esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Identification and Quantification of the Volatile Compounds in XYM] Green Tea
#### 3.2.2 Esters  
A total of seventeen esters were identified (Table 1), among which were six hexenyl esters, including cis-3-hexenyl hexanoate, (Z)-3-hexenyl octanoate, cis-3-hexenyl butyrate, cis-3-hexenyl valerate, (E)-2-hexenyl hexanoate, and cis-3-hexenyl crotonate. From tea shoots to drying, cis-3-hexenyl butyrate gradually decreased and reached its minimum level (154.4 \(\mathrm{\SIUnitSymbolMicro}\)/L) at drying (Figure 4a), which decreased by 88.5%. cis-3-Hexenyl hexanoate (Figure 4b) and cis-3-hexenyl valerate (Figure 4c) presented a similar trend during the manufacturing, and reached their minimum levels at drying, which decreased by 56.1% and 76.9%, respectively. The concentrations of the six hexenyl esters were the lowest at drying, and the hexenyl esters losses might be due to their high volatility. As shown in Figure 4d,f, the variations of methyl jasmonate and dihydroactinolide appeared a similar trend, and spreading significantly increased their concentrations. Integrated volatile metabolomics and transcriptomics confirmed that spreading could increase the concentration of methyl jasmonate [28]. The high-temperature fixing significantly resulted in the losses of methyl jasmonate and dihydroactinolide, which decreased by 45.5% and 50.5%, respectively. After scattering, the high temperature (115 \({}^{\circ}\)C) shaping promoted the increase in the concentrations of methyl jasmonate and dihydroactinolide. Being similar to the above esters mentioned, the concentration of methyl salicate significantly decreased from tea shoots (11,159.7 \(\mathrm{\SIUnitSymbolMicro}\)/L) to fixing (712.9 \(\mathrm{\SIUnitSymbolMicro}\)/L). Although the methyl salicate fluctuated from fixing to drying, there were no significant differences between them. The changing trend of methyl salicate in this study was consistent with the previous findings [20,30].  
Figure 3: Changes in the concentrations of linalool (**a**), trans-furan linalool oxide (**b**), trans-nerolidol (**c**) and isophytol (**d**) during the XYMJ green tea manufacturing.
## 3 Materials and Methods
### Generating Domain-Adapted Beat-Score Maps (DA-BSMs)  
After the WSL-based adaptation process, the adapted beat-type classifier is employed to generate a Domain-Adapted Beat-Score Map (DA-BSM) for the input ECG signal. The input signal is first segmented into a sequence of beat-centered segments, each of which is passed through the adapted classifier to produce a beat-type score vector. These vectors are temporally aligned and stacked in the order of occurrence, forming a two-dimensional We checked and added explanation matrix in which each column corresponds to a beat-type score vector and each row corresponds to the prediction scores for a specific beat type over all beat-centered segments. This DA-BSM encodes the time-varying distribution of the beat-type predictions, effectively representing the dynamic rhythm structure of the input signal. An example of this transformation from the ECG signal to the DA-BSM is illustrated in Figure 3.  
Since the beat-type classifier is adapted using weakly supervised learning with record-level labels, the resulting score maps contain features that are both robust to domain differences and sensitive to label information. The DA-BSM representation acts as an intrinsic feature map, capturing the underlying characteristics of the input ECG signals, thereby improving the generalizability of our method across diverse databases.  
The proposed method ensures that the beat-type classifier is optimized for each new database. For instance, in the SPH dataset with nine beat types, the DA-BSM method focuses on refining the features of the nine beat types present in the coarsely annotated SPH dataset. In contrast, for the remaining beat types learned from the MITDB but not present in the SPH dataset, the features undergo only minimal adjustments due to the preceding refinement process. This adaptation helps mitigate domain discrepancies arising from differences in measurement environments, sensors, and patient demographics, thereby enhancing the method's generalization ability across diverse databases.
