esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Analysis and the Results
### Time Complexity  
The CCA algorithm is divided into three steps. The first step is to categorize the data into a few categories; as a result, the step's time complexity is O(n). We need to use K-means to divide each class into some clusters based on similarity in the second step, which has a time complexity of O(n\({}^{2}\)). The final step is to quantify Cores across all clusters. Therefore, the final step is also needed O(n\({}^{2}\)). Therefore, the holistic time complexity is calculated as: O(n) + O(n\({}^{2}\)) + O(n\({}^{2}\)) = O(n\({}^{2}\)). Additionally, time complexity was calculated for the general form of the algorithm, assuming the fixed number of clusters k and excluding the number of iterations while executing the clustering to update Cores.
## 2 Background and Related Work  
In this section, we briefly discuss the UAV operating environment's modeling techniques, the pathfinding algorithms, and geometric- and sampling-based PP methods. The initial step of the global PP is to model the real environment with correct geometric shapes. It is closely linked to the choice of search algorithm because most search algorithms yield good performance when they are collectively employed with a particular environment's illustration. A comprehensive discussion about the performance impacts of distinct environment modeling techniques collectively tested with their respective search methods was given by [PERSON] et al. [52]. Many UAV operating environment methods have been discussed in the published studies. These modeling methods are categorized as RoadMap (RM), Cell Decomposition (CD), and Potential Fields (PF). [PERSON] et al. [53] presented a comprehensive survey on environment modeling techniques used in PP. Each modeling method differs in terms of the scale of space/time complexity, the modeling method's accuracy, and the path quality. For example, when the cell sizes are relatively small, CD-based methods yield poor path quality. In contrast, if the cells are too wide, they are vulnerable to very high time and space complexity. The PF-based methods are prone to getting trapped in local minima, and, thereby, solution quality can be degraded. After modeling the environment with a visibility/waypoint graph, a search algorithm is utilized for the graph's exploration in order to find a path.  
Most of the existing search algorithms explore and model whole maps during the PP that can lead to various overheads, such as resource-hogging, needless exploration of many parts of a map, and latency issues during pathfinding. Generally, they do not take advantage of the available useful knowledge related to obstacles' geometries from underlying environments in order to lower the complications in path computing. While finding a path from a provided graph, they mostly hold all edges that are visible in the memory, thereby memory requirements of these algorithms are high. Current bio-inspired search algorithms are vulnerable to pre-mature convergence by relying solely on the specified parameters that can lead to poor path quality. In addition, they were mostly tested in semi-urban environments, and their completeness property may yield infeasible results in realistic-urban environments. To address these technical problems, we proposed a new PP method for computing low-cost paths in order to facilitate UAV's aerial missions in urban environments.
## 2 Data Acquisition and Pre-Processing
### Data Extraction and Pre-Processing  
The car-following events were extracted by several simple rules. The ego vehicle and preceding vehicle were in the same lane. The relative distance \(L\) between the ego vehicle and preceding vehicle was no less than 120 m, and the ego vehicle speed was greater than 10 km/h. The duration of the following events was no less than 30 s to obtain sufficient data. Finally, 1104 following events were obtained, with an average of 33.45 events per driver, and the average duration was 45.7 s for each event.  
Three-sigma criterion was used to eliminate abnormal data caused by driver's operational irregularity and equipment unstably. The three-sigma criterion is expressed as  
\[|x_{i}-\overline{x}|>3\sigma \tag{1}\]  
where \(\overline{x}\) and \(\sigma\) represent mean and standard deviation of data, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Types of Data** & **Information** \\ \hline driver operation & brake/accelerator pedal position, turn signal, steering angle \\ ego vehicle states & speed, acceleration, location information, yaw angle speed, \\  & engine speed \\ preceding vehicle states & speed, acceleration, location information \\ subjective score & DSQ, RPQ \\ physiological signal & ECG, GSR, EEG \\ \hline \end{tabular}
\end{table}
Table 1: Data acquisition information.  
Figure 4: N–Back auditory feedback task diagram.  
Figure 3: Experimental Process.  
