esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Characterization of Purified PHA  
The polymer extracted from _B. licheniformis_ PL26 grown in production media was characterized through TGA, DSC, NMR and _Fourier transform infrared spectroscopy_ (FTIR).  
Figure 1 indicates TGA analysis to analyze the thermal decomposition of the extracted polymer through the thermogravimetric analyzer. The extracted polymer showed 0.28 g weight loss out of 0.35 g till 500 \({}^{\circ}\)C temperature. Mass change of 0.21 g PHA out of 0.35 g PHA was found in the temperature range of 225-325 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Concentration** \\ \hline Total Carbon content in fermentation medium & 0.7\% \\ \hline Total Carbon left in the supernatant after & 0.41\% \\ complete fermentation (96 h production age) & \\ \hline Carbon present in the biomass & 0.21\% \\ \hline Percentage carbon utilized & 30\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Percentage carbon utilization by _Bacillus licheniformis_.  
Figure 1: Thermogravimetric analysis (TGA) of purified PHA obtained from _Bacillus licheniformis_.  
The DSC analysis shown in Figure 2 indicates the melting temperature of the standard sample and the extracted polymer through sodium hypochlorite treatment. The thermal degradation was obtained at 268 \({}^{\circ}\)C for the obtained polymer and 250 \({}^{\circ}\)C of standard PHA from Sigma Aldrich.  
\({}^{1}\)H NMR spectra of the extracted polymer, standard PHB and standard 3 hydroxy valerate were found to be comparable with respect to each other. Prominent peaks were observed at \(\delta\) = 1.6 ppm for CH\({}_{3}\), \(\delta\) = 2.4 ppm for CH\({}_{2}\) and \(\delta\) = 5.2 ppm for CH group (Figure 3).  
Figure 3: Nuclear magnetic resonance (NMR) spectra of purified product along with standard PHB and standard 3 hydroxy valerate.  
Figure 2: Differential scanning calorimetry (DSC) of purified PHA from _Bacillus licheniformis_.  
Infra red (IR) spectra (Figure 4) showed intense peaks at 1724 and 1283 cm\({}^{-1}\) corresponding to -C=O and -CH group which are in correlation of peaks of standard PHA. Peaks at 1380, 1456 and 2932 correspond to -CH\({}_{3}\), -CH\({}_{2}\) and -CH group which are in correlation of peaks of standard PHA as shown in Figure 5.
# Pollution and Climatic Influence on Trees in the Siberian Arctic Wetlands
###### Abstract
Inserts: a satellite-derived view of the Norilsk city and smokestacks (upper) and vegetation within the air emissions zone (lower). An adverse strong by-effect of the Norilsk industry is air pollution by sulfur dioxide, nitrogen oxides, and heavy metals (Cu, Ni, Co). That caused dramatic decline and mortality of ground-cover vegetation (moss and lichen communities) and trees (primary larch, _Larix sibirica_ Ledeb.) [5; 6; 7; 8]. Thus, in 1990, larch decline and dieback were observed at the distances of 200 km and 80-100 km in the southwest direction from the Norilsk, respectively [9; 10]. Alongside air pollutions, the recent release of c. 20,000 tons of diesel oil occurred in May of 2020 [11]. The spill impact on the water terrestrial biota was a subject of recent studies [12]. We hypothesize that current climate change might mitigate adverse pollution influence on the trees and shrubs growth. The purpose of this study is an analysis of trees growth response to the combined influence of air pollution (SO\({}_{2}\) emissions) and climate change on the trees growing in arctic wetlands. In addition, we studied the influence of the diesel oil spill on the tree growth. We seek the answers to the following questions:  
* What was the impact of SO\({}_{2}\) emissions on the larch (_Larix sibirica_ Ledeb.) trees and shrubs' (_Salix_ spp. and alder, _Duschekia fruticosa_) growth? * How has climate warming influenced larch trees and shrubs' growth? * What are the temporal trends of the gross (GPP) and net (NPP) primary production and vegetation index NDVI (Normalized Difference Vegetation Index) within the zone of pollution influence?
