esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Step Classification with SVD Preprocessing of Distributed Massive Datasets in Apache Spark
## 5 Evaluation
### Higgs Dataset
#### 5.1.2 Analysis with Limited Number of Features  
Next, we repeated the same tests with the same settings, limiting the number of features that we used as input to both one-step and two-step classification experiments to the first 21 features.  
The results of the decision tree classifier are presented in Table 6. In the same context as before, as the dataset got larger, the better the classifier results were getting and the more complex the resulting tree. Some observed irregularities might have been caused due to the biased dataset since we performed random selection of rows. Specifically, the tree in the case of 5K rows was larger than the tree in the case of 10K for both one-step and two-step classifier, although in the latter to a lesser degree. In this case, the proposed two-step classifier performed the same as the formal classification method in terms of tree complexity and \(F1\) metric (about 2% better results for maximum cases), although it required about 32% more time to calculate the classification model. Again, the scaling of the classifier was not linear regarding computation time and dataset size.  
In Table 7, the results of the random forest classifier are presented. Again, as the dataset grew, the results were getting better and the resulting model was getting more complex in terms of the total number of nodes. Both one-step and two-step classification methods performed the same in terms of \(F1\) metric, with the model of the two-step classifier being a bit simpler, but it required more time to be calculated (about 5% in the case of 200K rows).  
Results from the binomial logistic regression classifier are depicted in Table 8. Both one-step and two-step classifiers performed almost equally in term of \(F1\) metric, while the computation time was still low. In addition, as we are already aware, the bigger the dataset, the more accurate the classifier got.  
In Table 9, the results of the gradient boosted trees classifier are depicted. Again, the two classification methods performed almost equally in terms of the \(F1\) metric, but like the random forest, the two-step method required more time for evaluating the model. As expected, as the dataset was getting bigger, the classification performance improved.  
Finally, in Table 10, the results of the multilayer perceptron classifier are presented. As expected, the results improved as the dataset size got larger. Moreover, in terms of performance, the two-step classification method provided approximately 5% less accurate results (in terms of \(F1\) metric), but required about 15% less time to calculate the model.
## 3 Information Rate  
The channel capacity limits the data rate of reliable transmissions in the system, which is a important figure of merit for information transfer. While capacity depends on the channel itself, there are different capacity bounds for phase or amplitude modulation. In this paper, we compare the impact of phase and amplitude modulation of a multisine signal on capacity and information transfer. The considered modulation schemes, and their corresponding information rate, are discussed next.
# The Machine-to-Everything (M2X) Economy: Business Enactments, Collaborations, and e-Goverance
## 2 The M2X Economy
### Running Case  
We introduce an example running case of the M2X Economy in order to provide the reader with a better understanding as well as the scope of M2X applications. The selected running case is illustrated in Figure 1 and belongs to the sub-set of vehicle-focused M2X applications, i.e., the vehicle-to-everything (V2X).  
In the future, people might not possess vehicles any more. Instead, vehicles may own themselves, or they are owned by the government, or private corporations [1]. We assume that [PERSON] requests a self-driving car (TaaS) to go from Point \(A\) to \(B\) and several route options exist for this. Figure 1 indicates that the fastest route option is expensive but also the most comfortable and equipped with toll gates. Alternatively, the less comfortable, cheaper option is via Point \(C\) and includes traffic lights and traffic congestion. [PERSON] may select her preferred option depending on her price range and on the urgency of reaching Point \(B\). Furthermore, we assume that the self-driving cars are able to communicate with each other as well as the traffic lights (infrastructure). It is also possible to buy a green-light phase for a faster commute to Point \(B\). Finally, Figure 1 shows an electric charging station near Point \(B\) that the self-driving cars may use for some amount of fee. In the described running case, assuming that time and money are important factors, [PERSON] may select from a range of possible options. On the one hand, she may choose the fastest and most expensive route to Point \(B\), or take the less comfortable and cheaper option via Point \(C\). Additionally, she can pay an extra fee and her car may negotiate for a green light at the traffic signals.  
