esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Baseline UDS-Based SSI
### Harmonic Enhancement  
The speech production principle dictates that a speech signal is generated by exciting a vocal tract transfer function via source signals that correspond to the spectral envelope and to the fine structure, respectively, in the frequency domain. The spectral envelope is typically represented using the MCCs, that are predicted from the non-acoustic signals in most SSI systems. The source signals originate either from white noise (for unvoiced regions) or from impulse train signals (for voiced regions). Artificially generated signals, such as white noise [12; 25; 30], an impulse train with a constant pitch period [12], and an impulse train with randomly perturbed pitch periods [12] were adopted as source signals in previous SSI methods. Such artificially generated source signals do not originate from natural speech, which leads to reproduced speech signals that sound unnatural.  
Figure 1: Block diagram of the baseline ultrasonic SSI system. (**Top**): Training procedure. (**Bottom**): Synthesis procedure.  
In the present study, we adopted a method for predicting the entire speech spectra (not only the spectral envelope), which avoided the problems associated with the use of an artificial source signal. The experimental results, however, showed that only spectral envelopes were estimated even when the target output was set as the entire speech spectra. An example of the predicted speech spectrum is shown in Figure 2. This result was commonly observed in most voiced regions. Using such an estimated spectra to synthesize the speech signals resulted in unclear, rough, and unnatural qualities, as experienced in previous studies that used the artificial source signals.  
To improve the quality of the reproduced speech signals without the use of artificially generated source signals, harmonic enhancement (HE) [40] was employed in the present study. The main objective of employing HE is to increases the natural quality and clarity of synthesized speech. This can be achieved by emphasizing the periodicity of speech that is typically observed in the voiced regions. The harmonic-enhanced signal \(\tilde{y}(t)\) is given by  
\[\tilde{y}(t)=y(t)+\sum_{k=-K}^{K}\gamma_{k}\cdot y(t-P(t)-k) \tag{4}\]  
where \(\{\gamma_{k}\}_{k=-K}^{K}\) denotes the coefficients of HE and \(2K+1\) is the number of taps of the HE filter. By changing \(\gamma_{k}\), it is possible to adjust the voicing strength. \(P(t)\) is the pitch period at time \(t\). The HE technique adjusts the periodicity of the speech signals that have already been synthesized without using artificial source signals. The usefulness of HE was verified in the previous US-based SSI method [18].  
Although it was apparent that the HE method allows for pitch error [18], a reliable estimation of the pitch period is essential for HE. Evaluation of the performance of pitch estimation and voiced/unvoiced decisions for the various modalities are described in the subsequent section. The accuracy of the ultrasonic signal was compared with that of other modalities so that the usefulness of HE adopted in the US-based SSI system was verified.
###### Abstract  
In this paper, we introduce a 5G-based multi-sensor platform for monitoring workpieces and machines. The prototype is realized within the EU-funded 5G-SMART project, which aims to enable smart manufacturing through 5G, demonstrating and validating new generation network technology in industrial processes. There are already state-of-the-art solutions, but with drawbacks such as limited flexibility, brief real-time capability, and sensors aimed at single applications. The 5G-SMART multi-sensor platform is designed to overcome these points and meet the requirements of Industry 4.0. The device is equipped with different sensors to acquire multiple data from workpieces and machines of the shop floor, wirelessly connected by 5G to the factory cloud. A hardware design description of the prototype is provided, focusing on the electronic components and their interaction with the microcontroller. Verification of the correct functioning of the board is given, with a basic library for the main peripherals used as a basis for the final firmware.  
