esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 2 Materials and Methods
### Skip Connection Layer Structure
Since the depthwise separable convolution contains depthwise convolution and point-wise convolution, Equation (6) can be expanded as follows:\[\mathbf{M}_{\mathrm{c}}(\mathbf{X}^{2})=\sigma\Big{\{}\mathbf{W}_{p1}\Big{[}\mathbf{ W}_{d1}\Big{(}\mathbf{X}_{\mathrm{avg}}^{2c}\Big{)}\Big{]}+\mathbf{W}_{p2} \Big{[}\mathbf{W}_{d2}\Big{(}\mathbf{X}_{\mathrm{max}}^{2c}\Big{)}\Big{]}\Big{\}} \tag{7}\]  
where \(\mathbf{W}_{d1}\) and \(\mathbf{W}_{d2}\) are the weights of the depthwise convolution, \(\mathbf{W}_{p1}\) and \(\mathbf{W}_{p2}\) are the weights of the pointwise convolution, and \(\mathbf{X}_{\mathrm{avg}}^{2c}=\mathrm{AvgPool}(\mathbf{X}^{2})\) and \(\mathbf{X}_{\mathrm{max}}^{2c}=\mathrm{MaxPool}(\mathbf{X}^{2})\) represent the results of global average pooling and global max pooling, respectively. In the SAM, based on Equation (5), multi-head self-attention is used to replace the original global average pooling and global max pooling operations, which accepts features from the CAM as input, and the improved spatial attention is calculated as follows:  
\[\mathbf{M}_{\mathrm{s}}(\mathbf{X}^{2c})=\sigma\Big{[}\mathrm{MultiHeadAttention }\Big{(}\mathbf{X}^{2c}\Big{)}\Big{]} \tag{8}\]  
where MultiHeadAttention represents the multi-head self-attention mechanism. Specifically, the multi-head self-attention is composed of N single-head self-attention components, where the feature vectors are mapped to \(\mathrm{K}_{\mathrm{n}}\) (key vectors), \(\mathrm{Q}_{\mathrm{n}}\) (query vector), and \(\mathrm{V}_{\mathrm{n}}\) (value vector), where n represents the number of self-attention mechanism heads. After \(\mathrm{K}_{\mathrm{n}}\) and \(\mathrm{Q}_{\mathrm{n}}\) undergo the Matmul operation (denoted as M) to produce a new matrix, the scaled dot-product operation (denoted as S) is applied, followed by another M operation with \(\mathrm{V}_{\mathrm{n}}\) to obtain the attention score matrix [28; 29], and then, the attention score and weighted sum value are calculated independently for each head, the outputs of the N heads are concatenated together, and finally linearly transformed through the forward propagation network to obtain the final output feature \(\mathbf{X}^{3}\). The improved U-Net model integrates the strengths of U-Net, novel convolutions, ASPP, and multi-head attention, boosting the network's feature extraction capabilities. This enables the network to capture high-level semantic information while paying attention to details, thereby enhancing target extraction capabilities.
## 2 Background
### Satellite orbital errors cancelling potential in space  
As mentioned above, the closer the parallelism assumption is to reality, the better the performances of the ISR estimator in terms of bias. This statement is true not only for the bias introduced by Equation (13) but also for the residual GNSS satellites orbital errors. Indeed, the capacity of the differential GNSS algorithms to cancel orbital errors depends on the angle between the steering vectors from the two users to a given GNSS satellite. Given the large orders of magnitude for a lunar differential GNSS scenario using Earth GNSS signals, it is of interest to understand how well the application of DGNSS for lunar orbiters can remove orbital errors. To this end, the angles \(\theta_{ij,k}^{s}\) as depicted in Figure 1b will be displayed over time as well as the residual on the orbital errors \(\Delta\varepsilon_{LOS,ij,k}^{s}\) after application of single differencing on the pseudorange observables. Its value is computed according to:  
\[\Delta\varepsilon_{LOS,ij,k}^{s}=||\varepsilon_{k}^{s}||(\cos\beta_{i,k}^{s}- \cos(\theta_{ij,k}^{s}+\beta_{i,k}^{s})), \tag{23}\]  
where \(\beta_{i,k}^{s}\) is the angle between the GNSS satellite position error vector \(\varepsilon_{k}^{s}\) and the unit steering vector from the _aided user_\(\mathbf{h}_{i,k}^{s}\).