Our running case--despite it simplicity--already covers a wide variety of M2X service enactments, i.e., TaaS, toll gate payments, battery electric vehicle (BEV) charging, road  
Figure 1: Self-driving M2X running case incorporating smart traffic lights and a traffic-congestion response, adapted from [1].  
space negotiations, smart parking, and traffic information provision. Nevertheless, they also only constitute a small subset of services within the M2X ecosystem.
## 1 Introduction
### Related Works
#### 1.1.1 Background Filtering-Based Methods  
Background modeling-based methods initially employ image filtering algorithms to estimate the background of an input image, followed by target detection through background subtraction. Principal filtering techniques include top-hat transformation filtering [4], bilateral filtering [5; 6], minimum two-dimensional mean square filtering [7], random walker [8], and anisotropic diffusion-based filtering [9; 10; 11; 12], among others. While background filtering approaches can achieve accurate background prediction under stable conditions, they often fail to produce satisfactory results in real-world scenarios where the background undergoes significant changes.
## 2 Results
### DksA Deletion Promoted Membrane Integrity in Y. enterocolitica  
Fatty acids serve as essential building blocks for the synthesis of phospholipids and lipopolysaccharidesaccharides found in the structure of the cell membrane structure. Disturbance in the metabolic pathways associated with fatty acids could alter the ratio of saturated and unsaturated fatty acids, thereby impacting the permeability of cell membranes [34]. To investigate the role of DksA in maintaining the integrity of the cell membrane structure, the permeability of the outer membrane and inner membrane of the WT strain, YEND strain, and complementing strain YEND-D were measured by NPN and ONPG assays.  
NPN is a hydrophobic fluorescent probe capable of binding to hydrophobic structures that are exposed when the outer membrane of a cell experiences heightened permeability. The binding leads to an increase in the fluorescence signal of the probe, and the resulting fluorescence intensity reflects the changes in the bacterial outer membrane permeability. As shown in Figure 3, following a 10-min incubation in vitro, the YEND strain exhibited lower fluorescence values than the WT strain. In contrast, the fluorescence values of the YEND-D strain returned to levels similar to those of the WT strain. These results indicate that the YEND strain exhibits greater outer membrane permeability. In addition, we measured the stability of the inner membrane of _Y. enterocolitica_, and we evaluated the stability of the inner membrane of _Y. enterocolitica_ using the ONPG. When the outer membrane of a cell is damaged, enzymes and nucleic acids can leak out, including \(\beta\)-galactosidase, which could hydrolyze ONPG to produce a yellow substance called o-nitrophenol. By quantifying the amount of color reaction, we can measure the leakage of extracellular enzymes and assess the stability of the bacterial inner membrane. Following a one-hour in vitro reaction, the absorbance value of the YEND strain was lower than that of the WT strain (Figure 3). These findings suggest that the inner membrane of the YEND strain was more stable and sustained less damage than the WT strain. Overall, the knockout of DksA resulted in greater integrity of both the outer and inner membranes of _Y. enterocolitica_.
# Bearing Capacity of Ring Foundations on Sand Overlying Clay
## 2 Problem Definition  
The description of the bearing capacity problem is shown in Figure 1. A rigid ring foundation, with internal radius (\(R_{i}\)) and external radius (\(R_{0}\)), is placed on a sand layer that lies over a deep bed of clay. The sand layer, which has a thickness (\(H\)), a unit weight (\(\gamma\)), and an internal friction angle (\(\varphi\)), is drained. The undrained shear strength of the clay is given by \(c_{u}\). The ultimate bearing capacity of ring foundations on sand overlying clay can be described in the following dimensionless form:  
\[\frac{p_{u}}{(\gamma R_{0})}=f\bigg{(}\frac{R_{i}}{R_{0}},\ \frac{H}{R_{0}},\ \frac{c_{u}}{(\gamma R_{0})},\ \varphi \bigg{)} \tag{1}\]  
where \(p_{u}\) is the average limit pressure defined by the following:  
\[p_{u}=\frac{Q}{\pi\Big{(}R_{0}^{2}-R_{i}^{2}\Big{)}} \tag{2}\]  
where \(Q\) is the ultimate vertical downward load.  
