esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results  
Three main aspects are examined in this section regarding the use of the suggested IMGO algorithm. Several applications are implemented as follows:  
* First, the performance of the IMGO algorithm is compared to the conventional MGO method utilizing benchmark functions from the CEC 2017 single-objective optimization competition set.
* Second, the comparisons are extended to consider 23 additional benchmarks.
* Third, the following three traditional engineering optimization problems are addressed by IMGO: the design of WBD, REBD, and PVD problems. After that, these outcomes are contrasted with those from other well-known algorithms that have been documented in the literature.
* Fourth, a real-world Egyptian power distribution network is utilized to test both MGO and IMGO. The goal is to minimize energy losses across the system by strategically placing photovoltaic (PV) and capacitor units.
* Lastly, the applications are extended for solving the parameter estimation problem of a PV cell by considering the commercial RTC France cell.
# A Transplantatable Frequency Selective Metasurface for High-Order Harmonic Suppression
## 1 Introduction  
Metasurfaces have been widely applied in manipulation of electromagnetic wave, such as radar radomes [1], spatial filters [2], reflect arrays [3], artificial impedance surfaces [4], polarization converters [5], and absorbing materials [6], etc., because of their properties of allowing uninhibited transmission of electromagnetic waves in specific frequency bands while suppressing transmission in other undesired bands. In general, a band-pass spatial filter based on metasurface is also called frequency selective surface (FSS) which is transparent within the operation frequency band of the antenna and opaque at other frequencies. However, most FSSs have multiple spurious transmission windows in higher operating frequency bands. And the high frequency harmonic phenomenon will severely interfere with other antennas and radars which operate at the h-armonic frequency bands especially on the low-observable or stealth platforms. As a result, the FSSs with harmonic suppression feature are important for practical applications.  
In [7; 8; 9], band-pass frequency selective surfaces with stop-band characteristics are reported. They use artificial absorbing coating, resistive high-impedance surface, and other techniques to achieve the harmonic absorbing properties. However, the FSSs in these designs are specific which means the technique of these harmonic suppression FSSs cannot be independent and transplanted. In this paper, a transplantable frequency selective metasurface element for harmonic suppression is designed. The frequency selective metasurface element (FSMSE) are integrated with two different types of band-pass FSSs to remove the spurious transmission windows outside of the desired operational pass-band, respectively. Full-wave simulations and experiments show feasibility of the harmonic suppression FSMSE, and measured results agree well with simulated ones.
# Research on Model-Based Fault Diagnosis for a Gas Turbine Based on Transient Performance
## 4 Conclusions  
In this paper, a gas turbine dynamic model was introduced to simulate its nonlinear dynamic behavior. The model was developed in the environment of Matlab/Simulink(MATLAB 2013a, The Mathworks, Inc., Natick, MA, USA). The start-up process was simulated via this model. The comparison of the simulation results to the field operating data showed the validity of the model and the advantage of using transient accumulated deviations. Some conclusions have been obtained as follows:  
1. A new, non-linear, model-based diagnostic method, using gas turbine transient measurements and a cuckoo search (CS) algorithm, was tested to diagnose a gas turbine before and after a washing process.
2. Diagnosis with transient measurements is more relevant than diagnosis with steady-state measurements, when gas turbine faults contribute little to performance deviation in steady-state conditions or gas turbine output fluctuates greatly.
3. Gas turbine component fault diagnosis using transient data can be more effective than using steady state data, owing to magnifying fault signatures and extending the tracking time to eliminate variable uncertainties.  
This study was co-supported by the National Natural Science Foundation of China (No. 51676182) and the National Natural Science Foundation of China (No. 51706132).  
