esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Color
Figure 3: Average values of the L* parameter of instant noodles with the addition of rice protein, hemp protein and TGase (CS—control sample, RP—rice protein, RP+1%TG—rice protein with 1% TGase, RP+2%TG—rice protein with 2% TGase, HP—hemp protein, HP+1%TG—hemp protein with 1% TGase, HP+2%TG—hemp protein with 2% TGase). Columns correspond to various additions of rice protein. Mean values denoted by different letters (**a–f**) represent statistically significant differences between variables (_p_ < 0.05). The addition of hemp protein and hemp protein and TGase had an impact on the value of the a* parameter of instant noodles. The lowest value of this parameter was achieved by the control sample (\(-1.38\)). For the other samples with additives, positive values of a* from 0.67 to 3.74 were observed. The applied additives caused the saturation to change from green for the control sample to red for all samples with additives. Similar observations i were noted by [PERSON] and [PERSON] n their research [58]. The b* parameter values of the tested instant noodles with the addition of rice protein ranged from 21.87-noodles with 5% rice protein-to 23.68-control sample (Figure 5). Analysis of the value of this color parameter showed that all tested instant noodles were saturated with yellow (positive values). Figure 4: Average values of the a* parameter of instant noodles with the addition of rice protein, hemp protein and TGase (CS—control sample, RP—rice protein, RP+1%TG—rice protein with 1% TGase, RP+2%TG—rice protein with 2% TGase, HP—hemp protein, HP+1%TG—hemp protein with 1% TGase, HP+2%TG—hemp protein with 2% TGase). Columns correspond to various additions of rice protein. Mean values denoted by different letters (**a**–**g**) represent statistically significant differences between variables (\(p<0.05\)). Figure 5: Average values of the b* parameter of instant noodles with the addition of rice protein,  
hemp protein and TGase (CS--control sample, RP--rice protein, RP+1%TG--rice protein with 1% TGase, RP+2%TG--rice protein with 2% TGase, HP--hemp protein, HP+1%TG--hemp protein with 1% TGase, HP+2%TG--hemp protein with 2% TGase). Columns correspond to various additions of rice protein. Mean values denoted by different letters (**a-c**) represent statistically significant differences between variables (_p_ < 0.05). Based on the results of the b* parameter, it was found that the addition of hemp protein and TGase influenced the value of this color parameter. All instant noodles with additives had a lower value than the control (23.68). The instant noodles obtained positive values from 16.04 (sample with 4% hemp protein addition with 2% TGase) to 20.47 (sample with 2% hemp protein addition). These were saturated with yellow. [PERSON] and [PERSON] [39] and [PERSON] and [PERSON] [59] also obtained instant noodles with a yellow saturation.
## 5 Discussion
### Performance Analysis of the Proposed Method
#### 5.1.2 Computational Complexity Analysis  
One complex multiplication was considered equivalent to six floating-point operations (FLOPs), and one complex addition was two FLOPs. The interpolation kernel length is \(N_{in}\), and one interpolation operation requires \(2(2N_{in}-1)\) FLOPs. Based on the processing flow in Section 3, the computational complexity of the proposed two-step azimuth phase compensation-based BP method could be calculated.  
\[I_{proposed}=N_{x}N_{y}N_{a}\bigg{(}4N_{in}-\frac{2}{N_{a}}+8\bigg{)} \tag{17}\]  
where \(N_{a}\) represents the equivalent number of pulses in the azimuthal dimension. The directions of \(x\) and \(y\) are consistent with the definitions in Figure 7. \(N_{x}\) and \(N_{y}\) represent the number of ground grid points in the corresponding directions. It can be observed that the computational load of this algorithm is mainly influenced by the size of the ground scene grid. Additionally, the above computational load is calculated starting from the signal in Equation (10), excluding the computational load of navigation signal preprocessing and range pulse compression.  
In the data processing of this work, the algorithm's parameters are set in Table 2.  
