esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time HIFU Treatment Monitoring Using Pulse Inversion Ultrasonic Imaging
## 2 Materials and Methods
### Proposed High-Intensity Focused Ultrasound (HIFU) Interference Elimination and Its Implementation
The proposed method for HIFU interference elimination was implemented in a commercial ultrasound research imaging scanner (Vantage 128, Verasonics Inc., Redmond, WA, USA) that has an option to change the polarity of transmit pulse. This option facilitated the transmission of ultrasound imaging signals with \(0^{\circ}\) and \(180^{\circ}\) phases. Note that this scheme allows the full bandwidth of an imaging transducer to be used for ultrasound transmission unlike the conventional rule inversion for tissue harmonic imaging because the harmonics generated by the ultrasound imaging signals do not have to be received. Additionally, this imaging scanner has the ability to write radio-frequency (RF) data to local buffer memory and add new RF data to the RF data previously stored in the local buffer memory, which is called the write/accumulation mode. The subtraction of two sets of received RF data in Equation (3) was implemented using the receive apodization function provided by the scanner and the write/accumulation mode as shown in Figure 2; the negative apodization means multiplying RF data by \(-1\), thus reversing the phase of the RF data. For one scanline, the RF signal received after the first transmission and reception step, i.e., \(r_{+}(t)\) is stored in the local buffer memory. In this step, the ultrasound transmission is conducted using the positive polarity of transmit pulse and the phase of the received RF data does not change because of the positive apodization (i.e., multiplying the RF data by +1). In the second transmission and reception step, the negative polarity of transmit pulse is used for ultrasound transmission and the phase of the received RF data is reversed using the negative apodization before adding the new RF data to the RF data previously stored in the local buffer memory. This procedure is repeated until all local buffer memories corresponding to a predetermined number of scanlines are filled with RF data. After this step, beam formation is performed to obtain the scanlines expressed as Equation (3). These scanline data are used to construct an ultrasound monitoring image by means of the echo-processing functions provided by the ultrasound research imaging scanner; a 41-tap band-pass filter with cutoff frequencies of 2.2 and 5 MHz is used to remove out-of-band noise of the scanline signals.
# Data Protection and Multi-Database Data-Driven Models
## 2 Preliminaries  
Data protection mechanisms are used when we need to share data with third parties. There are three main types of mechanisms [3; 4]: perturbative methods, non-perturbative methods, and synthetic data generators.  
Perturbative methods build a copy of the database in which the original data have been modified by introducing some kind of error. There are several algorithms with this goal. Examples include microaggregation, noise addition and multiplication, rank swapping, PRAM (Post RAndomisation Method), and transformation-based methods. In this paper, we use this type of method for protecting data. Different methods provide different guarantees and protection. For example, microaggregation is useful for providing \(k\)-anonymity [10; 11; 12]. In contrast, noise addition using Laplacian noise is useful for providing local differential privacy [13; 14; 15]. All methods are useful for providing privacy against reidentification [16] attacks.  
Non-perturbative methods build a copy of the database in which the original data are replaced by data of lower quality. Here, data are not erroneous but may lack detail. E.g., values can be replaced by intervals, or simply suppressed. Different methods may provide different privacy guarantees (e.g., \(k\)-anonymity). Nevertheless, as the type of data after protection is different to the type of data before protection, perturbative methods are easier to use. For example, the original data may be numerical but protected data may correspond to intervals. Therefore, we need to use machine learning algorithms that are able to deal with intervals (or alternatively post-process the data to replace intervals with values). For this reason, we have not used these methods here.  
Synthetic data generators [17] are based on building models of the data, and then data are replaced by artificial data generated by these models. Different methods exist, typically based on different machine learning models (e.g., generative adversarial networks, decision trees, support vector machines). We have not considered these approaches here, and we leave the analysis of this type of method for future work.
