esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[8] used RF, K-NN, and support vector classification (SVC) to predict ground conditions in tunnels using four TBM parameters namely; cutterhead torque, cutterhead thrust, cutterhead speed, and advance rate, and concluded that SVC outperformed the other techniques with an accuracy of 98%. They also indicated that out of the four TBM parameters analyzed, the cutterhead torque and thrust were found to better reflect the changes in rock types. Based on the Hydropower classification (HC) system, [9] used TBM operational data to train five predictive models: AdaBoost-CART, CART, SVC, ANN, and KNN, and concluded that AdaBoost-CART was the best model for predicting rock mass conditions. [PERSON] et al. [21] used ANN, SVM, KNN, and CART to develop geologic type recognition classifiers based on advance rate, cylinder thrust, cutterhead torque, and cutterhead rotational speed. [PERSON] and [PERSON] [22] proposed the multivariate sequence segmentation, abstraction, and classification (MSAC), a data-driven rock mass classification model, using the advance force, cutterhead torque, penetration rate, cutterhead rotations, advance speed, specific penetration, specific energy, and torque ratio. This study explores the suitability of two supervised machine learning algorithms, random forest (RF), and extremely randomized trees (ERT) in predicting the ground conditions on the tunnel face ahead of a TBM based on the Japanese Highway Classification System. RF and ERT harness the predictive capabilities of multiple decision trees. Different sets of predictors are used at each node; hence, the variance of the resulting model is significantly reduced compared to the individual regression trees. RF was selected for this analysis because it has been applied successfully in a wide variety of projects and has seen tremendous acceptance in many disciplines due to its tendency to decrease the models' variance [23]. ERT, on the other hand, is relatively unknown especially in the area of rock excavation but it was selected due to its high performance with less noisy data. In this study, TBM operating parameters namely; rate of penetration, cutterhead torque, cutterhead thrust force, cutterhead revolution per minute, hydraulic cylinder stroke speed, boring pressure, pitching, and motor amps were analyzed using the two ML algorithms to develop models for classifying the rock mass conditions in TBM tunnels. This research contributes to the ongoing research efforts towards developing reliable models that could be incorporated into TBMs to allow for on-the-fly characterization and classification of ground conditions in tunnel excavation as well as eventual automation of ground support systems selection.
# Numerical and Experimental Analysis of the Oil Flow in a Planetary Gearbox
## 1 Introduction  
Planetary gearboxes are widely used in mechanical transmissions with great advantages, such as high transmissible torque, power density, high reliability, and efficiency. These gearboxes are composed of four main components: the sun gear, the ring gear, the planet gears, and the planet carrier. The sun is an external gear and is coaxial with the ring gear, which, on the contrary, is an internal gear. The planets are external gears that engage with both the sun and the ring gears. Different arrangements of the sun, the planets, the planet carrier, and the ring gear are possible due to the kinematic degrees of freedom.  
Planetary gearboxes are used in several application areas, especially in industrial automation systems, wind power industry, and industrial gear units. For example, they are used in dynamic applications in robotics, where factors such as low backlash and a balanced load distribution are highly valued so to ensure a longer system lifetime and positioning accuracy. Typical applications include machine tools and self-propelled machines, packaging systems, conveyor belts, printing presses, and any context where extremely precise positioning control is required. Compared with other types of geared transmissions, planetary gearboxes allow to design a more compact system, a feature that is always appreciated by machine designers.  
In geared transmissions, it is crucial to ensure the proper lubrication of all components, especially when they operate at high speeds or must withstand heavy loads. In this work, a combined experimental-numerical approach is used to investigate the oil phenomena that occur in the FZG internal gear test rig [1; 2]. A special transparent housing cover was designed to make the front part of the gearbox visible. This allowed to capture the oil flow by high-speed camera recordings. To analyze the oil distribution, a CFD model was developed. A new mesh-handling technique was applied to build a time efficient simulation model. The CFD results show a high comparability with experimental results for a wide range of operation conditions. The potential of CFD for predicting oil flow is shown.
# Design Method of Primary Structures of a Cost-Effective Cable-Supported Photovoltaic System
###### Abstract  
Cable-supported photovoltaic systems (CSPSs) are a new technology for supporting structures that have broad application prospects owing to their cost-effectiveness, light weight, large span, high headroom, few pile foundations, short construction period, and symbiosis with fisheries and farms. Recently, a new CSPS with a much smaller settlement and stronger wind resistance was proposed. The new CSPS, with a 10% lower cost compared with traditional fix-tilted PV support, is a better alternative to traditional photovoltaic (PV) support systems. In this study, the failure models and bearing capacity of the primary structures of the new CSPS were investigated in detail using the FEM method, and a design method for the new structure was proposed based on the limit state design method. The results showed that the structure had a strong load-bearing capacity. Failure of the cables and triangular brackets are the two main types of failure of the primary structure. The cross-sectional area of the cables is the most important factor affecting the load-bearing capacity of the structure and directly affecting the failure modes of the CSPS. The numerical case verified that the proposed design method works well and that the designed structure has sufficient loading capacity and is cost-effective.  
