esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Taillight Detection and Light Signal Identification Based on Image Processing
### Taillight Detection
#### 4.2.1 Correlation Detection Based on Histogram of Oriented Gradients  
A histogram of oriented gradients (HOG) is a kind of feature histogram. For an image \(f(x,y)\), its gradient magnitude \(\left|\
abla f(x,y)\right|\) and direction angle \(\Phi\) at the point \((x,y)\) are defined as:  
\[\begin{array}{l}\left|\
abla f(x,y)\right|=mag(\
abla f(x,y))=(G_{x}^{2}+G_ {y}^{2})^{1/2}\\ \Phi(x,y)=\arctan(G_{y}/G_{x})\end{array} \tag{3}\]  
\(Gx\) and \(Gy\) respectively represent the gradient in the x direction and the gradient in the y direction.  
If we divide the interval \(\left[-\frac{\pi}{2},\frac{\pi}{2}\right]\) in the gradient direction in Equation (6) into \(k\) uniform intervals (bin), use bin\({}_{k}\) to represent the number \(k\) gradient direction and use \(K\) to represent the dimension values. Then the gradient magnitude weight projection function \(Q_{k}(x,y)\) in the number \(k\) gradient direction of the pixel point \(Q_{k}(x,y)\) can be expressed as:  
\[Q_{k}(x,y)=\begin{cases}\left|\
abla f(x,y)\right|&\varphi(x,y)\in\text{bin}_ {k}\\ 0&\varphi(x,y)\
otin\text{bin}_{k}\end{cases} \tag{4}\]  
\(k=0,1,\cdots K-1\)  
The gradient magnitude weight projection function \(Q_{k}(x,y)\) in Equation (4) is the gradient magnitude of the pixel, which reflects the edge information of the pixel to some extent. According to Equation (4), the gradient feature of each pixel point \((x,y)\) is a \(K\) dimensional vector. The gradient direction histogram of the image is the histogram statistics of the \(K\) dimensional gradient features of all the pixels in the image. The candidate taillight areas obtained by the image processing method is as shown in Figure 9.  
The correlation coefficients of the gradient histograms of the two frames in the X and Y directions are calculated, as shown in Table 5. Analyzing the correlation coefficient of the taillight candidate area, the taillight pair candidate coefficients on both sides have the highest gradient histogram correlation coefficient values in the X direction and the Y direction, which are considered to be the correct taillight pair matching results.  
Figure 8: Grayscale adaptive threshold segmentation results in HSV space.  
Figure 9: The taillight candidate areas extracted by the gradient feature.
## 4 Observation Simulations of GMR, GeoSTAR, and GIMS
### Simulation of the Observed Brightness Temperature TA of GMR, GeoSTAR, and GIMS
#### 4.2.3 Geostationary Interferometric Microwave Sounder (GIMS)
In this study, a Fortran script was developed to compile the simulated GEO-MW brightness temperature data into the BUFR format files of AMSUA using BUFRLIB (see more details in [[https://github.com/NOAA-EMC/NCEPLIBS-buff](https://github.com/NOAA-EMC/NCEPLIBS-buff)]([https://github.com/NOAA-EMC/NCEPLIBS-buff](https://github.com/NOAA-EMC/NCEPLIBS-buff)) (accessed on 21 March 2021)). This approach is a shortcut to assimilate simulated future observations using the WRFDA system, with the advantage that the data preprocessing steps, including quality control, data thinning, and bias correction, can be consistent with existing operational data assimilation schemes. In this study, the quality control procedures of AMSUA in WRFDA were applied to the GEO-MW brightness temperature data, including surface type check, absolute and relative departure check, cloud check, and rain check. Finally, only four channels of GEO-MW brightness temperature data, 4 to 6 and 8, could be assimilated by the WRFDA system.
## 3 Results and Discussion
### Batch Absorption Experiments
#### 3.2.3 Effect of Ionic Strength  
The effect of ionic strength (0-200 mg/L) on the absorption of Cu(II) by lime sand bricks was studied (Figure 6). The results indicate that at lower sodium chloride concentrations (i.e., 0-5 mg/L), the adsorption of Cu(II) by lime sand bricks decreased with increasing salt concentration, with the adsorption capacity decreasing from 0.59 mg/g to 0.56 mg/g. With the increase of NaCl concentration from 5 mg/g to 20 mg/L, the adsorption of Cu(II) increased rapidly from 0.56 mg/g to 0.62 mg/g. As the ionic strength further increases, the adsorption capacity of lime sand bricks to copper increases slowly. Ion exchange was suggested to be the main mechanism in the adsorption process. The initial depression of Cu(II) adsorption by ionic strength might be due to the competition of Na\({}^{+}\) ions with Cu cations for the same binding sites on the sorbent surface [40; 43]. The adsorption of Cu(II) was then increased with the increase of salt concentration, which can be explained by the fact that sodium chloride acts as an in situ regenerating agent for the sorbent via removal of oxygenated complexes as soluble chlorine complexes, thereby increasing the number of adsorption sites and, hence, the adsorption of the Cu(II) species [40].  
