esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Grid Synchronization for the Secondary Control Unit
### Frequency-Locked Loop  
Among recent advancements in grid synchronization for single- and three-phase systems, a new grid synchronization technique has been proposed in [160] called the frequency-locked loop (FLL) technique. This technique is realized in the stationary reference frame and is used to measure the input signal frequency rather than the grid voltage phase. The benefit of this technique is that it does not experience such abrupt phase angle changes. The authors in [161; 162] propose the second-order generalized integrator FLL (SOGI-FLL) by implementing two adaptive filters to produce a stationary reference frame that can auto-tune to the grid frequency. This method gives rise to a new structure, called the dual SOGI (DSOGI-FLL), which can estimate the symmetrical components of the grid voltage even under adverse grid conditions. The work in [163] introduces the enhanced FLL (EFLL) for faster dynamic response. This method proposes a generalized design framework of FLL via the Popov hyperstability criterion-based model reference adaptive control. Therefore, a simple structure and convergence speed of the FLL are developed. An adaptive frequency system based on limit cycle oscillator FLL (LCO-FLL) is proposed in [164]. This technique offers a high degree of invulnerability against phase-frequency shifts, harmonics, and voltage sags. Besides, it performs well in a highly polluted grid by ensuring an acceptable transient unto the synchronization process, which achieves the synchrony with the network from any initial condition set. A new multiresonant frequency adaptive synchronization method is proposed in [134; 165] that not only can estimate both positive- and negative-sequence components at fundamental frequency but also can detect the following sequence components at harmonic frequencies. This technique uses both harmonious decoupling networksof multiple SOCI and FLL, known as MSOGI-FLL. As a result, the system frequency becomes more adaptive in behavior. The benefit of this method is that the tuning process of different harmonics at the fundamental grid frequency can be deduced. Under highly distorted grid conditions, MSOGI-FLL contributes to detecting accurately with less computation time and low computational burden in symmetrical harmonic components.
## 2 Proposed Network
### Adversarial-Based Domain Adaptation for Anchor-Free Detector
The detailed structure and operations of the discriminator network is described in Table 2.  
Figure 7: Network architecture of LogoNet with domain adaptation setting. We provide these mid-level outputs of the source image (_Mid_\(X_{S}\)) and target image (_Mid_\(X_{T}\)) as inputs to the discriminator network to classify the inputs form source domain (_S_) or target domain (_T_). The classification loss (_Lcls_) is calculated to update the network weights of the discriminator network to increase the ability to distinguish the inputs into the respective domains. We assign source images (source-domain) with domain label '0' and target images (target-domain) with domain label '1'. The binary classification loss _Lcls_ (training objective of domain discriminator network) can be defined as:  
\[L_{cls}=\frac{1}{|X_{S}|}\sum_{i=1}^{|X_{s}|}L_{cls}(Mid\_X_{S}^{i},0)+\frac{1 }{|X_{T}|}\sum_{i=1}^{|X_{T}|}L_{cls}(Mid\_X_{T}^{i},1) \tag{13}\]  
where _Mid_\(X_{s}^{i}\)_ and _Mid_\(X_{T}^{i}\)_ are the mid-level features of the _i_th source training sample and the _i_th target training sample, respectively. \(|X_{s}|\) and \(|X_{T}|\) are sample numbers of source domain and target domain, respectively. Meanwhile, to bring the target domain (_T_) and source domain (_S_) distributions closer, we provide the mid-level output feature maps (_Mid_\(X_{T}\)) of the target image into the discriminator network and compute the adversarial loss (_Ladv_) by giving an inverted domain label, i.e., '0' instead of '1'). The adversarial binary classification loss _Ladv_ can be defined as:  
\[L_{adv}=\frac{1}{|X_{T}|}\sum_{i=1}^{|X_{T}|}L_{cls}(Mid\_X_{T}^{i},0) \tag{14}\]  
Adversarial loss is propagated to update the gradients of LogoNet framework, the objective loss function of the network is given in the following equation. \[L_{det}=L_{k}+\lambda_{size}L_{s}+\lambda_{off}L_{off}+\lambda_{adv}L_{adv} \tag{15}\]  
\(\lambda_{adv}\) is loss weight. We use a value of 0.001 in our experiments. This approach encourages the network to produce similar output feature maps distributions from target (_T_) to the source domain (_S_) by mocking the discriminator network. The task-specific detection network and the domain discriminator network are jointly trained in an end-to-end manner. During inference we do not need the discriminator network and the normal detection pipeline is used to perform the detection task so we drop the discriminator network.
