esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Blockchain Technologies in Verification of Software Metrics
## 3 Methodology
### Verification on Blockchain
#### 3.2.1 Blockchain Model Selection  
Numerous blockchain networks have been developed in the realm of blockchain technology. Bitcoin was one of the first blockchain platforms, primarily serving as a decentralized payment system with limited programmability [27]. In contrast, Hyperledger  
Figure 2: Blocks in blockchain network.  
Fabric is a permissioned blockchain designed for enterprise applications, emphasizing modularity and private transactions, though it lacks the decentralized openness characteristic of public blockchains [28; 29]. Another well-known blockchain is Cardano, which introduces a layered architecture and an alternative consensus mechanism, though it is still in the process of fully implementing smart contract functionality [30].  
Ethereum is one of the most well-known and widely used blockchains on the market, supporting smart contracts and decentralized applications (dApps). For the purposes of this research, the Ethereum blockchain was selected. Furthermore, the capabilities of Ethereum smart contracts are utilized for verifying and storing software metric values on the blockchain, ensuring immutability, transparency, and decentralized validation of the computed results.  
Each operation of executing smart contracts on the Ethereum network requires a certain number of gas units, which correspond to the computational resources needed to run instructions in the Ethereum Virtual Machine [31]. The price of one gas unit is expressed in Gwei (1 Gwei = ETH), while the total cost of a transaction is obtained by multiplying the gas used by the Gas price:  
Total = Gas used \(\times\) Gas price  
The gas fee mechanism ensures that anyone executing code on the network pays for the computational resources they use, thereby fairly compensating resource providers and discouraging unnecessary or wasteful operations [32]. This mechanism is particularly important in the context of software metric verification, as frequent writes and data updates can significantly increase the total cost, especially during periods of network congestion.
## 4 Simulation of Use Cases
### Design of the Generic Use Case
#### 4.1.1 Structure of the Simulation Use Cases: Production System Flow and Characteristics  
Firstly, this sub-chapter describes the general structure of the simulation models that are applied for all simulation models within the simulation use cases. The structure is generated to provide the necessary production system flow and characteristics in order to answer the research question. Thus, as can be seen in Figure 7, the structure considers a production system within a supply chain of suppliers-production system-distributors-retailers/customers serving as a generic framework applicable for any sector. Moreover, the production system consists of technical processes, that is, the execution of the transportation, warehousing and production of finished products, as well as of management processes, systems and organizational structures, from the operational to strategic levels.  
As a summary, all models have a set of suppliers, one raw materials warehouse, three production processes, one finished products warehouse, a set of distributors and three retailers serving end-customers each with a certain demand. All simulation models maintain this structure over the simulation period.
# Application Layer Protocol Identification Method Based on ResNet
## 3 Materials and Methods  
This paper proposes a novel method for network protocol recognition, which comprises the following four sections:  
1. Truncating and processing network traffic data.
2. Utilizing a delimiter determination algorithm based on information entropy to determine an optimal set of delimiters. This set is then used to extract features from the application layer data, generating a feature table.
3. Employing a composite-feature-based RGB image generation algorithm to generate images required for model training.
4. Fine-tuning and protocol type recognition using a pre-trained ResNet model.  
Unlike traditional methods, the proposed approach ensures both the accuracy of protocol recognition and eliminates the need for manual feature selection. The processing workflow of this method is illustrated in Figure 1.
## 4 Design Architecture
### Connection/Design Options  
The design has been constructed in a scalable way to enable an optimized implementation on different or multiple FPGAs and for several use cases. Figure 5 sketches two exemplary designs. Depending on the FPGA-architecture it can be useful to implement more than one DCU, e.g., for FPGAs containing SLRs. In this case (Figure 5a), a part of the static signatures (one for every processing element) is sent to every DCU and the dynamic signature is streamed between the daisy chained units. This implementation also allows the use of a different number of processing elements for each DCU, e.g., to save more space for the control structure in one SLR. Input signatures are streamed directly via a custom AXI slave interface or from an onboard DDR. Results of all DCUs are combined and send back to the AXI slave or DDR. Furthermore, it is possible to use more than one DDR. In example (**b**) (Figure 5b), four DDRs are used to provide the input data and store the results. The input signatures are stored in one DDR while the remaining three DDRs are connected to one DCU each to store the results.  
