esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Effect of the Temperature Gradient for Heat Transfer  
In thermodynamic performance analyses, the temperature gradient between heat sources/sinks and components, needed to realize the heat transfer process, is often neglected. Nevertheless, this can play a crucial role in the final selection of the most effective materials and components. For this reason, the achievable performance of the investigated working pairs when a reasonable \(\Delta\)T = 5 K was compared against the reference condition where \(\Delta\)T = 0 K. Figure 5 summarizes the results, both in terms of COP and specific energy, varying the waste heat and ambient temperature, using, AQSOA Z02/water as the reference working pair again for the sake of simplicity.  
As shown by Figure 5a,c, the effect induced by accounting for a \(\Delta\)T = 5 K for heat transfer in all AdHT components is extremely relevant for systems exploiting 40 \({}^{\circ}\)C of waste heat source temperature. Indeed, in this case, a heat upgrading effect, albeit limited, can be achieved only when the ambient temperature to which the process heat is dumped is below 10 \({}^{\circ}\)C. Only 10 K of GTL can be achieved at the minimum acceptable COP (i.e.,  
Figure 4: Coefficient of performance (COP) (**a**) and specific energy (**b**) for the working pair AQSOA 02/water, achievable for the Configuration I (one adsorbent bed) and Configuration II (two adsorbent beds), as a function of gross temperature lift (GTL).  
0.3) and quite low specific energy (i.e., 20 kJ/kg), when the ambient temperature is 5 \({}^{\circ}\)C. It has to be pointed out that the temperature gradient of 5 K is also considered in the heat recovery process between desorber and adsorber.  
Similar considerations are obtained when 80 \({}^{\circ}\)C of waste heat temperature are available, Figure 5b,d. Clearly, in this case, since the temperature difference between waste heat source and ambient heat is higher, the amount of refrigerant processed per each cycle is higher as well (see Figure 2), thus up to 52 K of GTL can be achieved at a COP of 0.3, even though with a very limited specific energy (i.e., below 50 kJ/kg). This demonstrates that, in practical applications, the right compromise must be sought between the needed GTL and the specific energy that can be extracted at the required temperature level, which cannot preclude from technological and economic aspects, which have to be taken into account to make the technology attractive for practical applications.  
Given the relevant role played by the temperature gradient to make the thermodynamic calculations reliable, a temperature gradient of \(\Delta\)T = 5 K will be always considered in the following analysis, focusing on the comparison among different working pairs. This can be assumed as a quite conservative approach, since good heat exchangers design can lead to smaller \(\Delta\)T in real applications. Nevertheless, it guarantees that the reported results have practical relevance for the selection of the most attractive working pairs for different operating conditions.
## 4 Conclusions  
In this work the quantitative concentration of oxygen atoms and carborne oxides compounds on the surfaces of HOPG, MWCNT, CSS and C\({}_{60}\) powders was determined. It was shown that the main source of oxygen content in the amount of approximately 2 at.% on the surface of HOPG, MWCNT, and C\({}_{60}\) powder is water condensed from the atmosphere in the form of an adsorbed water molecule and hydroxyl group. At the same time, a small amount of oxygen is contained in the form of carbon oxides on the MWCNT surface. The situation is fundamentally different for the CSS surface, where the concentrations of oxygen atoms as a part of carbon oxides, and in the form of adsorbed water molecules and hydroxyl groups are close and equal to 4-5% and 5-6%, respectively. The high content of adsorbed water on the CSS surface is maybe due to the strong roughness and high porosity of the surface, which, according to the BET method, was 425 \(\pm\) 30 m\({}^{2}\)[13]. The obtained data are important for the development of methods for the modification of carbon materials and the synthesis of nanostructured composites based on them.  
Conceptualization, D.V.S. and O.V.P.; research supervision, D.V.S., O.V.P., S.V.N. and V.N.S.; writing--original draft, D.V.S., O.V.P., S.V.N., A.S.V. and V.N.S.; XRD measurement and their data analysis, I.V.V., B.S.K. and A.M.O.; Raman spectroscopy measurements, S.I.I.; XPS and NEXAFS spectroscopy measurements and its data interpretation, D.V.S., S.V.N., O.V.P., A.S.V., K.A.B., R.N.S. and V.N.S. All the authors were actively involved in discussing the results, writing and drafting the manuscript. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the Grant of the President of the Russian Federation MK-3796.2021.1.2; by the Ministry of Science and Higher Education of Russia under Agreement N 075-15-2021-1351 in part of research on NEXAFS spectroscopy. The reported study was funded by RFBR, project number 19-32-60018, and the Komi Republic, project number 20-42-110002 r-a; the state research target for the G.A. Razuvaev Institute of Organometallic Chemistry of RAS, theme No. 45.4; the bilateral program of the RGBL at BESSY II.  
