esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Design  
Winter wheat and summer maize were planted on 20 October 2016 and 20 June 2017 in four 4 m \(\times\) 4 m plots (Figure 1b,c) at the Fangshan Comprehensive Experimental Station (39\({}^{\circ}\)35\({}^{\prime}\)N, 115\({}^{\circ}\)42.5\({}^{\prime}\)E), Beijing Normal University. Field campaigns were separately conducted from March 28 to May 19 of 2017 for wheat and from July 29 to September 29 of 2017 for maize. An irrigation amount of 60 mm was applied to each plot before winter (7 November 2016) to ensure the winter wheat could endure the freezing cold in the winter (the monthly average temperature between November in 2016 and February in 2017 was 1.3 \({}^{\circ}\)C). From mid-October of 2016 to late-March in 2017, about 55 mm accumulated precipitations were recorded by a micro-weather station adjacent to the experimental plots (Figure 2a). Without the influence of precipitation on wheat growth after green up (March 28 to May 19 of 2017), different irrigation treatments were applied over four plots of wheat to generate a drought gradient. For plot 4, no irrigation was applied to simulate an extreme drought condition. For the other three plots, different irrigation treatments were conducted four times, separately on 28 March, 22 April, 2 May, and 12 May 2017 (Figure 2b). More in detail, the same volume of irrigation with 60 mm was applied on 28 March over the three plots, while the irrigation amounts in the subsequent three practices were 60, 40, and 25 mm, applied separately to plot 1, plot 2, and plot 3, respectively. For plot 1, the accumulated irrigation was about 300 mm, which could largely satisfy the demand of evapotranspiration during the whole growth season of winter wheat [34; 35]. Thus, the wheat in plot 1 was unstressed, while the other three plots were subjected to water stress since their accumulated irrigation was much lower than 300 mm. Furthermore, the intensity of water stress was in an increasing order for plot 2, plot 3, and plot 4. Besides, no irrigation was applied over maize due to the rich precipitation after sowing. As shown in Figure 2a, the accumulated precipitation during the maize growth season was \(\approx\)360 mm, which is in the range of the water requirement of summer maize (350-400 mm) [36]. Therefore, maize in our field experiment did not suffer from water stress.  
Figure 1: The automated spectroscopy system and the field experimental design for wheat and maize. (**a**) Detailed device configuration for the system. (**b**,**c**) Description of the data collection and the location of each data acquisition equipment, respectively.  
Figure 2: (**a**) Precipitation during the period from 15 October 2016 to 29 September 2017, covering the time periods for the field campaigns for wheat and maize. (**b**) Gradient irrigation treatments applied over the four plots of wheat.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Type** & **Examples** \\ \hline Structural/ physical & Engineered and builtenvironment & Coastal protection and flood control structures, improved drainage, flood and cyclone shelters \\ \cline{2-3}  & Technological & New crop varieties, knowledge, hazard and vulnerability monitoring and schemes, early warning systems \\ \cline{2-3}  & Ecosystem-based & Ecological restoration soil conservation, control of overfishing, ecological corridors \\ \cline{2-3}  & Services & Social safety nets and social protection, essential public health services \\ \hline \hline \end{tabular} \
\begin{tabular}{c c} \hline \hline \multirow{4}{*}{Institutional} & Economic & Financial incentives, insurance, payment for ecosystem services, contingency funds in case of disasters \\ \cline{2-3}  & Laws and regulations & Urban planning legislation, building standards and practices, disaster risk reduction legislation, protected areas \\ \cline{2-3}  & National and government policies and programs & Adaptation plans, urban upgrading programs, disaster planning and preparedness, integrated coastal zone management \\ \hline \hline \end{tabular}
\begin{tabular}{c c} \hline \hline \multirow{4}{*}{Social} & \multirow{4}{*}{Educational} & Awareness raising and mainstreaming in education, participatory action research and social learning, knowledge sharing, knowledge sharing and learning platforms \\ \cline{2-3}  & & Development of hazard and vulnerability schemes, early warning and response systems, surveillance and remote sensing, climate services \\ \cline{2-3}  & Behavioral & Household preparation and evacuation planning, migration, soil and water conservation \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of adaptation measures and examples in coastal areas. Adapted from IPCC [18].
## 4 Simulation Results and Analyses  
### Simulation Setup  
The simulation was conducted in the environment of MATLAB/Win 7. Assuming that the CRN-aided IoT system exists with a multipath frequency selective fading channel, and that the channel gain of each subcarrier is subject to Rayleigh fading with an average channel gain of 1, then the parameters of the simulation are set as shown in Table 1.  
