esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Tropical-like cyclone (TLC or medicine) Ianos formed during mid-September 2020 over the Southern Mediterranean Sea, and, during its mature stage on days 17-18, it affected southern Italy and especially Greece and its lonian islands, where it brought widespread disruption due to torrential rainfall, severe wind gusts, and landslides, causing casualties. This study performs a sensitivity analysis of the mature phase of TLC Ianos with the WRF model to different microphysics parameterization schemes and initial and boundary condition (IBC) datasets. Satellite measurements from the Global Precipitation Measurement Mission-Core Observatory (GPM-CO) dual-frequency precipitation radar (DPR) and the Advanced Scatterometer (ASCAT) sea-surface wind field were used to verify the WRF model forecast quality. Results show that the model is most sensitive to the nature of the IBC dataset (spatial resolution and other dynamical and physical differences), which better defines the primary mesoscale features of Ianos (low-level vortex, eyewall, and main rainband structure) when using those at higher resolution (\(\sim\)25 km versus \(\sim\)50 km) independently of the microphysics scheme, but with the downside of producing too much convection and excessively low minimum surface pressures. On the other hand, no significant differences emerged among their respective trajectories. All experiments overestimated the vertical extension of the main rainbands and display a tendency to shift the system to the west/northwest of the actual position. Especially among the experiments with the higher-resolution IBCs, the more complex WRF microphysics schemes (Thompson and Morrison) tended to outperform the others in terms of rain rate forecast and most of the other variables examined. Furthermore, WSM6 showed a good performance while WDM6 was generally the least accurate. Lastly, the calculation of the cyclone phase space diagram confirmed that all simulations triggered a warm-core storm, and all but one also exhibited axisymmetry at some point of the studied lifecycle.  
Medicane Ianos; Mediterranean tropical-like cyclones; satellite observations; WRF model; initial conditions; microphysics sensitivity; Hart's phase space diagram +
Footnote †: journal: Remote Sensing  
0021
## 4 Target Identification Method between Star Trackers
### Identification of Targets Observed in the Different Orbital Arc  
The identification of targets observed in the different orbital arc segments is done based on the existing multiple star trackers in the respective orbital arc segments, and the star trackers can complete the solution of the positioning information to identify the same debris targets in different orbital arc segments space debris. The observation correlation of different orbital arcs is low, and the similarity cannot be used to extract the same debris targets. The initial orbital parameters of space debris can only be solved by the positioning information of each arc segment, and then the similarity of orbital parameters can be used to identify the target in different orbital arcs. The orbit determination algorithm will be described in the next section.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 7 Security Analysis
### Malicious-Attack Analysis
#### 7.2.4 Broken Authentication  
Attackers try to gain unauthorised access to online applications, in particular e-voting applications [40]. The possibility of successfully obtaining unauthorised access to e-voting systems during the election is a very serious issue which compromises the integrity of the election. In PVPBC, an authentication protocol based on blockchain is implemented to ensure valid and efficient authentication is carried out. The authentication process is maintained by the election authority in collaboration with the TTP by making use of a capability token which is verified and stored on the blockchain. As blockchain offers an immutable history, it is very challenging to forge the capability access token.
## 3 Engineering Example
### Numerical Analysis Based on the Response Displacement Method
#### 3.2.2 Calculating Working Conditions  
Investigations into nuclear power plant engineering reveal that the design value for the SL-2 level horizontal peak ground acceleration in China's coastal nuclear power plants typically ranges between 0.15 g and 0.30 g. For nuclear immersed tunnel conditions, it is essential to consider factors such as tunnel joint stiffness and varying peak ground accelerations. The seismic safety of immersed tunnels under diverse conditions must also be evaluated. This study simulates multiple scenarios to assess these aspects. Specifically, for the response displacement method in joint seismic performance analysis, peak accelerations of 0.15 g and 0.30 g and water stop stiffness values of 51 and 62 were selected, resulting in four distinct working conditions. Detailed information is presented in Table 2.
