esa-sceva/satcom-chunk-collection huggingface dataset:
# Radiometric Cross-Calibration of GF-1 PMS Sensor with a New BRDF Model
## Appendix A Computing Principle of EBT  
Thanks a lot for the reviewer's valuable reminding and suggestion. We use an example to illustrate the calculation of envelope of bright temperature (EBT) and the screening process of sunny day.  
1. Taking the data in the Table 11 as an example (the first line of the table is the day of year (DOY), and the second line is the bright temperature), the envelope of BT is calculated.
2. A scatter diagram is drawn by DOY as X-axis and BT as Y-axis. As shown in Figure 11.  
(3) Computing Principle of EBT  
Firstly, take the first point as the starting point, connect it with the first adjacent point on its right side, and draw an extension line. As shown in Figure A2a.  
Figure A2: Calculation process of envelope of bright temperature (EBT). (**a**) Process of EBT calculation; (**b**) Results of EBT calculation.  
Secondly, determine whether other points fall on the right side of the extension line. If a point falls to the left side of the extension line, the adjacent point is discarded. Then, connect the starting point with the next adjacent point, draw the extension line, and determine again whether all other points fall on the right side of the extension line, and so on, until all points fall on the right side of the extension line.  
At this point, the adjacent point is used as a new starting point, and the first adjacent point on its right side is connected, and the extension line is drawn to determine whether the other remaining points are on the right side of the extension line again.  
Thirdly, the envelope of bright temperature (EBT) can be obtained by repeating the above process, as shown in Figure 10(a).  
Finally, the points on the EBT are represented by red solid boxes, as shown in Figure 10(b).  
(4) Principle of sunny day data screening  
As shown in Figure 11, taking point A as an example.  
Firstly, the perpendicular line is made through point A, intersects with envelope BC at point D, and calculates the ordinate value of point D (which can be calculated by the coordinates of the three points of A, B, and C).  
Secondly, the difference \(\Delta\)t (\(\Delta\)t = BTD \(-\) BTA) between the ordinates (BT) of point A and point D is calculated. If \(\Delta\)t is less than 10 K, then point A is considered to be sunny day, and represented by a red solid box. Otherwise, if \(\Delta\)t is greater than 10 K, then this point is not sunny day.  
Finally, according to the above principles, other points within the envelope are judged separately. Then, the sunny day that meet the requirement (\(\Delta\)t < 10 K) can be screened out.
# Urokinase System in Pathogenesis of Pulmonary Fibrosis: A Hidden Threat of COVID-19
###### Abstract  
Pulmonary fibrosis is a common and threatening post-COVID-19 complication with poorly resolved molecular mechanisms and no established treatment. The plasminogen activator system, including urokinase (uPA) and urokinase receptor (uPAR), is involved in the pathogenesis of COVID-19 and contributes to the development of lung injury and post-COVID-19 pulmonary fibrosis, although their cellular and molecular underpinnings still remain obscure. The aim of the current study was to assess the role of uPA and uPAR in the pathogenesis of pulmonary fibrosis. We analyzed uPA and uPAR expression in human lung tissues from COVID-19 patients with pulmonary fibrosis using single-cell RNA-seq and immunohistochemistry. We modeled lung fibrosis in _Plau-/-_ and _Plaur-/_ mice upon bleomycin instillation and explored the effect of uPAR downregulation in A549 and BEAS-2B lung epithelial cells. We found that uPAR expression drastically decreased in the epithelial airway basal cells and monocyte/macrophage cells, whereas uPA accumulation significantly increased in tissue samples of COVID-19 patients. Lung injury and fibrosis in _Plau-/-_ vs. WT mice upon bleomycin instillation revealed that uPAR deficiency resulted in pro-fibrogenic uPA accumulation, IL-6 and ACE2 upregulation in lung tissues and was associated with severe fibrosis, weight loss and poor survival. uPAR downregulation in A549 and BEAS-2B was linked to an increased N-cadherin expression, indicating the onset of epithelial-mesenchymal transition and potentially contributing to pulmonary fibrosis. Here for the first time, we demonstrate that plasminogen treatment reversed lung fibrosis in _Plau-/-_ mice: the intravenous injection of 1 mg of plasminogen on the 21 st day of bleomycin-induced fibrosis resulted in a more than a two-fold decrease in the area of lung fibrosis as compared to non-treated mice as evaluated by the 42 nd day. The expression and function of the plasminogen activator system are dysregulated upon COVID-19 infection, leading to excessive pulmonary fibrosis and worsening the prognosis. The potential of plasminogen as a life-saving treatment for non-resolving post-COVID-19 pulmonary fibrosis warrants further investigation.  
