esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The CS technique has been widely adopted in signal-processing applications due to the faster data acquisition times and smaller storage spaces it offers. However, its adoption in some applications has been hindered by the fact that compressed data requires reconstruction before further signal-processing for analysis in either the time or the frequency domains, a process that features redundant transformations between the two domains. In this paper, we proposed a simpler framework that avoids full reconstruction of ultrasound CS data. Here, using an appropriate basis matrix to transform the signal to its sparse form, spectral properties can be directly extracted from compressed data, and the conversion to a complex analytic signal for detection of the envelope of the original measurement in the time domain is made more efficient. Simulations conducted with numerical TM phantoms confirmed that this framework is simpler and faster in execution times than the traditional framework, average 63.6% of the time required for the envelope detection and average 24.6% for the attenuation estimation at compression rates varying from 50% to 90%. For the estimation accuracy, the RMSEs of envelope detection and attenuation estimation were averagely reduced by 10% and 38% compared to traditional method at all compression rates. Hence, we suggest that the proposed framework offers a new standard for signal processing using ultrasound CS data, especially for small and portable systems, and applications that handle large datasets.  
Conceptualization, Y.K. and H.K.; methodology, Y.K. and H.K.; software, J.P.; validation, Y.K. and J.P.; investigation, Y.K. and H.K.; resources, H.K.; data curation, J.P.; writing--original draft preparation, Y.K.; writing--review and editing, Y.K. and H.K.; visualization, J.P.; supervision, H.K.; project administration, Y.K.; funding acquisition, H.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP); the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20174010201620); and research grant of Kwangwoon University in 2018.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CDM & Centroid Downshift Method \\ CS & Compressed Sensing \\ DCT & Discrete Cosine Transform \\ DFT & Discrete Fourier Transform \\ FFT & Fast Fourier Transform \\ IFFT & Inverse Fast Fourier Transform \\ QUS & Quantitative UltraSound \\ RF & Radiofrequency \\ RMSE & Root Mean Square Error \\ ROI & Region of Interest \\ TGC & Time Gain Compensation \\ TM-phantom & Tissue-Mimicking Phantom \\ \end{tabular}
## 4 Establishment of Contact Mechanics Model
### Conclusions  
(1) The contact state and porosity of rough sealing surfaces were analyzed. Through numerical calculation and simulation, it was found that when the topography parameters of two rough surfaces are the same or similar, the contact should be considered shoulder-shoulder rather than top-top.  
(2) The geometric model for the shoulder-shoulder contact and contact mechanical model for rough surfaces were established, and the deformation of asperity, the real contact area and contact load of rough sealing surface were discussed. By comparison with other methods, the present contact mechanics model was verified.  
(3) The change rules of porosity \(\phi\) with contact pressure \(p\) and topography parameters were obtained: Porosity decreases with the increase of \(p\); larger fractal dimension \(D\) and smaller fractal roughness \(G\) correspond to larger initial porosity but faster and largerdecrease of porosity; with the increment of \(D\), porosity increases first and then decreases on the whole, and smaller fractal roughness \(G\) corresponds to larger porosity reduction; as the fractal roughness \(G\) becomes bigger, all the porosity increases, and larger \(D\) corresponds to a larger difference and change of porosity.  
(4) The change rules of contact area ratio \(A_{\rm r}/A_{0}\) with contact pressure \(p\) and topography parameters were achieved: With the addition of contact pressure \(p\), \(A_{\rm r}/A_{0}\) increase, and the variation of \(A_{\rm r}/A_{0}\) is closer to linearity with smaller changing amplitude at smaller fractal dimension \(D\) and larger fractal roughness \(G\); with the increase of \(D\), \(A_{\rm r}/A_{0}\) increases gradually, and the growth rate is bigger at smaller \(G\) and bigger \(p\); as \(G\) becomes bigger, \(A_{\rm r}/A_{0}\) declines, and it declines more gently at smaller \(D\) and \(p\); the influence of fractal dimension \(D\) on \(A_{\rm r}/A_{0}\) is greater than that of fractal roughness \(G\).  