## 3 Experimental Setup
### Clustering Method  
To compare our model's results, we applied a k-means clustering algorithm. This algorithm uses the Euclidean distance because it minimizes the sum of squared distances between cluster elements and their centroids. It is a quick and widely used algorithm for detecting meaningful places in the literature [19]. However, its restriction to Euclidean distance limits its applicability to certain types of data.  
We also used the k-medoids clustering algorithm [37] as an alternative approach to place identification. Unlike k-means, k-medoids select actual data points as cluster centers and can work with any pairwise dissimilarity measure, offering both greater robustness to noise and outliers in the mobility data and more flexibility in distance metrics. To evaluate the impact of different similarity measures on clustering performance, we experimented with three distinct distance metrics:  
Figure 2: Manual labeling for (**a**) the 15 \(\times\) 15 subgrid and (**b**) the 4 \(\times\) 4 forest as ground truth values. Yellow: work, blue: home, red: forest. Geojson.io visualization.  
* Manhattan distance [30], which measures the absolute difference between coordinates along each dimension, making it particularly suitable for grid-based spatial data.
* Euclidean distance [30], which computes the straight-line distance between points in the feature space and serves as a standard baseline metric.
* Complexity-invariant distance (CID) [31], which incorporates the inherent complexity of the data into the distance calculation by adjusting for the variability in temporal patterns. This metric may be particularly valuable for CDR data where the complexity of mobility patterns varies significantly across different location types.  
The choice of distance metric can significantly influence the clustering results, as each metric captures different aspects of similarity between mobility patterns. This comparative analysis allows us to assess which distance measure best aligns with the semantic categories we aim to identify.
## 9 Conclusions  
In this study, we aimed to address the problems with mmWave massive MIMO V2X due to obstacle occlusion and mmWave transmission range limitation. Combining the advantages of FSO and RIS, a tensor-based joint beamforming scheme for ultrasonic and RIS-assisted for dual-hop hybrid FSO-mmWave massive MIMO of V2X was proposed. The V2X communication range was increased by the FSO link, and the problem of occlusion in vehicle LoS communication was solved using RIS-assisted V2X. Then, we utilized ultrasonic and RIS-assisted mmWave massive MIMO V2X. The ultrasonic-assisted RIS phase shift matrix was designed based on subspace self-organizing iterations based on the DOA information obtained from ultrasonics. The beam direction between the RIS and the vehicle during vehicle motion was tracked. Finally, the Tucker tensor decomposition was used to describe the high-dimensional beam space, and a cascaded channel decomposition method based on SVD was adopted to obtain the combined matrix beamforming combination matrix for BS and vehicle, which solved the problem of difficult rank solution corresponding to the CP tensor decomposition. The simulation results show that the spectral efficiency of the proposed method significantly improved, and the BER was lower compared with other methods. And it can solve the above problem very well.  
Conceptualization, X.Z. and Z.Z.; methodology, X.Z.; software, Z.Z. and J.L.; validation, X.Z. and L.T.; formal analysis, X.Z. and Z.M.; investigation, X.Z. and L.T.; resources, X.Z. and J.L.; data curation, J.L.; writing--original draft preparation, Z.Z.; writing--review and editing, Z.Z.; visualization, J.L.; supervision, X.Z.; project administration, X.Z. and Z.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Shanghai Capacity Building Projects in Local Institutions, grant number 19070502900, Science and Technology Commission of Shanghai Municipality, grant number 22142201900.  
Not applicable.  
The authors declare no conflict of interest.
