esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Method
### DNN Training and Optimization
#### 2.3.2 DNN Optimization Based on PSO  
As for the DNN models, the quantities of hidden nodes and hidden layers are the most significant parameters, which decide the ability of DNNs to capture useful information from massive input data. The architectures of a DNN model can be defined as follows:  
\[\text{DNN}[\text{param1};\text{param2}_{1},\cdots\text{param2}_{j};\text{param 3}] \tag{6}\]  
where parameters represents the number of input nodes, \(\text{param2}_{i}\) denotes the number of hidden nodes of it hidden layer, while \(\text{param3}\) stands for the number of output nodes.  
A lot of research reveals that too few hidden nodes usually make the network not competent enough for modelling the data, while too many hidden nodes may trigger some problems such as over-fitting and even lead to unreliable results at last [12]. However, until now, there is no mature theory that has been reported for computing the exact quantity of hidden nodes or layers, which remains the construction of DNN that is still an intractable task.  
In this paper, we propose to use the particle swarm optimization (PSO) algorithm to optimize the model parameters of DNNs. The particle swarm optimization algorithm can be regarded as a process for the global optimization of the population, which can be effectively applied to many optimization problems. The PSO algorithm adopted in this study can realize the establishment of the optimal DNN model through iterating the model parameters.  
The target parameters of the optimization include the number of nodes in the hidden layer, the order of training in the DNN model, and the number of trainings per level. Usually, the number of nodes in the input layer is less than half of the number of hidden layers, and the number of nodes in the current hidden layer is generally not less than 2 times that of the next layer. For example, if the DNN model has a hidden layer of 2, with L input nodes and 1 output node. L denotes the feature dimension after transformation, and is generally not greater than 6, then the values of the number of nodes in the  
Figure 2: Structure of DNN.  
two hidden layers can be set as 12 to17 and 6 to 8, respectively. The verification shows that when the order of the model training exceeds 8, the error generated by the model increases exponentially, so the order of the model training is set as 1 to 7. Additionally, for the number of trainings of each order, it is required to be divisible by the number of elements included in the input node, taking into account the performance of the computer and the time PSO algorithm need, the times of training are tentatively set as {500, 1000, 1250, 2000, 2500, 4000, 5000, 6250, 10,000, and 12,500}.  
After optimization, the parameters param2\({}_{1}\), param2\({}_{\text{j}}\) in Equation (6) will be determined and thus the optimal DNN model can be obtained.
## 2 Literature Review
### Clock Skew Measurements  
We have analyzed the skew of the clocks of IoT devices across a larger device population by selecting a production batch of 8581 devices for the test. Each device was individually tested by the following automated test procedure:  
1. Tested device generates a voltage pulse with duration of exactly 500,000 \(\upmu\)s. Pulse duration is generated based on internal crystal oscillator.
2. External tester with base accuracy \(\approx\) 1 ppm is used to measure real pulse width.
3. Tests (1) and (2) are repeated 10 times and mean value is calculated for each device.
4. Average of expected clock skew over 1,000,000 s of operation is calculated.  
All the devices were tested at room temperature after similar operation time. Possible measure inaccuracy like electrical pulse rises and fall times have been analyzed and were proven to cause less than 1 ppm inaccuracy. Figure 1 represents a histogram of population size divided by total device count also considered as probability (y axis) with given clock skew (x axis). The red line represents the fitted probability distribution function.  
The observed distribution is very near to the normal distribution. These results are consistent with the phenomenon observed in the literature [13; 14; 15; 29]. Based on this we believe that the normal distribution can be used to estimate the probability of the clock skew.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Attention Mechanism  
The Attention Mechanism is an encoder-decoder technique designed specifically for neural machine translation. This technology was developed to facilitate the translation of neural information into machine information using an encoder and a decoder. The input is transformed through the encoder into a fixed-length vector and then translated via the decoder [115]. In 2014, ref. [116] developed the first Attention Mechanism, which was modeled on the human visual Attention Mechanism. The Attention Mechanism acts similarly to the brain in terms of prioritizing specific elements and is beneficial for many domains, including image analysis [117], video analysis [118], machine translation [119], and others. Moreover, it is effective for time-series forecasting, and its diversified range of applications has been acknowledged by academics.  
