esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
1. The key influence on the precision of impact force course modelling was the frequency of the free vibrations (resonance frequency) of the force sensor and the grain size.
2. If the duration of the contact force pulse was longer than half of the sensor's own vibration period, the measurement signal coincided significantly with the contact force course. This occurred for large and soft grains with a higher moisture content.
3. If the duration of the contact force pulse was shorter than half of the sensor's own vibration period, the results of the contact force measurement were not reliable because the first cycle of the measurement signal lasted longer than the actual contact time. Moreover, with hard fine grain, the signal itself was always close to half a sinusoid of a period corresponding to the resonance vibration period of the force sensor, regardless of the shape of the contact force pulse resulting from the shape of the grain mechanical properties.
4. For large grains (e.g., maize, pea, and _Pisum arvense_) with a diameter greater than 5.0 mm, the modelling of the impact force by the sensor response signal with free vibrations of approximately 40 kHz could be considered adequate.
5. For fine grains (e.g., rape) with a diameter of less than 2.0 mm, the exact modelling could only be obtained by a sensor with a free vibration frequency exceeding 200 kHz. Piezoelectric force sensors available for sale have a free vibration frequency of 70 kHz, which does not allow the credible impact force value to be measured for fine grain e.g., rape and mustard.
6. To reliably measure the course of the impact force, impact force sensors of a special design with high resonance frequency should be used. The technological capabilities of manufacturers of such sensors allow resonance frequencies of up to 200 kHz.
7. The most crucial factors influencing the measurement accuracy of the grain impact force course were the grain dimensions (diameter) and the force sensor resonance frequency.
8. In DEM calculations, where the contact force course is of key importance, the empirical research results of the force courses for fine grain should be approached carefully, by only relying on theoretical models (rheological) of the force courses.  
Conceptualization, W.K. and J.M.; methodology, W.K.; software, W.K.; validation, J.M., J.S., L.G., and K.K.; formal analysis, J.S. and Z.S.; resources, J.M. and J.S.; writing--original draft preparation, J.M., W.K., and L.G.; writing--review and editing, Z.S., L.G., and K.K.; visualization, J.M.; project administration, L.G. and K.K., supervision, J.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by SBAD, grant number 0613/SBAD/4677.  
The authors declare no conflict of interest.  
Figure 12: The error course of the contact time (red line) and the maximum impact force (green line) depending on grain diameter.
## 4 Results and Discussions  
### Distribution Characteristics of Fatigue Strength \(\sigma_{w\
eq 9}^{*}\)  
Since the number of inclusions included within the critical volume of a specimen is unknown, the authors analyzed the distribution characteristics of the fatigue strength, \(\sigma_{w9}^{*}\), assuming several numbers of inclusions like \(n=2\), 3, 5, 6, 10, 20, and 40, respectively. Among them, the analytical results of the fatigue strength distribution obtained for \(n=2\), 5, and 20, by the method in the previous section are indicated in Figure 8 as typical examples. Figure 8a shows the probability density functions under these three cases of \(n=2\), 5, and 20, whereas Figure 8b indicates the cumulative distribution functions corresponding to the respective cases. Weibull parameters \(a\) and \(b\) in Equation (8) are provided to satisfy the condition of \(P[1\;\upmu\mathrm{m}<\rho<15\;\upmu\mathrm{m}]=0.8\). It is found that the peak (mode) and the standard deviation of the fatigue strength distribution tends to decrease with an increase in the inclusion number.  
Figure 8: \(Cont.\)  
Figure 7: Schematics of joint probability density function of \(\rho_{n}\) and \(\xi\).  
In order to investigate the effect of the condition for the dispersion of the inclusion size of \(\rho\), similar analyses were performed, giving some other conditions of \(P[1\;\upmu\text{m}<\rho<10\;\upmu\text{m}]=0.8\) and \(P[1\;\upmu\text{m}<\rho<5\;\upmu\text{m}]=0.8\). Among these analyses, only the results of the cumulative distribution functions are indicated in Figure 9, due to article page limit. As seen in Figures 8b and 9, median of the fatigue strength distribution tends to increase with a decrease of the dispersion of the inclusion size, while standard deviation of the fatigue strength distribution tends to decrease a little, depending on the decrease of the dispersion of the inclusion size.  