# Preassembly Cryogenic Drilling in Carbon Fiber Sandwich Sheets with Inner Foam Core
## 3 Results
### Variability Analysis
#### 3.2.2 Dimensional Accuracy of Holes  
The inlet diameter variation, \(\Delta\)Di, and its influencing factors are presented in Table 3. In this case, no interactions were eliminated from the initial ANOVA. Significant factors can be identified as main factor N, second-order factors N\({}^{2}\), and two interactions, FT \(\times\) N\({}^{2}\) and N\({}^{2}\)\(\times\) F, at the 95% confidence level (see \(p\)-value in Table 3). The major contribution was N\({}^{2}\) (33.59%), followed by N (18.72%) and N\({}^{2}\)\(\times\) F (12.60%) as main influencing factors. Note that the residual contribution is close to 3%.  
ANOVA was used to identify significant factors for the outlet diameter variation, \(\Delta\)Do, at the 95% confidence level (see Table 3). Factors with a \(p\)-value of less than 0.05 were the interactions between foam thickness and feed rate, FT \(\times\) F and FT \(\times\) F\({}^{2}\). It is noteworthy that no main factor is significant and, unlike \(\Delta\)Di, rotation speed is not an influencing factor, probably because of the strong effect of the fiber layer thickness. The main contribution to the variability comes from FT \(\times\) F\({}^{2}\) (29.11%) and FT \(\times\) F (22.86%). The residuals are larger than in \(\Delta\)Di, as the academic literature shows in other materials [1].
# Applying CRISPR Technologies for the Treatment of Human Herpesavirus Infections: A Scoping Review
###### Abstract  
Background: Human herpesvirusiruses are double-stranded DNA viruses of which eight types have been identified at present. Herpesvirus infection comprises an active lytic phase and a lifelong latency phase with the possibility of reactivation. These infections are highly prevalent worldwide and can lead to a broad spectrum of clinical manifestations, ranging from mild symptoms to severe disease, particularly in immunocompromised individuals. Clustered regularly interspaced palindromic repeats (CRISPR)-based therapy is an interesting alternative to current antiviral drugs, which fail to cure latent infections and are increasingly challenged by viral resistance. Objective: This scoping review aimed to summarize the current state of CRISPR-based antiviral strategies against herpesvirusirus infections, highlighting the underlying mechanisms, study design and outcomes, and challenges for clinical implementation. Design: A literature search was conducted in the databases PubMed and Web of Science, using both a general and an individual approach for each herpesvirusirus. Results: This scoping review identified five main mechanisms of CRISPR-based antiviral therapy against herpesvirus infections in vitro and/or in vivo. First, CRISPR systems can inhibit the active lytic replication cycle upon targeting viral lytic genes or host genes. Second, CRISPR technologies can remove latent viral genomes from infected cells by targeting viral genes essential for latency maintenance or destabilizing the viral genome. Third, reactivation of multiple latent herpesvirusirus infections can be inhibited by CRISPR-Cas-mediated editing of lytic viral genes, preventing a flare-up of clinical symptoms and reducing the risk of viral transmission. Fourth, CRISPR systems can purposefully induce viral reactivation to enhance recognition by the host immune system or improve the efficacy of existing antiviral therapies. Fifth, CRISPR technology can be applied to develop or enhance the efficiency of cellular immunotherapy. Conclusions: Multiple studies demonstrate the potential of CRISPR-based antiviral strategies to target herpesvirusirus infections through various mechanisms in vitro and in vivo. However, aspects regarding the delivery and biosafety of CRISPR systems, along with the time window for treatment, require further investigation before broad clinical implementation can be realized.  
