esa-sceva/satcom-chunk-collection huggingface dataset:
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Temperature Acquisition  
Knowing the most precise temperature possible is one of the most difficult tasks when designing a BMS. Not only the advantages and disadvantages of the various available sensor types (fully digital interface or analog) need to be accounted for, but also where to measure the pack's temperature. This leads to the required number of cell temperature sensors. Simulations might be necessary to find the optimal placement for a limited amount of sensors. Sometimes, it can also be important to acquire peripheral temperatures, such as those of the contactors, fuses or even the electric busbars that carry the battery pack's main current. The latter might only be necessary when assuming to manage a battery pack that has to operate at its thermal limit. When imagining a battery pack to be optimized regarding light weight, heavy copper busbars need to be omitted as far as possible, forcing the design engineer to handle probable thermal peaks during high power operation, thus requiring a thermal observation of busbars as well. Having discussed thermal aspects on peripheral systems, the next focus shall be the cell-based temperature within a battery pack. Usually, there is a temperature sensor to voltage sensor channel ratio of 2:3 down to 2:12, meaning that only two surface temperature sensors can be implemented for a cell-composition of 12 for the last example. These ratios are based on commonly available analog front-end IC [3].  
In general, a temperature requirement has to consider three use cases: charging, discharging and storage. Considering the safe operating range, the cell manufacturer should be consulted. lithium based batteries can not work properly in too low or too high temperatures ([4], p. 24, [5]). However, even within these limits, knowing the temperature properly is of high relevance. The important effect of lithium plating can occur in the normal temperature range at too high current rates. For avoiding lithium plating, temperature, voltage and current must be known precisely [6]. The requirement set should also include the significance of thermal time constants, such as the thermal anisotropic conductivities as well as the thermal capacitance as basic properties of the battery pack. Battery cells have a large thermal capacitance and a good thermal conductibility (in certain geometric paths), which is influenced and limited by boundary layers of thermal isolation (housing, geometry of cells, etc.) [7]. Misreadings and thermal blind spots can result if there are suboptimally placed temperature sensors.
## 4 Results  
### Apparent Soil Electrical Conductivity Measurement  
Figure 6 shows an example of the measured ECa of the salt patch, including a field photograph and 2D and 3D maps from HH and VV modes by Surfer software, at Ban Don Nam Sai, Ban Wang sub-district.  
For HH mode, as representative of topsoil, the high ECa is found in the upper left corner in Figure 6b,c. Meanwhile, the high ECa is found in the western part in Figure 6d,e for VV mode, as representative of subsoil. This observation indicates the spatial variation of ECa's intensity in different soil depths.  
Besides, Table 4 displays another example of the measured ECa with the essential statistical data of HH and VV modes in five land use types.  
\begin{table}
\begin{tabular}{l c l} \hline \hline
**Soil Salinity Severity Class** & **ECe (mS/m)** & **Effect on Crop Plants** \\ \hline Non-saline & 0–200 & Salinity effects negligible \\ Slightly saline & 200–400 & Yields of sensitive crops may be restricted \\ Moderately saline & 400–800 & Yields of many crops are restricted \\ Strongly saline & 800–1600 & Only tolerant crops yield satisfactorily \\ Very strongly saline & \textgreater{}1600 & Only a few very tolerant crops yield satisfactorily \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification of soil salinity severity based on FAO standards [59].  
Figure 6: Field photograph and 2D and 3D maps of HH and VV modes over salt patches at Ban Donor. Nam Sai, Ban Wang sub-district. (**a**) Salt patches. (**b**) 2D-ECa (HH). (**c**) 3D-ECa (HH). (**d**) 2D-ECa (VV). (**e**) 3D-ECa (VV).  
Table 4 shows the average measured ECa values of HH and VV modes in cassava and corn fields located in non-saline soil according to the FAO standard values. The average measured ECa values of bare land and paddy fields from the two modes were in slightly saline soil. On the contrary, average ECa values of the salt patch from the two modes were strongly saline.
