esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Therefore, we propose a classifier-level method known as Dual Logit Weight Perturbation (DLWP) loss for DL-based FD systems. Based on our approach, our main contributions can be summarized as follows:  
* We propose a classifier-level method called DLWP loss, which can be applied directly to DNNs, preserving the original dataset distribution during training while improving overall classifier performance on the imbalanced dataset. * We introduce logit weight vectors, a set of tunable network hyperparameters adjustable on a case-by-case basis, meant for regulating the levels of focus accorded to each of the distinct target classes during training. * We show that information from the imbalanced target class distribution can be strategically used to generate a suitable logit weight vector that predisposes a DNN to focus on minority samples during critical periods of training. * We introduce a training regime that facilitates switching between predefined logit weight vectors, one for each training phase of a DNN, achieving improved classifier performance on the minority samples while still effectively generalizing the entire dataset. * We propose a data-informed strategy for safety-related FD systems first by generating models that prioritize recall, followed by a call to action enlisting human experts for feedback on a subset of the results; model predictions with high uncertainty requiring further action. The remainder of this paper is structured as follows. In Section 2, we present a review of the literature and related work on methods used to address imbalanced dataset classification problems. In Section 3, we describe associated concepts from which we build upon our proposed method and weight vector selection strategies. We also present the implementation details, uncertainty estimation methods, datasets, and the evaluation techniques used for the experiments. In Section 4, we discuss the results of our experiments. Finally, in Section 5, we conclude the paper with an overview of our contributions and future work.
## 3 Results
### Linking MOC to Clay Minerals and Other Factors
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & **SOC** & **MOC** & **pH** & **Clay** & **Silt** & **Sand** & **MAP** & **MAT** & **SiO\({}_{2}\)** & **Al\({}_{O}\)**\({}_{3}\)** & **Fe\({}_{O}\)**\({}_{3}\)** & **CaO** & **Sa** & **Saf** & **Bacteria** & **Fungi** \\ \hline SOC & 0.99 & \(-\)0.70 & 0.64 & 0.43 & \(-\)0.55 & 0.57 & \(-\)0.08 & 0.01 & 0.43 & 0.38 & \(-\)0.28 & \(-\)0.33 & \(-\)0.37 & 0.70 & 0.60 \\ MOC & & \(-\)0.69 & 0.67 & 0.47 & \(-\)0.58 & 0.53 & \(-\)0.07 & \(-\)0.03 & 0.48 & 0.43 & \(-\)0.27 & \(-\)0.40 & \(-\)0.42 & 0.67 & 0.56 \\ pH & & & \(-\)0.22 & 0.06 & 0.04 & \(-\)0.21 & 0.20 & \(-\)0.50 & \(-\)0.36 & \(-\)0.07 & 0.74 & \(-\)0.02 & \(-\)0.01 & \(-\)0.54 & \(-\)0.51 \\ Clay & & & & 0.63 & \(-\)0.78 & 0.37 & 0.18 & \(-\)0.33 & 0.54 & 0.55 & 0.06 & \(-\)0.61 & \(-\)0.60 & 0.33 & 0.32 \\ Silit & & & & \(-\)0.95 & 0.59 & 0.36 & \(-\)0.72 & 0.17 & 0.86 & 0.40 & \(-\)0.59 & \(-\)0.79 & 0.16 & 0.10 \\ Sand & & & & & \(-\)0.57 & \(-\)0.37 & 0.64 & \(-\)0.26 & \(-\)0.82 & 0.61 & 0.76 & \(-\)0.20 & \(-\)0.15 \\ MAP & & & & & & 0.18 & \(-\)0.29 & 0.03 & 0.46 & 0.17 & \(-\)0.19 & \(-\)0.38 & 0.40 & 0.34 \\ MAT & & & & & & \(-\)0.30 & \(-\)0.31 & 0.19 & 0.29 & 0.02 & \(-\)0.09 & \(-\)0.65 & \(-\)0.69 \\ SiO\({}_{2}\) & & & & & & & & 0.08 & \(-\)0.65 & \(-\)0.84 & 0.62 & 0.70 & 0.08 & 0.17 \\ Al\({}_{O}\)\({}_{3}\) & & & & & & & & 0.41 & \(-\)0.37 & \(-\)0.71 & \(-\)0.56 & 0.33 & 0.45 \\ Fe\({}_{O}\)\({}_{3}\) & & & & & & & & & 0.20 & \(-\)0.75 & \(-\)0.93 & 0.17 & 0.18 \\ CaO & & & & & & & & & & \(-\)0.26 & \(-\)0.27 & \(-\)0.22 & \(-\)0.30 \\ Sa & & & & & & & & & & 0.91 & \(-\)0.17 & \(-\)0.20 \\ Saf & & & & & & & & & & & \(-\)0.18 & \(-\)0.19 \\ Bacteria & & & & & & & & & & & & 0.92 \\ Fungi & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Pearson’s correlation among soil physical-chemical, environmental factors, and MOC. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Zero-Order** & **MAP** & **pH** & **Clay** \\ \hline SOC & \(-\)0.25 & \(-\)0.40 \({}^{\star}\) & \(-\)0.08 & \(-\)0.33 \\ MOC & \(-\)0.26 & \(-\)0.41 \({}^{\star}\) & \(-\)0.10 & \(-\)0.34 \\ \hline Zero-order: without controlling any factors. \({}^{\star}\)\(p\) < 0.05, significant correlations. SOC: soil organic carbon; MOC: mineral-associated organic carbon. MAP: mean annual precipitation.  
