esa-sceva/satcom-chunk-collection huggingface dataset:
# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 3 Materials and Methods
### Experimental Design
#### 3.2.5 Testing Using Different Databases  
The optimal feature choice and a reduced list of classifiers were selected for the final framework development. The efficacy of this framework was tested by training the classifiers on a larger dataset and testing them on a different dataset. The latter dataset, except for the speech task, differed in terms of speakers, language, and acquisition environment, ensuring a more comprehensive assessment of the classifiers' performance and robustness. Furthermore, the influence of gender over the framework was examined by conducting replications of the experiment without any gender-based filters to the data. This allowed for a broader analysis of how the classifiers performed across both genders without any gender-specific constraints.  
Owing to the variations in data acquisition conditions, such as differences in equipment and variations in speaker-to-microphone distances, the features extracted from different databases exhibited discrepancies in their numerical ranges. Normalization has been used to address these differences and maintain uniformity between the features from both databases before initiating the experimentation where training and testing were performed using different databases. z-scores with zero mean and unit standard deviation were extracted from feature data using Equation (3) and used for training and testing.  
\[z_{ij}=\frac{x_{ij}-\mu_{i}}{\sigma_{i}} \tag{3}\]  
where, _x_-Feature value, _i_-Feature index, _j_-segment index, _u_-Mean, and _v_-Standard deviation.  
In addition to the normalization, a syllabic analysis approach is designed to emphasize more of the perturbations. The features extracted were transformed into covariances at a super segment level to do this. In the speech data, super segments were identified as the voiced components bordered by unvoiced/silent portions on both ends as explained earlier. Features extracted from all segments within a super segment were grouped and covariances of these feature groups were used for training classifiers. Figure 4 shows the method adopted to extract these covariances. C1 to Cn represent the feature covariances and subsequently used feature vectors from each super segment.  
Figure 4: Syllabic learning approach showing the original recording (blue) overlaid with voiced sections (red) in the figure at the top. One of the voiced sections, segmented pitch synchronously, is zoomed into and shown at the center.
## 2 Materials and Methods
### Modifications Performed to Original ALO RPA
#### 2.2.1 Electric Engine  
ALO RPA originally had a two-cylinder two-stroke reciprocating engine (see Figure 5). The new configuration, with an electric engine, needs to achieve at least the same power. The chosen electric engine for the retrofitting platform is the DC electric engine Plettenberg(c) Predator 37-6 brushless (BLDC) outrunner type, manufactured by Plettenberg(c) Elektromotoren GmbH and Co., Baunatal, Germany. It weighs only 1.9 kg, has a maximum power of 12 kW, allows a maximum current intensity of 308 A, and can work with 12-14-cell (3 V each) battery packs. The manufacturer also declares an efficiency of 87%. The main characteristics of the electric engine are shown in Table 7.  
Figure 5: Original ALO internal combustion engine with a two-blade wooden propeller.  
As shown, the engine has more than enough power to propel the RPA, even if it finally had an increase in total weight [72].  
The main feature of BLDC engines is the absence of brushes on switching for the transfer of power, since brushes produce friction, decrease performance, generate heat, are noisy, require periodic replacement, and; therefore, require greater maintenance [73]. Some of the advantages of the BLDC motors in regards to conventional DC motors are:  
* Best relationship speed torque engine.
* Greater dynamic response.
* Greater efficiency.
* Greater life time.
* Lower noise.
* Greater range of speed.  
In addition, the torque: size ratio is much greater, thus are very useful in work environments with reduced space. These motors develop their maximum torque from 0 rpm, so reduction gear is usually not needed, and it can be attached directly to a propeller. It has permanent magnets installed in the outer shell of the engine and the winding is attached to the shaft. These motors require ESC, which transform DC battery power into a three-phase alternating current and feed it to the windings in a certain sequence depending on the position of the rotor.
