esa-sceva/satcom-chunk-collection huggingface dataset:
# Possibilities of Evaluating the Effectiveness of Noise Barriers in Slovakia
## 4 Discussion
As a result of measuring the acoustic elements and poles of the barrier, we obtained both values of the sound insulation index in the one-third octave band in the range of 100-5000 Hz for acoustic elements, the pole, and the whole barrier, as well as the single-number rating of the sound insulation of the barrier as well as its individual elements. Based on the result (\(DL_{51,G}=28\) dB), we were able to assign the insulation index category D3 according to the EN 1793-2 standard, which means that the barrier is high quality and has satisfactory airborne sound insulation. It is worth noting that the value of the airborne sound insulation of acoustic elements (panels) has a value of \(DL_{51,E}=35\) dB(A), but the poles only have a value of \(DL_{51,P}=26\) dB(A), which gives the barrier a total value of 28 dB(A). A comparison of ISO 10847 and EN 1793-6 shows that the latter standard can directly evaluate the sound insulation index of the whole barrier structure, which is its advantage. The effectiveness of barriers can also be assessed based on the subjective perception of noise by the population using questionnaire surveys. Recently, a survey of railway noise annoyance in the Kysuce region (Northwest Slovakia) [33] was carried out. The results showed that people perceived noise disturbance, especially at night. Similar surveys are expected to be done in the region for road transport as well. The results of the subjective perception of the noise situation before and after the installation of noise barriers will show the effectiveness of these noise barriers. This type of research can be found in [34], where the subjective perception of noise was compared with the measured values before and after the noise control intervention. Such an assessment of the noise barriers' effectiveness can be considered comprehensive and is planned to be done in Slovakia.
## 5 Dataset Description  
Three publicly accessible databases were utilized in building the proposed method, and the details for each database are as follows:  
1. NLM (National Library of Medicine) dataset [16; 20; 21]: This was made by two publicly accessible datasets including the Montgomery County CXR set (MC) and Shenzhen (CHN) dataset. The MC dataset was compiled in collaboration with Montgomery County, Maryland, the United States' Department of Health and Human Services. The collection includes 138 frontal CXRs from Montgomery County's tuberculosis screening program, where 80 CXRs were normal cases while 58 CXRs had TB manifestations. The X-rays were taken with a Eureka stationary X-ray machine (CR) and provided as 12-bit gray level images in portable network graphics (PNG) format. Moreover, the Digital Imaging and Communications in Medicine (DICOM) format is also available upon request. The X-rays were either 4020 \(\times\) 4892 or 4892 \(\times\) 4020 pixels in size. The Shenzhen dataset was collected in collaboration with Shenzhen No. 3 People's Hospital, Guangdong Medical College, Shenzhen, China. The CXRs were from outpatient clinics and captured as part of the daily hospital routine within a 1-month period, mostly in September 2012, using a Philips DR Digital Diagnost system. The dataset contained 662 frontal CXRs, of which 326 belonged to normal cases while 336 had TB manifestations including pediatric X-rays (AP). The X-rays are provided in PNG format, and can vary in size, but is approximately 3 K \(\times\) 3 K pixels.
2. Belarus dataset [20]: The National Institute of Allergy and Infectious Diseases, Ministry of Health, Republic of Belarus, collected the Belarus Set for a drug resistance study. There are 306 CXRs in the dataset, representing 169 patients. The Kodak Point-of-Care 260 system was used to take chest radiographs with a resolution of 2248 \(\times\) 2248 pixels. All images in this database had been infected with tuberculosis.
3. RSNA dataset [20]: The RSNA pneumonia detection challenge dataset contains approximately 30,000 chest X-ray images, 10,000 of which were normal and the rest were abnormal as well as the lung opacity images. The DICOM format was used for all images. A total of 3094 normal images were taken from this database and the remaining 406 normal images were taken from the NLM database to create a normal database of 3500 chest X-ray images for this study.
