esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Conclusions  
In this work, we demonstrated a dispersion-managed, passively mode-locked, ultrashort pulse, Tm-Ho co-doped fiber laser using SWNTs dispersed in polyimide film. An in-line type spectral filter was developed to control the oscillation spectra. The net  
Figure 10: Variation of characteristics of output pulses as a function of net cavity dispersion when (**a**) film A and (**b**) film B were used, respectively. Green areas show the region where mode-locking was obtained only when spectral filter was not used, and orange area shows the region where mode-locking was not obtained.  
Figure 9: Characteristics of output pulses when \(D=+0.0503\) ps\({}^{2}\) and spectral filter was used, (**a**) optical spectra, (**b**) autocorrelation trace, (**c**) pulse train, (**d**,**e**) RF spectra of (**d**) fundamental frequency and (**e**) wide range.  
cavity dispersion was controlled using a normal dispersive fiber, and the characteristics of output pulses were investigated. Dependences on the spectral filter and SWNT films were, additionally, investigated.  
Mode-locking was obtained in wide dispersion conditions, from \(-0.319\) to \(+0.101\) ps\({}^{2}\). In the anomalous dispersion region, almost transform-limited, short soliton pulses were obtained. The spectral filter was effective in suppressing Kelly sidebands and the wavelength tunable operation. Around the zero-dispersion region, stable mode-locking was obtained when film B with a larger modulation depth was used. For the normal dispersion region, high power, dissipative soliton pulses with a wide spectral width were obtained for film A. The maximum average power was 75.7 mW. The spectral filter was effective in achieving a high power, stable operation in the normal dispersion region.  
A stable operation was observed experimentally, and it is expected that the developed Tm-Ho co-doped SWNT fiber laser will be useful for practical applications, such as biomedical imaging, spectroscopy, etc.  
Conceptualization, Y.S. and N.N.; methodology, Y.S. and N.N.; investigation, K.F., S.K. and N.N.N.; resources, Y.Z., Y.S., T.S. and N.N.; writing--original draft preparation, N.N.; writing--review and editing, N.N.; supervision, Y.S. and N.N.; project administration, Y.S. and N.N.; funding acquisition, N.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant Number 20H00350.  
Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.9. Scalability of IPSO in Multi-UAV Path-Planning  
To investigate the scalability of IPSO, we conducted experiments in a large scenario by increasing the number of UAVs to 18 and the number of obstacles to 24. The objective is to assess how the performance of the algorithms is influenced as the scale of the problem increases. As shown in Figure 14, IPSO showcases its effectiveness in handling larger problem sizes efficiently.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **PSO** & **ABC** & **CPSO** & **IPSO** & **GA** \\ \hline
**UAV1** & 58.3129 & 83.7895 & 92.8117 & 103.124 & 128.492 \\ \hline
**UAV2** & 55.0654 & 79.6591 & 89.2066 & 99.1185 & 126.294 \\ \hline
**UAV3** & 55.7387 & 79.4361 & 88.4537 & 98.2819 & 125.378 \\ \hline
**UAV4** & 56.3328 & 80.9401 & 89.2281 & 99.1423 & 126.707 \\ \hline
**UAV5** & 58.1598 & 84.3046 & 93.4992 & 103.888 & 128.856 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Average execution time.  
Figure 14: Two-dimensional view of paths for 18 UAVs navigating in environment filled with 24 obstacles.  
Figure 13: Average execution time.
# Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation
## 2 Materials and Methods
### The Mode Shape Damage Index (MSDI)
In this way, only mode shapes with a reasonable degree of correlation are considered, and the ones that do not match are excluded from the damage localization. If even small damages are present at the \(i\)-th node, the MSDI value becomes a nondimensional negative value, presented as an absolute value. Theoretically, if there is no damage, i.e., no changes in the stiffness of the system (assuming there is no change in mass and damping), the MSDI value equals 0 in each \(i\)-th node of the plate.
