esa-sceva/satcom-chunk-collection huggingface dataset:
# LLM-Based Cyberattack Detection Using Network Flow Statistics
## Appendix A Validation with Other Datasets  
Table 11 contains the confusion matrix for a model built with CIC-IDS-2017. A total of 0.05% malignant rows, 42 out of 89,975, were classified as benign, and 0.01%, 11 out of 89,944, benign network flows were classified as malignant. Therefore, we can assert there is a negligible percentage of misclassification. We detected the poorest results for the types of attacks 12 and 14, which are Infiltration and Heartbleed, respectively. For the first, the model only successfully classified 3 out of 7 network flows, which is 43%, and for the second, only 2 rows were classified as Infiltration. This represents 100% misclassification.  
Table 12 contains the confusion matrix for the trained model with BCCC-CIC-IDS-2017. A total of 0.07% malignant network flows, 49 out of 71,375, were classified as Benign, and 0.11% Benign rows of data, 75 out of 71,401, were classified as malignant. Therefore, there is a negligible percentage of misclassification using this dataset. We detected only one type of attack with negative results, specifically label 12, Web_SQL_Injection. In this case, 100% rows of data were misclassified, since 5 out of 5 network flows were classified as Benign.  
The comparison (Tables A1 and A2) between the confusion matrices for the models trained with CIC-IDS-2017 and its augmented dataset BCCC-CIC-IDS-2017, revealed an interesting finding. The first model successfully classifies 100% SQL Injection data and misclassifies 100% of Heartbleed data. However, quite the opposite happens with the second model, since it misclassifies 100% SQL Injection rows of data and successfully classifies 100% Heartbleed data.
## 3 Overview of Fuzzy Logic System
### Fuzzy Logic System Components  
In [31], [PERSON] introduced fuzzy logic that uses a range of input values and produces estimated results as output. As shown in Figure 2, a fuzzy logic system basically includes the following components:  
* A fuzzifier;
* An inference engine;
* A defuzzifier.  
During the fuzzification stage, precise values (inputs) are transformed into fuzzy values. Subsequently, the inference engine interprets the fuzzy values obtained from the fuzzifier into alternative fuzzy values using IF-THEN rules. In the defuzzification phase, fuzzy values are converted back into precise values (outputs). Three frequently utilized defuzzification approaches include the center of gravity method, height method, and mean of maximum method.
## Materials and Methods
### Evaluation of Cloud Top Heights  
The pixel value in each cluster was multiplied by the corresponding monthly SD and added the corresponding monthly mean in order to present the clusters in the original variable \(T_{b}\). A linear regression between temperature and height, using temperature expressed in Kelvin as the predictor, was conducted over reanalysis data in order to estimate cloud top heights (CTHs) from \(T_{b}\) values. The reanalysis database used for these calculations consisted of air temperatures and geopotential heights retrieved from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis webpage ([[https://www.esrl.noaa.gov/psd/data/gridded/data.ncep](https://www.esrl.noaa.gov/psd/data/gridded/data.ncep)]([https://www.esrl.noaa.gov/psd/data/gridded/data.ncep](https://www.esrl.noaa.gov/psd/data/gridded/data.ncep)). reanalysis.pressure.html) [64]. Each GOES pixel was associated to the closest grid point in the reanalysis. The regression was carried out for the later dataset between 1000 and 70 hPa so that information from both the troposphere and the lowermost stratosphere was considered. This was done for each of the 2036 individual dates and for the total number of 5,045,535 pixels in each image. The regression coefficients were used to associate \(T_{b}\) values with CTHs. Given that the instrument on board the satellite did not collect information from below the Earth surface negative CTHs that resulted from the regression were not included in the analysis.
