esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion  
### Effect of Various Heat Treatment Methods on the Arrona Profile of Basil Infusions  
Basil does not have one \"right\" aroma, and its aroma profile is heterogeneous [17]. Fresh basil aroma is a complex mixture of many volatile organic compounds (VOc) that are present in various proportions and have various odor activity values. Aroma quality depends on essential oil content and composition (mainly terpenoids and phenylpropanoids). The most abundant compounds in the aromatic profile of basil differed between authors: (Z)-3-hexenal, eucalypt, linalool, and eugenol were indicated by [PERSON] [29]; linalool, eucalypt, and trans-bergamotene were pointed out by [PERSON] et al. [12]; eucalypt, linalool, and eugenol were reported by [PERSON] et al. [11]; while linalool, eugenol, and cis-alpha-bergamotene, by [PERSON] and [PERSON] [30]. In this study, the similar VOC of fresh basil (eucalyptpol, linalool trans-ocime, \(\beta\)-linalool, and \(\beta\)-myrrence) were dominant. These discrepancies in the most plentiful volatiles are normal, as four major essential oil chemotypes in _O. basilicum_ were identified: estraged-rich; linalool-rich; methyl eugenol-rich; and methyl cinnamate rich [31]. The results of [PERSON] et al. [4] suggest that the most aroma-active compounds in Iranian and Turkish basil were linalool and estragole, which were present in significant quantities in our study. The compounds present in smaller amounts also results in a decrease in similarity to fresh basil. After heat treatment and independently of processes conditions, the volatile compounds profile was changed. In our study, the relative percentage content of eucalypt in the sous-vide samples was higher than in both the fresh samples and the conventionally infused. The dynamics of the changes are entirely different than in drying, where high temperature caused evaporation of eucalypt and many other VOc [32]. Unlike in this study, other authors [33] reported that the relative percentages of linalool and eucalypt increased with increasing heating time and temperature, but they used oils with dried basil. The most abundant compound in this study was eucalypt, of which the highest share was noted in SV\({}_{65}\) and the lowest in traditional infusions. In the study by [PERSON] et al. [7] on aromatic compounds of lemon verbena, concentrations of eucalypt and limonene decreased with temperature at the longer steeping times. In this study, heat treatment lowered the share of volatiles giving forest and wood notes in the profile, which is in accordance with the results of [PERSON] et al. [34], who found that high temperature can decrease the floral, woody, fatty, and sweet notes, and enhance the odor of green, roast, and fruity scents. In this study, basil infusions had a greater share of volatiles responsible for sweet, floral, and spicy notes, e.g., \(\beta\)-linalool and eugenol. Although no dramatic changes
# Three-Dimensional Path-Following with Articulated 6 DoF Robot and ToF Sensors
## 3 Results
### Experimental Verification  
To demonstrate the effectiveness of 3D path-following using an industrial robot controlled by the developed algorithm and the proposed path detection tool, tests were performed for four different path settings. Path 1 was tested twice and then tests for paths 2, 3, and 4 were performed. In each presented case, the task was successfully accomplished. Below, views of the recognised paths are visualised. In each figure, the robot is presented in the initial position, and the path is presented as a green line. Note that the figures show path positions as they were detected by the path detection tool and as they were represented in the algorithm. These are not the actual, real locations of the path but are close to them. Additionally, photos of some selected positions obtained by the robot during the program run are also presented. A full view of each run is presented in the video available as the Supplementary Data provided as [S2].  
Figure 10: Path-following algorithm schema.
