esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Loss Function  
Alongside model architecture advancements, significant progress has been made in designing loss functions for bounding box regression, aiming to improve intersection over union (IoU) optimization. Building upon the basic IoU loss, researchers developed GIoU [28] to address gradient vanishing in non-overlapping cases. Subsequently, DIoU [29] accelerates convergence through the centroid distance penalty, and CIoU [29] further optimizes bounding box fitting with aspect ratio constraints. However, these improvements remain limited to relative scale computations, proving inadequate for PCB defect detection with absolute size sensitivity. While SIoU [30] redefines directional penalties via vector angles, its width-height difference modeling still relies on normalized relative values. WIoU [31] balances easy and hard samples through dynamic focusing but lacks explicit geometric prior modeling. The recently proposed Shape-IoU [32] represents a significant advance in geometric-aware loss design: it introduces size-sensitive weight coefficients to strengthen absolute scale perception and directly constrains physical dimension matching through a shape distance term. By integrating absolute geometric attributes into loss calculations, this approach significantly enhances localization stability for PCB defects, ensuring precise detection in high-density backgrounds.
# Detection of Threats to Farm Animals Using Deep Learning Models: A Comparative Study
## 3 Materials and Methods
### Evaluation Metrics  
The results obtained from our study were measured and evaluated according to various criteria, including precision, recall, mAP50, and mAP50-95. We employed mAP50 as a metric to evaluate and compare different models. The training process involved assessing key performance metrics such as accuracy and loss. Accuracy provides an overall measure of model performance by calculating the proportion of data points correctly predicted relative to the entire dataset. Alongside accuracy, precision and recall metrics were also computed to enhance the quality and relevance of the outcomes. Precision assesses explicitly the accuracy of the classification of samples within the entire dataset and provides insights into the accuracy of the classification process. Conversely, recall focuses on correctly identifying positive examples among all positive instances within a dataset, which is crucial in evaluating a model's ability to capture all relevant instances. In target detection, the average precision (AP) for each target type and the mean average precision (mAP) for all targets are utilized to assess the detection effectiveness and performance of a model. AP is calculated as the area under the recall and precision curves, and its formula is as follows:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{4}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{5}\]  
\[AP=\frac{1}{100}\sum_{i=0}^{100}\text{Pr}\bigg{(}Re=\frac{i}{100}\bigg{)} \tag{6}\]  
\[mAP=\sum_{i=1}^{N}\frac{AP_{i}}{N} \tag{7}\]
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 2 Materials and Methods
### Construction of the Dynamic Compensator for the Fluxgate Magnetometer
#### 2.1.1 System Identification for the Fluxgate Magnetometer
In order to obtain the response signals of the magnetometer under magnetic fields of different frequencies, the current output frequency of the current source is gradually increased from 0.01 Hz of the lower frequency to 240 Hz of the higher frequency. Thus, magnetic fields of different frequencies are generated in the coil as the input magnetic field of the magnetometer. The amplitude-frequency characteristic curve and phase-frequency characteristic curve of the magnetometer are drawn according to the test results, as shown in Figure 4.  
It can be seen from Figure 4 that when the input magnetic field frequency increases, the output amplitude of the magnetometer decreases continuously, the amplitude attenuation becomes more and more serious, and the dynamic measurement error increases continuously. The time delay and phase difference between the output and input signals of the magnetometer are also increasing. In the test frequency band (0.01-240 Hz), the amplitude attenuation of the magnetometer is from 2% to 1.49%, which represents a 10-745 nT measurement error in the geomagnetic background field (average of 50,000 nT). Moreover, the maximum phase difference is \(-8.5508^{\circ}\). In Figure 4, the amplitude/phase-frequency characteristic curves of the magnetometer are consistent with the amplitude/phase-frequency  
Figure 4: Amplitude–frequency and phase–frequency characteristic curves of the fluxgate magnetometer: (a) amplitude–frequency characteristic curve; (b) phase–frequency characteristic curve. Figure 3: Ambient magnetic field of the shield chamber before/after filtering. characteristic curves of the first-order sensor [20]. Thus, the input and output data of the system are utilized to model the fluxgate magnetometer based on the system identification method. The equivalent transfer function of the first-order sensor system is expressed as [12]:  
\[G(s)=\frac{1}{\tau s+1} \tag{1}\]  
In Equation (1), \(\tau\) is the system time constant. Equation (1) needs to be converted into a difference equation since the data of the sampling system are discrete. The transfer function in the S-domain is converted to a discrete function in the Z-domain by bilinear Z-transformation, and then, the difference equation is obtained:  
\[(2\tau+T)y(k)+(T-2\tau)y(k-1)=Tx(k)+Tx(k-1) \tag{2}\]  
In Equation (2), \(k\) is the time of the discrete system, and \(T\) is the sampling period of the discrete system. \(y(k)\) and \(x(k)\) represent the output and input signals at the time \(k\), respectively. Thus, the system model of the magnetometer is established and provides input signals as the previous step of the dynamic compensator.
