esa-sceva/satcom-chunk-collection huggingface dataset:
In the framework of the Helmholtz Future Project ARCHES (\"Autonomous Robotic Networks to Help Modern Societies\"), a heterogeneous team of robots was prepared to cooperate and autonomously deploy tools and sensors for the exploration of an unknown terrain [19; 27; 28; 29; 30]. In addition to performing scientific measurements, the mission scenario also includes the collection of samples. The space analogue demonstration mission at the foothills of Mount Etna in Sicily, Italy, chosen as a site similar to barren extraterrestrial surfaces such as the Moon, was originally scheduled for summer 2020 but was postponed twice due to the COVID-19 pandemic and finally took place in summer 2022. The LRU2 is one of two similar rovers developed at DLR, participating in the space analogue demonstration mission of the ARCHES project [18; 19; 27; 28]. Weighing less than 30 kg, the rovers are powered by rechargeable batteries providing 200 Wh and can achieve speeds of over 7 km/h. Both LRU1 and LRU2 feature a pan-tilt camera head for navigation. In addition, LRU1 is equipped with multi-spectral and high-resolution cameras for scientific imaging, and LRU2 features a robotic arm with a docking interface to which standardized payload modules can be coupled. With its robotic arm, the LRU2 can deploy small tools, such as a hand for grasping [31], a WLAN repeater, or a box into which collected samples can be placed. The arm also enables the LRU2 to position engineering or scientific payloads in the field. In the mission scenario, various modules are fetched by the rover from the main spacecraft, the landing system. After picking up the desired payload box using its robotic arm, the LRU2 places the box in a transportation frame on its back to secure the payload for driving to the location where the module is needed. Upon arrival, the LRU2 can pick up the box from its transportation frame and utilize the payload's features at the location of interest. In this work, we present the design and development of the LIBS modular payload box for the LRU2. In contrast to ongoing and previously flown or proposed LIBS instruments for in-situ Solar System exploration [2; 6; 7; 12; 15], we investigate a system that can be picked up and used by a rover equipped with a robotic arm. While this approach limits the mass and volume of the instrument, it enables a flexible and modular approach to planetary surface science, in accordance with the science goal of the ARCHES project. In addition to detailing the hardware and software, we discuss preceding tests conducted at the Planetary Exploration Laboratory (PEL) at DLR Oberpfaffenhofen and the field test demonstration at Mount Etna, Sicily.
## 4 Results and Discussion
### Evaluation of UCS Tests
#### 4.2.2 Effect of Sawdust on Sulphate Resistance for 7 Days Curing  
Sulphate resistance of specimens prepared by adding SD to the specimens treated with 3%, 5% and 7% PC after 7 days curing time is shown in Figure 7. It is apparent from this figure that all samples showed an increasing trend in UCS with increasing SD content. For example, the values of UCS of 3% PC treated specimens increased from 710 kPa to 751 kPa with increasing SD content from 3% to 15%, respectively. Specimens treated with 5% and 7% PC showed the same increasing trend. UCS values increased from 715 kPa to 947.16 kPa and from 1050 kPa to 1391 kPa when PC content increased to 5% and 7%, respectively. Specimens exposed to the \(MgSO_{4}\) showed the same increasing trend in UCS values with increasing SD content, but with lower range compared to benchmark samples. From the data in Figure 7a, the UCS values of 3% PC treated specimens increased from 150 to 401 kPa with increasing SD content from 3% to 15%. The increasing trend was lower, at a range of 223.37 \(\leq\) UCS  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{2}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-4}  & **3\% PC = 180** & **5\% PC = 296** & **7\% PC = 352** \\ \cline{2-4}  & **UCS (kPa)** & **UCS (kPa)** \\ \hline
3\% & 241 \(\pm\) 2 & 322.3 \(\pm\) 2 & 391 \(\pm\) 4 \\
5\% & 302 \(\pm\) 2 & 360.8 \(\pm\) 4 & 458 \(\pm\) 2 \\
10\% & 349 \(\pm\) 4 & 399.9 \(\pm\) 2 & 502 \(\pm\) 3 \\
15\% & 401 \(\pm\) 2 & 472.6 \(\pm\) 3 & 574 \(\pm\) 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: UCS results before sulphate exposure in presence of SD (3 days curing).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{4}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-5}  & **3\% PC = Vanished** & **5\% PC = Vanished** & **7\% PC = Vanished** \\ \cline{2-5}  & **UCS (kPa)** & **Improvement (\%)** & **UCS (kPa)** & **Improvement (\%)** \\ \hline
3\% & 20 \(\pm\) 1 & - & 72.8 \(\pm\) 1 & - & 102 \(\pm\) 2 & - \\
5\% & 61 \(\pm\) 2 & - & 96.7 \(\pm\) 2 & - & 164 \(\pm\) 1 & - \\
10\% & 106 \(\pm\) 2 & - & 165.4 \(\pm\) 3 & - & 250 \(\pm\) 2 & - \\
15\% & 157 \(\pm\) 3 & - & 223.5 \(\pm\) 2 & - & 340 \(\pm\) 3 & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: UCS results after sulphate exposure in presence of SD (3 days curing).  
