esa-sceva/satcom-chunk-collection huggingface dataset:
# Control of Porosity in Parts Produced by a Direct Laser Melting Process
## 2 Experimental Procedures
### Materials  
Commercially-available Fe-powders (HKKSolution, Seoul, Korea) were used in the experiment, and their morphologies are illustrated in Figure 3. The powder size distribution and morphology influence the physical properties of the parts created. The particle size was classified by the sieve mesh size, using a Ro-Tap Sieve Shaker (HJ-2152). A laser diffraction particle size analyzer (LS\({}^{\text{TM}}\) 13 320 SW) was used to measure the particle size distribution. This device analyzes the particle sizes by dispersing the particles in an appropriate solvent and using laser scattering. The Gaussian-like particle size distributions are illustrated in Figure 4, while Figure 5 indicates the approximate major/minor axis ratio of the non-spherical powder. The average ratio was estimated to be approximately 1.6, regardless of particle size, and AISI D2 was used as a base plate.  
Figure 4: Particle size distributions of test powders.  
Figure 3: Powder morphology: (**a**) spherical and (**b**) mixed powder with non-spherical particles.  
Four types of powders were investigated for this analysis. Powder 1 was composed of fully spherical powder with an average particle size of 20 \(\upmu\)m. Powders 2, 3, and 4 were mixed powders composed of 50% spherical powder with an average particle size of 20 \(\upmu\)m and 50% non-spherical powder with average particle sizes of 20 \(\upmu\)m, 40 \(\upmu\)m, and 60 \(\upmu\)m, respectively. The powder characteristics are shown in Table 1.  
Figure 6 illustrates the initial stacked height after packing the same weight of non-spherical and spherical powders. The powder packaging density decreases with an increasing non-spherical powder size, while the stacked height increases. The measured apparent densities for powders 1, 2, 3, and 4 were 2.46 g/mm\({}^{3}\), 2.40 g/mm\({}^{3}\), 2.35 g/mm\({}^{3}\), and 2.31 g/mm\({}^{3}\), respectively. The apparent density decreases with the size misfit degree between the non-spherical and spherical powders.
# Minimum Frequency and Voltage Stability Constrained Unit Commitment for AC/DC Transmission Systems
## 5 Results and Discussion  
The proposed UC model and algorithm have been applied to the modified IEEE 39-bus and Jiangsu power grid in eastern China. The analysis has been conducted for a 24-h scheduling horizon with a time interval of 1 h. All the numerical simulations have been coded in Matlab with CPLEX.
# FPE-Transformer: A Feature Positional Encoding-Based Transformer Model for Attack Detection
## 1 Introduction
The Finformer model provides higher accuracy and efficiency in financial attack detection using the Transformer architecture compared to traditional models, validating the advantages of Transformers in the capture of complex attack patterns [13]. A Transformer model named FCNN-SE has achieved successful results in multi-dimensional traffic data to improve complex feature extraction processes [14]. This study introduces a novel Transformer-based feature positional encoding (FPE) Transformer model for attack detection. Unlike existing Transformer-based approaches, FPE-Transformer only uses encoders, and it includes an optimized classification head that follows these encoders. In addition, instead of traditional positional encoding adapted to text, it uses feature positional encoding. Feature positional encoding can better capture the relationships and temporal patterns among features, enabling more precise attack detection. Experimental results show that the FPE-Transformer model is successful in attack detection. The primary contributions of our work can be summarized as follows:  
* We reviewed existing Transformer models in the literature. * We introduced a novel feature positional encoding method, which uniquely calculates separate positional encodings for each feature in a dataset. * We introduced the novel FPE-Transformer attack detection framework, which combines temporal analysis with the feature positional encoding technique in a Transformer-based model. Our FPE-Transformer framework is a Transformer-based attack detection model that applies feature positional encoding to capture complex temporal patterns of individual features. By improving the ability to distinguish attack behaviors, this approach increases detection accuracy in network security applications. * We compared the performance of the classification results for Transformer models with various positional encodings. Section 2 examines Transformer models in the literature and their application for attack detection. Section 3 includes the FPE-Transformer model. Section 4 outlines the experimental findings. Section 5 presents recommendations for future research.
