esa-sceva/satcom-chunk-collection huggingface dataset:
# A Performance Analysis of Internet of Things Networking Protocols: Evaluating MQTT, CoAP, OPC UA
## 3 An Overview on IoT Communication Protocols and Frameworks
### Information-Centric Networking Approaches in IoT  
_Information-centric Networking (ICN)_ is emerging as a new stack which is being explored in multiple areas. However, it is in the field of IoT that ICN is gaining ground. There are several architectures being explored from an end-to-end perspective, being the most popular today the _Content centric Networking (CCN)_ architecture (2010, PARC and Partners), the _Named Data Networking (NDN)_ architecture (2014, coordinated by UCLA), and _Hybrid ICN (hICN)_ (2014), by Cisco. In what concerns relevancy for IoT, the most popular approach today is NDN as it supports all IoT basic requirements [22].  
CCN has been adopted by the _Internet Research Task Force (IRTF)_ working group _Information-centric Networking Research Group (ICNRG)_[23] and gave rise to both NDN and hICN. Both software architectures are therefore quite similar, being the main difference the fact that hICN is focused on IP interoperability, while NDN is focused on the development of clearer networking semantics, that support better the aspects that IP cannot cope by design, such as security, mobility.
# Blind Interleaver Parameters Estimation Using Kolmogorov-Smirnov Test
###### Abstract  
The use of error-correcting codes (ECCs) is essential for designing reliable digital communication systems. Usually, most systems correct errors under cooperative environments. If receivers do not know interleaver parameters, they must first find out them to decode. In this paper, a blind interleaver parameters estimation method is proposed using the Kolmogorov-Smirnov (K-S) test. We exploit the fact that rank distributions of square matrices of linear codes differ from those of random sequences owing to the linear dependence of linear codes. We use the K-S test to make decision whether two groups are extracted from the same distribution. The K-S test value is used as a measure to find the most different rank distribution for the blind interleaver parameters estimation. In addition to control false alarm rates, multinomial distribution is used to calculate the probability that the most different rank distribution will occur. By exploiting those, we can estimate the interleaver period with relatively low complexity. Experimental results show that the proposed algorithm outperforms previous methods regardless of the bit error rate.  
interleaver; blind interleaver parameters estimation; non-cooperative systems; linear block codes; channel coding +
Footnote †: journal: Sensors  
1
Footnote 1: Department of Electronic Engineering, Hanyang University, Seoul 04763, Korea; [EMAIL_ADDRESS] (S.W.); [EMAIL_ADDRESS] (C.C.)  
* Correspondence: [EMAIL_ADDRESS], Tel. +82-2-2220-4369
# Overview on Different Types of Solar Cells: An Update
## 3 Perovskite Solar Cells
### Lead-Free Perovskite Solar Cells  
The study of the properties of lead-free perovskite solar cells has been performed by many researchers. Methylammonium tin iodide (CH\({}_{3}\)NH\({}_{3}\)SnI\({}_{3}\)) could be used to replace lead due to its narrower band gap, eco-friendliness, wider visible absorption spectrum, and excellent mobility. [PERSON] and co-workers [144] described a solar cell made from glass/ITO (as an electron-transport layer)/n-type WS\({}_{2}\)/CH\({}_{3}\)NH\({}_{3}\)SnI\({}_{3}\)/p-type P\({}_{3}\)HT (as a hole-transporting layer)/Au. Tungsten disulfide (WS\({}_{2}\)) indicated a small, indirect band gap (1.3 eV), a big direct band gap value (2 eV), a high carrier mobility, and excellent conductivity. The homo-polymer poly (3-hexylthiophene), called P3 HT, showed a wide band gap, high hole mobility, robust hydrophobicity, high thermal stability, and relatively low cost. The experimental findings showed power conversion efficiency (due to number of carriers increased) and the short circuit current density increased (due to more photons falling on it); however, the fill factor and open-circuit voltage reduced (due to an increase in dark saturation current) with the increase in the film thickness (Figure 7). It is noted that more photons were absorbed when the film thickness was increased. These photons could penetrate deeper into the absorber layer, resulting in the production of more electron-hole pairs in devices. The photovoltaic parameters (fill factor = 81.59%, open-circuit voltage = 1.0997 V, power-conversion efficiency = 33.46%, short-circuit current density = 37.17 mA/cm\({}^{2}\)) were studied under optimized conditions. [PERSON] and co-workers proposed zinc magnesium oxide (ZnMgO) and MASnBr\({}_{3}\) as an electron-transport layer and hole-transport layer, respectively [145]. The photovoltaic behaviors (fill factor = 82.01%, open circuit voltage = 0.95V, power conversion efficiency = 26.33%, short circuit current density = 33.85 mA/cm\({}^{2}\)) were investigated in specific solar cells (Zn\({}_{0.75}\)Mg\({}_{0.25}\)O/CH\({}_{3}\)NH\({}_{3}\)SnI\({}_{3}\)/MASnBr\({}_{3}\)). [PERSON] and co-workers used ZnTe (as the hole-transport layer) to replace spiro-OMeTAD, which is a very expensive) material [146]. The power conversion efficiency reached 22.96% (simulation model) and 8.41% (fabrication model) based on the experimental results.
