esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Review Papers and Application Fields
#### 4.6.7 MCDA and Other Fields
Furthermore, stakeholder involvement was limited, usually only occurring during the weight assignment phase. This suggests that the practical implementation of stakeholder engagement in the evaluation process is not as developed as originally envisioned. TransportationKugemann and [PERSON] [136] focused on the evaluation of road transport fuels and vehicles and argued that MCDA is a suitable analytical tool for alternative fuel analysis. They identified 41 potential criteria for future research but also highlighted issues with the selection process, which lacks scientific basis and standardisation. FisheriesAndalecio [137] reviewed the application of MCDA methods in fisheries and observed that the use of hybrid MCDA is gaining traction over single MCDA methods due to its enhanced effectiveness. Green supply chainsBanasik et al. [138] investigated the design of green supply chains with MCDA. They concluded that this research field is rapidly emerging, although it is relatively new. Most publications focus primarily on production and distribution issues, with only a few inventory models incorporating environmental and waste minimisation factors.
# Non-Contact and Real-Time Measurement of Kolsky Bar with Temporal Speckle Interferometry
## Principles
### Principle of Temporal Speckle Interferometry In-Plane Measurement  
The principle of the in-plane displacement measurement used in this article is shown in Figure 2. When the object moves in the \(z\)-direction, the optical paths of both the reflected light and transmitted light change consistently [12]. Therefore, the speckle remains unchanged.  
When the object moves the \(y\)-direction, one of the optical path increases \(\Delta y(x,z,t)sin\alpha\) and the other reduces \(\Delta y(x,z,t)sin\alpha\). Therefore, the optical path difference is as follows:  
\[\delta=2\Delta y(x,z,t)sin\alpha \tag{15}\]  
The phase change of the reflected light and the transmitted light is:  
\[\Delta\varphi_{r}=\frac{2\pi}{\lambda}\delta=\frac{4\pi}{\lambda}\Delta y(x,z,t)sin\alpha \tag{16}\]  
where \(\lambda\) is the wavelength. \(\alpha\) is the incident angle. Before the motion of the object, the intensity function of the speckle is:  
\[I(x,y,t)=I_{0}(x,y)\{1+Vcos[\Phi_{0}(x,y)]\} \tag{17}\]  
Figure 2: The optical path of in-plane displacement measurement.  
where \(I_{0}\) is the average intensity of the interference field. \(V\) is the modulation visibility. \(\Phi_{0}(x,y)\) is the initial phase. Hence, after the motion of the object, the intensity function of the speckle is:  
\[I(x,y,t)=I_{0}(x,y)\{1+Vcos[\Phi_{0}(x,y)]\pm 4\pi\Delta y(x,z,t)sin\alpha/\lambda\} \tag{18}\]  
where \(\pm\) is the direction of the object movement.
# Experimental Study on Mechanical Properties of Graded EPS-Steel Fiber-Reinforced Concrete
## 4 Conclusions  
In this study, we analyzed the effects of different EPS and steel fiber dosages on the mechanical properties of graded EPS-steel fiber concrete made with gravel and crushed stone of different grades, through compressive and flexural strength tests. The main conclusions are as follows:  
(1) **Failure Modes and Ductility**: Graded EPS-steel fiber fine aggregate and pebble concrete across different strength grades exhibited similar failure modes under compressive and flexural loading. The incorporation of graded EPS significantly enhanced ductility under compression, with the optimal ductility observed at an EPS volume fraction of 25%. Steel fibers improved ductility under flexural failure, with the best performance achieved at a volume fraction of 2.25%.  
(2) **Compressive Strength**: A negative correlation was observed between the volume fraction of graded EPS and the compressive strength of concrete. The rate of strength reduction was more pronounced in the 15-25% range compared with the 25-35% range. This trend was consistent across fine aggregate and pebble concrete groups for all strength grades. Additionally, the uniaxial compressive strength at 7 days reached 60-75% of the strength at 28 days, highlighting the importance of early curing.  
(3) **Workability**: an increase in the volume fraction of graded EPS significantly reduced the workability of concrete, with the slump reduction showing an accelerating trend as the EPS content increased.  
