esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
The following are available online at [[http://www.mdpi.com/2076-3417/9/22/4805/s1](http://www.mdpi.com/2076-3417/9/22/4805/s1)]([http://www.mdpi.com/2076-3417/9/22/4805/s1](http://www.mdpi.com/2076-3417/9/22/4805/s1)), Figure S1: Effect of common ions (Ca\({}^{2+}\), Mg\({}^{2+}\), Na\({}^{+}\) and K\({}^{+}\)) on the removal efficiency of lanthanides using ground original sample, Figure S2: Adsorption capacity of (a) La(III) and (b) Eu(III) both in single and multiple solution by using L1 or L2, Figure S3: Langmuir isotherm for La(III) adsorption onto L2, Figure S4: Freundlich isotherm for La(III) adsorption onto L2, Figure S5: Langmuir isotherm fitting for Eu(II) adsorption onto L2, Figure S6: Freundlich isotherm fitting for Eu(III) adsorption onto L2, Figure S7: The kinetic models for La(III) adsorption onto L2, Figure S8: The kinetic models for Eu(III) adsorption onto L2, Figure S9: Plots of InK\({}_{\rm Q}\) vs. 1/T for the estimation of thermodynamic parameters obtained for the adsorption of (a) La(III) and (b) Eu(III) on L2, Figure S10: Plots of \(q_{\rm e}\) vs. In (\(q_{\rm e}\)/\(c_{\rm e}\)) for (a) La(III) and (b) Eu(III) on L2, Figure S11: Optimized structures of (a) [Eu(H\({}_{2}\)O)\({}_{8}\)]\({}^{3+}\) and (b) [Eu(EDTA)(H\({}_{2}\)O)]\({}^{-}\). Table S1: Coefficients of Langmuir and Freundlich isotherms for La(III) adsorption onto L2, Table S2: Coefficients of Langmuir and Freundlich isotherms for Eu(III) adsorption onto L2, Table S3: Kinetic parameters of Pseudo-first order and Pseudo-second order, Table S4: Kinetic parameters of Elovich model and Intraparticle diffusion, Table S5: Thermodynamic parameters for the adsorption of La(III) on L2, Table S7: Bond lengths of [Eu(H\({}_{2}\)O)\({}_{8}\)]\({}^{3+}\) shown in Panel (a) of Figure S3, Table S8: Bond lengths of [Eu(EDTA)(H\({}_{2}\)O)]\({}^{-}\) in Panel (b) of Figure S11, Table S9: Bond lengths of [Eu(H\({}_{2}\)O)\({}_{8}\)]\({}^{3+}\) shown in Panel (a) of Figure S11, Table S10: Bond lengths of [Eu(EDTA)(H\({}_{2}\)O)]\({}^{-}\) in Panel (b) of Figure S11. Experiment and writing, S.Z. ; data evaluation and supervision N.K. ; Calculation, K.M. ; measurements of Zeta-potential, H.O. This research was funded by Grant-in-Aid for Scientific Research Program (C), grant number 16K00599 from the Japan Society for the Promotion of Science and the project of the Jiangxi Provincial Department of Education, grant number GJJ180385. This research was also supported by a Fund for Promotion of Niigata University KAAB Projects from the Ministry of Education, Culture, Sports, Science and Technology, Japan. The authors are grateful to [PERSON] of Office for Environment and Safety, [PERSON] of the Facility of Engineering and [PERSON] of the Center of Instrumental Analysis in Niigata University for permitting the use of ICP-MS, FT-IR and XRD; and for giving helpful advice on measurements. Computations were performed by the Research Center for Computational Science, Okazaki, Japan. The authors declare no conflict of interest.
# Reduction of Phase Shifters in Planar Phased Arrays Using Novel Random Subarray Techniques
## 2 Random Subarray Design Methodology
### Planar Array Design from Optimal Linear Arrays  
An initial approach for addressing this problem is creating a planar array from a collection of linear arrays with subarrays. These sequences are optimal solutions of an exhaustive search performed in a search space of solutions created from a given set of subarrays \(K_{j}\) and a steering angle usually in the range of \([-12^{\circ}\leq\theta\leq 12^{\circ}]\) for optimal performance [14].  
Let \(S_{m}\) be a linear array that constructs each row of a \(N\times M\) planar array, where \(m\in[1,2,\ldots,M-1,M]\) represents the number of rows in the array. Then, the search space \(\mathcal{S}\) or number of possible solutions is defined by  
\[\mathcal{S}=\frac{(s+M-1)!}{M!(s-1)!}, \tag{8}\]  
where \(s\) denotes the number of sequences used to construct each row of the planar array. When a set of subarrays of sizes \(j\) is chosen, the number of possible combinations in which a sequence can be created is given by  
\[s=\frac{(PS_{m})!}{\prod\limits_{j\in J\subset\mathbb{N}}(K_{j})!}, \tag{9}\]  
\(PS_{m}\) is the number of subarrays according to (6).
