esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Telecommunication Tests
#### 4.2.1 4G and 5G Characterisation  
Figure 7 shows the comparison between 4G and 5G networks at physical and network layers. It describes the cumulative density function of each measured value for RSRP, RSRQ and SINR according to the network latency. This result makes it possible to determine the KPIs to guarantee the proper working of the critical services considering the signalling data characterisation. These KPIs are summarised in Table 3, including the latency obtained 99% of the measured time.  
Figure 7: RSRP, RSRQ and SINR measured CDF for 4G (**right**) and 5G (**left**).  
Considering the KPIs in Table 1, the maximum network round-trip time is set to a threshold of 500 ms (50% margin over the maximum gap measured of 1 s). Figure 7 shows that the RSRP shall be greater than \(-109\) dBm for a 4G network, whereas the NSA of 5G allows a decrease down to \(-120\) dBm.  
Moreover, it is also clear how the system is more stable as the signal levels increase. In terms of availability, an RSRP of \(-108\) dBm provides a latency lower than 200 ms 99% of the time versus \(-109\) dBm, which guarantees a threshold of 300 ms.  
Following similar reasoning, RSRQ and SINR can go down to \(-12\) and 10 dB, respectively, for 4G networks. Although in the 5G NSA network, no minimal values have been found for these KPIs, the SINR of 4 dB provides adequate performance of the transmission data, and RSRQ can provide satisfactory quality service with \(-13\) dB. This result shows a significant improvement in comparison to 4G networks.  
This study shows the minimal values to obtain sufficient signal levels to provide the required network parameters. However, in a process design, more restrictive values should be selected in order to increase the probability of fulfilling the requirements more often.
## 4 Results and Discussion
### Results Gained through Simulation Platform  
The peak value of the input AC source is taken as 36 V (25 rms voltage) at 50 Hz frequency. The duty cycle of the PWM-controlled switching transistor is set to '0.4' to obtain a 60 V peak AC voltage at the output. The value of the switching frequency is set to 50 kHz. The diode is a model with 0.8 V forward voltage and 5 mf internal resistance. The internal resistance of the switching transistor is chosen to be 80 mf. Load resistance of 50 \(\Omega\) is used to ensure safe currents for the switching devices. The internal parameters of the semiconductor devices (transistors and diodes) can be seen in the data sheets presented in the section containing the real results. The values of the input and output capacitors are set to 1 uF and 4.7 uF, respectively. A 1 mH value inductor is selected, which is inserted between the diode and transistor bridge.  
The plots of the output voltage with respect to the input voltage are depicted in Figure 9(a),b for voltage in-phase and out-of-phase operation. Here, the output voltages in both scenarios are almost two volts lower than their ideal value as some voltages are dropped internally in the semiconductor devices.  
In the same way, the value of the peak reverse voltage across the switching devices is verified through the simulation plots of Figure 11. Here, it can be observed that the maximum voltage across the switching devices of the transistor's bridge is raised to the output voltage level, while, in the case of the diode's bridge, it is restricted to only the input peak voltage level.
## 2 Methodology
### Model Establishment
#### 2.3.4 Simulation Setting  
The numerical simulation was carried out to generate the experimental conditions previously described (Figure 6). The numerical simulation replicated N = 113054 particles, particle spacing d\({}_{\text{P}}\) = 0.0025 m, water particle density \(\rho_{\text{w}}\) = 1000 kg/m\({}^{3}\), solid particle density \(\rho_{\text{s}}\) = 1400 kg/m\({}^{3}\). The dam body is isosceles trapezoid, the angle between the profile of the dam body and the horizontal direction is 20\({}^{\circ}\), the height of the dam body is 0.2 m, the length of the bottom surface of the dam body is 1 m, and the length of the dam crest is 0.45 m.  
In order to compare the effects of different interfacial forces on the formation process of dam-break debris flow, the following conditions were used to replicate different solid-liquid two-phase interaction forces.
