esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In the current digital era, Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) are evolving, transforming human experiences by creating an interconnected environment. However, ensuring the security of WSN-IoT networks remains a significant hurdle, as existing security models are plagued with issues like prolonged training durations and complex classification processes. In this study, a robust cyber-physical system based on the Emphatic Farmland Fertility Integrated Deep Perceptron Network (EFDPN) is proposed to enhance the security of WSN-IoT. This initiative introduces the Farmland Fertility Feature Selection (F\({}^{2}\)S) technique to alleviate the computational complexity of identifying and classifying attacks. Additionally, this research leverages the Deep Perceptron Network (DPN) classification algorithm for accurate intrusion classification, achieving impressive performance metrics. In the classification phase, the Tunicate Swarm Optimization (TSO) model is employed to improve the sigmoid transformation function, thereby enhancing prediction accuracy. This study demonstrates the development of an EFDPN-based system designed to safeguard WSN-IoT networks. It showcases how the DPN classification technique, in conjunction with the TSO model, significantly improves classification performance. In this research, we employed well-known cyber-attack datasets to validate its effectiveness, revealing its superiority over traditional intrusion detection methods, particularly in achieving higher F1-score values. The incorporation of the F3S algorithm plays a pivotal role in this framework by eliminating irrelevant features, leading to enhanced prediction accuracy for the classifier, marking a substantial stride in fortifying WSN-IoT network security. This research presents a promising approach to enhancing the security and resilience of interconnected cyber-physical systems in the evolving landscape of WSN-IoT networks.  
wireless sensor network (WSN); internet of things (IoT); security; cyber-physical system; intrusion detection; farmland fertility feature selection (F\({}^{2}\)S); deep perceptron network (DPN); tunicate swarm optimization (TSO) +
Footnote †: journal: Sensors
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 5 Results
### Results on ZH300
#### 5.1.4 Spatial Distribution Analysis  
Figure 6 shows the output result and error distribution of February 2017, in which the average RMSE of the month reflected the results of the PRD proposals in Table 2. Figure 6a shows the prediction output, in which all of the methods captured the global pattern compared to the ground truth. However, the low ZH300 value around the North Pole was only captured by the SP-CRN (LSTM) methods that considered the periodic-depth representation. The prediction results indicated that the periodicity component helped the models capture a wider range of ZH300 values. Moreover, the periodic-depth representation was more important than the nearby-time representation for capturing the lowest peak value.  
Figure 6b shows the spatial error distribution using the absolute error between ground truth and prediction results. The CNN, ConvGRU, and ConvLSTM results showed a large area of high error (100-150 m) around the tropical area. However, there was only a small error on the subtropical area of the CNN, ConvGRU, and ConvLSTM. Thus, the yearly change of this area was not much, which did not show a huge advantage for the model considering the periodicity components. The models that considered periodicity components, on the other hand, showed a much smaller error in the tropical area. The SP-CRN (GRU), SP-CRN (LSTM), and SP-CRN-1 could not capture the peak of low values in Figure 6a, in which they represented the high error distribution around the North Pole in Figure 6b. The SP-CRN-2 and SP-CRN-3, which considered only periodic-depth representation, showed some small area of high error distribution even when comparing SP-CRN-1+3 and SP-CRN-1+3-Meta with lower RMSE.
###### Abstract  
The growing complexity of natural disasters, intensified by climate change, has amplified the challenges of managing emergency shelter demand. Accurate shelter demand forecasting is crucial to optimize resource allocation, prevent overcrowding, and ensure evacuee safety, particularly during concurrent disasters like hurricanes and pandemics. Real-time decision-making during evacuations remains a significant challenge due to dynamic evacuation behaviors and evolving disaster conditions. This study introduces a spatiotemporal modeling framework that leverages connected vehicle data to predict shelter demand using data collected during Hurricane Sally (September 2020) across Santa Rosa, Escambia, and Okaloosa counties in Florida, USA. Using Generalized Additive Models (GAMs) with spatial and temporal smoothing, integrated with GIS tools, the framework captures non-linear evacuation patterns and predicts shelter demand. The GAM outperformed the baseline Generalized Linear Model (GLM), achieving a Root Mean Square Error (RMSE) of 6.7791 and a correlation coefficient (CORR) of 0.8593 for shelters on training data, compared to the GLM's RMSE of 12.9735 and CORR of 0.1760. For lodging facilities, the GAM achieved an RMSE of 4.0368 and CORR of 0.5485, improving upon the GLM's RMSE of 4.6103 and CORR of 0.2897. While test data showed moderate declines in performance, the GAM consistently offered more accurate and interpretable results across both facility types. This integration of connected vehicle data with spatiotemporal modeling enables real-time insights into evacuation dynamics. Visualization outputs, like spatial heat maps, provide actionable data for emergency planners to allocate resources efficiently, enhancing disaster resilience and public safety during complex emergencies.  
