esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 3 QR\(-\)LoRa Design  
### System Overview  
The system overview of QR\(-\)LoRa is shown in Figure 4, including the transmitting and receiving modules.  
**Tx.** First, the transmitter generates a standard LoRa chirp with the frequency encoded \(F_{0}\). Then, a ramp signal with the amplitude encoded \(T_{0}\) is generated. They are multiplied to form a QR\(-\)LoRa chirp. We organize the QR\(-\)LoRa package according to the LPWAN protocol.  
**Rx.** The receiver first identifies the QR\(-\)LoRa packet from the noisy received signal based on the Start-of-Frame Delimiter (SFD). To highlight the features of SFD, we make the envelope of _downchirp_ constituting SFD constant at 1. Then, the receiver determines the starting position of the data packet through the cross-correlation operation and separates the data packet. Next, Carrier Frequency Offset (CFO) compensation is performed on the separated data packets by leveraging the preamble. Finally, each chirp is read from the payload of the packet.  
The demodulation of QR\(-\)LoRa includes two steps: first, frequency decoding, and then amplitude decoding. The traditional dechirp is adopted to perform frequency demodulation. For the amplitude demodulation, first, the envelope of QR\(-\)LoRa is extracted. Then the differential operation is performed on the envelope to achieve the exact amplitude jump position, which carries the amplitude-encoded information.
# Machine Learning-Based Paging Enhancement in 5G Network
## 3 Proposed Machine Learning-Based Paging
### Proposed Paging
#### 3.3.1 Proposed Algorithm
In our work, the KNN model for classification was selected in the AMF of this paper owing to its high accuracy, simple code implementation, and low computation complexity compared to other models. The KNN model did not need any additional processing of the data, but the simple comparison with the reference provided a low-cost, simple but effective method. As shown in Figure 6, the proposed probabilistic paging algorithm applied with machine learning in the AMF was categorized into two functions, as follows: The first function obtained the UE profile information in the AMF and conducted a UE classifier model applied using machine learning. In the paging experiment, when paging was attempted toward the target UE, the weight ratio of the UE group, which was obtained by performing the UE classifier model, was used to select the appropriate gNodeB. The second function obtained the UE movement around the gNodeBs and then classified the UE movement acquired by the gNodeB. The transition probability for each gNodeB was sorted in descending order to perform the probabilistic paging method. By periodically performing these processes at the AMF, each gNodeB updated the transition probability using the latest UE movement data that was changed most recently. The paging method with KNN-based UE classification and UE movement prediction for gNodeBs was performed by substituting the second step of the typical paging range profile shown in Figure 7.  
Figure 5: Classification accuracy for UE grouping. Therefore, when the AMF used the gNodeB list for the second step of paging, the paging range was selected by the UE classifier applied with the KNN model and the probabilistic gNodeB list proposed herein. When the proposed paging was not activated, as shown in Figure 7, the AMF attempted paging based on the latest visited gNodeB list in terms of the second paging range.
## 3 Methodology
### Problem Definition
#### 3.1.3 Character Prediction Decoder  
This decoder is used to predict the character in the correct sentence \(Y\) based on the coded output H. Specifically, given each input character \(x_{i}\), we first project its coded output \(h_{i}\) into the character-specific feature space (\(GeLu\) is a Gaussian error linear unit activation function, and its derivative is continuous, which makes it easier to propagate the gradient when training a deep neural network, avoiding the problem of discontinuity of the derivative at zero, thus reducing the problem of gradient vanishing during training):  
\[h_{i}^{c}=GeLu(W^{c}h_{i}+b^{c}) \tag{1}\]  
The corresponding correct character \(\hat{y}_{i}\) is then predicted based on the projected output _Softmax_ is an activation function that normalizes a numerical vector into a probability distribution vector, where the sum of all probabilities is equal to 1. It is commonly employed as the final layer in neural networks, particularly for the output in multi-class classification problems. The Softmax layer is often used in conjunction with the cross-entropy loss function:  
\[p(\hat{y}_{i}|X)=softmax(W^{y}h_{i}^{c}+b^{y}) \tag{2}\]  
Figure 4: ChineseBERT framework.  
where \(W^{c}\in R^{D\times D},b^{c}\in R^{D}\) is the learnable parameter of the character-specific feature projection layer; \(W^{y}\in R^{V\times D},b^{y}\in R^{V}\) is the learnable parameter of the character prediction layer; and \(V\) is the vocabulary size.
