esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The damping ratio (\(D\)) is one of the key soil parameters in geotechnical issues where the soil is subjected to dynamic loads, like machines foundation, city tram and subway traffic, and driving of sheet pile or precast pile. Each of the abovementioned geotechnical problems is connected with significant damping, so its effect should be included in the dynamical analysis. Therefore, this article focuses on the damping phenomenon in cohesive soils from the capital of Poland, which is described by damping ratio (\(D\)). In this research, a set of the damping tests by free vibration method in resonant column device were conducted, and the influence of four selected factors, i.e., shear strain (\(y\)), effective stress (\(p\)'), plasticity index (\(PI\)), and void ratio (\(e\)) on damping ratio in wide strain range was investigated and discussed. Based on the laboratory tests, the shear strain has the most impacts on the damping ratio characteristics; the plasticity index and the effective stress also have a significant influence. Based on the performed analysis, the authors propose the empirical equations with two sets of variables, the first for low and medium cohesive soil (\(PI<20\%\)) and the second for very cohesive soils (\(PI>20\%\)).  
Keywords:laboratory studies; damping ratio; free vibration decay; cohesive soil; resonant column +
Footnote †: journal: Applied Sciences
## 3 EQPD-MAC Protocol Development
### Castalia Implementation and Simulation  
Both the TMAC [56] and the MPQ-MAC [30] protocols inspired the development of the EQPD-MAC. Castalia Simulator [57] provides the implementation of the TMAC protocol inside the MAC module directory. Every feature of EQPD-MAC protocol were implemented by modifying the TMAC protocol, which supports adaptive active/sleep time. TMAC protocol supports the activation timeout (_TA_). The internal construction of the node composite module is shown in Figure 7. Message passing is represented by solid arrows, whereas dashed arrows represent the basic function call.  
1. Priority is implemented inside \"ThroughputTest\" to support multi-priority data packets. \"ThroughputTest\" is an application module inside the Castalia directory. Each packet is assigned a random value between 0 and 1, representing its priority. P4 is the most important, whereas P1 is the least important priority packet.
2. Data fields such as source ID, destination ID, and sequence number are defined in the application layer. The actual end-to-end latency of the packet can be accurately measured by assigning priority in the application layer.
3. After that, to access the channel, another random number is generated inside the MAC module. If the random number is greater than p, then the sensor node can begin the data packet transmission.
4. The sink node first starts the waiting timer Tw to receive the Tx-beacons. The waiting timer had to be implemented inside the MAC module.
5. The sensor nodes send the Tx-beacons, and the beacon contains the priority number. The sink node checks the priority and selects a sensor node based on the higher priority. Such conditions of checking the priority have to be implemented inside the code.
6. The sink node sends an Rx-beacon containing the address of the selected sensor node, and the timer is canceled. The sensor node receives Rx-beacon and inspects if the beacon contains its address. If the source address matches the sensor address, the node sends the priority packet and waits for an ACK packet from the sink.
7. In Castalia, the TMAC module provides several basic functions such as startup, toNetworkLayer, timerFiredCallback. These functions should also include the priority field, ensuring that all nodes are aware of the priority while transmitting the packet.
8. AODV routing module also provides several basic functions such as startup, fromApplicationLayer, fromMacLayer, timerFiredCallback, toMacLayer. The priority field has to be implemented on all functions, ensuring that all nodes are aware of the priority while transmitting the packet.  
Figure 7: The node composite module.  
After that, AODV routing protocol was tested with the EQPD-MAC protocol and the performance was evaluated. The transmissions of the packets can be viewed as an output using the \"trace\" command inside the code. Additionally, the AODV routing protocol code is available [58].
