esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods  
### Sample Preparation and Volumetric Measurement  
Mesial roots of 45 extracted mandibular molars were collected with informed consent of the donors and approval by the ethics committee of the University of Regensburg (No. 19-1327-101, Ethics Committee, University of Regensburg). Criteria for inclusion were the absence of caries, complete root formation and patency of the root canals. To evaluate the canal anatomy, the root length was adjusted to 10 mm and high resolution uCT-scans were conducted (Phoenix v! tone! xs 240/180, Baker Hughes Digital Solutions, Houston, TX, USA). Three-dimensional reconstructions were generated at a voxel size of 12 um. The scans were performed with the D-Tube in \"fast scan\"-mode and a 0.1 mm copper filter, resulting in 2000 slices per sample. The detection limit for debris was set to 0.01 mm\({}^{3}\), which was determined with test bodies in preceding accuracy measurements.  
All roots were classified according to the criteria of [PERSON] et al. [37], and the root canal volume was quantified. For data acquisition and 3D-reconstruction, phoenix datos l x 2 software (Version 2.5.1, Baker Hughes Digital Solutions) was used. Moreover, volumetric analysis was performed using VG Studio Max software (Version 2.2.3, Volume Graphics, Heidelberg, Germany). The volume of interest (VOI) was defined from the coronal separating plane to the apical endpoint, which was determined with a hand file ISO 15 (K-files, VDW, Munich, Germany), 1 mm short of the anatomical root length. The length determination of the VOI was performed manually between the determined points with an accuracy of \(\pm\)0.1 mm in VG Studio Max software. Together with the outer contour of the roots, the VOI was defined. The resulting 3D-structure was subdivided into three compartments of equal height for further analysis. The uCT-scans and their processing were conducted by a trained professional (BS); data analyses were performed by two experienced evaluators (LK, MW). Subsequently, samples were allocated to three experimental groups (CNI, SAI, LAI) and stratified according to their volume and anatomical classification (n = 15).
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 3 Simulation of Hard Roof Blasting
### Material Parameters
#### 3.2.2 Explosive Material Parameter  
In the numerical simulation process, it is crucial to accurately describe the change course of pressure during detonation [20]. The high-energy explosive material model (\"MAT_HIGH_EXPLOSIVE_BURN) and the equation of state (\"EOS_JWL) are used to simulate the whole detonation process [21]. The JWL equation of state is a widely used empirical equation whose parameters are usually measured by cylinder tests. The EOS_JWL defines the pressure as:  
\[P_{\text{CJ}}=A(1-\frac{\omega}{R_{1}V})e^{-R_{1}V}+B(1-\frac{\omega}{R_{2}V})e ^{-R_{2}V}+\frac{\omega E}{V} \tag{14}\]  
where \(P_{\text{CJ}}\) is the pressure of the detonation products; \(A\), \(B\), \(R_{1}\), \(R_{2}\), and \(\omega\) are material constants (Table 2); \(V\) is the relative volume of the explosive; \(E\) is the internal energy of the explosive [22].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Density** & **Tangent** & **Elastic** & **Poisson’s** & **Yield** & **Compressive** & **Tensile** \\
**(g/cm3)** & **Modulus** & **Modulus** & **Ratio** \(\mu\) & **Stress (MPa)** & **Strength** & **Strength** \\ \hline
2.55 & 9.33 & 22.75 & 0.22 & 30 & 60 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Rock material parameters.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\rho\) & \(P_{\text{CJ}}\) & \(D\) & \(A\) & \(B\) & & & \(E\) \\
**(g/cm3)** & **(GPa)** & **(m/s)** & **(GPa)** & **(GPa)** & **(GPa)** & \(R_{1}\) & \(R_{2}\) & \(\omega\) & **(GPa)** \\ \hline \hline \(1.18\) & \(10.6\) & 3600 & 264 & 0.182 & 4.2 & 0.96 & 0.15 & 4.19 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Explosive material parameters.  
Figure 2: Schematic diagram of the calculation model.
