esa-sceva/satcom-chunk-collection huggingface dataset:
# Sandibular Molar Distalization in Class III Malocclusion: A Systematic Review
## 4 Discussion
### MMD via a Fixed Appliance  
In clinical practice, various mechanics with fixed orthodontic appliances can be used to implement the MMD. The most frequent is the use of intermaximillary elastics. Another technique involves the use of open spiral springs placed between specific brackets, which exert a distal force on the molars [35].  
[PERSON] et al. describe the use of the Multiloop Edgewise Arch Wire (MEAW) technique for dental compensation using MMD and the prevention of temporomandibular joint (TMJ) disorders. The MEAW technique uses L-shaped bending forces on the teeth. By applying a distal force to the L-shaped curve of the MEAW arch wire, the lower molars are tilted back towards the distal center and repositioned, helping to correct the anterior crossbite. This approach potentially reduces the risk of TMJ problems such as TMD symptoms or condyle displacement [28]. we inserted the bibliographic reference number next to the names, but unfortunately we could not make the zotero link in the numbers in this image  
[PERSON] et al. report a case of MMD with the use of fixed orthodontic appliances, intermaximillary elastics and open coil springs. The treatment resulted in a satisfactory facial profile and stable occlusion [36].  
[PERSON] et al. describe the non-surgical treatment of a TCM with lateral deviation via the distal movement of the mandibular arch and extraction of the third molars. The mandibular molars were moved into an upright position and distalized using a light continuous tip-back force and short Class III elastics [37].  
In addition to elastics and springs, modifications to the archwire can be made to incorporate distalizing components. For example, [PERSON] et al. use a sliding jig (S) combined with intermaximillary elastics. The use of SJ, a type of orthodontic mechanic, allowed for efficient forces to be applied to individual teeth, promoting controlled molar movement. Despite the challenges associated with patient cooperation, the treatment was successful in achieving functional occlusion and dental aesthetics [38].  
[PERSON] compared the results of orthodontic treatment with TADs and Class III elastics. TADs have been shown to cause the anti-clockwise rotation of the occlusal and mandibular planes, whereas Class III elastics can lead to the clockwise rotation of the mandible. The distalization achieved with TADs results in the body movement of the lower incisors and distal tilting of the lower molars. The amount of distalization achieved with TADs is generally greater than with Class III elastics. They declare that TADs offer a reliable method of distalization for the treatment of TCM [29].  
Is important to note that the success of MMD with fixed appliances depends on several factors, including the severity of the malocclusion, the patient's cooperation, and the orthodontis's expertise.
# Tilted Beam Fabry-Perot Antenna with Enhanced Gain and Broadband Low Backscattering
## 3 Simulation Results and Experimental Validation  
The simulated model of the designed F-P antenna is shown in Figure 12a. It consists of a 6 \(\times\) 6 unit cell grid. The excitation source is a square microstrip patch antenna having dimensions of 11 \(\times\) 11 mm\({}^{2}\). The input signal pin is displaced 2 mm towards the positive \(x\)-axis, from the center of the patch. It is well known that the conventional F-P cavity resonates at \(\lambda/2\), if \(\varphi_{PRS}=\varphi_{G}=\pi\). Therefore, from Equation (1), the cavity height (\(h\)) of the proposed design turns out to be 22.73 mm at the operating frequency. However, as the gradient PRS has varying reflection phases over its surface, the final cavity height was optimized to 20 mm, keeping under consideration the antenna matching, tilt angle, as well as the SLL performance. Antenna simulations were carried out using full wave analysis in HFSS. Fabricated prototype is shown in Figure 12b. The top and bottom patterns of the composite PRS dielectric are shown in Figure 12c,d, respectively. A hard foam-type material was precisely cut and used as an air gap spacer between the antenna and PRS, as well as between the PRS and absorptive FSS. A small amount of glue was applied to attach the spacers and the structure. Some taping was done additionally, across the four sides, in order to keep the structure strongly intact. This made the structure robust enough for testing and repeatability. The soldered RF resistors were high frequency resistors from Vishay. Anechoic chamber testing was performed for measuring far-field radiation and radar scattering properties. S-parameter measurement was done on Keysight's network analyzer. The increasing gradient was implemented along negative \(x\)-axis. The final optimized lengths of the square gradated patches along with their reflection phase values are given in Table 2. These gradient values were achieved by parametric optimizations of the patch sizes, and the corresponding reflection phases were extracted from the unit cell simulations. A different set of gradient values would be required in order to steer the main beam to a different tilt angle; therefore, a steering model developed in [28], which predicts the tilt angles, could be of great insight.
