esa-sceva/satcom-chunk-collection huggingface dataset:
# Generic Digital Twin Architecture for Industrial Energy Systems
## 3 Proposed Generic Digital Twin Architecture
An example could be a fault detection service, which can be implemented based on simple statistical models and indicate abnormal operating conditions of a plant. Diagnostic Services are services supporting, for instance, condition monitoring or root cause analysis of faults. They can build upon underlying Monitoring Services in combination with Simulation Services to gain more insight into the current state of the Physical Entity. Prediction Services are important for the DT to make decisions based on information about future events. Such services can be used, for instance, for predictive maintenance or the prediction of energy consumption. Also, external variables, like renewable energy production or prices at the energy market, can be predicted. Additionally, external prediction services can be integrated. The prediction results can be used by Control Services for realizing an optimized control or used to generate recommendations for the operating staff. Control Services have an influence on the operation of the plant via recommendations over Human-Machine Interface (HMI) or direct access to the process control. Control Services with direct access can bypass the Smart Data Service in the Information Layer to change the state of the physical entity without additional delay. Usually, Control Services make use of monitoring, diagnosis, and prediction services to achieve optimized operation. Thereby, the control strategy can change over time, caused by a reconfiguration of the Physical Entity or new objectives specified by the business logic. Reconfiguration Service. Reconfiguration means a rather static change of the fundamental properties of the Physical Entity by the DT itself. This has a significant influence on the context information inside the Shared Knowledge. Reconfiguration can be initialized through events or changed objectives inside the business logic, which resides inside the business layer. The Reconfiguration Service takes care of such changes inside the DT. Humans have to be informed about the current state of the Physical Entity as well as the DT itself to interact with it. Thus, an appropriate HMI is very important for almost every service of a DT. Business Layer--In this layer, the business logic resides, which can also orchestrate a large amount of DT. It defines the overall objectives (e.g., to reduce the risk of downtime or cost), which should be reached with the help of the DT. As this highly depends on the business strategy, it is not relevant for the design of the GDTA but only relevant via specific inputs.
# Advancing Road Infrastructure Safety with the Remotely Piloted Safety Cone
## 3 Results
### Use Case
#### 3.1.1 Positioning  
This test aimed to evaluate the RPSC's positioning accuracy and determine the GNSS error, thereby establishing a baseline for navigation reliability. Ground Control Points (GCPs) were previously measured and georeferenced with the Trimble R8 GNSS system. The RPSC, calibrated to receive RTK corrections, was positioned at these points. The positioning error was calculated by comparing the position reported by the Here3+ GNSS with the known location of the GCPs.  
Multiple RTK correction services were used to optimise accuracy: the Instituto Geografico Nacional (IGN) GNSS network, which provides high-precision positioning across Spain, and Hexagon's SmartNet, a global high-precision network. The tests were repeated with each service to ensure consistency in the results.
# PV-Powered Charging Station with Energy Cost Optimization via V2G Services
## 2 PV-Powered Charging Station with V2G Service
### Electric Vehicles
#### 3.4.1 V2G Mode  
The following constraints given below in (17)-(23) are included in the optimization problem for the EV users who accept providing V2G services.  
