esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset Preparation and Training Environment
### Training Environment and Parameter Setting  
This study utilized a desktop computer with an Intel Core i7-10700 CPU, RTX 3060 Ti GPU with 8 GB independent video memory, and 32 GB memory capacity as the model training and testing platform. The software environment comprised Windows 10, CUDA 11.6, and Pytorch 1.13.1 deep learning framework. During the model training, the initial learning rate was set at \(1.5\times 10^{-4}\), and the batch size was 32 with a total of 100 iterations. The Adaptive Motion Estimation (Adam) [46] algorithm was uniformly used as the optimization method to obtain the optimal training model. The cross-entropy loss function was employed to calculate the loss value, and the dynamic attenuation strategy was used to update the learning rate, reducing the learning rate by half every 20 iteration cycles.
# Enhanced Feature Selection via Hierarchical Concept Modeling
## 5 Results and Discussion
### The Performances of Prior Feature Selection Approaches
The layered structure of ANN models can leverage diverse feature sets, making it especially effective with IG and FS methods that capture non-linear relationships within the data. The highest classification accuracy occurs when feature importance aligns with each classifier's inherent strengths. For example, datasets with high inter-feature relevance perform well with BS and FS, which selectively retain essential features while discarding irrelevant ones. This alignment between feature importance and classifier capabilities ensures that the chosen features directly support classification goals, resulting in improved model performance.
# Secure Data of Industrial Internet of Things in a Cement Factory Based on a Blockchain Technology
## 4 Process Flow of the Proposed Architecture
### Data Storage  
Data storage starts after completion of the registration step for all of the sensors and actuators that the participants in the blockchain have networked successfully. The next step represents the process of storing data provided from these sensors and actuators to the blockchain network through lightweight nodes, as depicted in Figure 3. The process starts when new data from the sensors is fed to the STM32 board. The STM32 is responsible for preparing the received data (pre-possess) and sending it to the lightweight nodes in the blockchain service layer. The lightweight nodes (ARM Cortex-M4) check the registration ID of the device to verify it or deny the proposed transaction. If it is true, then it executes the ECDSA directly and generates the public and private key after encrypting the received data. The proposed transaction is sent to the blockchain network for storage purposes, after checking the PoAh, and a copy of all transactions is sent to the lightweight nodes to provide a backup of these transactions, that can be used in case of a single node crash. Thesmart contract and lightweight node interact with each other to execute the transaction. The smart contract responsible allows the transaction to store the encrypted data inside the blockchain network. Algorithm 4 summarizes the data collection and storage process.  
```
1 start:
2 STM32 start pre-process sensors data
3 STM32 send the processed information to lightweight nodes
4 ARM Cortex-M4 verifies the device through its registration ID
6 if (Device authentication = = true)
7 Execute ECDSA
8 Generate public and private keys
9 Submit proposed transaction to blockchain for data storage
10 if (PoAh = = true)
11 Store encryption data inside the blockchain network
12 else
13 Return error ( )
14 else
15 Deny transaction
16 Return error ( )
17 end: Go to step 1 for next process
```  
**Algorithm 4**Sensors Data Storage.
## 2 Materials and Methods
### Quantitative PCR (qPCR) Assay  
The total RNA from the small intestine was extracted while following the instructions of the Trizol kit (Thermo Fisher Scientific, Inc., Waltham, MA, USA). Both the purity and concentration of total RNA were measured via ultramicrospectrophotometer and the RNA concentration was standardized to one level (1 ug/uL). An RNA sample (1 ug/uL) was mixed with 1 uL (oligo) primer dT and 10 uL ultrapure water (Thermo Fisher Scientific, Inc., Waltham, MA, USA) and then mixed at 65 \({}^{\circ}\)C for 5 min. Then, the system was MIXED with 1 uL Ribolock RNase Inhibitor, 2 uL 100 mM dNTP mix, 4 uL 5\(\times\)Reaction buffer, and 1 uL Revert Aid M-mu/v RT, respectively. The whole 20 uL mixture was used to synthesize cDNA at 42 \({}^{\circ}\)C for 60 min, and 70 \({}^{\circ}\)C for 5 min. The target genes (Table 1, Thermo Fisher Scientific, Inc., Waltham, MA, USA) were reversely transcribed and amplified. The reaction conditions were: 95 \({}^{\circ}\)C degeneration for 15 min, 60 \({}^{\circ}\)C annealing for 1 h, 95 \({}^{\circ}\)C extension for 15 min, for a total of 40 cycles. GAPDH was used as a housekeeping gene and the relative expression level of the target gene was calculated via 2\({}^{-\Delta\Delta\text{CT}}\) method [21].  
