esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Results
### Simulation Results  
#### 4.3.1 Proposed Protocol versus LEACH and KMMDA  
As shown in Figure 4a, the proposed protocol showed a slower decline in the average residual energy over time, indicating more efficient energy usage compared to LEACH and KMMDA. As shown in Figure 4b, the proposed protocol maintained a higher number of surviving nodes for a longer period, suggesting that it better preserved the node lifetime and prolonged the network functionality. As shown in Figure 4c, the proposed protocol resulted in a consistently higher number of packets received by the base station, indicating a superior data transmission capability and network throughput. As shown in Figure 4d,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum number of iterations \(e_{max}\) & 2000 \\ Network communication rounds \(e_{p}\) & 2000 \\ Node initial energy \(E_{0}\) & 40 J \\ Length of water body \(l\) & 150 m \\ Node depth \(H\) & 50 m \\ Packet reception minimum sound level \(I_{0}\) & 1 uw \\ Non-cluster header packet size \(b^{CH}\) & 50 bits \\ Cluster header packet maximum \(b^{BS}\) & 500 bits \\ Node receives 1 bit of data energy & \(5\times 10^{-5}\) J \\ consumption \(e^{RX}\) & 0.2 s \\ Guard time slot \(t_{guard}\) & 1500 m/s \\ Underwater sound velocity \(v\) & 1500 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._the bar chart illustrates that the first node death occurred significantly later in the proposed protocol, demonstrating its effectiveness in energy distribution and management.  
In summary, the proposed protocol outperformed LEACH and KMMDA in these four metrics, leading to enhanced network performance and reliability.
# LCNS Positioning of a Lunar Surface Rover Using a DEM-Based Altitude Constraint
## 3 Moonlight and LCNS
### LCNS Payload  
The navigation payload in the LCNS satellites is responsible for broadcasting one-way ranging signals to the lunar users. Contrary to the Earth, the Moon does not have a significant atmosphere, removing the need for dual-frequency broadcasting. Furthermore, to avoid potential interference with Earth GNSS L-band signals (which may be used up to Lunar altitudes [28]) and in line with the Space Frequency Coordination Group (SFCG)'s R32 recommendation [33], the signals are transmitted using the S-band carrier frequency (2491.005 MHz). Finally, to keep the receiver implementation simple (note that lunar users may use both LCNS and Earth GNSS), a simple BPSK(5) (binary phase shift keying) modulation is used (similarly to the Galileo E6 BC signal). The signal characteristics are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Satellite** & **Semi-Major** & **Eccentricity** & **Inclination** & **Argument of** & **RAAN** & **True Anomaly** \\
**Number** & **Axis** & & & & \\ \hline
1 & 9750.73 km & 0.6383 & 54.33\({}^{\circ}\) & 55.18\({}^{\circ}\) & 277.53\({}^{\circ}\) & 123.42\({}^{\circ}\) \\
2 & 9750.73 km & 0.6383 & 54.33\({}^{\circ}\) & 55.18\({}^{\circ}\) & 277.53\({}^{\circ}\) & 0\({}^{\circ}\) \\
3 & 9750.73 km & 0.6383 & 61.96\({}^{\circ}\) & 121.7\({}^{\circ}\) & 59.27\({}^{\circ}\) & 180\({}^{\circ}\) \\
4 & 9750.73 km & 0.6383 & 61.96\({}^{\circ}\) & 121.7\({}^{\circ}\) & 59.27\({}^{\circ}\) & 0\({}^{\circ}\) \\ \hline \end{tabular}
\end{table}
Table 1: Kepler elements of the LCNS ELFO orbits. Note that the orbits have not been optimised and are therefore not representative of the Moonlight orbits. RAAN refers to the right ascension of the ascending node.  
The LCNS antenna boresight is pointed towards the centre of the Moon (i.e., nadir-pointing). As the distance between the surface and the Moon and the LCNS satellites is relatively small (compared to the Earth GNSS case), the EIRP (effective isotropic radiated power) can be kept small and is considered to be 15.02 dBW at boresight. The LCNS transmission antenna pattern is considered symmetric, and the off-boresight EIRP is provided in Figure 4. The transmission antenna pattern considered in this publication is based on internal ESA studies and aims to provide an example of a more representative pattern compared to previous publications from the same authors [29; 32; 34]. It is important to note that the final antenna patterns for LCNS are currently under assessment within the Phase A/B1 studies and are expected to be provide similar (or better) performances.