To summarise, the 7 days curing behaviour, for 3% of cement, the samples showed firstly increases up to 3% of sawdust and right after the behaviour was slightly increased with increase in cement percentage from 3% to 7%; then again, the increase in UCS values could be attributed to hydration reaction of the mixture. Once again, the sample without sawdust in 7 days of curing dissolved fully or had very low UCS value in sulphate attack.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Enzymatic Degradation of Polymers  
Extracellular and intracellular enzymes are produced by microorganisms which are involved in the biodegradation of plastic [46; 47; 86]. To degrade polymers, enzymes capable of breaking ester, ether, carbon-carbon, and amide bonds will be required [34]. Among these, oxidoreductase, peroxididases, transferases, and hydrolases have been reported to facilitate depolymerisation and hydrolysis reactions [87; 88]. The application of a variety of purified enzymes in plastic degradation has been studied [46; 89]. The advantage of using purified enzymes lies in the easier downstream recovery of degradation products and avoiding catabolism of products (typically the monomers) [90]. Acyl hydrolases are the most extensively studied enzymes for the degradation of polymers [91; 92; 93]. Cutinase, lipase, esterase, and PETase are widely reported to efficiently degrade polyesters like PET, PHA, and PLA [46; 94; 95]. They are produced by both bacteria and fungi involved in plastic degradation [46; 47]. The difference between lipases, esterases, and cutinases is a function of their substrate chain lengths [96]. Esterases catalyse the breakdown of ester bonds with chain lengths of less than 10 carbon atoms, and lipases hydrolyse esters with more than 10 carbon atoms in their chains [97; 98]. Cutinases can break ester bonds, and their active sites are more exposed to substrates due to the lack of a \"lid\" structure covering the active site [99]. Lipases also break ester bonds, but their active sites are covered by a lid which somewhat limits substrate access. The lid opens in the hydrophobic environment of the water/lipid interface [100; 101]. These enzymes belong to the \(\alpha/\beta\)-hydrolase superfamily [102] and contain a catalytic triad of Serine, Histidine and Aspartate. This catalytic triad facilitates nucleophilic attack on the carbonyl carbon of the ester bond, causing the hydrolysis of the polyester chain [103; 104; 105].
## 5 Conclusions  
1. This case study aimed to propose a novel approach to chemical reconnaissance system development. It proposed using machine learning, modelling algorithms and a contaminant dispersion model to combine signals from different sensors, and to reduce the false alarm rate. From proof of concept to a prototype, this study analysed the development of the EU-SENSE, specifically heterogeneous sensor nodes components, chemical agents to be detected, and EU-SENSE system architecture with situational awareness, computational, and network layers. The authors believe that the development of such devices will trigger new approaches to chemical reconnaissance. Effectiveness in a less predictable environment must be more rapid, reliable, and flexible. Therefore, more advanced solutions based on artificial intelligence should be developed to help CBRN first responders act more effectively;
2. The confidence of future system users will be built through special training on the system functionalities and use in the field. The training modes will rely on the active didactic methods: case study analysis and a simulation;
3. The proposed design of the system developed with the research and development activities can be used in other areas, namely in biological or radiological threat detection. Future directions of research and development works can be proposed in this area.  
