esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of River Velocity and Discharge Based on Video Images and Deep Learning
## 2 Theory and Methods
### Construction of Datasets  
The texture direction of the space-time images generated by the river video is chaotic, which is difficult to judge directly and accurately. Therefore, the ST images are converted into a frequency domain representation, as shown in Figure 2. The image feature extraction ability of deep convolutional neural network is utilized to obtain the mapping relationships from image space to angle space from a large number of training data, and then uses the learned relationship for angle prediction, which is a simple and efficient process with end-to-end implementation.  
At present, the STIV method based on deep learning lacks publicly available image datasets of the river, so a high-definition video camera is installed at a fixed position on the bank of the hydrological station to record river flow videos at different time periods to construct the datasets. The common texture angle of the STIs is [5\({}^{\circ}\), 85\({}^{\circ}\)]. Considering the difficulty of collecting river video in specific complex scenes and making the constructed dataset representative, four common scenes, including normal, exposure, turbulence, and blur conditions, are selected to generate STIs. Artificial spectrum labeling is adopted to label texture angle sizes, and unified integer as classification labels. The STIs generated under real river conditions contain a lot of noise, which may be completely treated as such datasets will make the model learn the noise patterns during training, and the angle values may be incorrectly detected, affecting the accuracy of the datasets. Therefore, some synthetic images created with Berlin noise are incorporated into the datasets, which are ideal STIs and have the characteristics of no noise interference and accurate known MOT values.  
Since it is not possible to generate STIs for all angles, it is necessary to augment the original images to increase the diversity and variability of training data. The data augmentation process involves the following steps: the images are rotated in 1-degree steps with the center of the images as the origin to generate 81 classes of space-time images with a given texture angle, and then these images are transformed into frequency domain representation with an image size of 224 \(\times\) 224 pixels for training. Figure 3 shows the images of the datasets in various scenarios randomly selected, and the sequence is normal, exposure, turbulence, blur, and synthesis. Table 1 displays the dataset's distribution.  
Figure 2: The process of FFT.
# Deep Cross-Network Alignment with Anchor Node Pair Diverse Local Structure
## 4 Experiment
### Baseline Methods  
We choose six network alignment methods as baselines to validate the effectiveness of our method. The comparison methods are listed as follows:  
* PALE [12] first learns the individual embeddings of two networks by existing single network embedding methods LINE. Then, it learns a mapping function to match the latent space of the two networks and predicts anchor node pairs by calculating the distance of embeddings.
* CrossMNA [16] uses two vectors to preserve the common features of the anchor nodes in different networks (inter-vector) and the specific structural feature for a node in its selected network (intra-vector) respectively. Additionally, it uses the inter-vector to align nodes in different networks.
* CAMU [15] learns network embedding by considering network structure and node attribute information. Additionally, then it learns a mapping function to decrease the representation distribution discrepancy of different networks.
* BRIGHT [35] builds a specific unified space using labeled anchor links as landmarks using random walk with restart (RWR), and then employs a common linear layer to determine the significance of the RWR scores at various dimensions.
* NeXtAlign [46] uses a special relational graph convolutional network (RelGCN) to encode the alignment consistency.
* DHNA [31] uses a variational autoencoder to learn node embeddings, and considers the different anchor nodes' degrees across networks.
# A Stackelberg Trust-Based Human-Robot Collaboration Framework for Warehouse Picking
## 3 Theoretical Foundations
### Probabilistic Model Checking  
Model checking is an automated verification technique used to ensure finite-state systems satisfy specific properties or specifications. Probabilistic model checking is a branch of this technique focusing on systems with probabilistic or nondeterministic behaviors. Unlike traditional model checking, which primarily verifies Boolean logic properties, probabilistic model checking quantifies the likelihood of certain system behaviors meeting given specifications. Probabilistic attributes capture uncertainty and randomness in the environment, as well as nondeterministic decision-making in models [34].  
Markov decision processes (MDPs) are commonly used for modeling and reasoning about agents' strategic behavior in stochastic environments. System properties are specified using temporal logics such as CTL (computation tree logic) or its probabilistic extension, PCTL (probabilistic CTL). The Partially Observable Markov Decision Process (POMDP) extends MDPs by introducing partial observability of the environment. It allows for modeling and analysis under conditions where agents' abilities to observe the environment are limited. PCTL is further extended to describe the behaviors of systems with probabilistic dynamics, incorporating reward structures and constraints [35].  
