esa-sceva/satcom-chunk-collection huggingface dataset:
# Modulation Characteristics of High-Speed Transistor Lasers
## 2 Inherent Advantages of TL
### Independent Control of Output Wavelength and Power  
For comparison, the structure diagrams of DML and TL are shown in Figure 2. For DML, changing the voltage changes the output power of the device because it changes the number of carriers injected into the active region. Further, changing the voltage also changes the output wavelength because it changes the band structure of the QW. In contrast, for TL, the amount of carrier injection in the active region is mainly controlled by the base-emitter voltage V\({}_{\text{BE}}\), while the energy band structure is mainly controlled by the base-collector voltage V\({}_{\text{BC}}\). Therefore, while changing the output power of the TL, the energy band structure is not significantly affected and the dispersion of the output wavelength as the power changes is reduced. It thus follows that the modulation ability of TL is better than that of DML [22].  
Figure 1: Two 5 nm InGaAs QWs are embedded in the p-type GaAs base region, which enhances the carrier confinement and radiative recombination ability of the base region. This can achieve both electrical gain and optical gain [19].
# High-Resolution, Broad-Range Detection Setup for Polarimetric Optical Fiber Sensors
## 4 Discussion
Using the properties of the Wollaston prism, it can be demonstrated that the measured phase difference \(\Delta\varphi\) is proportional (to a good approximation) to the birefringence \(\Delta n\) of the material from which the prism was made, i.e.,  
\[\Delta\varphi=k\cdot\Delta n. \tag{23}\]  
The measurement error \(\delta\varphi\) due to the temperature change is  
\[\delta\varphi=k\cdot\delta n. \tag{24}\]Combining Equations (23) and (24), we obtain  
\[\delta\varphi=\frac{\delta n}{\Delta n}\Delta\varphi. \tag{25}\]  
For \(\Delta\varphi=1000\) rad, the measurement error \(\delta\varphi\) is 0.11 rad/\({}^{\circ}\)C. This is a large value when compared with the resolution of the presented set-up (0.002 rad). Reducing this error would require either keeping the setup in a constant-temperature environment, or using a method in which the setup is periodically illuminated by a reference beam from a source whose wavelength is independent from temperature, and the arising fringe pattern is used to recalibrate the setup. Alternatively, designs based on an athermalized Mach-Zehnder interferometer or on [PERSON]'s mirrors can be considered. The functionality of the discussed interferometer can be implemented using a Mach-Zehnder interferometer, which offers three important advantages in this application. First, when an athermalized design is used, its sensitivity to temperature can be greatly reduced compared to that of the presented interferometer. Second, it can be aligned in such a way that the region of the localization of fringes coincides with the area image detector [46]. Third, the spacing of the interference fringes can be varied by changing the orientation of the mirrors of this interferometer. However, a potential disadvantage of this implementation is the presence of multiple reflective surfaces whose imperfections can introduce distortion into the fringe pattern, degrading accuracy or even preventing the correct operation of such a setup. Moreover, the mechanical stability of such a setup, as well as its susceptibility to vibration, can limit the usability of such a setup. In spite of this, further work will be performed on its implementation. Finally, the scope of application of the presented interferometer can be extended to sensors in which two beams of the same polarization interfere, such as those described in [47].
## 3 Experimental Results and Discussion
### Discussion on Real-Time Damage Monitoring  
It is clear from the above analyses that cracks and bolt failures were observed during the tests. From this research, we see the urgent need for crack monitoring and bolt loosening monitoring after a  
Figure 12: Stiffness degradation curve of each test specimen.  
seismic event. However, the strain gauge sensors used cannot monitor these types of damages in real time. During recent years, emphasis has been placed on structural health monitoring (SHM) [47; 48] in civil engineering [49; 50; 51]. An SHM system has the ability to monitor the health status of a structure in real time based on integrated sensors and to issue early warnings based on advanced algorithms to prevent catastrophic consequences [52; 53; 54]. A commonly used type of sensor in SHM is the PZT (lead zirconate titanate) [55; 56; 57; 58], a type of piezoceramic material with a strong piezoelectric effect. Enabled by the piezoelectric effect, a PZT transducer can be used as an actuator [59; 60] or as a sensor [61; 62]. PZT transducers have other advantages, such as wide bandwidth [63; 64; 65; 66], fast response, and low cost. Therefore, PZT transducers are often be used to generate and detect stress waves in an SHM system [67; 68; 69; 70].  
