esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Circuit Design of AC/DC Converter for T8 LED Tube
## 2 The Proposed LED Converter for a T8 LED Tube
### Ripple Free Stage  
Figure 3 shows the output ripple eliminator of the proposed LED converter for alleviating light flicker. An output ripple eliminator is a kind of common collector amplifier using an NPN Darlington configuration, which is one of the basic amplifier topologies, as a voltage follower. It consists of \(R_{E}\), \(C_{E}\), and a Darlington transistor \(Q\)[24; 25].  
Since the bias voltage of the base is immediately transferred to the LED anode, the impedance should be carefully chosen to find the optimized LED operation point in terms of power losses and light flicker elimination.  
Figure 4 shows conceptual waveforms of the proposed output ripple eliminator. To achieve a high power factor of the proposed LED converter, the output waveform inevitably contains a large ripple with double line frequency. This double frequency ripple is passed through the power stage and transferred to the ripple removal stage. Since the output voltage \(V_{o}\) is constant with output ripple, it can be expressed as:  
\[V_{O}(t)=V_{O,dc}+V_{O,ac}(t) \tag{6}\]  
Figure 3: The output ripple eliminator.  
where \(V_{O\_dc}\) and \(V_{O\_ac}(t)\) are the DC component and AC component with double line frequency ripple, respectively. Therefore, the base bias voltage of the Darlington pair has the same frequency ripple in steady state and the instantaneous value of bias voltage can be expressed as:  
\[V_{B}(t)=\ V_{B\_dc}+V_{B\_ac}(t)\ =\ (V_{O\_dc}-V_{CB\_dc})+[V_{O\_ac}(t)-V_{CB\_ ac}(t)] \tag{7}\]  
where \(V_{CB\_dc}\) and \(V_{CB\_ac}(t)\) are the DC component and AC component of the collector-base forward voltage, respectively. Considering the base-emitter forward voltage in the Darlington input stage, \(V_{LED}\) applied across LEDs can be obtains as:  
\[V_{LED}(t)=(V_{B\_dc}-V_{BE\_dc})+[V_{B\_ac}(t)-V_{BE\_ac}(t)] \tag{8}\]  
where \(V_{BE\_dc}\) is the DC value, and \(V_{BE\_ac}(t)\) is the AC value. Using [PERSON]'s voltage law, the AC component of bias voltage \(V_{B\_ac}(t)\) is determined from \(X_{E}\), \(Z_{E}\) and \(V_{O\_ac}(t)\),  
\[V_{B\_ac}(t)=\frac{X_{E}}{Z_{E}}.V_{O\_ac}(t) \tag{9}\]  
where \(Z_{E}=\sqrt{{R_{E}}^{2}+{X_{E}}^{2}}\) is the total impedance, and \(X_{E}=1/(4\pi f_{L}\cdot C_{E})\) is the impedance of \(C_{E}\). If the base-emitter forward voltage \(V_{BE}\) components are negligible, from Equations (8) and (9), the LED voltage can be derived as:  
\[V_{LED}(t)=(V_{O\_dc}-V_{CB\_dc})+\frac{X_{E}}{\sqrt{{R_{E}}^{2}+{X_{E}}^{2}}} \cdot V_{O\_ac}(t) \tag{10}\]  
Equation (10) shows that the output ripple eliminator can provide a DC value to the LEDs by adjusting the AC value to nearly zero, which means light flicker completely disappears from the LED tube.
## 3 Results and Discussion
### Influence of Soil Preparation  
On the same soil samples (10\({}^{-5}\) dilution, reading at 96 h, subtraction of both blank well and blank-corrected OD readings) and on the basis of previous experiments [7, 9], eight concentrations of Na\({}_{4}\)P\({}_{2}\)O\(\blacktriangledown\) 10 H\({}_{2}\)O ranging from 0.0% to 3.0% (_w/v_) (0, 0.6, 1.0, 1.4, 1.8, 2.2, 2.6 and 3.0%) were used for finding the optimal concentration to be used. Due to its high number of electric charges once in solution, it is known that this compound has the ability to disperse charged soil colloids. This is particularly important for soil microbiological assays, as bacteria are not only present on the surface of soil aggregates but also inside the aggregates, that is, on the micropore and macropore walls and in their internal spaces [6]. Figure 3 shows that with more than 1.8% (_w/v_) Na\({}_{4}\)P\({}_{2}\)O\({}_{7}\) soils did not show an increase in _AWCD_, _H'_ and \(E\), probably because the maximum possible number of bacteria was extracted from soil. Of course, this value could vary for different soil types but we can suggest that a concentration of around 2.0% (_w/v_) is necessary for not underestimating _AWCD_ values. The use of the appropriate concentration of Na\({}_{4}\)P\({}_{2}\)O\({}_{7}\) allows to overcome the technical problem in extracting the highest number of microorganisms from soil samples, that was one of the main remarks of this technique highlighted by [PERSON] et al. [10].  
