esa-sceva/satcom-chunk-collection huggingface dataset:
# A Parametric Computational Study of RC Building Structures under Corner-Column Removal Situations
## 4 Discussion
### Dynamic Amplification and Load Increase Factors (DAFs and LIFs)
All the values are around 1.10, slightly inferior to the 1.20-1.30 which the authors obtained in previous studies [13; 28] and which can be justified by (i) the fact that the present study considered experimental damping in the FE models and (ii) the small differences found in the reactions peak values between experimental and computational results. Model M_q9 again obtained a value of 1.00 because the load was exactly the yielding load. Model M_crt0.2 had a slightly lower value (1.04), again due to the slow removal of the column. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{3}{*}{**Model**} & \multicolumn{3}{c}{**Static Nonlinear**} & \multicolumn{3}{c}{**Static Linear**} & \multicolumn{3}{c}{**Dynamic Nonlinear**} & \\ \cline{2-10}  & **Max. Disp.** & **Axial Force** & **Max. Disp.** & **Axial Force** & **Max. Disp.** & **Axial Force** & **DAF\({}_{\mathrm{LD}}\)** & **LIF\({}_{\mathrm{LD}}\)** & **DAF\({}_{\mathrm{LF}}\)** & **LIF\({}_{\mathrm{LF}}\)** \\  & **[mm]** & **P2/P6 [km]** & **[mm]** & **P2/P6 [km]** & **[mm]** & **P2/P6 [km]** & & & & \\ \hline M\_Ref & 25.6 & 317.4 & 14.1 & 317.3 & 46.9 & 340.1 & 1.83 & 3.33 & 1.07 & 1.07 \\ M\_Ref2.5 & 20.6 & 316.0 & 14.1 & 317.3 & 31.3 & 346.4 & 1.52 & 2.22 & 1.10 & 1.09 \\ M\_Ref3.0 & 18.4 & 316.1 & 14.1 & 317.3 & 26.2 & 351.4 & 1.42 & 1.86 & 1.11 & 1.11 \\ M\_Ref3.0 & 26.9 & 317.9 & 14.1 & 317.2 & 27.2 & 350.6 & 2.70 & 5.16 & 1.10 & 1.11 \\ M\_Ref4 & 27.8 & 645.0 & 15.2 & 649.6 & 70.3 & 720.7 & 2.53 & 4.63 & 1.12 & 1.11 \\ M\_Ref7 & 30.7 & 1188.8 & 18.1 & 1152.1 & 77.7 & 1267.4 & 2.53 & 4.29 & 1.11 & 1.10 \\ M\_crt0.01 & 25.6 & 317.4 & 14.1 & 317.3 & 63.4 & 352.6 & 2.48 & 4.56 & 1.11 & 1.11 \\ M\_crt0.05 & 25.6 & 317.4 & 14.1 & 317.3 & 57.8 & 343.6 & 2.26 & 4.10 & 1.08 & 1.08 \\ M\_crt0.2 & 25.6 & 317.4 & 14.1 & 317.3 & 35.6 & 330.5 & 1.39 & 2.52 & 1.04 & 1.04 \\ M\_q9 & 76.3 & 411.4 & 18.7 & 399.3 & 556.8 & 398.6 & 7.30 & 29.78 & 0.97 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Dynamic Amplification Factors (DAFs) and Load Increase Factors (LIFs) for displacements (\(\mathrm{DAF}_{\mathrm{LD}}\) and \(\mathrm{LIF}_{\mathrm{LD}}\)) and forces (\(\mathrm{DAF}_{\mathrm{LF}}\) and \(\mathrm{LIF}_{\mathrm{LF}}\)).
# Coal Seam Thickness Prediction Based on Transition Probability of Structural Elements
## 4 Experimental Results and Analysis  
### Coal Seam Forecasting Based on Simulated Data  
In view of the complexity of the coal rock interface, the coal seam is normally modelled as the double cosine surface [21]:  
\[Z=2.4+0.2\times\sin\Bigl{(}\pi\times\frac{w}{100}\Bigr{)}-0.2\times\sin\biggl{(} \pi\times\frac{l}{100}\biggr{)}. \tag{17}\]  
Figure 8 shows the simulated surface. In this experiment, 30 sets of continuous data were randomly generated from the simulated surface as a training set and a test set. The average mining height was set to be 2.4 m.  