In this paper, five different values of the ratio \(R_{i}/R_{0}\), namely 0, 0.25, 0.33, 0.5, and 0.75, are taken into consideration, covering most problems of practical interest [32; 33; 34]. The value of \(H/R_{0}\) varies from 0.25 to 4, corresponding to different values of \(c_{u}/\gamma R_{0}\), namely 0.25, 0.5, 0.75, 1, 2, 3, 4, and 5. The internal friction angle (\(\varphi\)) is set to \(25^{\circ}\), \(30^{\circ}\), \(35^{\circ}\), \(40^{\circ}\), and \(45^{\circ}\)[16].
## 4 Experiments
### Visualization Results  
The first and fourth row in Figure 8 show a common difficulty in the COCO dataset. There are overlaps between a large number of objects, which are difficult to distinguish, such as pedestrians with backpacks and parallel zebras. It can be seen that RetinaNet and GA-RetinaNet produce numerous redundant and overlapping detection boxes, and these boxes originate from a large quantity of pre-defined anchor boxes with poor quality, which are difficult to remove with post-processing. However, our method produces concise and accurate detection boxes by virtue of an efficient anchor prediction mechanism, avoiding invalid results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Backbone** & **AP** & **AP\({}_{\text{50}}\)** & **AP\({}_{\text{75}}\)** & **AP\({}_{\text{S}}\)** & **AP\({}_{\text{M}}\)** & **AP\({}_{\text{L}}\)** & **GFLOPs** \\ \hline RetinaNet & ResNet-50 & 35.9 & 55.4 & 38.8 & 19.4 & 38.9 & 46.5 & 201.53 \\ GA-RetinaNet & ResNet-50 & 37.1 & **56.9** & 40.0 & 20.1 & 40.1 & 48.0 & 197.43 \\ DA-RetinaNet(ours) & ResNet-50 & **38.0** & 55.5 & **41.2** & **20.1** & **41.8** & **48.5** & 141.79 \\ RetinaNet & ResNet-101 & 39.1 & 59.1 & 42.3 & **21.8** & 42.7 & 50.2 & 277.6 \\ GA-RetinaNet & ResNet-101 & 38.4 & 58.5 & 41.3 & 21.0 & 41.1 & 49.7 & 273.5 \\ DA-RetinaNet(ours) & ResNet-101 & **40.0** & 57.9 & **43.5** & 21.5 & **44.1** & **51.2** & 217.86 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The compared results on MS COCO 2017 test-dev. We used AP, AP\({}_{\text{50}}\), AP\({}_{\text{75}}\), AP\({}_{\text{S}}\), AP\({}_{\text{M}}\), AP\({}_{\text{L}}\) to evaluate the performance of the three models and counted the floating-point computation (GFLOPs) of all models.  
the feature enhancement module, our model extracts the features of anchor regions and integrates the semantic information of the head and feet to obtain a complete detection box. The other rows are similar and our algorithm always outperforms other methods in detecting overlapping targets.
## 3 Results
### Test Scenario for using FFT Data  
The focus of this study was the proper wearing of safety helmets at construction sites. To distinguish between wearing and not wearing a helmet and then between properly and improperly wearing the helmet, the test scenario depicted in Figure 11 was created.  
First, a determination is made regarding whether or not the safety helmet is being worn. If the helmet is being worn, it is determined whether the helmet is being worn properly or improperly. For this purpose, it is necessary to classify the worker activities. In existing studies on sensor-based activity recognition, activities such as walking, standing, running, riding a bicycle, going up stairs, and going down stairs have been classified [23; 24; 25; 26]. Since the focus of this study was on worker activities at a construction site, the test scenarios were classified accordingly, as shown in Figure 12.  
Figure 11: Safety helmet state classification chart.  
