esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Schemes
### Specim Preparation  
In this study, the conventional triaxial test employed a visual in situ specimen preparation device [39] to directly prepare the glass bead specimens on the base.  
The primary steps of the in situ specimen preparation method are illustrated in Figure 2. After completing the specimen assembly on the base (Figure 2g), the procedure involves installing the pressure chamber, filling the confining pressure chamber with water, and degassing remove air bubbles from the pressure chamber (Figure 2h). The test can then commence.  
The specimen preparation process for the large-scale triaxial test was conducted in accordance with relevant specifications, and the main steps are illustrated in Figure 3. After the large triaxial specimen was prepared (Figure 3d), it was hoisted to the bottom plate of the triaxial apparatus (equipped with a pulley) using a gantry crane. Subsequently, the specimen was positioned beneath the top loading rod (Figure 3e), ensuring precise axial alignment with the top loading rod.  
Relevant studies have demonstrated that \(D_{\mathrm{r}}\) is a crucial factor influencing the mechanical behavior of granular materials [9]. The maximum and minimum dry density values of glass beads with different particle sizes, as shown in Table 1, further indicate that it is challenging to control specimen preparation based on a specific density value. \(D_{\mathrm{r}}\) is also a commonly used control index in the engineering of cohesionless soil. Therefore, this study maintains consistent \(D_{\mathrm{r}}\) for glass beads of different sizes.  
As shown in Table 1, the maximum and minimum dry density difference for large glass beads is just 0.06 g/cm\({}^{3}\). This is because the spherical shape of glass beads means  
Figure 3: Large triaxial specimen preparation process.  
Figure 2: In situ specimen preparation method.  
larger ones have a higher center of gravity, making them prone to rolling and instability. Consequently, large glass beads struggle to stay in a relatively loose state and tend to pack closely. Therefore, the relative density \(D_{\text{r}}\) of the specimens for both large and small sized glass beads was set at 0.8.  
The dry density of the specimens can be calculated according to Equation (1), and subsequently, the mass of the required test materials can be determined according to Equation (2). The mass of glass beads required for each layer can be obtained based on the number of layers during specimen preparation.  
\[\rho_{d}=\frac{\rho_{\text{dmax}}\cdot\rho_{\text{dmin}}}{\rho_{\text{dmax}}-D_ {r}\cdot(\rho_{\text{dmax}}-\rho_{\text{dmin}})} \tag{1}\]  
\[M=\rho_{d}\cdot V=\rho_{d}\cdot\frac{\pi R^{2}H}{4} \tag{2}\]  
where, \(\rho_{d}\) is the dry density of the glass beads; \(M\) is the total mass of the specimen; \(V\) is the volume of the specimen; \(R\) is the diameter of the specimen (61.8 mm/300 mm); and \(H\) is the height of the specimen.  
Both conventional and large-scale triaxial tests were conducted under consistent consolidated-drained (\(CD\)) conditions.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 4 Methodology
### Hierarchical Detection Framework  
Generally, NIDSs using graph embedding usually use coarse labels to predict whether the network flow is an attack or benign [14]. However, such a model is not able to further explore the attack type to facilitate network defenders in conducting research. [PERSON] et al. [13] classifies the attack fine-grained labels while training coarse-grained detection, and then trains a second model for attack type recognition, but this will cause a lot of training resources to be wasted.  
Therefore, we add both fine-grained and coarse-grained objectives to the training objective. For coarse-grained loss, \(L_{coarse}\) is calculated using the cross-entropy loss function:  
\[L_{coarse}=\text{CrossEntropyLoss}(\mathbf{\hat{y}}_{coarse},\mathbf{y}_{ coarse})\]  
For fine-grained loss the steps are as follows: First, create a mask \(\mathbf{M}\) to mark samples whose coarse-grained labels are positive (assuming the positive label is 1):  
\[\mathbf{M}_{i}=\begin{cases}1,&\text{if }\mathbf{y}_{coarse}(i)=1\\ 0,&\text{otherwise}\end{cases}\]  
If \(\sum_{i=1}^{N}\mathbf{M}_{i}=0\), that is, there are no samples with coarse-grained labels as positive, then the fine-grained loss \(L_{fine}=0\). If the opposite holds, perform the following steps:  
* Compute fine-grained prediction probability distribution \(\mathbf{P}\): \[\mathbf{P}_{ij}=\frac{\exp(\mathbf{\hat{y}}_{fine}(i,j))}{\sum_{k=1}^{C_{fine }}\exp(\mathbf{\hat{y}}_{fine}(i,k))}\] where \(i\) is the coarse-grained positive sample index and \(j\) is the fine-grained category index, \(j=1,2,\dots,C_{fine}\).
