esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods
### Project Hardware and Software  
Scripts and tools were written using the Python programming language. A combination of NumPy and Geospatial Data Abstraction Library (GDAL) were used to process the imagery and training data and convert them into multi-dimensional arrays, the format required for machine learning data processing.  
A combination of Python, Nvidia's CUDA [45], CUDA deep neural network library (cuDNN) [46], Keras [47], and Tensorflow [48] formed the basis for the deep learning part of the project. Because of the volume of data and the number of iterations required to train a model, efficient processing of the data is required.  
The Queensland Government's high performance computing (HPC) infrastructure consists of 2256 threads, 8.8 TB of memory and eight Nvidia Tesla V100 GPUs, used to process the training data, train the U-Net model, and to create the model inference (resulting area of banana plantations classified by the model once trained).  
Figure 1: The study area (**a**) and extents of training (**b**). Note the Tully River catchment was excluded from the training and reserved as the extent of the final classification.
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 2 LoRaWAN
### Network Topology  
A LoRaWAN network consists of the following fundamental elements, as illustrated in Figure 1: nodes, gateways, a network server, a join server and an application server. The structure of a LoRaWAN network follows the star-of-stars topology, where nodes are connected towards a central network server and an application server via multiple gateways. Such settings can improve data throughput, as variable RF channel conditions might temporarily become degraded on a single gateway.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Year** & **Physical Layer Attacks** & **Physical Layer Countermeasures** \\ \hline
[9] & 2020 & moderate & moderate \\
[34] & 2021 & moderate & moderate \\
[35] & 2020 & superficial & - \\
[37] & 2019 & moderate & moderate \\
[36] & 2020 & moderate & superficial \\
[38] & 2021 & - & - \\
**This work** & 2022 & **comprehensive** & **comprehensive** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Treatment of physical layer security topics in LoRaWAN security review papers.  
A LoRaWAN node initiates the network join procedure as well as taking care of secure data transfer towards the servers. In between the server infrastructure and the nodes, several gateways forward LoRaWAN data packets and MAC layer commands. During LoRaWAN packet reception, a gateway decides between forwarding and packet drop by inspecting the integrity of the received packet. Next to the LoRaWAN payload, additional information such as a time stamp or metadata, such as a received signal strength indicator (RSSI) value or a signal to noise ratio (SNR) value, are added to the forwarded message. The link between a gateway and the servers can be implemented by any available communication infrastructure (WLAN, 4G/5G, Ethernet). The network server (NS) handles all the essential features on the network level, such as the frame authentication, responding to MAC layer requests, the gateway selection for downlink messages and the data rate adaptation for the nodes. The gateway selection is based on the ranking of the recorded RSSI/SNR. Additionally, as a single LoRaWAN payload might arrive multiple times from many gateways, the NS performs deduplication of the messages. The join server takes care of the over-the-air activation procedure, during which a node is authenticated and the sessions keys are derived. Finally, the application server is utilized to decrypt the LoRaWAN messages and to store the payload data (and the metadata).
## 1 Introduction
### Aim of the Paper  
The analysis of the state of art revealed discordant values regarding the achievable accuracy of this type of sensor for bathymetry. Therefore, the lack of these data makes it difficult to frame the surveys conducted in more general scenarios and thus define application ranges with some geomatics sensibility. Based on these considerations, this paper proposes to analyse the following aspects:  
1. Tests to determine the accuracy and precision of shallow-water depth measurement and planimetric positioning of Deeper Smart Sonar (particularly CHIRP+ and Pro+ 2), implementation and evaluation of a system to synchronise the Deeper Smart Sonar with an external GNSS receiver via smartphone to improve the GNSS Single Point Positioning (SPP) performance in real-time, and a method to correct the GNSS positioning of the Deeper Smart Sonar in Post-Processed Kinematic (PPK) based on a spacetime approach and on the GPS Time (GPST) to further optimise positioning performance;
2. Building 3D mesh models of the environments surveyed with the Deeper Smart Sonar.  
In order to make a comparison with internationally accepted reference values, data collected during the tests and the aims achieved in the research were analysed first taking into consideration the standardised uncertainty values of the International Hydrographic Organization (IHO) [17] and then considering possible applications in science and engineering fields.
