esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Enhancing the Energy Efficiency of Thermal Energy Production  
Based on its key features, the analyzed DHS, which primarily serves household consumers and encompasses a range of thermal energy sources, can be classified as a fourth-generation system (4 GDH). However, this DHS has great potential to transition to the advanced fifth generation (5 GDH).  
The DHS at issue relies on low-power CHP units (up to 5 MW), which serve as cost-effective sources of both electrical and thermal energy. The presence of the CHPP as part of the DHS makes it possible to partially or completely satisfy the demand for electricity of consumers located in the DHS service area and distribute the surplus to the external electrical network [104; 105].  
The increase in the environmental friendliness of the DHS allows us to simultaneously address the challenges of enhancing the technical reliability and economic accessibility of thermal and electrical energy.  
In large cities with a high share of household consumers, the thermal energy supply from large TPPs reaches 80%. For example, in Copenhagen, Odense, and other cities in Denmark, this figure is 98%. The share of cogeneration heat production in large cities in Finland is close to 95%.  
The main potential for increasing the energy efficiency of electric and thermal energy production from the CHPP is concentrated in medium and small towns and populated localities. The main reason for this is the existence of various gas boiler houses of different sizes in these areas. These boiler houses serve as the primary sources of thermal energy in DHSs. The introduction of low-power CHPPs in these cities will improve the technical reliability and economic accessibility of the heat supply.
## 3 Results and Discussion
### Geometry Optimization of Synthesized Dyes  
The geometry-optimized structures of the synthesized dyes, which were obtained by DFT calculations using Gaussian09, are shown in Figure 1. The planarity of the dye molecule can be visualized in these structures where it could be assumed that the properties of substituents mainly affected the interactions between adjacent molecules and their fluorescence properties. As shown in **(a)** of Figure 1, the two phenyl rings around the ethylene linkage are twisted by 18\({}^{\circ}\) out of plane, whereas the analogue dye **SBnHe** was free of angular strain (0\({}^{\circ}\)) by an ethylene linkage, as seen in **(b)**, indicating that longer length of alkyl chain reduced angular strain within the core moieties which could be attributable to the larger interactions between a hexyl chain and an ethylene group [13]. Similarly, this effect was observed in **SB2 Bu** dye containing 2-butylamino substituents. They provided smaller surface areas due to the more spherical than straight-chain alkanes; thus, the greater twist angles were seen, such as 32\({}^{\circ}\), shown in **(e)**, for **SB2 Bu**. In the case of **SB2 Bu**, the less linear nature of the 2-butylamino substituents leads to greater hindrance and thus furnishes increased angle of twist of 32\({}^{\circ}\) as shown in **(e)**.  
As shown in **(c)** and **(d)**, much higher planarity of the anilino groups and benzoxazole rings was observed for analogue **SBnBe**, which can be explained by the increased \(\pi\)-conjugation throughout the phenyl and benzoxazole systems. Therefore, the twist angles between a benzoxazole and a phenyl ring were calculated to be 0\({}^{\circ}\) for **SBnBe**, whereas the corresponding angles were 35\({}^{\circ}\) for **SBnPy**. It is also of note that **SBnPy** dye was influenced primarily by its pyrrolidinyl groups to be in twisted form of an 'X' shape, as shown in **(d)**.  
The presence of acetamide groups in **SBnAc** led to significant steric hindrance as evidenced by a twist angle of 48\({}^{\circ}\) between phenyl ring and ethylene linkage of the stilbene moiety, as depicted in Figure 1f, in addition to a dihedral angle between the benzoxazole system and phenyl substituent of 19\({}^{\circ}\).  
Figure 1: The torsion angle of minimum energy geometry of **(a)** SBnBu, **(b)** SBnHe, **(c)** SBnBe, **(d)** SBnPy, **(e)** SB2 Bu, **(f)** SBnAc.