## 3 Results and Discussion
### Sensors Quality Evaluation  
The CV of NDVI simulation was combined with SNR as an evaluation parameter for sensor quality. SNR values ranged from 267 for Red and 293 for Near-Infrared bands of Spot 6/7 to 50 for Red and 50 for Near-Infrared bands of Dove satellites. This variability in SNR values led to important differences in CV of NDVI simulation. The CV of NDVI was 0.32% for Spot 6/7 and 1.85% for Dove, which represented the lowest and the highest values respectively. Free of charge satellites showed a low CV: 0.44% for Landsat 7/8 and 0.59% for Sentinel-2. The reference sensors for UAVs (Tetracam micro-MCA) and planes  
Figure 3: Comparison of different platforms for image acquisition considering only the price per hectare in 2020 (without fixed costs for take-off, minimum order area, elaboration, and processing costs).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Price per** & **Data Volume** & **Spatial** & **Radiometic** & **Revisit** & **CV of** \\  & **Hectare** & **per Hectare** & **Resolution** & **Resolution** & **Time** & **NDVI** \\ \hline  & **EUR min** & **KB ha\({}^{-1}\)** & **m** & **Bits Pixel\({}^{-1}\)** & **Days** & \\ \hline MR & 76.1 & 0.52 & 12.5 & 14.0 & 6.50 & 0.501\% \\ \hline HR & 434 & 21.8 & 1.55 & 12.4 & 3.14 & 0.737\% \\ \hline VHR & 2527 & 106 & 0.45 & 11.3 & 2.25 & 0.579\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average satellite cluster features.  
(Leica DMC III) showed a CV of NDVI simulation of 0.43% and 0.63%, respectively. Data reported in Figure 4 shows the SNR of each sensor and the relative CV of NDVI simulation.  
The CV of NDVI simulation was used to classify sensors into three groups. The first group had a CV up to 0.5%, and included UAV and plane reference sensors, some of the most recent on demand satellites, and free of charge satellites. The second group included a group of satellites less recently launched, which had CV of NDVI up to 1%. The third group included only the Dove satellite constellation, which showed a CV of NDVI simulation of 1.85%. The presence of free of charge satellites in the first group proves the high quality of the optics installed in the non-commercial satellites. On the other hand, the Dove nano-satellites constellation prioritized reduced size of satellite over optical quality. Reference sensors for UAVs and planes showed a low CV of NDVI simulation, demonstrating the optical consistency of these sensors. The relatively high value of CV of NDVI simulation of the Dove satellites may be caused by the postprocessing applied by PlanetScope (e.g., orthorectification), as highlighted by [PERSON] et al. (2016). The comparable value of CV of NDVI between Sentinel 2, UAV and plane images confirms a previous study ([PERSON] et al., 2017) which highlighted the consistency of these sources of data for crop monitoring.
## 3 Results
### Effects on Latent Heat  
Varying aerosol concentrations affect the content and vertical distributions of hydrometeors, which inevitably brings in different latent heat. Latent heating includes three parts: condensation heating; freezing heating; and deposition heating. Likewise, latent cooling includes evaporation cooling, melting cooling, and sublimation cooling. Figure 9 shows an average vertical profile of latent heating during the early stage of precipitation. It can be seen that the largest source of heating is condensation heating. Secondary sources are deposition heating, and freezing heating. The biggest source of cooling is the evaporation of cloud droplets (Figure 9a,b).  
