esa-sceva/satcom-chunk-collection huggingface dataset:
# Neural-Network-Assisted Polar Code Decoding Schemes
## 3 Neural Network-Assisted Polar Code Decoding Scheme
### Last Subcode Neural Network-Assisted Decoding (LSNNAD) Scheme  
Let \(N_{S}\) denote the code length of the last subcode neural network node (LSNNN). The main idea of this scheme is to identify the last subcode as LSNNN. Note that at this time, LSNNN may be one of the four traditional subcodes mentioned above, or it may be different from the above four subcodes. The type, length, position, and order are determined at the moment when the codeword is constructed. In this experiment, 0 and 1 are used to represent the frozen bits and information bits, i.e., the sequence \(information\_pos\in\{0,1\}^{N}\) of code length \(N\) is obtained after the codeword is constructed. When \(information\_pos=1\), the information bits are stored. Otherwise, the frozen bits are stored. In this experiment, all frozen bits are 0. The LSNNN identification flowchart is shown in Figure 9.  
The input \(N\) is the same as in Figure 1. Let \(D\) denote the position of bits and \(A\), an arbitrary subset of \(\{1,\cdots,N\}\), denote the position of information bits. \(I\) denotes the vector of the index, the initial content of which is in the range 0 to \(N-1\), and it represents the sequence position currently being processed. \(I[0]\) denotes the first element of the vector \(I\) and represents the starting position of the code being processed. \(D\), \(I\), and \(N\) constantly change during the calculation process depicted in the flowchart (Figure 9). \(D(0:N/2)\)  
Figure 9: LSNNN identification flowchart.  
denotes the first half of the vector \(D\), \(D(N/2:N)\) denotes the second half of the vector \(D\), and \(N_{Z}=N-N_{S}\) denotes the starting point of the NNN that is already fixed when the total code length and length of the NNN are determined. The output \(Re\) is a matrix with five rows, with the first to the last rows denoting the node type, the starting point, \(K\), \(N\), and the information position of every node, respectively. In the node identification process, the decoding depth of each node is recorded, i.e., the node position in the SC decoding tree is recorded. The decoding tree after dividing the subcodes according to the above rules is shown in Figure 10.
## 3 Result and Analysis
### Comparison with Previous Methods
Project 2 and Project 3: The selection results of previous methods show that the shield TBM is more appropriate, which is more consistent with the actual results. However, the previous methods did not establish a clear boundary between single-shield TBM and double-shield TBM. The method proposed in this paper can directly decide whether to use single-shield TBM or double-shield TBM. Based on the results of the comparative study and previous research results, it has been found that [PERSON]'s TBM selection method focuses on geological hazards and construction risks. In a tunnel with more adverse geology, it has a good selection decision effect. In addition to adverse geology, the method proposed in this paper also selects geological conditions and tunnel design indexes. Compared with previous studies, the TBM selection decision-making model is more comprehensive and more accurate.
# An Overview of Material Extrusion Troubleshooting
## 3 Troubleshooting of ME Parts
### Print Quality Problems
#### 3.3.6 Messy First Layer  
The first layer is messy or undesired lines are shown on the bottom surface of the part due to adhesion issues on the build platform, incorrect build platform levelling, or the print temperature is too high if the fine details on the bottom layer are not crisp. Solutions include the following:  
1. Wiping the build surface using 90% IPA solution or acetone to keep the surface grease-free.
2. Use an alternative build platform surface (refer to 3.1.2: (5) Poor build platform adhesion).
3. Run through the calibration of the first layer or recalibrate the build platform (refer to 3.1.2: (1) Incorrect built platform levelling).
4. Lower the printing temperature gradually by 5 \({}^{\circ}\)C each time until the details appear crisp.
## 2 Cable Model and Effective Length in 3D Twisted Conductors
### The Effective Length \(l_{\text{twist}}\) in 3D Models  
Accurate calculation of conductor resistance requires proper accounting of the true path length that current travels [1]. Unlike simplified 2D analyses (which often represent an ideal cross-section with negligible length), 3D twisted models explicitly incur a helix path for each conductor. The direct-current (DC) resistance \(R_{\text{DC}}\) thus depends on how much extra length is introduced by the twisting.
