esa-sceva/satcom-chunk-collection huggingface dataset:
# Convolution Accelerator Designs Using Fast Algorithms
## 3 Architecture Design
### Winograd Design  
The architecture of the matrix convolution based on the Winograd algorithm is shown schematically in Figure 7. In the figure, the design based on the Winograd algorithm is divided into two stages. In Stage 1, eight convolution modules are needed to complete the convolution between the input feature maps and the kernels. In Stage 2, the output results from the convolutions are summarized to obtain the output feature maps.  
Figure 6: Architecture of the matrix convolution based on the Strassen algorithm.  
Figure 7: Architecture of the matrix convolution based on the Winograd algorithm.  
The FilterNxN module utilizes the Winograd algorithm to complete the convolution. Figure 8 shows the architecture of the FilterNxN module, which adopts the same buffering strategy and module as the ConvNxN module. All the kernels are stored in registers.  
Figure 9 shows the dataflow of the FilterNxN module. We can see from the figure that the \(4\times 4\) data and \(3\times 3\) kernel are sent each clock cycle, which are first transformed according to Equation (A26). Element-wise multiplication is performed immediately following the transformation. Finally, the product is transformed using the matrix K and the matrix K\({}^{\text{T}}\). We can see from Equation (A26) that 16 multipliers are needed in this module. That is, the design in Figure 7 requires a total of \(8\times 16=128\) multipliers.
# Sliding Mode Thau Observer for Actuator Fault Diagnosis of Quadcopter UAVs
###### Abstract  
Fault diagnosis (FD) is one of the main roles of fault-tolerant control (FTC) systems. An FD should not only identify the presence of a fault, but also quantify its magnitude and location. In this work, we present a robust fault diagnosis method for quadcopter unmanned aerial vehicle (UAV) actuator faults. The state equation of the quadcopter UAV is examined as a nonlinear system. An adaptive sliding mode Thau observer (ASMTO) method is proposed to estimate the fault magnitude through an adaptive algorithm. We then obtain the design matrices and parameters using the linear matrix inequalities (LMI) technique. Finally, experimental results are presented to show the advantages of the proposed algorithm. Unlike previous research on quadcopter UAV FD systems, our study is based on ASMTO and can, therefore, determine the time variability of a fault in the presence of external disturbances.  
fault diagnosis; quadcopter UAV; fault-tolerant control; sliding mode observer; Thau observer
# Edge-Aware Dual-Task Image Watermarking Against Social Network Noise
## 4 Experiments
### Robustness Analysis  
To comprehensively evaluate the robustness of our watermarking scheme, we conducted extensive experiments on various types of noise attacks on COCO [51], Div2K [52], and WikiArt [53] datasets. As shown in Tables 4-6, we compare our model with several state-of-the-art methods, including HiDDeN [10], IGA [50], Stegastamp [13], MBRS [14], SSLW [34], De-END [15], Arwgan [35], and Flexmark [36], under different attack scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Methods**} & **Our** & \multirow{2}{*}{**FlexMark**} & **ARWGAN** & **De-END** & **SSLW** & **MBRS** & **StegaStamp** & **IGA** & **HiDDeN** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **S1** \\ **S1** \\ **S2** \\ **S3** \\ **S4** \\ **S5** \\ **S6** \\ **S7** \\ **S8** \\ **S9** \\ **S10** \\ **S11** \\ **S12** \\ **S13** \\ **S14** \\ **S15** \\ **S16** \\ **S17** \\ **S18** \\ **S19** \\ **S10**  
Our model demonstrates remarkable robustness across all attack types. For Gaussian noise attacks with varying intensities (0.01, 0.03, and 0.05), our method consistently achieves the highest extraction accuracy. On the COCO [51] dataset, our model achieves 99.97%, 99.95%, and 99.86% accuracy under increasing noise intensities, significantly outperforming the second-best method De-END [15] (99.94%, 98.58%, and 97.30%). A similar superior performance is observed on Div2K [52] and WikiArt [53] datasets.
