esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
## 1 Introduction
[PERSON] and [PERSON] [6] studied the compressive strength of cement stabilized soils; in their experiments, they used 3% and 6% of cement with various amount of fly ash. [PERSON] [18] studied the stabilization of pavement subgrade by using fly ash (5-20%) activated by 5% cement. [PERSON] [19] performed experiments on the stabilization of claye soils with 10% fly ash and 4% cement. In light of previous research, it was decided to prepare test samples with a fixed 3% cement content and varying percentages (5-25% by weight) of fly ash. Since the cement is only being used as an activator in this research, rather than a stabilizer, it was selected at its lowest percentage. In this study, cement and fly ash additives on the geotechnical properties of fine sand soil were experimentally investigated, which includes the effect of curing duration and the relative density of sand on the strength of the soil. In addition, the effect of the additives on the permeability of sand was considered. The optimum water content and maximum dry unit weight were obtained by compaction tests. Shear box, falling head permeability and CBR tests were performed to investigate the effect of additives on the geotechnical properties of the sand soil.
## 4 Discussion
### Oral Surgery Procedures and Bleeding Risk  
The dental procedures most at risk of bleeding are extractions, implant surgery, scaling and root planning, and surgical interventions aimed at the excision of oral lesions present on soft and hard tissues [5; 6; 7].  
Bleeding during oral surgery can become a major problem if it becomes significant, as it leads to decreased operator vision, prolonged post-operative bleeding, or hematoma formation, which can lead to bacterial infections. Although the risk of complications from hypovolaemia is generally associated with technical errors or preoperative evaluations, an accurate preventive anamnesis is always required to rule out the presence of congenital or acquired haemostatic disorders [5; 6; 7].  
Primary haemorrhage in dental surgery occurs during surgery and may be caused by traumatic procedures resulting in laceration of soft tissues and blood vessels, or due to infection or bone injury; post-operative haemorrhage, or reactionary bleeding, occurs 2-3 h after surgery and is common in patients with coagulation disorders or secondary haemorrhage. Finally, if bleeding occurs within 14 days after surgery, it is likely secondary bleeding caused by an infection. It is also possible to classify haemorrhage according to the area of origin: vascular, bone, and soft tissue [5; 6].  
Congenital haemostasis disorders are not uncommon: the deficiency leads to recurrent joint, mucosal and soft tissue bleeds, and intracranial and intraperitoneal haemorrhages can occur. The bleeding frequency and severity are proportional to the deficiency [5; 6]. Medical and dental management may differ depending on the bleeding disorder and the intervention expected.  
Even in the perioperative stage, the surgeon must be careful with some procedures such as the administration of local anaesthesia, due to the risk of bleeding. In fact, anaesthetic injections (both intramuose or truncular) can cause prolonged bleeding that can be uncontrollable with local measures and can have serious consequences: haemorrhage with anaemia until hypovolaemic shock [4; 5], conspicuous haematoma up to respiratory tract obstruction and an additional risk is bacterial superinfection of a haematoma formed as a result of oral surgery [4; 5].  
In cases of bleeding, the surgeon can act both locally and systemically. The systemic intervention involves the administration of fresh frozen plasma, platelets, or both; factor replacement therapy, intranasal desmopressin, oral or intravenous tranexamic acid or amino-d-caproic acid. Systemic therapy is generally reserved for patients who have systemic disorders affecting haemostasis [5; 6; 7].  
Local interventions include surgical procedures such as suturing the bleeding site or using haemostatic agents, including oxidized cellulose, gel foam, collagen fleeces, cyanoacrylate glue, and collagen or gelatin sponges [5].
## 3 Methodology
### Flow Recovery Methods  
Our analysis required the use of specially developed software called GSX [28] to perform flow recovery from large packet traces in pcap (PCAP--LibPCAP format specification available: ([[https://wiki.wireshark.org/Development/LibpcapFileFormat](https://wiki.wireshark.org/Development/LibpcapFileFormat)]([https://wiki.wireshark.org/Development/LibpcapFileFormat](https://wiki.wireshark.org/Development/LibpcapFileFormat)), 10 December 2024) format. GSX performs advanced flow recovery, including stateful reconstruction of TCP sessions, with additional feature engineering to calculate a broad variety of metrics, including features characterising payload (in this analysis, we focus primarily on TCP and UDP protocols). Payload entropy was calculated in both directions (outbound and inbound, with respect to the packet flow source; outbound meaning that the initiator of the flow is sending data to a remote entity. Here, source can be thought of as the end-point IP address).  
