esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Results
The confusion matrix for the three-class classification case is presented in Figure 6. Note most common misclassifications occur between the \"adjacent\" classes, i.e., between neutral and negative sentiments and between neutral and positive sentiments. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Methodology** & **Method** & **First Set** & **Second Set** & **Third Set** & **Fourth Set** \\ \hline \multirow{8}{*}{Single-model machine learning} & FFNN & 0.82 & 0.826 & 0.873 & 0.776 \\  & Linear regression & 0.845 & 0.801 & 0.863 & 0.790 \\  & KNN & 0.830 & 0.782 & 0.823 & 0.639 \\  & SVM & 0.845 & 0.801 & 0.863 & 0.790 \\  & Naive Bayes & 0.845 & 0.801 & 0.854 & 0.790 \\  & CART\({}^{\dagger}\) & 0.845 & 0.801 & 0.863 & 0.790 \\ \hline \multirow{8}{*}{Ensemble learning} & AdaBoost classifier & 0.844 & 0.800 & 0.863 & 0.790 \\  & AdaBoost regressor & 0.519 & 0.404 & 0.506 & 0.315 \\ \cline{1-1}  & Bagging classifier & 0.844 & 0.800 & 0.863 & 0.790 \\ \cline{1-1}  & Bagging regressor & 0.460 & 0.318 & 0.519 & 0.284 \\ \cline{1-1}  & ExtraTrees classifier & 0.844 & 0.800 & 0.863 & 0.790 \\ \cline{1-1}  & HistGradientBoost classifier & 0.844 & 0.800 & 0.863 & 0.790 \\ \cline{1-1}  & Stacking classifier & 0.819 & 0.826 & **0.873** & 0.776 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Accuracy of classifiers on the SemEval-2017 dataset (of two-class classification without considering the neutral class) with different sets of emotions. The best result is shown in bold. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification Methodology** & **Method** & **IMDB** & **Sentiment140** &
\begin{tabular}{c} **SemEval-2017** \\ **(w/o Neutral Class)** \\ \end{tabular} \\ \hline \multirow{8}{*}{Single-model machine learning} & FFNN & 0.773 & 0.728 & 0.873 \\  & Linear regression & 0.767 & 0.715 & 0.863 \\  & KNN & 0.760 & 0.655 & 0.823 \\  & SVM & 0.767 & 0.715 & 0.863 \\  & Naive Bayes & 0.766 & 0.715 & 0.854 \\  & CART\({}^{\dagger}\) & 0.767 & 0.715 & 0.863 \\ \hline \multirow{8}{*}{Ensemble learning} & AdaBoost classifier & 0.767 & 0.714 & 0.863 \\  & AdaBoost regressor & 0.423 & 0.177 & 0.506 \\ \cline{1-1}  & Bagging classifier & 0.767 & 0.714 & 0.863 \\ \cline{1-1}  & Bagging regressor & 0.332 & 0.047 & 0.519 \\ \cline{1-1}  & ExtraTrees classifier & 0.767 & 0.714 & 0.863 \\ \cline{1-1}  & HistGradientBoost classifier & 0.767 & 0.714 & 0.863 \\ \cline{1-1}  & Stacking classifier & 0.772 & 0.728 & **0.873** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Accuracy of classifiers on three benchmark (IMDB, Sentiment140 and SemEval-2017) datasets. The best result is shown in bold. Table 9 shows some of the examples of misclassifications. Note that many misclassifications may have occurred due to mislabeling of the original text in the dataset.
## 3 Misbehavior Scenarios and Alert Types
### Change of Lane (CoL)  
Change of lane alert (CoL) increases vehicle safety in a dense driving area. This helps to prevent fatal accidents that can occur when a car unexpectedly switches its current path on a roadway. Here, an attacker \(V_{i}\) in one lane raises a false CoL warning to the next \(V_{j}\) in another lane with the bid of moving ahead of it. This is an intentional attack in which CAV tries to obtain space in one lane and move ahead of CAV in another lane. This is illustrated in Figure 2, at time \(t\) with velocity \(v_{i}^{\prime}\), attacker \(V_{i}\) sends a false CoL warning to other CAVs for lane switching with current false location denoted as \((x_{i}^{\prime},y_{i}^{\prime})\) instead of its actual position\((x_{i},y_{i})\), as shown in Figure 2. The short distance between \(V_{i}\) and \(V_{k}\) prohibits \(V_{i}\) from changing lane, however, the false reported location makes the inter-vehicle gap between \(V_{i}\) and \(V_{k}\) appear too wide for \(V_{i}\) to change lane immediately with coordinates \((x_{i},y_{i})<(x_{i}^{\prime},y_{i}^{\prime})<(x_{j},y_{j})\).  