connected vehicle data; spatiotemporal modeling; disaster management; shelter demand prediction; geographic information science (GIS); big data analytics; transportation resilience; hurricane evacuation +
Footnote †: journal: Applied sciences  
Article
## 2 Implementation of GPU-MOC Method
### Implementation of MOC on a GPU  
The basic GPU-MOC parallel strategy is that the logical operations are conducted on a CPU and the floating-point operations are executed on a GPU. The logical operations like initializing data, allocating memory for arrays, transferring data between the host and device, and invoking MOC kernels, are executed on the CPU, because the CPU has many logical processing units and is efficient in disposing complex logical operations. The floating-point operations, including calculating the present time level values and updating them to the previous time level values, are conducted on GPU, because GPU is equipped with the layouts of pipelines and is efficient in disposing simple floating-point operations.
## 6 Conclusion
### Economic Validation
#### 6.5.2 Savings Calculation  
Savings were evaluated based on the reduction percentages demonstrated in the validation of the proposed design (current vs. proposed situations) concerning existing problems and their percentage incurrence assessment. The objective was to determine the total savings for the company concerning the costs currently generated.  
The costs generated in 2020 due to absenteeism amount to 120,315 PEN, where the implementation of the proposed design will generate a saving percentage of 35.68%, in other words, 42,929 PEN. Table 11 shows the results of the economic evaluation by optimistic, pessimistic and moderate scenarios.  
After the economic evaluations have been developed, it can be concluded that the project is viable in the different scenarios developed, since in all cases, a positive Net Present Value (NPV) or greater than 0 was obtained, and in all cases, it can be said that the benefits exceeded the costs incurred in the implementation of the project, which is viable and beneficial for the company.  
Taking the most positive NPV generated in a moderate scenario, it yields PEN 13,370.34. On the other hand, the investment is expected to be recovered in approximately eight months. Additionally, based on the cost-benefit ratio (C/B), it can be concluded that the project yields PEN 1.56 for every PEN invested. Finally, the average return at the point where the Net Present Value (NPV) equals zero is 80.98%.
# Area-Efficient Early-Termination Technique for Belief-Propagation Polar Decoders
## 4 Experimental Results
Thus, the additional circuitry for a polar code with a small code rate becomes negligible compared to the previous methods.
## 4 Discussion  
### Nutritional Value of Amaranth  
The values of CP, EE and CF in amaranth grain were higher than those in cereals. The high value for GE can be attributed to the high content of organic matter (850 g/kg) in amaranth grain. NDF was also high in the ADs, which probably decreased DMD in ADs containing a high level of amaranth grain. However, the increased NDF and ADF in amaranth grain was not enough to cause a decrease in the availability of energy in the ADs.  
Nutrient concentrations in amaranth seed have been determined previously [9; 19; 48; 49]. The concentrations of nutrients (CP, EE, CF, NFE and NFC) in our study were mostly within the range of reported values. Compared with our value of 3255 kcal/kg, UAG has previously been found to have AMEn values of 3145 kcal/kg [33], 2859 kcal/kg [20] and 2832 kcal/kg [25]. AMEn values for _A. hypochondriacus_, _A. cruentus_ and _A. hybridus_ have been reported to be between 2700 and 3600 kcal/kg [9]. [PERSON] et al. [33] reported AMEn values of untreated and autoclaved amaranth species (_A. edulis_) as 3145 and 3475 kcal/kg, respectively. [PERSON] and [PERSON] [22] determined the AMEn values of extruded _A. cruentus_ in broiler chickens as 3522 and 3415 kcal/kg DM, respectively. [PERSON] et al. [25] estimated the AMEn value of _A. hypochondriacus_, _either_ untreated or autoclaved, in broiler chickens as 2832 and 3133 kcal/kg dry matter, respectively. [PERSON] and [PERSON] [20] reported that the AME value based on the DM for untreated amaranth in the forms of flour, fat-free flour, milled containing persiperm, milled with bran and popped were as follows: 3210, 3090, 3680, 3060 and 2980 kcal/kg, respectively. The AME of these forms for autoclaved amaranth were 3040, 2940, 3100 and 3170 kcal/kg, respectively.  
