esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The algorithm considers multi-cell interference and selects antennas and user subsets to optimize the cell capacity. In reference [23], the joint optimization problem in non-orthogonal multiple access (NOMA) systems is investigated. For a multi-band, multi-user scenario, a joint antenna and user contribution algorithm is proposed, which jointly considers the contribution of each antenna's and each user's channel gain to the overall channel gain. The proposed method is shown to be more effective in scenarios with high user density and a large number of antennas. All the above studies involve antenna selection on base station side. [PERSON] et al. investigate antenna selection on user side in [24]. A user grouping strategy utilizing subspace similarity is proposed, with the goal of selecting users with better spatial compatibility to improve the system throughput. Furthermore, to address the issue of zero rates for certain users caused by user scheduling, a proportional fairness (PF) scheduling scheme is introduced. Table 1 lists the aforementioned related works and summarizes their system models, research objectives, and methods. From the above analysis, it can be seen that the joint antenna selection and user scheduling scheme suffers from the fact that the transmitter always tries to prioritize the allocation of resources to some users with the best channel conditions, thus neglecting the service quality for the remaining participants. This kind of service lacks fairness regarding the system, because some users with poor channel conditions may never be selected to participate in communication. Compared with traditional maximum-rate scheduling, which prioritizes users with high channel quality, PF scheduling exhibits a notable ability to balance system performance and user fairness. This scheduling strategy dynamically adapts to time-varying channel conditions by jointly considering both the current channel state and the historical resource allocation of users. In each scheduling instance, it compensates users who have not been served for an extended period. By doing so, PF scheduling achieves a trade-off optimal solution that ensures both a high system sum rate and fair service distribution among users. Moreover, PF scheduling is inherently compatible with complex network architectures such as orthogonal frequency division multiple access (OFDMA), multi-user MIMO, and 5G NR. Therefore, after investigating the low-complexity joint antenna selection and user scheduling algorithm JAS-OUS, we propose a transmission scheme named JAS-PFUS, which integrates PF scheduling to ensure fairness in user scheduling.
## 4 Analysis of Experimental Results
### Experiments on Rail Wear Calculation  
In order to verify the effectiveness of the ISS-4 PCS + KD-ICP two-stage point cloud alignment method proposed in this paper, a 400 mm long 60 kg/m model standard railroad track is selected for this experiment. This validation process uses a quantitative test method for wear values, and a numerical test is carried out at intervals of 40 mm; the 10 sets of wear data obtained are shown in the form of Table 7.  
As can be seen from Table 7, the wear values obtained using the ISS-4 PCS + KD-ICP method proposed in this paper as well as a conventional measuring ruler are on the small side, which is attributed to the fact that the selected 60 kg/m model rails are freshly polished new rails, and the amount of wear itself is small. The wear errors were analyzed: the maximum error on vertical wear \(\Delta\)H was 0.047 mm, that on horizontal wear \(\Delta\)M was 0.048 mm, and the maximum error on total wear W was 0.058 mm. The  
Figure 12: Rail section wear.  
maximum errors were all less than 0.06 mm, while the conventional wear calipers used had a reading error of 0.1 mm or less. It can be seen that the wear values measured by the ISS-4 PCS + KD-ICP method used in this paper are within the permissible error range and meet the testing requirements.
## 5 Conclusions  
Sulphate attacks are a major issue for infrastructures, especially in coastal lands, and cause significant damages every year. On the other hand, sawdust is an abundant by-product, and previous studies have shown its potential usage in civil engineering projects. As described earlier, the curing time was 3, 7, 28 and 90 days. The following conclusions can be drawn from this study:  
* The effect of SD was considered to be linear with decreasing effect on MDD and OMC. This means that optimum moisture content (OMC) decreased with increasing SD. At the same time, maximum dry density (MDD) was decreased with increasing SD. This result was attributed to lighter density of SD in this case compared to soil density.
* SD helped the samples to resist more while exposed to sulphate attack. This was concluded based on UCS peak results. It is noted that SD addition increased the resistance of samples by more than 3 times in some cases.
* Curing time affects the resistance of mixed soil against sulphate attack. The process can be attributed to the hydration process: With an increase in curing from 7 days to 90 days, proper hydration occurred, increasing the actual strength without exposure and increasing the resistance after exposure.
