esa-sceva/satcom-chunk-collection huggingface dataset:
# Realistic Simulation for Body Area and Body-To-Body Networks
## 4 Performance Evaluation
### Results
However, the impact of the  
Figure 13: Average packet delivery ratio at varying transmission power for one and three BANs (having three nodes per BAN). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Link** & \multicolumn{2}{c}{**Ear-Belt**} & \multicolumn{2}{c}{**Right Wrist-Belt**} \\ \hline
**Tx Power** & **CM3-B** & **E-CM3-B** & **CM3-B** & **E-CM3-B** \\ \hline
0 & \(-52.65\) & \(-80.0843\) & \(-52.02\) & \(-79.9807\) \\ \(-5\) & \(-57.69\) & \(-85.7168\) & \(-56.83\) & \(-85.6790\) \\ \(-10\) & \(-62.66\) & \(-90.4281\) & \(-62.03\) & \(-89.3149\) \\ \(-15\) & \(-67.65\) & \(-94.8671\) & \(-67.03\) & \(-94.2420\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average received power for ear and belt (with distance equal to 0.26 m) and right wrist and belt (0.334 m). transmission power variation from 0 dBm to \(-15\) dBm is significant in enhanced CM3-B (_i.e._, proposed deterministic approach). With regards to the PDR results of the semi-deterministic approach, it is more optimistic than the deterministic approach. It can be observed that, with reduced transmission power (_i.e._, \(-10\) dBm), the PDR is nearly 8% to 10% lower than at 0 dBm both for one BAN, as well as three BANs. In general, while moving from one BAN to three, there is a slight decrease in PDR (almost 4%), as can be observed in Table 10. However, at \(-15\) dBm, the PDR is reduced to about 50%. To conclude, the performance results of PDR are not realistic in the IEEE 802.15.6 proposed channel models, and the selection of transmission power needs to be done wisely. The results of the average end-to-end packet delay are presented in Figure 14. The delay is computed based on the actual packet delay and the propagation delay. For example, a data packet considering the maximum payload in the IEEE 802.15.6 standard (_i.e._, 256 bytes) with DQPSK modulation at 971.4 kbps and MAC and PHY headers takes 2.16 ms. It can be observed that the delay between the IEEE 802.15.6 proposed CM3-B and semi-deterministic approach is closely matched until \(-10\) dBm. However, at \(-15\) dBm, the semi-deterministic approach has almost a 1-ms higher delay. Please note that, at 0-dBm transmission power, for either one or three BANs, the results are almost comparable at millisecond granularity, and mainly, the difference is in the order of microseconds (\(\upmu\)s). Whereas, the deterministic approach has the highest delay among the three approaches. On average, it has a 1-ms higher delay, which continues to increase at lower transmission power levels. Finally, the detailed results are available in Table 10.
## 4 Effects of Shaft Shapes
### Mechanical Characteristics  
The natural frequency of the rotor is the main frequency at which the stator's vibration and noise occur. Therefore, when designing a high-speed motor, it is essential to analyze mechanical characteristics. In this chapter, the analysis of mechanical characteristics according to the shape of the rotor and the winding patterns was performed using an FEM.  
Figure 9 shows the natural frequency derived according to the driving speed based on the analysis result as a Campbell diagram. It was confirmed that the solid shaft model had a critical speed of 53,800 rpm, and the hollow shaft model had a critical speed of 26,500 rpm. Additionally, Figure 10 is the result of analyzing the stress that current exerts on the hollow shaft rotor for each frequency. Even in the hollow shaft model, it can be seen that the concentrated winding with less stress is more effective. Therefore, even if the hollow shaft was applied, there was no significant electromagnetic difference and it was confirmed that there was no driving problem, even if the mechanical characteristics were considered.  
Figure 10: Stress applied to the hollow shaft rotor according to the winding method for each frequency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Solid Shaft** & **Hollow Shaft** & **Unit** \\ \hline Input Current & 24.1 & 25.2 & A\({}_{\text{rms}}\) \\ Torque & 1.41 & 1.41 & N.m \\ Output Power & 12.16 & 12.15 & kW \\ Core Loss & 515.4 & 487.4 & W \\ Eddy current Loss & 275 & 289.7 & W \\ Copper Loss & 69.6 & 75.6 & W \\ Efficiency & 93.4 & 93.4 & \% \\ \hline \hline \end{tabular}
\end{table}
Table 5: High speed motor design specifications according to shaft shape.  
