esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Further and Newest Advances in RMS Applications  
Indeed, RMS is a rapidly emerging technique with medical applications in the early diagnosis of various types of pathologies, providing a unique \"fingerprint\" that enables the identification of characteristic chemical bonds [101].  
To date, its use in dentistry has been focused on in vitro evaluations, but new innovative applications are being developed to exploit RMS in vivo. The most important difficulty in using Raman in vivo is the dimension of the instrument and the cost; however, Raman spectrometers have recently been miniaturized to pocket portable devices and can be converted to remote control by optical fibers. Hence, the availability of solid correlations with well-established spectroscopic techniques makes the Raman approach a quantitative tool for dental diagnostics that fits the preventive management of oral care required in modern dentistry [86]. The early identification of individuals with overall low mineralization of the enamel may be a valuable screening tool for determining a group with a much higher than average risk of caries, allowing intervention before the progression of decay. Indeed, optical probes could be used in time-lapse on the same patient, measuring the analyzed parameters, such as M/M, C, and C/P, with a single measurement lasting a second. Such vibrational outputs could be also automatically converted into scores for clinical interpretation in a time-lapse fashion for individual patients [100].  
With the development of advanced intraoral probes for clinical practice, the quality and quantity of bone could be analyzed, helping clinicians in predicting the prognosis and success rate of implant osseointegration. Fine intraoral probes could also be used to study the composition and other critical features of the newly formed bone adjacent to the implant (osseointegrated bone), thus providing information on bone maturation, implant stability, and the bone-implant interface [13].  
Another potential application concerns the staging of osteonecrosis, which poses a challenging clinical situation since it is difficult to identify at an early stage [13, 102]. Indeed, RMS analysis may reveal over-mineralization and ultrastructural modifications of the apatite mineral in bone affected by osteonecrosis [102].  
Thanks to the sensitivity of these new Raman-based fiber optical probe technologies, early interception of both genetic dental anomalies and enamel and dentin lesions can be achieved [9, 38, 39]. Moreover, many studies have demonstrated the use of RMS to distinguish between normal, pre-malignant, and malignant forms of oral mucosa [37, 103, 104].
## 4 Simulation Results and Analysis
### Analysis of Average False Detection Rate
#### 4.3.2 Analysis of the Average False Detection Rate for Partial-Band Interference  
In the case of noise signals under partial-band interference, the number of frequencies exceeding the threshold outside the known interference band is recorded for each trial. The average of these values across all trials is then taken as the number of non-interference frequencies.  
As illustrated in Figure 9, the average false detection rates of the three algorithms also increase with the JNR at first, and then gradually level off, with the values being nearly identical across all the three methods (with similar trends as illustrated in Section 4.3.1).
## 2 Experimental Procedure
### Analysis of Variance (ANOVA)  
ANOVA is a statistical method used to compare the difference between means of groups. The results of this analysis are presented in the form of a table which shows the influence of the control factors in the experimental test. ANOVA table consists of various statistics such as sources (it contains control factors, error, and total of all sources), degree of freedom (\(DF\)) of data, sum of square (\(SS\)) of data, means sum of square (\(MS\)) of date, Fischer's F distribution (F-value), \(p\)-value, and percentage contribution of the controlling factors. The detailed explanation of the items in the ANOVA table is as follows:  
Source: It contains the control factors (between group or treatment), their interactions, error (within the group), and the total of all sources.  
SS: It is the sum of squares of the data. The total sum of squares (\(SS_{T}\)) is the sum of squares of the factors (\(SS_{M}\)) and the sum of squares of the error (\(SS_{E}\)) as:  
\[SS_{E}\;=\;SS_{T}\;-\;SS_{M} \tag{5}\]  
where, \(SS_{T}=\sum\Bigl{(}y_{ij}-\overline{y}\Bigr{)}^{2}\) and \(SS_{M}=\;n\sum\Bigl{(}y_{j}-\;\overline{y}\Bigr{)}^{2}\).  
Figure 5: Block diagram of the system input and output factors.  
