esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methodology
### Preprocessing the Categorized Signals  
Preprocessing is a crucial step in preparing the explicit and implicit signals for ML models. It ensures that the data are clean, structured, and ready for effective analysis. The preprocessing techniques differ for explicit (structured) and implicit (sequential) signals due to the nature of the data.
## 3 Dynamic Model
### Dynamic Model under Disturbance of Wind Field  
In the turbulent environment, the wind field force on the rotor of UAV is recorded as \(f_{wi},i=1,2,3,4\), \(f_{wi}=2\rho SV_{w}V_{i}\), \(\rho\) is the air density, \(S\) is the force bearing area of the rotor, \(V_{w}\) is the wind speed value, and \(V_{i}\) is the induced speed. The direction of the wind field force is consistent with the direction of the lift force. According to the derivation equation of the rotor lift in (9), the wind field disturbance amount \(w=\begin{bmatrix}w_{1}&w_{2}&w_{3}&w_{4}\end{bmatrix}^{T}\) can also be obtained. When the flight attitude of UAV changes slightly, the pitch angle and roll angle are approximately considered as \(\phi\approx 0\), \(\theta\approx 0\). Substitute it into (14) to acquire: \(\begin{bmatrix}\dot{\phi}&\dot{\theta}&\dot{\psi}\end{bmatrix}^{T}=\begin{bmatrix} p&q&r\end{bmatrix}^{T}\). Therefore, the dynamic model of UAV under turbulent disturbance is obtained as:  
\[\left\{\begin{array}{l}\ddot{X}=\frac{(\sin\psi\sin\theta+\cos\psi\sin\theta \cos\phi)(u_{1}+w_{1})}{m}-\frac{k_{1}\dot{X}}{m}\\ \ddot{Y}=\frac{(-\cos\psi\sin\phi+\sin\psi\sin\theta\cos\phi)(u_{1}+w_{1})}{m }-\frac{k_{2}\dot{Y}}{m}\\ \ddot{Z}=\frac{(\cos\theta\cos\phi)(u_{1}+w_{1})-mg}{m+1}-\frac{k_{3}\dot{Z}}{ m}\\ \ddot{\phi}=\frac{I_{Y}-I_{Z}}{I_{X}}\dot{\theta}\dot{\psi}+\frac{I_{W}}{I_{X} }-\frac{I_{C}\Omega_{x}}{I_{X}}\dot{\phi}-\frac{k_{4}\dot{\phi}}{I_{X}}+w_{2} \\ \ddot{\theta}=\frac{I_{Z}-I_{Y}}{I_{Y}}\dot{\phi}\dot{\psi}+\frac{I_{W}}{I_{Y} }-\frac{I_{C}\Omega_{x}}{I_{X}}\dot{\phi}-\frac{k_{5}\dot{\theta}}{I_{Y}}+w_{3 }\\ \ddot{\psi}=\frac{I_{Y}-I_{Y}}{I_{Z}}\dot{\phi}\dot{\theta}+\frac{I_{W}}{I_{Z} }-\frac{k_{5}\dot{\theta}}{I_{Z}}+w_{4}\end{array}\right.. \tag{15}\]
###### Abstract  
Ecological restoration programs are expected to control environmental deterioration and enhance ecosystem functions under a scenario of increasing human disturbance. The largest ecological restoration program ever implemented in China, the first round of the countrywide Grain for Green Program (GGP), finished in 2010. However, it is not known whether the ecological changes that resulted from the GGP met the restoration goal across the whole implementation region. In this study, we monitored and assessed the ecological changes in the whole GGP region in China over the lifetime of the first round of implementation (2000-2010), by establishing a comprehensive assessment indicator system composed of ecosystem pattern, ecosystem quality (EQ), and key ecosystem services (ESs). Remote sensing interpretation, ecological model simulations based on multi-source images, and trend analysis were used to generate land use and land cover (LULC) datasets and estimate ES and ESs indicators. Results showed that while forest increased by 0.77%, artificial land increased more intensely by 22.38%, and cropland and grassland decreased by 1.81% and 0.68%, respectively. The interconversion of cropland and forest played a primary role in ecosystem pattern change. The increase in ecosystem quality measures, including fractional vegetation cover (0.1459% yr\({}^{-1}\)), leaf area index (0.0121 yr\({}^{-1}\)), and net primary productivity (2.6958 gC m\({}^{-2}\) yr\({}^{-1}\)), and the mitigation of ecosystem services deterioration in soil water loss (\(-\)0.0841 t h yr\({}^{-1}\)) and soil wind loss (\(-\)1.0071 t h yr\({}^{-1}\)) in the GGP region, indicated the positive ecological change in the GGP region to some extent, while southern GGP subregions improved more than the those in the north on the whole. The GGP implementation other than climate change impacted ecological change, with contributions of 14.23%, 9.94%, 8.23%, 30.45%, and 18.05% in the ecological outputs mentioned above, respectively. However, the water regulation did not improve (\(-\)2283 t km\({}^{-2}\) yr\({}^{-1}\)), revealing trade-offs between ecosystem services and inappropriate afforestation in ecological restoration programs. Future GGP implementation should change the practice of large-scale afforestation, and focus more on the restoration of existing forest and cultivation of young plantings, formulating rational and specific plans and designs for afforestation areas through the establishment of near-natural vegetation communities, instead of single-species plantations, guided by regional climate and geographical characteristics.  