## 2 Materials and Methods
### Wettability of Hydrogel and Organogel  
To study the effect of the bio-ferment (B), extract (E), and pure silymarin (S) on all formulations' wettability, the Kruss 165 DSA100 drop shape analyzer (Filderstadt, Germany) was used. One drop of distilled water (4 \(\mu\)L) was placed on the preparation's surface, and the contact angle of the sessile drop method was measured using DSA4 software-controlled. The examination of the contact angle was conducted 5 s after placing the drop on the formulation. Measurements were made on the surface of the formulation with a layer thickness of 100 \(\upmu\)m. The formulations from ten different places were measured, and the outcomes were calculated by taking the average (c.a.av.) as shown in Figure 1a-h.  
Antioxidant Activity and Total Polyphenos Content of Silymarin, Bio-Ferment, and Extract 2.7.1. DPPH Radical Scavenging Assay  
To assess the antioxidant potential of silymarin, bio-ferment, and extract, the DPPH method was used [34]. The analyses were conducted on the Thermo Scientific GENESYS 50 apparatus at the wavelength \(\lambda\) = 517 nm. Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) served as a benchmark material. The antioxidant activity was expressed in mmol Trolox/L of appropriate cosmetic raw material, based on the resulting calibration curve y = \(-\)1.2463x + 1.0546 (R\({}^{2}\) = 0.999).  
First, a 1% ethanolic solution of silymarin was prepared. The antioxidant activity of the prepared solution and the obtained bio-ferment (B) and extract (E) was measured as follows: 2.85 mL 0.3 mmol/L alcoholic solution containing about 1.000 \(\pm\) 0.020 (at \(\lambda\) = 517 nm) of the DPPH radical was inserted into the tube, and 0.15 mL of the ethanolic solution of silymarin or cosmetic raw materials obtained (B or E) was added. Control samples devoid of antioxidants were generated using the same methodology. The tubes were wrapped in aluminum foil, sealed with a stopper, and then incubated for 10 min at room temperature. Three independent experiments were performed, and each sample was analyzed three times.
## 3 Results and Discussion
### Equilibrium and Kinetics Studies  
Preliminary studies have shown that the ADAV is more powerful compared to the TTAV samples in biosorption removal of TY. Therefore, the equilibrium and the kinetics studies were conducted on the ADAV. Equilibrium and kinetic studies play an imperative role in assessing the efficiency of adsorbents. The adsorption isotherms are very useful in determining the maximum quantity (\(q_{m}\)) that can be adsorbed, the pattern of adsorption on the surface of the biomass, the interaction between adsorbate and adsorbent's surface, and whether it is chemisorption or physisorption. On the other hand, the kinetic studies can be used to determine the rate of adsorption, the thickness of the formed layer around the sorbent, and whether the reaction is controlled by diffusion or adsorption mechanisms. The following sections reveal a discussion on the adsorption isotherms as well as the kinetics of the adsorption of TY on ADAV.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Aridity Calculation  
The Aridity Index is characterized as the ratio between PRE and potential Ep [18]. Given that this research utilizes precipitation and potential evapotranspiration data solely from the summer months spanning 2000 to 2022, the resulting metric is specifically designated as the Summer Aridity Index (\(SAI\)).  
\[SAI=\frac{\sum\limits_{i=6}^{8}PRE_{i}}{\sum\limits_{i=6}^{8}PET_{i}} \tag{1}\]  
In the equation, \(PRE_{i}\) and \(PET_{i}\) represent monthly values of summer precipitation and potential evapotranspiration, respectively (\(i=6,7,8\)), with units in mm. A higher \(SAI\) value indicates a more humid climate. We classified \(SAI\) into four levels: hyperarid (\(SAI\leq 0.4\)), arid (\(0.4<SAI<0.75\)), semi-arid (\(0.75\leq SAI<0.9\)), and semi-arid to semi-humid (\(SAI\geq 0.9\)).
