esa-sceva/satcom-chunk-collection huggingface dataset:
# Active Control of Low-Frequency Noise through a Single Top-Hung Window in a Full-Sized Room
###### Abstract  
The push for greater urban sustainability has increased the urgency of the search for noise mitigation solutions that allow for natural ventilation into buildings. Although a viable active noise control (ANC) solution with up to 10 dB of global attenuation between 100 Hz and 1000 Hz was previously developed for an open window, it had limited low-frequency performance below 300 Hz, owing to the small loudspeakers used. To improve the low-frequency attenuation, four passive radiator-based speakers were affixed around the opening of a top-hung ventilation window. The active control performance between 100 Hz and 700 Hz on a single top-hung window in a full-sized mock-up apartment room was examined. Active attenuation came close to the performance of the passive insulation provided by fully closing the window for expressway traffic and motorbike passing noise types. For a jet aircraft flyby, the performance of active attenuation with the window fully opened was similar to that of passive insulation with fully closed windows. In the case of low-frequency compressor noise, active attenuation's performance was significantly better than the passive insulation. Overall, between 8 dB and 12 dB of active attenuation was achieved directly in front of the window opening, and up to 10.5 dB of attenuation was achieved across the entire room.  
active noise control; natural ventilation; facade insulation; noise pollution +
Footnote †: journal: Applied Sciences
## 4 Examples and Comparisons
### Comparisons with Other Methods
**Case 2**  
The second comparison was performed on the process with pure time-delay  
\[G_{P2}(s)=\frac{e^{-s}}{s}. \tag{35}\]  
On the basis of the given process model, areas were determined from Expression (9):  
\[A_{0}=1,\ A_{1}=1,A_{2}=0.5. \tag{36}\]  
Then, the controller was designed according to Equations (12) and (14):  
\[K_{P}=0.56,\ K_{I}=0.12,\ b=0.5. \tag{37}\]  
Once more, the proposed MOMI method was compared to the presented tuning methods. The calculated PI controller parameters for the presented methods are given in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Process** & **MOMI** & **MS14** & **MS20** & **TA** & ** ALI** \\ \hline \(\mathrm{G_{P1}}\) & 9.4 & 8.5 & 11.0 & 11.6 & 30.5 \\ \(\mathrm{G_{P2}}\) & 6.1 & 21.7 & 4.7 & 8.8 & 11.3 \\ \(\mathrm{G_{P3}}\) & 21.3 & 35.3 & 30.3 & 27.0 & - \\ \(\mathrm{G_{P4}}\) & 13.5 & 23.2 & 21.3 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The 5% settling times for reference tracking. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Process** & **MOMI** & **MS14** & **MS20** & **TA** & ** ALI** \\ \hline \(\mathrm{G_{P1}}\) & 3.06 & 5.71 & 2.15 & 2.31 & 13.63 \\ \(\mathrm{G_{P2}}\) & 2.27 & 8.12 & 2.61 & 2.33 & 4.66 \\ \(\mathrm{G_{P3}}\) & 21.44 & 59.59 & 27.71 & 19.82 & - \\ \(\mathrm{G_{P4}}\) & 9.39 & 25.95 & 14.45 & - &Figure 14 compares the closed-loop responses for tracking and disturbance-rejection between the mentioned tuning rules applied to 2-DOF PI controllers. The shortest 5% settling time was obtained with the Ms20 method. The ITSE criterion for tracking performance revealed that the MOMI, TA, and Ms20 methods gave almost identical results, while Ms14 and ALI gave noticeably higher values. The TA method exhibited some undershoot, and the ALI method some overshoot. For disturbance-rejection performance, ITSE favoured the TA method, while Ms20 and MOMI were close to each other (MOMI ranked third).
