esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview of Tool Wear Monitoring Methods Based on Convolutional Neural Network
## 4 Information Data of Indirect Online Measurement on Tool Wear
### Cutting Force  
The cutting force change is closely related to the tool wear/damage conditions during the cutting process. The cutting force information is easy to obtain, and it responds quickly and sensitively to tool wear. The literature [40; 41; 42; 43] suggested that cutting force monitoring is a promising method. Machining centers and FMS commonly use the cutting force as information data to monitor tool damage. [PERSON] et al. [38] proposed a method of tracking cutting force model coefficients to monitor the wear condition of end milling tools in real-time. This method studied the correlation between the cutting force model coefficients and tool wear condition. [PERSON] et al. [44] established a method for identifying broken cutters in the milling process based on the timing feature of horizontal cutting force. [PERSON] et al. [45] adopted a rotating dynamometer to collect the cutting force in three directions and the torque of the rotating tool. [PERSON] et al. [46] proposed that the most reliable feature in the cutting process was the cutting force feature, which directly caused the generation of features such as vibration and acoustic emission. [PERSON] et al. [47] used a piezoelectric dynamometer to monitor tool wear conditions in the end-milling operations. [PERSON] et al. [48] applied support vector machine regression to realize turning tool condition monitoring based on the cutting force. The results showed that the model had a high prediction accuracy. [PERSON] et al. [49] created a semi-hidden Markov model considering tool wear continuity index to monitor tool wear. This model reduced the computational cost and improved the recognition accuracy. [PERSON] et al. [50] proposed a method for identifying the milling tool condition based on cutting force features, geometric information, and process information. It is suitable for single-piece and small-batch manufacturing processes, in which the geometry and cutting parameters were constantly changing.  
The disadvantage of the cutting force feature is that it is not suitable for processing large workpieces. It is affected by the low stiffness and low-frequency bandwidth of the workpieces. It interferes with the precise motion control of the machine tool spindle and worktable, reducing the rigidity of the machine tool [51; 52].  
The installation diagram of a cutting force sensor is shown in Figure 1.
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
###### Abstract
The following main contributions to the two IDRF phases are made:  
* In the offline phase, real-world moving trajectory data from Beijing taxis are employed to assess the use of the vehicle trajectory continuity algorithm to correct vehicle GPS trajectories, add data on vehicle arrival at intersections, and estimate the average vehicle speed for a road segment. The algorithm can rectify and analyze vehicle trajectories, resulting in the attainment of accurate information and better routing decisions. * The SDN architecture, which utilizes historical vehicle information to train a packet relay node model using the DQN, is adopted when designing the IDRF_DQN framework. The IDRF_DQN_RF framework, which combines IDRF_DQN with RF, is proposed to train a vehicle intersection traversal model using historical trajectory information. These two frameworks adopt the DQN to reduce excessive storage consumption and searching delays during Q-learning. * In the real-time phase, we propose algorithms to estimate the vehicle link stability, determine whether complete packet transmission can be carried out between the packet-carrying vehicle and the relay ones, and calculate the minimum segment delay, which is the delay when transmitting packets from one road segment to the next. The optimal packet transmission path can be found when considering both real-time and non-real-time vehicles. * We propose and adopt an integrated IDRF framework that combines IDRF_DQN_RF with real-time DQN-determined packet routing and exception-handling mechanisms. When new vehicles enter the network in real time, their information is added to the segment delay calculation. Then, the framework determines whether these new real-time vehicles can establish new packet relay paths by combining the relay node delay choices from the trained DQN model and the intersection relay probabilities computed through RF. Consequently, this integration enables the shortest end-to-end delay packet relay path for real-time vehicles between the source and the destination to be determined.
# Retrieval and Comparison of Multi-Satellite Polar Ozone Data from the EMI Series Instruments
## 3 Methods
### Deriving the Ozone Slant Column Density
(**a**) Measured (red) and reference (blue) spectra; (**b**) measured and fitted ozone optical densities; and (**c**) EMI-DQ01 RMS values of the DOAS fitting.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 3 System Architecture
### Interacting Multiple Model Filter Structure
#### 3.1.1 Interaction and Mixing  
Initially, the system can be considered as a discrete group of \(r\) models, which can be represented as \(M=\{M_{1},M_{2},\ldots,M_{r}\}\). The mixing probability \(\mu_{m}^{i|j}\) can be described as  
\[\mu_{m}^{i|j}=\frac{1}{\varepsilon_{j}}p_{ij}\mu_{m-1}^{i} \tag{8}\]  
in which \(\mu_{m-1}^{i}\) is the mode probability of model \(i\) in the preceding stage and \(p_{ij}\) is the mode switching probability matrix.  
