esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Gesture Recognition System with Gesture Spotting Based on Self-Organizing Maps
## 6 Conclusions  
This paper proposed a vision-based real-time dynamic hand gesture recognition system with a gesture spotting function. In order to recognize the dynamic gesture, the SOM-SOM-Hebb classifier was newly devised. To provide the spotting function, end of gesture was detected from transitions of the vector distance between input and winner neuron's weight vectors. This gesture spotting capability made the system much more practical.  
The proposed recognition algorithm was examined by simulation and real-time experiment. The results revealed that the system could recognize the nine types of gesture with an accuracy of 96.6%, which was better than that of other recognition systems. Other advantages of our system over the compared methods are its real-time operability and the gesture spotting function.  
Major drawback of the proposed system is the use of the color glove, and implementation of the hand segmentation without the glove is left for our future work. Another future research objective is to develop a hardware gesture recognition system with faster recognition speed, higher portability, and lower power consumption than those of PC implementation.  
Conceptualization, H.H. and Y.M.; methodology, H.H.; software, Y.I.; validation, Y.I. and H.H.; formal analysis, H.I. and H.H.; writing--original draft preparation, H.H. and Y.I.; writing--review and editing, H.I.; supervision, Y.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant Number JP18K11483, JP20K11999, and the Kansai University Fund for the Promotion and Enhancement of Education and Research, \"Development of System Analysis and Design Methods Integrating Model and Data Premised on IOT Technology.\"  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Input** & **Data Set** & **Accuracy (\%)** \\ \hline
[11] & 3D (Kinect) & RT, 8 gestures & 84.9 \\ \hline
[14] & Kinect & RT, 9 gestures & 96.25 \\ \hline
[15] & Kinect & RT, 36 gestures & 95.42 \\ \hline
[16] & time-of-flight & RT, 9 gestures & 95.1 \\ \hline
[17] & Leap Motion & Handcraft-gesture dataset, 10 gestures & 95.0 \\ \hline
[18] & Leap Motion & RT, 4 gestures & 97.5 \\ \hline
[24] & color+depth & SKIG & 97.7 \\  & color+depth+optical flow & ChaLearn & 98.6 \\ \hline
[26] & RGB+depth & SKIG & 98.89 \\ \hline
[27] & RGB+velocities & RT, 6 gestures & 97.0 \\ \hline
[28] & Vision (barehand) & Cambridge & 91.0 \\ \hline
[29] & Vision (barehand) & Cambridge & 86.0 \\ \hline
[31] & Vision (with colored glove) & RT, 9 gestures & 94.3 \\ \hline this work & Vision (with colored glove) & RT, Cambridge & 96.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of accuracies.
# Model-Based Deep Reinforcement Learning with Traffic Inference for Traffic Signal Control
## 5 Reinforcement Learning Based on Traffic Inference Model
### Method Design
It waits for the experience in the model replay to reach a certain number and then starts training. Similar to the model, the learner first calculates the priority for the experiences in the model replay and then randomly selects the experiences for training using priority as the probability. The formula for calculating the priority and loss is based on the algorithm used. The learner periodically passes the network parameters to the actor and to the model replay. It is worth mentioning that in the proposed algorithm, the actor, model, model replay, and learner are used in an asynchronous method. The advantage of this is that each part only needs to wait for the initial conditions to be met to start working, and it therefore no longer waits for synchronization of the other parts to work in each round of the loop. Specifically, the algorithm is divided into five parts: learner, actor, replay, model, and model replay. Except for replay, the other four parts run asynchronously, and the flow is shown in the pseudo-code Algorithms 1-4. ```