The Attention Mechanism might prioritize more significant input features while overlooking other features. This approach influences the way in which we pay attention to information from the outside world in a wise and sensible way, ignoring irrelevant information and emphasizing relevant information. Hence, the application of the Attention Mechanism allows us to process information more rapidly and accurately. The mathematical description and information distribution are expressed as shown in Equations (26) and (27):  
\[e_{i}=\tan h\ (w_{h}\ h_{i}+b_{n}h_{n}),\ e_{i}\in[-1,\ 1] \tag{26}\]  
Figure 9: The architecture of DBN [114].  
\[a_{i}=\frac{exp(e_{i})}{\sum_{i=1}^{t}exp(e_{i})},\ \sum_{i=1}^{t}a_{i}=1 \tag{27}\]  
Self-attention picks up both long- and short-term dependencies and focuses on various aspects of temporal patterns. It is an excellent candidate for time-series forecasting because of these benefits. It is utilized to personalize an attention layer, the parameters of which are specified via an optimization method [119].  
The Self-Attention Mechanism comprises a key matrix (\(K\)), a value matrix (\(V\)), and a query matrix (\(Q\)), as shown in Equation (28):  
\[T=K=V=Q \tag{28}\]  
The basic principle in the self-attention process is the scaled dot-product of attention (SDA). Two stages are involved in the process of determining similarity, which are (1) finding the dot product between \(Q\) and matching matrix \(K\) and (2) dividing it by the matrix \(K\) dimension. To acquire the attention expression, we utilized the SoftMax function to normalize the output before multiplying it by the matrix \(V\). Equation (29) shows how SDA operates.  
\[SDA(K,V,Q)=\textit{SoftMax}(\frac{QK^{T}}{\sqrt{d_{k}}})\ v \tag{29}\]
# Glucose Data Classification for Diabetic Patient Monitoring
## 5 Implementation  
This section describes in detail the implementation of our proposed system including the scheme, system illustration, hardware, and software installation.  
The flowchart of the system in Figure 5 shows the complete system from the measurement of glucose level in the patient until the final decision of the doctor.  
Figure 4: Arduino card.  
Figure 3: GSM modem.  
In this system, we describe the general scheme of the proposed system (Figure 6). The patient can perform a measurement of blood glucose using the blood glucose sensor. After the measurement, the results of the glucose level in the blood will be obtained for the patient. The measurement data is automatically transmitted by the blood glucose sensor that is connected in serial mode with the Arduino card. The data processed by the Arduino card's microcontroller is automatically transmitted to the GSM modem that has been integrated into the Arduino board to send the measurement results to a specialist doctor via SMS.
## 4 Results and Discussion
### Comparison of Shadow Detection Models
Figure 13: ROC curves of the proposed shadow detection system (i.e., MLP\({}_{1}\)), [PERSON]’s and [PERSON]’s approaches. Note that the MLP\({}_{1}\) achieved the highest AUC = 0.92 \(\pm\) 0.005.
## 2 Materials and Methods  
### Experimental Measurements  
Ozone was measured with a certified X-am 5000 multi-gas detector equipped with a metal oxide semiconductor XXS Drager ozone sensor (Draeger Safety S.P.A., Corsico, Milano, Italia). The sensor has a 0-10 ppm range with 0.01 ppm resolution. The multi-gas detector has an optional pump, with a 1.5 m sampling line, that was used to sample the atmosphere inside the dryer while it was working. Ozone data were used on the basis of a valid calibration and performance certificate from the manufacturer.  
