esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments
### Dataset and Preprocessing  
The dataset had a total of 50 samples collected with various process parameter combinations, with each sample consisting of four thermal time series data. The samples were divided into two categories according to the existence or non-existence of warping deformation. After printing a specimen, the warping deformation of the specimen was specified by measuring the angle \(\theta\) at each corner. The corner angle \(\theta\) is obtained using the following equation:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Printing Parameters** & **Value** & **Printing Parameters** & **Value** \\ \hline Material & ABS & Extrusion temperature (\({}^{\circ}\)C) & 240–275 \\ Filament diameter (mm) & 1.75 & Platform temperature (\({}^{\circ}\)C) & 50–90 \\ Nozzle diameter (mm) & 0.4 & Raster angle (\({}^{\circ}\)) & \(\pm\)45 \\ Layer height (mm) & 0.19 & Infill density (\%) & 30 \\ Printing speed (mm/s) & 66.7 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Printing process parameters.  
Figure 1: Images of the experimental setup (**left**) and the build platform with the four thermocouples (**right**).  
Figure 2: (**a**) The sketch of cuboid model and (**b**) method of measurement at each corner.  
\[\theta=tan^{-1}\left(\frac{y}{x}\right) \tag{4}\]  
where \(y\) is the corner height and \(x\) is the horizontal distance of the corner from the nearest center of the specimen, both measured using a digital vernier caliper. In this work, the center point in between corners 1 and 4 and corners 2 and 3 were considered as the centers of the specimen. The parts peeled away with an angle greater than \(1^{\circ}\) with the platform were considered to have warping deformation. If a specimen had more than one warped corner, it was classified as a warped specimen. Images of the produced specimens with and without warping deformation are shown in Figure 3.  
The thermal time series data extracted from the thermocouple sensors installed at the corners of the specimen with and without warping deformation are shown in Figure 4. As seen in the figure, TC1 and TC2, as well as TC3 and TC4, moved relatively similarly as their corner distance was closer.  
The noise data at both ends of the data were removed to extract the patterns of the time series data in a unified interval. The length of each time series data was set as 2640 steps to conduct a model learning and performance evaluation. Min-max normalization was also conducted on the data for more stable learning. In general, min-max normalization is used to prevent certain features from overwhelming others and to make models more stable to learn. The min-max normalization is calculated as in Equation (5).  
\[x^{\prime}=\frac{x\,-\,x_{min}}{x_{max}\,-\,x_{min}} \tag{5}\]  
where \(x\) is the original value, and \(x_{max}\) and \(x_{min}\) are the maximum and minimum values of each feature, respectively.  
Figure 3: Printed specimens (**a**) with warping deformation and (**b**) without warping deformation.
# Recent Advances in Muconic Acid Extraction Process
## 2 Muconic Acid Properties  
Organic acids are a series of widespread and frequently used compounds due to the important characteristics they possess: high solubility, hygroscopicity, biodegradability, and biocompatibility [27]. From the point of view of the importance of applications, fumaric acid, citric acid, itaconic acid, adipic acid, and muconic acid are short-chain organic acids containing one or more carboxylic groups, with a wide range of applications industrial and high market demand [27].  
Unsaturated dicarboxylic acids are of high industrial importance due to the presence of two double bonds and two carboxyl groups, which allow polymerization reactions to be carried out for the production of synthetic resins and biodegradable polymers [28]. Muconic acid is an unsaturated dicarboxylic acid, and its IUPAC name is 2,4-hexadienedoic acid [28], which, due to its chemical structure, presents itself in the form of three isomers (Figure 1). The three isomers differ in their chemical characteristics and physical properties, as well as their applications [29]. The physical properties of muconic acid are highlighted in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Property** & **Details** & **Ref.** \\ \hline Molecular mass & 142.11 g/mol & [30] \\ State of aggregation & Solid & [30] \\ Color & Light yellow & [30] \\ Molecular formula & C\({}_{6}\)H\({}_{6}\)O\({}_{4}\) & [30] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of muconic acid.  
The stereospecific configuration, reactive functional groups, and conjugated double bonds allow muonic acid to participate in a variety of chemical reactions yielding commercially important products (Figure 2) such as adipic acid [28] and terephthalatic acid [28], caprolactone [32], caprolactam [32], 3-hexenedioic acid [32], important monomers for obtaining nylon-6,6, polytrimethylene terephthalate, dimethyl terephthalate, polyethylene terephthalate, trimellitic anhydride, resins, polyesters, polyols, as well as other products used in food, pharmaceutical, cosmetic [28], and agrochemical industries [33].  