## 1 Introduction
If a 15-m resolution EVI product from ASTER red and NIR reflectances can be derived and made available, it should be very useful to scale up ground-based data to moderate resolution satellite data such as MODIS. Indeed, one frequent request from the GEO Grid system users is the provision of an ASTER EVI product that can be used in conjunction with the EVI from MODIS. The EVI is one of the two vegetation indices contained in the MODIS standard vegetation index products (MOD13 and MYD13 series) [8]. There are, however, issues with producing the EVI from ASTER data. The ASTER sensor does not have a blue band and, hence, the EVI equation (Equation (1)) is not simply applicable to ASTER data. Recently, two-band EVIs without a blue band have been proposed, of which compatibility to the original three-band EVI have been demonstrated for the MODIS sensor [15, 16]. These two-band EVIs without a blue band could be computed from ASTER data. However, the ASTER sensor characteristics differ from those of MODIS. In particular, differences in sensor spectral bandpasses between ASTER and MODIS red and near-infrared (NIR) bands can lead to systematic differences in their reflectance values (Figure 1) (e.g., [17, 18, 19]). Thus, even when a two-band EVI is computed from ASTER data, the compatibility of ASTER EVI to MODIS EVI needs to be examined and evaluated for their synergistic applications. In this study, we developed a new module, _i.e._, vegetation index module, to the GEO Grid subsystem (called the ASTER Grid system), which generates ASTER EVIs. We incorporated the two forms of two-band EVI found in [15, 16] into the module. Likewise, we also developed an algorithm to compute the original three-band EVI using ASTER red and NIR bands and MODIS blue band. Thus, the performance and compatibility of these three ASTER EVI products from the GEO Grid with MODIS EVI were assessed. In the reminder of this paper, we first describe the developed algorithm to generate the GEO Grid ASTER EVI products in Section 2. In Section 3, we describe the methodology used to assess the ASTER EVI products, followed by results (Section 4). Conclusions and discussions of this study are presented in Section 5.
## 3 Experimental Setup
### Optical Transmission and Backscattering Setup
The calibration procedure detected the optical diffusion and the maximum detection depth in the frozen/unfrozen tissue by measuring the variation in transmitted and backscattered intensities, for different embedded depths \(x\) in the tissue, which ranged from 0 to 3 mm of the illumination fiber. Thus, for the transmission measurements the moving illumination fiber in array-A, was carefully aligned with the central detection fiber (T4) in array-B. Similarly, for backscattering measurements the central and peripheral fibers in the fiber arrays-A were used to measure the optical diffusion in the tissue for different depths \(x\) of the illumination fiber. Therefore, with the source-fiber fully embedded in the tissue, and in near contact with the central fiber (fiber T4) of array 2, the tissue was subjected to freeze/thaw cycle. Data acquisition was initiated for ten seconds prior to cryogen application, so the transmitted and backscattered optical intensities for room temperature could be recorded. Liquid nitrogen was subsequently applied on side-A for about 20 s, reaching a surface temperature measured on side-A and side-B about \(-\)100 \({}^{\circ}\)C and \(-\)65 \({}^{\circ}\)C respectively. Subsequently, the source fiber was retrieved is steps of 100 \(\upmu\)m and the process was repeated until the illumination fiber reached the surface of the PS. Using this calibration procedure, the optical data from the two arrays, could be used to correlate the transmission, and backscattering intensities and hence the depth of the frozen tissue. It should be noted that in real life experiments, transmission measurements cannot be obtained, and these calibration results assisted in developing a procedure based on backscattering intensities solely from array-A to determine the depth of freezing as will be outlined below.
