esa-sceva/satcom-chunk-collection huggingface dataset:
# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 2 Materials and Methods  
### Robot Setup  
For the field test, we utilized a developed robot and an attached implement. The robot features a continuous track with a length and width of 1200 and 180 mm, respectively. The robot's overall dimensions (length, width, and height) are 3977, 1720, and 1460 mm, respectively, with a total weight of 1350 kg. It is powered by four 12 V, 63 Ah batteries. The attached implement (BG600, Bulls Co., Ltd., Daegu, Republic of Korea) is a standard ridge-forming machine with a length, width, and height of 1400, 1200, and 720 mm, respectively. Further details are provided in Table 1.  
Figure 1 illustrates the pre-setup for data acquisition from the robot and implement. To ensure accurate robot driving linearity, GPS data were collected using an RTK sensor (UM982, Unicore Communications, Inc., Beijing, China), and motion data were gathered using a motion tracker (MT1-1, Xsens, Enschede, The Netherlands). These data were integrated via an embedded system consisting of a Raspberry Pi 4 and stored on a laptop at the field site. The acquired implement data comprised three major parts: (1) torque, (2) revolution speed of the PTO shaft, and (3) GPS location.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine** & **Specification** & **Value** \\ \hline \multirow{6}{*}{Robot} & Company & Sungboo Co., Ltd. (Chilgok-gun, \\  & Gyeongsangbuk-do, Republic of Korea) \\ \cline{2-3}  & Model & SB-2000 \\ \cline{2-3}  & \multirow{2}{*}{Frame} & Dimension, mm (L \(\times\) W \(\times\) H) & 3977 \(\times\) 1720 \(\times\) 1460 \\ \cline{2-3}  & \multirow{2}{*}{Frame} & Weight, kg & 1350 \\ \cline{2-3}  & & Velocity, km/h & 1.5–3.0 \\ \cline{2-3}  & \multirow{2}{*}{Robot} & Type of brake & Electronic \\ \cline{2-3}  & Maximum distance of point search, mm & 2000 \\ \cline{2-3}  & Distance between identified point, mm & 200–1000 \\ \cline{2-3}  & \multirow{2}{*}{Battery} & Voltage, V & 12 \\ \cline{2-3}  & & Amplifier Hour, Ah & 63 \\ \cline{2-3}  & \multirow{2}{*}{Quantity, ea} & 4 \\ \cline{2-3}  & Track & Dimension, mm (L \(\times\) W) & 1200 \(\times\) 180 \\ \hline \multirow{6}{*}{Implement} & Company & Bulls Co., Ltd. (Seongju-gun, Gyeongsangbuk-do, \\  & & Republic of Korea) \\ \cline{2-3}  & Model & BG600 \\ \cline{2-3}  & Dimension (L \(\times\) W \(\times\) H) & 1400 \(\times\) 1200 \(\times\) 720 \\ \cline{2-3}  & Weight, kg & 350 \\ \cline{2-3}  & PTO revolution speed, RPM & 440–800 \\ \cline{2-3}  & Number of furrows, ea & 6 \\ \cline{2-3}  & \multirow{2}{*}{Target structure of ridge} & Width, mm & 1200 \\ \cline{2-3}  & Depth, mm & 150–200 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the robot and implement used for a field test.  
Figure 1: Data acquisition preparation for robot and implement.
# Carbon Dots with Up-Conversion Luminescence as pH Nanosensor
## 3 Results and Discussion
### Dependence of the UCL Intensity of Aqueous Solutions of CDs on the Power of the Exciting Radiation  
As discussed earlier, according to some photophysical models, the UCL of CDs is due to a two-photon absorption process. In this case, the CDs' UCL integrated intensity should depend quadratically on the power of the exciting radiation.  
To check this, the UCL spectra of aqueous solutions of CD-poly and CD-COOH were obtained for different values of the excitation radiation power, which varied in the range from 12 mW (5.1\(\cdot\)10\({}^{4}\) J\(\cdot\)cm\({}^{-2}\cdot\)s\({}^{-1}\)) to 83 mW (3.5\(\cdot\)10\({}^{5}\) J\(\cdot\)cm\({}^{-2}\cdot\)s\({}^{-1}\)). The dependences of the UCL integrated intensity of CD-poly and CD-COOH in water on the value of the pump power are shown in Figure 7. They are well approximated by a quadratic function.  
The obtained quadratic dependences for the intensity of UCL in aqueous solutions of CDs on the power of the exciting radiation prove the fact that the UCL of studied CDs is due to two-photon absorption. In following processing of the spectral data, the intensity of the CDs' UCL will be normalized to the square of the power of the exciting radiation.  
