esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Testing Environment and Configuration
### Application Setup  
As this experiment aims to compare energy consumption between an SCHC-enabled scenario and one without compression, two application setups were implemented, as shown in Figure 9. Black represents the flow without SCHC, while red represents the flow with SCHC.  
For the non-SCHC scenario, a Python application (Python App1) creates payloads, representing DLMS traffic. The generated traffic is sent to another Python application (Python App2) through a TUN interface. When this second application captures traffic, it forwards it over NB-IoT using the HL78 modem's AT commands interface. The packets sent by the modem over the NB-IoT network are received by a third Python application (Python App3), representing the application server. The application server can send packets back to the initial application (Python App1) using the same path. When DTLS is enabled, it is applied to the first app, generating the payloads.  
For the SCHC-enabled scenarios, we use the same application (Python App 1) to generate traffic. However, the C application (C App) receives the traffic in this case through the TUN interface. The C App, which embeds Acklio's SCHC, applies the SCHC operations and communicates with the HL78 modem through AT commands. The traffic is forwarded over NB-IoT just as in the previous scenario, but instead of reaching the end application directly, the traffic passes through Acklio's IPCore (SCHC server) for decompression. The end application can send back the packet using the same path, and the compression/decompression actions are reversed (the echoed traffic is compressed by Acklio's IPCore and decompressed by the C app). DTLS encryption is applied on the compressed packet, then a second simple compression step is performed to compress the DTLS header.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Config.** & **Channel Emulator** & **UE TX** & **Number of** \\
**ID** & **Output Power (dBm)** & **Power (dBm)** & **Repetitions** \\ \hline
0 & \(-\)20 & 0 & 1 \\ \hline
1 & \(-\)75 & 23 & 1 \\ \hline
2 & \(-\)85 & 23 & 32 \\ \hline
3 & \(-\)95 & 23 & 64 \\ \hline
4 & \(-\)110 & 23 & 64/128 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Channel emulator parameters.
###### Abstract
In our system, we used an easy-to-carry portable mobile device to visualize the AR scene because it is easy to rotate the viewpoint and view direction. Using several appropriate 3D building models, we can create a small city environment. On top of the city environment, virtual crowds must be placed so that they can interact with the environment similar to the real world. In this study, a technique for simulating a virtual crowd based on sketches is proposed to provide a feeling of liveliness in urban environment design. A critical problem of image-based markers in AR is that each marker can be applied to only a single virtual object. Therefore, to place many objects, the user will require to produce as many markers as the number of objects. In addition, pre-made markers are difficult to modify and have a fixed shape, making them unsuitable for constructing urban environments. To address these issues, we propose a CoR-SketchAR system that can add or remove a wide variety of user-drawn sketches representing the specific virtual objects as erasable marker pens on the whiteboard. The sketch drawing method is more convenient than the existing computer-based GUI method because pen and markers are more natural for human users. Figure 1 shows the CoR-SketchAR system. In this example, a user creates a crowd simulation environment by drawing sketches with dry-erase markers of different colors and shapes. The sketches were then converted into 3D buildings, highways, trees, and buildings with exits by recognizing their shapes and colors. Not only those 3D objects mentioned above but sketch-based paths can also be specified using a black dry-erase marker to control the movement of the crowd. The 3D human character models were then placed at the location of the crowd along the path. During animation, collisions between characters are prevented automatically. Experiments confirmed that the proposed system had a higher level of participation and accessibility than general 2D screen-based environmental authoring tools. Furthermore, the survey revealed that CoR-SketchAR was more useful and easier to learn than 2D-authoring tools. The higher accessibility and participation of the system is, the higher the user's sense of immersion in the environment they are creating, which also can increase the efficiency of completing the given tasks. The remainder of this paper is organized as follows. In Section 2, we introduce prior research approaches for applying AR and previous studies on augmented reality, including crowd simulation. In Section 3, we present detailed algorithms for shape recognition, environmental authoring tools, routing, and crowd generation. In Section 4, we perform a series of experiments for generating environments with the authoring tools we propose, and we conduct a comparative survey between the sketch-based AR environmental authoring tool and the computer-based environmental authoring tool. Section 5 examines the proposed method and discusses the future research directions.