In order to ensure the integrity of the structural elements in the moving process, the data at the edge of the coal-rock interface is gradually processed by RBF-Kriging interpolation.  
The coal seam data was then normalized to [0,1]:  
\[\overline{z}(x_{i})=\frac{z(x_{i})-min(z(x))}{\max(z(x))-min(z(x))} \tag{18}\]  
where \(z(x)\) is the original data, \(min(z(x))\) and \(\max(z(x))\) are the maximum and minimum values of \(z(x)\) respectively, \(\overline{z}(x_{i})\) are the normalized data. For the transition probabilities, no normalization  
Figure 8: Simulated coal seam surface.  
Figure 7: Flowchart of structural element transition probability based Gaussian process progression (STTP-GPR).  
operations were carried out, since they are in the range of [0,1]. The normalization operation can improve the speed and convergence of subsequent data prediction.  
Figure 9 shows the prediction results in 95% confidence interval obtained using the proposed STTP-GPR algorithm.  
From Figure 9, it can be seen that the trends of prediction data matches the real data very well. Overall, the maximum prediction error is less than 0.03 m and the average absolute error remains around 0.02 m. From Figure 8, it can be seen that the proposed STTP-GPR algorithm has a good performance in dealing with the locality change problem, which verifies that the spatial distribution of the coal seam that is near the prediction point plays an irreplaceable role in the prediction process. Processing the structural distribution of coal seams correctly is the key to improving the prediction accuracy of coal seams, and by expressing the structural distribution through transfer probabilities in local regions. Figure 10 shows the 3D visualization of the real surface and the prediction surface.
## 5 Experimentation, Results, and Analysis  
### Experimental Setup  
Our experimental setup is designed to provide a robust and efficient environment for training and evaluating the DCAT model. The hardware includes a high-end NVIDIA GPU A100, chosen for its advanced computational capabilities and speed, which is crucial for training large deep learning models like DCAT. Additionally, we use an Intel i9 processor, 64 GB of RAM, and 1 TB of SSD storage, ensuring fast processing and ample storage for our datasets and model checkpoints. The software environment is built on Ubuntu 18.04, offering stability and compatibility with various deep learning libraries. We utilize Python 3.6 or above, considering its widespread adoption in the data science community and support for the latest features and libraries.  
For the development and training of our model, we employ PyTorch 2.6.0, which provides a flexible and dynamic computation graph, facilitating easier debugging and a more intuitive design of complex models. The choice of PyTorch is further supported by its extensive ecosystem and active community, making it a reliable choice for deep learning applications. We use the Hugging Face Transformers library for implementing the GPT-3 component of our model, leveraging its state-of-the-art pre-trained models and ease of integration. For data manipulation and preprocessing, Pandas and NumPy are employed, given their efficiency in handling large datasets and their comprehensive set of functions for data analysis. This combination of cutting-edge hardware and versatile software provides an optimal platform for training the DCAT model, ensuring fast computation, efficient memory usage, and a smooth development process.
# Development of Potato Mass Estimation System Based on Deep Learning
## 2 Materials and Methods
### Potato Mass Estimation System
#### 2.1.1 Camera Calibration  
In all image-based works, correction is an essential and the most important step for reducing errors. Raw images photographed using a camera are distorted by the lens. The checkerboard detection method proposed by [PERSON] [19] is generally used to correct such distortions. Although this is a method that was first presented in 2000, it continues to be used in many image fields to this day as it has the advantages of a high accuracy and stability. Since the raw images of the camera developed in the present study were also distorted by the fisheye lens, distortion correction using the checkerboard detection method was carried out first. A checkerboard, on which 5 \(\times\) 8 squares of 35 mm in width and height are printed, was used, and distortion correction was carried out using OpenCV after photographing the board from various positions within the viewing angle, as shown in Figure 3. After the distortion correction, to match each pixel and the actual length, the actual length (mm) was divided by the number of pixels occupied by one square of the checkerboard to calculate the length per unit pixel.  
Figure 3: Checkerboard images for camera distortion calibration.  
Figure 2: Schematic diagram of potato mass estimation system.
