esa-sceva/satcom-chunk-collection huggingface dataset:
# An Extremely Close Vibration Frequency Signal Recognition Using Deep Neural Networks
## 5 Conclusions  
This study discusses the new novel extremely close vibration frequency signal recognition system using image-based DNN. Comparative analysis with traditional signal analysis methods, such as Fast Fourier Transform (FFT) and Short-Time Fourier Transform (STFT), was conducted using identical signals. Surprisingly, the results revealed inaccuracies in vibration frequency detection by FFT and STFT methods at specific times, highlighting the limitations of these conventional approaches.  
Key findings from the analysis include:  
* Simulation results showcase the remarkable accuracy and confirm the expectations of the proposed DNN model, achieving an impressive validation accuracy of 95.16%.
* The ability of the proposed system to discern extremely close frequency vibrations using an unmodified Fiber Bragg Grating (FBG) sensor demonstrates its high sensitivity and precision.
* Extensive testing data utilized by the proposed system enabled accurate identification of both single and superposition vibration frequency signals without relying on signal analysis methods.
* Utilizing a simple and cost-effective FBG experimental setup employing a single FBG further enhances the practicality and accessibility of the proposed system for vibration frequency detection.  
Moreover, the proposed system exhibits potential applicability across diverse vibration sensors and sources, capable of handling complex superposition vibration frequency signals with or without noise. This underscores the versatility and robustness of the proposed system approach in addressing various vibration monitoring needs across different engineering applications.  
**Author Contributions:** Conceptualization: M.P.J., C.-K.Y. and P.-C.P.; methodology: M.P.J. and P.-C.P.; data preparation: M.P.J., M.I.L. and P.-C.P.; software: M.P.J., M.I.L. and P.-C.P.; model validation: M.P.J., M.I.L. and P.-C.P.; formal analysis: M.P.J., Y.C.M., A.M.D. and P.-C.P.; investigation: M.P.J., C.-K.Y., A.M.D., Y.C.M. and P.-C.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Science and Technology Council, Taiwan, under Grant NSTC 112-2221-E-027-076-MY2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available in this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Utilization of Wool Integral Lipids to Determine Milk Fat Content in Suffolk Down Ewes
## 2 Materials and Methods
### Wool Analysis  
Wool samples from Suffolk Down ewes were collected from the superior left-side hind leg at 30 and 60 days in milk. The wool was cut near the skin and stored in individually labeled paper bags at \(-20\)\({}^{\circ}\)C until analysis. Prior to internal-lipid extraction, it is important to remove the surface lipids and contaminants, especially from wool; thus, the wool was cleaned by rinsing with dichloromethane, evaporated under nitrogen gas, and dried at 35 \({}^{\circ}\)C for 5 h [14,22]. A total of 1 g of clean wool was excised and ground in a mortar with liquid nitrogen. The wool fatty acids were extracted using a modified Folch method [19]. Briefly, total lipids from ground wool were extracted with 20 mL hexane/isopropanol (2:1 \(v/v\)). The samples were magnetically shaken and filtered. Lipids were retained in the hexane layer. After concentrating and evaporating the solvent with nitrogen gas, the extracted lipids were weighed to determine wool fat content [14]. For the formation of fatty acids methyl esters (FAMEs), 1.3 mL potassium hydroxide in 2 N methanol, and 0.8 mL n-hexane were added for lipid extraction, followed by shaking for 30 min. The supernatant was filtered with anhydrous sodium sulfate and transferred into a vial for direct injection of 1 \(\mu\)L into a gas chromatography model Clarus 500 (Perkin Elmer, Buckinghamshire, UK) equipped with a flame ionization detector (FID), autosampler, split injection, and a fused silica capillary column SPT\({}^{\mathrm{TM}}\) 2380 (60 m \(\times\) 0.25 mm \(\times\) 0.2 \(\mu\)m film thickness) (Supelco, Bellefonte, PA, USA). The following chromatograph temperature conditions were applied: The initial oven temperature was 150 \({}^{\circ}\)C (held for 1 min). This was subsequently increased to 168 \({}^{\circ}\)C, at a rate of 1 \({}^{\circ}\)C min\({}^{-1}\) (held for 11 min), and to 230 \({}^{\circ}\)C at 6 \({}^{\circ}\)C min\({}^{-1}\) (held for 8 min). The temperature of the injector and FID was 250 \({}^{\circ}\)C, and nitrogen was used as the carrier gas, at a flow rate of 1 mL min\({}^{-1}\). Finally, FAMEs were individually identified by comparison with the standard FAME Mix C4-C24 (Supelco, Pennsylvania, PA, USA), which contains 37 FAMEs, and analyzed under the same conditions. For the quantification of wool FAs, we used the internal standard nonadecanoic acid analytical standard (C19:0, Merck Darmstadt, Germany), which was added to each sample and the standard mixture.
