esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Models to Multi-Parameter Maximum Magnitude Prediction
## 2 Data and Preprocessing
### Time Window Settings  
In constructing an earthquake prediction model, the accuracy of certain parameters, like the \(b\)-value obtained using the least squares method, depends on the number of seismic events within the time window. If the number is insufficient, the calculated \(b\)-value will lose reliability due to significant errors. Therefore, the time window was set to ten years to ensure that a sufficient number of seismic events were available for analysis within the time window. In addition, two different training set-to-test set ratios were set up for comparison experiments, these being 7:3 and 8:2, respectively. Finally, the sliding window was set to one month to accommodate short-term seismic prediction needs (Figure 2).
# Detection and Classification of Different Weapon Types Using Deep Learning
## 3 Materials and Methods  
### Dataset and Pre-Processing  
Because there is no standard dataset for weapon detection and recognition, 5214 weapon images were prepared by downloading them from the internet. Downloaded weapon images must be of good quality images with different angles in order to be successful in detecting and recognizing real-life weapons. In addition, in order to achieve higher success accuracy from the developed neural network model, irrelevant objects in each weapon image were removed. As such, downloaded weapon images were examined individually and different computer application programs were used to make changes to the images, such as padding, masking, background cleaning, sizing, and rotation, according to the content.  
After preparing separate images for each weapon class, they were converted into a dataset. The weapon types and numbers of images belonging to the dataset used in the study are given in Table 1. The dataset comprised images of assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers (Figure 1). The images used in the dataset were transformed into gray color format at 144 \(\times\) 144 pixels using the Python programming language. Then, the images belonging to the dataset were labeled under the weapon class to which they belonged and grouped.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Weapon Class** & **Number of Images** \\ \hline Assault rifle & 927 \\ Bazooka & 211 \\ Grenade & 507 \\ Hunting rifle & 849 \\ Knife & 1076 \\ Pistol & 1170 \\ Revolver & 474 \\ \hline
**Total** & **5214** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weapon dataset classes and numbers of images.
# A Model Output Deep Learning Method for Grid Temperature Forecasts in Tianjin Area
## 2 Data and Organization
### Observational Data  
The observational data are the 2 m air temperature observation data of all 267 automatic stations in Tianjin provided by the Tianjin Meteorological Bureau. Each automatic station recorded the 2 m air temperature of its location hour by hour from January 2016 to November 2018. Thus, these observational data constitute the other part of the original dataset \(D_{0}=(X_{0},Y_{0})\), and this part is denoted by  
\[Y_{0}=\left\{y_{S,t,state}\right\}, \tag{2}\]  
where the ranges of values of _state_, \(t\) and \(S\) are \((1,2,\cdots,267)\), \((1,2,\cdots,24)\) and \((1,2,\cdots,1065)\). Because the maximum forecast lead time of model data is 10 days, each model sample requires 10 days of observation samples as the label of the model input. The EC data eliminated due to the lack of observation data is calculated for a total of 33 days. Accordingly, the time dimension parameter S of the numerical forecast data is specifically compressed to 1032.  
Because the automatic station data is not grid data in space, this paper uses inverse distance interpolation to obtain grid format observation data, as shown in Figure 2. Observation data of 267 automatic stations at 53 time steps are interpolated to form one data for supervision. Inverse distance weighting is the general term for a series of weighted average interpolation methods. Different strategies for setting weights will make some difference in the grid data after interpolation. This paper uses the Cressman strategy [39]. The Cressman strategy uses the ratio between distance of an observation from a grid cell and the maximum allowable distance in order to calculate the relative importance of an observation for calculating an interpolation value.
