esa-sceva/satcom-chunk-collection huggingface dataset:
# Power Profiling of Smart Grid Users Using Dynamic Time Warping +
## 4 Preliminaries and Background
### Power Profiling  
Power profiling includes analytical approaches that lead to predictive maintenance, abnormality detection, and fault detection. While most consumers show variable power consumption behavior, they typically exhibit a certain amount of repetitiveness [5; 60]. Consumers' consumption behavior is affected by their social, professional, and economic situations, the appliances they use, the time of day, and environmental factors such as weather and dwelling conditions. These factors can be considered random or complex phenomena [61].  
Power profiling is also used to predict and balance electricity purchases and sales. Poor estimation can be costly for a utility provider [62]. Personalizing power management and understanding electrical usage patterns help utilities send proper demand signals to the DR systems to balance the electrical load. Typically, consumers are clustered based on their load curves. Consumers in the same cluster have similar consumption patterns. By recognizing the load patterns of a new consumer, they can be classified into a particular consumer group, where similar rules are applied. Furthermore, abnormal usage can be detected for this new consumer with minimal information based on the load curves of their class [9]. Beyond load prediction and abnormal usage detection, analyzing a user's power consumption data for profiling may also lead to determining the working cycle of each residential electrical appliance [12].  
Processing DR data makes it possible to determine the patterns of individual electrical loads (e.g., operating schedules). Therefore, patterns of normal and abnormal operation are available for utility engineers [60]. These patterns are used in decision tree-based schemes for fault detection or security management [63]. For instance, an abnormal increase in usage at an unusual time (e.g., during midnight or vacation time) can indicate an energy theft attack or an operational fault and should be treated accordingly.  
For load management, data processing usually occurs locally in DR systems, including AMI and Non-Intrusive Load Monitoring (NILM) systems. NILM is a fundamental tool for extrapolating in-home activity. It disaggregates a consumption data stream into individual load signatures and matches them with reference signatures stored in a database to distinguish operating appliances such as refrigerators, air conditioners, and water heaters. NILM can identify specific electric device/appliance brands and might even identify malfunctioning appliances [64]. Therefore, power profiling can easily compromise consumer security and privacy. Credential extraction by power analysis attacks, authentication compromise by replay attacks and masquerading, and privacy breaches are potential security issues associated with power profiling [65].
# A New Tooth Profile Modification Method of Cycloidal Gears in Precision Reducers for Robots
## 3 Calculation of the Modification of Tooth Profile
### Modification Calculation for the Cycloid Method  
When a circle rolls along a straight line, the trajectory \(NN^{\prime}N^{\prime\prime}\) of Point \(N\) on the circle forms a cycloid, as shown in Figure 5.  
The mathematical expression of the cycloid is:  
\[\left\{\begin{array}{l}x=\pi r-r(\theta-sin\theta)\\ y=C-r(1-cos\theta)\end{array}\right., \tag{7}\]  
where \((x,\ y)\) is the coordinates of any point on the cycloid; \(r\) is the rolling circle radius; \(\theta\) is the rolling angle of the circle; \(C\) is the vertical coordinate of the starting point \(N\).  
According to Equation (7), there are two methods to determine the cycloid modification function. In the first method, the pressure angle \(\alpha\) as the \(X\)-axis and the modification \(\Delta L\) as the \(Y\)-axis meet the cycloidal function relationship. In Figure 5, the coordinates \((0,\ C-2r)\) of the point \(N^{\prime\prime}\) correspond to the coordinates \((\alpha_{0},\ \Delta L_{0})\) of reference point \(A\) in the modified coordinate system, and the coordinates \((\pi r,\ C)\) of the point \(N\) correspond to the coordinates \((\alpha_{tip},\ \Delta L_{tip})\), \((\alpha_{root},\ \Delta L_{root})\) of the tooth tip \(D\) and the tooth root \(E\), respectively. Then, the cycloidal relationship between the modification and the pressure angle can be established, as shown in Figure 6.  
Figure 5: Schematic diagram of forming a cycloid.  
