esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
By employing KNN and SVM classifiers, they achieved classification accuracies of 97.5% and 96.24%, respectively. In this context, the ability to classify kiwifruit based on their ripeness stage at harvest and their predicted behavior during storage represents a valuable tool for precision harvesting strategies. The integration of Vis-NIR spectroscopy with advanced classification algorithms enables the development of decision-support systems capable of guiding harvest operations. Such tools can discriminate fruits not only by their current quality attributes but also by their anticipated postharvest evolution, thereby allowing selective harvesting. Most of the literature has focused on long-term storage scenarios, with studies assessing internal quality attributes over extended periods ranging from 120 to 150 days [1; 22], with the aim of supporting storage management decisions and optimizing fruit destination. In contrast, few studies have explored the use of Vis-NIR spectroscopy to monitor quality evolution over shorter periods, up to 8 weeks [27]. This study aims to contribute to the assessment and classification of kiwifruit quality at harvest. Unlike conventional approaches, which mainly focus on data collected at or after harvest, this work also integrates spectral and physicochemical information gathered during the pre-harvest phase, allowing for the continuous monitoring of quality traits up to 60 days after storage. Using portable near-infrared (NIR) spectroscopy (900-1700 nm), the goal is to develop predictive and classification models of kiwifruit ripening based on key commercial parameters: soluble solids content (SSC) and firmness (FF). The ultimate aim is to improve the accuracy of ripening stage prediction and classification, thereby supporting more informed decisions regarding harvest timing and postharvest management, in a short- to medium-term perspective.
## 5 Proposed Model Architecture  
The MLP was set up and trained with seven input layers, four hidden layers, and two output layers. The hidden layers comprised 164, 64, 32, and 16 units, each utilizing the rectified linear unit (ReLU), as depicted in Figure 8. To prevent overfitting, a dropout rate of 0.2 was applied at the end. The input layer incorporates seven units to feed the extracted features to the model. The output layer consists of two units for detecting spoofing and estimating the ToA with ReLU activation. We utilized the mean square error loss function, Adam optimizer, and ran 800 epochs with a batch size of 25 samples.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviation** & **Description** \\ \hline \(S_{AB}\) & Total power received by AP \\ \(SNR_{NAP}\) & Signal-to-noise ratio at AP \\ \(\beta\) & Distance ratio \\ \(T_{PSAP}\) & Propagation time between spoofer and AP \\ \(T_{PNAP}\) & Propagation time between target and AP \\ \(d_{NAP}\) & Distance between target and AP \\ \(d_{SE}\) & Distance between spoofer and edge node \\ \(d_{EAP}\) & Distance from edge to AP \\ \hline \hline \end{tabular}
\end{table}
Table 4: Description of features in the developed dataset.  
Figure 8: MLP structure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mathbf{S_{AB}}\) & \(\mathbf{SNR_{NAP}}\) & \(\boldsymbol{\beta}\) & \(\mathbf{T_{p}(NAP)}\) & \(\mathbf{d_{NAP}}\) & \(\mathbf{d_{SE}}\) & \(\mathbf{d_{EAP}}\) & \(\mathbf{T_{p}(SAP)}\) & **Status** \\ \hline
0.0182 & 0.6685 & 0.0807 & 0.9787 & 0.9787 & 0.4904 & 0.2969 & 0.3194 & 0 \\
0.0181 & 0.6670 & 0.0813 & 0.9789 & 0.9789 & 0.6168 & 0.2952 & 0.3178 & 0 \\
0.0188 & 0.6665 & 0.0815 & 0.9780 & 0.9780 & 0.3642 & 0.2945 & 0.3170 & 0 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
0.0185 & 0.6646 & 0.0823 & 0.9784 & 0.9784 & 0.5627 & 0.2923 & 0.3150 & 1 \\
0.0177 & 0.6635 & 0.0827 & 0.9793 & 0.9793 & 0.9064 & 0.2911 & 0.3138 & 1 \\
0.0192 & 0.6622 & 0.0833 & 0.9775 & 0.9775 & 0.4269 & 0.2894 & 0.3121 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample of scaled data.  