## 3 Results and Discussion
### CleanSeq Detects and Eliminates Mutation Reads Efficiently as Compared with FastQ Screen  
To test the performance of CleanSeq regarding the detection and elimination of contaminated reads, we generated a series of simulated sequence datasets via dwgsim using a mixture of ten bacterial genome DNA sequences. As demonstrated in Figure 2, coverage (3\(\times\) or 30\(\times\)), the genomic mutation rate in the genome (0.01% or 0.0001%), and the similarity between the target and contaminating bacteria (0.42%, 7.73% or 82.99%) were considered. Although the results seemed to be species (genome)-dependent, the efficiency of contaminating reads detection (calculated according to Materials and Methods) was generally over 91%, except for _E. coli_ and _S. flexneri_, which were nearly indistinguishable at the species level, with a genome similarity of 82.99% [40; 41]. The same result was also obtained for the contamination degree in reads after cleanup which was kept nicely within 0-0.15% in either high or lower coverage, a mutation rate of 1 \(\times\) 10\({}^{-4}\) or 1 \(\times\) 10\({}^{-6}\), and a lower genome similarity and contamination rate. It should be noted that the decontamination efficacy (-42%) and correctness (-0.1%) dropped significantly in the condition when the similarity and mutation rate were both very high. Compared to FastQ Screen [16], the cleanup efficiency of CleanSeq was slightly higher as judged by the detection efficiency. The increase in efficiency results was minor in the contamination degree after cleanup, but it was still acceptable. The performance between CleanSeq and FastQ Screen was comparable as expected.
# Safety and Efficacy of Modern Stents in Patients with Metabolic Syndrome
## 3 Results
### Diagnosis and Presentation  
Patients with MS were more likely to present with STEMI than NSTEMI or UA (48.52% vs. 27.94% vs. 23.52%). Overall, 73.73% of STEMI patients underwent 2 nd generation DES stenting, while other stents were less used in patients presenting with ACS. Characteristics of patient's presentation at emergency department are described in Table 3. Patients presenting with cardiogenic shock, acute pulmonary edema or cardiopulmonary arrest on arrival had more chance of 2 nd generation DES stenting than 1 st generation DES or BMS stenting (78.94% vs. 21.05%). There was no statistically significant difference concerning the time to PCI between groups. Over 39.21% of patients underwent coronary stenting in less than 120 min. Moreover, almost equal proportion of patients underwent PCI in less than 2 h in both groups (39.07% vs. 39.62%). Echocardiography was assessed in all patients  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Clinical Factors** & \
\begin{tabular}{c} **2 nd Generation DES** \\ **(_n_ = 151_)** \\ \end{tabular} &
\begin{tabular}{c} **Other Stents** \\ **(_n_ = 53_)** \\ \end{tabular} & \(p\)-Value** \\ \hline Age (year), mean \(\pm\) SD & 60.53 \(\pm\) 10.65 & 62.94 \(\pm\) 11.81 & 0.17 \\ \(<\)50, \(n\) (\%) & 20 (13.24) & 11 (20.75) & 0.19 \\
50–69, \(n\) (\%) & 98 (64.90) & 27 (50.94) & 0.07 \\ \(\geq\)70, \(n\) (\%) & 33 (21.85) & 15 (28.30) & 0.34 \\ \hline Female, \(n\) (\%) & 45 (29.80) & 17 (32.07) & 0.76 \\ Male, \(n\) (\%) & 106 (70.19) & 36 (67.92) & 0.76 \\ \hline BMI (kg/m\({}^{2}\)), mean \(\pm\) SD & 33.68 \(\pm\) 4.11 & 33.18 \(\pm\) 3.19 & 0.42 \\ \hline Obesity, \(n\) (\%) & 151 (100) & 53 (100) & \\ Hypertension, \(n\) (\%) & 135 (89.40) & 51 (96.22) & 0.13 \\ Diabetes, \(n\) (\%) & 57 (37.74) & 25 (47.16) & 0.23 \\ Low HDL-C, \(n\) (\%) & 130 (86.09) & 42 (79.24) & 0.24 \\ Hypertriglyceridemia, \(n\) (\%) & 98 (64.90) & 26 (49.05) & 0.04 \\ \hline Prior ischemic heart disease, (_n_) \% & 27 (17.88) & 13 (24.52) & 0.30 \\ Prior MI, \(n\) (\%) & 13 (8.60) & 8 (15.09) & 0.18 \\ Prior CABG, \(n\) (\%) & 2 (1.32) & 1 (1.88) & 0.77 \\ Prior PCI, \(n\) (\%) & 15 (9.93) & 7 (13.20) & 0.51 \\ Prior stroke/TIA, \(n\) (\%) & 4 (2.64) & 0 & 0.23 \\ \hline \hline \end{tabular} Abbreviations: DES, drug eluting stent; SD, standard deviation; BMI, _Body mass index_; MI, myocardial infarction; CABG, coronary artery bypass graft; PCI, percutaneous coronary intervention; TIA, transient ischemic attack.  
