esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Derivation of the 10\(N-9\) Phase-Shifting Algorithm
### Fourier Representation of the \(10N-9\) Algorithm  
By using the Fourier representation [40] of the sample amplitude \(a_{k}\) and \(b_{k}\), the new algorithm can be better understood and visualized. For an \(M\)-sample algorithm, the sampling functions \(f_{1}\) and \(f_{2}\) in time domain can be described as  
\[f_{1}(\alpha)=\sum_{k=1}^{M}b_{k}\delta(\alpha-\alpha_{k}), \tag{13}\]  
\[f_{2}(\alpha)=\sum_{k=1}^{M}a_{k}\delta(\alpha-\alpha_{k}), \tag{14}\]  
where \(a_{k}\) and \(b_{k}\) are the sample amplitude and \(\delta(\alpha)\) is the Dirac delta function.  
The sampling functions in the frequency domain can be described by using Fourier transform as  
\[F_{1}(v)=\sum_{k=1}^{M}b_{k}\exp(-i\alpha_{k}v), \tag{15}\]  
\[F_{2}(v)=\sum_{k=1}^{M}a_{k}\exp(-i\alpha_{k}v), \tag{16}\]  
where \(v\) is the frequency variable and \(i\) is an imaginary unit. \(F_{1}\) is purely imaginary and \(F_{2}\) represents purely real functions based on the asymmetrical and symmetrical properties of the sampling amplitude and phase shift parameter [45].  
Figure 5 presents the sampling functions \(iF_{1}\) and \(F_{2}\) of [PERSON]'s \(2N-1\) algorithm. As mentioned above, this algorithm can compensate for not only the phase shift miscalibration but also the coupling error. However, the sidelobe still exists, with a value around \(5.5883\%\). A high sidelobe amplitude indicates significant vulnerability to the adjacent signal. Therefore, this algorithm is not appropriate for measuring a highly reflective sample.  
Figure 6 presents the sampling functions of the \(10N-9\) algorithm. The sidelobe is \(0.0007\%\) compared with \(5.5883\%\) of [PERSON]'s \(2N-1\) algorithm, which means that the \(10N-9\) algorithm has a strong sidelobe suppression ability. The sampling functions of \(10N-9\) have a zero gradient at relative frequency \(v=1\), which fulfills the requirements of the fringe contrast maximum condition [39]. The \(10N-9\) algorithm can also compensate for the bias modulation because the sampling function has zero gradients at relative frequency \(v=0\). Moreover, the sampling functions of \(10N-9\) have zero  
Figure 4: Shape of polynomial window function.  
gradients at \(v=2\), \(3\),..., \(8\), which means that the new algorithm can compensate for the coupling errors. In addition, to compensate for the coupling errors up to \(o(A_{m}\varepsilon_{8})\), the derivatives have zero gradients up to the eighth order.
## 5 Discussion
The corrected implementation did not substantially alter the ranking of feature sets, as the Katz, Petrosian, and Higuchi combination remained among the top-performing configurations. However, the accuracy dropped slightly (from 79.04% to 78.95%) due to the refined feature representation. The latter affected the overall accuracy of this combination considering the other methods in Table 5 and decreased the outcome from 79.1% to 79.0%. To clearly present the contribution of the newly introduced fractal dimension methods, we compared classification performance using feature sets consisting solely of traditional fractal dimensions (Katz, Petrosian, and Higuchi) against those integrating the new algorithms (box-counting, DFA, MFDFA, and correlation dimension). The baseline combination of traditional FDs (KFD vs. PFD vs. HFD) yielded a mean accuracy of 78.95%. When integrating the newly proposed FD methods (e.g., box-counting and correlation dimension), the mean accuracy notably improved to 79.16% (KFD vs. BCFD vs. CDFD). This incremental enhancement illustrates the specific value that new fractal dimension methods add by capturing additional complexity parts of EEG signals previously overlooked by traditional FD methods alone. Although our proposed method demonstrated enhanced classification accuracy through the integration of multiple fractal dimension (FD) techniques, an important practical limitation is the increased computational load inherent in using combined FD methods. Each fractal dimension computation (e.g., Box-counting, DFA, MFDFA, and correlation dimension) significantly adds to the complexity of feature extraction due to their iterative and multi-scale calculation processes, particularly when processing large-scale EEG datasets or aiming for real-time classification. This complexity may impose constraints on the real-world applicability of our pipeline, mainly in online and real-time brain-computer interface (BCI) scenarios where computational efficiency is crucial. While the findings generated from this study confirm the effectiveness of multi-dimensional fractal representations, different fractal dimension methods remain unexplored in the context of motor imagery BCIs. Future research should investigate alternative fractal complexity measures, which may offer complementary insights into the complexity of EEG signals.
