esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analytical Model
Note that, in order to model a complete cycle of compression (storing energy) and expansion (releasing energy) of the CAES system, the energy balance for the air domain--Equation (4)--needs to be rewritten as a function of the discharge mass flow rate (\(\dot{m}_{out}<0\)) and the air temperature within the reservoir, according to Equation (16):  
Figure 2:6  
Air mass flow rates of 0.22 kg s\({}^{-1}\) and \(-0.45\) kg s\({}^{-1}\) have been considered in the charge and discharge periods, respectively, for both RFP and steel sealing layers. The thermal properties and the volume of air, reinforced concrete, sandstone rock mass and sealing layers considered in the model are shown in Table 1[24; 25]. Note significant differences between the thermal conductivity of FRP and steel. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Specific Heat** & **Thermal Conductivity** & **Volume** & **Density** \\  & **(J kg\({}^{-1}\) K\({}^{-1}\))** & **(W m\({}^{-1}\) K\({}^{-1}\))** & **(m\({}^{3}\))** & **KN m\({}^{-3}\)** \\ \hline Air & 1006 & 0.0242 & 200.53 & 0.0117 \\ Reinforced concrete & 1000 & 1.60 & 195.26 & 23 \\ Sandstone & 711 & 5.00 & 2605 & 25 \\ Sealing layer (FRP) & 384 & 0.40 & 7.24 & 8.82 \\ Sealing layer (Steel) & 500 & 45 & 7.24 & 76.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Thermal properties and volume of air, concrete, sealing layers and sandstone rock mass.
## 3 Proposed Approach
### Class Weights
#### (ii) Multi-Class Classification  
The architecture outlined in Table 22 showcases the auto encoder model specifically designed for multi-class classification, with tailored configurations for both the NF-UNSW-NB15-v2 and CICIDS2017 datasets. The model begins with an input layer that accommodates the unique features of each dataset. The NF-UNSW-NB15-v2 dataset processes data with 27 features, while the CICIDS2017 dataset handles 35 features. This input layer serves as the entry point, setting the foundation for the model's processing. The encoder consists of three dense layers, with 128, 64, and 32 neurons respectively. Each layer employs the ReLU activation function, which introduces non-linearity and enhances the model's ability to capture intricate patterns and relationships within the data. This structure efficiently compresses the input data into a lower-dimensional latent space, extracting the most essential features for multi-class classification. The architecture culminates in the output block, where the NF-UNSW-NB15-v2 dataset's output layer consists of 10 neurons, and the CICIDS2017 dataset's output layer has 15 neurons. Both layers are activated by the softmax function, which generates class probabilities, allowing the model to classify the input data into multiple distinct categories. This design enables the model to address multi-class classification tasks effectively, distinguishing between various classes with high accuracy across both datasets.
# Review of GIS-Based Applications for Mining: Planning, Operation, and Environmental Management
## 4 Mine Operation Using GIS
### Mine Ventilation/Safety/Drainage System Design and Management
The analysis model identified hydrological parameters, including the flow accumulation of rainfall in sinks and catchments, and designed in-pit stormwater ponds with pumping facilities by considering an optimal pump capacity and pond storage. Finally, a flooding simulation was performed to determine the feasibility of pond design under concentrated rainfall conditions. This model could be utilized as a powerful tool for drainage system design.
## 1 Introduction
### Previous Work on Approximating Frequent Items  
Consider a data stream and, in particular, those data items whose timestamps fall into the last \(W\) time units (\(W\) is the size of the sliding window). An item (or precisely, an item name) is said to be a frequent item if its count (_i.e._, the number of occurrences) exceeds a certain required threshold of the total item count. [PERSON] and [PERSON] [6] were the first to study approximating frequent items over a sliding window under the synchronous model, in which data items arrive in non-decreasing order of timestamps. The space complexity of their data structure is \(O(\frac{1}{\epsilon}(\log\frac{1}{\epsilon})^{2}\log(\epsilon B))\), where \(\epsilon\) is a user-specified error bound and \(B\) is the maximum number of items with timestamps falling into the same sliding window. Their work was later improved by [PERSON] and [PERSON] [7] to \(O(\frac{1}{\epsilon}\log(\epsilon B))\) space. Recently, [PERSON] _et al_. [1] initiated the study of frequent items under the asynchronous model, and gave a solution with space complexity \(O(\frac{1}{\epsilon}\log W\log(\frac{\epsilon B}{\log W})\min\{\log W,\frac{1 }{\epsilon}\}\log|U|)\), where \(U\) is the set of possible item names. Later, [PERSON] _et al_. [12] gave a hashing-based randomized solution using \(O(\frac{1}{\epsilon^{2}}\log|U|)\) space. The space complexity is quadratic in \(\frac{1}{\epsilon}\), which is less preferred, but that is a general solution that can solve other problems like finding the sum and quantiles.  
