esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Analysis and Simulation Calculation
### Thermodynamic Model Parameter Calculation of the Nut-Rotating Ball Screw Transmission Mechanism
#### 3.1.2 Thermal Resistance Calculation  
The size of thermal resistance represents the ability to hinder heat transfer. According to heat transfer theory, in a one-dimensional steady-state heat transfer process, thermal resistance is divided into conduction resistance and convective heat transfer resistance.  
3. Calculation of conduction thermal resistance of thin-walled cylinders  
\[R_{\mathrm{C}}=\frac{\ln(d/D)}{2\pi l\kappa} \tag{59}\]  
where \(d\) is the outer diameter of the thin cylinder \((\mathrm{m})\); \(D\) is the inner diameter of a thin cylinder \((\mathrm{m})\); \(\kappa\) is the thermal conductivity of the material \([\mathrm{W}/(\mathrm{m\cdot K})]\); \(l\) is the axial length of a thin cylinder \((\mathrm{m})\).  
4. Calculation of Conduction Thermal Resistance of Solid Bodies  
\[R^{\prime}{}_{C}=\frac{l^{\prime}}{A\kappa} \tag{60}\]  
where \(l^{\prime}\) stands for heat transfer length of solid body \((\mathrm{m})\); \(A\) is thermal conductivity area vertical to the heat flow direction \((\mathrm{m}^{2})\).  
5. Calculation of convective heat transfer resistance  
\[R_{V}=\frac{1}{\alpha S} \tag{61}\]  
where \(\alpha\) stands for convective heat transfer coefficient \([\mathrm{W}/(\mathrm{m}^{2}\cdot\mathrm{K})]\) and \(S\) indicates the area of convective heat transfer \((\mathrm{m}^{2})\).
# Computational Simulation of 3D Fatigue Crack Growth under Mixed-Mode Loading
## 1 Introduction
Many fatigue crack problems identified to date by the literature use different computational approaches in simulation for simple and complex structures in 2D and 3D [2; 18; 19; 20; 21; 22; 23; 24]. An energy-based criterion was developed by [25] using additively manufactured materials to analyze the effects of material anisotropy on the behavior of fatigue crack growth. The criteria are established based on the concept of strain energy density in the anisotropic domain by including T-stress into the solution and considering the effects of the specimen geometry. Therefore, this work uses the ANSYS APDL 19.2 to predict the mixed-mode SIFs accurately and the associated fatigue life for the single edge cracked plate and compact tension shear specimens. In particular, three methods have been widely used to illustrate the fatigue analysis of materials: the method of fracture mechanics developed by [PERSON] and [PERSON] [26], the method of strain-life independently proposed by [PERSON] [27], and the method of stress-life proposed by [PERSON] [28]. The first approach was employed in this study for predicting fatigue life, whereby the crack tip could be described separately by the stress intensity factors. The primary motivation for this study was to make a significant contribution to the use of ANSYS as an efficient tool for modelling FCG under the condition of mixed-mode loading and monitoring the influence of the holes on the crack growth trajectory.
# Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things
## 4 WUR in Cellular Standards
### NB-IoT Wake-Up Signal  
Introduced in 3 GPP Rel. 15, NB-IoT paging events during extended discontinuous reception (eDRX) operation are prepended by the Narrowband Wake-Up Signal (NWUS) [13]. The NWUS is a short correlation-based sequence that is unique to a certain device or subset of devices. This allows the device to only need to monitor for NWUSs instead of full paging messages while in the idle state. Once the correct NWUS message is received, the device can wake up its main radio to demodulate the following paging message.  
Figure 6 shows the frequency-time resource grid and frame structure of LTE/NB-IoT. In the time domain, each 0.5 ms slot contains seven OFDM symbols. In the frequency domain, each resource block (RB) contains 12 consecutive subcarriers. The NWUS is a  
Figure 5: Power vs. SIR (only 79 out of 228 ULP RXs reported SIR) [15].  
