esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Polynomial Solvability
### Systematic Approach  
After one inserts potential (2) in Schrodinger Equation (11) it is possible to assume the existence of the even-parity and/or odd-parity QES bound-state solutions in the most general form of the standard normalizable polynomial ansatz such that, on the positive half-axis,  
\[\psi^{(QES)}(x)=\psi(x)=e^{-x^{2}/2+dx}\,\times\sum_{k=0}^{N}\,a_{k}x^{k}\,, \quad x\geq 0\,,\quad a_{N}\
eq 0\,. \tag{13}\]  
We may accept the normalization convention  
\[\left\{\begin{array}{ll}\psi(0)=1\,,&\psi^{\prime}(0)=0\,,\\ \psi(0)=0\,,&\psi^{\prime}(0)=1\,,\end{array}\right.\quad\text{i.e.,}\quad \left\{\begin{array}{ll}a_{0}=1\,,&a_{1}=-d\,,\\ a_{0}=0\,,&a_{1}=1\,,&a_{2}=-d\,,\end{array}\right.\quad\text{parity}=\text{ odd}\,. \tag{14}\]  
With \(a_{N+1}=a_{N+2}=\ldots=0\) and after appropriate insertions and elementary algebra the QES solvability of the model appears equivalent to the validity of the set of linear recurrences  
\[(E-1-2n)\,a_{n}+2d(n+1)\,a_{n+1}+(n+1)(n+2)\,a_{n+2}=0\,,\quad n=0,1,\ldots,N\,. \tag{15}\]  
From the last item with \(n=N\) we get the QES value of energy \(E=2N+1\) so that we are left with an \(N\)-plet of relations between the \(N-1\) unknown coefficients \(a_{2}\), \(a_{3}\),..., \(a_{N}\) and an unknown, QES-compatible value (or rather a multiplet of values) of the shift \(d=d^{(QES)}\).  
Starting from the choice of \(N=0\) and \(E=1\) we find that the odd solution cannot exist while the well known even-parity solution only exists at \(d=0\). In the next step with \(N=1\) and \(E=3\) the single constraint \(2\,a_{0}+2\,d\,a_{1}=0\) only admits the pure \(d=0\) harmonic oscillator in the odd-parity case while the first nontrivial QES solutions with \(d=\pm 1\) emerges in the even-parity scenario.  
Once we skip the harmonic-oscillator solutions and demand that \(d\
eq 0\) we may set \(N=2\) and \(E=5\) in Equation (15). Then we have to satisfy the set of two relations  
\[4a_{0}+2 da_{1}+2a_{2}=0\,,\quad\ 2a_{1}+4 da_{2}=0\,. \tag{16}\]  
This leads to the two easy odd-parity solutions with \(d=d_{\pm}=\pm 1/\sqrt{2}\) and \(a_{2}=-d\). They become accompanied by the equally easy even-parity solutions with \(a_{2}=1/2\) and \(d=\pm\sqrt{5/2}\).  