DF: It represents the degree of freedom of the data and can be represented similarly to the \(SS\) (Equation (6)). The total degree of freedom (\(DF_{T}\)) can be calculated as \(n\)-1, where \(n\) is the total number of data. The degree of freedom of factors (\(DF_{M}\)) and error (\(DF_{E}\)) are \(k\)-1 and \(n\)-\(k\) respectably.  
\[DF_{E}=\ DF_{T}-DF_{M} \tag{6}\]  
MS: It is the mean sum of the square of the data which can be calculated by dividing the \(SS\) by related DF. Mean sum of square of the factors and error can be calculates as \(MS_{M}=SS_{M}/k-1\), and \(MS_{M}=SS_{M}/n-k\) respectively.  
F-value: It is also called the F-test which is a ratio of two variances and compares the factors. Variances are measures of how much the date is scattered from the mean. It can be calculated by dividing the mean sum of the square of the factor (\(MS_{M}\)) by the error mean square (\(MS_{M}\)) as:  
\[F=\frac{MS_{M}}{MS_{E}} \tag{7}\]  
\(p\)-value: It helps to define the significance of the results in a statistical test to accept or reject the null hypothesis. Depending on the significance, the \(p\)-value expresses between 0 and 1. Smaller value provides a strong indication for rejecting the null hypothesis. Typically this value keeps \(\leq\) 0.05.  
% contribution: It is the ratio of the sum of the squares (\(SS_{M}\)) of the variable to the total sum of the square (\(SS_{T}\)) of all the variables [34]. It shows the influence of the particular variable (factor) in terms of percentage on the response of the product/process as:  
\[\%=\frac{SS_{M}}{SS_{T}} \tag{8}\]
## 3 The Proposed Method and Scenarios
### Overview of the System
#### 3.1.2 Overview of the Phases  
The phases in the overall system include the system initialization and registration phase, initial authentication phase, and handover phase. The registration phase has a function to register all of the entities, including the RSU, TAS, and vehicles. The initial authentication phase and handover phase are presented in Figure 2. In the initial authentication process, vehicles send the authentication request to the TAS, especially to the AS. If verified, the AS sends the ticket-granting ticket to the vehicles as a credential to obtain the Service Ticket from the TCS. With that service ticket, the vehicle is authenticated and can connect to the network through RSU1. While authenticating the vehicle, the TAS will generate the authentication message as the credential to the vehicle to perform the re-authentication in the handover phase. When leaving the RSU1 communication range, the vehicle will send the handover request to RSU2 by sending its authentication message. RSU2 has to check the credentials to match the data from the blockchain. RSU2 sends the authentication message request to the blockchain, and if it matches the vehicle will be re-authenticated. In this handover process, the vehicle does not need to perform the initial authentication phase anymore, which can reduce the authentication delay.
## 5 Results and Discussion  
### Networks Performance  
After the network training and validation, to further test whether the networks can be used by completely unknown author, we test the networks with new authors' signatures that are not present in the training and verification dataset stage (see Table 3). We then used the following metrics to evaluate the performance of our network: training, validation and testing accuracy; false rejection rate (FRR) or type I error (\(\alpha\) error); false acceptance rate (FAR) or type II error (\(\beta\) error), which are defined in Equations (3)-(5) [31]:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+ \text{TN}}\times 100\% \tag{3}\]  
\[\text{FRR}\left(\alpha\right)=\frac{\text{FN}}{\text{TP}+\text{FN}}\times 100\% \tag{4}\]  
\[\text{FAR}\left(\beta\right)=\frac{\text{FP}}{\text{FP}+\text{TN}}\times 100\% \tag{5}\]  
TP is the number of true positive, which indicates the genuine signatures are correctly classified as positive. FP is the number of false positive, which means the forged signatures are misdetected as positive. Likewise, we got false negative (FN) and true negative (TN). As a result, the higher accuracy and the lower error rate (FRR/FAR) can be considered as better performance. In our experiments, different results obtained from different training sample sizes (numbers of forgery authors and signatures for training) and network architecture, which are summarized in Table 4.