Model checking is crucial for designing and analyzing communication networks, stochastic algorithms, embedded systems, and other systems involving probabilistic decision-making and uncertainty.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 3 IoT Devices and Cloud Processing
### Data Transmission Technologies
#### 3.2.2 Sigfox  
Sigfox is a company based in France providing network connectivity within the IoT through its eponymous wireless technology. It was founded in 2010 with a focus on low-consumption IoT devices that require the transmission of small amounts of data over longer distances [87].  
Sigfox can operate in either simplex (one-way) or half-duplex (two-way) mode. An upload packet can comprise a maximum of 12 bytes of payload, while a download packet is limited to 8 bytes of payload. The operational range of this network reaches up to 40 km [87; 88]. Such a value is achieved by combining several techniques. The first of them is the use of ultra-narrowband (UNB); such a narrow frequency selection increases the signal-to-noise ratio (SNR). A combination of Gaussian Frequency-Shift Keying (GFSK) and Differential Binary Phase-Shift Keying (DBPSK) is used for signal modulation in the physical layer. Delivery reliability is increased by sending the same message multiple times. Duplicate messages are sent on randomly selected frequencies with random delays from the first one. Sigfox relies on a license-free band reserved for industrial, scientific and medical (ISM) purposes. The specific frequency then depends on the deployment region. In addition to the maximum transmission power, the regulation also sets a duty cycle limit. Because of this, the end device is limited to 140 messages per day in the case of sending and 4 messages in the case of receiving [88; 89].
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PSs**} & \multirow{2}{*}{\(\pi_{m}\)} & \multicolumn{3}{c}{**GA [22]**} & \multicolumn{3}{c}{**PSO [23]**} & \multicolumn{3}{c}{**GSA [16]**} & \multicolumn{3}{c}{**OGSA**} \\ \cline{2-13}  & & \(\phi_{m}\) & **PG** & **Profit** & \(\phi_{m}\) & **PG** & **Profit** & \(\phi_{m}\) & **PG** & **Profit** & \(\phi_{m}\) & **PG** & **Profit** \\ \hline
1 & 1.7365 & 0.020926 & 481.97 & 4466.11 & 0.022242 & 460.8 & 4367.64 & 0.021140 & 503.3 & 4923.36 & 0.021315 & 510.02 & 5102.39 \\
2 & 10 & 0.093054 & 19.58 & 31.85 & 0.085062 & 23.34 & 40.90 & 0.095375 & 24.9 & 52.94 & 0.118167 & 22.07 & 52.68 \\
3 & 7.1429 & 0.055503 & 84.31 & 344.03 & 0.070490 & 68.70 & 299.17 & 0.089745 & 58.3 & 280.91 & 0.086831 & 62.94 & 315.82 \\
4 & 10 & 0.093054 & 19.58 & 31.85 & 0.085062 & 23.34 & 40.90 & 0.095375 & 24.9 & 52.94 & 0.118167 & 22.07 & 52.68 \\
5 & 1.81 & 0.021983 & 455.45 & 4186.68 & 0.020178 & 504.3 & 4573.59 & 0.021931 & 481.7 & 4671.9 & 0.023482 & 459.83 & 4584.5 \\
6 & 10 & 0.093054 & 19.58 & 31.85 & 0.085062 & 23.24 & 40.90 & 0.09375 & 24.9 & 52.94 & 0.118167 & 22.07 & 52.68 \\
7 & 2.4390 & 0.025527 & 367.57 & 3124.72 & 0.027791 & 344.3 & 344.26 & 0.030105 & 330.1 & 3071.93 & 0.029132 & 349.05 & 3257.04 \\ MCP & & & 11.82 & & & & & & & & & & & \\ Total Profit for & & 12,217.07 & & & 12,459.12 & & & & & & & & \\ TPS & 12,217.07 & & & 12,459.12 & & & & & & & & & \\ Total PG for TPS & & 1448.05 & & & 1448.05 & & & & & & & & & \\ \(W_{g}\) (MW) & 51.95 & & & 51.95 & & & & & & & & & & \\ \(Q_{c}(\mu_{g})\) & 39,0609 & & 39.6227 & & & & & & & & & & & \\ \(U_{c}(\mu_{g})\) & 343.8484 & & & & & & & & & & & & & \\ \(IMC(W_{g})\) & 382.9093 & & & & & & & & & & & & & \\ Profit for WPS & 231.1397 & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimal bidding outcomes with wind power for the IEEE standard 57-bus. higher profit by utilizing OGSA. According to the result analysis, the OGSA technique is more effective, accurate, and modeling the bidding technique is possible for the IEEE30-bus and IEEE 57-bus systems, respectively.
