esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This research has proposed a new and analytical method to investigate the cutter edge temperature in trochoidal milling. This work has presented an approach to calculating the heat flux into the cutter edge. It has proposed a model of the cutter edge temperature using the Green function, and this approach is verified by comparing its result with that of a recognized test. Based on the engagement analysis in trochoidal machining, the cutter edge temperature history is calculated for the first time as far as we know.  
The proposed approach is effective in predicting the cutter edge temperature histories in both side and trochoidal milling process. During trochoidal machining, the maximum temperature is tightly related to the immersion angle, so is the minimum temperature. If the immersion angle increases, the cutter edge temperature will rise, and vice versa. Moreover, the temperature variation patterns in trochoidal machining become stable after a trochoidal cutting cycle. The cutting temperature in corresponding side milling also becomes stable after a trochoidal cutting period. These results will facilitate the choice of cutting parameters to control the cutting temperature in both side milling and trochoidal machining.  
Two reasons have been found to decipher the cutter life extended phenomena in trochoidal machining. The first reason is that the temperature in trochoidal machining is lower than the temperature of corresponding side milling. The second reason is that the thermal shock in trochoidal machining is much smaller than the thermal shock in corresponding side milling. These two factors together make the trochoidal machining excel the traditional side milling in cutting difficult-to-cut materials, in which cutting temperature is always high, and the cutter life is generally short.  
This research is original in terms of predicting cutter edge temperature in milling process. It can be used in machining parameter optimization and cutter design optimization.  
Q.D. methodology, validation, result analysis, and original draft preparation; [PERSON] supervision and project administration; [PERSON] came up with ideas and edited the content; [PERSON] reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundations of China, grant number 51775445; the Aeronautical Science Foundation of China, grant number 2017 ZE53053; and the major R&D project of Shaanxi Province, grant number 2019 GY-064 and 2016 KTZDGY4-02.  
The authors declare no conflict of interest.
## 3 Non-Thermal Technologies in Winemaking Process
### Other Non-Thermal Technologies in Winemaking Process
#### 3.4.2 Cold Plasma (CP) Effects on Wine Phenolics and Color Characteristics
Bruxellensis_ yeast after applying air and nitrogen APCP treatments, while a lower inactivation effect was achieved for bacteria _P. Pentosaceus_ and _A. Pasteurianu_. Also, there was no change in the morphology of the wood surface after APCP treatments. More recently, [PERSON] et al. [69] carried out research on the effect of CP treatments (2, 5, and 10 min, He/O\({}_{2}\) and He/N\({}_{2}\)) using a dielectric barrier discharge (DBD) jet system on red wine phenols and biogenic amines. The same authors also compared the impact of CP with the wine preservation effects using potassium metabosulfite (30 and 100 mg/L) and a combined method (CP with 30 mg/L potassium metabosulfite). The results showed that CP-treated wines (5 min, He/N\({}_{2}\)) had a 3.1% higher concentration of phenols than that achieved by potassium metabosulfite addition (100 mg/L). Additionally, the application of CP reduced the content of biogenic amines, with a combined CP treatment (10 min, He/O\({}_{2}\), 30 mg/L potassium metabosulfite) causing the highest degree of reduction. Another study by [PERSON] et al. [70] with the same methodology for CP treatment was carried out to establish the impact of CP on red wine physicochemical and biological characteristics in contrast to the conventional preservation and combined method effects. The long-term effect of the proposed methods was also considered. The results showed a decrease in phenolic compounds and antioxidant activity after storage, where the mildest changes were observed for methods with potassium metabosulfite and those with a He/N\({}_{2}\) gas mixture. Furthermore, CP treatments did not cause major alterations in wine color, whereas the highest microorganism reduction rate of 4.21 log number was achieved for 10 min by a combined method using He/O\({}_{2}\). In contrast to other non-thermal technologies, CP is still in the early stages of development and application in winemaking. Despite the scarcity of the literature in the field of oncology, the existing reports provide very promising results on the use of CP in terms of increasing the stability and quality of wine, reducing the amount of SO\({}_{2}\) and sanitizing wine barrels. However, as this technology is product specific and leads to a number of very complex chemical interactions [90], further experiments are needed to explore the chemistry between the generated plasma and the wine components, and to optimize the CP process conditions in order to minimize possible negative effects on the finished wine, and thus allow for the transfer of this technology from the laboratory to the wine industry.