## Appendix A Stability
### Stability Condition on Photoionization Effect  
Together the wave equation we take into consideration the differential equation \(\frac{\partial}{\partial t}\widetilde{D}(\overline{r},t)=\epsilon_{\infty}e_{ 0}\partial_{\overline{\overline{\overline{\sigma}}}t}\widetilde{E}(\overline{ r},t)+\sigma_{fe^{-}}(\overline{r},t)\widetilde{E}(\overline{r},t)\), which by means of the defined operators can be discretized as follows,  
\[\tilde{\sigma}_{t}\widetilde{D}^{s}=\epsilon_{\infty}e_{0}\tilde{\sigma}_{t} \tilde{E}^{s}+\sigma_{fe^{-}}\bar{\Delta}\tilde{E}^{s}\]  
Again we combine the equation with the discretized wave differential equation with the aim to determine the characteristic polynomial \(\Phi(y)=2\epsilon_{0}e_{\infty}(1+y\big{(}4\
u^{2}-2\big{)}+y^{2})+\Delta_{t} \sigma_{fe^{-}}(y^{2}-1)\) in which we apply the bilinear transformation \(y=\frac{\tau+1}{\tau-1}\). Therefore, we calculate the characteristic polynomial \(\Phi(r)=2\epsilon_{0}e_{\infty}\
u^{2}r^{2}+\sigma_{fe^{-}}\Delta_{t}r+2 \epsilon_{0}e_{\infty}(1-\
u^{2})\) for which the Routh-Hurwitz criterion can be applied. The first column of the Routh table are formed by the coefficients \(c_{1,0}=2\epsilon_{0}e_{\infty}\
u^{2}\), \(c_{2,0}=\sigma_{fe^{-}}\Delta_{t}\) and \(c_{3,0}=2\epsilon_{0}e_{\infty}(1-\
u^{2})\). The later coefficient provides the classical condition for stability. In addition, we know that \(\sigma_{fe^{-}}\) must be a positive magnitude so as to address the stability in the algorithm updating.
## 1 Introduction
### Motivations and Contributions  
In real-world scenarios, moving dim and small targets are situated in diverse backgrounds and exhibit varying intensities. For dimmer targets in simpler backgrounds, energy accumulation across adjacent frames enhances detection capability, as it amplifies weak target signals while suppressing transient noise. Conversely, for brighter targets in more complex backgrounds, temporal contrast modeling proves effective by leveraging the statistical differences between targets and their surrounding backgrounds.  
To address the limitations of temporal-domain and single-frame algorithms in information extraction, as well as the challenges posed by variations in target brightness and scene complexity, this work integrates single-frame spatial contrast and gradient features into the detection process. Specifically, we introduce two novel temporal-domain techniques. The first is Regional Intensity Accumulation (RIA). By accumulating target energy across consecutive frames, RIA enhances the detectability of dim targets while mitigating the impact of noise and background clutter. The second, Temporal Regional Contrast (TRC), models the temporal contrast between targets and their local backgrounds, effectively suppressing dynamic interference in complex scenarios. For spatial analysis, we propose the Gradient-Enhanced Local Contrast Measure (GELCM), which incorporates gradient direction and magnitude coherence into local contrast computation, further reducing false alarms caused by edges and structured backgrounds. Based on these components, we propose Spatial-Temporal Fusion Detection (STFD), a method that robustly combines spatial and temporal information for small target detection. STFD adaptively balances the contributions of RIA, TRC, and the GELCM based on scene complexity and target characteristics, ensuring superior performance across diverse scenarios.  
The main contributions of this paper are as follows:  
1. This work proposes Regional Intensity Accumulation and Temporal Regional Contrast, which are temporal detection methods based on temporal profile analysis that successfully characterize and address scenarios involving complex backgrounds and dim targets. These methods reliably obtain temporal detection results from multi-frame image sequences.
2. This work introduces the Gradient-Enhanced Local Contrast Measure, a spatial contrast detection method that integrates gradient direction and magnitude information. The GELCM effectively describes real-world targets, significantly enhances target detectability in complex backgrounds, and efficiently extracts spatial detection results from multi-frame images.
3. This study designs and integrates the GELCM (spatial detection) with TRC and RIA (temporal detection) into the Spatial-Temporal Fusion Detection method. The GELCM, TRC, and RIA complement each other, and their synergy in STFD enables effective target detection across diverse scenarios. The method demonstrates its effectiveness and robustness through evaluations in various scenarios and infrared sequence types.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 5 Performance Comparison  
This section studies the performance of the shaping architectures explained in Section 4. The used metrics are (i) finite-length rate loss at a fixed blocklength \(n\), (ii) finite-length information rates for BMD, and (iii) frame error rate (FER). Communication systems with limitations on peak power or peak-to-average power ratio are not considered here.