# Sand Production Characteristics of Hydrate Reservoirs in the South China Sea
## 2 Research Status Worldwide
### Experimental Study of Natural Gas Hydrate Sand Production in the Sea Area
It is recommended to carry out stage-by-stage graded sand prevention during sandy hydrate mining, i.e., to allow the output of small-grained sand at the initial stage to improve the pore-permeability conditions of the reservoir, and to strengthen the prevention and control of large-grained sand at the middle and late stages. It also helps to form effective sand arches, bridges and channels, whereas sediment morphology during mud hydrate reservoir mining is much larger and more prone to large-scale serious sand production problems [38; 39; 40]. For the hydrate reservoir in the Liwan Sea area in the northern part of the South China Sea as a reference object, the innovative proposal of using the two-channel acoustic method to monitor the distribution of solid particles in the samples. The results show that without the use of the sand-proofing device, under the production pressure difference of 1 MPa, the sand output accounted for 19% of the total amount of sand in the experiments, and the sand output problem is very serious. The best effect is achieved when D\({}_{50}\) = 11d\({}_{50}\), \(\omega\) = 3.8d\({}_{50}\) (D\({}_{50}\) refers to the median gravel particle size, the d\({}_{50}\) refers to the median particle size of reservoir particles, and \(\omega\) refers to the sieve size) the effect is optimal and recommended the use of 4 MPa pressure reduction and 400-mesh sand-proof screen mesh in practical mining [41]. Studies on the sand behaviour of hydrate-bearing reservoirs before, during and after decomposition show that the amount of sand produced before hydrate decomposition is small and is dominated by muddy chalk. After hydrate decomposition, the amount of sand produced increased and was dominated by chalk, fine sand and medium sand [42]. The above experimental results show that the driving force of the sand emergence process in hydrate mining is mainly the water flow in the pore space, and the factors affecting the sand emergence process include the magnitude of the pressure drop, the rate of the pressure drop, and the saturation of the hydrate. Early studies focused on the sand behaviour of sandy reservoirs, and the sand process in sandy reservoirs was mostly controllable in the experiments. With the success of the hydrate test mining project in the Shenhu Sea of the South China Sea, more attention has been paid to the sand release behaviour of muddy reservoirs. Existing research results show that mud reservoirs exhibit a higher risk of sand emergence due to finer sediment grains. At the same time, due to the elevated mud content, the sanding process in mud reservoirs is prone to forming special structures such as \"mud cake\" and \"mud skin\" in the near-well area, which can lead to clogging problems and affect gas production.
## 4 System Design and Goals  
This section presents the proposed design goals, the new message type, message sequence, HI message packet format, and a new technique for the MPR selection process. The current design specification for OLSRv2 [45] and OLSR [15] assumes mobile devices (such as mobile phones) have abundant redundant processing and storage capacity to handle frequent link- and route-building broadcasts/processing and the housekeeping or maintenance of their associated information bases. Unfortunately, this is not often the case. Therefore, this paper acknowledges that mobile devices have limited resources for OLSRv2 link sensing and information-based maintenance services. Thus, the proposed design emphasizes controlled broadcasts and ample periods for proper information base maintenance. These design goals will help to improve the OLSRv2 performance in link sensing, the MPR selection mechanism, and database maintenance techniques that will produce desirable results in dense and sparse static and/or mobile network scenarios.  
The primary design goals of this paper with respect to enhancing the OLSRv2 packet delivery ratio, reducing network energy consumption, and maintaining up-to-date information bases are as follows:  
* Network decongestion: OLSRv2 implementation is plagued by enormous packet loss and high processor loads due to uncontrolled broadcasts of control messages. However, enforcing listening periods before and between message broadcasts will reduce the frequent usage of the radio transmitter since nodes that transmit HELLO messages and fail to receive a timely response (due to an ongoing DB chores mode) will simply drop into listening mode. The reduced usage of the radio transmitter increases the energy available to the D2D nodes since transmitting consumes more energy than receiving.
* Information base maintenance: the proposed database (DB) chores mode allows OLSRv2 to commit link states and routes to information bases without sudden disruption from incoming messages on the OLSR address.
* MPR selection: the proposed MPR selection considers the status of the nodes to select the best node as the MPR for energy efficiency and link quality.
# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 2 Related Work
### One Stage MOT  
In order to balance the speed and accuracy of MOT, one-stage MOT methods have been continuously proposed. In JDE [5], object detection and appearance embeddings are allowed to be learned in a shared model. The competition of detection and tracking tasks impairs the learning of task-relevant representations, which impairs the performance of tracking. To address this issue, CSTrack [6] effectively motivates each branch to learn task-relevant representations. Previous work usually treats the ReID task as a secondary task, whose accuracy is severely affected by the main detection task. As a result, the network is biased towards the main detection task, which is unfair to the ReID task, which FairMOT [7] solves simply and effectively. In CorrTrack [8], adding spatiotemporal correlation to [7] makes the tracking results more robust. CenterTrack [9] is borrowed from [3] and implements end-to-end tracking by inputting the heatmap of the previous frame and the current frame.
## 3 Results and Discussion
### Oxidative Stability
If we compare the oxidative stability measured at the same temperature (120 \({}^{\circ}\)C) by the Rancimat and PDSC tests, it turned out that induction time values measured by PDSC were four times shorter compared to those measured by the Rancimat method. The differences could be related to the smaller sample size used in PDSC measurement (3-4 mg) in comparison to the Rancimat quantity of the sample (2.5 g) [41]. According to [PERSON] et al. [43], higher surface-volume ratio of the PDSC oil sample also played an important role leading to the shortened analyzed time. It may also be due to the fact that the oil in the PDSC test was oxidized with pure oxygen at a pressure of 1400 kPa, while in the Rancimat test, air containing approx. 21% oxygen at a flow of 20 L/h [41] was used. [PERSON] et al. [41] managed to obtain a statistically significant linear correlation (R > 0.99) between PDSC and the Rancimat values for hazelnut oil. The authors recommended PDSC as an appropriate objective method for assessing the oxidative stability of oil.
# Fully Symmetric Convolutional Network for Effective Image Denoising
## 4 Experimental Results
### Comparative Analysis of Algorithm  
The results of the proposed FSCN method were compared with three nonlocal, similarity-based methods (i.e., BM3D [2], WNNM [11], and PCLR [37]), and three discriminative training-based methods (MLP [13], CSF [27], and DnCNN [28]). The source codes were downloaded from the authors' websites and tested on the images with their default parameter settings. Ten common benchmark images were used for testing. A detailed comparison was made to evaluate the proposed model against the above competing algorithms on the standard test set.
# Real-Time FPGA-Based Balance Control Method for a Humanoid Robot Pushed by External Forces
## 3 FPGA-Based Balance Control Method
### External Force Detection  
The external force detection module is proposed and implemented to estimate an inclination angle \(\phi\) of the robot based on the sensor information \((\omega_{Gyro},a_{Acc})\) obtained from the gyroscope and accelerometer mounted on TKU-X robot. The information obtain from these two sensors is employed to infer the stepping strategy with which the humanoid robot could step to regain its balance. Because the value \(\omega_{Gyro}\) measured by the gyroscope will gradually diverge with time and the value \(a_{Acc}\) measured by the accelerometer will also change significantly when the robot swings, the Kalman filter [25,26] shown in Figure 5 is used to integrate the sensor information \((\omega_{Gyro},a_{Acc})\) obtained from the gyroscope and accelerometer to calculate a more accurate inclination angle \(\phi\). The main formulas are described by  
\[\phi(t)=A\phi(t-1)+B\omega_{Gyro}(t)+w(t) \tag{1}\]  
\[a_{Acc}(t)=H\phi(t)+v(t) \tag{2}\]  
where \(A\), \(B\), and \(H\) are the state transition matrix, the control input matrix, and the transformation matrix, respectively. \(\phi(t)\) is the state at time \(t\) is evolved from the state \(\phi(t-1)\) at time \(t-1\). \(\omega_{Gyro}(t)\) is the control input which is measured by the gyroscope and \(a_{Acc}(t)\) is the measurement which is measured by the accelerometer. \(w(t)\) and \(v(t)\) are, respectively, the process noise and the measurement noise.  
As shown in Figure 6, when the humanoid robot is subjected to an external force from the back or the front, an inclination angle \(\phi\) of the robot with respect to the vertical ground is generated. This inclination angle \(\phi\) is used to reflect the strength of this external force. A small value \(\phi\) means this external force does not have a significant impact on the robot, and a large value \(\phi\) indicates this external force may cause the robot to fall down and no longer be balanced. Therefore, if the external force comes from the back, a forward balance strategy is proposed so that the robot will take a step forward to maintain balance. Conversely, if the external force comes from the front, a backward balance strategy is proposed so that the robot will take a step backward to maintain balance.  