# Multi-IRS-Assisted mmWave UAV-BS Network for Coverage Extension
## 3 Architecture and System Model
### Architecture  
Figure 1 presents the overall system architecture this paper aims to develop. The depicted scenario assumes that all BSs in a cell are nonoperational due to a disaster such as an earthquake, hurricane, or lightning, thus necessitating the establishment of temporary communication areas. As discussed in the previous section, UAVs offer a readily deployable solution. However, using UAVs merely as access points for end-users would limit their utility to the communication range of the BS. To overcome this limitation, the role of UAVs as relay stations becomes essential.  
The figure illustrates two types of UAVs: access link UAVs, which provide an access link for end-user UEs, and backhaul link UAVs, which play as relay stations and facilitate backhaul connections between the access UAVs and neighboring undamaged BS. When access and backhaul UAVs operate autonomously, challenges arise, such as managing radio interference, ensuring adequate throughput, and avoiding redundancy of relay stations.  
To address these issues, coordinated operations of multiple UAVs become critical and require meticulous management and control. This is achieved by connecting all UAVs to the BS via a control plane. Small amounts of data, such as where the UEs are now, are also shared by using this control plane.  
Our primary goal is extending the access UAV coverage area. To transmit detailed damage information to victims and rescue workers, access UAVs should be capable of high-capacity communications to all the UEs in the disaster area. mmWave has the potential for high-capacity communication due to its wide bandwidth but suffers from severe propagation attenuation due to blocking. Therefore, in this paper, we consider reflecting and delivering radio waves using the IRS existing in the city. We create a virtual LoS from the access UAV through the IRS to improve the received SNR even for UEs with Non-LoS (NLoS). In particular, we discuss the creation of UE distributions based on real-world data, thus constructing a simulation environment modeled after a real city, determining communication paths using IRS, and optimizing the UAV three-dimensional location. Finally, the effectiveness of the proposed method is verified by numerical analysis.
## 4 Application to Classification Problems  
### Proposed Algorithm for Group-Sparsity Based Classification  
Since \(\ell_{2,1}\) regularizer in GSC is unfair for classes of different sizes (see Appendix A) while \(\ell_{2,0}\)-regularizer is not, our purpose is to use a better approximation of \(\ell_{2,0}\) as the regularizer. Therefore, we apply model (20) to group-sparsity-based classification. Following GSC, we can set \(\mathbf{W}=\mathbf{I}_{n}\) in (20).  
Inspired by WGSC [11] which well designs weights to enforce locality and similarity information of samples, we can also set the weight matrix \(\mathbf{W}\) according to (9). The classification algorithm is summarized in Algorithm 1.  
The \(\ell_{2,1}\)-norm regularized least squares problem in WGSC can be solved by a proximal gradient method [41]. Compared with it, the step 2 in Algorithm 1 for solving (20) requires at each update only one additional computation for \(\text{Prox}_{\gamma\|\cdot\|_{2,1}}\) (see (10) with \(w_{i}=1\)).
## 2 Class-D PA for Polar-Mode Architectures
### Envelope Nonlinearities in Polar-Mode RF PAs
#### 2.1.1 Class-D Complementary Voltage Switching  
The complementary voltage switching (CVS) topology [14; 15], as shown in Figure 3a, requires a low-Q resonator (\(L_{S}\), \(C_{S}\)) at its load network to allow for wideband operation. Consequently, the envelope input (\(v_{E}\)) baseband (independent term) component present at the resonator input (\(v_{R}\)) is defined as follows:  
\[v_{R}(t)=\frac{1}{2}[v_{E}(t)+v_{E}(t)s_{C}(t)] \tag{2}\]  
It cannot be fully suppressed by the low-Q resonator, resulting in some envelope leakage (\(v_{E}\) to \(v_{O}\)) at the output (\(v_{O}\)). This issue is also present in any single-ended PA topology.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 3 Bloch Sphere Quantum Bee Colony Algorithm
### Multi-Robots Path Planning Based on QABC
#### 3.3.2 Conflict Resolution Algorithm  
If the priorities of the robots are the same, then the robot with the shortest expected initial path will avoid the robot with the longest expected initial path for optimal efficiency.  
