esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization Method of Assembly Tolerance Types Based on Degree of Freedom
## 5 A DOF-Based Method for Screening and Optimizing Tolerance Items
### Process for Selecting Tolerance Types of Assembly Fautures of Assembly Parts  
According to the process of obtaining the control parameter degrees of freedom of the assembly in Section 4, it is known that a certain tolerance type is selected for the assembly feature surface, the DOFs of the tolerance zone of the tolerance indicates that it cannot constrain the displacement changes in the corresponding direction of the assembly feature surface, and its displacement changes may affect the geometric functional requirements of the assembly, which provides a basis for using the DOFs of the tolerance zone to screen and optimize tolerance types. For this purpose, the algorithmic optimizing of tolerance types based on DOFs is designed, as shown in Figure 5. The steps are as follows:  
Figure 4: Calculation flow of CPDF \(V_{0}\).  
* Step 1: Determine the tolerance zones of different tolerance types of the assembly feature features and their DOFs vector \(V_{i}\) (subscript i is the tolerance mark serial number).
* Step 2: Calculate the Boolean operation value between the DOFs vector \(V\)i and the CPDF vector \(V_{0}\) which can be represented by the vector \(V_{i}^{\prime}\), \(V_{i}^{\prime}\) is called the comparison freedom vector. Its calculation formula is shown in Formula (16): \[V_{i}^{\prime}=V_{i}\cap V_{o}\] (16)
* Step 3: Select tolerance types. Judge the relationship between the comparative DOF vector \(V_{i}^{\prime}\) and the CPDF vector \(V_{0}\). If the CPDF vector \(V_{0}\) is equal to the comparative DOF vector \(V_{i}^{\prime}\), it indicates that the \(i\)-th tolerance type of an assembly feature surface of the assembly part is the preferred tolerance type.
# A Personal Microcomputer as an Access Control Management Platform in Road Transport
## 6 Comparative Analysis, Recognition Problems and Future Development
### Recognition Problems  
All the main and additional goals of the described project have been fulfilled. It is possible to create an autonomous vehicle access control system using microcomputers and OCR technology. Despite the lower computing power compared to classic desktop computers, devices such as Raspberry Pi can efficiently operate on large databases, process images, and quickly analyse the data. However, it is not yet a perfect system. Although the average accuracy exceeds the minimum requirements, there are still some problems. The first is the lightning conditions--the entire surface of the licence plate must be illuminated evenly. This condition has to be implemented in the design process during the infrastructure implementation of the described system.  
Another, and perhaps the biggest, problem is the misrecognition of registration numbers caused by pairs of similar letters and digits in adjacent positions. Some rare but possible combinations of licence plate numbers may have an almost 0% chance of being correctly recognised by the system and consequently will not be allowed to enter a restricted area, even if they should be allowed to. This problem can be solved in two ways. The first is related to the idea of artificial intelligence and machine learning. The OCR engine used in the described project--Tesseract--is equipped with training tools. It brings the possibility of improving its accuracy, especially when working with specific fonts and the visual format of the image. Training consists of recognising text and comparing the results with the \"ground truth\"--pairs of text images and files specifying each character and its exact position in the picture. The training program runs through a predetermined number of iterations and modifies the text recognition process with each iteration to lower the error rate and thus achieve better accuracy. The results should be more proportional to the number of samples and iterations, but the training process would take much longer.  
However, the training process does not guarantee 100% accuracy. Optical character recognition and image processing are complex processes, especially when the input images are so different. There is always the possibility that some unforeseen errors will occur. The second and easiest way to solve this problem is to add an intercom to the system that enables communication between the driver and the person responsible for access control. This would ensure that the inspection process would be automatic most of the time. Still, in the event of a problem, the relevant person can manually check if the vehicle can enter the premises. This solution is currently used in all similar systems and will be used until the technology can maintain 100% accuracy and prevent errors.
# WorkloadGPT: A Large Language Model Approach to Real-Time Detection of Pilot Workload
## 6 Discussion
### Comparison with Previous Studies (Q3)  
In this section, this study is compared with previous research in terms of data type, feature selection methods, classification models, sample size, number of categories, real-time capability, number of participants, and accuracy (ACC). The comparison results are shown in Table 9.  
