esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Case Study: Position Control of a Rotor by PII\({}^{1/2}\)DD\({}^{1/2}\) Control
### Comparison of PID and PII\({}^{1/2}\)DD\({}^{1/2}\) Controls by Experimental Tests  
The simulation results of Section 8.2 were verified by means of the experimental set-up in Figure 11a, composed of a flywheel (inertial load) directly connected to a DC motor Kollmorgen AKM42G, with maximum continuous torque of 3.4 Nm. The overall moment of inertia of the rotor, composed of the motor rotor, joint, shaft, and flywheel, is \(J\) = 1.04 \(\times\) 10\({}^{-3}\) kg\(\cdot\)m\({}^{2}\). The no-load torque/speed characteristics of the rotor (i.e., the torque necessary to drive the rotor at constant speed) was measured and approximated by a linear characteristic with coefficient \(B\) = 1.45 \(\times\) 10\({}^{-3}\) Nms/rad. Therefore, the experimental setup is characterized by the same values of \(J\) and \(B\) considered in the simulations.  
The three controllers discussed in the previous sections were implemented in Simulink Desktop Real Time running on a PC. The same Simulink model performs the DTS and the ET by means of two parallel subsystems (Figure 11b). The overall control layout is shown in Figure 12: a National Instrument PCI-6259 DAQ card, driven by Simulink Desktop Real Time, reads the encoder signal \(\theta\) and generates the reference torque signal \(M\), which is sent to a Kollmorgen driver AKD-P00606; the current reference is obtained dividing \(M\) by the torque constant \(k_{t}\), and then used in the driver current loop.  
Figure 13 collects the time histories of the angle \(\theta\), of the error \(e_{\theta}\) and of the motor torque \(M\), comparing ET (continuous lines) and DTS (dashed lines). Table 2 summarizes the main results in terms of maximum and mean tracking error (\(e_{\theta,max}\) and \(e_{\theta,mean}\)), maximum torque (\(M_{max}\)), and control effort (\(E_{c}\)): \(e_{\theta,max}\) and is the maximum absolute value of the error, \(e_{\theta,mean}\) is the average absolute value of the error, and \(E_{c}\) is defined according to the following equation:  
\[E_{c}=\int\limits_{0}^{\infty}M^{2}dt \tag{26}\]  
Figure 11: Experimental layout: flywheel directly actuated by the brushless DC motor (**a**) and Simulink Desktop Real Time control scheme (**b**).  
Figure 12: Overall control scheme of the experimental layout.
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 4 Processing of UAV Fault Data
### Anomalies Time Window Setting Based on Fault Time Point Anchoring Method  
The experimental method was adopted for simulating GPS, accelerometer, engine (recoverable), and remote controller failures during one flight. There should be a reasonable division method to distinguish normal data from abnormal data. The method used in this paper is as follows: when the fault occurs, record it as \(T_{start\_i}\) and record the longitude, latitude, and altitude at this time. After the flight, go to the GPS information recorded in the log to find the longitude, latitude, and altitude when the fault occurred so as to determine the starting position of the faulty sample. Similarly, when the fault ends, record it as \(T_{end\_i}\), the longitude, latitude, and altitude at this time are also recorded to determine the location of the sample at the end of the fault. The mathematical description is shown in Equation (4). Table 6 shows the correspondence between the fault types represented by each class label and the fault window.  
\[\left\{\begin{array}{c}T_{start_i}=Record(T|\{Lat_{k},Lon_{k},Alt_{k}\})\\ T_{end_i}=Record(T|\{Lat_{j},Lon_{j},Alt_{j}\})\quad i\in[0,FlyTimes]\\ TimeWindow_{i}=T_{end\_i}-T_{start\_i}=Fault_{i}\end{array}\right. \tag{4}\]  
Among them, \(Lat_{k}\) and \(Lat_{j}\) refer to the latitude of the drone at a certain moment; \(Lon_{k}\) and \(Lon_{j}\) refer to the longitude of the drone at a certain moment; and \(Alt_{k}\) and \(Alt_{j}\) refer to the altitude of the drone at a certain moment. \(Record()\) refers to the function of recording the longitude, latitude, and altitude of the drone. \(TimeWindow_{i}\) refers to the time window of the \(i\)-th fault. \(FlyTimes\) refers to the number of failed flights/normal flights.
