esa-sceva/satcom-chunk-collection huggingface dataset:
# A Temperature Fiber Sensor Based on Tapered Fiber Bragg Grating Fabricated by Femtosecond Laser
## 4 Results and Discussion
For accuracy, the round-up temperature sensitivities are used. Compared to the reference FBG fabricated by femtosecond laser, the temperature sensitivity of the tapered FBG has improved from 11.0 pm/\({}^{\circ}\)C to 12.3 pm/\({}^{\circ}\)C.  
In order to discuss the reproducibility of the technique, we fabricated five tapered FBGs for comparison using the same fabrication conditions. The Fujikura 80S was used to fabricated fiber tapers with 25 \(\upmu\)m diameter and 1000 \(\upmu\)m length. For the femtosecond laser inscription, all of the five tapered FBGs were first order with grating pitches being 535 nm and grating lengths being 1000 \(\upmu\)m. Then the temperature behaviors of the five tapered FBGs were investigated in the aforementioned furnace chamber with a test temperature range of 30-350 \({}^{\circ}\)C. All five tapered FBGs exhibited a red shift during the heating processes, and a blue shift during the cooling processes. The Bragg wavelengths and temperature sensitivities of the five tapered FBGs are listed in Table 2.  
Notably, the temperature fiber sensors based on first-order tapered-FBGs can be fabricated by femtosecond laser with good reproducibility. Combined with the experimental data in Table 2, the Bragg wavelengths can be precisely controlled given the grating pitch and the length on the same kind of optical fiber. During cooling processes, the temperature sensitivities are slightly smaller compared to those in the heating processes. This is probably due to the fact that the thermal equilibrium takes longer during the cooling process. By prolonging the stabling period, the associated errors can be further improved.
## 3 Methods
### Completeness Magnitude (\(M_{\rm C}\)) and \(b\) Value Estimation  
The estimation of the completeness of earthquake catalogs is essential to the computation of \(b\) values, and the lowest magnitude of all earthquakes that are reliably detected in a space-time volume is defined as the completeness magnitude (\(M_{\rm C}\)) [39]. The lower the \(M_{\rm C}\), the higher the detection capability. Here, we use the MaxCurvature technique, which estimates the \(M_{\rm C}\) by locating the magnitude that is the highest frequency of events in the FMD. [PERSON] [40] showed that the MaxCurvature technique underestimates the \(M_{\rm C}\) in cases involving gradually curved FMDs and postulated that this underestimation tendency arises from spatiotemporal heterogeneities within the earthquake monitoring network. Therefore, we used the corrected MaxCurvature method with a correction factor of +0.2 [41], and the uncertainties were determined by bootstrapping.  
The least-squares method and maximum likelihood estimation are often used to calculate the \(b\) value, and the latter approach is considered more stable. In this work, the maximum likelihood estimation used to calculate the \(b\) value and its standard deviation [42; 43]:  
\[b\ =\ \frac{1}{\ln(10)(\overline{M}-M_{\rm C})} \tag{1}\]  
where \(\overline{M}\) is the average magnitude of earthquakes with \(M\geq M_{\rm C}\); \(M_{\rm C}\) is the cutoff magnitude. The confidence limit of the \(b\) value is expressed as follows:  
\[\sigma\ =\ \frac{b}{\sqrt{N}} \tag{2}\]  
where \(N\) is the number of earthquake cases of the given sample.
## 4 Defense Against False Data Injection
### Experiments
#### 4.2.2 Performance Metrics  
In this subsection, we outline the key metrics used to evaluate the performance of our FDI attack detector. A diverse set of metrics is employed to provide insights into various aspects of classification accuracy, helping to assess the strengths and weaknesses of each model.  
Accuracy measures the proportion of correctly classified samples (both true positives and true negatives) out of the total samples. It is defined as:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{15}\]  
where:  
TP (True Positives): Correctly classified positive samples.  
TN (True Negatives): Correctly classified negative samples.  
FP (False Positives): Incorrectly classified positive samples (false alarms).  
FN (False Negatives): Incorrectly classified negative samples (missed positives).  
Precision measures the proportion of positive samples that were correctly identified by the classifier. It is defined as:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{16}\]  
Precision is important when the cost of false positives is high, as it measures the reliability of positive predictions.  