## 2 Baseline Algorithm, SAR-SIFT: A SIFT-like Algorithm for SAR Images
### Key Point Detection  
A simple method for detecting key points on SAR images involves applying a logarithmic transformation to the image and then using the LoG-Harris detector of the SIFT algorithm. This approach can handle images with additive noise rather than multiplicative noise and can suppress false detection in high-reflectivity areas. Despite its simplicity, this method is not robust enough in noise handling and does not significantly improve the performance of the original LoG-Harris method. Key point detection examples on SAR images, affected by speckle noise unique to SAR images, indicate that key points do appear near corners but with poor localization. Compared to direct application on amplitude images, there are fewer scattered detections, and they also appear in both high- and low-reflectivity areas, with many false positives. Adjusting the parameters of multiple scales can reduce the number of false detections, but it also decreases the number of correctly detected key points.  
For the key point detector, the multi-scale space of the original image can be used instead of LoG scale space, which is represented by computing different scales \(\alpha_{m}=\alpha_{0}\cdot c^{m}\) (where \(m\in[0,\ldots,m_{max}-1]\)) obtained from the multi-scale function. Then the local extrema is selected as the key point candidate at each scale, and the bilinear interpolation using the SAR-Harris criterion near the local extrema to refine the key location of the point. Thresholds \(d_{\text{SH}}\) on multi-scale functions can filter edges and low contrast points, thereby obtaining the key points expressed from position \((x,y)\) and scale \(\alpha\). This method is called the SAR-Harris detector, which integrates the two steps of the LoG-Harris detector and does not use second-order derivatives. At the same time, it has the advantage of being independent of the dynamic range of the image.
## 3 Materials and Methods
### Shrinkage  
#### 3.2.1 Lasso Regression  
Variable selection is the basis of statistical learning that can be performed using shrinkage methods. The aim of these methods, such as lasso, elastic net, and group lasso, is to select some of the variables to make a new model. In this paper, these methods are used to reduce the number of RF trees to enhance the RF performance. In cases where the number of RF trees exceeds the number of observations (\(Ntree>N\)), shrinkage methods can be used to reduce the number of trees.  
Lasso is one of the most common variable-selection methods, introduced by [PERSON] [27]. Lasso regression reduces the residual sum of squares and minimizes the prediction error where the sum of the absolute values of regression coefficients are less than the constant value of t. This method is used in high-dimensional data as well as in multi-collinearity [28]. In the variable-selection process in which Lasso is used, some coefficients may be estimated at exactly zero and the non-zero coefficients stay in the model.  
Lasso tries to find the regression model that leads to the minimum residual sum of squares. The coefficients are limited by applying constraints to the regression model. This penalty is dependent on the tuning parameter of \(\lambda\). Suppose \((\mathbf{X},Y)\) is a dataset so that \(\mathbf{X}=\big{(}x_{1},\ldots,\ x_{p}\big{)}^{\prime}\) is the prediction variable and \(Y\) is the response variable. The objective function is as follows:  
\[\hat{\beta}^{lasso}=\frac{argmin}{\beta}\Big{\{}Y-\mathbf{X}\beta^{2}+\lambda \beta_{1}\Big{\}} \tag{5}\]  
where \(\beta_{1}=\sum_{j=1}^{p}|\beta_{j}|\) is \(\ell_{1}\)-norm of \(\beta\); \(\beta\) is the linear-regression coefficient; \(\lambda\geq 0\) is the tuning parameter that controls the amount of contraction. The value of \(\lambda\) is selected based on cross-validation. If \(\lambda\) is set to zero, then the lasso estimator will be the same as OLS. Generally, the increase in \(\lambda\) causes many coefficients to become zero [29].
