handle interactions with both mobile devices and the Quest 3. For mobile devices, it has an AR plane component to register tap events via ARCore, while, for the Quest 3, it has the XRSimpleInteractable component to detect controller input. When an interaction occurs, a portal prefab is instantiated at the selected location and, over time, expands smoothly to its final size, using a scaling animation. The portal prefab consists of a circular plane with the custom Mask layer and shader to display the virtual content behind it and a particle effect around its edges using the Effects layer (Figure 2). When the user steps through the portal, the system switches from the portal effect to the full virtual environment by checking the position of the user's camera. A trigger collider attached to the door portal detects the camera crossing through, and, using the dot product between the camera's position and the portal's plane normal, we determine which side of the portal the camera is on. If the camera is on the virtual side, the portal's custom layers are disabled, and the camera's culling mask is set to render the entire virtual scene, creating a seamless transition into the virtual space. **Weather and Lighting. ** To render real-time weather conditions at the cultural site, we integrated the OpenWeatherMap API into Unity3D. This API allows us to retrieve live weather data via simple HTTP requests. Each request is constructed as a URL containing the API key, endpoint (such as current weather or forecasts), and parameters like the city name or geographic coordinates. Once the request is sent, the API returns a JSON response with detailed weather data, including the temperature, humidity, and specific conditions like cloud coverage or precipitation. We parse these data in Unity and use them to adapt the virtual environment based on the real-world conditions at the site. Six weather conditions were modeled to demonstrate the environmental adaptability of the application: clear sky, drizzle, rain, thunderstorm, clouds, and mist. Each condition is visually represented using Unity's particle systems, optimized for low-resource usage on mobile devices. Rain types use simple, low-polygon raindrop effects, and mist is a combination of image-based fog and low-density particles used to simulate atmospheric diffusion, ensuring that each weather type is rendered with performance efficiency in mind. For the light direction estimation, we used a sun direction calculation based on the user's location, and the light intensity is derived from the weather conditions [24] and then applied to the virtual scene's directional light (Figure 3). Figure 2: Screenshot from the mobile application showcasing the portal feature. The user taps on the screen to open multiple portals, allowing them to see inside an otherwise inaccessible virtual site (Sacrification Church of Pyñama).
# Attention-Based Light Weight Deep Learning Models for Early Potato Disease Detection
## 1 Introduction
### Motivation  
* In CNN-based image classification tasks, feature extraction is achieved using filters or kernels which extract edges, shapes, and textures. These are used to produce more prominent features that aid in classifying different images. Since an image may contain multiple features, not all of which are relevant to classification tasks, a mechanism is needed to focus only on the relevant features. This is where the attention mechanism comes in; it prioritizes specific parts of an image that are crucial for distinguishing between classes while downplaying irrelevant areas. By assigning greater weight to distinguishing features, the attention mechanism enhances the model's ability to classify images accurately.
* In order to capture minute features in the image, a series of convolution layers are used because initial layers only capture prominent features and as we go deeper into the layers, the model is able to capture very specific features as well. A dense network has large number of trainable parameters, which increases the training time. Additionally, the size of the model increases, making it unsuitable for integration in devices with less computation power and memory.
* In a multi-layer CNN model, the large number of trainable parameters makes the model highly complex. This complexity increases the risk of overfitting, making the model difficult to train. Additionally, training a complex model is time consuming due to the need to compute optimal values for numerous parameters.
# Cloud Gaming Video Coding Optimization Based on Camera Motion-Guided Reference Frame Enhancement
## 4 Experiments  
### Experimental Setup  
#### 4.1.1 Datasets  
Most current datasets for deep learning only contain camera-captured natural images while we aim at cloud gaming videos with corresponding camera motions. Herein, we use the Air Light VR (ALVR) [52], a piece of software to stream SteamVR games to the standalone VR headsets of users, to establish a cloud gaming video dataset containing the rendering images and rotating camera motions. The camera motions are represented by quaternions. We select five representative popular games [53; 54; 55; 56] containing various scenes as the game sequence library, and the raw videos are selected across diverse types of content. Ten videos with 1000 frames are employed for training, and another three with 500 frames called Secret Shop, Robot Repair, and VRChat, are used for testing. The videos are cropped to a uniform size of 1024 \(\times\) 1024. Figure 5 shows several frames of the test set as examples.  
Figure 5: Examples of the test set. (**a**) Secret Shop. (**b**) Robot Repair. (**c**) VRChat.
