esa-sceva/satcom-chunk-collection huggingface dataset:
## Results and Discussion
### Phenolic Composition  
In Table 3 the phenolic compounds detected in the two varieties PdP and CIL, are reported at the two storage times considered above. The phenolic compounds contents detected in PdP0 and CIL0 were 38.54 and 29.63 mg/100 g, respectively. In the case of PdP6, the content of these bioactive compounds statistically decreased to 34.27 mg/100 g (11%), whereas for CIL1 the total phenols content increased statistically in comparison with the control experiment to 37.48 mg/100 g (20.9%).  
Upon analysing the contents of the individual phenolic compounds detected, chlorogenic acid and rutin (quercetin 3-O-rutinoside) were the most abundant phenolic compounds evidenced in these vegetable matrices. In particular the rutin contents detected in PdP0 and CIL0 were 7.37 and 4.41 mg/100 g respectively, and significantly increased during ripening in both cultivars. The amounts  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **TP (mg GAE/100 g** & **Rutin (mg/100 g** & **Quercitin (mg/100** & **Chlorogenic Acid** \\  & **Fresh Weight)** & **Fresh Weight)** & **g Fresh Weight)** \\ \hline PdP 0 & 38.544 \({}^{\text{a}}\)\(\pm\) 0.07 & 7.37 \({}^{\text{b}}\)\(\pm\) 0.01 & 0.95 \({}^{\text{b}}\)\(\pm\) 0.01 & 10.39 \({}^{\text{a}}\)\(\pm\) 0.66 \\ PdP 6 & 34.27 \({}^{\text{b}}\)\(\pm\) 0.11 & 8.98 \({}^{\text{a}}\)\(\pm\) 0.15 & 0.34 \({}^{\text{b}}\)\(\pm\) 0.01 & 7.34 \({}^{\text{b}}\)\(\pm\) 0.83 \\
**CIL 0** & 29.631 \({}^{\text{c}}\)\(\pm\) 0.97 & 4.41 \({}^{\text{d}}\)\(\pm\) 0.01 & 2.18 \({}^{\text{a}}\)\(\pm\) 0.03 & 7.94 \({}^{\text{b}}\)\(\pm\) 1.66 \\
**CIL 1** & 37.483 \({}^{\text{a,b}}\)\(\pm\) 0.19 & 5.63 \({}^{\text{c}}\)\(\pm\) 0.01 & 1.87 \({}^{\text{a}}\)\(\pm\) 0.03 & 7.16 \({}^{\text{b}}\)\(\pm\) 0.10 \\ \hline \hline \end{tabular} a-c different letters in the same column correspond to significant differences (\(p\) < 0.05).  
\end{table}
Table 3: Total phenolic (TP) compounds, rutin, quercetin and chlorogenic acid (mean \(\pm\) standard deviation) in tomato samples.  
were higher than those found by other authors [30] who reported an overall amount of 0.63 mg/100 g in green through red cherry tomatoes during vine ripening. This compound has been found to be the major flavonoid compound in ripened tomatoes in most studied cultivars due to its antioxidant properties and bioavailability to humans [31].  
The chlorogenic acid content detected in the sample PdP0 was 10.30 mg/100 g, which was significantly higher (23.5%) than that found in CIL in both studied storage periods and higher than that in PdP6. Quercitin was detected in small amounts decreasing from 0.95 to 0.34 mg/100 g (35.7%) in PdP. The values detected are in accordance with other studies [21] that reported a value of 8.52 mg/kg FW (i.e., 0.85 mg/100 g FW) of quercitin in PdP. In the samples stored for 6 months and 1 month, the detected amount of this antioxidant decreased in both varieties and ranged from 0.34 to 1.87 mg/100 g, respectively.
## 2 Economic Development and Air Pollution in the Beijing-Tianjin-Hebei Region  
### Status of Economic Development in the Beijing-Tianjin-Hebei Region  
The Beijing-Tianjin-Hebei region is at the stage of accelerated urbanization and modern industrialization. In the past five years, the economic development of the Beijing-Tianjin-Hebei region has been steadily improving. GDP (Gross Domestic Product) refers to the final results of the production activities of all resident units of a country (or region) calculated in accordance with the national market price within a certain period of time. It is often identified as the best indicator to measure the economic status of a country (or region). In 2018, the GDP of the Beijing-Tianjin-Hebei region reached 8.5 trillion yuan, an average annual increase of 6.6% over five years. In 2018, the urbanization rate of Beijing-Tianjin-Hebei region was 65.8%, an increase of 4.7% from 2014. The per capita disposable income of residents in Beijing, Tianjin and Hebei was 62,361 yuan, 39,506 yuan, and 23,446 yuan, respectively. The average annual growth rate was 8.8%, 8.2% and 8.9%. The economic quality of Beijing, Tianjin and Hebei has continuously improved, they are increasingly developing, and the conversion of development momentum has accelerated, laying a good foundation for achieving high-quality development. The rapid economic development in the Beijing-Tianjin-Hebei economic region has also brought about problems such as energy consumption, exhaust emissions from industrial enterprises, and increased emissions from automobile exhausts, which in turn affects air quality and the layout of the urban industrial structure [23]. At the same time, the acceleration of the urbanization process has led to increased environmental protection investment, improved greening coverage, and improved air pollutant control and governance capabilities brought about by technological advances, which will also reduce the air pollution [24]. There is a close relationship between industrial structure and air pollution. On the one hand, industrial structure upgrades, technological progress, and labor upgrades will help improve air quality. On the other hand, if resource utilization is ignored during this process, it will cause air pollution to occur again.