Example (**b**) is an appropriate solution for use cases where many results can be expected. To find only a few results in a huge database example (**a**) in combination with more DDRs to store the input signatures is a better choice. The design developed for this work uses one DDR and is connected similar to example (**a**).
## 3 Results and Discussion
### Principal Component Analyses (PCA)  
Figure 1 shows the results of principal component analyses (PCA) for the quality parameters of yeast strains after rehydration in the presence of individual waters (PTWAir, PTWN, CW). The first principal component PC1 described 33.54% of the total variance, while the second (PC2) described 24.16%. Positive PC1 loads were related to glycogen and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameters** & \multicolumn{2}{c}{**Viability of Biomass (\%)**} & **Trehalose (\% ww)** & **Glycogen (\% ww)** \\ \hline \hline \multirow{4}{*}{Sig.}  
glycogen after fermentation; negative loads were related to trehalose and trehalose content after fermentation. For PC2, positive loads were for acidity and the number of yeasts, while the other parameters had negative values. In the presented studies, the statistically significant results depend mainly on different used yeast cultures; however, they could also depend on the time of storage of individual yeast strains. The rehydration medium only slightly contributed to the presented results. Significant differences were noticed in the case of the PTWN medium, which also depended on the yeast strain. _Sc_ US-05 strain was characterized by a relatively high glycogen content compared to the other samples. _Sp_ W30/70 strain had high trehalose content and flocculation ability. In contrast, _Sc_ T58 strain had significantly higher saccharolytic activity, as well as glycogen after fermentation.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 4 Numerical Simulations
### Performance with Iterative LSQR Equalization  
In this section, the BER performance of the iterative LSQR equalizer for UP-OCDM is proposed. Two equalization methods based on LSQR algorithm for OFDM and respectively proposed in References [25; 27], are simulated over the same doubly selective channels with the normalized Doppler spread set as 0.1 and 0.3.  
The simulation results are given in Figure 4a,b. The blue curve is the BER of UP-OCDM system. The green curve and red curve are the BER of OFDM with the equalizers proposed in References [25; 27], respectively. We observe that outstanding performance can be achieved by the iterative LSQR equalizer for UP-OCDM under high Doppler scenarios than the equalizers for OFDM. Moreover, compared with the LSQR based equalization algorithms for OFDM, the complexity of the proposed equalization algorithm is reduced by \(\mathcal{O}(NlogN)\) because of the circulant structure of transform matrix \(\mathbf{\Phi}\), which means that the symbols vector \(\mathbf{d}\) can be directly recovered by applying the LSQR algorithm without an extra step of recovering the symbol vector \(\mathbf{d}\) from an intermediate variable which is estimated by LSQR first. Moreover, generally, the performance of the proposed algorithm gets better with the increase of iteration numbers, because the inversion error will be smaller with more iterations. However, it should be noticed that, in low SNR regions, a larger iteration number does not always lead to a better BER performance. This is because the proposed LSQR based does not require any statistics  
Figure 3: BER performance of the UP-OCDM and OFDM systems with band MMSE block equalizers under different bandwidth \(Q\) with the normalized Doppler spread of 0.1 (**a**) and 0.3 (**b**).  
information. When the SNR is low, a larger iteration number might cause severer noise amplification and lead to worse BER performance. Nonetheless, the performance of the proposed is better than the similar LSQR based equalizers in OFDM with less computational complexity by \(\mathcal{O}(NlogN)\). Moreover, compared with the MMSE block equalizer proposed in Section 3.3, the complexity is reduced to \(\mathcal{O}(iNMlogN)\) from \(\mathcal{O}(N^{3})\), while maintaining comparable performance.