## 1 Introduction
A proof-of-concept experiment was implemented to verify the concept, and the three microwave waveforms with repetition rates of 8 GHz were successfully generated. The RMSE between the generated and the theoretical were all less than 2%. The scheme only used a commercial off-the-shelf modulator to generate more kinds of waveforms, which features a simple structure and better universality.
## 2 Background and Related Work
### Feature Integration Module Using DenseNet  
Feature integration is a crucial aspect of image processing, and significant advancements have been seen with the development of CNNs. DenseNet (Densely Connected Convolutional Networks) has been particularly influential in this domain [27]. DenseNet features direct connections between two layers with the same feature map size, ensuring maximum information flow between layers. The direct connection architecture addresses the vanishing gradient problem, encourages feature reuse, and substantially reduces the number of parameters compared to traditional CNNs. As shown in Figure 2, DenseNet consists of multiple dense blocks. Each dense block includes a sequence of BN-ReLU-Conv1x1 and BN-ReLU-Conv3x3 layers. In this context, BN-ReLU-Convjxj denotes a layer sequence composed of a BatchNorm operation, a rectified linear unit (ReLU) activation, and a convolution with a kernel size \(3\times 3\).  
Recent works have leveraged DenseNet's feature integration capabilities to enhance various image-processing tasks. DenseNet was incorporated for image super-resolution [28], demonstrating improved performance due to the effective combination of shallow and deep features. In addition, DenseNet has been employed in medical image analysis for accurate lesion detection by integrating multi-scale features [29].  
By extracting multiple features through dense blocks and reusing these features, the network's representational capacity is significantly improved. This feature reuse strategy integrates the benefits of dense connections into the watermarking model, thereby improving resistance to various image distortions and attacks.
# Estimation of Inter-System Biases between BDS-3/GPS/Galileo and Its Application in RTK Positioning
## 1 Introduction
The above studies were conducted by considering different GNSSs separately, which is called the loosely combined model (LCM) of RTK since each GNSS needs a pivot satellite. Benefitting from the overlapping frequency, the compatibility and interoperability between various systems have become accessible. [PERSON] [24; 25] introduced the theory of inter-system bias (ISB) and found the ISB between GPS and Galileo was stable over time when their overlapping frequency observations are applied in the inter-system DD equation. In [PERSON] [26], the ISB between GPS and the Japanese Quasi Zenith Satellite System (QZSS) was evaluated as being so stable that the ISB can be safely neglected to maximize the redundancy of multi-system RTK. [PERSON] [27] compared the ISBs between BDS-3, GPS, and Galileo, and proved the interoperability between these GNSSs. [PERSON] [28] analyzed the ISB between GPS and BDS-3/Galileo with the overlapping frequency. The results indicated that when identical receiver types were applied, the ISBs were close to zero. In contrast, the ISB generally exists but is stable when different types of receivers were applied. The above research provides evidence that ISBs exist in inter-system difference observations, and the stability of ISB enables their calibration. After correcting the ISB in the inter-system difference observations, the tightly combined model (TCM) of RTK is established, which shares a common pivot satellite between overlapping frequencies. In this article, we first analyze the quality of the signals transmitted by BDS-3, GPS, and Galileo in terms of the noise level of both pseudo range and phase observations. Then, with many observations, we extract the ISBs of BDS-3, GPS, and Galileo at overlapping frequency to verify their short- and long-term stability. Afterwards, the positioning performance of single-system and multi-system RTK is analyzed in terms of positioning errors and SR. Finally, with the extracted ISBs, the performance of the multi-system TCM RTK is evaluated with different cutoff elevation angles in contrast to LCM RTK.
# Integrated Communication and Measurement System with BOC-Assisted OFDM
## 3 Reception of the Composite Signal
### Acquisition of the BOC Signal  
Acquisition algorithms are generally two-dimensional search processes of the code and Doppler dimensions [27]. The main function of the acquisition algorithm is to roughly estimate the code phase offset and Doppler frequency offset. The parallel code phase acquisition algorithm [28] is applied in this paper and is described below.  
First, the received signal is mixed with the local sine and cosine carrier signals to obtain the I/Q branches.  
Second, the FFT is applied to the \(I+jQ\) signal and to the local BOC signal and its conjugate.  