As illustrated in Figure 6, the expression of the first cycloid modification curve from the reference point \(A\) to the tooth tip \(D\) can be obtained as:  
\[\left\{\begin{array}{l}\alpha=\alpha_{tip}-\big{(}\Delta L_{tip}-\Delta L_{0} \big{)}\big{(}\theta-\sin\theta\big{)}\big{(}\alpha_{tip}-\alpha_{0}\big{)}/(2 \pi r)\\ \Delta L=\Delta L_{tip}-\big{(}\Delta L_{tip}-\Delta L_{0}\big{)}\big{(}1-cos \theta\big{)}/2\end{array}\right.. \tag{8}\]  
Similarly, by replacing \(\alpha_{tip}\) and \(\Delta L_{tip}\), respectively, with \(\alpha_{root}\) and \(\Delta L_{root}\), the first cycloidal modification curve expression from the reference point \(A\) to the tooth root point \(E\) can be obtained.  
In the second method, with the pressure angle \(\alpha\) and the modification \(\Delta L\) are, respectively, treated as the parameter angle \(\theta\theta\) and the \(Y\)-axis. Thus, the function relationship between the modification and the pressure angle can be established (Figure 7).  
As illustrated in Figure 7, the second cycloid modification curve expression from the reference point \(A\) to the tooth tip \(D\) can be determined as:  
\[\Delta L=\frac{\Delta L_{tip}-\Delta L_{0}}{2}\bigg{(}1-\cos\bigg{(}\frac{ \alpha-\alpha_{0}}{\alpha_{tip}-\alpha_{0}}\pi\bigg{)}+\Delta L_{0}\bigg{)}. \tag{9}\]  
Through a similar modeling method, the modification equations of the straight line method and catenary method can also be obtained.
## 4 Circuit Description
### Circuit Module Design
#### 4.2.2 Mixing Module  
In [20], they encountered two main difficulties when designing an analog circuit to realize the MWC. One is analog mixing with spectrally rich waveforms, and another is constructing the periodic waveforms with a required alternation speed. Similarly in SCMWC, to solve the first problem, one of the essential blocks is the mixer. Since \(p_{i}(t)\) spans a wide spectrum, the mixer should allow a wide range of LO frequencies. We choose the wideband active mixer chip ADL5801 produced by Analog Device. This kind of mixer uses a high linearity, doubly balanced, active mixer core with integrated LO buffer amplifier to provide high dynamic range frequency conversion from 10 MHz to 6 GHz [21]. The LO circuit exhibits low additive noise, resulting in an excellent mixer noise figure. As for RF, for the sake of optimal performance, a balun is chosen to convert the single-ended signal into two differential signals. The schematic of the modulator is shown in Figure 7b.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 4 Experiments
### Performance on Common Datasets
#### 4.4.5 Ablation Study
It proves that the SSIM-based loss does work. Since the SSIM compares the consistency between the estimated density map and the corresponding ground truth, the fusion loss function performs better than the merely L\({}_{2}\) loss.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 1 Introduction
### Contributions  
The purpose of this paper is to develop a technique for performing power control to mitigate interference in UAVs operating within 5G networks. To achieve this goal, a novel method of power control is proposed, deviating from conventional approaches. Specifically, this method regulates transmit power not only at the serving gNB but also controls transmit powers of interfering gNBs from a central location. This approach introduces a race condition where the serving gNB for one UAV can act as an interfering gNB for another user. To address this challenge, deep reinforcement learning is employed due to its proven effectiveness in resolving similar problems.  
The deep Q-learning (DQL) algorithm is considered a promising solution to the interference problem as it eliminates the need for reporting channel state information (CSI), thereby reducing overhead and resulting in a low-complexity system design. Under the DQL approach, UAVs report only their received signal-to-interference-and-noise ratio (SINR) and coordinates, while the gNB, supported by a cloud-based architecture, performs power control to mitigate interference. This approach involves both associated and interfering gNBs issuing power control commands, diverging from industry standards where only the associated gNB is involved. Building upon our previous work [12], this paper presents the following significant contributions:  
* Develops a power control optimization problem for the downlink direction to maximise the SINR experienced by the UAV.
* Develops a framework based on deep reinforcement learning (DRL) that enables the concurrent execution of multiple actions and dynamically adjusts power levels to achieve optimal SINR by utilizing data from a given dataset.  
Thus, the paper presents a comprehensive framework based on DRL to optimise the SINR experienced by UAVs through dynamic adjustment of power levels using data from a provided dataset. The paper follows a systematic approach, beginning with a literature review and identification of research gaps in Section 2. This is followed by  
Figure 1: 5G-connected UAVs.  
a discussion of the challenges posed by current interference mitigation techniques in Section 3. Section 4 provides a detailed analysis of 5G and UAV technology and their functionalities. In Section 5, the paper introduces DRL, its components, neural network architecture, and policy selection methods. Section 6 presents a comparison of existing interference mitigation techniques with the proposed solution and describes the proposed algorithm. The simulation environment, setup, and results obtained are discussed in Section 7. The paper concludes with a discussion on the impact assessment of the results in Section 8.