CRISPR-Cas; herpesvirusirus; antiviral therapy; gene therapy +
Footnote †: leftmargin=*] *
# Application of Fly Ash Obtained from the Incineration of Municipal Solid Waste in Agriculture
## 3 MSWI Treatment Methods
### Separation Processes  
Separation is done to reduce heavy metals, salts, and chloride from MSWI ash. According to [1], 90-95% of chlorides are removed by washing with water. Heavy metals (Cu, Pb, and Zn) are removed by washing with sulfuric and phosphoric acid [1].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & [35] & [38] & [39] & [40] \\ \hline SiO\({}_{2}\) & 20.33–44.90 & 13.60 & 44.40 & 4.48–24.84 \\ Al\({}_{2}\)O\({}_{3}\) & 9.26–17.0 & 0.92 & 27.50 & 1.56–12.43 \\ CaO & 10.36–18.20 & 45.42 & 11.50 & 16.6–39.9 \\ Fe\({}_{2}\)O\({}_{3}\) & 1.72–6.29 & 3.83 & 6.21 & 0.85–5.04 \\ MgO & 1.83–3.15 & 3.16 & 2.36 & 0.67–3.76 \\ K\({}_{2}\)O & 1.44–5.28 & 3.85 & 0.99 & 3.76–15.24 \\ Na\({}_{2}\)O & 2.03–7.70 & 4.16 & 1.38 & 2.71–9.17 \\ SO\({}_{3}\) & - & 6.27 & 1.01 & 7.03–14.30 \\ P\({}_{2}\)O\({}_{5}\) & 0.01–0.06 & 1.72 & 1.37 & 1.76 \\ MnO & 0.10–0.25 & - & - & - \\ TiO\({}_{2}\) & 1.14–2.34 & 3.12 & 1.79 & 1.06 \\ Cr\({}_{2}\)O\({}_{3}\) & - & 0.19 & - & - \\ CuO & - & 0.25 & - & - \\ ZnO & - & 2.32 & - & 0.71–3.11 \\ PbO & - & 0.57 & - & - \\ BaO & - & - & 0.42 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Oxide compositions in fly ash (FA) (wt%) (data from [35; 38; 39; 40]).  
Figure 3: Fly ash treatment methods (adapted from [1]).  
Heavy metals are extracted through the leaching process, and then they are recovered from the leach solutions using chelating agents.  
To extract heavy metals, biological leaching is used with strains resistant to acids and heavy metals, such as autotrophic Thiobacillus species and heterotrophic Aspergillus niger [42].  
The calcium oxalate precipitate loosens fly ash molecules, thus releasing the heavy metals. Bielaching with thiobacillus species is effective in the extraction of metals depending on the pH value (less than 4), the ash content up to 8%, and the sulfide content. Through chemical leaching and bielaching [43], metals such as Mg, Zn, Al, Mn, and Cr are removed from fly ash. Nd, Pb, and Co were leached with biological reagents, and Ni, Fe, and Cu were leached with chemical reagents [1].  
According to [PERSON] et al. [1], heavy metals from fly ash are removed by acid leaching.
## 4 Real-Life Application: AI in Judgment of Tires during Use
### Automated Tire Inspection  
Proovstation [28] offers an automated tire inspection system that uses AI algorithms to detect tire defects and anomalies in real time. The system uses advanced imaging techniques, including high-resolution cameras and machine vision technology, to capture detailed images of each tire's surface. AI algorithms then analyze these images, enabling fast and accurate detection of defects such as cuts, bulges, or tread wear. Automated tire inspection systems increase the efficiency and reliability of tire quality control processes, reducing human error and ensuring consistent and accurate assessments.  
By integrating AI into tire assessments during actual use, manufacturers and users can monitor tire condition, increase performance, and improve safety. These AI-based solutions provide practical information, facilitate predictive maintenance, and enable timely interventions to reduce the potential risk of tire wear or damage.