132-length complex Zadoff-Chu sequence and is sent in a single 1 ms subframe. The simplified 132-length sequence expression is  
\[z_{u}(n)=e^{\frac{(N_{ID}\pi n(n+1)}{131}} \tag{2}\]  
where \(N_{ID}\) indicates the group NWUS resource to which the UE is associated. The Zadoff-Chu sequence is encoded into the frequency domain and transmitted via 11 OFDM symbols, each with 12 subcarriers and 15 kHz spacing. The total bandwidth of the NWUS is 180 kHz. As a standard NB-IoT subframe is 14 OFDM symbols, the first 3 symbols of the NWUS are reserved (Figure 7). The NWUS contains a single bit of information, to wake up or not, determined by whether it meets the correlation threshold of the assigned NWUS on the device.  
The use of OFDM requires an FFT for demodulation. As a result, the power of digital baseband processing is often equal to or greater than the power of the receiver front end, with the majority of the power consumed by the FFT [41]. This high power motivates new hardware techniques to reduce the power of NB-IoT radios. In Section 6, we present a state-of-the-art NB-IoT WUR with an integrated digital baseband FFT optimized for NWUS detection.
## 4 Discussion
### Perturbations in the Mid Stance Phase  
The perturbations induced during the Mid Stance phase had minimal impact on the parameters studied compared to the previously mentioned perturbations. Notably, only this perturbation did not alter stride length compared to unperturbed gait. However, this finding contrasts with the [PERSON], et al. [43] report, which observed a significantly shorter step length under similar conditions.  
The most common strategy adopted in response to this perturbation involved rising onto the toes (Figure 3). This is evident in the notable deviations observed in ankle plantar flexion occurring within the intervals [23.47-51.17; 57.81-59.22]% of the gait cycle. Moreover, extreme torque values for plantar flexion increased by 118.18% compared to those recorded during undisturbed gait, marking the most significant change in this parameter of all the perturbations. Furthermore, there was a significant increase in extreme torque values for knee flexion--93.94%--compared to unaffected gait. Aligned with the findings presented in this study are the results of [PERSON], et al. [38]. [PERSON], et al. [38] demonstrated a noticeable decrease in ankle dorsiflexion and plantar flexion when locomotion was perturbed. However, it is worth noting that the authors analysed ranges of motion rather than angles.  
Before perturbation, as for perturbations in the Pre-Swing phase, some areas showed significant differences from the unperturbed gait cycle. These included changes in [0-9]% and [5-60]% of the gait cycle for ankle and knee kinematics, respectively. For kinetic parameters, the change regions were [2-4.22; 6.63-20]% and [6-22.1]% of the gait cycle for the knee and hip joint, respectively (Figure 6). It seems that the explanation for this phenomenon may be similar to that for perturbation in the Pre-Swing phase, and it relates to the premature activation of muscles in subconscious anticipation of a perturbation, as this type of perturbation was studied as the second one in the experiment. [PERSON], et al. [44], by applying the muscle synergy theory, demonstrated that humans are able to modify their motor control strategies, especially in terms of timing, when walking in unsteady conditions.
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 2 Methods for Estimating the Location of Radio Transmission Sources
### Estimation Method Using Phase Difference of Received Signals  
This section revisits a well-known method for estimating the location of a radio wave source. The direction of arrival of a radio wave is determined by using the phase difference between received signals at multiple elements based on the same principle as that of interferometry. For simplicity, we will explain the estimation of the direction of arrival when a plane wave is incident on two array antennas, as shown in Figure 3. When considering such a situation, the phase difference \(\Delta\phi\) between the received signals of arrays 1 and 2 can be expressed, as in Equation (1) [12].  
\[\Delta\phi=\frac{2\pi}{\lambda}d_{\text{array}}\text{sin}\,\theta \tag{1}\]  
If the complex received signal series of each antenna are \(x_{1}\) and \(x_{2}\), respectively, the phase difference \(\Delta\phi\) can be expressed as in Equation (2) [12].  