# Semantic Image Segmentation Using Scant Pixel Annotations
## 2 Related Work
### Active Learning for Segmentation  
In the iterative active learning approach, a limited number of unlabeled images are selected in each iteration for annotation by experts. The annotated images are merged with training data and used to develop the next segmentation model, and the process continues until the model performance plateaus on a given validation set. Active learning approaches can be broadly categorized based on the criteria used to select images for annotation and the unit (images, patches, and pixels) of annotation. For instance, in [21], FCNs are used to identify uncertain images as candidates, and similar candidates are pruned leaving the rest for annotation. In [22], the drop-out method from [23] is used to identify candidates and then discriminatory features of the latent space of the segmentation network are used to obtain a diverse sample. In [24], active learning is modeled as an optimization problem maximizing Fisher information (a sample has higher Fisher information if it generates larger gradients with respect to the model parameters) over samples. In [25], sample selection is modeled as a Boolean knapsack problem, where the objective is to select a sample that maximizes uncertainty while keeping annotation costs below a threshold. The approach in [21] uses 50% of the training data from the MICCAI Gland challenge (85 training, 80 test) and lymph node (37 training, 37 test) datasets; [22] uses 27% of the training data from MR images dataset (25 training, 11 test); [24] uses around 1% of the training data from an MR dataset with 51 images; and [25] uses 50% of the training data from 1,247 CT scans (934 training, 313 test) and 20% annotation cost. Each of these works produces a model with the same performance as those obtained by using the entire training data.  
The unit of annotation for most active learning approaches used for segmentation is the whole image. Though the approach in [25] chooses samples with least annotation cost, it requires experts to annotate the whole image. An exception to these are [24, 26, 27], where 2D patches are used as the unit of annotation. While active learning using pixel-level annotations (as used by SSPA approach) is rare, some recent works show how pixel-level annotations can be cost effective and produce high-performing segmentation models [28]. Pixel-level annotations require experts to be directed to the target pixels along with the surrounding context, and such support is provided by software prototypes, including those such as the PIXELPICK described in [28]. There are several domain-specific auto-annotators exist for medical images and authors have also developed a domain-specific auto-annotator for biofilms that will be released soon to that community.
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 6 Results of the Experiment  
To test the performance of the P2P distributed voltage control system, one needs to create a voltage rise (or drop) problem and solve it in a P2P fashion. To create a voltage rise problem in a laboratory-based microgrid, a high-power injection from the inverters back to the grid can be used. Alternatively, the impedance of the feeder depicted in Figure 2 can be oversized to create such a problem with low-power injection. In the following experiments, R1 and R2 are set to 8 \(\Omega\), L1 and L2 are set to 5 mH. Figure 9 shows the generation profile applied at both inverters. The active power generation starts at zero, and increases to a maximum of 1200 W. At the higher generation, the voltage is expected to rise above the maximum voltage limit. To comply with the European standard EN 50160, the voltage limits \(V^{\mathrm{max}}\) and \(V^{\mathrm{min}}\) are enforced to be \(\pm 10\%\) of the nominal phase voltage.  
Two experiments are carried out to compare the voltage profiles with and without voltage control. The comparison helps in quantifying the performance of the P2P voltage control.  
Figure 8: Illustration of the implemented synchronized protocol operation (TX: transmission, RX: reception).
## 4 Tracking
### Estimation
#### 4.2.1 Kalman Filter  
A Kalman filter can estimate the state of a linear system based on the system model. First, we consider two system models: the measurement model and the process model. The measurement model describes the relationship between the desired state and the measurement. The linear measurement model is expressed as follows:  
\[z(k)=C(k)x(k)+v(k) \tag{4}\]  
where \(z(k)\) is the measurement, \(x(k)\) is the desired state, \(C(k)\) is the measurement matrix, and \(v(k)\) is the measured noise.  