## 3 Results and Discussion  
### Feed Source Characterization  
The feed to the wastewater treatment plant was characterized based on pH, free chlorine, COD, protein, FOG, TS and TSS concentrations. It is important to know what kind of wastewater is being dealt with before any experiments are done, as this gives an idea on how it may affect the different membranes being studied. Concentrations of these various materials in the wastewater are depicted in Table 4. A total of 12 samples of the feed were collected on four different days and analyzed. The heterogeneity in the feed is seen in terms of standard deviation, and it also depends on the moment at which the samples are taken. The wastewater predominantly contains CIP solutions, which is why the pH is 11.32 \(\pm\) 0.55, i.e., on the alkaline side of the scale.  
The presence of free chlorine in the wastewater is due to the usage of chlorinated caustic cleaners, chlorine sanitizers, and salt use during product manufacturing. Levels of 5 \(\pm\) 1 ppm of free chlorine are very low, and won't affect the pores of an RO membrane. Thus, no pretreatment is necessary while using RO membrane. Dairy products finding their way down the drain from various pathways are the primary origin for COD, protein, FOG and solids in the wastewater. From Table 4, it is also seen that total suspended solids constitute only a very small part of the total solids. This means that the wastewater contains a huge amount of dissolved solids due to dumping of acid whey down the drain. Thus, the feed source characterization gives an overall idea on what is being dealt with.
## 7 Conclusions  
We suggested a new SRGM considering the start of actual debugging time for software affected by syntax errors. To compare it with several existing NHPP SRGMs, we applied two real data sets. Parameters for all models were estimated by the LSE method. In addition, common criteria were used to compare the goodness of fit in order to discuss the optimal model (Tables 5 and 6). The values of the criteria of the proposed model are better than those of the other SRGMs, as listed in Table 1. The values of AIC for both data sets were not the lowest compared to those of the other models, but the proposed model, from the point of view of the other criteria, was the most significant. Then, we applied a cost model to the new proposed SRGM, and found out how much each parameter is changed regarding coefficients, release time, and cost.  
In summary, the proposed model fits better than all the other models for both data sets. Through in Section 5, we found out variance of C\({}_{3}\) and C\({}_{4}\) affecting the field environment are greater than other coefficients. In order to establish the optimal release policy, it is necessary to subdivide the coefficients related to the field environment. As discussed in Section 6, b is the most sensitive parameter and T\({}_{0}\) is the most insensitive parameter (in Table 14). Overestimation of \(\alpha\), \(\beta\), T\({}_{0}\), and N and underestimation of a and b have to be avoided because they can lead to misestimation of the optimal release time.  
Recently, many researchers have studied software reliability model considering software development environment. Likewise, we studied software reliability model considering uncertainty of software development environment like syntax error. Then, we provided optimal release policies having minimized the total development cost for various environments. Therefore, when the other data sets and various environments are given, the proposed model is beneficial.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & \(\mathbf{-30\%}\) & \(\mathbf{-20\%}\) & \(\mathbf{-10\%}\) & \(\mathbf{0}\) & \(\mathbf{10\%}\) & \(\mathbf{20\%}\) & \(\mathbf{30\%}\) \\ \hline a & \(0.022837\) & \(0.014165\) & \(0.006637\) & \(0\) & \(-0.005927\) & \(-0.011273\) & \(-0.016140\) \\ b & \(1.739638\) & \(0.498721\) & \(0.187518\) & \(0\) & \(-0.118787\) & \(-0.197279\) & \(-0.251138\) \\ \(\alpha\) & \(-0.021859\) & \(-0.013792\) & \(-0.006561\) & \(0\) & \(0.006012\) & \(0.011565\) & \(0.016728\) \\ \(\beta\) & \(-0.021811\) & \(-0.013763\) & \(-0.006547\) & \(0\) & \(0.006000\) & \(0.011543\) & \(0.016696\) \\ T\({}_{0}\) & \(-0.000003\) & \(-0.000002\) & \(-0.000001\) & \(0\) & \(0.000001\) & \(0.000002\) & \(0.000003\) \\ N & \(-0.054793\) & \(-0.035748\) & \(-0.017538\) & \(0\) & \(0.016989\) & \(0.033519\) & \(0.049658\) \\ \hline \end{tabular}
\end{table}
Table 14: Sensitivity analysis for parameters of the optimal release time.  
