esa-sceva/satcom-chunk-collection huggingface dataset:
# Simulated Annealing for Resource Allocation in Downlink NOMA Systems in 5G Networks
## 5 Conclusions  
Simulated annealing (SA) is applied to optimize the resource allocation problem and to maximize the overall throughput in the downlink NOMA system. The SA algorithm is deployed to accomplish user assignment, determine the subchannel power, and apply the proportional power factor to the multiplexed users. The user pairing scheme is performed based on two configurations of the channel gain difference to ensure the scheme's optimality. The power allocation is used to assign the power for each subchannel and determine the power ratio for the multiplexed users on the same subchannel. The design tested the NOMA system with a minimum of users as 10 and a maximum of 60 users. The SA algorithm outperformed by 7% the NOMA-DC, 43% FTPA, and 48% OFDM schemes with less computational time.  
The SA also shows fast execution time for a large number of users and higher throughput achievement. The SA equal power allocation outperformed other power allocation schemes. It also mathematically proved that equal power allocation is the optimum power allocation scheme for the NOMA system. Moreover, according to NOMA concepts, the multiplexed users' proportional power ratio can be fixed to a fractional value that can reduce the optimization's complexity. The results show SA as a solution to both resource allocation problem and a way to maximize the Throughput in the NOMA-based 5G.  
Conceptualisation, O.A.; methodology, O.A.; software, O.A.; validation, O.A., M.B.R. and Z.B.Y.; investigation, O.A., M.B.R. and Z.B.Y.; writing-original draft preparation, O.A., M.B.R. and Z.B.Y.; writing-review and editing, O.A., M.B.R. and Z.B.Y.; visualisation, O.A., M.B.R. and Z.B.Y.; supervision, M.B.R.; project administration, O.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Application of Artificial Neural Networks to Ship Detection from X-Band Kompsat-5 Imagery
## 3 Methods
If ground-truth data, such as the automatic information system (AIS), are available, they can be used for training and verification. However, since in-situ data is not available in this study, the training set for the ANN approach is determined by using a statistical threshold technique. Training samples of the ship detection are determined from the range calculated from the intensity-differential image. The ship training samples (\(P_{S}\)) are selected as follows:  
\[P_{S}(i,j)=\left\{ \begin{array}{cl}1,&\quad if\;\;P(i,j)>MAX(P)-T_{S}\times Range (P)\\ 0,&\quad otherwise\end{array} \right., \tag{1}\]  
where \(P(i,j)\) is the pixel value at the range \(i\) and azimuth \(j\), \(T_{S}\) is the threshold value for the ship samples, \(MAX(P)\) is the maximum value of all of the pixels and \(Range(P)\) is the range, which is defined as \(MAX(P)-MIN(P)\). The non-ship training samples (\(P_{NS}\)) are selected by:  
\[P_{NS}(i,j)=\left\{ \begin{array}{cl}1,&\quad if\;\;P(i,j)<MIN(P)+T_{NS} \times Range(P)\\ 0,&\quad otherwise\end{array} \right., \tag{2}\]  
where \(T_{NS}\) is the threshold value for the non-ship samples. The threshold values of \(T_{S}\) = 0.4 and \(T_{NS}\) = 0.1 are used for this study, because the standard deviation of sea objects is a smaller value whereas that of ship objects is a higher value. The final ship detection is performed by applying a threshold value to the ship-probability map. The threshold value is estimated from the histogram of the ship-probability map. This step is done by finding a point that the two PDFs from the ship and non-ship objects cross.
## 4 Experimental Results
### Results and Analysis
#### 4.2.3 Results of Target Re-Identification  
The experimental content of this part was mainly using the proposed model TRTrack, a target re-identification method based on shot boundary object detection for SOT, and state-of-the-art models, including OSTrack-384 [19], MixFormer-L [18], and NeighborTrack-OSTrack [30], to conduct comparative experiments through the classroom video dataset CLV established in this paper. For these SOT models, OSTrack-384 (proposed by [PERSON] et al.) connects the template and the search region to bidirectional information flows to combine feature learning with interaction. MixFormer-L, developed by [PERSON] et al., introduced a mixed-attention module to build an information interaction channel between the template-search image pairs. NeighborTrack-OSTrack uses the confidence score to automatically utilize the information of neighbor regions that are not obscured. The above three models all achieve good performance in SOT tasks. The experimental results can reflect the performance of TRTrack for target re-identification, which is shown in Table 4.  
