esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The design of the experiments and the response surface methodology were applied to study the effect of these cutting parameters on the surface roughness. In high-speed end milling, these models do not account for the vibration of the spindle system. [PERSON] and [PERSON] [26] suggested a simulation algorithm and programming method to simulate the machined surface using an acceleration signal in high-speed end milling. Further studies on surface roughness have focused on modeling or algorithmic methods that rely on cutting parameters and signals, such as force or vibration. Throughout the research process, tool design parameters have not been fully considered because scholars mostly use indirect methods, such as the empirical method and response surface method. Additionally, related research on the direct modeling method is insufficient. Surface roughness, a process quality indicator that is closely related to the tool lifetime for machining needs, should not be considered an isolated design parameter. Instead, surface roughness should be linked to the tool lifetime, as well as the remaining useful life. The existing tool life definition, based on the failure criterion of tool wear [27; 28], cannot directly reflect whether the machining quality of a worn tool under different requirements still meets the demand. Moreover, related research on the remaining useful tool lifetime of a ball end mill does not directly identify any relationship between the tool design parameters, cutting parameters, and tool lifetime. Research on tool life modeling has mainly focused on early studies into the subject matter. [PERSON] and [PERSON] [28] developed mathematical models for the tool life in end milling steel using high-speed steel slot drills under dry conditions. The predictive models were developed in terms of the primary machining variables, such as the cutting speed, feed rate, and axial depth of cut, by response surface methodology. The method construction process mainly relies on response surface method. As a consumable part of the manufacturing front-end process, a reasonable processing plan of a tool, according to the processing demand and its service performance, will enable sufficient tool performance throughout its whole lifecycle. The ability to utilize the full life expectancy of a tool can undoubtedly significantly reduce production costs and even improve processing quality [28]. Compared with previous studies, this study innovatively relates the time-varying characteristics of tool wear to the quality of the workpiece to represent the time-varying characteristics of tool machining performance. Unified the tool life and machining performance. In addition, the model completely contains important design parameters and operating parameters of the ball end milling tool, so the research of tool service performance can be conducted with the goal of emphasizing additional useful tool design parameters, and the modeling process can provide the basis for determining the design of cutting tools for optimal performance, according to the processing requirements.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Dual-Domain Joint Channel Estimation
#### 4.3.2 CIR Estimation in DT Domain  
According to [17], the channel impulse response in the DD domain in Equation (3) can be rewritten as follows:  
\[h(\tau,v)=\sum_{l\in L}\sum_{q\in Q_{l}}h(l,q)\delta(\tau-lT/M)\delta(v-q \Delta f/N), \tag{19}\]  
where \(L=\{l_{i}\}\) is the set of distinct normalized delay shifts in the DD domain, \(Q_{l}=\{q_{i}|l=l_{i}\}\) is the set of normalized Doppler shifts for each path with normalized delay shift \(l_{i}\), and the \(l\)th delay tap Doppler response is  
\[h(l,q)=\left\{\begin{array}{ll}h_{i},&\text{if }l=l_{i}\;\;and\;\;q=q_{i}\\ 0,&\text{otherwise}\end{array}\right. \tag{20}\]  
Then, the UWA CIR is as follows:  
\[\tilde{h}(\tau,v)=\int_{v}h(\tau,v)e^{l2\pi v(l-\tau)}dv=\sum_{q\in Q_{l}}h(l,q)e^{l2\pi q\frac{\Delta f}{N}(t-\tau)}. \tag{21}\]  
Discretizing the channel after sampling at \(t=aT/M,0\leq a\leq NM-1\):  
\[\tilde{h}(l,a)=\sum_{q\in Q_{l}}h(l,q)e^{\frac{l2\pi q(a-l)}{MN}}. \tag{22}\]Setting \(a=m+nM\), according to [17], the input-output relationship with the channel in the DT domain can be defined as follows:  
\[\mathbf{y}(m,n)=\sum_{l\in L}\tilde{h}(l,m+nM)\mathbf{x}(m-l,n), \tag{22}\]  
where \(\mathbf{x}(m,n)\) and \(\mathbf{y}(m,n)\) are the transmitted and received symbols in the \(m\)th row, respectively, with size \(N\times 1\), as shown in Figure 1.  