## Materials and Methods
### Material Preparation and Final Selection  
Two absorber concepts with different materials and production technology were investigated in the initial research phase. The first concept was a paint with alcohol-based organic pigment. Its advantage was that it could be applied with a 50 mm wide felt tip marker. However, in the case of application on a 0.02 mm thick aluminum foil, the pigment layer was prone to leafing. A 0.3 mm aluminum plate was necessary to provide an optimal low-emissivity substrate. The second concept was a polyolefin laminate combined with manganese ferrite black spinel pigment (0.5 \(\upmu\)m mean diameter of particles). Due to its better mechanical stability, it was possible to incorporate the pigment in high-density polyethylene (HDPE) (5% by mass). The HDPE was processed on a semi-industrial blow-out line. The resulting foil was subsequently attached to a low-density polyethylene (LDPE) metalized laminate by hot pressing (20 min; 140 \({}^{\circ}\)C; 0.1 MPa).  
The second concept was selected to be more promising for further development after the initial spectral optical measurements based on the results of the tests, previous work experience, and a literature review (see Section 1). It was assumed that a polymer-based absorber foil would have advantages of easier transportability, and lower production costs and environmental impacts, compared to an absorber incorporating metal plates. This resulted in selection of biaxially oriented polypropylene (BOPP) and metalized polyethylene terephthalate (MPET) foils as possible substrate materials for the final absorber concept. Both are well-established materials (in the context of Central Europe) with suitable parameters. BOPP is more compatible with a polyethylene absorption layer, but it has lower thermal resistance. MPET has high tensile strength (150 MPa), stiffness, and temperature resistance (up to 140 \({}^{\circ}\)C), ensuring acceptable mechanical properties at the intended operating temperatures around 80 \({}^{\circ}\)C. This selection was followed by further development of the production process. Industrial roll-to-roll processing was selected because it is a readily available technology with an acceptable production rate (up to a few meters per second), sufficiently low costs, and sufficient pigment thickness control. It also enables reprocessing of technological waste and works without further materials, such as solvents in printing technologies (which is beneficial for the mitigation of environmental impacts).  
The final composition of the developed polymer-based solar absorber therefore consists of a polyethylene absorbent layer with 5% (by mass) inorganic pigment, an adhesive polyethylene bridge, vapor-coated aluminum, and a structural layer of MPET (see Figure 1). In standalone applications, a UV stabilizer should be incorporated, however, it was not used in the current development stage.
# Monitoring of Ionospheric Anomalies Using GNSS Observations to Detect Earthquake Precursors
## 2 Materials and Methods
### Model Implementation: From GNSS Doppler Observations to VTEC and VTEC Rate
#### 2.1.2 Parameterization of VTEC and VTEC Rate  
\(VTEC(t)\) and \(VTEC(t)\) are variables, functions of space (besides time), and hence to produce real-time estimation they have to be written as series of spherical harmonic functions depending on the latitude and longitude of IPPs ([PERSON], 2017). Since we are interested in using a local model, namely, a spatially limited case, we record the expansion on a spherical cap by means of the Adjusted Spherical Harmonic Analysis (ASHA) ([PERSON], 2017; [PERSON], 2017). Changing the domain of the expansion from the sphere to the cap means to define a new set of coordinates (the modified longitude and the colatitude), resulting in the following:  
* The coordinates refer to a cap with the pole corresponding to the center of the region of interest;
* The colatitude \(\theta\) is adjusted to \(\theta^{\prime}\) defined on a hemisphere \(\theta^{\prime}=s\theta\), with \(s=9^{\circ}/\theta_{0}\) (\(\theta_{0}\) is the cap half-angle in degrees); now, the colatitude is defined in the interval \([0^{\circ},90^{\circ}]\);
* The Legendre functions in the new coordinates (longitude \(\lambda\) and colatitude \(\theta^{\prime}\)) still have integer order and degrees.  
