esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Authentic Signature Identification Method Integrating Dynamic and Static Features
## 4 Results
### Feature Extraction
#### 4.1.1 Dynamic Feature Extraction  
When writing a signature, a series of movement tracks will be left. Each person's stroke characteristics, writing strength and speed will be different. Dynamic features are obtained by further processing the information on attributes, such as speed, time and pressure, obtained when writing online signatures. It has higher accuracy and can reflect the writing style of the writer to a certain extent.  
In the process of screening dynamic features, the obtained dynamic features are analyzed by a thermal map. The heat map shows the color shades corresponding to different correlation coefficients to explore the correlation between the identity of the writer and each feature; as in Figure 5, the selected features have larger values and lighter colors, the results indicate that the selected dynamic features are more effective.  
As a representative of the extremely strong embodiment of writing style, the number of strokes can show the connection of the written signature, as shown in Figure 6, which provides favorable conditions for identification. Especially in the imitation of complex signatures, the writing habits and psychological states of different signers will have a certain impact on the number of strokes.  
(2) Average pressure  
Figure 5: Heat map.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Dynamic Feature** & **Static Feature** \\ \hline StrokeSum & AspectRatio \\ AveragePressure & Area \\ HangTime & Center of Gravity \\ StrokeTime & SpindleDirection \\ SpeedMax & Quadrilateral defining \\ Signature structure \\ SpeedMin & ChainCode \\ \hline \hline \end{tabular}
\end{table}
Table 5: Feature extraction.  
Pressure is the force exerted on the paper by the individual through the nib when writing, as in Figure 7. The behavior of writing a signature is a dynamic process. As a continuous and hard-to-copy feature, pressure is difficult for forgers to accurately reproduce. Although there are different degrees of pressure values in continuous strokes, considering the number of strokes, the average pressure value of the signature is better.  
Total hang time  
Total time  
Figure 6: Total strokes.  
Figure 7: Pressure value.  
In the case of forged signatures, the time spent writing varies from author to author, as shown in Figure 9, which is directly related to the author's original writing speed, the difficulty of signing and the author's proficiency in forging signatures.  
The maximum velocity of the author's writing is shown in Figure 10. Velocity is a key feature that cannot be ignored when writing and it can express the natural degree and accuracy of a signature.  
Minimum velocity  
Figure 8: Hang time.  
Figure 9: Writing time.
## 4 Proposed Approach for Forecasting the Wind Power Intervals
### Wind Power Probability Interval Prediction
#### 4.2.1 Optimizing the Objective Function  
The selection of the objective function in PSO is very important because it affects the optimization results. Reliability and accuracy are two indices for evaluating the prediction interval. Reliability is defined as the probability that the actual observation falls within the prediction interval. The value should be as large as possible to make the prediction more reliable. Accuracy is used to predict the width of the interval, which should be as small as possible so that the prediction width is as narrow as possible. However, the two indices are contradictory; therefore, we construct a comprehensive optimization objective function \(F\) that takes both accuracy and reliability into account.  
\[\underset{\beta}{\text{min}}F=\sum_{i=1}^{n}\left[\gamma_{i}\left|PICE_{t}^{(a )}\right|+\varphi_{i}\left|PINAW_{t}^{a}\right|\right] \tag{11}\]  
where \(\gamma_{i}\) and \(\varphi_{i}\) are the weights of prediction interval coverage error (PICE) and prediction interval normalized average width (PINAW), and \(\left|\cdot\right|\) are the absolute values of PICE and PINAW. PICE = \(\left|\text{PINC - PICP}\right|\) (prediction interval nominal confidence (PINC) is the confidence level).  
Figure 4: The overall architecture of the proposed approach for probabilistic wind power forecasting.  
The prediction interval coverage probability (PICP) reflects the probability that the actual observation value \(t_{i}\) falls within the upper and lower bounds of the prediction interval:  
\[\text{PICP}=\frac{1}{N_{t}}\sum_{i=1}^{N_{t}}\kappa^{(\alpha)} \tag{12}\]  
where \(N_{t}\) is the number of predicted samples, and \(k\) is the Boolean quantity. If the predicted target value \(t_{i}\) is included in the upper and lower bounds of the prediction interval, then \(k=1\); otherwise \(k=0\). PICP should be close to PINC.  