industrial 5G devices; 5G use cases in smart production; smart manufacturing; smart sensor network; Industry 4.0; multi-sensor platform +
Footnote †: journal: _electronics_  
_Acticle_  
5G-Based Multi-Sensor Platform for Monitoring of Workpieces and Machines: Prototype Hardware Design and Firmware
# Image-Based Gimbal Control in a Drone for Centering Photovoltaic Modules in a Thermal Image +
## 2 PV Module Imaging System Using Drone Equipped with Dual Camera
### Gimbal Control Process to Reach Desired Angle and Position  
Figure 7 illustrates the process from image acquisition through the entire chain. The process involves (1) capturing the image from visible camera, (2) finding the PV module area using various image processing, such as threshold function, inRange function etc., (3) calculating horizontal angle and vertical position from the results of image processing, based on the Hough transform, etc., (4) calculating PID control value to obtain the proper PWM values, (5) transferring the calculated PWM values to GCB, and (6) GCB controlling BLDC motors as given values. This process will be repeated from phase 1 again as closed loop control.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 5 Experiments
### Ablation Study  
To evaluate the effectiveness of our spatiotemporal data refinement methodology and the DeepST-Net model, we conduct an ablation study. This study systematically compares the performance of models based on the same DeepST-Net architecture, with the only variation being the type of input data. The models are trained and evaluated under the following four distinct conditions: raw data, cleaned data, temporally refined data, and the fully enhanced dataset used in the DeepST-Net model. By systematically altering or excluding specific preprocessing techniques, this study identifies the most impactful steps for improving model performance.  
* **Raw:** This baseline model uses the raw TLC dataset without any refinement or feature engineering. Inputs include raw GPS coordinates and basic temporal variables, such as the starting hour (1-24), day (1-7), and month (1-12). This setup provides a benchmark for assessing the performance of the DeepST-Net architecture when trained on unprocessed data.
* **Clean:** This model is trained on a clean version of the dataset, where preprocessing steps such as noise reduction and outlier removal have been applied. The inputs remain consistent with the raw model, but the refinement improves data quality by addressing inaccuracies and inconsistencies. This setup highlights the role of data cleaning in enhancing predictive accuracy.
* **Temporal:** This model incorporates temporal refinement by clustering data into daily, weekly, and yearly intervals, capturing variations in travel time across different time scales. Inputs include refined GPS coordinates and temporal clusters, enabling the model to better account for periodic patterns and temporal dynamics.
* **DeepST-Net:** The fully enhanced Deep Spatiotemporal Network model integrates multiple refinement techniques to enhance travel time prediction accuracy, closely aligning with real-world scenarios. It combines refined features, GPS coordinates, temporal variables (starting hour, day, and month), and temporal clustering. This configuration leverages the strengths of each variant, aiming to achieve the highest level of prediction accuracy by capturing the spatial, temporal, and clustering aspects of the data.  
By systematically comparing these models, we gain valuable insights into the effectiveness of various data refinement and feature engineering techniques. This analysis forms a robust foundation for our refined methodology and prediction model. Including the raw data baseline allows us to isolate the impact of preprocessing techniques from the model itself, an aspect often overlooked in prior research. This perspective enables a clearer understanding of the individual contributions of data refinement and model architecture to accurate travel time prediction, ultimately contributing to more effective intelligent transportation systems.
## 1 Introduction
The proposed method is invariant to translation and illumination, and can detect subtle defects as small as one-pixel wide in a wide variety of nonperiodical patterns found in the electronic industry. [PERSON] et al. [9] proposed a novel cascaded convolutional neural network for Surface-Mount Technology (SMT) solder joint inspection, whose input is the whole SMT solder joint image rather than its features. Their method adaptively determines the regions of interest of the SMT solder joint image via a sliding-window scheme based on one kind of CNN. It utilizes the characteristic of CNN to combine the global and vital local information of the SMT solder joint image to make accurate predictions using a cascaded structure. [PERSON] and [PERSON] [10] proposed a hybrid approach which helps in producing zero- defect PCB by detecting the defects. This approach not only detects the defect, but also classifies and locates the defects. The hybrid approach uses referential and non-referential methods to analyze the PCB and this approach proposed an algorithm which involves image representation, image comparison, and image segmentation. Moreover, people in the industries tend to set a strict AOI threshold value to convert images from gray-scale to binary in PCB process and prevent underkills. In this regard, many histogram-based methods used a calculated global threshold, and this leads to serious over-screening, causing manual re-examination to still be required after AOI. [PERSON] and [PERSON] [11] used the mean and standard deviation of pixel values to compute the threshold. The adaptive threshold is more commonly used. [PERSON] et al. in [12] and also [PERSON] and [PERSON] [13] used the Otsu method to calculate the optimum threshold for pre-processed IC images using the bi-modal histogram. Except for one global threshold, [PERSON] et al. in [14] measured the threshold values for each individual pixel against their neighborhood. However, the histogram-based methods cannot work well when other highlighted components are included, such as markings and via-holds.