# Multi-Tag Fusion Localization Method Based on Geometric Constraints
## 2 Visual Tag Process
### AprilTag3 Detection  
In the pursuit of a pose estimation method well-suited for our application, AprilTag3 [33] has been identified as the optimal choice, predominantly attributable to its outstanding capabilities in delivering precise orientation results and elevated detection rates. AprilTag3, a sophisticated iteration within the AprilTag family, stands out for its open-source accessibility, high real-time processing capabilities, and enhanced accuracy.  
This advanced version has undergone significant optimization, resulting in a more robust algorithmic framework. Notably, these enhancements have led to substantial gains in computational efficiency and a reduction in memory usage, making AprilTag3 an attractive proposition for resource-intensive applications. Additionally, AprilTag3 supports an innovative format of two-dimensional codes, expanding its utility and applicability.  
Figure 1: Common visual labels: (**a**) ARTag; (**b**) ArUco; (**c**) AprilTag; (**d**) Stag.  
As depicted in Figure 2, the algorithmic process begins with an image filtering step that downscales the initial image to accelerate subsequent operations. This is followed by adaptive thresholding, which generates a binarized image that clearly delineates the tag boundaries against the background.  
The subsequent edge detection phase employs the union-find algorithm to segment the image into meaningful components, effectively filtering out irrelevant sections and thus enhancing the precision of the detection process. Quadrilateral fitting then identifies potential tags from clustered line segments through a calculated sequence of operations.  
To address perspective distortion, a perspective correction step is integrated, utilizing a transformation matrix to reconcile the disparity between the tag's anticipated and actual poses. The final stage involves image sharpening to mitigate blurriness, enabling the clear decoding of the tag ID and the determination of the tag's relative position to the camera.
# Deep Learning Model for the Inspection of Coffee Bean Defects
## 3 Results
### Experiment Settings  
The modifications performed in this study are described in the following text.  
* Feature extraction and dimensionality reduction  
Three improved convolutional layers and one regular convolutional layer were used. Each convolutional layer was connected to an improved pooling layer to enhance image features.
* Padding  
Padding was removed from all the convolutional layers.
* Activation Function  
The activation function was changed to the leaky ReLU from the regular ReLU.  
Three models were constructed for comparison. In the new network-dr model, only the dimensionality reduction function was modified; in the new network-dr-pv model, the dimensionality reduction and padding functions were modified; and in the new network-final model, all the functions were modified. Table 4 presents the configuration of the new network-final model.  
An optimized model should be highly accurate and generalizable so that it can be used to detect coffee bean defects in real life. In this study, the accuracy rate (6) was used to evaluate model training. Specifically, the accuracy of the models in extracting features from the images of each coffee bean type was calculated, and the kappa (7) was used to determine the homogeneity of model generalization. Confusion matrices were used to analyze the classification of each bean type to determine the efficiency of the constructed models in predicting the classification of each sample and to evaluate the error distribution.  
\[(Accuracy)=\big{(}t_{p}+t_{n}\big{)}/(t_{p}+f_{p}+t_{n}+f_{n}\big{)} \tag{6}\]  
where \(t_{p}\),\(f_{p}\), \(t_{n}\), and \(f_{n}\) are true positive, false positive, true negative, and false negative.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **Patch Size** & **Strides** & **Padding** & **Remark** \\ \hline Input layer & \(112\times 112\times 3\) & & & \\ Conv. \(1\) & \(7\times 7\times 32\) & \(2\) & Vaid & LRN, Leaky ReLU \\ Pool. \(1\) & \(3\times 3\times 32\) & \(1\) & Same & \\ Conv. \(2\) & \(5\times 5\times 72\) & \(2\) & Vaid & LRN, Leaky ReLU \\ Pool. \(2\) & \(3\times 3\times 72\) & \(1\) & Same & \\ Conv. \(3\) & \(3\times 3\times 96\) & \(2\) & Vaid & LRN, Leaky ReLU \\ Pool. \(3\) & \(3\times 3\times 96\) & \(1\) & Same & \\ Conv. \(4\) & \(3\times 3\times 72\) & \(1\) & Vaid & LRN, Leaky ReLU \\ Pool. \(4\) & \(3\times 3\times 72\) & \(1\) & Same & \\ Flatten \(6\) & \(7200\) & & & \\ Full connect\(7\) & \(512\) & & & Dropout \\ Full connect\(8\) & \(512\) & & & Dropout \\ Output layer & \(8\) & & & Dropout \\ \hline \hline \end{tabular}
\end{table}
Table 4: Configuration of the new network-final model.  
