esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Optical Detection of Aminoglycosodies
## 3 Colorimetric Assays
### Colorimetric Biosensor Strips  
Lateral flow assays (LFA) are one of the rapid detection technologies and are based on immunohistochemistry. A typical LFA strip contains four parts, including a sample pad, conjugate pad, detection pad, and adsorbent pad. Au NPs are the most commonly used labels in LFA. Antibodies, as the recognition elements, are immobilized on the surface of Au NPs. The immuno-nanoparticles are prestored in the conjugate pad. Both the test line and the control line are located in the detection pad. For the detection of AGs, a competitive immunoassay is commonly employed. LFA strips for the rapid detection of AGs are commercially available. In recent years, aptamers have been employed in LFA, owing to their short synthesis procedure, easy modification, and good stability. The aptamers are conjugated on the surface of Au NPs, and the complementary strands are immobilized on the test line. In the absence of target AG, the hybridization of aptamers with complementary sequences leads to the accumulation of Au NPs on the test line. In contrast, the presence of AG results in competitive binding, which inhibits the accumulation of Au NPs in the test zone. To achieve higher sensitivity, [PERSON]'s group employed Ag NPs as signal amplification elements to carry Au NPs (Figure 5A). With the aid of a scanning reader, quantitative detection of kanamycin can be achieved based on the optical density (expressed as peak area) of the test zone. The LOD was 0.0778 nM in buffer solution, while the visual detection limits in food were higher at 50 nM, 100 nM, and 200 nM in honey, milk, and milk powder samples, respectively. The lateral flow strips avoid multiple incubation and washing steps. The detection can be completed within 10 min [51]. In addition to LFAs, [PERSON] and [PERSON]'s group prepared a portable colorimetric biosensor strip for the determination of kanamycin by combining aptamer-immobilized electrospun nanofiber membranes with complementary single-stranded DNA-conjugated Au NPs. The competitive binding of kanamycin with the aptamer led to the dissociation of Au NPs and a decrease in the absorbance signal at 510 nm. The proposed colorimetric biosensor strips showed a low detection limit of 2.5 nM [52].
# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 4 Ethical Considerations  
There is a noted asymmetry in the literature on the ethics of AI in healthcare, with less attention granted to global health, particularly disease surveillance [4; 19; 60; 61]. Much of the AI-driven intervention research in global health has limited descriptions of the ethical, regulatory, or practical considerations specifically required for cases of widespread use or deployment at scale [59; 62]. Not least, there is the interconnectedness of individual and population levels of disease surveillance, diverse data types, and sources which are required for reporting and decision-support [17; 18; 19].  
Making AI systems transparent, fair, secure, and inclusive is essential in this context, but how these systems are interpreted and operationalized can vary [6]. The OECD has recognized that AI systems deployed in specific contexts require different approaches to policy-making and governance, and have produced an evaluation toolkit to address variations using scenario-based examples [63]. The OECD toolkit shares principles with increasingly pervasive responsible AI frameworks [59; 61; 64; 65], although this still remains a relatively unexplored area when applied to disease surveillance systems.  
The recent literature focusing on the COVID pandemic has only just touched on the potentially important application of responsible AI [59; 64; 65]. The Future Society in collaboration with the GPAI launched a responsible AI assessment framework aimed at applying a number of normative criteria to AI-related initiatives arising in the COVID-19 pandemic. This assessment is based on human rights, inclusion, diversity, innovation, and economic growth [59].  
In an earlier and wider-ranging study, a meta-analysis of ethical AI frameworks by [PERSON] et al. [66] resulted in the development of the AI4 People framework for a \"good AI society\". The five principles underlying the AI4 People framework are [66]:  
* Benefcience: promoting well-being, preserving dignity, and sustaining the planet
* Non-maleficence: privacy, security and \"capability caution\"
* Autonomy: the power to decide (whether to decide)
* Justice: promoting prosperity and preserving solidarity
* Explicability: enabling the other principles through intelligibility and accountability.  
Of particular relevance to this study is the grounding of the AI4 People framework in bioethics principles, which more closely aligns it with an ecosystem of agents, patients, systems, and environments [60; 61; 62; 66]. In applying the AI4 People principles in the context of global public health disease surveillance, we reflect on how such principles could inform both ethical and practical considerations for future disease surveillance system practices and applications.
