esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Population Balance Dynamics Simulation of Grinding under Impact Crushing
## 3 Results and Discussion
### The Population Balance Dynamics Simulation of Grinding under Impact Crushing of PyrrIhotite
#### 3.2.4 Study on Simulation Results of the Population Balance Dynamics of Grinding for Pyrrhotite  
As in Section 3.1.4, based on the obtained breakage distribution function \(b_{ij}\) and selection function \(S_{i}\) of pyrrhotite, combined with the Reid solution of the grinding overall balance equation [26], the yield of each product particle size generated by crushing the pyrrhotite under impact is calculated using MATLAB programming. The variation in the product yield with time for pyrrhotite with the feed particle size of 3.35 + 2.36 mm, \(-2.36+1.7\) mm, and \(-1.7+1.18\) mm is shown in Figures 8-10.  
From Figure 8, it can be seen that pyrrhotite with the feed particle size of \(-3.35+2.36\) mm is crushed to products of various particle sizes after impact grinding. Among them, the yield of the \(-2.36+1.7\) mm, \(-1.7+1.18\) mm, and \(-1.18+0.85\) mm particle sizes is higher, with the highest yield being \(19.80\%\), \(18.27\%\), and \(9.13\%\), respectively. The highest yield of the other particle sizes is \(5.54\%\). As evident in Figure 8, with an extensive range for the ordinate, the yield curve of the \(-0.85+0.6\) mm particle size is very smooth. With the expansion of the ordinate scale, as depicted in Figure 8b, the yield for each particle size at the 8 min mark does not rise above \(3.72\%\), and the yield curve is smoother. This indicates that the generation rate of these particle sizes of pyrrhotite under impact grinding is slower, but faster than that of quartz with the same particle size.  
From Figure 9, it can be seen that the pyrrhotite with the feed particle size of \(-2.36+1.7\) mm is broken into particles with a more uniform distribution than that with the feed particle size of \(-3.35+2.36\) mm. For the same product particle size, the yield of the \(-2.36+1.7\) mm feed is higher than that of the \(-3.35+2.36\) mm feed, and the generation rate is also faster. Compared with the quartz, the generation rate curve of the pyrrhotite with particle sizes of \(-0.425\) mm generated by crushing has a more obvious change in its trend, and its generation rate is faster. This is because pyrrhotite has a lower hardness than quartz, so it is easier to break it under impact to produce products of various particle sizes.  
From Figure 10, it can be seen that the pyrrhotite with the feed particle size of \(-1.7+1.18\) mm is broken into more uniform products of each particle size. For each product particle size of \(-0.425\) mm, the pyrrhotite has a higher yield than the quartz, and the variation trend of the formation rate curve is more significant. Among the three feed particle sizes, the \(E_{cs}\) of the \(-1.7+1.18\) mm feed is \(6.31\) kWh/t, and the \(E_{cs}\) values of the \(-3.35+2.36\) mm and \(-2.36+1.7\) mm feeds are \(0.81\) kWh/t and \(2.23\) kWh/t, respectively. Therefore, the fastest rate is obtained when crushing the pyrrhotite with smaller feed particle sizes into each particle size.
# Real-Time Stereo Visual Odometry Based on an Improved KLT Method
## 5 Conclusions  
This paper presents a novel algorithm for stereo visual odometry that avoids time-consuming feature detection and matching processes as much as possible based on feature inheritance, an adaptive KLT tracker, and a simplified KLT matcher, which can greatly reduce computational complexity without notably compromising the localization accuracy. Based on the average disparity, the translation velocity, and the yaw angle, the proposed method can jointly determine a suitable window size for the KLT tracker, which effectively mitigates the effect of scale distortion and affine transformation. Furthermore, an effective lever chain matching scheme can be employed to limit the drift error. In the experiments, the method presented here was tested on the KITTI odometry dataset and compared with other methods. According to the experimental results, although the translation error of the proposed SVO is slightly less accurate than some state-of-the-art methods, with an error of 1.14% against an error of 0.71% for the SOFT2, an error of 73% for the ORB-SLAM2, and an error of 0.98% for the FRVO, the proposed method can strike a good trade-off between efficiency and accuracy. Efficiency is achieved in that the system is able to run at 15 Hz on a single-thread CPU @ 3.0 GHz, outperforming even the parallel or multi-threaded approaches in the balance between high accuracy and low computational complexity.  