* Calculate the information entropy of each sample \(\mathbf{H}\): \[\mathbf{H}_{i}=-\sum_{j=1}^{C_{fine}}\mathbf{P}_{ij}\log(\mathbf{P}_{ij})\]
* Calculate the weight of each sample \(\mathbf{w}\): \[\mathbf{w}_{i}=\frac{1}{1+\exp(-\tau\mathbf{H}_{i})}\]where the \(\tau\) is the temperature coefficient.
* Normalize weights and calculate fine-grained losses \(L_{fine}\): \[\mathbf{w}_{i}=\frac{\mathbf{w}_{i}}{\sum_{i=1}^{N}\mathbf{M}_{k}}\mathbf{w}_{i}\] \[L_{fine}=\sum_{i=1}^{N}\mathbf{M}_{i}\mathbf{w}_{i}\text{CrossEntropyLoss}( \mathbf{\hat{y}}_{fine}(i),\mathbf{y}_{fine}(i))\]  
Finally, automatically balance the weights of the loss function. The total loss function \(L_{total}\) is a combination of the coarse-grained loss function and the fine-grained loss function.  
\[L_{total}=L_{coarse}+\frac{\sum_{i=1}^{N}\mathbf{M}_{i}}{N}L_{fine}\]  
The hierarchical design effectively addresses class imbalance by first filtering out the majority of benign samples through coarse-grained detection, thus reducing the imbalance ratio for fine-grained classification. The dynamic weighting mechanism based on information entropy further ensures that rare attack types are assigned higher training weights, preventing the model from being dominated by dominant attack categories. This dual strategy not only enhances detection efficiency but also significantly improves the model's ability to recognize rare attack types in imbalanced datasets.
## 4 Transient Stability Analysis
### The Phase Trajectory Method  
The steps for analyzing the transient stability of a system based on the phase trajectory method are briefly described as follows:  
_Step 1: Determine Initial Conditions:_  
To analyze the transient stability of the system, start by specifying the initial conditions of the state variables in the phase space. Under normal operating conditions, the system predominantly exhibits positive sequence components. Consequently, the initial states of state variables associated with negative sequence dynamics are set to zero.  
_Step 2: Solve Differential Equations:_  
Employ numerical simulation techniques, such as the Runge-Kutta method, to solve the system's differential equations. This involves iteratively updating the values of the state variables to trace the system's trajectory through phase space. The solution of these equations provides a dynamic representation of the system's evolution over time.  
_Step 3: Visualize Phase Trajectory:_  
Represent the system's trajectory in phase space using phase diagrams or phase plane plots. Each point on the diagram corresponds to the system's state at a particular moment, while the trajectory illustrates the system's temporal evolution. Visualization helps in understanding how the state variables interact and evolve.  
This method offers significant insights into system behavior and stability by analyzing how the system's state evolves along its trajectory. The transient stability is assessed based on whether the trajectory converges or diverges. Notably, the phase trajectory method provides reliable stability assessment results without being overly conservative.
# LoRa, Zigbee and 5G Propagation and Transmission Performance in an Indoor Environment at 868 MHz
## 3 Methodology  
### Ideal Narrowband Measurements at 868 MHz  
The study of the power attenuation of narrowband (NB) transmission is carried out following the scheme shown in Figure 3. This procedure consisted of generating a sinusoidal signal with the Rohde & Schwarz SMB100A signal generator, with a power of 0 dBm, and then amplifying it with a Mini Circuits (Brooklyn, NY, USA) ZHL-42 amplifier to have 30 dBm of transmission power before transmitting it with the antenna. The measurement of the received power at each measurement point was performed using the SA Anritsu MS2090A and using an antenna identical to the transmission one.  
Figure 1: File photo of the corridor.  
Figure 2: Floor plan underground level.  
Due to the fact that the measurements carried out with the SA vary rapidly between values, it was decided to configure a mean filter in the SA with a window of 100 samples. This filter was intended to obtain a static measurement value at each measurement point. The specific configuration used in the SA is listed in Table 2.