\(dx\) and \(dy\) represent the grid resolutions in the corresponding directions, both set to 5 m. Given the imaging scene of 1500 m by 800 m, the values for \(N_{x}\) and \(N_{y}\) could be determined. The calculated computational load for performing imaging operation was approximately 1.04 TFLOPs. The program was executed in MATLAB R2022b on a computer equipped with an Intel Core i7-10750H @ 2.60 GHz processor and 64 GB RAM. The proposed method's execution time was about 6241 s. It is worth noting that when combining the ionospheric scintillation error compensation (whether the proposed two-step processing method or the traditional PGA-based method) with the BP algorithm, the time consumed by BP imaging far exceeds that of the former. In other words, the computational load of the former can be almost ignored. Therefore, the computational complexity focuses solely on the deployed method in this paper, without comparing it to other possible algorithms.
## 3 Materials and Methods
### Analysis Based on Real Datasets  
#### 3.4.1 New Impression Rate  
In Section 3.2, we focused on executing a theoretical analysis of the impact of \(FC\). For this analysis, we used fixed values for the probabilities of getting new impressions. This section will study the probability of sending a new impression to a user based on the real data. For this purpose, we will use data from the five different companies.  
For studying the probability of getting a new impression, we use the number of previous impressions that the user has received. Figure 3 shows for each of the five clients the portion of users that receive the next impression based on the number of previous impressions received. The impression ratio grows at a very high pace for few impressions and starts to move slowly towards an asymptotic value between 0.9 and 1.0 after 10 impressions.  
It is important to explain why the ratio is low for a small number of previous impressions. This happens because for a low number of impressions, the major part of the users is still available, and there exists a greater chance to lose track of a large number of users as well, so no further impressions can be delivered.  
The red line in Figure 3 shows that the function \(\dfrac{x}{x+1}\) is a good approximation to measure the ratio. Therefore, we will use this function for numerical approximation calculations later in the article. Finally, note that although the shape of the distribution of the ratio of users per number of impressions is similar in all the 5 companies, the actual value of the ratio changes significantly among them.  
Figure 3: In the figure, we can see data for the five clients. There are points for the Impression Rates based on the number of previous impressions (for states with 100 users or more). On top of the IR is to draw the function \(\dfrac{x}{x+1}\) in red. With 2 dotted lines one over and other below with a 5% difference.  
Figure 2: Graphs showing all existing states and its transitions for each of companies that we have.
## 4 Discussion
### Comparison of Different Models
With a smaller dataset and faster computation, the GBDT model could generate a prediction algorithm, allowing better generalization and thus making it more appropriate for estimating the temporal variation of the Chl-a concentration than other models. Figure 10: Performance evaluation of Chl-a retrievals using B4 model (**a**), B1 + B4 model (**b**), B2 + B4 model (**c**), B3 + B4 model (**d**), FAI model (**e**), SVM model (**f**), GBDT model (**g**) and neural network model (**h**). The concentration of Chl-a in the coastal sea surface of the Beibu Bay of Guangxi obtained by remote sensing inversion ranged from 0 to 35 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{L}\), similar to the field survey results of [PERSON] and [PERSON] et al. (2016), indicating that the retrieval effect of the selected remote sensing inversion model for the Chl-a concentration was close to the field data, and the remote sensing inversion results had high reference significance.