PINAW is the average bandwidth index of the prediction interval and reflects sharpness. If the PINAW is too wide, it cannot effectively predict information of uncertainty.  
\[\text{PINAW}=\frac{1}{N_{t}}\sum_{i=1}^{N_{t}}\left[U_{t}^{\alpha}(x_{i})-L_{t }^{\alpha}(x_{i})\right] \tag{13}\]  
Adjusting the weight factor controls the degree of influence of different criteria on the optimization results.
## 3 Results and Discussion
### Ft-Ir  
The FTIR spectra of non-woven PET and poly (GMA-DVB-VBC) with different compositions are shown in Figure 4. The bands at 2923 and 2852 cm\({}^{-1}\) are assigned to the stretching vibration of -CH\({}_{2}\) and -CH groups. The C=O (-COO- in carboxyl group) is the band at 1719 cm\({}^{-1}\) and epoxy (-COC-) stretching vibration the band at 907 cm\({}^{-1}\)[23, 24]. The intensity of C=O and epoxy vibration bands increases as composition of GMA is increased from 15 wt% to 75 wt%. Bands at 1510 and 1608 correspond to C=C stretching vibration in aromatic ring [25]. GDV15 and GDV45 base membranes containing aromatic rings from VBC and DVB show more intensive bands compared to those of GDV75 base membrane. C-Cl stretching from VBC corresponds with the band at 1265 cm\({}^{-1}\) and its intensityincreases as the composition of VBC is increased from 15 wt% to 75 wt%. [26] These results indicate that monomers (GMA, DVB, and VBC) were successfully polymerized and combined with non-woven PET.  
Figure 4: FT-IR spectra of **(a)** non-woven PET; **(b)** GDV75; **(c)** GDV45; and **(d)** GDV15.  
Figure 3: SEM surface images of: (**a**) non-woven PET; (**b**) GDV45 base membrane; (**c**) enlarged surface of GDV45 base membrane; and (**d**) GDV45-TMA membrane; and SEM cross section image of: (**e**) GDV45-TMA.  
Among GDV membranes with varying compositions, GDV45 base membrane and membranes combined with different functional groups (TMA, TEA, TPA, and DABCO) were analyzed by FT-IR to confirm the quaternization of membranes listed in Figure 5. GDV45 base membrane was selected to confirm quaternion because this membrane has identical weight percent ratio between GMA and VBC (Table 1). Figure 5b-e each exhibit a broad band at 3400 cm\({}^{-1}\), which are ascribed to stretching vibration of -OH groups from epoxy ring opening reaction. The C-N vibration of function groups (TMA, TEA, and TPA) show new peaks at 1170 cm\({}^{-1}\). GDV45-DABCO presents C-N vibrations near 1614 cm\({}^{-1}\) and 1463 cm\({}^{-1}\)[27]. The bands at 1260 cm\({}^{-1}\) and 907 cm\({}^{-1}\) do not disappear although amine groups are introduced to the base membrane, the reason for which will be discussed in the IEC section. The results from Figure 4 show that functional groups (TMA, TEA, TPA, and DABCO) successfully developed chemical bonding between the epoxy ring from GMA and C-Cl from VBC on base membranes.
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
## 5 Experiments
### CESDD Vehicle Detection  
In this experiment, the CESDD vehicle detector is trained over 18,195 vehicle samples (including FV, PO, and LO), 33,218 background samples of GB, and 18,402 background samples of VP, all obtained from the KITTI2017 training set. For comparison, the DPM detector is obtained from voc-release5 site [34], which is named car_final.mat and trained from Pascal VOC 2007's trainval set.  
Qualitative test results from the proposed CESDD vehicle detector with and without mean-shift-based NMS are presented in Figure 8; qualitative comparison with DPM detector is presented in Figure 9. It is observed that the proposed CESDD vehicle detector is effective in these in-the-wild scenarios.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Training Time (s)** \\ \hline random forest & 46.6 \\ gradient boosted trees & 232.1 \\ discrete AdaBoost & 361.3 \\ linear-SVM & 960.7 \\ RBF-SVM & 10,731.3 \\ ours (CESDD) & 470.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training time comparison of CESDD-based classifier and other classifiers.  
Figure 8: Illustration of the effect of mean-shift-based non-maximum suppression (NMS) on CESDD-based vehicle detection. Purple boxes are initial detections from window classification process; cyan boxes are refined detections through the adapted mean-shift-based NMS. (**a**,**c**,**e**) show initial detections; (**b**,**d**,**f**) show corresponding refined detections.  
