esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
High-resolution visible remote sensing imagery and thermal infrared hyperspectral imagery are potential data sources for land-cover classification. In this paper, in order to make full use of these two types of imagery, a spatial-spectral-emissivity land-cover classification method based on the fusion of visible and thermal infrared hyperspectral imagery is proposed, namely, SSECRF (spatial-spectral-emissivity land-cover classification based on conditional random fields). A spectral-spatial feature set is constructed considering the spectral variability and spatial-contextual information, to extract features from the high-resolution visible image. The emissivity is retrieved from the thermal infrared hyperspectral image by the FLAASH-IR algorithm and firstly introduced in the fusion of the visible and thermal infrared hyperspectral imagery; also, the emissivity is utilized in SSECRF, which contributes to improving the identification of man-made objects, such as roads and roofs. To complete the land-cover classification, the spatial-spectral feature set and emissivity are integrated by constructing the SSECRF energy function, which relates labels to the spatial-spectral-emissivity features, to obtain an improved classification result. The classification map performs a good result in distinguishing some certain classes, such as roads and bare soil. Also, the experimental results show that the proposed SSECRF algorithm efficiently integrates the spatial, spectral, and emissivity information and performs better than the traditional methods using raw radiance from thermal infrared hyperspectral imagery data, with a kappa value of 0.9137.  
image fusion; thermal infrared hyperspectral imagery; conditional random fields; land-cover classification; emissivity +
Footnote †: journal: Computer Science  
1
## 2 Materials and Methods
### Anthocyanin Extraction, Hydrolysis, Identification, and Quantification  
The extraction and hydrolysis of anthocyanin were performed according to the method described by [PERSON] et al. [18] with some modifications. An aliquot (0.5 g sample) of fresh fruit tissue was snap-frozen using liquid nitrogen and ground into powder in a mortar and pestle. Afterwards, an extraction solvent made of 15 mL 95% ethanol and 1.5 mol/L HCl was added to the powder. Then the mixture was transferred to a 15 mL centrifuge tube and treated ultrasonically at 100 W for 30 min. The tube was centrifuged at 5000\(\times\) g for 5 min, and the residue was subjected to extraction for a second time. The two supernatants were pooled, and the volume was adjusted to 50 mL. A total of 3 mL supernatant was added into 1 mL 3 N HCl, and the reaction solution was incubated for 40 min at 90 \({}^{\circ}\)C and then placed in an ice box to cool. This hydrolyzed solution was filtered to obtain pure anthocyanins through a 0.45 \(\upmu\)m nylon filter membrane.  
Anthocyanin separation, identification, and quantification were performed following the procedures described by [PERSON] et al. [18], using HPLC (as mentioned above) equipped with a variable wavelength detector and a Zorbax SB-C18 column (i.d. 4.6 mm \(\times\) 250 mm, 5 \(\upmu\)m particle size). The mobile phase consisted of two solvents: 10% (_v/v_) aqueous formic acid (solvent A) and 10% (_v/v_) formic acid in methanol (solvent B) with a flow rate of 0.8 mL/min. The following linear gradient program was used: 90% solvent A for 5 min, 70% solvent A for 15 min, then 40% solvent A for 20 min, and returning to 90% solvent A for 30 min, with a column temperature maintained at 30 \({}^{\circ}\)C and an injection volume of 5 \(\upmu\)L. Six anthocyanin standards (delphinidin, petunidin, cyanidin, pelargonidin, peonidin, malvidin) were prepared before injection. Anthocyanidins were detected at 520 nm. The HPLC chromatogram of identified compounds and standards is shown in Supplemental Figure S5.