## 4 VRFormer
### Advanced Data Processing and Sensor Fusion Specifics
#### 4.6.1 Sensor Data Simulation with Extended Kalman Filtering  
VRFormer's data simulation, executed within the VRDeepSafety platform, mimics real-world sensory inputs, integrating LiDAR, camera, and radar simulations that model environmental noise and occlusion patterns. LiDAR data are processed through ray casting to create 3D point clouds, which are then enhanced with Gaussian noise to represent real-world variability. EKFs are applied to refine position and velocity estimates by continuously recalibrating them based on observed state deviations. This is specifically important in scenarios with unpredictable movement patterns. The EKFs is similar to the Kalman Filter but accounts for non-linearities in the data.  
The EKF prediction equations are given by the following:  
\[\begin{split}\hat{\mathbf{x}}_{t|t-1}=f(\hat{\mathbf{x}}_{t-1}, \mathbf{u}_{t-1})+\mathbf{w}_{t}\\ \mathbf{P}_{t|t-1}=\mathbf{F}(t)\cdot\mathbf{P}_{t-1}\cdot\mathbf{ F}(t)^{\mathbf{T}}+\mathbf{Q}(t)\end{split} \tag{29}\]  
where \(\hat{\mathbf{x}}_{t|t-1}\) is the predicted state, \(f(\hat{\mathbf{x}}_{t-1},\mathbf{u}_{t-1})\) is the non-linear state transition function, and \(\mathbf{w}_{t}\) is the process noise.  
The update equations for the EKF are the following:  
\[\begin{split}\mathbf{K}_{t}=\mathbf{P}_{t|t-1}\cdot\mathbf{H}(t) ^{T}\cdot\left(\mathbf{H}(t)\cdot\mathbf{P}_{t|t-1}\cdot\mathbf{H}(t)^{ \mathbf{T}}+\mathbf{R}(t)\ \right)^{-1}\\ \hat{\mathbf{x}}_{t}=\hat{\mathbf{x}}_{t|t-1}+\mathbf{K}_{t}\cdot \left(\mathbf{z}_{t}-h\left(\hat{\mathbf{x}}_{t|t-1}\right)\right)\\ \mathbf{P}_{t}=\left(\mathbf{I}-\mathbf{K}_{t}\cdot\mathbf{H}(t) \right)\cdot\mathbf{P}_{t|t-1}\end{split} \tag{30}\]  
where \(\mathbf{K}_{t}\) is the Kalman gain, \(\mathbf{H}(t)\) is the Jacobian of the measurement function, \(\mathbf{z}_{t}\) is the measurement vector, and \(\mathbf{R}(t)\) is the measurement noise covariance matrix. The update equations incorporate new sensor measurements to correct the predicted state. This iterative process allows VRFormer to process complex, non-linear data, providing accurate real-time position and velocity estimates that are critical for AVs navigation within VRDeepSafety.
## 3 Case Study
For the laboratory test and validation of the effectiveness of the proposed method, approximately 1.2 h of the total power curve (\(P_{T}\)) was emulated (see Figure 9). The choice of this specific segment of the curve for emulation was based on its highly variable behavior, allowing for a more accurate assessment of the proposed method. The goal was to test the method in real-time to obtain a more comprehensive evaluation of the V2G system behavior with the Li-Ion BESS. Notably, the Li-Ion BESS available in the laboratory was used to emulate the power supplied by the PV and WP systems. Figure 8: Wind power curve. Figure 7: High variability in daily PV power curve.
## 4 Case Study  
### Introduction to Testing System  
To verify the effectiveness of the proposed method, simulation experiments were conducted on an improved IEEE 33-bus distribution system, as illustrated in Figure 5. Two PV inverters were installed at buses 8 and 13, with capacities of 3 and 3 MVA, respectively.  
Two controllable capacitor banks were placed at buses 6 and 32, with capacities of 1 and 1 MVar. The voltage reference was set to 10.5 kV, and the voltage safety range was defined as [0.95 p.u., 1.05 p.u.].  
The experimental simulations were implemented using PyCharm 2023.1.2, with the PyTorch framework, Python 3.11, and the PyPower 5.1.16 library. The hardware platform used for training and testing included an NVIDIA RTX 4060 GPU (NVIDIA Corporation, Santa Clara, CA, USA) and a 12 th Gen Intel(R) Core(TM) i7-12650 processor (Intel Corporation, Santa Clara, CA, USA) operating at 2.30 GHz.  
The experiment used a 15-min scheduling interval, with load data and PV power output collected from a regional power grid at the same interval for three typical days as training data, as shown in Figures 6 and 7. The scheduling period was one day, equivalent to 96 time steps. The control hyperparameters of the Safe Graph DDPG algorithm are listed in Table 1.  