## 5 Discussion
### Prescribed Burning Emissions  
The prescribed burning emission estimations can differ from different methods (FINN or BlueSky) or fire activity data sources (adjusted FINN or permits). Prescribed burning emissions estimated by FINN or BlueSky have a high correlation for southeastern daily total emissions and a low correlation when making a fire event comparison. This result indicates that evaluating specific prescribed burning impacts on air quality would yield different results when the FINN or BlueSky method is employed. In this study, we used BlueSky to provide the prescribed burning emissions for the southeastern United States since the estimation of prescribed burning emissions in the CONUS (contiguous United States) from BlueSky differs from FINN in several ways. Firstly, BlueSky uses FCCS, which includes more detailed fuel types than FINN. Additionally, BlueSky separates wildfire and prescribed burning for emission calculation and integrates emission factors from laboratory or field studies concentrated in the United States [35], while FINN concentrates on global emission estimation and does not treat wildfire and prescribed burning differently. Moreover, BlueSky estimates fuel moisture, fuel consumption, and plume height by considering meteorological conditions. This information affects the magnitude and vertical structure of emissions. Meanwhile, FINN does not need meteorological conditions for emission estimation.  
Prescribed burning emission comparisons between adjusted FINN and permits show a similar correlation as burned area comparisons, which indicates that burned area estimation is important in prescribed burning emission estimations. For daily total emissions, adjusted FINN emissions are close to permit-based emissions in Georgia and South Carolina after applying the adjustment factor for the burned area. On the other hand, permit emissions are about 80% higher than adjusted FINN emissions in Florida. The reason for the low adjusted FINN emissions in Florida is that we developed the scaling factor to reduce FINN burned area based on all three states (Figure 3). A burned area comparison for Florida indicates a scaling factor to increase the burned area, which is a different pattern among these three states. For grid-based emission comparisons, the slope of the linear regression without the intercept is close to one since we adjusted burned area before BlueSky modeling. Without the burned area adjustment, the same slope was only 0.6 (Figure S24). This indicates that the FINN burned area adjustment mitigates the difference between FINN-based and permit-based emissions. This also implies that the adjustment factor derived from the slope of linear regression is robust, even though the R\({}^{2}\) of the linear regression between permit and FINN burned areas was low.
## 3 Results and Discussion
### Relationship between Coffee Acceptability and Volatile Compounds
#### 3.5.1 Relationship between Sensory Data and Volatile Compounds  
Overall, liking was negatively correlated with furfural (V3, \(r=-0.64\)), 5-methyl-2-furaldehyde (V7, \(r=-0.67\)), 4-vinylguaiacol (V31, \(r=-0.60\)), and only 3-methylphenol (V13, \(r=0.62\)) was positively correlated with overall liking ratings as a result of correlation analysis (Table 9). This result seemed to be due to the absence of strong flavors induced by volatile compounds, affecting the overall liking of consumers. Bitterness intensity was correlated with many volatile compounds such as pyrazines (methylpyrazine, V2, \(r=0.81\); 2,6-dimethylpyrazine, V5, \(r=0.80\); 2-ethyl-3-methylpyrazine, V9, \(r=0.68\); 3-ethyl-2,5-dimethylpyrazine, V17, \(r=0.66\); 2-ethyl-3,5-dimethylpyrazine, V18, \(r=0.69\); 2,5-diethylpyrazine, V19, \(r=0.72\); and 5H-5-methyl-6,7-dihydroxycoplecton[b]pyrazine, V25, \(r=0.79\)), pyrroles (1-methyl-1H-pyrrole-2-acetonitrile, V11, \(r=0.63\); 2-acetylpyrrole, V14, \(r=0.72\); and 2-acetyl-1-methylpyrrole, V16, \(r=0.63\)), and guaiacols (guaiacol, V20, \(r=0.74\); 4-ethylguaiacol, V29, \(r=0.74\); and 4-vinylguaiacol, V31, \(r=0.62\)), known to have roasted [1] nutty [26], smoke, and medicinal characteristics [32], respectively. Phenols (phenol, V8) and 2,4-di-tert-butylphenol (V34), which had burning and coal tar characteristics [32], were related to coffee ID intensity. Pyridine (V1, \(r=0.63\)) and pyrroles (2-acetylpyrrol, V14, \(r=0.71\); 2-acetyl-1-methylpyrrole, V16, \(r=0.68\)) were positively correlated with coffee ID intensity. The strong perception of coffee flavor might be influenced by volatile compounds, which have strong characteristics.
