esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Cloud Approach for Connected Vehicles
## 4 Analytic Model
### Message Dissemination
#### 4.2.1. RSU Distribution  
To be able to model the dissemination, we first have to determine how the RSUs are placed in the network. Figure 12 presents the different possible configurations for a segment.  
Figure 12b,c represent segments with only one RSU. Figure 12a represents a segment without any RSU, while Figure 12d represents a segment with two RSUs. Let us note that Figure 12b,c are actually the same.  
**Definition of \(X\)**  
We denote \(X_{C}\) as the variable indicating the presence or the absence of an RSU at a junction. \(X_{C}=1\) if there is an RSU and \(X_{C}=0\) else. Since a segment is composed by two junctions, we will work with \(X_{C}1\) and \(X_{C}2\). The probability for \(X_{C}\) to have an RSU is given by \(P(X_{C}=1)\) and the probability for \(X_{C}\) to not have an RSU is given by \(P(X_{C}=0)\). The probability of having an RSU on a junction follows the rule shown in Table 5.  
Figure 12: Possible Segment Configurations. (**a**) Configuration 1; (**b**) Configuration 2.1; (**c**) Configuration 2.2; (**d**) Configuration 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \(X_{c1}\) & \(X_{c2}\) & \(P(X_{c1})\) & \(P(X_{c2})\) \\ \hline Config. 1 & 0 & 0 & \(\frac{\varepsilon-\beta}{\varepsilon}\) & \(\frac{\varepsilon-\beta-1}{\varepsilon-1}\) \\ Config. 2.1 & 1 & 0 & \(\frac{\varepsilon}{\varepsilon}\) & \(\frac{\varepsilon-\beta}{\varepsilon-1}\) \\ Config. 2.2 & 0 & 1 & \(\frac{\varepsilon-\beta}{\varepsilon}\) & \(\frac{\beta}{\varepsilon-1}\) \\ Config. 3 & 1 & 1 & \(\frac{\beta}{\varepsilon}\) & \(\frac{\beta-1}{\varepsilon-1}\) \\ \hline \end{tabular}
\end{table}
Table 5: RSU Probability.  
**Definition of \(S_{X}\)**  
\(S_{0}\) is the probability for a segment to not have any RSU, \(S_{1}\) is the probability to have one, and \(S_{2}\) is the probability of having two RSUs. These probabilities are defined as follows:  
\[S_{0}=(\frac{\epsilon-\beta}{\epsilon})(\frac{\epsilon-1-\beta}{ \epsilon-1}) \tag{15a}\] \[S_{1}=2(\frac{\epsilon-\beta}{\epsilon})(\frac{\beta}{\epsilon-1})\] (15b) \[S_{2}=(\frac{\beta}{\epsilon})(\frac{\beta-1}{\epsilon-1}) \tag{15c}\]
# Seareed Exhibits Therapeutic Properties against Chronic Diseases: An Overview
## 5 Bioactive Compounds in Seaweed
### Phlorotannins  
Marine algae contain bioactive polyphenic molecules that can modulate biological properties, such as phlorotannins. Phlorotannins are tannin derivatives composed of phloroglucinol-based phenolics (1,3,5-trihydroxybenzene) that are synthesized through the acetate-malonate pathway [94]. They are thought to be the defense compounds in brown seaweeds with high concentrations (up to 25%), and are stored in special vesicles (physodes) [4]. A variety of phlorotannins has been discovered, including eckol, phlorofucoflueroeckol A, dieckol, 6,6-bieckol, 8,8-bieckol, 7-phlorocockol, fucodiphlorotheoth G, phloroglucinol, and bifuhalol [96; 97; 98; 99]. These compounds exhibit a variety of biological activities, including high antioxidant activity [100]; inhibition of \(\alpha\)-amylase, \(\alpha\)-glucosidase, and lipase, which are key enzymes in obesity and diabetes control [96; 101; 102]; and neuroprotective activities [97; 98]. Phlorotannins were found to be non-toxic in cell lines, invertebrates, microalgae, seaweeds, plants, animals (fish, mice, rats, and dogs), and humans [99].
