esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Based Low-Cost Magnetometer Calibration
## 2 Magnetometer Calibration Based on GNSS  
### Magnetometer Error  
Magnetometers suffer from multiple sources of stochastic and systematic errors. Systematic errors can be divided into two groups. In the first group, there are errors in the sensor itself, such as bias (\(b_{so}\)), nonorthogonality (\(N\)), and scale factor (\(S\)). In the second group, there are errors caused by the environment around the sensor, which can be divided into the influence of magnetically hard (\(b_{hi}\)) and soft iron (\(A_{si}\)). Figure 1 shows how individual magnetometer errors affect the resulting vector. Soft iron curves the magnetic field lines, thanks to which the sensor measures an ellipsoid instead of a circle. Hard iron is a permanent magnet that creates an ellipsoid (sphere) shift. We can write these errors in Formula (1), which we can rewrite (2). We can combine all errors into the calibration matrix with the size of \(3\times 3\) and then add the hard iron factor b [2] to the multiplication result. The Formula (1) can be rewritten to (3), where M is a \(4\times 3\) matrix.  
\[h_{m}=SN(A_{si}h+b_{hi})+b_{so} \tag{1}\]  
\[h_{m}=Ah+b \tag{2}\]  
\[h_{m}=Mh \tag{3}\]The magnetometer will be placed in the vehicle, which is mainly made of ferromagnetic material. Therefore, the calibration deviations will be high. Meanwhile, low-cost magnetometers provide a highly erroneous and noisy output (stochastic error).
## 1 Introduction
Such an approach aims at expanding knowledge and the proper interpretation of the course and distribution of one of the basic water properties, namely, temperature. The requirement of the availability of long-term data regarding inland water temperature is obvious due to the importance of this property for the entirety of processes and phenomena occurring in inland water. Therefore, studies have been undertaken with varied approaches, aimed at the reconstruction of water temperature. They are based on paleolimnological methods [21], apply mechanistic open source models [22], or use three estimators of mean annual water temperature depending on different measurement methodologies and periodicity [23]. Research conducted in the Anchor River watershed (Alaska) by [PERSON] et al. [24] shows that it is possible to reconstruct mean water temperatures in situ for small streams at the regional scale by means of the regression model in combination with Landsat or air temperature data. The prediction of water temperature in rivers with the progressing application of advanced modelling techniques is currently a well-developed research trend referring to the thermal regime of rivers [25; 26; 27]. Against the background of the commonly used factor of air temperature, this paper applies data on water temperature in lakes in the vicinity of the analysed rivers as an additional predictor. It was assumed that the approach would improve the modelling accuracy. The paper presents a reconstruction of water temperature for selected rivers in Poland in the period 2015-2020, based on air temperatures and water temperature in lakes. The detailed objectives are as follows: (1) determination of a set of input data optimal from the point of view of the reconstruction of river water temperature; (2) the selection of the optimal method for the reconstruction of water temperature in rivers among multiple linear regression (MLR), random forest regression (RF), and multilayer perceptrone network (MLP); (3) the identification of errors in the reconstruction of river water temperature; (4) the assessment of the effect of the results of data reconstruction on the estimation of long-term changes in the thermal regime of rivers.
## 3 Results
### Laboratory Testing
#### 3.1.2 Effect of Wet-Dry Cycles on Wind-Induced Soil Losses  
Figure 5 depicts the effect of wet-dry cycles on wind-induced soil losses of biomaterial-treated sand wetted with different rainfall intensities. Each bar represents the calculated mean of three replicates. It is apparent from the diagrams (Figure 5a-d) that (i) the soil losses varied for each wet-dry cycle and rainfall intensity, and (ii) the biomaterials behaved differently. Chicory vinuases and corn steep liquor showed similar characteristics at a rainfall intensity of 0.25 L/m\({}^{2}\). The lowest soil losses occurred after the first wet-dry cycle, while a considerable increase was observed after the fourth. Samples treated with decantation syrup behaved slightly differently, whereby the soil losses achieved their minimum at wet-dry cycle 3 and its peak at wet-dry cycle 4. By contrast, the weight losses of palatinose molasses-treated soil peaked at wet-dry cycle 1, then decreased and subsequently increased at wet-dry cycle 4 (to a similar level to wet-dry cycle 1). At higher rainfall intensities (1-4 L/m\({}^{2}\)), the mass loss data followed no clear trend. Either the soil losses per wet-dry cycle did not differ significantly, or there were isolated outliers. In part, samples showed almost no soil losses with increasing wet-dry cycles. A summary of the soil loss data after each wet-dry cycle is given in Table A3, Appendix A.
