esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 3 Materials and Methods
### \(kNN\) Search
#### 3.4.2 3D R*-Tree Searching  
MBR-based \(NN\) search algorithm is proposed in [36], in which two metrics \(MINDIST\) and \(MINMAXDIST\) are introduced to avoid traversing the entire tree in finding the \(k\) nearest neighbors. The algorithm described briefly in Algorithm 1, in which the symbols are defined as : \(p_{i}\) is the \(i\)-th dimensional coordinate value of point \(p\), \(R(s,t)\) is a \(MBR\) where \(s\) can be represented as \((x_{min},y_{min},z_{min})\), \(t\) can be represented as \((x_{max},y_{max},z_{max})\).  
Two metrics are defined as follows:  
\(MINDIST(p,R)=\sum\limits_{i=1}^{3}|p_{i}-r_{i}|^{2}\), where  
\[r_{i}=\begin{cases}s_{i}&\text{if }p_{i}<s_{i},\\ t_{i}&\text{if }p_{i}>t_{i},\\ p_{i}&\text{otherwise}.\end{cases}\]  
\(MINMAXDIST(p,R)=\underset{k=1,2,3}{min}(|p_{k}-rm_{k}|^{2}+\sum\limits_{i=1,i \
eq k}^{3}|p_{i}-rM_{i}|^{2})\) where  
\[rm_{k}=\begin{cases}s_{k}&\text{if }p_{k}\leq s_{k}+{t_{k}}^{2},\\ t_{k}&\text{otherwise}.\end{cases}\]  
and  
\[rM_{i}=\begin{cases}s_{i}&\text{if }p_{k}\geq s_{i}+{t_{i}}^{2},\\ t_{i}&\text{otherwise}.\end{cases}\]  
To date, the hybrid spatial index tree is constructed, and the search algorithm is designed. In the next step, we will show the superiority of the proposed hybrid spatial index method from the perspective of experimental data.  
```
0:\(current\_node\), query point \(p\), number of neighbors \(k\) if\(current\_node\) is leaf\(\_node\)then  compare distance from \(p\) to all points in \(current\_node\)  update the \(current\_best\)\(kNN\) and \(current\_bestdistance(\text{top }k)\) else  generate Active Branch List(ABL): sort the \(MBR\)s contained in \(current\_node\) according \(MINDIST\) in ascending order  compute \(kMM\): sort \(MINMAXDIST\) in ascending order and set \(kMM\) to be the \(k\)-th \(MINMAXDIST\), in other word, \(kMM=sorted(MINMAXDIST)[k]\) for i = 1:len(ABL) do if\(MINDIST\) of \(MBR[i]\) is greater than \(kMM\)then  discard \(MBR[i]\) and other nodes whose \(MINDIST\) is greater than \(MINDIST[i]\)  break endif endfor for i=1:len(ABL) do  search \(kNN\) in \(MBR[i]\) endfor for i=1:len(ABL) do if\(MINDIST\) of \(MBR[i]\) is greater than \(current\_bestdistance[k]\)then  discard \(MBR[i]\) and other nodes whose \(MINDIST\) is greater than \(MINDIST[i]\)  endif endfor endif
```  
**Algorithm 1**\(kNN\) search
## 2 Materials and Methods
### Data Sources and Reprocessing
#### 2.1.2 EVI Datasets  
The MODIS Terra/Aqua Vegetation Indices (MOD13C1/MYD13C1, V006) were combined to generate EVI time series from 2003 to 2016 at 8-day interval and 0.05\({}^{\circ}\) spatial resolution, which were available at [[https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)]([https://ladsweb.modaps.eosdis.nasa.gov/](https://ladsweb.modaps.eosdis.nasa.gov/)) (accessed on 15 March 2021). Global MOD13C1 and MYD13C1 are cloud-free spatial composites of MOD13A2 and MYD13A2 at 16-day intervals and 1 km spatial resolutions, respectively.