A commercially available condenser tumble dryer was used with no hardware modifications beside the ozone generator and ozone piping installation. The dryer was housed in a medium-sized empty room (25.5 m\({}^{3}\)), maintained at 24.5 \({}^{\circ}\)C, 45% relative humidity, with no openings, in agreement with the requirements for household air-cleaning electrical appliances [12]. The dryer was positioned on the floor, at 500 mm from the back wall, and the room was equipped with a computerized recirculation system, used to clean up ozone residuals after the experiments.  
Ozone generated in the dryer was produced with ozone generators (Shanghai Zero Industrial Co. Ltd., Shanghai, China) with a declared nominal production rate of >5, >25 and >60 mg/h. The actual production rate was verified by direct sampling of the generator input and output airflow, with the ozone gas detection system, after a ten-timesdilution with dry synthetic air, to keep the ozone output concentration within the sensor analytical range.  
The ozone half-life in the room conditions was obtained by experimentally measuring ozone decay. The ozone generators were placed on the dryer 1.5 m from the sensor and were turned on until the ozone concentration reached steady state. Then, the concentration was measured continuously for 12 h and the half-life was calculated by fitting exponential decay curves of ozone concentration over time.  
Ozone generators and dryer movements were controlled by the dryer logic board, after adapting the software for the desired cycling times.  
To assess the exposure, ozone concentration was measured in front of the dryer, approximately 300 mm from its door and 300 mm off the floor, simulating a child sitting or playing in front of the appliance. Ozone concentrations in the dryer were obtained by placing the sensor inside, when it was working without textiles. When textiles were present, the sensor sampling tube was placed in the middle of the dryer space. The textiles used were a 1.5 kg mixture of clean, dry, white cotton and pieces of wool fabrics.
## 3 QR Code Navigation and Construction of a Template Matching Library
### QR Code Navigation Process  
First, the AGV uses SLAM to navigate to the area near the specified QR code. Then, the AGV opens the QR code scanning function and switches to QR code navigation after scanning the QR code setting information. Then, the AGV approaches the direction of the center of the QR code until the scanning center of the AGV chassis coincides with the center of the QR code. The vehicle-mounted camera takes the position of the QR code center as the relative position, obtains the spatial coordinates through calculation and conversion, and uses the coordinate transformation to make the AGV scan the center of the QR code and realize normal movement. Figure 11 shows the flow chart of QR code scanning and recognition.  
To calculate the geometric information of objects in three-dimensional space, it is necessary to calibrate the camera. From this, the camera's internal parameter matrix A and R are obtained, and the camera's orientation relative to the world coordinate system is determined. Since the camera's focal length is not 0, and the matrices A and R are reversible, the actual coordinates of pixels in the photo in the AGV coordinate system are calculated using Formula (10).  
\[\left\{\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}=R^{-1}\cdot\left(A^{-1}\cdot\begin{bmatrix}u\\ v\\ 1\end{bmatrix}^{-1}\right)\right. \tag{10}\] \[\left.\left(u_{c},v_{c}\right)=\left(\frac{u_{1}+u_{3}}{2},\frac {v_{1}+v_{3}}{2}\right)\right.\]  
Figure 10: AMCL algorithm simulation: (**a**) trace of error; (**b**) trajectory map; (**c**) \(x\)-axis speed; (**d**) \(y\)-axis speed.  
When the central coordinate of the QR code \(p(u_{c},v_{c})\) is known, the coordinates \(\mathrm{P}(\mathrm{X}_{\mathrm{C}},\mathrm{Y}_{\mathrm{C}},\mathrm{Z}_{\mathrm{C }})\) of the car body coordinate system can be calculated. Due to the use of QR code three-point positioning, the coordinates of the three points of the QR code in the image \((u_{1},v_{1})\), \((u_{2},v_{2})\), \((u_{3},v_{3})\), and because the position of the required detection pattern is known, the coordinates of the center \(p(u_{c},v_{c})\) of the QR code can be calculated, and \(p(u_{c},v_{c})\) is substituted to obtain the coordinates \(\mathrm{P}(\mathrm{X}_{\mathrm{C}},\mathrm{Y}_{\mathrm{C}},\mathrm{Z}_{\mathrm{ C}})\) of the QR code in the coordinate system of the car body. Combined with the coordinate information of the current QR code in the entire map, the position of the AGV in the map can be determined. In Figure 12 (a) represents the relationship between the three coordinate systems, (b) AGV scanning for coordinate transformation.  