\[\mathit{disch\_min}\cdot 60/\mathit{\Delta t}\leq\sum V2G_{bin_{o}}(t_{i})\leq \mathit{disch\_max}\cdot 60/\mathit{\Delta t}\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}] \tag{17}\]  
\[V2G_{5W_{v}}(t_{i})\geq V2G_{bin_{v}}(t_{i})-V2G_{bin_{o}}(t_{i-1})\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}] \tag{18}\]  
\[G2V_{5W_{v}}(t_{i})\geq G2V_{bin_{v}}(t_{i})-G2V_{bin_{v}}(t_{i-1})\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}] \tag{19}\]  
\[G2V_{bin_{v}}(t_{i})+V2G_{bin_{o}}(t_{i})\leq 1\ \forall t_{i}\in[t_{arr_{v}},t_{ dep_{v}}] \tag{20}\]  
\[-P_{EV\_fast\_max}\cdot V2G_{bin_{o}}(t_{i})\leq p_{EV_{v}}(t_{i})\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}] \tag{21}\]  
\[p_{EV_{v}}(t_{i})\leq P_{EV\_fast\_max}\cdot G2V_{bin_{o}}(t_{i})\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}] \tag{22}\]  
\[p_{EV_{v}}(t_{i})-p_{EV_{v}}(t_{i-1})\leq\mathit{Limit}\cdot\mathit{\Delta t} /60\cdot G2V_{bin_{o}}(t_{i})\ \forall t_{i}\in[t_{arr_{v}},t_{dep_{v}}] \tag{23}\]  
where \(\mathit{disch\_min}\) (5 min) and \(\mathit{disch\_max}\) (15 min) are the minimum and maximum duration for the EV's discharge, while \(t_{arr_{v}}\) and \(t_{dep_{v}}\) are the time of arrival and departure of vehicle \(v\), respectively. \(G2V_{bin_{o}}\) and \(V2G_{bin_{o}}\) are binary decision variables for charging/discharging times; \(G2V_{5W_{v}}\) and \(V2G_{5W_{v}}\) are binary decision variables for the switching times between charging/stop and discharging/stop: \(p_{EV_{v}}\) is the EV charging power of vehicle \(v\); \(P_{EV\_fast\_max}\) is the fast charging power at maximum; and \(\mathit{Limit}\) is the ramp-up charging power (15 kW/min). Constraint (17) determines the discharging period of the EV, while Constraints (18) and (19) determine that the discharging and charging times of the EV, respectively, should be successive. Constraint (20) specifies that the EV can either charge, discharge, or be idle at any given time. Constraints (21) and (22) refer to the discharging and charging power of the EV, respectively. Lastly, Constraint (23) helps to reduce the charging fluctuations of the EV.
# Significant Wave Height Estimation from Space-Borne Cyclone-GNSS Reflectometry
## 3 Results and Evaluation
### Accuracy Assessment
#### 3.2.2 Comparing with Buoy Data  
In this paper, the linear parameters are calculated based on the ECMWF SWH model (significant height of combined wind waves and swell), then CYGNSS estimation data is compared with the SWH measurement data from National Data Buoy Center (NDBC) at some relevant buoy stations and the satellite altimeter data provided by AVISO. The NDBC provides an SWH observation per hour. Each SWH value is obtained from the 20-minute field observation sampling period. Further, for marine meteorology and ocean-atmosphere gas transfer studies, AVISO provides near-real time SWH and wind speed modulus. Also, the SWH data is processed using the last 2 days of available Interim Geophysical Data Record (IGDR) data for each satellite and are cross-calibrated using OSTM/Jason-2 as reference mission. The buoy stations used in this paper are mainly distributed close to (20N, 80W). The trajectory distribution of the buoy station and CYGNSS observation mirror point is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Date** & **Apr 05** & **Apr 10** & **Apr 15** & **Apr 20** & **Apr 25** & **Apr 30** \\ \hline
**Bias** & 0.0006 & \(-\)0.0064 & 0.0238 & 0.0132 & 0.0043 & 0.0032 \\ \hline
**RMSE** & 0.3231 & 0.2931 & 0.2949 & 0.3242 & 0.2861 & 0.3265 \\ \hline
**R** & 0.9087 & 0.9229 & 0.9343 & 0.9322 & 0.9473 & 0.9228 \\ \hline \end{tabular}
\end{table}
Table 2: Accuracy analysis of CYGNSS SWH estimates versus AVISO SWH (m).  
Figure 11: The difference distribution between the CYGNSS SWH estimate and AVISO satellite altimeter SWH observation.  
Figure 12: Distribution of mirror point trajectories for buoy stations and CYGNSS observations.  
According to the data matching method in Section 2.4, the discrete distribution of CYGNSS SWH estimates and buoy SWH measurements is shown in Figure 13. The fitted linear equation is:  
\[\text{SWH}_{buoy}=1.002 SWH_{\text{CYGNSS}}+0.0453 \tag{7}\]  
As can be seen from Table 3, the CYGNSS SWH estimate has a small deviation from the buoy SWH observation with 5 cm, and the RMSE is 0.2761. The correlation coefficient is 0.9539, which shows a good correlation between the CYGNSS SWH estimation and the buoy observation.