## 4 Results and Discussion
### Evaluation of UCS Tests
#### 4.2.4 Effect of Sawdust on Sulphate Resistance for 90 Days Curing  
The effect of SD content on UCS of PC treated specimens after 90 days curing time is shown Figure 9. From the data in Figure 9a, it is apparent that the UCS value of the benchmark specimens was enhanced with increasing curing time. For example, UCS values for 3%, 5% and 7% PC treated specimens increased from 1297 to 1925 kPa, from 1680 to 2240 kPa and from 1906 to 2450 kPa when 3% to 15% SD was added to specimens. \(MgSO_{4}\) exposed specimens exhibited the same increasing trend for \(q_{u}\) values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{3}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-4}  & **3\% PC = 841** & **5\% PC = 1026** & **7\% PC = 1347** \\ \cline{2-4}  & **UCS (kPa)** & **UCS (kPa)** & **UCS (kPa)** \\ \hline
3\% & 962 & 1194 & 1489 \\
5\% & 1090 & 1351 & 1781 \\
10\% & 1200 & 1509 & 1908 \\
15\% & 1407 & 1807 & 2159 \\ \hline \hline \end{tabular}
\end{table}
Table 8: UCS results before sulphate exposure in presence of SD (28 days curing).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**SD Content**} & \multicolumn{3}{c}{**Unconfined Compressive Strength, UCS (kPa)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**3\% PC = 120**} & \multicolumn{2}{c}{**5\% PC = 289**} & \multicolumn{2}{c}{**7\% PC = 409**} \\ \cline{2-5}  & **UCS (kPa)** & **Improvement (\%)** & **UCS (kPa)** & **Improvement (\%)** \\ \hline
3\% & 145 & 20.8 & 649 & 124.56 & 1105 & 170 \\
5\% & 210 & 75 & 871 & 201.38 & 1380 & 237 \\
10\% & 400 & 233 & 1006 & 248.1 & 1560 & 281 \\
15\% & 520 & 333 & 1275 & 341 & 1812 & 343 \\ \hline \hline \end{tabular}
\end{table}
Table 9: UCS results after sulphate exposure in presence of SD (28 days curing).  
Figure 9: Effect of sawdust addition on USC peak values (90 days curing): (**a**) 3% PC; (**b**) 5% PC; (**c**) 7% PC.  
Table 10 presents the UCS values before exposure to sulphate. The results clearly followed the same trend as the previous curing time (i.e., 3, 7, 28 days). The results showed that with increase in SD content, the UCS values increased accordingly. Table 11 also presents the values of UCS and _IMP_% after exposure to sulphate with inclusion of different SD%. The results also support that the _IMP_% increased with increasing in both cement percentage and SD percentage.  
Generally, the samples for 90 days more show the effect of sulphate resistance with higher percentage of cement. Once again, samples of 3% without sawdust easily dissolved, while with increasing sawdust percentage, a better resistance was observed.
## 3 Results and Discussion
### 3.2. Adrenaline Transfer in Complex Matrices  
To achieve our goal of developing a sensor capable of detecting Adr in cerebellum fluid, we extended our experiments to a more complex matrix: a Tris buffer solution made with Tris hydrochloride (Tris HCl), which closely mimics the composition of artificial cerebrospinal fluid. This buffer system is widely employed in neurochemical studies [37; 38; 39] due to its similarity to the ionic environment of biological matrices. In addition to Tris HCl, the buffer contains a mixture of salts, including NaCl, KCl, CaCl\({}_{2}\), NaH\({}_{2}\)PO\({}_{4}\), MgCl\({}_{2}\), and Na\({}_{2}\)SO\({}_{4}\), with the pH carefully adjusted to 7.4 using NaOH. This composition creates an environment that approximates the ionic strength and complexity of natural cerebrospinal fluid, making it an ideal matrix for studying Adr transfer and evaluating the performance of our sensor in physiologically relevant conditions.  
Given our earlier observation of potential shifts in CVs for Adr in Millipore DI water, we hypothesized that similar shifts might occur due to the individual components of the Tris buffer solution as ionic strength increased (Table S1). Such shifts could arise from interactions between Adr and the ions in the matrix, potentially influencing the Adr transfer behavior. To systematically evaluate this, we performed a stepwise analysis where we began with Tris HCl alone and sequentially added each salt to the solution. For each matrix composition, Adr was introduced, and CVs were recorded using both ionophores, DB24C8 and DB18C6, to investigate their behavior under these conditions.  
The stepwise addition of salts allowed us to isolate the contribution of each ionic species to the observed CV response. After each salt addition, the CVs corresponding to Adr transfer were analyzed, and any shifts in E\({}_{1/2}\) values were quantified. Figure 4 illustrates those shifts in CVs upon adding individual salts and Adr to the matrix. We also recorded the pH of each solution as we performed the stepwise addition (Table S1). While shifts in the E\({}_{1/2}\) values were observed for both ionophores after the addition of salts, the magnitude of these shifts was relatively minor compared to the pronounced shifts we observed during earlier experiments with varying pH values. These results suggest that while the ionic components of the Tris buffer can influence Adr transfer, their impact is less significant than that of pH changes.  