* From a microanalytical point of view, the hydration process while cement is present generally causes the UCS to increase with increased curing time.
* The decrease in strength of the mixture/soil after exposure to the \(MgSO_{4}\) was due to the decalcification process by substitution of the calcium in calcium silicate hydrate (CSH) with magnesium and production of the magnesium silicate hydrate (MSH), which is not cohesive.  
In this study, the effect of sawdust on improvement of sulphate exposed soils has been investigated, and the results showed that addition of sulphate is effective to reduce the destructive effects of sulphate.  
Conceptualization, A.C. and H.N.; methodology, M.K. and A.C.; validation, A.S.S., M.K. and A.C.; formal analysis, A.C.; investigation, S.M.; resources, A.C.; data curation, S.M.; writing--original draft preparation, A.C., M.K., S.M.; writing--review and editing, A.S.S.; visualization, S.M.; supervision, A.C.; project administration, M.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors acknowledge the use of Curtin University's Microscopy and Microanalysis Facility, whose instrumentation has been partially funded by the University and State and Commonwealth Governments.  
The authors declare no conflict of interest.
# Link and System-Level NOMA Simulator: The Reproducibility of Research
## 2 The NOMA Link-Level Simulator
### Receiver Design  
Figure 2b shows the receiver implementation of the MC-NOMA-based system with SIC. The first step to be performed at the receiver for OFDM-NOMA is CP removal. After this equalization is executed to undo the channel effects. The next step to be carried out on the equalized data is a fast Fourier transform (FFT), which converts the time-domain signal into a frequency-domain one. As NOMA is based on multiplexing of users' data in the power domain at the transmitter side via SC, multiuser detection (MUD) is required at the receiver side to de-multiplex the data of multiple users. SIC has been widely adopted as an MUD technique for NOMA-based systems in the literature [26; 27; 28; 29]. FEU with large power treats the NEU as noise and detects its signal with a high probability of success. On the contrary, NEU considers FEU as interference and thus, performs MUD in the form of SIC. The receiver design of the proposed NOMA simulator is also based on SIC. Different variants of SIC are considered in the proposed downlink NOMA simulator, which is discussed below.
## 2 Materials and Methods
### Development of Classification Models
#### 2.3.2 Soft Independent Modelling of Class Analogies (SIMCA)  
SIMCA was evaluated in the current study as a class-modelling technique [18]. Thus, samples of unknown origin were used to obtain a classification rule able to discriminate samples of unknown origin into the different classes established, based on the values of the different characteristics of the samples themselves. Thus, SIMCA builds class models based on independent PCA models performed only on samples belonging to each class under study (_Black_, _Red_ or _White_). The dimension of the individual PCA model is given by the number of principal components (PCs), determined by a cross-validation procedure. Samples do not have to be classified in only one of the above classes. The classification of new samples with unknown origin was calculated using the scores and the loadings of the created PCA model, taking into consideration the distance of the sample to the centre of the model (leverage), which provides information about the placement of the projected sample on the PCs, and the distance of the sample to the model defined by the PCs (S-distances).  
Classification ability of the models were expressed on terms of SE and SP [17]. Calibration results from all pre-treatments and spectra ranges evaluated are compiled in Table S2 (supplementary material), whilst results of the best fitting models (calibration and external validation) are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Commercial Category**} & \multirow{2}{*}{**Pre-Treatment**} & \multirow{2}{*}{**Range (nm)**} & \multirow{2}{*}{**LVs**} & \multicolumn{6}{c}{**Cross-Validation**} & \multicolumn{6}{c}{**External Validation**} \\ \cline{4-13}  & & & & **n** & **1-VR** & & **RMSECV** & **SE** & **SP** & **n** & **SE** & **SP** \\ \hline _Black_ & SNV-DE & 1000–2300 & 12 & 69 & 0.815 & 0.198 & 100.00 & 100.00 & 31 & 81.82 & 90.00 \\ _Red_ & SG 1/4,4,1 & 1000–2300 & 10 & 70 & 0.838 & 0.181 & 96.00 & 97.87 & 31 & 60.00 & 66.67 \\ _White_ & SG 1/4,4,1 & 1000–2300 & 10 & 70 & 0.790 & 0.245 & 100.00 & 95.65 & 31 & 70.00 & 71.43 \\ \hline _Black_, _Red_ and _White_ = commercial categories of raw used for manufacturing Iberian _chorizo_; SNV = Standard normal variate; DE = de-trending; SG = Savitzky-Golay derivates; LVs = latent variables; n = number of samples; 1-VR = coefficient of determination in cross-validation; RMSECV = root mean square error of cross validation; SE = sensitivity (%); SP = specificity (%). & \\ \hline \end{tabular}
\end{table}
Table 2: PLS-DA results of the best fitting equation for pre-sliced MAP Iberian _chorizo_ classification within the official commercial categories of the raw material (_Black_, _Red_ and _White_).
