esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Thus, the methodology for determining the energy-to-mass ratio entails a sequence of steps consisting of (i) computing the actuator's mass; (ii) determining the evolution of the force developed over the entire working stroke; (iii) computing the developed energy and (iv) computing the energy-to-mass ratio. Given its short running time, the computational methodology of the energy-to-mass ratio put forward in the paper also lends itself to other types of actuators.  
The following conclusions resulted in relation to the quality characteristics used and analyzed for the comparison of the two types of pneumatic actuators:  
* From the viewpoint of axial and radial dimensions, single-acting cylinders are more compact than pneumatic muscles;
* The masses of the two types of pneumatic motors of the same diameter do not differ significantly. Nevertheless, in the case of 40 mm diameter actuators, as the strokes required to be carried out and implicitly their lengths increase, the difference between the mass of the cylinder and that of the muscle increases, in the sense that the pneumatic muscles become increasingly lighter than the cylinders.
* The forces developed by pneumatic muscles are about 5- to 10-times greater than those developed by single-acting cylinders;
* The energy-to-mass ratios of the pneumatic muscles are about 4- to 9-times larger than those of single-acting cylinders.  
The results of the study prove that despite their radial and axial larger sizes, pneumatic muscles offer benefits from the viewpoint of developed forces and energy-to-mass ratios. In applications that require large forces and high energy efficiency, replacing single-acting cylinders with pneumatic muscles has become a necessity.  
The analysis of the energy-to-mass ratios confirm the assertions found in the literature related to pneumatic muscles that motors have an energy efficiency superior to that of single-acting pneumatic cylinders.  
The flexibility of pneumatic muscles, the large, developed forces and a high energy-to-mass ratio render these actuators an optimum solution for manufacturing exoskeleton devices. In addition, their light weight, portability, safe operation, reliability, compliance and easy integration in wearable assistive devices render pneumatic muscles a viable actuation alternative to such rehabilitation systems.  
Conceptualization, A.D. and T.D.; methodology, A.D. and T.D.; resources, A.D.; data curation, T.D.; writing--original draft preparation, A.D.; writing--review and editing, A.D. and T.D.; supervision, A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
###### Abstract  
Closed-loop control is desirable in direct energy deposition (DED) to stabilize the process and improve the fabrication quality. Most existing DED controllers require system identifications by experiments to obtain plant models or layer-dependent adaptive control rules, and such processes are cumbersome and time-consuming. This paper proposes a novel data-driven adaptive control strategy to adjust laser voltage with the melt pool size feedback. A multitasking controller architecture is developed to incorporate an autotuning unit that optimizes controller parameters based on the DED process data automatically. Experimental validations show improvements in the geometric accuracy and melt pool consistency of controlled samples. The main advantage of the proposed controller is that it can adapt to DED processes with different part shapes, materials, tool paths, and process parameters without tweaking. System identification is not required even when process conditions are changed, which reduces the controller implementation time and cost for end-users.  
additive manufacturing; direct energy deposition; closed-loop control; virtual reference feedback tuning +
Footnote †: journal: Applied Sciences
## Underlying Theories
### Link Scheduling  
In TSCH networks, the mechanism of scheduling becomes a very important part, because the nodes in the network will be active or sleep according to a predetermined schedule. As for what is meant by scheduling, in this research it is link scheduling, because the things to be set up are the timeslots in each slotframe. The scheduling system will generate a schedule that provides information for each node in what timeslot it must transmit, receive, or sleep. In order to save energy, when it gets a sleep turn, the nodes will turn off the radio system because it requires the most energy [27].  