_applied sciences_  
_Review_  
**Dentin Hypersensitivity: Etiology, Diagnosis and Contemporary Therapeutic Approaches--A Review in Literature**  
**[PERSON] 1,*\({}^{\copyright}\), [PERSON] 1 and [PERSON] 2**  
1 Department of Operative Dentistry, Faculty of Dentistry, School of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; [EMAIL_ADDRESS]  
2 Department of Endodontics, Faculty of Dentistry, School of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +30-2310999578  
**Abstract:** The aim of this review was to provide dentists with comprehensive information regarding dentin hypersensitivity. This includes presenting its etiology, outlining the process of diagnosis, discussing clinical management strategies, and exploring technical approaches aimed at alleviating sensitivity. Dentin hypersensitivity is characterized by distinctive short, sharp pain arising from exposed cervical dentin in response to various external stimuli. The etiological factors cause two specific changes in teeth. First, the dentin surface must be exposed and denuded, which requires the loss of enamel or gingival recession combined with the loss of cementum. The second condition is the opening of the dentin tubules to allow the sensory mechanisms in the pulpal area following stimulation of the dentin surface. The accurate diagnosis of dentin hypersensitivity before receiving therapies is critical for successful treatment. The diagnosis of the disease starts through investigating the medical history of the patient and examination. In the examination, some techniques such as air stream and water rinsing are used in order to simulate the stimulating factors and to determine the degree of pain of the patient. Numerous materials and methods have been proposed to reduce or alleviate sensitivity. These materials typically exert their effects by either sealing dentin tubules or disrupting the transmission of nerve impulses. Generally, the primary approach in addressing dentin hypersensitivity involves the utilization of toothpaste containing potassium salts and fluoride. Newly introduced materials and in-office methods for treating dentin hypersensitivity include bioactive glasses, iontophoresis, CPP-ACP, and lasers.  
bioactive glasses; dentin hypersensitivity; diagnostic methods; fluorine compounds; lasers; in-office treatments; etiological factors
# Linear Approximation Signal Detection Scheme in MIMO-OFDM Systems
## 3 Conventional Detection Schemes
### Zf  
The ZF removes inter-antenna interference (IAI) perfectly by multiplying pseudo-inverse channel matrix by received vector. The pseudo-inverse channel matrix \(\mathbf{G}\), which is a least square (LS) solution to estimate the transmit signal, is as follows,  
\[\mathbf{G}=\left(\mathbf{H}^{H}\mathbf{H}\right)^{-1}\!\!\mathbf{H}^{H}. \tag{3}\]  
Then, the estimated MIMO transmit vector \(\mathbf{\hat{X}}_{\text{ZF}}\) is as follows,  
\[\mathbf{\hat{X}}=\mathbf{G}\mathbf{Y}=\mathbf{X}+\overline{\mathbf{Z}}, \tag{4}\]  
where \(\overline{\mathbf{Z}}=\mathbf{G}\mathbf{Z}\) is a modified noise vector.
## 3 MIMO Radar Simulation Platform
### Noise and Data Receiver Module
The probability density function is:  
\[f(x)=\frac{x}{\sigma^{2}}e^{-\frac{x^{2}}{2\
u^{2}}},x>0 \tag{18}\]  
For the actual work environment, the radar signals will always be interfered with by other clutter when the working environment changes, including natural clutter such as the ground and waves, or artificial clutter such as chaff, corner reflectors, and so on. The noise type can be adjusted according to the requirements of different radar application environments. As can be seen from Figure 5, the signal generated in the previous step is multiplied with a complex signal in the real part and the imaginary part, respectively. The complex signal amplitude is 1 V, and the frequency \(F_{d}\) is the Doppler shift of the radar target. The clutter and noise of the Rayleigh distribution are generated by Gaussian signal generator. The difference is that the bandwidth is different from the amplitude. After the superposition, the target delay is added by a delay element and the distance of the target can be calculated. The resulting signal is a reflected signal with clutter, noise, and target information. The data-receiving module is one of the most important modules in MIMO radar. Through this module, MIMO radar will receive the signal reflected by the radar target and calculate the radar targets such as speed, distance, DOA, and other information through the process of matching filtering, detection, and beam forming. The structure of the data-receiving module of traditional MIMO radar system is shown in Figure 6.  
In Figure 6, three filters are listed as an example but, in an actual situation, the filters should be matched to the number of transmitter orthogonal signals, and the useful target information is filtered after the data-processing and digital beam-forming (DBF) processes. Assuming the MIMO radar can transmit \(M\) orthogonal waveforms and each link needs corresponding matching filter, then the \(M\times N\) matching filters will make the system very complex and expensive for maintenance. Figure 5: Schematic diagram of target and noise. Gaussian signal generator will generate the clutter and noise of the Rayleigh distribution and the interference will be added to the input data after the Doppler shift.