# Reduced Overhead Routing in Short-Range Low-Power and Lossy Wireless Networks +
## 5 Performance Evaluation
### ERPL's P2P Routing and Data Forwarding Performance Evaluation  
Here, we present a simulation and physical test-bed based protocol performance evaluation. We implemented ERPL's P2P routing and data forwarding algorithms in the Contiki operating system. Simulation-based performance evaluations were carried out using the widely used Cooja emulator [23] using the real program code for embedded devices. The test-bed based performance evaluation was performed using the FlockLAB [24] test-bed. The protocols use the objective function 0 [20]. In our Cooja setup, 50 Tmote sky motes were placed in a network that spans an area of 250 \(\times\) 250 m\({}^{2}\). There is a single gateway in our network. We evaluate the protocol's performance using different random and grid topologies. Moreover, we analyze the impact of the gateway's position and the nodes' data generation interval on the protocols performance. The general simulation parameters are stated in Table 1. The total duration of a single simulation is 1020 s. We categorize our performance benchmarks as follows: reliability, latency, and energy consumption. For reliability we measure and report mean packet delivery ratio (PDR), for latency we report mean per-packet end-to-end delay, and for energy consumption we report the total number of times P2P communication packets are relayed and the total number of re-transmissions. We compare our ERPL protocol against a RPL protocol implementation. The experiments were performed using storing MoP, because in storing MoP nodes can forward packets to the destination largely without involving the root node, hence we can obtain an upper bound on the protocols' performances.  
For MP2P communication, each node generates data packets. Nodes start generating data packets at 10 simulation seconds, and stop the generation of data packets at 1000 s. For P2 MP communication, the gateway starts generating data packets at 20 s, and stops the generation of P2 MP data packets at 1000 s. For each data packet, the gateway selects a destination node at random. Similarly, for P2P communication, each node starts P2P communication at 20 s, and stops P2P packet transmission at 1000 s. For each data packet, nodes randomly select the peer destination node. We repeated our experiments by varying the MP2P, P2 MP, and P2P communications packet generation interval. The packet generation interval is varied from 2 s to 8 s.
## 2 Effect of the Average Particle Size on the Model's Macroscopic Mechanical Behavior
### Effect of the Average Particle Size on the Compressive Strength  
The compressive strength of specimens corresponding to different \(L/d_{\text{ave}}\) is shown in Figure 5. As can be seen from Figure 5, the uniaxial compressive strength increases with a decrease in the particle size, and the average compressive strength increases by 62.7%, from 7.5 MPa to 12.2 MPa. However, the rate of increase gradually decreases when \(L/d_{\text{ave}}\) is greater than 50. In addition, with a decrease in the particle size, the difference in the compressive strength of specimens with different IDs is smaller. This indicates that an increase in \(L/d_{\text{ave}}\) will make the particles denser and more closely related to each other. Figure 6 shows the trend of the compressive strength variance with \(L/d_{\text{ave}}\). It can be seen that the variance in uniaxial compressive strength gradually decreases with an increase in \(L/d_{\text{ave}}\). When \(L/d_{\text{ave}}\) increases from 10 to 100, the variance decreases from 2.133 to 0.245, reducing by 88.51%, indicating that the strength dispersion decreases with the increase in \(L/d_{\text{ave}}\), and specimens tend to be more identical. The compressive strength range corresponding to each \(L/d_{\text{ave}}\) is shown in Table 3. The range decreases with the increase in \(L/d_{\text{ave}}\), and the percentage of the range of average strength also decreases continuously, falling to 10% when \(L/d_{\text{ave}}\) is 75 and below 5% when \(L/d_{\text{ave}}\) is 100.  