## 1 Introduction  
Traditional methods for the evaluation of the mechanical properties of pavement are usually costly, as core samples are drilled and shipped to a laboratory to test the corresponding performance. To avoid tedious work and detriment to the pavement by coring, non-destructive testing (NDT) techniques can be applied as a replacement, such as ground-penetrating radar (GPR), spectral analysis of surface wave (SASW), and ultrasonic testing technology (UT). Ultrasonic testing technology is one of the most widely used non-destructive testing methods, in which ultrasonic waves are excited by an ultrasonic transducer, propagated through a medium, and received by the transducer at the other end. These elastic waves include longitudinal and shear waves. In terms of longitudinal waves, particle vibration occurs in the direction parallel to the wave propagation, whereas for shear waves, particles vibrate in the direction perpendicular to the wave propagation. Acoustic parameters, like amplitude, wave velocity, and wave frequency, are directly related to the mechanical properties of the medium [1]. Thus, in a solid elastic isotropic medium, these waves are related to the elastic modulus (E), Poisson's ratio (\(\
u\)), and density (\(\rho\)), based on the given equations below.  
\[C_{L}=\sqrt{\frac{E(1-\
u)}{\rho(1+\
u)(1-2\
u)}} \tag{1}\]\[C_{S}=\sqrt{\frac{E}{2(1+\
u)\rho}} \tag{2}\]  
This ultrasonic wave is frequently used in rock and cement concrete materials and was recently applied in the research area of asphalt concrete, including identifying damage within asphalt concrete [1], quantifying fatigue life of asphalt mixture, evaluating crack-healing efficiency of asphalt pavement [2; 3], and determining the dynamic modulus of a specific asphalt mixture [3]. This technology is also able to monitor changes in the integrity of mixtures due to moisture conditioning [4].  
Wave propagation modeling is attracting more interest since it provides a way to evaluate the propagation pattern of a wave within a structure like asphalt pavement. Wave propagation modeling has been successfully performed using ANSYS [5] (Ansys Inc., Canonsburg, PA, USA), ABAQUS [6] ABAQUS Inc., Palo Alto, CA, USA), and LS-DYNA (Livermore Software Technology Corporation (LSTC), Livermore, CA, USA) [7]. Finite element method (FEM) was successfully applied in the modeling of three-dimensional propagation of a lead zirconium titanate (PZT) patch-induced Lamb wave through reinforced composites [8] and homogenous plates [9]. FEM modeling of directed Lamb waves, induced by piezoelectric patches bonded on the steel plate, was utilized in [10].
## 4 High-Speed On/Off Valve Orifice Erosion Influence Factors Analysis  
### Effect of Spool Taper Angle on Orifice Erosion  
The curve of the maximum erosion rate with the spool cone angle is shown in Figure 8. With the increase in the spool cone angle, the maximum erosion rate of the valve port grew linearly, and the maximum erosion rate of a spool cone angle of 120\({}^{\circ}\) was about four times that of a cone angle of 60\({}^{\circ}\). With data fitting by least squares, the relationship between the maximum erosion rate and the spool taper angle was obtained as \(R_{\text{erosion}}\ =1\times 10^{-10}\times(4.919x-163.2)\big{(}\text{kg}\cdot \text{m}^{-2}\cdot\text{s}^{-1}\big{)}\).  
The change in the spool taper angle led to a change in the valve orifice structure, and there were great differences in the impact behavior of the particles flowing through different cone angle orifices. Combining Figures 9-11 for analysis, we can conclude that:  
* When the spool taper angle was small, the flow of oil was smoother and could be discharged in time after flowing through the valve orifice. Thus, the particles hit the wall of the orifice less often and the impact angle was smaller;
* Figure 1 shows that when the impact angle of the particles was 20-50\({}^{\circ}\), the normal and tangential rebound coefficients were small, and the erosion damage of the particles on the material surface was great. The erosion angle of the particles on the taper angle of 90\({}^{\circ}\) and the 105\({}^{\circ}\) taper surface fell exactly in this angle range, resulting in serious erosion wear in the area near the sealing line of the taper angle of 90\({}^{\circ}\) and the 105\({}^{\circ}\) taper surface compared to other locations;
* With the increase in the spool taper angle, the valve orifice structure changed, causing increases in the particle number and speed, impacting the spool and valve seat, which intensified the erosion wear at the valve orifice and resulted in \"the larger the spool cone angle, the more serious the erosion wear of the valve port\" phenomenon.  