## 3 Results and Discussion
### Computational Overhead
#### 3.1.2 Encryption  
Figure 7 shows the comparison of encryption generation for different access schemes. It is observed that the time for generating keys increased concerning attributes. However, in multi-authority and attribute-based encryption, the encryption time is nine times higher than the proposed MCP-ABE-BG for 100 attributes. This shows that the proposed MCP-ABE-BG requires 0.02 s less to encrypt the data with the generated key for 100 attributes.  
Figure 6: Key generation time comparison.
## 5 Discussion  
### Results Analysis  
#### 5.1.1 The Result Analysis of TGC Price  
The TGC price reflects the market demand for renewable energy generation, and its fluctuations are significantly correlated with the TGC quota ratio and carbon emission cap target. When the TGC quota ratio increases, the growth rate of TGC prices accelerates. This result conforms to the basic law of market supply and demand: in the context of increasing demand for renewable energy, increasing the TGC quota ratio means an increase in demand for green certificates in the market, thereby driving up TGC prices [46]. On the contrary, reducing the TGC quota ratio will lead to a slowdown or stabilization of TGC price growth. Therefore, when designing the TGC quota ratio, policymakers need to fully consider the actual demand and supply capacity of the electricity market for TGC and avoid excessively loose or tight TGC quota ratios that may cause instability in TGC price [47]. The instability of TGC prices will lead to increased uncertainty in investment in the renewable energy market. This will affect the construction progress of new projects and potentially weaken the profitability of renewable energy enterprises, thereby affecting the stability of the electricity market and the achievement of carbon reduction targets. The carbon emission cap target is another crucial factor affecting TGC price. When the carbon emission cap target decreases, the growth rate of TGC price accelerates. This indicates that the tightening of carbon emission cap targets has forced the power industry to reduce its carbon emissions or rely more on TGC to fulfill emission constraints, thereby increasing demand in the TGC market. With the relaxation of the carbon emission cap target, companies are more inclined to enter the CET market and reduce their purchases of TGC, resulting in a slow growth rate of TGC price [27]. The fluctuation of TGC prices is not only a reflection of supply and demand but also closely related to the carbon emission cap target. Therefore, in policy design, policymakers need to pay attention to the supply-demand matching and price dynamics of the TGC and CET markets and flexibly adjust the TGC quota ratio in combination with the carbon emission cap target to ensure the coordinated development of the TGC and CET markets.
## 4 A Framework for Feature Curves on SPD Manifold
### Formulation of Short Video Recognition  
Video recognition is to identify the corresponding label of a query video \(V_{query}\) based on a number of sample videos \(\{V_{1},\cdots,V_{n}\}\) labeled with \(\{l^{V}_{1},\cdots,l^{V}_{n}\}\), which covers face recognition, scene classification, action recognition, and other different application directions. For example, the recognition of surveillance video in the field of the public is to match a query video \(V_{query}\) against the video library obtained by the monitoring system. The target to be recognized in the video can be the person, car, or even license plate involved in the case.  
This paper focuses on short video recognition. Short videos are now popular in social media with their high-frequency output and strong participation. Hence, video media have high requirements for content audit, information screening, and content recommendation. It is necessary to promote the research of short video recognition. To extract features for recognition, static images are normally described in terms of feature vectors, and videos are regarded as image sets. However, image set ignores the temporal feature of video.  
Spatial features in video dynamically change in the time dimension, and adjacent frames share similar spatial features. Different from long video, such as movies, the transition or conversion in short video between paragraphs and scenes are minor, which means the significant spatial features, i.e., ROI, may run through the whole short video. To extract spatial-temporal features from short videos, we proposed a framework focusing on the short video recognition model spatiotemporal trajectory of ROI in video as a family of feature curves on the SPD manifold.
## 4 Results
### Testing Processes
#### 4.2.3 Discussion  
The binary classification model, which distinguishes the diabetic retinopathy images from normal images, was suitable for building the rapid screening system. The robustness of model is a key performance index for a medical screening system. Our test results shown in Sections 4.2.1 and 4.2.2 indicated that the contrastive classification model for diabetic retinopathy was more robust than the traditional model.  