## 2 Experiments  
### Introduction  
The purpose of the experimental part is to provide suitable data for the numerical modelling of both the development and testing. The purpose of the experiments was to calibrate a numerical model developed for the solver Fire Dynamics Simulator (FDS) and then validate it.  
The experiment took place under laboratory-controlled conditions, in order to match the indoor conditions of a real building indoor space. The analyzed space is a real-scale enclosure, as recommended in fire safety research literature [22]. The area of the considered enclosure is A = 18.00 m\({}^{2}\). The experimental was carried out in the courtyard of the Building Services Engineering Faculty at the Technical University of Civil Engineering of Bucharest. A real-scale experiment is usually preferred, being characterized by better precision, compared to a reduced scale model. Our experimental study involved a real application, with complex parameters, which evolution could not be represented at reduced scale. In our case, for a different scale, it would have been difficult to find the appropriate similitude criteria for the simultaneous involved phenomena: air flows, fire reactions, and sprinkler activation.  
The experimental test chamber was equipped with burners, air inlet and outlet, ventilation system, liquified petroleum gas (LPG) ducts, thermocouples and data acquisition system, video survey cameras, a sensor for the unburnt gas leaks detection, and CO alarm systems.  
Three experiments were performed, the only parameter that differed being the air flow, extracted by means of the ventilation system. Further in the paper, the following notationswill be used for the three experiments: CALIB1 (used for numerical model calibration), CALIB2, and CALIB3 (both used for numerical model validation and testing).
## 3 Results and Discussion
### Texture Profile Analysis  
Figure 2 shows the texture attributes evaluated instrumentally in the experimental costeno-type cheeses. Control cheese (Q\({}_{C}\)) had the highest hardness and cohesiveness compared with reduced-salt cheese.  
[PERSON] et al. (2003) [37] reported that the decrease in the salt content of muenster cheese decreased its hardness. This effect was seen in the hardness results reported in this study, except for Q\({}_{1}\) (\(p\)-value \(<\) 0.05). Studies report that the firmness of the cheese increases as the level of fat decreases. This phenomenon indicates that the moisture content present in Q\({}_{1}\) (Figure 1) and the adjustment of the fat globules within the protein matrix may have affected this cheese [18; 37]. The hardness also significantly increases as the moisture content decreases, and the FDM content increases [14]. This phenomenon was observed in this study (Table 1).  
On the other hand, Q\({}_{2}\) cheese with lower addition of NaCl compared to Q\({}_{1}\) had a hardness with values similar to those reported in the control cheese (Q\({}_{C}\)) and the cheeses with partial substitution of NaCl for KCl (Q\({}_{4}\) and Q\({}_{5}\)). These similarities could be due to the increase in cooking temperature applied during the making of these cheeses (Table 1) [10; 14; 24].  
[PERSON] (2007) [38] indicated that the cooking process increases the hydrophobic junction's relative strength, resulting in particles' aggregation into more extensive and easily processed curds with higher density and strength. Consequently, there is an increase in the degree of serum separation.  
Concerning cohesiveness, [PERSON] and [PERSON] (2003) [32] reported that an increase in hardness creates more brittle and less cohesive cheese texture. Nevertheless, costeno-type cheese with maximum salt content (Q\({}_{C}\)) had the highest cohesion than reduced-salt cheese and cheeses with NaCl and KCl. However, it is impossible to establish statistical differences in this texture variable due to the sample size.  
