esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation
### Case 1: Public Bearing Dataset of Paderborn University (PU)  
#### 4.1.1 Dataset and Experimental Apparatus  
At present, most of the fault datasets of bearings both at home and abroad are based on vibration signals, so there are few datasets containing current signals. The public bearing dataset PU synchronously collects vibration and motor current signals that are presently widely used and is also known as the condition monitoring (CM) experimental bearing dataset. The test rig is shown in Figure 6 and is divided into several different modules based on its function: the motor, torque measuring shaft, rolling bearing, flywheel, and load motor. In the rolling bearing test module, the dataset is generated by replacing bearings with different damage types. All the test bearings are ball bearings of type 6203. Among them, there are 12 artificially damaged bearings (including 7 outer-race faults and 5 inner-race faults), 14 damaged bearings in the accelerated life test (including 5 outer-race faults, 6 inner-race faults, and 2 composite faults), and 6 healthy bearings. The motor phase current signal is obtained through the output current of the frequency converter, which is collected through a LEM CKSR 15-NP current transducer. Then, the current signal is converted from an analog signal to a digital signal at a sampling rate of 64 kHz. According to the operating parameters, four working conditions are set, and two of them are selected for the experiment, as shown in Table 3, according to the experimental verification requirements. Data for each working condition were collected 20 times, for 4 s each time, and saved as a MATLAB file whose name consists of the experimental condition and the special bearing code (such as N15_M01_F10_K001_1.mat).  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Model** & **Hyperparameter** \\ \hline \multirow{3}{*}{ISCV-ViT} & Max epochs = 200 \\  & Batch size = 32 \\  & Optimizer = [PERSON] (\(lr=2\times 10^{-3}\)) \\ \hline \multirow{3}{*}{ViT} & Max epochs = 200 \\  & Batch size = 32 \\  & Optimizer = [PERSON] (\(lr=2\times 10^{-3}\)) \\ \hline \multirow{3}{*}{ISCV-ResNet50} & Max epochs = 200 \\  & Batch size = 32 \\  & Optimizer = [PERSON] (\(lr=2\times 10^{-3}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameter settings of the three benchmark models.
# Volatile Organic Compounds in _Dactylorhiza_ Species
## 3 Results and Discussion  
The scent of _Dactyllorhiza_ orchids has been the object of some studies in the past. Analysis of _D. samucina_ showed the presence of limonene, \(\beta\)-myrcene, \(\alpha\)-pinene, \(\alpha\)-bergamotene, \(\beta\)-bisabolene, caryophyllene, and \(\beta\)-selinene [70]. Dichloromethane extracts of flowers of _D. incarnata_ showed the presence of 4-hydroxybenzaldehyde (16.88%), 4-hydroxybenzylalcohol (30.60%), and methyl 4-hydroxyphenylacetate (34.37%) [71]. Pentane, diethyl ether extraction of flowers of \(D\). _incarnata_ showed the presence of nonanal, 9-(Z)-heptacosene, 9-(Z)-nonacosene, tricosane, pentacosane, and heptacosane [72] The analysis of the scent of \(D\). _romana_ (red flower), obtained by head space analysis of the scent absorbed on Porapak Q, showed the presence of several components, including nonanal (9.19%), sabinene (10.89%), (E)-ocimene (15.63%), and linalool (7.99%) [31]. Such different results, obtained by using different analytical techniques, allowed us to analyze the scent of these species by using SPME (solid phase microextraction) technique. The results are reported in Table 1.  