\end{table}
Table 1: Baseline characteristics of patients according to stent type inserted.  
enrolled and echocardiographic measurements are summarized in Table 3. Laboratory findings and lipid levels of patients undergoing coronary stenting are shown in Table 4.
## 4 The Proposed Heuristic Scheme
### Service Mapping Algorithm  
This algorithm uses the decomposition selection algorithm to find the decomposition with minimum cost \(G_{s}\). Then, it retrieves end-to-end simple paths of physical network for each virtual path_ from the catalogue of physical paths \(C_{p}\). Thereafter, the function **pathGroupGenerator** generates the path group list \(PathGroupList\), in which each \(PathGroup\) of physical paths contains one candidate path for each path in \(P_{\ell 2e}\) of the selected decomposition. That candidate group of paths is passed to the path mapping algorithm with \(P_{\ell 2e}\). If the path mapping algorithm succeeded in embedding the service, the service mapping algorithm will return a _success_ state, otherwise, it will return a \(fail\) state. For more details refer to Algorithm 2.  
```
Data: S: Service request contains few decompositions, \(G\): Physical network graph, \(C_{p}\): Catalogue of physical paths. Result: service mapping state (success or fail).
1 begin
2\(ReqPathList\leftarrow\phi\ CandidatePathsList\leftarrow\phi\ G_{s}=DecompositionPathSelection(s_{dc})\ P_{\ell 2e}\gets \text{end-to-end paths of }G_{s}\)for\(path\in P_{\ell 2e}\)do
3\(I_{Path}\gets VirtType(path)\)/*\(\mathtt{virtualization\ technique\ types\ for\ nodes\ in\ path}\)*/
4\(CandidatePathsList\bigcup C_{p}(I_{Path})\)
5 end for /* Generate path Group list: */
6\(PathGroupList=\textbf{pathGroupGenerator}(CandidatePathsList)\ status=False\)for\(CandidatePathsGroup\in PathGroupList\)do
7\(status=\textbf{PathMapping}(P_{\ell 2e},CandidatePathsGroup)\)if\(status=True\)then
8 break
9 end if
10 end for
11 end for
12 if\(status=True\)then
13 Return: success
14 end if
15 else
16 Return:\(fail\)
17 end if
18
19 end for
```  
**Algorithm 2**Service Mapping Algorithm  
For example, if the selected \(G_{s}\) has two paths \(p_{1},p_{2}\in P_{\ell 2e}\) and the identification of them \(I(p_{1})\) and \(I(p_{2})\); and if \(C_{p}(I(p_{1}))=(P_{ab},P_{cd})\) and \(C_{p}(I(p_{2}))=(P_{ef},P_{gh})\). Thus,  
\[CandidatePathsList=\Big{(}C_{p}(I(p_{1}),C_{p}(I(p_{2}))\Big{)}\]  
and,  
\[PathGroupList=\Big{(}(P_{ab},P_{ef}),(P_{ab},P_{gh}),(P_{cd},P_{ef}),(P_{ad},P_{ gh})\Big{)}\]
## 2 Materials and Methods
### Sample Collection  
To provide average geochemical composition of urban TSP and its temporal changes, its accumulation was done in the same ten permanent monitoring sites (1-10, Figure 1) by exposing their passive samplers during four sampling periods P1, P2, P3, P4. The exposure of passive samplers during each period in 2020 lasted 6 weeks. The periods were successive: P1--04.03-15.04, P2--16.04-27.05, P3--28.05-08.07, and P4--09.07-19.08. Since sites 1 and 2, 3 and 4, and 8 and 9 are in close proximity, each of these pairs can be treated as one generalized (duplicate) site consisting of two sub-sites; thus, such 7 sites (3 duplicate and 4 single) are evenly distributed in the city. The duplicate sites 3 and 4 are close to iron ore stevedoring. Close to it, an additional 10 sites (11-20, Figure 1) were selected, the location of which around terminal B is denser. In these 10 sites, the accumulation of TSP was done using analogous passive samplers during the same P3 period as in 10 permanent monitoring sites. Therefore, 20 sites were sampled during P3.  
