esa-sceva/satcom-chunk-collection huggingface dataset:
# Load Forecasting with Machine Learning and Deep Learning Methods
## 3 Methodology
### Modeling
#### 3.2.6 Temporal Convolutional Networks  
The convolutional neural networks are also a derivative of ANNs, so the learning process is carried out in a similar way to that presented above, seeking the minimization of (10). CNNs and ANNs are also often utilized together, where the first layers of the problem correspond to the CNN. Theses layers filter data and find relevant information from the data, and the subsequent layers comprise the ANN. This is due to the CNNreducing the dimensions of the data, keeping important details and avoiding irrelevant ones. Therefore, if CNN layers are used too many times in the problem, important patterns are lost, thus resulting in forecast failure [73]. This characteristic allows the CNN to simplify the problem, making in contrast to the RNN, the training more efficient [74]. The training process is carried out by Conv-1D and flattening layers.  
Convolutional layers are the main structures of the Conv-1D. The process that this layer follows starts from an input vector, i.e., the input features, which is convoluted with kernels, i.e., the feature detector, to give as a result an output vector, i.e., the feature map. Thus, the feature detector moves over the input vector generating the dot product with the subregion of input vectors and obtains the feature maps as a matrix of the dot product. The feature detectors represent the weights of the previous deep learning methods presented. Instead of having individual weighs for each neuron, Conv-1D uses the same weights for all neurons. More feature detectors may result in better capture of input patterns. Nevertheless, this may cause some of them to take the same pattern, thus reducing the reliability of the method [75; 76; 77].  
The shape of the feature map depends on the application of the convolution between the input vector and the feature detector. Stride and the padding also have an impact in the process (16). Stride refers to the number of moves from the feature detector to the input vector, and padding corresponds to the addition of an outer edge of zeros to the input vector [78; 79].  
\[o=\frac{i-k+2p}{s}+1 \tag{16}\]  
where \(o\) is the feature map, \(i\) is the input vector, \(k\) is the feature detector, \(p\) is the padding, and \(s\) is the stride.  
Similar to the convolutional layers, flattening of layers reduces the dimensions of the convolved feature. This method is normally employed at the end of the convolution process. It transforms the feature map matrix into a single column that is fed to the ANN for processing [80; 81].  
Conv-1D offer several advantages for electricity demand forecasting. Some of them are as follows:  
* Scalability: It can handle variable-length input sequences, accommodating missing data.
* Robustness: It is robust against noise and outliers in the data since it can detect and filter the irrelevant patterns.
* Global context: It captures global information and dependencies across different time scales.
* Interpretability: It allows better understanding of the patterns that contribute to electricity consumption.
## 2 Proposed Antenna Design
### Fabrication and Measurement
The maximum gains of the proposed antenna are 0.3 dBi, 3.2 dBi, and 3.4 dBi at 26 GHz, 28 GHz, and 30 GHz, respectively. To confirm the feasibility, the antenna characteristics such as the operating frequency band, the maximum gain, the antenna dimensions, and the substrate material of the proposed antenna are compared to those of previous studies, as listed in Table  
Figure 4: The measured EM characteristics of the thick vehicle window glass substrate: (**a**) measurement setup; (**b**) dielectric constant and loss tangent according to the frequency. Figure 5: Reflection coefficient of the proposed antenna. Figure 6: Measured and simulated bore-sight gains of the proposed antenna with and without parasitic elements. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Research**} & \multirow{2}{*}{**Frequency Band**} & \multirow{2}{*}{**Maximum Gain**} & \multicolumn{2}{c}{**Antenna**} \\  & & & **Dimensions** & **Substrate** \\  & & & **(Width \(\times\) Length)** & **Material** \\  & & & \(\times\) Thickness)** & \\ \hline
[17] & 108–130 MHz & \(-\)7.47 dBi & \(850\times 480\times 3\) & Glass \\
[18] & 112.5–130 MHz & \(-\)7.58 dBi & \(1.2\times 0.42\) m\({}^{2}\) & Glass \\
[26] & 88–106 MHz & - & 52.6 cm (width) & Glass \\
[27] & 4.66–11.84 GHz & \(-\)0.15 dBi & \(50\times 17\times 1\) mm\({}^{3}\) & Glass \\
[28] & 24–30.6 GHz & \(>\)5 dBi & \(40\times 24\times 1.6\) mm\({}^{3}\) & FR-4 \\
[29] & 25–30 GHz & \(>\)8 dBi & \(20\times 16\times\) & \\  & & & 0.508 mm\({}^{3}\) & Nucleo NY9220 \\ Proposed antenna & 24.6–30 GHz & 2 dBi & \(25\times 25\times 3.2\) mm\({}^{3}\) & Glass \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparisons of antenna characteristics. Figure 7: Measured and simulated radiation patterns of the proposed antenna: (**a**) \(zx\)-plane at 28 GHz; (**b**) \(zy\)-plane at 28 GHz; (**c**) \(zx\)-plane at 29 GHz; (**d**) \(zy\)-plane at 29 GHz.
