esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model
### Phase I: Source to Relay Multiple Access Channel  
Let \(\mathbf{s}_{l}\in\Lambda\) be the message vector to be transmitted by \(l-\)th source (\(l=1,\ldots L\)), where \(\Lambda\subset\mathcal{L}\) and \(\mathcal{L}=\{\lambda=\mathbf{G}\mathbf{v}\mid\mathbf{G}\in\mathbb{R}^{n\times n },\mathbf{v}\in\mathbb{Z}^{n}\}\) is an \(n-\)dimensional lattice with generator, \(\mathbf{G}\), having components over time. The message vector satisfies the average power constraint:  
\[\frac{1}{n}E[\parallel\mathbf{s}_{l}\parallel^{2}]=1 \tag{1}\]  
Each transmitter precodes the message signal with precoder, \(w_{l}\in\mathbb{R}\), to obtain the precoded signal, \(\mathbf{x}_{l}\in\mathbb{R}^{n}\), as:  
\[\mathbf{x}_{l}=w_{l}\mathbf{s}_{l} \tag{2}\]  
The average power of the precoder satisfies the constraint, \(E[\parallel w_{l}\mid^{2}]\leq\gamma_{w}\), where \(\gamma_{w}>0\). The precoded signal satisfies the power constraint of:  
\[\frac{1}{n}E[\parallel\mathbf{x}_{l}\parallel^{2}]\leq\gamma_{w} \tag{3}\]  
If \(\gamma_{w}=1\), the original signal power is preserved after precoding.  
The channel output, \(\mathbf{y}_{R}\in\mathbb{R}^{n}\), observed at the relay is given by:  
\[\mathbf{y}_{R}=\sum_{l=1}^{L}h_{l}\mathbf{x}_{l}+\mathbf{z}_{R} \tag{4}\]  
Figure 1: System model.  
where \(h_{l}\in\mathbb{R}\) is the channel coefficient between transmitter \(l\) and the relay node and \(\mathbf{z}_{R}\) is i.i.d. (independent and identically distributed) Gaussian noise vector given by \(\mathbf{z}_{R}=\left[\begin{array}{cccc}z_{R1}&z_{R2}&\ldots&z_{Rn}\end{array} \right]^{T}\), \(z_{Ri}\sim\mathcal{N}(0,\sigma_{1}^{2})\). The channel coefficient vector is given by \(\mathbf{h}=\left[\begin{array}{cccc}h_{1}&\ldots&h_{L}\end{array}\right]\), and the channel is assumed to be quasi-static. The aim of the relay is to compute a linear combination of source signals given by:  
\[\mathbf{v}_{R}=\sum_{l=1}^{L}a_{l}\mathbf{s}_{l} \tag{5}\]  
where \(a_{l}\in\mathbb{Z}\) are integer coefficients chosen on the basis of \(h_{l}\). The linear coefficient vector is given by \(\mathbf{a}=\left[\begin{array}{cccc}a_{1}&\ldots&a_{L}\end{array}\right]\). The relay is free to choose these linear coefficients. Since the linear combination, \(\mathbf{v}_{R}\), is computed over \(\mathbb{Z}\), while the channel output is obtained over \(\mathbb{R}\), the linear coefficients, \(a_{l}\), are chosen in a way to efficiently exploit this channel output for this computation. Here, \(\mathbf{v}_{R}\in\Lambda^{\prime}\), where:  
\[\Lambda^{\prime}=\{\lambda^{\prime}\mid\lambda^{\prime}=a_{1}\lambda_{1}+a_{2 }\lambda_{2}+\ldots+a_{L}\lambda_{L},a_{i}\in\mathbb{Z},\lambda_{i}\in\Lambda\} \tag{6}\]  
Hence, \(\Lambda^{\prime}\) is also an \(n-\)dimensional lattice. The estimate of \(\mathbf{v}_{R}\) obtained at the relay using the decoder, \(\mathcal{D}_{R}:\mathbb{R}^{n}\rightarrow\Lambda^{\prime}\), is given by:  
\[\hat{\mathbf{v}}_{R}=\mathcal{D}_{R}(\mathbf{y}_{R}) \tag{7}\]  
It is assumed that each transmitter has the CSI of its own channel only, _i.e._, the channel between the transmitter itself and the relay.
