esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Outage Rate Analysis
### Outage Probability Versus Inter-Separation between Transmitters \(\Delta_{t}\)  
As discussed in Section 2.3, the greater \(\Delta_{t}\) makes macro-diversity gain increase, while the MISO link created by DAS suffers from higher path attenuation. For the reason, we delve into the impact of \(\Delta_{t}\) on the outage rate. In Figure 4, the horizontal axis represents \(\Delta_{t}\) from 1 to 50 m, while the vertical axis indicates the outage probability \(P_{out}\) in a logarithmic scale. For the comparison purpose, the simulation results with \(\rho=0\) and \(\rho=1\), which correspond to the curves with the circles and triangles respectively, are also illustrated. The results with \(0<\rho<1\) are indicated by the curves with squares. Furthermore, the blue solid and red dotted lines represent the results with MRC and SC,respectively. The outage probabilities with \(\rho=0\) and \(\rho=1\) simply increase, as \(\Delta_{t}\) increases, because their path losses monotonically increase with fixed correlation \(\rho\) of zero and one, respectively. However, for the same \(\Delta_{t}\), the curves with \(\rho=0\) show significantly smaller outage probabilities compared to those with \(\rho=1\), which can be explained by the different degree of macro-diversity.  
The curves with \(0<\rho<1\) determined by (10) first decrease for both MRC and SC, as \(\Delta_{t}\) increases, because of enhanced macro-diversity gain with smaller \(\rho\). Then, the outage curves increase again, as \(\Delta_{t}\) increases, since the path attenuation increases. Therefore, there exist an optimal \(\Delta_{t}\), which corresponds to the minimum outage rate, as shown in the graphs. The simulation results show that both MRC and SC schemes have the same optimal inter-separation \(\Delta_{t}^{*}\). The outage performance with \(0<\rho<1\) is always upper bounded by \(P_{out}\) with \(\rho=1\) and lower bounded by \(P_{out}\) with \(\rho=0\). In fact, as \(\Delta_{t}\) decreases, \(P_{out}\) becomes similar to \(P_{out}\) with \(\rho=1\). Furthermore, the outage rate approaches \(P_{out}\) with independent shadowing, as \(\Delta_{t}\) increases, which implies that \(\rho\to 0\).
## 4 Conclusions  
The analysis results show how some stator slot parameters can have a meaningful impact on the additional Joule losses occurring in hairpin windings. The considered slot parameters are: the slot-opening width, the stator tooth width, the polar shoe depths and the ratio between slot depth and slot width. In particular, the FEA results suggest that a proper design of the stator slot would include the following details:  
* Narrow stator teeth;
* Great polar shoes, in order to create the best flux density paths;
* Narrow slot-opening.  
Another relevant parameter is the slot depth to slot width ratio; in particular, it should feature a value of around 1.5.  
If the previous design's expedients are accurately combined, the slot design could be a good way to mitigate the consequences of skin and proximity effects on additional Joule losses. Figure 26 shows a final slot geometry for the rectangular shape that takes into account all of the design recommendations of this paper.  
Figure 24: Current Density module—widest opening.  
Figure 25: Colour plot of the Current Density.  
The geometry in Figure 26 also reduces the percentage difference between the impedance with the highest impedance conductor and the lowest impedance conductor, \(\Delta\)Z%. Figure 27 shows the \(\Delta\)Z% value of 6.23% at 100 Hz frequency, obtained with the optimal rectangular slot geometry. Because of the small difference, adopting parallel paths should be less burdensome for this slot geometry.  
**Author Contributions:** Conceptualization and methodology, C.B. and M.V.; validation, S.N. and D.B., investigation, C.B., M.V. and A.T.; data curation, A.T.; writing--original draft preparation, M.V.; writing--review and editing and supervision, G.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 26: Rectangular slot—best geometry.  
Figure 27: impedance comparison.
# Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics
## 4 Verification on Benchmark Datasets
### Regression
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & **Algorithm** & \(RMSE\) & **Training Time/s** & **Testing Time/s** \\ \hline \multirow{3}{*}{Abalone} & KELM & 0.0708 & 0.3725 & 0.0546 \\  & DKELMs & 0.0706 & 0.0310 & 0.0083 \\  & n-SVR & 0.0706 & 17.4184 & 0.0336 \\ \hline \multirow{3}{*}{Airfoil sound} & KELM & 0.0738 & 0.0364 & 0.0088 \\  & DKELMs & 0.0692 & 0.0295 & 0.0065 \\  & n-SVR & 0.0745 & 1.1691 & 0.0063 \\ \hline \multirow{3}{*}{Auto mpg} & KELM & 0.0727 & 0.0020 & 0.0003 \\  & DKELMs & 0.0728 & 0.0013 & 0.0002 \\  & n-SVR & 0.0743 & 0.0093 & 0.0006 \\ \hline \multirow{3}{*}{Bike sharing} & KELM & 0.0290 & 0.0066 & 0.0009 \\  & DKELMs & 0.0288 & 0.0028 & 0.0005 \\  & n-SVR & 0.0293 & 0.0409 & 0.0021 \\ \hline \multirow{3}{*}{Boston housing} & KELM & 0.0641 & 0.0040 & 0.0010 \\  & DKELMs & 0.0617 & 0.0037 & 0.0010 \\  & n-SVR & 0.0747 & 0.0630 & 0.0015 \\ \hline \multirow{3}{*}{Cpusmall} & KELM & 0.0232 & 1.4076 & 0.1563 \\  & DKELMs & 0.0229 & 0.0094 & 0.0182 \\ \cline{1-1}  & n-SVR & 0.0239 & 12.3305 & 0.1721 \\ \hline \multirow{3}{*}{White wine quality} & KELM & 0.1180 & 0.2140 & 0.0838 \\  & DKELMs & 0.1175 & 0.0658 & 0.0343 \\ \cline{1-1}  & n-SVR & 0.1178 & 0.3859 & 0.0703 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance of distributed KELM on regression benchmark datasets. Figure 3: Comparison on \(RMSE\) and testing time in regression benchmark datasets. (Red, blue and green respectively represent the original KELM, n-SVR and DKELMs algorithms. Different legend shapes represent different datasets).
# Recent Advancements in Anaerobic Digestion and Gasification Technology
## 1 Introduction  
According to the statistical review of world energy (2021), the COVID-19 pandemic was reported to have had a dramatic effect on the energy market. This contributed to the fastest rate of decline in primary energy and carbon emissions since the Second World War. Despite this report, the good news is that renewable energy continued to grow. For recording purposes, the current rate of primary energy consumption and carbon emission fell by 4.5% and 6.3%, respectively. This decline was the largest for primary oil since 1945 and the lowest level since 2011 for carbon emissions [1]. Renewable energy sources, such as wind, solar, and hydroelectricity, all grew despite the decline in overall energy demand. Statistically, renewable energy sources with the inclusion of biofuel and the exclusion of hydro rose by 9.7%, lower than the 10-year average of 13.4% per annum but with an increase to 2.9 Exajoules (EJ) in terms of energy. Solar and wind energy rose by 1.3 EJ and 1.5 EJ, respectively, thereby contributing to the largest renewable energy growth. Similarly, hydroelectricity in China improved by 0.4 EJ [1]. From the authors' point of view, it is worrisome that biomass energy was not mentioned specifically, as it accounts for more than one-third of primary energy.  
To this effect, biomass energy is regarded as renewable and sustainable energy. It is interesting that approximately 32% of the country's overall energy supply comes from biomass, and 70% of the national population is said to depend on it. In addition to natural biomass, the use of conventional waste biomass as a source of energy is another promising option [2]. One major way and option for achieving waste-to-energy focusing on biogas and syngas is through anaerobic digestion and gasification technology [3]. These technologies are regarded as mature biochemical and thermochemical processes and are currently receiving research interest. Its efficiency in the substrate and product separation as well asthe microbes' ecology of anaerobes test contribute to its recent attention. Without a doubt, this review will promote and contribute to the development of the practical application of both technologies as a hybrid system.
