esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Based Polymers with Potential for Biodegradability
## 3 Poly(lactic acid) (PLA) and Related Polymers  
Among the biodegradable polymers, poly(lactic acid) (PLA) is the one of the most important. PLA has been extensively studied for a wide range of applications covering disposable household items, food packaging, agricultural films, drug delivery systems, and implantable biomedical devices [110]. There has been an abundance of literature in recent years related to the production of lactic acid and PLA. There have been recent reviews on lactic acid related to production of lactic acid from lignocellulosic [111] production by fermentation processes [112], and lactic acid as a platform chemical for chemical synthesis [113]. For PLA, there have been many reviews of processing technologies [114], with some focused specifically on PLA foaming [115], PLA crystallization [116], and PLA-based nanocomposites [117]. Lactic acid (2-hydroxypropanoic acid) has two optically active configurations lactic acid (L-(+)-lactic acid and D-(-)-lactic acid), as shown in Figure 1[118]. Both L- and D-enantiomers are produced naturally [119].  
PLA is a linear, aliphatic polyester thermoplastic that is produced commercially by ring opening polymerization of lactic [120]. The ratios of L- and D-isomers influence the properties of PLA [116]. High molecular weight polymers are now possible due to advances in processing techniques greatly expanding the range of applications where PLA can be used [121].  
Figure 1: Isomeric forms of lactic acid.
# Abrasive Surface Finishing on SLM 316L Parts Fabricated with Recycled Powder
## 2 Experimental Procedure
### Machine Setup and Important Notes  
The Renishaw company offers two default sets of printing parameters, with laser power of 200 and 400 W. According to our findings, parts fabricated with the default set with laser power of 200 W have better overall mechanical properties [28]. The scanning parameters of this set are listed below in Table 1.  
From Table 1, it can be observed that a parameter, namely increment rotation angle, was set to 67\({}^{\circ}\). With this setup, the coordinate system of each layer is rotated by 67\({}^{\circ}\) to ensure that the inside area of the fabricated parts is filled with printed material as much as possible. However, if the rotation point is fixed, the resulting parts would be hollow in the middle. Thus, additional setup to randomize the rotation point for every layer is needed. This principle is explained in Figure 4 below. Notably, the below figure utilizes straight scanned tracks for better illustration, but is applicable as well for meander and stripes strategies.  
Additionally, two functions, namely Upskin and Downskin, available in Renishaw machine, were utilized to remelt the exposed surfaces, hiding the scan track patterns, subsequently, improving the smoothness of the specimens. The Upskin effect is illustrated below in Figure 5. It is worth mentioning that the exposed surfaces inherit the pattern of stacked layers, which is depicted in Figure 4, and Figure 5 is simplified for clearer illustration.  
The \"staircase\" effect and the Upskin-treated surfaces are further elaborated on in Figure 6. Remarkably, Figure 6 is valid as well for upside-down direction, as in the case of Downskin function. Differences in Downskin situation lie in the presence of support structures utilized for overhang details.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Laser power & 200 W \\ Hatch spacing & 0.11 mm \\ Scan speed & 650 mm/s \\ Preheat temperature & Ambient \\ Layer thickness & 50 μm \\ Increment rotation angle & 67\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fabrication parameters.  
Figure 4: Stacked layers with a fixed rotation point and randomized rotation points.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 4 Simulation Results
### Simulation Parameters  
The parameters adopted in the simulation experiment are summarized in Table 1. \(\eta\in(0,1)\) is the energy conversion efficiency, whose default value is 0.6 (For example, the 2.45 GHz microwave rectifier can achieve energy conversion efficiency much larger than 0.6 in the linear region [40]. Thus, we select energy conversion efficiency as 0.6 to perform our simulations.) \(\lambda_{\text{SR}}\) and \(\lambda_{\text{RD}}\) are the mean values of random variable \(Y\) and random variable \(X\), respectively, and we use \(\lambda\) for them for simplicity. \(m\) is the path loss coefficient with the default value of 2; \(R\in(0.5,2)\) is the minimum transmission rate required by the system with the default value of 1 bit/s/Hz. \(\rho\in(0,1)\) is the time assignment factor with the default value of 0.4. \(P_{\text{sum}}\) is the total transmit power of the source node, which has the range is \((20,40)\) dBm, and the default value is 30 dBm; \(K_{\text{SR}}\) and \(K_{\text{RD}}\) are Rise factors of the links the from \(S\) to \(R\) and from \(R\) to \(D\), respectively, and we use \(K\) for them for simplicity. In order to observe the influence of various parameters on the experimental results, the \(K\) is set to 3. \(E_{\text{Q}}\) is the threshold value of relay circuit energy with the default value of \(5\times 10^{-5}\) J. \(N_{\text{S}}\in(2,4)\) is the number of source node antennas with the default value of 2. \(N_{0}\) is the noise power, assuming that the noise power at the relay and destination nodes are both \(-25\) dBm.