Partial substitution of NaCl with KCl resulted in a difference in cohesiveness compared to control cheese (Q\({}_{C}\)). Various studies have demonstrated that mixtures of NaCl and KCl affect the cheese's texture [7; 8; 13]. KCl has a decreased ionic strength towards NaCl, which results in a decrease in the \"salinity\" or solubility of proteins, with a direct effect on the cheese matrix [8].  
Figure 2: Texture attributes of costeno-type cheese: (**A**) hardness; (**B**) cohesiveness. Q\({}_{C}\) (black bar), Q\({}_{1}\) (gray bar), Q\({}_{2}\) (diagonal striped bar), Q\({}_{3}\) (point bar), Q\({}_{4}\) (horizontal striped bar), and Q\({}_{5}\) (vertical striped bar). Error bars represent standard deviation. Student’s t-tests were performed to assess significance compared to the control cheese (Q\({}_{C}\)), where (*) corresponds to a \(p\)-value \(<\) 0.05.
# Endoscopic Optical Imaging Technologies and Devices for Medical Purposes: State of the Art
## 1 Introduction
The resolution in this case is still based on the number of fibers present in the bundle and can be increased using specific optical lenses that will be discussed later in the paper. The light beam can be scanned either by actuating an optical fiber at a resonance or non-resonance frequency, actuating a micromirror surface to deviate the light shining on it, or using a galvanometer scanner at the proximal end. The actuation of these components can be either performed using electrostatic, electrothermal, piezoelectric, electromagnetic, or shape memory alloy actuators. The size of an endoscopic device is highly limited by the size of the actuator used [3,6]. In addition to the actuation methods, the actuation frequency is an important parameter affecting the performance of the scanner. The scanning of the light in an endoscopic device must have a frame rate of over 5 Hz to overcome motion artifacts caused by the motion of the organs during respiration, peristalsis, and beating of the heart. The scanning of the target surface can be performed using a raster, spiral, Lissajous, circular, or a propeller pattern. In the raster scan, the fiber is scanned along one axis using a sinusoidal or triangular waveform and a step-sized increasing triangular wave in the other direction. In the spiral scan, the two axes are actuated with increasing sinusoidal waves with a 90\({}^{\circ}\) phase shift to sweep the area inside the spiral. In a Lissajous pattern, two axes are driven by two sinusoidal waveforms with different frequencies and phase shifts. Circular and propeller patterns are less frequently used in optical scanning devices [2]. This paper aims to describe the technology behind optical imaging devices used in medical applications. The paper is structured as follows: Section 2 describes some of the most used medical imaging technologies, including OCT, CM, nonlinear microscopy, and the PA imaging techniques. Section 3 discusses two different kinds of the optical scanners that are distinguished based on the imaging direction with respect to the axis of the device. Resonant, non-resonant, and semi-resonant scanning principles are reported in Section 4. Section 5 illustrates the different actuation methods along with their applications in optical imaging devices. Section 6 outlines the different scanning patterns followed during beam scanning. Section 7 reports some discussions and comparisons about different scanners available to date, along with some of their potential applications based on the literature study. Section 8 contains conclusions.
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 2 Related Work
### Pre-Processing Method of Machine Learning for Edge Detection  
[PERSON] et al. proposed a pre-processing approach of machine learning for edge detection with high accuracy [40]. In this approach, the quality of the image is improved by adjusting contrast and brightness, which results in effective edge detection without light control.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Operation** & **Input Generation** & **Advantages** & **Disadvantages** \\ \hline \multirow{4}{*}{[18]} & Image & Segmental Symbolic & Use of structural testing & \multirow{2}{*}{Images are compared manually} \\  & Reconstruction & & Evaluation Method & & quality of MR \\ \hline \multirow{4}{*}{[32]} & Image Smoothing, & \multirow{4}{*}{Segmental Symbolic} & Automatic framework for & \multirow{4}{*}{Structural testing is not used to check} \\  & Dilation and & & Evaluation Method & & the adequacy of source test cases \\ \cline{1-1}  & & & & output evaluation & \\ \hline \multirow{4}{*}{[33]} & Dilation & Random Input & Automatic the test oracle & \multirow{2}{*}{Structural testing is not used to check} \\  & Generation Method & & using SVM & & the adequacy of source test cases \\ \hline \multirow{4}{*}{[35]} & Image & \multirow{4}{*}{Random Input} & Use of structural testing & \multirow{2}{*}{Images are compared manually} \\  & Reconstruction & & & & \\ \cline{1-1}  & & & & quality of MR & \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._First, the dataset of ground truth images are used as input. In pre-processing, meaningful features are extracted from the image and perform machine learning (SVM, KNN, and MLP) to predict brightness and contrast for better edge detection. This approach is used for ISP pre-processing so that it can detect the boundary lines more accurately and improve the data processing speed when compared with the existing ISP.