On the other hand, the LSTM consists of three layers for sequence learning in addition to a fully connected layer and a regression layer as the output. The initial layer is composed of 128 hidden units, while the second and third layers consist of 64 and 32 units, respectively. Training was conducted with a mini-batch size of 25 over 800 epochs, as depicted in Figure 9. Throughout each phase, 80% of the data were allocated for training, 10% for testing, and an additional 10% for evaluation purposes. The LSTM employs the same optimizer and loss function as the MLP model. The model structure is designed to detect, estimate, and localize spoofing attacks, as depicted in Figure 10.  
Figure 10: Proposed architecture.  
Figure 9: LSTM structure.
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
## 5 Verification Experiment
### Thermal Error Test Piece Machining  
A novel test piece was designed to evaluate the effect of the proposed machine tool positioning the thermal error compensation method. As shown in Figure 14, the proposed test piece was designed to avoid the influence of tracking errors on its machining accuracy. The machining error is determined by the positioning error of the X-/Y-/Z-axis feed system and the geometric error of the machine tool. The geometric error of the machine tool is regarded as a constant, and the spindle of the machine tool was warmed up fully before machining to avoid the influence of the spindle's thermal error. Thus, any variation in the test piece's machining error with varying feed load is completely determined by the positioning thermal error of the feed system.  
The process of machining and measuring the test piece is shown in Figure 15. The machining accuracy of the test piece was measured using a high-precision three-coordinate machine ([PERSON] & WENZEL), and the measurement results are shown in Figure 16.  
After compensation, the maximum thermal error was reduced by 88.7%, 87.1%, and 56.7% in the X-, Y-, and Z-directions, respectively. Compensation was least effective in the Z-direction, mainly due to the Z-direction machining accuracy being greatly affected by the thermal error of the spindle and the thermal deformation of the column structure. This is also one of the problems to be studied and solved in the future.  
Figure 14: Custom test piece for evaluation of positioning thermal error.  
Figure 15: Machining and measurement of the test piece. (**a**) Clamping. (**b**) Machining. (**c**) Measurement.
# Reverse Circulation Drilling Method Based on a Supersonic Nozzle for Dust Control
## 5 Results and Discussion
### Observation of Shock Wave Structure  
Figure 12 show the experimental Schlieren pictures of the flows after the nozzle exit. With a constant value of \(\theta\) (32\({}^{\circ}\)), a clear under-expanded flow with shock wave can be observed in Figure 12a, and then the shock wave gradually blurred and the shock train length decreased as the increase of \(L_{\mathrm{s}}\). The phenomenon was basically consistent with the CFD result. Although higher pressure at the nozzle outlet is not beneficial to suction performance, and the formation of strong shock waves may result in irreversible energy losses and a nonuniform field [31], the higher flow speed at the nozzle exit presented stronger shear and entrainment processes, which achieved a better performance, finally. It seems that the effect of flow shear and entrainment is more important than that of pressure for the multiple-inclined supersonic nozzle structure.  
Figure 11: Comparison of predicted results and experimental data for \(\omega\) with different \(D_{\mathrm{f}}\).  
Clear images of the flow from nozzles with different \(\theta\) give a comparative picture from which qualitative inferences on the possible mechanisms can be inferred. As can be seen in Figure 12d-f, the length of the shock wave increased with the value of \(\theta\) and the shock train direction deviated from the nozzle axial. The flow in the unequal-length diffuser zone tended to be close to the wall of the longer side and made a deviation at the nozzle exit due to Coanda effect. When the deflection between the direction of supersonic nozzle exit and center passage wall was in a proper curvature range, the jet stream was easier to flow along the surface resulting in a shorter length of shock wave. It can also be inferred that the very long shock waves from multiple nozzles were likely to collide in the center passage, which led to a lower entrainment ratio. Nevertheless, too much deflection decreased the entrainment and mixture zone in center passage so that the entrainment ratio decreased as well.  
In general, the shock train length did not present a clear relationship with the entrainment ratio. The possible reason is that the design of multiple-inclined supersonic nozzles need a synthetic consideration of the flow shear and mixture, flow collision, and pressure.