Given an obtained path delay \(\hat{l}\), let \(m=m_{p}+\hat{l},\hat{l}\in L\), the input-output relationship of pilot signal can be expressed as follows:  
\[\mathbf{y}(m_{p}+\hat{l},n)=\sum_{l\in L}\tilde{h}(l,m_{p}+\hat{l}+nM)\mathbf{ x}(m_{p}+\hat{l}-l,n). \tag{23}\]  
Based on Equation (9), \(\mathbf{x}(m_{p}+\hat{l}-l,n)=0\) when \(0<|l-l^{\prime}|\leq l_{g}\) (i.e., \(0<|l-\hat{l}|\leq l_{g}\)), due to the zero-padding. Thus, the CIR can be obtained as follows:  
\[\hat{h}(l,m_{p}+\hat{l}+nM)=\frac{\mathbf{y}(m_{p}+\hat{l},n)}{\mathbf{x}(m_{ p},n)},\hskip 14.226378 ptfor\hskip 14.226378 pt\hat{l}\in L, \tag{24}\]  
where \(\mathbf{x}(m_{p},n)\) is the transmitted pilot signal. The UWA CIR in the DT domain can be represented using spline interpolation [22]:  
\[\hat{h}(l,a)=spline\_interpolate(\{\hat{h}(l,m_{p}+\hat{l}+nM)|n\in[0,N-1]\},[m_{ p}+\hat{l}:M:NM],[0:NM-1]). \tag{25}\]  
Finally, based on \(\hat{h}(l,a)\), the DD domain channel matrix \(\mathbf{H}\) can be obtained according to [17].
## 4 Simulations
### Multiple Data Streams on Shared Medium
Via the simulations, it was discovered that when the second stream started transmitting after five seconds (real-case scenario with delay transmission from different end-point equipment, e.g., Smart Meters), it will take 200 to 250 s before the streams are balanced in scenario 1A and from 150 to 200 s in scenario 1B. This suggests that if at least one stream transmits data with one-third of the bandwidth, it takes less time for the streams to balance. For the cases where two streams out of three start to transmit after five seconds, it will take approximately 100 s in scenario 2A and between 80 to 100 s in scenario 2B. Figure 14: Scenario 1B. Figure 15: Scenario 2A.
# An Approach to Trustworthy Article Ranking by NLP and Multi-Layered Analysis and Optimization
## 2 Related Work
### Trustworthiness in Article Discovery  
In article discovery, \"trustworthiness\" refers to the reliability and validity of information contained in the retrieved scholarly articles. Scientists must have confidence in the papers that they read and use in their research activities. The academic community is aware of the importance of reliability in article discovery. In the past decades, many research studies [15; 16; 17] have been conducted to address such concerns. In general, those works have developed their own systems and criteria to determine the trustworthiness of an article. These criteria comprise the standard of the peer review procedure, the credibility of the source, the credentials and work history of the writers, and the number of citations the article has received. Among those metrics, citation is a common way for academics to gauge whether a work is well-liked and influential.
## 3 The Friendly-Sharing Approach
### Buffer Management
#### 3.2.1 Message Forwarding  
Message forwarding refers to the order for extracting messages from the buffer in order to transmit them to the connected device. We considered the four methods graphically described in Figure 2a. More specifically:  
1. _Random:_ The messages are selected randomly from those stored in the buffer. This method is already implemented in the ONE simulator and it is configured by default.
2. _FIFO (First-In, First-Out)_: Messages are selected for forwarding according to their arrival order, i.e., the first message to be forwarded is the first message arriving at the mobile device (that is the oldest message, taking into account the reception time). From the figure: message \(M3\) arriving at time \(t_{n}\) will be forwarded first, message \(M1\) arriving at time \(t_{n+1}\) will be the next one to be sent, \(M1\) will be the following, and so on.