# Multi-formalism Models for Performance Engineering
## 3 Queueing Networks Techniques
### Timeout with Quorum based Join
#### 3.1.4 Model results  
We start considering requests arriving at rate \(\lambda=1.8\) r/s, and a timeout \(T=10\)s. It is interesting to compare the behavior denoted on the right of Figure 3, where the spikes generated by the services are not depending on a timeout variable and the scenario shown on the left, where the timeout is cutting the service times of the requests exceeding it.  
This effect is further investigated in Figure 4, where the response time distribution for different number \(K\) of VMs, ranging from 1 to 3, and an arrival rate of \(\lambda=0.9\) r/s is presented. Although for \(K=1\) the system is stable and characterized by an average utilization \(U=0.9\), most of the requests will undergo a timeout (the steep rises at time \(t=10\)s): users are then either served very quickly or quit the system otherwise. With \(K=3\), timeout almost never occurs, while \(K=2\) seems to be a reasonable tradeoff between deployment cost and user experience, where most of the requests are served on time, and only less than 10% are subject to timeouts.  
Figure 4: Distributions of response time with Timeout=10 s and \(\lambda=0.9\) r/s for a variable number \(K\) of VMs.  
Figure 3: Response times with (left) and without (right) timeout of 10 s, for \(\lambda=1.8\) r/s and \(K=2\) VMs.  
Figure 2: Model of a cloud application with user’s timeout.  
The effect of the deployment on a different number of VMs is then investigated in Figure 5, for increasing timeout durations. With only one VM (\(K=1\)), the users are almost always subject to timeouts, and its value basically defines the time requests will take before being discarded due to abandonment. Parallel deployments (\(K=2\) and \(K=3\)) are instead only marginally influenced by timeouts, since, in any case, most of the users will be served before the event occurs.  
Finally, Figure 6 studies the trade-off user satisfaction and deployment cost, by defining a metric: \(cost=1-U+P(Timeout)\) where \(U\) is the average utilization of the VMs and \(P(Timeout)\) is the probability that a timeout event occurs, as identified by the two loggers. When the system is deployed on a small number of VMs, the utilization is very high (hence, \(1-U\) is close to zero), but also the timeout probability is close to \(P(Timeout)\approx 1\). Conversely, when the system is deployed on a large number \(K\) of VMs, the time probability is close to \(P(Timeout)\approx 0\), but also the utilization is very low, making \(1-U\approx 1\). The best configuration of the system is thus the one that minimizes this cost. For an arrival rate of \(\lambda=0.9\) r/s, this occurs at \(K=2\), while, for \(\lambda=1.8\) r/s and \(\lambda=2.8\) r/s, at \(K=3\) and \(K=4\), respectively. Note that, in these cases, respectively, \(K=1\) and \(K=2\) could not have been used since in such circumstances the system would have not been stable. For \(\lambda=0.5\) r/s, the best solution seems to be \(K=1\), since having a second VM does not sufficiently decrease the timeout probability.
## 4 Numerical Analysis and Failure Mechanism of a Single-Crack Specim  
### Numerical Model and Parameter Analysis  
The stress field and stress intensity factor of a simulated single-crack concrete specimen were analyzed using the finite element program ABAQUS. The extended FEM was used to simulate the fracture propagation of a single-crack concrete sample. The two-dimensional computational model of the concrete sample (Figure 5) consists of 3040 grid elements. The mechanical parameters of the simulated material are listed in Table 4.  
