esa-sceva/satcom-chunk-collection huggingface dataset:
# New Landslide Disaster Monitoring System: Case Study of Pingding Village
## 3 Results
### Monitoring Data Analysis
#### 3.2.4 Slope Circle Monitoring Data  
The slop circle monitoring data from the Typhoon Sinlaku period are displayed in Figure 17. The figure shows two bigger inclinations, which indicate soil retaining wall inclinations caused by the increase in soil pressure resulting from an increase in the water of the soil after rainfall. The data return to the initial position after the water in the soil evaporates or runs off. The data were compared with those of the management values, and all of the slopes were found to remain in a stable condition and the set threshold of the vigilance values was not exceeded. Figure 18 shows a detailed data display during Typhoon Megi--International Number 1013 is the 13 th tropical cyclone named in the Pacific typhoon season in 2010, and was also the strongest storm of the year.  
Figure 13: Relationship between Hourly Rainfall and GPS displacements.  
Figure 14: The reporting process for slope land collapse disasters.  
Figure 16: Detailed data display of the retractable meter during Typhoon Sinlaku (17–18 October 2010).  
Figure 15: GPS-2 horizontal displacement quantities.  
Figure 17: Detailed data display of the slop circle during Typhoon Megi (17–25 October 2010).  
Figure 18: Relationship between rainfall and GPS displacement data.
# Analysis and Experimental Validation of Efficient Coded OFDM for an Impulsive Noise Environment
## 4 Experimental Planning and Test Bed Design
### Specifications  
We now present a brief description of the setup used for the experimental tests.  
\begin{table}
\begin{tabular}{c c c} \hline
**Dimensions** & **Air Cavity** & **Dielectric** \\ \hline Height (mm) & 0.25 & 3.6 \\ Width (mm) & 2.5 & 55 \\ Depth (mm) & 2 & 58 \\ \hline Relative permittivity (\(\epsilon_{r}\)) & 1 & 4 \\ \hline Conductivity (\(\sigma_{i}\)) & 0 & \(2.8\times 10^{-9}\) \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the air cavity.  
Figure 4: Impulsive noise from the generator bar at 16 kV.
## 5 International Standards and Solid-Induced Design Loads
### EN 1991-4 (2006) \"Eurocode 1: Actions on Structures--Part 4: Silos and Tanks\"  
Commonly called \"Eurocode\" [71], it is widely recognized as the world's most advanced standard of its kind, as well as the most comprehensive silo design code currently in use [27]. Based on the reliability of the structural arrangement and the susceptibility to different failure modes, Eurocode's provisions classify silos into three different action assessment classes (named action assessment class 1, action assessment class 2, and action assessment class 3), which help to determine the level and the sophistication of analysis. These classes take into account the storage capacity, the geometry, and the possible filling/discharging eccentricates. In addition, another classification based on the aspect ratio is considered by this standard. Thus, Eurocode proposes designs with essentially equal risk, in terms of load determination, which helps to provide logical treatment of different loads with varying complexity, e.g., the eccentric loads and discharge pressure. Therefore, this fact gives an advance over other international standards [108]. A noted insufficiency exists in covering some common load cases, such as the loads imposed by the grain swelling, expanded flow (combination of funnel and mass flow [102]), external equipment, and load variations due to inserting internals.  