In addition, a \(Z\)-score standardization method was used to standardize the selected variables of each event.  
\[\overline{y}_{n}^{(m)}=\frac{y_{n}^{(m)}-u_{n}}{\sigma_{n}},\ m=1,2,\cdots,M,\ n=1,2, \cdots,N \tag{2}\]  
where \(y\) = [_THW_, \(a_{e}\)], \(m\) is the number of events, \(n\) is the total number of drivers, \(n\) = 33, \(\overline{y}_{n}^{(m)}\) is the \(n^{th}\) driver's \(m^{th}\) event's variable. \(u_{n}\) and \(\sigma_{n}\) are the mean and covariance of all events for the \(n^{th}\) driver.
## 2 Materials and Methods
### Hydrogeological Monitoring of the Tunnel during the Construction Phase  
During the construction of the tunnel, an intensive hydrogeological monitoring was carried out to verify the working hypotheses. This monitoring consisted of continuous reading of the piezometric and forometric network installed in the study area. The construction of the tunnel caused a drop in water levels in the northern and central areas of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Flow in** & **Flow with** & \\  & **Natural** & **Draining** & **Variation** \\  & **Regime (L/s)** & **Tunnel (L/s)** & \\ \hline INPUT & Precipitation infiltration & 9.1 & 9.1 & - \\ \hline  & Ategorri stream (springs and & & \\  & diffuse discharges) & 4.1 & 0 & \(-100\%\) \\  & Urdinbide tunnel & - & 6.6 & - \\  & Etxanosolo stream (springs & 1.5 & 0.8 & \(-46\%\) \\  & and diffuse discharges) & 1.9 & 1 & \(-47\%\) \\  & Asketa spring & 0.8 & 0.2 & \(-75\%\) \\  & Sastratcu spring & 0.1 & 0 & \(-100\%\) \\  & Existing railway line tunnel & 0.7 & 0.5 & \(-28\%\) \\ \hline Total & 9.1 & 9.1 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the natural regime and a draining tunnel regime.  
the aquifer, and had a one-off impact on the municipal water intakes from the springs at the headwaters of the Ategorri stream.  
The magnitude of these impacts was related to the phase of greatest activity during excavation, where potential temporary impacts during construction would be maximum.  
The execution of preinjections and the advance excavation of tunnel tubes caused a depression of between 15 and 25 m compared with the over 50 m of maximum water column that existed above the tunnel level before the works (Figure 13).  
An extraordinarily dry and prolonged drought period that occurred in this area sensibly coinciding with the beginning of tunnel construction in the aquifer also contributed to the magnitude of these impacts.  
Figure 12: Schematic longitudinal profile of injection treatment. Stage situation.  
Figure 13: Evolution of the water level in the piezometers of the Autzagane aquifer. Evolution of the total precipitation in the area. The different colors correspond to different piezometers installed in the study area.  
The effectiveness of the waterproofing injections carried out, together with the recovery of the usual precipitation regime, partially reversed the impacts that the tunnel construction had caused on the natural functioning and use of the Autzagane aquifer. Since the commissioning of the tunnel in 2018, hydrogeological monitoring has been carried out during the operation phase. Since then, the maximum flows measured through the tunnel have been less than 2 L/s, far from the limit imposed to guarantee the functioning of the aquifer and the municipal water catchment (Table 4).
## 3 Methodologies
### Self-Organizing Map  
The SOM method is an artificial neural network approach that achieves clustering results through unsupervised learning [55]. This method is based on a competitive learning strategy that relies on the competition among neurons to gradually optimize the network, using neighborhood functions to maintain the topological structure of the input space, thereby facilitating self-organized training and clustering of similar neurons in a spatial distribution (Figure 7) [56]. In the SOM method, an input vector is assigned to the neuron unit with the most similar weight vector or the closest neuron, with appropriate metrics used to measure the distance between vectors. The general idea is that the weight vectors represented by each neuron are spatially correlated, thereby making the represented input vectors close, combined with a competitive strategy to identify the grouping forms of input data [57]. When a new input vector is introduced, the distance to each neuron's weight vector is calculated, and the optimal neuron is selected as the best matching unit (BMU), with neighboring weights adjusted accordingly [58]. According to the following update equation, the magnitude of adjustment decreases with time and distance to the BMU.  
\[W_{v}(t+1)=W_{v}(t)+\theta(v,t,d)\alpha(t)[D(i)-W_{v}(t)] \tag{1}\]  
Figure 6: Yanshan Iron Ore Mine big data integration and multi-dimensional ore-forming prediction process flowchart.  
