esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Formulation of the SC Recognition Problem  
_Maximum Amplitude Derivatives and a Decision-Making Rule_  
Let us consider the variables \(H_{xp}=H_{1p}\), \(H_{yp}=H_{2p}\), \(H_{zp}=H_{3p}\), and \(H_{0p}\), which are the components and the total intensity of the geomagnetic field for a certain time interval where SC is present, obtained from a set of magnetic observatories, where \(p\) is the serial number of the magnetic observatory, and \(p=1,\ldots,P_{0}\), \(P_{0}\) is the total number of magnetic observatories. Further processing is based on the normalized records \(H_{N,1p}(T(i))\), \(H_{N,2p}(T(i))\), \(H_{N,3p}(T(i))\), and \(H_{N,0p}(T(i))\), obtained by subtracting the mean and normalizing the maximum amplitude of variations to \(\pm 1\).  
The SC recognition procedure for the normalized variables is based on three stages.  
At the first stage, we estimate the first derivative of the discrete time series formed by the components of the geomagnetic field, which we denote as \(H_{N,4p}^{\circ}(T(i))\), \(r=0\), \(1\), \(\ldots\) 3, \(p=1,\ldots,P_{0}\) and obtained using the differentiating FIR filter (with a finite impulse-transient characteristic). We perform the differentiating FIR filtering using sliding averaging  
Figure 1: Plots of one-minute-averaged data of the components \(H_{x}\), \(H_{y}\), \(H_{z}\) and the total intensity \(H_{0}\) of the geomagnetic field strength vector with SC during 22 June 2015.
## 4 Numerical Results
### Results and Discussion  
To comprehensively evaluate the performance of the proposed distributed source camera identification approach, we considered several distinct scenarios. These included different identification environments, such as conventional settings and privacy-preserving contexts, as well as extensive testing across multiple datasets. Additionally, we assessed the approach in a real-world scenario, using images processed by various popular social media platforms to simulate practical conditions. To further illustrate the benefits of the distributed method, we compared its performance in both single-user and multi-user settings, highlighting the advantages of utilizing multiple secondary users in the identification process. In this work, we selected the method proposed by [PERSON] et al. [12] as the primary baseline for comparison, because it represents one of the most recent and state-of-the-art approaches in the field of source camera identification, particularly in encrypted environments. Furthermore, [PERSON] et al.'s method has already been used in extensive experiments and comprehensive comparisons with several widely used and representative methods, such as those based on PRNU. These results have consistently demonstrated the superior performance of their approach over previous baselines. Therefore, to avoid redundancy and unnecessary duplication of prior comparative studies, and to maintain a focused, fair, and meaningful evaluation, we adopted [PERSON] et al.'s method as the sole baseline in our experiments. This choice allowed us to directly assess the advantages of our proposed method against a strong and well-established benchmark, ensuring a clear and rigorous evaluation.
## 7 Conclusions  
In this review, we present an overview of the reported studies that use PMMA-based materials that were modified with silver nanoparticles for dentures or bone cements. The use of silver nanoparticles as an antimicrobial agent for surface modification to prevent bacterial and fungal adherence can serve as an interesting approach to prevent oral infections in patients. Moreover, the use of silver nanoparticles in bone cements can promote the optimal functioning of total hip prostheses, which are the most used prostheses. Finally, the use of silver nanoparticles can enhance the mechanical properties of PMMA-based materials, depending on the concentration, dispersion and even the synthesis method used on materials under this approach. According to the results of this review, the better strategy for embedding silver nanoparticles into a PMMA matrix seems to be mixing AgNPs with a monomer before the polymerization reaction. This strategy allows for control of some properties in areas that require improvement, such as nanoparticle concentration, shape and dispersion, depending on what kind of material is sought. This synthesis method is easy and simple to perform. Synthesis methods such as cluster beam deposition and pulsed laser deposition require more specialized equipment, which could be less efficient for synthesizing and obtaining the appropriate material. Standardized methods would allow us to compare materials in a more accurate way, and this fact can bring the development of materials with antimicrobial properties closer to their final clinical application in the dental field in the short term. For the success of dental applications at this moment, we need evidence from human clinical trials that this approach works appropriately.  