Figure 4: Stress–strain curves of model with different \(L/d_{\text{ave}}\).  
Figure 5: Relationship between the uniaxial compressive strength and \(L/d_{\text{ave}}\).
## 3 Proposed Method
### Backward Dropping Algorithm (BDA)  
The BDA is a greedy algorithm that searches for the optimal subsets of variables that maximizes the I-score through step-wise elimination of variables from an initial subset sampled using in some method from the variable space. The steps of the algorithm are as follows:  
1. _Training Set_ : Consider a training set \(\{(y_{1},x_{1}),\ldots,(y_{n},x_{n})\}\) of \(n\) observations, where \(x_{i}=(x_{1i},\ldots,x_{pi})\}\) is a \(p\)-dimensional vector of explanatory variables. The size \(p\) can be very large. All explanatory variables are discrete.
2. _Sampling from Variable Space_: Select an initial subset of \(k\) explanatory variables \(S_{b}=\{X_{b_{1}},\ldots,X_{b_{k}}\}\), \(b=1,\ldots,B\)
3. _Compute Standardized I-score_: Calculate \(I(S_{b})=\frac{1}{n\sigma^{2}}\sum_{j\in\mathcal{P}_{k}}n_{j}^{2}(\tilde{Y}_{ j}-\tilde{Y})^{2}\). For the rest of the paper, we refer o this formula as the influence measure or influence score (I-score).
4. _Drop Variables_: Tentatively drop each variable in \(S_{b}\) and recalculate the I-score with one variable less. Then drop the one that produces the highest I-score. Call this new subset \(S^{\prime}_{b^{\prime}}\), which has one variable less than \(S_{b}\).
5. _Return Set_: Continue to the next round of dropping variables in \(S^{\prime}_{b}\) until only one variable is left. Keep the subset that yields the highest I-score in the entire process. Refer to this subset as the return set, \(R_{b}\). This will be the most important and influential variable module from this initial training set.
# Advancing Digital Twin-Based Collision Avoidance:
## 4 Functional Safety for Wireless Systems
### Modeling Outage Frequency for Wireless Systems
#### 4.3.3 Two-Layer SGM/SGM/SGM for \(Pfh_{\text{D}}\)  
In this section, we consider the two-layer SGM/SGM/SGM model from [22] for the case of an internet-based safety system. For the outer states, for the edge and for the core, we use the numbers from the example bursty trace from [22]. For the access link, we use our own results from above.  
Figure 10: With the SGM, we receive the following distribution for our measurements. This is the recommended model for traffic only flowing through the access link.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Burst Length \(k\) & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline Probability \(p_{k}\) & 0.895 & 0.094 & 0.0099 & 0.001 & \(1.09\times 10^{-4}\) & \(1.1\times 10^{-5}\) & \(1.2\times 10^{-6}\) & \(1.3\times 10^{-7}\) & \(1.3\times 10^{-8}\) & \(1.4\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Burst loss distribution of our data set according to SGM.  
\[\text{Outer states} =\begin{bmatrix}0.9263&0&0.0632\\ 0&0&0\\ 0.1182&0&0.8818\end{bmatrix}\] Access Link SGM =\begin{bmatrix}0.956&0.044\\ 0.895&0.105\end{bmatrix}\] Edge SGM =\begin{bmatrix}1&0\\ 0&1\end{bmatrix}\] Core SGM =\begin{bmatrix}0.9682&0.0318\\ 0.9071&0.0929\end{bmatrix}\]  
We simulated this Markov chain with \(10^{7}\) events, resulting in the burst loss count in Table 6. We derive \(PFH_{D}\) by fitting the data and normalizing the resulting equation to  
\[PFH_{D}=3.38\cdot 10^{-3}\cdot e^{-T\cdot f}\cdot f\cdot 3600\]  
Figure 11 shows us that with \(T=1\,\mathrm{s}\), we would need \(T=14\,\mathrm{Hz}\) for performance level A and \(T=19\,\mathrm{Hz}\) for D. The additional possibility for loss on the core leads to a significant increase in total loss, which is why we would need a higher packet transmission frequency to reach the same \(PFH_{D}\) in a scenario which is otherwise unchanged.