# AISMSNet: Advanced Image Splicing Manipulation Identification Based on Siamese Networks
## 4 Experimental Results  
To evaluate the proposed image splicing detection algorithm, our own image database was used, which contains images captured in different environments and conditions, such as various scenarios or objects with lighting changes, to which different image splicing attacks were applied. This database contains 166 images, of which 68 do not contain manipulations and 68 are images modified by splicing attacks. The photos are in the RBG color model. Another database used was the Columbia1 image database, which is composed of 183 original images and 180 manipulated images. The image sizes range from 757 \(\times\) 568 to 1152 \(\times\) 768 and are uncompressed, in either TIFF or BMP formats [38]. The CASIA V2 image base was also used, which consists of 5123 images classified into 3295 images manipulated by copy-move attacks and 1828 by splicing distortion [39]. In addition, the Realistic Tampering Dataset is composed of 220 original and tampered images; all images are 1920 \(\times\) 1080 px RGB uint8 bitmaps stored in the TIFF format [40]. Finally, the MICC-F220 database was used to assess the effectiveness of the proposed splicing detection method in different scenarios. This database contains 110 original images and 110 modified RGB images in JPEG format (737 \(\times\) 492) [41]. Figure 4 shows some examples of the databases images.  
This section provides a comprehensive overview of the results of many experiments with the proposed splicing detector algorithm. The approach presented identifies manipulated regions. Our evaluation examines the system's performance and robustness against splicing attacks. For experimentation, it utilized a system equipped with an NVIDIA GTX 1650 graphics card and an Intel Core i7 processor operating at 3.4 GHz, running on the Windows 10 operating system. These specifications establish an environment conducive to the algorithm's effective operation within the PyTorch framework in Python.  
Figure 3: (**a**) Original image. (**b**) Image splicing tampering. (**c**) Ground truth. (**d**) Siamese neural network splicing detection. (**e**) Siamese neural network splicing detection with K-means algorithm refinement.  
Figure 4: Some examples of original (O) and tampered (T) images contained in the databases used for evaluating the proposed algorithm.
# Cognitive Interference Cancellation with Digital Channelizer for Satellite Communication
## 6 Numerical Results
However, if the interference is not cancelled out, the EVM increases with the interference power because the error is caused by the interference. To evaluate the interference detection performance of the proposed simple and improved methods, we show the false alarm and detection (or misdetection) probabilities in Figure 10a,b. Here, we set the power of the CW tone interference as -23 dB lower than the desired wideband signal and its frequency is \(\frac{200}{1024}\pi\) radians/sample. The total number of subchannels is 1024 as in the previous simulations. This means that the CW tone interference has 7 dB higher power than the desired signal in the subchannel with interference. The detection thresholds for the simple and improved approaches are set at \(\lambda=3\) and \(\lambda^{\prime}=6\), respectively. In Figure 10a, false alarm and detection probabilities are shown as the number of subchannel blocks, \(L\), increases. The simulation results (lines with markers), in addition to the analytical performance (only markers) for both the simple and improved methods are shown. As expected, the false alarm probability decreases and detection probability increases with the number of blocks, \(L\). When \(L\geq 10\), we can achieve nearly perfect detection performance. The improvedmethod requires a smaller number of subchannel blocks than the simple one in terms of both false alarm and detection probabilities. Roughly, one subchannel block can be saved with the improved method, with a cost of implementation complexity (or detection range of interference frequency). Additionally, the analytical results are well matched with the simulation ones. To investigate a more accurate performance of the interference detection, we plot the false alarm and misdetection probabilities in a log scale in Figure 10b. Figure 8: Quadrature phase shift keying (QPSK) constellation with and without interference cancellation depending on the interference power: (**a**) With interference cancellation in \(-25\) dB lower interference than the desired signal, (**b**) without interference cancellation in \(-25\) dB lower interference than the desired signal, (**c**) with interference cancellation in \(-5\) dB lower interference than the desired signal, and (**d**) without interference cancellation in \(-5\) dB lower interference than the desired signal. Figure 9: Error vector magnitude (EVM) performance with and without interference cancellation depending on interference power.
## 4 Conclusions  
In summary, we synthesized \(\alpha\)-NaYF\({}_{4}\)/\(\beta\)-NaLuF\({}_{4}\): Yb\({}^{3+}\), Er\({}^{3+}\) nanoparticles coated with oleic acid using a mild hydrothermal method, whose luminescence intensity was improved at 1.53 \(\upmu\)m nearly six-fold. Then, the nanoparticles were copolymerized with methyl methacrylate (MMA) to fabricate NaYF\({}_{4}\)/NaLuF\({}_{4}\): Yb, Er NPs-PMMA. The doped Er\({}^{3+}\) concentration was 2.8 \(\times\) 10\({}^{26}\) m\({}^{-3}\). NaYF\({}_{4}\)/NaLuF\({}_{4}\): Yb, Er NPs-PMMA was filled into a Si\({}_{3}\)N\({}_{4}\) slot waveguide as a core to promote the interaction between the light field and the Er-Yb codoped gain medium, and 980 nm was used as a pump light to improve the gain performance. The optimum size of the Si\({}_{3}\)N\({}_{4}\) slot waveguide with an H\({}_{\rm r}\) of 400 nm, W\({}_{\rm S}\) of 200 nm, and Wr of 400 nm were obtained by combining the confining factor and effective cross-sectional area via the finite difference method. A six-level system model of the Yb-Er codoped system was established, and the gain characteristics were simulated. Theoretically, when transmission loss was 3 dB/cm, a net gain of 8.2 dB could be obtained. With further optimization of loss and doped Er\({}^{3+}\) concentration, ultrahigh gain can be envisaged.  
