esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network Resource Awareness and Resource Status Prediction
### Construction of Resource Awareness Dataset  
#### 3.2.1 Integration of Awareness Data  
INT technology enables refined awareness of the resource status of all nodes in the network, providing data support for resource prediction. Due to the limited number of established LEO satellite networks, there are relatively few available open-source datasets for satellite network node resources. Terrestrial network traffic datasets and network resource datasets do not possess the complex time-varying characteristics and multidimensional heterogeneous features of satellite networks, making them unable to meet the resource prediction requirements in the current LEO satellite network scenarios. Network resource awareness datasets were therefore constructed in this work using simulation methods. The generation process of the awareness datasets is presented in Algorithm 1.  
```
Input: The number of the three types of services \(M_{\mathrm{per}}\), \(M_{\mathrm{band}}\), \(M_{\mathrm{c-s}}\); Time slot \(\mathrm{T}_{k}\); network resource status matrix \(\mathrm{R}_{i}\left(\mathrm{t}_{j}\right)\);
1 for\(i\gets 1\) to \(M\)do
2 Generate corresponding service sets for three types of services based on arrival models and number of services;
3 Generate resource demand vectors \(\mathrm{F}_{i}^{\mathrm{R}}=\left[\mathrm{B}_{i},\mathrm{C}_{i},\mathrm{S}_{i}\right]\) for each services;
4 end for
5 for\(j\gets 1\) to \(k\)do
6 Deploy services within the network, confirm the services across each node;
7 Update the on-board resource status based on the corresponding resource requirements of services;
8 Generate a network resource status matrix \(\mathrm{R}_{i}\left(\mathrm{t}_{j}\right)\) for the satellite;
9 end for
10 Collect and integrate the on-board status of resources across the entire network at each time slice \(\mathrm{R}^{\mathrm{t}_{j}}=\left\{\mathrm{R}^{\mathrm{t}_{1}},\mathrm{R}^{ \mathrm{t}_{2}},\ldots,\mathrm{R}^{\mathrm{t}_{j}}\right\},\)\(\mathrm{t}_{j}\in\mathrm{T}_{k}\);
11 Extract data and construct corresponding resource datasets based on three types of resources in chronological order. Output: Network resource awareness datasets.
```  
**Algorithm 1**Resource awareness dataset generation
## 7 Limitations and Future Work
#### Limitations of Fully Supervised Methods and Potential for Semi-Supervised Learning  
The current approach relies predominantly on _supervised learning_, where extensive labeled data are required to capture the variety of spoofing scenarios. This dependence on annotated training sets introduces a limitation in dynamic or evolving operational contexts, as spoofing techniques may deviate from those observed in the original dataset. A promising direction for addressing this shortcoming involves _semi-supervised learning_, wherein a relatively small set of labeled data is complemented by a larger repository of unlabeled data. Such methods allow the model to learn critical feature representations from unlabeled instances, thereby improving adaptability to unknown or rapidly shifting spoofing patterns. Semi-supervised strategies, for example, may include the pseudo-labeling of high-confidence predictions, consistency regularization, or hybrid classification-autoencoder frameworks. Future work will explore the benefits and practical implementation details of these semi-supervised approaches, especially for UAVs operating in dynamic environments where traditional fully supervised methods may be insufficiently flexible.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement
### Pulse Signal Processing  
Power frequency interference, respiratory movements, and bodily shaking introduced noise to pulse waves, necessitating the use of a third-order Butterworth bandpass filter and a 50 Hz notch filter to eliminate it. Figure 8 displays the pulse wave signals before and after filtering, while their amplitude-frequency characteristics are presented in Figure 9.  
Figure 8: The pulse waveform pattern: (**a**) raw signal; (**b**) filtered signal.  
Figure 7: Flowchart of Blood Pressure Measurement Method.  
As demonstrated in Figures 8 and 9, the filter design effectively removed baseline noise and power frequency interference while retaining the essential feature points of the pulse signal.  