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 3 Results and Discussion in Axle Recognition
### Toll Station Field Test Results  
To further verify the accuracy and robustness of the axle recognition model, the modular WIM system was installed at the entrance of Epang Palace highway toll station for field tests. The system included a data-collecting board and an upper computer. The data-collecting board obtained data from WIM sensors and transmitted them to the NVIDIA Jetson Xavier NX edge using an ethernet interface. And, the Transformer-GRU axle recognition model was deployed on the Jetson Xavier NX for a field test.  
The speed of the vehicles in this toll station lane (Figure 6) ranged from 0 to 30 km/h. In total, 9250 vehicles' axle recognition results were recorded. The actual axle number was determined through a manual review, and the model results are presented in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Weather** & **Number of** & **Number of** & **Accuracy of** \\
**Conditions** & **Vehicles** & **Incorrect Vehicles** & **Model** \\ \hline Rainy day & 4614 & 8 & 0.9983 \\ Sunny day & 4636 & 7 & 0.9985 \\ \hline Total & 9250 & 15 & 0.9984 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Field testing of the Transformer-GRU model.  
Figure 6: Axle recognition test site.  
The field test results showed that the accuracy is very similar between rainy and sunny days, and the accuracy improved by 0.33% compared with its performance on the test set. This improvement was primarily because two-axle and six-axle vehicles, which exhibited high accuracy with the Transformer-GRU axle recognition model, were the most frequently encountered at the toll station. The proportion of two-axle and six-axle vehicles had gradually increased, leading to a slight improvement in the model's accuracy. In addition, although the operating conditions for trucks at the toll station were more complex, with various special situations such as parking and reversing, the five weighing platforms could identify five times and obtain voting results from the five sampling signals of one vehicle, further enhancing accuracy. The axle recognition model demonstrated strong accuracy and robustness when vehicles traveled through the toll station.
# The Influence of Bearing Clearance on the Load Capacity of Gas Polymer Bearings
## 3 Results
### Run-Up Measurements  
Figure 6 shows the measured transient signals of the frictional torque (black line) and the rotational speed (light grey line) for two different bearing configurations. Figure 6a corresponds to the data for the configuration with a bearing clearance of 105 \(\upmu\)m and a static load of 10 N. In Figure 6b, on the other hand, the dataset of a configuration with a smaller clearance (25 \(\upmu\)m) and a higher static load (70 N) is presented. As the turbine is pressurised, the frictional torque (black line) increases over time until it reaches a peak. The rotational speed (grey line) remains at zero while the frictional torque reaches its maximum value. In the area of the drop in the frictional torque, the rotation of the shaft begins. Both graphs show similar characteristics up to the peak point. When the shaft begins to accelerate, the frictional torque rapidly decreases in a short amount of time. In the papers of [PERSON] et al. [6] and [PERSON] et al. [5], the frictional torque reached a local minimum after the bearing became airborne. The torque in Figure 6b shows a similar behaviour as that seen in these works, but in contrast, the torque does not increase after the lift-off event. An even larger deviation in the behaviour after lift-off is presented in Figure 6a. For the configuration with a 105 \(\upmu\)m clearance, a further decrease in the friction torque is detectable. Furthermore, the torque after lift-off does not show a local minimum. The minimum after the bearing becomes airborne was used as a criterion by [PERSON] [6] to determine the lift-off speed. It is not possible to name the exact reason for these differences, as there are a number of factors (e.g., temperature or material relaxation) that can influence the behaviour after lift-off. These factors will be discussed in Section 4.  