In TSCH networks, scheduling must be designed carefully because it will determine the success of the transmission. As an illustration, Figure 5a shows a network with six nodes, a tree topology network with a directed graph showing the links between nodes and its parents. If node E in the figure has a timeslot to send data to node D, D must be ready to receive the data in that slot. Figure 6b shows an example of an activity schedule for each node, starting from timeslot 0 to timeslot 5, using four offset channels. The assumption used in this example is that each node will send one data packet to the master node (node A) on each slotframe. Within the duration of one slotframe, all data in the network nodes must arrive at the master node. If the schedule in Figure 5b is carried out by the network node in Figure 5a, the process of transmitting, receiving, and collecting data and idling will be as shown in Figure 6.  
Figure 5: (a) TSCH network with tree topology and (b) example of link schedule for data collection.  
Figure 6: Transmit, receive, idle, and data collection processes.
# An Analysis of the Wind Parameters in the Western Side of the Black Sea
## 4 Discussion  
It is highly important to acknowledge that wind resources are directly related to wind speed not to mention that their productivity will be influenced by the climate. In order to assess the dynamics of the wind energy resources in the Black Sea, the 6 years of GeoEcoMar data (2015-2020) were considered for evaluation. To identify relevant trends, analysis of mean and maximum wind speed, as well as their seasonal variations were conducted. Moreover, an evaluation of the strongest winds recorded is presented.  
In order to highlight the distribution of the strongest winds recorded in the Black Sea region, the maximum wind speeds were considered for discussion in order to identify some energetic peaks. On the 18 January 2018, a maximum wind speed of 22.5 m/s was seen at 19.00. Signs of a cyclone were seen on the 9 January when the wind started to blow with 8 m/s from N-NE. On the 11 January, the wind speed was around 14 m/s with a direction of 67\({}^{\circ}\)-126\({}^{\circ}\). The wind speed had an average of 13 m/s in this period although the pressure continued to decrease. On 18 th at 19.00 the maximum peak of wind speed was 22.5 m/s, direction of 327\({}^{\circ}\) and pressure of 983.7 mbar. On the next day, the average wind speed was 12 m/s from the western sector. On the 20 th, 21 th, 22 th, 23 th above-normal wind speed values were recorded. On the 24 January, the cyclone was no longer present in the western sector of the Black Sea. To sum up, the cyclone started on the 9 January (8 m/s), had a maximum peak on the 18 th (22.5 m/s) and finished on 24 th (12.5 m/s).  
Following the same principle, the highest value of wind speed recorded from the entire study should be detailed. In this instance, the period is much shorter and with not so many well-defined severe weather parameters. On 8 January 2019 at 05.00, a maximum value of 25.5 m/s was recorded. Before this day, on the 5 th, the wind speed reached a value of 10.2 m/s. The next day, the wind speed average was 10.375 m/s from the north-western sector. On the 7 th, the wind had a similar direction and a speed average of 13.308 m/s. The pressure was in standard parameters. On the following day, the wind speed started to rise substantially, recording an average of 17.9 m/s. On the last day, the wind speed decreased to usual values resulting in completion of the extreme event reported.  
Analyzing the data obtained for the mean wind parameters in the present study, it can be seen that the overall wind speeds for each year are close, indicating that no significant variations will be present in the near or distant future, proving that the Romanian coastal zone can be a reliable solution for the offshore wind farms development.