Figure 6: The effect of ionic strength (NaCl) on the adsorption of copper from aqueous solution by lime sand bricks at 25 \({}^{\circ}\)C (\(C_{0}\) = 5 mg /L, solid-liquid ratio = 8 g/L, stirring speed = 135 rpm, pH = 5).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**adsorbent** & \(q_{\text{m}}\) **(mg/g)** & **References** \\ \hline Line sand bricks & 7.01 & this study \\ Limestone & 0.0145 & [42] \\ Cedar sawdust & 29.84 & [40] \\ Clinoptiolite & 1.64 & [38] \\ Zeolite & 5.10 & [39] \\ Clays & 9.58 & [41] \\ Fly ash & 1.39 & [35] \\ \hline \hline \end{tabular}
\end{table}
Table 5: The adsorption capacity of the adsorbent for copper in water treatment.
## 3 Experimental Results
### Impact Behavior  
#### 3.2.1 Energy Penetration  
Figure 6a-c display the energy-time history curves for different composite specimens at different drop levels. Each specimen was subjected to multiple impacts at different drop levels such that the first, second, and third impacts were at 50 mm, 100 mm, and 150 mm, respectively, measured from the upper end of the spherical steel ball up to the lower end of the impactor. It was observed that all tested ECC specimens did not fail or even crack under the effect of multiple impacts up to the maximum capacity of the testing machine. Also, it was revealed that among all tested specimens, ECC2-1 showed the best performance at low impact levels, as shown in Figure 6a. ECC2-1 sustained the impact load with better energy dissipation capability and lower penetration depth compared to all other specimens. A similar trend was observed at higher levels of impact, as displayed in Figure 6b,c. Incorporating fiber dosages higher than 2% PVA and 1% SMA, led to a general reduction in the energy dissipation capability and penetration resistance at all impact levels. This is ascribed to increased porosity and tendency of fiber clustering implemented with a high dosage of fibers.  
The computerized system, which was used in the data acquisition during impact, was programmed to calculate the energy dissipation capacity of the different ECC specimens through estimating the difference between the applied impact energy and that remaining in the system upon impact. The cumulative enhancement in energy dissipation capability of the ECC specimens is illustrated in Figure 7. It can be observed that the energy dissipation capability was generally improved by SMA fiber addition. For instance, the ECC2-0.5, ECC2-1, and ECC2-1.5 specimens achieved dissipated energy of 23.39%, 30.43%, and 11.53% higher than that of the control ECC specimens made with PVA fibers alone, respectively. This underscores the significant enhancement in impact resistance  
Figure 6: Typical Energy-penetration curves for different composite mixtures at different drop levels: (a) 1 st hit, (b) 2 nd hit, and (c) 3 rd hit.  
of the composite owing to SMA fiber addition. The accumulated impactor penetration depth in the different ECC specimens is illustrated in Figure 8. Only small penetration occurred upon impact loading. A general reduction in penetration depth was observed owing to SMA fiber addition. For example, the ECC2-0.5, ECC2-1, and ECC2-1.5 specimens had 6.3%, 10.1%, and 2.6% lower penetration depth compared to that of the control ECC2-0 specimens, respectively. This is ascribed to the resistance of the composite's fiber-matrix interfacial frictional bond. The ECC specimen incorporating 2% PVA and 1% SMA fibers achieved the highest energy dissipation capacity and lowest penetration depth among all tested specimens. Higher fiber dosage led to impact strength degradation due to increased porosity via fiber clustering, as evidenced by the authors elsewhere [6].
