esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
However, rarely these methods alone offer the opportunity to cope with the complete segmentation task and are usually complemented with pre- and post-processing procedure to overcome limitations of hand-craft features and improve spatial consistency in classification results. In line with this general perspective, the first objective of this study was to investigate whether BM segmentation may be approached successfully by two supervised ML classifiers belonging to feature-based and deep learning approaches, respectively. SVM and V-Net Convolutional Neural Network model [34] are selected from the literature as representative of the two approaches. Their performances, already assessed in several biomedical image segmentation studies, were evaluated and compared under critical conditions imposed by specific clinical radiotherapy domain which requires fast segmentation of multiple lesions of small size on single T1c magnetic resonance image. A second objective of the present work was the design of a software package implementing an integrated set of procedures for active support in BM segmentation within the radiotherapy workflow. It was designed to be user-friendly, with a right trade-off between automation and interactivity. The solutions investigated in this paper are an extension of those presented in a previous work [35] from which we inherit the idea of using segmentation procedures based on SVM. In the present work, experiments have been extended by using an enlarged dataset and by developing and evaluating a second segmentation procedure based on deep learning approach. The remainder of the paper is organised as follows. The next section illustrates previous works. Section 2 introduces the conceptual segmentation frameworks based on SVM and V-Net model, respectively. Section 3 describes the experimental evaluation and comparison of their performances using in-house collected datasets of different size. Section 4 illustrates the main features of the software package and the use of the system in clinical studies. Section 5 reports the discussion and conclusions.
## 3 Experimental Program
### Experimental Test
#### 3.3.3 Ultrasonic Pulse Velocity Method (UPVM)  
Ultrasonic Pulse Velocity Method (UPVM) uses the relationship between travel length (\(L\)) and time (\(T\)) of an ultrasonic pulse wave. The measurement is made using a transmitter and receiver. Evidently, less faults inside the concrete results in a high speed of the UP, whereas more faults (caused  
Figure 1: Non-destructive test for the concrete cylinder specimen. (**a**) Rebound hammer test; (**b**) Direct ultrasonic pulse velocity method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Mix** & \(f_{c}\) & **W/B** & **S/a** & **Water** & **Cement** & **Sand** & **Gravel** & **Fly-Ash** & **Silica** & **AE a** \\
**Types** & **(MPa)** & **(\%)** & **(\%)** & **(\%)** & **Water** & **Cement** & **Sand** & **Gravel** & **Fly-Ash** & **Fume** & **AE a** \\ \hline Mix 1 & 40 & 38.5 & 52 & 160 & 374 & 358 & 537 & 849 & 42 & - & 3.74 \\ Mix 2 & 60 & 31 & 51 & 165 & 479 & 328 & 492 & 809 & 53 & - & 4.52 \\ Mix 3 & 100 & 21 & 46.5 & 165 & 613 & 189 & 440 & 743 & 79 & 94 & 14.93 \\ \hline \end{tabular}  
a air-entraining admixture.  
\end{table}
Table 1: Mix proportions of concrete (unit: kg/m\({}^{3}\)).  
by pores, cracks or poor quality) reduces the UPV. Ultrasonic velocity is affected by the choice of measuring type such as the direct or indirect method. Indirect UPVM is usually used for in situ measurements and a calibration factor of 1.05 between direct and indirect UPVM was employed in a previous study [31]. The BS 1881-203 (1986) [32] suggests that the effect of steel reinforcement bars occurs parallel to direction of the pulse path. For \(a/L\), where \(a\) is the concrete cover and \(L\) is the length of the ultrasonic pulse path, the influence of the reinforcement becomes negligible when \(a/L\) reaches 0.2 to 0.25. In case where \(a/L\) is under 0.2, Equation (1) is employed to correct the influence of steel reinforcement on the ultrasonic pulse velocity. In this study, direct UPVM was employed to measure the concrete cylinders and core specimens as shown in Figure 1b. UPVM was conducted three times for each cylinder specimen and the data was averaged to give the representative value.  