# Near Real-Time Freeze Detection over Agricultural Plots Using Sentinel-1 Data
## 2 Study Sites and Datasets
### Datasets
#### 2.2.1 SAR Sentinel-1 Time Series
The radiometric accuracy of the S1 SAR backscattering coefficients is approximately 0.70 dB (3\(\sigma\)) for the VV polarization and 1.0 dB (3\(\sigma\)) for the VH polarization [51; 52]. Figure 3: Repeat cycle of the Sentinel-1 synthetic aperture radar (SAR) images in ascending “A” (afternoon, red) and descending “D” (morning, blue) modes for the month of October 2018: (**a**) study site A, and (**b**) study site B. The number next to each line represents the incidence angle for each acquisition. Finally, the backscattering coefficients \(\sigma_{\theta}^{0}\) acquired at different incidence angles \(\theta\) were normalized to a common reference incidence angle set at \(\theta_{\textit{ref}}=40^{\circ}\) using the square cosine correction equation [53; 54]:  
\[\sigma_{\textit{ref}}^{0}=\frac{\sigma_{\theta}^{0}cos^{2}\!\left(\theta_{ \textit{ref}}\right)}{cos^{2}\!\left(\theta\right)} \tag{1}\]
## 2 Materials and Methods
### Database  
In our study, we used the publicly available EEG dataset from the University of Bonn, as described by [PERSON] et al. [22]. The dataset has been extensively used to investigate various epilepsy states. It consists of five unique subsets (Z, O, N, F, and S) that are recorded using a 10-20 electrode placement system. Each subset contains one-channel 100 EEG segments of 23.6 s in length. The recordings were obtained at a sampling frequency of 173.61 Hz, resulting in 4097 samples for each EEG segment. Subsets Z and O were derived from recordings of five healthy volunteers, with Subset Z corresponding to segments collected with the eyes open and Subset O corresponding to segments collected with the eyes closed. N, F, and S subsets consist of EEG recordings taken from 5 epilepsy patients. Subsets N and F refer to seizure-free intervals during the interictal state. Note that Subset F includes segments recorded from the epileptogenic zone of the hippocampal formation, whereas Subset N signals were obtained from the contralateral hippocampal formation. Subset S includes EEG segments recorded during seizures (ictal state). Further, all the subsets were band-pass filtered to improve signal quality. The filter has a low cut-off frequency of 0.53 Hz and a high cut-off frequency of 40 Hz. To ensure the reliability of the dataset, an expert manually inspected all EEG segments for artifacts due to muscle activity and eye movements. The description of the dataset is shown in Table 1.  
In this study, we used three distinct cases of grouping the segment into classes, designed to address specific classification scenarios. The details of these cases are presented in Table 2 for a comprehensive overview. Case I refers to the identification of segments collected from healthy subjects from seizure segments, whereas Case II refers to the three-class identification of healthy, interictal, and ictal segments. Finally, Case III refers to the identification of healthy vs. epileptic patients. Considering three classes in the classification scenarios enables the study to provide a comprehensive analysis of EEG data, allowing for the differentiation of healthy, interictal, and ictal states. This multi-class approach con  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Healthy Subject**} & \multicolumn{2}{c}{**Epilepsy Subject**} \\ \hline \multirow{2}{*}{Eyes open} & \multirow{2}{*}{Eyes closed} & \multicolumn{2}{c}{Seizure-free interval (interictal state)} & \multirow{2}{*}{Seizure activity (ictal state)} \\ \cline{3-3} \cline{5-5}  & & Hippocampal formation & Hippocampal formation & \\ \cline{3-3} \cline{5-5}  & & formation & of the opposite hemisphere of the brain & \\ \hline Z & O & N & F & S \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed description of the database used in the study [22].  
Figure 1: Pipeline of the proposed methodology.  
tributes to a deeper understanding of epilepsy and its neural dynamics, which is valuable for both diagnostic and research purposes.
# A Novel Approach to Reach Impedance Matching in Wireless Power Transfer Systems
## 4 Experimental Results
In addition, the red and black lines represent the simulation results. Obviously, the transfer efficiency increased once the optimal load was reached. In particular, when the transmission distance was 40 cm, compared to before optimization, the transfer efficiency increased by nearly 11%. Figure 11b showed the optimal \(d_{12}\) and \(d_{23}\) at various distances in the case of reaching the optimal load. The experimental results were in good agreement with those calculated by Equation (9), which once again illustrates the accuracy of the theoretical analysis in this paper.