In the aforementioned equation, \(t\) represents the iteration number or round of the algorithm, \(D(i)\) denotes the input vector (where \(i\) is the sample size of the training dataset, ranging from 1 to \(n\)), \(\theta(v\),\(t\),\(d)\) is the neighborhood function, which depends on the distance to the BMU, and \(\alpha(t)\) is a monotonically decreasing learning coefficient.  
Ultimately, the neurons or output nodes can be graphically visualized and associated with groups present in the input multi-dimensional space, where the color scale represents the distances between neurons stored in the U matrix.  
This study is based on the theory of the K-means algorithm, utilizes Euclidean metrics to calculate distances between vectors, and employs the lowest Davies-Bouldin validity index to select the optimal clustering. To estimate the number of SOM nodes, a heuristic formula was used [59].  
\[m=5\sqrt{n} \tag{2}\]  
In the aforementioned equation, m represents the number of SOM nodes, and n denotes the number of samples in the input dataset.
# Susceptibility Mapping on Urban Landslides Using Deep Learning Approaches in Mt. Umyeon
## 3 Methodology
### Convolutional Neural Network  
Recently, CNN applications for classification and prediction in various fields are increasing [78]. As mentioned above, CNNs are one of the well-known DL methodologies that show strong performance in image processing fields based on an ANN [79]. Important features of raw data can be automatically extracted through CNN [80]. Researchers have developed various structures depending on the type or purpose of the data; the basic architecture consists of an input layer, multiple hidden layers, and an output layer. The difference between CNNs and simple neural networks is that the hidden layer is composed of one or more convolutional and pooling layers, which share weights with multiple layers, pooling, and local connections [40]. The convolution layer, composed of several convolution kernels, repeatedly extracts features of the original data. The pooling layer prevents overfitting by reducing the dimension of the feature map through downsampling after the convolutional layer. In addition, it has  
Figure 5: Model architecture proposed in this study: (**a**) Deep Neural Network, (**b**) kernel-based Deep Neural Network, and (**c**) Convolutional Neural Network.  
an advantage of fast processing speed by greatly reducing the number of parameters by interpreting the input data as an image.  
The CNN architecture in this study also consists of a convolution layer that learns convolution, and a pooling layer that improves computation performance by controlling overfitting and reducing the number of structures due to convolution through stable transformation [81]. In particular, the sizes of the convolutional kernel and the pooling kernel determine the scale of the convolution and pooling operations, respectively [82]. The activation function converts a linear relationship into a nonlinear relationship [83], and the optimizer iteratively updates the parameters at various learning rates. The loss function updates the weight by measuring the error between the predicted and actual values. In this study, the hyperparameter setting, a key step in building CNN architecture, is as follows in Table 1.  
In this study, ReLU was selected as an activation function to improve the nonlinear property of the network; hereby, landslide susceptibility with a value between 0 and 1 is finally calculated. Stochastic gradient descent (SGD) was used as the optimizer, and MSE was selected as the loss function. The max epochs, batch size, and learning rate were set to 300.00, 150.00, and 0.01, respectively. Like DNN, the weight decay was set to 0.03, so that the weights of all input neurons were reflected, and stable loss and accuracy could be calculated. Image kernel size was set to \(7\times 7\) in the same way as kernel-based DNN. The feature map extracted through the convolution and pooling layers was reconstructed by the connection layer, and a classification result was derived through a nonlinear activation function. Figure 5 shows the prediction process using the CNN classifier used in this study.