Flow collection is also possible in real-time by using common network tools and hardware [29; 30], using industry standard and extensible schemas, as provided with network flow standards such as IPFIX [31], and later versions of NetFlow. Depending on available resources, in-flight capture may differ, for example, by employing sampling and sliding window methods [32].
# Systematic Evaluation on Speckle Suppression Methods in Examination of Ultrasound Breast Images
## 2 Filtering Methods
### PDE-Based Filter  
#### 2.4.1 Perona-Malik Diffusion Filter  
PMD filter is a kind of filter based on the anisotropic diffusion theory, which can both de-noise and maintain boundaries. [PERSON] and [PERSON] [23] studied the isotropic diffusion filter or equivalent Gaussian smoothing, and pointed out that Gaussian blurring does not \"respect\" natural object boundary. Therefore, they introduced a diffusion coefficient which can automatically adjust the diffusivity according to varied image content. This simple diffusion model has been applied to de-speckling of medical ultrasound images [22].  
The PMD [23] filter is described as  
\[\left\{\begin{array}{l}\frac{\partial I_{g}}{\partial t}=div(c(x,y,t)\cdot \
abla I_{g})\\ I_{g}(t=0)=I_{0}\end{array}\right. \tag{14}\]  
In Equation (14), \(c(x,y,t)\) is diffusion coefficient which is chosen locally as a function for the magnitude of the gradient of the image, e.g., \(c(x,y,t)=g(\|\
abla I_{g}(x,y,t)\|)\), \(\
abla\) is the gradient operator.  
The function \(g(\cdot)\) is a non-negative monotone decreasing function with \(g(0)=1\). Two functions were proposed to model the diffusion coefficient  
\[g(\|\
abla I_{g}\|)=e^{-(\|\
abla I_{g}\|/K)^{2}} \tag{15}\]  
and,  
\[g(\|\
abla I_{g}\|)=\frac{1}{1+(\|\
abla I_{g}\|/K)^{2}} \tag{16}\]  
where \(K\) is a constant controlling the sensitivity of the diffusion coefficient to image gradient and it is usually chosen depending on optimized experiments of visual measurement.
## 3 TVMS Modeling of Gear Tooth Spalling  
The time-varying meshing stiffness model of the gear is the core of gear dynamic behavior simulation. Currently, the most popular modeling method regards the gear tooth as a variable cross-section cantilever beam and uses the potential energy method to derive the mathematical model. The time-varying meshing stiffness can be considered a combination of bending stiffness, shear stiffness, axial compression stiffness, and Hertzian stiffness. Thus, the total meshing stiffness can be expressed by  
\[k_{total}=\sum_{i=1}^{n}\frac{1}{\frac{1}{k_{b,j}}+\frac{1}{k_{b,j}}+\frac{1}{k_ {b,2i}}+\frac{1}{k_{s,1j}}+\frac{1}{k_{s,2i}}+\frac{1}{k_{s,1j}}+\frac{1}{k_{ s,2i}}}, \tag{26}\]  
where the subscript \(i\) denotes the \(i\)th pair of gears during meshing, and subscripts 1 and 2 denote the pinion and gear. Gear tooth spalling is localized damage around the pitch circle of spur gears. This paper considers the spalling fault with a rectangular defect that is \(a_{s}\times b_{s}\times c_{s}\) in size. Specifically, the geometric characteristics of the spalling fault are shown in Figure 2.  
