esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Manufacturing towards Industry 4.0: From For Now' to 'Four-Know'
## 3 Pipeline of Applying Machine Learning in Manufacturing
### Data Transformation  
Data transformation is the process of transforming unstructured raw data into data better suited for model construction. Data transformation can be broadly classified into mandatory transformations and optional quality transformations. Mandatory transformations must be carried out to convert the data into a usable format and then deliver the transformed data to the destination system. These include transforming non-numerical data into numerical data, resizing data to a fixed size, etc. It should be noted that data transformations are not always straightforward. Indeed, in certain situations data types can be interconvertible by leveraging specific processing techniques, as shown in Figure 6. For instance, univariate time series can be converted into image data using the Gramian Angular Field (GAF) or Markov Transition Field (MTF) [63] methods. Unstructured text data can be converted into tabular data via word embedding [64]. Tabular data can be transformed into image data by projecting data into a 2D space and assigning pixels, as in Deepinsight [65] or Image Generator for Tabular Data (IGTD) [66]. Image data are preferable for data analysis, as they allow the power of Convolutional Neural Networks (CNNs) [67] to be exploited.  
In real-world applications, data are normally high-dimensional and redundant. When performing data modelling directly in the original high-dimensional space, the computational efficiency can be very low. Hence, it is necessary to reduce the dimensionality in order to obtain better representation for data modelling. This is achieved by feature selection, which selects the most informative feature subset from raw data, or feature extraction, which generates new lower-dimensional features. After feature engineering, features are either manually designed, so-called \"handcrafted features\" [68], or automatically learned from data, so-called \"automatic features\". Handcrafted features are heavily dependent on domain knowledge, and normally have physical meaning. However, these features are highly subjective [69] and inevitably lack implicit key features [70; 71].  
By contrast, automatic features driven by data require no prior knowledge. Therefore, they have been gaining increasing research attention in recent years. Conventionally, automatic features are obtained by linear transformations such as Principle Component Analysis (PCA) [72] or Independent Component Analysis (ICA) [73]. However, with the development of Artificial Neural Networks (ANNs), direct learning of implicit features has become possible by optimizing the loss function. Thus, neural networks have gradually developed into an end-to-end solution where knowledge is directly learned from raw data without human effort. Typically, CNNs [74] and Recurrent Neural networks (RNNs) [75] are used for image data and time series data, respectively.  
A summary of typical features for different data types can be seen in Table 3.
# Energy-Efficient Subcarrier Pairing and Power Allocation for DF Relay Networks with an Eavesdropper
## 2 System Model and Problem Formulation
### System Model
Therefore, we define the total power dissipation in the network as follows [6]:  
\[P_{total}=\underbrace{\sum\limits_{i=1}^{K}P_{S,i}+P_{SP,S}}_{\text{ source power dissipation}}+\underbrace{\sum\limits_{j=1}^{K}P_{R,j}+P_{SP,R}}_{\text{ relay power dissipation}} \tag{8}\]  
where \(P_{SP,S}\) and \(P_{SP,R}\) denote the total static power consumption at the source and the relay nodes, respectively.
# A Synthetic Approach for Biosynthesis of Miquelianin and Scutellarin A in _Escherichia coli_
## 3 Results and Discussion  
### Selection of Plant UGTs and Their Activities  
Flavonoids are an important group of plant secondary metabolites and are usually present in the human diet in the form of glycosides. Glycosylation typically occurs in the final step of flavonoid biosynthesis, which is catalyzed by several UGTs displaying high substrate stereo- and regio-selectivity [24]. In this context, although plants could be considered to have multiple glycosyltransferases enzymes capable of conjugating diverse sugars to different classes of plant molecules, we cannot escape from the difficulties using them in microbial cell factory, as most of the eukaryotic enzymes are difficult to express functionally. Previously reported regiospecific and well-characterized plant glucuronosyltransferase (UGT88D8 and VvGT5) [25; 26] were selected to synthesize valuable glucuronic acid conjugated flavonoids in an industrially important bacterial strain _E. coli_. These UGTs were codon optimized prior to synthesize based on the _E. coli_ codon usage to facilitate functional expression and used in biotransformation. According to a previous report, UGT88D8 favored the substrate apigenin catalyzing glucuronidation at 7-OH position where as VvGT5 favored quercetin and conjugate glucuronic acid at 3-OH position. Thus, we tested these GCs with respective substrates. The HPLC-PDA and LC-MS analyses could easily detect the generation of apigenin 7-O-glucuronoid (scutellarin A) and quercetin 3-O-glucuronoid (miquelianin). Furthermore, we tested similar class of flavonoids (baicalein, luteolin, crysin) in vivo bio-transformation catalyzed by UGT88D8 and VvGT5 with myricetin, kampferol, fisetin, as substrates. When carrying out analyses in HPLC-PDA and LC-MS, we were unable to detect product peak(s) and unable to detect in mass (data not shown). This also supported that the aforementioned UGTs are regio-, stereo- as well as a substrate specific.
