esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Non-Thermal Technologies in Food Processing
### High-Pressure Processing
#### 3.4.3 Application of HPP in Juices and Beverages  
HPP technology has been used primarily to reduce pathogens and spoilage enzymes in juices [191; 200]. In addition, through this technique, the shelf life of the juices is extended, while it seems to be effective in maintaining the sensory and nutritional quality [224]. Enzymes such as ascorbic acid oxidase (AAO), polyphenol oxidases (PPO), and peroxidase (POD) are found naturally in fruit and vegetables and modify quality characteristics related to texture, taste, color, and nutritional value [225]. The enzymatic browning of juices is a result of high enzymatic activity, which causes the degradation of valuable bioactive compounds [226]. Both pasteurization and inactivation of these enzymes can be performed through the HPP treatment, although studies have shown that in relation to heat treatment, this technique is not as effective in the degradation of enzymes [227; 228].
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## Reasons for Software Defined Metro and Access Networks
### Cost Savings  
In order to be implemented, each technological solution needs to be economically attractive. In other words, the benefits introduced have to be compared with the cost of the technology needed to provide it. This is also true for telecommunication networks and, in particular, for metro and access networks, where network costs have to be amortized over a fewer number of users. Of course,  
Figure 4: Results of the 2015 EC public consultation on SND/NFV impacts on access services.  
deploying software-defined metro and access networks requires significant investments for the telecom industry which will generate a return in the following years thanks to the incomes allowed by benefits of re-configurability and programmability, as described in the previous paragraphs. In addition to this, the break-even point of these investments will be decreased by the cost reduction: the application of SDN offers significant reductions in capital expenditures (CapEx) and operational expenditures (OpEx), and minimizes a network total cost of ownership (TCO). It has been estimated that, for a high-scale tunable IP/optical network with 10\(\times\) traffic growth over five years, the TCO savings will be approximately 70% per-unit capacity (e.g., per gigabit), where over 40% of the benefit arises from SDN automation and network optimization [6]. By analogy, TCO savings can be guessed for software-defined metro and access segments.  
OpEx savings can be achieved since programmability enables efficient sharing of resources also in the metro and access segments. Re-configurability enables provisioning of resources on-the-fly, facilitating the dynamic service creation, and enhanced programmability can make such procedures even more efficient [15]. Also, CapEx savings can be achieved since an SDN-based approach allows the optimal use of existing network resources and scalability (the capacity allocated to a service can be scaled up or down easily). These features defer costly network upgrades and future investments.
# Construction of a Cutting-Tool Wear Prediction Model through Ensemble Learning
## 4 Results and Discussion
### Machine Learning
#### 4.5.1 Machine Learning Results  
In this section, machine learning metric scores are based on the results from the test dataset. Each model has different metric scores, as shown in Table 7 and Figure 17. Thedecision tree (DT) model performs the worst among all models, with an R\({}^{2}\) of 0.69132, RMSE of 0.58086, MSE of 0.3374, and MAE of 0.4311. This is speculated to be because the model is too simple to capture the complex relationships in the data. The extra trees model performs the best on all metrics, with an R\({}^{2}\) of 0.91737, RMSE of 0.30053, MSE of 0.09032, and MAE of 0.21134. This is likely due to the introduction of more randomness and the fact that the model's generalization capability could be enhanced. Different models exhibit different performances, indicating that various machine learning models have distinct performance advantages. This can also be observed in Figure 18, which shows scatter plots for each regression model. It is evident that extra trees, AdaBoost, RF, and XGB have better predictive results, while DT and SVR perform poorly, with higher errors compared to other models. The figure also illustrates that each model has a different trend in predicting results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Indictors** & **R\({}^{2}\)** & **RMSE** & **MSE** & **MAE** \\ \hline DT & 0.69132 & 0.58086 & 0.3374 & 0.43112 \\ \hline RF & 0.90902 & 0.31534 & 0.09944 & 0.22303 \\ \hline Extra Trees & 0.91737 & 0.30053 & 0.09032 & 0.21134 \\ \hline XGB & 0.90144 & 0.32822 & 0.10773 & 0.22051 \\ \hline LGBM & 0.78587 & 0.48379 & 0.23405 & 0.32589 \\ \hline AdaBoost & 0.91051 & 0.31276 & 0.09782 & 0.23232 \\ \hline SGD & 0.85313 & 0.40066 & 0.16053 & 0.2977 \\ \hline SVR & 0.70355 & 0.56924 & 0.32403 & 0.36844 \\ \hline LSVR & 0.81672 & 0.44758 & 0.20033 & 0.27107 \\ \hline MLP & 0.88668 & 0.35195 & 0.12387 & 0.28653 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparisons among different machine learning models.  