Figure 9: Qualitative vehicle detection results of our CESDD-based detector (**a**,**c**,**e**,**g**,**i**,**k**) vs. deformable part-based model (DPM) (**b**,**d**,**f**,**h**,**j**,**l**) on KITTI2017.
## 2 Materials and Methods
### Model Materials and Design  
Currently, there are some studies on cemented transparent soil [25; 26]. Nonetheless, cemented transparent soil and cement soil are different, and the sample preparation is complicated. Therefore, in the model of this test, washed kaolin was taken as the curing object [27], and 42.5 ordinary Portland cement was regarded as the curing agent. The plastic limit of kaolin was 33%, the liquid limit was 60%, and the pH value was 4.14. Soft soil with high water content can be prepared by mixing purified water with the kaolin liquid limit. The gridded hard crust model cannot be cast in transparent soil and hence was prefabricated in advance. Then, a mold made of 6 mm thick PVC cardboard splicing was designed to establish the model. Simultaneously, purified water was first added with 60% dry soil mass to kaolin, stirred into a flowing state, and then added with a cement of 8% and 15% wet soil mass, stirred evenly, and poured into a mold. After curing indoors for 7 days, all templates were removed, and finally the model was formed, as exhibited in Figure 3.  
For illustration purposes, the dimension symbol of the gridded hard crust is defined in Figure 4. A model with the ratio of hard crust thickness (t) to grid structure height (h) of 0.5 and the ratio of grid structure spacing (s) to grid structure width (d) of 2 was established to explore the destruction mode of gridded hard crust based on previous engineering experience. Besides, 2 cases were considered when the cement content was designed.  
Figure 3: (**a**) Molds for making concrete models and (**b**) Gridded hard crust model.  
Figure 2: Transparent soil test materials (**a**) U10 and (**b**) Titanium oxide.  
First, when the cement content was small (namely the modulus of the gridded hard crust was low), the deformation of the hard crust was large and coordinated with the deformation of the foundation, and the soil exerted more bearing capacity. Second, when the cement content was large (namely, when the modulus of the gridded hard crust was large), the deformation of the hard crust was small, the soil did not exert its bearing capacity, and the hard crust was destroyed. Then, two kinds of cement content (8% and 15%) were designed. Control groups with t/h = 0.5, 1, and s/d = 1 were placed to research the influence of structural differences on destruction modes. The test scheme is presented in Table 1. Among them, S, M, and L represent the ratio of s to d as 1, 2, and 3, respectively; N and P denote the ratio of t to h of 0.5 and 1, respectively; 8 and 15 indicate cement content. The width of the model is aligned with the model box at 20 cm.
## 3 Conceptual Model: Procurement Order Quantities Regulation for Shortage Scenarios
### Development of a MRP Simulation System  
First, a series of assumptions are defined to simplify the model so that we can focus on the objective of the simulation.  
* Manufacturing process not considered;
* Final demand without deviations;
* Material receipts can be used on the day of receipt for manufacturing;
* Quality failures not considered;
* Demand does not change if customer service is better or worse;
* Infinite warehouse, manufacturing, and procurement capacity.  
The defined points will be listed to make a comparison between the different scenarios within the simulation model possible.  
* Same demand, same demand patterns, and replicas;
* Same deviations in days of delivery delays;
* Same cost parameters.  
The simulation case study manufactures an item A from three units of component B and one unit of component C. Moreover, the simulation considers the procurement planning for the production plant of the Tier-1 supplier as shown in Figure 5. Therefore, the Tier-2 suppliers are suppliers of the Tier-1 manufacturer for the items B and C. Finally, the original equipment manufacturer is the client of the production plant in focus.  
The next step is to develop the simulation model based on the previous information. The simulation model is built based on data inputs, the simulation execution, and the data output as shown in Figure 6. Moreover, a new parametrization of the POQ to execute further scenarios is possible enabling the comparison of policies and the continuous improvement of the planning while considering disturbances.  
Based on the previous Figure 6, the model is developed consisting of six different areas or sub-models as is shown in Figure 7.  
Given a defined demand and production strategy, the modeler must define a time horizon and time units. In the case study, it has been decided to simulate a horizon of 11 weeks to evaluate influences at the tactical and operational levels with a time pass of 1 day.  
Moreover, behavior validation of the simulation model was performed with extreme-value tests such as, for instance, if there is no reception of components, then stocks will be zero and the service level will decrease or as, for instance, when there are double the procurement order quantities ordered, then there is an accumulation of stocks. Based on these facts among others, the simulation is validated.
