esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, a near real-time infrared bird target detection method is proposed. Firstly, Temporal Variation Filtering (TVF) is performed to the continuous frame images to  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**TVF**} & **GHPNet without** & **GHPNet with** & & & \\  & & **Maximum-** & **Maximum-** & **P (\%)** & **R (\%)** & **F1 (\%)** \\  & & **No-Pooling** & **No-Pooling** & & & \\ \hline \multirow{3}{*}{Bird} & ✓ & ✗ & ✗ & 78.0 & 72.7 & 75.3 \\  & ✓ & ✓ & ✗ & 90.2 & 89.0 & 89.6 \\  & ✓ & ✗ & ✓ & 91.2 & 89.8 & 90.5 \\ \hline \multirow{3}{*}{TIV} & ✓ & ✗ & ✗ & 77.6 & 75.4 & 76.5 \\  & ✓ & ✓ & ✗ & 86.4 & 82.5 & 84.4 \\  & ✓ & ✗ & ✓ & 88.3 & 87.4 & 87.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation Experiment.  
Figure 10: The target detection results in a complex background, (**a**) on our dataset, (**b**) on TIV dataset.  
extract the moving targets in the scene. Secondly, a Gaussian heatmap perception network (GHPNet) is proposed to predict the pixel-wise Gaussian heat map of the candidate region. Finally, the watershed algorithm is used to segment each target from the predicted heatmap. Experimental results show that the proposed method achieves the best infrared target detection performance on both sparse and dense cases.  
Conceptualization, F.Z.; methodology, F.Z., R.W. and S.S.; software, R.W., Y.C. and S.S.; validation, S.S.; formal analysis, S.S. and Y.C.; writing--original draft preparation, F.Z.; writing--review and editing, C.J. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (NSFC), grant number 52075435, and by the Key R&D Project of Shaanxi Province, China, grant number 2022 GY-305.  
Not applicable.  
The dataset TIV in this paper can be obtained from the task of Multi-view Multi-Object Tracking in the link: [[http://csr.bu.edu/BU-TIV/BUITV.html](http://csr.bu.edu/BU-TIV/BUITV.html)]([http://csr.bu.edu/BU-TIV/BUITV.html](http://csr.bu.edu/BU-TIV/BUITV.html)), accessed on 25 September 2014.  
The authors declare no conflict of interest.
# Link Quality Estimation for Wireless ANDON Towers Based on Deep Learning Models
## 3 Materials and Methods
### Estimation Model  
The multilayer perceptron is one of the most established deep learning models for non-linear classification and regression. These architectures are frequently used in the industry to model systems and predict phenomena due to their ease of implementation with relatively low computational requirements for training compared to other more complex methods [41]. In addition, an MLP is a suitable option for small workshops in developing countries such as Mexico, where computational resources are limited and commonly older than 10 years. The MLP estimation model proposed in the present paper has three hidden layers, as shown in Figure 7, with the ReLU as the activation function to avoid the vanishing gradient problem. The inputs to our MLP model were the transmission rate, the transmitter power, the distance from the transmitter to the receiver, the payload, and the bit error rate.  
Figure 6: Dataset structure from CSV file.  
Figure 7: The MLP model structure.  
In order to select the most suitable learning rate, we realized two random searches. The first search was performed in a broad range of 3 \(\times\) 10\({}^{-8}\)\(\leq\) LR \(\leq\) 3 \(\times\) 10\({}^{-2}\) for 5000 epochs, as shown in Figure 8, where we found the best LR = 1.65 \(\times\) 10\({}^{-5}\); then, we repeated the process within a finer range of 1.55 \(\times\) 10\({}^{-5}\)\(\leq\) LR \(\leq\) 3 \(\times\) 10\({}^{-5}\); this time, the model was trained for 10,000 epochs (Figure 9). In the first search, we used the confusion matrix to evaluate the model (Figure 8), and in the second search, we used the metric of accuracy against epochs (Figure 10). The accuracy metric was calculated by (3) where TP indicates true positives and TN indicates true negatives.  
\[Accuracy=\frac{TP+TN}{Total} \tag{3}\]  
Figure 8: Confusion matrix for first search learning rate tuning.
