esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Melody Extraction Based on Extreme Learning Machine
## 4 Experimental Results and Discussions
### Experimental Results on Test Sets  
The performance of the proposed method is compared with some typical methods, including Melodia [7], the source/filter model incorporated with the contour characteristics (BG1) [9], the harmonic cluster tracking (HCT) [29], the probabilistic model (PM) [6], the modified Euclidean algorithm (MEA) [10], and the particle filter and dynamic programming (PFDP) [11] methods. These methods were chosen since they are typical methods and we could get their source codes or Vamp plug-in to assure the results were unbiased. In more detail, we used the Vamp plug-in of Melodia, the source codes of BG1, HCT, and PM, provided by the authors. MEA and PFDP are two methods proposed by us before. The detailed results and discussions of these methods are elaborated in this subsection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Neuron Number** & \begin{tabular}{c} **Training** \\ **Accuracy (\%)** \\ \end{tabular} & \begin{tabular}{c} **ValidationAccuracy** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} &
\begin{tabular}{c} **Validation** \\ **Time (s)** \\ \end{tabular} \\ \hline
1000 & 73.56 & 67.40 & **193.16** & **11.77** \\
2000 & 77.79 & 68.80 & 594.48 & 24.73 \\
3000 & 80.20 & 69.45 & 1236.82 & 37.34 \\
4000 & 83.58 & 69.87 & 2300.94 & 50.33 \\
5000 & 84.91 & **70.04** & 3786.51 & 63.95 \\
6000 & 85.90 & 69.87 & 5698.43 & 71.48 \\
7000 & **86.35** & 69.59 & 8112.62 & 81.07 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Influence of hidden neuron number on the overall accuracy(OA).
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 5 Performance Evaluation
### Results and Discussion
#### 5.3.3 Average Handover Delay  
We measured the average handover delay experienced by the UE in two handover scenarios with different methods. Figure 6 gives the comparison results. We note that the average handover latency is independent of the buffer size. It can be seen from Figure 5(a),b that under the same buffer size, BDMM performs better than ProPull in terms of average handover delay and is similar to LPBMMS. The difference in average handover delay between BDMM and ProPull is reflected in the fact that BDMM knows the latest location of the UE sooner than ProPull. The update signaling of ProPull needs to go through the propagation process from the UE to the source access router. To obtain a new network address, BDMM needs to go through the propagation process of the Mobile Event Message from the source base station to the source access router and the process of the source access router querying the NRS for the new address (the query occurs in the cross-UPF handover scenario). NRS can provide low-latency deterministic query services. Therefore, in order to ensure handover delay, the NRS needs to be deployed as close to the access router as possible so that the access router can obtain the latest network address of the UE as soon as possible. When the LPBMMS prediction is successful, the predicted target base station detects the attachment of the UE and can directly forward the packet. Therefore, if the prediction is successful, the handover delay of LPBMMS is approximately the time from the UE leaving the source base station to it attaching to the target base station (\(T_{ST}\)), which is better than the handover delay of BDMM. In the case of LPBMMS prediction failure, the handover delay depends on the propagation delay of broadcast prefix signaling throughout the network and the transmission delay of subsequent packet forwarding to the new location. In this simulation topology, when the prediction success probability is 50%, the average handover delay of LPBMMS is better than that of BDMM.  
Figure 5: Comparison of weighted average packet loss rates in different scenarios: (**a**) cross-UPF handover scenario; (**b**) non-cross-UPF handover scenario.
# A New Sensor System for Accurate 3D Surface Measurements and Modeling of Underwater Objects
## 2 Materials and Methods
### 3D Model Generation  
Using the initial estimation of the motion trajectory by visual-inertial odometry, a registration strategy with several steps is used for further refinement of the trajectory.  
In preparation for the registration, the 3D data are filtered to reduce the size of the 3D point cloud and achieve an equalized spatial distribution of the chosen points.  