In the near future, the main question that needs to be addressed is whether polymeric materials are the optimal candidates to continue developing dental prostheses from. The efficiency of the production methods should also be questioned. In recent years, with advances in the area of tissue regeneration and with new production methods such as 3D printing, the field of dentistry faces great scientific and technological changes.  
The use of PMMA-AgNP-based materials in dental applications can promote better oral health in prosthesis users, helping to improve their quality of life.  
All authors made a significant contribution to the manuscript submitted: Literature revision, data analysis, revision of the manuscript and final approval of the submitted version, F.-A.J.C.; data analysis, drafting and critical revision of the manuscript and final approval of the submitted version, G.-C.R.; data analysis, drafting and critical revision of the manuscript and final approval of the submitted version, V.-S.G.; conception of idea, literature review, data analysis, revision of the manuscript and final approval of the submitted version, A.-T.L.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Flores-Arriaga JC thanks DGAPA-UNAM for postdoctoral scholarship.  
The authors declare no conflict of interest.
# On the Performance of Underlay Device-to-Device Communications
## 3 System Model  
In this section, a detailed system model is provided. In particular, we discuss the procedure operation of the whole networks, the large-scale path-loss, and small-scale fading. Additionally, three distinct power allocation schemes, i.e., the equal, random, and path-loss-based schemes, are provided. Then, based on the received signals, the signal-to-interference-plus-noise (SINR) at both D2D receivers and base station are formulated.  
Let us consider a single cell uplink cellular networks with \(N\) pairs of device-to-device users and cellular users, as shown in Figure 1. We assume that all nodes, i.e., the cellular and D2D users, are located inside the coverage area of the base station (BS) which is a disk with the center at the BS and \(O\) radius. It is noted that there is at most one node at a specific location under the considered networks. The D2D users are operated under the underlay protocol which permits them to continuously access the licensed spectrum provided that the aggregate interference at the base station is less than a predefined threshold [44]. The whole transmission takes place in one resource block (RB), where the D2D transmitters send their information to their dedicated D2D receiver (Here, an RB can be a time resource or a frequency resource [45]).  
Meanwhile, the cellular user also transmits its signals to the base station. (The present paper considers an RB (one time-lot) scenario. However, if the multiple RBs scenario is adopted, the cellular user, of course, is able to access all RBs. As for the D2D users, they can also access all RBs, provided that the interference created by the D2D networks measured on the cellular users is strictly guaranteed. Additionally, the derived mathematical framework in the present paper can straightforwardly apply to the multiple RBs scenario where all RBs have at least one active cellular user.) As a result, D2D receivers are subjected to interference from both D2D networks and cellular networks. The BS, however, only experiences interference from the D2D networks due to the orthogonal resource allocation at the cellular networks such as time-division multiple access (TDMA) and/or frequency-division multiple access (FDMA). As all transmissions take place simultaneously, a power control scheme is needed at the D2D networks in order to guarantee the interference and quality of service (QoS) at the cellular networks, as well as to enhance the performance of the D2D networks. Three distinct power allocation schemes are considered in the present paper and are given in detail in Section 3.2.