1. Conflict type; Conflict type I and the collision prediction are shown in Figure 2. Two robots travel in opposite directions on segment AB, and [PERSON]\({}_{2}\) is moving along BA, while [PERSON]\({}_{1}\) is moving along AB. [PERSON]\({}_{1}\) is predicted to collide with [PERSON]\({}_{2}\) at P point.  
In order to avoid collision, [PERSON]\({}_{2}\) passes in accordance with the established path, while S\({}_{1}\) defines the safety range around the collision course of the outer ring as the restricted area of S\({}_{1}\), according to the priority, and [PERSON]\({}_{1}\) waits for [PERSON]\({}_{2}\) to pass. The shadow area in the figure shows the superposition of the circular part with radius \(d\).  
2. Conflict type II  
When the paths of two robots engaged in circumferential collision walking intersect, a collision may occur. Suppose that [PERSON]\({}_{1}\) moves along the line S\({}_{1}\)P, and [PERSON]\({}_{2}\) moves along the line S\({}_{2}\)P; the path intersection is P. If [PERSON]\({}_{1}\) moves along the planned path to point C and [PERSON]\({}_{2}\) arrives at point D with the same speed, their direction of movement is \(d\). When the distance between two robots DC is less than the safe distance, that is \(0\leq d\leq a\), \(0\leq d\leq b\), they are expected to collide. Conflict type I and the collision prediction are shown in Figure 3. The collision prediction diagram is shown in Figure 4.  
Figure 2: (a) Collision type I; (b) collision prevention approach I.  
To avoid a collision, \(\text{S}_{2}\) with low priority actively avoids the robot \(\text{S}_{1}\) and waits at a safe distance, while \(\text{S}_{1}\) maintains its initial speed until it leaves the outer circle of \(\text{S}_{2}\). Finally, \(\text{S}_{2}\) continues along its original path and speed.  
3. Conflict type III  
Taking the position of the parked robot as the center of the circle, the circle corresponding to the radius and safety distance is divided into small areas, and the local route is designed according to the improved algorithm. To avoid collision, the robot path needs to change, as shown in Figure 5.
## 3 Review of Path-Planning Techniques for Data Harvesting
### Online Path-Planning Techniques  
The ever-changing nature of the oceanic environment underscores the need for online path-planning algorithms. Recently, numerous researchers have directed their efforts towards formulating solutions for dynamic path planning. Notably, a study outlined in [100] introduced a dynamic path-planning framework designed to facilitate informed decisions regarding the sequence in which AUVs should visit network nodes. These decisions are informed by real-time network status information. Furthermore, authors of [41] presented a dynamic value-based path-planning strategy. This strategy empowers AUVs to dynamically select data collectors to visit, with the objective of maximizing the value of information (VoI). Addressing the issue of low location accuracy associated with sensor nodes, the work in [99] proposed a grouping-based dynamic trajectory planning (GDTP) approach. This method detects sensor nodes' presence and orientations, subsequently grouping them based on a proposed common communication area model. Building on this foundation, authors of [98] extended the GDTP [99] concept to encompass tracking sensors mounted on turtles. In this adaptation, the AUV's cruising direction is dynamically determined to achieve the maximum expected payoff, considering both data collection and energy consumption despite inaccurate detection. In summary, when contrasted with TSP-variant models, dynamic methods offer significant enhancements in mission feasibility through their adaptive response to diverse scenarios. Nevertheless, the intricacies in dynamic planning pose challenges for researchers, including how to address issues such as Doppler frequency, multipath effects, latency, and the rate of data transmissions.
# Evaluating GNSS Receiver Resilience: A Study on Simulation Environment Repeatability
## Appendix A
### Visualization of Results  
The processed data are visualized by plotting the mean location deviation (\(\mu_{t}\)) as a function of time. To represent the temporal variability, a shaded region extending one standard deviation above and below the mean (\(\mu_{t}\pm\sigma_{t}\)) is overlaid on the plot. This graphical representation allows for a clear assessment of the average impact of jamming on location accuracy and the consistency of this impact over time. Furthermore, vertical lines are introduced at specific time points to indicate the start and stop times of the jamming signal, facilitating the visual correlation between the jamming events and the observed changes in location deviation statistics.  
