esa-sceva/satcom-chunk-collection huggingface dataset:
# From Brown-Field to Future Industrial Networks, a Case Study
## 4 Brown-Field Traffic Analysis
### Traffic Pattern Modeling  
The volume and dynamic mixture expands the scope of measurement from individual flows to groups of flows with a specific traffic class that is generated from a VLAN. The IT traffic is commonly assumed to have the burst traffic type, and OT traffic is generated more periodically. The sporadic traffic can be generated by both IT and OT from alarm and critical applications. The brown-field traffic was collected under normal operation with no emergency or alarm, so the data are not a proper representative for the sporadic class. The engineering documentation, along with exploratory analysis, were used to map VLANs in the network to the suggested data flow classes in the literature. Figure 5a,b show traffic dynamics from one of the OT-VLANs and one of the IT-VLANs, respectively. The OT-VLAN shows a smaller variance in accumulated data per second as well as the number of transmitted packets in unit time intervals when compared to the IT-VLAN. The smaller variance, however, does not reflect the expected cyclic pattern in the literature, Figure 6a,d. This difference is also true for the burst traffic of the IT-VLAN, Figure 6e, and the assumed pattern in literature, Figure 6c.  
Figure 4: Complexity of modeling communication flow between two devices with Discrete Time Markov Chain (DTMC) and packet-level details [7].  
These dissimilarities can be reasoned as the result of a more complex operation of the brown-field, which differs from the theoretical models. It can be argued that flows can be decomposed into their components to achieve the same representation as in the theoretical view; the counter-argument points at a simultaneously increased level of complexity that this approach initially wants to avoid. The same reasoning is also standing correct for the burst traffic. Moreover, the analysis of the flows from the brown-field IT-VLAN shows traces of IP addresses associated with OT-VLANs. This insight contradicts the common assumption of IT and OT isolation in the networks and the suitability of a single model per VLAN.  
While volume and dynamics mixture can reduce the complexity of flow characterization and flow-based profiling, by removing some levels of details and providing a more comprehensive level of abstraction for network modeling, it also falls short in terms of accuracy or completeness of input parameters when applied to the brown-field traffic. The sub-system view over the network flow, considering isolation due to virtualization, can simplify the modeling; nonetheless, it does not by any means simplify the classification of data flow within each of the VLANs.  
Figure 5: Traffic dynamics in the network for transmitted data and the number of transmitted packets in OT-VLAN (**a**) and in IT-VLAN (**b**).  
Figure 6: Data packet generation models in literature: (**a**) cyclic, (**b**) stochastic and (**c**) burst [20] vs. brown-field: (**d**) OT-VLAN (cyclic) and (**e**) IT-VLAN (burst).
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 3 Proposed Frequency Offset Estimation Scheme
### The Low-Complexity Implementation of the Proposed Scheme
Based on the simplified compensation, there are \(M(L_{1}+L_{2}+1)\) complex-division operations in calculating the coefficients \(\mathbf{Z}_{l}\) for all \(l\in\mathcal{L}\), and the number of complex-multiplex operations of Algorithm 1 is summarized in Table 2.  
It is noted that the frequency offset compensation still needs the maximum computing resources in Table 2. Nevertheless, the low-complexity implementation of the proposed scheme would be effective if \(M\) is much less than \(N\).
## 5 Conclusions  
In this study, we investigated the spatiotemporal patterns of vegetation growth, T\({}_{\rm min}\), T\({}_{\rm max}\), and T\({}_{\rm D}\) during the growing season between 1982 and 2017. Additionally, we probed the spatial heterogeneity of vegetation response to air temperature in different frozen soil types using multiple attribution analysis methods.  
The findings clearly indicate that T\({}_{\rm min}\) increases more significantly (0.223 \({}^{\circ}\)C de\({}^{-1}\); \(p<0.01\)) than T\({}_{\rm max}\) (0.145 \({}^{\circ}\)C de\({}^{-1}\); \(p<0.01\)), which has resulted in a significant decrease in the temperature difference between day and night (\(-0.078\)\({}^{\circ}\)C de\({}^{-1}\); \(p<0.01\)) over the past few decades. The principal conclusion of this study is that T\({}_{\rm min}\) emerges as the predominant temperature indicator influencing vegetation growth across the TP, effectively reducing the high risk of freezing injury and actively promoting the development of both vegetation and leaf growth throughout the growing season, and it also exerts significant influence over 63.3% of the area in NDVI and 61.4% of the area in GPP. Moreover, it has been demonstrated that air temperature exhibits the strongest explanatory power in seasonal frozen grounds, followed by permafrost regions and, finally, unfrozen grounds. The physiological mechanism of the asymmetric influence of T\({}_{\rm min}\) and T\({}_{\rm max}\) on vegetation growth has also been further revealed: T\({}_{\rm min}\) primarily regulates and promotes plant growth in drier and colder conditions at higher elevations, whereas T\({}_{\rm max}\) has a greater influence on vegetation growth over the eastern TP. This has an inhibitory effect on vegetation growth on the western TP. Our study provides evidence of the varying impacts of diurnal warming on vegetation, with different types of frozen ground exhibiting distinct responses. These findings could enhance our comprehension and explanation of the TP's unique ecosystem.  