Next we choose \(N=3\) and find that the odd-parity construction degenerates to the triplet of relations  
\[2+2\,d\,a_{2}+3\,a_{3}=0,\quad d+a_{2}=0,\quad a_{2}+3\,d\,a_{3}=0\]  
with solution \(a_{3}=1/3\), \(d=\pm\sqrt{3/2}\) and \(a_{2}=-d\). In parallel, the even-parity conditions  
\[-2\,d\,+2\,d\,a_{2}+3\,a_{3}=0,\quad 3-d^{2}+a_{2}=0,\quad a_{2}+3\,d\,a_{3}=0\]lead to the slightly less trivial quadruplet of eligible QES shifts  
\[d_{\pm,\pm}=\pm\sqrt{\frac{9\pm\sqrt{57}}{4}}\,. \tag{17}\]  
Each one of them defines the related two coefficients  
\[a_{2}=\frac{2d^{2}}{2d^{2}-1}\,,\quad a_{3}=\frac{2d}{6d^{2}-3}\,. \tag{18}\]
## 2 Miniaturization of the Microwave System  
### Conventional Microwave System  
Figure 3 shows the three-dimensional structure of the ECR ion source experimental platform equipped with a conventional microwave transmission system, the physical object, and the measurement results of the microwave transmission of this ion source. Among them, Figure 3a shows the three-dimensional structure of the experimental platform. The experimental platform includes a magnetron, an excitation cavity, a circulator, a three-pin tuner, a directional coupler, a high-voltage isolated waveguide, a matched waveguide, a source body, and a three-pass chamber. The platform can be used to measure the transmission of microwave systems and to perform preliminary experiments on gas ionization. Figure 3b shows a physical diagram of the experimental platform and microwave transmission measurements. By replacing the magnetron and excitation cavity with a waveguide coaxial converter and connecting it to a vector network analyzer, the microwave transmission of the platform can be measured. The match between the microwave system and the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Organization**} & **Microwave** & **Overall Size of the** & **Miniaturized** & **Power** & **Magnetic Field** & **Extraction** & **Voltage (\&V)** & **Beam (mA)** \\  & **Transmission** & **Mode** & **System (mm)** & **Component** & **(W)** & **Generation Mode** & **Voltage (\&V)** & **Beam (mA)** \\ \hline Peking & waveguide + & \textgreater{}600 (without & microwave system + & 180 & permanent & 40 & 21.7 \\ University [10, 11] & coaxial line & coaxial line length & discharge chamber & & magnet ring & 35 & 4.5 \\  & antenna + & \textgreater{}200 (without & microwave system + & 100 & permanent & 15 & 0.5 \\  & coaxial line & coaxial line length & discharge chamber & & & & \\  & Theoretical and & & \textgreater{}200 (without & microwave system + & 100 & permanent & 15 & 0.5 \\  & Experimental & & coaxial line length & discharge chamber & & & & \\  & Physics(ITEP) [12] & & & & & & \\ CEA/Saclay [13] & waveguide & \textgreater{}1000 mm & extraction system & – & focus coil & 50 & 48.5 \\  & & microwave system + & \textgreater{}300 & permanent & & & \\  & & discharge chamber & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ECR ion source parameters in accelerator facilities.  
Figure 2: Structural components of the physical computational model: (**a**) structural model for calculating the transmission of the miniature microwave system; (**b**) structural model of the source body for calculating the discharge inside the discharge cavity.  
discharge chamber is changed by adjusting the three-pin tuner to minimize microwave reflections throughout the system. At this moment, the reflection situation is represented by the S11 parameter and the standing wave ratio, which are, respectively, \(-\)20.213 dB and 1.2159.
## 4 Results of the Research  
### The Empty Car Analysis  
Stability maps for the motion of an empty wagon model are shown in Figure 5. As can be seen there, the properties of the model are regular. That is, for each of the tested routes, the critical velocities \(v_{n}\) were determined. Only stable stationary solutions appear for velocities lower than the critical value. Periodic solutions exist for velocities equal to and  
Figure 4: The idea of building a pair of bifurcation plots and the component elements of a stability map [7] for a CC with \(R=2000\) m: (**a**) example simulation of stationary solution on CC; (**b**) example periodic solution for CC; (**c**) bifurcation plot of \(1y_{lu}\,max\); (**d**) bifurcation plot of p-t-p \(y_{lu}\).  
Figure 5: Stability map of an empty gondola car: (**a**) absolute value of the leading wheelset’s maximum lateral displacement \(1y_{lu}\,max\); (**b**) p-t-p value of \(y_{lu}\) as a function of vehicle velocity.  
higher than the critical value \(v_{n}\). The values of the critical velocity \(v_{n}\) on each curved track increase as the curve radius decreases (Table 2). The smallest value is 37.2 m/s for an ST (\(R=\infty\)), and the highest \(v_{n}\) is 45.2 m/s for a CC with \(R=1200\) m. Stable periodic solutions still exist at velocities greater than 200 m/s on the ST (black line). The velocity ranges where periodic solutions for CCs occur are smaller than those for STs and decrease as the curve radius decreases (different line colours for particular \(R\) values). For each CC section and the final range of velocities, there is a bifurcation of periodic to stationary solutions, which are the stable ones. Potentially, this could be the effect of the action of centrifugal force. Only stable stationary solutions exist in the range of velocities lower than the critical value, with the \(\mid\!y_{ln}\!\mid\!max\) values increasing as the curve radius decreases. Therefore, the diagrams show results for a motion velocity higher than 30 m/s. The wheelsets not being centrally located on CCs below \(v_{n}\) (\(\mid\!y_{ln}\!\mid\!max\
eq 0\)) results from the motion conditions, including the applied superelevation of the track (Table 1).  
