esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Implementation and Learning Algorithm
### Random Forest Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value/Range** & **Unit** & **Description** \\ \hline Packet energy consumption & 0.01–0.2 & Joules & Total energy consumed for transmitted packets. \\ \hline Power consumption during the idle state & 0.001–0.05 & mW & Consumption of power by each node in the idle state. \\ \hline Signal-to-noise ratio range for training & \(-19\) to \(-6\) & dB & Signal-to-noise ratio ranges utilized for training tasks. \\ \hline Received signal strength indicator & \(-120\) to \(-80\) & dBm & Calculated at each node to evaluate the signal strength. \\ \hline Queuing time & 5–50 & ms & The waiting time spent in the queue until a packet is transmitted. \\ \hline Number of collisions & 0–5 per second & Count & Number of packet collisions affecting retransmission energy consumption. \\ \hline Packet loss & 0–10\% & Packets & The ratio of packet loss during transmission. \\ \hline Data rate & 0.3–50 & kbps & The data rate used by LoRA nodes. \\ \hline Distance range for training & 110 & km & The distance range within which nodes communicate during training. \\ \hline Bit error rate & \(1\times 10^{-14}\)–\(1\times 10^{-5}\) & - & Bit error rate for estimating the level of error during training. \\ \hline Number of iterations & \(7\times 10^{6}\) & - & The count of iterations achieved during model training. \\ \hline Signal-to-noise ratio threshold & \(-7.5\), \(-10\), \(-12.5\), \(-15\), \(-17.5\), \(-20\) & dB & Signal-to-noise ratio threshold utilized for transmission decisions. \\ \hline Maximum payload size & 242, 115, 51 & Bytes & The maximum payload size that can be handled by the LoRa node. \\ \hline Transmission delay & 5–100 & ms & Time delay from transmission to receipt operations. \\ \hline Retransmission count & 0–3 & Count & Number of times packets are retransmitted due to collision or error. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._where \(\text{PL}_{\text{pred}}\) refers to predicted lost packets, M is the number of trees in the forest, Tm is the output of each decision tree, and XPL represents the input feature vectors of each decision tree for the input (x) related to the packet loss [44].
## 2 Proposed System
### Simulation Configuration VLC Channel and Signal-To-Noise Ratio (SNR) Analysis
After calculating the DC gain for both direct and diffuse channels, the total received optical power at the PD is as follows [25]:  
\[P_{Total}=\sum_{1}^{4}P_{T}\left(H_{LOS}+\int H_{NLOS}\right), \tag{3}\]  
where \(P_{T}\) is the total transmitting optical power. To compute the SNR, we first determine the Gaussian noise from the output, which is the sum of shot noise \(\sigma_{S}^{2}\), thermal noise \(\sigma_{T}^{2}\), and inter-symbol interference \(\sigma_{ISI}^{2}\). However, the \(\sigma_{ISI}^{2}\) term can be removed, due to short transmitting duration. The total noise can be expressed as [22]:  
\[N_{total}^{2}=\sigma_{S}^{2}+\sigma_{T}^{2}. \tag{4}\]  
Assumed that the dark current noise is small, the shot noise variance due to the received signal and the background radiation is given by [22]:  
\[\sigma_{S}^{2}=2 qRP_{Total}B+2 qI_{B}I_{2}B, \tag{5}\]  