## 4 Hammer Impacting Energy Transfer Efficiency of Offshore SPT
### The Effect of Water Depth on the Hammer Impacting Energy Distribution of the Probe Rod  
The hammer impacting energy varying with the water depth at the top of the probe rod and water-soil contact surface is shown in Figure 6. The test results show that the water depth has a great influence on the hammer impacting energy distribution in the probe rod. The hammer impacting energy at the top test point increased with the rod length. For the test groups with water depths of 7.1, 8.9, 11.1, 11.9, 15.0, 17.1, 19.4, and 20.7 m, the hammering energies at the top test point are 399.7, 403.1, 408.1, 405.4, 403.2, 405.8, 408.1, and 409.6 J, respectively. The total potential energy of the hammer before falling in the SPT was 473.5 J, so the energy transfer efficiency at the top test point in each test group was 84.4, 85.13, 86.2, 85.6, 85.2, 85.7, 86.2, and 86.5%, respectively. The hammer impacting energy at the test point of water-soil contact surface decreases gradually with the increase in the probe rod length. Compared with those at the top test points, the hammer impacting energies decrease by 19.1, 20.8, 22.5, 22.2, 22.6, 23.3, 24.2, and 24.6%, respectively, and the attenuation value increases with the increase in water depth.  
By fitting the data in Figure 6, the relationship between the hammer impacting energy and probe rod length at the top of the probe rod and water-soil contact surface can be obtained:  
\[E_{r}=0.49\times l+391.14\ (\text{top test point}) \tag{3}\]  
\[E_{r}=-13.35\text{ln}(l)+348.89\ (\text{water-soil contact surface}) \tag{4}\]  
where \(l\) is the water depth (m).
## 4 Results and Comparative Analysis
### Comparative analysis and summary of results  
The results of this research show a prevailing difference between continuous and discrete prediction techniques. Most continuous methods tend to be more accurate with the high end of the biodegradability spectrum for which LOOCV MLR is the best performer (6.10% error), closely followed by LOOCV K-NN MLR (6.60% error). The best performer for the low end is MBR  
\begin{table}
\begin{tabular}{c c c c} \hline
**Technique** & **Low-B** & **High-B** & **All** \\ \hline
**LOOCV DT C5.0** & 9.6 & 6.2 & 7.9 \\
**LOOCV Logistic Reg.** & 3.2 & 3.1 & 4.7 \\
**LOOCV MBR** & 3.2 & 21.8 & 12.7 \\
**K-Means** & 3.2 & 6.2 & 4.7 \\
**Two-Step** & 16.1 & 3.1 & 9.5 \\
**Logistics-KV** & 3.2 & 6.2 & 4.7 \\
**Rule 1** & 6.4 & 3.1 & 4.7 \\ \hline \end{tabular}
\end{table}
Table 3: Percent Misclassification from Binary Prediction.  
with 10.63% error, while for overall, it is either trained MLR (12.99% error) or LOOCV MLR (13.8%). The latter is to be preferred since it enables the use of the largest data set available.  
All these continuous methods resulted in comparable maximum errors around 70%. This is due to the nonlinearity of the biodegradability process that is hard to predict using the linear methods applied. A more plausible alternative for improvement is provided by the K-NN MLR, which can capture a \"local\" linear model around the case to be predicted, thus capturing most of the biodegradability behavior locally. This state of highly nonlinear biodegradability process is a further rationale for using the binary dependent variable classification techniques, which were expected to be more accurate, although less precise. The best classification results derived from continuous models were achieved by K-NN MLR and ANN for the overall set with a 4.76% error. Both favored the low end with 0% and 3.23% error, respectively. The results from the direct binary methods show that the best method is the three-variable Logistic Regression, closely followed by Logistics-KV which performed better for the low end (3.23%), and K-Means-KV which fared better for the high end (3.13%). The simple classification Rule derived from this single-variable DT model achieved comparable results. All of these one-variable methods achieved the same accuracy for the overall set with 4.76% error. The application of this simple rule enables an effective classification of base oils into the slowly/highly biodegradability classes. This result is significantly superior to the one achieved by QSARs methods that achieved misclassification errors in the 40% to 90% [8].
## 4 Experimental Test Results  
This section discusses the structural behavior of the tested specimens in the form of slab vertical deflection and crack pattern. As mentioned earlier, fatigue pre-cracking was conducted under force control. The first hair flexural crack was observed at about 2.5 times the FLS loading (275.625 kN) underneath the wheel footprint area at the mid-span in the longitudinal direction (parallel to the supporting girders). The load was increased to three times the FLS load (330.75 kN) to increase the crack propagation beyond the wheel footprint area. The flexural crack width was 80 \(\upmu\)m at that static load. CHBDC specifies that the design-factored ultimate limit state (ULS) load of the deck slab is the multiplication of the CHBDC truck wheel load of 87.5 kN, load factor of 1.7, and dynamic load allowance of 1.40. This makes the factored design applied load ULS\({}_{1}\) = 87.5 \(\times\) 1.4 \(\times\) 1.7 = 208.25 kN. Interestingly, the pre-cracking monotonic load of 330.75 kN, at which a minor flexural crack appeared, is about 59% greater than the CHBDC factored design load of 208.25 kN.
