esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Initial Alignment Error Compensation for Shearer's SINS
### Fine Alignment Stage Based on Optimized UKF Algorithm
\tag{22}\]  
In Equation (21), \(\Omega_{U}=\omega_{ie}\sin L\), \(\Omega_{N}=\omega_{ie}\cos L\), and \(C_{ij}\) are the elements in the attitude matrix \(C_{b}^{n}\). The Gaussian white noise of \(N(0,Q)\) is \(W_{(t)}\), and \(W(t)=\big{[}\omega_{\delta V_{E}}\ \omega_{\delta V_{N}}\ \omega_{\varphi_{E}}\ \omega_{\varphi_{N}}\ \omega_{\varphi_{U}}\ 0_{5\times 1}\big{]}^{T}\). In the actual coal cutting of the shearer, it is assumed that its velocity remains unchanged along the \(Z\) axis, so \(\delta V_{E}\) eastward velocity error and \(\delta V_{N}\) northward velocity error in the horizontal direction are selected as the observed quantities. Then, the observation equation for the system is expressed as follows:  
\[Z=HX+V \tag{23}\]  
In Equation (23), \(H=[I_{2}0_{2}]\), and \(V\) is the system observation noise of \(N(0,Q)\) Gaussian white noise. We discretize Equations (17), (18), and (20) to perform a recursive calculation more effectively and conveniently using the UKF optimization algorithm. Then, we obtain  
\[X_{k}=O_{k,k-1}X_{k-1}+\Gamma_{k-1}W_{k-1} \tag{24}\]  
\[Z_{k}=H_{k}X_{k}+V_{k} \tag{25}\]  
All coefficient matrices satisfy the following relations:  
\[O_{k,k-1}=I+A_{k-1}T+\frac{1}{2!}A_{k-1}^{2}T^{2}+\frac{1}{3!}A_{k-1}^{3}T^{3 }+\ldots \tag{26}\]  
\[\Gamma_{k-1}=T\bigg{(}I+\frac{1}{2!}A_{k-1}T+\frac{1}{3! }A_{k-1}^{2}T^{2}+ \ldots\bigg{)} \begin{bmatrix}I_{5\times 5}&0_{5\times 5}\\ 0_{5\times 5}&1_{5\times 5}\end{bmatrix}  \tag{27}\]  
\[H_{k}= \begin{bmatrix}1&0&0&0&0&0&0&0&0&0\\ 0&1&0&0&0&0&0&0&0&0\end{bmatrix}  \tag{28}\]  
\[E\Big{\{}W_{k}W_{j}^{T}\Big{\}}=Q_{k}\delta_{kj} \tag{29}\]\[E\Big{\{}V_{k}V_{j}^{\mathrm{T}}\Big{\}}=R_{k}\delta_{kj} \tag{30}\]
# Fuzzy Decision Algorithm for Health Impact Assessment in a 5G Environment
## 5 Fuzzy Algorithm for the Assessment of 5G's impact on Health
### Fuzzy Modeling  
Fuzzy modeling has been widely applied to approximate non-linear models by aggregating linear models with both numeric and linguistic inputs and non-linear weighting functions [30; 31; 32; 33; 34]. Fuzzy modeling is a technique that uses fuzzy sets to model uncertain, imprecise, and/or vague information. Fuzzy modeling involves the following basic steps:  
1. Fuzzification:  
The first step in fuzzy modeling is to convert the inputs into fuzzy sets. This is acheived by assigning degrees of membership to each input based on their values.
2. Rule base creation:  
The rule base is a set of _if-then_ rules that represent the relationship between the inputs and the outputs. The rules are created based on expert knowledge or by using data-driven techniques such as clustering.  
Inference:  
Inference involves combining the rules to generate the output. This is achieved by using fuzzy logic operations such as _and_, _or_, and _not_.  
Defuzzification:  
The final step in fuzzy modeling is to convert the fuzzy output into a crisp value. This is achieved by using techniques such as the centroid method or the maximum method.  