where \(q\) is the electronic charge; \(R\) is the PD responsibility; \(I_{B}\) is the photocurrent due to background radiation; \(I_{2}\) is the noise bandwidth factor; and \(B\) is the equivalent noise-bandwidth of the PD. Figure 3: Design of prototype. (**a**) On-Off keying (OOK) modulation using Manchester encoding. (**b**) Time-division multiplexing (TDM) protocol. The thermal noise variance is given by [22]:  
\[\sigma_{T}^{2}=\frac{8\pi\kappa T_{k}}{G}\eta_{PD}AI_{2}B^{2}+\frac{16\pi^{2} \kappa T_{k}\Gamma}{g}\eta_{PD}^{2}A^{2}I_{3}B^{3}, \tag{6}\]  
where \(\kappa\) is the Boltzmann's constant; \(T_{k}\) is the absolute temperature; G is the open-loop voltage gain; \(\eta_{PD}\) is the fixed capacitance of PD per unit area; \(\Gamma\) is the FET channel noise factor, \(g\) is the FET transconductance; and \(I_{3}\) is the noise-bandwidth factor. Finally, the SNR for the LOS channel, diffuse channel, and overall channel are depicted in Figure 4, and the SNR equation is as follows [25]:  
\[SNR=\frac{R^{2}P_{Total}^{2}}{N_{total}^{2}}. \tag{7}\]
## 2 Materials and Methods
### Big-Leaf LUE-GPP Models
#### 2.5.1 EC-LUE Model  
The eddy covariance-light-use efficiency (EF-LUE) model was first proposed by [PERSON] et al. [37], who developed a LUE daily GPP model from EC measurements. Its reliability has been verified in many ecosystems [16]. The GPP in EF-LUE is estimated as follows:  
\[GPP=PAR\times fPAR\times LUE_{\text{max}}\times Min(T_{\text{s}},W_{\text{s}}) \tag{1}\]  
where \(LUE_{\text{max}}\) is the maximum LUE (g C m\({}^{-2}\) M\({}^{-1}\)) without any environmental stress, which is derived from leaf-scale measurements using a Li-6400 (Li-COR, USA) photosynthesis system. Specifically, an intensive leaf-scale field measurement was conducted in the peak growing season. The quantified maximum apparent quantum yield was close to 0.066 \(\upmu\)mol CO\({}_{2}\)/\(\upmu\)mol PPFD and very close to the 1.67 g C MJ\({}^{-1}\) based on an approximate conversion of 2.05-2.17 between MJ (\(10^{6}\) J) and mol PPFD [38]. The fraction of PAR absorbed by the canopy, known as \(fPAR\), is generally described by the Beers-Lambert exponential decay equation (fPAR = 1 - \(e^{-\mathrm{kLAI}}\)). \(T_{\mathrm{s}}\) and \(W_{\mathrm{s}}\) represent the temperature and moisture constraints on \(LUE_{\mathrm{max}}\), respectively, and \(Min\) (\(T_{\mathrm{s}}\), \(W_{\mathrm{s}}\)) indicates that \(LUE_{\mathrm{max}}\) is only affected by the maximum constraints of \(T_{\mathrm{s}}\) and \(W_{\mathrm{s}}\).  
\[T_{\mathrm{s}}=\left\{\begin{array}{cc}\frac{(T_{\mathrm{a}}-T_{\mathrm{min} })\times(T_{\mathrm{a}}-T_{\mathrm{max}})}{\left(T_{\mathrm{a}}-T_{\mathrm{ min}}\right)\times\left(T_{\mathrm{a}}-T_{\mathrm{max}}\right)+\left(T_{\mathrm{a}}-T_{ \mathrm{opt}}\right)^{2}}&T_{\mathrm{min}}\leq T_{\mathrm{a}}\leq T_{\mathrm{ max}}\\ 0&T_{\mathrm{a}}<T_{\mathrm{min}}\;or\;T_{\mathrm{a}}>T_{\mathrm{max}}\end{array}\right. \tag{2}\]  
\[W_{\mathrm{s}}=\frac{LE}{R_{\mathrm{n}}} \tag{3}\]  
where \(T_{\mathrm{min}}\), \(T_{\mathrm{max}}\), and \(T_{\mathrm{opt}}\) denote the minimum, maximum, and optimum air temperatures (\({}^{\circ}\)C), respectively, conducive to the photosynthetic activity of kiwifruit leaves; \(LE\) denotes the latent heat (W m\({}^{-2}\)) measured by EC; and \(R_{\mathrm{n}}\) denotes the net radiation (W m\({}^{-2}\)). In this study, \(T_{\mathrm{min}}\) and \(T_{\mathrm{max}}\) were set to 0 and 40 \({}^{\circ}\)C, respectively, and \(T_{\mathrm{opt}}\) was determined by least-square nonlinear fitting [37].
