esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### The Chemical Accident Recognition Equipment
#### 2.1.2 Long-Range Video Surveillance System (TORUSS-LR2000)  
The LRVS is composed of EO/IR multi-sensors, which can provide accurate information during both daytime and nighttime because the EO sensor converts light into an electrical signal, and the IR sensor detects surrounding structures by emitting or sensing IR radiation. Thus, the LRVS is mainly used to improve target identification, evaluate threats at a certain distance, and monitor targets. Owing to its excellent image stabilization and long-range imaging ability, this system is used for national defense and commercial purposes. It can be applied in different sectors, such as situational awareness evaluation, fire control systems, aviation homeland security, search and rescue, and intelligence, surveillance, and reconnaissance [23].  
The industrial complex chemical accident monitoring system was developed to monitor chemical accidents from a distance; thus, it must be able to accurately identify distant targets. The TORUSS-LR2000 was applied because of its suitable DRI characteristics, which indicate the degree to which a camera can generate an image of a specific target. Table 1 shows the DRI characteristics of the TORUSS-LR2000.  
The TORUSS-LR2000 is an intelligent LRVS that uses long-distance multi-sensors (EO/IR) and a high-precision driving device [24]. The FLIR Ranger HDC-1200 (United States) was used as the IR camera. The Ranger HDC-1200, together with the FLIR image processing engine, provide long-range thermal images and a 22x continuous optical zoom for constant situational awareness and target evaluation [25].  
The TORUSS-LR2000 outputs real or thermal images in black and white or rainbow colors. The thermal image can show gas or water vapor emitted from a stack, which is nearly invisible in a real image and to the naked eye. Figure 4 shows an image of the TORUSS-LR2000 installed at the Hwachi site; the thermal image underneath shows the heat generated from the stack, which is invisible in the real image.  
The TORUSS-LR2000 is normally operated in an auto-scan mode that automatically passes through predesignated points, storing still images from each section into the NAS and sharing them with the AI chemical accident detection model. Moreover, a function that automatically directs this system to the corresponding section was developed in the event that the SIGIS-2 detects chemical substances.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Detection** & **Recognition** & **Identification** \\ \hline EO camera & 19.8 km & 9.3 km & 6.0 km \\ IR camera & 18.8 km & 10.0 km & 5.5 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: DRI characteristics of the TORUSS-LR2000.  
Figure 3: Program mode of the SIGIS-2 installed at the Jeongyang site.  
Hence, the LRVS (TORUSS-LR2000) can identify targets (such as chemical facilities), recognize the shape of gas or water vapor through thermal imaging, enlarge the accident site, and measure the distance to allow location estimation.
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 3 Investigation of the Relationship Between Vertical Curves and Track Irregularities
### Impact of Vertical Curve Gradient and Length on Detection Accuracy  
The length, radius, and gradient of a vertical curve are interrelated, satisfying the relationship \(L=R(\arctan i_{1}-\arctan i_{2})\). By controlling one factor, the effects of variations in the other factors on detection accuracy can be examined. In this section, the vertical curve radius is held constant while the gradient and length are varied to assess their impact on detection accuracy. According to relevant design standards, the maximum single-sided gradient for intercity railways should not exceed 30%. The specific test conditions are outlined in Table 2.  
After obtaining the virtual dynamic responses under varying conditions of vertical curve gradient and length, the virtual irregularities were computed. Similar to the previous section, a 500 m segment was extracted in both directions from the centre of the vertical curve to ensure full coverage of the curve for a time and frequency domain analysis. The results are shown in Figure 5.  
Figure 5a shows that under varying vertical curve gradients and lengths, the standard deviation of error between the virtual and excitation irregularities remains largely consistent in magnitude, with variations in peak error locations attributable to changes in vertical curve length. Specifically, the error standard deviation stabilizes around 0.3 mm near the tangent-to-curve transition and 0.5 mm near the curve-to-tangent transition, indicating higher detection error near the latter. This suggests that gradient variations have minimal impact on the detection accuracy of virtual irregularities. Figure 5b demonstrates that the PSD curves of the virtual irregularities remain largely unchanged across different gradient and length conditions, with only noticeable differences from the excitation irregularities in the 100-120 m wavelength range. This discrepancy arises from the spatial cutoff frequency of the high-pass filter at 120 m, which does not fully eliminate the influence of the track alignment. In summary, gradient variations in the vertical curve have no significant impact on the wavelength components of the virtual irregularities, which aligns with the findings in [19].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Operating** & **Train Speed** & **Radius \(R\)** & **Length** & **Gradient** & **Single-Side** \\
**Conditions** & \(V\) (km\(\cdot\)h\({}^{-1}\)) & **(m)** & **L (m)** & **Difference \(\Delta i\) (\%o)** & **Gradient \(i\) (\%o)** \\ \hline
1 & & & 200 & 20 & \(\pm\)10 \\
2 & 120 & 10,000 & 400 & 40 & \(\pm\)20 \\
3 & & & 600 & 60 & \(\pm\)30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental conditions for variations in vertical curve gradient and length.
