esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The energy consumption of network nodes is balanced by changing cluster heads regularly; this is utilized to extend the life cycle of the network [19]. The foremost contribution of this research work is as follows:  
* For the low computational complexity and excellent stability, the ICOA is utilized in the WSN to choose the CH. Based on numerous objective values, ICOA selects the CH, such as node degree, residual energy, node centrality, distance to the BS, and distance to neighbors. * In WSN, IT can enable the rapid discovery of solutions. By utilizing the IJLFA, the shortest route between CH and BS is discovered. * Because of the optimal route generation and energy-efficient CH selection for the transmission of data, the network's lifetime is extended. Furthermore, by reducing the energy utilization of nodes while transferring packets, the overall packets expected by the BS are enhanced. The research paper is organized as follows: Section 2 presents a review of recent research in the field of cluster head selection and routing algorithms. In Section 3, the proposed methodology is elaborated. The simulation results of the proposed methodology are described in Section 4. Finally, the research is concluded in Section 5.
## 2 Theoretical Background
### PS-DHI for Stokes Vectors or Miller Formalism
Consequently, polarization-sensitive imaging of an incoherent scene or by using partially incoherent illumination should be implemented. Unfortunately, only a couple of work proposed the incoherent polarization sensitive holography/interferometry. For example, [PERSON] and [PERSON] [52] investigated a self-interference polarization holographic imaging (Si-Phi) method that allows real-time 3D imaging of an incoherent scene. The authors developed an in-line polarization holography configuration equipped with a polarization-resolving detector array; this setup allows a single shot acquisition of the complex-valued hologram and results demonstrated both 3D and real-time imaging capabilities. Even if the use of incoherent sources is still immature, future developments are expected in this field.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 5 Future Research Issues
### Dynamic Topology Control  
Due to the high-speed movement of UAV, the network topology changes dynamically, so we need to improve the topology control mechanism in FANET to ensure the network connectivity. For example, limited energy of nodes in FANET, making energy efficiency the primary design goal of almost all topology control algorithms. Moreover, flight distance and distribution range between nodes are relatively large, then the corresponding algorithm must have characteristics such as distributed and localized, which means node must be able to independently complete the local topology construction through localized information (neighbor node information within the scope of one hop or two hops), and form connected global topologies through the combination of local topologies. In addition, due to the poor flight environment, topology control algorithm is required to have better fault tolerance capability, adapting to the changes such as failure of nodes, new nodes joining the network and so on.
## 2 Materials and Methods
### Data Processing and Model Development
#### 2.4.2 Feature Wavelength Selection  
The goal of feature wavelength selection, which employs variable selection algorithms, is to identify the primary influencing factors of seed germination rate in NIR quantitative prediction model from a chemometric perspective. This procedure minimizes the effect of interfering variables on the model and heightens its predictive ability and stability. MATLAB2020a software was leveraged for feature wavelength selection and modeling. A comprehensive comparison was undertaken amongst an assortment of variable selection algorithms to identify the most effective method. This suite of algorithms included Synergy Interval Partial Least Squares (SiPLS), Backward Interval Partial Least Squares (BiPLS), Iteratively Retains Informative Variables, (IRIV) (IRIV), Variable Combination Population Analysis (VCPA), Competitive Adaptive Reweighted Sampling (CARS), Random Frog (RF), and combinations of these. Each was meticulously assessed to determine the optimal selection approach.
# SPEEDY Quantum Circuit for Grover's Algorithm
## 2 Related Work
### Quantum Background
#### 2.1.1 Quantum Gates  
Quantum gates exploit the quantum entanglement and superposition states of qubits [24; 25]. In quantum computing, the state of a qubit is changed with a quantum gate that can perform reversible operations. Figure 1 shows some of the quantum gates.  
1. \(NOT/X-gate\), \(\mathrm{X}(x)=\overline{x}\): The \(\mathrm{X}\) gate inverts the state of a single qubit.
