esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Applications and Background of Uwsn
### Background of UWSN
#### 2.2.3 Localization  
Most of the applications of UWSN require time and location of the sensed data. So, localization is very important to perceive the network architecture and also to collect the data accurately [22]. Localization underwater is challenging because radio frequency is highly attenuated in underwater  
Figure 2: Two dimensional (2D) UWSN architecture.  
conditions, thus GPS technology is not feasible there. In most cases, the exact positioning of all the nodes is not necessary. Rather, the exact locations of a few nodes are enough and these are called the reference nodes. Such localization schemes are broadly of two types--(i) Range based scheme, and (ii) Range free scheme.  
The range measurement is much more preferred when the acoustic channel is used instead of radio frequency or optical waves [37, 38]. To locate a node in the network, a range based scheme utilizes distance, angular values, time stamps and difference of time for a packet received or sent. Whereas a range free scheme rather uses an anchor node as a reference for locating any node within the network [39]. Researchers like [PERSON] et al., [PERSON] and [PERSON] and [PERSON] et al. have surveyed these localization schemes elaborately in References [40, 41, 42] but only small-scale networks are compatible with these solutions. [PERSON] et al. proposed the Dive and Rise (DNR) method for positioning [43]. It uses DNR beacons for localization of the sensor nodes. In this method, static anchor nodes are replaced by mobile DNR beacons. But the main downside of this method is that these DNR beacons are very costly and many of them are needed. [PERSON] et al. reduced the expenses to a great extent by decreasing the requirements of DNR beacons [44]. They replace the DNR beacons with nodes of 4 separate kinds: (i) water floating-buoys, (ii) detachable transceivers, (iii) nodes that are attached to the bottom, and (iv) regular sensor-nodes.  
It is presumed that the network is completely static and also the nodes of the network can measure the water levels. A pressure sensor always remains attached to this sensor nodes to perform this task.
# Bouncer: A Resource-Aware Admission Control Scheme for Cloud Services
## 3 Background and Challenges
### Admission Control Challenges in System Virtualization  
Due to the increasing size of computing systems, cloud admission control schemes are growing in complexity to a phenomenal degree. Below we highlight a few of the many challenges faced by these admission control schemes.  
* _Resource contention:_ Virtualized services and environments consistently need to verify whether there are enough resources available to satisfy the VM-level reservation (without interrupting VM kernel operations) or the VM-level reservations of other VMs running on that host.
* _Resource pre-emption:_ To maximize profits, service providers are keen to accept as many services as possible. This develops a need to define the effective number of requests that can be accepted by a resource provider while ensuring that QoS violations are minimized. Efficient mechanisms are required to provide these services and involve using pre-emption aware schemes.
* _Oversubscription:_ While oversubscription can leverage underutilized capacity in the cloud, it can also lead to overload. Workload-admission control avoids oversubscription effects by minimizing congestion, packet loss, and possible degradation of the user service experience.
* _Overhead and overbooking:_ VM contention issues on shared computing resources in data centers bring noticeable performance overheads and can affect the VM performance for tenants. Efficient resource consolidation strategies can address these issues by using various overhead mitigation techniques; a number of these are presented in [37].
# Effect of Curing Conditions on Microstructure and Pore-Structure of Brown Coal Fly Ash Geopolymers
## 6 Further Research Work  
The present research study shows that it was feasible to produce a geopolymer concrete with a compressive strength of over 30 MPa from Loy Yang brown coal fly ash. However, the studies raise concerns over the long term performance and commercial viability of large scale specimens. As such, further studies in the area of alkali-activation of brown coal fly ash are needed to assess the optimal curing conditions required for large scale structural elements to achieve the engineering performance required in order to produce a highly durable geopolymer concrete. In addition, the effect of the optimized elevated curing temperature and the duration of the environmental benefits require assessment.
