esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussions  
### Simulation Parameters  
The algorithms proposed in this study have undergone rigorous testing through simulation experiments across a spectrum range spanning from \(100\,\mathrm{MHz}\) to \(200\,\mathrm{MHz}\). The meticulous parameter settings employed throughout our simulations are comprehensively outlined in Table 1. Unless explicitly stated otherwise, these settings remain consistent across all the conducted simulation experiments. For the sake of detailed analysis, we partition the total available bandwidth \(\mathbf{B}\) into \(M=25\) distinct sub-bands, each possessing a bandwidth of \(b=4\,\mathrm{MHz}\). Consequently, the action sets for both the UAV and the jammer are delineated as \(\mathcal{F}_{U}^{c}\), \(\mathcal{F}_{j}^{c}=\{102\,\mathrm{MHz},106\,\mathrm{MHz},\ldots,198\, \mathrm{MHz}\}\), with cardinalities \(|\mathcal{F}_{U}^{c}|=|\mathcal{F}_{j}^{c}|=25\).
## 4 Effectiveness Under Disturbances:  
This study rigorously assessed the controllers' effectiveness under various power quality disturbances, such as three-phase balanced voltage swells, sags, and three-phase-to-ground faults. The results highlighted the FOPI-FOPI controller's capability to significantly reduce settling time by 30.5% to 56.1%, indicating its efficiency in stabilizing the regulated signal quickly, which is crucial for dynamic operations.
## 2 Elevation-Dependent Modeling of Satellite-Induced Code Bias
### Formulation of MP Combinations
The means for selection of linear factor \(a\) is that the geometric contribution (antenna movement, satellite orbit, and satellite clock), tropospheric delay, and ionospheric delay cancel out. As shown in Equation (3), the MP combinations mainly contain the float phase ambiguity, the multipath and noise errors and the SICB. Therefore, the long-term changes--namely, the elevation-dependent SICB variations--can be detected by the MP combinations.
# Real-Time Failure Prediction of ROADMs by GAN-Enhanced Machine Learning
## 4 Experimental Results and Discussion
### Model Evaluation
#### 4.3.1 Evaluation Results at 51 GHz Shifts  
Figure 17 shows the evaluation result at 51 GHz shifts with TRDS data only. The number of training data points used was 12,450 for each label. We successfully achieved very high accuracy (nearly 100%).
# Ascertaining the Ideality of Photometric Stereo Datasets under Unknown Lighting
## 1 Introduction
Many studies have been devoted to improving the application of PS to non-Lambertian conditions. In [21], the authors formulate the problem as a low-rank matrix factorization subject to sparse errors, due to the presence of shadows and specular reflections in data images. A statistical approach based on a hierarchical Bayesian approximation was used in [22] to simultaneously estimate the surface normal vectors and the experimental errors, by solving a constrained sparse regression problem. Learning-based methods were also employed to solve PS (see, e.g., [23; 24]). To manage near-light problems, models based on neural networks were presented in [25; 26], where they were stated under non-Lambertian conditions. Our approach is slightly different from the ones above. Here, we assume that the light source location is unknown, and point our attention to the lack of ideality in a dataset due to either the necessity of illuminating the observed surface with close lights, or to the fact that the surface is only approximately Lambertian. Indeed, in archaeological applications, which motivated this work, the assumption that requires the light sources to be located at a large distance (theoretically, at infinite distance) from the observed surface is an unacceptable constraint, as carvings and bas-reliefs are often located in narrow caves or excavations. Furthermore, the carvings are engraved in rock, which may not be an ideal Lambertian surface. To explore these aspects of the model, we assume that orthographic projection conditions are met and the position of light sources is unknown. Then, we determine the light position by the Hayakawa procedure and investigate the reason for its breakdown in the presence of nonideal data, i.e., images that excessively deviate from the above assumption. This also leads us to introduce a new nonlinear approach for identifying the light position, alternative to Hayakawa's one. Finally, we study the problem of ascertaining the ideality of a dataset. This is accomplished by defining some measures of ideality, which can assist a researcher in selecting the best subset of images from a redundant dataset. To demonstrate the performance of the new approaches, we apply them to both a synthetic dataset, where the position of the lights can be decided at will, and to an experimental one, which is not guaranteed to represent a Lambertian reflector. The plan of the paper is the following. Section 2 contains a review of the numerical procedures to solve the PS problem under both known and unknown lighting. In Section 3, we describe a new nonlinear approach to determine the light position directly from the available data. The ideality of a dataset is discussed in Section 4, where two indicators are introduced for detecting the presence of unideal images, and an algorithm is presented to extract a subset of pictures that better suit the assumptions of the PS model. The numerical experiments that investigate the performance of the algorithm are outlined in Section 5 and discussed in Section 6.
