esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Recent Applications  
Ion imprinted polymers have been employed for the extraction of metal ions in several kinds of samples with different preconcentration modalities and techniques of determination. Some of the recent applications of these materials during the last years are shown in Table 5. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Chemical Nature of IIP** & **Synthesis** & **Features/Properties** \\ \hline Organic & & The first IIP were organic in nature [35], using poly(ethyleneimine), poly(vinylpyridine), poly(vinyl alcohol) and polyacrylate for the recognition of Cu\({}^{2+}\), Fe\({}^{3+}\), Co\({}^{2+}\), Zn\({}^{2+}\), Ni\({}^{2+}\) and Hg\({}^{2+}\). & These materials have a rigid polymeric network that is highly crosslinked and chemically stable over a wide pH range. \\ Organic & & The is a wide variety of commercially available monomers. \\  & & The used in solid-phase extraction (SPE), separation membranes, and sensors. However, in SPE, organic polymers can shrink or swell when exposed to different solvents, with resistance to the passage of liquids, generating column packing. \\  & & Usually have a retention capacity of around 70 to 100\%. \\ \hline Inorganic & & Synthesized employing sol-gel polymerization. & Polymers synthesized in the presence of acid catalysts lead to the formation of particles with smaller size and aggregation, and greater surface area and porosity. \\  & & The have reduced stability in an aqueous medium with a low or high pH value. \\  & & The use of initiators is not decisive; therefore, the synthesis can take place in the presence of molecular oxygen with heating to 90 \({}^{\circ}\)C. & The adsorption/desorption kinetics becomes unfavorable due to the micropores that make up the material, resulting in the slow mass transfer of the analyte. \\ \hline Hybrids (Organic-inorganic) & & There are several possibilities for synthesis, based on two types of interaction: weak interactions between components, such as hydrogen bonds, van der Waals forces, \(\pi\)-\(\pi\) or electrostatic interactions; or by strong chemical bonds such as covalent or ionic-covalent bonds. \\  & & These materials are mainly prepared by sol-gel polymerization, using TEOS as a crosslinking agent and organic molecules capable of interacting with the metal ion for their incorporation into the polymeric network. In this case, the organic component is not polymerizable, as it is just covalently bound to the inorganic matrix. \\  & & Using other polymerization strategies, it is possible to associate the effect of vinylated organic monomers (coupling agents) with inorganic precursors to provide greater selectivity to the polymer. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: IIP classification according to the chemical nature of its components.
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 5 MPPT Algorithm and Simulation of TEG System  
The output performance of the TEG system is influenced by several factors, such as the temperature of the hot and cold ends, external load resistance, and connection mode of thermoelectric plates. As a result, the output characteristics of the system are nonlinear. Under certain external conditions, its output power is not stable with the load change, but there is a maximum power point and the corresponding voltage and current [19]. Therefore, it is necessary to control the operating point of the TEG system in the best position to maximize its effectiveness, which is called Maximum Power Point Tracking (MPPT) algorithm.  
The maximum output power can be maintained as long as the load resistance and the internal resistance of the TEG module is equal in the circuit. In fact, the measurement of dynamic resistance is very troublesome, and it is impossible to adjust the resistance manually [33], so the PWM signal is used to control the on/off of the switching tube. Figure 20 shows the maximum power output block diagram of TEG system. The TEG module is linked to the load via the DC-DC converter, which controls the switching tube by adjusting the duty cycle of PWM. This enables the TEG system to constantly approach its maximum power point, thereby regulating its operating point in real-time.
