esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Preconditioning and Tunnel Support on Strain Burst Potential
## 2 Assessment of Strain Burst Hazard
### Damage Initiation Spalling Limit (DISL) Approach  
The damage initiation spalling limit (DISL) approach is a method developed by [PERSON] [8] so engineers could simulate the brittle fracture response in conventional and readily available engineering design software. The method utilizes the generalized nonlinear Hoek & Brown failure criterion [43] to define the limiting stress envelopes, as presented in Figure 3. The lower bound strength corresponds to the damage initiation, while the upper bound strength corresponds to the crack interaction. A transitional curve for in situ rock strength extends between both bounds and is referred to as the spalling limit. In the figure, any stress path that remains below the damage initiation threshold will be elastic and incur no damage. This threshold involves a minimal confinement dependency (elevated cohesion and low friction), while the spalling limit is defined by elevated friction  
Figure 1: Predicted depth of failure using measured depths of failure [3]. R\({}_{\text{f}}\) is the depth of failure and a is the diameter of the tunnel [21].  
Figure 2: Failure depth predicted as a function of maximum wall stress, CI (typically 40–50% of UCS but measured independently during UCS testing) and in situ stress ratio K [3].  
and a cohesion loss [44]. For numerical purposes, the damage initiation limit can be assigned a \"peak\" parameter and the spalling limit corresponds to \"residual\" strength parameters. The damage initiation limit and spalling limit can be related to the damage zones, as shown in Figure 3. The damage initiation limit corresponds to the transition between the excavation influence zone (EIZ) and the outer excavation damage zone (EDZo), while the spalling limit corresponds to the transition between the inner excavation damage zone (EDZi) and highly damaged zone (HDZ).  
Various methods and discriminants exist to estimate the potential of rockburst occurrence. A simplified approach consists in evaluating a single-index empirical criterion to assess the rockburst hazard. These criteria are often based on the theories of strength, energy and stiffness. A review of some of these criteria and discriminants can be found in the work of [PERSON] & [PERSON] [46]. One of these discriminants is the \(\sigma_{max}\)/UCS ratio. Table 1 summarizes the values of the ratio with the associated rockburst potential. Over the years, thanks to the increase of historical cases and advances in numerical modeling, the values of the ratio and the associated rockburst potential have evolved. However, the range of values has stayed similar.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 6 Conclusions  
The Gardner algorithm and various improved algorithms are based on baseband signals for error detection. In this paper, we proposed an improved Gardner algorithm applied to the carrier signals based on the similarity between the carrier signal and the baseband symbols in the waveform. Compared to the Gardner algorithm, the CB-Gardner algorithm redefined the position of the error detection point, improved the error-detection  
Figure 9: BER for different SNRs with Gardner and CB-Gardner algorithms.  
equation, simplified the working process of the NCO, and resampled all passband sampling points using an interpolator. The improved algorithm performed error detection directly on the passband while eliminating the frequency offset and sampling-point bias caused by Doppler; thus, the estimation and compensation of CFO at the receiver side are no longer required, simplifying the processing flow. The error detection remained valid when adjacent symbols were the same, reducing the self-noise to a certain extent. CB-Gardner retains the real-time iterative property of the Gardner algorithm and can resist time-varying Doppler.  
The CB-Gardner algorithm was primarily designed for single-carrier BPSK signals under AWGN channels, particularly for wireless communication systems located in the medium-wave band, making it suitable for maritime wireless communication. Simulations showed that the performance of CB-Gardner against Doppler is about three times better than that of the Gardner algorithm. However, CB-Gardner requires a higher SNR for the system. The main improvement direction for CB-Gardner may be to continue reducing self-noise, making it suitable for QPSK and other modulation systems.  
Conceptualization, F.Q.; methodology, F.Q., Z.L. and X.T.; software, Z.L. and M.Z.; validation, F.Q., Z.L. and M.Z.; formal analysis, F.Q., Z.L. and M.Z.; investigation, Z.L.; resources, F.Q., X.T. and Y.W.; data curation, Z.L.; writing--original draft preparation, F.Q. and Z.L.; writing--review and editing, X.T. and M.Z.; visualization, Z.L. and X.T.; supervision, F.Q. and Y.W.; project administration, Y.W.; funding acquisition, F.Q., Y.W. and X.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Science Fund for Distinguished Young Scholars under Grant 62225114 and in part by the National Natural Science Foundation of China under Grant 62171405 and Grant 62101489.  
