esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Methods and Results
### Implementation Results of the NFT-MEP Model to Predict Additional Periods of Output Data  
From the previous experiment, the NFT-MEP model outperformed the other popular PM prediction models. However, this model predicts only one hour ahead of both types of PM. To implement the NFT-MEP model in real-world applications, information about PM one hour in advance was not sufficient for outdoor activity planning. This subsection implemented the NFT-MEP model to predict additional periods: two and three hours in advance. The implementation results are reported in Table 8. The results showed that as the length of the time periods increased, the model performance of the proposed model decreased for both types of PM and output data. However, the overall accuracy was more than 70 and 60% for three and five classes of output data, respectively. In addition, the F-score was more than 0.6 and MCC was approximately 0.5 for both types of PM.
## 4 Optimization Design
### Metamodel Construction  
To improve the optimization efficiency while ensuring high accuracy, 380 sample points were generated in the design space using LHD. A metamodel was developed to approximate the system response based on these samples [36].  
The predictive performance of the selected agent model is shown in Figure 14, in which the predicted and actual values of the two objective functions were compared. Among the metamodel candidates--including kriging (KRG), progressive response surface (PRG), and radial basis function (RBF) models--the regularized RBF model offered the best predictive performance for acceleration robustness, achieving a coefficient of determination \(R^{2}=0.9429\) and a test RMSE of 0.1454. For the side force objective, the interpolating RBF model yielded excellent accuracy with \(R^{2}=0.9998\) and a test RMSE of 128.3 \(N\). These results confirmed that the metamodel provided excellent accuracy and can be used for subsequent multi-objective optimization.  
Figure 13: Sensitivity analysis.
# Forage Biomass Estimation Using Sentinel-2 Imagery at High Latitudes
## 1 Introduction
Currently, to the authors' best knowledge, few studies are reported to have tested the use of Sentinel-2 to estimate forage biomass production (e.g., [9; 16; 30; 31]) and there are no relevant studies in Nordic countries where the revisit frequency of Sentinel-2 satellite constellations is higher due to the higher latitude, but on the other hand, the availability of the data is affected by the high occurrence of clouds [32; 33]. Thus, the aim of this study was to build and compare several regression models for forage biomass estimation using Sentinel-2 multispectral data in northern Sweden. The objective was to build regression models for dry matter yield (_DMY_) estimation using different approaches and compare their performance.
## 3 System Model
### Beamforming with Scheduling Process for 4 \(\times\) 4 MIMO Use-Case Scenario Analysis  
For the downlink PD-NOMA scenario, an SISO-based NOMA has been considered and discussed in the previous sections, which is extended for the MIMO-based NOMA scenario.  
In this scenario, consider a use-case having a BS with a Uniform Linear Array (ULA) of 4 antennas with half-wavelength spacing. The BS serves 5 clusters/pairs of a total of 10 legitimate users, with 2 users in each cluster. Each user is equipped with 4 receiving antennas. Assume the users in each cluster have almost the same angles with clusters equally spaced between \(-60^{\circ}\) to \(60^{\circ}\), i.e., \(-60^{\circ}\), \(-30^{\circ}\), \(0^{\circ}\), \(30^{\circ}\) and \(60^{\circ}\), concerning ULA.  
In our multi-user 4 \(\times\) 4 MIMO-NOMA scenarios, the beamforming technique proposed in [29; 30; 31], especially Zero Forcing-based Beamforming (ZF-BF), can be used to mitigate the interference caused by multiple clusters of users, where multiple users' clusters are simultaneously served within the same RB. Such interference can lead to performance reduction and signal quality degradation. In ZF-BF, the channel matrix is formed to represent a BS and clusters of users' channel conditions and is used to calculate the precoding matrix by pseudo-inverting the channel matrix. Each column of the precoding matrix represents the beamforming vectors. So, the beamforming vectors are designed in such a way that they carefully direct/beam/steer the transmitted signal spatially to the desired/targeted users, forcing nulls to undesired/untargeted users or users' clusters, thus without causing interference between them. In this scenario, having 5 clusters, ZF-BF can serve 2 clusters (with a total of 4 users) simultaneously, thus ensuring users within a cluster and inter-cluster do not interfere among themselves. Hence, ZF-BF helps improve the achievable sum rate, resource management and signal quality.  