Figure 12: Sensitivity analysis for parameters of the optimal release time.
## 4 Simulation Results and Discussion
### Case 1: Sensitivity Analysis for Criterion Variation Within \(\pm 5\%\)
#### 4.2.5 Scores Variation  
Table 13 presents the obtained score values for the three scenarios under consideration. Figures 5-7 visually compares these obtained satellite scores across the three scenarios with those of the initial case, respectively for SD-TOPSIS, Entropy-TOPSIS, and Importance-TOPSIS. We observe that as the number of varied parameters increases from Scenario 1 to Scenario 4, significant variations in the scores are observed at different rates for each  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Satellite ID** & \begin{tabular}{c} **Upload Speed** \\ **[Mbps]** \\ \end{tabular} & \begin{tabular}{c} **Download Speed** \\ **[Mbps]** \\ \end{tabular} & \
\begin{tabular}{c} **Ping [ms]** \\ \end{tabular} &
\begin{tabular}{c} **Packet Loss** \\ **[\%]** \\ \end{tabular} \\ \hline
1 & 10.97 & 198.39 & 47.52 & 0.0096 \\ \hline
2 & 12.46 & 215.81 & 63.92 & 0.1632 \\ \hline
3 & 13.31 & 206.83 & 64.21 & 2.97 \\ \hline
4 & 15.88 & 219.18 & 64.80 & 19.65 \\ \hline
5 & 14.95 & 222.93 & 46.36 & 0.0098 \\ \hline
6 & 13.98 & 233.57 & 55.41 & 3.414 \\ \hline
7 & 14.95 & 183.32 & 57.79 & 1.216 \\ \hline
8 & 12.72 & 214.9 & 55.31 & 13.81 \\ \hline
9 & 17.01 & 181.23 & 65.19 & 0.4752 \\ \hline
10 & 15.67 & 213.01 & 67.58 & 18.66 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Decision matrix of Scenario 3, for criterion variation within \(\pm 5\%\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Satellite ID** & \begin{tabular}{c} **Upload Speed** \\ **[Mbps]** \\ \end{tabular} & \begin{tabular}{c} **Download Speed** \\ **[Mbps]** \\ \end{tabular} & \
\begin{tabular}{c} **Ping [ms]** \\ \end{tabular} &
\begin{tabular}{c} **Packet Loss** \\ **[\%]** \\ \end{tabular} \\ \hline
1 & 10.97 & 198.39 & 47.52 & 0.0096 \\ \hline
2 & 12.46 & 215.81 & 63.92 & 0.1632 \\ \hline
3 & 13.31 & 206.83 & 64.21 & 2.97 \\ \hline
4 & 15.88 & 219.18 & 64.80 & 19.65 \\ \hline
5 & 14.95 & 222.93 & 46.36 & 0.0098 \\ \hline
6 & 13.98 & 233.57 & 55.41 & 3.4144 \\ \hline
7 & 14.95 & 183.32 & 57.79 & 1.216 \\ \hline
8 & 12.72 & 214.9 & 55.31 & 13.81 \\ \hline
9 & 17.01 & 181.23 & 65.19 & 0.4752 \\ \hline
10 & 15.67 & 213.01 & 67.58 & 18.66 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Random variation rates applied to packet loss, for criterion variation within \(\pm 5\%\).
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Slice-Based QoS Manager Solution
#### 4.6.5 Per-Flow QoS: IntServ  
Our per-flow slice-based QoS implementation adds queue settings and rules to reserve network bandwidth, taking advantage of egress traffic shaping to do so. Hence, the target interface is not only limited to a specific bandwidth, but instead, distinct known applications or services can be assigned a bit rate using IntServ classification [21]. Traffic shaping is configured using the QoS and queue OvS tables and uses OpenFlow in order to identify and separate services in the solution SDN environment.