## 5 Study Cases
### Flame Analysis  
To test the methodology proposed in this paper, the analysis of a malware 'Flame' has been made. It belongs to APT malware type, one of the most complex and dangerous malwares discovered today. The objective of this analysis is to obtain a comprehensive understanding of its operation, composition, modules, types of encryption, injection mechanisms, communications with its command and control system, data types, etc. A more complete analysis is beyond our purpose as it would be unfeasible in a limited time.  
The main objective of this malware is the theft of classified information (cyber espionage) on systems with Windows OS and using USB sticks to spread exfiltration data. Given its complexity, it has been considered to be developed by a state, which fall within the category of 'Advanced Persistent Threats'.  
Their level of complexity would be very high considering its size, the number of files it handles, languages and libraries used, different encryption methods used, or the time needed for its analysis. It means that so far can be considered the most complex existing type of malware.
## 3 Helicopter Emergency Medical Services and Air-MSU
### Aircraft Selection  
When selecting an appropriate aircraft for Air-MSU, the choices are between a fixed wing and a rotor wing. The former is capable of covering a larger distance with greater speed, although it requires a runway; the latter generally has a smaller coverage distance and a lower speed but does not require a runway, allowing for more flexible landing options. [PERSON] et al. [22] presented a table of existing helicopters for technical comparison, where it can be seen that the cabin size for helicopters used for HEMS tend to be smaller than those for search-and-rescue missions and military operations. Aeromedical rotorcrafts come in different sizes, each designed for different purposes and operating in different locations. When designing for Air-MSU, selecting a larger rotorcraft not only allows for a higher payload carriage but also a larger cabin room, easing the process of integrating a CT scanner and any additional equipment required into the nominated aircraft. Furthermore, having a larger cabin room also means more working space for both the medical staff during operation and personnel performing maintenance work for the CT scanner.  
In collaboration with the Australian Stroke Alliance, Agusta Westland 189 (Figure 2) was selected as the rotorcraft model for retrofitting a proposed CT scanner as it has a relatively large cabin size.  
Figure 2: AgustaWestland AW189. Reproduced under a Creative Commons Licence from [25].
# UV-Induced Aging in Thermochromic Pigment-Integrated Food-Grade Polymers: A Performance Assessment
## 2 Materials and Methods
### Tensile Property (Post-UV Exposure)  
In tensile testing, a regulated tensile (pulling) force is applied until the sample breaks to assess the mechanical properties of the polymer [68]. To determine the effects of UVA radiation on mechanical performance, tensile properties were analyzed on ISO 527-2:2012 [57] type 1 BA specimens. Five replicate specimens per formulation (PP98/TP2 and PP92/TP8) were tested after 0, 500, and 1000 h of exposure. The evaluated parameters included maximum tensile stress, Young's modulus, and strain at break, which are indicative of stiffness, strength, and ductility, respectively.  
Tests were conducted exclusively on samples aged under indoor UVA conditions (RS 559-934, 360 nm), as the QUV/Solar Eye device could not accommodate tensile molds. Unexposed control samples were included for comparison. Mechanical testing was performed on an Instron 3400 system (Instron, Norwood, MA, USA) using a 4 kN load cell and Bluehill v4.29 software (Instron, Norwood, MA, USA). Specimens (170 mm \(\times\) 10 \(\pm\) 0.2 mm \(\times\) 4 \(\pm\) 0.2 mm) were tested with a 25.4 mm grip separation at acrosshead speed of 10 mm/min. A Mitutoyo Absolute CD-6-ASX caliper (BCS Calibration, Laois, Ireland) was used for dimensional verification.
## 4 Results and Discussion
### Performance Analysis  
The detailed analysis and the comparative outcomes obtained are detailed in this section. Different methods are also considered in the evaluations to prove the performance outcomes of the proposed approach. The methods considered for comparison include random forest (RF), convolutional neural network (CNN), gated recurrent unit (GRU), and classical deep Q-learning network (DQN). A more detailed analysis of the outcomes is presented below:
# A Novel Decentralized E-Commerce Transaction System Based on Blockchain
## 3 Platform Design
### Platform Architecture
#### 3.3.1 Information Upload Stage  
1. The seller sends the detailed information of commodity to IPFS, including the name, country of origin, specifications, and then IPFS returns an address.
