esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Background_: Survival of dental implants in well-controlled Type 2 diabetes (T2 DM) was found to be comparable to that in healthy patients. However, to our best knowledge, there have been no studies of the bone histomorphometry of ooseointegration in patients with Type 2 diabetes. _Purpose_: To compare bone-implant-contact (BIC) and new bone formation between well-controlled Type 2 diabetes with HbA1c of less than 8% and healthy controls. _Methods_: 10 diabetic (T2 DM) patients and 10 healthy controls were selected. Each patient received a 2.5 mm \(\times\) 5 mm micro-implant in the maxilla, in either the premolar or first molar area. After 8 weeks of healing, the micro-implant was retrieved using a trephine bur and sent for bone histomorphometric analysis. A commercial titanium implant was immediately placed as the conventional treatment. _Results_: The mean BIC (30.73%) in T2 DM patients was significantly lower than in the healthy patients (41.75%) (\(p\) = 0.01). New bone formation around the implant surface was reduced in T2 DM patients (36.25%) compared to that in the control group (44.14%) (\(p\) = 0.028). The Pearson correlation coefficient revealed a strong correlation between increased HbA1c and decreased BIC (\(p\) < 0.05) and decreased new bone formation (\(p\) < 0.05). _Conclusions_: Within the limitation of this study, bone-to-implant contact and bone healing around dental implants in T2 DM patients were significantly lower than in healthy patients.  
Keywords: dental implants; type 2 diabetes; ooseointegration; obesity; bone histomorphometry +
Footnote †: journal: Applied
# Accurate Instance-Based Segmentation for Boundary Detection in Robot Grasping Application
## 2 Related Works  
### 2D Approach  
In recent years, many researchers, involving both 2D and 3D approaches, have been approved to upgrade the segmentation task. In particular, apart from Mask R-CNN, Unet [12] was designed for biomedical image segmentation. It consisted of a contracting path and an expansive path. The contracting path was a typical architecture of a convolutional network. The essential part was the expansive path, which aimed to upsample the feature map and improve the accuracy, in detail, in localization and segmentation.  
Improvement of the Mask-RCNN object segmentation algorithm [13] handled the poor masks of Mask R-CNN by combining with Grabcut [14]. However, Grabcut depends too much on the contrast of the input images and does not consider the detailed information in segmentation generally. Moreover, it can preserve the foreground but cannot classify it.  
Fast Vehicle and Pedestrian Detection Using Improved Mask R-CNN [15] introduced a novel Side Fusion feature pyramid network (SF-FPN) by adding a feature pyramid network (FPN) [16] to obtain more precise feature semantic information. This was the most important part that helped researchers increase the accuracy.  
Mask Scoring R-CNN (MS R-CNN) deals with problems that the predicted mask was not qualified to; in addition, it is confident. This model has a network block to learn the quality of the predicted instance masks and calibrate the misalignment between mask quality and mask score. Therefore, the model noticeably gained the instance segmentation performance.  
Mask-Refined R-CNN (MR R-CNN) introduced a new semantic segmentation layer that realized feature fusion by constructing a feature pyramid network and summing the forward and backward transmissions of feature maps of the same resolution. It was applied for the semantic segmentation, while the other FPN structures were selected for object detection. From this method, the network can determine the characteristics of various scales simultaneously and improve the sensitivity to the global and detailed information. In comparison to MS R-CNN and Path Aggregation Network (PAN) [17], MR R-CNN had a remarkable performance in the prediction of large objects.  
In summary, these above models based on 2D dataset input accomplish the appreciable improvement of Mask R-CNN detailed information segmentation. However, these models only consider the 2D feature of the image. It is hard to admit that these models are the complete solutions when practicing with dimly lit environments or obstructed objects.