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 1 Introduction
The polymer matrix in the NEG provides good mechanical support for the piezoelectric nanostructure. In addition, the NEG offers higher output potential compared with VINGs and LINGs. To improve the efficiency of PENGs, researchers have explored novel materials, such as sodium bismuth titanates (BNTs) and graphitic carbon nitride (g-C\({}_{3}\)N\({}_{4}\)), owing to their superior piezoelectric characteristics [16; 17]. The piezoelectric response depends strongly on the morphology of the piezoelectric material; for example, ZnO nanosheets and nanodiscs demonstrate superior piezoelectric performance compared with ZnO nanorods [18; 19]. To further improve performance, researchers have been developing new approaches, such as interfacial modifications and 3D printing techniques [20; 21]. Recently, the concept of a hybrid structure has gained the attention of researchers as it can harvest energy, such as mechanical stress and heat simultaneously from multiple sources, thus drastically enhancing the device's performance [22]. However, these approaches have not yet been thoroughly discussed. Therefore, the recent developments in the field of PENGs must be summarized. This review presents an in-depth summary of recent developments in PENGs. It compiles research on PENGs, including recent reports and different strategies, such as electrode modification, selective laser sintering (SLS), and direct ink writing (DIW), which are discussed in detail. Additionally, emerging hybrid structures and their evolution over the last few years are included. This article begins with different materials used in the fabrication process. More emphasis is placed on the different strategies that researchers have followed to improve output performance. Additionally, the potential applications of PENGs in biomedical fields are presented. Finally, we discuss the future outlook based on the knowledge acquired.
## 6 Numerical Model Calibration
### Calibration Based on HRR  
The accuracy of the numerical model calculated using MRE was performed for all 8 simulations. Among these simulations, only 6 produced results comparable to the experimental data or were completed within a reasonable processing time. Of the tested variants, the simulation labeled \"Sim 6\" (marked in green in Figure 16), which employs a cell size of 5 cm, a cumulative fire source area of 40.16 m\({}^{2}\), and an individualized domain subdivision as shown in Figure 9, offered the best compromise between result accuracy and computation time. As illustrated in Figure 16, the curve corresponding to Sim 6 closely follows the entire experimental HRR evolution, with no significant differences observed throughout the analyzed interval.  
\begin{table}
\begin{tabular}{l l} \hline
**Input Data** & **Output Data** \\ \hline Domain meshing type & Result Precision \\ \hline Cell size & Calculation Processing Time \\ \hline Surface area of the cribs & \\ \hline \end{tabular}
\end{table}
Table 1: Objectives of the domain refinement analysis.  
The accuracy of the model calculated using MRE over the entire 1280 s interval was 10.12%, which is considered within the acceptable error range. However, over the useful interval of 100 to 1234 s, representing approximately 92% of the simulation duration, the accuracy calculated with MRE was only 5.04%. The active phase is reproduced with very high accuracy (error 2.9%), while in the generalized phase, deviations increase slightly (6.5%), mainly due to cumulative uncertainties in fuel surface regression and ventilation dynamics. This suggests that significant discrepancies occur in the initial moments of the simulation, attributed to the slightly inaccurate prediction of the dead time.  
The discrepancies in HRR between the numerical model and the experiment are primarily attributed to several factors, with cell size being a significant contributor. Additionally, the use of a standard calorific power value of 17.5 MJ/kg for wood, based on  
Figure 16: Calibration based on HRR.  
Figure 15: RMSE matrix for various simulation configurations.  
the current regulations [11], introduced variability, as it was not feasible to measure this value for each wooden bar individually. Differences in wood properties, likely due to varying tree sources, further contributed to inconsistencies. These factors were recognized and incorporated within the acceptable margin of error to ensure the reliability of the HRR calibration.