## 2 Materials and Methods
### Evaluation Metrics  
In the context of multiorgan segmentation, five commonly used metrics were employed to assess the segmentation performance. The chosen metrics included DSC, IoU, HD, and two metrics comparing the volumes obtained with the ground truth and with the automatic segmentation: the mean of the differences using a Bland-Altman study and the correlation coefficient.  
DSC is a measure of the similarity between two sets. IoU is another measure of overlap between automatically segmented regions and reference regions. HD is a metric that quantifies the maximum distance between points in one set to the nearest point in the other set. It provides a measure of the dissimilarity between two sets of points.  
The Bland-Altman method used for the evaluation of the organ volume calculation is a statistical technique used to assess the agreement between two sets of measurements obtained with the predicted and ground truth segmentations. The coefficient of correlation is a metric that quantifies the correlation between two measures--in our case, the volumes of the segmented regions and the reference regions. It provides a measure of how well the volumes are preserved in the segmentation process.  
Higher DSC and IoU values indicate better results, while lower HD values also suggest better performance. The Bland-Altman method and the correlation coefficient provide insights into the agreement between the volumes of the segmented regions and the reference regions in multiorgan segmentation. All these metrics are computed in 3D, where the ground truths and masks are combined for each patient to create a 3D image. This approach provides a genuine assessment of the error between the masks and the ground truths. The metrics presented in Section 3 are all calculated in this manner to facilitate a comprehensive evaluation of the entire organ segmentation process.
# Probabilistic Resilience Analysis of the Icelandic Power System under Extreme Weather
## 3 Methodology  
### Overview  
Through the development of the extreme weather event, the progression of the strong wind front poses the unique challenge of simultaneously making electricity indispensable to the end-users and causing the outage of several transmission components, while also limiting the capacity to dispatch maintenance crews and perform restorative operations. Our methodology seeks to evaluate the system resilience in anticipation of such a challenge.  
We use historical weather-dependent failure rates along with forecasted wind speed data to model the anticipated threat. We evaluate, through time, the probability that any transmission component would survive up to \(t\) hours from the start of the storm, as well as the probability that it may fail within the next interval \(t+dt\). The detailed derivation of such weather-dependent survival and failure probabilities is presented in the following Section 3.2. Studying the evolution of said probabilities over time, we identify the moment of peak threat as the moment wherein the probability that the system remains still intact is extremely low, while the probability of realizing any further new outage is maximized (Notice here that we avoid modelling repairs. In the absence of credible repair rates under the extreme anticipated conditions, we take the conservative assumption that these operations are completed after the storm. Performing repairs is extremely difficult at times of a severe storm when several components may have failed, locations may be harder to reach, as well as on-site crew health and safety may be jeopardized).  
A resilient power system should be able to contain further degradation of electricity supply, irrespective of the component failures it has already sustained. In order to assess for this property, we compare the ability of the system to withstand the worst possible additional outage at three characteristic phases, and specifically: (i) a moment prior to the start the storm, (ii) the aforementioned moment of peak threat, and (iii) a moment at the end of the storm. For each one of these phases, we perform a Monte Carlo simulation, wherein the network state is randomly drawn simulating the uncertainty in the survival of transmission components. For each sampled state, we assess the amount of load that would have to be shed upon occurence of an additional worst-case outage by solving a bi-level attacker-defender model, presented in detail in the following Section 3.3. The resulting expected worst-case load shed (i.e., expected ability to withstand any additional failure) through the three phases of the storm is our probabilistic metric for assessing the system resilience.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 6 Conclusions  
We have proposed a decision-control approach with the event-triggered communication scheme for the problem of signal source localization. This proposed decision-control approach includes two levels. In the decision level, we have designed a particle filter approach, which is used to estimate the position of signal source. The designed particle filter can guide the movement of robots well under a search environment with big noises. At the control level, we have proposed a cooperative control approach with an event-triggered communication scheme. The proposed event-triggered communication scheme can save communication resources and lighten the communication burden. The simulation and experimental results have illustrated the effectiveness of the proposed decision-control approach.  
[PERSON] performed the simulations and experiments. [PERSON] proposed the methods, analyzed the data and wrote the paper. [PERSON] reviewed the paper and took on the task of project management. [PERSON] revised the paper and took on the task of project supervision.  