## 3 Results and Discussion
### ATR-FTIR (Attenuated Total Reflection Flourier Transformed Infrared Spectroscopy)  
It is known that adsorption includes physical adsorption and chemical adsorption. The former is due to the Van der Waals forces between oil/water and the PLA fiber on the fiber surface. The latter depends on the chemical bonds formed between oil/water and the PLA fiber. In order to investigate whether chemical adsorption existed, the PLA membranes were tested by the ATR-FTIR, which is an approach to test the chemical bonds via molecular vibration after the infrared light interacted with the molecules. As shown in Figure 7, the peak at 1757 cm\({}^{-1}\) is a stretching vibration mode of carbon-oxygen double bond (C=O), and the peak at 1183-1088 cm\({}^{-1}\) is the stretching vibration mode of carbon-oxygen-carbon ester group (C-O-C) [22; 23; 24]. An ester group is a kind of hydrophobic group, which can be combined with the oil molecule and hinders water molecules. This is the reason why PLA is hydrophobic.  
Figure 5: The wettability of fibers is presented: (**a**) the WCA of fibers prepared by the 21 G needle, (**b**) the OCA of fibers prepared by 21 G the needle, (**c**) the WCAs and OCAs of the fiber membranes for samples obtained using different needle diameters.  
Figure 6: The absorption time of an oil drop on the different porous fibers.
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 5 Results and Analysis
### Analysis  
Through the above experiments, the following conclusions can be drawn by analyzing the experimental results:  
* The MongoDB database uses the WiredTiger storage engine, which stores the data as disk files. When there are sufficient memory resources to cope with the storage and access requests in the cluster, higher performance can be obtained. Moreover, the network bandwidth will also influence the storage and access performance of the MongoDB cluster.
* From the perspective of shard key strategy, the paper chooses two different shard keys to conduct the experiments, though neither of them can guarantee optimal performance in both storage and access. Therefore, when designing the shard key strategy, practicality should be considered.
## 3 Result and Discussion
### Characterization of PGC/EYO Composite Films  
In this study, by adding EYO to the dressings, it is expected that the lecithin, unsaturated fatty acids, and vitamins in the oils can assist in cell regeneration. However, oils have a great impact on the properties of a dressing by altering its softness, firmness, and elongation. The surface of the PGC112 film was observed to be flat and smooth (Figure 3). The surfaces of the PGC/EYO films revealed that their surfaces were rough, uneven, and contained particles with varying diameters. The distribution and particle size of EYO particles increased in response to higher EYO ratios. This is because emulsified EYO droplets tend to collide with each other and agglomerate during emulsification, thus increasing the particle size [6]. The average size of PGC/0.25 EYO is 4.6 \(\pm\) 1.7 \(\upmu\)m and PGC/0.5 EYO is  
Figure 2: Weight remaining ratio of PGC composites in PBS at 37 \({}^{\circ}\)C.  
4.9 \(\pm\) 1.4 \(\upmu\)m. The resuls show that particle size was increased following EYO inclusion on the film.  
The FTIR spectrum of the PGC112 and PGC/EYO composite film can be seen in Figure 4. It is apparent that after incorporation of EYO into the PGC112 film, the predominant broad absorption at 3297.8 cm\({}^{-1}\) shifted to a higher wavenumber region of 3328.7 cm\({}^{-1}\) in the PGC/EYO0.5 film. This band is associated with the OH stretching of PVP. Because egg-yolk lecithin has a large amount of hydroxyl groups at 3200-3600 cm\({}^{-1}\)[28], it may strongly interact with the macromolecular chains of PVP, causing a blue shift. In addition, the spectrum of the PGC/EYO composites shows a new characteristic peak at 1744 cm\({}^{-1}\). This represents the C=O stretch band of esters in the EYO. However, as the content of EYO increases, the C\(-\)H stretching vibrations at 1600 cm\({}^{-1}\) also increase, though the water content and mechanical properties of the PGC112 and PGC/EYO films show no significant difference (Table 3). The degradation tests of the PGC and PGC/EYO composites show that the weight loss ratio is up to 30% within one week, and the addition of EYO has no effect on the degradation (Figure 5).  
