esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed MRGM Framework
### Message Encapsulating and Routing Service  
To encapsulate this message and route it over the WAN, the DDS routing service is activated and simply routes this message over the network router and delivers it to a predefined topic within a DDS  
Figure 2: The mechanism of the generic object-oriented substation event (GOOSE) repetition.  
domain in another network, where DDS subscribes are waiting to read data. This is possible just by defining the IP address of both DDS publisher and subscriber through the DDS routing service profile.  
As mentioned before, to meet the maximum end-to-end delay for R-GOOSE communication, specific network settings are needed. The DDS QoS profile contains control mechanism and policies for data transfer over the network. For example, it could control availability of data, life span, latency budget, etc. [31]. The message delivery time is defined by the latency budget, i.e., the index which shows the maximum allowed time span for the message delivery. To control this latency, message priority indices could be attached to time-sensitive and critical messages, therefore, the network switches transfer the received messages based on the priority policies, regardless of data queue. This means that in limited bandwidth networks with high message deliver latencies, which usually is due to the first come first serve policy, defining the message priority profiles could significantly enhance the critical message communications over the internet network [32]. The DDS supports three priority policies including round renin (RR), earliest deadline first (EDF), and high priority first (HPF). The RR is known as the simplest priority scheduling scheme which processes the messages in the order they have been received. It prioritizes the messages considering their latency budget scheduled in EDF policy, where the messages with less remaining time span are processed first. It is worth mentioning that the EDF is the default policy scheduled by the DDS. The combination of the RR and EDF could be scheduled by HPF policy, where the messages with the same latency budget are processed by the RR policy [31].
## 4 Estimation Techniques  
### Genetic Algorithm (GA)  
A search heuristic algorithm that was inspired by evolutionary theory is the Genetic Algorithm (GA). GA is frequently used to examine alternatives to optimization problems. For the problems that do not have a proven efficient solution (NP-complete problems) and very large search spaces, we often use genetic algorithm. In GA, the fittest generation has a greater chance of surviving in the following generation since we use GA to build several generations before arriving at the ultimate answer. The current population goes through cycles of crossover and mutation. Each generation has a solution, and the best or most useful generation is ultimately picked as the answer. The GA operations are shown in Figure 3. Based on the values of the population, the GA can be binary or real-coded. In this study, we used the real-coded GA.  
Figure 3: Genetic algorithm operations [34].  
Uniform crossover is used during this study. The offspring are calculated according to the following equations:  
\[y_{1i} =\alpha_{i}x_{1i}+(1-\alpha_{i})x_{2i} \tag{51}\] \[y_{1i} =\alpha_{i}x_{2i}+(1-\alpha_{i})x_{1i} \tag{52}\]  
Here, \(x_{1i}\) and \(x_{2i}\) are parents, \(y_{1i}\) and \(y_{2i}\) are offsprings, and \(\alpha\) is the set of real-valued random numbers which is uniformly distributed over the interval \([0,1]\). To obtain better results or expand the search space, \(\alpha\) can also be distributed over the interval \([-\gamma,1+\gamma]\), where \(\gamma\) is a small real number.  
To set the mutation parameters, we suppose that the variable \(x_{j}\) is changing to \(x_{j}^{\prime}\) according to the following relation:  
\[x_{j}^{\prime}=x_{j}+\delta \tag{53}\]  
\(\delta\) is a real-valued random number with any valid continuous probability distribution. In this study, we assumed that \(\delta\) has a normal distribution:  
\[\delta\,\sim\,(\mu,\,\sigma^{2}\,) \tag{54}\]  
The variance \(\sigma^{2}\) defines the mutation step size.  
Owing to its impressive performance, GA is widely used in optimization and classification problems ranging among fault diagnosis in PV cells [34], OFDM and OQAM synchronization estimation [35], OFDM modulation [36], classification problem solving [37], hyperparameter optimization of power amplifiers [38], optimal resource allocation in cloud computing [39], linear array antenna designing [40], robotic locomotion optimization [41], compressor-based characteristics estimation for a system's slow speed [42], and many more similar problems.
