esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Batch Leveled FHE Scheme
### Homomorphic Properties of the Batch FHE Scheme  
The following theorem summarizes the homomorphic properties of our scheme.  
Theorem 4.2: _For the Batch.RFHE scheme with parameters \(n\), \(q\), \(\|\chi\|<B\), \(L\):_  
\[4\cdot\delta_{R}{}^{L}\cdot(\delta_{R}+1)^{L+1}<\frac{q}{B}\]  
\(L\) _is homomorphic._  
Proof: Let \(n\), \(q\), \(\|\chi\|<B\), \(L\) be the parameters for Batch.RFHE, and compute \((pk,evk,sk)\)\(\leftarrow\) Batch.RFHE.KeyGen\((1^{L},1^{\lambda})\).  
Let \(\boldsymbol{c}_{1}\), \(\boldsymbol{c}_{2}\) :  
\[\langle\boldsymbol{c}_{1},(1,s_{i-1})\rangle =\Big{\lfloor}\frac{q}{q}\Big{\rfloor}\cdot m_{1}+e_{1}\;(mod\;q)\] \[\langle\boldsymbol{c}_{2},(1,s_{i-1})\rangle =\Big{\lfloor}\frac{2}{q}\Big{\rfloor}\cdot m_{2}+e_{2}\;(mod\;q)\]  
with \(\|\boldsymbol{c}_{1}\|\), \(\|\boldsymbol{c}_{2}\|\leq E<\left\lfloor\frac{q}{2}\right\rfloor/2\). Define \(\boldsymbol{c}_{\boldsymbol{add}}\leftarrow\mathrm{Batch.RFHE.Add}( \boldsymbol{c}_{1},\boldsymbol{c}_{2})\), \(\boldsymbol{c}_{\boldsymbol{mult}}=\mathrm{Batch.RFHE}\).  
\(\mathrm{Mult}(\boldsymbol{c}_{1},\boldsymbol{c}_{2})\), then:  
\[\langle\boldsymbol{c}_{\boldsymbol{add}},(1,\boldsymbol{s}_{i})\rangle =\Big{\lfloor}\frac{q}{q}\Big{\rfloor}\cdot\left[m_{1}+m_{2} \right]_{2}+e_{add}\;(mod\;q)\] \[\langle\boldsymbol{c}_{\boldsymbol{mult}},(1,\boldsymbol{s}_{i}) \rangle =\Big{\lfloor}\frac{2}{q}\Big{\rfloor}\cdot m_{1}\cdot m_{2}+e_{ mult}\;(mod\;q),\]  
From Lemma 4 and Lemma 5:\[\|e_{add}\|\leq 2\cdot(E+1)+E_{switchKey}\]  
\[\|e_{mult}\|<E\cdot\delta_{R}(\delta_{R}+1)+E_{switchKey}\]  
where \(E_{switchKey}=\frac{q\cdot B_{k}\cdot\delta_{R}}{p}+\frac{(\delta_{R}+1)}{2}\), \(p=q^{k-1}\) and \(B_{k}>\alpha^{1-\sqrt{k}}q^{k-\sqrt{k}}\cdot B^{\sqrt{k}}\).  
The additive noise is much smaller than the multiplicative noise, so we only observe the multiplicative noise. We choose the appropriate \(p\) so that the noise introduced by the key switching is smaller than that introduced via multiplication, and the noise term \(E_{switchKey}\) will be ignored in the following analysis. From Lemma 3, \(E_{0}=B\cdot(2\cdot\delta_{R}+1)\), and \(E_{i+1}=\delta_{R}(\delta_{R}+1)\cdot E_{i}\). \(E_{L}=\delta_{R}{}^{L}\cdot(\delta_{R}+1)^{L+1}\cdot B\) is obtained. If \(E_{L}<\left\lfloor q/2\right\rfloor/2\), the decryption is successful and Theorem 3 is proven. \(\square\)
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Limitations of Multi-Connectivity
#### 3.5.2 Cross-Layer Design  
This is critical in MC as this can cause to a complete failure of achieving the primary goals. Proper information sharing is required to achieve efficient usage of network resources and flexibility. Protocol layers are different with different technologies and have unique abilities and functionalities. All network resources have to be optimally utilized, and designing a cross layer is a challenge given the multiple factors affecting the transmission over wireless channels.