###### Abstract  
A body composition analysis has been used to investigate alterations on body tissues in children and adolescents diagnosed with human immunodeficiency virus (HIV) infection, such as low bone mineral content (BMC). The study aimed to developed equations to estimate the BMC in children and adolescents diagnosed with HIV based on a bioelectrical impedance analysis (BIA) using dual energy X-ray absorptiometry (DXA) as a reference method. This is a cross-sectional study conducted with children and adolescents diagnosed with HIV infection. Their body composition was evaluated by BIA and DXA. Linear multiple regression models were constructed to estimate the BMC. Sixty-four children and adolescents aged 8-15 years (12.22 \(\pm\) 2.13) who were diagnosed with HIV infection participated in the study. The models were substantially correlated with the BMC values, and they explained 82-91% of the BMC, with no differences between DXA and values as estimated by the models. The model with the highest estimate of the BMC (91% of explanation) and the lowest error (SEE of 0.011 kg) included the following variables: body mass, fat mass, total 50 kHz reactance, TCD4 lymphocytes, antiretroviral therapy type, and %TCD4 lymphocytes. The models had a clinically acceptable correlation and low SEE values. The BIA was suitable to estimate the BMC in children and adolescents diagnosed with HIV.  
acquired immunodeficiency syndrome; body composition; linear models; bone mass; validity; measurement error; youth +
Footnote †: journal: Applied Sciences  
1 Research Group in Kianthropometry and Human Performance, Sports Center, Federal University of Santa Catarina, Florianopolis 88040-900, SC, Brazil 1
# Peri-Implantitis Therapy Using Surgical Methods:
## 3 Results
### Effect of Interventions on Radiographic Bone Gain  
RBG in the [PERSON] et al. study [25], between baseline and five years after treatment in groups 1 (bone substitute material + membrane) and 2 (bone substitute material) was 1.3 \(\pm\) 1.2 and 1.1 \(\pm\) 1.2 mm, respectively, that was equal to 67.4% and 72.5% RBG in groups 1 and 2, respectively. The results of the [PERSON] et al. study [31] demonstrated that the mean bone loss at the initial visit was 6.92 \(\pm\) 1.26 mm, while this value at 12, 24, and 36 months after treatment was 2.85 \(\pm\) 0.73, 2.62 \(\pm\) 0.80, and 2.60 \(\pm\) 0.73 mm, respectively, demonstrating that the treatment caused a significant decrease in the mean bone loss (\(p\) > 0.01). In other words, RBG three years after treatment, compared to that of baseline, occurred by 62.4%. [PERSON] et al. [32] demonstrated that the crestal bone level in groups 1, 2, and 3 at the baseline was 4.98 \(\pm\) 0.42, 4.32 \(\pm\) 0.74, and 5.53 \(\pm\) 0.57 mm, respectively, while these values three years after treatment were 5.47 \(\pm\) 0.63, 4.74 \(\pm\) 0.90, and 6.39 \(\pm\) 0.58, respectively, indicating bone gain that was higher in group 3 compared with groups 1 and 2. Also, the results of the [PERSON] et al. study [33] demonstrated that marginal bone level prior to and two years after treatment were 5.1 and 5.3 mm, respectively, indicating bone gain by \(\sim\)3.8%. In addition, [PERSON] et al. [34] demonstrated that the treatment regimen containing a xenogenic bone grafting material combined with either CGF or CM could increase RBG compared to that of baseline by 39.2% at year three after surgery. Also, the results demonstrated that the treatment regimen in group 2 (bone substitute + CM), compared to that of group 1 (bone substitute + CGF), was more efficient in increasing bone gain by 39.9% and 28.3% at years one and three post-surgery, respectively, indicating the higher efficacy of bone substitute + CM, compared to bone substitute + CGF, in the bone regeneration (Figure 4).  
Figure 4: Radiographic bone gain (RBG) values after follow-up (i.e., after surgical treatment) compared to that of the baseline (before surgical treatment), reported by [PERSON] et al. [32], [PERSON] et al. [25], [PERSON] et al. [33], [PERSON] et al. [31], and [PERSON] et al. [34]. As these studies showed, the highest and lowest RBG values were observed in the [PERSON] et al. and [PERSON] et al. studies, respectively.
## 7 Conclusions  
This paper presents an automated transformer disassembly method leveraging multimodal sensing and cooperative control. By fusing 2D images and 3D point cloud data from a multi-view RGB-D camera, alongside deep learning models and advanced cutting technologies, the system demonstrates high accuracy and efficiency in disassembling transformers. The experimental results highlight the system's impressive disassembly success rate and safety, effectively handling key components like screws, copper plates, winding study, and silicon steel sheets while preventing damage to critical transformer components.  
