esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Medical Triage: A Predictive Model for Emergency Department Disposition
## 2 Materials and Methods
### The Modeling  
AutoML, or automated machine learning, applies machine-learning models to real-world problems through automated selection, composition, and parameterization. This automation can make ML processes more accessible and often faster and more accurate than manual methods. Institutions without specialized data scientists or machine-learning experts can use these platforms, available from third-party vendors or open-source repositories.  
In the medical field, AutoML is used for tasks such as disease prediction, medical imaging, genomics, and EHR analysis. These tools simplify the application of ML models, reducing the need for extensive ML expertise and allowing clinicians to focus on their primary tasks. Automation can increase efficiency, reproducibility, and potentially improve diagnosis and prognosis accuracy [25]. AutoML also supports personalized medicine by using patient-specific data for individualized treatment plans [26; 27].  
To ensure transparency in healthcare applications, our study incorporates Explainable AI (XAI) principles, allowing clinicians to understand and trust the model's predictions. XAI techniques, such as feature importance scores and model-agnostic explanations, clarify how and why predictions are made. Visualizations of feature impacts and decision logic help healthcare providers assess the model's recommendations.  
We also address the ethical implications of using automated systems in medical triage (Section 4), emphasizing our commitment to responsible AI use. Safeguards are in place to prevent misuse, and we continuously monitor the model's performance to align with ethical standards and clinical expectations.  
AutoML automatically identifies and applies the best machine-learning algorithm for a specific task, with \"optimal\" defined as achieving the highest performance based on a set of predefined criteria within a specified time frame [28]. Below are the steps in the machine-learning process that AutoML can automate, listed in the order they occur:  
1. Raw data processing;
2. Feature engineering and feature selection;
3. Model selection;
4. Hyperparameter and parameter optimization;
5. Deployment, considering business and technology constraints;
6. Evaluation metric selection;
7. Monitoring and issue detection;
8. Analysis of results.
# Enhancement of Heat Dissipation from the Hydraulic System Using a Finned Adaptive Heat Exchanger
## 1 Introduction
The geometrical parameters and material of the ribbed surface also play an important role in heat dissipation [16; 17]. The effects of the fin thickness, fin pitch and fin height on the thermo-hydraulic behaviour of the extended surfaces were studied by [18; 19]. Helical serrated finned tubes are effectively used in various thermal systems [20]. The previous literature has minimally investigated the heat dissipation from hydraulic hoses through the aluminium ribbed surfaces into the ambient air. Hydraulic circuits normally consist of an oil cooler but overheating of the operating fluid (oil) still occurs, especially in the summer months and in enclosed systems. The investigated adaptive heat exchanger has a new design and is practically applicable for heat dissipation from hydraulic systems of various industrial machines. The practical benefits of the AHE design include the possibility of mounting on the outer walls of the hydraulic pipes/hoses, thus ensuring an increased heat exchange surface and continuous cooling of the oil during machine operation. The installation of the AHE does not require the interruption of pipelines, during which unwanted aeration of the circuit and leakage of the working fluid would occur. The possibility to lengthen and shorten the length of the AHE using the individual heat exchanger segments brings high variability to different hydraulic systems as required. Adaptability in terms of adjusting the inner diameter of the clamp brings the possibility of application for different diameters of hydraulic pipes. The adaptive heat exchanger is evaluated based on the oil temperatures and surface temperatures on the pipes at the inlet and outlet of the adaptive heat exchanger and heat flow on the oil side. The adaptive heat exchanger mounted in the hydraulic circuit is compared to the system without it. In addition, the adaptive heat exchanger whose contact surfaces are covered with a copper-based thermal conductive paste is compared with the system with and without the adaptive heat exchanger.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 5 Clinical Applications of Image Fusion in Liver Cancer Diagnosis and Treatment
### Ultrasonography/MRI, Ultrasonography/CT and Ultrasonography/PET-CT
#### Utility of CEUS/PET-CT in Transarterial Chemoembolization Treatment of Liver Cancer
Here, we list one example to show the influence of image fusion of CUES and CT/MRI images in the real clinical application. The first example is from [PERSON] et al. [164]. They performed CT/MRI and CEUS fusion imaging to evaluate treatment responses of 157 patients. From the results, if only the US modal is used, there were 26.7% (41/157) of inconspicuous lesions observed in the enrolled patients. While if US modal is fused with CT/MRI, there were only 1.3% (2/157) of inconspicuous lesions observed. One image fusion example of CEUS and MRI is shown in Figure 3 from [PERSON] et al. [164].