\begin{table}
\begin{tabular}{c c} \hline
**Gene Name** & **Sequence** \\ \hline Cu/Zn-SOD & Forward: 5\({}^{\prime}\)-GAAGAGAG
## 2 Materials and Methods
### Model Calibration  
The next step was to initiate the model calibration process for the previously defined model. This procedure involved varying the above-mentioned hydraulic parameters for a simulation period and comparing the obtained results with certain available measurement points within the model's boundaries. The main goal was to obtain the best possible match between the calculated and observed values. To reproduce the groundwater regime at the given location as accurately as possible and then determine the potential impact of the structure on it, the entire year 2006 was taken as the simulation period. Transient simulation was carried out for 2006 as it was a year with extremely high water levels recorded at the hydrological station Novi Sad. For these purposes, a temporal change in the piezometric head was specified in the wells located on the boundaries of the model, and the water level graph for the year 2006 was specified on the boundary line between the modeled area and the Danube (Figure 4).  
After setting boundary conditions in the form of water levels in the wells and Danube River near Novi Sad for 2006, the goal was to determine flow parameters. The calibration process was carried out by comparing the calculated water level values with the measured ones in well S-102 for different filtration coefficient values, in order to achieve the best possible match between the compared values. It should be noted that the calibration was carried out without the piles, i.e., in the calibration process the piles were not implemented in the model. For the model defined in this way, the filtration coefficient of K = 250.0 m/day and the retention coefficient of S = 0.005 1/day were determined. The comparison of the calculated and measured water levels in well S-102 for the entire simulated period of the year 2006 is presented in Figure 5. The maximum deviation is approximately 70 cm, which is acceptable regarding groundwater levels. In this period, the piles did not exist. An example of the spatial distribution of the groundwater level is shown in Figure 6, along with the detail of the structure showing three control points: KT-1, KT-2, and KT-3, located at a distance of 3.5 m from the structure itself, which can assist in comparing the temporal distribution of the groundwater levels for the year 2006 and for the two cases, i.e., with and without the structure. This allows the control of the structure's impact on groundwater levels in its vicinity in the case of an unsteady state of the Danube regime and groundwater throughout 2006.  
Figure 4: Adopted water level graphs for the year 2006: (**a**) well S-112, (**b**) water level of the Danube near Novi Sad.
## 3 Experiments Results and Discussion
### Data Processing  
To the best of our knowledge, there is no standard dataset for insulator fault training and testing. Since aerial images of insulator faults are rare and not easy to collect, in order to obtain as many insulator faults images as possible, on the basis of the Chinese Power Line Insulator Dataset ('CPLID') [51], the simulated insulator faults samples were created by the use of the software Photoshop, which was used to erase the normal insulator strings and replace them with their nearby pixels. The samples of insulator faults with diverse scenes are shown in Figure 6.  
Specifically, 1331 simulated insulator fault images were collected using the above method. Then, the faults' position on the insulator in the aerial images were labeled by the tool Label-Image. Finally, an insulator faults dataset named 'InSF-detection' (Insulator  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configuration** \\ \hline CPU & Intel(R) Core(TM) i9-9900K, CPU/3.60 GHz \\ GPU & Nvidia GeForce GTX 3080/10 GB \\ Accelerated Environment & CUDA 11.1, cuDNN 8.0.5 \\ Visual Studio Framework & Open CV 3.4.0, Visual Studio 2017 \\ Training Framework & Dark-net \\ \hline \hline \end{tabular}
\end{table}
Table 4: The relevant experimental environment.  
Figure 6: The simulated samples of insulator faults with diverse scenes.  
