esa-sceva/satcom-chunk-collection huggingface dataset:
# Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
## 3 Results
### Model Results
The PLS-DA, DNN, and RP-CNN models demonstrated accuracy improvement in the two-group models compared to the three-group models. When examining the accuracy of the models in heat levels 1 and 2, it was found that among the three-group models, the LS-SVM and RP-CNN models achieved the highest accuracy, while the accuracy did not significantly differ between the PLS-DA and DNN models. For the two-group models, no significant difference in accuracy was found among all models. The PLS-DA, LS-SVM, DNN, and RP-CNN models demonstrated improved accuracy in the two-group models compared to the 3-group models. Figure 6 is the average of the accuracy of each model of the two-group model and the three-group model. Figure 6 shows the results of the LS SVM model with the optimal results in both two-group and three-group models. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{3-6}  & & \multicolumn{2}{c}{**Control Group**} & **Control Group** & **Heat Level 1** \\  & & **vs. Heat Level 1** & **vs. Heat Level 2** & **vs. Heat Level 2** \\ \hline \multirow{5}{*}{Three-group models} & PLS & Calibration & 62 & 76.67 & 55.83 \\ \cline{2-6}  & DA & Prediction & 63.75 & 80.25 & 55.75 \\ \cline{2-6}  & LS & Calibration & 100 & 100 & 100 \\ \cline{2-6}  & SVM & Prediction & 100 & 97 & 59.75 \\ \cline{2-6}  & DNN & Calibration & 76.08 & 73.33 & 61.42 \\ \cline{2-6}  & & Prediction & 63.5 & 63.25 & 50.25 \\ \cline{2-6}  & RP & Calibration & 70.75 & 60.5 & 69.42 \\ \cline{2-6}  & CNN & Prediction & 65 & 55.75 & 62.25 \\ \hline \multirow{5}{*}{Two-group models} & PLS & Calibration & 83.17 & 76.42 & 77.17 \\ \cline{2-6}  & DA & Prediction &
## 3 Results and Discussion
### The EYB Eyes Image Results
#### 3.1.3 The DWT with a Curvature-Based Method Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multirow{2}{*}{**DWT**} & \multicolumn{11}{c}{**Wavelet Coefficient and Curvature Combination**} & \multicolumn{11}{c}{**} \\ \cline{3-14}  & & **A-K** & **A-H** & **A-X** & **A-N** & **V-K** & **V-H** & **V-X** & **V-N** & **H-K** & **H-H** & **H-X** & **H-N** & **D-K** & **D-H** & **D-X** & **D-N** \\ \hline \multirow{11}{*}{SVM-1} & Harr Lv1 & 20.28 & 70.51 & 64.06 & 56.22 & 21.66 & 51.61 & 44.24 & 47.47 & 22.12 & 53.92 & 51.15 & 50.09 & 24.88 & 43.78 & 38.25 & 39.63 \\  & Harr Lv2 & 28.11 & 61.75 & 65.44 & 55.76 & 25.35 & 52.07 & 41.01 & 45.62 & 20.74 & 48.85 & 46.08 & 48.85 & 21.66 & 34.56 & 31.80 & 30.41 \\  & Spar2 LV1 & 22.58 & 74.65 & 64.06 & 69.12 & 22.58 & 46.54 & 44.24 & 43.78 & 26.73 & 45.16 & 38.71 & 42.40 & 29.95 & 28.57 & 37.33 & 32.72 \\  & Spar2 LV2 & 28.11 & 66.36 & 68.66 & 58.99 & 17.05 & 43.78 & 34.10 & 40.09 & 21.66 & 47.47 & 42.40 & 40.09 & 17.05 & 23.50 & 24.42 & 31.34 \\  & D2L Lv1 & 22.58 & 74.65 & 64.06 & 69.12 & 22.58 & 46.54 & 44.24 & 43.78 & 26.73 & 45.16 & 38.71 & 42.40 & 29.95 & 28.57 & 37.33 & 32.72 \\  & D2L Lv2 & 28.11 & 66.36 & 68.66 & 58.99 & 17.05 & 43.78 & 34.10 & 40.09 & 21.66 & 47.47 & 42.40 & 40.09 & 17.05 & 23.50 & 24.42 & 31.34 \\  & Sw22 LV1 & 23.50 & 70.97 & 65.90 & 68.66 & 22.58 & 44.70 & 44.24 & 38.25 & 25.35 & 45.16 & 46.08 & 41.01 & 26.27 & 25.81 & 32.72 & 35.48 \\  & Sw22 LV2 & 30.41 & 64.52 & 65.90 & 59.91 & 19.82 & 41.47 & 35.94 & 36.41 & 23.96 & 39.17 & 39.17 & 40.09 & 22.12 & 23.96 & 30.41 & 28.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The detailed accuracy (%) results of the combined DWT with a curvature-based method on the E\(\gamma\)B dataset for the eye images using _SVM-1_. The combined methods produced wavelet coefficients with two decomposition levels and curvature-based variables (e.g., _A-K_ (approximation with Gaussian curvature) until _D-N_ (diagonal with min principal curvature)).