The experimental results indicate that  
* The system achieves an average disassembly time of under 20 min, marking a significant reduction compared to traditional manual methods.
* The disassembly success rate stands at 98%, showcasing the system's adaptability to various complex structures and transformer types.
* Importantly, there was no damage to core components in any successful disassembly experiments, ensuring a safe disassembly process.  
The system notably enhances disassembly efficiency and simplifies operations through automated path planning and multimodal feature fusion technology. Its versatility allows it to adapt to various transformer types, presenting promising applications in waste transformer recycling.  
Conceptualization, L.W.; methodology, F.C.; software, Y.H.; validation, L.W.; formal analysis, F.C.; investigation, Z.Z.; resources, Y.H.; data curation, Y.H.; writing--original draft preparation, L.W.; writing--review and editing, Y.H. and K.Z.; visualization, K.Z.; supervision, Y.H. and Z.Z.; project administration, Y.H.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
This paper is funded by the State Grid Zhejiang Electric Power Science and Technology Project (5211 WF240001 Research on Key Technology of Fine Dismantling and Green Full-volume Recovery of Distribution Transformer Based on Artificial Intelligence).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors [PERSON] and [PERSON] were employed by the company State Grid Zhejiang Electric Power Co., Ltd. And authors [PERSON] and [PERSON] was employed by the company Zhejiang Huadian Equipment Testing and Research Insitute Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest
## 3 Materials and Methods
### Processing Techniques
#### 3.3.3 Sentinel-5P  
SO\({}_{2}\) emissions are recovered from the imaging spectrometer TROPOMI on-board the Sentinel-5P satellite (launched in 2017, data available since December 2018). It offers data with a revisit frequency of 1 day, at exceptionally high spatial resolution (7 \(\times\) 3.5 km\({}^{2}\)) and detection limit (respectively 13 and 4 times better than the heritage Ozone Monitoring Instrument (OMI)), thereby opening the possibility to detect volcanic SO\({}_{2}\) with an unprecedented precision [16]. SO\({}_{2}\) slant column densities are retrieved by TROPOMI using differential optical absorption spectroscopy (DOAS) in the UV spectra (312-326 nm). Final gas densities are provided as vertical column densities (VCDs), for 1 km thick boxes at three different altitudes: 0-1 km (planet boundary layer, PBL), 6.5-7.5 km (mid-troposphere), and 14.5-15.5 km (upper troposphere). ESA disseminates the data as near-real time (NRTI) products, available during 1 month after acquisition, after which they are replaced by Offline (OFFL) products. OFFL products are based upon consolidated calibration and auxiliary data and thus provide better data quality data compared to NRTI products.  
MOUNTS downloads Level 2 NRTI products, and processes them to retrieve SO\({}_{2}\) mass found in a 500 \(\times\) 500 km box centered around the volcano. Vertical column SO\({}_{2}\) densities are first converted from mol-m\({}^{-2}\) to the more commonly used Dobson Unit (DU), using a multiplication factor of 2241,15. SO\({}_{2}\) mass is then calculated following [72]:  
\[\text{MSO}_{2}=0.0285\sum\
olimits_{i=0}^{n}\text{A}_{i}\text{SO}2_{i}, \tag{1}\]  
where \(\text{MSO}_{2}\) is the mass of SO\({}_{2}\) (in tons), and A\({}_{i}\) and SO\({}_{2}\) are respectively the area (7 \(\times\) 3.5 km\({}^{2}\)) and the density (in DU) at each pixel i of the 0-1 km VCD (PBL). Pixels contaminated with SO\({}_{2}\) are successively isolated by creating a mask where DU \(>\) 1, to which is successively applied a morphological filter (i.e., erosion + dilatation operators, structuring element of 5 \(\times\) 5 pixels), which allows for removal of noisy pixels. The remaining pixel clusters are assumed to be of volcanic origin, and the resulting SO\({}_{2}\) mass is assigned to the volcano for the time series display.  