Comparing the two cases, there are only a few differences during the initial stage of precipitation (Figure 9c). At a height of 6 km, the evaporative cooling of cloud water is slightly larger in P-case than in C-case. Correspondingly, deposition heating is slightly larger in P-case than in C-case. This indicates that the higher cloud water content with smaller cloud droplets at high levels in P-case results in more evaporation, leading to the enhanced deposition process to produce ice-phase particles. That is to say, the WBF conversion proceeds faster with the polluted conditions. In addition, at a height of about 6 km in P-case, the freezing heating is a bit higher than that in C-case. This is mainly due to the more cloud droplets with smaller size in P-case being carried to higher levels (Figures 4 and 5), where they either freeze, or are accreted by ice-phase particles, releasing more latent heat (Figure 9a,b). It is noted that increased latent heating due to freezing and deposition creates a \"warmer bed\" for cold-cloud development in P-case.  
As the cloud system develops and cold-cloud processes strengthen, P-case ice-phase particles release more and more latent heat from freezing and deposition. By t = 16 h, P-case latent heating has  
Figure 10: Same as Figure 9, but for t = 21 h.  
already approached C-case latent heating (not shown); At t = 17 h, the warm-cloud latent heat profiles are similar, but cold-cloud heating in P-case is clearly stronger than in C-case (not shown). From here, a shift occurs, and P-case net latent heating begins to exceed C-case.  
Figure 10 gives the vertical profile of the average latent heating rate in the model domain during the maximum precipitation stage (t = 21 h). Both heating (due to condensation, deposition, and freezing) and cooling (due to sublimation, melting, and evaporation) increase dramatically in the two cases (Figure 10a,b). The height of the net latent heating maximum value has moved up to over 6 km (Figure 10c). This indicates that latent heating in cold-cloud processes increased markedly with the development of the precipitation system. Comparing the two cases, not only freezing and deposition heating, but also condensation heating are larger in P-case. This suggested that P-case water vapor content may already be greater than C-case. Meanwhile, evaporation, melting, and cooling in P-case are all greater than in C-case.
## 3 Theoretical Analysis
### Thermal-Hydraulic Performance
In order to ensure that the battery temperature does not exceed 45 \({}^{\circ}\)C, and the temperature consistency does not exceed 5 \({}^{\circ}\)C, the parameter range was estimated through theoretical calculations, as follows:  
* Five diameters of the main pipe were used: D = 12 mm (\(\delta\) = 0.484), D = 14.5 mm (\(\delta\) = 0.707), D = 17 mm (\(\delta\) = 0.973), D = 19.5 mm (\(\delta\) = 1.278), and D = 22 mm (\(\delta\) = 0.1627). * The boundary conditions for the simulation were as follows: The maximum current must not exceed 35 A, the maximum inlet temperature of the coolant must not exceed 30 \({}^{\circ}\)C, and the coolant flow must be greater than 5 L/min. Subsequently, three sets of currents (25 A, 30 A, 35 A), two sets of inlet coolant temperatures (25 \({}^{\circ}\)C, 30 \({}^{\circ}\)C), and two sets of coolant flow (5 L/min, 10 L/min) were orthogonally combined for simulation and verification.
###### Abstract  
As one of the 5G applications, rich communication suite (RCS), known as the next generation of Short Message Service (SMS), contains multimedia and interactive information for a better user experience. Meanwhile, the RCS industry worries that spammers may migrate their spamming misdeeds to RCS messages, the complexity of which challenges the filtering technology because each of them contains hundreds of fields with various types of data, such as texts, images and videos. Among the data, the hundreds of fields of text data contain the main content, which is adequate and more efficient for combating spam. This paper first discusses the text fields, which possibly contain spam information, then use the hidden Markov model (HMM) to weight the fields and finally use convolutional neural network (CNN) to classify the RCS messages. In the HMM step, the text fields are treated differently. The short texts of these fields are represented as feature weight sequences extracted by a feature extraction algorithm based on a probability density function. Then, the proposed HMM learns the weight sequence and produces a proper weight for each short text. Other text fields with fewer words are also weighted by the feature extraction algorithm. In the CNN step, all these feature weights first construct the RCS message matrix. The matrices of the training RCS messages are used as the CNN model inputs for learning and the matrices of testing messages are used as the trained CNN model inputs for RCS message property prediction. Four optimization technologies are introduced into the CNN classification process. Promising experiment results are achieved on the real industrial data.  