# Reducing Oscillations in Suspension of Mine Monorail Track
## 5 Conclusions  
The developed mathematical model of vertical oscillations of the monorail track allows setting the interconnection between the rolling stock parameters and dynamic loads in the suspension. As a result, the equations for oscillation amplitudes of the monorail elements were obtained and damping coefficient of the suspension was defined.  
Analysis of the obtained results shows that in order to reduce the vibration amplitudes of a suspended monorail mine, it is appropriate to use suspension systems for rolling stock and a monorail track, consisting of elastic elements. On the rolling stock, elastic elements can be installed between the undercarriage and the body, in the undercarriage--between its frame and wheels, as well as between the wheel axle and the wheel rim. The elastic elements of the monorail track can be located in the places where the suspension is fixed to the monorail beam or to the upper supports of the mine working.  
The change in the ratio between the natural frequencies of components of the mine suspended monorail and the frequencies of the disturbing forces is appropriate to produce by varying the rigidity of the elements of the monorail. The parameters required for this can be determined using the proposed method, and required rigidity of the monorail track is provided by embedding elastic supports into its suspension system.  
The obtained results allow setting reasonable parameters of the monorail fastening of the mine suspended monorail tracks. The proposed monorail suspension makes it possible to reduce the amplitude of the maximum suspension oscillations by 2-3-fold, and the dynamic loads formed during the movement of the rolling stock by 30-40%. It can also be used to modernize existing mine suspended monorails.  
Conceptualization and methodology, V.O.G. and N.V.M.; validation and formal analysis, R.V.K., V.A.K. and V.V.K.; investigation, V.A.K. and V.V.K.; resources, R.V.K.; data curation, L.V.I. and L.N.K.; writing--original draft preparation, V.O.G. and N.V.M.; writing--review and editing, R.V.K., V.A.K. and V.V.K.; visualization, L.V.I. and L.N.K.; supervision, V.O.G.; project administration, N.V.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available from the corresponding authors upon reasonable request.  
The authors declare no conflict of interest.
## 4 Experiments and Evaluation  
In this section, we present a comprehensive evaluation of our proposed watermarking scheme. Various aspects of the proposed scheme have been meticulously evaluated, including its robustness, imperceptibility, and computational efficiency. Additionally, we perform a comparative analysis against other related works to highlight the advantages and improvements offered by our approach. The results demonstrate the effectiveness and reliability of our watermarking method in practical scenarios.  
The images used in our experiments, as represented in Figure 2, were sourced from COVID-19 dataset ([[https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection](https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection)]([https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection](https://www.kaggle.com/datasets/fusicfenta/chest-xray-for-covid19-detection)) (accessed on 15 January 2024)). This dataset contains two types of chest X-ray images, one which represents COVID-19 infection and the other being normal. The images are gray-level images (only intensity channel), in the format of _JPG_, having variable sizes ranging from 1200 px to 2500 px.