Fault, InSF) was established including 1331 aerial images with 2104 faults in total. We randomly selected 809 aerial images as the training set, and designated the other 522 aerial images as the testing set; detailed information regarding the 'InSF-detection' dataset is shown in Table 5.
## 2 Results and Discussion
### Volatile Composition of Investigated Fruits and Vegetables
#### 2.1.5 Broccoli  
It was possible to identify a total of 88 volatile metabolites in broccoli samples, of which 59% were alcohols and 26% aldehydes, the most representative chemical classes. The volatiles with the greatest influence on the volatile composition of the broccoli are (\(Z\))-3-hexen-1-ol (136), hexanal (38), heptanal (62), nonanal (133) and pentanal (21) [56], (\(Z\))-3-Hexen-1-ol and hexanal contribute with 43.1% and 10.3%, respectively, for the total volatile composition. Broccoli is also characterized by organosulfur compounds, namely dimethyl disulfide (36) and methyl thiocyanate (99) formed from glucosinolates and amino acid precursors [57; 58]. As described by [PERSON] et al. [58], the main reactions responsible for the formation of VOCs in broccoli can be attributed to (i) the degradation of unsaturated fatty acids; (ii) enzymatic hydrolysis of sulfur-containing amino acids and glucosinolates resulting from the loss of cellular integrity; (iii) thermally induced glucosinolate and the degradation of sulfur-containing amino acids; and finally (iv) the Maillard reaction and its successive side reactions.
Sensors_**2012**, _12_, 3162-3185; doi:10.3390/s120303162  
_Sensors_  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
_Article_  
Monocular Camera/IMU/GNSS Integration for Ground Vehicle Navigation in Challenging GNSS Environments  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3 and [PERSON] 3**  
1 School of Earth and Space Sciences, Peking University, No. 5 Yiheyuan Road, Haidian District, Beijing 100871, China  
2 School of Instrumentation Science and Optoelectronics Engineering, Beihang University, Xueyuan Road No.37, Haidian District, Beijing 100191, China; E-Mail: [EMAIL_ADDRESS]  
3 Department of Aerospace Engineering Sciences, University of Colorado at Boulder, Boulder, CO 80309, USA; E-Mails: [EMAIL_ADDRESS] (S.B.); [EMAIL_ADDRESS] (D.A.)
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-303-492-4462.  
_Received: 27 January 2012; in revised form: 22 February 2012 / Accepted: 22 February 2012 / Published: 7 March 2012_  
**Abstract:** Low-cost MEMS-based IMUs, video cameras and portable GNSS devices are commercially available for automotive applications and some manufacturers have already integrated such facilities into their vehicle systems. GNSS provides positioning, navigation and timing solutions to users worldwide. However, signal attenuation, reflections or blockages may give rise to positioning difficulties. As opposed to GNSS, a generic IMU, which is independent of electromagnetic wave reception, can calculate a high-bandwidth navigation solution, however the output from a self-contained IMU accumulates errors over time. In addition, video cameras also possess great potential as alternate sensors in the navigation community, particularly in challenging GNSS environments and are becoming more common as options in vehicles. Aiming at taking advantage of these existing onboard technologies for ground vehicle navigation in challenging environments, this paper develops an integrated camera/IMU/GNSS system based on the extended Kalman filter (EKF). Our proposed integration architecture is examined using a live dataset collected in an operational traffic environment. The experimental results demonstrate that the proposed integrated system provides accurate estimations and potentially outperforms the tightly coupled GNSS/IMU integration in challenging environments with sparse GNSS observations.  
**Keywords:** sensor integration; extended Kalman filter; GNSS; strapdown mechanization; computer vision; tightly coupled integration
## 2 Previous Models
### Flame Submodel  
As discussed in the previous section, the calculation of non-local radiation strongly depends on the temperature and height of the flame, \(T_{f}\) and \(F\), respectively, which are initially considered model parameters depending on the type of fuel. These parameters have been improved by means of two submodels that delve into their physical meaning.