Not applicable.  
Analytical research was undertaken using equipment of NRC <<Kurchatov Institute--IREA Shared Knowledge Center, the Center for Studies in Surface Science of Research Park of St. Petersburg State University. The work was carried out using the equipment of the center for collective use \"Analytical Center of the IOMC RAS\" with the financial support of the grant \"Ensuring the development of the material and technical infrastructure of the centers for collective use of scientific equipment\" (RF--2296.61321X0017, No. 075-15-2021-670).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Second-Order Statistical Modeling of Current-Steering Electronic DACs (eDAC)
### Example: Analytic Models for 3-Bit (PAM8) eDACs of the ThWgt \(|\)BiWgt UNIP \(|\) BiP Types  
In this section, we specialize the heretofore developed analytic models of first-order and second-order statistical performance to the special cases of ThWgt \(|\)BiWgt UNIP \(|\) BIP eDACs with of \(B=3\) bits, i.e., \(C=8\) levels (PAM8). A total mean current budget of \(\langle I_{\text{tot}}\rangle=7\)mA is assumed. The unary current is taken as \(\langle I_{\text{u}}\rangle=1\)mA for both UNIP and BIP. The relative mismatch is taken as \(\sigma_{\text{u}}=10\%=0.1\), excessive in practical terms, yet useful to highlight the random mismatch variations for all currents--see the figures in Section 2.9--validating the analytic models of this subsection by Monte Carlo simulations. To recapitulate our specifications for the modelled eDAC instances:  
\[C=8,\ \ B=3,\ \ \langle I_{u}\rangle=1\ \text{mA},\ \sigma_{u}=0.1 \tag{196}\]  
ThWgt UNIP: use \(C\)-1 = 7 independent normally distributed current sources all of mean current \(\left\langle I_{\text{s}}^{\text{ThWgt-UNIP}}\right\rangle=\langle I_{\text{u}} \rangle=1\ \text{mA},\ \ s=0,1,2,\ldots,6\):(consistent with \(\langle I_{\text{tot}}\rangle=7\ \text{mA}\))  
\[\mathbf{I}^{\text{ThWgt-UNIP}}\equiv\left\{I_{6}^{\text{ThWgt-UNIP}},I_{5}^{ \text{ThWgt-UNIP}},I_{4}^{\text{ThWgt-UNIP}},I_{3}^{\text{ThWgt-UNIP}},I_{2}^{ \text{ThWgt-UNIP}},I_{1}^{\text{ThWgt-UNIP}},I_{0}^{\text{ThWgt-UNIP}}\right\} \tag{197}\]  
The resulting eight output levels distributions are shown in Figure 2b, evaluated by accumulating the seven source currents, \(J_{c}^{\text{ThWgt-UNIP}}=\sum_{s=0}^{c-1}I_{s}^{\text{ThWgt-UNIP}},\ c=1,2, \ldots,8\), yielding:\[ \begin{array}{l}J_{1}^{\rm ThWgt-UNIP}=0,\ \ J_{2}^{\rm ThWgt-UNIP}=I_{0}^{\rm ThWgt- UNIP},\ J_{3}^{\rm ThWgt-UNIP}=I_{1}^{\rm ThWgt-UNIP}+I_{0}^{\rm ThWgt- UNIP},\\ J_{4}^{\rm ThWgt-UNIP}=I_{2}^{\rm ThWgt-UNIP}+I_{1}^{\rm ThWgt-UNIP}+I_{0}^{\rm ThWgt -UNIP},\ldots,J_{8}^{\rm ThWgt-UNIP}=\sum_{s=0}^{7}I_{s}^{\rm ThWgt-UNIP}\\ \therefore\left\langle I_{c}^{\rm ThWgt-UNIP}\right\rangle=(c-1)\langle I_{u}\rangle,\ \sigma_{l_{c}}^{2}=(c-1)\sigma_{l_{u}}^{2}=(c-1)\sigma_{u}^{2}\langle I_{u} \rangle^{2}=(c-1)\cdot(0.1\ {\rm mA})^{2}\end{array}  \tag{198}\]  
The means, variances and STDs of the output levels vector are then  
\[\left\langle{\bf J}^{\rm ThWgt-UNIP}\right\rangle=\{0,1,2,3,4,5,6,7\}\langle I _{u}\rangle=\{0,1,2,3,4,5,6,7\}\ {\rm mA} \tag{199}\]  
\[ \begin{array}{l}\sigma_{\bf J}^{2}{}_{\rm ThWgt-UNIP}=\sigma_{u}^{2}\langle I _{u}\rangle^{2}\{0,1,2,3,4,5,6,7\}=0.01({\rm mA})^{2}\{0,1,2,3,4,5,6,7\}\\ \sigma_{\bf J}^{\rm ThWgt-UNIP}=\Big{\{}0,1,\sqrt{2},\sqrt{3},\sqrt{4},\sqrt{5 },\sqrt{6},\sqrt{7}\Big{\}}\sigma_{l_{u}}=.01\Big{\{}0,1,\sqrt{2},\sqrt{3}, \sqrt{4},\sqrt{5},\sqrt{6},\sqrt{7}\Big{\}}{\rm mA}\end{array}
# The Challenges of Advanced CMOS Process from 2D to 3D
## 4 Dopant Implantation in CMOS  
For decades, Ion implantation has been a major doping strategy for a planar CMOS device in S/D formation or threshold voltage adjustment. However, when the transistor dimension shrunk below 100 nm the extended S/D implantation needed to be more aggressive to inhibit the short channel effect [99]. In MOSFETs, shallow junction (USJ) was sought to follow the down-scaling geometrical rules.  