# Assisted Migration Field Tests in Canada and Mexico: Lessons, Limitations, and Challenges
## 2 Key Assisted Migration Definitions  
_Assisted migration_ (AM), as an \"umbrella\" term, has several different names, some of which have different meanings, but all of which refer to the translocation of individuals in anticipation of or in response to environmental change. General terms include managed relocation, managed translocation, and conservation translocation [32]. Terminology varies somewhat between fields, whether for commercial forestry management, ecological restoration, or endangered species conservation [33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45]. For the purpose of our discussion, we consider that in a forestry or tree species conservation context, assisted migration is usually considered to involve planting tree seeds from sources with climates that are slightly warmer than that of the planting site. By nudging tree populations in the direction of climate change, assisted migration is expected to help maintain forest health and productivity by restoring populations to climates where they are best adapted [37; 46; 47; 48].  
Assisted migration in general can be categorized into two forms (after [PERSON] et al. [49]):  
1. _Assisted migration within current geographic range_, for simplicity referred to herein as assisted migration within range, when it involves the movement of populations within a species' current natural distribution. It has also been referred to as assisted gene flow [40] or assisted population migration [37].
2. _Assisted migration outside of current geographic range_, referred herein as assisted migration outside of range, when it involves the movement of populations to locations just beyond a species' current distribution. This form of assisted migration has also been called assisted colonization [50] or assisted range expansion [37].  
A third form of assisted migration, _exotic translocation_, involves long distance movement (sometimes trans-continental) of a population or species to locations where it is not likely to migrate naturally within the foreseeable future [37]. While intensive plantation forestry in many parts of the world is focused on non-native species (e.g., pines in the southern hemisphere), there is little demand for exotic translocations in North America because of the large number of tree species in most locations, particularly in biodiverse regions of Mexico. Consequently, we have few data upon which to evaluate long distance transfers and will not address this topic further in this report.  
While assisted migration outside of range may be the form of assisted migration most commonly considered by the general public, and indeed by many forest scientists and foresters, the wide natural distribution of most North American tree species combined with the relatively short geographic and climate distances actually proposed for assisted migration [51] suggest that most AM in forestry will likely take the form of assisted migration within range.
## 2 Results and Discussion  
### Experimental Design, Model Fitting and ANOVA  
Initially, the four variables (temperature, substrate molar ratio, amount of biocatalyst, and added water) were evaluated measuring their effects on the initial reaction rate for the ultrasound-assisted butyl acetate synthesis catalyzed by MCI-CALB. The 27 experiments for the central composite design (CCD) with their results are shown on Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Treatment** & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \(\mathbf{X_{4}}\) & **Initial Reaction Rate (mmol\(\cdot\)L\({}^{-1}\)h\({}^{-1}\))** \\ \hline  
\hline \end{tabular} \(\mathrm{X_{1}}\): temperature; \(\mathrm{X_{2}}\): substrate molar ratio; \(\mathrm{X_{3}}\): amount of biocatalyst; \(\mathrm{X_{4}}\): added water.  
\end{table}
Table 1: Experimental design and results of CCD.  
The highest reaction rate (273.9 mmol:L^-1h^-1) was obtained in treatment 22 (45 \({}^{\circ}\)C, 3:1 alcohol:acid, amount of biocatalyst 10%, added water 0.5%), whereas the lowest activity was in treatment 21, with a reaction rate of 44.5 mmol:L^-1h^-1 (45 \({}^{\circ}\)C, 3:1 alcohol:acid, amount of biocatalyst 1%, added water 0.5%). In general, half of the treatments presented good reaction rates, over 200 mmol:L^-1h^-1, which represents almost 70% of conversion in 1 h. These were better results than those obtained using CALB for the butyl acetate synthesis in ionic liquids [23].  
According to [PERSON]'s statistical test for analysis of variance (ANOVA), the model was statistically significant and adequate to represent the actual relationship between the responses and the variables, as suggested by the model F-value (6.51) and the very low p-value (_p_ = 0.0012). The values of the determination coefficient, R2, and correlation coefficient, R, were, 0.88 and 0.94, respectively. This denotes a highly satisfactory representation of the process model and a good correlation between the experimental results and the theoretical values predicted by the model equation. The coefficients of variables were determined for the second-order polynomial model and the statistical significant (5%) are given below:  
\[\begin{gathered}\mathrm{Y}=232.46+19.91\mathrm{X}_{1}+47.24 \mathrm{X}_{3}-2.24\mathrm{X}_{4}-25.19\mathrm{X_{1}}^{2}-11.45\mathrm{X_{2}}^{ 2}-20.92\mathrm{X_{3}}^{2}-\\ 18.63\mathrm{X_{1}}\mathrm{X_{2}}-17.49\mathrm{X_{1}}\mathrm{X_{4}} +12.13\mathrm{X_{2}}\mathrm{X_{3}}-8.00\mathrm{X_{2}}\mathrm{X_{4}}-11.98 \mathrm{X_{3}}\mathrm{X_{4}}\end{gathered} \tag{1}\]  
where Y is the percentage conversion, and X1, X2, X3, and X4 are the coded values of temperature, substrate molar ratio, amount of biocatalyst and added water, respectively.