The earlier work on asynchronous data stream focused on a relatively simpler problem called \(\epsilon\)-approximate basic counting [10; 11]. [PERSON] _et al_. [1] improved the space complexity of basic counting to \(O(\frac{1}{\epsilon}\log W\log(\frac{\epsilon B}{\log W}))\). Notice that under the synchronous model, the best data structure requires \(O(\frac{1}{\epsilon}\log(\epsilon B))\) space [9]. It is believed that there is roughly a gap of \(\log W\) between the synchronous model to the asynchronous model. Yet, for frequent items, the asynchronous result of [PERSON] _et al_. [1] has space complexity way bigger than that of the best synchronous result, which is \(O(\frac{1}{\epsilon}\log(\epsilon B))\)[7]. This motivates us to study more space-efficient solutions for approximating frequent items in the asynchronous model.
# Stateflow-Based Energy Management Strategy for Hybrid Energy System to Mitigate Load Shedding
## 4 Execution of SF Chart and Operation Modes of HES
### The Grid_Connected_Mode  
The Grid_Connected_Mode is invoked for the normal system operation, i.e., the utility grid is the main source of energy to fulfill the load demand. Moreover, the load shedding is not expected to occur during this period. The execution of the Grid_Connected_Mode ensures the electricity supply to the load is not interrupted and at the same time takes advantage of the availability of the PV energy for economic benefits and supporting the  
Figure 10: EMS-based operation of HES.  
grid operation. The active state chart of Grid_Connected_Mode is shown in Figure 11a. It is assumed that in the absence of shedding, the grid has sufficient capacity to satisfy the load and charge the ESU. However, ESU charging through the grid takes place during the off-peak hours (excluding peak hours, i.e., 18-22 h). Meanwhile, it is possible for several events to take place during Grid_Connected_Mode. For example, if PV power is available, it is used to charge the ESU and contribute to the load. The utilization of available PV power takes place in the child states PV_to_ESU_Mode and PV_to_Load_Mode. Both are child states of PV_Mode. The decision on the transferred energy is based on the level of SOC of the ESU. If SOC\({}_{\text{C}}<\) SOC\({}_{\text{U}}\), charging the ESU is the first priority. Once the SOC\({}_{\text{C}}\) reaches the SOC\({}_{\text{U}}\), the remaining PV energy (PV_Surplus) is used to contribute to the grid power supply operation. The PV_to_ESU_Mode enables PV energy to be stored and consumed at a later stage when the PV is not available. In practice, this operation is realized using a DC-DC converter (with MPPT control) and a bi-directional DC charger. On the other hand, the PV_to_Load_Mode allows the surplus energy to support the grid operation, thus reducing the burden on the grid.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 1 Introduction
Studies have demonstrated that using MR guidance instead of traditional paper instructions can decrease workers' errors, misinterpretations, or overlooking of information [9]. Quantitative and qualitative research show that MR-based assembly guidance records lower task execution times, higher accuracy and usability, and reduction of cognitive load compared to other training formats [16].
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 5 Methodology
### Description of the Validation Procedure  
As commented above, three different scenarios targeting different effects were defined. Each one of these scenarios was evaluated for a range of radii (geometrical parameter of the scenarios). Furthermore, two different ceiling materials, presenting different reflectivity coefficients, and two frame arrival rates (saturated and unsaturated traffic) were also analyzed. The simulator was run 15 times in order to obtain statistically significant results, and the extracted metrics were compared to a MAC-only (no path loss) simulation.  
Considering all the different experimental setups 2160 simulations were carried out. Each one of these simulations recreated the behavior of an IEEE 802.15.7 VLC network during 100 s, generating between 4\(\cdot\)10\({}^{5}\) and 4\(\cdot\)10\({}^{6}\) messages per simulation (depending on the simulation parameters). This huge amount of information is enough to obtain consistent statistical conclusions. The simulations were carried out without parallelization in a workstation with an Intel Core i7 (1.99 GHz) and 16 GB of DDR3 RAM. For the non-saturated traffic scenarios each simulation was executed in approximately 50 s, whilst for the saturated traffic ones 170 s were spent per execution.  
