esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
There has been no feasible approach for predicting weight loss after bariatric surgery. This study develops wide and deep learning (WAD), a predictive and explainable artificial intelligence for weight loss after sleeve gastrectomy with medical image and non-image data, such as electronic medical records (EMRs). Prospective data came from 42 patients with sleeve gastrectomy at a university hospital. They were followed for one year after surgery. The dependent variable consisted of three categories: minimal, moderate, and significant change groups, classified based on postoperative percentage total weight loss (%TWL) in body mass index. A pair of 100 images and their non-image data came from each patient, with 4200 pairs from 42 patients in total. A WAD model was trained and tested with 3200 and 1000 pairs, respectively. Here, the WAD model combined a convolutional neural network (CNN) for image data and a linear layer for non-image data (EMR). The study population included 42 patients, with a mean age of 36.6 years (standard deviation SD 11.0) and a female proportion of 58% (26/45). On average, %TWL was 19.1 (SD 2.8), 27.3 (SD 2.2), and 35.1 (SD 4.7) for the minimal, moderate, and significant change groups, respectively. The corresponding accuracy outcomes were 61%, 100%, and 75% for the minimal, moderate, and significant change groups (average 71%). When the minimal and moderate change groups were combined, the accuracy was 100% for the combined group and 75% for the significant change group, with an overall average accuracy of 88%. Baseline HOMA2-B, insulin, and vitamin B12 were major predictors of %TWL. The optimal region of interest for predicting %TWL was found to be the entire cross-section above the diaphragm. In conclusion, WAD is an effective predictive and explainable artificial intelligence for weight loss following sleeve gastrectomy with image and non-image data. The most important predictors of postoperative weight loss were identified as baseline HOMA2-B, insulin, and vitamin B12 levels, while the key region of interest (ROI) in abdominal CT imaging was the entire cross-section located above the diaphragm.  
s 1 (2025) 15 2457 1
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 4 Applications of DLC Coatings
### Medical Applications of DLC Layers  
DLC films have received more attention within the last 20 years as candidate biomaterials due to their superior mechanical properties and biocompatibility. In addition, C in several structures has been utilized for a number of biomedical applications. Tissue can adhere well to C implants and support a robust interface. Additionally, in the presence of blood, a protein layer is formed, which prevents the formation of blood clots at the C surface [164; 165; 166]. C fiber inserts can advance the fast ingrowth of tissue [168; 169; 170; 171; 172; 173; 174; 175]. However, in recent years, several researchers have shown an interest in this application for the biomedical sector [176; 177; 178].
## 3 Discussion
### Regulation of Secondary Metabolite Pathways at Different Ammonium Conditions  
Phenols serve a wide range of functions as ROS scavengers, lipid peroxidation inhibitors, or electron donors [14; 19; 48]. However, whether phenols, especially phenolics, flavonoids, and anthocyanins, play any roles in ammonium assimilation for macrophytes, particularly _M. aquaticum_, remains unclear. KEGG analysis indicated that genes for phenylpropanol and flavonoid biosynthesis were significantly enriched (_p_ < 0.05) in the 7721 DEGs, which ranked the third and fourth most enriched, respectively. In this study, most genes involved in phenylpropanol biosynthesis, including cinnamoyll-CoA reductase (DN563398_c1_g1 and DN562056_c0_g1) and cinnamyl-alcohol dehydrogenase (DN562714_c1_g1 and DN559807_c1_g3), were significantly upregulated (_p_ < 0.05) at high concentrations of ammonium. This was also reported in previous studies [18; 19]. Accelerated synthesis of salicylic acid through the phenylpropanol pathway that utilized chorismate, which is the product of the shikimate pathway, as a precursor, indicated a hormone signaling regulation at high concentrations of ammonium [22]. The end products of phenylpropanol biosynthesis are different kinds of lignin, which is the constitutive components of the cell wall, plant mechanical support, and the defense system [18]. Additionally, the stimulation of the phenylpropanol pathway was considered to be a common response to some abiotic stress such as drought, salinity, ozone intoxication, and heavy metals [14; 16; 18]. Although lignin does not contain nitrogen, the