esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation  
### Simulation Setup  
As identified earlier, reliability, real-time data delivery and network lifetime are the three main QoS requirements for WBSN applications. To evaluate the improvement of QoS in WBSNs, particularly the improvement of reliability, real-time data delivery and network lifetime, we have used simulation. PLR, data delivery delay and energy efficiency or consumption are considered as the responsible metrics for the reliability, timeliness and network lifetime QoS requirements, respectively. Reliability is measured using the packet loss rate, or PLR, metric that is defined as the number of packets that cannot be recovered at the destination divided by the number of packets sent without any network coding. On the other hand, for the delay and the energy efficiency improvement, we have used the number of retransmissions needed for the mechanisms to receive and recover data. Usually, higher PER means more retransmissions and more delay and energy consumption at the relay nodes. Three mechanisms, the proposed one, the MP's one and no coding, were included in the comparative study. For the proposed scheme, three simulation results, one for the non-critical data, one for critical data and one for the overall or combined data, have been generated.  
The considered WBSN simulation scenario consists of 12 sensors nodes and four relay nodes. All of the sensors continuously sense and send their readings to the destination or PS through the assigned relay nodes. As this work aims to employ NC for the links between the relay nodes and PS, it is assumed that the sensor readings are reaching the relay nodes without any error. Once, the sensor readings are available at the relay nodes, they check the priority of the sensors, encode them accordingly and send them to the PS. Sensor priority settings can be dynamically changed based on the requirements. To demonstrate the dynamic priority settings, MATLAB's pseudo-random generator was used. The key components of the implementations include the encoding process that resides in the relay nodes, the recovery process and the sensor priority allocation process, which reside in the PS/MS. To mimic the wireless channel's dynamic behavior, random PERs were generated for the links between the relays and PS using MATLAB's pseudo-random generator. Based on these PERs, PS calculates the corresponding PLR and \(P_{\mathit{nep}}\) for each link and sends them to the relay nodes. If a packet from a sensor cannot be  
Figure 9: The working principle of the proposed error recovery mechanism.  
recovered, it is counted as lost, and relays in the next communication cycle send the next generation data from the sensor. As we have considered TDMA for channel access, each TDMA frame is considered as a cycle.  
To get consistent results, the mechanisms were simulated 3000 times. During each simulation time, the PER was varied between 1%-100%, and the averages of 3000 simulations were selected for the results.
## 2 Materials and Methods
### The Ability of the Listeners to Recognize Their Own Individual HRTF  
This experiment involved the assessment of various HRTFs by the participants, who were given the task of selecting their own individual HRTF from a group of four different HRTFs by listening to sound samples that were binaurally encoded using these HRTFs. The objectives of this experiment were two-fold: firstly, to serve as a training exercise for the participants, aimed at familiarizing them with head-tracked virtual audio and sensitizing them to pertinent acoustic cues. Secondly, the study aimed to indicate the potential benefits of individual HRTFs in a virtual audio system for each subject. This advantage may translate to improved performance in the primary focus of this research, namely, the accuracy of sound localization. Therefore, this experiment was performed before the localization accuracy experiment.  
The experimental setup involved seating the participants in front of a computer and equipping them with headphones and a head tracker. The experiment was conducted using a C++ program that utilizes the 3 DTI toolkit [28].  
Before the experiment began, the precise orientation of the head tracker relative to both the head and the world frame needed to be determined by asking the participants to not their heads for \"yes\" and \"no\". The experimental procedure consisted of 10 trials in total, wherein a virtual sound source was positioned directly in front of the participant, with its direction stabilized and updated in real-time based on head-tracking data. The participants were given the option to select between four randomized HRTFs by pressing a corresponding number on the keyboard and were allowed to change their choice for as long as they saw fit. The participants were instructed to choose the preferred HRTF according to the following criteria: \"Choose the HRTF which you think is yours, considering the natural feeling of the sound, the externalization of the sound, and it should sound like you are listening with your own ears, and not someone else's\". The \"natural feeling\" was additionally defined to the participants as the perception of a sound source that appears consistently in the same, well-localized direction, regardless of head rotation. The same audio files were played repeatedly throughout the trial, with the switching of the HRTF occurring instantaneously upon the participant's command via the keyboard. The trial concluded once the participant communicated their preferred HRTF via the keyboard. Two distinct stimuli were presented, alternating between consecutive trials:  
1. A woman reading a technical text in English;
2. A song by [PERSON].  
The same set of four randomized HRTFs was employed in each trial. The set consisted of the participant's own individual HRTF and the individual HRTFs of three other people who did not partake in the experiment. These three HRTFs were identical for all participants.