## Results
### Course of the Number of Lesions  
In total, 29 patients had a valid lesion count at baseline, at treatment completion as well as at two and four weeks after treatment completion. The courses of the inflammatory and non-inflammatory lesion counts are presented separately for the treated and the untreated side in Figure 3. The treated sides showed a significant decrease in inflammatory lesions (\(p=0.007\)) but not in non-inflammatory lesions (\(p=0.288\)). The untreated sides neither showed any reduction in inflammatory (\(p=0.241\)) nor in non-inflammatory lesions (\(p=0.554\)). No further significant effects were observed.  
Figure 3: Course of the number of inflammatory lesions (**a**) and non-inflammatory lesions (**b**). Blue = treated side; orange = untreated side. Mixed linear models (MLMs) were used to compare the number of lesions between the measurement time points. MLMs additionally included the fixed effects of the number of applied plasma treatments and the interaction of the time and number of applied plasma treatments (data not shown). Presented are the estimated/adjusted means with a corresponding 95% confidence interval (CI) out of four separate MLMs. In total, 29 patients had a valid lesion count; 25 patients had 10 plasma treatments, and 4 patients 8 treatments. Only inflammatory lesions on the treated side were significantly reduced from baseline to the last treatment (\(-30\%\), \(p=0.020\)), to the two-week follow-up (\(-43\%\), \(p=0.002\)) and the four-week follow-up (\(-48\%\), \(p=0.040\)).
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 3 Noise Modeling and Building the Training Sets
### Building the Training Sets  
The overall process involved in building training sets from noisy real-world inputs is illustrated in Figure 4. The first step is to extract noise samples from noisy speech signals to establish a noise dataset. After that, NM-GAN is trained to learn the noise distribution and generate many noise samples, followed by noisy speech synthesis.
# Application Research of a New Adaptive Noise Reduction Method in Fault Diagnosis
## 2 Basic Theory
### Optimized CEEMD Method  
CEEMD is an improved method based on the EMD method to add a pair of positive and negative white noise; there is also a mode aliasing phenomenon, and there is still noise in the decomposed IMF component. In order to eliminate the influence of noise on the decomposition results, this paper uses multi-scale sample entropy to remove the IMF with more noise components, reconstruct the signal, and perform the Fast Fourier transform(FFT) transform to obtain the fault feature signal to achieve the purpose of optimizing CEEMD. The flow chart is shown in Figure 3, the method is as follows:1. Employing the CEEMD method to decompose the original signal \(X(t)\) containing noise to obtain a series of IMFs.
2. The selection of the scale factor \(Q\) of the MSE will affect the multiscale sample entropy value of each IMF. According to the decomposition characteristics of the IMF, the noise content of each layer is different. In order to better distinguish each layer of signals, this paper chooses a scale factor of 1-20 and signals with different SNRs for simulation to determine the best scale factor \(Q\).
3. Substitute \(m=2\), \(r=0.25 SD\), and the \(Q\) that was determined in step (2) into the MSE, calculate the multiscale sample entropy value of 10,000 sets of random noise signals, and obtain the minimum value \(K\) of the noise multiscale sample entropy.
4. Calculate the multi-scale sample entropy value of each IMF component, compare the obtained value with \(K\), remove components greater than \(K\) value, and reconstruct the remaining IMFs components.
5. Perform CEEMD decomposition on the reconstructed signal again, and then calculate the multiscale sample entropy value of each IMF component.
6. Compare the MSE value of each IMF component with the minimum value \(K\), if there is more than \(K\) worth of IMF, repeat (2-6); otherwise, continue.
