esa-sceva/satcom-chunk-collection huggingface dataset:
## 10 Simulation Results and Discussion
### Electricity Cost vs. Appliance Waiting Time  
To achieve a lower electricity bill, smart users must operate all appliances according to the optimal schedules given by EMC. During the scheduling horizon, the start time of any appliance cannot be fixed due to the price variation in each hour. Therefore, the scheduling algorithm adjusts the starting time of those appliances that are categorized from the maximum cost saving perspective. However, this mechanism can save electricity cost, but can eventually disturb the end user lifestyle. Alternatively, the appliance scheduling algorithm can be designed to maximize the user comfort, but at maximum electricity cost. Therefore, these two objectives are contradictory and difficult to achieve simultaneously. The WDO algorithm is designed for those customers who are more conscious about electricity bill saving and can compromise on comfort. In Figure 7c,f, the clothes dryer and air-conditioner are scheduled for maximum electricity cost saving. In Figure 7c, the working of almost every appliance is rescheduled to the time slots having low electricity price. Bars with a greater width denote the user-specified time slots, while, bars with a smaller width show the rescheduled time slots and total number of operating hours. It is clear from Figure 7b that the air-conditioner does not start working immediately due to the high electricity price, and its working is rescheduled by EMC. Alternatively, starting the air-conditioner immediately can add high electricity cost. Therefore, we cannot start the working of all of the appliances immediately due to the capacity constraint to avoid high peaks on the grid side. In Figure 7b,d, appliances are scheduled along with starting time bounds and the RE source to increase the user comfort. Appliances turn on immediately after the scheduling process starts and after one hour of working, EMC reschedules the remaining working hours when the electricity prices are low. It is clear that the average appliance waiting time of the tumble dryer is low compared to the clothes dryer and air-conditioner.  
Figure 6: Time of use (TOU) signal.  
Figure 7a,e has starting time limits and shows scheduling without an RE source. The working of the microwave oven is rescheduled after completing the first three hours consecutively in order to provide user comfort, while the electric stove completes only the first hour, with the remaining hours rescheduled by EMC. Equation (1) gives the average waiting time of an appliance where the waiting time is inversely proportional to the electricity cost. If the price increases, the waiting time decreases, and _vice versa_. Therefore, we design the scheduling algorithms having minimum electricity cost and appliance waiting time.
# SnowMamba: Achieving More Precise Snow Removal with Mamba
## 3 Experiments  
### Experimental Configuration and Evaluation  
**Datasets.** This study validates the effectiveness of the proposed algorithm on three benchmark datasets: CSD [10], Snow100K [7], and SRRS [9]. For Snow100K and CSD,  
Figure 3: Structure of the Snow Caption Mamba (SCM) module. “x” denotes the multiplication operation, “+” denotes the addition operation, and “C” denotes channel-wise addition.  
100 images were randomly selected from their training sets as the validation set, with the remaining images used for training. For testing, 2000 images were randomly stratified from the Snow100K test set as the test set for this experiment, while the test set provided by the CSD dataset was used directly. For SRRS, the first 10,000 images were used as the training set, 100 images in the middle were selected as the validation set, and the last 2000 images were used as the test set.  
**Implementation Details.** The snow removal network designed in this paper adopts a four-level encoder-decoder progressive learning structure, which extracts snow image features at different scales through downsampling and upsampling. Different SCM blocks are set for feature learning according to the scale of the image at each level. The number of SCM blocks from the first to the fourth level is [2; 2; 9; 2], and the number of channels from the first to the fourth layer of the encoder-decoder is [48; 96; 192; 384]. The initial input image patch size is set to \(128\times 128\). As the input image patches increase in size, more image information is fed into the network, and the number of iterations for progressive learning is reduced. The correspondence between the two is shown in Table 1. During training, the model is implemented using the Pytorch framework on a single Nvidia RTX 3090, with the AdamW optimizer for updates and L1 Loss guiding the model in learning the snow removal process. The initial learning rate is set to \(10^{-4}\), which decreases to 0.7 times its original value as the input patches change, and is reduced to \(10^{-7}\) through cosine annealing.  
