esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sampling Information Potentials Based on Untargeted Data
The tested techniques with higher amounts of polymer gave better results in terms of concentration capacity, as expected; the approach with the highest TIC response, calculated over all UT peaks, was HSSE-PDMS/CPB (\(1.56\times 10^{7}\)), as indicated by the predominance of red colored spots on the heat-map. This was followed by DHS-TENAX (\(1.17\times 10^{7}\)) and then by DHS-PDMS (\(8.89\times 10^{6}\)). As expected, SPME-TRIF was the approach with the lowest overall TIC response. However, its coverage for key-analytes is quite good, as will be illustrated in the next section. These following preliminary considerations can be confirmed upon observing the HC results (Figure 3): SPME-TRIF clusters independently of the other approaches; HSSE with PDMS twisters (HSSE-TW1 and HSSE-TW2) and the combination of PDMS and Carbopack B (HSSE-PDMS/CPB) were all clustered together with the sub-cluster of the two PDMS devices, which differed in the amount of extraction polymer. Interestingly, both DHS approaches were closely clustered, as were MMSE-ODS/GC and HSSE-PDMS/EG, which, however, showed limited accumulation capacity. The next section will discuss sampling performance towards a selection of targeted analytes of interest for olive-oil sensory profiles.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 4 Performance Analysis
### Complexity Analysis  
Taking complex multiplication and complex addition required for each correlation coefficient calculation as examples, complexity of different detection algorithms is compared, as shown in Table 1.  
It can be seen from Table 1 that the complexity of DUWDC is lower than the traditional dual correlation detection algorithm, and its multiplication and addition times are both \(\frac{2}{N\times P}\) of the latter, where \(N\times P=D\) is the total number of DUW symbols, generally far greater than two. Compared to Hou\({}_{1}\)and Hou\({}_{2}\), the complexity of DUWDC is slightly improved, its multiplication times are \(\frac{D-1}{D-P}\) and \(\frac{D-1}{D-N}\) of Hou\({}_{1}\)and Hou\({}_{2}\), respectively, and the same is true for addition.
## 4 Controller Design
### Tracking Differentiator  
In engineering applications, differential methods usually obtain the derivative of the signal. However, when the noise mixed in the signal, this method will amplify the noise. The tracking differentiator is proposed to solve this problem. The tracking differentiator is designed, as follows [33].  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=-r^{2}sign(x_{1}-x_{in})\cdot(x_{1}-x_{in})^{\alpha}-rx_{2}\end{array}\right. \tag{29}\]  
where \(x_{in}\) is the input of TD, \(x_{1}\) is the tracking output of \(x_{in}\), \(x_{2}\) is the derivative of the input \(x_{in}\), \(r\) is the gain of TD, and it is positive, which determines the tracking speed of TD. \(\alpha\) is limited by \(0<\alpha<1\), which determines the approaching speed of TD.  
Figure 6: Block diagram of the proposed control strategy.
## 3 Results and Discussion  
### The Results of the Thermal Cycling Test of Ceramic Joint Specimens  
After the completion of the thermal cycling test with a duration of over 100 h, it was found that all the prepared ceramic joint specimens withstood high temperatures without significant visible alterations, cracks, or breakage, as shown in Figure 4. After a detailed observation, it was assumed that all of them remained completely undamaged and the ceramic bricks remained well bonded. This test clearly indicated good thermal stability and reliability in all the GC samples at temperatures up to 800 \({}^{\circ}\)C with satisfactory bonding ability, which is mandatory for their application either as thermoresistant adhesives for heating-device connections or as sealants for IT-SOFCs. Accordingly, the studied samples were considered to be completely safe from the aspect of thermostability for further examination.  
Nevertheless, on sample GC\({}_{10}\) (Figure 5), some slightly larger waste sludge particles were clearly visible due to the darker color, which indicated that a sieving of the components was needed in order to obtain a more uniform, finer material and a homogenous mixture with fully defined characteristics. Moreover, this was especially important since the values of mechanical properties of adhesive and sealant materials tend to decrease with increasing particle size [30]. Moreover, the sealants for IT-SOFCs generally consist of microsized particles [31; 32]. Therefore, prior to sample preparation, all the components in the GC compositions were additionally sieved through a 63 \(\upmu\)m laboratory test sieve and further experiments were conducted with samples containing particles of a size below 63 \(\upmu\)m.  
Figure 4: Ceramic joint specimens before (**a**) and after (**b**) thermal cycle of 100 h at temperatures in the range of 200–800 \({}^{\circ}\)C.
