esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
###### Abstract
The DL results found that the BER and SE against transmitted power showed the MIMO-NOMA enhanced the BER performance for the best user U4 from \(10^{-1.7}\) to \(10^{-5.2}\) at 80 MHz bandwidth (BW), and from \(10^{-1.5}\) to \(10^{-5}\) at 200 MHz for transmitting power of 40 dBm. In contrast, the SE performance for the best user U4 is enhanced from 24 \(\times\)\(10^{-3}\) to 25 \(\times\)\(10^{-3}\) bits/second/Hz at 80 MHz BW and from 19.8 \(\times\)\(10^{-3}\) to 20 \(\times\)\(10^{-3}\) bps/Hz at 200 MHz BW. Although the outcomes for the UL were obtained in terms of average capacity rate and OP versus SNR at 80, and 200 MHz BW, the MIMO-NOMA result showed that the average capacity rate for the best user U4 performance improves by 12 bps/Hz for 1 dB SNR and the OP is reduced by 15 \(\times\)\(10^{-3}\) for 80 MHz BW and by 12 \(\times\)\(10^{-3}\) for 200 MHz BW at an SNR of 0.17 dB. As the BW increased the BER, the average capacity rate increased while the SE and OP decreased. For both DL/UL NOMA with and without MIMO, closed-form expressions for BER, SE, average capacity rate, and OP were obtained. All users' performance, even those whose connections were affected by interference or Rayleigh fading channels significantly improved, when MIMO-NOMA was implemented.
## 3 The LBP-FFT-CNNs Model
### Computational Complexity Analysis  
While the integration of FFT and self-attention mechanisms enhances feature representation and model robustness, it inevitably introduces additional computational overhead. FFT transforms spatial image data into the frequency domain, which involves \(O(NlogN)\) operations per image, where \(N\) is the number of pixels. However, due to selective frequency component retention, the overall dimensionality is reduced before entering the convolutional layers, partially offsetting the added cost.  
The self-attention module, designed to capture global dependencies across feature maps, requires \(O(n2)\) time and space complexity for feature maps of size \(n\). While more computationally demanding than standard convolution, its inclusion significantly improves the model's ability to distinguish between background textures and obstacles under challenging conditions.  
To ensure practical feasibility, we apply both FFT and self-attention only at specific stages in the network. Additionally, experiments were conducted on a Raspberry Pi 4 platform to validate real-time performance, confirming that the processing speed remains acceptable for embedded applications in indoor positioning.
# A Lightweight and Partitioned CNN Algorithm for Multi-Landslide Detection in Remote Sensing Images
## 5 Results
### Comparison of Experiments' Results with Different Backbones  
To provide a more quantitative validation of the detection accuracy of our proposed method, we conducted a set of experiments comparing it to existing mainstream lightweight object-detection algorithms. We tested several lightweight backbone architectures, including ShuffleNet, PP-LCNet [43], EfficientNetB0 [44], MobileNetV3(Small), repVGG [45], GhostNet, and our PartitionNet with the same neck structures and loss functions.  
As shown in Table 4, while the models with ShuffleNet, MobileNetV3, and PP-LCNet as backbones exhibited favorable performance with respect to the number of parameters, FLOPs, and Latency metrics on the landslide dataset, our algorithm outperformed them in terms of the P, R, AP50, and AP50:95 accuracy metrics.  
Furthermore, compared to the CIoU loss function, we found that employing the SIoU loss function in YOLOv5s resulted in a 1.5%, 1.4%, and 2.2% improvement for AP50, AP50:95, and Precision, respectively. When comparing YOLOv5s-SIoU-PAN with LP-YOLO-PAN, we observed a decrease in AP50, but not exceeded 1.1%, and an increase in Latency, but a decrease in the parameters and FLOPs of 35.6% and 56.3%, respectively. Finally, we found that incorporating a VH block and attention module to the backbone, as well as replacing the PAN module with NewPAN resulted in an increase in the AP and a decrease in the Params when compared to LP-YOLO-PAN.