Subsequently, the above two signals are multiplied after the FFT and conjugation, and the IFFT is applied to the result of this multiplication.  
Finally, the modulus is taken to find the maximum correlation peak. When the maximum correlation peak exceeds a preset acquisition threshold, the acquisition is considered successful. Otherwise, the local carrier frequency is adjusted and the above process is repeated until the output maximum correlation peak exceeds the preset acquisition threshold. If all carrier frequencies are traversed and the preset acquisition threshold still is not exceeded, then the acquisition is considered to have failed. The block diagram of parallel code phase acquisition is shown in Figure 7.
## 4 Experimental Results and Discussion
### Performance Evaluation Metrics
#### 4.1.2 AP (Average Precision)  
Another way to evaluate outlier detection models is to use the average precision (AP). The AP measures the average precision across all possible thresholds, with a higher value indicating a better model. The AP is more suitable for outlier detection problems with rare anomalies or imbalanced data, as it focuses more on the positive class (anomalies) than the negative class (normal instances). However, it may not reflect the overall accuracy or specificity of the model, as it does not account for the true negatives or false negatives. Evaluating outlier detection models can be challenging, especially when you do not have labeled data or ground truth data to compare with. One of the possible ways to evaluate outlier detection models is to use external validation, which involves comparing the results with some other sources of information, such as domain experts, feedback, or historical data.  
Overall, 30% of the data in experiments is reserved for testing, while the remaining 70% is used for training. The area under the receiver operating characteristic (ROC) and average precision (AP) are used to obtain the average score from ten separate trials to assess performance.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 6 Measurement Results and Analysis
### Results Comparison  
In this section, the results of path loss and RMSDS are compared with the results of the previous studies reviewed in Sections 2.3 and 3. However, due to the inherent differences in the measured environment and the measurement and modeling methodology, e.g., the frequency and the range of measurements, the path loss and RMSDS may not be directly comparable. Hence, our approach cannot be generalized to all environments and frequencies. However, the effects of the environment on the channel characteristics can be observed from the similarities and contrasts in different propagation scenarios at different frequencies. The PLE values for our measurements and the previous conducted measurements in different indoor offices at 140 GHz [8, 33, 34, 35] are comparable and close to the free space PLE. For comparison with industrial measurements in the mmWave band below 100 GHz, the PLE values of 1.9 at 28 GHz [50] and at 60 GHz [44] in industrial environments are almost identical to our measurements.  
The studies of RMSDS at frequency bands above 100 GHz are very limited. It can be noted that the RMSDS values at indoor offices are less than 0.5 ns at 310 GHz and 140 GHz [30, 33], which are very low compared to our measured RMSDS. For measurements in the mmWave band at 28 GHz in industrial environments [50], the RMSDS was 23.1 ns, which is comparable with our maximum value of RMSDS. In [44], it was also found that the RMSDS mean values in light industrial environment were 13.7 ns and 13.4 ns at 28 GHz and 60 GHz, respectively, which are almost identical to our mean value of RMSDS at NTNU MANULAB.
## 4 Conclusions  
The stainless steel coatings with different Mo contents were prepared on the surface of a 45 steel substrate by laser cladding. The following conclusions can be obtained.  
1. With the increase of Mo content, the amount of M\({}_{7}\)(C, B)\({}_{3}\) decreases gradually, while the amount of M\({}_{2}\)B, M\({}_{23}\)(C, B)\({}_{6}\), and Mo\({}_{2}\)C gradually increases. When the Mo content is 4.0 wt. % and above, Mo\({}_{2}\)C phase appears in the coating.
2. The microstructure of the coatings is transformed from planar crystals to cell crystals, dendrites, and equiaxed crystals with the increase of distance from the fusion line. The amount of martensite decreases, while the amount of ferrite gradually increases with the increase of Mo content.
3. The microhardness decreases and the wear resistance of the coatings gradually increases with the increase of Mo content. The wear resistance of the 6.0 wt. % Mo coating is about 3.7 times that of the Mo-free coating.