## 4 The Proposed Scheme: LDAKM-EIoT
### Dynamic Node Addition Phase
#### 4.3.2 Dynamic Cloud Server Addition  
The upcoming procedure is required to add a new cloud server, say \(CS_{nj}\), in the required part of the IoT environment.  
**DC1.** The \(TA\) chooses a distinct unique identity \(ID_{CS_{nj}}\) for \(CS_{nj}\) and computes its pseudo identity as \(RID_{CS_{nj}}=h(n\ ||ID_{CS_{nj}})\) and \(ACS_{nj}=h(K_{EN-CS_{nj}}\ ||ID_{CS_{nj}}\ ||ID_{EN}\ ||RTS_{CS_{nj}})\) where \(RTS_{CS_{nj}}\) is the registration timestamp for \(CS_{nj}\). Moreover, \(K_{EN-CS_{nj}}\) is the shared secret key of the edge node \(EN\) and cloud server \(CS_{nj}\) generated by the \(TA\).  
**DC2.** The \(TA\) then stores the credentials \(\{RID_{CS_{nj}},\ ACS_{nj},\ ID_{CS_{nj}},h(\cdot)\}\) into the database of \(CS_{nj}\) prior to its deployment in the IoT environment.
## 4 Results and Analysis
### Lyapunov Exponent Analysis  
Lyapunov exponents are used to describe the typical chaotic system because they measure a system's predictability and sensitivity to changes in its starting conditions [27]. The proposed 3D Gauss map's Lyapunov exponent graph is shown in Figure 11. The pattern similarity of the 3D Gauss map in Figure 11 with the 1D Gauss map (from Figure 5) proves the successful generation of a 3D Gauss chaotic map.
###### Abstract  
A high-resolution catalog for the 2014 _M_s 6.5 Ludian aftershocks was constructed based on the deep learning phase-picking model (CERP) and seismic-phase association technology (PALM). A specific training strategy, which combines the advantages of the conventional short-long window average energy ratio algorithm (STA/LTA) and AI algorithms, is employed to retrain the CERP model. The P- and S-wave phases were accurately detected and picked on continuous seismic waveforms by the retained AI model. Hypoinverse and HypoDD were utilized for the precise location of 3286 events. Compared to the previous results, our new catalog exhibits superior performances in terms of location accuracy and the number of aftershock events, thereby enabling a more detailed depiction of the deep-seated tectonic features. According to the distribution of aftershocks, it can be inferred that (1) the seismogenic fault of the Ludian earthquake is the NW-trending Baogunao-Xiaohe Fault, (2) the Ludian aftershocks interconnected with the discontinuous NW-trending Baogunao-Xiaohe Fault, and they also intersected with the Zhaotong-Ludian Fault. (3) This suggests that the NE-trending Zhaotong-Ludian Fault may have been intersected by the NW-trending Baogunao-Xiaohe Fault, indicating that the Baogunao-Xiaohe Fault is likely a relatively young Neogene fault.  
deep learning; 2014 Ludian earthquake; aftershock catalog +
Footnote †: journal: Applied Physics Letters
# Leveraging IPv6 and ICMPv6 for Delay-Tolerant Networking in Deep Space
## 3 Architecture Proposal
### Custom ICMPv6 Messages
#### 3.2.3 Routing and Processing  
The proposed approach follows the BPv7 model as its primary solution. In this model, the destination IP address of the ICMPv6 message is set to the address reported as the sender in the packet, so that status reports are sent back to the original transmitter. This enables the sender or higher-layer protocols to assess the delivery status and decide if retransmission is required.