Firstly, previous studies have predominantly used high-interference devices for data collection. For instance, [PERSON], [PERSON] et al. [76] utilized EDA, ECG, and RESP data, while [PERSON], [PERSON] et al. [7] and [PERSON] et al. [8] employed EEG data. In contrast, this study utilized the low-interference eye movement indicator and seat pressure indicator, effectively reducing interference with pilot operations.  
Secondly, common feature selection methods in past research have included ANOVA, Pearson correlation coefficient, and discrete wavelet transform (DWT), which extract features by analyzing variance, correlation, and signal characteristics. This study, however, did not perform explicit feature selection but has adopted an end-to-end modeling approach, fully utilizing raw data and introducing individual difference cues during the construction of formatted instances. This enhances the model's cross-pilot generalization. This  
Figure 10: Evaluation results of cross-pilot generalization for different models.  
end-to-end method allows the model to accurately capture key features affecting workload classification, thereby improving the model's accuracy and consistency in complex dynamic scenarios.  
Additionally, the real-time detection of driving workload has been challenging in previous research. This study extracted features using a 10 s time window and input them into WorkloadGPT for classification, with a model response time of only 1.76 s, demonstrating strong real-time performance.  
Finally, this study employed a three-level cognitive workload classification standard (low, medium, high), with WorkloadGPT achieving an accuracy of 87.3%, significantly higher than the 70-80% range reported in previous studies [6, 7, 8]. Unlike prior models that relied on classical machine learning algorithms or ensemble structures, this study innovatively applies a large language model to address pilot workload classification, presenting a novel solution. Despite the limited sample size and number of participants in this study, the proposed GAN-Ensemble data augmentation method provides a robust foundation for model development.
###### Abstract  
Adaptation of handover parameters in ultra-dense networks has always been one of the key issues in optimizing network performance. Aiming at the optimization goal of effective handover ratio, this paper proposes a deep Q-learning (DQN) method that dynamically selects handover parameters according to wireless signal fading conditions. This approach seeks good backward compatibility. In order to enhance the efficiency and performance of the DQN method, Long Short Term Memory (LSTM) is used to build a digital twin and assist the DQN algorithm to achieve a more efficient search. Simulation experiments prove that the enhanced method has a faster convergence speed than the ordinary DQN method, and at the same time, achieves an average effective handover ratio increase of 2.7%. Moreover, in different wireless signal fading intervals, the method proposed in this paper has achieved better performance.  
ultra-dense network; handover parameters; deep Q-learning; digital twin; long short-term memory 2023  
2
# C-RAN Traffic Aggregation on Latency-Controlled Ethernet Links
## 3 Traffic Aggregation  
The exponential increase in mobile users and enormous bandwidth requirement by mobile applications emphasize the need for introducing a solution to increase the throughput. Traffic aggregation is one of the well-established concepts in the networking context and, in this section, the aggregation technique and how C-RAN can benefit from it will be explained in detail.
## 1 Introduction
The primary objective of this study is to evaluate the correlation between Vs30 estimates derived from HVSR inversion and those from ReMi surveys, as well as their fundamental site period. This comparison assesses GA's effectiveness as a supplementary Vs30 estimation method. Geomorphological data and available geological maps were used as additional references for site selection. By employing HVSR inversion with GA and integrating geomorphological data with site-specific calibration using ReMi data, this study presents a more efficient approach to Vs30 estimation. The method enables broader coverage with fewer data points while mitigating uncertainties associated with previous techniques. This method substantially reduces data acquisition time and logistical demands, offering a faster cost-effective alternative for seismic hazard mapping.