The 500 \(\times\) 500 km box size is chosen arbitrarily to capture large SO\({}_{2}\) plumes. However, several monitored volcanoes may be located within this area, potentially resulting in redundant SO\({}_{2}\) anomalies.  
Although visual inspection of the image helps identify the emitting volcano, future developments will focus on using wind models to discriminate the emission source, and therefore recover more accurate SO2 emissions for each volcano.
## 8 Taxonomy of Ad Fraud Prevention and Detection Methods
### Detection of Digital Advertising Fraud in Academia
#### 8.2.3 Action Fraud Detection  
As mentioned earlier, action fraud targets users' online business activities. Because cost-per-action advertising typically involves less risk than other advertising pricing models (advertisers only pay when they make a profit or a sale), advertisers are intensely interested in using it to assess their advertising costs vs. revenue; therefore, action fraud can directly impact the ad pricing, campaign planning and other components of the ad ecosystem [1]. To detect fraudulent affiliates in ad ecosystems, [PERSON] et al. [46] have introduced a method of monitoring transactions between the affiliate marketer(s) and their respective merchant website. The proposed model analyses patterns of activities to distinguish abnormal traffic from normal traffic. In particular, the detection process involves obtaining transaction data using an affiliate separation module in order to split up the transactions based on the affiliate source. The module then inspects each affiliate transaction to determine whether it matches a set of predefined parameters consistent with some known fraudulent activities. Subsequently, for each affiliate the detection model computes the percentage of suspicious transaction relative to all transactions, and the affiliate gets labelled'suspicious' if the percentage of suspicious transactions exceeds a predetermined threshold. To combat cookie stuffing-based affiliate fraud, [PERSON] et al. [61] have introduced AffTracker, a custom-built Chrome extension, to detect affiliate cookies and collect data from numerous crawled domains targeted by fraudulent affiliates. Using the detection module, they have been able to detect the merchants targeted by cookie stuffing fraud, and they have discovered a number of third-party affiliate networks implicated in stuffing scams. Particularly, they have found that large affiliate networks (such as CJ Affiliate--Commission Junction) provide a more significant opportunity for fraudulent affiliates to simultaneously target multiple merchants. The findings have also shown that in-house (in-house affiliate marketing refers to an affiliate program managed by a merchant using only an affiliate software alternative to an affiliate network. It allows the merchant to have a relationship with affiliates or to save on network commissions) affiliate programs are able to protect their affiliate programs better due to greater visibility into their activities and the revenue flow. Nevertheless, it should be acknowledged that running an in-house affiliate program requires additional investments. A comprehensive summary of the aforementioned techniques of fraud detection and prevention is provided in Table 3.
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### General Objective Functions  
Considering expressions (40) and (41), we can define the maximization functions, which we will optimize with the proposed algorithm. For the MRC scheme, the corresponding SNR maximization problem is as follows:  
\[\max_{\mathbf{x},\mathbf{y},\mathbf{z}}\sum_{t=1}^{T}\sum_{k=1}^{N_{\text{PD}}} \frac{P_{MRC}(\mathbf{x},\mathbf{y},\mathbf{z},t)}{N_{0}B}, \tag{41}\]  
where \(P_{MRC}(\mathbf{x},\mathbf{y},\mathbf{z})t\) depends on the type of mirror used.  
For the SBC scheme, the combined SNR is determined by the photodiode with the highest SNR at each time step. The objective function to maximize is the following:  
\[\max_{\mathbf{x},\mathbf{y},\mathbf{z}}\sum_{t=1}^{T}\max_{k=1}^{N_{\text{PD} }}\frac{P_{SBC}(\mathbf{x},\mathbf{y},\mathbf{z},t)}{N_{0}B}. \tag{42}\]  
As in MRC, \(P_{SBC}(\mathbf{x},\mathbf{y},\mathbf{z},t)\) depends on the type of mirror, using the respective equations for planar or convex mirrors.  