###### Abstract  
In the design phase of housing projects, predicting the settlement of soil layers beneath the buildings requires the estimation of the coefficient of soil compression. This study proposes a low-cost, fast, and reliable alternative for estimating this soil parameter utilizing a hybrid metaheuristic optimized neural network (NN). An integrated method of artificial bee colony (ABC) and the Levenberg-Marquardt (LM) algorithm is put forward to train the NN inference model. The model is capable of delivering the response variable of soil compression coefficient a set of physical properties of soil. A large-scale real-life urban project at Hai Phong city (Vietnam) was selected as a case study. Accordingly, a dataset of 441 samples with their corresponding testing values of the compression coefficient has been collected and prepared during the construction phase. Experimental outcomes confirm that the proposed NN model with the hybrid ABC-LM training algorithm has attained the highly accurate estimation of the soil compression coefficient with root mean square error (RMSE) = 0.008, mean absolute percentage error (MAPE) = 10.180%, and coefficient of determination (R\({}^{2}\)) = 0.864. Thus, the proposed machine learning method can be a promising tool for geotechnical engineers in the design phase of housing projects.  
Keywords:soil compression coefficient; neural computing; artificial bee colony; geotechnical engineering; Hai Phong city +
Footnote †: journal: Applied Sciences
# Missing Data Imputation in the Internet of Things Sensor Networks
## 4 Experiments
### Methodology
#### 4.2.2 K-Nearest Neighbour (KNN) Imputation  
We also employed the KNN algorithm in this study. The KNN method is a machine learning algorithm which approaches imputation by classifying the \(k\) closest observed values to missing values and using the average of these \(k\) nearest neighbours to impute missing values going by the distance measure between points [42]. Various distance factors have been applied to the KNN algorithm in research but for the purpose of this study, we used the Euclidean distance factor which is the most widely used measure that maximizes efficiency and productivity of the algorithm [43; 44]. The nearest neighbours to the missing data points were determined by calculating the distance factor between the missing data point (\(x\)) as well as the observed neighbour (\(y\)), as shown in Equation (1) below.  
\[Dist_{xy}=\sqrt{\sum_{k=1}^{m}(X_{i}-Y_{i})^{2}} \tag{1}\]
## 2 System Model
### Scalable Filtering Distortion  
The \"morphing\" between \(L\) states of payload distortion is achieved with a sinusoidal weight function \(\mathbf{w}(\kappa,L)=[w_{1}(\kappa,L),...,w_{L}(\kappa,L)]^{\rm T}\), where the \(l\)-th element is defined as  
\[w_{l}(\kappa,L)=\begin{cases}\frac{1}{2}\cos(\Omega(L)(\kappa-m(l,L))+\frac{1 }{2}&\text{if }|\kappa-m(l,L)|\leq\frac{1}{L-1}\\ 0&\text{else}\end{cases} \tag{13}\]  
with \(\Omega(L)=2\pi(L-1)\) and \(m(l,L)=(l-1)/(L-1)\). A filter with scalable distortions can be then described as  
\[H(f,\kappa,\mathcal{H})=\sum_{l=1}^{L}w_{l}(\kappa,L)H_{l}(f)=\mathbf{H}(f)\mathbf{w} (\kappa,L) \tag{14}\]  
with \(\kappa\in[0,1]\) being the distortion scaling coefficient; \(L=|\mathcal{H}|\); and the set of ordered filter functions \(\mathcal{H}=\{H_{1}(f),...,H_{L}(f)\}\), where \(H_{1}(f)\) is the filter applying the least distortions (e.g., \"all-pass\" filter), \(H_{L}(f)\) is the filter with maximal distortions, and the filters from \(H_{2}(f)\) to \(H_{L-1}(f)\) show an increase in filtering distortion in terms of band limitation and group delay. The scalable distortion OMUX filter in this study uses \(L=5\) filters. The first filter \(H_{1}(f)=1\) has an all-pass characteristic. The last filter \(H_{5}(f)\) is generated through a polynomial fitting of a measured transfer characteristic of a GNSS satellite payload. \(H_{2}(f)\) and \(H_{3}(f)\) have a pre-distorted Butterworth characteristic of fourth and sixth order, respectively. Furthermore, \(H_{2}(f)\) has two sides with 61.38 MHz and \(H_{3}(f)\) has the same bandwidth as \(H_{5}(f)\). Lastly, \(H_{4}(f)\) is a pre-distorted version of \(H_{5}(f)\), resulting in the same amplitude response but an all-zero phase response.
