esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mechanism of Low-Frequency Pressure Fluctuation
### Resonance Mechanism of Low-Frequency Pressure Fluctuation  
[PERSON] [30] postulated that periodic pressure pulsations in cavities result from acoustic resonances excited by edgetone feedback, and this theory has been applied by several researchers to investigate low-frequency pressure fluctuations in open-jet wind tunnels. It is generally accepted that significant low-frequency pressure fluctuations occur when the edgetone feedback frequency matches the natural frequency of some part of the wind tunnel structure [4; 6; 17; 18; 20; 26].  
The frequency of the edgetone is determined by both the length and the velocity of the jet. Since the jet length and plenum dimensions are usually comparable in magnitude, the edgetone feedback frequency is likely to be close to the vibration frequencies associated with the plenum, as well as certain frequencies of standing waves within the pipe loop. As a result, resonance occurs, resulting in significant low-frequency pressure fluctuations.  
Different wind tunnels may have different resonance excitation mechanisms. The potential excitation sources that can resonate with edgetone feedback include the standing wave modes of the wind tunnel circuit [4; 6; 7; 8; 9; 11; 19; 26], the reverberation modes of wind tunnel chambers [4; 6; 19; 26], the Helmholtz modes of wind tunnel chambers [6; 8; 26], and the pressure pulsation modes caused by the rotation of the wind tunnel fans [4]. Planar standing waves in the three directions of the plenum also serve as excitation sources for low-frequency pressure fluctuations in open-jet wind tunnels [22; 23; 25; 49].  
Edgetone feedback easily excites the standing wave modes of the wind tunnel circuit and the Helmholtz mode of the plenum, as confirmed by several studies [4; 6; 7; 8; 9; 19; 26]. The pressure pulsation generated by the rotation of the fan, on the other hand, does not easily resonate with the edgetone feedback due to its high frequency. The occurrence of a standing wave in the plenum is related to the ratio of plenum size to jet length [22].  
Table 1 provides an overview of the primary resonance mechanisms found in various wind tunnels as documented in the literature, along with the corresponding techniques for suppression. It can be seen that the standing wave of circuit loop is the most prevalent source of excitation.  
In the IVK-MWT wind tunnel, three modes share a common frequency of 18 Hz: the edgetone feedback mode, the 5 th order wind tunnel circuit loop mode, and the plenum longitudinal standing wave mode. The edgetone feedback mode is the only mode that involves energy injection, while the other two are passive modes without injection. Note that in Table 1, the author infers that the plenum planar standing wave of IVK-MWT and DNW-LLF wind tunnels serves as an excitation source that resonates with edgetone feedback. This inference is made based on literature data and is detailed in the fourth part of Section 2.2.2.  
\begin{table}
\begin{tabular}{c c c} \hline
# Muscle Activity of Superimposed Vibration in Suspended Kneeling Rollout
###### Abstract  
Training using instability devices is common; however, for highly trained athletes, a single device may not provide sufficient challenge. This study examines the effect of superimposed vibration in suspended kneeling rollout. Seventeen physically active participants performed the exercise with non-vibration, vibration at 25 Hz, and vibration at 40 Hz. Muscle activation of the pectoralis clavicularis, pectoralis sternalis, anterior deltoid, serratus anterior, infraspinatus, and latissimus dorsi was recorded during exercise, and the perception of effort was recorded after exercise (OMNI-Res scale). One-way repeated-measures analysis of variance (ANOVA) showed significant differences for the kneeling rollout (\(p<0.05\)). [PERSON]'s test showed significant differences in the OMNI-Res (\(p=0.003\)). Pairwise comparison showed significant differences in the anterior deltoid (\(p=0.004\)), latissimus dorsi (\(p<0.001\)), infraspinatus (\(p=0.001\)), and global activity (\(p<0.001\)) between the 25 Hz and non-vibration conditions. It also showed significant differences between the 40 Hz and non-vibration conditions for pectoralis sternalis (\(p=0.021\)), anterior deltoid (\(p=0.005\)), latissimus dorsi (\(p<0.001\)), infraspinatus (\(p=0.027\)), and global activity (\(p<0.001\)). The post hoc Conover pairwise comparison showed significant differences in the OMNI-Res only between the non-vibration and vibration at 40 Hz conditions (\(p=0.011\)). Superimposed vibration increases the muscle activation of the upper limbs when performing the suspended kneeling rollout.  