4. The corrosion resistance of the 45 steel substrate is significantly improved by the laser cladding coating. With the increase of Mo content, the corrosion resistance of the coating first increases and then decreases. The coating with 2.0 wt. % Mo has the best corrosion resistance.  
Figure 10: Potentiodynamic polarization curves of the substrate and four coatings with different Mo contents.  
**Acknowledgments:** The authors appreciate the financial support for this work from National Natural Science Foundation of China (No. 51675303), the Open Foundation of Key Laboratory of E&M (Zhejiang University of Technology), Ministry of Education & Zhejiang Province (No. EM2016070103), and the Tribology Science Fund of the State Key Laboratory of Tribology (SKLT2014B09, SKLT2015B09).  
**Author Contributions:** [PERSON] performed all experiments and wrote the paper; [PERSON] and [PERSON] designed the experiments and reviewed the paper; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Results
### The Optimization of Genetic Transformation Methods for D. zingiberensis
To accelerate the transformation process, buds were directly infected with _Agrobacteriumum_ following sterilization, which reduced the time spent in the 2,4-D-containing callus induction medium. It is worth noting that the sterilization process can damage the buds. Using sterile seedlings as the explant source can lower bud mortality during sterilization, thereby further improving transformation efficiency. Compared to leaves, stems, and roots, the buds of _D. zingiherensis_ not only exhibit lower contamination rates but also demonstrate faster and more efficient dedifferentiation, making them the optimal material for genetic transformation in this species. _DZHDZ32_ was cloned into the pCXUN-4 XHA vector and introduced into _Agrobacteriumum tumefaciens_ strain EHA105. Buds from WT plants were used for stable transformation. The infected buds underwent dedifferentiation and were subsequently redifferentiated to regenerate shoots and roots, ultimately yielding overexpressing transgenic plants (Figure 3). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Plant Species**} & \multirow{2}{*}{**Transformation Efficiency**} & \multicolumn{2}{c}{**Time**} & \multirow{2}{*}{**Target Gene**} & \multirow{2}{*}{**Reference**} \\  & & **(From _Agro-Infection to_** & & \\  & & **Regeneration of Complete** & & \\  & & **Transgenic Plant)** & & \\ \hline _D. zingiherensis_ & 46.8\% & 4 months & _DZHDZ32_ & In this study \\ _Dioscorea rotundata_ & 9.4 to 18.2\% & 3–4 months & _GUSA_ & [24] \\ _D. zingiherensis_ & Very low & Unknown & _DZFPS_ & [22] \\ _Dioscorea alata_ L. & 2.33\% & Unknown & _DRPDS_ & [23] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the efficiency of existing genetic transformation protocols in _Dioscorea_ species. Stable OE (_DZHDZ32_-overexpressing) lines were successfully generated. To further examine the relationship between _DZHDZ32_ expression and diosgenin accumulation, we selected two transgenic lines with the highest diosgenin content and analyzed _DZHDZ32_ expression in the roots, stems, leaves, and rhizomes. Expression analysis revealed the highest _DZHDZ32_ transcript levels in the leaves, with varying levels of upregulation across other tissues (Figure 4a), indicating tissue-specific expression of _DZHDZ32_ in _D. zingiberensis_. Correspondingly, these transgenic lines exhibited significantly elevated diosgenin levels in all tissues compared to WT plants, with the most pronounced increase observed in rhizomes (Figure 4b). Specifically, diosgenin content in the rhizomes of the two transgenic lines increased by 68.41% and 89.59%, respectively, relative to WT. These findings support the hypothesis proposed by [PERSON] et al. 2022, that diosgenin biosynthesis primarily occurs in the leaves, while rhizomes serve as the main storage organs [1].