This work was supported in part by the Zhejiang Provincial Natural Science Foundation of China under Grant LY18F030008 and the National Natural Science Foundation of China under Grants 61503108 and 61375104.  
The authors declare no conflict of interest.
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 2 Material and Methods
### Weather Station
#### 2.1.1 Central Unit, with an Air Pressure Sensor and Solar Panels  
All the sensors, except the absolute air pressure sensor and integrated solar panels, are connected to a central unit (Figure 2a). The central unit processes all the signals received over the wire, stores them, and sends them to the cloud via the network at 10 min intervals. The display shows basic information on the strength of the network signal, from the battery and various statuses to the operation of the modules and individual alarms. In the event of a network outage, the control panel stores the data in its memory, and only sends them to the cloud when the network is back up and the connection is working smoothly. The central unit also has the option of lightning protection by connection to a copper wire.  
The absolute air pressure sensor is embedded into the central unit. The absolute air pressure represents the actual air pressure in a given space and time. From it, the relative air pressure can be obtained, which is calculated, among other things, by using the altitude data. The accuracy of the sensor is illustrated in Section 2.1.3.  
The device has an integrated 1.7 watt solar panel with lithium batteries (Figure 2b). The device operates flexibly in the rural areas, still sending data with a weak cellular network signal. In case of problems connecting to the cloud at a particular time, it stores  
Figure 1: Location and the architecture of the meteorological station. (**a**) Satellite image of the meteorological station (source: Google Maps). (**b**) Meteorological station with illustrations of the employed sensors.  
Figure 2: Components 1/2.  
the data, and sends them all when the connection can be re-established. The battery life, without solar recharging, with hourly connections and 1 min logging, can last several months. The battery's life extends from 3 to 5 years under temperature conditions from \(-20\)\({}^{\circ}\)C to 40 \({}^{\circ}\)C. It is possible to use connection intervals as frequent as up to 10 min to send data to the cloud. This frequency is crucial for draining the battery and recharging it via solar panels, as in the winter, because of the fog, it is often the case that we need to set a less frequent connection interval between the device and cloud to ensure healthy operating conditions.
## 3 Results  
### Enhanced Removal of VOC  
As reported in our previous work [8], and as shown in Figure 6a, the application of an external electric field to an ACC enhances the MEK removal. In a test with the electric field, after 90 min, the applied potential was switched off, and the rise of the MEK concentration was noticed (Figure 6b). Similar behavior was found for the water vapor concentration.  
Fitted values of the Equation (7), relatively to the batch adsorption tests, are reported in Table 5.  
It is possible to observe that, with increasing the applied potential, it is not possible to clearly appreciate the difference of the amount of MEK adsorbed at the equilibrium (\(q_{e}\)), as well as the change of the partition coefficient (PC) because of the entity of the increase is of a few unit percent, in the same order of the experimental variability. However, this difference is appreciable when the electric field is switched off, as reported in Figure 6b, and it corresponds to an enhancement of 3.3% for the PC value, which implies an enhancement in adsorption affinity and adsorption ability [45]. Jointly with a slight increase of the adsorption capacity, it is possible to appreciate a tremendous increase  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Applied Potential** & \(\mathbf{C_{0}}\)** **MEK** & \(K\) & \(q_{e}\) & \(\mathbf{R^{2}}\) & **PC *** \\ \hline kV & mg m\({}^{-3}\) & min\({}^{-1}\) & mg m\({}^{-2}\) & & mol m\({}^{-2}\) Pa\({}^{-1}\) \\ \hline
0 & 710 & 0.11 & 1579 & 0.9997 & 0.097 \\
0 & 2150 & 0.17 & 4673 & 0.9956 & 0.095 \\
0 & 7100 & 0.09 & 15820 & 0.9966 & 0.097 \\
3 & 710 & 0.18 & 1544 & 0.9833 & 0.096 \\
3 & 2150 & 0.11 & 4638 & 0.9933 & 0.095 \\
3 & 7100 & 0.15 & 15,330 & 0.9947 & 0.094 \\
7 & 710 & 0.20 & 1568 & 0.9997 & 0.096 \\
7 & 2150 & 0.16 & 4711 & 0.9976 & 0.096 \\
7 & 7100 & 0.19 & 15,720 & 0.9941 & 0.096 \\ \hline \hline \end{tabular}  
* PC is referred to the commercially-available activated carbon cloth tested, normalized by the surface. To express the value normalized on mass, it is possible to divide partition coefficient (PC) by the ACC specific weight, reported in Table 4.  