# High-Fidelity Fin-Actuator System Modeling and Aeroelastic Analysis Considering Friction Effect
## 3 Modeling of the System
### Modeling of the Fin Structure  
Here, the Gladwell branch modal synthesis method was applied and used to model the fin structure. This method divides the modes into two branches--namely, the elastic branch and the rotational rigid branch [10; 45; 46], as illustrated by the schematic diagram in Figure 4. Note that the elastic modal branch, which is branch 1, was obtained by adding a spring to the root of the fin shaft in the direction of the \(x\) axis and the fin was clamped in the \(y\) and \(z\) directions.  
The dynamic equation of the fin structure is [47]:  
\[M\ddot{\textbf{x}}+C\dot{\textbf{x}}+K\textbf{x}=F \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Elastic Branch** & **Frequency** & **Modal Shape** \\ \hline
1 st mode & 50.3 Hz & \\
2 nd mode & 593.0 Hz & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Elastic modal frequencies and modal shapes of the fin.  
Figure 4: The modal synthesis method was used to build the structure model of the fin, where (**a**) is the elastic branch and (**b**) is the rotational rigid branch.  
for which the details of matrix \(M\), \(C\), \(K\), and \(F\) are shown in Appendix A. Moreover, the generalized coordinates vector is \(x^{\text{T}}\)\(=\)\([q_{\text{e}}\), \(\delta]\), where \(q_{\text{e}}\) is the displacement vector in generalized coordinates of elastic modes, and \(\delta\) is the rotation angle of the actuator output shaft.
## 4 Resource Allocation Mechanism
### Weighted Greedy Strategy for Illumination Pattern  
As described in Section 3.5, the traffic demand of each cell can be further expanded to \(\{\phi_{t,1}^{i},\phi_{t,2}^{i},\cdots,\phi_{t,t}^{i},\cdots,\phi_{t,T_{tt}}^{i}\}\) according to different waiting time. Since we set the expiration time limit of data packets as \(T_{ttl}\) in this paper, we define the demands that have waited for more than \(4/5\)\(T_{ttl}\) as urgent, which are denoted as \(d_{u,t}^{i}\) and are prone to fail to access due to timeout if not satisfied in time. The rest of the requirements are non-urgent as they can wait for an additional period of time, which is denoted as \(d_{mu,t}^{i}\). We first normalize the two types of demands of each cell by the following formula:  
\[nor\_d_{*,t}^{i}=\frac{d_{*,t}^{i}}{\sum\limits_{i=1}^{N}d_{*,t}^{i}} \tag{17}\]  
in which \(*=u\) represents urgent demands and \(*=nu\) represents non-urgent demands. In order to seek a trade-off between the total amount and urgency of the demand in each cell, we take the weighted sum of the two normalized demands to obtain the current traffic request of each cell as follows:  
\[d_{t}^{i}=\omega_{1}*nor\_d_{u,t}^{i}+\omega_{2}*nor\_d_{mu,t}^{i} \tag{18}\]  
where \(\omega_{1}\) is the weight of urgency, and the higher the weight, the more the system tends to respond to requests that are about to expire, ensuring a high access success rate. \(\omega_{2}\) is the weight of demand amount, the higher the weight, the more the system tends to allocate beam resources to the cell with the most traffic demand.  
After calculating the weighted demand of each cell in each time slot by the above formula, we use the greedy strategy to select the \(K\) cells with the largest \(d_{t}^{i}\) to form the illumination pattern of the current time slot. The Weighted Greedy Strategy (WGS) that selects the illuminated cells in a time slot is summarized in Algorithm 1. After completing the joint allocation of bandwidth and power for the current time slot in the second stage, update the traffic demand of each cell and determine the illumination pattern of the next time slot according to the WGS again, then repeat the process and iterate until the end of the entire beam hopping cycle.  