## 2 Experimental
### Characterization
#### 2.5.3 Thermal Conductivity Test  
Thermal conductivity (\(\lambda\), W/mK) was calculated according to equation: \(\lambda=\alpha\times C_{p}\times\rho\). Thermal diffusivity (\(\alpha\), mm\({}^{2}\)/s, measurement accuracy is 0.001 mm\({}^{2}\)/s) of sample disks (\(\varnothing\)12.7 \(\times\) 2 mm\({}^{3}\)) at 35 \({}^{\circ}\)C was measured by the laser flash method (LFA 447, NETZSCH Instruments Co., Shanghai,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sample** & **Composition (Mass Ratio)** & **Filler Content/(wt \%)** & **Density/(\(\times\)10\({}^{3}\) kg/m\({}^{3}\))** \\ \hline PCM & SBS/HDPE/paraffin = 30/10/60 & 0 & 0.800 \\ PCM-1 EGP & PCM/EGP = 100/1 & 0.99 & 0.800 \\ PCM-3 EGP & PCM/EGP = 100/3 & 2.91 & 0.810 \\ PCM-3 EGP & PCM/EGP = 100/5 & 4.76 & 0.822 \\ PCM-7 EGP & PCM/EGP = 100/7 & 6.54 & 0.834 \\ PCM-10 EGP & PCM/EGP = 100/10 & 9.09 & 0.849 \\ PCM-12 EGP & PCM/EGP = 100/12 & 10.71 & 0.858 \\ PCM-1 MWCNT & PCM/MWCNT = 100/1 & 0.99 & 0.807 \\ PCM-3 MWCNT & PCM/MWCNT = 100/3 & 2.91 & 0.824 \\ PCM-5 MWCNT & PCM/MWCNT = 100/5 & 4.76 & 0.832 \\ PCM-7 MWCNT & PCM/MWCNT = 100/7 & 6.54 & 0.859 \\ PCM-10 MWCNT & PCM/MWCNT = 100/10 & 9.09 & 0.870 \\ PCM-EGP\({}_{9}\)MWCNT\({}_{1}\) & PCM/EGP/MWCNT = 100/9/1 & 9.09 & 0.855 \\ PCM-EGP\({}_{8}\)MWCNT\({}_{2}\) & PCM/EGP/MWCNT = 100/8/2 & 9.09 & 0.866 \\ PCM-EGP\({}_{7}\)MWCNT\({}_{3}\) & PCM/EGP/MWCNT = 100/7/3 & 9.09 & 0.867 \\ PCM-EGP\({}_{9}\)MWCNT\({}_{5}\) & PCM/EGP/MWCNT = 100/5/5 & 9.09 & 0.860 \\ PCM-EGP\({}_{9}\)MWCNT\({}_{7}\) & PCM/EGP/MWCNT = 100/3/7 & 9.09 & 0.861 \\ \hline \end{tabular}
\end{table}
Table 2: The compositions of thermal conductivity-enhanced SSPCMs (shape-stabilized phase change material).  
\begin{table}
\begin{tabular}{c c} \hline
**Sample** & **\(C_{p}\)/(J/gK)** \\ \hline PCM & 2.42 \(\pm\) 0.19 \\ PCM-1 EGP & 2.38 \(\pm\) 0.15 \\ PCM-3 EGP & 2.22 \(\pm\) 0.14 \\ PCM-5 EGP & 2.23 \(\pm\) 0.10 \\ PCM-7 EGP & 2.28 \(\pm\) 0.13 \\ PCM-10 EGP & 2.35 \(\pm\) 0.17 \\ PCM-12 EGP & 2.34 \(\pm\) 0.17 \\ PCM-1 MWMCNT & 2.33 \(\pm\) 0.11 \\ PCM-3 MWCNT & 2.31 \(\pm\) 0.10 \\ PCM-5 MWCNT & 2.37 \(\pm\) 0.15 \\ PCM-7 MWCNT & 2.31 \(\pm\) 0.14 \\ PCM-10 MWCNT & 2.34 \(\pm\) 0.12 \\ PCM-EGP\({}_{9}\)MWCNT\({}_{1}\) & 2.31 \(\pm\) 0.16 \\ PCM-EGP\({}_{8}\)MWCNT\({}_{2}\) & 2.30 \(\pm\) 0.15 \\ PCM-EGP\({}_{7}\)MWCNT\({}_{3}\) & 2.27 \(\pm\) 0.14 \\ PCM-EGP\({}_{8}\)MWCNT\({}_{5}\) & 2.32 \(\pm\) 0.11 \\ PCM-EGP\({}_{3}\)MWCNT\({}_{7}\) & 2.36 \(\pm\) 0.17 \\ \hline \end{tabular}
\end{table}
Table 3: The specific heat values of the compositions of thermal conductivity-enhanced SSPCMs.  