Figure 1: False Emergency ELectric Brake Light (EEBL) alert.  
Figure 2: Change of Lane Attack Scenario.
## 3 Methods
### Loss Function Design  
The training process of the proposed model consists of two stage tasks: image denoising based on diffusion model and image recognition using Swin Transformer. The overall loss function is designed as a weighted sum of the individual loss terms for each task:  
\[\mathcal{L}_{\text{t}}=\lambda_{\text{d}}\mathcal{L}_{\text{d}}+\lambda_{\text {c}}\mathcal{L}_{\text{c}} \tag{33}\]  
where \(\lambda_{\text{d}}\) and \(\lambda_{\text{c}}\) are the weights to balance between the two losses.  
For the time-frequency distribution denoising task, in order to obtain a reverse diffusion model that can restore the corrupted time-frequency distributions back to clean distributions, during training we use images without added noise as input, and use the mean squared error (MSE) between the input image \(x\) and output image \(x^{\prime}\) as the loss term:  
\[\mathcal{L}_{\text{d}}=\text{MSE}(x,x^{\prime})=\frac{1}{n}\sum_{i=1}^{n}(x_{ i}-x_{i}^{\prime})^{2} \tag{34}\]  
The image recognition task is formulated as a classification problem. The cross-entropy loss between the predicted class probabilities \(y\) and ground-truth labels \(t\) is used:  
\[\mathcal{L}_{\text{c}}=\text{CrossEntropyLoss}(y,t)=-\sum_{c=1}^{C}t_{c}\log(y _{c}) \tag{35}\]  
where \(C\) is the number of classes.  
By optimizing this weighted multi-task loss, the model can be trained to balance the denoising reconstruction and recognition accuracy. The loss weights \(\lambda\) allow adjusting the difficulty of training each component.
# Study on Failure Mechanism of Mudstone Based on Digital Core and Digital Volume Correlation Method
## 2 Real-Time Loading CT Scanning Experimental System and Experimental Scheme  
### Real-Time Loading CT Scanning Experimental System  
The experimental system is mainly composed of two parts:  
1. CT scanning system: The minimum focus size of the CT experimental equipment is 3 \(\upmu\)m, which can distinguish the pore with 0.5 \(\upmu\)m aperture and the crack with 1 \(\upmu\)m width. After the loading equipment is fixed on the high-precision micro-CT mechanical rotation platform, the sample is in the best scanning position through the movement in X, Y, and Z directions.
2. Loading system: The loading system is a uniaxial loading equipment independently developed by the research group. The main structure includes an oil pump, motor, hydraulic cylinder, accumulator, tubing, displacement sensor, loading push rod, aluminum-magnesium alloy casing, spherical seat, and so on. The maximum output force of the cylinder of the loading device: Fmax = 40 KN, the cylinder stroke: S = 40 mm, the effective range of the displacement sensor: 40 mm, and the accuracy of the displacement sensor: 0.001 mm. After fixing it on the \(\upmu\)CT225 kVFCB CT mechanical turntable, the CT scanning of rock samples under the condition of real-timeloading and steady pressure can be realized, and the pressure control system of the loading system can realize digital pressure control, as shown in Figure 1.
## 3 Results and Discussion
Even in this case, the obtained precision (0.60) is very close to the mean precision of the teams (0.667). Figure 6: Comparison of the mean precision of the participants of the TREC2017 Precision Medicine Track (TREC) and the proposed method (EQ) in the top five retrieved documents. Figure 7: Comparison of the mean precision of the participants of the TREC2017 Precision Medicine Track (TREC) and that of the proposed method (EQ) in the top ten retrieved documents. Furthermore, of the top overall systems in Table 6[27], the proposed method surpassed the best team run [30] at P@5 (0.5448), P@10 (0.4448), and P@15 (0.3885), where we attained 0.579, 0.469, and 0.393, respectively. If we analyze the standard deviation of the best team results excluding the best team and duplicates, the values we get are 0.0175 (P@5), 0.0178 (P@10), and 0.0195 (P@15), while the proposed method improves them to 0.034 (P@5), 0.024 (P@10), and 0.005 (P@15). This means that our method obtains a significant improvement when analyzing the first five and ten documents, only managing to match the best team results at P@15. To better visualize this analysis, Figure 9 shows a comparison of the mean improvement of the teams and their respective previous team to the improvement of the proposed method and the best team results.