The estimated state is often variable, so it is necessary to consider the transfer mode of the state, which is called as the process model, which is generally expressed as the following with the linear transfer mode of the state  
\[x(k+1)=A(k)x(k)+w(k) \tag{5}\]  
where \(A(k)\) is the process matrix and \(w(k)\) is the process noise. It is clear that the characteristics of process noise and measurement noise play a crucial role in the performance of estimation methods. The simplest linear system is that the process noise \(w(k)\) and measurement noise \(v(k)\) are both Gaussian noise with a zero mean with a given covariance of \(Q(k)\) and \(R(k)\), respectively.  
To obtain the estimated state for such systems, a Kalman filter [75] is one of the most widely used estimation methods. Theoretically, a Kalman filter is an estimator for what is called the linear-quadratic problem, which is the problem of estimating the state of a linear dynamic system. The resulting estimator is statistically optimal with respect to any quadratic function of estimation error [76; 77]  
According to these equations, details of the Kalman filter algorithm are presented in Figure 6, in which it is evident that it works in a two-step process from the initial value \(\hat{x}(0|0)\). In the first step, denoted the prediction step, the Kalman filter produces prediction estimates \(\hat{x}(k|k-1)\) along with their uncertainties from the last estimate \(\hat{x}(k-1|k-1)\). The second step is the update step. Once the outcome of the next measurement, \(z(k)\), is obtained, the estimates \(\hat{x}(k|k)\) are updated using a filter gain \(K(k)\) based on \(\hat{x}(k|k-1)\).
## 3 Results
### Crop Phenotype Parameter Acquisition Performance
#### 3.2.2 Dynamic Performance  
The system dynamic response time includes the synchronous trigger time of the timing boards (the sum of the synchronization time between the system clock and the PTP clock and the relative error time between the PTP clocks), the data acquisition time, and the network transmission time. Among them, the network transmission time refers to the transmission time of public networks (such as 5G), and this part is not discussed in this study. As mentioned above, taking the PTP clock of timing board 1 as the reference (master clock), the root mean square values of the time differences between the system clocks of each timing board and the PTP clock of timing board 1 were all less than 130 ns. The length of the data acquisition time mainly depends on the internal interruption delay, software delay, and clock drift of the system, which are the main factors determining the system dynamic response.  
To obtain the crop data acquisition time, 100 potted Guofu 909 peppers were used as test samples. The three timing boards were synchronously triggered to continuously acquire 100 groups of laser point clouds, visible light images, and thermal infrared images  
Figure 8: Visible light image (upper left corner) and thermal infrared image (**bottom left corner**) at the beginning of acquisition; visible light image (**upper right corner**) and thermal infrared image (**bottom right corner**) at the end of acquisition.  
of the crops, and their respective acquisition times were recorded. The scatter plot of the acquisition times over 100 trials is shown in Figure 9.  
The root mean square values of the acquisition times of the laser point cloud, visible light image, and thermal infrared image were calculated as 299.20, 255.70, and 223.22 us, respectively. The dynamic response time of the fast mobile crop phenotype acquisition system is the sum of the synchronous trigger time of the timing boards and the data acquisition time, that is, 130 ns + 300 us.
## 2 Methods
### Development of hydro-optical models
#### 2.4.2 The North Sea hydro-optical model  
For the North Sea the intrinsic hydro-optical model (Figure 2) has been obtained under the ESA GMES MarCoast shipborne campaign encompassing cruises in the coastal areas of southern Norway. Water samples were collected to determine the concentration vector constituted by _chl_, total suspended matter (_tsm_), and absorption at 443 nm by yellow substance plus non-pigmented/detrital particulate matter. Concurrently with water sampling, surface reflectance, \(\rho_{u}(\lambda)\) was measured at six wavelengths (412, 443, 490, 510, 555, and 665 nm), where \(\rho_{u}(\lambda)\) is related to \(R_{rsw}\) via the coefficient of proportionality \(k=0.587\) sr\({}^{-1}\). A detailed description of this hydro-optical model is given elsewhere [22]. The water sampling and laboratory analyses were conducted in accordance with methods described in [23].