## 4 MM Estimation Theory for Positioning
\begin{array}{cc}r_{1}(X_{0})&\dots&r_{n}(X_{0})\end{array} ]\)
2. Multiple scale estimate functions are defined, however the following median function is employed to compute the initial scale value \(s_{0}\)[33], \[s_{0}=1.4826\times median(\mathbf{r}^{0})\] (20)
3. Compute initial scaled residual vector \(\stackrel{{\rightharpoonup}}{{\mathbf{r}}}_{s}{}^{0}=\mathbf{r}^{0}/s_{0}\). Use the weighting function \(w(\stackrel{{\rightharpoonup}}{{r}}_{s})\) of one method, Huber or Bi-square, shown in Table 1 to compute corresponding weights \(w_{l}\) for each observation. 4. Solve the estimated unknown \(X_{i}\) at the \(i\)-th iteration (\(i\) = 1, 2, 3,... ) by using the weighted least-squared method, \[X_{i}=\left(\mathbf{H}^{T}\Sigma_{i}\mathbf{H}\right)^{-1}\Sigma_{i}\mathbf{H }^{T}\rho\] (21) \(\mathbf{H}\) is the transform matrix composed of \(\mathbf{H}_{l}\), and \(\Sigma\) is the weighting matrix with the weights \(w_{l}(l=1,2,\dots,n)\) as diagonal elements. 5. Update residuals \(r_{l}(X_{i})=\rho_{l}-\mathbf{H}_{l}^{T}X_{i}\) with the new estimate \(X_{i}\), calculated in last step. Then compute scaled residual \(\stackrel{{\rightharpoonup}}{{r}}_{s,l}=r_{l}(X_{i})/s_{i-1}\). Note that we still use last-step's scale factor to get new scaled residual because updated scale factor can only be obtained later. 6. Use the objective function \(\mu(\stackrel{{\rightharpoonup}}{{r}}_{s})\) to compute transformed scaled residual \(\overline{\mathbf{r}}_{s,l}^{\mu}=\mu(\stackrel{{\rightharpoonup}}{{r}}_{s,l})\), and get a transformed scaled residual vector \(\overline{\mathbf{r}}_{i}^{\mu}=[ \begin{array}{cc}\overline{\mathbf{r}}_{s,1}^{\mu}(X_{i})&\dots&\overline{\mathbf{r}}_{s,n}^{\mu}(X_{i})\end{array} ]\). 7. Update scale factor \(s_{i}\) with Equation (20) but with the transformed scaled residual vector \(\overline{\mathbf{r}}_{i}^{\mu}\). After that go back to step 4 to start a new iteration. The first three steps are only used for initial estimate, and the last four steps are iteratively used for the later estimates.
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 3 Materials and Methods
### Model Configuration  
The total number of fine dandruff and perifollicular erythema images used in this study were 7300 and 6518, respectively. The original size of the images provided by AI-Hub was 640 \(\times\) 480, but they were resized to 224 \(\times\) 224 to fit the EfficientNet-B0 model.  
The model parameters for the fine dandruff and perifollicular erythema modeling were set to batch sizes of 16 and 24, respectively, and the class mode was set to \"sparse\". In the test data, the shuffle was set to \"false\" to allow for comparison between the predicted results and the actual labels.  
The training model used the pre-trained EfficientNet-B0. After adjusting the network weights, new layers were created so that the existing layers of EfficientNet-B0 could be re-used, and only the directly added layers were trained. EfficientNet ranges from B0 to B7 depending on the model size; however, considering the resources available, B0 was used for training in this study.  
The models for the diagnosis of fine dandruff and perifollicular erythema are shown in Figure 5. A GlobalAveragePooling2D layer was added to convert the 3D tensor to 1D and to average the feature maps. A BatchNormalization layer was then added to normalize the features, with a mean of 0 and a variance of 1. A fully connected layer, which was dense, was added to decide the number of neurons and the activation function based on user customization. To prevent model overfitting, a dropout layer was added to deactivate neurons. The loss function used for image classification was sparse categorical cross entropy, and the optimizer used was Adam. For the training of the fine dandruff data, the initial learning rate was set at 0.001, the step count for decreasing the learning rate was set at 300, and the reduction rate was set at 0.97 to set up a learning rate schedule. For the training of the perifollicular erythema data, the initial learning rate was set at 0.0001, the step count for decreasing the learning rate was set at 1500, and the reduction rate was set at 0.98 to set up a learning rate schedule.  
The training parameters of the model were set at 30 and 50 epochs, and an EarlyStopping callback was set up to stop the training if there was no improvement in the model's validation loss for 10 and 5 consecutive evaluations for the training of fine dandruff and perifollicular erythema, respectively.  
Figure 5: Models for the diagnosis of fine dandruff and perifollicular erythema.