# Study on the Damage Process and Numerical Simulation of Tunnel Excavation in Water-Rich Soft Rock
## 2 Staged Softening Process of Water-Rich Soft Surrounding Rock Based on Tunneling
### Evolution Equation of Permeability Coefficient  
In the coupling analysis of stress-seepage-damage, the permeability coefficient of the surrounding rock will vary with the degree of softening damage. The permeability coefficient of porous media can increase by 1-5 orders of magnitude after significant damage occurs [63]. To reflect the influence of the damage field on the permeability of rock mass, a jump factor \(\xi\) is introduced to calculate the permeability coefficient of the damaged rock mass.  
\[k_{D}=\xi k_{0} \tag{32}\] \[\xi=\left\{\begin{array}{cc}10&0\leq D\leq 0.1\\ \frac{1000-10}{0.9-0.1}(D-0.1)+10&0.1<D<0.9\\ 1000&0.9\leq D\leq 1\end{array}\right. \tag{33}\]  
where, \(k_{0}\) are the initial permeability coefficient of the rock mass, \(k_{D}\) is the permeability coefficient after damage, and \(D\) is the rock damage variable.
## 1 Introduction  
The solid rotor induction motors (SRIM) is applied in many fields due to its simple structure, high reliability, and good starting performance [1]. Toroidal winding structures, such as high-frequency inductors, transformers, and electric machines, are used in various applications because of their short end winding, low maintenance cost, and flexible speed regulation [2; 3; 4]. Applying toroidal winding to SRIM, results in a solid rotor induction motor with toroidal winding (TWSRIM) that combines the advantages of a solid rotor and toroidal winding can be obtained [5]. The equivalent circuit model is an important way to achieve motor design and performance analysis [6]. In order to quickly and intuitively analyze the performance of TWSRIM, it is crucial to establish an equivalent circuit model.  
composite SRIM consists of steel and copper layers. The common equivalent magnetic circuit method has difficulty analyzing the electromagnetic field and calculating the equivalent circuit parameters. Therefore, finite element methods (FEMs) are generally used to analyze composite SRIM [7; 8; 9]. However, FEMs require a large amount of calculation time. To address this disadvantage, Reference [10] proposed a multilayer theory to analyze a smooth solid rotor induction motor. Reference [11] provided a novel multi-slice and multi-layer method for a novel dual stator solid rotor axial flux induction motor. Reference [12] developed a semi-analytical 3D model based on Fourier analysis; this model can calculate the fringe field in a 3D slotted structure. Moreover, several complex propagation constants have been proposed to calculate composite rotor parameters [13; 14].  
Toroidal windings are widely used in various types of motors [15; 16; 17; 18; 19]. Reference [15] conducted a dynamic analysis of a toroidal winding switched reluctance motor (TSRM). Reference [16] proposeda new type of TSRM with a single, continuous, multi-wire winding and compared its performance with that of a conventional switched reluctance motor. Reference [17] developed a novel self-bearing motors based on a toroidally-wound brushless DC motor. To improve machine torque density and efficiency substantially. Reference [18] presented a novel toroidally wound permanent magnet machine. Meanwhile, a complex study of the performance of an outer rotor induction motor with multipole stator winding was performed in [19].  
In this paper, a novel solid rotor induction motor with toroidal winding (N-TWSRIM) is proposed. For the N-TWSRIM, composite multilayer theory is used to calculate the motor parameters. Section 2 presents the motor structure, main structure parameters and operating principle of N-TWSRIM. Section 3 introduces the established ECM, and the parameters are calculated with composite multilayer theory. Section 4 presents the analysis results for different copper layer thicknesses. Such results are compared with those of FEM. Section 5 shows the manufacturing and testing of a simple prototype. The correctness of ECM, FEM, and the operating principle are also verified. Section 6 provides the conclusions.