\end{table} 
Table 5: Partial correlations between the SOC fractions and MAT after controlling the related soil properties in grassland transects. positive effects on MOC. The most significant factor influencing SOC was the pH, which was followed by bacteria.
# Impacts of MT-HVDC Systems on Enhancing the Power Transmission Capability
## 4 Modeling and Computation Structure
### Control Layout for CIGRE B4 Test System
#### 4.1.2 Local Controllers  
MT-HVDC grid performance is highly dependent on the used control approach, while control relies primarily on the type of AC network connection and the configuration of the DC grid [8]. The \(P\)-\(V_{ac}\) control is deployed to regulate the AC voltages of WFs at precise level [11]. To generate stable AC voltage at 50 Hz, proportional integral (PI) control is used to reduce voltage error (\(\mathrm{e}=V_{\mathrm{WF}}\)* - \(V_{\mathrm{WF}}\)), which then employed as performance index [11; 46]. Table 6 lists the specifications for PI control parameters.  
As illustrated in Figure 5, \(V_{\mathrm{DC}}\) and \(Q\) are regulated at GS-VSCs for the evaluation of MT-HVDC grid. The DC OPF is executed in the dispatch center while generalized voltage-droop control for each VSC is done locally and at the primary level [44; 46]. Moreover, Figure 6 displays a droop controller structure used to regulate DC grid voltage. Where \(Vg^{*}\) pu and \(P^{*}\) pu are direct voltage and real power references, respectively, while \(k\) is the slope of the droop characteristic [47; 48].  
It is worth noting that the d-q control approach [49] is employed at the primary (VSC station) level. The OPF at dispatch center is performed in the time scale of second while the time constant for the primary control system is in the range of milliseconds. The computational cost of DC OPF is 1.64 s in this paper.  
An average two-level VSCs value model is used for MT-HVDC grid modeling, while WFs are demonstrated as fixed power sources.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{_PI Controller_} & \(\boldsymbol{T_{i}}\) & \(\boldsymbol{K_{p}}\) \\ \hline \(q\)-axis \(PI\) & 0.0067 & 0.48 \\ \(d\)-axis \(PI\) & 0.0067 & 0.48 \\ \(P\) controller & 0.0400 & 0.01 \\ WF AC voltage \(PI\) & 1.0000 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 6: PI controller parameters.
## 3 System Design  
In order to reasonably evaluate the operating status of the IS after it enters the orbit, the performance of the IS needs to be comprehensively tested on the ground. A traditional high-voltage levitation system has been designed and built, and corresponding experiments were performed on the IS of the Taiji verification technology satellite. In the experiments, because the X-plane needs to undertake the suspension high voltage, it is impossible to accurately measure the performance index in the direction of the non-sensitive axis (X-axis as shown in Figure 3). The resolution in this direction can only be estimated by evaluation. Therefore, we design a torsion pendulum facility to evaluate the non-sensitive axis noise of IS to complete the evaluation of the system.