Nonetheless, this model is very challenging and, as a result, for large networks, the calculation time will be dramatically increased [22]. Another method is also used to model the grounding system, which is known as the hybrid method [23; 24; 25], combined with the electromagnetic field theory and circuit concept. In these procedures, the main and leakage currents are involved in the calculations jointly. They use the quasi-static approximation and reflexive coefficients to solve the problem, in order to consider the air-ground intermediate effect [26]. In the hybrid technique to model the entire grounding rods, they are divided into \(n\) small part, and then the electric field is calculated at each point using the Maxwell equations. The solutions obtained are used to calculate the inductive, capacitive, and resistive components within the framework of theoretical circuit methods to increase the accuracy of computations. In some literature, the circuit-based models have been proposed without considering the effect of soil ionization, or some negative resistance parameters have been calculated which are physically unacceptable [27; 28]. In high frequency analysis, the soil ionization causes non-linearization of the transient behavior of the buried grounding system in the Earth. Currently, if these equipments are buried in multilayer soils with different special resistances and with different geometries such as cylindrical or spherical soils, the modeling of its electrodes, in order to analyze power networks in software packages, is practically impossible. Therefore, this paper presents a novel technique to investigate all these phenomena, in order to investigate the electromagnetic transient states of lightning strikes by fast relaxed vector fitting (FRVF) algorithm. Using this method, each arbitrary grounding system can be implemented and evaluated with just a few simple RLC admittance branches with a low-order state-space transfer function and frequency response in a very wide frequency range. Another feature of this process is its high precision and computational speed.
# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 3 Materials and Methods
### Metrics  
To allow comparison between architectures' fault localization abilities, performance metrics are defined. By making use of the Intersection over Union (IoU) of the predicted model's bounding boxes and the labeled ground truth bounding boxes, the amount of overlap can be quantified. Here, varying levels of confidence are set to determine classification. Through each value in the range [0, 1], precision and recall scores are calculated for the model. Precision is defined as the number of correct predictions, True Positives (\(TP\)s), out of the total number of positive predictions, true positives, and False Positives (\(FP\)s).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Training** & **Validation** & **Test** & **Total Labels** \\ \hline Crack & 4001 & 536 & 1096 & 5633 \\ Hole & 1092 & 215 & 353 & 1660 \\ Erosion & 867 & 173 & 283 & 1323 \\ \hline
**Total** & **5960** & **924** & **1732** & **8616** \\ \hline \hline \end{tabular}
\end{table}
Table 2: CAI-SWTB dataset label distribution.  
Figure 5: Samples from the labeled small WTB dataset. (**a**) Sample images containing erosion and hole faults; (**b**) Sample images containing crack faults; (**c**) Sample images containing no faults.  
Recall determines the true positive rate or sensitivity of the model by accounting for the true positives correctly identified out of the entire positive label distribution. Precision and recall equations are shown below.  
\[\text{precision}=\frac{TP}{TP+FP} \tag{1}\]  
\[\text{recall}=\frac{TP}{TP+FN} \tag{2}\]  
Upon deriving precision and recall metrics across diverse confidence thresholds for each class within the dataset, the area under the curve (AUC) can be computed. The AUC quantifies the Average Precision (AP) of the model for each respective class. By averaging these AP values across all classes, the mean Average Precision (mAP) is obtained. Furthermore, by adjusting the Intersection over Union (IoU) threshold, the mAP can be modified to enforce more stringent criteria for bounding box overlap. Specifically, mAP0.5 denotes the mAP where the predicted bounding box must overlap the ground truth by at least 50% to be deemed accurate. These metrics will be employed to evaluate and compare model performance in the subsequent section.
# A Two-Stage Kalman Filter-Based Carrier Tracking Loop for Weak GNSS Signals
###### Abstract  
For global navigation satellite system receivers, Kalman filter (KF)-based tracking loops show remarkable advantages in terms of tracking sensitivity and robustness compared with conventional tracking loops. However, to improve the tracking sensitivity further, increasing the coherent integration time is necessary, but it is typically limited by the navigation data bit sign transition. Moreover, for standard KF-based tracking receivers, the KF parameters are initialized by the acquired results. However, especially under weak signal conditions, the acquired results have frequency errors that are too large for KF-based tracking to converge rapidly to a steady state. To solve these problems, a two-stage KF-based tracking architecture is proposed to track weaker signals and achieve faster convergence. In the first stage, coarse tracking refines the acquired results and achieves bit synchronization. Then, in the second stage, fine tracking initializes the KF-based tracking by using the coarse tracking results and extends the coherent integration time without the bit sign transition limitation. This architecture not only utilizes the self-tuning technique of the KF to improve the tracking sensitivity, but also adopts the two-stage to reduce the convergence time of the KF-based tracking. Simulation results demonstrate that the proposed method outperforms conventional tracking techniques in terms of tracking sensitivity. Furthermore, the proposed method is compared with the standard KF-based tracking approach, proving that the proposed method converges more rapidly.  