Recall measures the proportion of actual positive samples that were correctly identified. It is defined as:  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{17}\]  
Recall is important when missing positive cases is costly, as it indicates the model's ability to identify all positive instances.  
F1-Score is the harmonic mean of precision and recall and is particularly useful in situations with imbalanced datasets. It is defined as:  
\[\text{F1-Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{18}\]  
The F1-Score provides a balance between precision and recall, making it particularly useful when both false positives and false negatives are important concerns.  
Receiver Operating Characteristic (ROC)- Area Under the Curve (AUC) measures the performance of the classifier across all possible thresholds. The ROC curve plots the True Positive Rate (Recall) against the False Positive Rate (FPR), and the AUC summarizes the performance. FPR is calculated as follows.  
\[\text{FPR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{19}\]  
A higher AUC indicates better performance, with a value of 1.0 representing a perfect classifier.  
False Alarm Rate (FAR) indicates how frequently the model incorrectly predicts a positive class (false positives). It is defined as:  
\[\text{FAR}=\frac{\text{FP}}{\text{FP}+\text{TN}} \tag{20}\]  
A lower FAR is desirable, particularly in applications like fraud detection or security, where minimizing false positives is essential to avoid unnecessary alerts or interventions.
# Escalator: An Autonomous Scheduling Scheme for Convergecast in TSCH
## 5 Analysis of Escalator  
This section analyzes the performance and limitations of Escalator. Firstly, we prove the conflict-freeness of the convergecast slotframe schedule using the wireless conflict model. Then, we evaluate the performance of Escalator through the analysis of the end-to-end delay and bandwidth.
## 3 UAV 6G Networks
### 6G Application Requirements
In the studies [50; 51; 60], communication technologies such as NR, 4G, 5G and futuristic 6G technology including other future revolutionized technologies including artificial intelligence, integrated radar, smart detectors, XR, autonomous systems such as UAVs, brain computer interface based on wireless and AI-based future communication models are studied and compared. 6G is anticipated to provide 100 times more coverage for connectivity and multiple times the performance. The pertinent revolutionary features in the upcoming 6G communication model are AI, machine learning, TeraHertz communication, drone connectivity through terrestrial and non-terrestrial networks [52; 65; 67] and wireless power transfer [53; 64]. This will serve max of 1 Tbps per device data transfer speed. Moreover, ensuring at least 1000 km/h mobility support to airborne vehicle flying in a densely populated metropolitan environment. The communication requirements for UAVs, as well as their capabilities and 6G functions, are listed below.
## 4 Numerical Simulation
### Damage Identification Based on the Proposed Method
#### 4.6.2 Damage Case 2  
In damage case 1, two large damages are considered, and the results are relatively accurate. In damage case 2, multiple smaller damages are considered, and the damage identification results are shown in Table 4. It can be seen from Table 4 that the results identified using the improved and traditional objective function are very close, which verifies the accuracy of the proposed method. Besides, they are both close to the theoretical values and the error is less than 0.03, which meets the requirements of damage identification. Therefore, the accuracy and validity of the proposed method are verified also in the case of multiple smaller damages.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No** & **Theory** & **The Improved Method** & **The Traditional Method** \\ \hline
1 & 1.0000 & 0.9999 & 0.9996 \\
2 & 0.6000 & 0.6084 & 0.5897 \\
3 & 1.0000 & 0.9999 & 1.0000 \\
4 & 1.0000 & 0.9998 & 0.9997 \\
5 & 0.7000 & 0.6846 & 0.7087 \\
6 & 1.0000 & 0.9998 & 1.0000 \\
7 & 1.0000 & 0.9997 & 1.0000 \\
8 & 1.0000 & 0.9896 & 0.9963 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of damage identification results using the improved objective function and traditional objective function.  
Figure 6: The identified damage extent.