## 3 Results and Discussion
### Temperature Stability
#### 3.1.3 The Temperature of the Bare Wall and the Wall after Coating Changes  
As shown in Figure 7, temperature measurements were conducted on both bare and painted block walls, with the room temperatures recorded at 16.2 \({}^{\circ}\)C and 20.4 \({}^{\circ}\)C, respectively. The data indicated that the temperatures behind the wall, both before and after painting, were similar, resulting in almost identical curves. In the temperature range of 10\({}^{\circ}\) to 50\({}^{\circ}\), the temperature behind the bare wall was consistently above the room temperature when exposed to the heat source. However, after applying a coat of paint, the temperature behind the wall decreased by approximately 3\({}^{\circ}\), with the most significant reduction observed at 60\({}^{\circ}\). For the bare brick wall and painted brick wall, with room temperatures of 16.2 \({}^{\circ}\)C and 20.6 \({}^{\circ}\)C, respectively, the temperatures behind the bare wall exceeded the room temperature from 10\({}^{\circ}\) to 80\({}^{\circ}\) when exposed to the heat source. After painting, the temperature behind the wall dropped, with the largest decrease at about 20\({}^{\circ}\), showing a reduction of 6.6 \({}^{\circ}\)C. Similarly, for the bare earth wall and painted earth wall, with room temperatures of 17.1 \({}^{\circ}\)C and 20.4 \({}^{\circ}\)C, respectively, the temperatures behind the bare wall were higher than the room temperature from 10\({}^{\circ}\) to 80\({}^{\circ}\) under heat source exposure. After painting, the temperature behind the wall fell by approximately 0.7 \({}^{\circ}\)C. These observations suggest that the optimal angle for applying the coating, which achieves the best insulation effect, varies among the three types of walls, indicating that the insulation effectiveness is influenced by both the wall material and the angle of the light source.  
Figure 7: The temperature of the three bare walls and the wall after one coat of paint changes with \(\theta\) when the wall is illuminated by the heat source (\(\theta\) is 10\({}^{\circ}\)-80\({}^{\circ}\), step size is 10\({}^{\circ}\)).
## 3 Data and Methods  
### Overall Workflow  
The main objective of this paper is to use the data of various types of submarine-landslide-hazard impact factors as the basis for regional classification of submarine landslide hazards after data preprocessing and machine learning modeling. The study in this paper can provide an exploration of the hazard classification of global submarine landslides. The workflow of this study is shown in Figure 2, and includes the following specific steps:  
The data of this study are obtained from long-term geophysical surveys and in situ monitoring in the study area by the First Institution of Oceanography, MNR [20]. After data collection, we should determine suitable geological factors as submarine-landslide-influencing parameters. Suitable impact parameters should be able to cover both the geological, hydrological, and human influential factors of landslides and be more readily available as common parameters.  
* Influencing parameter determination.  
Figure 1: Location map of the study area.  
Figure 2: The general concept of the research method.  
After determining the factors influencing the submarine landslides, each data point was interpolated separately so that the values of all influencing factors were included at each study site in the study area. Subsequently, the data were classified into several categories according to the characteristics of different influencing factors, and the category distribution maps were drawn.  
Classification-level data extraction.  
The coordinate values of the study-point locations are defined and the categories of each factor are extracted from the impact-factor classification map obtained in the previous step. The coordinate values of the study-point locations are defined and the categories of each factor are extracted from the impact-factor classification map obtained in the previous step. Thus, each study point corresponds to multiple classes of impact-factor parameters that can be used in the next step of unsupervised machine learning model training.  
Establishing unsupervised machine learning models.  
Establish 3 unsupervised machine learning models using k-means, spectral clustering, and hierarchical clustering. The different parameters in the model are first modeled several times, and subsequently, the model parameter with the best prediction is selected to build the final established model.  
Accuracy comparison.  
Compare the accuracy and rationality of different predicting results and choose the best one. Both the mathematical test metrics and the measured geological conditions should be used to test the models' accuracy. The Calinski-Harabasz index, silhouette index, and Davies-Bouldin index are used to calculate the mathematical accuracies. The liquefaction zonation is used to calculate the geological rationality.  
Influencing parameter analysis.  
Study the importance of all the landslide-influencing parameters by excluding them individually using the best model and test the accuracies with evaluating indicators.