###### Abstract
The objective of this paper is to fill the research gap in this area by proposing a novel SI IoV-based model utilizing vehicular networks to address the critical tradeoffs affecting efficient big data collection and transmission in an LS-WSN for an SC waste management scenario, which include: (1) finding the optimal number of data collector vehicles (DCVs) needed in the network, (2) finding the optimal number of data collection points (DCPs) for DCVs, and (3) partitioning the network into regions of multiple sizes and assigning a DCV to each region. We note that this approach represents a significantly delay-tolerant network (DTN) that employs different data delivery mechanisms including exploitation of vehicles for opportunistic data collection and transmission. Below, we further highlight the major contributions of this, work which are explained as follows:  
1. Due to issues of network complexity and cost implications as a result of the deployment of multiple data collector vehicles (DCVs), it is expedient to determine an appropriate number of DCVs. Thus, we employed an analytical approach to determine the optimal number of DCVs required in the network within a given time period, including the optimal trajectory for each DCV;
2. We considered the optimal number of data collection points (DCPs) for the DCVs, including their locations and data collection times at a given location;
3. For LS-WSN scenarios exploiting multiple DCVs, we first consider the need to partition the network into multiple finite regions using the improved _k-means_ algorithm and assign each region to a DCV using the principle of distance matrix between OPs and DCVs;
4. Simulation experiments using swarm intelligence algorithms are further provided to validate the analytically based method. The rest of this paper is structured as follows. Section 2 presents an IoV-based architecture for SC waste management strategies, and related works are presented in Section 3. Section 4 presents the system model and problem formulation. The proposed work is presented in Section 5. Section 6 presents simulation results and analysis, while Section 7 concludes the work.
# Dual Histogram Equalization Algorithm Based on Adaptive Image Correction
## 4 Analysis of Algorithm Results
### Objective Evaluation Indicators
#### 4.3.2 Peak Signal-to-Noise Ratio  
Peak signal-to-noise ratio (_PSNR_) can be used to compare the contrast enhancement effect of images. PSNR is a measure of image quality based on the definition of mean square error (_MSE_), which expresses the average of the differences between two images at each pixel point and is calculated as follows:  
\[MSE=\frac{\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}\left|X(i,j)-Y(i,j)\right|^{ 2}}{N} \tag{23}\]  
Figure 13: The simulation results (**above**) of the ‘scene 10’ image are presented along with its corresponding histogram (**below**).  
_PSNR_ is improved on the basis of _MSE_ and its value is greater than zero. The larger the value, the less distortion in the output image, the higher the contrast, and the more obvious the enhancement effect; it is calculated as follows:  
\[PSNR=10\log_{10}[\frac{\left(2^{N}-1\right)^{2}}{MSE}] \tag{24}\]  
where \(N\) is the total number of pixels; \(X(i,j)\) and \(Y(i,j)\) denote the input image and output image, respectively; and _MSE_ is the mean square error.
# Terrain Irregularity Sensing by Evaluating Feet Coordinate Standard Deviation
## 5 Experimental Terrain Roughness Evaluation
It is clear that when the robot starts to climb up the obstacle feet, the \(z\) coordinates begin to increase and decrease when the robot climbs down the obstacle. The measured feet \(z\) coordinates are very close to the natural obstacle's parameters, and all the feet coordinates change similarly, and their values are close. The largest feet coordinate deviation is 16 \(\pm\) 4 mm (red circle in Figure 10), which also shows the terrain roughness evaluation accuracy. By comparing this to the obstacle's height (40 mm), it amounts to a 60 \(\pm\) 10% lowest possible accuracy. Though such accuracy could seem low, it must be mentioned that it has the lowest received accuracy (front left) for just one gait. Using the feet \(z\) coordinates (data presented in Figure 10), we calculated the \(SD\) values for each step. By doing so, we can visualize how the \(SD\) changes when the robot is walking over an obstacle. In Figure 11, we can see that the \(SD\) increases when the robot starts to climb up the obstacle (Figure 11 at the 10 cm mark) and then it starts to climb down (Figure 11 at the 40 cm mark). The \(SD\) starts to decrease and becomes close to 0 when the robot is off the obstacle and fully (all legs) on the obstacle. The standard deviation \(SD\) maximum values deviated from the real one by 3.4 mm when climbing up/down the obstacle and 4.3 mm on flat terrain (see arrows in Figure 11). A maximum of the \(SD\) can reach up to 21.9 mm when three legs are on the obstacle while the other three are still on the ground. So, by estimating the terrain roughness using the \(SD\), there is 84.5% accuracy. Compared to feet \(z\) coordinate inaccuracies, the standard deviation is about 25% more accurate for estimating terrain roughness. By looking at the \(SD\) values, we can estimate  
Figure 10: Feet \(z\) coordinate average values during locomotion over a rectangular obstacle using different gaits with deviation compensation using robot body inclinations; uncertainty \(\pm\)4 mm. (**a**) Left front, (**b**) right front, (**c**) left middle, (**d**) right middle, (**e**) left hind, and (**f**) right hind legs. that the obstacle falls into the low-roughness terrain category because the \(SD\) values do not reach the maximum low-roughness terrain values (see Section 2).