Figure 9: Critical speed analysis results of two different rotors (**a**) solid shaft, (**b**) hollow shaft.
# Training-Free Acoustic-Based Hand Gesture Tracking on Smart Speakers
## 3 Materials and Methods
### Hand Tracking
#### 3.4.1 Tracking Model  
After position estimation, we convert the distance \(d\) and angle \(\theta\) to a point \((dcos\theta,\,\,dsin\theta)\) on the two-dimensional x-y plane. For the \(k^{th}\) chirp, the hand state vectors of the constant velocity (\(CV\)) and constant acceleration (\(CA\)) models can be represented as  
\[\mathbf{x}_{k}^{CV}=\left[p_{x},p_{y},\dot{p}_{x},\dot{p}_{y}\right]^{T} \tag{12}\]  
\[\mathbf{x}_{k}^{CA}=\left[p_{x},p_{y},\dot{p}_{x},\dot{p}_{y},\ddot{p}_{x}, \ddot{p}_{y}\right]^{T} \tag{13}\]  
where \(p_{x}\) and \(p_{y}\) are the coordinates of the hand's position, \(\dot{p}_{x}\) and \(\dot{p}_{y}\) are the hand's velocities along the x and the y directions, and \(\ddot{p}_{x}\) and \(\ddot{p}_{y}\) are the hand's accelerations along the x and the y directions. The hand trajectory state equations can be represented as  
\[\mathbf{x}_{k+1}^{CV}=F^{CV}\mathbf{x}_{k}^{CV}+w_{k}, \tag{14}\]  
\[\mathbf{x}_{k+1}^{CA}=F^{CA}\mathbf{x}_{k}^{CA}+w_{k}, \tag{15}\]  
where \(w_{k}\) is the process noise. \(F^{CV}\) and \(F^{CA}\) are the transition matrices, which can be represented as  
\[F^{CV}=\left[\begin{array}{cccc}1&0&\Delta t&0\\ 0&1&0&\Delta t\\ 0&0&1&0\\ 0&0&0&1\end{array}\right], \tag{16}\]  
\[F^{CA}=\left[\begin{array}{cccc}1&0&\Delta t&0&\frac{1}{2}\Delta t^{2}&0\\ 0&1&0&\Delta t&0&\frac{1}{2}\Delta t^{2}\\ 0&0&1&0&\Delta t&0\\ 0&0&0&1&0&\Delta t\\ 0&0&0&0&1&0\\ 0&0&0&0&0&1\end{array}\right], \tag{17}\]  
where \(\Delta t\) is the time increment. The measurement equations can be represented as  
\[\mathbf{z}_{k}^{CV}=H^{CV}\mathbf{x}_{k}+v_{k}, \tag{18}\]  
\[\mathbf{z}_{k}^{CA}=H^{CA}\mathbf{x}_{k}+v_{k}, \tag{19}\]where \(v_{k}\) is the measurement noise. \(H^{CV}\) and \(H^{CA}\) are the measurement matrices, which can be represented as  
\[H^{CV}=\bigg{[}\begin{array}{ccccc}1&0&0&0\\ 0&1&0&0\end{array}\bigg{]}, \tag{20}\]  
\[H^{CA}=\bigg{[}\begin{array}{ccccc}1&0&0&0&0\\ 0&1&0&0&0&0\end{array}\bigg{]}. \tag{21}\]
# Dual-Frequency Microwave Plasma Source Based on Microwave Coaxial Transmission Line
## 4 Summary and Discussion  
Based on the COMSOL Multiphysics simulation, a dual-frequency microwave plasma source is proposed and investigated. This dual-frequency microwave plasma source is based on the coaxial transmission line, and 2450 and 915 MHz microwaves are utilized in this study.  
In the experiment, the plasma was excited by a 2450 MHz microwave from Port 1 first, and then a 915 MHz microwave was inputted from Port 2 to adjust the plasma characteristics. The measured experiment results by the Langmuir probe show that the electron temperature gradually increases as the 915 MHz microwave power increases, and there is little change in electron density. In contrast, if there is a 2450 MHz microwave that is inputted from Port 2, the electron density increases as the microwave power increases, and there is little change in the electron temperature. These phenomena of dual-frequency microwave plasma are similar to dual-frequency RF plasma.  