The spalling of the gear tooth surface reduces the geometric length of the variable cross-section beam at the contact position, resulting in the change in the meshing stiffness. Figure 3 shows the two scenarios for gear tooth spalling when the tooth root circle is smaller than the base circle (scenario I) and the root circle is larger than the base circle (scenario II). Figure 3: Geometric tooth model of spalling spur gear. Figure 2: Schematic diagram of spalling spur gear. According to the length \(a_{s}\), width \(b_{s}\), and depth \(c_{s}\) of the spalling, the change in contact length, section area, and moment of inertia can be calculated as  
\[\triangle L_{x}=\left\{ \begin{array}{ll}a_{s},&u-r\leq x\leq u+r\\ 0,&\text{others}\end{array} \right., \tag{27a}\] \[\triangle A_{x}=\left\{ \begin{array}{ll}\triangle L_{x}c_{s},&u-r\leq x\leq u +r\\ 0,&\text{others}\end{array} \right.,\] (27b) \[\triangle I_{x}=\left\{ \begin{array}{ll}\frac{1}{12}\triangle L_{x}c_{s}^{3 }+\frac{A_{x}\triangle A_{x}(h_{x}-c_{s}/2)^{2}}{A_{x}-\triangle A_{x}},&u-r \leq x\leq u+r\\ 0,&\text{others}\end{array} \right., \tag{27c}\]  
According to the expression of gear tooth geometric parameters in Reference [12], we can further derive all gear stiffness components as follows: For scenario I, the bending stiffness, shear stiffness, and axial compression stiffness can be written by  
\[ \begin{array}{ll}\frac{1}{k_{b}}=-\int_{\varphi_{2}}^{\varphi_{3}}\frac{3a_{ x}\big{(}R_{b}-R_{f}\cos\varphi_{3}\cos\varphi_{1}-a_{x}\varphi\cos\varphi_{1}-b_{x} \cos\varphi_{1}\big{)}^{2}}{2 EL\Big{[}R_{b}\sin\varphi_{2}+r_{f}-\sqrt{r_{f} ^{2}-\left(x-d_{1}\right)^{2}}\Big{]}^{3}}d\varphi\\ +\int_{-\varphi_{1}}^{\varphi_{2}}\frac{3[1+\cos\varphi_{1}[(\varphi_{2}- \varphi)\sin\varphi-\cos\varphi]]^{2}(\varphi_{2}-\varphi)\cos\varphi}{E\Big{[} 2L[\sin\varphi+(\varphi_{2}-\varphi)\cos\varphi]^{3}-\frac{3\Delta I_{x}}{k_{b }^{3}}\Big{]}}d\varphi\end{array}
## 2 Materials and Methods
### Field Measurements  
Two successive field campaigns were conducted towards the end of July 2015 (18 th-25 th) and July 2016 (25 th-30 th) to measure LAI and other plant traits. Stratified random sampling was adopted and (x,y) coordinates were randomly generated in the grassland/saltmarsh area located at the eastern part of the island. To understand the variation of LAI through the two succeeding years, the sampled  
Figure 1: True colour composite of the Sentinel 2A image acquired on 30 July 2016 (bands 665, 560 and 490 nm) and the location of the study area (Schiermonnikoog Island, The Netherlands). The blue and red points demonstrate the distribution of sample plots in 2015 and 2016, respectively.  
Figure 2: Examples of grass composition observed in the Schiermonnikoog Island, The Netherlands.  
plots from 2015 and those of 2016 had about one-third of overlaps. In total 50 quadratic plots (30 plots in 2015 and 20 plots in 2016) with 20 m side length were sampled (Figure 1).  
In each sampling plot, several biophysical and biochemical traits were measured within five randomly selected subplots (1 \(\times\) 1 m), and their averages were calculated to represent the plots. In each subplot, 30 leaves were randomly selected and the CCM-300 chlorophyll meter was used to measure the leaf chlorophyll (\(\mathrm{C_{ab}}\)). Next, leaf samples were collected in each subplot and stored in zipped plastic bags and transferred to the laboratory to obtain the leaf dry matter (\(\mathrm{C_{m}}\)) and water content (\(\mathrm{C_{w}}\)). The soil reflectance was measured from a subset of subplots using a portable spectroradiometer (ASD), USA.  