```
1:collection of cells \(\{N\}\), set of demand of all cells \(D_{t}=\{D_{t}^{i}|i\in\text{N}\}\)
2:the set of illuminated cells \(\Phi\)
3:Initialize \(\Phi=\varnothing\), \(Count=0\);
4:for i=1 to N do
5: count the \(d_{u,t}^{i}\) and \(d_{uu,t}^{i}\);
6:endfor
7:Normalize two types of demand: \(nor\_d_{s,t}^{i}=d_{s,t}^{i}/\sum\limits_{i=1}^{N}d_{s,t}^{i}\);
8:Obtaion weighted demand: \(\bar{d}_{t}^{i}=\omega_{1}*nor\_d_{u,t}^{i}+\omega_{2}*nor\_d_{uu,t}^{i}\);
9:while\(Count<K\)do
10:\(i=\text{arg}\max\bar{d}_{t}^{i},i\in\{N\}\)
11: Add \(i\) to \(\Phi\) and remove \(i\) from \(\{N\}\)
12:\(Count=Count+1\)
13:endwhile
```  
**Algorithm 1** WGS for illumination pattern determination.
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 6 Conclusions  
In this paper, we propose an improved robust watermarking algorithm in DFT transform via spread spectrum DS-CDMA, optimizing the key operation parameters, particularly the amounts of bands and coefficients of frequency as well as the watermark strength factor using the PSO optimization algorithm. This research reveals several findings related to the use of the PSO algorithm to improve the performance of an invisible watermarking scheme. One of these is related to the design of fitness function of PSO. In this way, we show that instead of continuing the mainstream of the state-of-the-art in the use of the metric PSNR in the fitness function of the PSO algorithm, we can employ another visual quality metric such as VIF, which reflects perceptual distortions more precisely than the PSNR, and can help to obtain better performance results in terms of imperceptibility. Another finding is related to the kind and number of attacks considered in the fitness function. Analyzing a priori the attacks and the tolerance against which the conventional invisible watermarking has weaknesses, the design of the fitness function can focus only on improving the robustness against these attacks, assuming that against other the watermarking will keep the robustness. Another finding is the focus of the state-of-the art to increase the watermark payload neglecting the robustness against geometric distortions. Another critical finding is the few images considered in the testing performed by the current state-of-the-art, which may be a serious drawback to implement the watermarking algorithm in large-scale image datasets. In this way, a massive testing in terms of imperceptibility and robustness considering large image datasets should be compulsory, as in the case of our proposal that considers a dataset of 1000 color images with 24 bit/pixel resolution. Finally, in invisible watermarking, payload, imperceptibility, and robustness are not the only constraints to deal with but security is important too. To solve security issues in conventional DFT-based watermarking, the PSO algorithm has a fundamental role, finding the unique secret key to
# A Review of Knowledge Graph-Based Reasoning Technology in the Operation of Power Systems
## 3 Applications of Knowledge Graphs in the Power System Operation
### Power System Operation Application Scenarios for the KG
#### 3.2.2 Power Customer Service
[116] stored customer service history data in the Neo4j graph database. Knowledge extraction and knowledge management are performed in the built KB to build the KG platform application system. First, the Bi-LSTM model obtains the features of input questions and marquee attributes. Then, the attribute whose objective function is closest to the input question in KG is calculated. The attribute is linked to the answer to the query question. Ref. [111] studied power customer service robots. As an AI means in the back-end of intelligent robots, the KG is used to assist robots in providing consulting services to users through mobile applications in the form of text, voice, and video. In addition, KG technology can also be used in other business scenarios in the field of power marketing [117]. Taking electricity sales as an example, fluctuations in power sales are influenced by multiple factors such as business expansion, line losses, and power substitution. We can use the KG to show all of the factors affecting the change in electricity sales in a multi-dimensional way. Thus, the graph structure can quickly identify and locate abnormal fluctuations and provide corresponding solutions. When a power failure occurs, KG technology can contact the person in charge of the area according to the location of the failure and notify the customer in time. It will also bring new vitality to power marketing in terms of electricity bill payment reminders, family identity recognition, and personalized recommendations. Unlike previous AI customer service, KG-driven online service keyword entity matching is not just shallow similarity matching. Instead, it statistically analyzes the most interesting problems of users through big data technology and excavates customers' real needs through deep semantic understanding. For example, a special recommendation algorithm based on users' historical comments [118] combines with algorithmic models to provide users with the best solutions. The future customer service business of electric power systems will be dedicated to combining with various popular social media. Electricity customer service can interact with users in internet communities to determine their real needs and provide them with personalized and customized services [119].