Figure 8: Distribution patterns of fatigue strength.  
Figure 9: Comparison of cumulative distribution functions in respective conditions.  
Comparing these analytical distribution characteristics of the fatigue strength \(\sigma_{w\theta}^{*}\) in Figures 8 and 9 with the experimental result in Figure 1, the distribution feature of the fatigue strength \(\sigma_{w\theta}^{*}\) under the conditions of \(P[1\upmu\text{m}<\rho<15\upmu]=0.8\) and \(n=5\) was roughly in agreement with the experimental distribution aspect of the fatigue strength at \(N=10^{9}\). The most preferable number of inclusions, \(n\), is further discussed again in Section 4.4.
## 1 Introduction
### Paper Organization  
The rest of the paper is organized as follows: Section 2 provides the literature survey of the state-of-the-art works with the problems listed; Section 3 explains the specific existing issues along with the proposed research solutions; Section 4 details the system model in the proposed research along with the overall architecture; Section 5 specifies the proposed model with appropriate theoretical, mathematical, and diagrammatic explanations; Section 6 provides the experimental results with simulation setup, comparative analysis, and summary of the research; and, finally, Section 7 supplies a better conclusion for the proposed work.
## 2 Related Work
### ICN Multicast  
ICN transforms the conventional host-centric communication model into the information-centric communication model. ICN adopts location-independent naming, network caching, name-based routing, and other technologies, which can distribute content in the network more effectively and provide support for functions such as mobility and security. SomeICN solutions use NRS to manage the mapping between names and locators. Many NRS technologies have been proposed [35; 36; 37; 38].  
Many ICN architectures provide their multicast solutions. By merging requests for the same information object, some ICN solutions can natively support multicast without the need to explicitly calculate or construct a multicast tree, such as CCN/NDN, DONA, and NetInf. PURSUIT can achieve multicast by calculating and encoding the entire multicast tree into a single Bloom filter [15]. Named the Object Multicast Approach (NOMA) [39], it presents scalable push multicast services based on MobilityFirst. NOMA assigns a Globally Unique Identifier (GUID) to each multicast group and uses the global name resolution service (GNRS) to store and maintain the multicast tree by recursively mapping the multicast group GUID to the GUIDs of the multicast tree's branch nodes. NOMA uses a multicast service manager (usually a gateway) to centrally calculate the multicast tree. Thus, whenever the group membership changes, the multicast service manager needs to recalculate the multicast tree and update the information. MMF [40] is another multicast mechanism based on MobilityFirst, which assigns a unique Multicast GUID (MGUID) to each multicast group. MMF manages multicast groups by using GNRS to maintain the mapping between MGUID and the addresses of receivers. A receiver that wants to join the multicast group needs to send a join message to the multicast source, which informs the GNRS to insert the IP address of the receiver into the mapping. In MMF, the multicast source needs to deal with the joining and leaving requests of each receiver, which brings great pressure on the multicast source. Moreover, both NOMA and MMF are SBT-based multicast methods.
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 2 Methodology
### Threshold Filter  
The CNR and SNR, \(\alpha\), are quality indicators of the measurement and extend the data examination from only radial wind speed to two dimensions. Looking at individual measurement points in the radial-speed-carrier-to-noise-ratio diagram (\(u_{r}\)-\(\alpha\)-diagram) in Figure 1a, a correlation of CNR values and validity can be found. It can be seen that data points below the red line indicating a -24 dB level have high deviations in the range of -32 m/s to 32 m/s wind speed, thus, we assume that the points are invalid. The high scattering in this region may be caused by the LiDAR internal peak-fitting-algorithm of the frequency spectrum when there is no significant peak within the background noise. This results in a multimodal data distribution scattered around \(u_{r}=0\) m/s forming a comb shape. From this comb-shaped distribution the assumption arises that the peak-fitting-algorithm is not a homogenous process but is more attracted by certain frequencies, leading to a detectable accumulation at corresponding wind speeds.  
