esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint AP Association and Bandwidth Allocation Optimization Algorithm in High-Dense WLANs
## 5 Performance Evaluation
### Evaluation Results  
#### 5.2.1 Throughput and AP Utilization  
The aggregated throughputs of three kinds of algorithms are shown in Figures 3 and 4. Both the uniform and hotspot distributions are studied. As is shown, when the number of clients is 40, which means that the load is light and the clients' demands almost are fulfilled, the aggregated throughputs of the three algorithms are very close. With the increasing of client scale, the aggregated  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \(\gamma_{ij}(dB)\) & 6–7.8 & 7.8–9 & 9–10.8 & 10.8–17 & 17–18.8 & 18.8–24 & 24–24.6 & \textgreater{}24.6 \\ \hline \(R_{ij}(Mbps)\) & 6 & 9 & 12 & 18 & 24 & 36 & 48 & 54 \\ \hline \end{tabular}
\end{table}
Table 2: The relationship between the effective bit rates and the SINRs.  
Figure 2: Network simulation scenarios for different distributions (the different shapes represent 802.11 b and 802.11 g clients with different rates and the black plus signs denote APs). (**a**) The uniform distribution; (**b**) The hotspot distribution.  
throughput of SSF algorithm is much lower than that of the other two algorithms, and CAA-BD algorithm can achieve higher throughput than SSF and Categorized algorithm can do, especially, when the number of clients grows beyond 120 in the uniform distribution and beyond 60 in the hotspot distribution. This may be due to the impact of load imbalance among APs for SSF and Categorized algorithm. However, it is alleviated in the CAA-BD algorithm. When the number of clients is 200, the aggregated throughputs of CAA-BD algorithm in the uniform and hotspot distributions are respectively 13.1% and 32.4% higher than that of Categorized algorithm. In particular, in Figure 4, when the number of clients is beyond 120, the aggregated throughput of Categorized algorithm decreases drastically, which may be due to the overload of the APs in hotspots distribution. However, The Categorized algorithm does not make a fine-grained association decision.  
Figure 4: Aggregated throughput in the hotspot distribution.  
Figure 3: Aggregated throughput in the uniform distribution.  
The average AP utilizations of all algorithms in the uniform and hotspot distributions are shown in Figures 5 and 6. When the number of clients is 200, the average AP utilization of CAA-BD algorithm gets a significant increase, with 47.9% in the uniform distribution and 66.6% in the hotspot distribution respectively, compared with that of Categorized algorithm. It is obvious that the load balance is more significant in CAA-BD algorithm, rather than the other two algorithms. Accordingly, it also illustrates that the average AP utilization for CAA-BD algorithm may result in the high throughput.
# Combating Fake News with Cryptography in Quantum Era with Post-Quantum Verifiable Image Proofs
###### Abstract  
In an age of AI-generated content and deepfakes, fake news and disinformation are increasingly spread using manipulated or fabricated images. To address this challenge, we introduce Post-Quantum VerITAS, a cryptographic framework for verifying the authenticity and history of digital images--even in a future where quantum computers threaten classical encryption. Our system supports common image edits, like cropping or resizing, while proving that the image is derived from a legitimate, signed source. Using quantum-resistant tools, like lattice-based hashing, modified Poseidon functions, and zk-SNARK proofs, we ensure fast, privacy-preserving verification without relying on trusted third parties. Post-Quantum VerITAS offers a scalable, post-quantum-ready solution for image integrity, with direct applications in journalism, social media, and secure digital communication.  