ACI 313-16 (2016) \"Design Specification for Concrete Silos and Stacking Tubes for Storing Granular Materials and Commentary\"  
This standard [100] is directed to study the RC silos. Anyway, calculating methods of the loads exerted on silos should be independent from the construction material. This standard adopted [PERSON]'s theory [33] to calculate the static uniform filling pressure on walls. The discharge-induced pressure is computed by using a minimum value of the overpressure factor (\(C_{d}\)), assumed equal to 1.6. However, this is a rough estimation and it is relatively large if compared to the load magnifying factors (horizontal pressure discharge factor, \(C_{h}\), and wall frictional traction discharge factor, \(C_{w}\)) recommended by EN 1991-4 [71] and determined based on equations after considering the action assessment classes of the silo. The old edition of this standard (ACI 313-97) ignored the calculation of non-uniform pressure exerted by asymmetric flow and did not endorse any method for evaluation of the effect of the asymmetric flow. The current edition (ACI 313-16) mentioned two methods to deal with pressure induced by the asymmetric flow. In other words, it takes into consideration several aspects, such as the industry's experience of the professional design, the characteristic of flow pattern, the nature of the surfaces and the stored material, and suggests to use either flow channel method or eccentricity method. In this sense, Eurocode suggests different approaches to deal with the non-uniform pressure induced due to asymmetry, by considering patch loads or nonuniformly distributed pressure based on the silo classification, wall thickness, aspect ratio, and eccentricity.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 3 Quantized Precoding
### MagIQ and QCM
]\))
2:\(\mathbf{x}^{(0)}[.]=\mathbf{x}[. ]_{init}\)
3:\(\alpha^{(0)}=\alpha_{init}\)
4:for\(i=1:I\)do // iterate over OFDM block
5:for\(t=0:T-1\)do
6:\(\mathcal{S}=\{1,\ldots,N\}\)
7:while\(\mathcal{S}\
eq\varnothing\)do
8:if Algo = MAGIQ then
9:\((\mathbf{x}_{n^{*}}^{*},n^{*})=\operatorname*{argmin}_{\alpha_{n}\in\mathcal{X},n \in\mathcal{S}}\)
10:\(G\left(\mathbf{x}^{(i)}|0\right),\ldots,\mathbf{x}^{(i)}[t-1],\tilde{\mathbf{x}}\),
11:\(\mathbf{x}^{(i-1)}[t+1],\ldots,\mathbf{x}^{(i-1)}[T-1],\alpha^{(i-1)}\right)\)
12:else // Algo = QCM
13:\(n^{*}=\min\mathcal{S}\) // round-robin schedule
14:\(x_{n^{*}}^{*}=\operatorname*{argmin}_{\tilde{x}_{n^{*}}\in\mathcal{X}}\)
15:\(G\left(\mathbf{x}^{(i)}|0\right),\ldots,\mathbf{x}^{(i)}[t-1],\tilde{\mathbf{x}}\),
16:\(\mathbf{x}^{(i-1)}[t+1],\ldots,\mathbf{x}^{(i-1)}[T-1],\alpha^{(i-1)}\right)\)
17:endif
18:\(x_{n^{*}}^{(i)}[t]=x_{n^{*}}^{*}\) // update antenna \(n^{*}\) at time \(t\)
19:\(\mathcal{S}\leftarrow\mathcal{S}\setminus\{n^{*}\}\)
20:endwhile
21:endfor
22:\(\alpha^{(i)}=\frac{\sum_{t=0}^{T-1}\operatorname*{Re}\left(\mathbf{u}[t]^{H} \sum_{x=0}^{L-1}\mathbf{H}[\mathbf{r}[\mathbf{x}]^{x(i)}[t-\tau]\right)}{\sum_{t=0}^{T} \left\|\sum_{t=0}^{L-1}\mathbf{H}[\mathbf{r}[\mathbf{x}]^{\alpha(i)}[t-\tau]\right\|^{2}+ TK\sigma^{2}}\)
23:endfor
24:return\(\mathbf{x}[.]=\mathbf{x}^{(I)}[. ],\alpha=\alpha^{(I)}\)
25:endprocedure
```  
**Algorithm 1** MAGIQ and QCM precoding.
# Embedded One-Dimensional Orifice Elements for Slosh Load Calculations in Volume-Of-Fluid CFD
## 2 Materials and Methods
### Orifice Flow Losses
#### 2.1.2 Flow Contraction and Expansion Losses  
The pressure differential due to venturi such as contracting and expanding flow within the orifice is given as [34]  
\[\Delta p_{cont}+\Delta p_{exp}=K_{cont}\frac{\bar{\rho}_{u}\bar{u}^{2}}{2}+K_{ exp}\frac{\bar{\rho}_{d}\bar{u}^{2}}{2}\,, \tag{5}\]  
with  
\[K_{cont}=\left(\frac{1}{\sigma_{c}^{2}}-1\right), \tag{6}\]  
\[K_{exp}=\left(2-\frac{2}{\sigma_{c}}\right), \tag{7}\]  
and  
\[\sigma_{c}=\frac{A_{c}}{A_{o}}\approx\frac{\pi}{\pi+2}=0.611\,, \tag{8}\]  
where \(\bar{\rho}_{u}\) and \(\bar{\rho}_{d}\) are the average fluid density within the orifice--upstream and downstream, respectively--of the vena contracta and \(\sigma_{c}\) is the contraction coefficient, which in this work is approximated using [PERSON]'s method [39] and further motivated by [35; 40; 41]. Note that, since the orifices considered in this work are all of constant cross-sectional area, the average velocity \(\bar{u}=\bar{u}_{u}=\bar{u}_{d}\). Equation (6) is derived from the energy flux equation assuming steady state and constant enthalpy [31], while Equation (7) is derived from the steady state momentum flux equation [34; 35]. Using the approximate value of \(\sigma_{c}\) in Equation (8), \(K_{cont}=1.679\) and \(K_{exp}=-1.273\). The relatively larger positive value of \(K_{cont}\) shows how the fluid in the orifice is accelerated (and, therefore, the pressure reduced) without any losses up to the vena contracta. Similarly, the relatively smaller negative value of \(K_{exp}\) shows how only some of the pressure is recovered during expansion.