1:procedureActor(\(B,T\))
2:\(\theta_{0}\leftarrow\)Learner.Parameters()
3:\(s_{0}\leftarrow\)Environment.Initialize()
4:for\(t=1\)to\(T\)do
5:\(a_{t-1}\leftarrow\)ComputeAction(\(s_{t-1},\theta_{t-1}\))
6:\(<r_{t-1},s_{t},f_{t-1}>\leftarrow\)Environment.Step(\(a_{t-1}\))
7:LocalBuffer.Add(\(<s_{t-1},f_{t-1},a_{t-1},r_{t-1},s_{t}>\))
8:ifLocalBuffer.Size() \(\geq B\)then
9:\(\tau\leftarrow\)LocalBuffer.Get(\(B\))
10:Replay.Add(\(\tau\))
11:Replay.UpdateAverage(\(\tau_{f}\))
12:endif
13:Periodically(\(\theta_{t}\leftarrow\)Learner.Parameters())
14:endfor
15:endprocedure
```  
**Algorithm 1** Actor```
1:procedureModel(\(T\))
2:\(\theta_{0}\leftarrow\textsc{InitializeNetwork()}\)
3:for\(t=1\)to\(T\)do
4:\(\tau\leftarrow\textsc{Replay.Sample()}\)
5:\(l_{t}\leftarrow\textsc{ComputeLoss(\tau)}\)
6:\(\theta_{t+1}\leftarrow\textsc{UpdateParameters(}l_{t},\theta_{t}\texttt{)}\)
7:\(p\leftarrow\textsc{ComputePriorities(}\tau\texttt{)}\)
8:\(\textsc{Replay.SetPriority(}\tau_{id},p\texttt{)}\)
9:\(\textsc{Periodically(Replay.RemoveOutdated())}\)
10:endfor
11:endprocedure
```

**Algorithm 2** Model

```
1:procedureModelReplay(\(T\))
2:\(\theta_{0}^{L}\leftarrow\textsc{Learner.Parameters()}\)
3:\(\theta_{0}^{M}\leftarrow\textsc{Model.Parameters()}\)
4:for\(t=1\)to\(T\)do
5:\(\tau_{\tau=s^{\prime}}\leftarrow\textsc{Replay.Sample()}\)
6:\(\tilde{f}\leftarrow\textsc{Replay.GetAverage()}\)
7:\(\tau_{f}\leftarrow\textsc{Sample(}\tilde{f}\texttt{)}\)
8:\(\tau_{a}\leftarrow\textsc{ComputeAction(}\tau_{s},\theta_{t-1}^{L}\texttt{)}\)
9:\(\tau_{c,r,s^{\prime}}\leftarrow\textsc{TrafficInfer(}\tau_{c<s,f,a^{\prime}}, \theta_{t-1}^{M}\texttt{)}\)
10:\(p\leftarrow\textsc{ComputePriorities(}\tau\texttt{)}\)
11:\(\textsc{LocalBuffer,Add(}\texttt{)}(\texttt{)}(\texttt{)}(\texttt{)}(\texttt{)}( \texttt{)}(\texttt{)
## 3 Applications to a Waste Separation System
### Introduction to the Waste Separation Technology  
The technological scheme of the waste processing plant that was chosen to examine can be seen in Figure 8[29]. It operates with seven units. The pre-shredder has one input and one output, as it changes only the size distribution of the waste. Therefore, the compositions of its input and output are the same. All other units can be considered as flow splitters, those performing separation based on different principles. They have one input and two outputs in all cases. Magnetic and eddy-current separators aim to remove iron and aluminum from the waste, respectively. The drum screen extracts non-recyclable components (RDF: refuse-derived fuel) from the waste, those that go for burning to produce electricity. The air separator splits the rest of the waste according to its shape into 2D and 3D components. Finally, optical separators working in the near-infrared (NIR) spectra sort PET from HDPE, and paper from LDPE.  
Of course, the separation efficiency of the operation units is never 100%, which means that none of the output flows will be completely pure. The flow labels of Figure 8 refer to the component which gives the majority of that stream. In all the cases, the stream contains not only the targeted component to be removed in the current step, but also a small amount of the other components. On the other hand, the targeted component will not even be removed completely, some of it will go to the next unit(s). Separation efficiency (\(r_{j}\)) parameters represent the imperfection of separation in the units, which are not known due to a lack of related measurements, but should be known as part of the system model to be able to use it for the analysis and prediction of operation. Experience-based expert knowledge is a suitable alternative to measurements that makes it possible to estimate the unknown parameters, as will be introduced in Sections 3.2 and 3.3.