The functions \(VTEC\) and \(VTEC\) can be now written as follows:  
\[VTEC(\lambda,\theta^{\prime})=\sum_{k=0}^{K}\sum_{m=0}^{k}\left(g_{k}^{m}\cos m \lambda+h_{k}^{m}\sin m\lambda\right)P_{k}^{m}\left(\cos\theta^{\prime} \right), \tag{4}\]  
\[\begin{split} VTEC(\lambda,\theta^{\prime},\lambda,\dot{\theta}^ {\prime})&=\sum_{k=0}^{K}\sum_{m=0}^{k}\left[\left(g_{k}^{m}\cos m \lambda+h_{k}^{m}\sin m\lambda\right)P_{k}^{m}\left(\cos\theta^{\prime} \right)\right.\\ &\quad\left.\left.\left.\left.\left.\left.\left.\left(g_{k}^{m} \cos m\lambda+h_{k}^{m}\cos m\lambda\right)\lambda\dot{\lambda}P_{k}^{m} \left(\cos\theta^{\prime}\right)\right.\right.\right.\right.\right.\\ &\quad\left.\left.\left.\left.\left.\left(g_{k}^{m}\cos m \lambda+h_{k}^{m}\sin m\lambda\right)\frac{\partial}{\partial t}\left(P_{k} ^{m}\left(\cos\theta^{\prime}\right)\right)\right.\right]\right.\right.\end{split} \tag{5}\]  
where \(g_{k}^{m}\) and \(h_{k}^{m}\) are the estimable harmonic coefficients, \(P_{k}^{m}\left(\cos\theta^{\prime}\right)\) are the corresponding Legendre functions of order \(m\) and degree \(k\), and \(K\) is the degree at which the expansion is truncated. Now, the unknown parameters \(VTEC(t)\) and \(VTEC(t)\) are replaced by the new unknowns \(g_{k}^{m}\), \(g_{k}^{m}\), \(h_{k}^{m}\), and \(h_{k}^{m}\).
## 4 Results and Discussion
### Financial Fraud Detection Results
This unique advantage, rooted in the mathematical foundation of the adversarial training process and symmetric projection space, ensures that the proposed approach not only exceeds existing techniques in performance metrics but also delivers a highly secure and practical solution for financial fraud detection in privacy-sensitive environments.
# FusionTrack: Multiple Object Tracking with Enhanced Information Utilization
## 3 Methodology
### Training
#### 3.7.2 Tracklet Management  
We adopt a different tracklet management scheme during training. For the tracklet representation, an additional ground truth instance ID is stored in the tracklet to represent its identity. For the detection branch, we directly match the outputs with all ground truth objects using the Hungarian algorithm. A new tracklet is initialized if a detection matches a ground truth object and the no existing tracklet with the same ground truth instance ID is found. The tracklet management scheme for the tracking branch stays the same as MOTR.  
Figure 6: An example of batched query denoising. To enable parallel processing, queries of two frames from different clips are padded to the same length using negative queries to form a batched query. Low-score detections and randomly generated objects are used to initialize these fake tracklets.  
Compared to MOTR, we use all ground truth targets instead of only newborn targets to match the predictions of object queries, stabilizing the training of the detection branch. Such a design can also make the supervision of the detection branch identical to traditional detectors, making it easier to transfer knowledge from pretrained existing detectors. Compared to TransTrack, we directly forward the feature and prediction of the existing tracklet as MOTR, ensuring the continuity of the features.
# Multi-Tier Heterogeneous Beam Management for Future Indoor FSO Networks
## 3 MTH Beam Management Protocol
### Pointing, Acquisition, and Tracking (PAT)  
Each directional tier must account for the well known PAT requirements of directional communication technologies [36]. Pointing addresses the the need to direct an element's emission towards the desired UD. Pointing protocols may require the AP to scan for available UDs or use an alternative communication medium to make the system aware of the UD's presence [37; 38; 39]. After the pointing process has determined the UD's general direction, the acquisition process iteratively adapts the beam emission profile (i.e., width and direction) in order to concentrate emitted signal on the receiver. With an established connection, tracking monitors the UD's movement and potentially adapts the emission profile to maintain the link [40]. There are obvious tradeoffs between narrow and broad emission. Wider emission simplifies tracking for small scale variations in the UD's location. Narrow emission can increase system capacity, but it requires more frequent adaptation and high speed feedback to accommodate the small scale movement.