# A Hybrid MCDM Model for Live-Streamer Selection via the Fuzzy Delphi Method, AHP, and TOPSIS
## 3 Research Method
### Ahp  
AHP is a widely used MCDM approach that performs a pairwise comparison to establish the weights of the elements and the priority of the alternatives [46]. AHP is performed in seven steps, which are explained as follows [47]:  
1. Confirm the problem  
The scope of the research problems should be clearly defined and all the elements that may affect the problem should be considered.  
List all elements related to the problem  
All the elements related to the problem can be obtained through the Delphi method, brainstorming, and literature collection, without considering their order and relevance.  
3. Construct a hierarchical framework  
A hierarchical framework can be constructed to derive multiple levels according to the needs of the problem. There are no fixed methods for constructing a hierarchical framework. The uppermost layer of a hierarchical framework is the goal. Each of the bottom layers can include multiple elements, but it is recommended that they contain no more than seven elements because humans cannot compare more than seven elements simultaneously.  
4. Questionnaire design and survey  
AHP uses elements of the previous level of the hierarchical framework as the basis for evaluating elements of the current level. Based on the element at the upper level, any two elements at a certain level are compared to evaluate their relative contribution or importance. AHP involves the evaluation of the relative importance of elements of the same level through a pairwise comparison. The elements are rated using the following five scales on a scale of 1, 3, 5, 7, and 9, with 2, 4, 6, and 8 being the intermediate values. In other words, the participants use an element from the previous level as the evaluation benchmark to compare elements from each level through pairwise comparison on a scale of 1 to 9. The questionnaire must clearly state the questions for each pairwise comparison and include detailed instructions.  
5. Establish a pairwise comparison matrix  
The pairwise comparisons can represent the weights of elements.  
6. Calculate the consistency ratio  
To determine the appropriateness of the responses in the questionnaires, the consistency ratio of each pairwise comparison matrix is computed, and a ratio of less than 0.1 is considered acceptable.  
7. Calculate the element weight in each level, perform integration between levels, and finally determine the priority of the alternatives.  
There are several famous weighting means. Among them, AHP has been the most utilized, owing to its applicability and popularity [48]. As a result, we acquired the weight of each dimension and selection criterion based on AHP.
## 4 Results
### General Applications of RIP
#### 4.3.1 Sleep Studies and Detection of Apnea  
Obstructive sleep apnea (OSA) is a medical condition interlinked with obesity and hypertension that has become more prevalent in recent decades in both rich and developing nations [75]. OSA causes decreased quality of life and mortality in patients in some severe cases [76]. Apneas are defined by temporary cessation of respiration during sleep for \(\geq\)10 s, with \(\geq\)5 such events per hour, which corresponds to a reduction of \(\geq\)4% saturation in oxygenated hemoglobin. However, respiration does not completely cease in hypopnea, which is characterized by a reduction in respiratory airflow by 30-50% [77]. The severity of OSA is classified with the apnea-hypopnea index (AHI), with scores \(\geq\)5 for mild, \(\geq\)15 for moderate, and \(\geq\)30 for severe forms [53]. The American Academy of Sleep Medicine (AASM) recommends using an oronasal thermistor and a nasal cannula for AHI scoring in sleep studies [78], but these sensors might be uncomfortable for some people and can even interrupt their sleep. Patients can move them about or even remove them during sleep studies, making the sleep study data incorrect and unusable. Continuous positive airway pressure (CPAP), a prevalent treatment for obstructive sleep apnea, is depicted in Figure 5a, while Figure 5b depicts the airway obstruction caused by apnea. The authors of [12] proposed that the combination of tracheal sounds and RIP could be used as a reliable method for detecting and characterizing respiratory events without the need to place sensors on patients' faces and that it could be used as an alternative to or in addition to the recommended AASM sensors. RIP can monitor breathing asynchrony between the thorax and abdomen occurring in OSA and can aid in diagnosis of OSA, though asynchronous breathing is not exclusive to OSA [79]. Detection of sleep stages has also been studied with RIP, along with apnea detection, in [56], where a strong correlation was found between RIP and PSG (r = 0.91) for AHI estimation, and accuracy ranges between 57.1% and 75% were determined for the detection of sleep stages as well as wakefulness.  
