esa-sceva/satcom-chunk-collection huggingface dataset:
Samples with QWRs were based on 40 QW/barrier pairs made of strain balanced In\({}_{0.2}\)Ga\({}_{0.8}\)As/GaAs\({}_{0.85}\)P\({}_{0.15}\) on 6\({}^{\circ}\) misoriented GaAs substrate. Nominal QW thickness was 8 nm, and barrier thickness was 17 nm. The in-plane periodicity of the QWRs was 121 nm, the total height of the QWR was 13.6 nm, and the minimum thickness of the layer, at the boundary between one QWR and the next one, was 4.6 nm. Optical properties of QWRs were compared with a reference QW grown on the exactly oriented surface. Growth on the misoriented surface resulted in a broadening and a redshift of PL line from 1.26 eV to 1.22 eV. Radiative recombination was found to be less efficient in these QWRs than in reference QW grown on the exactly oriented surface. Formation of In\({}_{0.4}\)Ga\({}_{0.6}\)As QWRs was demonstrated during the MBE growth on the (311)A surface [24]. The average height of the wires was found to be 4 nm; the PL maxima were at about 1.34 meV. The enhanced linewidth of the PL lines (\(>\)30 meV) was attributed to the size distribution of the quantum wires and the quality of the InGaAs/GaAs interfaces. Recently, we have demonstrated a novel type of nanostructures that we refer to as quantum well-dots (QWDs). The QWDs represent a dense array of carriers localizing indium-rich regions formed inside In-depleted residual quantum wells by MOCVD deposition of 4-16 monolayers of In\({}_{\chi}\)Ga\({}_{1-\chi}\)As (0.3 \(<\)\(x\)\(<\) 0.5) on GaAs substrates (Figure 1). QWDs can be considered as nanostructures of mixed (0D/2D) dimensionality. The use of QWDs in single-junction GaAs solar cells enabled extending of long-wavelength edge of the spectrum of internal quantum yield from 860 nm up to 1100 nm, which resulted in a record high increment in the photocurrent (4.6 mA/cm\({}^{2}\) for 20 QWD layers) for terrestrial spectra [32], which is making QWDs promising for photovoltaic applications. Despite the rather interesting results obtained for photovoltaic converters based on various nanostructures of mixed dimensionality, including WoW, QWR, QWDs, etc. [25; 26], the reports on light-emitting devices were scarce [27; 31]. In this review article, we mostly focus on the application of QWDs to light emitting devices, describe their basic properties, and demonstrate fundamental advantages.
## 2 Model Description
### Numerical Model
#### 2.3.2 Governing Equations and Flow Models  
The solver utilizes the conservation of mass and momentum in all flow models which are used. The conservation of mass, also known as the continuity equation, is given by,  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot(\rho\mathbf{u})=0 \tag{10}\]  
where \(\rho\) is the density and \(\mathbf{u}\) is the velocity vector. The conservation of momentum is given by  
\[\rho\frac{\partial\mathbf{u}}{\partial t}+\rho(\mathbf{u}\cdot\
abla)\mathbf{ u}=-\
abla p+\
abla\cdot\tilde{\tau}+\mathbf{F} \tag{11}\]  
where p is the static pressure, \(\tilde{\tau}\) is the stress tensor, and \(\mathbf{F}\) is the volume force vector. The stress tensor can be described by  
\[\tilde{\tau}=\mu(\
abla\mathbf{u}+(\
abla\mathbf{u})^{T})-\frac{2}{3}(\mu \
abla\cdot\mathbf{u})\mathbf{I} \tag{12}\]  
where \(\mu\) is the viscosity and \(\mathbf{I}\) is the identity matrix. The SST flow model uses two additional transport equations, these are formulated in terms of the turbulent kinetic energy \"k\" and the specific dissipation rate \(\omega\),  
\[\rho\frac{\partial k}{\partial t}+\rho\mathbf{u}\cdot\
abla k=P-\rho\theta_{0 }^{*}k\omega+\
abla\cdot((\mu+\sigma_{k}\mu_{T})\
abla k) \tag{13}\]  
\[\rho\frac{\partial\omega}{\partial t}+\rho\mathbf{u}\cdot\
abla\omega=\frac{ \rho\gamma}{\mu_{T}}P-\rho\beta\omega^{2}+\
abla\cdot((\mu+\sigma_{\omega} \mu_{T})\
abla\omega)+2(1-f_{v1})\frac{\rho\sigma_{\omega 2}}{\omega}\
abla\omega\cdot\
abla k \tag{14}\]  
A full description of the SST model can be found in [33]. COMSOL uses a pseudo time-stepping method based on an adaptive feedback CFL controller. The controller is a multiplicative PID controller given by  
