esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Analysis
### BSCI Algorithm
If \(EE\left(\overline{N}\right)\geq EE\left(\overline{N}-1\right)\geq EE\left( \overline{N}-2\right)\), \(N_{opt}\) does not exist. If EE \(EE\left(\underline{N}\right)\geq EE\left(\underline{N}+1\right)\geq EE\left( \underline{N}+2\right)\), \(N_{opt}=\underline{N}\) and \(EE_{max}(N)=EE(\underline{N})\). If not in the above two cases, \(N_{opt}\) and \(EE_{max}(N)\) are obtained by using the binary search method. **Step 5:** output \(N^{\prime}\) and \(EE_{max}(N)\). Given the parameter values of the LED, PD, and IRS  
calculate the iterative range \(N=\left\{N\in\mathbb{N}^{+}|\underline{N}\leq N\leq\overline{N}\right\}\) according to Formulas (48) and (49). calculate \(R(N)\) with the iterative range of N according to Formula (19). for \(N=\underline{N}:1:\overline{N}\)  
if \(EE\left(\overline{N}\right)\geq EE\left(\overline{N}-1\right)\geq EE\left( \overline{N}-2\right)\)  
\(N_{opt}\) does not exist;  
break;  
else if \(EE(\underline{N})\geq EE(\underline{N}+1)\geq EE(\underline{N}+2)\)  
\(N_{opt}=\underline{N}\);  
\(EE_{max}(N)=EE(\underline{N})\);  
else  
\(N_{opt}\) and \(EE_{max}(N)\) are obtained by using the binary search method;  
end if  
end for  
output \(N_{opt}\) and \(EE_{max}(N)\)  
According to the BSCI algorithm, when \(EE(N)\) increases or decreases monotonically in the range of \(N\), the required result can be obtained only by one conditional decision. When \(EE(N)\) increases monotonically first and then decreases monotonically, the inflection point can be found quickly by using the binary search method. Compared with the Bubble Sort method, the amount of computation is greatly reduced and the computational efficiency is improved.
# Dynamic Control Architecture Based on Software Defined Networking for the Internet of Things
## 2 Related Works
Beside, a modified low-energy adaptive clustering hierarchy (LEACH) clustering protocol is proposed in [27]. In particular, the same concept of the LEACH scheme [28], i.e., sensor nodes organised into clusters, is adopted with the selection of a cluster head. The clustering mechanism is applied to distribute supply energy among sensors, according to the time division multiple access (TDMA) based MAC protocol, which is further modified by introducing a threshold value for cluster head selection with the simultaneous switching of different power levels among nodes. Several papers in the literature consider bio-inspired clustering algorithms. Ant Colony Optimization (ACO) is presented in [29], where the cluster heads selection is implemented as it happens with the ant pheromone level. The Artificial Bee Colony algorithm is proposed in [30] to monitor the state of the sensor nodes in a typical SDN architecture through the Controller which selects the best path. The wide use of software-defined WSNs (SDWSNs) to reconfigure the networks, even after their displacement, led the authors of [31] to consider an efficient Particle Swarm Optimization (PSO) algorithm based on the residual energy of the nodes and the transmission distance. A green routing algorithm using fork and join adaptive PSO (FJAPSO) is addressed in [32] to operate in SDWSN. The considered scenario presents a classical topology, where sensor nodes are divided into clusters and each cluster has its own CH. However, to maximise the lifetime of sensor nodes and allow the adaptation to scalable networking scenarios, the FJAPSO acts on two levels by optimising jointly the number of control nodes and the best suited position of all the control nodes with respect to the Control Server (CS) and between CHs and sensor nodes. The Whale Optimization Algorithm, which considers the residual energy, communication cost and nodes density, is proposed in [33] to enhance the network lifetime. The aforementioned works typically consider a well-known hierarchical routing algorithms relying on the partition in clusters and the selections of the proper CHs. Moreover, CHs are highly energy consuming nodes, since they are requested to transmit to the Sink over a single hop, especially if they are located far from the Sink. Conversely, this paper proposes a routing approach which allows dynamically optimising in real time the end-to-end flows delivery through a real time-estimation of network status providing an effective load balancing of the network. It easily adapts to the conditions of flexibility and scalability required by a SDN-IoT network and helps to achieve better results, as shown in Section 4.