# Complete Genome Report of a Hydrocarbon-Degrading _Sphingobium yanoikuyae_ S72
## 2 Materials and Methods
### Bacterial Growth in and Degradation of Hydrocarbons  
_S. yanoikuvae_ S72 was inoculated in different Bushnell Haas (BH) agar media (composition: magnesium, 0.20 g; calcium chloride, 0.02; monopotassium phosphate, 1.00 g; dipotassium phosphate, 1.00 g; ammonium nitrate, 1.00 g; ferric chloride, 0.05 g; agar, 20.0 g) already supplemented with either biphenyl, toluene, xylene, naphthalene, or phenanthrene, at different concentrations of 20, 40, 80, 100, and 150 \(\mathrm{\SIUnitSymbolMicro mL}\) for each hydrocarbon. The final bacterial concentration in the medium was approximately 3.0 \(\times\) 10\({}^{7}\) CFU/mL as determined in the laboratory. Each culture was incubated at 30 \({}^{\circ}\)C for 72 h. The highest concentration of each hydrocarbon tolerated by S72 was selected as the concentration for the degradation studies of each hydrocarbon used in this study.  
The hydrocarbon-degrading test was carried out in 5 different BH liquid media with each containing one out of the five hydrocarbons (naphthalene, phenanthrene, biphenyl, toluene, or xylene) as the sole carbon source. A 1 mL inoculum of _S. yanoikuvae_ S72 was added to 40 mL of each of the 5 Bushnell Haas broth (BH) already supplemented with one of the hydrocarbons unless otherwise stated, in a 250 mL flask. The culture was then incubated in a rotatory incubator at a revolution of 180 rpm at 30 \({}^{\circ}\)C for 25 days. The bacterial growth was checked every 48 h and growth was determined using the optical density readings obtained from the spectrophotometer. A control containing each of the hydrocarbons tested in liquid medium without a bacteria inoculum was used as the blank for each assay. The cultures were withdrawn on the 12 th and 25 th day to determine the extent of hydrocarbon degradation by the isolate. The metabolites formed from the degradation of the hydrocarbons were determined with a gas column chromatography-mass spectrophotometry (GC-MS) analysis.
## 4 A Two-Stage Optimal Scheduling Model for Wind-PV-Thermal-Storage System
### Intraday Optimal Dispatching Model
#### 4.2.1 Thermal Units Power Output Correction Model  
Considering the influence of wind, solar, demand response, and load uncertainty on optimal dispatching, the output correction models of thermal power units can be divided into the following two types.  
* The Deterministic Optimal Scheduling Model  
In the intraday dispatching, thermal power units are usually in deep peak regulation state. In this model, the uncertainty of wind power and photovoltaic power generation prediction is not concerned, and the output of thermal power units that have been started is revised with the objective of minimizing the intraday adjusted cost of thermal power units. The model is as follows:  
\[\min\Delta f=\sum_{t=1}^{T}\sum_{i=1}^{N_{\text{G}}}C_{\text{IN},i,t}(P_{ \text{DEV},i,t}) \tag{28}\]  
\[C_{\text{IN},i,t}(P_{\text{DEV},i,t})=x_{i}+y_{i}P_{\text{DEV},i,t}+z_{i}P_{ \text{DEV},i,t}^{2} \tag{29}\]  
\[P_{\text{DEV},i,t}=\left|P_{\text{G},i,t,\text{DA}}-P_{\text{G},i,t,\text{IN }}\right| \tag{30}\]  
where \(C_{\text{IN},i,t}\) is the system scheduling cost function of thermal units, \(P_{\text{DEV},i,t}\) is the deviation power caused by day-ahead scheduling power \(P_{\text{G},i,t,\text{DA}}\) and intra-ahead power, and \(x_{i}\), \(y_{i}\), \(z_{i}\) are respectively the intraday dispatching cost coefficients of the \(i\)th thermal power unit.  