# An Under-Sampling Array Signal Processing Method Based on Improved Hadamard Matrix
## 3 Improved Hadamard Matrix
### Improved Hadamard Matrix
The improved Hadamard matrix is a deterministic measurement matrix. On the one hand, in the array signal processing, if there are several sensors error receiving or failure, we can use CZ method to zero the corresponding column vector element in the measurement matrix to remove invalid or useless data and itself interference from the array; On the other hand, in the process of compressing and under-sampling the array, as shown in Figure 3, the relationship between the array signals before and after under-sampling compression is established through the improved Hadamard matrix, the unsampled array data and redundant information are removed by the method of CZ, to accelerate the reconstruction speed and enhance the performance of target estimation. After the above-mentioned array compression zeroing method, there is still irrelevance between the columns of the measurement matrix \(\Phi\), and when the number \(N\) is close to the number \(U\), it can still basically satisfy the RIP. However, the selection of \(U\) and \(N\) in the improved Hadamard matrix proposed above still has certain application conditions. The size of its construction dimension \(U\) must satisfy an integer multiple of 2, that is, \(size(U)=2^{r},r=1,2,\cdots\), therefore, our reconstructed \(N\) needs to be as close to \(U\) as possible. From the Hadamard matrix of \(U\times U\), and through the above introduction method, after selecting a sub-matrix of size \(M\times N\) composed of \(M\) rows and \(N\) columns, which still has strong non-correlation and partial orthogonality.
## 3 Results
### Water Holding Capacity  
No significant differences were observed for WHC. However, it should be mentioned that some indications of improved WHC were observed for groups supplemented with antioxidants. Namely, an important effect size was observed for cooking loss (lower) in pigs supplemented with sweet chestnut wood extract and hops (Hedge's \(g>0.8\); Figure 1). Similarly, an important effect size (Hedge's \(g>0.8\)) for lower processing losses at the end of salting was noted in hams of pigs supplemented with hop cones.  
\begin{table}
\begin{tabular}{c c} \hline
**Category of Traits** & **Traits with \(p\)-Values for the Treatment Group Effect** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Carcass traits \\ \end{tabular} } & Warm carcass weight (\(p=0.34\)), Fat thickness over _glutens_\(mins\) (\(p=0.42\)), Meat percentage (\(p=0.83\)), Green ham weight (\(p=0.03\)), \\  & Ham fat thickness (\(p=0.37\)), Belly weight (\(p=0.26\)) \\ \hline \multicolumn{3}{c}{Water holding capacity} \\ \hline LT muscle & Drip loss (\(p=0.41\)), Thawing loss (\(p=0.36\)), Cocking loss (\(p=0.14\)) \\ Ham & Water loss after salting (\(p=0.10\)) and ripening (\(p=0.57\)) \\ Belly & Water loss after salting (\(p=0.76\)) and ripening (\(p=0.99\)) \\ \hline \multicolumn{3}{c}{Chemical composition} \\ \hline LT muscle & Dry matter (\(p=0.98\)), IMF (\(p=0.33\)), Protein (\(p=0.78\)) \\ Dry-cured ham—BF muscle & Dry matter (\(p=0.54\)), IMF (\(p=0.79\)), Protein (\(p=0.80\)), NPN (\(p=0.39\)), Proteolysis index (\(p=0.27\)), Salt (\(p=0.52\)), \(a_{w}\) (\(p=0.35\)) \\ Dry-cured ham—SM muscle & Dry matter (\(p=0.70\)), IMF (\(p=0.91\)), Protein (\(p=0.72\)), NPN (\(p=0.43\)), Proteolysis index (\(p=0.39\)), Salt (\(p=0.51\)), \(a_{w}\) (\(p=0.45\)) \\ Dry-cured belly & Dry matter (\(p=0.18\)), IMF (\(p=0.76\)), Protein (\(p=0.99\)), NPN (\(p=0.93\)), Proteolysis index (\(p=0.61\)), Salt (\(p=0.42\)) \\ \hline \multicolumn{3}{c}{Objective color} \\ \hline LT muscle & Lightness (\(p=0.32\)), Redness (\(p=0.60\)), Yellowness (\(p=0.85\)), C (\(p=0.37\)), L\({}^{\circ}\) (\(p=0.72\)) \\ Dry-cured ham—BF muscle & Lightness (\(p=0.94\)), Redness (\(p=0.80\)), Yellowness (\(p=0.48\)), C (\(p=0.89\)), L\({}^{\circ}\) (\(p=0.89\)) \\ Dry-cured ham—SM muscle & Lightness (\(p=0.20\)), Redness (\(p=0.94\)), Yellowness (\(p=0.06\)), C (\(p=0.18\)), L\({}^{\circ}\) (\(p=0.08\)) \\ Dry-cured ham—fat & Lightness (\(p=0.79\)), Redness (\(p=0.67\)), Yellowness (\(p=0.89\)), C (\(p=0.71\)), L\({}^{\circ}\) (\(p=0.17\)) \\ \hline \multicolumn{3}{c}{Oxidative stability} \\ \hline LT muscle & Carbonyl groups (\(p=0.92\)) \\ Backfat & Racimat (\(p=0.28\)) \\ Dry-cured ham—BF muscle & TBARS (\(p=0.51\)) \\ Dry-cured ham—fat & Racimat (\(p=0.38\)) \\ Dry-cured belly & Racimat (\(p=0.38\)) \\ \hline \multicolumn{3}{c}{C*: Chroma; \(h^{c}\): Hue angle; LT: _Longissimus thoracis_; IMF: Intramuscular fat; BF: _Bicps femoris_; NPN: Non-protein nitrogen; \(a_{w}\): Water activity; SM: _Senimembranosus_; TBARS: Thiobarbituric acid reactive substances.} \\ \end{tabular}
\end{table}
Table 2: A list of traits with \(p\)-values for the treatment group effect.
# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
## 4 Implementation for Relation Classification  
Our approach introduces the semantic and structure information from FrameNet and knowledge graphs as prior knowledge to guide the distantly supervised relation extraction. In this section, we discuss how to train the knowledge guided relation extraction model. First, the word embedding (words in sentences and in LUs) and KG embedding are pre-trained by using the GloVe [38] tool and the knowledge graph embedding module, respectively. Then, the sentence embedding model can be trained by using the word-level attention mechanism based on the relation indicator of LUs. Finally, the bag-level representation of the sentence bag can be obtained by using the sentence-level attention mechanism. We adopt a pairwise margin-based ranking loss function [39] as the optimization target of our knowledge-guided graph convolutional network model.  
Given a text corpus \(D\) and a knowledge graph with relation set \(R\), the model aims to predict a relation type for each sentence bag in the textual corpus, which assigns a semantic matching score to each sentence bag as for how well the bag expresses a candidate relation. The vector representation of each bag \(\mathbf{r}_{b}\) can be obtained by using the models proposed in aforementioned section. During the training stage, we learn the vector representation \([W^{R}]_{r}\)for each relation label \(r\). To this end, we calculate the semantic matching score between each bag-level representation \(\mathbf{r}_{b}\) with each relation type, which is formalized as follows,  
\[S(x)_{r}=\mathbf{r}_{b}^{\mathsf{T}}\cdot[\mathbf{W}^{R}]_{r}, \tag{15}\]  
where \(\mathbf{W}^{R}\in\mathbb{R}^{d\times|R|}\) is a randomly initialized relation matrix whose columns represent different relation labels, and \(|R|\) is the number of the predefined relation types. In order to train the model, we define a loss function and optimize it over all instance in the training set \(D\),  
\[\begin{split}\mathcal{L}=& log(1+exp(\gamma(m^{+} -S(x)_{r^{+}})))\\ &+log(1+exp(\gamma(m^{-}+S(x)_{r^{-}}))),\end{split} \tag{16}\]  
where \(\gamma\) is a scaling parameter, \(m^{+}\) and \(m^{-}\) are hyper parameters. \(S(x)_{r^{+}}\) and \(S(x)_{r^{-}}\) represent the semantic matching score between \(\mathbf{r}_{b}\) with the corresponding actual relation type \(r^{+}\) and false relation type \(r^{-}\), respectively. In the training stage, we choose \(r^{-}\) with the highest false score as the negative relation type. We employ SGD optimizer to optimize the loss function and utilize \(L2\)-norm \(\beta\|\theta\|_{2}^{2}\) to prevent over-fitting, where \(\theta\) is the parameter set.