## 3 Results and Discussions
### WoA-BPNN Modeling
After parameter tuning, a single hidden-layer neural network with 10 neurons was chosen, resulting in a final structure of 4 \(\times\) 10 \(\times\) 2. The whale algorithm was set with a population size of 30 and a maximum of 50 evolutionary iterations to find the optimal weight threshold for the BPNN. Initial parameters included 500 iterations, a learning rate of 0.01, and a training target of 0.0001. Predictive performance was assessed using scoring indices, allowing for the development of the laser-induced hybrid hardening hardened layer process prediction model in MATLAB. Figure 6 shows the fitness evolution of the hardened layer's quality (depth and hardness). After optimizing the weights and thresholds via WOA, these values were applied to the BPNN. By the 50 th generation, the fitness value for hardened layer depth reached 0.0256, while hardness fitness reached 0.0626. Figure 5: The topological structure of the BPNN. After training, the depth coefficient of determination of the neural network model optimized by the whale algorithm was 0.995, and the hardness coefficient of determination was 0.996. The RMSE, MAE, and MAPE of the depth of the WOA-BPNN model were calculated by Equations (1)-(3) to be 0.099 mm, 0.022 mm, and 0.697%, respectively, and those of the hardness of the WOA-BPNN model were 1.734 HV\({}_{0.3}\), 0.596 HV\({}_{0.3}\), and 0.787%, respectively.
## 6 Results and Discussion
### Bit Error Rate for Different Amplitudes  
Considering different values of the transmitter signal amplitude, Table 3 presents the results of the BER for specific experimental setup parameters and the four best-performing pixels. The results are ordered in descending order. It can be observed that for low amplitude values, while some pixels received the data without errors, others performed poorly. This behavior contrasted with the greater amplitude values, where all the pixels performed without error.  
For low values of the transmitter signal amplitude, it is unlikely that the demodulator could separate events caused by the signal from those caused by the noise. Additionally, considering a given stimulus contrast, the probability that a pixel outputs an event is not spatially and temporally uniform, resulting in varied performances of the pixels [29]. Finally, the use of higher amplitudes generates a sufficient contrast threshold difference required to trigger events, effectively stimulating a greater portion of the pixels.  
\begin{table}
\begin{tabular}{c|c c c c c c c c c} \hline \hline
**Amplitude [Lux]** & **D = 0.1** & **D =0.2** & **D = 0.3** & **D = 0.4** & **D = 0.5** & **D = 0.6** & **D = 0.7** & **D = 0.8** & **D = 0.9** \\ \hline A = 13 & \(1.29\times 10^{-1}\) & \(6.25\times 10^{-2}\) & \(3.17\times 10^{-2}\) & \(<\)1.25 \(\times 10^{-4}\) & \(2.08\times 10^{-2}\) & \(1.42\times 10^{-1}\) & \(2.39\times 10^{-4}\) & \(1.80\times 10^{-1}\) & \(8.00\times 10^{-1}\) \\ A = 24 & \(7.56\times 10^{-2}\) & \(3.67\times 10^{-2}\) & \(1.10\times 10^{-2}\) & \(<\)1.25 \(\times 10^{-4}\) & \(<\)1.25 \(\times 10^{-4}\) & \(1.91\times 10^{-3}\) & \(2.86\times 10^{-3}\) & \(2.96\times 10^{-2}\) & \(7.96\times 10^{-1}\) \\ A = 75 & \(6.44\times 10^{-2}\) & \(6.89\times 10^{-2}\) & \(5.96\times 10^{-3}\) & \(<\)1.25 \(\times 10^{-4}\) & \(<\)1.25 \(\times 10^{-4}\) & \(6.92\times 10^{-3}\) & \(<\)1.25 \(\times 10^{-4}\) & \(5.51\times 10^{-2}\) & \(7.21\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The bit error rate is presented for different duty cycle (\(D\)) values of n-pulse modulation, transmitting 8000 bits. The distance between the transmitter and receiver was 200 cm. The transmitter operated at a frequency of 200 Hz. The maximum number of pulses for n-pulse modulation (\(n_{\text{max}}\)) was set to 4. Additionally, the receiver’s bias sensitivity was configured to “default”.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 4 FESS Applications  
Flywheel applications range from large scale at the electrical grid level, to small scale at the customer level [8; 9]. A high power and capacity is reached by arranging flywheels in banks, rather than by using large machines [62]. The best and most suitable applications of flywheels fall in the areas of high power for a short duration (e.g., 100 s of kW/10 s of seconds) [6], when frequent charge-discharge cycles are involved [8]. The most common applications are power quality such as frequency and voltage regulation [2; 63], pulsed power applications for the military [61], attitude control in space craft [61], UPS [18], load levelling [2], hybrid and electric vehicles [18; 61], and energy storage applications [61]. As part of energy storage applications, flywheels perform storage applications both at the grid, as well as at the customer level. A brief description of some common applications associated with flywheel energy storage systems will now be given.