## 2 Materials and Methods  
### Study Area and Data  
This paper selected a trail strip census in Binhu District, Wuxi City as the experimental area (Figure 1). Wuxi is a typical city in the Yangtze River Delta region of China. It ranked 18 th among the top 100 cities in China, with 10.5118 trillion gross domestic product (GDP) and 6,553,300 residents in 2017. Binhu District is dominated by secondary and tertiary industries, among which the township of Hudai has a tax sale of 20.107 billion. BinHu District was rated as one of the top 100 areas with investment potential in China in 2018, in which representative industries include the Internet of Things (IoT), Computer Aided Industry Design (CAID), software service outsourcing, and so on. The experimental area is located in the Wuxi Hudai Industrial Zone, which borders Taihu Lake in the southeast. It is the only peripheral town in BinHu District, and is thus relatively independent of the core urban area, where the population is dense, and the industry mainly relies on electronics, light, and textiles. The experimental area was used to research the carrying capacity pressure of the service facilities contained 14 communities and 13,600 residents. This paper collected Internet map data (route planning API, POI data, and remote sensing images) and LBS data (Tencent LBS data). A detailed introduction about the data volume, data descriptions, and data applications can be found in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Category** & **Dataset Name** & **Data Content** & **Data Application** & **Access Method** \\ \hline \multirow{4}{*}{Internet map data} & Route planning API & Three route planning methods, included driving, riding, and walking, requested opening API to obtain corresponding planned path. & Calculating travel time costs & Requesting from [[https://lbs.amap.com](https://lbs.amap.com)]([https://lbs.amap.com](https://lbs.amap.com)) \\ \cline{2-5}  & POI data & POI data was requested from Amap in BinHu District in 2018, including schools, banks, and restaurants. & Representing different service facilities & Requesting from [[https://lbs.amap.com](https://lbs.amap.com)]([https://lbs.amap.com](https://lbs.amap.com)) \\ \cline{2-5}  & Basic geographic data & Amap platform provides remote sensing image to request. & As map background & Downloading from [[https://ditu.amap.com](https://ditu.amap.com)]([https://ditu.amap.com](https://ditu.amap.com)) \\ \hline \multirow{2}{*}{LBS data} & \multirow{2}{*}{Tencent LBS data} & Requesting real-time population locations of Tencent one time per hour from 9 April 2018 to 22 April 2018; & Reflecting spatial distribution of real population & Crawling from [[https://heat.qq.com/](https://heat.qq.com/)]([https://heat.qq.com/](https://heat.qq.com/)) \\  & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A detailed description of the data used in the paper. API: application programming interface, LBS: location-based service, POI: point of interest.
## 2 Materials and Methods  
### Study Area  
The Bor na Czerwonem bog is located in Southern Poland in the Lesser Poland Voivodeship within the Nowy Targ commune. The nature reserve at this site was established in 1925, mostly owing to the efforts and determination of Prof. [PERSON]. Initially, the reserve covered 5 ha of the bog [13]. Since 1948, the entire bog area has been owned by the state, and at present, it is managed by the Nowy Targ Forest Inspectorate. It belongs to the so-called Orava-Nowy Targ Peatlands, located within the Orava-Podhale Depression macroregion, in the Orava-Nowy Targ Basin mesoregion [14; 15] which is located in the immediate vicinity of the city of Nowy Targ and the montane river Bialy Dunajec. The location of the bog is marked by the geographic coordinates 49\({}^{\circ}\)27\({}^{\prime}\)31.91\({}^{\prime\prime}\) N and 20\({}^{\circ}\)02\({}^{\prime}\)29.14\({}^{\prime\prime}\) E. The studied bog represents a domed raised bog. At its highest point, it reaches an elevation of 617.3 m a.s.l., and the elevation of its surroundings is lower by about 2.2 m. It is about 1000 m long and 500 m wide (Figure 1). The central part of the reserve comprises the bog dome, occupying an area of about 36 ha. At present, the entire reserve area is about 114.66 ha, and its buffer zone is 68.4 ha [16].  