Initially, USJ was realized by low ion implant energy and followed by a traditional rapid thermal process (RTP or spike annealing). A series of low energy high current ion implanter were also developed to improve the throughput of the tools [100; 101]. While the gate length was further scaled to 45 nm node and beyond, the transient enhanced diffusion (TED) effect of the dopant was more and more apparent and a desired junction depth (International Semiconductor Technology Development Roadmap(ITRS) required) could not be achieved by just low energy implantation. Some novel solutions e.g., co-implantation, cryogenic implantation or Cluster implant were developed to reduce the dopant's diffusion in the substrate during activation step [102; 103; 104; 105; 106]. For co-implantation, carbon was proved to be an appropriate species with good control of dopant diffusion in both silicon or germanium based transistors [107; 108].  
On the other hand, the annealing technology was also improved e.g., laser anneal and flash anneal can activate the dopant within milliseconds [109; 110]. These techniques can not only reduce the thermal budget of the annealing process, but also improve the activation level of the dopant. Nowadays, these techniques have been used as standard source/drain thermal treatment in CMOS fabrication. In recent years, a new thermal treatment method so-called microwave annealing is found to be useful for shallow junction formation. This technique offers the annealing process at rather low temperatures with less dopant diffusion [107; 111]. Since the temperature of activation is lower than 500 \({}^{\circ}\)C, it could be meaningful for Ge based devices in future.
# Conservative Interference Injection to Minimize Wi-Fi Sensing Privacy Risks and Bandwidth Loss
## 5 Proposed Contention Model for Defence Against Wi-Fi Sensing
### Throughput of CSMA/CA WLANs
We illustrate 5 curves corresponding to the devices propagating packets of varying lengths. From the vertical spread of the various curves, we note that the throughput varies as a function of the packet size. This is symptomatic of the greater channel utilisation due to larger packet sizes since the stations spend less time in the inter-frame periods such as DIFS and SIFS. We further note that the decay in throughput appears to be independent of the size of the packets being transmitted. Overall, the total channel throughput decreases slightly as the number of stations increases, presumably due to the increased overhead of the distributed access protocol. This result represents the throughput in bits per second. We recall from Section 3 that CSI for Wi-Fi sensing is captured on a per-packet basis. Hence, we propose a new expression for the available CSI data rate as the quotient of throughput with the length of the transmitted packets, divided by the total number of stations as in Equation (10). \[R_{\rm CSI}=\frac{1}{N}\frac{\tau}{L} \tag{10}\]  
We compute \(R_{\rm CSI}\) for the throughputs derived earlier and plot the results below. In Figure 9, we illustrate the CSI data rates for an 802.11n network with an increasing number of stations in the channel. The x-axis represents the number of stations transmitting in the channel, and the y-axis represents the effective CSI data rate of the channel. Each curve represents a simulation with packets of varying lengths. We note that a packet  
Figure 8: Throughput of 802.11n networks with increasing users and different packet lengths. length of 700 bits corresponds to ICMP ping packets, which are commonly used for Wi-Fi sensing applications. We observe firstly that the CSI data rate reduces as the number of 802.11n users increases. This is initially a sharp decrease by a factor of 0.5 when the usage increases from one to two users, and the rate of decrease slows down subsequently. We further note that the CSI data rate is maximised for the smallest packet lengths, with the solid blue curve representing the highest CSI data rate for ping packets. This result reflects the smaller time spent occupying the channel per packet, and hence, more packets can use the channel resulting in a higher CSI data rate. Overall, the analytical results in this section support the notion that interference from other devices can reduce the effective CSI data rate by virtue of the 802.11n medium access control.