## 4 Proposed Model
### Proposed Hybrid Model
#### 4.1.1 Window Size Manipulation  
Window size manipulation represents a TCP/IP parameter regulating network traffic, and it determines how many data can be sent at a time in a network connection. It is possible to utilize window size manipulation to detect abnormal behaviors in network traffic and prevent cyberattacks. Malicious users can manipulate the window size to perform denial of service (DoS) attacks. In the TCP/IP protocol, the window size determines how many data segments can be sent without acknowledgment. A malicious user can cause the target system to be overloaded or the network traffic to be corrupted by changing this window size. To explain this with an example, the \"TCP Zero Window\" attack is such an attack type. In the attack mentioned above, the attacker sets the window size determined by the receiver to zero, causing the sender to stop sending data and essentially freeze the network  
Figure 4: The overall framework of the proposed hybrid model.  
connection. This situation can cause the target server to be out of service or severely disrupt the network traffic. In other words, an attacker can acquire important information about the state and functioning of a target system by monitoring and analyzing the network traffic. We can disrupt this flow of information and render the attacker's actions more difficult by manipulating the window size. The AI model can be trained to identify actions violating normal functioning in the ICS dataset. If an anomaly is detected, the model can manipulate the window size in the ICS's network traffic, making it challenging for an attacker to monitor the flow of information in the system.
## 4 Implementation and Results
### Evaluation Metrics  
By implementing our integrated manufacturing application that was based on HCPS 2.0, improvements in the productivity, quality, sales, and profit growth of a company were realized using the evaluation indicators to measure whether each goal was achieved. Target  
Figure 11: The configuration of the data gathering system.  
Figure 10: The software configuration.  
values were presented in the first half of 2021 and the improved indicators were set and managed for each year or specific period. Table 1 shows the KPIs that were presented in the first half of 2021 and the facility utilization rate represents the increase/decrease in the number of operating hours for production equipment, based on there being 24 h in a day. Excluding downtime due to decreases in order volume, the operation rate was calculated as (actual facility operating time/worksite working hours) \(\times\) 100. The actual operating time refers to the time that the equipment spent emitting laser beams and the working hours are based on there being 24 h in a day. When unprocessed downtime occurred due to insufficient order volumes, the unprocessed downtime was calculated separately and managed as a key performance indicator for the sales department, which was the time that was registered on the production calendar but excluded downtime due to unavoidable external events or when there were no products to be processed. The process defect rate represents the number of defects in relation to the production input per process unit and was calculated as the number of generated defects/process input. The unit of production is the production scale meter and the unit of defective quantity is the defective square meter.  
To store the facility's performance data in the form of machine log files in real time, the IoT platform communicated with the PC that was installed in the facility in real time and successfully transmitted the data to the designated manufacturing management data server (DAQ, MES or edge computer) using IP communication. To analyze the outcomes, real-time monitoring was carried out using data visualization, which consisted of inspection progress by unit, LOT status by process, progress status by LOT, and facility operation/non-operation status items. The log file status by facility, log transmission status by facility, and facility data management were all aspects of IoT data management. Each abnormal occurrence and quality inspection data point used the prediction/maintenance status and a real-time decision support system was designed and developed to implement a production site prediction/maintenance system by linking the task assignment status with the work order by facility item, as well as abnormal occurrence management. We then checked that it worked properly.
# High-Precision Roller Supported by Active Magnetic Bearings
## 2 Automatic Alignment System Using Magnetic Bearings
### Principles of the Automatic Alignment System Using Magnetic Bearings  
Figure 2 shows the testing setup for assessing the automatic alignment performance of the precision roller for printing. Radial magnetic bearings capable of controlling the radial position and force were installed at both ends of the roller, and a thrust magnetic bearing was placed at the right end of the roller to control the axial movement. Inside the radial magnetic bearings, gap sensors were attached in the x-axis and y-axis directions to measure the displacement of the rotor in real time. Through the feedback of the gap sensors on the measured roller displacement, the position of the roller inside the air gap was controlled. The roller proposed in this study can move up to \(\pm\)0.2 mm in the radial direction from the center of the radial magnetic bearings and the force and position of each of the left and right magnetic bearings can be controlled. The position of the radial bearings can be used to adjust the force exerted by the roller and the load cells provide force feedback of the contact force and alignment of the roller with respect to the element the roller is in contact with. Figure 3 is a conceptual diagram of the automatic alignment of the roller and shows the definition of alignment used in this paper. If the printing forces are the same, it is assumed that alignment is achieved.  
Figure 1: Schematic diagram of magnetic bearing control system.  
Figure 2: Precision roller testing setup with magnetic bearings for automatic alignment performance assessment.
## 3 Equation Fitting
### Pre-Fitting  
Prior to data fitting, the element abundance records were systematically segmented to ensure meaningful classification. Among the 791 records, 29 records met the criterion of SiO\({}_{2}\)\(\leq\) 33. In the remaining 762 records, a subset of 54 records was identified with MgO > 3.9. From the subsequent set of 708 records, 342 records were selected based on WIG \(\leq\) 65, effectively distinguishing samples with lower weathering intensity. The remaining 366 records were categorized based on Na\({}_{2}\)O content, with 229 records having Na\({}_{2}\)O \(\leq\) 3 and an additional 137 records falling outside this range (Figure 1).  
