esa-sceva/satcom-chunk-collection huggingface dataset:
# Coal and Gangue Recognition Method Based on Local Texture Classification Network for Robot Picking
## 3 ITC-Net Method
### Local Texture Classification with CNN Components  
The classification process is local sampling, predicting, and voting. In predicting part, a VGG16-based [23] CNN model is designed and implemented as the local texture classifier. This network is trained with preprocessed cutting images from Dataset1_train and tested on those from Dataset1_test. Details and test results of the model are shown in Table 2.  
It is worth noting that this model is set for the classification of the local texture images instead of the full-scale, complete images. Though the local texture classification accuracy  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Description** \\ \hline Input & (256, 256, 3) \\ \hline Output & 0: coal texture 1: gangue texture \\ \hline Activation function & Output layer: SIGMOID \\  & Other: RELU \\ \hline Loss function & Binary cross entropy \\ \hline Validation & 5-fold validation \\ \hline Random dropout threshold & 0.5 \\ \hline Epoch(s) & 20 \\ \hline Batch size & 60 \\ \hline Initial parameters & ImageNet \\ \hline \multirow{2}{*}{Other} & Random rotation, \\  & zooming, cut, brightness, and contrast change are \\  & imposed before training. \\ \hline Test accuracy & 82.33\% \\ \hline Test recall rate of coal textures & 83.76\% \\ \hline Test recall rate of gangue textures & 81.49\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed information of CNN component.  
to a certain extent determines the overall success of the LTC-Net method, it is not the only direct indication of the final recognition results since the original intention of the LTC-Net method is to reduce the instability in classification and improve the robustness.  
The test results shown in Table 2 from the above section testified the CNN component's validation in terms of classification of the local texture images from coals and gangues with a test accuracy above 80%. It is then applied in the recognition process described in Figure 4. For each full-scale image, \((n-2)\times(n-2)\) classification results will be given by the \"Classification\" procedure. In the following process, \"Count and Judge\" sum the results and compare them to a threshold value t. If the threshold is reached, a final recognition result of gangue is given; otherwise, coal. This process is depicted by Algorithm 2, and the value of \(t\) is discussed in Section 3.6.  
```
Input: \((n-2)\times(n-2)\) local texture images \(l\) sampled from one original full-scale image (the output of Algorithm 1), a trained local texture classification CNN model \(m\), and a pre-calculated threshold \(t\). \(c=0\) for\(i=1\) to \((n-2)\times(n-2)\)do resize([\(l\)], (256, 256, 3)) if\(0==m\).predict([\(l\)])do \(c=c+1\) if\(c>t\)do output GANGUE else output COAL
```  
**Algorithm 2** Full-scale recognition.
## 3 Results
### Research Results and Analysis of Carbon Emission Risk of Land Use
#### 3.3.2 Analysis of Spatial Variation Characteristics of Land Use Carbon Emission Risk  
During the study period, the carbon emission risk of land use in the study area showed obvious regional distribution characteristics. Micro-risk areas are mainly concentrated in counties and districts of Chengde City. Due to their abundant forest resources and relatively limited industrial activities, these areas demonstrate low levels of carbon emissions and maintain a favorable ecological environment. In contrast, high-risk areas are predominantly found in the counties and districts of Zhangjiakou City, which experience heightened environmental risk as a result of accelerated urbanization, industrialization, and increased carbon emissions. Furthermore, severe risk areas are identified in the counties and districts of Baoding City, indicating a pressing need for these regions to implement effective carbon emission management strategies.  
In 2010, the study area was in the early stages of economic development, with land use carbon emission risk primarily found in micro-risk and moderate-risk areas. During this time, many regions had not yet undergone large-scale industrialization and urbanization, resulting in relatively low carbon emission levels and a stable ecological environment. For further details, see Figure 5.  