## 3 Research Design and Method
### Introduction to the Theory
#### 3.1.3 Evaluation System  
An evaluation system is a set of indicators containing rules for assessing, measuring or comparing things or behaviors. It usually consists of multi-level indicators, from the whole to the details, which are used to assess various aspects or characteristics of an object and provide a quantitative or qualitative evaluation of it. The construction of the evaluation system involves synthesizing the data processing results of the multi-round consultation questionnaires and the suggestions made by the experts, modifying the initially established evaluation indicators, which subsequently leads to the formation of a sound system of evaluation indicators [27]. This process of using a questionnaire is usually carried out in the form of a literature analysis, taking into account the attributes of the subject in the study through the initial formulation of the evaluation indicators and the use of back-to-back correspondence between experts, which ensures the independence of their opinions [28]. Scholars usually construct an evaluation model and design the index of an evaluation system by means of the Delphi method and then use the hierarchical analysis method to determine the weights of the evaluation indexes of IT quality of service [29]. For example, when constructing an evaluation system for information security, the guideline layer can be divided into five parts, namely hardware security, information security, software security, management security, and environmental security, while for the evaluation system of creative open data competition works, the first-level indicators are set as information content, functional design, interface design, system performance, open data development and utilization, and the degree of compliance with the competition rules [30]. [PERSON] et al. pointed out that for the evaluation of creative competition entries, they have the following first-level indicators: information content, functional design, interface design, system performance, data development, competition rules, etc. [31]. The design of such an evaluation system can reflect the characteristics and quality requirements of the evaluation object more comprehensively and improve the accuracy and credibility of the evaluation.
## 3 Analysis Methods  
### Satellite Atomic Clock Model and Noise Characteristic  
The atomic clock model usually contains three parameters [24]:  
1. Initial clock offset, \(a_{0}\), which represents the clock difference at time \(t_{0}\);
2. Initial frequency offset, \(a_{1}\), which represents the relative frequency deviation at time \(t_{0}\);
3. The frequency drift, \(a_{2}\), which represents the linear change of relative frequency deviation.  
The atomic clock system can be considered as a numerical integrator that requires the three parameters mentioned above to form the oscillator, the schematic of which is shown in Figure 4. In terms of self-characterization and the influence of the working environment, the stochastic differential equation of a satellite atomic clock can be generally expressed as follows:  
\[\begin{split}\Delta t(t)&=a_{0}+a_{1}t+\tfrac{1}{2} a_{2}t^{2}+\tfrac{A}{2\pi f_{0}}\sin(2\pi f_{0}+\varphi)|_{0}^{t}\\ &\qquad+\sigma_{1}W_{1}(t)+\sigma_{2}\int_{0}^{t}W_{2}(s)ds+ \sigma\varepsilon(t)\end{split} \tag{1}\]  
* \(\tfrac{A}{2\pi f_{0}}\sin(2\pi f_{0}+\varphi)\Big{|}_{0}^{t}\) expresses the periodic component in the phase, which was shown in the figure as environmental effects;
* \(W_{1}(t)\), \(W_{2}(t)\) are two independent Wiener processes as oscillator noise in the figure, representing the random component of the clock offset, respectively;
* \(\sigma\varepsilon(t)\) is the observation noise.  
Figure 3: The Efficiency of the Clock Data after MAD Processing.  
In Equation (1), \(a_{0}\), \(a_{1}\), \(a_{2}\) could be regarded as constant values in a short period of time; the periodic components produced by the orbital motion of the satellites usually have certain rules, especially for the main cycles. \(W_{1}(t)\), \(W_{2}(t)\) usually represent white frequency modulation noise (WHFM) and flicker frequency modulation noise (FLFM), respectively. The stability equation of the clock offset, taking Allan variance as an example, can be expressed as follows:  
\[\sigma_{y}^{2}(\tau)=3\sigma^{2}/\tau^{2}+{\sigma_{1}}^{2}/\tau+\frac{1}{3}{ \sigma_{2}}^{2}\tau+\frac{1}{2}d^{2}\tau^{2}+A^{2}\frac{\sin^{4}(\pi f_{0}\tau )}{(\pi f_{0}\tau)^{2}} \tag{2}\]  
The main operational characteristics of a satellite atomic clock can be obtained from the above analysis, the phase output of which is determined by a combination of frequency, drift rate, periodic terms due to the environment, and noise. The relevant performance metrics include fitting residuals, frequency drift rate, and frequency accuracy. In addition, the noise characteristics are mainly determined by the frequency stability, and the periodic term can also cause an abnormal protrusion in frequency stability over a certain time period. The above analysis allows an intuitive understanding of the satellite atomic clock output pattern, enabling the design of a reasonable performance analysis scheme.