Every 3D point cloud is registered sequentially against its predecessors using an iterative closest point algorithm (ICP), with the goal of local optimization of the trajectory and improvement of the resulting 3D map. For this, a metascan created from a sliding window of registered preceding scans is used to provide more structure during ICP registration. The search radius is selected to be quite small (a few centimetres), depending on the velocity of the sensor, to reduce the risk of rough registration errors because of the limited field of view and the expected weak geometric structure of the scene.  
Because the remaining residual errors accumulate and the existing drift of visual-inertial odometry is not completely eliminated, a second registration with a continuous-time ICP method is performed in the next step. The basic idea is that the error of the trajectory in temporal proximity of a considered pose is negligible. The trajectory is then split into subsections, and several successive 3D scans around a chosen reference scan are combined to form a partial map. These partial maps are again registered against their predecessors. The change in pose of a reference scan is then distributed to the poses between two reference scans to maintain the continuity of the trajectory. For small changes, a linear distribution (translation) or SLERP (rotation) is sufficient. To correct the accumulated drift, loops are detected and closed. For this purpose, the poses of the aggregated submaps are optimized, and the changes are subsequently analogously distributed to the individual poses. The resulting map is provided for live visualization during the measurement process. Post-processing of the data to create the final 3D point cloud is performed with continuous-time SLAM [50].
## 2 Materials and Methods
### Instrument  
A cavity ring-down spectrometer (CRDS; Model G2301, Picarro, Inc., Santa Clara, CA, USA) was used to measure atmospheric CO\({}_{2}\) and CH\({}_{4}\) mole fractions continuously. The air was collected from the inlet on the roof of the building at CRCS (-5 m above the ground) into the Picarro G2301 spectrometer which was placed in the room at 25 \({}^{\circ}\)C. Both CO\({}_{2}\) and CH\({}_{4}\) mole fractions were reported as atmospheric dry air fractions. The Picarro G2301 spectrometer was calibrated by the standard gas (traced to WMO CO\({}_{2}\) X2007 and CH\({}_{4}\) X2004A scale) obtained from China Meteorological Administration before and after the campaign. Furthermore, another working standard was used to check the drift at CRCS. Meanwhile, a WS500 Weather station (Lufft GmbH, Fellbach, Germany) was used to measure temperature, relative humidity, pressure, wind speed, and direction close to the sampling inlet on the roof of the building at CRCS.  
A portable Fourier transform spectrometer (EM27/SUN, SN134, Bruker Optical GmbH, Ettlingen, Germany) was used to measure solar spectra at a resolution of 0.5 cm\({}^{-1}\). CO\({}_{2}\), CH\({}_{4}\), O\({}_{2}\), and H\({}_{2}\)O spectra were measured by an InGaAs (Indium Gallium Arsenide)  
Figure 1: Location of the CRCS observation site in the Gobi Desert in Dunhuang, western China. The subfigure (**B–D**) are the enlarged area in the red box of subfigures (**A–C**) respectively. The base map was obtained from Geoq, and the base figures were obtained from Google Earth, Image Landsat/Copernicus, Image 2021 © CNES/Airbus; Image © 2021 Maxar Technologies.  
detector between 5000 and 12,000 cm\({}^{-1}\), while CO spectra were measured by an extended InGaAs detector below 5000 cm\({}^{-1}\)[12]. The EM27/SUN was placed on an outdoor desk (\(\sim\)1 m above the ground) at the CRCS. The EM27/SUN was installed manually in the morning of the sunny days and moved back to the building in the later afternoon.  
CO\({}_{2}\) and CH\({}_{4}\) mole fractions were continuously monitored by Picarro G2301 spectrometer from 15:00 on 26 June 2021 to 8:00 on 20 July 2021, as well as the meteorological parameters by the weather station. Because EM27/SUN requires manual operation and can only be observed on sunny days, there are 12 days (26 and 28 June, 7, 8, 11, 12, 14-19 July) observations of XCO\({}_{2}\), XCH\({}_{4}\), and XCO at CRCS.
