esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: This paper aims to complement the latest contribution in the literature that provides estimates of physiological parameters of a dynamic model for the elbow time profile during walking while linking them to a neurodegenerative disorder (Parkinsons's disease) characterized by motor symptoms. An upper limb model is here proposed in which an active contractile element is included within a model, viewing the arm as a double pendulum system and muscles as represented by a Kelvin-Voight system. All model parameters characterizing both the shoulder and the elbow of each subject are estimated via a gradient-like identifier whose exponential convergence properties are determined by a non-anticipative Lyapunov function, ensuring robustness features. Methods: Joint angle data from different walking subjects (healthy subjects and patients with Parkinson's disease) have been recorded using an IMU sensor system and compared with the joint angles obtained by means of the proposed model, which was adapted to each subject using available anthropometric knowledge and relying on the estimated parameters. Results: Experiments show that the reconstruction of shoulder and elbow time profiles can be definitely achieved through the proposed procedure with the estimated stiffness parameters turning out to constitute objective and quantitative indices of muscle stiffness (as a pivotal symptom of the pathology), which are able to track changes due to the therapy. Conclusions: The same dynamic model is actually able to capture the main features of the upper limb movement of both (healthy and pathological) walking subjects, with its parameters, in turn, characterizing the nature and progress of the pathology.  
upper-limb swing modeling; shoulder and elbow angles; stiffness identification; contractile element; Parkinson's disease
# Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis
## 3 Experimental Setup  
### Real Seismic Data  
The proposed methodology was rigorously tested using real seismic data from Mexico, specifically, a pre-stack seismic time migration cube. The survey area encompassed 141.47 km\({}^{2}\) and featured 491 inlines and 461 crosslines. The data spanned a time range of 1251 ms, with a sampling rate of 4 ms and a bin size of 25 m. Each CDP consisted of approximately 40 traces. Furthermore, the dataset was enriched by incorporating well-log information obtained from five drilled wells.  
A pre-processing stage, described in Section 2, was meticulously applied to prepare the raw data for training, resulting in two subsets: one labeled and one unlabeled, as seen in Table 3.
# Effect of Stiff Hangers on the Longitudinal Structural Behavior of Tied-Arch Bridges
## 3 Stiff Hangers
### Pinned vs. Stiff Hangers  
In some cases, it not strictly necessary for stiff hangers to be fully fixed at both ends. The rotation either at the bottom or the top end can be released--a decision made by the designer as a step of the design process. Figure 9 compares four configurations: for fully fixed stiff hangers, hangers pinned at the top end, the bottom end or at both ends (and composed of cables). The cross-section considered for the stiff hangers is an all-steel rectangular hollow box, 800 \(\times\) 800 \(\times\) 20 mm.  
For the most adverse load combination (\(q_{1}\)), it can be easily seen that when half of the deck is loaded, the bending moments both at the arch and at the deck can be reduced by stiffening the hangers. In fact, the minimum bending moments are always achieved for hangers fixed at both ends. Hangers with only one pinned end are not as efficient as fully fixed hangers. For example, when the hangers are pinned at the arch end it can be seen that the bending moment diagram is smooth for the arch, whereas it has a saw-teeth form for the deck, and vice versa. This is due to the introduction of concentrated bending moments at the fixed end (the one at the bottom) of the hangers.  
However, for the live load \(q_{3}\) acting on the whole deck, the positive bending moments near the springings are higher for the fully fixed hangers. This effect can be explained because of the structural behavior of Vierendeel trusses. This is a possible drawback of stiff hangers, and the process of design of the structure must consider this fact.  
The stiff hangers are also a very efficient way to reduce the deck deflections. Figure 10 shows the effect of the hanger stiffness on the deflections of the deck under \(q_{1}\) and \(q_{3}\). The maximum reduction is always achieved for hangers fixed at both ends. Besides, for the same hanger configuration (i.e., for hinges located either at the top or at the bottom), an 800 \(\times\) 800 \(\times\) 20 hanger, with higher flexural stiffness, is more efficient when reducing the deflections than a 400 \(\times\) 400 \(\times\) 20 hanger, with lower flexural stiffness.  
Figure 8: Bending moment diagrams for pinned and fixed stiff hangers (800 \(\times\) 800 \(\times\) 20 mm). Live load distributed on half deck, \(q_{1}\) (**a**,**c**), and the whole deck, \(q_{3}\) (**c**,**d**), both for cables (**a**,**b**) and stiff hangers (**c**,**d**).  