In a fuzzy modeling process, the first step is to define the problem and identify the variables that are relevant to the decision. Next, these variables are described using fuzzy sets, which assign degrees of membership to each variable. After defining the fuzzy sets, the experts make a set of _\"if-then\"_ rules for the variables in order to arrive at a decision. Finally, a decision is made by aggregating the results of the rules using a defuzzification process, which converts the fuzzy output into a crisp, numerical value.
# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
## 2 Study Area and Dataset
### Remote Sensing Data  
Five years of eight-day GLASS LAI and _f_PAR products, provided by the Center for Global Change Data Processing and Analysis of Beijing University, were used as input data to the remote sensing-based model ([[http://glass-product.bnu.edu.cn/](http://glass-product.bnu.edu.cn/)]([http://glass-product.bnu.edu.cn/](http://glass-product.bnu.edu.cn/))). The LAI products were obtained from time-series remote sensing data using general regression neural networks (GRNNs). The GLASS _f_PAR products were then generated from the GLASS LAI products, which were without missing values and spatially complete [26]. To compare the GPP of the original and the optimized MOD_17 model, MOD 15A2 and MOD17A2 products were downloaded from NASA LAADS ([[http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov)]([http://ladsweb.nascom.nasa.gov](http://ladsweb.nascom.nasa.gov))). The MOD15A2 products included eight-day LAI and _f_PAR maximum value composite (MVC) products, derived directly from MODIS Reflectance and ancillary data (e.g., land cover type, background). The MOD17A2 simulated eight-day composites of the GPP and annual NPP.  
All the GLASS and MODIS products have a resolution of 1 km. The standard preprocessing procedure for the production of these original data includes georeferencing of the original images using the nearest neighbour algorithm.  
The elevation of the forest flux site was obtained from ASTER GDEM. Ensuing, slope and aspect were extracted. The soil map was provided by the Data Center for Resources and Environmental Sciences of the Chinese Academy of Sciences.
## 3 Results and Discussion  
### XRD Pattern of ZrO\({}_{2}\) NPs  
The crystalline nature and phase purity of the resultant powder were assessed through room temperature XRD measurements (Figure 1). All the diffraction peaks of the ZrO\({}_{2}\) sample were indexed according to the joint committee on powder diffraction standards (JCPDS) card number 80-0965. The XRD pattern of the investigated sample confirmed the tetragonal phase (space group = P42/nmc). No additional peaks were found in the synthesized powder XRD pattern. The average crystallite size was calculated for the most intense diffraction peaks using [PERSON]'s formula:  
\[D=\frac{K\lambda}{\beta\text{Cos}\theta} \tag{1}\]  
where \(\beta\) (in radian) is a half maximum full width of XRD peaks, \(K\) = 0.94 is the shape factor, \(\lambda\) = 1.54178 A for Cu-K\({}_{\alpha}\) X-rays, and \(\theta\) is the diffraction angle (in degree) corresponding to each plane. Crystalline size (\(D\)) was found to be \(80\pm 5\) nm for chemically synthesized ZrO\({}_{2}\).
## 3 Background and Preliminaries
### Anomaly Detection in Wireless Sensor Networks  
Ensuring sensor data quality is crucial for right decision making. Cryptographic and key management techniques are not sufficient to ensure the integrity of data as they cannot protect sensor nodes from insider attacks such as data fabrication. Therefore, anomaly detection models are designed to detect any abnormal behavior in sensor data streams. The following subsections describe the concepts, challenges and requirements of anomaly detection in WSNs.
# A Networked Sensor System for the Analysis of Plot-Scale Hydrology
## 3 Results and Discussion
### WSN Challenges and Utility in Hydrology
#### 3.5.4 Heterogeneous Note Reprogramming  
The ASWP WSN deployment consists of three different more platforms as well as multiple application versions (corresponding to various external sensors attached to individual motes). As an exploratory and evolving WSN deployment, the network application needs to be updated frequently to test new protocols and parameter configurations. Over-the-air reprogramming approaches become a natural choice since manually reprogramming the motes is cumbersome. The heterogeneous nature of the developed WSN with motes operating in LPL makes the existing reprogramming tools infeasible [52; 53].  
