esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 4 Conclusions  
The deep learning-based interference monitoring method proposed in this scheme outperforms other deep learning methods. Other deep learning methods typically only detect the interference styles, treating the problem as a classification problem. This method treats interference detection and parameter identification as target detection problems, and the main advantages are as follows:  
At present, the interference signal detection and identification methods based on deep learning mainly aim at the interference signal detection and classification under the condition of single channel, but fail to realize the interference signal detection and classification under the condition of multi-channel broadband. At the same time, the existing research methods mainly identify the types of interference signals through correlation detection algorithms, which are not likely to identify the basic parameters such as the starting and ending frequency, energy and bandwidth of interference signals. Compared with the existing methods, the interference signal feature extraction and pattern classification algorithm based on deep learning proposed in this paper can complete the detection and classification of broadband interference signals and the parameter identification of interference signals.  
In conclusion, the interference detection proposed in this scheme has excellent detection performance, high detection accuracy and small rate of error in parameter prediction, which can be used for communication satellite channel monitoring.  
Conceptualization, Y.Z. and J.Q.; methodology, J.Q.; software, J.Q.; validation, J.Q. and F.Z.; formal analysis, J.Q.; investigation, K.W. and J.Q.; resources, C.D.; data curation, J.Q., C.D. and J.Q.; writing--original draft preparation, J.Q.; writing--review and editing, J.Q. and Y.Z.; visualization, J.Q.; supervision, Y.Z.; project administration, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques  
This section presents energy conservation techniques aimed at prolonging the lifespan of WSNs and evaluates their effectiveness in LWSNs. Energy conservation techniques are principally involved with energy savings at the sensor node level or network level [59; 60; 64; 67]. This can be done by implementing techniques that are aimed at minimizing energy consumption during network activities and/or implementing schemes that involve switching off node components that are not temporarily needed or dynamically adapting their power consumption [68]. The reason is that a large amount of energy is consumed by the node components (CPU, radio, sensor, etc.) even in idle mode [36]. Hence, energy conservation strategies primarily target minimizing the energy consumption of the communication, processing, and sensing subsystems to extend the lifetime of WSNs. Figure 6 presents a taxonomy we developed for classifying the energy conservation techniques that extend the WSN's lifetime. In our proposed taxonomy, we broadly classified energy conservation schemes for extending the WSN's lifetime into node-level-focused and network-level-focused techniques. On the one hand, node-level energy savings are achieved by reducing the energy consumption of the different sub-components of the sensor node (processor, transceiver, and sensors) via the use of duty cycling (power management), data-driven schemes (data reduction and energy-efficient sensing), or mobility-based schemes (mobile sink/relay). On the other hand, energy savings at the network level are achieved by reducing the energy consumption of a subset of nodes in the WSN via topology control schemes (location-driven and connection-driven schemes) or energy-efficient routing protocols (cluster-based, data-based, and geographic-based routing). The aim of the network-level energy-saving techniques is to maintain the network with a maximum node life [54]. Table 3 presents the different categories and sub-categories of energy conservation techniques for extending the WSN's lifetime. For each technique, the strategy adopted, the components affected in order to achieve energy savings, the layer where the technique is implemented, and its suitability for LWSNs are presented. Table 4 presents an evaluation of the energy conservation technique discussed in this subsection. The pros and cons of each technique and an analysis of how the efficiency of these techniques could be applied to LWSNs are presented, while a detailed discussion is provided in Section 7. This can be helpful to engineers by assisting them in making the best decision during the design of energy-efficient LWSN monitoring systems. Figure 6: Taxonomy for classifying energy conservation techniques in WSNs.