# Development of Fire Consequence Prediction Model
## 3 Configuration of the Prediction Model
### Composition of Accident Consequence Data  
Figure 8 depicts a scenario for predicting the accident consequence. It is assumed that in a situation where LNG is transported from the LNG storage tank to the LNG-fueled ship via the FGS system, a hole is created in the pump of the LNG tank, and a fire occurs when the leaked gas meets an ignition source. The hole size was predicted through this accident scenario using the data from the monitoring system, and the accident consequence was predicted using the predicted hole size and leakage situation.  
In Figure 8, D1 and D2, respectively, denote the leakage detection sensors for detecting the gas concentration and time when gas is detected in the FGSS room of an LNG-fueled ship. M1, M2, and M3 denote the sensors for monitoring LNG flow from the LNG storage tank to the piping line. These sensors detect the transport volume, speed, and pressure of the LNG, respectively. V1 represents the emergency shutdown (ESD), which is activated when leaked gas is detected by D1 or D2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Set** & **Error** \\ \hline Training set & 5.29903\% \\ Test set & 2.87421\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Errors of hole size prediction.  
Figure 8: Liquefied natural gas (LNG) bunkering accident scenario.  
Figure 7: Mean square error (MSE) of the hole size prediction data.  
A fire analysis was performed for the leakage situation using the Phast 7.11 analysis program to generate data, and the corresponding result is shown in Figure 9, which depicts the thermal radiation according to the damage distance. The vertical axis represents the radiation level at the fire point, and the horizontal axis represents the damage distance of the fire. Two hundred scenarios of leakage from the piping line pump in the FGSS room based on the hole size, gas concentration, and leakage time were converted into data.  
It is assumed that sensors D1 and D2 in the scenario detected values in the range of 50,000-150,000 PPM, which is the concentration of gas (5-15%) that can cause a fire in the air in the simulation. Furthermore, Time 1 and Time 2 were converted to data for the time at which 50,000 PPM was detected by D1 and 150,000 PPM was detected by D2, respectively.  
The effect distance at a thermal radiation of 12.5 kW/m\({}^{2}\), which can cause injury to a person at the accident location, and the maximum thermal radiation that can occur when a gas cloud is generated at Time 2 and PPM 2 were converted to data. The number of data points for the accident consequence prediction model was 200, and training was performed by dividing the data into a training set (85% of the data) and test set (15% of the data).  
The input and output data ranges of the features of the accident consequence prediction model are summarized in Table 3.
# PhotoMatch: An Open-Source Tool for Multi-View and Multi-Modal Feature-Based Image Matching
## 3 Experimental Results and Discussion
### Results
#### 3.5.1 Multi-View  
The results for each case and image matching are outlined in Table 1.  