###### Abstract  
This paper presents a CMOS wideband amplifier operating in the full Ka-band, with a low gain variation. An inductive neutralization is applied to the amplifier to compensate for the gain roll-off in the high-frequency region. Neutralization inductance is carefully determined considering the tradeoff between stability and gain. To achieve a low gain variation over the full Ka-band, the amplifier employs the frequency staggering technique in which impedance matching for three gain stages is performed at different frequencies of 26, 34, and 42 GHz. The experimental results show that a peak gain of 13.2 dB is achieved at 39.2 GHz. The 3 dB bandwidth is from 23.5 to 41.7 GHz, which fully covers the Ka-band. Especially, the gain ripple of the amplifier is only 13 \(\pm\) 0.2 dB over a wide bandwidth from 26.2 to 40.2 GHz. The input and output return loss values are better than \(-\)10 dB from 26.3 to 40.1 GHz and from 25.3 to 50 GHz, respectively. The DC power consumption is 18.6 mW.  
Keywords:Ka-band; CMOS amplifier; inductive neutralization technique; frequency staggering technique; wideband flat gain +
Footnote †: journal: Applied Sciences
## 2 Study Area and Datasets
### Satellite Imagery
#### 2.3.3 Time of Imagery  
The image employed in these tests was captured on 1 June 2009. RapidEye's sun-synchronous orbit and a 12:13 UTC image capture would correspond with low tide in Dublin Bay on that day (12:05 UTC). There were three important considerations when choosing the date of the imagery: ensuring the image corresponded as closely as possible with the date of the INFOMAR MBES survey, coinciding with low tide to minimize the distance light would have to penetrate through the water column and a high illumination angle which would have the same effect.  
Figure 2: Study imagery, a RapidEye RGB Image of Dublin Bay, East coast of Ireland. 1 June 2009 at 12:13 UTC.
###### Abstract  
The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study's innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.  
PV system; battery storage; MPPT; fuzzy MPPT; PSO; GA; jellyfish optimization Article
## 4 Results and Discussion
### Performance on Full Data  
As the results described above show that the model limited to vehicle headways on the same line performs the best in predicting trajectory positions, this version of the model was chosen and ran on a large dataset containing 56,679 and 12,187 training and testing samples for the westbound direction, respectively. In the eastbound direction, the dataset consisted of 107,831 and 22,953 training and testing samples, respectively. In both directions, a moderate improvement in per-epoch validation performance can be noticed. More importantly, a regularisation effect of the larger dataset and consequently a larger number of batches is evident Figures 15 and 16. This can be seen in the less volatile validation curves. A similar reduction in volatility can be seen in the ETA validation performance, especially in the westbound direction. This can be explained by the smaller size of the dataset and, thus, the smaller number of batches within this dataset. From these results, it can be concluded that larger datasets have a two-fold benefit. It results in a reduction in validation volatility and thus generalisation error and, secondly, a moderate improvement of overall prediction performance. This highlights the need for very large datasets if data quality is poor. As a consequence, this results in very long training times. A possibility to further improve the performance of the proposed model structure is to include synthetic data during the training process. This has the caveat that it is unlikely that any network interaction can be incorporated into synthetic models, as these tend to be unknown factors.
# Comparison of PCR Primers for Analyzing Dentirifying Microorganisms in the Hyporheic Zone
## 2 Materials and Methods
### PCR Amplification Testing of Denitrification-Associated Genes  
To test whether denitrification-associated genes were amplified from the samples using a QIAamp DNA Mini Kit (QIAGEN, Hilden, Germany), where the nucleic acid was extracted from sixteen aqueous environmental samples (three groundwater, three stream water, and ten hyporheic water samples) taken from areas where denitrifying microorganisms were expected to be present. The extracted nucleic acid was quantified using a NanoDrop(tm) 2000C and used as template DNA for the sample testing. The PCR primer sets selected for each denitrification-associated gene were applied to analyze the amplification of specific bands. The PCR mixture had a total volume of 20 \(\upmu\)L and contained AccuPower(r) HotStart PCR PreMix (Bioneer, Seoul, Korea), 1 \(\upmu\)L of template nucleic acid, 2 \(\upmu\)L of primers (1 \(\upmu\)L of forward, 1 \(\upmu\)L of reverse), and 17 \(\upmu\)L of nucleic-acid-free water. The PCR was performed according to the conditions described in the references listed (Supplementary Table S1).