## 4 Empirical Illustrations
For these two cases, it is not easy to say which methodology is better. This study then used Phase 2 to minimize the total tardiness for Cases 3 and 20 with 11 days of makespan, which is equal to the results of LWST. The resulting total tardiness values are 4320 and 138 days, which are both better than that of the LWST. Therefore, the proposed methodology can outperform the LPT and LWST for the proposed identical PMS problem. In Table 3, it can also be seen that LWST outperformed the LPT in all 20 cases. The company in the case study wants to relax the constraints imposed by the mold exchange. Adding another team that can perform a mold exchange is one option (although the two teams would still have to share the crane). This research then modified Equation (10) to be Equation (16). The results of the proposed methodology are shown in Table 4.  
\[\sum_{j=1}^{J}\sum_{K=1}^{K}C_{jkl}\leq 2\qquad\forall d \tag{16}\] \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{3}{c}{**Phase 1**} & \multicolumn{3}{c}{**Phase 1 + Phase 2**} \\ \cline{2-5}
**No.** & \(\mathbf{M}\) & \(\mathbf{T}\) & \(\mathbf{M}\) & \(\mathbf{T}\) \\ \hline  
& 9 & 2841 \\ \hline \end{tabular}
\end{table} 
Table 2: The experimental results. In Table 4, it can be seen that adding another team that can perform a mold exchange can reduce the makespan for all cases, except Case 20 (in which the results are the same). The total tardiness can also be improved in all cases, and the tardiness can even be reduced to zero in 11 cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**\# Team of Mold Exchange**} & \multicolumn{2}{c}{**Improvement**} \\ \cline{2-7}
**No.** & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**(\%)**} \\ \cline{2-7}  & \(\mathbf{M}\) & \(\mathbf{T}\) & \(\mathbf{M}\) & \(\mathbf{T}\) & \(\mathbf{M}\) & \(\mathbf{T}\) \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 4: Comparison among teams of different numbers for the mold exchanges. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Proposed Methodology**} & \multicolumn{2}{c}{**LPT**} & \multicolumn{2}{c}{**LWST**} \\ \cline{2-7}
**No. ** & \(\mathbf{M}\) & \(\mathbf{T}\) & \(\mathbf{M}\) & \(\mathbf{T}\) & \(\mathbf{M}\) & \(\mathbf{T}\) \\ \hline  
The total computing times were highly dependent on the mathematical model of Phase 2. According to observations during LINGO 17's run, the times required to result in the minimal total tardiness are illustrated in Figure 6. It can be found that the results were within 2 min for nine cases. However, more than 10 min are required in five cases (1, 12, 13, 17, and 18).
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 2 Data and Methodology
### Methodology
#### 2.2.2 Diffusion-Based Saliency Model
Get the foreground mask map \(\mathbf{M}^{o}=\big{(}m_{i}^{o}\big{)}_{N}\), and calculate \(d_{ii}^{o}=\exp\big{(}-m_{i}^{o}\big{)}\), \(\mathbf{D}^{o}=\big{(}d_{ii}^{o}\big{)}_{N\times N}\), with the other elements outside the diagonal as 0. According to the definition of the construction graph, \(S_{init}\) calculates the new degree matrix \(\mathbf{D}^{c}\) and the new weight matrix \(\mathbf{W}^{c}\). Combining \(\mathbf{M}^{o}\) to obtain the refined saliency map, the refined saliency map is generated by the proposed optimization method in Reference [20]. \[\mathbf{g}^{*}=\min_{\mathbf{g}}\frac{1}{2}\bigg{\{}\mathbf{g}^{\mathbf{T}} \bigg{[}\mathbf{D}^{c}-\mathbf{W}^{c}+\mu_{2}\bigg{(}\mathbf{I}-\frac{\mathbf{ D}^{c}}{\
u^{c}}\bigg{)}\bigg{]}\mathbf{g}^{\mathbf{+}}\|\mathbf{g}-S_{init}\|^{2}+ \mathbf{g}^{\mathbf{T}}\mathbf{D}^{o}\mathbf{g}\bigg{\}}. \tag{18}\]  
The three terms in the formula define different constraint terms. The first two terms correspond to the improved manifold ranking algorithm, the first term is to maintain the continuity of the salient values, so that the detected building target is a whole rather than scattered small areas. The second item is to constrain the difference between the refined saliency map and the saliency map before refinement must not be too large, to maintain the consistency of the detection results, which can play the role of mutual supervision of the two detection results. The third item is to reflect the role of removing the background noise of the bounding box proposal, which can suppress the external environment area that does not belong to the bounding box proposal and highlight the target area of the building. The optimal solution of Equation (18) is expressed as:  