Conceptualization, G.H., X.L. and M.Z.; software, X.L.; formal analysis, X.L. and M.Z.; writing--original draft preparation, X.L. and M.Z.; writing--review and editing, G.H., X.L. and M.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 61905088) and the Science and Technology Development Plan of Jilin Province (No. 20200401051 GX).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 9: (**a**) Energy level transitions for Er\({}^{3+}\)-Yb\({}^{3+}\) codoped systems; (**b**) The gain versus coordinate pump power at 980 nm for different waveguide lengths; (**c**) The gain versus coordinate pump power at 980 nm for different W\({}_{\rm S}\); (**d**) The gain versus coordinate pump power at 980 nm for different W\({}_{\rm r}\).
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 4 Proposed Fuzzy Logic Dijkstra-based Routing Algorithm
### Cluster Formation Phase  
In this phase, the sensor nodes are grouped in the form of clusters by using \(K\)-means clustering algorithm. This algorithm is widely used to group the data into \(K\) clusters. In the proposed algorithm, the value of \(K\) is calculated by Equation (9) [31]. As a result, the network domain is divided into \(K\) clusters with minimum inter-cluster and maximum intra-cluster similarity.  
\[C_{K}=\sqrt{\frac{n}{2\pi}}\cdot\frac{F}{d_{BS}{}^{2}}\cdot d_{th} \tag{9}\]  
where \(n\) denotes the number of sensor nodes, \(F\) represents the network domain size, and \(d_{BS}{}^{2}\) symbolizes the average node distance from the BS. This clustering algorithm comprises several iterations and steps that are described as follows:  
Step I: Calculate the value of \(C_{K}\) using Equation (9) to obtain the desired number of clusters according to the network parameters.  
Step II: Randomly select one of the sensor nodes to be the first initial cluster center: \(C_{1}\). Select another node located far away from \(C_{1}\) to be the second cluster center: \(C_{2}\) such that the distance between both the cluster centers is much greater than the transmission range \(r_{t}\) of \(C_{1}\). Similarly, select another node located far away from \(C_{1}\) and \(C_{2}\) to be the third cluster center: \(C_{3}\) such that the distance between the cluster centers is the maximum of the min (\(d(C_{3},C_{1}),d(C_{3},C_{2})\)) [32]. Select all the \(C_{K}\) initial cluster centers in the mentioned sequence.  
Step III: Calculate the distance between each sensor node and the initial cluster centers using the Euclidean distance given by Equation (10).  
\[d_{n-C}=\sqrt{\sum_{i=1}^{n}(S_{i}-S_{C})} \tag{10}\]  
where \(d_{n-C}\) represents the distance of the sensor nodes to the cluster center, \(S_{i}\) symbolizes the position coordinates of the \(i\)th node, and \(S_{C}\) denotes the position coordinates of the cluster centers. Once the distances are calculated, each node is associated to the closest cluster center.  
Step IV: Determine the new cluster centers by calculating the mean value of all the sensor nodes in respective clusters.  
Step V: Repeat Step III with the new cluster centers. If the cluster centers assigned to the nodes change, then repeat Step IV, else stop the algorithm. These steps are summarized in Algorithm 1.
# Indoor Localization Using 6G Time-Domain Feature and Deep Learning
## 3 ResNet with SLAB and CLAB
### Spatial Local Attention Block (SLAB)  
SLAB is designed to enhance spatial feature representation by capturing spatial dependencies. As shown in Figure 4, SLAB begins by applying both average pooling and max pooling along the channel dimension of the input feature map (\(C\times H\times W\)), generating two spatial feature maps of size \(2\times H\times W\). These two maps are then concatenated to form a unified spatial representation. The concatenated feature map is processed through a convolutional layer, followed by a sigmoid activation function to generate a spatial atten  
Figure 3: Channel local attention block (CLAB).  