# Wettability of Carbonate Reservoir Rocks:
## 3 Materials and Experiments
### SCAL Tests and Fluids  
Plugs were first saturated with a synthetic brine, i.e., a NaCl solution with salinity of 190 g/liter. They were then kept in the brine for at least ten days to reach ionic equilibrium conditions between the rock sample and the brine. Several pore volumes of brine were next injected at a constant pressure. The recorded data were used to calculate the absolute permeability of the samples by employing Darcy's law. After that, the primary drainage water-oil capillary pressure curve tests were conducted at 50 \({}^{\circ}\)C using the centrifuge method where dead crude displaced the brine at a series of centrifuge speeds until reaching the irreducible brine saturation. Next, the core plugs were kept in the crude oil at the reservoir temperature of 70 \({}^{\circ}\)C for almost two weeks to restore their native-state wettability. We flushed the cores twice with the crude oil during the aging process to enhance a uniform wettability in the samples. To alleviate the injectivity issues and possible permeability impairment phenomena that were likely in the next-step flooding tests, the dead reservoir oil was replaced by a synthetic oil (n-Decane). We injected decalin as a buffer fluid between synthetic oil and crude oil to prevent any potential asphaltene precipitation and deposition. Consequently, the unsteady-state brine displacing oil tests were conducted at room temperature and continued until no more oil was produced. The collected data were used to calculate the relative permeability curves using the method of [PERSON] and Roszelle [73].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Plug No.** & **Depth (m)** & **Porosity (\%)** & **Air Permeability (mD)** & **RQI (\(\mu\)m)** & **GHE* Class** \\ \hline
1 & 2775.67 & 17.45 & 1.67 & 0.097 & 3 \\ \hline
2 & 2776.08 & 19.38 & 2.98 & 0.123 & 4 \\ \hline
3 & 2781.26 & 17.27 & 2.03 & 0.10  
After the relative permeability tests, the core plugs were centrifuged at room temperature where synthetic oil displaced water to attain irreducible brine saturation. Afterwards, spontaneous and forced displacement processes were carried out. The volume of brine freely imbibed into the plug was measured in the Amott cell, followed by brine displacing oil centrifugal displacement at several rotational speeds to reach the residual oil saturation where forced displacement capillary pressure curves were also determined. Then, the volume of oil freely imbibed into the plug was measured in the Amott cell, followed by oil displacing brine centrifugal displacement at several rotational speeds to reach irreducible water saturations where the corresponding forced displacement capillary pressure curves were also determined. Measurements on three samples (plugs no. 8, 16, and 18) had questionable results that were rejected after quality control. The properties of the fluids used are shown in Table 2.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 4 Results and Discussion
### Training Phase of Weld Seam Detection using YOLO Algorithms
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Initial Value**} & **Optimized** & **Optimized** \\  & & & **Value** & **Value** \\  & & & **(YOLOv4)** & **(YOLOv5)** \\ \hline
1 & Learning rate & 0.01 & 0.0121 & 0.0108 \\
2 & Momentum constant & 0.93 & 0.937 & 0.98 \\
3 & Weight decay & 0.0005 & 0.00039 & 0.00035 \\
4 & Class loss & 0.5 & 1.09 & 1.42 \\
5 & IoU\_target & 0.2 & 0.2 & 0.2 \\
6 & Anchor\_target & 4 & 4.6 & 3.88 \\
7 & Epochs & 50 & 200 & 200 \\
8 & Dataset Split ratio & 60-20-20 & 60-20-20 & 60-20-20 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Initial and final training parameters of different YOLO algorithms. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**S. No**} & \multirow{2}{*}{**Parameter**} & **Scaled** & \multirow{2}{*}{**YOLOv5**} & **YOLOv4** & **Faster** & \multirow{2}{*}{**RCNN**} \\  & & **YOLOv4** & & **DarkNet** & **RCNN** & \\ \hline
1 & Precision & 0.76 & 0.967 & 0.47 & 0.5 & 0.788 \\