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 1 Introduction
However, this design exhibits a correlation between the response outputs, even with different challenge inputs. This potential correlation is attributed to the inevitable mismatch in parasitic composition along the input signal paths to the two inverter arrays in the layout design. This mismatch results in the PUF cell becoming independent of the challenge input and producing a fixed response output. This paper proposes an operation timing control scheme to eliminate the mismatch of challenge and word-line inputs in order to solve this problem. The proposed timing scheme requires a modification of the structure by adding adjunct NMOS to eliminate the influence of the challenge input on the SRAM operation in generating a random response output. Subsequently, a symmetrical inverter array arrangement technique is used to maximize the effects of process variation. Simultaneously, the pre-charge input signal is also symmetrically connected to the two inverter arrays to minimize the delay mismatch. The remainder of this paper is organized as follows. Section 2 describes the structure and operation of the conventional SRAM CRP PUF in [22], along with an analysis of the existing issues. Section 3 introduces the proposed SRAM CRP PUF, incorporating a symmetrical layout design technique, and presents the simulation results that demonstrate its effectiveness. Section 4 discusses the implementation of the SRAM CRP PUF system. Section 5 details the measurement results of our PUF using prototypes deployed on a 180 nm CMOS process, presenting a comparison with the existing state-of-the-art PUFs. Lastly, Section 6 concludes our work.
# Artificial Intelligence of Things for Solar Energy Monitoring and Control
## 3 AIoT Applications in PV Systems: A Comprehensive Review
### Applications of AI in PV Systems
#### 3.1.3 Energy Forecasting and Optimization
[106] & Comparison of physical models and NN-based statistical models & Day-ahead & Neural Networks & Solar irradiance, air temperature, cloud cover, humidity & Demonstrated NN models’ & Sensitive to weather variability and RMSE of 10.5\% over physical models’ 12.45\%; real-time correction & Sensitive to weather variability and RMSE of 10.5\% across physical models’ 12.45\%; real-time correction & Sensitive to weather variability and RMSE of 10.5\% across physical models’ 12.45\%; real-time correction & Sensitive to weather variability and RMSE on NWP data quality. \\ \hline [PERSON] et al. [107] & Daily prediction of global solar radiation on a horizontal surface & Day-ahead ANN & Clearness indices, solar radiation time series & Improved accuracy by 5–6\% over traditional models like ARIMA; nRMSE = 37\% ( winter), 15\% ( summer); R\({}^{2}\) \(>\) 0.99 validation. & Dependent on high-quality time series data. \\ \hline [PERSON] et al. [108] & Developed a MLP model of solar-tilted global irradiation from horizontal ones & Short-term (5 min) & ANN (MLP) irradiation, declination, angles & Horizontal & Achieved high accuracy (nRMSE of 8.81\%) for tilted surfaces; input sensitivity; validated on 2-year dataset. & Limited generalizability to different regions and inclinations. \\ \hline [PERSON] et al. [109] & Estimating and monitoring the power generated by a PV module & Day-ahead & ART & PV power data, system parameters & Enhanced accuracy for nonlinear PV system behavior; real-time monitoring; tested on 1-year data. & Requires high-quality system-specific data for accurate estimates. \\ \hline [PERSON] et al. [110] & Comparison of GRNN and FFBP for output forecasting & Day-ahead & GRNN, FFBP & Temperature (max, min, mean), solar irradiance & FFBP showed superior accuracy compared to GRNN. & Limited scalability for larger datasets or variable conditions. \\ \hline [PERSON] and [PERSON] [111] & Feature extraction with kNN and ANN models for short-term solar irradiance forecasting & Short-term (15 min–2 h) & kNN + ANN Global
## 4 Materials and Methods
### Chemometrics Analysis  
Identified volatile compounds and extract ion chromatogram (EIC) data generated by XCMS were both subjected to chemometrics analysis by OPLS-DA to compare MVOCs profiles, by means of SIMCA-PTM software (Version 13.0, UMetrics AB, Umea, Sweden). Before constructing the OPLS-DA model, data were normalized using a Pareto scaling strategy to reduce the impact of artifacts and noise in the models, which is positive for the model's predictive ability [60]. For evaluation of the model performance, two parameters were calculated, namely the R\({}^{2}\) representing total explained variance and cumulative Q\({}^{2}\) that represents the fraction of the variation of Y which can be predicted by the cross validation model [30]. Potential biomarkers were chosen from VIP generated from the OPLS-DA model. This variable selection method was described by [PERSON] and [PERSON] [61]. The higher the absolute value of VIP, the more important the corresponding variable [26]. Furthermore, potential markers identified by VIP were screened out by \(t\)-test (\(p\)-values below 0.05).
