esa-sceva/satcom-chunk-collection huggingface dataset:
# Stress-Dilatancy Behavior of Alluvial Sands
## 1 Introduction  
A characteristic feature of soils and other geomaterials is the change in volume caused by shear deformations. This phenomenon is called dilatancy. Stress-dilatancy, or more precisely, stress ratio-plastic dilatancy, plays a crucial role in the elastoplastic modeling of geomaterials [1; 2; 3].  
The best-known is [PERSON]'s stress-dilatancy relationship for sand, which takes the critical state as the reference state. The critical state for sand is reached at large shear strains [4]. In the critical state, non-uniform stresses and strains are observed in the tested sample due to the formation of a shear band in dense sand samples and the barreling of loose sand samples [5; 6; 7; 8]. The non-uniform sample deformation during shearing is a fundamental difficulty in correctly identifying the soil strength and stress-strain relationship [6]. The shear strength of sand, dilatancy, and critical state angle depend on the size and shape of sand particles [9]. The anisotropy of the sand in the tested sample may result from the method of sample preparation in the laboratory [7; 9]. Transverse anisotropy is observed in gravity-deposited soils. Therefore, a sand sample taken in situ or formed by the dry pluviation method has transverse anisotropy [10].  
The Frictional State Concept (FSC) was formulated by [PERSON] [11]. The general stress ratio-plastic dilatancy relationship obtained from the FSC is defined by the critical frictional state angle (\(\phi^{\circ}\)) and two parameters of the FSC (\(\alpha\) and \(\beta\)). These two parameters represent the deviation in the dissipated energy in the current state from the energy dissipated in the critical frictional state (CFS) as the reference state. The CFS can be treated as an extension of the critical state. The critical state angle and critical frictional state angle are equal for sands (\(\phi^{\circ}=\phi_{\text{cs}}\)).  
In this work, the conventional drained triaxial compression test results of alluvial sand were analyzed in light of the FSC. It is shown that the dilatant failure states and the failure state are slightly different.
# Testing an \"IoT\" Tide Gauge Network for Coastal Monitoring
## 2 IoT Tide Gauge Network Design  
There are many tide gauge networks around the world, for example, national networks covering whole coastlines and localized networks often associated with port operations. These use a variety of different communication methods (cellular, ethernet, telephone lines, etc.) to transmit the data to users [15; 16]. These tide gauges are often sparsely spread along coastlines, and some port authority data are not always publicly available.  
The \"Internet of Things\" (IoT) tide gauge network tested here is being developed specifically to support morphology monitoring. However, it could be easily adapted to fill in gaps and contribute to larger data projects such as Smart Cities [17] or Smart Coastlines [18]. It uses the IoT ultra narrowband wide area network, which combines low-power communication with good range, an ideal combination for coastal locations away from major infrastructure.  
The IoT, tide gauge network, is required to provide accurate sea-level for coastal monitoring at a number of locations (for duplication and redundancy), in both near-real-time mode and \"delayed-time\" mode. An IoT device on a beach needs to store data in memory when communications are inhibited by being underwater and then begin to send that data when exposed to air during the ebb tide. In addition, each tide gauge in the network should have relatively low-power consumption to be capable of long deployments.
## 3 Model Construction
### Analysis of Simulated Backscattering Coefficient as Functions of Roughness and Soil Moisture
#### 3.3.2 Effect of Soil Moisture \(mv\) on the Backscattering Coefficient Using CIEM Simulations  
The relationship between soil moisture and backscattering coefficient was analyzed based on a CIEM simulated database under the following conditions: incidence angle \(\theta\) is set to 33.5\({}^{\circ}\); frequency \(f\) is set to 5.405 GHz; _Hrms_ is 1.4 cm; corresponding empirical correlation length _lopt_ can be calculated according to Equations (3) and (4); \(mv\) (10%, 50%) at intervals of 0.5%.  
In Figure 4, when the surface roughness and radar configuration are fixed, there are different fitting relationships in VV and VH polarizations using two function expressions (linear and logarithmic). R\({}^{2}\) of the logarithmic relationship in VV and VH polarizations is 0.96, and the linear value is 0.85.  