COVID-19; pulmonary fibrosis; urokinase uPA; urokinase receptor uPAR; plasminogen; scRNA-seq +
Footnote †: journal: Journal of Molecular Sciences
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
###### Abstract  
This study provides an in-depth analysis of GAF-Net, a novel model for video-based person re-identification (Re-ID) that matches individuals across different video sequences. GAF-Net combines appearance-based features with gait-based features derived from skeletal data, offering a new approach that diverges from traditional silhouette-based methods. We thoroughly examine each module of GAF-Net and explore various fusion methods at the both score and feature levels, extending beyond initial simple concatenation. Comprehensive evaluations on the iLIDS-VID and MARS datasets demonstrate GAF-Net's effectiveness across scenarios. GAF-Net achieves state-of-the-art 93.2% rank-1 accuracy on iLIDS-VID's long sequences, while MARS results (86.09% mAP, 89.78% rank-1) reveal challenges with shorter, variable sequences in complex real-world settings. We demonstrate that integrating skeleton-based gait features consistently improves Re-ID performance, particularly with long, more informative sequences. This research provides crucial insights into multi-modal feature integration in Re-ID tasks, laying a foundation for the advancement of multi-modal biometric systems for diverse computer vision applications.  
person re-identification; gait recognition; feature fusion; deep learning; computer vision +
Footnote †: journal: _Algorithms_
# Passenger Flow-Oriented Metro Operation without Timetables
## 3 Mathematical Description
### Objective Function  
The waiting time for all passengers(WAT) can be used to evaluate the operation strategy. It includes the waiting time of passengers who are taken to their destination and the passengers who are left in the station, which means that passengers who cannot be taken by the last train become a penalty factor of this problem. Thus, the operation strategy should balance taking more passengers and faster arrival. Based on Equations (1)-(14), the objective function can be formulated below:  
\[\begin{split} WAT=\sum\limits_{i=0}^{I}\sum\limits_{s=0}^{S-1}& \Bigg{[}p_{i,s}^{left}(d_{i,s}-d_{i-1,\,s})+\sum\limits_{t=d_{i-1,s}+1}^{d_ {i,s}}\lambda_{s}(t)(d_{i,s}-t+1)\Bigg{]}\\ &+\sum\limits_{s=0}^{S-1}\Bigg{[}p_{I,s}^{left}(T-d_{I,\,s})+ \sum\limits_{t=d_{I,s}+1}^{T}\lambda_{s}(t)(T-t+1)\Bigg{]}\end{split} \tag{15}\]  
In real-time operation, passenger flow's average entering speed will keep changing; the operation strategy needs to be optimised continually. However, it is impossible to adjust the station stop strategies that have already passed by the train. Thus, to optimise the operation based on detected variations in real-time, the departure time generated should be compared with the detecting time. If the departure time is earlier than or equal to the detecting time, it could not be optimised, and these departure times will be regarded as parameters in the objective function in future optimisation. Otherwise, the departure time is still a variable that could be optimised in real-time:  
\[d_{i,s}^{real-time}=\left\{\begin{array}{ll}d_{i,s}^{old}&t_{D}\leq\ d_{i,s}^{ old}\\ d_{i,s}^{new}&t_{D}>d_{i,s}^{old}\end{array}\right. \tag{16}\]  
As the constraints need to be met to guarantee the safety of the system, the optimisation function of RPOM based on nonlinear integer programming can be given as follows:  
\[\left\{\begin{array}{ll}\min WAT=\sum\limits_{i=0}^{I}\sum\limits_{s=0}^{S-1 }[p_{i,s}^{left}(d_{i,s}^{real-time}-d_{i-1,s}^{real-time})+\\ \sum\limits_{t=d_{i-1,s}^{real-time}+1}^{d_{i,s}^  
describes passengers' journeys between specific stations. For metro infrastructure, the input data mainly focus on the constraint conditions in the last section, which contains the running time, departure interval levels, different dwell time levels, and train capacity.