## 4 Experimental Verification and Results Analysis  
In the preceding sections, we systematically introduced the MSIDBO algorithm to address the multi-agent cross-domain collaborative task allocation problem, and established corresponding mathematical models for target and control allocation. To verify the effectiveness and superiority of these theoretical models and the MSIDBO algorithm in practical applications, this section provides detailed experimental verification and analysis. By designing cross-domain collaborative task scenarios of varying scales and simulating the task allocation process in real-world environments, we compare the performance of the MSIDBO algorithm with other classical and improved algorithms. This section focuses on key performance indicators such as convergence speed, solution accuracy, and real-time performance. Through comprehensive and detailed experimental validation, we aim to demonstrate the significant advantages of the MSIDBO algorithm in solving cross-domain collaborative task allocation problems, analyze its optimization mechanisms, and identify applicable scenarios, thereby providing strong support for subsequent practical applications.
###### Abstract  
The quality of micro-features in various technologies is mostly affected by the choice of the micro-fabrication technique, which in turn results in several limitations with regard to materials, productivity, and cost. Laser beam micro-machining has a distinct edge over other non-traditional methods in terms of material choices, precision, shape complexity, and surface integrity. This study investigates the effect of laser fluence and pulse overlap while developing microchannels in alumina ceramic using an neodymium-doped yttrium aluminum garnet (Nd:YAG) laser. Microchannels 200 \(\upmu\)m wide with different depths were machined using different laser peak fluence and pulse overlap (percentage of overlap between successive laser pulses) values. It was found that high pulse overlaps and fluences should be avoided as they give rise to V-shaped microchannels i.e., 100% bottom width errors. The optimal peak fluence range was found to be around 125-130 J/cm\({}^{2}\) corresponding to 3-5 \(\upmu\)m depth per scan. In addition, channels fabricated with moderate pulse overlap were found to be of good quality compared to low pulse overlaps.  
microchannels; alumina; micromachining; fluence; laser +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Methods  
#### 2.4.1 Classification Tree (CT) Model  
Classification tree (CT) analysis is based on dichotomous partitioning of data at certain thresholds of the value of explanatory variables, which determine the branch a particular sample will follow [26], and is considered robust with limited samples of remotely-sensed data [27]. The steps in our analysis for developing a CT model for mapping aquatic vegetation include: (1) selecting appropriate spectral variables according to spectral characteristics of aquatic vegetation; (2) determining the structure of the CT model and correct thresholds of the variables by unsupervised classification; and (3) assessing the classification accuracy of the resulting CT model. It can be difficult to identify water and types of aquatic vegetation with only a single reflectance band as shown in Figure 2. Spectral indices from band combination have been determined to be more suitable for vegetation classification than a single band for vegetation classification. In addition, studies have indicated that some image transform methods, such as principal components (PC) and tasseled cap (TC), can extract and strengthen some image information [28,29], and can be used to map wetlands [18]. Therefore, we used four PC bands and four TC bands, along with PC and TC transforms which were generated with the transform module of ENVI software, and additional spectral features (SFs) obtained from HJ-CCD images, and their potential for identifying emergent vegetation, floating-leaved vegetation and submerged vegetation. We conducted preliminary exploratory analyses to select a set of variables (Table 3) as input to develop a CT model.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Sensor Type** & **Blue Band** & **Green Band** & **Red Band** & **Near-Infrared Band** \\  & **(Band 1)** & **(Band 2)** & **(Band 3)** & **(Band 4)** \\ \hline HJ-1A/1B & 430–520 & 520–600 & 630–690 & 760–900 \\ CCD & 430–520 & 520–600 & 630–690 & 760–900 \\ TM/ETM+ & 430–520 & 520–600 & 630–690 & 760–900 \\ \hline \end{tabular}
\end{table}
Table 2: Band wavelengths (nm) of the Environment and Disaster Reduction Small Satellite Charge-Coupled Devices (HJ-1A/1B CCD) compared with those of Landsat sensors (TM/ETM+).