At the beginning of the execution, all user nodes were marked as un-associated and all the internal variables of the simulation modules were set to their default state. Once the simulation started, the nodes began to perform association requests until they were properly notified by the coordinator node. After a successful association, each node started to generate 2000 bit packets (payload size) at the rate defined by the scenario under evaluation. Each packet was handled as commented in Section 4, using a frame buffer of 50 packets.  
Each event that occurred during the 100-s simulation was logged into a file, describing the emitter, the receiver, the received power (if applicable), the event type, and a timestamp. All this information was used by the data analysis stage to extract the metrics of this work, which are commented in the following subsection.  
Figure 10: Impact of the amount of rays on the outcomes of the Modified Monte Carlo Ray tracing (MMCRT) simulation. (**a**) illustrates DC channel gain whilst (**b**) corresponds to bandwidth.
###### Abstract  
A quasi-static finite cavity cylindrical expansion model is proposed to investigate the lateral boundary effect during long-rod penetration. Analytical solutions for the cavity pressure in the elastic-plastic stage and plastic stage are obtained, through which a decay function for the lateral boundary effect is constructed. The resistance of the target with finite diameter \(R_{\mathrm{T}}\) is obtained by multiplying the decay function with the resistance of infinite target \(R_{\mathrm{T}}\)*. Then, a theoretical model for the penetration into the metal targets with finite diameters by long rods is proposed by substituting \(R_{\mathrm{T}}\) into the Alekseevskii-Tate model. The proposed model is verified by comparison with existing experimental results. Furthermore, the effect of the target diameter, initial impact velocity and yield criterion on the penetration depth and resistance are investigated. The results showed that the lateral boundary effect should be taken into account if the ratio of the target radius to the rod radius (\(r_{\mathrm{H}}/R_{\mathrm{P}}\)) is less than 25.  
Footnote *: Correspondence: [EMAIL_ADDRESS] (X.T.); [EMAIL_ADDRESS] (B.Z.)  
impact dynamics; long-rod penetration; quasi-static finite cylindrical cavity expansion model; lateral boundary effect; target resistance; metal target
## 2 Experiments
### Sample Preparation  
Seven different bituminous coal samples that were actually used in a coal-fired power plant in Korea were prepared. For the reference data, the elemental concentration was determined from the conventional industrial chemical analysis. The concentrations of carbon, hydrogen, and nitrogen were determined by a C/H/N elemental analyzer (5E Series, Daeduck Analytical Research Institute). The concentration of the sulfur was measured via a 5E-S3200 Coulomb sulfur analyzer. The concentration of oxygen was calculated by subtracting the concentration of the remaining elements and the concentration of ash from 100%.  
The elemental concentrations of each coal sample are shown in Table 1. The method that is widely practiced to evaluate the higher heating value (HHV) of coal, which is known as the calorific value, is by using [PERSON]'s equation. [PERSON]'s equation uses the weight fractions of carbon, hydrogen, oxygen, and sulfur to predict the higher heating value. [PERSON]'s equation is as follows [20]:  
\[HHV(\text{kcal/kg})=8080C+34460(H-\frac{O}{8})+2250S \tag{1}\]  
where C, H, O, and S are the concentrations of carbon, hydrogen, oxygen, and sulfur, respectively. Therefore, we presented 4 main components to obtain the calorific value in Table 1. From Equation (1), the higher heating value of coal is a positive function of the carbon, hydrogen, and sulfur concentrations. From Table 1, the coal samples were mainly composed of about 65 to 75% carbon. Carbon and hydrogen are the dominant components used to evaluate the calorific value.  
Figure 1: Schematic diagram of LIBS device; (**a**) spectrometer and (**b**) optics.  
The coal samples were initially pulverized into a powder form. By using a scale (HR-250A), 0.3 g of each coal sample was prepared and pelletized into a 13 mm-diameter and a 2 mm-thickness under a pressure of 10 tons using a hydraulic press (PN 181-1110) as shown in Figure 2 (left). To improve the measurement accuracy of the samples, 100 points (10 \(\times\) 10) were measured for each delay time from 0.2 to 2 us by 0.2 us, as shown in Figure 2 (right).