\[T=\frac{L}{V_{s}}+2a\sqrt{\frac{V_{s}^{2}-V_{c}^{2}}{V_{s}V_{c}}} \tag{1}\]  
where,  
\(V_{c}=\) ultrasonic pulse velocity in the plain concrete  
\(V_{s}=\) ultrasonic pulse velocity in the steel
## 2 RDFTA (Repairable Dynamic Fault Tree Analysis)
### CSP Logic Gate of RDFTA  
\(S_{x(i)}(t)\) represents the normal working probability of the CSP gate input event \(x_{i}(t)\) at time \(t\), \(F_{x(i)}(t)\) is the failure state probability of the CSP gate input event \(x_{i}(t)\) at time \(t\), \(\mu_{x(i)}\) is the maintenance rate of the input event \(x_{i}(t)\), and \(\lambda_{x(i)}\) is the failure rate of the input event \(x_{i}(t)(i=2)\). \(S_{y}(t)\) represents the normal working probability of the CSP gate output event \(y(t)\) at time \(t\), \(\mu_{y}\) is the maintenance rate of the output event \(y(t)\), and \(\lambda_{y}\) is the failure rate of the output event \(y(t)\).  
A conversion rate matrix can be derived from Figure 3 as follows:  
\[A=\begin{bmatrix}-\lambda&\lambda&0\\ \mu&-\lambda-\mu&\lambda\\ 0&\mu&-\mu\end{bmatrix}. \tag{4}\]  
According to the Formula State Matrix (4), the differential Equation (5) can be solved:  
\[\left\{\begin{array}{l}\left(P_{0}^{\prime}(t),P_{1}^{\prime}(t),P_{2}^{ \prime}(t)\right)=\left(P_{0}(t),P_{1}(t),P_{2}(t)\right)A\\ \left(P_{0}(0),P_{1}(0),P_{2}(0)\right)=\left(1,0,0\right)\end{array}\right., \tag{5}\]  
where \(P_{0}^{\prime}(t),P_{1}^{\prime}(t),P_{2}^{\prime}(t)\) is the derivative of \(P_{0}(t),P_{1}(t),P_{2}(t)\).  
As can be seen from Figure 3, \(S_{y}(t)=P_{0}(t)+P_{1}(t)\).  
1. Quantitative operation rule  
In the CSP logic gate, the input event \(x_{i}(t)(i=1,2,\ldots,n)\) and output event \(y(t)\) have two states: state \(0\) is the normal working state, while state \(1\) is the fault state. According to the relationship between the input and output events \(x_{i}(t)\) and \(y(t)\), a list of the quantitative operation rules of the CSP gate can be obtained, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & \(x_{1}(t)_{1}\) & \(x_{2}(t)\) & \(\ldots\) & \(x_{n}(t)\) & \(y(t)\) \\ \hline \(1\) & \(0\) & \(0\) & \(\ldots\) & \(0\) & \(0\) \\ \(2\) & \(0\) & \(1\) & \(\ldots\) & \(1\) & \(1\) \\ \(3\) & \(1\) &The quantitative calculation formula of the CSP gate is as follows:  
\[\left\{\begin{aligned} S_{y}(t)=1-P_{2}(t)=\frac{\lambda\mu+\mu^{2}}{ \lambda^{2}+\lambda\mu+\mu^{2}}-\frac{\lambda^{2}\big{(}s_{2}e^{s_{1}t}-s_{1}e^ {s_{2}t}\big{)}}{s_{1}s_{2}(s_{1}-s_{2})}\\ F_{y}(t)=\frac{\lambda^{2}}{s_{1}s_{2}}+\frac{\lambda^{2}}{s_{1}(s_{1} -s_{2})}e^{s_{1}t}+\frac{\lambda^{2}}{s_{2}(s_{2}-s_{1})}e^{s_{2}t}\\ \lambda_{y}=\sum\limits_{i=2}^{n}\lambda_{i}\\ \mu_{y}(t)=\frac{\lambda_{y}\times S_{y}(t)}{\big{(}1-S_{y}(t) \big{)}}\end{aligned}\right. \tag{6}\]  
where \(\Delta(s)=s^{2}+2(\lambda+\mu)s+\big{(}\lambda^{2}+\lambda\mu+\mu^{2}\big{)}\), \(s_{1},s_{2}\) are the two roots of \(\Delta(s)=0\), and \(s_{1},s_{2}=-(\lambda+\mu)\pm\sqrt{\lambda\mu}<0\).
