esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion and Summary
### ANN SHM Highlights  
* The ANN-based SHM systems showed good damage identification results using the modal information extracted from the structural acceleration responses with less training time compared to the proposed CNN and SVM, and simplicity in implementation.
* The selection of damage-sensitive features include the use of natural frequencies, modal shapes, damage index based on modal strain energy, FRF estimation, IMF or a combination of them.
* The removal of environmental effects and noise is also encouraged by signal processing techniques, such as filters (Kalman filter), and transforms, such as WT, EMD and HHT. Indeed, natural frequencies are susceptible to effects by temperature changes [23].
* The need for a large number of sensors is a drawback for ANN-based SHM system implementations. Analytical or data-driven estimation of unmeasured locations in the structure can enrich the input data to improve structural damage identification, as shown in [76]. The use of a sensor network allows the application of data fusion techniques that mitigate redundant signal information and anomalous sensor data.
# A Passive RF Testbed for Human Posture Classification in FM Radio Bands
## 3 Feature Engineering and Classification
### Classification Methods  
Regarding the classification methodologies, we exploited three different classification techniques:  
* The sum of distances to all clusters' points;
* Support Vector Machine (SVM);
* K-Nearest Neighbors (KNN).  
In the first classification method, for each value pair obtained from the two selected features, we compute the total distance as the sum of all distances between that input and all points of the class cluster in the calibration dataset. Then, the method outputs the total distances for all clusters, and the cluster achieving the smallest total distance is considered as the predicted class. We used a total of 9 different distance metrics to compute this method: city block (or Manhattan), Euclidean, standardized Euclidean, squared Euclidean, cosine, Chebyshev (or infinite \(L_{p}\) norm), Canberra, Bray-Curtis, and Mahalanobis.  
Regarding the SVM classification technique, we used three different kernel types in the algorithm: linear, polynomial (3 rd degree), and Radial Basis Function (RBF).  
The KNN method is adopted using \(N=1,10,20,30,40,50,60\) number of neighbors and, additionally, exploiting six distance metrics, namely city block, euclidean, cosine, Chebyshev, Canberra, and Bray-Curtis. For each distance metric, we computed the KNN for every different \(N\).  
For both the SVM and KNN classifiers, we used the _Scikit-learn_ Python free software ML library [19] to train and classify the data.  
Considering the offline classification step, only the 180 s datasets were used. This means that we used the data from 22 different datasets for this particular step. We performed an offline classification in which each classifier was trained with the labeled data received from the calibration data. The two selected features are computed for each 25 kS sliding window of each test portion. After computing the two features, a 2D feature output was constructed by placing the value of one feature on one axis and the value of the other feature on the other axis, as shown in Figure 8.  
Figure 8: Classification results obtained with Feature6 and Feature2 (obtained with the secondary-Lab_Class201 dataset).  
As described in Table 11, for a 180 s dataset, 560 feature outputs can be computed for each class. However, since only the test portions of each dataset are considered for the classification step, \(560-60=500\) feature outputs are computed for each class. Note that the 500 feature outputs are used to obtain 500 system classifications. Sample averaging was adopted, and 10 2D feature outputs were stored before feeding the classifier. This means that the classifier was fed with the 2D median of the accumulated feature outputs. Therefore, the total number of classifier predictions for each class can be summarized as described in Table 14.
# MMNET: A Multi-Modal Network Architecture for Underwater Networking
## 2 Network Architecture
### MMNET Multi-Application Organization  
With the increasing demand for underwater applications, the number and types of applications that need to be carried by UWSNs is increasing, and it is more difficult for different underwater applications to propose UWSN architecture for application organization. Researchers have carried out a variety of explorations based on traditional UWSN architecture, which cannot solve the organizational problems between different applications [1; 6; 7; 27]. As shown in Figure 6, NFV virtualizes the traditional hardware-based functions, integrates the physical resources and provides them to multiple applications, so that each application can share the physical resources while isolating from other applications, and each application does not interfere with each other in space and time. Through NFV, MMNET can make many different applications share network resources.  
Figure 4: Implement of control forwarding of MMNET routing.  
Figure 5: Routing implementation of control forwarding separation.  
Each application can also choose its own physical layer, MAC layer and net layer according to their own needs, which can not only meet the diversified personalized needs of each application for the network, but also achieve the mutual non-interference among multiple networks, thus solving the organization problem of network applications.