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 3 Results  
### Spatio-Temporal Drought Patterns  
The drought temporal patterns are presented in Table 2, indicating the percentage of the polygon area in each biome that was affected by monthly anomalous values of \(P\), \(SM\), \(SSM\), \(ET\), and \(TWSA\). The results showed how the hydrological variables represented drought in each region, the time lag of the anomalies, in which period the anomalous situation started, and the length of time taken to return to a normal state. On the other hand, Figure 3 represents the drought spatial patterns focusing on the temporal effects of the phenomenon. Figure 3: Area affected by monthly anomalous values from \(-2\) to \(2\) for precipitation (\(P\)), surface soil moisture (\(SM\)), subsurface soil moisture (\(SSM\)), evapotranspiration (\(ET\)), and terrestrial water storage anomalies (\(TWSA\)), for the Amazon, Cerrado and Pampa. Figure 2: Methodological flowchart applied in this study. For the Amazon in 2015, the first negative anomalies (z-score \(\leq-1\)) occurred in SON for \(P\), \(SM\), and \(SSM\), considering that \(TWSA\) already showed a z-score \(\leq-1\) in a smaller percentage of the area (35%) in JJA 2015. \(P\) covered a major percentage of the area with anomalous values (\(\leq\)-1) in SON 2015 (79%), while anomalous values of \(SM\) and \(SSM\) affected the greatest percentage of the area in DJF 2015 (44% and 60%, respectively). \(TWSA\) continued with a z-score \(\leq-1\) until JJA 2016, reaching up to 88% of the area affected, and being the last variable to return to normal conditions. For this drought, no \(ET\) anomalies were observed in the MOD16 dataset. Thus, drought patterns in 2015-2016 were associated with a meteorological drought, later turning into agricultural and hydrological droughts. The 2010 event in the Amazon showed a different behavior in relation to that of 2015, with a larger area affected by anomalies in \(SM\) and \(SSM\) than by \(P\) anomalies in MAM 2010. \(SSM\) continued with negative anomalies in part of the area (32%) in JJA 2010. On the other hand, negative \(ET\) anomaly values were found in SON 2010, which were not observed in 2015. In this same period, however, there were negative \(TWSA\) anomaly values affecting a much smaller area than in 2015 (22%). The Cerrado showed a more expressive effect of drought propagation (Figure 3), while the negative anomalies followed a pattern that started in DJF 2013-2014 by affecting \(P\) (26%), followed by effects in \(SM\), \(SSM\), and \(ET\) in the following months, and finally reaching the \(TWSA\) variable. The MAM 2014 data already showed a larger part of the area (88%) affected by \(TWSA\) anomalies, while the entire area was affected by negative \(TWSA\) anomaly values (z-score \(\leq-1\)) in December 2014 to February 2015. Further, the Cerrado had a longer period (especially from 2014 to 2015) with part of the area affected by negative anomalies
## 3 Numerical Results and Discussion  
This section presents our experimental results and the corresponding discussions.  
We begin by providing a comprehensive introduction to the simulation settings, including the methodology for setting the experiment parameters and generating the experimental data. Subsequently, we present and analyze the experimental results.  
To verify the hypothesis that the proposed dual-driven MIMO detector outperforms the OAMP-Net and some other detectors, we chose the bit error rate (BER) as the evaluation metric to assess performance. The reason why BER was chosen as the evaluation metric instead of the symbol error rate (SER) is that we considered the role of demodulation; the symbol estimation error does not completely lead to the bit estimation error, while the bits truly determine the quality of the communications, as stated in [25; 26; 27; 29; 30].  
According to Equation (4), we find that the MIMO detection process is affected by channel \(\overline{\mathbf{H}}\) and noise \(\overline{n}\). Thus, we compare the BERs of the dual-driven MIMO detector and other detectors under different noise powers and channel estimation errors. To test the convergence of the proposed dual-driven detector, we compare the BERs of the dual-driven detector and the OAMP-Net with different layers with different learning rates.