## 3 Confidential Transmission Methods for High-Speed Power Line Carrier Communications
### Confidential Transmission Method Based on Generalized Two-Dimensional Polynomial Chaotic Mapping
#### 3.3.3 Confidential Transmission Scheme for High-Speed Power Line Carrier
The ciphertext \(\chi^{\prime\prime}\) acts as an information carrier, transmitting the bits \(\chi_{2l}\) in the first time slot. The second time slot consists of two parts: the first part is the information transmitted in the previous time slot, \(\chi_{2l}\chi_{i-H}^{\prime\prime}\), serving as the message carrier, followed by the transmission of the bits \(\chi_{2l+1}\) in the second time slot. The latter part is the high-speed power line carrier communication ciphertext of the current time slot, \(\chi_{i}^{\prime\prime}\), which satisfies  
\[\chi_{(2l+1)H}^{\prime\prime}=\chi_{2(l+1)H}^{\prime\prime}\,\forall l\in\{0, \pm 1,\cdots\} \tag{9}\]  
Receiving end: When the receiver obtains the high-speed power line carrier communication ciphertext, it decrypts and recovers the original information bits using the key derived from the generalized two-dimensional polynomial chaotic mapping in Section 3.3.2. The RM-DCSK confidential transmission scheme primarily relies on chaotic delay characteristics and non-coherent demodulation techniques. The received ciphertext deviates from the original due to noise interference during transmission across different high-speed power line carrier communication networks. In this paper, the additive white Gaussian noise (AWGN) channel model is adopted as the primary communication medium, and the channel interference is modeled as additive white Gaussian noise \(\varphi_{i}\). Therefore, the received ciphertext \(c_{i}\) is expressed as  
\[c_{i}=\chi_{i}^{\prime\prime}+\varphi_{i} \tag{10}\]  
Decryption is carried out using the key pairs generated by the generalized 2D polynomial chaotic mapping of Section 3.3.2 to decode \(c_{i}\), thereby recovering the original information bits and completing the confidential transmission of high-speed power line carrier communication.
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 5 Conclusions  
In this study, we proposed the Cross-layer Fusion and Edge-Aware network (MSEANet) to address challenges in polyp segmentation, including insufficient extraction of contextual information and insensitivity to high-frequency edge details across multiple scales. To tackle these issues, the EFE module was designed to prioritize the extraction of edge information from features rich in spatial details during the early stages of the encoder. Concurrently, the CCF module was introduced to integrate global context information across multiple scales and receptive fields, enhancing the model's understanding of polyp morphology.  
The features extracted by the EFE and CCF modules were subsequently passed to the SEA module, where high-frequency edge features were effectively fused with global context features. This fusion improved the model's sensitivity to edge information, addressing a critical limitation in existing polyp segmentation methods.  
Comprehensive experiments on three benchmark datasets--Kvasir-SEG, Kvasir-Sessile, and BKAI--validated the effectiveness of MSEANet. The results demonstrated significant improvements in segmentation performance across all key evaluation metrics, highlighting MSEANet's potential for clinical applications in colonoscopy. These findings underscore the value of combining edge-aware strategies with multi-scale context fusion for advancing polyp segmentation technology.  
Although our model performs well on multiple datasets, real-time performance and more complex samples are required in clinical environments. Therefore, our future work will explore model lightweighting and multimodal data fusion to improve model robustness and real-time performance so it can perform better in diverse and complex clinical environments.  
Conceptualization, B.L. and C.S.; Formal analysis, M.Z.; Methodology, C.S.; Resources, B.L.; Software, C.S.; Supervision, B.L. and M.Z.; Validation, M.Z. and C.S.; Writing--original draft, B.L. and C.S.; Writing--review & editing, B.L. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This study received no external funding.  
The data involved in the experiments were downloaded from their respective official websites.  
The authors declare no conflict of interest.