2. \(CNOTgate\), \(\mathrm{CNOT}(x,y)=(x,\,x\oplus y)\): One of the two input qubits becomes the control qubit, and the other becomes the target qubit. When the control bit is set to one, the state of the target qubit is inverted. If the control qubit \(x\) is one, target qubit \(y\) is inverted.
3. \(Toffolgate\), \(\mathrm{Toffoli}(x,y,z)=(x,\,y,\,x\cdot y\oplus z)\): Two of the three input qubits become the control qubits, and the other becomes the target qubit. When all control bits are one, the state of the target qubit is inverted. If both control qubits (\(x\) and \(y\)) are one, the target qubit \(z\) is inverted.
4. \(SWAPgate\), \(\mathrm{SWAP}(x,y)=(y,\,x)\): This changes the state of two qubits.
## 3 Results
### Pcnn
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Age** & **BMI** & **PSA** & **GS** & **PT** & **ECE** & **SV** & **PNI** & **LNM** & **SM** \\ \hline Age & 0 & 1.42 & 0 & 7.63 & 0.94 & 0.47 & 9.95 & 0.94 & 9.47 & 8.06 \\ BMI & 1.42 & 0 & 1.42 & 11.37 & 0.94 & 0.95 & 8.05 & 2.84 & 6.63 & 9.95 \\ PSA & 0.01 & 2.85 & 0 & 5.21 & 0 & 0.48 & 6.16 & 0.48 & 1.89 & 8.06 \\ GS & 9.94 & 6.64 & 2.37 & 0 & 4.27 & 1.43 & 9.47 & 3.78 & 13.26 & 15.17 \\ PT & 3.78 & 3.8 & 1.42 & 11.84 & 0 & 2.37 & 8.53 & 0.94 & 5.68 & 2.84 \\ ECE & 1.89 & 2.37 & 1.89 & 5.21 & 0.48 & 0 & 8.05 & 1.89 & 2.36 & 5.21 \\ SV & 4.26 & 9.48 & 4.26 & 17.06 & 4.27 & 3.8 & 0 & 3.78 & 4.26 & 8.06 \\ PNI & 4.26 & 1.9 & 1.89 & 8.05 & 2.38 & 3.8 & 5.21 & 0 & 4.74 & 8.53 \\ LNM & 4.73 & 2.37 & 4.26 & 14.21 & 1.9 & 0 & 11.37 & 0.47 & 0 & 2.84 \\ SM & 8.05 & 5.22 & 4.74 & 9.47 & 0 & 1.9 & 18.95 & 1.41 & 7.1 & 0 \\ Threshold & 4.26 & 4.00556 & 2.47222 & 10.0056 & 1.68667 & 1.68889 & 9.52667 & 1.83667 & 6.15444 & 7.63556 \\ \multirow{2}{*}{**Candidate**} & **LNM, SM** & **GS, SM** & **SM** & **SV, LNM** & **GS** & **PNI** & **ESA, SM** & **ECE** & **Age, GS** & **Age, BMI, SV** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of partial correlation analysis. PSA, prostate-specific antigen; BMI, body mass index; GS, Gleason score (sum); PT, pathology T stage; ECE, extracapsular extension; SV, seminal vesicle; PNI, perineural invasion; LNM, lymph node metastasis; SM, surgical margin.