The current market price of muonic acid is USD 1.5/kg, and the global market size (worldwide production of 79,000 t/year) is expected to be over USD 110 million in 2024 [33].
## 3 Blockchain and Tracking Technologies in the Raw Material Sector
### Considerations on the Value of the Data in a Blockchain Environment
\begin{table}
\begin{tabular}{c c} \hline \hline Typical Use Cases & A barcode is a machine-readable optical label that contains information about the item to which it is attached or assigned to. A QR code is a matrix type of barcode. The use of bar and QR codes is mostly associated with fast moving consumer goods for supermarket check-out, but is also widely used in the early stages of the supply chain. Within the mining industry, barcodes and QR codes can, for instance, be attached to bags of ore, ingots, and even containers to identify specific lots and verify provenance [25]. Moreover, unlike RFID labels for example, bar- and QR codes can be generated and distributed electronically, e.g., via e-mail or mobile phone. \\ \hline Robustness & Barcode and QR codes usually are quite robust. For example, certain materials may allow that the bar or QR code is directly laser-printed on the material. Thus the robustness often correlates with the material of choice. This also means that a smelting procedure can interrupt the tracking of objects that are labeled with a laser-printed barcode or QR code. \\ \hline Range & The range of a barcode or QR code is limited to its visibility, unless it is transmitted by supportive means. It requires a scanner or other reader to read the code and translate it into data for further processing. \\ \hline Energy Requirements & Besides the energy needed to print and read the code, there are usually no further energy requirements. \\ \hline Data Related Capabilities & The data capacity of the barcode or QR code may be limited, depending on the type of code and the standard(s) applicable to it, for example in terms of data type, version and correction level. But usually it is used just to link to the relevant information via an URL. Problems can relate to difficulties reading a barcode or QR code, for example due to errors in code printing, surface contamination, water, humidity, varying lighting levels [3]. \\ \hline Other Aspects & Barcode and QR code solutions both can be relatively low cost and low tech, which can make them very practical concerning most use cases for raw materials tracing and sourcing scenarios. However, to provide visibility and compatibility with the applicable standards, barcodes and QR codes require a minimum space to be printed on. This can exclude them from being printed directly on bulk material or material of smaller size. A solution to this may again be bags or other containers. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Characteristics of Barcodes and QR Codes.
## 2 Related Work
A knowledge-based model for smart city services mapped static and dynamic data to smart city ontology in [29]. Automatic knowledge extraction of non-taxonomic relations from smart city documents is demonstrated in [30] by combining semantic graph-based method with context information. Formal concept lattices are constructed for smart city problems [31] to extract knowledge that can be utilized for smart city services. To summarize, previous studies on smart city initiatives focused on understanding the common elements of smart city initiatives rather than evaluating smart city initiatives from a multi-level granularity perspective. Smart city initiatives are embedded in higher level initiatives and policies including country level and international initiatives. It is important to analyze the initiatives at different levels of abstraction to improve alignment of initiatives to higher level objectives. The objectives of this paper are (1) to provide a knowledge elicitation methodology for multi-granular analysis of text-based policies and initiatives generated by different administrative levels. Such an automated method would support decision makers and analysts in balanced assessment of policies pointing to gaps, and more effective alignment of policies at higher levels, (2) to demonstrate the methodology in evaluation of smart city initiatives generated from multi-level administrative levels to understand alignment and variations among the initiatives. Main methodology utilized in previous research on smart city initiatives is based on use of surveys, interviews, and comparative study of case studies of various smart city initiatives across the world. A recent study of smart city initiative analysis [13] utilized content analysis and text mining to analyze smart city initiatives but the methodology did not focus on use of formalized methods. While there are studies of text document analysis for smart cities utilizing formalized methods, these studies aim to extract knowledge for smart city applications and services rather than use the methodology for analysis of smart city initiatives. This study provides a formalized methodology to evaluate smart city initiatives and differs from other studies in terms of constructing a formalized semantic network structure of smart city initiatives to establish a common framework for analysis of initiatives written at different administrative levels including international, national, and regional.