# Effect of Prestress Levels and Jacking Methods on Friction Losses in Curved Prestressed Tendons
## 1 Introduction
[13] published a summary of a study conducted at Pennsylvania State University and the University of Texas at Austin regarding the effects of unflushed emulsifiable oils on corrosion, bond, and friction losses. The friction tests in the study showed that if the tendon was stressed when the oil was fresh, the lubrication decreased the friction coefficient by approximately 15% in the rigid steel pipes and high-density polyethylene (HDPE) ducts. Furthermore, the friction coefficient for the HDPE ducts was significantly lower than the value recommended by the American Association of State Highway and Transportation Officials (AASTHO) [14]. Previous studies have mainly investigated the effects of both duct material and emulsifiable oilson friction losses in post-tensioned concrete. Structural engineers use the wobble and friction coefficients, which are typically presented as constant values in the design codes, to evaluate friction losses [15; 16], even though the friction between a prestressing tendon and its sheath can be greatly affected by the prestress levels and prestressing methods. Therefore, more studies pertaining to the evaluation of friction losses using full-scale PSC girders are required in order to design the PSC girder and evaluate its flexural behavior accurately. This study evaluated the effects of the prestressing methods and prestress levels on the friction losses of a curved prestressing tendon. Two 30-m long full-scale PSC girders were fabricated and used for the friction loss tests. The tests evaluated the effects of varying the prestress levels from 13% to 45% of the ultimate tensile strength of a prestressing tendon with jacking at one end or at both ends. The test results were used to analyze the effects of the experimental variables on the friction losses of prestressing tendons mounted in a sheath tube of a full-scale PSC girder.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## Appendix D ROC-AUC Measure  
The Area Under the Curve (AUC) for the Receiver Operating Characteristic (ROC) curve (ROC-AUC) evaluates the ability of a binary classifier to distinguish between positive and negative classes. Given a batch of size \(N\) where each sample corresponds to a flattened predicted matrix \(\hat{y}^{(n)}\), denoted by \(\hat{y}^{(n)}=\texttt{flatten}(\hat{Y}^{(n)})\in[0,1]^{|V|^{2}}\), and a flattened ground truth matrix \(y^{(n)}\), denoted by \(y^{(n)}=\texttt{flatten}(Y^{(n)})\in\{0,1\}^{|V|^{2}}\), the ROC curve is constructed by varying a threshold \(t\in[0,1]\) and computing the True Positive Rate (TPR) and False Positive Rate (FPR):  
\[\text{TPR}(t)=\frac{\sum_{i}|\mathcal{L}(\hat{y}^{(n)}_{i}\geq t \text{ and }y^{(n)}_{i}=1)}{\sum_{i}|\mathcal{L}(\hat{y}^{(n)}_{i}=1)}, \qquad\text{FPR}(t)=\frac{\sum_{i}|\mathcal{L}(\hat{y}^{(n)}_{i}\geq t \text{ and }y^{(n)}_{i}=0)}{\sum_{i}|\mathcal{L}(\hat{y}^{(n)}_{i}=0)} \tag{11}\]  
where \(|\mathcal{L}(\cdot)\) is the indicator function. The AUC is then computed as the integral:  
\[\text{ROC-AUC}(y^{(n)},\hat{y}^{(n)})=\int_{0}^{1}\text{TPR}(t)\;d\text{FPR}(t) \tag{12}\]  
which can be approximated [108] with \(P=\sum_{i}|\mathcal{L}(\hat{y}^{(n)}_{i}=1)\) the number of positive samples and \(N=\sum_{i}|\mathcal{L}(\hat{y}^{(n)}_{i}=0)\) the number of negative samples, as follows:  
\[\text{ROC-AUC}(y^{(n)},\hat{y}^{(n)})=\frac{1}{PN}\sum_{i:\hat{y}^{(n)}_{i}=1 }\sum_{j:\hat{y}^{(n)}_{j}=0}|\mathcal{L}(\hat{y}^{(n)}_{i}>\hat{y}^{(n)}_{j}) \tag{13}\]  
To evaluate our models, we used the batch-averaged AUC, computed as follows:  
\[\text{ROC-AUC}_{\text{batch}}=\frac{1}{N}\sum_{n=1}^{N}\text{ROC-AUC}(y^{(n) },\hat{y}^{(n)}) \tag{14}\]
## 3 Results
### Analysis of Regional Drought Characteristics of Converted Land Uses in the Shendong Mining Area
#### 3.4.1 Change Region Meteorological Drought Characteristics  
The characteristics of meteorological drought exhibit distinct patterns during the pairwise conversion processes among grassland, cultivated land, and bare land. As shown in Figure 13, overall, the frequency of meteorological drought in regions undergoing land use changes fluctuated around 1, indicating a relatively high probability of meteorological drought in the study area. The duration of meteorological drought shows significant variations: when cultivated land is converted to grassland or bare land, the duration is less than 5 months; conversely, when grassland is converted to cultivated land or bare land, the duration exceeds 5 months. This suggests that the seasonal characteristics of cultivated land have a notable influence on drought duration. Meteorological drought intensity generally remains above 0.9, indicating a consistently high intensity.  