## 5 Experiment
### Experimental Results and Analysis
#### 5.4.5 Analysis of Temporal Correlation and Heterogeneity Effects  
The demand for shared bicycles is correlated with temporal changes, and whenever factors with periodicity and seasonality vary, the demand also changes accordingly. However, when temporal heterogeneity factors come into play, it can have a significant impact on demand. In the decoder part of the LGDMGCN model, through the combined design of multiple time feature data sources and temporal attention, the model can dynamically adjust the corresponding changes in demand based on external features. Considering the backdrop of the COVID-19 pandemic, shared bicycle demand fluctuates in response to the variations in pandemic cases. Figure 10 illustrates the correlation between the daily confirmed COVID-19 cases and the demand for bike-sharing predicted by the LGDMGCN model. The solid purple line represents the daily confirmed COVID-19 cases, while the dashed blue line represents the corresponding predicted demand for shared bicycles. Each data point on the blue line corresponds to the model's forecast for the day, demonstrating how demand reacts to changes in pandemic severity. The chart clearly shows the trend of predicted reduction in demand during periods of increased COVID-19 cases, and vice versa. Although there is a slight observable lag, it essentially reflects the trend of temporal heterogeneity effects on bike-sharing demand during the pandemic.  
We also computed the MAE for each model during both the severe periods (November) and the relatively mild periods (June) of the COVID-19 pandemic.  
As shown in Figure 11, overall, the performance of the models in November, when the COVID-19 pandemic was severe, was worse than in June, when the pandemic was relatively stable. Specifically, the LSTM, STGCN, DGCRN, and AttConvLSTM models exhibited significant performance discrepancies between these two periods, indicating that these models struggled to capture demand fluctuations during severe pandemic conditions. In contrast, the STSeq2 Seq model demonstrated smaller performance differences across the periods, possibly due to its PAM module effectively capturing dynamic changes in the time series. The LGDMGCN model, benefiting from the inclusion of COVID-19 case data and temporal attention mechanisms, achieved the best performance in both periods.  
Figure 9: Dynamic adjacency matrix weighted heatmaps at different times.
# A Hierarchical Federated Learning Algorithm Based on Time Aggregation in Edge Computing Environment
## 3 Dynamic Weighted Hierarchical Federated Learning Algorithm
### Federated Average Algorithm
Assuming that the data from all client devices in the system are uniformly distributed, the objective function of Equation (1) can be rewritten as:  
\[F(\omega)=\sum_{i=1}^{N}\frac{|D_{i}|}{|D|}F_{i}(\omega). \tag{2}\]  
Before the \(t\)-th round of global training, the central server sends the current global model parameter \(\omega^{t}\) to the participating client device \(i\) as the local model parameter \(\omega^{t}_{i}\) for local training. The client optimizes this parameter through \(E\) rounds of local iterations using the SGD method as the local optimizer. The participating client device \(i\) then calculates the local model parameter \(\omega^{t}_{i}\) in the \(l\)-th local iteration and updates it using the following equation:  
\[\omega^{t}_{i}=\omega^{(l-1)}_{i}-\eta\
abla F_{i}\Big{(}\omega^{(l-1)}_{i} \Big{)}, \tag{3}\]  
where \(\eta\) is the preset learning rate, and \(\
abla\) is the gradient calculation symbol of the model parameter \(\omega\). After local training, the central server performs average weighted aggregation of all the collected local models to produce the global model update:  
\[\omega^{t+1}=\sum_{i=1}^{N}\frac{|D_{i}|}{|D|}\omega^{t+1}_{i}=\omega^{t}- \eta\
abla\sum_{i=1}^{N}\frac{|D_{i}|}{|D|}F_{i}(\omega^{t}_{i}). \tag{4}\]  
After multiple rounds of global and local iterations, a joint training model with precision \(\theta\) is obtained, and the global model is extended to all participating clients, where \(\big{\|}\
abla F(\omega^{t})\big{\|}\leq\theta\leq\big{\|}\
abla F(\omega^{t-1 })\big{\|}\). The efficiency of federated learning is affected by the duration of local training and model transmission. Communication involves uploading the local model and downloading the global model. In this study, only the uplink time is considered because the uplink bandwidth is significantly lower than the downlink bandwidth. Additionally, the network bandwidth of the central server is limited, which means that the number of parallel client connections it can accept is also restricted. When the transmission model dimension is high or the number of connected client devices is large, the communication process can consume significant resources [21].