\[\Delta\phi=\tan^{-1}\frac{\text{Im}\{E[x_{1}x_{2}^{s}]\}}{\text{Re}\{E[x_{1} x_{2}^{s}]\}} \tag{2}\]  
where \(\text{E}[\cdot]\) denotes an ensemble average operation, \(\lambda\) is the wavelength of the carrier wave, \(d_{\text{array}}\) is the antenna spacing, \(\text{Re}\{\cdot\}\) denotes the real part, and \(\text{Im}\{\cdot\}\) denotes imaginary  
Figure 3: Array antenna.  
part. When the antenna spacing is 1/2 of the carrier wave wavelength, \(\theta=\sin^{-1}\left(\frac{\Delta\phi}{\pi}\right)\) from Equation (1), and the direction of arrival \(\theta\) can be obtained in the range \(-\frac{\pi}{2}<\theta<\frac{\pi}{2}\) by determining the phase difference \(\Delta\phi\). In a previous study [13], in order to improve the estimation accuracy, the direction of arrival was obtained by using several pairs of two-element arrays to obtain (in advance) the complex reception response \(a_{i}(\theta)\) to the arriving wave from the angle \(\theta\) of element i and introducing the evaluation function shown in Equation (3) [12]. When \(P(\theta)\) reaches its maximum, \(\theta\) is consistent with the direction of arrival.  
\[P(\theta)=\frac{1}{\left|\frac{E[x_{1}x_{2}^{*}]}{\sqrt{E[|x_{1}|^{2}]E[|x_{2 }|^{2}]}-\frac{a_{1}(\theta)a_{2}(\theta)^{*}}{|a_{1}(\theta)||a_{2}(\theta)|} \right|^{2}}} \tag{3}\]  
\[[a_{1}(\theta),a_{2}(\theta)]=\left[1,exp\left(-\frac{2\pi d_{\text{array}}}{ \lambda}sin\theta\right)\right] \tag{4}\]  
The estimated direction of arrival is then used to estimate the location of the radio source by triangulation, as shown in Figure 4.  
This method provides highly accurate estimation when there is a line of sight (LoS) from the antenna to the source of the radio wave. However, when estimating the direction of arrival in urban areas, multiple waves are received due to reflected waves. The problem with this method is that the direction of arrival cannot be estimated properly at that time.
## 4 V2H and V2G
### Technological Developments
#### 4.3.2 The Netherlands  
\"Resourcefully\", the Amsterdam-based environmental consultancy, released the \"Amsterdam Vehicle 2 Grid\" project (Figure 6a) in 2013 to interconnect EVs, solar energy generation, and individual customers via the smart grid and household information management system [68]. Taking advantage of the V2G system, Resourcefully presented the following results to the public:  
1. The energy self-sufficiency was significantly increased, so the zero-emission energy autonomy of participants was improved (from 34 to 65%);
2. The frequency of energy exchanges between participants and the grid was significantly reduced (by 45%);
3. The utilization efficiency of electrical energy storage capacity reached 93%;
4. The loss of energy due to conversion throughout the process of storing energy into DC batteries and regaining power from them amounted to 80%;
5. The battery capacity consumption for the two-year use was negligible (6-7%).
# Towards Agent Organizations Interoperability: A Model Driven Engineering Approach
## 2 Organizational Models for MAS
### Modeling Dimensions
#### 2.1.1 Fundamental Aspects of Systems  
In general, designed systems exhibit some fundamental aspects that, from an engineering standpoint, are natural candidates for modeling. Firstly, there is the _functional behavior_ of the system--the input (stimulus) to output (response) relations that couple the system to external elements composing the environment in which the system is situated. In modeling this aspect, the system is commonly depicted as a black box whose internal constitution, at first, does not matter. What really matters is that the environment imposes functional requirements, such as operations or tasks, that the system as a whole is supposed to perform. Further, these functional requirements may be subdivided in a recursive way until reaching atomic operations or tasks arranged in a given ordering (dependency graph). Later, when the innards are determined, the actual execution of the atomic operations or tasks can be associated with specific components of the system and their interactions. Modeling the functional behavior is a common practice both in developing computer systems and in representing organizational processes. Modeling techniques such as DFD (Data Flow Diagram) [20], and the activity diagrams of UML (Unified Modeling Language) [21] are typical examples of that.  