## 3 Results
### ART Sorting Algorithm
Figure 12 summarizes the results that were achieved with the different classification models, based on the synthetic training data with different noise levels. The obtained TNR (true negative rate) and TPR (true positive rate) values, which indicate the amount of correct classifications for the good parts and for the defective ones, vary slightly with the random number sequences used for the geometric definitions and for applying noise. The  
Figure 11: Classification results for 1000 virtual connecting rods each being either undamaged (IO) or featuring a 2 mm through-hole anywhere in the smaller cylinder ring (NIO/R1), the larger cylinder ring (NIO/R2), or the bar area (NIO/B), based on the classification models F and F-G2 both derived from training data without additional noise (blue: classified correctly, red: misclassified). Figure 12: Overview of the classification results for virtual connecting rods being either undamaged or featuring a defect anywhere in the part with a size between 1 mm and 3 mm, based on various classification models derived from training datasets with different noise levels. From Figure 12, it can be seen that the good parts were classified correctly in all of the considered cases. In contrast, the classification results for the defective parts depend strongly on the classification model configuration and on the noise level of the synthetic training data. The additional use of the geometric data as linear or even interactional model input significantly improved the detectability of the smaller defects, but the defect detection became worse the more noise that was applied to the training data. Depending on this, an acceptable defect detection with a detection rate of at least 95% is given for defects of 1 mm or smaller, or only for defects significantly above 3 mm size. For the training data set with the medium noise level, the theoretical defect detection limit is between 1.5 mm and 3 mm, depending on the applied model configuration. Although improved modeling, such as including higher polynomial eigenfrequency terms, implementing more frequencies, or even using other classification types, may slightly improve the defect detection, there may be a limit when using the eigenfrequencies alone. This is because the spectral shifts are not only an indication of a defect, but they are also caused by tolerable structure configurations. This makes it challenging from a certain point to find a clear quality conclusion from a given set of eigenfrequencies without any further component information. Nevertheless, the results achieved on the virtual parts indicate a good chance of separating the defective machine-made parts from the good ones, at least above a certain defect size.
# Applications of Machine Learning in Cyber Security: A Review
## 3 The Cyber Security Research Landscape  
To begin to examine the utility of AI/ML in CS, it is important to first examine the research landscape. These are the main domains in which CS research is advancing, organized in order of relevance to AI/ML.  
* Advanced Threat Detection and Response: New methods of identification of patterns and anomalies, indicative of potential security threats, enhance the capabilities for detecting and responding to threats [5].
* IoT security: The quick integration of Internet of Things (IoT) devices into the digital ecosystem necessitates secure frameworks to mitigate inherent vulnerabilities [6].
* Regulatory compliance and standards: Compliance with cyber security norms and regulations (e.g., General Data Protection Regulation (GDPR)) is paramount in practices and software [7, 8].
* The role of encryption: The evolution of encryption technologies, including the development of quantum-resistant algorithms, is critical [9].
* Blockchain for security: The blockchain's decentralized framework ensures the integrity and transparency of transactions and data exchanges in cyber security infrastructures [10], as well as identity management and secure communication.
* Cyber security awareness and training: This is vital for reducing the susceptibility to human errors, which cause a significant proportion of security breaches [11].
* Incident response planning: This is characterized by well-defined protocols and responsibilities, demonstrating preparedness and resilience [12].  
By nature, some domains are more closely related to AI/ML by virtue of the availability of data (items enumerated at the top), while others necessitate human interaction and are thus unaffected (items at the bottom). Regulatory compliance affects AI/ML applications more intrinsically, and this is further discussed in the remainder of this paper. The following section further splits the AI/ML-relevant research domains by type of cyber security.
## 3 Results
### Chloride Ion Penetration  
Figure 7 shows the results of chloride ion permeability testing. The targeted permeability of 4000 C or less was satisfied by all of the mixes. The values for the Plain, GS 50%, GG 50%, GG 100%, and GS 50%/GG 100% mixes were 3483, 3458, 3351, 3421, and 3232 C, respectively. The permeability increased with an increasing amount of air-cooled slag aggregate.  