LAI and average leaf angle (ALA) were non-destructively measured using a widely used optical instrument, the LAI-2200C Plant Canopy Analyzer. The instrument can be used under any daytime sky conditions [87], and makes some assumptions, such as random spatial distribution of leaves, that affect the measurements accuracy. The degree to which these assumptions are met will affect the measurements of LAI and ALA. The reader is referred to [87] for the detailed description of the instrument. In each subplot, six below and two above-canopy radiation measurements were made with the sun behind the operator and using a view restrictor of 45\({}^{\circ}\). The LAI of each plot was then calculated as the average LAI measurements of its subplots.  
The LAI-2200 measurements correspond to the plant area index (PAI) [27]. The PAI includes photosynthetic and non-photosynthetic components [88]. During the measurements, when non-photosynthetic components (dried leaves) were present within the plot, the LAI readings were modified considering the percentage of green (and dead) leaves which was estimated by visual inspection in each plot (LAI = (LAI\(\mathrm{readings}\)\(\times\) fraction of green leaves)), and hereafter these measurements are referred as LAI values. Table 1 summarises the statistics of the LAI values measured in summer 2015 and 2016 in the study area.
## 3 Results and Discussion
Nevertheless, the comparative effects of the treatment solutions (brine, TWW, and FW) on groundwater salinity could be fairly estimated on the basis of the following assumptions and simplifications: (1) Most of the added solutes on the unpaved roads during the dry season reach the groundwater. (2) Under long-term and steady state conditions, the average amounts of the Cl\({}^{-}\) that were added on the unpaved roads or pumped with the water from the aquifer are equal. (3) The annual average volume of the water that is pumped from the aquifer and that which recharges the aquifer are equal. According to the above conditions, a mass balance model (Equation (3)) was used to predict the effects of brine, TWW, or FW applications on the long-term annual average salinity increase (\(\Delta C_{d}\)) in the pumped water from the Yarkon-Taniimir aquifer. \[\Delta C_{cl}=\text{A}\cdot\frac{P_{cl}}{WR} \tag{3}\]  
where A is the long-term average area (m\({}^{2}\)) of all the unpaved roads located above the Yarkon-Taniimir aquifer and treated with the treatment solutions (brine, TWW, or FW). The \(P_{cl}\) is the long-term average amount (mg m\({}^{-2}\)/yr) of the Cl\({}^{-}\) that was added on the A area, and is calculated by Equation (2). \(WR\) is the long-term average of the water volume (L/yr) that was pumped from the Yarkon-Taniimir aquifer. The \(\Delta C_{cl}\) values of the pumped water from the Yarkon-Taniimir aquifer, which were calculated by Equation (3), are presented in Figure 5 for the various treatment solutions (brine, TWW, FW), and two different \(A\) values. For these calculations, a \(WR\) value of 400 \(\times\) 10\({}^{9}\) L per a year was used (a long-term annual average of the pumped water from this aquifer), as were \(A\) values in the years 2016 and 2017, which were used as two examples. The long-term annual average values of the \(\Delta C_{cl}\) in the pumped water from the Yarkon-Taniimir aquifer following the TWW, brine, and FW applications were 1.6, 0.8, and 0.4 mg/L, respectively, in the first example (2016), and 1.2, 0.58, and 0.32 mg/L, respectively, in the second example (2017) (Figure 5). These \(\Delta C_{cl}\) values in the first and second examples under the application of TWW were approximately two and four times higher than under brine and FW applications, respectively. In spite of these large differences in the \(\Delta C_{cl}\) values between the various treatments (Figure 5), the long-term annual average additions of Cl\({}^{-}\) concentration in the groundwater following the three treatment solutions were relatively low and ranged between 0.32 to 1.6 mg L\({}^{-1}\) in the two examples (Figure 5). Figure 4: The annual loaded amounts (\(P_{j}\)) of Cl (upper part), Na (middle part), or Ca+Mg (bottom part), on the unpaved roads following the treated wastewater (TWW), brine and fresh water (FW) applications (the Y-axes’ scales are different).
# Effect of Corrosion on the Hysteretic Behavior of Steel Reinforcing Bars and Corroded RC Columns
## 2 Experimental Procedure
### Corrosion Damage Estimation  
Upon completion of the mechanical tests, all reinforcing bars were revealed and carefully removed out of the concrete specimens to assess their corrosion damage level, expressed by means of their percentage mass loss.