# Improved Application of Hyperspectral Analysis to Rock Art Panels from El Castillo Cave (Spain)
## 2 Materials and Methods
### Visualization Improvement  
Visualization enhancement methods are processed independently in order to create image transformations that decorrelate and rescale the noise in data (a process known as noise whitening) resulting in transformed data in which the noise has unit variance and there is no band-to-band correlation. Different methods have been applied in order to create the transformed images.
## 5 Results and Discussion  
### The NPUSCH Channel: A Detailed Discussion  
As can be seen in Figure 3, where the obtained profiles have been visualized, the detection success ratio (DSR) was accepted as a measure of performance, defined as a percentage of successfully received messages in the investigated uplink channels, before any retransmission happened. Various numbers of repetitions are marked as N1, N4, N16, N64, and N128 in the figure.  
As for the analysis of the results, the following conclusions can be immediately drawn:  
* Within each MCS, a separation between points on the five curves, corresponding to the same DSR level, differed within the range of 1.7-4.3 dB, with an average difference of 2.97 dB and a standard deviation \(\sigma\) = 0.57 dB, as is also shown in Figure 4a. These values indicate the impact that each increase in the number of repetitions by a factor of four will have on the NB-IoT uplink performance;
* Average differences between profiles corresponded to the same values of Nrep, but were obtained for different MCSs, varying in the range of 1.6-3.6 dB, with an average difference of 2.7 dB and a standard deviation \(\sigma\) = 0.31 dB, as is also shown in Figure 4b. These outcomes demonstrate the MCS's impact on NB-IoT performance;
* A separation between the profiles corresponding to extreme values of Nrep (i.e., 1 and 128) equaled 9.6 dB for MCS0, 10.4 dB for MCS5, and 11.9 dB for MCS10. The results appear to indicate that, though not remarkably, the number of repetitions seemed to have a greater effect on the higher-order MCS than on the lower-order MCS;
* It took 10.8 dB for each DSR profile to make a complete transition from 0% to 100%, with the maximum standard deviation from this figure equal to 1.6 dB.
# Low-Cost Sensor Based on SDR Platforms for TETRA Signals Monitoring
## 3 Results and Discussion
### Results of the Laboratory Testing
Figure 13: Adjacent channel attenuation measured by R&S FSV13 analyzer. In the case of SDR platforms, a resolution of 2048 points was used for the FFT calculation. Even in this situation, the results obtained with the SDR NI USP 2930 platform are comparable to those provided by the R&S FSV13 reference analyzer. The ADALM Pluto SDR platform has an attenuation of approximately 30 dB for adjacent level 1 channels and approximately 35 dB for adjacent level 2 channels. These attenuations may be insufficient in the case of field measurements, especially in the case of strong channels in the immediate vicinity of the TETRA emission sources. For example, a TETRA channel with a power of \(-40\) dBm will produce adjacent channel levels of \(-60\) dBm when measured with the ADALM Pluto SDR platform. This quantity can lead to misinterpretations regarding the presence of TETRA signals in different frequency bands. A solution of increasing the attenuation of the adjacent channel consists in improving the frequency resolution, by increasing the number of FFT points of calculation. Table 4 compares the attenuations of the adjacent channel for measurements made with the SDR ADALM Pluto platform for the values of the frequency resolution of: 2048 points, 4096 points and 8192 points, respectively. We observe that the attenuation of the adjacent channels for a resolution of 8192 points is about 60 dB. This size can be considered sufficient, even in the case of strong TETRA channels. For example, for a TETRA channel with a power of \(-40\) dBm, an adjacent channel level of less than \(-100\) dBm will result.
# Drone-Based Vibration Monitoring and Assessment of Structures
## 2 Background  
### Accelerometer-Based Sensing  
Accelerometers have countless uses, from flipping the screen orientation on smartphones to keeping planes on their trajectory in flight. Accelerometers also play a crucial role in Structural Health Monitoring (SHM). Researchers have created many methods of determining the condition of a structure using the vibration signature [2]. However, hardware implementations of such methods are limited due to multiple factors including size, cost, and power requirements. The high-sensitivity, low-noise electronics required to make such measurements are relatively large, expensive, and require experienced personnel to operate [2]. Attempts to make more compact systems and increase the resolution and sensitivity are included in the references of this work [3; 4]. However, these compact systems can be labor-intensive and dangerous to deploy in remote areas. Such areas also make it impossible to connect these systems to the power grid, creating a need for wireless and power-independent sensor packages.  