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 4 Multi-Sensor Fusion Positioning Algorithm Based on BICI and IMM  
The motion pattern of platform is typically hard to obtain in actual scene. The filter based on a single model sharpens the deterioration of positioning accuracy and divergences when motion model noise variance exceeds the conservative upper bound in the strong mobility scenario. The accurate estimation can be obtained by a multi-model algorithm with known covariance in actual scene. There are few studies on the estimation with uncertain time-varying noise variance. Combined with BICI and IMM, BICI-IMM multi-sensor fusion positioning algorithm improves the positioning accuracy.  
The process of the traditional IMM algorithm is divided into four steps: model reinitialization, model filter, model probability updating and estimation fusion [34]. The model filter designs for a single sensor cannot meet the multi-sensor fusion positioning. Therefore, we introduced a local model and global filter to replace model filter based on BICI algorithm. Meanwhile, a sequential method was employed for the likelihood function in the model probability updating phase.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithms** & **Positioning Error (m)** \\ \hline SCI & 0.88 \\ BCI & 0.88 \\ SICI & 0.90 \\ BICI & 0.71 \\ SCI (fix) & 0.88 \\ BCI (fix) & 0.88 \\ SICI (fix) & 0.90 \\ BICI (fix) & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: RMS of positioning error.  
Figure 5: The positioning error CDF of three-dimensional motion.
## 2 Methodologies and System Description
### Independent Components Analysis, ICA  
The concept of ICA was introduced in the early 1980s by [PERSON], [PERSON] [18] in order to solve the BSS problem. As can be seen in Equation (3), the observations are considered as linear combinations of independent sources  
\[x=As \tag{3}\]  
where \(x\), \(s\), and \(A\) are the observation vector, the vector of independent sources, and the mixing matrix, respectively. The main goal is to find the separator matrix \(W\), from observation vector \(x\) to form the estimated sources vector, which is yield as follows  
\[\hat{s}=Wx \tag{4}\]  
where \(\hat{s}\) is the vector of estimated sources. \(W\) is obtained by the PSO method with fast convergence in this paper.
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 2 Materials and Methods
### Types of Interventions and Control  
The 15 included studies that focused on rehabilitating the upper extremities post-stroke used two robotic-training protocols: (a) motor imagery brain-computer interface (MI-BCI) (six studies) and (b) robot-assisted therapies (nine studies) (e.g., robotic devices on the wrists and arms). Nine out of the fifteen upper limb studies used unilateral tDCS protocols (i.e., anodal tDCS on the ipsilateral hand area within the M1), whereas six studies applied bilateral tDCS protocols (i.e., anodal tDCS on pisilesional hand area within the M1 and cathodal tDCS on contralesional hand area within the M1). In total, 12 studies administered multiple sessions of robotic training combined with tDCS protocols (e.g., 10-36 sessions), and only 3 studies used a single session of the combined rehabilitation protocols. Concerning the robotic devices, fourteen studies applied end-effector type devices and only one study used an exoskeleton-type device. Moreover, five specific types of joint
# Design and Analysis of a Novel Variable Stiffness Joint Based on Leaf Springs
## 2 Principle of the VSJ  
### Analysis of Stiffness Regulation Principles  
The proposed working principle of VSJs is shown in Figure 1. The VSM mainly consists of an input shaft (the area where the dashed and thin curves are located), leaf springs, and an output shaft, connected in series. One end of the leaf spring is fixed to the output shaft's chuck, and the other end is free, thus forming a cantilever beam structure. By adjusting the distance between the slider (connected to the input shaft) and the fixed end of the leaf spring, the effective length of the leaf spring participating in the transmission can easily be changed, thereby adjusting the stiffness between the input and output sides of the entire VSJ. From a theoretical perspective, when the slider is at the free end of the leaf spring, the stiffness of the VSJ is at its minimum, while the elastic deflection (maximum deflection) is at its maximum. As the slider is adjusted towards the fixed end of the leaf spring, the length of the leaf spring involved in the actual transmission gradually decreases, and the output stiffness of the VSJ gradually increases. When the slider moves to the fixed end of the leaf spring, the effective length of the leaf spring involved in the transmission can be considered to be equal to zero. At this point, the input shaft and the output shaft are equal in terms of the effect of the rigid gear transmission, whereas its compliance is the lowest. Furthermore, by arranging multiple leaf spring parts (connected in parallel) uniformly around the output axis, not only can the minimum stiffness value be changed (changing the tuning bandwidth), but the load capacity of the entire VSJ can also be improved, thereby acquiring certain reconfigurable characteristics. This bears important positive significance for the VSJ to adapt to application scenarios involving different loads [38].  