**Author Contributions:** [PERSON] and [PERSON] have proposed a research method based on fault diagnosis for transient performance of gas turbine, the simulation and experiment data are designed, analyzed and processed, and the manuscript is written. [PERSON] has offered help for the simulations. [PERSON] has proposed the resiving suggestions of manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Time** & **Process** & \(D_{GC}\) & \(D_{EC}\) & \(D_{GC}\)/\(D_{EC}\) & \(D_{GT}\) & \(D_{ET}\) & \(D_{GT}\)/\(D_{ET}\) & \(D_{GP}\) & \(D_{EP}\) \\ \hline \multirow{2}{*}{Before Washing} & Steady-state & 5.79 & 1.70 & - & 4.85 & 2.03 & - & 0.88 & 1.53 \\  & Transient process & 7.83 & 4.62 & - & 5.71 & 2.10 & - & 3.28 & 1.89 \\ \hline \multirow{2}{*}{After Washing} & Steady-state & 2.02 & 1.48 & - & 2.03 & 1.30 & - & \(-\)0.17 & 1.07 \\  & Transient process & 1.54 & 1.41 & - & 2.56 & 1.42 & - & 2.95 & 1.76 \\ \hline \multirow{2}{*}{Improvement} & Steady-state & 3.77 & 0.29 & 13.0 & 2.82 & 0.73 & 3.86 & 1.05 & 0.44 \\  & Transient process & 6.29 & 3.21 & 1.96 & 3.15 & 0.57 & 5.53 & 0.33 & 0.13 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison result of diagnostic results based on steady-state analysis and transient process analysis (%).
## 2 Theoretical Background
### Optimization Algorithms
#### 2.2.4 Sin-Cos Optimization Algorithm (SCA)  
The SCA algorithm is a metaheuristic optimization technique inspired by the trigonometric functions of sine and cosine. Proposed by [PERSON] in 2016, this algorithm seeks to find optimal solutions to various optimization problems by iteratively updating a population of candidate solutions [58]. An initial population of \(N\) candidate solutions is  
Figure 1: Illustrative figure comparing the parameters of four optimization algorithms: PSO, GWO, WOA, and SCA. The shaded regions indicate shared parameters among the optimizers, highlighting the similarities in their configurations. Notably, GWO and WOA have closely aligned parameters, with WOA incorporating an additional parameter \(b\), which distinguishes it from GWO.  
generated randomly within the search space. The position of each candidate solution in the population will be updated using the following equation [58; 59; 60]:  
\[\mathbf{x}_{i}(t+1)=\begin{cases}\mathbf{x}_{i}(t)+r_{1}\sin(r_{2})|r_{3}\mathbf{ G}(t)-\mathbf{x}_{i}(t)|&\text{ if }r_{4}<0.5\\ \mathbf{x}_{i}(t)+r_{1}\cos(r_{2})|r_{3}\mathbf{G}(t)-\mathbf{x}_{i}(t)|&\text{ if }r_{4}\geq 0.5\end{cases}\]  
where \(r_{2}\), \(r_{3}\), and \(r_{4}\) are random numbers, while \(\mathbf{G}\) denotes the destination or target point, representing the global best position. The variable \(r_{1}\) serves a similar purpose as the variable \(a\) in the GWO algorithm, allowing the SCA algorithm to transition between exploration and exploitation phases. As \(r_{1}\) decreases linearly throughout the iterations, the SCA algorithm initially focuses on exploring the search space and then shifts to refining current solutions in the later iterations (i.e., exploitation phase). Additionally, \(r_{2}\) indicates the angle of the Sine and Cosine functions, varying from zero to \(2\pi\). The parameter \(r_{3}\), which lies in the range of \([0,1]\), acts as a random weight for the destination, while \(r_{4}\), also within \([0,1]\), facilitates the switch between sine and cosine movements.
# A Low Cost BLE Transceiver with RX Matching Network Reusing PA Load Inductor for WSNs Applications
## 2 Proposed Front-End with RX Matching Network Reusing PA Load Inductor
### The Summarized Merits of this Proposed Front-End  
There are some merits of the proposed front-end with RX matching network reusing PA load inductor: (1) Only one inductor is employed in this front end. It is used for PA load in TX mode, and reused as a matching component in RX mode. Thus, the chip area has been reduced significantly; (2) The front-end has integrated the TRX-switch and matching network, and reduced the number of external components and thus reduced the cost of WSNs nodes; (3) RX and TX performance have not been deteriorated: for one, the impedance of LNA and PA can be optimized respectively using the proposed scheme; for the other thing, nonlinear components, such as CMOS switch, have not been adopted in the signal path of matching network; (4) It is free of TX reliability issues, because the PA sees the RX path as a high impedance compared with 50 \(\Omega\) and the input voltage swing of the LNA is very small.