## 1 Introduction
In the study conducted by [PERSON] et al. [29], the accuracy of IDW, OK, and Spline interpolation methods was investigated for the interpolation of ZTD values. According to the results, IDW and OK methods were found to be more successful than the Spline interpolation method. According to the study conducted by [PERSON] [30], a method based on least square estimation was used to predict residual tropospheric delays. The spatial prediction root mean square error (RMSE) of the residual ZTD values was found to be approximately 1.5 cm. In [31], a new model was developed based on special harmonic functions taking seasonal and diurnal variations into consideration that provides tropospheric delay corrections for user location without temperature, pressure, and humidity measurements. In the analysis, the grid point values were horizontally interpolated to the user position and the mean RMS value was found to be 3.8 cm. In another study conducted by [PERSON] et al. [13], interpolation of the troposphere delay was performed based on the feed-forward neural network model. In this study, artificial neural network tests were carried out using the differences between total troposphere delay obtained from the Hopfield [3] model and total troposphere delay obtained from the GNSS technique. They employed the model to predict the ZTD values in a network where the GNSS station is absent. According to the results, the suggested model can improve the ZTD prediction accuracy by more than 90% compared with the Hopfield model. As seen from the studies, spatial interpolation of ZWD with high accuracy is still a difficult issue, especially at distances of 70 km and above. The Multilayer Perceptron model with back-propagation learning architecture, which is a powerful nonlinear approach for meteorological predictions, was successfully applied in the aforementioned studies. Therefore, a three-layer feed-forward neural network model based on meteorological parameters was used in this study for the spatial interpolation of wet delay. The main purpose of the study is to enhance the interpolation accuracy of ZWD in order to contribute GNSS positioning and weather forecasting applications based on a newly designed neural network structure by using meteorological parameters (P: pressure, T: temperature, T\({}_{\text{M}}\): weighted mean temperature, E: water vapor pressure). The determination of the meteorological parameters was carried out by evaluating the correlation coefficients between ZWD and meteorological parameters. In order to evaluate the contribution of the parameters to the interpolation performance, six different ANN models were tested by subtracting meteorological parameters from the artificial neural network model one by one. The details of ANN models and the performance evaluation of the neural network design based on meteorological data are given and discussed in the following sections.
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 1 Introduction  
General image fusion mainly includes multi-focus, multi-modal, and multi-exposure image fusion, among which multi-focus images contain color and gray multi-focus images, and multi-modal images contain medical multi-modal and infrared and visible images. With the updating and iteration of sensors, the sources of image acquisition are becoming increasingly diverse. In many scenarios, it is often necessary to use multiple sensors to jointly image the target scene to meet different practical needs. In order to make full use of multi-focal and multi-modal images obtained by multi-sensors, image fusion technology comes into being and plays an important role in photography, machine vision, medicine, military, and other fields.
## 3 Results and Discussion  
### HS-SPME-GC-MS Analysis of Volatile Compounds  
The development of quick and sustainable methods for the extraction of volatile compounds from plant materials such as the HS-SPME is important. The rapid sampling shortens the whole length of the analysis, while the avoidance of organic solvents reduces its environmental impact. HS-SPME coupled with the GC-MS analysis of the aerial parts of _P. spinosa_ exhibited 48 volatile compounds, accounting for 98.1% of the whole volatile extract. The identified compounds, their retention indexes, and their relative proportions are presented in Table 1. A representative chromatogram is shown in Figure 1.  
The volatile profile was dominated by sesquiterpene hydrocarbons (65.8%), followed by monoterepene hydrocarbons (16.9%). Oxygenated monoterepenes were less represented, similar to oxygenated sesquiterpenes (8.3% and 6.5%, respectively). Phenylpropanoids (0.5%) and apocarotenes (0.1%) were found as trace components. Considering the single identified compounds, the aerial parts of _P. spinosa_ were characterized mainly by \(\beta\)-chamigrene (16.2%), \(\alpha\)-selinene (12.8%), \(\beta\)-pinene (10.6%), and \(\beta\)-caryophyllene (9.2%). Some other compounds, such as trans-\(\gamma\)-cadinene (5.2%), alloaromadendrene (4.4%), and germacrene D (4.3%), were found. Figure 1: Exemplificative chromatogram obtained from the GC-MS analysis of the volatile extract obtained from _P. spinosa_ by HS-SPME.