## 3 Use Cases
Although VLOS-operated robots can be controlled using direct wireless links, teleoperated robots are comparable to BVLOS in UC11 (without links to UTM). The operation of UAVs has some similarities to UGVs, although, especially in wooded areas, providing coverage at ground level is more challenging than at drone flight altitudes of up to 120 m. For semi-autonomous robots (ambiance awareness, plant row detection, or driving according to a sowing map with high-precision GNSS positioning), the needs include:  
* communication for, e.g., the uploading of work scenarios and updates to the knowledge base (weed patterns, pest presence symptoms, trees or signs of infestations that are found);
* remote computing for offloading the local processing onboard the robot;
* transmission of information (using video streams) to the robot operator or supervisor. **UC14 Support for field personnel:** To support field workers in the era of Industry 4.0, online access to the supporting ISs is a given, e.g., a personal terminal (tablet) receiving information from a GIS based on the current position (soil property maps and water relations, sown plants or forest inventory, historical records regarding agricultural treatments at the site, previous photos, etc. ), enabling the quick familiarization by the staff member of the situation without the necessity of their previous presence onsite. In the future, the wide application of AR technology is expected to enrich observed images with contextual information (e.g., labels of recognized objects and their components, descriptions of their properties, operation schemes, etc.). AR imposes high demands for connectivity and extremely high-quality video streaming in RT, with a maximum latency of 20 ms for good QoS [81] (cybersickness becomes perceptible above 40 ms and significantly intensifies above 75 ms [82]). A list of the occurrences of the described use cases in particular AFBW sectors is presented in Table 1.
# A Credit-Based Congestion-Aware Incentive Scheme for DTNs
## 5 Performance Evaluation
### Performance Comparison
#### 5.2.5 Influence of Message Generation Interval  
As shown in Figure 17, it can be seen that the delivery ratio increases with the increase of message generation interval. When the message generation interval is larger, the total messages generated in the network will be smaller, and nodes can have enough buffer and bandwidth to forward messages, so the delivery ratio is higher. In the direct scheme, a node only forwards its own messages, so the delivery ratio is lowest. The delivery ratio of CBCAIS is much higher than SMART, Multicent, and GTA when message generation interval is small. This is because when the message generation interval is small, more nodes will be congested, and the CBCAIS scheme can effectively reduce the impact of congestion.  
Figure 18 shows the impact of message generation interval on delay. It can be seen that delivery delay increases for all schemes as the message generation interval increases. When the message generation interval is small, more messages are generated in the network and many messages with longer delay will be dropped due to the limited buffer. Because the messages with longer delay can be forwarded when the message generation interval is larger, the delivery delay will increase as the message generation interval increases. The average delivery delay of CBCAIS is slightly larger than SMART, Multicent, and GTA. This is because more messages are forwarded to nodes with more free buffer but a slightly larger delivery delay to avoid being dropped.  
Figure 17: Delivery ratio for varying message generation interval.  
Figure 18: Delivery delay for varying message generation intervals.  
As shown in Figure 19, the overhead ratio of the direct scheme is always 1. For the other schemes, the impact has two aspects. On the one hand, as message generation interval increases, nodes can have enough bandwidth to forward messages, so the number of forwards will increase, leading to an increase in the overhead ratio. On the other hand, nodes will also have enough buffers to deliver more messages, leading to a decrease in the overhead ratio. When the first impact outweighs the second one, the overhead ratio increases, otherwise the overhead ratio decreases. The overhead ratio of CBCAIS is lower than SMART, Multicenter, and GTA schemes.  
Figure 20 shows the impact of message generation interval on energy. It can be seen that the energy decreases for all schemes as the message generation interval increases. This is because when the message generation interval is larger, the total messages generated in the network will be fewer, which leads to less energy consumption. Because of the effective message forwarding, the energy consumption of CBCAIS is slightly higher than in the SMART, Multicenter, and GTA schemes. The energy consumption of the direct scheme is the lowest.  
