esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Modified Reverse Arrangements Test  
Similar to the RA test, the MRA test was applied into adjacent 100, 200, 500, and 1000 ms segments with 10 sub-segments and the mean square value was calculated for each sub-segment. Theoretically, the mean square values from adjacent segments of a stationary signal are independent observations from a random variable. Thus, time trends in the mean square values may be due to a source of signal nonstationarity [32]. The test then requires the calculation of the number of times, \(A\)--beginning with the first mean square value \(y_{1}\) in the sequence of 10 ms sub-segments \(y_{i}\)--that each subsequent mean square value \(y_{1},y_{2},y_{3},...,y_{n}\) is less than \(y_{1}\). This process was then repeated for \(y_{2},y_{3},...,y_{n-1}\). A z-score could then be calculated using the following equation:  
\[z=\frac{A-\left[\frac{n(n-1)}{4}\right]}{\sqrt{\frac{n(2n+5)(n-1)}{72}}} \tag{2}\]  
where \(A\) = total number of reverse arrangements in the EMG signals, and \(N\) = total number of data points. The null hypothesis for this test was that the mean values \(y_{i}\) in the signal were independent observations from a random variable. The alternative hypothesis was that the mean values \(y_{i}\) that make up the signal were related and part of a significant trend underlying the signal. To reject the null hypothesis at \(p\leq 0.05\) requires a z score of \(z\geq 1.96\) or \(z\leq-1.96\).  
Both tests were carried out utilising MATLAB software. By performing both tests, the EMG signals were then classified as stationary and non-stationary according to the z-score for each trial set. For each window size and subject, the percentage of the stationary signals was calculated using the following equation:  
\[\text{Percentage of the stationary}=\frac{N_{z}}{N_{t}} \tag{3}\]  
where \(N_{z}\) is the number of stationary segments with respect to a window size, \(N_{t}\) is the total number of segments with respect to window size, and \(i\) is the index of the EMG signals.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Low Latency Applications
#### 12.1.2 Latency in Transponders  
The advantage of HCF as a low-latency transmission medium will be lost when high dispersion or interference from higher-order modes degrades the signal received and Forward Error Correction (FEC) is employed, introducing latency due to signal processing.  
There are multiple variants of FEC. For example, the use of the Reed-Solomon (544, 514, 15) GF(210) code known as KP4 FEC (with \"hard\" decision) introduces latency of up to 200 ns in transponders with 25 Gb/s streams on each optical carrier and of up to 100 ns in devices with 50 Gb/s streams [116]. This corresponds to latency saved by installing 130 m and 65 m of HCF, respectively, and negates the advantage of HCF in a data center or similar facility if other transponders working over conventional fiber without FEC are available.  
Latency introduced by optical transport network (OTN) equipment carrying 10 Gb/s and 100 Gb/s streams is considerably larger due to extensive signal processing. According to report of tests performed at ADVA [117], latency introduced by a 10 Gb/s transponder with RS (255,239) FEC per ITU-T G.709 standard was 6.3 \(\upmu\)s with FEC and 2 \(\upmu\)s without FEC for one-way transmission. Additional functions required in OTN networks add some 5 \(\upmu\)s of latency in a 10 Gb/s transponder. The latency of a 100 Gb/s transponder 100 Gb/s utilizing newer type of FEC with \"soft\" decision (SD-FEC) varied within the 5.4-7.3 \(\upmu\)s range depending on number of iterations set; the latency can be reduced at the expense of a less effective error correction. The latency introduced by these transponders is in the 2-15 \(\upmu\)s range, equal to latency saved by the deployment of an HCF cable 1.3-10 km long.  
For latency-critical applications like in data centers, dedicated 10 Gb/s transponders were developed, where FEC and other latency-generating functions like management channel are absent, or can be deactivated [118]. Certain 10 Gb/s transponders from the Infinera XTM family have their latency reduced to 4-10 ns.