# Vision-Based Pavement Marking Detection and Condition Assessment--A Case Study
## 4 Case Study Results
### Worn Condition Assessment  
To validate the proposed approach for condition assessment, a roughly 2 km long road segment was further retrieved randomly. To generate a benchmark for their worn condition, images were first superimposed onto a grid and the worn percentage of line marking instances in each image frame were manually measured by the author. The minimum spacing of the underlying grid is five pixels, and therefore, the manual measurement could possibly reach an accuracy of one pixel. Comparisons between the manual measurement and the results of the proposed method are plotted on line charts. Figure 10 presents the result comparison charts for LINE_1, the nearest line marking to the camera to the left, and LINE_3, the farther boundary of the adjacent lane, as illustrated in Figure 7a. The horizontal axis represents the corresponding SLK (Straight Linear Kilometer) number of each frame, as recorded in MRWA's management system, while the vertical axis is the estimated worn condition in terms of percentage.  
Figure 10: Cont.  
Figure 9: Other noises affecting classification, including: (**a**) occlusion from other vehicles; (**b**) curb; (**c**) road-side railings.  
In Figure 8, the worn condition evaluated by the proposed method fits the worn condition assessed through manual measurement, though the former tends to present a higher worn percentage than humans do. For example, it is noted that in Figure 10a, between SLK 27.55 and SLK 27.65, there exists a peak indicating extensive wear of line marking. By referring to the original video data, we found considerable tracing nuts on the marking, as shown in Figure 11a. Similar deteriorations of line marking can be found at SLK 28.23-28.33 (for LINE_1), SLK 28.41-28.47 (for LINE_2 and 3, as shown in Figure 10b, etc. However, there are false warnings for poor marking conditions in both charts (e.g., SLK 27.89-27.99 for LINE_1 and SLK 28.34-28.39 for LINE_3). Such errors are mainly attributed to the color-based feature extractor, which is subject to the changing illuminations and shadows of road-side objects. Figure 12 shows image frame at SLK 27.93, where yellow continuity lines are reduced to only its edges and thereby, received incorrect worn condition results.  
In terms of efficiency of the assessment, the manual approach took 3 h per 100 image frames, while the proposed method took less than one minute. Since there is no benchmark readily available in terms of the wear issues of line marking, the proposed method represents a potential option for industrial applications due to its capability to provide instructive information on the condition of pavement markings (e.g., distribution of worn segment), while taking considerably less time.  
Figure 11: Example frames of worn line marking. They are located at (**a**) SLK 27.59; (**b**) SLK 28.45, separately.  
Figure 10: Comparison of worn condition assessment results. (**a**) Result comparison for Line 1; (**b**) result comparison for Line 2; (**c**) result comparison for Line 3.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 3 LoRa Technology Overview
### LoRaWAN  
LoRaWAN is a network protocol designed for LPWAN applications (i.e., requiring a wide range of coverage and low power consumption, in which usually the end devices are powered by batteries) that specifies the medium access control layer (MAC) and the application layer. The latest version of the protocol released by the LoRa Alliance is version 1.1 [28] that guarantees backward compatibility with its predecessor 1.0, the first version of the protocol.