Figure 3: Surface morphology of (**a**) PGC112, (**b**) PGC/0.25 EYO, and (**c**) PGC/0.5 EYO composite films by field emission scanning electron microscope (FESEM). The yellow arrow indicates the position where the EYO particles are dispersed.  
Figure 4: FT-IR spectrum of PGC and PGC/EYO composite films.  
Figure 5: Weight remaining ratios of PGC and PGC/EYO composites in PBS at 37 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **EYO (wt\%)** & **Water Content (\%)** & **Young’s Modulus (MPa)** & **Tensile Strength (MPa)** \\ \hline PGC112 & 0 & 92.8 \(\pm\) 0.1 & 927 \(\pm\) 13 & 62.3 \(\pm\) 2.2 \\ PGC/0.25 EYO & 0.25 & 92.9 \(\pm\) 0.3 & 878 \(\pm\) 26 & 66.8 \(\pm\) 2.6 \\ PGC/0.5 EYO & 0.5 & 93.8 \(\pm\) 0.9 & 963\(\pm\) 105 & 67.2 \(\pm\) 4.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Composition of PGC/EYO films and characterization.
# Forest Fire Risk Forecasting with the Aid of Case-Based Reasoning
## 3 Forecasting the Risk of Forest Wildfires Using a Case-Based Approach
### Data  
The database includes more than 45,000 records describing information about thermal points identified as a result of analysis of satellite images for 2017-2020. This information is used as initial data. A thermal point is a significant increase in temperature on the earth's surface registered at the time of the satellite's passage in comparison with neighboring sites. A thermal point can indicate burning of garbage, a man-made process, a fall, a man-made  
The visibility zone of the Alice-SK satellite complex extends from the Urals in the west to the Far East in the east of the Russian Federation, which provides monitoring of the fire-hazardous situation throughout Irkutsk Oblast.  
The first stage of the monitoring system is direct reception of data from the NOAA satellite via the Alice-SK satellite complex, and then, several stages of data processing are carried out (demodulation, synchronization, decoding, etc.). At the final stage, analysis is performed by the FirePro software, ver.1 ISTP SB RAS, Irkutsk, Russia (Institute of Solar-Terrestrial Physics of the Siberian Branch of the Russian Academy of Sciences) [60; 61; 62] to identify thermal points (Figure 3), where the data obtained in automatic mode are further corrected by the operator to reduce possible errors in the algorithm and stored in the database that provided information for this study. At the same time, information about all linear and areal objects (thermal points, roads, rivers, etc.) is stored in the WKB (well-known binary) format.
## 3 Parametric Study
### Effect of Stationary Vehicle Frequency
#### 3.1.1 Vehicle Frequency Lower Than any Bridge Frequency  
The first case considers a stationary vehicle with a vertical frequency of 1.59 Hz, which is less than the bridge's first frequency of 2.08 Hz. The mass of the stationary vehicle was 200 kg and the simulation stiffness was 20,000 \(\mathrm{N/m}\). The moving vehicle properties were as mentioned earlier with a speed of 10 m/s and frequency of 3.25 Hz. The true bridge response, stationary vehicle response, and its contact-point response are shown in Figure 5a. The stationary vehicle response is very smooth, which implies that it does not contain higher modes of vibration. On the other hand, the contact-point response is in good agreement with the bridge vibration. Figure 5b shows the FFT of the acceleration response of the bridge, contact-point, and stationary vehicle. The stationary vehicle acceleration spectrum does not clearly show the fundamental bridge frequency of 2.08 Hz. Higher modes of vibration do not even exist in the stationary vehicle response; however, the contact-point acceleration spectrum shows the fundamental bridge frequency clearly and shows the third mode of vibration. The second mode of vibration at 8.33 Hz is barely visible in the bridge response spectrum as shown in Figure 5b. That occurs because the second mode shape of a simply supported bridge is zero at the mid-span location. When the stationary vehicle is located at other locations, for example, at the quarter-span location, the second mode will be visible as will be demonstrated in Section 3.3. Finally, the fourth mode of frequency is visible in the bridge response but it is not detected by the contact-point response. This trend occurs for all cases as will be shown below. This occurs due to the numerical inaccuracies that are produced when numerically calculating the second derivative of the stationary vehicle's acceleration response. It is important to note that the frequency of the stationary vehicle is almost eliminated in the contact-point response. Furthermore, it is not completely eliminated due to numerical derivation inaccuracy.