## 2 Predictive Maintenance Overview  
Throughout the past decades, according to the technology improvement, the manufacturing industry was also developing from one generation to another [8; 9; 10; 11]. Figure 1 shows the evolution in maintenance techniques and objectives for implanting a certain level of intelligence into equipment and systems. When considering the past literature and reflection of industrial live out [9; 12; 13; 14; 15], maintenance has been considered as the main factor in running a sustainable industrial business by optimizing the entire lifecycle of the entities from beginning to end of their lives. Maintenance has drastically transformed in compliance with the technology requirements and market demands.  
In the fourth industrial revolution, the IoT took manufacturing to the next level, commonly known as industry 4.0, which is able to compensate the older approaches by enabling maximizing the useful life of equipment [11; 16; 17; 18; 19] through real time health monitoring. This can be achieved through empowering the automated and digitized monitoring process by using integrated electronics, information technology, and standards protocols capabilities to smart systems [20; 21; 22; 23; 24]. The term \"smart\" infers the capability of an object to flexibly, effectively, and safely collaborate with another object in order to share information that is allied to the adjacent surroundings.  
In order to meet the customer demand and cope with new technology, the organizations that utilize this type of equipment also need to adopt new technology to step up and maintain their equipment. Even though the maintenance techniques revolve from one level to another according to the evolution in technology, each level techniques contain different activities to keep equipment in its best operating status by maximizing their reliability and assuring their availability.  
For more innovative and competitive workflow, organizations need to continuously access useful data and be able to transmute them into information to adjust their works for better performance. Similarly, today's maintenance technology needs evidence to perform any action on operational equipment. The data from such equipment are crucial and the powerful evidence regarding their health status relate to their health trend observing, diagnosis, forecast, and planning for their reliability; hence, deciding on which component to repair or replace and when.  
Taking into consideration the cost that is associated to premature replacement and of sudden failure, the plan on spare parts ordering, quantity, and time could be done conveniently. By gathering increased data, the maintenance team shall be informed on cheap solutions that lead to improved reliability, lower downtime, rarer coincidences, and failures.
# Telepresence Social Robotics towards Co-Presence: A Review
## 3 From Telepresence to Co-Presence Design
### Co-Presence Design  
Mechanisms that contribute to enhancing co-presence in telepresence robots should consider the robot-side systems and the remote user (robot's operator) side solutions. Robotic interfaces support interactions between the robot and the local user (bystander) and between the robot and the remote user (operator). Human-robot interfaces can be classified into sight, hearing, touch, and body-sensing technologies. Technological advances include robust robot sensory (vision, face and expression recognition, object recognition, activity identification, pressure, touch, temperature, speech understanding, sound localization, etc.), acting (mobility, proxemics, gestures, gazing, facial expressions, speech synthesis, etc.), reasoning (localization, planning, context awareness, grasping, etc.), and appearance (familiar, unfamiliar, human-like, and mechanical) [3,122,123].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** & **Robotic Telepresence System** & **Application Area** & **Expression or Manipulation** & **Cost** \\ \hline
[117] & Kubi & Office, education & Pan 300\({}^{\circ}\), tilt 900\({}^{\circ}\)(screen) & USD 675.00 \\
[118] & TableTop TeleMe & Office, education & Pan 360\({}^{\circ}\), tilt head (screen) & USD 3995.00 \\
[119] & SelfieBot & Office, education & Pan 180\({}^{\circ}\), tilt 180\({}^{\circ}\) head (screen) & USD 195.00 \\
[120] & Meeting Owl Pro & Office & Static 360\({}^{\circ}\) camera (1080p) & USD 999 \\
[121] & Robovie mR2 & Research & Expression, arms, gestures, eye blinking (cameras), 18 joints (3 in each eye), 18 servo motors & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Unmovable robotic telepresence (RP) systems.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**References** &
\begin{tabular}{} \end{tabular} & **Application** & **Expression or Manipulation** & **Navigation Features** & **Cost** \\ \hline
[22] & PRoP & Research & Laserpointer, 2 DOF hand and arm & - & - \\
[111] & FURo-i & Home & No & Bumpers & USD 1800.00 \\
[112] & MeBot & Research & 3 DOF neck for screen and 3 DOF arms & Collision prevention, cliff sensor & - \\
[113] & Origibot & Research & Tiltable head for screen, 1 DOF arm (180\({}^{\circ}\)), 2 DOF gripper & No & Low cost \\
[114] & Nao & Research & Humanoid, 25 DOF, tiltable head, arms, legs, 4 directional microphones and speakers, 2 cameras & No & – \\
[114] & Pepper & Research & DOF (head: 2, shoulder: 2, elbow: 2, wrist: 1, hands (5 fingers): 1, waist: 2, knees: 1, base: 3 wheels), 2D and 3D cameras and sonars & Autonomous navigation, bumpers & USD 30,000.00 \\
[115,116] & GrowMeUp & Eldery research & Robot expression, directional microphones and speakers, 2D and 3D cameras, sonars, touchscreen & Obstacle avoidance, autonomous navigation, service, expression, behaviors & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Mobile robotic telepresence (MRP) systems: research-oriented solutions.
