esa-sceva/satcom-chunk-collection huggingface dataset:
# Pallet Recognition with Multi-Task Learning for Automated Guided Vehicles
## 3 Experiments  
### Datasets  
There are two types of labels; 11 classes for angle and seven classes for distance (i.e., a total of 77 labels for each combination.) We obtained a total of 385 images taken from five types of AGV workplaces. By moving the AGV in each place, we obtained 77 images for angles and distances. For the test dataset, 77 images were sampled by stratified random sampling. Two images with the same label were not included in the test set as shown in Figure 4a,b so that all 77 images have different labels. As for the remaining images, 38 and 270 images, respectively, were used as the validation and training datasets. Because it is generally known that a deep learning model requires a large training dataset, we increased the training data in two ways. One was to take one more picture with an additional light turned on in the same place. The new images have the same labels as the original images. The second method was to flip the images horizontally, and these images have also flipped labels. By combining these two methods, a total of four image types are possible as shown in Figure 5. To verify the effect of each, the datasets were created in four ways as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Dataset** & **Validation Dataset** & **Test Dataset** \\ \hline
270 & 38 & 77 \\ (Default) & 38 & 77 \\
540 & 38 & 77 \\ (Default + flipped) & 38 & 77 \\
1080 & 38 & 77 \\ (Default + light on + flipped) & 38 & 77 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of images used for training, validation, and test dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Architectures**} & \multicolumn{3}{c}{**Number of Parameters**} \\ \cline{2-4}  & **Two Single Models** & **Multi-Task Model (Proposed)** & **Ratio (\%)** \\ \hline AlexNet & 114.08 M & 111.61 M & 97.83 \\ VGG11 & 257.62 M & 248.39 M & 96.41 \\ ResNet-18 & 22.37 M & 11.19 M & 50.02 \\ ResNet-50 & 47.05M & 23.54 M & 50.03 \\ ResNet-101 & 85.04 M & 42.54 M & 50.02 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the number of parameters between the multi-task model and two single models.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Multi-Task Model (VGG11)**} \\ \hline \multirow{2}{*}{\(\lambda\)} & **0.1** & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** & **0.9** \\ \hline \multirow{2}{*}{Exact match ratio} & 0.65 & 0.68 & 0.67 & 0.70 & 0.70 & **0.79** & 0.77 & 0.72 & 0.60 \\  & (0.08) & (0.06) & (0.06) & (0.09) & (0.07) & **(0.08)** & (0.07) & (0.08) & (0.09) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the hyperparameter \(\lambda\) for multi-task model. Comparisons of results using single-task models and multi-task models, in terms of exact match ratios. The best values for each architecture are in bold. The mean exact match ratio and standard deviations (in parentheses) are reported.
# Evaluation of the Indoor Air Quality in Governmental Oversight Supermarkets (Co-Ops) in Kuwait
###### Abstract  
Examining the indoor air environment of public venues, especially populated supermarkets such as Co-Ops in Kuwait, is crucial to ensure that these venues are safe from indoor environmental deficits such as sick building syndrome (SBS). The aim of this study was to characterize the quality of the indoor air environment of the Co-Ops supermarkets in Kuwait based on investigation of CO\({}_{2}\), CO, NO\({}_{2}\), H\({}_{2}\)S, TVOCs, and NMHC. On-site measurements were conducted to evaluate these parameters in three locations at the selected Co-Ops, and the perceived air quality (PAQ) was determined to quantify the air's pollutants as perceived by humans. Moreover, the indoor air quality index (AQI) was constructed for the selected locations, and the ANOVA test was used to analyze the association between the observed concentrations among these environmental parameters. At least in one spot at each Co-Op, the tested environmental parameters exceeded the threshold limit set by the environmental agencies. The PAQ for Co-Op1, 2, and 3 are 1.25, 1.00, and 0.75 respectively. CO\({}_{2}\) was significantly found in an association with CO, H\({}_{2}\)S, and TVOCs, and its indoor-outdoor concentrations were significantly correlated with R\({}^{2}\) values ranges from 0.40 to 0.86 depending on the tested location.  