Characteristic dust (CD) was collected quite close (\"right next door\") at the sites of stevedoring of iron ore, apatite, phosphoric, and potassium fertilizers (analytical data of appropriate samples, named as _B'Fe_, _C'ap_, _C'ph_, and _D'KCl_ will be given in \"Results\"). Everywhere TSP sampling was done using laboratory-made passive samplers (Figure 2) and only the sample of waste incineration ash (_named as F*Wa_) was swept from the chimneys.  
Figure 2: Passive sampler for particulate matter. Roof (1), protective net (2), collection plates (3), fixing bracket (4).  
Samplers were designed with considerations from other research works, with protection from rain and the effects of external factors such as birds or heavy debris. The sampler consisted of a roof (1), a protective net (2), working sections (3), and mounting brackets (4). Disposable Petri dishes were used for sample collection. The area of the single Petri plate was 5674 mm\({}^{2}\). Petri dishes were arranged in working sections (3). The Petri dishes were protected from large elements and birds by a net (2). The roof (1) protected the Petri dishes from rain. If water entered the working section, it was collected together with the dust sample using a syringe. The samplers were attached to the electric or light poles at a height of 3 m by means of mounting brackets (4). During the sampling and analysis, extreme caution was ensured in order to avoid contamination of the samples with metallic laboratory tools. Each TSP sample was placed on a microscope slide and a drop of distilled water was added for covering. Photographs were taken using microscope \"Nikon Eclipse Ci-L\" with a magnification lenses 40\(\times\).
## 4 Conclusions  
The impact of using waste broken glass as a partial substitute for fly ashes on the geopolymer's physical characteristics was investigated in the presented study. The aim was to analyse its usefulness for geopolymers production. The following conclusions can be formulated based on the tests performed.  
1. The addition of waste glass can improve the mechanical performance of the geopolymer. It is possible to achieve maximum compressive strength if the waste glass content is no higher than 20%.
2. It is possible to produce geopolymer containing waste glass as a partial replacement for fly ash with compressive strength similar to the cement mortar containing class 32.5 Portland cement.
3. The addition of waste glass significantly improves the flexural strength of the geopolymers, which is a consequence of the physical properties of the glass and the non-regular shape of its grains.
4. The higher content of the reactive silicon oxide and aluminium oxide produced as a result of adding glass waste intensifies polycondensation, improving the mechanical performance.
5. All tested geopolymers have demonstrated good insulating ability compared to the cement mortar. The results of thermal conductivity tests on the geopolymers indicate that adding 10% glass waste leads to a slight increase in \(\lambda\) (from 0.358 to 0.397 W/mK), which can be attributed to the higher thermal conductivity of glass particles compared to the geopolymer matrix. At higher waste glass content (20% and 30%), \(\lambda\) values decrease, probably due to changes in the microstructure of geopolymers associated with the distribution of pores in the composite.