## 2 STFT Algorithm and Hardware Architecture
Therefore, designing an STFT processor that supports a variable overlap ratio using an MDC FFT processor requires a single FFT processor instead of four SDF FFT processors, as shown in Figure 4.  
Figure 2: Window overlap scheme for (**a**) 0% overlap, (**b**) 25% overlap, (**c**) 50% overlap and (**d**) 75% overlap.
## 4 Performance Evaluation  
In this section, the effectiveness of the proposed method is verified using numerical results and field test results, respectively. For the field test, the experiment is carried out using the self-developed multi-channel passive radar (McPR) system based on the DVB-S signal.
Applied sciences  
Reducing Aidity of Tropical Acid Soil to Improve Phosphorus Availability and _Zea mays_ L. Productivity through Efficient Use of Chicken Litter Biochar and Triple Superphosphate  
[PERSON] 1, [PERSON] 1,2,3,*, [PERSON] 1, [PERSON] 3, [PERSON] 1 and [PERSON] 1  
Department of Crop Science, Faculty of Agriculture and Food Sciences, Universiti Putra Malaysia Bintulu Campus, Sarawak 97008, Malaysia; [EMAIL_ADDRESS] (A.M.); [EMAIL_ADDRESS] (A.A.); [EMAIL_ADDRESS] (N.M.); [EMAIL_ADDRESS] (A.V.J.)  
Agriculture and Environment, Borneo Eco-Science Research Center, Faculty of Agriculture and Food Sciences, Universiti Putra Malaysia Bintulu Sarawak Campus, Sarawak 97008, Malaysia  
Institute of Tropical Forestry and Forest Products (INTROP), Universiti Putra Malaysia, Selangor 43400, Malaysia; [EMAIL_ADDRESS]  
Correspondence: [EMAIL_ADDRESS]  
Received: 9 January 2020; Accepted: 10 February 2020; Published: 20 March 2020  
**Featured Application: Authors are encouraged to provide a concise description of the specific application or a potential application of the work. This section is not mandatory.**  
Phosphorus is a macronutrient which plays an important role in plant metabolism, growth, and development. However, in tropical acid soils, P fixation is high because of significant amounts of Al and Fe ions. Al and Fe ions can reduce diffusion of P into plant roots. Low absorption of P at initial growth of most plants causes stunting and slow growth of plant leaves. This process reduces photosynthesis. Chicken litter biochar (CLB) had been used on tropical acid soils to improve total P, available P, organic P, and inorganic fractions of P. Moreover, CLB is able to reduce exchangeable acidity, Fe, and Al ions in mineral acid soils because of the reactive surfaces of this organic amendment. However, there is dearth of information on the effects of the right combination of CLB and triple superphosphate (TSP) on the aforementioned soil chemical properties and crop productivity. To this end, the objectives of this study were to improve P: (i) Availability in a mineral acid soil and (ii) uptake, agronomic efficiency, and dry matter yield of _Zea mays_ L. using the right amounts of TSP and CLB. Combinations of 75%, 50%, and 25% CLB (based on recommended 5 t ha\({}^{-1}\)) and TSP (based on recommended P fertilization for maize) were evaluated in a pot study. Selected soil chemical properties, maize plants nutrient uptake, growth variables, and dry matter yield were determined using standard measures. Results showed that 25% and 50% biochar of 5 t ha\({}^{-1}\) with 75% TSP can increase soil P availability, recovery, agronomic use efficiency, and dry matter yield of maize plants. These optimum rates can also reduce P fixation by Al and Fe ions. Therefore, soil and maize productivity can be improved by using CLB (25% and 50% of 5 t ha\({}^{-1}\)) and TSP (75% of conventional rate) to increase nutrients availability especially P.  