Keywords: indoor air quality; sick building syndrome; perceived air quality; air pollution +
Footnote †: journal: Applied Sciences
# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 1 Introduction
However, recent studies show that the traditional deterministic models struggle to capture the non-linear relationship between the concentration of contaminants and their sources of emission and dispersion [21; 22; 23; 24], especially in a model application in regions of complex terrain [25]. To tackle the limitations of traditional models, the most promising approach is to use statistical models based on machine learning (ML) algorithms. Statistical techniques do not consider physical and chemical processes and use historical data to predict air quality. Models are trained on existing measurements and are used to estimate or forecast concentrations of air pollutants according to predictive features (e.g., meteorology, land use, time, planetary boundary layer, elevation, human activity, pollutant covariates, etc.). The simplest statistical approaches include Regression [26], Time Series [27] and Autoregressive Integrated Moving Average (ARIIMA) [28] models. These analyses describe the relationship between variables based on possibility and statistical average. Well-specified regressions can provide reasonable results. However, the reactions between air pollutants and influential factors are highly non-linear, leading to a very complex system of air pollutant formation mechanisms. Therefore, more advanced statistical learning (or machine learning) algorithms are usually necessary to account for a proper non-linear modelling of air contamination. For instance, Support Vector Machines [29], Artificial Neural Networks [30], and Ensemble Learning [31] have been applied to overcome non-linear limitations and uncertainties to achieve better prediction accuracy. Although statistical models do not explicitly simulate the environmental processes, they generally exhibit a higher predictive performance than CTMs on fin spatiotemporal scales in the presence of extensive monitoring data [32; 33; 34]. Different machine learning approaches have been used in recent years to predict a set of air pollutants using different combinations of predictor parameters. However, with a growing number of studies, it is puzzling why a certain algorithm is chosen over another for a given task. Therefore, in this study we aim to review recent machine learning studies used in atmospheric pollution research. To do so, the remainder of the paper is organized into three sections. First, we explain the method used to select and scan relevant journal articles on the topic of machine learning and air quality, which conforms to the PRISMA guideline. This section also describes the strategy used to analyze the manuscripts and synthetize the main findings. Second, the results are presented and discussed from a general to a detailed and synthetic account. Finally, the last section draws conclusions on the use of machine learning algorithms for predicting air quality and the future challenges of this promising approach.
# Research on Seismic Signal Analysis Based on Machine Learning
## 2 Application of Machine Learning Method in Seismology
### Machine Learning Methods
#### 2.2.2 Unsupervised Learning Methods  
Unsupervised learning methods can directly learn patterns in datasets based on the similarity between samples without relying on known examples [20]. K-means algorithm is one of the most commonly used unsupervised classification methods in data mining. The K-means algorithm is a partition-based clustering algorithm, which uses distance as a measure of similarity between data objects. In other words, the smaller the distance between data objects, the higher their similarity, and the more likely they are to be in the same cluster. There are many ways to calculate the distance between data objects. The K-means algorithm usually uses Euclidean distance to calculate the distance between data objects [38].  
Improving clustering performance usually can be done by reducing the dimensionality of the data and performing the clustering in the feature space instead of the data space [19]. In this paper, on the basis of predecessors, we propose a collaborative learning model suitable for the task of seismic sensing signal classification, that is, a two-step model of a pre-trained knowledge base based on supervised learning labels and an unsupervised learning method. This paper showed that this method can predict and obtain better recognition accuracy. Specifically, we used the MiniRocket method to directly extract the important features in the seismic sensing signals, and then used the K-means and cluster head methods for classification operations. The results showed that compared with the direct use of the unsupervised classification method, our method of MiniRocket feature extraction in MiniRocket combined with unsupervised classification proposed in this paper achieved great improvements and improved the classification accuracy of earthquake events.
