esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Evaluation
### Parameter Calibration  
Testbed experiments were conducted to find \(LQI_{th}\) and \(E_{th}\), since they can be used to evaluate the link quality in real-world deployments in the presence of interference and moving people, as expected for IoT scenarios. Additionally, in testbed experiments, the energy consumption can be calculated with real hardware used by many networks.  
The experiments were carried out in the 2 nd floor of the Computer Engineering Department at the Federal University of Para. The experiments consisted of 10 SunSPOT motes [31] in a grid topology of 3 \(\times\) 3 and the BS on the edge, as depicted in Figure 2. The proposed grid topology is able to provide a minimum number of 5 neighboring nodes for each node.  
The experiments were carried out in an indoor environment, as shown in Figure 3. In this scenario, the nodes were deployed on the roof of the rooms and in the corridor. The experiments were conducted in a real world environment in the presence of continuous movement of people and other wireless network devices, which caused noise and interference.  
As mentioned earlier, most of the IoT applications require reliable communications systems to allow the end-user to analyze the accurate collected data by the sensor nodes. We assume that most of the IoT applications need a PDR that is higher than 80% as QoS requirements (this value can be easily changed for different applications). In this context, experiments were conducted to find the LQI threshold for this purpose. Based on the results shown in Figure 4, we selected \(LQI_{th}\) = 220.  
Figure 3: Testbed scenario on Computer Engineering Department  
Figure 2: Testbed topology and connectivity map.  
Experiments were also conducted to find the _E\({}_{th}\)_ in the real hardware since it is possible to analyze energy consumption in a real device. Figure 5 shows five possible values for the energy threshold varying from 2 to 10. Values of _E\({}_{th}\)_ higher than 6 reduce the network lifetime. This occurs because of the low frequency of the RADV notifications, which provides an unbalanced consumption of network resources, degrades the performance of the load balancing mechanism, and the use of multiple routes. As result, nodes located at potential energy holes take a longer time to be detected. On the other hand, _E\({}_{th}\)_ = 2 has the highest lifetime of all, exceeding the value 4 by 2 min.  
Figure 4: Testbed experiment to find _LQI\({}_{th}\)_.  
Figure 5: Testbed experiment to find _E\({}_{th}\)_.
## 2 Materials and Methods
### Test Bench and Engine  
The test bench hosts the engine, together with the following facilities:  
* SCHENK DYNA3 asynchronous dynamometer, which imposed the brake of the engine by simulating the corresponding resistance force along with the real driving cycles. The resistance force applied by the brake is considered aerodynamic, friction, and inertia forces that would be exerted on the vehicle.
* Type K class B thermocouples.
* Kistler 4045A5 piezoresistive pressure sensors.
* Horiba MEXA-7100 gas analyzer, which measured volumetric concentrations of O\({}_{2}\), THC, NOx, CO\({}_{2}\), and CO at the exhaust tailpipe. CO\({}_{2}\) concentration was also registered at the intake manifold, downstream of the EGR junction to estimate the EGR rate.
* AVL 439 pocameter, to obtain the particulate mass, the opacity has been measured, and later it was traduced to soot density [30] through Equation (1). \[\rho_{\text{soot}}\Big{(}\frac{\text{mg}}{\text{m}^{3}}\Big{)}=7.5353+235.23\; \text{[k]}-176.45\;\text{[k]}^{2}\] (1) In addition, k is calculated with Equation (2): \[\text{k}=\frac{-\ln\Big{(}1-\frac{\text{N}}{100}\Big{)}}{\text{L}}\] (2) where: \[\bullet\quad\text{L}=\text{measuring length}.\]
* N = opacity (%)  
After calculating the soot density, it is possible to obtain the PM mass flow, through the exhaust mass flow.  
A 2.18 L displacement, 126 kW Euro 6 turbocharged four-stroke diesel engine of four cylinders has been used to perform all the tests. The engine was equipped with a high-pressure exhaust gas recirculation system which also has an intercooler before the engine intake. To control the intake temperature, a water charge air cooler (WCAC) is submerged in a coolant tank and a PID controller regulates the coolant flow to keep the required air intake temperature.  
The aftertreatment system was composed of a diesel oxidation catalyst (DOC) responsible for oxidizing THC and CO, and a diesel particulate filter (DPF) to minimize the PM emissions. Considering that the DPF requires periodic regenerations, a specific cleaning procedure was performed between two consecutive cycles. This strategy complemented the passive oxidation promoted by cerium dioxide added to the fuel.  