# A Routing Algorithm Based on Real-Time Information Traffic in Sparse Environment for VANETs
## 2 Related Work  
### Topology-Based Routing Protocols  
Many researchers regard topology as the basic element of routing protocols and design some routing protocols based on topology. In topology-based routing protocols, each node maintains a routing table for forwarding packets from source to destination. For Destination Sequenced Distance Vector Routing (DSDV) [18], each node in the network topology maintains the topology information about all the other nodes, and exchange information with neighbors at fixed time intervals to ensure the validity of topology information. The discovery and maintenance of routing tables are proactive, based on time intervals, and using broadcasts. When a message needs to be sent, the source node can quickly find the routing path from the routing table, and then quickly forward the packet to the destination node. This scheme provides a relatively low network delay but results in substantial network overhead in the process of exchanging information while the network scale is large. Ad-hoc On-Demand Distance Vector (AODV) [19] is a reactive routing protocol, which establishes the path only after the node wishes to engage in communication. This lightens the burden on the network. In route discovery, the source node sends packets to the destination node by flooding. The relay nodes forward the packet until the destination node receives it. In the whole routing discovery process of AODV routing, a lot of network overhead is generated.
## 3 Results and Discussion
### BP Neural Network Structure Based on Improved Grey Wolf Algorithm
#### 4.1.4 A Neural Network Improved by Grey Wolf Algorithm Based on Circle Chaotic Mapping  
In this paper, an improved CCM Grey Wolf algorithm for a BP neural network and a BP neural network without improvement were compared by predicting the tool wear and the delamination factors. Figure 13 shows the flow chart of the BP neural network improved by the Grey Wolf algorithm based on CCM. According to the analysis in Sections 3.1-3.4 above, the cutting speed, feed rate, number of processed holes, thrust force and vibration amplitude have significant effects on the tool wear and delamination factors. These factors are selected as the input layer of the neural network. The output layer comprises the VB of tool wear and the entrance and exit delamination factors. The diagram of the BP neural network structure is shown in Figure 14.  
Figure 14: BP neural network prediction model.  
Figure 13: Flow diagram of the improved neural network structure [34].  
According to empirical Formula (3), the number of hidden layer neurons \(m\) is determined as follows:  
\[m= \sqrt{n+l}+\alpha \tag{7}\]  
where \(n\) is the number of input layer neurons, and \(l\) is the number of output neurons. \(\alpha\) is a constant between 1 and 10. According to Figure 13, when \(\alpha=8\), the number of hidden layer neurons \(m\) is 11. The transfer function between the layers is the tan-sigmoid function.
# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 3 Materials and Methods
### Post-Processing  
It was necessary to mask higher values in BR, as these corresponded to a small number of pixels that were not representative of the study area and were associated with false positives, such as urban areas. This step was taken because, when applying color slices, these higher values distorted the scale, making it difficult to accurately interpret the distribution of the values within the area of interest. Masking these outliers ensured that the resulting visualization was more representative and better aligned with the study's objectives. The masked values can be observed in Table 2.  
The Color Slices tool was applied using the Color Slices function in the ENVI software. This tool allows selecting data ranges and assigning colors to highlight specific areas in an image. Automatically, the data is organized into 16 intervals, which we will refer to as classes. Each class represents a specific interval of digital number (DN) or reflectance values, and is assigned a unique color. While cool colors represent the lowest values, warm colors represent the highest values.
# Screening of Scaffolds for the Design of G-Quadruplex Ligands
## 2 Materials and Methods
### Cytotoxicity in Human Cell Lines
#### 2.5.2 MTT Assay  
The in vitro cell proliferation was determined using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT) (Sigma-Aldrich, St. Louis, MO, USA) assay. The A549 and NHDF cells were plated in 96-well culture plates with density of 1 \(\times\) 10\({}^{4}\) cells/mL in the appropriate culture medium. After 48 h of adherence cells were treated with the different compounds in the study for 72 h. At the end of incubation, the medium was removed and replaced with MTT solution and further incubated at 37 \({}^{\circ}\)C for 4 h. Then, the MTT solution was removed, and the resulting formazan crystals were dissolved with 100 uL of DMSO. The absorbance was measured at 570 nm using a microplate reader Bio-rad Xmark spectrophotometer (Bio-Rad, California, USA). The cytotoxicity was normalized control conditions (untreated cells). Each experiment was performed in quadruplicate in two independent experiments.
