esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Antioxidant Responses in Leaves of Salt-Stressed Plants Are Induced by GmPgb1  
Limitation of oxidative damage is a tolerance trait in several forms of abiotic stress, including salinity [43]. The cellular level of ROS is determined not only by their production, but also by the antioxidant system composed by an array of enzymes. These include superoxide dismutase (SOD), scavenging superoxide; catalase (CAT) and ascorbate peroxidase (APX) participating in the detoxification of H\({}_{2}\)O\({}_{2}\); and enzymes required for the turnover of ascorbic acid, dehydroacorbate reductase (DHAR), monodehydroacorbate reductase (MDHAR), and glutathione reductase (GR).  
Expression analyses of the genes encoding these enzymes and responsive to abiotic stress [37] were performed. The foliar expression of _SOD1_ and _CAT1_ increased after 6 h of Na\({}_{2}\)SO\({}_{4}\) stress (Figure 6A). This increase was more pronounced in the GmPgb1(S)17 plants and lower in the the GmPgb1(RNAi)23 line relative to the WT. A similar profile was also observed for _CAT2_ despite an earlier (0-6 h) increase in expression. Salinity induced _SOD3_ in the three genotypes, and after 24 h, the expression of this gene was greatest in the GmPgb1(S)17 plants, intermediate in the WT plants, and lowest in those suppressing _GmPgb1_ (Figure 6A).  
Transcript levels of the enzymes participating in glutathione and ascorbate metabolism were also measured. Salinity increased the foliar expression of _APX1_, especially in the GmPgb1(S)17 line (Figure 6B). No major differences between the lines were observed for _MDHAR1_ and 4 despite an increase in expression in the WT leaves after 12 h. The expression of _DHAR1_ increased between 0 and 6 h independently of the genotype; at 24 h, a higher expression level was observed in leaves, upregulating _GmPgb1_. Of the two _GR_ genes, the expression of _GR1_ did not exhibit pronounced fluctuations during the Na\({}_{2}\)SO\({}_{4}\) treatment, while a gradual suppression of _GR2_ was recorded in the GmPgb1(S)17 line. This contrasted with the WT and the GmPgb1(RNAi)23 line which showed an increase in _GR2_ transcripts after 6 h (Figure 6B).  
To further interpret the changes in transcript levels, the activities of the antioxidant enzymes were measured in leaves of plants grown for 24 h under control or Na\({}_{2}\)SO\({}_{4}\) conditions. The activities of many enzymes, similar under control conditions among the three genotypes, were induced by salt stress (Figure 7). Relative to the WT and GmPgb1(S)23 lines, the foliar activities of APX, DHAR, and CAT were higher in the GmPgb1(S)17 line under salinity stress. The three lines exhibited similar activity values for MDHAR and GR, while the activity of SOD was slightly reduced in the GmPgb1(RNAi)23 line when compared to the values of the other two lines exposed to stress (Figure 7).
# Visual Tracking Control of Cable-Driven Hyper-Redundant Snake-Like Manipulator
###### Abstract  
The cable-driven hyper-redundant snake-like manipulator (CHSM) inspired by the biomimetic structure of vertebrate muscles and tendons, which consists of numerous joint units connected adjacently driven by elastic materials with hyper-redundant DOF, performs flexible kinematic skills and competitive compound capability under complicated working circumstances. Nevertheless, the drawback of lacking the ability to perceive the environment to perform intelligently in complex scenarios leaves a lot to be improved, which is the original intention to introduce visual tracking feedback acting as an instructor. In this paper, a cable-driven snake-like robotic arm combined with a visual tracking technique is introduced. A visual tracking approach based on dual correlation filter is designed to guide the CHSM in detecting the target and tracing after its trajectory. Specifically, it contains an adaptive optimization for the scale variation of the tracking target via pyramid sampling. For the CHSM, an explicit kinematics model is derived from its specific geometry relationships and followed by a simplification for the inverse kinematics based on some assumption or limitation. A control scheme is brought up to combine the kinematics with visual tracking via the processing tracking errors. The experimental results with a practical prototype validate the availability of the proposed compound control method with the derived kinematics model.  