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## 2 Tracking Error Estimation
### Traditional Discriminator  
There is a variety of discriminator algorithms for tracking error estimation. The ATAN discriminator and ATAN2 discriminator are the research objects for carrier phase error and carrier frequency error estimation in this paper. Because they are optimal (maximum likelihood estimator) at both high and low signal to noise ratio (SNR) and their slopes are signal amplitude dependent [11]. As for code phase error estimation, the most popular NC-EMLE discriminator is the research object. The ATAN, ATAN2, and NC-EMLE discriminator algorithms are expressed in Equations (12)-(14), respectively, as follows:  
\[\overline{\delta\varphi}_{\rm ATAN}=\arctan\frac{\rm Q_{P}}{\rm I_{P}} \tag{12}\]  
\[\delta f_{\rm ATAN2}=\frac{\arctan\!2(P_{\rm cross},P_{\rm dot})}{2\pi\cdot T_{ \rm coh}} \tag{13}\]  
\[\delta\tau_{\rm NC-EMLE}=(1-d)\frac{E-L}{E+L} \tag{14}\]  
where \(\overline{\delta\varphi}_{\rm ATAN}\) is carrier phase error estimation (cycle), \(\delta f_{\rm ATAN2}\) is carrier frequency error estimation (Hz), \(\delta\tau_{\rm NC-EMLE}\) is code phase error estimation (chip), the definitions of \(P_{\rm cross}\), \(P_{\rm dot}\) are shown in Appendix A. Plug the mathematical models of coherent/non-coherent integration, which have been shown in Table 1, into the Equations (12)-(14), these discriminator algorithms can be reformulated as:  
\[\overline{\delta\varphi}_{\rm ATAN}=\arctan\frac{A\cdot D\cdot R(\hat{\sigma} \tau)\sin\c(\delta fT_{\rm coh})\sin(\overline{\delta\varphi})+n_{\rm QP}}{A \cdot D\cdot R(\hat{\sigma}\tau)\sin\c(\delta fT_{\rm coh})\cos(\overline{ \delta\varphi})+n_{\rm IP}}=\overline{\delta\varphi}+n_{\rm ATAN} \tag{15}\]  
\[\delta f_{\rm ATAN2}=\frac{1}{T_{\rm coh}}\!\arctan\frac{B(n)B(n-1)\sin( \delta\varphi_{n}-\delta\varphi_{n-1})+C_{1}(n)}{B(n)B(n-1)\cos(\delta\varphi_{ n}-\delta\varphi_{n-1})+C_{2}(n)}=\delta f+n_{\rm ATAN2} \tag{16}\]  
\[\delta\tau_{\rm NC-EMLE}=(1-d)\frac{A|\sin\c(\delta fT_{\rm coh})|\cdot\{\sum \limits_{i=1}^{N_{\rm QP}}R(\hat{\sigma}\tau-\frac{d}{2})-\sum\limits_{i=1}^{N_ {\rm NP}}R(\hat{\sigma}\tau_{i}+\frac{d}{2})+N_{\rm nc}n_{\rm IP}-N_{\rm nc}n_{ \rm PL}}}{A|\sin\c(\delta fT_{\rm coh})|\cdot\{\sum\limits_{i=1}^{N_{\rm NP}}R( \hat{\sigma}\tau-\frac{d}{2})+\sum\limits_{i=1}^{N_{\rm NP}}R(\hat{\sigma}\tau _{i}+\frac{d}{2})+N_{\rm nc}n_{\rm IP}+N_{\rm nc}n_{\rm IP}}}=\delta\tau+n_{ \rm NC-EMLE} \tag{17}\]  
where \(B(n)\), \(C_{1}(n)\), \(C_{2}(n)\) are derived and defined in Appendix A, and \(n_{\rm ATAN}\), \(n_{\rm ATAN2}\), and \(n_{\rm NC-EMLE}\) are estimation noise of corresponding discriminator. It should be noted here that the discriminator
# Practical Real-Time Phase Drift Compensation Scheme for Quantum Communication Systems
## 2 Methods
Here, we define \(\Delta e_{i}=e_{i}-e_{i-1}\) to roughly evaluate the correctness of the previous modulation direction, which is represented by \(\varepsilon_{i}\), and thus determine whether to change the modulation direction for this time by  
\[\varepsilon_{i}=\left\{ \begin{array}{cc}\varepsilon_{i-1},&\Delta e_{i}\leq 0 ||i>0\\ -\varepsilon_{i-1},&\Delta e_{i}>0||i>0\\ 1,&i=0\end{array} \right.. \tag{9}\]  
Therefore, Equation (7) can be substituted with the subsequent formula  
\[\Delta V_{i}=\varepsilon_{i}\frac{V_{\pi}}{\pi}\arccos\frac{1-2e_{i}}{1-2e_{0 }}, \tag{10}\]  
and the driving voltage value of the next round can be easily obtained by  
\[V_{i+1}=V_{i}+\Delta V_{i}. \tag{11}\]In order to achieve a more stable operation for the system, we also introduced the Proportional-Integral-Derivative (PID) algorithm to calibrate the driving voltage with  
\[\Delta V_{i}^{\prime}=K_{P}\Delta V_{i}+K_{I}\sum_{n=1}^{i}\Delta V_{n}+K_{D}( \Delta V_{i}-\Delta V_{i-1}), \tag{12}\]  
where \(K_{P},K_{I},K_{D}\) are the coefficients of the PID algorithm, and Equation (7) can be adjust to be  
\[V_{i+1}=V_{i}+\Delta V_{i}^{\prime}. \tag{13}\]  
To conclude, the main flowchart of the proposed scheme is illustrated in Figure 1.
