esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Semantic Understanding of Object Layer
### Local Area Semantics
#### 4.2.2 Feature Mapping  
After constructing the visual dictionary, it is necessary to encode and map the local features of the image, and to represent the semantic information of the image by transforming the local features into some organized form of visual words. In addition, there are some problems such as the low efficiency of dictionary generation, serious quantization errors and the lack of spatial information of visual words. Furthermore, the image semantic representation based on the visual dictionary is a linear representation, which only performs well in the case of the classifier with the nonlinear kernel, such as support vector machine (SVM). This will undoubtedly reduce its usefulness, making it difficult to apply to large-scale data set classifications. In recent years, a semantic representation based on feature mapping has attracted more attention. Feature mapping is used to quantize and code the visual features according to the visual words and generate the representation of the visual features in the visual dictionary.  
Vector quantization (VQ) is simple and convenient, but its constraint conditions are too strict, resulting in the lack of information after visual feature quantization. To overcome this shortcoming, the sparse regularization approach can be used to loosen the constraints in the VQ, which translates into a sparse coding [65; 66]. Sparse coding (SC) uses a sparse regularization method to reduce quantization errors and improve the uniqueness of feature coding. However, sparse coding is only a shallow learning model with a single hidden layer. The visual dictionary acquired by shallow learning lacks the selectivity of features, which will reduce the semantic resolution of image content. On the basis of SC, the local-constrained linear coding (LLC) is proposed [67]. The sparsity of feature coding cannot guarantee its locality, while the locality of feature coding can guarantee its sparsity. As a result, LLC is more efficient and has a better refactoring effect and local smooth sparsity [68].
## 3 Results
### Classifier Optimization  
The classifier was trained with the SVM, RF, and MLP methods. After training with the 3630 new features, it is found that the MLP classifier obtained the best accuracy of 92.0%, which is much better than the SVM classifier (84.0%) and the RF classifier (74.0%). Consequently, the MLP classifier was further optimized.  
Weight parameters would affect the accuracy of the MLP classifier. Figure 5 exhibited the false predictions ((a), (c), (e)), and corresponding receiver operating characteristic (ROC) curves ((b), (d), (f)) of MLP classifier trained with'sgd', 'adam', and 'lbfgs' as solver, respectively. Clearly, the MLP classifier with 'lbfgs' as the solver performs the best and only has four false predictions.
# Distributed Energy Sharing for PVT-HP Prosumers in Community Energy Internet: A Consensus Approach
## 5 Problem Formulation and Algorithm
### Design of Algorithm  
The local electric and thermal power mismatches are denoted as \(\xi_{i}^{E}(t),i\in V_{e}\) and \(\xi_{i}^{H}(t),i\in V_{t}\), respectively. \(\rho_{e}(t)\) and \(\rho_{t}(t)\) represent the gain parameters. The final errors \(\varepsilon_{\xi^{E}}(t)\), \(\varepsilon_{\xi^{H}}(t)\) and \(\varepsilon_{\lambda^{E}}(t)\), \(\varepsilon_{\lambda^{H}}(t)\) are for the power mismatch and incremental cost (utility). To simplify the description of algorithm, \(t\) is omitted in the following part, i.e., \(\xi_{i}^{E}\) denotes \(\xi_{i}^{E}(t)\). The detail of the algorithm can be found in Algorithms 1 and 2. ```
1:Set parameters: \(a^{\prime}\), \(b^{\prime}\), \(v_{i}\), \(\omega_{i}\), \(COP\), \(\phi\), \(\beta\), \(\gamma\), \(\delta\), \(\theta\), \(\varphi\)
2:For\(i=1\)