Due to the absence of a local minimum, another criterion was defined. In Figure 6, the time derivative of the normalised friction torque (red dashed line), also normalized by its maximum absolute value, is also displayed with the measured signals. The normalised time derivative increases as the turbine becomes pressurised until the shaft begins to accelerate. During the transition phase from static to air friction, the derivative forms a downward peak. After lift-off, the change in frictional torque is much slower, despite the rapidly increasing rotor speed. The fallback of the time derivative is used to find a criterion when the lift-off has finished. In the following, a 95% fallback of the derivative after the downward peak is defined as a criterion. As shown in Figure 6, this level is arbitrary, but very practical. Its application detects the lift-off region very well.
# Data Privacy and Ethical Considerations in Database Management
## 6 Conclusions and Future Work
### Future Work  
In future work, additional studies should determine the effectiveness of various data protection techniques by ensuring ongoing compliance with privacy regulations, investigating new anonymization and pseudonymization methods, and implementing monitoring and auditing systems.  
Additionally, it would be interesting to consider ways to make the database structure more efficient, possibly looking to further minimize the negative impact of additional security measures. These can be accomplished using more advanced data processing methods or optimizing database queries and operations.  
Future research could also focus on the following:  
Exploring advanced encryption techniques that balance security and performance more effectively.  
Investigating the use of blockchain technology for enhancing data integrity and traceability in healthcare databases.  
Developing more sophisticated anonymization techniques that better preserve data utility while ensuring privacy.  
Studying the long-term impacts of these data protection measures on healthcare outcomes and research capabilities.  
Exploring the ethical implications of emerging technologies like artificial intelligence and machine learning in healthcare data management.  
Investigating ways to implement the \"right to be forgotten\" in healthcare databases while maintaining data integrity and complying with retention requirements.  
Finally, it is imperative to stay updated with data privacy and security regulation changes, and monitor new technological developments and best practices in data management. These will ensure that the system remains adapted to the ever-evolving needs and requirements of the digital healthcare environment.  
Writing--original draft, E.P., J.R. and H.J.; Supervision, M.A. and P.M.; writing--review and editing, M.A., P.M., C.W., P.V. and J.S. All authors have read and agreed to the published version of the manuscript.  
This work is funded by National Funds through the FCT--Foundation for Science and Technology, I.P., within the scope of the project Ref. UIDB/05583/2020. Furthermore, we thank the Research Center in Digital Services (CISeD) and the Instituto Politecnico de Viseu for their support. [PERSON] thanks the national funding by FCT--Foundation for Science and Technology, I.P., through the institutional scientific employment program contract (CEECINST/00077/2021).  
Data is contained within the article.  
The authors declare no conflicts of interest.
###### Abstract  
Using in situ near-surface observations of solar-induced chlorophyll fluorescence (SIF) and gross primary productivity (GPP) of a subtropical evergreen coniferous forest in southern China, this study analyzed the dynamics of SIF, GPP and their environmental responses, and explored the potential of SIF in characterizing the variation of GPP. The results showed that SIF and GPP have similar diurnal and seasonal variation and both reach the highest value in summer, indicating that the SIF can be applied to indicate the seasonal variation of GPP for the subtropical evergreen co-inferous. With the increase in temporal scale, the correlation between SIF and GPP becomes more linear. The diurnal variations of both SIF and GPP were characterized by photosynthetically active radiation (PAR), the seasonal variations of SIF and GPP were influenced by air temperature (Ta) and PAR. Probably due to the absent of drought stress during the study period, no significant correlation was detected between soil water content (SWC) and either SIF or GPP. With the in-crease in Ta, PAR or SWC, the linear correlation between the SIF and GPP gradually decreased, and when Ta or PAR was relatively higher, the correlation between SIF and GPP become weakly. Further research is still needed to illustrate the relationship between SIF and GPP under drought condition which occurred frequently in this region based on longer observation.  