PV modules; flexible support; numerical simulation; wind-resistant design; design method
## 4 Experimental Results
### Firewall Performance under Controlled Network Traffic
This can be explained since the 'AXI 1G/2.5G Ethernet Subsystem' IP module introduces a delay of 12 clock cycles to process the Ethernet frame. The measured value of \(\lambda\) as a function of the packet size is reported in Figure 8 for both the average value and the standard deviation. The measured \(\lambda\) resulted in comparable values for the different sizes of packet, with the case of a packet size of 750 bytes producing a slightly higher value. The average value of \(\lambda\) for the different packet sizes was calculated as 33.2 bytes/s. Thus, the used memory increases from 5 kB to 12 kB in an average time of 215.8 s. When the used memory threshold of 12 kB is reached, the firewall stops receiving packets, while the packets in the data memory are transferred until the used memory decreases below 5 kB. At this time, the firewall returns to being functional. Considering the worst-case scenario when all packets in the data memory are transmitted and the firewall data throughput is 0.811 Gb/s (in the case of 100-byte size), the time needed for the used memory to decrease under 5 kB can be estimated as 70.71 \(\upmu\)s. This means that the firewall is not operative for a negligible part of the time (3.29\(\cdot\)10\({}^{-5}\)%).
# Water Quality Evaluation and Analysis by Integrating Statistical and Machine Learning Approaches
## 4 Results and Discussion
### Comparative Discussion and Implications  
The integrative approach used in this study, combining statistical techniques with hybrid machine learning models, is validated by recent literature emphasizing the effectiveness of ensemble and diagnostic-supported modeling. Our findings, particularly the significance of DO and AN, are consistent with the results by [46, 47, 49], reaffirming the ecological importance of these parameters across different water systems. The RF model's strong performance further aligns with studies that favor ensemble classifiers for robust and interpretable WQI prediction. While our Decision Tree Regression model underperforms in terms of R2, this is consistent with observations in environmental datasets where limited size or variability affects regression accuracy. Ensemble methods like Gradient Boosting or XGBoost may offer better generalization in future work. The dimensionality reduction using PCA also supports more efficient modeling, as demonstrated in chemometric and ecological applications across prior research. Overall, the consistency of our results with published studies enhances the confidence in our methodological approach and supports its potential application for national-scale water quality monitoring.
# Drone and Controller Detection and Localization: Trends and Challenges
## 5 Conclusions  
This study has reviewed the most recent techniques for UAV/drone and its controller detection, classification, and localization. Cost-effectiveness, precision, accuracy, reliability, and real-time processing are among the factors considered while developing UAV detection systems. After discussing the high-level architecture of UAVs and security concerns, a comprehensive review of radio frequency, radar, acoustic, electro-optic, and hybrid systems for UAV detection is presented. The UAV detection systems employ different algorithms and techniques depending on the applications for detecting, classifying, locating, tracking, and alerting. To address the challenges, meet market needs, and improve reliability, employing a hybrid fusion of multiple cues, such as radio frequency, radar, acoustic, and visual sensors, can enhance detection performance.  
Conceptualization, J.Y. and M.G.; methodology, J.Y., M.A. ([PERSON]), M.Y., T.B., E.A.S. and M.G.; validation, J.Y., H.Z., M.A. ([PERSON]), M.Y., T.B., E.A.S., A.G. and M.G.; formal analysis, J.Y., H.Z., M.A. ([PERSON]), M.Y., T.B., E.A.S., A.G. and M.G.; investigation, J.Y., H.Z., M.A. ([PERSON]), M.Y., T.B., E.A.S., A.G. and M.G.; resources, [PERSON], H.Z., E.A.S., M.A. ([PERSON]) and M.G.; data curation, [PERSON], H.Z., M.A. ([PERSON]), M.Y., T.B., E.A.S., A.G. and M.G.; writing--original draft preparation, J.Y., E.A.S. and M.G.; writing--review and editing, [PERSON], H.Z., M.A. ([PERSON]), M.Y., T.B., E.A.S., A.G, M.A. ([PERSON]) and M.G.; visualization, J.Y., M.A. ([PERSON]), M.Y., T.B., E.A.S. and A.G.; supervision, J.Y., M.A. ([PERSON]) and M.G., project administration, J.Y. and M.G.; funding [PERSON], J.Y., M.A. ([PERSON]) and M.G. All authors have read and agreed to the published version of the manuscript.  