China). Density (\(\rho\), g/cm\({}^{3}\)) was calculated by the mass (measurement accuracy is 0.0001 g)/volume of samples, as shown in Table 2.
# Data Analysis, Simulation and Visualization for Environmentally Safe Maritime Data
## 5 Experimental Evaluation
### Evaluation of the Inference Performance  
In this experimental setting, we are mostly interested in measuring the inference ability of the hybrid Bayesian network in one selected output parameter, namely _danger level_. Certainly, the network is able to simultaneously forecast the future values for all output parameters, but for reasons of readability, the aforesaid was selected. Since the majority of similar works utilize linear regression analysis and neural networks, a benchmark of the performance of the proposed method is provided against Linear Regression and Multi-layer perceptron Neural networks (NN). The method of 10-fold cross validation was used, in which the dataset is iteratively partitioned into two distinct subsets, a train and a test set, using a 9:1 ratio. Algorithms are trained using the former set and their forecasting models are tested on the latter. This step is repeated 10 times using different train and test subsets. The result is the average performance over the 10 experimental runs. About performance evaluation, the following metrics were used:
# An Embedded Sensor Node Microcontroller with Crypto-Processors
###### Abstract  
Wireless sensor network applications range from industrial automation and control, agricultural and environmental protection, to surveillance and medicine. In most applications, data are highly sensitive and must be protected from any type of attack and abuse. Security challenges in wireless sensor networks are mainly defined by the power and computing resources of sensor devices, memory size, quality of radio channels and susceptibility to physical capture. In this article, an embedded sensor node microcontroller designed to support sensor network applications with severe security demands is presented. It features a low power 16-bitprocessor core supported by a number of hardware accelerators designed to perform complex operations required by advanced crypto algorithms. The microcontroller integrates an embedded Flash and an 8-channel 12-bit analog-to-digital converter making it a good solution for low-power sensor nodes. The article discusses the most important security topics in wireless sensor networks and presents the architecture of the proposed hardware solution. Furthermore, it gives details on the chip implementation, verification and hardware evaluation. Finally, the chip power dissipation and performance figures are estimated and analyzed.  
sensor node; security; cryptography; processor; system-on-chip +
Footnote †: journal: Sensors
# CA-BSN: Mural Image Denoising Based on Cross-Attention Blind Spot Network
## 3 Methods
### Feature Extraction Network Construction
#### 3.1.1 Densely Dilated Residual Block Construction  
The dilated convolution extends the field of view of the convolution filter over the feature map by adding dilation between neighboring filter pixels during convolution, using the expanded sense field to allow the network to extract more contextual information and reduce computational cost. To fully utilize the information extracted from the convolution of each layer, we propose a densely dilated residual block (DDR), which combines densely connected residual blocks and dilated convolution under the guarantee of the denoising performance of the mural image, which enhances the information exchange between the layers and obtains richer feature information while reducing the number of references. The structure of densely dilated residual block (DDR) is shown in Figure 2.  
First, the local input feature \(I_{l}\in R^{H\times W\times C}\) is sequentially passed through two dilated convolution layers, \(Conv_{r=2}(.)\) and \(Conv_{r=4}(.)\), with a convolution kernel size of 3 \(\times\) 3, where \(r\) denotes the dilated rate, the convolution kernel 3 \(\times\) 3 in size is the most commonly used filter size in image denoising, and the feature obtained from each pass through the dilated convolution layer and the feature of the previous layer are channel-summing using \(concat(.)\). After the two-layer feature extraction process, an overall channel-summing is performed to obtain the feature information \(O_{l\_cat}\in R^{H\times W\times 5C}\). The complete calculation process is shown in Equation (1):  
\[O_{l\_cat}=concat(Conv_{r=4}(concat(Conv_{r=2}(I_{l}),I_{l})),concat(Conv_{r=2 }(I_{l}),I_{l}),I_{l}) \tag{1}\]  
Then, feature fusion and channel processing are performed using 1 \(\times\) 1 convolution \(Conv(.)\) on \(O_{l\_cat}\).  