The table shows that compared with OSTack-384, MixFormer-L, and NeighborTrack-OSTrack, the TRTrack model proposed in this paper has an excellent performance in the classroom video dataset CLV, and the metric ET, ED, and TD are almost optimal in 16 videos. The average value of ET is 2.5 times, and the sum value of it is 40 times, which is less than 3.19 times and 51 times of the second-best model NeighborTrack-OSTrack, indicating that TRTrack possesses good tracking stability and is not prone to tracking errors. The average value of ED is 4.3 s, and the sum value is 68.75 s, which is an order of magnitude less than other popular algorithms, indicating that TRTrack has a high tracking accuracy. The metric TD contains the average and sum value of the time interval between the target's reappearance after leaving the shot and being tracked again in a video. For other tracking algorithm models, TD has the order of tens of seconds and hundreds of seconds, accounting for most of the tracking error duration, namely the metric ED. This is enough to show that the re-identification of the target after leaving the shot is a crucial object-tracking problem, and the poor ability in this aspect of current SOT models is the major source of tracking
## 2 Materials and Methods
### Scanning Electronic Microscopy (SEM) Observations  
Three twigs of _Ulex parvitforus_, (3 individuals) at different phenological stages (young and mature) were observed with a Zeiss scanning electron microscope, SEM EVO 15 (with lanthanum boride source), in wet mode.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Index** & **Abbreviation** & **Formula** & **Reference** \\ Normalized Phaeophytinization Index & **NVI** & (R415 \(-\) R435)/(R415 \(+\) R435) & [19] \\ \hline Normalized Difference Vegetation Index & NDVI (N) & R565 \(-\) 708/R565 \(+\) K708 & [20] \\ \hline Normalized Difference Vegetation Index & NDVI (N) 2 & (R730 \(-\) R759)/(R730 \(+\) R759) & [20] \\ \hline Normalized Difference Vegetation Index & NDVI (N) 3 & (R717 \(-\) R770)/(R717 \(+\) R770) & [20] \\ \hline Normalized Difference Vegetation Index & NDVI (N) 4 & (R720 \(-\) R839)/(R720 \(+\) R839) & [20] \\ \hline Water Index & WI & R900/R970 & [21] \\ Normalized Difference Vegetation Index & NDVI & (R800 \(-\) R680)/(R800 \(+\) R680) & [22] \\ Photochemical Reference Index & PRI & (R531 \(-\) R570)/(R531 \(+\) 570) & [23] \\ Solar Induce Fluorescence A proxy & SIFa & & R470/R530 & [24] \\ \hline Solar Induce Fluorescence B proxy & SIFb & R685/R850 & [24] \\ \hline Enhanced vegetation index & EVI & 25 \(\times\) [(NIRref \(+\) REDFT)/ & [25] \\  & & (NIRref \(+\) 6 \(\times\) REDFT \(-\) 75 \(\times\) BLUEref) \(+\)1 & [25] \\ Normalized Difference Index & NDI & (R850 \(-\) R710)/(R850 \(+\) R680) & [26] \\ Normalized Difference Red Edge & NDRE & (R800 \(-\) R720)/(R800 \(+\) R720) & [19] \\  & Red Edge Index & RE & R740/R720 & [27] \\ Red Edge Chlorophyll Index 1 & **CI1** & & (R800/R740) \(-\) 1 & [28] \\ Modified Chlorophyll Absorption in & MCARI & [(R700 \(-\) R1510) \(-\) 0.2(R700 \(-\) R550)] \(\times\) (R700/R1510) & [29] \\  & Nitrogen Planar Domain Index 2 & NPDI2 & (CI2 \(=\) CI2 MINN)/(CI2 MAX \(=\) CI2 MINN) & [30] \\ \hline Red Edge Chlorophyll Index 2 & CI2 & (R740/R550) \(-\) 2 & [30] \\ \hline \end{tabular}
\end{table}
Table 1: Selected vegetation indices in the literature.
# Pyramid Spatial-Temporal Graph Transformer for Skeleton-Based Action Recognition
## 4 Experiments
### Comparisons with Other Approaches
#### 4.4.2 Experiments on the NTU-RGBD 120 Dataset  
The performance of the NTU-RGBD 120 dataset was also evaluated on both the X-sub and X-set benchmarks. As shown in Table 6, the PGT performed better than most recent works. Compared with the four-stream shift-GCN, the accuracy of our PGT was 0.6% and 1.2% higher on the X-sub and X-set benchmarks, respectively, while compared with the three-stream RA-GCN [13], our PGT achieved 5.4% higher accuracy on the X-sub benchmark and 6.1% higher accuracy on the X-set benchmark.  