# Intelligent Detection of Underwater Defects in Concrete Dams Based on YOLOv8s-UEC
## 2 Methodology
### Evaluation Indicators  
In this study, the performance of the proposed network is evaluated using the evaluation metrics of the object detection task, including Precision (\(P\)), Recall (\(R\)), \(F_{1}\) index, mean category average precision (\(mAP\)), and detection speed FPS, which are calculated as follows:  
\[P=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FP}} \tag{10}\]  
\[R=\frac{\mathrm{TP}}{\mathrm{TP}+\mathrm{FN}} \tag{11}\]  
\[F_{1}=\frac{2\times P\times R}{P+R} \tag{12}\]  
\[AP=\int_{0}^{1}P(R)\mathrm{d}R \tag{13}\]  
\[mAP=\frac{\sum_{q=1}^{Q}AP(q)}{Q} \tag{14}\]  
In Equations (10) and (11), TP is the number of correctly detected samples, FP is the number of incorrectly detected non-defective samples, and FN is the number of defective samples that were not detected. In Equation (12), \(F_{1}\) is the harmonic mean of precision and recall. The Average Precision (\(AP\)) is the area enclosed by the \(P\) and \(R\) curves and their axes. In Equation (14), \(Q\) represents the category, and \(mAP\) is the mean value of the \(AP\) for different categories. In this study, the evaluation metrics are \(mAP_{0.5}\) and \(mAP_{0.50.95}\). \(mAP_{0.5}\) is obtained when the IoU is set to 0.5, while \(mAP_{0.50.95}\) is the average of 10 mAP values calculated by incrementally increasing the IoU from 0.5 to 0.95 in steps of 0.05.
# Full-Scale Measurements of Wind Characteristics on a High- Rise Building during Typhoon Sarika
## 3 Wind Characteristics of Typhoon Sarika
### Power Spectrum of Wind Speeds  
The power spectrum of wind speeds can be expressed by dimensionless power spectral density function \(fS_{u}(f)/\sigma_{u}^{2}\), and \(S_{u}(f)\) is the power spectrum of wind speeds. Many scholars have conducted significant studies on the power spectrum of wind speeds, including Von Kaimal spectrum, Davenport spectrum, Simiu spectrum and Harris spectrum. [PERSON] et al. [15] and [PERSON] and [PERSON] [5] proposed that the power spectrum of wind speed generally followed a Von Karman-type spectrum during typhoons. Therefore, Von Karman-type spectrum were selected to compare with the measured power spectra of wind speeds. The longitudinal and lateral power spectrum of wind speeds are predicted by Von Karman-type spectrum as follows:  
\[\frac{fS_{u}(f)}{\sigma_{u}^{2}}=\frac{4X_{u}}{\left(1+70.8X_{u}^{2}\right)^{2 }}\;X_{u}=\frac{fL_{u}^{x}}{U} \tag{13}\]  
\[\frac{fS_{v}(f)}{\sigma_{v}^{2}}=\frac{4X_{v}(1+755.2X_{v}^{2})}{\left(1+283.2X _{v}^{2}\right)^{11/6}}\;X_{v}=\frac{fL_{v}^{x}}{U} \tag{14}\]  
Figure 11: The variation of gust factors obtained at different sample duration and gust duration. (a) \(G_{u}\) with respect to \(t_{\mathrm{g}}\); (b) \(G_{v}\) with respect to \(t_{\mathrm{g}}\); (c) \(G_{u}\) with respect to \(T\); (d) \(G_{v}\) with respect to \(T\).  
Figure 10: The relationship between gust factor and turbulence intensity. (a) Longitudinal gust factors; (b) Lateral gust factors.  
where \(\sigma_{u}\) and \(\sigma_{v}\) are the standard deviations of fluctuating wind in the longitudinal and lateral direction, respectively; and \(L_{u}^{x}\) and \(L_{v}^{x}\) are the integral scales of fluctuating wind in longitudinal and lateral directions, respectively. The values \(X_{u}\) and \(X_{v}\) are the dimensionless frequency of fluctuating wind in longitudinal and lateral directions, respectively. In this study, power spectrum of wind speeds of sample duration at 10-min mean wind speed of 9.09 m/s, 15.14 m/s, and 20.02 m/s are analyzed. Figure 12a-d shows that the Von Karman-type spectrum was consistent with the predicted value of the measured fluctuating wind power spectrum in longitudinal and lateral directions. The Von Karman-type spectrum has a slightly higher prediction of the longitudinal power spectrum of wind speeds when \(X_{u}\) in the range of 10\({}^{-2}\)-10\({}^{-1}\). Moreover, the measured longitudinal power spectrum of wind speeds decays faster than the lateral power spectrum of wind speeds, and the Von Karman-type spectrum has a slightly higher prediction of the longitudinal power spectrum of wind speeds when \(X_{u}\) is in the range of 10\({}^{-1}\)-10\({}^{1}\).
