esa-sceva/satcom-chunk-collection huggingface dataset:
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 5 Experimental Design and Results
### Experimental Results
#### 5.2.3 Stability Experiment Analysis  
For localization algorithms, stability is to a certain extent related to the maximum error within the relevant time. To further validate the performance of LIF-M, we carried out static error tests and dynamic error tests, with the results shown in Tables 3 and 4, respectively.  
Figure 8: Partial three-dimensional trajectory comparison diagram.  
Figure 7: Unstructured experimental map from M2 DGR (Multi-modal and Multi-scenario Dataset for Ground Robots).  
Figure 9: Yaw variation diagram of the robot’s forward path.  
In the static error experiments, datasets of approximately 100 s were selected to test algorithm error in a scenario with no movement. Most of the data were clustered to obtain the reference origin, with the most distant point recorded as the static error point. The algorithm publishes the robot's pose at a frequency of 10 Hz. For the circular area set used for static error calculation, enough points were obtained within 100 s to estimate the maximum error produced by the robot. According to the data in Table 3, the static error ranged from 0.03 m to 0.05 m, which is considered favorable for a versatile localization algorithm.  
For the dynamic error experiments, datasets of approximately 50 s were selected, mostly involving straight-line motion tests. Quadratic regression was used to fit the absolute path of the state set. The maximum value of the dynamic error point was recorded as the point farthest from the reference path. For a moving robot, 50 s of attitude data is enough to generate a path to calculate algorithm stability under dynamic conditions. While more time may make the trajectory longer, it does not affect the actual error. According to the data in Table 4, the dynamic error ranged from 0.005 m to 0.06 m. There were variations in the velocities between different groups, leading to a relatively large deviation between the lower and upper bounds; however, the overall dynamic error meets the requirements of the localization system.
# Remote Control of a Mobile Robot for Indoor Patrol
## 6 Conclusions  
This study presents integration of image processing techniques, fuzzy theory, wireless communications, and smartphone to a wheeled mobile robot (WMR) for real-time moving object recognition, tracking and remote surveillance. The WMR uses a webcam to capture its surroundings. The WMR calculates the relative position of the target object through image processing and distance computation algorithms. Fuzzy system is applied to robot control. In this study, three cases of experiments are given. In the first case, a PC and a smartphone are utilized to control directly the WMR's forward, backward, left-turn, and right-turn movements. The second case focuses on target tracking control. The WMR can track a specific target by the HSV algorithm and fuzzy controller. The target can be clearly seen on the smartphone via the webcam on the WMR. In the third case, the WMR is applied to surveillance usage. The WMR can be controlled remotely by a smartphone via wireless communications. The WMR uses infrared sensor and fuzzy controller for obstacle avoidance  
Figure 31: Captured images on smartphone. (**a**) Captured image at Figure 29a position; (**b**) Captured image at Figure 29b position; (**c**) Captured image at Figure 29c position; (**d**) Captured image at Figure 29d position.  
Figure 30: Smartphone remote control site.  
and wall-following control. The WMR can perform indoor patrol and monitor its surroundings. The home site conditions can be clearly seen on the smartphone. Experiments show that the proposed control design and system integration of the wheeled mobile robot works well for indoor patrol.  
This study is partially supported by the National Taiwan Ocean University.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 1 Introduction
### How Diatom Are Species Distinguished?  
Diatoms' identification and classification are not trivial tasks, even for the experienced diatom. The main obstacles derive from the great number of species, the similarities between species--cryptic speciation--and the differences within species--mainly arising from the current stage of life cycle and others that are environmentally driven [15]. The main features used to identify and classify diatoms rely on the morphology (see Figure 2) and ornamentation of their valves [8; 16; 17], such as:  
* Shape and symmetry of the valves;
* Number, density, and orientation of the striae formed by the alignment of several pores or areolas;
* Morphology of the central groove or raphe--involved in mobility--in the most pennate diatoms, especially its central area and terminal endings, and so on.  
Diatomists identify diatoms by following two methods [8]:  
1. Matching the specimen under study with a picture of a known diatom. The correct identification relies on a trained eye to interpret the subtle differences between the specimen and the reference picture.
2. Working through a decision tree about the presence/absence of morphological features. In this case, the identification concludes with the best match of features considered by the classification  
Figure 2: (**a**) Examples of the shape diversity of diatom valves (not at the same scale); (**b**,**c**) features of a pennate valve (source: AQUALITASpublic database [18]).  
key. Overall, this is the most effective approach when multiple access keys are used, where each feature is numbered in a table with a list of the coded status of each feature.
