esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The SLAM Framework of Low-Cost LiDAR and Vision Fusion
### Loop Detection  
Loop detection is a core problem in SLAM. Recognizing the past places and adding loop pose constraints to the pose graph can effectively reduce the cumulative error and improve the positioning accuracy. SLAM algorithms based on LiDAR are often unable to detect the loop effectively, because scan data can only describe the obstacle structure on the LiDAR installation plane, which usually lack of unique features of the scene. In other words, there may be multiple places that have very similar scan data to be detected, such as long coordinator, office workplace card, rooms with similar structure, etc. The rich texture features of visual images can just make up for this defect in our work.  
In this paper, BoW is applied to construct the dictionary corresponding to the key-frames by visual features. Because the number of images captured by building indoor maps is too large, and the adjacent images have a high degree of repeatably, the first step is to extract the key-frames. The key frame selection mechanism is as follows: (1) It has passed 15 frames since the last global relocation; (2) the distance from the previous key-frame has been 15 frames. (3) key frames must have tracked at least 50 three-dimensional feature points.  
Among them, (1) (2) is the basis of its uniqueness, because in a short period of time the characteristics of the field of vision will not change significantly (3) to ensure its robustness, too few map points will lead to an uneven calculation error.  
For each key-frame, the ORB image features and their visual words in BoW dictionary are calculated and saved as a bag of features. As a result, a key-frame stores the robot pose and bag of features, as well as the scan data obtained in that pose. It should be denoted that the key-frame here is applied for loop detection or relocation, the robot pose of each key-frame is updated after global optimization.  
During the loop detection procedure, for an incoming image frame, the bag of feature is calculated and matched with all possible previous key-frames (possible means the key-frame should be a period of time before, and close enough in an estimated pose during SLAM procedure). When the current image frame is sufficiently similar to a key frame image by match the bag of feature, closing loop possibility is considered. Then, the current frame and that key frame are matched by both the scan data and image data, and the error in formula (6) is also calculated. If the matching error is small enough, the loop is confirmed and the matching result is added as constraints to the graph optimization framework, so that the accumulative errors can be eliminated.
# Development of a Staggered PCD End Mill for Carbon Fiber Reinforced Plastic
###### Abstract  
This work presents a PCD (polycrystalline diamond) end mill with a new staggered structure for the milling of CFRP (carbon fiber reinforced plastic). The magnitude and direction of cutting force is decreased and changed by side-edge re-configuration of the structure. The flute and insert pocket of the staggered PCD end mill are designed considering the tool's stiffness and welding process. The milling process and machining defects of the staggered PCD end mill are analyzed, and the structural parameters of the staggered PCD end mill are determined. The staggered PCD end mill is fabricated by the process of wire cutting, NC (Numerical Control) machining, high-frequency induction brazing, welding, grinding and passivation. Milling tests of multidirectional CFRP with the staggered PCD end mill is conducted on CNC (Computerized Numerical Control) milling machine. The milling force is measured by a dynamometer. A range analysis of the numerical results of milling force is conducted after milling test, and the influence of milling parameters on milling force is analyzed. A regression model of the milling force is built and verified by experiment. The effects of fiber cutting angle on milling force are obtained through milling CFRP with different fiber orientation angles.  