## 6 Performance Evaluation
### Experimental Set Up
#### Microsecond Accuracy in FTSP Implementation for TinyOS  
WSN synchronization protocols are designed to determine a global network clock. In order to achieve this, the local clock of the mote has to be obtained with the lowest accumulated error. Maroti and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Beacon\_Rate & 30 s \\ Max\_Entries & 3 elements \\ Root\_Timeout & 5 periods \\ Ignore\_Root\_Msg & 3 periods \\ Entry\_Valid\_Limit & 3 elements \\ Entry\_Throwout\_Limit & 500 \(\mu\)s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in FTSP, AT (Adjusted Temperature)-FTSP and A2T (Advanced Adjusted Temperature)-FTSP.  
[PERSON] [3,10] demonstrated that time-stamping the packets in the MAC layer reduces the uncertainty times, and thus, errors are minimized.  
There are several FTSP implementations, but, none of the published ones use the timers of TelosB motes in TinyOS to obtain microsecond accuracy. The original version of FTSP for TinyOS only supports millisecond accuracy. In this work, the FTSP protocol implementation for TinyOS has been improved. With this improvement, microsecond accuracy has been reached, and the results of AT-FTSP and A2T-FTSP can be compared with FTSP in microseconds. To do so, several tasks have been performed. FTSP implementation for TinyOS is based on IEEE 802.15.4 by using the CC2420 communications stack. The above mentioned stack has been developed to provide support in motes with the Texas Instruments CC2420 radio. By default, the stack for the CC2420 does not support MAC time-stamping with microsecond accuracy, and thus, it is necessary to modify it. The CC2420x stack of TinyOS can be used. This communication stack uses the _rfxlink_ as the communication driver. In the TinyOS tree, this library is located in _/tinyos/lib/rfxlink_. CC2420 xActiveMessageC library has been used for the required data encapsulation, while keeping compatibility with CC2420x stack.  
As has been described in Section 5.1, the microsecond accuracy is obtained by using the internal DCO of the MSP430, which controls the timer where the local time is stored. TinyOS is responsible for the dynamic recalibration of the clock of the DCO, adjusting its working frequency (increasing or reducing it) according to the external 32 Khz oscillator frequency.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 2 Databases and Model Evaluation Metrics
### Standard Databases
The text dataset is 52,230 images of text lines containing 2703 classes of characters, which are segmented by the line from 5090 texts containing 1,350,000 characters and divided into a training set of 41,781 images and a test set of 10,449 images. The contents of the text are mostly news reports on the Internet and a part of ancient poems, and the text dataset also provides detailed inter-character segmentation annotation information. Since the test set in CASIA-HWDB is relatively close to the training set in font style and text content, most experiments use the ICDAR 2013 [14] competition set as the test set to judge the performance of the algorithm. The dataset is written by 60 people and contains 3432 text image samples. In addition, the SCUT-COUCH dataset [13] can also be used for HCCR and HCTR tasks. It is a handwritten dataset covering single words, phrases, text lines, numbers, letters, and symbols published by the South China University of Technology. Creating a dataset requires a significant investment of human and material resources, and collecting and annotating a large number of samples are challenging tasks. Therefore, there are few research efforts to create high-quality datasets with sample diversity and sufficient sample size. Two large handwritten datasets have been released in recent years, the HCC dataset HITHCD-2018 [15], collected by the Harbin Institute of Technology in 2019, and the HCT dataset SCUT-HCCDoc [16], collected by South China University of Technology in 2020. The HITHCD-2018 dataset includes 21,000 Chinese character categories and 20 million Chinese character images, significantly expanding the scope of the previous HCC dataset and covering the entire list of GBK character set specifications. The SCUT-HCCDoc dataset contains 12,253 images of documents taken by the camera, with 116,629 lines of text and 1,155,801 characters. The data in this dataset are unconstrained text data due to the different conditions of camera capture, different scenes of text sources, differences in the length and orientation of text lines, etc. This dataset is also more diverse.
## 3 Results
### Spectral Characterisation  
After the spectra were collected, the reflectance data were smoothed using the Savitzky-Golay method to prevent noise from interfering with the experimental results, as shown in Figure 4. The main characteristics are described in the following:  
1. The surface-cultivated soil in the hilly regions has a high overall reflectance. Around 1400 nm, 1900 nm, and 2200 nm, there is an obvious water vapor absorption band, which is mainly caused by the vibration of water molecules in the soil [43].
2. From the reflection characteristics of sand, silt, and clay, the reflectivity increases with the increase in wavelength in the visible band, while in the near-infrared band, it has a higher reflectivity and tends to stabilize, with a reflectivity of up to 60%. After 2200 nm in the short-wave infrared band, the reflectivity shows a downward trend. The reflectance characteristics of three different types of soil particles are very similar. In hilly areas, the spectral mixing degree of clay and silt rich in illite and montmorillonite is higher, while the reflectance of sand rich in quartz and feldspar is slightly higher with a higher mixing degree with silt and clay [26].  