\[kappa=\frac{p_{0}-p_{e}}{1-p_{e}} \tag{7}\]  
where \(p_{o}\), is the relative observed agreement among raters, and \(p_{e}\) is the hypothetical probability of chance agreement.
# Structural Correlation Based Method for Image Forgery Classification and Localization
## 6 Experimental Results
### Evaluation Metrics
#### 6.2.2 Metrics for Image Forgery Classification and Localization  
Since we concurrently classify image forgery types and localize the forged regions, the evaluation is performed in both image and pixel levels.  
To quantitatively evaluate the performance of forgery localization, we adopt two metrics for tampered regions of a classified tampered image [19], localization precision \(L_{P}\) and localization recall \(L_{R}\), which are defined as follows:  
\[L_{P}=\frac{\text{\# correctly detected pixels}}{\text{\# all detected pixels}}. \tag{18}\]  
\[L_{R}=\frac{\text{\# correctly detected pixels}}{\text{\# all tampered pixels}}. \tag{19}\]  
Similarly, we define the classification precision \(C_{P}\), and recall \(C_{R}\) at image level:  
\[C_{P}=\frac{\text{\# correctly detected tampered images}}{\text{\# all detected tampered images}}. \tag{20}\]  
\[C_{R}=\frac{\text{\# correctly detected tampered images}}{\text{\# all tampered images}}. \tag{21}\]  
In order to balance between precision and recall, we consider both of these quantities by computing their harmonic mean, called localization \(F\)-measure, as follows:  
\[L_{F}=\frac{2L_{P}L_{R}}{L_{P}+L_{R}}. \tag{22}\]  
\[C_{F}=\frac{2C_{P}C_{R}}{C_{P}+C_{R}}. \tag{23}\]  
The metrics precision, recall, and \(F\)-measure at pixel level are used for all 3 datasets in this work. Nevertheless, the metrics at image level are only used to evaluate performance of the proposed method in MICC-600 and GRIP datasets. To evaluate the classification performance in CASIA 2, which contains 3 classes, we use confusion matrix.
## 2 Materials and Methods
### Workflow and Model Implementation
#### 2.4.4 Inference and Heat Maps  
After training the U-Net model and fine-tuning the network parameters in an iterative process, the best-performing model and corresponding weights were used to identify the aquatic vegetation on hold-out/OOB satellite data at the pixel level. With new input image feeds, our model outputs four layers of pixel-by-pixel segmentation, including floating vegetation, emergent vegetation, water, and land. Each output pixel prediction is accompanied by a probability of the object belonging to one of the four classes. For visualization purposes, we combined four layers of segmentation and color-coded each layer to form a single inference image. The inference image displaying the spatial distribution of the aquatic floating vegetation identified through field observations as the most suitable snail habitat can then be used for examining disease transmission risk [7].
## 3 Results and Discussion
### Monitoring the Volatile Compound Profiles in Seaweed Fermentation
#### 3.2.2 Laminaria
Moreover, 2-heptanone (436.59 \(\mathrm{\SIUnitSymbolMicro g}\)/L) was formed after fermentation with BS, and its content was dominant at around 33% of total ketones (1308.42 \(\mathrm{\SIUnitSymbolMicro g}\)/L) in the fermented _Uliva_ (Table 1). The difference in volatile compounds between the SC and the unfermented group based on PC2 is shown in Figure 4A. Based on the loading plot (Figure 4B), 1-hexanol is in the positive axis of PC2, which indicates its contribution to the difference in volatile compounds between the SC and the unfermented _Uliva_. This result might correspond to the formation of 1-hexanol (289.18 \(\mathrm{\SIUnitSymbolMicro g}\)/L) after SC fermentation (Table 1). Based on our GC-O analysis, the odor intensity of 1-hexanol, described as floral and sweet, is enhanced by SC fermentation. Therefore, 2-heptanone might contribute to the difference between  
Figure 2: Heat map of the volatile compounds in the brown seaweed _Laminaria_ sp. fermented by various microorganisms. Colors indicate the Z-score distances to the mean levels of the observations. A Z-score greater than 1.645 (red color) or lower than \(-\)1.645 (blue color) is significant at the 0.05 level. UF: unfermented; BS: _B. subtilis_; SC: _S. cerevisiae_; LA: _Lb. acidophilus_; LB: _Lb. delbrueckii_ subsp. _bulgaries_; LC: _Lb. casei_. _Ulva_ fermented with BS and unfermented _Ulva_. Furthermore, 1-hexanol plays an important role in the fermentation of _Ulva_ with SC compared with the unfermented _Ulva_ (Figure 4). These results are consistent with the PCA of chemical groups shown in Figure 3.  