## 2 Materials and Methods
### Physicochemical and Geochemical Characterization  
The pH value was measured by a pH meter (PE28-Standard, Mettler Toledo, Shanghai, China) at a solid-liquid ratio of 1:2.5. Organic carbon content (Corg) in the samples was analyzed by potassium dichromate oxidation [34; 35]. Total nitrogen content (TN) was determined using an elemental analyzer (EA300, EuroVector, Milano, Italy). Total phosphorus (TP) content was determined by the molybdenum blue colorimetric method (UV3200, Shimadzu, Japan). Aluminum oxide (Al\({}_{2}\)O\({}_{3}\)), calcium oxide (CaO), ferric oxide (Fe\({}_{2}\)O\({}_{3}\)), silicon dioxide (SiO\({}_{2}\)), magnesium oxide (MgO), sodium oxide (Na\({}_{2}\)O), and potassium oxide (K\({}_{2}\)O) contents of the samples were determined by X-ray fluorescence spectrometer (XRF, Axios Max, PANalytical, Almelo, The Netherlands).  
Figure 1: Locations of the study area and samples collected in this study. Abbreviations: US = unremediated smeling slag: RS5 = smelting slag heap remediated after 5 years; RS10 = smelting slag heap remediated after 10 years; UT = unremediated tailings; RT10 = tailings piles remediated after 10 years.
## 4 Results and Discussion
### Actual Energy Efficiency
It means that reserves are within the following domains: the optimal deadweight and type of ships, the excluding empty voyages, the enhancing of load factor, etc. Ship-owners should optimize types of vessels and their dead weight. Barges may be a suitable type of vessel in national conditions. Sppers and ship-owners are both interested in excluding empty trips. It allows shippers to cut transport costs and ship-owners to increase profit. It is quite natural that, in this case, the energy consumption for cargo transportation is reduced. The enhancing of load factor is a function of the following: deadweight, type of vessel, waterway depth, and logistics. Dredging of waterways is the responsibility of authorities and businesses. Appropriate ships and logistics are the responsibility of shippers and ship-owners. Shipping companies are looking for more energy-effective and cost-effective solutions for navigations [52; 53; 54]. The e-Navigation concept may be a solution. This concept has been developed by the International Maritime Organization (IMO) and the International Association of Marine Aids to Navigation and Lighthouse Authorities (IALA) [55]. It helps to ensure the efficiency and safety of inland waterway traffic [56; 57].
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 7 BackCom: Range Extension and Reliability
### Body Area Sensor Networks Lsing BackCom  
A body area sensor network enables the connectivity of on-body sensing devices for the measurement of different body parameters. Enabling BackCom for on-body sensors allow communication on the principle of backscattering, between body-mounted sensing tags and commercial WiFi and Bluetooth radios [138]. Figure 46 shows a Google lens as an example of a body area sensor, which uses BackCom to transmit data. This was invented by Google in 2014. This lens can measure glucose level from tears [10]. These type of devices are useful for monitoring diabetic patients for the timely injection of insulin. Other applications include the monitoring of cardiac patients in case of cardiac arrest. Furthermore, secure on-body BackCom based on signature was proposed for on-body propagation without any change in hardware [209]. [PERSON] et al. suggested ReMIX, which shows deep tissue BackCom and localization [210]. ReMIX utilizes circuit non-linearities to avoid interference by shifting the frequency of reflected signals.  