Extracting these feature points is vital for accurate calculation of blood pressure values. To locate the peak points, the findpeaks function in Matrix Laboratory (MATLAB, version 2021b) was employed, and a new waveform was obtained by subtracting the existing pulse data from the maximum peak value. The trough value of the original waveform was determined by locating the peak points in the new waveform. Figure 10 shows the localization of the peak and trough points, and this method was effective in identifying them accurately.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
## 4 Results
### MOO Comparisons
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Clus Algo** & **Accuracy** & **F1** & **Kappa** & **QE** & **WSS** & **BSS** & **Total** \\ \hline M-Fuzzy & **51** & **45** & **47** & **29** & **34** & 70 & 276 \\ M-BBPSO & **54** & **54** & **54** & 61 & 57 & 66 & 346 \\ M-QPSO & 57 & 56 & 58 & 73 & 63 & 56 & 363 \\ SP-QPSO & 62 & 76 & 69 & 67 & **30** & 105 & 409 \\ BBPSO & 67 & 62 & 67 & 102 & 111 & **13** & 422 \\ CM-BBPSO & 83 & 77 & 82 & 95 & 102 & **52** & 491 \\ IB-Fuzzy & 99 & 88 & 96 & **52** & 101 & 86 & 522 \\ IB-QPSO & 96 & 100 & 101 & 104 & 101 & 104 & 606 \\ \hline MG-BBPSO * & 76 & 84 & 80 & 57 & 49 & 85 & 431 \\ MG-QPSO * & 73 & 85 & 77 & 94 & 67 & 113 & 509 \\ MG-Fuzzy * & 99 & 88 & 91 & 69 & 105 & 73 & 525 \\ \hline \hline \end{tabular}  
* No results for 2 or more datasets.  
\end{table} 
Table 5: Sum rank of algorithms based on performances on 6 metrics on all datasets with best two sum rank values presented in bold. In Table 8, we compare the performance of CM-BBPSO with other reported algorithms in terms of Accuracy and F1 score for five UCI datasets [15]: CMC, WOBC (Cancer), Iris, Glass and Wine. Where results are not available, we left it blank. While CM-BBPSO was not found to be the best in any of the datasets, it was found to outperform certain algorithms in a few datasets. CM-BBPSO outperformed the following algorithms in the respective datasets: MOAC in CMC and Iris, VAMOSA in WOBC and Wine and CBPSO in CMC. This demonstrates that CM-BBPSO performs competitively, and this result is promising. However, HT-MOC, MOFC-TMS, GenClustMOO and ES-NMPBFO outperformed CM-BBPSO in the available common datasets.
# Adaptive Noise Cancellation Based on Time Delay Estimation for Low Frequency Communication
## 4 Results and Discussion
### Simulation Results
#### 4.2.2 Noise Cancellation Performance  
In this paper, the noises in the primary input and the reference input are in the same amplitude and the time delay is set to be 2 \(T_{s}\). The length of filter coefficient vector is set to be 4. In the MCC algorithm, the Gaussian kernel size \(\sigma\) is 2 and the positive scalar \(\mu\) is 0.5. In the LMS algorithm, the positive scalar \(\mu\) is 0.0002. Figure 12 shows the square error of the filter coefficient vector of LMS algorithm and MCC algorithm when mixed SNR in the primary input is \(-23\) dB. The MCC algorithm has faster convergence speed and smaller square error than the LMS algorithm in the impulsive noise. Figure 13 shows the square error of signal of LMS algorithm and MCC algorithm when mixed SNR in the primary input is \(-23\) dB. In the steady state, the LMS algorithm has much bigger square error than the MCC algorithm when the pulses occur.  
To compare the noise cancellation performance between LMS algorithm and MCC algorithm at different mixed SNR levels, the noise is kept at the same magnitude and the magnitude of communication signal is changed. During this process, the parameters of LMS algorithm and MCC algorithm are not changed. Figure 14 shows the MSE (Mean Squared Error) of filter output at different mixed SNR levels. The mixed SNR has different effects on LMS algorithm and MCC algorithm. As the mixed SNR increases, the MSE of LMS algorithm slightly decreases. However, the MSE of MCC algorithm increases when the mixed SNR increases, because the increased amplitude of communication signal has a greater impact on the Gaussian kernel function. To obtain smaller MSE, the Gaussian kernel size should be changed. Nevertheless, the MSE of MCC algorithm is much smaller than that of the LMS algorithm.  
Figure 11: Time delay estimation variance at different mixed SNR levels.  
Figure 12: Square error of filter coefficient vector when mixed SNR in the primary input is \(-23\) dB.