first step in the phenylpropanol pathway, driven by phenylalanine ammonia lyase (PAL), requires Phe whose synthesis was significantly enhanced (_p_ < 0.05) at high concentrations of ammonium (Figure 1). PAL genes (DN559452_c0_g1) were more expressed at high concentrations of ammonium in this study while ammonium released from Phe by PAL could be further assimilated by GS [16]. These results indicated a close correlation between nitrogen assimilation and phenylpropanol biosynthesis. Under nitrogen deficiency conditions, the phenylpropanol production was adjusted to the anthocyanin pathway, which could help plants adapt to nitrogen deficiency conditions [16; 19; 48]. In this study, genes for anthocyanins biosynthesis, such as anthocyanin synthase (DN555985_c2_g1), anthocyanin reductase (DN559950_c0_g4), and leucoanthocyanidin reductase (DN555793_c0_g1), were significantly upregulated (\(p<0.05\)) at low concentrations of ammonium, and the correspondingly enriched anthocyanin pathway might be a strategy for _M. aquaticum_ to adapt ammonium limiting conditions [48]. The enhanced synthesis of phenylpropanoids and their derivatives such as phenolics, anthocyanins, and lignin at different ammonium concentrations further emphasized the importance of these secondary metabolites for _M. aquaticum_ adapting to various ammonium concentrations.
# Green IoT: A Review and Future Research Directions
## 8 Potential Future Directions
By dynamically adjusting power usage based on usage patterns, network conditions, and energy availability, these algorithms can help to achieve the optimal balance between power consumption and performance, leading to longer battery life and improved sustainability. 5. _Wireless charging_ The development of more efficient wireless charging technologies is an important area of research that can significantly improve the energy efficiency of charging electronic devices, leading to a reduction in environmental impact. Wireless charging is becoming increasingly popular, as it allows users to charge their devices without the need for cables or connectors. However, traditional wireless charging technologies have some limitations, including their energy efficiency. Research is ongoing to improve the efficiency of wireless charging, which can downscale the required energy to charge devices and decrease the environmental impact. One approach to improving the efficiency of wireless charging is to optimize the power transfer between the charger and the device being charged. This can be achieved through the use of advanced circuitry and control algorithms that monitor and adjust the power transfer to ensure that the charging process is as efficient as possible. Another approach to improving the efficiency of wireless charging is to increase the distance over which charging can occur. This can be achieved through the use of resonant wireless charging technologies that use magnetic resonance to transfer power over a greater distance. This technology can be used to charge multiple devices simultaneously, making it ideal for charging in public spaces such as airports and coffee shops. In addition to improving the efficiency of wireless charging, research is also ongoing to develop more sustainable wireless charging technologies. For example, some researchers are investigating the use of solar-powered wireless charging systems, which use photovoltaic cells to convert sunlight into electrical energy. These systems can be used to charge devices even in remote or off-grid locations, making them ideal for outdoor and wilderness environments.
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 2 Methods and Materials
### Region Growth Layer-by-Layer Clustering  
Region growth layer-by-layer clustering is the process of aggregating a group of elements into a larger region [31]. In this paper, the results of trunk detection are used as the initial clustering center. Then, the fuzzy C-means algorithm is used to classify the remaining point clouds. The cluster center obtained at layer \(i\) is used as the initial cluster center at layer \(i+1\) for layer-by-layer clustering until the points in each layer of point clouds are divided.  
The fuzzy C-means algorithm is shown in Equation (5), where m is the fuzziness, N is the number of points in this layer, C is the number of clusters, and \(x_{i}\) represents the \(i\)th sample with 3D attribute. In addition, \(u_{ij}\) represents the membership degree of sample \(x_{i}\) belonging to cluster \(j\) (Equation (6)). And \(c_{j}\) is the center of cluster \(j\) (Equation (7)) with 3D attribute. \(\parallel x_{i}-c_{j}\parallel\) is a measure of Euclidean distance.  