As we continue future work, we plan to use PZT patch transducers, which can be easily bonded onto the outer surface of structures, to monitor the onset and development of cracks. From this study, we know the approximate locations for potential cracks. Two PZT patches will be strategically placed across the potential location. The so-called active sensing method [71; 72; 73; 74] will be adopted to monitor the cracks. In the active sensing approach, one PZT patch will be used to generate a stress wave that can propagate along a structure, and another PZT patch can also be used as a sensor to detect the stress wave. If a crack, which functions as a stress relieve [75; 76], is formed, the energy of the stress wave that travels across the crack will be greatly reduced, based on which the onset and development of the crack on the structure can be monitored in real time. Using the active sensing approach, bolt loosening can be detected by surface-bonded PZT on the bolt head and on the structure since a bolt loosening event will significantly alter the stress wave propagating between the two PZT patches. In our future tests, we will explore the use of PZT patches to quickly evaluate structural integrity by detecting cracks and bolt loosening after a seismic event.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Estimate**} & \multirow{2}{*}{\begin{tabular}{c} **IQ Frame** \\ **Rate (kHz)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Corr. Lag** \\ **(μs)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Nyquist** \\ **Velocity** \\ **(mm/s)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Corr.** \\ **Frame** \\ **Rate (kHz)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Averaging** \\ **Interval** \\ **(Samples)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Averaging** \\ **Interval** \\ **(ms)** \\ \end{tabular} } \\ \hline sTx3 HRI & 10 & 100.0 & 246.4 & 10 & 11 & 1.100 \\ sTx7 HRI & 4.286 & 233.3 & 105.6 & 4.286 & 5 & 1.167 \\ dTx6 HRI & 5 & 200.0 & 123.2 & 5 & 5 & 1.000 \\ sTx3 LRI & 30 & 33.3 & 739.2 & 30 & 31 & 1.033 \\ sTx7 LRI & 30 & 33.3 & 739.2 & 30 & 31 & 1.033 \\ dTx6 LRI PW & 30 & 33.3 & 739.2 & 15 & 15 & 1.000 \\ dTx6 HRI PW & 30 & 33.3 & 739.2 & 5 & 5 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The relevant slow-time quantities are given below. Note that in the case of the pairwise (PW) estimates in the last two rows the correlation frame rate differs from the in-phase and quadrature (IQ) frame rate. This is a characteristic of the pairwise estimation schemes. sTx3: three-angle single transmit sequence; sTx7: seven-angle single transmit sequence. dTx6: three-angle, double transmit sequence. temporal non-uniformity of the sidelobe motion. The interleaved compounded dataset (dTx6 HRI PW), offered a compromise between full compounding (dTx6 HRI) and no compounding (dTx6 LRI), as it tracks well in low velocity regime, but not quite as well in the higher velocity regime.
# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
###### Abstract  
The prediction of reservoir and caprock thickness is important in geological evaluations for site selection for aquifer underground gas storage. Therefore, high-resolution seismic identification of reservoirs and caprocks is crucial. High-resolution time-frequency decomposition is one of the key methods for identifying sedimentary layers. Based on this, we propose a least squares constrained spectral analysis method using a greedy fast shrinkage algorithm. This method replaces the traditional Tikhonov regularization objective function with an L1-norm regularized objective function and employs a greedy fast shrinkage algorithm. By utilizing shorter window lengths to segment the data into more precise series, the method significantly improves the computational efficiency of spectral analysis while also enhancing its accuracy to a certain extent. Numerical models demonstrate that compared to the time-frequency spectra obtained using traditional methods such as wavelet transform, short-time Fourier transform, and generalized S-transform, the proposed method can achieve high-resolution extraction of the dominant frequencies of seismic waves, with superior noise resistance. Furthermore, its application in a research area in southern China shows that the method can effectively predict thicker sedimentary layers in low-frequency ranges and accurately identify thinner sedimentary layers in high-frequency ranges.  