A normalizing factor \(\overline{\varepsilon}_{j}\) can be denoted by  
\[\overline{\varepsilon}_{j}=\sum_{i=1}^{r}p_{ij}\mu_{m-1}^{i} \tag{9}\]The initial mixed state estimate \(x_{m-1}^{0,j}\) and its covariance \(P_{m-1}^{0,j}\) for each filter can be expressed as  
\[x_{m-1}^{0,j}=\sum_{i=1}^{r}\mu_{m}^{i|j}x_{m-1}^{+,j} \tag{10}\]  
\[P_{m-1}^{0,j}=\sum_{i=1}^{r}\mu_{m}^{i|j}\bigg{[}P_{m-1}^{+,j}+\left(x_{m-1}^{ +,j}-x_{m-1}^{+,j}\right)\left(x_{m-1}^{+,j}-x_{m-1}^{+,j}\right)^{T}\bigg{]} \tag{11}\]  
in which \(x_{m-1}^{+,j}\) and \(P_{m-1}^{+,j}\) are the final mean and covariance for a single model \(i\), respectively.
# Quantitative Analysis of Forest Water COD Value Based on UV-vis and FLU Spectral Information Fusion
## 4 Results and Analysis  
### Spectral Feature Selection  
As shown in Figure 1a, the UV-vis absorption of the water sample is mainly concentrated in the 200 nm to 300 nm band [36]. We note that the absorbance curves after 300 nm overlap without significant spectral features. To reduce the redundant information, we use the SPA and CARS algorithms to select the feature wavelengths. Based on the SPA algorithm, the minimum RMSECV of 0.1502 can be obtained when the selected wavelength point is 6, which achieves the best result [37]. The selected wavelength points are shown in Figure 2a. For the CARS algorithm, the number of selected variables gradually reduces with the increased iterations, as shown in Figure 2b. However, the RMSECV value exhibits a non-monotonic trend that decreases first and then increases. The reduced RMSECV indicates that some useless information in spectral data has been eliminated first. Meanwhile,the increased values indicate that some important information may be lost during modeling. Based on the CARS algorithm, the RMSECV can reach a minimum value of 0.2631 when the number of iterations is 23, as shown in Figure 2c. During the feature-selection process, 29 wavelength points are selected.  
Accordingly, we also use the SPA and CARS algorithms for the feature selection of the pre-processed FLU emission spectra. Based on the SPA, the minimum RMSECV of 0.3341 can be obtained when the selected wavelength point is 9, as shown in Figure 3a. From Figure 3b,c, RMSECV reaches the minimum value of 0.304 in 24 iterations based on the CARS algorithm. During the feature-selection process, 34 wavelength points are selected.
## 3 Results
### Analysis of EQI and USI in City-Level
The positioning to the right implies that the cities in the CYRD experience a high level of urbanization. PRD, on the other hand, demonstrates a relatively lower intensity of urban expansion. Several cities in the CYRD, such as Zhangjiakou, Wenzhou, and Zhaoqing, show significant improvements in EQI despite having lower levels of urbanization. Notably, most cities in the three urban agglomerations are concentrated in areas where the absolute value of USI is small. This suggests that the government has acknowledged the ecologically  
Figure 6: EQI values of each city in the three major urban agglomerations in 2001 and 2020.  
damaging effects of urbanization and has taken proactive measures. Consequently, the intensity of ecological deterioration is kept at a lower level despite the high intensity of urban expansion. Although the CYRD has experienced the largest area of ecological deterioration, many cities in this region have smaller absolute USI values, indicating successful efforts to reconcile urban economic development and ecological protection. However, certain cities, such as Tangshan, Langfang, and Qinhuangdao, exhibit low intensity of urban expansion but high intensity of ecological damage, necessitating close attention to ecological quality and the implementation of active measures. Effective strategies may include establishing protected areas to safeguard vegetation and biological resources, regulating industrial pollution emissions, and rigorously addressing encroachments on ecological land. In the cases of Beijing and Shanghai, Shanghai demonstrates relatively less ecological quality deterioration despite similar urban expansion intensity with Beijing. Consequently, the smaller absolute value of Shanghai's USI indicates better performance in reconciling the human-land conflict.