## 3 Results and Discussion  
### Fermentation Kinetics  
The fermentation kinetics analysis of strawberry juices of four cultivars is shown in Figure 1. The Brix value result showed that three strawberry cultivars (except BX) almost accomplished alcohol fermentation on the sixth day, and TZ was close to complete fermentation on the fourth day, whereas cultivar BX was not able to finish fermentation until the tenth day (Figure 1A). The results of residual sugar content and alcohol content were consistent with the Brix values. Except for cultivar BX, the other three cultivars were close to complete fermentation on the sixth day, with an alcohol content of over 10% \(v/v\), while the fermentation process of cultivar BX was relatively slow (Figure 1B,C). And the results of glucose and fructose content elucidated that all four cultivars depleted glucose on the sixth day, and then fully utilized fructose around the tenth day (Figure 1D,E).  
There are many factors determining the fermentation rate, such as the fermentation condition and yeast inoculation methods. It is a trend to improve the aroma of fruit wine and reduce the alcohol content through mixed fermentation applying non-saccharomyces  
Figure 1: Fermentation kinetics analysis of different cultivars of strawberry wine. (**A**) Brix; (**B**) residual sugar content; (**C**) ethanol content; (**D**) glucose content; (**E**) fructose content; BX: Snow White; TCL: Sweet Charlie; TZ: Tongzhougongzhu; ZJ: Alkihime.  
cerevisiae [28]. In addition to inoculating non-saccharomyces cerevisiae for mixed strain fermentation [29], freeze-dried immobilized kefir culture [30] and high-temperature treatment [31] can also reduce the alcohol content of fruit wine; however, the effective promotion of fermentation strategies to industrial applications is still a problem to be solved [13]. For strawberry wine, further research is needed regarding fermentation condition optimization and microbial strain selection, after designating the wine-making cultivar.
## 3 Experimental Test Procedure
### RGB+D Fusion
#### 3.3.3 Point Cloud Processing  
Product of laser scanning is a point cloud. For each point in the cloud 3D coordinate (\(x\), \(y\) and \(h\)) are determined in the used coordinate system, defined by the instrument setup. The point cloud processing consists of calculating theodolite angles (\(Hz\) and \(V\)), 3D distances (\(d_{3D}\)) and 2D distances (\(d_{h}\)) from the camera center to each point of the cloud based on the 3D coordinates of points and calculated coordinates of the camera center. 2D and 3D distances were calculated from 3D coordinates of matched points in different epochs using the formula for Euclidean distance between two points in Cartesian space.  
Theodolite angles (\(Hz\) and \(V\)) of each point in point \(i\) cloud are defined as:  
\[\begin{split} Hz_{i}&=arctan\left(\frac{x_{cc}-x_{ i}}{y_{cc}-y_{i}}\right),\\ V_{i}&=arcsin\left(\frac{d_{3D_{i}}}{d_{h_{i}}} \right),\end{split} \tag{4}\]  
where \(x_{cc}\) and \(y_{cc}\) are the coordinates of the camera center, in the defined coordinate system.
# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 6 Evaluation
### Compared with Existing Work  
The distributed DETO algorithm proposed in this work is compared with two different offloading schemes: the PGOA [19] scheme and DEFO [8] scheme.  
1. PGOA: In [19], they considered the multi-device and multi-MEC scenario of an SCN integrated with MEC and formulated a distributed overhead minimization problem, proposed as the potential game-based offloading algorithm (PGOA), which minimizes the overhead of each user. However, they did not distinguish the subtasks among the applications, and they treated them as an entire task. Thus, they always offloaded these subtasks as a whole to the edge network.
2. DEFO: In [8], the author investigated the problem of fine-grained task offloading in edge computing. They proposed a distributed earliest finish time offloading scheme (DEFO) for subtasks from different users, which considered not only the computation workload of the subtask but also the dependency among subtasks in DAG. However, they did not consider utilizing the processor idle time to duplicate predecessor tasks to further reduce the completion time of the application.  
Figure 3: The convergence of the distributed DETO algorithm.  
Figure 4 shows the average delay for different network scales in terms of edge servers. The number of edge servers is increased from 1 to 7, and there are twenty users in the edge network. It can be inferred that the average delay will reduce with the increasing number of the server since the users could utilize more edge computation resources (taking more CPU cycles). As the PGOA did not distinguish the subtasks among the application, it always achieves a higher average delay than the fine-grained offloading DEFO and DETO algorithms.  
Through utilizing the processor idle time to duplication for certain critical subtasks, our DETO algorithm can further reduce the makespan of all the applications. Figure 5 shows the average delay for the different numbers of users for the same network scale. The number of users grows from 1 to 12, and there are only two edge servers in the network. We find that the average delay will increase with the competing users, and our algorithm can also achieve the minimum delay compared to the other two offloading strategies.  