CFRP; staggered PCD end mill; milling; milling force +
Footnote †: journal: Applied Sciences
## 4 Results and Discussion
### Data Visualization  
A random set of sample data was selected for visualization and analysis to verify the optimized SL-CNN classification model and its practical classification performance. In addition, three grassland feature classification models, namely GDIF-3D-CNN, DIS-O, and LGFEN, were used to visualize the same samples for a comparative study. In the paper, to emphasize the real data on the ground after the model classification, the experimental markers (mats and small flags) part of the RGB color image captured by the DJI Phantom 3 Pro UAV was displayed, as shown in Figure 15f. The visualization and local zooming results of the classification of SL-CNN and the contrasting models are displayed in Figure 15b-e. After comparing the visualization results with ground survey data, the study's results revealed that the DIS-O model had the worst overall classification performance, the GDIF-3D-CNN model had more pixel classification errors, and the LGFEN model misclassified more Stipa brevifora as Artemisia frigida. The predicted classification results of the SL-CNN model were the most consistent with the actual spatial distribution of the features and retained the spatial characteristics of the features effectively. This showed that the model had a high generalization ability and could meet the classification needs of desert grassland vegetation taxa.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GDIF-3D-CNN** & **DIS-O** & **LGFEN** & **SL-CNN** \\ \hline Kappa \(\times\) 100 & 95.933 & 95.033 & 98.293 & 98.735 \\ OA (\%) & 97.480 & 96.922 & 98.942 & 99.216 \\ AA (\%) & 96.972 & 93.304 & 98.158 & 98.442 \\ Train time (s) & 49 & 63 & 233 & 367 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the classification models of desert grassland features.  
Figure 15: Comparison of classification visualization results. (**a**) Original image; (**b**) SL-CNN; (**c**) LGFEN; (**d**) GDIF-3D-CNN; (**e**) DIS-O; and (**f**) RGB.  
While the primary focus of this study is on the desert grasslands of Inner Mongolia, its findings can offer fresh perspectives for ecological and environmental studies on a global scale. Additionally, the research could provide valuable theoretical references for similar studies conducted in other regions, signifying its significant contribution to understanding the functions of desert grassland ecosystems.
# On Implementing a Two-Step Interior Point Method for Solving Linear Programs
## 1 Introduction  
### Background  
The following linear optimization (LO) problem is considered:  
\[(P)\quad\min\{c^{T}x:Ax=b,x\geq 0\},\]  
and the corresponding dual  
\[(D)\quad\quad\max\{b^{T}y:A^{T}y+s=c,s\geq 0\},\]  
where \(A\in\mathbb{R}^{m\times n}\), \(x,c,s\in\mathbb{R}^{n}\) and \(y,b\in\mathbb{R}^{m}\).  
LO is an essential tool used widely in theoretical and practical science and engineering domains. Its applications extend to various fields such as operations research, engineering, economics and combinatorics. LO finds numerous applications across various fields. For instance, it is used in \(\mathcal{L}_{1}\)-regularized support vector machines (SVMs) [1], basis pursuit (BP) problems [2], sparse inverse covariance matrix estimation (SICE) [3], non-negative matrix factorization (NMF) [4] and maximum a posteriori (MAP) inference [5].  
The Interior-Point Method (IPM) is a powerful optimization technique that has been widely used in the field of LO. It offers an alternative approach to solving LO problems compared to traditional simplex methods. [PERSON] [6] described the first efficient algorithm for the IPM. [PERSON] and [PERSON] [7] introduced a class of self-concordant barrier functions and extended the algorithm to a more general type of optimization problems, including convex programming, non-linear complementarity problem (NCP), semidefinite optimization (SDO) and second-order cone optimization (SOCO) problems.
## 2 Theoretical Background
### Genetic Algorithm  
Many artificial intelligence techniques try to imitate the behavior of a biological system. These types of techniques are known as bio-inspired techniques and the GA falls into this classification since it tries to emulate the principles of natural evolution [35]. Figure 1 presents the flow chart for the computation of the GA. The first step consists of randomly generating the initial population. In this step, it is necessary to define proper boundaries to ensure that the individuals of the population remain within an acceptable range. Next, a fitness evaluation is performed. At this point, the performance of every individual has to be assessed. This task is carried out using a maximum value of a fitness function, \(maxF(x)\), whose value depends on the evaluation of several performance criteria considered in an objective function, \(J(x)\). The integral of the absolute error (IAE) is one of the most common performance criteria applied on the GA and is based on a signal error, \(\varepsilon(t)\), as specified by (3) and (4):  
\[maxF(x)=1/J(x), \tag{3}\]  
\[IAE=\int_{0}^{\infty}\bigl{|}\varepsilon(t)\bigr{|}=J(x). \tag{4}\]  
After evaluating every individual, a criterion that allows performing a hierarchical selection must be set. The purpose of the selection consists of classifying the individuals by function of their fitness value to select those individuals with the best attributes. GA is an iterative process, thus, it is necessary to define a stop condition that determines when the process must be finished. If the stop condition is not reached, a new population is generated through the crossover and mutation operations, and the process is repeated from the fitness evaluation. The best individual of the old population goes directly to the new population; the rest of the individuals are obtained by applying the crossover and the mutation operations between the best individual and the other individuals of the old population. Finally, the mutation operation consists of a random replacement of certain individuals in the population according to a mutation probability, \(Pm\).
