esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the model rationalization in our method section, the presented results reveal the impact of incorporating the improved TimesNet model, the SARIMA module, and the VMD module on the final model metrics, further validating the rational scientific basis of our prediction model. While the primary focus of this study is on predicting subway passenger flow, the proposed hybrid SARIMA-TimesNet model is designed as a versatile time series prediction framework. Due to its ability to effectively capture both linear and nonlinear components, the model can be applied to a wide range of domains that exhibit complex temporal patterns. For instance, in the energy sector, the model could be used to forecast consumption trends or electricity demand by accounting for both seasonal fluctuations and unexpected surges. In financial markets, it could help predict stock prices, exchange rates, or market indices by modeling inherent volatility and cyclical behavior. Moreover, in meteorology, the model could assist in predicting weather patterns by capturing recurring seasonal shifts and abrupt changes. These examples underscore the adaptability and robustness of the SARIMA-TimesNet model, making it suitable for any field where time series data involve a combination of predictable cycles and irregular variations. The structure of this paper is outlined as follows: The passenger group prediction model for subway flow data, tailored to their characteristics, will be described in detail in Section 2; Section 3 will compare the experimental results of various models and analyze the functionality of each block in our proposed method; Section 4 will provide a conclusive summary; and the final section will discuss future research directions.
## 2 Materials and Methods
### Methods
#### 2.3.2 Multi-Class MMFT Framework  
A novel multi-class MMFT is presented in this section to evaluate the effect of multi-class cross-subject classification on transfer learning performance. The proposed framework adapts the idea of a two-class cross-subject classification for MMFT in multi-class classification problems, using the three-class and four-class classification as examples.  
Figure 3 gives a detailed overview of the proposed multi-class MMFT framework that performs transfer mapping, utilizing features acquired from multiple source domains to improve the prediction rate of individual target domains based on a three-class and a four-class problem. The proposed multi-class MMFT framework is composed of four modules that form part of transfer learning:  
* Distribution means alignment (DMA). Multi-class MMFT firstly performs DMA for multi-class domains through rank of domain (ROD) utilizing KL divergence to evaluate similarities between source and target domain, mainly to align the distribution mean of each domain on (SPD) manifold.
* SPD manifold feature extraction. After domains are aligned, tangent space features are extracted from multi-class SDs and TD, respectively.
* Grassmann manifold feature learning. Once tangent space features have been extracted, the geodesic flow kernel is utilized to learn feature mapping.
* Classification utilizing MMFT classifier. After Grassmann manifold feature learning, the MMFT classifier is used to predict TD labels using knowledge from SDs. The overall procedure of the multi-class MMFT is summarized in Algorithm 2.  
```
Input: Multi-class source domains samples ; Multi-class target domain samples ; regularization parameters, number of iterations. Output: Predict multi-class labels ; for target domain
1: Calculate the covariance matrices for both \(z\) source domains and target domain, apply DMA, get aligned covariance matrices and.
2: Get tangent space features form SPD manifold.
3: Learn Grassmann manifold features for \(z\) source domains and target domain.
4: Four-class pseudo labels for target domain.
5: for \(n=1,\ldots,N\) do
6: Multi-source classifier.
7: for \(j=1,\ldots,z\) do
8: Construct kernel K using features.
9: Calculate  using multi-class labels \(\hat{y}_{S}^{(j)}\) and \(\hat{y}_{t}\)
10: Compute \(\alpha\) to obtain \(f_{j}\) trained by \(j\)-th source domain via the representer theorem.
11: Quantified voting for target domain, \(f=f+f_{j}\).
12: end for
13: multi-class pseudo labels \(\hat{y}_{t}=f(Z_{t})\), update \(\hat{y}_{t}\).
14: end for
15: return multi-class labels \(\hat{y}_{t}\) for target domain.
```  
**Algorithm 2** Multi-class MMFT  
Figure 3: Proposed multi-Class MMFT framework.
## 5 Major Equipment and Technologies
### Cloud Computing  
Growers can use cloud facilities to access data from predictive analytics agencies so that they can find the right products based on their exact needs. Cloud computing offers growers a knowledge-based repository that contains valuable information and experience related to farming methods and equipment options available on the market. The scheme can be further extended to include access to consumer databases, supply chains, and billing systems to make it more active.  
Obviously, cloud-based services offer ample opportunities but also bring new challenges. First, various sensors are being developed and utilized in smart farming, each with its unique data format and semantics. Second, most decision support systems are application-specific. On the other hand, farmers may need access to various systems for specific applications, such as soil monitoring. Considering these two situations, cloud-based decision support systems not only need to handle the diverse data and their formats, but they should also be able to configure these formats for various applications.