## 3 Proposed Method
### Random Forest  
\"Random forest\" is a type of ensemble learning using decision trees that is a well-known and widely used model. It can be used for class classification, regression, clustering, etc. This method uses multiple decision trees and obtains results by majority vote of the prediction results of each decision tree. This kind of learning method using multiple models to improve performance is called ensemble learning and it is widely used in addition to random forests.  
Random Forest creates decision trees by randomly selecting data samples, obtains predictions from each tree, and chooses the best solution by voting. For example, if five decision trees are created and three out of five are \"A\" and two are \"B\", then \"A\" is selected. This is called \"majority logic\". In addition, when it comes to regression problems, if five are predicted to be [4; 8; 10; 12; 14], it will be predicted with a mean of 9.6 and a median of 10.  
Random forests have various applications such as recommendation engines, image classification, and feature selection. Therefore, it is a highly accurate and popular algorithm. The four steps of the algorithm are:  
1. Select a random sample from the given dataset;
2. Build a decision tree for each sample and obtain a prediction result from each decision tree;
3. Vote for each prediction result;
4. Select the prediction result with the most votes as the final prediction.  
The advantages of the random forest algorithm are as follows:  
1. In random forests, each decision tree has different characteristics and can make complex decisions;
2. Compared with decision trees, there is no problem of overfitting. The main reason is the average of all predictions is taken to cancel out the bias;
3. Random forest can also handle missing values. There are two ways to handle these: using the median value to replace the continuous variable and computing the proximity-weighted average of the missing values;
4. Importance can be obtained. This helps select the features that contribute the most to the classifier.  
On the other hand, the disadvantages of this algorithm are as follows:  
1. Random forests are slow to generate predictions due to multiple decision trees. For every prediction, every tree in the forest must make a prediction on the same input and perform a vote on it. This entire process takes time.  
Compared with decision trees, the models are harder to interpret because there are multiple trees.
## 2 Materials and Methods
### Selection of Relevant Variables for FSV Estimation  
For the MLR method, the \"redun\" function was used to avoid collinearity. The parameter \(R^{2}\) was set to 0.9. Then, the variables were further filtered based on their significance (i.e., \(P<0.05\)) and variance inflation factor (i.e., \(\text{VIF}>10\)). For the machine learning methods, in order to obtain the best fit model, the selection of the relevant variables for the FSV estimation was carried out using the \"variable selection using random forests\" (VSURF) package [83]. All the extracted data were analyzed in order to obtain the variable importance measures (VIM) based on the VSURF package in the R 3.5.3 software (R Core Team, Vienna, Austria). Importance type 1 represents the percentage increase in the mean square error (PercentIncMSE), and importance type 2 represents the increase in the NodePurity (IncNodePurity); each had the ability to evaluate the importance of variables [83]. The selected variables were then used in RF modeling and SVR modeling.
## 2 Characterization of Bacterial Biofilms
### The Occurence of Bacterial biofilms  
Bacterial biofilms are capable of adhering to a wide variety of surfaces, both biotic and abiotic, which can be observed in every natural environment, such as streams, lakes, and oceans, as well as on medical devices and in human tissues (for example, teeth or heart valves) [18]. Recent investigations suggested the occurrence of biofilms in extreme environments, such as acid mines (pH close to 0), thermal springs, and the \"desert-like\" lake ice cover of Lake Bonney, Antarctica [10]. Due to their widespread occurrence, biofilms represent a major threat causing infectious diseases and economic losses. The National Institutes of Health (NIH) revealed that over 80% of microbial infections in the body are associated with biofilms (program announcement PA-03-047) [16]. The formation of biofilms in healthcare settings is extremely problematic. Biofilm has been found on medical devices and prostheses, water lines and tubing, endoscopes, intravenous and urinary catheters, and wounds [19; 20; 21]. From these surfaces, microbes can readily spread to patients and cause acute infections.  