# Method for Recognition of Communication Interference Signals under Small-Sample Conditions
## 5 Simulation and Analysis
### Experiment on the Impact of Generated Sample Quantity on Accuracy  
To further explore the impact of small sample sizes on recognition rates, real and generated samples were mixed in different proportions for sample augmentation. The dataset was expanded with 30, 60, and 90 generated samples per category and then fed into the C-ResNet network for identification. The accuracy of communication interference signal recognition before and after sample augmentation is shown in Figure 9.  
Figure 8: The confusion matrix for (**a**) the CNN classification network; (**b**) our proposed method.  
From the above figure, it is evident that under the condition of having 98 real samples per category in the training set and at a JNR of 10 dB, the recognition accuracy for communication interference signals is 79.37%. By incorporating generated data to increase the sample size, the deep neural network is able to more thoroughly exploit the underlying features of the data. After augmenting the real samples with 30, 60, and 90 additional generated samples per category, respectively, the recognition accuracies for communication interference signals at a JNR of 10 dB improved to 88.29%, 92.87%, and 96.01%, respectively, indicating an overall increase of 15.64% in recognition rate before and after sample expansion. The experimental results demonstrate that the WDCGAN-SA+C-ResNet model proposed in this article effectively addresses the problem of low recognition rates for communication interference signals under small sample conditions.
## 3 Methods  
The AFT-SAM model proposed in this paper consists of five main components: pre-conditioning, feature extraction, encoders, an adaptive attention mechanism fusion module, and a decoder. The network structure is illustrated in Figure 1. Below, we provide a detailed description of each component.
## 2 MCT Operations Description
To provide the negotiated vessel handling rates, the MCT operator has to allocate the required amount of handling equipment for each vessel, including quay cranes for loading containers on the vessel and unloading containers from the vessel, internal transport vehicles (e.g., yard trucks, automated guided vehicles, straddle carriers, automated lifting vehicles) for transfer of containers from the seaside to the marshing yard, gantry cranes for handling containers in the marshing yard, and other miscellaneous equipment. The handling time of vessel \(i\) at berthing position \(j\) (\(T_{ij}^{HT},\ i\in I,\ j\in J\)--measured in h) is determined based on the number of containers to be handled for vessel \(i\) and the handling productivity, negotiated with the liner shipping company. Furthermore, the MCT operator must allocate the storage space in the marshing yard for containers, delivered by each vessel. Generally, the storage space is allocated as close as possible to the berthing position, where a given vessel is originally planned to be assigned for service (the latter term is also known as \"preferred berthing position\" in the BSP literature [6; 11]). Because of changes in the original berth schedule, a given vessel may be assigned for service at an alternative berthing position (e.g., to reduce the waiting time of that vessel in case if its \"preferred berthing position\" is occupied by another vessel at the moment). Due to changes in the original berth assignment, the vessel handling time will increase since the internal transport vehicles will be required to travel longer distances between the alternative berthing position and the assigned storage space in the marshing yard (as compared to the travel distance between the \"preferred berthing position\" and the assigned storage space). Based on the contractual agreement with the MCT operator, the liner shipping company requests a specific departure time (\(T_{i}^{D},\ i\in I\)--measured in h). It is critical for the liner shipping company to ensure that each vessel will leave the MCT at the scheduled time to avoid potential delays at the consecutive ports, which must be visited by that vessel. The MCT operator may be even expected to pay penalties to the liner shipping company in case of late vessel departures [6]. To design an efficient berth schedule, the MCT operator must account for priority of the arriving vessels. The priority of vessels will be assigned using weights (\(W_{i},\ i\in I\)--a real-valued number, varying from 0.10 to 1.00). The latter methodology has been widely used in the BSP literature [6]. The overall objective of the MCT operator is to develop the optimal berth schedule, which will allow minimizing the total weighted vessel waiting time, the total weighted vessel handling time, and the total weighted vessel late departures.
## 4 Establishment of Double-Coupled Duffing Weak Signal Detection System
### Amplitude Normalization and Sweep Detection of Double-Coupled Duffing System  
In practical engineering applications, the value size of the collected sensor data will be different, and the signal with unreasonable energy size will make the system unable to detect normally. Before detection, it is necessary to normalize the signal amplitude. In this paper, after extensive simulation calculations, the energy of the signal to be measured is normalized to the same as the white noise with a variance of 0.01. At this amplitude, the double-coupled Duffing system has good detection performance for periodic signal components in the tested signal.  
