esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Study Selection, Data Extraction, and Thematic Analysis  
A total of 83 studies were initially identified. After the removal of 10 duplicate records, 73 studies remained for initial screening. These were assessed based on predefined inclusion and exclusion criteria, resulting in the exclusion of 23 studies due to reasons such as lack of methodological transparency, irrelevance to healthcare soundscapes, non-peer-reviewed format, language restrictions, or lack of full-text availability. Following this, 50 studies met the criteria and were retained for further analysis. To enrich the dataset and ensure comprehensive thematic coverage, 10 additional articles were sourced through Google Scholar using the same Boolean keyword combinations applied in the primary database search. The selection of these articles was based on their relevance to dental or healthcare acoustic environments, full-text availability, and compliance with the predefined inclusion criteria. These additions specifically addressed gaps in areas like biophilic architecture, patient-centered sound design, and perception of acoustic environments in clinical and dental settings. In total, 60 studies were included in the final synthesis and subjected to thematic content analysis based on metadata such as authorship, publication year, study type, methodology, outcomes, and relevance to acoustic optimization in healthcare. In Figure 1, the PRISMA flow chart of the study is presented. The review adhered to the PRISMA 2020 guidelines, ensuring methodological strength, transparency, and comprehensive reporting ([[https://www.prisma-statement.org/prisma-2020-flow-diagram](https://www.prisma-statement.org/prisma-2020-flow-diagram)]([https://www.prisma-statement.org/prisma-2020-flow-diagram](https://www.prisma-statement.org/prisma-2020-flow-diagram)), accessed on 16 April 2025) [23]. All included literature was subjected to thematic content analysis. Key data points, authorship, year, study type, methodology, results, and relevance to acoustic optimization were extracted and tabulated. Table 1 was used to classify findings and map the diversity of approaches across disciplines. This triangulation ensured comprehensive coverage of clinical, psychological, architectural, and sustainability-related dimensions of healthcare and dental soundscapes. Figure 1: The PRISMA flow chart of the study.
## 6 Simulation Results and Evaluation of Proposed Methods
### Simulation Results of AFCT Method  
Figure 15 shows the simulation results when we used the information of four different sensing motes in the required condition mentioned in our AFCT method in Section 5.1. We used a simple formal majority voter like the majority voter in Section 3.3. The maximum error was in the order \(10^{-10}\), implying that in 200 times of target tracking in 400 seconds of simulation run, no target tracking error was encountered. This method uses the sensing information of four sensor nodes in the required conditions. For this purpose, sometimes the sensing information of each sensor node was broadcasted in two hops distance to neighboring sensor nodes.  
As Figure 15 shows we have some peak errors in the results. Although the magnitude of this peak errors is in the order of \(10^{-10}\) and ignorable but it shows existence of small outliers. In Section 3.2 we explained our proposed method for solving simultaneous equations. The coefficient matrix of Equation (6) as mentioned earlier is as follows:  
\[m=\begin{bmatrix}2(x_{2}-x_{1})&2(y_{2}-y_{1})\\ 2(x_{3}-x_{1})&2(y_{3}-y_{1})\end{bmatrix} \tag{43}\]  
For solving these simultaneous equations we must compute the inverse of this coefficient matrix. If the determinant of this matrix is zero, then this matrix is singular and is not invertible and the system of simultaneous equations of target tracking does not have any answer. This condition holds when the first row of matrix \(m\) is dependent on the second row, and this condition in turn holds when the three sensing nodes with positions \(p_{1}(x_{1},y_{1})\), \(p_{2}(x_{2},y_{2})\) and \(p_{3}(x_{3},y_{3})\) are located in a straight line. Incomputation of the inverse matrix \(m^{-1}\) we need to use the inverse of the determinant of matrix \(m\). Determinant of matrix \(m\) tends to zero when the positions of three sensor nodes tend to reside on a straight line. Therefore the target tracking error increases when the three sensing nodes are close to a straight line. In our simulations all sensor nodes were spread randomly with uniform distribution in a field. When a target passed from a part of the field containing three sensing nodes whose positions were close to a straight line, then the target tracking error increased. We can control this error propagation by adding another condition that if the positions of three sensing node are close to a straight line more than a threshold value, we discard the target tracking results. We did not apply this elimination in our simulations and that is why some peak errors exist in our reported results. We deliberately left out these peaks in our results in order to show that the magnitude of error propagation of our proposed method in ideal conditions is very low. For the same reason we will have peak errors in the RAF method too.