## 4 Application Results
### The Case of Injection Molding Process
On the Tennessee Eastman (TE) dataset, Incremental Pyraformer-DCCA completed training in 1.41 s, outperforming Fedformer-DCCA (4.31 s) and Autoformer-DCCA (2.21 s) while achieving a comparable or slightly faster performance than other baselines. Similarly, in the Continuous Stirred-Tank Reactor (CSTR) experiment, Incremental Pyraformer-DCCA trained in 4.19 s, whereas LSTM-DCCA required 5.30 s, Fedformer-DCCA 9.00 s, Crosformer-DCCA 5.68 s, Autoformer-DCCA 4.75 s, and GRU-DCCA 4.20 s.  
The test times were significantly lower, demonstrating near real-time applicability. For instance, in the injection molding scenario, Incremental Pyraformer-DCCA processed new samples in approximately 0.03 s, a speed comparable to the most efficient baseline methods. Across all datasets, the inference times remained between 0.02 and 0.04 s, highlighting the method's feasibility for real-time deployment. These results confirm that our proposed approach achieves efficient training and fast inference while maintaining robust predictive performance. More importantly, the method remains computationally competitive and can be deployed on standard GPU hardware without incurring prohibitive runtime overhead.
## 3 Fuzzy Agent-Based Model
### Behavioral Functions of a Fuzzy Agent  
\[\Phi_{(\Omega_{k})}: (E_{\alpha_{i}}\cup I_{\alpha_{i}})\times\Sigma_{\alpha_{i}} \rightarrow\Pi_{\alpha_{i}} \tag{3}\] \[\Phi_{(\Delta\alpha_{i})}: \Pi_{\tilde{\alpha}_{i}}\times\Sigma_{\alpha_{i}}\rightarrow \Delta_{\alpha_{i}}\] (4) \[\Phi_{\Gamma(\alpha_{i})}: \Delta_{\alpha_{i}}\times\Sigma\rightarrow\Gamma_{\alpha_{i}} \tag{5}\]  
Equations (3)-(5) define key functions for a fuzzy agent \(\tilde{\alpha}i\) within a fuzzy multi-agent system \(\tilde{M}\alpha\), specifically as follows:  
* Equation (3) represents the observation function, which maps the set of observed fuzzy events \(E_{\tilde{\alpha}i}\) and interactions \(I_{\tilde{\alpha}i}\), along with the agent's state \(\Sigma_{\tilde{\alpha}i}\), to its fuzzy perceptions \(\Pi_{\tilde{\alpha}i}\).
* Equation (4) defines the decision-making function, mapping fuzzy perceptions \(\Pi_{\tilde{\alpha}i}\) and agent state \(\Sigma_{\alpha_{i}}\) to fuzzy decisions \(\Delta\tilde{\alpha}_{i}\).
* Equation (5) describes the action function, which maps fuzzy decisions \(\Delta_{\alpha_{i}}\) and the overall system state \(\Sigma\) to the agent's fuzzy actions \(\Gamma_{\tilde{\alpha}i}\).  
Here, \(E_{\tilde{\alpha}i}\) denotes the set of fuzzy events observed by the agent, \(I_{\tilde{\alpha}i}\) represents its fuzzy interactions, \(\Sigma_{\tilde{\alpha}i}\) is the agent's fuzzy state space, \(\Pi_{\tilde{\alpha}i}\) represents the set of fuzzy perceptions, \(\Delta_{\alpha i}\) denotes the fuzzy decisions made by the agent, \(\Gamma_{\tilde{\alpha}i}\) represents the agent's fuzzy actions, and \(\Sigma\) refers to the overall state of the fuzzy multi-agent system.