According to [PERSON] [17; 18], the bog arose on a Last Glacial river terrace (Wurm, Weichselian, MIS 2-4), at the foot of a scarp of a Mindel-aged terrace (Elsterian, MIS 12-16). The origin ages of the peatlands of the Orava-Nowy Targ Basin vary. The research by [PERSON] [19] indicates they are no older than 10,000 years. There are exceptions, however, for instance, the Grel mire, whose base was dated at the Oldest Dryas [20], or Puscizna Rekowianska (Wielka), with a record extending back to the Younger Dryas [21]. Due to the exploitation of the bog, as well as its drainage and climate changes, the bog area has shrunk significantly: from 0 to 35% in one part and by 35-70% in the other part [17; 18]. The bog developed on clayey covers, and the age of its base is estimated at ca. 7000 years (6930 \(\pm\) 240 BP) [22]. Studies on adjacent sites indicate that peatlands within the Orava-Nowy Targ Basin, including Bor na Czerwonen, are underlain by gravels and clays of the Czarny Dunajec fan, formed during the Last Glacial [23]. The peat deposit was formed within a linear depression, and the bog evolved from an initial mesotrophic mire to a raised, typical ombrogenous bog. The bottom layer was produced by a _Sphagno-Picetum_ assemblage, as evidenced by numerous trunks [24], probably of _Picea abies_ and/or _Pinus sylvestris_, occurring in this layer. The peat-forming process was initiated in aquatic conditions, under embryo-soligenous supply during the Atlantic period of the Holocene. The peat comprised within the dome is, according to [PERSON] [22], of the transitional type. Our own observations indicate a raised type for the bog, with a clearly delimited dome and lagg, mostly fed by precipitation and partly by shallow groundwater [17].
## 4 Design and Reconstruction of Sliding Mode Fault Observer (SMFRO)
### Robust Multiple Fault Reconstruction  
The discontinuous switch \(\
u_{\rho}\), i.e., sliding mode strategy Equation (14), makes the observer deviation equation produce a sliding mode motion to ensure the robustness of the system to the external disturbance, but it will inevitably produce chattering and bring high frequency interference to the system. When the sliding mode motion reaches \(\dot{e}_{y}=e_{y}=0\); i.e., \(0=A_{3}e_{1}(t)+\
u_{eq}-F_{2}f_{a,s}(t)\), where \(\
u_{eq}\) is the equivalent output control. Because \(A_{1}\) has a stable pole, it makes \(\lim\limits_{t\to\infty}e_{1}=0\), then \(\
u_{eq}\to F_{2}f_{a,s}(t)\). In order to reduce the chattering phenomenon, the continuous function approximation method is used to approximate the sliding mode and Equation (17) is obtained [33]:  
\[\
u_{eq}=-\rho\|P_{2}\|\frac{P_{2}CL_{n}e_{y}}{\|P_{2}CL_{n}e_{y}\|+\sigma} \tag{17}\]  
where \(\sigma\) is the normal number with less accuracy. The equivalent output control Equation (17) makes the reconfiguration system robust, and the robust fault reconstruction expression is as follows:  
\[\hat{f}_{a,s}(t)=-\rho\|P_{2}\|(F_{2}^{T}F_{2})^{-1}F^{T}\frac{P_{2}CL_{n}e_{ y}}{\|P_{2}CL_{n}e_{y}\|+\sigma} \tag{18}\]  
The robust fault reconstruction expressions for the actuator and the sensor are Equations (19) and (20):  
\[\hat{f}_{a}(t)=[\begin{array}{cc}I_{q}&0\end{array}]\hat{f}_{a,s}(t) \tag{19}\]  
\[\hat{f}_{s}(t)=[\begin{array}{cc}0&I_{h}\end{array}]\hat{f}_{a,s}(t) \tag{20}\]