# Recent Progress in Multiplexed Single-Photon Sources
## 3 Multiplexed Single-Photon Sources
### Temporal Multiplexing
Another promising type of multiplexing is relative multiplexing that aims at synchronizing multiple photons at a convenient time bin, or in the spatial mode that requires the least switching [124; 125; 126]. This solution is useful in experiments where such synchronized indistinguishable photons are needed. Figure 2: Schematic figures of temporally multiplexed SPSs based on (**a**) a storage loop and (**b**) binary-divided delays. HGs are heralded sources, and SWs are optical switches. The various delay times realized by the various delay lines are also indicated in the figure.
# Yeast-Based Magnetic Biocomposite for Efficient Sorption of Organic Pollutants
## 2 Materials and Methods
### Equilibrium Studies  
An important stage of research when removing pollutants using sorption methods is to determine the time after which the balance between the sorbent and the sorbentic is established.  
Equilibrium tests were conducted as follows: 0.1 g of YL-M-L-AC was weighed into propylene containers and 20 cm\({}^{3}\) of methylene blue solution with concentrations of 50, 100, 150, 200, and 250 mg/dm\({}^{3}\) were added. The prepared samples were placed on a laboratory shaker for 24 h at a 25 \({}^{\circ}\)C. After the process was completed, YL-M-L-AC was separated from the MB solution using an external magnetic field (neodymium magnet) and the MB content in the solution was examined. All tests were repeated in triplicates and the results were averaged. After analyzing the MB content, the sorption capacity qt and qe were determined using Equation (1) and the degree of MB removal:  
\[\text{q}_{\text{t(e)}}=\frac{\left(\text{C}_{0}-\text{C}_{\text{t(e)}}\right) \cdot\text{V}}{\text{w}} \tag{1}\]  
\[\text{R}_{\text{e}}=\frac{\left(\text{C}_{0}-\text{C}_{\text{e}}\right)}{\text {C}_{0}}\cdot 100\% \tag{2}\]  
where: qt--mass of adsorbed MB at time \"t\" (mg/g), qe--mass of adsorbed MB at equilibrium (mg/g), C0--initial concentration of MB in the solution (mg/dm\({}^{3}\)), Ct--concentration of MB after time \"t\" (mg/dm\({}^{3}\)), Ce--concentration of MB at equilibrium (mg/dm\({}^{3}\)), V--volume of solution (dm\({}^{3}\)), w--mass of YL-M-L-AC used during the sorption process (g), Re--degree of MB removal from the solution in a state of equilibrium (%).  
Adsorption isotherms are useful in showing the interaction of adsorbate with the adsorbent and are crucial for optimizing its use. The correlation of equilibrium data using empirical equation is important for the interpretation and prediction of adsorption data [18]. Four mathematical models were used to describe the experimental data:
# Implementation of a Lossless Moving Target Defense Mechanism
## 5 Protocol-Independent Packet Processors Programming
The data extracted by the parser must be reattached to the portion of the message that has not undergone processing. During deparsing, it is not necessary to add back the exact number of bits that were removed from the packet during the parsing process. This flexibility means that the outgoing packet from the node or NIC card can have not only altered data in the headers but also headers of varying lengths. It is also possible to encapsulate a protocol unit in another protocol.
## 3 Proposed Optimal Orchestration Process
### Algorithm Performance Analysis
#### 3.4.2 Message Bus Implementation and Performance  
The message bus implementation utilised Apache Kafka deployed at the mobile edge, configured with three broker nodes to ensure high availability. The system employed 12 partitions per topic with a replication factor of 3, optimising for both performance and reliability. This configuration achieved remarkable performance metrics, including average message latency of 3.8 ms and throughput of 100,000 messages per second, while maintaining 99.999% delivery reliability.