## 5 Results of the Foamed Poly(Lactic Acid)  
### Scanning Electron Microscopy  
We evaluated the cell structure of the manufactured foam samples based on scanning electron microscope (SEM) images. Figure 8 shows characteristic SEM images of the foam structure. They indicate that foaming took place in the case of both polymer matrices and all three foaming agents (Tracel3170, Tracel4200 and Luv_9537). Hyd_3168 applied at 190 \({}^{\circ}\)C could not form a cell structure in the case of either PLA matrix. This corresponds to the TGA results, which showed that Hyd_3168 could not form effective gas at 190 \({}^{\circ}\)C in 330 s (the modelled extrusion time) and therefore a cell structure could not form.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Code** & **PLA Used** & **CBA Used** & **CBA Dosing** & **Note** \\ \hline
[-] & [**Type**] & [**Type**] & [**wt\%**] & [-] \\ \hline
2003\_ref & 2003D & - & 0 & extruded once \\
8052\_ref & 8052D & - & 0 & extruded once \\
2003\_3170 & 2003D & Tracel3170 & 2 & - \\
8052\_3170 & 8052D & Tracel3170 & 2 & - \\
2003\_4200 & 2003D & Tracel4200 & 2 & - \\
8052\_4200 & 8052D & Tracel4200 & 2 & - \\
2003\_Luv\_9537 & 2003D & Luv\_9537 & 2 & - \\
8052\_Luv\_9537 & 8052D & Luv\_9537 & 2 & - \\
2003\_Hyd\_3168 & 2003D & Hyd\_3168 & 2 & - \\
8052\_Hyd\_3168 & 8052D & Hyd\_3168 & 2 & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: The designation of the samples and their recipes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Code** & **T\({}_{\text{melt}}\)** & **P\({}_{\text{melt}}\)** & **Pressure Drop** \\ \hline
**-** & \({}^{\circ}\)**C** & **bar** & **MPa** \\ \hline
2003\_ref & 196 & 74 & 7.3 \\
8052\_ref & 196 & 43 & 4.2 \\
2003\_3170 & 196 & 32 & 3.2 \\
8052\_3170 & 195 & 21 & 2.0 \\
2003\_4200 & 196 & 14 & 1.3 \\
8052\_4200 & 196 & 13 & 1.2 \\
2003\_Luv\_9537 & 196 & 18 & 1.7 \\
8052\_Luv\_9537 & 196 & 34 & 3.3 \\
2003\_Hyd\_3168 & 197 & 56 & 5.5 \\
8052\_Hyd\_3168 & 196 & 46 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Manufacturing parameters.  
Figure 7: The surface of 2003_3170, 2003_4200 and 8052_3170, 8052_4200 foam structures manufactured at 190 \({}^{\circ}\)C.
# High-Precision Image Editing via Dual Attention Control in Diffusion Models Without Fine-Tuning
## 4 Method Overview
### Attention Control
#### 4.3.1 Controlling the Cross-Attention  
Cross-attention layers are designed to capture interactions between different sequences. In this case, we weight the attention map from the original prompt and the attention map from the modified prompt and replace the modified attention map. This modification can better control which parts of the input sequence should be attended to, generating a specific desired outcome, as shown in Figure 4. Specifically, the attention map \(M_{\mathbf{Z}_{t}}\), obtained from the generation of the initial prompt \(\mathbf{p}\), can be injected into the generation process of the modified prompt \(\mathbf{p}^{*}\). This allows the synthesis of an edited image while preserving the structure of the input image. In detail, suppose the following:  
\[LCA(\mathbf{Z}_{t},\mathbf{p},t,s) \tag{9}\]  
represents a step in the denoising process, where the output is the noisy image \(\mathbf{Z}_{t-1}\) and the attention map \(M_{\mathbf{Z}_{t}}\). Subsequently, our diffusion step will be expressed as:  
\[LCA(\mathbf{Z}_{t}^{*},\mathbf{p}^{*},t,s)\{M_{\mathbf{Z}_{t}^{*}}\leftarrow \alpha\cdot M_{\mathbf{Z}_{t}}+(1-\alpha)\cdot M_{\mathbf{Z}_{t}^{*}}\} \tag{10}\]  
Figure 3: The denoising U-Net’s attention downsampling block. The attention upsampling block attaches a ResNet block and a Transformer block, where downsampling is replaced by upsampling. Additionally, the final attention-based upsampling block does not perform any upsampling.  
