esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Field Models of Fiber Reinforced Concrete for Structural Applications
## 3 Models of Fiber Reinforcement
### Semi-Discrete Representation of Fibers
#### 3.2.3 Transport Behavior: Model Verification  
The discrete (volumetric) approach and proposed semi-discrete approach are compared for the case of a fiber(s) embedded in a plate of homogeneous material. Domain discretization for the discrete approach is shown by the Voronoi diagram in Figure 4a. A similar diagram is used for the semi-discrete approach, but without the structured arrangement of cells representing the fiber. For both cases, the fiber is centrally positioned and aligned with the direction of flow, which is induced by a potential difference \(\Delta\phi=1\) acting across the model domain. The top and bottom surfaces act as perfect insulators. For the purposes of illustration, the fiber has aspect ratio \(l_{f}/d_{f}=50\) and conductivity ratio \(\sigma_{f}/\sigma_{m}=1000\). By comparing potential values along the plane of symmetry (Figure 4b,c), close agreement between the model results is evident. The semi-discrete approach is versatile in that fibers of different length, orientation, etc., can be placed in the domain without modification of the background mesh representing the matrix phase, as shown by the results in Figure 4d. Notably, the random lattice discretization of the matrix phase of the material does not produce artifacts in the contour plots. This lack of artificial heterogeneity in the background lattice allows for more precise descriptions of the fiber contributions to composite behavior.  
Figure 4: Planar lattice modeling of fiber inclusions: (**a**) discrete representation of a single fiber aligned with the direction of flow; (**b**) corresponding potential distribution; (**c**) potential distribution based on semi-discrete fiber approach; and (**d**) application of semi-discrete approach to multiple fibers.
## 1 Introduction
WSNs are typically used in dynamic and complex environments and are becoming important in a large variety of real applications, from scientific to military domains, such as surveillance or disaster recovery, monitoring and other applications. An example of their successful use is the environmental monitoring: nodes with sensing capabilities, wireless communication and computation are deployed in the physical environment and cooperatively monitor a variety of physical-environmental conditions [5; 6; 7]. A communication task is correlated to the sensing activity of each node in order to send data (the measured values) to a central elaboration system. Such system is usually the company application (CRM, ERP, business intelligence system, _etc._) that is responsible for handling the data received from the remote devices. The paper is organized as follow: Section 2 presents the state of the art about energy monitor solution; Section 3 introduces the architectural solution; Section 4 focuses on technological issues; Section 5 describes the real scenario; Section 6 presents the implementation issues of the proposed application; Section 7 reports an analysis of the experimental results obtained from the real testbed. The last section summarizes and provides future developments.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 2 Laser-Matter Interaction in the Femtosecond Regime
### Double Pulse fs-Laser Ablation and Ion Yields  
The characteristics of fs laser pulses permit applications of more complex irradiation schemes such as short delay pulse sequences or tailored pulse shapes. One of the arrangements, known as double pulse fs-ablation, was particularly theoretically and experimentally particularly well-explored due to its application in material processing and synthesis [83; 84; 85], chemical analysis by LIBS [86], and was recently applied in fs-LIMS [87; 88; 82]. Femtosecond double pulse (DP) irradiation offers better control over ionisation yields and polyatomic species produced in laser ablation [87; 88]. This control is achieved by tuning the laser pulse energy of each laser pulse and inter-pulse delays (ps range) [85].  
The mechanisms of femtosecond double-pulse laser ablation were investigated by modelling (e.g., hydrodynamic simulation, molecular dynamics) [89; 90] and by plasma diagnostics [91; 92; 93]. In hydrodynamic simulation methods, one can identify a pressure/density wave produced by the first laser pulse that propagates into the sample. Behind this wave, a tensile stress wave is formed and propagated through the liquid layer which results in the mechanical fragmentation and ablation of the layer. When the delay is much shorter than the electron-ion relaxation time, only these two waves appear. When the delay is on the order of the relaxation time, the second pulse creates the second wave, thus reducing the intensity of the first tensile wave which also causes the depth of the ablation crater to decrease. For delays longer than the electron-ion relaxation time, the second pulse reheats and decelerates the ablated material, and the ablation crater is then formed by the first pulse only with the crater depth smaller than that of the single pulse ablation alone [90]. The results of this study show that ablation can be suppressed due to the formation of the second shockwave.  