Figure 12: Land use changes in the Shendong mining area from 2001 to 2020.  
Figure 13: Regional meteorological drought characteristics for land use conversions in the Shendong mining area.  
Specifically, the extent of mutual conversion between cultivated land and grassland is substantial. The frequency and duration of converting cultivated land to grassland are lower compared to converting grassland to cultivated land, suggesting that reclamation activities may exacerbate drought occurrence to some extent. Conversely, ecological protection measures such as returning farmland to grassland can help mitigate drought occurrences. The frequency of drought associated with grassland conversion to bare land is lower than that of bare land conversion to grassland, but has a longer duration. This indicates that reduced surface vegetation cover can intensify the adverse effects of meteorological drought. Additionally, while the frequency and duration of drought are lower for conversions from cultivated land to bare land compared to conversions from bare land to cultivated land, the intensity of meteorological drought across all types of land use changes remains high, underscoring the significant impact of meteorological drought on this region.
# Hyperspectral Molecular Orientation Mapping in Metamaterials
## 2 Experimental: Setups and Samples
### Gold-Polyimide-Gold MIM Metasurface and Properties
(**a**) FTIR absorbance \(Abs=-\lg(T)\) map (x-y values in pixels) of metal-insulator-metal (MIM) metasurfaces at a single wavenumber [cm\({}^{-1}\)] (wavelength [nm]) (see geometry in the top inset). Diameters \(d\) ranged from 500 nm (pattern #1) to 1600 nm (#22) with \(\Delta d\) nm the diameter increment changing from 10 to 100 nm (\(d\) is marked on the map). The triangular lattice period of the nanodisk pattern was \(\Lambda=1.5d\). Thicknesses of MIM layers: Au film was 200 nm, Au nanodisk was 50 nm, and polyimide layer was 60 nm, single pixel on the sample was \(25\times 25\)\(\upmu\)m\({}^{2}\). (**b**) The integrated absorbance over multiple pixels of the #18 area at four polarisation angles 0, \(\frac{\pi}{4}\), \(\frac{\pi}{2}\), \(\frac{3\eta}{4}\) vs. wavenumber. 0\({}^{\circ}\) is the horizontal x-axis on the image. diffusivity for the in-plane and out-of-plane modes were observed in an industrial grade polyimide [29]. It is expected that anisotropy of polyimide nanoffilm can be exploited for polarisation properties of MIM emitters at IR wavelengths.
# A Comparison of Segmentation Methods for Semantic OctoMap Generation
## 3 Methods  
### Semantic OctoMap Generation  
We implemented an ROS2 node to enhance LiDAR point clouds with semantic class information. A schematic visualizing the process can be found in Figure 1. Topic synchronization is employed to ensure the processing of corresponding camera images and point clouds. The image first undergoes undistortion and rectification, followed by resizing and normalization using ImageNet dataset weights. This step aims to keep consistency with the format and distribution of the training data. After tensor conversion, semantic segmentation is performed using an ONNX-format model. The format was selected for its compatibility with both greenPython and greenC++ nodes and its efficient inference time. The implementation was carried out using Python 3.10 and C++11.  
In the following stage, the point cloud is projected onto the segmentation mask using transformation between LiDAR and camera frames. Points, along with their corresponding semantic class information, are then organized into a separate point cloud structure and published to a dedicated topic. We also integrated a filtration mechanism to control which classes of objects can be included in the point cloud. This feature is valuable for excluding dynamic objects (e.g., as pedestrians or cyclists) or elements of classes that cannot logically exist in the map. During the experiments discussed in the subsequent sections, no filtering was applied.  
The final step involves updating the OctoMap. We utilized the OctoMap implementation available in the octomap_server package [34], which supports the additional point cloud features such as color. We leverage this to label voxels via pseudo-colors encoding class labels. However, this method can lead to color blending when spatial regions are inconsistently assigned class labels, resulting in color mixing and the formation of \"intermediate\" labels. When evaluating the map, points that cannot be unambiguously assigned to any class are filtered out using a heuristic based on maximum color difference from the reference values listed in Table 1. Beyond the semantic point cloud, the map requires information about the current robot's pose in the global frame coordinates to include the points. The complete processing cycle, from receiving data on ROS topics to updating the OctoMap, requires between 30 and 60 ms, depending on the neural network architecture  
Figure 1: Diagram representing the proposed workflow of the semantic OctoMap generation pipeline.  
and the number of points involved in processing. The input consists of 65,536 points, while the number of labeled points varies between 11,000 and 16,500, determined by the number of valid distance measurements in the LiDAR scan. The node implementation is designed to support adaptation to other datasets without requiring substantial code modifications.