## 3 Results and Discussion
### Monitoring of the Added Strains  
The development of the added strains was monitored at several cheese production steps collecting 347 isolates which were identified and typed using a polyphasic approach combining microscopic inspection and RAPD-PCR analysis. This approach is commonly applied to monitor the added starter cultures in dairy products [20; 42]. Microscopic inspection confirmed that all isolates were cocci with cells organized in short chains, typical of lactococci [15]. The direct comparison of the polymorphic profiles clearly showed the dominance of _L. lactis_ Mise36, Mise94, Mise169 and Mise190 both in control and experimental cheees, excluding any negative influence of GPP.  
Figure 4: Differentiation of lactic acid bacteria (LAB) isolates from pasteurized ewes’ milk. (**a**) Dendrogram obtained with combined randomly amplified polymorphic DNA (RAPD)-PCR patterns of the LAB strains identified; (**b**) phenotypic grouping of the LAB isolates based of morphological, physiological and biochemical traits. Abbreviations: _E., Enterococcus_; _Ls., Limosilactobacillus_; R, rod; C, coccus; s.c., short chain; n.d., not determined.
# Explainable Lightweight Block Attention Module Framework for Network-Based IoT Attack Detection
## 3 Proposed Methodology
### Feature Extraction by Pyramid Atrous Attention Module  
The attention network plays a crucial role in extracting effective features from the preprocessed data. This sort of network is built upon encoder and decoder structures, consisting of five stages. The initial three stages employ \(1\times 1\) convolution layers for the convolution process, while the subsequent two stages utilize atrous convolution with 3 \(\times\) 3 convolution layers. A ReLU layer is introduced between the two convolution layers to generate nonlinear representations, capturing low-level specific features.  
To upscale the high-level feature maps within all residual blocks, the deconvolution technique is employed. Ensuring uniform feature map sizes is necessary for conducting feature fusion operations. Subsequently, the convolutional block attention module (CBAM) [5] is incorporated into the lateral connections to fine-tune the feature maps layer by layer. This integration aids in reducing false detections and enhancing feature extraction accuracy.  
The CBAM module holds the capability to enhance network feature learning and can be seamlessly integrated into any network architecture. In our approach, we incorporated a CBAM module after each dense block to refine the features and bolster the network's ability to represent features effectively. Please refer to Figures 2 and 3 for a visual representation of this process.  
Overall, the attention network leverages encoder and decoder structures, employing convolution and atrous convolution layers to extract features. The deconvolution technique is used to upscale feature maps, and the CBAM module is added to adjust feature maps and improve feature representation accuracy. This comprehensive approach enhances the network's capability to learn and represent features accurately.
## 2 Methods
### Object-Based Image Analysis (OBIA)  
OBIA was chosen to classify land use and land cover (LULC) from the Pleiades image. With OBIA, a remote sensing image is subdivided into image segments consisting of neighboring pixels [9]. A multi-resolution segmentation approach was applied for the delineation of image objects [10; 11]. In this bottom-up approach, region merging is applied starting from pixel level. The segmentation process was enhanced by an edge layer, derived with the Canny edge operator [12; 13]. The resulting image segments were classified with a rule-based approach. The parameters used for the ruleset were the spectral information of the Pleiades image, the three first principle components and the Normalized Differenced Vegetation Index (NDVI). A thematic road layer (revised OpenStreetMap layer [14]) was used to delineate traffic infrastructure from building objects. Of the resulting LULC classification, only the class representing built-up objects was considered for further processing. Based on the building class thresholds (Table 1) and the segment properties, a preliminary classification of the building footprints was conducted.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 1 Introduction
### Contributions and Outline of the Paper  
This review paper primarily aims to investigate the following:  
* To conduct a comprehensive study to determine security issues emerging from the increased usage of mobile devices in cloud computing, identifying significant concerns and potential vulnerabilities.
* To review the scalable and secure connection for controlling and protecting data flow between mobile devices and cloud-based services while assuring the integrity, availability, and security of the data.
* To provide a detailed overview of the existing SDN-NFV and CASB strategies and how they are implemented in MCC.  