## 2 Study Area  
The Karapinar region, covering 2940 km\({}^{2}\), is a sub-basin located within the semi-arid Konya Closed Basin (KCB) in the Central Anatolian Plateau, at an elevation of 980-1080 m (Figure 1). Most of the Karapinar region is characterized by an arid climate. There is also a desert area in the southern parts of the region characterized by a low precipitation level, annual mean of 265 mm/yr (based on rain record from 1964-2015), which is the lowest in Turkey. Moreover, the evaporation in the region is generally higher than the precipitation [38]. The Karapinar area has been used for agricultural production for centuries, mostly by dry farming (non-irrigation). However, since the early 2000s, irrigated agrarian activities, mainly for sugar beet and corn, which rely on groundwater extraction, have increased the agricultural productivity of the area [33].  
Figure 1: (**a**) Location of the study area on a Land Use/Land Cover map. Yellow and red circles mark the locations of old and recent sinkholes, respectively. Blue and Red frames show the footprints of the COSMO-SkyMed and Sentinel-l, respectively. Grey frames mark the locations of Figures 5 and 6. (**b**) Yarm oglu Sinkhole. (**c**) A sinkhole formed in the beet field on 23 August 2016. (**d**) Some surface fissures. (**e**) A sinkhole formed in the cornfield in September 2018.  
During the last two decades, changes in agriculture patterns induced various environmental problems, including land subsidence, sinkholes formation, and surface fissures in the region. For example, there were 332 sinkholes in Karapinar and its surroundings by 2020 [37]. These environmental problems arise due to groundwater over-pumping, which is needed for irrigated agriculture [33]. Previous studies estimated that irrigation has been conducted using roughly 92,000 groundwater wells, including 66,000 that have been used illegally, throughout the KCB [35; 39]. We estimated a total of 6000 wells in the Karapinar sub-basin alone.
## 6 Conclusions  
The MVDR beamforming-based underwater acoustic source localization techniques often encounter many unfavorable factors in the ocean environment, such as direction error, random wavefront fluctuation, hydrophone position error, hydrophone gain error, etc. These unfavorable factors lead to signal self-cancellation problems and severe performance degradation of the MVDR beamformer. Therefore, the signal self-cancellation problem is generally considered to be a disadvantage of the MVDR beamformer and is suppressed. On the contrary, by exploiting the signal self-cancellation phenomenon, this paper proposes a high-precision DOA estimation method for the underwater acoustic sources. First, the beam response of the MVDR beamformer in the direction interval is calculated according to the steering vector model. Then, the pseudo spatial power spectrum is calculated using the unit circle mapping technique. Finally, to reduce the computational complexity, the online calculation process of the pseudo spatial power spectrum is given. The computer simulation results show that the SSC-MVDR algorithm can obtain satisfactory direction estimation accuracy under the conditions of low intensity of acoustic source, strong interference and noise, and less array snapshot data. At the same time, computer simulation also gives reasonable suggestions for the width of the direction interval.  
Figure 7: DOA estimation accuracy results obtained by the SSC-MVDR algorithm when the width of the direction interval varies between 1 and 40 degrees, and the deviation of the center of the direction interval from the true DOA is equal to 0, 2, and 4 degrees, respectively.  
**Author Contributions:** Conceptualization, J.W.; Methodology, F.W., Y.C.; Software, F.W.; Writing--original draft, F.W.; Writing--review and editing, F.W.  
