esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Command and Control
### Components Communication
#### 4.3.1 Communication Model  
The publish-subscribe model has been adopted for communication of these highly decoupled components and to increase the flexibility of the architecture. The components may register as publisher or subscriber of some message type at run time, hence avoiding a system set up phase. Thus, the broker is a central element for integration.  
The message flow relies on topics, which are strings indicating the scope (e.g., the system name), the type of the message, and the result type. More specifically, for the DEXTER deployment at Anagnina, the topics are structured as:  
* dexter/sensorResult/[resultType], where [resultType] corresponds to: MIC and EXTRAS detection for the sensors; to 2D Video result, which is further specified with the region of detection of the commuter, e.g., dexter/sensorResult/Video2D/Init; and to the C&C component triggers following MIC and EXTRAS data fusion e.g., dexter/sensorResult/MICSuspect and dexter/sensorResult/alarm;
* dexter/[state], where [state] is any state of the C&C built situation that may be of interest to display on user interfaces, for example of the security room or for system demonstration, e.g., dexter/threat and dexter/normal are used to publish the final result for a commuter, positive and negative respectively, aggregating the sensor detections and 2D Video images of each commuter;  
Figure 7: Grafana-based monitoring dashboard displaying aggregated metric results of the BCT days.  
* dexter/alarmResult, published by the Alarm management system client to notify the C&C and other security systems whether the commuter in the image on the smart glasses has been recognized by the guard in the real environment.  
As shown in Figure 2, Sensor 1 and Sensor 2 are the only publishers of detection results, whereas the INSTEAD components are configured as follows.  
The 2D Video publishes messages from each camera containing label and image of any intercepted commuter crossing the pre-defined lines. Moreover, the 2D Video may subscribe to receiving Sensor 1 messages to later use them to support prioritize tracking and reidentification of suspect commuters. However, this feature has not been used in the experimented system.  
The C&C Data fusion component subscribes to messages by all the components. Furthermore, it publishes trigger messages and final state results.  
The 3D Video component subscribes to receiving tracking hand-over trigger messages and directly communicates the commuter location data to Sensor 2.  
The Security client subscribes to alarm and other result information messages and publishes the result on the recognition of the threat commuter.
# A Review on Automated Detection and Identification Algorithms for Highway Pavement Distress
###### Abstract  
The global expansion of road networks and the aging of infrastructure have intensified the need for efficient pavement distress detection technologies to ensure road safety and sustainability. While traditional manual inspections are time consuming and labor-intensive, recent advances in automated systems have improved detection precision. However, challenges persist, including limited accuracy, poor generalization across datasets, and high computational demands for pixel-level segmentation. This review systematically examines the evolution of pavement distress detection, covering three key phases: manual inspection, semi-automated systems, and non-destructive automated methods. We analyze advancements in image acquisition (e.g., 2D to 3D, ground to aerial platforms) and processing techniques (e.g., threshold-based segmentation to deep learning), highlighting critical trade-offs between speed, accuracy, and scalability. Our findings reveal that, while modern systems excel in controlled environments, their real-world performance remains inconsistent due to varying imaging conditions and underrepresented distress types. To address these gaps, we propose four future directions: (1) enhancing environmental adaptability through multi-sensor datasets, (2) optimizing datasets via self-supervised learning, (3) deploying lightweight models on edge devices for real-time analysis, and (4) integrating predictive maintenance frameworks. These strategies aim to shift pavement management from reactive repairs to proactive, data-driven decision making, ultimately supporting smarter infrastructure systems.  
p 2025  
Aerial Vehicle) photogrammetry. These systems can capture road surface images with millimeter-level precision. Despite these advances, automated pavement distress detection and classification still face challenges. Current recognition algorithms perform poorly, often achieving under 70% accuracy in distress identification. This level of performance fails to meet practical engineering needs.  
This review systematically explores the technological evolution of road surface detection. It focuses on the combined progress of image acquisition systems and intelligent processing algorithms during semi-automated and automated non-destructive phases. By analyzing both technological advances and current limitations, this study provides insights for developing fully automated pavement inspection systems. The discussion covers four key aspects: (1) development stages of road surface detection, (2) progress in image acquisition technologies, (3) comparative evaluation of intelligent recognition algorithms, and (4) major technical challenges and future research directions in road surface detection.