# Measurement of Heat Flow Transmitted through a Stacked-Screen Regenerator of Thermoacoustic Engine
## 4 Results and Discussion  
### Regular Flow Channel Regenerator  
Figure 2 presents relations between the heat flow rate \(|Q|\) and the axial mean oscillation velocity amplitude \(|\langle V\rangle|\) at the hot end of the regenerator region for the ceramic honeycomb catalyst. The experimental \(|Q|\) values were obtained using oscillation frequencies of 5 Hz and 200 Hz. Results show that the experimental \(|Q|\) increases quadratically with \(|\langle V\rangle|\), as expected from Equation (3). For comparison, we evaluated heat flow rate \(|Q|\) and \(|Q_{\text{D}}|\) as a function of \(|\langle V\rangle|\) by inserting the relation \(P=0.3\varphi_{\text{m}}a\phi\,|\langle V\rangle|\), the experimental angular frequency \(\omega\) and thermal properties of the gas at hot end temperature \(T_{\text{H}}\) into Equation (3). The temperature gradient, \(dT_{\text{m}}/dx\), was approximated with the ratio of temperature difference \(\Delta T\) over the regenerator length (=35 mm), and also half of the hydrodynamic diameter, \(d_{\text{h}}/2=0.34\) mm, was used as the channel radius. The cross-sectional area \(A\) was determined as \(A=\phi\pi R^{2}\), where \(R\) is the tube radius. Results show that \(|Q|\) and \(|Q_{\text{D}}|\) are mutually close, as described in the preceding section. Furthermore, good agreement is observed between the theoretical heat flows and the measured ones. These results confirm the validity of the thermoacoustic theory. It is noteworthy that the agreement between theoretical and experimental \(|Q|\) is still good, even when \(|\langle V\rangle|\approx 1\) m/s with 5 Hz. The corresponding displacement amplitude becomes as large as 31 mm, which is comparable to the regenerator length (=35 mm). This result indicates that the thermoacoustic theory that assumes a uniform channel is still useful for such a large amplitude regime.
## 5 Experiment
### Experiment Results  
#### 5.2.1 Main Results  
The results on LC-QuAD 2.0 are shown in Table 1. Numbers are evaluated on the test set. Our proposed TSET model achieves state-of-the-art performance. In comparison to the T5 baseline model of equal size, the TEEST model achieves superior performance across all sizes, as measured by both the QM and answer \(F1\) metrics. Specifically, for the small-size models, TSET-small improved the \(F1\) score by 1.6% points, reaching 94.0%, and enhanced the QM score by 1.7% points, achieving 92.0%. Similarly, for the base-size models, TSET-base improved the \(F1\) score by 1.6% points, reaching 95.0%, and enhanced the QM score by 1.8% points, achieving 93.1%.  
Table 2 shows the experimental results on the QALD-9 plus and QALD-10 datasets, where TSET surpasses the baseline methods and achieves the state-of-art QM and answer \(F1\) score. On the QALD-9 plus dataset, TSET achieved a 61.76% QM score and a 75.85% answer \(F1\) score, which is a large absolute improvement with the T5 baseline method. On the QALD-10 dataset, TSET attained a QM score of 40.05% and an answer \(F1\) score of 51.37%, establishing a new state-of-the-art performance. We use bold font to highlight the highest score and improvement in each indicator.  
Overall, our proposed TSET model achieves better results than other models on these three widely used datasets, demonstrating the TSC objective's effectiveness in the pretraining period.
## 5 Intelligent Diagnostic Technology for Pile Inspection
### Existing Challenges in AI Integration for PIT  
Following a systematic review of current research, the integration of AI into PIT continues to encounter challenges that impede its broader implementation in engineering practice.  