Specifically, analysis of the scatter plot between SiO\({}_{2}\) and lgCr (Figure 1a) revealed two clustering patterns, prompting an initial differentiation based on SiO\({}_{2}\) < 33. Notably, the majority of records with SiO\({}_{2}\) < 33 were identified as carbonatites or marbles. Subsequently, the relationship between MgO and lgCr (Figure 1b) exhibited a marked change in trend, leading to further classification of the samples with SiO\({}_{2}\)\(\geq\) 33 based on MgO > 3.9. According to this classification criterion, the majority of the abundance data for records with MgO > 3.9 correspond to mafic and ultramafic rocks. The remaining records primarily consisted of soil, stream sediment, and rock samples, necessitating further classification. The WIG values served as a reliable metric for segmenting samples into categories based on  
Figure 1: Illustration of the stepwise classification of the 791 element abundance records. (**a**) The initial subset of samples with SiO\({}_{2}\) < 33 (29 records), along with the remaining data (762 records). (**b**) The next subset of samples, excluding those already classified in (**a**), with MgO > 3.9 (54 records), resulting in a remaining total of 708 records. (**c**) Those with MgO > 3.9 identified in (**b**), with the remaining samples subsequently separated with WIG \(\leq\) 65 (342 records). (**d**) Samples with WIG \(\leq\) 65 (342 records) as shown in (**c**), with the remaining samples then distinguished into two categories based on Na\({}_{2}\)O \(\leq\) 3 as 229 records with Na\({}_{2}\)O \(\leq\) 3 and 137 records with higher Na\({}_{2}\)O.  
weathering intensity, effectively distinguishing those with WIG \(\leq\) 65 as weathered samples (Figure 1c). Notably, lower WIG values indicate higher weathering intensity. The final classification step focused on the remaining records, which were mainly rock samples, and the scatter plot between Na\({}_{2}\)O and lgCr (Figure 1d) indicated a further division into low and high Na\({}_{2}\)O categories, based on Na\({}_{2}\)O \(\leq\) 3.
# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 4 Results
### Technological Innovations
#### 4.3.3 Privacy-Preserving and Secure Decentralized Access-Control System for Low-Emission Zones [33]  
This study introduces a novel decentralized access-control system for LEZs that combines privacy-preserving mechanisms with blockchain technology to overcome the limitations of centralized access-control schemes. It emphasizes user privacy, non-repudiation, and fraud prevention, aligning with the evolving needs of modern LEZ implementations. Its key contributions are as follows:  
* Decentralized Control: The system replaces centralized entities with a blockchain-supported network for managing vehicle accesses and payments, enhancing transparency and reducing the risk of privacy violations.
* Privacy Preservation: User anonymity is preserved using pseudonyms that can be renewed regularly, ensuring the non-traceability of users' actions unless fraudulent behavior is detected.
* Blockchain and Smart Contracts: Access events are processed as blockchain transactions using smart contracts, enabling automated fee calculation and payment while ensuring data integrity.
* Non-Repudiation and Fraud Control: The system incorporates cryptographic proofs to prevent unauthorized access and ensures that dishonest users can be identified without compromising the privacy of compliant drivers.
* Scalable and Adaptable Design: Integrating onboard units (OBUs) and cryptocurrency mixing services ensures scalability, making the system adaptable for urban environments with varying traffic densities.  
This system provides a foundation for implementing dynamic LEZs that require real-time data processing and user trust by decentralizing access control and enhancing privacy. Its blockchain-powered approach ensures reliable, equitable, and privacy-focused management of urban emission zones.
# Urokinase System in Pathogenesis of Pulmonary Fibrosis: A Hidden Threat of COVID-19
## 4 Materials and Methods
### Protein Extraction, SDS-PAGE Electrophoresis and Western Blot  
Cells were collected by centrifugation for 10 min at 800g. Cell pellets were resuspended in NETN buffer (150M NaCl, 1 mM EDTA, pH 7.5 50 mM Tris, 0.5% NP 40, protease and phosphatase inhibitor cocktails), sonicated for 10 s at 30%, incubated on ice for 30 min, and centrifuged at 4 \({}^{\circ}\)C at 12,000\(\times\)\(g\) for 10 min. The supernatant was transferred into a new pre-cooled microcentrifuge tube, and the cell pellet was discarded. Protein quantification was performed using the Pierce(tm) BCA Protein Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA) on the NanoDrop 2000C (Thermo Fisher Scientific, Waltham, MA, USA). After measuring the concentration, cell lysates used for Western blot were supplemented with Laemmli buffer (LDS) and 0.1M DTT, then heated at 95 \({}^{\circ}\)C for 10 min.  
