esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sem  
The BSE images with EDS results are shown for polished samples of CG and ROG, taken at 28 days, in Figure 6. In general, in the viewing area in a BSE image, the brightness of the area increases as the concentration of heavy elements increases ([PERSON] et al., 2019); thus, by using the gray scale, different phases can be distinguished in the BSE image.  
Figure 6 illustrates the gray scales and surface textures of gypsum in the ye'elimite-dominant phase (i.e., anhydrous CSA) and the ettringite-dominant phase (i.e., reaction products of CSA cement) as follows: (1) gypsum and anhydrous CSA showed greater brightness than the reaction products; (2) although the sample surfaces were polished, the gypsum crystals, anhydrous CSA particles, and the areas of reaction products showed distinctive texture surfaces. Thus, using the identified gray scales and surface textures, phase identification was conducted for the BSE images.  
Figure 5: Chloride contents in hardened paste samples at 28 days.  
Although Figure 6 showed different particle sizes of gypsum and anhydrous CSA between CG and ROG, when considering all BSE images for other areas observed using SEM in addition to these figures, no significant difference was found between the two samples in terms of microstructure. The overall spaces were filled with gray regions, which mostly consisted of reaction products.
## 5 Discussion
### Limitations in Modeling ET under Sparse Vegetation Cover Conditions  
The generated daily ET data produced by TSEB-SM showed good agreement with the ground measurements at the grassland and cropland sites with relatively homogeneous surface conditions, but TSEB-SM did not generate fully reliable estimates of ET over sparse vegetation cover conditions, where the modeled daily ET did not capture intermittent high values in rainy days at the Huazhaizi site and peak values that occurred during the middle of the growing season at the Sidaoqiao site. The TSEB-SM model yielded better performance at these dry surface sites when using more accurate surface soil moisture data such as ground measurements or SMAP retrievals as model inputs [36; 37]. This indicates that the deficiencies in the TSEB-SM product reported here are primarily due to the satellite soil moisture data input to the model.  
The model inputs of soil moisture (2003-2019) were derived by downscaling the microwave soil moisture products of AMSR-E and AMSR-2 (which have an extremely coarse spatial resolution of 36 km) down to 1 km using the MODIS reflectance and LST data. The rapid increases in soil moisture introduced by rainfall events were not captured by the microwave observations in the region of interest. This is because rainfall is not necessarily region-wide in these areas, so rain-covered areas are likely to be of a different scale when compared to the coarse-resolution microwave soil moisture products driving the downscaling. Underestimates in the downscaled soil moisture contributed to the underestimation of daily ET at the high points when the rainfall events occurred at the Huazhaizi site. In addition, in the coarse microwave satellite pixel where the Sidaoqiao site is located, the flux tower was installed in a shrubland area surrounded by sparse woodland and very dry bare soil as land cover. These dry surfaces result in lower soil moisture values de  
Figure 7: The annual ET of three products and observations from 2014 to 2018 at the Arou and Daman sites.  
rived by AMSR-E and AMSR-2 when compared with the value derived from a SMAP pixel that mostly consists of vegetation-covered land cover around the flux tower. As a result of spatial mismatching, the downscaled soil moisture data used in this study were lower than those downscaled from SMAP soil moisture products. For the purposes of this long-duration study, the only soil moisture products available for 2000-2020 were the AMSR-E and AMSR-2 microwave products.
# On Blockchain-Enhanced Secure Data Storage and Sharing in Vehicular Edge Computing Networks
## 2 Background
### Vehicular Edge Computing Networks  
Vehicular edge computing networks (VECNETs) are an extended concept of the conventional vehicular ad hoc networks (VANETs), where an additional edge layer is the main difference that distinguishes between VANETs and VECNets. The idea of VECNets is to combine the conventional vehicular network (VANET) and mobile edge computing (MEC) technology that was introduced and standardized by ETSI Industry Specification Group (ISG) in December 2014 [15]. MEC aims to enhance user experience with high bandwidth, low latency, and real-time communications [15]. Furthermore, it reduces delay and response time on decision-making computation by leveraging the edge node as local server infrastructure. Recent studies have been proposed for the integration between VANETs and MEC. In [16], the authors suggested a joint load balancing and offloading problem in VECNets to improve network effectiveness. The authors of [17] proposed DREAMS, an efficient distributed reputation management in VECNets. In [18], the authors introduced a new mechanism to reduce resource sharing cost while increasing quality of service (QoS) parameters with mixed-integer nonlinear programming (MINP) formulation.  