fake news; post-quantum cryptography; zk-SNARKs; misinformation; image provenance verification
# Deep-Learning-Based Approach for IoT Attack and Malware Detection
## 2 Datasets
### CIC IoT 2023 Dataset
\begin{table}
\begin{tabular}{c l c c c} \hline \hline
**No** & **Class Name** & **Train** & **Test** & **Validation** \\ \hline
1 & Backdoor\_Malware & 2253 & 483 & 482 \\
2 & BenignTraffic & 768,737 & 164,729 & 164,729 \\
3 & BrowserHijacking & 4101 & 879 & 879 \\
4 & CommandInjection & 3786 & 811 & 812 \\
5 & DDoS-ACK\_Fragmentation & 199,573 & 42,766 & 42,765 \\
6 & DDoS-HTTP\_Flood & 20,153 & 4319 & 4318 \\
7 & DDoS-ICMP\_Flood & 5,040,353 & 1,080,076 & 1,080,075 \\
8 & DDoS-ICMP\_Fragmentation & 316,742 & 67,873 & 67,874 \\
9 & DDoS-PSHACK\_Flood & 2,866,329 & 614213 & 614,213 \\
10 & DDoS-BSFTINFOod & 2,831,700 & 606,793 & 606,792 \\
11 & DDoS-SYN\_Flood & 2,841,433 & 608,879 & 608,878 \\
12 & DDoS-SlowLoris & 16,398 & 3514 & 3514 \\
13 & DDoS-SynonymousIP\_Flood & 2,518,697 & 539,721 & 539,720 \\
14 & DDoS-TCP\_Flood & 3,148,367 & 674,650 & 674,650 \\
15 & DDoS-UDP\_Flood & 3,788,601 & 811,843 & 811,843 \\
16 & DDoS-UDP\_Fragmentation & 200,848 & 43,039 & 43,038 \\
17 & DNS\_Spoofing & 125,238 & 26,837 & 26,836 \\
18 & DictionaryBruteForce & 9145 & 1960 & 1959 \\
19 & DoS-HTTP\_Flood & 50,305 & 10,780 & 10,779 \\
20 & DoS-SYN\_Flood & 1,420,184 & 304,325 & 304,325 \\
21 & DoS-TCP\_Flood & 1,870,011 & 400,717 & 400,717 \\
22 & DoS-UDP\_Flood & 2,323,017 & 497,789 & 497,789 \\
23 & MITM-ArpSpoofing & 215,315 & 46,139 & 46,139 \\
24 & Mirai-green\_flood & 694,306 & 148,780 & 148,780 \\
25 & Mirai-green\_flood & 526,177 & 112,752 & 112,753 \\
26 & Mirai-udpplain & 623,403 & 133,586 & 133,587 \\
27 & Recon-HostDiscovery & 94,065 & 20,157 & 20,156 \\
28 & Recon-OSScan & 68,781 & 14,739 & 14,739 \\
29 & Recon-PingSweep & 1583 & 339 & 340 \\
30 & Recon-Portscan & 57,599 & 12,343 & 12,342 \\
31 & Sollinjection & 3671 & 787 & 787 \\
32 & Uploading\_Attack & 876 & 188 & 188 \\
33 & VulnerabilityScan & 26,167 & 5607 & 5608 \\
34 & XSS & 2692 & 577 & 577 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The amount of training, test, and validation data for different attack types in the CIC IoT 2023 dataset. smart devices and attack simulations, enhancing research on IoT security vulnerabilities and solutions.
## 5 Discussion
In [15], individual, direct transfer, and repurposed transfer were compared. The results showed that repurposed transfer learning that retrained a previously created model with another dataset provided the best results and a significant accuracy improvement. Finally, other studies compare the innovative federated learning approach versus the individual and centralized, as seen in [14; 25], concluding that individual models tend to achieve high accuracy, while centralized learning may have more dispersion, and federated learning even more. Our research consolidates and extends the discussions present in the literature. However, our review is the only one that compares together all these approaches (individual learning, direct transfer learning, repurposed transfer learning, soft-voting and stacking ensemble transfer learning, and federated learning) using different sizes of datasets, providing a specific guide according to data availability. In summary, direct transfer and soft-voting zero-shot approaches are suggested when an institution do not have data. When an institution begins to obtain data and has small-size datasets, they can use pretrained models, such as repurposed transfer learning, obtaining very good performance. If an institution has larger quantities of data, maintaining and training an individual model is the primary option, as well as considering the stacking approach to improve collaborations between institutions and predictive capability. Finally, we provide some general key findings derived from our experimentation:  
* When there are privacy concerns, the most immediately appealing alternative to individual models seems to be federated learning. However, this approach never improves on the baseline. There are some transfer approaches (such as stacking or repurposed transfer learning) that can be used in those cases since they improve on the baseline and do not have to share data. * In terms of performance, it seems better to train models on the source context and continue with the training process on the target (repurposed) than to merge all data together in a centralized approach. * When the resources needed for each approach are important, one positive aspect of centralized and federated learning is that only one model is trained and maintained. However, other approaches--not based on external centralized servers, such as stacking or repurposed transfer learning--need to deal with a large number of models, since the source models need to be created and more importantly, infrastructure and resources are needed for training and managing the models in the target contexts, which may lead to high resource consumption there.