Figure 17: Performance comparisons among different machine learning models.  
Figure 18: Scatter plots showing the relationship between predicted values and ground truth across different machine learning models: (**a**) DT, (**b**) RF, (**c**) extra trees, (**d**) XGB, (**e**) LGBM, (**f**) AdaBoost, (**g**) SGD, (**h**) SVR, (**i**) LSVR, (**j**) MLP.
# Effect of Fermentation Technology and Storage Time on the Quality of Salami-Type Sausages
## 2 Materials and Methods
### Measurement of pH Level and Water Activity  
The pH level of the salami-type sausages was determined according to the ISO 2917 standard [29]. A Testo 206-pH2 pH-meter (Testo SE & Co. KGaA, Titisee-Neustadt, Germany), equipped with a combined glass-calomel electrode, was used. The samples were examined on 0, 21, 42, and 63 days of cold storage. Before each measurement cycle, the pH meter was calibrated against standard pH 4 and pH 7 buffers. Measurements were performed three times for each sample.
## 3 Design and Implementation Results
### Data Plane
The key things here are that (1) the data plane refers the forwarding-related information from the Shared Memory, not from the kernel space, and (2) the data plane forwards itself without the help of the kernel network stack.
# Estimation of Sedimentary Rock Porosity Using a Digital Image Analysis
## 4 Conclusions  
The porosity of three different rocks (sandstone, siltstone, and coal) were studied using a series of laboratory tests (MIP) and digital analysis using high-quality SEM images and the PSAC technique. Based on the obtained results, the following major conclusions can be drawn:  
* The majority of sandstone and siltstone pores were larger than 1000 nm in diameter. The sandstone had a minimum pore size of 3000 nm, while the siltstone had a minimum pore size of around 15 nm. The coal pore size was found to be linear between 10,000 nm and 3 nm, according to the MIP test in which the maximum pressure of 400 MPa was applied. Furthermore, DV/Dd is more appropriate to be used for characterizing PSD data for sedimentary rock.
* The data from the SEM image agree with the results from the MIP tests. The minimal pore size of sandstone was around 1000 nm, whereas the minimum pore size of the siltstone was approximately 10 nm.
* The linear relationship between the two-dimensional porosity (from PACS) and the three-dimensional porosity (from MIP) seems to exist with a multiplier of 0.88 for the sandstone and siltstone. For these two rocks, quartz was the dominant mineral, which created uniform void space.  
The combination of PACS analysis and MIP tests can provide useful insights into the pore size distribution of rock. However, inconsidences (especially for more porous rocks like coal) can exist when SEM images at higher magnifications are analysed. In this case, the porosity obtained from the image analysis appears greater than the porosity measured in the laboratory. Overall, the results can be processed directly in the field of rock science, or as a preparation or comparative phase for in-depth research, saving time and effort and enhancing the precision of tests. Here, it is useful to select photographs that require appropriate magnification, since these images will have a significant impact on the data inaccuracy.  
Conceptualization, Q.T. and I.G.; methodology, Q.T.; software, Q.T.; validation, Q.T. and I.G.; formal analysis, Q.T.; investigation, Q.T.; resources, I.G.; data curation, Q.T.; writing--original draft preparation, Q.T.; writing--review and editing, I.G.; visualization, Q.T.; supervision, I.G.; project administration, I.G.; funding acquisition, I.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.