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 2 Materials and Methods
### Edge Detection
#### 2.3.3 Canny Edge Detection  
To perform edge detection on the preprocessed target images, the Canny edge detection method included the following four steps: The first step was to smooth the image. We constructed a filter with a Gaussian function, performed convolution filtering on the images, removed noise, and obtained smooth images. The second step was to calculate the gradient magnitude and gradient direction of the images. The gradient magnitude and gradient direction of the smoothed images were calculated by the finite difference in the first-order partial derivatives. The third step was to perform non-maximum suppression on the gradient amplitude. To determine the edge, it was necessary to keep the point with the largest local gradient and to suppress the non-maximum value, that is, we set the non-local maximum value point to zero in order to obtain a refined edge. If the gradient value of the neighborhood center point was larger than the value of the two adjacent points along the gradient direction, the current neighborhood center point was determined as a possible edge point. Otherwise, it was assigned a value of zero, and the pixel point was judged as a non-edge point. The fourth step was double threshold detection. After applying non-maximal suppression, the remaining edge pixels provided a more accurate representation of the true edges in the images. However, some edge pixels were still caused by noise and color variations. To account for these spurious responses, edge pixels must be filtered out with weak gradient values, as well as edge pixels with high gradient values. This was achieved by choosing high and low thresholds. If the gradient value of an edge pixel was higher than the high threshold, it was marked as a strong edge pixel. If the gradient value of an edge pixel was less than the high threshold and greater than the low threshold, it was marked as a weak edge pixel. If the gradient value of an edge pixel was less than the low threshold, it was suppressed.
## 2 Rescue Process of TPM for SRP
### Predicting the Trajectories of PDTs  
For medium/distant maritime rescue, it is worthy to predict the trajectories of the PDTs first. The drift trajectory can be influenced by winds, currents, and the physical properties of the PDT. Assuming that the ocean wind speed is \(V_{\text{rad}}\), the ocean flow speed is \(V_{\text{ref}}\), and the wind speed modification factor is \(\gamma\). Then, for the maritime rescue mission, the drift speed of PDTs can be given by:  
\[V_{\text{drift}}=V_{\text{ref}}+\gamma\cdot V_{\text{rad}} \tag{1}\]  
Figure 1: The surface rescue process of amphibious aircrafts based on TPM for medium/distant maritime rescue.  
Assuming that the initial position of the PDT is \(s_{0}\) at a certain time, \(t_{0}\), the ocean wind speed and the ocean flow speed do not change in a short time, \(\Delta t\), and the position of the PDT after a short time can be predicted by:  
\[s_{drift}=s_{0}+\int_{t_{0}}^{t_{0}+\Delta t}V_{drift}dt \tag{2}\]  
Considering that the ocean wind speed and the ocean flow speed, etc., are uncertain, and that the \(\Delta t\) cannot be infinitely small, Monte Carlo experiments were used in this study which could simulate these uncertainties [41]. Additionally, in an actual rescue process, the lifeboat would have a certain range of rescue capabilities. Although the predicted location was not the accurate position, the PDTs could be rescued by the lifeboat within a little amount of time after it reached the predicted position of the PDTs. Therefore, the average predicted result of the Monte Carlo experiments was used to find the predicted trajectory of each PDT to improve the successful rescue rate. As shown in Figure 2, four examples of trajectory prediction were given.
# Potential of a SAR Small-Satellite Constellation for Rapid Monitoring of Flood Extent
## 2 Methods
### Design of SAR Small-Satellite Constellation
#### 2.1.2 Orbital Elements  
To determine the orbital elements of each satellite constituting a virtual constellation, we applied two-line element (TLE) sets of an existing SAR-equipped small satellite ICEYE-X2. The details are explained in Appendix B.
# Passive Radar-Based Parameter Estimation of Low Earth Orbit Debris Targets
## 4 Self-Mixing Signal Processing  
To obtain useful Doppler information for a particular target, an efficient signal processing scheme must be implemented along with multiple receivers in an array. Figure 4 displays a block diagram outlining the proposed processing steps.  
The received signal must first pass through a low-noise amplifier (LNA) to increase the signal power. This step is especially necessary for GPS signals that are incident on the earth's surface at \(-134\) dBm [23]. Next, the square law detector (SLD) squares any input voltage. The low-pass filter (LPF) will then remove the center frequency. The high-pass filter (HPF) or direct current (DC) blocker will remove any DC frequencies. After the analog-digital converter (ADC) passes the signal to the host PC system, the Fast-Fourier Transform (FFT) can be performed to observe any Doppler shifts. The black-outlined boxes represent analog components, the red-outlined box represents the digital component, and the blue-outlined boxes represent processes that can be implemented either through analog or digital components. Such a system may be realized by a software-defined radio (SDR) in series with an LNA and SLD. The Ettus X310 (National Instruments, Austin, TX, USA), for example, has an onboard field programmable gate array (FPGA) [39] that may be tasked with signal acquisition to identify the space vehicle number(s) and satellite-induced Doppler shift(s). Afterward, the signals are downsampled and passed to the host PC for data binning and FFT processing.  