# Diamond-Like Carbon (DLC) Coatings: Classification, Properties, and Applications
## 3 Characterizations/Properties of DLC Coatings
### Thermal Stress: DLC Films
[PERSON] [101, 102, 111] examined the stress for DLC deposition on Si and many sheets of steel prepared by a DC glow-discharge source. The stress did not seem to be identified with the H content of the film, which contradicted earlier assumptions made by [PERSON] [97], [PERSON] [100], and [PERSON] [112]. In any case, the stress expanded at low temperatures at that point leveled at higher temperatures. [PERSON] et al. [107, 108] analyzed the strain effect on diamond films using X-ray diffraction. In the view of understanding the methods of coating deposition by using sputtering deposition techniques [113], [PERSON] et al. [114] discussed whether the shifting and broadening of the Raman line from film was due to stress. They observed that the diamond Raman line was moved by 2.5 cm-1 to a higher frequency, which could be clarified by stress of 9.0 \(\times\) 10\({}^{8}\) Pa, which agreed with other measurements in Table 3. So far, in the view of bonding aspect, only adhesion strength has been considered, and it improved the mechanical properties of DLC films. This was resolved to a limited extent by the strength of the bonding between substrate and film [115, 116, 117, 118, 119]. The strength of this combination was based on their thermodynamic properties, as given in Table 4. \begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Compound**} & \(\Delta\)**H\({}^{\circ}\)f (25 \({}^{\circ}\)C)** & \(\Delta\)**G\({}^{\circ}\)f (25 \({}^{\circ}\)C)** & \multicolumn{2}{l}{**Linear Coeff. of Thermal**} \\  & **(kcal mol\({}^{\circ}\))** & **(kcal mol\({}^{\circ}\))** & **Expansion** & **Reference** \\  & & & **(at 25 \({}^{\circ}\)C) \(\times\) 10\({}^{\circ}\)C\({}^{-1}\)** & \\ \hline Alc\({}_{3}\) & -30.9 & -29 & \multicolumn{1}{c}{} & [120] \\ CoC\({}_{3}\) & 9.5 & 7.1 & \multicolumn{1}{c}{} & [120] \\ Crc\({}_{2}\) & -21 & -21.2 & 8.0 & [120, 121] \\ Crs\({}_{2}\) & -9 to –29 & -11 to –31 & \multicolumn{1}{c}{} & [122] \\ FeC & 5.0 & 3.5 & \multicolumn{1}{c}{} & [120] \\ MoC & 34.4 & \multicolumn{1}{c}{} & 5.95 & [121] \\ MoC\({}_{2}\) & -14 & -14.1 & \multicolumn{1}{c}{} & [123] \\ MoC & -11.5 & -12.5 & 5.48 & [121, 123] \\ MoC & \multicolumn{1}{c}{} & 7.8 to 9.3 & [94] \\ NiC & 11.0 & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & [120] \\ SiC & -16.5 & -15.9 & 4.63 & [121, 123] \\ TaC & -34.6 & -34.6 & 8.2 & [121, 123] \\ TaC & -32.3 to –53 & -35 to –56 & 7.1 & [94, 122] \\ TaC & -47 & -47 & \multicolumn{1}{c}{} & [123] \\ TiC & -43.8 & -43.8 & 6.52 & [121, 123] \\ TiC & -44.1 & -45.8 & 8.0 to 8.6 & [94, 122] \\ UC & -21.1 & \multicolumn{1}{c}{} & 9.47 & [121, 122] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermodynamic properties of carbides
## 2 Materials and Methods  
### Preparation of Materials and Samples  
For the purposes of this study, the materials selected were commercially sourced PEEK, PPTA, and PTFE. PEEK was procured from Victrex, Lancashire, UK, characterized by an average particle size of 48 um and a density of 1.35 \(\times\) 10\({}^{-3}\) kg/m\({}^{3}\); PTFE obtained from DuPont in Wilmington, DE, USA, featured an average particle size of 150 \(\upmu\)m and a density of 1.38 \(\times\) 10\({}^{-3}\) kg/m\({}^{3}\). Finally, PPTA was sourced from Teijin in Osaka, Japan, with specifications including an average particle size of 50 \(\upmu\)m and a density of 2.16 \(\times\) 10\({}^{-3}\) kg/m\({}^{3}\). The components of PEEK-based composites are shown in Table 1.  