X.L. ([PERSON]): Conceptualization, methodology, data curation, writing--original draft preparation, visualization, investigation. K.Z.: supervision, writing--reviewing and editing. X.L. ([PERSON]): supervision, writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript. This study was supported by the Fundamental Research Funds for the Central Universities (B220204014 and B220203051).  
**Data Availability Statement:** Only public data duly referenced are used in the present study.  
**Acknowledgments:** The author warmly thanks the reviewers for their help and dedication.  
**Conflicts of Interest:** The author declares no conflict of interest.
## 3 Experimental Results
### Slip Behavior  
Micrometer calipers are adopted to measure the slip values at the loading end and free end (Figure 5a). The slip value at the free end is equal to the value of concrete compression deformation by head bearing. The slip value at the load end contains the concrete compression deformation and slip between the headed bar and concrete as well as the tensile deformation of headed bar. So, the value of slip between the headed bar and concrete can be determined from the measured slip value at the load end subtracted by the superposition of measured slip value at the free end and the headed-bar tensile deformation. Similarly, the average values of a total of twelve measurement points for three specimens of the same design parameters are adopted in the following discussion.
## 4 Simulation Results  
To assess the MC-MDCSK-MCS system's performance, some simulation results are demonstrated. It is assumed that the chaos sequences are from one dimension of the fractional-order Chen chaos system, which possesses mean and variance as \(\text{E}[\text{x}_{\text{j}}]=0.17\) and \(\text{Var}[\text{x}_{\text{j}}]=3.6\times 10^{-6}\), respectively.  
In Figure 5, the comparison between the simulation results and the theoretical results over the AWGN channel and the Rayleigh fading channel is shown. The main characteristics include \(M=16\), \(N_{\text{c}}=10\), \(n=20\), and \(\alpha=0.9\). Here, we consider a three-path Rayleigh fading channel with \(E[\alpha_{1}^{2}]=E[\alpha_{2}^{2}]=E[\alpha_{3}^{2}]=\frac{1}{3}\) and the time delay \(\tau_{1}=0,\tau_{2}=2T_{\text{c}},\tau_{3}=4T_{\text{c}}\). Obviously, the simulation results agree with the theoretical results of the two channels.  
In Figure 6, the performances among MCS-MDCSK in [14], DCSK in [31], and the proposed systems are compared over the AWGN channel. All simulation results are obtained based on MATLAB R2016a. For all systems, the length of the chaotic signal is set as \(N_{\text{c}}=10\). In the proposed system and the MCS-MDCSK system, the length of the Walsh code sequence is 512, and the number of bits per symbol is set to 4, that is, \(M=16\). In the proposed MC-MDCSK-MCS system, the number of subcarriers is \(n=20\), and the fractional order of the Chen chaos system is \(\alpha=0.9\). In Figure 6, it can be observed that MC-MDCSK-MCS shows better performance.  
In Figure 7, to show the comparison of the MC-MDCSK-MCS system given three fractional orders over the AWGN channel, BER performances are plotted with \(M=16\), \(N_{\text{c}}=10\), and \(n=20\). Clearly, it can be noticed that the impact of the fractional order is negligible. Fortunately, the confidentiality of MC-MDCSK-MCS system improves with the fractional order as the range of fractional orders is extensive.  
Figure 5: Comparisons of the simulation results and the theoretical results over the AWGN and Rayleigh fading channels with \(M=16\).  
In Figure 8, to show the comparison of the MC-MDCSK-MCS system given four \(N_{c}\) over the AWGN channel, BER performances are plotted with \(M=16\), \(\alpha=0.9\), and \(n=20\). Clearly, it can be observed that MC-MDCSK-MCS shows a little better performance when the length of fractional-order chaotic signal \(N_{c}\) is smaller.  