## 3 Methodology
### Component Complementarity Analysis
#### 3.7.1 Analysis of RSENet  
The main goal of the RSENet is to generate an approximation of the rain trace map, which is a key feature to distinguish rain traces from background details. The rain trace extraction process can be considered as an extraction task of high-frequency features aimed at capturing the rain trace structure in the image. Wavelet transform is used as a preprocessing step to decompose the image into different frequency components, helping to separate the high-frequency component (rain traces) from the low-frequency component (background information). This is shown in Equation (13).  
\[I_{input}=I_{LF}+I_{HF} \tag{13}\]  
where \(I_{input}\) is the input image, the image is decomposed into a series of high-frequency and low-frequency components by wavelet transform, \(I_{LF}\) represents the low-frequency background, and \(I_{HF}\) represents the high-frequency rain traces. The RSENet utilizes the high-frequency component, \(I_{HF}\), to generate an approximated rain trace map, \(I_{rain}\), which will be used as the basis of the subsequent module for the deraining process.
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 2 Materials and Methods
### Data Collection  
This study was founded on an extensive, two-decade-long collection of water-quality data from an urban lake. The collection period spanned from January 2000 to December 2020. Water samples were systematically collected once a month, resulting in a total of 252 observations over the course of 20 years.  
The monthly sampling was designed to capture the temporal variations in water quality due to seasonal changes and varying anthropogenic activities throughout the year. The time frame chosen for the study also allows us to analyze and understand the long-term trends and impacts of urbanization on the water quality of the lake.  
The water-quality indices that were collected include Dissolved Oxygen (DO), Biological Oxygen Demand (BOD), pH, Turbidity, Nitrate, and Phosphate, among others. These indices were selected for their sensitivity to various pollution sources and their standard use in assessing water quality [24; 25; 26].  
To ensure reliable and consistent data, all water samples were analyzed following the standard methods laid out by the American Public Health Association (APHA). These universally accepted procedures provide a solid framework for the assessment of water quality and are designed to yield reliable and accurate results. Adhering to these methods also ensures that our results can be compared with studies from other regions or time periods that also follow APHA guidelines [27; 28].  
Dissolved Oxygen and pH were determined through electrometric methods, Biological Oxygen Demand was determined by 5-day BOD test, Turbidity was measured using a nephelometric method, while Nitrate and Phosphate concentrations were determined through spectrophotometric analysis [29; 30].  
The compiled dataset, including a range of water-quality parameters measured consistently over 20 years, provides an unprecedented opportunity to apply machine learning techniques to predict water quality in an urban lake. The extensive dataset not only increases the robustness of the predictive models but also allows the extraction of complex relationships and trends that would be undetectable in shorter or less comprehensive datasets [14; 31].
## 5 Conclusions  
We have shown that the algorithm based on the spectral method can obtain the dispersion characteristics of non-axisymmetric modes in the fluid-filled tube accurately and efficiently. Compared to the commercial software COMSOL, it consumes far fewer resources to obtain the same results. The algorithm is suitable for dealing with a multi-layer tube model, and it has no demands on the dimension of the tube and wall thickness. In addition, the algorithm can find all the axial propagation modes, while some of the low-order modes have been found to be missing in the relevant literature.  
The limitations of this paper are that the spectral method algorithm proposed here worked under the assumption that the tube wall material was homogeneous and isotropic. For future work, the algorithm should be adjusted to deal with damped, inhomogeneous and anisotropic problems. In addition, the experiment of wave propagation in a fluid-filled elastic tube should be carried out to validate the numerical algorithm.  
Conceptualization, Z.G. and Q.L.; methodology, Z.G.; software, Z.G.; validation, Z.G.; formal analysis, Z.G.; investigation, Z.G.; resources, Q.L. and D.S.; data curation, Z.G.; writing--original draft preparation, Z.G.; writing--review and editing, D.S. and R.T.; visualization, Z.G.; supervision, Q.L. and D.S.; project administration, Q.L. and D.S.; funding acquisition, D.S. and R.T. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline Dimension & \(b\) (cm) & \(h_{1}\) (cm) & \(h_{2}\) (cm) & \(h_{3}\) (cm) \\  & 50 & 5 & 5 & 5 \\ \hline \multirow{2}{*}{PVC} & \(v_{p}\) (m/s) & \(v_{s}\) (m/s) & \(\rho_{s}\) (kg/m\({}^{3}\)) & \\  & 2388 & 1060 & 1380 & \\ \hline \multirow{2}{*}{Iron} & \(v_{p}\) (m/s) & \(v_{s}\) (m/s) & \(\rho_{s}\) (kg/m\({}^{3}\)) & \\  & 5893 & 3230 & 7800 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: The parameters of the water-filled multi-layer elastic tube.  
Figure 13: Phase velocity curves of modes propagating in the water-filled multi-layer elastic tube.  
