esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Clock Generation and Time Distribution System for Hyper-Kamiokande +
###### Abstract  
The construction of the next-generation water Cherenkov detector Hyper-Kamiokande (HK) has started. It will have about a ten times larger fiducial volume compared to the existing Super-Kamiokande detector, as well as increased detection performances. The data collection process is planned from 2027 onwards. Time stability is crucial, as detecting physics events relies on reconstructing Cherenkov rings based on the coincidence between the photomultipliers. The above requires a distributed clock jitter at each endpoint that is smaller than 100 ps. In addition, since this detector will be mainly used to detect neutrinos produced by the J-PARC accelerator in Tokai, each event needs to be timed-tagged with a precision better than 100 ns, with respect to UTC, in order to be associated with a proton spill from J-PARC or the events observed in other detectors for multi-messenger astronomy. The HK collaboration is in an R&D phase and several groups are working in parallel for the electronics system. This proceeding will present the studies performed at LPNHE (Paris) related to a novel design for the time synchronization system in Kamioka with respect to the previous KamiokaNDE series of experiments. We will discuss the clock generation, including the connection scheme between the GNSS receiver (Septentrio) and the atomic clock (free-running Rubidium), the precise calibration of the atomic clock and algorithms to account for errors on satellites orbits, the redundancy of the system, and a two-stage distribution system that sends the clock and various timing-sensitive information to each front-end electronics module, using a custom protocol.  
Keywords:neutrinos; detection; timing; atomic clocks; GNSS +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
most importantly at long term, the construction of a new far detector Hyper-Kamiokande (HK) [4] on the symmetric site to SK with respect to the beam axis (same off-axis angle). It will be based on the same technology as SK, but eight times larger and with new photomultipliers (PMTs), a new digitizer, and a new timing system which will be the main topic of this paper. With the start of the data collection planned for 2027, the increased statistics and performance of HK will allow for the measurement of the CP violation phase, the determination of neutrino mass hierarchy, higher precision in measuring the atmospheric neutrino oscillation parameters, and will open the door to other neutrino detection challenges such as Supernovae bursts and diffuse background, or neutrinos' time of flight measurements as in [5].
## 4 Security Attacks
### Application Layer
#### 4.5.4 Dictionary Attack  
A dictionary attack [50] involves an adversary systematically attempting to guess a target node's password by trying a large number of likely possibilities. This attack becomes feasible when the target employs weak or easily guessable passwords. The effectiveness of such an attack is directly related to the randomness and strength of the hash generator used to secure the password. To mitigate dictionary attacks, it is essential to enforce strong password policies and implement multifactor authentication schemes, which add additional layers of security beyond just a password.
## 2 Related Work
### Multi-Person Pose Estimation in Images
#### 2.1.1 Two-Stage Approaches  
The two-stage approaches can be further divided into bottom-up and top-down methods.  
* Bottom-Up Approaches: These approaches start by detecting all possible keypoints in an image and then group them to form individual skeletons. Several improvements have been made in bottom-up approaches, such as optimizing keypoints regression [8] and improving heat map prediction methods [9]. However, these approaches often ignore global information from other body parts and the overall target, relying solely on keypoints detection. They also face challenges in handling scale variation in images with small individuals.
* Top-Down Approaches: In contrast to the bottom-up approach, top-down approaches first detect the bounding boxes for all persons in the image and then perform single-person pose estimation within each detected bounding box. However, when the detection target is severely occluded, the deviation caused by bounding box detection can lead to failure. To address this issue, subsequent work has explored techniques based on Convolutional Neural Networks (CNNs) or transformer frameworks. For example, an Image Guided Progressive Graph Convolutional Networks (GCN) module [10] was proposed to infer the position of invisible joints based on action types and image context. However, top-down approaches typically involve two stages: initial pose estimation and pose correction, which affect their efficiency. Transformer-based methods, such as ViT-Pose [11], offer simplicity, scalability, and flexibility, but their larger model size and input resolution hinder their effective application to video-based pose estimation. A common limitation of top-down approaches is that their inference time increases with the number of people in the image.
* A hybrid solution [6] that combines the two approaches has been explored, where bottom-up pose estimation models are used as detectors instead of classic object detectors. These models provide detection boxes and pose proposals as conditions for subsequent attention-based top-down models. However, the performance of different sampling strategies for these conditions, such as empirical sampling and generative sampling, requires further exploration.
