esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulation Study of a New Magnetorheological Polishing Fluid Collector Based on Air Seal
## 2 Geometric Structure of MR Polishing Collector  
### Principle of MR Polishing Liquid Collection  
A sketch of a magnetorheological polishing device is shown in Figure 1a. The polishing fluid flows on the surface of the polishing wheel; then, the polishing fluid is controlled by the magnetic field generator to polish the workpiece. After polishing, the polishing fluid is returned to the collector by the polishing wheel. A traditional collector is shown in Figure 1b. The collector has a circular magnetic seal ring. The used polishing fluid is brought into the open cavity of the collector through the diversion inlet by hindering the circular magnetic seal ring. Then, the polishing liquid in the open cavity is inhaled by the negative-pressure air through the collecting duct. As mentioned above, serious wear of polishing wheel occurs when using a circular magnetic seal. Thus, a new air seal collector is developed here. Figure 1c shows a sketch of the proposed collector. Pressurized air is projected into the open cavity through pneumatic nozzles to hinder the polishing fluid. Pressurized air serves as an alternative to the circular magnetic seal ring to prevent the polishing liquid from flowing out of the open cavity along the polishing wheel.  
Figure 1: Schematic diagram of the inverted MR polishing device. (**a**) MR polishing schematic, (**b**) traditional magnetic seal collector, (**c**) air seal collector.  
Figure 2 shows a traditional collector and air-sealed collector. Figure 2a,b are depictions of traditional collectors, whereas Figure 2c is the new air-sealed collector. The difference between the three is that the traditional collector uses a magnetic seal and scraper for collection, whereas the new collector is based on the nozzle forming an airflow seal for collection.
# The Application of Spectral Entropy to P-Wave Detection in Continuous Seismogram Analysis
## 2 Materials and Methods
### Drift Correction and the Detrending Sensitivity Analysis  
Raw seismic traces often exhibit baseline drift due to instrument responses, local environmental effects [23], and installation bias. This slow-varying component injects excessive low-frequency energy into the power spectral density, thereby inflating the spectral entropy even at pre-event intervals. As a result, the entropy contrast \(\Delta H\) between the noise and signal becomes attenuated, reducing the detection sensitivity.  
Removing the drift restores the frequency-localized characteristics of the noise window, allowing spectral entropy to capture the structural complexity introduced by seismic arrivals more reliably. In terms of the SNR, according to [24], the estimated variance ratio of the signal amplitude in half of the window before and after the P-wave is increased after the detrending procedure Figure 5.  
Figure 4: The dependence of the spectral entropy gain \(\Delta H\) on the window length, using the _pentropy_ function with customized _pspectrum_ parameters: a 14.5 s spectral window and 80% overlap. The window size for signal analysis varies from 20 to 60 s, centered around the P-wave. Each point represents the mean \(\Delta H\) for a given window size, with 95% confidence intervals on the normal distribution. The results show that the entropy estimates stabilize when the analysis window exceeds 40 s.  
After analyzing the seismograms from seven stations, a slow drift was identified at some locations, such as BTLS and JNKS. To eliminate this drift and remove low-frequency noise, a ninth-order Butterworth high-pass filter with a cutoff frequency of 2 Hz was applied. This filter effectively suppressed trends and microseismic noise while preserving the key high-frequency characteristics of the P-waves, crucial for their accurate detection. The comparison revealed that detrending significantly improved the separation between the noise and signal domains. The average SNR increased from \(5.41\pm 0.69\) dB to \(14.57\pm 1.20\) dB, with a 95% confidence interval across two stations. The average \(\Delta H\) increased from \(0.0056\pm 0.0021\) dB to \(0.1061\pm 0.0103\) dB. These results confirm that drift correction enhances the robustness of detection and supports its inclusion in future implementations of the method.
# Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance
## 4 Privacy-Preserving System Design  
In order to achieve the identified protection goals under the previously presented attack and trust model, we develop and implement _4 Crypt_--A privacy-preserving video documentation system for smart manufacturing applications. Our system allows the continuous monitoring and logging of critical manufacturing steps using off-the-shelf video equipment, while still protecting the privacy interests of recorded workers. For this, 4 Crypt automatically encrypts all captured video footage and securely distributes the encryption keys to a set of trusted individuals using a secret-sharing protocol. Decrypting the videos is only possible with the consent of both the recorded worker and at least one of the employee's trusted representatives. This application of the 4-eyes principle prevents the employer from pressuring workers into yielding keys required to decrypt videos without legitimate cause. As a result, the 4 Crypt system protects the recorded worker from illegitimate surveillance and covert performance evaluation, while still allowing the employer to reap the benefits of an automated video documentation and workflow recognition. Furthermore, 4 Crypt provides strong cryptographic security guarantees against malicious interfering and tampering with the privacy-protection modules. Workers can verify the integrity of the 4 Crypt system by using their personal smartphone on a dedicated NFC-reader, which prevents the system operator from disabling the encryption layer or extracting any encryption keys. This is achieved by adding a trusted platform module (TPM) to each system and by implementing a remote attestation protocol over NFC.  
With 4 Crypt we extend the basic version of our smart video documentation system presented in Section 2 by a number of privacy-protection components, most importantly an encryption/decryption module as well as a TPM-based attestation service. The resulting system design is presented in Figure 3 with new components shown in yellow.  
The main idea of 4 Crypt is to add a transparent encryption layer into the video processing pipeline, which implements a hybrid encryption scheme that restricts the employer's access to captured video files. In the following sections we describe the three main tasks of 4 Crypt and how they were solved in greater detail. More concretely, 4 Crypt needs to:  
1. Record, encrypt and securely store videos in a way that even the system operator cannot access them.
2. Retrieve a stored video with consent from both the recorded worker and at least one of the employee's trusted representatives.
3. Offer a way of verifying the integrity of the protection components before a worker begins with a monitored work step.
# Post-Earthquake Fire Resistance in Structures: A Review of Current Research and Future Directions
## 6 Behavior of Materials at Elevated Temperatures
Fire intensity is another critical factor, with the time to spalling being heavily dependent on how intense the fire is. For instance, under a high-intensity fire scenario, spalling in slabs can occur in as little as 2 min [70]. Cracks in concrete, often resulting from seismic activity, significantly alter the material's behavior at elevated temperatures. Larger crack widths, which indicate severe initial damage, are associated with higher temperatures within the concrete member during a fire. While normal-strength concrete may resist spalling, cracked and damaged concrete members are much more vulnerable. The presence of cracks can lead to localized temperature variations within the concrete when exposed to fire. Numerical simulations have shown that as the projected length of a crack increases, higher temperatures tend to concentrate near the crack's surface [15]. This uneven temperature distribution complicates the analysis and prediction of concrete's behavior in post-earthquake fire scenarios. In summary, the intricate interplay of physical and chemical changes in concrete at elevated temperatures, exacerbated by pre-existing damage from seismic events, necessitates a nuanced understanding of its behavior. This understanding is crucial for accurately predicting structural performance in post-earthquake fire conditions and ensuring the safety and resilience of structures subjected to such extreme events.
## 3 Numerical Algorithms and Computational Methodologies
### Pressure Correction  
Unlike for compressible flow, where empirical relations such as the ideal gas law allow for a direct/explicit pressure-linked equation, the solution processes for pressure-linked equations for incompressible fluid flow on the other hand are complicated by the reality that there is no explicit pressure equation to solve. Numerical solutions for pressure-linked equations of incompressible fluid flow have led to the development of various techniques to overcome this difficulty. The current investigation employs the Pressure-Implicit-with-Splitting-of-Operators (PISO) algorithm, see for example [56], to deal with this challenge. The PISO algorithm can be summarised as follows.  
1. Initialise the field variables: velocity \(\mathbf{U}\), pressure \(p\), polymeric stresses \(\mathbf{\tau}\), and temperature \(T\).