Figure 45: BackCom-based interactive clothing.  
Figure 46: BackCom-based lens.
## 3 Materials and Methods
### Object Tracking Algorithm  
It is indispensable to implement a lightweight, straightforward, and effective tracking algorithm. The main reason is that object detection is a computationally expensive part of the entire process. One of the straightforward algorithms to track detected objects is to calculate the distance between center points of the current and previous rectangle or intersection between them. Direction detection is the product of tracking information. It is based on analyzing trajectory and calculation of the difference between center points.  
One of the main aspects is to maintain the information of the vehicle that moves in the ROI and make sure that it is the same and not another vehicle. The entire process is based on object detection. Therefore, this operation has to be stable in terms of confidence score. The feature should not have large deviations. Many algorithms currently exist. It holds true that the better the algorithm, the more computationally demanding it is. One of the priorities we consider is computational ease. Thus, object detection has the highest priority in terms of maximum load on the resources. The other load should be minimized as much as possible. For this reason, we propose an algorithm based _on distance_ and _intersection area_ calculation. The entire process depicts Figure 4.  
Object detection produces bounding boxes in the form of a rectangular shape. The goal is to compute two substantial values:  
1. _Calculation of intersecting area_--This includes the calculation of the intersecting area of the valid values between the bounding box of the current frame and the previous frame. Detection loss is marked as an invalid value. Only intersecting rectangles are considered to be the same. On the contrary, deformed bounding boxes by overlapping are not marked as identical;
2. _Calculation of distance_--Calculation of the Euclidean distance of the valid values between the center points of a bounding box of the current frame and the previous frame. Vehicles are identical when the distance between their center points is less than the determined value. We have to bear in mind that it may be difficult to determine the appropriate value. Therefore, the intersecting area is implemented, which is not subject to deformation.  
In addition, we set the size of the tracking history. Once the position update of monitored objects occurs, we add a new object based on the above criteria, and such a candidate has to appear at least in the two frames.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 3 Microarchitectural Side Channel
Another mitigation technique for preemptive scheduling and SMT side channels involves refreshing the microarchitectural state during context switches to clear any potential traces left by the victim. For example, several works have proposed CPU cache refreshing during context switches [49; 52; 53]. **Decoupling** involves eliminating the correlation between secret data and the observable leaked information. For widely prevalent timing side channels, the most general mitigation strategy is the application of constant-time programming principles [54; 55; 56]. This approach ensures that the execution time of instruction sequences remains consistent, regardless of the different microarchitectural states or execution paths they may take. However, all these mitigations effectively reverse certain microarchitectural optimizations, inevitably leading to some degree of performance degradation. Thus, the existing defenses against microarchitectural side channels represent a trade-off between efficiency and security.
_applied_  
_sciences_  
_Article_  
\(\mathbf{k_{f}}\) Evaluation in GFRP Composites by Thermography  
**[PERSON] \({}^{1,\star}\), [PERSON] \({}^{1}\)\({}^{\copyright}\) and [PERSON] \({}^{2}\)\({}^{\copyright}\)**  
\({}^{1}\) Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; [EMAIL_ADDRESS]  
\({}^{2}\) ENEA, Centro Ricerche di Brindisi, c/o Cittadella della Ricerca S.S.7 Appia km 706, 72100 Brindisi, Italy; [EMAIL_ADDRESS]  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]  
**Abstract:** Since the presence of a notch in a mechanical component causes a reduction in the fatigue strength, it is important to know the \(\mathbf{k_{f}}\) value for a given notch geometry and material. This parameter is fundamental in the fatigue design of aeronautical components that are mainly made of composites. \(\mathbf{k_{f}}\) is available in the literature for numerous types of notch but only for traditional materials such as metals. This paper presents a new practice, based on thermographic data, for the determination of the fatigue notch coefficient \(\mathbf{k_{f}}\) in composite notched specimens. The innovative aspect of this study is therefore to propose the application on composite materials of a new thermographic procedure to determine \(\mathbf{k_{f}}\) for several notch geometries: circular, U and V soft and severe notches. It was calculated, for each type of notch, as the ratio between the fatigue limits obtained on the cold and hot zone corresponding to the smooth and notched specimen, respectively. Consequently, this research activity provides, for the first time, a little database of \(\mathbf{k_{f}}\) for two particular typologies of composite materials showing a fast way to collect further values for different laminates and notch geometries.  