# A Mobile LiDAR-Based Deep Learning Approach for Real-Time 3D Body Measurement
## 3 Proposed Algorithm for Mobile 3D Body Circumference Measurement
### Merging Keypoints and 3D Data
According to Table 1, the summation-then-constant method demonstrated superior accuracy, reducing MAE from 6.8 cm to 5.2 cm while improving the coefficient of determination \(R^{2}\) from 0.76 to 0.84. This result suggests that combining front and side circumferences before applying a single correction factor provides a more robust estimation approach. Figure 2:5 Figure 2:6 Figure 2:7 Figure 2:8 Figure 2:9 Figure 3:0 Figure 3:1 Figure 3:2In this study, the derived regression constant exhibited consistently high performance, irrespective of body type (e.g., lean or obese). This result indicates that the combination of front and side circumferences effectively captures an individual's body shape and may maintain a certain level of measurement accuracy across diverse ethnicities and nationalities. Nevertheless, to thoroughly validate this finding, future research should establish a large-scale dataset encompassing a broad range of ethnic and national groups to evaluate the generalizability of the regression constant.
# Optimization Algorithms in SDN: Routing, Load Balancing, and Delay Optimization
## 4 Specific Routing and Traffic-Related Algorithms in SDN
### Load Balancing Algorithms
For example, if a link becomes congested or fails, the SDN controller can immediately reroute traffic to avoid the affected area, maintaining optimal performance without manual intervention. The need of using load balancing involves the capability to make accurate traffic predictions, so in consequence for the purpose of implementing an efficient algorithm in SDNs, it is very important to be able to predict with high accuracy changes related to traffic patterns and loads. Recurrent Neural Networks (RNNs) were proposed by [PERSON] et al. [29] in order to predict the traffic in general networks like data or internet networks. The anticipation of the volumes and patterns of the traffic over time was the goal of this paper. [PERSON] et al. [30] researched mobile app traffic prediction at the packet-level granularity using advanced Deep Learning techniques. The study analyzed various neural network architectures like Conventional Neural Networks, Recurrent Neural Networks, and Composite Neural Networks to enhance prediction accuracy. The study discovered important improvements compared to other methods such as Higher-order Markov Chains and Random Forest Regressor, predicting traffic characteristics on datasets generated by different categories of mobile applications. The research highlights the potential of Deep Learning in network traffic prediction, bringing valuable insights and comparative analyses for predictive model optimization.
## 2 Materials and Methods
### Data Sources  
In recent years, the NTL data sources obtained from the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) and the Suomi National Polar-orbiting Partnership-Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) have been the most commonly used [16], both of which are provided by the National Oceanic and Atmospheric Administration (NOAA) of the United States. The DMSP-OLS dataset provides global NTL maps from 1992 to 2013 with a spatial resolution of 30 arc-seconds, while the NPP-VIIRS dataset provides global NTL maps from 2012 up to the present with a spatial resolution of 15 arc-seconds. Compared with the DMSP-OLS dataset, the NPP-VIIRS dataset possesses higher spatial resolution and avoids the impact of brightness saturation problems. Furthermore, the NPP-VIIRS dataset is continuously updated for future research. Therefore, the annual VIIRS nighttime lights version 2 (VNL V2) product of the NPP-VIIRS satellite created from monthly cloud-free composites was selected as the data source of this study, which can be downloaded from [[https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)]([https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)) (accessed on 20 September 2023).  
The NTL remote sensing images for provincial-level administrative regions of China from 2013 to 2023 were obtained and preprocessed by projection conversion, noise removal, and outlier processing for subsequent research and analysis [35]. Taking the NTL images from 2021 as examples, Figure 2 shows the original and preprocessed NTL images of various provincial-level administrative regions of China. Meanwhile, the TOVAFAF for the corresponding provincial-level administrative regions from 2013 to 2023 were obtained from the website of the National Bureau of Statistics of China. It should be mentioned that  
Figure 1: The provincial-level administrative regions in China.  
the TOVAFAF is expressed in the form of currency, which is calculated by multiplying the output of agriculture, forestry, animal husbandry, fishery, and their by-products by the price of their respective unit products. In this paper, the unit of the TOVAFAF for each provincial-level administrative region is expressed in 100 million yuan.