\[J_{m}=\sum_{i=1}^{N}\sum_{j=1}^{C}u_{ij}^{m}\parallel x_{i}-c_{j}\parallel^{2}, \tag{5}\]  
\[u_{ij}=\frac{1}{\sum_{k=1}^{C}\left(\frac{\parallel x_{i}-c_{j}\parallel}{ \parallel x_{i}-c_{k}\parallel}\right)^{\frac{2}{n-1}}}, \tag{6}\]  
\[c_{j}=\frac{\sum_{i=1}^{N}u_{ij}^{m}.x_{i}}{\sum_{i=1}^{N}u_{ij}^{m}}, \tag{7}\]  
Figure 1: Clustering process of improved density-based spatial clustering of applications with noise (DBSCAN) algorithm.  
Fuzzy C-means algorithm is a process of iteratively calculating the membership degree \(u_{ij}\) and cluster center \(c_{j}\) until they reach the optimum. For a single sample \(x_{i}\), the sum of its membership degree for each cluster is one, as shown in Equation (8).  
\[\sum_{i=1}^{c}u_{ij}=1, \tag{8}\]  
The termination condition of iteration is shown in Equation (9):  
\[max_{ij}\Big{\{}\Big{|}u_{ij}^{(k+1)}-u_{ij}^{k}\Big{|}\Big{\}}<\varepsilon, \tag{9}\]  
where \(k\) is the number of iteration steps and \(\varepsilon\) is the error threshold. The above formula means that the degree of membership will not change greatly if the iteration continues. That is, the local optimal is achieved.  
In the process of clustering with fuzzy C-means method, the initial membership matrix \(U_{0}\) is calculated by the initial clustering center \(C_{0}\), and the \(V_{t}\) corresponding to the minimum of the objective function \(J_{m}\) is obtained iteratively. Then, the fuzzy membership matrix \(U_{t}\) is calculated by the clustering center \(V_{t}\). Set the fuzziness \(m=2\), the number of iterations is 30, and the critical value of iteration \(\varepsilon\) is 0.0001. Divide each sample data into the corresponding category with the largest membership degree. Set to one layer every 0.5 m. Cluster center obtained at layer \(i\) is the initial cluster center of layer \(i+1\) for layer-by-layer clustering, until the points in each layer of point clouds are divided.
# Integrating Interactive Detection of Code Smells into Scrum: Feasibility, Benefits, and Challenges
## 6 Online Validation: Insights from a Controlled Experiment
### Results
#### 6.3.2 Code Smell Detection (Phase 2)
This means developers are encouraged to analyze and correct code smells as  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**1a Release**} & \multicolumn{4}{c}{**2a Release**} \\ \cline{2-7}  & & \multicolumn{4}{c}{**Scrum + ID**} \\ \hline  & **TP** & **FP** & **FN** & **TP** & **FP** & **FN** \\ \hline Team 1 & 11 & 3 & 6 & 9 & 3 & 4 \\ \hline Team 2 & 12 & 2 & 7 & 8 & 2 & 4 \\ \hline Team 3 & 13 & 3 & 6 & 9 & 2 & 5 \\ \hline Team 4 & 15 & 4 & 7 & 8 & 2 & 4 \\ \hline Total & 51 & 12 & 26 & 34 & 9 & 17 \\ \hline Average & 12.7 & 3 & 6.5 & 8.5 & 2.3 & 4.25 \\ \hlinesoon as they are identified, even before they propagate and become more complex. This practice results in the early detection of issues and prevents false alarms from accumulating. Additionally, the proposed approach involves close collaboration among team members, including developers, testers, and project managers. During code inspections, all members can collaboratively review and discuss the smells found, allowing a more comprehensive view and a better understanding of the code context. These results are closely aligned with those of [PERSON] et al. [10]. _The approach decreases the number of False Negatives (FN)_. The experimental results revealed that the teams using the approach identified 26 (first delivery) and 17 (second delivery) False Negatives, while the teams without the approach detected 38 (first delivery) and 33 (second delivery) False Negatives. This indicates that using the ID technique can reduce the number of FN by over 30%. It is important to mention that the statistical results related to FN (alpha = 0.05, \(p\) = 0.00389, \(W\) = 0, MD = \(-\)3.62, \(Z\) = \(-\)2.520) obtained by the experimental tasks were significant using the Wilcoxon test [38]. Several reasons explain this reduction in FN with the use of the proposed approach. Firstly, the approach emphasizes code inspection and early smell detection. By adopting continuous inspection practices and collaborative reviews, teams can detect more smells representing maintenance problems due to improved awareness and skills in smell detection [3; 10]. It is important to mention that a previous study argues that a major reason that causes smells in software systems is poor technical skills of developers and lack of awareness towards writing high-quality code [3]. This allows these smells to be addressed before they become FN, meaning issues that would go unnoticed without the intervention of the ID technique. Additionally, the approach promotes a shared responsibility mindset for code quality. In Scrum, teams are encouraged to take ownership of software quality and strive for excellence in development. Using the approach, developers become more aware of the importance of identifying and resolving code smells that represent maintenance problems. This reduces FN as developers are more attentive and committed to ensuring code quality.