###### Abstract
However, there are few research results on the mechanical properties of the Permian Lucaogou Formation shale oil reservoirs in the Jimsar sag, Junggar Basin. Based on mechanical experiments, the data points of strength parameters and elasticity parameters can be obtained, which have the characteristics of dispersion and only cover a small section of the formation. For the heterogeneous reservoirs, however, data points obtained from the experiments are scattered and cannot represent the rock mechanical properties of the entire reservoir interval. The shale oil reservoirs from the Permian Lucaogou Formation in the Jimusar Sag of the Junggar Basin are of a heterogeneous type [15]. Thus, it is difficult to obtain the mechanical property profile of the rocks continuous along the entire reservoir interval. Logging is an effective means to obtain information on the rock mechanical properties in continuous formations [16; 17; 18]. However, logging data cannot directly provide the rock mechanical properties of rocks. In order to solve the problem, there are numerous correlations between strength or static elastic parameters and physical parameters (such as wave velocity, density, and porosity, etc.) that have been proposed in the literature [19; 20; 21; 22; 23]. The studies indicated that the empirical expressions in the literature have certain application limitations, and different empirical expressions have different application ranges; that is, there is a set of suitable empirical relations for predicting the rock mechanical properties in a specific formation or a specific rock type, so that there are limitations in the direct application of the empirical relations in a certain region to other regions. That may be because many of the same factors including pore size, degree of cementation, mineralogy, shape and spatial arrangement of grains, etc., influence rock mechanical and physical properties [24]. Therefore, local empirical relationships between rock mechanical properties and physical parameters in a specific formation need to be developed. This is the reason why numerous empirical equations were proposed by previous workers. In general, the correlation between rock mechanical and physical properties of a specific formation is based on laboratory tests. In addition, there are no reports of empirical equations for predicting the rock mechanical properties of shale oil reservoirs from the Permian Lucaogou Formation in the Jimusar Sag of the Junggar Basin. The main objective of this paper was to investigate the rock mechanical properties, including strength and static elasticity parameters. The regression analysis method was applied to obtain the relationships between physical and rock mechanical properties. On this basis, new empirical relationships between rock mechanical properties were developed, which can be used to directly obtain a continuous profile of these properties of the shale oil reservoirs from well logging data.
## 5 Existing Solutions
### Lessons Learned  
After the review of numerous works that included variations of federated learning and Blockchain in the IoT environment, the following points summarize existing solutions:  
* Most of the found work focuses on applying federated learning with Blockchain with a disregard to applying methods to detect poisonous attacks.
* We found that most work is reputation-based rather than noise-based to prevent the occurrence of poisonous attacks.
* The application scope of federated learning and Blockchain integration heavily focused on industrial, medical, and communications area.
## 4 Proposed Model
### Proposed Evaluation Metrics  
#### 4.3.1 Predicted Rating Level (P.L.)  
Since we endeavored to improve the accuracy of RSs, we suggested a new evaluation measure, named predicted rating level determination, which divides the ratings into:  
* The positive level (_POS_): is represented when the rating is _r > (max/2)_; where \"max\" means the maximum score rating in the dataset.
* The average level (_AVG_): when is _r = (max/2)_; and
* The negative level (_NEG_), when is _r < (max/2)_.  
We supposed that, if the predicted and original ratings were both at the same level, the proposed model's outputs were accurate, and the model was capable of selecting an appropriate neighborhood.