Figure 8: The curve of the maximum erosion rate with the spool cone angle (inlet pressure—5 MPa, outlet pressure—4 MPa, valve opening—0.2 mm, oil contamination level—10, particle size range—1–10 μm).  
Figure 11: Distribution of erosion rate on axisymmetric surface (unit: kg\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\)).  
Figure 10: Distribution of erosion rate at different spool taper angles (unit: kg\(\cdot\)m\({}^{-2}\)\(\cdot\)s\({}^{-1}\)).  
Figure 9: Velocity traces at different spool taper angles (unit: m/s).  
The maximum velocity of fluid flow at the orifice increased from 39.6 m/s at the taper angle of 60\({}^{\circ}\) to 44.4 m/s at the taper angle of 120\({}^{\circ}\), and the maximum velocity of particles increased from 83.6 m/s at the taper angle of 60\({}^{\circ}\) to 112 m/s at the taper angle of 120\({}^{\circ}\). Therefore, reducing the angle of the spool taper angle reduced the velocity of particles hitting the wall, thus reducing the erosion damage of particles on the wall.
###### Abstract  
Although multiple efforts have been made to model global navigation satellite system (GNSS)-reflectometry (GNSS-R) delay-Doppler maps (DDMs) over land, there is still a need for models that better represent the signals over land and can enable reliable retrievals of the geophysical variables. Our paper presents improvements to an existing GNSS-R DDM model by accounting for short-wave diffraction due to small-scale ground surface roughness and signal attenuation due to vegetation. This is a step forward in increasing the model fidelity. Our model, called the improved geometric optics with topography (IGOT), predicts GNSS-R DDM over land for the purpose of retrieving geophysical parameters, including soil moisture. Validation of the model is carried out using DDMs from the Cyclone GNSS (CYGNSS) mission over two validation sites with in situ soil moisture sensors: Walnut Gulch, AZ, USA, and the Jornada Experimental Range, NM, USA. Both the peak reflectivity and the DDM shape are studied. The results of the study show that the IGOT model is able to accurately predict CYGNSS DDMs at these two validation sites.  
GNSS; GNSS-R; CYGNSS; DDM; electromagnetic modeling; soil moisture Article  
Improved Geometric Optics with Topography (IGOT) Model for GNSS-R Delay-Doppler Maps Using Three-Scale Surface Roughness
# Concrete Gas Permeability: Implications for Hydrogen Storage Applications
## 2 Experimental Development  
### Materials  
The materials used in the experimental study include different gases, helium, nitrogen, oxygen, and argon, which have been tested on different batches of concrete specimens (Table 1) conditioned at different relative humidities.  
Three types of conditioning of specimens were tested:1. Air: Specimens were placed in the oven at 50 \({}^{\circ}\)C, as described in the UNE 83966 procedure [32], and weighed daily until there was no further mass variation. They were then left under laboratory conditions (20 \(\pm\) 2 \({}^{\circ}\)C and relative humidity above 45%) for two to four days before testing.
2. Controlled relative humidity (RH): specimens conditioned to 65% RH.
3. Saturated: specimens were submerged in water and weighed daily until there was no mass variation (100% RH).  
Both types of cement used in our samples contribute to specific properties and performance characteristics tailored to their respective applications in precast elements with high early-age strengths and slip-form concrete. They enhance early-age strength, durability, workability, and sustainability, addressing some of the requirements for H2 storage. While these mixes may not represent the only types used for such structures, they were chosen based on practical considerations and realistic scenarios.  
As mentioned, specimens with cold joints were tested. These joints were fabricated by filling half of the specimen mold with one batch of concrete and then filling the other half with another batch on a different day, using the same mixing proportions. The time lapse between the two pours was one day, allowing the first pour to harden sufficiently to support the second layer but not fully cure, thereby creating a cold joint. After 24 h, it could be assumed that the first layer of concrete had reached its initial set but was still somewhat green. The cold joints were parallel to the gas flow.