Figure 4: Spectral profile of soil samples.  
Figure 3: Distribution of sample particle sizes in the USDA texture triangle.
## 2 Related Work
### Object Tracking
[35] introduced the FPNS-MOT, which integrates a Siamese architecture with a feature pyramid network [36]. It computes a similarity vector between features from two different inputs and then updates tracks using an interactive selection of the maximum scored pair of tracks and measurements. FPNS-MOT outperformed the aforementioned methods on the MOT challenge benchmarks [27] with an inference time of 10 Hz. [PERSON] et al. [37] enhanced the performance of the Deep-SORT [11] object feature extractor with a Siamese architecture. In addition, it introduced optical flow [38] in the motion module, improving the object association accuracy. In summary, Table 1 presents the main characteristics of the aforementioned tracking by detection MOT methods.
## 2 Materials and Methods
### Denoising  
Physiological signals are nonlinear, nonstationary, and quasi-periodic time series. However, they are very sensitive to different types of noise that can distort their morphological and spacing properties, leading to misdiagnosis and inappropriate treatment of patients. For this reason, the ECG signals obtained from the participants were subjected to various processes and cleared of noise to become understandable. In this study, a Daubechies mother wavelet 'dB9' at decomposition level 4 was used to remove interference noise. This mother wavelet is recommended in the literature as the most effective noise removal method for ECG signals [28]. The MATLAB Wavelet Toolbox 3.0.1 was used for the noise removal procedure.
# Physicochemical and Molecular Properties of Spelt and Wheat Starches Illuminated with UV Light
## 3 Results and Discussion
### Morphology  
SEM pictures were taken for the native starches and for starches irradiated for 50 h, which are provided in Figure 2.  
The wheat starch consisted of small and large granules. The shape of these granules were different, and the large ones resembled a flattened spheroid, while the small granules possessed a spherical shape. As for the spelt starch, intermediate granules were additionally present, which is in line with the results presented by other authors [28]. The surface of the investigated grains was smooth, though some grooves and channels could be registered. The occurrence of pores and surface channels also varied for different types of granules and depends on the dimension of the grains [29]. Modification with UV radiation for 50 h (Figure 2B,C) did not significantly change the surface structure of the spelt starch grains. Nevertheless, a higher dose of energy caused small granules to aggregate into larger particles.  
Observations from the analysis of the wheat starch images (Figure 2E,F) indicated partial degradation of the starch shell, particularly the larger granules, and that smaller granules stuck to the larger ones. The amylose level was higher in larger wheat starch granules, while smaller granules exhibited a higher lipid content, [28] which may result in the greater absorption of UV radiation by larger granules and thus their faster degradation. These observations may indicate the possibility of enzyme interference during the enzymatic hydrolysis process and a significant rising up in the hydrolysis rate for wheat starch irradiated for 50 h. Other research data showed that illumination with UV light led to insignificant changes in the morphology of the examined starch granules [22].  
Figure 2: SEM images of the native spelt (**A**) and wheat (**D**) starches and of the spelt and wheat starches irradiated for 50 h (**B**,**E**, respectively) taken at \(\times\)500 magnification and at \(\times\)1000 (**C**,**F**, respectively).
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 2 Methodology
### Arbitrary Channel Shape Generation  
In the case of an external flow, there are no major issues in analyzing it by moving the object so that the center of mass is located in the middle of the CFD solving domain. However, in the case of internal flows, where the pipe interacts across the entire domain, the flow pattern changes depending on the order in which they are connected. Since the fully convolutional network produces feature maps only with partial information of the entire image, there can be issues in learning internal flow datasets that have the mentioned characteristics.  
We suggest a data generation method to test the learning capabilities. This method allows for the independent generation of the top and bottom surfaces and also allows for the cross-sectional area of the pipe to vary through a distribution function. Additionally, it allows for the presence of steps and bumps at random positions within the domain.  
For this, two serial pipes biased up and down from the center were joined together as in Figure 1. Each serial pipe was constructed by connecting a rectangle aligned with the inlet velocity direction and the space between them in a linear manner. We did not use Bezier curves or sine functions when designing the shape because the flow flowed parallel to the wall without flow separation or vortex generation along this function shape. Examples created through this method are shown in Figure 2.  
Figure 1: Example of upper and lower serial pipe in dataset.  
Figure 2: Samples of generated channel.  