## 4 Conclusions  
In this research, the ability of a CW to remove OM and TC from water was investigated in a continuous scheme with various HRTs (1 day and 2 days) with varying initial DOC concentrations and TC concentrations. In different experimental runs with variable concentrations of OM operating during HRT-1 day CW treatment can have a high tolerance of variation of DOC concentrations in influent water and maintain stable effluent water quality. The discrepancy in the removal efficiency of OM between HRT-1 day and HRT-2 day was minor. This indicates that most of OM may be reduced on the first day of operation. However, HRT played an important role in the removal of ammonia in this experiment. The removal efficiency of NH\({}_{3}\)-N increased considerably with the increase of HRT.  
In terms of TC removal, CW showed an excellent performance in reducing TC in water, especially in the first two hours of the operating period, when more than 80% of the TC was removed from the influent water. In addition, TC removal efficiencies of 90.20 \(\pm\) 1.02%, 87.60 \(\pm\) 2.48% and 95.42 \(\pm\) 0.73% were obtained from these trials with 1, 5, 10 mg/L of initial TC concentration respectively, indicating that the CW system was effective in eliminating TC.  
In terms of applying the combination of CW and PCR (TiO\({}_{2}\)/\(\alpha\)-Al\({}_{2}\)O\({}_{3}\), UVA) in treating water containing OM, the findings of this research showed an improvement in the quality of the effluent. The concentration of sCOD in effluent water was estimated to be compliant with THM standards if chlorination was applied to the treated water. However, the increase in treatment efficiency of the combined system compared to that of CW alone was minor. Therefore, further research on the combination of CW and photocatalytic oxidation needs to be done to enhance the performance of this combined system.  
Although the combined system decreased OM and TC successfully in this study, there are still a few limitations which must be taken into account. There is a great need to illuminate the black box CW to gain performance data for both the adsorption process and microbial activity. Also, the contribution of the plants to the overall removal process should be considered to enhance the treatment performance of CWs for specific compounds. In addition, other water quality parameters, for example, microorganisms and DBP (such as THMs) should be investigated. Further experimental investigations with longer operation time periods are required to provide more comprehensive and convincing evidence which will help gain more insights into the removal mechanism of target compounds in combined CW and PCR systems.  
Conceptualization, K.-C.C.; Data curation, H.T.T.N.; Formal analysis, H.T.T.N.; Methodology, K.-C.C.; Supervision, K.-C.C.; Validation, H.-R.C. and K.-C.C.; Writing--original draft, H.T.T.N.; Writing--review & editing, H.T.T.N., H.-R.C. and K.-C.C.  
This research received no external funding.  
The authors declare no conflict of interest.
# An Overview of the Alternative Use of Seaweeds to Produce Safe and Sustainable Bio-Packaging
## 2 Potential Algal Bioactive Compounds for Bioplastic Production  
The accumulation of petroleum-based plastic on land and into oceans is of growing concern to society, as it can result in physical issues for organisms that ingest plastic or are entangled in fishing nets or plastic ropes, and also lead to the indirect consumption of microplastic previously ingested by animals subsequently consumed by humans [42]. The accumulation of microplastics can lead to an accumulation in the organism of toxic chemical compounds, incorporated into/onto plastics. According to [PERSON] et al. [43], these chemical substances can accumulate in higher trophic levels, and thus into seafood, creating health issues for humans. Entanglement and ingestion of microplastic debris can be lethal or subt lethal, causing reduced food particle capture and swallowing, impaired reproduction ability, loss of sensitivity or mobility, the inability to escape from predators, and/or decreased growth. [PERSON] and [PERSON] [44] reported that sea turtles, marine mammals, and all types of sea birds are species most negatively affected by entanglement in, and ingestion of, plastic pollution. New materials based on biopolymers might provide a sustainable solution to replace synthetic plastic with edible and non-harmful bioplastic. Various studies have already proven that bioplastic can be produced from starch, crops, and microalgae [45; 46; 47; 48; 49]. The use of cultivable marine resources will avoid the use of land and resources destined for crops; it also potentially avoids deforestation, as seaweed cultivation is performed in outdoor or laboratorial conditions [50; 51; 52]. Moreover, the large-scale production of seaweeds reduces costs and increases compound availability.  