Conceptualization, G.G. and Z.D.; methodology, G.G.; software, G.G.; validation, G.G., Z.D. and Y.D.; formal analysis, G.G.; investigation, G.G.; resources, G.G. and Y.D.; data curation, G.G. and Y.D.; writing--original draft preparation, G.G.; writing--review and editing, G.G.; visualization, G.G.; supervision, Z.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 2 Materials and Methods
### Methods
#### 2.3.4 Validation with In Situ Data  
GRACE GWS estimates (mm/year, equivalent water height) were validated using in situ measurement from ANA (m/year) from 1700+ wells, converted to comparable units using aquifer areas. Validation focused on six aquifers (Table 1), ensuring robust correlation analysis.
## 4 Review of Existing Methods and Capabilities
### Path Planning
#### 4.1.3 Target Search and Surveillance
The method required each UAS to perform independent receding horizon feedback control that relied on its own information along with any received remote information from neighbor vehicles to plan the required search path.
## 3 Methodology
### Use CRITIC for Weight Calculations  
CRITIC determines the weights for criteria based on contrast intensity (relative to the variability in the data) and correlation or conflict between criteria [67] and is applicable to ensure a balanced and data-driven weighting system. The weight increases when the standard deviation is higher, indicating greater discriminative power in the criterion. Conversely, a lower correlation with other criteria manifests less redundancy and increases the weight. We obtained the weights for the criteria by calculating the product of standard deviations and the sum of the complements between criteria.  
Steps:  
1. Construct the decision \(X=\left[\xi_{ij}\right]_{m\times n}\) and organize alternatives \(A_{i}\)\((i=1,2,\ldots,m)\) and criteria \(C_{j}\)\((j=1,2,\ldots,n)\) with their corresponding values. \(\xi_{ij}\) is the value of the \(j\)th criterion for the \(i\)th alternative. \[X=\begin{array}{cccc}A_{1}&C_{1}&C_{2}&\ldots&C_{n}\\ A_{2}&\left[\begin{matrix}\xi_{11}&\xi_{12}&\ldots&\xi_{1m}\\ \xi_{21}&\xi_{22}&\ldots&\xi_{2m}\\ \vdots&\vdots&\vdots&\vdots\\ \xi_{m1}&\xi_{m2}&\ldots&\xi_{mm}\\ \end{matrix}\right]\end{array}\] (1)
2. Normalize the decision matrix: By using min-max normalization, the scale of values and units of measurement for each criterion can be made independent, keeping everything within the same range, which facilitates comparison and calculations. \(\xi_{j}^{min}\) and \(\xi_{j}^{max}\) are the minimum and maximum values of the \(j\)-th criterion across all alternatives. For maximization criteria: \[\overline{\xi}_{ij}=\frac{\xi_{ij}-\xi_{j}^{min}}{\xi_{j}^{max}-\xi_{j}^{min} },i=1,2,\ldots,n;j=1,2,\ldots,m\] (2) For minimization criteria: \[\overline{\xi}_{ij}=\frac{\xi_{j}^{max}-\xi_{ij}}{\xi_{j}^{max}-\xi_{j}^{min} },i=1,2,\ldots,n;j=1,2,\ldots,m,\] (3) where \(\xi_{j}^{max}=\max_{j}\bigl{\{}\xi_{1j},\xi_{2j},\ldots,\xi_{mj}\bigr{\}}\); \(\xi_{j}^{min}=\min_{j}\bigl{\{}\xi_{1j},\xi_{2j},\ldots,\xi_{mj}\bigr{\}}\). In any case, all normalized coefficients \(\xi_{ij}\) are in the interval \([0,1]\).