Biofilms may be composed of only a single or of different types of microbial species. Interestingly, microorganisms which are attached to dental plaque potentially leading to dental caries consist of _Pseudomonas aerobicus_ and _Fusobacterium nucleation_. They are the main agents of gingivitis and periodontitis [22]. Moreover, contact lenses are mainly colonized by _Escherichia coli_, _Pseudomonas aeruginosa_, _Staphylococcus aureus_, _Staphylococcus epidermidis_, _Serratia_ spp., and _Proteus_ spp. bacteria and various species of the _Candida_ genus. Additionally, venous catheters can be contaminated by _P. aeruginosa_, _Enterobacter_ spp., and _Klebsiella_ spp., while urinary catheters can be contaminated by _E. coli_, _Enterococcus faecalis_, _S. epidermidis_, _P. aeruginosa_, _Proteus mirabilis_, _Klebsiella pneumoniae_, and other Gram-negative bacteria [9].  
Examples of biofilm-associated infections also include lung infections in cystic fibrosis, caused by _P. aeruginosa_, eventually leading to lung failure [23]. Biofilms are also the root of many problems in the food industry. Microbes that occur in food preparation and water distribution systems can result in contamination of food products, causing persistent and chronic bacterial foodborne infections. Pathogens such as _E. coli_, _Salmonella_ spp., _Shigella_ spp., _S. aureus_, _Vibrio_ spp., _Campylobacter jejuni_, _Clostridium_ spp., and _Listeria monocytogenes_ form biofilms in the food processing environment [15].
## 4 Discussion  
The main objective of this research was to analyze and compare the EMG activity of the muscle groups of the lower limbs produced during the execution of the leg press with a traditional SLP machine, and OFE-SLP and BIOFIT-LP machines. The results showed that in all three machines, the most activated muscle group was the vastus lateralis, followed by the rectus femoris and tibialis anterior. These results coincide with those found in a similar study, in which a high activation of the vastus lateralis, as well as the rectus femoris and tibialis anterior, was observed during the use of an SLP [22, 27, 40]. In light of these results, the performance of exercises involving simultaneous ankle and knee extension, regardless of the small biomechanical differences between exercises, involves the same major muscle groups [11]. This corroborates the findings of recent studies, in which muscle electrical activity was analyzed in exercises such as the SLP, the leg press at 45\({}^{\circ}\), and the squat, finding that they all activate the same major muscle groups, despite their differences in execution, as they are all lower limb press exercises [1, 11, 14]. Furthermore, as all exercises are performed on stable, guided machines, the core musculature may be more involved, as they rely less on the stabilizing musculature, as compared to exercises with free weights [41]. Therefore, it is possible to proceed to the comparison of the machines with each other. In this regard, an outstanding result of the present investigation was that when comparing machines with the same percentage of intensity, more specifically the EMG activities measured in the SLP and the BIOFIT-LP, similar percentages of activation were found both at 60% and 75% of the 1 RM in the main muscle groups involved, in the full stroke, and in the concentric and eccentric phases.
## 4 Measurement Results and Performance Analysis
Thanks to the use of 16 array elements, the radiation pattern performance of this system is superior compared to some of the above referenced papers using beamforming for GNSS systems. For example, in [14], a 2 \(\times\) 2 antenna array is implemented using SDR software, MUSIC. This antenna array achieves a maximum gain of approximately 11 dBi, a half-power beamwidth (HPBW) of 60\({}^{\circ}\), an SLL of less than 9 dB when the main beam is steered to 30\({}^{\circ}\), and a first-null-to-peak difference of 17 dB. In contrast, the proposed 16-element array achieves a significantly higher maximum gain of 14.9 dBi, a narrower HPBW of 30\({}^{\circ}\), an SLL of 15 dB when the beam is pointed at boresight, and a first-null-to-peak difference exceeding 35 dB.