## 1 Introduction
### Basics of the Smart City
#### 1.1.1 The Smart Grid  
The smart grid is a future electrical network encompassing four major domains: bulk generation, transmission, distribution, and consumer segments. Moreover, uninterrupted communication between the domains is essential for the smart grid system [24]. Furthermore, the Internet of Things (IoT) and artificial intelligence (AI) are also integrated with the power grid in the case of the smart grid system [24]. In addition, the smart grid is not only a sustainable, cost and power-effective power supply solution, but it also provides high-quality, low losses, as well as a safe and secure power supply [25; 26; 27].
## 3 Results
### Characterization of the MPs
#### 3.2.2 Characterization of the MPs by FTIR  
ATR-FTIR equipment was used to identify the functional groups present in the different layers of the face mask. The spectra of the inner and outer layers were very similar (see Figure 3). The two layers showed similar bands in the range from 2800 to 3000 and from 1335 to 1490 cm\({}^{-1}\); these bands are characteristic of polypropylene (PP) [63; 64]. The bands at 2950 and 2870 cm\({}^{-1}\) were attributed to asymmetric and symmetric stretching vibrations of the methyl group (CH\({}_{3}\)), respectively, while the bands at 2915 and 2840 cm\({}^{-1}\) were attributed to asymmetric and symmetric stretching vibrations of the methylene group (CH\({}_{2}\)), respectively [63; 65]. The FTIR spectra also showed two intense bands at 1452 and 1375 cm\({}^{-1}\) caused by symmetric and asymmetric deformation vibrations of CH\({}_{3}\), respec  
\begin{table}
\begin{tabular}{c c c c} \hline
**Indicator** & **Values** & **Indicator** & **Values** \\ \hline Texture & Clay loam & N-NO\({}_{2}\)\({}^{-}\) & 6.10 \(\pm\) 0.80 \\ \hline pH & 7.50 \(\pm\) 0.05 & N-NO\({}_{3}\)\({}^{-}\) & 24.98 \(\pm\) 0.08 \\ \hline EC & 0.19 \(\pm\) 0.01 & N-NH\({}_{4}\)\({}^{+}\) & 18.17 \(\pm\) 1.90 \\ \hline WHC & 102.84 \(\pm\) 5.20 & UA & 213.45 \(\pm\) 4.60 \\ \hline TOC & 1.22 \(\pm\) 0.02 & DHA & 264.41 \(\pm\) 21.10 \\ \hline OM & 2.12 \(\pm\) 0.04 & FDA & 42.72 \(\pm\) 2.50 \\ \hline TN & 0.24 \(\pm\) 0.05 & MBC & 272.94 \(\pm\) 7.30 \\ \hline C/N & 5.08 & & \\ \end{tabular}
\end{table}
Table 2: Physicochemical and biological characterization of agricultural soil.  
tively [63; 66]. Finally, the band at 1165 cm\({}^{-1}\) was attributed to C-C asymmetric stretching, CH\({}_{3}\) asymmetric rocking, and C-H motion vibrations [63; 66].  
[PERSON] et al. [63] characterized the different parts of three types of face masks using FTIR and found that all three were composed of PP layers, results that are in agreement with those found in the present study, i.e., that the layers of the face masks used here were made of PP fibers.
# Smart Clothing Framework for Health Monitoring Applications
## 3 Proposed Framework for Smart Clothing (Leggings)
### Integration between Sensor and Textile
#### 3.6.2 Integration at the Fibre Level  
Researchers from Massachusetts Institute of Technology (MIT) have produced optoelectronic semiconductor-embedded fibres that are woven into soft fabrics using light-emitting diodes (LEDs) and diode photodetectors. The fibre was heated in a furnace during the drawing process where the polymer was liquefied to make a long fibre with the diodes lined up along its centre, connected by copper wires. These fibres can be used for communication and safety, military lighting applications, monitoring physiological parameters, including measuring vital statistics, and monitoring healing, such as in pulse meters and smart bandages [139].
## 4 Collaborative Distributed Scheduling Approach
### Two-step Collaborative Distributed Scheduling Approach
#### 4.3.1 Scheduling Process  
For T-CDSA, nodes shifting to the Sleep state are not restricted to the condition that their queue be empty. For a CSMA/CA based MAC protocol, the MAC layer will cease the data transmission and discard the data packet if retries for failed transmission attempts equals to a specific threshold [26]. For a RTS/CTS based CSMA/CA protocol, another threshold also restricts the number of retries for RTS frames. Therefore, in T-CDSA, we introduce a new state -- Failure (F) -- to represent the state that either of these limits is reached. The F state is partly caused by serious collisions in the communication channel. It is also partly caused by the Sleep state of the next-hop, which disables the ability to receive the next-hop. For the former case, the node should consider whether it should re-transmit the data packet again or go to Idle state. For the latter one, it should choose whether it goes to Sleep or not.  