Not applicable.  
Code for data analysis is available on request from the authors.  
The authors declare no conflict of interest.
## 1 Introduction
The dry powders with a highly porous structure are important characteristics of powders obtained by both SFD and FD [24]. Although the porous wall structure is advantageous in terms of solubility and flowability, it could be disadvantageous in the case of long-lasting release of aroma [25]. In this study, three different encapsulation techniques were examined in terms of the encapsulation of the aroma compound of _Pistacia treebinthus_ L. fruit oil. SD has been chosen due to the most used encapsulation technique. FD and SFD techniques carried out at low-temperature conditions have been chosen since the volatile compounds might be more retained at the end of the encapsulation process. The choice of proper wall material is the first and most critical step affecting the final powder characteristics and encapsulation efficiency. There are a number of natural and synthetic polymers available as wall materials with their own advantages and disadvantages. Gum acacia (Arabic) has been addressed as an effective wall material for the encapsulation of oils and volatiles with high volatile retention due to its film-forming and emulsifying properties [26; 27]. However, there are some challenges, such as limited availability, high cost, and impurity in gum acacia used in encapsulation. Therefore, researchers are looking for combinations of wall materials that provide good flavor retention by partially replacing gum acacia with another wall material. In this respect, a combination of gum acacia with maltodextrin, one of the commonly used wall materials, has been successfully used in encapsulation of 2-acetyl-1-pyrroline, a major flavor component of aromatic rice [28], pine flavor [29], Lippia sidoides essential oil [26], nutmeg essential oil [30], cinnom essential oil [31], pterodon emarginatus essential oils [32], ethyl butyrate as the model flavor [33] and sulfur aroma compounds [34]. PTFO is a unique oil not only with its fatty acid composition but also with its aroma composition. However, the number of studies on the utilization of treebinth fruit oil is limited. It is essential to find alternative forms of this valuable oil to increase its use. In this respect, encapsulation of PTFO could expand its possible uses. Although there is only one study [35] on microencapsulation of PTFO, the study focuses on encapsulation efficiency and color characteristics of the powder. To the best of our knowledge, there is a lack of studies on the aroma composition of PTFO and the aroma retention of powders obtained by different encapsulation techniques. This is a significant issue to be studied because it can be a promising alternative for the food and cosmetic industry with a similar scent to pistachio nuts. Therefore, the objectives of this study were (1) to investigate the aroma composition of PTFO, (2) to investigate the effect of different MD:GA ratios on emulsion properties, and (3) to determine the effect of encapsulation techniques (SFD, SD, and FD) on powder characteristics and aroma retention.
## 3 Influence of Key Parameters on the Response
### Definition of the Nominal Parameter Values and of the Benchmark Case  
As stated above, the purpose of this section is to analyse the effect that variations of single parameters have on the resulting frequency shift produced by the strip. This investigation starts from a set of nominal values for each of the three parameter choices outlined above. The \"nominal values\" and the correspondent explored variations are summarized in Table 1. It should be noted that the nominal values related to mesh size and number of master contact points (points 1 and 2) were chosen in order to ensure a high-fidelity representation of the contact patches. These values are the result of a convergence study whose outcome is summarized in Figures 5 and 6 and which will be further commented upon in the following sections. Deviations from the nominal values are investigated with the intent of assessing whether designers can achieve an adequate representation even if requirements on mesh size and number of retained contact nodes are relaxed.  
On the other hand, the contact parameter values were chosen based on the authors' previous experience on rigid dampers subjected to contact pressures similar to the strip's [39]. Since no experimental data on strip dampers is available, the influence of contact parameter variations will be explored both by increasing and decreasing the contact parameters' nominal values.  