In the rapid screening system for diabetic retinopathy, the diabetic retinopathy images and the normal images were considered as the similar within the classes and dissimilar between the classes, respectively. The proposed contrastive classification model learned the representations of the fundus images with the rules that simultaneously maximized the agreement between similar images and maximized the disagreement between dissimilar images. In contrastive learning, the representations for the same type of samples were more compact, and the representations for the different type of samples were more discriminative. Hence, the model built by the contrastive learning method was more robust. The representations of the fundus images were learned from the unlabeled images and then fine-tuned with the labeled images. Our test results showed that the contrastive learning method was suitable for establishing a robust classification model for diabetic retinopathy.  
In image classification, contrastive learning is primarily used for unsupervised or semi-supervised representation learning. Recently, the contrastive learning has been applied to build a supervised classification model and obtain high accuracy results. The supervised contrastive learning was used to classify product images [30], and it was also applied to build a classification model for underwater acoustic communication modulation [31]. The robustness of the supervised contrastive classification model was analyzed in [32]. The results in this work showed that the features learned by supervised contract learning were aggregated into different clusters separately. The supervised contrastive learning method can obtain more distinguishing features, in which the distance of samples with the same label is less, while the distance of samples with a different label is more. Thus, the boundary of the classification model can be more easily determined in the the supervised contrastive learning method, and the supervised contrastive learning obtains better robustness. Similar to [32], our results showed that we can build a robust classification model for diabetic retinopathy by using the supervised contrastive learning method.
## 4 Recommendation on Improving the Efficiency of Intensity Measures  
In this section, we analyze efficiency and sufficiency of IMs by computing statistical correlations between EDPs (maximum inter-story drift ratio) and ground motion IMs from a set of near-field records in the NGA West2 database. We consider 2D reinforced concrete (RC) frame buildings with different natural periods and perform non-linear time history analysis to obtain inter-story drift ratios.  
For each IM value, we investigate correlations of EDPs over magnitude, epicentral distance, and site class for different natural periods of structures. We then examine correlations using multi-parameter linear regression models to assess the change in efficiency and sufficiency characteristics. Our results confirm and augment previous findings, while we also provide recommendations on how the efficiency and sufficiency of IMs can be improved. We discuss the ground motion database, intensity measures, and building models in the following sub-section.
## 3 Path Planning Algorithm Based on the Offset Curve  
The VRML (virtual reality modeling language model) [20] is used in our system, since it contains both the geometric data and the color data. In our previous work [15], the adaptive filling algorithm based on the section method (AFSM) was used, which includes two parts, the adaptive-thickness slice (ATS) and the self-adaptive printing filling (SAPF). However, the AFSM is not the best solution, since the printing points could not fully fill the theoretical filling area (TFA). Therefore, a new method based on offset curve path planning (OCPP) is proposed.
## 2 The Signal Clustering Idea in a Radio Spectrogram  
Typically, in signal analysis for a single radio channel, various classification methods, including accurate or heuristic algorithms, can be successfully applied, such as those operating on a radio spectrogram. However, the challenge remains to develop solutions that can analyze signals in real time in the wideband radio spectrum, including those that can simultaneously analyze multiple detected signals (sometimes dozens to hundreds) within the analyzed bandwidth. Therefore, there is a need for the preliminary selection of radio signals that are of interest from the perspective of a specific scenario of a wideband cognitive spectrum analyzer operation.  
The clustering of radio signals in the wideband spectrum is a process aimed at distinguishing signals with similar characteristics, such as received power, modulation, bandwidth, duration, the same radio fingerprint [9; 10; 11], or originating from the same direction. One of the fundamental tasks requiring a preclassifier is the detection of signals from an anti-eavesdropping and jamming countermeasures radio station through the use of frequency-hopping spread spectrum (FHSS) spectrum spreading.  
Examples of goals for the clustering of FHSS signals include the need for the autonomous determination of specific hopping frequencies, examining the vulnerability of TRANSEC (TRANmission SECurity) measures, and autonomously avoiding collisions or jamming through real-time spectrum monitoring.  