6. Utilising the waste glass is economical and environmentally friendly as it leads to diminished energy consumption, lower grinding and sorting costs, and reduced use of natural raw materials.  
Figure 9: Flexural strength of geopolymers and reference mortar RCM after 28 days of curing.  
**Author Contributions:** Conceptualization, K.C.; methodology, K.C., J.S. and E.H.; formal analysis, K.C., E.H. and J.S.; investigation, K.C. and J.S.; supervision, E.H.; writing--original draft preparation E.H. and J.S.; writing--review and editing, E.H., J.S. and K.C. All authors have read and agreed to the published version of the manuscript.  
Financially supported by Minister of Science under the \"Regional Initiative of Excellence\" (RID) programme.  
Not applicable.  
**Data Availability Statement:** All additional data are available after personal communication with the authors.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors
## 2 Mathematical Model of SPIM
### Power Losses in SPIM  
The electrical losses of SPIM are as follow:  
1. Stator copper losses. Copper losses in the main and auxiliary stator windings are given by [21]: \[P_{cus}=I_{m}^{2}R_{sm}+I_{a}^{2}R_{sa}\] (9) Substituting the auxiliary winding current in the right-hand side of (9) with the expression \(I_{m}/Z_{t}\) (as per (1)), the stator copper losses can be expressed as: \[P_{cu}=I_{m}^{2}\Bigg{(}R_{sm}+\frac{R_{sa}}{\left|Z_{t}\right|^{2}}\Bigg{)}\] (10)
2. Rotor copper losses. Copper losses in the rotor winding can be written as: \[P_{cur}={l_{r}^{\prime}}^{2}R_{r}^{\prime}=I_{m}^{2}R_{r}^{\prime}\Bigg{|} \frac{1-j\frac{a}{Z_{t}}}{\frac{R_{r}^{\prime}}{s}+jL_{tr}^{\prime}\omega_{e} }+\frac{1-j\frac{a}{Z_{t}}}{\frac{R_{r}^{\prime}}{2-s}+jL_{tr}^{\prime}\omega_ {e}}\Bigg{|}^{2}\] (11)
3. Core losses. Core losses which include hysteresis and eddy current losses are given by [21]: \[P_{fe}=c_{fe}\Big{(}E_{mf}^{2}+E_{mb}^{2}\Big{)}\] (12) Then, the aforementioned equation can be expressed in terms of \(I_{m}\) as follows: \[P_{fe}=\frac{c_{fe}}{4}\Bigg{(}\left|Z_{f}\right|^{2}\left|1-j\frac{a}{Z_{t}} \right|^{2}+\left|Z_{b}\right|^{2}\left|1+j\frac{a}{Z_{t}}\right|^{2}\Bigg{)}I _{m}^{2}\] (13)
4. Stray load losses: Stray load losses are due to changes made in the flux distribution and eddy currents given by the following empirical expression: \[P_{str}=c_{str}{l_{r}^{\prime}}^{2}\omega_{e}^{2}\] (14) It can further be expressed in terms of \(I_{m}\): \[P_{str}=c_{str}{l_{m}^{2}}\omega_{e}^{2}\Bigg{|}\frac{1-j\frac{a}{Z_{t}}}{ \frac{R_{r}^{\prime}}{s}+jL_{tr}^{\prime}\omega_{e}}+\frac{1-j\frac{a}{Z_{t}} }{\frac{R_{r}^{\prime}}{2-s}+jL_{tr}^{\prime}\omega_{e}}\Bigg{|}^{2}\] (15) By summing all the losses expressions in terms of \(I_{m}\) and factoring, it yields: \[P_{el}=I_{m}^{2}A_{2}\] (16)  
In this equation, \(A_{2}\) is a variable being function of motor parameters, the applied frequency and slip, written as:  
\[A_{2}=g(\omega_{e},s) \tag{17}\]
## 2 Related Work  
Efficient enhancement of sensor coverage is a very important topic, especially for many modern systems that are modeled in WSN. Therefore, the sensing coverage problem has been put forward by various studies over the years in different methods and levels to be discussed in order to efficiently term the target of sensor coverage. In the studies of the sensor coverage problem, some emphasize the maintenance of coverage with at least a certain value, such as \(k\)-coverage, and some strengthen the comprehensive coverage enhancement.  