By configuring the pipe as above, we created different flow rates over converging and diverging channels, bumps and cavities at the walls, and objects in the channels. The height \(H\), width \(W\), and the \(x\) and \(y\) positions of each pipe were chosen as the independent variables to be determined before constructing a biased serial pipe, using discrete uniform and normal distributions. Detailed specifications are shown in Figures 3 and 4.  
The size of the domain used in the experiment was set to \(192\times 192\). The number of pipes was selected by generating a random integer between three and seven, and the hyperparameter \(\sigma\) was set to 20. In addition, to create a higher serial pipe, the lower serial pipe was inverted in the \(y\)-direction, following the aforementioned procedure.  
Figure 4: Flow diagram for determination of the Width and x positions of rectangles consisting of lower serial pipes. From left to right: (**a**) spaces of pipes and intervals, (**b**) x position and width of pipes. The superscript * represents a variable used as an intermediate step to determine the actual value.  
Figure 3: Flow diagram for determination of the height and y positions of rectangles consisting of lower serial pipes. From left to right: (**a**) height, (**b**) y position. The superscript * represents a variable used as an intermediate step to determine the actual value.
# A Label-Free Aptamer-Based Fluorescent Assay for Cadmium Detection
## 1 Introduction  
Heavy metal pollution is a very serious environmental problem, and regulations are becoming stricter to address this. To implement action plans for the prevention of water pollution, methods for the detection of pollutants are required. Heavy metals are increasingly problematic in drinking water. Cadmium (Cd) is one of the most toxic elements among the heavy metals, and is widely used in fertilizers, fuels, plastics, toys, and several industrial products [1; 2]. Cd tends to accumulate in the kidneys throughout a person's life. According to the Centers for Disease Control and Prevention, Cd ranks seventh out of 275 hazardous substances in the environment [3]. Thus, it is necessary to develop effective, convenient, and inexpensive methods for the detection of Cd.  
Many methods have been developed to detect Cd, including inductively coupled plasma mass spectrometry, atomic absorption spectrometry, and electrochemical and fluorescence sensing techniques [4; 5; 6; 7]. Among these methods, fluorescence techniques are highly reliable and sensitive for metal ion detection. However, most fluorescence-based Cd\({}^{2+}\) sensors are quenched on coordination with large quantities of Cd\({}^{2+}\), and they often depend on an irreversible Cd\({}^{2+}\)-dependent chemical reaction to turn on the fluorescence [8; 9]. In addition, these systems use organic solvents, and construction of the methods is generally time consuming and needs complex design. As possible alternatives, aptamers and fluorescent PicoGreen (PG) have attracted attention. Aptamers are single-stranded nucleic acid sequences that are selected via a process known as systematic evolution of ligands by exponential enrichment. They are utilized as molecular recognition elements that can bind various targets with high affinity and specificity [10]. PG is an asymmetric cyanine dye that does not fluoresce when free. On binding to double-stranded deoxyribonucleic acid (dsDNA), it exhibits a more than 1000-fold fluorescence enhancement, but no significant fluorescence change is observedwhen it binds to single stranded DNA [11]. Therefore, an aptamer sensor using PG as the fluorescence enhancement dye might obtain better specificity and detection limit. The aim of the present study was to use aptamers and PG to design a simple, sensitive, and universal fluorescent sensing strategy for Cd\({}^{2+}\) detection.
# Real-Time Anomaly Detection with Subspace Periodic Clustering Approach
## 6 Experimental Analysis and Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multirow{2}{*}{**Algorithms**} & \multicolumn{3}{c}{**Evaluation Metrics**} & \multirow{2}{*}{\
\begin{tabular}{c} **Execution Time** \\ **(in Seconds)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Periodic Clusters** \\ **Obtained** \\ \end{tabular} } \\ \cline{3-6}  & & **Recall** & & **Precision** & **F1-Score** & **(in Seconds)** & **Periodic Clusters** \\ \hline
1 & \(k\)-means & 0.8701 & 0.8501 & 0.8600 & 95 & \(\times\) \\ \hline
2 & IF model & 0.7300 & 0.7502 & 0.7400 & 64.5 & \(\times\) \\ \hline
3 & SC & 0.6645 & 0.6420 & 0.6530 & 149.5 & \(\times\) \\ \hline
4 & HDBSCAN & 0.3899 & 0.3793 & 0.3850 & 150 & \(\times\) \\ \hline
5 & ACA & 0.7410 & 0.7010 & 0.7200 & 54.4 & \(\times\) \\ \hline
6 & LOF & 0.90401 & 0.9000 & 0.9020 & 47.6 & \(\times\) \\ \hline
7 & SSWLOFCC & 0.9280 & 0.9499 & 0.9390 & 40 & \(\times\) \\ \hline
8 & PCM & 0.7430 & 0.7810 & 0.7600 & 88 & \(\times\) \\ \hline
9 & OnCAD & 0.8450 & 0.8353 & 0.8400 & 102 & \(\times\) \\ \hline
10 & MICA & 0.9832 & 0.9770 & 0.9800 & 68 & \(\times\) \\ \hline
11 &
\begin{tabular}{c} Proposed \\ Approach (RADSPCA) \\ \end{tabular} & 0.9860 & 0.9801 & 0.9830 & 88.5 & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative performances analysis of RADSPCA with some well-known existing methods using Kitsune [65] dataset. periodicity. Thus, the execution time of RADSPCA depends mostly on the dataset sizes and the number of periods associated with a cluster in its lifespan. Moreover, the RADSPCA's execution time in seconds is compared against that of \(k\)-means [19], IF model [61, 62], SC Algorithm [54], HDBSCAN algorithm [63], ACA Algorithm [54], LOF algorithm [54], SSWLOFCC algorithm [54], PCM algorithm [10], OnCAD algorithm [46], and MCA Algorithm [9] and the results are presented using a bar diagram in Figure 5.  