3. Calculate standard deviation \(\sigma_{j}\) and correlation coefficients \(r_{jk}\) for criterion \(C_{j}\)\((j=1,2,\ldots,n)\): The variability allows for understanding the impact of the criterion on the evaluation of an alternative, considering that the higher the variability, the more this criterion influences the relevance of the assessment. \(r_{jk}\) contains the coefficients of the linear correlation of the vectors \(\xi_{j}\) and \(\xi_{k}\  
in comparison indicates that it has a greater capacity to discriminate between the alternatives under evaluation.
4. Compute the CRITIC weights: \[W_{j}=\sigma_{j}\times\sigma_{j}=\sigma_{j}\times\sum_{k=1}^{n}\left(1-r_{jk}\right)\] (5) \(W_{j}\) represents the composite weight assigned to criterion \(j\).
5. Obtain the relative weights: \[w_{j}=\frac{W_{j}}{\sum_{k=1}^{n}W_{k}}\] (6) The CRITIC-derived weights \(w_{j}\) are used in both TOPSIS and WASPAS for weighted evaluation.
## 3 Privacy Considerations for Marine Network Protocols  
Even though the focus of this paper is on security considerations, privacy considerations exist for vessels that may prevent some security objectives from being realized. Privacy considerations can vary from identifiability to access to personal data communications [17]. For example, where anonymity is required, anonymous broadcast communications may be necessary even at the expense that these communications can be spoofed or even replayed. Similarly, intrusion detection systems often parse raw and if possible unencrypted data [18]. Access to such systems inevitably leads accessing data that may contain personal communications or otherwise critical communications (e.g., business affairs).
# Protocol for Energy-Efficiency in Networked Control Systems Based on WSN
## 5 Energy Saving Protocol
### Energy saving Scheme by Trade-Off Approach  
The use of the MJLS framework to represent a plant connected to the controller through a semi-reliable network enables the design of stabilizing controllers with theoretical guarantees of performance. By employing a similar Markovian model, it is also possible to design \(\mathcal{H}_{\infty}\)[6; 7; 8; 13] and \(\mathcal{H}_{2}\)[10] filters connected through semi-reliable networks to the plant. However, related works [6; 7; 8; 10; 13] consider only the case of complete availability of the Markovian modes and that the measurement signals are provided by a single source of information. This meansthat the loss process could only be modeled by a Bernoulli transition probability matrix of order two, differently from the case handled in this paper, where the fragmentation of the signal vectors requires a transition probability matrix of higher order. Consequently, the partial mode-dependent approach is considered more adequate.  
In the aforementioned works the decision variable used to optimize the use of network resources is the maximum number of transmissions per packet \(L\). Thus, adapting the filtering approach for the control problem discussed in the Section 4, one has  
\[\forall\mathcal{N}_{U}^{\mathcal{R},\mathcal{S}}\in\Psi\ \ \text{using}\ \ L=\epsilon.\]  
As discussed in Section 3.3.1, if \(\epsilon\) is not limited, the success probability is given by \(P_{S}=1\) and \(\mathcal{E}(M)\) is the maximum possible value of the global number of transmissions, such that this setup corresponds to the classical control, without packet loss [6].  
The trivial approach to save energy resources in a communication network is \(L=0\), meaning that there is no transmission and, consequently, no energy consumption. However, not transmitting the control signal is not possible due to obvious reasons. In order to choose a proper value for \(L\), the designer must define how much performance loss (in terms of the \(\mathcal{H}_{\infty}\) norm) is acceptable (when compared with the classical case: no packet loss) to achieve the energy savings that seems appropriate. A theoretical formulation of this problem is presented in the following lemma.  