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 3 Experimental Setup
### Evaluation Framework
#### 3.3.2 Overlapped Speech Detection Performance  
Given the significant challenge that overlapped speech poses in real-world conversations, we specifically evaluate the system's capability to detect and handle such segments. This evaluation employs standard classification metrics: precision (P), recall (R), F1-score, and accuracy. These metrics provide insights into both the system's ability to identify overlapped speech segments and its potential bias toward single-speaker detection.
# Accurate Needle Localization in the Image Frames of Ultrasound Videos
## 3 Experimental Evaluation and Results
### Experimental Results  
Figure 6a-c presents image frames extracted from three ultrasound videos acquired during needle insertion in _ex vivo_ bovine muscle specimens. The gold standard needle trajectory and tip are indicated using yellow arrows. The needle trajectory and tip that are predicted by the proposed method are shown in Figure 6d-f. In addition, Figure 7a-c shows image frames extracted from three _in vivo_ ultrasound videos taken during ultrasound-guided fine needle aspiration biopsy of thyroid nodules. The needle trajectory and tip that are predicted by the proposed method are presented in Figure 7d-f. These figures demonstrate the capability of the proposed method to accuratly detect the needle under various ultrasound echogenicities and needle visibility conditions.  
Figure 5: The needle axis error.  
Figure 6: (**a–c**) _Ex vivo_ ultrasound images acquired for needles inserted in bovine muscle specimens. The gold standard needle trajectory and tip are indicated using yellow arrows. (**d–f**) The needle trajectory (shown as a red line) and tip (shown as an orange circle) predicted by the proposed method.  
Figure 7: (**a–c**) _In vivo_ ultrasound images acquired during ultrasound-guided fine needle aspiration biopsy of thyroid nodules. The gold standard needle trajectory and tip are indicated using yellow arrows. (**d–f**) The needle trajectory (shown as a red line) and tip (shown as an orange circle) predicted by the proposed method.  
The performance results obtained by the proposed method for the _ex vivo_ ultrasound videos are provided in Table 1. In addition, Table 1 shows the number of image frames for each ultrasound video. The proposed method was able to localize the needles with failure rates of 0% for the seven _ex vivo_ ultrasound videos. Furthermore, the mean angular errors are between 0.3\({}^{\circ}\) and 0.7\({}^{\circ}\), the mean axis errors are between 0.2 mm and 0.7 mm, and the mean tip errors are between 0.4 mm and 0.8 mm. The mean run times of the proposed method are between 0.51 s and 0.53 s.  
Table 2 presents the performance results achieved by the proposed method for the seven _in vivo_ ultrasound videos. Table 2 also shows the number of image frames for each ultrasound video. The failure rates obtained by the proposed method for all _in vivo_ ultrasound videos are equal to 0%. Additionally, the mean angular errors are between 0.2\({}^{\circ}\) and 1.0\({}^{\circ}\), the mean axis errors are between 0.3 mm and 1.0 mm, and the mean tip errors are between 0.3 mm and 1.1 mm. The mean run times of the proposed method are between 0.42 s and 0.44 s.