## 3 Numerical Results and Complexity Analysis
### Complexity Analysis  
Computation complexity is derived from equalizer vectors. Symbol estimates in terms of complex multiplications and additions as summarized in Table 2. The conventional FD-MMSE equalization algorithm as given by (19) requires \(M\) complex multiplications to estimate the equalizer symbols. In the proposed FD-MMSE equalizer scheme, to suppress the MMSE estimation error, additional operations are required to be configured including the \(K\)-point IFFT operation and \(nK\)-point FFT operation. Hence, its computational complexity in terms of cost is higher in comparison with conventional MMSE and LMSE schemes [21]. As a result, the additional computational cost consists of \(K[n\log_{2}(nK)+\log_{2}(K)]\) complex additions and \((K/2)[n\log_{2}(nK)+\log_{2}(K)]\) complex multiplications.  
Figure 8: BER performance comparison of the proposed MMSE-FDTE and LMSE equalization algorithms of uplink NB-IoT systems using BPSK with different numbers of iterations.  
Figure 9: BER performance comparison of the proposed MMSE-FDTE and LMSE equalization algorithms of uplink NB-IoT systems using QPSK with different numbers of iterations.  
The complexity of the LMSE equalizer is approximately similar to the DFT-based scheme in [20]. Besides, the performance of the proposed FD-MMSE turbo equalization operating at a low SNR region (i.e., SNR \(<\) 0 dB) is better than the corresponding turbo equalization proposed in [22] in uplink NB-IoT systems.
## 5 Statistical Analysis of the Protocols Based on Subsets of Responses and ECC
### Error Management in the Cryptographic Keys
#### 5.3.2 Error Management of Collisions  
We developed and analyzed a method to experimentally detect collisions. The approach is based on the observation that when a collision occurs between a response from the subset and a response from the full set positioned in one of the states of \"0\" of K, that there is also a match with a response positioned in one of the states of \"1\" of K; this response from the subset then contains multiple matches.  
Conversely, without collision, each response from the subset has one (and only one) match in the full set of responses. After experimentally finding the \(\rho\) positions of the subset with multiple matches, we conclude that all other positions (the number of which is f-\(\rho\)) are error-free. This significantly limits how many possible keys have to be tested in a methodology similar to that described by the RBC. For example: if one response of the subset sees two matches, then the first of the two can be a state of \"1\": while the second one is a state of \"0\" (and vice-versa). This leaves only two possible keys, which is quickly validated by computing F with Equation (7). If \(\rho\) responses from the subset create one collision each, then the number of possible keys per response is 2. This results in total \(2^{\rho}\) possible keys required to be checked; a lower value than the number of cases checked by the brute force RBC for the same number of errors. The latencies for 256-bit long keys K are listed in Table 5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Errors** & **0** & **1** & **2** & **3** \\ \hline AES—Latencies (s) & \(10^{-8}\) & \(2.5\times 10^{-6}\) & \(3.2\times 10^{-4}\) & \(2.2\times 10^{-3}\) \\ \hline Dilithium—Latencies (s) & \(10^{-5}\) & \(2.5\times 10^{-3}\) & 0.3 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Latencies to verify authenticity with RBC.
## 2 You-Ye-Lin landslide
### Landslide of You-Ye-Lin
#### 2.3.4 Geophysical Survey  
As illustrated in Figure 7a, four transects (_RP-1_: longitudinal transect and _RP-2-RP-4_: transverse transects) were set up for 2-D electrical resistivity imaging survey in the study area. The _RP-1_ (250 m long and adjacent to _A-A'_ profile in Figure 4) was aligned from _SE_ to _NW_ close to the monitoring point _B09-W_ and _B10-W_. The purposes of the survey are to inspect the distribution pattern of the groundwater and to provide information for the design of the subsurface drainage system.  