The ST-TR-AGCN [14] also applies a self-attention mechanism in its architecture. However, multi-head self-attention is added separately for spatial graph convolution and temporal graph convolution with a two-stream network similar to the work in [10]. The core ST-GCN unit was also adopted in our graph embedding to obtain the local information in the vertex domain. Furthermore, our work took a pyramid transformer as the main structure to give full play to the long-range attention ability of the temporal-spatial joints. Concretely, our PGT applied both spatial-temporal transformer and joint transformer design for long-range correlations in both the spatial and temporal domains. The accuracy of the PGT was 3.8% and 3.8% higher than the work in [14].  
The performance of the GCN-based method, efficient-GCN [15], was better than our method on this dataset. As far as we are concerned, we paid more attention to the lightness of the network, particularly in the adjustment of the hyperparameters of the model. The FLOPs of the four-stream PGT were 4 GFLOPs, nearly one-fourth the amount of the efficient-GCN. Therefore, on a small dataset such as NTU-RGBD 60, our performance was even better on the CV benchmark. If the computational complexity of our pyramid network is improved, then the experimental results will be further increased as well.
Sensors_**2006**, \(6\), 1383-1410  
_Review_  
**Recent Electrochemical and Optical Sensors in Flow-Based Analysis**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 1, [PERSON] 2 and [PERSON] 1**  
\({}^{1}\) Sensors Research Unit, Department of Chemistry, Faculty of Science, Chulalongkorn University,  
Phayathai Road, Patumwan, Bangkok, 10330, Thailand  
\({}^{2}\) Department of Chemistry, Faculty of Science, Srinakharinwrot University, Bangkok, 10110, Thailand  
* Author to whom correspondence should be addressed; E-mail: [EMAIL_ADDRESS]  
_Received: 8 August 2006 / Accepted: 9 October 2006 / Published: 24 October 2006_  
**Abstract:** Some recent analytical sensors based on electrochemical and optical detection coupled with different flow techniques have been chosen in this overview. A brief description of fundamental concepts and applications of each flow technique, such as flow injection analysis (FIA), sequential injection analysis (SIA), all injection analysis (AIA), batch injection analysis (BIA), multicommutated FIA (MCFIA), multisyringe FIA (MSFIA), and multiumped FIA (MPFIA) were reviewed.  
**Keywords:** Flow injection analysis (FIA), FI, SIA, AIA, BIA, MCFIA, MSFIA, MPFIA, electrochemical detection and optical detection  
**Contents**  
**1. Introduction**  
**2. Flow-based electrochemical detection**  
**2.1.** _Amperometric detection_  
**2.2.** _Potentiometric detection_  
**2.3.** _Other electrochemical detection_  
**3. Flow-based optical detection**  
**3.1.** _Atomic absorption spectrometry (AAS)_  
**3.2.** _ICP-AES, ICP-OES, and ICP-MS_  
**3.3.** _Spectrophotometry_
## 3 Synthetic Examples
### The Double Hyperbolic Chirped Signal  
In order to futher verify the effectiveness of the SSGST method in dealing with non-stationary signal whose time-frequency characteristics is more complicated, we apply it to the double hyperbolic chirped signal (Figure 3), whose main frequency hyperbolic increases.  
Figure 3: The double hyperbolic chirped signal.  
Figure 2: Time-frequency spectra of the double linear chirped signal based on (**a**) Continuous Wavelet Transform (CWT), (**b**) Generalized S-transform (GST), (**c**) Synchrosqueezing continuous wavelet transform (SSCWT), and (**d**) Synchrosqueezing generalized S-transform (SSGST).  
The results generated by the CWT, GST, SSCWT, and SSGST methods are shown in Figure 4. The time-frequency represents in Figure 4a,b resulted from the CWT and GST show poor energy concentration. However, as can be seen from Figure 4c,d, both SSCWT and SSGST provide higher resolution time-frequency result than CWT and GST, and they are good approximations of the signal's instantaneous frequency. Compared with the SSCWT result, the SSGST representation shows an obviously higher energy concentration (see Figure 4c,d).  
According to the comparison of the SSGST method with the CWT, GST, and SSCWT methods, we find that the SSGST method has better performance in improving time-frequency resolution and accuracy of time-frequency resolution. Moreover, there is no spurious frequency in the time-frequency spectrum of SSGST; thus, high-resolution time-frequency analysis results can be obtained accurately by the SSGST method.