Accelerometers are electromechanical devices that detect the change in acceleration in a given direction. Piezoelectric (PE) and Integrated Electronics Piezoelectric (IPEP) accelerometers use internal crystals and the piezoelectric effect to generate a charge related to the acceleration introduced to the system. There are also piezoresistive accelerometers that vary their resistance in relation to an input acceleration. The third common type of accelerometers are the Capacitive Microelectromechanical Systems (MEMSs). These accelerometers have a suspended microstructure, embedded in the Integrated Circuit (IC), with a small mass that is free to vibrate and measure the change of capacitance of the microstructure due to acceleration [5]. DC coupling of capacitive MEMS accelerometers allows for the detection of low-frequency signals, which is critical when measuring the vibration signature of large structures, which typically resonate at frequencies below \(1\) Hz. Furthermore, MEMSs are capable of detecting steady-state acceleration [6; 7] in addition to allowing the integration and double integration of their outputs to determine velocity and displacement, which is important for quantifying the stresses and loads on structures. Figure 1 compares performance versus qualitative cost estimates for the three main types of accelerometers. For this work, a MEMS accelerometer was chosen for its reliability, affordability, and ease-of-use.  
Figure 1: Comparison of performance versus cost for the three main types of accelerometers.
# Empirical Evaluation on Utilizing CNN-Features for Seismic Patch Classification
## 4 Experimental Study
### CNN Models Implemented  
Figure 2 and Table 1 show the details of the CNN architecture designed for the experiment. This CNN consists of two convolutional (and subsampling: SUB) layers, and one FC layer, which is one of the smallest required scales for our goal. The parameters involved in the CNN are listed in Table 1.  
Figure 2: The architecture of the CNN model.  
In Table 1, the architecture is implemented with two different models, a conventional sum-pooling CNN and an improved O2P-pooling CNN, which are referred to as \"CNN-sum\" and \"CNN-O2P\", respectively. Details related to the models are as follows: In CNN-sum, the number of output maps of the two C layers was set to 4 and 8, respectively, but, in CNN-O2P, the number was adjusted to 8 and 16, as indicated by the parentheses in the fourth column of the table.  
Second, in the two SUB layers, sampling was performed by sum-pooling, which reduces the dimension of each axis by half. At this time, the CNN-sum directly connected the output (which was reshaped to a vector) of the second SUB layer to the FC layer, but, in CNN-O2P, O2P-pooling was additionally implemented, and the obtained result was connected to the FC layer. In all these operations, pooling was performed with a stride of 2.  
Third, the last component at the bottom of both models is the FC layer, and the number of input neurons of this layer is defined as the number of weights output from the previous layer. The CNN-features we are trying to extract consist of these input neurons that connect directly to the FC layer. Therefore, the dimension of the feature vector can be determined by adjusting the number of these neurons.  
Fourth, each model deals with the two-class problem of classifying the seismic image patches as normal and abnormal. Therefore, it is necessary to adjust the number of neuron units in the output layer when expanding to multiclass problems. In addition, a soft-max function is required to obtain the normalized probabilities for each class.  
Finally, the parameters for learning the CNN models were experimentally set to a learning rate of 1, a momentum of 0.5, a batch size of 10, and the number of epochs was set to 100. The number of epochs was optimized by referring to the learning curve to prevent overfitting.
# Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement
## 4 Experiments
### Experimental Results
#### 4.3.4 Speech Spectrogram  
Figure 6 is the spectrogram of the synthesized speech enhanced by the RRSENet model, \"t\" is the number of recursion of RRSENet. From Figure 6, noisy speech is more disturbed than clean speech. In the spectrogram of the enhanced speech, as the number of recursion increases, the noise reduction effect of the speech is better. The enhanced speech spectrogram works very well, and speech quality is preserved intact. Through the analysis of the spectrogram, the effectiveness of RRSENet is proved.  
To demonstrate the practicality of the RRSENet algorithm proposed in this chapter, five speech segments with ambient background noise were recorded in a real-life scenario with a sampling rate of 8 kHz. when using the RRSENet algorithm for speech enhancement, firstly, the adoption rate of the recorded speech was resampled operationally so that the adoption rate of the speech changed to 16 kHz, because the sampling rate of the speech during model training was 16 kHz. Secondly, after the enhancement of the speech, the sampling rate of the speech is downsampled to 8 kHz. Finally, the speech is visualized as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & \multicolumn{2}{c}{**PESQ**} & \multicolumn{2}{c}{**STOI**} \\ \hline
**Model** & **RLSENet** & **RRSENet** & **RLSENet** & **RRSENet** \\ \hline t1 & 2.151 & 2.135 & 81.92 & 81.54 \\ t2 & 2.170 & 2.238 & 81.89 & 83.13 \\ t3 & 2.189 & 2.244 & 89.37 & 84.26 \\ t4 & 2.230 & **2.268** & **84.56** & **84.59** \\ t5 & **2.262** & 2.245 & 84.56 & 83.48 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of RLSENet and RRSENet under unseen noise conditions. BOLD indicates the best result for each case.  