# An Improved PIN Diode Model Design for a Tunable Frequency Selective Absorber
###### Abstract  
An enhanced PIN diode model designed for a tunable frequency absorber is proposed, enabling continuous and adjustable absorption across the X-band frequency range. This new structure consists of four primary components: a frequency selective surface (FSS) layer, a dielectric substrate, an air spacer layer, and a metal substrate. Unlike previous designs, the PIN diode model introduced here offers significant advancements. It not only allows for the switching between absorption and reflection states but also enables precise tuning within the absorption state itself, providing a higher degree of control over the system's performance. The unique arrangement of the PIN diodes in this design involves placing them between adjacent units of the structure. This strategic placement eliminates the need for complex and bulky bias networks, which are commonly used in conventional designs. As a result, it also minimizes the electromagnetic interference that often arises from bias lines, thereby improving the overall system efficiency and reliability. The proposed model, therefore, reduces the complexity of the absorbing system while enhancing its performance and tunability. Experimental results obtained through free-space measurements were conducted to validate the proposed design. The results show an excellent agreement with the simulation data, confirming that the improved structure can achieve continuous, tunable absorption with high precision. These findings suggest that the new PIN diode-based model could be a promising solution for a wide range of applications in dynamic frequency management and electromagnetic wave absorption systems.  
m +
Footnote †: journal: Applied Sciences
# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Experiment Protocol
#### 2.1.1 Calibration Phase  
In the calibration phase, the cognitive-driven gain controller system was individually calibrated for each participant using the EEG responses for a scenario with two competing speakers (see Figure 2). Participants were cued by an arrow on a screen to listen attentively to one of the speakers while recording the ongoing EEG responses. Participants were also instructed to minimize eye movement and blinking, which may cause EEG artifacts. The EEG responses were recorded during four sessions, lasting 30 min in total. The first and the second session each lasted 10 min, while the third and the fourth session each lasted 5 min. For the first and the third session, the participants were cued to attend to the left speaker, whereas, for the second and the fourth session, the participants were cued to attend to the right speaker. Following each session, the participants were asked to fill out a questionnaire consisting of multiple-choice questions about the stories uttered by the speakers. There was one question per minute of each story. The questionnaire was used to check whether the participants attended to the cued speaker. After the fourth session, there was a short break. During this break, the recorded EEG responses were used to calibrate the cognitive-driven gain controller system (see Section 2.5), individualized per participant.
## 3 System Design  
The purpose of this study is to develop a mobile IAPS that combines scene information and automatically categorizes different image types based on their various aesthetic standards. This capability fills a gap in the research by solving the problem of ordinary users needing the ability to apply advanced post-processing techniques to their images to improve their aesthetic quality. The overall system architecture is shown in Figure 2 and includes four main parts: scene recognition, model lightweighting, multi-attribute IAP, and functional demonstration.  
The main challenge of IAP lies in identifying image attributes that are highly correlated with aesthetics and applying these attributes to image processing. Aesthetic attributes are closely related to photography principles. Because it is difficult for a computer system to recognize and interpret every photographic rule, choosing specific aesthetic attributes based on existing criteria is necessary. This study considers the following criteria to determine the necessary aesthetic attributes.  
**Generality:** Although image sharpness is a factor specific to each photograph, the excessive enhancement of sharpness in certain scenes (e.g., smooth textures) can lead to the loss of image details, resulting in distortions and reduced quality. Therefore, we focus on universality and generality when choosing image attributes.  
Figure 2: Overview of the image aesthetic processing system.  
**Relationship with photographic rules:** Because our purpose is to enhance aesthetics by improving multiple image attributes, we leverage those that are frequently used in professional photography and strictly follow the general rules thereof.  
**Clear definition:** Photography best practices and rules are usually expressed through examples rather than formulas or oral expression. To achieve automated processing of image aesthetics, we chose image attributes that could be defined as well as possible: contrast, color saturation, and portrait beauty.