# Digitized Reconfigurable Metal Reflectarray Surfaces for Millimeter-Wave Beam-Engineering
## 1 Introduction  
A reflectarray antennas is made of an array of reflected phase elements and a feeder, which efficiently re-radiate electromagnetic waves. Printed-patch reflectarray is becoming considerable a substitute to a curved reflector due to numerous advantages such as its ease of transportation and fabrication, and low profile and weight [1]. Despite of these advantages, the antennas suffer from the major drawbacks which inherently have substrate loss and narrow bandwidth characteristics. Recently, metal-only reflectarray antennas have been studied as promising antennas because the antenna does have any dielectric loss [2; 3; 4]. A metal reflectarray antenna is manufactured by the phase-controlled metallic unit cell. The reflection phases of unit cell are simply controlled by the depth or height at the end of each waveguide. The reflectarray is an alternative to a phased-array antenna, which can provide the possibility of utilizing reconfigurable beam-forming for many applications such as synthetic aperture radar, automotive radars, security and medical imaging systems, and satellite communications.  
Technological advancement involved in millimeter-wave bands, expedited by upcoming solid-state sources and detectors, has attracted a great deal of interest in many different research areas and industrial applications. Thousands of phase elements of millimeter-wave reflectarray can be calculated for the meditated wave front focused at a desired location. The phase elements of a reflectarray antenna have been realized with MEMS switches [5], diodes [6; 7], liquid crystal [8; 9], dielectrics [10; 11], and height of array [12; 13]. Furthermore, research on reflective array antennas having a functional role such as low-profile dual circularly polarization [14] and intelligent reflecting surface [15] has been recently reported.  
This paper describes the design of digitized beam-aimed metal reflectarray antennas in a millimeter-wave region. The phase of metal groove elements is digitized by the number of bits and also calculated by a conventional beam-forming method [16]. This paper has the advantage of being able to manufacture a beam-aimed antenna in a millimeter wave band by using a relatively simple machining method. In order to save the time and cost required for production, the phase adjustment value of each element is digitized. The research results of this paper can be directly utilized as practical examples such as functional reflective surfaces for the changing channel path and increasing channel distance in millimeter wave and 6G band communication and the choice for high-gain beam-steering antennas in millimeter-wave regions.
## 2 Results
### Verification of Aroma Extraction Methodology through HS-SPE
#### 2.2.3 Solvent Ratio in the Elution of Target Analytes from the Sorbent Material  
The solvent ratio used in the elution of target analytes from a sorbent material is a critical factor in solid-phase extraction (HS-SPE). In this process, a stationary phase or sorbent material, in this case, a LICHROLUT EN resin packing, is used to retain the target analytes present in the sample selectively. The choice of stationary phase and solvent ratio in elution is based on the polarity and chemical properties of the analytes, as well as the characteristics of the sample matrix.  
The elution solvent must be capable of efficiently desorbing analytes from the sorbent while minimizing interference from other components in the sample. Using an appropriate elution solvent significantly improves the recovery and selectivity of the extraction process.  
The solvent ratio can affect the representativeness of extracts obtained from the matrix. Increasing the polarity of the elution solvent mixture does not always result in greater elution of polar analytes. It results in a stronger retention of non-polar analytes on the sorbent, leading to inefficient elution. Moreover, increasing the polarity of the elution solvent can intensify interference from other sample components that co-elute with the target analytes, thus compromising the selectivity of chromatographic separation. Therefore, selecting an elution solvent with optimal polarity for the analytes and the sorbent is crucial.  
The results are detailed in Table 3, which shows that the optimal solvent ratio for the elution of analytes, which demonstrated the best results, was 95/5, aligned with the initially employed methodology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flow (mL/min)** & **Total Area** & **Number of Peaks** \\ \hline
80 & 745.9 & 60 \\
100 & 783.0 & 60 \\
120 & 789.1 & 61 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of HS-SPE extraction flow verification (total area and number of spikes obtained by evaluating the extraction times).