## 2 Results and Discussion
### Desorption  
The efficient desorption of the adsorbed compounds using ethanolic solutions was proposed. Despite the fact that methanol could provide higher elution yields than ethanol [6] a food grade solvent was preferred. The process was optimized by means of a central composite design of experiments, since a systematic variation of parameters has been recommended for the recovery of purified plant extracts enriched in certain target compounds [7]. The matrix with the real values of the independent variables temperature and ethanol concentration, and the correspondent coded variables (T, E) is shown in Table 2. The experimental values for the objective functions obtained for the resins selected on the basis of their potential for selectively retaining the target compounds present in the diluted stream from winery wastes are shown in Tables 2-4. The maximal phenolics desorption yields were 67, 62 and 51%, respectively, for HP20, SP207 and XAD16 HP, whereas the sugar desorption yields were 22, 31 and 19%, respectively. As a general trend, desorption of phenolic compounds is favoured by higher ethanol concentrations than desorption of sugars. The effect of temperature on the desorption yields depends on the resin and does not present a definite trend. Similar recovery yields have been reported during the alkaline elution of resins used for the simultaneous removal of phenolic compounds and polar anions from citrus peel juice and molasses [5] and for ethanol elution of phenolics from apple juice [15]. The range of phenolic content of the desorbed products was 44-51% for HP20, 35-51% for SP207 and between 30-47% for XAD16 HP. The range of sugar content of the desorbed extracts was 24-29%, for HP20, 22-31%, for SP207 and 16-26% for XAD16 HP. The highest phenolic purity for HP20 was obtained for experiment 7, operating at 25 \({}^{\circ}\)C and eluted with 88% ethanol. An expected correlation between the phenolic content of the desorbed extracts and the radical scavenging capacity was observed. The ABTS radical scavenging capacity of the desorbed product ranged from 8.9 to 13.0 mM Trolox for HP20, from 11.2 to 13.3 mM Trolox for SP207 and from 7.6 to 14.0 mM Trolox for XAD16 HP. In order to compare the efficieny of the desorbed product, these values can be referred to the activity of one gram of the dry desorbed product. These activities ranged from 1.94 to 2.63 grams of Trolox _per_ gram of desorbed product for HP20, from 1.97 to 2.66 g/g for SP207 and from 1.5 to 3.1 g/g for XAD16 HP. The ABTS radical scavenging capacity of one gram of BHT (butylated hydroxytoluene) was equivalent to 1.80 g Trolox, one gram of BHA (butylated hydroxyanisole) was comparable to 2.06 g Trolox and gallic acid to 4.93 g Trolox [10]. The effects of the independent variables were evaluated by a Student t-test and the significance of the models by F-test. The coefficients for the linear, quadratic and interaction effects of T and E on the objective functions are shown in Table 5 for the selected resins.
# IoT OS Platform: Software Infrastructure for Next-Gen Industrial IoT
## 1 Introduction  
The concept of the Internet of Things (IoT) was proposed by [PERSON] [1] and formally established in 2005 [2], and has been developing for over two decades. Compared with other new-generation information technologies such as big data, cloud computing, and artificial intelligence, the IoT presents a characteristic where \"applications precede theory\": the widely recognized three-layer IoT architecture (perception layer, transport layer, and application layer) is too generalized and insufficient to guide practice; subsequent IoT architectures proposed are limited to the interpretation of existing applications in some industries and fail to reach a consensus within the industry [3].  
Industrial IoT (IIoT) is a key area of IoT application. By applying IoT technology in the industrial field, combining connection, perception, analysis, and control methods, it achieves intelligence, automation, and efficiency in industrial production [4; 5; 6]. In various scenarios of the IIoT, the architecture design of IoT systems is often specialized for a particular project, lacking generalization standards [7; 6; 8], which affects the maintainability and scalability of IIoT systems [3]. Traditional IIoT systems usually can only meet the existing needs for a short time after project delivery and cannot cope with new demands, unless by building a new IoT system to meet new needs [9]. The recurrence of the above situation has led to the formation of data silos among various IoT systems. Lower-level IoT systems are unable to integrate all the data perceived by physical objects in a unified manner, affecting the upper-level analysis and decision-making, increasing the cost of enterprise digitalization, and thus slowing down the pace of industrial digitalization [10; 11].  