## 7 Results and Recommendations
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Criteria** & **Results** & **Recommendations** \\ \hline
**Topicality** & & \\  & & Most of the content is still valid and up to date. & * Update RFC versions and check for changes. \\  & & Some RFCs have newer versions, see Section 4.2 & * Consider coverage of RFCs published after 2010. \\  & & RFCs published after 2010 are not covered. & * Reconsider coverage of outdated or discouraged methods (e.g., NAT-PT). \\  & & Some methods and techniques are outdated. & * Watch out for new security breaches. \\  & & * Watch out for changes in IETF standards. \\ \hline
**Completeness** & & \\  & & High completeness (0.82). & * Watch out for new RFCs. \\  & & High weighted completeness (0.81). & * Watch out for new RFCs. \\  & & Strong focus on broad coverage of IPV6-relevant topics. & \\  & & Many missing RFCs are later than 2010. \\ \hline
**Depth** & & \\  & & Very detailed explanations for specifications. & * Include practical examples for concrete infrastructures. \\  & & Generally well aligned with importance ratings of the survey. & * Distinguish between different security requirements. \\  & & Highly informative character. & * Increase depth for DHCPv6. \\  & & Average depth of covered RFCs at 0.54 & * Increase depth for ND and IPsec. \\  & & * Coverage of ICMPv6 could be reduced. & * Increase depth for DNS security issues. \\  & & * Increase depth for Routing Protocols. \\  & & * Increase depth for Dual Stack. \\ \hline \end{tabular}
\end{table} 
Table 10: NIST—Results and recommendations. As can be seen in the comparison of the two guides, the depth of covered RFCs is actually not as bad as it seems when analyzing the overall depth. Nonetheless, content depth could generally be increased by incorporating more practical examples, and also the main IPv6 specification could be covered in more detail. This is even more important for IPsec, DHCPv6, DNS security issues, Multihoming, Routing Protocols, Dual Stack and Tunneling. These are all relevant topics and should be explained in more detail and with examples. Besides these results and recommendations, there are also other aspects to look at. In particular, the BSI guide features very well designed deployment scenarios, which are worth mentioning, but could not be evaluated with the formal scores used in this study. Also, the NIST guide has some qualities that can not be captured by only using completeness and depth. Table 12 lists the pros and cons of the two guides, taking the so far not covered aspects into consideration.
## 4 Results and Discussion
### Comparison with Previous Studies
Compared to the data points from the previous studies, the current data shows deviations of the membrane characteristics due to the absolute pressure differences in this study. It can be seen that high water vapor permeability is measured when the experimental conditions are not considered. However, the selectivity value is relatively low, hence, if the corresponding membrane is used in a system configuration, an additional device will be required to prevent a pressure rise in the compressor due to permeated nitrogen.
# Review of Communication Technologies for Electric Vehicle Charging Management and Coordination
## 6 Physical Layer Security
In fact, frequency hopping spread spectrum (FHSS) is a traditional anti-jamming technique that makes the signal more resilient to jamming by spreading its frequency onto a much wider bandwidth, thereby allowing the original signal to 'hop away' from the jammed spectrum [111]. Alternatively, MIMO interference cancellation techniques cancel out jamming signals by treating them as noise while adjusting the legitimate signal direction to optimize the reception and decoding performance [111]. In [112; 113], jamming signals are proposed to be utilized as artificial noise (AN) that disturbs the reception of eavesdroppers and enhances the physical layer security of legitimate transmissions. Collectively, the different PLS techniques discussed in this section can be effectively employed to enhance the security of vehicular communications, particularly for EV charging coordination and management, while acknowledging the rapid temporal channel variations due to vehicular mobility.
# Robustness Analysis of the Collective Nonlinear Dynamics of a Periodic Coupled Pendulums Chain
## 3 Numerical Examples
### Overall Discussion  
Three stable solutions are obtained at several frequencies in the multistability domain due to modal interactions between the responses of two coupled nonlinear pendulums, generated by the collective dynamics. However, up to six stable solutions are obtained if a three coupled pendulums chain is considered.  
In presence of uncertainties, the stability of the system is enhanced. Indeed, a multiplicity of stable solutions is obtained and the multistability domain is wider in terms of frequency range and response amplitude. Moreover, the nonlinearity of the system is strengthened by increasing the ratios of the frequency intervals by the amplitude intervals computed between the extreme bifurcation points in the multistability domain.  
The robustness of the attractors of different modes and modal branches is illustrated by the dispersion analysis of the basins of attraction and the quantitative study of the evolution of the distribution of these attractors from deterministic case to the case \(\delta_{l}=5\%\). Modal localization is generated by widening one or more special modal branches more than the others.  