## 5 Field Test Configuration and Results
### Credibility Evaluation Results  
The credibility evaluation methodology outlined in Section 3 was employed to quantitatively analyze and compare the results of the real-vehicle tests (Real) and PG-based VILS tests (Sim). Six test scenarios (A-F), each representing a distinct driving behavior, were evaluated from three perspectives: parameter-based, scenario-based, and dynamics-based. Based on the results, we examined how consistently the XILS platform performed in terms of reliability and fidelity, before integrating the findings to assess its overall credibility. Finally, we reviewed the limitations of the evaluation metrics used and their potential scope for enhancement, gaining insights regarding future research directions.
# RHLP-18T: A Radiation-Hardened 18T SRAM with Enhanced Read Stability and Low Power Consumption
## 3 Simulation Results and Analysis
### Stability Comparison
#### 3.1.3 WSNM Comparison  
Write Static Noise Margin (WSNM), or Write Margin, measures how easily the cell can be successfully written to with the intended value. It is typically found by analyzing VTCs during the write operation. Similarly to HSNM and RSNM, WSNM is defined as the side length of the largest square that fits between two curves. A higher WSNM generally means better writeability. The proposed RMLP-18T cell has a WSNM of 305 mV (Figure 7). While 305 mV is sufficient for successful writes under normal conditions, it is lower than some competitors like HPHS12T (478 mV) or RHBD14T (450 mV). This suggests that writing to the cell might be easier (potentially faster), but the safety margin against noise during writing is smaller. This is likely a consequence of the effective write-assist mechanism (using WWL and P11/P12), making the lower WSNM a trade-off for potentially better writeability at the expense of write stability margin.  
Figure 6: RSNM comparison between the proposed RHLP-18T cell and previous works.
# Interaural Coherence Estimation for Speech Processing in Reverberant Environment
## 3 Numerical Experiment and Results
### Computation Time  
Various speech preprocessing and speech recognition techniques are performed in real time; hence, a low computation time of IC estimation is required to add a coherence-based technique. The computation time was verified for 16-kHz speech samples with various lengths from 0 to 8 s and compared for the two IC estimation methods. The IC of the conventional method was calculated sequentially over time. Based on MATLAB, the average computation time was calculated 100 times for each speech length, and the results are shown in Figure 7.  
Increasing the length of the applied speech increased the computation time, and the proposed method always showed less computation time. In this numerical experiment, the proposed method was found to be superior to the conventional method in terms of computation time, and as the speech became longer, the computation time difference also increased.
# Enhancing IoT Data Dependability through a Blockchain Mirror Model
## 3 The Mirror Model  
Before delving into the formal details of the Mirror Model, the context within which the model is supposed to work should be clear. In particular, our argumentation takes place in three correlated domains (Figure 1):  
* Reality;
* Representation;
* Trust.  
Reality is the domain where things actually exist and act. In the domain of Reality, we refer to entities, actions, reactions, events and so on. The Representation domain is the model of Reality we are interested in. In Representation, we refer to objects, relationships, attributes, functions, transformations and so on. The Trust domain is where objects in the Representation domain are made dependable and we refer to asset, transactions and immutable logs. In other words, Reality is where things objectively exist, independently of any efforts made by humans to design or control the Reality, Representation is a model conceived by architects or engineers or scientists to depict the aspects of interest or concern in the Reality and, finally, Trust is a structure of validation of the Representation of Reality whose aim is to make the Representation and its data dependable.  
Trust in not the domain that makes Reality directly dependable, but it is the means to make the Representation directly dependable. Through the Trust and Representation domains we can act indirectly on the Reality to make it more reliable and secure for the benefit of humans and the environment in which they live. Trust is evidently the domain of blockchain for the simple reason that blockchain enforces the necessary mechanisms of immutability and consensus and makes available an immutable history of stored events. The Reality is connected to the Representation by the efforts of the designer while the Representation and Trust domains are connected by the Mirror Model.  