###### Abstract  
The photochemical reflectance index (PRI) is a proxy for light use efficiency (LUE), and is used in remote sensing to measure plant stress and photosynthetic downregulation in plant canopies. It is known to depend on local light conditions within a canopy indicating non-photosynthetic quenching of incident radiation. Additionally, when measured from a distance, canopy PRI depends on shadow fraction--the fraction of shaded foliage in the instantaneous field of view of the sensor--due to observation geometry. Our aim is to quantify the extent to which sunlit fraction alone can describe variations in PRI so that it would be possible to correct for its variation and identify other possible factors affecting the PRI-sunlit fraction relationship. We used a high spatial and spectral resolution Aisa Eagle airborne imaging spectrometer above a boreal Scots pine site in Finland (Hyytiala forest research station, 61\({}^{\circ}\)50\({}^{\prime}\)N, 24\({}^{\circ}\)17\({}^{\prime}\)E), with the sensor looking in nadir and tilted (off-nadir) directions. The spectral resolution of the data was 4.6 nm, and the spatial resolution was 0.6 m. We compared the PRI for three different scatter angles (\(\beta=19^{\circ}\), 55\({}^{\circ}\) and 76\({}^{\circ}\), defined as the angle between sensor and solar directions) at the forest stand level, and observed a small (0.006) but statistically significant (\(p<0.01\)) difference in stand PRI. We found that stand mean PRI was not a direct function of sunlit fraction. However, for each scatter angle separately, we found a clear non-linear relationship between PRI and sunlit fraction. The relationship was systematic and had a similar shape for all of the scatter angles. As the PRI-sunlit fraction curves for the different scatter angles were shifted with respect to each other, no universal curve could be found causing the observed independence of canopy PRI from the average sunlit fraction of each view direction. We found the shifts of the curves to be related to a leaf structural effect on canopy scattering: the ratio of needle spectral reflectance to transmittance. We demonstrate that modeling PRI-sunlit fraction relationships using high spatial resolution imaging spectroscopy data is suitable and needed in order to quantify PRI variations over forest canopies.  
multi-angular airborne imaging spectroscopy; photochemical reflectance index; shadow fraction; sunlit fraction; Scots pine; hyperspectral imaging; spectral information divergence +
Footnote †: journal: Remote Sensing
## 4 Model Establishment and Evaluation
### Model Establishment  
#### 4.1.1 AE Model and AE-ED  
As mentioned in Section 2.2, an autoencoder (AE) can reconstruct data. In the process of the encoder mapping the input data to the feature space, the characteristic information of the data, including the data distribution, is stored in this feature space. The process of decoding is similar to reconstructing data according to the characteristic information.  
The AE is the feature extractor, which can extract representative features from the original data, used to describe the properties, structures, and characteristics of the data. After training the AE model using the training set, the characteristic information of the training set is saved in the AE model, which can reconstruct the training set data very well. For each new piece of input data of the test set, we can feed it into the AE model to reconstruct the data. If the reconstructed data is very different from the new input data, then the data similarity between the new input data and the training set is low; otherwise, the data similarity is high. In order to quantify the difference between the new input data and reconstructed data, we define a metric named the AE-ED, which stands for \"the Euclidean Distance between input data and the reconstructed data from the autoencoder model\". The calculation formula of the AE-ED is given in Section 2.2.  
We chose continuous well interval data to test and evaluate the model, and the data used are all from Well X in the Middle East. First, we established the AE model and calculated the data similarity of Well X. The results are shown in Figure 6.  
The blue line represents the AE-ED value. For each new piece of input data of Well X, we can calculate its AE-ED value according to the above method. The red dotted line is the AE-ED threshold. The AE-ED value quantitatively expresses the data similarity, and the AE-ED threshold is the key judgment condition of the hybrid model. They will be mentioned in the establishment of the hybrid model based on data similarity below.  
Figure 6: The AE-ED value of Well X.