## 5 Conclusions  
In this work, a novel superpixel-based multi-scale Siamese graph attention network (MSGATN) is proposed for change detection in high-resolution remote sensed imagery. In the proposed method, superpixel segmentation is exploited to aggregate homogeneous difference information to construct heterogeneous change information for a better recognition of multi-scale changed land cover. In addition, multi-scale superpixel-constructed graphs are introduced to a graph attention network (GATN) in a Siamese framework, which further facilitates the cognition of multi-scale objects for the GATN, thus improving the performance. The proposed MSGATN is validated over two widely used change detection datasets, and compared to several comparative change detection methods. Corresponding results indicate that the proposed method outperforms other methods over all selected evaluation metrics.  
In the future work, efforts can be made to achieve a more fine-grained changed land cover annotation in an unsupervised framework, which can be less time-consuming and laboring in practical applications.  
Conceptualization, W.S. and F.J.; methodology, F.J. and H.Z.; validation, H.Z.; investigation, J.L.; writing--original draft preparation, W.S.; writing--review and editing, W.S. and F.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (61906148).  
The authors declare no conflict of interest.
# Robust Optimization for Household Load Scheduling with Uncertain Parameters
## 2 Mathematical Model
### Constraints
#### 2.3.2 Interruptible Loads  
Interruptible loads (_IL_) are allowed to be stopped during their working time so that load shifting can be achieved. Electric vehicles (EVs) and clothes washers (CWs) are two examples of this category of load. The constraints for interruptible loads can be expressed as follows:  
\[x_{IL,i}=\left\{\begin{array}{ll}0&\forall i\in[1,b_{IL})\cup(e_{IL},N],i\in N ^{+}\\ 0\;\mbox{or}\;1&\forall i\in[b_{IL},e_{IL}],i\in N^{+}\end{array}\right. \tag{11}\]  
\[\sum\limits_{i=b_{IL}}^{e_{IL}}x_{IL,i}=l_{IL} \tag{12}\]  
\[P_{IL,i}=x_{IL,i}*p_{IL}^{r} \tag{13}\]  
where \(x_{IL,i}\) is the working status of interruptible loads (0--off, 1--on); \([b_{IL},e_{IL}]\) represents the allowable working period; \(l_{IL}\) is the required working time to; and \(p_{IL}^{r}\) is the rated power.
## 4 POD with Ambiguity Resolution
### Mathematical Models
#### 4.1.4 Integer Ambiguity Validation  
Several integer ambiguity resolution methods have been proposed including the integer rounding, integer bootstrapping, integer least squares, and partial ambiguity resolution methods [64]. The integer rounding approach is used in this research to resolve the WL and NL ambiguities. The ambiguity validation procedure is presented here for clearness. The reader can refer to [65] for more details.  
Let \(b\) be the float WL or NL ambiguities and it is assumed that the probability density function for \(b\) is:  
\[P(b|n)=\frac{1}{\left(2\pi\right)^{1/2}\sigma}\exp[-\frac{(b-n)^{2}}{2\sigma^ {2}}] \tag{12}\]  
where \(n\) is the true integer ambiguity, \(\sigma\) is the formal uncertainty from the adjustment. Let \(I\) be the nearest integer of \(b\). In the ambiguity rounding procedure, it should be determined whether to fix the ambiguity to \(I\) or leave it as float. The deviation  
\[x=b-I \tag{13}\]  
must fall in the interval [\(-\)0.5, 0.5]. If the true integer ambiguity \(n\) is not equal to the nearest integer \(I\), the probability of wrong ambiguity resolution can be calculated using Equation (14):  
\[Q_{0}=\sum_{m=1}^{\infty}\left[erfc(\frac{m-x}{\sqrt{2}\sigma})-erfc(\frac{m+ x}{\sqrt{2}\sigma})\right]\leq\alpha \tag{14}\]  
\[erfc(x)=\frac{2}{\sqrt{\pi}}\int_{x}^{\infty}e^{-t^{2}}dt \tag{15}\]  
where \(\alpha\) is the allowable rate of wrongly fixed ambiguity, \(erfc\) is the complementary error function. In order to eliminate some extremes, a decision function is defined:  
\[d(x,\sigma)=T/Q_{0} \tag{16}\]  
\[T=\left\{\begin{array}{cc}0,&|x|\geq T_{1}\sigma\sigma\geq T_{2}\\ (1-\frac{|x|}{T_{1}})(3T_{2}-3\  
resolution. When \(d(x,\sigma)\) is greater than 1000, the ambiguity will be fixed to the nearest integer [28; 66].