## 3 Results
### Limitations of VMFDI  
The VMFDI, with a 1 km resolution, effectively monitors regional drought, particularly in agroecosystems, and is more sensitive than the SPEI, GRACE-DSI, and individual indicators such as VPD, SM, and SIF. However, it has some limitations. The GOSIF downscaling product used in this study lacks high spatial resolution and long-term coverage, which may introduce uncertainties. Future work will refine downscaling techniques, such as the use of machine learning, to minimize these effects. Additionally, while the VMFDI was applied at the monthly and annual scales in this study, future research could explore multi-scale assessments to enhance decision support for agriculture and water management. A further validation of the VMFDI across diverse regions and agroecosystems is also needed. Ultimately, these limitations can be addressed by improving data quality and optimizing the model. As remote sensing technologies advance, the VMFDI has the potential for more accurate drought monitoring across larger regions and over extended time periods.
# A GIS Partial Discharge Defect Identification Method Based on YOLOv5
## 5 Case Study  
This chapter analyzes the effectiveness and practicality of the YOLOv5 algorithm in the field of GIS partial discharge defect detection. The trained GIS partial discharge defect detection model is applied to the field inspection.  
The partial discharge online monitoring of a 330 kV GIS substation found that there was an abnormal UHF signal at the insulated basin location of a bus section. The signal is concentrated in the positive and negative half-period of the working frequency, and has certain symmetry, low number of discharges, low cycle repeatability, and scattered discharge amplitude. The partial discharge signal is monitored by adding UHF sensors to the insulated basin of the GIS busbar, and the PRPD map is obtained. The PRPD map signals of different locations and the identification results of GIS partial discharge defects are shown in Figure 13.  
After determining the type of partial discharge and the location of partial discharge of GIS equipment, equipment disassembly and inspection were carried out. By inspecting each metal conductor and insulator piece by piece, the technicians finally found multiple obvious bubble-like projections on the concave side surface of the second basin insulator of busbar II, as shown in Figure 14.  
Figure 12: Multi-defect detection results. (**a**) metal tip defects, free metal particle defects and insulating void defects, (**b**) floating electrode defects and insulating void defects, (**c**) insulating void defects and free metal particle defects, (**d**) metal tip defects and insulating void defects.
# Interest Flooding Attacks in Named Data Networking and Mitigations: Recent Advances and Challenges
## 3 Interest Flooding Attack: Concept and Taxonomy  
An IFA in NDN represents a significant security challenge, exploiting the unique architecture of NDN to degrade network performance and deny service to legitimate users [18]. Unlike traditional IP-based DDoS attacks that overwhelm targets with excessive data traffic, IFAs manipulate NDN's stateful forwarding plane by inundating routers with a high volume of interest packets for non-existent or unpopular content. This forwarding exploitation leads to the saturation of PIT in routers, consuming critical resources such as memory and processing power, and ultimately hindering the network's ability to process legitimate requests [18].  
The stateful nature of NDN's PIT, which records each forwarded interest packet until the corresponding data packet is returned or the request times out, is central to the architecture's efficient data retrieval [6]. However, this recording feature also introduces vulnerabilities, as malicious actors can exploit it by sending interests that cannot be satisfied, causing the PIT to overflow and legitimate interests to be dropped [8], as shown in Figure 2 where a malicious consumer and a producer collude, bypassing IFA detection by replying to malicious interests, keeping PIT entries occupied. This PIT state contrasts with the stateless forwarding in traditional IP networks, where routers do not maintain such per packet state, making them less susceptible to this specific type of attack [18].  
_Collusive Interest Flooding Attacks (CIFAs)_ are a behavior pattern within the broader category of IF, where multiple malicious consumers coordinate their interest traffic to intensify the strain on network components, especially the PIT. CIFAs involve deliberate synchronization among attackers more than IFAs, which stem from isolated or loosely timed flooding attempts. They may request the advertised content of each other or use closely related name prefixes to simulate plausible communication patterns. This coordination enables them to bypass basic rate controls and evade detection methods that rely on isolated traffic anomalies. As shown in Figure 2, CIFAs differ visually from standard IFAs through the presence of multiple interacting sources that form a loop of unresolved interests, rather than a one-directional flood. The taxonomy in Table 1 and Figure 3 accounts for the CIFAs. We do not isolate them as a separate category. Their mitigation shares overlap with existing strategies for distributed IFAs, and treating them as a behavioral variant offers a more practical framework for both detection and response.