To meet the construction needs of IIoT systems and avoid the predicament of traditional IoT system construction, we propose the concept of an IoT operating system platform. As a software infrastructure for the next generation of IIoT, the IoT OS platform follows the design philosophy of classic operating systems, positioning the platform as the core of the IIoT system: managing various entities downwards and providing service support for IIoT applications upwards. This platform can improve the disadvantages of traditional IoT system design, cater to the needs of industrial industry digital transformation, and deliver IIoT systems with greater specificity and scalability.  
The remainder of this article is organized as follows: Section 2 elaborates on the related work and current research status of IIoT system construction. Section 3 presents the definition and specific architecture of the IoT operating system platform. Section 4 introduces how to carry out IIoT construction based on the IoT operating system platform through case studies. Section 5 is a summary and outlook of the IoT operating system platform.
# Dispersion Curve Interpolation Based on Kriging Method
## 3 Methods
### Application in the Changbai Volcano  
The parameters we finally chose are shown in Table 2. How these parameters were determined will be described in more detail in Sections 4 and 5.  
We generated a group of points to be predicted, where the grid spacing was 0.03\({}^{\circ}\)\(\times\) 0.03\({}^{\circ}\). The longitude started at 127.66\({}^{\circ}\), and latitude was from 41.66\({}^{\circ}\), with a difference of 0.01 both in longitude and latitude. The purpose was to compare the interpolation results with the original image. In the ambient noise tomography result (as shown in Figure 2), we consider that the results with DWS less than 5 are inaccurate. We obtained 226 points with dispersion curves as input data by forward modeling. Then, we applied universal kriging for prediction, following the steps described in Section 3.2. Finally, we used CPS330 to inverse the 1D V\({}_{5}\) model through these dispersion curves, combining them into a 3D model beneath Changbai volcano. Figure 4a-c show the results after kriging interpolating.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & \\ \hline Method & Universal Kriging \\ \hline Variogram & Spherical \\ \hline Distance & 15,000 (m) \\ \hline Angle & 265 (\({}^{\circ}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in interpolation.  
Figure 3: Determination of the deviation angle.  
Figure 4: Results at different depths: (**a–c**) Interpolated results with grid translation; (**d–f**) original results inverted by CPS330; and (**g–i**) combination of the interpolation results and the original results (inverted by CPS330).
## 5 Case Study
### Case 2: Estimation Points Outside the Limits  
In this case study, there is no wind in the environment, so the wind speed is lower than normal speed (mean = 7.14 m/s). Hence, the wind speed meets following conditions:  
\[f(v)=(\frac{v}{32.5})e^{-(\frac{v}{65})^{2}}\ \ \ \ \ v\geq 0\ \, \tag{56}\]  
\[h(v)=\begin{cases}0&0\leq v<3.5\\ \frac{1}{46}(v-3.5)&3.5\leq v<18\,,\\ 0.315&18\leq x\end{cases} \tag{57}\]  
where \(k_{p}=2\), \(c_{p}=65\), \(x_{ci}=3.5m/s\), \(x_{co}=18m/s\), \(P=0.315 MW\), and the photovoltaic data and gas load 3 are the same as those in Case 1.  
Just as in Case 1, the relevance between each power plant and photovoltaic power plant is added using the Nataf transformation. Figure 8 shows the results of points that are outside of the boundary constraints, and Tables 4 and 5 show the errors in the variance and mean. It can be seen that the outside-of-range point has great impact on 2-PEM and 3-PEM. In these cases, the average error of the variance is more than 20%. The impacts on 5-PEM and 7-PEM are smaller.  
For 2-PEM, 3-PEM, 5-PEM, and 7-PEM, the computation quantities are 12, 18, 25, and 37, respectively.  
Figure 7: Comparison of Monte Carlo results with and without the Nataf transformation for Case 1. (**a**) Voltage results for each node. (**b**) Mean pressure for the natural gas network. (**c**) Enlargement of key points. (**d**) Cumulative probability of node 5 in the power network. (**e**) Cumulative probability of node 5 in the gas network.  