**Keywords:** composites; fatigue; geometric effect; notch sensitivity; thermography
## 4 Results and Discussion
### Training and Testing Time  
Figure 9 shows the variations of ARF and KNN models in terms of total running time, which include both training time and testing time.  
It is observed that ARF requires an average time of 56.53 s for training and testing, whereas the average training and testing time for KNN is 28.43 s.  
These findings can be attributed to the fact that the two algorithms have fundamental differences. KNN relies on relatively simple techniques based on determining the K-Nearest Neighbors and assigning the most common class label as the new label for the data point [30]. ARFs, on the other hand, are ensembles of decision trees that combine  
Figure 9: Training and testing time for ARF and KNN.  
decisions from various trees and aggregate their results to obtain the final output [6, 11]. Consequently, ARF might require higher training and testing time than KNN.
## 3 Results and Discussion
### PXRF (Portable X-ray Fluorescence): Spatial Survey
& 70.8 & 68.8 & 16.7 & 84.9 & 35.8 & 108.0 & 91.8 & 38.1 & 7.8 & 10.5 & 8.7 & 7.7 & 7.9 & 78.2 & 13.8 & 10.2 & 8.2 & 35.0 & 10.1 & 13.7 \\ \% & & & & & & & & & & & & & & & & & & & \\ Skewness & 1.9 & 1.5 & 0.9 & 1.9 & 1.2 & 1.5 & 2.5 & 0.9 & \(-\)0.7 & \(-\)0.5 & \(-\)0.9 & \(-\)1.2 & \(-\)0.2 & 1.2 & \(-\)0.3 & \(-\)1.0 & \(-\)0.8 & 4.5 & 1.2 & \(-\)0.9 \\ Kurtosis & 3.8 & 2.2 & 0.9 & 3.7 & 1.2 & 2.1 & 9.5 surface (i.e., higher CI values), the more anthropized/contaminated the soil itself. However, generally stronger correlation coefficients were found between \(\gamma\)-rays dose rate and the content levels of several PTEs measured in the depth of 0-10 cm (Table S1A), including Pb (\(-\)0.372 **), Cu (\(-\)0.228 **), Zn (\(-\)0.265 *) and As (\(-\)0.308 *). In the next depth, 10-45 cm, higher correlations were found between \(\gamma\)-rays and some PTEs, such as Zn (\(-\)0.374 **) and Cu (\(-\)0.315 **), while a new correlation was found with Ni (\(-\)0.242 *) (Table S1B). The opposite trends between \(\gamma\)-rays dose rate and Pb, Zn, Cu, As and Ni were clearly due to the allochthonous source of these elements, mainly contained in anthropogenic waste materials, while \(\gamma\)-rays emissions were higher for autochthonous volcanic materials and elements. Indeed, positive correlation coefficients were identified at depths of 10-45 cm between \(\gamma\)-rays dose rate and V (0.511 **), Sr (0.491 **), Ti (0.451 **), Mn (0.436 **), K (0.390 **), Nb (0.319 **), Zr (0.317 **) and Rb (0.283 *), while correlation with Th (0.261 *) was found only in 0-10 cm of depth. On the whole, these results were consistent with the generally high values of \(\gamma\)-rays dose rates emitted by volcanic materials, which are naturally enriched with minerals, bearing K, Ti, Mn, Sr, Zr and Th. The significant correlations found between \(\gamma\)-rays dose rate and anthropogenic elements were due to their concentration in the upper 10 to 40 cm of soil. Indeed, according to previous works [77; 78; 79], 90% of \(\gamma\)-rays radiation is emitted from the first 20 to 30 cm of rock and from the first 30 to 50 cm of soil depth. Therefore, the contribution from deeper rocks and soils was negligible. Figure 7: Maps of PTE distribution, measured by PXRF. (**A**) Cd at 0-15 cm; (**B**) Cd at 15–40 cm; (**C**) Pb at 0–15 cm; (**D**) Pb at 15–40 cm; (**E**) As at 0–15 cm; (**F**) As at 15–40 cm.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### Mobile Band-Based Technologies  
Mobile technologies are widely deployed and used worldwide, as we can see in Figure 2. Although white zones remain, mobile networks may well be the widest wireless networks deployed to provide Internet access. The 3 rd Generation Partnership Project (3 GPP) consortium, which defines and proposes standards for mobile technologies, recently proposed an amendment to existing mobile standards. These amendments aim to make mobile communications usable for Internet of Things (IoT) applications, in respect to IoT special constraints, mainly in terms of computation power and energy restriction.  