# A 28 nm Bulk CMOS Fully Digital BPSK Demodulator for US-Powered IMDs Downlink Communications
###### Abstract  
Low-invasive and battery-less implantable medical devices (IMDs) have been increasingly emerging in recent years. The developed solutions in the literature often concentrate on the Bidirectional Data-Link for long-term monitoring devices. Indeed, their ability to collect data and communicate them to the external world, namely Data Up-Link, has revealed a promising solution for bioelectronic medicine. Furthermore, the capacity to control organs such as the brain, nerves, heart-beat and gastrointestinal activities, made up through the manipulation of electrical transducers, could optimise therapeutic protocols and help patients' pain relief. These kinds of stimulations come from the modulation of a powering signal generated from an externally placed unit coupled to the implanted receivers for power/data exchanging. The established communication is also defined as a Data Down-Link. In this framework, a new solution of the Binary Phase-Shift Keying (BPSK) demodulator is presented in this paper in order to design a robust, low-area, and low-power Down-Link for ultrasound (US)-powered IMDs. The implemented system is fully digital and PLL-free, thus reducing area occupation and making it fully synthesizable. Post-layout simulation results are reported using a 28 nm Bulk CMOS technology provided by TSMC. Using a 2 MHz carrier input signal and an implant depth of 1 cm, the data rate is up to 1.33 Mbit/s with a 50% duty cycle, while the minimum average power consumption is cut-down to 3.3 \(\mu\)W in the typical corner.  
BPSK modulation; energy harvesting; implanted medical devices; ultra-low-power, ultrasonic wireless link +
Footnote †: journal: Electronics  
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Footnote †: journal: Electronics  
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Footnote 0: 0
## 4 Conclusions  
In conclusion, a novel nebulized discharge plasma generated at atmospheric pressure is employed to detect the Cu element by AES. Plasma is generated near needle electrodes and aerosol is introduced from above the electrode gap by a pneumatic nebulizer, which avoids damage to the fragile device. The discharge image also indicates that a stable glow discharge is obtained. Under optimal conditions, the rotational and vibrational temperatures are calculated by the spectra of N\({}_{2}\) (C\({}^{3}\)T\({}_{u}\)-B\({}^{3}\)T\({}_{l}\)S\({}_{l}\), \(\Delta\)v = \(-\) 2), which are about 2190 K and 3165 K, respectively. From the gas temperature evolution of nebulized discharge, it can be seen that the temperature obtained is nearly symmetrical and has higher energy at both ends. When the electrode gap is 2 mm and the flow rate of solution is 2 mL/min, the optimal experimental conditions of nebulized discharge are HNO\({}_{3}\) solution with pH = 3.59,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Element** & **Working Curves** & \(r^{2}\) & \(s\) & LOD (mg/L) \\ \hline Cu & I = 5297c + 7845 & 0.9975 & 145.759 & 0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analytical performance of APNDP for detecting the Cu element.  
Figure 11: Working curve for Cu.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **LOD (mg/L)** & **Methods** & **LOD (mg/L)** \\ \hline This work & 0.083 & ELCAD-AES [25] & 0.109 \\ LCGD-AES [12] & 0.380 & u-LCGD-AES [35] & 0.47 \\ ICP-AES [5] & 0.04 & Standard in drinking water [36] & 2.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of LOD obtained by this work (APNDP) with other techniques for detecting trace heavy metal Cu.  
5.6 kV applied voltage, 1.68 L/min Ar flow rate, and 2% (V/V) ethanol. Among them, the applied voltage effects the energy efficiency and the number of high energy electrons and excited atoms, the gas flow rate affects the area of the discharge region and the solution-nebulized efficiency, and the solution pH value and organic matter changes the composition of the solution and some reaction processes of trace heavy metal Cu detection. Finally, the emission intensity of Cu I (3d\({}^{10}\)4p-3d\({}^{10}\)4s, 324.75 nm) is affected. Under the experimental conditions, the LOD of Cu is 0.083 mg/L and has a good linearity of detection. This work can detect trace heavy metal Cu directly, and after solution nebulization, the Cu element is easier to be excited under the action of plasma.  
Writing--original draft, H.Y. ([PERSON]); Writing review & editing, H.Y. ([PERSON]), S.L., W.W. and D.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52077026 and 11965018), the Science and Technology Development Fund of Xinjiang Production and Construction (2019 BC009) and the Fundamental Research Funds for the Central Universities (DUT21 LK31).  
Not applicable.  
The authors declare no conflict of interest.
# Image Semantic Segmentation Fusion of Edge Detection and AFF Attention Mechanism
## 2 Method
### Hybrid Model of Improved PSP-Net Network Combined with Improved HED
#### 2.1.4 Experiment of Hybrid Model
In the first and fourth rows of the scene, we can see that the edges of large objects are continuous, and the edges of the \"sofa\" and \"bed\" in the figure are more continuous. Experiments Based on CamVid Dataset  
Our semantic and edge feature fusion network, together with FCN-8s, Seg-Net, PSP-Net, and Deeplabv3+, were used on the CamVid dataset for the object segmentation experiment. The experimental results are shown in Table 4.  