**Keywords:** agronomic efficiency; development; dry matter yield; growth; phosphorus availability; phosphorus recovery; maize; Al and Fe; plant metabolism
# Toughened Vinyl Ester Resin Reinforced with Natural Flax Fabrics
## 3 Results and Discussion
### Resin Characterization
The D9500-2 group showed slightly higher values compared to the D9500-1 group, with both outperforming non-tughened vinyl ester, which indicates the efficiency of CSR particles [36]. Post-curing in Dion 1% consistently led to a modest increase in fracture toughness, as observed in D9500-1-25-70-3 and D9500-1-25-90-1. The limited improvement in KIC is likely due to the reduced ductility of an excessively crosslinked resin [37]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Resin Identification**} & \multicolumn{3}{c}{**Tensile Tests**} & \multirow{2}{*}{
\begin{tabular}{c} **Chrapy Impact** \\ **Strength (k/m\({}^{2}\))** \\ \end{tabular} } \\ \cline{2-3}  & **Stress at Break (MPa)** & & **(GPa)** \\ \hline D9500-1-25 & 29.29 \(\pm\) 2.44 & 1.50 \(\pm\) 0.58 & 4.07 \(\pm\) 2.05 \\ \hline D9500-1-25-70-1 & 26.62 \(\pm\) 3.79 & 2.40 \(\pm\) 0.52 & 4.00 \(\pm\) 1.01 \\ \hline D9500-1-25-70-3 & 35.24 \(\pm\) 4.93 & 2.66 \(\pm\) 0.96 & 2.72 \(\pm\) 0.58 \\ \hline D9500-1-25-90-1 & 38.80 \(\pm\) 4.85 & 2.40 \(\pm\) 0.56 & 2.84 \(\pm\) 0.55 \\ \hline D9500-2-25 & 39.72 \(\pm\) 2.44 & 2.42 \(\pm\) 0.58 & 1.40 \(\pm\) 0.21 \\ \hline D9500-2-25-70-1 & 45.76 \(\pm\) 3.79 & 2.35 \(\pm\) 0.52 & 1.17 \(\pm\) 0.14 \\ \hline D9500-2-25-70-3 & 35.62 \(\pm\) 4.93 & 2.72 \(\pm\) 0.96 & 1.66 \(\pm\) 0.23 \\ \hline D9500-2-25-90-1 & 36.54 \(\pm\) 4.69 & 2.82 \(\pm\) 0.41 & 2.16 \(\pm\) 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Dion 9500 mechanical characterizations. Figure 3: Fracture toughness of Dion 9500: (**a**) 2% Butanox; (**b**) 1% Butanox. For the tensile properties impact and fracture toughness, a post-cure at 90 \({}^{\circ}\)C for 1 h yielded the best compromise results.
## 4 Experiments
### Efficiency Analysis  
To show the efficiency of DESTformer with the multi-view attention mechanism in the seasonal domain and multi-scale mixed attention mechanism in the trend domain, we compare the memory cost and time cost in the training process with state-of-the-art models, including Informer, Autoformer, and FEDformer. As shown in Figure 3, DESTformer achieves \(O(L)\) complexity in both time and space efficiency. In addition, it shows superior efficiency in long-term time series forecasting tasks.
## 3 The Structure of the Decision Support Platform
### The Profiling Module  
#### 3.3.1 Working Assumptions  
The module contains an original clustering-based method to estimate the typical energy consumption profiles. Different sampling steps (a quarter-hour, half-hour, or hour), which lead to 96, 48, or 24 values of energy consumption, depending on the setting of the smart meter installed to each SME, can characterize the daily energy consumption profiles. The assumption related to the approximation of hourly consumption was adopted in the profiling process for energy consumption [52]. According to this assumption, the energy consumption characteristics are unknown, but the TECPs can be used. This situation is encountered at the small consumers from the category of micro, small, and medium-sized enterprises, which absorbs a maximum power up to 100 kW. The shape of these profiles is also influenced by season, the category of equipment or installations, and the activity type [52]. However, a high number of energy consumption profiles can create complex issues in the identification of the consumption patterns.  
The proposed method can arrange the energy consumption profiles in representative groups (consumption patterns), based on certain similarities. A typical energy consumption profile (TECP) will correspond to each consumption pattern, such that each SME can have one or more TECPs, depending on the activity type (e.g., small, medium, and high).