## 2 Methods
### State-Augmented EKF Design
This design both supports the previously defined dynamic system for the random walk model, and transfers the current position error vector to the previous spot in the error state vector during the propagation. In this design, the delta observables are directly related to the position increment as a constraint of the current position, relative to the previous position. Based on the phase-smoothing approach in the position domain, this EKF design can maximize the effectiveness of such measurements without assumptions about the dynamics of the vehicle [5] and control the growth of position errors. The correspondingly adopted measurement model in this study is described in the following subsection.
## 3 Results
### Evaluation Results with Blood Vessel Scatterers  
Figure 8 shows the examples of US images of the blood vessel scatterers obtained with the linear, matrix, and optimized arrays under four different conditions: two types of position errors (0 mm and 3 mm) and two types of angular errors (0\({}^{\circ}\) and 20\({}^{\circ}\)). US images obtained under all error conditions where both positional and angular errors were present are illustrated in Figures S7-S9. The measurement errors of the vessel inner diameter from the ground truth value of 6 mm are displayed in each image. As with the imaging of the wire and cyst scatterers, the blood vessel was most clearly visualized with the linear array under the condition of no positional or angular errors, and the measurement error of the vessel diameter was \(-1.0\) mm. However, when positional and angular errors are present, the longitudinal plane of the blood vessel could not be visualized, and the measured vessel inner diameter was significantly smaller compared to the ground truth. The cause of the large error is that the imaging cross-sections was displaced from the longitudinal plane of the vessel due to the positional and angular errors. In the images of the matrix array, there were little variety in the image qualities regardless of the positional and angular errors, and the measurement errors of the vessel diameter were smaller compared to the linear array. However, the qualities of the US images were low, and the vessel walls, particularly the upper walls, were unclear due to the grating lobe artifacts. In the images of the optimized array, although the image quality is lower than the linear array with no positional and angular errors, the upper and lower vessel walls were clearly visualized under all conditions, and the variation in the measurement errors of the vessel diameter was small, with all errors being around \(-0.5\) mm.  
Figure 7: Examples of ultrasound images of cyst scatterers obtained with the linear, matrix, and optimized arrays. The left column shows the element arrays, and the top row shows the positional relationship between the imaging plane and the element arrays. The dynamic range of the ultrasound images is 25 dB. The average CNR of the cysts are displayed on each image.
# Fractional View Analysis of Acoustic Wave Equations, Using Fractional-Order Differential Equations
## 2 Preliminaries Concepts  
**Definition 1**.: _Laplace transformation of \(\rho(\eta)\), \(\eta>0\) represented as [13]_  
\[Q(s)=\mathcal{L}[\rho(\eta)]=\int_{0}^{\infty}e^{-s\eta}\rho(\eta)d\eta.\]  
**Theorem 1**.: _The convolution of Laplace transform is_  
\[\mathcal{L}[\rho_{1}\times\rho_{2}]=\mathcal{L}[\rho_{1}(\eta)]\times\mathcal{ L}[\rho_{2}(\eta)],\]  
_here \(\rho_{1}\times\rho_{2}\), define the convolution between \(\rho_{1}\) and \(\rho_{2}\),_  
\[(\rho_{1}\times\rho_{2})\eta=\int_{0}^{\tau}\rho_{1}(\tau)\rho_{2}(\eta-\tau)d\eta.\]  
Laplace transform of fractional derivative  
\[\mathcal{L}\left(D_{\eta}^{\beta}\rho(\eta)\right)=s^{\beta}Q(s)-\sum_{k=0}^{n -1}s^{\beta-1-k}\rho^{(k)}(0),\quad n-1<\beta<n.\]  
_where \(Q(s)\) is the Laplace transformation of \(\rho(\eta)\)._  