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 3 Methodology
### Temporal Dynamics Modeling  
To capture the multi-scale temporal characteristics of the workload data, the temporal dynamics modeling module decomposes the temporal feature matrix \(X\) into short-term trends \(X_{T}^{\text{trend}}\) and periodic patterns \(X_{T}^{\text{period}}\). This decomposition provides precise temporal representations for the framework. The implementation details of these modules are given in [23] as follows:  
_(a) Short-Term Trend Modeling:_ The short-term dynamics are modeled using an autoregressive approach:  
\[X_{T}^{\text{trend}}\approx\sum_{h=1}^{M}B_{h}\cdot X_{T-h}, \tag{7}\]  
where we have the following:  
* \(M\): Number of past time steps considered for short-term dynamics.
* \(B_{h}\): Autoregressive coefficients that capture the influence of previous time steps.  
This formulation leverages neighboring historical data to model transient workload fluctuations.  
_(b) Periodic Pattern Modeling:_ The periodic characteristics are modeled as  
\[X_{T}^{\text{period}}\approx\sum_{l=1}^{N}A_{l}\cdot X_{T-lm}, \tag{8}\]  
Figure 1: Framework of TDDRMF.  
where we have the following:  
* \(N\): Number of past periodic patterns considered.
* \(m\): Length of the periodic cycle.
* \(A_{j}\): Regression coefficients for periodic dependencies.  
This model captures recurring workload patterns over longer-term periodic cycles, such as daily or weekly trends.  
_(c) Temporal Regularization_: Obviously, the associated parameters \(A\) and \(B\) play a key role in modeling the dynamic behaviors, so Equation (3) is re-expressed as  
\[R_{\text{AR}}(X|A,B)=\frac{1}{2}\sum_{t=d+1}^{T}\|X_{t}-X_{t}^{ \text{trend}}-X_{t}^{\text{period}}\|_{F}^{2}+\frac{\eta}{2}\|X\|_{F}^{2}, \tag{9}\]  
where \(d=max\{M,N*m\}\), and we have the following:  
* The first term minimizes the reconstruction error of \(X_{t}\) using its trend and periodic components.
* The second term applies Frobenius norm regularization to \(X\), constraining its complexity and improving generalization.  
While temporal dynamics modeling focuses on capturing multi-scale workload patterns over time, it is equally critical to represent the intrinsic relationships among microservices. To this end, we introduce a nuclear norm regularization on the microservice matrix \(W\), which ensures a compact and interpretable representation of microservice dependencies.
## 3 Results and Discussion
### Seismic Loss Assessment  
Figure 11a,b provide the calculated direct seismic loss curves for the examined tunnel in terms of \(PGA\) and \(PGV\), respectively. The fundamental trends of seismic loss curves are in line with the corresponding fragility functions. Using the derived loss curves, the repair cost of each tunnel ring (per meter) could be calculated for various seismic levels. For example, suppose a metro line is exposed to an earthquake intensity of \(PGA\) = 0.5 g. In that case, the total repair cost for the examined tunnel could easily be found to be 180,000 RMB from Figure 11a, indicating that rather essential repairs would be required for each tunnel segment. Similar conclusions may be drawn by examining Figure 11b, where the \(IM\) is given in terms of \(PGV\). In this way, the proposed direct seismic loss curves can be employed in the quantitative risk assessment of the metro tunnel network subjected to seismic loading.