Figure 5: Block diagram of Kalman filter used for TKU-X.  
Figure 6: Two types of the external force for the humanoid robot. (**a**) From the back. (**b**) From the front.
## 5 Discussion
### Threats to Validity  
There are still several potential limitations in our study which are shown as follows.  
1. Quality and quantity of datasets for empirical study might be insufficient. Although we have collected more than 100 datasets for illustrating the distribution of sample size and imbalanced ratio in most of SDP datasets, and 29 datasets for investigations in empirical study. It is still hard to confirm if these datasets are typical to reflect characters of SDP data
2. The generalization of our method might be limited. The method we proposed focus on binary classification, it improves the performance of predicting if a sample (software model) has any defects but cannot predict the number of defects in it. More types of defect datasets should be considered in the future to reduce the threats.
3. The performance evaluating metrics we selected might be partial. There are many metrics such as PD and MCC have been used in binary classification for SDP research. At the same time, F1 is also widely used in SDP, but we do not employ it as it is proved to be biased and unreliable [63]. Although we have considered to select evaluating metrics from two aspects, overall performance and one-class accuracy, however, the limited number of metrics still pose some threats to the construct validity.
4. Practical significance of LIMCR in software engineering might be extended. Project members can obtain information on possible defect-prone modules of the software before failure occur by using defect prediction technique, LIMCR has not been applied to predict defect classes/severities [64]. In addition, it is worth studying the performance of LIMCR with different prediction models (within a single project, cross project predictions) [65]. Meanwhile, how to cooperate with instance deletion, missing values replacement, normalization issues mentioned in [66] and defect prediction cost effectiveness [67] also needs further research.  
Figure 6: Result of LIMCR with different precision of variable \(D\).
## 2 Materials and Methods
### Equating All Pertinent Costs
Because it is assumed that all inspections will occur at moments \(M_{n}\equiv t_{n}\), STW will hence extend backwards in time by \((M_{n}-P_{n})\). If a potential failure is noticed to be in progress within a STW, the equipment unit will be scheduled to stop, the component will be promptly removed or repaired, and the incurred cost will be only \(C_{rp}\). If, on the contrary, a potential failure occurs within a UTW and is not noticed, a functional failure will happen with all the undesired consequences \(C_{op}\). If no potential failure occurs until moment \(T_{p}\), then the component will be replaced or recovered at this very moment at a cost \(C_{rp}\). Figure 2: Safe time windows \(P_{n}\)-\(M_{n}\) (grey), moment \(H\) (today), moment \(T_{p}\) (overhauling). Arrows represent inspection moments (authors’ figure). This stochastic economic assessment model consists of four different costs spread over the period \(H\)-\(T_{p}\):  
* Inspections cost supposed to be performed at moments \(M_{n}\);
* Opportunity cost due to the built up of production degradation;
* Repair cost of a potential failure, which may or may not happen;
* Consequence cost of a functional failure (repair and loss of production), which may or may not happen. An optimal solution can be found after a few trials (scenarios) making \(R(t_{n}\,\text{1}\,t_{n-1})\) vary and keeping \(T_{p}\) constant, or vice-versa, if interest exists. The best scenario is found when \(\overline{C_{h}}\) is a minimum.
## 4 Experiments
### Performance Analysis Under Different Traffic Conditions  
To evaluate the adaptability of Hybrid MARL under varying traffic conditions, we simulate different vehicle arrival rates and measure model performance. While all models experience performance degradation under high congestion, Hybrid MARL maintains a significant advantage over baselines, demonstrating robustness and scalability. Figure 6 Impact of different traffic intensities on waiting time. Hybrid MARL consistently achieves lower waiting times across different congestion levels.  