Figure 3: CIEM simulations of the backscattering signal and _Hrms,_ empirical correlation length _lopt_ in different fitting relationships (logarithmic and exponential) in VV and VH polarizations: (**a**) VV, logarithmic; (**b**) VH, logarithmic; (**c**) VV, exponential; (**d**) VH, exponential.  
The results show that the logarithmic relationship is better between soil moisture and backscattering coefficient, which can be expressed as follows:  
\[\sigma_{pq}(\mathrm{dB})=C\ln(mv)+D \tag{11}\]  
where \(C\) and \(D\) are the empirical coefficients, which can be calculated by a statistical regression analysis.
# An Improved MbICP Algorithm for Mobile Robot Pose Estimation
## 3 Results
### Real-World Applications  
The first experiment was ingenious because it allows for matching real-word scans without requiring the ground truth, but generating scans with a specific overlapping percentage by randomly removing a certain number of points is risky and does not always conform to reality. Therefore, we conducted a second experiment, which involved a run with a mobile robot on the 4 th floor of the School of Resource and Environment Science Building, Wuhan University. This building consisted of three narrow corridors and two corners, forming a U-shaped room. Stationary structures, such as offices, stairs and toilets, were distributed along the corridors. In addition, dynamic obstacles such as umbrellas and walking people were present in this scenario. We applied the two algorithms to match each pair of successive scans and output many transformed cloud points and a trajectory. This experiment was challenging because the overlapping percentage of successive scans from rotation and range holes could not be estimated in advance.  
Figure 14 depicts the matching results from the two algorithms. Obtaining the exact transformation data for each step during the experiment was difficult [1]. The ground truth and numeric values are thus actually unavailable in real-world applications because of a lack of information regarding actual robot poses, which is commonly the case in current relevant studies [1,10]. The results of the improved algorithm were clearly better than those of the standard algorithm because the former could adequately fit successive scans and the generated point clouds accurately presented the indoor structure. In contrast, the matching results from the standard algorithm suffered from several obvious mismatches around stairs and corners. Thus, the behavior of the improved algorithm was generally better than that of the standard algorithm under more realistic conditions.  
Figure 14: _Cont._  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Overlapping Ratio (\%)**} & \multicolumn{2}{c}{**Translation Error (mm)**} & \multicolumn{2}{c}{**Rotation Error (\({}^{\circ}\))**} \\ \cline{2-5}  & **Improved** & **Standard** & **Improved** & **Standard** \\ \hline
100 & 0 & 0 & 0 & 0 \\
90 & 1.373 & 6.518 & 0.022 & 0.279 \\
80 & 5.289 & 38.940 & 0.113 & 0.832 \\
70 & 12.142 & 43.226 & 0.283 & 1.528 \\
60 & 18.582 & 64.040 & 0.657 & 1.892 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Deviation in the translation and rotation.
## 3 Methodology
### Three-Phase Voltage Conversion Method Using Power Semiconductors  
This paper explains the theoretical method of converting a single-phase voltage to three-phase voltage and the rationale for using semiconductor switching devices. It was stated that most three-phase loads consist of motor loads, as three-phase induction motors are utilized to achieve greater power output. The motor load used in the test in the PDS was a DOL motor without a separate control function. In addition, a pump motor used for farming was manufactured separately. Since the test must assume the worst-case scenario,  
Figure 3: Rotary phase converter.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Strength** & **Weakness** \\ \hline Static Phase & Simple circuit configuration & Unstable performance under heavy load conditions \\  & Low implementation cost & Limited power capacity \\  & & Dedicated transformer required \\ \hline Rotary Phase & High reliability & Loudness, noise \\  & Resistant to load changes & Maintenance \\  & & Dedicated transformer required \\ \hline Electronic Phase & High-quality three-phase & \\  & voltage output & \\  & Three-phase voltage balance & High initial cost \\  & High conversion efficiency & Complex electronic circuits \\  & Voltage stabilization & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of three methods.  