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 2 PLL Principle and Chip Introduction  
### Basic Principle of PLL  
A phase-locked loop (PLL) is a phase feedback control circuit that adjusts its internal oscillator's frequency and phase using an external input reference signal [12]. It mainly comprises a phase detector (PD), a loop filter (LF), a frequency divider (FD), and a voltage-controlled oscillator (VCO) to form a closed-loop system. Figure 1 shows a typical schematic of the phase-locked loop [13].  
\(K_{VCO}\) is the voltage control sensitivity, \(K_{d}\) represents the voltage transfer coefficient, \(F(s)\) describes the filter transfer function, and \(G(s)\) represents the open-loop gain of the loop circuit. As shown in Figure 1, the PLL is a phase-negative feedback system where the input phase is \(\theta_{i}(t)\), the VCO feedback output phase is \(\theta_{o}(t)\), and the phase error is \(\theta_{e}(t)\). The phase relationship is described as follows [14]:  
\[\theta_{e}(t)=\theta_{i}(t)-\theta_{o}(t)=\theta_{i}(t)-K_{VCO}K_{d}\frac{F(p) }{pN}sin\theta_{e}(t) \tag{1}\]  
where \(p\) is the derivative operator. When \(\theta_{e}(t)\) is small enough, \(sin\theta_{e}(t)\) can be approximated as \(\theta_{e}(t)\). \(K_{VCO}K_{d}=K\) (1) can be transformed into the following complex frequency domain:  
\[s\theta_{e}(s)=s\theta_{i}(s)-\frac{KF(s)\theta_{e}(s)}{N} \tag{2}\]  
From (1), we have  
\[\theta_{o}=K\frac{F(s)}{sN}(\theta_{i}-\theta_{o}) \tag{3}\]  
From (1) and (3), the closed-loop transfer function of the loop circuit is obtained in terms of the transfer function \(F(s)\) as follows [12]:  
\[H(s)=\frac{\theta_{o}}{\theta_{i}}=\frac{K_{VCO}K_{d}F(s)}{sN+K_{VCO}K_{d}F(s)} =\frac{G(s)}{1+G(s)} \tag{4}\]  
where  
\[G(s)=\frac{K_{VCO}K_{d}F(s)}{sN} \tag{5}\]  
The loop filter is critical to the overall design of the frequency synthesizer. It can pass DC signals and suppress high-frequency AC signals, interference, and harmonic components. The loop filter type and bandwidth determine essential parameters of the output signal, such as phase noise, lock time, and loop stability. Although it has a simple circuit structure, it significantly influences the operation of the whole system.  
Figure 1: PLL schematic.
## 3 Methodology
### Architecture  
As shown in Figure 1, the architecture is structured into three layers: the Presentation Layer (user interface and visualized results), the Agent Layer (agents for environmental and species objects), and the Interaction Layer (interactions between agents generating simulation results). This structure enhances modularity and maintainability.  
The information flow begins with the GUI receiving user input data or parameters, which triggers the initialization of the environment and species agents. These agents then enter the Interaction Engine, where they engage in dynamic interactions, resulting in updates to their states. The Simulation Controller subsequently generates results and directs further cycles according to a predetermined schedule. This process is repeated iteratively until the simulations are complete. Upon completion, the Simulation Controller sends the results to the visualization module, which presents the findings through the GUI.  
We can abstract the interaction flow into a Markov decision process (MDP) [35]. An MDP is 5-tuple \(\mathbb{M}=\langle\mathcal{S},\mathcal{A},\mathbf{R},\mathbf{T},\gamma\rangle\), where the \(\mathcal{S}\) is the state space, the \(\mathcal{A}\) is the action space, \(\mathbf{R}:\mathcal{S}\times\mathcal{A}\rightarrow\mathcal{R}\) is the reward function, \(\mathcal{R}\) is the reward set, \(\mathbf{T}:\mathcal{S}\times\mathcal{S}\rightarrow\mathcal{S}\) is the state transition function, and \(\gamma\) is the discount factor.  
An agent at the current state, \(s_{t}\), chooses an action, \(a_{t}\), and give this action to the environment. Then, the environment responds to a reward, \(r\), through the reward function \(\mathbf{R}(s_{t},a_{t})\) and the next state \(s_{t+1}\) via the state transition function \(\mathbf{T}(s_{t},a_{t})\). This process runs constantly until the end of the episode, \(t=T\in\mathcal{T}\).  