## 4 Results and Discussion
### Sports Identification and Playing and Watching Intention  
The findings indicated that the desire to play a sport was more strongly connected to sports identification than the intent to watch it. This was evidenced by correlation coefficients of 0.463 and 0.596 for the relationship between sports identification and the intention to watch, which were lower than the coefficients of 0.784 and 0.742 for the relationship between sports identification and the intention to play in the pre- and post-surveys, respectively. This aligns with the convergent and discriminant validity analysis, as the AVE for sports identification (Table 3) was not greater than its shared variance with playing intention.  
Additionally, among those attending the baseball camp with spectatorship intervention, there was no significant correlation found between sports identification and the intention to watch. This suggests that an increase in the desire to watch did not proportionally enhance sports identification, particularly among three attendees. Without these three, the relationship coefficient (0.644) became high and significant. Overall, this pattern suggested that while increasing the desire to watch sports could generally strengthen sports identification, the relationship might not be straightforward for everyone. Individual differences and the specific context of engagement might influence how these variables interact.
## 4 Results
### Performance Experiment  
#### 4.1.1 Breast Ultrasound Report Classification Model  
To evaluate the effectiveness of the proposed breast ultrasound report classification model, the model was trained using the Mix-Photo and GenPic datasets. The best parameters were selected according to the validation set, while the model's true performance was evaluated using the test set. Moreover, alternative methods and models were trained on the Mix-Photo dataset and contrasted with the proposed approach, as illustrated in Table 3.  
The table compares the work on breast ultrasound classification tasks in recent years, and all the work is based on deep learning models to complete the classification task. It can be clearly seen from the table that our method has achieved the best performance in all indicators. The accuracy is 0.8931, recall is 0.8865, specificity is 0.8957, \(F1\) score is 0.8942 and AUC is 0.9453. This indicates that our method has higher classification performance compared with other methods. In addition, compared with other works, the basic ResNet50 network has shown better performance in all indicators. The feature extraction module of this article is also based on the ResNet network, which reflects that the feature extraction ability of the ResNet network can significantly improve the performance of medical image processing task-related models.
# Corrosion Susceptibility and Microhardness of Al-Ni Alloys with Different Grain Structures
## 2 Materials and Methods
### Electrochemical Tests  
To carry out the electrochemical tests, the specimens were cut to obtain 1 cm\({}^{2}\) working electrodes with different grain structures. One electrode with columnar grains and one with equiaxed grains was obtained for each alloy composition.  
Cyclic potentiodynamic curves were carried out using a velocity of 10 mV/min. The sweep of the curves was initiated about 300 mV below the open circuit potential towards the anodic zone and was reversed upon reaching a current of 1 mA/cm\({}^{2}\). After the tests, the affected areas were analyzed using an FEI Quanta200 SEM, FEI Company, NE Dawson Creek Drive Hillsboro, New York, United States of America, with an EDS detector of the Electron Microscopy and Microanalysis Service (SeMFi-LIMF)--Faculty of Engineering, UNLP, Argentina (of the Electronic Microscopy and Microanalysis Service (SeMFi-LIMF)--Faculty of Engineering, UNLP, Argentina). The open circuit potential of the alloys was also measured, E\({}_{\text{oc}}\), for a period of 3 h.  
Electrochemical Impedance Spectroscopy spectra were acquired in a frequency range of 10\({}^{5}\)-10\({}^{-1}\) Hz. The potential amplitude was set to 10 mV\({}_{\text{rms}}\) around the OCP. All the EIS data was fitted by the Gamry Echem Analyst(r).  
The electrochemical tests were carried out in a 3-electrode cell, according to ASTM G-5 Standard [31], with a platinum counter electrode, a saturated calomel reference electrode and alloy-working electrodes.  