2. For the LCR approach, solve for the conformation tensor \(\mathbf{D}\) and \(\mathbf{\Theta}=\log\mathbf{D}\).
3. For the DEVSS approach, solve directly for the polymer stresses, \(\mathbf{\tau}\).
4. Solve the momentum equations for the intermediate velocity field, \(\mathbf{U}^{**}\).
5. Using the intermediate velocity, \(U^{**}\), estimate a new pressure field \(p^{**}\). Subsequently, perform a correction of the intermediate velocity field and obtain the new velocity \(\mathbf{U}^{***}\), which must satisfy mass conservation.
6. The updated velocity \(\mathbf{U}^{***}\) is then used to compute the polymer-stresses \(\mathbf{\tau}^{**}\) and temperature \(T^{**}\) via the stress constitutive equations and energy equations, respectively.  
Go to step 1 with the field variables \(\mathbf{U}\), \(p\), \(\mathbf{\tau}\), \(T\), respectively, replaced with \(U^{**}\), \(p^{**}\), \(\tau^{**}\), and \(T^{**}\) and repeat the steps until the required accuracies are achieved or until the required number of iterations is reached.
###### Abstract  
Integration of a dual-fluid heat exchanger with a photovoltaic/thermal (PV/T) system has become increasingly important because it not only significantly reduces the photovoltaic (PV) solar cells' temperature but also produces additional thermal energy. In this study, energy, exergy, and economic analyses of a dual-fluid (water/air) PV/T system are performed in comparison with a reference PV module and single-fluid PV/T systems for the climate of Cheonan, South Korea. Daily and yearly performance evaluations of all of the aforementioned PV/T systems were carried out through experimentation. Based on the experimental findings, the economic feasibility of the dual-fluid PV/T system is assessed in the context of its financial benefits over both a shorter and longer period of time. Results show that the energy and exergy efficiencies of the dual-fluid PV/T system are significantly higher than those of single-fluid PV/T systems, by 20% and 11%, respectively. In addition, relevant to local domestic electricity price, the cost of energy is reduced by 80%, 60%, and 45% with the dual-fluid PV/T system and water and air type PV/T systems, respectively. Furthermore, using the dual-fluid PV/T system, extra revenue is generated through carbon credit by mitigating CO\({}_{2}\) emissions into the atmosphere.  
outdoor testing; dual-fluid PV/T system; technical analysis; economic study +
Footnote †: journal: Applied Sciences
# AI-Enabled Compressive Spectrum Classification for Wideband Radios
###### Abstract  
Cognitive radio is a promising technology that emerged as a potential solution to the spectrum shortage problem by enabling opportunistic spectrum access. In many cases, cognitive radios are required to sense a wide range of frequencies to locate the spectrum white spaces; hence, wideband spectrum comes into play, which is also an essential step in future wireless systems to boost the throughput. Cognitive radios are intelligent devices and therefore can be opted for the development of modern jamming and anti-jamming solutions. To this end, our article introduces a novel AI-enabled energy-efficient and robust technique for wideband radio spectrum characterization. Our work considers a wideband radio spectrum made up of numerous narrowband signals, which could be normal communications or signals disrupted by a stealthy jammer. First, the receiver recovers the wideband from significantly low sub-Nyquist rate samples by exploiting compressive sensing technique to decrease the overhead caused by the high complexity analog-to-digital conversion process. Once the wideband is recovered, each available narrowband signal is given to a cyclostationary feature detector that computes the corresponding spectral correlation function and extracts the feature vectors in the form of cycle and frequency profiles. Then profiles are concatenated and given as input features set to an artificial neural network which in turn classifies each NB signal as legitimate communication with a specific modulation or disrupted by a stealthy jammer. The results show a classification accuracy of about 0.99 is achieved. Moreover, the algorithm highlights significantly high performances in comparison to recently reported spectrum classification techniques. The proposed technique can be used to design anti-jamming systems for military communication systems.  