The resistivity profile (_RP_) before and after Typhoon Megi (2016/09/25-09/28) were established for comparisons to show the approximate accumulation pattern of the infiltrated rainwater and to be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Typhoon** & **Observation** & **Initial** & **Maximum** & **Groundwater** & **Cumulative** \\
**Well** & **Groundwater** & **Groundwater** & **Level (m)** & **Level Rise** & **Rainfall** \\ \hline \multirow{2}{*}{\begin{tabular}{c} Soudelor (2015/Aug./06-09) \\ \end{tabular} } & _B08-W_ & \(-\)11.60 & \(-\)7.39 & 4.21 & 314 \\ \cline{2-6}  & _B09-W_ & \(-\)34.00 & \(-\)21.19 & 12.81 & 252 (mm/day) \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Dujuan (2015/Sep./27-29) \\ \end{tabular} } & _B08-W_ & \(-\)6.88 & \(-\)6.72 & 0.16 & 194 \\ \cline{2-6}  & _B09-W_ & \(-\)35.22 & \(-\)24.32 & 10.90 & 33 (mm/hr) \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Megi (2016/Sep./27-28) \\ \end{tabular} } & _B08-W_ & \(-\)16.20 & \(-\)15.00 & 1.20 & 484 \\ \cline{2-6}  & _B09-W_ & malfunction & malfunction & malfunction & 345 (mm/day) \\ \cline{2-6}  & _B12-W_ & \(-\)16.90 & \(-\)12.97* & 3.93 & 49 (mm/hr) \\ \hline \multicolumn{6}{c}{**\(-\)12.97 m\({}^{*}\)**} \\ \end{tabular}
\end{table}
Table 1: Groundwater rise during typhoon events.  
Figure 6: Displacement profiles of inclinometers and the potential sliding surface (PSS) along _B-B’_ profile (after Typhoon Megi, 2016/09/25–09/28).  
used as a design reference of drainage wells. Figure 7b,c display the pseudo 3D electrical model of the study area with electrical resistivity \(\rho_{\text{a}}\) (\(\Omega\)-m) contours before and after Typhoon Megi. Because of the influence of building structures, groundwater, and rock fragments, the electrical resistivity distribution is rather scattered (\(\rho_{\text{a}}\) =15-500 \(\Omega\)-m). Comparing Figure 7b,c, the groundwater level is most likely situated in the colluvium at a depth of 5.0-30.0 m. The water-bearing stratum might be situated at a depth around 5-17 m and the upper boundary of the sliding body might be close to the residential area _RA-1_ (near the monitoring point _B12-W_).
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 3 The Binary DFT  
Let \(x_{n}\) be a complex number. Its most general finite precision representation can be expressed as  
\[x_{n}=\sum_{l=-M_{1}}^{M_{2}-1}[x^{(r)}{}_{n,l}+j\cdot x^{(i)}{}_{n,l}]\cdot 2^ {l} \tag{5}\]  
where \(x^{(r)}\) and \(x^{(i)}\) represent real and imaginary parts of the complex number, respectively, while \(b\) is the number of bits used to encode each sample. In detail, \(M_{1}\) is the number of bits used to represent the fractional part of each sample, while \(M_{2}\) is its integer part; clearly, \(M_{2}=b-M_{1}-1\).  
In the rest of the paper, the hypothesis that the real sequence is assumed as the generalization to the complex case is straightforward.  
In the stated simplified hypothesis, by a simple substitution of Equation (5) in Equation (1) we have  
\[X_{k}=\sum_{n=0}^{N-1}\sum_{l=-M_{1}}^{M_{2}-1}x_{n,l}\cdot 2^{l}\cdot W_{N}^{kn} \quad k=0,1,\ldots,N-1 \tag{6}\]  
and hence  
\[X_{k}=\sum_{l=-M_{1}}^{M_{2}-1}2^{l}\sum_{n=0}^{N-1}x_{n,l}\cdot W_{N}^{kn} \quad k=0,1,\ldots,N-1 \tag{7}\]  
If we denote with \(l\)-th bit slice in the following set of samples with \(BS_{l}=(x_{0,l},\)\(x_{1,l},\)\(\ldots,\)\(x_{N-1,l})\) with \(l=-M_{1},\ldots,M_{2}-1\), the DFT of the \(l\)-th bit slice is defined as  
\[X_{k,l}=\sum_{n=0}^{N-1}x_{n,l}\cdot W_{N}^{kn}\quad k=0,\ldots,N-1 \tag{8}\]  
It is evident from Equation (7) that the computation of the \(k\)-th Fourier transform coefficient can be obtained by calculating separately the DFT of each bit slice and adding these partial results weighted by a proper power of two.  
Since the generic \(x_{n,l}\) value is either zero or one, the DFT slice coefficients result in the sum of all the \(W_{N}^{kn}\) vectors at which a bit slice sample equal to one occurs.  
