esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Nine-Phase Induction Motor Drives  
The schematic diagram of a nine-phase motor fed from two-level VSI is shown in Figure 1. The motor has a star-connected stator winding with a single neutral point, n. The switching state of a leg-\(j\) in the nine-phase VSI (\(j=a\), \(b\), \(c\), \(d\), \(e\), \(f\), \(g\), \(h\), \(i\)) is determined by a switching function \(S_{j}\in\{0,1\}\), which is defined as: \(S_{j}=0\) when the upper transistor of the corresponding leg \(j\) is OFF, and \(S_{j}=1\) when it is ON. Therefore, the state of the inverter switches can be determined by the switching vector, \(\underline{S}\), which contains the switching function of each leg of the inverter  
\[\underline{S}=\begin{bmatrix}S_{a}&S_{b}&S_{c}&S_{d}&S_{e}&S_{f}&S_{g}&S_{h}& S_{i}\end{bmatrix} \tag{1}\]  
The nine-phase VSI has 512 \(\left(2^{9}\right)\) switching vectors for connecting the output phases to the positive or negative dc-rails [24]. These switching combinations can be analyzed in five groups, as listed in Table 1. The groups are represented as \(\{k,l\}\) where \(k\) and \(l\) are the numbers of phases connected to the positive and negative rails of the dc-link, respectively. The corresponding instantaneous phase to neutral voltages of the symmetrical nine-phase machine can be expressed based on the inverter switching functions by  
\[v_{jn}=V_{dc}\left(S_{j}-1/9\sum_{k=a}^{i}S_{k}\right) \tag{2}\]  
where \(V_{dc}\) is the dc-link voltage.  
These voltages are mapped on four subspace planes, namely \(\alpha\beta\), \(x_{1}y_{1}\), \(x_{2}y_{2}\) and \(x_{3}y_{3}\). The output voltage's fundamental component relates to the first plane, whereas the other harmonic components map into the other planes [22]. The transformation matrix given in (3) is used to calculate the \(\alpha\beta\) and \(xy\) components from the phase variables, where \(\alpha=2\pi/9\). Application of (3) in conjunction with (2) results in voltage representation in the planes of Figure 2, where the dots represent space vectors' tips [25].  
\[\begin{bmatrix}v_{\alpha}\\ v_{\beta}\\ v_{x_{1}}\\ v_{y_{1}}\\ v_{x_{2}}\\ v_{y_{2}}\\ v_{x_{3}}\\ v_{y_{3}}\end{bmatrix}=2/9\begin{bmatrix}1&\cos(\alpha)&\cos(2\alpha)&\cos( 3\alpha)&\cos(4\alpha)&\cos(5\alpha)&\cos(6\alpha)&\cos(7\alpha)&\cos(8\alpha )\\ 0&\sin(\alpha)&\sin(2\alpha)&\sin(3\alpha)&\sin(4\alpha)&\sin(5\alpha)&\sin(6 \alpha)&\sin(7\alpha)&\sin(8\alpha)\\ 1&\cos(2\alpha)&\cos(4\alpha)&\cos(6\alpha)&\cos(8\alpha)&\cos(\alpha)&\cos( 3\alpha)&\cos(5\alpha)&\cos(7\alpha)\\ 0&\sin(2\alpha)&\sin(4\alpha)&\sin(6\alpha)&\sin(8\alpha)&\sin(\alpha)&\sin(3 \
# Effect of Camera Choice on Image-Classification Inference
## 4 Results and Discussion
### Visual Summary of Effect of Camera Choice  
Figure 21 illustrates stacked bar charts showing the proportion of images classified as a cup or a coffee mug by the pretrained Resnet-18 model as a function of distance for all scenarios, Vessel 2 and Vessel 5, and the Angetube 962A, Logitech C920e, and Microsoft LifeCam cameras. Only the top prediction class of an image is considered. The three cameras have a very similar FOV.  
The pretrained Resnet-18 model is much more likely to classify the images of Vessel 2 as a cup rather than a coffee mug in both Scenarios 1 and 2, so there is no clear classification dependence on camera or scenario in this case. In contrast, the pretrained Resnet-18 model is much more likely to classify the images of Vessel 5 as a cup rather than a coffee mug in Scenario 1 and as a coffee mug rather than a cup in Scenario 2, irrespective of the source camera. Therefore, the source camera has no clear effect on the top prediction made by the model in these scenarios, but the classification ranking is highly dependent upon the scenario.  