## 4 Experiment and Results
### MOO Evaluation
#### 4.1.2 Hyper-Volume  
The hyper-volume results are presented in Figure 5. We observed that SC-MOPSO slightly outperformed NSGA-III in terms of hyper-volume, while NSGA-II produced a result of approximately 2.4 \(\times 10^{4}\). Reading these figures together with that for set coverage showed that SC-MOPSO outperformed NSGA-II in terms of set coverage, while it outperformed NSGA-III in terms of hyper-volume.
# Approximation of the Riesz-Caputo Derivative by Cubic Splines
## 2 Materials and Methods
### The Cubic B-Spline Basis on Finite Intervals  
Our aim is to approximate the solution to Equation (1) by a cubic spline, i.e., a piecewise polynomial of degree 3. Cubic spline on the finite interval \([0,L]\) can be represented as a linear combination of a given cubic spline basis [34; 35].  
Without loss of generality, we assume \(L\) to be an integer greater than or equal to 4 and choose integer knots in the interval \([0,L]\):  
\[x_{0}=x_{1}=x_{2}=x_{3}=0\,,\]  
\[x_{j}=j-3\,,\quad 4\leq j\leq M-4\,,\]  
\[x_{M-3}=x_{M-2}=x_{M-1}=x_{M}=L\,.\]  
where \(M=L+6\). We observe that the endpoints of the interval are repeated 4 times, meaning that they are knots of multiplicity 4. The internal knots \(x_{j}\), \(4\leq j\leq M-4\), are simple knots.  
The cubic B-spline basis  
\[\mathcal{N}=\{N_{k}(x),0\leq k\leq L+2\},\qquad x\in[0,L],\]  
is formed by \(L+3\) basis functions, 3 edge functions for each endpoints plus \(L-3\) internal functions. The left- and right-edge functions have index \(0\leq k\leq 2\) and \(L\leq k\leq L+2\), respectively, while the internal functions correspond to the values \(3\leq k\leq L-1\).  
We recall that the basis functions \(N_{k}\), \(0\leq k\leq L+2\), are non-negative and compactly supported with  
\[supp(N_{k})=[\max(0,k-3),\min(k+1,L)],\qquad 0\leq k\leq L+2.\]  
Moreover, the basis \(\mathcal{N}\) is centrally symmetric, i.e.,  
\[N_{k}(x)=N_{L+2-k}(L-x),\qquad 0\leq k\leq L+2, \tag{5}\]  
and satisfies the endpoint conditions:  
\[N_{k}(0)=\delta_{0,k},\qquad N_{k}(L)=\delta_{L+2,k},\qquad 0\leq k\leq L+2, \tag{6}\]  
where \(\delta_{jk}\) denotes the Kronecker delta.  
For the convenience of the reader, the explicit expressions of the basis functions are given in Appendix A. For details on the construction of spline bases, see [35; 36].  
The function system \(\mathcal{N}\) can be adapted to any sequence of equidistant knots by dilation:  
\[\mathcal{N}_{h}=\{N_{k}\Big{(}\frac{x}{h}\Big{)},0\leq k\leq M_{h}\},\qquad x \in[0,L], \tag{7}\]  
where \(M_{h}=\frac{L}{h}+2\). Figure 1 shows the basis for \(L=1\) and \(h=\frac{1}{8}\).  
Figure 1: The cubic spline basis \(\mathcal{N}_{h}\) on the interval \([0,1]\) with space step \(h=1/8\).
# Multi-Focus Image Fusion Based on Decision Map and Sparse Representation
## 2 Proposed Fusion Scheme
### Decision Map  
The above clarity score maps are binarized by a given threshold \(K_{1}\) and denoted as \(S^{\prime}_{A}\) and \(S^{\prime}_{B}\), as shown in Figure 4a,b (the focused pixels are marked as yellow, defocused pixels are marked as blue). It can be observed that there may be some misjudment areas caused by misclassification in the focused area or the defocused area. Morphological techniques are used to filter out these misclassifications to obtain the standard normalized clarity score maps. The results are shown in Figure 4c,d and denoted as \(S^{\prime\prime}_{A}\) and \(S^{\prime\prime}_{B}\). Thus, we can determine the location of the uncertain area when the focused areas of Figure 4c,d overlap.  