# Evaluation of 5G Positioning Based on Uplink SRS and Downlink PRS Under LOS and NLOS Environments
## 1 Introduction
Despite these efforts, comprehensive evaluations of positioning performance using UL-SRS and DL-PRS remain to be explored. A review of the existing literature reveals that the majority of prior research has focused on either downlink PRS or simulated uplink conditions. However, a unified performance comparison is lacking. To address this gap, our work presents a detailed evaluation and comparison of UL-SRS and DL-PRS positioning accuracy under LOS and NLOS conditions, highlighting the practical implications of using both signals for enhanced 5G localization. The main contributions of this paper are as follows  
* Assess the performance of UL-SRS and DL-PRS signals for 5G UE positioning, particularly in urban environments under LOS conditions. * Examine the behavior of UL-SRS and DL-PRS signals in NLOS scenarios by identifying key challenges and quantifying their impact on positioning accuracy. * Analyze and compare the effectiveness of UL-SRS and DL-PRS to establish a reliable performance benchmark for 5G positioning. This research contributes to the field by expanding the understanding of their functionalities, providing a novel perspective on 5G UE localization. The findings of this study indicate that there is considerable potential for the substantial improvement of positioning accuracy and robustness in future 5G networks. The structure of this paper is as follows: In Section 2, the methodology is presented in detail, with comprehensive discussions on various aspects of the proposed approach. Specifically, Sections 2.1 and 2.2 outline the 5G signal structure and configuration, thereby defining the key parameters for UL-SRS and DL-PRS transmission. Section 2.3 focuses on the design of NR SRS, explaining its structure, resource allocation, and signal properties tailored for precise uplink positioning. In Section 2.4, the design of the NR PRS is discussed in detail, including its role in downlink-based positioning. In Section 2.5, the TOA measurement is described. Finally, Section 4 presents the simulation results, providing an analysis of the proposed methodology.
# On the Energy Performance of Iridium Satellite IoT Technology
## 5 Evaluation
### Device Lifetime  
We next evaluate the theoretical lifetime of an Iridium device, based on (7), and considering a battery capacity of 2400 mAh, as a function of \(T\) and \(p\).  
As shown in Figure 9, lifetime increases asymptotically with \(T\). The maximum lifetime was 3.39 years. Remarkably, a lifetime of one year could only be achieved for values of \(T\) greater than 100 min. As \(T\) decreased below 100 min, device lifetime decreased abruptly. For example, the device lifetime for \(T\) = 10 min was only 43.8 days.  
As expected, device lifetime also decreases with \(p\) due to the greater current consumption caused by retries. The impact of a non-zero \(p\) on asymptotic device lifetime, for practical link qualities, is negligible.
###### Abstract  
The quadratic unconstrained binary optimization (QUBO) problem is categorized as an NP-hard combinatorial optimization problem. The variable neighborhood search (VNS) algorithm is one of the leading algorithms used to solve QUBO problems. As neighborhood structure change is the central concept in the VNS algorithm, the design of the neighborhood structure is crucial. This paper presents a modified VNS algorithm called \"B-VNS\", which can be used to solve QUBO problems. A binomial trial was used to construct the neighborhood structure, and this was used with the aim of reducing computation time. The B-VNS and VNS algorithms were tested on standard QUBO problems from [PERSON] and [PERSON], on standard max-cut problems from [PERSON], and on those proposed by [PERSON], [PERSON], and [PERSON]. Finally, Mann-Whitney tests were conducted using \(\alpha=0.05\), to statistically compare the performance of the two algorithms. It was shown that the B-VNS and VNS algorithms are able to provide good solutions, but the B-VNS algorithm runs substantially faster. Furthermore, the B-VNS algorithm performed the best in all of the max-cut problems, regardless of problem size, and it performed the best in QUBO problems, with sizes less than 500. The results suggest that the use of binomial distribution, to construct the neighborhood structure, has the potential for further development.  
QUBO; max-cut; VNS; neighborhood; binomial Article
## 2 Materials and Methods
### Measurements  
Measurement on the artificial head (mouth) as well as recordings of speaking people (that were reading the text) were performed by a Behringer ECM 8000 microphone at a distance of 1 m between the receiver (microphone) and sound source (i.e., artificial mouth/or person). The position of the microphone in the room was chosen according to the requirements of standard ISO 9921:2003 [26]. During the recordings, participants were asked to not move their heads, to ensure a constant distance from microphone.  
In addition to the microphone-based recordings, parallel skin vibrations in the suprasternal notch were measured by a miniature IEPE accelerometer (MMF, KS95C.10), charged by an ICP sensor signal conditioner (PCB, MODEL 480C02). These data served as a reference signal for the possible normalization of variations in speech intensity [27].  