# Perspectives and Challenges in Robotic Neurorehabilitation
## 3 Current Trends of Robot-Aided Neurorehabilitation
### Multimodal Assessment of Recovery  
The development of the robotic devices treated in this work is pivotal not only for therapy administration but also to accomplish another objective for the neuroscientific, medical, and rehabilitation engineering communities: The quantitative assessment of sensorimotor performance. Therefore, in addition to the kinematic and kinetic monitoring during gait, balance, or upper limb control, it is also necessary to assess the outcomes and the potential long-term retention of a neurorehabilitation protocol. As reported by [PERSON] et al. [165], providing a reliable assessment of the sensorimotor components is important in order to optimize the patient's chance of recovery. Despite this well-established evidence, these quantitative assessments by means of robotic devices are not continuously performed during clinical practice. Hence, the increased accuracy given by objective measurements of the performance is discarded in favor of objective clinical scales that are biased by the experience and the ability of the clinicians, leading to results that lack reliable measurement of the patient's impairment. Moreover, subtle sensory and motor abnormalities are hardly detected by clinical measurements. In this framework, rehabilitation robotics are also able to improve the clinical evaluation, at least by placing the quantitative assessment side by side with the standard clinical evaluations.  
Then, many of the neurorehabilitation studies that employed emerging robotic solutions did not account for a complete characterization of brain activity. Some studies exist [166; 167; 168], but further efforts are required [166] to characterize the neural basis of the sensorimotor function recovery driven by novel robotic devices. An unmet interest is understanding how the structural and functional properties of the brain are sculpted by the rehabilitation program. In fact, characterizing the brain reorganization, together with an adequate assessment of the behavioral performance, might help in a quantification of the recovery processes driven by the robot, in terms of both the ability to restore movements and to restitute pre-disease onset brain function [148]. Modern non-invasive neuroimaging techniques strike in, giving the possibility to assess brain activity changes pre/post and during the administration of robot-aided rehabilitation protocol.  
In the next sections, we first describe current assessment procedures based on clinical scales, and we then address: (i) The quantitative assessment of sensorimotor performance; and (ii) neural and muscular correlates of sensorimotor performance.
# A Nested U-Shaped Residual Codec Network for Strip Steel Defect Detection
## 5 Experiments and Discussion  
### Experimental Setup  
We verify the performance of our model on the public strip steel dataset SD-saliency-900 [14], which includes 900 cropped images with a size of 200 \(\times\) 200 pixels. Furthermore, in this dataset, there are three defects, namely inclusions, patches, and scratches.  
Inspired by [44], we use a standard training set to compare the proposed method with various deep models. This training set contains 810 images, of which 540 images (180 images per defect type) are randomly picked from the original dataset and 270 noise images (90 images per defect type) are obtained by using the disturbing method of salt and pepper noise (\(p=10\%\)). Similar to [44], in the training process, each image is resized to 256 \(\times\) 256 and randomly cropped to 224 \(\times\) 224, and then normalized by \((1-\mu)/\sigma\). The parameters of the encoder network are initialized through initialization strategy [45] instead of using a pretrained ResNet-34 [29]. The batch size was set to be 8, and the number of training steps was 50K due to the fact that the loss converges after 50K iterations without adopting a validation set, as shown in Figure 6. We use the RMSprop optimizer [46] as the optimizer with a learning rate equal to 0.001 and an alpha equal to 0.9. During the test, we first adjust the image to 256 \(\times\) 256 and then input it into the network to obtain its saliency map. To keep the resolution of the input and output images consistent, bilinear interpolation is used to restore the saliency map.  
We implement the proposed model using the Pytorch framework [47] and train all experiments with an NVIDIA TITAN Xp GPU. With the acceleration of the GPU, the whole training process takes approximately 7 h. When testing, our model only requires 0.035 s to process a 200 \(\times\) 200 image.
## 2 Graphene and Graphene-Related Materials
### Graphene and Related Materials, Characterization
#### 2.2.3 Advanced Microscopic Techniques  
Microscopic techniques are necessary to achieve proper characterization of a graphene-related material or composite.  
Few-layer graphene flakes can be successfully investigated using atomic force microscopy (AFM) due to a thickness in the range of 0.34-1.2 nm [71; 72]. However, this technique cannot be routinely used to scan large areas. In addition, AFM imaging provides topographic images only, which are unable to distinguish the number of layers for GO. However, pristine graphene and GO can be distinguished based on the different thicknesses using AFM imaging. Besides thickness and imaging characterization, different AFM modes can be used to study the mechanical [73], frictional, electrical, magnetic, and elastic properties of graphene nanosheets.  
