esa-sceva/satcom-chunk-collection huggingface dataset:
# COSMIC-2 Mission Summary at Three Years in Orbit
## 2 Mission Status and Milestones
### Data Processing Centers  
There are two data processing centers (DPCs) operated by UCAR's COSMIC Program and the Taiwan Central Weather Bureau (CWB). Both receive the level 0 downlink data and perform processing to level 1 and 2 products in near real-time (NRT) with the COSMIC Data Analysis and Archive Center (CDAAC) software framework [17]. The CWB additionally runs the TROPS system [18]. The DPCs are configured and maintained to provide automated processing with high availability.  
The processing steps are as follows:  
* Data intake and level 0 (mission/instrument specific) to level 1 (generic) data format conversion;
* Precise orbit determination (POD) using dual frequency pseudorange and phase with fixed precise orbits and clocks for the GPS constellation, using the aft POD antenna by default, typically achieving a precision of 10-15 cm 3D RMS [19; 20];
* Neutral atmosphere RO processing including computation of excess phase, bending angle and refractivity retrieval, and 1D-var retrieval using the European Centre for Medium-Range Weather Forecasts (ECMWF) operational short-term forecast as background [1; 21];
* Ionosphere processing including (1) ionospheric excess phase and electron density profiles [14], (2) absolute TEC for GPS and GLONASS [15; 22], (3) computation of scintillation amplitude, phase indices and geolocation, (4) IVM plasma composition, temperature, density, and velocity [16];
* Quality control, packaging in external data formats as required, and delivery of products to users [17]. In addition to NRT processing, the DPCs run post-processing on a monthly basis. The main differences versus NRT processing are:
* Collection of all available data (including downlinks delivered too late for NRT) into 24 h batches;
* Utilization of International GNSS Service Final GNSS orbits and clocks (the highest precision product);
* Daily POD using a 24 h arc;
* Utilization of the ERA5 short term-forecast as the 1D-var background. Finally, from time to time, all data are reprocessed using consistent algorithms and configuration in a manner similar to post-processing to benefit climate applications of the data. For COSMIC-2, this is being done the first time in summer 2022 for data going back to 1 August 2019. The data are expected to be released in the second half of 2022.  
Figure 1: COSMIC-2 ground station locations. Circle markers denote downlink stations and diamond markers indicate downlink/uplink capable stations.
# Seatbelt Detection Algorithm Improved with Lightweight Approach and Attention Mechanism
## 3 Methodologies
### Seattleblt Classification Based on RST-Net
#### 3.3.1 Lightweight Residual Network  
The overall idea utilized in this paper is to utilize the scaling coefficient in the Batch Normalization (BN) as a criterion for channel selection, evaluating the importance of channels without demanding tailored hardware. The scaling coefficient and network weights are first trained concurrently, and L1 regularization is subsequently introduced to penalize the scaling coefficient. Finally, the convolution channel with a small scaling coefficient is pruned. This pruning process helps obtain a lightweight network by removing redundant or less important channels. In particular, the training objective of our approach is presented by Equation (5):  
\[L=\sum_{(x,y)}l(f(x,w),y)+\lambda\sum_{\gamma\in\Gamma}g(\gamma) \tag{5}\]  
where \((x,y)\) represent the input and objective, respectively; W represents the trainable values; the former item corresponds to regular training loss; \(g(\gamma)=|\gamma|\) denotes L1-norm; and \(\lambda\) represents the penalty coefficient that regulates the value of the loss function.  
Batch normalization [39] is a classical improvement approach in most contemporary CNNs, typically being added after each convolution in Res-Net. Specifically, the purpose of BN is to facilitate aggregation and enhance the capacity of generalization and robustness, as formulated by Equation (6):  
\[\hat{z}=\frac{z_{in}-\mu_{B}}{\sqrt{\sigma_{B}^{2}+\epsilon}};z_{out}=\gamma \hat{z}+\beta \tag{6}\]  
Figure 4: Activation function diagrams for Mish, Relu, and Leaky Relu functions.  
where \(\mu_{B}\) and \(\sigma_{B}^{2}\) denote the average and regular variance of input features, respectively; \(\gamma\) and \(\beta\) represent the trainable scale coefficient and bias, respectively; and the utilization of \(\epsilon\) is to avoid situations where the regular variance is zero.  