# A Novel Spectral-Spatial Classification Method for Hyperspectral Image at Superpixel Level
## 3 Experimental Results
### Classification Results  
In Table 1, the lowest classification accuracy (77.63%) is obtained by SVM because only the spectral features of pixels are used for classification in this method. In contrast, the classification accuracy of the EPF is improved by about 10% due to the application of spatial information. In particular, the classification accuracy provided by SSC-SL raises nearly 20%. The classification result of the superpixel-level SSC-SL is better than those of other three spectral-spatial methods PF, IFRF and SuperPCA. This indicates that the application of superpixel in classification is helpful to improve classification accuracy. The reason is that the superpixel-level classification method not only effectively merges the spectral and spatial information, but also has a good denoising effect on HSI. The classification result of PCA-SLIC on Oats shows that this method divides it into two or more  
Figure 4: Overall accuracy of three datasets with variation of segmentation scale. superpixels, and there is always a misclassified superpixel. However, the improved SLIC algorithm can aggregate class Oats into a superpixel. The classification accuracy of 97.18% on Indian Pines dataset indicates that the introduced measurement, segmentation technique, and classification method are effective for unbalanced remote sensing datasets. Figure 5 presents the classification maps of these six methods on the Indian Pines dataset. The statistical results of these six methods on Salinas dataset are shown in Table 2. According to the OA index value, our algorithm achieves a better classification result of 99.03%. The classification result of EPF is slightly better than that of SVM in terms of OA (91.68% vs. 89.16%). It may be explained by the fact that the class boundaries of this dataset are relatively neat so that the advantage of EPF is not highlighted. The IFRF is superior to EPF and SuperPCA on this dataset. Part of the reason is that the transform domain recurrent filtering plays a good role in denoising in IFRF. It is easy to see from Table 2 that the classification accuracy of each class is greater than 95.5% in our classification result, except class Fallowroughlow (90.17%). It is worth noting that our method is still superior to
# Design of Multi-Modal Ship Mobile Ad Hoc Network under the Guidance of an Autonomous Ship
## 2 Related Research
### Ship Communication Network  
Some studies have explored the application of internet of things technology in maritime affairs, so there is the concept of ship internet, which connects ships, ports, and even related equipment and infrastructure. There has been further development for such emerging applications, such as route planning and optimization, collaborative collision avoidance decision-making [14], automatic berthing, unberthing, etc., can be assisted by the ship's internet [15]. [PERSON], [PERSON] et al. [16] introduced the architecture of ship internet and analyzed how to use it to improve shipping efficiency and security. The network proposed in this paper is a ship-to-ship network, which can be regarded as a part of the ship internet. IALA and IMO have proposed a VHF Data Exchange System (VDES) initiative, aiming to create a more spectrum-efficient marine MTC (Machine Type Communication) system to achieve global coverage [17; 18]. [PERSON], [PERSON] et al. [19] has modeled the Offshore Communication Network (OCN), and by solving the problem of effective connection maintenance and recovery between ship nodes, it provides maritime wireless internet between fishing vessel networks and solves the problem of communication between fishing vessels and the coast. Based on studying the unmanned detection ship at sea, [PERSON], [PERSON] et al. [20] built a network system of unmanned detection ships at sea by using the technical characteristics of a wireless self-organizing network for reference. Through simulation, we can see the advantages of the ad hoc network in ship networking.
## 3 Model Comparisons
### Comparison Criteria and Parameter Estimation Method
#### 3.1.3 Parameter Estimation Method  
Once the analytical expression for \(m(t)\) is derived, the parameters in \(m(t)\) can be estimated by using the maximum likelihood estimation (MLE) method or the least square estimation (LSE) method. Though estimates from MLE are consistent and asymptotically normally distributed as the sample size increases, sometimes the estimations may not be obtained especially under conditions where \(m(t)\) is too complex. Here we turn to LSE methods to estimate the models' parameters.  
The sum of the squared distance is given as follows:  
\[L=\sum\limits_{i=1}^{n}\left(y_{i}-\hat{m}(t_{i})\right)^{2} \tag{15}\]  
where \(y_{i}\) is the cumulative number of faults detected or corrected in time (0, \(t_{i}\)), and all failure data are denoted in the form of pairs (\(t_{i}\),\(y_{i}\)) (\(i=1,2,\ldots,n\)\(>\)\(t_{1}<t_{2}<\cdots<t_{n}\)).  