3:Initialization: Set \(\lambda_{i}(0)\), \(P_{i}(0)\), \(\xi_{i}^{E}(0)\),\(\mu_{i}(0)\), \(H_{i}(0)\) and \(\xi_{i}^{H}(0)\) as follows: \[ \begin{array}{ll}H_{i}(0)=0,i\in V_{t}\end{array} \] (29) \[ \begin{array}{ll}P_{i}(0)=0,i\in V_{e}\end{array} \] (30) \[ \begin{array}{ll}\mu_{i}(0)=\left\{\begin{array}{ll}(F^{\prime}(H_{i}))- \lambda_{i}(0)K_{CHP},i\in V_{CHP}\\ \frac{\partial I_{i}(H_{i})}{\partial H_{i}},i\in V_{P}\end{array}\right.\end{array} \] (31) \[ \begin{array}{ll}\lambda_{i}(0)=\left\{\begin{array}{ll}2a_{i} ^{\prime}P_{i}+b_{i}^{\prime},i\in V_{G}\\ \frac{\partial I_{i}(P_{i})}{\partial P_{i}},i\in V_{P}\end{array}\right.\end{array} \] (32) \[ \begin{array}{ll}\xi_{i}^{H}(0)=0,i\in V_{t}\end{array} \] (33) \[ \begin{array}{ll}\xi_{i}^{E}(0)=\left\{\begin{array}{ll}0,i\in V_{G}\cup V _{CHP}\\ -PV_{i},i\in V_{P}\end{array}\right.\end{array} \] (34)
4:Execute Algorithm 2: Calculate the optimal power and incremental costs (utilities), i.e., \(\lambda_{i}(t)\), \(P_{i}(t)\), \(i\in V_{e}\), \(H_{i}(t)\), \(i\in V_{t}\). 5:IF\(t>24\)
6:End For
7:Else\(t=t+1\)
```  
**Algorithm 1** Algorithm for participants in the CEI```
0: While true
1:\(k=1\)
2: Update incremental cost (utility): 1) Update \(\mu_{i}\) according to \[\mu_{i}(k+1)=\sum\limits_{j\in V_{t}}x_{ij}\mu_{j}(k)+\rho_{t}\xi_{i}^{H}(k),i \in V_{t}\] (35)  
2) Update \(\lambda_{i}\) according to \[\lambda_{i}(k+1)=\sum\limits_{j\in V_{e}}r_{ij}\lambda_{j}(k)+\rho_{e}\xi_{i}^{ E}(k),i\in V_{e}\] (36)
3: Update thermal power \(H_{i}\) according to: when \(i\in V_{CHP}\), \[H_{i}(k+1)=\arg\min[F_{i}(H_{i}(k))-\lambda_{i}(k+1)K_{CHP}-\mu_{i}(k+1)H_{i}( k)],H_{i}(k)\in[{H_{i}}^{min},{H_{i}}^{max}]\] (37) \[H_{i}(k+1)=\left\{ \begin{array}{l}H_{i}(k+1),H_{i}(k+1)K_{CHP}\geq 0. 3 Cap\\ 0\hskip 28.452756 pt,H_{i}(k+1)K_{CHP}<0.3 Cap\end{array} \right.\] (38)  
when \(i\in V_{P}\), \[ \begin{array}{l}[H_{i}(k+1),P_{i}(k+1),P_{i}^{hp}(k+1)]=\arg\min[I_{i}(k)+ \lambda_{i}(k+1)(P_{i}(k)+P_{i}^{hp}(k)-PV_{i})\\ \hskip 142.26378 pt+\mu_{i}(k+1)(H_{i}(k)-P_{i}^{hp}(k)COP)],\\ H_{i}(k)\in[{H_{i}}^{min},{H_{i}}^{max}],P_{i}(k)\in[{P_{i}}^{min},{P_{i}}^{max }],P_{i}^{hp}(k)\in[{P_{i}}^{min},{P_{i}}^{max}]\end{array}
## 4 Discussion
### Instrument Design and Cutting Efficiency  
In an in vivo study, there was a significantly lower mean torque when a triangular cross-section was used than when an off-centered rectangular cross-section was used [21]. Use of a convex triangle cross-section exhibited the highest torque or force compared with the use of triangle and S-shaped [11]; parallelogram and modified-convex triangle [20];  
Figure 4: Journals in which articles included in the review were published (by percentage). “Proc. Inst. Mech. Eng. H” refers to “Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine”.  
and triangle, S-shaped, and modified-rectangle cross-sections [15]. Asymmetric triangle cross-sections produced lower forces and similar torque compared with symmetric triangle cross-sections [19].  
A constant-tapered instrument exhibited lower torque than a regressive-tapered instrument [21]. Instruments with sharp blades exhibited significantly higher screw-in forces than those with radial lands [15].  
Instruments with shorter pitch lengths produced higher torque and higher forces [11; 12; 13] because dentin/resin chips can accumulate between the flutes, diminishing cutting efficiency, which may require that greater apical forces be applied [12]. Medium or large pitch lengths produced low torque, which were correlated with better cutting efficiencies [11; 12]. However, another study found the opposite--that pitch length did not influence the generation of screw-in forces [14]. Differences in pitch lengths within apical 2 mm of the tip of the instruments were also found to be unlikely to affect screw-in forces [15]. Small helical angles reduced the amount of screw-in force applied and torsional stress generated when instruments with the same size and cross-section were used [13]; however, use of a variable helical angle design did not have an effect on screw-in force [15; 20].  