## 3 Results
### Measurement of Methane and Isoprene Production on Wastewater  
Once growth was achieved in SWW, cells were transferred to wastewater biosolids pre- and postdigest effluent (before and after second-stage anaerobic digestion) from the Teresa Street Water Resource Recovery Facility in Lincoln, NE (Figure 3). Microcosms with and without methanol supplementation were incubated for 5 days at 35 \({}^{\circ}\)C, after which time methane and isoprene yields were quantified (Table 1). Methane was detected from predigest effluent whether or not methanol was added, indicating that substrates for methanogens (both wild and engineered strains) were present in the effluent (Figure 4).  
Figure 2: Engineered methanogens adapted to synthetic wastewater. (**a**) Inoculated synthetic wastewater (SWW) in anaerobic Balch tubes. (**b**) Confirmation of live engineered methanogens in SWW under 400 \(\times\) optical magnification. Viable methanogens are irregular cocci (0.5–1 \(\upmu\)m diameter) that fluoresce blue through a DAPI filter when UV illuminated.  
to SWW cultures, which was similar to the isoprene yield achieved by isolated strains grown in defined medium [9]. Based on these data we estimate up to 0.77 \(\pm\) 0.25% of dissolved organic carbon in postdigest effluent was recovered as isoprene (Table 1). These data suggest engineered _M. acetivorans_ can compete with wild methanogens in anaerobic digesters and isoprene can be detected in biogas from municipal waste. In pure batch culture the _M. acetivorans ispS+_ strain produces 0.89 mmol isoprene per mol methanol per gram of cells [9], which is 180 \(\times\) the yield of engineered _Synechocystis_ cyanobacteria growing on CO\({}_{2}\)[25]. In comparison, _E. coli_ and _Saccharomyces_ have been engineered to produce 352 and 175 mM isoprene, respectively, from glucose under fed-batch conditions [26; 27]. It is unknown whether any of these engineered strains can synthesize isoprene from digester effluent as has been demonstrated by _M. acetivorans_ in this work. Additional studies are needed to develop a commercially viable process that optimizes isoprene recovery from wastewater digester biogas streams.
# Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review
## 4 Deep Learning in Plant Water Stress Assessment
### Evaportranspiration Forecasting  
ET forecasting can bring important insights into future climate conditions which can be beneficial in assessing plant water stress and irrigation planning. It can also compensate for the lack of data generated, due to less sensors being installed as a result of high cost and limited manpower. Recently, DL ability to consider temporal features in the data has been utilized for accurate ET forecasting. [PERSON] and [PERSON] [84] proposed a CNN-LSTM combination for multi-step ahead prediction of daily ETo with relatively good performance achieved in comparison with traditional ML models and CNN and LSTM alone. [PERSON] et al. [85] also explored the use of three different architectures of CNN in the prediction of ETo time series. Using real climatic data, the models were able to predict daily ETo with lower uncertainty. [PERSON] et al. [86], on the other hand, considered a hybrid BiLSTM to forecast short-term daily ETo from meteorological data collected in the semi-arid region of central Ningxia, China. The results showed that the model was able to improve the forecast performance constantly compared to the general model.  
DL has also been investigated as a highly capable techniques for ETc forecasting based on historical field measurement data. [PERSON] et al. [87] conducted an attempt to estimate ETc for maize using two-years field measurements of ETc data using a lysimeter and TCN. The new form of DL, comprised of LSTM and DNN, was able to estimate maize ETc based on plant height, air temperature, relative humidity, solar radiation, leaf-area index and soil temperature better than the DNN and LSTM method alone. [PERSON], et al. [88] used the DNN model to estimate and predict ETc for several locations of wheat cultivation in Egypt based on recorded historical and future meteorological data. The results showed high correlation between ETc values from the standard FAO-56 method and the values predicted from the DL model.
# A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission
## 3 Human-Machine Collaborative Self-Judgment Framework for Safety of RVD  
### System Framework Design  
The self-judgment process for rendezvous and docking safety is divided into the following two parts: safety knowledge base construction and safety prediction.  
The purpose of the safety knowledge base construction is to learn the knowledge base. In addition, the generation of training samples is mainly based on the 6 DOF relative position and attitude data generated using the relative dynamics model of the spacecraft. The self-judgment framework for the safety of the rendezvous and docking is shown in Figure 2. We first establish the attitude dynamics model of the multi-body spacecraft and dynamics model of the lunar orbit [28]. Then, under each relative position and attitude, an image sequence is generated according to the internal parameters of the camera and the mechanical structure of the docking surface of the target. After feature selection and matching, a set of rules that can be used for training a decision tree of the safety knowledge base can be obtained. Finally, we can obtain the safety judgment knowledge base by training the decision tree.  