Seaweed-derived biological compounds most suitable to produce biofilms or bioplastics are alginate, carrageenan, and agar [53; 54; 55].
## 4 Experiment and Analysis
### Complexity Comparison Experiment  
We systematically evaluated the overall efficiency of PointNet, PointNet++, SPG, RandLA-Net, and LDE-Net on Sequence 08 of the SemanticKITTI dataset. Table 4 quantifies the total time, parameters, and mIoU of different methods. PointNet has the fewest parameters and faster inference times. Since PointNet is a feature extraction for the whole point cloud, it is not suitable for 3D point cloud semantic segmentation in large-scale environments. Then, PointNet++ added local feature extraction specifically, and the inference time, parameter number, and mIoU also increased a lot. SPG has fewer parameters, but it takes the longest time to process point clouds due to the expensive steps of geometric partitioning and supergraph construction. Compared with RandLA-Net, LDE-Net once again enhances the ability to extract local features. Its model complexity and inference time increased, but it has some advantages in terms of the mIoU. Thus, LDE-Net improves the mIoU by 1% with a slight increase in computation time.
# Magnetic Field Effect on the Handedness of Electrodeposited Heusler Alloy
## 1 Introduction  
In magneto-electrochemistry (MEC), electrochemical experiments are carried out with the application of a magnetic field in the electrochemical cell. Synergic effects are observed upon changing the superimposition direction of the electric and magnetic fields, causing complex hydrodynamics conditions in the cell, as well as the emergence of unique electronic properties in both bulk reagents and electrodes [1]. From a historical point of view, [PERSON] observed for the first time the effect of the magnetic field intensity and direction on electrochemical process [2]. Nonetheless, MEC experiments form a limited niche and rarely addressed the measurement procedure.  
The synthesis of new compounds that allow the modulation of the magnetic field is a hot topic [3, 4]. Fairly elegant and interesting papers are present in the literature which aim to assess the effect of a magnetic field on both bulk and interfacial properties in electrochemical systems. The interesting results report on the \"chiral texture\" that is observed in electrodeposited films grown under the influence of an applied magnetic field: metals (Cu, Ag, Fe, etc.); alloys; and conducting polymers (polyaniline, etc.). Such chiral structures are useful in technological applications such as enantio-selective recognition processes [5, 6].  
So far, most of the studies in the literature have used an external magnetic field to generate chiral structures or special hydrodynamic conditions. Of late, [PERSON] et al.,discovered a revolutionary \"chiral induced spin selectivity (CISS)\" effect: the electron transmission through chiral systems depends on the spin, giving rise to a net magnetic moment (with the associated magnetic field) [7], and references therein cited. The idea was recently demonstrated via an innovative Hall device that serves as the working electrode in an electrochemical cell, and is able to give information on the correlation of spin selectivity and the electrochemical process without an externally applied magnetic field [8].  
This article aims to explore the effect of applying a magnetic field during massive electrochemical deposition (the metallic deposit has a thickness in the 350 to 450 nm range) of a Heusler alloy. The Heusler alloy is a family of magnetic compounds with a face-centred cubic crystal structure and formula X\({}_{2}\)YZ, where X and Y are transition metals and Z is a p-block element [9; 10]. The Heusler alloy is interesting for many applications from data storage [11] to spintronics [12]. In this work, we performed the electrodeposition of the Co-Fe-Sn system that was previously obtained galvanostatically [13; 14] and potentiostatically [15] by other authors in the absence of an external magnetic field.