Figure 4: FSR Signal Processing Scheme for Doppler extraction.  
Figure 3: Simulated STFT of the BCE received signal.
## 3 Proposed Architecture
### Attention Based Deep Layers  
To utilize the knowledge from the weighted word representation layer, we used three region CNN networks, given the word representation as input, and fed into a convolutional layer based on each tweet. The convolutional word vector matrix is calculated through an \(F_{n}\) weighted matrix defined as \(w\in R^{t\times m}\) frequently to capture the inherent and local features, and \(t\) is a word vector selected in the matrix of \(F_{n}\) as follows:  
\[h_{i}=f(V_{i:j+t-1}\times W[i]+b_{i}) \tag{4}\]where \(f\) is the activation function for the nonlinearity, \(W\) represents the matrix's weight, \(b\) is the bias, and \(h\in R_{n-t+1}\) is for the generation of a feature map by a \(t\) word vector. Once convolutional layers produce feature maps, the max-pooling layer applies to minimize the dimensions of the dataset and extracts the most important features as shown in the equation:  
\[p_{i}=Max[h_{i}] \tag{5}\]  
where the \(P_{i}\in R^{n-t+1/2}\) is the feature map obtained after the max-pooling layers. Max-pooling is applied to features from the different CNN layers filter to extract equally essential features, but it is not focused on semantics and polarity importance. Therefore, we devote attention to emphasize the importance of each feature on the CNN-generated features as follows:  
\[A_{i}=\frac{exp(p_{i})}{\sum_{i}exp(p_{i})} \tag{6}\]  
The above equation shows the calculation of max-pooling attention, and \(A_{i}\) is the generated attention score for each feature context \(p_{i}\)[12].  
The Bi-LSTM network is applied to the attention score's output to learn the feature context. We use Bi-LSTM to generate final features by sequentially processing the map. The last feature map is obtained from the final feature context \(p_{i}\) from CNN and \(A_{i}\) attention scores.  
We used Bidirectional _LSTM_ to consider both forward and backward features parallel and simultaneously concatenate the hidden state of two LSTMs representing each position. Equation (7) represents forward, and (8) represents backward LSTMs:  
\[\overrightarrow{h}_{t_{lstm}}=\widehat{LSTM}(c_{t-1},h_{t-1},A_{i}) \tag{7}\]  
\[\overleftarrow{h}_{t_{lstm}}=\widehat{LSTM}(c_{t-1},h_{t-1},A_{i}) \tag{8}\]  
where \(c_{t}\) and \(h_{t}\) represent the hidden states and memory cell, respectively; however, the \(h_{t-1}\) and \(c_{t-1}\) represent the previous states of _LSTM_ function, and \(A_{i}\) is the attention score as an input vector in the _LSTM_ network. We now obtain an annotation for each input vector by concatenating the forward and backward context in Equation (9):  
\[h_{t_{lstm}}=LSTM[\overrightarrow{h}_{t_{lstm}},\overleftarrow{h}_{t_{lstm}}] \tag{9}\]  
\(h_{t_{lstm}}\) is the concatenating output of forwarding (\(\overrightarrow{h}_{t_{lstm}}\)) and backward (\(\overleftarrow{h}_{t_{lstm}}\)) extracts a long dependencies feature. The extracted feature of the entire sentence is \([\overleftarrow{h}_{t_{lstm}},\overleftarrow{h}_{t_{lstm}}]\). In this way, the forward and backward contexts can be considered simultaneously.