## 4 Analysis of Response Model Results
### Response Surface Model  
From the previous analysis, it is evident that the total fracture length \(F_{4}\) is influenced not only by the three individual factors \(E_{1}\) to \(E_{3}\) but also by the interactions among these factors. The interaction surface plots for the three factors on \(F_{4}\) were generated, with irrelevant factors set to their middle values, as shown in Figure 10. Figure 10 illustrates that the interaction surfaces of in situ stress-explosive load and stress difference-explosive load are quite similar; both decrease with increasing in situ stress or stress difference and increase with rising L-CO\({}_{2}\) phase transition explosive load. In contrast to the explosive load's influence, the impacts of in situ stress and stress difference on the construction of the borehole fracture network are monotonically negative and distinct, with their interactions significantly reducing the network's development. As seen in Figure 10a, with increasing in situ stress and stress difference, \(F_{4}\) significantly decreases, and at lower in situ stress levels, the influence of in situ stress on \(F_{4}\) is notably greater than that of stress difference.  
Figure 9: Influence of univariate factors on response variable.  
However, as in situ stress increases, the influence of stress difference on \(F_{4}\) gradually intensifies. As in situ stress rises from 5 MPa to 15 MPa, the range of explosive load's effect on \(F_{4}\) shifts from 1058-1580 to 214-746, with the change range slightly increasing from 522 to 532. When stress difference increases from 0 to 10 MPa, the explosive load's effect range on \(F_{4}\) reduces from 1249-1695 to 607-1224, with the change range rising from 446 to 617. This is primarily due to the significant suppression of fracture network expansion under high-stress conditions, which may even hinder successful formation. Therefore, after increasing the explosive load, the formation and expansion of the fracture network demonstrate certain expansion and enhancement effects.
# Design and Experiments of Autonomous Path Tracking Based on Dead Reckoning
## 2 Autonomous Driving Based on Dead Reckoning
### Error Measurement Based on Image Processing
#### 2.3.1 Distance Calibration  
As the measurement target is the distance between the center of the rear wheel and the lane line, only calculating the longitudinal height of the detected lane line in the image is necessary. For the accurate calculation of the actual distance that corresponds to each vertical height in the image, the GoPro must be calibrated for distance.  
The distance calibration method can be seen in Figure 6. Firstly, the scale marks representing distance were drawn on the ground, and each scale line was spaced 20 cm apart. The scale line was at the horizontal center of the GoPro image. Secondly, the vertical coordinates corresponding to each scale line in the image were found manually. Finally, the distance between each of the two scale lines was linearly interpolated, and the actual distance corresponding to each vertical pixel coordinate in the image was then calibrated.
## 5 Case Study
### Algorithm Implementation  
The TM algorithm was implemented in Matlab R2021b. The Text Analytics Toolbox was used for the LDA algorithm both for modeling and prediction and the presentation of results through appropriate graphs (word-cloud graphs). It is worth noticing the availability of particular application programs interfaces (APIs) that allow integration between the Matlab and Python environments.  
The first step is to extract and save the textual information contained in the.json file in a specific Matlab variable. We decided to keep track during this phase, in addition to the text of the phone call, of the'speaker id' label. Short or empty transcriptions were removed, as they are useless in the TM algorithm. The pre-processing steps are as in the following pre-processing algorithm (Algorithm 1).  
```
form=1:Mdo 1.tokenizedDocument(\(d_{m}\)) 2.lower(\(d_{m}\)) 3.erasePunctuation(\(d_{m}\)) 4.steming(\(d_{m}\)) 5.removeWords(\(d_{m}\)) /removeStopWords(\(d_{m}\)) 6.bagOfWords(\(D\)) 7.tifid(BoW) endfor
```  
**Algorithm 1**Pre-processing.  
Step 4, stemming, is the most important, as it significantly reduces the dimensional complexity of a text analysis problem. An example of stemming in the Italian natural language is as follows. The words \"disdire\", \"disdetto\", \"disdetta\" (the Italian for \"service cancel\") in the stemming step produce the following result:  
_Stemming_  
\"disdire\" -> \"disd\"  
\"disdetto\", \"disdetta\" -> \"disdett\"  
With reference to step _tfid_ in Algorithm Pre-processing, following Figure 5 shows the result on a portion of the matrix (10 documents and 10 words). Consider the words \(w_{3}\) and \(w_{7}\) for the document \(d_{4}\). The second word has a higher number of occurrences than the first (7 versus 6 in the bag-of-words table); following the TF-IDF correction, the weight associated with the most recurring word is about half that of the least recurring word. This happens because, limited to the small portion of the corpus represented, the word \(w_{7}\) is also used several times in the other 9 documents, while the word \(w_{3}\) is missing in many of them.  