Figure 5 shows the stress nephogram of specimen No. 9 under normal conditions. Table 5 lists the changes in the maximum circumferential tensile stress and radial shear stress at the crack tip for the specimens. The initiation angles obtained in the experimental tests and numerical simulations are shown in Table 6. Figure 6 compares the stress-strain relationships between the numerical and experimental results. The numerical solution shows a linear relationship before the peak strength. However, the stress-strain from the experiments is nonlinear and experiences compaction at the early loading stage. The numerical method can simplify complex stress-strain relations of cracked concretes. Figure 7 shows the computational results of the fracture propagation of the single-crack concrete sample. The SIF at the crack tip changes the pre-crack angle. The SIF at the crack tip becomes high and then low with the pre-crack angle. The SIF arrives at the maximum value when the pre-crack angle is 45\({}^{\circ}\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & \
\begin{tabular}{c} **Elastic Modulus** \\ **E (GPa)** \\ \end{tabular} & **Friction Coefficient \(u\)** &
\begin{tabular}{c} **Poisson’s Ratio** \\ \(\mathbf{v}\) \\ \end{tabular} \\ \hline Concrete & 1.354 & 0.12 & 0.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mechanical parameters used in numerical modeling. Figure 5: Numerical model of single-crack concrete sample with \(\beta\) = 45\({}^{\circ}\) and \(d\) = 3.0 mm. (**a**) Numerical model of single-crack sample; (**b**) Stress distribution of single-crack. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & **Crack Angle \(\beta\)** & \begin{tabular}{c} **The Initiation Angles** \\ **of Experimental Work** \\ **(\({}^{\circ}\))** \\ \end{tabular} & \begin{tabular}{c} **The Initiation Angles** \\ **by Numerical** \\ **Simulation (\({}^{\circ}\))** \\ \end{tabular} & \
\begin{tabular}{c} **The Direction of the** \\ **Maximum** \\ **Circumferential** \\ **Stress (\({}^{\circ}\))** \\ \end{tabular} &
\begin{tabular}{c} **The Direction of the** \\ **Maximum** \\ **Shear Stress (\({}^{\circ}\))** \\ \end{tabular} \\ \hline
1 & 15 & 11 & 13 & 13 & 0 \\
2 & 30 & 47 & 47 & 0 \\
3 & 45 & 68 & 63 & 0 \\
4 & 60 & 73 & 66 & 65 & 0 \\
5 & 75 & 69 & 70 & 73 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Initiation angles derived from experimental tests and numerical simulation.
## 1 Introduction  
Exercise immunology, the science that studies the effects of exercise on the immune system, is an emerging field that is gaining increasing attention in the field of oncology physiotherapy [1; 2]. Historically, physiotherapists working with patients with cancer have primarily focused on physical functioning. Over time, the role of the physiotherapist has broadened towards a movement specialist who also has a role in improving general health. Research has shown that exercise can have significant immunological effects, which can be an underlying mechanism for many health outcomes, although this area of research remains relatively underexplored [2; 3; 4].  
The immunological effects of exercise are dependent on the type and workload of the training [2]. This implies that physiotherapists can differentially influence immune functioning (IF) by adjusting the training's frequency, intensity, timing, and type (also known as FITT parameters). This is particularly relevant for physiotherapists working in oncological rehabilitation, i.e., the multidisciplinary approach aimed at helping individuals with cancer to manage and recover from the impairments caused by the disease and its treatments [5]. Since cancer treatments often have immunosuppressive effects, the IF of these patients can be more vulnerable. This means that oncology physiotherapists face the challenge of finding the right balance regarding physically challenging patients without overreaching. Knowledge regarding the optimal FITT parameters to support IF would therefore contribute to optimization of physiotherapy treatment.  
With a growing field of research regarding exercise immunology in oncology, the question arises if and how this knowledge can be incorporated into the oncology physiotherapy practice. Current guidelines do not provide guidance on this yet [6; 7]. This lack of direction presents clinical challenges, such as uncertainty in how to tailor exercise interventions to support immune function. To integrate this subject into the clinical reasoning of oncology physiotherapists, it is essential to understand the current role of IF in oncology physiotherapy. Additionally, physiotherapists' thoughts on this subject are crucial. Here, IF is defined by the authors as the dynamic activity of the immune system, encompassing both innate and adaptive mechanisms. It consists of a complex network of cells, tissues and organs that protect the body from harmful internal and external influences, with the aim of maintaining homeostasis and promote recovery [8; 9]. This study aimed to assess the perspectives of oncology physiotherapists on IF in oncology physiotherapy.