(4) **Tensile Failure and Flexural Strength**: The tensile failure process of graded EPS-steel fiber concrete can be divided into four stages. Each 0.25% increase in steel fiber volume fraction resulted in a flexural strength improvement of 14.35-17.61%, with a maximum growth rate of approximately 27%. The distribution of the growth rates was relatively dispersed.  
(5) **Strength Relationship and Predictive Model:** A conversion relationship between compressive strength and flexural strength was established using a correction equation incorporating the flexural-to-compressive strength ratio coefficient. The model achieved a high correlation coefficient (R) of 0.989, demonstrating strong predictive accuracy. A  
Figure 10: Prediction accuracy of flexural strength of graded EPS–steel fiber-reinforced concrete.  
method was also proposed for determining the maximum flexural-to-compressive strength ratio under the combined influence of graded EPS and steel fibers, providing valuable guidance for material design optimization.  
Conceptualization, Y.W. and X.Z.; methodology, Y.W.; software, Y.W.; formal analysis, Y.W. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the funding from the National Natural Science Foundation of China (Grant Nos. 51978248).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors declare no competing interests.
# Measurement of Absolute Acoustic Nonlinearity Parameter Using Laser-Ultrasonic Detection
## 4 Experimental Results
### Validations  
A conventional piezoelectric detection method was used to validate the experimental results for the proposed laser-ultrasonic detection method. This method was composed of two parts: a relative \(\beta^{\prime}\) parameter measurement and calibration [20]. In the former, a through-transmission method [20] with a pair of 2.25 MHz and 5 MHz piezoelectric transducers as transmitter and receiver was used to measure the \(\beta^{\prime}\) parameter. Here, except for the change in the receiver, the experimental procedure was virtually the same as the aforementioned laser-ultrasonic detection. In the latter, a pulse-echo method [34] with a 5 MHz piezoelectric transducer, which corresponded to the receiver in the through-transmission method, was conducted to obtain a transfer function; this was used to calibrate the electric voltage amplitude measured from the piezoelectric receiver to the absolute displacement amplitude.  
The \(\beta_{Cu}\) and \(\beta_{Al}\) values obtained from the piezoelectric detection method are listed in Table 2 with those obtained using the laser detection method. The reference ranges of the previously published studies are listed together. The \(\beta_{Cu}\) and \(\beta_{Al}\) values obtained from the piezoelectric detection were 3.2 and 5.0, respectively. The experimental results for the laser and piezoelectric detection methods demonstrated acceptable agreement within a deviation of approximately 4%. These values were within the range of previously published values. This 4% deviation could be unavoidable when considering the differences in the experimental environment, detector size, and surface roughness. These results indicate that the proposed method is suitable for measuring the absolute \(\beta\) parameter independent of the material type in a noncontact and nondestructive manner. Furthermore, the results indicate that this laser-ultrasonic detection method could be applicable even on rough surfaces corresponding to general milling machined surfaces (approximately sub-micrometer scale).
# Singular Value Decomposition Channel Estimation in STBC MIMO-OFDM System
## 5 Simulation Results
Figure 6:3 Comparisons of BER performance in the case of 16 QAM modulation at the static Brazil B channel. have a low complexity. However, these two methods could not provide a good BER performance in multipath channels. In the MMSE channel estimation method of Equation (11), \(R_{\textbf{HH}}\) is a \(N\times N\) channel autocorrelation matrix. The inverse of \(R_{\textbf{HH}}\) is a \(N\times N\) matrix. The inverse operation of \(R_{\textbf{HH}}\) has a high calculation complexity. The CFR \(\hat{H}_{\text{LS}}^{n,\beta}\) obtained from the LS channel estimation method is a \(N\times 1\) vector. Hence, the MMSE channel estimation method needs the N\({}^{3}\) point multiplication and has the highest calculation complexity among these four channel estimation methods. In the SVD channel estimation method of Equation (13), after the SVD operation of the channel autocorrelation matrix \(R_{\textbf{HH}}\), \(\textbf{U}\) and \(\textbf{V}\) are \(N\times N\) matrices. \(\Delta\) is a \(N\times N\) diagonal matrix. Since the number of significant taps is much lower than the number of subcarriers, there will be only a few significant singular values in \(\Delta\). Hence, the first \(l\) values in the diagonal of \(\Delta\) are retained and other values are set to zero. \(l\) is the length of CP. The SVD method simplifies the channel estimation complexity and it only needs the \(l^{3}\) point multiplication. Although the SVD of the channel autocorrelation matrix reduces the number of multiplication for the channel estimation, obtaining the SVD of the autocorrelation matrix by itself is computationally complex like the inverse operation. The SVD operation of the channel autocorrelation matrix has a certain complexity, but it only decomposes the channel autocorrelation matrix once. With the increases of SNR, the SVD operation is not performed. However, the inverse operation of the channel autocorrelation matrix is required for every change of SNR. The inverse operation of the \(N\times N\) channel autocorrelation matrix also has a high complexity when the number of subcarriers \(N\) is large. Hence, the proposed SVD method has a lower calculation complexity than the MMSE method.