Generally, the architecture of VECNets consists of a user layer, an edge layer, and a cloud layer [19; 20]. In the user layer, vehicles are equipped with onboard units (OBUs) to communicate with the roadside unit (RSUs) in the edge layer to access network services. There are two types of communication in vehicular networks: vehicle to vehicle (V2V) and vehicle to infrastructure (V2I). In the case of V2V communications, vehicles communicate with a neighboring vehicle using OBUs. OBUs are equipped with simple computation capabilities to collect data from sensing devices. Then, V2I, which consists of OBUs and RSUs, establishes a connection with the help of dedicated short-range communication (DSRC) in a single or multi-hop communication [21]. Here, RSUs are deployed along the road acts as an edge node, which provides wireless communications from roadside infrastructure to vehicles [22]. Next, the edge layer consists of several nearby RSUs responsible for temporary data storage that then deliver the data periodically to the cloud layer. A central cloud server is a permanent data center that can provide massive data storage and execute complicated computing tasks in the cloud layer. This server manages all the edge nodes to offers the service in VECNets.
# Air Quality Predictions through Mathematical Modeling for Iron Ore Mine Project
## 3 Methodology
### Air Quality Assessment
#### 3.3.1 Impact on Air Quality during Construction Phase  
During the construction phase of an iron ore mine (IOM) project, emissions originate from equipment movement and site-specific dust-generation activities like grading, and earth and foundation works. The vehicles and equipment deployed produce exhaust emissions, potentially increasing pollutants like SO\({}_{2}\), NOx, SPM, CO, and hydrocarbons. Concerns arise about air quality during dry months due to dust emissions, for reasons mentioned earlier, and explain the reason for selecting data for such a period for the analysis.  
The construction activities cause local variations in SPM levels, but impacts are temporary, marginal, and reversible, with predominantly inorganic dust that settles quickly at the site, posing minimal toxicity [33]. Although effects are confined to project boundaries, proactive measures like vehicle maintenance, wetting roads, and construction sites and introducing vegetation significantly reduce these impacts. These measures demonstrate a proactive approach to addressing environmental consequences during construction. How  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sl. No.** & **Model Name** & **Advantages** & **Disadvantages** & **Citation** \\ \hline
1 & AERMOD & Regulatory applications, applicable to various source types, complex terrain & Complex, less suitable for short-term assessments & [14; 15] \\
2 & CALPUFF & Multi-layered model, long-term assessments, considers complex terrain, land-use changes & Not suitable for short-term assessments & [22] \\
3 & HYSPLIT & Trajectory model, regional transport patterns, local dispersion modeling & Too complex, not suitable for short-term assessments & [23; 24] \\
4 & CMAQ & Comprehensive regional model, multiple pollutants, and complex atmospheric processes & Not suitable for short-term assessments & [25] \\
5 & ISCST3 & The gaussian plume model approach, needs less input data, less complicated, versatility for short-term area source modeling, suitable for flat terrains & Flat-terrain assumption may not be fully representative. & [8] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Survey of air quality prediction models for the study of IOM.  
ever, the construction phase, while causing temporary localized air quality impacts, offers an opportunity for effective mitigation of the particulate matter through conscientious practices and interventions.
###### Abstract  
Background: The purpose of this study is to compare bone union and soft-tissue healing in titanium implants with sandblasted, large-grit, acid-etched surfaces (SLA group) and femtosecond laser-treated surfaces (FEMTO group) in a rabbit model. Methods: Implants were inserted into rabbit tibiae, and implant stability, soft-tissue healing, and microscopic analyses (micro-CT and biopsy) were conducted. All animals maintained normal weight and health post-surgery. Results: Hemostasis was achieved at the laser incision site on the surgery day, but healing was slower compared to conventional methods. Micro-CT showed no significant differences in new bone formation or inflammatory tissue infiltration between groups. Tissue biopsy revealed slightly higher bone-implant contact in the FEMTO group compared to the SLA group, though not statistically significant. Conclusion: These findings suggest that femtosecond laser surface treatment may provide bone union comparable to or better than SLA treatment, though laser-assisted soft-tissue incisions heal more slowly.  