SIF; GPP; dynamic changes; environmental responses; temporal scale +
Footnote †: journal: plants  
0023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 4 Ground Roll Attenuation via NA-MEMD
### Field Data  
To further examine the ground roll attenuation performance of the NA-MEMD method, we processed multicomponent seismic data from land seismic acquisition. The data included two horizontal components and one vertical component. Figure 6a-c shows the raw seismic data that are recorded X, Y, and Z components from left to right, respectively.  
Figure 4: The ground roll attenuation results via NA-MEMD: (**a**) the horizontal component; (**b**) attenuated result of panel (**a**); (**c**) ground roll separated from panel (**a**); (**d**) the vertical component; (**e**) attenuated result of panel (**d**); (**f**) ground roll separated from panel (**d**).  
Figure 5: Comparison of the number of IMFs for the vertical component: reconstructed result using (**a**) the 3 rd–7 th IMFs; (**b**) The 3 rd–6 th IMFs; (**c**) the 3 rd–5 th IMFs; ground roll separated using (**d**) the 8 th IMF; (**e**) the 7 th and 8 th IMFs; and (**f**) the 6 th–8 th IMFs.  
It can be seen that seismic data in the near offset are contaminated by strong ground roll. We added three noise-assisted signals and decomposed data into eight IMFs. The ground roll with low velocity exists from the 112 th to the 170 th trace. Linear events with low frequency also ruin reflection waves. For each trace, we removed different numbers of IMFs. For the traces with ground rolls, we removed the first three highest index IMFs. For the rest, we removed only the first two highest index IMFs. Figure 6d-f displays the ground roll attenuation results via the NA-MEMD method for the raw three-component data, respectively. Figure 6g-i shows the ground roll and linear events separated with a low-frequency scale. From Figure 6d-f, we can see that the ground roll is successfully suppressed from the original data in Figure 6a-c.  
To further analyze the ground roll attenuation performance of the NA-MEMD method on field data, we compared it with the F-K filter method. Figure 7a,b displays the attenuation results and differences using F-K attenuation method for the vertical component, respectively. In Figure 6f,i, we can see that the ground roll is well attenuated by the NA-MEMD method. However, the F-K method suppresses the ground roll by cutting operation in the frequency-wavenumber domain, resulting in deletion of the low-frequency content of the reflection wave. Therefore, some weak reflection event energy remains in the difference section, as shown in Figure 7b. Figure 8a,d displays the waveform and amplitude spectra from the 90 th and 135 th traces, respectively. Although both methods successfully  
Figure 6: The ground roll attenuation results for field 3C data via NA-MEMD: (**a–c**) the raw data, (**d–f**) the attenuated results, and (**g–i**) the separated ground roll of three-component seismic data, respectively.  
suppress the ground roll and linear interference, the NA-MEMD method preserves the reflection wave better in the low-frequency range (<10 Hz), which is important for later high-resolution imaging.
# Traffic-Aware Optimization of Task Offloading and Content
## 3 System Model and Problem Formulation
### Execution Time and Energy Consumption Model
#### 3.2.3 Execution Time and Energy Consumption of Cloud Server Computing  
The cloud server \(c\) is situated at a greater distance from the task source compared to the edge server, resulting in potential latency. Hence, we opt to incorporate the cloud computing model for task processing only under specific conditions. These conditions include cases where the task demands extensive computational resources that surpass the processing capacity of the edge server or in scenarios where the edge server is already operating at its maximum capacity due to concurrent multitasking. Hence, the time \(TC_{q}^{i,n}\) of the task offloaded to cloud server \(c\) is defined as  
\[TC_{q}^{i,n}(t)=\alpha_{i,n}^{c}\times(\frac{d_{q}^{i,n}}{v_{n,c}^{i}(t)}+ \frac{c_{q}^{i,n}}{fc_{c}}+\frac{o_{q}^{i,n}}{v_{c}}) \tag{5}\]  