Figure 10: Deflections for stiff hangers.  
Figure 9: Pinned and stiff hangers comparison: Bending moments at the arch and the deck.
## 2 Materials and Methods
### Ultrasonics: Cure Process  
Ultrasonics were utilized to non-destructively measure the sound speed of the adhesive during its curing process. To perform this testing during the adhesive's curing process, the adhesive was first mixed and then placed in a PLA 3D printed mold. The mold had dimensions of 20 mm in height, 22 mm in width, and 12.5 mm in thickness. The mold's wall thickness was 1.5 mm, except for the locations where the transducers were placed, which had a thickness of 0.5 mm. A function generator (AFG31052; Tektronix, Beaverton, OR, USA) was utilized to generate a sine burst of 500 kHz at 10 volts peak-to-peak. This frequency was selected after observation over a range of frequencies as it produced the highest output signal for this particular adhesive material. A Tukey window with a cosine fraction, r, of 0.4 was used to taper the input function consisting of five cycles [46]. The burst was transmitted and detected by transducers placed on each side of the sample. Transducers (V133-RM and V154-RM, Olympus, Center Valley, PA, USA) of 2.25 MHz were used for evaluating the longitudinal and shear sound speeds, respectively. An ultrasonic couplant (Echo Ultrasonics, Bellingham, WA, USA) was used to ensure proper contact between the sample and the transducers. The resulting waveforms were recorded every two minutes using an oscilloscope (MDO32; Tektronix, Beaverton, OR, USA). Figure 1a shows a diagram of the ultrasonics setup, where the sample is depicted in blue and the transducers, in green. The output waveforms were then used along with the excitation waveform to calculate the sound travel time across the sample utilizing the cross-correlation method [30]. It is important to mention that the delays introduced to this travel time by the transducers and the PLA mold, where the adhesive was placed, were accounted for prior to using Equation (1) to calculate the sound speed in the material:  
\[c=\frac{d}{t} \tag{1}\]  
where \(d\) is the thickness of the sample and \(t\) is the sound travel time. The law of propagation of uncertainty was used to obtain the error associated with the velocity calculations [47]. This method accounts for the uncertainty associated with the resolution of the oscilloscope to plot the output waveforms (9 ns), which were used to obtain the sound travel time, and the uncertainty associated with the digital micrometer which was used to evaluate the thickness of the sample (0.01 mm).
## 6 Results  
### Experimental Study Results  
After the completion of the project, the participants were asked to fill out the survey questions. We analyzed the information we gathered from the participants via questionnaires. The results of the surveys are analyzed below. It should be noted that these participants are not professionals. This experiment group with no familiarity with low-code development rated their experience on a scale of 1 to 10, 1 being the hardest and 10 being the easiest, and they were asked to answer questions.  
We can deduce from the following results stated in Figure 8 that half of the participants who have never developed in a low-code development environment do not find this environment very easy. On the other hand, it is seen that 64% of the participants have a positive opinion about the framework that we have prepared. Moreover, 86% of respondents think that they understood the framework well. This is encouraging feedback that suggested that we were able to explain our methodology and tools to the participants well. We see that after the domain-oriented development processes, 79% of them find it easy to generate the products through different configurations corresponding to low-code development.  
When we asked the participants about their experiences regarding the transitions among models, we encountered the results in Figure 9. The answers we obtained are not very surprising. Transitions between the feature model and BPMN model cover the creation of the complete BPMN and the preparation of the base and partial BPMNs. In summary, it includes drawing a business process model covering the entire domain and more. For this reason, only 57% of people think that this transition is easy. On the other hand, operations involving method calls from the BPMN model were found easy by 86%. We think that this is due to the fact that method calls from within the tasks defined in the BPMN model can be made through the easy-to-use GUI of the framework.  
Figure 8: Easiness of the framework.  
One of the things we were most curious about during the experiment was the time spent. The answers we received to the questions we asked for this purpose are given in Figure 10. A total of 62% of respondents said they spent less than or equal to 10 h developing their webinar system domain. When generating the products with different configurations, 86% explained that they spent less than or equal to 6 h. We inferred that the time spent here is due to an immature domain because the framework completes the creation process of products developed in different configurations in milliseconds. However, the participants, who probably did not have a chance to develop a mature domain, encountered errors, corrections, etc. So, this step may have taken a long time.