GNSS carrier tracking; high sensitivity; Kalman filter; reduce convergence time +
Footnote †: journal: Sensors
# Analysis of Bluetooth RSSI for Proximity Detection of Ship Passengers
###### Abstract
A significant distance often separates the transmitter and receiver in proximity detection. The focus of proximity detection is on proximity. Two smartphone devices are pretty close to one other in practice. Proximity detection aims to discriminate between distant and near situations, not compute distance. Significantly, unlike previous proximity detection approaches, the proximity detection discussed in this study does not add closeness via sensing distance but instead directly uses RSSI. After distance is no longer a factor, the scenarios for proximity sensing are relatively similar, whether in a ship or a typical interior setting. As a result, the relevant conclusions gained through the dataset utilized in this study also apply to the ship environment. RSSI can be considered as sensor data referring to indoor locations. However, the characteristics of RSSI such as Bluetooth have been rarely studied. This is true despite the vast knowledge about RF phenomena and received signal properties in indoor environments. Since RSSI is not intended to be used as a position sensor, there may be inherent performance limitations in using RSSI to determine position. Its statistical distribution is expected to differ from other indoor radio propagation studies that use sophisticated equipment such as vector signal analyzers to collect received signal data. The main objective of this study is to understand the various properties of RSSI associated with proximity detection through statistical data analysis of RSSI. It is important to perform basic mathematical statistics on RSSI in this study. The apparent outcome is attained, and the results are declared by common sense. Our paper does not only obtain this result alone but also other exciting developments. Our paper analyzes Bluetooth RSSI signals available to the public and compares RSSI signals in two distinct poses: standing and sitting. These features can improve accuracy and provide an essential basis for creating algorithms for proximity detection. This enhances accurate identification of close contacts and enables COVID-19 to be stopped further within a ship.
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
### How IRS realizes Backscatter Communication  
Backscatter communication is a technique that allows wireless nodes to communicate without an active radiofrequency (RF) component. It was first introduced in 1948, and is now gaining strides in internet of things (IoT) networks thanks to its low-power and spectrum-efficient advantages. It is comprised of two devices: a tag and a reader. The tag can harvest existing RF energy from received signals, then modulate and reflect those signals to convey information to the reader [10].  
IRSs are often compared to relays and standard backscattering technology [11]. The former are differentiated because they have active transmission modules such as power amplifiers [2]. The latter is distinguished through its communications operation, wherein a reader transmits a signal to the a tag which then reflects a modulated signal back to the reader; this requires self-interference cancellation at the reader, and thus applications are mostly limited to short-range wireless sensor networks such as wearable IoT devices. On the other hand, there is a need for medium/long range wireless sensor networks which can monitor and observe large-scale phenomena at fine spatiotemporal resolutions, which can be achieved with the aid of IRSs. These sensor nodes are separated by long distances over a large geographical location, making the low power consumption of these sensors paramount for cost reduction. A fine spatial resolution of the sensing region requires a large number of sensors, each with its own set of observations. This incurs an issue in scheduling the ISN, which can be resolved using channelization, that is, a controlled access technique. For a WSN dedicated to environmental monitoring, systematic scheduling or channelization is a necessity, as multiple sensors transmit continuous streams of data. As such, random access protocols ( e.g., ALOHA, slotted ALOHA, and CSMA/CA), which work best with bursty traffic where users transmit on rare occasions, are bound produce to lower system performance [12]. Next, we discuss and compare transmission power and range in a typical WSN and an IRS-equipped WSN.
# Coalition Formation Game for Cost-Efficient Multiparty Payment Channel in Payment Channel Networks
## 3 Background
### Payment Channel Imbalance  
Asymmetric PBT flows can cause a channel imbalance that induces a balance deficiency and degrades the payment throughput in the PCN. Figure 2 depicts the dynamics of tokens at three channels with two asymmetric PBT flows \(F1\) and \(F2\). The PBT \(F1\) moves two tokens toward the counterparty every second, while \(F2\) moves backward only one token. This asymmetric PBT flow eventually causes balance depletion for one of the channel users.  
Figure 1: A PBT from the user \(A\) to the user \(C\) via the user \(B\). For the atomic operation of the PBT, each user pays conditionally by locking the amount with a new conditional BP. Later, the user settles the payment by unlocking with a new complete BP after receiving the proof of payment reception, the pre-image (\(S\)) of a hash value #S.  
We investigate the PCN performance with the imbalanced PBT flows as in Figure 2 using a discrete-time PCN simulator [34]. For the experiment, unlimited tokens are given to the payers, \(A\) and \(D\), while the other two PSPs, \(B\) and C, have fixed deposits. Each payment amount of the PBTs is configured as 50% of the initial channel deposit. The message propagation and processing delay follow an exponential distribution of 10 and 1 ms, respectively. The time lock is set as 3 s for each transaction.  