where \(v_{n,c}^{i}(t)\) denotes the data offload rate from the \(n^{th}\) vehicle to the cloud server within region \(i\) at time slot \(t\) and \(v_{c}\) is the backhaul transmission rate of edge server \(c\). Furthermore, the energy consumption of the cloud server is:  
\[EC_{q}^{i,n}(t)=\alpha_{i,n}^{c}\times(c_{q}^{i,n}\times(fc_{c})^{2}\times \varsigma+\frac{o_{q}^{i,n}}{v_{c}}\times\delta^{c}) \tag{6}\]  
Based on the analysis, the execution time for the task on the \(n^{th}\) vehicle in region \(i\) can be calculated according to the equation below:  
\[TT_{q}^{i,n}(t)=max(TL_{q}^{i,n},TM_{q}^{i,n},TC_{q}^{i,n}) \tag{7}\]Regarding energy consumption, the calculation for the task executed on the \(n^{th}\) vehicle in region \(i\) can be expressed as follows:  
\[ET_{q}^{i,n}(t)=EI_{q}^{i,n}+EM_{q}^{i,n}+EC_{q}^{i,n} \tag{8}\]
## 3 Communication Results by Experiments
### Description of Experiments
The length of the received signal after time-reversal transmission at the digitizer is twice as much as the length of the received signal attained from _Step 1_. 3. _Step 3 & 4: Data transmission via TR-PPM._ Timing acquisition is not conducted in our experiment, because the PXI system synchronizes the transmission and reception process between the arbitrary waveform generator and the digitizer. A fixed time offset can be determined. Here, we focus on data transmission. The time reversed signal, \(y(-t)\), obtained in Step 2 is employed as a basic waveform for information data transmission. The information data are a stream of binary digits (_i.e._, zero's and one's) and are coded by the pulse position modulation scheme, which takes the form of: \[s(t)=\sum_{j=0}^{M-1}ky(-t)\delta(t-jT_{f}-c_{j}\Delta)\] (33) where \(c_{j}\in\{0,1\}\) is the binary data to be transmitted, \(T_{f}\) is the frame time to send one bit data, \(\Delta\) is the additional time shift to distinguish between the pulses carrying the bit, zero or one. \(\delta(\cdot)\) is the Dirac delta function. The energy-normalization coefficient is given by \(k=\sqrt{E_{p}/E_{y}}\), where the total energy for signal \(y(-t)\) is given by \(E_{y}=\int_{-\infty}^{\infty}|y(-t)|^{2}dt\). In our experiments, the information data was a series of \(M=1,000\) binary numbers, zero's or one's, randomly generated by LabView programming. The frame time is determined by the prescribed transmitting data rate, \(R\), as follows: \[T_{f}=\frac{1}{R}\] (34) The tests were carried out at the data rates of \(10\) kbps, \(20\) kbps, \(50\) kbps and \(100\) kbps, which correspond to the frame time of \(100\,\mu s\), \(50\,\mu s\), \(20\,\mu s\) and \(10\,\mu s\), respectively. In order to restore the transmitted bit data, the time shift should be larger than the full width of the single Gaussian pulse. Here, we use one fourth of the frame time as the time shift, _i.e._, \(\Delta=\frac{1}{4}T_{f}\). The parameters for the TR-PPM test are summarized in Table 1. The recorded length is mainly determined by the length of the modulated time reversal signal. Here, we use twice the signal length for signal recording at the digitizer.
# Vulnerability Analysis of the China Railway Express Network Under Emergency Scenarios
## 3 Methodology
### Complex Network Indicators of CR
#### 3.1.1 Degree and Degree Distribution  
In CRN, the failure of a node or interruption of an edge may lead to changes in both the network topology and cargo flow. Primary nodes within the CRN are evaluated using metrics such as node degree, node betweenness, and node strength. Conversely, edge weight and edge betweenness are used to assess the importance of key routes within the CRN.  
To describe a CRN, the node degree (\(k\)) refers to the number of edges directly connected to the node. The degree distribution \(p(k)\) represents the probability of nodes with a degree of \(k\) among all nodes [21].  
\[p(k)=\frac{n(k)}{\sum_{j=1}^{\infty}n(j)} \tag{1}\]  
where \(n(k)\) represents the number of nodes with degree \(k\), and \(n(j)\) represents the number of nodes with degree \(j\). In complex network models, if the degree \(k\) of nodes in the network and the degree distribution \(p(k)\) follow a power-law distribution, then the network exhibits scale-free characteristics [22].