**Evaluation Metrics.** This work conducts a detailed qualitative analysis and comparison of the proposed desnowing algorithm on three public synthetic datasets and the real-world dataset provided by Snow100K, showcasing the desnowing effects of different methods through intuitive visual demonstrations. To further validate the effectiveness of the proposed algorithm, commonly used image quality evaluation metrics, including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) [26], Learned Perceptual Image Patch Similarity (LPIPS) [27], and Deep Image Structure and Texture Similarity (DISTS) [28], are employed for the quantitative assessment of the experimental results. These evaluations demonstrate the superiority and robustness of the proposed algorithm.
# Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
## 3 Results and Discussion
### Comparison with Other Oil Film Classifications of Shipborne Radar  
[PERSON] et al. [34] proposed a global threshold method for the identification of oil films in shipborne radar images. Based on the gray distribution matrix, [PERSON] et al. [35] proposed a dual-threshold method (gray and area) to detect oil spills. [PERSON] et al. [48] used an iterative method and an adaptive threshold to obtain the oil spills. These methods were used for comparisons with our method (Figure 19). The classification of oil films is based on the relative dark area, and the gray values of oil films are different  
Figure 17: Oil spill imaging mechanism of the shipborne radar images. Oil film reduces the backscatter of the shipborne radar image.  
Figure 18: Comparison with other local adaptive thresholds of Figure 1b showing (**a**) [PERSON]’s method, (**b**) [PERSON]’s method, and (**c**) the [PERSON] method.  
in diversified wave areas. To reduce the misjudgments of oil films, [PERSON]'s and [PERSON]'s methods therefore needed to adopt a lower global threshold value, but they still misinterpreted the long-distance weak waves as oil spills. In [PERSON]'s method, the adaptive threshold was applied and better results were achieved, but some oil films were missed. In addition, their iterative method forgoes efficiency. Our result (Figure 12b) is closer to the expert visual interpretation than the three aforementioned methods. The results show that the local adaptive threshold is more suitable for the extraction of oil spills in shipborne radar images.
## 4 Model Development
### Robot Manipulator Forward Kinematic Model  
A widely used method for defining and generating reference frames in robotic applications is the Denavit-Hartenberg (D-H) convention [29]. In this approach, each robotic link is associated with a Cartesian coordinate frame \(O_{i}X_{i}Y_{i}Z_{i}\). According to the D-H convention, the homogeneous transformation matrix \(A_{i}^{i-1}\), which represents the transformation from frame \(i-1\) to frame \(i\), can be decomposed into a sequence of four fundamental transformations:  
\[A_{i}^{i-1}=Rot_{z_{q_{i}}}Trans_{z_{d_{i}}}Trans_{x_{a_{i}}}Rot_{x_{a_{i}}}\]  
Expanding the transformation into its matrix form :  
\[A_{i}^{i-1}=\left[\begin{array}{cccc}c_{q_{i}}&-s_{q_{i}}&0&0\\ s_{q_{i}}&c_{q_{i}}&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&d_{i}\\ 0&0&0&1\end{array}\right]\left[\begin{array}{cccc}1&0&0&a_{i}\\ 0&1&0&0\\ 0&c_{a_{i}}&-s_{a_{i}}&0\\ 0&s_{a_{i}}&c_{a_{i}}&0\\ 0&0&0&1\end{array}\right] \tag{12}\]  
\[=\left[\begin{array}{cccc}c_{q_{i}}&-s_{q_{i}}c_{a_{i}}&s_{q_{i}}s_{a_{i}}&a _{i}c_{q_{i}}\\ s_{q_{i}}&c_{q_{i}}c_{a_{i}}&-c_{q_{i}}s_{a_{i}}&a_{i}s_{q_{i}}\\ 0&s_{a_{i}}&c_{a_{i}}&d_{i}\\ 0&0&0&1\end{array}\right] \tag{13}\]  
\[\text{Note }:c_{q_{i}}\equiv\cos(q_{i}),\ c_{a_{i}}\equiv\cos(\alpha_{i}),\ s_{q_{i}} \equiv\sin(q_{i}),\ s_{a_{i}}\equiv\sin(\alpha_{i}) \tag{14}\]  
The parameters \(q_{i}\), \(a_{i}\), \(\alpha_{i}\) and \(d_{i}\) define the link and joint characteristics of the robot. Here, \(a_{i}\) is the link length, \(q_{i}\) is the joint rotational angle, \(\alpha_{i}\) is the twist angle, and \(d_{i}\) is the offset between consecutive links. The values for these parameters are determined following the procedure outlined in [29].  