Our developed MobileDeluge [52] is a novel hand-held mobile over-the-air more reprogramming tool for outdoor WSN deployments (Figure 14). MobileDeluge builds a new control layer on top of Deluge [99]. It enables and disables Deluge services on demand, allowing for the selection of a subset of motes as targets when initiating a reprogramming task. It then disables LPL in the targets for fast dissemination of the new application image, which usually consists of thousands of packets. The targets are also configured in a different radio channel to avoid interference with the rest of the network. MobileDeluge currently works with the motes within a one-hop range to avoid forwarding a bulk code image over intermediate nodes for more energy conservation. Please see [52] (and the references herein) for more details.  
MobileDeluge has significantly reduced the time and labor required to update the application in the outdoor WSN testbed. The manual reprogramming procedure would consist of getting the enclosure from the tree, opening the box, attaching the mote to the laptop and uploading the new application. For example, it usually takes a few days to reprogram the whole ASWP testbed (i.e., 104 motes). With MobileDeluge, in contrast, the reprogramming can be finished within one afternoon.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 4 Security Issues of UWSNs
### Security Systems  
With the development of technology, security issues have been taken more seriously. Much research on specific security issues has been undertaken, and moreover, relatively mature security systems for WSNs have been proposed. To the best of our knowledge, security protocols for sensor networks (SPINS) [89] is the first security system for WSNs, which was proposed by [PERSON] on 2002. SPINS can provide data authentication, replay protection, and communication cost is low. Based on SPINS, extended security frameworks were proposed [90; 91]. TinySec [92] is another lightweight link layer security architecture for WSN, which can provide authentication, integrity and confidentiality. Some research utilized [93] or extended [94] TinySec for WSNs. Beyond these two widely used security systems, there are also some other security systems, such as Minisec [95] and SHARP [96].  
Although much security research mentioned above has been undertaken for UWSNs, almost all of them focused on a specific issue and have not been empirically verified. In [62], the existing secure communication protocols were discussed and compared carefully in detail.  
As far as we can ascertain, there is still no practical, system-level security system for UWSNs. Hence, this will be a challenging topic and new trend for UWSNs.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 3 Wireless Technologies
### Wireless Fidelity (Wi-Fi)
In addition, the combination of time-based and RSS measurements can be used, such as in the \"WiNar\" system [30], which utilizes the RTT and RSS as fingerprinting features, aiming to address challenges of the indoor environment, such as multipath, NLoS signal attenuation, and interference. It was concluded that the average localization error for both scenarios, RTT only and hybrid RTT-RSS, was almost the same. Likewise, in [34], RTT-based ranging was found to accurately reflect the true distance between the stations and the APs for both LoS and NLoS conditions.
## 3 Results and Discussion
### Number of Decision Trees and Their Maximum Depth  
Figure 9 illustrates the relationship between the average RMSE from ten-fold cross-validation and the number of decision trees, with the maximum depth of decision trees (D) set to 12 and the maximum number of features set to three. Initially, the RMSE is high due to the low complexity of the machine learning model, but it rapidly decreases as the number of decision trees increases. While there are occasional fluctuations, the RMSE generally shows a downward trend before slightly rising as the number of decision trees increases. The model achieves its best performance and lowest RMSE when the number of decision trees is set to 18. Overfitting can occur when a model with a large number of decision trees is trained on a small dataset, leading to an increase in RMSE. If the number  
Figure 8: EM field cumulative distribution function plots.  
Figure 7: Comparison of the estimated and real numbers.  
of decision trees exceeds 18, further increases in the number of trees and computation time will not lead to a reduction in RMSE (Figure 10).  
Figure 10: Map showing the study areas.  
Figure 9: The connection between the number of decision trees and the RMSE of ten-fold cross-validation.  
By limiting the maximum depth of decision trees, we were able to prevent overfitting in the random forest regression model used to estimate the electric field intensity around 5G base stations. As shown in Figure 11, both the number of decision trees and the maximum depth of the trees have a similar impact on the model's performance. Initially, as the maximum depth of the decision trees increases, the RMSE decreases. However, after a certain point, the RMSE begins to rise again. Through analysis, we determined that a maximum decision tree depth of 12 yields the lowest RMSE (0.5928). The relationship between RMSE and parameter values can help guide the selection of optimal key parameters for a machine learning model.