Figure 2: Static phase converter.  
it is not possible to identify the problems of an STPC in the case of motors that include inverter control functions or have a low inrush current. In this paper, tests are conducted under various load conditions and fault scenarios to assess whether there are any difficulties in using an STPC in the PDS where actual loads are connected, and the results are derived. A brief explanation of the STPC topology is a combination of a single-phase converter and a three-phase inverter. The single-phase voltage was first converted to DC voltage through the single-phase converter and then converted to a three-phase AC voltage using the three-phase inverter. A power utility company must supply power to the load by maintaining the standard voltage and frequency. The three-phase voltage generated by the STPC must conform to the standard voltage and frequency, so the topology and control logic must be configured accordingly. To convert a single-phase voltage to a three-phase voltage, it must be converted to DC and then converted back to AC. Each of the three phases must have the same magnitude, with a phase difference of 120 degrees. To convert to three phases, an AC-DC-AC conversion process must be performed. To directly perform AC-AC conversion, the voltage must be changed like a transformer, and to change the frequency or phase, a DC conversion process must be performed in the middle. This concept is as illustrated in Figure 4.
## 3 Results and Discussion  
Active vibration isolation experiments were conducted on a single generator set running at 1500 rpm, which is the normal operating condition. Measurements were made with the active control system switched both off and on, and the results are presented in Figures 7 and 8. Figure 7a shows the spectra of square root of the mean square acceleration responses above and below the isolators, with and without the active control system operating. The dominant responses at integer multiples of half engine orders are evident, and as these orders are contained in the reference signal, the active control system can target these frequencies. It is clear from Figure 7a that above about 20 Hz, the passive isolators are very effective in attenuating vibration transmission to the hull over the whole of the frequency range, with an average reduction of about 30 dB at most frequencies. With the exception of the frequency of 12.5 Hz, which corresponds to the first half engine order, the active control system results in vibration reduction at frequencies corresponding to all the half orders. For the vibration levels beneath the isolators, there is a small degree of amplification at frequencies corresponding to first half order and the fourth order, and a negligible change at frequencies corresponding to the sixth, seventh, and eighth orders. At frequencies corresponding to all the other half orders, the active control system is effective, but its efficacy varies between the engine orders.  
Figure 6: The impulse responses of the secondary paths modeled by FIR filters: (**a–f**) are the first to sixth secondary paths respectively.  
The spectrum of the hydrophone output is shown in Figure 7b for the active control system switched both off and on. The results show that in general, the half engine orders dominate the spectrum, with the active control system being effective at some frequencies but has a detrimental effect at others. To see more clearly the effectiveness of the active control system, the reductions at the frequencies corresponding to the half orders for the accelerations above and below the isolators and the acoustic pressure in the water, are plotted in Figure 8. It can be seen that all vibration levels and the sound pressure level have marginally increased at 12.5 Hz, which corresponds to the first half-order. At higher frequencies, up to the sixth engine order the reductions in vibration above the isolator range from between 5 and 10 dB. The reductions in vibration below the isolators is more modest, which is probably because the actuators only act on this vibration through the isolators by controlling the vibration above the isolators. The reductions in the sound pressure are even more modest, with some amplification occurring at some frequencies, especially at the sixth and a half order and the seventh order. Caution should be exercised in drawing strict conclusions concerning the sound reduction, however, as only a single point measurement was made in this case, whereas estimates of the global vibrations were made using six accelerometers above and below the isolators.
## 3 Results  
### Frequency Analysis Model Versus Healthy Subjects  
The tests with the TIP (see Figure 3) model show very clearly the effects of the connected Interference. The prominent peak at 0.3 Hz originates from the connected disturbance from the movement of the thorax in the respiratory process. The second prominent peak at 1.3 Hz results from the connected disturbance of the circulating pulse wave. The collection of frequencies from 0 to 0.2 Hz can be explained by a very slow drift in the hip joint. In the model, the hip joint and the position of the upper body were changed by 1.5\({}^{\circ}\) within 20 s.  