To compute the transformation from the end-effector frame \(O_{6}X_{6}Y_{6}Z_{6}\) (denoted as {E}) to the base frame \(O_{0}X_{0}Y_{0}Z_{0}\) (denoted as {O}), we multiply the individual transformations along the kinematic chain:  
\[T_{6}^{0}=A_{1}^{0}A_{2}^{1}A_{3}^{2}A_{4}^{3}A_{5}^{4}A_{6}^{5} \tag{15}\]  
Furthermore, the transformation matrix from the base frame {O} to the end-effector frame {E} can be derived by taking the inverse of \(T_{6}^{0}\):  
\[T_{6}^{6}=(T_{6}^{0})^{-1} \tag{16}\]  
If a point \(P^{O}\) is defined in the base frame, its coordinates in the end-effector frame \(P^{E}\) can be found using:  
\[P^{E}=T_{0}^{6}\ P^{O} \tag{17}\]  
Equation (17) describes a coordinates transformation process from the base frame to the end-effector frame.
# Design and Implementation of a Six-Degrees-of-Freedom Underwater Remotely Operated Vehicle
## 2 Mathematical Model
### Coordinate System  
The analysis of the kinematic and kinetic models is a part of the ROV's modeling. To create the six-DOF nonlinear dynamics model of the ROV, we first introduced the coordinate system and specifications of its motion parameters based on two reference coordinate systems (Figure 1).  
The ROV has two vertical thrusters and four horizontal thrusters. Thus, the following state vectors are utilized with the six-DOF equation of motion [20; 21]: \(\eta=\left[\eta_{1}^{T}\ \ \eta_{2}^{T}\right]^{T}\) illustrates the linear positions and Euler angles vector of the ROV in the earth-fixed (EF) frame \(E-X_{E}Y_{E}Z_{E}\), an explanation of the linear position \(\eta_{1}=[x,y,z]^{T}\) and the angular position \(\eta_{2}=[\phi,\theta,\psi]^{T}\); The ROV's linear position is indicated by the letters \(x\), \(y\), and \(z\); \(\varphi,\theta\), and \(\psi\), roll, pitch, and yaw, three Euler angles, respectively; \(v=\left[v_{1}^{T}\ \ v_{2}^{T}\right]^{T}\) represents the velocities vector in the body-fixed (BF) frame \(B-XYZ\), explaining the linear velocity \(v_{1}=\left[u,v,w\right]^{T}\) and the angular velocity \(v_{2}=\left[p,q,r\right]^{T}\).
## 2 Related Work
Deep learning based on convolutional neural networks (CNNs) has made significant advances, offering advantages in efficiency and accuracy over traditional machine learning. For instance, based on improving the MobileNetV2 model [44], the application of transfer learning achieved a fruit image classification accuracy of 99%, highlighting the significant improvement in the proficiency of deep learning-based object detection algorithms [45; 46; 47; 48]. Therefore, the latest object detection algorithms in deep learning have also been applied to tomato yield detection. [PERSON] et al. [49] constructed the tomato detection model RC-YOLOv4, improving the detection accuracy of tomatoes in natural environments. [PERSON] et al. [50]proposed an improved tomato cluster counting method that combines object detection, multi-object tracking, and specific tracking area counting to reduce the misidentification of background tomatoes, proposing YOLOv5-4D with fused RGB and depth images as inputs. Although the aforementioned studies using color or shape features and machine learning have made some progress in fruit detection, there are still some issues with tomato detection: (1) changes in natural lighting conditions have a significant impact on the color features of tomatoes, leading to unstable performance of color-based detection methods; (2) leaf and branch occlusion, overlapping between tomatoes, and cluster growth make it difficult for detection algorithms to identify tomatoes accurately; (3) improvement in small target detection has not been achieved; (4) although some methods have improved detection accuracy, the detection speed cannot meet real-time requirements. This study proposes an RT-DETR-Tomato model for tomato object detection, aiming to balance detection speed and accuracy in complex environments. The main improvements of this model are as follows: (1) reconstruction of the RT-DETR model backbone: By replacing the original ResNet50 backbone of the RT-DETR model with the Swin Transformer, the model can capture richer global dependencies and contextual information due to its hierarchical Transformer structure. This capability allows it to process complex images and scenes with a stronger feature representation compared to traditional convolutional networks, and to handle features at different resolutions, thereby enhancing the model's detection accuracy. (2) Integration of the BiFormer Vision Transformer with dual-level routing attention: The BiFormer enhances the ability to extract features of small objects. The BiFormer technology achieves more flexible, content-aware computation allocation. Additionally, since BiFormer focuses on a small subset of relevant tokens in a query-adaptive manner without dispersing attention to irrelevant ones, it ensures good performance and high computational efficiency. This work not only provides an effective solution for tomato detection but also offers a reference for developing detection technologies for other crops, promoting the advancement of agricultural automation and intelligence.