Finally, the skip connection is used to transfer the shallow feature information to the deeper convolutional layer to output the local feature \(O_{l}\in R^{H\times W\times C}\). The computational procedure is shown in Equation (2):  
\[O_{l}=I_{l}+(Conv(O_{l\_cat})) \tag{2}\]  
After the above steps of feature extraction and feature fusion, we obtain the locally extracted information of the image, ensuring the denoising performance of the mural image.
# Robust Static Structural System Identification Using Rotations
## 4 Using Redundant Rotations for Parameter Estimation
### Verification for a Simply Supported Bridge
#### 4.2.1 Case 1: Parameter Estimation for a Local Region  
The load case was the same as the one in Section 3.1. The target parameter was \(EI_{2}\) and five rotations (\(w_{5}\)-\(w_{9}\)) were measured; 200 samples were generated for both error levels of 5% and 10%, and 200 estimations were carried out for each error level; 10(\(=C_{2}^{2}\)) essential sets were able to identify \(EI_{2}\) as two rotations were sufficient to identify \(EI_{2}\). In strategy 3, the final estimation was the average of the 10 estimations from these sets. The boxplots of the estimations using the four strategies and the respective essential sets are depicted in Figure 8. The first four columns are the results for the proposed strategies while the last 10 columns are the results for respective essential sets. Great bias in the mean and a large variation are seen for estimations obtained using strategy 1. This is due to the formation of the observability equation. It is noted that the values of the rotations are mainly fed into the coefficient matrix. Additionally, the solutions of the equations are verysensitive to the values of the entries in the coefficient matrix. When strategy 1 is used, the solution is obtained using the least squares method. In such cases, the solution obtained from an ill-posed coefficient matrix may be quite far from reasonable values. Hence, this strategy is no longer investigated in the subsequent studies. In the case of strategies 2-4, the estimations are unbiased and robust.  
Regarding the dispersion in the estimations, the best result was observed in the case of imposing compatibility conditions (strategy 2). The extents of dispersion corresponding to strategies 3 and 4 were also well controlled. After the outliers were ruled out, the c.o.v. of the estimations decreased from 0.016 (strategy 3) to 0.014 (strategy 4) at the error level of 5%. A decrease from 0.034 to 0.028 was also observed at the error level of 10%. The distributions of the estimations using strategies 2-4 at error levels of 5% and 10% are depicted by the probability density curves in Figure 9. It is seen that the deviations in the estimations are bounded by the error levels.
## 2 Results
### Antibiofilm Activity  
To investigate the antib biofilm activity of the peptides, we initially analyzed their ability to affect biofilm formation at different concentrations. Figure 4 shows that both peptides at 1/2 MIC greatly inhibited the capacity of the EDL933 strain to form biofilm on polystyrene plates, while a weaker effect of the peptides was obtained towards the K12 strain.  
Figure 4: Antibacterial and antib Biofilm activity of the peptides on _E. coli_ strains. Planktonic growth (white bars) and biofilm formation (black bars) were quantified in modM9 (CTR) and in modM9 in the presence of 2 or 1 uM for Esc(1-21) and 16 or 8 uM for Esc(1-18) on the EDL933 (**A**) and K12 (**B**) strains, respectively. The bacterial culture was grown for 24 h in 96-well plates at 28 \({}^{\circ}\)C. The optical density (OD) of the planktonic cell suspension was measured at 595 nm to evaluate the bacterial turbidity before its removal from each well of the microtiter, while the OD of the biofilm was measured after CV staining of adherent cells at the same wavelength of 595 nm (see Section 4). Data are presented as mean \(\pm\) SD.  
Figure 3: Membrane perturbation assay performed with Sytox Green dye in the presence of Esc(1-18) and Esc(1-21) on the EDL933 strain. Time 0 indicates the addition of the peptide. Data points are taken from a single experiment representative of three independent experiments. Controls (Ctrl) are cells not treated with peptides.  
Interestingly, both peptides turned out to be more potent in inhibiting biofilm formation rather than the growth of the planktonic form; in fact, the planktonic growth was not affected at the concentration used in the biofilm assay.
## 4 Influence Factors on Damage Location Identification
### Influence of Road Surface Roughness on Damage Identification  
During on-site inspection, the uneven road surface will inevitably have a greater impact on the response of the vehicle, thereby affecting the identification of the bridge damage's location. Using random simulation in Section 2.5 to generate grade A-C road roughness class, the impact of road irregularities on bridge damage identification was studied. The working condition 3 in Section 3.3 (single damage; damage location: 20 m; damage degree 30%) was selected for calculation, and discrete wavelet transform was performed on the difference in acceleration response of the vehicle body to perform bridge damage identification, as shown in Figure 12.  