Given this necessary introduction of the three basic domains, the Mirror Model may be defined through a simple formal notation [17] whose aim is to provide conciseness, rigor and precision. The intricacies of the IoT reality, the corresponding modeling and the very technical knowledge needed in blockchain may be synthesized in a discourse that highlights the essential principles, abstracting from the accidental details of implementation. The Mirror Model is subdivided in three parts:  
* the Mirror, that sets the formal foundations to connect an object from the Representation domain to a corresponding asset in the Trust domain;
* the Horizontal Trust, that establishes the property that a collection of objects must possess in order to be trusted;
* the Vertical Trust, whose aim is to maintain trust in an object over time in the Representation domain.
# An Analysis of Methods and Metrics for Task Scheduling in Fog Computing
## 7 Literature Review of Problem Solution Techniques
### Reinforcement Learning Based Algorithms  
[PERSON] et al. [54] propose a reinforcement learning-based task-scheduling scheme for vehicular edge computing networks. The scheme aims to minimize the processing delay of all tasks by assigning the tasks to the edge device, a mobile edge computing servers (fog node), or the cloud server. While their deep Q-learning algorithm may enhance the effectiveness of solutions for complex scenarios with many vehicles, the scheduling approach may not adapt well in dynamic environments.  
[PERSON] et al. [27] have employed an alternative approach based on reinforcement learning (RL) to reduce latency and enhance the security for both fog nodes and Internet of Things (IoT) tasks.  
[PERSON] et al. [72] formulate the task scheduling problem in fog computing as a DAG task scheduling problem in a heterogeneous computing system. Reinforcement learning is used to solve the problem. They consider three main performance metrics in the evaluation phase: schedule length ratio, speedup, and scheduling algorithm's running time.  
[PERSON] et al. [74] presented a novel cloud scheduling approach utilizing a reinforcement learning-based model, which effectively employs fog resources to alleviate the burden on the cloud. Their study primarily focuses on understanding user request behavior, and their proposed decision model accounts for temporal and spatial factors. However, their placement decision policy requires refinement concerning the selection of services to incorporate.  
[PERSON] et al. [8] proposed a novel concurrent reinforcement learning placement approach for optimizing service delivery in fog and cloud environments. The proposed model incorporates the users' attributes, such as distance and service usage intensity, as constraints in the placement process. They are motivated by a need to address service delivery issues by achieving optimal distribution of service loads between fog and cloud computing platforms while maintaining a high service quality during service delivery.  
[PERSON] et al. [64] has developed an alternative task scheduling policy that relies on reinforcement learning principles. This study aims to reduce both the overall computational latency and communication costs. The proposed solution relies on a two-level neural network (NN). The first-level neural network assesses whether the data stream can be processed within the resource-constrained edge/fog environment or whether it should be sent directly to the cloud. The second level NN then schedules the tasks received from the first level NN to the fog layer, considering the available fog devices.
## Appendix B  
### The Clear Air Atmosphere Attenuation  
The calculation process of the electromagnetic wave propagation attenuation model is as follows:  
\[L_{\mathrm{s}}=\gamma_{0}r_{0}+\gamma_{w}r_{w} \tag{1}\]  
where \(\gamma_{0}\) and \(\gamma_{w}\) represent the ground attenuation rates of oxygen and water vapor. \(r_{0}\) and \(r_{w}\) are the equivalent path lengths of oxygen and water vapor, respectively.  