where \(\mathbf{p}\) is the original prompt, \(\mathbf{p}^{*}\) is the editing prompt for the modified image, and \(s\) is the random seed. To prevent randomness, the random seed needs to be fixed. This is due to the nature of diffusion models, where even with identical prompts, two different random seeds can lead to entirely different outputs. We do not directly substitute the attention map of the original image \(M_{\mathbf{Z}_{t}}\) for that of the edited image \(M_{\mathbf{Z}_{t}^{*}}\). Instead, a linear combination of both is achieved by introducing a parameter \(\alpha\), which controls the weight between the original and edited attention maps. This parameter provides a flexible method to adjust the extent of detail retention. Direct substitution could lead to a loss of subtle yet critical structural information present in the original image, resulting in unnatural artifacts in the generated image. The linear combination method allows for more precise control over which aspects should be emphasized or preserved, especially in local modification tasks. This method better maintains overall consistency and integrates two attention maps via smooth transitions, thereby retaining original characteristics while introducing new elements. Consequently, this leads to a more harmonious and natural editing effect.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 4 Results and Discussion
### Setup and Implementation Details  
#### 4.2.1 Evaluation Measures  
Because the accuracy can be a misleading measure when the data are highly imbalanced [43], we evaluated our model in terms of P, R, and F1-score, as follows:  
\[\text{P}=\frac{\text{TP}}{\text{TP}+\text{FP}}, \tag{22}\]  
\[\text{R}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{23}\]  
\[\text{F1}=\frac{2\times\text{P}\times\text{R}}{\text{P}+\text{R}}, \tag{24}\]  
where TP (True Positive), TN (True Negative), FP (False Positive), and FN (False Negative) represent the numbers of samples correctly classified as anomalous, correctly classified as normal, incorrectly classified as anomalous, and incorrectly classified as normal, respectively.
## 4 Case Study and Discussion
### Discussions  
The proposed revenue model for wind farms could reflect the current RPS policy in Korea as shown in results of case 1. From the other results, applying the REC multiplier in off-peak periods could be considered as a factor in reducing power shortages. This incentive would have a positive effect on mitigating wind power fluctuations. It might be also act as an economic strategy for power system operators since the additional revenue did not increase significantly, even when the magnitude of the REC multiplier was increased more than three-fold. Hence, the proposed incentive mechanism is a win-win proposition for wind farm and power system operators.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Month**} & **Case 1 \& Case 2** & **Case 2-1** & **Case 2-2** & **Case 2-3** \\  & **[103 Won]** & **[103 Won]** & **[103 Won]** & **[103 Won]** \\ \hline
1 & 56,027 & 56,080 & 57,360 & 58,513 \\
2 & 62,766 & 62,787 & 63,804 & 64,331 \\
3 & 57,057 & 57,068 & 58,164 & 59,255 \\
4 & 36,255 & 36,262 & 37,324 & 38,263 \\
5 & 21,344 & 21,354 & 22,494 & 23,558 \\
6 & 32,043 & 32,060 & 33,132 & 34,094 \\
7 & 42,325 & 42,589 & 43,782 & 44,851 \\
8 & 29,355 & 29,642 & 31,059 & 32,360 \\
9 & 27,149 & 27,430 & 28,834 & 30,055 \\
10 & 39,275 & 39,293 & 40,318 & 41,292 \\
11 & 35,039 & 35,054 & 36,126 & 37,171 \\
12 & 38,463 & 38,488 & 39,574 & 40,688 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Total net revenue results.  
Further studies are required to analyze the operations and control strategies of ESSs since the proposed model was mainly designed for simulating revenue generation. The above results would be greatly influenced by the operation conditions of the ESS including SOC and charging and discharging rates. Although wind farm operators had the chance to gain more revenue as the REC multiplier increased, the additional revenue was limited since the amount of charging/discharging power was limited by these conditions. This limitation was considered as a factor in slight revenue increase despite a significant increase in the REC multiplier. Moreover, the proposed model could not show the optimal REC multiplier for both wind farm operators and power system operators. Therefore, future studies need to be designed to construct an optimization problem that reflects the detailed operations of the ESS and determines the optimal REC multiplier to control the power fluctuations.
## 4 Performance Evaluation of Offshore Communications
### Power Coverage  
Based on the measurement channel impulse response (CIR), the received power can be obtained by calculating the square value of the amplitude of the CIR. It represents the real signal level received by the receiver after the transmission of the surrounding environment under the set parameters, including the gain of antennas, the height of the base station and the power of the transmitting equipment. It can contribute to predicting the effective coverage range, which is important to the wireless network planning and base station deployment.  
Figure 6: Propagation path loss. (**a**) SSI measurement, (**b**) S2S measurement.  