## 4 Proposed Framework
### Interoperability and Scalability
#### 4.4.1 Interoperability  
The usage of shared ontology \(O\) eases interoperability; it allows encoding information provided by heterogeneous IoT devices by the same semantic model. These steps are followed by the edge servers, which interact with the ontology as follows:  
* **Ontology alignment**: Links the raw data fields \(f_{i}\) from Internet of Things gadgets to the associated ontology features \(o_{i}\) as follows: \[M:f_{i}\to o_{i},\quad\forall f_{i}\in A_{j},\ o_{i}\in O\] (21)
* **Unit standardization:** Converts a uniform baseline from observations in different units. For example, temperatures are converted from Fahrenheit (\(T_{F}\)) to Celsius (\(T_{C}\)) as follows: \[T_{C}=\frac{5}{9}(T_{F}-32)\] (22)
* **Metadata enrichment:** Ensures semantic coherence within the system by appending context information to every single information piece, such as timestamps, geolocations, and device identifiers.  
At the edge server stage for federated learning, our ontology-driven method enables the smooth integration along with the effective utilization of diverse IoT device data.
# Progress in Transparent Nano-Ceramics and Their Potential Applications
## 5 Other Transparent Nano-Ceramics
### Summary  
With the in-depth research on transparent nano-ceramics, electro-optic and scintillation transparent nano-ceramics have also been found to have great application prospects. The hot-pressing two-step approach and sol-gel spin-coating method are two commonly utilized preparation procedures for the former. The compactness of ceramic materials can be considerably improved by sintering mixed oxide during the preparation process. The temperature and electric field strength, for example, have an effect on the transmittance of optoceramics, according to the current study. However, one of the next important research topics will be how to manage these parameters to change the transmittance of optoceramic materials. Scintillating transparent nano-ceramics, on the other hand, have a wide range of applications in the medical field, physics, and industry due to their scintillation function. The majority of scintillation transparent ceramics research now focuses on the preparation method, powder size, material characteristics, and so on. Following a review of the literature, it was discovered that the majority of scintillation transparent ceramics are made using the co-precipitation process, with scintillation qualities increasing via doping with scintillation ions (mostly rare earth ions Ce\({}^{3+}\), Pr\({}^{3+}\), etc.). Furthermore, the specific application of scintillation transparent ceramics and how to improve the transparency of ceramics (through a reasonable sintering method) and light output (through the doping of luminescent ions) will be the research direction and technical difficulties of the future.
###### Abstract  
A new design of 3 degrees of freedom (DOF) piezoelectric ultrasonic motor (USM) is introduced in this paper. The concept of this design is to incorporate a spherical rotor between two piezoelectric transducers. Each transducer is coupled with a flange, and it operates like a unimorph structure. Such a design of the transducer allows to increase the amplitude of the vibrations and to generate the higher torque and driving force used to achieve 3 DOF rotary motion of the spherical rotor. The proposed USM may be used for humanoid robots, optomechanical systems, or small satellites. This USM consists of several components, is lightweight and reliable. Numerical analysis and experimental studies were performed to validate the feasibility of this drive, to find out proper resonant frequencies for the unimorph, and optimize the shape of the flange. Experimental studies were accomplished to validate the results of the numerical analysis and to validate the operating principles of the piezoelectric motor.  
piezoelectric actuator; 3 DOF actuator; spherical rotor; ultrasonic motor; unimorph; FEM; piezoelectric transducer; piezoelectric drive; precise rotation
# Resolving Persistent Packet Collisions through Broadcast Feedback in Cellular V2X Communication +
## 3 Broadcast Feedback on Packet Collisions
### Feedback Operation
#### 3.3.2 Feedback Consumer Side  
When every broadcast receiver processes a broadcast packet, it examines any feedback information piggybacked on it. If the feedback points to the resource in which the receiving vehicle transmitted earlier, the vehicle immediately commences the standard SPS resource reselection procedure even if the resource reservation counter (RC) has not reached zero. Due to the broadcast feedback, the resource reselection is invoked at all the vehicles that caused the given packet collision event. The persistent collision is thus terminated as soon as a third vehicle provides collision feedback. The next section discusses how piggybacking can be implemented in the current standard framework.