In order to verify the performance of our proposed solution method using the PA-Jaya algorithm, the simulation model was run 10 times under the same parameter settings, and the average value  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Description** & **Parameter** & **Value** \\ \hline BER & \(p_{e}\) & \(10^{-5}\)\(-\)\(10^{-1}\) \\ Transmit BER & \(\delta\) & 5 dB \\ Noise spectral density power & \(N_{0}\) & \(10^{-7}\)\(\mathrm{W}/\mathrm{Hz}\) \\ Interference factor & \(S_{k,n}\) & \(10^{-6}\)\(\mathrm{W}\) \\ Subcarrier bandwidth & \(W_{c}\) & 0.315 \\ Total system upper power limit & \(P_{\mathrm{total}}\) & \(1\)\(-\)\(30\)\(\mathrm{W}\) \\ User-acceptable maximum interference limit & \(\frac{j_{\mathrm{th}}}{r_{e}}\) & \(10^{-3}\)\(-\)\(10^{-2}\)\(\mathrm{W}\) \\ The number of secondary users & \(K\) & 8 \\ Population size & \(M\) & 30 \\ Total number of subcarriers & \(N\) & 64 \\ The number of subpopulations & \(S\) & 3 \\ The iteration interval of subpopulation communicate & \(Q\) & 10 \\ The number of inner loop & \(T\) & 10 \\ Total number of iterations & \(D\) & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameters.  
was obtained. Moreover, the computational performance was also compared between our solution method and other popular algorithms, including SA [20], GA [21], PSO [22; 23], DE [24], ICO [25], and traditional Jaya [33]. The detailed parameter settings for each algorithm can be found in Table 2. It is noted that for our proposed PA-Jaya algorithm, the initial population is randomly generated, and each subpopulation is updated by asynchronous Jaya iteration, as shown in Algorithm 2. Then, information exchange between different subpopulations takes place at regular intervals, and the overall process can be seen in Algorithm 3.  
Moreover, for the user fairness issue discussed in this article, the user fairness proportion factor is randomly generated. Then, we compare our proposed PA-Jaya algorithm with the algorithms ROOT-FINDING [39] and LINEAR [51]. Our solution to this issue can be divided into two parts. The first part is the allocation of subchannels, which is the same as that in Ref. [50]. In the second part, we use the PA-Jaya algorithm to allocate the appropriate power to the channels of different users while meeting the required constraints. As the fitness function is set as Equation (17), and the minimum user rate proportionality is set to 0.99, the process of conducting PA-Jaya and setting the parameters are consistent with the former issue.
# Calculation of 1/f Fluctuation from Sound Signal and Comfort Evaluation
## 2 Calculation Method for Fluctuation Coefficient of the Sound Signal
### Fluctuation Coefficient of the Amplitude  
Changes in the amplitude value of the sound signal were calculated using the root mean square (RMS) of the time history of the sound signal data. The calculation interval of the amplitude value was 25 ms, which was the same as that of the frequency fluctuation. Figure 4a shows the time history of RMS of sound signal every 25 ms. The spectrum was calculated using the discrete Fourier transform, as defined in Equations (1)-(4). The time history of the RMS was used to analyze the fluctuation, which provides the change in amplitude. The fluctuation coefficient \(\lambda\) was defined by calculating the spectral slope from 0.05 Hz to 0.5 Hz. In this study, we defined the fluctuation coefficient of the amplitude \(\lambda_{a}\), which was calculated using the above method.  
Figure 4: The calculation result of the amplitude fluctuation coefficient. (**a**) The time history of the average amplitude of the sound signal. (**b**) Spectrum of amplitude (\(\lambda_{f}=-1.134\)).  
Figure 3: The calculation result of the frequency fluctuation coefficient. (**a**) The time history of the average frequency of the sound signal. (**b**) Spectrum of frequency (\(\lambda_{f}=-2.148\)).  
Figure 4b shows a result of the spectral slope obtained by the equations. The amplitude fluctuation coefficient \(\lambda_{a}\) was \(-1.134\).
## 7 Limitations  
While the developed simulation model offers significant advancements in energy consumption estimation for electric vehicles, several limitations should be acknowledged regarding our outcomes.  
**Data dependency:** Simulation relies heavily on experimental data, which may have limitations in terms of data availability, accuracy and representativeness. If the empirical data used are not fully representative of all driving conditions or vehicle configurations, the simulation results may not generalize well to different real-world scenarios.  
Figure 15: Residual error comparison.  
**Subsystem interactions:** Although the model includes key subsystems, such as the powertrain, steering, lighting and HVAC, the interactions between the subsystems are highly simplified. A more detailed examination of real-world interactions, such as dynamic changes in power consumption, the effects of wiring impedance, sudden weather changes, traffic conditions or driver behavior, is not provided by the model in its current form, which significantly limits the applicability of the model.  