## 4 Simulation Results  
### Simulation Settings  
In this section, the simulation results performed with Matlab for the proposed RIS-assisted RSMA scheme are presented. The 3D SCM channel was considered for the system. The UEs with a height of 1.5 m were randomly located at a distance of 5 m (50 m, 5 m). The positions of the BS and the RIS were, respectively, (0, 0, 25 m) and (50 m, 5 m). The distance-based path-loss model was considered as \(PL(d)=L_{0}d^{-\beta}\), where \(L_{0}\) is the reference path loss at a distance of 1 m, d is the distance between the end nodes, and \(\beta\) is the path-loss exponent. The ratio coefficient between the LoS power and non-LoS power was set to 3 dB, and the inter-antenna and inter-RIS element spacing were set as half a wavelength. Table 1 shows the other simulation parameters.
### Contribution of the Paper  
The major contributions of this paper are defined with respect to the key objectives of improving network lifetime and reducing delay in a 3D-UWSN with the assistance of AUVs. The contributions are the following:  
* Clustering and optimal CH selection with multi-objective spotted hyena optimization (MO-SHO), which formulates fitness using a sensor's lifetime, degree and centrality. The elected CH takes responsibility for allotting sleep slots and redundancy eliminations on the sensor nodes by taking into consideration some parameters, such as sensor's energy and hop counts, to make an efficient decision on whether the sensor status is to be executed while sleep, awake or idle, and the Hassanat distance metric is used for similarity data measurement for redundancy elimination;
* The data gathering AUVs predict their position dynamically by executing di-factor actor-critic path (DACP) prediction. The mid-point-oriented central position is determined to collect data from four CHs. The ability of the CH is identified by evaluating buffer, data collection delay, received signal strength indicator (RSSI) and data size in DACP prediction;
* A three-step (TS), inter-cluster routing method is executed only when the waiting time for the AUV is exceeded. The TS method firstly discovers the route, secondly, filters long routes and, lastly, validates the route. The fuzzy-LeNet method for route validation is used for estimating energy consumption, fairness, synthesis speed and efficiency;
* Emergency event data are transmitted immediately without any delay by means of selecting a forwarder with heavier weights estimated by distance, load and residual energy.
## 4 Privacy-Preserving Smart Metering with Authentication Capability
### Scheme Construction
#### 4.2.4 Verification  
When the concentrator receives \(L_{n}\) and \(\sigma\) from \(s_{n}\), the aggregate signature \(\sigma\) has the form  
\[\sigma=(g^{xr}\cdot\prod_{i}H_{2}(ID_{i})^{\alpha_{2}}\cdot H_{1}(ID_{i})^{ \alpha_{1}\cdot H_{3}(ID_{i}||c_{i})},g^{r},g^{x}),\]  
where \(r=\sum_{i}r_{i}\) and \(x=\sum_{i}x_{i}\). The concentrator then parses \(\sigma\) as \((\sigma_{1},\sigma_{2},\sigma_{3})\), and runs the \(Vf\) algorithm to check if  
\[e(\sigma_{1},g) \stackrel{{?}}{{=}} e(\sigma_{2},\sigma_{3})\cdot e(\prod_{i=1}^{n}H_{2}(ID_{i}),g^{ \alpha_{2}})\cdot\] \[e(\prod_{i=1}^{n}H_{1}(ID_{i})^{H_{3}(ID_{i}||c_{i})},g^{\alpha_ {1}}).\]  
If so, the algorithm returns 1; otherwise, it returns 0.