## 4 Experiment Results and Analysis
### Generalization Experiments  
To assess the performance and robustness of MDD-YOLOv8, we conducted generalization experiments using four additional SAR image datasets. These datasets were constructed using satellite remote sensing SAR images and contain a single category: ships. For comparison, we selected models from the previous experiment, including YOLOv5s, YOLOv8n, YOLOv9t, and RT-DETR-l. These models were chosen because their parameter sizes range from lightweight to heavy, making them representative of the object detection models currently in use. The results are visualized using the original images provided in the datasets.  
From Table 9, the first row lists the names of the datasets, while the second row displays the original images with red markers indicating the target locations. The remaining rows show the experimental results for each model. In the first line, for an image containing three ships, only RT-DETR-l and our MDD-YOLOv8 successfully detect all targets in the SAR Ship dataset, while the other three models miss some. The second line presents an image with a laid-up ship, which is challenging to distinguish. In this case, only MDD-YOLOv8 produces the correct result, though the marker frames the target incompletely. The third line is similar to the first, with no further details necessary. In the final line, the image contains an incomplete target that may mislead the algorithms, causing YOLOv5s and YOLOv9t to miss the detection. In summary, the results from the generalization experiments demonstrate that MDD-YOLOv8 outperforms other models, particularly in its adaptability to the unique features of SAR images.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **P (\%)** & **R (\%)** & **mAP@50 (\%)** & **mAP@50:95 (\%)** & **Parameters (M)** & **FLOPs \textasciitif** & **F1** \\ \hline YOLOv5n & 79.6 & 69.4 & 72.2 & 42.2 & 2.18 & 5.8 & 0.73 \\ YOLOv5s & 81.3 & 73.5 & 76.6 & 47.0 & 7.81 & 18.7 & 0.77 \\ YOLOv6n & 79.1 & 65.7 & 69.1 & 42.3 & 4.16 & 11.5 & 0.71 \\ YOLOv8n & 79.6 & 70.1 & 72.0 & 44.6 & 3.01 & 8.1 & 0.74 \\ YOLOv9t & 74.6 & 67.6 & 67.4 & 41.4 & 1.73 & 6.4 & 0.70 \\ RT-DETR-l & 82.1 & 62.3 & 67.2 & 38.9 & 31.99 & 103.4 & 0.69 \\ YOLOv10n & 77.5 & 66.6 & 67.2 & 39.8 & 2.70 & 8.2 & 0.71 \\ YOLO11n & 78.2 & 66.4 & 68.9 & 40.6 & 2.58 & 6.3 & 0.71 \\ GS-YOLO [35] & 79.1 & 73.0 & 75.2 & 44.8 & 6.21 & 14.2 & 0.75 \\ DGSP-YOLO [36] & 80.9 & 67.5 & 72.7 & 45.5 & 3.77 & 9.5 & 0.73 \\ SHIP-YOLO [37] & 86.1 & 67.5 & 73.9 & 44.2 & 2.81 & 7.4 & 0.75 \\ ODConv2-YOLO-SAHI [38] & 78.8 & 68.8 & 69.8 & 40.9 & 3.31 & 8.7 & 0.73 \\ Ours & 87.7 & 76.1 & 78.9 & 47.0 & 3.64 & 12.4 & 0.81 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The results of comparison experiments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Origin** & **Yolov5s** & **Yolov8n** & **Yolov9t** & **R-detr-l** & **Ours** \\ \hline SAR Ship & & & & & \\
[39] & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 9: The results of generalization experiments.