electromyography; instability; overhead; suspension training; upper limb +
Footnote †: journal: Applied sciences
## 6 Discussion  
### All-in-One and Hierarchical Segmentations  
There are numerous image segmentation [72] and classification [73] algorithms. Machine learning techniques are especially important because the segmentation by training is possible. An alternative approach based on the analysis of images and design of entire segmentation algorithm manually, by long trial and error sequences, is very difficult for complex image content. Parallel processing is possible for machine learning, so different configurations of machine learning algorithms may be automatically trained and tested for the optimization of segmentation performance.  
Two main approaches are applied for the design of image segmentation systems using machine learning algorithms:  
* All-in-One approach, where a single machine learning algorithm is used for overall image processing. Such an approach could also be used for the final classification purposes, so the input is the image and the output is the recognized class.
* Hierarchical approach, with well-defined processing stages where some of them could be machine learning-based and others could be typical image processing algorithms, such as filters.  
The solution proposed in this paper for the segmentation of autopsy lung images uses the hierarchical approach with the HistAENN and additional preprocessing.
## 3 Data and Methods  
### Data Acquisition  
Immediately after the Wenchuan earthquake, an emergency project, Monitoring and Assessment of the Wenchuan Earthquake Disasters by Remote Sensing, was initiated by the Chinese Academy of Sciences (CAS). A large number of remote sensing images of various types were acquired between 15 and 28 May 2008, for disaster assessment and rapid monitoring and evaluation of the severely damaged areas. The spatial resolution of these images ranged from 0.5 to 0.8 m depending on the flight altitude. During the subsequent years, airborne optical remote sensing images covering most of the heavily affected areas were again acquired in 2009, 2010, and 2011 to monitor the process of environmental restoration and reconstruction [38]. In our study, we used airborne optical images from 2008 to 2011 acquired from the Center for Earth Observation and Digital Earth (CEODE), CAS, to analyze the hazards development and ecological recovery in the earthquake-affected areas. Table 1 shows the flight task details.  
The spectral bands of the acquired images include blue, green, and red. The airborne sensors had a high spatial resolution, ranging from 0.3 to 0.7 m. However, the large data size and non-uniform image resolution made the images difficult to manage, thus after acquiring the images, CEODE resized the images to a uniform resolution. In addition, a systematic orthorectification of all the airborne images was conducted using the 25-m DEM data interpolated from 1:50,000 scale topographic maps [38], except for the year of 2010 because of the lack of Position and Orientation System (POS) data.  
Auxiliary data used in this study included a Sichuan administrative vector map; 30-m resolution Digital Elevation Model (DEM) data (scene ID: srtm_57_06 and srtm_58_06) obtained by the Shuttle Radar Topography Mission (SRTM) and acquired for this study from the Land Cover Facility, University of Maryland; the China geological map, produced by the China Geological Survey, to acquire the lithology  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Flight Year** & **Flight Data** & **Sensor** & **Image Resolution** & **Number of Images** & **Flight Area (km\({}^{2}\))** \\ \hline
2008 & 15 May–28 May & ADS40 & 0.5–0.7 m/2 m & 99 & 36,744 \\
2009 & 16 May–03 June & ADS80 & 0.5 m/5 m & 108 & 33,266 \\
2010 & 18 April–04 May & UCXp & 0.3 m/4 m & 78 & Approximately 10,000 \\
2011 & 17 May–07 June & UCXp & 0.3 m/4 m & 57 & 20,125 \\ \hline \hline \end{tabular} Note: the number before “/” in the “image resolution” column refers to the original spatial resolution, the number after “/” refers to the spatial resolution after resizing.  
\end{table}
Table 1: Remote sensing data acquired over the earthquake-damaged area.  
Figure 1: Airborne imagery across the earthquake region (background: Landsat TM image in 2007).  
data; and 30-m resolution Landsat TM images from 2007 (entity ID: LE71290382007262 PFS00 and LE71300382007141 SGS00) acquired from the United States Geological Survey to reflect the land cover before the earthquake.