We denote the DFT of bit slides with binary DFT (BFT). The BFT evaluation for the generic bit slice through expression (8) can be simplified by exploiting the symmetry properties of kernel values. In Figure 1, an example of the complexity reduction that could happen is indicated for an eight-point BFT slice. A bit slice of eight bits is the input of the computation for a chosen \(k\) value. Considering the complex Gauss plane, twiddle factors are represented as vectors and bits set to one sum up to define the BFT value for the specific \(k\). Opposite values give a zero contribution to the sum so that a simplification stage can be proposed to reduce the computational load of the BFT evaluation.
## 3 Comparative Analysis of the Existing Hash Chain-Based Authentication Approaches  
As described in Section 2, improvements to the DSA-based VMF message authentication processes of the existing SHA-1 are necessary, and their integrity and availability must also be secured for various limited battlefield networks. Therefore, additional requirements must be satisfied, including authentication-related elements provided in the SPI of the existing VMF authentication header; that is, data origin authentication (whether the data transmitted by the sender have been forged along the path), connectionless integrity (limited connectionless configuration for detecting any modifications or retransmissions of the tactical data while preventing hostile nodes from analyzing VMF messages, thereby preventing the identification of the sender's or receiver's tasks), and non-repudiation with proof of origin and proof of delivery. In addition, adaptability to other constraints, such as operational security (covertness), intermittent connectivity, risk of capture and compromise, and limited resources based on low bandwidth, frequency, and device energy, is required in order to operate in rapidly changing tactical network environments [28].  
Accordingly, additional security requirements specific to VMF-based CNR networks for a hash chain-based authentication scheme including T-OTP are preemptively defined so as to achieve functional authentication stability over a certain level and high tactical and strategic diversity. Based on these requirements, this approach contains a comparative analysis of prior studies related to hash chains in terms of their differences, unique features, advantages, and limitations, as well as their potential for being applied to VMF-based limited tactical networks.
# Fabrication of Novel CeO2/GO/CNTs Ternary Nanocomposites with Enhanced Tribological Performance
###### Abstract  
Increasing demands of multi-functional lubricant materials with well distributed nanoparticles has been generated in the field of oil lubrication. In this study, one-dimensional (1-D) acidified multi-walled carbon nanotubes (CNTs) and two-dimensional (2-D) graphene oxide (GO) sheets were dispersed together under an ultra-sonication condition to form CNTs/GO hybrids and the corresponding CNTs/GO hybrids decorated with uniform zero-dimensional (0-D) cerium oxide (CeO2) nanoparticles were prepared via a facile hydrothermal method. The tribological performance of CeO2/CNTs/GO ternary nanocomposite was systematically investigated using a MS-T3000 ball-on-disk tester. The results demonstrated that CeO2/GO/CNTs nanocomposites can effectively reduce the friction of sliding pairs in paraffin oil. Moreover, the oil with 1 wt% of CeO2/GO/CNTs exhibited the best lubrication properties with the lowest friction coefficient and wear scar diameters (WSD) compared with adding only GO nanosheet, CeO2, and CeO2/CNTs hybrid nanocomposite as lubricant additives. It is concluded that due to the synergistic effect of 0D CeO2, 1D CNTs, and 2D GO during sliding process, a dimensionally mixed CeO2/GO/CNTs nanocomposite exhibits excellent lubricating properties, providing innovative and effective additives for application in the field of lubrication.  
multi-walled carbon nanotubes; graphene oxide; cerium oxide; lubricating oil additives 2019 9 170 10 3390/app9010170
# TKEO-Enhanced Machine Learning for Classification of Bearing Faults in Predictive Maintenance
## 3 Experimental Procedure
### Data Splitting and Standardization  
The data were divided into training and testing sets in an 80/20 ratio, ensuring stratification according to fault categories. Stratified sampling ensured that the distribution of target labels remained consistent between the training and testing datasets, mitigating potential biases caused by class imbalance.  
To eliminate the influence of feature magnitudes, all data were standardized using the StandardScaler method. Standardization transformed the features to zero mean and unit variance.
# Robust Hybrid Beamforming Scheme for Millimeter-Wave Massive-MIMO 5G Wireless Networks
## 3 Proposed Algorithm  
On the basis of the traditional MMSE algorithm, an alternating MMSE (Alt-MMSE) algorithm is proposed by using the orthogonality of the digital matrix and the idea of an iterative update. The algorithm first designs the initial digital matrix, iterates on this basis to update, and finally obtains a hybrid beamforming matrix.