The benchmark case is the bladed disk shown in Figure 1. The disk, whose diameter is approximately 800 mm, belongs to a turbine for power generation and, as such, it is rotating at 3600 rpm.  
Figure 4: (**a**) Example of Free, Full Stick and Nonlinear forced response of the blade-strip system. (**b**) Resonance frequency values as a function of the excitation level \(|\mathbf{F_{c}}|/|\mathbf{F_{ex}}|\). For \(|\mathbf{F_{c}}|/|\mathbf{F_{ex}}|=15\), the peak is flat and multiple values are plotted in the figure to reflect this multiplicity.  
The disk is excited, unless stated otherwise, along the second bending mode (i.e., neck-bending mode which sees the blade bending about its axis while remaining within the disk plane). In this condition, the strip influence on the frequency shift is maximum. The frequency of this second bending mode, obtained in stick condition and using the \"Nominal values\" from Table 1, \(f_{stick}\) = 1320 Hz is here used as a reference to normalize other frequency values.
# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 6 Conclusions and Future Prospects  
This review paper summarizes the main aspects to consider in the design of autonomous wireless sensor networks. An overview about possibilities of energy supply for WSNs including different solutions developed for energy transfer and energy harvesting from ambient sources is provided and discussed. Thereby, we highlight the concept of designing hybrid converters solutions in terms of multi-harvester and multi-sources. Energy supply through wireless charging also has several challenges which are illustrated.  
In fact, not only the energy supply is needed but energy saving at the network level should be considered within the network design as well, which is discussed in this review paper in the last section. To overcome these high challenges, an interlocked interaction between different specialists should take place including material science, physics, system design, electronics and communication to realize necessary novel solutions.  
In the future, based on novel developments in the fields of system design as well as semiconductors and microelectronic, we can expect that a battery-free WSN will have much more functionality with ultra-low energy budgets. This includes the sensors, their interfaces as well as signal processing and communication circuits. From another perspective, thanks to significant developments of material science, more efficient energy harvesters can be developed and can be better integrated into systems and structures. For example, flexible nanogenerators can be built using nanomaterials and polymers [136; 137], 3D structures [138] or fiber for an improved integrability in textiles for wearables [139]. These are not the only perspectives. In addition, energy management concepts and components are evolving towards more intelligence and less energy consumption. In a few investigations, harvester and energy management can be coupled together such that control signals for the energy management are realized by the harvester, e.g., directly from the mechanical vibration of the source by a mechanical switch [79].  
O.K. contributed by conceptualization, methodology, original draft writing, visualization, editing and funding acquisition. S.B., S.N., F.D., G.B., S.K., D.E.H., M.B.A., A.B. and C.V. contributed by writing of sections, reviewing, visualization and editing. All authors have read and agreed to the published version of the manuscript.  
The publication of this article was funded by Chemnitz University of Technology.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 The Signal Adaptive Iterative Optimization Resampling (SAIOR) Algorithm  
In this section, the SAIOR algorithm is proposed based on the characteristics of PFM interference signals and LEO constellation signals. This algorithm eliminates the influence of LEO satellite signals in the input signal and utilizes optimized sampling to concentrate the PFM interference energy, which is originally dispersed over a wide bandwidth, into a few frequency points in the newly sampled data. This effectively reduces the damage to the desired signal during the anti-jamming process and overcomes the challenges that traditional PFM interference suppression algorithms fail to address under the condition of receiving LEO satellite signals with a single antenna.