**Definition 2**.: _The Riemann-Liouville fractional integral operator of order \(\beta\geq 0\) of a function \(f\in C_{\mu}\), \(\mu\geq-1\) is described in [21]_  
\[I_{\zeta}^{\beta}g(\zeta)=\frac{1}{\Gamma(\beta)}\int_{0}^{\zeta}(\zeta-s)^{ \beta-1}g(s)ds,\]_where \(\Gamma\) represent the gamma function as,_  
\[\Gamma(\beta)=\int_{0}^{\infty}e^{-\xi}\xi^{\beta-1}d\xi\qquad\beta\in\mathbb{C}.\]  
**Definition 3**.: _The fractional derivative of \(g(\eta)\) in the Caputo sense is defined as_  
\[D^{\beta}g(\eta)=\frac{\partial^{\beta}g(\eta)}{\partial\eta^{\beta}}=\begin{cases} I^{m-\beta}\left[\frac{\partial^{m}g(\eta)}{\partial\eta^{\beta}}\right], \quad\text{if }m-1<\beta<m,\quad m\in\mathbb{N}\\ \frac{\partial^{m}g(\eta)}{\partial\eta^{\beta}},\quad\beta=m.\end{cases}\]  
**Lemma 1**.: _If \(\tilde{m}-1<\beta\leq\tilde{m}\) with \(\tilde{m}\in\mathbb{N}\) and \(g\in\mathbb{C}_{\eta}\) with \(\eta\geq-1\), then_  
\[I^{\beta}I^{\alpha}g(\xi)=I^{\beta+\alpha}g(\xi),\qquad a,\beta \geq 0.\] \[I^{\beta}\xi^{\lambda}=\frac{\Gamma(\lambda+1)}{\Gamma(\gamma+ \lambda+1)}\xi^{\beta+\lambda},\qquad\beta>0,\lambda>-1,\quad\xi>0.\] \[I^{\beta}D^{\beta}g(\xi)=g(\xi)-\sum_{k=0}^{m-1}g^{(k)}(0^{+}) \frac{\xi^{k}}{k!},\quad\text{for}\quad\xi>0,\tilde{m}-1<\beta\leq\tilde{m}.\]  
**Definition 4**.: _Function of Mittag-Leffler, \(E_{\alpha,\beta}(\eta)\) for \(\alpha,\beta>0\) is defined as_  
\[E_{\alpha,\beta}(\eta)=\sum_{k=0}^{\infty}\frac{\eta^{k}}{\Gamma(k\alpha+\beta )},\qquad\alpha,\beta>0,\quad\eta\in\mathbb{C}.\]
# Modelling the Structure and Anisotropy of London Clay Using the SA_BRICK Model
###### Abstract  
Several constitutive models had been developed by other researchers to cover the main features of mechanical behaviour of natural overconsolidated clays, such as the nonlinear stress-strain response at small and large strains, and the recent stress history effect. Kinematic hardening models include these features to facilitate realistic predictions of soil-structure interaction. This paper presents the further development of a kinematic hardening model BRICK that includes anisotropy and the influence of soil structure on the mechanical behaviour of a natural clay. High quality laboratory tests were used to calibrate the input parameters of the model in a single element configuration, and a documented boundary value problem of tunnel excavation was used to validate the model in finite element calculations. A comprehensive comparative study between the predictions of different kinematic hardening models, using two different software packages, was carried out. It was observed that the SA_BRICK model is in fair agreement with the observed data and gives improved predictions in comparison to other kinematic hardening models, particularly in terms of narrowness of the settlement trough above the tunnel. Advanced predictions of ground deformations caused by tunnel excavations can be effectively used to mitigate possible damage of existing structures affected by tunnelling in an urban environment.  
Keywords:constitutive modelling; natural stiff clays; kinematic hardening; numerical analyses; tunnel excavations +
Footnote †: journal: Applied Sciences
## 3 Experimental Setup
### Hardware Implementation
#### 3.1.2 Photovoltaic (PV) Panels  
The PV panels convert solar energy into electricity, which is used either within the building or supplied to the main power grid. Any surplus energy is stored in the R-ESS for later use during high-demand periods. The PV panels consist of monocrystalline silicon panels with a generation capacity of 24 kW, offering high efficiency and long-term durability.  
Figure 3: Hardware implementation of the experimental setup in the Daekyung Engineering building, with power lines and communication lines indicated between each component (AMI: advanced metering infrastructure, KEPCO: Korea Electric Power Corporation, PCS: power conversion system, PV: photovoltaic, and V2G: vehicle-to-grid).