For a reduction of up to 97.5% of computing time compared to the LHS method, the gPCE method allows satisfying approximations of the frequency responses and the basins of attraction. These approximations are more accurate for the frequency responses since the dispersion analysis of the basins of attraction is more thorough. For high dispersion levels (>2%), some limits of the gPCE method are reached.
## 4 Experiments
### Ablation Study  
We conducted ablation experiments on the TKY dataset to evaluate the individual impact of each component on the overall performance of the model. Specifically, four experiments were conducted: (1) the remove geo-categorical-based POI-group-embedding model (denoted by \"w/o GCP\"); (2) the remove time and category information model (denoted by \"w/o Time & Cat\"); (3) the remove the multi-head attention mechanism module (denoted by \"w/o multi-head attention\"); (4) the use only the fully connected layer model (denoted by \"Fully Connected\"); (5) the full model. The results are shown in Table 3.  
As shown in Table 3, the full model has the best performance. For the rest of the components, the results show that the GCP plays a larger role in the overall performance than the other components. For example, without GCP embedding, the TOP-1 accuracy dropped from 19.92% to 18.29%. Other components also contributed to the final recommendation outcome. For example, with no time or category embeddings, the TOP-1 accuracy dropped from 19.92% to 19.62%. Multi-head attention mechanisms also play an important role in the model, enhancing the representation of the model by performing multiple independent attention operations in parallel and then merging the outputs. Without multi-head attention mechanisms, the TOP-1 accuracy dropped from 19.92% to 18.84%. To sum up, the ablation experiment proves the effectiveness of the attention-based time-embedding module and geo-categorical-based POI-group-embedding module developed in this study.
## 1 Introduction
Two different groups of micro-energy harvesting devices, i.e., SiO\({}_{2}\)/ZnO/SiO\({}_{2}\) micro-energy generators (SZS-MGs) and ZnO/SiO\({}_{2}\)/ZnO micro-energy generators (ZSZ-MGs), were fabricated by stacking both SiO\({}_{2}\) and ZnO thin films on top of each other, and subsequently characterized. With a particular interest in the fabrication of flexible devices, all the ZnO and SiO\({}_{2}\) thin films were deposited on flexible indium tin oxide (ITO)-coated polyethylene naphthalate (PEN) substrates using a radio frequency (RF) magnetron sputtering technique. The effects of the thickness and position of the deposited SiO\({}_{2}\) films on the device performance were investigated by observing the variations in the output voltages in comparison with the ZnO single-layer device used as a control sample. As a result, compared to the ZnO single-layer device, all the ZSZ-MGs showed much better output voltages, while all the SZS-MGs showed slightly better output voltages. Among the ZSZ-MGs, the highest output voltages were obtained from the ZSZ-MG with the SiO\({}_{2}\) thin films deposited at a deposition power of 150 W. It was also found that the output voltages were strongly dependent on the position and thickness of the SiO\({}_{2}\) thin films in the ZnO/SiO\({}_{2}\)-stacked multilayer devices, in addition to the processing conditions.
# An Overview on Deep Learning Techniques for Video Compressive Sensing
## 2 Compressive Sensing
### Reconstruction Algorithms  
The reconstruction process is the key to efficiently incorporate compressive sensing in real-world applications. Therefore, designing and implementing new optimization algorithms is the major concern of CS researchers. These algorithms can be categorized into several categories. In this section, we will cover the main two types of the recovery algorithms in CS: convex optimization algorithms and greedy algorithms.
## 4 Conclusions  
This study explores the potential of DG of PV electricity on a broiler poultry farm with various capacities of displacing grid power consumption under two energy pricing scenarios. The power demand of a broiler poultry farm raising four broiler flocks over a year was influenced largely by weather conditions, with lower power demand in winter months and higher demand in warmer seasons. It also exhibits higher daytime demand than nighttime. Broiler farms lack the flexibility of scheduling growing stages or managing power demand. Using the System Advisor Model, we simulated PV-generated electricity of three different sizes of on-site installations and how different energy pricing schemes affect the PV system profitability. Under a net energy metering (NEM) scenario, a 70 kW\({}_{\text{P}}\) system would reduce 85% of annual farm electricity consumption, resulting in a payback of 7 years. Under the net energy billing (NEB) scenario, the smallest system of 35 kW\({}_{\text{P}}\) resulted in an 85% self-consumption ratio but only offset less than half of the farm's annual power consumption. With avoided-cost rate pricing penalties of NEB compared to NEM, the 35 kW\({}_{\text{P}}\) system's profitability was only slightly lower than the NEM's profitability of similar size as, at that system size, only 15% of electricity produced by the solar system was sold back to the grid. The larger 105 kW\({}_{\text{P}}\) system yielded the highest net present values under both NEM and NEB scenarios but led to longer payback periods with NEB as expected. The results suggest that DG or less than utility-scale projects are less likely to be adopted on-farm under NEB and, if so, system sizes for farm-size projects will likely be smaller.  