# Turbulence: A Significant Role in Clear-Air Echoes of CINRAD/SA at Night
## 4 Results
### Comparison of the S-Band and X-Band
It means that the two radars should observe the same group of scatterers  
Figure 15: Histograms of the radar products of the CINRAD/SA (S-band) and X-POL (X-band), respectively, on 2 May 2021 at 1300 UTC for a height ranging between 300 m and 1.2 km. with the same scattering characteristic, moving from upstream (XFS) to downstream (SDX) the whole night, continuously. Although \(Z\) values from atmospheric insects may have asymmetric patterns which depend on the angle between the radar beam and the main orientation of the biological scatterers, the time-height cross-section of the \(Z\) values will not be affected because the cross-sections are counted by the whole coverage volume of the radar observation and are not dependent on the azimuth. Thus, the asymmetric patterns cannot stain the ratio between the \(Z\) values of the CINRAD/SA and X-POL. It is already known that, according to Equation (6) and Equation (10), the \(Z\) difference in the turbulence of the S-band (2870 MHz, 10.45 cm) and the X-band (9455 MHz, 3.17 cm) is about 19.0 dB and 21.4 dB, respectively. Since many creatures are within the size threshold for Mie scattering and have different RCSs at different wavelengths, the reflectivity relationships to the wavelength are far from straightforward. Meanwhile, wide discrepancies are found between the characteristics of clear-air echoes and the law of biological activities. Therefore, this proves that the Beijing CINRAD/SA echoes are mainly caused by turbulence. The DWR statistics for the region of Beijing reveal that, from 1 May 2021 to 20 May 2021, 58% of the DWR was distributed between 18 dB and 24 dB, which is the characteristic interval of turbulence. Moreover, the clear-air echoes maintain the features of the variations in the \(Z\) values and the VAD analysis. It means that nearly 58% of the echoes were probably caused by turbulence and proves that the influence of turbulence is erroneously ignored in clear-air echoes. Almost all these echoes did not show the classic features of Bragg scattering, such as a low \(Z\) and zero \(Z_{DR}\). These echoes could wrongly be identified as bio-scatterers and cause a misestimation of biomass. The number of flying creatures observed by the weather radar was much smaller than previously thought.
# Automatic Detection of Construction Workers' Helmet Wear Based on Lightweight Deep Learning
## 5 Conclusions  
To reduce the risk of head trauma to workers during construction work at high-risk workplaces such as construction sites, it is critical to develop an algorithm that can automatically and robustly detect helmet wear.  
In this paper, we designed a novel one-stage lightweight end-to-end convolutional neural network aimed at identifying whether people on a construction site are wearing helmets or not. This algorithm achieves high accuracy while significantly reducing computational costs and can meet the real-time performance required for real-world detection. The designed neural network first utilizes GhostNet, a lightweight network, as the backbone feature extraction network. It uses its inexpensive operation to make the model lighter overall while ensuring efficient automatic feature extraction. Secondly, we designed a multi-scale segmentation and feature fusion network (MSFFN) in the feature-processing stage to improve the algorithm's robustness in detecting objects at different scales.  
In contrast, the design of the feature fusion network can enrich the diversity of helmet features, which is beneficial to improving the accuracy of helmet detection when distance changes, viewpoint changes, and occlusion phenomena occur. For the attention module, we propose LRCA-Netv2, an improved version of LRCA-Net, and it has a good performance improvement over the previous one. Finally, the mAP and FPS of the proposed lightweight helmet-wearing detection network evaluated on the dataset reached 93.5% and 42, respectively. Our model has excellent performance compared to other methods.  
Figure 14: Limitations of the proposed method (blue circles represent false or missed objects), covering objects including (**a**) tiny objects, (**b**) severely under-illuminated or individual poses, and (**c**) obscured.  
This work provides new ideas for improving existing helmet wear detection algorithms and model lightweight efforts. Future developments will involve expanding the variety of construction site detection objects, especially for small objects, and focusing on such working directions as improving the loss function, combining modules with adaptability, and combining helmet detection with tracking techniques for monitoring other objects.  
Conceptualization, H.L. and S.S.; methodology, H.L.; software, H.L.; writing--original draft preparation, H.L.; writing--review and editing, S.S.; visualization, H.L.; supervision, S.S.; project administration, S.S.; funding acquisition, S.S. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A1B02011625).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Dispensing Robot System and Laser Spot Location Principle
### Laser Imaging Spot Location  
The laser spot location algorithm is the most important key technology to improve measurement accuracy. The gray centroid method [27, 28] and function fitting method [29, 30, 31, 32] are the mainstream methods. Under ideal conditions, the laser spot energy presents a Gaussian distribution. The function fitting method fits the Gaussian distribution of the spot energy by a simplified mathematical function, which can effectively and quickly obtain the position of the imaging point on the CMOS. In practice, however, due to optical distortion and the structure of the optical path, the imaging waveform was tilted and stretched, which led to a skewed distribution. Therefore, the function method causes some calculation deviation. In contrast, the gray centroid method can calculate the centroid of distorted waveforms to achieve the spot subpixel location.  