Figure 4: Principal component analysis plots of the volatile compounds in the green seaweed _Ulva_ sp. fermented by various microorganisms: (**A**) score plot (**B**) loading plot. UF: unfermented; BS: _B. subtilis_; SC: _S. cerevisiae_; LA: _Lb. acidophilus_; LB: _Lb. delbrueckii_ subsp. _bulgaricus_; LC: _Lb. casei_. Figure 3: Principal component analysis plots of chemical groups of the volatile compounds in the green seaweed _Ulva_ sp. fermented by various microorganisms: (**A**) score plot (**B**) loading plot. UF: unfermented; BS: _B. subtilis_; SC: _S. cerevisiae_; LA: _Lb. acidophilus_; LB: _Lb. delbrueckii_ subsp. _bulgaricus_; LC: _Lb. casei_. Sulfur-conta, sulfur containing compounds; benz, benzene derivatives.
# Validation of a Useful Tool for Screening for Overweight and Obesity in Pre-Adolescents
## 1 Introduction
There were significant differences in overweight and obesity values with respect to sex; girls were 1.5% more overweight than boys, but in terms of obesity, boys were 4.6% more obese than their female counterparts. At the national level, in Spain, the ALADINO study carried out in 2019 [14; 15; 16] showed the prevalence of overweight and obesity in children aged 6-9 years across 276 schools in all Spanish regions with a sample of 16,665 schoolchildren. The prevalence of overweight was 23.3% (21.9% in boys and 24.7% in girls) and the prevalence of obesity was 17.3% (19.4% in boys and 15.0% in girls) using WHO growth standards for infants [17]. Thanks to these two studies, ALADINO 2015 and ALADINO 2019, it can be observed that there is a stable trend in the prevalence of overweight, and a minimal but visible decrease in the prevalence of childhood obesity in Spain. Given the introductory background, there is the possibility of sex differences in the factors that determine the occurrence of overweight or obesity in childhood. In this regard, it seems that the stage of sexual development coincides with a generalised weight gain in both boys and girls, with differences between them. Other important factors are food, obesogenic environments, eating patterns and physical activity. In this sense, it would be advisable to have a tool to determine whether overweight or obesity is sensitive to these factors. Regarding the screening for childhood overweight and obesity, body mass index percentiles linked to cut-off points for children can be used as a reference. These percentiles vary according to age and sex; reference tables for height-for-age and weight-for-age in boys and girls were obtained from the WHO [18]. In this context, a new effective tool for detecting childhood overweight and obesity would be desirable. The mid-upper arm circumference (MUAC) tape would provide the simplicity of a single measurement, which is quick, inexpensive, without the need for additional devices and usable in any setting, such as school, primary care or community,so that early nutritional strategies can be implemented to tackle this pandemic. Arm circumference measured with MUAC tape can be used as a useful tool for nutritional screening for overweight and obesity, both in childhood and adolescence. The tape is plasticised, used on the right arm, which is naked and relaxed, and the measurement is taken at the midpoint between the tip of acromion and olecranon processes [19; 20; 21; 22].
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Data Processing  
Once the data streams from the aircraft, the GNSS reference stations, the kinematic GNSS data for independent elevation validation and the weather data have been collected the data reduction or processing to produce the final products can be accomplished. Despite the fact that the description of the data processing steps presented in the following subsection might seem to flow in a linear fashion, the processing is far from linear. Processing ALS observations generally is cyclical in nature. Information obtained from the last step in the process may, and often does, indicate that the results will be improved if changes are made to any of a number of previous steps, including even the earliest steps such as determining the aircraft trajectory from the GNSS and IMU (Inertial Measuring Unit) data. The process may cycle back and forth between refinement of the aircraft trajectory and generating raster images, or between raster processing and ground return classification. Another aspect that needs to be emphasized is that, despite the fact algorithms and software development have reached a point where it is possible to do almost fully automated ALS data processing, ALS data product generation is still an art as much as a science and it takes significant experience to produce research quality datasets.
# IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey
## 3 IoT Devices and Cloud Processing
### Data Transmission Technologies
#### 3.2.3 Nb-IoT  
Narrowband IoT is another LPWAN technology available today. It was developed by the 3 GPP organization, with the first NB-IoT standard defined in 2016. This communication network is based on and is directly linked to the GSM/LTE cellular network standard. Modifications consist of changes in the original technology to best meet the requirements for IoT end-device network connectivity (such as low power consumption, low cost or the transmission of small amounts of data over longer distances). Compared to the LTE protocol, NB-IoT only allows half-duplex at a bandwidth of 180 kHz. Modulation schemes more complex than \(\pi/4\)-QPSK are not supported. Not all parts of the E-UTRA specification are implemented either. Thanks to this aspect, the receiver is simpler and therefore less energy-demanding and lower in price [90; 91; 92].  
The standard allows transmission using both unlicensed bands and licensed GSM/LTE brands in one of the following operation modes:  
* Stand-alone operation--When using this mode, NB-IoT is the exclusive user of the selected frequency band.
* Guard-band operation--In this mode, NB-IoT data are transmitted through an unused frequency band next to, or between, LTE bands (the so-called guard band).
* In-band operation--LTE data together with NB-IoT data share the same carrier. This can have a negative impact on the quality of both services [90; 93].  
The existing LTE network provider often only needs to modify the software to operate the NB-IoT network if it decides to deploy the guard-band or in-band model. This approach is also recommended by the 3 GPP organization itself. The usable distance of the IoT node from the base station is up to 10 km [85]. The payload of one message is limited to 1600 bytes. There is no limit on the number of messages transferred per day when using the licensed band. NB-IoT relies, like the LTE protocol, on orthogonal frequency division multiplexing (OFDM) to transmit downlink messages. In the case of uplink transactions, OFDM is replaced by single-carrier frequency division multiple access (SC-FDMA). This reduces the value of the peak-to-average power ratio (PAPR) [94].
# Optimized Analytical-Numerical Procedure for Ultrasonic Sludge Treatment for Agricultural Use
## 4 Integration of an Innovative Approach in the Treatment of Sludge in Sewage Treatment Plants  
Laboratory-tested sonication treatment grafted inside the plant (see Figure 2) lasting up to 150 min, with the usual sampling carried out every 5 min (measuring COD, temperature, and PDI), was performed on an 8-liter sludge sample, applying a 230 V amplitude voltage at 50 Hz (constant frequency throughout the treatment duration), with a maximum flow rate of 50 L/min, with a contact time (total time the slurry remains in contact with a chemical reagent) per liter of 2.40 s. Four units with a peak power of 1.7 A were used to produce US waves to achieve the ideal US frequency of 150 kHz, corresponding to maximum cavitation. As can be seen from Table 2, the data obtained do not reveal any incompatibilities of sludge use for agricultural purposes, both in terms of heavy metals and the possible presence of bacteria (Salmonella). It is worth noting that both the treatment duration (150 min) and the contact time of 2.40 s depend on the sludge composition [60]. The prototype is powered by four sets of US generators, each with a maximum draw of 1.7 A to ensure the ideal frequency of 15 kHz, leading to the maximum detected cavitation (usually achievable between 1.5 A and 1.7 A). Table 3 summarizes the operational parameters of sonication, while the experimental apparatus is displayed in Figure 3.  
Therefore, we consider proceeding with sludge humidity reduction using a thermal approach. We propose a physical-mathematical model that couples temperature with humidity to accurately quantify the volume of liquid and condensate inside the sludge treatment tank.  
Figure 2: Integration of the proposed approach (enclosed by the green dashed line) into the sludge treatment process within a wastewater treatment plant (red dashed line). Section (**a**) represents the conventional wastewater treatment chain, including pretreatment, sedimentation, biological treatment, and sludge management. Section (**b**) highlights the proposed approach, combining monitoring by humidity and temperature sensors with a sonication system aimed at sludge moisture reduction (sludge humidity reduction with \(\Delta T\)).