\[CFL_{n+1}=\Big{(}\frac{e_{n-1}}{e_{n}}\Big{)}^{k_{p}}\Big{(}\frac{tol}{e_{n}} \Big{)}^{k_{l}}\Big{(}\frac{e_{n-1}/e_{n}}{e_{n-2/e_{n-1}}}\Big{)}^{k_{d}}CFL _{n} \tag{15}\]  
where \(k_{p}\), \(k_{i}\), and \(k_{d}\) are the controller parameters, \(e_{n}\) is the nonlinear error estimate for step \(\mathbf{n}\), and \(tol\) is the target error estimate. A lower limit of \(CFL\geq 1\) is used and convergence is not accepted until \(CFL_{n}\geq CFL_{\infty}=10^{4}\), where \(CFL_{\infty}=10^{4}\) is the steady-state CFL number.
# TransECA-Net: A Transformer-Based Model for Encrypted Traffic Classification
## 2 Related Work
### Comparison of GNNs and Transformers for Sequence-Aware Classification  
In the field of encrypted traffic classification, graph neural network (GNN)-based methods typically construct packet nodes and temporal-relationship edges to capture traffic interaction features. However, these methods face three key challenges in practical applications: First, static graph structures struggle to effectively characterize the dynamic temporal characteristics and implicit correlations of network traffic, potentially introducing noise or erroneous assumptions. Second, the local perception nature of traditional neighborhood-aggregation mechanisms limits their ability to model global dependencies across sessions. Third, large-scale traffic graphs significantly increase the computational complexity of neighborhood aggregation, making real-time classification challenging.  
By contrast, Transformer-based architectures demonstrate distinct advantages: Their inherent sequence-modeling capabilities naturally align with the temporal characteristics of traffic data, enabling the direct processing of raw packet sequences or statistical features without manual graph construction. The self-attention mechanism captures long-range behavioral patterns in encrypted traffic through the parallel computation of dependencies between arbitrary positions, while the multi-head attention mechanism adapts to heterogeneous feature spaces via multidimensional dynamic feature interactions. This synergistic mechanism of global context awareness and dynamic feature fusion provides a more robust solution for encrypted traffic classification.
## 4 Results and Discussion
### Effect of the Circuit Number on the Evaporator and Condenser Performance
Figure 6 presents the average values of the overall heat transfer coefficient and temperature differences for all 24 tubes in the FTHX. In general, the overall heat transfer coefficient decreased with as the circuit number increased, since more circuits led to a lower refrigerant velocity and a smaller refrigerant-side heat transfer coefficient. The temperature difference increases with the circuit number, because a lower refrigerant velocity also led to a smaller pressure drop (also shown in Figure 4) and further reduced the corresponding temperature drop in the liquid-vapor two-phase area. For the same heat transfer area, therefore, the FTHX capacity was determined by the trade-off between the overall heat transfer coefficient and the temperature difference. For the evaporator, the capacity increased first and then decreased, indicating that the increase in temperature difference overwhelmed the decrease in overall heat transfer coefficient with one to four circuits, but the opposite occurred with four to six circuits. For the condenser, however, the capacity decreased monotonously, denoting that the decrease in overall heat transfer coefficient was always the dominant factor. Figure 4 shows that as the circuit number increased from one to four, the overall heat transfer coefficient was reduced by only 4.8% in the evaporator mode but by 47.2% in the condenser mode. This dramatic difference resulted from the fact that the refrigerant flowrate in the FTHX generally increased in the evaporator mode but decreased remarkably in the condenser mode, as shown in Figure 4.  