To limit points that are outside of the boundary constraints, the power transformation and equality constraints transformation methods are used to recalculate Case 2. Tables 6-8 show the results of limiting the wind speed estimation points. It can be seen when the power transformation method works, the two methods have similar accuracy, but the equality constraint transformation method has wider applicability. In general, both methods effectively improve the accuracy of calculation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Relative Error** & **7-PEM (\%)** & **5-PEM (\%)** & **3-PEM (\%)** & **2-PEM (\%)** \\ \hline mean & 0.2399329 & 0.6347321 & 1.6014625 & 2.0714407 \\ variance & 13.499378 & 15.0755443 & 22.7094732 & 27.6881084 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average relative error of the gas network.  
Figure 8: Case 2 results. (**a**) Mean pressure for the natural gas network. (**b**) Variance in pressure for the natural gas network. (**c**) Mean pressure for the power network. (**d**) Variance in pressure for the power network.
# An Item Retrieval Algorithm in Flexible High-Density Puzzle Storage Systems
## 4 Analysis and Results
### Comparison of Our Algorithm with Previous Studies  
In this subsection, we aim to compare the result of our proposed approach with the results of the contemporary efficient algorithm introduced by [PERSON] and [PERSON] [6]; they found the optimal ARM for a puzzle network including one retrieval point at the lower left corner of the puzzle, one white cell at the retrieval point and one black cell. This optimal ARM, \(N^{*}\), can be calculated as  
\[N^{*}=\left\{\begin{array}{ll}6i+2j-13&\text{if }i>j\\ 8i-11&\text{if }i=j\\ 6j+2i-13&\text{if }i<j\end{array}\right.\]  
where \(i\) and \(j\) are the row and the column of the black cell counted from the retrieval point. In Table 2, we compare the results of our algorithm to the above optimal formula.  
As Table 2 shows, our algorithm obtains the same results as the formulation of [PERSON] and [PERSON] [6]. The main reason is that in our algorithm, we followed their main conclusion stating that the optimal puzzle must make the maximum number of 3 moves possible to retrieve the called items.  
[PERSON] and [PERSON] [23] studied how the location of the white cells impacts the average retrieval time for two configurations: (i) random distribution of empty cells and (ii) input/output distribution where white cells are at the retrieval point. We compared the results of our algorithm with their results for these two configurations. As Figure 19 shows, our algorithm outperforms their results in the input/output distribution configuration. However, the results are similar in the random distribution configuration (Figure 20).  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Case Number**} & **Black Cell Location** & **Mathematical Optimum** & **Software Program** \\  & **(i,j)** & **Number of Movements** & **Simulation Results** \\ \hline
1 & 5,5 & 29 & 29 \\
2 & 15,15 & 109 & 109 \\
3 & 25,25 & 189 & 189 \\
4 & 50,50 & 389 & 389 \\
5 & 5,15 & 87 & 87 \\
6 & 15,25 & 167 & 167 \\
7 & 25,35 & 247 & 247 \\
8 & 35,50 & 357 & 357 \\
9 & 15,5 & 87 & 87 \\
10 & 25,15 & 167 & 167 \\
11 & 35,25 & 247 & 247 \\
12 & 35,50 & 297 & 297 \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of our results with the optimum mathematical solution obtained by [6].  
Figure 18: The effect of the number of white cells on the ARM in a 25 \(\times\) 25 puzzle.
# Conformal Titanyl Phosphate Surface Passivation for Enhancing Photocatalytic Activity
## Materials and Methods  
### Material Synthesis  
The facile surfactant assisted sol-gel process was conducted by using titanium(IV) isopropoxide (TTIP, 97%, Aldrich), polyethylene glycol (PEG, M\({}_{n}\) 300, Aldrich), phosphoric acid (85 wt%, Aldrich) and methyl alcohol (anhydrous, Aldrich). The titanium precursor solution with concentration of 0.35 M was prepared by dropping the TTIP in the methyl alcohol solvent slowly (with the rate of 1 \(\mu\)L/s) during the vigorous stirring over 600 rpm. After fully dissolving, 3.5 M PEG was immediately injected in the precursor. Then, 0.1 M phosphoric acid was mixed with the solution. The thermal annealing process at 750 \({}^{\circ}\)C for 2 h was carried out to obtain the TP-TiO\({}_{2}\). For the bare TiO\({}_{2}\), the same experimental protocol was utilized without the phosphoric acid.