# Collaborative Beamforming with DQN for Interference Mitigation in 5G and Beyond Networks
## 2 Related Work
Although machine learning approaches for beamforming have been studied recently, there is still a significant problem in creating low-complexity, distributed algorithms that can adapt to mobile circumstances and yet achieve near-optimal performance. To solve the multi-objective optimization issue of unmanned aerial vehicle (UAV) enabledcollaborative beamforming in air-to-ground communication systems, The findings in [14] devised a multi-agent deep reinforcement learning method. By optimizing UAV locations and excitation current weights, their suggested HATPRO-UCB algorithm simultaneously maximizes transmission rate and minimizes energy consumption, outperforming conventional techniques in dynamic situations. Using tensor decomposition and a channel, frequency, and temporal attention (CFTA) method, the authors in [15] created an attention-deep reinforcement learning strategy for joint beamforming optimization in reconfigurable intelligent surface (RIS) assisted mmWave massive MIMO vehicle-to-everything (V2X) communications. By combining tensor-based channel modeling with DRL-based optimization of RIS phase shift matrices and \(BS\) beamforming, their suggested approach tackles the problems of fast time-varying propagation and dynamic vehicular networks, showing enhanced performance for cooperative automated driving applications. The study in [23] developed an improved adaptive model for MIMO systems. They collected multipath RSS training data across line-of-sight and non-line-of-sight conditions. Using these data, their model adaptively steers beams based on estimated user locations to improve localization accuracy. Simulations demonstrate reduced root mean square error close to Bayesian bounds, outperforming support vector machine (SVM) and K nearest neighbor (KNN) approaches, proving the effectiveness of adaptive beamforming for robust 5G outdoor localization. Further research should actively explore the collaborative development of learning techniques tailored for beamforming across different antenna array configurations. Although many promising approaches exist [11, 24, 25], beamforming techniques would benefit from continued collaborative efforts to pursue further improvements. However, finding the optimal method for practical scenarios with low-resolution phase shifters and ADCs remains an open challenge. Existing approaches often rely on specific system configurations and lack generalizability across different architectures [22]. Thus, there is still room for further development in this area.
## 4 Multi-Energy Microgrid Reliability Assessment
### Reliability Evaluation Indexes
The supply and demand comparison matrix \(\mathbf{Q}\) can be built accordingly. The elements in matrix \(\mathbf{Q}\) are Boolean variables. The elements \(Q_{j,k}\) (\(j=1,2,\ldots N\); \(i=1,\ldotsldots,8\); \(k=e,h,c\)) in the matrix represent the \(j\)-th sampling, \(i\)-th scene obtained, and discrimination of the \(k\)-th energy source. The distinguishing method of power energy, e.g., in the matrix is as follows:  
\[Q_{j,i,e}=\left\{ \begin{array}{cc}0&P_{\text{Load}}^{\text{elec}}>C_{\text{ M},i,e}+C_{\text{S},i,e}\\ 1&P_{\text{Load}}^{\text{elec}}\leq C_{\text{M},i,e}+C_{\text{S},i,e}\end{array} \right. \tag{22}\]  
The energy supply expectation of power, heat, and cold energy types in the microgrid are calculated by Equations (23)-(25), respectively:  
\[EES^{\text{elec}}=\frac{1}{N}{\sum_{j=1}^{N}Q_{j,i,e}} \tag{23}\]  
\[EES^{\text{heat}}=\frac{1}{N}{\sum_{j=1}^{N}Q_{j,i,h}} \tag{24}\]  
\[EES^{\text{cold}}=\frac{1}{N}{\sum_{j=1}^{N}Q_{j,i,c}} \tag{25}\]  
where \(N\) is the number of Monte Carlo simulated sampling time periods. (2) LOEEThis index denotes the gross loss of energy due to unit failure or outage of a certain energy source in the statistical time period. The unit is MW\(\cdot\)h/_a_. A higher LOEE suggests that the energy source is greatly influenced by unit failure, and that energy supply reliability is low. The index is calculated by Equations (26)-(28). \[LOEE^{\text{elec}}=\frac{8760}{T}\sum_{j=1}^{N}LOEE_{i}^{\text{elec}} \tag{26}\]  
\[LOEE^{\text{heat}}=\frac{8760}{T}\sum_{j=1}^{N}LOEE_{i}^{\text{heat}} \tag{27}\]  
\[LOEE^{\text{cold}}=\frac{8760}{T}\sum_{j=1}^{N}LOEE_{i}^{\text{cold}} \tag{28}\]  
where \(T\) is the evaluation time cycle. (3) SAIDI  
SAIDI refers to the duration of insufficient supply of a certain type of energy caused by unit failure or outage in the statistical time period. The unit is h/_a_. Longer duration means a larger impact of unit failure and low-energy supply reliability. This index is calculated by Equations (29) and (31):  
\[SAIDI^{\text{elec}}=\frac{8760}{T}\sum_{j=1}^{N}SAIDI_{i}^{\text{elec}} \tag{29}\]  
\[SAIDI^{\text{heat}}=\frac{8760}{T}\sum_{j=1}^{N}SAIDI_{i}^{\text{heat}} \tag{30}\]  
\[[PERSON]^{\text{cold}}=\frac{8760}{T}\sum_{j=1}^{N}SAIDI_{i}^{\text{cold}} \tag{31}\]  
(4) Energy supply reliability economic indicator  
This study determined energy supply reliability economic indicators by calculating the LOEE by type with corresponding prices [27]. The index value reflects the economic loss expectation caused by equipment failure to the multi-energy microgrid system. It is calculated as follows:  
\[C_{\text{RE}}=w^{\text{elec}}LOEE^{\text{elec}}+w^{\text{heat}}LOEE^{\text{ heat}}+w^{\text{cold}}LOEE^{\text{cold}} \tag{32}\]  
where \(w^{\text{elec}}\), \(w^{\text{heat}}\), and \(w^{\text{cold}}\) are the energy loss values of electricity, heat, and cold, respectively. The specific values can be extracted from the references [18].