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
## 3 Circuit Implementation
### Rvb  
The high-speed clock signal of the comparator, which can reach several GHz, imposes stringent requirements on the reference voltage settling time on the CDAC top plate. Typically, the settling process must be completed within a few hundred picoseconds. This requires the unity-gain frequency of the RVB to reach several GHz, which will consume a significant amount of current, even more than the ADC core circuit. To address this issue, this design uses an on-chip source follower [22; 23] structure as the RVB for the four reference voltages.  
The open-loop output stage structure provides a small output impedance, a wide bandwidth, low noise, and a small area. The following Figure 16 shows the proposed RVB. The output stage of the IQ channels replicates the second stage of the closed-loop operational amplifier, and the size of the output transistor M1R in both IQ channels is ten times that of M1. Moreover, in order to address the impact of power supply noise on the reference voltage, the output stage is implemented as a cascode structure, with VB providing the bias voltage for the stacked transistors, resulting in an improved power supply rejection ratio (PSRR). Additionally, RC filters have been incorporated into the design to minimize the effects of operational amplifier output noise.  
Figure 14: The proposed SAR Logic.  
Figure 15: The proposed tri-state gate and corresponding timing diagram of the window-opening strategy.  
With the help of redundancy technology, the output stage current is determined to be 1 mA. This design also uses a switch controlled by the signal EN_RVB to separate VREFN and VSS. Since there is only a single-point connection between VREFN and VSS on the layout and the wiring matching VREF1/2/3/4 with VREFN is in parallel, this can make the ratio between the four positive reference voltages VREF1/2/3/4 and the negative reference voltage VREFN more matched. In addition, since the mismatch between the resistors (4R, 2R, and R) at the output of RVB and the CDAC mismatch are equivalent in terms of their effects, it is necessary to ensure good matching by increasing the area of these resistors. Furthermore, dummy resistors are added around these resistors in the layout process to further improve the matching between the VREF1/2/3/4 and VREFN. The VBG, which is equal to 1.2 V and tracks the bandgap voltage, is insensitive to temperature and process variation. The output stage supply voltage of the RVB is 1.6 V.  
Table 2 summarizes the detailed noise contribution of the proposed SAR ADC, assuming no CDAC mismatch. The thermal noise and quantization noise were obtained through calculation, while the comparator input referred noise and reference noise were obtained from post-layout noise simulation.
# Backdoor Attack Against Dataset Distillation in Natural Language Processing
## 1 Introduction
into a compact distilled dataset during an initial phase, then carries out backdoor attacks against downstream models trained with the distilled data. Building on this idea, we introduce a backdoor attack strategy targeting dataset distillation within the NLP domain, which we refer to as _Backdoor Attack Method against Dataset Distillation in NLP (BAMDD-NLP)_. This method involves embedding triggers into the distilled dataset during the upstream model's distillation process in order to execute a backdoor attack on downstream models. Specifically, BAMDD-NLP incorporates triggers into the original dataset and alters the labels of compromised samples at the early stage of distillation. It does not alter existing dataset distillation algorithms, but instead utilizes them directly to generate backdoored synthetic data that preserve the trigger information. To assess the effectiveness of our proposed backdoor attack method, we performed comprehensive experiments across four benchmark datasets, two commonly used model architectures, and two distinct techniques for dataset distillation. Furthermore, we conducted ablation studies to investigate factors that may affect attack performance. Lastly, we evaluated our method's efficacy against three defense strategies. Our experimental findings reveal that these defenses are insufficient to counter our approach. Our primary contributions can be summarized as follows:  
* To the best of our understanding, this research represents the inaugural backdoor attack targeting dataset distillation within the NLP domain. Our approach involves embedding triggers into the distilled dataset during its creation in the upstream phase and subsequently executing a backdoor attack on downstream models that are trained using this modified dataset. * We introduce the BAMDD-NLP backdoor attack method. Our comprehensive experiments show that BAMDD-NLP can deliver impressive attack performance. * We conduct a series of ablation experiments to evaluate the attack performance of BAMDD-NLP under different settings. The results from these experiments highlight the robustness of our proposed attack method across different configurations. * We assess our attack against three different defense mechanisms. The findings from these experiments show that these defense strategies are ineffective at mitigating our attack method. The rest of the paper is organized as follows: first, we introduce background knowledge and related work in Section 2; then, in Section 3, we provide a detailed description of the threat model adopted in our attack method and outline the implementation process; in Section 4, we present the performance of our experimental method on multiple network architectures and datasets and analyze the results to better understand the effectiveness of our proposed attack; finally, Section 5 summarizes and concludes the paper.