# Overlapping Community Detection of Bipartite Networks Based on a Novel Community Density
## 5 Experimental Evaluation
### Experiments on Artificially Generated Networks
Table 3 shows the decrease rate of NMI under different \(N\). \begin{table}
\begin{tabular}{c c c c} \hline \(\lambda\) & **DSNE** & **CBG\&BEN** & **LOCD** \\ \hline
0.5 & 54.9\% & 64.1\% & 18.1\% \\
0.6 & 24.4\% & 27.9\% & 48.8\% \\
0.7 & 2.1\% & \(-3.3\%\) & 15.6\% \\
0.8 & 2.0\% & 6.25\% & \(-15.9\%\) \\
0.9 & 2.0\% & 4.0\% & 56.0\% \\ \hline \end{tabular}
\end{table} 
Table 2: When \(N=256\), NMI decreasing speed with noise increasing (\(\lambda\) decreasing) under different algorithms. Figure 7: Density Sub-community Node-pair Extraction (DSNE) algorithm uses NMI to evaluate experimental results under different \(N\) conditions. According to Table 3 and Figure 7, when \(N=256\), with the increase of noise, i.e., the decrease of \(\lambda\), NMI decreases more slowly. When \(\lambda>0.7\), and decreases rapidly when a hovers around 0.5. When \(N+128\), NMI's value showed a strong downward trend when \(\lambda\) hovered around 0.6. When \(N=64\), NMI showed a strong downward trend when \(\lambda\) hovered around 0.9. Therefore, we find that the increase of the number of network edges \(N\) is negatively related to NMI and \(\lambda\); that is to say, more noise is needed to make the algorithm imprecise. When the value of \(N\) is 64, 128 and 256, the corresponding average decline rates of NMI are 29.6%, 22.6%, and 17.%, which indicates that with the increasing value of \(N\), the decline rate of NMI is getting slower and slower, that is, the stability of the algorithm is getting better. Besides, with the increase of \(N\), there is more and more structural information in the network, and the average value of NMI is also increasing. In other words, the matching degree between the community detected by the algorithm and the real community is higher.
# Communication Manager for Hyper-Connected RPAS Environments
## 4 Validation: From the Lab to the Air  
### Laboratory Ground Truth  
This section presents the results of two PoC experiments deployed to validate the CIM and showcase its potential to handle communication facilities in multi-RPA environments.  
A testbed was set up at the 5 TONIC laboratory to support both PoCs. The main objective of this testbed is to experience new developments and potential improvements (e.g., CIM progress, new communication alternatives) during the project period before trialing them on prototypes onboarded into real RPAs and on real-use cases. To this end, mini ITX computers (each with 8 CPUs, 8 GB of RAM, and 128 GB for storage) were used, providing an ideal platform for development. The network configurations and developments performed over these devices are relatively smoothly transferred to smaller devices that can be embedded, such as Raspberry Pi single-board computers, as demonstrated in our previous work [30].  
This testbed (following the system explained in Section 3) includes an RPS and two RPAs. All of them have the CIM installed and have two available interfaces. At the 5 TONIC laboratory, there is cellular communication (4G/LTE commercial deployment and 5G New Radio) and Wi-Fi. Because the flight tests to be conducted in the Labyrinth project (June 2023) will take place in locations where commercial 5G has not yet been deployed, it was decided to use commercial 4G/LTE connectivity to give verisimilitude to the experiments. On the other hand, the Wi-Fi channel and the movement of the RPAs (flight trajectories) were emulated using the Virtualized Environment for multi-UAV network emulation (VENUE) [25] software platform. The Wi-Fi channel could have been configured with the built-in Wi-Fi interface of the devices (and will be like that in real scenarios, quickly reaching up to 100 m with the proper antenna, according to our tests). However, we would not be able to emulate the RPAs' movements in that case in the laboratory tests. Accordingly,with the native Wi-Fi, we would not have been able to test the changes between one-hop and two-hop connectivity or the Wi-Fi connectivity unavailability use case in the laboratory.