# Processing of AZ91D Magnesium Alloy by Laser Powder Bed Fusion
## 4 Conclusions  
The research performed in this study revealed the basic possibilities of AZ91D alloy processing using the LPBF process. During the research, process parameters were investigated, leading to the most suitable configuration. The resulting parameters led to a fine internal structure for the volume sample, competitive mechanical properties and minimization of internal porosity. Based on the abovementioned experiments and analyses, the following findings have been concluded:  
* The process parameters of the SLM technology for the alloy AZ91D were developed and tested. With the developed parameters, a relative density of 99% and more was achieved for the cube samples. The final configuration, which resulted in low porosity and a fine, uniform microstructure, consisted of the following parameters: \(LP\) = 180 W, \(LS\) = 612.5 mm/s, \(HD\) = 0.133 mm and \(LT\) = 0.05 mm.
* The results of the tensile test showed very good mechanical properties of the material (\(Y_{s}\) = 181 MPa, \(UTS\) = 305 MPa and \(A_{5,65}\) = 5.2%). Compared to the literature data, sample 7-1 showed more than twice higher ductility.
* Fractographic analysis performed after the tensile tests revealed that the damage mechanism is of low-energy ductile character with a fine dimple morphology.
* Light microscopy of the etched samples was used to obtain basic information about the microstructure of the processed material. The microstructure showed the typical structure of solidified melt pools in the longitudinal direction. In addition, the directivity of the solidified pools of the given laser trajectory was observed in the transverse direction.
* A detailed SEM analysis revealed a fine \(\beta\)-Mg\({}_{17}\)Al\({}_{12}\) phase presence within the solidified melt pools and in the areas of the fusion boundaries separating the individual pools. EDS microanalysis was used for the determination of the chemical composition of the phases in the processed material.
* The studied alloy AZ91D showed a loss of wt.% of Mg after LPBF processing due to evaporation. The other alloying elements did not significantly change, and the resulting chemical composition was in the range of the ASTM standard for AZ91D alloy.  
Conceptualization, J.J. and O.C.; methodology, K.N.; software, K.N.; validation, O.C., J.J., L.P. and D.K.; formal analysis, K.N.; investigation, K.N.; resources, S.G. and S.S.; data curation, J.J.; writing--original draft preparation, K.N., O.C. and J.J.; writing--review and editing, K.N., J.J., O.C., D.K., L.P., S.G. and S.S.; visualization, J.J.; supervision, L.P. and D.K.; project administration, L.P. and D.K.; funding acquisition, D.K., S.G. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Commission within the framework of the INTERREG V-A Austria-Czech Republic in the project \"ReMaP\" (Interreg project no. ATCZ229).  
Not applicable.  
The authors declare no conflict of interest.
## 3 The Proposed Model Architecture
### MSCA-Res2 Block
softmax function is used to normalize the channel attention feature vector \(z_{i}\) of each feature map, thereby obtaining the multi-scale channel adaptive weight \(W_{i}\):  
\[W_{i}=Softmax(z_{i})=\frac{exp(z_{i})}{\sum_{i=1}^{n}exp(z_{i})}\ \ \ \ (i=1,2 \cdots n) \tag{12}\]  
After this step, each scale's feature map is adaptively weighted based on its contribution to the task, leading to more effective feature representation. Finally, we multiply the corresponding scale feature map \(F_{i}\) with its adaptive weight \(W_{i}\). To ensure that the number of channels remains the same at each layer, we concatenate in the channel dimension, yielding the multi-scale channel adaptive feature map \(Y\), which can be expressed as:  
\[Y=Concat[W_{1}F_{1};W_{2}F_{2};\ldots;W_{n}F_{n}] \tag{13}\]  
We embed the MSCA module in Res2 Net, as shown in Figure 3. From the above analysis, our proposed MSCA-Res2 Block module pays attention to multi-scale space and channel information locally. It allows information to interact better and makes the network better adapt to the extraction of complex contextual information in dialect identification.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 3 Experimental Setup and HEM Algorithm
### Hardware
#### 3.1.5 Room Occupancy  
Much energy is wasted through the operation of appliances in a room while no one is inside. AC units and lights should particularly be turned off when a room is unoccupied. This is the reason for employing CO2 and motion sensors in the proposed HEM system. The use of the two sensors affords a two-fold determination of the occupancy of a room. The PIR motion sensor sends a value of 1 when motion is detected inside the room, which indicates that the appliances should be kept on for an additional 5 min. This time duration is used to address the problem that might arise from someone being in the room but without a detected motion, possibly due to an outrage of the PIR sensor trigger. During the 5-min. time gap, the CO2 sensor reads the concentration of CO2 in the room. If a continuous increase is detected, the central controller would maintain the occupancy status of 1, regardless of the motion sensor detection. Conversely, the central controller would wait for the next communication from the motion sensor if there is a decline in the CO2 concentration. If it changes to 0, a vacant room decision would be taken.  