In 2015, economic development and the rapid acceleration of urbanization led to significant changes in the risk levels of certain counties and districts. Fuping County and Laiyuan County transitioned from micro-risk areas to low-risk areas, while Wanquan District, Xuanhua District, Yangyuan County, Huai'an County, and Tang County shifted from medium-risk areas to high-risk areas. This shift indicates that these regions are experiencing increased environmental pressure due to rapid economic development. Additionally, Quyang County has moved from a high-risk area to a severe-risk area, highlighting the substantial challenges related to land use and environmental management.  
Compared with 2015, Huai'an County changed from a high-risk area to a moderate-risk area in 2020, indicating that the area has taken effective measures in carbon emission management and ecological restoration and successfully reduced the carbon emission risk. Quyang County has transitioned from a severe risk area to a moderate risk area, likely due  
Figure 5: Distribution map of carbon emission risk levels.  
to policy adjustments, optimization of the industrial structure, or the implementation of ecological protection measures that have helped reduce its carbon emission risk.  
Overall, the carbon emission risk within the study area has undergone dynamic changes throughout the study period. This not only mirrors the intricate relationship between regional economic development and environmental protection but also underlines the diverse approaches and outcomes of counties and districts in addressing carbon emission challenges. In the future, continuing to strengthen ecological protection in the region will be crucial for further reducing carbon emission risk.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 3 Satellite Attitude Control System Evaluation Indicator Set Construction
### Performance Evaluation Indicator Set
Figure 3: Schematic diagram of composite attitude angles. The attitude angles of the satellite before and after the attitude maneuver are \(RotA\cdot\delta\) and \(RotB\cdot\delta\), respectively. According to the spatial geometry relationship, the combined attitude angle of the satellite maneuvering process can be calculated from the rotation matrix:  
\[\theta=ar\cos(\frac{trace\big{(}RotARotB^{T}\big{)}-1}{2}) \tag{6}\]  
In Equation (6), \(trace\) represents the trace of the solving matrix. If \(t_{0}\) is the start time of the maneuver and \(t_{1}\) is the end time of the maneuver, then the angular velocity of the maneuver \(\omega\) is:  
\[\omega=\frac{\theta}{t_{1}-t_{0}} \tag{7}\]  
(5) Attitude control response time. Attitude control response time \(t\) refers to the time required for the satellite to start performing the corresponding attitude adjustment after receiving the attitude control command, in which the attitude and target attitude errors need to be satisfied within the pointing accuracy range (\(\pm\)5%). (6) Attitude control response time. Attitude control energy consumption refers to the energy consumed by the satellite attitude control system, according to the principle of dynamics, the control torque of the flywheel can be calculated as [15]:  
\[M=J\alpha=J\frac{d\omega}{dt} \tag{8}\]  
In Equation (8), \(M\) represents the control torque, \(J\) represents the rotational inertia of the flywheel, which is a constant value. \(\alpha\) represents the angular acceleration of the flywheel, which can be calculated from the flywheel rotational speed. Setting the satellite for attitude control during the time period \([t_{1},t_{2}]\), the energy consumed by the flywheel is:  
\[\rho(\theta,T)=Rot(x,\alpha+\Delta\alpha)Rot(y,\beta+\Delta\beta)Rot(z,\gamma+ \Delta\gamma) \tag{2}\]0  
The moments calculated for each of the four flywheels are integrated and summed to give the attitude control energy consumption as:  
\[\rho(\theta,T)=Rot(x,\alpha+\Delta\alpha)Rot(y,\beta+\Delta\beta)Rot(z,\gamma+ \Delta\gamma) \tag{2}\]1  
In Equation (10), \(E_{1},E_{2},E_{3},E_{4}\) represent the attitude control energy consumption of the four flywheels, respectively.