Figure 2: ResNet with CLAB and SLAB.  
tion map of size \(1\times H\times W\). This spatial attention map highlights essential regions in the feature map, allowing the network to focus on significant spatial features.  
SLAB applies average-pooling and max-pooling operations and concatenates them along the channel dimension to produce the feature maps. The feature maps are passed through the final sigmoid activation layer to produce the probability of where to focus. In other words, SLAB is designed to identify the most important positions in the given input feature map.  
Finally, a cascade structure, where channel attention is applied first, followed by spatial attention. This sequential configuration encourages the model to pre-select informative feature maps and subsequently refine them spatially, enhancing localization accuracy even in complex indoor environments.
## Materials and Methods
### Illumination Condition (IC)  
The incidence angle (i), is the angle between the normal to the pixel surface and the solar Zenith angle (z) [34; 35] (Figure 5). The radiance detected by the sensor after interacting with surfaces of different slopes and aspects is dependent on this incidence angle (i). If (i) is low, the directions of the sun and the sensor are closer and radiance will be higher, the scene will be well illuminated and shadows due to topography minimized. Conversely, if (i) is large, the light will reach the surface in an oblique direction, illumination will decrease in the scene and more shadows will be cast due to the topography effect. Illumination condition (IC) is the cosine of the incidence angle (i) and ranges from 0 to 1, respectively, with 0 being values for poorly illuminated and 1 for well-illuminated areas. IC is calculated in Equation (3).  
\[\cos\mathrm{i}=\mathrm{Illumination\ Condition\ (IC)}=\cos\mathrm{z}\times\cos \mathrm{s}+\sin\mathrm{z}\times\sin\mathrm{s}\times\cos\left(\mathrm{a}- \mathrm{o}\right) \tag{3}\]  
where z is the solar zenith angle; s is the terrain slope; a is the solar azimuth angle and o is the terrain aspect [8]. To calculate slope and aspect we used a DEM, the Shuttle Radar Topography Mission (SRTM) which is also available as a dataset in GEE (ID: 'USGS/SRTMGL1_003') [36]. Zenith and azimuth angles are available in the metadata of each Landsat image. A script was created in GEE using SRTM and metadata solar angles in the image to calculate IC at the pixel level and incorporate it in each image as a new band.  
Figure 4: Dates expressed as year and day of year when the 39 selected images were available for the analysis.  
Figure 5: Terrain and solar angles involved in the calculation of the incidence angle (i) and illumination condition (IC).
# Detecting Atypical Values and Their Influence on Blast-Induced Seismic Measurement Results
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**B1** & \multicolumn{3}{c}{**Weight (kg) for Conf. Int. (\%)**} & \multicolumn{3}{c}{**Ratio**} \\ \hline
**Distance (m)** & **68.27** & **95.45** & **100.00** & **68.27/100.00** & **95.45/100.00** \\ \hline
10 & 0.487 & 0.469 & 0.306 & 1.59 & 1.53 \\ \hline
20 & 1.946 & 1.876 & 1.226 & 1.59 & 1.53 \\ \hline
30 & 4.379 & 4.22 & 2.758 & 1.59 & 1.53 \\ \hline
40 & 7.785 & 7.503 & 4.904 & 1.59 & 1.53 \\ \hline
50 & 12.16 & 11.72 & 7.662 & 1.59 & 1.53 \\ \hline
60 & 17.52 & 16.88 & 11.03 & 1.59 & 1.53 \\ \hline
70 & 23.84 & 22.98 & 15.02 & 1.59 & 1.53 \\ \hline
80 & 31.14 & 30.01 & 19.62 & 1.59 & 1.53 \\ \hline
90 & 39.41 & 37.98 & 24.83 & 1.59 & 1.53 \\ \hline
100 & 48.66 & 46.89 & 30.65 & 1.59 & 1.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data from the calculated permissible charge weight per delay for the required distances from the blast, with the chosen maximum PPV for both confidence intervals (68.27% and 95.45%) and the full dataset, and their correlation. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Borehole**} & \multicolumn{4}{c}{**Remaining Measurement Points after Excluding Outliers for**} \\  & \multicolumn{4}{c}{**Confidence Intervals (\%)**} \\ \cline{2-5}  & **68.27** & **95.45** & **99.73** & **99.99** & **100.00** \\ \hline B1 & 4/7 & 6/7 & 7/7 & 7/7 & 7/7 \\ \hline B2 & 2/7 & 6/7 & 7/7 & 7/7 & 7/7 \\ \hline B3 & 3/7 & 5/7 & 7/7 & 7/7 & 7/7 \\ \hline B4 & 2/8 & 6/8 & 8/8 & 8/8 & 8/8 \\ \hline B5 & 5/8 & 6/8 & 8/8 & 8/8 & 8/8 \\ \hline B6 & 5/8 & 7/8 & 8/8 & 8/8 & 8/8 \\ \hline B7 & 3/8 & 7/8 & 8/8 & 8/8 & 8/8 \\ \hline B8 & 2/8 & 8/8 & 8/8 & 8/8 & 8/8 \\ \hline B9 & 5/8 & 6/8 & 8/8 & 8/8 & 8/8 \\ \hline B10 & 4/8 & 6/8 & 8/8 & 8/8 & 8/8 \\ \hline B11 & 4/8 & 6/8 & 8/8 & 8/8 & 8/8 \\ \hline B12 & 4/7 & 6/7 & 7/7 & 7/7 & 7/7 \\ \hline B13 & 4/7 & 6/7 & 7/7 & 7/7 & 7/7 \\ \hline B14 & 5/7 & 6/7 & 7/7 & 7/7 & 7/7 \\ \hline B15 & 4/7 & 6/7 & 7/7 & 7/7 & 7/7 \\ \hline B16 & 4/6 & 5/6 & 6/6 & 6/6 & 6/6 \\ \hline B17 & 2/5 & 5/5 & 5/5 & 5/5 & 5/5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number of measurement points for all boreholes after the exclusion of the atypical values. Due to the large amount of data, in Table 7 for each borehole, only the ratios between both confidence intervals (68.27% and 95.45%) and the full dataset are presented.
# Robust Grey Relational Analysis-Based Accuracy Evaluation Method
## 3 Robust Grey Relational Analysis-Based Accuracy Evaluation Method  
The essence of traditional grey relational analysis (GRA) lies in evaluating the similarity of the spatial curves formed by time series data based on the distances between corresponding data points. However, this method has a fundamental issue: it does not consider the relative distances between the two sequences, leading to inaccuracies in the evaluation of sequence correlation. As illustrated in the case shown in Figure 2, where \(X_{0}\), \(X_{1}\), \(X_{2}\), and \(X_{3}\) represent different data sequences, the traditional GRA method exhibits the following problems:  
Figure 1: Grey correlation-mean square distance-accuracy conversion model.  
Figure 2: Reference example.  
Intuitively, when comparing the consistency of sequences \(X_{1}\) and \(X_{2}\) with sequence \(X_{0}\), it can be observed that sequence \(X_{2}\) should exhibit better consistency with \(X_{0}\) than with \(X_{1}\). This leads to \(\gamma(X_{0},X_{1})<\gamma(X_{0},X_{2})\). However, in the case where the differences between the data points of sequences \(X_{1}\) and \(X_{2}\) and the reference sequence \(X_{0}\) are equal or opposite values, the traditional grey relational analysis (GRA) yields \(\gamma(X_{0},X_{1})=\gamma(X_{0},X_{2})\), which contradicts the actual observation.
2. For sequences \(X_{1}\) and \(X_{3}\) that exhibit a parallel trend, where the displacement difference at each sampling point is a constant, the minimum differences \(\underset{k}{\min}|X_{1}(k)-X_{3}(k)|\) and maximum differences \(\underset{k}{\max}|X_{1}(k)-X_{3}(k)|\) between the two sequences are equal. In this scenario, the traditional grey relational analysis (GRA) method yields \(\gamma(X_{1},X_{3})=1\), which fails to account for the relative distances between the sequences, leading to a result that does not align with the actual observations.
3. There are cases where the data points of the sequences are relatively close to each other; however, due to the significant difference in the range (the difference between the maximum and minimum values) of the sequences, the grey relational coefficient is low, which, in turn, results in a low overall grey relational degree.  
In this paper, we address the first issue by introducing the expected valve crossing rate combined with an interpolation function to calculate the corrected distance points of the sequences. This approach increases the weight of the grey relational coefficient in the valve crossing point domain. For the second and third issues, we propose a bias tolerance control index to filter and screen time series data, assess the proximity of data sequences, and construct a distance weighting function. This function corrects the distances and geometric shapes of time series data on a small scale to enhance the robustness of the grey relational degree evaluation results. We combine the grey relational degree, mean square deviation distance, and accuracy model to achieve accuracy evaluation, and propose a robust grey relational analysis-based accuracy evaluation method (RGRA-AEM).