A state, \(s\in\mathcal{S}\), consists of multiple observational dimensions, with each dimension representing an ecological model. However, when the number of dimensions is limited, the state, \(s\), may fail to fully capture the dynamics of the environment. In this case, the agent receives an observation, \(o\), where the dimensionality of \(o\) is smaller than that of the true state, \(s\). This leads the agent to face a partially observable Markov decision process (POMDP) [36] problem. Incorporating more ecological models increases the number of dimensions, thereby mitigating the issue of partial observability.
# Modeling and Experimental Verification of Induction Heating of Thin Molybdenum Sheets
## 2 Contactless--Induction Heating of Thin Molybdenum Sheet  
Investigation of the possibilities of induction heating is presented based on highly accurate thermal simulation models. Initially, experimental measurement (Figure 1) of the molybdenum sheet's induction heating was performed to verify the accuracy of the simulation model. Main parameters that are clinical for the settings of the induction heating even if the simulation is considered are:  
* the operating frequency of the electromagnetic field
* value of the coil's current
* distance (gap) between the heating coil and Mo sheet  
Figure 1: Experimental set-up of induction heating of Mo sheet, **Right—**the 3D projection of the heating coil with Mo sheet and required assembly, **Left—**the physical prototype of proposed induction heating system.  
In the next sections, the design of the simulation model of induction heating of thin molybdenum sheet is presented together with the parametrical investigation of operational condition. Using the proposed model, it will be possible to investigate optimal settings related to the induction heating process.
## 5 Numerical Simulations
### Noiseless Inversions  
Complex visibilities \(V\) have been simulated according to (1) for a typical scene \(T\) over the ocean [40], as illustrated in Figure 7. They have been inverted according to (9), and the retrieved brightness temperature distributions \(\widetilde{\mathcal{T}}\) have been apodized with a Blackman apodization window for comparison with \(\mathcal{T}\), which is at the same angular resolution and apodized with the same Blackman window.  
Figure 6: Variations of the condition number \(\kappa(\widetilde{\mathbf{G}})\) of the regularized modeling matrix \(\widetilde{\mathbf{G}}\) of the cross-shaped array shown in Figure 4 with the shortest spacing \(d\) between the 167 elementary antennas. This figure has to be compared to Figure 3a as here again two regions are well-separated: in the first one \(\kappa(\widetilde{\mathbf{G}})\) is almost constant, whereas in the second one it is a growing function of the inverse of the shortest spacing. The frontier between these regions is estimated between \(0.704\lambda\) and \(0.705\lambda\).  
Shown in Figure 8 are the error maps \(\tilde{\mathcal{T}}-\mathcal{T}\) when the shortest spacing \(d\) is set to \(0.69\lambda\) or to \(0.71\lambda\). As expected, the reconstruction error is almost equal to 0 in both cases, whatever the polarization. In this ideal noiseless situation, the retrieved brightness temperature maps are not sensitive to the spacing between the antennas.
# Compact Quad-Port MIMO Antenna with Ultra-Wideband and High Isolation
## 1 Introduction
The decoupling approach, which combines polarization diversity techniques with the introduction of a decoupling gap, has been proposed for the design of UWB MIMO antennas and can improve the isolation to more than 27.8 dB per port. The validity of this decoupling approach has been validated by simulation analysis and measurements. With stable gain and good diversity properties, the proposed MIMO antenna can be widely adopted in UWB communication systems and also used for satellite mobile communications, fixed communications and live television. The main content of this paper is organized as follows: In Section 2, an antenna capable of coverage operation in the ultra-wideband regime is designed by principle analysis and simulation optimization, and the design principle, evolution process, critical parameters and results of far-field simulations of the antenna are analyzed and discussed. Section 3 is based on the design and analysis of a compact four-cell MIMO antenna based on UWB antenna elements and achieves low coupling and miniaturization through polarization diversity and the introduction of a decoupling gap. Section 4 is the test and verification work for the broadband characteristics and low coupling characteristics of the four-cell MIMO antenna, which mainly carries out the test work of S-parameters and far-field radiation pattern, and analyzes and illustrates the diversity characteristics of the MIMO antenna, including: ECC, DG, MEG, TRAC, etc.
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 2 Overview of WSNs
### WSN Communication
#### 2.4.2 Multihop Communication  
Using multihop communication reduces transmission distance, thus reducing energy loss in order to increase the service life of the network. In multihop transmissions, network architecture plays a major role. Multihop network architectures are typically divided into flat or hierarchical architectures. Figure 5 illustrates this type of communication.