Finally, a selective catalytic reduction (SCR) to reduce NOx emissions was integrated into the exhaust line. Nevertheless, the AdBlue injection was deactivated in all tests. Therefore, although the gas analyzer was placed downstream of the aftertreatment system, raw NOx emissions were monitored.  
The engine layout and pressure, temperature, and polluting sensors installed in it are shown in Figure 1 The Acquisition frequency was always 1 Hz (as RDE regulation imposes) for all the pollutant emissions, dynamic variables, temperatures, pressures, and mass flows.
# Green Shipping--Multifunctional Marine Scrubbers for Emission Control: Silencing Effect
## 3 Results and Discussion
### Simple Expansion Chamber
#### 3.1.1 Experimental Transmission Loss Assessment  
As previously highlighted, different parameters influence the measurement setup, so the evaluation of its accuracy becomes mandatory.  
Figure 10 compares the analytical (calculated with Equation (5)) and experimental TL values as a function of frequency for the simple expansion chamber (Figure 6a).  
Table 3 compares the frequencies at which the minima (\(f_{min}\)) and the maxima (\(f_{max}\)) of the TL occur, as calculated with Equations (6) and (7), respectively. Delta represents the percentage difference between the analytical and experimental data.  
Table 4 compares the amplitudes of the experimental and analytical TL. Delta represents the difference between the analytical and experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Analytical** & **Experimental** & **Delta** & **Analytical** & **Experimental** & **Delta** \\ \(n\) & \(f_{min}\) & \(f_{min}\) & \(f_{min}\) & \(f_{max}\) & \(f_{max}\) & \(f_{max}\) \\  & **(Hz)** & **(Hz)** & **(\%)** & **(Hz)** & **(Hz)** & **(\%)** \\ \hline
0 & 0 & 0 & 0.0 & 170 & 220 & +29.4 \\
1 & 340 & 348 & +2.4 & 510 & 508 & \(-\)0.4 \\
2 & 680 & 685 & +0.7 & 850 & 919 & +8.1 \\
3 & 1020 & 1050 & +2.9 & 1190 & 1226 & +3.0 \\
4 & 1360 & 1382 & +1.6 & 1530 & 1520 & \(-\)0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between analytical and experimental \(f_{min}\) and \(f_{max}\) of TL for the simple expansion chamber.  
Figure 10: Comparison of analytical and experimental TL curves for the expansion chamber.  
A perusal of the data reported in Tables 3 and 4 reveals differences of less than 3% between analytical and experimental \(f_{min}\) and differences of less than 5 dB between the analytical and experimental amplitudes of TL; accordingly, the above reported fitting criterion [34] is satisfied.  
A somewhat worse fit is observed for the \(f_{max}\), which can be attributed to the irregular features of the TL peaks shown in Figure 10, which are reasonably caused by the discontinuity introduced along the pipe by the bushing (Figure 8) used to connect the expansion chamber and the impedance tube, as described in Section 2.2. The irregular feature of the experimental TL will be studied in the next section using the assessed FEM model.  
However, considering the fundamental importance of the \(f_{min}\) being measured correctly, as previously explained, the adopted experimental setup can be recognized as sufficiently accurate in the selected frequency range from an engineering point of view.
# An Adaptive Model Predictive Voltage Control for LC-Filtered Voltage Source Inverters
###### Abstract  
The three-phase inductor and capacitor filter (LC)-filtered voltage source inverter (VSI) is subjected to uncertain and time-variant parameters and disturbances, e.g., due to aging, thermal effects, and load changes. These uncertainties and disturbances have a considerable impact on the performance of a VSI's control system. It can degrade system performance or even cause system instability. Therefore, considering the effects of all system uncertainties and disturbances in the control system design is necessary. In this respect and to tackle this issue, this paper proposes an adaptive model predictive control (MPC), which consists of three main parts: an MPC, an augmented state-space model, and an adaptive observer. The augmented state-space model considers all system uncertainties and disturbances and lumps them into two disturbance inputs. The proposed adaptive observer determines the lumped disturbance functions, enabling the control system to keep the nominal system performance under different load conditions and parameters uncertainty. Moreover, it provides load-current-sensorless operation of MPC, which reduces the size and cost, and simultaneously improves the system reliability. Finally, MPC selects the proper converter voltage vector that minimizes the tracking errors based on the augmented model and outputs of the adaptive observer. Simulations and experiments on a 5 kW VSI examine the performance of the proposed adaptive MPC under different load conditions and parameter uncertainties and compare them with the conventional MPC.  