# Nitrogen Oxide Removal by Coal-Based Activated Carbon for a Marine Diesel Engine
## 2 Experimental System and Methods
### Materials and Methods  
The coal-based columnar AC used in this paper was purchased from Henan Huanyu Carbon Co., Ltd., Xuchang, China. Its diameter, length, and specific surface area are 1.5 mm, 2-4 mm, and 326 m\({}^{2}\)/g, respectively. The AC was washed with deionized water before the experiments to remove impurities on the surface and then dried in a drying oven at 110 \({}^{\circ}\)C for 12 h. The types and contents of oxygen-containing functional groups on the AC surface were detected by the Boehm titration method. The total gas volume was 2 L/min with N\({}_{2}\) as the carrier gas and 30 g of AC was used in each group of experiments. (The corresponding volume of gas per volume of catalyst and per hour, i.e., VVH, was 2500 h\({}^{-1}\).) The exhaust gas temperature of large low-speed two-stroke marine diesel engines is  
Figure 1: Experimental system of activated carbon denitration. 1. Pressure reducing valve 2. Filter 3. Three-way valve 4. Mass flow controller 5. Check valve 6. Stop valve 7. Mixing chamber 8. Tubular heating furnace 9. Temperature controller 10. Flue gas analyzer 11. Exhaust gas absorption device.  
generally lower than 300 \({}^{\circ}\)C [4]. Therefore, the temperature range studied in this paper was from room temperature (15 \({}^{\circ}\)C) to 300 \({}^{\circ}\)C.  
The NOx concentration in the exhaust gas of marine diesel engine is generally about 1000 \(\upmu\)L/L, and NO accounts for the main part. When the NO/NO\({}_{2}\) molar ratio is 1, the fast SCR reaction Equation (2) takes place, and the reaction rate is the fastest. So, the concentrations of NO and NO\({}_{2}\) were both 500 \(\upmu\)L/L in the fast SCR reaction. The total concentration of NO and NO\({}_{2}\) remained 1000 \(\upmu\)L/L. When NH\({}_{3}\) was needed, NH\({}_{3}\) was added at an ammonia-nitrogen ratio of 1 depending on the initial NOx. The O\({}_{2}\) concentration was 5% when needed. We do not pay special attention to the physical adsorption time of AC in this paper. The discussion of the denitration efficiency of AC in the following refers to the removal efficiency when the outlet NOx concentration is in a near-stable condition. The near-stable denitration efficiency is then defined as follows:  
\[\eta=\frac{C_{\text{in}}-C_{\text{out}}}{C_{\text{in}}}\times 100\% \tag{4}\]  
Here \(\eta\) is the NOx removal efficiency of AC, %; \(C_{in}\) is the inlet NOx concentration, \(\upmu\)L/L; \(C_{out}\) is the outlet NOx concentration, \(\upmu\)L/L.
# Automatic Bridge Crack Detection Using a Convolutional Neural Network
## 3 Experimental Results and Discussion
### Experimental Results
#### 3.1.5 Comparison of ASPP Modules Placed at Different Locations  
In order to find the relationship of the detection performance and the site of the ASPP module, we placed the ASPP module behind the 2 nd convolutional layer (conv2), the 3 rd convolutional layer (conv3) and the 4 th convolutional layer (conv4). The testing results are shown in Table 5.  
In Table 5, when the ASPP module is placed behind conv3, the best detection effect is obtained. The reasons are as follows: (1) The main function of ASPP is to incorporate feature information in different receptive fields and multi scales, rather than extracting information. Therefore, when the ASPP module is placed in the first few layers, the effect is not ideal, because there is not a sufficient feature extraction process. This explains why the ASPP module does not work well when placed behind conv2. (2) In the image classification task, the input image is finally down-sampled to a \(1\times\) N size (N is the number of categories of image classification), and the image resolution of the last few  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Epochs** & **Test Accuracy** \\ \hline
[2; 4; 6] & 300 & 89.45\% \\
[2; 4; 8] & 300 & 96.37\% \\
[3; 6; 9] & 300 & 90.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of different atrous rates in the ASPP module.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Epochs** & **Test Accuracy** \\ \hline with ASPP & 300 & 96.37\% \\ without ASPP & 300 & 90.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model comparison with or without the ASPP module.  
layers is also very small (e.g., the input feature map size of conv4 in the proposed model is \(7\times 7\)). When the ASPP module is placed behind conv4, the feature map of the input ASPP module is much smaller than the feature map with the ASPP module placed after conv3, and a large amount of feature information is lost, so the detection accuracy is greatly reduced. For example, if there is a crack feature with size \(8\times 8\) in the image, the feature may be lost after 16 times down-sampling. This is why ASPP is not placed on the last few layers.