However, in Scenario 3, the model classifies images as follows:  
Figure 21: Proportion of images classified as a cup or a coffee mug as the top prediction as a function of distance for different scenario, drinking vessel, and camera combinations.  
Figure 20: Selectivity \(\sigma\) (i.e., the mean absolute difference between prediction probabilities for cup and mug classes) comparison for cameras with similar FOV (scenario: Scenario 3).  
* Images of Vessel 2 and Vessel 5 acquired by the Angetube 962A are much more likely to be classified as cups rather than coffee mugs.
* Images of Vessel 2 and Vessel 5 acquired by the Microsoft LifeCam are much more likely to be classified as coffee mugs rather than cups.
* Images of Vessel 2 and Vessel 5 acquired by the Logitech C920e are sometimes classified as cups and sometimes classified as mugs.  
These findings are confirmed in Table 2, which shows the top prediction proportions for cups, coffee mugs, and all other classes for different scenario, drinking vessel, and camera combinations summarized over all distances. Hence, the source camera has a very significant effect on the top prediction made by the model in Scenario 3. This illustrates the complex relationship between model, camera, and scenario for image-classification inference.
# Wind Loads on Overhead Sign Structures: A Comparative Study
## 4 Results
### Drag and Force Coefficients for Porous Panels  
Introducing uniform porosity decreases the drag on a flat plate or wall as part of the flow passes through the surface, which reduces the pressure difference between the windward and leeward faces. The drag coefficient for a porous panel is usually calculated using a porosity factor, \(K_{p}\), which is dependent on the solidity of the plate. Equation (1) provides a linear expression for porosity. If the panel is not linearly related to the plate solidity, Equation (2) can be applied. Applying a porosity value of 0.75 to Equation (2) yields the following:  
\(K_{p}=1-(1-0.75)^{2}=0.9375\). Using the drag coefficient value for the standard panel produces the expected drag coefficient for the porous cases: \(C_{D,\text{Kp}}=1.2\times 0.9375=1.125\). This number is not the exact value of the drag coefficient, and given that the panels have different geometry of porous air slots and holes, the drag coefficient may vary among the models; however, it should return a meaningful value. Similarly, using the pressure coefficients in Table 1 and applying them to Equation (1), one can obtain the drag coefficient for the porous panels. Table 1 lists the calculated non-linear and linear drag coefficients and the CFD drag coefficient of the standard solid and porous panels. Since the curved panel has no porous qualities, the drag coefficient is directly compared to the results from the standard sign panel. The reductions in the drag force for the 28 holes, 200 holes, 10 slots, and curved panels are 15.6%, 5.8%, 11.3%, and 21.8%, respectively. Based on these results, the curved panel experiences the minimum drag, followed by the 28 holes panel.
# Collecting Sensed Data with Opportunistic Networks: The Case of Contact Information Overhead
## 5 Evaluation
### Evaluation Methodology  
In order to improve overall network performance, encounter-based forwarding approaches rely on contact information collected on the go to make forwarding decisions. Such information is often exchanged in summary vectors during encounters in order to remain up-to-date. We experiment with PRoPHET routing protocol in order to show the impact of the overhead incurred during this exchange.  
The performance of our proposed PoiFord is evaluated based on selected metrics by incorporating it into PRoPHET and comparing the results before and after the incorporation. We also experiment with HERO to show that location-based forwarding approaches do not incur contact information overhead but may suffer low delivery guarantees. Configuration settings for PRoPHET are in accordance with the values given by [PERSON] et al. (2019), which are 0.98 for \(\gamma\) and 0.75 for \(e_{init}\). For PoiFord, the radius, \(r\), for maintaining encounter-based routing information is taken as 10 m, and location-based information is maintained at hourly intervals in a location table of 24 time slots. HERO collects a GPS reading every hour for maintaining a location table of 7 days, and the region that records the most locations is considered the home region.  
Since we evaluate routing performance through simulations, node mobility needs to portray realistic human movement properties. While real-world traces represent actual movement scenarios, they are less suitable for this evaluation due to: (i) the lack of realistic node density and encounter frequencies in city-wide scenarios; and (ii) the inability to change network properties such as node population and geographical area without interfering with the encounter opportunities between nodes. Hence, we resort to using a synthetic mobility model that provides the desired level of flexibility and is also able to reproduce realistic properties of human movement.  
The remainder of this section is organized as follows. Section 5.1.1 presents the simulation set-up. In Section 5.1.2, metrics for evaluating the performance of PRoPHET, PoiFord and HERO are presented. Then Section 5.1.3 models PoiFord's contact information overhead for theoretical analysis and comparison with PRoPHET.