In a LIMS application, femtosecond double-pulse laser ablation can improve the quantitative chemical analysis and further improve the resolution of chemical depth profiling [87; 88; 82]. In addition, at delays in a range of tens of ps, DP leads to a significant reduction of polyatomic ions, by reheating produced plasma and ionising neutral gas, which reduces isobaric interferences and an increase of ion yields up to a factor of 10-50 compared to single pulse femtosecond ablation [87; 88; 82].
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 2 System Model  
The proposed system, with a generic multilane traffic light system is shown in Figure 1. It is assumed that there are no large vehicles running ahead of the receiver, and the receiver is placed on the exterior of the vehicle, at the bottom-center of the windshield. Therefore, there will be a Line-of-Sight (LoS) link between the receiver and the traffic light LED. The optical signals are intensity modulated (IM) in all colors. The duration of the ON (1s) cycle followed by the OFF (0s) cycle of transmitting information is short enough that the LED appearance is always ON to be humanly imperceptible, so, it will not affect the traffic control. In this paper, the LED half power semi-angle is 15\({}^{\circ}\), which is the same as the actual LED light [12]. The receiver vertical inclination angle is \(\theta\), and the field-of-view (FOV) angle is \(\Psi_{\rm c}\). The position of the vehicle is expressed by the distance in the direction of the lane, x, and the distance in the direction of the width, y. A vehicle on the first lane with a LED light is in position y = 0 m. If the width of the lane is 3.5 m and the width of the vehicle is 1.8 m, a vehicle on the second lane is positioned in location y \(\leq\)4.1 m. Table 1 summarizes the system model design parameters.  
The LoS path, d, between the transmitter and the receiver is given as [13]  
\[\mathrm{d}=\sqrt{x^{2}+y^{2}+z^{2}}. \tag{1}\]  
The angle \(\varphi\) in Figure 1 represents the irradiant angle, which is the angle between the irradiance ray and the line of the transmitter surface normal, while the angle of incidence, \(\psi\), is the angle between the incident ray and the line of the receiver surface normal. H\({}_{\mathrm{I}}\) = 5.3 m represents the height of traffic light, H\({}_{\mathrm{r}}\) = 1 m represents the height of the receiver itself from the road, and z is the height difference between H\({}_{\mathrm{I}}\) and H\({}_{\mathrm{r}}\), as used in [12; 13].  
Both angles \(\varphi\) and \(\psi\) are calculated, respectively, from Figure 1 as [13]  
\[\varphi=\arccos\left(\frac{x}{d}\right) \tag{2}\]  
\[\psi=\arccos\left(\frac{\sin(\theta+\arctan(z/x))\sqrt{x^{2}+z^{2}}}{d}\right). \tag{3}\]
# High-Frame-Rate 3-D Vector Flow Imaging in the Frequency Domain
## 2 Materials and Methods
### Experiments  
The experimental tests were based on the ULA-OP 256 [38] (Figure 4), which is a research scanner with 256 independent channels, characterized by full programability, high flexibility, and access to data at any point of the RX chain. In addition, it embeds high-speed programmable devices, such as field-programmable gate arrays (FPGAs) and digital signal processors (DSPs), for real-time processing and the implementation of custom, computationally intensive algorithms. For this work, the ULA-OP 256 was used to transmit plane waves and acquire raw RF channel data, which were then postprocessed in Matlab.  
In the experiments, a homemade phantom was used, in which a blood-mimicking fluid flew in a cylindrical Rilsan(r) pipe immersed in a water tank, as shown in Figure 4. The fluid was obtained by stirring 3 g of 10 \(\upmu\)m polyamide spherical particles (Orgasol(r), Arkema Inc., Philadelphia, PA, USA) in 2 L of demineralized water until a homogeneous suspension was achieved (density of \(2.6\times 10^{10}\) scatterers/mm\({}^{3}\)). The flow was forced by a peristaltic pump connected to a pulsation dampener, capable of further reducing possible turbulences. Considering the entrance length of the tube (70 mm), parabolic flow was guaranteed by setting the peak velocity approximately at 7.5 cm/s. The distance and the angle of the Vermon probe with respect to the pipe were set through a custom holder connected to a positioning system.  