# Unsupervised Detection of Changes in Usage-Phases of a Mobile App
## 4 Results and Discussions
### Case Studies  
Dividing a stream of screenshots into distinct usage-phases is not easy. Figure 1 provides an example. In this example, it is possible to group Figure (a), (b), and (c) into one usage-phase (because they represent searching) or make a group of Figure (b) and (c) as they show search results. Because it is ambiguous to make distinct groups from such screenshots in Figure 1, we refer to these cases as ambiguous cases and observe the performance considering ambiguous cases. Table 4 reports the performance on datasets considering ambiguous cases. From 13,272 screenshots, seven-hundred thirty-six screenshots were designated as ambiguous cases. As expected, the performance on the ambiguous cases (numbers in parentheses in Table 4) was poor. The performance on datasets excluding ambiguous cases was slightly better than the reported performance in Table 2 because screenshots corresponding to ambiguous cases amounted to only 5.5% of the whole screenshots. Graph based methods require multiple nodes and edges to construct a graph. If a screenshot contains too few UI widgets (Figure 2) or the number of successfully recognized UI widgets is too small, graph based methods are likely to produce poor performance. Table 5 reports the performance on datasets excluding cases generating too small graphs (a graph with less than three nodes) and the performance on datasets corresponding to too small graphs (numbers in parentheses in Table 5). From Tables 2 and 5, we can see that the performance on datasets excluding small graphs was better than performance on datasets considering whole screenshots. However, the improvement in performance was not so impressive due to the small number of screenshots corresponding to small graphs (4.5% of all screenshots). Besides precision, recall, and accuracy, the number of estimated usage-phases gave insight into the performance of the proposed methods. Table 6 provides the average number of estimated usage-phases (the number of manually labeled usage-phases is provided in Table 1). The performance in Table 6 informs us that methods based on SIFT (when utilizing the \"mean\" threshold), graph entropy (when utilizing the \"empirical threshold\"), and hypothesis testing were too sensitive.
## 4 Future Directions
### Open-Source Ecosystems and Interoperability Standards  
To mitigate vendor lock-in, open-source low-code frameworks are gaining traction. Platforms like vf-OS exemplify this trend by providing modular architectures that integrate with legacy systems and third-party tools. Academic efforts [54] enable cross-platform data harmonization, reducing integration costs in collaborative manufacturing environments. These developments are critical for increasing system flexibility and reducing long-term dependencies on proprietary solutions, which can otherwise impose constraints on scalability and system evolution.  
Standardization initiatives are also critical. The Adaptive Integrated Digital Architecture Framework (AIDAF), proposed in [PERSON] et al. [16], establishes governance models for hybrid low-code architectures, ensuring compliance with industry-specific regulations like AS9100 and ISO 27001. Notably, AIDAF integrates Design Thinking and Agile Software Development (ASD) to align low-code application development with enterprise-wide digital strategies. In practice, as demonstrated in the pharmaceutical case study, AIDAF's four-phase adaptive cycle--Prototype Development, Contextualization, Architecture Review, and Realization--enables structured risk management, addressing concerns such as vendor lock-in, interoperability, and operational continuity. For example, Power Apps, though a proprietary solution, was strategically evaluated and approved via AIDAF's Architecture Board review, with specific mitigation procedures implemented to counter long-term lock-in risks. Thus, frameworks like AIDAF not only promote technical standardization but also embed governance and compliance deeply into the development lifecycle of citizen-built applications.
# Visible Light Communication System Technology Review: Devices, Architectures, and Applications
## 6 VLC Application
### Visible Light ID and Position System  
Visible light can be used as an identification system and to determine indoor position, similar to a global position system (GPS). For instance, if we were standing in room 15 of a certain building, the system could be used to identify the room number and its building [126; 6].  
Figure 17: The propagation distance of different wavelengths in (**a**) clear ocean water, (**b**) a highly turbid harbor.  
Figure 18: VLC using in information displaying signboards. Reprinted with permission from [135]. Copyright 2019, American Chemical Society.
## 1 Introduction
* MAB is applied to predict the optimal frame size according to the channel quality. * Bandwidth estimation based on the ACK interval is used for tracking the wireless channel quality. * The MAB scheme is compared with the extended Kalman filter (EKF) [3], which is better in the current research of adaptive packet length. The rest of the paper is organized as follows: in Section 2, we discuss related work. Then, in Section 3, we formally describe the system model. Section 4 outlines MAB and the packet size adaption and then analyses its performance in Section 5. A simulation is described in Section 6. We comment on future work in Section 7.