Figure 12: Classification of static, moderate, and dynamic activities of workers in test scenarios.  
Figure 10: Results of Fast Fourier Transformation (FFT) conversion of walking activity according to whether or not the safety helmet was worn properly.  
In this study, 11 typical main activities of workers at a construction site were identified and were divided into static, moderate, and dynamic activities. The static activities include sitting, standing, and resting and generally involve minimal upper and lower body movements. Resting allows the upper body to move more freely than sitting. The moderate activities include hammering and painting. Here, the lower body is generally static, but the upper body is moving to perform a task. Finally, the dynamic activities include walking, running, going up stairs, going down stairs, transporting objects, and climbing ladders.  
The data sampling period of the sensor was 8 Hz, and a data frame was created to allow data sampling to occur 128 times every 32 s. Every 128 data items constituted a set, and 100 sets of raw data were collected regarding the proper and improper wearing of safety helmets according to activity. The tests were performed on three men and two women to consider a variety of body conditions. Each subject performed each activity for 2-30 min, depending on whether the chinstrap was fastened or not. When the safety helmet was properly worn (On), the harness was adequately tightened around the head, and the chinstrap was 2 cm vertically from the tip of the chin of the subject. When the safety helmet was improperly worn (Off), the harness was loose, and the chinstrap was not properly fastened.
## 2 3 GPP Vehicular Communication Standards
### LTE and NR V2X Modes
On the other hand, in SL mode 2, similar to LTE C-V2X mode 4, UEs can establish a communication link outside the coverage area of a gNB. Additionally, 4 sub-modes are defined [9; 10]: mode 2(a), mode 2(b), mode 2(c) and mode 2(d). In mode 2(a), UEs automatically select the resources to be used. In mode 2(b), on the other hand, a UE can assist other UEs in the selection of resources for transmission. In mode 2(c) it is differentiated whether the operation is performed outside the network coverage area (out-of-coverage operation) or in coverage area (in-coverage operation). For the out-of-coverage operation mode, mode 2(c) provides a pre-configuration of one or multiple transmission patterns, mapped to each resource pool. For the within coverage area mode of operation, it is assumed that the gNB indicates the location of one or multiple transmission patterns. Finally, in mode 2(d), a UE participates in resource selection for other users. Additionally, for NR C-V2X, 3 modes of message transmission between UEs have been defined: unicast, groupcast and broadcast. As illustrated in Figure 2, in unicast mode a UE communicates only with another UE. By groupcast a UE can transmit messages to one or more UEs in its vicinity, e.g., in a platoon of vehicles, the lead vehicle transmits messages to the platoon members. In broadcast mode, a UE transmits messages to all surrounding vehicles that are within the transmission coverage area. In NR C-V2X a UE can support the 3 modes of information transmission previously indicated. As shown in Figure 2, a lead vehicle can support groupcast transmission to the members of a platoon, and at the same time it can perform a unicast transmission to another surrounding vehicle that does not belong to the platoon. Figure 1: LTE V2X modes. Figure 2: NR V2X transmission modes.