# A Micro-Processor-Based Feedback Stabilization Scheme for High-Q, Non-Linear Silicon Resonators
###### Abstract  
Stabilization of silicon micro-resonators is a key requirement for their inclusion in larger photonic integrated circuits. In particular, thermal refractive index shift in non-linear applications can detune devices from their optimal working point. A cavity stabilization scheme using a micro-processor-based feedback control loop is presented based on a local thermal heater element on-chip. Using this method, a silicon \(\pi\)-phase shifted grating with a cavity Q-factor of 40 k is demonstrated to operate over an ambient temperature detuning range of 40 \({}^{\circ}\)C and injection wavelength range of 1.5 nm, nearly 3 orders of magnitude greater than the resonant cavity linewidth.  
silicon micro-resonators; Bragg gratings; non-linearities; stabilization +
Footnote †: journal: Applied
## 2 Literature Review
### Resources Allocation in FL  
At present, there are a large number of resource allocation studies on the energy-saving optimization of FL in wireless communication scenarios [26; 27; 28; 29; 30]. Here, we focus on resource allocation in the IoV scenario.  
In the IoV, the research on resource allocation mostly aims at high performance or energy efficiency. Among them, the research on resource allocation with high performance as the optimization goal focuses more on performance indicators, delay, and transmission rate. [PERSON] et al. jointly carried out calculation and URLLC resource allocation in [31] to ensure the stability of the C-V2X network. [PERSON] [32; 33] used the deep reinforcement learning method to allocate resources. [PERSON] et al. allocated communication resources in [32] to ensure the low transmission delay of users, and [PERSON] et al. allocated V2V resources in [33] to seek the optimal transmission bandwidth and power according to the interaction between V2V communication link agents and the environment.  
The research on resource allocation with high energy efficiency as the optimization goal focuses more on energy consumption. [PERSON] [34] proposed a cellular network uplink energy-saving transmission scheme based on V2X relay communication. [PERSON] [35] studied the resource management problem of maximizing vehicle energy efficiency and proposed a new resource allocation scheme based on Lyapunov theory.  
In the research on resource allocation, the above papers have either considered high performance or high energy efficiency, but there exist few studies that have combined the two goals to optimize them. Moreover, few studies simultaneously consider optimizing both the data quantity and resources for VUs.
# Towards Client Selection in Satellite Federated Learning
## 5 Simulation
### Experimental Environment, Datasets, and Hyper-Parameters  
The construction and simulation of constellations in this study were carried out using STK 12.2 and Python 3.9, and client access times (start and end) were obtained via the generation of files in JSON format. We built the SFL using Python 3.9, Pytorch 2.0 with CUDA 11.7. The experiment was executed on a personal computer equipped with Windows 11-64 bit system (Microsoft, Washington, DC, USA), and 64 GB of memory, a 36-thread Intel-i9-10980 XE CPU (Intel, Shanghai, China), and an NVIDIA RTX 3090 graphics card (ASUS, Taipei, China) with 24 GB of memory.  
The ResNet-50 [30] model was applied as the backbone network in this experiment, which consists of five stages each with a convolution and identity block. Each convolution block has three convolution layers and each identity block also has three convolution layers. The ResNet-50 has over 23 million trainable parameters, which is challenging to train but is capable of providing highly accurate results.  
The Fashion-MINIST [31] and CIFAR-100 [32] datasets were applied during the experimental phase. Fashion-MINIST is a dataset comprising 28 \(\times\) 28 grayscale images of 70,000 fashion products from 10 categories, with 7000 images per category, whose training set has 60,000 images and the test set has 10,000 images. CIFAR-100 has 100 classes containing 600 images each and there are 500 training images and 100 testing images per class, which include animals, foods, vehicles, flowers, etc.  
In the experiment, the training set was randomly distributed across the clients after being shuffled, and an overlap coefficient \(\zeta\) was set to allow for the possibility of identical data among different clients. The test set was deployed on the server side for evaluating the aggregated model. The term \(\zeta=1\) signifies that all clients possessed identical datasets, whereas \(\zeta=0\) implies that no client datasets overlapped. In one of our experiments, the \(\zeta\) was set at 0.2, indicating that each client contained 20% of the data common to one or more other clients.  
We have established 5 epochs for local training and a total of 200 epochs for global training. When clients have finished the five epochs of local learning, they immediately send their results to the PS. For model evaluation, the ratio coefficient \(\sigma\) was set to 0.2. In a single-round of training, the maximum number of client systems that can be selected is capped at 40. On the client side, there were three rounds of localized training, with the learning rate being fixed at \(\eta=0.01\) and the batch size configured to 32.