cable-driven; visual tracking; kinematics; correlation filter +
Footnote †: journal: Applied Sciences
## 2 Astrophysical Conditions
The disadvantage of GEO is the long distance, which creates huge signal power requirements, resulting in huge and very heavy satellites, which are expensive in themselves and expensive to launch. On the other hand, the advantage of LEOs is the proximity to Earth, which translates to substantially lower signal power requirements. Reduced demand for signal strength allows the use of smaller satellites. The second issue is latency. The radio signal to and from GEO needs 2 x 120 milliseconds to go,impacting real-time communication. On the other hand, the radio wave to and from LEOs takes only 2 \(\times\) 2 milliseconds [25; 28; 29; 35].
## 2 Materials and Methods
### Numerical Simulation
#### 2.2.4 Power Characteristics Analysis  
For the dynamic characteristics of the floating body structure, a dry modal analysis method was used. The comparison curves of the first 20 orders of self-oscillation frequencies between the structural model of the offshore airport and the model of [PERSON] et al. (2016, 2017) are given in Figure 5; as can be seen from the figure, the two models almost overlap in the first 4 orders with small self-oscillation frequencies, and gradually start to increase from the 4 th order. The maritime airport model reaches the second growth inflection point at order 15 with a frequency of 0.26 Hz, the [PERSON] et al. model reaches the second growth inflection point at order 14 with a frequency of 0.15 Hz, and both reach similar frequencies at order 16 and 17. From the 17 th order onwards, the frequency shows a substantial increase, while the latter model frequency is greater than that of the maritime airport model, which is mostly due to the different connection methods of the models. The frequencies in the first 20 orders are less than 2.0 Hz.  
The natural mode of the oversized floating body of the offshore airport in air, which is the dry mode and the corresponding orders, is given in Table 4. As can be seen from Table 4, the low-order vibration pattern of the structure is mainly bending, with different degrees of vertical bending occurring at orders 7, 8, 9, and 10, corresponding to self-oscillation frequencies of 0.017, 0.046, 0.091, and 0.148 Hz, respectively. Transverse bending occurs at order 11 with a frequency of 0.182 Hz, longitudinal torsion occurs at orders 13 and 18 with frequencies of 0.275 and 0.835 Hz, respectively, and order 16 presents a superimposed mode of torsional bending and vertical bending with a frequency of 0.491 Hz.  
Figure 5: Frequency comparison of offshore airport models.
# SS3 DNet-AF: A Single-Stage, Single-View 3D Reconstruction Network with Attention-Based Fusion
## 3 Proposed Methods
### Ablation Study
#### 4.3.1 Effectiveness of Attention Mechanism: Single-Stage, Two-Stage, and Multi-Level Fusion  
The attention mechanism in the fusion module was evaluated by comparing a single-stage design to two-stage and multi-stage variations. The multi-stage approach shows improved reconstruction accuracy, and it was observed that removing the attention mechanism from the fusion module led to a significant decrease in performance, as the network struggled to capture details of the object effectively. Figures 4-6 present the 3D reconstruction results for various fusion and attention configurations. In Figure 4, the first column shows the input 2D images of different aircraft. The second column presents the results of direct feature fusion, which produces scattered and incomplete 3D shapes. The third column shows reconstructions using a single-layer attention mechanism, which moderately improves 3D structure but shows limitations in capturing shape details. The fourth column incorporates a two-layer attention mechanism, leading to better-defined shapes with enhanced spatial consistency. Lastly, the fifth column presents results from the proposed multi-level attention mechanism, achieving the most accurate and detailed reconstructions.  
Figure 4: Comparison of 3D reconstruction results under various fusion and attention configurations. The input images (first column) are followed by reconstructions using direct feature fusion (second column, incomplete shapes), single-layer attention (third column, moderate improvement), two-layer attention (fourth column, enhanced spatial consistency), and multi-layer attention (fifth column, most accurate reconstruction).