After the preliminary training of the model, a global threshold \(\hat{r}\) is introduced to examine whether feature channels will be pruned. Figure 5 displays the procedure of the channel pruning technique. Eventually, we acquire a narrower RS-Net with fewer parameters compared to the original Res-Net through utilizing the channel pruning approach, which can be directly used for recognition missions based on CNNs, making it more accessible and practical for applications.
# Modified Equation of Shear Strength with Respect to Saturation
## 2 Theory  
### Bishop Shear Strength Model  
The shear strength of unsaturated soil put forward by [PERSON] [12] is well-suited to characterize the effect of suction on the unsaturated soil, and has been widely adopted in many geotechnical engineering applications. [PERSON] extended the definition of soil to unsaturated soil and modified the shear strength equation as follows:  
\[\tau_{f}=c^{\prime}+(\sigma-u_{a})tamb^{\prime}+\chi(u_{a}-u_{w})tan\phi^{\prime} \tag{1}\]  
where \(\tau_{f}\) is the shear strength; \(c^{\prime}\) is the effective cohesion; \(\phi^{\prime}\) is the effective angle of internal friction; \((\sigma-u_{a})\) is net normal stress on the failure plane at failure; \((u_{a}-u_{w})\) is the matric suction; \(\sigma\) is the total normal stress; \(u_{a}\) is the pore-air pressure; \(u_{w}\) is the pore-water pressure; \(\chi\) is a parameter dependent on degree of saturation. The extended Mohr-Coulomb failure envelope is shown in Figure 1.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 5 Experiments and Results
### In-Depth Analysis
#### 5.3.8 Processing Speeds  
For real time application, processing speed is just as vital as prediction accuracy. The average CPU processing times of our method and its individual components are listed in Table 4 (on an Intel Xeon E5 processor). The method performs the full analysis with a good real-time speed for a video resolution of 640 \(\times\) 480 pixels. The only bottleneck is the face finding and modelling step, which is modular w.r.t the rPPG pipeline and can be swapped out for a faster implementation.
## 3 Results and Discussion
### Flow Curves of Tested Film-Forming Solutions
#### 3.1.4 High-Methylated Apple Pectin Solutions with Pumpkin Puree  
Figure 1D shows the flow curves of solutions of highly methylated apple pectin with the addition of pumpkin puree. Additionally, in the case of these composite solutions, it was found that they are shear-thinning liquids without yield points, and the course of changes was described by the Ostwald [PERSON] model (1). At the same time, it was also observed that the apparent viscosity of solutions of highly methylated apple pectin with the addition of pumpkin puree increases with increasing concentration of the solutions, and the values range from 0.0460 to 0.1468 Pa-s. As in the case of soy protein isolate solutions with the addition of pumpkin puree, the parameter \(n\) in the case of highly methylated apple pectin solutions increases with the increase in the solution concentration.  
[PERSON] et al. [25] studied the rheological properties of carrot puree with the addition of 0.8% highly esterified pectin at a shear rate of \(\sim\)50 s\({}^{-1}\). They obtained a viscosity of 3.6 Pa-s, which indicates that the viscosity of the solution is significantly influenced by the hydrocolloid concentration and the level of puree addition, which was much lower in this study (40%). [PERSON] et al. [32] studied the rheological properties of peach puree jams with the addition of low-methyl pectin (0.4%) and sugar at 20 \({}^{\circ}\)C. The addition of sugar in jams ranged from 10 to 30%. The pH was adjusted to 3.4 with citric acid. The Herschel-Bulkley model was used to describe all flow curves. The apparent viscosity of peach jams ranged from 1.51 Pa-s for a jam with a 10% sugar concentration to 3.4 Pa-s for a jam with a 30% sugar concentration. The difference in the viscosity and nature of the tested jams, compared to the solutions of high methylated pectin with the addition of pumpkin puree tested in this study, results from the fact that peach jams, despite the lower content of pectin in the solution, gelled thanks to the addition of sugar. The difference in viscosity may also be because no water was added in the production of the jam, only pure peach puree.