thickness prediction; aquifer underground gas storage; spectral analysis; constrained least squares; fast shrinkage algorithm Article  
15 November 2024  
Revised: 30 January 2025  
Accepted: 31 January 2025  
Published: 18 July 2025  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints. _Appl. Sci._**2025**, 158034. [[https://doi.org/10.3390/appl5148034](https://doi.org/10.3390/appl5148034)]([https://doi.org/10.3390/appl5148034](https://doi.org/10.3390/appl5148034))  
**Copyright:** 62025 by the authors.  
Unionsec MDPT, Basel, Switzerland.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license  
([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Electromagnetic Radiation Shielding Using Carbon Nanotube and Nanoparticle Composites
## 6 Example: Design of a CNT-Nanoparticle Material for Photon Attenuation
Data for the C-Fe-W (24/6/70) material are as follows. The Atomic Number and Fraction by Weight are: carbon \(Z\) = 6: 0.24, iron \(Z\) = 26: 0.06, and tungsten \(Z\) = 74: 0.7. Density carbon = 2.26 g/cc, density iron = 7.87, density tungsten = 19.28 g/cc. Volume fraction carbon = 0.24, volume fraction iron = 0.06, volume fraction tungsten = 0.7. Equivalent density = densityC \(\times\) vol fraction C + densityFe \(\times\) vol fraction Fe + densityW \(\times\) vol fraction W = 14.5106 g/cc. Table 3 gives attenuation coefficients for the C-Fe-W material. Figure 4b shows the relative intensity, \(I/I_{o}\), of photons with energies between 0.010 MeV and 1 MeV as they penetrate increasingly thick C-Fe-W targets, where \(Io\) is the initial intensity of the photon beam, and \(I\) is the intensity after penetrating the carbon target having a thickness of \(x\) cm. W increases the relative photon linear attenuation coefficient as compared to C alone. The mass attenuation coefficient of the C-Fe-W material is larger than for C at low energy levels and similar at a high energy. This points to the need to improve photon shielding at higher energy levels. This might be done by using  
Figure 3: Radiation attenuation characteristics of C. (**a**) Mass attenuation coefficient (cm\({}^{2}\)/g) versus photon energy (MeV) for C. Figure generated using XCOM code [87]. (**b**) Attenuation ratio of low-energy photons in a pure carbon target. \begin{table}
\begin{tabular}{l l l l l l} \hline Energy (MeV) & 0.01 & 0.05 & 0.08 & 0.5 & 1.0 \\ \hline Mass attenuation coefficient (cm\({}^{2}\)/g) & 78.64 & 4.33 & 5.54 & 0.122 & 0.065 \\ \hline Linear attenuation coefficient (1/m) & 1141.1 & 62.79 & 80.40 & 1.776 & 0.946 \\ \hline \end{tabular}
\end{table} 
Table 3: Attenuation coefficients for C-Fe-W material. smaller-size nanoparticles and a fabric material that is thick with many layers with a large number of interfaces due to the nanoparticles.