## 3 UAV Specification and Wind Farm Specification
### UAV-Based Inspection System, Testbed, and Evaluation Domain Environment  
Specialized Applications for Remotely Piloted Aircraft Systems (RPAS)--the following items were used during the drone flight across the wind farm and were manufactured by SZ DJI Technology Co., Ltd. headquartered in Shenzhen, China:  
* DJI Matrice 200V2 RPAS Drone (Primary of RGB);
* Sufficient TB55 Intelligent Batteries;
* Additional WB37 Controller Intelligent Batteries;
* High-Speed SD Cards DJI;
* Matrice 210V2 RPAS Drone (Secondary for Thermal);
* Matrice 210V2 RPAS Drone (Secondary for LiDAR);
* DJI ZenMuse L1 LiDAR sensor;
* DJI ZenMuse X5S (FC6520) Imaging Sensor;
* F-stop: f/5.6;
* Exposure time: 1/320 s;
* ISO speed: ISO-100;
* Exposure Bias: +0.7 step. As part of the inspection system, the pilot positions the DJI Matrice 200V2/210V2 RPAS drone at the base of the turbine and sends it up to inspect the turbine autonomously. The drone takes off, and while in flight, it creates a three-dimensional model of the turbine in real-time using cameras and sensors mounted on the drone. The GPS-provided corresponding coordinates of the UAV are used to precisely geo-locate the wind turbine
generator, which can be obtained from the Zenmuse L1 LiDAR sensor and the DJI ZenMuse XS5 (FC6520) Imaging sensor mounted on the respective drone platforms.  
The drone collects laser and RGB flight data on the turbine to aid in the creation of a more accurate model of the wind turbine. The data allows for the precise location and size of the damage to be determined.  
Based on the WTG component and material identifier, the inspection results can categorize the damage. For the two-megawatt wind turbine, the process takes about 15 min. When the drone lands, the pilot picks it up, loads it into the back of the pickup truck, and drives to the next turbine to repeat the process. The pilot connects the drone and tablet to the internet at the end of the day, and all data is spontaneously transferred to the analytics stage, where the client or owner of the wind farm can access and interact with their data. Figure 1 depicts the entire scheme developed for UAV-based inspection of a utility-scale wind farm.  
The wind farm owner analyzes and processes the laser and RGB image data collected by drone inspections. To analyze and pinpoint defects on wind turbines, image analyses are performed using computer analytics and object detection technology. The computer vision platform runs a sequence of high-resolution RGB images of the Wind Turbine Generators, and it also generates a report on wind turbine blade anomaly detections using the software application platform.  
Both laser and RGB images are processed in this article. The correlative image processing and analytics functions use these raw images to distinguish the features of wind turbine blade damage. Since there are limited, publicized accessible trained techniques for this type of experiment, the images are manually processed by labeling each image that contains wind turbine blade damage in order to prepare it for image training.
## 2 Data Acquisition  
To conduct the experiment, AE signals of different faults are collected from the collaborative experimental setup at Intelligence Dynamic Lab (Gyeonsang National University, Korea). Figure 1 depicts the block diagram of the experimental setup for collecting acoustic emission (AE) signals from bearings. In this experimental setup, an induction motor is connected to a drive-end shaft, and a non-drive-end shaft is attached to the drive-end shaft through a gearbox with a 1.52:1 gear reduction ratio. Both the drive-end and non-drive-end shafts use cylindrical rolling element bearings (model FAG NJ206-E-TVP2), where the number of rolling elements is 13, the contact angle (\(\alpha_{\text{cangle}}\)) is 0\({}^{\circ}\), and the roller diameter (\(B_{d}\)) and pitch diameters (\(P_{d}\)) is 9 mm and 46.5 mm, respectively [27]. Figure 2 presents the parts and measurements of cylindrical rolling element bearing. In this setup, the general-purpose wide-band frequency AE sensor (WS\(\alpha\)) from Physical Acoustics Corporation (PAC) is used [28]. The operating frequency is 100-1000 kHz, peak sensitivity is 55 dB, and resonant frequency 125 kHz of the AE sensor. The AE sensor is attached at the top of the non-drive-end bearing house and the collected signals are sampled at 250 kHz using a PCI-2 based system [28]. Figure 3 presents the picture of the experimental setup and data acquisition system. Bearing faults are seeded defects or cracks of different sizes (small size crack: 3 mm and big size crack: 12 mm) as shown in Figure 4, where fault names are Bearing Crack Outer (BCO), Bearing Crack Inner (BCI), and Bearing Crack Roller (BCR).  