## 3 Results and Discussion
### Mechanical Properties of PP and Its Composites  
Figure 11a,b shows the tensile strength and the elongation at break of the pure PP and composites. All data points regarding both the tensile strength and the elongation at break are the mean values of 10 test data. Further, the 10 test data are within the error of the measurements. As can be seen from Figure 11a, the tensile strength values of the pure PP, PP-1%LDH, PP-3%LDH, PP-5%LDH, PP-7%LDH, PP-10%LDH, and PP-20%LDH are 12.6, 20.5, 14.4, 15.8, 12.2, 10.6, and 9.7 MPa, respectively. This result indicates that the tensile strength values of PP-1%LDH, PP-3%LDH, and PP-5%LDH are much higher than that of the pure PP, especially PP-1%LDH. As shown in Figure 11b, the elongation at break values of PP-1%LDH, PP-3%LDH, PP-5%LDH, PP-7%LDH, and PP-10%LDH are 761%, 837%, 909%, 922%, and 1015%, respectively. The above elongations at break values are gradually improved with the increase of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH in composites. Furthermore, these values are much higher than 654% of the pure PP. In particular, PP-10%LDH is 361% higher than PP. The tensile strength and the elongation at break indicate that Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH can be used as reinforcing and toughening agents of PP.  
It is noteworthy that when Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH loading reaches 20% in composite, the elongation at break value of PP-20%LDH is decreased to 575%. Simultaneously, the tensile strength values of composites show gradual reduction when Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH loading is more than 7% in composites. These mechanical test results indicate that the interfacial adhesion between Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH and the PP matrix is better when a small amount of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH was added into composites. Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH particles and the PP matrix together moved to deformation under the action of external tensile stress. Thus the strength and ductility of the composites were improved because of the increase of the effective interface between Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH and the PP matrix. However, when Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH exceed a certain amount, the excessive and agglomerate rigidity particles of Ni\({}_{0.2}\)Mg\({}_{2.8}\)Al-LDH in turn led to a decrease of the tensile strength and ductility of the composites.  
Figure 10: Thermogravimetric analysis (TG) and differential thermogravimetry (DTG) curves of PP and its composites; (**a**) TG curves; and (**b**) DTG curves. Deriv.: differential of TG curves.
## 4 Discussion  
Most of the existing studies defined an outlier as an outlier only when it was significantly different from the normal value, and only pre-processing methodologies for related outliers were presented. There are not enough studies on cases where there is no significant difference between the outlier and the normal value. As the importance of body weight in the lifelong is highlighted, it is important for digital healthcare research, but research on the preprocessing methodology for body weight is also highlighted. However, there are also not enough of specialized preprocessing studies for data with periodicity and small daily variability, such as body weight data. Therefore, this study proposes an outlier generation method suitable for the characteristics of body weight lifelong data.  
To demonstrate the superiority of the proposed algorithm, lifelogs of 59 users were collected over 4 months. In these logs, 1.06% of the total data are outliers. The experiments showed that the proposed method performed better than using the traditional SMOTE method and the latest CTGAN method. Specifically, both F1 scores and AUC scores were improved in the SVM, XGBOOST, and CATBOOST algorithms, and significantly improved compared to no preprocessing.  
However, this study had potential instability limitations in data collection caused by external factors such as battery shortage and network instability that may occur in the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **None** & **SMOTE** & \begin{tabular}{c} **SMOTE +** \\ **ENN** \\ \end{tabular} & \begin{tabular}{c} **SMOTE +** \\ **TOMEK** \\ \end{tabular} & \
\begin{tabular}{c} **SMOTE +** \\ **CTGAN** \\ \end{tabular} &
\begin{tabular}{c} **CTGAN** \\ **Algorithms** \\ \end{tabular} \\ \hline SVM & 0.207 & 0.878 & 0.886 & 0.828 & _0.888_ & **0.986** \\ XGBoost & 0.207 & 0.818 & 0.822 & _0.968_ & 0.805 & **0.987** \\ CatBoost & 0.323 & 0.965 & _0.969_ & 0.888 & 0.948 & **0.987** \\ \hline \hline \end{tabular}
\end{table}
Table 4: F1 score of the classification algorithm according to each data augmentation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **None** & **SMOTE** & \begin{tabular}{c} **SMOTE +** \\ **ENN** \\ \end{tabular} & \begin{tabular}{c} **SMOTE +** \\ **TOMEK** \\ \end{tabular} & \
\begin{tabular}{c} **SMOTE +** \\ **Algorithms** \\ \end{tabular} &
\begin{tabular}{c} **CTGAN** \\ **Algorithms** \\ \end{tabular} \\ \hline SVM & 0.812 & 0.952 & _0.956_ & 0.953 & 0.936 & **0.995** \\ XGBoost & 0.938 & 0.965 & _0.968_ & 0.962 & 0.938 & **0.986** \\ CatBoost & 0.990 & 0.968 & _0.992_ & 0.971 & 0.983 & **0.998** \\ \hline \hline \end{tabular}
\end{table}
Table 5: AUC score of the classification algorithm according to each data augmentation.  