# Research on Data Link Channel Decoding Optimization Scheme for Drone Power Inspection Scenarios
## 5 Conclusions  
This paper explores the application of drones in power line inspection, addressing the challenges of detection and maintenance difficulties caused by the large-scale deployment of power facilities. It also proposes the use of drone clusters as data transmission relays to solve the problem of remote communication between patrol drones and ground servers. Due to the large amount of power data collected by drones, in order to improve the reliability of large-scale power data transmission on the drone data link, this study focuses on designing channel long-code decoding schemes for drone data links. LDPC codes are considered the best choice for decoding long codes in drone data links due to their excellent performance. By combining DL with the traditional LDPC normalized minimum sum (NMS) decoding algorithm, we propose a novel shared-parameter neural-network-normalized minimum sum decoding algorithm based on codebook quantization (shared-NNMS-CQ). This decoding algorithm introduces a shared-parameter method and weight quantization method to reduce computational complexity while ensuring decoding performance. Through experiments, it has been proven that the proposed method is a good decoding scheme for drone data link channel decoding.  
Conceptualization, H.Y.; methodology, H.Y. and K.Z.; software, H.Y.; validation, K.Z., X.Z., Y.Z., and B.C.; formal analysis, B.Y., Y.L., and C.M.; investigation, H.Y. and K.Z; writing--original draft preparation, H.Y.; writing--review and editing, W.G. and H.Y.; visualization, H.Y.; supervision, W.G., S.S., and G.L.; project administration, H.Y., K.Z., and W.G.; funding acquisition, S.S. and G.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research in this article is supported in part by the State Grid Corporation Headquarters Science and Technology Project (Project Code: 5108-202218280A-2-410-XG) and in part by Beijing New Generation Information and Communication Technology Innovation Project (Project Code: Z231100005923026).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** We declare no conflicts of interest with any individual or organization.
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 1 Introduction
### Privacy Concerns  
The FRS is an innovative approach that seeks to provide a privacy-aware paradigm for model training by avoiding direct exposure to real user data [11; 12]. However, there are still some core concerns that need to be addressed in order to make this approach fully effective. These concerns include finding the exact privacy-preserving technique that not only provides the trustworthiness of the data, but also achieves higher accuracy. Moreover, the scalability of the approach must be further explored in order to make it applicable to a wide range of use cases:  
* **User-side concerns**: the primary privacy concern of a user in an FRS is that personal data are shared across multiple entities [13; 14]. These data may include sensitive information such as name, address, age, gender, and financial information. If these data are not properly secured and shared securely, this could lead to a potential violation of an individual's privacy [15]. In addition, the user may be unaware of how his/her data are being used, leading to a potential breach of his/her privacy. On the other hand, an FRS can itself pose privacy risks due to the potential for profiling. This means that the user's data are used to build a profile of his/her interests and activities, which can then be used to create targeted advertisements or other manipulative tactics. This can lead to a potential violation of an individual's right to privacy.
* **Server-side concerns**: An FRS is vulnerable to attacks by malicious actors, who can disrupt the federated system by manipulating user ratings, injecting false information, and taking over the federated system [16; 17]. Attackers can also employ various techniques, such as data poisoning, distributed denial-of-service attacks, and brute force attacks, to disrupt the system [18]. Furthermore, attackers may be able to access user data through unencrypted channels or modify the recommendations generated by the system to suit their interests.
## 3 Methodology for Data Pre-Processing
### Preprocessing
#### 3.2.4 Sequentializing  
Recurrent neural networks, such as GRUs or LSTMs, are built using sequence data as input. The sequence length is the same length as the section on which the model can fit the behavior. The lengths of sequences have a direct effect on the memory and speed necessary for computation. Considering that battery effects are highly time-dependent, a long sequence length is desirable. The internal effects of diffusion, charge transfer, electrochemical double layer and conductance have different time dependencies. Time constants for modeling these effects range from milliseconds to hours and are dependent on the SOC and temperature.  
As a trade-off between computational cost and model accuracy, the sequence length was chosen as 128 data points. Data were logged with a sampling rate of 10 Hz, which means that each sequence  
Figure 2: Training data distribution of (**a**) temperature and (**b**) current after over- and undersampling.  
represents the last 12.8 s of recording. Sequentializing was performed with a shift of one-step, such that no information was lost.  
**4. Battery Modeling and Rnn Hyperparameter Tuning**  
Selecting a smart input feature enables simple utilization in vehicle applications. The model accuracy is highly dependent on the chosen hyperparameters and model architectures.