# A Sun-Tracking Algorithm for Satellite-Borne Spectrometers Based on the Orbital Motion Model
## 2 Spectrometer System  
The incident angle of sunlight on the working orbital plane of the spectrometer varies within a range of about 47\({}^{\circ}\) in a year, which is much larger than the effective field of view of the spectrometer. The spectrometer system is designed as shown in Figure 1, in accordance with the working characteristics of the spectrometer. Spectrometers with two certain spectra are mounted on a two-dimensional turntable. The deviation angle of the sun relative to the field of view of the spectrometer, which is caused by the orbital motion of the satellite, is compensated by adjusting the angular position of the turntable such that the optical axis of the spectrometer can be aligned with the sun in real-time. To realize the tracking of the sun, the two-dimensional turntable is directly driven by a permanent magnet synchronous motor (PMSM). The angular offset of the sun relative to the optical axis is measured by the tracking sensor, and the angular velocity of the turntable and the position of the rotor are measured using the absolute encoder to realize the vector control of the PMSM.  
When the spectrometer system is operating, the azimuth and pitch angular positions of the turntable are adjusted first to point at the sun, such that the sun lies in the effective field of view of the tracking sensor. Then, the two-axis angular positions are adjusted further in real-time to achieve sun tracking according to the measured angular offset. Only when the sun deviates from the center of the field of view of the spectrometer at a small angle can the two spectrometers obtain effective spectral data. The lower the tracking error, the better the performance of the optical system. Therefore, high-precision sun tracking and a stable two-dimensional turntable are imperative for the effective operation of the spectrometer.  
The azimuth and pitch axes of the two-dimensional turntable are approximately orthogonal to each other, so the control of the two axes is independent. Vector control based on the magnetic linkage orientation of the rotor is adopted for the motor drive, and a typical control system for a single axis is shown in Figure 2. The control loop comprises a position loop, speed loop, and current loop, and a proportional-integral (PI) controller is adopted in each loop to realize closed-loop control. The current loop controls the acceleration of the turntable by controlling the stator current of the torque motor; its bandwidth is much larger than that of the speed loop and the position loop. The feedback speed is estimated from the position encoder using the cascading finite difference method (FDM) with a low-pass filter, and the speed loop ensures the stable operation of the turntable. The position loop obtains the angular offset from the tracking sensor to ensure that the turntable tracks the sunlight during closed-loop control [19].  
Figure 1: Schematic of the spectrometer system.
# Speech-Based Support System to Supervise Chronic Obstructive Pulmonary Disease Patient Status
## 2 Materials and Methods
### Extraction of Speech Features  
The recordings were performed with portable recorders that provided files in DVI ADPCM (digital visual interface/adaptive differential pulse-code modulation) format,with a sampling rate of 48 kHz and a bit rate of 192 kbps. The original files were converted into PCM WAV (pulse-code modulation/waveform audio file format) for their further processing with the Praat speech analysis software [26].  
f0, voice quality parameters such as jitter and shimmer, and pauses in COPD analysis were included following existing literature [1; 19; 27]. Prosodic information was also included, accounting for the variation of f0 (range and slope), and other rhythm features such as speech rate, articulation rate, and syllable duration. For the extraction of f0 and its related features, the auto-correlation method in Praat was used with an interval of 10 ms and a Hanning window of length 40 ms. Duration and speech rate features were extracted by adapting the Praat script found in [28], without relying on transcriptions, and thus were language independent.
## 3 Proposed Non-Linear Current Control Scheme of Grid Connected Inverter with LCL Filter
### DC-Link Voltage Controller  
As the DC-link voltage control is not the concern of this paper, the conventional DC-link voltage control scheme is presented in this subsection for the ease of reference. As shown in (13), both \(i_{gd}\) and \(P_{dc}\) affect the dynamics of the DC-link voltage. To remove the dynamic dependency to \(P_{dc}\), it is a commonly used strategy to add \(P_{dc}\) as a feed-forward to the control system [32]. Thus, \(i_{gd}\) is the variable which is solely used for control of DC-link energy. Equation (65) represents the proper reference value of \(i_{gd}\),  
\[i_{gd}^{ref}=\frac{P_{dc}}{\frac{3}{2}v_{gd}}+\frac{K_{p}s+K_{i}}{-\frac{3}{2} v_{gd}s}(W_{c}^{ref}-W_{c}) \tag{65}\]  
where, \((K_{p};K_{i})\) are proportional and integral gains of the PI controller, respectively.  
Using the reference value of \(i_{gd}^{ref}\) in (65), the closed loop transfer function of the DC-link energy loop is as given by (66).  
\[W_{c}=\frac{K_{p}s+K_{i}}{s^{2}+K_{p}s+K_{i}}W_{c}^{ref} \tag{66}\]  
With reference to (66), the fast step response, high system bandwidth, zero steady state error, and proper robustness in presence of uncertainties in the system parameters are achieved by a proper setting of \((K_{p};K_{i})\) values.