**Lemma 5**.: _For a given MJLS (1) associated to the transition probability matrix \(\mathbb{P}=[p_{ij}]=[p_{j}]\) computed according to the procedure presented in Section 4.2 with a finite \(L\) for some node \(\mathcal{N}_{U}^{\mathcal{R},\mathcal{S}}\), if there exists a full-order dynamic output-feedback controller such that the closed-loop MJLS (13) satisfies Lemma 1, then this controller assures saving network resources when compared with the classical control design._
# Efficient On-Off Keying Underwater Acoustic Communication for Seafloor Observation Networks
## 3 Receiver Design Adapted to Channel
### Iterative Threshold Estimation (ITE) Algorithm
For each iteration, the main complexity of the ITE algorithm lies in the sort operation in step 4 and the summation in step 8, leading to the total complexity of \(\mathcal{O}(NK\log K+NK)\). ```
1:\(Y_{i}\); \(\{L_{\text{ACC}}^{n-1}\left(u_{i}\right)\}\); \(D^{n}\),\(D_{H}^{n}\) are initialized as all 0 vectors of length \(K+1\); \(D_{l}^{n}\) is the \(l\)-th element of the vector \(D^{n}\); \(\{\cdot\}_{DE}\) represents the decision operation. 2:\(B^{n}\)
3:for\(n=0:N-1\)do
4:\(L_{\text{c}}^{0}\left(u_{i}\right)=Y_{i}^{2}-\text{median}(Y_{i}^{2})\) // First LLR bias
5:if\(n\geq 1\)then
6:\(\{\left[L_{\text{c}}^{n-1}\left(u_{i}\right)^{\prime}\right\}\), \(P^{n-1}=\text{sort}(\{L_{\text{c}}^{n-1}\left(u_{i}\right)\})\) // sort() is the sort function in MATLAB
7:\(\{L_{\text{ACC}}^{n-1}\left(u_{i}\right)\}_{\text{DE}}^{\prime}=\{L_{\text{ACC }}^{n-1}\left(u_{i}\right)\}_{\text{DE}}(P^{n-1})\) // Permutation
8:\(D^{n}(\{L_{\text{ACC}}^{n-1}\left(u_{i}\right)\}_{\text{DE}}^{\prime}\geq 0)=1\)
9:\(D_{H}^{i,n}=\sum_{i=1}^{l-i}D_{l}^{n}\) //\(D_{H}^{i,n}\) is the \(i\)-th element of the hamming distance vector \(D_{H}^{n}\)
10:\([D_{H}^{i,n},j]=\min(D_{H}^{n})\) // The minimum hamming distance \(D_{H}^{j,n}\) appears at the \(j\)-th position
11:\(B^{n}=\{L_{\text{c}}^{n-1}\left(u_{i}\right)^{\prime}\}(j)\)
12:endif
13:endfor
14:return\(B^{n}\)
```  
**Algorithm 1** Search Algorithm of ITE
## 2 Related Basic Work
### DPC Algorithm  
The DPC algorithm [18] introduces the concepts of the local density and the center offset distance, which can make it easier to understand the principle of the DPC algorithm. The algorithm works as follows: (1) it chooses the clustering center with both larger local density and center offset distance, and all other points in the cluster surrounding the clustering center. (2) The clustering centers are far from each other.  