Figure 11: QR code scanning recognition process.
# Perceived Brightness and Resolution of Holographic Augmented Reality Retinal Scan Glasses
## 2 Materials and Methods
### Retinal Scan Glasses
#### 2.1.1 Brightness in Retinal Raster Scanning  
The human eye detects retinal illuminance as brightness. Retinal illuminance \(\mathrm{E_{R}}\) is defined as luminous flux \(\mathrm{\Phi_{V}}\) per retinal area \(\mathrm{A_{R}}\).  
\[\mathrm{E_{R}}=\frac{\mathrm{\Phi_{V}}}{\mathrm{A_{R}}} \tag{1}\]  
Figure 1: **(a)** BML500P RS glasses prototype with light engine in the right temple. **(b)** Transparent holographic optical element embedded in the right spectacle lens.  
Display brightness is usually denoted in terms of luminance L, which is defined as the luminous flux \(\Phi_{\rm V}\) emitted into a solid angle \(\Omega\) by a light source with area A\({}_{\rm S}\).  
\[\rm L=\frac{\Phi_{\rm V}}{\Omega\,A_{\rm S}} \tag{2}\]  
Luminance is a measure for the light source, while the perceived brightness depends on the illuminance on the retina. When comparing Lambertian displays with each other, luminance can be used as a proxy for perceived brightness because luminance changes lead to proportional retinal illuminance changes.  
This is, however, not valid for a comparison between Lambertian and RS displays. The size of the human pupil influences the luminous flux \(\Phi_{\rm V}\) from a Lambertian display but not from an RS display, as shown in Figure 2.  
For this reason, measuring the luminance of an RS image with a luminance camera leads to inconsistent results: the larger the entrance pupil of the luminance camera (to which it is calibrated using a Lambertian light source), the smaller the measured RS luminance.  
In the first part of the Results Section, we show that it is nevertheless possible to calculate an equivalent luminance for RS displays. This luminance depends on the RS luminous flux, RS field of view, and human pupil size and is directly comparable with the luminance of Lambertian displays.
# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 2 Materials and Methods
### Spectra Preprocessing and Augmentation Methods  
Before training the model, we first used interpolations to remove the backscattering peak of the 405 nm laser observed during testing. Traditional oil film thickness measurement methods rely on fluorescence intensity and thus require calibration before measurements. However, in real-world environments, wind and waves cause fluctuations in fluorescence intensity that can generate false signals and degrade the accuracy of thickness inversion models. To mitigate this interference, we applied min-max normalization to scale the fluorescence spectra of oil films with different thicknesses to the [0, 1] range, enabling the model to focus on differences in spectral distribution related to film thickness.  
Furthermore, spectral acquisition in practical settings is often affected by environmental and instrumental errors, which introduce noise and interference [38]. Traditional chemometric methods require preprocessing steps, such as noise removal, to improve the signal-to-noise ratio. In this study, to demonstrate the adaptive noise handling capabilities of our proposed framework, we evaluated the performance of the multi-task framework, traditional deep learning models, and chemometric models under various conditions: without preprocessing, with Savitzky-Golay (SG) filtering [39], standard normal variates (SNVs) [40], and normalization. The classification and regression results were statistically analyzed.  