Moreover, the scheduling process proposed in [32; 33], specifically the round-robin scheduling process, can be utilised by a BS to serve clusters of users simultaneously during each time slot. After serving a cluster of users, the BS selects the next cluster of users in a round-robin sequence, thus managing equal access for each cluster to channel over time and promoting user fairness. Hence, in our case, with 4 \(\times\) 4 MIMO-NOMA, 2 clusters with 2 users each (with a total of 4 users) can actively acquire channel access at a given time slot. The remaining cluster users will get their turn for the channel evenly over the following time slots. Imperfect CSI and SIC are not taken into consideration in this scenario.  
It is worth noting that joint dynamic user clustering, beamforming and a scheduling process can be mathematically modelled for MIMO-NOMA in a tactile communication infrastructure, which is beyond the paper's scope and will be included in our future work.
## 6 Conclusions and Future Directions
### Main Conclusions
#### 6.1.4 Practical Value Fully Demonstrated  
This study not only achieves theoretical innovation but, more importantly, demonstrates significant practical value:  
It provides feasible technical solutions for regions lacking traditional detection equipment. With the development of new transportation modes such as connected vehicles, ride-hailing vehicles, and shared vehicles, trajectory data acquisition becomes increasingly accessible, and this method can precisely utilize such data to extract traffic parameters, avoiding expensive detection equipment investments.  
The developed toolchain features high reusability and automation. The dual-platform implementation based on Python and MATLAB combines the advantages of both tools. Detailed code comments, standardized interface design, and flexible parameter configuration enable convenient method portability to other projects.  
Extracted parameters can be directly applied to signal control optimization. Parameter types, granularity, and accuracy all meet practical signal control requirements, serving as inputs for various optimization algorithms. This practicality represents an important design objective and a direct manifestation of the method's value.
###### Abstract  
The purpose of this work was to develop a highly selective, sensitive, and reliable method for multi-residual analysis. A three-dimensional microporous reduced graphene oxide/polypyrrole nanotube/magnetic hydrogel (3D-rGOPFH) composite was synthesized and utilized as a magnetic solid-phase extraction (MSPE) sorbent to preconcentrate thirteen insecticides, including five organophosphorus (isocarbonbohs, quinalphos, phorate, chloryprifos, and phosalone), two carbamates (pirimor and carbaryl), two triazoles (myclobutanil and diniconazole), two pyrethroids (lambda-cyhalotrhin and bifenthrin), and two organochlorines (2, 4\({}^{\prime}\)-DDT and mirex), from vegetables, followed by gas chromatography-tandem mass spectrometry. This method exhibited several major advantages, including simultaneous enrichment of different types of insecticides, no matrix effect, high sensitivity, and ease of operation. This is ascribed to the beneficial effects of 3D-rGOPFH, including the large specific surface (237 m\({}^{2}\) g\({}^{-1}\)), multiple adsorption interactions (hydrogen bonding, electrostatic, \(\pi\)-\(\pi\) stacking and hydrophobic interaction force), appropriate pore size distribution (1-10 nm), and the good paramagnetic property. Under the optimal conditions, the analytical figures of merit were obtained as: linear dynamic range of 0.1-100 ng g\({}^{-1}\) with determination coefficients of 0.9975-0.9998; limit of detections of 0.006-0.03 ng g\({}^{-1}\); and the intra-day and inter-day relative standard deviations were 2.8-7.1% and 3.5-8.8%, respectively. Recoveries were within the range of 79.2 to 109.4% for tomato, cucumber, and pakchoi samples at the fortification levels of 5, 25, and 50 ng g\({}^{-1}\). This effective and robust method can be applied for determining multi-classes of insecticide residues in vegetables.  