# Consumer's Attitude towards Display Google Ads
## 1 Introduction
### Hypotheses Development and Research Model
#### 1.2.5 Consumer Attitude towards Display Google ads  
Advertisements must be a combination of three elements, which are credibility, trustworthiness, realism, and originality to create a positive consumer attitude [45]. Unfamiliar or irrelevant brands in an ad affect customer attitudes [46]. Analyzing consumers' attitudes is an important factor when examining the consumer's response toward an advertising message [24]. Google ads' keywords content determines customers' attitudes and the effectiveness of the ad promotion [11]. Google ads privacy influences Google ad Settings, which affects attitudes toward the websites [47]. Pull marketing affects customers' intention to use social media. Customer perceptions about products and services are either a good image or a bad image affect customers' attitudes toward Google Ads [12]. Advertisements through social media tools increase consumer liking, awareness, and action affecting consumer attitudes [13]. Google and Facebook ads affect brand awareness and customers' buying decisions [15]. Google ads improve customer contact and attitudes, which affect customers' behavior [14]. Organizations target customers through social media advertising to increase information and attitudes toward products and services [16]. Positive customerattitudes toward Google ads increase trust and customer satisfaction [18]. For the current research, consumers' attitudes towards Display Google ads are the indicators of feelings and reactions of the consumer based on the factors that were listed earlier towards the advertisement. In light of this discussion, limited studies focused on consumer attitudes resulting in the intention to click on Display Google ads. Accordingly, the following hypothesis has been completed:  
**H8.**_Consumer attitude towards Display Google ads has a significant positive influence on intention to click on Display Google ads._  
The suggested hypotheses are directed to test how Display Google ads' originality, relevance, credibility, and prior experience affect customer's attitudes toward Display Google ads, then Intention to click.
## 4 Discussion of System Configuration and Performance Analysis  
### System Configuration Performance  
Figure 3 presents the design concept schematic diagram with the specifications as outlined in Table 2. The technology is considered more straightforward to install and reasonable in cost. ZigBee device works on radio frequency (RF) requiring a low to medium data rate, long battery lifetime,  
Figure 2: Block diagram of distributed energy resources (DERs) thermal condition monitor.  
and low module power [48; 58; 60; 61; 100; 101]. The architectural hardware design is classified, as the transmitter and receiver sections to work as a transceiver. The transmitter is considered as Arduino ATMEGA328P microcontroller with GSM/ZigBee and sensor nodes, whereas the receiver is at the DMS-DMU during the reporting stage. The design has sensors attached proximately to the components frames, or appropriate probes used where possible. The concept is developed to sense thermal behaviors of solar PV modules, wind turbine rotatory compartment, distribution line cables, protection control units, and storage batteries with inverters. The sensors and wireless communication link work in complement with the microcontroller to continuously monitor the conditions in both standard and abnormal status. The LM35 picks temperature signals after the initialization of the sensors with each node establishing a protocol link for real-time data exchange with ZigBee via the microcontroller. Each of the sensors continuously checks for the data in parity order on real-time [102; 103; 104; 105]. At the receiving end, the other side of the ZigBee setup receives real-time data for decoding and analysis. The data received is an action controlled by the microcontroller and interface with gateway application layer. Limits are set and defined for sensor nodes configuration and performance according to threshold requirements. After checking the thermal condition thresholds status is acknowledged as normal or abnormal. If the data is out of the defined threshold range, status is abnormal. After the temperature measurement comparison, system issues an \"Alert\" action for an abnormal condition. SMS alert/notification sent and displayed on the LCD or a PC for details of the status information per configuration setup. The setup uses serial communication modem link to make voltage levels compatible with the microcontroller to receive the data serially, and to capture the real-time data on the LCD or PC display. Both the abnormal or normal data are shown at the field and the DMS-DMU.
# Classification of Logging Data Using Machine Learning Algorithms
## 3 Method  
This study includes 2 stages (see Figure 2).  