# Evaluation of Eccentric Compressive Behavior Between Active and Passive Preloading in CFST
## 3 Definition of the Specimens  
### Influencing Parameters  
To quantitatively compare the performance of the preloaded CFST columns, five determining factors had to be considered, which were the preloading methods, eccentric ratio (\(e/r\)), concrete cubic compressive strength (\(f_{\rm cu}\)), steel yield strength (\(f_{\rm y}\)), and steel tube thickness (\(t\)). Except for the abovementioned four representative preloaded CFSTs, the remaining four categories of influencing factors were 0.2, 0.5, 0.8, and 1.1 for the eccentric ratio [42], C25, C35, and C45 for the concrete cubic compressive strength, 275 MPa, 355 MPa, and 420 MPa for the steel yield strength, and 2 mm, 4 mm, and 6 mm for the steel thickness. Additionally, consistent with [PERSON]'s work, the diameter and the full length of the specimen were 250 mm and 1000 mm, respectively.
# Evaluation of Combat Helmet Behavior under Blunt Impact
## 3 Results
### Flat Anvil Surface
#### 3.1.2 Head Injury Criterion (HIC)  
The results for the different configurations are summarized in Table 7. The maximum HIC is 1245 in the frontal zone, whereas the minimum HIC is 868 for rear impacts according to the NHTSA criteria, the HIC threshold is 1000. Therefore, the only configuration that overcomes the limit value is in the frontal impact leading to severe injuries. However, all values are lower than the European Standard, EC ER22/05, in which the HIC threshold is 2400.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Head Model**} & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**PLA (g)**} & \multicolumn{2}{c}{**AIS head**} & \multirow{2}{*}{**AIS Scale**} \\  & & & & & **(Equation (6))** & \\ \hline \multirow{4}{*}{**Head surrogate**} & Top & 263 & yes & 2.99 & 3 \\  & Frontal & 270 & yes & 3.03 & 3 \\  & Rear & 210 & Yes & 2.61 & 2 with a 61\% of probability to produces level 3 \\  & Lateral & 252 & yes & 2.91 & 3 \\ \hline \multirow{4}{*}{**Human head**} & Top & 293 & No & 3.18 & 3 \\  & Frontal & 258 & Yes & 2.95 & 3 \\ \cline{1-1}  & Rear & 236 & Yes & 2.8 & 2 with 80\% of probability to produces level 3 \\ \cline{1-1}  & Lateral & 223 & Yes & 2.7 & 2 with a 70\% of probability to produces level 3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results for an impact velocity of 4.4 m/s for the head surrogate and the human head.  
Figure 10: The acceleration evolution comparison of both head models used for each impact location. (**a**) Top impact; (**b**) Frontal impact; (**c**) Rear impact; (**d**) Lateral impact.  
For the human head configurations, the maximum HIC is reached at the top location, i.e., HIC = 2198, and the minimum HIC is located in the rear zone, i.e., HIC = 995. No serious injuries are found for rear impact according to NHTSA. Nevertheless, for the other zone, the severity of injuries can be caused. According to the EC ER22/05 standard, all the results are within the ranges allowed for this standard.  
The risk curves for both configurations are defined to relate the HIC value to the AIS level, according to [PERSON] et al. (2014), and are shown in Table 7. According to our results, for all locations with the head surrogate, a high probability of level 1 and 2 injuries is achieved. However, the probability of AIS \(\geq\) 3 is reduced by approximately 50% for all locations, except for the case of front impact, where tthe probability is 77%. The worst-case impact is found in the top localization since the probability of AIS 6 is 50%. AIS 6 is associated with the death of an individual.
# Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
## 3 Experiments Results and Analysis  
### Experiments Scheme  
The experiments for step, ramp and square wave faults are carried out to evaluate the method performance. We add the faults to the 1 st gyroscope in 500 s. The gyroscope error contains bias, scale factor error, installation error, random walk. The parameters used in experiments are shown in Table 2.