# Development of Continuum Robot Arm and Gripper for Harvesting Cherry Tomatoes
## 3 Tomato Recognition System
Table 1 shows the results of calculating the mAp(mean average precision), mAr(mean average recall), and f1 score metrics:  
Figure 13: Testing of the tomato recognition in a real environment. Based on this table, we see that the value of the YOLOv5 metrics shows a very good result compared to Mask R-CNN. Thus, it can be argued that the Mask R-CNN algorithm showed a poor result in training since the values of mAP = 0.13 and F1 score = 0.23 are low. To measure the distance between a tomato and the camera, we used a measurement method by using a single camera (Figure 14). It allows calibrating the relative position of the gripper tool for perfect grasping of the tomato without any damage (Figure 15). The camera generates a one-to-one relationship between the object and the image. Using this principle, we can deduce a distance from the camera to an object (\(d\)) from known parameters: focal length (\(f\)), the radius of the tomato in the image plane (\(r\)), and the radius of the tomato in the object plane (\(R\)) with \(d=f\times\frac{R}{r}\). However, a drawback of this method is size limitation, this means that the program for the recognition requires information about the real size of the object and compares with predefined size of it. For instance, in this research, we applied this method only to cherry tomatoes of about 30 mm in diameter. This means that the distance measurement is applicable only for tomatoes with about a 30 mm diameter [35]. \begin{table}
\begin{tabular}{p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline
**Value of Metrics** & **YOLOv5** & **Mask R-CNN** \\ \hline
**mAp** & 0.90 & 0.13 \\ \hline
**mAr** & 0.87 & 0.82 \\ \hline
**Fiscore** & 0.89 & 0.23 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison table of YOLOv5 and Mask R-CNN. Figure 14: Tomato detection architecture. Figure 15: The gripper and camera allocation and the way to measure object location by a single camera [36]. (**a**) CAD view of gripper and camera structure. (**b**) Principal working diagram of the camera for distance measurement.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 4 MROR System and Network Model
### Mobility-Enhanced Channel Availability Modeling
#### 4.2.1 PU Protection
Finally, by multiplying the average time \(n\) has to utilize a channel \(i\) (_i.e._, Equation (17)) by the average fraction of areas where channel \(i\) is available (_i.e._, Equation (8)), we can arrive at the spatio-temporal availability of channel \(i\), \(\varpi_{i}=\Omega_{i}\omega_{i}\).
###### Abstract
Existing research has shown that sparse vehicle trajectories result in lost trajectory details, adversely  
Figure 1: The automatic vehicle identification system. Figure 2: AVI trajectory (**left**) and GPS trajectory (**right**). affecting subsequent research and applications. Therefore, to make AVI trajectories useful, reconstructing the sparse vehicle automatic identification trajectory is necessary. The challenge of sparse trajectories requires advanced reconstruction methods, as traditional mathematical methods such as interpolation are often ineffective in accurately simulating the complex motion patterns between sparse data points. Furthermore, the historical data of automatic identification do not include information about the vehicle's motion, making it impossible to learn the vehicle's position changes from its own data. Although road network matching methods are commonly used for the reconstruction of automatic identification trajectories, it can be challenging to obtain accurate road network data and keep them up to date. Consequently, reconstructing these trajectories without road network data remains a significant challenge. Addressing the above challenges, this paper proposes an attention-based spatial-temporal reconstruction model for vehicle trajectories. The model first reconstructs the spatial trajectory of the vehicle and further proposes a time series prediction method based on the historical trajectory and an attention mechanism. The spatial trajectory of the vehicle is used to predict the time series within the trajectory, and then the complete spatial-temporal vehicle trajectory is reconstructed. Overall, our contributions can be summarized as follows. 1. Aiming at the reconstruction problem of target trajectories, we propose a two-step reconstruction method that first reconstructs the spatial trajectory and then reconstructs the spatial-temporal trajectory. 2. We propose a time series prediction method based on historical trajectories and an attention mechanism, which considers the impact of the target's activity cycle and the surrounding status and predicts the time series within the trajectory based on known spatial trajectories. It then reconstructs the complete spatial-temporal vehicle trajectory. 