Figure 3: Mean values (_n_ = 5) \(\pm\) SD of (**A**) _AWCD_, (**B**) _H' and (**C**) \(E\) measured a 96-h incubation in dilutions (\(\sim\)10\({}^{4}\) CFUs mL\({}^{-1}\)) of soils from the experimental olive orchard. The dilutions were obtained using different Na\({}_{4}\)P\({}_{2}\)O\({}_{7}\)\(\blacktriangledown\) 10 H\({}_{2}\)O concentrations.
# Remote Spectral Identification in the THz Band with Reflection Spectroscopy in an Open Atmosphere
## 2 Materials and Methods
### Transmission Setup  
Based on the conducted analysis, we used commercially available equipment to build the setup for the THz transmission spectroscopy. Figure 1 shows a diagram of the experimental setup. The source of radiation used was the solid-state OPO delivered by MSQuare. It worked in the 0.7-2.5 THz range with nonlinear characteristics providing a 53 Hz pulse repetition rate. The produced pulses had about a 20 ns time length, 50 GHz bandwidth, and 10 nJ maximum energy, corresponding to a 10W maximum amplitude. The OPO generated two output signals: the optical idler signal with a wavelength of approximately 1067 nm and the terahertz signal [39; 40]. The idler pulse was coupled into the optical fiber and used as a trigger for data acquisition. The THz beam generated by the OPO was slightly divergent, with a diameter of approximately 20 mm (1/e2). It was collimated by two 2\" concave-plane lenses with a 15 mm focal length in the 2f configuration. Using a polarizer, the THz beam was limited to the linear vertical polarization, which is relevant for the spectroscopy of some materials. To avoid detector saturation, a set of attenuators was placed after the polarizer. The THz beam with the appropriate parameters was directed onto the sample in a holder. The signal transmitted through the sample was reflected by the 8\" off-axis mirror to the detector. The terahertz signal was detected by the HEB [41; 42]. The sensing component was a 4 nm thick bridge made of niobium nitride (NbN) integrated with a planar logarithmic spiral antenna on a substrate of highly resistive silicon. The aperture of the detector input was limited to 1-2 deg. The signal directed to the bolometer was filtered by a window made of high-density polyethylene (HDPE), with the pass band in the range of 0.3-3.0 THz. The filtered signal was collected by a silicon lens and focused on the NbN bridge. This is a reconfigured and optimized version of the setup from [43].  
During the measurements, technical issues arose. A second polarizer was added to fine-tune the signal amplitude in addition to using attenuators with four set values. The delivered attenuators had to be remeasured due to significant attenuation deviations from the nominal values. The divergence of the lower frequency part of the signal was high enough to pass around the polarizer, bounce from metal elements, and produce unwanted interference in the measured spectra. To eliminate this, an iris with an additional blockade was placed in the focal point shared by the two lenses.
## 4 Progress in the Understanding of Illuminated B-O LID Mitigation Processes  
### Role of Hydrogen  
Hydrogen is well-known to passivate some metallic and non-metallic defects in silicon [99; 100]. [PERSON] first identified the critical role of hydrogen in the permanent deactivation of B-O defects in 2009 [101]. In particular, solar cells with an antireflection coating consisting of hydrogen-free low-pressure chemical vapour deposited (LPCVD) silicon nitride (SiN\({}_{x}\)) saw a degradation of performance with exposure to illumination attributed to B-O defects but no subsequent recovery with prolonged illuminated annealing. In contrast, cells coated with hydrogen-containing plasma-enhanced chemical vapour deposited (PECVD) silicon nitride (SiN\({}_{x}\):H) displayed a subsequent recovery of opencircuit voltage and efficiency with illuminated annealing. Subsequent work by various groups has provided strong evidence for the role of hydrogen in the permanent deactivation process. [PERSON] et al. showed that the degree of bulk hydrogenation in a remote hydrogen plasma (instead of the commonly used PECVD SiN\({}_{x}\):H layers as hydrogen source) correlates with the kinetics of passivation [102] as well as the expected release of hydrogen from boron-hydrogen pairs [87]. [PERSON] et al. [103] and [PERSON] et al. [104] showed that the firing step mainly responsible for contact formation has a significant impact on the kinetics of passivation most probably as it determines the degree of hydrogenation of the silicon bulk. Also, it was observed that increasing the hydrogen content in dielectric layers can lead to an increase in the hydrogen concentration within the silicon, which correlates well with an acceleration of the passivation [105; 106; 107; 108; 102].