Galaxy Watch environment. This is expected to be resolved through more sophisticated preprocessing. For example, in the case of sleep, there is a way to reduce distortion by analyzing individual sleep cycles and generating derived variables.
## 2 Mathematical Formulation  
This section presents the mathematical formulation developed to optimize the operation of BESSs within AC MGs. The proposed model seeks to minimize energy losses and CO\({}_{2}\) emissions, incorporating the technical constraints of a distributed generation environment with energy storage. The location and sizing of the BESS units are assumed to be known beforehand. Model validation is carried out based on the self-powered and grid-dependent scenarios reported in [31].
## 3 Preliminaries
### Software-Defined Networking (SDN)  
Just like the IoT, SDN is a networking technology introduced at the beginning of this century. The literature on SDN is abundant; we mention some points in this section for completeness.  
SDN is an evolution of the classic network model into a network defined by applications. SDN architecture separates the network control (control plane) and forwarding functions (data plane), enabling the network control to become directly programmable and centrally managed. This programming is done via SDN controllers instead of classical Internet protocols. Centralization allows the controller to maintain a global view of the network and control it through standards such as OpenFlow, which is a protocol defined by the Open Networking Foundation to transfer forwarding rules from the controllers to the routers using APIs. We use in our work the most common way of programming SDN networks, where applications give abstract rules to controllers, which translate them into commands for the network equipment concerned, the SDN router.  
To justify our choice of the SDN architecture, we start with the observation that global security solutions are more efficient and effective when they are centralized, as SDN is. Further, SDN is a system designed for efficient networking, so its use for data security will be efficient. Finally, we will see that SDN allows a straightforward translation of our labels into rules for controllers and then routers. Many types of controllers and routers exist in practice, but our approach appears to be feasible for any of them. The use of SDN limits our approach to centrally controlled IoT systems, but the resulting efficiency makes it valuable in such contexts.  
Reviews of SDN and its use for security can be found in several papers, e.g., [PERSON] et al. [22], a review paper that focuses on the use of SDN specifically for security in the IoT. Other research that proposes SDN-based security frameworks for the IoT has been mentioned in Section 2. However, the main concerns of this literature are the management and deployment of security policies, identity management, and detection or prevention of vulnerabilities, intrusions, or attacks; these subjects are outside of this paper's scope.  
SDN has been adopted in various systems and contexts, both in research and industrial practice. Google uses SDN in its B4 WAN [23] to optimize bandwidth usage and resource allocation between its data centers. Microsoft employs SDN in its Azure cloud platform [24] to manage network resources efficiently, providing scalable and reliable cloud services. GENI [25] is a research initiative that uses SDN to create a flexible and programmable testbed for experimenting with future internet architectures and services. Researchers use GENI to explore new networking paradigms and applications. Several other applications of the SDN concept are found in theory and practice, hence the importance of our research.
## 2 Wireless Sensors for Industrial Manufacturing  
To achieve high-quality manufacturing processes, such as the milling of highly complex components, fast detection of deviations in the movement of the milling tool and a subsequent reaction within a few milliseconds are critical. Machine-integrated sensors can record process data and send this information to the machine's controller. Applications involving advanced data analytics and real-time condition monitoring require cloud-based systems. Due to the complex machining process, with a lot of movement of workpieces and work tools, having a wired sensor system is impractical as wires can restrict motion, risk entanglement, and are prone to wear or damage in such dynamic environments. The current wireless options, such as 4G or Wi-Fi, do not provide the low latency, reliability, or bandwidth needed for industrial applications. Such industrial communication needs high reliability and a low latency wireless connection [15; 16]. This article investigates a use case for anomaly detection for milling processes using intelligent sensor systems. The use case was designed to support benchmarking the suitability of 5G and FRER integration for industrial applications.