rich communication suite (RCS); 5G; anti-spam; hidden Markov model (HMM); convolutional neural network (CNN) +
Footnote †: journal: Applied  
10 June 2021  
Accepted: 6 July 2021  
Published: 9 July 2021
## 2 Double Distribution TLBM
### Energy Distribution Function  
In the double distribution LB model, an energy distribution function is considered independent of the density distribution in order to mimic the energy transport. The following energy distribution function is employed in our study  
\[g_{i}(\mathbf{x}+\Delta\mathbf{x},t+\Delta t)-g_{i}(\mathbf{x},t)=-\frac{1}{ \tau_{g}}(g_{i}(\mathbf{x},t)-g_{i}^{\text{eq}}(\mathbf{x},t)) \tag{15}\]  
where \(g_{i}(\mathbf{x},t)\) is the energy distribution in the \(i\)-th discretized direction, and \(\tau_{g}\) is the relaxation time for energy. The equilibrium distribution \(g_{i}^{\text{eq}}\) is defined as  
\[g_{0}^{\text{eq}} = -\frac{2\rho c_{\text{p}}T}{3}\frac{\mathbf{U}^{2}}{c^{2}}, \tag{16}\] \[g_{1,2,3,4}^{\text{eq}} = \frac{\rho c_{\text{p}}T}{9}[\frac{3}{2}+\frac{3\mathbf{e}_{i} \cdot\mathbf{U}}{2c^{2}}+\frac{9(\mathbf{e}_{i}\cdot\mathbf{U})^{2}}{2c^{4}}- \frac{3\mathbf{U}^{2}}{2c^{2}}],\] (17) \[g_{5,6,7,8}^{\text{eq}} = \frac{\rho c_{\text{p}}T}{36}[3+\frac{6\mathbf{e}_{i}\cdot \mathbf{U}}{c^{2}}+\frac{9(\mathbf{e}_{i}\cdot\mathbf{U})^{2}}{2c^{4}}-\frac{ 3\mathbf{U}^{2}}{2c^{2}}]. \tag{18}\]  
In the above equations, \(c_{\text{p}}\) is the heat capacity at constant volume (assumed to be 1 in our study). This model differs from the one proposed in [23], where both viscous dissipation and the compression work done by the pressure were taken into account. The influence of viscous dissipation can be evaluated via the Brinkman number Br, which is defined as \(\text{Br}=\
u\mathbf{u}^{2}/(\alpha T_{0}-\alpha T)\), where \(T_{0}\) and \(T\) represent temperature of wall and fluid, respectively. The importance of compression work is reflected by the Mach number, that is \(\text{Ma}=|\mathbf{U}|/c_{s}\). In the problem of TFE, both Br and Ma are usually very small. Hence, viscous dissipation and compression work are negligible in our study. The resulting energy distribution function is significantly simplified. Furthermore, the second order truncation error can be absorbed in the relaxation time \(\tau_{f}\) and \(\tau_{g}\). It is noticed that for the multiphase flow, the density difference between liquid and vapor phases is large, which deteriorates the numerical stability in the thermal flow simulations. Hence, another variable \(g^{\prime}\) is introduced, that is \(g^{\prime}=g/\rho\). Now \(g^{\prime}\) is considered in the steaming and collision steps. Furthermore, the equilibrium distribution of \(g^{\prime}_{i}\) can easily be extracted from Equations (16)-(18). The temperature \(T\) is then computed via  
\[T=\sum_{i=0}^{8}g^{\prime}_{i}. \tag{19}\]  
The thermal diffusivity \(\alpha\) is expressed as \(\alpha=2c_{s}^{2}(\tau_{g}-0.5)\Delta t\).