# Golgi Metal Ion Homeostasis in Human Health and Diseases
## 2 Ca\({}^{2+}\)/Mn\({}^{2+}\) Transporters and Channels  
As one of the most ubiquitous second messengers, Ca\({}^{2+}\) plays a critical role in a variety of intracellular signaling events in the cell. The resting concentration of Ca\({}^{2+}\) in the cytoplasm is normally maintained at around 100 nM, while the extracellular level is in low millimolar range. To maintain the low cytosolic concentration, most of the Ca\({}^{2+}\) is stored in the lumen of the endoplasmic reticulum (ER) and the Golgi. Correlative laser scanning confocal fluorescence microscopy (LSCFM) and ion microscopy revealed that the Golgi is capable of storing up to 5% of total cellular Ca\({}^{2+}\), depending on the cell type, and is more resistant to Ca\({}^{2+}\) depletion than other cellular organelles [27]. The maintenance of high Ca\({}^{2+}\) levels in the Golgi lumen is facilitated by Golgi-residing Ca\({}^{2+}\) ATPases (transporters), Ca\({}^{2+}\) release channels, and Ca\({}^{2+}\) binding proteins. This section focuses on these proteins in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metal Ion** & **Disease** & **OMIM** & **Gene** & **Protein** & **Clinical Features** \\ \hline \multirow{6}{*}{Caz\({}^{2+}\)/Mn\({}^{2+}\)} & Brody myopathy (BRM) [3] & 601003 & _ATP2A1_ & SERCA1 & Early onset of muscle function disorder characterized by muscle & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & Acrokeratosis verruciformis (AKV) [4] & 101900 & _ATP2A2_ & SERCA2 & Early onset keratinization disorder affecting the & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & Darier disease (DD) [5] & 124200 & _ATP2A2_ & SERCA2 & Early onset keratinating disorder characterized by small & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & Halley-Hailey disease (HHD) [6] & 169600 & _ATP2C1_ & SPCA1 & A skin disease causing persistent blistines and superbansal cell & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & Spinocerebellar ataxia 15 (SCA15) [7] & 606658 & _ITPR1_ & IP3R 1 & A neurological condition characterized by progressive gait & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & Spinocerebellar ataxia 29 (SCA29) [8] & 117360 & _ITPR1_ & IP3R 1 & Early onset cerebellar ataxia causing slowly progressive or & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & [PERSON] syndrome (GLSP) [9] & 206700 & _ITPR1_ & IP3R 1 & A congenital neurological disorder characterized by the & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } \\  & Anhidrosis, isolated, with normal sweat & & & & & \\  & glands (ANHD) [10] & 106190 & _ITPR2_ & IP3R 2 & A disorder characterized by absence of respiration and & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ Ca\({}^{2+}\)/Mn\({}^{2+}\) & & & & & & \\  & Malignant hyperthermia 1 (MHS1) [11] & 145600 & _RYR1_ & RyR1 & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & &  
the maintenance of Ca\({}^{2+}\) and Mn\({}^{2+}\) homeostasis in the Golgi as well as their significance in Golgi function.
# Wavelength Selection for Satellite Quantum Key Distribution
## 2 Transmittance and Spectral Radiance
### Key Assumptions and Limitations of MODTRAN Simulations  
MODTRAN assumes that the atmosphere is described in several layers up to a 100 km altitude using one of six different reference model atmospheric profiles which specify gas content per layer. The layers are defined at their boundaries by temperature, pressure density, etc., and the MODTRAN program interpolates between layers. MODTRAN includes several atmospheric gases, aerosols, and cloud and rain models. Water vapour is the major factor in cloud formation and in determining transmission of laser light through the atmosphere. Cloud models incorporate parameters such as cloud type, altitude and thickness, optical properties, water content, temperature, and multiple cloud layers. The scattering term is modelled using Mie theory, which describes the scattering of electromagnetic radiation by spherical particles. The absorption term is modelled using the Beer-Lambert law, which describes the absorption of radiation by a medium. Atmospheric refraction and surface reflection and emission are also accounted for, as well as solar and lunar irradiance.  
Despite its major success and widespread use in modelling the atmosphere, MODTRAN has certain limitations. MODTRAN6 uses a line-by-line (LBL) algorithm, which allows the transmittance to be computed at an arbitrarily small wavelength resolution using a high-resolution transmission molecular absorption database called HITRAN. This LBL radiative transfer model is very intense and requires significant time and computer power to solve. Also, while the MODTRAN program includes several atmospheric models, real-world atmospheric environments are much more complicated. MODTRAN assumes that the concentration of atmospheric constituents varies with altitude, but not horizontally. Lastly, in terms of adaptability, the MODTRAN program has only a few aerosol models, which yields a risk that the aerosol parts become inaccurate when studying conditions beyond these models [46; 47].  