## 2 Materials and Methods
### Energy Consumption  
As above mentioned, during the air sampling period (August 2016 to July 2017), this study also collected the data of energy consumption in the three ORs with different dehumidification air conditioning systems. Energy consumption monitoring of the dehumidification air conditioning systems included energy consumption on both the air (fan, heat pump, and other equipment) side and water side. The former was directly measured using a direct-reading digital multimeter (ADTEK, CPM-20, New Taipei City, Taiwan) to record electricity consumption (kWh). Meanwhile, for the latter, the thermal energy related to the chilled and hot water sides were directly measured using a calorimeter (including the flow meter and water supply/return thermometer) ([PERSON], EL1000 + WL500S-CY, Bristol, UK). The calorimeter was used with the electromagnetic induction principle. The accuracy of the calorimeter was \(\pm\)0.2% of readings in the measurement range of 0.1-12 m/s for bi-directional flow, current, and pulse output. Two pumps for delivering the liquid desiccant in the LDAC system and the heater for regeneration in the RDAC were also included to record electricity consumption. Data were recorded every 15 min for a year, regardless of whether ORs were in use or not (i.e., at night or on holidays).  
The energy consumption values of the chilled water sides were converted into electricity consumption (kWh) using the following formula:  
\[Y\left(\text{kWh}\right)=a\left(\text{kW}\right)/b\left(\text{3.51 kW/RT}\right) \times c\left(\text{0.85 kWh/RT}\right) \tag{1}\]  
where \(Y\) = electricity consumption, \(a\) = thermal power related to chilled water, \(b\) = conversion factor from RT to kW, and \(c\) = operating efficiency of chilled water.  
In this study, the energy consumption of the dehumidification air conditioning system in each OR was calculated using electricity consumption divided by the area of the OR.
## 2 Methodology and Data  
The proposed hybrid NAR-SVR model employs two parallel processes to enhance forecast accuracy. The method's workflow, outlined in the subsequent sections, comprises the following steps: Initially, an a priori estimate for the observed variable is generated using a selected NAR network. Subsequently, SVRs are utilized to predict the error time series stemming from the optimal NAR network's predictions on the observed variable. This correction process refines the network's forecasts. Finally, the performance of the hybrid NAR-SVR model is compared to that of NAR, SVR, and other classical methods for univariate time series forecasting. Within the framework of the NAR network, the configuration of key hyperparameters is established through a dual consideration. Firstly, the NAR network architecture involves determining the number of nodes, thereby shaping the network's architecture. Secondly, the selection of activation functions plays a pivotal role in capturing intricate nonlinear associations within the data. Similarly to its significance in NAR networks, in the SVR model the number of inputs serves as a foundational determinant of the ML framework. In both models, the optimal configuration of lags, nodes, inputs, and activation functions is carefully selected to minimize both root mean square error (RMSE) and mean absolute error (MAE) within the training dataset.
# Automatic Era Identification in Classical Arabic Poetry
## 4 Experiments and Results
### Experiment 1: Era Classification--Two Periods  
Our first experiment was designed to explore the extent to which the poems' eras can be identified using an automated learning approach. After consulting a domain expert, we considered the following two main periods: 'Abbasid and pre-'Abbasid, where the latter contains the pre-Islamic and the _mubadram_ poets (explained previously) and the Umayyad period. The classification into these periods was made according to the periods in which these poets lived. These periods, including the date of death and sometimes date of birth of each poet, are sharply determined in the biographical books that deal with these poets (see, for example, the biographical entries for the poets as they appear in _The Encyclopaedia of Islam_). Overall, there are 1685 poems in our dataset that were composed during the 'Abbasid period, and 1703 poems that belong to the pre-' Abbasid period. Notably, learning from such a label-balanced set of examples is beneficial, as equally rich evidence is available to learn about both classes.  
Table 4 details the results of the 10-fold CV experiments using the different classifiers. The table details the precision, recall, and F1 performance for the poems associated with each era and summarizes the performance using a weighted average over the target classes (As discussed, there is a roughly equal number of examples per class in this case). As we can see in Table 4, the SVM classifier yields the best classification results for each of the individual periods, as well as on average, with an F1 score of 0.887. An important property of SVMs is that they only consider a subset of the features that best distinguish between examples of different classes and can, therefore, gracefully handle large feature spaces. The voted perceptron algorithm achieves the second-best F1 performance, 0.835, and logitboost yields an F1 of 0.821.