It has been found from Figure 5 that most of the aforesaid algorithms depend not only on dataset sizes but also on dimensions, and some are inefficient with high-dimensional data. However, RADSPCA is less dependent on the dimension of the dataset and is a bit dependent on the sequence of time intervals associated with every cluster, which is a negligible factor. In fact, Figures 6 and 7 further validate that the RADSPCA's execution time grows linearly with respect to the increase in dimension if the dataset size is kept constant. Also, RADSPCA runs in cubic time. Figure 5: Comparative analysis of all the aforesaid methods in terms of time complexity. Figure 6: The execution time with respect to the dimension using KDDCUP’99 dataset [64].
## 1 Introduction
In [10], European Telecommunications Standards Institute's (ETSI) Satellite Communication and Navigation (SCN) group of the Technical Committee Satellite Earth Stations and Systems (TC SES) suggested to elaborate a firm and common standard for GNSS-based location systems. The group addresses interference from the perspective of robustness to interference, interference localization, and GNSS denial survival in addition to formulating location systems' minimum performance requirements. In particular, [11] discusses the GNSS based applications and standardization needs more inclusively. In addition, the Resilient Navigation and Timing Foundation urges for development of standards for interference-resistant receivers to include ARAIM (Advanced Receiver Autonomous Integrity Monitoring) and RAIM to protect, toughen, and augment GNSS [12]. The international aviation community has also been considering updating standards regarding GNSS aviation receivers, improving their ability to withstand interference from repeaters, pseudolites, and jammers [13]. In order to address the need from various GNSS stakeholders, the STRIKE3 consortium proposed a draft standard for GNSS receiver testing against real-world interferences detected at different STRIKE3 monitoring sites, along with a test campaign following the guidelines from the drafted receiver test standard. This paper discusses how the receiver testing is done and presents some of the results obtained within STRIKE3 testing activity. In particular, the paper addresses mass market and professional grade receiver testing. The test platform and the test methodology are described in Sections 2 and 3, respectively. Section 4 provides an overview of the receiver configuration and the simulated GNSS scenario settings, whereas Section 5 presents the five threats chosen for the tests. Results of the tests carried out are presented and analyzed in Section 6 and, finally, conclusions are drawn in Section 7.
# Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps
## 3 Improved A* Algorithm Based on Local Obstacle Rate and Dynamic Neighbourhood
### Modifications to the Evaluation Function  
#### 3.4.1 Modifying the Distance Function  
The heuristic function has a great influence on the efficiency and accuracy of the algorithm, and the traditional heuristic function mainly uses three kinds of distance functions. Among them, Manhattan distance is applied to four directions, Euclidean distance is used on eight directions, and diagonal distance is applied on unlimited direction selection. The Manhattan distance is often larger than the actual distance, and the Euclidean distance is often smaller than the actual distance, so we chose the diagonal distance as the calculation of distance. Let the length of one cell in the raster map be 1, then the diagonal length is root two, and the diagonal distance is publicised as follows:  
Let the start node be \(\mathrm{S}(x_{1},y_{1})\), the current node be \(\mathrm{N}(x_{n},y_{n})\), and the target node be \(\mathrm{E}(x_{2},y_{2})\), then we can obtain  
\[h(\mathrm{n})=\left(\sqrt{2}-2\right)\times\min(abs(x_{n}-x_{2}),abs(y_{n}-y_ {2})+abs(x_{n}-x_{2})+abs(y_{n}-y_{2}) \tag{3}\]