3. _Oldest:_ With this approach, the message with lower TTL value will be forwarded first. In the example shown in the Figure, \(M3\) will be sent first, the next message will be \(M1\) with TTL value \(t_{n+1}\), etc. (note, that this approach is different to the _FIFO_ approach, as _FIFO_ considers the arrival time of the message, while _Oldest_ considers the TTL.)
4. _Smallest:_ This approach aims at first sending the smallest messages. In the figure, we can see that the sending queue is organized according to the messages size, i.e., \(M3\) with size \(S_{n}\) will be forwarded first than message \(M1\) with a message size of \(S_{n+1}\), since \(S_{n}<S_{n+1}\).  
On the original version, ONE only implements the _Random_ and _FIFO_ policies. The other two policies were implemented by the authors, as detailed in the following section.
## 3 Results and Discussion  
### Changes in the Nutritional Composition of the Fish Broth  
#### 3.1.1 Water-Soluble Proteins, Fats and Total Sugars  
Nutrients such as the proteins, fats and sugars in fish broth are important indicators of the flavor and nutrition of the broth. As shown in Figure 1A, the water-soluble protein contents increased over time in both the boiling water treatment and the thermo-ultrasonic treatment groups. The protein contents of the thermo-ultrasonic treatment group were significantly higher than in the boiling water group. The protein concentration for 30 min of thermo-ultrasonic treatment was 0.93 g/100 mL, which was similar to the result of 90 min of boiling water treatment. As shown in Figure 1B, the fat contents of the two groups increased as time went on, with the values for the thermo-ultrasonic treatment group (2.69 g/100 g) being larger than the boiling water group at 120 min (1.56 g/100 g). It is worth noting that the fat content of the thermo-ultrasonic treatment group reached 1.76 g/100 g at 30 min, which even exceeded the fat content of the boiling water group at 120 min. As shown in Figure 1C, the trend in the total sugar content was consistent with the protein. The total sugar content of the thermo-ultrasonic treatment group reached 24.74 mg/100 mL at 120 min. It is worth noting that the total sugar content of the thermo-ultrasonic treatment for 30 min was almost the same as that of the boiling water group for 120 min. Therefore, the fish broth processing method using thermo-ultrasonic treatment can reduce the extraction time of nutrients going into a broth. The results of nutrient extraction in the boiling water treatment group indicated that relatively high temperatures could promote the content and the nutrient extraction rate in fish broth processing, which is also known as the thermic effect [32]. By comparing the data from the two treatment groups, it was evident that thermo-ultrasonic treatment had a significant effect on the extraction of nutrients in fish broth processing. [PERSON] et al. [20] concluded that the shock waves and shear forces generated by an ultrasound can disrupt macromolecular nutrients in ground bone, allowing for an easier dissolution and delivery of nutrients.
# A Watermarking Protocol Based on Blockchain
## 3 Main Challenges  
One of the main challenges in designing watermarking protocols consists of accurately defining the role played by TTPs in the purchase transactions, since TTPs could collude with the other parties involved in the protocols [17; 20; 40] so as to impair them. In this regard, the best solution would be to totally eliminate TTPs from protocols. However, such a solution is not always possible, since protocols often need TTPs to validate specific data, or some phases of the protocol, or, for example, the plug-ins that have to be downloaded and installed in the buyers' web browsers to complete the purchase transactions [22; 23]. Furthermore, when TTPs play a limited role in the protocols, buyers end up being forced to perform complex security actions to complete the purchase transactions, and this makes the protocols impractical for the web context [17; 18; 19; 20; 41; 42; 43; 44].  
The watermarking protocols presented in [22; 23; 24] do not completely eliminate the TTP, but they carefully exploit it without assigning it a central role in order to simplify the buyer participation in the protocols. In particular, the TTP participates only in the initial phase of the protocols and restricts its role to the generation of a number of tokens needed to unambiguously bind the chosen content to the buyer, the seller and the ongoing purchase transaction.  