## 4 The Proposed Approach: DIRECT-NSGA-II
### Constraint-Handling Technique  
Simulated binary crossover and polynomial mutation are employed to generate offspring and enhance population diversity. A repair operator is applied to convert infeasible solutions into feasible ones. Specifically, for a newly generated individual \((x_{1}^{\text{low}},\ldots,x_{m}^{\text{low}},x_{1}^{\text{up}},\ldots,x_{m}^{ \text{up}})\), if \(x_{i}^{\text{low}}<x_{i}^{\text{l}}\), then it is replaced by its lower bound, that is, \(x_{i}^{\text{low}}=x_{i}^{\text{l}}\), and if \(x_{i}^{\text{up}}>x_{i}^{\text{u}}\), then it is replaced by its upper bound, that is, \(x_{i}^{\text{up}}=x_{i}^{\text{u}}\). Moreover, if \(x_{i}^{\text{low}}>x_{i}^{\text{up}}\), then a swap move is performed, that is, \(x_{i}^{\text{low}}\gets x_{i}^{\text{up}}\) and \(x_{i}^{\text{up}}\gets x_{i}^{\text{low}}\).  
This paper incorporates the constraint-handling method from [39]. More precisely, the normalized sum of constraint violations for all individuals in the current population is calculated as follows:  
\[\text{Violation}(\mathbf{x}^{\text{low}},\mathbf{x}^{\text{up}})=\sum_{i=1}^{p}\frac{ \max\left[(f_{i}^{l}-f_{\text{min},i}(\mathbf{x}^{\text{low}},\mathbf{x}^{\text{up}})),0\right]}{|f_{i}^{l}|},\]  
Here, \(|\cdot|\) denotes the absolute value, and \(f_{\text{min},i}(\mathbf{x}^{\text{low}},\mathbf{x}^{\text{up}})\) represents the value of the objective function \(f_{i}^{*}(\mathbf{x}^{\text{low}},\mathbf{x}^{\text{up}})\) obtained using the DIRECT algorithm. Subsequently, individuals are classified based on overall constraint violations by comparing them pairwise. If both individuals have zero violations, we apply standard domination ranking. Otherwise, the individual with the lower (or zero) overall violation dominates the other.
## 3 Evidential Reasoning Algorithm
### Input Data Analysis
#### 3.4.1 Continuous Variable as Input Data  
Suppose we evaluate the condition of a system component by measuring the quantity \(x\), which can take continuous values (e.g., breaker switch-on time, the moisture content in the transformer oil, etc.). Due to various factors in the measurement procedure, several successive measurements differ from each other. The measurement results behave according to their inherent statistical distribution. For most technical systems, the distribution is approximated by a normal or Gaussian distribution with the parameters \(\overline{X}_{i}\sigma^{2}\) (mean value and standard deviation). The question as to how to transform the measured value into a qualitative assessment arises [1].  
According to the manufacturer's recommendation and the user's experience, the observed components can be classified into good or bad, i.e., classes to which we can add qualitative ratings. The classes are defined by ranges of measured size. Of course, the boundaries between classes cannot be precisely defined, so ratings will inevitably overlap. Instead of defining fixed interval boundaries, the mean and variance of the measured value are determined for each class individually.  