## 3 Results and Discussion
### Volatile Compound Profile of Non-Fermented and Fermented Apple Juice and Cider Samples
A description of the odours of the VC 1-butanol; acetic acid, butyl ester; butanoate <butyl->; acetic acid, hexyl ester; 1-octanol; and nonanoic acid is given above. In addition. butyrate <ethyl has a sweet, fruity, tutti frutti, lifting, and diffusive odour. 2-Pyrazoline, 3-ethyl-1-isopropyl- has a sharp musty odour like rubbing alcohol and that of n-decanoic acid is described as unpleasant, rancid, sour, fatty, and citrus. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**RT, min**} & **Volatile Compound, \% from the** & **C-No.1** & **C-No.2** & **C-No.3** & **C-No.4** \\  & **Total Volatile Compounds** & & & & \\ \hline
4.373 & 3-methylbutan-1-ol & 27.9 \(\pm\) 0.3 & 22.9 \(\pm\) 0.2 & 29.1 \(\pm\) 0.2 & 18.9 \(\pm\) 0.2 \\
6.159 & Ethyl 2-hydroxypropanoate & nd & 2.06 \(\pm\) 0.18 & nd & 1.32 \(\pm\) 0.24 \\
7.594 & 1-Hexanol & 6.41 \(\pm\) 0.27 & 5.97 \(\pm\) 0.31 & 9.22 \(\pm\) 0.34 & 5.27 \(\pm\) 0.30 \\
10.492 & Hexanoic acid & 1.45 \(\pm\) 0.14 & 1.58 \(\pm\) 0.13 & 1.66 \(\pm\) 0.11 & nd \\
11.022 & Hexanoic acid, ethyl ester & 1.99 \(\pm\) 0.17 & 4.25 \(\pm\) 0.29 & 3.86 \(\pm\) 0.22 & 3.78 \(\pm\) 0.29 \\
12.803 & 1-Octanol & 2.13 \(\pm\) 0.16 & 1.27 \(\pm\) 0.17 & nd & 1.09 \(\pm\) 0.09 \\
13.911 & Phenethyl alcohol & 11.6 \(\pm\) 0.2 & 7.93 \(\pm\) 0.42 & 9.99 \(\pm\) 0.38 & 8.47 \(\pm\) 0.38 \\
15.383 & Butanedio acid, diethyl ester & 8.76 \(\pm\) 0.09 & 4.78 \(\pm\) 0.31 & nd & 4.78 \(\pm\) 0.20 \\
15.472 & Octanoic acid & nd & 7.11 \(\pm\) 0.36 & 4.04 \(\pm\) 0.23 & nd \\
15.69 & Butanoic acid, hexyl ester & 0.101 \(\pm\) 0.009 & nd & nd & 12.5 \(\pm\) 0.08 \\
15.783 & Ethyl octanoate & 1.78 \(\pm\) 0.15 & 8.04 \(\pm\) 0.43 & 6.77 \(\pm\) 0.41 & 5.89 \(\pm\) 0.29 \\
16.158 & 4-Octanol, 2,4-dimethyl- & 1.40 \(\pm\) 0.12 & nd & nd & nd \\
17.174 & 1,3-Octanediod & 3.46 \(\pm\) 0.21 & 0.780 \(\pm\) 0.027 & 1.47 \(\pm\) 0.26 & 0.541 \(\pm\) 0.031 \\
17.338 & Nonanoic acid & 4.77 \(\pm\) 0.32 & 2.07 \(\pm\) 0.14 & 6.47 \(\pm\) 0.32 & 5.40 \(\pm\) 0.32 \\
17.444 & 1-Decanol & & 2.13 \(\pm\) 0.20 & nd & nd \\
19.322 & 3-Allyl-6-methoxyphenol & 3.30 \(\pm\) 0.29 & 0.630 \(\pm\) 0.011 & nd & 0.376 \(\pm\) 0.025 \\
19.358 & \(n\)-Decanoic acid & 1.96 \(\pm\) 0.15 & 6.91 \(\pm\) 0.30 & 7.45 \(\pm\) 0.51 & 11.3 \(\pm\) 0.1 \\