# Research on a Real-Time Driver Fatigue Detection Algorithm Based on Facial Video Sequences
## 2 Approach
### Driver Fatigue Recognition Features
#### 2.3.2 PERCLOS: An Effective Cue for Driver Fatigue Detection  
Percentage of eyelid closure over the pupil over time (PERCLOS) is defined as the degree of eye closure within a certain time. It is an effective method to measure the fatigue state. PERCLOS judges fatigue driving by the time proportion of eye closure. Because the size of eyes varies from person to person, the area of eyes changes dynamically due to the influence of the scene and head movement, and the opening degree of eyes is relative to its maximum opening state. The time can be converted into the number of video frames for calculation, and the degree of drowsiness of the driver can be judged according to the  
Figure 5: Eyes or mouth landmarks and the values of EAR/MAR; (**a**) eyes landmarks have significant differences (open or closed); (**b**) mouth landmarks have significant differences (open or closed); and (**c**) the values of EAR or MAR at open and closed states.  
PERCLOS threshold [35]. When the proportion of eye closure in a certain time interval exceeds the threshold, it is considered as the fatigue state. \(PERCLOS\) can be computed by:  
\[PERCLOS=\frac{N_{drow\sin ess}}{N_{total}} \tag{4}\]  
where \(N_{drow\sin ess}\) is the number of drowsy video frames and \(N_{total}\) is the total number of video frames in a specific time.  
In the online identification module, \(N_{total}\) is 300 frames. If \(PERCLOS\) is greater than the threshold, where the threshold is 0.8 in this paper, the driver is assessed as driving drowsily.
# A Monarch Butterfly Optimization for the Dynamic Vehicle Routing Problem
## 3 The Proposed Algorithm for DVRP
### Initial Population  
In accordance with [PERSON] et al. (2015) and many similar works, a working day is split into \(n_{ts}\) equal-length time slices, and in each time slice a static VRP is solved, with the aim of servicing the known customers so far. We postpone the arrival of a new request to the end of the current time slice and optimize the next time slice. So, a cut-off time (\(T_{co}\)), is considered, as introduced in [PERSON] et al. (2015), which postpones orders after \(T_{co}\) to the next working day. In addition, an advanced commitment time, (\(T_{ac}\)), is also employed to allow a driver to respond to new orders prior to the time of processing the order itself. The first partial static VRP created at the beginning of the working day (\(T_{1}\)) consists of all static customers which are left over from the previous working day, as defined by the \(T_{co}\) parameter. We apply a random approach to generate an initial feasible solution (\(S\)). Initially, \(S\) consists of \(k-1\) virtual depots, and then we try to update those routes by inserting customers randomly. This process is repeated until all customers have been assigned to a route. Each individual is defined as a random permutation of these known customers and existing routes. Hence, at each slice, all butterflies have the same length and contain the same numbers (customers' and vehicles' identifiers). All butterflies will be verified against the capacity and feasibility rules described above.  
The next static problem considers all orders received during the previous time slice as well as those which have not been visited by drivers yet. In our simulation, each vehicle (\(k\)) starts from the location of the last visited customer (\(i\)), with a departure time (\(dt_{i}\)) corresponding to the end of the service time for this customer (\(dt_{i}=b_{i}+s_{i}\)), and with a remaining capacity equal to the capacity left after serving all previously visited customers.  
A decision to wait or go occurs when a vehicle finishes servicing a customer. The algorithm must choose between keeping the vehicle waiting at its current location or beginning to move towards the next customer. Assume that in \(l\)-th time slice (\(TS_{l}\)), the vehicle (\(k\)) has finished serving the customer (\(i\)) and is ready to serve the next customer (\(l+1\)) in the route (\(R_{k}\)). If it satisfies the inequalities (9), the vehicle will wait at the customer until \(T_{l}\) (\(T_{l}=l^{\star}T/n_{ts}\)), and the wait time (\(w_{i}\)) at the customer is defined in Equation (10)  
\[dt_{i}<T_{l}\ \wedge\ b_{i+1}> T_{l}\ \wedge\ T_{l}+t_{i,i+1}<z_{i} \tag{9}\] \[w_{i} =T_{l}-dt_{i} \tag{10}\]