voltage source inverter (VSI); model predictive control (MPC); adaptive control; adaptive observer; sensorless operation; augmented state-space model; microgrid (MG) +
Footnote †: journal: Applied Sciences
## 5 Building ZTD Height Change Grid Model Based on ERA5
### Model Accuracy Verification  
To verify the experimental results, the fitted ZTD deviation and root mean square error are calculated. In this case, the ZTD is obtained using the integration of the 2021 ERA5 barometric stratified data set as the reference true value, and the least-squares exponent is used to fit the ZTD with an accuracy of mm level. The average deviation on the 37-layer isobaric surface for all grid points is 6 mm, and the average root mean square error is 8.5 mm. The root mean square error is shown in Figure 7.  
It can be seen from Figure 7 that the least squares index fitting ZTD has high accuracy in China, with the root mean square error within 12 mm. The fitting accuracy is related to the latitude. The higher the latitude, the higher the fitting accuracy. According to the data, the air in the low latitude area contains more water vapor, and the climate is relatively humid, so it is difficult to accurately estimate ZWD.
## 2 Materials and Methods
### Sensor Node Components
The ADXL344 was selected as the low-power accelerometer because of its low-cost, low-power consumption, and wide bandwidth. The accelerometers of both sensor nodes were glued on the pipe surface using hot glue, and wires were used to connect them to the ESP32 MCU. Table 1 presents a comparison between the ADXL344 and LSM9 DS1 accelerometers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Accelerometer Resolution**} & \multirow{2}{*}{\begin{tabular}{c} **Bandwidth** \\ **(Hz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Sensitivity** \\ **(LSB/g)** \\ \end{tabular} } & \multirow{2}{*}{**Sensing Range**} & \multirow{2}{*}{\
\begin{tabular}{c} **Noise Floor** \\ **Level** \\ **(\(\upmu\)g/Hz)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Current** \\ **Consumption** \\ **(\(\upmu\)A)** \\ \end{tabular} } \\ \hline ADXL344 & 13 & 0–1600 & 4096 & \(\pm\)2 g, \(\pm\)4 g, \(\pm\)8 g, \(\pm\)16 g & 530 & 23 \\ \hline LSM9 DS1 & 16 & 0–400 & 32,768 & \(\pm\)2 g, \(\pm\)4 g, \(\pm\)8 g, \(\pm\)16 g & N.A. & 600 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of ADXL344 and LSM9 DS1 accelerometers. Figure 2: Setup model depicting hardware interfacing of the sensor node’s components and power measurement device.
# Emulsions Stabilized with an Electrostatic Complex of
## 3 Results and Discussion
### Measurement of Air/Water Interface Tension  
Figure 3 shows the air/water interfacial tensions at different concentrations of QCNF, Gel, HC\(-\)Gel, and OC\(-\)Gel solution. The interfacial tension of the buffer solution (i.e., PBS (10 mM, pH 7.4)) was around 71.8 dyne/cm, almost the same as that of water, 72 dyne/cm. The interfacial tension hardly changed when the concentration of QCNF increased up to 1 mg/mL, suggesting that QCNF was not interface\(-\)active. QCNF is a bundle of CNF and it has trimethyl ammonium groups on its surface. Accordingly, the fiber is  
Figure 2: \({}^{1}\)H NMR spectrum of Gel (**a**), HC\(-\)Gel (**b**), and OC\(-\)Gel (**c**).  