The working environment chosen is VS code, with a base kernel Python 3.10.2. The NLTK--Natural Language Toolkit library was used The library contains all the built-in functions necessary to perform the preprocessing. In particular, the normalization of words is carried out through a Python stemmer built for the Italian natural language. The.py script takes as input the three.csv files exported by Matlab (data, agent and customer). The LDA algorithm, coded in Matlab R2021b ran on a 2.6 GHz Intel Core i7 with 16 GB of memory.
## 5 Performance Evaluation
### Throughput Analysis by Varying the Transmit Power Level  
Figure 4 shows the average D2D throughput over transmit power applying our proposed method, distributed reinforcement learning and random allocation of resources. All the methods follow the same trend that with the increase of transmit power, D2D throughput increases. Our proposed reinforcement learning outperforms others at every level of transmit power due to the appropriate learning of transmission power assignment to the resource blocks. Our proposed method increases D2D throughput by 6.2% as compared with the distributed reinforcement learning. On the other hand, random allocation shows the lowest performance as usual due to the allocation of resources without adaptiveness.  
Figure 5 shows the trade-off between D2D throughput and cellular user throughput when varying the transmit power to these values [0.0569, 0.0741, 0.0800] after applying the proposed cooperative reinforcement learning, random allocation, and distributed reinforcement learning where the results are grouped into three sets for each method considering 12 D2D users. For all methods, we can observe the same trends, for example, with the increase of transmit power; D2D throughput increases but cellular user throughput decreases which show the typical phenomena of D2D communication. When the transmit power of the D2D device increases, the D2D throughput also increases. But this provides an impact of interference level to the cellular users which prov  
Figure 3: (a) Average system throughput over number of iterations (b) Convergence of learning algorithms.  
throughput. Our proposed method outperforms all methods in terms of D2D and cellular user throughput. For example, when the transmit power is equal to 0.0569, our proposed learning algorithm provides a D2D throughput equal to 3.80, and the cellular user throughput is equal to 2.912. On the other hand, for distributed reinforcement learning, the D2D and the cellular user throughput are 3.53 and 2.734, respectively which is lower than our proposed method. Moreover, random allocation of resources provides the least amount of D2D and cellular user throughput with values equal to 2.62 and 2.27, respectively.
# Fractal Metasurfaces and Antennas: An Overview for Advanced Applications in Wireless Communications
###### Abstract
Ongoing research in these areas continues to explore new designs, materials, and fabrication techniques to exploit their full potential. The aim of this paper is to review the progress made in the study and application of fractal antennas and metasurfaces and compare existing fractal-based antennas and metasurfaces, highlighting their benefits and limitations. Finally, a comprehensive analysis of innovative reflectarrays and metamaterial unit cells is presented and the challenges and future developments of these electromagnetic structures are discussed. This paper is organized as follows. In Section 2, several fractal antenna elements are illustrated and discussed. Some novel fractal-based reflectarray configurations are illustrated in Section 3, highlighting their performance and advantages. Fractal metasurfaces and their applications are discussed in Section 4. The conclusions are outlined in Section 5.
## 2 Materials and Methods
### Materials
#### 2.1.4 EC Flux Data  
The performance of MOD17A2H GPP on eighteen EC flux tower sites, including evergreen needleleaf forests (ENF), evergreen broadleaf forests (EBF), deciduous broadleaf forests (DBF), mixed forests (MF), croplands (CRO) and grasslands (GRA), was evaluated in this study (Table 1). These eighteen sites are Virasoro (AR-Vir), Alice Springs (AU-ASM), Riggs Creek (AU-Rig), Whoro (AU-Whr), Lonzeee (BE-Lon), Vielsalm (BE-Vie), Santarem-Km83-Logged Forest (BR-Sa3), Ontario- Groundhog River (CA-Gro), Billy Kriz-grassland (CZ-BK2), Hainich (DE-Hai), Soroe-LilleBogeskov (DK-Sor), Grignon (FR-Gri), Puechabon (FR-Pue), Ankasa (GH-Ank), Renon/Ritten (IT-Ren), Sardinilla Pasture (PA-SPs), Curtice Walter-Berger cropland (US-CRT) and Walnut Gulch Kendall Grasslands (US-Wkg). The detailed information for each site is available at the FLUXNET-Fluxdata website [42].  
