esa-sceva/satcom-chunk-collection huggingface dataset:
# Throughput Analysis of IEEE 802.11 WLANs with Inter-Network Interference
## 6 Conclusions  
This paper has proposed the analytical expressions of one-to-one WLAN's throughput under the impact of inter-network interference. The analysis, which uses the airtime concept, can provide each network throughput by expressing the simultaneous transmission time, carrier-sensing duration, and frame-existence probabilities concerning each network. We have applied the analytical expressions in three scenarios, including the two string topologies and a grid one. The validity of the analytical expressions has been confirmed from the quantitative agreements between the analytical and simulation results.  
Conceptualization, H.S.; Formal analysis, J.L. and H.S.; Funding acquisition, K.N.; Investigation, T.A.; Methodology, Z.L. and K.N.; Supervision, Z.L., T.P. and Y.-j.C.; Writing original draft, J.L.; Writing review and editing, J.L., K.N. and H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by JSPS KAKENHI Grant Number 19K20251. Additionally, [PERSON] is supported by the Leading Initiative for Excellent Young Researchers (LEADER) program from MEXT, Japan.  
Figure 7: Network characteristics of analytical (lines) and simulation (plots) results as a function of the offered load for Scenario 3. (**a**) Throughput. (**b**) Frame-existence probability. (**c**) Carrier-sense airtime.
# An Improved Particle Swarm Optimization Algorithm for Data Classification
## 2 Materials and Methods
### Opposition Rank Base Inertia Weight  
All the inertia weight has a significant influence on the performance of the standard PSO algorithm [56]. Inertia weight is the effective parameter that maintains the velocity of particles. Inertia weight is vital for balancing local search, known as exploitation (for lower values), and global search, also known as exploration (for higher values). Researchers have proposed many variants of it. This paper adopted a rank-based strategy to solve the problems. Particle inertia weight was updated with the rank-based inertia weight strategy. Opposition Rank-based strategy adjusted inertia weight according to particle fitness value and assigned a fitness rank to each particle. The most suitable particles near the best position will move slowly, while the fast-moving particles far away from the best position will continue to explore. The suitable particle is selected from either current or their opposite. It has improved local and global search at the same time. To find the rank base inertia weight of each particle, we calculated it as follows:  
\[W_{(i)}(t)=W_{max}+\left(\left(\left(W_{min}-W_{max}\right)\right)/n\right)*R_ {(i)}(t) \tag{6}\]  
where \(n\) is the size of the population and \(R_{(i)}(t)\) is the fitness rank of \(i\) particle. The slowly moving particles are the fittest particles that have the lowest inertia weight and fitness rank 1. The fast-moving particle has the highest fitness value rank, and the highest inertia weight is assigned. Maximum inertia weight (\(W_{max}\)) was set at 0.9, and 0.4 was the minimum inertia weight. Below Figure 8 show the feed forward neural network.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction
### Network Setup: Multicast Groups  
Due to the limitations of the operating environment, the ability to perform _secure_ multicast transmission is a significant advantage, and may save bandwidth compared to multiple unicast transfers of the same message to different addressees.  
We assume that all network nodes are available for personalization, such as preloading requisite cryptographic material by a single entity before the network is deployed in the target environment. This corresponds to, e.g., military and civilian use of swarms of UUVs. In particular, it may be assumed for the purposes of unicast transmission that for any pair of distinct nodes \(i\) and \(j\), there is a symmetric link key \(K_{ij}\) that is preloaded on both nodes and unknown to all other nodes. This key establishes a secure link between nodes \(i\) and \(j\).  
We associate the nodes with integer identifiers; for the most part, we shall think of a fixed designated sender \(S\) and consider the set of \(n\) receivers with identifiers \(1\) through \(n\). We denote this set of (potential) receivers with \(\mathcal{N}=\{1,\ldots,n\}\). Given this notation, we remark that \(n\) link keys must be stored (preloaded) per node. This is not a problem for networks of moderate size due to the availability of low-cost non-volatile memory.  