While we assume that high data-density regions (HDDR) contain valid measurement points by the assumption of self-similarity and comparing means with the ultrasonic anemometer velocity measurements, here indicated by yellow and green regions in Figure 1b, we think that there is no indication based on the measurement distribution that data belonging to HDDR below a lower CNR limit, here \(\alpha_{le}=-24\) dB, is invalid (Figure 1a).  
Main challenge of LiDAR data filters is the distinction of valid data from overlaid invalid scattered data. Outliers could have a real physical meaning, however, they may fall far away from the HDDR.  
The threshold filter is commonly applied on CNR values of a data set. Data points beyond a certain range, will be filtered out. The low end edge, \(\alpha_{le}\), indicates the level of signal gain where it is assumed that no information can be extracted anymore, while the upper edge, \(\alpha_{ue}\), filters out hard targets with high backscattering.  
\[\alpha_{le}\leq\alpha\leq\alpha_{ue} \tag{1}\]  
where \(\alpha\) represents CNR values of a valid measurement points. Depending on the manufacturer, the recommended \(\alpha_{le}\) and \(\alpha_{ue}\) vary.
# Design of an Eye-in-Hand Smart Gripper for Visual and Mechanical Adaptation in Grasping
## 3 Materials and Methods
### Range of Motion for Double-Section Structures  
An adaptive finger model composed of two identical four-bar linkage structures, as illustrated in Figure 6b, is investigated in this section. Keeping \(L\) as the reference linkage length of the second linkage structure, \(h_{1}\) as the input linkage length, \(h_{2}\) as the output linkage length, and the length ratio between \(h_{2}\) and \(h_{1}\) as \(R\), this adaptive finger also possesses the same notation for the initial angular values \(\varphi\) of the input and \(\psi\) of the output, and the corresponding angle increments \(\delta_{1}\) and \(\delta_{2}\). Due to the identical structure of each four-bar linkage in this adaptive finger, the same value for each parameter is applied to each linkage structure. To determine the effect on the range of motion for the linkage parameters, scatter 2-D plots are presented in Figure 11 for the case of \(h_{1}~{}=~{}1.0L\), with length ratio \(R\) set at 0.5, 1.0, and 1.2 in (a) to (c) to show the fingertip's positions. For the \(R\) = 1.0 case, one can observe that the ratio between the output and input angles is maintained regardless of the \(\theta_{5}\) value changed. It can be seen in the scatter plots that larger ranges of motion can be achieved when \(R\) < 1 in the double four-bar linkage robotic finger.  
Figure 10: Effect of \(h_{1}\) on torque magnification in a single four-bar linkage finger structure: (a) \(h_{1}\) = 0.2\(L\), (b) \(h_{1}\) = 0.5\(L\), (c) \(h_{1}\) = 1.0\(L\), (d) \(h_{1}\) = 1.5\(L\), and (e) \(h_{1}\) = 2.0\(L\).  
Figure 9: Angle magnification \(M_{\Theta}\) as a function of linkage length ratio \(R\) = \(h_{1}\)/\(h_{2}\) in a single four-bar linkage finger structure: (a) with various \(h_{1}\) ranging from 0.2 to 2.0\(L\) with fixed initial \(\varphi\) and \(\psi\) angles 90\({}^{\circ}\), and (b) with various initial angle \(\varphi\) and \(\psi\) ranging from 30\({}^{\circ}\) to 150\({}^{\circ}\) when \(h_{1}\) = 0.5\(L\).  
