esa-sceva/satcom-chunk-collection huggingface dataset:
# The Suppressive Activity of Water Minosa Extract on Human Gastric Cancer Cells
## 1 Introduction  
Gastric cancer has a high rate of incidence and mortality worldwide, causing a severe public health problem [1]. This type of cancer can develop from damages caused by stomach diseases, such as a _Helicobacter pylori_ infection, Epstein-Barr virus, gastric polyps, and chronic gastritis. In addition, other factors, such as gender, age, smoking, and especially familial and personal diet, also increase the risk of gastric cancer development [2]. Currently, therapies for the treatment of gastric cancer mainly include chemotherapy, radiation therapy, and gastrectomy [3]. A healthy diet contributes to the prevention and management of gastric cancer [4].  
It was evidenced that a healthy diet with fruits, vegetables, edible mushrooms, and cereals can reduce the risk of cancer development [2]. Among them, _Neptunia oleraca_, an aquatic vegetable and edible plant belonging to the family of Fabaceae, has been studied for its anti-cancer property. For the first time, [PERSON] and colleagues successfully isolated pheophorbide A and B from _N. oleraca_ leaves. These compounds were reported to suppress the Epstein-Barr-virus-induced tumor promoter's activation of human B lymphoblastoid tumor cells at IC\({}_{50}\) values of 3.3 and 4.5 uM, respectively. The anti-tumor activity of these compounds was comparable with that of curcumin (3.1 uM) [5]. Recently, the methanol extract of _N. oleraca_ was shown to downregulate the expression of Bcl-2, c-Myc, and pERK in human leukemia. Meanwhile, it enhanced the expression of a cleaved PARP molecule and induced an apoptotic cell population [6]. These studies indicated that _N. oleracea_ possesses a potential inhibition on hematological malignancies. Although the potential anti-cancer activity of _N. oleraca_ was evidenced, its effects were reported only on leukemia. Therefore, the inhibitory activity of _N. oleraca_ on a wide range of other cancer cell lines should be further evaluated. Notably, [PERSON] and colleagues mentioned that youngshoots of _N. olercea_ can be used to treat gastritis problems [7]. As the result, a question is raised whether _N. olercea_ is able to protect against the initiation and development of gastric cancer. Up to now, there are no reports on the anti-cancer activity of _N. olercea_ on gastric cancer cells. Accordingly, this study investigated the inhibitory effect as well as the active mechanism of _N. olercea_ against gastric cancer cell proliferation via an in vitro experimental model.
# Effects of Voice and Lighting Color on the Social Perception of Home Healthcare Robots
## 5 Conclusions  
An understanding of the social perceptions of healthcare robots can enable manufacturers to develop healthcare robots that will be accepted by users and that can easily integrate into human society. A healthcare robot's social cues affect social perceptions of the robot. In the present study, the RoSAS was used to evaluate the effects of two social cues, namely voice type and lighting color, on the social perceptions of a healthcare robot. The results indicated that voice affected the social perceptions of the healthcare robot. The participants of this study provided high warmth ratings to the robot when it had a child voice or an adult female voice, and they provided a high competence rating to the robot when it had an adult male voice. Matching between the visual feedback and verbal interactions of current intelligent robots is essential for increasing their acceptance by users. In this study, the lighting color of the healthcare robot was controlled to analyze its effect on social perceptions of the robot. The robot obtained high warmth scores when warm colors were used for lighting as visual feedback, whereas it obtained high competence scores when a cool color was employed. The robot was perceived to be warm and competent when white lighting was used as visual feedback.  
The findings of this study offer insights regarding the consistency in the social perceptions triggered by a robot's social cues. Mismatches in robot voice and lighting color can cause user discomfort, which results in low user acceptance of the robot. The present results indicate that the user acceptance of healthcare robots can be increased by optimizing their voice and lighting color. On the basis of the findings of the present study, further research will be conducted on the effects of other social cues of robots on social perceptions.  
X.D.: writing--review and editing; L.Y.: supervision; K.W.: methodology; J.N.: writing--original draft preparation; investigation. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangdong Social Science Association (grant number GD20 XYS22) and Guangdong Polytechnic Normal University (grant number 2021 SDKYB026). The APC was funded under grant GD20 XYS22.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Not applicable.  