# Analytical Model and Feedback Predictor Optimization for Combined Early-HARQ and HARQ
## 2 System Model  
### e-HARQ and HARQ Schemes  
An e-HARQ prediction was introduced in order to reduce RTT in stringent URLLC transmissions. The general aim of an e-HARQ scheme is to provide an ACK/NACK feedback on the decodability of the received signal ahead of the end of the actual transmission process. Apparently, the performance of the prediction-based scheme directly depends on the selection of the algorithm that provides the feedback. In [16], the authors introduced the prediction algorithm, which exploits the substructures of the LDPC codes to start feedback calculation already on partially received codewords, and therefore, provides an excellent opportunity to send ACK/NACK report before the full reception of the codeword. These subcodes are formed by selecting a subset of rows and columns from the parity-check matrix. Let us study the main differences between the HARQ scheme and e-HARQ approach that exploits various LDPC codes lengths, shown in Figure 1.  
Figure 1: Time diagram comparison between LDPC-based e-HARQ and HARQ schemes.  
Here, we consider an example of a packet transmission length \(N\) equal to 6 OFDM symbols. Let us assume \(n_{0}\) to be a number of propagation time units between the gNodeB (or gNB) and the UE, where a time unit aka microslot corresponds to the duration of an OFDM symbol. The e-HARQ scheme starts prediction process as soon as \(n_{1}\) OFDM symbols are received at the UE and generates feedback respond in \(n_{2}\) microslots. Consequently, the gNB will get it in \(2n_{0}+n_{1}+n_{2}\) time units from the beginning of the transmission, and then the first retransmission may start. To preserve the general terms, we suppose that actual transmission length is \(n_{3}\) OFDM symbols with the corresponding processing of \(n_{4}\) microslots. So the retransmission based on HARQ scheme can be performed in \(2n_{0}+n_{3}+n_{4}\) microslots, as shown in Figure 1.  
The HARQ timeline is dominated by the processing time, which may scale with the Transmission Time interval (TTI) length [20], and the propagation time, which is independent of the TTI length. The given scalability principle is taken into consideration by equating \(n_{1}=n_{2}\) and \(n_{3}=n_{4}\). We suppose propagation time \(n_{0}=0\), keeping in mind URLLC as a use case with UEs closely located to gNB. Furthermore, we assume various LDPC subcode lengths, e.g., in Figure 1a \(n_{1}+n_{2}=n_{3}\), which means that at time moment \(n_{3}\) we already have a e-HARQ FeedBack (eFB) message at gNB, and may initialize the retransmission process. In Figure 1b prediction is based on 4 OFDM symbols, which corresponds to \(n_{1}+n_{2}=n_{3}+d\), where \(0\leq d<N\) is a time offset, which is measured in OFDM symbols. By incrementing \(d\) the accuracy of the e-HARQ predictor obviously rises at the cost of a smaller number of possible retransmissions. Note that e-HARQ prediction can be performed on very small subcodes, even less than a half of the transmission length. However, due to the high e-HARQ prediction error, it is out of scope of this work.
# Research on Replacing Numerical Simulation of Mooring System with Machine Learning Methods
## 1 Introduction
For turret-moored FSPO, Metocean and GPS data from an FPSO were used to train Kriging and neural network models, and the identified mooring system change was used to predict a mooring line failure event [18]. For the tension leg platform (TLP), ref. [19] used deep neural networks (DNN) to detect mooring line damage based on simulation data using Charm3D (a fully coupled nonlinear hull/mooring/riser program from Texas A&M University, College Station, TX, USA). DNN was also used to detect mooring line failure based on the data from floater-mooring coupled hydroelastic time-domain numerical simulations for the submerged floating tunnel (SFT) [20]. Until now, time-domain numerical simulation has been a common practice in both mooring system design and detection. To replace this time-consuming approach, the most recent research focused on the neural networks, and only a few assessed other methods, such as Kriging and random forest. This paper investigates five popular machine learning algorithms, including random forest, extremely randomized trees, K-nearest neighbor, decision tree, and gradient boosting decision tree, to substitute time-domain mooring numerical simulation, using the CALM case as an example. In addition, a sampling strategy for global and local data is proposed to select the numerical basis cases.