## 3 Results and Discussion
### The Fourier Transform Infrared (FTIR) Test
#### 3.2.1 Oxygen-Containing Compounds  
The oxygen-containing compounds are alcohols and phenols, ethers, aliphatic and aromatic ketones, esters and carboxylic acids and anhydrides [30]. Carbonyl stretching is one of the popular known bands used to evaluate the ageing of bitumen [6; 31; 32]. It is well known that development and increase in the carbonyl groups indicates an increase in the number of large molecules in the bituminous binder, resulting in higher stiffness and more solid-like behavior as a result of ageing [6]. It is usually the most intense band in the spectrum, and depending on the type of C=O bond, it occurs in the 1830-1650 cm\({}^{-1}\) region. It may be absorbed above 2000 cm\({}^{-1}\), under consideration at 2923 cm\({}^{-1}\) and 2856 cm\({}^{-1}\)[33; 34]. Aliphatic and aromatic ketones show carbonyl bands at 1730-1700 and 1700-1680 cm\({}^{-1}\), respectively, while aromatic aldehydes produce carbonyl bands in the range of 1720-1680 cm\({}^{-1}\)[30].  
The carboxylic acid observed at a wave number of 1694 cm\({}^{-1}\), representing the formation of C=O, is responsible for increasing bitumen hardening; researchers have stated that the formation of the carbonyl group is correlated to an increase in the asphaltene after ageing [6; 9; 35]. Oxidation creates carbonyl compounds by oxidizing aromatic compounds. The formation of carbonyl groups results in an increased asphaltene fraction, which has been proven to be the one of main reasons for an increase in the binder viscosity [6; 35].  
The absorbance peak at the wave number of 1694 cm\({}^{-1}\) represents the carbonyl acid absorbance value for the modified binders before and after ageing and is presented in Figure 4, where Figure 4,b show the FTIR spectrum results of the band and its absorbance values, respectively. It is notable that an ageing effect was observed for all the modified binders, where the band of carboxylic acid was formed. However, the formation of this band varied between STA and LTA for the binders, showing that the most sensitive binder was 0 NCMB, where the value of the absorbance peak was higher than 2 NCMB and 4 NCMB after STA, by 39.02% and 29.27%, respectively. This means that an improvement against ageing occurred after adding NC particles. In addition, after LTA, the absorbance peak for 0 NCMB was higher than for the rest of the modified binders, but not as much as after STA, where the increase of absorbance for 0 NCMB was higher than 2 NCMB and 4 NCMB by 6.45% and 9.68%, respectively. The formation of carboxylic acid at a wave number of 1694 cm\({}^{-1}\) was varied due to the addition of NC particles and ageing. The improvement in oxidation ageing resistance for 2 NCMB and 4 NCMB might be related to a unique property of NC, namely heat absorption. NC particles absorb heat and therefore reduce the effect of temperature on the bitumen structure during ageing and protect it from oxidation. This fact has been observed in previous studies where NC has been investigated [28; 29; 36].
# GAN Training Acceleration Using Frechet Descriptor-Based Coreset
## 4 Experiments and Results
### Experiment II: Validation Experiments Using 3D Human Kidney MR Images
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **Entire Dataset** & \begin{tabular}{c} **IED-Coreset** \\ (\(k\) = 10)** \\ \end{tabular} & \begin{tabular}{c} **FDD-Coreset** \\ (\(k\) = 10)** \\ \end{tabular} & \
\begin{tabular}{c} **FDD-Coreset** \\ (\(k\) = 20)** \\ \end{tabular} &