\begin{table}
\begin{tabular}{c c} \hline
**Imaging/Flow/Processing Parameters** & **Value** \\ \hline Central transmission (TX) frequency (\(f_{0}\)) & 3.7 \(\ast\)–7 \(\ast\) MHz \\ Pulse repetition frequency (PRF) & 500 Hz \\ Pixel step (\(\delta_{z}\times\delta_{x}\times\delta_{y}\)) & (\(0.02\times 0.3\times 0.3\)) mm \\ \hline Beam-to-flow angles (\(\delta_{D}\), \(\delta_{A}\)) & [(90,0),(85,0),(80,0),(90,45),(85,45),(80,45)]\({}^{\circ}\) \\ Barycenter depth (\(z_{0}\)) & 16 mm \\ Peak velocity (\(\hat{v}_{max}\)) & 7.5 cm/s \\ \hline No. of frequencies \({}^{\$}\) (\(k_{r}\cdot k_{r}\)) & [3; 10] \\ z-axis bandwidth (\(B_{z}\)) & 0.74 \(\ast\)–1.4 \(\ast\) MHz \\ Kernel size (\(\mathrm{M_{B}\times N_{B}\times P_{B}}\)) & (\(1.5\times 2.5\times 2.5\)) mm \\ \hline \end{tabular} \({}^{\ast}\) Vermon probe \({}^{\ast\ast}\) Capacitive Micromachined Ultrasonic Transducer (CMUT) probe \({}^{\$}\) Refer to Appendix A.  
\end{table}
Table 1: Imaging, flow, and processing parameters.  
Figure 4: On the left, the ULA-OP 256 scanner with the Vermon probe used for the experiments. On the right, the schematic diagram of the flow phantom.
## 3 SMTRPM Design
### Hardware System  
The patient node is the most important entity in the hardware system. The patient node can sense, sample, and process one or more biometric signals. For this research, the initial hardware system includes prototype sensors to sense body temperature (BT) and heart rate (HR). These two variables are initialized manually by the nurse. The prototype SMTRPM measures only the BT and HR to demonstrate the concept of smart remote monitoring. However, more sensors could be added.  
Each patient node is based on an Arduino Uno, which is an open source microcontroller platform for electronic prototyping that provides a flexible interface between hardware and software [15]. The Arduino Uno platform is used to acquire, process, and transmit the biometric signals. Additionally, a WiFly shield (roving networks) is incorporated into each Arduino to provide WiFi communication; this capability is necessary for communication with other patient nodes, the nurse server, and the smartphone.  
The SMTRPM is a prototype that is expected to be used for hospitalized patients in PHIs. It is assumed that the patients' mobility within their rooms will be limited. The sink node (the Arduino Uno with the WiFly shield) is attached to the patient through a flexible band attached to the forearm, as shown in Figure 2. The sink node must be placed inside an antistatic bag and has the capability to operate with external 9 V batteries. Therefore, the SMTRPM allows the patient to be mobile within a room.
# Capacity Bounds for Dense Massive MIMO in a Line-of-Sight Propagation Environment
## Appendix D Approximated Distribution of the Total Power Gain of the Channel Matrix
The proof is concluded by replacing Equations (15) and (19) into the definitions of \(\kappa\) and \(\theta\). \[ \begin{split}\mathbb{E}[\text{C}_{\text{inst.}}^{2}]& =K\Bigg{\{}\mathbb{E}\left[\log_{2}^{2}\left(\frac{\rho M\eta}{d_ {k}^{2}}\right)\right]+(K-1)\mathbb{E}\left[\log_{2}\left(\frac{\rho M\eta}{d_ {k}^{2}}\right)\right]^{2}\Bigg{\}}\\ &=K\Bigg{\{}\mathbb{E}\left[\log_{2}^{2}\left(\frac{\rho M\eta}{d_ {k}^{2}}\right)\right]-\mathbb{E}\left[\log_{2}\left(\frac{\rho M\eta}{d_{k}^ {2}}\right)\right]^{2}\Bigg{\}}\\ &=\frac{K\left(\frac{\pi^{2}b+4a\ (R-cR-R_{min}+dR_{min})+8b+4 cR(c-2)-4d(d-2)R_{ min}}{b}-K^{2}\left(a+\frac{2(R-cR-R_{min}+dR_{min})}{b}\right)^{2} \right)}{\log^{2}(2)}.\end{split}  \tag{17}\]  
\[ \begin{split}\mathbb{E}\left[\log_{2}^{2}\left(\frac{\rho M\eta} {d_{k}^{2}}\right)\right]&=\int_{R_{min}}^{R}\log_{2}^{2}\left( \frac{\rho M\eta}{d_{k}^{2}}\right)\frac{1}{R-R_{min}}dr\\ &=\frac{a^{2}b+4a\ (R-cR-R_{min}+dR_{min})+8b+4 cR(c-2)-4d(d-2)R_{ min}}{b\log^{2}(2)}.\end{split}  \tag{18}\]  
\[ \begin{split}\sigma_{\text{C}_{\text{inst.}}}^{2}& =\frac{K\left(\frac{b\log^{2}(c)+4a\ (R-cR-R_{min}+dR_{min})+8b+4c(c-2)R-4d(d-2)R_{ min}}{b}-K(K+1)\left(a+\frac{2(R-cR-R_{min}+dR_{min})}{b}\right)^{2} \right)}{\log^{2}(2)}\end{split}  \tag{19}\]
## 5 Results
### Validation by Comparison with Experiments
#### 5.2.2 Results for Experiments Involving Rain  
As Figure 14 shows, the results of the simulation are comparable with the reference lidar results. For Livox and Aeye, the number of detected points is almost constant at 100% throughout the experiment, with the exception of an unexplainable outlier at 20 mm/h in the Livox experiment. The work in [11] just tested a precipitation rate of 55 mm/h for an unknown reason, and in this experiment, the mean detection rate of the points is around 100%. For precipitation rates above 100 mm/h, the results for the Aeye and Livox experiments are very different; the detection of the Livox lidar collapses to around 40%, as well as the outlier, whereas the detection of the Aeye is still around 100%. For these differences, the following root causes can be hypothesised:  
* The particle distribution for the rain in the Cerema test facility is not known. As shown in Figure 5, the extinction coefficient is very dependent on the actual particle distribution. The rain generator might not be constant enough to produce the exact needed particle distribution throughout its operating range and test volume.