# Fronthaul Design for Wireless Networks
## 2 Background on C-RAN and Fronthaul Communication Technologies and Related Work  
In order to contextualize the topics address in this paper, this section presents a description of the C-RAN architecture, which enables pointing out the role of the fronthaul segment within the mobile network. Moreover, typical fronthaul communication technologies are also overviewed and compared, namely MRT, FSO, and FO. The section ends with a review of related work.
## 2 Device Structure and Parameters  
Figure 1 shows the silicon tunnel FET structure used in the simulations. Double gate and tunnel region underneath the gates are adapted to enhance gate controllability and tunneling current drivability. Tunnel region thickness (4 nm) and source overlap length (20 nm) are used, respectively.  
In addition, equivalent oxide thickness (EOT) of 1 nm and body thickness of 20 nm are applied to assume high-\(\kappa\) dielectric and double gate structure. The drain is underlapped with the gates to suppress ambipolar current at off-state and it can be easily formed with conventional self-align process. Other detailed parameters used in the technology computer-aided design (TCAD) simulations are described in Table 1. Among the parameters, only spacer thickness and dielectric constant are variable to optimize the electrical performances of the tunnel FET. All device evaluations are performed by commercial tools of SentaurusTM (Synopsys, Mountain View, CA, USA) [25]. For accurate analysis on the tunneling current, the dynamic nonlocal BTBT model is activated in the whole regions, and used with experimentally calibrated Kane's parameters, which are F\({}_{0}\) = 1 V/m and P = 2.5 for indirect BTBT,  
Figure 1: The schematic of the proposed tunnel field-effect transistor (FET), which have double gate, asymmetric spacers, and a tunnel channel region to improve the tunneling current.  
\(\text{A}_{\text{Si}}=4\times 10^{14}\) cm\({}^{-3}\cdot\)s\({}^{-1}\) and \(\text{B}_{\text{Si}}=9.9\times 10^{6}\) V\(\cdot\)cm\({}^{-1}\), respectively [26; 27]. Furthermore, the Slotboom model is applied to consider the impact of heavy doping on bandgap narrowing in the source region.  
Moreover, fermi statistics, drift-diffusion, and the Shockley-Read-Hall recombination model are also used. However, trap-assisted tunneling and gate leakage current are ignored for this work because it is focused on the effects of the asymmetric spacer technique.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 3 Rotational Stiffness of Corrugated Steel Flange Joint
### Rotational Stiffness of Joint
#### 3.3.2 Rotational Stiffness of CSFJ  
Based on the definition of the rotation of the CSFJ, the relationship between the deflection (\(\omega\)) and the rotation (\(\theta\)) of the CSFJ is established, the vertical load (\(F\)) is converted into the bending moment (\(M\)), and then the vertical load-deformation curve is transformed into the rotation-bending moment characteristic curve of CSFJ, as shown in Figure 12.  
Figure 12 illustrates that the moment-rotation relationship of the CSFJ exhibits a two-stage linear behavior under both positive and negative bending moments. The rotational stiffness of the CSFJ is evaluated using an elastic-plastic constitutive model, which includes two distinct phases: (1) Elastic phase: The moment-rotation curve approximates a straight line, where the slope represents the rotational stiffness of the CSFJ. In the elastic phase, the CSFJ structure experiences minimal joint opening. For the model dimensions of 380 mm \(\times\) 140 mm \(\times\) 5 mm, the rotational stiffness in the elastic phase is \(K_{1}^{+}\) = 761.9 kN\(\cdot\)m/rad under positive bending moments and \(K_{1}^{-}\) = 1125.0 kN\(\cdot\)m/rad under negative bending moments. (2) Plastic phase: Upon entering the plastic phase, a significant joint opening occurs, and plastic zones near the bolt holes become evident in the flange  
Figure 11: Schematic diagram of CSFJ rotation.  
plates. The rotational stiffness of the joint decreases compared to the elastic phase. For the model dimensions of 380 mm \(\times\) 140 mm \(\times\) 5 mm, the rotational stiffness in the plastic phase is \(K_{2}^{+}\) = 263 kN\(\cdot\)m/rad under positive bending moments and \(K_{2}^{-}\) = 142 kN\(\cdot\)m/rad under negative bending moments.