Compared with FCN-8s, the segmentation accuracy of our semantic and edge feature fusion network is improved by 13.1%, and the processing speed is also greatly improved. Compared with Seg-Net, PSP-Net, and DeeplabV3+, the segmentation accuracy is improved by 8.7%, 6.6%, and 1.4%, respectively. The segmentation results based on the CamVid dataset are shown in Figure 11. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Method** & **Input** & **BaseNet** & **MIOU(Val)** & **MIOU(Test)** & **FPS** \\ \hline
**Seg-Net** & 2048 \(\times\) 1024 & VGG-16 & 67.5 & 66.2 & 30.1 \\
**PSP-Net** & 2048 \(\times\) 1024 & ResNet50 & 69.0 & 68.3 & 72.3 \\
**Deeplabv3+** & 2048 \(\times\) 1024 & Xception & 75.6 & 73.5 & 74.8 \\
**FCN-8S** & 2048 \(\times\) 1024 & VGG-16 & 62.2 & 61.8 & 8 \\ Our semantic and edge & 2048 \(\times\) 1024 & MobileNet3 & 76.1 & 74.9 & 51.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and speed of image semantic segmentation network on CamVid dataset. Figure 10: Segmentation results on the NYU-V2 dataset figure. The results in Figure 11 show that our semantic and edge feature fusion network can segment objects such as \"street\" and \"building\" better, and the edge is more continuous. This is because the edge features are fully utilized in the improved HED network. As can be seen from the semantic segmentation results of different weather conditions, our method can accurately segment \"car\", \"building\" and other objects. However, the segmentation effect on small objects needs to be improved. Therefore, on the base of our semantic and edge feature fusion network, the AFF attention mechanism is introduced to solve the problem of imprecision of small objects.
## 3 Problem Description
### Energy Consumption Modeling  
Server power consumption is mainly affected by CPU, memory, disk storage, and bandwidth; of these, CPU has the largest contribution to server power consumption and accounts for most of the energy consumption. For simplicity, we consider only the impact of CPU on power consumption. In this model, the power consumption of the server fluctuates depending on the usage of the CPU on this server. According to [20], it can be seen that when the server changes from zero load (0%) to full load (100%), the power consumption of the server has a linear relationship with CPU utilization, and the power consumed by the server at zero load is 67% of the power consumed at full load. The power consumption \(W_{i}\) of a physical machine \(P_{i}\) can be calculated as follows:  
\[W_{i}=(W_{\max}-W_{idle})U_{cpu}^{i}+W_{idle} \tag{1}\]  
where \(W_{\max}\) and \(W_{idle}\) indicate the power consumption when the server is fully loaded and idle, respectively; \(U_{cpu}^{i}\) is the CPU utilization rate on the physical machine \(P_{i}\).
## 2 Materials and Methods  
### Materials  
Iron(III) chloride hexahydrate (FeCl\({}_{3}\cdot 6\)H\({}_{2}\)O), ammonium acetate (NH\({}_{4}\)Ac), sodium acetate anhydrous (NaAc), ethylene glycol, anhydrous ethanol, trisodium citrate dihydrate, aqueous ammonia solution (NH\({}_{3}\cdot\)H\({}_{2}\)O, 25%), \(N\),\(N^{\prime}\)-dimethylformamide (DMF), aniline, 2,2-azobisisobutyronitrile (AIBN), \(N\)-isopropyl acrylamide (NIPAm), acrylic acid (AA), 2-hydroxyethyl methacrylate (HEMA) were purchased from Sinopharm Chemical Reagent Co., Ltd. (Shanghai, China). \(N\),\(N^{\prime}\)-Methylene-bisacrylamide (MBA), \(N\)-methylolacrylamide, diisopropyl azodicarboxylate (DIPA), triphenylphosphine, \(N\)-hydroxyphthalimide (NOP), hydrazine monohydrate were purchased from Aladdin (Shanghai, China). Methacryloxypropyltriethoxysilane (MPS), bovine serum albumin (BSA), asialofetuin from fetal calf serum (ASF), myoglobin from horse heart (MYO), lysozyme (LYS), sodium periodate (NaIO\({}_{4}\)), ammonium bicarbonate (NH\({}_{4}\)HCO\({}_{3}\)), urea, MALDI matrix (\(\alpha\)-cyano-4-hydroxycinnamic acid, CHCA) were all obtained from Sigma (St. Louis, MO, USA). Acetonitrile (ACN, 99.9%, chromatographic grade) and trifluoroacetic acid (TFA) were purchased from Merck (Darmstadt, Germany). The glycerol free peptide-\(N\)-glycosidase (PNGase F, 500 units/\(\upmu\)L) and SDS-PAGE molecular weight standards (6.5-175 kDa) were from New England Biolabs (Ipswich, MA, USA). Sep-Pak C18 columns were from Waters (Shanghai, China). Human serum was provided by Fudan University Shanghai cancer center and stored at -80 \({}^{\circ}\)C before analysis. Water used in experiments was ultrapure water prepared using a Milli-Q50 SP Reagent Water System (Millipore, Bedford, MA, USA).