By taking derivatives of (15) with respect to each parameter, and setting the results equal to zero, we can obtain several equations for the proposed model as follows:  
\[\frac{\partial L}{\partial a}=\frac{\partial L}{\partial a}=\frac{\partial L }{\partial A}=\frac{\partial L}{\partial c}=\frac{\partial L}{\partial r}= \frac{\partial L}{\partial b}=\frac{\partial L}{\partial\beta}=0 \tag{16}\]  
After solving the above equations simultaneously, we can obtain the least square estimates of all parameters for the proposed model.  
As noted, solutions of the above Equation (16) are extremely difficult and require either graphical or numerical methods. Under the help of MATLAB, the calculation of the parameters is not a critical problem, though adding additional parameters to make the software reliability model more complex makes the work of parameter estimation more difficult.
## 1 Introduction
[32] was also employed to estimate the channel of the OTFS system recently. A CS estimation scheme for OTFS channels with sparse multipath was introduced in [33]. In [34], the author combined Fibonacci search and OMP together to estimate the channel with fractional delay and Doppler characteristics. Along with deep learning developing rapidly in recent years, scholars have also carried out some work to introduce deep learning into OTFS channel estimation field [35; 36; 37; 38; 39]. [PERSON] et al. combined the time-domain method and deep learning method together in [40], with low complexity and good flexibility. Nonetheless, it is worth acknowledging that the methods mentioned earlier can mostly be seen as block-wise channel estimation methods. Thus, the performance of these methods may deteriorate severely in low signal-noise ratio scenes. We introduce a symbol-based adaptive method that estimates the channel of the OTFS system in the DD domain by improving proportionate normalized least mean squares (IPNLMS) [41]. Generally speaking, the symbol-based channel estimation method could generate an estimated channel in each adaptation step, which is usually used to estimate and track the time-varying channels [42]. In the DD domain in the OTFS system, the channels could be seen as invariant after phase compensation during one OTFS symbol. Then, these estimation results generated by multiple adaptation steps can be regarded as multiple noisy samples related to the same channel. Noticing this characteristic, we propose a new denoise strategy scheme which is composed of three processes to enhance the accuracy of adaptive channel estimation. The superiority of the proposed method is finally verified by simulation and lake experiment.
## 3 Proposed Approach
### Initial Implementation and Understanding of the Data Flow  
The initial stage was to test an initial set of processes, confirm the gaps identified during the literature review, and then analyse the volume and type of information produced throughout a highway project that involved the construction of a 700 m long viaduct. Several components were fed into the project, and these are summarised in Figure 2. There was a particular focus on analysing the outcomes of implementing the BS 1192 suite of standards on the project. It has been noted that the ISO 19650 standards have superseded the BS 1192 suite of standards since the inception of this research project. However, the validity of the results from this research stage has not changed, as the transition from one standard to the other is relatively simple, with subtle variances such as those in terminology rather than overall processes [8].  
Figure 1: The overarching solution overview: the process of collecting information, refining processes, and developing the prototype.  
Figure 3 shows the areas this piece of research covers. The aim was to implement the processes according to the standards, analyse the breakdown of the AIM, identify the challenges faced when creating it, and determine the potential problems that could have been faced during operation.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis
### PI Controller Simulation Results  
The output current of the grid-connected inverter when a PI controller was used in the current control loop is shown in Figure 8a. During the change in the reference current command at 0.05 s, the shape of the current waveform was slightly distorted during the transient, but quickly recovered to maintain a sinusoidal shape. The change in \(d\)-axis current means that the output power of the inverter changed from 500-1500 W, as shown in Figure 8b. There was an overshoot of 40% in the active power response, which was slightly higher than expected for a PI controller designed with a phase margin of 45\({}^{\circ}\). This can be attributed to the peaks from the switching of the converter. Such an overshoot can be critical for larger systems, as the inverters need to be over-sized, which ultimately increases the cost. The overshoots can theoretically be minimized in the control design phase, but would result in a slower speed response. The reactive power, however, remained constant at around 50 Var (average) throughout the simulation as the \(q\)-axis reference was kept at 0 A. There was a short transient pickup in the reactive power when the active power changed.