Figure 6: Spectrogram of synthesized speech enhanced by the RRSENet model.  
Figure 7: Spectrogram of real-world speech enhancement. The first row is the spectrogram of real-world noise, the second row is the spectrogram of real-world speech, the third row is the spectrogram of the enhancement speech
## 6 Conclusions
### Conceptual and Empirical Contributions  
This research's generated model investigates the major challenges of using BIM. These challenges may be used by policymakers, such as government agencies and construction industry regulators, to develop a strategy for increasing BIM use in the AECO sector. The study began by assessing the most significant challenges to implementing BIM in the building industry. This lays the groundwork for further research into the challenges of implementing BIM in the AECO sector. In order to increase BIM acceptance in Nigeria or other developing nations, the theoretical constructs emerging from this study would be useful in constructing a mathematical tool for determining the BIM implementation hurdles that need to be overcome. The research also achieved several important theoretical and practical advances, including the following:  
* The study makes a theoretical contribution by illuminating new ideas that can be included in the existing framework. For instance, challenges to implementing BIM have an effect on BIM adoption and understanding at all stages of a project's lifespan.
* While several studies have been conducted on the subject of BIM deployment in industrial nations, research on the topic in Nigeria is still in its infancy. This research fills that need by focusing on the most pressing issues impending the widespread implementation of BIM and the factors that are directly related to those issues.
* The study's model is the first predictive model to assess the impact of BIM implementation hurdles on BIM utilization and awareness across the AECO industry's project lifecycle. Hopefully, this resource will accelerate the spread of BIM in underdeveloped nations. This contribution is empirical since it focuses on doing what no previous research has performed: evaluating the theoretical linkages between two variables (\"BIM implementation hurdles\" and \"BIM usage and awareness in project lifecycle\").
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 9 Manufacturing of Hollow-Core Fibers
### Reduction in NANF Attenuation  
The lowest attenuation reported for NANF (0.22 dB/km @ 1625 nm, [49]) and DNANF (0.17 dB/km @ 1550 nm, [13]) is comparable to that if solid-core SMFs operating at the same wavelengths [30; 31]. However, it is not lower. While theoretical predictions of confinement loss give values \(\leq\)0.001 dB/km [48; 50], there are other loss mechanisms presented in Sections 6.2 and 6.3, primarily:  
1. Leakage of light due to imperfections in cladding;
2. Surface Scattering Loss (SSL), typically 0.02-0.05 dB/km;
3. Microbending loss--specific to each fiber design;
4. Absorption by water vapor and other gas contaminants.  
The lowest possible attenuation was estimated at \(\approx\)0.05 dB/km [61], unless a new design of NANF with reduced power density at the cladding inner surfaces to reduce the SSL appears.  
Recommended solutions in manufacturing may include the following: (a) tighter control of geometry of capillaries to reduce confinement loss; (b) flushing newly drawn fiber with inert gas free of water vapor, methane, propane, and other absorption-inducing gases; and (c) thicker, two-layer (soft/hard) protective coating to minimize fiber microbending in cables.
# Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption +
## 6 Conclusions and Future Works  
Herein, we propose a blockchain-based EHR management scheme with PRE. Owing to the security of the blockchain, the stored EHRs can be delivered only to authorized users, such that the integrity of the records is guaranteed. In addition, PRE ensures the confidentiality of the encrypted EHRs. In our system, the proxy server is separated from the blockchain system; therefore, the plaintexts of the EHRs will not be visible to the blockchain outsourcing company if the system manager does not reveal a user's private key. Based on security analysis and the experimental results, we confirmed the security and usability of the proposed scheme. Using the proposed scheme, privacy-sensitive information in the EHRs will be maintained by only trustable members; therefore, the security and privacy of the healthcare data are guaranteed.  