## 1 Introduction
The following steps should be taken to successfully implement the proposed solution: dataset balancing, attribute, and feature dimension reduction, building a training-testing model, and building a decision-making model. To evaluate the strategy's effectiveness, this paper uses the Kaggel Amazon access control policy dataset and many experiments before and after the dataset balancing phase. There are two methods for evaluating performance metrics: firstly, contrasting the performance of policy decision-making, the flexibility of access control and the processing time of various machine learning (ML) algorithms. Second, comparing benchmark performance and time with previous methods determines the solution's effectiveness. The experimental findings indicate that the suggested solution can achieve an Accuracy of 95% in policy decision-making, and the time cost of PDP-based ML technology is 0.15, measured in seconds as the scale of policy increases. This research makes several contributions, including:  
* Analyse and evaluate existing decision-making approaches in access control. * Enhance access control decision-making based on the attribute-based access control (ABAC) model to solve the abovementioned problems. In particular, a permission determination engine based on machine learning is proposed. The authorization request for PDP issues is classified as a binary classification either authorized or denied. * Improve the current access control by modifying the policy decision point (PDP) to be distributed and dynamic based on machine learning (ML) technology instead of an access decision based on predetermined rules and a centralized structure. * Optimise the Accuracy of PDP-based ML technology by balancing the data set and conducting numerous experiments before and after data processing to determine the optimal strategy. * Conducted an extensive evaluation of the solution using the Amazon access control policy dataset, which provides strong evidence of its effectiveness and robustness. * Compare the proposed method to other machine learning methods applied to the same dataset. The solution's effectiveness can also be measured by (benchmark metrics) PDP performance and cost time for access decision-making using several state-of-the-art methods. * Introduce a solution that can protect and preserve policy information privacy during permission decisions through the policy decision point, which is independent of policy administration. It implies that the PDP and PAP are primarily independent. Also, it could provide secure, efficient, and lightweight access control. The remaining sections of the paper are organised as follows: Section 2 discusses the motivation and problem statement. In Section 3, the background and related concepts are described. Next, Section 4 reviews relevant studies on access control and decision-making techniques. Section 5 proposes a model and explains the implementation steps in detail. Section 6 explains and analyses the experiments. Next, Section 8 includes the Discussion and finally, the conclusion is included in Section 9.
# Prediction of Soil Compaction Parameters Using Machine Learning Models
## 2 Materials and Methods
### Soil Database  
In the research, 168 sets of soil sample data were collected. In the Gunaydin [17] literature, nine different soil types were gathered from the small dams constructed in the vicinity of Nigde in Turkey, which were high-plasticity inorganic clay (CH), medium-plasticity inorganic clay (CI), low-plasticity inorganic clay (CL), clayy gravel (GC), silty gravel (GM), high-compressibility inorganic silt (MH), medium-plasticity inorganic silt (MI), low-plasticity inorganic silt (ML), sandy clay (SC), and 126 sets of data were obtained through the Proctor compaction test. In the study by [PERSON] et al. [29], different natural soil types are collected from different geologic locations in India, including high-plasticity inorganic clay (CH), medium-plasticity inorganic clay (CI), low-plasticity inorganic clay (CL), high-compressibility inorganic silt (MH), medium-plasticity inorganic silt (MI), low-plasticity inorganic silt (ML), sandy clay (SC), ML-CL, sandy silt (SM), and MI-CL, and 42 sets of data were obtained through the Proctor compaction test.  
In this database, there are six features, which are fine content (content of particle size less than 0.075 mm, clay and silt, FC), sand content (S), specific gravity (SG), liquid limit (LL), and plastic limit (PL). Table 1 shows that the soil samples in this database have a fine content of 13-98%, a sand content of 2-80%, a specific gravity of 2.58-2.85, a liquid limit of 23.1-115%, a plastic limit of 13.68-45.3%, an OMC of 7.6-36.8%, and an MDD of 12.6-20.51 kN/m\({}^{3}\). Then, in order to evaluate the generalization ability of the model, 134 (80%) and 34 (20%) sets of soil sample data were randomly selected as training and test sets from the soil database. The training set is used for the training of the model and the test set is distinguished from the training set for testing and validating the performance of the model.  
Randomized division is a data division method commonly used in machine learning. It can reduce assessment bias, improve model generalization, and ensure objectivity, accuracy, and reliability in model assessment.
## 3 Integration of Sensors and Geometric Processing  
Accurate geometric processing is a crucial task in the data processing workflow for UAV datasets. Fundamental steps include the determination of the sensor interior characteristics of the sensor system (interior orientation), the exterior orientation of the data sequence (position and rotation of the sensor during the data capture), and the object geometric model to find the geometric relationship between the object and the recorded radiance value.  