# Design of Wing Root Rotation Mechanism for Dragonfly-Inspired Micro Air Vehicle
###### Abstract  
This paper proposes a wing root control mechanism inspired by the drag-based system of a dragonfly. The previous mechanisms for generating wing rotations have high controllability of the angle of attack, but the structures are either too complex or too simple, and the control of the angle of attack is insufficient. In order to overcome these disadvantages, a wing root control mechanism was designed to improve the control of the angle of attack by controlling the mean angle of attack in a passive rotation mechanism implemented in a simple structure. Links between the proposed mechanism and a spatial four-bar link-based flapping mechanism were optimized for the design, and a prototype was produced by a 3D printer. The kinematics and aerodynamics were measured using the prototype, a high-speed camera, and an F/T sensor. In the measured kinematics, the flapping amplitude was found to be similar to the design value, and the mean angle of attack increased by approximately 30\({}^{\circ}\) at a wing root angle of 0\({}^{\circ}\). In the aerodynamic analysis, the drag-based system implemented using the wing root control mechanism reduced the amplitude of the force in the horizontal direction to approximately 0.15 N and 0.1 N in the downstroke and upstroke, respectively, compared with the lift-based system. In addition, at an inclined stroke angle, the force in the horizontal direction increased greatly when the wing root angle was 0\({}^{\circ}\) at the inclined stroke angle, while the force in the vertical direction increased greatly at a wing root angle of 30\({}^{\circ}\). This means that the flight mode can be controlled by controlling the wing root angle. As a result, it is shown that the wing root control mechanism can be applied to the MAV (micro air vehicle) to stabilize hovering better than the MAV using a lift-based system and can control the flight mode without changing the posture.  
biomimetic robot; micro air vehicle; flapping; drag-based system; dragonfly +
Footnote †: journal: Applied Physics Letters
# A Cyber-Physical System for Wildfire Detection and Firefighting
## 3 Materials and Methods
### YOLO Object Detection
#### 3.2.2 YOLOv5: Object Detection for UAV Component  
To extend the capabilities of our system, we incorporate YOLOv5 into the UAV component. YOLOv5 offers a streamlined architecture and optimized data augmentation strategies, making it ideal for resource-constrained UAVs. Leveraging its efficiency, YOLOv5 empowers the UAV to perform real-time fire detection and localization, enhancing aerial surveillance and coordination with the UGV. The integration of YOLOv5 within the UAV component showcases its advantages in terms of inference speed and its ability to handle resource limitations.  
By harnessing the strengths of YOLOv4 in the UGV component and YOLOv5 in the UAV component, our unified system achieves robust object detection capabilities and seamless integration within the overall framework. Moreover, the collaboration between UGVs and UAVs, facilitated by YOLO algorithms, enables real-time fire detection and localization, providing an effective solution for forest fire monitoring and detection.
## 3 Periodontitis: Pathogenesis and Early Diagnosis
### Early Detection and Salivary Diagnosis of Periodontitis  
Early detection of periodontitis would allow prevention of irreversible tissue damage, consequently improving treatment outcomes. In fact, early detection of periodontitis makes early treatment possible, preventing CAL and bone loss, which are irreversible once destroyed [23]; in addition, periodontal therapies, including scaling, root planing, and antimicrobial therapy, are more effective when conducted at early stages [24].  
Moreover, it is known that periodontitis is linked with systemic health, in particular with cardiovascular disease, diabetes, and adverse pregnancy outcomes. Early detection of periodontitis may help reduce systemic inflammatory condition and reducing risks of the correlated systemic implications [25]. Finally, intercepting periodontitis at early stages implicates early management, which reduces the need for complex and costly treatment, such as surgery or prosthetic rehabilitation [26].  
A promising means of early detection of periodontitis is the analysis of salivary samples. Saliva has the following advantages: it contains biomarkers, its collection is easy and non-invasive, and it provides real-time information. Saliva contains a wide array of potential biomarkers, including cytokines (e.g., IL-1\(\beta\), IL-6), enzymes (e.g., MMP-8), immunoglobulins, and microbial DNA/RNA, reflecting the health status of the patient, including periodontium condition [27]; its collection is simple, painless, and does not require specialized doctors, making it ideal for routine screening and epidemiological studies [28]; salivary diagnostics can provide real-time information on disease status, progression, and response to therapy [29]. All the characteristics of salivary diagnosis of periodontitis are very advantageous, making it a good potential periodontitis diagnostic tool.  
Figure 2: Schematic representation of pathogenesis of periodontitis. From healthy periodontal tissues, microbiota overgrown leads to a reversible condition called gingivitis, which consists of inflammation of gums mediated by innate immunity with bleeding and increased cervical fluid flow. In presence of dysbiosis gingivitis can get worse, leading to periodontitis, in which is a condition in which there is destructive inflammation mediated by acquired immunity with irreversible attachment and bone loss.
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 3 Methodology
### Experimental Setup  
Experiments were conducted using a cloud-based infrastructure on Google Colab, leveraging Tesla GPUs for accelerated training. Tensorflow 2.18.0 was the primary framework used for model development, training, and evaluation, owing to its flexibility and strong community support. TensorFlow was additionally used for validation and visualization tasks.  