# Participatory Exhibition-Viewing Using Augmented Reality and Analysis of Visitor Behavior
## 4 Data Analysis
### Distance Analysis  
The test statistic of distance was t(61) = 0.361, \(p\) > 0.05. That is, at the \(\alpha\) = 0.05 level of significance, the distances covered in exhibition-viewing and message modes showed no significant differences. The \"viewing distance\" and \"message distance\" data reflect how far participants were from artworks when viewing or messaging, respectively. The large standard deviations for both \"viewing distance\" and especially \"message distance\" indicate a wide variation in participant behavior. This variability might suggest that while some participants chose to interact with, or message about, artworks from closer distances, others did so from much farther away, leading to a wide range of recorded distances.  
The large standard deviation in \"message distance\" could raise questions about data reliability, as it suggests participant behavior varied widely. However, the standard error of the mean helps address these concerns by indicating how far the sample mean (of either \"viewing\" or \"message\" distances) is likely to be from the true population mean. Smaller standard errors suggest the sample means are a reliable estimate of the population means, despite the wide variability among individual measurements.
## 5 Case Study
### Analysis of Identification Effect
#### 5.2.2 Model Identification Results  
Based on the model settings detailed in Section 5.2.1, the CAS is carried out first in this section. According to the analysis in Section 4.2.5, the ROS method was not used to process the dataset in this stage, and the original datasets \(\mathbf{D}_{\text{train}}^{\text{CAS}}\) and \(\mathbf{D}_{\text{valid}}^{\text{CAS}}\) were used directly; this was to limit the adaptation of the model architecture to features of minority class examples, which can reduce the ability to identify the general examples accurately. CAS was conducted using the computing resources and datasets mentioned above, and 50 generations of training required 0.3444 GPU days. The final cell architecture is shown in Figure 10.  
When cells are stacked to form the MTGDI-CNN, the model's performance will be affected by different numbers of stacked cells and training epochs. Therefore, when performing FMV on the model constructed by the cell shown in Figure 10, this study set different numbers of stacked cells (\(n=[8,16,24]\)) and training epochs (\(\text{epoch}=[300,600,\dots,1500]\)) with a fixed random seed (0) for training and testing. A comparison of the \(F_{2}^{\text{TGD}}\) and training time for different hyperparameter combinations is illustrated in Figure 11. For a convenient illustration, the model is denoted by \(M_{(n,\text{epoch})}\), and all models with \(n\) layers are denoted by \(M_{(n,\ast)}\).  
Figure 10: **(A)** Normal cell architecture obtained from CAS; **(B)** reduction cell architecture obtained from CAS. (the transparent circles represent the input/output nodes, the normal circles represent the intermedia nodes, the arrows means the links between nodes and number 0-3 means the order of the intermedia nodes).  
As demonstrated by the results in Figure 11A, the performance growth of the \(M_{(8,*)}\) models did not correlate optimally with the number of training epochs, indicating that the training of these models for 300 epochs was sufficient. The performance of the \(M_{(16,*)}\) and \(M_{(24,*)}\) models increased relatively with an increase in the training epoch, indicating that deeper models require more training epochs. When the model training converges, its comprehensive performance has a positive correlation with the number of stacked cells, and \(M_{(16,*)}\) demonstrates a limited comprehensive performance improvement relative to \(M_{(8,*)}\); however, the improvement effect of \(M_{(24,*)}\) is not apparent compared with \(M_{(16,*)}\). As indicated in Figure 11B, the training time was proportional to the number of stacked cells and training epochs. The model with the superior performance was \(M_{(16,600)}\), featuring a \(F_{2}^{\text{TGD}}\) value of 0.8838. The precision and recall of LLs at each level of \(\mathbf{D}_{\text{valid}}^{\text{FMV}}\) are listed in Table 1. Evidently, after effective training, the model proposed in this study could use actual field data to identify LLs on the metro track and distinguish their severity levels.