In the process of preparing samples of PEEK and PEEK composites, a method involving compression molding was employed. First, the raw material was fully dried in an electric thermostatic drying oven and stirred with an oscillator for 10 min to mix evenly. Thereafter, the blended powder mixture was processed into preforms using an electric tablet press. These preforms were then carefully placed in a sintering mold, previously treated with a release agent, and subjected to heat. The final step involved optimizing the samples through die-grinding, machine polishing, and ultrasonic cleaning to obtain a usable test sample (15.7 \(\times\) 10 \(\times\) 6.3 mm). An important aspect of this procedure is the application of pressure in phases. First, the pressure is applied at half of the designated level and sustained for 10 min to guarantee overall stability. This is followed by increasing the pressure to the intended final level and maintaining it for another 10 min to complete the compression. Then, a gradual heating method is employed, beginning with preheating to 200 \({}^{\circ}\)C and maintaining this temperature for 30 min to ensure thorough mold filling before reaching the final temperature (350 \({}^{\circ}\)C, 360 \({}^{\circ}\)C, 370 \({}^{\circ}\)C, 380 \({}^{\circ}\)C, 390 \({}^{\circ}\)C) and maintaining it for another 30 min, and finally allowing the sample to cool in the furnace. This preparation process is depicted in Figure 1.
# Regressed Terrain Traversability Cost for Autonomous Navigation Based on Image Textures
## 3 Traversability Cost Prediction  
### Algorithm Architecture-Overview  
Using data received from sensors, the mobile robot will build a model of the environment to predict terrain vibrations necessary to enable safe autonomous navigation. The overall system architecture is given in Figure 5. The architecture consists of the sensors available on the robot, which are a mono-vision system, an acceleration sensor, and odometers. The camera is responsible for gathering images of subsequent terrains. The inertial unit measures acceleration signals generated while traversing. The wheel odometer tracks the position of the mobile platform over time. The proposed method follows offline and online processes to achieve traversability cost prediction.  
In the offline process, sensor information required for generating the traversability cost predictor is collected. Multiscale analysis measures a low-level image feature that is contrast distance feature to localize irregularities in terrain images. In terrain non-uniformity detection (TNUD), the resulting feature map from the multiscale analysis, is tested to determine whether the terrain nature is homogeneous or non-homogeneous. Region of Interest (ROI) localization extracts image regions traversed by the mobile robot based on its physical features and the camera model. The texture analysis calculates the texture features using the fractal dimension. Using the acceleration signal, the vibration analysis calculates the motion feature. Based on the output of TNUD, image and motion features are used to approximate either the function for uniform or non-uniform terrains.  
In the online process, the same steps accomplished in the above mentioned offline process are executed to obtain the terrain image properties, that is, the terrain image properties, texture feature  
Figure 4: Differential drive mobile robot.  
Figure 5: Overview of the proposed traversability cost prediction.  
and terrain category. Based on the terrain category class, the vibrations of the subsequent terrain are inferred using texture information and the learned function.
## 3 SP-Rife Algorithm  
The SP-Rife algorithm is proposed to enhance the precision of frequency estimation and improve the algorithm's noise resistance by combining spectral refinement and phase angle interpolation. The algorithm is primarily divided into three parts: spectral refinement, the definition of the frequency deviation factor, and phase angle interpolation estimation.
## 4 Implementation
### User Study
#### 4.3.3 Study 2  
The first study revealed the optimum combinations of the parameters of force concentration, which maximized the directional sensation. The second study tested how well the participants could feel the degree of weight generated from force concentration. We adjusted optimal combinations of rotation angle intervals and rotational speeds to elicit the desired weight sensations. Participants were told that the second test was to evaluate how well they could feel the different weights generated by the DFF. Instructions were provided, explaining that the participant should correctly guess the degree of weight generated by the DFF. The study was conducted in a controlled lab setting with an experiment mediator. There were three degrees of weight used for this study (i.e., 1 = light; 2 = middle; 3 = heavy). Participants received a 2-min tutorial session to learn to feel the weights generated and were blindfolded as in the first study. The combinations of parameters affecting the force concentration were based on three rotation angle intervals (\(\pm 30^{\circ}\); \(\pm 60^{\circ}\); \(\pm 90^{\circ}\)) and 3 degrees of weight (1 = light; 2 = middle; 3 = heavy). In total, 45 combinations (3 tests per weights per interval) were used (Figure 10). The participants audibly spoke the rendered direction when DFF performed each combination. Then, the direction data was recorded by the experiment mediator.  