## 4 Discussion
Thus, the controlled dissolution of set CAp granules supports bone formation during both the early and long-term stages of treatment. The combination of CSH and CAp granules at an optimized ratio demonstrates superior performance, offering improved handling and promoting both early to long-term bone formation compared to other type of graft materials. Due to its adjustable degradation rate, calcium release, and setting function, it outperforms conventional materials in bone-grafting applications, where preventing granule migration and ensuring early tissue integration are critical concerns. In a clinical scenario, these findings suggest that the combination of CSH and CAp granules may eliminate the need for a barrier membrane as it facilitates cell penetration and enables straightforward bone reconstruction. This approach effectively addresses challenges associated with conventional materials, which often require a barrier membrane to prevent particle migration. Previous studies have demonstrated the potential of combining CSH and CaP materials for enhanced bone repair. [PERSON] et al. reported that injectable \(\beta\)-TCP/CSH granules offer improved handling and bone repair capabilities due to their reversible setting reaction, which facilitates early-stage bone integration and scaffold stability [45]. Similarly, [PERSON] et al. reviewed the biphasic calcium sulfate/HAp system, emphasizing its dual-phase degradation behavior, which provides a balance between rapid resorption of calcium sulfate and the longer-term structural support of HAp [46]. These findings align with the performance of the set CSH/CAp granules, where a CSH/CAp ratio of 0.82 demonstrated controlled degradation, structural integrity, and an increased intergranular space post-dissolution, making it a promising candidate for bone reconstruction without the need for additional barrier membranes. The limitations of this study should be acknowledged. First, the experiments were conducted in vitro using a saline solution, which does not fully replicate the physiological environment. The in vivo behavior of set CAp granules, including their interactions with enzymatic activity and cellular processes, remains unexplored. Additionally, this study focused on early-stage dissolution and structural changes over six days, and longer-term investigations are necessary to assess the gradual integration and remodeling of the graft material within bone defects. Lastly, while \(\mu\)-CT images provided valuable insights into the expansion of intergranular spaces, a more detailed analysis of pore interconnectivity and its impact on cell migration and nutrient flow is required to better understand the performance of the material as a scaffold.
## 4 Simulation Results and Analysis
Therefore, deploying IRS in HST communication systems requires a careful consideration of both the transmit power and the number of IRS elements to achieve optimal system performance. Figure 4: EE versus the number of BS antennas. Figure 5: EE versus the number of IRS elements at different transmit powers. Figure 6 illustrates the variation in the system energy efficiency with BS transmit power for \(M=32\) using the proposed AO-EE algorithm at different numbers of IRS reflecting elements. From Figure 6, it can be observed that, as the BS transmit power increases, the system energy efficiency initially improves and then reaches a plateau for different numbers of IRS reflecting elements. Furthermore, under the same BS transmit power, the system energy efficiency increases with the growth in the number of IRS reflecting elements. This is because a larger number of IRS elements can reflect more received signals from the BS, leading to an increase in system energy efficiency. In summary, the results in Figure 6 demonstrate that increasing the BS transmit power and the number of IRS reflecting elements can enhance the system energy efficiency in an IRS-assisted HST communication system. Figure 7 illustrates the energy efficiency advantages of an IRS-assisted HST communication system over a traditional multi-antenna HST communication system. In the traditional HST communication system, increasing the number of BS antennas can improve the system's energy efficiency. In Figure 7, three scenarios are considered: 1. Setting the number of BS antennas to \(M=16\) and increasing the number of IRS reflecting elements; 2. Setting the number of IRS elements to \(N=16\) and increasing the number of BS antennas; 3. No IRS, and increasing the number of BS antennas. It can be observed that the system energy efficiency with IRS assistance is higher than that without IRS, indicating that increasing the number of IRS reflecting elements is more effective in improving system energy efficiency than increasing the number of BS transmit antennas. Therefore, IRS can economically and effectively enhance the system energy efficiency. Furthermore, the proposed AO-EE algorithm's system energy efficiency outperforms Baseline Scheme 1, proving the effectiveness of the proposed algorithm. Figure 6: EE versus transmit power for different numbers of IRS elements.