Finally, the initial decision map is obtained by  
\[\widetilde{D}(x,y)=\left\{\begin{array}{ll}1,&\text{if }S^{\prime\prime}_{A}(x,y)=1 \,\text{and}\,S^{\prime\prime}_{B}(x,y)=0\\ 0,&\text{if }S^{\prime}_{A}(x,y)=0\,\text{and}\,S^{\prime}_{B}(x,y)=1\\ 0.5,&\text{otherwise}\end{array}\right. \tag{2}\]  
as shown in Figure 4e, where the white pixels indicate the uncertain area. In order to make the size of the decision map consistent with the source images, the upsampling operation is also carried on to the initial decision map.  
The next target is to generate the final decision map. As mentioned above, there is still an uncertain area in the initial decision map \(\widetilde{D}\). To obtain the final decision map, further analysis and processing of the uncertain area is needed. We use the spatial frequency method to divide the pixels of the uncertain area in the initial decision map \(\widetilde{D}\) into two categories--focused and defocused--to obtain the final decision map containing only the focused area and the defocused area. The spatial frequency method can be described as  
\[SF(x,y)=\sqrt{\iint\limits_{\Omega}\left(\triangledown_{x}I\right)^{2}}+ \sqrt{\iint\limits_{\Omega}\left(\triangledown_{y}I\right)^{2}} \tag{3}\]  
where \(I\) is the input image, \(\Omega\) is a \(7\times 7\) window centered on the point \((x,y)\), and \(\triangledown_{x}\) and \(\triangledown_{y}\) represent the horizontal and vertical differences of the pixel points, respectively. The larger the spatial frequencyvalue, the higher the clarity value of the point. Thus, points in the uncertain area of the initial decision map \(\overline{D}\) can be classified according to the following decision rules:  
\[\overline{D}(x,y)=\left\{\begin{array}{cc}1,&\text{if }SF_{A}(x,y)>SF_{B}(x,y)\\ &0,&\text{otherwise}\end{array}\right.. \tag{4}\]  
Assuming that the spatial frequency values of the corresponding uncertain pixel points in the two source images are \(SF_{A}(x,y)\) and \(SF_{B}(x,y)\), respectively, and \(SF_{A}(x,y)>SF_{B}(x,y)\), the pixel point can be determined as the focus point, and vice versa. Based on this, the final decision map \(D\) can be obtained, as shown in Figure 4f.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 3 Experimentation and Performance Evaluation
### Experimental Results and Analysis
#### 3.2.1 Detection of Target Area Overlap Occlusion  
As shown in Figure 10, with the disease area of the maize leaves overlapped and obscured, we can see that the three algorithms (with the exception of MFF-CNN) missed the overlapping leaves in the upper left corner of the image. Both DETR and CenterNet did not perform very well in the detection of the remaining disease areas in the images.  
DETR is a transformer-based target detection framework that transforms target detection into a simple set prediction problem to achieve end-to-end target detection. However, it has a long training time, misses the detection of small target diseases that appear densely in the middle of maize leaves in the experiment, and has insufficient detection accuracy for dense, small targets. CenterNet is an anchor-free method by direct prediction of the centroid coordinates of objects. When there are multiple objects in overlapping centers, they will be misidentified as one object. Further, CenterNet predicts that if the centroids of two objects also overlap in down-sampling during the prediction process, they will also be mistaken for one object. When there are dense targets in the detection area, such as maize leaves close together and overlapping occlusions, CenterNet will mismatch the key points and exhibit poor performance.  
In our proposed MFF-CNN, the X average pool and Y average pool aggregate feature along two spatial directions obtains a pair of feature maps with perceptual capabilities in different directions, which helps the network to locate the target of interest more accurately. Thus, the MFF-CNN performed best in the complex case of overlapping shading of diseased areas of maize leaves, achieving a mAP of 0.486.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **0.5** & **1** & **2** & **3** & **4** & **8** & **Avg. [0.5, 1, 2, 4, 8]** \\ \hline MFF-CNN & 25.59 & 37.05 & 47.93 & 56.13 & 61.27 & 71.01 & 49.83 \\ Yolov5s & 25.14 & 36.49 & 46.40 & 53.06 & 58.11 & 67.93 & 47.85 \\ DETR & 24.23 & 34.86 & 46.85 & 54.59 & 59.64 & 70.09 & 48.38 \\ CenterNet & 21.71 & 30.54 & 43.51 & 50.36 & 56.85 & 68.83 & 45.30 \\ Faster RCNN & 18.38 & 29.01 & 39.28 & 45.77 & 51.44 & 62.88 & 41.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitivities (%) at various false positives (FPs) per image.