In the experiments using the artificial head, pink noise was played through the loudspeaker charged by a laboratory amplifier (Bruel and Kjaer, Type 2706) in the mouth opening, simulating an artificial mouth. All the devices were connected to the PC via DAQ (Behringer, U-PHORIA UMC404 HD).  
In the experiments with people, two kinds of texts were inherited from logopedic exercises for Slovak language pronunciation learning. The first one was focused on training the pronunciation of sibilants, while the second one did not contain any sibilants. The idea behind this was to investigate the influence of the presence of sibilants on the speech produced by a talking person, and the difference between the effect of mask wearing on the speech in these two scenarios. The length of both texts was very similar, and the typical  
Figure 1: Photo of the laboratory (**left**) and artificial head with loudspeaker placed in the mouth opening (**right**).  
time necessary to read the text was about 30 s. In order to reduce the effects of variations in sound pressure level during the reading, variations in speed of reading and other random artefacts, each person was asked to read the texts 3 times. Next, the average result per person was considered.
## 4 Conclusions  
The purpose of this work is to provide an accurate evaluation method for virtual image inpainting, specifically by assessing different activation functions in deep learning models used for restoring Greek temples. Using GANs, we focus on reconstructing missing architectural elements in images of temples with varying degrees of ruin. To ensure robust evaluation, we employed both objective and subjective methods. Objective evaluation used mathematical metrics on both whole images and reconstructed parts, while subjective evaluation involved an architect's expertise to validate these results.  
Our findings indicate that the ReLU activation function outperforms others. [PERSON] and Leaky ReLU did not perform well for this application, and Swish, although performing poorly in mathematical metrics, was close to ReLU in professional evaluations. This discrepancy suggests that current mathematical metrics do not fully capture human visual perception.  
Several limitations were identified, such as overfitting in frontal and rear perspectives due to minimal differences between them and issues with lighting and material contrast affecting depth perception. This is particularly relevant when two columns with different degrees of ruin are viewed from the same perspective but are processed as a single column. Additionally, in some images, diffuse lighting significantly impacts the contrast, resulting in reduced visibility of walls forming a corner. These led to inaccuracies like rounded corners and transparency problems.  
Future work will apply this method to more complex cases, such as Mudejar churches, which present greater architectural diversity. The composition and significant heterogeneity among the various architectural elements in these churches add substantial complexity to the current problem of virtual restoration. Additionally, we aim to propose new evaluation metrics that better capture both the accuracy of architectural reconstruction and image quality, minimizing artifacts. Ultimately, we plan to validate our approach with real images, advancing toward reliable tools for cultural heritage restoration.  
Conceptualization, A.M.M., A.N. and G.F.; Formal analysis, A.M.M., A.N., G.I.S. and C.P.-C.; Funding acquisition, A.J.G.-T.; Investigation, A.M.M. and A.N.; Methodology, A.M.M., A.N. and A.J.G.-T.; Project administration, E.D.-M. and A.J.G.-T.; Software, G.F.; Supervision, A.J.G.-T.; Validation, E.D.-M., G.I.S. and C.P.-C.; Writing--original draft, A.N.; Writing--review and editing, A.M.M., E.D.-M., G.I.S., C.P.-C. and A.J.G.-T. All authors have read and agreed to the published version of the manuscript. This work was supported by Project PID2021-126633 NA-I00 supported by MICIU/AEI/10.1-3039/501100011033 and FEDER, UE.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 4 Discussion
### Measurements  
The experiments were performed with a Nd:YAG laser with a wavelength of 1064 nm (Dualis VP, Fotona d.o.o., Ljubljana, Slovenia). The laser beam, with a diameter of 6 mm, was directed at an angle of about 35\({}^{\circ}\) to the surface of the specimen. The pulse had a top-hat shape, while fluences of 20 and 40 J/cm\({}^{2}\), both in combination with a duration of 30 ms, were used. The irradiated area was partially fixed by an aperture made of transparent plexiglass; temperature measurements on the surface were taken at approximately 1 s before the pulse, during the pulse, and approximately 4 s after irradiation using a high-speed IR (infrared) camera (SC7500 FLIR Systems, Boston, MA, USA) with a 50 mm lens and a nominal temperature sensitivity NEDT of 20 mK. Radiometric images were captured at a frequency of 1000 Hz, and a different spot on the skin was irradiated each time.  