# Kinematic Modelling and Experimental Validation of a Foldable Pneumatic Soft Manipulator
## 4 Experiment Design and Validation  
### Experiment Design  
The prototype of the foldable pneumatic module is made up of a pneumatic actuator and a straight arm. The pneumatic actuator is fabricated by selective laser sintering (SLS) 3D printing technology with nylon composite power. It consists of three independent inflatable bellows. The two sides of the pneumatic actuator are fixed by two rigid constraint planes to ensure the three bellows remain in the same plane. The straight arm consists of an inner soft retractable bellows and an outer skin. The outer skin is made of soft fiber fabric which can protect the inner soft bellows and limit the elongation of the inner bellows. The straight arm is in a contracted state when it is not inflated; after inflation, the inner bellows stretches axially to a fixed length limited by the outer skin, as shown in Figure 6.  
In Figure 6a,b, the contraction state and the expanded state of the pneumatic soft module are shown respectively. In Figure 6c, the diagram of the pneumatic control system is shown. The pneumatic control system includes a Festo motion terminal VETM, a BECKHOFF PLC controller, a computer, an air supply, electrical wires, and air pipelines. VTEM is able to control eight pipelines at a proportional pressure with four piezo pilot valves integrated in a valve terminal. The CX9020 BECKHOFF PLC controller is used to offer multi-pipelines air pressure control for the VTEM. The experimental system consists of a pneumatic control system, a measurement system, and a pneumatic soft module, as shown in Figure 7a. The measurement system includes an electronic total station and a force gauge system. On the soft manipulator, nine observation points are set and recorded in order to obtain the shape deformation of the total soft module. Five observation points are on the pneumatic actuator with equal distance, while four observation points are on the straight arm, as shown in Figure 7b. The positions of the nine observation points are recorded by using the electronic total station. The position and status of the pneumatic actuator can be obtained through measuring the position of the five observation points. The number 6 observation point is set at the junction of the pneumatic actuator and the straight arm, which is the starting point of the straight arm. Two observation points are set in the middle section of the straight arm, and one observation point is arranged at the end of the straight arm. The position and status of the straight arm can be obtained through measuring the position of the four observation points.  
Figure 6: Prototype of the foldable pneumatic soft module.
# Cyber-Physical Systems for Water Supply Network Management: Basics, Challenges, and Roadmap
## 3 CPS Architecture and Applications  
### CPS Conceptual Overview  
CPSs are complex next-generation engineering systems integrating embedded computing, control, and communication technologies to monitor a physical environment [25]. Several architectures of a CPS have been reported in the published literature. For example, [PERSON] et al. [23] proposed a bottom-view, four-layer architecture, which comprises of a physical, network, processing, and an application layer. The physical layer encompasses distributed sensors and actuators units for real-time measurements and control. The network layer, which uses wired and wireless communication technologies, offers the basis for data interaction and exchange of information among the units. The processing layer is the data management center, which stores, organizes, and manages the information obtained from the network layer, while the application layer enables the management of resources and real-time monitoring. In [PERSON] et al. [24], a three-layer service-oriented CPS architecture having a physical, network, and service layer was proposed. Similar to the research studies in [21; 23; 31; 32; 33], the physical layer has a module for sensor and actuator nodes, the network layer has the network modules, while the service layer is equipped with the resource and service modules. Considering these studies, a CPS may be perceived as a technology that utilizes the theory of communication, computing, and control to monitor and influence a physical environment. Therefore, as illustrated in Figure 2, a CPS comprises of the physical process, sensing and actuation system, communication networks, and decision system.  
The physical processes are the environment to be monitored or controlled using sensors and actuators. The sensors are usually arranged in a sensor node for seamless communications. The acquired information from the physical process is sent to the cyber systems (where decisions are made) through a communication network. Wireless communication technologies are mostly used in this case. The following sections discuss the components of a typical CPS architecture.  