\begin{tabular}{c} **FDD-Coreset** \\ (\(k\) = 30)** \\ \end{tabular} \\ \hline \(PSNR\) & 12.539 \(\pm\) 0.143 & 13.000 \(\pm\) 0.753 & 12.084 \(\pm\) 0.423 & 15.758 \(\pm\) 0.373 & 11.489 \(\pm\) 0.216 \\ \(DER\) & 0.091 \(\pm\) 0.037 & 0.124 \(\pm\) 0.131 & 0.171 \(\pm\) 0.156 & 0.219 \(\pm\) 0.061 & 0.052 \(\pm\) 0.037 \\ Precision & 0.941 \(\pm\) 0.013 & 0.759 \(\pm\) 0.069 & 0.671 \(\pm\) 0.056 & 0.923 \(\pm\) 0.018 & 0.827 \(\pm\) 0.024 \\ Recall & 0.855 \(\pm\) 0.033 & 0.781 \(\pm\) 0.052 & 0.771 \(\pm\) 0.042 & 0.720 \(\pm\) 0.049 & 0.845 \(\pm\) 0.032 \\ F-score & 0.896 \(\pm\) 0.018 & 0.766 \(\pm\) 0.030 & 0.715 \(\pm\) 0.028 & 0.807 \(\pm\) 0.029 & 0.835 \(\pm\) 0.014 \\ Dice & 0.825 \(\pm\) 0.017 & 0.548 \(\pm\) 0.046 & 0.662 \(\pm\) 0.014 & 0.502 \(\pm\) 0.039 & 0.671 \(\pm\) 0.022 \\ _IoU_I & 0.702 \(\pm\) 0.024 & 0.379 \(\pm\) 0.044 & 0.495 \(\pm\) 0.015 & 0.336 \(\pm\) 0.035 & 0.505 \(\pm\) 0.025 \\ _PB_ (Ground Truth: 0.519) & 0.538 \(\pm\) 0.279 & 0.609 \(\pm\) 0.302 & 0.577 \(\pm\) 0.308 & 0.583 \(\pm\) 0.290 & 0.610 \(\pm\) 0.298 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison (avg \(\pm\) std) of BlobGAN trained on entire dataset (1000 Random Samples), IED-Coreset (\(k\) = 10) and proposed FDD-Coreset (\(k\) = 10, 20, 30) on 3D synthetic images. differences between the results of these three training approaches and stereology data are also listed in Tables 5 and 6. As shown in Tables 5 and 6, \(N_{gdom}\) and mean \(aV_{gdom}\) derived from the BlobGAN trained on the FDD-Coreset with \(k=10\) provides a smaller difference ratio with stereology than the BlobGAN trained on the IED-Coreset with \(k=10\). While the BlobGAN trained on the entire dataset has the smallest difference ratio with stereology, it takes 96% more computational time than the FDD-Coreset with \(k=10\), as shown in Table 4. We could conclude that the FDD-Coreset not only significantly reduces the training time, but also maintains the approximate performance of glomerular segmentation compared with training on the entire dataset.
## 3 Results and Discussion
### Techno-Economical Analysis
These factors contribute to its stronger financial indicators, including a gross margin of 11.65%, an ROI of 62.61%, and an IRR of 53.36%, which reflect the process's ability to convert investments into substantial returns. The faster payback time (1.60 years) and significantly higher NPV (USD 103,815,276) indicate that ethanol extraction not only recovers its costs quickly but also generates long-term value. The disparity in performance arises from ethanol extraction's ability to handle larger production volumes, achieve better cost efficiencies, and capitalize on economies of scale, which offset its higher capital and operating costs. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameter** & **Methanol Extraction** & **Ethanol Extraction** \\ \hline Investment charged to this project & USD 21,633,680 & USD 27,867,186 \\ Annual operating cost & USD 131,959,686/year & USD 207,987,258/year \\ Annual revenues & USD 134,902,668/year & USD 235,414,870/year \\ Unit production ref. rate & 84.31 kg MP \({}^{1}\) /year & 147.13 kg MP \({}^{1}\) /year \\ Unit production cost & USD 1,565,095/kg MP \({}^{1}\) & USD 1,413,588/kg MP \({}^{1}\) \\ Unit production revenue & USD 1,600,000/kg MP \({}^{1}\) & USD 1,600,000/kg MP \({}^{1}\) \\ Gross margin & 2.18\% & 11.65\% \\ Return on investment (ROI) & 12.75\% & 62.61\% \\ Payback time & 7.84 years & 1.60 years \\ Internal rate of return (IRR) & 8.20\% & 53.36\% \\ Net present value (NPV, at 7.00\%) & USD 1,749,872 & USD 103,815,276 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Main product, PCB.  
\end{table} 
Table 5: Comparison of economic parameters for methanol and ethanol extraction methods. Therefore, the large-scale production of PCB using ethanol extraction demonstrates substantial profitability relative to the investment and the ability to generate immense added value even after recovering the initial investment, strongly supporting the economic viability of this approach; moreover, the optimized PCB extraction method using ethanol developed in this study proved to be more productive than the conventional methanol extraction method, while also eliminating additional disposal costs, making it a viable model for industrial-scale applications.