In addition, a large number of smaller irregular peaks in the range around 4 Hz can be determined. Comparing the TIP model with the DIP model they cannot be identified, and the entire spectrum has a significantly stronger attenuation from 1.5 Hz. These higher power components are due to the movements of the knee joint, which oscillates minimally in conjunction with the spring-damper system. The difference between the areas under the spectrum is 12.845 Nm\({}^{2}\). However, in the range 0-1.5 Hz the characteristics of the spectra are similar and the areas under the spectrum differ by 0.36 Nm\({}^{2}\).  
From this finding, the question arises whether this behavior can also be shown on the spectrum in test persons. During the test person measurement in the main study, it could be shown that some test  
Figure 3: Comparison of the spectrum of a controlled TIP- (red) and DIP-model (blue).  
persons could not fully push through the knee when standing on one leg, and thus could only stand with a slight knee flexion. To answer this question, the tracks of the test persons in whom this behavior was observed are compared with CoP tracks of comparable test persons without this observation.  
Table 2 shows that the mean value of the area under the spectrum in the range of 4-8 Hz is increased in subjects with knee flexion and the group shows greater variability. The result of this investigation is shown in Figure 4 as an average spectrum. In picture part A \"Test persons with knee flexion\", it can be seen clearly that in the range above 4 Hz increased peaks can be observed. In contrast, the spectrum of the group without knee flexion greater than 3 Hz shows only minor frequency components.  
The relationship shown in Figure 4 was examined for its statistical significance. The results are shown in Table 3. From the results it can be shown that there is a statistically significant difference in the area under the spectrum in the range of 4-8 Hz for the two groups investigated. The subjects without knee flexion have a lower power level in this area of the spectrum.
# Design of Parabolic Off-Axis Reflector Optical System for Large Aperture Single Star Simulators
## 2 Principle and System Parameters
### Analysis of System Indexes and Determination of Main Parameters
The design index requirements for the optical system of the large aperture single star simulator are shown in Table 1.  
The optical system index data shows that the system has a larger aperture and a longer focal length while the relative aperture is smaller. Therefore, choosing a two-mirror system is relatively simple, and the commonly used two-mirror systems are reflective and refractive-reflective two-mirror systems [23]. Chromatic aberration is difficult to avoid due to the inconsistent transmission of the lens to different wavelengths. In contrast, the reflective system does not produce chromatic aberration since the beam does not transmit through the lens. The lens material limits the wavelengths of refractive-reflective and transmissive systems, while reflective systems can be used in broader spectrum optical systems by coating the mirrors with suitable materials [24]. The structure of off-axis two mirrors was chosen for the system design since there can be no obstruction in the system center. Additionally, due to the small field of view of the optical system of this single star simulator, one parabolic reflector was chosen for the system design [25; 26]. The theoretical study, simulation design, and experimental verification of the parabolic off-axis reflective optical system are carried out in the following.
## 3 Results and Discussion
### Failure Modes  
Taking compressive strength testing as a representative case, this study systematically analyzed the failure modes of printed and cast specimens under different loading directions across varying flowability ranges (Table 2). The results demonstrate that the failure patterns of cast specimens showed minimal dependence on flowability, primarily exhibiting vertical and diagonal cracks along edges and outer surfaces. This behavior correlates strongly with the 45\({}^{\circ}\) principal tensile stress trajectories induced by the confinement effect [41].  