Protein samples (20 \(\upmu\)g) and prestained molecular weight markers (PageRuler(tm) Pretained Plus Protein Ladder, Thermo Fisher Scientific, Waltham, MA, USA) were resolved on 12-well precast SDS-PAGE gels (4-12%) (NuPage, Carlsbad, CA, USA) in MOPS Running Buffer (NuPage, Carlsbad, CA, USA). Proteins were transferred onto 0.45 \(\upmu\)m PVDF membrane (GE Healthcare, Chicago, IL, USA) in a transfer buffer (0.025M Tris, 0.192M Glycine, 20% ethanol) at 90V at + 4 \({}^{\circ}\)C for 2 h. Nonspecific binding was blocked in 5% non-fat dried milk in Tris-buffered saline, 0.1% Tween-20 (TBST) for 1h at room temperature.  
Proteins were probed at +4 \({}^{\circ}\)C overnight with the following primary antibodies: rabbit anti-uPAR (1:500, Abcam, Cambridge, UK, ab103791), rabbit anti-N-cadherin (1:1000, Abcam, ab76011), rabbit anti-E-cadherin (1:500, Abcam, Cambridge, UK, ab40772), mouse anti-\(\upbeta\)-actin (1:1000, control of protein load, Santa Cruz, Biotechnology, Dallas, TX, USA, cat. # sc-81178). Membranes were washed with TBST and incubated with the appropriate peroxidase-conjugated secondary antibodies (g goat anti-mouse IgG-HRP (Santa Cruz Biotechnology, Dallas, TX, USA, cat. # sc-2005), donkey anti-rabbit IgG-HRP (Santa Cruz Biotechnology, Dallas, TX, USA, cat. # sc-2313)) in 1:1000 dilution for 2 h at room temperature, and followed by washing in TBST. Proteins were visualized using Immobilon Western Chemiluminescencet HRP Substrate (Millipore, Burlington, MA, USA) and ImageQuant LAS 4000 mini (GE Healthcare, Chicago, IL, USA) for Western blot imaging and analysis. Densitometric analysis of blots at non-saturating exposures was performed using ImageJ. The original uncropped Western blot images are presented in Figure S4.
## 3 System Framework Overview
### Application Layer  
The application layer of the proposed model consists of two cloud components which involve the Software as a Service (SaaS) features, providing all required software applications for the interaction of end user with the system. The applications are centrally hosted, accessed by users remotely and licensed on a subscription basis.
# Investigation of High-Sensitivity pH Sensor Based on Au-Gated AlGaN/GaN Heterostructure
## 3 Results and Discussion
### I-V Characteristics and Current Sensitivity  
Figure 7 presents the output current-voltage (I\({}_{\text{DS}}\)-V\({}_{\text{DS}}\)) characteristics of the Au-gated AlGaN/GaN pH sensor with W/L = 160/40 \(\upmu\)m in the three pH standard buffered solutions at the V\({}_{\text{Ref}}\) from \(-\)3 to 1 V (1 V/step). The drain-source voltage (V\({}_{\text{DS}}\)) was swept from 0 to 5 V. The sensor exhibits good current saturation characteristics. The magnitude of the V\({}_{\text{DS}}\)  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sv (mV/pH)** & **pH Range** & **Sensing Membrane** & **Ref.** \\ \hline
58.59 & 4–9.18 & Au & This work \\
57.7 & 4–9 & Al\({}_{2}\)O\({}_{3}\) & [6] \\
57.8 & 4–9 & Al\({}_{2}\)O\({}_{3}\) & [9] \\
55.81 & 4–9 & AlGaN & [11] \\
57.66 & 4–12 & ZnO & [13] \\
55.2 & 2–12 & Al\({}_{2}\)O\({}_{3}\) & [14] \\
55.7 & 4–9 & AlGaN & [15] \\
53.3 & 4–10 & GaN & [16] \\
50 & 4–8 & GaN & [21] \\
49 & 2–11 & AlGaN & [22] \\
55.5 & 4–9 & AlGaN & [23] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of potential sensitivity with previously reported AlGaN/GaN pH sensors.  