## 4 Results and Discussion
### Discussion  
The results by themselves only show how the system behaves, but not if the performance is efficient or poor. In order to answer that question other conditions must be considered. As explained before, this work has been developed in the framework of the WoO project. This project has some requirements regarding the response time of a service, especially for events. In the WoO deliverable that defines the requirements of the system there is a strict condition regarding the system time response: it shall not exceed 1 min. So, when considering the results it can be affirmed that the proposed virtualization complies with the requirements of the system.  
Although we have not planned to strictly follow EN 54-5, this standard also establishes some limits that can be used to measure the response of our composed approach to a fire detector. Considering the operating characteristics of the SunSPOT devices, we can consider them what [16] refers as a class A detector. For a class A1, the normative requires a response time between 1 min and 4 min and 20 s. Again, the results of the tests in both situations are below the requirements, so it can be concluded that the proposed virtualization model complies also with this requirement.  
Some features of the deployment can be enhanced. The usage of a SQL database, for instance, is responsible for the longest delay in the virtualization of an existing event source. In order to save time in the registration part of the system, other structured data storage should be considered.  
There is also another issue that can be observed in the actions where a wireless communication is required. In the event generation, for instance, it is quite easy to see that from time to time there is an increase in the time required. This extra amount of time is due to communication issues, and as explained in the previous section, this seems to be related to the update of the routing tables.  
Another usual question is about the usage of REST for an event driven architecture. While REST is an effective a way of transferring the representation of the state of some resource, it does not make compulsory the usage of a specific protocol, although it was born from HTTP. So the final idea about this matter is that the REST paradigm can be followed by some other protocols, even those which provide a guarantee of delivery.
## 2 Materials and Methods
### Statistical Analysis  
Data analysis was performed with SPSS version 20.0 (IBM, Chicago, IL, USA) for Windows. A descriptive analysis was carried out for the quantitative variables, and the means and standard deviations were used. For the qualitative variables, an analysis of frequencies and percentages was carried out. The intraclass correlation coefficient (ICC) and the 95% confidence intervals (95% CI) were calculated to establish the test-retest and inter-rater reliability of the CVA-CVapp. A calculation of the test-retest and inter-rater reliability ICCs by subgroups was also performed. The criteria suggested by [PERSON] (1999) for the interpretation of reliability were used. ICCs below 0.40 represent poor reliability, ICCs from 0.40 to 0.75 represent moderate reliability, and ICCs above 0.75 indicate excellent reliability. In addition, the calculation of the standard error (SEM) and the minimum detectable change (MDC) were performed. The calculation of SEM was established according to the formula: SD \(\times\)\(\surd\)1-ICC. Additionally, the calculation of a minimum level of detectable change (MDC95% CI) was performed according to the formula: (SEM) \(\times\) z-score at the two-sided 95% confidence intervals (z = 1.96) \(\times\)\(\surd\)2.  
The Pearson's correlation coefficient was calculated to determine the strength of the relationship between both instruments (Kinovea and CVA-CVapp); the correlation was interpreted as strong if it was greater than 0.70, moderate if it presented a value between 0.50 and 0.70, and low if it was less than 0.30 [37].
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
## 3 EV SRM Drive
### DC Source-Povered Fixed DC-Link Voltage SRM Drive  
#### 3.1.1 Power Circuit  
In the SRM drive shown in Figure 3, the DC-link is directly powered using a commercial DC power supply with fixed voltage. The constituted system components are summarized as follows:  
1. SRM: three-phase, 12/8, 380 V, 2000 rpm, 2.2 kW.
2. Asymmetric bridge converter formed using six IGBT modules CM100 DY-12H (Mitsubishi Company) (\(V_{CES}=600\) V, continuous current \(I_{C}=100\) A, peak current \(I_{CM}=200\) A).
3. Dynamic brake constructed by an IGBT module CM100 DY-12H and a braking resistor with \(R_{dB}=50\)\(\Omega/300\) W.
4. Dynamic load: FW and DCG are coupled to the SRM shaft to serve as its mechanical load. FW is added to increase the stored kinetic energy for the ease of performing regenerative braking.