**Funding:** This research was funded by National Natural Science Foundation of China (61601209), Natural Science Foundation of Jiangxi Province, P.R. China (20171 BAB202003), and Science and Technology Research Project of Education Department, Jiangxi Province, P. R. China (8140).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conducting the Cyber-Attack Scenario and Evaluating the Results
### Rogue PLC  
The Rogue PLC has been implemented in the open-source program OpenPLC [23]. The software was developed according to the IEC 61131-3 standard [38], which defines the 5 PLC programming languages (Ladder Logic--LD, Structured Text--ST, Instruction List--II, Function Block Diagram--FBD, and Sequential Function Chart--SFC). It is divided into two main parts: the Editor and the Runtime. The Editor is where programs are created. The Runtime can be embedded in low-cost microcontrollers such as the ones of the Arduino family, or in a generic target like a Soft-PLC (Windows or Linux). In addition, there is a web-based platform to define, monitor, and manage the program to be executed and the various PLCs in use. A ladder program was designed in the OpenPLC Editor that enabled manual control of the cyber-attack, via simple circuitry based on the Arduino board ESP8266 NodeMCU v1.0 ESP-12E (button PB1 ON--attack activated; button PB2 ON--attack interrupted). This Arduino board was connected to the Wi-Fi network of the test environment [39]. To link the variables defined in the program to the Modbus memory IO target variables; to manipulate only the desired Holding Registers in it, without messing with the other addressing areas, and to have access to more analog outputs, we followed the OpenPLC addressing conventions and created 3 slaves (one of Device Type ESP8266 and two of  
Figure 12: CD Press CTRL module commented out. Figure 13: CC control variables communications activated. Device Type Generic TCP), as can be seen in Figure 14 and according to the settings shown in Table 5.  
The ladder program logic is as follows. When the attack is triggered (the normally open button PB1 is physically pressed on the arduino board), the consecutive Holding Registers corresponding to CC_PumpSpeedCmd (Hccpspeedcmd at 400,225 and Lecpspeedcmd at 400,226) are written with the values 17,046 and 0000 (FLOAT 7.5E+01, or 75) on the ModRSsim2 Server. When the attack is stopped, the CC_PumpSpeedCmd registers are written with the values 17096 and 0000 (FLOAT 1.0E+02, or 100). The registers related to CC_PumpOnOffCmd (Hccponoffcmd at 400,285 and Lecponoffcmd at 400,286) are kept at 16,256 and 0000 (FLOAT 1.0E+00, or 1) throughout the operation. The MOVE instruction was used to transfer the content of the operand at input IN to the operand at output OUT when the Input EN is ON. The local variables used are shown in Table 6 and the implemented ladder program in OpenPLC Editor is shown in Figure 15.  
Figure 14: ESP8266 NodeMCU v1.0 OpenPLC address mapping.
## 4 Conclusions  
This study was based on the principle of deformation-induced martensitic transformation in austenitic stainless steel, and a nano-crystalline layer dominated by the martensitic phase was constructed on the surface of 304 stainless steel. The diphasic control of the nanocrystalline layer was carried out by heat treatment (500 and 550 \({}^{\circ}\)C), and the anti-cavitation erosion performance of the nanocrystalline layer before and after heat treatment was studied. The following conclusions can be drawn:  
1. The Vickers hardness of the nanocrystalline layer is as high as 595 \(\pm\) 18 HV\({}_{0}\)-1, and after heat treatment at 500 and 550 \({}^{\circ}\)C, the Vickers hardness decreases to 521 \(\pm\) 15 HV\({}_{0}\)-1 and 433 \(\pm\) 10 HV\({}_{0}\)-1, respectively. This may be due to the reverse transformation of austenite.
2. The cumulative mass loss of the 304-Nano, N-500 \({}^{\circ}\)C, and N-550 \({}^{\circ}\)C samples in deionized water are 1/7, 1/5, and 1/3 of that of the 304 sample, respectively. This may be related to its ultra-high density grain boundaries of the nanocrystalline layer.
3. The reduction in hardness is the main reason for the decrease in the cavitation erosion resistance of the nanocrystalline layers after heat treatment.  
Methodology, N.H.; Investigation, N.H.; Writing-original draft, N.H., Y.T.; Writing-review & editing, Y.T., R.Y., X.C.; Conceptualization, X.C.; Supervision, H.L., T.X., X.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Zhejiang Provincial Natural Science Foundation of China (grant #LZ22E090001), and Ningbo 3315 Talents Program (grant #2020A-29-G).  
Not applicable.  
**Data Availability Statement:** All data are available in the main text.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 5 Experimental Results and Discussion  
In this section, we evaluate the performance of JVFRS-CO-RA-DQN in terms of the cost under different network conditions, the delay and analysis accuracy under different minimum frame resolutions, and the convergence performance. We first describe the parameter settings before delving into the simulation results.
## 2 The Principle of the Synthetization Approach
### The Proposed Synthesization Approach for Simplification  
To simplify the conventional topology [28] and decrease the number of extra variable parameters, TLs were used to substitute CLSs in this paper.  