## 3 Monitoring Data Collection and Preparation  
The data used in this analysis can be split into three categories: (i) data from the 3D dilatometers placed on the SAL detachment plane; (ii) climatic variables and (iii) seismicity (Figure 2).  
Figure 1: Location of the study area: (**a**) the El Hierro Island with a simplified geological map showing the location of the TM-71 3D dilatometers and showing the direction of the axis (X, Y, Z) of the measurement: grey rectangle shows the location of Figure 8c; (**b**) dilatometer HIE2 located underground on the upper scap of the SAL; (**c**) dilatometer HIE3 located in the open air on the left side of the SAL.
# Study on Airflow Field Distribution and Dust Distribution Characteristics at Blast Piles
## 3 Results and Discussion
### Dust Dispersion Distribution Patterns at Blast Piles
#### 3.3.2 Dust Transport and Dispersion Patterns Under Shoveling Disturbances  
After blasting, the blast piles not only generate dust due to natural airflow but also during the shoveling operations by large machinery. Therefore, this section focuses on studying the distribution of regular patterns of dust migration at the site of shoveling blast piles. Various shovel models' lifting and pressing speeds were obtained through data collection, as shown in Table 6. Based on this, the initial velocity of dust release is determined.  
Since the dust concentration trends and particle trajectories during shoveling are consistent with those regular patterns when the blast piles are stationary, the figures for dust concentration and particle distribution at 50 s and 300 s are omitted. The dust concentration and dust particle at the 600 s moment are shown in Figures 16 and 17, respectively.  
Comparing the dust concentration figures at stationary blast piles at the same moment, it is evident that under all wind speeds, the concentration and impact range of dust at shoveling piles are beyond stationary piles, especially in high-concentration zones filled in red. This indicates that throughout shoveling, the dust endowed with an initial velocity travel farther while dispersing wider and more susceptible entrained by airflow, thus leading to more severe dust pollution while shoveling operations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Shovel Model** & **Lifting Speed (m/s)** & **Pressing Speed (m/s)** \\ \hline WK-10 & 1.37 & 0.67 \\ WK-20 & 1.58 & 0.54 \\ WK-35 & 0.82–1.60 & 0.58–0.65 \\ WK-55 & 1.58 & 0.75 \\ WK-75 & 0.92–1.84 & 0.68–0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Some parameters of the shovel model.  
Figure 16: Variation in shovel dust concentration at 600 s under different wind speeds (Red: Large particle size; Green: Medium particle size; Blue: Small particle size).  
Comparing the shoveling particle distribution figures of stationary piles, it can be observed that overall dust particle distribution remains consistent, which indicates that the initial velocity of dust during shoveling has little impact on its motion compared to the airflow speed. However, by the time dust particles obtain the initial velocity, the number of them at the same location increases, meanwhile the travel distance of larger particles also relatively increases. The movement direction of dust at the blast pile is primarily determined by the wind flow, with the initial velocity from shoveling providing auxiliary assistance to dust movement.