## 1 Introduction
Second, we derive the ergodic link capacity of orthogonal space-time block coding and spatial multiplexing with zero-forcing receivers for MN MIMO systems, using the results from [35] as a starting point. The resulting capacity expressions depend not only on the MIMO transmission scheme, antenna correlation and signal-to-noise power ratio as in the SN systems, but also on the path gain and transmit antenna set selected from multiple sensor nodes. Based on these models, spatial MIMO mode adaptation and antenna set switching algorithms are proposed. The important differences in the proposed scheme from the previous SN MIMO adaptations [29, 30, 31, 32, 33, 34, 35] are: that (i) it considers statistical antenna subset selection; and (ii) we exploit structure in the spatial correlation matrices, due to the difference in sensor node locations. The proposed scheme is especially suitable for mobile WMSNs, because mode switching decisions are made statistically on a slower timescale, thus requiring less inter-sensor node coordination. To demonstrate the improved spectral efficiency of the proposed MN MIMO, we consider a numerical example with two sensor nodes, where a mobile sink moves from one sensor node to another, called a boundary region, and which is similar to a handoff region in cellular systems. We plot the ergodic link capacity for each spatial transmission mode as a function of distance for a given correlation model. We show improvements in spectral efficiency at the boundary region of as much as 50% _versus_ strict SN MIMO processing. The rest of the paper is organized as follows. In Section 2, we introduce the target MN MIMO system and channel models. In Section 3, we analytically derive the ergodic capacity of MN OSTBC and SM schemes. The proposed MN MIMO switching scheme in mobile WMSNs is presented in Section 4. Finally, we provide simulation results and concluding remarks in Sections 5 and 6, respectively.
## 2 Results and Discussion
### High Temperature-Triggered Regulatory Events in Koshiiukari Rice Seeds of Grades 2 and 3
#### 2.4.5 Starch Metabolism  
Starch metabolism is a key process in regulating rice quality and germination [23; 33]. Two key enzymatic processes involved in this pathway are related to starch hydrolysis and sucrose synthesis. Here, in both Y2 and Y3, few amylase genes (Os08g36910.2 and Os09g28400.1) were found to be commonly upregulated (supplementary Table S3, Metabolism overview; and Figure 5). In addition, one more gene related to amylase (Amy1) (Os02g52700.1) was also found to upregulated in Y3 alone. It was previously found that upregulation of Amy1 related genes produced chalky grains by degrading the starch reserves in the ripening grains [24; 25; 26]. It appears to be more in Y3 as multiple copies of such genes are upregulated. On the other hand, cleavage of sucrose is very important for accumulation of starch in the seeds. This is mediated by sucrose synthase (Susy) genes which converts sucrose to UDP glucose. The effect of heat stress could suppress these genes and was observed here. A gene related to the Susy family (SUS4, Os07g42490.1) was downregulated in both Y2 and Y3. Downregulation of this gene indicates a reduction in starch content and indirectly contributing to grain chalkiness along with amylases.
# A Distributed Sensor System Based on Cloud-Edge-End Network for Industrial Internet of Things +
### Contributions  
This paper presents a distributed sensor data generation and fusion system based on a cloud edge network. The main contributions of this work can be summarized as follows:  
* This paper presents a distributed sensor self-network, which challenges the traditional edge sensor-central computer network method. It uses a three-level network approach that integrates cloud server platforms to optimize the network structure. It solves the problem of the high cost and low reliability of existing data collection schemes in the current Industrial Internet of Things environment.
* The scheme realizes the real-time monitoring of industrial field data and visualizes them in various dimensions, levels, and granularities. It helps enterprises make better decisions and manage industrial sites.
* The system is equipped with an advanced data preprocessing system that can use neural networks to clean, filter, and tag data. The system can generate complete industrial datasets and make them public, which solves the problem of the current lack of datasets in the Industrial Internet of Things.  
The rest of this paper is structured as follows: In Section 2, we present the system model of the distributed smart cloud edge network. Section 3 describes the system architecture of the distributed sensor system. We then introduce the feature fusion of multisensor data from distributed sensors in Section 4. In Section 5, we summarize the main contributions and conclusions of this paper.