Another fundamental aspect is the _internal structure_ of the system. In contrast, to model the internal structure of a system means to represent it as a transparent box. It means to represent the break down of the system in its constituent parts (components and subsystems) and the relations interconnecting these parts. Like functional modeling, modeling the internal structure of a system is a recurring theme in system design. In software development, for example, the class diagrams and the component diagrams of UML serve to this purpose. In the case of human organizations, a traditional form of structural modeling is the creation of organograms describing the divisions, roles and hierarchical relationships existing inside an organization.  
A third candidate for modeling is the _structural behavior_ of the system. Roughly, it consists of the \"movement\" of the internal structure of a system towards the realization of some desired functional behavior. Thus, when modeling the structural behavior, we also see the system as a transparent box. What we try to represent is the ordering of interactions occurring over time among the constituent parts of a system. These interactions make the system work, i.e., perform some expected task or operation in its environment. Examples of this type of modeling are the sequence and collaboration diagrams of UML.
# Dropped Object Impact Analysis Considering Frequency and Consequence for LNG-FPSO Topside Module
###### Abstract  
Recently, quantitative risk assessment (QRA) has been widely used as a decision-making tool in the offshore industry. This study focused on analyzing dropped objects in the design of a modern offshore platform. A modified QRA procedure was developed for assessing production module protection against accidental external loads. Frequency and consequence analyses were performed using the developed QRA procedure. An exceedance curve was plotted, and a high-risk management item was derived through this process. In particular, simulations and experiments were used to verify the difference between the potential and impact energies according to drop orientation. When the object dropped in a specific orientation, the impact energy was confirmed to be up to 4.7 times greater than the potential energy. To reflect the QRA results in structural design, the proposed procedure should be used to calculate the maximum impact energy. The proposed procedure provides a step-by-step guide to assess the damage capacity of a production area as well as the damage frequency and consequences.  
quantitative risk assessment; dropped objects; frequency analysis; consequence analysis; impact analysis; offshore topsides; liquefied natural gas-floating production storage and offloading (ING-FPSO) +
Footnote †: journal: Applied Sciences  
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22 July 2021  
18 August 2021  
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more qualitative than quantitative, identifying accident risks through methods such as hazard and operability analysis (HAZOP), failure mode and effects analysis (FMEA), and defined risk probabilities, causes, and consequences of accident risks in a deterministic manner. Similarly, a study on marine fire hazard identification (HAZID) was conducted by analyzing the results of a questionnaire appropriate for the analytic hierarchy process (AHP) technique [7]. However, over time, following the accumulation of accident data and development of technologies, methods such as computational fluid dynamics (CFD) were proposed as probabilistic rather than deterministic methods of evaluating the probability of accident occurrence and risk. Simulations of fires, gas diffusion, and explosions have allowed for relatively more rational quantitative assessment of risk consequences.
## 5 Conclusions  
On the basis of the obtained results, no obvious correlation between the plate roughness and the determined value of the friction sensitivity can be established. Therefore, currently, it is not justified to introduce a requirement for a roughness measurement preceding the Peters test into the standard. However, it is recommended to include information on the components used in the test reports.  
Nevertheless, discrepancies between various types of plates are still being observed. This may be caused by another valid feature of used components that have not been examined in this study, e.g., the hardness of ceramics or variations in thickness. However, RRTE results have shown that the limitation of the types of plates may help lower the differences in the obtained sensitivity. Despite this, the total unanimity of the results is practically impossible to achieve, where the role of the human factor and subjectivity in the assessment of the test results seems to be currently unavoidable. Therefore, inter-laboratory comparison tests are crucial for determining the overall level of sensitivity of the tested explosives.  