The purpose of safety prediction is to predict the safety level of the rendezvous and docking process. In the process of safety prediction, the features of the images taken by the camera are matched with the simulated image's features. After the feature matching is manually evaluated, if there are more than six feature points forming an effective match, these features and the safety knowledge base are used to judge the safety level of the rendezvous and docking.
# Provision of Energy- and Wavelength-Efficient Traffic
## 4 Simulation and Performance Evaluation
### Analysis of the AWUR  
The AWUR is an important parameter related to wavelength efficiency. Given the traffic requests and the used wavelengths, the AWUR can be calculated by  
\[\text{AWUR}=\frac{\sum\limits_{d\in V}\sum\limits_{f\in[1,F_{k,d}]}b^{f}_{k,d}}{ \Omega(\sum\limits_{w}{(\sum\limits_{i,j\
eq j}{\mu_{ij}^{w}}+\sum\limits_{i^{ j},j^{\prime}\
eq j^{\prime},P_{i^{\prime}\cap P_{ij}}=\varnothing}{\mu_{i^{ j^{\prime}}}^{w}})})} \tag{20}\]  
In Figure 3, the comparisons of the AWUR between the DLG, TPTG, DLG_GA, and TPTG_MA under different network sizes are presented. It can be observed that the AWUR of the TPTG_MA is obviously higher than that of other algorithms. In terms of heuristic, the proposed TPTG outperforms the DLG heuristic in improving the AWUR under different network sizes, which implies that more wavelength savings are achieved via the TPTG. For the six-node topology, the available links are limited. When the traffic intensity is beyond 300 Erl, the four algorithms achieve the same AWUR as shown in Figure 3a. This is because most of the wavelength capacity has been occupied and the traffic grooming algorithm has no effect on the improvement of AWUR.  
As the network size increases, we can observe a decrease in the AWUR under the same traffic intensity in Figure 3b.c. This is due to an increasing number of available wavelengths. Compared with the DLG_GA, the proposed TPTG_MA achieves higher AWUR, at most 18% improvement in Figure 3b and 23% improvement in Figure 3c. As the traffic intensity increases, Figure 3c witnesses a quick increase in the AWUR for the TPTG_MA, particularly when the traffic intensity is beyond 450 Erl. This is because the proposed TPTG_MA tries to groom as many traffic requests as possible into the existing wavelengths instead of occupying new wavelengths.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Bandwidth range of a traffic request & 20 Mbps-300 Mbps \\ Capacity of a sub-wavelength & 2 Gbps \\ Capacity of a wavelength & 10 Gbps \\ Ports for traffic aggregation & 40 \\ Ports for optical/electric conversion & 20 \\ Total optical/electric ports & 20/60 \\ Energy consumption per port & 15/15/5/10/20 W \\ \(E_{\omega}/E_{\omega}/E_{agg}/E_{d\
eq d}/E_{TX}\) & 15/15/5/10/20 W \\ \hline \hline \end{tabular}
\end{table}
Table 1: Major simulation parameters.  
Figure 3: Comparisons of the AWUR between the DLG, TPTG, DLG_GA, and TPTG_MA under (**a**) 6-node topology, (**b**) 12-node topology, and (**c**) 22-node topology.
## 4 Experimental Results  
In order to evaluate the feasibility, reliability and accuracy of the proposed system tests have been carried out. These tests comprised measuring the light intensity throughout the entire day and later analyzing the data, obtaining the sunrise and sunset times and then applying the algorithm explained in Section 2.2.  
The sunlight measures were obtained from the NOAA Public FTP server [26]. This dataset comprise data dating back to 1995. Each entry documents the date, UTC time and sun variables values such as downwelling and upwelling global solar radiation, direct radiation, diffuse radiation, infrared, atmospheric pressure, _etc_. The files also provide the coordinates and elevation of the station above sea level where the measures were obtained. These are not underwater measurements, but serve as a realistic substitute.  
Only the downwelling global solar radiation was used in the experiment, since it is the most representative of light intensity combining both direct and diffuse radiation. Furthermore, radiation is the one which the most low cost sensors measure. The downwelling global solar radiation is expressed in \(W/m^{2}\) where theoretically the minimum value is 0. However, the sensors employed by NOAA are thermopile-based, and it is not unusual for these solar instruments to register small negative signals at night. This offset is attributed to the thermopile cooling to space.