## 5 Validation Procedures and Results  
In this section, we perform a simulation to evaluate the efficiency of the proposed secondary authentication framework. To quantitatively express the performance of the proposed framework, we evaluate the communication cost as the performance of secondary authentication in the simulated environment. The ideal requirement of the 5G network's communication latency is 1 ms or less of end-to-end round-trip time [29]. However, to simulate the real-world latency of the 5G network is very challenging because to meet the ideal latency in real-world communication is infeasible. Thus, to concentrate on the NAS signaling storm problem, we define the communication cost as the amount of traffic at each network domain. In this experiment, we compare the performance of proposed our scheme with the secondary authentication scheme that is introduced in the 5G system release 15 specification [30] published by 3 GPP.  
To evaluate the number of packet transmission, we separated the type of transmission according to the participating nodes on each communication. Figure 4 shows the types of communication in the authentication mechanism of 5G. Communication between user equipment(UE) and serving network(SN) is wireless communications and performed in the access stratum (AS). Communications between nodes of SN such as AMF, SMF, SAF, and ADMF use wireless or wired communication and is performed in both edge and fog layer. Communications between serving network node(AMF, SMF, SAF, and ADMF) and home network node(UPF, AUSF, and DN-AAA) are the non-access stratum(NAS) communications. Moreover, some communication is performed within the home network nodes. According to these types of communication, we expressed the cost of each communication in Table 3.  
The NAS signaling storm affected by the number of NAS and HN traffics. Thus, \(n(T_{NAS})+n(T_{HN})\) affects the degree of NAS signaling storm problem. Moreover, the number of AS and SN traffics directly affects the user experiences. Thus, \(n(T_{AS})+n(T_{SN})\) means the availability of user on the 5G network. We evaluate the proposed secondary framework using these two perspectives: the degree of NAS signaling storm and user availability.
## 2 General Features of ClyA
### Transcriptional Regulation  
The transcriptional regulation of _clyA_, which has been well-studied, is somewhat complex in that it involves several known regulators and is subject to the influence of environmental factors (Table 1).  
Transcription of _clyA_ is normally prevented by the heat-stable nucleoid-structuring (H-NS) protein [28; 29], which is a global transcriptional regulator that influences the expression of approximately 5% of all genes in _E. coli_. The direct influence of H-NS on transcription is almost universally negative, often regulating gene expression in response to environmental conditions [30; 31; 32]. Therefore, _clyA_ transcription is only derepressed and activated in H-NS-deficient strains, which is why ClyA is sometimes called the \"silent hemolysis\". In addition to derepression in the context of H-NS deficiency, _S._ Typhimurium SlyA and _E. coli_ MprA, both of which are members of the MarR family of transcriptional regulators, activate _clyA_ transcription [12; 13; 24]. H-NS and SlyA share a binding site in the _clyA_ promoter region; therefore, overexpression of SlyA can activate _clyA_ expression by antagonizing H-NS-mediated transcriptional repression [28; 33]. It has recently been reported that (p) ppGpp, an important nucleotide that acts as a secondary messenger in the stringent response to stress, also affects _clyA_ expression in a SlyA-independent manner [34]. Two other transcriptional regulators, cyclic AMP receptor protein (CRP) and fumarate and nitrate reduction regulator (FNR), also positively regulate _clyA_ expression by binding at the same site in response to two distinct environmental signals, oxygen starvation (for FNR) and glucose starvation (for CRP) [23; 29; 33; 35]. The ClyA-derived hemolytic activity of _E. coli_ has been shown to be strongly enhanced under anaerobic conditions [23]. In _S._ Typhi, _clyA_ is part of an operon that also contains the _taiA_ gene and is located in the _Salmonella_ pathogenicity island, and _clyA_ expression is activated by the PhoPQ two-component system [36]. Another study showed RpoS-dependent transcriptional upregulation at a low pH or high osmolarity, as well as oxygen depletion-independent regulation, of _clyA_[37]. In addition, CRP can downregulate _clyA_ expression in _S._ Typhi [37]. Later work by [PERSON] indicated that RpoS is the central regulator in the _clyA_ regulatory network, integrating the CRP and PhoP signaling pathways [38]. Thus, the mechanisms regulating _clyA_ expression seem to differ among bacterial species, at least between _E. coli_ and _S._ Typhi. In addition, _clyA_ expression is modulated by several global regulators via a variety of complicated mechanisms and is influenced by multiple environmental signals.