## 3 Results and Discussions
### Pozzolanic Reactivity
#### 3.3.2 Portlandite Consumption  
Thermogravimetric analysis (TGA) was used to quantitatively evaluate the portlandite content (CH) in the blended cement pastes at 3, 7, 28 and 91 days, as a measure of pozzolan reactivity. Generally, the higher the Ca(OH)\({}_{2}\) content remaining after hydration, the lower the reactivity of the material [23]. Figure 6 shows the formation of portlandite in the pastes with varying calcined clay and silica fume content. The reference cement paste was observed to exhibit a consistent rise in portlandite content up to 91 days of hydration. This was expected and in line with the results obtained by the heat of hydration test. The extent of hydration reaction and the amounts of tricalcium silicates and dicalcium silicates  
Figure 5: (a). Heat of evolution of blended cement. (b). Cumulative heat of blended cement.  
account for this high portlandite content in the reference cement, which could influence mechanical strength development [40]. However, the portlandite content at early ages in the control sample was found to be lower than all the blended cement samples. This is because pozzolanic reactivity at early ages is known to proceed slowly [18; 41]. This behaviour has also been reported by [PERSON] and [PERSON] [42]. Partial replacement of cement with 30% calcined clay and silica fume caused significant reductions in the portlandite content of the two hydrated pastes at all curing ages. Between the two incorporated mineral admixtures, the paste prepared with only silica fume was found to contain less portlandite content at the end of the curing period. The portlandite content remaining in 30% silica fume pastes after 3, 7, 28 and 91 days hydration were 7.7%, 13.3%, 12.8% and 8.6%, respectively, less than that of the 30% calcined clay pastes. This is due to the lower kaolinite content of the clay which consequently affects pozzolanic reactivity, leaving some amounts of Ca(OH)\({}_{2}\) unreacted [43].  
Pastes containing a ternary blend of silica fume and calcined clay showed a relatively higher degree of reactivity, resulting in lower portlandite content after hydrating for 91 days. Portlandite content ranged between 10.6% and 17.4%. It is noteworthy that pastes containing higher silica fume content recorded lower portlandite content. This is attributed to the high silica content and large surface area of the silica fume which lead to improved reactivity and the production of extra calcium silicate hydrate gels (C-S-H) [44]. Two factors are responsible for the overall decrease in portlandite content in the blended cement pastes. Firstly, the clinker component is significantly reduced due to pozzolan replacement, which invariably reduces the tricalcium silicate (C\({}_{3}\)S) content, slowing down the hydration process. Secondly, the calcined clay and silica fume have high contents of amorphous silica which propels pozzolanic reaction and consumption of portlandite [45].
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 3 Methods
### Texture Mapping  
After computing the projected 2D bounding box and aligning it with the original elliptical Gaussian region, the next step is to apply texture mapping (see Algorithm A2). This allows each Gaussian primitive to inherit the shape and detail of a brushstroke texture, enhancing the artistic expressiveness of the rendering.  
To ensure smooth texture mapping, we use bilinear interpolation, a technique that blends texture values from neighboring pixels to create a smooth transition, avoiding sharp edges and aliasing artifacts. We assign \(UV\) coordinates to the four corners of the bounding box, which determine how the texture image is mapped onto the Gaussian primitive. Given a texture image of width \(W\) and height \(H\), the normalized \(UV\) coordinates \((u,v)\) are mapped to discrete texture space as follows:  
\[x =u\cdot(W-1)\] \[y =v\cdot(H-1), \tag{12}\]  
where \(u\) and \(v\) are computed using the spatial relationships between the projected Gaussian region and the texture's coordinate system:  
\[u =\frac{1}{2}(1+\frac{R(\theta)_{x}}{Axis})\] \[v =\frac{1}{2}(1+\frac{R(\theta)_{y}}{Axis_{y}}). \tag{13}\]  
However, since \(x\) and \(y\) may not align exactly with integer pixel positions, direct sampling could cause aliasing or sharp edges in the rendered texture. To prevent this, we apply bilinear interpolation, which blends values from nearby pixels to achieve a smooth transition between them. The interpolated texture value is calculated by blending the texture values from the four neighboring pixels as follows:  
\[V= (1-w_{x})(1-w_{y})V_{00}+w_{x}(1-w_{y})V_{01}\] \[+(1-w_{x})w_{y}V_{10}+w_{x}w_{y}V_{11}, \tag{14}\]  
where \(V_{ij}\) represents the texture values at the four neighboring pixels, and \(w_{x}\) and \(w_{y}\) are the interpolation weights based on the distance between the target sampling point and the pixels' positions.  