7. The signal is FFT transformed to obtain the fault signal.
## 3 Results and Discussion
### Rheological Characterization and Curing Behavior of Hybrid Reinforced EPDM Compounds
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{
\begin{tabular}{c} **Swelling Degree** \\ **(\%)** \\ \end{tabular} } & \multicolumn{2}{c}{**Gel Content**} & \multicolumn{2}{c}{**M\({}_{\mathbf{c}}\)**} \\  & & **(\%)** & & **(\%)** & & **(g/mol)** \\ \hline  & **0 h** & **24 h** & **0 h** & **24 h** & **0 h** & **24 h** \\ \hline PF–ZDA & 154 \(\pm\) 0.8 & 142 \(\pm\) 2.6 & 97 \(\pm\) 0.1 & 99 \(\pm\) 0.1 & 953 \(\pm\) 4 & 821 \(\pm\) 22 \\ PF–ZDMA & 166 \(\pm\) 1.5 & 148 \(\pm\) 0.4 & 97 \(\pm\) 0.1 & 99 \(\pm\) 0.1 & 1075 \(\pm\) 16 & 872 \(\pm\) 2 \\ PF–TRIM & 147 \(\pm\) 5.2 & 141 \(\pm\) 0.6 & 97 \(\pm\) 0.2 & 99 \(\pm\) 0.1 & 896 \(\pm\) 15 & 823 \(\pm\) 4 \\ PF–7 PO & 192 \(\pm\) 3.4 & 180 \(\pm\) 1.1 & 96 \(\pm\) 0.3 & 99 \(\pm\) 0.2 & 1433 \(\pm\) 48 & 1230 \(\pm\) 11 \\ PF–5 PO & 226 \(\pm\) 0.8 & 206 \(\pm\) 0.2 & 93 \(\pm\) 0.3 & 97 \(\pm\) 0.2 & 1944 \(\pm\) 15 & 1571 \(\pm\) 7 \\ Belt–Ref & 137 \(\pm\) 0.4 & 135 \(\pm\) 0.2 & 100 \(\pm\) 0.1 & 100 \(\pm\) 0.1 & 703 \(\pm\) 15 & 674 \(\pm\) 3 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Swelling degree of hybrid-reinforced EPDM compounds prior to and after postcuring for 24 h at 150 \({}^{\circ}\)C.  
Figure 3: Schematic representation of (**a**) curing rheogram, (**b**) tensile properties, (**c**) storage modulus, and (**d**) tan\(\delta\) based on DMTA results of EPDM–PF compounds. compounds. Further, the gel content is reduced for vulcanizates without coagents. This is attributed to the scavenging effect of PF on the rubber cross-linking and therefore reduced curing efficiency. The curing process continues during the post-curing step, resulting in enhanced gel content for all samples. As expected, the calculated cross-link density is lowest for solely PF-reinforced vulcanizates. The addition of coagents results in a significant enhancement of the cross-link density. However, similar to the previous section, ZDMA results in low cross-link density for the nonpostcured samples. Postcuring significantly impacts the PF-reinforced compounds, which indicates further curing of the novolac resin. These results demonstrate the positive influence of coagents on the state of cure and are in good agreement with the MDR analysis. However, postcuring is assumed to yield a further increase of cross-link density for all compounds. This increase can be attributed to aging-induced chain scission, which contributes to the formation of new cross-links of the EPDM matrix or further curing of the PF resin [35, 36].
## 3 Traditional Energy Detection Algorithm and Principle
There are two kinds of calculations of the threshold value \(x\): (1) known false alarm probability, and (2) known missed detection probability. The specific calculation method is as follows:  
\[\rho=(\sigma_{n}^{2}+\sigma_{x}^{2})^{2}\left(N-Q^{-1}\left( \begin{array}{c} P_{m}^{\rho}\end{array} \right)\sqrt{2N}\right) \tag{8}\]  
\[\rho=Q^{-1}\left( \begin{array}{c}P_{f}^{\rho}\end{array} \right)\sqrt{2N \sigma_{n}^{2}}+N\sigma_{n}^{2} \tag{9}\]  
Figure 5 shows the energy detection diagram of the double threshold, the detection principle of which is similar to that of the single threshold. The decision rules of the double-threshold energy detection algorithm are as follows: if the energy value of signal \(y\) is greater than \(\rho_{h}\), the main user signal exists. If the signal energy value \(y\) is less than \(\rho_{l}\), then the main user signal does not exist. When the signal energy value \(y\) is between two thresholds, it is an uncertain region. Three performance indicators are shown as follows:  
\[P_{f}=P(y>\rho_{h}\left|H_{0}\right.)=Q(\frac{\rho_{h}-N\sigma_{n}^{2}}{\sqrt {2N\sigma_{n}^{2}}}) \tag{10}\]  
\[P_{m}=P(y<\rho_{l}\left|H_{1}\right.)=Q\left(\frac{N\left(\sigma_{n}^{2}+ \sigma_{X}^{2}\right)-\rho_{l}}{\sqrt{2N\left(\sigma_{n}^{2}+\sigma_{x}^{2} \right)^{2}}}\right) \tag{11}\]  
\[P_{d}=P(y>\rho_{h}\left|H_{1}\right.)=Q\left(\frac{\rho_{h}-N\left(\sigma_{n} ^{2}+\sigma_{X}^{2}\right)}{\sqrt{2N\left(\sigma_{n}^{2}+\sigma_{X}^{2} \right)^{2}}}\right) \tag{12}\]  
Figure 4: Energy detection diagram of a single threshold. Figure 5: Energy detection diagram of the double threshold.