* The Uncertain Optimal Scheduling Model  
In this model, the day-ahead dispatching plan is determined, and there are errors in the wind, solar, and load forecasting on different time scales, so the output of thermal power units in day-ahead dispatching is variable. This model takes average adjustment cost as the objective function, and details are as follows:  
\[\min\Delta f=\sum_{t=1}^{T}\sum_{i=1}^{N_{\text{G}}}C_{\text{IN},i,t}\left( \widetilde{P_{\text{DEV},i,t}}\right) \tag{31}\]  
\[C_{\text{IN},i,t}\left(\widetilde{P_{\text{DEV},i,t}}\right)=x_{i}+y_{i} \widetilde{P_{\text{DEV},i,t}}+z_{i}\widetilde{P_{\text{DEV},i,t}}^{2} \tag{32}\]  
\[\widetilde{P_{\text{DEV},i,t}}=\left|P_{\text{G},i,t,\text{DA}}-\widetilde{ P_{\text{G},i,t,\text{IN}}}\right| \tag{33}\]  
where \(\widetilde{P_{\text{DEV},i,t}}\) is the varying deviation power; \(\widetilde{P_{\text{G},i,t,\text{IN}}}\) is the intraday power varying due to the prediction errors of wind power and photovoltaic power stations.  
In intraday scheduling, when the prediction error of the actual wind power output \(P_{\text{W},i}\) is \(\mu_{\text{W}}\) (%), the constraint of actual wind power output changes into:  
\[P_{\text{W},j,\max}^{\min}=P_{\text{W},j,\max}-\left(\frac{\mu_{\text{W}}}{1 00}\times P_{\text{W},j,\max}\right) \tag{34}\]  
\[P_{\text{W},j,\max}^{\max}=P_{\text{W},j
## 4 Test
### Results and Discussion  
We applied the newly proposed equation (Equation (3)) and the previous equation (Equation (2)) reported by [PERSON] et al. [24] to test their fitting results, which are illustrated in Figure 3.  
With respect to the results of the 138 samples calculated with Equation (3), 136 samples were located within the logarithmic difference range of \(\pm\)0.2 (Figure 3a), accounting for 98.6% of the total samples (Table 3). Specifically, for the 20 rock samples from [PERSON] et al. [33], Cr geochemical background values were calculated using Equations (3b), (3d) and (3e), with logarithmic residuals ranging from \(-\)0.11 to 0.26. Except only one outlier, the other 19 samples were within the logarithmic difference range of \(\pm\)0.2, accounting for  
Figure 3: Testing results of 138 rock and soil samples on Equation (3) (**a**) and Equation (2) (**b**). The 45\({}^{\circ}\) diagonal line with lines deviating by \(\pm\)0.2 indicates the geochemical background value with its error range [24; 33; 34; 35; 36].  
95%. The 58 rock samples from [PERSON] et al. [34] were assessed with Equations (3b) and (3e), yielding logarithmic residuals between \(-0.15\) and \(0.10\), with a fitting accuracy of 100%. For the 10 carbonate samples from [PERSON] et al. [35], Equation (3a) was used. Except only one outlier with a logarithmic error of 0.36, the others derived logarithmic residuals ranging from \(-0.09\) to 0.17, with a fitting accuracy of 90%. For the 50 soil samples from [PERSON] et al. [36], Equations (3c) and (3d) were applied with logarithmic residuals between \(-0.13\) and \(0.20\), indicating an accuracy of 100%. In summary, the five sub-equations of Equation (3) were all tested on the 138 samples, where Cr concentrations ranged from 6.3 to 256 \(\mathrm{\SIUnitSymbolMicro g}\)/g with an accuracy of 98.6% on the criterion of logarithmic error of \(\pm 0.2\).  
Of the 138 samples, 10 carbonatite rock samples from [PERSON] et al. [35] had WIG values larger than 120, which is not suitable for Equation (2). Therefore, 128 samples were tested on Equation (2), reported by [PERSON] et al. [24], and their results are illustrated in Figure 3b. The 50 soil samples from [PERSON] et al. [36] were all well predicated with the logarithmic error of \(\pm 0.2\). However, the 78 rock samples from [PERSON] et al. [33] and [PERSON] et al. [34] were clearly outside of the logarithmic error of \(\pm 0.2\), indicating the unsuitability of Equation (2).  
The comparison results between Equations (2) and (3) indicate that the newly fitted Equation (3) not only has a wider practicality but also has a higher accuracy. The results of the testing indicate that the normal samples fall within the background region defined by the equation, and the application of Equation (3) yields the geochemical background values for commonly observed geological materials in nature. This approach is applicable to not only a single geological sample commonly found in nature but also a dataset of samples (regardless of its distribution and statistical parameters) for determining their geochemical background values of Cr.