Texture mapping, combined with bilinear interpolation, allows for the seamless application of brushstroke textures onto Gaussian primitives. This approach effectively prevents artifacts such as sharp edges or aliasing, enhancing rendering quality and transforming simple Gaussian primitives into expressive artistic elements that reflect the characteristics of the brushstroke style.
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 3 Backscatter Communication with FMCW
The total received signal is  
\[r(t)=H\mathrm{e}^{i(\varphi^{\prime}-2\pi\Delta f\cdot t)}\times\sum A(n- \Delta n)e^{i\theta(n-\Delta n)}g(t-\Delta t-nT_{s})+n^{\prime}(t)+\sum_{k=1} ^{K}H_{k}e^{i(\varphi_{k}-2\pi\Delta f_{k}\cdot t)} \tag{8}\]Compared with the traditional backscatter communication system, the difference is the carrier signal. The FMCW signal is used instead of a single-tone CW signal. This result in a frequency offset \(\Delta f\) in \(r(t)\). When there is no tag in the view, the proposed system can work normally as an FMCW radar. To avoid unintentional jamming between the FMCW radar signals and communication signals from tags, as well as distinguish signals from different tags, signature codes are used. This will be discussed in the next section.
## 2 Methodology
### Observation Data Description  
3.1 Moderate Resolution Imaging Spectroradiometer (MODIS) Fraction of Absorbed Photosynthetically Active Radiation (FPAR)  
The Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR algorithm is based on 3-D radiative transfer theory. Inverse Look-Up-Tables (LUTs) are generated for six major biomes defined by MODIS; model configurations for each biome differ from each other by fractional coverage, structural characteristics including canopy height, leaf type, and soil color. Measured surface reflectances (atmospherically corrected bidirectional reflectances) at a maximum of seven spectral bands are used for the inversion [29]. The MODIS daily product is computed daily at 1 km resolution globally. The maximum FPAR value (across the eight days) is selected for the eight-day product [21]. Based on the eight-day product, [PERSON] [41] produced an improved product with cloud-contaminated pixels gap-filled.  
3.2 Fraction of Photosynthetically Active Radiation (FPAR) 3g/Leaf Area Index (LAI) 3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS)  
GIMMS FPAR3g/LAI3g is computed based on improved versions of MODIS FPAR/LAI [42, 43] and GIMMS NDVI3g generated from the Advanced Very High Resolution Radiometeters (AVHRR) using an artificial neural network. GIMMS NDVI3g and its long-term (June 1981-2011) global coverage at frequent intervals provide a unique opportunity to explore vegetation long-term dynamics [44]. It is therefore used as the sole dataset for long-term trend evaluation. However, the AVHRR data lacks correction for aerosol scattering and water vapor absorption [45], resulting in possible atmospheric artifacts in the GIMMS data sets.
## 6 Simulation Results
### Power Management Analysis  
The power management capability of the proposed switch can be analyzed through two key aspects: power harvesting efficiency and transmission power requirements. As demonstrated in Figure 8, SWIPT mode achieves optimal energy harvesting when the source node is positioned near the relay, with harvesting efficiency decreasing as the source-relay distance increases. This spatial dependency of energy harvesting directly influences the switch's mode selection decision.  
Figure 11 further illustrates the system's power management strategy across different source locations. When operating in optimal positions (\(y=1\) to \(y=4\)), the switch predominantly utilizes SWIPT mode, harvesting sufficient energy (often exceeding 2 J) to support transmission requirements. However, in challenging positions (\(y=5\) and \(y=6\)), where harvesting efficiency decreases, the switch intelligently transitions to ABCS mode to maintain communication while minimizing power consumption.  
The relationship between harvested power and transmission requirements can be observed through the algorithm's decision-making process. When the energy harvested through SWIPT meets the minimum energy threshold requirement, and the data rate conditions are satisfied, the switch maintains SWIPT operation. In scenarios where these conditions cannot be met, particularly in positions farther from the relay, the switch transitions to ABCS mode. This transition reflects the system's ability to adapt its power management strategy based on real-time conditions, ensuring efficient operation across varied warehouse locations while maintaining communication capability.