Figure 5: Modified IEEE-33 system.  
Figure 6: Photovoltaic active power profiles over three consecutive days.
## 3 The Proposed Scheme
### Sentiment Estimation  
Bayesian theorem, as a supervised learning technique, is commonly applied to the sentiment analysis. In this paper the Multinomial Naive-Bayes (MNB) is employed as the classifier to predict the polarity of the tested documents. Assuming that tested document_d_ is represented as a vector _<w\({}_{1}\)_,..., _w\({}_{|W|}\)_>, the MNB estimates the label of the Maximum A Posteriori (MAP) category and the probability of the category membership of \(d\) for category_c using Equations (23) and (24) [50]:  
\[c_{map}=arg\underset{c\in\mathcal{C}}{\max}(P(c|d)) \tag{23}\]  
\[\begin{array}{ll}P(c|d)&=\frac{P(d|c)P(c)}{P(d)}\\ &\propto P(c)P(d|c)\\ &=P(c)\prod_{i=1}^{|W|}P(w_{i}|c)\end{array} \tag{24}\]Here \(W\) is the number of unique words in document_d, \(w_{i}\) represents the \(i\)-th word of \(d\), and \(C\) is the set of all the possible labels. \(P(w_{i}|\,c)\) is the conditional probability of word_w\({}_{i}\) (\(1\leq i\leq|W|\)) belonging to document_d_ with the category label_c. Observe from Equation (24) that multiplying \(|W|\) conditional probabilities may cause floating point underflow, which results in incorrectly predicting the sentiment. Hence, the addition of the logarithms of the probabilities is used instead of the multiplication of the probabilities as follows.  
\[c_{map}=argmax_{c\in C}[log\,P(c)+\sum_{1\leq i\leq n}log\prod_{i=1}^{|W|}P(w _{i}|c)] \tag{25}\]  
The prior probability, \(P(c)\), is computed as:  
\[P(c)=N_{c}/N \tag{26}\]  
Here \(N_{c}\) is the number of documents in category_c, and \(N\) is the entire number of documents in the training data. Note that incorporating the feature weights into both the classification formula and conditional probability model improves the performance of the classifier of Naive-Bayes [51]. Therefore, the conditional probability in Equation (25) is modified as:  
\[P(w_{i}|c)=\frac{W(w_{i},G_{\theta},c)\cdot cf_{i}\cdot\lambda(c_{j},c)+1}{ \sum_{i=1}^{n}\sum_{j=1}^{n}W(w_{i},G_{\theta},c)\cdot cf_{i}\cdot\lambda(c_{j },c)+|V|} \tag{27}\]  
Here \(W(w_{i},G_{\theta},c)\) is the importance degree of word in terms of inter-class and intra-class distribution, and it is computed based on Equations (21) and (22). \(\lambda(c_{j},c)\) is a binary function judging whether the two input parameters are equal or not. It is defined as:  
\[\lambda(c_{j},c)=\left\{\begin{array}{l}1,c_{j}=c\\ 0,\text{otherwise}\end{array}\right. \tag{28}\]  
As the feature weights are incorporated into the MNB model for calculating the probability of the target document belonging to category_c, the class label with the highest probability is selected as the most suitable one for that document. Note that the synonyms are separately assigned the feature weight to reflect its significance, causing a respective effect in sentiment analysis.
# A Novel Integrated Fault Diagnosis Method Based on Digital Twins
## 2 Related Works
### Digital Twin  
Digital twin was initially conceptualized as a bridge between physical entities and virtual simulations. With the rapid development of industrial big data and information industries, digital twin has found widespread application in manufacturing and production, for instance, a fault diagnosis method based on digital twin technology, utilizing deep transfer learning algorithms to analyze the operational states of machine tools for fault detection and diagnosis [7]. In terms of a hybrid predictive maintenance approach for CNC machine tools driven by digital twin technology, this method integrates digital twin models with big data to predict the lifespan of critical components in CNC machine tools [8]. These digital twin methods provide robust support for fault diagnosis, enhancing both the efficiency and accuracy of fault diagnostic processes in various industrial applications.
# Infrastructure BIM Platform for Lifecycle Management
## 4 Infrastructure BIM Platform  
In this section, the infrastructure of the BIM platform framework, combining BIM and state-of-the-art techniques, is proposed based on a literature review. The Integrated Definition for Function Modeling (IDEF0) [61], which is a methodology that provides complex ideas via easy concepts through simple boxes and arrows, was developed to clearly define the activities of the infrastructure BIM platform. Simple examples of five main activities of the infrastructure BIM platform are then sequentially described.  