The OES method is used to investigate the physical mechanism of these phenomena, and the OES results show that the particles at different energy levels presented different responses to different frequencies. Thus, different frequencies could selectively control the plasma characteristics. If a high-power wide bandwidth frequency tunable microwave source is used as the power source from Port 2, the plasma electron density and electron temperature would be controlled, respectively and precisely, in the dual-frequency microwave plasma source. This exploratory research would improve the operation frequency of dual-frequency microwave plasma sources from RF to microwave.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app11219873/s1](https://www.mdpi.com/article/10.3390/app11219873/s1)]([https://www.mdpi.com/article/10.3390/app11219873/s1](https://www.mdpi.com/article/10.3390/app11219873/s1)), Table S1: Intensity of Ar I characteristic peaks, Figure S1: Intensity of Ar I characteristic peaks versus microwave power inputted from port 2 under pressure of 80 Pa and 700 W 2450 MHz microwave at Port 1.  
[PERSON] and [PERSON] contributed to the overall study design, analysis, and writing of the manuscript. [PERSON], [PERSON], [PERSON] and [PERSON] provided technical support and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China under 2019 YFA0210202, the National Natural Science Foundation of China under Grant 61971097 and 62111530054.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Effect of U-Shaped Sheet Pile Section Size
#### 4.2.1 Effect of Cross-Sectional Dimensions on the Bearing Characteristics of the U-Shaped Piles  
(1) Horizontal displacement of the USCSP  
Figure 9 indicates the variation curves of the horizontal displacement of piles under different cross-sectional dimensions of the U-slab pile. Compared with type II and type III, type I has the smallest cross-sectional size, the least flexural modulus, and the largest horizontal displacement at the top of the pile. When the excavation deepens from 1 m to 2 m, the horizontal displacement of the type I sheet pile increases from 10.3 mm to 18.9 mm, the horizontal displacement of the type II sheet pile increases from 6.6 mm to 12.6 mm, and the horizontal displacement of the type III sheet pile increases from 6.4 mm to 11.3 mm.  
(2) Internal force of the USCSP  
Figure 10 shows the pile stress distribution curves corresponding to different USCSP cross-section sizes. With the increase in the pile section size, the flexural section modulus increases and the maximum tensile and compressive stresses in the pile gradually decrease. When excavating to 1 m, the maximum tensile stress in the corresponding section of I plate is 1566.4 kPa, the maximum tensile stress in the corresponding section of the II plate is 457.4 kPa, and the maximum tensile stress in the corresponding section of the III plate is 419.8 kPa; when excavating to 2 m, the maximum tensile stress in the corresponding section of the type I plate is 2853.6 kPa, the maximum tensile stress in the corresponding section of type II plate is 891.1 kPa, and the maximum tensile stress in the corresponding section of the type III plate is 793.1 kPa. It can be seen that when the excavation is at 2 m, the maximum tensile stress of the corresponding section of the type I plate has exceeded its tensile strength, so it is not suitable to be used in this project.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Cross-Section Type** & **Type I Plate** & **Type II Plate** & **Type III Plate** \\ \hline Plate thickness (mm) & 120 & 160 & 200 \\ Cross-sectional width (mm) & 1000 & 1200 & 1500 \\ Cross-sectional height (mm) & 500 & 900 & 1200 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cross-sectional dimensions of different types of U-plates.  
Figure 9: Displacement of piles with different U-plate sizes. (**a**) Excavation at 1 m. (**b**) Excavation at 2 m.