Figure 6: S\({}_{\text{V}}\) of the Au-gated AlGaN/GaN pH sensor.  
influences the electric field within the heterostructure, thereby affecting the carrier mobility and the saturation current. When the V\({}_{\text{Ref}}\) becomes more negative, the difference in the I\({}_{\text{DS}}\) across various pH levels (such as pH = 4.0 and pH = 9.18) decreases. This is because the more negative voltage changes the electric field, decreasing the sheet electron density within the 2 DEG, thereby leading to weaken the sensor's response to pH variations.  
In practical applications, the transconductance (G\({}_{\text{m}}\)) and output current are more effective parameters to integrate with subsequent circuits, where current sensitivity (S\({}_{\text{A}}\)) is essential. The S\({}_{\text{A}}\) of the sensor is defined as:  
\[S_{A}=\frac{\Delta I_{DS}}{\Delta pH}=G_{m}S_{V} \tag{3}\]  
where \(\Delta\)I\({}_{\text{DS}}\) is the change of drain-to-source current and \(\Delta\)pH is the change of pH value. Using Equation (3) to calculate the results from Figure 7, the current sensitivities of the sensor are shown in Figure 8 with different V\({}_{\text{Ref}}\) values.  
Figure 7: I\({}_{\text{DS}}\)-V\({}_{\text{DS}}\) of the Au-gated AlGaN/GaN pH sensor with W/L = 160/40 \(\upmu\)m in the three pH standard buffered solutions at different V\({}_{\text{Ref}}\).  
Figure 8 shows the S\({}_{\rm A}\) of the Au-gated pH sensor achieves 372.37 uA/pH (V\({}_{\rm DS}\) = 3.5 V, V\({}_{\rm Ref}\) = 0 V) with a linearity of 99.99% in the pH range from 4.0 to 9.18. The findings indicate that the sensor exhibits high sensitivity to microliter solutions.
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 2 Materials and Methods
### Incorporation of the Sharpness-Aware Minimizer (SAM)  
The training procedure for mainstream convolution-free transformer architectures, as mentioned above, relies heavily on extensive pretraining [22] or a variety of strong data augmentations [29], leaving many hyperparameters to tune [22]. When trained from scratch on a regular-sized dataset without using advanced data augmentations, the ViT network and its variants usually yield inferior accuracy to CNNs of similar size. This is because transformer architectures tend to converge at extremely sharp local minima [29]. To address this issue, the sharpness-aware minimizer (SAM), which can explicitly avoid sharp minima, was put forward. [PERSON] et al. [22] pointed out that by explicitly regularizing the loss geometry through SAM, transformer-related models enjoy much flatter loss plateaus and improved generalization regarding accuracy and robustness.  
Figure 1: Illustration of TransCNN (modified after [24]). DW Conv represents depthwise separable convolution; TDB: Two-branch Downsampling Block; IRB: Inverted Residual Bottleneck; \(\times\) Li means that the H-MHSA + IRB block is repeated Li times; S denotes the stride of the convolution; H and W denote the height and width of the input image, respectively; SiLU is the nonlinear activation function.  
To be specific, SAM seeks to find the parameter \(w\) whose neighbors all have lower training loss \(L_{train}\) by formulating a minimax objective:  
\[\underbrace{min}_{w}\underbrace{max}_{||\epsilon||_{2}\leq\rho}L_{train}(w+\epsilon) \tag{1}\]  
where \(\rho\) is the size of the neighborhood ball. Without loss of generality, here, the \(l_{2}\) norm is used for its strong empirical results, and the regularization term is omitted for simplicity [29]. The exact solution of the inner maximization is as follows:  
\[\epsilon^{*}=\arg max_{||\epsilon||_{2}\leq\rho}L_{train}(w+\epsilon) \tag{2}\]  
As \(\epsilon^{*}\) is difficult to obtain, we employ an efficient first-order approximation:  
\[\begin{split}\hat{\epsilon}(w)&=\underbrace{\arg max _{Ltrain}(w)+\epsilon^{T}\bigtriangledown_{w}L_{train}(w)}\\ &=\rho\bigtriangledown_{w}L_{train}(w)/||\bigtriangledown_{w} L_{train}(w)||_{2}\end{split} \tag{3}\]  
Under the \(l_{2}\) norm, \(\hat{\epsilon}(w)\) is simply a scaled gradient of the current weight \(w\).  
After computing \(\hat{\epsilon}\), SAM updates \(w\) based on the sharpness-aware gradient as:  
\[\bigtriangledown_{w}L_{train}(w)\big{|}_{w+\hat{\epsilon}(w)} \tag{4}\]  
SAM is an optimization algorithm often used in the training of pure transformer architectures. However, we found that it also performed well when training our transformer-CNN combined model.