# Low-Altitude Sensing Model: Analysis Leveraging ISAC in Real-World Environments
## 3 Multi-BS Networking with Cellular-like Topology
The research findings above provide crucial theoretical foundations for constructing a multi-BS networked cellular-like sensing topology model and designing the key parameters. In the process of building a low-altitude ISAC network, achieving seamless 3D sensing areas is the primary task. Based on the relationship between station spacing \(d\) and coverage radius \(R\), it is necessary to plan the station spacing and the cell radius precisely. The relationship is that \(d\leq R\), and when \(d=R\), a specific networked sensing 3D structure can be formed. Through such a design, on the one hand, the continuity of the sensing area can be ensured. On the other hand, signal interference in overlapping areas can be effectively reduced, enhancing the overall stability and reliability of sensing. From the perspective of vertical coverage height, the networking modes at different altitude layers differ significantly. A standard hexagonal cellular topology was adopted at the altitude limit, as this classic structure ensures continuous signal coverage. As the height gradually decreased, the hexagonal structure underwent stretching deformation, leading to overlapping between cells. The increase in overlapping areas reduced the probability of missed signal detection and improved the accuracy of low-altitude sensing. Meanwhile, when designing the antenna FOV of base stations, the influence of the local station's vertical FOV \(\alpha\) must be fully considered. This parameter directly determines the position of the outer edge of the tower top blind spot at the maximum coverage height. Consequently, it influences the requirements for neighboring stations to cover the local station's tower top blind spot. When considering the coverage of neighboring stations over the local station's tower top blind spot, the beam gain of neighboring stations is a critical factor that cannot be overlooked. Under the given station spacing \(d\), the angle by which the beam deviates from the normal direction must be carefully addressed. Only through precise control of this angle can the sensing capability of neighboring stations meet the requirements, effectively fill the local station's tower top blind spot, minimize sensing blind spots to the greatest extent, and thereby significantly enhance the overall performance of a low-altitude ISAC network. Figure 7: Multi-BS networked blind spot coverage by neighboring stations.
## 2 Circular Patch Antenna Design  
The first step towards the goal of electrical size reduction of the proposed antenna is the design of a circular patch antenna operating at 6.24 GHz based on the basic patch antenna design procedure [18; 29]. The initial operating frequency is chosen as 6.24 GHz because CDNG-MTM tuning would end up in final operating frequencies in WLAN application bands of 2.4 GHz and 5.2 GHz. The designed structure is shown as Figure 1. The prototype of the antenna is fabricated on an FR4 substrate. The antenna is fed with a microstrip line feed having feed-width of 1 mm and feed-length of 3.1 mm. Detailed dimensions of the circular patch antenna are given in Table 1. The simulated and measured return loss characteristics of the circular patch antenna are plotted in Figure 2.
# Design and Implementation of Composed Position/Force Controllers for Object Manipulation
## 5 Grasping Stage  
As has been stated in Section 1, the grasping stage refers to the action of closing the gripper to take and manipulate the object properly. In order to arrive at this stage, some considerations have to be taken into account.  
Generally, mobile robotic manipulators dispose of a perception system that provides the object's information (position, shape, color, etc.) to the arm control system. The perception system can be composed of cameras, laser range finders, depth cameras, or a combination of these sensors. Once the perception system recognizes an object, it sends its information to the position and arm controllers to do the pre-grasping stage. In this work, those stages were realized. In other words, an object's size and position were calculated and the arm was driven to reach the object. However, due to uncertainty in: (a) the relative position of the mobile base, (b) associated with onboard sensors, and (c) due to the control and motion of the arm's joints, the position of end-effector can be slightly different than planned. Therefore, the object's position relative to the gripper can be one of those shown in Figure 4. Namely, the object could be placed in the center, shifted to the right, or shifted to the left between the two fingers. To avoid perception obstruction, as commonly happens with mobile robots, it was decided to not use visual surveying, and instead we used gripper sensor grasping control. The robotic arm used in this work has two-independent fingers with angular motion. Each finger was provided with a piezoelectric force sensor, imposing then, the use of one controller for each finger.
## 2 Materials and Methods
### Circadian Stimulus
#### 2.4.2 Measuring Campaign  
As noted in the introductory section, circadian rhythms typically begin developing at around six months of age. Consequently, the classroom for infants younger than six months was excluded from this research. Therefore, classroom B was analyzed to represent their respective categories.  
The methodology for analyzing the circadian stimulus was divided into two distinct phases. The first phase involved monitoring the current lighting conditions in the selected classrooms over 11 weeks. To collect the necessary data, specialized equipment was deployed. The PCE-CSM 8 Spectrophotometer (400 to 700 nm, accuracy of \(\Delta\)E-ab 0.2) by PCE Iberica S.L, Spain) was used to analyze the color properties of the classroom facades (RGB values, points a, b, d, e), while the L-100 Lux Meter by PCE Iberica S.L, Spain (0.1 k to 3000 kk, accuracy of \(\leq\)2.5% \(\pm\)1 LSB) was employed to determine the glass transmissivity of the windows (point c) and to perform a set of measurements in the ground of the room (point b), as can be seen in Figure 5. These values were then incorporated into a detailed model of the classroom using Velux Daylight Visualizer (v2.8.4), a lighting simulation software designed for precise daylight analysis. Measurements were used to validate the simulation model produced with this tool.  