# A New Effective Narrowband Active Noise Control System for Accommodating Frequency Mismatch
## 4 Simulation Results and Discussion  
In this section, some representative simulations are conducted to prove the fast convergence and better noise reduction in the proposed NANC algorithms under FMs. The mean square error (MSE) \(E(e(n)^{2})\) in dB is obtained to compare the performance of the above three NANC systems. We consider three tonal noise frequencies \(f_{i}=[50\ 100\ 150]:\ (i=1,2,3)\) in the primary noise, where \(100\ \mathrm{Hz}\) and \(150\ \mathrm{Hz}\) are the harmonic of \(50\ \mathrm{Hz}\), and then convert all three frequencies to the digital ones by \(2\pi f_{i}/f_{s}\). A hundred independent trials were performed to evaluate the mean square behavior of all of the systems. The optimal step-size is chosen to reach the minimum MSE for each algorithm, respectively. All the basic conditions are listed in Table 2. This section may be divided into subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
# Dual-Branch Luminance-Chrominance Attention Network for Hydraulic Concrete Image Enhancement
## 3 Methods
### Luminance Adjustment Branch (LAB)  
The LAB is responsible for adaptively enhancing the luminance of low-light hydraulic concrete images. At its core lies the LAHAB, the structure of which is illustrated in Figure 4. To effectively extract luminance features, the LAHAB adopts a multi-branch attention mechanism operating on the Y channel, integrating both global and local luminance perception for comprehensive modeling.  
The Global Attention Block (GAB) is constructed based on the Swin Transformer architecture [41], with its core comprising multi-head self-attention (MHSA). The GAB is responsible for capturing global luminance representations, enabling the model to understand the overall brightness distribution and structural context of the image. Inspired by [42], the input luminance feature map is first partitioned into non-overlapping windows, followed by window-based self-attention and shifted window operations to model long-range dependencies. We adopt the feature separation operation to divide the query (Q), key (K), and value (V) into \(n\) equal parts and obtain \(\{Q_{1},Q_{2},\ldots,Q_{n}\}\), \(\{K_{1},K_{2},\ldots,K_{n}\}\), and \(\{V_{1},V_{2},\ldots,V_{n}\}\). The calculation formula for each attention head is as follows:  
\[Attention(Q_{i},K_{i},V_{i})=Soft\;\text{max}\Bigg{(}\frac{Q_{i}K_{i}^{T}}{ \sqrt{d_{k}}}\Bigg{)}\times V_{i},i\in 1,2,\ldots n \tag{4}\]  
where \(d_{k}=\frac{C}{n}\) and \(C\) represents the number of channels.  
The Y-channel data are convoluted to obtain the luminance features map, then processed by different attention modules. Finally, they are adaptively fused. The process is as follows:  
\[F_{Y}=\lambda F_{GY}+(1-\lambda)F_{LY} \tag{5}\]  
\[F_{Y}=GAB(Conv1(Y)) \tag{6}\]  
Figure 4: The framework of the LAHAB. (**a**) Luminance-Aware Hybrid Attention Block. (**b**) Global Attention Block. (**c**) Multi-head self-attention. (**d**) Channel Attention Block. (**e**) Spatial Attention Block.  
\[F_{LY}=Conv3(( CAB(Conv3(Conv1(Y))))+(SAB(Conv3(Conv1(Y))))) \tag{7}\]  
where \(\lambda\) is an adaptive fusion parameter, the value of which is updated automatically during the update process. The initial value of \(\lambda\) is set to 0.5. \(F_{Y}\) represents the luminance-aware feature, \(F_{GY}\) represents the global luminance feature, and \(F_{LY}\) represents the local luminance feature.  
To complement the global perception with finer detail understanding, we further design a Channel Attention Block (CAB) and a Spatial Attention Block (SAB), following the principles proposed in [43; 44], respectively. These two modules focus on local luminance features, such as crack boundaries, shadows, and texture variations, which are critical for highlighting defect regions. The CAB and SAB are connected in parallel, allowing the model to simultaneously attend to inter-channel relevance and spatial saliency. Convolutional layers with a kernel size of 3 are applied both before and after the attention modules to refine feature representation and ensure local consistency.