## 4 Methodology
### Dataset Description  
In the Senselet++ platform [13], an IoT-driven end-to-end sensing platform was designed for the University of Illinois Urbana-Champaign's (UIUC) scientific cleanroom laboratories (Figure 5 shows a cleanroom semiconductor lab at UIUC). This IoT sensing platform consists of a CPS for the critical high-end components, including lab instruments and infrastructure, within the cleanroom laboratory. It monitors various physical factors such as temperature (in Celsius), humidity (in %), pressure (in Bar) and rate of airflow (in meters/second to measure quantity of air being moved) using low-cost sensors. Each cleanroom component is equipped with its own CPS, comprising a data acquisition system tailored to the specific requirements of the component. For more details on the data acquisition unit, please refer to our previous work on Senselet++ [13]. Additionally, it is important to note that our current work is not focused on building the IoT system itself. Instead, our approach was designed to work with any existing IoT-driven CPS platform that exhibits the existence of concept drift, which poses considerable challenges in developing ML models, as their learning performance may progressively degrade due to data distribution changes. Subsequently, our aim is to demonstrate a framework for an advanced, online adaptive learning model that can detect and react to concept drift that occurs in IoT-driven CPS data streams. We narrow down the scope of the extensive CPS subsystems to the three most important components of cleanrooms: the Fumehood, Vacuum Pump, and Karl SussLithography Unit. The CPS connected to each of these cleanroom components collects time-series data from various sensors, as discussed below:  
* **Fumehood:** Data from temperature, humidity, and airflow are recorded, providing a comprehensive view of the environmental conditions within the Fumehood.
* **Vacuum Pump:** Temperature sensor data (in Celsius) are aggregated to monitor the operational health and detect any thermal anomalies that may indicate malfunctions or efficiency issues.
* **Karl Suss Lithography Unit:** Both temperature (in Celsius) and humidity sensor data (in %) are gathered to ensure that the system operates within optimal conditions for precise manufacturing processes.  
These collected data form the basis for developing digital twins for each component's CPS, enabling real-time drift detection and continuous adaptive learning for highly precise anomaly classification. Figure 5(a)-c illustrate the distribution of data for the Fumehood's temperature sensor and humidity sensor and the Vacuum Pump's temperature data.
# RF Energy Harvesting Wireless Communications:
## 3 RF-EHWC Hardware
### Concerns in the Design of Energy Harvesting Circuits
As shown in Figure 12, through carefully arranging the crossover region of multiple energy harvester, the RF-EHWC system can efficiently harvest energy with a wide range of incident power.
# Machine Learning Applications for Short Reach Optical Communication
## 4 Machine Learning Applications for Indoor Optical Wireless Communication and Positioning
### Machine Learning Applications for Indoor Optical Wireless Communication System
#### 4.1.3 Other Machine Learning Based Applications in Indoor OWC System  
In addition to the applications mentioned above, which focus on signal processing, the ML technique has also been applied in other aspects of indoor OWC systems. In [137], a ML-based feature separation network for transmitter fingerprinting (TF-FSN) to distinguish illegal transmitters and to improve the physical layer security in the indoor VLC system has been proposed.  
The proposed neural network structure is depicted in Figure 15. The feature separation network consists of three parts: the feature extractor (FE), the label predictor (LP) and the feature classifier (FC). The FE includes three residual stacks which contain two residual units, and each residual unit contains one convolutional layer, one batch normalization layer and one ReLU activation layer. Then, the outputs are fed into a fully connected layer for dimension reduction, whose output are further evenly divided as the inputs of the LP and FC, respectively. The LP works as a fully connected layer, which classifies the high-dimensional private feature into different labels. At the FC, a loss function with the soft subspace orthogonality constraint is used to optimize the training process, so that the interference of channel effects can be eliminated from the extracted private features. This algorithm is verified by experiment, showing 92.65% and 98% accuracy of identification and verification, respectively. The system also shows strong robustness at different distances over a wide range of SNR.  