**Definition 4** (Cutoff distance).: _Set \(d_{ij}\) to represent the similarity between two different samples \(i\) and \(j\) of the dataset \(D\). Traditional algorithms usually use Euclidean distance to measure the similarity of two samples. Let \(R\) represent the sequence of \(d_{ij}\) arranged in the order from the smallest to the largest, \(R_{i}\) representing the \(i\)th index of \(R\). Set \(d_{c}\) as the cutoff distance, \(p\) as the cutoff percentage; \(d_{c}\) of the DPC algorithm is determined by \(p\), and its calculation can be expressed as \(d_{c}=r_{p\times n}\), where \(n\) represents the total number of samples in the dataset._  
**Definition 5** (Local density).: _Set \(x_{i}\) as the \(i\)th sample of the dataset \(D\), \(\rho\) as the local density, and \(\rho_{i}\) to represent the local density of each point. There are two methods to define the local density [33]: if all attribute types of the dataset \(D\) are discrete, Equation (5), which represents the calculation method of truncated kernel, can be used; if the attribute types of the dataset \(D\) are continuous, the soft statistics method, which is based on Gaussian kernel, can be used, and its formula is given by Equation (6) [29]._  
\[\rho_{i}=\sum_{j}\chi(d_{ij}-d_{c}),\chi(d)=\begin{cases}0,d>0\\ 1,d\leq 0\end{cases} \tag{5}\]  
\[\rho_{i}=\sum_{j}\exp(-(\frac{d_{ij}}{d_{c}})^{2}) \tag{6}\]  
**Definition 6** (Center deviation distance).: _Set \(\delta\) as the center deviation distance and \(\delta_{i}\) as the center deviation distance of each point. The center deviation distance represents the minimum distance between point \(x_{i}\) and the set of points whose local density is locally larger than \(x_{i}\) For the point \(x_{i}\) with the largest local density value, \(\delta_{i}\) represents the maximum distance between that point and other points. The definition of center deviation distance can be expressed as the mathematical Equation (7) [29]._\[\delta_{i}=\left\{\begin{array}{l}\min_{j}\{d_{ij}\},\rho_{j}>\rho_{i}\\ \max_{j}\{d_{ij}\},otherwise\end{array}\right. \tag{7}\]
# Hardware-Based Architecture for DNN Wireless Communication Models
## 3 Background
### Non-Linear Noise in MIMO OFDM Systems  
Although the MIMO OFDM systems provide high data rates and bandwidth efficiency, their drawback is the high PAPR problem, especially when a PA with a low dynamic range is deployed at the RF front end of the transmitter of these systems. Therefore, a method is used to clip the signal before passing through the PA to ensure that the PA operates in the linear region. However, the original signals are affected, then the transmitted signals are distorted.  
A simple solution to this problem is to limit the operation of the PA into the linear region with a large power backoff, but this adversely reduces the power efficiency. In this paper, we apply DNN models to improve the receiver performance and recover the original signals from the clipped signals. The clipped signal is explained in the following formulas and presented in [15].  
\[S(n)=\begin{cases}CL,&\text{for }S(n)\geq CL\\ S(n),&\text{for }-CL\leq S(n)\leq CL\\ -CL,&\text{for }S(n)\leq-CL\end{cases} \tag{9}\]  
\[CR=\frac{CL}{\sigma} \tag{10}\]  
where CL denotes clipping level, CR corresponds to clipping ratio, and \(\sigma\) is the root mean square (RMS) power of the OFDM signal. For example, if CR = 1, it means a signal is cut off at the RMS power level. Alternatively, if CR = 2.24, it means the CL is about 7 dB higher than the RMS power level.
## 3 Finite Element Simulation Analysis
### Analysis of Influencing Factors of Fatigue Life of Brazed Joints  
#### 3.3.1 Cyclic Displacement Effects  
Figure 16 shows the change in the damage to the cohesive elements under cyclic displacements X = [0.4 mm, 0.5 mm, 0.6 mm]. When the number of cycles reached 17,466 (point A in Figure 16), the damage of cohesive elements corresponding to cyclic displacements X = [0.4 mm, 0.5 mm, 0.6 mm] were 0.049, 0.215, and 0.243, respectively. When the number of cycles reached 26,800 (point B in Figure 16), the damage of cohesive elements corresponding to cyclic displacements X = [0.4 mm, 0.5 mm, 0.6 mm] were 0.085, 0.433, and 0.817, respectively. This indicates that when the number of cycles is the same, the total damage D of the cohesive elements increases with the increase in cyclic displacement X, and the fatigue life decreases.  
Table 6 lists the crack initiation and fatigue life under different cyclic displacements. When the loading cyclic displacements X = [0.4 mm, 0.5 mm, 0.6 mm], the corresponding fatigue life is 144,500, 52,800, and 44,000 cycles, respectively. This indicates that as the cyclic displacement increased, both the crack initiation life and fatigue life decreased.  