(5) The findings of this paper could be further verified by comparing the porosity obtained by the established model with the results obtained by 3D reconstruction and finite element analysis, and the leakage or lubrication of sealing surface also could be studied, all which need further in-depth research.  
Conceptualization, Q.Y. and J.S.; data curation, Q.Y.; formal analysis, Q.Y. and Z.J.; funding acquisition, J.S. and Z.J.; investigation, Q.Y.; methodology, Q.Y. and J.S.; project administration, J.S.; resources, Z.J.; supervision, J.S.; validation, Q.Y.; writing-original draft, Q.Y.; writing--review & editing, Q.Y., J.S. and Z.J. All authors have read and agreed to the published version of the manuscript.  
This work is financially supported by National Natural Science Foundation of China (No.52075268), National Key Research and Development Program of China (No.2018 YFB2000800) and Project of Huai'an Science and Technology Bureau (No.HAB202069).  
Not applicable.  
The authors declare no conflict of interest.
# Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis
## 3 Dataset Description
### Graph Edit Distance  
Graph Edit Distances (GEDs) [15; 22; 23] are dissimilarity measures belonging to the wide family of edit distances, extending a set of consolidated and well-known dissimilarity measures defined on sequences (e.g., the Levenshtein distance [24]) towards graphs. If in a discrete finite domain, such as the string domain, the edit distance can be simply evaluated as the minimum number of insertions, substitutions and deletion of letters needed to transform a given string into a target one, the same does not hold where annotations of atomic objects can be equipped with arbitrary data structures. In other words, the \"exactness\" of a match is always guaranteed in the string domain (i.e., two letters are the same or not), but it is rarely guaranteed in the graph domain (i.e., only in cases when nodes and/or edges are equipped with categorical attributes).  
Amongst the several GEDs available in the literature, our choice fell on the node Best Match First (nBMF) GED. In short, the nBMF similarity measure starts by matching (in a greedy manner) most similar nodes first, where the similarity between nodes is evaluated thanks to a suitable dissimilarity measure \(d^{\text{node}}(\cdot,\cdot)\). Then, possible edges between each pair of previously-found similar nodes are searched for in both graphs to be compared: if found, this counts as an edge substitution whose cost is given by a suitable dissimilaritymeasure \(d^{\text{edge}}(\cdot,\cdot)\) defined on the edge labels domain. The difference between the order of the two graphs accounts for the nodes' deletion and insertion, whereas the existence of induced edges in only one of the two graphs yields an edge deletion or insertion. Full mathematical details on nBMF can be found in [25].  
As will be clear in Section 3.4, in this work, we deal with graphs whose nodes are equipped with MAC addresses and whose edges are equipped with a 6-elements real-valued vector. Therefore, we let \(d^{\text{node}}(n_{1},n_{2})\) be the discrete distance (i.e., the dissimilarity is 0 or 1 depending on whether the two nodes \(n_{1}\) and \(n_{2}\) share the same MAC address or not) and we let \(d^{\text{edge}}(e_{1},e_{2})\) be the \(\ell_{1}\)-norm between the feature vectors of two given edges \(e_{1}\) and \(e_{2}\).
## 6 Purpose-Based Encryption
### Evaluation
#### 6.6.1 Performance  
The performance depends on two factors: the security levels and the numbers of key attributes.  
1. _Security levels:_ The ABE scheme computes bilinear pairing operations. The National Institute for Standards and Technology (NIST) has recommended the sizes for secure settings of parings and their validity period [77]. Our implementation also follows these key size settings of NIST for both AES and ABE.  
Figure 16: (**a**) The shopping service; (**b**) The delivery service.  