* Raindrops could possibly flow down the lidar window, thus deflecting the path of the laser beam, causing the rate of detected points to drop. This could explain the Livox outlier at 20 mm/h and above 100 mm/h.
# Sillcom: A Communication-Efficient Privacy-Preserving Scheme for Indoor Localization
## 8 Evaluation Results
### Micro Benchmarks  
In this section, we present comprehensive benchmarking results across multiple parameter configurations.
## 4 Discussion
### Limitations  
Several limitations of this study should be considered. First, although we aimed to recruit a diverse sample of participants by following a recruitment matrix that considered demographic diversity, our study population was of limited age and gender diversity. All our patient participants were male, and most of the care partners were female. This sampling limitation is largely due to the demographic diversity imbalance within the VA population. The VA population has a higher median age compared to the general ESRD population (64 vs. 41 years old, respectively), and there is a far greater number of men than women (89% vs. 11%, respectively), and is predominantly white race (80%) [83, 84, 85]. In terms of the broader ESRD patient population, the disease disproportionately affects men more than women (60% vs. 40%, respectively), affects older individuals more than younger people (85% of ESRD patients are at least 45 years old), and affects black individuals more than individuals of other races (78.6% higher than the next group) [84]. Studies suggest that such disproportional distribution of ESRD between individuals may be linked to several factors including physiological differences such as gender hormones [86], socioeconomic status [87], and lifestyle-related factors that induce the risk of obesity, diabetes, and hypertension contributing to the disease development [88]. Given this information, the findings may not reflect the perspectives of all patients with ESRD and their care partners. Mobile hemodialysis experiences with bi-directional monitoring of patients' vital signs and disease symptoms may be different across different user populations. We acknowledge that our study population may represent minority groups in terms of race and level of education. We highlight the importance of overcoming challenges that may exist in establishing a mobile dialysis care program across different socioeconomic groups. Secondly, while this study explored both patients' and care partners' perspectives, it did not include clinicians' points of view. Clinicians' opinions are an important consideration when designing a medical device and designing and implementing new monitoring systems and procedures.
# A Stochastic Bulk Damage Model Based on Mohr-Coulomb Failure Criterion for Dynamic Rock Fracture
###### Abstract  
We present a stochastic bulk damage model for rock fracture. The decomposition of strain or stress tensor to its negative and positive parts is often used to drive damage and evaluate the effective stress tensor. However, they typically fail to correctly model rock fracture in compression. We propose a damage force model based on the Mohr-Coulomb failure criterion and an effective stress relation that remedy this problem. An evolution equation specifies the rate at which damage tends to its quasi-static limit. The relaxation time of the model introduces an intrinsic length scale for dynamic fracture and addresses the mesh sensitivity problem of earlier damage models. The ordinary differential form of the damage equation makes this remedy quite simple and enables capturing the loading rate sensitivity of strain-stress response. The asynchronous Spacetime Discontinuous Galerkin (aSDG) method is used for macroscopic simulations. To study the effect of rock inhomogeneity, the Karhunen-Loeve method is used to realize random fields for rock cohesion. It is shown that inhomogeneity greatly differentiates fracture patterns from those of a homogeneous rock, including the location of zones with maximum damage. Moreover, as the correlation length of the random field decreases, fracture patterns resemble angled-cracks observed in compressive rock fracture.  