## 2 Results and Discussion  
### Preparation of the Catalytic System  
The PS-NHPI heterogeneous catalyst was obtained by immobilizing NHPI on commercially available chlorophyll polystyrene (PS-Cl) resins (Table 1). The resins (carriers) were characterized by elemental analysis and SEM, as well as by FT-IR spectroscopy applying an attenuated total reflectance (ATR) method. The results of the elemental analysis showed that the functional group content in the PS-Cl-1, PS-Cl-3, and PS-Cl-4 did not differ from the values provided by the manufacturer. Two resins, PS-Cl-2 and PS-Cl-5, were found to contain slightly more functional groups than reported by the vendor  
Figure 2: Catalyst systems tested and compared in this work, i.e., with and without ionic liquid. (a) PS-NHPI (b) PS-NHPI@CoCl\({}_{2}\)@[emim][OcOSO\({}_{3}\)]. The FT-IR spectra allowed for identification of the deformation vibration bands (\(\delta_{\text{C-CI}}\)) at approximately 1265 cm\({}^{-1}\) and the bands corresponding to the stretching vibrations (\(\
u_{\text{C-CI}}\)) at around 823 cm\({}^{-1}\), which are characteristic of the chloromethyl group (Table 1). The SEM analysis showed that the resins have a consistent circular grain shape within the size range specified by the manufacturer (Figures S1-S5). According to the procedure described in [24], trimellitic anhydride was first immobilized on a PS-Cl supports through formation of ester bonds, and in the second reaction step, the N-OH group was formed following a reaction with hydroxylamine hydrochloride (Scheme 2). The obtained heterogeneous catalysts PS-NHPI were characterized using FT-IR analysis (Figure 3, Figures S11-S14, Table 2), elemental analysis (Table 2), and SEM (Figures S6-S10). For example, characteristic bands are observed in the FT-IR spectrum of the PS-Cl-2 carrier (Figure 3, Table 1) at 3025, 2920, 1602, 1510, 1493, 1451, 1421, 1265, 1111, 1019, 908, 823, 760, 699, and 674 cm\({}^{-1}\). In the FT-IR spectrum of PS-NHPI-2 (Figure 3, Table 2) there is an additional a broad band at 3398 cm\({}^{-1}\) corresponding to the stretching vibrations of the N-OH group (\(\
u_{\text{OH}}\)), an intense band at 1720 cm\({}^{-1}\) of the carbonyl groups' stretching vibrations (\(\
u_{\text{C-O}}\)), and a band at 1236 cm\({}^{-1}\), which is characteristic of the stretching vibration of a C-O single bond (\(\
u_{\text{C-O}}\)), thus confirming the presence of an ester group. Elemental analysis indicated the presence of nitrogen, which verifies the immobilization of NHPI on the support (Table 2). The amount of NHPI immobilized on the support ranged from 1.70 to 2.63 mmol/g.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 4 Discussions
### Further Improvements for the Bottom-Up Inversion  
As described Section 2, in the \(N_{e}\) inversion the modified measurement vector is a differential _hTEC_ profile relative to its linear variation determined from \(h_{t}\) = 20-50 km. This change yields two advantages: (1) significantly reducing the impact of \(F\)-region residuals on the \(D/E\)-region \(N_{e}\) retrieval; and (2) making the \(D/E\)-region \(N_{e}\) retrieval less dependent on absolute error or accuracy of the _hTEC_ measurement. Apart from these changes, the core inversion formulation (i.e., Equation (11)) is the same in both approaches.  
The same principle is applied to the _TEC_ retrieval using _HTEC_ differences between the measurement at \(h_{t}\) = 60 km and those at \(h_{t}\) < 220 km (Section 2.3.4). This \(\Delta\)_hTEC_-based _TEC_ retrieval will allow a full use of the high-rate RO data for both neutral atmospheric and ionospheric applications, and the retrieved _TECs_ agree reasonably well with the ground-based observations in the case where the RO profile top reaches 220 km. Because the number of RO profiles is generally greater and the RO data quality (i.e., SNR and _hTEC_)  
Figure 20: As in Figure 20, but for August 2008, observed by COSMIC-1, GRACE, TSX, and SAC-C.  
is better than POD measurements, the high-rate RO profiles with a higher top will attract more attention in the dual applications for atmospheric and ionospheric sciences.  