# Sixty-Nine-Element Voice Coil Deformable Mirror for Visible Light Communication
## 4 Discussion
### Response Time  
For VCDM, our electromagnetic actuator development aims to increase its electromagnetic force and achieve a response speed as fast as possible [24]. The mass of the magnet mover is 4.6786 mg, and the initial velocity is 0 m/s. The response time of the VCA obtained using Maxwell is shown in Figure 8. The acceleration of the magnet mover is proportional to the current. All magnet movers can reach the set positions within 0.65 ms.  
Figure 7: Temperature and thermal deformation of VCDM’s thin mirror as a function of current. (**a**) The temperature of the thin mirror is a function of currents. The inset shows the temperature of the thin mirror when the current is 0.06 A. (**b**) The thermal deformation of the mirror surface as a function of currents. The inset shows the deformation of the thin mirror when the current is 0.06 A. (**c**) The inset shows the deformation of the thin mirror when the current is 0.06 A. (**d**) The temperature of the thin mirror is 0.06  
The first resonance frequency of the VCDM is mainly related to the mirror and the actuator stiffness. When the mirror material is selected, the mirror stiffness is determined by the mirror thickness. In addition, the stiffness of the VCA is dominated by the spring. As shown in Figure 9, the first resonance frequency is positively correlated with the spring stiffness for the same mirror thickness. However, large spring stiffness leads to a small electromagnetic force. Therefore, a mirror thickness ranging from 20 \(\upmu\)m to 50 \(\upmu\)m is preferable.
## 3 Results and Discussion
### Humidity Sensors and Humidification  
An automated test procedure for characterizing the humidifier and testing the sensor accuracy was implemented and executed. This procedure repeatedly activates the humidifier for a fixed short duration followed by a measurement phase, where the change in humidity is monitored. Figure 11b shows the course of this automated procedure and the captured measurements.  
The humidity values from Sensor S1 deviate significantly from the reference measurement device (R1) due to self heating of the sensor (3 \({}^{\circ}\)C). To obtain correct humidity readings \(H\) from the sensor, the manufacturer recommends correction to the obtained raw humidity data \(H_{\rm raw}\) in percentage relative humidity (RH) as  
\[H=H_{\rm raw}\cdot e^{\left(k_{1}k_{2}\left(\frac{T_{\rm raw}-T}{(k_{2}+T_{ \rm raw})(k_{2}+T)}\right)\right)}, \tag{4}\]  
where \(k_{1}=17.62\) and \(k_{2}=243.12\,^{\circ}\)C are coefficients for correction supplied by manufacturer, \(T_{\rm raw}\) is the temperature reading in \({}^{\circ}\)C from the sensor, and \(T=T_{\rm raw}-3\,^{\circ}\)C is the compensated temperature in \({}^{\circ}\)C. Data S1-Corr in Figure 11b represents the sensor data after applying this correction for error compensation. Sensor S2 data also show deviation and this is due to its slow response and narrow measurement range (10-90% RH). The humidifier (ultrasonic atomizer) could raise the humidity inside the chamber to 100% within 2 min. This translated to a conversion rate of \(0.01\times 10^{-3}\,\rm kg\,s^{-1}\).
###### Abstract  
Winter catch crops are promoted in the European Union under the Common Agricultural Policy to improve soil health and reduce nitrate leaching from agricultural fields. Currently, Member States often monitor farmers' adoption through on-site inspections for a limited subset of parcels. Because of its potential for region-wide coverage, this study investigates the potential of Sentinel-2 satellite time series to classify catch crops at the field level in Flanders (Belgium). The first objective was to classify catch crops and identify the optimal model and time series input for this task. The second objective was to apply these findings in a real-world scenario, aiming to provide reliable early-season predictions in a separate target year, testing early-season performance and temporal transferability. The following three models were compared: Random Forest (RF), Time Series Forest (TSF), and a One-Dimensional Convolutional Neural Network (1D-CNN). The results showed that, with a limited field-based training dataset, RF produced the most robust results across different time series inputs, achieving a median F1-score of >88% on the best dataset. Additionally, the early-season performance of the models was delayed in the target year, reaching the F1-score threshold of 85% at least one month later in the season compared to the training years, with large timing differences between the models.  
c 2024 16420[[https://doi.org/10.3390/rs16244620](https://doi.org/10.3390/rs16244620)]([https://doi.org/10.3390/rs16244620](https://doi.org/10.3390/rs16244620))