The values of the double four-bar linkage adaptive finger's initial angles \(\varphi\) and \(\psi\) can slightly affect the fingertip's positions in the X-Y coordinates, but have a large influence on the input angle range, as well as the relationship between the input and output angles. As shown in Figure 12, for \(h_{1}~{}=~{}1.0L\) and \(R\) = 0.5, with initial angles \(\varphi\) and \(\psi\) both set to \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\), the initial angle value alters the relationship between the input and output angles in the adaptive finger. Consequently, larger \(R\), \(\varphi\), and \(\psi\) values lead to a significant decrease in the range of motion of the robotic finger.
# Non-Newtonian Behaviors of Crumb Rubber-Modified Bituminous Binders
## 4 Conclusions  
Non-Newtonian behaviors of highly modified bituminous binders challenge the traditional interpretation of viscosity measurements of blends. Crumb rubber modified bitumen and binders with wax additives show apparent shear-thinning behaviors at certain temperatures. The shear-thinning mechanisms of different binders can be attributed to three types: polymer solutions/melts, suspensions and dispersions. Zero shear viscosity of different binders calculated using the Cross model and yield stresses derived based on the Herschel-Bulkley model are two important parameters to characterize the non-Newtonian behaviors of bitumen. It is emphasized that characteristics of zero shear viscosity and yield stress are highly temperature-dependent. The Arrhenius equation is only valid to model the temperature dependence of viscosity in the Newtonian region. The viscosity-temperature dependence of bitumen should be described with distinct equations in different temperature regions, namely Newtonian and non-Newtonian regions.  
The testing temperature range in this study covers the average pavement temperature and high pavement temperature. For further research, viscosities of different bituminous binders tested at a wider temperature range (including mixing temperatures around 135 \({}^{\circ}\)C) and using different measuring systems (e.g., concentric cylinder, cone-plate) in DSR (Dynamic Shear Rheometer) are needed to examine the repeatability and accuracy of the model predictions. Systematic approaches to measure the yield stress of bituminous binders can be developed to compare with that predicted by the model.  
Conceptualization, X.L.; Formal analysis, H.W.; Methodology, P.A. and T.S.; Project administration, T.S.; Supervision, X.L. and T.S.; Writing--original draft, H.W.; Writing--review and editing, H.W. and X.L.  
This research received no external funding.  
The authors acknowledge RUMAL for providing testing materials. The first author would like to thank the China Scholarship Council for financial support.  
The authors declare no conflict of interest.  
Figure 7: Logarithmic complex viscosity as a function of the reciprocal of temperature (**a**) at shear rate of 0.01 rad/s; (**b**) at shear rate of 100 rad/s.
# Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network
## 1 Introduction  
Urban rail transit has become an important part of urban traffic, with the advantages of large capacity, high speed, low land occupation, energy conservation and environmental protection. The global scale of urban rail transit lines keeps rising, and China is at the climax of network construction. However, with the completion of some lines, not all rail transit systems can operate at high efficiency, and problems such as overcrowding of railway lines and transfer hubs are gradually emerging. Beijing subway, for example, reached 856.2 km in network operation length in 2021 and ranked second in the world, facing several typical problems, including insufficient network planning, overly conservative allocation of transport capacity, and prominent imbalance of passenger flow. An essential research priority is how to fully exploit the efficiency of transport services supplied by urban rail transit under network conditions and ensure its sound and quick development. It is also the basis for constructing a sustainable urban transport system, offering a theoretical foundation for improved scientific planning, cost savings, and better passenger service.
## 2 First Step of One-Bounce Scattering Signal Identification Algorithm
### Identification under Condition of Two BSs
Hence, the setting of identification threshold \(D\) depends on the trade-off between false alarm probability and alarm dismissal probability.