# Feasibility of Wave Simulation in Typhoon Using WaveWATCH-III Forced by Remote-Sensed Wind
## 2 Data and Method
### Metric Parameters  
Two parameters, root-mean-squared error (RMSE) and scatter index (SI) [41; 42], are employed for the error analysis of the variables, i.e., the maximum wind speed \(V_{max}\), SWH, and PWP:  
\[\text{RMSE}\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{(X_{i}-Y_{i})}^{2}}} \tag{4}\]  
\[\text{SI}=\frac{1}{\bar{Y}}\sqrt{\frac{\sum_{i=1}^{n}{\left[{\left({X_{i}-\bar {X}}\right)-\left({Y_{i}-\bar{Y}}\right)}\right]}^{2}}{n}} \tag{5}\]  
where the \(n\)-elements \(X_{i}\) and \(Y_{i}\) represent the modeled and measured data. The correlation coefficient (Cor) and the squared error (Err) are defined as  
\[\text{Cor}\;=\;\frac{\sum{P_{k}Q_{k}\Delta k}}{\sqrt{\sum{P_{k}^{2}Q_{k}^{2} \Delta k}}}, \tag{6}\]  
\[\text{Err}\;=\;\frac{{}^{2}\Delta f}{\sqrt{\sum{P_{f}^{2}\Delta f\sum{Q_{f}^{ 2}\Delta f}}}}. \tag{7}\]  
where \(P_{f}\) and \(Q_{f}\) represent the one-dimensional wave spectrum in terms of wave frequency \(f\) simulated by the WW3 model and measured by SWIM, respectively.  
A diagrammatic sketch of this study is shown in Figure 3.
## 2 Related Works
However, owing to the nature of gyroscopic sensors, errors can occur ([PERSON] et al., 2018), and these errors become large when they are integrated, thereby causing angular drifts ([PERSON] et al., 2018). Therefore, accurately determining the degree of tilt is difficult using the gyroscope data obtained from an IMU sensor. To resolve this, in this paper, we used gravity acceleration data instead of gyroscopic data to accurately determine the degree of neck tilt. The existing studies on correcting FHP conditions are as follows: [PERSON] et al. (2018) used corrective exercises with biofeedback, and--after four weeks of corrective exercises--the average craniovertebral angle (CVA) of 35 participants increased by \(\sim\)4\({}^{\circ}\) (a low CVA angle reflects FHP); [PERSON] et al. (2018) used four weeks of neurodevelopmental therapy and cervical spine exercises to increase the mean CVA of 12 participants by 4\({}^{\circ}\); and [PERSON] et al. (2018) used Chiropractic BioPhysics (2018), a stretching method, to increase the average CVA of 33 participants by more than 10\({}^{\circ}\) over three months. While corrective exercises and stretches are effective, these are usually difficult to perform independently without professional help. Additionally, exercises require sustained commitment and considerable time to physically correct bad posture. However, previous studies have proven that wearable sensors can help improve posture in everyday life to easily achieve similar results as are obtained with specialized exercises and in less time.
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 3 Results
### Statistics and Analysis
#### 3.1.3 Optimal Spectral Information Combination  
After studying the importance of each spectral information element individually, it is essential to determine the best combination of these elements that yields the best results for organ detection. It should be noted that combination 4 consists of the top 10 pieces of information for organ detection. Based on results from Figure 8, these elements are: B1, B2,  
Figure 8: Bar charts illustrating the importance of each spectral information element (Sentinel-2 bands and spectral indices).  