## 4 Stiffening Effect
\begin{array}{c}\left[-\frac{6}{l^{2}}E_{0}\quad\frac{2}{l}E_{0}\right] \begin{bmatrix}v_{i}(t)\\ \varnothing-\frac{6}{l^{2}}E_{l}-\omega^{2}\quad\frac{4}{l}E_{l}\end{bmatrix} \begin{bmatrix}v_{i}(t)\\ \theta_{i}(t)\end{bmatrix}=\begin{bmatrix}\varnothing-\frac{6}{l^{2}}E_{0}- \omega^{2}&-\frac{4}{l^{2}}E_{0}\\ -\frac{6}{l^{2}}E_{l}&-\frac{2}{l^{2}}E_{l}\end{bmatrix}\begin{bmatrix}v_{i-1} (t)\\ \theta_{i-1}(t)\end{bmatrix},\end{array}  \tag{47}\]  
where \(E_{0}=\varnothing\Big{(}\frac{m_{p}L}{\rho}+\frac{L^{2}-s_{0}^{2}}{2}\Big{)}\), \(E_{l}=\varnothing\Big{(}\frac{m_{p}L}{\rho}+\frac{L^{2}-s_{0}^{2}}{2}\Big{)}\). Regarding the first element, the constraints of the proximal end are defined as \(v_{0}(t)=0\) and \(\theta_{0}(t)=0\). Accordingly, the relationship between \(v_{1}(t)\) and \(\theta_{1}(t)\) is obtained as  
\[ \begin{array}{c}\left[\varnothing-\frac{6}{l^{2}}\varnothing\left(\frac{m_{p }L}{\rho}+\frac{L^{2}-l^{2}}{2}\right)-\omega^{2}\right]v_{1}(t)+\frac{4}{l} \varnothing\bigg{(}\frac{m_{p}L}{\rho}+\frac{L^{2}-l^{2}}{2}\bigg{)}\theta_{1} (t)=0.\end{array}  \tag{48}\]  
As the \(N\)th element, Equation (18) is rewritten as  
\[\ddot{v}_{N}(t)-\frac{EI}{m_{p}}\bigg{[}\frac{12}{l^{3}}v_{N-1}(t)+\frac{6}{l ^{2}}\theta_{N-1}(t)-\frac{12}{l^{3}}v_{N}(t)+\frac{6}{l^{2}}\theta_{N}(t) \bigg{]}=0. \tag{49}\]  
After iterative mathematical manipulations, the lateral deformation of the \(N\)th element can be deduced with the transfer formula Equation (47) and the boundary condition Equation (48). The natural frequency is determined by substituting the deformation result into Equation (49). To verify the analysis results using FEM in the paper, a simulation is implemented, and the natural frequency feature is discussed below. For comparison, the flexible link is divided into a set of the different number of elements as \(\{4,6,8,10\}\). The relationship of the fundamental frequency and three factors (i.e., angular velocity, payload mass, and length of the flexible link) with respect to the number of elements are illustrated in Figure 9. The first subfigure (a) shows the variation of the fundamental frequency with different angular velocity under the condition of payload mass as 1 kg and the beam length as 1 m. It is worth noting that the frequency becomes higher as the angular velocity increases. In other words, it also reflects the effect of the angular velocity on the stiffness of the flexible link. The next two subfigures (b) and (c) illustrate the effect of payload mass and length of flexible beam. It is evident that the frequency decreases with larger payload and longer beam length. Moreover, the iterative process costs more in computation time when the number of elements is larger than 10, while the corresponding results show no significant difference.
## 2 Signal Processing Principles
### Poisson Statistical Properties in Optical 4 FSK Communications  
In optical wireless communication systems employing 4 FSK, the transmission process is inherently random due to the discrete nature of photon arrivals. When the optical signal is modulated to one of the four specified frequencies \(f_{a}(a\in\{1,2,3,\,4\})\), the receiver measures the received intensity by photomultiplier tube (PMT) and converts the incident photon flux into discrete photoelectron signals. Since the photon arrival process obeys Poisson distribution, its mathematical model can be expressed as follows:  
\[P(S_{i}|\lambda_{i})=\frac{\lambda_{i}^{S_{i}}e^{-\lambda_{i}}}{S_{i}!} \tag{1}\]  
where \(S_{i}\) is the observed photon count at the \(i\)-th frequency channel, and \(\lambda_{i}\) denotes the expected photon count at a given the transmitted data. When \(i=s,\,\lambda_{s}\) corresponds to the expected photon count for transmitting bit value 1. When \(i=b,\,\lambda_{b}\) represents the number of environmental noise photons. The \(\lambda\)-value expressions for both are as follows:  
\[\lambda_{s}=\frac{P_{t}\cdot F(f_{c},t)\cdot\eta}{L\cdot R\cdot hv} \tag{2}\]  
\[\lambda_{b}=\frac{J}{R} \tag{3}\]  
where \(\eta\) represents the detector quantum efficiency. The sequence composed of probability density functions for different frequencies \(f_{c}\) is represented by \(F(f_{c},t)\), which is a function of time \(t\). \(h\) represents [PERSON]'s constant, \(v\) represents the signal frequency, and \(R\) denotes the transmission rate of the code elements in the system. \(J\) is the number of noisy photons in the environment at 1 s time. In this decoding scheme, the photon probability distribution for the case of different frequency photons can be obtained by determining \(\lambda\), thus providing theoretical support for channel decoding.  