Although the role of the TTP is rather restricted in the protocols described in [22; 23; 24], it has to be further limited if the main goal is to develop an innovative watermarking protocol suited for the current web context. In this regard, blockchain technology represents a challenge to achieve such a goal. In fact, it can be exploited in the proposed protocol with the aim of securely tracking the purchase transactions in a public ledger that can be updated by automatically executing smart contracts without resorting to the control of a TTP [26; 27; 28; 29]. Thus, the TTP involved in the proposed protocol can act as a simple and trusted web distributor of secure tokens needed to complete the purchase transactions of protected digital content. In fact, it is not a WCA, even though it has to behave as a TTP in the sense of a common certification authority (CA) [45; 46; 47].  
The adoption of blockchain technology to strongly restrict the role of TTP makes it necessary to accurately design and code the smart contract that controls the execution of the proposed watermarkingprotocol and validates each purchase transaction. In fact, this represents a relevant practical challenge well documented in the literature, since the code that implements the contract, once it has been released, can no longer be modified or updated. Therefore, if the code of the contract is incorrect or gives rise to a problem during use, it ends up impairing the entire protocol [48].
# Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot
## 6 Experimental Study
### Large Curvature Surface Constant Force Control  
To ensure the force control effectiveness of the force control algorithm for curved surface parts, the force control platform shown in Figure 12c was adopted. The desired contact force was set as \(f_{d}=-5\) N and the running time was 10 s. The control parameters of the CIC, AVIC, and DPAVIC methods were set as:  
* CIC: \(m=1,b=8,k=0\).
* AVIC: \(m=1,b=8,k=0,\sigma=0.003\).
* DPAVIC: \(m=1\), \(b=5\), \(k=0\), \(\sigma=0.01\), \(k_{p}=0.2\), \(k_{d}=0.05\), \(k_{i}=0.001\), \(r=100\), \(h=0.01\).  
Figure 15 shows the response curves of the end compensation displacement and the contact force. It can be found that when CIC and AVIC are adopted, the overshoot of the  
Figure 14: Force control results of slope surface.  
Figure 13: Force control results of flat surface.  
system is large, 10.26% and 7.25%, respectively, and the force control error is 0.336 N and 0.252 N. However, the overshoot and force error of DPAVIC are 1.51%, within \(\pm\)0.2 N, respectively. Although the system response is slow with the DPAVIC method, the adjustment time is shorter.
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 5 Datasets