The infrastructure BIM platform was composed of five main activities: planning and BIM model design (A01); 4D/5D BIM-based construction management and monitoring (A02); scan-to-BIM using data gathered from as-built infrastructure by unmanned robots (A03); deep learning-based automated damage evaluation and mapping (A04); and annual grading of infrastructure for lifecycle management (A05), as shown in Figure 2. The infrastructure BIM platform is a web-based lifecycle management platform for the implementation of CDE. The framework includes the lifecycle management of infrastructure, from planning to demolition. First of all, a 4D/5D BIM model is designed using the 2D design model, pre-construction schedule, and a WBS/Organization Breakdown Structure (OBS)/Cost Breakdown Structure (CBS) by stakeholders (A01). Construction is then systematically performed using the 4D/5D BIM model (A02). The 4D/5D BIM model is shared with stakeholders as an international standard format (IFC and XML). At the completion stage, texture mapped BIM models are established using scanning data that were gathered by a sensor embedded in an unmanned robot (A03). Damage in the gathered data is automatically detected using a damage-trained deep learning network (A04). A damage mapped BIM model is then established by mapping detected damage to the BIM model. Infrastructure is graded regularly and managed by the law of each country. Therefore, the BIM model is updated regularly via repeated model updates, and the outdated BIM model becomes the reference model (A05). Details of the featured parts of the infrastructure BIM platform are sequentially explained.
## 2 Experiment Description
### Terrestrial Experiment
#### 2.1.2 Test Drive Details  
The driven trajectory in Hannover (Figure 1c) represents typical characteristics of an urban area that are strong shadowing caused by buildings and variable velocities with standstill phases at traffic lights and crossings. The measurement starts with a standstill phase of approximately 10 min in a parking lot (label A). The test route led through streets with four-storey residential buildings of roughly 20 m height as well as through the inner city ring, which has a more open sky characteristic. The trajectory marked with the red box (label B) was repeatedly driven five times with approximately seven minutes per round. The details of the marked points on the urban trajectory map and their approximate GPS times are listed in Table 1. The total duration of the experiment is about two hours. Each receiver was recording GPS, GLONASS and Galileo code, phase and Doppler observations as well as signal strength with a sampling rate of 1 Hz. It must be noted that an elevation cut-off angle was not applied while recording the data. The geodetic-grade receivers have the capability to track multiple frequencies simultaneously while the high sensitivity receivers can only track the L1 frequency of each GNSS system. The receiver independent exchange format (RINEX) specifiers of the phase observation types recorded on single or multiple frequencies from different GNSS systems using different receiver types are listed in Table 2[27]; corresponding code ranges, Doppler and signal strength were also recorded. Note that the frequency bands (L1, L2 and L5) for different GNSS systems are slightly different.  
Figure 1: Kinematic experiment in Hannover. (**a**) Measurement setup: two JVD and two u-blox (UBX) receivers connected via a GNSS splitter to a geodetic antenna. One of each receiver types is connected to an atomic clock (MAC SA35), SWG refers to signal wave generator and a Novatel SPAN receiver was logging inertial measurement unit (IMU) data. (**b**) Vehicle used during the experiment. (**c**) Measurement route. For details of the marked points, cf. Table 1. Map presentation taken from [26].  
The reference trajectory was computed using data from the iMAR FSAS IMU and the GNSS reference station (referred to as MSD8) located at the rooftop of IFE. The ground-truth was determined in post-processing mode using the TerraPOS software [28].
## 3 Results and Discussion
### Erosion Induces Phase Transformation  
To further clarify the phase transformation before and after erosion, the FRH with the highest wear resistance under 90\({}^{\circ}\) was surveyed by SEM and TEM. The erosion subsurface was divided into two regions, consisting of an erosion-affected region on the top and \(\alpha\)-Al matrix below (Figure 10). In the former region, the high temperature during erosion induced Si nanoprecipitation. The compositions of the Al, Si, and O depth changes in the Al matrix were detected by the EDS of TEM (Figure 11a-d). The results revealed two parts: (1) Si gradually decreasing while away from the erosion surface and the (2) O concentration increasing in the erosion-affected region, indicating Al oxidization to Al\({}_{2}\)O\({}_{3}\).  
Figure 10: (**a**) The schematic diagram of the erosion-affected region and \(\alpha\)-Al matrix, the SEM image of (**b**) the erosion-affected region, and the (**c**) \(\alpha\)-Al matrix.  