The authors declare no conflict of interest.
# Properties and Functionality of Cereal Non-Starch Polysaccharides in Breadmaking
## 4 The Role of NSPs in Bread Making
### The Influence of NsPs on Bread Crumb Hardness  
It has been shown (Table 3) that the addition of 0.5-1% AXs to wheat, rye, or gluten-free bread significantly reduces crumb hardness, compared to bread without the addition of NsPs [15, 134, 155, 155, 163]. The addition of cross-linked AXs to rye bread in amounts of 1-12% reduced the hardness of the bread crumb up to 88%. On the other hand, the addition of hydrolyzed AXs to rye bread in the amount of 1-15% reduced the hardness of the bread crumb up to 92% [83, 154]. This is due to the fact that AXs increase bread volume and crumb porosity and also help retain water in the bread crumb, which consequently affects its hardness [177].  
It was shown that the addition of 0.8% \(\beta\)-glucans to wheat bread reduced the crumb hardness after 1 day of bread storage by 30% [166]. Soluble oat fiber containing 70% \(\beta\)-glucans was added to the wheat bread, and the bread with the addition of 10% to 12% of such fiber was characterized by lower crumb hardness compared to the control bread, which was baked without fiber addition [178]. The addition of 2% galactomannans from carob seeds to wheat bread also caused a decrease in crumb hardness [169].  
In addition, other NsPs may reduce crumb hardness, but the effectiveness of individual polysaccharides and the mechanisms behind this process are not well studied.
## 2 Materials and Methods
### Soil and Plant Analysis  
After each of the four harvests, ryegrass tops were dried, ground, and analyzed for Total Kjeldahl Nitrogen (TKN). Samples were digested in concentrated H\({}_{2}\)SO\({}_{4}\) with a titanium-based catalyst using a SpeedDigester K-436 (Buchi Labortechnik, Flawil, Switzerland), then steam-distilled (K-355 distillation unit, Buchi Labortechnik, Flawil, Switzerland) into boric acid solution with a Tashiro indicator before titration with HCl to determine ammonia content. After the finalization of the experiment, soil samples were analyzed for pH and electrical conductivity EC (1:5 H\({}_{2}\)O), along with total soil nitrogen.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Scenario**} & \multicolumn{2}{c}{**S1**} & **S2** & **S3** & **S4** & **S5** & **REF CM** \\ \hline
**Dosage Nr** & **kg N/ha** & **g N/pot** & **mg N/kg TS Soil** & \multicolumn{6}{c}{**g Fertilizer/pot**} \\ \hline
1 (normal) & 20 & 0.033 & 0.023 & 1.027 & 1.157 & 1.080 & 0.962 & 0.814 & 1.100 \\ \hline
2 & 70 & 0.116 & 0.079 & 3.596 & 4.048 & 3.779 & 3.368 & 2.850 & 3.851 \\
3 & 120 & 0.198 & 0.135 & 6.164 & 6.940 & 6.479 & 5.773 & 4.886 & 6.602 \\ \hline
4
## 3 Materials and Methods
### Data Acquisition

Table 2: Dataset description. Vegetation indices such as NDVI, EVI, and FPAR exhibit strong seasonal correlations with precipitation and soil moisture, capturing the impact of climatic variability on groundwater recharge dynamics [56; 57; 58]. Additionally, temperature and LST show moderate negative correlations with deformation data, suggesting potential subsidence in areas with groundwater depletion [59; 60]. These findings emphasize the relevance of integrating vegetation parameters, soil moisture, and surface deformation data as critical predictors for groundwater variation analysis, given their pronounced responses to seasonal and climatic variations. Moreover, 54 piezometric boreholes were installed across the Naqsh-e Rustam-Persepolis-Marvdasht aquifer system, a semi-arid region underlain by calcareous soils (limestone-dominant), alluvial deposits, and clay-rich Quaternary sediments. Boreholes were split into training (70%) and testing (30%), preserving hydrostratigraphic representation (e.g., Bakhtiari Conglomerate and Asmari Formation). Training sites focused on agriculturally intensive zones with high irrigation-linked groundwater variability, while test sites targeted peripheral areas with sparse monitoring. The partitioning ensures model generalizability across heterogeneous lithologies critical to regional groundwater dynamics; the spatial location of those piezometers is presented in Figure 1b.