# Marine Internet for Internetworking in Oceans: A Tutorial
## 5 Internetworking Strategies of Marine Internet
### Integrated Coastline Network (ICN)
#### 5.1.3 Adaptive Service Coverage  
Broadcast and point-to-point communications can be jointly applied to provide cost-effective services following the distribution of marine Internet users. Usually, user distribution near the coastline or in busy water canals are relatively high, and broadcast communication is preferable. In remote water areas, marine Internet users are distributed sparsely, and usually crowd in moving ships. In this case, point-to-point communication using directional antennas can focus the signal to the corresponding water areas with reduced interference to other areas.  
Particularly for shore-to-ship communication, a tracked point-to-point communication link can be established for long range high-speed communication. That is, a TBS and a VBS establish a point-to-point link using directional antennas. As the ship is moving, the antennas also rotate accordingly to maintain the best point-to-point communication quality. If the ship is moving out of the effective coverage of the current TBS, a handoff will be triggered to find another TBS to maintain the service. This is feasible since the TBS can have the position information on a ship though the AIS or the report of the ship, while ships usually move at relatively slow velocity. A ship in progress of communication with a TBS can also relay signals for its neighboring terminals or vessels.  
The following polices can be used by either a terminal or a VBS to select a communication link:* A terminal selects a communication link with the best channel quality and acceptable link cost. If a terminal (e.g., in a small boat) is located in or near a vessel equipped with a VBS, the terminal links the VBS in the following cases: (i) it cannot communicate with any other TBS; or (ii) the link quality is much better than that of any TBS link without incurring extra cost.
* A VBS selects a TBS link with sufficient channel quality and acceptable quality of service (QoS) to avoid extra latency caused by communication relay via other VBSs, since a TBS is usually much more powerful than a VBS.  
To efficiently support inter-BS communications, a spectrum band should be allocated to create a dedicated channel, with which BSs are organized into a mesh network, and directional antennas can be used to improve the communication quality.
## 2 Materials and Methods
### Measurement of Metrological Variables  
Photosynthetic photon flux density (PPFD), air temperature (T\({}_{\rm A}\)), relative humidity (RH) at 2 m above the ground, soil temperature (T\({}_{\rm S}\)) at 5 cm depth, and soil water content (SWC) at 10 cm depth were measured from 2 April to 29 October in the evergreeniferous forest and mixed forest and from 7 May to 25 October in the deciduous broadleaved forest. PPFD was measured at 5 min intervals, and other factors at 30 min intervals. All measurements in the deciduous broadleaved forest were conducted in both canopy and gap areas. PPFD was measured with a quantum sensor (SE-SQ110, Apogee Electronics, Santa Monica, CA, USA), T\({}_{\rm A}\) and RH with a temperature sensor and an RH datalogger (HOBO MX2300, Onset Computer, Bourne, MA, USA), T\({}_{\rm S}\) with temperature dataloggers (HOBO MX2204, Onset Computer), and SWC with a soil moisture sensor (SM150, Delta-T, Cambridge, UK). A solar radiation shield (RS3-B, Onset Computer) shaded the T\({}_{\rm A}\) and RH sensor from direct sunlight. The quantum sensor in the deciduous broadleaved forest canopy area broke immediately after installation, so we used data acquired from a sensor built into a leaf area index analyzer (MIJ-15 LAI/P, Environmental Measurement Japan, Co., Ltd., Fukuoka, Japan) established 2 m above the ground from 7 May.