## 5 Long-Short-Range Communication
### Short-Range Communication
#### 5.2.1 Low-Energy-Based Bluetooth  
Low-energy-based Bluetooth technology was developed by a special interest group to supply efficient energy standards that can be adopted by battery-based (coin-cell) wearable devices. The main objective of this technology is to activate IoT, connecting small interfacing devices to processing devices such as smartphones. Low-energy Bluetooth can be implemented using a star topology, which is quite useful for healthcare purposes. The master node behaves as the master of said topology, with the sensors attached. The sensors do not require direct communication with each other. The range of this technology is around 150 m in open conditions [82], with a shorter range in non-ideal scenarios.  
This technology has quite a low delay of 3 ms and a data rate capacity of 1 Mbps [83], making it suitable for use in wideband healthcare systems in which nodes are physically proximal and extremely low delay is ideal in the case of emergency health situations. It operates in the 2.4 GHz band, which is also suitable for classical Zigbee and Bluetooth. This band sometimes generates some noise, but with the help of frequency hopping in channel selection and redundancy checking, the interface can be minimized to maintain its robustness, making it suitable for healthcare systems [84]. Power consumption is extremely low, at about 180 mAH, and the system can run for 18 h continuously. If the sensor sends information 2880 times, i.e., every 30 sec, then the battery can function for around 18-20 years. The use of a low-power program and careful hardware construction make it suitable for different healthcare purposes.
## 5 Performance Evaluation
### Single-Spot Deployment Scenario  
In this deployment scenario, we first investigate the throughput gain of MLO. Then, we present the coexistence performance and evaluate it using a new metric.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 11 Transmission Performance of HCFs
### Hc-PbGF  
The record parameters reported by [PERSON] et al. [111] for a 1550 nm single-wavelength DP-QPSK link with a 19-cell HC-PBGF (single core, no shunts) were 100 Gb/s (excluding FEC) at a distance of up to 55.8 km, and 40 Gb/s up to 74.8 km, with pre-FEC BER \(\leq\)5 \(\times\) 10\({}^{-2}\) in both cases. The transmission distance was limited by the interference from crosstalk between the modes. In a real network, the length of un-repeated data link with low-loss HC-PBGF fiber (3 dB/km + 2 \(\times\) 1 dB for HCF-SMF joints at both ends, see Sections 10.1 and 10.4) transceivers with a 28 dB loss budget and a 2 dB loss margin would be loss-limited to 8 km. This may suffice for fronthaul links in 5G radio networks, but not for typical telecom networks, even in a medium-sized town. Additionally, the latency added by FEC would possibly exceed the reduction in latency in the fiber (Section 12.1.2).  
[PERSON] et al. [22] conducted a DWDM transmission test over 3.18 km long HC-PBGF fibers with six shunts (OFS AccuCore) in installed duct cable, sending 10 Gb/s channels at 33 wavelengths from 1545.32 nm to 1558.17 nm with a spacing of 50 GHz. The transceivers used NRZ line code without FEC to minimize the link's latency. BER was 10\({}^{-15}\) for all channels, but attempts to add more channels at adjacent frequencies resulted in error floor due to the rise in fiber dispersion (CD or PMD) at wavelengths close to the edges of its narrow low-loss band (Figures 4 and 8). AccuCore fiber is presented in Sections 4.2 and 4.3.  
The second experiment appears to reflect the best applications for HC-PBGF fibers, single-channel or low-capacity WDM links spanning short distances (\(\leq\)4 km) and operating at medium data rates without FEC, for latency-sensitive applications. Considering the fact that (a) the experimental link was dispersion-limited, and (b) the peculiar characteristics of chromatic dispersion of fiber used (Figure 8), one can expect much faster  
Figure 22: Fusion splicing of two HC-PBGF fibers with molten glass capillary: (**a**) fiber alignment, (**b**) push and (optional) low-temperature fusion without deformation of photonic cladding, (**c**) sliding of glass capillary over fiber contact location, (**d**) melting of glass capillary.  
single-channel transmission, e.g., 100 Gb/s, at short distances like 200 m. This is enough for most connections in a data center, LAN or campus network.
## 4 The Proposed Framework
### The Methodological Framework
#### 4.1.3 Dataset Creation and Setting the Number of Clusters  
Each raster cell, now containing both LST and NDVI values, is treated as a multi-dimensional data point. This integration enables the FCM algorithm to simultaneously analyze the relationship between temperature and vegetation density.  