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Vibration  
Vibration feature monitoring has a low cost, easy assembly, and a periodic shape similar to the cutting force. Thus, it is widely used in tool wear monitoring [53; 54; 55]. During the cutting process, the workpiece rubs against the worn side of the cutting edge, causing vibrations of different frequencies. There are two methods for vibration feature monitoring. The first is to divide the amplitude into high and low parts and compare the amplitude of the two parts during cutting. The second is to divide the amplitude into several independent bands and use a microprocessor to continuously record and analyze the bands for monitoring the wear on the tool flank surface. [PERSON] et al. [56] collected the original high-frequency vibration features and performed batch standardization and slice processing. These authors input a hybrid model based on a one-dimensional convolutional neural network and extreme learning machine and output the milling cutter wear condition. [PERSON] et al. [57] reported that the vibration acceleration feature change on the spindle can distinguish different tool wear conditions during micro-milling. [PERSON] et al. [58] used a three-axis IEPE accelerometer mounted on the spindle housing to collect the vibration acceleration feature of face milling. [PERSON] et al. [59] employed a laser vibrometer to collect vibration displacement features to monitor the tool wear in the face milling process and achieve a high monitoring accuracy. [PERSON] et al. [60] analyzed the morphological component characteristics and sparse characteristics of the vibration feature in the high-speed milling process, and extracted the vibration characteristics to monitor the tool wear condition. [PERSON] et al. [28] proposed that the change in tool wear condition affects the vibration status change during milling. [PERSON] et al. [61] confirmed the linear relationship between the milling force with the eddy-current displacement caused by tool holder swing. Additionally, the eddy-current displacement feature was extracted to monitor the milling cutter wear condition. [PERSON] et al. [62] used an accelerometer to measure the workpiece vibration feature during milling and performed a 5-layer feature decomposition by a wavelet packet to obtain the energy spectrum. Then, it was input into the classic convolutional neural network to realize the recognition of tool wear condition.  
The disadvantage of the vibration feature is that it is limited by the installation position and interference factors (e.g., cutting fluid) during the collection process, which leads to errors, sensor data loss and complex feature filtering [28; 38].  
The installation diagram of a vibration sensor is shown in Figure 2.  
Figure 1: An installation diagram for cutting force sensor (drawn from the literature [43]).
# Communication-Efficient Vertical Federated Learning
## 3 Proposed Method  
The proposed method, as shown in Figure 2, is based on feature extraction techniques that reduce the dimensionality of data by removing redundancy. The feature extraction methods generally obtain new generated features by combining and transforming the original feature set, thus giving it a new latent representation. In a vertical federated setting, clients possessing relevant disjoint data are interested in training a global machine learning model without exposing their raw data to each other. One of the clients is assumed to have labeled data (guest party) and the rest (host parties) have unlabeled data. The objective of the guest party is to be able to use the data from the host parties in order to perform better predictions for incoming new data, without compromising the privacy of data for itself as well as for other clients. Algorithm 1 shows this proposed method in pseudo code.
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 5 Multi-Planetary Detection, Communications, and Defense: Live Long and Prosper
### Equipping the SCADN with Mitigation Technologies  
On top of the initial development and deployment of the SCADN, some further proactive strategies of mitigation can be utilized to reduce the probability of catastrophic consequences of the impact of space objects. Conventionally speaking, the mitigation techniques, or collision avoidance techniques, are developed based on metrics such as technology readiness, failure risks, operation feasibility, performance, and cost [153]. There have been various methods proposed to change the course of an asteroid or comet [154], which can be categorized by various types of attributes such as the type of mitigation, approach strategy, and energy source, as summarized and concluded in Table 2.  