**Model validation scope:** Validation mainly involved comparing simulation results with real data and two regression models. While this provides some insight, it does not give a complete overview of all available regression techniques or other potential influencing factors, especially for different vehicle models or manufacturers. A comparison of the model performance with a wider range of regression models or other prediction techniques could provide a more comprehensive assessment of the strengths and limitations of the model.  
**Model scope:** While many important vehicle subsystems are included in the model, other factors and modules contribute to the energy consumption of electric vehicles, such as tire friction, road surface type, regenerative braking efficiency or simply the use of a multimedia unit. Including additional subsystems or refining the model to account for these factors can improve the accuracy and reliability of the predictions.  
In conclusion, while the simulation model provides a promising approach to energy consumption estimation for electric vehicles, its limitations in terms of data dependency, subsystem interactions, model validation scope and the model scope considered suggest areas for further refinement and research.
## 3 Methodology
### Artificial Neural Network Model  
In this work, ANNs were used to estimate \(\rm GHUVE\) through the Levenberg-Marquardt Back-Propagation (LMBP) algorithm. The ANN architecture adopted for this purpose consists of a single hidden layer and a single output, as outlined in a previous publication [28; 29]. In the input layer, each neuron is a meteorological variable. Determining the optimal number of neurons in the hidden layer is currently unknown, but it is acknowledged that this number should not exceed the number of neurons in the preceding layer [51]. Therefore, if the input layer has only one variable and thus a single neuron, the hidden layer can have only one neuron. If the input layer has two meteorological variables, the hidden layer can have either one or two neurons. Similarly, if the input has three variables and, therefore, three neurons, the hidden layer can have three, two, or one neuron/s, and this trend continues for additional variables in the input layer. The iterative process and the fitting are explained elsewhere [28].  
Four ANN models were generated and tested in this work, one for all skies and three for each sky type (clear, intermediate, and overcast), considering the selected meteorological variables shown in Table 3. Table 5 shows the goodness of fit for each ANN model according to the described statistics. It can be observed that in four cases, a good determination coefficient was obtained, R\({}^{2}>0.95\). The nRSME obtained varied between 11.9% and 21.3%. The best results were obtained for clear skies (R\({}^{2}>0.98\) and nRMSE \(<12\%\)).
## 2 Theoretical Framework
### Performance Metrics  
The fundamental challenge for a metric error measure is that it condenses a large amount of data into a single value. Evaluating the performance of a forecasting method is achieved by comparing the actual values with the predicted ones. A typical approach is to use a specific criterion to measure the error of the predicted value. Subsequently, the efficiency of the forecasting method is evaluated based on how close the predicted and actual values are. The most frequently used error index, MSE [6; 32], was used in the present study, which is defined as  
\[\text{MSE}=\frac{\sum\limits_{t=1}^{n}\left(F_{t}-y_{t}\right)^{2}}{n}\]  
where \(y_{t}\) and \(F_{t}\) are the actual and predicted values at time \(t\), respectively, and \(n\) is the number of data points.
## 2 Results and Discussion
### Volatile Compounds Analysis
#### 2.2.4 Terpenes and C13 Nor-Isoprenoids  
Terpenes and C13 nor-isoprenoids which are derived mainly from the grapes could be from the free aromas or the glycosidic aroma precursors and are known to be varietal aroma compounds [23; 26]. As can be seen from Table 2, a total of 9 compounds were identified in this experiment. Compared to the control wine, clarification treatments significantly influenced the total concentration of terpen and C13 nor-isoprenoids compounds, and among them, BT fining showed the greatest influence with a 32.36% decrease. This finding was in contrast with [PERSON] et al. [24], who found bentonite showed a low effect on the loss of terpenes, but the doses of bentonite were much lower than this experiment. On the contrary, wines treated by SP and CF showed a lower decrease with 10.34% and 13.36%, respectively. linalool, \(\alpha\)-terpineol, and citronellol were the dominant terpen identified. The concentration of linalool was significantly influenced by CF and BT treatments, while the concentration of \(\alpha\)-terpineol and citronellol only showed significant decrease in MF wine. However, there was no observed difference in other treatments. \(\beta\)-damascenone and geranyl acetone belonged to C13 nor-isoprenoids, surprisingly, the influence of clarification treatments on these compounds was in controversy. Fining agents, both BT and SP treatment influenced geranyl acetone significantly, while mechanical clarification CF and MF treatments showed a significant impact on \(\beta\)-damascenone concentration.