# A New Big Data Benchmark for OLAP Cube Design Using Data Pre-Aggregation Techniques
## 3 Method
### The Apache Kylin Case
#### 3.3.1 Cube Design for Apache Kylin
Therefore, these dimensions will appear together in all possible combinations between the joint group and remaining dimensions. Therefore \"Derived\" Dimensions and Aggregation Groups are powerful tools to reduce Curse of Dimensionality effects. Considering these optimizations and the complete architecture of Kylin, we summarize in the following the design steps and principles to be applied for the OLAP cube implementation:  
1. **Source system selection:** If batch processing is required, we recommend using Apache Hive due to its proved capabilities to store and maintain analytical data models for Big Data scenarios. In case of a real-time scenario, we recommend using Kafka. 2. **OLAP cube definition:** First the user must identify the tables and the relationship between them over the data model stored in the source system chosen. Secondly, to reduce the effects of the dimensionality curse [25], it is very important to determine the optimal type for each dimension column. Finally, for those dimensions defined as \"Normal\", we have to create one or more aggregation groups and apply to them the possible optimizations allowed. To this end, we recommend conducting a previous study of end-users querying patterns, to achieve a trade-off between pre-aggregation stored in the OLAP cube and the aggregation required during query execution time. Therefore, for our benchmark implementation, we have to study the set of 30 SQL queries proposed to identify such querying patterns.
## 2 Materials and Methods
### Calculations  
To assess the desalination performance of the retrofitted MFRO system, commonly utilized metrics such as the permeate flux \(J\), solute rejection \(\O\), permeate flux ratio \(\gamma\)%, and recovery rate \(R\), along with the specific energy consumption (\(SEC\)), were employed. The calculation of these indices, which related to the system as a whole, was based on data derived from parameters such as the inlet water salinity, produced water salinity, inlet pressure, outlet pressure, and current and voltage measurements. These data provide the basis for accurate evaluation and analysis.  
The permeate flux (\(J\), L/(m\({}^{2}\cdot\)h)) is defined as the volume of water that passes through one square meter of the reverse osmosis membrane per hour.  
\[J=\frac{V}{S\times t} \tag{2}\]  
where \(V\) is the water permeation volume at time \(t\) (L), \(S\) is the effective membrane area of 1.8 m\({}^{2}\), and \(t\) is the sample collection time (h).  
Solute rejection refers to the extent of the difference in the solute concentration between the permeate and the feedwater when subjected to the RO membrane. It serves as a measure of the membrane's effectiveness in removing solutes.  
\[=\frac{C_{0}-C_{T}}{C_{0}}\times 100\% \tag{3}\]  
where \(C_{0}\) refers to the solute concentration (mg/L) in the permeate, while \(C_{T}\) represents the solute concentration (mg/L) in the feedwater.  
The permeate flux ratio (\(J\)%) refers to the ratio of the permeate flux at time \(t\) to the initial permeate flux:  
\[J_{\gamma_{n}}=\frac{J\text{v}t}{J_{vt0}} \tag{4}\]  
where \(J_{vt0}\) refers to the initial moment of water flux (L/m\({}^{2}\cdot\)h, liters per square meter per hour); and \(J_{vt}\) refers to the water flux at time \(t\) (L/m\({}^{2}\cdot\)h).  
The recovery rate (\(R\)) is defined as the percentage of water infiltrated relative to the total amount of water treated by the system after time \(t\).  
\[R=\frac{V}{V_{0}}\times 100\% \tag{5}\]  
where \(V_{0}\) is the total volume (L) of feedwater that has been treated during \(t\) hours of operation.  
\(SEC\)[38; 39] represents the amount of energy required to produce one liter of water, expressed in kW\(\cdot\)h/L.  
\[SEC=\frac{W}{V/t} \tag{6}\]  
\[W=\frac{P_{wp}}{R}+W_{E} \tag{7}\]  
\[W_{E}=\frac{U\times I\times T_{1}\times\eta}{1000} \tag{8}\]  
where \(W\) represents the overall energy consumption of the treatment system per hour (kW\(\cdot\)h), \(P_{wp}\) is the operating pressure provided by the booster pumps (MPa), \(W_{E}\) is the energy consumption to produce one ton of water for the electric field (kW\(\cdot\)h), \(U\) refers to the operating voltage of the system (\(V\)), \(I\) refers to the operating current of the system (A), \(T_{1}\) refers to the treatment time (1 h), and \(\eta\) refers to electrical energy conversion efficiency, taken as 0.8.