Traditional maximum intensity detection methods are based on single-sample maximum decision, but due to the randomness of photon counting, these methods are prone to noise interference under low SNR conditions, leading to significant BER increases. Therefore, this paper proposes a codeword-level photon accumulation and maximum likelihood estimation method, which enhances decoding reliability by statistically accumulating over the entire symbol duration.
# Study on a Landslide Segmentation Algorithm Based on Improved High-Resolution Networks
## 4 Results and Discussion
### Discussion  
First, the test results show that the model proposed in this paper has higher accuracy compared to the baseline model, indicating better performance in landslide segmentation tasks. Moreover, the model demonstrated superior segmentation results when dealing with old landslides and small-scale landslide segmentation.  
Secondly, when compared to the other models, the U-Net achieved an MIOU of 82.72%, MPA of 91.19%, and F1-score of 90.18% on the dataset in this paper. Deeplabv3+ attained an MIOU of 83.65%, MPA of 90.68%, and F1-score of 90.75% on the same dataset. PspNet, on the other hand, recorded an MIOU of 84.09%, MPA of 91.79%, and F1-score of 91.07% on this dataset. Specifically, all performance metrics of the model proposed in this paper surpassed those of the other models on the dataset, demonstrating the superior performance of this model in landslide segmentation tasks. Furthermore, both PspNet and the model in this paper utilize a parallel structure, suggesting that this structure may offer advantages in landslide detection.  
Finally, this paper compared the number of parameters and the computation of the DHRECA-Net with the other models. The results indicate that the number of parameters in the DHRECA-Net was only 39.6% that of U-Net, 18% of Deeplabv3+, and 21.1% of PspNet. Additionally, the computation required for the DHRECA-Net was only 8.42% of that of U-Net, 22.8% of Deeplabv3+, and 32.1% of PspNet. These findings demonstrate that the model proposed in this paper achieved a superior performance while utilizing fewer parameters. Particularly for scenarios in which deployment on power-constrained devices is necessary, the algorithm presented in this paper demonstrated significant advantages. Through parameter and computation optimization, the model can efficiently operate in resource-constrained environments, presenting a more effective solution for practical applications.
# Flexible Phased Antenna Arrays: A Review
## 1 Introduction
### Problem Statement  
Although the FPA concept encompasses a wide spectrum of shape-adaptive array architectures, much of the existing literature has focused on individual implementations orspecific technologies, rather than providing a comprehensive comparison across different solutions. Given the rapid evolution of enabling materials, manufacturing processes (both subtractive and additive), and design methodologies, a comparative analysis is needed to identify the key differentiators and limitations of each approach. Such analysis can be useful to guide future developments by clarifying performance metrics, integration challenges, and areas where further innovation is required. Accordingly, the following Section 2 presents different manufacturing techniques exploited for producing FPA; Section 3 highlights the materials used for their realization; Section 4 focuses on the major challenges identified in the field of FPA; while Section 5 presents a comparative analysis of the various technologies based on key performance parameters. Finally, Section 6 concludes the review, trying to guide the reader through the future trends of this promising field.
_Appl. Sci._**2015**, \(5\), 1211-1220; doi:10.3390/app5041211  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Microwave-Assisted Synthesis and Antifungal Activity of Some Novel Thioethers Containing 1,2,4-Triazole Moiety  
**[PERSON] 1,\({}^{\dagger}\), [PERSON] 2,\({}^{\dagger}\), [PERSON] 3, [PERSON] 3, [PERSON] 3,*, [PERSON] 2,* and [PERSON] 4,***  
1 College of Life Science, Huzhou University, Huzhou 313000, China; E-Mail: [EMAIL_ADDRESS]  
2 Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100014, China; E-Mail: [EMAIL_ADDRESS]  
3 College of Chemical Engineering, Zhejiang University of Technology, Hangzhou 310014, China; E-Mails: [EMAIL_ADDRESS] (H.-K.W.); [EMAIL_ADDRESS] (Z.-H.S.)  