# IoT-Based Implementation of Field Area Network Using Smart Grid Communication Infrastructure
## 3 Related Work
### Weather Monitoring System  
The IoT-based prototype was developed for both local and wide area networks. The developed prototype is used for the measurement of weather parameters such as temperature and humidity. These parameters are essential for crop cultivation. The DHT11 sensor is used for the sensing and measurement of temperature and humidity. It uses a thermistor and a capacitive humidity sensor for sensing. These data are directly fed to Pin 2 of Arduino Uno. Arduino Uno is programmed for a webserver application. An HTML webpage was designed for the monitoring of humidity and temperature. When the client enters an IP address of the server, humidity and temperature are displayed on a local network. The dynamic host configuration protocol enables a server to automatically assign an IP address to connected clients. The developed prototype was used for the monitoring of temperature and humidity in a local area network. An ethernet shield is used for the Arduino webserver application. The designed prototype includes temperature readings in both Celsius and Fahrenheit. Monitoring humidity and temperature can also be useful to switch an agricultural load to different energy sources depicted in the previous prototype. For example, if temperature is low and humidity is higher, then the solar photovoltaic system cannot serve the load and the system can be switched to the grid for smooth operation of farming machineries. The developed design uses IEEE 802.3 and IEEE 802.11 standards. Figure 7 shows the design of the weather monitoring system for field area networks. Figure 8 shows the flow chart of the weather monitoring system. Figures 9 and 10 show a snapshot of readings of the developed system in Celsius and Fahrenheit, respectively.  
Figure 7: Design of the weather monitoring system for smart farming.  
Figure 8: Flowchart of the weather monitoring system.
## 2 Study Area and Experimental Data
### Data  
#### 2.2.1 FLUXNET Data  
FLUXNET 2015 is the latest version of the FLUXNET dataset. Compared with previous datasets, FLUXNET v.2015 improves the protocols of data quality and the pipeline of data processing [28]. The Net Ecosystem Exchange (NEE) in the FLUXNET 2015 dataset was gap-filled with the marginal distribution sampling (MDS) method [29]. It was then partitioned into Ecosystem Respiration (RECO) and GPP using the daytime fluxes method (_DT) [30] and the nighttime fluxes method (_NT) [29]. The quality flags in FLUXNET 2015 are given values ranging from 0 to 1, indicating the percentage of high quality gap-filled and measured data [12]; 1 represents the highest quality and 0 represents the poorest quality [31].  
As shown in Table 1, there are 12 sites in the FLUXNET datasets located in the study area, including 7 wetland sites, 2 forest sites, a grassland site, a shrulands site, and a permanent snow and ice site. This study used GPP (GPP_NT_VUT_REF) estimated  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site\_ID** & **Site\_Name** & **Country** & **Latitude (\({}^{\circ}\) N)** & **Longitude (\({}^{\circ}\) E)** & **Land Cover** & **N** \\ \hline FI-Lom & Lompolojankka & Finland & 67.9972 & 24.2092 & WET & 71 \\ GL-NuF & Nuuk Fen & Greenland & 64.1308 & \(-\)251.3861 & WET & 105 \\ GL-ZaF & Zackenberg Fen & Greenland & 74.4814 & \(-\)20.5545 & WET & 42 \\ RU-Che & Cherski & Russia & 68.6130 & 161.3414 & WET & 33 \\ SJ-Adv & Adventdalen & Svalbard and Jan Mayen & 78.1860 & 15.9230 & WET & 23 \\ US-Atq & Atqasuk & USA & 70.4696 & \(-\)157.4089 & WET & 88 \\ US-Ivo & Ivotuk & USA & 68.4865 & \(-\)155.7503 & WET & 68 \\ FI-Sod & Sodankyla & Finland & 67.3624 & 26.6386 & ENF & 371 \\ US-Prr & Poker Flat Research Range & USA & 65.1237 & \(-\)147.4876 & ENF & 98 \\  & Black Spruce Forest & & & & & \\ GL-ZaH & Zackenberg Heath & Greenland & 74.4733 & \(-\)20.5503 & GRA & 137 \\ RU-Cok & Chokurdakh & Russia & & 70.8291 & 147.4943 & OSH & 107 \\ SJ-Blv & Bayelya, Spitsbergen & Svalbard and Jan Mayen & 78.9217 & 11.8311 & SNO & 13 \\ \hline \hline \end{tabular}  
* WET: wetlands; ENF: evergreen needleaf forests; GRA: grasslands; OSH: open shrulands; SNO: permanent snow and ice. N means the numbers of the data points of the sites after quality control.  
\end{table}
Table 1: Basic information regarding the FLUXNET sites of the Arctic.  
Figure 1: (**a**) Location of the FLUXNET sites used in this study. The description of these sites is provided in Table 1. The base map is the land cover types of the Arctic. (**b**) The elevation distribution of the Arctic.  
from the night-time method with a daily temporal scale. The selection of GPP followed two criteria: (1) the quality flags were larger than 0.5; and (2) the difference between the GPP derived using the night-time method (GPP_NT_VUT_REF) and the day-time method (GPP_DT_VUT_REF) was lower than 50%. After filtering the data based on the two criteria, daily GPP were temporally aggregated to generate the 8-day averaged GPP, matching the temporal resolution of MOD17A2H.