Figure 4: The delays for different networks.  
Figure 5: The delays for different users.  
In order to verify the effectiveness of our proposed game-theory-based DETO algorthe ithm, we compare the running time and the average delay with the centralized DETO algorithm.  
Figures 6 and 7 depict the results and show the comparison between them. The X-axis denotes the number of users. Considering the exponential computational complexity of centralized DETO algorithm, the number of servers is set to 2, and the number of users increases from 1 to 8. As illustrated in these figures, although the centralized algorithm achieves a slightly higher average delay, it has much a higher computation efficiency and provides a workable solution in the ultra-dense network.
## 4 Problem Statement
### System Model  
The proposed model includes four main entities in the communication, namely patient \(P_{l}\), server \(S_{m}\), patient \(P_{j}\) and server \(S_{n}\), who are communicating in a multi-server U-healthcare environment. As depicted in Figure 1, the patient \(P\) logs into and accesses services from multiple servers \(S\). The DNA-based U-healthcare services include monitoring of body fluids, virus control, understanding disease mechanism at the molecular level, etc. [3]. The IoLT network consists of multiple DNA-reading sensors worn by \(P\). The sensing data containing DNA samples produced by the sensors are transmitted to \(P\)'s mobile device with the support of a wireless technology, e.g., Bluetooth, Wi-Fi or Zigbee. Thereafter, a sequencing process is run by a tiny sequencer connected to the mobile device. Through an open internet, the sequences produced are sent to \(S\) (e.g., doctors, data scientists, etc.) for further processing and analysis services. For example, in the monitoring of body fluids mentioned, \(S\) is allowed to keep an eye on their \(P\)'s health via blood, sweat and saliva samples. The analysis results would be transmitted back to \(P\) upon their specific request. Some related services, for instance, a WBSN, can be integrated to improve the overall healthcare process and possible medical treatments. It is recommended that the communications in the proposed system model are aided with 5G or 6G mobile technology to achieve a truly real-time healthcare process [22]. Mobile devices (smart phones, tablets, etc.) are now known for their simplicity, robustness and advanced connectivity, with many brands supporting 5G. They would also support 6G, which is expected to be introduced in 2030 [33].  
Figure 1: System model of the proposed protocol.  
In the model, two patients \(P_{i}\) and \(P_{j}\) want to share personal medical information or U-healthcare data with each other. For example, a family member may wish to know the health status of another one. Since this communication is carried out via a public channel, data security and patient privacy become prominent concerns. To this end, the proposed protocol allows \(P_{i}\) and \(P_{j}\) to compute an authenticated E2E session key used to protect their communicated messages. My work designs a three-factor authentication mechanism where a patient uses a mobile device and a smart card to register with respective servers. \(P_{i}\) carries out a SC-SSO, which uses a single set of registered credentials to log into the system and directly communicate with \(P_{j}\) through the assistance of \(S_{m}\) and \(S_{n}\).
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 4 Some Studies of Fitts' Law in HCI Scenes
### Touchscreen Scenes  
Touchscreen technology has become pervasive in entertainment, education, and industrial applications, catering to diverse needs in daily life [44; 45]. Touchscreens are typically classified into small and large screens based on size and handling convenience: small screens, like those on cell phones and smartwatches, allowing for one-handed operation; or larger screens, such as those on tablet PCs and large touchscreen displays, usually requiring two-handed operation or additional support like a stand. Notably, touchscreens integrated into vehicles present a unique case due to their large size and the inherent safety concerns associated with driver distraction. Design considerations for automotive touchscreens prioritize user safety by minimizing distractions, as tapping on the screen while driving could compromise the driver's focus and lead to unsafe behavior.
## 4 Results and Discussion
### Discussion  
Overall, the two-phase heuristic outperforms the MILP model of [PERSON] and [PERSON] (2018), while providing solutions that are at least as good as those by LAHC or GATS or MSB for most instances. This is most evident in the test cases of Sections 4.2.1 and 4.3. Although the two-phase heuristic does not dominate in terms of solution quality and speed, it is more robust than LAHC, as evidenced by having zero or lesser percentage makespan standard deviations for most test cases.  