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.2.3 Smart Cities  
Smart city initiatives significantly enhance urban management, public safety, and citizen engagement tackling serious problems thanks to the VR-AI-IoT combinations. In such environments with the use of IoT networks vast amounts of real-time data from urban infrastructure are collected easily and with great accuracy while AI algorithms analyze them to provide insights and predictions. When fed with all the data and predictions, VR is able to give city planners immersive simulations and assist them in decision-making and crisis management [78]. UAV fleets will play a major role especially when equipped with IoT sensors and AI algorithms that enable them to monitor traffic, detect emergencies, and provide situational awareness to authorities while delivering instant first aids avoiding terrestrial traffic [75]. VR simulations further prepare first responders for various scenarios, improving response time and minimizing risks.
# Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT
## 3 Proposed Method
### IoT-Based ECG Framework
#### 3.1.1 DSR Protocol  
The DSR protocol operates as a source-routing mechanism within the IoT cloud network, enabling the transmission of packets across multiple hops among network nodes. This is particularly useful when the distance between nodes exceeds the range of direct transmission. The protocol involves the initiation of a packet's transmission via an originator node, which determines a specific sequence of nodes through which the packet must pass to reach its intended destination.  
In the DSR protocol, when a source node (S) seeks to transmit data to a destination node (D), it first checks its route cache to determine whether a direct route to D exists. If such a route is absent, S broadcasts a route demand message to its neighboring nodes. These neighboring nodes, acting as intermediaries, either respond with route information through a route reply message if they possess a suitable route to D or they forward the request to their own neighboring nodes [35].  
This iterative process continues until the route demand message reaches either D or a node that holds a viable route to D. Upon receiving a route reply message, S stores the route information in its cache, enabling subsequent data transmission along the established route.  
It is important to note that the DSR protocol prioritizes route discovery only when there are data to be sent to a destination. Each node maintains a list of routes in its cache, and a node will request a route from its neighbors if it lacks a route to a specific location. The protocol's core mechanisms include route discovery and maintenance, and while it offers simplicity and efficiency, it has limitations in handling large networks and susceptibility to specific types of routing loops and attacks.  
Due to its characteristics, the DSR protocol is most effective in small-scale, ad hoc networks, where minimizing overhead takes precedence over scalability and robustness. The operation of the DSR protocol is summarized in Figure 3, outlining the steps from route discovery to successful data transmission, with additional nodes identified alongside the source node (S) and destination node (D) for clarity.  
* The source node (S) intends to transmit data to the destination node (D).
* S verifies its route cache to determine the availability of a route to D.
* If S does not possess a route to D in its cache, it broadcasts a route demand message to its neighboring nodes.
* Each neighbor that receives the route request message checks its own route cache to see whether it has a route to D. If it does, it sends a route reply message back to S with the route information. If it does not have a route to D, it forwards the request to its own neighbors.
* This procedure carries on until the request is received by either D or a node with a route to D.
* When a route reply message is received by S, it records the route in its cache and uses it to send the data to D.
## 2 Problem Description  
The ASC task is to classify audio data into one of known acoustic scene classes, e.g., airport, indoor shopping mall, or metro station, as shown in Figure 1. The ASC tasks have been investigated to help understand an environment with sound signals. ASC tasks have a relatively long history among machine-learning-based sound signal analyses. The challenge on DCASE, which is one of the famous competitions related to the analysis of sound signals, has dealt with the ASC problem since 2013 [26]. Nowadays, the task has been developed for complicated and target-specific problems.  
One of the interesting problems of the ASC task is implementation on mobile devices in recent ASC research. To achieve this goal, two issues should be considered: The performance should not be affected by the difference in response between devices, and the complexity of the ASC model should be as small as possible. Therefore, ASC models have been developed to process input signals from multiple devices with a certain complexity limit, as shown in Figure 1.  
As mentioned in Section 1, there are several techniques to reduce the complexity of existing ASC models, but they are not of interest in this paper. Our problem is to develop a method that can enhance an ASC model without using additional model complexity. In order to develop the method, we focus on self-ensemble techniques of semi-supervised learning tasks.