## An Efficient Data-Gathering Routing Protocol for Underwater Wireless Sensor Networks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3, [PERSON] 4, [PERSON] 5 and [PERSON] 1**  
\({}^{1}\) COMSATS Institute of Information Technology, Park Road, Islamabad 44000, Pakistan;  
E-Mails: [PERSON] (N.I.); [EMAIL_ADDRESS] (A.A.);  
[PERSON]ESS] (M.I.K.)  
\({}^{2}\) College of Applied Medical Sciences, Department of Biomedical Technology, King Saud University,  
Riyadh 11633, Saudi Arabia; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Cameron Library, University of Alberta, Edmonton, AB T6G 2J8, Canada;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Internetworking Program, FE, Dalhousie University, Halifax, NS B3J 4R2, Canada;  
E-Mail: [EMAIL_ADDRESS]  
\({}^{5}\) Institute of Space Technology, Islamabad Highway, Islamabad 44000, Pakistan;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +92-300-579-2728.  
Academic Editor: [PERSON]  
_Received: 15 July 2015 / Accepted: 11 November 2015 / Published: 17 November 2015_  
to prevent the gateways from overloading. Moreover, gateways are rotated with the passage of time to balance the energy consumption of the network. To prevent data loss, AEDG allows dynamic data collection at the AUV depending on the limited number of member nodes that are associated with each gateway. We also develop a sub-optimal elliptical trajectory of AUV by using a connected dominating set (CDS) to further facilitate network throughput maximization. The performance of the AEDG is validated via simulations, which demonstrate the effectiveness of AEDG in comparison to two existing UWSN routing protocols in terms of the selected performance metrics.  
Keywords:underwater wireless sensor networks; routing protocol; mobile sink; autonomous underwater vehicle; lifetime +
Footnote †: journal: Journal of Applied Soft Matter
## 2 Materials and Methods  
Over a period of 6 days, on 12 and 17 February 2024, windshear reports were received from pilots of aircraft landing on the third runway from the west, i.e., runway corridor 07 LA. It appears from the LIDAR pictures that the windshear may be related to terrain-induced airflow disturbances arising from Lantau Island. However, due to limitations of the measurement range of the LIDAR under cloudy skies, this point is not well established using the LIDAR data themselves.  
The use of numerical weather-prediction models to simulate low-level windshear over the airport has proven to be a valuable tool for investigation (e.g., [11, 12]). The methodology has been applied to other airports with complex terrain, such as [1]. The numerical Regional Atmospheric Modeling System (RAMS) model, version 6.3, is used in this study. The configurations can be found in the paper by [13]. The model is nested from the global forecast system of the National Centers for Environmental Predictions. Five nests are used so that the innermost domain contains the HKIA area only with a horizontal resolution of 40 m [14]. A turbulence parameterization scheme is adopted in the innermost three domains for the large eddy simulation (LES) mode of RAMS. This scheme has been shown to perform well despite it having been developed for a rather long time (e.g., [15]).  
No new data are assimilated into the model. The simulation is only performed as dynamic downscaling. The Hong Kong terrain is adopted from the Lands Department of the Hong Kong Government with a spatial resolution of around 30 m. As shown in previous studies, including the one by [13] above, the major mountain features in the HKIA area have been well represented in this model configuration.  
This paper attempts to document the two windshear events and investigate their cases, namely, whether they are related to the Lantau terrain, which is located at least 4 km from this runway. If this is the case, the Lantau terrain, which has a maximum height of only 1 km, may have wakes extending to quite a far distance away, so that the runway corridor 07 LA is still affected by them and has significant headwind changes. With the accumulation of more windshear reports over 07 LA over a period of a few years, we may then compare the frequency of terrain-induced windshear over this new 07 LA with that of the original 07 LA (i.e., landing on the central or second runway, which was the original north runway of HKIA, from the west). Because a statistical analysis cannot be performed at the moment, the current study aims at establishing the impact of the Lantau terrain on significant windshear over 07 LA, e.g., examining how far away the wake of the terrain could extend.