A crucial aspect of running FCM is identifying the optimal number of clusters. The study utilizes various validity indices, including the Xie-Beni, Fukuyama-Sugeno, and Davies-Bouldin indices, to determine the optimal number of clusters (C). This step is vital for ensuring that the clustering results are meaningful and accurately reflect the underlying data patterns. The use of validity indices represents one of the innovative aspects of our method, as in other clustering approaches, the choice of the number of clusters is often arbitrary and made a priori, which may not accurately capture the nature of the data.  
After a careful analysis of the scientific literature, it was observed in [38] that the validity indices used in this study, including Xie-Beni, Fukuyama-Sugeno, and Davies-Bouldin, are among the most applied in the context of the fuzzy C-means algorithm.  
This alignment with established practices not only reinforces the robustness of the methodology but also enhances the credibility of the results, as these indices have been shown to effectively evaluate clustering performance in various contexts.
# Life Cycle Assessment of Medical Waste Management: Case Study for Istanbul
## Global Warming Potential [%]  
### Acidification Potential  
Nitrogen oxide and sulfur dioxide emissions from the incineration process are the main contributors to acidification potential [31]. The percentage of acidification potential in each scenario is shown in Figure 9. Both scenario 2 (incineration + steam sterilization + landfill) and scenario 4 (waste segregation at generation points/waste minimization + incineration + sterilization + landfill) had the least impact, measuring 0.53 and 37 kg SO\({}_{2}\)-Eq, corresponding to 13.46% and 9.42%, respectively, since the incineration process was implemented in the lowest proportion. The acidification potential in scenarios 0 (sterilization + landfill + incineration) and 3 (waste segregation at generation points/waste minimization + landfill + incineration) showed a slight increase, with similar impacts of 0.76 kg SO\({}_{2}\)-Eq, corresponding to 19.28% and 19.19%, respectively. Although the incineration ratios in these scenarios were close, the sterilization process in scenario 0 increased the acidification potential somewhat due to emissions from LNG used as fuel. The greatest acidification impact was observed to be 1.52 kg SO\({}_{2}\)-Eq, corresponding to 38.64%, in scenario 1 (incineration of all medical waste + landfilling of non-hazardous municipal solid waste). Similarly, the studies in [25; 35] reported that incineration had a greater impact on acidification compared with landfilling. This situation is attributed to the more frequent use of the incineration process compared to other scenarios, resulting in the highest emissions of sulfur dioxide and nitrogen oxides. While the incineration process is regarded as the main contributor to acidification potential, the use of LNG as fuel in the steam sterilization process and emissions from transportation also contribute to the acidification potential [33].  
Figure 8: The percentage of global warming potential for each scenario.
## 2 Materials and Methods
### Laboratory Investigations
#### 2.3.3 Raman Spectroscopy  
Raman spectroscopy was employed to identify pigments produced by isolated colonies (S\({}_{6a}\)--isolates 1, 3, and 5), as well as directly on micro-samples (S\({}_{5a}\), S\({}_{6a}\), S\({}_{7}\), S\({}_{10}\), S\({}_{11}\)) collected from green or pink-stained areas. Raman spectra were acquired using a B&W Tek micro-Raman spectrometer (Newark, DE, USA), equipped with a 785 nm laser source (nominal power 300 mW). The backscattered light was dispersed by an 830 lines/mm grating, and the Raman signal was detected by a silicon CCD sensor thermoelectrically cooled to \(-5\)\({}^{\circ}\)C. The nominal spectral resolution is approximately 5 cm\({}^{-1}\). Spectral acquisitions in the range of 1500-3200 cm\({}^{-1}\) were performed using 20\(\times\) and 40\(\times\) objectives. For each sampled point, 50 accumulations were collected, with integration times ranging from 3 to 10 s, depending on the response and structure of the individual sample. The laser power was optimized for each examined specimen to optimize the signal while avoiding sample degradation. All measurements were collected after automatic subtraction, referred to as \"dark,\" to remove any interference from the instrument, substrate, or external light sources. Origin software 8.5 (OriginLab Corporation, Northampton, MA, USA) was used for treatment analyses, including baseline correction and smoothing.