In this work, the MODTRAN Atmospheric Generation Toolkit was used to replicate the atmospheres of each scenario [48; 49]. MODTRAN6 was used to clarify the wavelength ranges available for QKD by determining the losses resulting from atmospheric absorption and scattering. For satellite QKD to help establish the quantum network, systems must yield secret key rates in a variety of atmospheric conditions and geographical locations. Two very different scenarios are considered here: the summer subtropical climate of Tucson, Arizona, is compared with the winter temperate climate of Waterford, Ireland. MODTRAN was used to simulate these atmospheres and determine the transmittance and spectral radiance across a range of wavelengths. Spectral radiance is highly dependent on the sun's position in the sky relative to the Earth. Therefore, we specify a location and time. Tucson is used to represent an ideal location for satellite communications whereas the wet and cloudy skies of Waterford represent regions where satellite communication is more challenging.
# A Distinctive Explainable Machine Learning Framework for Detection of Polycystic Ovary Syndrome
## 3 Materials and Methods
### Feature Selection
#### 3.3.1 Harris Hawks Optimization (HHO)  
Harris Hawks Optimization is a population-based algorithm that uses stochastic components to find the optimum parameters of a dataset. The algorithm considers the ability of Harris Hawks to form chasing patterns based on the prey's escaping dynamics [36]. This method consists of two phases exploration and exploitation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Sr. No** & **Attributes** & **Mean** & **Std** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline
1 & PCOS (Y/N) & NA & NA & 0 & NA & NA & NA & 1 \\ \hline
2 & Age (yrs) & 31.43068 & 5.411006 & 20 & 28 & 31 & 35 & 48 \\ \hline
3 & Weight (Kg) & 59.63715 & 11.02829 & 31 & 52 & 59 & 65 & 108 \\ \hline
4 & Height (cm) & 156.4848 & 6.033545 & 137 & 152 & 156 & 160 & 180 \\ \hline
5 & Pulse rate (bpm) & 73.24769 & 4.430285 & 13 & 72 & 72 & 74 & 82 \\ \hline
6 & RR (breaths/min) & 19.24399 & 1.688629 & 16 & 18 & 18 & 20 & 28 \\ \hline
7 & Hb (g/dL) & 11.16004 & 0.866904 & 8.5 & 10.5 & 11 & 11.7 & 14.8 \\ \hline
8 & Cycle (R/I) & NA & NA & 2 & NA & NA & NA & 4 \\ \hline
9 & Cycle length (days) & 4.94085 & 1.49202 & 0 & 4 & 5 & 5 & 12 \\ \hline
10 & Pregnant (Y/N) & NA & NA & 0 & NA & NA & NA & 1 \\ \hline
11 & FSH (mlU/mL) & 14.60183 & 217.0221 & 0.21 & 3.3 & 4.85 & 6.41 & 5052 \\ \hline
12 & LH (mlU/mL) & 6.469919 & 86.67326 & 0.02 & 1.02 & 2.3 & 3.68 & 2018 \\ \hline
13 & FSH/LH & 6.904917 & 60.69198 & 0 & 1.42 & 2.17 & 3.96 & 1372.83 \\ \hline
14 & Waist (inch) & 33.84104 & 3.596894 & 24 & 32 & 34 & 36 & 47 \\ \hline
15 & Waist-Hip Ratio & 0.891627 & 0.046135 & 0.76 & 0.86 & 0.89 & 0.93 & 0.98 \\ \hline
16 & AMH (ng/mL) & 5.620634 & 5.876742 & 0.1 & 2.01 & 3.7 & 6.9 & 66 \\ \hline
17 & PRL (ng/mL) & 24.3215 & 14.97039 & 0.4 & 14.52 & 21.92 & 29.89 & 128.24 \\ \hline
18 & PRG (ng/mL) & 0.610945 & 3.808853 & 0.047 & 0.25 & 0.32 & 0.45 & 85 \\ \hline
19 & Weight gain (Y/N) & NA & NA & 0 & NA & NA & NA & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics of some of the attributes present in the dataset.  