# Efficacy of TENS in Cervical Pain Syndromes: An Umbrella Review of Systematic Reviews
## 4 Discussion
Furthermore, when the administration of TENS is administered together with another intervention, it does not seem to show substantial differences with the comparison group in all the outcomes considered, both in the short and in the medium term. All patients who underwent TENS were suffering from acute or chronic neck pain with mechanical, myofasical, and traumatic (whiplash's syndrome) etiology. TENS is one of the possible therapeutic strategies that can be proposed to the patient with acute or chronic cervical pain, although it is not the therapy that allows us to achieve the best results. Eight systematic reviews analyze the frequency parameter, and most of them use values included in the range from 2 to 100 Hz [20; 21; 24; 25; 26; 28]. The two extremes of this range represent the frequencies, respectively, used in the endorphin TENS (1-5 Hz) and in the conventional TENS (60-100 Hz), that is the two most used modalities of application in clinical practice. The data reported regarding the current intensity are lacking and inaccurate, and none of the revisions reports specific intensity values, thus limiting the search for the most suitable current intensity value for the treatment of patients with acute or chronic neck pain. The duration of the pulse was in the range 40 \(\upmu\)s-250 \(\upmu\)s [20; 21; 24; 25; 28]. The extremes of this range represent, again, the pulse duration values usually used, respectively, in the endorphinic TENS (<60 \(\upmu\)s) and in the conventional TENS (>60 \(\upmu\)s). The duration of the single session, in most of the studies, varies from 10 to 30 min, while the duration of the entire treatment program is usually between 2 and 6 weeks. Further studies are needed to define more precisely the duration of the treatment. Some studies carry out the follow-up evaluation up to 6 months later [24; 26; 27; 28], others at 3 months [19; 22], others still carry out it immediately after treatment [21] or up to 12 months after the end of the therapeutic program [17]. The positioning of the electrodes represents therapeutic information often neglected in the various reviews; despite this it is good practice that they are placed on the painful region. There is very low-quality evidence that demonstrates the efficacy of TENS in chronic pain in the trapezius muscle. The heterogeneity of the rehabilitation protocols used during the development of the studies remains a fundamental problem to standardize and protocol the rehabilitation treatment. Implications for Future Research and Clinical PracticeIt is important to seek clear scientific evidence and high-level recommendations due to their relevance in clinical practice. The main limitation of the study is due to the excessive heterogeneity of the studies, especially in relation to the different methods and rehabilitation protocols in patients suffering from acute and chronic neck pain.
## 3 Results and Discussion
### Method Application to Real Samples  
Using the proposed method, 125 serum samples collected from healthy volunteers in Henan Province were measured. For each volunteer, the paired serum and urine samples were collected and the 125 urine samples have already been tested and included in our recent study on ZEN internal exposure assessment that was based on a total of 301 urine samples [33].  
For the paired urine samples, ZEN, \(\alpha\)-ZOL, \(\beta\)-ZOL and ZAN were detected. ZEN showed the highest detection rate of 75.2%, followed by \(\beta\)-ZOL (21.6%), \(\alpha\)-ZOL (5.6%) and ZAN (1.6%). Alpha-ZAL and \(\beta\)-ZAL were absent in all these urine samples. The mean concentration and range of each analyte were as following: ZEN (mean 0.31 ng/mL, range ND-3.68 ng/mL), \(\beta\)-ZOL (mean 0.084 ng/mL, range ND-1.32 ng/mL), \(\alpha\)-ZOL (mean 0.051 ng/mL, range ND-2.64 ng/mL) and ZAN (mean 0.019 ng/mL, range ND-0.518 ng/mL). The results were comparable with those reported for other countries, normally with the mean concentration between 0.009-1.82 ng/mL [22; 23; 26; 32; 33; 34; 35; 36; 37].  
On the contrary, among the serum samples, only one was found to be positive for ZEN after enzyme digestion, whose chromatogram was presented in Figure 4. ZAN, \(\alpha\)-ZOL, \(\beta\)-ZOL, \(\alpha\)-ZAL and \(\beta\)-ZAL were all absent. It can be attributed to the rapid clearance and excretion of ZEN from blood after oral intake.  