## 3 Methodology
### Specim Details and Data Collection  
Two RC slabs (designated as Specim A and B) measuring 91.4 cm \(\times\) 45.7 cm \(\times\) 12.7 cm (36 in \(\times\) 18 in \(\times\) 5 in) were cast with a No. 4 black rebar (12.7 mm diameter) embedded at the center of the cross-section of the slabs, as shown in Figure 2. The concrete mixture is one typically used by NDOT (Nebraska Department of Transportation) and has a specified strength of 4000 psi.  
Researchers have used different concentrations of NaCl in their studies. For instance, [PERSON] et al. [38] investigated the effect of Cl\({}^{-}\) on segments of a precast concrete tunnel lining, immersed in 3, 3.5, and 10% NaCl solutions. Various tests were conducted on cylindrical cores extracted from reinforced concrete and steel fiber-reinforced concrete segments.  
Figure 2: Test specimens: (**a**) illustration with dimension; (**b**) actual specimen before submersion.  
It was observed that the difference in the chloride penetration for NaCl concentrations of 3 and 3.5% was insignificant. In contrast, the 10% NaCl concentration specimens showed significant effects from chloride exposure. Based on this prior work, 10% NaCl is also used in this study.  
Ten equally spaced spots, 7.6 cm (3 in) apart and starting at 15.2 cm (6 in) from the edge of specimen were tested with UGWL, while five spots 15.2 cm (6 in) apart were tested with HCP (Figure 3). The data were collected once every 3 days for the first 30 days, and every 6 days for the rest of the process. Measurements collected from UGWL method were analyzed in the frequency domain as change in amplitude.
## 3 Results and Discussions
### Dynamic Oil Recovery  
Figure 12 shows the dynamic oil recovery with injected pore volume (PV) at different flooding stages with different technologies. The increase in oil recovery is recorded in Table 5. The increases in oil recovery during bottom water flooding and water flooding processes are around 35% and 20%, respectively. However, there are different values in increased oil recovery during nitrogen flooding and foam-assisted nitrogen flooding. A total of 39.7% of increased oil recovery is achieved after foam-assisted N2 flooding, while nitrogen gas technology contributes 27.34% of oil recovery. It can be seen that the nitrogen gas flooding technology has a relatively fast EOR rate at the beginning stage compared with foam-assisted N2 flooding technology. This result is attributed to the lower flow resistance of nitrogen gas possessing a rapid expansion rate. However, once the gas channeling is formed, the oil recovery is almost stable. The foam slug with higher flow resistance can enlarge the sweep efficiency of N2 and suppress the gas channeling formation. In addition, the surfactants released from foam can reduce the IFT and improve the oil washing efficiency. Thereby, foam-assisted nitrogen flooding technology has better potential for EOR in fracture-vuggy reservoirs compared to nitrogen gas flooding technology.
## 3 Results
### Optimization of the Desorption Conditions  
In the desorption procedure, the desorption time and the desorption temperature are critical factors that influenced the extraction efficiency. At first, the effect of the desorption time from 0.5 min to 2.5 min was examined (Figure 6a). The results show that the extraction efficiency increased continuously with the increase in the desorption time. However, a too long desorption time resulted in the peak broadening of PAEs. So, a 2 min desorption time was chosen for the further experiments. Second, the effect of the desorption temperature was evaluated from 260 \({}^{\circ}\)C to 290 \({}^{\circ}\)C (Figure 6b). The higher the temperature, the higher the peak area of the five PAEs. Considering the maximum temperature tolerance of the HP-5 column, 280 \({}^{\circ}\)C was selected as the desorption temperature.