## 3 System Architecture
### The MAB Problem  
At each time step \(t\in\{1,2,\ldots\}\), the MAB agent pulls (selects) and uses arm \(I_{t}\in\mathcal{K}\), \(\mathcal{K}=\{1,2,...,K\}\), according to a policy \(\pi\) and, as a consequence, the environment returns a corresponding reward \(X_{t}(I_{t})\in\Theta\) with \(\Theta=[0,1]\) to signal how beneficial it was to pull arm \(I_{t}\).  
In this article, we address a specific class of non-stationary bandit problems, in which the reward distributions are piecewise-stationary and may abruptly change at unknown time steps; hereafter, change-points. To cope with this type of distribution, the rewards for each arm \(i\in\mathcal{K}\), \(\{X_{t}(i)\}_{\forall t\in\{1,2,\ldots\}}\), are a sequence of independent random variables from potentially different distributions, which are unknown to the agent and could abruptly change over time (time-dependent). \(\mu_{t}(i)\) is the expected reward of arm \(i\).  
The policy performance \(\pi\) depends on the regret, i.e., the expected difference between the policy's collected rewards and the rewards that would have been gathered by the optimal policy (an oracle pulling the arm with the largest expected reward) over a time horizon \(T\):  
\[\mathbb{E}_{\pi}\left[\sum_{t=1}^{T}\biggl{(}\max_{i\in\mathcal{K}}(\mu_{t}(i ))-\mu_{t}(I_{t})\biggr{)}\right], \tag{1}\]  
where \(\mathbb{E}_{\pi}\) is the expectation under policy \(\pi\).  
Figure 1: Proposed architecture of a programmable system augmented by an MAB learner agent for online optimum end-to-end dynamic routing path.
# Enhancing IoT Data Dependability through a Blockchain Mirror Model
## 1 Introduction  
One subtle aspect of the alert recently raised by Dr. [PERSON] about a \"science crisis\" [1] is the observation that current machine learning algorithms may discover patterns in data that exist only in data but not in the real world. This is particularly true of data obtained by external sources where a validation process is barely exposed or accomplished. The emphasis here is not in external sources but in the lack of a credible validation process of data. This is \"a fortiori\" true in the case of data generated by the Internet of Things (IoT). In fact, the IoT relies (and will rely) heavily on Artificial Intelligence (AI) techniques to provide security and reliability [2]. The infrastructure network of devices that forms the base of any IoT system is a remarkable data producer and AI is aimed at discovering patterns in these data that, in turn, may provide hints for detecting or preventing security breaches or provide working optimization or reliability (e.g., detecting those devices that are not working properly).  
However, these desirable benefits are gained if the data produced by the IoT are dependable. Data from a sensor are a representation of the state of the world and should not be confused with reality. For instance, a thermometer could measure a body temperature of 40 \({}^{\circ}\)C while the real body temperature is 36 \({}^{\circ}\)C. This discrepancy could be caused, for example, by a defect in the thermometer or by an incorrect procedure of measuring. At the same time, we must have certainty that this particular thermometer applied to this particular subject at that particular time produced a measure of 40 \({}^{\circ}\)C. We have to be certain of the data values produced by the sensors (eventually not corresponding to afaithful representation of the reality), i.e., that these data have been not modified or altered in any form. This is a fundamental requirement to accomplish computations or investigations that may amend errors and yield practical results. In other words, we need a mechanism to render the representation of data produced by IoT networks trustable. This is where blockchain comes into play [3; 4; 5]--providing data immutability and consensus among peers when data are updated.  
The article is organized as follows: In Section 2 we present the state of the art related to the trust of data and the contribution of blockchain in the IoT domain. In Section 3 we propose a simple formal model named \"the Mirror Model\" where a measure (or a generic data) coming from an IoT network is associated (mirrored) with a corresponding registration in a blockchain network, thus providing dependability to data which, in turn, may be safely used in any subsequent AI pattern discovery or reasoning. In Section 4 we sketch a possible implementation of the Mirror Model, and in Section 5 we draw some short conclusions.