## 1 Introduction
### Our Contribution  
To address the issue of step effects in smooth areas and the loss of edge information during image reconstruction using traditional regularization methods, this paper proposes a SOGTV algorithm for EIT. The proposed method demonstrates superior performance compared with traditional algorithms:  
* We propose a SOGTV regularization algorithm that can more effectively smooth the noise of EIT images while preserving the edge information of key lung structures. Compared with traditional algorithms, the new algorithm preserves edges more precisely when processing EIT data.
* To address the dual problem in the SOGTV regularization model, we propose an improved ADMM that combines Nesterov gradient descent and variable orientation multiplier (ADMM) methods. Our algorithm solves the model by leveraging the equivalent form of SOGTV.
* To evaluate the effectiveness of the algorithm, we initially utilized simulation data based on EIDORS for conducting the simulation experiments. Subsequently, we employed the physical model of the circular water tank that we developed to validate our findings. The imaging results clearly demonstrated that our algorithm is capable of accurately identifying the perturbation position of the acrylic cylinder.
# Method to Measure Light Spot Position in Beacon-Less Inter-Satellite Communication System
## 4 Selection of the Modulation Frequencies  
The response speed of the ATP system depends in part on the sampling speed and data length. Furthermore, from Equation (20), we can see that data length \(N\) is proportional to the sampling frequency and inversely proportional to the modulation frequency. Therefore, reasonable selection of the sampling frequency and the modulation frequencies is needed to reduce the amount of data. According to the simulation in Section 3.4, the selection of the frequencies is divided into the follow steps:  
Step 1: determine the sampling frequency \(f_{s}\), the modulation frequency \(f_{1}\) and the effective data length \(N\);  
Step 2: make the modulation frequency \(f_{2}\) scan at a certain step in the selected frequency band, and record the results of CCR method at each frequency point.  
Step 3: select a frequency value for \(f_{2}\) from the frequency points at which CCR can detect the correct amplitude value of photocurrent of the beacon signal.  
In the following experiment, the sampling frequency was 100 MHz, \(f_{1}\) was 280 kHz, and the sampling data length was 5000 points. We chose two frequency values for\(f_{2}\) to verify the validity of the proposed method, one frequency is 200 kHz and another is 300 kHz.  
Figure 3: The detected amplitude \(A_{1}\) by cyclic cross-correlation (CCR) and normal cross-correlation (NCR) at different \(f_{2}\) frequencies.
## 4 Preparation of Plasmonic Thin Film Sensors
### Reactive Magnetron Sputtering Deposition
Reactive magnetron sputtering is characterized by the addition of reactive gases, for example, oxygen (O\({}_{2}\)) and nitrogen (N\({}_{2}\)), to the \"working\" gas (usually argon, Ar). Those gases are mentioned as \"reactive\" since they are introduced to chemically bond to the sputtered atoms. In this way it is possible to produce complex metallic oxides/nitrides thin films with a specific stoichiometry, such as aluminium oxide (Al\({}_{2}\)O\({}_{3}\)), aluminium nitride (AlN), titanium dioxide (TiO\({}_{2}\)), copper oxide (CuO), and many more [146; 152]. The magnetron sputtering systems used most employ a primary vacuum pump (e.g., oil-sealed rotary vane, diaphragm, or piston) to produce a medium vacuum, i.e., lower than 1 Pa in the deposition chamber. After this pressure is reached, a high/ultrahigh vacuum pump (e.g., turbomolecular, ion, oil diffusion, or cryogenic pump), backed up by a fore-vacuum pump (for example, the primary vacuum pump) produces a base pressure lower than \(10^{-4}\) Pa in the deposition chamber. When this pressure is reached, the working gas (e.g., Ar) and, if needed, the reactive gases (e.g., N\({}_{2}\) or O\({}_{2}\)), are introduced (Figure 12) using mass flow controllers. Then, after configuring the desired parameters (e.g., current, potential, power, frequency, pulse width, time on, etc. ), the power source (e.g., DC, RF, or pulsed DC power source) is turned on to generate and maintain the plasma, and the sputtering deposition process occurs. In addition, to prepare nanocomposite thin films where Au nanoparticles are embedded in the oxide matrix, the sputtering target is initially modified with small pieces (disks/\"pellets\") of the desired metal, as depicted in Figure 12.