Figure 4: Peak shifts observed for Adr transfer in different solution matrices in the presence of DB24C8 (orange) and DB18C6 (yellow) upon stepwise addition of Adr.  
This finding is critical because it validates the use of the Tris buffer for further studies. Despite the minor shifts observed with individual salt additions, the buffer system remains robust and reflective of the physiological environment. Moreover, the Tris buffer's composition ensures stability and reproducibility for calibration studies, making it a suitable medium for investigating the Adr transfer process and optimizing sensor performance.
## 3 Proposed Approach
### Whole-Word Masking (WWM)  
Traditional word masking methods in pre-trained language models (e.g., BERT) frequently cause fragmented or partial word masking, where sub-words are independently  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Initialize From** & **Data and Training Steps** \\ \hline BERT-Base & - & Wikipedia + BookCorpus 1M steps \\ BioBERT-Base Discharge Summary BERT & BERT-Base BERT-Base & Dishcharge Summary Notes 150K steps \\ Bio+Discharge Summary BERT BlueBERT & BioBERT-Base BERT-Base & Discharge Summary Notes 150K steps \\  & PubMed 5M steps + MIMIC 200K steps \\ \hline Discharge Summary CRPT & BERT-Base & Discharge Summary Notes 200K steps \\ Bio+Discharge Summary CRPT & BERT-Base & PubMed 3M steps + Discharge Summary \\  & Notes 200K steps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration of BERT-Based Models [Standard hyper-parameters (steps, epochs, LR, batch size, seq-len) were identical for every model].  
Figure 4: Contrastive Learning of Representations. Positive and negative sentence pairs are mapped into a latent space through BERT and a projection head. Positive pairs (consecutive or semantically aligned sentences) are drawn closer, while negative pairs (non-consecutive or semantically unrelated sentences) are pushed apart, enforcing a margin m.  
masked. Such fragmentation can impair the model's ability to fully capture semantic meaning, particularly in medical texts characterized by multi-token terms (e.g., \"hematuria\"). WWM directly addresses this issue by masking entire words rather than isolated sub-tokens, thereby preserving the linguistic integrity and contextual unity of domain-specific vocabulary.  
This technique was initially introduced in ERNIE and later adopted by BERT, demonstrating improved performance across various NLP tasks. In our Discharge Summary CRPT pre-training, we integrated WWM into the Masked Language Modeling phase, ensuring that multi-token clinical terms remain semantically cohesive. Figure 5 contrasts Random Masking (RM) with WWM, illustrating how RM inadvertently splits tokens like \"#am\" and \"##mon,\" thus obscuring the original word structure (e.g., \"phil #am ##mon\"). WWM, in contrast, masks the entire word at once, preventing vital medical terms from being diluted into multiple sub-tokens.  
By employing WWM, our approach achieves more context-aware representations of complex medical terminology, a critical advantage in tasks where terminological precision (e.g., diagnoses or treatments) directly impacts model performance. Indeed, this cohesive masking strategy proves especially crucial for discharge summaries and clinical notes, where the correct interpretation of multi-token medical entities can significantly influence downstream applications such as patient outcome prediction, information extraction, and clinical decision support.
# Review of Physical Layer Security in Integrated Satellite-Terrestrial Networks
## 5 Future Directions
### Beamforming  
As the environment is complex in an ISTN, a careful combination of analog and digital beamforming, i.e., hybrid beamforming, should be considered to balance complexity and performance. Developing deep learning-based beamforming that can dynamically learn about this complex environment should be explored. Minimizing power allocation with beamforming to maximize the secrecy capacity, energy efficiency, and user fairness should be given consideration. Based on channel conditions, user demands, and security requirements, we should investigate the adaptive schemes for robustness and improved system performance. Robust beamforming schemes should take into account channel estimation errors while minimizing interference.  