## 3 Results and Discussion
### Bioactivity of the Microalgae Extracts
#### 3.4.1 Total Phenolic Content (TPC) and Antioxidant Activity (AA) of the Microalgae Extracts
[44] reported a TPC of 0.894 \(\pm\) 0.20 mg GAE/g DW for a 70% hydroethanolic extract of _Odontella_ sp., which is significantly lower than the value obtained in the present study (31.8 \(\pm\) 5.3 mg GAE/g DW using a 90% hydroethanonic solution. For a chloroform/methanol extract (8 mg/mL), EC50 values were 1.821 \(\pm\) 0.25 and 5.463 \(\pm\) 0.47 mg/mL for the ABTS and DPPH assays, respectively--both higher than those observed for the hydroethanolic extract. For _Porphyridium_ sp., [PERSON] et al. [55] analyzed an extract prepared by cellular lysis (0.1 mg/mL), reporting IC50 values of 45 \(\upmu\)g/mL (DPPH) and 658.81 \(\upmu\)g/mL (ABTS). [PERSON] et al. [56] investigated _Porphyridium aeruginosa_ and _Porphyridium sordium_ in the ABTS assay, obtaining values of 67.95 \(\pm\) 0.36 and 32.48 \(\pm\) 0.04 mg TE/100 g extract, respectively. The IC50 values in the DPPH assay were 157.53 \(\pm\) 0.35 and 286.24 \(\pm\) 0.25 mg/mL, respectively. For _Tetraselmis_ sp., [PERSON] et al. [57] obtained a TPC of 20.45 \(\pm\) 1.87 and 25.19 \(\pm\) 1.26 mg GAE/g DW in water and methanolic extracts, respectively. These are notably higher than the value recorded in the present study (0.57 \(\pm\) 0.01 mg GAE/100 mg DW using a 90% hydroethanonic extract). They also evaluated AA via ABTS and DPPH assays, reporting no detectable activity for the water extract at concentrations of 1, 5, and 10 mg/mL. The methanolic extract showed no ABTS activity, but it exhibited DPPH activity of 17.29 \(\pm\) 0.35% and 24.94 \(\pm\) 0.71% at 5 mg/mL and 10 mg/mL, respectively. It is important to highlight the considerable variability in units and methodologies used in literature, which complicates direct comparisons. Nevertheless, it is evident that extraction conditions--including the choice of solvent, the extraction method (e.g., infusion, maceration), the solvent polarity, and the use of techniques like heating, enzymatic treatment, microwave irradiation, and ultrasound-- greatly influence the composition and resultant bioactivity of extracts. These factors can substantially affect both the antioxidant activity and total phenolic content of the microalgal extracts.
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 3 Biocompatible 3D Printing Materials
### Bioinks
GelMA has also been combined with methacylated polyvinyl alcohol (PVA-MA) and a visible light photoinitatior to produce a bio-resin for digital light processing (DLP) lithography. This approach allows for freeform fabrication, without the constraints of producing lattices, and with resolution down to 25-50 \(\upmu\)m. Human bone marrow derived MSCs was seeded into the bio-resin prior to printing and showed \(>\)85% viability 24 h after UV polymerization. GelMA was shown to be an essential addition to PVA-MA for long-term (28 days) cell survival [36]. Components of ECM such as collagen, fibrin and gelatin have been used to recapitulate the ECM for cell culture scaffolds for decades, but many of the chemical and biological signals from whole ECM are missing. Tissue-derived decellularized ECM (dECM) has the potential to be a very functional bioink; however, the decellularization process often renders it very mechanically soft, so it requires blending with synthetic or natural materials to increase its integrity following printing [59]. In a recent study, decellularized dentin was prepared by first removing the pulp and enamel from molars, followed by grinding and decalcification. It was then mixed with sodium alginate and SCAPs to produce a printable bioink. Increasing the concentration of dentin matrix molecules caused a significant increase in the upregulation of dentin markers ALP and RUNX2 [60].