# Analysis of Bulb Turbine Hydrofoil Cavitation
## 1 Introduction
They [16] performed a multipoint and multiobjective optimization design procedure, achieving efficiency improvements up to 0.91%. The optimization procedure included the 3D inverse design method, CFD analysis, and multipoint and multiobjective optimization technology. Runner blades are central turbine elements, where energy is transferred from the fluid to the generator shaft. Blade sizes and angles, including hydrofoil selection, contribute to the blade design. The blade design is often a trade-off between efficiency, cavitation development, and cavitation erosion. A pressure difference between the suction and pressure sides is limited by cavitation, while at the same time the high pressure difference enables energy transfer from water to the generator. If the turbine is at full load and the water level is low in the reservoir, the pressure on the suction side may decrease and locally drop below the vapor pressure. Cavitation on the runner blades may appear in several locations. These are the turbine blade leading edge, the tip, the pivot, the gap between the blade tip and the discharge ring, the blade suction side close to the trailing edge, and the cavitation near the draft tube cone [7; 17]. For the abovementioned cavitation types, the root causes are suboptimal runner blade design and hydrofoil selection for the selected operating conditions. Cavitation is extensively studied in hydraulic turbine machinery; among these, few studies have employed the visualization method [6; 7]. On bulb turbines' blades, the initial cavitation most often appears at the location with the highest local speed i.e., at the blade tip [18]. We show an experimental analysis of a modification to the hydrofoil design of bulb turbine blades so as to resist cavitation during continuous operating conditions at high flows. The paper shows the cavitation characteristics of two bulb turbine hydrofoils. The improvement from the existing to modified hydrofoil is discussed based on visualization analysis in the cavitation channel. Furthermore, we show a comparison of the modified runner blades with the unmodified blades in terms of the turbine output. The paper shows marked improvement in cavitation occurrence between existing and modified hydrofoils, and a confirmed regression model of the void fraction.
## System Model  
The MEC network consists of the UEs and \(N\) BSs equipped with the MEC server. The BSs are distributed on a finite two dimensional (2D) regular grid network, each with a supporting radius of R (as shown in Figure 2). Let \(\mathcal{N}=\{1,2,\ldots,N\}\) denote the index set of MEC servers, and \(l_{n}\) represents the location of BS \(n\) equipped with the MEC server. The mobile UEs create a total \(M\) task for offloading into MEC servers, denoted by the set of \(\mathcal{M}=\{1,2,\ldots,M\}\). Let \(l_{m}\) signify the location of task \(m\) created by a user with mobility. Due to the densified deployment of BSs, it is assumed that each task \(m\) can be served by multiple BSs. The set of admissible BSs to location \(l_{m}\) can be represented as  
\[\mathcal{A}(m)=\{n|\parallel l_{n}-l_{m}\parallel\leq R,\forall n\in\mathcal{ N}\} \tag{1}\]The users decide which candidate BSs \(\mathcal{A}(m)\in\mathcal{N}\) are appropriate to offload computational tasks based on the utility cost. It is also assumed that only one BS is responsible for computing each task \(m\). Hence, users will choose one BS station from \(\mathcal{A}(m)\) for each task \(m\). Due to large task sizes, each task \(m\) can be divided into many sub-tasks. An online learning approach is considered to manage the user mobility. Any random mobility model can be applicable and suitable. The system model has been divided into three sub-models: computation task model, network model, and mobility model. The explanations of each sub-model follow.
# Active Control of Low-Frequency Noise through a Single Top-Hung Window in a Full-Sized Room
## 2 Materials and Methods  
### Experimental Setup  
The active control system was installed on a single 60 cm \(\times\) 40 cm top-hung window above a full-sized apartment window, as shown in Figure 1a. A single reference microphone (PylePro PLM3, Pyle Audio Inc., New York, NY, USA) sensed the primary noise from a single loudspeaker (8341A, Genelec Oy, Isalmi, Finland), located in an adjoining 270 cm \(\times\) 340 cm \(\times\) 260 cm chamber outside the window, as depicted in Figure 1b. The reference signal was fed into the controller (NI 8135, National Instruments, Austin, TX, USA), which drove four control loudspeakers (T3-2190S, TB Speaker Co. Ltd., Taipei, Taiwan) to actively minimize the sum-of-the-squared pressures at four error microphones (PylePro PLM3).  