Cooperative beamforming can be explored to enhance secrecy performance and mitigate eavesdropping threats. Physical layer jamming could be considered to degrade the eavesdropping channel. This will improve the security of legitimate communication. Information leakage can be minimized by privacy-preserving beamforming techniques. Secure data aggregation methods should be developed for ISTNs. A mechanism to improve trust among network nodes and manage user reputations should be investigated to enhance security and reliability. Furthermore, cross-layer solutions and trade-offs can be explored for improved system performance.
## 4 Conclusions  
In summary, we have introduced a numerical approach to calculate the DOC distribution of a Schell-model type GPCB in any transverse plane perpendicular to the propagation axis during free-space propagation. This method first represents the GPCB as a sequence of random electric fields by introducing white noises into it. In this representation, each realization of random fields obeys the propagation law described by Huygens-Fresnel integral formula. Then, we use a convolution approach to obtain the corresponding realization of random fields in the output plane. At last, the information on DOC is acquired through evaluating the fourth-order correlation function of random fields in the output plane. The prominent advantage of this numerical approach is that it can conveniently calculate the DOC of any partially coherent Schell-mode beam, even when it is impossible to obtain using analytical expressions for the CSD function. Two numerical examples of the evolution of DOCs for two types of GPCBs with propagation distance were investigated in detail. One demonstrated that our approach is valid, by comparing our results with those obtained with the analytical formula. The other example showed the influence of the initial field amplitude and correlation function on the evolution of the DOC. The analytical formula for the CSD function cannot be applied to the beam in the second example. Our method can also deal with the DOC of a GPCB propagation through a paraxial ABCD optical system or atmospheric turbulence. We believe that this numerical approach may find applications in optical communication and optical encryption.  
P.M. (Data curation and Writing--Original Draft); B.K. (Writing--Review); R.K. (Data curation); C.L. (Supervision and investigation); J.Z. (software); X.P. (investigation); C.M. (formal analysis); Y.E.M. (software); Y.Z. (formal analysis and validation); F.W. (Supervision, Writing--Review, Editing and validation); Y.C. (Supervision, Writing--Review, Editing and validation).  
This research was funded by the National Natural Science Foundation of China (NSFC) (91750201, 11874046, 11525418); Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions; Qing Lan Project of Jiangsu Province; Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_2024). China Scholarship Council (CSC) (201706920085).  
The authors declare no conflict of interest.  
Figure 4: Density plots of the modulus of the square of the degree of coherence of a general partially coherent beam at a propagation distance \(z=1000\) m, with the initial coherence width \(\delta_{0}=0.32\) mm in (**a**,**d**), \(\delta_{0}=2.35\) mm in (**b**,**e**), and \(\delta_{0}=4.8\) mm in (**c**,**f**). The \(p\) function used in (**a**–**c**) is the soccer patter shown in Figure 2a, and the \(p\) function used in (**d**–**f**) is the circular function shown in Equation (15).
# Immersive Virtual Reality Experience of Historical Events Using Haptics and Locomotion Simulation
## 1 Introduction
### The Antikythera Mechanism  
The Antikythera Mechanism is an ancient Greek hand-powered orrery, considered to be the first analog computer [28]. Its use was to predict astronomical positions and eclipses of various celestial objects decades in advance. It could also track the four-year cycle of the ancient Olympic games. This artifact was among wreckage retrieved from a shipw off the coast of the Greek island Antikythera in 1901 [29]. The wreck also contained numerous statues, coins, and other artifacts dating back to the fourth century BC. The mechanism components found were merged as one lump, and after inspection and conservation efforts it became known that the artifact consisted of various gears. The main fragment and other findings of the Antikythera mechanism are on display at the National Archaeological Museum in Athens.  
We chose the specific event because it is a historical acquisition of international interest, and it also provides the opportunity to visit an underwater site of cultural interest. The integration of multisensory interaction and gamification elements enhances the immersion and educational experience.
# Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination
## 3 Results  
### Results from the First Experiment Using Drabensted's Algorithm for a Combined Signal  
The four signals of a measurement obtained from the first experimental setup, as shown in Figure 1, are demodulated and the resulting velocity signals are displayed in Figure 3. In addition, the combined signal derived from the velocity signals of CH1-CH4 with Equations (1) and (2), is also pictured.  
In Section 3.2, a detailed explanation of the implemented algorithm for raw signals can be found, which is applicable to velocity signals as well.  
For the channels CH1-CH4, strong peaks (due to signal dropouts) are visible in the velocity signals. In our application with forced signal dropouts, we know that at any time a channel exists, where a signal without any disturbances can be detected. This fact can generally be assumed for any signal, as the signal dropouts caused by the laser speckle effect are stochastically independent [21].  