For the first four multi-view datasets (Figure 5a-d), all of them corresponding to non-planar environments, R2D2 was the best algorithm in terms of accuracy, while ASIFT was able to obtain a higher number of matches with a lower accuracy. The exception was the boulder multi-view dataset (Figure 5d), where ASIFT achieved the highest accuracy, as the environment does not represent important challenges for matching. For the facade's multi-view dataset (Figure 5a), none of the algorithms (hand-crafted and learning-based) were able to obtain acceptable results as a consequence of the low overlap and repetitive features. Only R2D2 provided the best result, with 52.5% of correct matches (Table 1). The fifth multi-view dataset (Figure 5e) represents a favourable photogrammetric acquisition with high overlap and low geometric differences. In this case, the hand-crafted algorithms outperform learning-based algorithms, in terms of both accuracy and the number of correct matches (Table 1). For the last multi-view dataset (Figure 5f), with images with considerable geometric differences covering a wall, ASIFT was the only hand-crafted algorithm capable of computing a high number of accurate matches. Neither the other hand-crafted algorithms, nor the learning-based algorithms, provided acceptable results (Table 1). Although the performance of hand-crafted methods is guaranteed for multi-view datasets with high overlap and favourable conditions, for challenging environments (i.e., important geometric variations) the image matching is not always successful. The experimental results show that some learning-based algorithms, such as R2D2, are capable of outperforming classical, hand-crafted methods for challenging datasets with low overlap and low-resolution images. Due to its capacity to avoid areas with low reliability, R2D2
## 2 Literature Review
### Analysis Techniques
#### 2.5.2 Neural Network  
The neural network can be explained as a significant parallel distributed processor, consisting of simple processing units that are naturally inclined to store experimental knowledge and to provide access for use [117]. Moreover, a neural network is considered to be similar to the human brain and is capable of attaining new knowledge from its surroundings by implementing the learning process. Then, the synaptic weights store this acquired knowledge [117]. Following this, the learning algorithm uses sample data for altering the synaptic weights of the neural network in an orderly fashion in order to achieve the design objective [117]. Moreover, the neural network offers numerous benefits than traditional statistical methods. Such benefits include non-linear and linear neural networks to enable the assessment of non-compensatory decision processes, and it can help attain the input and output mapping without requiring specific distribution concerning the output or input [118]. Furthermore, the adaptivity of the neural network suggests that it is able to address the data generation process in terms of structural changes and that it is not difficult to re-train it according to environmental changes [118,119]. It has also been noted that neural networks surpass traditional compensatory models such as multiple, discriminant, and logistic regression analyses [118,119]. However, despite the fact that neural network has been implemented in studies in different fields such as economics [120], customer loyalty [121], wearable healthcare devices [122], and consumer choice [119,123], few studies have focused on its information systems applications [124]. Hence, the present study will first utilize PLS-SEM to determine the constructs that have strong relationships with the adoption of CC in HEIs, and then implement the non-compensatory neural network model for foreseeing the adoption of CC in HEIs according to the critical adoption variables.
###### Abstract  
Traditional Simultaneous Localization and Mapping (SLAM) (with loop closure detection), or Visual Odometry (VO) (without loop closure detection), are based on the static environment assumption. When working in dynamic environments, they perform poorly whether using direct methods or indirect methods (feature points methods). In this paper, Dynamic-DSO which is a semantic monocular direct visual odometry based on DSO (Direct Sparse Odometry) is proposed. The proposed system is completely implemented with the direct method, which is different from the most current dynamic systems combining the indirect method with deep learning. Firstly, convolutional neural networks (CNNs) are applied to the original RGB image to generate the pixel-wise semantic information of dynamic objects. Then, based on the semantic information of the dynamic objects, dynamic candidate points are filtered out in keyframes candidate points extraction; only static candidate points are reserved in the tracking and optimization module, to achieve accurate camera pose estimation in dynamic environments. The photometric error calculated by the projection points in dynamic region of subsequent frames are removed from the whole photometric error in pyramid motion tracking model. Finally, the sliding window optimization which neglects the photometric error calculated in the dynamic region of each keyframe is applied to obtain the precise camera pose. Experiments on the public TUM dynamic dataset and the modified Euroc dataset show that the positioning accuracy and robustness of the proposed Dynamic-DSO is significantly higher than the state-of-the-art direct method in dynamic environments, and the semi-dense cloud map constructed by Dynamic-DSO is clearer and more detailed.  
SLAM; VO; static environment assumption; CNNs; semantic segmentation; dynamic environments; direct method; sliding window optimization
# NBTI and Power Reduction Using an Input Vector Control and Supply Voltage Assignment Method
## 2 Preliminaries
### Path-Based NBTI Model  
The propagation delay of a logic gate \(D_{g}\) is dependent on many factors, such as load capacitance, input transition time, and the \(V_{th}\) of an internal transistor. In this paper, we assume that the delay of a complex gate is proportional to the delay of a standard inverter gate, and the delay of a logic gate can be modeled similar to the alpha-power law of an inverter [28], which is shown as:  
\[D_{g}=C_{L}\times V_{dd}/I_{D}=A/\left(V_{gs}-V_{th}\right)^{\mu} \tag{6}\]  
where \(V_{dd}\) is the supply voltage, \(I_{D}\) is the drain current, \(C_{L}\) is the load capacitance, \(V_{gs}\) is the voltage between the gate and source terminals, \(A\) is a technology-dependent factor, and \(\mu\) is a measure of velocity saturation. In order to analyze the NBTI effect, it is necessary to acquire the relationship between the gate delay increase (\(\Delta D_{g}\)) and the \(\Delta V_{th}\). Therefore, we use the one-order Taylor series expansion of Equation (6) to express the function relationship between \(\Delta V_{th}\) and \(\Delta D_{g}\).  