## 5 Experimental Results
### Experimental Results
The most relevant results reported in Table 5 on VGG16 can be summarized as follows:  
* again, the ensemble methods outperform the stand-alone CNNs. As was the case with ResNet50, 15 ReLU strongly outperforms (\(p\)-value of 0.01) the stand-alone CNNs with ReLU;
* among the stand-alone VGG16 networks, ReLU is not the best activation function. The two activations that reach the highest performance on V6616 are ReLU (\(k=4\)) with \(maxInput=255\) and GaLU with \(maxInput=255\). According to the [PERSON] signed rank test, there is no statistical difference between ReLU, ReLU (\(k=4\)), \(\text{MI}=255\), and GaLU, \(\text{MI}=255\);
* interestingly, ALL with \(maxInput=1\) outperforms eALL with \(p\)-value of 0.05;
* Stoc_4 outperforms 15 ReLU with \(p\)-value of 0.01, but the performance of Stoc_4 is similar to eALL, ALL (\(maxInput=1\)), and Selection. Considering both ResNet50 and Vgg16, the best AF is MeLU (\(k=8\)), \(\text{MI}=255\). It outperforms ReLU with a \(p\)-value 0.1 in ResNet and a \(p\)-value of 0.16 in VGG16. Interestingly, the best average AF is a learnable one that works even on small/midsize data sets. Figure 6 provides a graph reporting the average rank of different AFs and ensembles for VGG16. As with ResNet50 (see Figure 5), it is clear that ensembles of AFs outperform the baseline 15 ReLU and stand-alone networks. With VGG16, the performance of Stoc_4 is similar to eALL and Selection. Figure 5: Average rank (lower is better) obtained by different AFs and ensembles coupled with ResNet50 (light blue represents stand-alone methods and dark blue, ensembles). To further substantiate the power of different AFs in ensembles with small to midsize data sets, in Table 6, we show a further batch of tests comparing 15 ReLU and the ensembles built by varying the activation functions to the performance of five different CNN topologies, each trained with a batch size (BS) of 30 and a learning rate (LR) of 0.001 for 20 epochs (the last fully connected layer has an LR 20 times larger than the rest of the layers (i.e., 0.02)) using Matlab2021b. Note that these parameters are slightly different from those of the previous tests. We did not run tests using VGG16 due to computational issues.
# Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks
###### Abstract  
LoRaWAN is a low-power wide-area network (LPWAN) technology that is well suited for industrial IoT (IIoT) applications. One of the challenges of using LoRaWAN for IIoT is the need to collect data from a large number of devices. Polling is a common way to collect data from devices, but it can be inefficient for LoRaWANs, which are designed for low data rates and long battery life. LoRaWAN devices operating in two specific modes can receive messages from a gateway even when they are not sending data themselves. This allows the gateway to send commands to devices at any time, without having to wait for them to check for messages. This paper proposes various polling mechanisms for industrial IoT applications in LoRaWANs and presents specific considerations for designing efficient polling mechanisms in the context of industrial IoT applications leveraging LoRaWAN technology.  
LoRaWAN; low-power wide-area network (LPWAN); scalability; network optimization Article Polling Mechanisms for Industrial IoT Applications in Long-Range Wide-Area Networks Future Internet
# Towards Lunar In-Situ Resource Utilization Based Subtractive Manufacturing
## 3 Results
### Water Jet Machining of Lithium Silicate  
First, parameter adjustments were necessary for the lithium silicate samples because of the deviations compared to the aluminum samples (Sections 2.5 and 2.6). In fact, the lithium silicate samples have a greater thickness, and the material has higher hardness. Either way, all three states of lithium silicate are suitable for both cutting and surface removal with the abrasive water jet process using regolith simulate as the abrasive. Only the lithium silicate in nucleation and crystallization conditions (representing high-strength ceramics) could inertially be processed to a limited extent, as samples broke in the clamping during processing, despite the low process forces. Moreover, the materials tended to crack at pressures of 300 MPa (Table 17), resulting in a water pressure reduction to 200 MPa. The qualitative evaluation (Figure 17) shows a larger kerf width on the jet entry side (0.5-0.6 mm) of the aluminum material. Furthermore, a larger taper is visible, which is in line with the state of the art for water jet machining of hard materials. Either way, the notched sample in Figure 17 shows a uniform cutting depth.  
Figure 16: Characteristic cut surface without typical grooves of the water jet process with associated process parameters. Nevertheless, the opposing trends require an application-specific trade-off, which favors cutting quality (roughness) or productivity, depending on individual requirements.  
Figure 17: Examples of lithium silicate machining, (**a**) cut kerf, (**b**) surface structuring.