## 6 Discussion  
Vertical histopathology represents the current \"gold standard\" approach to microscopically evaluate most biopsied and/or surgically excised skin samples, while HHSs are traditionally used for the diagnosis of some alopecia subtypes, as they allow pathologists to better visualize the follicular and perifollicular areas [1,24]. However, most of the commonly used diagnostic techniques in dermatology, including dermoscopy and RCM, take a horizontal point of view of the skin [1,25,26] that differs from the vertical plane of conventional histopathology. Based on this, the use of HHSs has been described in the literature and proposed as a novel method to strongly validate the diagnostic accuracy of RCM and dermoscopy [1,14,20,23,24,25,26]. Importantly, the use of HHSs is aimed not to replace conventional histopathology but to further validate RCM features, highlighting cellular analogies between RCM and HHSs [1]. This strong correspondence has also been found in some skin tumors [1,24,25], but, in our opinion, it can be particularly useful in the management of inflammatory diseases [14,20,23,26]. Some considerations have to be made: the demonstrated correlation between RCM features and HHSs in skin diseases reported in Table 1 may avoid the use of multiple biopsies for diagnostic confirmation, may allow a better selection of the sampling site, and, in selected cases, may solve differential diagnosis issues in case of diseases with similar clinical presentation.  
In conclusion, although vertical histopathological sections still remain the best approach to obtain a certain diagnosis of most cutaneous disorders, the present review emphasizes the validity of RCM in the clinical evaluation of some inflammatory skin diseases, such as psoriasis, DLE, and eczematous dermatitis, as it provides clinicians with the same morphological features that can be seen via horizontal histopathology. In addition, the recent introduction of deep-learning-based frameworks able to transform in vivo RCM images into virtually stained hematoxylin-and-eosin-like images, if confirmed by large case series, might further expand the usefulness of RCM in inflammatory skin disorders [27].  
In our experience, RCM represents a strong diagnostic aid that supports and improves the clinical and dermoscopic findings in both inflammatory and neoplastic skin diseases; it may also assume a decisive diagnostic role for diseases with overlapping clinical and dermoscopic features.  
Figure 3: Stratum Spinosum. (**A**) Intraepidermal dark vesicular areas (asterisks) containing small mildly refractive cells (yellow arrows) and broadband intercellular spaces (red arrows) are seen under RCM. (**B**) RCM features correlate with spongiosis, vesicles, and mild lymphocytic exocytosis, seen on horizontal histopathology (hematoxylin and eosin: original magnification 200\(\times\)).
# Transfer Learning Analysis for Subvisible Particle Flow Imaging of Pharmaceutical Formulations
## 3 Results and Discussion
### Inter-Vial and Intra-Vial Variability  
We tested protein particles, which are the most stress-sensitive of the seven particles, to demonstrate the robustness of our classifier to both inter-vial and intra-vial variability in protein particles. Intra-vial variability was validated by using untrained images in the vial where the training set images were located. This was validated for a total of six groups, of which the first group was the test set of 2000 protein particles, and the other five groups each contained 3000 images. The results shown in Figure 5a indicate little variation in the accuracy of the new protein particles in the same vial--the accuracy was above 98% in all cases.  
Figure 4: ROC curve and area under the curve of the model and each class. The ROC curve is the graphical display of sensitivity (TPR) on the \(y\)-axis and 1-specificity (FPR) on the \(x\)-axis for varying cut-off points of test values. An area under the curve closer to 1 indicates better performance of the test. ROC: receiver operating characteristic.  
Figure 5: Intra-vial and inter-vial variability of protein particles. (**a**) Accuracy of test protein image data within the same vial as the training set. Dataset number 1 means the test set containing 2000 test images; numbers 2–6 each contained 3000 test images in the same bottle. (**b**) Accuracy of test protein image data between different vials. Each vial number means an individual vial containing 5 \(\times\) 3000 protein images; Vial 1 was where the training data were located. The accuracy was averaged over five sets of images. The test images above were not used in training.  
Figure 5b demonstrates the variability in the accuracy of protein particles within different vials using 3000 \(\times\) 5 images from each of the different vials that entered the network. The results of the study show that the intra-vial variability was significantly higher than the inter-vial variability, as the accuracy fluctuations were more pronounced, as the fluctuations in accuracy were more pronounced. Nevertheless, the accuracy rates in different vials were over 92%, indicating that the protein particles were also robust for vial-to-vial differences. Overall, in our study, the CNN classifier is robust for both inter-vial and intra-vial variability of protein particles. This shows the network structure used is able to tolerate minor variations in the input image data, and has some generalization ability.