\[\Delta D_{g}=\frac{\mu\times D_{g}}{V_{gs}-V_{th0}}\times\Delta V_{th} \tag{7}\]  
where \(V_{th0}\) is the transistor's original threshold voltage, and \(D_{g}\) is the fresh delay of the gate. Equation (7) shows that there exists a linear relationship between \(\Delta D_{g}\) and \(\Delta V_{th}\). In order to measure the error of Equation (7), we conduct HSPICE simulation on some basic gates with a predictive technology modeling (PTM) 65 nm model [11], e.g., an NAND and NOR gate, and the error is below 2% which can meet our requirement.
## 3 Risk Assessment-Based Dynamic Access Model
### Comprehensive Decision-Making  
In the comprehensive decision-making of risk assessment and policy rule detection, the results of policy rule detection are mainly used, and risk assessment is supplemented to make decisions on requests. Mainly divided into the following situations:  
1. When the detection result of the policy rule is _deny_, the request is rejected regardless of the risk assessment result, that is, the comprehensive decision \(H\) is _deny_.
2. When the policy rule detection result is _allow_, the decision is based on the risk assessment result: when the requested risk assessment value is \([0.0,...,0.4]\), it indicates that the request risk value is low, the system resources are in a safe state, and the comprehensive decision is _allow_; When the risk assessment value of the request is greater than 0.8, it means that the request risk is very high, and the comprehensive decision is _deny_; when the risk assessment value of the request is \([0.4,...,0.8]\), it means that the request risk value is high and the system resources are in a relatively unsafe state. The configuration determines whether the decision result is _deny_ or _allow_. For example, if the administrator sets a low-risk tolerance of the system, the comprehensive decision is _deny_, and if the risk tolerance is high, _allow_.
# DTV Essential Hidden Area Decoder for DTV Broadcasting
## 2 The Conventional DEHA Analysis
### Motion Effect and Video Quality with DEHA  
Motion estimation (ME) and motion compensation (MC) play a crucial role in MPEG video compression algorithms. These techniques are essential for achieving efficient video coding. In the context of the last line of macroblocks (LML), the horizontal motion vectors in the bar mode exhibit similarities to those in the reference mode. However, a notable distinction arises in the vertical motion vectors of the bar mode, which are predominantly restricted to 0. This restriction is due to the vertical boundary of the bar mode within the LML, as depicted in Figure 5a. Understanding the characteristics and limitations of motion vectors in different modes is vital for accurately representing and compensating motion in MPEG video compression.  
In accurate motion estimation, the average sum of square error (MSE) after motion estimation (ME) is either 0 or a very small value. However, due to the restriction of vertical motion vectors in the bar mode DEHA, the MSE values after ME in the last line of macroblocks (LML) are depicted in Figure 5b. It can be observed that the histograms of black bars mode and white bars mode are highly similar. There exist minor differences between DEHA modes in terms of lower MSE values. Notably, the bar mode exhibits more perfect matching blocks (i.e., MSE is 0 and marked as 'O' on the \(y\)-axis) compared to the reference mode. Despite the slight variations in MSE after ME between DEHA modes, the video quality of the active region in the LML is higher for the bar mode due to the utilization of smaller mQuant values, as illustrated in Figure 6a. These findings highlight the impact of DEHA modes on video quality in the LML.  
Figure 5: Motion effect in 17.5 Mbps MPEG CBR of 25 fps CrowdRun sequence: (**a**) Histogram of vertical motion vector and (**b**) Histogram of MSE after ME.  
The video quality assessment of the CrowdRun sequence was conducted to enable a precise comparison of quality within the 1920 \(\times\) 1080 active regions, measured using the Peak Signal-to-Noise Ratio (PSNR) of the Y component. Figure 6b illustrates the results, with frames marked along the dotted diagonal line representing the corresponding quality levels. In all conditions tested, the average quality achieved using the proposed method was either equal to or slightly better than that of the original MPEG-2 and MPEG-4 AVC video.  