## 5 Discussion
We investigated the effects of different diversity values. When the value is close to 0, more overlapping keywordsare produced, for example, _urban heat island research_, _urban heat island study_, and _urban heat island phenomenon_, as shown in Figure 4 in Section 3.2. On the other hand, when the value is close to 1, diverse and general keywords emerge such as _determinant variable_, _main contribution_, and _planning practice_. We decided to use a value of 0.5 as suggested by other similar studies [61; 62; 63]. However, the diversity parameter can be adjusted to meet the needs of specific applications [64]. There are some ways to further improve keyword extraction, which have not been explored by this study. Semi-supervised learning, unlike the supervised approaches that require labeled datasets for training, benefits from the portion of labeled data to improve its accuracy. The study by [PERSON] et al. (2008) [65] showed that semi-supervised learning trained on both labeled and unlabeled data achieves a competitive performance compared to supervised learning. However, there are only a few semi-supervised solutions for keyword extraction at the moment [39]. Another way is to filter unwanted keywords. We note that some of our keywords are place names such as _Los Angeles_, _Manila_, and _Texas Gulf Region_ in group 2 in Figure 5(a). They were extracted because they occur frequently in the abstracts. In most cases, urban place names indicate the location a study takes place; however, they can be filtered out using named entity recognition techniques. Finally, the evaluation of keyword extraction results is difficult and time-consuming because it typically requires human intervention to reach an agreement [66]. One is that domain experts use human-labeled datasets to judge the keyness of a word. Because no domain experts were available in this study, we treat the keyword grouping as the evaluation task with the silhouette score as the metric and present the keyword groups whose silhouette scores are positive. We tried topic coherence metrics such as UCI measure [67] or UMass measure [68] on the keyword grouping results, and found that these measures give different results because they use different metrics, e.g., UMass relies on word co-occurrences [1; 69; 70] and our approach on semantic similarity. We also attempted to use the context vector-based topic coherence metric proposed by [PERSON] and [PERSON] (2013) [71] and the results are aligned with the silhouette scores, where the keyword groups with negative silhouette scores receive low scores.
# Reconstruction of Rainfall Field Using Earth-Space Links Network: A Compressed Sensing Approach
## 2 Principles of Reconstructing Rainfall Field  
When rainfall happens in the atmosphere, microwave communication signals will be significantly attenuated by the effect of scattering and absorption caused by raindrops, and rainfall is the most major meteorological factor affecting the quality of microwave communication signals. The cleverness of using ESL to retrieve rainfall is that it transforms the interference of rain attenuation in the communications field into a favorable condition for obtaining atmospheric parameters in the meteorological field. The rainfall retrieved by a single ESL represents the average rainfall at the location of the link. Furthermore, when there are multiple ESLs forming an ESL network, it can be used to retrieve the distribution of the sparse rainfall in the area and then reconstruct the rainfall field in the area through a reconstruction algorithm.
# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 3 How Does Computer Vision Improve the Accuracy of Disease Diagnosis  
Computer vision algorithms excel at detecting subtle patterns and anomalies in medical images that may be invisible to the human eye [89]. By analyzing vast datasets of medical images, these systems can identify minute details crucial for accurate diagnosis, particularly in complex cases like cancer or neurological disorders [114, 115]. One of the major advantages of computer vision is its ability to maintain consistent accuracy, regardless of external factors like fatigue or distractions that can affect human performance [92]. This leads to more reliable interpretations of medical images and helps minimize diagnostic errors. Computer vision enables earlier and more accurate detection of diseases, which is critical for improving treatment outcomes [100]. For example, algorithms can detect early-stage conditions like diabetic retinopathy, glaucoma, and age-related macular degeneration in ophthalmology [116; 117; 118; 119; 120]. This early intervention capability can significantly impact patient prognosis and treatment effectiveness.  
Studies have shown that computer vision can achieve remarkable accuracy in disease detection [117; 121; 122; 123]. For instance, a system for detecting pneumonia in chest X-rays achieved 85% accuracy [124; 125; 126; 127; 128]. In lung cancer detection, a computer vision system demonstrated 95% accuracy compared to 65% for trained physicians [129]. Japanese researchers found that computer vision could increase cerebral aneurysm detection by up to 13% in MRI scans [130; 131; 132]. Computer vision systems can simultaneously analyze multiple aspects of medical images, providing a more holistic view of a patient's condition [133]. This comprehensive approach helps identify complex patterns and relationships that might be missed in manual analysis.  
By using consistent criteria and algorithms, computer vision helps standardize the diagnostic process across different healthcare settings. This scalability ensures that high-quality diagnostic capabilities can be extended to underserved or remote areas, improving overall healthcare accessibility. Computer vision significantly improves diagnostic accuracy by enhancing detection capabilities, reducing human error, enabling earlier interventions, and providing a more comprehensive and standardized analysis of medical images [134; 135]. As these technologies evolve, they promise to revolutionize disease diagnosis and improve patient outcomes across various medical specialties.