c 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).  
os cognitive radios; wideband spectrum sensing, compressive sensing; stealthy jammer; signals classifications; cyclostationary feature detector; artificial neural networks; anti-jamming systems
# Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding +
### Network Coding Approaches
A notable difference of BNC from the generation-based RLNC is that BNC decodes the batches jointly such that the decoder does not need to receive all the packets within every batch. The small memory requirement of BNC enables hardware acceleration to boost the real-time performance [65; 66; 67]. The ordinary RLNC can be regarded as a \"BNC with a single batch\", so that we can perform recoding on the data until the transmission is completed. When there are multiple batches, one has to specify the number of recoded packets to be generated for each batch. The information carried by each batch, known as the \"rank\" of the batch, jointly affects the throughput of BNC. More specifically, the achievable rate of BNC is upper bounded by the expectation of the ranks of the batches arriving at the destination node [68]. Therefore, our goal is to design a recoding scheme that preserves more \"ranks\" across the batches.
## 5 Docking Analysis of AMPK with Flavonoids
All six flavonoids interact with the \(\alpha 1\) subunit of AMPK, indicating a common site of action that could be crucial to their role as AMPK activators. However, the strength of these interactions varies significantly, as evidenced by the differing docking scores, suggesting that not all flavonoids are equally effective in activating AMPK. The binding interactions predominantly involve hydrogen bonds and pi-alkyl interactions, which are common across the flavonoids, but the specific contact residues differ, leading to variations in binding affinity and possibly activation potency. The docking results suggest that these flavonoids could activate AMPK through direct binding to the \(\alpha 1\) subunit, thereby inducing the necessary conformational changes for activation. While some flavonoids, like baicalein and dihydromyricetin, exhibit strong binding and potentially more effective activation, others like alpinumiosflavone show weaker interactions, which might translate to less potent AMPK activation. Further experimental validation is required to determine whether these flavonoids activate AMPK through the same or different mechanisms compared to known direct activators like A-769662. Compared to established direct AMPK activators such as A-769662, which have well-characterized mechanisms of action, these flavonoids offer a diverse range of interactions and potential activation pathways. While A-769662 primarily interacts with the \(\beta 1\) subunit, most of the flavonoids in this study target the \(\alpha 1\) subunit, suggesting an alternative activation route. This difference could lead to varying therapeutic outcomes, making these flavonoids interesting candidates for further research in the development of AMPK-targeted therapies. Molecular docking studies, while useful for predicting how small molecules interact with proteins, have certain limitations. They are typically conducted in simplified environments and do not fully reflect the complexity of the human body. Factors like protein flexibility, the presence of cofactors, and the surrounding cellular environment are often not considered, which can lead to inaccurate predictions of how a molecule will behave in vivo. Additionally, docking studies provide static snapshots of interactions, missing out on the dynamic conformational changes that occur in real biological systems. Therefore, experimental validation through in vitro and in vivo studies is necessary to confirm docking predictions.
# ICN-Based Enhanced Content Delivery for CDN
## 3 Proposed Design
### Details
#### 3.4.2 Design of IG and EG  
IG and EG are the most important components of the ICN service layer. As the interchange points between ICN and HTTP, the HTTP-ICN gateways must be compatible with both the HTTP and ICN paradigms. Figure 4 shows the design diagram of the HTTP-ICN gateway.  
Figure 4: The design diagram of the HTTP-ICN gateway.  
Figure 3: ID-based ICN protocol stack.  
The HTTP server on IG parses and translates an HTTP request into an ICN chunk request with a protocol conversion mechanism. The chunk request is subsequently handled by the ICN client. Vice versa, upon receiving the chunk response on the ICN client, IG wraps the data in the HTTP payload and sends the HTTP response back to the CDN server. Similarly, EG receives an ICN chunk request, translates it into an HTTP content request and extracts data from the HTTP response payload to generate the ICN chunk response. The ICN client and server run on the ICN protocol stack described in the above section.