## 3 Materials and Methods
### Water Immersing Characteristics  
The samples were prepared using a REY-CC-101-00B drying oven and a custom-made vacuum saturation device. The sandstone samples were placed in the drying oven, where the temperature was set to 108 \({}^{\circ}\)C. After drying for 24 h, the samples were removed and cooled to room temperature. Two samples were then selected and placed in the vacuum saturation device for water absorption testing using the manual weighing method. During the first six hours, the samples were removed every half hour, and the surface water droplets were wiped off before weighing with an electronic balance. When the weight increment difference between measurements was less than 0.01 g, the samples were considered to have reached water saturation. The water content of each sample was calculated using the following equation:  
\[W_{t}=\frac{R_{t}-R_{0}}{R_{0}}\times 100\% \tag{1}\]  
where \(W_{t}\) is the water content of the sample at time \(t\) (%), \(R_{0}\) is the mass of the sample when it was removed from the drying oven (g), and \(R_{t}\) is the mass of the sample at time \(t\) (g). The average water content of the sample as a function of immersion time is shown in Figure 9.  
As shown in Figure 9, the water absorption process of the top-coal sandstone can be divided into four stages: a rapid absorption stage (0-6 h and 0-3 h), during which the absorption curve rose sharply as water was quickly absorbed through a large number of highly permeable pores and fractures; a slow absorption stage (6-14 h and 3-10 h), when the absorption curve became less steep as the initial pores were filled and the absorption rate gradually decreased as the water spread into the rock through the smaller pores and microfractures; a saturation absorption stage (14-19 h and 10-20 h), when the absorption curve flattened out as the rock approached saturation, indicating that the internal pores and fractures were nearly filled with water and the absorption reached its maximum; and a long-term absorption stage (20 h and 19 h onward), when the curve remained flat, suggesting the presence of tiny pores or the need for pressure equilibrium within the pores, which caused a prolonged period of minimal water absorption. The average saturated water content of the sandstone was 1.54%.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 4 Building a QKD Emulation System to Test Protocols
### Modifications to Include Two Wavelengths  
#### 4.2.1 Hardware  
We worked with Thorlabs to add a second wavelength to their existing kit and then made a few other modifications for automation. Thorlabs featured our customized version on their website (www.thorlabs.com; search for \"Quantum cryptography analogy demonstration kit\" and see the \"User Application\" tab). They generated the graphics shown in Figure 5, which compare the original kit with our modification. Figure 6 shows this physical kit with our custom modifications, before automation. We doubled all components, but used a 520 nm laser for the second laser. We also included dichroic mirrors to combine and split the beams so that only one quantum channel is necessary between Alice and Bob. A dichroic mirror functions by transmitting wavelengths above a threshold and reflecting wavelengths below the same threshold. The customized kit components play the same role as the original single wavelength kit, except with the additional wavelength, we are able to implement ternary and quaternary protocols to test the error rates. We also automated the custom kit with stepper motors, gears, and an Arduino Mega microcontroller. By mounting a 3D printed gear on the face of each half-wave plate and a gear on the shaft of each stepper motor (see Figure 7 for an image of the design), we are able to accurately turn the half-wave plates and automate the polarizing rotation process. We also directly connect the photodetectors to the Arduino to automate the read process.  
Figure 5: The original kit and our custom version with two wave plates (generated by Thorlabs). The half wave plate (HWP) is the same as the polarization rotator in the original diagram. PBS stands for polarizing beamsplitter. See their website (www.thorlabs.com).