Figure 6: _Cont._  
Furthermore, we analyze traffic flow stability, which quantifies fluctuations in vehicle speeds and queue lengths. Figure 7 visualizes the real-time traffic flow stability for Hybrid MARL and baseline models. The results indicate that Hybrid MARL produces smoother traffic flow, reducing the probability of sudden congestion spikes.  
As shown in Figure 6, Hybrid MARL exhibits more stable traffic movement, reducing fluctuations in congestion patterns.  
Figure 6: Traffic flow stability comparison.
## 2 Signal and Channel Model  
Before presenting the proposed method, we first introduce the signal model used in this paper. We begin this section by deriving the expression of multipath CTF which characterizes the multipath channel in the frequency domain, and then briefly introduce the principle of the direct-FFT method.
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 5 System Testing
### Comparison of Pattern-Recognition Methods  
In order to illustrate the superiority of the proposed method in feature extraction and pattern-recognition compared with the traditional method, this paper compared the recognition results with the NanoEdge AI classifier. NanoEdge AI extracts features using the feature parameter method, combined with the literature references [22; 28; 30; 31]. Firstly, the collected data was normalized, and then the amplitude features of the discharge pulse, time-domain features of the discharge, frequency-domain features of the discharge, and skewness features were extracted from the data, among other attributes. The features from each group of data were integrated to generate an unsigned one-dimensional array with a length of 1080 elements, which was used as the input for the network model. By collecting 4000 sets of data for model training for each of the board-board, ball-board, and pin-board models, the best classification model was selected from the classification library and then transferred to the STM32F769 NI microcontroller for model recognition. The two different approaches were executed on the embedded side for testing, using an insulation thickness of 4.5 mm and a discharge voltage of 8 kV, and the final recognition accuracy comparison was obtained through 200 sets of test experiments, as shown in Table 8.  
As can be seen from the above table, the overall accuracy of CNN between the two identification methods reached 98.17%, which was 11.47% higher than that of the NanoEdge AI classification algorithm. Among the three insulation types, board-plate discharge had more data on the PRPD map and its discharge characteristics were more obvious and easier to distinguish. In contrast, ball-plate discharge and tip discharge were relatively more challenging to recognize. The unique network structure of the CNN model allowed it to deeply extract data features, effectively capturing detailed information and learning a broader range of comprehensive and abstract features from the grayscale map.
# Spatial Small Target Detection Method Based on Multi-Scale Feature Fusion Pyramid
## 3 Method  
### Dataset Construction and Processing  
In order to ensure the performance of the small target detection algorithm, the space small target database is needed for training, and also, the database is the basis for algorithm performance testing. Existing spatial datasets include the SPEED dataset, URSO dataset SPEED+ dataset, etc. However, the existing aerospace datasets mainly focus on the relative attitude estimation of spacecraft, which does not satisfy our data requirements for small target detection tasks in space. More work has been performed on spacecraft dataset construction in recent years, such as [30; 31], among others, who have constructed spacecraft datasets, and no more research has been performed on small targets in space.  
To meet our requirements for the database of space small target detection tasks, we constructed data from 8000 space small target images. These include three types of objects, small objects, spacecraft, and space debris, in order to cover as many different types of common natural or man-made small targets in space as possible. The image data were obtained by building a space environment simulation scene in 3D modeling software and rendering it. Firstly, we collected high-precision 3D models of 300 small objects and 10 spacecraft in open sources and constructed an 8000 \(\times\) 2000 star map background based on real space background data. Among them, the former is used to simulate the small targets of small objects and spacecraft in space. Since there are fewer open data on space debris, we edited and modified the spacecraft models to obtain their local models for debris processing, so as to simulate space debris targets. The latter contains a large number of stars widely distributed in space, which is used to construct the long-range space background beyond the target. Then, we used the collected data to construct a variety of small space targets through 3D modeling software, set the parameters of light reflectivity and refractive index of different materials in the three types of object models, and designed different light source positions and intensities to simulate the real space lighting environment in space. Finally, we selected multiple shooting angles, considered the different observation angles of small targets in space, and automatically shot them through the script function integrated into the software to achieve the construction of the database. Some images of the database are shown in Figure 1.