So, in light of the tests carried out on the car model in an empty state, the requirements regarding motion stability for operating velocities (less than 120 km/h \(\approx\) 33.33 m/s) are met. The critical velocities on particular tested routes are not lower than the maximum operational velocity. Periodic solutions with \(\mid\!y_{ln}\!\mid\!max\) values up to approximately 0.006 to 0.007 m appear for the critical and greater than critical velocities. This means that self-exciting vibrations of the wheelsets may occur in the modelled system. Still, the basic motion of the vehicle (along the track) is also possible at a velocity over the critical value.
# Multi-Objective Order Scheduling via Reinforcement Learning
## 3 Problem Description
### Markov Decision Process Formulation  
The continuous decision method based on PDR uses a series of steps to solve JSSP instances. In each step, a set of qualified operations (that is, operations for which previous operations have been scheduled) is first identified. The specific PDR is then applied to calculate the priority index of each qualified operation, and the one with the highest priority is selected for scheduling. Traditional PDRs design the priority by rules, such as selecting the shortest processing time from a set of operations. As mentioned above, solving a job dispatching instance can be viewed as the task of determining the direction of each disjunction. Therefore, we consider the dispatching decisions made by PDRs as actions of changing the disjunctive graph, and formulate the underlying MDP model as follows.  
State. The state \(s_{t}\) of the disjunctive graph \(G(t)=(O,C\cup D_{u}(t),D(t))\) presents the solution up to time \(t\), where \(D_{u}(t)\subseteq D\) contains all the disjunctive arcs that have been assigned a direction up to \(t\) and \(D(t)\subseteq D\) includes arcs which are not dispatched. For each node \(O\) in the graph, we record the recursively calculated value for each target and a binary record \(I(O,s_{t})\), the value of which is be 1 if the node is scheduled in \(s_{t}\):  
\[T_{LB}(O_{ij},s_{t}) =T_{LB}(O_{i,j-1},s_{t})+t_{ij}, \tag{1}\] \[F_{LB}(O_{ij},s_{t}) =F_{LB}(O_{i,j-1},s_{t})+f_{ij},\] (2) \[C_{LB}(O_{ij},s_{t}) =C_{LB}(O_{i,j-1},s_{t})+c_{ij}, \tag{3}\]  
where \(t_{ij}\) is the process time for this operation, \(f_{ij}\) is the degree of completion, and \(c_{ij}\) is the cost of this operation. We calculate the target by only considering the precedence constraints from its predecessor.  
Action. An action \(a_{t}\) is one executable operation in a decision step \(t\).  
Rewards. The reward function is vital for reinforcement learning. At each juncture, the agent selects an action based on the present state according to its policy and the environment generates a reward signal that is the goal the agent wants to maximize during the action selection process.
# An Improved Low-Density Parity-Check Decoder and Its Field-Programmable Fate Array Implementation
## 4 Improved LDPC Decoder Hardware Design and Implementation
### Verification Module  
After the decision module completes its work, it outputs 672 codewords that are stored in 24 shift registers. When the control module sends an enable signal to the verification module, it retrieves the codeword positions corresponding to the non-zero elements of each row in the parity-check matrix. Using XOR operations, the module multiplies the decision codewords with the row vectors of the parity-check matrix.  
Each clock cycle performs row vector multiplication for 12 rows, and all row vector multiplications are completed within 28 clock cycles. The outcomes of the XOR operations are documented. If all results are zero, decoding is successful. A decoding success signal is sent, and the first 336 bits are extracted as the decoded output.  
If the sum of XOR results is not zero, a decoding failure signal is sent, the iteration count is incremented, and the next decoding iteration is initiated.