### Datasets Analytics
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **LN** & **OLN** & **YCBV** & **T-L** & **KIT** & **CAM** & **RE** & **TEj** \\ \hline SSP-Pose [34] & & ✓ & & & & ✓ & & \\ MV6D [30] & & ✓ & & & & & & \\ [PERSON] et al. [36] & & ✓ & & & & & & \\ GDR-NET [25] & ✓ & ✓ & ✓ & & & & & \\ DCL-Net [67] & ✓ & ✓ & ✓ & & & & & \\ EPro-PnP [52] & ✓ & & & & & & & \\ RANSAC Voting [72] & ✓ & ✓ & & & & & & \\ FS6D-DPM [28] & ✓ & & & ✓ & & & & \\ GPV-Pose [29] & ✓ & & & & & & & ✓ \\ [PERSON] et al. [35] & ✓ & & & & & & & \\ OVE6D [31] & ✓ & ✓ & & & ✓ & & \\ PoET [43] & ✓ & ✓ & ✓ & & & & \\ [PERSON] et al. [70] & ✓ & ✓ & ✓ & & & & \\ Primitive Pose [71] & & & & & & & & \\ PVNet [32] & ✓ & ✓ & ✓ & ✓ & & & \\ SC6D [33] & & & & ✓ & ✓ & & \\ ZebraPose [37] & & & ✓ & ✓ & & & \\ Gen6D [38] & ✓ & & & & & & \\ CRT-6D [41] & & & ✓ & ✓ & & & \\ [PERSON] et al. [46] & ✓ & ✓ & & & & & \\ StereoPose [82] & & & ✓ & & & & \\ DR-Pose [76] & & & ✓ & & & & ✓ & \\ PLuM [47] & & & ✓ & & & & \\
3 DNEL [73] & & & ✓ & ✓ & & & \\ Yolo 7 [84] & ✓ & ✓ & & & & & \\ Improved CDPN [77] & ✓ & ✓ & & & & & \\ DFTr network [42] & ✓ & ✓ & ✓ & & & & \\ SwinDePose [83] & ✓ & ✓ & ✓ & & & & \\ [PERSON] et al. [39] & & & ✓ & & & & \\ HS-Pose [44] & & & & & & & ✓ & \\ MSDA [79] & & & & & ✓ & & \\ C2 FNET [75] & ✓ & ✓ & & & & & \\ Improved PVNet 2 [78] & & & ✓ & & & & \\ [PERSON] et al. [48] & & & & & & & & ✓ \\ SE-UF-PVNet [49] & ✓ & ✓ & & & & & \\ Improved PVNet [45] & ✓ & ✓ & & & & & \\ SASA-PVNet [80] & ✓ & ✓ & & & & & \\ [PERSON] et al. [54] & ✓ & & & & & & \\ [PERSON] et al. [87] & ✓ & ✓ & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._dataset is also prominently used, with 13.36% of the models validating their performance on it. These three datasets serve as the primary benchmarks for evaluating a 6D pose estimation model, underscoring their significance in the field. Other datasets such as T-LESS, KITTI, CAMERA25, REAL, and TEJANI have been employed less frequently, with usage rates ranging from 0.46% to 5.99% and a 24% used also other datasets.
## 3 Industrial Consumption, BESS, and Grid Modelling
### BESS Model  
The BESS model includes both a battery model and a power electronic converter model, the latter being necessary to manage the battery behaviour and to connect the battery to the AC grid.
# ODEM-GAN: An Object Deformation Enhancement Model Based on Generative Adversarial Networks
## 3 Approach
### Network Architecture of the ODEM-GAN Module
#### 3.2.1 ODEM-GAN Module
Specifically, \(z\) is a 64-dimensional vector that is a uniform distribution of \([-1,1]\). We take the average of the feature patches along the channel dimension; then, the shape of the feature patches on \(PF_{3}\) will change from \([n,n,c]=[8,8,256]\) to \([8,8,1]\) and flatten it into a 64-dimensional vector. Finally, the 64-dimensional vector is concatenated with \(z\) to obtain a new 128-dimensional vector as the input of the generator. The 128-dimensional vector achieves a shape of feature patches \([n,n,c_{f}]=[8,8,512]\) by upsampling (the upsampling strategy uses nearest neighbor interpolation) and three convolution modules. As shown in Figure 9, the number of channels of the convolutional layer is the same as the multiple of the resolution increase. There are two upsamplings, and \(n_{c}=\frac{c_{f}}{2^{2}}=128\) in Figure 9. The parameters of generator \(G\) are shown in Table 1.  