From Sections 3.1 and 3.2 it can be concluded that the mechanisms for nonlinear (photoluminescence in the result of two-photon absorption) optical properties of CDs, as with linear ones (classic photoluminescence in the Stokes region [34; 44]), depend on the type and composition of surface functional groups.
## 4 System Model and Preliminaries  
### Network Model  
The proposed ATLAS-MAC assumes that several sensors are attached or implanted on/in a human body. Unlike existing TDMA MAC protocols for WBANs, we consider a clustered network as shown in Figure 2 where each sensor node belongs to a cluster and transmits the acquired data to the gateway through a respective cluster-head. Hence, the proposed MAC is designed for two types of communication [6,8], namely, _cluster-head to gateway_ (Ch-to-G) and _sensor to cluster-head_ (S-to-Ch).  
Assume there are \(N\) nodes, \(C\) clusters and only one gateway node (denoted by \(G\)) in the network. The \(i\)-th cluster is identified by \(C_{i}\) and its cluster-head is given by \(Ch_{i}\), such that \(Ch_{i}\in N\). The \(j\)-th sensor node in the \(C_{i}\) cluster is denoted as \(S_{j}^{i}\). Based on the IEEE 802.15.4 standard, the protocol operational time is divided into consecutive time intervals, alternatively called a superframe or cycle (given by \(T_{c}\)).
[34] opposed a gear fault diagnosis method based on variational mode decomposition (VMD) and SOM neural networks based on kurtosis criteria, which has a good effect on gear fault diagnosis. [PERSON] et al. [35] proposed a fault diagnosis method based on integrated empirical mode decomposition (EEMD) time-frequency energy and SOM neural networks, and the diagnosis results of this method have good visibility. The input of high-dimensional data may have an impact on subsequent diagnostic efficiency and accuracy. Therefore, the reduction and feature extraction of high-dimensional process data are of great significance before fault identification [36]. Principal component analysis (PCA) is a typical method for feature extraction and data analysis, and an effective tool for dimensionality reduction analysis [37]. [PERSON] et al. [38] obtained the finite sample approximate result of CDM-based PCA through matrix perturbation and obtained the final estimate of CDM-based PCA. [PERSON] et al. [39] improved the diagnostic accuracy by combining PCA and contribution analysis for fault isolation. This paper presents a health management method based on an improved gated loop unit and self-organizing mapping neural network. In the fault prediction stage, the multi-layer GRU neural network prediction model is established, and the attention mechanism is introduced into the GRU neural network, which improves the long-term dependence problem of the GRU neural network, improves the model performance, and provides the explanation and interpretability. At the same time, the Nadam optimizer is used to update the model parameters, which improves the convergence speed and generalization ability of the model, making it suitable for solving the prediction problem of large-scale data. In the fault diagnosis stage, the competitive learning mechanism is used to perform cluster analysis on different kinds of data, find the winning neurons, change the weights of the connections between the winning neurons and the input layer, make similar input variables similar to the weights of the connections between the output neurons, cluster similar input variables into the same class, and output the results through the SOM competition layer. In addition, the PCA method is used to find the most important features affecting the whole from the high-dimensional features to improve the expression ability of features, reduce the complexity of training, and achieve data dimensionality reduction. The rest of this article is structured as follows. The second part analyzes the fault of a servo drive system and determines three main fault types. The third part reviews the basic theories and techniques of LSTM and SOM and discusses their limitations. The fourth part introduces and analyzes the health management process of the machine tool servo system and introduces the Attention-MLGRU and PCA-SOM methods in detail. The fifth part introduces the process of predictive model selection and analyzes the equipment operation data set based on a health management system, including fault prediction and fault diagnosis. Finally, the conclusion is given in the sixth part.
## 3 Materials and Methods
### Revocation Phase  
All the idle devices or non-responding device's keys are revoked by the \(FS\). To re-initiate authentication, such devices need to solve the challenges using Mode 1 and, if again un-successful, then later using Mode 2. An \(FS\) maintains a list of revoked client \(C\)'s keys and allows re-authentication using either the challenge in Mode 1 or Mode 2. The basic purpose of this key revocation phase is to maintain high security within the hash-chain fog/edge protocol.
# Shapley's Value as a Resource Optimization Strategy for Digital Radio Transmission over IBOC FM
## 2 Materials and Methods
### IBOC FM Spectrum for Hybrid Mode  
In the hybrid model, the digital signal is transmitted in the primary main sidebands on both sides of the FM signal. In this model, each sideband of the IBOC FM system comprises 10 frequency divisions allocated between carriers 356 to 545 and between \(-\)356 to \(-\)545. Carriers 546 and \(-\)546 serve as reference carriers. Furthermore, the level of the digital subcarriers describes a total power of 23 dB below the nominal power of the analog FM carrier [2]. Figure 1 illustrates the carrier distribution for the hybrid mode.