# Improvements in Rock Mass Description for Stope Design by Geophysical and Geochemical Methods
## 4 Laser-Induced Breakdown Spectrometry (LIBS) for Geochemical and Geometallurgical Characterization
### Results  
The preliminary analysis demonstrated the potential value of LIBS remote scanning analysis for delineating ore and geochemical alterations while highlighting the challenges encountered in mining conditions, such as accumulated dirt, dust, and moisture complicating the interpretation of the LIBS signals.  
Comminution tests were conducted on a gold deposit to investigate the relationship between geometallurgical properties, crushability, and grindability. More detailed information on the comminution study is reported in [31]. The point load test was found to be a useful tool for determining ore hardness, in contrast to the more complex bond ball mill test. To further explore this relationship, a machine learning model was trained using data from LIBS and the point load test. The results were compared and demonstrated a strong correlation between the point load test and LIBS data, as depicted in Figure 4, validating that the LIBS signal can be used for geometallurgical characterization of rock mass.  
Figure 4: Point load test correlation with point load value predicted from LIBS data.  
Figure 3: Diagrams shows scatter plots of the LIBS pellet measurements and the mine laboratory data for the linear correlations for S, As, Fe, and Cu. Each X-axis is the LIBS measurement mean value in counts, the Y-axis displays the mg/kg analyzed by ICP-AES (inductively coupled plasma–atomic/optical emission spectroscopy). The figure on the right denotes the concentration of aluminum of drill cores scanned with LIBS overlayed on top of a photo of the same cores (see [29] for additional information).
## 4 Discussion
When the \(\mathrm{a_{w}}\) value was similarly high as in the bars we tested (0.945), it demanded a pH value of up to 4.75, yet when the pH value was as high as in our results (6.0), it demanded an \(\mathrm{a_{w}}\) of about 0.92. Many scientific studies [58; 59; 60; 61] report the inhibitory effect of LAB on the growth of _L. monocytogenes_. However, in our study, such a relationship was not detected in bars when stored at 6 \({}^{\circ}\)C. Despite a noticeable increase in the LAB count on day 14 at a storage temperature of 6 \({}^{\circ}\)C and the corresponding decrease in pH value, there was no decrease in the _L. monocytogenes_ count. An explanation for this result could be the negative impact of lactic acid bacteria metabolites (decrease in pH) on the growth of _L. monocytogenes_[58]. Based on the results analysed above, we conclude that refrigeration storage of up to 4 \({}^{\circ}\)C of uncured, vacuum-packed RTE meat bars with dried plasma does not promote the growth of _L. monocytogenes_. When analysing the obtained data, the 6 \({}^{\circ}\)C limit may be considered as a temperature threshold, the exceeding of which could result in the multiplication of _L. monocytogenes_ in the tested bars. However, taking into account the safety margin, as well as other irrational behaviour of the consumer (including non-compliance with the temperature regime indicated on the label), this limit should be set even as low as 4 \({}^{\circ}\)C. A similar approach, taking into account potential breaks in the cold chain, is also shown by other researchers, who propose a conservative approach to classifying foods with respect to microbiological criteria and increasing requirements for FBOs despite optimal conclusions for the sub-temperatures studied [62]. Following the EURL guidelines on challenge tests to determine the growth of _L monocytogenes_ in food is beneficial; the research results can be compared if standard methods are used and provide the knowledge necessary for hazard analysis and risk assessment. This study shows that there are factors other than pH, \(\mathrm{a}_{\mathrm{w}}\) and APC that affect the ability of _L. monocytogenes_ to grow in a given type of food, highlighting the need to validate each type of product individually [63]. It should be noted that, due to the innovative nature of the food product, which was the object of our research and the specifics of the production technology, it is not possible to directly relate the results obtained to source data that would unequivocally confirm or exclude our findings.
## 4 The Proposed Development Model: Holistic Security and Accessibility Layered Architecture (HSALA)
### The Data Layer and Data Security Layer  
The application architecture's foundation is referred to as the Data Layer and it deals with the storage and management of data. This layer makes use of databases such as MySQL or PostgreSQL, and NoSQL [48] databases like MongoDB. The Data Layer has the objective of storing and retrieving the information in the safest and fastest way possible.  