Keywords:insecticides; vegetables; gas chromatography-tandem mass spectrometry; magnetic solid-phase extraction; three-dimensional microporous composite +
Footnote †: journal: Applied Soft
# Yield Prediction of Winter Wheat at Different Growth Stages Based on Machine Learning
## 2 Materials and Methods
### Winter Wheat Yield Prediction  
To assess the performance of various machine learning algorithms in yield prediction, initially, we constructed models using data spanning the entire growth period. We partitioned the county-level modeling factors for each growth stage of winter wheat, calculating the mean values of these factors during the corresponding growth stage as feature variables for model construction. Data from 2012 to 2019 served as the training set for training the models, including the ensemble voting model based on ridge, random forest, and gradient boosting, along with 10 other commonly used machine learning models. The data from 2020 were used as the validation set and the data from 2021 as the test set in the evaluation of the predictive accuracy of the models.  
To explore the yield prediction potential across different growth stages, we chose machine models with high accuracy throughout the entire growth period and the ensemble voting model constructed in this study for modeling analysis at each growth stage. In this process, each class of modeling factors was partitioned based on the winter wheat growth stage and used as a set of feature variables for model construction. We continued to use data from 2012 to 2019 as the training set, 2020 as the validation set, and 2021 as the test set for data partitioning. The algorithms were then employed for yield prediction at each growth stage, followed by accuracy validation.
## 4 Experimental Results  
### Datasets  
In our experiments, we use four popular benchmark datasets to evaluate the performance of our models: Animals with Attributes 2 (AwA2) [29], Caltech-UCSD Birds-200-2011 (CUB) [30], Oxford Flowers (FLO) [2], and Attribute Pascal and Yahoo (APY) [31]. The APY dataset is a coarse-grained dataset consisting of 20 seen classes and 12 unseen classes, each with 64 annotated attributes. The AwA2 dataset is commonly used for animal classification and consists of 40 seen classes and 10 unseen classes, each annotated with 85 attributes. The FLO dataset contains 102 flower classes, 82 seen classes, and 20 unseen classes. The CUB dataset contains 200 species of birds, of which 150 species are seen classes and 50 species are unseen classes. The semantic embeddings of FLO and CUB are 1024-dimensional character-based CNN-RNN features [32] extracted from the fine-grained visual descriptions (10 sentences per image). The details of each dataset are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** &
\begin{tabular}{c} **Visual** \\ **Dimension** \\ \end{tabular} & **Attribute** & **Seen** & **Unseen** \\ \hline CUB & 2048 & 1024 & 150 & 50 \\ FLO & 2048 & 1024 & 82 & 20 \\ AWA2 & 2048 & 85 & 40 & 10 \\ APY & 2048 & 64 & 20 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the datasets used in our experiments, including the dimensions of visual features (visual dimension), the dimensions of semantic vectors per class (attribute), seen class size (seen), and unseen class size (unseen).
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Parson  
The Parson algorithm is a variant of the Kolter and Jaakkola algorithm [46]. The algorithm is designed to be a semi-supervised learning algorithms. This means that the algorithm does not need to train on the data from the concrete house prior to deployment, but it still requires some prior knowledge. The idea is to have a database of general appliance models that tells how specific appliances mostlikely will behave. This model will be able to detect the device, and do some unsupervised special training to better fit the concrete appliance.  
The algorithm is based on a kind of HMM called \"difference HMM\" since they take the step difference in the data and use as the observed output \(Y_{t}\). In a normal HMM there is only emitted one output for each hidden state in a chain. In the Parson algorithm the model is changed to output two states, \(X_{t}\) and \(Y_{t}\), from the hidden states.  
The \(Y_{t}\) is corresponding to the step in power that is observed since the last sample. It is assumed that the only thing that can cause a step in power is if an appliance change states from one to another. The \(x_{t}\) is the constant power consumption the appliance would have in the state. This is used to filter out the appliance if the power consumption observed is smaller than this threshold.  
It is assumed that the power consumption of a state in the general model of an appliance can be modeled as a normal distribution. This can be illustrated as in Equation (11).  
\[w_{t}|_{S_{t}}\rightarrow\mathcal{N}(\mu_{S_{t}},\sigma_{S_{t}}^{2}) \tag{11}\]  
This shows how the power consumption of an appliance is modeled as a normal distribution. The step Power \(Y_{t}\), can also be modeled as a normal distribution that is created from the change between two states as shown in Equation (12).  