Figure 4: Faulty bearing parts with different crack severities. (**a**) BCO (bearing crack on the outer surface) 3 mm, (**b**) BCI (bearing crack on the inner surface) 3 mm, (**c**) BCR (bearing crack on the roller surface) 3 mm, (**d**) BCO 12 mm, (**e**) BCI 12 mm, (**f**) BCR 10 mm.  
Figure 1: Block diagram of the experimental setup.  
Figure 3: (**a**) Bearing fault signal collection test rig and (**b**) data acquisition system.  
Figure 2: Cylindrical rolling element bearings, (**a**) parts of the bearing, (**b**) measurement of bearing.
# SQRT: A Secure Querying Scheme of Routing Table Based on Oblivious Transfer
## 1 Introduction
A multi-party secret sharing scheme, TRIDENT is designed in [11], where the share of the product and its hash value is generated among multiple participants in the online stage and sent to other parties to check the consistency, which leads to a linear relationship between the storage overhead and the number of participants. In [12], a route selection scheme based on connectivity, delay, and trust (CDT) is proposed to help user equipment obtain good connectivity-delay-trust performance and prevent potential attacks from malicious routing nodes. However, if the user equipment is a malicious attacker trying to detect the network topology, the above scheme cannot guarantee the anonymity of the routing nodes, and the malicious attacker can sniff the identity and location information of all the routing nodes. In [13], a secure route optimization scheme based on decentralized identifiers (DIDs) is provided to defense against denial-of-service attacks at the routing layer. There is a defect in the secure routing scheme where the directory server masters each routing node through which the user equipment information transmission path passes. Therefore, it is obviously not applicable to the honest but curious model of the directory server. Based on the analysis of the schemes proposed in latest literature, the authors provide Table 1, which demonstrates the technical advantages and disadvantages of some current routing querying protocol schemes. A qualitative analysis shows the innovation and contribution of the SQRT scheme in the paper. The remainder of this paper is organized as follows: Section 2 describes the system model of secure query of the routing table in anonymous networks; Section 3 presents the \(N_{k}\)-out-of-\(N_{k}+\Delta\) oblivious transfer protocol and its application in the secure query of the routing table; Section 4 analyzes the security of SQRT scheme; Section 5 provides the simulation environment, the numerical results, and some discussions, and Section 6 concludes this paper.
## 2 Materials and Methods
### Image Denoising Based on Group Sparse Representation  
#### 2.3.1 WNNM Model  
The traditional sparse representation model requires learning in a dictionary. It is much more complex and it ignores the relations of sparse coding patches. As a result, ref. [31] made use of the robust principal component analysis (RPCA) model based on group sparsity representation. The nuclear norm is used in the RPCA model. It is displayed by  
\[\widetilde{X}=argmin||Y-D*X||_{F}^{2}+\gamma||X||_{*} \tag{7}\]  
Ref. [24] has expressed that the nuclear norm is the matrix rank. The larger eigenvalue stands for detailed information about images. In the NNM model, all eigenvalues are processed with a soft threshold shrinkage operator. It leads to over-smoothness in the restored image. So, the article presents the weighted nuclear norm model (WNNM) that could improve the denoising result for the uneven shrinkage. The original problem could be transferred to expressions such as  
\[\widetilde{X}=argmin||Y-D*X||_{F}^{2}+\gamma||X||_{w,*} \tag{8}\]  
The primary sparse representation based on the WNNM model considers the local sparsity and it did not construct the relationship on the whole image. The computation is complex. Thus, the group sparse representation (GSR) is applied to the image's sparse representation. Through the fusion of the local sparsity and similarity of these patches, ref. [32] forms the learning dictionary and improves the performance. In the GSR model, many overlapping image patches could be attained from single frame \(Y\) according to some searching steps. The patches are described by \(Y_{i},i=1,2,\ldots,n\). The size of each patch is \(\sqrt{m}\times\sqrt{m}\). They form many kinds of vectors and constitute the new group \(S_{i}\). Then, the image matrix \(Y_{i}(Y_{i}\in R^{m*K})\) is displayed in  
\[Y_{i}=\{y_{i,1},y_{i,2},\ldots y_{i,K}\} \tag{9}\]  
\(Y_{i}\) includes all similar image patches. In single frame, all similar group matrices could be defined by  
\[Y=\{Y_{1},Y_{1},Y_{1},\ldots Y_{N}\} \tag{10}\]  
The original function could be transformed by  
\[\widetilde{X_{i}}=argmin\sum_{i=1}^{n}(\frac{||Y_{i}-D_{i}X_{i}||_{F}^{2}}{2} +||X_{i}||_{*}). \tag{11}\]  
The \(X_{i}\) stands for the coefficient matrix of each group \(Y_{i}\)[33]. \(||.||_{F}^{2}\) means the Frobenius norm. \(||.||_{*}\) is the nuclear norm.  