# An Approach to Incorporating Implicit Knowledge in Object Detection Models
## 3 Problem Formulation and Preliminaries
### Modeling Implicit Knowledge in Object Detection Models  
A single-step object detection model generally consists of three parts: the backbone, responsible for feature extraction; the neck, which performs multi-scale feature fusion; and the head, which detects the position and class of objects based on the features extracted by the previous two parts.  
During the feature fusion process, the object detection model concatenates feature maps or feature vectors from different layers along the channel dimension, transforming features from different layers to the same size through interpolation or convolution. Shallow features, closer to the input data, typically have higher resolution and more detailed information, while deeper features contain higher-level semantic information and global context, albeit with lower spatial resolution but capturing broader contextual information. Since the feature maps from different layers contain varying information, sampling and concatenation may lead to feature aliasing issues [27], resulting in information loss. Therefore, we chose to incorporate implicit knowledge into the model during the feature fusion process to optimize feature representation, as illustrated in Figure 4.  
Let \(C_{i}(i\in 1,\ldots,n)\) denote the original feature maps input into the feature pyramid network (FPN) and the pyramid attention network (PAN) structures. The fused feature maps after fusion that compute \(h\) are represented as \(P_{i}(i\in 1,\ldots,n)\), where \(i\) represents the level of the feature maps. Therefore, the FPN and PAN stages can be expressed as  
\[P_{i+n}=h(C_{i},C_{i+1},\ldots,C_{i+n}) \tag{8}\]  
The incorporation of the fusion operation, which integrates implicit knowledge into the model, can be represented as  
\[P_{i+n}=h\big{(}C_{i}\mathbin{\textcircled{\char 37}}g_{\varphi}(z_{i}),C_{i+1} \mathbin{\textcircled{\char 37}}g_{\varphi}(z_{i+1}),\ldots,C_{i+n}\mathbin{ \textcircled{\char 37}}g_{\varphi}(z_{i+n})\big{)} \tag{9}\]
# Closing Force Evaluation of a Sample Return Capsule for a Phobos Sample Return Mission
## 4 Testing Set-Up  
To evaluate the necessary closing force, an electromechanical universal testing machine, Instron 3369 Dual Column Testing System, equipped with a 50 kN load cell was used (Instron, Norwood, MA, USA). The evaluation of the necessary closing force was made for a stroke of 8 mm, which corresponds to the insertion path stroke and a feed rate of 1 mm/s. In the real case, the insertion path is realized by releasing the compression springs of the closing mechanism. To properly dimension the compression springs of the closing mechanism, several tests are required to determine the most suitable geometry of the piston and to gather data regarding the closing force value considering different operational temperatures.  
Verification Philosophy  
Tests were conducted for both kinds of seals (Type I and Type II), for different configurations, such as:  
* Device equipped with large seal;
* Device equipped with small seal;
* Device equipped with both seals.  
Figure 11: Sample Container nominal positions during the Vault closing operations: (**a**) SC in the start position of the closing operation, (**b**) SC at the end of the closing operation (Vault closed and sealed).  
Figure 12: The device with seals mounted; (**a**) the starting position of the old piston in the cylinder; (**b**) the starting position of the new piston in the cylinder.  