## 4 Discussion
### Validating the Model with Unseen Data

Table 9: Collection of ground truth data for maize plants using the traditional approach. and PENemble 4 predicted yields around 258.2 kg to 258.75 kg, both showcasing error rates below 10%. Other models, including M estimators, linear regression, ridge regression, and other PENemble models, had predictions ranging from 259.45 kg to 263.05 kg, with error rates from 10% to 11%. For Plot 3, with an actual harvest of 387.82 kg, the traditional breeder's method estimated a yield of 342.67 kg, resulting in a 12% error. Most of the ML models and the PENemble methods predicted yields between 345.22 kg and 348.57 kg, with errors between 10% and 11%. In summary, this table provides an insightful overview of the accuracy of various predictive methods against actual harvest yields across different plots. While the traditional breeder's method tended to yield higher error rates, especially for Plot 1, the ML models and PENemble methods exhibited lower error percentages, which underscores their potential for more accurate yield predictions.
# Development of an Electronic Smart Safe Box Using Private Blockchain Technology
## 3 Materials and Methods
### Hardware Components
#### 3.2.9 GPS Tracker  
By using the GPS tracker system, users can track the box in the GPS tracking system offering the safe box's current location at any given time and blockchain for data logs for more security. The GPS module needs an internet location to update the security box location. This means that the system is provided with a GSM SIM card to let the administrator see the updated location.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 3 Enabling Technologies of Sixth Generation Networks
### Networking  
Networking at both the link and networking layers in 6G is expected to exhibit great flexibility. As noted above, ITU-T expectations for 2030 go beyond a wider adoption of IPv6 (and relevant ossifications), and they expand to a more dynamic and adaptive communication [52]. The control-data split introduced by the softwarization in the Internet is undoubtedly a positive step in this direction.  
There are other aspects to consider. For example, the advances in the physical layer and the topology entail more sophisticated link-layer management modes. On one end, there is the complexity of the connection possibilities at the physical layer that needs to \"structured\" and scheduled. On the other, there is the need to interact with upper layers more closely (i.e., a stronger cross-layer coupling) to attain higher performance levels. For both, there is also the consideration of increased connectivity for IoT/MTC.  
Similarly, changes are needed in layer 3 (L3) to handle the increased connectivity from both physical and logical entities, i.e., virtualized instances. Meanwhile, emphasis on reliability will have to extend to \"high-precision networking\", whereby low-to-zero tolerance is exercised in supporting service delivery requirements [53]. These extensions are inevitable if proper support is to be provided for URLLC services and their evolutions. There is also a need to go beyond what can be offered by efforts such as time-sensitive networking, especially as they were designed for traditional and wired operations.  
Another inevitable expectation is complementing the control-data split with a knowledge plane [54, 55]. This plane would include functionalities and procedures where the Internet gathers its understanding and reasoning and shares it across the different domains. Realizing the knowledge plane requires efficiency and robustness in knowledge representation and sharing. The evolution of content distribution networks through information-centric networks allows for multimodal access to knowledge, namely through traditional IP addressing, geographical addressing, and content labeling, among others [56]. A relevant view of 6G networks is that of \"semantic communications\" [57].
## 8 Comparison with Other Emerging Interconnect Technologies
### Inter-Chip Wireless Communication in Neuromorphic Computing  
Neuromorphic Computing enabling deep learning is emerging as the computation backbone for many applications [116]. Deep learning neural networks require several layers of artificial neurons where the number of interconnections between neurons scale exponentially with increase in the number of layers [117]. Typically, the neurons have a degree of connectivity in cortical networks in the order of 10,000. Therefore, traditional interconnection methodologies with physical interconnects will be incapable of realizing this kind of networks. Moreover, the communication between neurons in different layers need to be reconfigurable where a neuron communicates with different neurons at different times. Therefore, wireless interconnections render themselves most suitable for creating the communication backbone for such deep neural networks with reconfigurable interconnections. While the IBM TrueNorth [118] is among the most notable examples of neuromorphic chip, true computation power will require the integration of multiple neuromorphic chips to provide the scalability. In that case, the chip-to-chip interconnection network will become a bottleneck. This is because each individual chip will be neuromorphic, providing the performance per watt similar to the human brain while the communication between the chips will use traditional interconnect mechanisms. Therefore, novel inter-chip interconnections utilizing reconfigurable wireless links will provide the flexibility and scalability in performance needed for multi-chip neuromorphic computing platforms. Thorough investigation of the specific deep neural network will aid in determining the required data rates and power envelopes, thereby guiding the design choices for the wireless interconnection architecture including topology, physical layer, and MAC.  
Thus, IoT, Industry 4.0 and Neuromorphic Computing present new opportunities for chip-to-chip wireless interconnections. However, these new paradigms may introduce their own challenges.