# Autonomous Wireless Sensor Networks in
###### Abstract  
Until recently data acquisition in integrated pest management (IPM) relied on manual collection of both pest and environmental data. Autonomous wireless sensor networks (WSN) are providing a way forward by reducing the need for manual offload and maintenance; however, there is still a significant gap in pest management using WSN with most applications failing to provide a low-cost, autonomous monitoring system that can operate in remote areas. In this study, we investigate the feasibility of implementing a reliable, fully independent, low-power WSN that will provide high-resolution, near-real-time input to a spatial decision support system (SDSS), capturing the small-scale heterogeneity needed for intelligent IPM. The WSN hosts a dual-uplink taking advantage of both satellite and terrestrial communication. A set of tests were conducted to assess metrics such as signal strength, data transmission and bandwidth of the SatCom module as well as mesh configuration, energetic autonomy, point to point communication and data loss of the WSN nodes. Finally, we demonstrate the SDSS output from two vector models forced by WSN data from a field site in Belgium. We believe that this system can be a cost-effective solution for intelligent IPM in remote areas where there is no reliable terrestrial connection.  
wireless sensor networks; integrated pest management; spatial decision support system; LoRa; satellite communication; _Aedes albopictus_ Article
## 2 Newly Proposed Evaluation Method and Analysis Procedure
### Detailed Procedure of CRITIC-GRA Model to Optimize Stop Dimensions
#### 2.2.2 Optimization of Stope Dimensions Based on the CRITIC-GRA Model  
The dimension parameters of the excavated stopes have great influences on the key economic indicators of mining operations, including ore loss rate, ore dilution rate, mining efficiency, production capacity, mining costs, time cycle of stoping and backfilling, etc. The determination of stope dimension parameters is a crucial aspect in mining operations. Generally, larger stope sizes result in better economic indicators including annual production and mining costs, but with compromised stope stability. Therefore, it is of great significance to optimize the selection of stope dimension parameters in mining operations by comprehensively considering economic and safety factors.  
The proposed CRITIC-GRA model is used to construct a step-by-step method for the optimization of stope dimension parameters. Firstly, the CRITIC method is used to calculate the weight values of the above selected safety and economic indicators, including the safety indicators such as the stability probability for side walls and roof via the Mathews graph, the safety indicators such as the maximum displacement, plastic zone volume and maximum principal stress calculated via FLAC3D simulations and the economic indicators such as the mining cost index and stope production capacity calculated with the mining design index and actually verified index in mining operations. Then, the weight matrix of each factor is used to calculate the ranking of each preset scheme with different combinations of length, width and height. After that, the grey correlation coefficient of each factor in each dimension scheme is assessed via the GRA method. Finally, the optimal selection on the stope dimension scheme is obtained based on the unique criterion of the grey correlation degree, i.e., the product of the weight values and the grey correlation coefficient for each factor in each dimension scheme.
###### Abstract  
Accelerated soil erosion by water and wind involves preferential removal of the light soil organic carbon (SOC) fraction along with the finer clay and silt particles. Thus, the SOC enrichment ratio in sediments, compared with that of the soil surface, may range from 1 to 12 for water and 1 to 41 for wind-blown dust. The latter may contain a high SOC concentration of 15% to 20% by weight. The global magnitude of SOC erosion may be 1.3 Pg C/yr. by water and 1.0 Pg C/yr. by wind erosion. However, risks of SOC erosion have been exacerbated by the expansion and intensification of agroecosystems. Such a large magnitude of annual SOC erosion by water and wind has severe adverse impacts on soil quality and functionality, and emission of multiple greenhouse gases (GHGs) such as CO\({}_{2}\), CH\({}_{4}\), and N\({}_{2}\)O into the atmosphere. SOC erosion by water and wind also has a strong impact on the global C budget (GCB). Despite the large and growing magnitude of global SOC erosion, its fate is neither adequately known nor properly understood. Only a few studies conducted have quantified the partitioning of SOC erosion by water into three components: (1) redistribution over land, (2) deposition in channels, and (3) transportation/burial under the ocean. Of the total SOC erosion by water, 40%-50% may be redistributed over the land, 20%-30% deposited in channels, and 5%-15% carried into the oceans. Even fewer studies have monitored or modeled emissions of multiple GHGs from these three locations. The cumulative gaseous emissions may decrease at the eroding site because of the depletion of its SOC stock but increase at the depositional site because of enrichment of SOC amount and the labile fraction. The SOC erosion by water and wind exacerbates climate change, decreases net primary productivity (NPP) and use efficiency of inputs, and reduces soils C sink capacity to mitigate global warming. Yet research information on global emissions of CH\({}_{4}\) and N\({}_{2}\)O at different landscape positions is not available. Further, the GCB is incomplete and uncertain because SOC erosion is not accounted for. Multi-disciplinary and watershed-scale research is needed globally to measure and model the magnitude of SOC erosion by water and wind, multiple gaseous emissions at different landscape positions, and the attendant changes in NPP.  