As initially stated, the tests should be performed at operational temperatures, \(-40\)\({}^{\circ}\)C and \(80\)\({}^{\circ}\)C. Following the ECSS-E-ST-33-01C standard [19], the test temperatures shall take into consideration a \(\pm 5\)\({}^{\circ}\)C margin and also the worst-case scenario. Thus, by considering both the operational temperatures and ECSS standards, the tests were performed at \(-20\)\({}^{\circ}\)C (as an intermediate temperature evaluation), at \(-60\)\({}^{\circ}\)C (worst case scenario-the minimum operational temperature is \(-45\)\({}^{\circ}\)C), at \(20\)\({}^{\circ}\)C (environmental temperature) and \(+70\)\({}^{\circ}\)C. Testing at \(+85\)\({}^{\circ}\)C could not be performed due to a limitation of the test rig that could not maintain a stable elevated temperature. The temperature during the test was measured using a type T thermocouple (S.C. CAOM S.A, Pascani, Romania). As specified before, two piston shapes (conical and toroidal) were used for the force evaluation. The tests with only one seal (large or small) were performed to determine the behaviour for each independent seal, but for the compression spring dimensioning, the results obtained when the device was equipped with both seals were used. For each test, a test sequence was followed and conducted to ensure the traceability and repeatability of the test. The test is divided into several phases, as described in Table 3.
## 2 Fibre Design Philosophy  
Under compressive loading, concrete usually fails and is quite brittle. Besides conventional reinforcing, which leads to a more ductile failure mode, fibre reinforcement might also be an option to prevent concrete components from sudden failure. Steel fibres are the most common material in terms of fibre reinforcement. The design of steel fibres rests upon the material properties of the concrete (i.e., the bond behaviour) and the fibre material (i.e., the tensile strength) itself. Besides the better compressive behaviour of fibre-reinforced concrete, fibre reinforcement leads to a post-cracking tensile strength that might be considered in terms of tensile loading.  
Ultra-high-performance concrete with a compressive strength of more than 150 N/mm\({}^{2}\) usually requires very high fibre contents, leading to a relatively high post-cracking tensile strength. The design of UHPC-suited high-strength micro steel fibres, i.e., the geometry and the tensile strength, should lead to fibre pullout instead of fibre rupture under tensile loading. Therefore, the fibre material's tensile strength has to be higher than the bond strength between the micro steel fibre and UHPC.  
Under short-term static loading, in most UHPFRC components under bending, shear, and torsion, the desired fibre pullout always occurs [6; 15; 16].  
As weight-reduced UHPFRC structures are more susceptible to fatigue, the performance of micro steel fibres under cyclic loading has to be considered additionally. Here, the occurrence of the desired fibre pullout is not entirely clear yet. [PERSON] [12] assumed that cyclic loading causes fibre pullout under the top load and slips back into the concrete channel by elastic rebound under the bottom load. The cyclic pulling out and slipping back abrades the fibre's surface. After a certain number of load cycles, the abrasion progresses so far, and there is no longer any \"bond\"; thus, the fibres pull out of the concrete matrix. Furthermore, it might also be possible that a so-called \"tribocorrosion\" (the combination of wear and corrosion) of fibres in the crack area occurs. This combination might lead to a faster decreasing cross-sectional area of the fibre, and an unexpected material failure might happen.
## 5 Conclusions  
In this study, the effects of mineral grain shape on the mechanical properties of LDB granite were investigated by means of uniaxial compression simulations. Based on the CLUMP method in PFC2D, three different grain shapes were constructed: strip, triangle, and square. The relationship between the grain shapes and rock mechanical properties was analyzed, and the main conclusions are as follows:  
1. The elastic modulus and peak strength of rock are affected by the mineral grain shape. In our study, the model with strip-shaped grains had the largest elastic modulus and peak strength, followed by the triangular grain and square grain models. The mechanism of grain shape effect on the peak strength and elastic modulus was mainly achieved by affecting the porosity of the model. The increasing porosity led to a smaller peak strength and elastic modulus.
2. Flatness and roughness can describe the overall contour and surface morphology of mineral grains well. The effects of grain flatness and surface roughness on rock peak strength are the opposite. A flat grain helps to increase rock strength, while rock strength is reduced due to increased porosity if the grain has higher roughness. Further, the relationship between the peak strength and the cohesion of rock is positively correlated.