Figure 7: Overall accuracy results of organ detection for each time using LightGBM and single temporal Sentinel-2 Level 1C data.
# Slotted E-Shaped Meta-Material Decoupling Slab for Densely Packed MIMO Antenna Arrays
## 2 Methodology of Design and Measurement Analysis  
In this section, the coupling conduct of the array components is investigated in detail. The two sorts of coupling phenomenon are: the surface wave phenomena that is limited inside the substrate, and the space wave phenomena that is identified with the near-field or the reactive field coupling and is confined outside the substrate over the coupled patches. The coupling conduct is explored for three distinct arrangements, which are without Decoupling Slab (DS), with Meta-material DS (MM-DS), and with Slotted MM_DS (S-MM-DS).
## 3 Results and Discussion
### Model-Parameter Optimization  
Figure 6 shows parameter-optimization examples for the PLSR model. The first four latent variables (number of principal components) capture the maximum X and Y variance for over 80% of the data, the percentage variance decreased after four latent variables (Figure 6a). It is important to note that only the first four latent variables (LVs) are useful for predicting the chemical components. Comparing the latent variable plot against the root-mean-square error (RMSE) showed that the error value dropped at the fourth latent variable (Figure 6b). Therefore, it was concluded that only four latent variables were essential in the PLSR model for obtaining a good prediction accuracy with a lower error rate, while avoiding the risk of under-fitting or over-fitting the model.  
In contrast to PLSR, an examination of the RMSC value is also essential for optimizing the SVR parameters. Figure 7 presents the SVR-parameter optimization in the form of a counter-plot. The counter-plot displays the C and \(\gamma\) parameters against the RMSE values. Grid searching, as a recommended process, was applied to tune the parameter sets (C and \(\gamma\)). Here, the RMSC value was  
Figure 5: Mean absorbance spectra of three different meat types (sirloin, tenderloin, and turkey).  
Figure 6: Example cross-validation method for optimizing a partial least squares (PLS) model: (**a**) Plot for the X-variance captured by the model; (**b**) Plot of latent variables (LVs) vs. root-mean-square error (RMSE) (the arrow indicates the optimized LVs with the lowest root-mean-square current (RMSC)).  
calculated for each combination of C and \(\gamma\) for every grid point; the grid point providing the smallest RMSE value is the best combination of the parameter sets. It can be seen that the grid marked with an arrow shows the lowest error for both parameters.
## 1 Introduction
To further improve the accuracy of LotHps-based models, we propose a variable-weighted difference training (VDT) strategy, which uses an existing ReLU-based model to guide the training of the LotHps-based model. Specifically, the difference between the activation layer outputs of the LotHps-based model and the ReLU-based model, as well as the difference between their final layer outputs, are  
Figure 1: DLaaS based on homomorphic Encryption (Taking the classification of Skin-cancer Images as an example). added to the loss of the LotHps-based model, with a weight function p(x) to smoothly transition between the two different terms. This strategy enables the LotHps-based model to achieve higher accuracy in the early stages of training and prevents overfitting to the ReLU-based activation layer outputs, leading to lower final accuracy. In summary, our contributions are as follows:  
* We propose a low-degree Hermite deep neural network framework (called LHDNN), which employs a set of low-degree trainable Hermite polynomials (referred to as LotHps) as activation layers in the DNNs. In addition, LHDNN integrates a novel weight initialization and regularization module with LotHps, ensuring a more stable training process and a stronger model generalization ability. * We propose a variable-weighted difference training (VDT) strategy that uses the original ReLU-based model to guide the training of the LotHps-based model, thereby improving the accuracy of the LotHps-based model. * Our extensive experiments on benchmark datasets MNIST, Skin-Cancer, and CIFAR-10 validated the superiority of LHDNN in inference speed and accuracy on encrypted data. In the rest of this paper, we discuss related work in Section 2. The knowledge related to homomorphic encryption is introduced in Section 3. In Section 4, we present the proposed LHDNN and the variable-weighted difference training (VDT) strategy. Relevant experiments are conducted in Sections 5 and 6. Finally, we provide a summary of the entire paper in Section 7.