## 3 Theoretical Penetration Model and Model Verification
### Validation of the Theoretical Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(v_{0}\) (m/s) & \(r_{0}\)/\(R_{\rm P}\) & \(P_{\rm e}\) (mm) * & \(P_{\rm max}\) (mm) & \(P_{\rm max2}\)[25] & \(P_{\rm max3}\)[26] & \(P_{\rm max4}\)[23] \\  & & & & & (mm) & (mm) & (mm) \\ \hline
1037 & 35.16 & 64.6 & 57.44 & 116.52 & 115.53 & 117.07 \\
1042 & 35.16 & 41.6 & 57.90 & 117.17 & 116.17 & 117.72 \\
1174 & 35.16 & 67.5 & 69.61 & 133.54 & 132.40 & 134.11 \\
1193 & 35.16 & 50.7 & 71.22 & 135.77 & 134.61 & 136.33 \\
1216 & 35.16 & 50.7 & 73.14 & 138.42 & 137.24 & 138.98 \\
1284 & 35.16 & 78.8 & 78.61 & 145.94 & 144.70 & 146.49 \\
1337 & 35.16 & 61.8 & 82.64 & 151.48 & 150.19 & 152.01 \\
1411 & 35.16 & 106.1 & 87.91 & 158.73 & 157.38 & 159.23 \\
1515 & 35.16 & 76 & 94.58 & 167.96 & 166.53 & 168.40 \\
1802 & 35.16 & 94.3 & 108.50 & 187.81 & 186.20 & 187.97 \\
1813 & 35.16 & 120 & 108.92 & 188.41 & 186.80 & 188.56 \\
2052 & 35.16 & 113.9 & 116.01 & 199.00 & 197.29 & 198.80 \\
2204 & 35.16 & 124.6 & 119.00 & 203.43 & 201.68 & 202.96 \\
2255 & 35.16 & 137.4 & 119.80 & 204.57 & 202.81 & 204.00 \\
2476 & 35.16 & 137.9 & 122.34 & 207.70 & 205.92 & 206.66 \\ \hline \hline \end{tabular}  
* \(P_{\rm e}\) are the experimental results of penetration depth in [24]; \(P_{\rm max}\), \(P_{\rm max2}\), \(P_{\rm max3}\), and \(P_{\rm max4}\) are penetration depths calculated by the proposed model and the models in [23; 25; 26], respectively.  
\end{table} 
Table 3: Summary of initial penetration conditions, penetration depths, and calculation results of different theoretical models [24]. Figure 4: Comparison of results of the normalized target resistance with numerical results in [23]. Table 4 shows the mean percentage error, root mean square error, and mean absolute error of the theoretical models. The results show that the proposed model has higher accuracy and better applicability.
# Optimization of Intelligent Maintenance System in Smart Factory Using State Space Search Algorithm
## 2 Materials and Methods
### State Space Search Algorithms for IMS Framework  
The maintenance cycle for automatic machine maintenance in contemporary factories primarily employs a cyclical maintenance approach, typically scheduled every 3, 6, 9, or 12 months. During these maintenance cycles, all components on the replacement list are substituted with new parts to prevent breakdowns and machine stoppages. However, several key issues arise from this method:  
1. Parts that have not yet reached the end of their service life are replaced within the same cycle.
2. The process of replacing a large number of components often necessitates several days of machine downtime.
3. The requirement to replace numerous spare parts can lead to a significant waste of the maintenance budget.
4. There is a lack of measuring instruments for assessing parts, resulting in equipment that may not be operational at all times.  
However, the service life of components must also be considered. To address this issue, it is essential to employ an algorithm that identifies the shortest path to a solution. Nevertheless, relying solely on the shortest path approach may not always yield effective results, as both costs and risks must also be taken into account [3]. Based on the aforementioned challenges, a flowchart for the state space search algorithms can be developed, as illustrated in Figure 15.  
Figure 15: State space search algorithms flowchart.  
From Figure 15, the workflow begins with identifying the machines that require maintenance. Each category encompasses several identical sub-machines. The next step involves selecting spare parts that have reached their planned maintenance cycle. After determining the optimal time for maintenance, the subsequent step is to select each period and compare the results. The next step involves the algorithm optimizing maintenance cycles to assess risks associated with the selected parts. If the risk assessment indicates that the selected part meets acceptable risk criteria, the proposal will be forwarded to the relevant parties for approval. If the risk is deemed unacceptable, the process will loop back to present a new selection route.