An array of six microphones (GRAS 40 PH, GRAS Sound & Vibration A/S, Holte, Denmark) was distributed across the entire 287.5 cm \(\times\) 525 cm \(\times\) 260 cm room to evaluate the global control performance in the entire room, as denoted by the microphones numbered from 1 to 6 in Figure 2. These six microphones were arranged based on the recommendations in ISO 16283-3:2016 [22]. The seventh observation microphone (GRAS 40 PH) was mounted in the center of the error microphone array, which was 30 cm from the window aperture, to evaluate the performance at the error microphone positions.  
As the chamber where the primary source was located was designed as a reverberation chamber, both doors were left open to release some of the acoustic modes. The doors in the receiving room were kept closed during the experiment.
# A Practical Deep Learning Architecture for Large-Area Solid Wastes Monitoring Based on UAV Imagery
## 3 Methods
### Segmentation Model
#### 3.2.4 Feature Fusion Module  
Two feature fusion mechanisms are designed to promote information interaction between branches in this study: the first involves downsampling and addition, where spatial features are passed to the contextual feature branch; the second mechanism involves upsampling contextual features, multiplying them with spatial features to obtain semantic features, which are then summed up with the original spatial features and passed to the spatial feature branch. This mainly involves multi-level fusion of DCR boundary information and GSM semantic information. The innovation of this part includes:  
1. Different preprocessing scales. The GSM block is \(1/16\) in stage 1, \(1/32\) in stage 2, and the DCR is \(1/8\) in both stages. Since the global semantic information covers the entire image, the GSM block can focus on highlighting global semantic features in low-resolution feature maps, while spatial detail information mainly concentrates on local regions. The high-resolution feature maps allow the DCR to focus on highlighting spatial detail information. The separation and extraction mechanism makes feature extraction more efficient.
2. Multi-level fusion and complementary information. By conditioning the learning of the GSM block on DCR features, and conditioning the learning of DCR on SWR features, for the contextual branch, downsampling and global semantic information extraction will result in the loss of feature map detail information. However, spatial features from high-resolution can effectively supplement the detail information, addressing the blurring of feature maps caused by information loss. For the spatial feature branch with low-level detailed features, it typically contains a large amount of noise and redundant information. After communication, it can obtain high-level global semantic information at the low level and weight the detail information based on the importance of semantic information.
3. Improving the joint training-prediction accuracy of the GSM block and DCR block. Joint training framework refers to a training approach where multiple components are trained together in an integrated manner. Due to the dependency between the two branches, integrating them into a joint training framework can better utilize their complementary information, enhancing the model's expressive power and prediction accuracy. It also reduces the reliance on the sample size.
# Development and Characterization of Tailored Polyurethane Foams for Shock Absorption
## 2 Materials and Methods
### Processing of Polyurethane Foams (PUR)
#### 2.2.2 Influence of the Catalyst  
Four catalysts were used while keeping the NCO/OH proportion. Since the resulting foam was in a semirigid shape, whatever the type and mass of the catalyst, adding A33 alone at 5% of the polyol (POPE) mass resulted in a relatively good expansion reaction, which created an intact semirigid substance (Figure 3a). Adding A33 and EO at 5 and 0.5% of POPE resulted in a speedy cream expansion of fewer than 12 s (Figure 3b). Lastly, the essential components were not even correctly combined. For both catalysts, adding A33 and EA1 at 2.5% of POPE resulted in a versatile substance with slight expansion due to  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multirow{2}{*}{**Materials**} & **PUR\_01** & **PUR\_02** & **PUR\_03** & **PUR\_04** & **PUR\_05** & **PUR\_06** \\ \cline{3-8}  & & **PUR1.05/** & **PUR1.1/1.25/** & **PUR1.04/1.25/** & **PUR1.08/0.97/** & **PUR1.05/1.89/** & **PUR1.01/** \\  & & **0.93/0/0** & **10.52/0** & **10.43/0** & **3.52/0** & **0/10.53** & **1.74/0/0** \\ \hline \multirow{8}{*}{A} & Polyol (wt.