Multicast encryption keys are much harder to preload, as their number is exponential in \(n\) and corresponds to the possible subsets of \(\mathcal{N}\). Each subset \(\mathcal{R}\subseteq\mathcal{N}\) with more than one element is a potential _multicast group_, and dedicated key material must be available for it. This is bad asymptotically, but also unacceptable for the tiny networks we consider; indeed, for a single sender and \(20\) potential receivers, each receiver already belongs to \(2^{19}-1\) multicast groups. Thus, for \(256\)-bit keys (\(128\)-bit security level against quantum adversaries), each node would have to store \(\approx 16\) MB.  
As already mentioned, multicasting is complicated due to the key management and group setup as well as to the problem of source (sender) authentication. In the case of one-to-one communication using link keys, authentication is implicit. If a node \(j\) successfully decrypts a message using \(K_{ij}\) (we assume authentication encryption), the recipient can be certain that it originated from the node \(i\). For multicasting, this argument does not work, and additional mechanisms must be designed.
# Parameter Calibration and System Design of Material Lifting System for Inclined Shaft Construction
## 2 Lifting System Parameter Calibration Method
### Drum Parameters Calibration  
According to Safety Requirements for Mine Hoists and Mine Hoisting Winches (GB20181-2006), the ratio of hoisting system drum diameter and wire rope diameter should be greater than 60. The number of layers of wire rope winding on the drum according to the following provisions: when the hoisting system to lift personnel or materials, winding layer for 2 layers; when dedicated to lifting materials, winding layer for 3 layers. The design of this paper is dedicated to lifting materials, so it takes 3 layers.  
Therefore, the drum diameter \(D\) is as in Equation (4):  
\[D\geq Md \tag{4}\]  
where \(d\) is the wire rope diameter (mm); \(M\) is the scale factor, 60.  
The width of the roller \(B\) is calculated as in Equations (5) and (6):  
\[D_{p}D+\frac{k-1}{2}\sqrt{4d^{2}-\left(d+\varepsilon\right)^{2}} \tag{5}\]  
\[B=\frac{H+30+(n^{\prime}+3)\pi D}{k\pi D_{p}}(d+\varepsilon) \tag{6}\]  
where \(D_{p}\) is the average winding radius of wire rope (mm); \(k\) is the number of winding layers, 3; \(H\) is the lifting height (m); \(n^{\prime}\) is the number of wrong rope circle; \(\varepsilon\) is the wire rope circle gap (mm).  
Physical diagram of steel wire rope and drum is shown in Figure 1.
## 4 Conclusions  
For timely grasping the development trend of agricultural economics, this paper attempts to explore the relationship between NTL data and the total output value of agriculture, forestry, animal husbandry, and fishery (TOVAFAF), so as to establish the forecasting model for the TOVAFAF in various provinces of China using NTL remote sensing data and machine learning algorithms. The conclusions are as follows:  
(1) The constructed TNLI, LPQI, LPRI, and NLSDSI indices are correlated with the TOVAFAF at the extremely significant level;  
(2) The ELM and PSO-ELM models established based on multiple NTL indices can better explore and characterize the potential nonlinear relationship between NTL data and the TOVAFAF than all the models established based on single NTL index and the MLR model established based on multiple NTL indices;  
(3) The PSO-ELM model achieves the best forecasting effect in 2023 with the MRE value of 32.20% and the R\({}^{2}\) values of the linear relationship between the actual values and the forecasting values for 0.6460.  
Figure 8: The linear relationship and the mean relative error (MRE) between the true values and the forecasting values of (**a**) TNLI-power exponential, (**b**) LPQI-power exponential, (**c**) ELM, and (**d**) PSO-ELM in 2023.  
The PSO-ELM model established based on multiple NTL indices in this paper can effectively forecast the TOVAFAF in various provincial-level administrative regions of China on the whole. However, the forecasting accuracy still needs further improvement. In the future research, we will explore the temporal features from NTL remote sensing images to construct NTL indices. And the ELM and PSO algorithms will be improved to deeply explore the potential relationship between NTL data and the TOVAFAF, so as to establish a forecasting model with higher accuracy.  
Data curation, R.Y.; methodology, R.Y.; supervision, Q.Z. and L.X.; validation, T.W. and Y.Z.; writing--original draft preparation, R.Y.; writing--review and editing, R.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Central Public-interest Scientific Institution Basal Research Fund (No. JBYW-All-2024-46).  