A scanning electron microscope is a type of electron microscope that produces images and analyses the elemental composition (SEM-EDX) of a sample by scanning the surface with a focused beam of electrons. The electrons interact with atoms in the sam ple, producing various signals that contain information about the surface topography and the composition of the sample. This technique is mostly used for the morphological characterization.  
Transmission electron microscopy is a microscopy technique in which a beam of electrons is transmitted through a specimen to form a see-through image. The specimen is usually either an ultrathin section of the sample (less than 10 nm thick) or a suspension on a grid. The resolving power (the minimum distance between two points for which they can be distinguished as such and not as one) is about 0.2 nm, that is about 20 times greater than the best SEM. Energy dispersive X-ray (EDX) spectra can also be collected to provide information about the elements existing in the samples [74]. It is possible to observe the metal catalyst trapped inside the lattice structure of graphene, and in some cases, it is also possible to distinguish functional groups anchored onto the surface of GO. This technique is used for the morphological characterization of graphene [75].
## 3 Methods
### Random Forest (RF) Classification on GEE Platform  
GEE provides a serial of classification algorithms, including Minimum Distance (MD) [58], Random Forest (RF) [59], Support Vector Machine (SVM) [60], Regression Trees (CART) [61] and Naive Bayes (NB) [62]. According to previous studies, SVM and RF have better performance than other classification algorithms [63; 64; 65]. SVM algorithm performs good with limited training samples, however, RF performs better as training sets get larger [66]. RF is conceptually similar to tree-based learners, and they share same advantages [67]. RF can exhibit higher accuracy and efficiency even facing the unfavorable conditions of noise interference. Moreover, RF classifier shows better performance than other classifiers in land cover mapping using long time series images [68; 69]. Therefore, RF classifier was employed in this study.  
In this study, the composed NDVI, NDWI and NDBI products were also employed as spectral attributes of each pixel, the GLCM features of NDWI, NDBI, VV and VH were used as texture attributes, and the ETOPO1 value was applied as a topographic attribute. A feature vector of 60 dimensions (3 spectral + 4 images \(\times\) 14 measures/image texture + 1 topographic) was obtained. A RF classifier on GEE platform was trained according to existing reference [70], in which the number of trees (\(n_{tree}\)) was set to 100, the maximum depth was unlimited, the minimum sample number of each tree node was 1, and the number of features for each tree was set as the square root of the variable number.
# Smart Preliminary Channel Access to Support Real-Time Traffic in Wi-Fi Networks
## 5 Results
### Impact of the TXOP Limit Value  
Let us fix the number of RTA STAs: \(M=5\); the RTA traffic period: \(T_{period}=20\,\mathrm{ms}\); and \(\sigma=10\,\mathrm{\SIUnitSymbolMicro s}\). Consider the dependences of delay quantile and efficiency on the TXOP Limit value. Figure 5 shows the monotonic increase in delay quantile for PCA and Tuned EDCA over the entire range of TXOP Limit. At the same time, for the Smart PCA, the quantile reaches a constant value starting from some value of TXOP Limit. With a long TXOP Limit, the reservation intervals are large enough, and other RTA STAs transmit data instead of waiting for them. In this case, condition (1) is not met, and the Smart PCA performs a constant delay. At low TXOP Limit, condition (1) is met, so the RTA STA does not have time to transmit in the channel reserved by another RTA STA. So, the growth of TXOP Limit causes an increase in the waiting time for another RTA STA reservation and hence an increase in delay.  
The efficiency of the Tuned EDCA increases monotonically because as the TXOP Limit increases, both the portion of successful transmissions and the portion of payload in one transmission increase. The efficiency in the PCA and the Smart PCA increases, reaches the peak, and then decreases, because in addition to the effect described above, with an increase in TXOP Limit, the values of \(T_{\text{PCA}}\) and \(T_{\text{SmartPCA}}\) increase. As a result, RTA transmissions, including PCA procedure, take more channel time. A slight difference between the efficiency for the PCA and the Smart PCA is related to the difference in the values of \(T_{\text{PCA}}\) and \(T_{\text{SmartPCA}}\).