## 3 Review of PLC Systems
### Evolution of PLC Technology and Systems
* The non-stoppable evolution of DSL technologies, together with the consolidated HFC alternatives made Internet access interest vanish, even if projects like Open PLC European Research Alliance (OPERA) [41,42] led most of the research and development (R&D) initiatives around BB PLC for the access segment. * BB PLC interest was reoriented for a segment that could complement the BB Internet access penetration, and many initiatives (Homeplug [43], Universal Powerline Association (UPA) [44], HD-PLC Alliance [45] and HomeGrid Forum [46]) developed solutions to provide BB PLC connectivity. Standardization bodies consolidated these developments in the form of standards for in-home, both in IEEE 1901 [47] and ITU G.hn [48]. * However, the interest of utilities on BB PLC did not vanish with the Internet access deception. OPERA 2 [49] managed to re-orient the know-how gathered in BB PLC in the access segments, to search for electricity operational applications of BB PLC. Thus the possibility to communicate Secondary Substations (SSs) among them through BB PLC,
leveraged existing knowledge, and BB PLC products that could serve in-home environments, were adapted to produce industrial solutions that together with coupling units for MV cable connectivity, managed to provide deployable solutions for MV SS connectivity [50]. As a consequence, a group of state-of-the-art PLC technologies and solutions exist today. They have been developed and applied in different segments of the grid and for different purposes. Some of them are even well standardized and have the needed industry support.
### Homology Modeling and Site-Directed Mutagenesis  
Sequence alignment was carried out with CLC Sequence Viewer 7. The sesquiterpene synthases (sTPSs) were used including PatTps177 (_Pogostemon cablin_, Patchouled synthase, Q49 SP3.1), Nt5 EAS (_Nicotiana tabacum_, 5-epi-aristolochene synthase, 5 EAS_A), AaADS (_Artemisia annua_, amorpha-4,11-diene synthase, Q9 AR04.2), AaGAS (_Artemisia annua_, germacrene A, ABE03980.1), CsTPS1 (_Citrus sinensis_, valencene synthase, AAQ4608.1), GaXC14 (_Gossypium arboretum_, (+)-8-cadinene synthase, Q39760.1), GhTPS1 (_Gossypium hirsutum_, (-)-germacrene D synthase, NP_001314061.1), PatTpsA (_Pogostemon cablin_, \(\gamma\)-curcumene synthase, Q49 SP7.1), SIGCS (_Solanum lycopersicum_, germacrene C synthase, AAC39432.1), ScGAS (_Solidago candensis_, germacrene A synthase, CAC36896.1), StVS(_Solanum tuberosum_, vetispiradiene synthase, NP_001275171.1), VvVAS (_Vitis vinifera_, Valcencene synthase, Q6Q3H2.1), ZmTPS10 (_Zea mays_, (E)-\(\beta\)-farresene synthase, NP_001105850.2), ZSS1 (_Zingiber zerumbet_, \(\alpha\)-humulene synthase, B1B1U3.1).  
A three-dimensional model of TaPS was acquired on the SWISS-MODEL server (www.swissmodel. expasy.org) using Nt5 EAS (PDB ID: 5 EAS) as the template. The 3D structure of \(\beta\)-patchoulene was generated by Chem3D. The docking of \(\beta\)-patchoulene-TaPS was generated using AutoDock (autodock.scripps.edu) [38]. The docking results were visualized with PyMOL.  
Site-directed mutagenesis was carried out using the Stratagene QuikChange kit following the manufacturer's instructions. The mutants were confirmed by sequencing and the confirmed constructs were directly used for recombinant expression as described above.