Figure 16: Changes in cohesive unit damage under different cyclic displacements.  
Figure 15: Damage accumulation curve.  
Figure 17 and Table 7 show the crack propagation length under different cyclic displacements. The smaller the cyclic displacement, the smaller the crack propagation rate, and the longer the fatigue life.  
The cyclic displacement X = [0.4 mm, 0.45 mm, 0.5 mm, 0.6 mm, 0.7 mm] was selected to simulate and analyze the crack propagation of butt-brazed joints under different cyclic displacements.  
Figure 18 shows the fatigue-life fitting curves under different displacement loads, which were fitted using an exponential function as follows:  
\[y=5458.97x^{-3.545} \tag{8}\]  
where \(y\) represents the number of cycles, and \(x\) represents the cyclic displacement (mm). The fatigue life of the butt-brazed joint has a nonlinear relationship with the cyclic displacement, with a goodness-of-fit of 0.97, indicating a good fit. The results show that the fitting equation can predict the fatigue life of butt-brazed joints under different cyclic displacements.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Serial Number** & **Cyclic Displacement/mm** & **Fatigue Crack** & **Fatigue Life/Cycles** \\ \hline
1 & \(U=0.4\) & 118,750 & 144,500 \\
2 & \(U=0.5\) & 35,200 & 52,800 \\
3 & \(U=0.6\) & 28,000 & 44,000 \\ \hline \end{tabular}
\end{table}
Table 6: Crack initiation life and fatigue life under different cyclic displacements.  
Figure 17: Comparison of fatigue crack propagation under different cyclic displacements.
# R-PointNet: Robust 3D Object Recognition Network for Real-World Point Clouds Corruption
## 2 Related Work
### Point Cloud Recognition Network  
With the remarkable advancement of 2D-based computer vision model architectures, there has been a rise in the emergence of 3D point cloud-based recognition methods that involve the proposition of various architectures and operations. Notably, three of the most typical approaches are based on 3D voxels, multi-view projection images, and point clouds [22].  
Some early studies converted cluttered point clouds distributed in space into 3D voxel grid representation and then used refined 2D or 3D convolutional networks to achieve shape classification [23]. PointGrid [24], which combined points and voxels, can extract the local features of 3D objects better and achieve great results. However, point clouds suffer from a certain degree of original information loss in the process of voxelization preprocessing due to the resolution. Meanwhile, these methods are highly memory-consuming and generally inefficient in network training with 3 DCNN. In contrast, MVCNN [25] takes a set of views of a 3D object by 12 cameras with different viewpoints and then inputs them into the convolutional neural network to obtain the features of each view, and then the features of multi-view images are maximally pooled to obtain the global features and classification. GVCNN [26] first groups different views, then aggregates view features in groups to obtain inter-group features and finally weights different inter-group features by the learned weights to obtain global features before doing classification. Although the multi-view-based approach has achieved good results, there is no definite conclusion about how the number of views should be determined. In addition, multiple views cannot completely cover the 3D objects, especially when the objects have an occlusion situation, which poses a great challenge.  
PointNet [8] pioneered extracting each point feature by multilayer perceptron and integrating all point features to obtain the global features of the point cloud with max-pooling, and then performs classification prediction through a fully connected layer, which achieves good classification results. PointNet++ [9] used grouped feature extraction to solve the problem of not being able to capture the local features of point clouds. PointCNN [27] and RSCNN [28] refactor the traditional pyramid CNN to improve the local feature learning for point cloud recognition. DGCNN [12] constructs a dynamic graph of point cloud data based on graph data structure for representation learning. More recently, PCT [13] incorporates Transformer [29] blocks into point cloud learning achieving a state-of-the-art performance.  
In conclusion, the current point cloud recognition networks mostly focus on enhancing accuracy on clean datasets by architectural innovations, to some extent ignoring the corruption of real-world point clouds.