2. The seller launches the address and basic information of the commodity into the platform, including the name, unit price, and pay a certain deposit.
3. The smart contract uploads the address and information to the blockchain, and the deposit is stored in the smart contract.  
Figure 3: E-commerce system platform architecture.  
Blockchain feeds back the upload status information. Based on the price adjustment of commodity, the deposit is deposited in the smart contract. If an accident happens during the transaction, the buyer can get compensation from the deposit first. During the sale of commodity, if the commodity needs to be taken off from platform due to selling out, the seller can take the initiative to cancel the smart contract with unconcluded-transaction, and the rest of the deposit will be returned to seller's account except for the gas consumed. The introduction of deposit mechanism increases the cost of false transactions of sellers and effectively prevent the false transactions.
###### Abstract  
A significant gap in the known methods for assessing the levels of metal artifacts in X-ray computed tomography and approaches to their reduction is an almost complete disregard for the physical nature of this artifact--the proximity to zero of radioscopic transparency. The proposed work fills this gap. A mathematical model has been developed for evaluating metal artifacts in X-ray computed tomography as applied to the geometry of a parallel beam. The simulation model was transformed into an algorithm, and a Mathcad program was designed to simulate images of the internal structure of the test objects. The algorithm for estimating the studied artifact includes the stages of generating sinograms and estimating the distributions of the linear coefficient over the sections of the object based on the back projection method with filtering. The efficiency of the metal artifacts simulation algorithm is demonstrated in the example of symmetric and asymmetric objects with low- and high-density inclusions and inclusions from materials with high atomic number values. The possibility of reducing metal artifacts with the help of a rational choice of the maximum energy of X-ray radiation and the ADC bit depth is illustrated. For example, for an aluminum cylinder 200 mm in diameter with a central cylindrical cavity 80 mm in diameter, cylindrical inserts 12 mm in diameter with material densities from 1.5 g/cm\({}^{3}\) to 10 g/cm\({}^{3}\), and effective atomic numbers of materials from 13 to 47, the numerical simulation method proved the following: it is practically unattainable to significantly reduce the level of metal artifacts by increasing the ADC capacity to the maximum X-ray energy of 160 keV; the desired effect is achieved by simultaneously increasing the maximum X-ray energy to 225 keV and the ADC capacity to 24 or 32; increasing the maximum X-ray energy from 160 keV to 225 keV leads to an increase in the energy absorbed in the material of the test object by 26%. The results of this research can be used at the design stage of X-ray computed tomography systems designed to control objects with fragments of low radiation transparency.  
X-ray computed tomography; metal artifacts; ADC bit depth; density; effective atomic number; simulation modeling 2023
# A Review of the Scheduling Problem within Canadian Healthcare Centres
## 4 Hospital Scheduling in Depth
### Operating Room Scheduling
#### 4.3.1 Constraints  
With all of the considerations an operating room has the constraints tend to not be divisible into neat categories as the previous types of schedules. There are sometimes multiple different schedules being created in tandem and this leads to more broad constraints.  
* Patients with a high priority must be scheduled within the planning period [2; 37].
* If additional space is available non-priority patients can fill it [2].
* Doctor's should be allowed to set a preference regarding the type of patients they see [41].
* Each surgeon can only work at one hospital [2].
* Total surgical time does not exceed total OR time [2; 34; 37].
* Each OR is assigned to one specialty [34].
* Each OR must have the same specialty every week [34].
* No specialty can be over-represented [34].
* Surgeries can only be performed in the appropriate OR with the appropriate specialty [2; 34; 41].
* Patient throughput must meet a specified threshold [34].
* Beds should be minimized [34].
* Operating rooms and specialties must be compatible in aspects such as equipment and specialty requirements [34].
* Time must be allotted for patient recovery [34].
* Overtime should be avoided if at all possible [41].
* Surgeries cannot go over the predicted time based on surgeon skill and standard duration [41].