**Keywords:** Grain for Green Program; ecological monitoring and assessment; land use and land cover; ecosystem services; trade-offs; remote sensing
# Resource Allocation Schemes for 5G Network: A Systematic Review
## 5 Discussion
### Q4. Which Metrics and Parameters Are Considered during Resource Allocation in 5G?
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metrics** & **References** \\ \hline Response Time & [119] \\ \hline End-To-End Delay & [100,149] \\ \hline Delay & [94,103,104,106,115,116,124–126,148,153] \\ \hline Throughput & [89,93,96–98,104,106–108,110,115,120–126,131,137,142,144,150,158] \\ \hline Packet Loss & [142,147] \\ \hline Latency & [102,103,109,115,121,128,123,134,147,148,151] \\ \hline Overhead & [95,114,122,130] \\ \hline Jitter & [96] \\ \hline Availability & [93,121] \\ \hline Spectral Efficiency & [89,90,92,95,97,105,112,113,117,127,136–138,155,156,158] \\ \hline Fairness & [89,93,96,98,106,107,113,114,132,137,138,144] \\ \hline Outage Ratio & [89,94,145] \\ \hline Sum Rate & [90,94,101,103,113,118,135,146,154,158] \\ \hline Energy Efficiency & [86,90,97,103,110–112,114,118,124,134,136,138–144,152] \\ \hline System Performance & [91,95,99–101,105,108,113,117,122,132] \\ \hline Low Complexity & [91,95,96,98,99,107,108,110,114,117,130,132] \\ \hline Power Allocation & [86,91–95,97,110,111,113,118–120,132,134,139,152] \\ \hline Reliability & [102,124,128,133,155] \\ \hline Time Required for RA & [104] \\ \hline Scalability & [108] \\ \hline Interference & [100,112,113,119,120,132,146,152,154,156] \\ \hline Power Consumption & [126,156] \\ \hline Feasibility & [128] \\ \hline Energy Consumption & [129,143] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Metrics used in 5G Resource Allocation. Table 6 illustrates the domains used for this literature review consisting of fronthaul, C-RAN, H-CRAN, backhaul. Figure 8: Year-wise analysis of metrics used in 5G resource allocation. Figure 9: Analysis of metrics used for 5G resource allocation. Figure 10 presents the number of papers reviewed in this extensive literature review that discussed downlink and uplink resource allocation from the perspective of fronthaul, C-RAN, backhaul, and HC-RAN. The number of reviewed papers was 32 for fronthaul, 13 for CRAN, 5 for Backhaul, and 4 for HC-RAN for downlink schemes while the uplink schemes papers reviewed were 22, 7, 3, and 2, respectively.
## 2 Material and Methods
### Metataxonomic Analysis  
Total DNA was extracted from each sample using the DNEasy PowerSoil Pro Kit (Qiagen, Hilden, Germany), following the manufacturer's recommendations. After extraction, the DNA was quantified using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). The V3-V4 variable regions of the bacterial 16S rRNA gene were amplified from the total DNA extracted, using primers 341F and 805R. To amplify the ITS region of the fungi, primers 3F and 805R were used [17], both coded with Nextera indices, according to the manufacturer's instructions (Illumina Inc., San Diego, CA, USA). The  
Figure 1: Geographic distribution of coffee processing regions in Brazil with a focus on the sampling area in Santa Catarina state.  
samples were sequenced in pairs (2 \(\times\) 250 bp) using the MiSeq v2 reagent kit and run on a MiSeq platform (Illumina, San Diego, CA, USA). The raw reads obtained during sequencing were analyzed using the parameters of the QIIME (Quantitative Insights into Microbial Ecology) software. Short (<100 bp) and low-quality sequences containing more than one ambiguous base (N) were eliminated. Subsequently, the high-quality reads obtained were aligned with the SILVA database [18] using the UCLUST method [19].  