## 4 Optimal Control Modeling for the UAV-UGV Formation-Maintenance Controller
### Optimal Formation-Maintenance Controller Design
#### 4.2.1 PI Optimal Formation-Maintenance Controller Design
\[J=\int_{t_{0}}^{t_{f}}\Big{[}\mathbf{Y}^{T}\mathbf{Q}_{Y}\mathbf{Y}+(\mathbf{U}+\mathbf{\tilde{W}})^{ T}\mathbf{R}(\mathbf{U}+\mathbf{\tilde{W}})+\mathbf{U}_{1}^{T}\mathbf{R}_{1}\mathbf{U}_{1}\Big{]}dt \tag{31}\]  
Assuming that the perturbation \(\mathbf{\tilde{W}}\) is a slow variable, it follows that  
\[\dot{\mathbf{\tilde{W}}}=\mathbf{0} \tag{32}\]  
Then, we have  
\[\mathbf{U}_{1}=\dot{\mathbf{U}} \tag{33}\]  
Thus, the quadratic optimal output performance index of the system (21) can be described as  
\[J=\int_{t_{0}}^{t_{f}}\Big{[}\mathbf{Y}^{T}\mathbf{Q}_{Y}\mathbf{Y}+(\mathbf{\tilde{U}}+\mathbf{ \tilde{W}})^{T}\mathbf{R}(\mathbf{\tilde{U}}+\mathbf{\tilde{W}})+\mathbf{\hat{U}}^{T}\mathbf{R}_{1 }\mathbf{\hat{U}}\Big{]}dt \tag{34}\]  
For the optimal control quantity (27), the following equation can be obtained by combining it with (33):  
\[\mathbf{U}_{1}^{*}=\dot{\mathbf{\tilde{U}}}^{*}=-\mathbf{R}_{1}^{-1}\mathbf{B}_{1}^{T}\mathbf{P} \mathbf{X}_{1} \tag{35}\]  
where \(\mathbf{P}\) is the minimum solution of the Riccati optimal control equation for achieving the performance index in Equation (26). Then, Equation (35) can be further expanded as  
\[ \begin{split}\dot{\mathbf{\tilde{U}}}^{*}&=-\mathbf{R}_{1 }^{-1}\Bigg{[}\begin{array}{cc}\mathbf{0}\\ \mathbf{I}\end{array}\Bigg{]}\Bigg{[}\begin{array}{cc}\mathbf{P}_{11}&\mathbf{P}_{12}\\ \mathbf{P}_{21}&\mathbf{P}_{22}\end{array}\Bigg{]}\Bigg{[}\begin{array}{cc}\mathbf{X} \\ \mathbf{\tilde{U}}^{*}+\mathbf{\tilde{W}}\end{array}\Bigg{]}\\ &=-\mathbf{R}_{1}^{-1}\mathbf{P}_{21}\mathbf{X}-\mathbf{R}_{1}^{-1}\mathbf{P}_{22}\Big{(}\mathbf{ \tilde{U}}^{*}+\mathbf{\tilde{W}}\Big{)}\end{split}  \tag{36}\]  
The above equation is an indicator of the minimum optimal control amount required to achieve the zero-valued set point in Equation (26).
# Distributed Inference Models and Algorithms for Heterogeneous Edge Systems Using Deep Learning
## 3 System Model and Problem Description
### Problem Description  
(1) Problem Definition  
In DNN-based distributed inference within an edge-computing system, latency determines the user experience, whereas energy consumption is a critical factor affecting user costs. Both latency and energy consumption are simultaneously considered to establish the following utility function based on the normalized weighted sum of these two factors:  
\[Q_{\pi}=\lambda_{t}T^{pre}+\lambda_{e}E^{pre} \tag{12}\]where \(\pi=\{a_{1},a_{2},...,a_{i},...,a_{N}\}^{T}\) represents the partitioning decision, and \(\lambda_{t},\lambda_{e}\in[0,1]\), with \(\lambda_{t}+\lambda_{e}=1\), are the weights representing the influence of latency and energy consumption on the workload-partitioning decision.  
Typically, the smaller the inference task assigned to edge device \(U_{i}\), the smaller the response time of the WD and the corresponding energy consumption of \(U_{i}\). Accordingly, the optimal combination of inference latency and energy consumption is modeled as the minimization utility function, as shown in Equation (13):  
\[\arg\min_{a_{i}}Q_{\pi} \tag{13}\]  
\[s.t.\left\{\begin{array}{ll}\text{C1}:&a_{i}\geq 0,a_{i}\in\mathbb{Z},\forall i \in N\\ \text{C2}:&\sum\limits_{i\in N}a_{i}=H\\ \text{C3}:&T^{pre}\leq\Delta_{\text{max}}\end{array}\right. \tag{14}\]  
where \(Q_{\pi}\) represents the minimized utility function, and \(a_{i}\) is the workload assigned to edge device \(U_{i}\). Constraint C1 specifies that each \(a_{i}\) in the partitioning decision must be a positive integer. Constraint C2 ensures that the sum of all values in the partitioning decision equals height \(H\). Constraint C3 defines the maximum allowable inference latency for the task.  