## 4 Proposed Algorithm
### Framework of the NSACOWDRL  
The framework of the NSACOWDRL is given in Figure 3. The NSACOWDRL consists of two stages: fully searching the solution space to generate Pareto solutions and robust selection of Pareto solutions.  
(1) Stage 1: Solution Space Characteristic Exploration and Pareto Solution Generation  
Step 1: Initialize the algorithm parameters, including pheromone matrix, distance matrix, neural networks, etc. The initial number of iterations is 0.  
Step 2: Obtain the set of feasible routes according to the solution construction strategy (see Section 4.2 for details).  
Step 3: The routings are stratified by nondominated sorting based on the reference points, and N routings are selected for this iteration.  
Step 4: The pheromone matrix is updated according to the pheromone update strategy.  
Figure 3: Framework of the NSACOWDRL.  
Figure 2: The optimization process of the NSACOWDRL.  
Step 5: When the iteration is a specific value, the network of the DDQN is trained through the nondominated solutions, and the N paths are obtained by the trained network to participate in the next iteration through the selection of the actions.  
Step 6: The number of iterations plus 1.  
Step 7: If the maximum number of iterations is reached, output the solution set, otherwise skip to step 2.  
(2) Stage 2: Robust Multiobjective Optimization  
Step 1: The Pareto solutions obtained in stage 1 are partitioned by weights that are uniformly distributed in the solution space.  
Step 2: Each solution is assigned to the weight closest to this solution.  
Step 3: Through Monte Carlo simulation, the Monte Carlo feasible times for each solution is obtained to quantify the feasibility of the solution.  
Step 4: The set of solutions belonging to each weight is then selected according to feasibility, respectively, to obtain the set of Pareto solutions for each weight. Robust solutions that take diversity into account are obtained.
## 3 Congestion Prediction Model
### Data Transmission Time
#### 3.5.2 Network Type Classification  
The values of \(\alpha\) and \(\beta\) are expected to vary by the network type. Thus, the optimal \(\alpha\) and \(\beta\) values can be found as in Section 3.5.1 after dividing the networks by speed and number of channels, as shown in Table 4.
## 3 Solution Approach
### Localization of Possible Faults  
In a power grid \(G=(V,E)\), let \(V_{G}\), \(V_{L}\subseteq V\) represent the set of generators and loads, respectively, and \(E\) represents the set of branches. If the system is attacked by uplink or downlink spoofing attacks, the control center cannot receive the alerting signal.  
**Definition 1**.: _The distance of Node \(i\) and \(j\): the distance \(L_{ij}\) of node \(i\) and \(j\) is defined as the shortest path from node \(i\) to \(j\)._  
**Definition 2**.: _The possible Fault Set of branch \((i,j)\) : a set of nodes \(S=\{g_{1},g_{2},\ldots,g_{m},l_{1},l_{2},\ldots l_{n}\}\), \(1\leq m\leq|V_{G}|\), \(1\leq n\leq|V_{L}|\) is called a possible fault set of branch \((i,j)\), if \(S\) satisfies the following two conditions simultaneously: (1) for \(\forall g_{k}\in S\) and \(\forall p\in V_{G}\), there are \(g_{k}\in V_{G}\) and \(L_{ig_{k}}\leq L_{ip}\) (or \(L_{igk}\leq L_{jp}\)); (2) for \(\forall l_{w}\in S\) and \(\forall q\in V_{L}\), there are \(l_{w}\in V_{L}\) and \(L_{il_{m}}\leq L_{iq}\) (or \(L_{il_{w}}\leq L_{jq}\))._  
Algorithm 1 is proposed to identify the possible fault set of branch \((i,j)\).  
```
1:Input the overloaded branch \((i,j)\)
2:For \(1\leq r\leq|V_{G}|\), \(r\
eq i,j\)
3:Calculate the distance \(L_{ri}\) and \(L_{rj}\), respectively (not including the path \((i,j)\))
4:Identify nodes that have the shortest distance with node \(i\)
5:Identify nodes that have the shortest distance with node \(j\)
6:End
7:For \(1\leq r\leq|V_{L}|\), \(r\
eq i,j\)
8:Calculate the distance \(L_{ri}\) and \(L_{rj}\), respectively (not including the path \((i,j)\))
9:Identify nodes that have the shortest distance with node \(i\)
10:Identify nodes that have the shortest distance with node \(j\)
11:End
12:Combine steps 4, 5, 9, and 10, get the possible fault set \(S\)
```  
**Algorithm 1**Identify the possible fault set  
**Remark 1**.: _Algorithm 1 is used to deal with the situations in which an overload is caused by uplink or downlink spoofing attacks. It should be pointed out that Algorithm 1 will be used \(n\) times if there are \(n\) overloaded branches._
## 1 Introduction  
Induction machines that facilitate the production processes and related services lead to enormous changes in human lifestyles. Induction machines are extensively used in all aspects of industrial, commercial, domestic, utility and special-purpose commercial markets owing to their rugged configuration, low cost, versatility, reasonably small size, and their operation with an easily available power supply. However, induction machines suffer from a low efficiency and low power factor that means the loss of energy is high. This issue is viewed as an important disadvantage because of the energy cost and global energy concerns.  