2. _Key attributes_: The decryption of the ABE scheme has \((2l+1)\) pairing operations for \(l\) key attributes, and the pairing is an expensive operation. By using the segment tree [74], we can reduce any numerical ranges of size N by a set of size \(log_{2}(N)\) key attributes. Also, we present the access time policy in a \"day\" unit, which takes at most 10 key attributes. The \"day\" unit is not as precise as \"millisecond\". However, most examples of data retention in the P3P [2] are in days, so we think the \"day\" unit is reasonable.  
We have tested the performance with various data sizes (i.e., 1 byte, 1 MB, 10 MB, and 100 MB) and with various security levels (i.e., 80, 112, 128, 192, and 256 bits). Figure 17a shows the performance result on a CPU i7-8650U, 1.90 GHz, 16 GB RAM. First, we notice that various data sizes in the same security level (i.e., 1 byte, 1 MB, 10 MB) affect the encryption and decryption time minimally. This is because we use AES to encrypt and decrypt the data. Second, the performance is fast for the following security levels: 80, 112, and 128 bits. To give an illusion, for 128 security bits, it took 377 ms and 284 ms to encrypt and decrypt 1 MB, respectively. However, above this level, the performance degrades significantly.  
Figure 17b shows the token generation (TAT and PAT in total) is very fast for 112 and 128 security bits but slow for 192 security bits (9 seconds). Higher security levels also increase the token size slightly. For 112 and 128 security bits, the tokens add additional overhead to the protocol SAML and OAuth (in total) with 10 KB and 15 KB, respectively. We think this token overhead is acceptable.  
In summary, we think the performance is acceptable for 112 and 128 security bits. However, for 192 bits, service consumers will experience a delay in their transactions (around 12 seconds in total). For 256 bits, the performance is not acceptable. To improve the performance, we may implement an ABE scheme that is built on Type F curves [78], which is known to provide faster performance at these levels [79].
## 5 Discussion  
Two different mechanical models were used to calculate the stability of the surrounding rock under the same support scheme. The calculation accuracy of the two models and the reasons for the deviation are compared and analyzed according to the calculation results and actual monitoring data.  
Comparative Analysis of Numerical Calculation Results of Original Plan and Monitoring Data of Surrounding Rock Deformation  
When the strain-softening characteristics of a rock mass are not considered, the rock mass is considered to be an ideal elastoplastic body, and the failure process of the rock mass obeys the Mohr-Coulomb criterion, on the basis of which the shaft-surrounding rock-supporting structure is designed. According to the calculation results (Figure 11), only the original support scheme 2 meets the stability requirements of the surrounding rock. When radial displacement of the rock mass reaches 33-34 mm, the surrounding rock and supporting structure are in balance. On-site monitoring showed that the supporting structure of the actual original supporting plan 2 produced three different degrees of damage, and the surrounding rock had no obvious convergence trend. The supporting structure was unstable at 18 days after construction, and deformation of the rock mass continued to increase until 23 days after construction. On-site monitoring data showed that the surrounding rock deformation only started to slow down when the surrounding rock deformation reached 300 mm. The numerical calculation results deviate from the on-site monitoring results.
# The Role of AI-Based Chatbots in Public Health Emergencies: A Narrative Review
## 7 Challenges and Limitations of AI Chatbots in Infectious Disease Outbreaks
### Amplification of Misinformation  
Epidemics often trigger an \"infodemic\"--an overabundance of information, including rumors and misinformation [118]. AI chatbots, if not properly monitored, can unintentionally amplify false information due to their perceived authority, leading to harmful behaviors and undermining public health efforts [123]. Detecting misinformation is challenging because it evolves rapidly and is often context-dependent. While machine learning algorithms for misinformation detection exist, their integration into conversational AI systems remains underexplored [124].  
To mitigate misinformation, it is necessary to integrate advanced NLP models capable of identifying false information in chatbot responses [125]. Chatbots should also cross-reference trusted databases from health organizations to ensure the reliability of the information provided [105]. Socio-technical measures, such as user reporting mechanisms and transparent disclaimers, foster a collaborative and trustworthy environment [126]. Partnering with fact-checking organizations can ensure that chatbots remain up-to-date with verified corrections [127].  