# Ion Mobility-Mass Spectrometry Approach for the Comparison of Sheep and Goat Milk Lipidomes
## 2 Materials and Methods
### Im-QTOF-MS Analysis  
Milk samples were analyzed with a 6560 drift-tube ion mobility QTOF-MS connected to a 1290 UHPLC system (Agilent Technologies, Palo Alto, CA, USA). A volume of 1.0 uL was analyzed using a Kinetex 5 um EVO C18 100 A, 150 mm \(\times\) 2.1 um column (Agilent Technologies, Palo Alto, CA, USA) heated at 50 \({}^{\circ}\)C and with a flow rate of 0.4 mL/min. For the positive ionization mode, the mobile phase consisted of (A) water containing 10 mM ammonium formate (60%) and acetonitrile 40% and (B) a 10 mM ammonium formate solution containing 90% isopropanol and 10% acetonitrile. For positive ionization mode, the separation gradient was 60% A, followed by a linear decrease to 50% A in 2 min, then at 1% in 5 min for 1.9 min and then equilibration for 1 min. In the negative mode, the mobile phase used varied only for the use of 10 mM ammonium acetate.  
The mass spectrometry parameters were as follows: mass range, 50-1700 m/z; gas temperature, 200 \({}^{\circ}\)C; nitrogen 10 L/min; nebulizer gas nitrogen, 50 psig; sheath gas temperature, 300 \({}^{\circ}\)C; sheath gas flow, 12 L/min; capillary voltage, 3500 V for positive and 3000 V for negative; nozzle voltage, 0 V; fragmentor, 150 V; octapole, RF 7550 V; skimmer, 65 V; capillary voltage, 3.5 kV; collision energy, 20 eV in positive and 25 eV in negative mode; mass precursor per cycle = 3; threshold for MS/MS 5000 counts. Nitrogen worked as a drift gas with a trap fill time and a trap release time of 2000 and 500 \(\upmu\)s, respectively. An Agilent reference mass mixture was used during the chromatographic run for mass calibration. Experimental data were obtained using the auto-MS/MS method consisting of an iterative mode with a mass error tolerance of 10 ppm and a retention exclusion tolerance of 0.2 min. In the iterative auto-MS/MS method, the sample was injected several times, while precursors previously selected for MS/MS fragmentation were rejected on a rolling basis [19].
## 2 Ensemble Neural Network and Signal Graph-Based SEI
### Signal Graph and Improved Graph Convolution
#### 2.1.2 Improved Graph Convolution for the Signal Graph  
The formula for the original graph convolution can be defined as:  
\[H^{l+1}=GConv(H,W)=\sigma(LH^{l}W^{l})=\sigma(D^{-\frac{1}{2}}AD^{-\frac{1}{2 }}H^{l}W^{l}) \tag{1}\]  
where \(H\) is the output of the \(l\)-th layer graph convolution, \(W\) is the parameter matrix of the \(l\)-th layer graph convolution, \(A\) is the adjacency matrix of the graph, \(D\) is the degree matrix of the adjacency matrix \(A\). In the original signal graph, all the adjacent nodes have the same impacts on the central node. However, different adjacent nodes have different impacts on the central node due to their different positions in the signal sequence [19].  