2 & Recall & 0.983 & 0.96 & 0.47 & 0.5 & 0.841 \\
3 & mAP & 0.973 & 0.987 & 0.534 & 0.54 & 0.92 \\
4 & F1 score & 0.85 & 0.96 & 0.54 & 0.5 & 0.813 \\
5 & GloU loss & 0.023 & 0.017 & 0.317 & 0.541 & 0.00089 \\
6 & Object loss & 0.071 & 0.022 & 0.302 & 0.5084 & 0.133 \\
7 & Class loss & 0.015 & 0.0046 & 0.34 & 0.212 & 0.169 \\
8 & Training Time (h) & 0.901 & 0.710 & 0.534 & 1.5 & 0.583 \\
9 & GPU memory & 5.51 GB & 1.89 GB & 1.70 GB & 6.7 GB & 1.5 GB \\
10 & Total Loss & 0.109 & 0.0436 & 0.959 & 1.261 & 0.303 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance metrics of different YOLO algorithms during weld shape detection. Figure 12: Mild steel plates with different shape numbers used for YOLO training and real-time experimental validation. comparatively smaller with the value of 0.0436, and it was high for Faster RCNN with the value of 1.261. This shows that, for the weld dataset, YOLOv5 performed better than the other algorithms.
# Coarsely Quantized Decoding and Construction of Polar Codes Using the Information Bottleneck Method
## 5 Space-Efficient Information Bottleneck Successive Cancellation List Decoder
### Message Alignment for Successive Cancellation List Decoder
The decision level cluster indices \(t_{i}=[t,i]^{T}\) are clustered into aligned indices \(t^{*}\) such that the relevant information \(I(U;T^{*})\) is maximized. It is pointed out that we are free to choose the alignment cardinality \(\left|\mathcal{T}^{*}\right|\). The choice of \(\left|\mathcal{T}^{*}\right|\), however, affects the performance of the quantized decoders, as seen in the next section. Moreover, the aligned decoding tables at the decision level require a memory of \(\left|\mathcal{T}\right|^{2}\cdot Q_{t^{*}}\) or \(2\cdot\left|\mathcal{T}\right|^{2}\cdot Q_{t^{*}}\) bits instead of \(\left|\mathcal{T}\right|^{2}\cdot Q_{t}\) or \(2\cdot\left|\mathcal{T}\right|^{2}\cdot Q_{t}\) bits, respectively, where \(Q_{t^{*}}=\log_{2}\left|\mathcal{T}^{*}\right|\). Therefore, when \(\left|\mathcal{T}^{*}\right|\) is chosen to be larger than \(\left|\mathcal{T}\right|\), the size, i.e., the number of elements in each decoding table at the decision level remains unchanged, but the memory required for each decoding table increases since \(Q_{t^{*}}>Q_{t}\).
## 5 The SNR of MFIR Filters
### The SNR and Optimal Ordering for MFIR Filters
#### 5.3.1 General MFIR SNR Expressions
The round-off noise performance of an MFIR filter approximating a complex-conjugate pole pair filter  
* is significantly better (up to 20 dB) than the noise performance of its corresponding IIR filter when the approximated poles are situated in the neighborhood of the real axis;
* is far less pole angle \(\theta\) dependent in comparison with the corresponding IIR filter;
* is up to 2.5 dB better for infinity bound scaling than for absolute bound scaling (using reverse ordering);
* is always better for L\({}_{2}\) bound scaling than for the other scaling methods (obeys Equation (44));
* is pole magnitude dependent, but not that much as the corresponding IIR filter;
* is fairly insensitive to an extra MFIR filter stage (typically 1 dB);
* is very sensitive to the stage ordering for absolute and infinity bound scaling;
* is ordering independent in case of L\({}_{2}\) bound scaling;
* is in general better in reverse ordering than in forward ordering, except for pole angles in the neighborhood of \(\pi/2\);
* is for pole angles in the neighborhood of \(\pi/2\), for absolute and infinity bound scaling, better in forward ordering than in reverse ordering (It was already remarked in Section 4.2, from a theoretical point of view, that this situation could occur. );
* can be up to 6 dB worse than the corresponding IIR filter for pole angles in the neighborhood of \(\pi/2\). The width of this region and the magnitude of the difference decreases however with increasing pole magnitude ;  
In the normal range of pole magnitudes that are considered for MFIR approximations (\(\left|r\right|>0.9\)), the noise performance is in general (depending on the pole angle) better than for the corresponding IIR filter.