Figure 6: Comparisons of the BER performances between the proposed MC-MDCSK-MCS system and other non-coherent chaotic communication systems over the AWGN channel.  
Figure 7: BER performance of the proposed MC-MDCSK-MCS with different fractional orders over the AWGN channel.
# Study on the Residual Strength of Nonlinear Fatigue-Damaged Pipeline Structures
## 5 Conclusions  
This study achieved the computability of the residual strength of nonlinear damaged pipeline structures and verified the accuracy of the theoretical load-bearing ultimate model for nonlinear damaged sections by constructing a finite element analysis model of dynamic material strength with damage. The radial deformation of nonlinear damaged and uniformly damaged pressure pipeline structures was compared. The main conclusions are summarized as follows:  
(1) The residual strength and ultimate internal pressure strength of nonlinear damaged pipelines show a rapid decrease followed by a slow decrease with damage accumulation.  
Figure 20: Maximum radial deformation.  
Finally, the residual strength loss is about 29%, and the ultimate internal pressure strength decreases by about 18%. After damage, the pipeline has a heterogeneous strength structure, and the residual strength of each layer in the section decreases to varying degrees with damage accumulation, and the degree of decrease is positively correlated with its distance from the neutral layer.  
(2) Under internal pressure conditions, the plastic strain of nonlinear damaged pipelines initially occurs on the inner wall in the vertical direction and then extends outward in a \"straight line\" form. Under tensile conditions, the tensile displacement increases with damage accumulation, and the deformation of the load-loading surface shows concave characteristics. Plastic deformation first occurs in its outermost layer and then extends towards the neutral layer.  
(3) Compared to uniformly damaged pipelines, the maximum radial deformation of a nonlinear damaged pipeline under internal pressure conditions is smaller than that of a uniformly damaged pipeline.  
Conceptualization, W.W.; methodology, W.W.; software, R.Z. and F.F.; validation, R.Z.; formal analysis, J.C.; investigation, Y.Z.; data curation, W.W.; writing--original draft preparation, R.Z.; writing--review and editing, W.W.; visualization, Y.Z.; supervision, J.C. and Y.C.; project administration, W.W.; funding acquisition, W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under contact Nos. 51901180 and 52274006, the Scientific Research Program Funded by the Shaanxi Provincial Education Department under contact No. 21 JP096, and the Postgraduate Innovation and Practice Ability Development Fund of Xi'an Shiyou University under contact No. YCS22212026.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 2 System Model
### Chromatic Dispersion Effect  
In this work, it is assumed that the FSO link under consideration uses a simple modulation format such as on/off keying (OOK) modulation with non-return-to-zero (NRZ) pulses which can be approximated using those with a Gaussian profile. Due to the fact that the signal propagates as a longitudinal chirped Gaussian pulse envelope, with many spectral components which are traveling with different group velocities, the pulse amplitude, \(B\), and pulse width, \(T_{pw}\), evolve along the propagating distance, \(z\), [8; 22; 35; 36]. Thus, the instantaneous, normalized irradiance due to the GVD influence, \(I\), is expressed as follows [22; 37; 38]:  
\[I=B\exp\Bigl{[}-\bigl{(}T/T_{pw}\bigr{)}^{2}\Bigr{]}, \tag{2}\]  
where \(T\) is the retarded time, which is defined in the usual way, for many physical models [37], and \(T\) = 0 describes the center of the pulse at any point of the link path,\(B(z)=\left[1+2T_{0}^{-2}C\beta_{2}z+T_{0}^{-4}\big{(}C^{2}+1\big{)}\beta_{2}^{2}z ^{2}\right]^{-1/2}\) and \(T_{pw}(z)=\left[T_{0}^{2}+2C\beta_{2}z+T_{0}^{-2}\big{(}C^{2}+1\big{)}\beta_{2}^ {2}z^{2}\right]^{1/2}\), where \(T_{0}\) is the pulse width at the transmitter's origin (\(z=0\)), \(C\) is the chirp parameter, where \(C=0\), \(C>0\), and \(C<0\) correspond to unchirped, up-chirped, and down-chirped pulses, respectively, and \(\beta_{2}\big{[}ps^{2}/km\big{]}=1.75n(\omega)P_{h}\big{(}\pi c^{2}\lambda T_{h }\big{)}^{-1}\times 10^{15}\) is the GVD parameter, where \(n(\lambda)=1+77.6\times 10^{-6}\Big{(}1+7.52\times 10^{-3}\lambda^{-2}\Big{)}P_{h} /T_{h}\) is the refractive index, \(\omega[rad/s]=2\pi\times 10^{6}\upsilon\lambda^{-1}\) is the angular frequency, \(\upsilon\) (m/s) is the speed of light in the medium, \(c\) is the speed of light in vacuum, \(\lambda\) (um) is the operating wavelength, \(P_{h}[mbar]=2.23\times 10^{-6}\big{(}44.41-h\times 10^{-3}\big{)}^{5.256}\) and \(T_{h}[K]=288.19-6.49\times 10^{-3}h\) denote the pressure and the temperature conditions, respectively, and \(h\) (m) is the altitude of the studied horizontal terrestrial FSO link [22; 37; 38].  