In typical signal clustering solutions, Key Indicators (KIs) are utilized, which are specific metrics based on which signals can be compared to each other and then assigned to specific groups. However, this requires the development of these indicators and often the application of complex algorithms that analyze at least several parameters of the radio signal. In this article, we present a different, innovative semi-supervised method for clustering radio signals in the wideband spectrum, which does not require defining KIs for this purpose.
## 3 Problem Description
### Effective Covered Grids and COST  
Let \(|\bullet|\) be the number of elements in \(\bullet\) and the sensing radius of the sensor located at \((x,y)\) be \(r(x,y)\). In WSN(S, AREA, RADIUS, COST), the effectively covered grids ECG of the sensor in \(S\) located at \((x,y)\)\(\epsilon\) AREA is the set of grids inside the circle under radius \(r(x,y)\), i.e.,  
\[\text{ECG}(r(x,y))=\{\,p\,\mid\,\text{grid }p\text{ is inside in CIRCLE}(r(x,y)\cap\text{AREA }\,\}, \tag{1}\]  
where CIRCLE(\(r(x,y)\)) is the circle with center \((x,y)\) and radius and \(r(x,y)\).  
For example, in Figure 1, assume the sensor A located at (5, 75) is with \(r(\text{A})=r(5,75)=10\), and 255 grids are inside the circle under radius 10, i.e., the number of grids in \(\{\,p\,\mid\,\text{grid }p\text{ is inside in (the circle with center and radius (5, 75) and \(r(5,75)\)) }\,\}\) is 255. However, the area we are interested in is only in AREA = [0, 99) \(\times\) [0, 99). Hence, these grids that are out of these range should be removed, i.e., \((-1,75)\), \((-2,75)\), \((-3,75)\), \((-4,75)\), \((-5,75)\), \(\ldots\), and 50 grids are removed because of this and only \(\,\mid\,\text{ECG}(r(5,75)=10)\,\mid\,=255\) grids are left.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(i\) & \((x,y)\) & Radius Level & \(r(x,y)\) & COST(\(r(x,y)\)) \\ \hline A & (5, 75) & 1 & 1 & 1 \\  & & 2 & 3 & 2 \\  & & 3 & 5 & 3 \\  & & 4 & 10 & 4 \\ B & (12, 75) & 1 & 2 & 1 \\  & & 2 & 5 & 2 \\ C & (25, 50) & 1 & 1 & 1 \\  & & 2 & 2 & 2 \\  & & 3 & 3 & 3 \\  & & 4 & 4 & 4 \\  & & 5 The total ECG of a whole WSN is calculated based on Equation (1) after removing these grids outside AREA or in the intersection ranges of sensors as follows:  
\[\cup\text{ECG}(r(x,y))\text{ for all sensors located in }(x,\ y)\text{ with radius }r(x,\ y) \tag{2}\]  
The total grids covered by all sensors is:  
\[|\cup\text{ECG}(r(x,y))|. \tag{3}\]  
For example, \(|\)ECG(\(r\)(A) = 10) \(|\) = 255 as shown before, \(|\)ECG(\(r\)(B) = 8) \(|\) = 193, and \(|\)ECG(\(r\)(C) = 5) \(|\) = 69. There are 124 grids in ECG(\(r\)(A) = 10) \(\cap\)ECG(\(r\)(B) = 8) and ECG(\(r\)(A) = 10) \(\cap\)ECG(\(r\)(C) = 5) = ECG(\(r\)(B) = 8) \(\cap\) ECG(\(r\)(C) = 5) = \(\emptyset\), where sensors A, B, and C are located at (5, 75), (12, 75), and (25, 50), respectively. We have:  
\[\begin{split}|\text{ECG}(r(\text{A})&=10)\cap \text{ECG}(r(\text{B})=8)\cap\text{ECG}(r(\text{C})=5)\\ &=255+193+69-124=393.\end{split} \tag{4}\]  
Moreover, from COST in WSN(\(S\), AREA, RADIUS, COST), if the cost of the sensor located in (\(x,y\)) with radius \(r(x,y)\) is COST(\(r(x,y)\)), the total cost to have the above deploy plan for all sensors in \(S\) is:  
\[\sum_{(x,y)}\text{COST}(r(x,y)). \tag{5}\]  
For the same example in Figure 1, based on Table 1, the cost to have \(r\)(A) = 10, (B) = 8, and \(r\)(C) = 5 is COST(\(r\)(A) = 10) = 4, COST(\(r\)(B) = 8) =3, and COST(\(r\)(C) = 5) =5. The total cost to achieve this is:  
\[\text{COST}(r(\text{A})=10)+\text{COST}(r(\text{B})=8)+\text{COST}(r(\text{C}) =5)=12. \tag{6}\]