Taxonomic allocation and the generation of operational taxonomic units (OTUs) were carried out using a cut-off point of 97% sequence identity. The number of OTUs obtained in the metagenomic analyses was used to calculate the diversity (Shannon and Simpson) and richness (Chao) indices in the Past program, version 4.03.
# HCLE: Hybrid Clustering Approach for Extending WBAN Lifetime
## 5 Simulation Results and Analysis
### Experimental Testbed Setup  
Our experimental testbed was established within an academic unit's laboratory. The network topology in our testbed comprises 10 sensors, denoted as node 1 through node 10, and a central sink node identified as node 0. The system structure of our network is illustrated in Figure 4. To collect data from sensor nodes, we employed Crossbow MICAz motes running the TinyOS embedded operating system in our testbed, each equipped with a 1.2-inch-long monopole antenna with a wavelength of one-quarter inch. These motes were programmed to transmit sensing (i.e., physiological) data to the sink node at 3-min intervals. Communication among the nodes was established using the Chipcon CC2420 (Chipcon, Oslo, Norway) transceiver operating at 2.4 GHz, which facilitates data exchange among sensors.  
In our network, the sink node (node 0) serves as the gateway, receiving data from the sensors and relaying it to the backend servers. This sink node utilizes USB connectivity to establish communication with the cloud, facilitate configuration, and supply power. Therefore, it could be connected to any PC using a USB cable, as shown in Figure 4. All sensor nodes, except the sink node, were powered by AA batteries and had sufficient power (i.e., RSSI) to communicate with each other and the sink node. The transmission power in our testbed was configured within the range of 0 dBm (1 mW) to \(-25\) dBm  
Figure 4: System architecture.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Initial energy of the sensors & 4 joules \\ Number of sink nodes & 1 \\ Number of sensor nodes & 10 \\ Packet size (\(k\)) & 127 bytes \\ Reference distance (\(d_{0}\)) & 10 cm \\ Frequency (\(f\)) & 2.4 GHz \\ Path loss coefficient (\(\eta\)) & 3.38 \\ Standard deviation (\(\sigma\)) & 4.1 dB \\ Mean (\(\mu\)) & 0 \\ Energy threshold (\(Th\)) & 2 joules \\ Number of serving nodes for parent node (\(\tau\)) & 4 \\ Total number of rounds (\(R\)) & 35,000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental parameters.  
(0.003 mW) to minimize interference and conserve radio power consumption. The primary objective of this testbed was to assess the performance of the WBAN and gather additional data for future investigations.  
In wireless networks, high RSSI values indicate reliable data transfer capabilities. Thus, our work leveraged the data collected from the sensor nodes in our testbed to model RSSI using a log-normal distribution. The choice of log-normal distribution was validated through a goodness-of-fit assessment using a QQ-plot, confirming its suitability for our data as shown in Figure 5. The log-normal distribution, widely applied in this domain, offers a probability density function (PDF) that effectively characterizes the distribution of RSSI values. By adopting this approach, we gain a deeper understanding of RSSI behavior and its implications within our work.
# Designing a C-ITS Communication Infrastructure for Traffic Signal Priority of Public Transport
## 3 Methodology and Results
### Capacity and Delays on the Pilot Underground Railway Section U5  
The analysis and evaluation of how and whether delays in the aboveground section of the U5 increased or decreased is an essential and fundamental basis for evaluating the acceleration of public transport vehicles and local rail transportation in the current state. On the basis of this information and procedure, the existing potential can be estimated and developed using the new concept.  
The evaluation of the delays was time-differentiated with regard to the following criteria:  
* Mean day of the week from Tuesday to Thursday;
* A mean over typical Saturdays;  
Figure 4: Comparison of the radio propagation based on the estimate (**left**) and the measurements (**right**) in the southern section of the U5 route.  