Figure 5: Tube-by-tube overall heat transfer coefficients and temperature differences in the FTHX. In short, the evaporator exhibited the highest capacity with four circuits because of the trade-off between the decreased overall heat transfer coefficient and the increased temperature difference. The condenser, however, showed a monotonously reduced capacity, since the overall heat transfer decreased greatly and always dominated the capacity decrease as the circuit number increased. Therefore, this contradiction caused a difference in the optimal number of circuits for the evaporator and the condenser. Next, we demonstrated that the reversely variable circuitry in the FTHX could address this problem.
## 3 New Hybrid Opportunistic Scheduling
Uplink User Selection: Upon receiving \(N-M\) uplink scheduling metrics except for the selected downlink MSs in \(\mathcal{S}^{(d)}\), the BS selects \(M\) uplink MSs having the smallest uplink scheduling metrics. That is, for \(m\in\{1,\cdots,M\}\), the BS selects \[\phi_{m}=\operatorname*{arg\,min}_{j\in\{1,\cdots,N\}\setminus\left(\mathcal{S }^{(d)}\cup\{\phi_{l}\}_{l=1}^{n-1}\right)}\gamma_{j},\] (7) which eventually results in the set of selected uplink MSs \(\mathcal{S}^{(u)}=\{\phi_{1},\cdots,\phi_{M}\}\). Then, each MS in \(\mathcal{S}^{(u)}\) is ready for transmitting its uplink packets. 6. Uplink ZF Beamforming at the BS: To decode uplink packets, the BS applies ZF receive filtering by nulling out the uplink interference without CSI at the transmitter. For the proposed opportunistic scheduling method, we assume that each MS \(j\in\{1,\cdots,N\}\setminus\mathcal{S}^{(d)}\) can estimate the MS-to-MS interference \(\gamma_{j}\) by overhearing feedback signals sent from the downlink MSs to report their scheduling metrics to the BS. Moreover, it is worthwhile to address the fundamental differences between our approach and two different types of scheduling methods for full-duplex systems as follows. Remark 1: _In [25], instead of ZF beamforming, random receive beamforming for decoding uplink packets is employed at the BS. In [26], a set of downlink MSs is selected to eliminate both the downlink interference and MS-to-MS interference, whereas a set of uplink MSs is arbitrarily chosen._
## 3 Results and Discussion
### Characterization of the Biochar
#### 3.3.4. Fourier Transform Infrared Spectroscopy (FTIR)  
The NH\({}_{4}\)+-N adsorption capacity of biochar depends not only on the specific surface area, but also on the contents of functional groups on its surface. The FTIR spectra of NaOH-activated and KOH-activated biochars are shown in Figure 8. Interestingly, NaOH-activated biochar exhibited a significantly different FTIR spectrum compared with the biochar activated by KOH. No obvious absorptions were found from the FTIR spectrum of NaOH-activated biochar in the range of 4000-400 cm\({}^{-1}\), indicating the relative absence of functional groups on its surface. However, the KOH-activated biochars presented obviously enhanced absorption peak amount and intensity: a broad band at about 3420 cm\({}^{-1}\) attributed to hydroxyl group stretching vibration [12; 17], two bands at 1590 cm\({}^{-1}\) and 1384 cm\({}^{-1}\) assigned to the stretching vibration of asymmetric and symmetric COO- of deprotonated carboxylate functional groups [49], and a 1045 cm\({}^{-1}\) band caused by a C-O-C stretching mode [17]. Thus, the FTIR results indicated that the KOH-activated biochar had much more functional groups, e.g., hydroxyl groups and carboxyl groups, which are critical for the adsorption of NH\({}_{4}\)+-N by electrostatic interaction [50]. Moreover, Boehm titrations were applied to analyze the oxygen-containing functional group contents of the two obtained biochars. The result showed that carboxyl and lactonic groups were present in KOH-activated biochar at 0.21 mmol/g and 0.08 mmol/g, respectively, much higher than the levels in NaOH-activated biochar (0.03 mmol/g and 0.02 mmol/g).  