## 3 Quality of Service
### Open Issues
While the standards provide the basic mechanisms to support differentiated QoS, the detailed specification to support operation in a multi-hop network is outside the scope of the standard. Although mechanisms that support QoS in multi-hop networks were developed for other applications, e.g. the Internet, their reuse is limited as they do not provide sufficiently strong real-time guarantees and are mainly adapted to multimedia streams, which differ from data streams in sensor networks. Further research is therefore mandatory to provide an exhaustive description of the required mechanisms to support QoS in multi-hop networks.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Safety Consideration  
The maximum allowable power incident in the in-body antenna is limited by issues related to patient safety. The specific absorption rate (SAR), which represents the amount of energy deposited per unit mass of tissue, is usually accepted as the most suitable scientific measure in compliance with international guidelines. The IEEE C95.1-1999 standard confines the average SAR over any 1 g of tissue in the shape of a cube to less than 1.6 W/kg (SAR\({}_{\text{ig, max}}\leq\) 1.6 W/kg) [56], which is followed by the FCC in the US. The international commission on non-ionizing radiation protection (ICNIRP) standardizes the limit of SAR averaged over 10 g of contiguous tissue to be less than 2 W/kg [57]. To comply with ICNIRP guidelines, the IEEE C95.1-2005 standard limits the average SAR over any 10 g of tissue in the shape of a cube to less than 2 W/kg (SAR\({}_{\text{10g, max}}\leq\) 2 W/kg), which is followed by the European Union [58].
## 4 Numerical Analysis
### The Excavation Process and Current Stability Analysis
This region can be aptly termed the elastic core region of the sidewall. In Figure 8, \"shear now\" indicates that elements have entered shear failure during the current calculation step, while \"tensile now\" suggests that elements are currently experiencing tensile failure during the respective calculation step. \"shear past\" indicates a previous state of shear failure. \"tensile past\" denotes a previous state of tensile failure. Compared with the plastic state, the IMASS model's sloss indicator more accurately reflects the failure process of the brittle rock mass. Figure 9 presents the distribution of the sloss index at the final excavation state. On both sides of the remaining sidewall, a region with sloss index values ranging from 0.95 to 1.0 was observed. This indicated that the stress within this zone had just exceeded the peak strength and had entered the stage of strength degradation. As the strength of the elastic core was still sufficient to bear the transferred stress from excavated room, the strength degradation did not escalate rapidly. Consequently, the sloss index remained slightly below 1.0. Figure 8: Plastic region evolvement at different excavation states. Figure 9: Rock damage conditions (sloss index) at final excavation state. The regions characterized by low slos index values can be perceived as potential rupture surfaces. The stress within these regions was very close to or even surpassed the rock's peak strength. While they might not experience immediate destruction, there is a high likelihood of further damage, rupture, and spalling failure from the sidewall to the empty room over the medium to long term. The rock mass within the potential rupture surface of the sidewall is referred to as the elastic core region, which is distributed in a \"vertical barbell\" form. This elastic core plays a pivotal role in bearing the overburden pressure over the long term. Figure 10 illustrates the variation in the vertical displacement of the surrounding rock mass at different excavation stages. The roof of the excavated room initiated collapsing when the excavation length reached 8 m. The final maximum settlement of the roof was approximately 3 cm, while the maximum floor bulge was less than 1 cm. By the final stage, the sidewall remained stable, experiencing a maximum compression of less than 1 cm. In essence, noticeable deformation did not occur in the abandoned mining area throughout the excavation process, whether it was the roof or the floor. Moreover, horizontal displacement of the rock mass was less prominent than the vertical displacement, suggesting that both the roof and the sidewall were capable of maintaining stability in the excavation stage.