## 1 Introduction  
Diabetes mellitus is a metabolic disorder classified into insulin-dependent type 1 diabetes mellitus and non-insulin-dependent type 2 diabetes mellitus (T2 DM) [1]. T2 DM is a chronic condition characterized by continued hyperglycemia due to compromised glucose uptake into cells, a consequence of decreased insulin sensitivity. Previous studies have shown that persistent hyperglycemia can cause diseases in organs such as the liver, kidney, and heart. In particular, diabetes-induced liver damage accumulates over time, culminating in liver diseases such as fatty liver and non-alcoholic fatty hepatitis [2, 3]. According to several studies, T2 DM severely affects glucose metabolism and oxidative stress in the liver. First, under fasting conditions, the liver breaks down glycogen and produces glucose to maintain normal blood glucose levels. After a meal, liver cells synthesize glycogen to regulate blood glucose. When levels of blood glucose are high, it is taken up by liver cells via GLUT2, followed by its conversion into glucose-6-phosphate by glucokinase (GK), which is synthesized as glycogen. Conversely, under low blood glucose level, glucose-6-phosphatase (G6 PC) and phosphoenolpyruvate carboxykinase (PEPCK) drive gluconeogenesis for glucose synthesis [4, 5, 6]. Second, recent studies have shown that the overproduction of reactive oxygen species (ROS), which results in oxidative stress, is involved in insulin resistance. Thus, compounds with antioxidant activity have been shown to improve blood glucose levels in T2 DM patients [7, 8]. Currently available pharmacological agents for the treatment of T2 DM have limited tolerability, decreased efficacy, and cause side effects such as vomiting, weight gain, diarrhea, and hypoglycemia. Therefore, the identification of natural compounds suppressing the underlying molecular causes of T2 DM is of great necessity [9; 10].  
Rice germ is known to help lower blood glucose. Apart from \(\gamma\)-aminobutyric acid (GABA) and \(\gamma\)-oryzanol, rice germ also contains a large amount of phenolic acids, including ferulic acid. Fermented rice harbors larger amounts of phenolic compounds when compared to raw rice. In particular, ferulic acid levels are significantly increased during fermentation [11; 12]. This phenolic compound is known to have antidiabetic, antioxidant, anti-inflammatory, as well as anticancer effects. According to previous studies, ferulic acid inhibits oxidative damage to cell membranes and DNA by scavenging ROS such as nitrogen peroxide, superoxide, and hydroxyl oxygen [13; 14; 15]. [PERSON] et al. found that ferulic acid suppressed hyperglycemia by inhibiting lipid peroxidation by scavenging ROS in streptozotocin - induced Sprague-Dawley (SD) rat [16]. Moreover, ferulic acid was shown to decrease levels of thiobarbituric acid reactive substances (TBARS) and upregulate the activity of antioxidant enzymes such as superoxide dismutase (SOD), catalase, and glutathione (GSH) [17; 18]. This study investigated the effects of fermented rice gum extracted with 30% ethanol or 50% ethanol on T2 DM based on hepatic glucose metabolism and oxidative stress in C57 BL/KsJ-db/db mice.
## 3 Experiments
### Dataset Descriptions  
This study evaluates the performance of three predictive models with vibration signals generated during milling operation of a CNC machine. The flowchart of the experimental platform is shown in Figure 4. First, vibration signal data are acquired from an acceleration sensor when the cutter starts to mill the workpiece. Subsequently, three predictive models are trained using the input of vibration signal data. The evaluation of the training and prediction results will be used to compare the models. Figure 5 shows the experimental setup; sensory data are obtained from the X and Y directions of the accelerometer (50 g) attached in a spindle tool as the vibration signals for analysis. For simplifying the analysis of the correlation between the vibration signals and the surface roughness in the intelligent predicted model considered here, the machine parameters are set at the special milling conditions as follows:  
1. The material of the workpiece is Medium-Carbon Steel S45C, and the material of bull end billing tool is AlTiN Coated Carbide with axial depth of cut (ap) of 2 mm and radial depth of cut (ae) of 10 mm;
2. The final finish milling depth of 10 \(\upmu\)m in this process was used to obtain the vibration signals in the experiment;
3. The center spindle speed is set at 7000 rpm;
4. 10 KS/s is chosen as a sampling rate, such that the raw vibration data sampling rate is 10 k in one second;
5. The selected five seconds of sample data are taken between 63 s and 68 s, corresponding to the end of the milling process. The vibration signals sampled with the 5-s time interval are highlighted by the red box in Figure 6. Two-axis accelerometers are used in this experiment; thus, x- and y-axial vibrations are produced during milling operation. The x- and y-axial vibrations are  
Figure 3: The framework of the one-dimensional convolutional neural network (1-D CNN) model.  
converted to Fourier spectra, as shown in Figures 7 and 8. The vibration signal features in the spectrum signals of the x and y accelerations are partially consistent in processing time. However, the spectral features from the x-axis have rich feature information that is more sensitive in the milling processing. For simplicity, in this study, only vibration signals in x-axial direction are used as model inputs to predict the surface roughness.