## 4 Numerical Results and Discussion  
### Stability Analysis of Small Clear-Distance Tunnels under Different Excavation Schemes  
Different excavation schemes were selected when tunnelling was carried out under different surrounding rock levels and buried depths. Given that the project was a small clear-distance tunnel and the minimum clear distance in the model was 1.6 m, to ensure the stability of the construction interruption surface, the excavation scheme needed to be optimized and analyzed. Therefore, commonly used excavation schemes, such as full section, step, CD, and double wall heading methods were simulated. Figure 4 shows the layout of each excavation scheme. In each excavation scheme, the main line of the tunnel was excavated first with footage of 5 m, and the staggering distance between the main line and the ramp was 15 m. The section 20 m away from the main line tunnel portal was selected as the target section for analysis.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Materials** & **Density (kg/m\({}^{3}\))** & **Elastic Modulus (GPa)** & **Poisson’s Ratio** & **Cohesion (MPa)** & **Friction Angle (\({}^{\circ}\))** \\ \hline Surrounding rock & 2200–2300 & 6–10 & 0.25–0.3 & 2.0 & 39–50 \\ Primary lining support & 2500 & 30 & 0.20 & / & / \\ Secondary lining support & 2500 & 32.5 & 0.15 & / & / \\ Temporary support & 7900 & 200 & 0.30 & / & / \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of the numerical model.  
Figure 4: The layout of each excavation scheme: (**a**) Step method; (**b**) Full section method; (**c**) CD method; (**d**) Double wall heading method.
# Impact of Die Radius in a Streamlined Die during Wire Drawing
## 4 Results  
### Comparison of Strain  
As shown in Figure 6, the effective strain of the wire increased with the distance from the center regardless of the die radius [36]. However, the effective strain profiles were quite different depending on the die radius, as shown in Figure 7a. As the die radius increased, the uniformity of the effective strain of the wire increased along the radial direction. The effective strain of the wire fabricated by the die with a radius of 128 was more uniform than that of the wire manufactured by the conventional die. Furthermore, the effective strain of the wire by the die with a radius of 64 was similar to that of the wire by the conventional die. Regardless of the die shape, the center area had a similar theoretically calculated effective strain (\(\epsilon\)) based on the nominal area reduction using the following Equation:  
\[\epsilon=ln\left(\frac{A_{i}}{A_{f}}\right) \tag{5}\]  
To evaluate the level of inhomogeneity with the die radius, the strain inhomogeneity factor (\(SIF\)) was calculated using the following equation:  
\[SIF=\frac{S_{\text{max}}-S_{\text{min}}}{S_{\text{min}}} \tag{6}\]  
where \(S_{\text{max}}\) and \(S_{\text{min}}\) are the maximum and minimum effective strains, respectively. As shown in Figure 7b, the \(SIF\) decreased with increasing die radius, and the specimens subjected to the 128-radius die had the smallest deviation of effective strain, indicating that a larger die radius improves the homogeneity of the wire along the radial direction.  
Figure 6 also compares the shear strain of the wire with the die radius. The profiles of shear strain along the radial direction exhibited a pattern similar to that of the effective strain, as shown in Figure 7c. All wires had the maximum shear strain near the surface and the minimum shear strain at the center, which is consistent with the results of a previous study [37]. The shear strain of the wire increased with decreasing die radius because additional redundant work in the surface area was restrained with increasing the die  
Figure 5: Comparison of measured and simulated (**a**) drawing force and (**b**) center temperature of the wire during the conventional wire drawing process used in this study.  
radius [38]. In addition, the shear strain of the wire fabricated by the die with a radius of 128 was more uniform than that of the wire manufactured by the conventional die, which is confirmed by the result of the maximum shear strain with the die radius as shown in Figure 7d.  
Figure 6: Comparison of contour in effective strain and shear strain of the wire with the die radius.  
Figure 7: Comparison of (**a**) effective strain profile along the radial direction, (**b**) _SIF_ value, (**c**) shear strain profile along the radial direction, and (**d**) the maximum shear strain of the wire with the die radius.