# A Scheduling Scheme for Improving the Performance and Security of MU-MIMO Systems
## 4 Scheduling Schemes
### Proposed Scheduling Scheme
In addition, the total system bandwidth is typically greater than \(B_{c}\), thus, subcarriers separated by a frequency interval greater than \(B_{c}\) will be affected by channel gains (or equivalently flat fading) that are independent [50]. Consequently, the channel gain for a user may be weaker in some subcarriers, but stronger in others. Therefore, a UT that does not transmit on a subcarrier can transmit on another, which guarantees that all users within the same cell can transmit.
###### Abstract  
The industrial use of nanoparticles is rapidly increasing in agricultural products, and it causes numerous effects on plant growth and seed germination. There are limited studies about the uptake, accumulation, and effect of nanoparticles on plant growth. Moreover, there are no studies that have been able to measure the biological activity of seeds' interiors under nanoparticle treatment prior to germination. Hence, in this study, the possible size-defendant effect of alumina nanoparticles on lentil (_Lens culinaris_) seed germination was monitored using biospeckle Optical Coherence Tomography (bOCT) at an early stage before germination. bOCT is a non-contact, non-destructive, and non-invasive image modality developed by researchers to visualize internal biological activities in vivo. Previous studies by researchers using the technique have demonstrated its potential to observe the effect of different stimuli on seed germination at an early stage before germination occurs. In the present study, the possible size-defendant effect of 200 nm and 2000 nm alumina nanoparticles (ANPs) on lentil seed germination was examined at a concentration of 100 mg/L. The results imply that the AlNPs could enhance lentil seed germination and seedling growth compared to control treatments, and the effect is more significant as the particle size decreases. This is believed to be due to the enhancement of energy metabolism under exposure to AlNPs.  
alumina; nanoparticles; lentil; biospeckle; Optical Coherence Tomography; seed germination; seedling growth 2023
# Sentinel-1 and -2 Based near Real Time Inland Excess Water Mapping for Optimized Water Management
## 2 Materials and Methods
### Data  
The presented inland excess water mapping workflow integrates different remote sensing datasets with auxiliary vector and raster data. The satellite images were obtained from the scientific data hub of the Copernicus Earth observation program of ESA [44].
## 5 Energy-Efficient Resource Matching Algorithm for D2D Relay Communications
### Two-Dimensional Matching
#### 5.1.1 Preference Establishment  
In the two-dimensional matching problem, D2D TRs on one side propose to establish pairs with RBs on the other side based on their preference lists. As mentioned above, the RB selection for the second hop D2D link is independent of relay selection. Thus, regardless of which RS is selected, D2D TR \(m\) prefers to be matched with the RB that maximizes the reciprocal of interference power received by RX \(m_{r}\), i.e.,  
\[V_{m,c_{s}}=\frac{1}{P_{c_{s}}d_{c_{m}}^{-\alpha}l_{s,c_{s},m_{r}}^{2}}, \tag{9}\]  
which is defined as the preference value of TR \(m\) on RB \(c_{s}\). By temporarily pairing each D2D TR pair with each RB, the reciprocal of the interference corresponding to each TR-RB pair can be obtained. The preference value of TR \(m\) on each RB in descending order is denoted as \(\hat{E}_{m}=\{\hat{E}_{m,1},\hat{E}_{m,2},...,\hat{E}_{m,C}\}\)which is firstly calculated as (9), and the corresponding RB is \(\hat{\mathcal{O}}_{m}=\{\hat{O}_{m,1},\hat{O}_{m,2},...,\hat{O}_{m,C}\},\forall\hat {O}_{m,i}\in\mathcal{C}\), \(i\in[1,C]\), which is defined as the preference list of TR \(m\). The set \(\hat{\mathcal{E}}_{m}\) and \(\hat{\mathcal{O}}_{m}\) will be updated in each matching iteration.