Traditional deep learning methods often require large datasets to learn feature relationships. Since there are no publicly available datasets for oil spill spectra and the collected data are limited, data augmentation is necessary to simulate real variations in spectral data. This helps improve model generalization under different operational conditions. In this study, we applied multiplication, noise addition, and random shift transformations to augment the dataset. The original dataset was split into training, validation, and test sets at a ratio of 3:1:1. Data augmentation was performed on the training set. Multiplication was adjusted using \(\pm\)0.1 times the standard deviation of the training set, simulating variations in spectral intensity caused by sensor sensitivity and baseline shifts. Noise addition ranged from 1% to 5% of the training set's standard deviation, mimicking spectral variations due to environmental light and instrument noise in real scenarios. Random shifts, limited to within 5 nm, were applied to simulate wavelength calibration differences between instruments. These shifts enhance model adaptability to various spectrometers, as calibration methods may cause slight wavelength offsets in measured spectra. For each spectral sample, the augmentation process was repeated nine times, increasing the dataset size from the original 4200 samples to 26,880.
# Cluster-Centered Visualization Techniques for Fuzzy Clustering Results to Judge Single Clusters
## 2 Clustering Methods
Algorithms like hierarchical clustering, or DBSCAN, need a distance matrix, whereas Gaussian and other mixture models, as well as \(k\)-means clustering and its fuzzy version, work directly with data vectors in the multidimensional space. Of course, distance matrices can also be based on the data vectors, for instance, by computing the pairwise Euclidean distances. But they can also be based on other distance measures, e.g., one-minus correlation. The membership degrees in fuzzy clustering are based on relative distances--for details, see Equation (1) in Section 2--leading to the advantage that one can recalculate distances from the membership degrees and use the distances for visualization techniques, no matter whether these distances are Euclidean distances, locally scaled Euclidean distances as in the Gustafson-Kessel algorithm [7] or abstract distances derived from correlations. For our cluster visualization techniques, we exploit the interdependence between membership degrees and distances. But the most important aspect of our visualization techniques is that we do not try to visualize the clustering result as a whole, but for single selected clusters.
# Rational Design and Modification of NphB for Cannabinoids Biosynthesis
[PERSON]  
1 New Comerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China, [EMAIL_ADDRESS] (W.X. ); [EMAIL_ADDRESS] (X.C.) [PERSON]  
1 New Comerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China, [EMAIL_ADDRESS] (W.X.C.) [PERSON]  
1 New Comerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China, [EMAIL_ADDRESS] (W.X. ); [EMAIL_ADDRESS] (X.C.) [PERSON]  
1 New Comerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China, [EMAIL_ADDRESS] (W.X.C.) [PERSON]  
1 New Comerstone Science Laboratory, Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an 710072, China, xiawenhao@synbilab. and extensive land use [18; 19]. Consequently, synthetic biology has become an attractive approach to synthesize cannabinoids more efficiently [20; 21; 22; 23]. The prenyltransferase (NphB) is the first step in combining geranyl diphosphate (GPP) and olivetolic acid (oa) to produce cannabigerolic acid (CBGA) [22]. NphB is capable of catalyzing the C- and O-geranylation of a variety of aromatic compounds [24; 25]. It can catalyze the geranylation of OA at the C3 position to form CBGA (minor) and at the O2 position to form 2-O-geranylolivetolic acid (2 OG, major) [20]. Numerous studies have reported that enhancing its product specificity and kinetic performance enables the efficient synthesis of CBGA [26; 27; 28]. This is the common precursor of cannabinoid compounds in the cannabis biosynthetic pathway [29; 30; 31]. CBGA could be further converted into CBDA, THCA, CBD, THC, and other cannabinoids [32; 33; 34]. Although CBGA biosynthesis and some non-natural analogs have been achieved in yeast- and cell-free catalysis [27; 35], the low catalytic activity for NphB still is a critical rate-limiting factor in the synthesis process. There is a strong need for further research into its catalytic mechanisms to improve yields. In this study, we employed the rational design and modification of NphB to enhance the biosynthesis of various cannabinoid scaffolds. Additionally, we revealed its catalytic mechanism that results in the synthesis of different cannabinoids by NphB mutants. These results lay the foundation for synthesizing rare cannabinoid compounds.