\end{table}
Table 5: Results of the experimental batch adsorption kinetic data fitting.  
Figure 6: (a) Methyl Ethyl Ketone concentration in the absence and presence of the electric field. (b) MEK concentration in the test where the electric field was first switched on and then off.  
of the mass transfer coefficient (_K_), doubling for the sample under the electric field. The amounts of MEK adsorbate at the equilibrium, normalized on weight, are comparable with the one found in the literature [49; 50] if it is considered that the activated carbon amount in the present composite is approximately 44%.
# Comprehensive Review of RF MEMS Switches in Satellite Communications
## 2 Advanced Technologies and Mechanisms in RF MEMS Switches
### Contact Mechanisms
#### 2.2.1 Capacitive RF MEMS Switches  
Capacitive RF MEMS switches operate based on capacitive coupling, where the switching of states is achieved by moving the electrical part into or out of contact with the transmission line. Compared to ohmic switches, they are typically used in higher frequency scenarios [48; 49] because they have better isolation and lower insertion loss at higher frequencies [50], such as the millimeter wave band (30-300 GHz). In high-frequency applications, the capacitive coupling characteristics of these switches enable them to effectively transmit high-frequency signals while maintaining high isolation and low insertion loss. Additionally, these switches are easier to miniaturize and lightweight, meeting the strict space and weight requirements of high-frequency circuits.  
Challenges commonly faced by capacitive MEMS switches include dielectric stiction/breakdown and mechanical impact caused by high actuation voltages, hindering their commercialization. The actuation and capacitive areas of RF MEMS switches can be designed separately [51], as shown in Figure 2a. Such switches can be integrated into RF systems without additional circuits to isolate the DC voltage, cleverly avoiding damage to the system from high actuation voltages.  
Flexible RF MEMS switches, with advantages such as conformability, low cost, and impact resistance [52; 53; 54; 55], ensure high-performance, high-integration, and lightweight devices. They are suitable for satellite/aerial radar or IoT communication systems, especially in satellite communication applications where large curvature bending or scalable conformal integration is required. Flexible RF MEMS switches are basic structural units in flexible RF MEMS switch matrices [13; 56] and reconfigurable MEMS antennas [57], such as island bridges connecting MEMS units. In the system, each individual switch acts as a node within the island bridge structure [58] and needs to adapt to a certain degree of curvature. [PERSON] et al. [8] designed a flexible RF MEMS switch, as shown in Figure 2b, achieving a certain degree of bending radius through miniaturized electronic components. When the curvature varied from 0 mm\({}^{-1}\) to 0.10 mm\({}^{-1}\), the RF characteristics of this switch were insensitive to bending deformation. However, when the switch is in the On state and the curvature exceeds 0.10 mm\({}^{-1}\), its performance rapidly deteriorates. Currently, it has not been confirmed that flexible RF MEMS can maintain stable RF performance under conditions of significant bending [59; 60]. This is mainly due to the difficulty in reconciling the manufacturing processes of MEMS with high performance [61; 62; 63].
## 2 Laser-Generated Ultrasound
### Design and Structures
#### 2.2.1 Fiber-Optic Transmitters
It may find attractive applications in all-fiber and embedded ultrasound systems for imaging and monitoring. For example, the corrosion detection is demonstrated using fiber-optic LGUS transmitters [64]. Through the characterization of the samples with different corrosion rates, effective identification of the corrosion level is achieved. The miniature device can be embedded into a structure for applications to structural health monitoring. Besides nondestructive evaluation, minimally invasive endoscopic ultrasound imaging is another promising application [65; 66].