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 3 Basic Algorithms
### Aca
#### 3.1.2 Update PC  
In the classical ant colony system, the global pheromone update operation is usually performed after each search iteration. Not only do the pheromones evaporate naturally, but the magnitude of evaporation also depends on the evaporation coefficient. Concurrently, every time an ant traverses any particular pathway, it emits fresh pheromones, and the amount deposited during each traversal is directly proportional to the length of the trail. The updated Formulas (4) and (5) modify the PC along the trails to guide the next ants towards optimal routes.  
\[\tau_{ij}(t+1)=(1-\rho)\tau_{ij}(t)+\sum_{k=1}^{m}\Delta\tau_{ij}^{k},0<\rho<1 \tag{4}\]  
\[\Delta\tau_{ij}^{k}=\left\{\begin{array}{l}\frac{Q}{L_{k}},\emph{The nth ant has passed along the path from }i\emph{ to }j.\\ 0,\emph{Others}.\end{array}\right. \tag{5}\]  
In Equations (4) and (5), \(\tau_{ij}(t)\) represents the PC on the path between node \(i\) and node \(j\) at time \(t\), \(\Delta\tau_{ij}^{k}\) is the new PC produced by the \(k\)th ant on this path, and \(\tau_{ij}(t+1)\) is the concentration for the next round; \(\rho\) is the evaporation factor of the pheromone; \(Q\) represents the total number of pheromones; and \(L_{k}\) is the total length of the path taken by ant \(k\).
## 3 A Recommender of Tourist Activities Based on Clustering and Rule Mining
### Cluster Analysis
#### 3.3.2 Clustering Parameters  
The clustering process was done using the scikit-learn [58] Python library. The choice of parameters used in the cluster analysis was the following:  
* _Algorithm_. The k-means algorithm was selected because of its speed and ability to work with large data sets.
* _Feature scaling_. In cluster analysis it is necessary to ensure the data are scaled appropriately, as features having different scales would affect the clustering process negatively. The clustering features were standardised using the _Z-Score_.
* _Number of clusters (k)_. The k-means algorithm requires the number of clusters as an input parameter. Clustering is by nature an unsupervised analysis process, and therefore, the optimal number of clusters is case-dependent. In this work we were not concerned about having equally sized clusters with clear dividing boundaries, but rather clusters that represent different combinations of the clustering features in order to create user profiles with different interests and contexts. After some experimentation, we found \(k=25\) clusters was a suitable number for our data set.  
Figure 5 shows a graphical representation of each cluster with a different colour; features are on the x-axis and the mean value of each is on the y-axis. The clusters display different combinations of peaks across the clustering features, showing high heterogeneity (especially in the activity interest features). On the contrary, temporal features do not exhibit great differences among the clusters. The high heterogeneity in activity features allows the recommendation of different POIs across clusters.
## 3 Results
### Three-Satellite Constellation
#### 3.1.2 Near-Simultaneous RO Inside and Outside of Heavy Precipitation  
The example presented in Figure 8 highlights the case where there is (at least) one RO sounding inside heavy precipitation and (at least) one sounding outside, the occurrences of which permit the analysis of relationships between soundings inside heavy precipitation and in its immediate environment. For assessing the probability of this condition across a one-year period, there are 21,500 events where at least one of the observations crosses detectable precipitation, and 15,500 that have at least one observation crossing detectable precipitation and one crossing undetectable precipitation. While scientific inferences regarding the vertical moisture structure can be gathered from the events where all soundings are inside precipitation, for this study, we are primarily focused on the events with (at least) one RO sounding inside heavy precipitation and (at least) one sounding outside. The distribution of the distances between the observations with these desired observing conditions is shown in Figure 10.  
Figure 9: A depiction of the horizontal extent of the rays corresponding to RO ray paths whose bending falls below a fixed height (shown here at 6 km). The lower rays project a longer horizontal distance relative to upper rays.  
The joint distribution in Figure 10a exhibits a bimodal shape, arising from the fact that the constellation consists of three satellites. Therefore, the occurrences of sounding inside and outside precipitation can come from two consecutive satellites (e.g., RO-1 and RO-2), or the leading and trailing satellites (RO-1 and RO-3). Along with the histogram of the distance separation in Figure 10b, the histogram for \(\alpha\) is plotted in Figure 10c. For this orbit configuration, most of the observations are confined within \(20^{\circ}<\alpha<160^{\circ}\).