## 2 Main Types of Combustible Waste
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{6}{c}{**Ultimate Analysis (wt\%)**} & \multicolumn{6}{c}{**Proximate Analysis (wt\%)**} \\ \cline{2-13}  & **C** & **H** & **O** & **N** & **S** & **Moisture** & **Volatile** & **Fixed** & **Carbon** & **Ash** & **Calorific Value** & **Ref.** \\ \hline Shenhua coal & 69.55 & 3.74 & 10.14 & 0.83 & 0.25 & 8.28 & 29.55 & 54.96 & 7.21 & 27.07 & [38] \\ \hline Samca coal & 75.9 & 5.3 & 12.27 & 0.7 & 5.8 & - & 36.9 & - & 22.8 & - & [39] \\ \hline Coal gangue & 17.5 & 1.26 & - & 0.56 & 1.28 & 0.75 & 15.07 & 16.31 & 68.62 & 4.82 & [40] \\ \hline Coal slime & 87.2 & 5.1 & 4.5 & 2.1 & 1.1 & - & 23.1 & - & 26.5 & 24.83 & [41] \\ \hline Semicoke powders & 69.12 & 1.35 & 10.33 & 0.89 & 0.71 & 0.7 & 15.74 & 67.36 & 16.9 & - & [54] \\ \hline Pyrolytic carbon black & 93.5 & 2.84 & \textless{}0.01 & 0.46 & 3.2 & - & - & - & 25 & 26 & [55] \\ \hline Textile dyeing sludge & 15.53 & 3.44 & 16.47 & 2.43 & 1.38 & 1.37 & 36.53 & 1.35 & 60.75 & 5.99 & [56] \\ \hline Waste soot & 74.6 & 1.6 & - & 0.2 & 1.35 & 68.6 & - & - & - & 28.1 & [57] \\ \hline Sewage sludge & 24.83 & 3.31 & 14.39 & 4.47 & 1.13 & 97.95 & 42.74 & 5.39 & 44.58 & 0.77 & [58] \\ \hline Sewage sludge & 13.22 & 2.91 & 19.7 & 2.12 & 0.57 & 5.29 & 31.31 & 2.06 & 61.34 & 5.215 & [59] \\ \hline Coking sludge & 24.48 & 3.15 & 23.68 & 2.36 & 0.94 & 78.97 & 45.48 & 9.14 & 45.38 & 8.49 & [60] \\ \hline Brewery wastewater sludge & 17.6 & 2.93 & - & 2.41 & - & 2.33 & 37.72 & 0.09 & 59.86 & 6.56 & [61] \\ \hline Waste lubricating oil & 83.53 & 13.32 & 2.83 & 0.15 & 0.17 & - & - & - & - & [62] \\ \hline Mineral waste oil & 83.2 & 13.0 & 1.2 & - & 1.2 & - & - & - & - & [39] \\ \hline Lubricating Oil Wastes & 83.2 & 13 & 1.2 & - & 1.2 & - & - & - & - & 44.33 & [63] \\ \hline Waste lubricating oil & 84.02 & 13.31 & 1.92 & - & 0.75 & - & - & - & - & [46] \\ \hline Waste cooking oil & 71.84 & 10.14 & 17.71 & 0.06 & 0.01 & 0.08 & 99.15 & 0.56 & 0.24 & 39.24 & [64] \\ \hline Oily sludge & 63.9 & 7.3 & 25.3 & 1.2 & 2.3 & 33.4 & 69.3 & - & 21.2 & 23 & [44] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Properties of the components used for the preparation of fuel mixtures. Figure 1: Calorific value of wastes and their average global generation rate (according to [13; 34; 35; 36; 37]).
# A Glider-Assisted Link Disruption Restoration Mechanism in Underwater Acoustic Sensor Networks
## 3 The Glider-Assisted Link-Disruption Restoration Mechanism
### The LDR-GS Mechanism  
The details of LDR-GS mechanism follows the steps below:  
1. The static sensor node \(s_{i}^{j}\) sends data packet sign (\(s_{i}^{j}\)), which contains the node's own position information, to its cluster head node \(h_{i}\) every time period.
2. If the cluster head \(h_{i}\) receives the sign (\(s_{i}^{j}\)) packet sent by \(s_{i}^{j}\), it indicates that there is no disruption of all links between static node \(s_{i}^{j}\) and the cluster head \(h_{i}\). If the cluster head \(h_{i}\) does not receive the sign (\(s_{i}^{j}\)) packet sent by \(s_{i}^{j}\) but receives the sign (\(s_{i}^{j+1}\)) packet of the next hop \(s_{i}^{j+1}\), then it judges the link \(e_{j+1}\) between \(s_{i}^{j}\) and \(s_{i}^{j+1}\) to be disrupted and the link disruption identifier in \(h_{i}\) therein becomes \(\delta_{j,i+1}=1\). If the cluster head \(h_{i}\) does not receive the sign (\(s_{i}^{j+1}\)) packet sent by \(s_{i}^{j=+1}\), then it does \(j=j+1\) and continues to judge until the cluster head can receive from the next hop. Then the cluster head can judge the disrupted link \(e_{ab}\), where \(b=a+1\). If there are some simultaneous link disruptions, the glider will prioritize the link closest to the cluster head node to ensure the connectivity of more nodes.