# Nanomaterials in Bone Regeneration
## 4 Nanoparticles
### Tantalum Nanoparticles  
Tantalum is a metal that has very high corrosion resistance and outstanding biocompatibility, making it a promising material for orthopedic and dental implants. Compared to the most commonly used titanium (density 4.5 g/cm\({}^{3}\)), pure tantalum is very heavy (density 16.6 g/cm\({}^{3}\)). Contrary to titanium which is used as a bulk, tantalum can be manufactured as a porous material with many advantages. Porous tantalum has optimal mechanical properties and shows excellent cellular adherence and bone ingrowth resulting in better fixation of the implant. The clinical application of porous tantalum in bone tissue engineering has been reviewed by [PERSON] et al. [120].  
Tantalum nanoparticles (Ta-NPs) are also gaining attention. They can be produced in the human body as wear particles from tantalum implants. [PERSON] et al. explored the effects of Ta-NPs on the MC3T3 cell line and found that a low concentration of Ta-NPs promoted the proliferation of osteoblasts, while concentrations higher than 25 ug/mL caused osteoblast damage via oxidative stress and reduced mitochondrial membrane potential [121]. Wear particles can contribute to aseptic implant loosing also due to proinflammatory properties. [PERSON] et al. compared cytotoxicity, ROS production, and inflammatory response of macrophages induced by TiO\({}_{2}\)-NPs and Ta-NPs. They found that TiO\({}_{2}\)-NPs triggered these adverse effects in a dose-dependent manner. Ta-NPs caused no apparent cytotoxicity and induced much less release of proinflammatory cytokines and only negligible ROS generation compared to TiO\({}_{2}\)-NPs. It indicates that regarding nanoscale wear fragments, tantalum may be superior to titanium for the manufacturing of implants [122].  
There are also efforts to use Ta-NPs to improve polymers used as bone substitutes. [PERSON] et al. in 2015 modified the surface of polyetheretherketone (PEEK) with Ta\({}_{2}\)O\({}_{5}\) nanoparticles by plasma immersion ion implantation. It increased the elastic modulus, making it similar to that of human cortical bones. Osteogenic properties of the material were evaluated using BM-MSCs and by in vivo studies on Sprague Dawley rats. The modified surface showed the enhanced osteogenic differentiation of MSCs, and better osseointegration was observed in vivo [123]. Recently, [PERSON] et al. investigated reinforcement of PEEK with Ta-NPs. Composites containing 3 wt% and 5 wt% of Ta-NPs showed better mechanical properties (elastic modulus and compressive strength), an increase in adhesion of cells on the surface, and also exhibited enhanced bone formation in animal studies [124].
# Correction of Directional Effects in VEGETATION NDVI Time-Series
## 3 Methodology
### BRDF Correction
#### 3.2.2 Compositing  
To avoid introducing any delay on the timing of key phenological stages, we considered new observations as if they were acquired in the last ten days of the accumulation period. If there is sufficient new information, i.e., three new clear observations at least, the BRDF inversion uses only those. On the other hand, if there are not enough new data (less than 3) acquired during the last ten days, the entire dataset of observations accumulated over 16 days is used. This approach is what we called an adaptive window, implemented to ensure that NDVI key phenological stages are not too much garbled by data accumulation. If no clear observation is available in 16-days, the inversion is not achieved. After assembling the set of clear observations, the median date of observations used in the BRDF modelling is computed. The latter is provided as an independent layer (time-grid) and indicates the most representative date of the observations ensemble used to compute the BRDF-adjusted NDVI. The analyses of BRDF-adjusted NDVI time-series presented in Section 4 use the aforementioned time-grid.
# Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
## 1 Introduction
### Preparation of Data for the System  
#### 1.3.1 Creation of Disease Vectors  
This section discusses the process of creating disease vectors from the medical interviews, the classification of responses and the data sources. Patient interviews identified key symptoms and transformed them into diagnostic vectors, assigning values to the responses. A description of the disease classes that are diagnosed by the system based on patient responses is also presented, and a discussion of how response vectors are interpreted, where each number value in the vector reflects the patient's response to the questions. Questionnaires, anonymous epics and medical literature were used to collect data to support the accuracy and efficiency of the diagnosis.  