## 2 Drill Core Fidelity Performance Model
### Effect of Back-Rake and Side-Rake Angle on Fidelity Performance  
The back-rake angle, \(\theta\), represents the angle at which the PDC bit presses into the rock vertically along the coring bit. It is a crucial design parameter for the coring bit, as it affects the contact angle between the cutting edge and the rock and the transmission of forces, Physical view of the deep coring drill bit is shown in Figure 1. A suitable back-rake angle not only increases drilling pressure and cutting speed but also reduces the retention time of rock chips on the cutting-edge surface, preventing a decline in cutting performance due to frictional heat generated between the rock chips and the PDC piece. The literature [20] shows that the back-rake angle is generally designed to be 0-30\({}^{\circ}\).  
When the back-rake angle is slight, the contact area between the rock and the drill bit decreases, resulting in larger cutting forces, leading to the poorer surface quality of the rock sample and reduced fidelity performance. On the other hand, a larger back  
Figure 1: Physical view of deep coring drill bit.  
-take angle provides a larger contact area. It reduces cutting forces, making it easier to maintain the straight movement of the core barrel during coring, thus achieving better core fidelity [21; 22; 23]. However, an excessively large back-rake angle can increase friction and heat generation during coring, affecting drilling and breaking efficiency.  
The side-rake angle, \(\beta\), refers to the counterclockwise rotation of the PDC drill bit when positioned horizontally, as shown in Figure 2. The side-rake angle directly affects the magnitude and direction of the lateral forces acting on the cutting chips. During the rock-breaking process, the side-rake angle of the cutting teeth primarily operates in the radial direction, exerting thrust on the rock chips and preventing their accumulation at the bottom of the hole. This preservation of chip accumulation affects the quality of the drilled core and the efficiency of drilling and coring. From the literature [24], the side-rake angle is generally designed to be 0-25\({}^{\circ}\).  
When the side-rake angle is slight, the contact area between the cutting tooth and the rock diminishes, making the cutting tooth more susceptible to lateral forces from the rock. This can result in excessive lateral vibration of the cutting tooth during rock breaking, thereby affecting the fidelity performance of the core. Conversely, when the side-rake angle is large, the cutting teeth are more prone to rock impact, leading to increased tooth wear and a higher amount and retention time of rock chips. These factors negatively impact the surface quality of the core, ultimately reducing core fidelity.  
Therefore, when designing a fidelity core drill bit, it is essential to consider the characteristics of the actual rock sample and structural parameters, such as the back-rake and side-rake angles. This approach ensures the acquisition of the best fidelity core and improves the fidelity performance of the core.
## 3 Status and Application  
The technique of interferometric synthetic aperture radar is to find the surface change by comparing two radar images acquired at different times or positions. It is mainly divided into repeat-pass interference technology and single-pass interference technology, which provide critical observational capabilities for natural disaster monitoring, glacial dynamics assessment, hydrological resource management, land use/cover change detection, etc., and also provides data support for the study of climate models, marine coral reef degradation, and other fields.
## 4 Discussion  
The use of ground-based and drone-based GPR has its advantages and disadvantages. For example, ground-based GPR data will, in principle, have higher reflection amplitudes because the antenna is closer to the ground. However, the data quality depends on slight surface morphology, mainly when the GPR is used in agricultural environments. In addition, using ground-based GPR over a large area and grid is time-consuming. Significant efforts are required to maintain the grid parameters (e.g., the distance between nearby lines and their exact direction).  