# Application of Improved LightGBM Model in Blood Glucose Prediction
## 3 Experimental Results and Discussion
### Comparative Experiments  
Two groups of comparative experiments were set in the study. The first group of comparative experiments was as follows: comparing the HY_LightGBM model optimized by the Bayesian hyperparameter optimization algorithm with the LightGBM model, the XGBoost model, and the CatBoost model without parameter optimization. The second group of the comparative experiments was as follows: the comparison among the LightGBM model with parameter optimization by genetic algorithm, the LightGBM model with parameter optimization through random searching algorithm, and the LightGBM model with parameter optimization by the Bayesian hyper-parameter optimization algorithm used in this study. The comparative experiments verified the improvement in the prediction accuracy of the HY_LightGBM model optimized by the Bayesian hyper-parameter optimization algorithm and the feasibility of the HY_LightGBM model.
# Impact of Device Topology on the Performance of High-Speed 1550 nm Wafer-Fused VCSELs
## 3 Results and Discussion
### Small-Signal Modulation
A further increase in the modulation bandwidth seems to be associated with an increase in the resonant frequency and a decrease in damping.
## 3 Methodology
### Chi-Squared Automatic Interaction Detection--CHAID  
Chi-squared automatic interaction detection (CHAID) is a decision tree technique used primarily for identifying significant relationships between categorical variables. It is commonly applied in areas like market research, social science, and other fields where classification tasks are involved. CHAID works by repeatedly splitting the data into mutually exclusive subsets based on the most significant independent variable, measured by the chi-square statistic [132].  
The process starts by evaluating all potential splits for each predictor variable. For each predictor, the algorithm assesses whether the relationship between the independent variable and the target variable is statistically significant. It merges categories of the predictor variable that do not significantly differ in relation to the target variable. The merging continues until all significant differences are accounted for, with the chi-square test determining the statistical significance. Once the best predictor is selected, the dataset is split accordingly, and the process is repeated for each branch of the tree. Unlike other decision tree algorithms such as CART or C4.5, CHAID can produce trees with more than two branches at each node, making it a multiway split approach. This is particularly useful when dealing with categorical variables with multiple levels, as it allows for more nuanced classifications. Additionally, CHAID is non-binary and can handle missing data, which adds to its flexibility [132].
# The Failure Law and Combined Support Technology of
## 5 Field Test and Effect Analysis
### FLAG3D Numerical Calculation Verification
#### 5.2.2 Simulation Result Analysis  
As shown in Figure 12, the stress level within 4.3 m of both sides of the roadway under the original support is lower than the stress of the original rock. The peak stress of the surrounding rock is located at 6.4 m of the roadway side, and the stress concentration coefficient is 1.6. After multi-stage strengthening combined support, the area below the original rock stress is reduced by 64.6% compared with the original support, and the peak value of surrounding rock stress moves 2.15 m to the roadway surface. The statement suggests that the supporting scheme enhances the stress distribution in the surrounding rock, improves its overall strength, effectively prevents pressure transfer to deeper areas, and contributes to maintaining roadway stability.  
The plastic failure range of the roadway is significantly reduced after combined support, as shown in Figure 13. The fracture zone range of the roadway side and roof decreased from 1.75 m to 0.63 m under the original support. In comparison, the floor also decreased from 2.87 m to 2.32 m, with a reduction in the plastic softening zone range. Additionally, there was a decrease in the \"Tension\" failure area on the roadway surface, indicating effective prevention of tension shear failure of surrounding rock.  
From Figure 14, it is evident that the original supporting roadway has undergone significant section deformation. The roof and floor have deformed by 224 mm and 335 mm, respectively, while the two sides have deformed by 243 m. This deformation poses a severe  
Figure 12: Stress distribution nephogram before and after the combined support: (**a**) original support; (**b**) Combined support.  
Figure 13: Plastic zone characteristics before and after the combined support: (**a**) original support; (**b**) Combined support.  
threat to the stability of the roadway. However, after adopting combined support measures, the surrounding rock's deformation is controlled. The top and bottom plates' deformations are now at 143 mm and 152 mm, respectively, while that of both sides is 130 mm. Compared with the original support, they are reduced by 36.2%, 54.6%, and 46.5%, respectively. The results show that the combined support method can effectively prevent large deformations of the surrounding rock mass in soft rock.