In comparison with the report of [PERSON] et al. [9], _A. hybridus chloroplasts_ in our study had less CP (168 g/kg) than the corresponding value for _A. hypochondriacus_ and twice the CF content (101 g/kg) compared with _A. hybridus_, _A. cruentus_ and _A. caudatus_ (60, 40 and 50 g/kg, respectively). The high CF content would probably decrease the available energy from _A. hybridus chlorostachys_ for poultry. However, [PERSON] et al. [49] evaluated the chemical composition of _A. hybridus_ and reported that it was a rich source of protein (170 g/kg), which is consistent with the present study. The CP value for amaranth seeds in the present study was in the range of CP values for amaranth seeds in different species (140-170 g/kg) [50]. [PERSON] et al. [25] reported CP, EE, CF and ash concentrations of 168, 58, 60 and 26 g/kg, respectively, for _A. hypochondriacus_. These are close to the corresponding values in this study, except for CF and ash. Amaranth grain is two times richer in CP and EE than corn, which is the basic energy provider in RD.  
The digestibility of dry matter (DM) in monogastric animals, particularly poultry that have short and less sacculated intestinal tracts, is greatly affected by the fibre fraction in the feed. As shown in Table 1, the NDF value in amaranth grain is high, containing cellulose and hemicellulose that are indigestible by broilers.
## 3 Data Preparation
### Data Selection and Preprocessing  
The Stanford-MIT-Toyota dataset experiences complex charging conditions, which exhibit richer characteristics of lithium-ion batteries than its smooth discharge process. The intuitive reflection of this can be observed through the voltage and temperature curve changes in Figure 4. Therefore, the complete charging voltage curve and temperature curve are selected to verify the RUL prediction method proposed in this study. The data selection of the Oxford dataset is also the same as the Stanford-MIT-Toyota dataset.  
To ensure the data accurately reflect the battery aging characteristics and to facilitate the inspection of the RUL prediction method, data preprocessing is required. In the Stanford-MIT-Toyota dataset, there exists a small amount of outlier data that stems from occasional measuring instrument faults and periodic time information peaks during testing. These outliers cannot objectively reflect the nature of the battery itself and, therefore, require filtering. The Oxford dataset has had the anomalous cycle removed directly from the dataset before release.  
Next, the data are resampled. During the cycle of the battery, the time elapsed in a series of charging curves obtained is different. For example, in the Stanford-MIT-Toyota dataset, the No. 11 battery spent 27 min on the first full charge, while the 761 st charge took about 21 min. Hence, the number of data points is also different for each curve. To prevent the feature from biasing towards the charging curve with more data points, resampling is required so that each data sample has the same dimension. Since there is a certain error in the sampling time interval of the Stanford-MIT-Toyota dataset, an interpolation operation is performed first before resampling. In this way, the values of each charging curve can be aligned in time intervals. It is worth mentioning that the sampling interval of the Oxford dataset is stable at 1 s, so the interpolation process can be omitted. Afterward, resampling the charging curve can obtain a dataset with a uniform dimension.  
Finally, the voltage and temperature variables in the charging curve are normalized, respectively. The method is as follows  
\[x^{\prime}=\frac{x-\min(x)}{\max(x)-\min(x)} \tag{20}\]  
Since the voltage ranges and test temperature ranges of the batteries used in the two datasets are different, normalization should be performed within their respective value ranges to eliminate unnecessary errors caused by differences in the value ranges.  
Figure 5: Charging voltage and temperature curve of Cell7. (**a**) Voltage. (**b**) Temperature.
# Forecasting Cost Risks of Corn and Soybean Crops through Monte Carlo Simulation
## 2 Materials and Methods
### Monte Carlo Simulation
#### 2.5.1 Simple Exponential Smoothing (SES)  
SES is used for time series forecasting when the data show no trend or seasonal patterns. It smooths the data using a weighted average of past observations. More recent observations are given more weight--Equation (2).  
\[\hat{y}_{t+1}=\alpha y_{t}+(1-\alpha)\hat{y} \tag{2}\]  
where Equation (2) is the forecast for the next period, \(y_{t}\) is the actual value at time t, and \(\alpha\) (with \(0<\alpha<1\)) is the smoothing parameter.