# Modern Design of Carrier for Overhead Conveyor
## 3 Results
### Measuring Electricity Consumption  
The electricity consumption was measured by the company that used the conveyor. Consumption measurement is very common in technical practice [37; 38]. The measurements were carried out on an empty conveyor. It is assumed here that the conveyor consumption will increase linearly with increasing load.  
First, measurements were made on the conveyor, where all the original carriers were replaced with new carriers, see Figure 27. The carriers were then loaded with the difference in weight by which the carriers were lightened and the consumption measurement was again taken to represent the consumption before conveyor optimization.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Carrier** & **Weight** \\ \hline Original carrier & 24.20 kg \\ New carrier & 11.75 kg \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison weight of carriers.  
Figure 27: Overhead conveyor with new carrier.  
The consumption value indicated in Table 5. Is the value of consumption when considering three shift operation.  
In the context of the total consumption of a factory like this, which is quite demanding in terms of energy, the savings are not essential in the overall calculation. In percentage terms, however, the saving is not insignificant and is 48.5%.  
Associated with the reduction in electric energy consumption is a reduction in CO\({}_{2}\) emissions. The emission reduction savings have been calculated by the conveyor operating company with respect to its energy mix. The saving, assuming continuous operation for 50 weeks per year, is 1931 kg CO\({}_{2}\).
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### One-Dimensional Convolutional Neural Network  
Figure 4 shows the 1D-CNN network structure diagram, which includes the input, convolution, pooling, fully connected, and output layers [17].  
Figure 4: Structure diagram of one-dimensional convolutional neural network.  
In the input layer, the multi-source heterogeneous sensor information group acts as the input of the network. In the convolution layer, multiple groups of convolution kernel performed convolution operations to the previous layer's output, extracted its information features and used a nonlinear activation function to output the results [18]. In the pooling layer, the output features of the convolution layer are screened and filtered using the pooling function, and the maximum or average value of the perception domain is obtained as the output feature map [19]. In the fully connected layer, the feature output of the previous layer is the input data of this layer, and the fully connected network is established using preset labels, which integrate the distinguished local features of the convolution layer and pooling layer. In the output layer, the previous layer's output is calculated using the Softmax function [20], and the results are taken as the corresponding probability value of each category of labels, where the label corresponding to the maximum probability value was the final recognition result.
## 2 Related Works  
There have been a few on-going research efforts concerning multipath routing for secure data collection presented in literature. For example, the SPREAD algorithm in [4] is used to find multiple most-secure and node-disjoint paths. A modified Dijkstra algorithm is used to find the top-K most secure node-disjoint paths iteratively. The H-SPREAD algorithm [5] improves the SPREAD algorithm by simultaneously accounting for both security and reliability requirements. The work in [6] presents distributed Bound-Control and Lex-Control algorithms, which compute multiple paths respectively. [PERSON] _et al._ in [3] presented an approach for secure data collection by using a (\(t\), \(n\))-threshold secret sharing algorithm and randomized multipath routes. A packet is broken into shares, which are sent to the sink through randomly generated paths. In their simulation, they use a fixed source node to evaluate the approach in simulation, while we extend their simulation with a collection of source nodes. [PERSON] and [PERSON] in [7] propose a routing protocol that uses multipath alternately as the path for communicating between two nodes. The protocol defends against some specific attacks like selective forwarding by advertising an attractive route to the destination. [PERSON] _et al._ in [8] propose an intrusion-tolerant routing protocol for WSNs. They try to preserve WSN security by using one way hash chains and nested keyed message authentication codes, as well as multipath routing. [PERSON] _et al._ in [9] presented a multipath secure routing protocol for WSNs. However, their approach requires the acknowledgement between every pair of nodes in routing. Therefore, it will cause large overhead to the data collection of the WSN.  
Compared with the existing works in this field, our approach use a novel tracing-feedback mechanism, which makes full use of the routing functionality of WSNs to improve the quality of data collection. The secure paths here are potentially safe for data collection. Therefore, routing via secure paths is much more secure than random multipath routing. The major difference between our approach and the existing multipath methods is that the process of constructing secure paths causes little overhead to sensor nodes and the algorithms are easy to implement and perform in resource-constrained WSNs.