The never studied before \(D\). _viridis_ contains, as its main components, verbenone (28.86%) and carophyllene (25.67%), while other compounds present in relevant amounts are \(\beta\)-terpineol (9.48%) and \(\delta\)-cadinene (6.94%) (Table 1 and Figure 3). In the scent of \(D\). _romana_ several compounds were found: \(\beta\)-ocimene (18.69%, found also in [31]), pentadecane (18.40%), \(\alpha\)-farnesene (14.65%), and isopropyl 14-methylpentadecanoate (14.32%) (Table 1). Previous works published on the aroma of \(D\). _incarnata_ found phenolic compounds, in one case, while long chain alkanes and alkenes, were found in another case [71,72]. SPME analysis of the scent detected the presence of a mixture of hydrocarbons, but with lower molecular weight than those found in [72]. Tetradecane (11.07%), pentadecane (28.40%), hexadecane (19.53%), and heptadecane (17.33%) were the main components, together with \(\alpha\)-cubenene (11.48%) (Table 1). _D. saccifera_ contained, as a main component, carophyllene (17.38%), while other significant compounds were hydrocarbons, pentadecane (6.43%), hexadecane (6.13%), and heptadecane (5.08%) (Table 1). Finally, the aroma components found in \(D\). _sambucina_ were carophyllene (12.90%, found also in [70]), \(\beta\)-sesquiphellandrene (32.16%, Figure 4), 4,5-di-_epi_-aristolochene (10.18%, Figure 4) (Table 1). Figure 4: Main components of the scent of \(D\). _sambucina_. Figure 3: Main components of the scent of \(D\). _viridis_.
## 5 RF Feature Extractors
### Teager-Kaiser Energy Operator (TKEO)  
The Teager-Kaiser energy operator (TKEO) is a transform which can estimate the instantaneous energy of a signal, and thus may uncover features that are distinguishable in power or energy. The TKEO transform \(\mathcal{T}_{TKEO}\) of a complex signal \(r(n)\) is defined as [58]  
\[\mathcal{T}_{TKEO}(n)=|r(n)|^{2}-\frac{1}{2}\bigg{(}r^{*}(n+1)r(n-1)+r(n+1)r^{* }(n-1)\bigg{)} \tag{19}\]  
where \(r(n)\) is the complex sampled signal and \(r^{*}(n)\) is the conjugate of \(r(n)\).  
TKEO has been previously used in the context of RFF in GNSS in [3] with promising results. It is also one of the feature extractors selected in our study.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 7 Future Research Directions
### Energy Charging Efficiency  
Energy constraints are a significant obstacle in any UAV communication scenario. Subsequent advancements in battery technology, such as improved lithium-ion batteries and hydrogen fuel cells, have enhanced energy charging to extend flight durations by using renewable energy sources such as solar power. The efficacy of energy charging, however, is significantly reduced due to longer removal time and irregular energy access. To improve charging productivity, novel energy transmission enhancements (such as energy beamforming using multi-antenna techniques and dispersed multi-point wireless power transfer (WPT)) are of great interest. The more important point to emphasize here is that addressing mobility management will improve power consumption efficiency. The goal of UAV mobility management is to reduce unnecessary HO processes, which in turn reduces the HO rate and head over signaling, saving more power and increasing energy efficiency.
# Modified 16-Quasi Log Periodic Antenna Array for Microwave Imaging of Breast Cancer Detection
## 6 Conclusions  
The design, simulation, and fabrication of a portable, lightweight, and compact PLPA element was presented in this paper. The 50 \(\times\) 40 mm\({}^{2}\) PLPA characteristics were investigated and verified for microwave imaging. There exists a modified sensor element in the proposed MIS, and the designed system consists of 16 antenna elements operating at 2 to 5 GHz. The back scattered signals collected by the circular antenna array elements are processed using DAS algorithm in MATLAB to reconstruct the image to detect the presence of breast tumor. An artifact removal method based on rotation subtraction was applied in this technique. PLPA array-based MIS presents an excellent performance, making it one of the most efficient methods for detecting tumor cells.  
Conceptualization, A.S. and H.R.; methodology, A.S., N.S. and R.M. software, A.S. and M.S.; validation, A.S., R.M. and H.R. formal analysis, A.S., R.M. and H.R.; investigation, A.S., N.S., R.M. and H.R.; resources, A.S., M.S. and H.R.; data curation, A.S. and H.R., writing--original draft preparation, A.S.; writing--review and editing, A.S., N.S. and H.R.; visualization, A.S. and R.M.; supervision, H.R.; funding acquisition, H.R. All authors have read and agreed to the published version of the manuscript.  