Figure 1: Schematic diagram of the effective length adjustment mechanism for the leaf spring.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 2 Theoretical Background
### Experimental Verification of the Simulation Method  
We conducted an experimental validation of the simulation method on a nickel alloy rough surface. The surface geometrical morphology was scanned by AFM, as shown in Figure 1, which was obtained from our previous work [11]. More details of the sample can be found in the literature [11].  
A commercial MMSE (ME-L, manufactured by Wuhan E-optics Technology Co., Ltd., Wuhan, China) was adopted to measure the Mueller matrix of the sample. The incident wavelength, \(\lambda\), was in the range of 200 nm \(\leq\lambda\leq\) 1000 nm, and the spot diameter was 200 \(\upmu\)m. The incident and azimuthal angles were fixed at \(\theta\) = 55\({}^{\circ}\) and \(\varphi\) = 30\({}^{\circ}\), respectively.  
The Mueller matrix calculated by simulation is perfectly non-depolarizing, while the experimental Mueller matrix has depolarization effects induced by many factors. In order to perform the experimental verification of the FDTD method, we need to convert the Mueller matrix measured in the experiment to a non-depolarizing one. The following expression can be used for the conversion [34]:  
\[M=[M_{\rm S}-(1-\rho)M_{D}]/\rho \tag{22}\]where \(\mathbf{M}_{\text{S}}\), \(\mathbf{M}\), and \(\mathbf{M}_{\text{D}}\) represent the experimental, non-depolarizing, and totally depolarizing Mueller matrices, respectively. Here, \(\mathbf{M}_{\text{D}}\) is defined by [34]  
\[\mathbf{M}_{D}=\begin{bmatrix}1&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&0&0&0\end{bmatrix} \tag{23}\]  
The parameter \(\rho\) can be solved by the following expressions [34]:  
\[\rho=\left(d^{2}+e\right)^{1/2}-d \tag{24}\]  
\[e=\left(m_{22}-m_{12}-m_{21}\right)/3 \tag{25}\]  
\[\begin{array}{l}f=\left[\left(m_{22}-m_{12}-m_{21}\right)^{2}+\left(m_{13}-m _{23}\right)^{2}+\left(m_{14}-m_{24}\right)^{2}\right.\\ \left.+\left(m_{31}-m_{32}\right)^{2}+\left(m_{41}-m_{42}\right)^{2}+\left(m_{ 34}-m_{43}\right)^{2}+\left(m_{33}+m_{44}\right)^{2}\right]\end{array} \tag{26}\]  
By associating Equations (23)-(26) with Equation (22), we can obtain the non-depolarizing Mueller matrix from the one measured.  
A comparison of the measured and simulated Mueller matrix elements of the actual sample with a rough surface is shown in Figure 2. The results of the measurements and simulations are displayed by green solid lines and pink dotted lines, respectively. The range of the vertical coordinates of all elements is \(-1\leq y\leq 1\). Because the surface of the sample is micro-rough, we can observe that the values of the off-diagonal elements are so small that they are nearly equal to zero. It can be seen that the simulated results agree well with the experimental results, as measured by MMSE.  
Figure 1: Morphology of the nickel alloy rough surface scanned by AFM, which was obtained from the literature [11].