Up to now, very limited data are available in this area as collected in Table 3. ZEN and its metabolites were hardly detected in human serum or plasma [32; 38], except for some patients [39; 40]. They were commonly detected in animal serum/plasma after oral administration of high amount of ZEN [4; 24; 28; 29]. Compared to theses previously reported works, the method developed here possessed a higher sensitivity for ZEN and its derivatives in blood, which can be applicable for disease research and toxicokinetic study. Additionally, the results of paired serum-urine samples in this study suggested that for ZEN biomonitoring and internal exposure study for healthy people, urine would be a preferred medium because of the higher concentration and longer lifetime.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**County** & **N** & **Positive** & **LOD/LOQ** & **Means/Medians** & **References** \\  & & & **(ng/mL)** & **(Ranges, ng/mL)** & \\ \hline Germany & 50 & 0 (ZAN, & 1.0/1.0 & \textless{}LOD & [38] \\ Italy & 233 & 5.4/e (ZEN) & 1.0 & 0.1(\textless{}LOD–3.9) & [39] \\ Italy & 63 & 6 (ZEN, & 0.025/0.05 (ZEN, \(\alpha\)-ZOL), & 0.106 (\textless{}zOL, 0.934 (ZEN) & [40] \\  & & \(\beta\)-ZOL) & \(\beta\)-ZOL) & \(\textless{}LOD\) & [32] \\ USA & 30 & 1 & — & \textless{}LOD & [32] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Occurrence of ZEN biomarkers in human serum/plasma.  
Figure 4: Chromatograms of a positive sample (**a**) and a negative sample (**b**).
# Time to First Fix Robustness of Global Navigation Satellite Systems: Comparison Study
## 4 Discussion  
Based on the test results provided above, the robustness of the TTFF for the different GNSSs is discussed here. This test reproduces typical conditions of mass-market applications, relying on the main band of all GNSSs, i.e., B1, L1/E1, and G1, and a low-cost receiver. Thus, the absolute values obtained can only be used as reference for those cases with similar conditions. For this reason, since all four GNSSs have undergone the exact same test conditions, the aim here is to perform a comparative analysis between them.
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 7 Results
### Traces  
The workload traces needed to feed the different forecasting and optimization algorithms can be obtained from the different sources, including public dataset repositories (e.g., Google Workload Cluster Traces [68], the Azure Public Datasets [69], the Grid Workload Archive [70], or the Alibaba Cluster Trace Program [71], among others), production traces obtained from some real companies, or synthetic traces generated by certain workload generator applications (e.g., Predator [72], Locust [73], etc.). Using public datasets has several advantages compared to the alternatives. Public datasets are typically curated, cleaned, and anonymized to ensure privacy and security; furthermore, they facilitate fair comparisons and reproducibility of experiments. On the other hand, accessing production traces from real companies can be challenging due to confidentiality concerns, legal restrictions, and the need for collaboration or data-sharing agreements. Synthetic traces generated by workload generators may not accurately represent real-world workload characteristics and patterns. Since there are no publicly available workloads with sufficient representation of distributed edge infrastructures, as noted in previous studies [74; 75], in this work, we opted to use two well-known cloud VM traces from public datasets. Specifically, we utilized data from the Azure Public Datasets [76] and the GWA-T-12 Bitbrains traces sourced from the Grid Workload Archive [77].  
The VM traces available in the Azure Public Datasets encompass a representative subset of the first-party Azure VM workload within a specific geographical region. These first-party workloads consist of both internal VMs utilized for research/development and infrastructure management, as well as first-party services provided to third-party customers, such as for communication, gaming, and data management. The time-series data derived from the Azure VM trace V1 span a duration of 30 days, commencing from 16 November 2016, and concluding on 16 February 2017. For each VM, this trace records the capacity provisioned for this VM in terms of its cores, memory, and disk allocations. Additionally, it collects CPU usage data reported every five minutes.  