An increase in CO2 inside the room is detected by a decrease in the voltage reading of the sensor. In the present study, the CO2 sensor was placed in the experimental room and its readings were monitored during different known occupancy statuses of the room to set an appropriate voltage threshold. A reading above the threshold indicated vacancy, and vice versa. The flowchart presented in Figure 12 describes the vacancy identification process. Figure 13 shows the CO2 sensor readings during different occupancy statuses of the room over a 24-h period. When the CO2 concentration in the room settles at a certain level, it is always below the set threshold, whereas the occupancy of the room always takes it above the threshold.  
Figure 12: Room vacancy identification process.
## 2 Materials and Methodology
### Fault Diagnosis Methodology
#### 2.2.3 Input Layer Adaptation  
Knowing how the data have been structured, the next step is to configure a data warehouse. For this purpose, Matlab specifically has the \"fileEnsembleDatasore\" func  
Figure 8: Signal and scalogram of a fault in the outer race of a bearing.  
Figure 7: Signal and scalogram of a fault in the inner race of a bearing.  
tion, which specializes in the development of algorithms used in predictive maintenance. When using this data store, it is necessary to specify the path where the files are located, their extension, as well as define the data variables that will be available in the store (\"DataVariables\"), the variables that describe the conditions or labels of the data (\"ConditionVariables\") and the property that lists all the variables that will be available in the data store (\"SelectedVariables\").  
Similarly, a custom function is used by calling the \"ReadFcn\" function in the code and assigning it the arbitrary name \"@readCWRUBearing\". This will facilitate the task of reading and processing the data, simplifying and optimizing the code.  
Next, the one-dimensional vibration signals are converted into scalograms, and the images are archived for use in the training process. Each scalogram is sized to 227 by 227 by 3, which matches the input format required by the SqueezeNet neural network. To improve the programming code, a helper function called \"convertSignalToScalgogram\" has been designed that takes the signal in its raw state and splits it into multiple segments. After the program is executed, a folder called \"train_image\" is automatically generated in the directory where Matlab is working. All scalogram images corresponding to the bearing signals in the folder called \"train_data\" will be stored in the folder \"train_image\".  
To avoid overtraining, the data have been segmented into training and validation sets. For this purpose, using the Matlab function \"splitEachLabel\", 80% of the images located in the \"train_image\" folder are used for the training phase, and the remaining 20% are used for the validation set.
## 2 Data Network Infrastructure for HSS
#### DNIHSS Communication Components
The DNIBI functions as a proxy between the HSS module and the DNIHSS. Figure 7 presents a representation of the DNIBI components and Figure 8 shows the flow of the asynchronous data exchange commands. Figure 5: HSS module data exchange command frame. Figure 8: DNIBI flow of the asynchronous data exchange commands. Figure 6: DNIHSS blocks. Figure 7: DNIBI components. The DNIBI is divided in two areas: one responsible for communicating with the HSS module and other responsible for the interface with the DNIHSS. When a HSS module needs to send a data exchange command, it connects to the Communication Interface unit, delivering the data and destination address. The Communication Interface creates a message frame and sends it to the Data Encryption unit. At the same time, if the data exchange command type is flagged by the NMS for activity control, a communication is initiated by the Communication Interface with the Blockchain Service unit and a Blockchain is initiated. In the Data Encryption unit, the data are encrypted and delivered to the Message Framework Creator, where a data exchange command frame is created. Then, the command frame is delivered to the DNIB Interface unit, and if needed, an activity is added to the previous created Blockchain by the DNIB Blockchain Service. The DNIBI connects to the closest DNIB and sends the data exchange command frame. When a data exchange command frame is delivered from the DNIHSS to the HSS module, the data flow follows the opposite path with only two exceptions: an activity is added to the Blockchain by the Message Framework Creator unit when it delivers the data to the Data Encryption unit and by the Data Encryption unit when it receives the data.