The chlorine ion permeability ratios for the Plain, GS 50%, GG 50%, GG 100%, and GS 50%/GG 100% samples were 1.00, 0.99, 0.96, 0.98, and 0.92, respectively. Relative to the Plain mix, mixes that contained air-cooled slag aggregates had lower chloride ion permeabilities. The fly ash and blast-furnace slag powder in all of the mixes filled the  
Figure 7: Test results for chloride ion penetration.  
micropores present in the air-cooled slag aggregates and concrete. The GS 50%/GG 100% composition had an air content that was 8% lower than that of the Plain mix, which was consistent with such pore filling. The chlorine ion permeabilities of the experimental samples all met the 4000 C maximum design criterion. Therefore, we do not anticipate any issues in the field concerning chloride ion permeability.
# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
Additionally, deep learning techniques require large amounts of labeled training data, and acquiring high-quality, accurately, real-world labeled datasets is both time-consuming and costly, which limits their widespread application. To address the aforementioned issues, this paper proposes a LOFAR spectrogram denoising method for underwater acoustic signal based on simulation enhancement. By utilizing ocean noise collected during sea trials, and combining it with line spectrum simulation data, we construct a large-scale labeled training dataset required for model training. This dataset is then used to train an effective deep convolutional denoising model. The trained model is directly applied to the sea trial data for denoising, allowing line spectrum to be prominently extracted on the time-frequency spectrogram, facilitating target feature analysis and recognition. The rest of paper is organized as follows: An enhanced simulation algorithm and convolutional denoising model that can significantly denoise environment noise in LOFAR spectrogram is proposed in Section 2. The experiment settings and datasets that used in this paper are elaborated in Section 3. In Section 4, quantitative results of experiments are presented. Finally, conclusions are summarized in Section 5.
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 3 Problem Formulation  
This section first introduces some necessary concepts and mathematical symbols, and then describes the task of the propagation-based recommendation method.  
**Definition 1**.: _Recommender system. A recommender system involves the set \(\mathcal{U}\) of \(m\) users and the set \(\mathcal{V}\) of \(n\) items. For the system, a user-item interaction matrix, \(\mathbf{Y}\in\mathbb{R}^{m\times n}\), is constructed according to the users' implicit feedback, where an element \(y_{uv}=1\) represents that user \(u\in\mathcal{U}\) has interacted with item \(v\in\mathcal{V}\) (e.g., clicking, watching, or purchasing), which is generally considered to indicate that the user likes the item; otherwise \(y_{uv}=0\)._  
**Definition 2**.: _Knowledge graph \(\mathcal{G}\). A knowledge graph is defined as a set of triples, that is, \(\mathcal{G}=\{(h,r,t)\mid h,t\in\mathcal{E},r\in\mathcal{R}\}\), where \(\mathcal{E}\) is the set of entities, \(\mathcal{R}\) is the set of relations, and each triple \((h,r,t)\) describes the relation \(r\in\mathcal{R}\) from the head entity \(h\in\mathcal{E}\) to the tail entity \(t\in\mathcal{E}\). Note that \(\mathcal{R}\) contains relations in both canonical direction (e.g., DirectorOf) and inverse direction (e.g., DirectedBy). In the graph, an entity is represented as a node, and a relation is represented as a directed edge (link) from the head entity node to the tail entity node._  
This paper uses a set \(\mathcal{A}=\{(v,e)\mid v\in\mathcal{V}\) can be aligned with \(e\in\mathcal{E}\}\) to describe the alignments between items in the recommender system and entities in the knowledge graph. An element \((v,e)\in\mathcal{A}\) indicates that item \(v\) can be aligned with entity \(e\). The task of the propagation-based recommendation method is formulated in Definition 3.  