3. If the link disruption flag \(\delta_{ab}\) is 0, the glider completes the data forwarding and continues to collect data according to the original running track. If there is a link-disruption, that is the link disruption flag \(\delta_{ab}\) is not all 0, \(h_{i}\) sends a repair (\(e_{ab}\)) packet containing the location information of the two nodes to \(g_{i}\) when \(g_{i}\) periodically approaches \(h_{i}\) for data forwarding, and then starts scheduling the glider to repair the link disruption.
4. If \(g_{i}\) receives the repair (\(e_{ab}\)) packet, the glider's link interrupt flag becomes \(\delta_{ab}=1\). The disrupted link is repaired by selecting the appropriate trajectory according to the motion characteristics of the glider \(g_{i}\).
5. After \(g_{i}\) completes the link-disruption restoring command issued by \(h_{i}\), the glider's link interruption flag \(\delta_{ab}\) becomes 0, indicating that the link \(e_{ab}\) repair task delivered by the cluster head has been completed. In this case, \(g_{i}\) returns to the original position again and sets the link disruption flag of \(h_{i}\) to 0, and continues the environmental monitoring task.
## 4 Discussion
### EO Studies per Thematic Area: Limitations, Research Gaps, and Future Directions
Future studies should focus on the development of protocols which will guide researchers and stakeholders towards the selection of the most suitable algorithm or method based on the specific characteristics of their research. Services related to early warning and decision support systems will upgrade EO applications, especially in thematic areas dealing with hazards and human health [113; 117; 180]. The improvement of existing services or the continuous development of new systems will help stakeholders and the relevant authorities to take the appropriate actions aiming at reducing the number of human casualties and severe economic losses. Similar systems will be extremely useful in the thematic areas of atmosphere and agriculture, with applications regarding extreme weather warnings and irrigation support. Research in specific thematic areas is surprisingly limited. The Mediterranean Sea is one of the busiest seas in the world, accounting for 20% of seaborne trade and over 200 million passengers [214]. Studies on ship detection and more particularly of those related to illegal activities were absent. In addition to this, the Mediterranean Sea is exposed to many risks such as rising CO\({}_{2}\) emissions, pollution, marine litter, and collisions. However, this is not reflected by the number of studies carried out in the marine safety and security thematic area. We speculate that the reason for the low number of studies in this thematic area may be twofold: restrictions on data sharing which are often applied in the sea region due to security reasons and the insufficient resolution and revisiting time of existing non-commercial satellites which limit their ability to detect objects and the movement of these objects in time. Similarly, EO applications in the cultural heritage thematic area are very rare (1.4% of all EO applications) with the insufficient resolution of existing non-commercial satellites being identified as the main limitation. Finally, we expect that the EO research in the energy thematic area will attract more attention in the following years due to the gradual transition from fossil fuel to renewable energy.
## 3 Design of the Unit Structure of the Main Reflector in the Deployable Reflector Antenna
### Performance Evaluation According to Honeycomb Core of Reflector Antenna
applying cores with different densities to each of the four parts of the main reflector; and (Case 2.4) applying cores with different thicknesses to each of the four parts of the main reflector. For each core design, the stacking sequence of Case 1.1 is equally applied to individually evaluate the structural influence of the core shape. In this subsection, modal analysis is implemented to compare the weight and fundamental mode frequency of the main reflector with the honeycomb core of Case 2.1-Case 2.4. The clamped boundary condition is applied at the hinge point to simulate the deployed condition. The weight and mode frequencies for each case are shown in Table 4. The mode frequencies of Case 2.2 consisting of a low-density core uniformly and Case 2.3 consisting of cores with different densities to each part are relatively higher than those of the other cases. However, in order to manufacture the honeycomb core of Case 2.3, the manufacturing process requires additional steps to interconnect the cores with different densities and it is difficult to maintain the quality of 24 main reflectors manufactured by complex processes. As shown in Table 4, in terms of weight, rigidity, and manufacturability, the design of Case 2.2, which applied a uniform low-density core, is the most suitable honeycomb core for the main reflector.