Figure 2: Holistic view of a cyber-physical system (CPS) architecture.
## 2 System Configuration  
Figure 3 illustrates the robot, sensors, and controllers used in this study. The Scout2.0 robot from Agile X (Dongguan, China), which can be controlled via an open-source ROS (Robotics Operating System Melodic) platform ([[https://www.ros.org/](https://www.ros.org/)]([https://www.ros.org/](https://www.ros.org/))), was chosen for its versatility across various industrial applications [18]. The robot is driven by four fixed wheels, employing a skid-steer mechanism. The distance between the wheels is 0.49 m, and the width is 0.69 m, allowing longitudinal and lateral control through the adjustment of wheel RPMs. The total weight is approximately 60 kg, and the robot can carry up to 50 kg of payload. Table 2 show the specifications of the YDLiDAR G6 (Shenzhen EAI Technology Co., Ltd., Guangdong, China). The object-following sensor used was the G6 model from YDLiDAR, which has a 360\({}^{\circ}\) field of view and an angular resolution of 0.1\({}^{\circ}\), with each particle being measured at 10 kHz. The minimum detection range is 0.12 m, and the maximum range is 16 m. The sensor was mounted at a height of 0.4 m from the ground, facing forward.  
While single-channel 2D LiDAR-based leg detection has been widely studied, particularly in mobile robots and pedestrian tracking applications [11; 19; 20], the application of such methods to small mobility platforms for agricultural and outdoor environments remains underexplored. Existing research focuses primarily on high-performance, multi-channel sensors and urban environments [12; 21], leaving a gap in the development of cost-effective, low-spec sensor solutions for rugged terrains. This study addresses this gap by demonstrating a novel approach to integrating single-channel 2D LiDAR sensors for pedestrian leg detection in compact mobile platforms, tailored specifically for agricultural applications. The controller used in this study was the NVIDIA Jetson AGX Orin board (NVIDIA Corporation, Santa Clara, CA, USA), running on Ubuntu 20.04 LTS and ROS [22]. A Python 3.8-based program was developed for sensor data acquisition and analysis, enabling object-following and PD control in both longitudinal and lateral directions.  
Figure 3: The controller, sensors, and robot platform used in the experiment.
# Recent Case Histories of Carbon-Neutral Activity Using Ground Improvement Technology in Japan
## 1 Introduction  
The Earth's average surface temperature has shown unusually rapid increases over the past century, primarily due to the greenhouse gases released as people burn fossil fuels. Greenhouse gases, including not only carbon dioxide (CO\({}_{2}\)) but also methane (CH\({}_{4}\)), dinitrogen monoxide (N\({}_{2}\)O) and chlorofluorocarbon (CCl\({}_{3}\)F) gases, accounted for about 50,000 million tons of CO\({}_{2}\) globally in 2018, and 1240 million tons in Japan [1]. This global warming has led to record-breaking heat waves, torrential rains and droughs on a global scale in recent years. For this reason, people around the world are more keenly aware of the need to reduce greenhouse gas emissions.  
The construction industry is one of these sources of greenhouse gas emissions, and accounts for about 43% of Japan's carbon dioxide (CO\({}_{2}\)) emissions. Since cement, one of the most frequently used materials in construction, is a relatively inexpensive, mass-produced, safe and durable material, it has been used in building and infrastructure for more than 100 years. It is made of limestone, which contains a large amount of carbon dioxide. In the cement manufacturing process, a large amount of carbon dioxide is emitted during the firing of the limestone. More than 30% of Japan's carbon dioxide is emitted in the entire course of construction works during the production of cement and concrete in Japan.  