In the exploratory phase, the Harris Hawk must monitor a particular area and prey. In HHO, the Harris Hawks can be considered search agents or candidate solutions, out of which the best solutions become near optimum. The exploratory phase constitutes the hawks or search agents monitoring all search space, enhancing the randomness of HHO.The transition from the exploratory to the exploitory phase is based on the target's energy [37]. The target comprises the optimal solution (Best features). After deploying the HHO optimizer as a feature selection technique, 15 of the most significant features were extracted. Out of which 8 were non-invasive features. The selected features are presented in Table 3.
###### Abstract  
Time difference of arrival (TDOA) method and beamforming method are often individually utilized in the localization of acoustic emission source of a plate. Based on the performances of the two methods, a novel joint localization method for acoustic emission source is proposed in this paper. Firstly, the performances of TDOA method and beamforming method are studied based on the simulation signals. Then, aiming at the advantages and disadvantages of these two methods, a joint localization method is proposed. Finally, the performances of joint localization method are verified by simulation and experiment. Both simulation and experimental results show that the accuracy of the joint localization method is improved, and the calculation amount is greatly reduced in comparison to the TDOA method and the beamforming method.  
Keywords:time difference of arrival; beamforming; joint localization; acoustic emission +
Footnote †: journal: Applied Sciences
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 3 System Model  
D2D communication over IoT-based 5G networks can be modeled as a graph \(G(N,L)\), in which \(N\) refers to the number of devices and \(L\) refers to the number of wireless links among the devices. \(l(a,b)\in L\) denotes the link among two devices, i.e., \(a\in N\) and \(b\in N\). The transmission of data packets is feasible when two neighbors' devices share links with each other, whereas relay devices are utilized when the two devices disassociate from each other. The proposed MBLCR scheme considers the mobility, battery, link quality status, and contention window size of relay devices in the efficient and optimal path selection from the source to destination devices. The following subsections describe the evaluation of relay devices' mobility, battery, link quality status, and contention window size.  
Figure 3: Challenges and solutions of MBLCR scheme.
# Fully Open-Source Meeting Minutes Generation Tool
## 5 Evaluations
### Performance of Audio-to-Text Function  
The _audio-to-text function_ utilizes the _Whisper_ model to convert _audio-to-text_ and evaluates performance using _WER_. Audio is normalized to a sampling rate of 16 kHz. Despite the various speeches from non-English-speaking countries, including Vietnam, Congo, Singapore, Malaysia, and Indonesia, with varied duration, the _Whisper_ model demonstrates averaged low _WER_, as shown in Table 8.  
Table 8 shows the results. It demonstrates that the _Whisper_ model consistently achieved low _WER_ values across different accents on all the slides. This emphasizes _Whisper_'s robustness in handling varied accented speakers. However, some slides that contain the main information (e.g., slide 2 and slide 3) show slightly higher _WER_ values. This behavior may be attributed to the technical terms used in the presentation. However, the model continues to deliver accurate transcriptions on the other slides that are comparable to professional human transcribers [60].  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Topic** & **Seconds** & **F1 Score** & **Execution Time (s)** \\ \hline Artificial Intelligence & 350 & 100\% & 38.57 \\ Biotechnology & 329 & 100\% & 50.47 \\ Blockchain & 404 & 100\% & 47.17 \\ Cybersecurity & 397 & 100\% & 42.16 \\ Renewable Energy & 413 & 100\% & 42.37 \\ Environmental & 365 & 100\% & 39.62 \\ Healthcare & 328 & 100\% & 35.01 \\ Quantum Computing & 272 & 100\% & 27.56 \\ Data Science & 373 & 100\% & 39.86 \\ Space Exploration & 490 & 100\% & 46.96 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _SSIM_ algorithm results for per-second approach.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Accents** & **Slide 1** & **Slide 2** & **Slide 3** & **Slide 4** & **Slide 5** \\ \hline Vietnam & 1.30\% & 1.58\% & 2.17\% & 0.82\% & 1.31\% \\ Congo & 1.32\% & 1.75\% & 2.45\% & 1.04\% & 1.60\% \\ Singapore & 0.89\% & 1.74\% & 2.37\% & 0.98\% & 1.43\% \\ Malaysia & 1.35\% & 1.57\% & 2.30\% & 0.98\% & 1.64\% \\ Indonesia & 1.36\% & 1.76\% & 2.41\% & 1.79\% & 1.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Averaged _WER_ on each slide from different English accents.