# Dynamic Measuring Method of Laser Beam Incident Angle for Laser Doppler Vibrometer
## 5 Error Analysis and Discussion
### Other Factors  
The result of the dynamic measuring device is also affected by other factors, such as the angular velocity \(\omega\) of the motor. The measuring process requires the motor speed \(\omega\) to remain constant during the measuring process. Before this experiment, the speed \(\omega\) was monitored in a targeted manner. The monitoring results show that most selected motor speeds can keep the preset speed unchanged and occasionally deviate from 1-2 rpm. The influence of this deviation is negligible relative to the set value up to several thousand revolutions. Therefore, this article no longer considers the influence of motor speed fluctuation.  
In addition, the environmental temperature is also an important factor affecting the measurement system. However, this effect is considered to be negligible when conducting a short-term indoor test.
## 5 Simulation Examples
### Estimation Accuracy Performance Simulation  
Two signal DOAs are (90\({}^{\circ}\), 81\({}^{\circ}\)) and (90\({}^{\circ}\), 85\({}^{\circ}\)), respectively, both the signal frequencies are 6 GHz. Figure 6 displays the root mean square error (RMSE) of the eight-element R-MUSIC, 10-element R-MUSIC, five-element UCA MUSIC, and eight-element UCA MUSIC _versus_ the SNR from 2 to 14 dB with snapshots \(L=100\). While Figure 7 displays the resolution probability of the four methods _versus_ the snapshots from 16 to 144 with SNR = 10 dB. Every data is averaged over 200 Monte Carlo simulations. It can be seen from Figure 6 and 7 that the RMSE of the proposed algorithm approximates to the CRB and the RMSE of the four methods is decreasing with increasing snapshots. Besides, the  
Figure 4: Successful resolution probability _versus_ SNR.  
Figure 5: Successful resolution probability _versus_ snapshots number.  
estimation accuracy performance of the proposed eight-element R-MUSIC is better than that of the five-element UCA-MUSIC, but which is lower than that of the eight-element UCA-MUSIC. However, the estimation accuracy of the 10-element R-MUSIC is better than that of the eight-element UCA-MUSIC.
# Neural Machine Translation of Electrical Engineering with Fusion of Memory Information
## 4 Experiment
### Dataset and Parameter Settings  
As this article focuses on low-resource neural machine translation in the field of electrical engineering, the data used in the experiment must have strong specificity and professionalism. Therefore, we collected 190,000 parallel bilingual corpora from professional books [20; 21; 22; 23], equipment manuals, references, and related papers in the relevant field as the dataset for the experiment.  
We used the open-source system OpenNMT [24] to implement the baseline model transformer. Regarding data preprocessing, we limited the sentence length in the corpus to within 100, meaning that sentences longer than 100 were filtered out. The vocabulary size of both the source and target languages was set to 44,000. Jieba and NLTK were used for Chinese and English word segmentation, respectively. The training and testing sets each contained 2000 pairs of bilingual parallel sentences. During the training process, the word vector dimension and the hidden layer dimension of the encoder and decoder were set to 512, the batch size was set to 64, the Adam optimization algorithm was used, and the dropout probability of the neurons was set to 0.1. A total of 25,000 steps was trained in this experiment, and the model was validated every 1000 steps. The beam search method was used during decoding, with a beam size of five and the remaining parameters using the default settings of OpenNMT. All parameters in the experiment were consistent, and the translation results were evaluated using BLEU [25]. The input and output channels of the GRU were both set to a dimension of 512. This means that the input and output vectors are both 512-dimensional, ensuring consistency and compatibility. The choice of this dimension was based on experimental considerations and the specific requirements of our model architecture. The number of layers in the GRU was kept consistent with the number of layers in the encoder. This ensured that the information flow and transformations between the layers remained synchronized throughout the model. The activation functions used for the update gate and reset gate in the GRU were sigmoid and tanh, respectively. The sigmoid function was employed to calculate the update gate, which determines how much of the previous hidden state should be retained, while the tanh function was utilized for the reset gate, which controls how much of the previous hidden state should be forgotten.