Figure 7 gives the power coverage range based on the S2I measurement. It shows that the received power varies from \(-92.3\) dB to \(-41.3\) dB with the measurement distance. The minimum received power is about \(-92.3\) dB at the Tx-Rx distance of 2500 m, which can still support smooth communication.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 3 Thermal Bridge Detection Method Using Neural Network  
This study proposes a method for automatically detecting thermal bridges in building envelopes from thermal images. Figure 1 shows the method proposed in this study. First, the areas with thermal anomalies in the envelopes are clustered. Thermal anomaly area clustering involves three processes. The first process is clustering according to the temperature distribution, wherein areas with similar temperatures are considered as one cluster. Second, only clusters considered to be abnormal temperature distributions are segmented among the clusters with different temperature distributions. The results of classifying thermal anomaly areas are considered one cluster, even if the physical distance is distant, because clustering was performed by temperature distribution, as specified above. In the third process, clustering according to distance is performed, to recognize physically separated areas as different defects. The results of thermal anomaly area clustering allow us to obtain physically separated clusters with an abnormal temperature distribution. Features to detect thermal bridges are extracted from these individual clusters. Because thermal bridges have long vertical or horizontal shapes, this study selected linear distribution and complex boundaries as the features. After extracting the features, a pre-trained thermal bridge detection model is used to recognize areas corresponding to thermal bridges within the thermal image.  
Figure 1: Proposed thermal bridge detection method using neural network.
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### Comparing IoT Technologies and IoT Enablers
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Impact on (Battery, Device Cost per Domain 1**} \\ \hline
**Technology** & \begin{tabular}{c} **Network** \\ **Topology** \\ \end{tabular} & \begin{tabular}{c} **Network** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Time-Based** \\ **Positioning** \\ \end{tabular} & \begin{tabular}{c} **Power-Based** \\ **Positioning** \\ \end{tabular} & \begin{tabular}{c} **Space-Based** \\ **Positioning** \\ \end{tabular} & \begin{tabular}{c} **Achievable** \\ **Positioning** \\ **Accuracy 2** \\ \end{tabular} & \
\begin{tabular}{c} **Most** \\ **Suitable** \\ **Domain** \\ \end{tabular} &
\begin{tabular}{c} **Accuracy** \\ **Studies** \\ \end{tabular} \\ \hline WirelessHART & mesh & LR/LP-Short range & **++++** & **+,+** & **++,++** & Medium & Power \\ \hline WiFi802.11 af & star & HR/HP-Long range & **+, +** & **+,+** & **++,++** & High & Time \\ \hline WiFi802.11 ah/HaLoW & star & LR/LP-Long range & **+,+** & **+,+** & **++,++** & High & Time \\ \hline WiFi802.11 az & star & HR/HP-Short range & **+,+** & **+,+** & **++,++** & High & Time \\ \hline WiFi802.11g (V2X) & mesh & HR/HP-Short range & **+,+** & **+,+** & **++,++** & High & Time & [26,27] \\ \hline Wirepas & mesh & HR-Long range & **++++** & **+,+** & **++,++** & Medium & Power \\ \hline WiSUN & mesh & LR/LP-Long range & **++++** & **+,+** & **++,++** & Medium & Power & \\ \hline ZigBee/ZigBee-NaN & mesh & LR/LP-Long range & **++++** & **+,+** & **++,++** & Medium & Power & [28, 29, 30] \\ \hline Z-Wave & mesh & LR/LP-Long range & **++++** & **+,+** & **++,++** & Medium & Power or Space \\ \hline \hline \end{tabular}  
* \({}^{1}\) (+, +): low impact, (++, ++): medium, (+++, ++): high impact 2 assuming implementation without external sensors, such as GNSS.  
\end{table} 
Table 1: _Cont._In addition, we have found that network-centric positioning solutions are being favored as opposed to device-centric ones, which is often related to the limited resources at the end nodes. However, a centralized architecture places an additional burden on the network capacity and latency as the number of devices grow. For many of the IoT systems, a centralized architecture will have difficulties accommodating real-time location systems, especially due to the strict latency requirements of such systems. Integration with other high-capacity technologies, such as WiFi and 5G, could decrease the latency at the expense of per unit cost and power consumption. The support of positioning updates at very sparse intervals ought to be feasible for many IoT technologies, which will certainly find its application in several niche markets, especially if the positioning system is supported fully by battery-powered networks over a span of multiple years.