# Unrepeated 240-km 64-QAM Transmission Using Distributed Raman Amplification over SMF Fiber +
###### Abstract  
We present a theoretical and experimental investigation of unrepeated transmission over standard single-mode fiber (SMF-28) using several schemes of distributed Raman amplification, including first, second, and dual order. In order to further extend the transmission distance, we utilize advanced bidirectional higher-order ultra-long Raman fiber laser-based amplification, where we use fiber Bragg gratings (FBGs) to reflect Stokes-shifted light from the secondary pumps. Our work demonstrates the possibility of transmission up to 240-km span length with a total span loss of 52.7 dB. Here, we use a 28-Gbaud signal using a 64-quadrature amplitude modulation (QAM) modulation format. Our results highlight the contribution of nonlinear compensation using digital back propagation in a digital signal processor (DSP) code at the receiver.  
distributed Raman amplification; digital backpropagation; unrepeated 64-QAM transmission 2020 10 1433 10.3390/app10041433
## 2 Methodology
### The Proposed Method of SRS Images Generation
#### 2.1.1 2D Ship Image Generation from 3D Ship Model  
The essence of the 3D ship model in practical applications is a 3D point cloud. Therefore, we can use the following set of 3D coordinate points to represent the 3D ship model:  
\[S=F(\begin{array}{cccc}X_{i}&Y_{i}&Z_{i}\end{array})=\Big{\{}\big{[}\begin{array} []{cccc}X_{i}&Y_{i}&Z_{i}\end{array}\big{]}^{T}\Big{\}}(i=1,2,\cdots,n) \tag{1}\]  
The principle of pinhole imaging, which is used to convert 3D data into a 2D image, is as follows:  
\[\big{[}\begin{array}{cccc}u_{i}&v_{i}&1\end{array}\big{]}^{T}\cong K[\begin{array} []{cccc}R&t\end{array}]\big{[}\begin{array}{cccc}X_{i}&Y_{i}&Z_{i}&1\end{array} \big{]}^{T} \tag{2}\]  
Figure 1: The main workflow of this paper. 1 is made by the 3D simulation software, and 1 is designed according to the appearance of the real ship in the real experiment scene. After adjusting the posture and the size of the 3D simulation ship data with the 3D simulation software, 1 is projected onto the 2D plane through a pinhole imaging model, and 2 is derived. Then, we superimpose the foreground target (2D ship), which is extracted from 2 to the real scene background, so we obtain 3, and we can also obtain 4 by the proposed method. The final task 5 is to train and test the SRS image with different CNN algorithms.  
where \([\begin{array}{ccc}X_{i}&Y_{i}&Z_{i}\end{array}]\) are the 3D coordinates of any point on a 3D target ship, and \([\begin{array}{ccc}u_{i}&v_{i}\end{array}]\) is the coordinate of the corresponding image point. \(K\) is the camera's internal parameter matrix; it can be expressed by an upper triangular matrix, as follows:  
\[K=\left[\begin{array}{ccc}f_{u}&\xi&u_{0}\\ 0&f_{v}&v_{0}\\ 0&0&1\end{array}\right] \tag{3}\]  
where \(f_{u}\) and \(f_{v}\) represent the focal length of the camera in terms of pixel dimensions in the \(u\) and \(v\) direction, respectively, and \([u_{0},v_{0}]\) is the principal point in terms of pixel dimensions. The size of the ship in the image is changed by adjusting the focal length of the camera. Besides the internal parameter matrix of the camera, Formula (2) also involves the rotation matrix and translation vector. The rotation matrix represents the camera's shooting angle, and the translation vector represents the shooting position of the camera. The ship images from different perpectives can be generated by changing the shooting angle, location of the camera, and focal length. Therefore, we obtained abundant target ship images.  
Through the imaging Formulas (1)-(3), 3D points cloud in a 3D ship model can be converted into 2D image data. Therefore, the point set \(f(u_{i},v_{i})\) can also be used to represent the ship's coordinates in the image. The representation of the point set \(f(u_{i},v_{i})\) is as follows:  
\[f(\begin{array}{ccc}u_{i}&v_{i}\end{array})=\Big{\{}[\begin{array}{ccc}u_ {i}&v_{i}\end{array}]^{T}\Big{\}}(i=1,2,\cdots n) \tag{4}\]  
Next, we need to extract the points belonging to the simulated ship from the 2D image point set, and add these points to the real experimental scene background.