When a node sends out a data packet to its next-hop, it will get one of two results accordingly. One result is that it receives the ACK frame, which means the transmission is successful and it can transmit the rest of packets in its buffer or change to another state. Another result is failure to receive the ACK frame and change to the F state. For the latter result, the node can also have two operation choices. One is going to state Ix for the re-transmission attempt or receiving the incoming data packets. Another operation is going to Sleep state because its next hop is in Sleep mode and its child nodes have transmitted all packets in their FIFO. Thus a node goes to Sleep state on the condition that it has transmitted all its data packets in FIFO queue or it is in F state. When the sleep node wakes up, the length of its FIFO queue reflects which state it will transfer to. If the FIFO queue is empty, which means it went to Sleep because it has sent out all of its data packets, it will transfer to the I\({}_{0}\) state. On the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathbf{S_{i}}\) & \(\mathbf{S_{i+1}}\) & \(\mathbf{P(S_{i+1}|S_{i})}\) \\ \hline S & I\({}_{0}\) & 1 \\ I\({}_{0}\) & S & \(1-v\) \\ R & T & \(m\times\beta\) \\ I\({}_{\mathrm{x}}\) & T & \((1-v)\times\beta\) \\ T & T & \((1-v)\times\beta\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scheduling Probabilities of O-CDSA.  
contrary, the node will go to Ix state if it went to sleep due to F state. The new working state diagram is illustrated below in Figure 6.  
To schedule states of a node efficiently, the node should estimate the Sleep behavior of its parent node in addition to the communication channel and the behavior of its child nodes. Therefore, we introduce two new variable \(g\) and \(g_{p}\) to denote the probability of a node and its parent node from F state to Sleep state. The behavior model of child nodes and the dynamics of channel are same as that in Section 4.2.2.
## 3 Results
### Accuracy, Specificity, Sensitivity, NPV, and PPV  
In detecting separated files (group 1), diagnostic accuracy for the observers ranged from 0.750 to 0.896 for digital periapical images and from 0.576 to 0.773 for CBCT images. Moreover, the accuracy values calculated based on the observers' average for the detection of separated files was 0.950 for digital periapical radiography and 0.747 for CBCT (Table 1). In general, for detecting separated files, accuracy, sensitivity, specificity, PPV, and NPV were higher in digital periapical radiographs than in CBCT.  
Figure 2: Digital periapical radiographs with (**a**) perpendicular and (**b**) distal horizontal angles and CBCT images in (**c**) sagittal and (**d**) axial views of a tooth with strip perforation of the mesiobuccal root canal.  
In detecting strip perforation (group 2), the diagnostic accuracy for the observers ranged from 0.727 to 0.932 for digital periapical images and from 0.900 to 0.960 for CBCT images. Moreover, the accuracy values calculated based on the observers' averages for detection of strip perforation were 0.855 for digital periapical radiography and 0.955 for CBCT (Table 2). On average, for detecting strip perforation, accuracy, sensitivity, specificity, PPV, and NPV were higher in CBCT than in digital periapical radiographs.
## 6 Recent Developments and Innovative Approaches
### Solar-Driven Photocatalytic Systems  
The shift toward solar-driven photocatalysis reflects a broader push for sustainability. Solar reactors, such as flat plate, parabolic trough, and compound parabolic concentrators, are being tested for various water matrices. These systems are particularly attractive for remote or low-resource settings, where centralized water treatment infrastructure is lacking.  
Challenges remain in maintaining consistent performance under fluctuating solar irradiance and weather conditions. Integrating solar energy into photocatalytic systems has garnered significant attention for sustainable water treatment, especially in remote or resource-limited areas. Recent advancements have focused on various reactor configurations to optimize solar energy utilization.  