# Nonlinear Analysis and Reliability Analysis of Multilink Mechanism Considering Mixed Clearance
## 1 Introduction
In contrast, the Newton-Euler method primarily focuses on motion discrepancies in specific mechanisms with particular clearances, the Lagrange approach struggles when handling complex dynamic problems involving multiple kinematic pairs and clearance couplings, and the finite element method mainly concentrates on local structural characteristics, making it difficult to thoroughly analyze the system's overall dynamic behavior. The increased complexity of hybrid clearance modeling stems from the compound effects of combined revolute and prismatic clearances, which introduce intricate dynamic interactions and strong coupling characteristics among various clearances. Moreover, to accurately evaluate key performance indicators of mechanical systems, the constructed model must maintain high precision and reliability, necessitating precise simulation of multiple influencing factors--a requirement that further escalates modeling difficulty. Consequently, the analytical framework established in this study holds both innovative and practical significance. In summary, there is limited research on the kinematic nonlinear characteristics and reliability of mechanisms, with most studies focusing on static reliability. In fact, nonlinear characteristics can better analyze the random and irregular motion of mechanisms, while the reliability of dynamic accuracy can reflect the reality of the various mechanisms better. However, research on nonlinear characteristics and reliability of mechanisms is typically conducted on simple mechanisms with a single clearance; there is less analysis of the nonlinear characteristic effects and reliability of complex mechanisms with mixed clearances. So, this article takes a multilink mechanical press mechanism as a research object, based on domestic and international research results and existing literature on the rigid body dynamic response analysis of mechanisms with clearances. The research contents are as follows. Section 2 establishes clearance models for joints and simulates normal contact force and tangential friction force at the clearances using the L-N model, an improved Coulomb friction model. Section 3 develops a dynamic model for a rigid multilink mechanism with mixed clearances based on the Lagrange multiplier method. Section 4 establishes a dynamic accuracy reliability model for mechanism based on stress-strength interference theory. Section 5 analyzes the dynamic responses of various types of clearances on the rigid multilink mechanism, as well as the effects of different clearance values and driving speeds on the dynamic responses of the rigid multilink mechanism with mixed clearances. Section 6 analyzes the influence of mixed clearances on the nonlinear characteristics of rigid multilink mechanisms at varying clearance values and driving speeds. Section 7 analyzes the reliability of multilink mechanisms with mixed clearances at different clearance values and driving speeds. Section 8 summarizes the entire paper.
# The Observed Impacts of Wind Farms on Local Vegetation Growth in Northern China
###### Abstract  
Wind farms (WFs) can affect the local climate, and local climate change may influence underlying vegetation. Some studies have shown that WFs affect certain aspects of the regional climate, such as temperature and rainfall. However, there is still no evidence to demonstrate whether WFs can affect local vegetation growth, a significant part of the overall assessment of WF effects. In this research, based on the moderate-resolution imaging spectroradiometer (MODIS) vegetation index, productivity and other remote-sensing data from 2003 to 2014, the effects of WFs in the Bashang area of Northern China on vegetation growth and productivity in the summer (June-August) were analyzed. The results showed that: (1) WFs had a significant inhibiting effect on vegetation growth, as demonstrated by decreases in the leaf area index (LAI), the enhanced vegetation index (EVI), and the normalized difference vegetation index (NDVI) of approximately 14.5%, 14.8%, and 8.9%, respectively, in the 2003-2014 summers. There was also an inhibiting effect of 8.9% on summer gross primary production (GPP) and 4.0% on annual net primary production (NPP) coupled with WFs; and (2) the major impact factors might be the changes in temperature and soil moisture: WFs suppressed soil moisture and enhanced water stress in the study area. This research provides significant observational evidence that WFs can inhibit the growth and productivity of the underlying vegetation.  
wind farm impact; vegetation growth; vegetation index; satellite observations; GPP; land surface temperature; land use change +
Footnote †: journal: Remote Sensing  
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