* A mean over typical Sundays. Furthermore, the evaluations were carried out for all trips/voyages that took place on the specified days on the basis of data records of the onboard computers of the VGF AVM system. For further chronological differentiation, the data records were evaluated in hourly slices. Result 4: The \"outgoing\" evaluation provided factual evidence that vehicles were already leaving the tunnel with delays. It was also shown that, on average, delays built up for vehicles, especially in the first section of the U5. Even on route and confined to its own tracks, a slight reduction in delays was possible, but this did not compensate for the losses resulting from the first section. The trips on the evaluated Saturdays and Sundays showed similar behavior. It is necessary to note that the delays for vehicles exiting the tunnel were much lower on Saturdays than on weekdays. On Sundays, for example, the trains left the tunnel with almost no delays and on time, and then they built up slight delays, especially in the first section. The \"incoming\" evaluation showed that almost all vehicles started without delay. This means that the buffer times within the turning facilities were adequate, and it was a rare occurrence that a vehicle carried over a delay from the previous round, bringing it into the current trip. Subsequently, it was observed that the delays built up. The trips on the evaluated Saturdays and Sundays showed similar behavioral integrity, i.e., a rough punctual start at the turning facility and a gradual build-up of delay.
## 3 Results and Discussion
### Evaluation of the Therapeutic Capacity of the DOX-NP (7)  
In order to verify the enhanced anticancer effect of doxorubicin conjugated to DOX-NPs (7), the proliferation inhibition was tested by measuring the cell-mediated reduction of sodium resazurin, a standard colorimetric and quantitative method that determines the cell viability on the cell lines studied [33]. A549, H520, and MDA-MB-231 cells were treated with a range of different NPs concentrations for 96 h. Free DOX as well as nanoparticles without the drug loaded (NAKED-NPs (1)) were use as positive and negative controls, respectively. As shown in Figure 3a,b, the cell proliferation inhibition efficacy of DOX-NPs (7) exhibited a strongly dose-dependent pattern after culture for 96 h. It is important to remark that the naked NPs (1) without doxorubicin have no effect on the cell viability of the three cells lines (Figure 3b). Half the maximal inhibitory concentration (IC50) was determined (Figure 3a). The IC50 value of DOX-NPs (7) was calculated to be 42 nM in the A549 cells, 68 nM in the H520 cells, and 58 nM in the MDA MB 231 cells (Figure 3a), which correspond to 3061, 5012, and 4325 NPs added per cell, respectively (Supplementary Figure S5, see Supplementary Material). DOX-NPs (7) were found to be considerably more efficient than free DOX on an equimolar basis (IC50 = 100 nM in A549, 186 nM in H520, and 120 nM in MDA MB 231 cells) (Supplementary Figure S6, see Supplementary Material). These results are in agreement with previously reported studies with free doxorubicin [13; 14]. These values indicated that the doxorubicin conjugated to the nanoparticles reduced the amount of drug required to achieve the IC50 value with respect to free doxorubicin treatment, suggesting that the nanoformulation had an enhancement effect. The higher cytotoxic activity of the DOX nanoformulation when compared with the free DOX, especially in the range of higher drug concentrations, is presumably due to selective pH stimuli release of the drug and the accumulation within tumor cells.  
In order to evaluate the impact of the nanoformulation on the required dose of the drug to achieve the therapeutic effect, the effect on cell viability of the treatment with DOX-NPs (7) compared to a similar concentration of free doxorubicin was measured. In particular, cells were treated with a dose of 40,000 NPs/cell of DOX-NPs (7) corresponding to a concentration of 500 nM of doxorubicin. As seen in Figure 3c, DOX-NPs (7) showed a greater cytotoxicity than free doxorubicin under cell culture conditions, the results being statistically significant with a \(p\)-value of \(<\)0.0001. These results show that the release of the doxorubicin conjugated to the nanoparticles was sufficient to successfully inhibit the cell proliferation of the three cancer cell lines tested. DOX-NPs (7) showed a therapeutic efficiency twice that of free doxorubicin, with results comparable to other nanoformulations [11; 13]. This fact suggests that this nanodevice is a promising tool for the selective release of drugs.
# Redefining Archaeological Research: Digital Tools, Challenges, and Integration in Advancing Methods
## 2 Traditional Methods vs. Digital Tools  
### Traditional Data Acquisition Methods in Archaeology  
The data acquisition methods used in archaeology have historically relied on field observations and measurements that often involved manual drawing on paper. Data collection, analysis, and visualization included a manual survey in the field to record site locations and features, drawing site plans and excavation details, and keeping records with notes, sketches, and photographs. Excavation-based methods such as trial excavations were invasive, costly, and often risked damage to archaeological features. Moreover, they were able to provide detailed information for only small areas. The use of topographic instruments such as theodolites usually accompanied on-site excavations, offering archaeologists data with geographic references [2]. A common practice, alongside topographic measurements, was the analysis and interpretation of aerial photographs for archaeological purposes. Early aerial photography improved the research processes, but the images were often qualitative and lacked detailed analysis. This process relies on individual observation and depends primarily on the user's level of expertise [3]. In general, these approaches were time-consuming, prone to inaccuracy, and, more significantly, made it difficult to analyze large datasets or integrate multiple data sources. Any statistical analysis of the spatial relationships among sites used basic tools that often limited the scope and precision of interpretations.  