Figure 1: Technologies’ maximum data rate over urban range.  
Figure 2: LTE-Advanced (LTE-A) coverage map [10].  
There are two main mobile IoT technologies that are deployed and widely used right now: enhanced Machine-Type Communication (eMTC), and Narrow-Band IoT (NB-IoT). Both are derived from the LTE 3 GPP standard. Two other technologies could be potential candidates: Extended Coverage GSM IoT (EC-GSM-IoT), and 5G. These technologies are going to be presented in more depth further in this section.
## 4 Parametric Analysis
#### 4.1.1 Influence of the Embedded Depth of Different Retaining Piles  
To analyze the influence of the embedded depth of the retaining pile on the deformation of the existing subway station and track, according to the different embedded ratios of the retaining pile, five working conditions of the embedded depth of 6 m, 7 m, 8 m, 9 m and 10 m were set to simulate the excavation of the foundation pit, and the embedded depth of the retaining pile was 8 m in the initial condition. The maximum vertical and horizontal deformations of the station and track structures under various conditions are shown in Figure 25.  
With the increase in the embedded depth of the retaining pile, the maximum vertical displacement of the adjacent subway station and track gradually decreases, and the amplitude of the change in the deformation value gradually decreases. When the embedded depth of the foundation pit retaining pile increases from 6 to 8 m, the maximum vertical deformation of the station and track clearly changes, and when the embedded depth of the foundation pit retaining pile increases from 8 to 10 m, the maximum vertical deformation changes little. When the embedded depth of the foundation pit retaining pile increases from 6 to 10 m, the maximum deformation values of the station and track structure decrease by 2.18 and 2.15 mm, respectively. The maximum vertical displacements of the station and track structures decreased by 28%, 39%, 45% and 48% and 32%, 43%, 48% and 51%, respectively.  
The maximum horizontal deformation of the station and track decreases with increasing embedment depth of the retaining pile. When the embedded depth of the foundation pit retaining pile increases from 6 to 10 m, the maximum horizontal displacement of the station and track structure decreases by 27%, 36%, 41% and 43% and 31%, 39%, 42% and  
Figure 25: Maximum vertical and horizontal displacements of the station and track structures under different embedding depths.  
45%, respectively. The horizontal displacement of the station and track structure is more sensitive to the change in the embedded depth of different retaining piles.  
Through the deformation analysis of station and track structures under different embedded depths of retaining piles, it can be concluded that increasing a certain embedded depth of retaining piles can reduce the deformation of station and track structures by nearly 50%. When the embedded depth of the retaining pile is 8 m, the deformation of the station and track structure is no longer obvious. Considering the economic benefits of foundation pit construction and reducing the difficulty of construction, the embedded depth of the foundation pit retaining pile can be selected as 8 m; i.e., the embedded ratio is 0.31.