This paper attempts to resolve the following questions:  
1. What unique security concerns are arising due to the increasing use of mobile devices for cloud computing, and what types of solutions might successfully mitigate these challenges?
2. How can an organised distributed firewall and controller system offer full security for mobile devices and their interactions with cloud services?
3. What techniques and design concepts should be used to provide an effective and reliable system for controlling and protecting data flow among mobile devices and cloud resources?
4. To what extent do the established distributed firewall and controller systems improve mobile device and cloud service security?  
**Outline of the paper:** The remaining part of this paper is organised as follows: Section 2 discusses the criteria and methods used to evaluate the security and performance of SDN, NFV, and CASBs. Section 3 details the principles, functionalities, advantages, and challenges of each technology within the MCC context. In Section 4, the research challenges and future research trends are discussed. Section 5 summarises the key findings of the review, reaffirming the necessity for advanced security frameworks in MCC.
# Low-Frequency Sound Absorption in Air Pipelines and Water-Filled Pipelines Based on Metamaterials
## 2 Design Paradigm and Theoretical Method
### Structure of the Low-Frequency Asymmetric Absorption Metamaterials for Air Pipeline  
Figure 2a illustrates the schematic diagram of the suggested metamaterial designed for asymmetric absorption. The metamaterial is constructed from a rectangular pipe with attached HRs cascading in the z-direction. The pipe's side lengths are established as \(l_{\mathrm{x}}\) = 50 mm and \(l_{\mathrm{y}}\) = 50 mm. Figure 2b depicts the cutaway perspective of the asymmetric absorbing metamaterial. In the schematic, \(r_{\mathrm{i}}\) and \(l_{\mathrm{ni}}\) denote the radius and length of the \(i\)th HR neck, and \(i\) (=1, 2, 3, 4, 5, 6, 7, 8) signifies the HR count (as listed in Table 1). \(D_{\mathrm{i}}\) and \(l_{\mathrm{ci}}\) refer to the diameter and depth of the \(i\)th HR cavity. \(l\) = 56 mm indicates distance between two successive HRs. Figure 2c illustrates the operating principle of an asymmetric sound-absorbing metamaterial. The metamaterial features two sides, designated as side 1 (left) and side 2 (right) for HR-1 and HR-2, respectively. When waves are incident from left to right, the metamaterial demonstrates efficient absorption of the wave energy. Conversely, when these sound waves traverse from the other side, the metamaterial turns highly scattering, leading to the reflection of most of the input sound waves.  
All HRs in this paper are numbered, and the corresponding geometric parameters are listed in Table 1.  
Figure 2: (**a**) Schematic diagram of the asymmetric sound-absorbing metamaterials. (**b**) The cutaway view of the asymmetric sound-absorbing metamaterials along with geometric parameters. The thick red and blue arrows represent the directions of the incident sound. (**c**) Illustration of the asymmetric sound-absorbing metamaterial principle. I: incident; R: reflected; T: transmitted.
## 5 Results and Discussion
### Model Evaluation Using Test Images  
Figure 19 compares the validation performance of each model-architecture combination using the different augmentation techniques. Specifically, Figure 17(a) illustrates the AP50 and AP50:95 results, while Figure 17(b) presents the corresponding IOU metrics. Overall, contrast augmentation emerged as the most consistently beneficial choice, providing reliable accuracy gains across diverse backbones and aiding generalization to unseen images. However, shearing augmentation occasionally surpassed contrast augmentation in specific architectures, indicating that while contrast augmentation is a safe universal option, shearing augmentation can deliver even better performance under certain conditions.  
Figure 20 compares the validation results to the test results for these final, optimized models to assess potential overfitting. Here, the validation and test scores remained closely aligned, suggesting that neither the Faster R-CNN with ResNet-101 nor YOLOv10 with PVT showed large performance differences between the two data splits. In other words, the model retained its efficacy outside the training or validation domain, thereby indicating minimal overfitting. Since both the validation and test scores were consistent, the figure confirms that the observed gains--particularly those from contrast augmentation--are reliable and not artificially inflated by model over-adaptation to the validation data.  