Figure 11: Wavelet transform coefficients at different vehicle speeds.  
Figure 12: Wavelet transform coefficients under different road smoothness grades.  
Analyzing Figure 12, it can be obtained that the damage location of bridge can be accurately identified under different road surface roughness, which shows that the road surface roughness has little influence on the bridge damage identification method. Under ideal road conditions, the use of acceleration response difference can effectively reduce the impact of roughness on the damage results, but it cannot be completely eliminated.
# Performance Assessment of a Renovated Precast Concrete Bridge Using Static and Dynamic Tests
## 5 Comparisons of Calculation and Test Results to Verify and Validate the FEM Model
### Natural Frequencies and Mode Shapes
Specifically, ten sine waves appeared between the times \(t_{1}\) = 13.72 s of the first peak and \(t_{2}\) = 19.56 s of the last peak. Thus, the third natural period \(T_{d}\) of the structure can be calculated; the result is \(T_{d}\) = 0.584 s. The third natural frequency related \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**No.** & \begin{tabular}{c} **Mode Shape** \\ **(Direction)** \\ \end{tabular} & \begin{tabular}{c} **Natural Frequency** \\ **(Measured)** \\ \end{tabular} & \
\begin{tabular}{c} **Natural Frequency** \\ **(Calculated)** \\ \end{tabular} &
\begin{tabular}{c} **Cross-MAC (Modal** \\ **Assurance Criterion)** \\ \end{tabular} & **Figure** \\ \hline
\begin{tabular}{c} Global in X \\ direction \\ \end{tabular} & N/A 1 & 1.16 & N/A1 & N/A 1 \\
3 &
\begin{tabular}{c} Global in Z \\ direction \\ \end{tabular} & 1.71 & 1.76 & 0.99 & Figure 9 \\
5 &
\begin{tabular}{c} Global in Z \\ direction \\ \end{tabular} & 3.78 & 3.50 & 0.93 & Figure 10 \\
7 &
\begin{tabular}{c} Global in Z \\ direction \\ \end{tabular} & 5.21 & 5.33 & 0.94 & Figure 11 \\
10 &
\begin{tabular}{c} Global in Z \\ direction \\ \end{tabular} & 8.27 & 7.9 & 0.92 & Figure 12 \\ \hline \hline \end{tabular}  
* N/A—not available from measurements.  
\end{table} 
Table 6: Comparison of natural frequencies and mode shapes. Figure 11: The 7 th identified mode shape (**a**) measurement, (**b**) FEM model. to vibration in the vertical (Z) direction is then equal to 1.71 Hz. The same result of the third natural frequency was obtained from measured accelerations (Table 6). The identified natural frequencies from two independent devices (complex measurement system vs. IBIS-S radar) confirmed the precision of the performed measurements. However, some mode shapes could not be extracted from the measured acceleration data because of the relatively small number of accelerometers used, especially in the lateral (Y) direction.
## 3 Integrated Adaptive Security Framework for IoT (IASF-IoT)
### AI-Driven Security Orchestration
Furthermore, Q-learning's scalability makes it suitable for implementation, particularly in resource-constrained environments like the IoT, unlike some computationally intensive deep learning approaches. The model also offers a degree of interpretability through its Q-table, providing security analysts with insights into the decision-making process. This makes Q-learning an ideal choice for managing security in a complex and dynamic environment such as the IoT. The IASF-IoT architecture, as illustrated in Figure 1, demonstrates a layered and modular integration of core security technologies. At the heart of the framework is the AI-Driven Security Orchestrator, as shown in Table 2, which continuously analyses data from cloud services and orchestrates the coordination between identity management, cryptography, autonomous threat response, and privacy-preserving mechanisms. Each module plays a specific role while contributing to the overall adaptive behavior of the system. For instance, the orchestrator dynamically configures Digital Twins based on detected threats and collaborates with the blockchain layer to enforce identity verification. Meanwhile, adaptive content authentication operates in tandem with quantum-resistant encryption to secure edge communications. Federated learning supports distributed model training, informing both the orchestrator and edge-centric security enforcement in real time. Figure 1: The Integrated Adaptive Security Framework for IoT (IASF-IoT). The AI-Driven Security Orchestrator manages, integrates, and coordinates modular components across core, distributed, and edge security layers. Arrows indicate data, control, and feedback flows between components such as Digital Twins, federated learning, and the quantum-resilient authentication module.