Not applicable.  
The NPP-VIIRS nighttime light remote sensing images in this paper can be obtained from [[https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)]([https://eogdata.mines.edu/products/vnl/](https://eogdata.mines.edu/products/vnl/)) (accessed on 20 September 2023).  
We acknowledge the NPP-VIIRS nighttime light remote sensing images for the research in this paper.  
The author declares no conflicts of interest.
## 3 Semantic Service Description and Context-Based Orchestration
### Construction of Ontologies
#### 3.2.3 Base Ontology  
The base ontology is the core of our domain ontology and serves two basic purposes. First, it provides the framework in which the other modules can be linked together in a way that the whole domain level profits from the interaction of them. To this end, the base ontology integrates further sub-ontologies using the OWL-import mechanism. Its second task is to serve as an interface of the domain level to the upper ontology level. Its head concepts _Operation_, _Device_, _Product_ represent the well-known breakdown of manufacturing in the subunits \"process\", \"resource\" and \"product\", but with the special emphasis of devices as resource. The mapping to the AVILUS base ontology is performed using the _owl:equivalentClass_ attribute. The complexity of our base ontology is held to a minimum, enabling great flexibility in the design of existing modules and easy addition of further modules.
# Centrosymmetric Optical Vortex
## 2 Basic Theory  
Firstly, we studied the generation process of the proposed CSOVs. To realize a centrosymmetric light field, the phase of the proposed CSOVs was combined with four local spiral phases of the conventional OVs with TC (\(l_{1}=-l_{2}=l_{3}=-l_{4}\)), where \(l_{1}\), \(l_{2}\), \(l_{3}\), \(l_{4}\) are the TC of the spiral phases located in the areas I, II, III, IV, respectively. For simply expressing the TC of the CSOV, we define a parameter, named as phase reconstruction factor (PRF), which possesses the relation PRF = \(l_{1}=-l_{2}=l_{3}=-l_{4}\). The parameter PRF is an integer, which is different from the average TC [(\(l_{1}+l_{2}+l_{3}+l_{4}\)) / \(4\equiv 0\)]. As shown in Figure 1 (c1 and c2), the OAM states of the CSOVs are decomposed [23; 24]. The OAM states of the CSOV are independent of the four TCs (\(l_{1}\), \(l_{2}\), \(l_{3}\), and \(l_{4}\)) and a maximal probability distributed at the states of \(l^{\prime}=0\) which are due to the centrosymmetric OAM states. Moreover, for the conditions of PRF \(>0\) and PRF \(<0\), the light fields have the same profile and OAM states but orthogonality, shown in Figure 1. In addition, owing to the azimuthal energy flow distribution, the light field distributions revolve in far field which lead to interference in the local areas [25]. To realize a controllable interference area, we used an axicon to regulate the rotation angle of the OV in far field [26]. The CSOV can be defined by the following formula  
\[E(\rho,\theta)=\exp[-ik(n-1)\alpha\rho]\exp\left[i\sum_{n^{\prime}=1}^{4}rect \bigg{(}\frac{2\theta}{\pi}-\frac{5}{2}+n^{\prime}\bigg{)}l_{n^{\prime}}\theta\right] \tag{1}\]  
where (\(\rho\), \(\theta\)) denotes the polar coordinates, \(k\) is the wave number, \(\alpha\) and \(n\) are the cone angle and refractive index of the axicon, respectively. The rect(.) is the rectangular function which is used to realize the selecting and reconstructing of the four local spiral phases, and \(l_{n^{\prime}}\) is the TC which determines the local OAM of the CSOV. Particularly, according to the definition of the TC [27; 28], the TC of the CSOV given by \(L=l_{1}+l_{2}+l_{3}+l_{4}\) which is constant equal to 0.
# Fatigue Life Prediction of High Modulus Asphalt Concrete Based on the Local Stress-Strain Method
## 8 Case Study
### Results  
#### 8.2.1 Nominal Strain  
According to the traffic axle load and varied temperature range, nominal strains at the bottom of HMAC were calculated using the finite element software, see Tables 12 and 13.