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
###### Abstract  
In recent years, wireless sensor networks (WSNs) have been widely applied to sense the physical environment, especially some difficult environment due to their ad-hoc nature with self-organization and local collaboration characteristics. Meanwhile, the rapid development of intelligent vehicles makes it possible to adopt mobile devices to collect information in WSNs. Although network performance can be greatly improved by those mobile devices, it is difficult to plan a reasonable travel route for efficient data gathering. In this paper, we present a travel route planning schema with a mobile collector (TRP-MC) to find a short route that covers as many sensors as possible. In order to conserve energy, sensors prefer to utilize single hop communication for data uploading within their communication range. Sojourn points (SPs) are firstly defined for a mobile collector to gather information, and then their number is determined according to the maximal coverage rate. Next, the particle swarm optimization (PSO) algorithm is used to search the optimal positions for those SPs with maximal coverage rate and minimal overlapped coverage rate. Finally, we schedule the shortest loop for those SPs by using ant colony optimization (ACO) algorithm. Plenty of simulations are performed and the results show that our presented schema owns a better performance compared to Low Energy Adaptive Clustering Hierarchy (LEACH), Multi-hop Weighted Revenue (MWR) algorithm and Single-hop Data-gathering Procedure (SHDGP).  
Article  
Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 1 Introduction  
Primary dentition plays a crucial role both functionally (as it guides the eruption of permanent teeth) and aesthetically [1]. Dental emergencies and traumatic dental injuries (TDIs) are well-documented in the guidelines of the International Association of Dental Traumatology (IADT) [2, 3, 4]. Epidemiological studies consistently demonstrate a high incidence and prevalence of TDIs, accounting for approximately 50% of cases [2, 5], as confirmed by a recent meta-analysis [5]. Consequently, TDIs pose a significant global health problem, particularly due to their potential long-term consequences on permanent teeth [6].  
Furthermore, traumatic events in primary dentition, such as extrusive luxation trauma and root and alveolar fractures, can lead to occlusal interferences that require prompt treatment [3]. These traumatic events often result in tooth extraction, posing challenges for the eruption and alignment of future permanent teeth [7].  
The primary maxillary central incisors are the teeth most frequently involved, with an incidence rate of up to 80% [8]. The most frequent primary TDIs are extrusion, intrusion (64%), and avulsions (92%) [9; 10].  
Additionally, there is a notable lack of studies on deciduous root fractures in the existing literature. Root fractures are relatively rare, with an incidence rate of approximately 2% [9]; they typically occur due to severe trauma in the crown area near the gum line or even in correspondence to the alveolar bone [10].  
Intra-oral radiographic examination is crucial for accurate diagnosis, as it reveals root fractures as a line of horizontal radiolucency.  
Fractures of the middle and apical third of the root are the most common.  
The standard treatment involves splinting and occlusal stabilization [11; 12; 13; 14]. According to [PERSON] et al. (1998), conservative therapy has shown a good success rate, despite the challenges associated with performing splinting and limited cooperation from pediatric patients [15].  
Long-term follow-up requires meticulous clinical and radiographic evaluations, particularly to monitor the onset and progression of root resorption, pulp necrosis (PN), and pulp canal obliteration (PCO). PCO is characterized by the gradual progressive obliteration of the canal walls, which is radiographically visible as a reduction of the endodontic space [16]. This retrospective study aims to analyze the outcomes of two homogeneous samples with deciduous root fractures to determine whether treatment with orthodontic splitting (utilizing orthodontic brackets and wires) compared with a control group treated only with composite fixation splinting to adjacent teeth yields a more favorable prognosis for the affected tooth and influences the onset and timing of resorption in the fractured root.