Based on the above GSR model theory, we consider putting it into the IR image denoising. The IR image with additive noise can be expressed by  
\[Y=X+N \tag{12}\]  
\(X\) is the original image and \(N\) is the added noise. The problem is thought to be the restoration of the original image without noise. The GSR model in image denoising is used to complete the optimization of all similar patches. We can use the WNNM based on GSR to solve the problem which is described in  
\[\widetilde{X}_{i}=argmin\sum_{i=1}^{n}(\frac{||Y_{i}-X_{i}||_{F}^{2}}{2}+||X_{i} ||_{w_{i},*}). \tag{13}\]
## 3 Results and Discussion  
### Microparticle Characterization  
The morphology of MPs was evaluated by scanning electron microscopy (SEM) (Figure 1). In all the produced formulations, spherical particles were obtained. Interestingly, the presence of POLOX in the organic phase of the emulsion, even in a low amount (0.50:5.9 F68:F127:PLGA weight ratio), had a fundamental impact on the resulting particle morphology. Indeed, the surface of POLOX-containing MP formulations was smooth and non-porous (Figure 1A,B,D,E), while evident surface pores were highlighted in the case of MPs consisting of PLGA only (Figure 1C,F). This is consistent with the results obtained in a previous publication [14], in which we showed that HA spontaneously arranged toward the W\({}_{2}\) phase, due to the amphiphilic nature of POLOX, which enhances the affinity between W\({}_{2}\) and the O phase.  
The mean diameter, BSA entrapment efficiency and yield of the obtained formulations are reported in the following Table 2.  
Importantly, POLOX presence in the MP bulk had a major influence also on the particle sizes (Figure 1, Table 1). In actual fact, the diameter of MPs decreased in the presence of the poloxamers: the average diameter of the MPs composed of PLGA alone was around 30 \(\upmu\)m, while in the presence of the poloxamers, MPs of 14-15 \(\upmu\)m were obtained. Protein encapsulation efficiency (EE) was between 43 and 81% in all cases, as shown in Table 2. In particular, EE was found to be significantly higher when the overall polymer concentration in the organic phase was 24% \(w/v\). This result can be reasonably explained considering that the higher polymer concentration leads to an increase in the viscosity ofthe organic phase of the emulsion and therefore reduced shear stress during the emulsion. This can be reasonably associated with the slower diffusion of BSA towards the external aqueous phase [24] and, overall, the increased EE of the protein.
# Rapid Measurement and Identification Method for the Geometric Errors of CNC Machine Tools
## 4 Stepwise Identification of Geometric Errors  
As discussed above, by scanning an arm of the L-shape standard piece, 4 degrees-of-freedom information of the motion axis, including the two-dimensional angle and two-dimensional displacement, can be obtained by the proposed system. In addition, the L-shape layout of the composite modules provides a reference for the measurement of the squareness error. Under the guidance of the integrated error model, the measurement path can be planned reasonably, and then the identification steps for geometric errors are established. For three-axis MTs, by placing the L-shape standard piece in the XOY, XOZ and YOZ planes, the measurement and identification of all 21 geometric errors can be achieved.  
The relative positions and postures of each module are calibrated before measurement, taking the standard piece in the XOY plane as an example to illustrate the stepwise identification method of geometric errors.