# Intent to Adopt Location Sharing for Logging Safety Applications
## Appendix A
\begin{tabular}{c c} \hline
**Theme** & **Survey Question** \\ \hline TPB & 9. How likely or unlikely are the following results if personal location-sharing devices are used on logging operations? \\  & * Improving overall workplace safety \\  & * Developing a dependence on the technology \\  & * Finding injured coworkers faster \\  & * Causing distraction from other work activities \\  & * Knowing when coworkers might need help \\  & * Causing workers to feel like they are being watched or monitored \\  & * Knowing when coworkers are in safe or unsafe areas \\ TPB & 10. How good or bad are the following results of using personal location-sharing devices on logging operations? \\  & * Improving overall workplace safety \\  & * Developing a dependence on the technology \\  & * Finding injured coworkers faster \\  & * Causing distraction from other work activities \\  & * Knowing when coworkers might need help \\  & * Causing workers to feel like they are being watched or monitored \\  & * Knowing when coworkers are in safe or unsafe areas \\ TPB & 11. How likely or unlikely are _you_ to use personal location-sharing devices (PLDs) in the following ways on active logging operations? \\  & * Using PLDs with local, automatic location sharing for hand fallers \\  & * Using PLDs on all ground workers and heavy equipment \\  & * Using GPS-based PLDs and geofences for general situational awareness \\ TPB & 12. Within your company or organization, how much influence do you have in whether personal location-sharing devices get used? \\ TPB & 13. How likely or unlikely do you think _other loggers_ are to use personal location-sharing devices (PLDs) in the following ways? \\  & * Using PLDs with local, automatic location sharing for hand fallers \\  & * Using PLDs on all ground workers and heavy equipment \\  & * Using GPS-based PLDs and geofences for general situational awareness \\ Background & 14. What is your age? \\ Background & 15. How many years have you worked in the logging industry? \\ Background & 16. What is your current job? _Please select all that apply._ \\  & * Hand Faller \\  & * Chaser/Hooker \\  & * Equipment Operator \\  & * Truck Driver \\  & * Owner \\  & * Other--please specify \\ Background & 17. On which of the following types of operations do you usually work? _Please select all that apply._ \\  & * Mechanized ground-based logging (feller-buncher and skidder or shovel) \\  & * Partially mechanized ground-based logging (hand faller and skidder or shovel) \\  & * Cable logging \\  & * Other--please specify \\ \hline \end{tabular}
# Application of Machine Learning to Express Measurement Uncertainty
## 2 Methods
### Measurement Uncertainty of ML Model  
In supervised learning, the training set consists of input-output pairs. The given set can be viewed as if a corresponding output value was measured for each input value. Generally speaking (without noise), one input corresponds to one output. The original output values from the training set can be considered the exact values, while the values obtained by the ML model are the measured values. Since only one output value is measured for one input value, it is impossible to express the measurement uncertainty of type A. However, it is possible to express the measurement uncertainty of type B because only one measurement is needed.  
One of the common sources for determining type B measurement uncertainty is the measurement instrument manufacturer's instructions. It contains the maximum errors determined by the manufacturer for specific ranges of the measured quantity. The analogy with the ML model would be the maximal \(L_{1}\) loss on the training dataset (Figure 2).  
Assuming the uniform distribution, the standard type B measurement uncertainty of the ML model is equal to standard deviation \(s\):  
\[u=\sqrt{u_{A}^{2}+u_{B}^{2}}=u_{B}=s=\frac{\max\limits_{(x,y)\in E}L_{1}(y, \hat{y})}{\sqrt{3}}, \tag{11}\]  
with the degree of statistical certainty of 57.7%.  
Additional sources of type B measurement uncertainty mentioned in Section 2.1 and analytically presented in [29] can also find an analogy with the ML model. For example, uncertainty due to the measurement's device indication (scale) reading by the operator can be analogous to taking a lower number of significant digits or inadequate rounding of predicted values by the ML model. The ML models are executed on appropriate hardware that optionally observes the environment through actuators. The CPU calculation uncertainty, the actuator measurement uncertainty, etc., can also be additional sources of type B measurement uncertainty of the ML model. These additional sources were not considered in the study.