3. We use a real AVI dataset from Chongqing to verify the effectiveness of the model. The experimental results prove that, compared with the traditional method, the method in this paper can reconstruct the spatial-temporal trajectory of the vehicle more accurately. The remainder of this paper is structured as follows. Section 2 provides an overview of related works in trajectory reconstruction. In Section 3, we introduce the data used in this paper and the relevant definitions. Section 4 describes the method used to construct the trajectory. Section 5 showcases the experimental results, and Section 6 concludes the paper.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Obtrusiveness  
The number of wearable sensors used in a system can be associated with the level of obtrusiveness of the system. It is known that having more sources of data in a system will provide more information. However, there is the disadvantage of decreased subject comfort as the number of sensors increases. Additionally, not all sensors are completely wireless since there are sensors that require the use of wires or electrodes, such as the EMG devices, to extract information, also affecting the level of obtrusiveness. Researchers often encounter this problem: accuracy versus subject comfort. They have to decide to either build a system with high accuracy using multiple sensors or build a system with lower accuracy and less data using fewer sensors. Less sensors allows the subject to be more comfortable and avoids interfering with trial performance or daily activities. Decreasing the number of sensors can be beneficial in terms of complexity, cost, and the amount of data to process. From the studies included in this review, 33 had a low level obtrusiveness. On the other hand, 10 were considered to have a medium level obtrusiveness, and 13 high level obtrusiveness.
## 3 Results
To quantify curdlan, the bacteria were grown in liquid cultures for 3 days with ADB using glucose as a carbon source. The results show that curdlan production by C58 was not detected after growth in ADB, while C58 PphaP produced about 0.1-0.2 g/L curdlan. Curdlan production by 31749 in ADB was also about 0.2 g/L, while production by 31749 PphaP in ADB reached 1.6 g/L. Thus, under these conditions, PphaP improved curdlan production about 8-fold in strain 31749 and forced C58 to produce about 0.2 g/L curdlan under conditions where it would normally not produce any. Figure 5: PphaP increases curdlan production in minimal medium. Bacteria were grown in a 96-well plate assay to reveal a temporal profile of the onset of curdlan production. Fluorescence from aniline blue/curdlan of the cultures was measured once every three hours for 60 h using an automated Tecan reader setup. For the details of this setup, see the materials and methods. Cultures include the C58-derivatives _crdA_ knockout (KO), C58 wild type (WT) and C58 PphaP for (**A**) and likewise for the 31749-derivatives (**B**). Error bars indicate standard deviation from 8 wells.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 3 Methodology
### Point-Line Feature Extraction Network
#### 3.1.2 Key-point Detection Module  
As shown in Figure 2, the key-point detection module consisted of three point perception modules and two \(3\times 3\) convolutions with a stride length of 1. A batch normalization layer and ReLU layers were added between each convolutional layer. The output vector was processed through a sigmoid activation function, so that the pixel values of the saliency map were between 0 and 1. Then, through the key-point perception module and convolution processing, the convolution operation was used to discriminate whether the \(8\times 8\) area prediction contained key points. Finally, the key points were detected by using the nonmaximum suppression (NMS) method in the key-point generation module (PG).  
The point-aware module was used to capture the relationship between key points. As shown in Figure 4, the key-point extraction branch embedded a context enhancement module, which improved the feature expression ability. The output feature \(y^{p}\)\(\in\)\(\mathbb{R}^{W\times H\times D}\) was obtained by fusing the convolutional features of different scales, which took \(y^{p}\)\(\in\)\(W\)\(\times H\times D\) as input. The above process was defined as:  
\[y^{p}=W_{1}[x^{p},BN(W_{1}x^{p}),BN(W_{2}x^{p}),BN(W_{3}W_{1}x^{p})] \tag{12}\]  
where \(x^{p}\) is the input feature, \(W_{1}\), \(W_{2}\), and \(W_{3}\) are convolution operations with sizes of \(1\times 1\), \(2\times 2\), and \(3\times 3\), respectively. \(BN\) is a normalization, and \([]\) is a splicing operation.  