## 4 Discussion
### Analysis of Image Texture  
The texture parameters adequately described the texture of the membranes observed with ESEM microscopy (Table 7).  
Regarding contrast, LBM FL had the highest values compared to LBM FC. High contrast values measured in the LBM FC are associated with the fibrillar microstructure formed in the membranes. This is attributed to a greater presence of fibers, which causes the contrast values to increase, which indicates a high degree of local variation of pixels within the image [17].  
The highest value of the entropy parameter (9.35) corresponded to LBM FC30, which indicates an inhomogeneous surface. The lowest entropy values were obtained with LBM FL30, which means that its surface texture is more homogeneous.  
Finally, the fractal dimension values for all LBMs can be associated with the roughness observed in their respective images, where LBM FC30 (more homogeneous surfaces) had the highest fractal dimension value (2.6452), while low values were observed. (2.5261) for LBM FL70 (heterogeneous surfaces). A roughened surface becomes more susceptible to decay and may fragment. Ultraviolet radiation from sunlight initiates a wide variety of chemical changes in surfaces, which causes them to deteriorate, thus losing their usual appearance and also their mechanical properties, as the strength characteristics are degraded [69]. High roughness could reduce UV light transmission through higher light reflection.  
The entropy/fractal dimension relationship values for LBM FC30 (3.5496) and FL70 (3.5539) are related to images with a high degree of complexity and roughness, while LBMFC 50 (3.5242) and FC70 (3.5289) have lower values, which are associated with simpler and smoother images. This fact is associated with the structural complexity observed in the topographic images of the LBM surfaces. This was mentioned in previous works, in which the effect of the addition of surfactants and optical parameters on the microstructure of hydroxypropyl methylcellulose films was studied [70]. Using microscopic characterizations, the complexity of the film surfaces was evaluated using the fractal dimension parameter.  
Therefore, it can be said that the texture analysis of images is a complementary tool for qualitative studies of the morphologies of the surface of biomaterials, in addition to having the advantages of being an economic and fast tool, which allows the analysis of samples with larger surfaces [30].
# Ocean Remote Sensing Techniques and Applications: A Review (Part II)
## 2 RS Applications in Ocean
### Iceberg
#### 2.1.3 Scatterometer  
As discussed in Part I of this review paper, scatterometers have two main architectures: fan-beam and pencil-beam. Considering their properties, each has its own benefits and limitations for iceberg studies. The fan-beam systems allow for the investigation of the scattering mechanism, while the pencil-beam systems enable narrow azimuthal sampling with broader coverage.  
It was also mentioned that \(\sigma^{\circ}\) could be used to distinguish different targets in the ocean environment. Regarding iceberg studies from RS data, seawater has a lower \(\sigma^{\circ}\) value than icebergs, which typically can be applied to discriminate between these features. The contrast between seawater and iceberg allows us to locate and monitor icebergs in the oceans. Generally, an iceberg can be efficiently identified by homogenous high \(\sigma^{\circ}\) values due to the volume scattering of iceberg constituents. For instance, the daily SeaWinds scatterometer data were collected and processed using the resolution-enhancement technique to detect and track large icebergs [44]. The authors only considered large tabular icebergs due to the low resolution of scatterometer data. The icebergs were identified as high-backscattered objects within lower-backscattered surroundings (e.g., SI and seawater) in daily images, enabling real-time positioning of icebergs. The detection and tracking results, which were validated by in situ observation of the National Science Foundation (NSF) ships and high-resolution satellite imagery, demonstrated the high potential of SeaWinds scatterometer data for large iceberg tracking. Additionally, [PERSON] and [PERSON] (2018) developed a new consolidated database for the Antarctic icebergs by combining daily positional data from Brigham Young University and weekly tracking data from the National Ice Center (NIC). Currently, Brigham Young University comprises data from several scatterometers (e.g., Advanced SCATerometer (ASCAT) and OceanSat SCATerometer (OSCAT)), and the NIC contains optical and infrared data for the iceberg studies. The size and rotational patterns of the detected icebergs are also estimated from \(\sigma^{\circ}\) values of scatterometers using the automatic contour estimation method.