[PERSON] et al. [143] investigated the performance of a solar-driven slurry photocatalytic membrane reactor (PMR) for the advanced removal of organic pollutants from municipal wastewater. Utilizing iron-doped titania as the photocatalyst and a polysulfone-based membrane for separation, the system achieved chemical oxygen demand (COD) removal efficiencies ranging from 66 to 85% under simulated solar light and from 52 to 81% under natural sunlight over a 7-hour irradiation period. The results demonstrated the enhanced photocatalytic activity afforded by iron doping and established solar-powered PMRs as an effective, low-energy, and environmentally friendly alternative for municipal wastewater treatment. [PERSON] et al. [167] designed and implemented a parabolic trough solar concentrator (PTSC) aimed at enhancing the efficiency of solar distillation processes for water purification. The study addressed critical challenges in PTSCs, including optical efficiency and thermal performance, and proposed solutions to improve their applicability in water treatment. The findings highlighted the potential of PTSCs in harnessing solar energy for efficient and sustainable water purification, particularly in regions with high solar irradiance. [PERSON] et al. [168] developed a numerical study focused on the particle flow and solar radiation transfer in a compound parabolic concentrator (CPC) photocatalytic reactor designed for hydrogen production. The research developed a comprehensive simulation model to analyze the natural convection flow field, temperature distribution, and radiative power absorption within the reactor. The results provided valuable insights into the design and optimization of CPC reactors, emphasizing their effectiveness in utilizing solar energy for photocatalytic applications.  
Figure 13c illustrates the key reactor configurations utilized in solar-driven photocatalytic water treatment, including flat plate, parabolic trough, and compound parabolic concentrators. These systems are designed to harness solar energy efficiently and are especially suited for decentralized applications in remote or resource-limited regions. By integrating sunlight as a renewable energy source, these reactor designs support low-carbon, energy-efficient solutions for addressing water contamination challenges.
## 3 Experimental Results
### Degradation Characteristics of Normalized Dynamic Shear Modulus  
The degradation characteristics of the strain-dependent normalized dynamic shear modulus (\(G/G_{0}\)) are essential for predicting and characterizing the non-linear dynamic response of the soil. Figure 7 exhibits the normalized shear modulus degradation curves of the MICP-treated sands at low strains. As shown in Figure 7a, the degradation of \(G/G_{0}\) with \(\gamma\) in the higher confining stress is much slower for the same treatment time.  
After MICP treatment, the \(G/G_{0}\) of the calcareous sandy sample decreases quickly with increasing strain due to the gradual deterioration of the bonding between sand particles. This indicates that the non-linear dynamic characteristic of MICP-treated calcareous sand is more pronounced, which has also been reported for other types of treated soil [5; 38; 38; 40]. In addition, the strain sensitivity of samples with longer treatment times is more evidence indicating that the brittleness of MICP-treated calcareous sands grows during the treatment process.  
Figure 6: Initial shear modulus versus (**a**) confining stress, (**b**) treatment time, and (**c**) unconfined compressive strength.  
The Hardin-Drnevich model [49] (Equation (8)) has been widely applied to describe the nonlinear relationship between the \(G/G_{0}\) and \(\gamma\). It allows the normalized shear modulus reduction to be expressed by two parameters:  
\[G/G_{0}=1/(1+(\gamma/\gamma_{\rm r})^{\zeta}) \tag{8}\]  
where \(\gamma_{\rm r}\) is the reference shear strain, defined as the shear strain corresponding to \(G/G_{0}=0.5\), and \(\zeta\) is the curvature coefficient.  
All the experiments in this study reveal that the \(G/G_{0}\)-In\(\gamma\) curves of MICP-treated calcareous sands are well-fitted by the Hardin-Drnevich model, where _R-square_ is greater than 0.98 in all cases. Table 4 lists the parameters of the Hardin-Drnevich model. The value of \(\zeta\) ranges from 0.83 to 0.99 and decreases as the confining stress improves. Generally, the reference shear strain is less when the confining stress and treatment duration are reduced.  
Figure 7: Effect of (**a**) confined pressure and (**b**) treatment time on normalized shear modulus.  
Figure 8 depicts a best-fit hyperbolic curve for all the data points (\(G/G_{0}\)-ln\(\gamma/\gamma_{\rm r}\)) when the shear strain \(\gamma\) is divided by the reference shear strain \(\gamma_{\rm r}\). The fitting parameter \(\zeta\) is 0.9. The _R-square_ value of 0.99 indicates that the model can be utilized to describe the behavior of stiffness attenuation in both treated and untreated calcareous sand. Hence, only one parameter, \(\gamma_{\rm r}\), can adequately represent the degradation characteristics of the normalized dynamic shear modulus.