## 6 Uses of UAVs in Agriculture Advancement
### Plant Species Detection/Identification  
Recently, UAVs have begun to identify plant species, particularly those that are very few on the planet. UAVs are the best tools for this task, as they can fly in very remote places with limited physical access. According to the National Tropical Botanical Garden (NTBG), Hawaiian calyx _Hibiscadelphus woodii_ was found on a vertical cliff face using a drone, which was believed to have become extinct in 2009 [(161; 162)]. Precision farming desperately requires spatial information on crop density when making decisions during several applications. Quantities and plant numbers not only reflect crop yields, but they can accurately assess outcomes and determine the fate of a crop. [PERSON] and [PERSON] [(163)] counted corn plants digitally using drones. Additionally, [PERSON] et al. [(164)] proposed a technique to use UAVs to evaluate wheat plants' density at the seedling phase and captured images using a Sony ILCE\(\alpha\)5100 LRGB camera. Crop monitoring is a daunting task and is inefficient due to the large coverage area. Compared with satellite images used in the past, UAVs provide all farm results by providing real-time monitoring data, so they are further accurate and cost-effective. Microdrones' + m accessory kit offers aerial imaging capabilities to monitor moisture content, crop nutrients status, and other essential factors [(165; 166)].
## 3 System Configuration  
As shown in Figure 8, the system contains an ABB-IRB6700 6-axis industrial robot (1) and a suction cup matrix (2) installed on the end of the robot to grab the diffuser plate (5). The vision system mainly consists of a SPES-CISRTLU-LW01 computer and two Basler2500-16 gm cameras with ring-shaped lights (3) installed on horizontal 2D mobile servo modules (4), which can adapt to different BLM (6) sizes. The Vision software (Version3.2.2) interface is shown in Figure 9.  
Figure 8: (**a**) Configuration of robot vision automatic assembling system; (**b**) captured image of DP corner. (1) ABB-IRB6700 6-axis industrial robot, (2) suction cup matrix, (3) Basler2500-16 gm cameras with ring-shaped lights, (4) horizontal 2D mobile servo modules, (5) diffuser plate, (6) BLM.  
Figure 9: Vision software interface. Green and yellow colors are used by the software to help the operator identify important information, green for correct information and yellow for warning information.  
The dissembling process consists of offline and online phases.  
Offline phase: (a) Template building: One image is utilized from each camera to build a template of the DP corner. (b) Two cameras observe the robot grasping the DP and moving through translation and rotation once each. Through this process, the transformation from the image coordinate system to the robot coordinate system can be calibrated.  
Online phase (Figure 10): (a) When the BLM finishes the previous process on the assembly line and stops at the current station, the robot receives the assembly signal from PLC and controls the servo module to carry the camera to the pre-calibrated position. (b) The cameras first acquire images and detect the outer corner of the BLM, and then jointly estimate the BLM pose; finally, the vision system sends the pose to the robot. (c) The robot blindly picks up the DP from the loading area and moves to a fixed position under the cameras; then, the cameras detect DP corners and jointly estimate the DP pose, which will be be sent to guide the robot to align the DP to the BLM. This aligning process will run several times until the deviation between the DP and the BLM is small enough.
## 3 Performance Analysis of Bearings Based on an Analytical Method  
### The Reynolds Equation for Gas Bearings  
When designing bearings, comprehending various parameters is crucial; the parameters encompass load-carrying capacity, stiffness, flow rate, and stability [28; 29]. Certainly, the distribution of gas pressure within the clearance of the bearing is critical to its performance. The pressure distribution equation can be derived from the equations governing gas motion, continuity, gas state, and energy [30].  