## 3 Results and Discussion
### Galvanostatic Charge/Discharge Measurements  
Initial lithiation/delithiation curves of the SiCN-S composites are shown in Figure 5. Two typical lithiation voltage plateaus of sulfur are clearly visible at around 2.4 and 2.0 V during the lithiation process. This behavior is related to the reaction of S\({}_{8}\) with Li to form soluble polysulfides Li\({}_{2}\)S\({}_{\rm x}\) (4 \(\leq\) x \(\leq\) 8) and solid Li\({}_{2}\)S\({}_{2}\) and Li\({}_{2}\)S [6; 15; 33]. The relevant reaction equations are as follows [34]:  
\[{\rm S_{8}+2e^{-}+2 Li^{+}\longrightarrow Li_{2}S_{8}} \tag{1}\]  
\[{\rm Li_{2}S_{8}+2e^{-}+2 Li^{+}\longrightarrow 2 Li_{2}S_{4}} \tag{2}\]  
\[2 Li_{2}S_{4}+4e^{-}+4 Li^{+}\longrightarrow 4 Li_{2}S_{2} \tag{3}\]  
\[4 Li_{2}S_{2}+8e^{-}+8 Li^{+}\longrightarrow 8 Li_{2}S \tag{4}\]  
Nevertheless, the two voltage plateaus of SiCN-S-800 (2.33 and 2.03 V) reveal a hysteresis, compared with other two composites (2.36 and 2.06 V), relating to a higher polariza  
Figure 4: SEM images and EDS elemental mapping of SiCN-S-800 (**a–c**), SiCN-S-900 (**d–f**), SiCN-S-1100 (**g–i**). Yellow represents Si, purple represents S.  
tion [35; 36]. This phenomenon is also seen in the delithiation curves (2.35 V of SiCN-S-800 vs. 2.23 V of SiCN-S-900 and SiCN-S-1100), which is discussed in terms of sample SiCN-S-800, which has the lowest degree of graphitization among all composites based on the Raman results. This, in turn, results in the lowest conductivity of the free carbon phase in sample SiCN-S-800 [37]. Initial lithiation capacities and corresponding columbic efficiencies of all SiCN-S composites are listed in Table 2. The initial lithiation capacity decreases from sample SiCN-S-800 to sample SiCN-S-900 and then increases in sample SiCN-S-1100. This behavior is explained by the amount of accessible sulfur embedded in the ceramic matrix, which decreases from SiCN-S-800 to SiCN-S-900 with decreasing SSA of the ceramic matrix. With increasing pyrolysis temperature, the degree of graphitization increases, resulting in an enhanced electrical conductivity of the SiCN matrix and, hence, higher initial lithiation capacity of the sulfur. All composites exhibit around 85% of initial coulombic efficiency, which indicates that the characteristic \"shuttle effect\" is less pronounced in all samples in the beginning of cycling.  
Figure 5: Lithiation curve of samples SiCN-S-800, SiCN-S-900, and SiCN-S-1100 (**a**); delithiation curve of samples SiCN-S-800, SiCN-S-900, and SiCN-S-1100 (**b**).
## 3 Methodology
### Overall Framework of BPNN Fusion Model
#### 3.1.3 Structure and Training Design of BPNN  
To model complex relationships, BPNN consists of input, hidden, and output layers, each containing multiple neurons. The network adjusts connection weights by learning from the training data. Figure 3 illustrates the structure of the neural network.  
The goal of minimizing the loss function is achieved through the backpropagation process, which utilizes an optimization algorithm to update the network's weights and biases. The loss function, defined by the following formula, represents the squared difference between predicted output and actual output, commonly known as _MSE_. To mitigate the risk of overfitting, an L2 regularization term is added to the loss function. The loss function is finally expressed as:  
\[C(w,b)=\frac{1}{2n}\sum_{x}\left(\|\hat{y}(x)-y(x)\|^{2}\right)+\frac{\lambda} {2n}\sum_{w}w^{2} \tag{6}\]  
where \(C(w,b)\) represents the loss function, where \(w\) and \(b\) denote the weights and biases, respectively, \(n\) is the number of training samples, \(\sum_{x}\) indicates summation over all training samples \(x\), \(\hat{y}(x)\) is the predicted output of the neural network on sample \(x\), \(y(x)\) is the actual output, \(\lambda\) is the regularization parameter, and \(\sum_{w}\) represents summation over all weights \(w\).