For both MRC and SBC, the optimization is subject to physical constraints on the mirror placement to ensure that mirrors are placed within the bounds of the indoor environment, denoted as  
\[(\mathbf{x},\mathbf{y},\mathbf{z})\in\text{Room Dimensions}. \tag{43}\]  
Furthermore, there are mirror-specific reflection properties, denoted as  
\[\theta_{ij}\leq\theta_{\text{max}}, \tag{44}\]where \(\theta_{\max}\) is the maximum allowable reflection angle for the mirrors.
## 1 Introduction
The similar spectral smoothing technology finds extensive applications in other signal processing fields, such as audio signal processing (noise reduction, echo cancellation, and audio enhancement), image processing (image restoration, denoising, and super-resolution imaging), communication systems (improving signal transmission reliability and efficiency by resisting multipath interference and frequency-selective fading), as well as biomedical imaging, radar signal processing, seismic data processing, and astronomy data analysis [19; 20; 21; 22; 23; 24; 25; 26]. The introduction of PSSD is also of far-reaching significance, making a substantial contribution to promoting the development and application of the eLoran system and related fields, mainly involving the following three aspects. Firstly, in terms of technological innovation, the PSSD algorithm brings new ideas and methods for eLoran demodulation technology. It integrates advanced signal processing concepts and introduces a combined time-frequency domain processing approach, expanding the boundaries of signal processing and leading the technological development direction in this field. Secondly, in terms of system performance improvement, it significantly enhances the stability of the eLoran system in harsh environments, improves timing accuracy and information transmission capabilities, endows the system with stronger environmental adaptability, and meets users' demands for high-precision PNT services. Thirdly, in terms of promoting industry development, it helps to promote the upgrading of eLoran-related industries, provides new technical support for industrial development, drives the development of related fields such as communication and navigation,promotes technological integration among various fields, and cultivates professional talents, providing guarantees for the sustainable development of the industry. In the following sections, we will elaborate on the principles and implementation of the PSSD algorithm, as well as present the experimental results and performance analysis.
## 4 Diesel Power System
### System Composition and Work Style  
AAOISE's diesel power generation system is mainly comprised of the diesel generator set, ac-dc converter, dc-dc converter, super capacitor, storage battery, and transmission cable, as is shown in Figure 2.  
In the power module cabin, the power generation units have two sets of devices with exactly the same redundant configuration, with three HATZ1B40 diesel engines in each set. Each engine connects with an alternator and each alternator joins with an ac-dc converter. When the diesel engine starts, the generator transforms the three-phase 220 V AC which flows into the ac/dc converter into the 220 V dc. Then, it is transmitted to the instrument module through the bus bar between two cabins. The maximum output power is about 1.8 kW created by a diesel generator.  
The fuel of the engine is aviation kerosene. The fuel tank capacity is about 4.5 tons, locating under the diesel engine. Generator units are fixed on the top of the tank to reduce vibration measures. Each engine has two lubricant oil tanks of 200 L, with a diesel engine and generator unit beside.  
Every engine generator equips a controller directed by the SMC--only one engine generator is in operation and the others are in rest at any time. The generator unit works in turn, controlled by SMC which has the duty of retaining each generator unit in proper working condition, extending service life and saving fuel consumption. SMC makes all decision according to the device working time and the actual power loss.  
In the equipment module cabin, the 220 V dc from the power module is divided into two parts. One is converted into 24 V dc through two redundant dc/dc converters, whose output is 10 amperes current. The other is converted into 50 Hz/220 V ac through redundant dc/ac converters with 150 W output power, even 300 W in peak. Besides, two sets of battery packs are located in the equipment cabin with a capacity of 320 Ah in each set.  
The 24 V dc is also needed in the power module, including controllers and the super capacitor charging. As the power source of the start motor of diesel engine, the super capacitors can reach 12 V/300 A instantaneously during discharging. The 24 V dc is sent back to the power module cabin through the bus bar between the two modules.  
Figure 2: Diesel generator power system structure.