In Figure 12a, the nanoscale grains were comprised of the erosion-affected region, at a depth of approximately 0.4 um, with a clear boundary segmentation of the erosion-affected region and \(\alpha\)-Al matrix. In mapping the results of the EDS, Si was solid-dissolved in both regions (Figure 12b) compared to the EDS results in Figure 12b, which revealed that Si has a low solubility in the \(\alpha\)-Al matrix. Therefore, the Si solution diffraction pattern could not be observed in Figure 12e.  
Figure 12: (**a**) TEM image of FIB cross-section view, (**b**) EDS elemental mapping images, TEM bright field image of (**c**) the \(\alpha\)-Al matrix, (**d**) an erosion-affected region, corresponding to the SAED pattern of (**e**) the \(\alpha\)-Al matrix, and (**f**) an erosion-affected region.  
The bright field and SAED patterns of the \(\alpha\)-Al matrix and erosion-affected region are shown in Figure 12c-f. The sampling positions of Figure 12e,f are, respectively, marked by the circles in Figure 12c,d. All lattice distortion is caused by a solid solution of Si and Figure 12e. The phase transformation schematic is exhibited in Figure 13. The erosion-affected region was composed of fine grains by erosion; the SAED pattern presented the diffraction consisting of multiple crystal planes (Figure 12f). Compared with \(\alpha\)-Al grains in the matrix, extremely fine grains can serve as a resistance effect to protect the particle impact and difficultly forming such pits after erosion. These results not only corresponded with FRH coming as the highest erosion rate at high angles but also reflected the grain orientation changing in the XRD results.
# Chalcogenide Thin Films--Holographic Media for Augmented Reality Devices
## 4 Experimental Results and Discussion
### Chalcogenide Glasses with Optimized Compositions
#### 4.2.3 Relief Recording at the Second Series of ChGs Samples  
It was known from previous publications (see, e.g., [20]) that characteristic exposures for the relief gratings formation are in the range of kJ/cm\({}^{2}\). Therefore, this experiment required higher power densities than that for holographic recording of volume gratings. For the relief formation, the same UV laser (He-Cd, 325 nm) was used with \(+45^{\circ}/-45^{\circ}\) recording beams polarization configuration. Such orthogonally-polarized beams ensure low grating contrast but high surface relief grating modulation and DE [20]. The need to rotate polarization of the recording beams restricted application of the Lloyd recording setup. Therefore, for this experiment, typical [PERSON] interferometer was assembled with nonpolarizing beam-splitting cube. Bragg angle was set again to about \(10^{\circ}\); thus, the period  
Figure 14: Kinetics of volume gratings recording for studied Ge-S samples. Recording and probing beams are \(s\)-polarized.  
Figure 13: Lloyd setup for volume gratings recording.  
of the relief grating should be the same as the period of above described volume gratings. Figure 15 shows the scheme of the recording setup.  
Power density at the sample was measured to be 2.1 W/cm\({}^{2}\). Therefore, exposure of 1 kJ/cm\({}^{2}\) could be achieved at 475 s. Efficiency of the grating was estimated with the same probing beam (s-polarized 532-nm laser diode, 396 \(\mu\)W). It should be noted that a part of a focused probing beam was coupled into the substrate plate and traveled away in the waveguide regime, thus leading to energy losses in the diffracted beam.  
Relief gratings were successfully formed at all samples. Measurement of the DE was initiated after 1 kJ/cm\({}^{2}\) of the recording exposure was achieved. As it is seen from Figure 16, four samples demonstrated nearly the same linear kinetics for the relief grating formation. However, sample Ge\({}_{30}\)S\({}_{70}\) seemed to have nonlinear-growing kinetics. At 5 kJ/cm\({}^{2}\) almost all samples were able to achieve the same efficiency as that for the volume gratings.  
Figure 16: Diffraction efficiency dependence on the recording exposure for the samples under investigation (surface relief gratings recording). Recording beams are \(+45^{\circ}\)/ \(-45^{\circ}\) linear polarized, and the probing beam is \(s\)-polarized.  
Figure 15: Experimental setup for relief grating recording. L, He-Cd laser; TS, telescope; BS, beam splitter; M, mirror; R\({}_{1}\), 90\({}^{\circ}\) rotator; R\({}_{2}\), R\({}_{3}\), 45\({}^{\circ}\) rotators; ChG, chalcogenide sample; D, laser diode; Le, lens; PM–power meter.