# Blockchain Enabled Transparent and Anti-Counterfeiting Supply of COVID-19 Vaccine Vials
## 3 Methodology
### System Architecture  
The system architecture of the proposed solution consists of four layers: user layer, smart contract layer, network layer and data layer as shown in Figure 1.  
Figure 1: System architecture of the proposed solution.  
The data layer and network layer are part of the blockchain environment whereby the data layer acts as a core layer that is responsible for the storing and chaining of data whereas the network layer's task is to perform transmission of data, data validation, consensus mechanisms and peer-to-peer networking in the blockchain network. The smart contract layer contains all the contracts that are responsible for every event done in the network and the smart contracts layer also acts as the core of the complete system that encapsulate the coding and delivers the functions to the system [47]. Smart contracts include the code and deployment for vaccine distribution tractability and anti-counterfeiting. The user layer, also called the application layer, allows the recipient, healthcare workers and all the peers in the vaccine distribution system to interact with the interface of the blockchain network.
## 4 Results and Discussions
### Additional Experiments  
To further demonstrate the effectiveness of the proposed methods, we carry out experiments on GC10 dataset from AI Studio [51]. The GC10 dataset is a small dataset which includes 2294 images from weld assemblies. It consists of nine kinds of surface defects and a welding line, which includes Crease, Crescent gap, Inclusion, Oil spot, Punching, Rolled pit, Silk spot, Waist folding, Water spot and Welding line. The annotations are provided in GC10, which marks the class and location of each defect in images. Conventionally, the GC10 is divided into a training set containing 1836 images and a testing set containing 458 images.  
Table 4 shows that all the metrics of accuracy are improved compared with the baseline GA-RetinaNet. However, we also find that ACA-Net achieves a FPS of 5.9 on GC10 dataset, just 0.2 FPS lower than the baseline GA-RetinaNet. Nonetheless, ACA-Net is obviously far faster than artificial visual inspection on the GC10 dataset. The results further confirm the effectiveness of our methods.  
\begin{table}
\begin{tabular}{c l c c c c c c c c} \hline \hline  & **Method** & **Backbone** & \(AP\) & \(AP_{\mathbf{50}}\) & \(AP_{\mathbf{75}}\) & \(AP_{\mathbf{S}}\) & \(AP_{\mathbf{M}}\) & \(AP_{L}\) & _FPS_ \\ \hline \multirow{6}{*}{one-stage} & YOLOv3 [42] & Darknet-53 & 0.284 & 0.661 & 0.209 & 0.337 & 0.238 & 0.270 & 18.8 \\  & YOLOF [49] & ResNet-50 & 0.330 & 0.675 & 0.265 & 0.303 & 0.277 & 0.372 & 18.6 \\  & YOLOX-1 [50] & CSPDarknet & 0.373 & 0.726 & 0.326 & 0.330 & 0.309 & 0.456 & 16.6 \\  & SSD [37] & VGG-16 & 0.319 & 0.692 & 0.237 & 0.200 & 0.266 & 0.395 & 18.1 \\  & GA-RetinaNet [25] & ResNet-50 & 0.355 & 0.728 & 0.290 & 0.231 & 0.283 & 0.437 & 8.6 \\  & ACA-Net (ours) & ResNet-50 & 0.373 & 0.746 & 0.332 & 0.306 & 0.302 & 0.440 & 7.6 \\ \hline \multirow{6}{*}{two-stage} & TridentNet [26] & ResNet-50 & 0.401 & 0.753 & 0.390 & 0.371 & 0.337 & 0.474 & 4.9 \\  & Faster R-CNN [35] & ResNet-50 & 0.383 & 0.731 & 0.370 & 0.289 & 0.320 & 0.459 & 10.6 \\ \cline{1-1}  & GA-Faster R-CNN [25] & ResNet-50 & 0.391 & 0.756 & 0.370 & 0.343 & 0.325 & 0.465 & 7.5 \\ \cline{1-1}  & ACA-Faster R-CNN (ours) & ResNet-50 & 0.403 & 0.764 & 0.380 & 0.440 & 0.336 & 0.461 & 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results.  