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Nickel Oxide Films  
The nickel oxide nanostructured films were deposited on glass slides at room temperature [155]. The band gap (3.71 to 3.67 eV) and resistivity (4.1 to 802.1 \(\Omega\cdot\)cm) were observed to strongly depend on the film thickness. The annealing effect on the properties of films was investigated by [PERSON] and co-workers [156]. Research findings showed that the crystal and surface properties were well-developed in the annealed films. The band gap (3.3 to 3.11 eV) was reduced by increasing the annealing temperature from 200 \({}^{\circ}\)C to 400 \({}^{\circ}\)C. Nickel oxide films could be used in supercapacitor applications due to the highest specific energy (64.8 Wh/kg) and excellent specific capacitance (1341 F/g); they showed long-term cycle stability with 90% capacitance retention after 1000 deposition cycles [157]. Nickel (II) chloride was used to produce films on glass substrates as described by [PERSON] and co-workers [158]. The obtained films were polycrystalline and consisted of platelet-type morphology. The films showed a good response (72%) to 5000 ppm liquefied petroleum gas at lower operating temperatures.
## 2 Introduction to IPv6
### Dns  
The Domain Name System (DNS) has not changed much for IPv6. In general, it works similar for IPv6 as it does for IPv4. It is possible to query for both IPv4 and IPv6 addresses disregarding the type of the network. The resource record for IPv6 is called AAAA record (Quad A). Each AAAA record can only store one IPv6 address so that some hosts could have multiple AAAA records. In an answer all of these addresses would have to be included. For reverse lookup, a new domain _ip6.arpa_ was defined.  
Some older applications might have problems handling 128 bit addresses when expecting a 32 bit address. Since multiple addresses could be returned, services must be able to handle this. Another issue could arise if fragmentation of the answer is needed, since fragmentation of IPv6 might not be allowed in some networks. Other security implications are basically the same as with IPv4. There are no new IPv6 specific protection mechanisms because Transaction Signatures TSIG [38] and DNS Security Extensions DNSSEC [39, 40, 41, 42, 43] are also applied in the context of IPv4 [3].
## 2 System Description  
MVDS deployments utilize directive antenna systems to transmit signals to end-users efficiently. However, the inherent radiation characteristics of these antennas, particularly the presence of secondary lobes, present a potential source of unintended interference to other satellite communication systems (as shown in Figure 1). This section delineates the fundamental architecture and operational parameters of MVDS, followed by a description of geostationary Earth orbit (GEO) satellite systems, which are inherently vulnerable to interference from terrestrial transmitters such as MVDS. Subsequently, an overview of the key interference mechanisms relevant to the system analysis undertaken in this paper will be presented.  
Figure 1: _Cont._  
To enhance the readability of the analysis, a comparison between MVDS and GEO satellite systems is provided in Table 1, focusing on aspects relevant to potential interference.
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
###### Abstract  
The Chinese Area Positioning System (CAPS) is a new positioning system developed by the Chinese Academy of Sciences based on the communication satellites in geosynchronous orbit. The CAPS has been regarded as a pilot system to test the new technology for the design, construction and update of the BeiDou Navigation Satellite System (BDS). The system structure of CAPS, including the space, ground control station and user segments, is almost like the traditional Global Navigation Satellite Systems (GNSSs), but with the clock on the ground, the navigation signal in C waveband, and different principles of operation. The major difference is that the CAPS navigation signal is first generated at the ground control station, before being transmitted to the satellite in orbit and finally forwarded by the communication satellite transponder to the user. This design moves the clock from the satellite in orbit to the ground. The clock error can therefore be easily controlled and mitigated to improve the positioning accuracy. This paper will present the performance of CAPS-based relative positioning and velocity estimation as assessed in Beijing, China. The numerical results show that, (1) the accuracies of relative positioning, using only code measurements, are 1.25 and 1.8 m in the horizontal and vertical components, respectively; (2) meanwhile, they are about 2.83 and 3.15 cm in static mode and 6.31 and 10.78 cm in kinematic mode, respectively, when using the carrier-phase measurements with ambiguities fixed; and (3) the accuracy of the velocity estimation is about 0.04 and 0.11 m/s in static and kinematic modes, respectively. These results indicate the potential application of CAPS for high-precision positioning and velocity estimation and the availability of a new navigation mode based on communication satellites.  