Three-dimensional visualizations included hand-drawn illustrations and physical models. Detailed drawings and cross-sections were created to represent artefacts and structures, but these methods often lacked consistency. Physical models, such as plaster casts, provided three-dimensional representations but were fragile, labor-intensive, and difficult to relocate. Overlayed photos, either terrestrial or aerial, resulted in 3D models that offered a limited sense of depth. In addition, manual measurements were prone to errors and could only provide partial data on the three-dimensional aspects of features.  
Finally, traditionally, archaeologists kept their records in notebooks, often accompanied by sketches and measurements. Site locations and features were plotted on paper maps using tools like a compass and basic topographic instruments like a theodolite, but this process was slow and prone to errors. Data from field surveys were manually logged and later transferred to databases, a time-consuming task that introduced potential transcription errors.
## 4 Conclusions and Outlook
However, minimizing the SiN interlayer thickness without affecting the electrical performance of the GaN device layer during the growth of diamond in harsh environments remains a significant challenge. Room-temperature SAB is an alternative method to solve the problem of bonding GaN to diamond. A low TBR of \(\sim\)10.9 m\({}^{2}\) K/GW has been achieved by mixing Si atoms into the Ar ion beam during the SAB process, with TBR expected to be further reduced by using other interfacial layers, such as SiC and SiN\({}_{\rm X}\). In conclusion, a decrease in the TBR between the GaN and substrate is crucial for thermal management in both GaN-on-SiC and GaN-on-diamond semiconductor devices. Using an interlayer is an effective method to reduce TBR by enhancing the bonding between GaN and the substrate. However, the optimization of the growth method and growth conditions to minimize the defect density near the interface, reduce the phonon scattering rate, and enhance the interfacial bonding to increase the phonon transmission probability across the interface needs further investigation. Furthermore, the SAB method is a promising candidate for replacing the conventionally used CVD growth method. However, the number of studies on this topic is still limited, and further investigation is required. Conceptualization, supervision, writing--original draft preparation: T.Z. ; supervision, project administration: F.N., T.W., H.K. and T.Z. ; writing--review and editing: M.X., Z.C., C.Z., H.K., F.N., Y-J.W., Y.X., H.W., M.S., W.W., Y.Z., X.L., Y.S., Y.J., S.G., T.H., T.M., A.O. and T.W. All authors have read and agreed to the published version of the manuscript. This work was supported by a JSPS Core-to-Core Program grant (JFJSCCA20200005), a JSPS Grant-in-Aid for Scientific Research (B) (22H01530), a JSPS Grant-in-Aid for Scientific Research (C) (21K04886), and a research grant from the Murata Science Foundation. Not applicable. The authors declare no conflict of interest.
## 3 Results and Analysis
### Predicted and Measured Compressive Strength Relationships  
#### 3.4.1 The Nonlinear Regression Model (NLR)  
The connection between the anticipated and actual compressive strengths of normal concrete, including polymer content, is shown in Figure 6a. The most significant parameters affecting compressive strength were curing time and cement content. The following formula was derived for the NLR model with numerous changeable parameters (Table 5).
## 3 Results and Discussion
### Practical Implications  
Comparing the two options, we observed that water reclamation projects for the irrigation of plants and green areas are considerably more cost-effective than those for industrial water supply. Therefore, the irrigation of plants and green areas is considered a recommended entry point for water reclamation in industrial parks due to its high NPVs, IRRs, and BCRs and short PPs (<3 years). However, the water demand for irrigation in industrial parks is generally low, not more than 23% of the WWTP effluent, based on the actual amount of irrigation water in industrial parks in Chonburi (Table S9). Therefore, another water reclamation option, such as reclaimed water for industrial water supply, can be combined to achieve a higher percentages of water reuse.  
In this study, a wide range of discount rates was applied in the feasibility studies to provide an overall picture of how environmental benefits affect the feasibility of water reclamation projects. It was not our intent to perform feasibility studies for specific projects. Investments in water reclamation projects could be made by different companies with different discount rates. Given that each company may have vastly different financial costs, lending interest rates, and/or shareholder returns, discount rates are likely to vary widely. It is recommended that the results of this study be used with regard to appropriately selected discount rates.