In an examination using similar-sized instruments with different cross-sectional designs, higher rake angles (negative) produced higher torque and forces [11]. In another study, however, instruments with positive rake angles produced greater screw-in forces than those with negative rake angles [15]. Instrument size influenced both torque and force when instruments with similar rake angle were tested [12].  
Thus, design had an impact on torque and force--shaping instruments with shorter pitch lengths produced greater values, helix angle and rake angle affected the results, and higher cutting efficiencies were associated with lower outcomes. However, care should be taken in interpreting the results of studies that compared instruments with different geometric features because of the inherent limitation that a particular geometric parameter may influence multiple mechanical properties of Ni-Ti rotary instruments [2; 11; 12; 62; 63; 64; 65; 66; 67; 68], such as bending, cutting efficiency, and stress and strain distribution pattern, and torque and force generation should be interpreted as the cumulative effect of those influencing factors.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 4 Application Implementation
### Occupancy Monitoring  
For indoor occupancy monitoring, the LiB IoT system was equipped with a passive infrared sensor (PIR), a light dependent resistor and a temperature and humidity sensor DHT11 and housed in a 3D printed box as in Figure 7. The ATmega8 microcontroller was used and wireless communication was achieved via a Zigbee RF module Xbee S1 [61]. All the components were chosen to keep the overall power consumption low. Figure 8 depicts the system configuration from a higher-level view. From Table 1, the total amount of current consumed by the system is 66.4 mA when the device is transmitting or in active mode, whereas during sleep state it reduced to 11.5 mA, consuming about 1/6 th of the active state consumption.  
Figure 8: System Architecture for Occupancy Monitoring.  
Figure 7: LiB prototype for occupancy monitoring.  
DHT11 [62] stays in a low power state until communication is initiated by the microcontroller and the conversion process starts. During standby mode, the sensor only consumes around 100 \(\upmu\)A. After the communication is initiated by the microcontroller the sensor sends a 40-bit packet containing the temperature and humidity data. The light dependent resistor changes its resistance according to the ambient light level and the analog voltage generated by the accommodating resistor divider circuit is converted to a digital signal by the 10-bit ADC in ATmega8 [63]. Without encryption, the data collected by the sensors are vulnerable to malicious intent. Attackers might monitor the occupancy in the house and find out when the house or establishment is empty and carry out crimes without any obstruction. Encrypting the data ensures that the data is safe from attacks.  
A 3.7 V 200 mAh Lithium Ion battery is used to power the circuit and TP4056 charge controller is used for charging the battery. A 3.3 V low dropout linear regulator MCP1700 is used to provide a reference voltage for the ADC in the microcontroller connected to the external reference pin of the microcontroller. As the battery voltage reduces with prolonged use, the supply voltage cannot serve as accurate ADC reference voltage. Data accumulated from the sensors is sent to the internet gateway Raspberry Pi via Xbee SI and then to Thingspeak cloud server for storage and analysis. The dashboard of the cloud service provides simple data visualization, graphs, and features such as device discovery, integration of things to the web, RESTful interaction with the platform, security, and web messaging, and tools such as MATLAB for data analytics.
## 3 Results and Discussion  
### Sea Buckthorn Leaves Characteristic  
The sea buckthorn, especially its fruits, is well-known for its nutritional and therapeutic value and has been extensively studied in scientific research. While the leaves are less valued, they also possess high nutritional content. The chemical composition of sea buckthorn leaves used in this study is presented in Table 1. In this study, dietary fiber, carbohydrates, and crude protein were predominant in sea buckthorn leaves (38.73, 27.85, 15.40 g/100 g, respectively), which confirms their potential as a valuable nutritional resource. The obtained results of sea buckthorn leaf proteins and dietary fiber content align with [PERSON] et al. (2017), who reported that these values range between 15.66-20.74 g/100 g and 9.59-12.14 g/100 g, respectively, depending on the specific plant cultivar and the year of harvest. While [PERSON] et al. [36] described a lower content of protein (14.48%).  
Sea buckthorn leaves could also be considered to be a source of fat, and in this study, the fat fraction was found to be predominantly composed of polyunsaturated fatty acids (60.96% of total fats), including omega-3 and omega-6 fatty acids, which are essential for nutrition and have important physiological functions [37]. [PERSON] et al. [35] and [PERSON] et al. [36] reported lower content of fat in sea buckthorn leaves.  