The improvement of induction machine efficiency was tried through an optimal design of these motors. However, due to several inherent limitations, it is difficult to improve the efficiency of induction machines significantly. An alternative solution is to replace the induction machines with high-efficiency permanent magnet synchronous motors (PMSMs). An important obstacle for ordinary PMSMs is they need an inverter to start that is not economical for many single speed applications. To overcome this problem, the squirrel-cage bar-equipped PM motors--called line start permanent magnet synchronous motors (LS-PMSMs)--have been introduced. LS-PMSMs also allow for the reaching of super premium efficiency levels [1;2]. The structure and configuration of the three phase-four pole LS-PMSM are depicted in Figure 1.  
An LS-PMSM is made up of a stator and a hybrid rotor with a squirrel-cage and pairs of permanent magnets. In this machine, when the rotor is operated from a standstill, squirrel-cage bars generate a suitably high starting torque, enabling the direct-on-line movement of the rotor. The reverse-rotating fields of the air gap reduces in a squirrel-cage rotor when there is an unbalancing in the load condition or there is a fluctuation in the rotation speed. Otherwise, if there is no squirrel-cage in the rotor, these conditions lead to significant losses, and no reduction in the reverse-rotating fields is observed [3].  
In the industry, LS-PMSMs are exposed to unavoidable stresses; for example, electrical, environmental, mechanical and thermal stresses. These stresses produce some failures and imperfections in different parts of the LS-PMSMs. Usually, the generated faults disrupt the operation of the LS-PMSMs, degrade the manufacturing quality, and therefore result in significant cost penalties. Maintenance costs can be greatly reduced with the application of an efficient fault detection method, as this avoids spontaneous downtimes. The application of LS-PMSM is growing gradually, although the industry is still lacking an accurate fault detection criterion for maintenance policies of the motor.  
The aim of this paper is to extend a motor startup methodology to the detection of a broken rotor bar (BRB) in LS-PMSM. In this method, a novel approach of deriving the amplitude of the left sideband harmonic (LSH) in a specific sub-band frequency is presented for BRB. Using discrete wavelet transform, LSH is extracted from the current spectrum and is used as an input for entropy features.
# Targeted Gene Sequencing, Bone Health, and Body Composition in Cornelia de Lange Syndrome
## 3 Results
### Body Composition and Bone Health
Considering that the normal ranges differ according to age and sex, only one adult with CdLS had hypocalcemia (calcium 2.08 nmol/L, normal range 2.2-2.5 nmol/L), hypophosphhemia (phosphorus 0.68 nmol/L, normal range 0.81-1.45 nmol/L), and a low level of ALP (43 IU/L, normal range 60 IU/L). Moreover, following international guidelines that classify serum concentrations of 25-OH-vitamin D values above 75 nmol/L as sufficient [33], seven individuals with CdLS were below this cutoff, and in only one of the oldest individuals the decrease was remarkable (39.94 nmol/L). Moreover, they also showed the lowest values of calcium, phosphorous, and ALP, accompanied by the highest values of PTH. Regarding the skeletal features or treatments affecting bone metabolism, none of the nine individuals had restricted mobility, used inhaled glucocorticoids, or took vitamin D supplements. However, most of the individuals, except one, had gastroesophageal reflux, necessitating the use of proton pump inhibitors for 33.3% of them [3]. In our sample, only one participant was diagnosed with epilepsy and was using anticonvulsants (levetiracetam and oxcarbazepine) during the assessments. Nonetheless, this participant did not have significantly decreased levels of vitamin D (55.66 nmol/L) [34]. One individual was under treatment with serntraline and two with risperidone due to aggressive behavior. Regarding possible anomalies of the spine, only one adolescent with CdLS had low-grade scoliosis without requiring treatment at present, and another reported low back pain in isolation. Neither vertebral nor other fractures in any individual with CdLS were found, except for a patient who had suffered a humerus fracture caused by aggressive behavior.