In Figure 3, the \(i\)th CLS is substituted by a single TL for example, where \(Z_{i}\) and \(\theta\) are the characteristic impedance and electrical length of the single TL. \(Z_{evi}=2Z_{i}\) and \(Z_{\alpha li}=0\) must be maintained. Then, the \(ABCD\) matrix of TL can be rewritten as  
\[\left[\begin{array}{cc}A_{i}&B_{i}\\ C_{i}&D_{i}\end{array}\right]=\left[\begin{array}{cc}\cos\theta&j\sin\theta Z _{evi}/2\\ 2j\sin\theta/Z_{evi}&\cos\theta\end{array}\right] \tag{17}\]  
The \(ABCD\) matrix form of the total topology will not be changed. In other words, when any single CLS (\(i\)th CLS) can be substituted by a single TL, Equation (4) can also be derived, and the characteristic impedances in all polynomial functions (\(a_{p}\), \(b_{p}\), \(c_{p}\) and \(d_{p}\)) will be slightly different because of \(Z_{evi}=2Z_{i}\) and \(Z_{\alpha li}=0\). Meanwhile, one variable parameter will be consumed, and the number of variable parameters will decrease to \(N-3\). Furthermore, the consumption of more variable parameters, different positions, and more CLSs can be substituted by TLs, where the maximum number of substituted TLs is no more than \(N-2\).  
Based on the discussion above, the general procedure of the proposed synthetization approach in Figure 4 is summarized as follows: (1) according to the actual requirements, determine the section number \(N\) in the conventional \(\Pi\); (2) determine the number of CLSs needing to be substituted; (3) select the substituting positions of the CLSs; (4) calculate the \(ABCD\) matrix; (5) calculate all the characteristic impedances by the Chebyshev response; (6) if the results cannot meet the design requirements, go back to (2) and re-design the circuit.  
Figure 4: The proposed synthetization approach for \(N\)-section IT.  
Figure 3: The \(i\)th CLS is substituted by a single TL.  
In summary, for \(N\)-section IT (\(N\geq 2\)) in Figure 2, the basic principle of substituting TL for CLS is summarized as follows:  
(I) There are \(2N\) parameters in the \(N\)-section IT [28];  
(II) \(N+2\) design conditions are used to derive the Chebyshev response; thus, \(N-2\) variable parameters can be designated arbitrarily;  
(III) Consuming one variable parameter, a single TL could substitute one CLS to maintain the Chebyshev response. In other words, maximum number of TLs will reach to \(N-2\);  
(IV) After running through all the positions of substituted TLs in every possible combination, the number of the substitutable cases are listed in Table 1, and the total number is:  
\[C_{N}^{1}+C_{N}^{2}+\ldots+C_{N}^{N-2}=2^{N}-N-2 \tag{18}\]
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
## 5 Experiments and Results
### Domain Adaptation  
Within the problem at hand in this work, transfer learning should be considered not only in order to initialize the model considering a different language (English) for which there is a large volume of data, but also considering that there is limited access to data within our target (European Portuguese) and also to a variant of our target (Brazilian Portuguese). Thus, it could be questioned whether the use of instances from a dataset composed mostly of Brazilian Portuguese can be helpful for the transfer learning process between the English-optimized model and the European Portuguese target.  
In order to evaluate this transfer learning domain adaptation, we proceeded with the addition of new combinations of MLS and SpeechDat datasets to add to the mixed datasets present in Table 11. These were used to train models using transfer learning. All combinations of the datasets and the corresponding WER performances can be seen in Table 14. These results correspond to the best performance attained for each (MLS, SpeechDat) mixture and cover 16 different points in the (MLS, SpeechDat) space where the number of MLS instances, \(n\) is in the range \(0\leq n\leq 37,498\) and SpeechDat in the range \(69,660\leq n\leq 107,158\). The results are depicted in Figures 4 and 5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Test Set** & **Instances** \\ \hline SpeechDat & 22,961 \\ ENG & 28,539 \\ MLS & 906 \\ SpeechDat + MLS & 23,867 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Audio instances from each test set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MLS Instances** & **SpeechDat Instances** & **WER** \\ \hline  & & 69,660 & 0.26626 \\
0 & 88,409 & 0.05776 \\  & 107,158 & 0.05032 \\ \hline  & & 79,035 & 0.08266 \\
9374 & 97,784 & 0.05320 \\ \hline  & & 69,660 & 0.07130 \\
18,749 & 88,409 & 0.05602 \\  & 97,784 & 0.05408 \\  & 107,158 & 0.05398 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Best WER performance for all tested combinations of MLS and SpeechDat datasets.