Figure 5: (**a**) Patient asleep while using a CPAP device. (**b**) OSA patient with tissue blocking the airway.
## 2 Simulation Model Development
### Fatigue Life Evaluation Method
#### 2.5.3 Damage Sum  
In order to evaluate the fatigue life, an S-N curve is required. To apply a more accurate S-N curve, an S-N curve was created using the results of previous studies which performed fatigue life tests using the same model as the spiral bevel gear used in this study. In the previous study, three samples of spiral bevel gears were used to evaluate the fatigue life based on the ALT. In this study, the S-N curve was obtained by calculating the bending stress using Equations (5)-(7), based on the test results of the previous study, as shown in Figure 5. The number of cycles of the S-N curve was calculated considering the rotational speed and the service life of the tractor. The coefficient of determination (R\({}^{2}\)) of the obtained S-N curve was 0.99.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \(Y_{Fa}\) & 2.89 \\ \(Y_{Sa}\) & 1.58 \\ \(Y_{\varepsilon}\) & 0.63 \\ \(Y_{K}\) & 1.027 \\ \(Y_{LS}\) & 0.963 \\ \(K_{A}\) & 1 \\ \(K_{V}\) & 1 \\ \(K_{F\beta}\) & 3.37 \\ \(K_{Fa}\) & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Major parameters for calculating the tooth root bending stress.  
Figure 5: S-N curve of spiral bevel gears obtained based on fatigue life test results presented in previous studies [41].  
The damage sum can be calculated based on the Palmgren-Miner cumulative damage rule, using the S-N diagram and the load spectrum of the measured data, as shown in Equation (8) [34; 42]. This method assumes that the total life of the tractor can be estimated by adding the percentage of life consumed at each stress level.  
\[D=\sum_{i=1}^{k}\frac{n_{i}}{N_{i}} \tag{8}\]  
where \(D\) is the damage sum, \(n_{i}\) is the number of cycles of measured data at the \(i\)th stress level, and \(N_{i}\) is the number of cycles of the S-N curve at the \(i\)th stress level.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 2 Materials and Methods  
### Cells and Cell Culture  
The flexibility of CAPs has enabled them to be applied in the internal clinical treatment [26]; therefore, the human embryonic kidney cell line was employed to conduct the primary experiments, considering the possible CAP applications in viscus therapy. Before CAP treatment, the cells were grown in Dulbecco's Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum and 1% Penicillin-Streptomycin. The cells were not treated by CAPs until the logarithmic growth phase was reached. Before the treatment, the cells were observed by the XD biological microscope (Sunny Optical Technology Co., Ltd., Ningbo, China) to make sure that they were in the logarithmic growth phase. Then, the cells were seeded near confluence in 96-well plates (5000 cells per well). Additionally, the cells were then kept in the cell incubator (Thermo Fisher Scientific Co., Ltd., Waltham, MA, USA) for 24 h with 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) prior to the CAP treatment. The cell line, the reagents, and their sources of procurement used in this study are listed in Table 1.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 7 Security Analysis
### Image Entropy Analysis  
Commonly, entropy is defined to judge whether the complexity or randomness is strong enough. Information entropy plays an essential role in measuring randomness of information. Image entropy can act as a reference index of information randomness of an image. Each pixel in either channel r, channel g, or channel b of an RGB image has an intensity value or gray value between 0 to 255, and the ideal entropy value of such encrypted message is 8, which means that the information that is contained in such a massage is arbitrary. High values of image entropy also represent that the ability to resist statistical analysis. We use Equation (26) to calculate image entropy.  
\[H(x)=-\sum\limits_{i=1}^{L}P(x_{i})\text{log}_{2}\,P(x_{i}) \tag{26}\]  
where \(x_{i}\in{1,2,3,\cdots,L}\) represents the gray value of pixels, and \(P(x_{i}),0\leq P(x_{i})\leq 1\), \(\sum\limits_{i=1}^{L}P(x_{i})=1\) is the probability of gray value \(x_{i}\).  