First, the log data are preprocessed and data dumps are generated (stages 1 and 2 in Figure 2). In this case, at first, the data of those wells that contain the required logging information are manually selected (1). Each well is represented by a table file. The resulting set of table data is processed so that input data and target columns are formed from the entire set of columns. Then, the data of each well are converted into a floating data window format and saved as dataframes. The interpretation of logging data by an expert is carried out not by the absolute values of the logging curve, but by its shape. Of particular importance are the minima, maxima, and inflection points.  
The expert classifies the rock interlayers with a minimum thickness of 0.1 m. At the same time, the distance between the recording electrodes of the logging tool is 1 m; therefore, it is advisable to use sections of the curve with a depth of at least one meter (10 points) for classification. In practice, the expert can consider the shape of a curve of a larger size. Therefore, the features of the logging tool and the data acquisition process suggest the possibility of using floating data windows.  
In other words, floating data windows allow the shape of the well log to be considered, thereby simulating the interpretation process performed by an expert.  
The test and training datasets are formed from different dataframes. To ensure cross-validation, the dataframes are split into test and training ones many times. The resulting test and training sets form dumps written to a disk. A set of such dumps with different floating window sizes and different splits into training and test sets form a UWL dataset.  
The UWL dataset is available at the link in the Data Availability Statement section.  
Secondly, the dumps are loaded and the classifiers are trained and evaluated to solve the problems of identifying reservoir oxidation zones (ROZs) and lithological classification (LC) (3 and 4 in Figure 2).  
Cross-validation can be performed, or the data dumps can be used separately. The Data Availability Statement contains a link to the data dumps and an example program for processing them.  
Further, the above-described processing stages are considered in detail.
# Crowd Monitoring and Localization Using Deep Convolutional Neural Network: A Review
## 2 Search Methodology and Taxonomy Level
### Taxonomy Level  
The entire taxonomy level of crowd monitoring has been shown in the form of flow chart. Basically the crowd related research approaches have been categorized into two domains based on the literature review namely crowd management and crowd monitoring. Then we have made categories of crowd monitoring i.e., counting, localization and behavior. Focusing on crowd monitoring, we just review the  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & **Description** & **No.of Images** & **Resolution** & **Min** & **Ave** & **Max** & \begin{tabular}{c} **Overall** \\ **Count** \\ \end{tabular} & **Accessibility** \\ \hline UCSD & People counting & 2000 & 238 \(\times\) 158 & 11 & 25 & 46 & 49,885 & Yes \\ \hline MALL & People counting & 2000 & 320 \(\times\) 240 & 13 & - & 53 & 62,325 & Yes \\ \hline UCF\_CC\_50 & Density estimation & 50 & Variable & 94 & 1279 & 4543 & 63,974 & Yes \\ \hline WorldExpo’10 & \begin{tabular}{c} Cross scene \\ crowd counting \\ \end{tabular} & 3980 & 576 \(\times\) 720 & 1 & 50 & 253 & 199,923 & Yes \\ \hline Shanghai Tech A,B & Crowd counting & 482 & \begin{tabular}{c} Variable, \\ 768 \(\times\) 1024 \\ \end{tabular} & 33 & 501 & 3139 & 241,677 & Yes \\ \hline UCF-QNRF & \
\begin{tabular}{c} Crowd counting \\ and localization \\ \end{tabular} & 716 &
\begin{tabular}{c} 400 \(\times\) 300 to \\ 9000 \(\times\) 6000 \\ \end{tabular} & 9 & 123 & 578 & 88,488 & Yes \\ \hline CUHK & Crowd behavior & 1535 & Variable & 49 & 815 & 12,865 & - & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of datasets.  
crowd management and did not categorize it further. After that, the counting section has been divided into two parts namely density estimation and people counting. In localization there are three main sub categories i.e., counting and localization, estimation and localization and anomaly detection and localization. Finally, the behavior category has been divided into three sub categories i.e., individual behavior estimation, anomalous behavior detection and normal and abnormal behavior detection. The taxonomy level of crowd monitoring (crowd counting, localization and behavior) has been shown in Figure 2.