\[\mathbf{g}=\bigg{[}\mathbf{D}^{c}-\mathbf{W}^{c}+\mu_{2}\bigg{(}\mathbf{I}- \frac{\mathbf{D}^{c}}{\
u^{c}}\bigg{)}+\mathbf{D}^{o}\bigg{]}^{-1}S_{init}. \tag{19}\]  
The saliency map obtained by combining the bounding box proposal is expressed as:  
\[S_{final}=Norm(\mathbf{g}). \tag{20}\]
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
###### Abstract  
In 2019 the Canadian Space Agency initiated development of a dedicated wildfire monitoring satellite (WildFireSat) mission. The intent of this mission is to support operational wildfire management, smoke and air quality forecasting, and wildfire carbon emissions reporting. In order to deliver the mission objectives, it was necessary to identify the technical and operational challenges which have prevented broad exploitation of Earth Observation (EO) in Canadian wildfire management and to address these challenges in the mission design. In this study we emphasize the first objective by documenting the results of wildfire management end-user engagement activities which were used to identify the key Fire Management Functionalities (FMFs) required for an Earth Observation wildfire monitoring system. These FMFs are then used to define the User Requirements for the Canadian Wildland Fire Monitoring System (CWFMS) which are refined here for the WildFireSat mission. The User Requirements are divided into Observational, Measurement, and Precision requirements and form the foundation for the design of the WildFireSat mission (currently in Phase-A, summer 2020).  
wildfire; wildfire management; satellite design; wildfire monitoring; wildfire detection; air quality; carbon emissions; user requirements; wildland fire; forest fire; earth observation; remote sensing 2020205081; doi:10.3390/s20185081
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion
### IEEE-39 Bus System  
The modified IEEE-39 bus system, containing 9 generating and 2 HVDC transmission, is depicted in Figure 3. The rated capacity of two HVDCs are all 1000 MW, accounting for 29.4% of the total generator capacity 4800 MW. Data of generators and HVDC, and load, are shown in the supplementary. The following cases are considered to examine the efficiency of the proposed model. Then economic analysis, HVDC power consumption, frequency stability, and transmission power after HVDC blocking is analyzed.  
Case 0: UC solution with AC transmission constraints.  
Case 1: SCUC solution with minimum frequency limits constraints.  
Case 2: SCUC solution with minimum frequency limits constraints and security constraints after secondary frequency regulation.  
Figure 2: The flow chart of solving the proposed UC model.
## 2 Materials and Methods  
### EVAR-eaSE Description  
The EVAR-eaSE software was developed in MATLAB (version R2019a, The MathWorks, Inc., Natick, Massachusetts, United States) a technical computing language used mostly for high-performance numeric calculations and visualization, that provides many thematic libraries, including image processing tools, and easy implementable Graphic User Interface (GUI) mechanisms.  
EVAR-eaSE is composed of three processing modules and corresponding GUI:  
1. Upload Window (UW): handles the uploading of DICOM (Digital Imaging and Communications in Medicine) files, e.g., computed tomography angiography (CTA) scans, the image pre-processing, the visualization of CTA volume rendering, and the selection of a region of interest (ROI).
2. Segmentation Window (SW): shows a contrast-enhanced version of the ROI selected in the previous window, and provides the ROI segmentation both for the pervious (lumen) and thrombotic part of the AAA; segmentation results are displayed in real time as contours on multi-planar volume reconstruction.  
3. Display and Computation Window (DCW): displays the 3D models resulting from AAA segmentation and provides measurements useful for planning, including the optimal number of coils for SE.  
The functions of each module are summarized in the framework reported in Figure 1, and are described more in detail in the following paragraphs.