Physical and chemical adsorption are two main factors for effective NH\({}_{4}\)+-N adsorption by biochar [17]. The physical adsorption capacity depends mainly on the adsorbent porous structure, since the adsorbate is captured through nonselective Van der Waals forces [17]. Nevertheless, chemical adsorption relates to the properties of the adsorbate and the contents of functional groups on the adsorbent surface. Thus, for physical adsorption, the adsorption capacity relies primarily on the specific surface area of the adsorbent, which quantitatively represents the porous structure [21]. Here, the NaOH-activated biochar had higher specific surface area compared with the KOH-activated biochar but lower NH\({}_{4}\)+-N adsorption capacity, suggesting that physical adsorption is not the main factor in NH\({}_{4}\)+-N adsorption by the biochar. According to the FTIR results, KOH-activated biochar has a much higher content of functional groups compared with NaOH-activated biochar and, accordingly, has higher adsorption capacity. Therefore, chemical adsorption is the main reason for the better NH\({}_{4}\)+-N removal ability of KOH-activated biochar, which was also confirmed by the activation energy from the kinetics results.  
Figure 7. Thermogravimetric curves of sorghum distillers grain (SDG), NaOH-activated biochar, and KOH-activated biochar.
## 4 Conclusions  
Three Ag(I) complexes with pyridine ligands were synthesized and well characterized using X-ray single crystal diffraction analysis, elemental analysis and NMR spectra. The solid state structure of [Ag(2-pyridone)NO\({}_{3}\)]\({}_{\rm n}\) is peculiar and best understood using network topology analysis giving the unusual **ins**-net. Their antimicrobial properties were compared with 17 antibiotics. [Ag(2-pyridone)NO\({}_{3}\)]\({}_{\rm n}\) has specific activity against three clinically isolated strains of _Ps. Aeruginosa_ compared to the tested antibiotics. This group of silver(I) pyridine complexes have relatively high biological activity against a broad spectrum of MDR (multi drug resistance) and ATCC standard bacteria as well as the fungus _C. albicans_. We hope to find promising substances to be used in wound dressing applications. Also, molecular packing analyses for complexes **1**-**3** were performed with the aid of Hirshfeld analysis. The net charges at silver atom are 0.6222, 0.7284, and 0.6852 instead of 1.0000 due to the interactions with the ligand groups coordinating it. The molecular docking of the studied complexes shows several types of interactions with the pocket including hydrophilic, hydrophobic, salt bridges, and \(\pi\)- stacking interactions.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/14/4853/s1](http://www.mdpi.com/2076-3417/10/14/4853/s1)]([http://www.mdpi.com/2076-3417/10/14/4853/s1](http://www.mdpi.com/2076-3417/10/14/4853/s1)), Figures S1-S3: \({}^{1}\)H and \({}^{13}\)C NMR spectra of complexes **1**-**3**. Figure S4: Distorted tetrahedral coordination of the silver atoms in [Ag(2-pyridone)NO\({}_{3}\)]\({}_{\rm n}\). Figure S5: Hydrogen bond interactions. Figure S6: X-ray structure with atom numbering for complexes 1-3. Tables S1-S4: X-ray crystal data for the complexes **1**-**3**.  
Conceptualization, A.M.A.B. and S.M.S.; data curation, A.M.A.B., A.B., J.H.A., M.M.S., and Z.U.-H.; formal analysis, J.H.A., M.M.S., Z.U.-H., and S.M.S.; funding acquisition, A.B.; investigation, S.M.S.; methodology, M.M.S. and S.M.S.; software, J.H.A. and Z.U.-H.; supervision, S.M.S.; writing--original draft, A.M.A.B., A.B., Z.U.-H., and S.M.S.; writing--review and editing, A.M.A.B., A.B., and S.M.S. All authors have read and agreed to the published version of the manuscript.  
The research was funded by King Saud University, Researchers Supporting Project Number (RSP-2020/64).  