Table 9 lists the experimental results of information entropy of both original images and signal \(Y_{c}\), and it implies that all the values calculated based on signal \(Y_{c}\) are approaching 8, albeit with various entropy values of original images.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Image** & **Channel r** & **Channel g** & **Channel b** & **Average** \\ \hline trailer & 0.078644 & 0.099305 & 0.088672 & 0.088874 \\ hallway & 0.052972 & 0.058863 & 0.051743 & 0.054526 \\ kids & 0.094172 & 0.112676 & 0.120774 & 0.109207 \\ llama & 0.090918 & 0.081103 & 0.127626 & 0.099882 \\ car & 0.107737 & 0.049603 & 0.063837 & 0.073726 \\ football & 0.048435 & 0.068405 & 0.051792 & 0.056211 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Correlation values for concealing analysis. The compression rate is 0.7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Entropy of Original Image**} \\ \hline
**Image** & **Channel r** & **Channel g** & **Channel b** & **Average** \\ \hline trailer & 7.6132 & 7.3457 & 7.1752 & 7.3780 \\ hallway & 7.1481 & 7.2374 & 6.704 & 7.0298 \\ kids & 7.2444 & 7.0481 & 6.8418 & 7.0447 \\ llama & 7.6599 & 7.2238 & 7.3275 & 7.4037 \\ car & 7.6715 & 7.6285 & 7.6061 & 7.6353 \\ football & 6.5350 & 6.6437 & 6.9785 & 6.7190 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Entropy of Signal \(Y_{c}\)**} \\ \hline
**Image** & **Channel r** & **Channel g** & **Channel b** & **Average** \\ \hline trailer & 7.9890 & 7.9886 & 7.9891 & 7.9889 \\ hallway & 7.9881 & 7.9885 & 7.9889 & 7.9885 \\ kids & 7.9879 & 7.9882 & 7.9886 & 7.9882 \\ llama & 7.9877 & 7.9884 & 7.9886 & 7.9882 \\ car & 7.9883 & 7.9883 & 7.9880 & 7.9882 \\ football & 7.9887 & 7.9882 & 7.9883 & 7.9884 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Experimental results of image entropy. The compression rate is 0.7.
## 5 Simulation Experiments and Results Analysis
### Results and Analysis of the Experiment
It can be seen that the scheme of this paper > CFN > DNS, and DNS had the lowest scheduling success rate due to the lack of service information. The success rate of this solution is about 10-23% higher than that of the CFN, and the advantage increased with the increase in load. This is because the domain division mechanism of this paper provides a certain degree of guarantee for the quality of service and can select the local nearest optimal cluster for task processing. The CFN lacks a domain division mechanism and only selects the optimal cluster. The optimal cluster will be selected multiple times, causing network and cluster overload, resulting in scheduling failure. Figure 9 is a performance comparison of this paper and the CFN architecture under different algorithms. It can be seen that, corresponding to the analysis in Section 4.1, the performance of the BWM-TOPSIS algorithm proposed in this paper was slightly higher than that of the traditional EWM-TOPSIS method, while the performance of the CFN was just the opposite. Notably, even under less favorable conditions, this paper outperformed the CFN's best performance, highlighting the superiority of the proposed architecture. Figure 9: Comparison of the success rate of scheduling. (**a**) Comparison of different architectures under the same subjective weighting method (BWM). (**b**) Comparison of this paper and CFN architecture under subjective weighting method (BWM) and objective weighting method (EWM).