## 5 Results
### Validation of Theory, Simulations and Flatsat Results
(**d**) Complex chirp (300 kHz wide, 5 s long, and period 16.67 ms) transmitted. onto the RF carrier at 435 MHz, without shifting it 100 kHz, so that it could fit in the 3 dB bandwidth of the receiver. To detect at which time window a signal becomes stationary, the time statistics of the moments can be analysed. In Figure 4, the normalised LME second moment, for different signals, is shown. It compares theoretical results (blue dashed lines), simulation results for all frequency bins (blue circles), and flatsat results (all frequency bins in black asterisks, and the bin with the highest power with a red cross). It shows that the time variability of the local averages decreases when the window size increases. In the AWGN case (Figure 4a), the normalised second moment for LME decreases following the result in Equation (A13) and converges to \(\pi/4\) after some time for all frequency bins. For the CW (Figure 4b), the values are stable and do not change significantly with the time windows since a CW is a stationary signal at all time scales for the bin with the carrier, which is the bin with the highest power. The rest of the bins are the sidelobes of the signal. The lower the sidelobes, the lower signal to noise ratio, and the signal approaches the properties of an AWGN. The pulsed signal curve (Figure 4c) changes the behaviour considerably, depending on whether the time window is smaller than the pulse length (\(T_{p}\)) and/or the pulse repetition period (\(T_{r}\)). When the time window is larger than the pulse repetition period, the curve flattens out. The last two data points in the curve correspond to 13.6 ms and 27.3 ms, and, since the curve is converging, it indicates that the pulse repetition period is in that  
Figure 4: Comparison of the normalised LME second moment in theory, simulations and measurements with the flatsat. The pulse and the chirp signal have a pulse length of 5 s and a period of 16.67 ms. (**a**) Normalised Local Mean Estimator (LME) second moment for AWGN. (**b**) Normalised LME second moment for a CW. (**c**) Normalised LME second moment for a complex pulse. (**d**) Normalised LME second moment for a complex chirp. range. The indication in the graph is validated since the pulse repetition period is 16.67 ms. To be able to obtain a better estimation of the period, more time windows would be needed. In Figure 5, the difference between the values of consecutive windows normalised is shown. For the AWGN, the curve has a decreasing exponential trend, and, for the CW, the values are zero for the highest frequency bin. There should not be any variation between time windows for a continuous signal. In the case of the pulsed signal, the peak is in between the fifth (\(T_{5}=3.4\) ms) and sixth point (\(T_{6}=6.8\) ms). Since the pulse transmitted was 5 s long, the curve is as expected. Furthermore, all theoretical, simulation, and flatsat values in Figure 5 are similar, validating the algorithm.
## 2 Materials and Methods
### Live/Dead Differentiation of Campylobacter in Meat Rinse Samples  
For live/dead differentiation of _Campylobacter_ using ddPCR or qPCR, meat rinses were divided into two aliquots, each with a volume of 1 mL. One of these aliquots was treated with PMA dye (Biotin, Fremont, CA, USA) at a final concentration of 50 \(\upmu\)M. This aliquot was incubated for 15 min at 700 rpm at 30 \({}^{\circ}\)C in the dark by covering the incubator with aluminum foil. Subsequently, it was transferred to phAST Blue equipment (genIUL instruments, Terrassa, Spain) for 15 min light exposure. This procedure was performed to cross-link PMA to DNA in dead bacteria at room temperature. Following that, both the PMA treated and the untreated aliquots were centrifuged at 16.000\(\times\)_g_ for 5 min at 4 \({}^{\circ}\)C. The supernatant was discarded and the cell pellet was either directly subjected to DNA extraction or frozen at \(-\)20 \({}^{\circ}\)C until DNA extraction.  
The total amount of _Campylobacter_, including live- and dead-cells, was determined from the aliquot without PMA treatment, and it served as control for the PMA efficiency in reducing the dead cell signal. Only the results from PMA treatment were directly comparable to the results from the classical microbiological method. This enabled the quantification of the number of viable _Campylobacter_ cells. For every sample, the dilution with the highest viable _Campylobacter_ count was considered as the final result, considering different factors, including initial dilution of the sample in peptone water, elution volume for DNA extraction, total reaction volume, and the DNA volume used in ddPCR. All results were converted into log\({}_{10}\). _Campylobacter_ positive results were interpreted in two different categories: above the limit of PHC (log\({}_{10}\) 3.0 live counts/mL) and below the limit of PHC.
# Improved Visual SLAM Algorithm Based on Dynamic Scenes
## 2 Related Work
### Deep Learning-Based Feature Extraction  
Recent research has highlighted how deep learning can enhance various elements of SLAM, including feature extraction, loop detection, and mapping.  