**Keywords:** global carbon budget; soil organic carbon erosion; deposition; gaseous emissions; enrichment ratio; soil depletion; preferential removal
# Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review
## 7 Landslides  
Landslides are a recurring hazardous event consisting of the down slope movement of soil, rock, and organic material. There are different triggering factors to landslides, such as rainfall and changes in ground water levels, rapid snow melts, or earthquakes, and therefore different quantities can be monitored to detect and predict landslides, primarily displacement but also weather parameters in rainfall triggered landslides [18]. A Landslide EW system can be deployed at different scales (regional, national, or a more local scale), and IoT and MEMS based systems can be used to reduce costs and allow the installation of denser sensor networks. Rainfall is often a triggering factor for landslides, and as such it is useful to integrate available weather data or weather forecasts with the data obtained using the on-site sensors. Figure 5 illustrates an example implementation of a landslides Early Warning System for rainfall induced landslides.  
Figure 5: Example implementation of a Landslides Early Warning System based on the IoT and underwater sensors.
## 5 Applications of Nanocellulose
### Nanocellulose in Printed Electronics  
Paper is a desirable material for printed electronics because it is formed of cellulose, which is a recyclable, renewable, biodegradable and non-toxic substance. Nanocellulose obtained from cellulose has recently been shown to have enormous potential applications in flexible-printed electronics. Since NC has the ability to build self-standing thermally stable films, it has been used to create translucent and smooth substrates for printed electronics [129]. The performance of printed devices can be enhanced while staying environmentally benign by using nanocellulose to regulate the surface, speed up ink-drying and hold functional ingredients [130]. Through the inventive use of nanocellulose as the substrate and the transfer printing technique, [PERSON] et al. established a type of ultrathin and reusable liquid metal circuit [131]. Likewise, cellulose nanofibers were applied as substrates for printed electronics by [PERSON] et al., where the findings indicated that the conductivity of printed lines on kaolin surface is greater than the conductivity on precipitated calcium carbonate surface [132]. Such materials prepared from nanocellulose might not function as well as their pure inorganic equivalents; however, the low cost and renewability of materials for products with short lifespans make them coforriendly and compatible. Itis necessary to conduct more research on substrate development to enable back-to-back printing through the production of nanocellulose-based paper in printed electronics.
## 4 Experimental Outcomes
### Performance Module
#### 4.1.2 Train and Validation Loss Trends  
The training and validation losses fluctuate with respect to each epoch. All models were run at different values of epoch starting from 10 up to 50; however, for the purpose of clarity and concision, only the results obtained considering the 50 epochs are illustrated.  
The loss curves demonstrate important trends to monitor in order to clearly distinguish the working mechanism of the CNN architectures over the repeated iterations. In Figure 6, it is distinctive to visualize the loss on both train and validation sets (first fold) for the best, i.e., DenseNet-161, and the worst, i.e., SqueezeNet, CNN architectures in the case of _No Aug_ configuration.  
Although the SqueezeNet shows decreasing loss on both train and validation sets in Figure 6b, the training loss curve becomes constant right after fewer epochs in DenseNet-161 in Figure 6a. Moreover, the validation curve depicts increasing behavior after fewer than ten epochs for the DenseNet-161. Such behavior could be motivated by the huge number of parameters that might cause the overfitting problem on the train set.  
The train and validation loss curves considering the advanced data augmentation configuration are provided in Figure 7. The augmentation helped the DenseNet-161 to overcome the increasing validation loss, as shown in Figure 7a. This evidences that incorporating on-the-fly data augmentation solved the overfitting issues. However, the SqueezeNet struggles to keep the loss low, as depicted in Figure 7b, and ends up with even worse performance than the no augmentation configuration. Differently from DenseNet-161, the SqueezeNet does not seem to take advantage of the on-the-fly augmentation, possibly due to lower number of parameters.  
Additionally, the reported behavior of the loss trends on both train and validation sets is comparable to the other folds. With the intention of concision, authors decided to depict the outcomes of the best and the worst performing architectures, i.e., DenseNet-161 and SqueezeNet, respectively, in terms of AUC and loss.  
Figure 6: The train and validation loss for the fold = 0 of cross-validation. (**a**) DenseNet-161 with _No Aug_ configuration; (**b**) SqueezeNet with _No Aug_ configuration. The reported behavior of train and validation loss trends is comparable to that of the other folds.