## 9 Discussion
### Guidelines for Further Analysis and Dimensioning  
This section accompanies the calculations by offering a guide for a thorough analysis, including exergy considerations. It also outlines the steps for designing the evaporator and condenser.  
Exergy analysis is pivotal when assessing and selecting an appropriate working fluid, where the most favorable choice presents minimal exergy destruction during the heat transfer process. The practical dimensioning includes the determination of the mass flow rate of the working fluid and the corresponding sizing of the heat exchangers.  
Exergy analysis  
\[\dot{E}_{k}=\dot{E}_{ph}+\dot{E}_{ch}+\dot{E}_{po}+\dot{E}_{ki} \tag{28}\]  
\[\dot{E}_{i}+\dot{E}_{Q}=\dot{E}_{o}+\dot{L}_{N}+\dot{E}_{des} \tag{29}\]  
\[\dot{E}_{Q}=\sum_{k=1}^{n}\dot{Q}_{k}\cdot\left(1-\frac{T_{0}}{T_{k}}\right) \tag{30}\]  
\[\dot{E}_{des}=T_{0}\cdot\left[\sum_{o=1}^{n}\dot{m}_{o}\cdot\overline{s}_{o}- \sum_{i=1}^{n}\dot{m}_{i}\cdot\overline{s}_{i}-\sum_{k=1}^{n}\frac{\dot{Q}_{k }}{T_{k}}\right] \tag{31}\]  
\[\psi=1-\sum\delta_{k} \tag{32}\]  
\[\delta_{k}=\frac{\dot{E}_{des,k}}{\sum\Delta\dot{E}_{in}} \tag{33}\]  
The sizing of the heat exchangers further complements the analysis. Figure 7 schematically displays the transfer from the waste heat source to the ORC.  
\begin{table}
\begin{tabular}{c c} \hline
**Data Center (DC)** & \\ \hline Location & Beijing (China) \\ IT rooms & 3 \\ IT rooms area [m\({}^{2}\)] & 1897.2 \\ Racks per room & 340 \\ Power IT equipment [MW] & 12.9 \\ Power auxiliary systems [MW] & 0.97 \\ Cooling system & Water-cooled chillers \\ Chillers COP [-] & 5.5 \\ DC power usage effectiveness [-] & 1.295 \\ \hline \end{tabular}
\end{table}
Table 5: Main characteristics of reference data center [109].  
Expression (34) determines the mass flow rate of the hot fluid (\(\dot{m}_{h}\)) resulting from the DC cooling process required in the ORC system with 1 kg/s of the working fluid (\(\dot{m}\)). Following the principles of thermal engineering, the size (\(l_{cwa}\)) of the evaporator is calculated per mass flow rate of the hot fluid, as in (35).  
Similarly, the principles apply to sizing the condenser. Equations (36) and (37) are used to calculate the mass flow rate of the cold fluid (\(\dot{m}_{c}\)) for condensation (relative to 1 kg/s of the working fluid) and the size (\(l_{cond}\)) of the evaporator (per mass flow rate of the cold fluid), respectively.  
Evaporator sizing (per kg of hot fluid)  
\[\frac{\dot{m}_{h}}{\dot{m}}=\frac{h_{o,boil}-h_{i,boil}}{\eta_{hs}\;c_{p,h}\;( T_{h,i}-T_{h,o})} \tag{34}\]  
\[\frac{l_{cwa}}{\dot{m}_{h}}=\frac{\left[\eta_{hs}\;c_{p,h}\;(T_{h,i}-T_{h,o}) \right]}{U_{cwa}\;(\pi\;d)\;(\Delta T_{ln}\;)} \tag{35}\]  
Condenser sizing (per kg of cold fluid)  
\[\frac{\dot{m}_{c}}{\dot{m}}=\frac{h_{i,cond}-h_{o,cond}}{\eta_{hs}\;c_{p,c}\;( T_{c,o}-T_{c,i})} \tag{36}\]  
\[\frac{l_{cond}}{\dot{m}_{c}}=\frac{\eta_{hs}\;c_{p,c}\;(T_{c,o}-T_{c,i})}{U_{ cond}\;(\pi\cdot d)\;(\Delta T_{ln}\;)} \tag{37}\]
###### Abstract  
This paper presents the design of an H-band planar antenna array with broad bandwidth and narrow beam width for an IR-UWB radar application. The basic single wideband microstrip antenna is achieved by adding slots and the inset-fed technique. Then, we proposed a planar antenna array on a limited area that obtains an essential narrow beamwidth for the radar of a Non-Contact Human Vital Signs Detection application. The experimental and simulated results of the microstrip antenna array are in good agreement. The measured results show that the proposed antenna array exhibits a wide impedance bandwidth of 10.7% at around 7.5 GHz and a narrow beamwidth of 40 degrees vertically and 50 degrees horizontally, respectively.  