Therefore, each class \(n=1\),... \(N\) is given rating \(H_{n}\), mean value \(\overline{X}_{n}\) and standard deviation \(\sigma_{n}^{2}\). Parameters \(\overline{X}_{n}\) and \(\sigma_{n}^{2}\) are estimated differently depending on the type of measured quantity, type of device, manufacturer's recommendation, fault statistics and the experience of the assessor.  
To measured value \(x\), qualitative rating \(H_{n}\), with a certain degree of belief \(\beta_{n}\) is added. The degree of belief \(\beta_{n}\) is defined by the Gaussian distribution with parameters \(\overline{X}_{n}\) i \(\sigma_{n}^{2}\):  
\[\beta_{n}\sim\ \mathfrak{b}_{n}=\frac{1}{\sigma_{n}\sqrt{2\pi}}e^{-\frac{(x- \overline{\mathfrak{b}}_{n})^{2}}{2\sigma_{n}^{2}}} \tag{29}\]  
It is reasonable to assume that the measurement of physical quantities is an exact procedure and that there is no indeterminacy of the results. Therefore, the sum of all probabilities \(\beta_{n}\) is unified to a value of 1:  
\[\sum_{n=1}^{N}\beta_{n}=1 \tag{30}\]  
\[\beta_{n}=\frac{b_{n}}{\sum_{n=1}^{N}b_{n}} \tag{31}\]  
The normal distribution assumes a certain probability for each measured value, so each rating would also have a certain probability. For the sake of simplicity, ratings where the probability is lower than a certain value (e.g., 0.05) can be ignored.  
For attributes where the deviation from a certain ideal value is measured, which can be both positive and negative, the qualitative assessment is proportional to the absolute value of the difference between the ideal and the measured value.  
The ideal trip delay time of the other two poles after the first one is \(t_{0}=7\) ms. The maximal deviation time for a pole is \(\Delta t=2\) ms. The variable that is observed and evaluated is therefore the absolute value of measured time \(t\) and ideal time \(t_{0}\):  
\[\Delta t=\left|\mathfrak{t}-\mathfrak{t}_{0}\right| \tag{32}\]
## 4 Discussion  
The results of the present study show the great potential of satellite SAR data to contribute to the monitoring of the tidal Wadden Sea area. Visual image interpretation of TerraSAR-X data combined with extensive in situ data enable the detection and observation of various large-scale surface structures and characteristic habitats. This is to be emphasized as the smooth and dynamic relief of the Wadden  
Figure 12: Time series 2009–2015 of tidal area imaged by TerraSAR-X, HRS: (**a**) 21/07/2009, asc., 111 cm < NHN and photograph of 14/07/2008; (**b**) 16/07/2011, asc., 152 cm < NHN; (**c**) 14/06/2014, desc., 132 cm < NHN and photograph of 17/10/2014; (**d**) 19/04/2015, desc., 166 cm < NHN. (1) mud flat; (2) depression; (3) area of patchy mussel bed; (4) area of solid mussel bed.  
Sea, influenced by variable water levels and weather conditions, places great demands on classification methods in general.