In this regard, the standard graph convolution formula was improved in this study. Each adjacent edge of the adjacent matrix was given a weight parameter to adjust the impact of different adjacent nodes on the central node in the summation and updating. The weight parameter of the adjacent edge is determined by the following formula:  
\[k(d)=\frac{1}{\sqrt{2\pi}}\exp(-\frac{d^{2}}{2\sigma^{2}}) \tag{2}\]  
In the formula, \(k\) represents the weight parameter of the adjacent edge; \(d\) represents the distance between an adjacent node and the central node in the signal sequence; and \(\sigma\) is the shape parameter. The distance between the adjacent node and the central node is inversely proportional to the impact of the former on the latter and vice versa. The set of the weight parameters is written as a weight matrix \(K\) (\(length\times length\)). The following formula exhibits the relation between the weight matrix \(K\) and the element \(k_{ij}\):  
\[K\!=\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!cipal diagonal, \(k_{|d|}\) controls the value of the diagonal element. Finally, weight matrix \(K\) and adjacent matrix \(A\) were used to calculate the Hadamard product and assign corresponding weight to each adjacent edge so as to control the impact of the different adjacent nodes on the central node in its updating. Therefore, the improved graph convolution for extracting features from the signal graph is written as the following formula:  
\[H^{l+1}=\mathit{Gonv}(H,W)=\sigma(D^{-\frac{1}{2}}K\odot AD^{-\frac{1}{2}}H^{ l}W^{l}) \tag{4}\]  
where \(H\) is the output of the \(l\)-th layer graph convolution, \(W\) is the parameter matrix of the \(l\)-th layer graph convolution, \(A\) is the adjacency matrix of the graph, \(D\) is the degree matrix of the adjacency matrix \(A\), and \(K\) is the proposed weight matrix.
## 3 LoRa and Localization Algorithms
### Overview of LoRa
#### 3.1.6 Packet Error Rate (PER)  
PER is the total number of received packets divided by the number of error packets after forward error correction (FEC). A packet is a data unit used in a radio transmission that is subject to FEC.
## 5 Discussion  
The SWAT model has undergone optimization and development over the past two decades. However, as a model reliant on numerical computations, it is difficult to fully demonstrate the real and complex flow environment. Notably, the refinement of the SWAT model at an hourly scale remains a work in progress, with its practical application area yet to be comprehensively validated. It needs to pay special attention to the potential underestimation of flood peaks when using the SWAT model for hourly simulation and flood peak forecasting. To enhance the simulation accuracy, we recommend establishing a more extensive station network to obtain long-term hourly precipitation and hydrological observations in critical river basins. Furthermore, while the utilization of the SWAT model for water quality simulation at an hourly scale is currently limited, it holds significant promise for applications in water quality simulations at this temporal scale.  
Examining the October 23 rd flow instance in Figure 6 offers insight. Despite a flood peak of 800 m\({}^{3}\)/s occurring at 01:00 on that day, neither the Fuzhong nor the Fukuyama precipitation observatory registered an exceptionally large rainstorm. This disparity can be attributed to the vast watershed area and regional disparities in rainfall patterns. Such occurrences, wherein areas devoid of precipitation observations experience high-intensity rainfall, pose challenges to hourly simulations in accurately reflecting true flood peak values. Precipitation data, a pivotal input influencing runoff simulation within hydrological models, substantially shape the precision of the final flood simulation outcomes.  
In some instances, the measured and simulated peak occurrences diverge by several hours. The two highest measured flood peaks lag behind their simulated counterparts by approximately 4 to 5 h. This temporal discrepancy arises from the influence of watershed topography on the timing of flow generation and convergence when precipitation transpires. The inherently uncertain nature of this process contributes to hourly simulated flood peaks, occasionally misaligning with actual peaks.