## 3 Proposed Method Based on MSDWF and DWSC  
The proposed basic structure based on MSDWF and DWSC is shown in Figure 4. Its structure consists of SAM, DWSC equipped with a residual connection, convolutions, BN, pooling, and Sigmoid, and a fully connected layer (FCL). The bearing vibration signal is a time series vector, and its internal data have an extremely high correlation. For the multi\(-\)sensor vibration signals obtained by different sensors, SAM is first used to find out the associated information between the internal data of the vibration signal and strengthen the interaction information between the data to obtain the signal sequence with more obvious characteristics. Then, the weighted data of each sensor are convoluted by DWC respectively to fully extract the unique features and realize the decoupling of the channel correlation of the multi\(-\)sensor data. Then the output results are processed by PWC to obtain the spatial features hidden in the multi\(-\)sensor data and to completely decouple the spatial correlation. The detailed fault diagnosis structure based on MSDWF and DWSC can be seen as follows:  
Figure 3: (**a**) Multi\(-\)channel convolution, (**b**) depth\(-\)wise separable convolutions.  
Step 1: Sensors are arranged at different positions of mechanical equipment to obtain vibration signal information at different spatial positions. When the number of sensors arranged is \(n\), the obtained vibration data can be indicated as \(S=\left\{s,\,s_{2},\,\ldots,\,s_{n}\right\}\), where \(s_{i}\) represents the data obtained by the \(i\)th sensor.  
Step 2: The data obtained by each sensor are input into SAM for weighting, in which a group of sensor data as a vector sequence is weighted, and the weighted data can be expressed as \(w=\left\{w_{1},\ w_{2},\,\ldots,\,w_{n}\right\}\), where \(w_{i}\) represents the weighted data of the \(i\)th sensor.  
Step 3: The weighted data are divided into two sets for training and test, and then the training data are input into DWSC to realize the decoupling of channel correlation and spatial correlation. After that, the rich features extracted by DWSC are subject to a series of operations such as convolution, BN, pooling, and Sigmoid to characterize the advanced features further. Then, FCL is used to process the obtained advanced features for classification.  
Step 4: The test data are input into the trained model for testing and to obtain the test results and analyze the results.  
Step 5: The different comparative experiments were carried out to verify the advantage and performance of the method proposed in the paper, and the contrast results were analyzed.
# To Cache or Not to Cache
## 2 Background and Motivation
### The Fear of Missing Out  
Classic caching solutions are _reactive_ and this impedes their ability to react to workload behavior changes. Many incorporate the notion of eviction history to evaluate the importance of an item [3; 5; 18]. Their post-mortem evaluation of evictions, while valuable, is a belated reaction to the workload's effect on the cache, compared to a direct reaction to the workload itself. Of LRU, ARC, and LIRS, LIRS reacts the most directly to the workload itself through its focus on reuse distance.  
Non-datapath caching algorithms, owing to the available flexibility of not having to perform cache updates, invariably embody a _fear of missing out_ on responding in a timely fashion to changes in workload behavior. Unfortunately, reacting as a consequence of accesses to items that were evicted requires that items must first enter the cache. Furthermore, if the number of cache hits is not increasing, only having information about the evicted items can obscure the reason for low performance with a limited view of the workload. To respond to workload changes rapidly, observing accesses to newly requested items are crucial. In particular, keeping track of recent accesses that resulted in cache misses allows us to understand what the cache is \"missing out\" on. Here, timely knowledge of workload behavior helps improve the accuracy of the filtering mechanism. We assert that the rate of access to recent cache missed items provides crucial, complementary information about the workload that allows for better understanding of short-term workload behaviors.  
**FOMO** is a general non-datapath admission policy that is capable of improving hit rate and write rate consistency by utilizing a simple model that avoids the issues present within the mARC state model. **FOMO**'s Miss-History captures the reuse of recently missed items and does not require the items to be inserted into the cache _first_. As we shall show in the next section, the design of **FOMO** focuses on comparing the cache hit rates and reuse rate of recent cache misses to better understand the general workload behavior.