The bottom-up inversion algorithm has several similarities to the Abel inversion in formulation. A further development with this algorithm will be to apply the inversion technique to the 1 Hz POD measurements, some of which have the RO profile reaching to \(h_{t}=100\) km or lower. Although these \(hTEC\) profiles may not have the same quality as the high-rate RO data, they can serve as a surrogate to verify the \(D\)/\(E\)-region \(N_{e}\) and \(TEC\) retrievals from the bottom-up method. If both POD and RO data can be used independently for the \(TEC\) retrieval, it will increase the sampling and coverage by a significant amount. Previous \(N_{e}\) retrievals from the POD \(hTEC\) have been struggling in the lower ionosphere, showing large oscillations in the \(N_{e}\) profile [38]. Preliminary results show that the sequential optimization inversion performs better than the onion-peeling approach for retrieving \(N_{e}\) retrieval without artificial oscillations in the lower ionosphere.  
Although it is feasible to jointly retrieve the \(N_{e}\) profile from the 1 Hz POD and high-rate RO \(hTEC\) measurements, the LEO-PRN pair in both observations needs to be consistent. For COSMIC-2, there are \(\sim\)50% of the POD profiles that can be paired to the high-rate RO profile. Out of these matches, approximate 50% are overlapped in tangent heights. As a result, there are only \(\sim\)25% of the RO profiles that may be used for jointly retrieving \(N_{e}\) from the \(D\)-region to \(F\)-region.
# Emerging Technologies for Advanced Power Electronics and Machine Design in Electric Drives
## 2 Topologies of Electric Drives
### Multiphase Electric Drive Topologies
#### 2.3.2 Common Multiphase Drive Configurations  
Typically, machines in multiphase drives use five or six phases; however, seven-, nine-, twelve-, and fifteen-phase drives have also been reported, especially for marine applications. The most common multiphase drives are five-phase induction [91] and synchronous drives [92; 93; 94; 95], as well as six-phase induction [96] and synchronous drives [80].  
Five-phase machines with concentrated windings offer the possibility of increasing torque generation by injecting harmonics into stator current [97]. In a machine with phase number \(n\), all odd harmonics in the range from one to \(n\) can interact with the spatial harmonics of the magnetomotive force (MMF) and generate an additional torque. The five-phase machine is very interesting in terms of operation reliability, especially as operation can be guaranteed even with double phase faults. As a result, five-phase drive systems are increasingly being used in more electric aircraft (MEA), where the priorities are to maximize power density, efficiency, and reliability [98].  
Of particular interest are multiphase machines with \(3\times k\) phases (e.g., 6, 9, 12,...), where windings are configured as several three-phase winding sets with \(k\) isolated neutral points [99]. This so-called multiple three-phase machine is particularly advantageous as it allows the use of conventional three-phase inverters for the supply. In this way, a readily available, proven three-phase technology can be used, reducing costs and development time [100]. Another advantage of the multiple three-phase drives is their enhanced fault tolerance due to the inherent modularity of the three-phase subunits. In the event of an open-phase fault, either on the machine or inverter side, the affected three-phase subunit (comprising the three-phase winding set and the inverter) can simply be disconnected from the DC link. Reconfiguring the drive after a fault is thus very straightforward [101].  
Figure 9: Different options for star-connected \(n\)-phase machine: (**a**) \(n\)-phase machine with 1 neutral point, (**b**) multiple \(m\)-phase machine with \(k\) neutral points, and (**c**) open-end winding \(n\)-phase machine. The selected option determines the topology of the multiphase drive, which is shown on the right for a 6-phase drive.  
Multiple three-phase drives also allow power sharing between the winding sets. The machine can be connected to multiple three-phase power sources or loads simultaneously, allowing active and reactive power to be transferred between the sets of three-phase windings without affecting the operation of the machine. The concept, which is primarily motivated by applications in microgrids and electric vehicles, has been successfully implemented in PMSM and IM drives [102].