Suppose the path elevation angle is \(\theta\), and the smaller and larger heights of the radiating source antenna and the reconnaissance receiver antenna are \(h_{1}\) and \(h_{2}\), respectively. \(r_{0}\) and \(r_{w}\) can be calculated as follows:  
\[r_{0}=\left\{\begin{array}{c}\dfrac{h_{0}(e^{-\frac{h_{1}}{R_{0}}}-e^{- \frac{h_{1}}{R_{0}}})}{sin\theta},10^{\circ}\leq\theta\leq 90^{\circ}\\ \dfrac{\sqrt{a_{e}h_{0}}[F(\chi_{1})e^{-\frac{h_{1}}{R_{0}}}-F(\chi_{2})e^{- \frac{h_{2}}{R_{0}}})}{cos\theta},0^{\circ}\leq\theta<10^{\circ}\end{array} \right.. \tag{2}\]  
\[r_{w}=\left\{\begin{array}{c}\dfrac{h_{w}(1-e^{-\frac{h_{1}-h_{2}}{hw}})}{sin \theta},10^{\circ}\leq\theta\leq 90^{\circ}\\ \dfrac{\sqrt{a_{e}h_{0}}[F(\chi_{1}^{{}^{\prime}})-F(\chi_{2}^{{}^{\prime}})e ^{-\frac{h_{1}-h_{2}}{hw}}]}{den},0^{\circ}\leq\theta<10^{\circ}\end{array} \right.. \tag{3}\]  
where \(h_{0}\) and \(h_{w}\) are the equivalent heights of oxygen and water vapor. \(a_{e}\) is the equivalent radius of the Earth. \(h_{0}\) and \(h_{w}\) can be calculated as follows:  
\[aah_{0}=\left\{\begin{array}{c}6,f\leq 63 GHz\\ 6+\dfrac{40}{(f-118.7)^{2}+1},63 GHz<f\leq 350 GHz\end{array}\right.. \tag{4}\]  
\[h_{w}=h_{w0}[1+\dfrac{3}{(f-22.2)^{2}+5}+\dfrac{5}{(f-183.3)^{2}+6}+\dfrac{2. 5}{(f-325.4)^{2}+4}],f<350 GHz \tag{5}\]  
where \(h_{w0}\) is the water vapor coefficient, and the value is 1.6 for sunny days and 2.1 for rainy days.  
The calculation equation of \(F(\chi)\) can be represented as follows:  
\[F(\chi)=\frac{1}{0.661\chi+0.339\sqrt{\chi^{2}+5.51}} \tag{10}\]  
\[\chi_{i}=cos\theta[sin\theta tan^{2}\theta\sqrt{\frac{a_{e}}{h_{0}}+\sqrt{\frac {a_{e}}{h_{0}}}sin^{2}\theta+\frac{2h_{i}}{h_{0}}+\frac{h_{i}^{2}}{2a_{e}h_{0} }}],(i=1,2) \tag{11}\]  
\[\chi_{i}^{{}^{\prime}}=cos\theta[sin\theta tan^{2}\theta\sqrt{\frac{a_{e}}{h_{ w}}+\sqrt{\frac{a_{e}}{h_{w}}}sin^{2}\theta+\frac{2h_{i}}{h_{w}}+\frac{h_{i}^{2}}{2a_{e}h _{w}}}],(i=1,2) \tag{12}\]
## 4 Discussion
### Limitations  
This study had few limitations in its experimental environment, designed to simulate high-wall climbing, though it may have differed from natural high-wall settings, which may have introduced measurement inaccuracies. The lack of control over participants' baseline activity levels meant general physical activity outside the experimental setting may have influenced the results, potentially affecting the generalizability of the findings to more sedentary or highly active populations. Additionally, some participants struggled to complete the required number of pull-ups during the PSTP sessions due to their individual fitness levels. The experimental group underwent the PSTP, and prior to the training, each participant's 1 RM for the PSTP exercises was tested to determine their maximum number of repetitions. As the training period progressed, participants' 1 RM values improved. However, trainers had to frequently remind participants to adjust the load, which presented a limitation of this study. Although BPUs were employed as an effective training tool, this exercise may have limitations in fully capturing all aspects of upper-extremity strength. Finally, because the participants lacked experience in climbing-related sports or competitions, their improvement threshold was higher, potentially influencing the overall results.
## 6 Imaging Radar
### Real-Time Processing  
Due to the narrow antenna lobe in the Ka band, the imaging radar is planned to also have a tracking capability to ensure optimum target illumination. Therefore, real-time processing of the acquired signals is required. Since arbitrary waveforms should be used, pulse compression using the deramping technique is not feasible. In order for any pulse compression filter to be applied, the received signal must be sampled in its entire bandwidth. Maximizing the pulse energy by increasing the pulse length to the maximum limit of the amplifier gives us a large number of samples per pulse which have to be Fourier transformed using a fast Fourier transform (FFT) for fast filtering. The calculation of one thousand FFTs per second with a size of about one million points is not reasonable to implement in a field-programmable gate array (FPGA). On the other hand, GPUs are very flexible and capable of handling these calculations on large datasets. The selected GPU-based solution enables a flexible data processing chain that implements pulse compression and tracking filters and even real-time ISAR imaging algorithms.