# Autonomous Manipulator of a Mobile Robot Based on a Vision System
## 2 System Description
### Positioning Based on a Vision System  
The system was developed with the assumption that the position of the appropriate markers on the panel and the location and type of objects are known. In the panel shown in Figure 3, there are two different markers, and the center of the left marker has the global coordinate system. In this case, the maximum number of points for estimation position is eight, so it increases the accuracy.  
The operation of the system begins with searching for markers and determining the position of the gripper relative to the coordinate system on the panel, as shown in Figure 4. When the cycle starts, the operator does not interfere with the robot's work. The starting position of the manipulator is arbitrary, provided that at least one marker in the frame is visible. After determining the position, we calculated how much the robot must move in each axis of the tool coordinate system and how much it should rotate in relation to these axes in order to position itself at a base point in front of the panel. When the intended position is reached, the markers are detected again and the position relative to which the next movements are determined is calculated. Before the robot completes the task, it successively arrives at several predetermined points where the position is calculated. It increases the accuracy because the farther the camera is from the markers, the less accurately the position is computed. At intermediate points, previous errors are eliminated. The last measurement is performed just in front of the object so that the marker is visible. Marker detection only takes place at fixed points while the robot does not move. Thanks to this, the image is of good quality and the movements between the points do not have to have speed limits. The algorithm of marker detection is shown in Algorithm 1. There are several necessary steps starting from image conversion (color version to greyscale version), passing throw marker contour detection (only rectangle shapes are allowed), and ending with checking coded in marker number and numbers database stored in manipulator memory. In the case of determining the manipulator distance from the marker, we used the algorithm and the fact that the marker frame has known dimensions and shape. 2D image points and their 3D correspondences are taken only from the outer edges of the marker frame. We also check the thickness of the marker frame and its code. The positions of the markers located on the panel are known relative to the global coordinate system, so in the PnP algorithm, all detected marker points could be used.  
Figure 3: View from the camera mounted on the gripper with recognized markers and marked switches.  
If no markers are detected at an intermediate point, the robot will move relative to the previously calculated position.
## 4 Assessment of the Developed Approach
### Assessment of Structural Element Reconstruction
#### 4.3.2 Attribution of Standard Deviations to Reconstructed Walls  
For each created wall, a quality index is calculated. It deals with the standard deviation of the unsigned distances between points and the reconstructed wall. Figure 16 presents wall point clouds colourized with the standard deviations associated to walls for one floor of each dataset.  
For the individual house, the mean of standard deviations associated to walls or portions of walls is 9 mm whereas for the office building the mean of standard deviations is 8 mm. This means that on average, the walls are reconstructed with a precision of 1 cm. The visualisation of standard deviations thanks to a colour-bar allows locating rapidly walls or portions of walls for which standard deviations are distinguished from those of the other elements. For example, for the garden level of the individual house, an element point cloud is colourized in red and emerges from the other elements (Figure 16a). It deals with a small portion of wall with a standard deviation of 1.9 cm which was likely not correctly reconstructed or which is not really a portion of wall.  
Figure 15: (**a**) Superimposition of _obj_ file (in red) with a 2D floor plan in _dug_ format—2 nd floor of the office building. (**b**) Zoom on the map.  
Assuming that there is no significant noise in the data, the factors that influence the standard deviation of a wall are the size of the wall, the number of planes composing it and the amount of points which emerges from the wall (objects leaning against walls). A small wall with a high standard deviation indicates a problem occurred during the reconstruction. However, for a long wall composed of several planes belonging to different rooms (typically the case of a corridor wall or an indoor facade wall) the standard deviation seems not be sufficient to conclude about the quality of the reconstruction. The distances between points and model have to be analysed more specifically.
# Binary Decision Support Using AHP: A Model for Alternative Analysis
## 3 The Proposed Binary AHP
### Use of the Transposed Pairwise Comparison Matrix  
The proposed Binary AHP introduces a distinctive feature: experts use the transposed pairwise comparison matrix to enhance the decision robustness and impartiality. When experts recalculate the priority vectors from the perspective of the opposite alternative by transposing the original comparison matrix, they achieve several benefits:  
* They validate the consistency and symmetry of the assigned weights, ensuring the model does not unintentionally favor one alternative.
* They facilitate direct and transparent comparisons between the alternatives, strengthening the decision process's reliability.
* They avoid the need to reconstruct the entire model for each alternative, thereby optimizing the workflow.
* They help identify and correct inconsistencies in expert judgments, increasing the decision quality.  
This dual-perspective analysis is particularly valuable in binary decision contexts, where fairness and methodological rigor are paramount.
## 5 Model Validation
### Results  
Finally, the results from UNICORN and the analytical model are compared to see if the predicted values agree with measured values according to Equation (27). Figure 9 shows the results.  