## 4 Results
### Channel Capacity  
In this subsection, the channel capacity performance corresponding to the proposed V2I-VLC propagation model is discussed. Figure 10 illustrates the relationship between the channel capacity (maximum data rate) and the transmission distance based on (22), and also the simulation curves. First, it achieves the highest capacity at all transmission distances in clear weather conditions due to better average path loss performance compared to foggy weather. Second, transmission distance has a great impact on the channel capacity of the proposed V2I-VLC propagation model. For example, under clear weather conditions,  
Figure 8: Received optical power corresponding to the proposed V2I–VLC propagation model under moderate fog conditions.  
Figure 9: Total received optical power corresponding to the proposed V2I–VLC propagation model under different weather conditions.  
the channel capacity at \(I_{V_{1}-PD}=20\) m is about 87.4% lower than the channel capacity at \(I_{V_{1}-PD}=10\) m. Additionally, when \(I_{V_{1}-PD}=40\) m (compared to \(I_{V_{1}-PD}=10\) m), this ratio reaches 98.9%. Furthermore, with the increasing transmission distance, the effect of weather conditions on the channel capacity becomes less significant. For instance, Figure 10 shows that when \(I_{V_{1}-PD}=10\) m, the channel capacity of the moderate and dense fog is about 18.6 Mb/s and 21.9 Mb/s lower than that of the clear weather, but, when \(I_{V_{1}-PD}=20\) m, the difference is only 3.6 Mb/s and 5.0 Mb/s. It reveals that, for the proposed V2I-VLC propagation model, the transmission distance is a more influential factor than the weather conditions in terms of the performance of the channel capacity.
###### Abstract  
Building-integrated photovoltaics (BIPV) requires tools that improve and facilitate simulating and predicting the system's output energy. The efficiency of a photovoltaic (PV) system can be determined by the performance ratio (\(PR\)), which relates the actual system's output energy to the theoretical output according to the installed power and the solar irradiation, thus accounting for the power losses the PV system undergoes. Among the different parameters affecting \(PR\), module temperature and the angle of incidence of irradiance are the most dependent on the BIPV application due to the varied module positioning. This paper assesses the suitability of several BIPV temperature models and determines the angular losses for any possible module positioning. The proposed methodology is easy to replicate and results in polar heatmap graphs to estimate \(PR\) at the desired location as a function of the tilt and azimuth angles of the modules. The calculations require irradiance, ambient temperature, and wind speed data, which can easily be obtained worldwide. Dynamic sky conditions are addressed through filters that smooth out quickly changing input data to avoid high and low peaks. The developed graphs are helpful in the decision-making process for BIPV designs by allowing the designer to estimate the expected \(PR\) of the BIPV system for any possible position of the modules on the building envelope, reducing the effect of uncertainties and resulting in more accurate and better predictions of the system's output energy. The method applied to a BIPV facade in Madrid showed a deviation of less than 3% between the estimated and monitored \(PR\)s; the \(PR\) values ranged between 0.74 and 0.82, depending on the BIPV application and module position.  
Building-integrated photovoltaics; performance ratio estimation; PV system losses modelling; BIPV module temperature +
Footnote †: journal: Applied sciences  
Article
## 4 Radiation Pressure-Driven PhC Cavity Sensors  
In addition to the mechanically tunable PhC cavities, sensing applications can also be realized using radiation pressure-driven PhC cavities. Along with the high-Q optical mode in a PhC cavity, the mechanical mode can be excited at the same time. The optical and mechanical modes are efficiently coupled to each other, resulting in strong optomechanical coupling. A radiation pressure rises from the optomechanical coupling in the interaction of optical and mechanical cavities. The essential effect of the optomechanical coupling is the fact that the optical force induced by the mechanical displacement of cavity modulates the state of light. In this section, sensing applications based on PhC cavity optomechanics for high precision detection of displacement, mass, force, acceleration, etc. are reviewed.
# Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies +
## 8 Conclusions  
Physical modeling of musical instruments is still a challenge for not only physicists, acousticians, and instrument makers, but also for engineers from the digital audio community. An electric guitar is not an exception.  
This paper focuses on nonlinear modeling of a pickup of an electric guitar capturing vibrations of ferromagnetic steel strings. Two main issues are raised: how we should proceed to measure the nonlinear properties of the pickup and what model we should use. An experimental set-up, in which a piece of steel string is attached to a shaker, is proposed. It allows for driving the pickup by shaker vibrations in a controlled way (string displacement perpendicular to the pickup). The shaker is driven by a Synchronized Swept Sine signal and a Generalized Hammerstein model of the pickup is estimated. Next, a simple Hammerstein model, usually used to represent pickup nonlinearities, is derived from the Generalized Hammerstein model and the validity of the Hammerstein model is verified for a pickup operating in a realistic way with a string excited by a hammer-like impact. Even if the experimental bench for model identification has a limited working frequency range (15-500 Hz), the estimated Hammerstein model precisely predicts the signal output.  
In future work, the model can be used to synthesize different kinds of existing pickups (single coil pickups, humbuckers). However, two important questions need to be answered: is the Hammerstein model sufficient for all the single coil pickups on the market, and, if yes, would their corresponding coefficients \(\alpha_{n}\) differ? Is the conclusion of this work also valid for other types of pickup such as humbuckers? Moreover, a focus on a larger frequency excitation range might bring more insight on whether a Generalized Hammerstein model would be more beneficial over a simple Hammerstein model.  
The results of this study shows that the Generalized Hammerstein model does not bring much more complementary information in a low-frequency range (15-500 Hz) and that the simple Hammerstein model is a very good compromise between precision and complexity.  
This research was funded by the Region Pays de la Loire within the Le Mans Acoustic Project. The paper is an extended version of [46], and the winner of the best paper award.  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] analyzed the data; and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
# An Efficient MAC Protocol for Blockchain-Enabled Patient Monitoring in a Vehicular Network
## 4 Results and Analysis  
To evaluate the performance of our proposed scheme, a simulation environment is created, where vehicles are registered as a patient's vehicle before the start of their journey. These vehicles remain in contact with RSUs throughout their journey. The mining node is placed adjacent to each RSU and acts as an \(MN\), and all patients' vehicles are connected with it in a star topology. Patient vehicles' data are transmitted to its associated \(MN\) in a dedicated time slot.  
A simulation environment is created in MATLAB that comprises 5 to 15 patients having adaptive data requests in each session of a round. The initial data slot duration is 83.3 ms with a data capacity of 2000 bits. This network is simulated for two different cases with varying data requests. Each case comprises a round of ten sessions. A complete set of simulation parameters are shown in Table 3. Simulations results are compared with state-of-the-art BS-MAC protocol and First Come First Serve (FCFS).  
Figure 6: Optimal vehicular node selection criteria.
# A PEKS-Based NDN Strategy for Name Privacy
## 4 Details of Implementation
### Producer Operations  
In key generation, \(KeyGen(1^{k})\) algorithm is used and announces the public parameters \(G_{1}\), \(G_{2}\), \(H_{1}\), \(H_{2}\) as well as Producer's public key \(P_{pub}=[\)g, h = \(g^{a}]\). Figure 7 shows that the producer also stores trapdoor \(T_{Name_{j}}=/\)Trapdoor(\(P_{priv}\), \(N_{1}\))/.../Trapdoor(\(P_{priv}\), \(N_{1}\)) and content name \(Name_{k}=/N_{1}/.../N_{k}\) in the Producer's trapdoor table, where \(N_{j}\) is a name component.  
When an interest packet arrives at the producer, it uses Test (\(P_{pub}\), C, \(T_{N}\)) to check incoming the interest name component by component with the use of the trapdoor table. If the data name is found, it then creates a data packet and replies to the consumer. It then starts to disseminate the trapdoor, which corresponds to the incoming interest name, to the network.