Accurate position and orientation information is required to compute the location of each pixel on the ground. Full-size airborne hyperspectral sensors follow the pushbroom design, and some can use a survey-grade differential GNSS receiver (typically multi-constellation and dual frequency capabilities) and IMU to determine the position and orientation (pitch, roll, and heading) of the image lines. When post-processed against a GNSS base station established over a survey mark at a short baseline (within 5-10 km), a positioning accuracy of 1-4 cm can be achieved for the on-board GNSS antenna, after which propagation of all of the pose-related errors typically results in 5-10 cm direct georeferencing accuracy of UAS image data. This can be further improved by using ground control points (GCPs) measured with a differential GNSS rover on the ground or a total station survey [94; 95; 96; 97]. One of the challenges in hyperspectral data collection from UAVs is the limitation in weight and size of the total sensor payload. Survey-grade GNSS and IMU sensors tend to be relatively heavy, bulky, and expensive, e.g., fiber optic gyro (FOG) IMUs providing absolute accuracy in orientation of <0.05\({}^{\circ}\)[98]. Development in microelectromechanical systems (MEMS) has resulted in small and lightweight IMUs suitable for UAV applications; however, traditionally, the absolute accuracy of these MEMS IMUs has been relatively poor (e.g., typically \(\sim\)1\({}^{\circ}\) absolute accuracy in pitch, roll, and yaw) [98]. The impact of a 1\({}^{\circ}\) error at a flying height of 50 m above ground level (AGL) is a 0.87-m geometric offset for a pixel on the ground. If we consider the key benefit of UAV remote sensing to be the ability to collect sub-decimeter resolution imagery, then such a large error is potentially unacceptable. The combined error in pitch, roll, heading, and position can make this even worse. There is an important requirement for the optimal combination of sensors to determine accurate position and orientation (pose) of the spectral sensor during acquisition (which also requires accurate time synchronization), or an appropriate geometric processing strategy based on image matching and ground control points (GCPs).
## 1 Introduction
Researchers found that the primary fungi in the fermented grain of sauce-flavor _baijiu_ produced in the Guizhou region include _Pichia_, _Saccharomyces_, _Thermoscusus_, and _Wallemia_, while those in the fermented grain of sauce-flavor _baijiu_ produced in the Beijing region are _Isatchenkia_, _Cryptococcus_, _Thermoscus_, and _Thermoyces_[9; 13; 19]. The microorganisms in the fermented grains are deeply involved in the saccharification of raw materials, the degradation of harmful substances, ethanol production, and the formation of flavor substances [5; 20]. Clarifying the differences in microbial community composition and their contribution to the characteristic flavor substances in the brewing process of sauce-flavor _baijiu_ produced in different regions is the key to unraveling the formation process of flavor substances. Although the Beijing and Guizhou regions are both production areas for sauce-flavor _baijiu_, they are geographically distant and have significant differences in their environmental conditions [16]. Our previous studies found that the microbial community compositions and flavor compounds in the fermented grains of the fifth, sixth, and seventh rounds during the brewing process of sauce-flavor _baijiu_ in Beijing and Guizhou regions had significant differences, ones that were closely related to the differences in environmental microorganisms [13; 16]. However, it remains unclear if there are differences in the microbial community composition and profile of flavor compounds during the third-round fermentation process in the Beijing area, being crucial for sauce-flavor _baijiu_ products. The current study aimed to investigate the temporal profile of microbial community and flavor compounds in fermented grains of sauce-flavor _baijiu_ produced in the Beijing region during the third round of fermentation and to compare it with sauce-flavor _baijiu_ produced in the Guizhou region to provide fundamental information for comprehending the formation of regional characteristic flavors.
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 2 Methods
### KLT Optical-Flow Method
#### 2.1.3 KLT Optical-Flow Method under Pyramid  
It can be seen from Section 2.1.2 that if the assumption of small motion is not met, the Taylor expansion of Equations (4) and (5) cannot be carried out. The image pyramid [32] referred by the KLT optical-flow method is used in this paper, which can effectively solve the above problem. Suppose there is an 800 \(\times\) 800 image, the displacement range of the target point in this image is 32 \(\times\) 32. Now the image is reduced to 400 \(\times\) 400, the displacement range of the target point is reduced to 16 \(\times\) 16, and the assumption of small motion can be met again according to this principle.  