It is worth noting that down-chirped longitudinal Gaussian pulses are initially shrinking instead of spreading up to a critical distance, \(z_{c1}\), expressed as follows [37]:  
\[z_{c1}=\frac{|C|T_{0}^{2}}{\beta_{2}(C^{2}+1)}. \tag{3}\]  
After that distance, the pulse spreads as an up-chirped one. The original dimensions of that pulse are restored at a second distance, \(z_{c2}=2z_{c1}\), where \(I_{z=0,T=0}=I_{z_{c2},0}\)[37]. Figure 1 illustrates the time profile dispersion of the propagating pulse for opposite chirp parameters and various distances for a given wavelength.  
Furthermore, the pulse amplitude and pulse width as a function of propagation distance, z, are depicted in Figure 2.  
Figure 1: Pulse irradiance profiles, \(I\), overtime, for several propagation distances and chirp parameters.
## 4 Fuzzy Logic Based Collaborative Task Offloading
### Fuzzy Inference Engine  
To determine the fuzzy output based on the above-mentioned fuzzy input variables, fuzzy rules are used. One important indicator used in our model is a fuzzy set or the linguistic variables: small, medium, and large (Table 3). Different linguistic variables are used to represent the input and output variables of FLS. A linguistic variable is defined using three parameters or triplets \((V,X,\Omega_{V})\), where \(V\) is a variable such as task size, delay sensitivity of task, network delay, etc.; \(X\) is the range of values of the variable, and \(\Omega_{V}\) is a finite or infinite set of fuzzy sets [55]. According to Figure 6a, the linguistic variable for task size can be defined as follows:  
\[\textit{Linguistic variable for task size}=\begin{cases}\alpha=\textit{Task Size}\\ X=\Re^{+}\\ \Omega_{\alpha}=(\textit{Small},\textit{Medium},\textit{Large})\end{cases} \tag{12}\]  
The mathematical description of the above-mentioned input variable, \(\Omega\), and their respective linguistic variables are given below:  
\[\Omega_{\alpha}(\chi)=[\xi_{\alpha}^{\textit{Sml}}(\chi),\ \xi_{\alpha}^{\textit{Md}}(\chi),\ \xi_{\alpha}^{\textit{Lrg}}(\chi)] \tag{13}\]  
\[\Omega_{\beta}(\chi)=[\xi_{\beta}^{\textit{Low}}(\chi),\ \xi_{\beta}^{ \textit{Md}}(\chi),\ \xi_{\beta}^{\textit{High}}(\chi)] \tag{14}\]  
\[\Omega_{\gamma}(\chi)=[\xi_{\gamma}^{\textit{Lt}}(\chi),\ \xi_{\gamma}^{ \textit{Nor}}(\chi),\ \xi_{\gamma}^{\textit{Hry}}(\chi)] \tag{15}\]  
\[\Omega_{\delta}(\chi)=[\xi_{\delta}^{\textit{Srt}}(\chi),\ \xi_{\delta}^{ \textit{Md}}(\chi),\ \xi_{\delta}^{\textit{Lrg}}(\chi)] \tag{16}\]  
\[\Omega_{\theta}(\chi)=[\xi_{\theta}^{\textit{Lt}}(\chi),\ \xi_{\theta}^{ \textit{Nor}}(\chi),\ \xi_{\theta}^{\textit{Hry}}(\chi)] \tag{17}\]  
For combining and evaluating the fuzzy rules, fuzzy inference is used. A fuzzy variable is the output of the inference system which is used for the process of defuzzification step. Fuzzy rule consists of a series of simple IF-THEN rules along with a condition and conclusion. For example, IF the task size is medium **AND** the delay sensitivity is low **AND** local SBS-MEC VM utilization is normal **AND** network delay is medium **AND** neighboring SBS-MEC VM utilization is heavy, THEN offload to the local edge server. In this simulation, the total number of fuzzy rules used is 243 because there are five input variables which include 3 linguistic terms.The example of fuzzy rules is depicted in Table 5.