As part of our research, we conducted a series of medical interviews with volunteer patients to collect anonymised clinical data. During each interview, questions were asked about the symptoms, the circumstances of the symptoms and the patient's feelings about specific complaints such as chest pain, its location, nature and intensity. The patients' responses were then transformed into vectors in which each column corresponded to a specific question, while the last column represented the final diagnosis made by the doctor based on the information collected. In order to assess the effectiveness of the classification of the different conditions, these vectors were analysed using the Monte Carlo Cross Validation (MCCV) method with 25 repetitions. This analysis allowed an estimation of the diagnostic system's effectiveness in diagnosing diseases by central and other classes, allowing an assessment of how accurately the diagnosis of selected diseases can be predicted from medical interview data. The test parts simulated previously unseen values. We chose a variational Monte Carlo method due to the low availability of samples for each class. A sample of interview questions and an example of a vector, representing one patient's responses, are shown below to illustrate the structural distribution of the data collected in the study.
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 4 Implementation Strategy
### DTN Module Implementation  
The DTN Module operates as an event-driven system that processes packets based on the state of the network and the requirements of the delay-tolerant protocol. Its implementation involves several components working in harmony to provide robust DTN functionalities. No strict guideline is available as of now for its implementation, but sticking to the structure outlined in the previous section, it is suggested to rely on already existing work and customize it for our needs. For instance, a wide variety of implementations of CGR are available, so an implementation from scratch of the Routing Function is not necessary. What is more relevant to discuss is its integration with LwIP. This is achieved through a custom hook mechanism tailored to intercept targeted traffic at essential points in the LwIP processing pipeline. The hook must be implemented within functions such asip_input() or ip_forward() in the LwIP stack. These functions are responsible for processing inbound packets and forwarding packets not destined for the local node, respectively. The hook inspects each packet and determines whether it meets the criteria for DTN processing. Criteria may include checks on the destination IP address, protocol fields, or application-layer identifiers.
###### Abstract
[26] presented a temporal convolutional network-residual self-attention mechanism (TCN-RSA), where the RSA scans through the global information and discovers local useful information, thus achieving the function of enhancing useful information and suppressing redundant information. The above method provides ideas for solving the problem of defects in rail inspection data. This paper presents a new approach to deriving the RUL of railway rails from the vibration information caused by wave abrasion and other injuries in heavy railways and to construct a feature extraction module, CNN-BiLSTM, which extracts the temporal features of the data and effectively extracts the forward and reverse dependencies of the data without requiring significant familiarity with the failure mechanisms of the rails. This effectively overcomes the limitations of the above literature in performing more comprehensive feature extraction from the data, including the challenge of using expert knowledge to build accurate damage-tracking models. Different attention mechanisms have satisfactory results. However, the self-attention mechanism in the above literature involves a large number of weight matrix operations; the optimization of the weight matrix optimization is usually difficult in traditional deep learning methods, the gradient of the error function must be back-propagated layer by layer, and the error function has a poorer optimization effect or the gradient disappears after back-propagation on the weight matrix. To address this problem, the RSA module is constructed, which solves the above problem by adding the residual self-attention mechanism with residual connection; at the same time, the RSA can also obtain the internal correlation features, which effectively improves the expressive ability of the model. The proposed method is validated and analyzed by experimenting with the vibration data collected by our team in a railway section. The contributions are as follows:  
1. A CNNBiLSTM-RSA model is proposed to establish an end-to-end prediction model between monitoring the data and the remaining service life of rails by using indirect data such as vibration signals caused by rail damage. A case study of the proposed method is carried out on different types of rail damage and roll bearings, and the CNN-BiLSTM-RSA has a better nature in terms of prediction accuracy, as well as a certain generalization ability. 2. The CNN-BiLSTM feature extraction module is constructed to make full use of the advantages of both to enhance the feature extraction capability, which can adaptively extract features and reduce the influence of artificial factors to a certain extent. The rest of the paper is structured as follows: The framework of the presented prediction method is presented in Section 2, and the individual components are describedin detail. Section 3 presents the rail data and details the experimental results and related analyses. Finally, the conclusions are presented in Section 4.