The sea buckthorn leaves also have high mineral content, which was in accordance with [PERSON] et al. [35], who described that mineral range between 4.06 and 5.79 g/100 g. The mineral profile of sea buckthorn leaves showed high concentrations of calcium (6.5% of all minerals), which is essential for nutrition [38]. [PERSON] et al. [39] also reported that calcium is the predominant macroelement in sea buckthorn leaves.  
Sea buckthorn leaves exhibit a notably high nutritional value and are distinguished by an elevated concentration of polyphenolic compounds, which are present in greater quantities in the leaves compared to the berries [40]. As a result, increasing attention is being directed toward the extraction and utilization of these bioactive compounds in the development of functional food products.
## 6 Conclusions  
In the present work, the transmittance of ultrasonic wave was numerically simulated by means of elastodynamic finite integration techniques (EFIT). Two distinct families of glasses, the fluorophosphate and the aluminosilicate glasses, were used as model systems to evaluate the applicability and effectiveness of the pulse-echo simulation. The relevant elastic constants were determined from the longitudinal and transversal sound velocities. The calculated and the experimental elastic properties were compared and correlated with the structure of these glasses. The comparison between experimental and simulation results showed low deviations supporting the concept that the numerical simulation of the pulse-echo ultrasonic inspection signals is effective and applicable for the study of the elastic properties of amorphous glasses as well as crystalline bulk materials. Elastic properties are important parameters related to the mechanical behavior of solids and for this reason their prediction is an important achievement. Their evaluation through computational models could lead to the reduction in the cost of manufacturing materials while simultaneously improving their properties. Furthermore, it has been found that using this technique, one can determine the precise location and size of a possible defect in the studied samples by comparing the experimental and simulation results.  
The latter can prove to be extremely important when one needs to monitor the performance of vitreous materials that are used either in optical or optoelectronic applications. The above can also stretch to defense and space applications where vitreous materials are commonly used due to their enhanced properties regarding their crystalline counter parts. Having a fast, non-destructive, and reliable technique can provide significant reductions in maintenance costs, eliminate failures due to early detection of structural flaws at the atomic level and ensure the optimum operation of critical optoelectronic and other components and infrastructures.  
Conceptualization, A.G.K.; methodology, A.G.K.; validation, A.G.K., N.K.N. and P.S.; formal analysis, A.G.K., N.K.N. and P.S.; investigation, P.S. and A.G.K.; resources, A.G.K.; data curation, P.S., N.K.N. and A.G.K.; writing--original draft preparation, A.G.K.; writing--review and editing, A.G.K., N.K.N. and P.S.; supervision, A.G.K.; project administration, A.G.K.; funding acquisition, A.G.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are available upon request from the corresponding author.  
The authors declare no conflict of interest.
## 1 Introduction  
Environmental issues have been receiving increasing attention in recent years, particularly those related to water pollution, which remains a major global concern, as clean water is essential for sustaining life. Among the most common pollutants found in aquatic environments are dyes from textile industries [1], pharmaceutical residues excreted by the human body [2], and microplastics originating from plastics [3], among others. To address these challenges, semiconductor materials have been increasingly explored for their potential as photocatalysts capable of degrading pollutants under light irradiation. However, water treatment becomes significantly more challenging when addressing real industrial effluents. These wastewaters are typically complex and variable in composition, often containing mixtures of dyes, chemical additives, organic compounds, and heavy metals, depending on the industrial source. As a result, many photocatalytic studies have focused on model pollutants such as methylene blue (MB), tetracycline, and rhodamine B (RhB). In contrast, studies employing real wastewater as the target pollutant remain relatively scarce, despite their crucial importance for practical and scalable applications. Recent reviews have highlighted the catalytic potential of iron-based materials in environmental remediation processes. For instance, nanoscale zero-valent iron (nZVI) systems have been successfully applied in advanced oxidation processes (AOPs) for the degradation of organic contaminants, demonstrating both reactivity and environmental compatibility in real wastewater conditions [4]. Similarly, magnetite-based catalysts (Fe\({}_{3}\)O\({}_{4}\)) have attracted attention for their excellent catalytic activity, magnetic recyclability, and low toxicity, reinforcing the viability of iron-based oxides in sustainable catalytic systems [5]. While these systems often rely on composite formation or magnetic recovery, this study focuses on the potential of simpler iron-based semiconductors--iron molybdate (Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\)) and iron tungstate (FeWO\({}_{4}\))--as visible light-driven photocatalysts. These materials combine structural stability, natural abundance, and environmental safety, making them promising candidates for real-world wastewater treatment without the need for doping or heterojunction engineering. Table 1 summarizes previously reported degradation efficiencies of Fe\({}_{2}\)(MoO\({}_{4}\))\({}_{3}\) and FeWO\({}_{4}\) photocatalysts against standard reference pollutants, including dyes and tetracycline. These contaminants were selected because dyes are commonly used as model molecules to evaluate photocatalytic performance, while tetracycline is a widely prescribed antibiotic considered an emerging pollutant and frequently studied in the context of iron-based photocatalysis.