The training pipeline involved standardized hyperparameters to ensure fair comparison across models mentioned in Table 1. Key configurations include the following:  
* Optimizer: Adam optimizer, selected for its adaptive learning rate and fast convergence properties [6].
* Learning Rate: \(1\times 10^{-4}\) for all models, including the hybrid.
* Batch Size: 32 images per batch, balanced for efficient GPU utilization and memory stability.  
Figure 1: Top row: Real images from the dataset. Bottom row: AI-generated fake images. (**a**) Real 1 (**b**) Real 2 (**c**) Real 3 (**d**) Fake 1 (**e**) Fake 2 (**f**) Fake 3.  
Loss Function: Cross-Entropy Loss, appropriate for binary classification tasks.  
The Adam optimizer [30] was consistently applied for all models, and training was performed under the same computational setup for reproducibility. Notably, the SE-ResNet50 hybrid model--despite being trained for only 10 epochs--demonstrated faster convergence and stronger generalization compared with its baseline counterparts, due in part to the effectiveness of transfer learning and the attention-enhanced SE blocks.  
These choices were based on iterative tuning and ensured optimal convergence while maintaining training stability and consistency across architectures.
# Robust Sampling Frequency Offset Estimation for OFDM over Frequency Selective Fading Channels
###### Abstract  
Digital radio mondiale (DRM) is a terrestrial radio broadcasting standard to replace existing analogue AM and FM broadcasting, which is based on an orthogonal frequency division multiplexing (OFDM) technique. This paper focuses on the issue of estimating a sampling frequency offset (SFO) in OFDM-based broadcasting systems under frequency selective fading channels. In order to design a robust SFO estimation scheme and to benchmark its performance, the performance of the various conventional SFO estimation schemes is discussed and some improvements on the conventional estimation algorithms are highlighted. The simulation results show that such a design enhances the robustness of the proposed scheme against frequency selective fading.  
digital radio mondiale; orthogonal frequency division multiplexing; sampling frequency offset; frequency selective fading +
Footnote †: journal: Applied Sciences
## 3 Discussion of the Noise Results  
### Experimental Validation of the Baseline Airfoil  
The measured acoustic spectra of the baseline NACA 0012 airfoil with a nonporous solid trailing edge under different free-stream velocities are presented in Figure 6 for geometric angles of attack of \(\alpha=0^{\circ}\), \(\alpha=5^{\circ}\), and \(\alpha=10^{\circ}\).  
Using the criterion of [PERSON] et al. [18] that an airfoil tonal noise is detected as the SPL value exceeds the broadband hump by 10 dB or greater, it can be easily observed that the experimental results have several characteristic features similar to data from the  
Figure 5: Sketch of the experimental setup (not to scale).  
literature: Tonal noise is sensitive to changes in the free-stream velocity and the geometric angle of attack. For the NACA 0012 airfoil with a sharp, nonporous trailing edge inclined at \(\alpha=0^{\circ}\), tonal noise is only detected at low free-stream velocities from 10 m/s to 40 m/s (see Figure 6a), while tonal noise disappears, and the acoustic spectrum is basically broadband at high free-stream velocities. As the geometric angle of attack increases to \(\alpha=5^{\circ}\), tonal noise is detected from the baseline NACA 0012 airfoil for all free-stream velocities. As shown in Figure 6b, the peak SPL of the tonal noise first increases and then decreases with the free-stream velocity at this angle of attack. Through observing Figure 6c for \(\alpha=10^{\circ}\), it is seen that the free-stream velocity at which tonal noise begins to appear increases to 40 m/s. It is agreed that a laminar separation area on either the pressure side or the suction side of the airfoil trailing edge is a necessary condition for the generation of strong tonal noise resulting from instability [19; 20; 21]. Moreover, it is found that the state of the boundary layer flow (laminar or turbulent) close to the airfoil trailing edge is significantly dependent on both model parameters (such as chord length and airfoil camber) and flow parameters (free-stream velocity and angle of attack) [22; 23; 24]. Therefore, the aforementioned sensitivity of the airfoil tonal noise stems from the complex nature of the laminar separation in the trailing edge boundary layer. Figure 7 summarizes the tonal noise regimes of the baseline NACA 0012 airfoil in this experiment, compared with the predicted tonal envelope of [PERSON] et al. [25] based on their theoretical methods. It can be seen that the tonal regimes are all in the envelope curve, which indirectly confirms the validity of this experiment and thus supports the subsequent parametric study in the following subsections.