## 3 The Signal Adaptive Iterative Optimization Resampling (SAIOR) Algorithm
### Signal Adaptive Iterative Cancellation  
Due to the high signal-to-noise ratio (SNR) of LEO satellite signals upon reception (generally 15-30 dB), these signals can severely affect the estimation of PFM interference parameters such as carrier frequency and modulation period. Therefore, before estimating the parameters of PFM interference, it is necessary to eliminate the high-power LEO satellite signals and isolate the PFM interference to facilitate subsequent parameter estimation. The SAIOR algorithm draws inspiration from the SCCI algorithm proposed in [34], adaptively and iteratively approximating the power spectrum of the LEO satellite signals to remove the influence of their power from the input signal.  
Through analysis, the power spectrum of the input signal (signal and noise) in the LEO satellite signal reception scenario follows a chi-squared distribution, thereby enabling the derivation of the primary expression between the input signal power spectrum and signal power spectral density, which establishes an approximation model:  
\[Y_{e}(f)=aG_{s}(f)+b \tag{10}\]  
where \(Y_{e}(f)\) represents the estimated power spectrum of the input signal, and \(G_{s}(f)\) represents the power spectrum of the signal.  
Let the error between the input signal power spectrum \(P(f)\) and the model estimation \(Y_{e}(f)\) be:  
\[e(a,b)=\sum\limits_{f=1}^{N}\left(aGs(f)+b-P(f)\right) \tag{11}\]  
where N is the number of FFT points; the mean square error (MSE) is expressed by:  
\[\begin{split}& e(a,b)^{2}=\sum\limits_{f=1}^{N}\left(aGs(f)+b-P(f) \right)^{2}\\ &=a^{2}\sum\limits_{f=1}^{N}Gs^{2}(f)+Nb^{2}+2 ab\sum\limits_{f=1} ^{N}Gs(f)+\sum\limits_{f=1}^{N}P^{2}(f)-2a\sum\limits_{f=1}^{N}Gs(f)P(f)-2b \sum\limits_{f=1}^{N}P(f)\end{split} \tag{12}\]  
Using the gradient descent method, with the minimization of the MSE between \(P(f)\) and the model estimate \(Y_{e}(f)\) as the criterion, multiple rounds of adaptive iteration are performed. This process eliminates the portions of the interference spectrum that exceed the true value of the signal power spectrum, ultimately achieving the goal of making the final iterative estimate of the signal power spectrum identical to the true value. The estimated values of parameters a and b in Equation (10) are then obtained, and the mean power spectrum estimate \(Y_{e}(f)\) of the input signal is derived. Next, the mean power spectrum \(Y_{e}(f)\) obtained from the estimation model is subtracted from the input signal power spectrum \(P(f)\). This can be considered as removing the power spectrum values of the LEO satellite signals from the input signal, leaving approximately only noise and PFM interference signals. At this point, the next step of estimating the interference signal parameters can proceed.
# Insertion Performance Study of an Inductive Weft Insertion System for Wide Weaving Machines
## 4 Results Analysis and Validation
### Experimental Verification  
To validate the accuracy of the analytical and simulation calculations, a prototype of the wet insertion guideway was manufactured, and an experimental platform was set up, as shown in Figure 9. The experimental platform consisted of a variable frequency drive, a weft insertion guideway, a shuttle conductive plate, a tension sensor, and a sliding rail. The support force provided by the sliding rail replaced the suspension force of the superconductor, and a tension sensor was used to measure the traction force of the wet insertion guideway. The output voltage of the variable frequency drive used in the experiment is given by Equation (19), with a frequency ranging from 0 to 100 Hz. The structural parameters of the experimental platform are listed in Table 1.  
Figure 10 depicts the variation of the traction force exerted by the weft insertion guideway on the conductive plate with the frequency of the power source when the conductive plate thickness is 5 mm, the air gap thickness is 10 mm, and the slip ratio is 1. As seen in Figure 10, the data results for the traction force from the analytical calculations, simulation calculations, and experimental measurements align closely with changes in the power source frequency. This confirms the accuracy of the simulated model for the weft insertion guideway and conductive plate established in this study. These findings hold significant implications for the design of an inductive weft insertion guideway in wide-width looms.  
Figure 8: Traction force–current frequency relationship.  
Figure 9: Weft insertion guideway experimental platform.