## 3 Numerical Results
### Case of a Mattress of 100 mm on Reinforced Soil (R100)
#### 3.2.1 Soil Arching  
Figures 15 and 16 show a slight decrease in the pile efficacy with respect to the number of loading cycles in tests C1_R100 and C2_R100. A reasonably good agreement between the experimental and both numerical results is observed. Similarly to the former studies [1; 2; 3; 5; 10; 29], the arching effect decrease under the number of load cycles is observed.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cyclic Loading Tests**} & \multicolumn{3}{c}{**Test C1\_D100**} & \multicolumn{3}{c}{**Test C2\_D100**} \\ \cline{2-7}  & **1 st Cycle** & **30 th Cycle** & **50 th Cycle** & **1 st Cycle** & **30 th Cycle** & **50 th Cycle** \\ \hline Experimental & 2 & 7.5 & 9.6 & 0.2 & 2.8 & 3.6 \\ \hline Numerical (HYP model) & 2.2 & 8.4 & 10.1 & 0.2 & 3.4 & 4.2 \\ \hline Numerical (CYsoil model) & 4.1 & 18.7 & 20.0 & 2.4 & 9.2 & 9.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: A comparison of tests C1_D100 and C2_D100 in terms of cumulative settlement with the specific number of load cycles (in millimeter).  
Figure 14: Experimental and numerical cumulative settlements in test C2_D100.  
As compared to those mentioned in Figures 9 and 10, the pile efficacy in slab the case nearly double those without slab, 82% compared to 43%. The existence of a rigid slab on mattress-reinforced soil results in an increase in the pile efficacy.
## 3 Methodology
### Basic Definitions of Traffic Spatiotemporal Parameters  
A comprehensive analysis of the travel time and the travel time under free-flow speed can be used to determine the traffic operating status, as shown in the first part of Equation (1), which judges the traffic operating status from the time dimension, or in a macroscopic sense. In urban road conditions, the interactions between vehicles, such as vehicle-vehicle spacing parameters and traffic control, are important factors leading to changes in traffic flow status. As shown in Figure 1, vehicles are distributed within the lanes upstream and downstream of the intersection. Therefore, a comprehensive analysis of vehicle queue lengths and travel time parameters can be used to evaluate the road traffic operating status.  
Defining the traffic spatiotemporal state as TP, using the traffic state evaluation method based on the free flow speed and travel time, and accounting for the uneven distribution of road density, the improved evaluation formula for the length of the vehicle queue in the upstream and downstream sections of the intersection during the analysis period is given by Equation (1):  
\[\begin{array}{ll}TP&=a\cdot\frac{T_{v}-T_{vf}}{T_{vf}}+(1-a)\cdot\left( \frac{L_{u}}{L_{u}+L_{d}}\cdot\frac{L_{up}}{L_{u}}+\frac{L_{d}}{L_{u}+L_{d}} \cdot\frac{L_{dp}}{L_{d}}\right)\\ &=a\cdot\frac{T_{v}-T_{vf}}{T_{vf}}+(1-a)\cdot\left(\frac{L_{up}}{L_{u}+L_{d}} +\frac{L_{dp}}{L_{u}+L_{d}}\right)\\ &=a\cdot\frac{T_{v}-T_{vf}}{T_{vf}}+(1-a)\cdot\frac{L_{up}+L_{dp}}{L_{u}+L_{ d}}\end{array} \tag{1}\]  
where \(\alpha\) is the weight of the travel time parameter, \(T_{v}\) is the travel time of the road section, \(T_{vf}\) is the travel time of the road section under free flow speed, \(L_{u}\) is the length of the upstream road section, \(L_{d}\) is the length of the downstream road section, \(L_{up}\) is the length of the vehicle queue in the upstream road section, and \(L_{dp}\) is the length of the vehicle queue in the downstream road section.
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 1 Introduction
For Galileo, it is recommended to use E1 and E5a dual-frequency signals and the ephemeris corrections are referenced to the E5a F/NAV navigation message. For GLONASS, it is advised to use L1 OC and L3 OC dual-frequency signals and the ephemeris corrections reference is to be determined (TBD). BDSBAS DFMC message mainly contains GNSS ephemeris and clock error corrections, integrity messages, clock-ephemeris covariance matrix, degradation parameters, and SBAS satellites ephemeris and almanacs. Compared with SF service, the DFMC service increases the SBAS availability and performance by direct mitigation of ionospheric delay with dual-frequency and inclusion of additional GNSS constellations such as BDS, Galileo and GLONASS, aiming to achieve CAT-I precision approach. The DFMC service is intended to support any combination of constellations, including only a single constellation operation. At the current test stage, the BDSBAS DFMC service provides BDS and GPS augmentation information, and will gradually increases the dual-frequency augmentation information for the Galileo and GLONASS systems. Table 1 shows the comparison of the BDSBAS SF service and the DFMC service. The BDSBAS SF service has already begun to provide initial service since 31 July 2020. Reference [20] shows the BDSBAS general design, system time datum, coordinate reference system and signal characteristics, [21] analyzed the pseudorange bias errors of BDSBAS monitoring receivers and their effect on the performance of the BDSBAS service. A general aviation flight test performance of BDSBAS was given by [22]. We focus to introduce the BDSBAS architecture design, user algorithms of broadcast messages and preliminarily evaluated the system performance of accuracy, integrity and availability in accordance with ICAO standards based on monitoring receivers distributed in the China service area with the real observations.
# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 2 Materials and Methods
### Network Architecture  
The architecture selected for the task of pollen classification using the features provided by the Rapid-E device is a multiple input convolutional neural network (CNN) [11], as depicted in Figure 2. Because the features are very heterogeneous, care has to be taken so that a neural network is able to use all the feature types. For this reason, a convolutional neural network [12] architecture was selected. In Figure 2, the architecture overview is presented. This architecture can be broken down into the following 4 main components: three feature extractors (FE), one for each of the three types of features, and a fully connected dense neural network [13] that uses the concatenated outputs of the feature extractors to make a classification.  
The feature extractors can be further broken down into convolutional blocks. Each block is built using one or more identical convolution layers with ReLU activations [14],  
Figure 1: The three features provided by the Rapid-E instrument. From left to right: scattering image, fluorescence spectrum and lifetime signal.  
followed by a batch normalization layer [15] and a max pooling layer [16]. After each max pooling layer the number of filters in the convolution layer is doubled to allow the model to cope with the reduction in the dimensionality introduced by the max pooling operation.
# EvoSplit: An Evolutionary Approach to Split a Multi-Label Data Set into Disjoint Subsets
## 6 Application to Large Image Data Sets
### Results  
The measures of the application of the different methods to split these data sets are shown in Tables 10-12. In almost all the cases (in bold), any evolutionary approach, either single-objective or multi-objective, either constrained or unconstrained, performs better than the official or stratified splitting methods. Similarly to the results shown for traditional smaller data sets, MOEA shows the best combined results for the Label Distribution and the Label Pair Distribution in almost all the cases. For the Microsoft COCO and the OpenImages data sets the results for those measures are improved by one or more orders of magnitude with respect to the official splits, i.e., those offered by the providers of the data set.  
With these data sets it is even clearer the effect of using the constrained approach, in which the goal is to include all the labels in every fold. The introduction of the constraint allows to obtain \(FZ\) and \(FLZ\) equal to zero for the OpenImages data set, while their values for the official split are 1 and 9 respectively. \(FLZ\) is dramatically reduced for the Imagenet data set (361 vs. 19 using MOEA).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Official** & **Random** & **Stratification** & \multicolumn{3}{c}{**Evolutionary Algorithm**} \\ \cline{4-7}  & & & **LD** & **LPD** & **MOEA** \\ \hline
**LD** & \(2.42\times 10^{-4}\) & \(5.06\times 10^{-4}\) & \(2.54\times 10^{-4}\) & \(\mathbf{3.10\times 10^{-5}}\) & \(2.76\times 10^{-4}\) & \(\mathbf{1.51\times 10^{-5}}\) \\ \hline
**LPD** & \(7.24\times 10^{-6}\) & \(1.28\times 10^{-5}\) & \(\mathbf{5.66\times 10^{-6}}\) & \(1.22\times 10^{-5}\) & \(\mathbf{6.48\times 10^{-6}}\) & \(\mathbf{3.96\times 10^{-6}}\) \\ \hline
**ED** & \(0\) & \(0\) & \(934\) & \(0\) & \(0\) & \(0\) \\ \hline
**FZ** & \(0\) & \(1\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline
**FLZ** & \(0\) & \(1\) & \(0\) & \(0\) & \(0\) & \(0\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Evaluation of the different splitting approaches of the Microsoft COCO data set. In bold the results that are better than the official method, usually employed in the literature. The results of the constrained alternative is not presented as the unconstrained approach obtains \(FZ\) and \(FLZ\) measures equal to zero.