Figure 19: Overhead ratio for varying message generation interval.  
Figure 20: Energy for varying message generation intervals.
# Error Dynamics Based Dual Heuristic Dynamic Programming for Self-Learning Flight Control
###### Abstract  
A data-driven nonlinear control approach, called error dynamics-based dual heuristic dynamic programming (ED-DHP), is proposed for air vehicle attitude control. To solve the optimal tracking control problem, the augmented system is defined by the derived error dynamics and reference trajectory so that the actor neural network can learn the feedforward and feedback control terms at the same time. During the online self-learning process, the actor neural network learns the control policy by minimizing the augmented system's value function. The input dynamics identified by the recursive least square (RLS) and output of the critic neural network are used to update the actor neural network. In addition, the total uncertainty term of the error dynamics is also identified by RLS, which can compensate for the uncertainty caused by inaccurate modeling, parameter perturbation, and so on. The outputs of ED-DHP include the rough trim surface, feedforward and feedback terms from the actor neural network, and the compensation. Based on this control scheme, the complete knowledge of system dynamics and the reference trajectory dynamics are not needed, and offline learning is unnecessary. To verify the self-learning ability of ED-DHP, two numerical experiments are carried out based on the established morphing air vehicle model. One is sinusoidal signal tracking at a fixed operating point, and the other is guidance command tracking with a morphing process at variable operating points. The simulation results demonstrate the good performance of ED-DHP for online self-learning attitude control and validate the robustness of the proposed scheme  
error dynamics; dual heuristic dynamic programming; recursive least square method; online self-learning; attitude control
## 6 Experiments
### Experimental Results
Figure 3 depicts the relationship between precision and recall for different concept weight thresholds. If we do not set any threshold on the weight of the concepts in the long-term profiles, the original approach achieves the best precision; _i.e._, precision is 10.86% for a recall of 72.94%, followed by topic modeling (precision: 10.79% and recall: 72.94%) and n-gram modeling (precision: 10.42% and recall: 76.82%). Overall, at concept weight thresholds of less than 0.5, topic modeling and n-gram modeling approaches have resulted in lower precision, as additional concepts included in the profiles (_i.e._, concepts representing topics and n-grams derived from experts' micro-contributions) have contributed to more noise in the profiles (demonstrated by a higher recall). Increasing the concept weight threshold to 0.5 will result in an increase in the precision achieved by all methods, with n-gram modeling achieving the highest precision (18.35%), albeit at the expense of the recall (49.96%). Subsequently increasing the threshold will result in further improvements to the precision achieved by all approaches, however at the expense of lower recall values. The best precision is achieved by the topic modeling approach at the concept weight threshold of 1.2 (34.08%) followed by the n-gram modeling approach (32.47%) and the original approach (27.68%). The results indicate that a higher accuracy is achieved by topic modeling and n-gram modeling approaches by setting an appropriate concept weight threshold, where the noise is reduced. Increasing the concept weight threshold over and above 1.2, results in a significant decrease in both the precision and recall values achieved by all methods; this is due to the exclusion of a large number of concepts with weights below such high thresholds. Experimental results indicate that at concept weight thresholds greater than 0.4, topic modeling and n-gram modeling approaches have consistently achieved higher accuracy in comparison to the original approach. Topic modeling has demonstrated the highest precision at the threshold of 1.2, although at the expense of recall. Overall, the N-gram modeling approach has achieved the highest accuracy  
Figure 3: Precision-recall curve at different concept weight thresholds. (F-score: 31.94%) at the concept weight threshold of 1. The enhanced accuracy is due to the fact that the n-gram modeling approach derives n-grams by taking into account _word order_ and _context_ (_higher precision_) and _multiple words_ and _phrases_ (_higher recall_).