**Definition 3**.: _The task of the propagation-based recommendation method. As described in [4; 5], given the user-item interaction matrix \(\mathbf{Y}\) and the knowledge graph \(\mathcal{G}\), the recommendation task is to predict the probability that user \(u\in\mathcal{U}\) will interact with item \(v\in\mathcal{V}\) that the user has not interacted with before. More specifically, the goal of the task is to learn a prediction function \(y_{uv}=\mathcal{F}(u,v\mid\Theta,\mathbf{Y},\mathcal{G})\), where \(y_{uv}\) represents the predicted probability that user \(u\) will interact with item \(v\), and \(\Theta\) denotes the model parameters of function \(\mathcal{F}\). Given a target user, the recommendation method sorts the predicted probabilities in descending order to generate a top-N recommendation list for the target user._
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Hybrid Graph Embedding Module  
The sign language recognition system uses wearable gloves to create a spatial-temporal network from keypoint detection outputs. These inputs comprise six-axis IMU sensor hand-position keypoints, finger curvature sensor finger-joint keypoints, velocity vector motion characteristics, acceleration data, temporal sequence data, and confidence scores for each keypoint. The inputs are arranged into S, T, and Combined Feature Vectors. Each input frame has 21 keypoints (one wrist + five fingers \(\times\) four joints), six motion parameters (three linear + three angular), confidence ratings, and a timestamp. The data structure is a float-timestamp matrix with nodes representing keypoints, edges representing spatial and temporal correlations, and edge weights calculated. These input data create the sign language recognition feature representation using spatial graph convolution, temporal attention mechanism, and feature fusion. Confidence scores affect feature reliability weighting. The system generates sign language recognition features using spatial graph convolution, temporal attention, and feature fusion from these input data.  
In Figure 4, to understand the temporal dynamics of movements, the Hybrid Graph Embedding Approach uses a combination of methods, including 3D keypoint extraction from sensor data frames, building a Spatial-Temporal Graph, learning representations of spatial features, and computing attention weights. The feature representation step is responsible for fusing the output of various modules and generating the final feature vector via weighted summing of multi-scale features. This combined graph-based modeling and Adaptive Convolutional Network strategy aims to improve the accuracy and resilience of sign language recognition by capturing the complex spatial-temporal interactions inherent in sign language communication. It gives a thorough overview of the proposed methodology in Figure 4, showing the flow of information and mathematical formulas underpinning each essential component.
## 4 Results  
### Style Similarity Between AI and Human Texts  
As described in Section 3.3, we applied the AV model on the four configurations shown in Figure 3. Table 2 shows the average model scores for each configuration. For example, GPT2 was prompted with 120 fanfiction pairs, which gave us 120 GPT2-generated documents. The human-human score indicates the average of the model scores across the 120 human fanfiction pairs. Since each fanfiction pair is written by the same author, all human-human scores have a higher score (of around 0.8). Note that since we had a slightly different number of document pairs for each LLM, the human-human average scores are slightly different in each row.  
The human-AI column shows the average of scores across the human-written fanfiction and the corresponding AI-generated version. For GPT2, this number is higher, but lower than the human-human score. This indicates that the writing style of GPT2-generated texts is somewhat similar to that of the original author, but not as similar as the other writings from the same author. On the other hand, for GPT3 and ChatGPT, the human-AI score is low, indicating that the style of the AI-generated fanfiction is very different from the original prompts, and that there is little style transfer from the prompts to the generated texts. The human-AI score for LLaMA-generated texts were somewhat mixed, indicating that there is some stylistic similarity that has been carried over from the source text.  
The GPT3 family LLMs have high scores for both the AI-AI comparison and the AI-AI (different author) comparisons. This indicates that the style is very similar across documents generated by the LLMs, even when the prompts for the documents came from different authors. This suggests that GPT3 and ChatGPT have their own unique styles. Since the AI-AI scores are slightly higher than the AI-AI (different author) scores, it is possible that some style transfer may have occurred from the original author to the generated text.  
Both GPT2 and LLaMA scores are somewhat mixed, with a higher AI-AI similarity score than the human-AI score but a lower AI-AI (different author) score. This suggests that the style is a mix of both the human author and one that is unique to GPT2/LLaMA.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Doc. Pairs**} & \multirow{2}{*}{**Human–Human**} & \multirow{2}{*}{**Human–AI**} & \multirow{2}{*}{**AI–AI**} & **AI–AI** \\  & & & & & **(Diff.** \\  & & & & & **Auth.)** \\ \hline GPT2 & 120 & 0.823 & 0.718 & 0.780 & 0.642 \\ GPT3 & 106 & 0.811 & 0.299 & 0.973 & 0.953 \\ ChatGPT & 95 & 0.800 & 0.232 & 0.984 & 0.977 \\ Llama 2 & 101 & 0.810 & 0.413 & 0.716 & 0.546 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The average AV scores for each LLM, under the different configurations described in Figure 3.