# Demystifying Quantum Gate Fidelity for Electronics Engineers
## 4 Quantum Gate Fidelity in Matrix Form
 \tag{35}\]  
The substitution of Equations (34) and (35) into Equation (29) yields:  
\[F=\frac{\mathit{Tr}\big{\{}H^{\dagger}H\big{\}}+\mathit{Tr}\big{\{}A^{\dagger }A\big{\}}+\left|\mathit{Tr}\{H\}\right|^{2}+\left|\mathit{Tr}\{A\}\right|^{2} }{n(n+1)} \tag{36}\]  
By emphasizing that \(A_{\mathit{TOT}}=H+A\), you can write:  
\[\mathit{Tr}\Big{\{}A_{\mathit{TOT}}A_{\mathit{TOT}}^{\dagger}\Big{\}}= \mathit{Tr}\Big{\{}\left(H+A\right)\left(H+A\right)^{\dagger}\Big{\}}= \mathit{Tr}\big{\{}\left(H+A\right)\left(H-A\right)\} \tag{37}\]  
where it has been emphasized that, by definition, \(H^{\dagger}=H\) and \(A^{\dagger}=-A\). Due to the above-emphasized property \(\mathit{Tr}\{AB\}=\mathit{Tr}\{BA\}\), it holds that \(\mathit{Tr}\{HA\}=\mathit{Tr}\{AH\}\). Thus, Equation (37) can be shortened as follows:  
\[\mathit{Tr}\Big{\{}A_{\mathit{TOT}}A_{\mathit{TOT}}^{\dagger}\Big{\}}= \mathit{Tr}\{HH\}-\mathit{Tr}\{HA\}+\mathit{Tr}\{AH\}-\mathit{Tr}\{AA\}= \mathit{Tr}\Big{\{}HH^{\dagger}\Big{\}}+\mathit{Tr}\Big{\{}AA^{\dagger}\Big{\}} \tag{38}\]  
From Equation (31), you can observe that similar matrices own the same trace:  
\[ \begin{array}{l}\mathit{Tr}\{D_{H}\}=\mathit{Tr}\Big{\{}\left(U_{H}^{-1}H \right)U_{H}\Big{\}}=\mathit{Tr}\Big{\{}U_{H}U_{H}^{-1}H\Big{\}}=\mathit{Tr} \{H\}\\ \mathit{Tr}\{D_{A}\}=\mathit{Tr}\{A\}\end{array}  \tag{39}\]  
Equation (39) shows that the trace of \(H\) (\(A\)) is real (imaginary), because the entries of \(D_{H}\) (\(D_{A}\)) are real (imaginary). Consequently, you can write:  
\[\left|\mathit{Tr}\{A_{\mathit{TOT}}\}\right|^{2}=\left|\mathit{Tr}\{H+A\} \right|^{2}=\left|\mathit{Tr}\{H\}+\mathit{Tr}\{A\}\right|^{2}=\left|\mathit{ Tr}\{H\}\right|^{2}+\left|\mathit{Tr}\{A\}\right|^{2} \tag{40}\]  
The substitution of Equations (38) and (40) into Equation (36) yields:  
\[F=\frac{\mathit{Tr}\big{\{}A_{\mathit{TOT}}A_{\mathit{TOT}}^{\dagger}\big{\}}+ \left|\mathit{Tr}\{A_{\mathit{TOT}}\}\right|^{2}}{n(n+1)} \tag{41}\]  
It is now worth emphasizing that the operator describing a quantum gate is unitary and thus represented by a unitary matrix. Since \(A_{I}\) and \(A_{R}\) are unitary operators, being the operators of a quantum gate, the operator \(A_{\mathit{TOT}}\), which is their product, is also unitary and thus \(A_{\mathit{TOT}}^{-1}=A_{\mathit{TOT}}^{\dagger}\). Equation (41) therefore takes the following form:  
\[F=\frac{\mathit{Tr}\{\{I\}+\left|\mathit{Tr}\{A_{\mathit{TOT}}\}\right|^{2}}}{ n(n+1)}=\frac{n+\left|\mathit{Tr}\big{\{}A_{I}^{\dagger}A_{R}\big{\}}\right|^{2}}{ n(n+1)} \tag{42}\]  
Equation (42) provides the mathematical expression for the fidelity of a quantum gate exploiting an n-level quantum system, as it has been calculated for a general qudit. In the case of a one-qubit gate (e.g., X Pauli gate), n = 2, since there are two energy levels, while for a two-qubit gate (e.g., CNOT gate), n = 4 because there are four energy levels [22].