Figure 7: Comparison of detection results on NEU-DET. (**a**) Groundtruth, (**b**) YOLOv3, (**c**) YOLOF, (**d**) YOLOX-l, (**e**) SSD, (**f**) GA-RetinaNet, (**g**) ACA-Net (ours), (**h**) TridentNet, (**i**) Faster R-CNN, (**j**) GA-Faster R-CNN, (**k**) ACA-Faster R-CNN (ours).
## 4 Conclusions  
In summary, we used a vacuum filtration method to fabricate a metallic single-walled carbon nanotube-based (m-SWCNT) saturable absorber. Nonlinear characterization of the 300 nm thin film yielded a modulation depth of 4.9%, while the linear transmission measurement presented the material as usable in a 1-3 um wavelength span. We experimentally showed that a single m-SWCNT saturable absorber is capable of initiating mode-locking in fiber lasers operating at 1559, 1938, and 2082 nm. All three investigated lasers generated stable pulses with sub-800 fs durations. We highlighted that the same m-SWCNT saturable  
Figure 6: Optical spectrum (**a**) and autocorrelation trace with a sech\({}^{2}\) fitting (**b**) of the generated pulses.  
Figure 7: Radio frequency spectra measured around the fundamental mode and in a 1.25 GHz span (inset) of an erbium- (**a**), thulium- (**b**), and holmium-doped fiber laser (**c**).  
absorber was used without failure in all three wavelength regions. Our results complement the current research on versatile and wideband saturable absorption in carbon nanotubes.  
Conceptualization, J.S.; thulium laser design, D.T. and G.S.; experimental investigation, M.P.; carbon nanotube film fabrication, A.D. and M.Z.; writing--original draft preparation, M.P. and A.D.; writing--review and editing, J.S. and G.S.; visualization, M.P. and A.D.; supervision, J.S. All authors read and agreed to the published version of the manuscript.  
The Polish Ministry of Science and Higher Education (IP2015 073774) and The National Science Centre (2015/18/E/ST7/00296). One of the authors ([PERSON]) received financial support from the Polish National Science Centre in the form of a doctoral scholarship (2019/32/T/ST7/00223).  
The authors declare no conflict of interest.
# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
## 3 Materials and Methods
### Radiomic Feature Extraction  
Radiomics is the process of quantitatively identifying all the properties of tumor volume. Radiomic features are examined in two categories: semantic (size, shape, location, etc.) and agnostic (first-order features, texture, wavelet, etc.) [46]. Agnostic radiomic features of tumor images were extracted using open-source PyRadiomics python package [47]. PyRadiomics enables the processing and extraction of radiographic features from medical image data.  
We examined the effectiveness of 3D wavelet radiomic features (LLH, LHL, LHH, LLL, HLL, HLH, HHL, HHH) belonging to six different matrices in brain cancer grade estimation. These matrices are as follows:  
* First Order (FIRST ORDER): Describes the individual values of voxels obtained as a result of ROI cropping. These are generally histogram-based properties (energy, entropy, kurtosis, skewness).
* Gray Level Co-occurrence Matrix (GLCM): Calculates how often the same and similar pixel values come together in an image and takes statistical measurements according to this matrix. These resulting values numerically characterize the texture of the image [48].
* Gray Level Run Length Matrix (GLRLM): Defined as the number of homogeneous consecutive pixels with the same gray tone and quantifies the gray-level studies [49].
* Gray Level Size Zone Matrix (GLSZM): Properties based on this matrix assign voxel counts according to the logic of measuring gray-level regions in an image.
* Neighboring Gray Tone Difference Matrix (NGTDM): Digitization of textures obtained from filtered images and their fractal properties. Mathematical definitions of these properties are evaluated independently of the imaging method.
* Gray Level Dependence Matrix (GLDM): Number of bound voxels at x distance from the central voxel.  
A total of 144 first-order features and 600 texture features, or a total of 744 3D wavelet features, were extracted with 3D Slicer. We applied a statistical analysis method to select the strongest features.