## 4 Convolutional Neural Network (CNN): Modeling and Training  
Despite the possible simplifications in the vehicle's geometry and materials, the estimated run-time of a simulation is still high when considering the full model, which is generally more representative of the real scenario. Testing the antenna's behavior on all possible locations of the vehicle's roof would be impractical, if not impossible, so the search for better placement is performed only considering the vehicle roof, thereby losing accuracy but saving computational time.  
As a way of not losing that accuracy without spending a lot of time, it is proposed to obtain the antenna's behavior in some specific placements for the complete and simplified models of the vehicle, as shown in Figure 1a, and based on this, use machine learning toolsto predict precise (full model), Figure 1b, radiation patterns given simplified entries (roof model only).  
The thirteen antenna placements considered to obtain the data for the machine learning model are shown in Figure 2, based on Figure 1a. Thus, using a search optimization algorithm, we can use a simplified model of the vehicle's geometry to obtain faster responses from CST MWS and then use machine learning to approximate the radiation pattern to a more accurate solution, as shown in Figure 3.  
Figure 1: Vehicle model preparation in CST MWS software to obtain the data. (**a**) Simplified vehicle model, just the roof. (**b**) Full car model.  
Figure 2: Thirteen initial data points (fitting model) of the vehicle roof.  
For the CNN, the antenna placements on the roof of the car (Figure 2) for the full and simplified vehicle models were entered into CST MWS, and the realized gain (RG) and axial ratio (AR) were extracted with MATLAB according to the elevation angle \(\theta\), which ranged from \(0^{\circ}\) to \(180^{\circ}\), and azimuth angle \(\phi\), which ranged from \(0^{\circ}\) to \(360\), in units of \(1^{\circ}\). The RG and AR are expressed in \(ABi\) and \(AB\), respectively.  
In fitting the model, values obtained from a less accurate electromagnetic model (Figure 1a) were considered as input of the CNN, and a more precise model (Figure 1b) was considered as the output or target of the CNN. It was programmed in Python. The architecture of the model is shown in the next section. As input we have the RG and AR matrices with \(\theta\times\phi\) sizes, and as output we have the same size matrices predicted to be used for the search optimization algorithms.
## 4 Experimental Evaluation of Performances
### Community Anomaly Detection in Synthesis Data  
The evaluation of the anomaly detection models is not an easy task due to the rarity of suitable datasets that include anomalies and the lack of ground truth that informs us which data points are true anomalies. Therefore, synthetic datasets are generally made for performance evaluation. These datasets are used to compare a model's behavior on real data against the generated data. We used generated synthetic datasets of different sizes and attribute numbers based on [22]. For the reproduction of the properties observed in real networks, the graph is generated by following a power-law distribution. The node attributes are divided into relevant and irrelevant attributes by 50%. For relevant attributes, nodes were assigned values from a Gaussian distribution, while for the irrelevant attributes, nodes were assigned values from a uniform distribution. To ensure no deviating values were present in the relevant attributes, a hyper-ellipsoid was used to cut the tails of each Gaussian distribution. For anomalous nodes, the values of their attributes were manipulated to be random values outside the range defined by the hyper-ellipsoids of their communities. The anomaly ratio is 10%, which determines the number of anomalous nodes in the communities. The anomalies are only detected by considering combinations of at  
Figure 5: Quality and runtime evaluation for the Book network.  
Figure 6: AUC and ROC curve of the Book network.  
least two relevant attributes. Each synthetic dataset contains two files: the graphml file contains the graph with its nodes attributes, and the true file contains the ground truth of nodes, where 1 indicates anomalous nodes and 0 indicates normal nodes. For the evaluation of the model with respect to the increasing numbers of attributes, we use synthetic datasets with 1000 nodes and different attributes 2, 10, 20, 40, 60, and 80. For the evaluation of the model with respect to increasing the network's size, we use the synthetic datasets with ten attributes and different numbers of nodes. For the parameter setting, we set our model parameter to be the half-number of attributes to measure the effect of attribute increase, while we used the same setting in the real network experiments for the other models.