# Using GPS Trajectories to Adaptively Plan Bus Lanes
## 4 Experiments  
### Data Collection  
We selected Nanshan District, Shenzhen City, as the research area to test the model. The road network data were downloaded from OpenStreetMap, which is a website that provides free geographic data worldwide. Here, we used urban road network data and city point-of-interest (POI) data, including commercial areas, residential areas, and bus stations. Figure 2 shows the research area and the road network.  
The GPS trajectory data of buses were derived from the traffic management department of Shenzhen and were collected in March 2014. These data mainly include vehicle plate numbers, routes, and locations, as well as the acquisition times of GPS collection. Table 1 shows six example records.  
Because bus lanes are mostly tidal lanes (bus lanes that are activated only during peak hours) [38], the bus trajectory data are selected from morning and evening peak times for two time periods (with the morning peak selected from 7:00 to 9:00, and the evening peak from 17:00 to 19:00), and the data spanned 7110 roads with 1952 bus trajectories.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Plate Number** & **Routes** & **Longitude** & **Latitude** & **Acquisition Time** \\ \hline BL8213 & 0E182 & 114.0091 & 22.58673 & 2014/3/6 7:00:06 \\ BN3221 & M3433 & 113.9472 & 22.5839 & 2014/3/6 7:00:01 \\ B90816 & M3583 & 113.969 & 22.558 & 2014/3/6 7:00:05 \\ BM7988 & 03620 & 113.9453 & 22.52448 & 2014/3/6 7:00:02 \\ BF7292 & 06824 & 113.905 & 22.53237 & 2014/3/6 7:00:02 \\ BK4648 & B7364 & 113.9529 & 22.59186 & 2014/3/6 7:00:01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of bus GPS data.  
Figure 2: Research area overview.
## 1 Introduction
The novelty of this work is (i) to build a generic (applicable to other buildings) ML model able to accurately approximate an advanced PL model, (ii) use the ML estimation in combination with a GA for wireless network planning, and (iii) obtain deployment solutions with similar, or better, performance than existing heuristic tools (e.g., the one developed in [12]), but in less time. We show that an advanced, but computationally expensive, path loss model, that builds on the physical properties of propagation, can be well approximated by a ML-based PL model that allows much faster PL estimations for a given link. Such a quick, but still accurate, PL model unlocks the possibility of more thorough exploration of the optimization space to find the optimal network deployment (number and location of APs). It is shown that the proposed approach is accurate (i.e., is able to provide the required coverage, according to the complex PL model), is 15 times faster than a heuristic optimization algorithm (and, thus, more flexible towards recalculations with adjusted optimization settings), finds a better solution (with fewer APs), and allows accounting for additional constraints, such as striving for maximal received power. The remainder of this paper is organized as follows. Section 2 discusses related works on PL estimation and network optimization approaches. Section 3 describes the scenarios in which our generic model is designed, the method used to estimate the PL, and describes the principles of a GA to optimize network deployment. Section 4 describes the performance of PL estimation using the ML algorithm. Also, we investigate the quality of the resulting network planning solution, by comparing the results obtained with an existing heuristic tool in terms of the number of APs, the time to make the planning, and the coverage percentage of the ML-GA-based network design. Finally, Section 5 presents the conclusions of this research.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 4 Proposed Scheme and Power Control
### Power Control and Pilot Assignment  
To clearly confirm the performance of the proposed algorithm, this paper adopts an equivalent gain power control algorithm as in [8; 25] instead of an additional power control algorithm. In the user-centric cell-free massive MIMO model, the power control coefficient between AP \(l\) and user \(k\) can be written by  
\[\eta_{lk}=\eta_{lk}=\Big{(}\sum_{k^{\prime}=1}^{K}\alpha_{lk^{\prime}}\gamma_ {lk^{\prime}}\Big{)}^{-1}. \tag{14}\]  
When the number of users is very large, the use of duplicate sequences between users is inevitable. Therefore, in such a situation, a proper method for allocating pilots efficiently is required as [8].