This mathematical framework provides a rigorous approach to analyze the effects of jamming on GNSS location accuracy by quantifying the central tendency and dispersion of the location deviation over time, while effectively handling missing data points inherent in such challenging measurement scenarios.
## 4 Materials and Methods
### Aroma Analysis  
Aroma profiles of all samples were determined on Agilent 7890B gas chromatograph equipped with Agilent 5977A mass spectrometer (Agilent Technologies, Santa Clara, CA, \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**pH Range**} & **Operating** & **Maximum** & **Salt Rejection** \\  & & **Temperature (\({}^{\circ}\)C)** & **Pressure (MPa)** & **(\%)** \\ \hline RO98 pHt & 2–11 & 5–60 & 5.5 & \(\geq\)98 \({}^{1}\) \\ NF & 3–10 & 5–50 & 5.5 & \(\geq\)99 \({}^{2}\) \\ \hline \hline \end{tabular} \({}^{1}\)Measured on 2000 ppm NaCl, 1.6 MPa, 25 \({}^{\circ}\)C. \({}^{2}\)Measured on 2000 ppm MgSO\({}_{4}\), 0.9 MPa, 25 \({}^{\circ}\)C.  
\end{table} 
Table 6: RO and NF membrane characteristics. USA). Solid-phase microextraction (SPME) was used as an extraction method. In 10 mL glass headspace vial 5 mL of sample was mixed with 1 g of NaCl. For quantification of aroma compounds 5 \(\upmu\)L of internal standard (myrtenol in concentration of 1 mg/L) was injected in each sample. Prepared vials with samples were heated on magnetic stirrer at 40 \({}^{\circ}\)C at 300 rpm. The adsorption time on the SPME fibre coated with polydimethylsiloxane/divinylbenzene (PDMS/DVB) sorbent (65 \(\upmu\)m, Supelco, Bellefonte, PA, USA) was 45 min. Further, the SPME fibre was transferred into GC injector port where the volatiles were desorbed at 250 \({}^{\circ}\)C for 7 min, splitless mode. In the GC oven, HP-5 MS (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m) column was installed and following method was applied: oven was heated from 40 \({}^{\circ}\)C (held for 10 min) to 120 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, then to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, using helium (He) 5.0 (purity 99.999%) as carrier gas (1 mL/min). The temperatures of MS Source and MS Quad were set at 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. Mass range (\(m/z\)) was from 40 to 400 and the ionization energy 70 eV. Obtained peaks and mass spectra were compared with NIST (National Institute of Standards and Technology, Gaithersburg, MD, USA) and Wiley mass spectral database. For each compound, linear retention index was calculated [48], using a C7-C30 saturated alkanes standards analysed under same GC/MS conditions. Samples were analysed in triplicates and the results were expressed as average value. The limit of detection (LOD), limit of quantitation (LOQ) and mass to charge ratio (\(m/z\)) for monitored compounds are presented in Table 7. Single compound quantitation was obtained by single ion monitoring (SIM) mode. The LOD and LOQ represent the lowest amount of an analyte in a sample that can be detected or quantified, respectively [49; 50]. They have been estimated using the instrumental signal to noise ratio (SNR) of 3:1 for LOD and 10:1 for LOQ. Signal to noise ratio was calculated by Agilent's MassHunter software.
## 4 Model Validation and Discussion
### Model Parameters and Validation
According to Figure 6, the slope and intensity of the initial viscoelastic phase increased with the increase in matrix suction, indicating significant effects of the moistening field on the mechanical properties of unsaturated soil. The hardening capacity and strength of unsaturated soil increased with the increase in confining pressure, indicating that the change in the stress field has a significant effect on the mechanical properties of unsaturated soil. Therefore, the model proposed in this study performed well in describing the triaxial shearing strain-hardening test data of unsaturated soil. For the proposed model, mechanical properties of unsaturated soil under 100-300 kPa confining pressure are verified in this paper. For the mechanical properties of unsaturated soil under other confining pressures, more test data are needed for further verification.