This project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia under grant No. (KEP-PhD-14-135-42). The authors, therefore, acknowledge with thanks to DSR technical and financial support.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Statistical Evaluation of Indicators  
In the present study, the coefficient of determination (\(R^{2}\)), root mean square error (\(RMSE\)), mean absolute percentage error (\(MAPE\)), and residual prediction deviation ratio (\(RPD\)) were used to evaluate the various models. The calculation Formulas (6)-(9) of each indicator are shown below:  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})}^{2}}{\sum\limits_{i=1} ^{n}{(\overline{y}_{i}-y_{i})}^{2}} \tag{6}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{(\hat{y}_{i}-y_{i})}^{2}}} \tag{7}\]  
\[MAPE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\left|\frac{\hat{y}_{i}-y_{i}}{y_{i}} \right|}} \tag{8}\]  
\[RPD=\frac{SD}{RMSE} \tag{9}\]  
where \(\hat{y}\) is the predicted value of the sample, \(\overline{y}\) is the mean value of the measured sample, \(y\) is the measured sample value, \(n\) is the number of samples, expressed by \(i=1\), \(2\), \(\cdots\), \(n\), and \(SD\) is the standard deviation. Models with high \(R^{2}\) and \(RPD\) and low \(RMSE\) and \(MAPE\) are considered to be suitable for quantitative inversion.
## 1 Introduction  
Wireless sensor network (WSN) plays a significant role in constructing IoT systems, which is composed of many sensor nodes in a multi-hop self-organizing manner. Due to the properties of easy connectivity and high data rate, WSN has been widely used in diverse fields including the military, industry, agriculture and so on [1, 2, 3]. However, as it is battery powered, the lifetime of a sensor node is limited by the battery capacity.  
With the development of green energy, energy harvesting (EH) techniques are introduced into the WSNs. Driven by the EH techniques, nodes can intake energy from the ambient environment (e.g., solar, wind, RF radiation, etc.) and recharge their batteries during operation, which can improve the energy efficiency and extend the lifetime of the network [4, 5]. At present, many routing algorithms have been proposed for energy harvesting wireless sensor networks (EH-WSNs) which focus on exploiting the harvested energy and optimizing energy efficiency [6, 7, 8, 9].
# Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model
## 2 Methodology
### Data Clustering  
#### 2.3.1 Feature Vector Extraction  
The main purpose of introducing the feature extraction here is to try to classify the data based not only on the data value but also on the similarity of the underlying dynamics. This thinking is formally supported by the assumption that if a PWA map is locally linear, then a small subset of points \(u_{k}\) which are close to each other will likely be in a similar region. Therefore, at first, a transformation from the data space to the feature vector space based on the local dynamics of the data is carried out. This transformation from the data space to the feature space further separates the data, thereby making it easy for the data to be clustered, thus highlighting the reason why feature vector extraction was performed on the data. It should be noted that the variable names or definitions here are particular to only this sub-section. The procedure for this transformation is as follows:  
Given \((y(j),x(j))\) a set of sample data for \(j=1,2,\ldots,n\), the neighboring \(c\) data points in the \((y,x)\) space, where \(c\) is a positive integer less than \(n-1\), is collected for each \((y(j),x(j))\) to generate a local data set \(LDs_{j}\) and calculate the feature vector \(\xi_{j}\), where the index \(j\) denotes the order in the data space. The feature vector is given by  