## 5 Application of Machine Learning Model Evaluation Methods
### Model Training and Validation
#### 5.3.1 Evaluation Index  
In terms of evaluation indicators, this paper selected the accuracy rate, F1 value, PR curve and ROC curve as indicators to measure the performance of the XGBoost model, aiming to evaluate the effectiveness and practicability of the proposed method more comprehensively.  
**TP** represents the number of samples whose real labels are positive and are predicted to be positive by the model; **FP** represents the number of samples whose real labels are negative examples but are predicted to be positive examples by the model; **FN** represents the number of samples whose real labels are positive but are predicted to be negative by the model; **TN** represents the number of samples whose real labels are negative examples and are predicted to be a negative examples by the model.  
Where the accuracy rate is expressed as:  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+FN+TN} \tag{2}\]  
Figure 4: Overview of the architecture.  
Express the accuracy rate as:  
\[\text{precision}=\frac{TP}{FP+TP} \tag{3}\]  
Express the recall rate as:  
\[\text{recall}=\frac{TP}{TP+FN} \tag{4}\]  
Express the F1 value as:  
\[\text{F1}=\frac{2*precision*recall}{precision+recall} \tag{5}\]  
The PR curve describes the changes in _precision_ and _recall_, and its area under the curve reflects the performance of the XGBoost model. The larger the area under the curve is, the better the performance of the model is, which indicates that the data fitting is sufficient. Also, it can explain that from a physiological point of view, it is feasible to use machine learning methods to predict the comprehensive evaluation of urban tunnel body landscape design quantitatively or qualitatively for driving.  
The ROC curve is used to evaluate the model's predictive ability. In the curve, the closer the ROC curve is to the upper left corner of the coordinate axis, the better the prediction effect is; the closer the ROC curve is to the diagonal, the worse the prediction effect is; and the larger the area under the ROC curve is, the better the prediction effect of the model is.
## 3 A Stepwise Description of the Proposed Model  
The developed attack detection model comprises three essential phases.  
* Initially, features including \"kurtosis, variance, moments, mutual information, symmetric uncertainty, information gain ratio, and relief-based features\" are derived.
* These features are then subjected to optimized GRU and BI-LSTM that recognizes the presence of attacks.
* Here, the weights of BI-LSTM are optimally tuned via SU-CMO.  
Figure 1 depicts the overall framework of the proposed model below.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Author** & **Deployed Schemes** & **Features** & **Challenges** \\ \hline [PERSON] et al. [1] & ML algorithm & High accuracy & Some security issues were not considered \\ \hline [PERSON] et al. [10] & APSO-CNN & Effective and reliable & Do not differentiate complicated tasks from interruption \\ \hline [PERSON] et al. [11] & JRip classifier & Higher performance & \\  & Improved accuracy & Only a particular dataset is preferred \\  & High detection rate & & \\ \hline [PERSON] et al. [12] & NN & Higher accuracy and performance & Layers specification is not provided \\ \hline [PERSON] et al. [13] & MLP & High level of performance & Test only for attack detection \\  & High ranking of feature & but not efficiency \\ \hline [PERSON] et al. [14] & EASH & Attain a higher accuracy rate & Tested for detection rate only \\ \hline [PERSON] et al. [15] & ML-F & High accuracy & Two attack categories were not considered \\ \hline [PERSON] et al. [16] & Markov Decision & High accuracy for a feature set & ANN was not detected accurately \\ \hline [PERSON] et al. [17] & LSTM, CNN & Higher accuracy rate & Higher training time \\ \hline [PERSON] et al. [18] & CNN, GRU, LSTM & Higher detection rate & Supplementary details issues are not provided. \\ \hline [PERSON] et al. [19] & Random Forest, Knn & Offers extensive review & Comparison results are not provided \\ \hline \end{tabular}
\end{table}
Table 1: Reviews on conventional IoT attack detection models.  
Figure 1: The overall framework of the adopted model.