Conceptualization, Y.L. and M.P.; methodology, Y.L.; software, Y.L.; formal analysis, Y.L.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, M.P.; project administration, Y.L.; funding acquisition, Y.L. and M.P. All authors have read and agreed to the published version of the manuscript.  
The research in this study was supported in part by the National Institute of Food and Agriculture, U.S. Department of Agriculture Hatch project under access No. 2666.  
Data Availability Statement: Data used in this study will be available upon request.  
The authors declare no conflicts of interest.  
Figure 12: Net present values over system’s useful life of 25 years as a result of two billing arrangements with three system sizes. NEM: net energy metering; NEB: net energy billing.
## 2 Geological Background and Sampling
### Sample Petrological Characteristics  
The fresh granodiorite (Figure 5A, TS-10-15) is composed of quartz (25%), feldspar (~45%), biotite (~15%), and amphib (15%). Near the skarn zone, plagioclase in granodiorite exhibits slight sericitization, and new minerals such as chlorite and diopside (0.1-0.2 mm) become more prominent, while Fe-rich amphibole and biotite minerals disappear (Figure 5B, TS-10-14). Moving further from the intrusion, garnet and pyroxene minerals increase in prominence, and chlorite disappears (Figure 5C); however, occasional chlorite veins are found within garnet. In this area, garnet and pyroxene are the primary minerals, with the proportion of garnet being five times greater than that of pyroxene. The proportion of garnet can increase to over 90% as one moves further from the intrusion in the eastern direction, where most garnets are euhedral, defining the garnet-enriched outer skarn zone. Chalcopyrite (cpy) veins also occur within cracks in the garnet (Figure 5E, TS-10-6). Chloritization and epidotization are evident in the core of garnet (Figure 5F, TS-10-5). As one continues further from the intrusion, fine-grained pyroxene appears, and its proportion relative to garnet gradually increases (garnet:pyroxene < 1:5) (Figure 5G, TS-10-4). Cpy and magnetite growth and abundance increase at the boundaries of the main minerals (Figure 5H, TS-10-3; Figure 5J, TS-10-2). This area has experienced copper mineralization with a copper content of 2 wt.%. Calcite constitutes over 99% of the marble in this field (Figure 5L, TS-10-1).  
From preliminary observations of specimens, we noted that the skarn profile ranges from rock mass, weak alteration zone, skarn zone, and mineralization zone (mainly Cu mineralization), to fully developed marble surrounding rock. Later stages of low-temperature and superficial weathering alteration were not developed. This profile is similar to the classic skarn profile [2] and serves as a good example for the study of skarnization processes.  
Figure 5: Micrographs of rocks at different positions in Tongshan skarn profile. (**A**) non-altered granodiorite; (**B**) diopside-epidote skarn; (**C**) garnet-rich skarn (garnet:diopside > 5:1); (**D**) early garnet cut by later garnet vein; (**E**) garnet mineral boundary filled with brass vein; (**F**) garnet decomposed and replaced by chlorite; (**G**) diopside-rich skarn (garnet:diopside < 5:1); (**H**) diopside rich zone fine vein disseminated distribution, magnetite and chalcopyrite coexist; (**I**) diopside rich skarn with chlorite; (**J,K**) disseminated distribution of diopside rich zone and reflection of chalcopyrite; (**L**) marble belt. (Cc-calcite, Ccp-chalcopyrite, Mag-magmagnetite, Chl-chlorite, Di-diopside, Grt-garnet, Ep-epidote, Pl-plagioclase, Qz-quartz, Bt-biotite. (**E,H,J**) were captured under reflected light, while the remaining were under transmitted light.)