When the double-coupled Duffing system is used for signal detection, a certain frequency difference is allowed between the signal to be measured and the built-in policy force of the system, so the Duffing system can detect a signal in a narrow frequency band each time. The width of this frequency band is related to the amplitude of the signal to be measured: the larger the amplitude, the wider the frequency band. For weak periodic signals with an amplitude of 0.001, the band width that can be detected is 0.003 \(\omega\), where \(\omega\) is the frequency of the signal to be measured. In order to ensure the robustness of the algorithm, the sweep step is set to 0.002 \(\omega\). When using the double-coupled Duffing system for bearing fault diagnosis, the bandwidth is used as the step size for frequency scanning to realize fault signal identification. Due to the complex working conditions in the actual machining process, there may be a certain deviation between the actual fault signal frequency and the theoretical value, but this deviation is very small. Therefore, when carrying out bearing fault diagnosis, it is only necessary to sweep the frequency of the neighborhood of the calculated theoretical value to complete the fault identification, which greatly reduces the amount of calculation and is conducive to the establishment of the real-time monitoring system.
## 3 Results and Discussion
### Basic Physical Properties of the Gluten-Free Bread with the Addition of Hemp Inflorescence
The \(\Delta\)E for all the tested samples with HI additions exceeded five, indicating a significant color deviation. All the color parameters changed significantly owing to a significant difference in the color of the hemp inflorescence addition from the rice flour that was used as the main raw material in the recipe. The dried hemp inflorescence had a green-brown color, which influenced the color of the bread significantly. Significant changes and darkening of the bread color were observed in other studies where hemp seed cake and hemp seed grit were used as bread additives (with only 1% of the addition) [26]. When cookies with hemp flour were tested, similar results were obtained [51]. The L* value decreased significantly when the ratio of raw and roasted hemp flour was increased from 0% to 20%. The crusts and crumb colors of the breads made with extruded hemp-rice flour were significantly darker than the control bread [49]. According to other authors [24], the addition of a hemp product (particularly 15% and 25% hemp flour) as the second auxiliary ingredient of bread results in an interesting color of the crumb due to the content of natural sugars and organic acids. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Addition of Hemp** & \multicolumn{4}{c}{**Crumb Color Values**} \\
**Influorescence (\%)** & **L*-Value** & **a*-Value** & **b*-Value** & **C*-Value** & **h\({}^{*}\)-Value** & **\(\Delta\)E** \\ \hline
0 & 75.17 \(\pm\) 0.63 d & \(-\)0.02 \(\pm\) 0.03 a & 13.59 \(\pm\) 0.20 a & 13.59 \(\pm\) 0.20 a & 90.10 \(\pm\) 0.12 b & \\
1 & 65.83 \(\pm\) 0.26 c* & 0.79 \(\pm\) 0.09 b* & 19.39 \(\pm\) 0.39 b* & 19.41 \(\pm\) 0.39 b* & 87.68 \(\pm\) 0.24 a* & 11.0 \\
2 & 64.98 \(\pm\) 0.64 c* & 0.86 \(\pm\) 0.06 b* & 21.34 \(\pm\) 0.33 c* & 21.36 \(\pm\) 0.33 c* & 87.69 \(\pm\) 0.09 a* & 12.8 \\
3 & 57.78 \(\pm\) 0.64 b* & 1.64 \(\pm\) 0.06 c* & 24.95 \(\pm\) 0.33 d* & 25.01 \(\pm\) 0.33 d* & 86.24 \(\pm\) 0.09 a* & 20.8 \\
4 & 56.35 \(\pm\) 0.86 b* & 1.79 \(\pm\) 0.10 c* & 26.54 \(\pm\) 0.26 e* & 26.60 \(\pm\) 0.26 e* & 86.15 \(\pm\) 0.17 a* & 22.9 \\
5 & 51.53 \(\pm\) 0.61 a* & 2.14 \(\pm\) 0.10 d* & 28.22 \(\pm\) 0.42 f* & 28.31 \(\pm\) 0.42 f* & 85.66 \(\pm\) 0.17 a* & 27.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The colors of the gluten-free breadcrumbs with the addition of hemp inflorescence. Figure 2: External appearances (**a**) and cross-sections (**b**) of the breads with different hemp inflorescence contents.