## 2 Experimental Set-Up and Test Methodology
### Measurement System and Uncertainties  
The plant is fully instrumented to be able to measure the main energy parameters of operation. The measurement devices and their uncertainty are detailed in Table 2. The refrigeration cycle incorporates eight T-type thermocouples, three pressure gauges, one Coriolis mass flow meter,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Heat Exchanger** & **Type** & **Model** & **Heat Transfer Area (m3)** & **Ref. Side** & **SF Side** \\ \hline Condenser & brazed plate & B25-THx40 & 2.39 (Ref. side) & 0.002166 & 0.00228 \\ \hline Secondary fluid & brazed plate & BSTHx20 & 0.414 (Ref. side) & 0.000351 & 0.00039 \\ \hline Cabinet evaporator & finned-tube & \
\begin{tabular}{c} 1520 length, \(5/8^{\circ}\) tube, 3 circuits \\ (13.13 m, 13.09 m, 13.13 m) \\ \end{tabular} & 1.765 (Ref. side) & 0.0078 & - \\ \hline Cabinet SF HX & finned-tube &
\begin{tabular}{c} 1520 length, \(5/8^{\circ}\) tube, 3 circuits \\ (13.13 m, 13.09 m, 13.13 m) \\ \end{tabular} & 1.765 (SF side) & - & 0.0078 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical data of heat exchangers.  
Figure 2: Schematic diagram of indirect expansion refrigeration system.  
a volumetric flow meter and a digital wattmeter. The cabinet incorporates five M-test packages according to ISO 15502 for measuring the product temperature, two T-type thermocouples to measure the impulsion and return temperatures of inside air to the heat exchanger, one combined humidity-temperature sensor and a digital wattmeter. For the operation with direct expansion, two pressure gauges and two T-type thermocouples are placed at the inlet of the cabinet, and for the indirect arrangement, two additional immersion T-type thermocouples are used to measure the secondary fluid temperature in the cabinet heat exchanger and two at the brazed plate heat exchanger. The cabinet is placed inside a climatic chamber, having another combined humidity-temperature sensor. All sensors are gathered using two cRIO-9074 data acquisition systems.
## 4 Evaluation of the Platform and Discussion
### Impact and Usability Evaluation
#### 4.4.2 Usability Questionnaire  
The satisfaction of the users with the energy AR system was measured through the system usability scale (SUS) questionnaire [20] which is the most used questionnaire for measuring perceptions of usability. This questionnaire provides a reliable tool for measuring usability and its reliability has been proved in hundreds of publications. This method has recently been successfully employed in the evaluation of AR applications [21; 22; 23].  
According to the authors of [24], a good SUS score for each individual should be above 68 (SUS score \(>\) 68). As shown in Table 2, the calculated SUS score for the conducted experiment is 80 and its standard deviation is 0.72. This means that the SUS score is not only above average, but it is also very close to 80.3, and according to the SUS score rank, when the score is 80.3 or higher, the system obtains the grade A, which means people love your system and will recommend it to their friends. It is worth highlighting the standard deviation value, which shows all the interviewed users have the same feeling.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Q1** & **Q2** & **Q3** & **Q4** & **Q5** \\ \hline Mean & 4.56 & 4.25 & 4.19 & 3.06 & 4.19 \\ Normalized Mean & 89.06 & 81.25 & 79.68 & 51.56 & 79.69 \\ Std & 0.63 & 0.86 & 0.98 & 1.34 & 0.75 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Impact questionnaire results (\(n=16\)).
# Wave Diffraction by Metamaterial-Coated Wedges: The UAPO Solution for Skew Incidence
## 1 Introduction
Since the method requires the evaluation of the field, which is reflected from the structure, the reflection coefficients need to be evaluated for each wedge surface that is involved in the scattering mechanism. The equivalent transmission line (ETL) model is here applied to this purpose in accordance with [1; 2; 13; 20], and the results are used to formulate the PO currents on the surfaces. Opportune approximations, analytical manipulations and evaluations, and the application of an asymptotic procedure permit us to extract the UAPO diffracted contribution from the scattering integral part that is associated with each wedge surface if this last is lit by the incident plane wave. In other words, each surface contributes to the diffracted field only if it interacts with the propagating plane wave. Section 2 is devoted to the formulation of the PO equivalent surface currents incorporating the reflection coefficients that are related to each wedge surface, whereas the UAPO solution for the diffracted field is proposed in Section 3. The radio frequency (RF) unit of Comsol Multiphysics(r) is applied in Section 4 to test the efficacy of the proposed approach. The conclusions are presented in Section 5.