## 3 Results and Discussion
### Integrated Findings  
The water flow rates profoundly influence wetlands' filtration and purification capacity. A reduced water flow promotes the aggregation, flocculation, and sedimentation of particles from AMD-affected water, leading to their settling and formation of a bottom sediment layer. This sediment layer remains in continuous contact with surface water, the primary carrier of pollutants stemming from AMD [63; 64].  
Wetland macrophytes undergo decomposition in oxygen-depleted, flooded environments, enriching the OM content within the bottom sediment [1; 68; 73]. Clay minerals, renowned for their robust ion exchange capacity, interact with OM, which metals have a strong affinity for [3; 70]. Moreover, sediment characteristics such as the pH and ORP are influenced by root exudates containing oxygen and other nutrients [1; 68]. Elevated pH and ORP values facilitate the precipitation and sorption of metals like Mn and Fe, often observed as Fe plaques on root surfaces [85; 95; 96].  
Microbial communities thrive in the nutrient-rich environment provided by accumulations in the rhizosphere, modifying metals and sulfur compounds through oxidation and reduction reactions during metabolic activities [77; 97]. Mobilized metals and sulfur compounds are subsequently released into the bottom sediment and readily absorbed into macrophyte root tissues [98].  
The success of this passive treatment system hinges on the intricate interplay among various wetland compartments, all interdependent within the anoxic and potentially toxic environment created by AMD [15; 77] Figure 10 provides a simplified schematic representation of this phenomenon in the current study, illustrating the dynamic interactions within the wetland environment.
## 3 Results and Discussion  
We performed simulations for all 36 test cases together with the reference test case as mentioned in Table 7 during the entire summer months. The analysis of the results is presented in the following three sections. First, we illustrate the general characteristics of the usage of PCM spackling in Section 3.1. Subsequently, in Section 3.2, we present the analysis of the cooling load reduction and finally, Section 3.3 reveals the energy saving aspects of the MPCM enhanced spackling material in combination with NV.
## 3 Proposed Methodology  
In this work, we have proposed a facial expression recognition system (FERS) in the diversified fields of applications, such as e-Healthcare, social IoT, emotion AI, and cognitive AI. The block diagram of the proposed method is demonstrated in Figure 2. Since these fields belong to interdisciplinary research areas, the algorithms and techniques employed during the implementation of these frameworks are interconnected. Thus, the proposed FERS will be used as the common platform for analyzing expressions in the applications of these frameworks. Furthermore, the implementation of a basic FERS is discussed in the following paragraphs.  
A facial expression recognition system generally consists of face representation, feature extraction, and classifier components. Regarding the importance of face recognition in computer vision research areas, we have proposed robust, efficient, and accurate deep convolution neural network (CNN) models for facial expression recognition systems. Here, an image \(\mathcal{I}_{m\times n}\) with a valid face region is used as an input to the system. Our objective is to predict the types of expressions, such fear, anger, disgust, surprise, sadness, happiness, and neutral, from the input face region \(\mathcal{I}_{m\times n}\). The proposed facial expression recognition system (FERS) has been implemented in four steps: (i) pre-processing, wherein the bounded box face region is detected from the input image using the tree structure part model [38]; (ii) feature extraction, wherein the global generic CNN features are extracted from the detected bounding box face region and are prepared for the next level through the deep learning model; (iii) the representations are further modified by using multi-stage progressive image resizing followed by transfer learning methods, wherein image augmentation and fine-tuning of parameters have also been adopted; and (iv) classification, which concerns predicting the type of expression classes of the facial region. Each of these steps is described in the block diagram of the proposed system, which is presented in Figure 2. Recently, deep convolution neural network (CNN) techniques have been successfully developed to learn discriminative features in various fields. It is widely being used in deep FER representation. Deep FER suffers from the over-fitting problem due to the lack of sufficient training samples, age variations, head poses, identity bias, and illumination variations. The proposed method focuses on these issues and overcomes the computational complexity of the proposed system.