Figure 3 shows the success and failure probability of payment, which depends on the balance of the PBT flows. Asymmetric flows by different payment intervals cause significant transaction failures significantly below 1 s due to a higher PBT rate of \(F1\). Meanwhile, symmetric flows with 1 s have no failed transactions. The number of successful transactions as throughput is limited by a flow with a lower PBT rate, which decreases as the \(F1\) rate diminishes, as shown in the figure.  
Consequently, the PC performance relies on the channel balance, which is dynamically changed with the PBT flows \(x_{ij}\). Furthermore, the channel deposits should be larger than the amount of each PBT, even for balanced PBT flows. We propose an MPC solution that maximizes the throughput and minimizes payment delay under unbalanced PBT flows and limited channel deposits.
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 2 EMS Measurement and Prediction  
The injection experiment on the UAV's data link is carried out to test its electromagnetic sensitivity (EMS) under different EMI parameters, such as signal types, intensities, and frequencies. The operating signal of the data link is transmitted from the ground control station with a communication mode of binary phase shift keying (BPSK). The types of EMI signals investigated in this paper are continuous wave (CW) and white Gaussian noise (WGN), which are common in the actual interference scenarios, as well as the BPSK interference, whose parameters are close to the communication parameters of the data link. Due to the radio frequency (RF) front-end filter followed by the receiving antenna, the data link is the most sensitive to the EMI, with its frequency deviation within \(\pm\)5 MHz from the center frequency f0 of the operating signal. Therefore, the data link's state parameters and I/Q data are collected under the above three types of interference signals with a frequency range from \(f_{0}-5\) MHz to \(f_{0}+5\) MHz and a bandwidth range within 10 MHz.
## 3 Results and Discussion
### Travel Time  
As mentioned in Section 2.1, the candidate sites were selected from the central regions that met both risk classification and response time criteria. Constrained by the segmentation of the map, a total of 5000 candidate points were randomly selected along urban roads. These candidate points were placed as close as possible to road intersections to ensure efficient emergency responses. The routing API interface of Baidu Maps was utilized to obtain more realistic travel routes and times for fire and rescue operations. A code that could retrieve real travel routes and times between each protected unit and candidate fire station point was programmed. As illustrated in Figure 6, for each candidate site, the program based on Baidu Map API could swiftly determine the travel time of the units with different risk classifications based on street distribution. It is important to note that the central points in Figure 6c were simplified as the estimated travel time for the entire unit, serving as the foundation for site selection evaluation.
# Reliability and Efficiency of Metamodel for Numerical Back Analysis of Tunnel Excavation
## 4 ANNs-Based Metamodel  
The five ground parameters, namely \(\gamma\), UCS, GSI, m\({}_{i}\), and K, were used as the input parameters, and the vertical displacement of the tunnel crown was used as the output parameter to develop the metamodels using ANNs. [PERSON] et al. [22] noted that it was appropriate to use two hidden layers for ANNs; accordingly, two hidden layers were used in this study. The optimum model was determined by grid search for the number of nodes and activation functions used in each hidden layer. For the grid search, the numbers of nodes were determined using the five equations shown in Table 3, where N\({}_{i}\) is the number of input parameters. The number of nodes used for the grid search was 8 (rounding off at 7.5), 11, 15, 20, and 25. As activation functions, sigmoid, hyperbolic tangent, ReLU, and Leakyrelu functions were for the grid search and \(\alpha\) = 0.2 was used for Leakyrelu. The training, verification, and test datasets were generated by dividing each dataset into 60%, 15%, and 25% of the total for each group. The early stopping condition was a situation in which the minimum value of the loss function was not updated for 500 iterations. Root mean square error was used as the loss function, as expressed in Equation (4). Here, \(\delta_{pi}\) and \(\delta_{ti}\) are the predicted and target vertical displacement of the tunnel crown. A total of 400 models (5 \(\times\) 5 \(\times\) 4 \(\times\) 4) were generated for each group through grid search, and the  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Number of Value** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Group A & O & - & - & - & O & - & - & - & O \\ Group B & O & - & - & O & - & - & O & - & - & O \\ Group C & O & - & O & - & O & O & - & O & - & O \\ Group D & O & O & O & - & O & O & - & O & O & O \\ Group E & O & O & O & O & O & O & O & O & O & O \\ \hline \hline \end{tabular}
\end{table}
Table 2: Numbers of the values used in each group.  
Figure 3: Cross section of the numerical analysis.  
model with the lowest derived value of the loss function among the 400 models was selected as the optimal model for the group. This optimal model for each group is a metamodel representing FLAC.  
\[\text{Loss}\ =\sqrt{\frac{1}{n}\sum_{i=1}^{n}\delta_{ti}-\delta_{pi}} \tag{4}\]