The GWA-T-12 Bitbrains dataset includes performance metrics obtained from approximately one thousand VMs within a distributed data center operated by Bitbrains. Bitbrains specializes in managed hosting and business computation services for enterprises, catering to prominent customers such as major banks (ING), credit card operators (ICS), insurers (Aegon), and more. The time-series data recorded in the Bitbrains traces were collected at 5 min intervals, spanning a duration of 30 days from 12 August 2013, to 11 September 2013. For each VM, this trace records the capacity provisioned for this VM in terms of number of CPU cores, CPU MHz, and memory allocations. Additionally, it collects data about CPU usage, memory usage, disk read/write throughput, and network input/output throughput, reported every five minutes.
## 3 Mechanical Overview
### Heat Dissipation  
Within the joint module enclosure, continuous heat generation from the servos might cause overheating and failure. Moreover, the PLA material has poor heat conductivity capabilities (0.13 W/mK) and continuous locomotion could eventually lead to high temperatures within the joint module. To minimise this risk of overheating, the _Dynamixel XMA30W-210T_, a COTS actuator produced by _ROBOTIS_[28], is selected to actuate each joint module. Each actuator is enclosed in an aluminium casing providing a good rate of heat transfer. As shown in Figure 4, each actuator is fixed to a specifically designed element which allows the surface area to remain exposed to the climate inside the module. The heat transfer performance of the selected actuator is qualitatively shown in Figure 7 by using a heat map. The motor was run for 50 min at a constant velocity of approximately 57.25 RPM. The servo was run without load continuously in one direction and monitored at 0 min, 25 min and 50 min, respectively. This qualitative experiment shows reasonable heat dissipation even after a medium/long running time. Extensive testing with different loads is still needed to assess the heat performance of _Serpens_ in a real operational scenario.  
Figure 6: The proposed design of the elastic gears for _Serpens_. The figure shows: the housing (**left**); the base (**middle**); and the cogwheel (**right**). The shaft (not depicted in this figure) runs through all parts. Compression springs are placed in the chamber of the cogwheel on each side of the outset of the base, providing passive-compliance.  
Figure 7: The heat transfer performance of the selected actuator is qualitatively shown using a heat map. The motor was run for 50 min at a constant velocity of approximately 57.25 RPM. The servo was run without load continuously in one direction and monitored at 0 min, 25 min and 50 min, respectively.
# Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions
## 3 Results and Discussions
### Practical Applications  
Based on the results, integrating the insights from machine learning models like ANN and RF is crucial for building a smart grid. Understanding the training time and feature importance is critical. These data can facilitate the design of real-time energy optimization strategies. The information from the models could be used to create dynamic models which are data-driven. If it is computationally feasible, RF could be used for rapid real-time predictions, but if high accuracy is required, the model can switch to ANN. The RF model can use both to give data-driven optimization. The optimization algorithm will then send a command to the grid to adjust the power of generation and distribution. The scalability of the forecasting model across diverse building types and energy demand profiles is a key consideration. For traditional buildings, the model can be adapted using historical energy consumption data and incorporate features like occupancy patterns and weather conditions. However, for near-zero-energy buildings (nZEB), the model needs to be refined with additional parameters like renewable energy generation, building envelope characteristics, and advanced HVAC systems. The model should be scalable and adaptable to different building types and energy demand profiles. The dynamic nature of nZEBs requires more sophisticated feature sets, including renewable energy production and storage, along with granular energy consumption data to ensure accurate predictions and optimal performance.
## 6 DRRF and Real-time Traffic  
Based on the discussion so far, we have introduced a Deficit Round Robin with Fragmentation (DRRF) and its extensions including the Generalized Weighted Fairness criterion basically to support both Best Effort (BE) and non real-time Polling Service (nrtPS) classes for IEEE 802.16e Mobile WiMAX. However, Mobile WiMAX also supports a real-time Polling Service (rtPS) class [1,3]. For the formal two classes, the performance metrics are based on fairness and throughput guarantees. For rtPS, an additional QoS parameter is the delay (maximum allowable delay) constraint and optional delay jitter (delay variation).  
In this section, we focus on an investigation of a well-known scheduler for real-time traffic in wired networks, _i.e._, EDF (Earliest Deadline First), applied in IEEE 802.16 WiMAX Networks. Note that a Deficit Round Robin (DRR) scheduling algorithm is commonly used to schedule non real-time traffic basically to maintain the expected throughput and fairness in a downlink direction [3]. We compare the results with DRRF (Deficit Round Robin with Fragmentation), as we described earlier, as well as a combination of the two.