## 3 Literature Review
### Understanding of Digital Capability
#### 3.3.2 Dimension of Digital Capability  
A review of the literature reveals ongoing debate among scholars regarding the definition of digital capability, leading to a lack of consensus on its structural dimensions. Some researchers conceptualize digital capability as a unidimensional construct, but the majority view it as a multidimensional concept. [PERSON] et al. (2017) [53], using qualitative data from four case studies, were pioneers in classifying digital capability into three key dimensions: analytical, connectivity, and intelligence capabilities. [PERSON]; et al. (2020) [54], through interviews with chief digital officers across various industries, identified three dimensions: digital innovation, data analytics, and customer engagement. [PERSON] and [PERSON] (2018) [55] proposed a two-dimensional structure comprising IT infrastructure capability and information management capability, emphasizing both technical and managerial aspects.  
[PERSON] and [PERSON] (2019) [56] extended the traditional dynamic capability framework by introducing three dimensions of digital capability: digital sensing, digital seizing, and digital transformation, offering a systematic perspective on sensing and responding processes. Building on this, we propose that digital capability relies on robust digital infrastructure, advanced data analytics, and the application of analytical insights to strategic decision making, aligning with [PERSON] et al. (2022) [57], who categorize digital capability into three dimensions: digital infrastructure capability, digital analysis capability, and strategic support capability.
# A Review on Internet of Things for Defense and Public Safety
## 3 Target Scenarios for Mission-Critical IoT
### Energy Management  
The U.S. DoD is already reducing its demand on facility energy by investing in efficiency projects on its installations [72]. The introduction of data and predictive algorithms can help to understand usage patterns better and significantly decrease military's energy costs.
# Modeling and Numerical Computation of the Longitudinal Non-Linear Dynamics of High-Speed Elevators
## 4 Study of the Nature and Law of Elevator Dynamics
### Effect of Load on Elevator Response  
To investigate the impact of load on the time-domain response of the high-speed elevator, we selected the parabolic-linear control strategy. We then modified the load conditions of the car in the model to represent empty, half-loaded (800 kg), and fully loaded (1600 kg) scenarios. Subsequently, we calculated and compared the elevator's time-domain response for each case. The results are presented in Figure 12.  
As shown in Figure 12, during acceleration, the load minimally influences the time-domain response of the high-speed elevator. The responses of the three conditions are relatively similar at this stage. However, as the speed reaches the evaluated speed, the impact of the load on the elevator's time-domain response becomes more pronounced. The elevator with a full load exhibits the highest fluctuation with the lowest frequency, while the empty elevator has the lowest fluctuation with the highest frequency. This effect persists until the deceleration phase. During the deceleration phase, the response fluctuations of the half-load and full-load conditions are similar to each other, but both are larger compared to the no-load condition.  
Figure 11: Time domain response under four different control strategies. (**a**) Triangular control strategy; (**b**) trapezoidal control strategy; (**c**) parabolic control strategy; (**d**) parabolic–linear control strategy.
## 3 Experiments and Results
### Data Sets
#### 3.1.1 Massachusetts Buildings Data Set  
The Massachusetts building data set [37] consists of many 1 m resolution RGB aerial images with the size of 1500 \(\times\) 1500 pixels. The original data set contains 137 images for training, 10 images for testing, and 4 images for validating. For computational convenience, in the data preprocessing stage, we cropped all of the images into 512 \(\times\) 512 pixels and added a flip operation for data augmentation. Through augmentation, there were 4700 images for training and 144 images for validating. Lastly, we transformed labeled images to grayscale images with pixel values of 0 and 1. Some samples are shown in Figure 6.