**The design of discriminator \(D\). ** As shown in Figure 10, the discriminator adopts an autoencoder structure. It consists of two parts: an encoder and a decoder. The encoder encodes the input (the samples created by the generator and online deformable sample creation module) into a set of multidimensional feature vectors, while the function of the decoder is the opposite of the encoder. This restores the multidimensional feature \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Output Size**} & \multirow{2}{*}{**Act.**} & \multicolumn{2}{c}{**Layer**} \\  & & **(Height, Width, Channel)** & & **(Filter\_h, Filter\_w, Num, Stride)** \\ \hline feature-patch + \(z\) & (128, -, -) & - & - \\ \hline full connect layer & \multicolumn{2}{c}{\((2\times 2\times n_{c_{c}}\), -, -)} & - \\ \hline \multirow{2}{*}{module1} & conv1 & (2, 2, \(n_{c}\)) & Relu & (3, 3, \(n_{c}\), 1) \\  & conv2 & (2, 2, \(n_{c}\)) & Relu & (3, 3, \(n_{c}\), 1) \\ \hline \multirow{2}{*}{module2} & conv1 & (4, 4, 2\(n_{c}\)) & Relu & (3, 3, 2\(n_{c}\), 1) \\  & conv2 & (4, 4, 2\(n_{c}\)) & Relu & (3, 3, 2\(n_{c}\), 1) \\ \hline \multirow{2}{*}{module3} & conv1 & (8, 8, 2\({}^{2}n_{c}\)) & Relu & (3, 3, 2\({}^{2}n_{c}\), 1) \\  & conv2 & (8, 8, 2\({}^{2}n_{c}\)) & - & (3, 3, 2\({}^{2}n_{c}\), 1) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters of generator \(G\). Figure 9: The structure of generator \(G\). vectors generated by the encoder to the shape of the original input. Therefore, the output dimension of the autoencoder is the same as its input dimension. The decoding structure used in this article is the same as the generator \(G\). The encoder is the inverse process of the decoder. The parameters of the discriminator \(D\) are shown in Table 2.
## 3 FLAC3D Model Building
### FLAC3D Grid Convergence Analysis  
In order to ensure the accuracy and reliability of the FLAC3D numerical simulation results, it is necessary to judge whether the mesh density is sufficient by gradually refining the mesh and observing the changes in the calculation results, so as to avoid the error of the simulation results caused by a too sparse or too dense mesh.  
In the convergence analysis of the FLAC3D mesh, it is necessary to select the appropriate initial mesh first. The initial mesh should be as uniform as possible and properly densified in the key areas to ensure that the influence of mesh density changes on the calculation results can be clearly observed in the subsequent refinement process. For example, in the simulation of slope stability, the stress concentration areas such as slope toe and slope surface should be refined.  
Next, the convergence is evaluated by gradually refining the grid and observing the changes in the calculation results. The specific steps include the following:  
1. The first refinement: local refinement based on the original grid, such as halving the side length of the original grid.
2. Calculate and compare the results: compare the results of the two calculations before and after, and observe the changes in the main output parameters (such as stress and displacement).
3. Repeat refinement: If the results after the first refinement are still not ideal, continue to calculate the finer mesh and repeat the comparison process until the mesh refinement no longer significantly affects the calculation results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Component** & **Depth** &
\begin{tabular}{c} **Internal Friction** \\ **Angle** \\ **m** \\ \end{tabular} & **Cohesion** & **Mass Density** & **Bulk Modulus** & **Shear Modulus** \\  & **m** & \(\circ\) & **kPa** & **kg/m\({}^{3}\)** & **Pa** & **Pa** \\ \hline Plain fill & 10.0 & 24.0 & 8.0 & 1600 & \(4.40\times 10^{7}\) & \(2.00\times 10^{7}\) \\ Large-diameter multi-disc soil anchor & - & - & - & 2500 & \(2.78\times 10^{10}\) & \(1.79\times 10^{10}\) \\ Equal-diameter soil anchor & - & - & - & 2500 & \(2.78\times 10^{10}\) & \(1.79\times 10^{10}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model-related parameters.  
In the convergence analysis of FLAC3D grid, the following three points should be paid attention to:  
1. To ensure that the model is a continuous whole, the number of grids of adjacent sections must be the same and share one node. If there is an integer multiple relationship, the \"attach\" command can be used to repair it.
2. Appropriate model parameters should be set before simulation analysis, and the parameters can be adjusted by the \"set larger\" command to avoid the problem of non-convergence of calculation.
3. FLAC3D adopts the explicit Lagrangian algorithm, and it is necessary to pay attention to the deformation and movement of the grid during the calculation process. If the mesh deforms greatly, it may be necessary to adjust the calculation parameters or redivide the mesh.