The comprehensive coverage-enhancing studies can be classified into the following strategies, such as:  
1. Sensor (node) deployment method: coverage enhancing using a sensor deployment model in a mobile WSN [21; 24], target coverage-enhancing with minimum cost using sensor deployment by a harmony search in a WSN [22], and sensor deployment to improve coverage with minimum cost in a transport WSN [23];
2. Sensor energy strategy: evaluation of the effect of energy-depleted nodes to improve the energy efficiency for coverage-enhancing [44];
3. Maximization of the perception range of a single sensor node: coverage-enhancing while minimizing the number of sensors in a 3-D WSN [45]; and
4. Network connectivity: coverage-enhancing by a sensor-connected design with energy consideration [46].  
For the maintenance of coverage with at least a certain value, sensor deployment and routing design are the two famous methods to enhance coverage, such as sensor deployment considering the energy to improve coverage in WSNs [25], and sensor deployment using the greedy heuristic method to improve the coverage in WSNs [26].
## 3 Results and Discussion  
### Compressive Strength  
Figure 2 displays the compressive strengths of the C-UHSCC samples mixed with the inorganic pigments. Compressive strengths were measured at 3, 7, and 28 days, and the average strength values of the three specimens of different ages were used. Error bars exhibited a 95% confidence level. The compressive strength test results revealed  
Figure 1: Photographs of HSCCs. Top row: manufacturing process; bottom row: manufactured specimens with different pigment types.  
that the specimen strength differed according to the inorganic pigment color. The initial hydration reactions of all the specimens were maximized through a 72 h steam-curing process, which accelerated the initial strength-development rate [29]. For specimen P, which had no pigment, the strength at 28 days of age showed negligible change from that at an early age measured after the completion of steam curing. This result indicates that the maximum strength was developed for specimen P because of the completion of all hydration reactions [30]. For the red specimens, the strength development was higher than that of specimen P as the pigment content increased with age; this was because the filling effect of the Fe\({}_{2}\)O\({}_{3}\) component in the red pigment contributed to the strength development [20]. Compared with the red specimens, the green specimens exhibited lower strength; however, their strength gradually increased with age. Although the water used for mixing was adsorbed on the pigments during the initial mixing process, the hydration reaction progressed under steam curing. As adequate amounts of moisture and heat were continuously supplied under atmospheric pressure to the specimens, further strength development was achieved [12; 31; 32; 33; 34].  
For all colors except red, the strength tended to decrease with increasing inorganic pigment content. However, the red specimens with 10% pigment content exhibited higher strengths than those with lower pigment contents. Strengths higher than that of specimen P were observed as the pigment content increased because the Fe\({}_{2}\)O\({}_{3}\) particles in the red pigment were nanosized. The nano-Fe\({}_{2}\)O\({}_{3}\) particles could partially fill the pores of the binder particles [35; 36]. Although a slightly different tendency was observed in the compressive strength results of a previous study that used a maroon marble powder additive containing an Fe\({}_{2}\)O\({}_{3}\) component [20], water curing was used, and the difference in results could be attributed to the difference between the water-curing and steam-curing environments. The specimens mixed with the pigments exhibited compressive strengths higher than 90 MPa at 28 days of age, but those with the green pigment exhibited the lowest strength.  
Figure 2: Compressive strength at each age and fracture images of specimens. The strengths at different ages are indicated in the bar diagram, and the error range is within the 95% confidence interval.