## 4 Experimental Results and Discussion
### Output Characteristics of Q-Switched Laser Based on PSM-SA
This surge in repetition frequency coincides with an increase in peak power, reaching a maximum of about 12.5 mW. The continuous decrease in pulse width is a notable observation, primarily attributed to the enhanced bleaching effect of the SA [43; 44]. However, it is important to acknowledge that there are inherent limitations to further reducing the pulse width. This limitation arises from the saturation effect inherent to the gain fiber within the system and the constraints imposed by the damage threshold and conversion efficiency of the PSM-SA itself. The minimum value of the pulse width is approximately 22.3 \(\upmu\)s. In terms of pulse energy, it exhibits a pronounced upward trend in tandem with the pump power's ascent, gradually stabilizing as it approaches its maximum value of approximately 28 mJ. The relationship between these parameters illustrates the dynamic interaction of output power, repetition rate, peak power, pulse width and pulse energy in the laser system with the change in pump power, reflecting the characteristics and limitations of the process of PSM-SA, providing a comprehensive understanding of its operational dynamics. Their relationship is:  
\[E_{p}=E_{ave}/f=\Delta t\times P_{\text{peak}} \tag{3}\]  
where \(E_{p}\) is single-pulse energy, \(P_{ave}\) is average output power,\(f\) is pulse repetition frequency, \(\Delta t\) is pulse width, and \(P_{peak}\) is output peak power. A novel PSM-SA operating at a wavelength of 1562 nm is successfully prepared, enabling the achievement of stable Q-switched output in an ultrafast fiber-laser system. After repeated experiments, our experimental phenomenon is stable and the results are reproducible. Furtherly, there exists a compelling avenue for further refinement and optimization of the PSM-SA. One pivotal objective is the reduction of its unsaturated loss, a factor that can significantly enhance the potential of the laser system. By addressing this aspect, we can open doors to increased mutual oscillation mode and mode selection within the laser cavity [45]. This optimization endeavor holds the promise of improving the Q value of the laser system, potentially even leading to the achievement of mode locking. Therefore, our current achievement with the PSM-SA marks just the beginning of a journey filled with possibilities. It has illuminated the innovative potential of photonic crystal technology and its application in advanced laser systems, setting the stage for future breakthroughs and advancements in this dynamic field.
## 3 Datasets and Methodology
### Datasets
#### 3.1.2 SRTM DEM  
The Shuttle Radar Topography Mission (SRTM) dataset, jointly developed by the National Aeronautics and Space Administration (NASA) and the National Geospatial-Intelligence Agency (NGA), provides freely accessible global elevation data [19]. Covering approximately 80% of the Earth's land surface, the SRTM dataset offers a spatial resolution of 30 m and a vertical accuracy of \(\pm\)16 m [20]. In this study, the SRTM Digital Elevation Model (DEM) was employed to eliminate topographic phase components induced by terrain variations during interferometric processing [21].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Orbit Altitude (km) & 700 \\ Revisit Period (days) & 12 \\ Incidence Angle (\({}^{\circ}\)) & 29–46 \\ Resolution (m) & 5 \(\times\) 20 \\ Swath Width (km) & 250 \\ Polarization Mode & VV + VH \\ Orbit Numbers & 142,106 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of Sentinel-1 satellite.  
Figure 1: Study area scope.
## 4 Neuoprotective Peptides and Their Therapeutic Application in HIE
### Studies Using CARPs in Animal Models of HIE
#### 4.4.6 P5-Tat  
P5 is a 24-amino acid peptide derived from the C-terminal region of the p35 protein (KEAFWDRCLSVINLMSSKMLQINA\({}_{254-277}\), net charge +0.9). Following cleavage of p35 by calpain, a cleavage fragment known as p25 can bind to cyclin-dependent kinase 5 (Cdk5) and alter its cellular location and stimulate its activity [137], which is known to be associated with neuronal cell death [138; 139; 140]. Administration (3,660, 7,320 and 9,761 nmol/kg: IP) of a modified P5 peptide (TFP5: KEAFWDRCLSVINLMSSKMLQINAVARARRAARR; net charge +5.9) can improve outcomes in Parkinson's disease, Alzheimer's disease, and adult ischemic stroke models in rodents [140; 141; 142].  
A dose-response assessment (0.23, 2.30, 5.76 and 11.5 nmol/kg; IP) of P5-TAT (KEAFWDRCLSVINLMSSKMLQINA-YGRKKRRQRRR, net charge +8.9) when administered 30 min before hypoxia in P7 rats demonstrated that all doses, except 0.23 nmol/kg, significantly reduced cerebral infarct volume at 48 h [143]. In addition, P5-TAT, when administered (11.5 nmol/kg; IP) 24 h after hypoxia, significantly improved behavioral outcomes up to 3 weeks post-HI. However, P5-TAT treatment following HI did not have any effect on the levels of Cdk5 activators, p35, p25 or p29, despite suppressing cleavage of caspase-3.