All the potentials mentioned in the present research are vs. SCE (saturated calomel electrode). Measurements were carried out in 0.5 M NaCl solution deaerated by bubbling with nitrogen at room temperature. The equipment used is a Gamry Reference 600(r) potentiostat.
# On the Energy Performance of Iridium Satellite IoT Technology
## 2 Related Work  
This section reviews existing work that is related to the present paper. This section is divided in three parts, focusing on the following three areas, respectively: (i) satellite IoT from a broad perspective [3; 4; 5; 6; 7], (ii) Iridium satellite technology [8; 9; 10; 11; 12; 13; 14; 15; 16], and (iii) energy performance of IoT technologies [17; 18; 19; 20; 21; 22].
# Sparse Parabolic Radon Transform with Nonconvex Mixed Regularization for Multiple Attenuation
###### Abstract  
The existence of multiple reflections brings difficulty to seismic data processing and interpretation in seismic reflection exploration. Parabolic Radon transform is widely used in multiple attenuation because it is easily implemented, highly robust and efficient. However, finite seismic acquisition aperture of seismic data causes energy diffusion in the Radon domain, which leads to multiple residuals. In this paper, we propose a sparse parabolic Radon transform with the nonconvex \(L_{q_{1}}-L_{q_{2}}(0<q_{1},q_{2}<1)\) mixed regularization (SPRT\(L_{q_{1}}-L_{q_{2}}\)) that constrains the sparsity of primary and multiple reflections to overcome the energy diffusion and improve the effect of multiple attenuation, respectively. This nonconvex mixed regularization problem is solved approximately by the alternating direction method of multipliers (ADMM) algorithm, and we give the convergence conditions of the ADMM algorithm. The proposed method is compared with least squares parabolic Radon transform (LSPRT) and sparse parabolic Radon transform based on \(L_{1}\) regularization (SPRT\(L_{1}\)) for multiple attenuation in the synthetic data and field data. We demonstrate that it improves the sparsity and resolution of the Radon domain data, and better results are obtained.  
multiple attenuation; parabolic Radon transform; \(L_{q}\) regularization; sparse inversion Article
# Evaluation of Waste Blends with Sewage Sludge as a Potential Material Input for Pyrolysis
## 1 Introduction
The low heating value of SS dry matter is 17-18 MJ/kg for raw SS, 14-16 MJ/kg for activated SS, and 8-12 MJ/kg for stabilized SS [19]. Therefore, we assume that with mixing SS with other waste materials (various types of plastics, waste tires, and paper rejects), the energy use of SS will increase. When preparing alternative fuels via the synergetic disposal of waste, it is advisable to concentrate on the disposal of waste occurring in large quantities, as well as to select waste materials originating close to the source, so as to eliminate transportation, storage, and handling costs. Further, indicative calorific values of the original waste products are summarized. Plastic waste can be converted into valuable fuels [20; 21]. Plastics meet the requirement of widely used packaging materials. These materials are also suitable for handling and transport, and their material properties allow for easy molding. Plastics are polymeric substances that can be divided into two main types. First, those that soften when heated are called thermoplastics, which account for 80% of plastics currently in production, e.g., high-density polyethylene (HDPE), low-density polyethylene (LDPE), polyethylene terephthalate (PET), polypropylene (PP). The second type of plastics that harden when heated are called thermosets [22]. In terms of energy potential, the high heating value of plastics is 46.5 and 44 MJ/kg for PE and PP respectively, and 22.8 MJ/kg for polyester (PES) fibers [23; 24]. The remaining waste materials used for the experiments were used tires and paper rejects. Used tires account for a significant share of all rubber waste. The high heating value of tires is similar to that of black coal (30 MJ/kg) [25]. The paper and pulp industries create large amounts of solid waste and wastewater that have to be treated. The methods for converting paper mill waste into energy are discussed [26]. As for the energy potential of paper, its low heating value is 14.11 MJ/kg [27]. It is necessary to find technical solutions that will enable the efficient management of the described waste in order to reduce environmental pollution and meet the requirements of the circular economy [28]. One of the technologies available to deal with these types of waste is pyrolysis. A number of articles have focused on the co-pyrolysis of sewage sludge with other types of feedstocks, such as agricultural waste [29; 30], wood waste [31] or lignite [32]. The aim of this article is to describe the energy parameters and simulate the thermal decomposition at the laboratory scale of individual waste materials and novel blends (i.e., different types of waste plastics, paper rejects and polyester waste from tire processing) with SS in an inert atmosphere. This research is important for learning about the discrete processes taking place during pyrolysis, and how the composition of different waste blends changes. Appropriately selected waste blends will be further tested in a semi-pilot plant pyrolysis unit for their energy recovery.