\[Y_{t}|_{S_{t},S_{t-1}}\rightarrow\mathcal{N}(\mu_{S_{t}}-\mu_{S_{t-1}},\sigma _{S_{t}}^{2}+\sigma_{S_{t-1}}^{2}) \tag{12}\]  
To model the constraint that the meter only can be on if the power consumption present in the signal is higher than the requirement for the state is an additional constraint added to the emission probability. This constraint is mathematically described in Equation (13).  
\[P(w_{t}|_{S_{t}}\leq x_{t}|S_{t})=\int_{-\infty}^{x_{t}}\mathcal{N}(\mu_{S_{t} },\sigma_{S_{t}}^{2})dw \tag{13}\]  
where \(x_{t}\) is the measured power consumption and \(w_{S_{t}}\) is the appliance power consumption constraint. This makes the probability going towards zero if the total power draw is too little and towards 1 if there is more than enough power. This is a loose constraint since too high power draw also could be created by other appliances. To minimize the impact this have for the algorithm is the mean power draw subtracted from the data before searching for other appliances.  
In the implementation validated in this paper are all appliances modeled as Type-1. The algorithm is allowed to train on the house data, to better fit the general models to the appliances and create the initial database.
## 3 Results and Discussion
### Viscosity  
A Brookfield viscometer measured the dynamic viscosities under six different temperature levels. As mentioned earlier, this characteristic is essential in pavement production and application. It is most likely that the PCM evaporated completely at 150 \({}^{\circ}\)C. It was noted that when the evaporation temperature of the PCM was exceeded, the binder started to smell pungent. As for the 5%PCM binder, the test was conducted at different temperatures ranging from 85 \({}^{\circ}\)C to 135 \({}^{\circ}\)C. Moreover, to explore the evaporation impact on the viscosity of all other binders (PG 58-28, PG 70-28, 5%GPCM, 7%GPCM, 5%GPCM-SBS, and 7%GPCM-SBS), the test was conducted under all temperatures, which ranged from 90 \({}^{\circ}\)C to 165 \({}^{\circ}\)C with an increment of 5 \({}^{\circ}\)C.  
Figure 9 shows the measured viscosity results (log scale) drawn versus temperatures. It was noted that for the 5%PCM binder at 90 \({}^{\circ}\)C, approx. 25% of the PCM evaporated as explored in the TGA test, with only a 10.6% reduction in the viscosity compared to the control binder. It is worth mentioning that, when testing the 5%PCM binder at temperatures above 90 \({}^{\circ}\)C, there was no effect on the viscosity of the 5%PCM binder compared to the control binder. This increase can be attributed to the fact that 100% of the PCM evaporated.  
On the other hand, adding 5% and 7% GPCM to the base binders, PG 58-28 and PG 70-28, led to a significant decrease in the viscosities at 105 \({}^{\circ}\)C and above, as shown in Figure 9, which can meet the temperature requirements to produce warm mix asphalt (WMA). For higher temperatures, the viscosity of the modified binders was lower than that of the base binder. For example, the viscosity of the 7%GPCM binder at 150 \({}^{\circ}\)C was recorded at 0.152 Pa\(\cdot\)s, whereas the control binder at the same temperature was 0.214 Pa\(\cdot\)s. From a designer's point of view, further attention should be drawn to the reduction in the viscosity of the binders, since it can negatively affect asphalt mixture properties and performance at high temperatures, i.e., rutting resistance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Base Binder** & **Additive** & **Mixing Temperature** \({}^{\circ}\)C & **Mixing Time** \\ \hline PG 58-28 & PCM & 80–90 \({}^{\circ}\)C & 20 min \\ PG 58-28 & GPCM & 90–110 \({}^{\circ}\)C & 20 min \\ PG70-28 & GPCM & 130–150 \({}^{\circ}\)C & 20 min \\ \hline \hline \end{tabular}
\end{table}
Table 5: Binder preparation temperatures/mixing time for different additives.
# Analysis of the Efficiency of Slope Evacuation for Dense Crowds in Urban Street-Type Public Spaces
## 4 Evacuation Scenario Setup
### Evacuation Scenarios
#### 4.4.2 Range of Street Width  
As the width of the street changes, the openness of the pedestrian's field of vision and the degree of perception of the surrounding environment will be affected. Therefore, in this section, we set the street widths of 1 m, 2 m, 3 m, 4 m, 5 m, 6 m, 7 m, 8 m, and 9 m to simulate the effect of street width on evacuation under different crowd densities. In order to reduce the error, the simulation was performed three times for each width.