The ML technique has also been applied to the handover between LiFi access points (APs) and WiFi APs in a heterogeneous indoor wireless system, where a fully-connected neural network with one hidden layer is utilized [138]. Four features including the user's speed, the separation between LiFi APs, the height of LiFi APs, and the number ratio between LiFi APs and WiFi APs are selected as the input parameters. The tanh activation function is used at the output layer to capture nonlinear effects in the system. Simulation results show that compared to the received signal strength (RSS)-based and trajectory-based handover methods, using the ML based approach can improve the user throughput by up to 260% and 50%, respectively. The Table 3 provides a summary of current ML based applications in indoor OWC system.  
Figure 15: The structure of the proposed feature separation network.
# Analysis of Behavioral Characteristics of Smartphone Addiction Using Data Mining
###### Abstract  
In 2016, the number of mobile phone subscriptions worldwide had surpassed the total world population; moreover, the number of smartphone addicts is increasing each year. Thus, the objective of this study is to analyze smartphone addiction by considering the differences between smartphone usage patterns as well as cognition. Our proposed method involves automatically collecting and analyzing data through an app instead of using the existing self-reporting method, thereby improving the accuracy of data and ensuring data reliability from respondents. Based on the results of our study, we observed that there is a significant cognitive bias between the self-reports and automatically collected data. As a result of applying data mining, among the six criteria out of the total 24 items of the questionnaire, the higher the \"recurrence\" item, the higher the addiction; further, \"forbidden\" item 1 had the largest effect on addiction. In addition, the input variables that have the greatest influence on the high-risk users were the number of times the screen was turned on and real-use time/cognitive-use time. However, the amount of data and time of smartphone usage were not related to addiction. In the future, we will modify the app to obtain more accurate data, based on which, we can analyze the effects of smartphone addiction, such as depression, anxiety, stress, self-esteem, and emotional regulation, among others.  
Keywords:data mining; smartphone addiction; actual usage data; smartphone usage patterns +
Footnote †: journal: Applied Sciences
# Ammonia-Oxidizing Bacterial Communities in Tilapia Pond Systems and the Influencing Factors
###### Abstract  
This study investigated ammonia-oxidizing bacterial communities in water and surface sediments of three tilapia ponds and their relationship with differences in the ponds, monthly variations in the water, and the physico-chemical parameters. Samples were collected from ponds with different stocking densities, after which DNA was extracted, 16S rRNA genes were amplified, the Illumina high-throughput sequencing was performed, and then the Silva and FunGene databases were used to investigate the ammonia-oxidizing bacterial communities. In total, 308,488 valid reads (144,931 in water and 163,517 in sediment) and 240 operational taxonomic units (207 in water and 225 in sediment) were obtained. Further analysis showed that the five genera of _Nitrosopira_,  
significant effects on pond ecosystems since it has strong digestive ability [10] and fast growth rates. Further, the faster accumulation of uneaten feeds and feces may lead to the faster release of ammonia into the system. In addition, due to the habit of nest-digging, tilapia cultures could accelerate the exchange of microbes between the surface sediments and water. Thus, we hypothesized that the ammonia-oxidizing bacterial communities in the water and sediment of tilapia pond ecosystems may respond to cultures of tilapia in different ways and present specific relationships with some physio-chemical parameters. The knowledge about this topic is of significance, as it will help to find ways of reducing ammonia in water. However, studies on the community structures of ammonia-oxidizing bacteria in tilapia aquaculture ponds and their influencing factors have not yet been conducted or reported. Based on our previous research on bacterial communities in water [11] and surface sediments of the same tilapia ponds [12], the same datasets were assigned to the FunGene database to analyze the ammonia-oxidizing bacterial communities in both water and surface sediments. Thus, research on this topic not only supports tilapia pond cultures, but is also an important supplement to the research on the ecology of ammonia-oxidizing bacteria.  
In this study, we determined the composition and bacterial species diversity of ammonia-oxidizing bacterial assemblages in both water and surface sediments of tilapia aquaculture ponds with different stocking densities during the whole cultivation season. Our primary focus was to gain an overall understanding of the ammonia-oxidizing bacterial community structures and carry out a preliminary investigation on whether the environment affects the bacteria.