The authors would like to extend their sincere appreciation to Researchers Supporting Project Number (RSP-2020/64), King Saud University, Riyadh, Saudi Arabia.  
The authors declare no conflicts of interest.
## 3 Synchronous Control Strategy Based on Real-Time Impedance Detection
### Radiation Efficiency of the Antenna  
The apparent power, S, of the antenna feeder system includes the active power, P\({}_{\rm a}\), and the reactive power, Q. In this paper, P\({}_{\rm a}\) is the antenna radiated power [4].  
\[{\rm P_{a}=R_{a}\times\stackrel{{\bullet}}{{\rm I}}}^{2} \tag{11}\]  
\[{\rm Q=}\bigg{|}\omega{\rm L-\frac{1}{\omega C_{a}}}\bigg{|}{\times\stackrel{{ \bullet}}{{\rm I}}}^{2} \tag{12}\]  
\[\eta=\frac{P_{a}}{P_{a}+Q}=\frac{R_{a}}{R_{a}+\bigg{|}\omega{\rm L-\frac{1}{ \omega C_{a}}}\bigg{|}} \tag{13}\]  
It can be seen from Figures 9 and 10 that when the accuracy of \(L_{0}\) and \(L_{l}\) is n \(\times\) 10\({}^{-2}\)mH (n is a positive integer, for example, n = 1, which means that the inductance is N), the variation range of antenna radiation efficiency is about 50% to 95%, and the variation range of antenna radiation efficiency is large and the decline is obvious. When the accuracy of \(L_{0}\) and \(L_{l}\) is n \(\times\) 10\({}^{-3}\)mH, the variation range of antenna radiation efficiency is about 91% to 100%.  
Figure 8: The power factor angle, \(\varphi\), of the antenna feeder system when the carrier frequency of the MSK signal is \(f_{l}\) (**a**) The power factor angle, \(\varphi\), of the antenna feeder system when the accuracy of \(L_{0}\) reaches n \(\times\) 10\({}^{-2}\)mH; (**b**) The power factor angle, \(\varphi\), of the antenna feeder system when the accuracy of \(L_{0}\) reaches \(n\times\) 10\({}^{-3}\)mH.  
Figure 9: The antenna radiation efficiency when the carrier frequency of the MSK signal is \(f_{\rm h}\). (**a**) The antenna radiation efficiency when the accuracy of L\({}_{0}\) reaches n \(\times\) 10\({}^{-2}\)mH; (**b**) the antenna radiation efficiency when the accuracy of L\({}_{0}\) reaches n \(\times\) 10\({}^{-3}\)mH.  
Because \(\Delta L=L_{l}-L_{0}\), Table 4 can be obtained. \(\Delta\)L is obtained by combining the inductor array (L\({}_{\text{Cn}}\),n \(\in\) N+), which is mainly controlled by the controller and the demodulated symbol signal. Table 4 is the number table required by the look-up table method. The inductor array combination corresponding to \(\Delta\)L can be determined according to the actual engineering, such as installation space, cost, etc., which will not be further elaborated in this paper.
# Assessment of Thermal Comfort in an Electric Bus Based on Machine Learning Classification
## 4 Discussion
### Comparison of Parameter Sets  
In Figure 11, the median score of every model is shown as well as the parameter set used. As a baseline, a red dashed line represents the median accuracy obtained by the PMV model with a threshold at 0.5 and a red dotted line for the PMV model with a threshold  
Figure 10: Confusion matrix for \(SMVRbf\) model using parameter set \(S_{2}\).  
at 1.5. It can clearly be seen that with machine learning the \(ANN\) and the \(SVM\) models showed a similar accuracy compared to the PMV 0.5 model.  
Within the better performing models, it can be clearly noticed that the subset \(S_{2}\) had the best accuracy, achieving up to an improvement of 1.67%. This might be counter intuitive as one would expect that using more parameters would lead to more passenger information to use in order to make a better prediction, which is exactly the opposite of what is observed.  