\[\xi_{j}=((\theta_{j,1}^{LD})^{\intercal},(\theta_{j,2}^{LD})^{\intercal},m_{j} ^{\intercal})^{\intercal} \tag{2}\]  
where \(((\theta_{j,1}^{LD})^{\intercal},(\theta_{j,2}^{LD})^{\intercal})^{\intercal}\) and \(m_{j}\) are the local parameters in the local ARX model and the mean value of the data \(u\), respectively, in the \(LDs_{j}\), and are calculated as follows:  
\[\theta_{j,l}^{LD} =(\phi_{j}^{\intercal}\phi_{j})^{-1}\phi_{j}^{\intercal}y_{LDs_{ j}}, \tag{3}\] \[m_{j} =\frac{1}{c}\sum_{u\in LDs_{j}}u, \tag{4}\]  
where \(y_{LDs_{j}}(c\times 1;l=1,2)\) is the \(LDs_{j}\) output samples, and \(\phi_{j}\) is denoted by  
\[\phi_{j}=(u_{1}\;u_{2}\ldots u_{c})^{\intercal},\text{for }u\in LDs_{j}. \tag{5}\]  
Finally, for each feature vector \(\xi\), a covariance matrix \(R_{j}\), which is used as a weighting matrix for computing the dissimilarity between feature vectors used in the clustering procedure, is computed as:  
\[R_{j}=\begin{bmatrix}V_{j,1}&0&0\\ 0&V_{j,2}&0\\ 0&0&Q_{j}\end{bmatrix} \tag{6}\]  
where  
\[V_{j,l} =\frac{SSR_{j,l}}{c-(9+1)}(\phi_{j}^{\intercal}\phi_{j})^{-1} \tag{7}\] \[SSR_{j,l} =y_{LDs_{j},l}^{\intercal}(I-\phi_{j}(\phi_{j}^{\intercal}\phi_{j })^{-1}\phi_{j}^{\intercal})y_{LDs_{j},l}\] (8) \[Q_{j} =\sum_{u\in LDs_{j}}(u-m_{j})(u-m_{j})^{\intercal}. \tag{9}\]
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products  
[PERSON]  
1 Department of Hydrology & Atmospheric Sciences, University of Arizona, Tucson, AZ 85721, USA  
These DEMs and point clouds are becoming increasingly common for assessing earth system processes and hazards in high mountain environments due to the submetre precision that is achievable [17; 24; 25].  
UAV surveys in the Himalaya are generally limited in spatial extent and by the costs and administration associated with permits, licensing, transportation, and field deployment (Table 1). However, flexible flight altitudes and survey times enable tailored data collection to requirements such as achieving a specified spatial resolution or minimising terrain shadows. The widespread low-cost availability of UAVs also means there are emerging opportunities to crowdsource imagery following disasters. For example, UAV-derived products, such as DEMs and orthophotos, can be used to map and monitor landslide evolution [26; 27] and quantify socioeconomic disruption [28]. Commercial satellite images were released for crowdsourced landslide mapping following the 2015 Gorkha earthquake in Nepal [5; 29]. However, their acquisition was dependent upon cloud-free periods coinciding with orbital revisit times, and coincident DEMs were unavailable for topographic change detection. UAVs provide a rapid means of collecting georeferenced imagery, and although spectral bands are often limited to red-green-blue, UAVs offer a flexible platform that can be fitted with other sensors (e.g., thermal and near-infrared).  
In this study, we compare the use of fine-resolution UAV-derived topography to open-access DEM products in a high-mountain environment. Our objectives are to (1) assess the utility of the different DEM products for high-mountain hazard assessment and management, including flooding, landslide volume, and landslide-dammed lake volume; (2) use a fine-resolution DEM to determine whether river-blocking landslides that formed on the Marsyangdi River following the 2015 Gorkha earthquake posed a downstream inundation hazard; and (3) outline a workflow for data acquisition and analysis following a natural disaster.