The Reynolds equation [31] can be simplified to:  
\[\frac{\partial}{\partial x}\left(\frac{\partial\mu^{3}}{\mu}\frac{\partial p}{ \partial x}\right)+\frac{\partial}{\partial y}\left(\frac{\rho h^{3}}{\mu} \frac{\partial p}{\partial y}\right)+Q\delta=0 \tag{1}\]  
\[Q=\frac{24\eta R^{2}}{\rho_{a}p_{a}c^{3}}\rho\bar{\sigma} \tag{2}\]  
In the specified formula, \(p\) indicates the gas pressure measured in MPa, \(\mu\) refers to the gas's dynamic viscosity, \(\rho\) is the density of the gas, and \(h\) points to the thickness of the film. \(Q\) is considered the correction term for flow rate. If \(\delta\) is 1, it is at the throttle hole, and if \(\delta\) is 0, it is at the non-throttle hole.  
Setting the initial boundary conditions for pressure, the bearing throttle hole, and the bearing pressure is crucial. Specifically, the initial pressure boundary condition is outlined as follows:  
\[\left\{\begin{array}{ll}P=P_{a}&\text{Bearing boundary}\\ P=P_{d}&\text{Orifice boundary}\end{array}\right. \tag{3}\]  
In the formula, \(P_{a}\) represents the bearing outlet pressure, while \(P_{d}\) denotes the gas supply pressure at the throttle orifice.
## 4 Discussion  
### How Do We Clinically Differentiate Nevi from Melanoma?  
It is the observation of the present authors that patients get alarmed when they encounter nevi, which resemble melanoma due to similarities in the colour of the lesions; namely, both have a bluish-black colour [36]. Nevi may also make the patients invariably think of melanoma that behaves aggressively, especially mucosal melanoma. From a scientific viewpoint, some nevi such as cutaneous cellular and atypical cellular blue nevi are significant as they may clinically and histopathologically resemble melanoma in addition to being a precursor to the development of cutaneous melanoma [1]. The ABCD rule, which assesses 'asymmetry, borders, colour and diameter' in the clinical diagnosis of melanoma, has been validated for skin lesions [36]. However, it is not clear whether this same principle could be applied to intraoral lesions as well. Therefore, the present study is conducted to present the clinico-pathological characteristics of OMN, including intraoral blue nevi, to reiterate the indolent nature of the lesions. In contrast to OMN, oral mucosal melanoma comprises aggressive lesions with an extremely poor outcome [37; 38; 39; 40; 41; 42; 43; 44].  
The literature review and the present series reveal that DBN or common blue nevi are indolent lesions that appear as single, smooth, sharply circumscribed and round-to-oval lesions that generally do not exceed more than 1 cm in their greatest diameter [9; 10; 11; 12; 13; 14; 15; 16]. However, it is also important to keep in mind that the large size should not preclude a diagnosis of DBN, as a case report describes an intraoral blue nevus that exceeded 5 cm in diameter. In addition, [PERSON] et al. [9] show that only approximately 15% of the lesions included in their series of OMN exceeded more than 2.5 cm in size. Thus, by applying the ABCD rule, namely, the diameter of the lesion, it could be concluded that an overwhelming majority of OMN including intraoral blue nevi do not exceed >6 cm in diameter and could be used as a reliable clue to exclude melanoma.  
When the blue nevus was first described by [PERSON], he coined the term due to the blue-black colour of the lesions [2]. The Tyndall effect is generally thought to be responsible for the blue colour of the lesion, which occurs from melanocytes situated deep in the dermis or lamina propria in the mucosa. The reason for colour variations observed in the blue nevi could be attributed to the presence or absence of melanocytes in the overlying epithelium/epidermis, the depth at which melanocytes are present and the number of melanin-containing melanocytes present in the lesion [31]. Intraoral blue nevi have also shown a range of colours that vary from blue to blue-black or grey [9; 10; 11; 12; 13; 14; 15; 16] (Table 1). However, none of the blue nevi described in reports were found to show colour variegations, which is another clue to differentiate these OMN from melanomas.