## 2 Theoretical Analysis of Mass Concrete Strain Monitoring  
### Temperature Effect in the Strain Response of Concrete  
According to the different generation mechanism, the strain response in a concrete structure is mainly composed of the following parts: strain caused by load, strain caused by temperature change, strain caused by concrete shrinkage and concrete creep strain:  
\[\varepsilon=\varepsilon_{lord}+\varepsilon_{temperature}+\varepsilon_{ shrinkage}+\varepsilon_{crep} \tag{1}\]  
The structural strain caused by a dead load can be regarded as constant in a short period of time. The structural strain caused by a live load, such as wind load, is random and has no obvious trend. The shrinkage and creep strain decay exponentially and change very slowly. For the early stage of monitoring, this term can be ignored. Therefore, the trend of the early strain of concrete mainly depends on the temperature effect. For the temperature effect analysis of a mass concrete structure, it can be simplified as a simply supported beam with one end elastic constraint [16].  
As shown in Figure 1, part of the displacement of the beam is constrained by the spring \(K_{L}\). The unconstrained displacement is \(\delta_{1}\) and the constrained displacement is \(\delta_{2}\). The free displacement \(\_L\) should be the sum of the two parts, that is:  
\[\Delta L=\delta_{1}+\delta_{2} \tag{2}\]  
The relationship between the strains is:  
\[\varepsilon_{2}=-\delta_{2}/L \tag{3}\]  
\[\varepsilon=\alpha\Delta T=\varepsilon_{1}+\varepsilon_{2} \tag{4}\]  
In the formula, \(\varepsilon_{1}\) and \(\varepsilon_{2}\) are the actual strain and stress strain of the elastic restrained beam subjected to temperature. The stress and strain of the structure under uniform temperature are calculated as:  
\[\varepsilon_{2}=-\frac{\alpha}{1+\frac{EA}{K_{L}L}}\cdot\Delta T \tag{5}\]  
The external constraint condition \(K_{L}\) and its own stiffness \(EA\) are fixed. Formula (5) shows that there is a linear relationship between the temperature strain and temperature of the structure.
# Is One Teacher Model Enough to Transfer Knowledge to a Student Model?
## 3 Proposed Methods
### Analyzing [PERSON]'s Approach  
The base idea of [PERSON] et al. [2] was to use a simple linear layer to convert the teacher's features into student's features. There are two potential problems with this approach. The first problem is that they use twice the epochs compared to a normal training (\(2\cdot N\) epochs): the first \(N\) epochs only using the MSE loss function, which serves to transfer the knowledge from the teacher to the student, while with the other \(N\) epochs, in which the teacher and also the translator are removed, only the cross-entropy loss function is used to fine-tune the student. This approach can decrease the accuracy test of the student in case a student topology suitable for the problem cannot be found, compared to the student alone without any teacher. Our hypothesis is that the problem with this approach lies in the first training phase in which the knowledge is transferred and where the classification objective is also missing, which could lead to further worsening of the translated features compared to the original ones. In particular, this last problem should be caused by the translator. The second potential problem is that the solution adopted by [PERSON] et al. does not work with all types of networks used as students, but requires that one finds the suitable model for each teacher and also for each domain in which this approach is used.  
To understand which of the problems listed above are real, we propose the following two potential solutions. Our first idea is to do only one training cycle (\(N\) epochs), in which both tasks carried out by the [PERSON]'s model (transfer learning and fine-tuning) are performed, this time simultaneously and not in succession. Since we suspect that another problem is in the translator, our second experiment is to eliminate the translator and make sure that the student has a features layer with the same shape as the features layer of the teacher. This way, we overcome the problem of information loss in the transfer learning phase adopted by [PERSON] et al.  
The hypotheses formulated above were experimentally validated, and in Table 1, the summary results are shown, where it can be seen that the solution described above leads to an increase in accuracy of all the different neural models used and for each dataset used.