19.974 & Ethyl decanoate & 0.622 \(\pm\) 0.041 & 6.16 \(\pm\) 0.41 & 2.86 \(\pm\) 0.19 & 3.65 \(\pm\) 0.26 \\
21.576 & 2.6-_bis_(1,1-dimethylethyl)-2,5- & 2.29 \(\pm\) 0.13 & 1.43 \(\pm\) 0.11 & 1.52 \(\pm\) 0.10 & 1.45 \(\pm\) 0.11 \\
22.275 & 2,4-_bis_(1,1-dimethylethyl)phenol & 5.65 \(\pm\) 0.28 & 3.52 \(\pm\) 0.15 & 2.50 \(\pm\) 0.21 & 3.78 \(\pm\) 0.25 \\
29.199 & 7,9-Di-_tert_-butyl-1-oxaspiro(4,5)deca- & 3.41 \(\pm\) 0.25 & 2.34 \(\pm\) 0.21 & 3.75 \(\pm\) 0.26 & 2.58 \(\pm\) 0.19 \\  & 6,9-diene-2,8-dione & -2,8-dione & -2.04 \(\pm\) 0.21 & 3.05 \(\pm\) 0.26 & 2.58 \(\pm\) 0.19 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Volatile compound profile (% from the total volatile compounds) of cider samples.
# Experimental Study on Multi-Cell Counting Using an Inertial Microfluidic Device
## 2 Materials and Methods
### Sample Flow Preparation
#### 2.3.2 Sample Preparation Process  
A total of 0.0125 g of microparticles was added to the prepared fluid medium in specified ratios, followed by 20 min of sonication to achieve uniform dispersion [41]. This sample preparation method ensured that the fluid dynamics and particle interactions closely mimicked physiological conditions, providing a controlled environment to study the sorting efficiency of different cellular compositions under various flow conditions [42].  
\begin{table}
\begin{tabular}{c c c} \hline
**RBC Ratio** & **Condition/Disease** & **Explanation** \\ \hline
600:1 & Normal [36] & This is the typical ratio in healthy human blood, reflecting a normal immune response. \\  & & \\
400:1 & Inflammatory disease, infection [37] & A decrease in the RBC ratio due to an increase in WBCs indicates the body is fighting an infection or dealing with inflammation. \\
250–300:1 & Severe inflammation, chronic infection [38] & A significantly lower ratio may indicate severe infections or chronic inflammatory diseases. \\  & & \\
100–200:1 & Leukemia, severe sepsis [39] & A very low ratio suggests serious conditions like leukemia, where WBC production is abnormally high, or severe sepsis. \\  & & \\
50–100:1 & Acute leukemia [36] & WBC count can become so high that it drastically reduces the RBC ratio. \\  & & \\ \(<\)50:1 & Advanced leukemia, other hematologic malignancies [40] & In very advanced cases, the WBCs can almost outnumber RBCs, indicative of severe hematologic malignancies. \\ \hline \end{tabular}
\end{table}
Table 1: Cell ratio vs. diseases.
# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### Physical layer security (PLS) in RIS-NOMA  
RIS is also critical in NOMA networks for enhancing privacy and security concerns in a network due to the potential to autonomously change the wireless propagation environment. Ensuring a good secrecy rate in a multi-user NOMA scenario, where the reliability of some of the legitimate users' channels from a transmitter is inferior to the wiretap channel is a challenging task [154]. This encourages the use of RISs in NOMA to improve of physical layer security [155]. Theoretically, the direct and reflected pathways can be added at the user and subtracted at the eavesdropper by placing RIS close to the authorised NOMA users or eavesdropper and effectively developing the passive beamforming [156]. Therefore, a positive secrecy rate is attained in RIS-NOMA networks by reducing the eavesdropper capability of signal reception and improving the legitimate signal reception capability. Knowing eavesdropper's CSI is crucial for effective beamforming design, which is almost impossible to do, to enable RIS-NOMA secure communication. In [151], the authors examined secure transmission in an RIS-NOMA network while considering a real-world eavesdropping scenario with an inadequate eavesdropper CSI.  
Channel estimation in RIS-NOMAIt is crucial to have the CSI, because NOMA employs SIC to detect the signals. In order to simplify the implementation of SIC's ability to identify the signal at the receiver, it is assumed that the majority of NOMA-related applications have perfect CSI. There exists only a few studies in the literature that concentrate on NOMA applications with imperfect CSI [157; 158; 159]. The huge RIS reflecting elements, their unique hardware limitations, and the multi-user aspect of NOMA make channel estimation in RIS-NOMA systems is a complex challenge. On the basis of RIS connection with devices, there are two types of RIS channel estimation algorithms that have been proposed in the literature: semi-passive and completely passive. Devices (such as inexpensive sensors) are embedded into the RIS's reflective components in semi-passive sensing. Therefore, signals from the BS or users to the RIS channels can be calculated depending on their received pilots by allowing the BS and each user to transmit pilot signals. For instance, in [160] a new reflection pattern at the RIS was devised for OFDMs to help the access point in estimate a channel based on the user-received pilot signals. Similar to this, Ref. [161] proposed a unique pilot-based channel estimate framework using a limited pilot symbols for an RIS-assisted multi-user communication system.
## 3 Results and Discussion
### Physicochemical Characterization of SHGCs
#### 3.1.1 Total Solids and Moisture Content  
Total solids (TS) content ranged from 58% to 68% WW, with statistical analysis revealing significant differences among samples, specifically between SHGC-C and SHGC-U, as well as SHGC-PP and SHGC-U (\(p\) = 0.0495). The TS content in SHGC-U decreased by 10%. In general, these values are lower than those reported by [PERSON] et al. [56] for Spanish goat's cheese \"Babia-Laciana\" (71.7 g\(\cdot\)100 g\({}^{-1}\) after 30 days of ripening) and by [PERSON] et al. [57] for Greek goat's cheese \"Xinotyri\" (80.88 g\(\cdot\)100 g\({}^{-1}\) after 22 days of ripening). However, the TS value (57.66 g\(\cdot\)100 g\({}^{-1}\) after 30 days of ripening) reported by [PERSON] et al. [58] for SHGC is comparable to that of SHGC-U but lower than that of SHGC-C (68.22% WW).  
Moisture content ranged from 33% to 43% WW, with significant differences observed between SHGC-C and SHGC-U, as well as SHGC-PP and SHGC-U (\(p\) = 0.0495). These values were lower than those reported by [PERSON] et al. [59] for \"Taleggio\" cheese without supplementation after 35 days of ripening (46% on the surface and 52% in the core) and by [PERSON] et al. [60] for \"Gredos\" raw goat's milk cheese (59%) produced in central Spain.  
As noted by [PERSON] et al. [6], the incorporation of seaweed into the curd is often associated with higher moisture content or lower total solids, which is consistent with our findings. This effect may be attributed to enhanced whey retention promoted by the hygroscopic properties of seaweeds. However, it is important to note that total solids and moisture content are highly dependent on oven type, drying techniques, temperature, and drying duration [61]. Additionally, differences in cheese preparation methods, both in this study and in the referenced literature, may influence the observed results.