Figure 18: Difference between each technique and baseline (IOU).
## 1 Introduction
Recently, various deep learning-based super-resolution models have been successfully applied to the processing of astronomical images. For instance, [PERSON] et al. (2020) [14] enhanced the clarity of SDO/HMI images using two deep learning models, and the results were consistent with those from the Hinode magnetograms. [PERSON] et al. (2022) [15] provided solar EUV images with higher temporal resolution for space weather forecasting and solar physics research through super-resolution technology. [PERSON] et al. (2023) [16] further confirmed the occurrence of magnetic reconnection within the braided structures of the corona by applying super-resolution processing to SDO/AIA images. While the high-resolution coronal images provided by Hi-C have led to new discoveries for researchers, its intermittent observation times and limited coverage of solar regions pose certain limitations. In contrast, SDO/AIA can provide continuous and comprehensive coronal observation data. Hi-C captured solar coronal activities at wavelengths of 193 angstroms and 172 angstroms, while AIA continuously observes the entire sun at wavelengths of 193 angstroms and 171 angstroms. Due to the similarity of their central wavelength data, it is necessary to register and calibrate the images to achieve better super-resolution results. Image registration effectively eliminates heterogeneity in the original data, allowing us to use popular deep learning neural networks to learn the mapping relations of images, thereby enhancing the spatial resolution of AIA images to match the level of Hi-C. In this paper, we utilized SDO/AIA images as low-resolution samples and Hi-C images as high-resolution samples. We constructed our dataset using SIFT image registration technology, simulating the scenario in actual scientific research where different devices capture solar astronomical images concurrently. Inspired by the ESRGAN [17] model, which offers decent image perceptual quality but performs poorly in pixel metrics, result ing in excessive smoothing effects during the super-resolution reconstruction of coronal images, there was a loss of coronal details, particularly in texture-rich regions. Additionally, the ESRGAN model employs a substantial number of dense connections, which leads to a large parameter size and, consequently, the model training requires extensive computational resources. We proposed a new super-resolution method, SAFCSRGAN, and designed a hybrid channel-spatial attention mechanism that enhanced the model's capability to capture subtle coronal features. We also optimized the loss function, significantly improving the model's performance on key image quality assessment metrics and surpassing other models in perceptual quality. Compared to ESRGAN, our model's parameters were reduced by 21%. Experimental results confirmed the effectiveness of our proposed method.
# A Circularly Polarized Antenna Array with Gain Enhancement for Long-Range UHF RFID Systems
## 1 Introduction
Series-fed arrays have been already proposed in the literature [15; 16], but in this case, the series configuration was employed to implement the SR method. Moreover, the feed network was designed with substrate-integrated coaxial line (SICL) technology [17; 18; 19]. It is shown that the two-level SR feed network implemented with SICL technology not only guarantees the achievement of an enhanced gain performance with respect to one-level configuration (or with respect to microstrip technology feed network because of a reduced insertion loss), but also minimizes the gain degradation within the working bandwidth, yielding to a wider 3 dB gain bandwidth. The microstrip patch height and array interelement distance were optimized to maximize the gain and guarantee the required bandwidth. A prototype was fabricated with a size of \(440\times 440\times 22.5\) mm\({}^{3}\) (\(1.32\times 1.32\times 0.068\)\(\lambda^{3}\)), and the measurement results show a peak gain of 12.5 dBic, and an overlapped bandwidth (defined as the spectrum portion in which \(|\)S\({}_{11}|\leq-10\) dB impedance bandwidth, AR bandwidth, and 3 dB gain bandwidth overlap) of 18.2% (828-994 MHz), which covers the universal UHF RFID 840-960 MHz bandwidth). Furthermore, even if not experimentally proven, it is shown that better peak gain performance can be achieved at the expense of a bandwidth reduction. A comparison with other existing high-gain wideband CP antenna arrays reveals that the proposed antenna offers an excellent trade-off between gain and antenna size, also demonstrated by comparing the theoretical limits of antennas in terms of \(Q\)-factor and aperture efficiency.