As a result, it can be concluded that the utilization of bar modes in DEHA does not significantly impact image quality or bit generation in MPEG CBR video compression. Furthermore, since the LML is located far from the center and falls within the transient effect area [1], the video quality of the active region in the LML is not prominently visible to the viewer. This realization forms the foundation of the main idea behind the utilization of DEHA, specifically via the combination of black-and-white bar DEHA modes.
# Quality of Service Based NOMA Group D2D Communications
## 2 System Model
### Interference Distribution
: See Appendix B. Note that, due to the presence of interferers in the network, similar to [20; 28], the derived outage probability in Equation (8) is a function of variables \(\varphi_{m}^{\max}\) and interference Laplace transform \(\mathcal{L}_{I}\). This is different from existing works that analyse NOMA wireless systems under no interference where outage probability is mainly a function of \(\varphi_{m}^{\max}\). For reference, please see [14; 27; 29]. Furthermore, it is worthy to note that the current outage analysis approach remains valid if more complex cluster models (Poisson cluster process, etc.) are adopted for modeling spatial distributions of DTs and DRs. In that case, the \(\mathcal{L}_{I}\) term will be replaced by the Laplace transform of the interference for the adopted model. Intuitively, \(\mathcal{L}_{I}\) is performing a form of scaling in Equation (8) and hence the conclusions are expected to remain the same if the spatial distribution model(s) of the DTs and DRs is changed. However, the exact impact on outage probability when more complex cluster processes model is adopted for modeling DT and DR locations would require further study, and we plan to investigate them in our future work.
## 3 Own Studies
### Planning and Production Cycle  
The planning and production cycle and life cycle of the 3D-printed hand exoskeleton (our own version) are shown in Figure 2.  
Figure 1: Selected 3D-printed exoskeletons mentioned in the article: (**a**) elbow exoskeleton, (**b**) hand exoskeleton, and (**c**) hand in hand exoskeleton.  
Figure 2: Planning and production cycle of the 3D-printed hand exoskeleton (own version).  
The cycle includes the following stages:  
* Stage 1: Data collection based on combined indications for exoskeleton use, functional diagnosis, the results of diagnostic imaging, and 3D scans of the part of the patient's body on which the exoskeleton is to be fitted. Digitization of this stage eliminates the need for additional, often manual measurements of the user and the impact of the associated inaccuracy/uncertainty;
* Stage 2: Design based on pre-programmed exoskeleton templates in Computer Assisted Design (CAD) software, taking into account the results of the analyses from Stage 1 and the therapy goals derived from the rehabilitation plan and the patient's priorities (patient-centered therapy);
* Stage 3: Testing, using the results of materials research and optimization (including artificial intelligence-based optimization) of the selected materials and their characteristics (lightness, strength, direction of action, and chemical properties) to produce 3D test prints, followed by operational testing and functional testing of the patient in the exoskeleton to check the optimum fit of the exoskeleton to the user;
* Stage 4: Validation, involving testing the use of the exoskeleton by the user under the controlled conditions of the Activities of Daily Living Laboratory. Here, under the supervision of specialists, the user performs all the activities envisaged for later use at home, including those resulting from the rehabilitation plan, hence the need to integrate the exoskeleton with rehabilitation methods and tools. At this stage, the final adjustments of the exoskeleton are made before it is issued to the user;
* Stage 5: Exploitation, covering the normal use of the exoskeleton at home (if necessary, adjustments are also made for hospital or outpatient settings), monitoring its wear, and providing technical support and modernization as needed--e.g., due to wear or damage to the exoskeleton, changes in the user's health, launching a new version on the market, or better supporting the user;
* Stage 6: Modification and recycling, including significant changes to the mechanical parts and software of the exoskeleton. This process goes beyond servicing to replacement and recycling of the unit as part of the Life Cycle Assessment, as well as monitoring and the implementation of conclusions from the above analyses for the planning and production cycle of the exoskeleton (e.g., considering the fastest wearing elements or necessary structural changes).  
The LCA assessment can be carried out in accordance with the methodology already tested by us [25]. Technical control of the exoskeleton is carried out at all stages of production in accordance with the Industry 4.0 paradigm.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### Performance Considerations
The objective of this case study is to perform a theoretical validation of HSALA by comparing it with the classic model across critical parameters, such as accessibility features, cybersecurity measures, scalability potential, user experience, and other relevant factors. The analysis aims to demonstrate the superiority of HSALA in addressing contemporary challenges in web application development.