Compared to the MILP model of [PERSON] and [PERSON] (2018), better or equally good solutions were obtained within a shorter time for some instances, supporting the use of decomposition to first generate machine-operation assignments. The importance of preemption is reflected in Section 4.2.1 where better schedules are generated for certain test instances, whereby the ratio of average loaded travelling time to the average processing time is less than 0.25.
## 2 The Experimental Equipment and Materials  
### The Experimental Equipment  
The major components of the laser cladding system for the structured CBN grinding wheel are the fiber laser, powder feeder, KUKA robot, and ultrasonic vibration system. The maximum output laser power is 500 W. The laser wavelength is 1020 nm. The power instability is less than or equal to \(\pm 5\%\), and the diameter of the beam on the surface is 1.1 mm. An ultrasonic generator, a transducer, and a variable amplitude rod make up the ultrasonic vibration system. The optimal trajectory program designed using the PQart software (V10, Beijing C.H.L Robotics Co., Ltd., Beijing, China) is imported into the KUKA robot's control system. The grinding wheel substrate is simultaneously connected to an ultrasonic vibration system via a rod with a configurable amplitude. The KUKA robot and the ultrasonic vibration system collaborate to finish the laser cladding process. Figure 1 shows the ultrasonic vibration-assisted, structured CBN grinding wheel laser cladding system. Its specific parameters are shown in Table 1.  
The maximum ultrasonic amplitude of the BRANSON 2000 bdc ultrasonic generator is 15 \(\upmu\)m. The power ratio of the ultrasonic generator can be adjusted between 0% and 100%. Since the amplitude and power ratio are linearly related, the amplitude can be adjusted indirectly by adjusting the power ratio. The microstructures of the printing layer were examined by laser confocal microscopy (LEXT OLS4100, Olympus Corporation, Tokyo, Japan), and the specific parameters can be seen in Table 2.  
Figure 1: The laser cladding system: (**a**) the experimental equipment; (**b**) the system diagram.
# Survey of Artificial Intelligence Model Marketplace
## 5 Model Tracking and Protection
### Model Tracking
#### 5.1.2 Tools and Techniques for Model Tracking  
Integrating these performance metrics and tools ensures that the AI marketplace delivers consistent value to its customers. Some of the tools include:  
* _Digital Watermarking:_ This ensures compliance with intellectual property agreements and prevents unauthorized duplication or usage.
* _Blockchain Technology:_ It provides a decentralized and immutable ledger to record model ownership, usage history, and transactions. This enhances transparency and trust in AI marketplaces.
* _API Usage Monitoring:_ It tracks the number and nature of API calls made to the model, identifying patterns of misuse or unusual activity. These insights are invaluable for service-level agreement (SLA) compliance.
* _Anomaly Detection Systems:_ It detects unusual patterns in model predictions or usage, flagging potential tampering or unauthorized activities.  
Figure 7: Performance metrics in the model value chain.  
* _Regular Model Retraining:_ It incorporates periodic updates or retraining mechanisms using new data to adapt the model to evolving contexts and preserve its utility.
* _Logging and Monitoring Dashboards:_ Tools like Grafana or Prometheus provide real-time insights into performance metrics, enabling rapid responses to detected issues.  
Continuous monitoring of latency, accuracy, and error rates guarantees reliability, while blockchain and watermarking enhance transparency by providing verifiable proof of ownership and compliance. Retraining mechanisms ensure adaptability to evolving environments, and anomaly detection safeguards security by preventing tampering or misuse. Additionally, resource utilization monitoring promotes cost efficiency, ensuring optimal operations and reducing unnecessary expenses for both providers and customers.
## 3 Proposed Methods Based on Convolutional Neural Networks
### Spatio-Temporal Convolutional Neural Network Version 1 (ST_CNN_v1)  
The first proposed model, called spatio-temporal convolutional neural network version 1 (ST-CNN-v1), is implemented using the Keras deep learning library [34]. The model architecture is defined in Figure 3.  
ST-CNN-v1 consists of a one-dimensional convolutional layer with 64 filters, a kernel size of two, and stride of one. Next, a max-pooling layer was added for feature extraction, with a pooling size and stride of two. Afterwards, a flattened layer can be found, and its output is passed through a densely connected layer with a rectified linear unit (ReLU) [35] activation function. The final layer features a single unit modelling the predicted solar radiation value. The Adam [36] optimizer is used for minimizing the mean absolute error (MAE--measuring the average absolute difference between the predicted values and the actual values) loss function.