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Feedstock
#### 3.1.1 Substrate
Algae as a substrate can circumvent the issue of food vs. fuel, provide an excellent alternative to the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feedstock** & **Methane Formation Stoichiometry** &
\begin{tabular}{c} **Methane** \\ **Concentration, \%** \\ \end{tabular} \\ \hline Carbohydrate & \(\left(C_{6}H_{10}OS\right)_{n}+nH_{2}O\to 3 nCH_{4}+3 nCO_{2}\) & 50 \\ Lipid & \(C_{50}H_{90}O_{6}+24.5H_{2}O\to 34.75 CH_{4}+15.25 CO_{2}\) & 69.5 \\ Protein & \(C_{16}H_{24}O_{5}+14.5H_{2}O\to 8.25 CH_{4}+3.75 CO_{2}+4 NH_{4}^{+}4+4 HCO_{3}^{-}\) & 68.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stoichiometry of biogas potential determination from various feedstock components [25]. biogas industry, and can offer a synergistic benefit of coproducing various chemicals and value-added products including biogas [38]. So far, the biomethane potential of various algae species has been documented in a number of past studies [39; 40; 41]. However, a host of problems are identified, including cell wall resistance for degradation, production of toxic substances, increasing level of pH in case of high C:N ratio algae species, LCFA inhibition [42], and NH\({}_{3}\) inhibition [37; 43]. To overcome part of these challenges, pretreatment, i.e., washing [44], maceration [44], thermal [45], sonic [45], and mechanical [46] treatment along with codigestion [47] may be implemented. Animal manures are a common source of organic material used as feedstock for AD. Using manures for biogas production reduces anthropogenic gas emissions, which would have otherwise released during storage [48]. Depending on factors such as animal species, breed, growth stage, feed, amount, and type of bedding, the biomethane potential (BMP) of animal manure can vary widely. Manure provides essential nutrients (micro and macro) for bacterial proliferation [49], buffer capacity for degradation of low nitrogen substrates, and controlling the level of VFA during AD. Due to its many advantages, most of the anaerobic digesters in Europe [50] are based on animal manure as a substrate. However, the biogas potential of manure is somewhat reduced by its recalcitrant material content, especially the biofbers or the embedded bed materials (e.g., straw). To achieve accelerated conversion of the recalcitrant fraction, manure is often suggested to undergo pretreatment before utilization for biogas production.
## 2 Materials and Methods
### Extraction and Determination of Total Phenolic Content and Antioxidant Activities
#### 2.4.2 Determination of TPC and Antioxidant Activities  
TPC and antioxidant activities were evaluated using a modified version of our previously published method [27]. In summary, TPC was determined using the Folin-Ciocalteu method and was expressed as milligrams of gallic acid equivalents per gram of dried seed weight (mg GAE/g) using gallic acid as a standard. The antioxidant activities, including DPPH\({}^{\bullet}\) scavenging activity, reducing power (RP), and ABTS\({}^{\bullet+}\) scavenging activity, were determined using in vitro colorimetric assays. The DPPH\({}^{\bullet}\) scavenging activity and RP were determined as milligrams of ascorbic acid equivalents per gram of dried seed weight (mg AAE/g) using ascorbic acid as a standard reference. Additionally, the ABTS\({}^{\bullet+}\) scavenging activity was calculated as mg AAE/100 g.
## 4 Implementation, Results, and Discussions
### Application Navigation and Features
#### 4.1.2 Hub Menu  
Once the application launches, the user will encounter the Hub menu, which allows them to choose an option of interest from the four possible menu options, which are: (i) shoulder and arm; (ii) forearm and hand; (iii) hip and thigh and (iv) leg and foot to explore (Figure 2(a)). However, as each option of interest was developed similarly besides its particular models or content, only the \"shoulder and arm\" option is presented in this paper for the sake of simplicity. The original prototype included four individual applications to be carried out for each option of interest, which were eventually integrated to form a Hub. The application was integrated due to the similarities in navigation and design with the exception of the \"realistic\" models and its particular content within each option of interest. The Hub Menu was then updated regarding orientation, application name and background (Figure 2(b)).
# A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic
## 3 Coherence Factors Values Estimation
### Case of Correlated Error Signals
Additionally, based on the data presented in Figure 4, it can be seen that the absolute value of the coherence coefficient decreases with increase in the ratio of the expanded uncertainty values of the analyzed pair of signals. The same property was demonstrated by the experiments, the results of which are presented in Figure 2.  
Figure 3: The value of the coherence coefficient depending on the value of the correlation coefficient (case for equal values of the standard deviation of signals). Figure 4: The value of the coherence coefficient depending on the value of the correlation coefficient and the ratio of the standard uncertainty values of the analyzed signals with the same shapes of the probability density function. In conclusion, to implement the discussed method effectively, the initial step involves estimating the resultant expanded uncertainty for all sets of correlated error signals. Various approaches can be employed for this purpose, for example, utilizing the method outlined in the study to estimate coherence coefficients based on the insights from Figures 3 and 4, determining correlation coefficient values analytically per the works of [15], or following the method elucidated in the guide [1]. Subsequent to determining the parameters for the resultant error signal in the correlated signal grouping, Equation (7) can be applied for coherence coefficients estimated according to Equation (14). Notably, in scenarios of complete correlation within the analyzed signals, the coherence coefficient will be \(1\) for full positive and \(-1\) for full negative correlation, serving as a crucial feature of the method.