# Messy Broadcasting in Grid
## 1 Introduction
: _Definition 5 is useful since it is easy to see that \(t_{i}(G,\ u)=\max_{v}\{t_{i}(G,\ u,\ v)\mid v\in V(G)\}\) for \(i=1,2,3\), where \(V(G)\) is the vertex set of graph \(G\)._  
This paper investigates the messy broadcast time and optimum schemes in two-dimensional grid graphs when the originator is one of the corner vertices in Section 3.2. The optimum broadcast time and scheme are found for messy models \(M_{1}\) and \(M_{3}\), which consequently gives a bound for \(M_{2}\) as well. Figure 1: An example that shows the differences between messy models. The drawn schemes are optima. It is easy to verify that \(t_{1}(u)=3\), \(t_{2}(u)=4\), \(t_{3}(u)=6\). (a) \(t_{1}(u)\geq 3\), (b) \(t_{2}(u)\geq 4\), (c) \(t_{3}(u)\geq 6\). **Definition 6** (Grid). : _A \(d\)-dimensional grid graph of dimensions \(n_{0},n_{1},\ldots,n_{d-1}\) denoted by \(G[n_{0}\times n_{1}\times\ldots\times n_{d-1}]\) is a graph whose vertices are \(d\)-tuple of non-negative integers \((v_{0},v_{1},\ldots,v_{d-1})\), where \(0\leq v_{i}\leq n_{i}-1\) for all \(0\leq i\leq d-1\). The edges connect \(d\)-tuples which differ in exactly one coordinate by one; see Figure 2._  
**Definition 7** (0 vertex). : _Let us call the corner vertex of a grid with all \(0\) coordinates, the \(0\) vertex._  
Our analysis will be based on vertex \(0\) being the originator of the broadcast. The results, however, are applicable to any corner vertex due to the existence of an automorphism that can map vertex \(0\) to any corner vertex. Studying broadcasting within a grid structure is essential due to its prevalent use in computer networking systems, especially in parallel computing. Grid topology is favored for its robustness, scalability, fault tolerance, simplicity, and efficiency. Before delving into the grid, this paper first examines the messy broadcast time and optimum schemes under model \(M_{3}\) for the infinite grid graph in Section 3.1. Analyzing the infinite grid is beneficial as it offers valuable insights into the broadcast problem within a borderless grid. In this scenario, the conventional concept of broadcast time becomes irrelevant; instead, we focus on \(t_{3}(0,v)\), which serves a comparable purpose to that described in Remark 1.
## 4 Discussion
Problems associated with hybrid models are inherited from empirical models since hybrid models integrate the methods of empirical models. There are other models which were less frequently reported in the literature. These models include the radiative transfer (GORT) model [60; 61], geometric optical mutual shadowing (GOMS) model [62; 63], geometric optical and a two-layer canopy-reflectance model (ACRM) [64; 65], Kuusk-Nilson forest reflectance model [66; 67] and discrete anisotropic radiative transfer (DART), which is a 3D canopy reflectance model developed by [68]. The above-mentioned models were developed to describe the effect of a three- and two-dimensional canopy structure on radiation, these models treated vegetation canopy as randomly distributed tree crowns which absorbed and scattered radiation passing through the crown. A review by [69] concluded that combining new algorithms and complementary information from various sensors lead to the development of better global LAI products. In terms of comparison of satellite LAI with model simulated LAI, different types of data validation and comparison methods were used. The most commonly used data comparison and validation method was a non-contact method that measured proportion of light penetrating through canopy using a plant canopy analyser known as Li-Cor LAI-2200;this handheld device was found to be easy to use, less laborious and less time-consuming compared to destructive tree harvesting and non-destructive litter collection [70]. Other examples of data comparison and validation methods included the use of hybrid instruments such as multiband vegetation imager, which is an optical method that was used by [71] to validate LAI estimated from LiDAR data. There are a number of global LAI products that were developed. For example, MODIS, MERIS, which are medium to coarse resolution data, are the most widely used satellite sensors to generate global LAI products. Ref. [9] reviewed the quality of MODIS (MOD15A2), Copernicus PROBA-V (GEOV1) and the recent EUMETSAT Polar System (EPS) LAI products for croplands LAI estimation. They found that the quality of LAI data from the above-mentioned satellite data were closely related to Sentinel-2 and Landsat 7/8 LAI data (R\({}^{2}\) = 0.90, RMSE = 0.50). Other popular LAI products include GEOV2 LAI, Global Land Surface Satellite (GLASS) LAI and GLOBMAP LAI. The availability of satellite data which combine high revisit time with high spectral and spatial resolution fast tracked the development and validation of sophisticated models for LAI estimation. The current focus on improving the robustness of RTMs will lead to the development of even better and more accurate LAI products.