The layer adjacent to the Data Layer is the Data Security Layer that prevents the unauthorized access of the database by encrypting the data. This layer implements techniques like data masking [49] to protect sensitive information when accessed by non-privileged users by substituting the original data with anonymized values. It also utilizes database encryption and database activity monitoring tools to reinforce data security.  
The implementation of the Data Security Layer ensures that confidential and sensitive information is guarded from unauthorized access and breaches. It provides compliance with regulations like GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) [50], safeguarding data integrity and confidentiality.  
Figure 2 presents an overview of the techniques, mechanisms and tools of the above-mentioned layers, and illustrates their interconnections:  
Figure 1: The HSALA model, consisting of five interconnected layers, each paired with an adjacent security layer.
## 5 Issues Relating to the Fundamental Design Aspects
### Charging Optimization  
Determining the most efficient methods for charging UAVs is important, given the criticality of many data-gathering applications. Energy waste can occur due to the UAV's movement to the charging station [90]; therefore, the UAV's visit to the charging station should be properly planned. This was explored in [90], where the UAV gets charged via a charging station. The authors aimed to reduce the visit times to the charging station to minimize the energy waste incurred.  
The UAV's energy can be constrained, and ground sensors or IoT nodes are usually energy-constrained. To address the energy problem in ground nodes, some researchers consider a different dimension to energy harvesting, particularly where ground nodes can harvest RF energy from UAVs for transmission [85]. For instance, in [85], the UAV performswireless energy transfer for powering sensor nodes while collecting data from the devices. The UAV takes off from the data center and then flies to sensor nodes to transfer energy. The harvested energy is used to upload data to the UAV.
## 3 Results and Discussion
### Health Risk Assessment
Unlike the estrogenic and antistrogenic activities, Flt and Pyr had high contributions to the total, estimated, binding potential (13.8 \(\pm\) 3.6% and 13.8 \(\pm\) 4.5%, respectively), and 6 OH-Chr/3 OH-BaA had a low contribution to the total, estimated, binding potential (5.8 \(\pm\) 4.5%). OH-PAHs contain a phenol group, which is considered an important functional group for binding with hER\(\alpha\), but not necessary for exhibiting antistrogenic activity [30]. Previous research has suggested that the binding affinity to hER is not the only factor affecting the estrogenic and antistrogenic activities. Figure 5: Daily relative estrogenic activities of atmospheric OH-PAHs in April, August, and November 2017, and February and April 2018 in Kanazawa. Figure 6: Daily relative antistrogenic activities of atmospheric OH-PAHs in April, August, and November 2017, and February and April 2018 in Kanazawa. The current results indicate that the high binding potentials of several atmospheric PAHs and OH-PAHs to the hER are not directly connected to their estrogenic and antiestrogenic activities. Instead, the results imply that the endocrine disruption risk with PAHs and OH-PAHs arises because of their relatively high atmospheric concentrations. OH-PAHs are intermediate metabolites of PAHs in organisms [48], and are of concern because they are endocrine disrupting chemicals [30]. Consequently, exposure to PAHs in the environment could result in endocrine disruption caused by OH-PAHs. However, over time, the OH-PAHs are metabolized into conjugated forms, such as the glucuronic acid conjugated form, by detoxification enzymes in the liver [10; 49] and excreted mainly via urine [35; 50]. These conjugated forms can be transported through the vascular system, but they generally have higher water solubility and lower toxicity than the non-conjugated forms. Therefore, metabolized OH-PAHs may have low health risks. However, it is also possible that OH-PAHs in the atmosphere can be taken in via inhalation and directly enter the vascular system through alveoli. This means that OH-PAHs from the atmosphere could enter the body without being metabolized in the liver and then may cause endocrine disruption. These atmospheric PAHs and derivatives have high risks for lung cancer and respiratory disease [51; 52; 53; 54]. Therefore, endocrine disruption caused by OH-PAHs can occur both indirectly through exposure to atmospheric PAHs, and directly through inhalation of atmospheric OH-PAHs. [PERSON] et al. [55] reported that OH-PAHs contributed to the in vitro estrogenicity of ambient particulate matter. Therefore, the monitoring of atmospheric OH-PAHs and the assessment of their estrogenic and antiestrogenic activities should be performed.