Chinese Area Positioning System (CAPS); relative positioning; velocity estimation 18 1528; doi:10.3390/s18051528
## 2 Wireless Communications with Energy Harvesting
### Channel Model  
We denote \(g\) as the channel power gain between S and D, and \(g_{ms}\) as the channel power gain between S and the \(m\)th transmit antenna of T. We assume slow flat \(\kappa\)-\(\mu\) shadowed fading channels. More specifically, a parameter set \((\mu,\kappa,\chi,\delta_{tr})\) specifies totally \(g_{tr}\) with \(g_{tr}\in\{g_{ms},g\}\) where \((\kappa,\mu,\chi)\) were discussed in Section 1 and \(\delta_{tr}=\mathbb{E}\{g_{tr}\}\) with \(\delta_{tr}\in\{\delta_{ms},\delta\}\) rotates the corresponding channel power. In line with [24], the PDF and the CDF of \(g_{tr}\) are respectively expressed to be  
\[f_{g_{tr}}(w)=\sum_{n=0}^{N}\frac{Q_{n}}{\mathrm{Y}_{tr}^{\chi_{n}}\Gamma(\chi _{n})}w^{\chi_{n}-1}e^{-\frac{\mu}{\Psi_{tr}}}, \tag{1}\]  
and  
\[F_{g_{tr}}(w)=1-\sum_  
power for transmission in Phase 2 converted from \(E\) is \(\frac{E}{(1-\beta)U}\). Conforming to the NLEH paradigm in [18], S transmits information in Phase 2 with the power as  
\[\begin{split} P&=\left\{\begin{array}{cc}\frac{\eta \beta\tilde{P}}{1-\beta}\sum\limits_{m=1}^{M}g_{ms}&,\beta\tilde{P}\sum\limits_{ m=1}^{M}g_{ms}\leq\ \iota\\ \frac{\eta\beta\iota}{1-\beta}&,\beta\tilde{P}\sum\limits_{m=1}^{M}g_{ms}>\ \iota \end{array}\right.\\ &=\left\{\begin{array}{cc}GH&,H\leq\ R\\ J&,H>R\end{array}\right.\end{split} \tag{3}\]  
where \(\iota\) is the power saturation threshold, \(G=\frac{\eta\beta\tilde{P}}{1-\beta}\), \(J=\frac{\eta\beta\iota}{1-\beta}\), \(R=\frac{\iota}{\beta\tilde{P}}\), and \(H=\sum\limits_{m=1}^{M}g_{ms}\).  
It should be noted that Equation (3) reflects the characteristic of the NLEH. Indeed, the output power of the NLEH is \(GH\), which is proportional linearly to its input power when the input power is below \(\iota\); otherwise, the output power of the NLEH is saturated at \(\iota\). Furthermore, we note that as \(\iota\) is high (\(\iota\rightarrow\infty\)), the NLEH reduces to the linear energy harvesting (LEH).  
In Phase 2, S transmits its information \(x\) with transmit power \(P\) to D where \(\mathbb{E}\Big{\{}\big{|}x\big{|}^{2}\Big{\}}=1\). By accounting for HWi, D receives the signal to be [23]  
\[y=h\Big{(}\sqrt{P}x+v\Big{)}+\varsigma, \tag{4}\]  
wherein \(\varsigma\sim\mathcal{CN}(0,\sigma)\) is additive noise at D, \(h\) is the channel gain, \(v\sim\mathcal{CN}(0,\rho P)\) is HWi at S and D where \(\rho\) is the total HWi at S and D.  
The signal-to-interference plus noise ratio (SINR) for D to restore \(x\) from Equation (4) is  
\[\Lambda=\frac{gP}{\rho gP+\sigma}. \tag{5}\]  
where \(g=|h|^{2}\) is the channel power gain.  
It is drawn from Equation (5) that HWi plays a role as an interference source generating the quantity of interference as \(\rho gP\). This interference causes performance degradation in comparison to hardware perfection.