Key research areas include developing real-time misinformation detection in AI chatbots, understanding the impact of misinformation on user behavior, and maintaining user trust through transparent content moderation policies [128]. Ethical considerations also require collaboration with policymakers to ensure responsible chatbot deployment, emphasizing transparency and accountability [111].
## 2 CMOS and MTJ Technologies Operating at Cryogenic Temperatures  
### CMOS Devices Under Cryogenic Operation  
Standard CMOS technology can operate across a wide temperature range, but commercially available process design kit (PDK) models lose accuracy at cryogenic temperatures (below 200 K) where key transistor properties are significantly impacted [31; 32]. As the operating temperature goes down to the cryogenic levels, transistors exhibit an increase in threshold voltage, carrier mobility, and saturation velocity, as well as a higher sub-threshold slope [32; 33].  
Early studies often relied on cryogenic-aware transistor models based on existing research or simulations [34; 35; 36]. In contrast, this work uses a 65 nm CMOS BSIM4.7-based transistor model, calibrated for cryogenic operation down to 77 K. The calibration was carried out via silicon measurements from liquid nitrogen-cooled test chips. Our calibrated model exhibits very high accuracy at cryogenic temperatures.  
Figure 1 compares the nMOS transistor current-voltage characteristics at both room (300 K) and cryogenic (77 K) temperatures for three scenarios: the original foundry PDK model, our calibrated model, and actual silicon measurements. While both models closely match the experimental data at room temperature, the calibrated model demonstrates a substantial reduction in average error at 80 K. The non-calibrated commercial PDK model suffers from errors of about 18% and 70% for drain-source current (\(I_{\text{DS}}\)) versus drain-source voltage (\(V_{\text{DS}}\)) and for \(I_{\text{DS}}\) versus gate-source voltage (\(V_{\text{GS}}\)), respectively. In comparison, our calibration reduces the average error in current-voltage characteristics to less than 2% for \(I_{\text{DS}}\) versus \(V_{\text{DS}}\) and less than 10% for \(I_{\text{DS}}\) versus \(V_{\text{GS}}\).  
Figure 1: Current–voltage characteristics of CMOS transistors at 300 K and 80 K: (**a**,**b**) drain–source current (\(I_{\text{DS}}\)) as a function of drain–source voltage (\(V_{\text{DS}}\)) for various gate–source voltage (\(V_{\text{GS}}\)); (**c**,**d**) \(I_{\text{DS}}\) as a function of \(V_{\text{GS}}\) for various \(V_{\text{DS}}\). Data refer to an nMOS transistor with a channel length of 60 nm and a width of 1 um, with the body–source voltage (\(V_{\text{BS}}\)) set to 0 V (reproduced from our previous work [30]).
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 2 Materials and Methods
### Analysis
#### 2.6.2 Structure from Motion Multi-View Stereo Photogrammetry (SfM-MVS)  
We used Pix4D Mapper v4.6.4 (Pix4D, Prilly, Switzerland) for the SfM-MVS workflow (Figure 8). The inputs into the workflow were the jpg photographs acquired by UAS. The final products, namely the dense 3D point cloud, the orthomosaic and the digital surface models (DSM) are the result of a multi-step process. In the first step, Pix4D optimizes both internal (e.g., focal length) and external (e.g., orientation) camera parameters and carries out an automatic triangulation and bundle block adjustment [65]. Keypoints, representing points in each photograph with a unique and identifiable texture, are located. These keypoints are then matched between neighbor photographs. The sparse point cloud (output of this first step) is generated through a modified scale-invariant feature transform (SIFT) algorithm [66; 67]. In step two, the point cloud is densified with a multi-view stereo (MVS) photogrammetry algorithm [68]. The output of this step, the dense point cloud, is one of final products used for further analysis. In step 3, the dense point cloud is interpolated with an inverse distance weighting (IDW) algorithm to generate the DSM in raster format. The input photographs and the DSM are used to generate an orthomosaic without perspective distortion. The DSM and orthomosaics are the other two final products used and discussed in this study.