microstrip antenna; antenna array; IR-UWB radar; narrow beamwidth; wide bandwidth +
Footnote †: journal: Applied Sciences  
antennas in a limited footprint. Furthermore, an antenna bandwidth broadening technique is call for to secure 800 MHz of bandwidth. In previous literature, a defected ground structure [11], monopole [12], U-slot [13], and slot line-fed microstrip [14] techniques have been proposed. In this work, two slots are simply designed in the antenna to divert the currents and to introduce a secondary resonance for bandwidth enhancement.  
In what follows, Section 2 outlines the design and optimization of a single antenna and the two-slot bandwidth enhancement technique. Based on the optimized single antenna, Section 3 expands the design to a 2 \(\times\) 2 antenna array. The PCB layouts of the radiator and feed are explained in detail, and the array simulation performance is analyzed and discussed. Having acquired the optimized antenna array design, a couple of prototypes were fabricated, and their measurement results are summarized in Section 4. The measured results show that the antenna array is operating well at the target bandwidth of 7.1-7.9 GHz, and the half-power beamwidth (HPBW) is limited around 40-50\({}^{\circ}\) with a high gain up to 8.4 dBi. Section 5 concludes the paper.
## 4 Experiment and Analysis of Experimental Results  
### Introduction of the Test Rig  
In order to compare the simulation results with those obtained experimentally, the simulation parameters are identical to the test rig parameters, which are listed in Table 2. The experimental platform shown in Figure 14a is used in this paper to carry out our experimental verification. The testbed is mainly composed of four parts: the driving motor, the speed sensor, the planetary gearbox, and the brake. The motor is used as the input to drive the entire system. Three planet gears are equally spaced in the gearbox and share the load from the brake. All parts are connected by the elastic couplings, which effectively reduces the influence of minor manufacturing and installation errors. The faulty sun gear is shown in Figure 14c. During the experiment, the input speed of the motor is 1000 rpm, the sampling frequency is 12,800 Hz. The characteristic frequencies of the planetary gearbox are listed in Table 3.  
Figure 14: Experimental platform and specimens: (**a**) planetary gearbox test rig; (**b**) the planetary gearbox; (**c**) the sun gear with 3 mm fault.  
Figure 12: Simulated signal of the planetary gearbox with a broken tooth on the sun gear: (**a**) waveform; (**b**) frequency spectrum; (**c**) spectrum near the meshing frequency.  
Figure 13: Frequency domain curves of sun gear with different broken sizes.
## 3 Time-Domain Analytical Solution of the Received Signal  
Next, the signal generator applies a square wave voltage signal to the excitation coil. We perform a time-domain analysis of the output voltage signal from the receiving coil and the magnetic induction intensity signal generated by the actual square wave current signal.
# Measurement of Instantaneous Mass Flow Rate of Polypropylene Gasification Products in Airflow
## 2 Experimental Setup
### Measurements  
In test fires, gas flow parameters were recorded both upstream and downstream of the PP sample. The measured parameters upstream of the PP sample included the stagnation pressure \(P_{\text{0}}\) in the facility storage vessels as well as gas flow total pressure \(P_{\text{0 in}}\) and temperature \(T_{\text{0 in}}\) at the inlet to the combustion chamber of the GG. The measured parameters downstream of the PP sample included the total pressure \(P_{\text{0 out}}\) and stagnation temperature \(T_{\text{0 out}}\) provided by pressure sensor and three tungsten-rhenium thermocouples upstream of the sonic nozzle at GG outlet. In addition, a static pressure sensor was placed in the nozzle throat, which allows measuring the static pressure \(P_{\text{out}}\) to control a local flow Mach number.  
For pressure measurements, RPD-I pressure sensors with measurement ranges up to 1.0 and 2.5 MPa were used. The rated measurement error of the pressure sensors was 0.2%. The actual pressure measurement error was established by the results of numerous calibrations of the sensors in the expected pressure range. In this case, to control the set pressure, a PDE-020I reference pressure transducer was used with a basic relative error of \(\pm\)0.02%. The resulting estimate of the actual pressure measurement error did not exceed 1%.  
For temperature measurements, tungsten-rhenium thermocouples were used. To convert the electrical signal of thermocouples into temperature readings, a standard calibration table was used. The temperature measurement error did not exceed 5%.  