## 2 Materials and Methods
### Testing
#### 2.3.3 Electrical Conductivity Measurements  
The electrical conductivity in the direct current (d.c.) of the investigated samples was assessed via a two-contacts method using a Metrohm-Autolab potentiostat, model PGSTAT302N operated in galvanostatic mode and a Keithley 6517A electrometer connected to Keithley 8009 test fixture (Keithley Instruments). Measurements were conducted at room temperature (23 \(\pm\) 1 \({}^{\circ}\)C) and the electrical conductivity values represent an average of at least five repetitions.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 4 Control Algorithms
### Manual/Automatic Mixed Control Based on Impedance Control  
In the teleoperation assembly system, the end of the robot was mounted with a 6-DOF force/torque sensor to measure the force between the gripper and the environment. Based on this force sensor, an impedance controller was developed. As a compliance control method, the Cartesian impedance control does not control the desired position directly. Instead, it converts the error between the desired force and the real contact force into a position command, thus achieving the desired contact force. The Cartesian impedance control was designed for achieving remote compliance assembly based on the force/torque sensor.  
The Cartesian impedance controller establishes a mass-damper-spring relationship between the Cartesian position \(\Delta\)x and the Cartesian force \(F_{d}\), expressed by Equation (4):  
\[m_{d}\Delta\ddot{x}+b_{d}\Delta\dot{x}+k_{d}x=F_{d} \tag{4}\]  
\[\Delta x=[x-x_{d}] \tag{5}\]  
where \(m_{d}\), \(b_{d}\), and \(k_{d}\) are the virtual inertia, damping, and stiffness, respectively, and \(x\) and \(x_{d}\) represent the real and desired position, respectively.  
The strategy of manual/automatic mixed control strategy based on Cartesian impedance control is shown in Figure 3. When the task starts, the motion-tracking device is operated by the operator, and the command position is transmitted to the remote robot or the virtual environment. During the period from the starting point to the target point, the manual operation mode is adopted. The operator controls the remote manipulator to approach the target position flexibly with the assistance of vision and human intelligence. According to the requirement that enables automated hole finding [31], when the operator observes that more than half of the cross-sectional area of the rod is projected in the hole, the signals for control mode conversion are sent by the operator, and the automatic assembly method is carried out. The robot arm moves gradually toward the current axis direction and realizes hole searching through planar Archimedes spiral search strategy. Then, the remote robot accomplishes automatic and compliant peg-in-hole assembly with the Cartesian impedance control algorithm.
## 1 Introduction
It is not clear that these displacements are mainly related to the structural configuration or incoherent ground motions or the analytical method itself. For instance, the displacements at structural supports might cause a change of the structural configuration, which could result in a leap of the structural vibration modes to an extent. When the differences of displacements at several structural supports are great enough, this mode leap could not be neglected in the structural response. Besides, all the elements of an ideal structural model synchronously work under a ground motion. But, in the real situation, a structure presents two different features: (a) the structure undergoes a relatively low-intensity ground motion in the linear phrase of structural vibration; (b) all the parts of the structure could achieve their working states, only when the ground motion reaches to a certain intensity. This implies that the MSRS method might overestimate or underestimate the anti-seismic ability of the structure, even in its linear analysis. As mentioned above, an updating concept is to make the long-span and multi-support structures keep in a linear/elastic working state, even under a strong shock. In addition, although these structures simply consist of a large-span beam and two piers, their response under dynamic loading is complex and difficult to accurately calculate. Hence, the improvement on the existing analytical techniques is expected to address the issues raised in long-span and multi-support structures. In view of the problems mentioned above, a modified MSRS method in this study is proposed to calculate the seismic response of long-span or multi-support structures under incoherent ground excitations more concisely and accurately, which can provide the useful reference to the rational seismic design of the bridge with multi-supports. Three ancillary methods are tried inclusive of making the structural displacement vector, making infinite stiffness members at supports, and increasing the degrees of freedom at structural supports. Furthermore, this study tries to obtain the collective structural response spectrum and structural power spectrum from the equation of the motion taking ground motion displacements as the input; meanwhile, it proposes the simplified structural power spectrum.
# RMRRASL: Robust Matrix Factorization with Robust Adaptive Structure Learning for Feature Selection
## 3 Robust Matrix Factorization with Robust Adaptive Structure Learning
### The Proposed RMFRASL Model
#### 3.1.2 RASL Model  
Although the proposed RMFFS model can implement feature selection, it does not sufficiently consider the local structure relationships among original data, which may lead to unsatisfactory performance. Therefore, a robust structure learning method based on the self-representation capability of the original data is proposed, which can learn non-negative representation coefficients to indicate the structural relationships among original data. In order to learn more discriminative non-negative representation coefficients, an error metric function with \(L_{21}\)-norm is imposed. Hence, the objective function of RASL is defined as follows:  
\[\begin{array}{l}\min\partial(Z,W)=||Z^{T}-Z^{T}W||_{2,1}\\ \text{s.t. }W\geq 0\end{array}, \tag{10}\]  
where \(W=[w_{1},w_{2},\ldots,w_{n}]\in R^{n\times n}\) denotes the non-negative representation coefficient matrix. The element of matrix \(W\) with a larger value means that the correlation between two data is more remarkable.