## 3 Experimental Platform of the Hybrid Thermal Management System  
### Hardware Designs of the IHTMS  
The hardware design of the IHTMS is shown in Figure 5. The emulated waste heat of dual energy sources was generated by two heaters inserted in two storage tanks (Heat(1) and Heat(2)), where two programmable power supplies provided required instant heating power controlled by LabVIEW on a PC. The inner-loop or outer-loop structure was governed by three electromagnetic valves. The pump and proportional valve determined the coolant flow rates for two heat sources that two flow meters were equipped with before the entrances of Heat(1) and Heat(2). Meanwhile, four temperature sensors before and after Heat(1) and Heat(2) supervised the heat source input-output temperatures. Two other temperature sensors were set at the entrance and exit of the heat exchanger to calculate the heat dissipation capability. An industrial PC with thermal management control strategies received the signals of the temperatures to determine the voltages of the radiator, proportional valve and the coolant pump. A data logger recorded all tested data for further applications. The detailed specifications of the key components are summarized in Table 1.  
Figure 4: The IHTMS simulator on the Matlab/Simulink platform.  
Figure 6 demonstrates the completed experimental platform of the IHTMS. Label a denotes a PC with LabVIEW system; label b represents two programmable power supplies to provide heating power to the heaters in label c, the two stainless tanks. Label d is the rapid-prototyping controller with the rule-based control strategies. Label e is the proportional valves; label f is the heat exchanger; label g is the coolant pump; label h is the data logger; label i is the AC inverters; label j is the flow meters; label k is the data logger; and label l is the three electromagnetic valves for inner and outer loops. With the arrangement of these key components, the simulation results of the simulator can be compared with the experimental data from the platform in Figure 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Product Model** & **Specification** & **Manufacturer** \\ \hline DC Heating Appliance & W & 1000 W & Ching-Ta Heating, Taiwan \\ Programmable Power Supply & PSW80–40.5 & 1080 W & Good Will Instrument, Taiwan \\ Temperature Transmitter & TRH-300 & 0–100 \({}^{\circ}\)C & TECPEL, Taiwan \\ Heat Exchanger & OC-1405 & 43.18 \(\times\) 28.57 \(\times\) 5.7 cm & TECPEL, Taiwan \\ Coolant Fan & SA12038B2H & 113 CFM & STK, Taiwan \\ Coolant Pump & U85B1 & 4000 mL/min & United victory scientific, Taiwan \\ Proportional Valve & AN-01-AMD-360 & 0–5 V & Anco, Taiwan \\ Solenoid Valve & 1304–15 & 1/2 in PT & Anco, Taiwan \\ Micro-Box & Micro-Box 220 & – & Terasoft, Taiwan \\ Variable-frequency Drive & JNTHBCBA0001 BE-U- & 2 HP/1.5 kW & TECO, Taiwan \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of key components.  
Figure 5: Hardware designs of the IHTMS.
# A Reinforcement Learning Approach to Speech Coding
## 4 Speech Coding as Reinforcement Learning  
Before we develop the reinforcement learning structure for speech coding, we need to outline the basic speech coding problem. The idea is that the speech signal (the Environment) can be observed at the Encoder and the operations in the Encoder must come up with a low bit rate representation that can be sent over a digital communications channel to the Decoder in order to reconstruct the speech. Note that the bit rate constraint actually can map directly into an energy or power constraint, which are the kinds of constraints usually placed on control sequences in stochastic control. Although the bit rate constraint can seriously limit the representation that can be sent, the reconstructed speech quality and intelligibility must be sufficient to satisfy the User; therefore, there is an Encoding process that generates a bit rate sequence that is used at the Decoder to reconstruct the speech. We start the discussion with a development of the Encoding process and then specify the Decoder that reconstructs the speech.  
A block diagram of the speech encoding process as a reinforcement learning problem is shown in Figure 1. The Environment or System is human speech so \(s(k)\) represents the input speech samples, and the goal of the Reinforcement Learning System is to develop a control sequence \(u(k)\) that will drive a system model to approximate the input speech subject to a rate constraint on the number of bits per second that is used to represent \(u(k)\). The rate constraint is an implicit indication that the System Model to be controlled is actually the Decoder in the User's mobile phone at a remote location.  
The problem setup thus requires the development of:  
* A System Model;
* A Cost Function;
* A Cost-to-Go function;
* A Control Policy.  
We begin with the System Model.