In Figure 9, it can be observed that the discrepancy between measured and predicted standard deviations are bounded by 0.0002 B1 primary code chips, which translates into a prediction accuracy of 0.0293 m, less than half of the threshold value of 0.66 m. This proves the validity of the analytical model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sections** & **Parameters** & **Values** \\ \hline Satellite Transmitter (TX) & TX bandwidth \(B_{T}\) & 30.69 MHz \\  & Target signal & BDS B1 \\ \hline Atmospheric Effects, multipath \& RF interference & none & Not Applicable \\ \hline  & Pre-correlation Bandwidth \(B_{r}\) & 4 MHz \\  & Sampling frequency \(f_{s}\) & 62 Msps \\ RFFE & Number of quantization levels \(N_{Q}\) & 4 \\  & Noise floor & \(-203.5\) dBW/Hz \\  & Power level at antenna & \(-160\) dBW \\ \hline  & DLL one-sided noise bandwidth \(B_{L}\) & 1 Hz \\  & Coherent Integration Time (CIT) \(CT\) & 20 ms \\ Software Receiver & Non-coherent Integration Time (NIT) \(NT\) & 1 \\  & Discriminator type & Dot product \\  & Early-late spacing \(\Delta\) & 0.5 chips \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters used in model validation.  
Figure 9: Measured vs. predicted tracking standard deviation.
## 5 Experimental Results
### IDS Results  
For experimentation of the performance of the proposal, we have considered a well-known standard dataset, i.e., NSLKDD data-set [37], which is freely available and mostly used for intrusion detection problems. It has 148517 instances and 42 attributes, including the class label. It also has 22 types of attacks. In NSLKDD dataset the considered number of normal packets was 77,054, and the number of anomaly packets was 71,463, giving a total of 148,517 packets. In total, 70% of data were used for training and 30% for testing. Specifically, the training data dimensions were 103,961 packets, and the testing dataset dimensions were 44,556 packets.  
Diverse performance metrics were considered to evaluate the suggested solution, including precision, recall, F1-measure and accuracy. These performance metrics are based on true positive (TP), false positive (FP), false negative (FN) and true negative (TN). Additionally, accuracy was employed to measure how many instances were correctly  
Figure 14: Comparison of last node dead (LND) for (100 m \(\times\) 100 m) and (200 m \(\times\) 200 m) for LEACH, LEACH-LMNN, EESR, EESR-LMNN, Sub-LEACH and Sub-LEACH-LMNN protocols.
# In-Depth Co-Design of Mental Health Monitoring Technologies by People with Lived Experience
### Monitoring Technologies in Mental Health Care  
Monitoring of consumer mental health and well-being is already a feature of contemporary clinical mental health care. Indeed, Australian community mental health clinicians have a legal responsibility to monitor the well-being and recovery progress of consumers in their care. Digital mental health interventions that streamline monitoring processes can reduce organizational labor and can also potentially scale up easily at a low cost [8].  
Examples of monitoring within DMHIs abound, including:  
1. Mobile applications that enable consumers to regularly self-monitor and report treatment outcomes, in turn helping to improve those outcomes [9; 10];
2. Real-time, longitudinal collection of patient data to inform decision-making and facilitate early intervention [10; 11; 12; 13];
3. Consistent and accessible, on-demand recovery support for remote communities enabled by ongoing digital monitoring of consumer wellbeing [14; 15];
4. Objective measures of medication adherence, derived from electronic medical records (EMRs) [16; 17];  
However, such services are still not widely used in community care settings [18].
## 4 Data Extraction
### Text Tokenization  
Since audio chunks will be tokenized in terms of phonetic units, alignments must be performed at the phonetic level. Thus, the text to be aligned with each audio chunk is passed through an in-house bilingual grapheme-to-phoneme (G2P) converter [36; 37] to obtain a sequence of phonetic units (the reference sequence). The G2P converter is based on two dynamic dictionaries for Basque and Spanish, initialized on Wikipedia and updated with each new word found in transcriptions. Each word in these dictionaries is mapped to a nominal pronunciation in terms of the reduced set of acoustic units by applying a language-specific set of pronunciation rules [42; 43]. When a known word is encountered, the G2P converter outputs the pre-stored pronunciation. Conversely, when an unknown word is detected, pronunciation rules are employed to derive its phonetic baseform, which is then stored in the appropriate dictionary. If a word is either unknown or exists in both lexicons, a choice must be made between the two languages, Basque or Spanish. This decision is based on the context: the language appearing the most in a window around the current word is chosen. This strategy is found to be effective in practice, leading to very few errors. Acronyms are assumed to be spelled; acronyms not following this rule are listed in the dictionaries. Finally, the words added to the dictionaries after processing the transcription corresponding to each audio chunk are supervised and validated by a human expert.