# STDecoder-CD: How to Decode the Hierarchical Transformer in Change Detection Tasks
## 2 Materials and Methods  
### Relevant Datasets  
To evaluate our proposed CD models, a series of comparative experiments were designed on CDD (Change Detection Dataset), PIESAT-CD, and CD_Data_GZ.  
CDD is a classic dataset for testing the performance of CD models. It contains 11 pairs of RGB images taken in different seasons obtained by Google Earth [4], and their spatial resolution is 3 cm/px to 100 cm/px. In this dataset, 10,000 image pairs are used for the training set, and 3000 pairs are used for each of the validation and testing sets.  
PIESAT-CD is an open-source dataset released on 29 October 2021 at the 4 th Chinese Conference on Pattern Recognition and Computer Vision. The dataset is available at [[https://captain-whu.github.io/PRCV2021_RS/dataset.html](https://captain-whu.github.io/PRCV2021_RS/dataset.html)]([https://captain-whu.github.io/PRCV2021_RS/dataset.html](https://captain-whu.github.io/PRCV2021_RS/dataset.html)) (accessed on 30 October 2021). Based on Gaofen-2 RS images covering the main busy cities of China, PIESAT-CD provides 6000 bitemporal image pairs with a size of 512 \(\times\) 512 \(\times\) 3 in.png format for training, 2000 pairs for validation, and 2000 for testing. Their spatial resolution is approximately 0.8-1.0 m/px. PIESAT-CD only detects changes in buildings, and other changed objects are omitted from the analysis. The CD labels (ground truth) were manually annotated by RS image interpretation experts who are employed by PIESAT International Information Technology Limited. Each sample in the dataset was annotated by one expert and then double-checked by another to produce a publicly available CD dataset. However, a close look at all of the samples showed that many of them are still inaccurately labeled; e.g., there are missing objects, object-labeling errors, and objects with misregistration errors in the training dataset.  
The CD_Data_GZ dataset, which is an open-source dataset released by [9], includes Google image pairs covering the outskirts of Guangzhou, China, from 2006 to 2019, as well as detection tags of changed buildings. Their spatial resolution is 0.55 m/px. CD_Data_GZ provides 20 RGB image pairs with sizes from 1006 \(\times\) 1168 to 4936 \(\times\) 5224, and they were cropped into 3860 non-overlapping tiles with a size of 256 \(\times\) 256 in.jpg format. Then, we removed non-changed image pairs to avoid the \"change versus non-change\" imbalance phenomenon. Finally, 1110 image pairs with positive and negative instances were used for training, and 82 image pairs with a size of 512 \(\times\) 512 were used for testing.
## 2 WSN for Monitoring of Column Shortening in High-Rise Buildings
### Wireless Sensor Network (WSN) System
#### 2.1.4 Wireless Sensor Network (WSN)  
As can be seen in Figure 2, WSN is based on a one-tier topology, sending collected data from sensor nodes to master node. However, communication problems can result from obstacles, including both structural and non-structural components within the building, and therefore, a tree topology can be employed at locations where repeater nodes are installed to transmit data between the sensor node and the master node. The tree topology is extensively applied in SHM to improve the limitation of one-tier topology, such as the power consumption and range of wireless communication [23, 24, 25]. Data can be collected through bidirectional communication between sensor nodes and master nodes; however, this communication can consume a substantial amount of power compared with unidirectional communication. The power consumption is a very important issue for the long-term automatic monitoring of buildings.  