_Forests_**2015**, \(6\), 1779-1809; doi:10.3390/f6061779  
_forests_  
**ISSN 1999-4907**  
www.mdpi.com/journal/forests  
_Article_  
**Simulation of CO\({}_{2}\) Fluxes in European Forest Ecosystems with the Coupled Soil-Vegetation Process Model \"LandscapeDNDC\"**  
**[PERSON] 1, [PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1, [PERSON] 1 and [PERSON] 1,2**  
Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), Kreuzeckbahnstr. 19, 82467 Garmisch-Partenkirchen, Germany;  
E-Mails: [PERSON]SS] (S.M.-H.); [PERSON].-R.);  
[PERSON]ESS] (D.K.); [PERSON]S] (S.K.); [PERSON]RESS] (E.H.);  
[PERSON]RESS] (R.K.); [PERSON]-B.)  
International Livestock Research Institute (ILRI), P.O. Box 30709, Nairobi 00100, Kenya  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-8821-183-124; Fax: +49-8821-183-247.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 25 March 2015 / Accepted: 25 May 2015 / Published: 28 May 2015_  
**Abstract:** CO\({}_{2}\) exchange processes in forest ecosystems are of profound ecological and economic importance, meaning there is a need for generally applicable simulation tools. However, process-based ecosystem models, which are in principal suitable for the task, are commonly evaluated at only a few sites and for a limited number of plant species. It is thus often unclear if the processes and parameters involved are suitable for model application at a regional scale. We tested the LandscapeDNDC forest growth module PnET (derived from the Photosynthetic / EvapoTranspiration model) with site-specific as well as multi-site calibrated parameters using independent data sets of eddy covariance measurements across a European transect. Although site-specific parametrization is superior (\(r^{2}\) for pooled Gross Primary Production (GPP) during calibration period: site-specific = 0.93, multi-site = 0.88; \(r^{2}\) for pooled Net Ecosystem Exchange (NEE) during calibration period: site-specific = 0.81, multi-site = 0.73), we show that general parameters are able to represent carbon uptake over periods of several years. The procedure has been applied for the three most dominant European tree species _i.e._, Scots pine, Norway spruce and European beech. In addition, we discuss potential model improvements with regard to the sensitivity of parameters to site conditions differentiated into climate, nutrient and drought influences.  
**Keywords:** LandscapeDNDC; process-based ecosystem model; parametrization; carbon exchange processes; model evaluation; site conditions; Scots pine; Norway spruce; European beech
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 5 Linear Programming-Based Mathematical Formulation
### Throughput Maximization  
To maximize the throughput, we take the objective function along with its linear constraints to get the optimal results. The mathematical formulation is shown in Equation (12).  
\[Maximum\Sigma_{r=1}^{rmax}Thr(r)\quad\forall r\in r_{max}. \tag{12}\]  
Constraints of the objective function are given in (12a)-(13).  
\[C_{1}:E_{tx},E_{rcp}\leq E_{i} \tag{12a}\]  
\[C_{2}:E_{tx}\leq E_{re} \tag{12b}\]  
\[C_{3}:TX_{n}\leq TX_{max} \tag{12c}\]  
\[C_{4}:D_{ij}\leq D_{ij}^{max} \tag{12d}\]  
\[C_{5}:Minimum\Sigma_{r=1}^{rmax}B_{Frw}^{r}. \tag{12e}\]  
Figure 5: Feasible region: energy minimization.  
Equation (12a) ensures that the energy required for transmission and reception should be less than the initial energy \(E_{i}\) of the node. Equation (12b) shows the constraint that transmission energy \(E_{tx}\) ought to be less than the residual energy \(E_{re}\). Equation (12c) ensures that in order to receive a good quality signal, the data packet ought to be transmitted within its maximum transmission range \(TX_{max}\); where \(TX_{n}\) is the transmission range of the node and \(TX_{max}\) is the maximum transmission range of the node. Equation (12d) maintains a threshold of distance between sender \(i\) and receiver \(j\) for successful communication. Equation (13) shows the restriction that the load on forwarder nodes and nodes that have less residual energy ought to be minimum; where \(B_{Frw}\) is the bandwidth of forwarder nodes.