This paper is part of a research project funded by the Office of Research and Sponsored Programs (ORSP) at Abu Dhabi University through a research fund (Grant number 19300564).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Scanning Electron Microscopic Analysis  
The surface morphology of the different biochars (CCB500, CCB600, and CCB700) is illustrated in Figure 3. The SEM images of all biochar samples indicate that the surface is heterogeneous, with an irregular shape, featuring fissures and a multitude of pores that come in different sizes, which increase with the pyrolysis temperature [35]. CCB700 particles, indeed, appear the most porous, with several cracks, which may play a crucial role in the adsorption of target dye molecules. At the surface level, CCB500 appears spongy. Small mineral particles appear to be deposited on the surface and inside the cracks in CCB600 and CCB700 particles. According to the XRD analysis, Ca peaks increase with the pyrolysis temperature (Figure 2b); thus, mineral particles appear to be more abundant in CCB700 than in CCB600.  
Figure 3: SEM images of the biochar samples at 5 and 100 \(\upmu\)m.  
The importance of large pores and cracks in dye adsorption is evident from the SEM images of CCB600 particles after adsorption, which can be found in the Supplementary Materials. Notably, the particle size and shape are not significantly modified upon adsorption, but at higher magnification, cavities are evidently filled.
## 2 System Model and Problem Formulation  
In this section, we present the system model for a general RIS-aided downlink CF communication network. Subsequently, we formulate the joint transmit power and BS selection problem.
# A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals
## 2 Methods
### PPG Signal Preprocessing  
Various types of noise often accompany PPG signals, acting as interference during the collection process [19]. These noises mainly include baseline drift, which arises from respiratory fluctuations and the instability of amplification circuits; power line interference, originating from AC power sources; electromyographic noise, resulting from limb tremors and muscle contractions; and motion artifacts, caused by changes in the optical measurement due to bodily movements.  
To enhance the quality of PPG signals, we used a 3 rd-order bandpass Butterworth filter. Choosing a 3 rd-order bandpass Butterworth filter can balance frequency selectivity and phase response during the filtering process, reducing distortion and signal delay. The design of this filter takes into account the characteristics of the PPG signal. It sets the high-pass cutoff frequency to 8 Hz, which can effectively filter out high-frequency interference caused by electromyographic noise and power frequency drift. The low-pass cutoff frequency is 0.5 Hz, which can effectively filter out low-frequency interference caused by baseline drift. The filtered PPG signals exhibited a significant quality improvement, forming the basis for subsequent identity authentication tasks. Furthermore, the filtered PPG signals underwent amplitude normalization to address amplitude variations in the signals. This step ensured a unified dynamic range of the signals at 1, further enhancing the accuracy and stability of the identity authentication process.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 4 Operational Semantics
### Transition Rules
#### 4.2.1 Sending a Service Request  
An IoT service is initiated by a client service which sends a service request \(\rho\) to the directory service to create a new service. The client's requirements are expressed in that request, \(\rho\). The same service request can also be used to subscribe to an existing service.  
The following transition shows how a client service sends a service request to the directory service:  
\[\begin{array}{l}\big{\langle}\llbracket I:\ [R[send(s_{d},(\rho,s))]\rrbracket_{s_{ d}},\ \alpha:\mathcal{O}]_{s},\ S\mid C\mid M\big{\rangle}\to\\ \big{\langle}\llbracket I:\ [R[nil]]_{s_{d}},\ \alpha:\mathcal{O}]_{s},\ S\mid C\mid \langle s_{d}\Leftarrow(\rho,s)\rangle,\ M\big{\rangle}\end{array} \tag{5}\]  
where \(s\) is a client service, \(s_{d}\) is the directory service, \(\rho\) is a service request, \(a_{s}\) is's coordinator actor, and \(send(s_{d},(\rho,s))\) sends message \((\rho,s)\) containing the received \(\rho\) and the client's name to the directory service \(s_{d}\). This leads to the creation of message \(\langle s_{d}\Leftarrow(\rho,s)\rangle\) on the right hand side, and actor \(a_{s}\) continues execution.