# Human Factor Issues in Remote Ship Operations: Lesson Learned by Studying Different Domains
## 4 Results  
The results of the literature review are classified as 13 main human factor issues which are shown in Table 2: (1) situation awareness (SA), (2) high workload, (3) trust, (4) boredom, (5) fatigue, (6) skill degradation, (7) human-machine interface, (8) lack of direct sensory information in the SCC, (9) communication challenges, (10) decision-making, (11) linguistic problems, (12) cultural problems, and (13) teamwork. These issues affect human-human and human-machine interactions. The sections below present the results of the each of these issues.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Human-Machine Interactions**} \\ \hline
**Authors** & **Domain** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** \\ \hline
**[PERSON] et al.** [10] & Shipping & \(\checkmark\) & \(\checkmark\) & & & & & \(\checkmark\) & & & & & \\
**[PERSON] et al.** [22] & Shipping & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & & \(\checkmark\) & & \(\checkmark\) & & & \\
**[PERSON] et al.** [7] & Shipping & \(\checkmark\) & \(\checkmark\) & & & & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & \\
**[PERSON] et al.** [23] & Shipping & \(\checkmark\) & \(\checkmark\) & & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & \(\checkmark\) & \(\checkmark\) \\
**[PERSON] et al.** [24] & Shipping & \(\checkmark\) & & & & & & & \(\checkmark\) & & & & \\
**[PERSON] et al.** [25] & Shipping & & & & & & & & & & & & \\
**[PERSON] et al.** [26] & Shipping & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Human factor issues in remote operations.  
Figure 1: Process flow for identifying human factors (HFs) affecting shore control centers (SCCs) from the literature.
# Masked Style Transfer for Source-Coherent Image-to-Image Translation
## 4 Experimental Results
### Evaluation Metrics  
In order to evaluate our model, we use the Frechet inception distance (FID) [36] for image quality and the learned perceptual image patch similarity (LPIPS) [37] to measure diversity in the generated results. More specifically, the FID metric measures the distance between two distributions, and in our case, it is used in order to measure the distances between generated images, e.g., generated cat images and the test set that contains real images, i.e., real cat images. So intuitively, a low value of FID means that two distributions are similar. Indeed, given two Gaussian distributions \((m,C)\) and \((m_{w},C_{w})\), the FID is computed as follows:  
\[d^{2}((m,C),(m_{w},C_{w}))=||m-m_{w}||_{2}^{2}+\text{Tr}(C+C_{w}-2(CC_{w})^{ \frac{1}{2}})\]  
The learned perceptual image patch similarity (LPIPS) calculates the perceptual similarity between two images. The LPIPS essentially computes the similarity between the activations of two image patches for some pre-defined and pre-trained network. This measure has been shown to match human perception well. A low LPIPS score means that image patches are perceptually similar. Indeed, given two patches \(x\) and \(x_{0}\), their distance is computed as follow:  
\[d(x,x_{0})=\sum_{l}\frac{1}{n_{l}!}\sum_{h,w}||w_{l}\odot(g^{l}_{hw}-g^{l}_{ 0 throw})||_{2}^{2}\]  
where \(g^{l}_{hw}\) and \(g^{l}_{0 throw}\) are the stacked features extracted from the patches. These features are normalized, and the distance between them is modulated by a learned weight vector \(w_{l}\) that adjusts the contributions of different feature channels [37].  
Since the main contribution of our model is to perform source-coherent translation, which aims to improve diversity for images generated with the same reference, the evaluation is designed as follows:  
* Firstly, we randomly select one image for every domain as reference;
* Secondly, given a set of source images, we generate samples with those reference images;
* Thirdly, we compute the FID and LPIPS (with consecutive pairs of images);Finally, we repeat this evaluation phase 10 times in order to remove randomness from the results.  
It is worth emphasizing that we decided to not use the StarGANv2 FID algorithm, which calculates the FID by using ten references for each domain, because we want to improve the diversity of the results generated from a single reference. Therefore, the FID is computed with only one reference per domain in order to evaluate the quality of the images generated with our method.