# An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties
## 2 Structural Time-Variant Reliability Model with Random Variables  
Generally, time-variant reliability models are an account of the generalized force model and contain one random variable. Let \(g\) be the limit state function composed of the general random process of structural resistance \(R(t)\) and the stochastic process of structural load effect \(S(t)\). This function, which can be linear or nonlinear, can be given as:  
\[g(R(t),S(t))=R(t)-S(t) \tag{1}\]  
where \(t\) is the time variable. Generally, the stochastic process \(S(t)\) of structural load effect contains the permanent load \(\mathbf{A}\) (usually \(n\)-dimensional random vector) and the stochastic process \(Q(t)\) of the variable load. Accordingly, Equation (1) can be rewritten as follows:  
\[g(R(t),Q(t),\mathbf{A})=R(t)-\mathbf{A}-Q(t) \tag{2}\]  
In practical engineering problems, the common degradation forms of structural resistance stochastic process \(R(t)\) include exponential and logarithmic degradation forms, which can be mathematically expressed through Equations (3) and (4), respectively.  
\[\varphi(R_{0},t)=R_{0}\exp(-\varepsilon t) \tag{3}\]  
\[\varphi(R_{0},t)=R_{0}[1+\ln(1-\varepsilon t)] \tag{4}\]  
where \(R_{0}\) and \(\varepsilon\) denote the initial resistance and the attenuation coefficient, respectively. Based on the definition of reliability, reliability \(P_{s}(t)\) and failure probability \(P_{f}(t)\) of the structure within the design service period \(T\) can be expressed in the form below:  
\[P_{s}(t)=P\big{\{}g(R(t),Q(t),\mathbf{A})>0,t\in[0,T]\big{\}} \tag{5}\]  
\[P_{f}(t)=P\big{\{}g(R(t),Q(t),\mathbf{A})<0,t\in[0,T]\big{\}} \tag{6}\]
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 1 Introduction
magnetron sputtering of two distinct layers was also reported by [PERSON] et al. [41; 42; 43]. The composite films were composed of nanometer particles with an \"island structure\" [44]. The inorganic layer was deposited in all cases from a metallic Zn target using O\({}_{2}\) as reactive gas. Therefore, to the best of our knowledge, no other research groups except ours have reported on the ion beam sputtering (IBS) simultaneous deposition of ZnO-Teflon(r)-like (ZnO-CF\({}_{x}\)) nanocomposites. In the present work, we report on new insights into the IBS co-sputtering of a Teflon-like thin film containing well-dispersed inorganic nanophases, starting from ZnO and PTFE ultrapure bulk targets, as we have already described before [45]. The aforementioned materials have been characterized by transmission electron microscopy (TEM), atomic force microscopy (AFM), and X-ray photoelectron spectroscopy (XPS). Hydrophobic properties were evaluated, as a function of ZnO loading, with dynamic water contact angle measurements. In particular, in this study, we focused on the in-depth characterization of ZnO-CF\({}_{x}\) composites along with post-deposition treatments able of improving the composites' final physicochemical properties and stability, which will pave the way for their real-life application in the textile industry.