A lot of ground improvement techniques have been developed and applied to improve the physical and mechanical properties of soil to achieve stability improvement, ground settlement control, reinforcement, liquefaction prevention, etc. These techniques use a lot ofnatural materials such as sand and crushed stone and industrial products such as cement. In order to reduce the environmental impact and to economize the techniques, many kinds of industrial waste and by-products have been beneficially used in many types of ground improvement techniques since the 1980s. At that time, they were not recognized as having contributed to CO2 reduction. In response to the growing awareness of carbon neutrality in recent years, it is necessary to further promote initiatives such as the beneficial use of the industrial waste and by-products that have been in use so far and the development of new materials and construction methods to reduce CO2 emission.  
In this paper, recent developments in and applications of environmentally friendly ground improvement techniques are briefly introduced.
# Integral Seismic Risk Assessment through Fuzzy Models
## 2 Linear Methods When Used to Build Composite Indices  
### Two Fundamental Disadvantages of Linear Methods  
The use of linear methods to form a composite index has different limitations, such as compensability, preference dependence and interpretation of associated weights. The assumption of preferential independence among indicators states that any contribution or influence made by an individual indicator to the overall composite is discernible at all times: a packaged and isolated capsule of information that can be propagated along with different information types, without any change of its nature. When this assumption is accomplished, simple additive methods of aggregation can be used [19]. Their use, however, may lead to a scheme of full compensability, where poor performance of some of the indicators can be compensated by high performances of other indicators. Geometric aggregation, even if less compensatory, still maintains a nonrestrictive use of weights as representations of importance or significance coefficients that can translate as diminishing or compensating for the influence of a particular dimension performing poorly with another dimension with higher performance [19; 20]. The use of geometric aggregation is the method of preference when compensability needs to be avoided since a geometric aggregation, in addition to lowering compensability, rewards more those indicators with higher scores, which can be used as a way to direct attention over those indicators with lower ranks, to increment the overall score [21].  
Preferential independence should be assumed only for those indicators, among which no phenomena of conflict or synergy exist and where any possible trade-off ratio between indicators is independent of the values of the remaining indicators [22]. Therefore, an integral risk index must be dependent on the good performance or decline of all of its constituent dimensions, and therefore any risk level should reflect any bad performance of one or more of its components. Conversely, a good or satisfactory integral risk value should be the result of the solid performance of all of its dimensions, both structural and social [23].  
Using non-compensatory logic and multi-criteria analysis is a suitable way to obtain satisfactory results when two or more performances are compared. Nonetheless, these methods largely rely on ranking schemes and do not try to explain the nature of the indicator's interrelationships. One of the consequences is the way in which each indicator's contribution is accounted for: regardless of the value of the difference between the contribution of two indicators, whether larger or smaller, the ranking between them remains the same.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results  
Analyzing the applications of LoRaWAN and the devices used in those applications is important for several reasons:  
* Knowledge Acquisition: First of all, conducting a literature review allows new researchers to gather the existing knowledge and insights about LoRaWAN applications. It can help to understand the current state of research, identify trends, and gain a comprehensive understanding of the subject matter.
* Identifying Gaps and Research Opportunities: A literature review enables the identification of gaps or areas that have not been extensively studied; by analyzing the existing scientific papers, it is possible to identify unanswered research questions or areas where further research is required.
* Validation and Evaluation: Scientific papers provide valuable insights and data on the applications of LoRaWAN and the devices used. By reviewing and evaluating these papers, you can assess the methodologies, experimental setups, and results presented. This evaluation helps you validate the findings and conclusions, ensuring that your work is based on solid scientific evidence and contributes to the overall reliability of the field.
* Keeping Up with Emerging Trends: The field of LoRaWAN applications is continuously evolving. By reviewing scientific papers, you can stay up to date with the latest advancements, emerging trends, and innovative approaches. This knowledge of current research allows you to incorporate the latest findings into your work and ensures that you are aware of the state of the art in the field.  
For the aforementioned reasons, the present work presents a literature review of the scientific papers on the applications of LoRaWAN and the devices used for helping new researchers acquiring knowledge in the area, identifying research gaps, validating findings, and staying informed about emerging trends.