Drone-based GPR measurements employing RTK technology are less time-consuming and avoid issues with line positioning. Unlike ground-based GPR systems, the drone-based setup navigates without surface obstacles, facilitating a smoother and more precise scanning process. This is crucial for studies conducted over rugged terrains or unmanaged agricultural fields. Moreover, the drone's ability to fly over terrain and capture data without disruptions contributes to a more efficient and comprehensive subsurface mapping. Our study demonstrates that maintaining a relatively low flight altitude (1 m above the Earth's surface) results in a maximum exposure area at a depth of 2 m, which is twice that of ground-based GPR, while preserving the resolution of linear targets (refer to Table 1). However, the amplitude of the reflections is diminished in comparison to ground-based radargrams.  
Combining two GPR methodologies yields overlapping outcomes, reinforcing the quality and dependability of archaeological discoveries. Moreover, the likelihood of uncovering significant findings is heightened by cross-referencing and validating data gathered via two distinct GPR measurement techniques. Another noteworthy outcome of our study is the effective use of drone-based GPR in clayey soil. The successful application in this challenging environment highlights the adaptability and reliability of drone-based GPR technology. This finding suggests that the methodology can be employed in other locations characterized by clayey soil, broadening the applicability of drone-based GPR in archaeological and geophysical investigations. Overall, the study reinforces GPR's standing as a fast, cost-effective, and dependable solution for archaeological subsurface surveys, with drone-based technology demonstrating particular promise in addressing challenging terrains.
## 4 Results  
### Analysis and Modeling with the Comprehensive Model  
From the battery discharging behaviors of Figure 2a-d, fitting curves for the variation of the end of discharging time can be constructed. It is easier to observe the temperature and current dependence of the CE graphically. The curve fitting results are shown in Figure 5, which depicts the difference between measured results and fitted curves. Since the relationship of the CE and temperature in the equivalent circuit model follows the [PERSON] law, while the current obeys [PERSON]'s law, the CE of the equation has the form of:  
\[\eta_{c(T)}=a_{1}e^{(b_{1}\cdot T)}+a_{2}e^{(b_{2}\cdot T)} \tag{12}\]  
where \(T\) is the temperature, and \(a_{1}\), \(b_{1}\), \(a_{2}\), and \(b_{2}\) are the fitting parameters.  
The parameters \(a_{1}\), \(b_{1}\), \(a_{2}\), and \(b_{2}\) of the batteries measured are plotted in Figure 6. It can be clearly seen that the parameters vary with Peukert's law. By observing the trends of the parameters under the same temperature and different discharge currents in Figure 6, the parameters can be fitted to:  
\[\begin{array}{l}a_{1(I)}=p_{1}\cdot l^{k_{1}}\\ b_{1(I)}=p_{2}\cdot l^{k_{2}}\\ a_{2(I)}=p_{3}\cdot l^{k_{3}}\\ b_{2(I)}=p_{4}\cdot l^{k_{4}}\end{array} \tag{13}\]  
where \(I\) is discharging current; parameters \(p_{1}\), \(p_{2}\), \(p_{3}\), and \(p_{4}\), \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\) are fitting parameters. The values are listed in Table 4.  
Figure 5: Fitting curves for different discharging temperature and discharging C rates.  
By incorporating all the values of Table 4 for Equation (13) to find the related parameters \(p_{1}\), \(p_{2}\), \(p_{3}\), and \(p_{4}\), \(k_{1}\), \(k_{2}\), \(k_{3}\), and \(k_{4}\), we can obtain the predicted values of the parameters \(a_{1}\), \(b_{1}\), \(a_{2}\), and \(b_{2}\) for Equation (12).  
Equation (12) is composed of two exponential terms, which correspond to the pair of RC circuits in Figure 4b, where an exponential term represents the slow RC network; the other exponential term represents the fast RC network.  
Comparing the experimental results to Equations (12) and (13), the mean absolute percentage errors (MAPE) defined as  
\[MAPE=\frac{\sum_{t=1}^{n}\big{|}\frac{A_{t}-F_{t}}{A_{t}}\big{|}}{n}\cdot 100\% \tag{14}\]  
are listed in Table 5. In Equation (14), \(A_{t}\) is the measured data of cycle 1 of Tables 1-3, \(F_{t}\) is the fitted data, and \(n\) the number of data readings.  
Figure 6: The Peukert law with respect to current can be derived by fitting to obtain the trends of the parameters: (**a**) trend of \(a_{1}\), (**b**) trend of \(b_{1}\), (**c**) trend of \(a_{2}\), and(**d**) trend of \(b_{2}\).