## 3 Earthquake Early Warning System for Bursa Province (Turkiye) and Its Surroundings  
### Earthquake Early Warning System Algorithm (EPIC)  
The EPIC algorithm is a regional point source algorithm [32]. The system will alert if P waves are detected by at least four stations and at least 40% of the stations are in the P phase wavefront area. Earthquake data is transmitted from Giralp sensors at stations to Scream software in.gcf format via LTE modem. It is then transmitted from Scream to Earthworm (the seismic data collection and automatic earthquake processing software) with the help of Scream2 ew 7.6 software and is used by EPIC. When estimating the location of earthquakes, a grid search method is used in which the difference between the travel time calculated and observed is minimized. At this stage, the one-dimensional layered global velocity model AK135 [33] was preferred as the velocity model. The magnitude of the earthquake is obtained by the peak displacement (\(Pd\)) and the predominant period (\(T_{p}^{max}\)) calculated from the data at different stations [31]. As time progresses, new data continues to arrive, and all parameters (such as occurrence time, location, and size) are updated more accurately [29].  
To ensure fast and reliable alerts, an EEWS needs to have a well-designed seismic network and algorithm that provides the longest possible warning time, accurate classification of shaking intensity, and a low rate of false or missed warnings. The optimal design of the network depends on various factors, including the seismotectonics of the region, available resources, and budget constraints [31; 34]. In the case of Bursa province's EEWS, 128 stations were utilized (99 being accelerometers and 29 being seismometers) (Figure 2). The station design was strategically placed and reinforced with existing stations to establish a reliable system. Accelerometers were installed in natural gas distribution points and public institutions' gardens, while velocity sensors were placed in areas far from residential zones to allow for the closure of natural gas distribution valves and the cessation of heavy or sensitive machinery [35]. [PERSON] [35] also noted that a high station density could decrease the time required for a solution and warning.  
Figure 2: Station distribution in Bursa province and surrounding. The stations of YUBAM, AFAD, and Bursagaz are shown in red, blue, and orange colors, respectively. Triangle and circle refer to the type of sensor.
## 5 Discussion
### Limitations and Future Work  
Several limitations were noted in our ES identification framework. Due to the availability of datasets, we calculated the ESV using the equivalent factor method. However, results could vary across different methods and cause differences in ES extraction results, as  
Figure 12: Comparison between extracted ecological source areas and policy-designated protected areas, with the wildlife spatial distribution in 2017 (**a**) and different areas between nature reserves and ecological source sites extracted in this study (**b**).  
described in the Discussion section. A more meaningful approach involves contrasting the disparities in the ESV derived from diverse calculation methods that could be developed for ES extraction in the future.  
Additionally, although previous studies have achieved the ideal result using spatial prioritization tools [88] on a different scale, ES extraction must be combined with the trade-offs and synergies between ecosystem services and other aspects of in-depth research. We hope that subsequent research can improve this research with trade-off models and other methods in the future.  
Furthermore, although the kNDVI may represent primary productivity and further help us understand carbon sinks to some extent [8], there are some discrepancies between it and the other parameters of terrestrial ecosystems (e.g., GPP, NPP) due to the potential spectral saturation effect. The kNDVI alone may not be sufficient to detect ecological source changes driven by carbon conditions. Research to enrich the identification framework with critical parameters for characterizing terrestrial ecological processes could be conducted.  
Despite these limitations, our findings provide valuable insights into applying a multi-perspective approach to ecological source identification.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 2 Low-Power Rectifiers
### Matching Network Enhancement
#### 2.2.3 Direct Antenna-to-Rectifier Matching  
Conventional rectenna designs typically match the antenna and rectifier output to \(50\)\(\Omega\) separately before interconnection. An efficient design approach is to design an inductive antenna to conjugately match the capacitive impedance of the diode, thereby eliminating the matching network [71; 72; 73; 74]. In [71], a broadband and high-efficiency rectenna based on this technique is designed, achieving over 60% efficiency within the ranges of \(0.9\)-\(1.1\) GHz and \(1.8\)-\(2.5\) GHz. A novel patch rectenna structure without impedance matching networks is introduced in [73]. The patch antenna is directly connected to the rectifier through a metallic via at the position where the antenna and rectifier's input impedance are  
Figure 8: The block diagram of the F3 MPPT controller [68].  
Figure 9: Layout of wide-load-range rectifier based on self-bias impedance compensation [70].  
conjugately matched, as shown in Figure 10. Ref. [74] proposed an omnidirectional flexible triple-band rectenna for low-power RF energy harvesting without any impedance matching network, which achieved rectification efficiencies of 33.3%, 21.68%, and 15% at 0.915 GHz, 2.45 GHz, and 3.5 GHz, respectively, under an input power of \(-\)20 dBm (Figure 11).