## 2 Methods
### Spacing Policy  
As shown in Equation (6), the distance error, \(e_{\text{d}}\), is crucial in the problem formulation. To ensure a safe distance between vehicles, it is essential to define an appropriate spacing policy. Various strategies can be employed, ranging from the simplest approach, such as maintaining a constant safety distance, to more sophisticated methods that incorporate velocity or acceleration terms into the safety distance [36]. In this study, the inter-vehicular safety distance is determined by the following law:  
\[\delta_{\text{safe}}=d_{0}+t_{h}v_{\text{ego}} \tag{8}\]where \(d_{0}\) represents the desired distance at standstill and \(v_{\text{ego}}\) is the speed of the ego vehicle. \(t_{h}\) is the time headway, i.e., the elapsed time between the front of the lead vehicle and the front of the ego vehicle passing the same point on the roadway, assuming that both vehicles move at a constant relative speed.  
In the spacing policy, \(\delta_{\text{safe}}\) is the distance computed using the desired time headway, which in this work is set to be \(t_{h}=1.4\). Consistently, with Equation (8), a constant time headway implies a safety distance growing linearly with respect to the velocity. Reference [37] suggests time headway in the interval [1 s, 2 s] based on safety and road capacity concerns.  
Finally, the distance error, first introduced in Section 2.2, is defined as the difference between the current inter-vehicular distance \(\delta\) and \(\delta_{\text{safe}}\):  
\[e_{\text{d}}=\delta-\delta_{\text{safe}}=d_{lead}-d_{\text{ego}}-(d_{0}+t_{h }v_{\text{ego}}). \tag{9}\]
# Upscaling Gross Primary Production from Leaf to Canopy for Potato Crop (_Solanum tuberosum_ L.)
## 2 Materials and Methods
### Leaf-Level Measurements  
At the leaf scale, we used an infrared gas analyzer IRGA LICOR 6800 IRGA system (LI-COR Biosciences, Lincoln, Nebraska, USA) to measure (1) the net photosynthetic rate at leaf scale (A\({}_{\text{n}}\)) [\(\mu_{\text{mol}}\) (CO\({}_{2}\)) m\({}^{-2}\) s\({}^{-1}\)] as a function of PPFD, ranging from 50 to 2000 \(\mu_{\text{mol}}\) (photon) m\({}^{-2}\) s\({}^{-1}\) (An-I curves); and (2) An-I curves were recorded in three strata of the plant canopy: the upper stratum (topmost unshaded leaves), the middle stratum, and the low stratum (bottommost shaded leaves). Measurements were taken at 28, 36, 41, 52, 66, 71, 77, 91, 105, 117, and 130 days post planting (DPP) between 9:00 and 11:00 am in three plants. On the same days, the accumulated leaf area index (LAI) was also determined for each layerof the plant canopy in 15 randomly selected plants using an ACCUPAR LP-80 ceptometer (METER Group, Inc., Pullman, WA, USA) with an external Apogee SQ110 PPFD sensor from 0 to 4000 \(\upmu\)mol (photon) m\({}^{-2}\) s\({}^{-1}\) and 80 probe PPFD sensors at the measurement bar. LAI data for the three canopy layers (upper, middle, and low) were fitted to third-degree and fourth-degree polynomial regressions.  
Leaf insertion angles were determined using a compass and protractor, marking the leaf inclination angle. Subsequently, an analysis of the distribution of foliar angles was carried out according to [30; 45] to determine the mean density angle (MDA) and the value of the ratio of the horizontal to vertical axis of the ellipsoid or parameter \(x\) using the following equation:  
\[x=1.7433025+\left(\frac{162.22048}{MDA}\right) \tag{2}\]  
The extinction coefficient (K) between 8:00 and 17:30 h was estimated from the ellipsoidal equation [30] that relates the variation of K with the vertical and horizontal projections of the canopy elements (parameter \(x\)) and the zenith angle \(\psi\):  
\[K=\frac{\sqrt{x^{2}+\tan^{2}\psi}}{x+1.774\,\left(x+1.182\right)^{-0.733}} \tag{3}\]
# Characteristics and Vaporization Potential of Fermentation Waste of Greengage (_Prunus mume_)
## 4 Discussion  
The greengage variety \"Nangao\" from Dayi (Sichuan, China) was used in this work. As a fruit well known to be rich in bioactive compounds, the main characteristics of the different greengage varieties had already been investigated [42; 43; 44]. Being different in a narrow range, they were quite comparable in total content of acid, sugar, vitamin C, proteins, amino acids, flavonoids and phenols. Those comparative investigations ensured that this work would provide important guidance for the majority of greengage wine breweries even though different varieties are employed in fermentation.  