Traditional feature extraction methods perform feature analysis based on local image regions, detecting key points with significant changes or unique characteristics. SLAM systems have extensively utilized techniques like SIFT and SURF. However, these methods still lack stability and are unable to meet the requirements of certain complex scenarios. The core of deep learning-based feature extraction methods is to effectively learn hierarchical feature representations of images through convolutional neural networks (CNNs). By employing multiple layers of convolution and pooling operations, these methods progressively extract features from low levels to high levels, ranging from edges and textures to more complex semantic features such as SuperPoint [18].  
CNN-SLAM [19] replaced the traditional feature point matching method in LSD-SLAM with a CNN-based approach, achieving more robust results. In 2017, [PERSON] et al.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequences** & **Image Segmentation** & **Object Detection** \\ \hline Example algorithm & SegNet, Mask R-CNN & YOLO, SSD \\ Advantages & Accurate dynamic feature & Good real-time performance \\  & point rejection & Static feature points are \\ Disadvantages & Poor real-time performance & over-rejected \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of common methods for dynamic SLAM algorithms.  
introduced the Flowodometry [20] algorithm, which uses CNNs to estimate motion poses from sequences of optical flow images. SuperPoint is a compact feature extraction technique that leverages an end-to-end trained convolutional neural network for detecting and describing feature points. In 2018, [PERSON] et al. [21] proposed the geometric correspondence network (GCN), which combines a convolutional neural network (CNN) for extracting shallow features with a bidirectional recurrent convolutional neural network (RCNN) [22] for extracting deep features, enabling the model to capture complex pixel relationships and generate semantically meaningful feature representations. The following year, [PERSON] et al. improved upon GCN and proposed GCNv2, which is based on a low-resolution generation strategy. By reducing image resolution, GCNv2 increases the number of channels that capture more feature information, significantly improving its efficiency in key point detection and descriptor generation.
# Simultaneous Measurement of Refractive Index and Flow Rate Using a Co\({}^{2+}\)-Doped Microfiber
## 1 Introduction  
In recent decades, on the basis of the technology developments of femtosecond laser processing [1, 2], CO\({}_{2}\) laser heating [3, 4], chemical etching [5, 6, 7, 8], and flame heating [9, 10], various microfiber fabrication methods have been proposed. Microfiber-based sensors often have many important advantages, such as good flexibility, mechanical strength, compact size, quick response and high sensitivity [11, 12, 13], so they have many good applications, such as biological applications [8, 14, 15, 16] and chemical analysis [12, 17].  
Liquid refractive index (LRI) measurement plays an important role in chemical [12], biological [14, 15] and other applications. Various microfiber-based LRI sensors have been proposed, as they have high sensitivity, a robust structure, and have many applications for biosensors, chemical sensors, etc. [11]. Previous scientific research studies include fiber-optic interferometers [3, 10, 18], tilted fiber Bragg gratings (TFBGs) [14], long-period gratings (LPGs) [15], and many kinds of microfiber Bragg gratings (MFBGs) [1, 19]. All of these previous studies bring us many useful microfiber-based sensors with specific designs.  
The liquid flow rate (LFR) measurement is also a paramount index for many applications, such as medical and biological analysis [20]. In the application of optical fiber biosensors, due to the herringbone microfluidic chip's capture efficiency being highly dependent on the flow rate, its accuracy and efficiency can be significantly improved by the measurement of flow rate [21, 22].  
\"Hot-wire\" is the most famous microfiber flowmeter [23, 24, 25]. For many applications, not only is the LFR needed, but also other parameters, such as LRI and temperature [19]. Sometimes simultaneous measurements may be necessary, and the measurement parameters should not be cross-impacted.  
In this paper, based on the Co\({}^{2+}\)-doped microfiber Bragg grating sensor (Co-MFBGS), an integrated LRI and LFR sensing sensor has been presented and experimentally demonstrated, which can measure both parameters simultaneously. The Co-MFBGS reflection spectrum provides a well-defined core mode resonance as well as two cladding mode resonances; the wavelength shifts of two kinds of resonances have quadratic non-linear relationship to LRI and LFR. The temperature cross-impact to the LRI measurement can be eliminated by monitoring the wavelength interval change between the core mode and cladding mode. Additionally, due to the non-radiation effect of Co\({}^{2+}\)-doped fiber, the Co-MFBGS can be heated by a pump laser; by interrogating the core mode's wavelength shift caused by the heat exchange effect, the LFR can be measured. The proposed integrated optical fiber sensor has potential value in chemical, medical and environmental applications.