## 4 Experimentation
### Evaluations
#### 4.4.2 Time Metrics  
In Figure 5, we observe the total time users spent on the study, with the frequency of users divided into time intervals. Most users who completed the study spent between 0 s and 5 min 36 s on it; this might suggest that those who spend time on the survey are more likely to complete it, indicating that the study is designed in such a way that it can be completed in a shorter period. The presence of users who did not complete the survey at each time interval suggests that there are points of difficulty or disinterest that led users to drop out of the study.  
In addition, we must convert the raw data into statistical metrics to make sense of the data and to detect patterns. Thus, the mean time to complete the task, the median time to complete the task, the number of users above or below, and the standard deviation of the time to complete the task with the percentage of users who completed the task correctly were calculated. Table 8 shows the statistical metrics obtained for time and errors during the study.  
Times  
* Average Times: The average time is approximately 797.51 s. This suggests that, on average, users take about 13 min and 29 s to complete the task or activity in question.  
Figure 4: Completion rates (average success and failure rate).  
Figure 5: Total time users spent on the study.  
* Median Time: The median time is approximately 759.28 s. This indicates that half of the users take less than 12 min and 65 s to complete the task, while the other half take longer.
* Mode of Times: The mode of the times is 0:10.62 s. This could indicate a group of users who do not complete the task.
* Standard Deviation of Times: The standard deviation of the times is approximately 531.25 s. This suggests that there is significant variability in user times. Some users may be very fast, while others may be slower.  
Errors
* Average Number of Errors: The average number of errors is approximately 2.19. This indicates that, on average, each user makes about two errors in completing the task.
* Median of Errors: The median of the errors is approximately 2. This suggests that half of the users make two or fewer errors while the other half make more.
* Standard Deviation of Errors: The standard deviation of the number of errors is approximately 1.77. This suggests that the number of errors made by users can vary significantly. Some users may make very few errors, while others may make more errors.  
These statistics suggest a wide variability in the timing and number of user errors. Some users are faster and more accurate, while others may take longer or make more errors. Figure 6 shows a histogram with a fitting curve representing the frequency distribution of times, measured in seconds, visualizing the distribution of time participants have taken in some
## 4 Simulation Environments
### Simulation Parameters  
The study is conducted on a simulated 2 km long highway segment with 3 lanes in each direction, totaling 6 lanes. Vehicles in the simulation maintain a mean speed of 140 km/h, with a standard deviation of 3 km/h. The system operates under the 5G-NR-V2X network with an OFDM numerology set to \(\mu=1\), corresponding to a subcarrier spacing (SCS) of 15 kHz and a symbol time (ts) of 0.5 ms. The traffic type is aperiodic, with variable data size. The 5G-NR-V2X Mode 2 radios are configured to function within a 20 MHz channel in the 5.9 GHz ITS band, using QPSK modulation. Subchannels are organized into 12 resource blocks (RBs) each, resulting in 4 available subchannels per time slot. The transmission power is adjustable between 0 and 23 dBm, while the receiver sensitivity is set to \(-103.5\) dBm. The simulation accounts for a proximity broadcast range of 500 m, with the resource reservation interval (RRI) set to 100 ms. The operational mode remains unmanaged, with an initial RSRP threshold of \(-128\) dBm and a retention probability (P) of 0 for the SB-SPS scheme. These settings are detailed in Table 3.  
The evaluation in this study utilizes a variety of V2X message types and simulation parameters to assess the performance of different scheduling mechanisms [28]. The key message types considered are Cooperative Awareness Messages (CAMs) and Collective Perception Messages (CPMs), as illustrated in Table 4. CAMs, defined in ETSI TS 103 900 V2.0.0 (July 2022), provide continuous updates on vehicle status and kinematics, typically at a frequency of 1-10 Hz with a payload size of around 400 bytes. CPMs, described in ETSI TS 103 324 V2.1.1 (Jun 2023), enable the sharing of sensor-based environmental perceptions, including detected and classified objects. These messages are also transmitted at a frequency of 1-10 Hz, with payloads of up to 1000 bytes [29].