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 4 Experiments
### Network Configuration
#### 4.2.2 For Identification  
The focus here is on implementing MFCC and GMM concurrently. The main feature was MFCC with tuned parameters with delta-MFCC as a secondary function. In addition, to train our model, we employed GMM with extra fine-tuned parameters. We performed this identification on the separated speaker's output dataset from the previous separation block. We achieved an excellent result on these datasets, with 96% accuracy on the self-prepared dataset. Combining MFCC and GMM in tandem showed great accuracy in identification results in performing speaker recognition tasks.
## 4 A Focus on Relevant Applications of Apta-POF-SPR Sensors
### Virus Detection  
The growing and spreading of the global population on the world caused in the last century an increase in the incidence of pathogens. There is, therefore, the requirement  
Figure 5: Apt-THR layer aged in PBS solution at 4 \({}^{\circ}\)C (circles) [4] or \(-\)20 \({}^{\circ}\)C (stars). Data are reported as mean value of two samples. Error bars represent the standard deviation. Exposition time: 0.5 s.  
for rapid and efficient biosensors in order to detect viruses early and allow immediate counteractions. In this context, aptamers found several applications [84; 85; 86]. Recently, we tested the PEG-SAM-based aptasensor for the detection of the receptor-binding domain (RBD) of the SARS-CoV-2 spike glycoprotein [71]. A longer aptameric sequence was used (51 nucleotides with respect to 29 for THR and 26 for VEGF), and a thorough characterization was performed in order to ensure the right recognition.  
The interface reported in Figure 6a was tested not only on the specific target (SARS-CoV-2 Spike protein) but also on aspecific targets such as other proteins (bovine serum albumin (BSA), AH1N1 hemagglutinin protein, and MERS spike protein) and in diluted human serum (50%) [4; 71]. A limit of detection in the nanomolar range was achieved (see Figure 6), confirming the good performances of our aptamer-based optical platform. Figure 6b shows the SPR spectra obtained at different concentrations of the SARS-CoV-2 Spike protein [4].  
A preliminary test was performed using real samples (positive Universal Transport Medium (UTM) swab sample), as described in [4] and shown in Figure 6c. A clear wavelength red shift was observed only on positive samples, confirming the ability of the aptamer-based platform to recognize the target.  
Figure 6: _Cont._
## 3 Results and Discussion
### Characterizations of Tyrosine/Chitosan/rGO Biocomposites
#### 3.1.2 Cyclic voltammetry  
The four types of electrodes were tested in a solution containing 5 mM Fe[CN\({}_{6}\)]\({}^{4-}\) in 0.1 M KCl, at a scan rate of 100 mVs\({}^{-1}\). As shown in Figure S3, chitosan/rGO/SPCE and tyrosinase/chitosan/rGO/SPCE had higher oxidation peak current levels compared to those of bare  
Figure 1: SEM images of: (**A**) bare screen-printed carbon electrode (SPCE), (**B**) reduced graphene oxide (rGO) /SPCE, (**C**) chitosan/rGO/SPCE, and (**D**) tyrosinase/chitosan/rGO/SPCE.  
Figure 2: (**A**) ATR-FTIR spectra of chitosan modified electrode with tyrosinase (blue line) and without tyrosinase (purple line). (**B**) Cyclic voltammetry (CV) responses (at a scan rate of 100 mVs\({}^{-1}\)) of the different electrodes in the presence of 10 \(\upmu\)M of dopamine (DA).  
SPCE, indicating enhanced electroactivity owing to the presence of chitosan. A slight decrease in peak current level was noted after the immobilization of tyrosinase. These findings agree with those obtained in previous studies [26; 44].
# Enhanced Multiple Speakers' Separation and Identification for VOIP Applications Using Deep Learning
## 3 Model Description
### Real-Time Audio Pre-Processing  
In this part, authors recorded their mixed audios from their microphone and saved it in a NumPy array using the python-sound device module.  
This module, combined with the wavio and scipy modules, converts this useful data type for sound processing into WAV format for storage.  
The recorded audio files will be used as an input to the separation process module as shown in the next section. The time for these audio files is 7 s for each mix file, with sampling frequency 8 kHz.
# A Survey on Robot Semantic Navigation Systems for Indoor Environments
## 2 Indoor Robot Semantic Navigation Systems
### Vision-Based Semantic Navigation Systems  
Vision-based semantic navigation systems can be implemented using either RGB (Red Green Blue) cameras or RGB-D (RGB-Depth) cameras. RGB-based systems can recognize objects in the area of interest, while RGB-D-based systems can also measure the distance to recognized objects. RGB-D cameras are more useful for semantic navigation applications, as they provide better semantic information for indoor robot navigation.