Keywords:dental implants; dental lasers; femtosecond laser; osseointegration; titanium surface engineering +
Footnote †: journal: Applied sciences
# Micromachining in Powder-Mixed Micro Electrical Discharge Machining
## 2 Micro-EDM (\(\mu\)-Edm)  
The machining mechanism of that in \(\mu\)-EDM is similar to the EDM process. During the machining process, unwanted material is removed by rapid sparking between the tool and the workpiece, which are separated by dielectric fluid to provide insulation. As the tool electrode is approaching the workpiece, the intensity of the electric field is increasing, and at some point it becomes higher than the dielectric strength, resulting in the generation of sparks between the electrodes [36]. To achieve small material removal, in \(\mu\)-EDM, discharge energy per single pulse is reduced. Since there is no physical contact between the tool and the workpiece, followed by the characteristic of low-discharge energy, the machining force of \(\mu\)-EDM can be ignored [37]. The resistor-capacitor (RC)-type pulse generator is preferred in \(\mu\)-EDM because it is easy to have a pulse with a short discharge duration to produce high-quality microholes and better dimensional accuracy, which becomes an important factor in micromachining [38; 39; 40]. However, due to arcing caused by normal pulse discharge and insufficient pure dielectric deionization [41], a modified machining process by introducing specific powder to the dielectric fluid, which is known as powder-mixed \(\mu\)-EDM, was conducted.  
Unlike the conventional EDM processes, power-mixed \(\mu\)-EDM utilizes a semi-conductive material in the form of powder which is mixed into the dielectric liquid. The added powder can organize themselves into series of bridges between the tool electrode and workpiece [42]. The conductivity of the powder decreases the insulating strength of the dielectric fluid, which gives an early breakdown in the gap [43]. Figure 3 shows the difference between conventional EDM and power-mixed \(\mu\)-EDM. The dispersion of powder in the machining gap contributes in a series of discharges to improve the material-removal rate without degrading the surface quality.  
This paper divides the research and development of power-mixed \(\mu\)-EDM into four sections: powder characteristics, applications of power-mixed micro-electric discharge machining (PM \(\mu\)-EDM), the variations of PM \(\mu\)-EDM and the challenges of PM \(\mu\)-EDM, as shown in Figure 4.  
Figure 2: Mechanism of powder-mixed EDM: (**a**) the bridge formation; (**b**) the spark initiation of because of the breakage of chain; (**c**) the explosion leading to zigzag particle motion and (**d**) the re-bridging.
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 2 Data and Preprocessing
### Data  
The earthquake catalog data are from the China Earthquake Networks Center ([[http://data.earthquake.cn](http://data.earthquake.cn)]([http://data.earthquake.cn](http://data.earthquake.cn))) (accessed on 1 April 2024) and range from 1 January 1970, to 31 December 2019, a total of 50 years. The dataset has five features: date, longitude, latitude, magnitude, and depth. Since the completeness of \(M_{\rm L}\geq 3\) seismic events is high, the seismic events with \(M_{\rm L}\geq 3\) were selected for this study based on the extent of the study area. Table 1 shows the number of moderate to strong earthquakes (\(M_{\rm L}>5\)) that occurred in the region during the study period, including aftershocks.
## 2 Collisions and the Coefficient of Restitution (\(CoR\), \(e\))
### Methods for Determining the Coefficient of Restitution (CoR, e)
#### 2.2.1 Velocity-Based Method  
This approach employs high-speed cameras or velocity sensors to measure the pre- and post-impact velocities along the collision axis. For a strictly normal impact and rebound, _CoR_ is obtained directly from Equation (2) [17].  