## 4 Experiments  
To verify the generality and effectiveness of the MTO framework proposed in this paper, we select well-represented single-objective algorithms: GA [45], PSO [46; 47], DE [48], and ABC [49] to combine with IMTO. The comparative experiments are performed on a series of MTO problems to demonstrate:  
1. IMTO can significantly outperform corresponding baseline solvers.
2. In terms of the optimization knowledge transfer, IMTO outperforms the multifactorial optimization framework.
3. IMTO can adapt to different task similarities and promise high transfer effectiveness.
## 3 The Results of the Bibliometric Analysis  
### Publication Trends  
Figure 4 illustrates the annual number of papers with strong relevance to the new technology of USVs, and shows the predicted trends for the future. It is evident that the number of annually published papers has overall steadily increased since about 2014. According to the dataset retrieved from WoS, the earliest paper directly on USVs, written by Portuguese scholars in English, was published in September 2003 [42]. The number of articles in Chinese was relatively low before 2007; this period can be regarded as the initial stage of the internationalization of USV research in China. Additionally, Figure 4 shows that the total number of articles published worldwide has seen an approximately curvilinear growth since 2013. Therefore, 2014 can be considered a pivotal year in the internationalization of new technology in USV research. After that, the number of publications assumes a trend of growth, in companion with a polynomial simulating curve colored in orange, showing that it is likely to keep this tendency and to reach a summit in 2023 or years afterwards.  
To obtain more insights into the concurrence of citations and publications, the time trends of publications in terms of publications and citations are also presented in Figure 4. The results show an overall increasing trend of research on USVs during the period from 2000 to 2023. This trend can be approximately divided into the following three stages: in (1) 2000-2013, the number of publications was minimal and increased slowly, producing fewer than 15 publications; in (2) 2014-2019, publications were driven by several special reports on USVs [43] and commitments using some new methods [44], and the research interest in considering USVs as promising auxiliary strategies had begun to increase. During this stage, the number of publications and citations increased from 11 to 57 and from 50 to 325, respectively; from (3) 2020-present, there is a stable stage in development, as the number of publications is always higher than the previous ones, along with a peak at 64 in 2022, during which time the average annual publication reached 40-60 documents.  
Note that the soaring increase in citations clearly demonstrates the concentrated focus in this field and forecasts the popular direction of USVs.
## 4 Analysis of Approaches to Modelling Agricultural Systems
### Artificial Intelligence  
Artificial Intelligence (AI) is an interdisciplinary approach consisting of a set of algorithms trying to mimic human intelligence. AI is already widely used for fraud detection and prediction models, image recognition patterns, spam filters, speech and audio recognition (Google, Siri, Cortana, Alexa, etc.) and others [107]. Machine learning algorithms and deep learning tools are commonly used to automatically learn from data, such as sensor data or databases, to recognize complex patterns and make intelligent decisions based on data [108]. AI allows for analysing big data quickly [109], regardless of complexity (Figure 2). In agricultural sciences, AI is widely used for predicting crop yields [110], for precision agriculture [107], agriculture automation [111], disease detection, and weed, crop,livestock, water, soil, and irrigation management [112; 113] by supporting, for example, better management practices for irrigation as well as pesticide and nutrient application.  
Moreover, AI is often used to analyse multi-temporal Remote Sensing data [114], and with new satellite mission initiatives (e.g., the Trishna initiative--Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment), it is a very promising tool for more systematic use of precision agriculture and a deeper understanding of environmental processes at real-time (e.g., like monitoring water status of continental ecosystems, improving the understanding of crop water requirements and water balances). The analysis of this data has the potential for better irrigation management and earlier potential drought warnings.  
**Strengths**:  
* At a regional to national scale, the AI approach is well designed for big data exploration and sense-making from various datasets that can help to inform policy.
* AI approach is widely used for prediction and management.
* AI allows coupling various types of data and is powerful for big data analysis.
* AI techniques improve classification and prediction.  
**Weaknesses**:  
* AI modelling techniques require high skills, large datasets, and complex training procedures.
* AI approach allows for learning problems very well, but the generalisation is not possible beyond the boundaries of data and model developed (for anything untrained).
## 3 Results and Discussion
### Optimal Capacity  
First, we needed to calculate the bandwidth necessary to satisfy the demand for network resources in the ENST campus. We created another tool using MATLAB for this task. Figure 5 summarizes the key inputs and outputs data for the calculation of the necessary bandwidth in the network.  
As indicated in Figure 4, based on input data and information, we estimated that a total bandwidth of 1198.8 Mbp/s for each Wi-Fi cell is required.