## 2 Research Context
### Product Architecture
#### 2.3.1 Integral Architecture  
Integral architecture is employed with the aim of maximizing a product's performance and minimizing production costs [5; 13]. It is suited to a market that accepts few compromises in product performance. This architecture consists of clusters of dense or highly interconnected parts serving multiple functions and possessing interfaces that do not facilitate interchangeability [5].  
The difficulty of interchangeability makes integral architecture more suited to MP or custom production that does not aim at reusing the design for creating a diverse range of products.
## 2 Materials and Methods
### Statistical Analysis  
#### 2.4.1 JNB Optimization  
The JNB method was adopted to separate the tidal flats from the ocean and land in our study area. The JNB method is a statistical analysis which can sectionalize data by minimizing each class's average deviation from the class mean while maximizing each class's deviation from the means of the other classes [28]. In this study, the algorithm began the process of dividing the ordered \(\gamma^{0}\) values in terms of the corresponding tide heights. Secondly, the algorithm calculated the sum of squared deviations from the class means (SDCM) and the sum of squared deviations from the mean of the complete array data  
Figure 2: Temporal distribution of tide heights from October 2014 to July 2020. The gray line shows tide heights. The blue dots denote the acquisition times of the Sentinel-1 SAR images used in our tidal flat topography (MHHW: mean higher high water, MLLW: mean lower low water, HAT: highest astronomical tides, and LAT: lowest astronomical tides).  
set (SDAM). The calculations had to be repeated using different breaks in the dataset to determine which set of breaks had the smallest in-class variance. The goodness of variance fit (GVF) index was indicative of the class's fitness obtained from the JNB classification.  
\[\text{GVF}=1-\text{SDCM/SDAM} \tag{1}\]  
The GVF ranges from 0 to 1, where 0 indicates the worst and 1 is the best case. Therefore, tidal flats tend to be close to 1 whereas land and ocean are close to 0. This is supported by the fact that the radar backscattering coefficients in tidal flats are readily divided into two classes. Generally, the \(\gamma^{0}\) shows high values when tidal flat topography is higher than tide height and low values when tidal flat is covered by water.
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 3 Methodology
### Method 1: Timecode Latency Measurement
Secondly, since both the original video and the decoded reconstructed video are sent to the time delay measurement device, this method is limited to local measurement and cannot be used for long-distance online real-time measurement.
## 3 Results and Discussion
### Interface Structures
When y reaches up to 0.875, the Fermi level still exists in the spin down conduction band, and there is much overlap between the spin up and spin down bands, and the energies of the spin up and spin down bands are almost the same, making the spin polarization seriously drop to only 3%. Hence, the doping structure TiFeCo\({}_{0.125}\)Fe\({}_{0.875}\)Si can be regarded as pseudo-semiconductor. As the impurity Fe concentration further increases up to y = 1, the spin up and spin down bands completely overlap, and there is an energy gap of 0.418 eV that exists in both spin up and spin down band, and the structure Fe\({}_{2}\)TiSi therefore converts to a semiconductor. Figure 8: The band structures of TiFeCo\({}_{1-y}\)Fe\({}_{y}\)Si. The AMM and TMM of Fe doped TiFeCoSi as a function of impurity Fe concentration is exhibited in Figure 9. When impurity Fe concentration increases from 0.125 to 0.75, AMM of Co atom has a slight increase, while that of the Fe atom descends obviously. In addition, the absolute value of AMMs of TiB and impurity Fe are weakened. It should be noted that when the impurity Fe concentration y increases to 0.875, the AMM of Co atom suffers a sharp decline, and AMMs of all atoms are extremely close to zero, owing to the fact that TiFeCo\({}_{0.125}\)Fe\({}_{0.875}\)Si shows a pseudo-semiconductor property. Furthermore, AMMs of Co and Fe are positive values, while that of TiB and impurity Fe are negative values, indicating that doping structure TiFeCo\({}_{1-y}\)Fe\({}_{y}\)Si is a ferrimagnet when y ranges from 0.125 to 0.75. As the impurity Fe concentration further increases up to y = 1, AMMs of all atoms are zero. As a result, Fe\({}_{2}\)TiSi is a nonmagnetic semiconductor.