Recent examples include the work [18], in which soil particles were treated (\(\mathcal{O}\) 2-3 mm) as point masses and a maize kernel as the target surface. Particles were dropped from heights of 10, 15, 20 and 25 cm, the collision was filmed at high speed, and CoR was computed as the ratio of separation to approach velocity. A similar velocity method, augmented with a fixture that varied the reflection angle of maize seeds, was reported in [19; 20].  
[PERSON] et al. [21], for example, used two electromagnets to release kernels from 155, 190, 240 and 300 mm onto granite, stainless steel, brass and rubber blocks. Two orthogonal high-speed cameras (1000 fps) captured the impacts, and CoR was calculated from Equation (2) [17].
# Pharmacokinetics of Marine-Derived Drugs
## 3 Pharmacokinetics Studies in Animals
### Distribution
#### 3.2.6 Sea Urchin-Derived Compounds  
The distribution period of echinochrome A (**17**) was shorter than the elimination period. Echinome A was detected in the cornea, crystal of the eye, vitreous humor, retina, and intraocular fluid after both subconjunctival and parabulbar injections in rabbits. The highest concentration of echinochrome A was found in the choroid with AUCs of 256.26 and 193 \(\mu\)g-h/mL after subconjunctival and parabulbar injections, respectively. The T\({}_{\rm max}\) varied between 0.35 and 1.6 h after subconjunctival injection and 0.57 and 1.3 h after parabulbar injection. Subcutaneous injection resulted in a high accumulation of the drug in the choroid, cornea, and retina with MRTs of 2.5, 2.43, and 2.08 h, respectively. After parabulbar injections, the maximal accumulation of the drug was observed in the crystal of the eye, retina, and vitreous humor with MRTs of 2.15, 1.96, and 1.64 h, respectively [124]. Later, [PERSON] et al. confirmed that echinochrome A can cross the blood-ocular barrier in rabbits after intravenous, subconjunctival, and parabulbar injections, and was detected in the vitreous humor and intraocular fluid [143].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{3}{c}{**Animals/Dose (mg/ kg/ administration)**} & \multicolumn{6}{c}{**Tams (0a)**} & \multicolumn{6}{c}{**Cans (ug/ kg/)**} & \multirow{2}{*}{**Reference**} \\  &  
The MRT of GPP in plasma after i/v injection in rats was 1.11 h. A more prolonged MRT of 5.58 h was observed after i/n administration. Repeated doses of i/n administration led to an increase in MRT of approximately 10-fold. After i/n instillation, the maximal concentration of GPP was found in the nose mucosa (Cmax = 53.66 \(\mu\)g/g, AUC = 248.75 \(\mu\)g-h/g, MRT = 8 h), followed by the spleen (Cmax = 2.53 \(\mu\)g/g, AUC = 28.90 \(\mu\)g-h/g, MRT = 10.2 h) and adrenal glands (Cmax = 2.67 \(\mu\)g/g, AUC = 27.06 \(\mu\)g-h/g, MRT = 21.42 h). The concentrations in striated muscle, kidneys, and liver were significantly lower (Table 3) [30].  
The MRT of LES in plasma after peroral administration in rabbits (5-25 mg/kg) was relatively prolonged (10.7-14.3 h). However, the difference was not statistically significant with increasing dose [29].
## 5 The Hybrid Predictor
### ARIMA Model  
The goal of this model is to minimize prediction error for linear time series data by capturing temporal causality. Temporal causality refers to a cause-and-effect relationship in which past data influence future outcomes in a time-ordered sequence. In ARIMA forecasting, this means that the value of a variable at time \(t\) depends on its own past values (\(t-1,t-2,\ldots\)) rather than on external factors. ARIMA \((p,d,q)\) is specifically designed to model this relationship by analyzing historical data. The parameters are defined as follows:  
* p: the number of lagged observations included in the model, also called the AR/Auto-Regressive term.
* d: the number of order differences that the time series data needs to achieve stationarity, also called the I/Integrated term.