\%) & 62.58 & 59.09 & 58.44 & 62.17 & 58.83 & 65.94 \\  & Glycerin & & 1.38 & 2.19 & 2.15 & 2.29 & 2.28 & 1.01 \\  & Dichloromethane & & & & & & & \\  & (wt.\%) & 0.74 & 0 & 0 & 0 & 1.24 & 0.82 \\  & Silicone (wt.\%) & 0.83 & 2.07 & 1.46 & 1.54 & 1.24 & 2.75 \\  & PEG (wt.\%) & 1.25 & 0 & 0 & 0 & 0 & 1.11 \\  & Catalyst & & & & & & \\  & BDMAEE & 0.93 & 1.26 & 1.26 & 1 & 1.79 & 1.76 \\  & (wt.\%) & & & & & & \\  & Bentonite & 0 & 10.52 & 10.43 & 3.52 & 0 & 0 \\  & (wt.\%) & & & & & & \\  & Alumina & 0 & 0 & 0 & 0 & 10.53 & 0 \\  & (wt.\%) & & & & & & \\ \hline B & PMDI (wt.\%) & 32.29 & 24.87 & 26.26 & 29.49 & 24.09 & 26.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantity of the prepared polyurethane foams (in wt.%).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & \multirow{2}{*}{**NCO/OH**} & **NCO (mol)/** & **NCO (mol)/** & **NCO (mol)/** & **Shore A (A)** & **Apparent Density (at RT)** \\ \hline PUR\_01 & \(1.05\) & 44.55 & 3.1 & 39.32 & 50 & 0.32 \\ PUR\_02 & \(1.1\) & 44.55 & 3.1 & 37.4 & 37 & 0.07 \\ PUR\_03 & \(1.06\) & 44.55 & 3.1 & 38.92 & 51 & 0.23 \\ PUR\_04 & \(1.04\) & 44.55 & 3.1 & 39.73 & 76 & 0.23 \\ PUR\_05 & \(1.08\) & 44.55 & 3.1 & 38.15 & 47 & 0.18 \\ PUR\_06 & \(1.01\) & 44.55 & 3.1 & 41 & 85 & 0.34 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The various ratios used during the PUR formula optimization.  
low reaction acceleration (see Figure 3c). The inclusion of A33 and TMD at 5% and 0.5% of POPE, respectively, resulted in an exceedingly slow growth with a 320-s improvement. Therefore, it can be inferred that the benefit of adding TMD is the position of an expansion retardant. Still, its crucial downside is the reaction with isocyanates to urea, which is why the spray looks very sticky (Figure 3d).
## 2 Related Work
### Secure Data Aggregation and Authentication Modeling  
Secure data aggregation and authentication ensure network reliability and energy efficiency are maintained in the UVWSN in CBND. Therefore, due to the unreliable nature of the USN channel and packet drop deployment found in secure MAC protocols, including SEFSC, SDA, and EEST, our proposed SDAA protocols employ secure data aggregation and authentication-optimized methods based on cluster-based network design (CBND). Based upon this, we modeled a cluster network in [22] for our proposed SDAA protocol to evaluate data reliability and compare the performance of the network deployment with the other secure MAC protocols, EEST, SDA, and SEFSC.  
Therefore, our proposed SDAA protocol employs optimized secure data aggregation and authentication methods to use trustworthiness or data integrity to provide data reliability deployment in CBND for UVWSN Consequently, we can deploy UVWSN to monitor and inspect vehicles, including ship structures [23]. For the proper authentication of individual USNs (underwater sensor nodes) in the network, the SDAA protocol utilizes the base station (BS). It deploys cluster heads (CH) and cluster nodes (CN) in the UVWSN.  
Figure 1 depicts the secure data aggregation and authentication optimized arrangement deployment in CBND that utilizes two steps: The first involves labels 1-4. These steps involve the aggregation process. In step 1, CH sends a registration request to the gateway. In step 2, the request is decrypted and retrieved, and in step 3, the GW generates the hash value of the data. In step 4, the decision of the hash value is determined by CH, if it is yes or no. Secure authentication of cluster heads (CHs) follows. The authentication process also utilizes protected data aggregation (found in the next section).  
In the second step, which involves labels 5-9, gateways (GWs) authenticate CHs. The authentication process is required to confirm that the operating CH in each cluster is a valid USN. Based upon these steps, the CH node must also not be compromised. This ensures that the entire cluster is in safe operation. Each USN is used to protect the data transmissions during the authentication process. It uses symmetric encryption (shared key encryption process) and sends the data to the CH. The data are then aggregated securely and transmitted to the base station (BS). Timestamp values are processed whenever compromised data or MNA (malicious node attacks) are detected, confirming the secure network operation.