###### Abstract  
This paper introduces a detailed procedure to utilize the high temporal and spatial resolution capabilities of an unmanned aerial system (UAS) to document vegetation at regular intervals both before and after a planned disturbance, a key component in _natural disturbance-based management_ (NDBM), which uses treatments such as harvest and prescribed burns toward the removal of vegetation fuel loads. We developed a protocol and applied it to timber harvest and prescribed burn events. Geographic image-based analysis (GEODIA) was used for the classification of UAS orthomosaics. The land cover classes included (1) bare ground, (2) litter, (3) green vegetation, and (4) burned vegetation for the parieir bum site, and (1) mature canopy, (2) understory vegetation, and (3) bare ground for the timber harvest site. Sample datasets for both kinds of disturbances were used to train a support vector machine (SVM) classifier algorithm, which produced four land cover classifications for each site. Statistical analysis (a two-tailed \(t\)-test) indicated there was no significant difference in image classification efficacies between the two disturbance types. This research provides a framework to use UASs to assess land cover, which is valuable for supporting effective land management practices and ensuring the sustainability of land practices along with other planned disturbances, such as construction and mining.  
UAS; GEODIA; NDBM; burn management; disturbance ecology; wildfire; remote sensing; planned disturbances +
Footnote †: journal: Advances in Neural Information Processing Systems
# Recent Progress on Underwater Wireless Communication Methods and Applications
## 2 Main Methods of Underwater Wireless Communication
### Alternative Underwater Communication Technologies
Firstly, signal bandwidth is inherently narrow, as the TENG's output is highly dependent on mechanical actuation frequency and material pairing. This restricts achievable data rates. Secondly, modulation schemes remain relatively rudimentary, often relying on ON-OFF keying or mechanical triggering, which lacks robustness under variable hydrodynamic conditions. Finally, system integration with standard underwater communication infrastructure is still in its infancy, as the generated signals differ from conventional acoustic or electromagnetic waveforms. Despite these challenges, the low energy requirement, stealth properties, and potential compatibility with bio-inspired sensors make TENGs a promising direction for future research, particularly in passive communication networks and environmentally constrained scenarios. In conclusion, research on underwater electric field communication highlights its unique advantages in short-range, high-reliability scenarios. It is expected to become an important supplementary method for underwater communication, with applications in robot collaboration, environmental monitoring, and underwater exploration.
# Optimisation of Cable Dome Structure Design for Progressive Collapse Resistance
## 2 Dynamic Response and Model Analysis of Progressive Collapse  
### Cable Dome Structure Case  
The structure analysed in this study was the roof of Yi Jin Huo Luo Qi sports centre in Inner Mongolia, which is constructed using a Geiger cable dome with a span of 71.2 m and a rise of 5.5 m, with the hoops divided into 20 equal segments. Each cable-truss frame consists of 13 members arranged axially symmetrically to the centre: an outer diagonal cable (DC1), a middle diagonal cable (DC2), an inner diagonal cable (DC3), an outer ridge cable (RC1), a middle ridge cable (RC2), an inner ridge cable (RC3), an outer strut (OS), a middle strut (MS), an inner strut (IS), an outer hoop cable (OHC), an inner hoop cable (IHC), an inner upper string of tension hoop (IUS), and an inner lower string of tension hoop (ILS). The design load is 0.4 kN/m\({}^{2}\), and a hinged support is fixed to the compression hoop beams. Figure 1 depicts the plan and cross section of the roof, and Table 1 illustrates the cross-sectional parameters and initial prestress of each component. The elastic moduli of the tension cables and struts are 160 GPa and 206 GPa, respectively.
## 4 Results and Discussion  
The verification experiment mainly includes three parts. Firstly, the raw signal should be preprocessed by statistical filter and then is reconstructed into the new data to train the SAE model. Secondly, the treated signal is utilized to verify the effectiveness of the proposed method and the signal without statistical filter is also used to make the comparison to explain the necessity of the filter process. Finally, traditional machine learning (BPNN and SVM) and classical deep learning are applied to the fault diagnosis. The results not only proved that SAE is better than traditional neural network in the aspect of the big data for the sliding bearing both effectiveness and accuracy but also it proved that the stepwise intelligent fault diagnosis method can effectively overcome the difficulties that the faults in sliding bearing have no obvious distinguishing features.
# Performance Analysis of a Color-Code-Based Optical Camera Communication System
## 3 Test and Results  
In this study, various tests were conducted to analyze the performance of the color-coded VLC system. The effects of RGB and HSV color spaces on data transmission rate and communication distance are examined. In addition, the effects of the distance between the color channels on the color detection performance were also investigated. Figure 12 shows an overview of the test setup.  
Figure 11: Size and color values of transmitter channels.  
Figure 10: Monitor angle.