polar, and it can be totally wetted with water; thus, it would hardly have an amphiphilic property. The Gel solution decreased to 64.1 dyne/cm in its interfacial tension when the concentration increased to 1 mg/mL. The decrease rate was rapid in the early stage and slow in the later stage. The saturated decrease with increasing concentration is typical of a surface\(-\)active agent solution. Gel is known to be surface\(-\)active and used as an emulsifier for oil\(-\)in\(-\)water emulsions [29; 30; 31]. The HC\(-\)Gel solution decreased to 64.3 dyne/cm when the concentration increased to 1 mg/mL, and its interfacial tension also decreased in a saturated manner. There was no significant difference in the interfacial tension between the Gel solution and the HC\(-\)Gel solution in the full range of concentrations tested. The hexanoyl group can provide a lipophilic property to gelatin, but it seemed to too short in its hydrocarbon chain to increase the interfacial activity of gelatin. The interfacial tension profile of the OC\(-\)Gel solution resembled those of the Gel solution and the HC\(-\)Gel solution. However, the interfacial tension of the former solution was significantly lower than those of the latter solutions. For example, the minimum interfacial tension of the OC\(-\)Gel solution was 57.5 dyne/cm, much lower than those of the Gel solution and the HC\(-\)Gel solution (i.e., 64.1 and 64.3 dyne/cm, respectively). The octyl chain of the octanoyl group is a lipophilic hydrocarbon chain, and it would be able to increase the amphiphilicity of gelatin, accounting for why OC\(-\)Gel was more interface\(-\)active than Gel and HC\(-\)Gel.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 3 Results and Discussions
### Hybrid Instrument Simulator
#### 3.3.2 Hybridization Signals on the Ground  
In order to validate the hybridization algorithm implemented in the simulator, the results obtained in terms of sensitivity with the on-ground lab prototype (see Figure 17) are reproduced with the simulation and reported in Figure 22. In this on-ground experiment, for each measurement cycle, the electrostatic accelerometer was used to determine the interferometric fringe index, and the electrostatic accelerometer bias was evaluated thanks to the atomic interferometer. The results show clearly, in the hybrid instrument signal (red curve), the correction of the electrostatic accelerometer drift (black curve) using the long-term stability of the atomic interferometer (blue curve), while the electrostatic accelerometer, with a sampling rate of 1 kHz, allows the exploration of the high-frequency region inaccessible to the atomic instrument because of its cycling time.
# Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar +
## 4 Results
### Experimental Results
#### 4.2.4 Analysis of Evaluation Metrics
On the other hand, both the training and test sets in the dataset contain far more background than targets, making it more difficult for the detector to detect targets and more prone to generating false alarms, thus allowing more false alarms to be suppressed. The acquisition of real sonar data is often challenging, and in most cases, the sonar datasets that researchers can collect are almost exclusively similar to datasets like SAS450; they are small in scale, and the background in the sonar images occupies significantly more space than the targets. This is a common issue in the field of underwater target detection. Therefore, the improvement of detection performance using this method on such small-scale datasets holds great reference significance for many researchers who do not have the capability to obtain higher-quality datasets. Moreover, if we simply compare the average false alarm rates \(\overline{P_{fa}}\) of the three detectors over the confidence interval [0.1,0.8], Detector C only reduces the false alarm rate by 3.91% and 6.73% compared to Detector A and Detector B, respectively, and Detector F only reduces the false alarm rate by 18.50% and 3.07% compared to Detector D and Detector H, respectively. This indicates that the training strategy proposed in this paper not only achieves a reduction in false alarm rate, but also improves it while considering the target recall rate. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detector** & **F1** & **P/fa** & **\(\overline{P_{fa}}\)** & **T** \\ \hline Detector A & 0.80 & 0.592 & 0.458 & 0.489 \\ Detector B & 0.83 & 0.460 & 0.471 & 0.278 \\ Detector C & 0.86 & 0.434 & 0.440 & 0.473 \\ Detector D & 0.44 & 0.900 & 0.892 & 0.149 \\ Detector E & 0.37 & – & – & 0.032 \\ Detector F & 0.52 & 0.727 & 0.727 & 0.248 \\ Detector G & 0.34 & 0.733 & 0.623 & 0.124 \\ Detector H & 0.71 & 0.750 & 0.775 & 0.529 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation metrics comparison for Detectors. It is worth noting that Detectors G and H, which were developed by transferring features from another set of sonar data, showed a 2.27% reduction in \(P_{f_{af}}\) for Detector G compared to Detector H, but a 19.61% reduction in \(\overline{P_{fa}}\). Moreover, when comparing Detector G to Detector F, although there was a 14.31% reduction in \(\overline{P_{fa}}\), the \(P_{f_{af}}\) rate increased by 0.825%. This suggests that although training with higher-quality sonar datasets using the DCD method can achieve a reduction in false alarm rate, it is not achieved while balancing the target recall rate.