## 2 Materials and Methods  
### Assessment of Contaminated Soil Characteristics and Pollution Levels  
The test soil was sourced from the area surrounding of the Huilong Arsenic Smelting Plant in Zhongshan County, located in Guangxi, South China, and situated at 24.46\({}^{\circ}\) N, 111.28\({}^{\circ}\) E, with an elevation of 173 m. The sampling area was shown in Figure 1. Soil sampling was executed as per the methods described by [PERSON] [36], with 30 samples collected from the shallow layer (0-20 cm), mixed well, and stored in 14 cm \(\times\) 20 cm sealed bags, each weighing 1 kg. After air-drying for 24 h, the soil samples were sifted through a sieve of 10 mesh (2 mm) and reserved for further use.  
The primary physicochemical attributes of the soil included a pH value of 6.25, a moisture content of 12.28%, an organic matter content of 13.5 g/kg, an available nitrogen content of 663 mg/kg, a phosphorus content of 360 mg/kg, a potassium content of 64 mg/kg, and a cation exchange capacity of 6.37 meq/100 g.  
The contamination assessment, detailed in Table 1, focused on the site's heavy metal levels. Cd and As were the primary concerns, exceeding the threshold for safe agricultural use. While Cd displayed mild pollution levels, As was significantly more concerning, showing severe pollution levels. The pollution index (PI) [37] was calculated using the following formula:  
\[PI=\frac{Ci}{Si} \tag{1}\]  
where \(C_{i}\) (mg/kg) denoted the metal concentration of the soil, while \(S_{i}\) (mg/kg) was the environmental quality standard for soil.
## 3 Results  
### Real-World Application Results  
This chapter presents the results of the different evaluation procedures that were presented in Section 2.2.2. In Figure 5, we see a comparison between model predictions and actual values. To look at the behavior of the methods in more detail, the graph refers only to a window of 3000 data points. All three BML methods consistently overshot the actual values, while the OML algorithm generally provided accurate predictions. The first 500 predictions showed clear oscillations of the actual values. Especially with these data points, batch learning evaluation methods have difficulty making usable predictions. It is clear to see how the OML algorithm responded flexibly to the fluctuations and delivered accurate predictions.  
\begin{table}
\begin{tabular}{l r r r l} \hline \hline
**Parameter** & **Default** & **Lower Bound** & **Upper Bound** & **Description** \\ \hline grace\_period & 200 & 10 & 1000 & Number of samples a leaf observes between the splits. \\ \hline max\_depth & 20 & 2 & 20 & Maximum depth of the tree. \\ \hline delta & \(1\times 10^{-7}\) & \(1\times 10^{-10}\) & \(1\times 10^{-6}\) & The significance level for the Hoeffding bounds is calculated by \(1-\delta\). \\ \hline tau & 0.05 & 0.01 & 0.1 & Threshold below which a split will be forced to break ties. \\ \hline leaf\_prediction & 0 & 0 & 2 & The prediction mechanism at the leaves (0—mean, 1—model, 2—adaptive). \\ \hline leaf\_model & 0 & 0 & 2 & The regression model that is used (0—linear regression, 1—PA-regressor, 2—perceptron). \\ \hline model\_selector\_decay & 0.95 & 0.9 & 0.99 & The exponential decaying factor applied to the learning models’ squared errors if the leaf prediction mechanism is ‘adaptive’. \\ \hline splitter & 0 & 0 & 2 & The splitter for defining thresholds and performing splits (0—EBSTS, 1—TBSTS, 2—QO). \\ \hline min\_samples\_split & 5 & 2 & 10 & Minimum number of samples a branch must have, resulting from a split. \\ \hline binary\_split & 0 & 0 & 1 & If True, only binary splits are allowed. \\ \hline max\_size & 500 & 100 & 1000 & Maximum size of the tree in MB. \\ \hline memory\_estimate\_period & \(1\times 10^{6}\) & \(1\times 10^{5}\) & \(1\times 10^{6}\) & Number of instances between memory checks. \\ \hline stop\_mem\_management & 0 & 0 & 1 & If true, stop growing the tree if maximum size (max\_size) is reached. \\ \hline remove\_poor\_attrs & 0 & 0 & 1 & If true, disable poor attributes. \\ \hline merit\_preprune & 0 & 0 & 1 & If true, enable merit-based pre-pruning. \\ \hline \hline \end{tabular}
\end{table}
Table 2: A list of the hyperparameters considered for the tuning of the Hoeffding Tree Regressor. The default values, along with their respective lower and upper bounds, are provided, accompanied by a short description of each hyperparameter.