# An Improved Visual SLAM Based on Map Point Reliability under Dynamic Environments
## 3 Experiments
### Discussions about the Real-Time Performance  
To discuss the real-time performance of the proposed V-SLAM method, some comparisons are given, which are listed in Table 5. Here only the methods which have provided the values of time consumption are listed in this comparison.  
In Table 5, the time in our SLAM versus ORB-SLAM3 is the average mean time in Table 6. As we can gather from Table 5, our average elapsed time per frame is only 0.23 ms more than ORB-SLAM3. Among the compared SLAM, SLAM based on deep learning cannot run at the rate of 30 frames per second. Only DSLAM and our SLAM can run at that rate with CPU in the dynamic environments. On average, our SLAM consumes 13.14 ms less per frame than DSLAM. DynaSLAM has a better performance than our SLAM in highly dynamic sequences, however, it takes 736.48 ms per frame, far more than our SLAM. This shows that our SLAM has more plasticity than other SLAM, which can be further improved based on our SLAM at 30 frames per second.  
To further analyze the real time of the proposed method and ORB-SLAM3, the details of the time consumption per frame in the nine sequences are given in Table 6. As we can see from the table, the average mean time consumption per frame in our SLAM system is higher by about 0.23 ms than that of ORB-SLAM3, but the average median time consumption per frame in our SLAM system is less by about 0.1 ms compared to ORB-SLAM3. So, the overall  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Sequence**}} & \multicolumn{6}{c}{**ATE.RMSE of Trajectory Alignment (m)**} \\ \cline{2-9}  & **Co-Fusion [52]** & **MaskFusion [34]** & **DynaSLAM [28]** & **EM-Fusion [29]** & **DS-SLAM [30]** & **ROS-SLAM [23]** & **Our Method** \\ \hline \multirow{2}{*}{slightly} & fr2/4/person & - & - & - & - & - & **0.0074** \\  & fr2/s/static & 0.011 & 0.021 & - & 0.090 & **0.0065** & 0.0084 & 0.0066 \\  & fr3/s/xyz & 0.027 & 0.031 & **0.015** & 0.370 & - & - & 0.0151 \\  & fr3/s/rry & - & - & - & - & - & **0.0219** \\  & fr3/s/half & 0.036 & 0.052 & 0.017 & 0.032 & - & - & **0.0161** \\ \multirow{2}{*}{highly} & fr3/w/static & 0.551 & 0.035 & **0.006** & 0.014 & 0.0081 & 0.0206 & 0.0079 \\  & fr3/w/xyz & 0.696 & 0.104 & **0.015** & 0.066 & 0.0247 & 0.0571 & 0.0243 \\  & fr3/w/rry & - & - & **0.035** & - & 0.4442 & 0.1604 & 0.1121 \\  & fr3/w/half & 0.803 & 0.106 & 0.025 & 0.051 & 0.0303 & 0.0807 & **0.0181** \\ \multicolumn{1}{c}{Average} & 0.3540 & 0.0582 & **0.0188** & 0.1038 & 0.1028 & 0.0654 & 0.0255 \\ \hline \hline \end{tabular} Note: The best results are shown in bold. Not all papers provide results for all sequences, and some methods just provided the three decimal places of data.  
\end{table}
Table 4: The ATE.RMSE on the TUM benchmark, compared with the V-SLAM methods based on deep learning.  
real-time performance of the proposed method is very near to ORB-SLAM3. However, the accuracy of our method is obviously better than ORB-SLAM3.
# Underwater Glider Propulsion Systems VBS Part 1: VBS Sizing and Glider Performance Analysis
## 3 General Operation States of Underwater Gliders  
In the Handbook of Ocean Engineering [16], the underwater gliders are described as a winged vehicle propelled by buoyancy changes, for which the mechanical force of locomotion, necessary to overcome the drag of the vehicle as it moves through a fluid medium, is supplied by gravitational  
Figure 1: Chart of the glider performance analysis method focused on VBS sizing.  
force in the form of net buoyancy (positive or negative). The change in the net buoyancy is generated with the VBS system, which is the main propulsion system of the underwater gliders.  