Conceptualization, M.G.-K., N.K. and H.K.-E.; methodology, M.G.-K., N.K. and H.K.-E.; resources, G.G., L.S., N.K., H.K.-E. and M.U.; writing--original draft preparation, M.G.-K., G.G., L.S. and M.U.; writing--review and editing, M.G.-K., G.G., L.S., N.K., H.K.-E. and M.U.; visualization, M.G.-K. and L.S.; supervision, G.G., N.K. and H.K.-E.; project administration, G.G. and L.S.; funding acquisition, G.G. and L.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Horizon 2020: European Union Programme for Research and Innovation, grant agreement ID 787031.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Path Planning of Mobile Robots with an Improved Grey Wolf Optimizer and Dynamic Window Approach
## 6 Experiments Simulation and Analysis
### Dynamic Obstacle Avoidance Experiment
Figure 12: Dynamic Obstacle Avoidance Experiment 2. (**a**) Global optimal path; (**b**) the robot stops to avoid the obstacle; (**c**) the robot proceeds toward the goal; (**d**) the robot reaches the goal. To further investigate the impact of additional obstacles on dynamic obstacle avoidance performance, we introduced five dynamic obstacles in map 2. Specifically, the first obstacle has start and end points at (6, 6) and (6, 1), the second at (7, 1) and (5, 7), the third at (14, 7) and (7, 5), the fourth at (15, 7) and (8, 5), and the fifth at (26, 23) and (17, 13). The mobile robot's start and end points are (1, 1) and (30, 30), respectively. The experimental results are presented in Figure 14, where Figure 14a shows the globally optimal path identified by the PAGWO algorithm. In Figure 14b, the mobile robot begins local path planning and detects two dynamic obstacles at (3, 2), approaching from the left and right, respectively. As a result, the robot halts and performs obstacle avoidance. In Figure 14c, after ensuring safety, the robot resumes movement. After a short distance, it detects two oncoming dynamic obstacles. In Figure 14d, the robot is seen avoiding the dynamic obstacles by skirting to the right and quickly returning to its original global path. Finally, in Figure 14e, the mobile robot encounters the last dynamic obstacle, evading it to the left. As shown in Figure 14f, the robot successfully avoids all dynamic obstacles and reaches the endpoint along the preferred path. Figure 13: Dynamic Obstacle Avoidance Experiment 3. (**a**) Global optimal path; (**b**) the robot stops to avoid the obstacle; (**c**) the robot proceeds toward the goal; (**d**) the robot approaches the second obstacle; (**e**) the robot is bypassing to the right; (**f**) the robot reaches the goal. The current experimental results clearly demonstrate that our improved PAGWO-IDWA algorithm significantly enhances obstacle avoidance compared to traditional methods, effectively handling scenarios where dynamic obstacles appear consecutively. This reactive strategy ensures safe navigation and validates the algorithm's practical utility in complex dynamic environments. However, we also recognize that when multiple dynamic obstacles simultaneously enter the detection range, the current approach may encounter processing limitations. Future work will address these challenges by incorporating predictive models--such as Reinforcement Learning--to anticipate obstacle trajectories and further improve avoidance performance in high-density dynamic settings.
## 1 Introduction
The capabilities of this technology will also be explored to try to improve the efficiency and accuracy in the identification of different classes according to the age of the same species of mollusk. This research arises under the premise of implementing an artificial vision system for the detection, counting, and classification of bivalve larvae. The focus is on developing a computer vision model for the classification of cheek larvae according to their size; specifically, we classified them into 40, 100, and 180 \(\upmu\)m (which are the average sizes of the individuals in their 3 key steps of mussel growth, and the data manufacturers need to understand when it is the right time to perform the insemination). The goal is, therefore, to develop, train, and test a specific computer vision model to differentiate the key characteristics of the mussel larvae individuals, so that automatic detection, accounting, and classification can be performed with no manual intervention in a small fraction of the time a human could perform this task. There are several premises that must be taken into account:  
* The individuals to be detected can be in multiple positions and orientations, so a dataset will be needed, i.e., a set of images, in this case, large and varied enough to avoid overfitting the model. * This technology has undergone multiple advances in recent years, but there is great difficulty when it comes to detecting small objects. To the untrained eye, the differences between the classes to be detected are not obvious. Being aware of this, we must establish some \"limits\" of our work and that is that we must always work with the same zoom and always under the most similar conditions possible to those that have been used to train the model. As a result, the goals for carrying out the work were as follows:  
* Implementation of an artificial vision system capable of analyzing images containing the different cheek larvae and distinguishing the characteristics for their classification. * Creation of image data augmentation techniques to increase the number of samples to train the model. * Evaluation of the model's ability to perform, count, and classify the model, both by software and by traditional manual analysis techniques. * Optimization of the model to maximize accuracy and efficiency in the identification of individuals, exploring possible adjustments and improvements in the algorithm. * Develop a software tool for counting the predicted classes in order to be able to handle and work with the collected data. These goals for the identification, accounting, and classification of mussel larvae are integrated within the purpose of achieving an artificial vision model capable of differentiating between an indeterminate variety of species and the species under study. The manuscript is organized as follows: Section 2 describes the materials and methods that have been used both in the Marine Research Center belonging to Xunta de Galicia and the University of Vigo; Section 3 depicts the results obtained and provides a discussion; and finally, Section 4 introduces the conclusions.
# Design, Challenges and Developments for 5G Massive MIMO Antenna Systems at Sub 6-GHz Band: A Review
## 3 5G Antennas as a Candidate for Massive MIMO Technique at Sub-6 GHz
### Sub-Arrays  
High gain, a stable radiation pattern, and a wider frequency band are three of the most critical requirements for 5G antennas. Due to the fact that a single-element antenna cannot fulfill these requirements, a multi-element array antenna has been designed to fulfill them. The following is a discussion of the two types of sub-array configurations, both of which were considered by the MIMO technique (multi-ports).