Therefore, this study uses unidirectional communication in which data are transmitted from sensor nodes to a master node. In the case of unidirectional communication, the master node and repeater node must always be on standby in order to receive data since it is not known when the sensor nodes will send data. This requires a large amount of energy consumption. This problem can be solved by repeating the sleep-action process, activating it when data collection is required and subsequently changing it back to sleep mode. The wireless nodes in this system, including sensor nodes, repeater nodes, and master nodes, possess the power-saving capability of alternating between sleep mode and action mode. The sleep-action mode for each wireless node is independently repeated to minimize power consumption. However, the independent operation of each wireless node results in unavoidable data loss. To simultaneously consider data loss and power consumption in this study, the sensor nodes are set to perform the action mode more frequently than are the master nodes. For example, if the sensor nodes transmit data to the master nodes at intervals of thirty minutes and the master nodes are operational for one hour and are then turned off for the next three hours, the master nodes can receive a minimum of four measurement data from the sensor nodes per day. A small 2,700 mAh battery can be used for a minimum of 200 days if 48 measurements are performed each day.
## 5 Conclusions  
It had been considered that the origin of absorption change of the F-monomer aqueous solution was J-aggregates formation induced by light gradient force of laser [1; 2]. The two main pieces of evidence were as follows. First, the increase in the broad absorption spectral range of the F-monomer aqueous solution was able to be attributed to J-aggregates (\(N<20\)) formation. Second, the broad increase in absorption of the PVA sample was smaller than that of the F-monomer aqueous solution. This result was consistent with the fact that F-monomer molecules in PVA matrix cannot freely move to form J-aggregates.  
It is clarified here that the absorption change of the F-monomer aqueous solution with [NaOH] = 1 mM observed in the present experiment was induced by the triplet excited-state absorption and SA effects due to the triplet state formation. There are three pieces of evidence for the statement. (1); The shape of the DAS in ref. [1; 2] is similar to that of the DAS reported in ref. [3; 4; 5], where the absorption changes were assigned to be induced by the triplet excited-state absorption and SA effect due to the triplet state formation in TPPS monomers. (2); The broad increase in absorption of the PVA sample was clearly observed in the present experiment (Figure 4a). (3); \(|\Delta A|\) of the F-monomer aqueous solution with [NaOH] = 1 mM at 412 nm increased by 4.6 times immediately after N\({}_{2}\) bubbling (Figure 2b). The lifetime of the triplet state was extended by the decrease in the dissolved oxygen concentration, and the triplet excited-state absorption and SA effects increased.  
\(|\Delta A|/A\) in the water-MeOH mixed solution of TPPS monotonically decreased with MeOH concentration at 1 mM NaOH and increased by a factor of more than 50 in the highest case at 10 mM NaOH. The former result can be explained by an increase in dissolved oxygen concentration [47], but the latter result is not readily explained at present. We would like to point out the possibility of a decrease in the dissolved oxygen concentration due to the addition of a tiny amount of NaOH. Although a further detailed study is required, our result opens a unique possibility of optical nonlinearity enhancement and of a structural change of mixed solution by adjusting the concentrations of alcohol and base/acid.  