# Cross-Domain Transfer Learning for Natural Scene Classification of Remote-Sensing Imagery
## 3 Materials and Methods
### Proposed Framework for Natural Scene Classification
#### 3.2.2 Pre-Trained Model Selection  
We deployed ResNet-50 and VGG-19 model architectures for cross-domain transfer-learning experiments. ResNet-50, as its name suggests, comprises 50 layers and it contains residual blocks which add input to the output of the block with the help of skip connections. It was introduced by Microsoft Research [15] in 2015 and has since become one of the most widely used and influential deep-learning models. The following are the key features of the ResNet-50 model.  
_Residual Learning:_ ResNet-50 came up with the idea of residual learning [15; 27], which solved the vanishing gradient problem in deep neural networks. The vanishing gradient problem refers to the observation that as deep networks are trained, their performance starts saturating and then degrades rapidly. Residual learning tackles this issue by introducing skip connections (shortcut connections) which enable the network to learn residual representations rather than learning underlying representations directly. These skip connections (see Figure 7) enable the gradients to pass through the network directly, addressing the vanishing gradient problem and facilitating the training of extremely deep networks.  
_Architecture:_ ResNet-50 is comprised of different layers including convolutional layers, batch normalization layers, pooling layers, and finally, dropout and fully connected layers. The architecture is characterized by residual blocks, which are comprised of multiple convolutional layers with skip connections. Each residual block contains a shortcut  
Figure 7: Residual Learning Building Block [15].  
connection that skips one or more layers and merges the input directly with the output of the block, allowing the network to learn residual representations. The architecture gradually reduces spatial dimensions while increasing the number of channels, enabling the network to capture hierarchical features at different scales. We used the following model architecture (see Figure 8) for training on the NaSC-TG2 dataset.  
_Pre-training on ImageNet:_ ResNet-50, like other deep-learning models, benefits from pre-training on large-scale datasets. ResNet-50 is initialized with weights pre-trained on the ImageNet dataset. ImageNet is a large-scale dataset which contains 1.4 million labeled images belonging to 1000 classes. Pre-training on ImageNet allows the model to learn rich and generalizable features which are transferable to various computer vision tasks with limited labeled data.
# Rod-Airfoil Interaction Noise Reduction Using Gradient Distributed Porous Leading Edges
## 2 Experimental Set-Up and Data Processing
### Data Acquisition and Processing  
As shown in Figure 1, far-field noise was measured by a single microphone at a polar angle of 90\({}^{\circ}\) and a distance of 1 m away from the jet-flow centerline, in the mid-span plane of the airfoil. Sound pressure data were acquired at a sampling rate of 51.2 kHz with a sampling duration of 30 s. The SPL of the far-field sound pressure (reference pressure \(p_{ref}=2\times 10^{-5}\) Pa) was computed using a Hanning window with a block size of 8192 samples and an overlap of 50%, yielding a spectral resolution of 6.25 Hz. The overall sound pressure level (OASPL) of the noise radiation was computed by integrating the SPL between 100 Hz (cutoff frequency of the anechoic chamber) and 25.6 kHz (Nyquist frequency).  
In the acoustic measurements, the noise data at the three angles of attack of the airfoil (i.e., \(\alpha=0^{\circ}\), \(10^{\circ}\), and \(20^{\circ}\)) and three streamwise gaps (i.e., \(L=100\), 140, and 200 mm) were measured for the baseline rod-airfoil configuration, in which the main body of the downstream airfoil was equipped with three aluminum leading edge strips. We recorded the acoustic time signals under three typical operating conditions (i.e., \(\alpha=0^{\circ}\) at \(L=100\) and 200 mm, and \(\alpha=10^{\circ}\) at \(L=200\) mm) for the porous leading edges due to the limited time of the wind tunnel was available. The noise levels of the isolated rod at \(L=100\) and 200 mm were also measured to check the contribution of the airfoil to the rod-airfoil interaction noise.