# An Automatic 3D Point Cloud Registration Method Based on Biological Vision
## 4 Research Methodology
### Automatic Registration  
As shown in Figure 6, the reference and test point clouds consisted of five cones. There exists a rigid transformation between the reference and test point clouds, as expressed in Equation (21). The ideal rotation matrix and translation vector are notated as \(\widehat{R}\) and \(\widehat{T}\), respectively. The calculated rotation matrix and translation vector are notated as \(\widehat{R}\) and \(\widehat{T}\), respectively. Rotation and translation errors under Gaussian noise with different intensities were used to evaluate the algorithm's robustness and accuracy. The rotation error \(\varepsilon_{r}\) is defined as follows:  
\[\varepsilon_{r}=\arccos\left(\frac{trace\left(\widehat{R}\widehat{R}^{\text{ T}}\right)-1}{2}\right)\frac{180}{\pi}. \tag{33}\]  
The translation error \(\varepsilon_{T}\) is defined as follows:  
\[\varepsilon_{T}=\frac{\|\widetilde{T}-\widehat{T}\|}{d_{res}}, \tag{34}\]  
where \(d_{res}\) is equal to the average mesh resolution (\(d_{res}\) = 1 mr). To test the influence of Gaussian noise with different intensities on registration performance, Gaussian noise was independently added to the X-, Y-, and Z-axes of each scene point. The standard deviation and step size were the same as in Section 4.1. In the experiment, the corresponding Euler angle of \(\hat{R}\) was [40\({}^{\circ}\), 40\({}^{\circ}\), 40\({}^{\circ}\)], and the rotation sequence was \"XYZ\". \(\widehat{T}\) was [237 mr, 166 mr, \(-\)144 mr].
# Influence of Surface State in Micro-Welding of Copper by Nd:YAG Laser
###### Abstract  
Laser welding of copper is characterized by low and unstable light absorption around 1000 nm wavelength. Combination of high thermal conductivity and low melting point makes it difficult to obtain good welding quality and leads to low energy utilization. To improve efficiency and welding quality, a technique to enhance process stability using 1064 nm wavelength Nd:YAG laser has been proposed, and absorption rate and molten volume in laser micro-welding were discussed. Since the surface state of specimen affects absorption phenomena, effects of surface shape and surface roughness were investigated. Absorption rate and molten volume were increased by creating appropriate concave holes and by controlled surface roughness. Stable micro-welding process with deep penetration and good surface quality was achieved for transitional processing condition between heat conduction and keyhole welding, by enhanced absorption rate.  
absorption rate; copper; heat conduction; keyhole; laser welding; Nd:YAG laser
## 2 Materials and Methods
### DSPE Protocol for Phenolic Acids  
For the extraction of phenolic acids from aqueous samples, a four-step DSPE protocol was established including sorbent conditioning, sample loading, washing and analyte elution. In the first step, 10.0 \(\pm\) 0.2 mg of solid sorbent was weighed into a 2 mL centrifuge tube. Subsequently, 0.5 mL of conditioning solution was added, and the mixture shaken on a thermomixer (5 min, 1200 rpm, 25 \({}^{\circ}\)C). After centrifugation (5 min, 14,000 rpm, 23 \({}^{\circ}\)C) the conditioning solution was discarded, and the polymer washed with 1 mL of 5 vol.% MeOH in a water solution by applying the same shaking and centrifugation settings as before. An amount of 1 mL of the sample was added, and the mixture shaken (15 min, 1200 rpm, 25 \({}^{\circ}\)C) and centrifuged (5 min, 14,000 rpm, 23 \({}^{\circ}\)C) with the supernatant analyzed by HPLC-DAD. The residual sorbent was washed with 1 mL of 5 vol.% MeOH in a water solution. The analyte elution was obtained by adding 1 mL of eluting solution and shaking (15 min, 1200 rpm, 25 \({}^{\circ}\)C). After centrifugation (5 min, 14,000 rpm, 23 \({}^{\circ}\)C) the supernatant was analyzed.  
To maximize the phenolic acid recoveries, the conditioning and eluting solution were optimized. Furthermore, the effect of the extraction time on the adsorption equilibrium was investigated to minimize the duration of the extraction process. These DSPE experiments were performed with the poly(n-VIM/C\({}_{6}\)-bis-VIM) sorbent with a monomer to crosslinker ratio of 11.50 to 1.