# SCHC over LoRaWAN Efficiency: Evaluation and Experimental Performance of Packet Fragmentation
## 3 SCHC over LoRaWAN  
This section describes the layer model of the SCHC standard and explains the concept of fragmentation used in SCHC. We review the modes of operation necessary to fragment a packet and show the types of SCHC messages to transport the original packet fragments. Finally, we explain how SCHC fragmentation works when operating over LoRaWAN.
## 2 Existing Leak Detection and Localization Methods
### Methods Based on Model Estimation
#### 2.2.5 Standing Wave Difference Method  
Standing wave difference method (SWDM) is a promising leak detection method for water pipeline transmission systems, which is based on the technology used in electrical engineering to determine-leak point position [28]. Its principle is that SWDM utilizes sinusoidal excitation of the pipeline at one end using an oscillator and simultaneous measurement of head and discharge. Every discontinuity of the pipe impedance (_i.e._, the ratio between head fluctuation and discharge) reflects incident waves, creating residual standing waves. The distance between the excitation site and pipeline discontinuity is determined by the analysis of the respective resonance frequencies.
## 4 Results
### On-Grid Operation
#### 4.1.3 Switching between Operating Modes  
The test of switching between the operating modes consisted in switching between the grid supply and the island with a non-linear load connected (resistive load connected through a six-pulse rectifier). Power failure was simulated by opening the K1 contactor. The restoration of the supply voltage was simulated by turning on the contactor K2.  
The voltage and current waveforms of one load phase and the voltage controlling the switching contactor were recorded (Figure 14--switching to off-grid mode, Figure 15--switching to on-grid mode). The voltage waveform of the contactor K1 allows one to assess the speed of operation of the contactor itself, which affects the time of physical switching between operating modes.  
Figure 12: Voltage and current waveforms when 12 kW load is switched on. 1—grid phase voltage; 2—load phase current; 3—generator phase current 4—grid phase current. Source: author’s development.  
Figure 13: Waveforms of voltage and current during switching on the 12 kW load (enlargement of fragments of waveforms in Figure 12): (**a**) switching on the load; (**b**) reducing the connection power to zero. 1—grid phase voltage; 2—load phase current; 3—generator phase current 4—grid phase current. Source: author’s development.  
Taking into account the time of switching on the voltage supplying the contactor after the failure and the return of the grid voltage, the switching time is shorter than 20 ms. The voltage is restored in approx. 40 ms.
# Diversity Balancing for Two-Stage Collaborative Filtering in Recommender Systems
###### Abstract  
Conventional recommender systems are designed to achieve high prediction accuracy by recommending items expected to be the most relevant and interesting to users. Therefore, they tend to recommend only the most popular items. Studies agree that diversity of recommendations is as important as accuracy because it improves the customer experience by reducing monotony. However, increasing diversity reduces accuracy. Thus, a recommendation algorithm is needed to recommend less popular items while maintaining acceptable accuracy. This work proposes a two-stage collaborative filtering optimization mechanism that obtains a complete and diversified item list. The first stage of the model incorporates multiple interests to optimize neighbor selection. In addition to using conventional collaborative filtering to predict ratings by exploiting available ratings, the proposed model further considers the social relationships of the user. A novel ranking strategy is then used to rearrange the list of top-N items while maintaining accuracy by (1) rearranging the area controlled by the threshold and by (2) maximizing popularity while maintaining an acceptable reduction in accuracy. An extensive experimental evaluation performed in a real-world dataset confirmed that, for a given loss of accuracy, the proposed model achieves higher diversity compared to conventional approaches.  
recommender systems; collaborative filtering; diversity; multi-interest; ranking function +
Footnote †: journal: Applied Sciences