## 5 Discussion and Open Research Issues
### QoS in I2O WBANs  
#### 5.1.1 Candidate Radio Technologies in I2O WBANs  
An I2O WBAN is a type of wireless body area sensor network (WBASN) and centers on the human intra-body region using low power consumption [3; 9]. There are several potential communication techniques in this area such as Bluetooth, WiFi, Zigbee and near field communication (NFC) [4]. Moreover, wireless coexistence communication technologies in I2O WBAN, such as LP-WiFi, are also promising candidates for future research [38].
# Reviewing CAM-Based Deep Explainable Methods in Healthcare
## 5 Experiment
### Datasets  
HAM10000 [66]: This dataset pertains to dermatoscopy and is sourced from the Medical University of Vienna. It comprises a diverse collection of skin disease images from various demographics, focusing primarily on pigmentary disorders. The conditionscovered include, but are not limited to, various cancers such as melanoma and hemangioma. Additionally, the dataset provides mask segmentation images. In total, it contains 10,015 images.  
Diabetic Retinopathy Arranged: The second dataset pertains to diabetic retinopathy, comprising 25,800 retinal images. These images are categorized on a scale from 0 to 4, with the numbers representing the severity of the condition. A higher number indicates more severe pathological changes, whereas a 0 rating signifies a normal, healthy state without any pathological alterations. This dataset is exclusively focused on classification tasks and does not include segmentation tasks. The dataset address is [[https://www.kaggle.com/datasets/amaneo/diabetic-retinopathy-resized-arranged/data](https://www.kaggle.com/datasets/amaneo/diabetic-retinopathy-resized-arranged/data)]([https://www.kaggle.com/datasets/amaneo/diabetic-retinopathy-resized-arranged/data](https://www.kaggle.com/datasets/amaneo/diabetic-retinopathy-resized-arranged/data)) (accessed on 15 April 2024).  
Breast Ultrasound Images Dataset [67]: This dataset features ultrasound images of women's breasts, designed for use in diagnosing and classifying breast cancer. The contributors to this collection range in age from 25 to 75, totaling 600 individuals. Images are organized into three categories reflecting breast cancer severity: normal, benign, and malignant. The set includes 780 images, all provided with corresponding mask images. On average, each image measures 500 by 500 pixels.  
Brain Tumor Classification (MRI) [68]: This dataset comprises medical diagnostic images used for brain tumor diagnosis, featuring a total of 394 images. These images are categorized into four distinct classes based on different types of brain tumors.  
Chest X-ray Images (Pneumonia) [69]: This dataset consists of chest X-ray diagnostic images, categorized into two types based on pneumonia diagnosis: normal and pneumonia. It includes a total of 5863 images, all derived from pediatric patients aged 1 to 5 years old.  
In this study, image datasets were divided into training and validation subsets at a 70:30 ratio. The training subset underwent preprocessing such as random rescaling to 224 \(\times\) 224 pixels, horizontal flips, and normalization using ImageNet's mean and standard deviation. Conversely, the validation subset images were first resized to 256 \(\times\) 256 pixels, then center-cropped to 224 \(\times\) 224 using the same normalization. These processes enhance the model's resilience against image variations and ensure data alignment with the original training conditions of pretrained models. Moreover, the training subset used random sampling, and the validation subset was shuffled before each iteration to ensure thorough and randomized data loading.
## 1 Introduction
### Motivation  
Multicopter unmanned aerial vehicles (M-UAVs) utilize multiple propellers to provide thrust perpendicular to the direction of the body, enabling vertical takeoff and landing (VTOL). Horizontal movement is not directly driven by the thrust of the propellers but is achieved by tilting the body to change the direction of the propeller thrust. There is a similar issue with yaw motion, which is achieved through the moment difference between propellers rotating in opposite directions. This incomplete actuation results in low efficiency in horizontal flight and yaw motion. A tiltrotor quadcopter unmanned aerial vehicle (UAV) named TJ-FlyingFish has addressed these issues through experiments in water [1]. Water is a higher resistance medium compared to air and amplifies the disadvantages of the quadcopter as an underactuated system. Therefore, the wrench that M-UAVs can apply to the environment is limited. The main reason for these deficiencies in M-UAVs is the coupling between the propellers and the body. Excessive body attitude angles affect stability, necessitating limiting the propeller's attitude. Thus, research on tiltrotor multicopter unmanned aerial vehicles (TM-UAVs), which aims to decouple the propellers from the body, has gradually emerged.