## 3 The Study
### Scenarios Implementation
#### 3.4.2 Network B: Two-Tier Architecture  
In this scenario, the \(S\) sensors in the water are organized in \(G\) groups, each coordinated by an aggregator. The aggregators are connected to \(E\) eNodeB cell towers of the LTE network, and the server. As before, the sensors monitor the water's \(pH\), then they transmit the measurements to the aggregator. The aggregator collects the data, aggregates them into a report containing the average, the standard deviation, and the maximum and minimum values of such measurements, and transmits them to the server. In this scenario, the alarm condition involves the server sending commands to aggregators to increase how often they transmit when an alteration in the data is detected. At the same time, the server requests a change in the format of the aggregators' packets from sending aggregated statistics to sending all the (node ID, \(pH\) value)-pairs for each node that the aggregator coordinates.
## 3 Momentum-Based Motion Detection
### MRSS-based Handoff Algorithm  
Based on the user motion state detection results, an MN can decide to activate or terminate its interfaces to save power and reduce handoff dropping rate in a network discovery process. In the MRSS-based handoff algorithm and the DMRSS-based handoff algorithm, a pre-defined handoff threshold (\(TH_{HO}\)), a network discovery threshold (\(TH_{ND}\)) and three network discovery modes are defined.  
In the general threshold based handoff algorithm, when the EWMA of the current servicing base station is lower than \(TH_{HO}\), the MN must execute the handoff procedure immediately to avoid disconnection. When EWMA is lower than \(TH_{ND}\), the MN should perform network discovery. \(TH_{ND}\) must be higher since an MN must turn on all of its interfaces in time to perform network discovery procedures such as base station searching, association, Authorization, Authentication and Accounting (AAA), IP address configuration, and other high layer signaling functions, before switching to a new network. However, using a high EWMA threshold certainly increases power consumption. Therefore, the following three network discovery modes are defined in the MRSS/DMRSS-based handoff algorithm to reduce power consumption.  
* **NON_ND mode**: this mode is used when an MN is approaching an AP or BS. Therefore, network discovery is not needed.
* **ND mode**: this mode is used when an MN is leaving the associated AP or BS. Therefore, timely activation of interfaces is critical for detecting all available wireless networks.
* **SEMI_ND mode**: this mode is applied when an MN is stationary. If \(EWMA<TH_{HO}\), the MN must activate all of its interfaces to perform network discovery and handoff procedure. Otherwise, the MN terminates the network discovery process.  
Figure 3 shows the flow chart of the DMRSS-based handoff algorithm. When an MN connects to an AP, the EWMA and MRSS are measured and the user motion is continuously determined. When the EWMA is smaller than \(TH_{ND}\), the MN checks its motion state. If the MN is in an approaching state, the network discovery mode is set to the NON_ND mode. If the MN is in a leaving state, the network discovery modes is set to an ND mode. If the MN is in a stationary mode, the network discovery modes is set to the SEMI_ND mode. During the network discovery process, a dwell-time mechanism [9] is utilized to avoid the ping-pong effect. If the MN is in the ND mode, continuously leaves the current serving AP and approaches the new AP until the dwell timer expires, the MN can switch its connections to the new AP.
## 4 Economic Analysis
### Cost Calculation of the System Designed with a Base Generation Power Source (Proposed Study)  
For a system with a base power source, the capacity of the fuel cells was set as 35 kW. The capacities of the PV power generation system and the ESS were set as 442.15 kW and 1375.96 kWh, respectively. Therefore, the total system cost was estimated to be USD 1,243,874.70 when the unit cost for each system component shown in Table 4 was applied.
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 3 Materials and Methods  
### Proposed Framework  
This section is composed of six subsections. First, the Apnea-ECG dataset and the preprocessing step are described. In this step, the number of signals used, sampling frequency, denoising method, data segmentation, and the derivation of the RR interval and R-wave amplitude (RAMP) signals from the ECG segments are explained. Afterward, linear and nonlinear analysis methods are applied to extract features and fuse three different feature sets using an early fusion strategy. Then, the optimal features are selected from the fused feature vector. Finally, these features are used as input to the four different types of classifiers for discriminating normal and apnea events. The flow diagram of the proposed technique is illustrated in Figure 1.  
Figure 1: Block diagram of the automated sleep apnea detection algorithm.