Keywords:bulk damage; brittle fracture; rock fracture; random fracture; Mohr-Coulomb; Discontinuous Galerkin +
Footnote †: journal: Applied Physics Letters
# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 4 Experiment
### Evaluation Metric  
We specify mean absolute error (MAE) and root mean square error (RMSE) to measure the performance of the proposed approach and other baselines. Lower values for these two metrics suggest a more accurate prediction. They can be defined as follows:  
* Mean Absolute Error: MAE is the average of the absolute values of the deviations of all individual observations from the arithmetic mean. \[MAE=\frac{\sum_{u,s}\abs{Q_{u,s}-\hat{Q}_{u,s}}}{N}\] (9)
* Root Mean Square Error: RMSE indicates the deviation between the observed value and the truth. \[RMSE=\frac{\sum_{u,s}\big{(}Q_{u,s}-\hat{Q}_{u,s}\big{)}^{2}}{N}\] (10) where \(Q_{u,s}\) is the QoS value actually recorded by user \(u\) calling service \(s\), \(\hat{Q}_{u,s}\) is the predicted QoS value when user \(u\) calls service \(s\). \(N\) represents the size of the number in the test set.
## 3 Design Consideration on Radiation-Grafted AEM
### Radiation Source  
The source of radiations can be from electromagnetic radiations such as gamma (\(\gamma\))-ray, X-ray, ultraviolet (UV), or charged particles such as electrons and beta particles [13; 62]. The highly penetrative characteristic of radiation also allows it to introduce uniform and controllable active sites for grafting initiation, depending on the radiation species, dose rate, irradiation dose, properties of base polymer and monomer, as well as grafting environment. In addition, there are fewer shape and structure restrictions on the polymer matrix for radiation grafting, which enables it to be applied on preformed polymers without any post-shaping issues [77]. The electron beam and gamma (\(\gamma\))-ray are the two common sources used in radiation grafting. The electron beam is usually applied for the pre-irradiation method, where a high dose rate of electron beams is irradiated on the polymer backbone to produce free radicals. Meanwhile, gamma (\(\gamma\))-ray is preferably applied in the simultaneous irradiation grating because it is the most penetrating of electromagnetic waves among the rest, but the grafting time is relatively longer than that of the electron beam [74]. However, gamma(\(\gamma\))-ray offers much greater grafting efficiency by higher penetration [78]. Thus, the required grafting degrees are achievable at lower doses with gamma(\(\gamma\))-ray in comparison to the electron beam. Furthermore, gamma(\(\gamma\))-ray is more suitable for the preparation of grafted materials in large quantities for industrial enactment which relatively more economic, simpler, and robust. Irrespective of this, the selection of radiation source was still subjected to the targeted application and vital specification attached within the material functional scope. A membrane with a low ionic exchange capacity requires much lower grafting degrees merely on the surface of the polymeric materials. In these circumstances, the electron beam will be the most appropriate choice, as the penetration of electrons is considerably limited. While, contrastingly, gamma (\(\gamma\))-ray fits well for the preparation of membrane with a high ionic exchange capacity for conferring higher grafting degrees.
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 4 Observation and Results
### Comparison with Other Methods  
The model was trained and evaluated on the CICDDoS2019 dataset, which contains various types of DDoS attacks. The results obtained from the proposed hybrid model are shown in Table 3 and Figure 15, which show an accuracy of 0.9981, precision of 0.9996, recall of 0.999, and an F1 score of 0.9993. These results outperformed several existing models such as GRUs [13], CyDDoS [30], DDoSNet [31], Meta-classification [32], Convolutional Neural Networks [33], and Klamn backpropagation NN [34]. The accuracy of the proposed model was higher than all other models, including those that used deep learning techniques. The comparison with the existing models showed that the proposed hybrid model achieved superior performance due to the combination of different deep learning techniques, which enabled the model to capture both spatial and temporal information of the data. The model's ability to process data in multiple domains and its high sensitivity to anomalies allowed it to outperform the other models. The proposed model's performance was also enhanced by the use of the CICDDoS2019 dataset, which contains a large number of samples and a wide variety of DDoS attacks.  
The results of our evaluation demonstrate that our hybrid algorithm performs exceptionally well and meets our expectations. This is confirmed by the comparison conducted in the same dataset, which shows that the hybrid algorithm consistently outperforms the individual algorithm. Therefore, our hypothesis that the hybrid approach would be superior in most cases has been supported by the data.