Conceptualization, E.T.; methodology, E.T., S.I., and T.K.; validation, S.I.; formal analysis, S.I.; investigation, S.I.; writing--original draft preparation, S.I.; writing--review and editing, E.T. and T.K.; supervision, E.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors acknowledge technical support and advice by [PERSON].  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Spheroid Classification Workflow  
We created our classification workflow using separate modules for object detection and grid optimization, as shown in Figure 4. For the tested segmentation models (Unet++ or Mask R-CNN), the input RGB image is used in its original form or converted into its luminance representation for the detection of the individual objects within the Petri dish. Independent of the segmentation, our workflow predicts the grid lines based on the detected grid within the Petri dish. Based on the resulting segmentation mask, our workflow uses the detected grid within the Petri dish to individually analyze each grid cell. Since it is not possible to find multiple valid objects within one cell, except finalized spheroids, our classification focuses on the monolayer and forming spheroid stages. For each detected object (monolayer and forming a spheroid), the calculated confidence score provided by the neural network is used to make the decision on which class should be applied for the entire cell. Since mirroring and cut-offs of the Petri dish at the border of the dish may negatively influence the grid cell detection, our workflow weights scores near the cell center higher than border objects using a Gaussian distribution into account. Additionally, a second weight takes the relative size of the detected object compared with the combined size of all detected objects within the grid cell into account. Both weights lead to a preference for large connected objects that reach the center of the grid cell. Based on the resulting score, the highest-scoring object's class is used to classify each detected object within the cell. This approach also leads to a correct size measurement of the whole grid cell objects, which would otherwise be wrongly detected as multiple objects due to insufficient separation of segmented cells since neural networks often fail to separate close or overlapping objects. This grid cell-based class correction and filtering leads to the filtering of all wrongly detected objects outside the Petri dish, as well as invalid reflections of objects occurring on the borders of the Petri dish. After each grid cell is processed, the resulting segmentation mask is combined with the unfiltered and unprocessed mask of finalized spheroid objects.
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 3 Proposed Method
### Classification Algorithms
#### 3.2.3 Logistic Regression  
Logistic regression is a regression model that can be utilized when the dependant value (i.e., output class) is categorical. It uses a logistic function to express the relationship between the dependant and the independent (i.e., features of each class) value. Apache Spark supports both binomial and multinomial logistic regression; for example, assuming we have \(K\) output classes, then one class is chosen as pivot, in following \(K-1\) models are created and the class with the largest probability among the \(K-1\) models is considered as the result.
# Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora
## 4 Map-Reduce: Approach and Frameworks
### The Map-Reduce Approach  
The Map-Reduce programming paradigm was introduced in [7]. The goal of the authors was to separate the definition of the (possibly simple) computation from the (possibly complex) problem of parallelizing the execution. In fact, the authors argued that most of problems concerned with data transformation are conceptually simple problems; however, their simplicity disappears when they must be parallelized, in order to get an answer in a reasonable time when very large data sets (such as the case of Big Data) are processed. The idea is to define a programming abstraction based on two functional primitives, by means of which to express the transformation. It will be responsibility of the execution layer to handle hard issues of parallel execution.  
Let us give a short description of the approach. Two primitives are available for programmers to write procedures: _Map_ and _Reduce_.  
* _Map primitive:_ this primitive is a programmer-defined function that processes (usually) small parts of input data in order to generate a set of intermediate key/value pairs. Keys will play the role of grouping values in the next Reduce step.
* _Reduce primitive:_ this primitive is a programmer-defined function that takes the set of key/value pairs generated in the Map phase and aggregate pairs with the same key.  
Programmers have only to define the two functions, i.e., how to generate key/value pairs and what aggregation to compute from a set of aggregated pairs. It is the responsibility of the underlying execution layer to perform the following activities:  
* distributing data to computing nodes;
* collecting key/value pairs;
* distributing key/value pairs to nodes, in order to aggregate them.  
As an example, consider the generation of the inverted index. Given a data set \(ds\) and the set of terms appearing in its schema and meta-data, the Map phase builds pairs \((term,ds\_id)\); the Reduce phase aggregates all pairs into the inverted index, in such a way a term is associated with the list of identifiers of data sets that contain that term (in fact, we implemented the indexing phase by means of Map-Reduce as well [3], but its description is outside the scope of the paper).  
Iterative Algorithms  
The Map-Reduce paradigm is particularly suitable for expressing simple transformations that can be depicted as a non-cyclic graph. An example could be building an inverted index (as previously discussed): only one Map-Reduce flow is necessary.  
However, many transformations and computations are necessarily iterative processes, where each iteration relies on outputs produced by the previous iteration. Such algorithms can be described by means of the Map-Reduce paradigm as well: each iteration is a single Map-Reduce flow; the overall computation consists of as many Map-Reduce flows as the number of iterations.