Even though underwater gliders can operate in different modes such as spiraling motion [22] or virtual mooring [30; 31; 32]. The basic modes are based on their movement in the vertical plane (\(xz\)), generating the characteristic \"sawtooth pattern\" in each cycle of operation as shown in Figure 2, while sampling the physical and chemical parameters of the water with respect to the operating depth (water column sampling) through the installed scientific sensors and the sampling configuration designated by the glider pilot. The sensors that are most commonly installed in these vehicles are the CT (conductivity-temperature) and the CTD (conductivity-temperature-depth) sensor, with which the operating density of the vehicle is indirectly estimated during the mission.  
In Figure 2, four of the most important operating states of a glider are shown. These states correspond to the different state changes of the VBS and other vehicle subsystems which are used to generate the forward movement at a resultant glide angle.
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 5 Discussions
### Improvement Directions
\begin{array}{l}\delta x=\frac{D_{2}(1+\cos\theta_{1})-D_{1}(1+\cos \theta_{2})}{\sin\theta_{2}-\sin\theta_{1}+\sin(\theta_{2}-\theta_{1})}\\ \delta y=\frac{D_{1}\sin\theta_{2}-D_{2}\sin\theta_{1}}{\sin\theta_{2}-\sin \theta_{1}+\sin(\theta_{2}-\theta_{1})}\end{array} \right. \tag{43}\]  
where \(D_{a}\) (\(a=1\), 2) is the two-way displacement estimated by the \(a\)-th surveillance antenna and \(\theta_{a}\) is equal to arctan [\((x-x_{0}^{a})/y\)] with \(x_{0}^{a}\) being the center position of the \(a\)-th synthetic aperture. At last, it should be pointed out that, although it is assumed to be stationary, the TV satellite indeed has some movements [58]. In the current study with the focus on imaging a local area during a short-term measurement, it has been found that the displacement estimation error is small and thus the phase error caused by the movement of the TV satellite has not been considered. When a large observation scene is to be imaged and long-term measurements are considered, the plane wave approximation may not be suitable and the TV satellite position should be determined in advance to get better imaging and displacement estimation results. Figure 21: Displacement vector estimation by PB-GB-SAR with two surveillance antennas.
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 2 Materials and Methods
### Agronomic Aim and Crop Classification  
We conducted a systematic analysis to classify the agronomic aim, and the analyzed crop systems. Based on the scope of each study, a post hoc categorization was performed, assigning studies to one of ten application areas:  
* Crop mapping;
* Crop phenology prediction;
* Disease control;
* Disease identification;
* Disease prediction;
* Disease resistance assessment;
* Early disease detection;
* Plant health monitoring;
* Yield prediction;
* Generic.  
The same approach was applied to categorize the crops in which the algorithms are used. Specifically, 16 categories were established as follows:  
* Apple;
* Citrus;
* Coffee/tea;
* Forest trees;
* Fruits;
* Grapes;
* Maize;
* Nuts;
* Oil trees;
* Oil seeds;
* Rice;
* Small grains;
* Tomato/potato;
* Tropical;
* Vegetables;
* Generic.  
Studies with a broad or nonspecific focus were grouped under the \"Generic\" category for both application and crop type.  
Figure 8 shows the combinations of crops and agronomic aims that were found in the analyzed articles. The interest towards disease identification is dominant, while few papers treat other agronomic aims. Regarding the combination with crops, the group tomato/potato has the highest number of papers, 27, on disease identification, with a further 8 papers where the focus is on early disease detection. Maize (22), rice (18), and small  
Figure 7: Histogram showing the usage of the algorithm typology in the analyzed studies.  
grains (16) follow in the disease identification category, confirming the relevance of cereal studies. It should be noticed that 54 papers concentrate on disease identification without focusing on a specific crop, opening the method applicability to a wide range of cultures.