# Performance Impact of Optimization Methods on MySQL Document-Based and Relational Databases
## 5 Performance Tests--Results and Analysis
### Delete Operation  
#### 5.4.1 Hard Delete  
The hard delete operation of all customers of a business is presented in Table 13.  
\begin{table}
\begin{tabular}{c} \hline \hline
**Relational MySQL: Select with Two Left Joins and Different Functions** \\ \hline @Query(value = “select b.name, u.name, count(c.id) as clients from business b left join user u on b.id = u.business_id left join client c on u.id = c.user_id where (b.deleted is null or b.deleted = false) \\ \multirow{2}{*}{and (u.deleted is null or u.deleted = false) and (c.deleted is null or c.deleted = false) group by u.id order by u.created_at”, nativeQuery = true);} \\ \hline \hline
**Document-based MySQL: Select with two left joins and different functions** \\ \hline DocResult res = collection.find(“deleted is null or false”).orderBy(“created_at”).fields(“name as businessName”, “user.name as userName”, “user.id as userId”, “count(user.client.name) as clients”).groupBy(“userId”).execute(); \\ \hline \hline \end{tabular}
\end{table}
Table 12: Select operation with two left joins and different functions.  
Figure 12: Performance time (ms) for the select operation with two left joins and different functions.  
In the case of using document-based MySQL, deleting elements can be conducted very simply, using the _remove()_ command which takes the filtering condition of the elements as a parameter; therefore, all the elements that satisfy this filter will be deleted. In relational MySQL, a join is needed to obtain all the clients, which are then deleted. As the actual deletion of elements is conducted in document-based MySQL using the predefined _remove()_ command, it is very fast, whether it has optimizations or not, with the times increasing slightly with the increasing number of elements.  
In relational MySQL, execution times are longer and increase significantly with the number of elements due to the joins between tables, with the search for elements taking a long time. After applying the optimization methods on the databases, response times are significantly reduced, as they now use added indexes, achieving a 50% improvement in relational MySQL, and a 10% improvement in document-based MySQL, as shown in Figure 13.
## 2 Materials and Methods
### HRT Imaging  
The parameters of ONH were measured with confocal scanning laser ophthalmoscopy (Heidelberg retina tomograph II (HRT2); Heidelberg Engineering, GmbH, Dossenheim, Germany). In brief, HRT uses the technology of confocal scanning laser ophthalmoscope to acquire a series of scans of ONH. The 3-dimensional tomographic image of the ONH was reconstructed based on the scan images. Only the images with standard deviations of less than 30 um were used in this study. The disc contour lines for all subjects were determined by one experienced operator (KL.Q.). The measurements of ONH were automatically generated by the software integrated into the machine. The standard reference plane is defined as 50 um posterior to the mean retinal height between 350 and 356 degrees along the contour line.
## 3 System Model and Problem Statements  
### System Model  
In this paper, the system model is a typical wireless sensor network based on duty-cycled, which is similar to the model in Ref. [61]. There are \(n\) sensor nodes randomly deployed in a two-dimensional network. The radius of this network is \(R\) and the node density is \(\rho\). And the communication range of each node can be seen as a circular area with a radius of \(r\), with the sender node as the center. Therefore, in the process of transferring from the initial sender to the sink, the packet may pass through multiple nodes. This means that each node can be either a receiver or a sender. According to the communication range and corresponding method of selecting relay nodes, the candidate forwarding nodes for each node can be determined. Since the energy of the node is limited, the method of duty-cycling based on the asynchronous protocol is used here. In this method, the state of nodes converts between two modes, namely, sleep modes and active modes. Each node wakes up at different times. When a sender node has a packet to transmit, if its candidate forwarding nodes are not in active mode, it must wait until at least one of its forwarders wakes up.  
In the entire network, packets or events are randomly distributed. Therefore, the probability of each sensor node's perception of a packet is equal. Likewise, for each node, the probability that it generates data is equal.