## 2 Materials and Methods
### Material Characterization
#### 2.4.4 Thermal Analysis  
Differential scanning calorimeter (DSC) was used to determine the phase transitions of the PHBV samples. Measurements were performed in a DSC 1 Star\({}^{\rm e}\) System analyzer (Mettler-Toledo GmbH, Greifensee, Switzerland) operating under a nitrogen atmosphere (20 mL/min). To this end, 5-7 mg of each sample was weighted in aluminum pans and heated from \(-40\) to 200 \({}^{\circ}\)C to remove the thermal history, cooled to \(-40\)\({}^{\circ}\)C to determine the crystallization temperature (T\({}_{\rm c}\)), and then heated (second heating step) to 200 \({}^{\circ}\)C for determining the glass transition temperature (T\({}_{\rm g}\)) and melting temperature (T\({}_{\rm m}\)). The heating and cooling rates were set at 10 and 50 \({}^{\circ}\)C/min, respectively. The degree of crystallinity (X\({}_{\rm C}\)) was measured using the second heating scans and following Equation (7):  
\[{\rm X_{C}}=\left[\frac{\Delta{\rm H_{m}}-\Delta{\rm H_{cc}}}{\Delta{\rm H_{ m}^{0}}\cdot(1-w)}\right]\cdot 100 \tag{7}\]  
where \(\Delta{\rm H_{m}}\) (J/g) is the melting enthalpy, \(\Delta{\rm H_{cc}}\) (J/g) is the enthalpy of cold crystallization, \(\Delta{\rm H_{m}^{0}}\) (J/g) represents the theoretical enthalpy of a fully crystalline sample of PHBV with a value of 146.6 J/g [37], and the term \(1-w\) represents the weight fraction of PHBV.  
Thermal degradation of the PHBV films was assessed by thermogravimetric analysis (TGA) in a thermogravimetric analyzer (TGA 1 Star\({}^{\rm e}\) System analyzer, Mettler-Toledo GmbH). Film samples, with a weight of 3-5 mg, were subjected to a heating program from 25 to 700 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min under a nitrogen atmosphere (10 mL/min). The initial or onset temperature (T\({}_{\rm onset}\)), corresponding to the temperature yielding a mass loss of 5%, the temperature at the maximum degradation rate (T\({}_{\rm deg}\)), and the residual mass at 700 \({}^{\circ}\)C were determined by analyzing the TGA curves and their first derivative thermogravimetry (DTG) curves. All thermal tests were performed in triplicate.
## 2 Materials and Methods
### Sensor Specifications  
**(A) Pressure, Temperature, and Humidity (PTH) Sensor.** Operating voltage: 1.8-3.3 V DC; pressure range: 300 to 1100 hPa (0.3-1.1 bar); temperature range: \(-40\)\({}^{\circ}\)C to 85 \({}^{\circ}\)C; temperature resolution: 0.01 \({}^{\circ}\)C; temperature accuracy: 1 \({}^{\circ}\)C; relative humidity range: 0-100% RH; relative humidity accuracy: \(+-3\)%; ultra-low power consumption; sampling frequency: 157 Hz (max.).  
**(B) Motion sensor.** These sensors are capable of detecting when a person approaches a specific area. They are placed at different strategic points of the house to know if the user has carried out all the actions that he/she is supposed to do during his/her daily routine. Upon the information gathered by the sensors, the system can create a table of activities per day, week, and month.  
**(C) Gyroscope sensor.** These are placed on the doors at the user's home. The functionality of these sensors is to detect when the user has received a visit and how long the visitor stayed. It is also possible to visualize when the user has left home and how long the house remained unattended.  
**(D) Noise sensor.** Input variable: DC 3.3-6 V; sensitivity: adjustable; digital output when detecting sound.  
**(E) Level sensor.** It is an electronic device that measures the height of any object. Generally, this type of sensor works as an alarm that is triggered when the threshold level has been reached.  
**(F) Light sensor.** It is a device with many applications. It is especially useful to make a much more efficient use of the energy at home, adapting the luminaries' power to the existing ambient light.  
**(G) Air quality sensor.** It allows for measuring the quality of the air. This sensor warns of high concentrations of CO\({}_{2}\) gases in the air, which affects people's health.  
Figure 3: Router node and radio frequency nodes’ circuits, schematics, and connections.