Moreover, by focusing on greengage fermentation waste, the bioactive compounds found in this work are quite consistent with the previously mentioned work on greengage fruit [45; 46; 47]. Though they have different compositions, the key bioactive compounds (linalool, terpineol, \(\beta\)-ionone, neochlorogenic acid and chlorogenic acid) were present and well identified in the samples of this work and those in the literature. This not only consolidated the previous revelations about bioactive greengage compounds, but also implied that they were partially retained in the fermentation waste.  
As for the potential applications of fermentation waste, various industrial sectors can be involved. Although the total phenol content and total flavonoid content of the fermentation waste extract were lower than the fresh greengage extract, they were still high enough to be recovered. According to the literature [48; 49], the extract of such bioactive compound richness can deliver human health benefits such as enhancing immunity, suppressing tumors, resisting infection, reducing cholesterol and delaying aging. Therefore, it may serve as the active ingredient in medical or cosmetic products.  
In addition, the extract proved to have a strong antibacterial ability that was thermal stable. This characteristic may contribute in the food industry as food preservative or additive, or in the production of personal hygiene products such as mouthwash. The results of this work may also inspire further applications of such fermentation waste for sustainable development.
# Study on the Method of Moving Load Identification Based on Strain Influence Line
## 4 Verification by Experiment
### Analysis of Experiment Results
#### 4.2.1 Speed Identification  
Firstly, the vehicle speed was identified. Assuming that the vehicle crosses the bridge with a constant speed, the integral Equation (17) of strain influence line can be obtained by modifying Equation (6):  
\[A=\int_{-\infty}^{+\infty}\varepsilon(x)dx=\int_{-\infty}^{+\infty}\varepsilon(t )vdt=v\int_{-\infty}^{+\infty}\varepsilon(t)dt \tag{17}\]  
As shown in Figure 14, it corresponds to the starting point \(t_{1}\) of the wave peak when the front axle of the vehicle contacts the bridge, and it corresponds to the ending point \(t_{2}\) when the rear axle of the vehicle leaves the bridge. Then the vehicle speed \(V\) can be calculated according to the time difference and the driving distance, as shown in Equation (18):  
\[v=\frac{d}{\Delta t}=\frac{L+x}{t_{2}-t_{1}} \tag{18}\]  
where, \(x\) is the vehicle wheelbase, \(L\) is the bridge length. In this experiment, the vehicle weight was 16.95 kg, and the vehicle speed was divided into nine levels by changing the speed of traction motor, as shown in Table 3. Meanwhile, each experimental condition was repeated three times. According to the above method, the strain data of 3# beam bottom were used to identify the vehicle speed, and the results are shown in Figure 15. It can be seen that the average relative errors of the speed identification were smaller than \(\pm\)4%, and they were within an acceptable range, which shows the great performance of the method.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 3 Experimental Results of RF Wireless Power Transmission  
### Single-Band RF WPT Experiment  
RF WPT experiments using the single-band (either 2.4 or 5.8 GHz) were conducted, as shown in Figure 1a,b, using the developed dual-band RF WPT system presented in Section 2. During the RF WPT experiment using the frequency of 2.482 GHz, every 2.4 GHz radiating element in the Tx array was _on_ and every 5.8 GHz radiating element was \(off\) (specifically, they are terminated by 50 \(\Omega\)). Likewise, during the experiment of transmitting 5.73 GHz power, every 5.8 GHz element in the Tx array was _on_ and every 2.4 GHz element was \(off\). The _on_ or \(off\) state of the radiating elements in the Tx array can be adjusted using  
Figure 12: Dual-band (2.4 and 5.8 GHz) stacked patch antenna: (**a**) configuration; (**b**) top view of the fabricated antenna.  
Figure 13: Simulated and measured results of the dual-band patch antenna: (**a**) reflection coefficients; (**b**) realized gain; (**c**) axial ratio.  
a GUI program on a control PC connected to the Tx module. In the RF WPT experiment, the RF wireless power received by the Rx antenna was measured using an Agilent E4440A spectrum analyzer. Finally, the measured results are compared with the calculated results using Equation (4).