**Funding:** This research was funded by the National Natural Science Foundation of China, Grant No.12274100, Project Name: Research on low-frequency sound propagation characteristics of fluid-filled elastic tubes. The funder is Prof. Dr. [PERSON]. It was also funded by Key Laboratory of Underwater Acoustic Countermeasure Technology Open Fund Project, No. JCKY2020207 CH02. The funder is Prof. Dr. [PERSON].  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to laboratory regulations.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Low Power Multi-Hop Networking Analysis in Intelligent Environments
## 2 Intelligent Systems at Home
### Bluetooth Applied to Intelligent Systems
All in all, real world applications designers in intelligent environments need models of the main metrics of wireless networks, so that they can determine the application range of a specific wireless standard. In fact, low power modes with a defined period of inactivity are almost mandatory in intelligent environments. Since theoretical calculations or simulation engines cannot foresee all the real-world issues, empirical models are required to analyze the viability of specific wireless applications [41]. Just a few empirical models on latency have been already obtained in Bluetooth multi-hop networks [20; 42]. As Bluetooth does not support synchronous links in scatternets, what is needed are empirical models for the latency in scatternets with ACL links in low power modes.
# On-the-Fly Olive Tree Counting Using a UAS and Cloud Services
## 4 Results and Discussion
### Performance of Color-Based Segmentation  
Classification performance metrics (TP, FP, FN, precision, recall, and \(F_{1}\) score) of color-based segmentation are shown in Table 2. The numbers in parentheses give the same metrics, but excluding those errors in the detection of trees that are on the edge of the image. The number of discrepancies ranges from zero to a maximum of nine (out of 139) trees, mainly consisting of FN, i.e., undetected trees. Further analysis of the FN shows that most of them (about 68%) come from trees located on the edges of the image. As the next subsection will show, these failures on the edges of the image are solved in the global counter, since they will be detected in the overlapping images. Only two images have FP equal to one, which translates into a precision of near 100%. On average, the algorithm achieves a \(F_{1}\) score of 98.7% (99.5% if borderline errors are not computed). The worst case is given for image DJI_0232 (shown in Figure 10), where six of the nine failures are due to trees that are on the edge of the image. The three remaining discrepancies are many-to-one associations. These errors occur mainly when the area pattern does not fit the disk template used in the template-matching step, which assumes a circular shape of a given radius and surrounded by a specific space. Thus, the algorithm fails to detect a tree when it is too close to other trees. The work in [22] focused on a similar problem, which is taking stock of the number of palm trees within a plantation. They also highlighted that the template matching algorithm works well in areas where \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Color-Based**} & \multicolumn{5}{c}{**Stereo Vision-Based**} \\ \hline
**Image** & **GT** & **Count** & **AE** & **Image** & **GT** & **Count** & **AE** \\ \hline DJI\_0225 & 107 & 106 & 1 & DJI\_0225-DJI\_0226 & 75 & 52 & 23 \\ DJI\_0226 & 115 & 114 & 1 & DJI\_0226-DJI\_0227 & 79 & 56 & 23 \\ DJI\_0227 & 117 & 115 & 2 & DJI\_0227-DJI\_0228 & 82 & 50 & 32 \\ DJI\_0228 & 115 & 115 & 0 & DJI\_0228-DJI\_0229 & 82 & 58 & 24 \\ DJI\_0229 & 119 & 117 & 2 & DJI\_0229-DJI\_0230 & 84 & 56 & 28 \\ DJI\_0230 & 125 & 120 & 5 & DJI\_0230-DJI\_0231 & 86 & 57 & 29 \\ DJI\_0231 & 130 & 125 & 5 & DJI\_0231-DJI\_0232 & 89 & 56 & 33 \\ DJI\_0232 & 139 & 130 & 9 & DJI\_0232-DJI\_0233 & 95 & 60 & 35 \\ DJI\_0233 & 141 & 138 & 3 & DJI\_0233-DJI\_0234 & 101 & 53 & 48 \\ DJI\_0234 & 143 & 142 & 1 & DJI\_0234-DJI\_0235 & 97 & 57 & 40 \\ \hline
**MAE:** & & & **2.90** & **MAE:** & & & **31.50** \\ \hline
**RMSE:** & & & **3.89** & **RMSE:** & & & **32.41** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the number of olive trees counted by the color-based and the stereo vision-based segmentation algorithms. Results are compared with respect to the number of hand-annotated olive trees (GT: ground-truth, AE: absolute error, MAE: mean absolute error, RMSE: root mean squared error).
# Subtitling 3D VR Content with Limited 6 DoF: Presentation Modes and Guiding Methods
## 4 Results: User Tests
### Results
#### 4.3.3 Final Questions  
Finally, participants were asked general questions about the relevance of subtitling for hearing users, and of VR subtitling in particular (Table 8).  