## 3 Results
### Evaluation of Model Performance for the GPP Flux in Different Ecosystems
The performance of the used models with the best internal functions for GPP forecasting among the training, validation, and testing periods in the five stands is presented in Table 3. It is apparent from the table that the ANN1 model in the testing period gains higher \(R^{2}\) and IA values and lower RMSE and MAE values than those of other optimal models at both AT-Neu and CA-Obs, which is similar to the ANN6, ANN9, and ANFIS10 models at SE-Deg, US-Bo1, and CA-Oas, respectively. This is also confirmed by Figure 1b. Additionally, it can be seen in Table 3 that the applied models atCA-Obs generally have higher \(R^{2}\) and IA values and lower RMSE and MAE values than those of the optimal models at the other sites. The observed values of the daily GPP and corresponding simulated values by the optimal models at the five sites are compared in Figures 2 and 3, in the form of scatter plot and time series graph, respectively. As can be seen in Figure 2, the fit lines of the four best models at the CA-Oas site are closer to the ideal line (1:1 line) in comparison with those of the optimal models at other sites, while the four best models at the CA-Obs site have a higher \(R^{2}\) value, indicating that it provides less scattered GPP estimates for the CA-Obs site than the optimal models for other sites. It is evident from Figure 3 that most of the estimated values from the optimal models at each site closely follow the corresponding measured ones. A noteworthy point is that the ANN1 model at CA-Obs underestimates the peaks during the growing season in the validation and testing periods. However, the ANN6 model at the SE-Deg site overestimates the peaks in the testing period. Figure 1: Model performance rankings of gross primary productivity (GPP) estimation in the testing period: (**a**) for five different sites and four different machine learning methods with their respective internal functions (130 models); (**b**) for four different machine learning methods with their respective internal functions (26 models) at each site. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Stand**} & \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Training**} & \multicolumn{6}{c}{**Validation**} & \multicolumn{6}{c}{**Testing**} \\ \cline{2-13}  & & \(R^{2}\) & **IA** & **RMSE** & **MAE** & \(R^{2}\) & **IA** & **RMSE** & **MAE** & \(R^{2}\) & **IA** & **RMSE** & **MAE** \\ \hline \multirow{4}{*}{AT-Neu} & ANN1 & 0.8312 & 0.9529 & 2.2268 & 1.4728 &  
Figure 2: Daily GPP observed via the eddy covariance method and modeled by the four different machine learning models with the best internal functions in the testing period at the five sites: (**a**) AT-Neu; (**b**) SE-Deg; (**c**) US-Bo1; (**d**) CA-Oas; (**e**) CA-Obs.
# Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics
## 4 Case-Study: Application to the Design of AGVs
### PHASE 1: SysML Modeling
#### 4.1.2 Behavioral Modeling  
The automated guided vehicles (AGVs) are used for automated factory logistics, such as the transportation of raw materials or final products. They are used for the management of the flow of resources between the point of origin and the point of destination in order to meet certain stakeholders' requirements, for example, companies or customers [6].  
The analysis of the behavior of the AGVs begins with the description of the scenarios of use and the missions to be accomplished by the AGVs. In SysML language, the scenarios of use can be modeled using SysML use-case diagrams. The AGV mission can be modeled with a state-machine diagram as is detailed below.  
**AGV scenario:** An AGV usually transfers a pallet of goods from an automated production line. The pallet must be brought to the shipping area. Sometimes pallets have to be stored in a warehouse that is composed of a set of racks and shelves or a set of block storage areas. Figure 11 represents this AGV scenario.  
**AGV missions:** A mission must be accomplished by an AGV. It is started with a task defined as a sequence of segments of the route map to be followed by AGVs. Thereafter, each trip is made by an AGV to move a pallet of goods from one place to another. Indeed, the loading and unloading operations are carried out at the beginning and the end of each journey. To apply the swarm aspect, each AGV performs the mission in parallel with the other AGVs (i.e., throughout the mission, each AGV performs a task of the mission). This mission can be defined using the state machine diagram shown in Figure 12.  