1. Data scarcity and quality issues. High-performance artificial intelligence models, particularly end-to-end deep learning models, rely on large-scale, high-quality labeled datasets. However, PIT field data has two key limitations: (1) a scarcity of defect samples, which leads to overfitting; (2) signal contamination caused by environmental noise (such as construction vibrations) and complex field conditions (such as deformation caused by layered soil) impairs the reliability of training data. While simulated datasets can supplement field data, the gap between simulation and real-world physical conditions remains unaddressed.
2. Signal interpretation challenges in non-stationary environments. PIT signals are inherently non-stationary, and AI models often face challenges in generalizing across diverse noisy environments. Existing denoising techniques can enhance signal quality but necessitate manual parameter adjustment, restricting their seamless integration with end-to-end AI pipelines.
3. Interpretability and regulatory gaps. Deep learning models function as \"black boxes\" and fail to elucidate how specific signal features correlate with defect types. This contradicts engineering safety requirements, which mandate that decisions be traceable to physical principles. Additionally, global PIT standards (such as ASTM D5882 and JGJ 106-2014) lack explicit guidelines for validating AI tools, leading to regulatory ambiguity and hindering industry-wide adoption. The large volumes of labeled data required to train AI often involve sensitive engineering information (such as geological profiles and construction defect records), impeding cross-project data sharing and compromising the robustness of model validation.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture
### SEU Recovery Analysis  
The recovery analysis is described based on the assumption that the cell is storing a logic '1'. Two internal nodes and two storage nodes exist in WRTU-16T, and only two nodes--Q and S0--are considered sensitive to SEU. Therefore, two single-node upset scenarios (at Q and at S0) and one simultaneous multiple-node upset case, in which recovery from radiation-induced upsets is enabled by inherent electrical feedback and circuit structure, are examined.
## 1 Introduction
### Inventory Management Methods with Policy-Based RL  
Policy-based RL methods update the policy function during the interaction with the environment via gradient descent and make decisions based on the policy function. [PERSON] et al. (2016) employed Approximate State-Action-Reward-State-Action (SARSA, a value-based RL algorithm) and three slightly different REINFORCE (a policy-based RL algorithm) algorithms on a two-level supply chain environment, extending RL methods to a multi-node single-item mode supply chain inventory management problem. The environment consisted of a factory and one to three warehouses with a horizon of 24 periods. The environment state was composed of inventory level and demand over the last two periods, and the agent's action was factory production and product transportation. The environment reward was profit, considering warehouse maintenance costs and inventory backlogs. Based on this work [PERSON] (2017) added lead time delivery cycles to the scenario, using DQN to handle discrete action. The environment state consisted of inventory, production, transportation, and the last m (hyperparameter) demands. The agent's action was factory production and product transportation. The environment reward was profit considering operational costs.  
[PERSON] and [PERSON] (2018) constructed a four-level supply chain environment for a single material in a multi-node system. They used a single-agent algorithm to solve the multi-agent problem. However, in practical problem environments involving multi-agent participation, agents often cannot access all the environment information, making such methods somewhat impractical. The lack of necessary information exchange among agents leads to unstable environments.
# A Method for Building a Mixed-Reality Digital Twin of a Roadheader Monitoring System
## 2 Architecture of Digital Twin Monitoring System
### Overall Architecture of Digital Twin Monitoring System
#### 2.1.5 Network Transmission System  
The network transmission system is the transmission channel for data communication among the various sub-components within the digital twin framework and is the \"blood system\" of the entire digital twin monitoring system of the roadheader, providing data interaction for the dynamic real-time bidirectional interaction mapping of the entire system. The network transmission system transmits the operation information collected by the multi-valued attribute physical entity to each subsystem and sub-module of the digital twin monitoring system of the roadheader through transmission channels such as industrial Ethernet, fieldbus, intelligent gateway, and wireless network, based on communication protocols such as TCP/IP, MODBUS, and LonTalk serial communication, combined with 5G communication, industrial Internet, and Internet of Things technologies, to realize the information interaction and mapping feedback among the subsystems and sub-modules.  
The above five modules work collaboratively to achieve the purpose of real-time monitoring of the roadheader. The process is shown in Figure 2.