## 4 Results
### Main Use Case and per Class Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Exp.** & **Model** & **Training Dataset** & **Evaluation Dataset** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & FFNN & ChatGPT & Indeed & 0.85 & 0.85 & 0.85 & 0.85 \\ \hline
2 & BERT & ChatGPT & Indeed & 0.85 & 0.85 & 0.85 & 0.85 \\ \hline
3 & FNN & Indeed\_60 & Indeed\_40 & 0.84 & 0.86 & 0.84 & 0.84 \\ \hline
4 & BERT & Indeed\_60 & Indeed\_40 & 0.85 & 0.88 & 0.86 & 0.86 \\ \hline
5 & FNN & Indeed augmented & Indeed\_40 & 0.85 & 0.86 & 0.85 & 0.85 \\ \hline
6 & BERT & Indeed augmented & Indeed\_40 & 0.92 & 0.92 & 0.92 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Prediction accuracy and model performance for all experiments per class. was well-constructed and contributed positively to the model's training. In contrast, the F1 scores for the ChatGPT dataset showed slight variability, potentially reflecting the unique language patterns and context of the generated resumes. As anticipated, the FFNN exhibited shortcomings in accurately categorizing specific classes due to the presence of overlapping descriptions within the resumes. Conversely, the BERT model effectively addressed these instances of overlap because it benefited from pretrained contextual embeddings and its enhanced ability to capture language nuances, which gave it a significant advantage in text classification tasks. In both the FFNN and BERT models, the presence of classes with fewer samples (sparse classes) had a noticeable impact on the accuracy results. These classes, such as landscape and tellers, exhibited lower F1 scores compared to classes with more training data. The introduction of the Indeed augmented dataset significantly improved the F1 scores for these sparse categories in both models. The Indeed augmented dataset, which added more examples and diversity to the training data, enabled the models to better capture the nuances and features of these underrepresented classes. This enhancement was particularly evident in the BERT model's results, where the F1 scores for landscape and tellers showed notable improvements. Finally, the sparse classes with limited training samples initially had a negative impact on accuracy in both the FFNN and BERT models. However, the use of the Indeed augmented dataset mitigated this effect by providing additional training examples, resulting in improved F1 scores for these sparse classes. The Indeed augmented dataset played a crucial role in helping the models to better capture and classify the under-represented classes. Figure 2: FFNN overall F1 score results for main use case.
# Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag
## 5 Prototype Realization and Measurements  
For realizing a prototype of the designed harmonic tag, we use a substrate made of Teflon, which has \(\epsilon_{r}=2.1\), \(tan\delta=0.0027\), and thickness \(h=1.5\) mm. The different metallic components that constitute the circuit and the antenna (microstrips, patches, and ground planes) are made with 35 \(\upmu\)m thick adhesive copper. The layouts for metallic components are realized using the Secabo C60 IV cutting plotter. Photos of the prototypes can be seen in Figure 14. Inductors, capacitors, and the Schottky diode (HSMS-2850) are soldered using traditional tin alloy soldering. Prototypes of the dual-band matching circuit and the final annular antenna are realized separately in order to check their behavior separately before the final assembly (Figure 14a,b).
# The Use of Drones in Spain: Towards a Platform for Controlling UAVs in Urban Environments
## 4 Proposed System
Only the message information with the highest timestamp is displayed, so the outdated information will not be displayed. If a message is received in a disorderly manner, it is inserted into a message queue in an orderly manner in the corresponding position. Messages with flight orders follow a similar pattern. However, in the case the pilot indicates a flight mode change, a A SYN-ACK model is followed in the message with an order. Thus, the platform does not only provide security but can also be used in legal issues, which is a very important aspect. The messages can be used in a judicial process since the use of blockchain assures that information has not been altered and if such attempt has been made it can be easily detected. Figure 2: Developed software: (**a**) ground control station; and (**b**) recorded telemetry system. The messages exchanged between the UAV and the platform (both with the controller software and with the pilot software) follow a similar scheme to that used by the well-known MAVLink protocol (secured version [17]), although the protocol designed for communication is smaller in terms of the type of messages that can be exchanged, since their payload can be configured according to the parameters of the header, with the freedom to send structured information with a single type. More specifically, there are message types for transmitting flight orders, flight modes, sensor information (adaptive type and payload), UAV position and orientation, pilot identification, UAV identification, flight configuration, UAV configuration and UAV type. All the software has been developed in Java, so it can be run on different platforms. It has been successfully tested on MacOS, Windows and Ubuntu. Although there are three independent applications (pilot, controller and telemetry display), there are layers common to all of them that are integrated following the scheme presented in Figure 3.  
In the \"TX/RX\" layer, the functionality of processing the received information is implemented, thus it is integrated in the three designed applications. However, the real-time information is processed by the pilot and controller software. The \"Navigation\" layer, which is also integrated in the pilot's software and the controller's software, including all the algorithms for translating the pilot's movements into flight orders, as well as algorithms for translating autonomous flight movements into flight orders. The functionality of the \"Input\" layer allows the pilot to directly indicate the movements they want to make, through a gamepad or by creating waypoints on a map. Therefore, this layer is only integrated into the software used by the pilot to control the ship. The \"Visualization\" layer is incorporated into all three software applications. It presents all the information that is to be displayed in the UAV. The \"Export\" layer is responsible for generating the corresponding telemetry files so that they can be processed by the telemetry visualization software after each flight. Figure 3: Developed software modules.