* The surface roughness (Ra) in the milling processing was measured offline by a 2-D surface roughness measurer (SV-3200 Series). Ra value is defined as Equation (9), where \(h(x)\) is the surface waviness profile and \(L\) is the measured length. Figure 9 presents the plots and definitions of the surface roughness profile. Figure 10 shows the measurement system of surface roughness. \[Ra=\frac{1}{L}\int_{0}^{L}\lvert h(x)\rvert dx\] (9)  
Figure 4: Experiment Platform.  
Figure 5: Illustration of the experimental milling operation setup.  
Figure 6: The selected signal window.  
Figure 7: Fourier spectrum distributions of the X-axial accelerator.  
Figure 8: Fourier spectrum distributions of the Y-axial accelerator.  
Figure 9: The plot of the surface roughness profile.
# Dynamic-RPL: Enhancing RPL-Based IoT Networks with Effective Support of Dynamic Topology Management
## 5 Design  
The design of Dynamic-RPL is introduced in this section in detail. It is based on simply maintaining an RPL network with two separate instances. One is an instance containing the single or multiple DODAGs in the RPL network. The other one is a management instance established among only the relevant nodes in the network. These are referred to as the local-instance and global-instance, respectively. The former is a normal RPL instance formed with any number of normal RPL DODAGs (local-DODAGs). It can apply any local OF for optimizing data traffic routing according to specific-application requirements. The latter is a distinct instance with a single-DODAG topology (global-DODAG) formed among specific nodes in the network (global-nodes). It runs a customized OF (global-OF) and special parent selection strategy to facilitate dynamic topology management. It enables cooperative communications over the network to make informative topological adjustment decisions and address adverse network situations in a dynamic and distributed manner.  
In Section 5.1, a description of the global-DODAG establishment is presented. The modified global-parent selection strategy is then described in Section 5.2 in addition to introducing a new OF for the global-DODAG. In Section 5.3, the dynamic multi-DODAG algorithm is illustrated. Section 5.4 explains how inter-routing can be managed over the network. Finally, example scenarios demonstrating the functionality of the Dynamic-RPL are presented in Section 5.5.
# A Review of Tunable Orbital Angular Momentum Modes in Fiber: Principle and Generation  
[PERSON]  
The State Key Laboratory of Information Photonics and Optical Communications, Beijing University of Posts and Telecommunications, Beijing 100876, China; [EMAIL_ADDRESS] (L.F.); [EMAIL_ADDRESS] (S.W.); [PERSON]IL_ADDRESS] (W.L.); [EMAIL_ADDRESS] (J.Q.); [EMAIL_ADDRESS] (H.G.); [EMAIL_ADDRESS] (X.H.); [PERSON]DDRESS] (Y.Z.)  
*Correspondence: [EMAIL_ADDRESS] (Y.L.); [EMAIL_ADDRESS] (J.W.)  
[PERSON] Abstract  
Orbital angular momentum (OAM) beams, a new fundamental degree of freedom, have excited a great diversity of interest due to a variety of emerging applications. The scalability of OAM has always been a topic of discussion because it plays an important role in many applications, such as expanding to large capacity and adjusting the trapped particle rotation speed. Thus, the generation of arbitrary tunable OAM mode has been paid increasing attention. In this paper, the basic concepts of classical OAM modes are introduced firstly. Then, the tunable OAM modes are categorized into three types according to the orbital angular momentums and polarization states of mode carrying. In order to understand the OAM evolution of a mode intuitively, three kinds of Poincare spheres (PSs) are introduced to represent the three kinds of tunable OAM modes. Numerous methods generating tunable OAM modes can be roughly divided into two types: spatial and fiber-based generation methods. The principles of fiber-based generation methods are interpreted by introducing two mode bases (linearly-polarized modes and vector modes) of the fiber. Finally, the strengths and weaknesses of each generation method are pointed out and the key challenges for tunable OAM modes are discussed.  
orbital angular momentum; tunable OAM; Poincare sphere; state of polarization +
Footnote †: journal: Applied Physics Letters