The facade of the Faculty of Education building, attached to the nursery in the courtyard area (north-facing--azimuth of 7\({}^{\circ}\)) and arranged parallel to it, is located at 12.18 m and has a height of 17 m. The facade's surface color, quantified using its RGB values, was determined to be 195, 193, and 187, reflecting its light-grey tone. Similarly, the reflectance properties of the outdoor surfaces in the adjacent courtyards were analyzed. The first courtyard ground exhibited RGB values of 117, 117, and 113, indicating a medium-grey surface, while the second courtyard ground showed darker tones with RGB values of 79, 78, 71. These reflectance data points were integrated into the simulation to ensure an accurate representation of the interplay between external reflected light and the interior lighting environment of the classroom.  
Figure 4: Circadian stimulus according to illuminance values and the resulting SPDs.  
The glass used in the classroom windows was measured to have a transmissivity of 0.758. The interior wall surfaces were also analyzed for their reflective properties. The upper portion of the wall exhibited RGB values of 239, 238, and 185. In contrast, the lower portion of the wall was found to have RGB values of 156, 182, and 185.
## 3 A Centralised Mapping Service
The Map Generation makes it easy to predict values at points where there is no sensing device. The routing service encapsulates the communication architecture used for data delivery to the sink. The mapping service can be on top of any routing protocol, however, in this work, the ROL/NDC routing protocol [19] was used for data communication. ROL/NDC has proven to be an energy efficient and robust routing protocol. It is already implemented, which allowed the authors to direct the implementation efforts on the Map Generation service. Finally, the application module contains the entire code specific to the user application functionality. Section 6.1 presents a sample mapping application.
# An Improved Cutting Force Model for Ultrasonically Assisted Grinding of Hard and Brittle Materials
## 2 Model Development  
### Kinematic Analysis of Diamond Grains  
In axial ultrasonically assisted grinding, the removal of materials can be thought of as the combination of the actions of the whole active abrasive particles, and the basic kinematics of UAG is shown in Figure 2. Taking the single abrasive grain into consideration, an abrasive grain consists of rotational motion, ultrasonic vibration, and feed motion. Therefore, the motion trajectory of an abrasive grain in UAG can be expressed as  
\[S(t)=\left[\begin{array}{c}-r\cdot\cos(\omega\cdot t)\\ r\cdot\sin(\omega\cdot t)+v_{F}t\\ A\cdot\sin(2\pi ft)\end{array}\right] \tag{1}\]  
where \(r\) is the rotational radius of the abrasive grain, \(\omega\) is the angular velocity of the abrasive grain (\(\omega=2\pi\text{nr}/60\), \(n_{r}\) is the rotation speed of the tool), \(v_{F}\) is the feed rate, \(A\) is the vibration amplitude, \(f\) is the ultrasonic vibration frequency. Figure 3 shows the trajectory of an abrasive particle in UAG.  
Figure 1: Wear of the tool surface after grinding.  
The velocity and acceleration of abrasive grains can be obtained by differentiating Equation (1), which can be derived as  
\[v(t)=\left[\begin{array}{c}r\cdot\omega\sin(\omega\cdot t)\\ r\cdot\omega\cos(\omega\cdot t)+v_{F}\\ 2\pi f\cdot A\cdot\cos(2\pi ft)\end{array}\right] \tag{2}\]  
\[a(t)=\left[\begin{array}{c}r\cdot\omega^{2}\cos(\omega\cdot t)\\ -r\cdot\omega^{2}\sin(\omega\cdot t)\\ -4\pi^{2}f^{2}\cdot A\cdot\sin(2\pi ft)\end{array}\right] \tag{3}\]
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 4 Optimization and Simulation Model
### Performance-Improving Strategies  
Penalizing synchronization and duration violations helps select more robust routes for both mother and daughter vessels, but does not necessarily reflect real-world operations of a feeder network service. In our optimization-simulation model, duration violations propagate from one week to another. In contrast, real-world vessel operators would try to either avoid a delay (e.g., by using more robust routes) or recover from the delay (e.g., by speeding up) before it causes any violations. To further improve the quality of the solutions, we propose two types of performance-improving strategies that mimic real-world operations and are integrated into the solution approach: enhancing the robustness of the routes and recovery from delays.