3. Here, the generated microcracks were mainly of the tension type, and the model with square grains had the maximum ratio of tensile cracking, followed by the triangular grain and strip-shaped grain models. The shape of the mineral grain also had a significant effect on rock mechanical behaviors at the postpeak stage. The stress drop became slower with an increasing surface roughness because the interlocking restrained the slip and rotation of grains on the fracture surface.  
However, there were some defects in the modeling. The grain shapes adopted in this study were simple and single, while real mineral grain shapes are very complex. Since the CLUMP method can realize the construction of complex grain shapes, future works should connect this study to real rocks. In addition, three-dimensional grain shape effects should also be investigated in future studies.  
[PERSON] and [PERSON] designed the theoretical framework; [PERSON] wrote the basic code of the program and designed the numerical simulation; [PERSON] checked the simulation results.  
This research was funded by the National Natural Science Foundation of China (Grant Nos. 41572312, 41672321), China Postdoctoral Science Foundation (Grant Nos. 2018M630204, 2019T120133), and China Scholarship Council (Grant No. 201804910293) to the third author.  
Figure 13: Stress–strain curves of three different grain shape models (case study of model with shape number 6).
# Internet of Things Meets Computer Vision to Make an Intelligent Pest Monitoring Network
## 2 Material and Methods
### Computer Vision  
Computer Vision (CV) is an interdisciplinary research area that processes digital images or videos to extract information [21]. Currently, CV is strongly based on artificial intelligence, namely, on machine learning and deep learning models that replace human-visual tasks, such as animal counting and crop or pest monitoring. In the specific context of this work, we can divide CV into image classification and object detection. Typically, image classification is used to classify an image, but it can be adapted to classify multiple objects in the same image. For that, we have to segment the objects in the image, extract their most relevant features, and then apply the classification model to each object. Object detection models incorporate all those steps into a single model and allow for the classification of multiple objects in the same image.  
In pest monitoring, the goal is usually to detect and count the pests in each trap, and not to holistically classify the trap; hence, object detection models are usually more suitable. There are two main types of object detection models: (1) two-phase models, such as the Faster Region-Convolution Neural Network (Faster R-CNN) and (2) one-phase models, such as You Only Look Once (YOLO) [22].  
Figure 2: Network topology (adapted from [18]).
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## Preliminaries
### Fundamental Concept of the Energy-Efficiency
#### 3.3.1 Energy Consumption for Hop-by-Hop Networks  
For the multi-hop network where the Hop-by-Hop transport scheme is implemented, it is possible to model the transmission failure and success using a Bernoulli stochastic process, where \(P_{\text{S}}\) and \(\mathcal{E}(M)\), that respectively represent the success rate and the expected number of global transmissions in the network, can be theoretically obtained by using the following lemma.  
**Lemma 4**: _For a system in which a transport scheme Hop-by-Hop is implemented, the packet arrival success probability is given by:_  
\[P_{\text{S}}=[1-(1-p)^{L}]^{N} \tag{15}\]  
_and the mathematical expectation of the global number of transmissions depends on the variables \(L\), \(N\) and \(p\), given by:_  
\[\mathcal{E}(M)=\left\{\begin{array}{ll}\frac{[1-(1-p^{2})^{L}]}{p^{2}(1+p)^ {-1}}\left[\frac{1-[1-(1-p)^{L}]^{N}}{(1-p)^{L}},\right]&\text{if $L<\infty$,}\\ \frac{N(1+p)}{p^{2}},&\text{if $L$ unlimited}.\end{array}\right. \tag{16}\]  
_where \(p\), \(N\) and \(L\) represent, respectively, the probability of transmission success, the number of communication links and maximum number of transmissions per packet._Equations (15) and (16) impose that the probability of success of a data packet transmission and the probability of success of the acknowledgment packets must have the same value. The proof for Equation (16) in Lemma 4 appears in [6], such that it can be calculated using a computational package [43] (\(HBH\) (\(p_{1}\), \(p_{2}\), \(L\), \(N\)) for R Package 'hopbyhop' with \(p=p_{1}=p_{2}\)).