## 4 Efficiency Boundary Prediction of PSiP
It can be seen that the main power losses at low and high frequencies are from switches and inductors, respectively. Therefore, the directions of further reduction of power losses at low frequency and high frequency are different. At low frequency, the sizes of air-core inductors are slightly increased to reduce their DC and AC resistance values. At high frequency, advanced processes are applied to reduce the parasitic capacitance values of Si LDMOSFETs. According to the above analysis, for reducing power loss to improve switching frequency, PSiPs need to optimize the size of the integrated Si MOSFET in advanced processes and optimize the winding structure of air-core inductors. At a high enough switching frequency, air-core inductors are integrated in the chip, which represents the trend from PSiP to PwrSoC. Reducing power loss is also a heat dissipation requirement for the highly integrated PwrSoC package. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Process** & _fs_ (MHz) & _R\({}_{on}\) (m\(\Omega\))_ & _C\({}_{ISS}\) (pF)_ & _C\({}_{RSS}\) (pF)_ & _C\({}_{OSS}\) (pF)_ \\ \hline \multirow{4}{*}{350 nm} & 1 & 35 & 295 & 147 & 355 \\  & 10 & 112 & 93 & 47 & 112 \\  & 100 & 354 & 29 & 15 & 35 \\ \hline \multirow{4}{*}{180 nm} & 1 & 22 & 208 & 104 & 253 \\  & 10 & 69 & 66 & 33 & 80 \\  & 100 & 217 & 21 & 10 & 25 \\ \hline \multirow{4}{*}{90 nm} & 1 & 18 & 176 & 88 & 179 \\  & 10 & 57 & 57 & 28 & 57 \\ \cline{1-1}  & 100 & 179 & 17 & 9 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Parameters of Integrated Si MOSFETs with Optimal Sizes at Different Frequencies. According to Figures 11b and 12b, the efficiency boundaries of PSiPs are predicted and verified in Figure 13 [42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61]. In order to eliminate the effect of input and output voltages on efficiency, the efficiencies in the literature are normalized according to the following:  
\[\eta_{st}=\frac{P_{o}}{P_{o}+(P_{ind}+P_{switch})\cdot\frac{V_{out}}{V_{in}} \cdot\frac{\mathrm{SV}}{1.8\mathrm{V}}}=\frac{\eta}{\eta+(1-\eta)\cdot\frac{ V_{out}}{V_{in}}\cdot\frac{\mathrm{SV}}{1.8\mathrm{V}}}, \tag{22}\]  
where \(\eta_{st}\) and \(\eta\) are efficiencies with and without normalization. In Figure 13, the efficiencies found in state-of-the-art research are lower than those of the predicted efficiency boundary, and most of them are close to the boundary, which verifies the prediction based on the power loss analysis. The predicted errors are mainly from power losses of the equivalent series resistor and controller. The above optimized measures reduce power losses and improve efficiency, which provides roadmaps for achieving high-frequency PSiPs. Figure 12: Power losses of inductors, \(P_{ind}\) and \(P_{ind}/P_{o}\), with switching frequency and power level of (**a**) \(P_{ind}\) (**b**) \(P_{ind}/P_{o}\).
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios
## 3 Ground Target 2D Localization Scheme  
In PRs, echoes received from targets are characterized by low power. For GPS-based PRs, the power budget is even more restrictive, because even the direct reference signal is at or below the noise level, which jeopardizes the detection and localization of small targets, such as terrestrial ones. To increase the power of target echoes and enable target localization, a two-stage spatial filtering approach is designed by taking advantage of a surveillance array system.  
The proposed approach is based on [20] in which multiple beams were simultaneously generated in the frequency domain to perform target detection and bistatic tracking considering the spatial integration gain of conventional beamforming techniques. After the detection stage, a second spatial processing step, based on direction-of-arrival techniques, was applied to produce estimates of higher angular accuracy that can be used to obtain the location of targets in a local Cartesian space. In this work, the spatial processing scheme is slightly modified to avoid target tracking in the bistatic domain, and perform target localization directly by Cartesian tracking after target angular information extraction. In addition, the first orthogonal beamforming stage is complemented with overlapping beams to improve the SNR of the target echo over the entire coverage area.  
The functional block of the proposed processing scheme is depicted in Figure 3 where two block groups can be differentiated: the first part of the scheme carries out target detection and parameters estimation in the bistatic domain taking into consideration the beamforming gain of conventional beamforming techniques applied to the RDMs of the individual antennas; on the second part high accuracy DoA techniques are applied to provide targets azimuth estimations and allow accurate target location in Cartesian space. The computation of individual CAFs, which are required as input for this processing scheme, has already been considered in Section 2.2, while the two-stage digital array processing and the target detection and localization algorithms are presented in the following subsections.