4 Ecology Institute of Shandong Academy of Science, Jinan 250014, China  
\({}^{\dagger}\) These authors contributed equally to this work.  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (X.-H.L.); [EMAIL_ADDRESS] (B.-J.L.); [EMAIL_ADDRESS] (Y.-G.Z.); Tel./Fax: +86-571-8832-0832 (X.-H.L.).  
Academic Editor: [PERSON]  
_Received: 8 October 2015 / Accepted: 11 November 2015 / Published: 16 November 2015_  
**Abstract:** A series of novel thioether derivatives containing 1,2,4-triazole moiety were designed and synthesized from 4-chlorophenol and ethyl 2-chloroacetate as starting materials by multi-step reactions under microwave irradiation, and their structures were characterized by \({}^{1}\)H-NMR, MS and elemental analysis. The antifungal activity of title compounds was determined. The results indicated that some of title compounds exhibited moderate antifungal activity.  
**Keywords:** thioether; 1,2,4-triazoleole; microwave assistant synthesis; antifungal activity
# Analysis of Quantum-Classical Hybrid Deep Learning for 6G Image Processing with Copyright Detection
## 2 Literature Review
### Deep Learning in Image Processing with Copyright Detection
#### 2.1.3 Copyright Detection Techniques  
Copyright detection has become increasingly important in the digital age, especially with the growing prevalence of computer-generated content and the rapid increase in digital images. Various technologies and algorithms are employed to effectively identify potential copyright infringements. For example, methods such as image hashing and machine learning techniques, including convolutional neural networks (CNNs), analyze visual features and assess similarity against a database of copyrighted materials. One effective approach is perceptual image hashing, which creates unique identifiers for images,enabling efficient comparison and detection of unauthorized use. As copyright detection technologies continue to advance, they play a crucial role in protecting digital assets, reducing financial risks, and ensuring that original creators receive appropriate recognition for their work.
## 3 Numerical Validations of Stiffened Ship Panel Strain Reconstruction
### Results and Discussion  
1. Comparison of Strain Contour Diagrams for Transverse T-Sections Obtained by FEM and SF-iFEM;  
For the 1 # and 2 # transverse T-sections of the stiffened ship panel, the reconstructed strain cloud diagram for Case 1 exhibited a substantial degree of consistency with the corresponding simulated strain cloud diagram, as visually presented in Figure 9.  
Figure 8: Strain field reconstruction accuracy for three different load magnitudes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Magnitude of**} & \multicolumn{2}{c}{**Magnitude of**} & \multirow{2}{*}{**RMSE [\(\mu\)\(\varepsilon\)]**} & \multirow{2}{*}{**MRE [\%]**} \\  & **Load on Point A** & & & **Load on Point B** \\  & **[KN]** & & **[KN]** & \\ \hline Case A & 10 & 5 & 4.96 & 4.53 \\ Case B & 10 & 7 & 4.54 & 3.81 \\ Case C & 10 & 9 & 4.24 & 3.29 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Strain field reconstruction accuracy for different magnitudes of applied loads.  
* Comparison of Strain Reconstruction Errors between iFEM and SF-iFEM;  
For Case 1 and Case 2, a comparative analysis of strain reconstruction errors along Path1, Path2, and Path3 between the conventional iFEM and the proposed method outlined in this paper are depicted in Figures 14 and 15, with a summarized presentation in Table 4.  
Table 4 revealed a notable enhancement in strain reconstruction accuracy for both Case 1 and Case 2 when comparing the proposed method with the conventional iFEM based on node displacement vectors. This improvement could be attributed to the inherent limitations in conventional iFEM, which reconstructed the structural strain field by solving for the node displacement vector. In scenarios where a substantial error existed between the node displacement vector and the actual displacement, this discrepancy adversely affected the accuracy of the structural strain field reconstruction.  
In contrast, the approach introduced in this paper adopted the node strain vector as the fundamental unknown, reconstructing the structural strain field by solving for it. This strategic choice mitigated the impact of inaccuracies in the node displacement vector on the overall accuracy of strain field reconstruction.