## 2 Materials and Methods
### Ground Measurements  
To support the remote sensing analysis in this study, we compared the MODIS data with the Large-Scale Biosphere-Atmosphere (LBA) dataset. We used eddy covariance flux tower data for the land-surface temperature (LST) and GPP variables ([[https://daac.ornl.gov/LBA/guides/CD32_Brazil_Flux_Network.html](https://daac.ornl.gov/LBA/guides/CD32_Brazil_Flux_Network.html)]([https://daac.ornl.gov/LBA/guides/CD32_Brazil_Flux_Network.html](https://daac.ornl.gov/LBA/guides/CD32_Brazil_Flux_Network.html))), and ground-based reflectance measurements for EVI/Albedo data, in the JPRB area in the year 2002 ([[https://daac.ornl.gov/LBA/guides/ND01_Pasture_Spectra.html](https://daac.ornl.gov/LBA/guides/ND01_Pasture_Spectra.html)]([https://daac.ornl.gov/LBA/guides/ND01_Pasture_Spectra.html](https://daac.ornl.gov/LBA/guides/ND01_Pasture_Spectra.html))), and in the CSP-ANP in the same year ([[https://daac.ornl.gov/LBA/guides/LC19_Field_2002.html](https://daac.ornl.gov/LBA/guides/LC19_Field_2002.html)]([https://daac.ornl.gov/LBA/guides/LC19_Field_2002.html](https://daac.ornl.gov/LBA/guides/LC19_Field_2002.html))) (Table 1).  
The analysis of the variables considered averages of 8 and 16 days from daily data. We apply a simple linear regression between each MODIS variable and the field data, discarding no-data values. Given the low availability of field measurements, we use the entire period available. However, the GPP variable had few days available, while the reflectance variable had several points distributed in the same period. Details of the flux tower dataset and JPRB/ANP-CSP ground measurements can be found respectively in [38; 39; 40].
# Edge AI for Real-Time Anomaly Detection in Smart Homes
### Contributions  
This study contributes to the field of smart home automation and Edge AI by the following:  
1. Proposing a novel edge-based architecture for real-time anomaly detection tailored to the constraints and requirements of smart home environments.
2. Developing lightweight machine learning models that balance detection accuracy with computational efficiency, facilitating deployment on typical smart home devices.
3. Conducting comprehensive evaluations using simulated IoT data to validate the proposed framework's performance and provide insights into its practical applicability.  
This research addresses the challenges associated with on-device anomaly monitoring in smart homes. It aims to enhance the security, efficiency, and reliability of smart home systems, contributing to the broader adoption and trust in IoT technologies.  
Our framework distinguishes itself from prior work by integrating a hybrid detection architecture with quantized inference and dynamic thresholding strategies, all tested on realistic embedded hardware platforms.  
The remainder of this paper is structured as follows: Section 2 reviews related work in IoT anomaly detection, edge computing, and federated learning, highlighting existing gaps. Section 3 introduces the proposed Edge AI-based anomaly detection framework, detailing its architecture, data processing pipeline, and anomaly detection models. Section 4 describes the experimental setup, including dataset generation, model training, and evaluation metrics. Section 5 presents the results and discussion, comparing the performance of Isolation Forest and LSTM Autoencoder regarding accuracy, computational efficiency, and energy consumption. Section 6 concludes the paper by summarizing key findings and outlining future research directions, such as adaptive learning, transformer-based models, and real-world deployment considerations.