## 6 Conclusions  
In this study, we analyzed the responses of GPP, SIF, APAR\({}_{\text{NDVI}}\) and APAR\({}_{\text{EVI}}\) to drought during the 2009 drought event in Victoria. For general Victoria region, satellite SIF had a more consistent decline with GPP losses induced by drought stress than did APAR\({}_{\text{NDVI}}\) and APAR\({}_{\text{EVI}}\). We also analyzedand compared the responses of these vegetation variables to drought for the main vegetation types in Victoria. APAR\({}_{\text{NDVI}}\) and APAR\({}_{\text{EVI}}\) had obvious lagged responses compared with SIF in evergreen broadleaf forest land. The spatial and temporal pattern results indicate that the sensitivity of SIF to drought is stronger than those of APAR\({}_{\text{NDVI}}\) and APAR\({}_{\text{EVI}}\) for this 2009 drought event in Victoria.  
To better illustrate the applicability of SIF for monitoring drought effects, we estimated the sensitivities of vegetation variables to meteorology anomalies using the ARx model, and analyzed the consistency of GPP anomaly and vegetation proxies anomalies for the main ecosystem types in Victoria. The ARx model results indicate that GPP and SIF are more sensitive to temperature stress for general Victoria region than are APAR anomalies. Compared with APAR anomalies, SIF anomaly can be explained better by meteorology anomalies for evergreen broadleaf forest land. In addition, GPP anomaly has a more significant relationship with SIF anomaly than with APAR\({}_{\text{NDVI}}\) and APAR\({}_{\text{EVI}}\) anomalies, especially in evergreen broadleaf forest land. Thus, SIF can accurately track and evaluate vegetation productivity variations under meteorology stress. SIF provides a powerful tool for accurately monitoring and assessing the response of plant productivity to drought.  
All the authors made significant contributions to the work. [PERSON] and [PERSON] conceived the study, designed the experiments, and wrote the paper; [PERSON] made important contributions to the research method, data analysis and manuscript revision; [PERSON] conducted the part of the experiments.  
This work was supported in part by National Key R&D Program on Monitoring, Early warning and Prevention of Major National Disaster 2017 YFC1502802, by the National Natural Science Foundation of China under Grant 41501394 and Grant 41501473, and in part by the Youth Innovation Promotion Association CAS under Grant 2017086 and by the Scientific Research Satellite Engineering of Civil Space Infrastructure Project under Grant 2016K-10.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Data  
#### 2.2.1 Sentinel 2 Imagery Acquisition  
This study utilized Sentinel-2 multispectral imagery for the period 2018-2023, chosen for its free availability, spatial resolution (10 m, 20 m, and 60 m depending on the band), and frequent revisit time, making it ideal for monitoring LULC changes. The Sentinel-2 MSI: Multi-Spectral Instrument Level-2A collection [30] was accessed through the Google Earth Engine (GEE) platform, using the Cloud Score+ dataset [31]. These products include radiometric and geometric corrections, providing highly accurate geolocated images without the need for further preprocessing, such as atmospheric correction.  
The Cloud Score+ dataset functions as a quality assessment (QA) processor for optical satellite imagery, effectively removing clouds and cloud shadows while identifying relatively clear pixels. It employs two quality bands, cs, and cs_cdf, which rate each pixel's usefulness about surface visibility on a continuous scale from 0 to 1, where 0 denotes \"not clear\" observations and 1 denotes \"clear\" observations.  
To mitigate the impact of extensive cloud cover typically observed in tropical regions, median composite images were created from a group of images within a specific time interval. Sentinel-2 images were filtered from the collection over 4 months, from the beginning of May to the end of September, corresponding to the dry season in the Amazon region. This temporal selection was made to minimize cloud cover interference and ensure the highest quality data for analysis.  
The resulting dataset consists of six annual median composites, corresponding to the years 2018 to 2023. These composites provide a consistent and cloud-free representation of the study area, enabling accurate detection and analysis of LULC changes over the study period. The use of median composites helps reduce the influence of outliers and temporary land cover changes, providing a more stable representation of the landscape for each year.
# Study on Underwater Target Tracking Technology Based on an LSTM-Kalman Filtering Method
## 3 Simulation  
We simulated the transmitted and received signals of an active sonar system in the marine environment and established a moving target trajectory diagram, which was based on a two-dimensional plane.  
Firstly, the target of the time series was compared using the target detection model to judge whether it was the same target. Secondly, the Kalman filtering method was combined with the LSTM network to obtain the optimal estimation result. Finally, we drew the tracking trajectory of the moving target. Figure 3 shows the simulation flowchart.