Essentially, Equation (13) is an ILP problem with a large decision space, similar to other complex optimization problems.
## 2 System Description and Its Mathematical Modeling
### Maximum Power Point Tracking  
In the MPPT control based on the inverter, the MPPT algorithm can be implemented in two different ways. In the first one, the MPPT controller provides the reference current directly and this has to be tracked by the grid current. The MPPT controller provides a current reference amplitude which is multiplied by a sinusoidal template synchronized to the grid voltage provided by a Phase Locked Loop (PLL). Then, an error between the grid current and its reference is finally processed by a current controller. Another way to implement the MPPT control is that its output is considered a reference voltage for the PV voltage and the grid current reference amplitude is provided by PV voltage controller. This is modulated by the sinusoidal template synchronized to the grid voltage provided by a PLL and then the grid current error is processed by the controller. In both schemes, the PWM control signal is an input to the pulse width modulator  
When designing the controller for a single-stage PV system with different loops, it must be taken into account the fact that the settling time of the PV voltage must be shorter than the sampling period of the MPPT control in order to ensure stability of the tracking algorithm. Many MPPT control algorithms can be used such as Perturb-and-Observe (P&O) algorithm and Incremental Conductance (INC) technique. All of them can be implemented by a fixed or a varying or adaptive sampling period. With fixed sampling period algorithms, this period determines the MPP tracking speed. The larger the sampling period, the faster the tracking and vice versa. In order to obtain both speed and accuracy, variable and adaptive sampling period algorithm can be used. When the PV voltage is far away from  
Figure 4: PV curves for different values of irradiance \(S\) in the range (200,1000) W/m\({}^{2}\) and temperature \(\Theta=25\)\({}^{\circ}\)C and for different values of temperature in the range (20,60) \({}^{\circ}\)C and for irradiance \(S=1000\) W/m\({}^{2}\).  
the MPP voltage, a large sampling period is used. Close to the MPP, a small sampling period is used to guarantee the tracking accuracy. Another important parameter for the MPPT is the perturbation amplitude which must be selected much lower than the DC value of the parameter to be perturbed.
# Testing Multi-Frequency Low-Cost GNSS Receivers for Geodetic Monitoring Purposes
## 1 Introduction  
The natural as well as built environment is continuously subjected to various forces in nature that may cause alterations in the shape and size of the environment. In some cases, those deformations may lead to damage and safety-of-life hazards. In order to prevent the worst-case scenario, manmade structures and natural hazards are monitored with geodetic sensors and methods on a basis of repeated geodetic surveys [1; 2]. Traditionally, sensors and methods of terrestrial geodesy have usually been applied, however, with the availability and development of satellite geodesy, especially Global Navigation Satellite Systems (GNSS); the latter has also been recognized as a methodology for deformation monitoring [3; 4; 5].  
The possibility to work continuously, remotely, in real-time, or with post-processing has resulted in a wide use of GNSS technology for geodetic monitoring [3; 4]. In general, high-precision, but expensive GNSS receivers are used to determine 3D displacements. In cases of different construction objects,such as bridges, dams, and viaducts, the geodetic GNSS receivers have been shown to be suitable in terms of accuracy and have been classified as proper sensors for geodetic monitoring [3; 4; 5; 6; 7; 8; 9]. A study in [5] emphasized that geodetic receivers were accurate enough for estimating displacement in case of dam monitoring, however, compared to the pendulum, where a long observation period is considered, geodetic GNSS receivers were inferior.  
Although geodetic receivers meet the quality criteria for monitoring tasks, their high price calls their use into question where there is a higher risk of instrument damage. An alternative solution may be seen in using low-cost GNSS receivers that have been shown to achieve high accuracy over short baselines [10; 11; 12; 13; 14].