A male subject, aged 25, was enrolled in the study with Fitzpatrick II skin type and dark hair. The study was conducted according to the guidelines of the Declaration of Helsinki and was approved by the Medical Ethics Committee of the Republic of Slovenia(protocol number 0120-365/2017-9); the subject signed an informed consent form. Temperature rises on the skin surface were recorded in different areas of freshly shaved forearm skin, hand-cleaned with cotton wool and ethanol, and finally air-dried. This anatomical location was chosen because of its easy accessibility for the experimental setup and ease of handling. Experiments were also undertaken using a PlatSil SiliGlass type C tissue-mimicking phantom with known optical and thermal properties [49]. All personnel present during the laboratory experiment wore appropriate eye protection.
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
## 2 Project-Oriented Numerical Algorithm for Impedance of Pile-Group Foundation
### Calculation of the Impedance Functions  
The motion equation of the cap-pile group-soil system under harmonic load can be expressed as:  
\[\mathbf{m}\ddot{\mathbf{u}}(\text{t})+\dot{\mathbf{c}}\dot{\mathbf{u}}(\text{t})+\mathbf{k}\mathbf{u} (\text{t})=\mathbf{p}\cdot\exp(\text{i}\,\omega\text{t}) \tag{8}\]  
where \(\mathbf{m}\), \(\mathbf{c}\), and \(\mathbf{k}\) are the mass matrix, damping matrix, and stiffness matrix, respectively; \(\mathbf{u}\) is the displacement vector; and \(\mathbf{p}\) is the force vector whose frequency is \(\omega\).  
Assuming that:  
\[\mathbf{u}(\text{t})=\overline{u}\cdot\exp(\text{i}\,\omega\text{t}) \tag{9}\]  
by substituting Equation (9) into Equation (8), it can be obtained that:  
\[\mathbf{k}_{\text{c}}\overline{\mathbf{u}}=\mathbf{p} \tag{10}\]  
where:  
\[\mathbf{k}_{\text{c}}=k-m\omega^{2}+\text{i}\,\omega\mathbf{c} \tag{11}\]  
Figure 2: Three-dimensional sketch of the adopted artificial boundary.  
Then, \(\overline{u}\) can be obtained through the decomposition of complex matrix \(k_{\mathrm{c}}\).  
By applying the unit harmonic loads with different frequencies at the center of the cap top in the vertical, transverse, longitudinal, transverse rotation, and longitudinal rotation directions, the dynamic flexibility matrix of the pile foundation can be obtained by calculating the corresponding displacement, which can be expressed as:  
\[F(\omega)=\left(\begin{array}{cccc}f_{Z,Z}(\omega)&0&0&0\\ 0&f_{Y,Y}(\omega)&f_{Y,\mathrm{RX}}(\omega)&0&0\\ 0&f_{Y,\mathrm{RX}}(\omega)&f_{\mathrm{RX,RX}}(\omega)&0&0\\ 0&0&0&f_{\mathrm{X,X}}(\omega)&f_{\mathrm{X,RY}}(\omega)\\ 0&0&0&f_{\mathrm{RY,X}}(\omega)&f_{\mathrm{RY,RY}}(\omega)\end{array}\right) \tag{12}\]  
where the subscripts Z, Y, X, RX, and RY represent the vertical, transverse, longitudinal, longitudinal rotation, and transverse rotation directions, respectively. Finally, the impedance functions of the pile-group foundation can be obtained in virtue of the inverse properties of the dynamic flexibility matrix and dynamic stiffness matrix, as follows:  
\[S(\omega)=F(\omega)^{-1} \tag{13}\]