The result of the test showed that the participants could correctly answer the various degrees of weight generated by the DFF (correct answer = 79.6%; SD = 0.09). The light degree of weight showed the highest correct rate (88%), followed by the heavy degree of weight (76.9%) and the middle degree weight (74.1%). The participants could feel the difference of all the three degrees of weight. Additionally, the rotation angle interval did not have a significant effect on correct answer rates in the light and middle degrees of weight (Mean \(\pm 30^{\circ}\) interval = 84.3%, SD = 0.11; Mean \(\pm 60^{\circ}\) interval = 86.1%, SD = 0.13). However, the correct answer rate was less in the \(\pm 90^{\circ}\) interval with 68.5% (SD = 0.2). It can be seen that when the interval is longer than a certain threshold, the resolving power for distinguishing the force direction is decreased.
## 7 Conclusions  
1. A theoretical model of short-distance coal seam floor failure and a mechanical model of inclined gob-side roadway were established. The influence depth of the residual coal pillars in the upper coal seam on the floor is 27 m, and the influence ranges of the stress concentration in coal pillars and the low-stress area of the inclined coal seam are 11 m and 12.6 m, respectively. With reference to the on-site production geological condition, the coal pillar width is preliminarily determined to be in the range of 6-8 m.
2. Numerical simulation analysis reveals that when the width of coal pillars is smaller than 6 m, the internal stress of coal pillars is distributed in a single-peak shape, and stress is concentrated near the roadway. When the width is 8-14 m, the internal stress concentration position of coal pillars moves to the gob side, showing an asymmetric bimodal distribution of high stress on the gob side and low stress on the roadway side.
3. Through numerical simulation calculation, the residual coal pillar has a certain impact on the mining of the #1 coal seam, and the influence range of the stress concentration is 10.5 m, which is almost consistent with the theoretical calculation of the concentrated influence range of the remaining coal pillar of the coal seam, which is 11 m, verifying the accuracy of the theoretical calculation. As the coal pillar width rises, the overall peak stress increases first and then decreases. When the coal pillar width reaches 8 m, the coal pillars have an overall strong bearing capacity.
4. The gob-side entry driving in inclined short-distance coal seams should adopt the technology of pressure-yield anchor net cable support. The technology boasts good overall support, realizes the safety and stability of roadway support and provides a reference for mine roadway support under similar conditions.  
Conceptualization, F.H.; Methodology, W.Z.; Software, J.S.; Formal analysis, X.X.; Data curation, W.Z.; Writing--original draft, W.Z.; Writing--review & editing, D.W. and Y.W.; Supervision, F.H.; Project administration, F.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No.51974317), and the Fundamental Research Funds for the Central Universities (202 ZYJSNY09).  
The data used to support the findings of this study are included in the article.  
The authors declare no conflict of interest.  
Figure 14: Effect drawing of on-site support.
# Analysis of Grating Lobe Effects on GEO DSC Distributed Antennas
## 3 Analysis of Grating Lobes in DSC Distributed Antennas
### Grating Lobe Suppression
#### 3.3.1 Spatial Grating Lobe Suppression  
The suppression of spatial grating lobes is primarily achieved through the design of ultra-sparse and non-uniform antenna array layouts, which break the periodicity of uniform arrays and eliminate the grating lobes [19; 20]. The antenna layout can be either a linear or planar array. Various intelligent optimization algorithms are employed to optimize the antenna positions as design variables. Simulation results indicate that when the baseline length of the satellite array remains constant, the variation in the number of satellites significantly affects the sidelobe level of the beam, while the impact of the minimum satellite spacing on the sidelobe level is not significant. Table 1 presents the optimized sidelobe levels for different values of the number of satellites \(N\) and minimum satellite spacing \(dc\). It is worth noting that the effectiveness of this method in suppressing grating lobes comes at the cost of increasing the number of satellites.