Figure 5: Gas generator with pyro-charge-assisted ignition.  
The data acquisition system was based on the National Instruments NI PCI-6255 board. The system has 80 differential measuring channels. The bit depth of the analog-to-digital converter (ADC) was 16 bits. The maximum sampling frequency was 1.25 MHz. In the experiments, a sampling frequency of 1000 samples for each channel was used. The frequency characteristics of the measuring equipment including pressure and temperature sensors as well as the data acquisition system were measured by applying preset pulse signals. It is shown based on the results of such dynamic tests that the operation frequency of the measuring equipment used ranges from 0 to 100 Hz. Since the characteristic frequencies of the processes under investigation do not exceed 10 Hz, dynamic measurement errors were not considered and were not taken into account.  
In addition to measuring gas pressure and temperature, the change in the mass of the PP sample was measured by weighing the sample and its separate blocks before and after test fires. The error in mass measurements was 0.05 g.  
The experimental study included more than 100 experiments. In the following, we discuss only 9 selected test fires with typical results.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 3 Approach
### Multi-Modal SCD Model  
Speaker change detection in audio streams can be addressed using a multi-modal approach that integrates audio and text data. Figure 1 illustrates the proposed multi-modal SCD model, which uses both audio and text to find when speakers change during a meeting. First, the multi-talker meeting audio is split into 1 s pieces. These pieces go through a pre-trained model that learns important sounds (using CNN and Transformer), and it creates a speech feature embedding, a short summary of each audio piece. The audio is converted into text using an automatic speech recognition system, rather than using ground-truth transcripts, which makes the approach more practical. The text is split into tokens and processed with a pre-trained language model to create a text embedding. Both the speech and text embeddings are then combined in a fusion layer. After that, a classification layer uses the combined information to decide whether a speaker change happens at each time step.  
This model employs two pre-trained large models: (i) large audio models to extract acoustic features from raw audio; (ii) large language model to derive semantic information from the transcript. The combination of these modalities enhances the accuracy of detecting speaker transitions.
# Initial Characterization of PDMAEMA: Styrene Porous Polymer Monolithic Morphologies
## 2 Materials and Methods  
DMAEMA:Styrene Polymers: Representative polymers A, B, and C were selected as representative samples (Figure S1). The casting solutions contained 10 mL of 2.5 mM sodium phosphate buffer (at pH 8.8-8.9), 30 mL of propanol, 60 mL of acetonitrile, and 250 mg of both 2,2'-azobisisobutyronitrile (AlBN) as the free-radical polymerization initiator, and acrylamide-2-methyl-1-propanesulfonic acid (AMPS) to support electroosmotic flow initiators. All three preparations consisted of 3.5 mL of the casting solution, which contained either a dibasic (A/B) or monobasic (C) 2.5 mM sodium phosphate buffer, and 1.7 mL of DMAEMA:Styrene in predetermined ratios. Polymer A was 75:25 (v:v) DMAEMA:Styrene; Polymers B and C were 85:15 (v:v) DMAEMA:Styrene. Polymers A and C received 1.00 mL DI water to the polymerization solution; Polymer B received 0.75 mL of DI water to the polymerization solution. The solutions were sonicated for 10 min to degas and ensure homogeneity. The various polymer solutions were then sealed in air-tight scintillation vials and submerged in a 90 \({}^{\circ}\)C water bath for a period of 48 h.  
Spectroscopic Sample Preparation: IR spectra (Nicolet iS10, Thermo Scientific Smart iTR, Madison, USA) and UV/Vis spectra (Varian 50 Bio UV/Vis, Cary Instruments, Palo Alto, USA) were taken of the 0.01 M HCl (pH 2.0) and 0.01 M NaOH (pH 11.9) solutions that contained submerged portions of the polymer samples (A, B, and C). Spectra of the exposed solutions were obtained every other hour, over the course of seven hours. IR spectra were also collected for solid samples of the polymers.  
Scanning Electron Microscope: The untreated polymer samples were air-dried, transferred onto a conductive and adhesive carbon tape mounted on an aluminum stage. The treated polymer samples were rewetted with either a drop of 0.1 M HCl (pH 1.1) or 0.1 M NaOH (pH 12.6) on top of the dried polymer. The samples were then allowed air dry for at least four hours.  
Morphological characterization of the samples was performed using SEM (Phenom XL, Nanoscience Instruments, Waltham, USA) working in the high vacuum mode at 1 Pa. Viewing was performed at a working distance of 10 mm, with a voltage of 15 kV and a spot size of 5.