# Towards Single 2D Image-Level Self-Supervision for 3D Human Pose and Shape Estimation
## 2 Related Works
### Three-Dimensional Human Pose and Shape Estimation  
Followed by progresses in the field of 3D human pose estimation, simultaneously estimating the human body pose and shape has become a recent trend [10; 33; 34; 35; 36; 37; 38; 39; 40; 41]. There are two main approaches for estimating 3D human poses and shapes (i.e., meshes): one is the optimization-based methods [9; 42; 43], and the other is the regression-based methods [5; 6; 7; 8; 36; 37]. SMPLify [9] is one of the representative optimization-based methods. It first estimates the 2D skeletons from the images and fits the graphical 3D body model (i.e., SMPL [13]) to it using the optimization method. [PERSON] et al. [5] proposed the deep learning-based regression framework. This is an end-to-end framework for reconstructing a 3D human mesh from a single RGB image. [PERSON] et al. [36] proposed neural body fitting. This framework generates 12 semantic human body parts with a semantic segmentation CNN. From these semantic human body parts, pose and shape parameters of SMPL are estimated and a 3D human mesh is reconstructed. [PERSON] et al. [37] proposed a framework that estimates heatmaps related to pose parameters of SMPL and silhouettes related to shape parameters of SMPL simultaneously. Using these heatmaps and silhouettes, a 3D human mesh is reconstructed. A method [44] has also been introduced that exploits the merits of both optimization-based and regression-based methods for 3D mesh estimation. The temporal dynamics of the human body and shape have also been incorporated in the framework of [6]. The framework used to overcome the adaptation of a pre-trained model of human mesh estimation to the out-of-domain field was proposed in [8]. In addition, non-parametric body mesh reconstruction methods have been proposed [38; 45]. [PERSON] et al. [38] proposed an encoder-decoder network: first, a decoder is trained to predict a body silhouette from the SMPL parameter input; second, an encoder is trained on a real image and corresponding silhouette pairs while the decoder is fixed. [PERSON] et al. [45] proposed end-to-end human pose and mesh reconstruction with a transformer [46].
###### Abstract  
This study aimed to evaluate and compare the effects of heat-killed and live _Lactobacillus_ on mice with diabetes induced by high-fat diet with streptozotocin (STZ). Results based on body weight and liver pathological changes, oral glucose tolerance test, and related serum index (AST (aspartate aminotransferase), ALT (alanine aminotransferase), MDA (malondialdehydehyde), TNF-\(\alpha\) (tumor necrosis factor \(\alpha\)), INS (insulin), and GC (glucagon) and gene expression of IL-1\(\beta\) (Interleukin 1\(\beta\)), IRS-1(Insulin receptor substrate 1), GLUT-4 (glucose transporter type 4), PPARy (peroxisome proliferators-activated receptor \(\gamma\)), and SREBP-1c (sterol-regulatory element-binding protein-1c) levels indicated that _Lactobacillus fermentum_ (LF) and _Lactobacillus plantarum_ (LP) could increase the average weight, alleviate the degree of damage in the liver, and improve the glucose tolerance of mice with diabetes. LF and LP also participated in the downregulation of AST, ALT, MDA, TNF-\(\alpha\), INS, and GC in serum, as well as the inhibition of IL-1\(\beta\), TNF-\(\alpha\), IRS-1, GLUT-4, PPAR\(\gamma\), and SREBP-1c expression. These regulating effects were remarkable, and the regulating effect of the live group was significantly better than that of the heat-killed group. This study suggested that LF and LP can significantly alleviate liver damage and hepatic insulin resistance in mice with diabetes and that the acting mechanisms of LF and LP were related to cellular components and their activities.  
_Lactobacillus fermentum; Lactobacillus plantarum; Chinese pickle; streptozotocin; diabetes_