## 3 Structure of SA-UBA
* **User behavior audit:** As shown by the yellow line in Figure 1: (1) Firstly, the user operation images and operation-related metadata are passed to the User Behavior Audit subsystem; these data are generated in the Secure Access Proxy and collected by the User Behavior Collection module. (2) The Text Information Extraction module extracts the text information in the user operation images and outputs the audit data to the Audit Database. The deep-learning-based scene text-reading model is used in this step to obtain more accurate audit data. (3) The User Behavior Analysis module identifies the users' operations by analyzing the audit data. This module divides user behavior into four levels to accurately determine privileged operations and respond in time. (4) The result of the user behavior analysis is sent to the Secure Access Proxy. When the user is found to perform an illegal operation, the proxy will block the operation in time.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 4 Discussion
### Advantages of AGLNM  
By comparing the performance of the AGLNM with other models, it can be seen from Figure 3 that the experimental performance of the adaptive graph learning network model proposed in this paper is obviously better than other models, especially in the Bohai sea data set with a stronger spatial correlation, regardless of the strength of the spatial correlation in the data set. This shows that the AGLNM proposed in this paper can not only adapt to different data sets, but can also mine well and utilize the spatial dependence of SST.
## 4 Modeling Method with Examples of Integration Flows  
The _Integrated services_ view shows both static and dynamic aspects of communication. The structural aspect is modeled using a UML component diagram and encompasses the components of cooperating IT systems and the message broker or service bus component. Services are modeled using interfaces realized by components. The most important are the interfaces provided by the components representing IT systems. In these components, the functions necessary to perform the integration flow are implemented. The message queue or service bus component performs flow actions but forwards calls for execution to the system component. That component realizes interfaces, which means that it provides the implementation for interface functions. Therefore, the system component realizes calls for services invoked on the interface. All components are crucial for modeling the dynamic aspect of integration. In integration flow diagrams, components are represented as partitions and services are used as endpoints.  
In presented examples, integration flows show the context of a single message transfer from the source application to the destination application, service, or device. The first example shows a message flow of sending orders from the Brokerage House (BH) application to the Stock Exchange (SE) system. Figure 6 presents an integration flow diagram (the specialized version of a UML activity diagram) for the _Send order to the Stock Exchange_ flow. This example uses both icons and stereotype names for messaging patterns.  
Figure 6: The _Send order to the Stock Exchange_ integration flow.  
Figure 7 presents the UML component diagram in the context of the _Create order in the SE_ use case.  
In the UML component diagram, two additional stereotypes from the Service-Oriented Architecture Modeling Language have been used: \(<<\)Consumer\(>>\) and \(<<\)Provider\(>>\). They can be used to clearly denote the difference between the component that realizes the interface (\(<<\)Provider\(>>\)) and the component that uses the function from the interface (\(<<\)Consumer\(>>\)). Both components cooperate using a message queue. The SE system enables stock exchange trading. A purchase and sale transaction in the SE system is concluded when two opposite buy and sell orders for the same company appear on the trading market. In addition, in both orders, the buyer agrees to pay the price proposed by the seller.  
The second example concerns sending the request for energy produced to IoT devices. Renewable energy prosumer communities exchange energy to optimize usage and cost of energy. Those communities are managed using blockchain networks. The flow illustrates the use of various IoT message protocols.  
Figure 8 presents the integration flow diagram for the _Send Request for Energy Produced_ integration flow. The second example uses only icons for messaging patterns.  
Figure 8: The _Send Request for Energy Produced_ integration flow.  
Figure 7: UML Component diagram for BH and SE systems cooperation.
## 3 A Prototype System for Pedestrian Positioning  
A block diagram of the proposed system is presented in Figure 1. From the hardware point of view, the system is made up of three parts: a PC platform, stereo camera connected through FireWire interface and an electronic module housing a GPS receiver and 6 DOF sensor. The data was acquired by walking through the University Campus with the stereovision camera attached to the chest of an experimenter. The laptop stored data from the electronic module and stereo camera. Then the data was processed off-line on a PC.