# Joint Pricing, Server Orchestration and Network Slice Deployment in Mobile Edge Computing Networks
## 4 The Three-Stage Stackelberg Game
### The Double-Layer BDQ Model for the NPL and RAL
#### 5.2.3 The Illegal Action Modification Method  
In BDQ, each branching network individually evaluates the action value of all possible sub-actions and outputs the sub-action with the maximum state-action value. The sub-actions from different branches may conflict with each other or be ineffective in combination. We call this an \"illegal situation\". There are two illegal situations that may occur in our scheme: (1) In the NPL, slice \(n\) requires \(M\) branches, and the branch corresponding to \(x_{m,n}\) determines whether slice \(n\) is deployed on base station \(m\) or not, which means that the relationship of slice \(n\) with each base station is determined by a different branch. Thus, there is a probability that the actions of all the branches corresponding to slice \(n\) are 0,  
Figure 4: The evaluation network of BDQ.  
i.e., \(\sum_{m=1}^{M}x_{m,n}=0\). In this case, no base station deploys slice \(n\), which is not allowed; (2) Similarly to the NPL, there is a probability in the RAL that the actions of all the branches corresponding to \((n,q)\) are 0, i.e., \(\sum_{m=1}^{M}y_{n,q}^{m}=0\). This is not allowed when there are users in \((n,q)\) who need to be served, i.e., \(u_{n,q}>0\). There may be many illegal actions in the early training stage of BDQ. Merely applying negative penalties would affect the convergence performance. To this end, we design an illegal action modification method based on the branching structure to mask illegal actions in Algorithm 2.  
Specifically, in the NPL, suppose there is a slice \(n\) that encounters the case where \(\sum_{m=1}^{M}x_{m,n}=0\) (line 2). We need to find all the branches corresponding to \(\{x_{m,n}|m\in\mathcal{M}\}\), i.e., \(F^{n}\). The action of the branch with the largest state-action value is modified to 1 (lines 3-4). In this way, we ensure that at least one base station deploys slice \(n\), and that base station corresponds to the highest state-action value. Similarly, in the RAL, suppose that \((n,q)\) encounters the case where \(u_{n,q}>0\) but \(\sum_{m=1}^{M}y_{n,q}^{m}=0\) (line 8). We need to find all the branches corresponding to \(\{y_{n,q}^{m}|m\in\mathcal{M}\}\), i.e., \(F^{n,q}\). Then, we set the action of the branch with the largest state-action value to 1 (lines 9-10). For ease of description, we will call the BDQ with illegal action modification as modified-BDQ (MBDQ).  
```
1:The modification method for NPL:
2:Input:\(\hat{a}_{1}^{NP}=\{[h_{m}^{t}]_{M\times 1},[x_{m,n}^{t}]_{M\times N}\}\)
3:for all\(n\in\mathcal{N}\)do
4:if\(\sum_{m=1}^{M}x_{m,n}=0\)then
5:\(\hat{f}_{1}=\underset{f\in F^{n}}{\operatorname{arg\,max}}\hat{Q}\Big{(}s_{t} ^{NP},\hat{a}_{t}^{NP,f};\mathbf{\varphi}\Big{)}\).
6:\(\hat{a}_{t}^{NP,\hat{f}_{1}}=1\).
7:endif
8:endfor
9:endfor
```  
**Algorithm 2** The illegal action modification method
## 4 Results and Analysis  
The results presented in this section are based on a one-month simulation conducted for spare parts groups of 50, 200, and 300 items. To provide robust validation for our findings, we performed 30 independent simulation runs for each scenario. In each run, the actual demand was generated stochastically to reflect real-world uncertainty, while the model only had access to its deterministic forecast. Different random seeds were used to ensure demand variability across the runs. All reported metrics for these scenarios show the mean and the 95% confidence interval. The proposed optimization model's performance is evaluated against three distinct benchmarks:  
* **(s,S) Policy:** A standard inventory control policy from the academic literature, serving as a rigorous algorithmic benchmark.
* **Business as Usual:** The current, manual practice in the case study warehouse, serving as a practical baseline.
* **Simulation with Known Demand (Simulation KD):** A theoretical, perfect-information case, corresponding to the \"dynamic oracle\" benchmark discussed in the literature [20], used to establish an upper bound on performance.  