From the FLUXNET-Fluxdata website, we downloaded the newest FLUXNET2015 dataset to obtain site meteorological data and GPP measurements during 2001-2014 for each flux tower site. The daily meteorological data (T\({}_{\it argg}\), T\({}_{\it min}\), PAR and VPD\({}_{\it argg}\)) were calculated from the half-hourly site meteorological measurements, and the daily GPP values were calculated from the half-hourly flux-derived GPP. We then obtained the eight-day site-based GPP by accumulating the daily calculations to ensure that our data remained consistent with the 8-day interval of the MOD17A2H GPP products.
## 4 Discussion
### Limitations and Future Work  
The study of relevant influencing factors for UAs in the primary stage of development is a challenge, as these areas are characterized by fewer cities, limited study samples, slower development, a singular industry focus, and insufficient population carrying capacity. Consequently, the network structure constituted by the big data of the floating population in these UAs is considered immature, less stable, and poses challenges for a thorough understanding of influencing factors. However, it is acknowledged that this approach might not entirely capture the multifaceted characteristics of UA network structures, given that urban linkage networks are complex entities encompassing various elements such as population, economy, information, and transportation. In addition, the current status and differences in the development of urban agglomerations in different countries and regions can have a complex impact on the network structure of urban agglomerations, and thus the evolution of the network structure of other urban agglomerations on a global scale needs to be further analyzed. Future investigations could employ analogous methodologies to examine other facets of urban networks, including economy, information, knowledge, and beyond. Subsequent studies should aim to address these limitations and delve more deeply into these issues.
## 2 Materials and Methods
### Dataset  
The Human Connectome Project (HCP) [38] is a consortium that studies brain connectivity in healthy adults and releases all relevant data. It provides MR images collected in many modalities, including the structural T1 and diffusion-weighted images employed throughout this study. HCP T1-weighted acquisition was performed using a 3D magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence (repetition time (TR)/echo time (TE) = 2400/2.14 ms, flip angle (FA) = 8\({}^{\circ}\), field of view (FOV) = 224 mm, 0.7 mm isotropic resolution, 256 slices), whereas the diffusion-weighted images were acquired using a Steiskal-Tanner monopolar diffusion-encoding scheme (TR/TE = 5500/89 ms, FA = 160\({}^{\circ}\), FOV = 210 mm, 1.25 mm isotropic resolution, 111 slices, b-values = 1000/2000/3000 s/mm\({}^{2}\) with 90 non-collinear gradient directions for each b-value and 18 b0 volumes) [38]. The consortium provides minimally pre-processed data for both modalities [39]. T1-weighted MRI data were corrected for gradient, readout, and bias field distortions, whereas dMRI data includes b0 intensity normalization and correction for susceptibility-induced b0 fields, eddy current, subject motion, and gradient distortions.  
Although the HCP dataset encompasses data from more than 1200 subjects, a total of 1065 subjects had scans for both diffusion- and T1-weighted MRI. As 2 subjects were discarded due to issues with the registration process, the dataset used throughout this study includes 1063 subjects.  
The dataset is fairly balanced with respect to subjects' sex, and has an age distribution from 22 to 35 years (Figure 5).  
Figure 4: Overview of data processing pipeline and its use for model training: Computation of diffusion tensor maps, registration of images to diffusion space, automated and manual mask generation, registration of masks to diffusion space, CNN training, and fine-tuning.
# Temperature Sensing with Nd\({}^{3+}\) Doped YAS Laser Microresonators
## 3 Materials and Methods
### Temperature Calibration  
The emission spectrum response to the temperature of the YAS:Nd\({}^{3+}\) doped glass was obtained using the experimental set-up described in Reference [9]. The sample was locatedinside a tubular horizontal furnace, where the excitation of the Nd\({}^{3+}\) ions was carried out from one side of the furnace with a 532 nm continuous wave diode pumped solid state laser (DPSSL) at 8 mW to avoid optically heating the sample. The luminescence from the sample was collected from the opposite side of the furnace and was analyzed with a high-resolution CCD spectrograph (Andor SR-3031-B CCD Newton DU920N manufactured in Ireland). A high pass filter was placed in the emission collection side to eliminate the excitation beam. The heating was performed at a rate of 1 K min\({}^{-1}\) from room temperature to 473 K. The temperature calibration was obtained employing the FIR technique on the emission spectra.