The specific principle of KLT optical-flow method under the pyramid is as follows. For each image, the original image is taken as layer 0, and the image reduced by 2\({}^{\text{L}}\) times in length and width is taken as layer L. The obtained image is superimposed from bottom to top to generate the Gaussian pyramid shown in Figure 3. The displacement value of the target point on the highest layer is calculated in the way of the previous section, which is taken as the initial value of the optical-flow calculation of the next layer to calculate the accurate displacement value of this layer. The calculated displacement value is transmitted to the next layer again, so as to calculate to the lowest layer (level 0) to obtain the real displacement value.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 4 Interference Compensation Algorithm
### Coefficient Tuning Independent of Environmental Information  
Section 4.1 presents the magnetometer compensation method under the interference conditions of the external magnetic field, carrier residual magnetic field, magnetizing magnetic field, and others. However, regarding the final result, Equation (18) cannot uniquely determine the compensation coefficient. Therefore, it is necessary to determine the compensation coefficient separately.  
If the geographic coordinate system is selected as the reference coordinate system, the north component of the geomagnetic vector is generally positive, i.e., the north component of \(B_{e}^{r}\) is positive.  
\[B_{e}^{r}(1)>0 \tag{27}\]  
From Equations (19), (25) and (27), we obtain:  
\[\omega v(1)=X(1)>0 \tag{28}\]  
From Equation (28), the value of the compensation coefficient can be uniquely determined.  
This method can determine the compensation coefficient, but also has limitations. The geographic coordinate system must be selected as the reference coordinate system. Even if this restriction is relaxed, it is necessary to clearly determine the position of the selected reference coordinates relative to the geographic coordinate system. This is inconvenient for practical application. Next, we introduce a compensation coefficient tuning method that does not depend on environmental information.  
By using the information of the carrier itself to adjust the compensation coefficient, we can avoid dependence on environmental information. In actual application scenarios of magnetometers, the UAV body generally avoids the use of magnetized materials up to the maximum possible extent. Therefore, the absolute value of each element of matrix \(k_{i}\) is significantly less than 1, such that the main diagonal elements of matrix \((I_{3\times 3}+K_{i})\) are greater than 0. From Equations (19) and (25), we obtain:  
\[\begin{split} K=\left[\begin{array}{ccc}X(4)&X(5)&X(6)\\ X(7)&X(8)&X(9)\\ X(10)&X(11)&X(12)\\ \end{array}\right]\\ =\omega\left[\begin{array}{ccc}v(4)&v(5)&v(6)\\ v(7)&v(8)&v(9)\\ v(10)&v(11)&v(12)\\ \end{array}\right]\end{split} \tag{29}\]According to the definition of \(K\):  
\[I_{3\times 3}+K_{i}=K^{-1}=\frac{1}{\omega}\begin{bmatrix}v(4)&v(5)&v(6)\\ v(7)&v(8)&v(9)\\ v(10)&v(11)&v(12)\end{bmatrix}^{-1} \tag{30}\]  
It is evident from Equation (30) that the value of \(\omega\) is the value of all elements (greater than 0) on the main diagonal of the matrix \(K^{-1}\). After calculating \(\omega\), we obtain:  
\[B_{pk}=\omega\begin{bmatrix}v(13)\\ v(14)\\ v(15)\end{bmatrix} \tag{31}\]  
The final compensation result can be written as:  
\[\begin{array}{c}K_{i}=K^{-1}-I_{3\times 3}\\ B_{p}=K^{-1}B_{pk}\end{array} \tag{32}\]  
From Equations (16), (29), (31) and (32), the geomagnetic vector in the reference frame after compensation can be obtained as:  
\[B_{m}=CB_{e}^{r}+B_{p}+K_{i}CB_{e}^{r}=(I_{3\times 3}+K_{i})CB_{e}^{r}+B_{p} \tag{33}\]