Over 90% of participants believed both that subtitles are beneficial for hearing users and that an appropriate subtitling of VR content is a relevant feature to be explored and provided. Participants were also asked about the situations that could benefit from the use of subtitles, in their opinion, and the following answers were obtained: when consumers do not speak the content language (33.3%); for language learning and improvement (25%); in noisy environments (20.8%) or if/when the audio volume is low (16.7%); to train reading skills (16.7%); to understand/obtain the spelling of specific uncommon words and names (12.5%); and when the audio quality is not good (8.3%).  
Finally, participants were encouraged to make final free comments, of which, key comments were: subtitles helped them to understand the story (25%), in fast action scenes it was difficult to read subtitles (16.7%), and that hybrid modes (e.g., combining Modes B and C) may help to overcome the impact of distance and viewing perspectives, but also to increase the immersion when the speakers are within the current FoV (20.8%).
# Process-Oriented Stream Classification Pipeline: A Literature Review
## 8 Discussion  
Since data stream classification can be applied to a multitude of different application areas, there are many diverse research directions. Although this yields the benefit of using potential synergies with other domains, it also complicates finding standard concepts and terms. Especially in the area of drift detection and the classification algorithms themselves, it is noticeable that different taxonomies exist. This diversity makes it difficult for newcomers to the field to get started. In this work, we give a broad overview of work in the field of data stream classification. Thus, the main contribution of this work is to assist users in this research field by structuring the literature according to the classical stream classification process--the so-called stream classification pipeline. We provide an overview of the most influential works for each process step and link appropriate summaries and benchmarking papers. Finally, we also highlight future research directions, as well as current research in the field. Our literature search encompassed 320 papers in the end.  
While it becomes clear from earlier works that models have been adopted from the offline setting, current research directions in the field are increasingly focusing on specific solutions to data stream classification. Here, predominant are the topics of concept drift detection and adaptation, evaluation, and issues around imperfect data, such as class imbalance or missing values. In addition, high-dimensional data and complex classification models such as deep neural networks offer room for improvement. Another research strand that gets almost no attention is the automated (hyper-)parameter configuration of stream classification algorithms. Since the underlying data stream is potentially volatile in real-life, automatically adjusting the configuration would considerably increase the classifiers' quality.  
All in all, it can be said that the scenario of stream classification opens a diverse field of research. Today, there is still no common sense in some steps of the pipeline. Therefore, scientists in the field should always keep in mind the different research focuses and statuses of the disciplines.  
Conceptualization, L.C. and J.S.P.; Methodology, L.C. and J.S.P.; Formal Analysis, L.C. and J.S.P.; Data Curation L.C.; Investigation, L.C. and J.S.P.; Writing--Original Draft Preparation, L.C. and J.S.P. and J.B.; Writing--Review and Editing, H.T. and P.K.; Visualization, J.S.P. and L.C.; Supervision, H.T. and P.K.; Project Administration, L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Project DemoResil (FKZ 005-1709-0001, EFIRE-0801431) funded by the German ministry of culture and science and the BMBF-funded project Hybrid (funding ref. 16 KIS1531K). The authors acknowledge support from the European Research Center for Information Systems (ERCIS), and the Topical Program \"Algorithmization and Social Interaction\" of the University of Munster.  
Not applicable.  
The authors declare no conflict of interest.
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results and Discussions
### Scanning Electron Microscopy  
Microstructure of the mortar specimen cured for 28 days were analyzed via scanning electron microscopy. Figure 9 displays the SEM images of the control specimen and the specimens containing various types of ashes. Figure 9a depicts the SEM image of REF mortar specimen. Mortars without WSA presented a porous microstructure with an increased amount of plate-like portlandite (CH). Contrary to this, the inclusion of WSA densified the microstructure of the mortars. This improvement was caused due to the pozzolanic and filler effects of WSA particles. In particular, the inclusion of fine-sized ash particles demonstrated enhanced densification as compared to the coarse-sized ash particles. For instance, from the SEM images (Figure 9b,d), the prevalence of dense structured C-S-H gel is visible. Accordingly, these specimens displayed a refined and dense microstructure due to pozzolanic and filler effect of finer ash particles. On the other hand, the microstructure of the mortar specimens made with W2 type ash (Figure 9c) depicted more porosity and reduced amount of C-S-H gel in comparison with specimens made with W1 and W2 type ashes. These results confirm that the inclusion of finer WSA particles improved the microstructure of the mortars. It is because of this reason, compressive and flexure strength increased and pore structure of the mortar specimens improved which resulted in higher UPV values and reduced total porosity. In summary, pozzolanic and filler effects and heterogeneous nucleation were responsible for the improved performance of various ash particles [24; 37; 42].  
Figure 9: SEM images of (**a**) control, (**b**) W1/20, (**c**) W2/20, and (**d**) W3/20 mortar specimens at 28 days.