## 2 Control Plant--Wastewater Treatment Plant and Aeration System  
The Swarzewo WWTP consists of three parts of wastewater treatment: mechanical, biological and chemical. This paper concerns the second part and one SBR with the capacity of 6400 m\({}^{3}\)/day. Wastewater treatment is based on the activated sludge method. Figure 1 presents a single SBR cycle. It consists of the following phases: filling, biological reactions (aerobic, anaerobic), sedimentation, decantation and idle state. The excess sludge is removed and used as garden soil in the form of compost. The Baltic Sea is the effluent receiver [21].  
Maximum acceptable pollutant concentrations of effluent, according to the water-law permit for Swarzewo WWTP, are: chemical oxygen demand (COD) = 125 g O\({}_{2}\)/m\({}^{3}\), total nitrogen (\(N_{tot}\)) = 15 g N/m\({}^{3}\), total phosphorus (\(P_{tot}\)) = 2 g P/m\({}^{3}\).  
Figure 1: Phases of the SBR operating cycle: 1) filling 2) biological reactions (aerobic, anaerobic) 3) sedimentation 4) decantation 5) idle state.  
The biological processes were modelled by applying ASM2d [22]. It is a popular mathematical description of biological processes at WWTPs. The overall model was calibrated according to the plant data records. The model was implemented in a simulation package, SIMBA [23], in order to obtain information data from the controlled plant. The verification of the modelling results was satisfactory they were used for control design [22].  
As mentioned above, air is necessary for the monitoring of biological processes. It is supplied to the SBR by means of an aeration system. It consists of blowers, pipes and diffusers (see Figure 2). The aeration system supplies the SBR with oxygen by means of compressed air. From the control point of view, it is a complex, non-linear, dynamic and hybrid system.  
The paper [24] presents the general methodology of process modelling of the aeration system. This model was applied and adapted to the case study aeration system. It was described by several linear and nonlinear algebraic and differential equations [22]. The model of the aeration system was implemented in the Matlab environment and was verified based on real data records from Swarzewo WWTP. Since the verification of modelling results proved satisfactory, they were used for control design.
# Reflection Characteristics Measurements of Indoor Wireless Link in D-Band
## 4 Experimental Setup
According to (3), in order to calculate the reflection coefficients, the received power in LoS propagation is also tested under the same experimental conditions as reflection measurements, including transmitted power, distance, frequency, experimental environment, etc., as demonstrated in Figure 4c. Finally, the dataset of the average received power of five measurements in reflection and LoS paths are obtained to calculate the reflection coefficients by (3). As mentioned in Section 3, with these measured reflection coefficients, the optimal relative permittivity and surface roughness of different materials can be estimated, and then the reflection characteristics will be discussed. Figure 4: (a) Design of reflection measurements, (b) Angle ruler, (c) Design of LoS propagation.
# VFLD: Voxelized Fractal Local Descriptor
## 1 Introduction
Unlike traditional descriptors that are sensitive to noise or small variations in point cloud density, fractal dimensions are more resistant to these disturbances due to their statistical nature and focus on pattern complexity. This paper proposes the Voxelized Fractal Local Descriptor (VFLD), a novel local descriptor for 3D data--point clouds--that is based on the computation of the fractal dimension. The work introduces a number of novelties regarding state-of-the-art 3D feature descriptors: our approach is agnostic to the number of points in the input cloud, sensor of choice, and noise up to a level; it is more accurate than Fast Point Feature Histogram, which is the most used 3D feature descriptor of the state-of-the-art; and it does not require training whatsoever, in contrast to learning-based feature description methods, so no datasets are needed. The VFLD may be used for 3D object recognition and 3D registration, which we explore in this work. Specifically, the main contributions of this work are as follows:  
* A novel local descriptor for 3D data based on the computation of the fractal dimension. * A study of the effects of different point cloud densities and the presence of noise for local descriptors, showing that our method performs well under these conditions. The rest of the paper is organized as follows. First, we review a number of significant related works in Section 2. We then describe our approach in Section 3. In Section 4, we show the results of the experiments conducted to validate our proposal. Finally, we draw the conclusions and discuss the limitations and future work in Section 5.