# Mineral Powder Extraction by the Natural Drying of Water from the Public Springs in Borsec
## 1 Introduction
Thus, carbon dioxide emanations are propagated through the andesite and pyroclastic fissures which conduct them to the adjacent dolomitic formations, acting as an inert gas-lift to the mineralized aquifer where they react with the water molecules causing the formation of carbonic acid specific to the sparkling water [11; 12]. The fumaroles' gas-lifting of CO\({}_{2}\) acts as a dynamic pressurizing of the aquifer, which is determined to burst out at the surface forming natural sparkling mineral water springs. These valuable sparkling mineral water springs were noticed from the XVIII century, being mentioned in the Transylvanian Principate mineralogical treaty elaborated by [PERSON] in 1767 published in Cluj-Napoca (e.g., Claudiopolis), as was mentioned by [PERSON] [1]. The first commercial approach regarding the Borsec sparkling mineral water was developed by [PERSON] in 1804 by a bottling facility using ceramic vials. The health benefit of the Borsec mineral water was rapidly observed and a curing house was built during the XVIII century and continuously developed up to today. Despite the springs' celebrity, there were not any studies conducted or any articles published regarding the mineral dissolution and re-crystallization of the Borsec waters. Therefore, the main aim of the present research is the evaluation of mineral extraction during the crystallization processes within the Borsec sparkling mineral water public springs (which did not belong to the bottling company) and their advanced characterization. The mineral amount's dependence on seasonality requires investigation through two campaigns: one held during the late autumn related to low rainwater and another held in spring related to high rainwater levels. Thus, the water infiltration's influence on the mineral amount can be considered. The determined physicochemical parameters of the collected water samples require correlation with the mineral formation during natural drying. The crystallized samples were characterized in a complex manner by using X-ray diffraction (XRD) coupled with polarized light microscopy (POM), and their morphological aspects were combined with elemental characterization which was effectuated by scanning electron microscopy (SEM) coupled with elemental analysis (EDX). It is expected that such complex investigations correlated with the water properties will evidence significant aspects regarding the mineral powder extraction and its potential applications. We chose five springs having the greatest water flow among the public springs in Borsec; the other ones have very low flow and are not set up for public access.
# A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection
## 4 Experiments  
### Experimental Setup  
**WIDER FACE [28]:** This dataset contains 32,203 images and 393,703 annotated faces. The dataset is split into training (40%), validation (10%) and testing (50%) set. Validation set and testing set are divided into 'easy','medium' and 'hard' subset. With its rich multi-scale and occluded faces, WIDER FACE is one of the most challenging public face datasets. We trained our model on the training set and evaluated it on the validation set.  
**FDDB (Face Detection Dataset and Benchmark) [29]:** FDDB is one of the most popular face detection evaluation platforms in the world. It contains 2845 images with 5171 faces. When evaluating our method on FDDB, we convert the face annotation area to rectangle.  
**Training settings:** We use weights pre-trained on ImageNet [25] to initialize the original classification network. Then, we fine-tune our network on the WIDER FACE training set. Stochastic gradient descent algorithm is used to train the network, batch size is set to 32. Learning rate is 0.0001 at the beginning and momentum is 0.9. After 80 k iterations, learning rate is reduced by 10 times and the total iterations are 100 k. We trained and evaluated our method on a Titan X GPU.
## 1 Introduction
Thus, a combined sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system is first proposed in this study as a novel high-efficiency CHP system. In this study, a preliminary design and analysis of a CHP system is carried out, in which the topping cycle is a sCO\({}_{2}\) cycle driven by a nuclear reactor to generate electric power and the bottoming cycle is a LiBr-H\({}_{2}\)O AHP to recover the waste heat of topping cycle for producing heat. Firstly, the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system is proposed and investigated in terms of energy, exergy, and exergoeconomics. A single sCO\({}_{2}\) cycle, a sCO\({}_{2}\)/DH system, and a sCO\({}_{2}\)/ammonia-water AHP system are compared with the sCO\({}_{2}\)/LiBr-H\({}_{2}\)O AHP system in order to show its advantages. Then, parametric analysis is performed to reveal the influences of several key system parameters, namely the turbine inlet temperature, compressor pressure ratio, generator temperature and evaporator temperature, on the power generation, heating, and overall performances for these systems. Finally, performances of these systems are optimized and then compared by utilizing the particle swarm optimization (PSO) algorithm.