In contrast, printed specimens displayed distinct anisotropic failure characteristics. Under X direction compression, the failure mode resembled that of cast specimens, dominated by vertical and diagonal cracks. However, at low flowability, vertical cracking prevailed due to the parallel orientation of interlayer and intralayer interfaces relative to loading direction. The insufficient interfacial filling caused by high yield stress and plastic viscosity at low flowability resulted in weak interfacial bonding strength, facilitating crack propagation along interfaces when principal tensile stresses encountered interlayer and intralayer defects. This phenomenon diminished significantly with increasing flowability and improved interfacial bonding. For Y and Z direction compression, stress concentration around connected pores at interlayer and intralayer junctions [42] in low-flowability specimens led to pore-connecting cracks. Medium-flowability specimens exhibited reduced pore connectivity, making weak interfaces the dominant factor. Since both Y and Z direction loading involved interfaces either perpendicular or parallel to the load, the interaction between principal tensile stresses and interfaces induced transverse or vertical shear failures, forming step-shaped cracks. High-flowability specimens showed enhanced interfacial bonding and reduced porosity, resulting in more uniform stress distribution and ultimately demonstrating cast-like 45\({}^{\circ}\) diagonal cracking patterns.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{1}{c}{**Loading Direction**} \\  & \multicolumn{1}{c}{**Type**} & \multicolumn{1}{c}{**Type**} \\  & \multicolumn{1}{c}{**Type**
## 2 Materials and Methods
### RGB+D Data from Multi Stations
#### 2.1.1 Multi Sensor Total Stations  
MS offers a unique advantage for the engineering surveyors by encompassing multiple measuring sensors in one instrument. It combines the measuring abilities of the classic total station equipped with robotic functionalities with built-in RGB sensors and a laser scanning function.  
Total stations by themselves are multi-sensor systems that provide highly accurate angle and distance measurements to a signal, i.e., prism and, less precisely, prismless, to nearly any other surface by laser measurements. Further functions are tilt correction by 2-axis inclinometers, automatic target recognition and tracking by image sensors and drive. These instruments are called robotic total stations (RTS).  
In recent years, all significant surveying manufacture equipment has started to offer RTS with the addition of at least one integrated image sensor. Its original function was to collect additional data about the measured points. In the literature, these types of total stations are called image assisted total stations (IATS). There are several different models of integration of image sensors into the RTS. Image sensors can be built into the telescope, onto the telescope and onto the eyepiece of the telescope of the RTS [10]. The best integration is when a coaxial camera is built into the telescope, which then uses the optics and magnification of the telescope. The coaxial camera's axis coincides with the telescope axis, and the camera center is the same as the instrument center. Using the telescope magnification results in a lower ground sampling distance (higher spatial resolution) of the image compared to the overview camera, albeit at a smaller field of view [17]. Most manufacturers use a 5-megapixel Complementary Metal-Oxide-Semiconductor (CMOS) sensor in their total stations.  
In addition to image sensors in the newer generations of IATS, manufacturers have started implementing laser scanner functions. The most significant disadvantage of the scanners integrated into total stations, when compared to terrestrial laser scanners (TLS), are their relatively slow rate of measuring data. Uniformly across all major manufactures, TLS measures data at a rate of around 1 MHz, while an integrated scanner usually measures data at a rate of 1 kHz, which results in long scanning times. The Trimble total station SX10, which is used for the testing by the author, as shown on Figure 1, has pushed the limit of scanning rate up to 26.6 kHz [18]. The relevant specification of the Trimble SX10 for the test is shown in Table 1.
# A Hybrid Exact-Local Search Approach for One-Machine Scheduling with Time-Dependent Capacity
## 2 Problem Description  
A detailed description of the problem exists in [6], so we give a brief description in this section. The problem involves \(n\) jobs and one machine with a certain capacity that varies over time. Each job \(i\) has a duration \(P_{i}\) and a due date \(D_{i}\). All jobs are available from the start time (\(t=0\)) and consume one unit of the machine's capacity for the period in which the job will eventually be scheduled. Once a job starts, it cannot be preempted and should continue execution until completion. It is imperative that the capacity of the machine is not exceeded at any time. Finally, the objective that should be minimized is the total tardiness of all jobs, which is computed based on the due dates of the jobs. If job \(i\) completes execution before its due date, it does not affect the cost. Otherwise, it imposes a cost equal to \(c_{i}-D_{i}\), where \(c_{i}\) is the completion time that job \(i\) assumes in the schedule. The mathematical formulation of the problem is presented in Section 7.  
The problem \((1,Cap(t)||\sum t_{i})\) is NP-hard, since problems \((1||\sum t_{i})\) and \((P||\sum t_{i})\) (\(P\) denotes a known number of identical machines), which are known to be NP-hard [7], can be reduced to it.