* q: the number of lags of the forecast error used in the forecast model, also called the MA/Moving Average term. ARIMA \((p,d,q)\) is formulated then according to Bowerman and O'Connell's notion [28] as follows: \[(1-\sum\limits_{i=1}^{p}\phi_{i}L^{i})(1-L)^{d}yt=c+(1+\sum\limits_{j=1}^{q} \theta_{j}L^{j})\epsilon t\] (5) where \(L\) is the Lag operator, \(\phi_{i}\) is (AR), \(\theta_{j}\) is (MA), \(\epsilon_{t}\) is the white noise error term and \(d\) is the differencing order used to achieve stationarity (I). The ARIMA model uses these three components--AR, MA, and I--to capture and identify linear temporal patterns in the data. Specifically, the AR component defines the relationship between the current value and its past (lagged) values, the I component ensures stationarity by removing seasonality and trend, and the MA component corrects the prediction based on past forecast errors.  
During the training process, we use the Akaike Information Criterion (AIC) [28] to select the optimal parameters \((p,d,q)\). The AIC is defined as:  
\[AIC=-2\text{ln}(L)+2k \tag{6}\]  
where \(L\) is the likelihood of the model, and \(k=p+q+1\). To achieve a better model fit and improve accuracy, we minimize the AIC value. The final step is to compute the residuals \(r_{t}\) as:  
\[r_{t}=y_{t}-\hat{y} \tag{7}\]  
where \(y_{t}\) is the actual number of function requests at time \(t\). These residuals are the input that the RF works on and aims to correct.
# Volatile Composition of Smoked and Non-Smoked Iranian Rice
###### Abstract  
In this work, the volatile profiles of smoked and non-smoked Iranian rice were identified, and their relative abundance was calculated and compared. Headspace solid-phase microextraction together with gas chromatography-mass spectrometry (SPME-GC-MS) were used to extract and identify the volatile compounds. The main groups of volatiles in Iranian rice were aldehydes, ketones, phenol derivatives, furans, linear hydrocarbons, esters and terpen. The chemical family aldehydes was the most abundant one in the profile of non-smoked rice, while phenol derivatives and furans predominated in smoked samples. This study is the first one reporting comparative data of volatile compounds between smoked and non-smoked Iranian rice.  
aldehydes; furans; GC-MS; HS-SPME; _Oryza sativa_; phenol derivatives +
Footnote †: journal: _foods_  
0000-000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-00000-00000-00000-00000-00000-000000-000000-000000-00000000000000000000000000000000000  
collection/concentration [8]. The separation is conducted by gas chromatography with flame ionization or a mass spectrometer [9]. Static headspace (SHS) is an effective technique for simple and rapid quantitative analysis of the aroma compound of rice, in which the sample headspace is automatically brought directly to the gas chromatograph (GC) [10]. However, [PERSON] et al. demonstrated that headspace solid-phase microextraction (HS-SPME) is more sensitive than SHS for the identification of volatile compounds from the packaged materials [11]. Also, [PERSON] et al. investigated the volatile compounds of rice using SPME combined with gas chromatography and mass spectrometry (GC-MS); this method proved to have several advantages, including easy preparation, quick analysis, no use of organic solvents and low cost [12]. The extraction temperature and exposure time are the most important factors in these techniques [10; 11].  
\"Hashemi\" is the most important aromatic rice cultivar in Iran [3]; also \"Sadri\" is considered a rice genotype with a strong aroma [13]. Moreover, there are studies about the \"Domsiah\" aromatic rice cultivar, where the main volatile compounds were nonanal (18.2%), hexanal (14.3%), 2-benzoyl-6,7-dimethoxy-4-methylidene-2H-1,3-benzothazine (7.8%), epilophodione (7.0%), and 1-methyl-4-(1-methyletheryl)cyclohexene (6.6%) [14]. In general, the volatile compounds that have been identified in rice are alcohols, aldehydes, ketones, esters, hydrocarbons, heterocyclic compounds and organic acids, with the predominant family being aldehydes [12].  
Even though there are several studies about the volatile profiles of rice, there is scarce information on the aromatic components of smoked rice. Consequently, the purpose of this study was to isolate, identify, and compare the volatile profiles of smoked and non-smoked rice samples from different Iranian cultivars. Fifteen rice cultivars four of which were smoked, were evaluated by HS-SPME combined with GC-MS.