## 2 Theoretical Background
### Organizational Maturity  
Maturity can be defined as \"the state of being complete, perfect or ready\" [21]. Maturity is referred to as the state of growth, level of excellence, as in [22], where the process of bringing something to maturity means bringing it to a state of full growth, and to improvement and excellence [23].  
Naturally, the term \"maturity\" is used in psychology and other human sciences, but it is also used in management in the context of quality [24]. Since the 1970s, maturity models have been recognized as an important concept for the improvement of organizations. [PERSON] was among the first to propose, in 1979, a quality management model with five levels of maturity [25]. To set the guidelines or references to the development or perfection, maturity models have been developed.  
Maturity models are now widely spread in Project Management (PM), Knowledge Management (KM), Information Systems (IS) and Supply Chain Management (SCM) industries [26]; more than one hundred maturity models have been published as guidance towards business excellence, as seen in Figure 1.  
Maturity models in literature have different characteristics and structure; they can be of moderate or high complexity, and maturity levels can be described in simple or complex terms.  
Probably the most disseminated maturity model is the Capability Maturity Model (CMM) developed by researchers at the Software Engineering Institute of the Carnegie Mellon University [24].  
In the field of Industry 4.0, many researchers have developed their own maturity models. For example, [PERSON] et al. [27] developed a model for assessing Industry 4.0 maturity with nine  
Figure 1: Published maturity models by area [26].  
dimensions defined and 62 characteristics assigned to them. The dimensions: \"Products\", \"Customers\", \"Operations\", and \"Technology\" have been determined to assess the basic enablers. Additionally, the dimensions \"Strategy\", \"Leadership\", \"Governance\", \"Culture\", and \"People\" have been depicted for including organizational aspects into the assessment.  
Each maturity model is based on the assumption that the maturity or quality of a process or project directly correlates with its success [28] as the term maturity is directly related to excellence [29]. Achieving excellence is a process that requires taking specific initiatives. To facilitate the process and organize the way to excellence, maturity levels are distinguished. Completing certain requirements means reaching the predefined level of maturity. Various maturity models are structured differently, with four up to seven levels, with the lowest level representing lack of signs of the approach the maturity is referred to, while the highest level represents full implementation of the approach; its deep understanding.
# Distributed Satellite Relay Cooperative Communication with Optimized Signal Space Dimension
## 1 Introduction
Based on the signal space reconstruction model, the cooperative node space compression method is designed to get rid of interference management constraints. The joint design of beam forming of multiple access nodes is realized. 2. Adaptive scheduling of multi-interactive cooperative transmission. The general processing strategy for complex transmission mode and its application in multi-directional relay transmission are studied.
# Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
## 3 Results
### Nine Elements Array  
In this part, the nine-element array shown in Figure 4c, will be characterized according to its main properties. This array features total dimensions of 14 \(\times\) 38.5 mm\({}^{2}\).  
A comparison between simulation and measurements of the reflection coefficient of this antenna is presented in Figure 11. In this figure both curves have a good agreement,  
Figure 10: Variation of the Gain and Efficiency of the 7-element array over the frequency.  
Figure 9: Radiation pattern of the 7-element log-periodic microstrip antenna array in the two main planes \(\phi\) = 0\({}^{\circ}\) and \(\phi\) = 90\({}^{\circ}\) for different frequencies: (**a**) f = 27 GHz, (**b**) f = 28 GHz, and (**c**) f = 29 GHz.  
and assuming the already defined convention of S\({}_{11}\)\(<\) \(-\)10 dB, this array has an operating bandwidth of 12.2 GHz, from 21.71 GHz to 33.91 GHz, corresponding to almost 44% of the design center frequency.  
The radiation pattern at different frequencies, 27 GHz, 28 GHz and 29 GHz is shown in Figure 12. As with the other structures, it maintains a good agreement between simulations and measurements, clearly identifying and defining the main radiation lobe.  
Figure 11: Simulated and measured reflection coefficient of the 9 elements antenna array.  
Figure 12: Radiation pattern of the 9-element log-periodic microstrip antenna array in the two main planes \(\phi\) = 0\({}^{\circ}\) and \(\phi\) = 90\({}^{\circ}\), for different frequencies: (**a**) f = 27 GHz; (**b**) f = 28 GHz; and (**c**) f = 29 GHz.  
Regarding the evolution of gain and efficiency with frequency, the curves are shown in Figure 13. Efficiency has a well-defined and stable behavior between approximately 22 GHz and 33 GHz, always with values above 75%, in some zones on the way to 90%. Relating to the gain, it presents a minimum of 6 dBi in the beginning of the band, but mostly it is better than 9 dBi, reaching very close to 12 dBi around 28 GHz.