As observed, the energy source of the nuclear explosive device can provide the most effective approach to cope with PHOs of various sizes for either short-notice or long-notice threats, particularly against solid objects. However, many NEOs are believed to be loosely held together by gravity as \"flying rubble piles\" and thus cannot be effectively handled by the method of kinetic impactors or nuclear explosive devices. The indirect methods such as gravity tractor, focused solar energy, laser ablation, and ion beam shepherd might take more time to alter the PHO's trajectory but require early development and deployment, such as traveling to the PHO's proximity in advance, for the space rendezvous. Furthermore, the PHO deflection means of gravity tractor and focused solar energy and the deployment procedure of the DART mission are illustrated in the following Figure 18, respectively. Figure 18: Illustration of various mitigation technologies of: (**a**) gravity tractor [155]; (**b**) focused solar energy [156]; (**c**) the deployment scheme of DART mission [157]; and (**d**) the conceptual design of solar sail [158]. In addition, some other proposed deflection methods include wrapping the PHO in a sheet of reflective plastic such as aluminized polyethylene terephthalate (PET) as a solar sail; dusting the PHO with titanium dioxide (white) to alter its trajectory via increasing the reflected radiation pressure or with black to alter its trajectory via the Yarkovsky effect;
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Machine Learning Methods for Yield Prediction
#### 2.3.3 Distance-Based Models  
This study also employed the instance-based k-nearest neighbors regressor (KNeighborsRegressor). KNeighborsRegressor is an instance-based learning method that makes predictions by calculating the distance between new samples and those in the training set [46]. In this research, three nearest neighbors (n_neighbors = 3) and the Minkowski distance metric (\(p\) = 2) were used. This approach is particularly suitable for analyzing datasets with smaller sample sizes or low-dimensional feature subsets.
## 2 Materials and Methods  
### Study Sites  
In the study, we used three rockfall deposit sites, all located in the Julian Alps in the NW part of Slovenia (Figure 1). Two of the rockfalls are located in glacial alpine valleys--Trenta (rockfall Kekec) and Krnica (rockfall Krnica)--while the third (rockfall Mangart) is located on the mountain pass, below the mountain Mangart. This part of the Alps is geotectonically part of the Southeastern Alps, the thrust unit of the Julian Alps that consists of sediment rocks (mainly from Lower Triassic and Cretaceous, and predominantly from Upper Triassic): limestone and dolomite, with limestone layers commonly passing into dolomite in vertical and lateral directions [52; 53]. Faults in this area have transverse Dinaric direction (NE-SW), while significant faults are also present in the Dinaric direction (NW-SE).  
The rockfall deposits at the Kekec site consist of massive and bedded dolomite and limestone (Carnian), at the Krnica site of bedded dolomite and limestone (Anisian-Ladinian; Triassic), while at Mangart Pass, they consist of platy and bedded micritic and crinoid limestone with chert (Malm) from the Jurassic period. The specialty of this area (i.e., Mangart Pass) is that these beds are of restricted dimensions in the Julian Alps and represent tectonically confined rests of unconformably overlying limestones from the Upper Triassic period (massive limestone) [54].  
All release areas are characterized by steep, vertical slopes and walls from where the rockfall material was deposited directly below the walls, steeper slopes, or lower down in the valleys. The terrain is rough with great elevation and slope values variations which can be observed in Figures 2 and 3. The rockfall runout area was the smallest for the Kekec rockfall (4800 m\({}^{2}\)), followed by Mangart (13,000 m\({}^{2}\)) and Krnica (35,000 m\({}^{2}\)). At all three rockfalls rock deposits, volumes vary from the size of the rocks on the scree slopes of up to 100-200 m\({}^{3}\) at the Krnica rockfall, up to 30 m\({}^{3}\) at the Mangart rockfall, and up to 70 m\({}^{3}\) at the Kekec rockfall. The conditions of surveying all three rockfalls are, therefore, extremely exposed, as the rocks deposited in the rockfall runout area can still be transported down the slope, and as there is always a possibility of new rockfall events occurring. Even though larger rockfall events in these sites have occurred in the past, the activity of falling rocks continues which can be observed by the colors of the rocks (brighter color of rocks indicate more recent events in comparison to darker deposits that are past events), and based on the vegetation (e.g., rocks covered by moss and grass; pioneer tree and shrub species).  
Figure 1: Locations of rockfalls (Slovenia) that were selected as study sites.  
Figure 2: Three-dimensional models of rockfalls (**a–c**) and photos from the field (**d–f**) of Kekec (**a,d**), Krnica (**b,e**), and Mangart (**c,f**).