###### Abstract
To the authors' knowledge, this contribution is unique as no other works are present in the literature modeling the DoA with respect to the elevation angle, i.e., a crucial parameter in satellite communication. Thus, it serves as an advantage, allowing the authors to study the performance of DoA techniques, especially at higher DoA angles, such as closer to \(-(\pi/2)\) and \((\pi/2)\), with respect to the antenna boresight and elevation angle. Furthermore, this paper studies the impact of spatial correlation in the channel on well-known DoA estimation techniques via comprehensive comparative analysis. As a significant part of this contribution, the authors introduce a signal model incorporating correlation in satellite communication. Some studies have presented spatial signal correlation models in satellite communications to analyze the performance metrics, such as the bit error or symbol error probability, outage probability, ergodic capacity, etc. However, this work presents and adapts a spatial signal correlation model for studying DoA estimations. The estimation performance evaluation considers using root mean square error (RMSE) measurements for different satellite communication link conditions through extensive Monte Carlo simulations. The paper is organized as follows: Section 2 reviews the challenges in DoA estimation methods for satellite communication and addresses the importance of an accurate satellite channel model. Section 3 considers the satellite geometries for DoA estimation in both uplink and downlink scenarios for two different satellite systems, i.e., GEO and LEO satellites. For uplink communication, the study utilized spherical geometries to determine the satellite's look angles and range, whereas for downlink communication, basic geometries were employed for satellites. Section 4 presents the signal model for both uplink and downlink scenarios for the aforementioned satellite systems. Notably, for LEO satellite systems, additional parameters, such as the satellite's altitude, velocity, and Doppler shift (due to the satellite's motion), were taken into account. In Section 5, two DoA estimation techniques are outlined: the classical delay and sum (DAS) method and subspace-based multiple signal classification (MUSIC) algorithms [5]. The aim is to assess the ability of different DoA estimation techniques to accurately estimate the DoA in a multiple-antenna-based single receiver system while operating under diverse channel impairments. The impairments that are examined in this paper comprise additive white Gaussian noise (AWGN), i.e., thermal noise, Rician fading [13] with channel-phase random variables, and spatial correlation in multi-antennas [14]. The efficacy of the proposed technique is evaluated through comprehensive Monte Carlo simulations in MATLAB. To validate the precision of the simulations, Section 6 presents the Cramer-Rao lower bound (CRLB) as a performance measure for the AWGN scenario. Section 7 provides a detailed analysis of the numerical outcomes. Finally, Section 8 summarizes the key findings of this study.
## 2 Study Area and Dataset  
### Study Area  
The QTP extends from the southern margin of the Himalayas in the south to the northern margin of the Kunlun, Altun and Qilian Mountains in the north, the Pamir Plateau and the Karakoram Mountains in the west, and the western segment of the Qinling Mountains and the Loess Plateau to the east and northeast. The plateau is approximately 2800 km long from east to west and 300-1500 km wide from north to south. The Chinese portion of the QTP extends from 26\({}^{\circ}\)00\({}^{\prime}\)12\({}^{\prime\prime}\) N to 39\({}^{\circ}\)46\({}^{\prime}\)50\({}^{\prime\prime}\) N and from 73\({}^{\circ}\)18\({}^{\prime}\)52\({}^{\prime\prime}\) E to 104\({}^{\circ}\)46\({}^{\prime}\)59\({}^{\prime\prime}\) E (Figure 1a). The QTP is the largest plateau in China and the highest plateau in the world; hence, it is commonly known as the \"Rood of the World\" and the \"Third Pole\". Moreover, the QTP is the highest and youngest natural geographical unit in the world with a close combination of horizontal and vertical zonality, with an elevation generally between 3000 and 5000 m above sea level and an average elevation exceeding 4000 m (Figure 1b). The QTP is the source of many great rivers that run through East, Southeast and South Asia, and the average annual temperature in its hinterland is below 0 \({}^{\circ}\)C, with the average temperature in the warmest month being lower than 10 \({}^{\circ}\)C in large areas. The plateau promotes tremendous species and ecosystem diversity and provides a variety of important ecosystem services, such as water, timber, rangeland and recreational opportunities, for more than 1.5 billion people. However, the region is extremely vulnerable to climate change and human activities [34], which have led to significant LULC, including lake shrinkage and expansion, permafrost loss, grassland degradation, desertification, deforestation and urbanization.