A few shortcomings exist in our study. For example, when an authorized user obtains the EHR from the file server through the blockchain system and the proxy server, the user can own the record indefinitely, which may result in another privacy issue. If the user sends the obtained EHR to another person that is not granted access, then the privacy of the EHR owner will be invaded. To overcome this issue, additional privacy-preserving schemes are required. In future studies, we will research and develop a scheme that solves privacy and sharing problems simultaneously.  
Data curation, Y.K. and S.-O.L.; Project administration, Y.-H.P.; Writing--original draft, Y.-H.P.; Writing--review & editing, K.K. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Ministry of Trade, Industry & Energy (MOTIE), the Korea Institute for Advancement of Technology (KIAT) through the Encouragement Program for The Industries of Economic Cooperation Region (P0014681), and by Seoul R&BD Program (CT200018, Seoul Campus Town Technology R&D Project).  
Not applicable.  
The authors declare no conflict of interest.
## 7 Discussion and Open Questions
### Risk Analysis in Digital Twin-Based Validation Frameworks
#### 7.4.4 Operational and Deployment Risks  
Post-deployment, the DT is leveraged for continuous monitoring and predictive maintenance. However, this phase involves risks related to reliance on telemetry data quality, which may be affected by sensor failures or communication losses, model drift over time as physical system behavior deviates due to aging or environmental factors, and inadequate responsiveness of the DT to emerging fault conditions, leading to missed or late anomaly detection [49].  
Examples are as follows:  
* A fleet of vehicles equipped with headlamp DTs experienced a sudden drop in anomaly detection accuracy after a firmware update altered sensor calibration characteristics. The DT models, based on prior calibration data, failed to adapt promptly, delaying fault detection and potentially risking driver safety;
* Telemetry loss may occur due to poor cellular coverage during vehicle operation, resulting in gaps in DT data streams;
* Model drift caused by environmental factors such as extreme temperature variations not accounted for in the initial DT calibration may occur.  
Operational risk management must include validation of telemetry integrity, adaptive model updating mechanisms, and fail-safe procedures to handle detected anomalies.
## 6 Conclusions  
To improve the performance of NLOS communication for UAV payload communication, we design an NLOS communication scheme based on CP/CS-SC-FDE. We first investigate the reason behind the generation of LISI in NLOS communication. The simulation shows that under the NLOS channel, there is a time offset with the value \(E\), i.e., the  
Figure 10: The BER curve of CP/CS-SC-FDE compared with CP-SC-FDE in COST207-BU channel and TDL-C channel.  
Figure 9: The BER curve of CP/CS-SC-FDE compared with CP-SC-FDE in Rayleigh fading channel.  
number of symbol intervals between the PwMP and the first path, which causes the LISI in NLOS communication. Then, the frame structure of conventional SC-FDE with CP (CP-SC-FDE) is improved, and the UAV NLOS communication scheme based on CP/CS-SC-FDE is designed. Furthermore, a channel estimation algorithm applicable to this scheme is proposed. The numerical results show that this communication scheme can eliminate the ISI in NLOS communication. Compared with the conventional CP-SC-FDE system, this scheme can achieve excellent performance in the Rayleigh channel, COST207-BU channel, and TDL-C channel. In the CP/CS-SC-FDE system, the BER result is similar to that under ideal synchronization. Therefore, this NLOS communication scheme based on CP/CS-SC-FDE can be applied to UAV payload communication in an NLOS scenario where the LOS path is blocked.  
Conceptualization, P.W. and X.X.; methodology, P.W.; software, P.W. and Q.L.; formal analysis, P.W. and R.W.; resources, P.D. and P.W.; data curation, Q.L., X.X. and P.W.; writing--original draft, P.W. and R.W.; writing--review and editing, P.W.; visualization, P.D. and Q.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
NLOS non-line-of-sight  
UAV unmanned aerial vehicle  
CP cyclic prefix  
CS cyclic suffix  
SC-FDE single-carrier frequency domain equalization  
ISI intersymbol interference  
LIST later intersymbol interference  
CNPC control and non-payload communication  
PC payload communication  
GBS ground base station  
PISI preceding intersymbol interference  
PwMP path with maximum power  
FAP first arrival path  
BER bit error rate  
SNR signal-noise ratio  
ML Maximum Likelihood  
MF Matched Filter  
RIS reconfigurable intelligent surfaces  
DFT Discrete Fourier Transform  
CIR channel impulse response  
TO time offset  
IDFT Inverse Discrete Fourier Transform  
LS Least Square  
AWGN additive Gaussian white noise