## 5 Implementation Steps
### Security Framework Objective Function (SRF-OF)  
The trust concept is implemented as a new OF in RPL-lite protocol, called security framework objective function (SRF-OF). The SRF-OF algorithm for choosing the best parent is presented in Algorithm 4. First, nodes check if neighbours are acceptable as parents. An acceptable node has low link metrics or path cost. Next, checks are done to avoid blacklisted and malicious nodes that were detected in previous attempts. A neighbour node with high trust value and smaller rank than the current node's rank is selected as the parent (lines 17-21). In case Algorithm 4 reaches the last condition (line 27), it returns the parent with the lowest ETX value. The last condition as well as the whole SRF-OF implementation includes appropriate mechanisms to achieve stability in parent selection and to avoid unnecessary parent switches. It is important to have a stable network and minimise parent switches to reduce energy consumption overhead. The trust value for each neighbour is computed in Algorithm 5. Specifically, monitored nodes receive the packet at time \(T\) from SRF-IDS, extract metrics from the packet for a specific neighbour, and store the measurements to the corresponding variables. For the trust calculation, a node uses the formula shown in Section 4.3, which takes into account the actual number of packets sent to the neighbour until time \(T\) to the number of packets forwarded by neighbour and captured by SRF-IDS detectors until time \(T\). SRF-OF utilises the following metrics during best parent calculations: trust value, rank, and ETX. A combination of these metrics would allow nodes to choose the most trusted and reliable parent. SRF-OF is implemented in Contiki-NG for both benign and malicious nodes. SRF-IDS uses the default MRHOF as the objective function, and the rest nodes use SRF-OF.
# Groundwater Mixing Process Identification in Deep Mines Based on Hydrogeochemical Property Analysis
## 3 Methodologies  
### Calculation of the Mixing Ratio of the Mixing Groundwater  
Piper diagram is used to identify the chemical type of all the groundwater samples and to calculate the groundwater mixing ratios in different boreholes [30]. The principle of this graphical method is to classify groundwater chemical types by plotting the milligram equivalent percentage (mEq%) of all the conventional groundwater chemical ions [31; 32; 33], including calcium (Ca\({}^{2+}\)), magnesium (Mg\({}^{2+}\)), sodium (Na\({}^{+}\)), potassium (K\({}^{+}\)), bicarbonate (HCO\({}_{3}\)\({}^{-}\)), sulphate (SO\({}_{4}\)\({}^{2-}\)), and chloride (Cl\({}^{-}\)), in two triangle fields (the anions and the cations), and then projecting the two ternary plots onto a diamond [34]. The diamond is a matrix transformation of a graph of the anions ((sulfate + chloride)/total anions) and cations ((sodium + potassium)/total cations) [35].  
When all the groundwater samples are plotted in the diamond field of the piper diagram, the hydraulic connection can be simply identified and the mixing ratio can also be preliminarily calculated. The hypothesis of the graphical calculation is that the dilution, concentration, and transformation of chemical substances will not occur after mixing. At the same time, the preconditions are that the mixture plots should be located between two parent solution plots, and the concentration of the mixture should be greater than the least concentration of the parent solutions and less than the greatest concentration as shown in Figure 4.  
Figure 3: Locations of sampling points and karst collapse columns in the study area. S #1 and S #2 are potential karst collapse columns, while #1 and #2 are certain karst collapse columns.  
The quantitative relationship between the mixture and its parent solutions is shown as [30]:  
\[D_{\text{a}}/D_{\text{b}}=(V_{\text{b}}\cdot C_{\text{b}})/(V_{\text{a}}\cdot C_{ \text{a}}) \tag{1}\]  
where \(D_{\text{a}}\) is the distance between mixture plot C and parent solution plot A, while \(D_{\text{b}}\) is the distance between mixture plot C and parent solution plot B. \(V_{\text{a}}\) and \(V_{\text{b}}\) are the volume of parent solution plot A and parent solution plot B; \(C_{\text{a}}\) and \(C_{\text{b}}\) are the concentration of parent solution plot A and parent solution plot B.  
The linear dependence of the volumes is described as:  
\[V_{\text{c}}=V_{\text{a}}+V_{\text{b}} \tag{2}\]  
where \(V_{\text{c}}\) is the volume of the mixture.  
The mixing ratio can be calculated by:  
\[\left\{\begin{array}{l}R_{\text{a}}=(D_{\text{b}}\cdot C_{\text{b}})/(D_{ \text{a}}\cdot C_{\text{a}}+D_{\text{b}}\cdot C_{\text{b}})\\ R_{\text{b}}=(D_{\text{a}}\cdot C_{\text{a}})/(D_{\text{a}}\cdot C_{\text{a}}+D _{\text{b}}\cdot C_{\text{b}})\end{array}\right. \tag{3}\]  
where \(R_{\text{a}}\) and \(R_{\text{b}}\) are the mixing ratio of parent solution A and parent solution B.  
The result of the mixing ratio can be used as the initial value for the simulation by PHREEQC.