We can confirm this by examining the combined signal (VeloComb from [21] in Figure 3). In contrast to the individual velocity signals (CH1-CH4), the vibration of the shaker at 100 Hz is clearly visible in the combined signal.  
The functionality of the algorithm can also be shown in the spectral results of this measurement shown in Figure 4. It should be mentioned that the detected frequency at 100 Hz has approx. The same amplitude for all signals; however, the noise level of the combined signal is significantly lower.  
Figure 3: Velocity signals of the individual channels (CH1–CH4) and the combined velocity signal (VeloComb.); (RBW = 2.5 Hz); additional detailed view of a small section to better show the combined signal.
## 3 Results
### Pollution Level in Groundwater Due to Localization of Piezometers
#### 3.2.3 BOD/COD Ratio  
The BOD/COD ratio is considered a valuable indicator of organic matter degradation in a landfill [43]. According to [PERSON] and [PERSON] [15], if the BOD: COD ratio in water is found to be higher than or equal to a value of 0.8, the water is said to be highly polluted.  
Based on our studies, it can be stated that before the closure of the vertical barrier, the highest values of BOD/COD ratios were observed in piezometer P-9 indicating groundwater pollution in that area. Mean values of BOD/COD ratios detected in the remaining piezometers vary from 0.379 (piezometer P-10A) to 0.539 (piezometer P-4). For comparison, the BOD/COD values obtained in studies conducted at a landfill site located in Finland [44] were in the range of 0.44-0.53. According to [PERSON] et al. [45], these values can be compared to untreated domestic wastewater, for which the BOD/COD ratio varies from 0.4 to 0.8.  
After the closure of the vertical barrier (November 2000), all of the mean BOD/COD ratio values became lower than 0.18. Change of the BOD/COD ratios of several orders of magnitude indicates significant improvement of the groundwater quality in the study area. On the other hand, COD indicates the total organic matter content of an effluent, both biodegradable as non-biodegradable, whereas BOD only measures the biodegradable fraction. Thus, the lowest ratio BOD/COD indicates that it has a lower proportion of biodegradability.  
Comparison of the minimum, maximum, mean and standard deviation of the BOD/COD ratios obtained for all piezometers during the entire monitoring period are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Piezometer**} & \multicolumn{2}{c}{**BOD/COD Ratio before the Closure of the**} & \multicolumn{4}{c}{**BOD/COD Ratio after the Closure of the**} \\  & \multicolumn{2}{c}{**Vertical Barrier**} & \multicolumn{4}{c}{**Vertical Barrier**} \\ \hline  & **Min** & **Max** & **Mean** & **STD** & **Min** & **Max** & **Mean** & **STD** \\ \hline P-2A & 0.370 & 0.663 & 0.490 & 0.132 & 0.003 & 0.352 & 0.119 & 0.119 \\ P-4 & 0.279 & 1.055 & 0.539 & 0.448 & 0.001 & 0.481 & 0.105 & 0.127 \\ P-6A & 0.260 & 0.673 & 0.493 & 0.206 & 0.011 & 0.469 & 0.118 & 0.123 \\ P-7A & 0.370 & 0.498 & 0.415 & 0.072 & 0.007 & 0.384 & 0.144 & 0.121 \\ P-9 & 0.560 & 2.062 & 1.561 & 0.708 & 0.005 & 0.610 & 0.140 & 0.155 \\ P-10A & 0.368 & 0.390 & 0.379 & 0.016 & 0.004 & 0.487 & 0.139 & 0.136 \\ P-11A & n.d. & n.d. & n.d. & n.d. & 0.004 & 0.445 & 0.133 & 0.139 \\ P-12 & n.d. & n.d. & n.d. & n.d. & 0.011 & 0.671 & 0.146 & 0.137 \\ P-14A & 0.270 & 0.560 & 0.454 & 0.150 & 0.016 & 0.543 & 0.155 & 0.119 \\ P-17 & n.d. & n.d. & n.d. & n.d. & 0.012 & 0.439 & 0.171 & 0.132 \\ \hline \hline \end{tabular} Note: n.d. (not detected).  
\end{table}
Table 3: BOD/COD ratio for groundwater samples collected in the study area.  
Figure 10: Temporal changes of the COD level in piezometers located in the area of adjacent facilities.