Figure 11: AGV scenario.  
Figure 12: AGV missions in SysML.
## 3 Undifferentiated Cells (Stem Cells)
### Adult Stem Cells
#### 3.4.7.1 General Information  
Human skeletal muscle has been recently recognized as a valid source of progenitor or stem cells. This tissue contains two distinct stem cells including satellite cells (myogenic precursors) and muscle-derived stem cells (MDSCs), which are primarily responsible for muscle regeneration [354]. Although satellite cells exhibit multi-potentiality and self-renewal capabilities, they are committed to the myogenic lineage. They are located under the basal lamina of mature skeletal muscle fibers. Compared to the satellite cells, the MDSCs are considered stem cells, which are not restricted to the myogenic lineage and can differentiate into multiple lineages. Currently, these cells are named  
Figure 10: Representation of S-MSCs location in the knee joint and their potential in differentiation. Reproduced with some modifications from Reference [349].  
mesenchymal stem cells due to their ability to differentiate into the mesodermal phenotypes as well as to express MSCs' specific markers such as CD73, CD90, CD105, and CD44 [355].  
Even though stem cells derived from bone marrow and adipose tissue are considered two of the most useful cells for clinical application, they suffer from the lack of continuous availability and less efficiency for bone regeneration after freezing, respectively. Therefore, the human muscle-derived cells are proposed as an alternative stem cell source, which is able to differentiate in vitro and form bone and cartilage tissues in vivo without being affected by the previously mentioned limitations [356; 357]. MDSCs secrete trophic factors that may stimulate and regulate endogenous mechanisms of tissue regeneration [358]. Having these properties, MDSCs are used in different tissue engineering strategies to accelerate the regeneration of damaged tissues. The isolation of the MDSCs consists of a minimally invasive procedure known as a needle biopsy of various muscles [359].
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 2 First-Order Ambisonics (B-Format)  
Ambisonics is unique in being a total systems approach to reproducing or simulating the spatial sound in all its dimensions [17] and the Ambisonic encoder decomposes the sound field at a particular point of space on the orthogonal basis of spherical harmonics functions [21]. The B-Format is the first-order Ambisonics, which encodes the directional information of a given three-dimensional sound field into four channels called W, X, Y, Z, where W is the omini-directional channel, X, Y and Z are the three figure-of-eight directional channels [22]. For a point sound source \(p(n)\) assimilated to a plane-wave coming from azimuth \(\phi\) and elevation \(\delta\), where \(n\) denotes the time index, the decomposition of the sound field on these four channels can be formulated as follows [22,23]:  
\[\left[\begin{array}{c}W(n)\\ X(n)\\ Y(n)\\ Z(n)\end{array}\right]=\left[\begin{array}{c}\frac{1}{\sqrt{2}}\\ \cos(\phi)\cos(\delta)\\ \sin(\phi)\cos(\delta)\\ \sin(\delta)\end{array}\right]p(n). \tag{1}\]  
Ambisonic-encoded signals carry the spatial information of the entire sound field. To drive the loudspeaker signals with a particular layout, an Ambisonic decoder is needed to transform the B-format signals into D-format (loudspeaker signals). The decoding matrix used in this paper can be found in [24]. To show the characteristics of the B-format signals and their corresponding echo components received by a microphone intuitively, the waveforms and spectrograms of a randomly chosen sample are shown in Figure 1. In the left panel of Figure 1, from top to bottom, Figure 1a-d plots the time-domain W-, X-, Y- and Z-channel signals of B-format recording, respectively, and Figure 1e plots the echo signal. In the right panel of Figure 1, Figure 1f-j plot spectrograms of Figure 1a-e.  
Figure 1: Waveforms and Spectrograms of B-format and echo signals. **(a,f)** B-format W-channel signal, **(b,g)** B-format X-channel signal, **(c,h)** B-format Y-channel signal, **(d,i)** B-format Z-channel signal, **(e,j)** echo signal.