Our proposed model, **Simulation with Predicted Demand (Simulation PD)**, is driven by the ADP framework with machine learning forecasts. The findings illustrate the benefits of integrating predictive demand models and buffer area strategies into warehouse operations.
## 6 Conclusions
### Nomenclature  
\begin{tabular}{l l} \(A\) & heat transfer area (m\({}^{2}\)) \\ \(c\) & specific heat capacity (kJ/(kg\(\cdot\)K)\({}^{-1}\)) \\ \(\dot{E}\) & exergy flow rate (kW) \\ \(e\) & specific exergy (kJ/kg\({}^{-1}\)) \\ \(h\) & specific enthalpy (kJ/kg\({}^{-1}\)) \\ \(\dot{m}\) & mass flow rate (kg/s\({}^{-1}\)) \\ \(P\) & pressure (MPa) \\ \(\dot{Q}\) & heat transfer rate (kW) \\ \(s\) & specific entropy (kJ/(kg\(\cdot\)K)\({}^{-1}\)) \\ \(T\) & temperature (\({}^{\circ}\)C) \\ \(V\) & volume (m\({}^{3}\)) \\ \(U\) & heat transfer coefficient (W/(m\({}^{2}\cdot\)K)\({}^{-1}\)) \\ \(\dot{W}\) & power (kW) \\
**Greek Symbols** \\ \(\eta\) & efficiency (\%) \\ \(\Delta T\) & temperature difference (\({}^{\circ}\)C) \\
**Subscripts** \\
0 & ambient state \\
1,2, et al. & state points \\ C & compressor \\ D & destruction \\ ex & exergy \\ i & inlet side \\ k & component \\ liq & liquid \\ o & outlet side \\ s & isentropic \\ stor & storage \\ T & turbine \\
**Abbreviations** \\ ESS & energy storage system \\ CAES & compressed air energy storage \\ PHS & pumped hydro energy storage \\ AA-CAES & advanced adiabatic compressed air energy storage \\ LAES & compressed liquid air energy storage \\ ICAES & isothermal compressed air energy storage \\ CCES & compressed carbon dioxide energy storage \\ TC-CCES & trans-critical carbon dioxide energy storage \\ SC-CCES & supercritical carbon dioxide energy storage \\ TES & thermal energy storage \\ LCES & compressed liquid carbon dioxide energy storage \\ PC-CCES & carbon dioxide phase-change energy storage \\ SSC-CCES & trough solar heat storage supercritical compressed CO\({}_{2}\) energy storage \\ STC-CCES & trough solar heat storage trans-critical compressed CO\({}_{2}\) energy storage \\ TCES-HP & trans-critical compressed CO\({}_{2}\) energy storage system combined heat pump \\ C & compressor \\ Cond & condenser \\ CST & cold salt tank \\ CWT & cold water tank \\ Evap & evaporator \\ F & fuel \\ \end{tabular}
# Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving
## 2 Related work
### Deep Learning Method  
Convolutional neural networks are widely used in areas such as image classification and target detection. In addition, researchers have also applied them to image enhancement [15; 16; 17; 18]. [PERSON] et al. [19] combined convolutional neural networks with retinex theory to propose MSR-net for low-light image enhancement. [PERSON] et al. [20] proposed a conditional generative adversarial network to address infrared image enhancement, which can avoid background noise being amplified and further enhance contrast and details. [PERSON] et al. [21] proposed a trainable end-to-end system named DehazeNet, which takes a hazy image as input and outputs its medium transmission map, which is subsequently used to recover a haze-free image via an atmospheric scattering model. [PERSON] et al. [22] proposed a neural network named a multi-scale error feedback network to enhance low-light images. [PERSON] et al. [23] presented an innovative target attention deep neural network to achieve discriminative enhancement in an end-to-end manner. The above study illustrates that CNNs are useful for being able to enhance the contrast of infrared images.