Applying sound and light detectors during sensitivity to friction tests may partially eliminate the above-mentioned human factor, resulting in more compliant results being obtained. Due to the development of 3D printing, another possibility for minimizing the discrepancies is to use printed plates--3D printers enable obtaining more regular grooves in technical ceramics used as filament. In each case, further tests are required.  
Although the explosives tested in this study met the requirements of the standards for their approval in each case, it must be taken into account that, for some of the explosives, the results will place them on the borderline of admissibility, which may be problematic for explosives manufacturers (identical product may exclusively be approved by some certifying laboratories). Hence, further investigation of this issue is necessary to improve the comparability of the obtained results and indirectly increase the safety of the explosives used.  
Conceptualization, M.P.; validation, S.N. and M.P.; formal analysis, S.N. and M.P.; investigation, S.N. and M.P.; resources, M.P.; writing--original draft preparation, S.N.; writing--review and editing, S.N.; visualization, S.N. and M.P.; supervision, M.P.; project administration, M.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The paper contains all original work and findings from the conducted study. Further questions can be addressed to the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BAM & Bundesanstalt fur Materialforschung und -prufung \\ HMX & High Molecular weight Explosive (octogen) \\ PETN & Pentaerythnitol Tetranitrate (penthrite) \\ RRTE & Round-Robin Test on Explosives \\ RDX & Royal Demolition Explosive (hexogen) \\ \end{tabular}
## 2 Literature Review
### Solution Algorithms  
In multi-echelon distribution network design and planning problems, the combined multi-layer decisions could dramatically increase the size of the search space, especially when additional real-world factors (the capacity, costs, uncertainty, etc.) are included. As a result, the design of algorithms for real-life instances has been challenging for practical applications and theoretical research. Given its inherent multi-layer structure, it is beneficial to adopt decomposition methods that can simplify the original problems and solve them efficiently. The Lagrangian relaxation-based method [43] is one of the most frequently used decomposition techniques. For instance, [44] used Lagrangian relaxation to decompose a location-inventory problem and provide a lower bound rule, then the branch-and-bound algorithm was used to obtain feasible solutions. To solve a distribution planning problem, [45] presented a Lagrangian substitution-based solution approach to transform the original nonlinear model into a mixed-integer linear programming model with univariate (solvable) concave models. [PERSON]. [46] also developed a Lagrangian relaxation solution framework to decompose the network design problem into closely related knapsack and time-dependent least cost path problems. Although there are other decomposition-oriented applications in the field of supply chain management [18; 19; 28; 47], theoretically rigorous and computationally reliable decomposition techniques are much needed for different complex scenarios [21]. Comparatively, commonly used heuristic methods aim to find a close-to-optimal solution rapidly in integrated production-distribution problems. To name a few: adapted imperialist competitive algorithm (AICA), variable neighborhood search (VNS) algorithm [22], genetic algorithm (GA) [28; 30], and ant colony (AC) algorithm [24]. Ref. [35] utilized the VNS, Tabu Search (TS), Keshtel Algorithm (KA), Water Wave Optimization (WWO), and Particle Swarm Optimization (PSO) to solve a tri-level location-allocation model for forwarding/reverse supply chain systems. Furthermore, different commercial software packages are also developed to solve production-distribution planning problems [2; 17; 32; 48]. Table 1 compares key modeling components in some closely related literature. Most multi-echelon DND models in the existing literature follow a linear structure in which lateral-transshipments within the same echelon is not considered [22; 33] or there is no single sourcing strategy [6]. In the work of [17], they used a general linearization technique in quadratic assignment problems to address the quadratic term in the objective function. Our proposed approach is developed from the perspective of quadratic assignment problems, with more emphasis on the problem decomposition scheme and branch-and-bound algorithms with domain-specific lower bound rules. Table 2 describes the key differences between this study and two highly related papers [6; 17] in terms of model structure and solution methods.