## 1 Introduction
UHIs are identified where temperature warming patterns for urban areas are significantly higher than the rural surroundings. UHIs are due to structural and land cover differences between urban and rural areas and are generally measured through a climate-based classification system called Local Climate Zone (LCZ) [17]. This classification defines 17 unique area types based on the physical and thermal properties of their surface. LCZ maps are widely used in urban climatology since they can explain the contribution of urban surface characteristics to heat fluxes [18; 19]. These maps are usually derived from the supervised classification of satellite imagery, eventually in combination with other EO products such as Digital Elevation Models (DEMs) [20]. The World Urban Database and Access Portal Tools (WUDAPT) [21; 22] offers detailed guidelines on how to perform LCZ classification and provides access to LCZ maps of different cities. Literature has seen many authors developing studies on the correlation of land cover and temperature, e.g., [23; 24]. However, these have mainly focused on the analysis of surface temperature rather than air temperature [25]. Despite air temperature and surface temperature are generally correlated measures, they have a different physical meaning. Surface temperature is a measure of the radiation emitted by the land surface, thus it can be acquired by satellite sensors. Air temperature is instead a ground-based measure which is strongly connected to atmospheric, seasonal, and geographical conditions [26]. Most important, it represents the characterizing factor of UHIs and, as such, it is considered to evaluate their intensity and persistence [27]. The present study focuses on the urban area of Milan (Northern Italy), where no similar studies have been carried out so far. The proposed analysis aims to provide insights into the underlying effect of land surface features on the air temperature by leveraging geo-data exploratory procedures. This enables to answer questions such as how and how much each LCZ class is correlated to local air temperature observations as well as to highlight strengths and limits of the considered geo-data in achieving the analysis goal. The remaining of the paper is structured as follows. Section 2 introduces the area chosen to analyze the relation between urban land cover and temperature, as well as the data sources and software technology used. In Section 3 the methodology undertaken to preprocess the datasets, compute the LCZ maps and correlate them with temperature observations are presented in detail. Section 4 discusses the results achieved. Section 5 concludes the paper by outlining the main findings of the study.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
As shown in Figure 1, The architecture consists of three parts; the first part is the video capture channel, which is used to pre-process the input video stream, such as decoding and de-framing, and store the raw video data into off-chip storage, the second part is the deep learning acceleration core, which is used for neural network inference and consists of a data loading kernel, a computational kernel, and a data unloading kernel, the third part is the scheduling system, which is used to schedule the components within the acceleration core and also runs the embedded operating system. The second and third parts use the Advanced eXtensible Interface Memory Map (AXI-MM) bus for data interaction, while the components within the acceleration core cache small amounts of data through the on-chip Block Random Access Memory (BRAM) and use the BRAM bus or registers for internal data interaction. The data in the acceleration core is first loaded into the data load buffer through the data loading kernel. The scheduling state machine in the computation kernel transfers the data to the computation matrix. After a round of computation, the data offloading kernel writes the data in the data storage buffer back to off-chip storage through the AXI-MM bus. In brief, the main contributions of our proposed architecture are as follows:  
1. We propose a parallel acceleration scheme for the three leading operators in CNN: convolutional layer, pooling layer, and fully connected layer. We improve the inference speed of CNN by a parallel acceleration in three dimensions: input channel parallelism, output channel parallelism, and pixel parallelism. We adopt a data address remapping strategy to match the bandwidth needs of parallel computation of FPGAs. 2. We propose an architectural optimization strategy of the deep learning acceleration core to improve the efficiency of data stream transfer. The task scheduling is designed in a pipelined manner. Data accessing and parallel computing are carried out simultaneously to avoid data flow operations affecting the computational efficiency of the parallel acceleration core. 3. The three-level data cache structure of off-chip storage, on-chip storage, and registers is proposed. The on-chip storage is sliced to provide sufficient data access bandwidth for parallel computing units. The implementation strategy of the computation matrix across clock domains is adopted to reduce the DSP resource occupation rate to half of the original one and improve the utilization of DSP resources on FPGAs.