# Unveiling Biological Activities of Marine Fungi: The Effect of Sea Salt
## 4 Discussion
Although filamentous fungi are also known to produce antioxidant activity, they are less explored. Some studies of filamentous fungi, such as or _Aspergillus_, _Chatomium_, _Caldosporium_, _Colletotrichum_, _Epicoccum_, _Lecanicillium_, and _Penicillium_, revealed that they are also sources of antioxidants in the form of their secondary metabolites [21,91]. All major naturally occurring antioxidant classes, including DPPH, phenols, and flavonoids, were found in the methanolic extracts of each fungal species studied. The highest antioxidant activity, including phenols, ortho-phenols, and flavonoids, was observed for the extract of _C. rubrum_ (MUM 19.39/CMG 28) when cultivated without sea salt, which may be a strain-specific characteristic. Phenolic compounds represent the largest category of natural products and are the most widely reported to exhibit antioxidant activity [44]. To deal with different stress situations, fungi produce phenolic compounds that act as natural lipophilic radical scavengers [92]. With flavonoids, these compounds provide fungi with natural defenses and protection from oxidative stress-caused damage against the free radicals, metal chelators and pro-oxidants [93]. Some studies revealed that antioxidant capacity in filamentous fungi was attributed mainly to their phenolic content [94]. Even though methanolic extracts of all fungal species studied were shown to possess antioxidant activities, extraction methods, solvents, time, temperature, and pH are crucial factors in obtaining natural antioxidants from different sources [92]. However, there are other mechanisms through which antioxidants can show their activity than by direct reaction mechanism [92]. We showed that these extracts from fungal marine species prove to also be a rich source of antioxidants components. According to FTIR analyses, the main differences found in functional groups between saline and non-saline culture conditions were chitin and ergosterol, lipids, and carbohydrates. Recently, [PERSON] et al. [95] showed changes in the membrane lipid composition of _Emericellopsis alkalina_ under different concentrations of NaCl. The present study suggests that sea salt applies a selective pressure on the activity profiles of the fungi assayed, and induces an alteration in the chemical composition of fungal cell walls.
## 3 Results and Discussion
### Residual Pesticides in 40 Korean Agricultural Soils  
Table 7 shows the results of the multiresidue pesticide analysis of each soil sample. Based on these results, the pesticide with the highest detection frequency in Korean paddy soils (40 sites) was tricyclazole, and the detection range was 0.003-0.034 mg kg\({}^{-1}\). The pesticide was detected in 30 out of 40 soil samples, thus, the detection frequency reached 75% (Table 7). Tricyclazole [5-methyl-1,2,4-triazole0(3,4-b)(1,3)benzothazole] is a triazole-typed systemic fungicide that is absorbed by foliage and roots, and is translocated to xylem  
and apoplasts in plants [26]. In Korea, it is only registered and used for controlling southern corn-leaf blight and rice blast diseases [27]; however, there is no domestic allowable standard for its residue in other crops. According to the risk assessment of pesticide residues in the soil, it has been reported that tricyclazole was mostly detected in soil samples in agricultural cultivated lands [28]. Tricyclazole in the residual pesticides was most frequently detected in Korean paddy soils because it is a fungicide that is used quite often, as 46,915 kg was released in 2017 in Korea, accounting for 5.6% of the fungicide used for paddy rice [29]. With this large consumption, tricyclazole has a long mean half-life (DT\({}_{50}\)) value of 305 days in surface soil, while the DT\({}_{50}\) value of difenoconazole in paddy soil ranged from 2.82 days to 23.26 days [30; 31]. These two factors can be attributed to extended tricyclazole presence in the paddy soil. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Ulsan Soils** & **Determined** & **Determined** & **Pohang Soils** & **Determined** & **Determined** \\  & **Pesticides** & **Amounts (ug kg\({}^{-1}\))** & **Pesticides** & **Amounts (ug kg\({}^{-1}\))** \\ \hline Ulsan 1 & Tricyclazole & 0.013 & Pohang 1 & - ** & - ** \\ \hline  & & & & Dimethomorph & 0.005 * \\  & & & & Imidacloprid & 0.016 \\  & & & & Mandipropamid & 0.003 * \\ Ulsan 2 & Tricyclazole & 0.001 * & Pohang 2 & Endosulfan & 0.040 \\  & & & & Chlorantraniliprole & 0.038 \\  & & & & Emamectin & 0.002 \\ \hline Ulsan 3 & Tricyclazole & 0.004 * & Pohang 3 & Alachlor & 0.009 \\  & & & & Procymidone & 0.004 * \\ \hline  & & & & Oxadiazon & 0.057 \\  & & & & Tricyclazole & 0.005 * \\  & & & & Thifluzzamide & 0.005 * \\ \hline  & & & & Carbendarzim & 0.002 * \\  & & & & Oxadiazon & 0.016 \\  &