To reduce the influence of random noise and increase the weight value near the peak, in this paper, the square weighted gray centroid method is adopted to calculate position \(x\) of the laser spot on the CMOS, as shown in Equation (2), where \(x_{i}\) is the position on the CMOS and \(y_{i}\) is the waveform intensity. In addition, we convert the position \(x\) from the pixel coordinates to the world coordinates by the calibration method of linear piecewise interpolation, and finally obtain the measured value.  
\[x=\frac{\sum_{i}^{N}x_{i}\cdot y_{i}^{2}}{\sum_{i}^{N}y_{i}^{2}} \tag{2}\]
# Prediction Model for Shield Tunneling Roll Angle and Pitch Angle: A PCA-PSO-LGBM Approach
## 2 Methodology  
### Principal Component Analysis  
Principal Component Analysis (PCA) is an unsupervised machine learning algorithm that transforms multiple variables into a few uncorrelated variables, known as principal components. The primary goal of PCA is to simplify the data structure by reducing the dimensionality of the dataset while retaining as much variance information from the original data as possible. Therefore, PCA can reduce computational load, highlight the main sources of variation in the data, and aid in understanding the data structure. In machine learning, PCA is used for feature extraction to improve algorithm performance and reduce computational cost. The implementation process of PCA is as follows.  
Assume we need to reduce the dimensionality of a dataset with m samples and n features to k dimensions (\(k<m\)). Let this sample set be denoted as matrix \(X\):  
Figure 1: Schematic diagram of shield roll angle and pitch angle.  
\[X=\begin{bmatrix}X_{11}&X_{12}&\ldots&X_{1n}\\ X_{21}&X_{22}&\ldots&X_{2n}\\ \ldots&\ldots&\ldots&\ldots\\ X_{m1}&X_{m2}&\ldots&X_{mn}\end{bmatrix}_{m\times n}, \tag{1}\]  
where each row represents a sample and each column represents a feature.
* Step 1: Calculate the mean value for each feature. For all samples, subtract the corresponding mean from each feature to obtain the centered matrix \(X\): \[X=\begin{bmatrix}X_{11}-\overline{X_{1}}&X_{12}-\overline{X_{2}}&\ldots&X_{1n }-\overline{X_{n}}\\ X_{21}-\overline{X_{1}}&X_{22}-\overline{X_{2}}&\ldots&X_{2n}-\overline{X_{n}} \\ \ldots&\ldots&\ldots&\ldots\\ X_{m1}-\overline{X_{1}}&X_{m2}-\overline{X_{2}}&\ldots&X_{nm}-\overline{X_{n}} \end{bmatrix}_{m\times n},\] (2) where \(\overline{X_{i}}\) is the mean value of all samples for each column in Equation (1), that is \[\overline{X}_{i}=\frac{1}{m}{\sum_{j=1}^{m}X_{ji}(i=1,2,\ldots n)},\] (3)
* Step 2: Calculate the covariance matrix of the centered matrix \(X\): \[C=\frac{1}{m-1}X^{T}X,\] (4)
* Step 3: Perform eigenvalue decomposition on the covariance matrix \(C\), calculating the eigenvalues \(\lambda_{k}\) and the corresponding eigenvectors \(v_{k}\): \[Cv_{k}=\lambda_{k}v_{k},\] (5)
* Step 4: Arrange the eigenvectors in descending order from left to right according to their corresponding eigenvalues, forming a matrix. Take the first \(k\) columns to form matrix \(W\), which is a \(n\times k\) dimensional matrix.
* Step 5: Calculate the reduced dimensionality features of the samples by \(Y=XW\), resulting in a \(m\times k\) dimensional matrix.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### The Identity Provider
#### 4.2.4 Roles Assignment  
Although policies (in terms of _Permissions_, _Roles_ and their relationships) are defined in the _PAP_, it is in the _IdP_ where the mapping among them and users, _Devices_ and/or groups of them is made. _Roles_ are assigned to _Devices_ in the scope of the _Service_, so a given _Device_ may have different roles depending on the application.
# Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities
## 3 Measurement Results and Analysis
### The Time Series  
In body-to-body communications, it is hard to categorize the communication link as LOS or NLOS, but more as a periodic transition between the two states. The periodicity depends on the dynamics involved with the specific activity, related to the movements of the body parts and posture changes [11] of the subjects involved. Our collected data has shown that there is a periodic fluctuation of received signal power regardless of the scenario or activity involved. Even when the arms where the transceivers are attached appear to be stationary as in cycling activity, the periodic movement of the body as the subject pedals gives a similar effect. This underlines the significance of the movement of the body in BBWN channels. Figure 3 shows the measured results for Scenario 1, where the periodicity is seen in both running and cycling activities, in which running shows sharper transitions due to movements of the arms. Similar observations are seen in Scenario 2 performed in this measurement campaign. Other contributing factors in the channel properties are the changes in the local environment,  
Figure 2: Investigated scenarios of the BBWN measurement campaign. Activities involved were running and cycling. (**a**) Scenario 1, subject behind the other; (**b**) Scenario 2, subject beside the other. The nodes are attached to the side of the upper arms with a radiation pattern away from the body.  
and the relative position of the subjects as they perform their activities. This leads to a large scale fading observed by the change in local mean as shown in Figure 4. The local mean was calculated using a sliding window with length of 750 samples, corresponding to 3 s (approximately three cycles of body motion).
## 6 Simulation Analyses
### The Relationship Between Orbit Determination Accuracy and Measurement Accuracy  
The measurement data obtained by the radar measurement equipment are the azimuth angle A, elevation angle E, and range \(\rho\). There are many factors that affect measurement accuracy, among which atmospheric disturbances and irregularities in the ionosphere are important factors.  
Atmospheric refraction causes radio waves to bend, resulting in the apparent distance and elevation angle measured by radar differing from the actual distance and elevation angle. To address the errors in radar ranging and angle measurement caused by atmospheric refraction, researchers have focused their efforts on two main areas: first, studying how to obtain atmospheric refraction indices that more closely match actual conditions [19; 20]; second, proposing more accurate and efficient models and correction methods for radio wave refraction errors [21; 22].  
The accuracy of measurement data affects the accuracy of precise orbit determination and thrust parameter estimation. Taking the pre-identification process of tangential thrust parameters as an example, we will analyze the impact of angular measurement and range measurement accuracy on the accuracy of thrust parameter pre-identification.
# A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone
## 1 Introduction
They are inexpensive, easy to install and replace, low-maintenance, do not pose privacy concerns, and require minimal computation (see Ref. [9] for a comparison with other sensors). On the negative side, they provide very simple and limited information, and the data processing algorithms must compensate this drawback. In this context, the objective of the present study is to propose a solution for long term ADL monitoring and detection of behavior changes, based exclusively on binary sensors, which is low cost, scalable, adaptable, and protects the privacy of the users. The contributions of this research can be summarized as follows:  
1. We propose an abstracted model of the residential living space, reduced to a collection of Behaviorally Meaningful Places (BMPs), represented as points located symmetrically in a generic Cartesian space. By eliminating all the details regarding the surface of the living space, the type and position of the furniture and appliances, and the specific locations of the sensors, this model creates a common ground for monitoring the ADL in almost any residential environment. 2. We describe a method to encode the sensor data in the form of a series of activity maps that embed information about the intensity and the spatial distribution of the activities. 3. We show that the respective activity maps can be automatically analyzed to detect changes in the behavior of the monitored persons, by comparing the activity map of the current time slice with the data previously recorded in a reference time interval. 4. We propose a method to reduce the number of false alerts based on fuzzy logic. The next section is a brief review of the closely related work found in the literature with a focus on the solutions based on binary sensors. Section 3 contains the detailed description of the method, and Section 4 presents the results obtained by applying the proposed method on two public datasets. Finally, Section 5 is reserved for discussion, and Section 6 presents the conclusions.