# An Efficient Anomaly Detection System for Crowded Scenes Using Variational Autoencoders
## 3 Method  
### Overall Scheme  
Anomaly detection is the identification of events with low probabilities, which represent the irregular shape or motion patterns in the video frames. Thus, identifying the irregular appearance and motion patterns is the essential issues in anomaly detection. Because of the insufficiency of the labels for the anomaly frames and pixels, the supervised learning based feature extraction methods barely work, in despite of the succes in other tasks with specific categories. Therefore a semi-supervised method is required to model the normal patterns including background of scene and the regular shape and motion. The work-flow of the proposed detection method is outlined in Figure 1.  
Figure 1: Appearance and motion representations of each receptive field are learned by our convolutional variational auto-encoder (VAE) first. Then for each receptive field of the scene, a multivariate Gaussian is modelled to fit the feature vector during training. At test time, the negative log-likelihood of each receptive field is computed using the corresponding multivariate Gaussian.  
In our work, a series of frames instead of a single frame are used as the input and each training frame series consists of several normal frames. Then, a convolution based variational auto-encoder is constructed to learn appearance and motion representations of the normal frame series, as a method of semi-supervised learning. According to the convolutional neural network, the feature vector at each location of the feature map, which is the output of the encoder, is considered as the appearance and motion representations of its corresponding receptive field. Then, for the feature vectors of the receptive fields at the same location in all inputs, a multivariate Gaussian is modelled to fit them so that each receptive field at different locations has the own corresponding Gaussian model. Once the encoder network and the Gaussian models are trained, receptive fields of low probability under the corresponding Gaussian are considered abnormalities. Given a test frame series, the feature vector of each receptive field is extraced by the encoder network, and its negative log-likelihood under its own Gaussian is computed. In the following we describe the proposed system in detail.
## 4 Experimental Results
### Experiment II: Individual Locomotion Activity Classification Accuracies  
One of the best options to find out how accurately the proposed system classifies an individual locomotion activity is the confusion matrix. We plotted the confusion matrices for all the concerned datasets. The confusion matrix for MobiAct_v2.0 is given in Figure 12.  
Figure 11: Real-Life HAR: individual ROC curves for all the classes, class 1 = walking, class 2 = active, class 3 = inactive, class 4 = driving.  
Figure 12 shows that the lowest recognition accuracy was 70%, and the corresponding locomotion activity was forward lying, while the highest accuracy, that is, 99%, was achieved by the car step-in activity. All other locomotion activities were also predicted with pretty decent accuracies. Following a similar pattern, we analyzed individual accuracies for the Real-World HAR with the help of the confusion matrix given in Figure 13.  
Considering the Real-World HAR, the lowest classification accuracy was for the sitting activity, that is, 73%, while jumping and running were classes that were always predicted correctly as their recognition accuracy was 100%. All other activities were also predictable with appreciable accuracy. Last, but not the least, the individual locomotion activity classification accuracies for the Real-Life HAR dataset are provided in the confusion matrix shown in Figure 14.  
The Real-Life HAR confusion matrix shows that all four activities were classified with exceptional accuracy. The walking and active classes were predicted with 95% confidence, while the driving and inactive classes were predicted 100% accurately.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reinforcement Learning Energy-Efficient Routing Protocols (RL-Based E2 RPs)
#### 5.4.1. Energy-Efficient Delay-Tolerant Q-Learning-Based Routing Protocol (E2 DTQRP)  
The first E2 RP, which is based on the Q-learning technique for delay-tolerant underwater networks (E2 DTRPQ), was presented by the author in [155]. A communicating data packet that is ready to be transmitted by a sensor Rx node characterizes the state of the network. Three factors are used to create the reward function: (i) Distance from relay node to sink node, (ii) Neighbor node density and remaining energy. According to the first criteria, a communicating node will be rewarded a high priority if it chooses a repeater node which is closest to the sink node. According to the second criteria, a communicating node will be rewarded on the basis of communicating node next layer and present layer density. A communicating node in the network, which selects a forwarder node will be awarded with a high priority, which resides in a high-density region that made easier forwarding process in the network. According the third criteria: A communicating node in the network that selects one specific communicating node, which have high remaining energy will be awarded a high communication priority. Furthermore, all communicating data packets are provided a deadline value, which is based on data packet priority. The communicating data packets are forwarded within the data packets deadline. Those data packets that are close to nearest deadline will be transmitted with most priority. The simulation results depict that, the suggested protocol achieves great performance in terms of energy efficiency and high data packet delivery.