## 2 Materials and Methods
### Physical and Mechanical Characterization of MBCs
#### 2.3.4 Thermal Conductivity  
Steady-state heat transfer properties may be measured by a number of standardized test methods. Taking into consideration the expected low thermal conductivity of MBCs, the EN 12667:2004 [51] method was applied to measure the coefficient of thermal conductivity (\(\lambda\)). The method is based on the so-called guarded hot plate at a temperature difference of 10 \({}^{\circ}\)C. A specimen of size 150 \(\times\) 150 \(\times\) 50 mm was placed between the hot and the cold plates and centered below the temperature gauges metering area of 100 \(\times\) 100 mm. An additional lateral insulation of EPS was provided to complete the measurements space of \(300\times 300\) mm. The coefficient of thermal conductivity \(\lambda\) (W/m\(\cdot\)K) was calculated as follows:  
\[\lambda=\frac{\Phi\cdot d}{A\cdot(T_{1}-T_{2})} \tag{6}\]  
where \(\Phi\) is the average power supplied to the metering section of the heating unit, \(T_{1}\) is the average specimen's hot side temperature (+20 \({}^{\circ}\)C), \(T_{2}\) is the average specimen's cold side temperature (+10 \({}^{\circ}\)C), \(A\) is the metering area (0.0001 m\({}^{2}\)), and \(d\) is the average specimen's thickness (ca. 50 mm).
## 2 Basic Theory  
### KPCA  
As a nonlinear principal component model, KPCA which can get more reasonable result of index reduction compared with PCA, can effectively solve the nonlinear relationship among variables, and is effectively used in multi-index comprehensive analysis [25]. This method can compress the information contained in a great quantity of index variables into a few comprehensive variables that can reflect the original information features. In addition, by analyzing the index of the inclusive variables, it can process the nonlinear relationship between the variables and minimize the loss of the original data information [26]. The basic steps are as follows [27,28].  
We set a combination of random vectors containing \(N\) random variables, thereinto, \(x_{k}\in R^{N}(k=1,2,\ldots,m)\), and m stands for the input sample size. That is: the initial input sample data set is \(M=[a_{1},a_{2},\ldots,a_{n}]^{T}=[b_{1},b_{2},\ldots,b_{m}]\). By projecting to the space through nonlinear mapping, the dataset is projected to: \(M=[\phi(b_{1}),\phi(b_{2}),\ldots,\phi(b_{m})]\), and it satisfies \(\sum\limits_{k=1}^{m}\widetilde{\phi}(b_{i})=0\).  
Get the covariance matrix according to the definition of covariance:  
\[C^{F}=\left(\begin{array}{ccc}C_{11}&\cdots&C_{1n}\\ \vdots&\ddots&\vdots\\ C_{n1}&\cdots&C_{nn}\end{array}\right) \tag{1}\]  
\[C=\frac{1}{m}\sum\limits_{i}^{m}\phi(b_{i})\phi(b_{i})^{T} \tag{2}\]  
Solve:  
\[C^{F}W^{F}=\lambda^{F}W^{F} \tag{3}\]  
Get it's the eigenvalue and the eigenvector, wherein \(\lambda^{F}\) is an eigenvalue; \(W^{F}=\sum\limits_{k=1}^{M}\alpha_{k}\widetilde{\phi}(x_{k})\) is the feature vector which is corresponding to the eigenvalue.  
\[K\big{(}x_{k},x_{j}=\phi(x_{k})\cdot\phi(x_{j})\big{)} \tag{4}\]  
Get \(\widetilde{K}\) by matrix centralization:  
\[\widetilde{K}=K-I_{n}K-KI_{n}+I_{n}KI_{n} \tag{5}\]  
wherein, \(I_{n}\) is \(n\times n\) the matrix, satisfy simultaneously \(I_{i}\), \(j=1/n\), the Formula (3) is simplified to:  
\[m\lambda^{F}M=\widetilde{K}M \tag{6}\]  
After the above calculation, we can use the method of principal component analysis in traditional PCA to calculate the projection of a data point on the eigenvector, and finally the kernel principal component of the point can be obtained.