# An Optimized Genetic Algorithm-Based Wavelet Image Fusion Technique for PCB Detection
## 2 Related Work
[PERSON], [PERSON], [PERSON] and [PERSON] explores GA in multi-objective energy system optimization, addressing Pareto front accuracy, convergence, uniqueness, and dimension reduction, proposing effective quantitative methodologies [25]. Recent advancements have introduced novel frameworks such as hybrid genetic algorithms for feature selection and super-resolution algorithms for medical image enhancement, combining discrete wavelet transforms with adaptive feature selection to improve CT image quality [26,27]. Furthermore, Wavelet-based fusion techniques continue to evolve, as demonstrated by [PERSON] and [PERSON], who improve wavelet-based image fusion by incorporating weighted averages of great boost and CLAHE, enhancing image quality by reducing blurring and artifacts [28]. [PERSON] et al. improve fusion techniques for real-time surveillance, presenting a wavelet-based multi-focus fusion approach that uses method noise and anisotropic diffusion [17]. [PERSON] et al. develop the Deep-AF model, which integrates CNN-based decision maps, sparse coding, and cross-modality transitions to improve multimodal medical image fusion [29]. Wavelet transforms also have a significant role in enhancing image clarity and feature preservation across various fields. The integration of Particle Swarm Optimization (PSO) and Stationary Wavelet Transform (SWT) has been proposed to optimize parameters for better edge resolution and detail preservation [30]. Additionally, [PERSON] et al. propose an image fusion method using PSO and Dual-Tree Discrete Wavelet Transform (DTDWT), optimizing fusion weights to improve fusion quality [31]. Therefore, image fusion techniques, particularly wavelet-based methods, combine complementary information to improve image quality. Integrating GAs optimizes fusion by dynamically selecting the best parameters, enhancing detail preservation. In PCB detection, a GA-based wavelet fusion approach significantly improves clarity, aiding in the detection of subtle defects and enhancing fault analysis, thus offering substantial practical value for industrial applications.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 8 Viruses
### Entomopoxvirus  
Entomopoxviruses (EPV) are a subfamily of Poxviridae, which includes several important vertebrate viruses, such as smallpox. EPVs are occluded DNA viruses that replicate in the cytoplasm and consist of three genera, containing 31 different species. EPVs have been found to infect several coopteran, lepidopteran, orthopteran, and dipteran species, including several scarabs [235, 244]. In fact, _Vagiovirus_, a type of EPV, was first discovered in cockhafer beetles (_M. melolonttha_) in France by [PERSON] [245], and subsequently by [PERSON] [246]. A similar virus was later discovered in _Ohtomius batesi_ in Australia [247, 248] and in two other Australian scarab populations, can beetle (_Demmelopida albolitrium_) and _Aphodius tasmaniae_[249]. This EPV has most recently been found in rose beetle (_Adoretus verstuts_) in Fiji [250] and in a cockhafer (_M. melolonthae_) population in Turkey [251]. Thus far, EPVs have not been found in any scarab species in the U.S.  
While the potential of EPVs as viable biocontrol options is still in development, they have many characteristics that make them good candidates. EPVs have been found to be infectious at all insect life stages [236], and while the infection period of EPVs can be quite long, at upwards of 40 weeks [236], a strain found in Turkey was particularly virulent, causing mortality as quickly at 15 days after infection [251]. Some evidence suggests that long-term inoculation is achievable, as [PERSON] and [PERSON] [194] found that _Vagoiavirus_ could become established in field plots by introducing artificially infected larvae. However, much more work is needed to identify EPVs present in U.S. scarab populations and to develop techniques for efficient inoculation, which may involve the use of vectors such as parasitoids or nematodes. Some EPVs are able to replicate in insect cell lines [252], making future research on these viruses more feasible.  
Although EPVs have been isolated from a range of different insect species, epizootics of EPV are rare and have only been reported in winter websworm (_Ocnogyna baetica_) in Spain [253], a midge population (_Chironomus decorus_) in California [254], and rose beetles (_Adoretus versutus_) in Fiji [250]. As EPV is not commercially available there is limited information on its ability to persist in field populations. However, [PERSON] and [PERSON] [154] found consistent EPV infection rates and control of cockchafer grubs in plots over a four-year period, suggesting that EPV infections may persist for long periods.
## 2 Materials and Methods
### Quantitative Assessment of Image Quality  
Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated for each scan using the following procedures:  
\[\text{SNR}=\frac{\text{Mean\;Hounsfield\;Unit}(\text{HU})}{\text{Noise}} \tag{1}\]  
Mean HU and noise were measured using Radiant DICOM viewer (version 2023.1) by drawing a circular 0.5 cm\({}^{2}\) region of interest (ROI) in the biggest lung nodule. Standard deviation of the measured HU represents the image noise [17].  
\[\text{CNR}=\frac{\text{Mean\;Hounsfield\;Unit}(\text{HU})_{\text{module}}- \text{Mean\;HU}_{\text{background\;lung}}}{\text{Noise}} \tag{2}\]  
The mean HU was measured in the biggest nodule compared to the background lung tissue, taking care to avoid surrounding vessels. Image noise was calculated as the standard deviation from a 0.5 cm\({}^{2}\) ROI in the lung tissue [40]. Figure 3 demonstrates an example of the measurements taken to calculate CNR.  
Figure 3: Measurement of CNR with ROI in largest nodule and background lung tissue.