This could be explained by what is called the curse of dimensionality, which is particularly relevant for a small dataset as ours. The idea behind the curse of dimensionality is that with an increasing number of parameters, the volume of the parameter space grows exponentially, which makes the available data sparser within this higher dimensional space [46]. Also, increasing the parameter space dimension increases the degrees of freedom a classifier can set its boundaries to, and without enough data this could lead to over-fitting [46].  
The fact that high accuracy can be achieved, even without hyperparameter tuning using the parameter set with the least parameters is of mayor significance. This means that in order to predict the TS of a passenger it would suffice to know the air temperature \(T_{a}\) and the relative humidity \(RH\) in the cabin, which are easier and more reliable measurements to take, especially when compared to the additional radiant temperature \(T_{r}\), air velocity \(V_{a}\) and clothing insulation \(I_{cl}\) needed for the PMV model. This would also drastically simplify data collection to create larger datasets needed to improve future ML models. Another reason for our positive results could be related to the fact that we simplified the task into a binary classification problem, because of our small dataset. It may be possible that subsets with higher dimensions could be better when multi-labeled classification is performed, for which larger datasets with representative TS across the whole ASHRAE scale would be required.
## 3 Moving Virtual Sensing Algorithms
### The remote moving microphone technique
Estimates of the primary disturbances, \(\tilde{\mathbf{d}}_{p}(n)\), at the \(M_{p}\) physical error sensors are first calculated using  
\[\tilde{\mathbf{d}}_{p}(n)=\mathbf{e}_{p}(n)-\tilde{\mathbf{y}}_{p}(n)= \mathbf{e}_{p}(n)-\tilde{\mathbf{G}}_{pu}\mathbf{u}(n). \tag{64}\]Next, estimates of the primary disturbances, \(\tilde{\bar{\mathbf{d}}}_{v}(n)\), at the spatially fixed virtual locations, \(\bar{\mathbf{x}}_{v}\), are obtained using  
\[\tilde{\bar{\mathbf{d}}}_{v}(n)=\tilde{\mathbf{M}}\tilde{\mathbf{d}}_{p}(n). \tag{65}\]  
Estimates, \(\tilde{\bar{\mathbf{e}}}_{v}(n)\), of the total virtual error signals at the spatially fixed virtual locations, \(\bar{\mathbf{x}}_{v}\), are calculated as  
\[\tilde{\bar{\mathbf{e}}}_{v}(n)=\tilde{\bar{\mathbf{d}}}_{v}(n)+\tilde{\bar{ \mathbf{y}}}_{v}(n)=\tilde{\mathbf{M}}\mathbf{e}_{p}(n)+(\tilde{\mathbf{G}}_{ vu}-\tilde{\mathbf{M}}\tilde{\mathbf{G}}_{pu})\mathbf{u}(n). \tag{66}\]  
As shown in Fig. 10, estimates, \(\tilde{\mathbf{e}}_{v}(n)\), of the virtual error signals at the moving virtual locations, \(\mathbf{x}_{v}(n)\), are now obtained by spatially interpolating the virtual error signals, \(\tilde{\bar{\mathbf{e}}}_{v}(n)\), at the spatially fixed virtual locations, \(\tilde{\mathbf{x}}_{v}\). The performance of the remote moving microphone technique has been experimentally investigated in an acoustic duct, at an acoustic resonance [10; 12]. In the acoustic duct, the virtual microphone moved sinusoidally between a virtual distance of \(v=0.02\) m and 0.12 m with a period of 10 s. Minimising the moving virtual error signal using a feedforward control approach achieved greater than 34 dB of attenuation at the moving virtual location. This is 20 dB of attenuation greater than that achieved by minimising the error signal at a fixed physical microphone at \(v=0\) m or a fixed virtual microphone at \(v=0.02\) m. [PERSON] et al. [56] then extended the remote moving virtual microphone technique to generate a virtual microphone capable of tracking the ear of a rotating artificial head inside a three-dimensional cavity. For \(\pm 45^{\circ}\) head rotations with a period of 10 s, between 30 dB and 40 dB of attenuation was experimentally achieved at the ear of the rotating artificial head at an acoustic resonance.