## 3 Experiments and Results
### YOLO--You Only Look Once  
One of the possibilities of computer vision is the ability to detect objects, which consists of locating a particular object, a region of interest, in a larger image. This task is a more advanced problem than classification and is more comprehensive because it also involves classification, adding the possibility of locating the classified object in the image. YOLO, short for \"You Only Look Once\", uses a neural network for real-time detection due to its enormous speed and accuracy in detection. Ref. [14] created this network architecture in 2015. YOLO has evolved through several versions and is currently on version 8.  
The YOLO proposal is an end-to-end connected network for making bounding box predictions with their probabilities for classes simultaneously, differing from previous algorithms that reused classifiers for detection. This algorithm uses a single, fully connected layer for predictions. In contrast, others, such as Faster RCNN, detect possible regions of interest using networks of proposed regions followed by recognition of these regions separately. YOLO uses a single interaction to propose the regions of interest, while other methods perform several interactions for the same image.  
The algorithm uses an image as input and then a simple CNN to detect objects in the image. Figure 2[14] shows the architecture of the CNN model used by YOLO.  
A transfer learning technique is used to abbreviate learning and is found in the initial twenty layers of the network. This initial learning is performed with ImageNet, in a cluster of fully connected layers. Performance is improved by applying convolution. Finally,the network uses a fully connected layer to determine the location probabilities of the bounding boxes.  
Initially, the image is divided into an S \(\times\) S grid. The cell located in the object's center will detect the object. Each cell will predict the bounding box and confidence scores, which define the model's confidence that the bounding box contains the object and with what accuracy. YOLO uses the concept of intersection over union (IoU) to define the most representative bounding box.  
YOLO uses non-maximum suppression (NMS). This step is applied in post-processing to improve accuracy and efficiency in detecting the target object. Since several boxes can be defined for the same object, they overlap with small changes in their location and size. This step then eliminates incorrect or redundant boxes for each object.
# Deep Learning-Based Step Size Determination for Hill Climbing Metaheuristics
## 3 Methodology  
### Machine Learning-Assisted Hill Climbing Algorithm  
The novel method proposed is based on the assumption that domain-specific knowledge about the search space can help the HC method (especially the determination of the \(\epsilon\) distance of each iteration). Algorithm 2 presents the idea of the Machine Learning-assisted Hill Climbing method. The algorithm has the following modifications:  
* The additional \(NN\) parameter presents a neural network trained on the specific \(S\) search space. Its training is discussed in Section 3.4.
* The additional \(L\) queue contains the domain-specific information gathered by the current run of the HC algorithm.  
* Line 7 appears as the prediction of the neural network is used to determine the next \(\epsilon\) value. The prediction is based on the already existing information stored by \(L\).
* This variant assumes a simple Feedforward Neural Network (FNN) with a fixed sized input (\(NN_{input\_size}\)). The detailed architecture and the training of the FNN are described in Sections 3.2-3.4. The neural network is not able to predict before the necessary information has been collected; therefore, the algorithm uses the same steps as the original HC for the first few iterations.
* At the end of each iteration, the current fitness value and \(\epsilon\) values are stored in \(L\) (line 10). It is enough to store only the information of the last few iterations, based on the input size (line 8).  
```
1:functionHillClimbingSteepestAscent(\(S,f,\epsilon,NN\))
2:\(L\leftarrow\emptyset\)
3:\(p\longleftarrow S\)
4:\(stuck\leftarrow\textit{false}\)
5:while\(\leftarrow\)stuck do
6:if\(L.Length=NN_{input\_size}\)then
7:\(\epsilon\gets NN(L)\)
8:\(L.Dequeue()\)
9:endif
10:\(q\leftarrow\)argmin(s) \(\{x\in S\mid|x-p|=\epsilon\}\)
11:if\(f(q)<f(p)\)then
12:\(p\gets q\)
13:\(L.Enqueue(\left[\,f(p),\epsilon\,\right])\)
14:else
15:\(stuck\leftarrow\textit{false}\)
16:endif
17:endwhile
18:return\(p\)
19:endfunction
```  
**Algorithm 2** Machine Learning assisted Hill Climbing Steepest Ascent algorithm  
The presented algorithm uses a fixed step size. However, it is obviously possible for any of the already known methods to be used to handle the first few iterations (for example, the BF method). It is also possible to design an alternating version of the algorithm, making some steps based on the BF method, after following some steps based on the prediction of the FNN, and switching back to the BF method for a while.