## 4 Case Analysis
### Analysis of User Travel Mode
Figure 6 illustrates the changes in decision-making regarding travel times for reserved users in Scenarios 2 and 3. The results indicate that in Scenario 2, users' willingness to adjust travel times was not pronounced, with most adjustments being within 10 min. This lack of significant adjustment could be attributed to insufficient incentives to alter habitual travel times. Consequently, the demand for travel during peak periods remained concentrated in a few key time windows, leading to underutilization of road resources during other periods. In contrast, Scenario 3 shows a more vital willingness among users, especially those with higher time values, to adjust travel times. This suggests that the incentive mechanism motivates users to modify their travel plans to accommodate the requirements of reserved travel. Figure 6: The changes in decision-making regarding travel times for reserved users.
# Optimization of Xylose Recovery in Oil Palm Empty Fruit Bunches for Xylitol Production
## 4 Optimization  
The optimization study was conducted to evaluate the pretreatment process, temperature, pretreatment time, and SL of OPEFB for xylitol production which required high xylose recovery for economic reasons.  
The results in previous sections suggested that optimization should be performed with a goal to maximize xylose recovery in solid and liquid pretreated OPEFB. This pretreated OPEFB would be used further in the hydrolysis process followed by fermentation for xylitol production. The dark green in the middle of the contour plot in Figure 5a,b shows the area of the highest total xylose recovery (\(>\)0.07 g/g of biomass). The range of optimal solid loading, temperature, and time values indicates that maximum  
Figure 4: Effects of pretreatment time and temperature on xylose recovery (**a**) in solid pretreated OPEFB hydrolysate, and (**b**) in liquid pretreated OPEFB hydrolysate, at an SL of 10%.  
xylose recovery ranged from 5%-15%, 155-178 \({}^{\circ}\)C, and 22-60 min. However, the optimization resulted in 43% xylose yielded from OPEFB xylan, which corresponds to 0.075 g/g biomass. The optimum process parameters were at a temperature of \(165\pm 5\)\({}^{\circ}\)C, with SL of 7% and a time of 60 min. This was validated at a temperature of \(165\pm 5\)\({}^{\circ}\)C, with SL of 7% and a pretreatment time of 60 min. The validation showed that 35% of xylose yielded from OPEFB xylan, which corresponds to 0.061 g/g of biomass. Even though the relative standard error of data was 8.4%, the validation yield of xylose from OPEFB xylan was 24% lower than that in the model. ANOVA results showed that the coefficients of determination (\(R^{2}\)) for xylose recovery in the pretreatment of solid OPEFB and liquid were 80.61% and 61.99%, respectively, which showed a deviation between the model and the results of the validation.
## 3 Methodology
### SVM Classification Model
#### 3.2.2 Kernel Function  
The abovementioned principle is a method for calculating the linear separable time of a given sample. However, the given sample point is usually linearly inseparable in practical research. In the case of linear inseparability, the main idea of an SVM is to map the input sample points to their own higher-dimensional eigenvector space through the kernel function. Then, the optimal classification surface is constructed in the space. It can not only complete high-dimensional spatial operations in low-dimensional space but can also effectively avoid dimensional disasters [34] (Figure 5).  
Some of the most commonly used kernel functions are as follows [35]:  
(1) Radial basis function (RBF), which is defined by Equation (16),  
\[\mathrm{K}(x_{i},\ x)=e^{-\gamma\cdot\|x_{i}-\alpha\|^{2}} \tag{16}\]  
(2) Polynomial function (PF), which is defined by Equation (17),  
\[\mathrm{K}(x_{i},\ x)=\left(ax_{i}^{T}x+b\right)^{c} \tag{17}\]  
(3) Sigmoidal function (SF), which is defined by Equation (18),\[\mathrm{K}(x_{i},\ x)=\tanh\!\left(ax_{i}^{T}x+b\right) \tag{18}\]  
In this study, the radial basis function (RBF), which is widely used at present, is selected for classification.