Figure 4: The point-aware module. BN means batch normalization.  
Figure 3: The dual attention module. The attention mechanism is adopted to adaptively aggregate different features, where the weights are normalized with the softmax function.
# A Strategy for Integrated Multi-Demands High-Performance Motion Planning Based on Nonlinear MPC
## 5 Conclusions  
This paper introduces the HMD strategy to formulate a high-performance NMPC planner tailored for integrated motion planning that accommodates multiple driving demands. To evaluate the effectiveness of the HMD-based planner, several typical scenarios are devised and analyzed. Simulation results show that during the lane-keeping task, the HMD-based planner achieves a 74.5% boost in efficiency, with a minimal speed error of under 0.8% compared to the TMO. In the context of lane changing and overtaking tasks, the HMD enhances solving efficiency by 68.5%, while simultaneously enhancing driving safety. Moreover, it reduces the maximum jerk by 58.8% compared to the TMO. In a particularly challenging task involving emergent obstacle avoidance, the HMD successfully executes safe motion planning, whereas the TMO-based approach falls short.  
In the future, we plan to incorporate the motion uncertainty of obstacles in the environment and the measurement uncertainty of vehicle states into consideration. We aim to investigate the design theory of integrated decision and control methods with a strong emphasis on stochasticity.  
Figure 16: Acceleration, time to collision, and solving time of HMD and TMO. (**a**) Longitudinal acceleration. (**b**) Lateral acceleration. (**c**) Time to collision. (**d**) Solving time.  
Figure 17: Evaluation results of different evaluation functions in scenario 3.  
**Author Contributions:** Conceptualization, Y.H. and X.M.; data curation, B.W.; formal analysis, Y.H.; investigation, Y.H.; methodology, Y.H.; project administration, G.C.; resources, B.W.; software, X.M.; supervision, Q.Z.; validation, X.M.; visualization, H.Z.; writing--review and editing, X.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Beijing Nova Program (No. 20230484432). The funder is Beijing Municipal Science & Technology Commission.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The simulation data can be obtained from the corresponding author upon request. The data are not publicly available due to privacy.  
**Conflicts of Interest:** Author [PERSON] and [PERSON] were employed by the company Inner Mongolia Dian Tou Energy Corporation Limited South Opencast Coal Mine Tongliao. Author [PERSON] and [PERSON] were employed by the company State Power Investment Corporation Research Institute. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Literature Review
### Second-Hand Trading Applications  
Second-hand goods trading platforms provide a marketplace for exchange and communication between the owners of idle items and buyers. This not only extends the lifespan of products but also reduces the need for manufacturing new products [21]. Given their impact on sustainability and economic benefits for consumers, there is currently extensive research on second-hand trading apps. [PERSON] and [PERSON]'s study focuses on the second-hand trading apps Xianyu and Hongbulin, analyzing the differences in interaction modes and their impact on user experience to enhance the overall user experience of second-hand trading apps [22]. [PERSON] and [PERSON] developed an electronic trading platform for second-hand goods based on the Android platform. Utilizing the MVP architecture pattern during platform construction, they achieved a highly maintainable, API-oriented second-hand trading platform software [23]. [PERSON] explored the relationship model between changes in user needs and dimensions of user flow experience in the design of internet shopping platforms, subsequently designing an internet shopping platform based on user flow experience [24]. [PERSON] et al. employed the extended Theory of Planned Behavior (TPB) to study consumers' willingness to purchase second-hand products and visit online and offline second-hand shops. [PERSON] and [PERSON] proposed a second-hand shoppers' motivation scale to evaluate the motivations of second-hand shoppers [25].  
In summary, research on second-hand trading platforms primarily focuses on user experience, platform design and implementation, and consumer purchase intentions. Although these studies provide effective solutions for optimizing the use of second-hand trading apps, research on the importance of usability metrics specific to the characteristics of second-hand trading apps remains insufficient. Therefore, this paper aims to develop usability metrics from a sustainability perspective, considering the unique characteristics of second-hand trading apps.