## 5 Existing Security Mechanisms
### Cryptography  
As wireless medical sensor networks deal with sensitive physiological information, strong cryptographic functions (_i.e._, encryption, authentication, integrity, _etc._) are paramount requirements for developing any secure healthcare application. These cryptographic functions provide patient privacy and security against many malicious attacks. Strong cryptography requires extensive computation and resources, therefore selecting appropriate cryptography are a challenging task for resource hungry medical sensor nodes that can provides maximum security whilst utilizing the minimum resources. Further, the selection of cryptography system depends on the computation and communication capability of the sensor nodes. Some argue that asymmetric crypto systems are often too expensive for medical sensors and symmetric crypto systems are not versatile enough [79]. However, applying the security mechanisms to resource constrained medical sensors should be selected based on the following considerations: _Energy_: how much energy is needed to perform the crypto functions. _Memory:_ how much memory (_i.e._, read only memory and random access memory) is needed for security mechanisms. _Execution-time:_ how much time is required to execute the security mechanisms.
## 3 Results  
The first proposed question, \"What relevant literature can be found when investigating the sustainability and SD in HEIs and where is this literature published?\", can be treated as two sub-questions: (i) \"What relevant literature is found when investigating sustainability and SD in HEIs?\" and (ii) \"Where is this relevant literature on sustainability and SD in HEIs being published?\" Relevant literature identified consisted of 190 focal articles. The detailed results of the analysis of the 190 focal articles are presented later in the Results section. To address the second sub-question, we determined that the majority of the articles (126) were published in _Journal of Cleaner Production_ and _International Journal of Sustainability in Higher Education_ (Figure 2).  
Most of the remaining articles were published in the journals _Sustainability, Environmental Education Research_, and _Higher Education Policy_. Figure 3 shows the distribution of focal articles over time, clearly indicating that the amount of research in this field is increasing.  
Fewer articles appeared to be published in 2020 due to the time frame in which the literature search was conducted, i.e., March and April 2020. Research conducted after this period was not considered in this literature review; hence, the low number of articles in 2020 does not reflect the number of publications for the entire year. Taking the regional context into account, most articles targeted HEIs in North America (USA and Canada), South America (Brazil), Asia-Pacific (Australia and China), Europe (Germany, Spain, Portugal, Sweden, and Belgium), and South Africa.  
Figure 2: Distribution of focal articles in journals (>two published articles in a journal).  
Figure 4 depicts the distribution of the focal articles by category. The results show that the institutional framework, research and assessment, and reporting in the context of sustainability and SD in HEIs were the most heavily investigated areas.  
In order to address the second proposed question, \"What themes are addressed in the literature on sustainability and SD in HEIs?\", as well as the third proposed question, \"What research gaps are identified in the literature on sustainability and SD in HEIs?\", an overview of the articles in each category is presented in the next part of the Results section. The categories are organized into the following subsections: institutional framework, campus operations, education, research, outreach and collaboration, SD through on-campus experiences, and a final category of assessment and reporting.  
Figure 4: Distribution of focal articles by category.  
Figure 3: Distribution of focal articles over a 20-year time frame.
###### Abstract  
Power systems that are known as the most complex systems encounter different types of disturbances and emergence events. To operate such systems in a stable mode, several control protection techniques are in need. Frequency plays a vital role in power systems and needs to be properly maintained in a permissible level. To this end, under-frequency load-shedding (UFLS) techniques are used to intercept the frequency decline when a system encounters a severe disturbance. In this paper, a novel, wide-area measurement system (WAMS)-based optimal UFLS technique is proposed. The system frequency response (SFR) model is identified online based on the real-time measurements collected by phasor measurement units (PMUs). Then, the SFR model is used to design a new optimal multi-stage UFLS scheme. Imperialist competitive algorithm (ICA), which is a powerful evolutionary computing method, is then adopted for solving the suggested multi-stage UFLS optimization problem. The applicability of the proposed method is shown on a practical test system. The effectiveness of the proposed optimal multi-stage UFLS scheme is verified by several simulation and comparison scenarios.  
under-frequency load-shedding; wide-area measurement system; frequency protection; interconnected power systems; frequency control; optimal UFLS; power deficit; frequency prediction; disturbance estimation; UFLS; WAMS; PMU