esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Model for Enhancing the Adoptability of Continuous Descent Approach at Airports
## 3 CDA Adoptability and Aircraft Descent Times
### Estimation of Aircraft Descent Time
#### 3.2.2 Drag Model  
Drag is the aerodynamic force acting on an aircraft body in terms of air resistance to aircraft motion through air. Similarly, to the lift force, the aerodynamic drag, \(D\), is the product of the dynamic pressure and drag coefficient, as follows:  
\[D=C_{D}\frac{1}{2}\rho V^{2}S \tag{6}\]  
The drag coefficient is given by the sum of zero-lift, \(C_{Do}\), and induced drag, \(C_{Di}\), coefficients, where the latter is a quadratic function of the lift coefficient, as follows:  
\[C_{D}=C_{Do}+C_{Di}C_{L}^{2} \tag{7}\]  
Typically, \(C_{Do}\) and \(C_{Di}\) are functions of the aerodynamic configuration of the aircraft flight phase. Generally, drag coefficients are functions of the aircraft's Mach number and the Reynolds number (\(Re=\rho VL/\mu\), where \(\mu\) is the absolute viscosity coefficient of air). For each aerodynamic configuration, BADA models these coefficients as constants to provide computations for altitude and speed profile thresholds at pre-determined flight phases (i.e., takeoff, initial climb, clean, approach, and landing).
## 2 Related Works
### Position-Based Dynamics
#### 2.2.2 Key Formula of Position-Based Dynamics Algorithm  
The main formula of the PBD algorithm is related to how to solve constraints based on location.  
Update location to meet constraints The position of the object in the PBD algorithm is updated according to the following equation: \[\Delta Pi=-\lambda\
abla C(Pi)\] (1) where \(Pi\) is the position of vertex \(i\), \(C(Pi)\) is the position-based constraint, \(\
abla\) is the gradient of the constraint, and \(\lambda\) is the Lagrange multiplier that regulates the strength of the constraint. This equation is a method of adjusting the position of an object to satisfy the constraint and calculates the final position by repeatedly projecting the constraint.
2. Distance constraint The distance constraint between the two vertices \(i\) and \(j\) is defined as \[C(Pi,Pj)=\parallel Pi-Pj\parallel-d\] (2) where \(d\) is the desired distance. To satisfy the corresponding constraints, each vertex is updated as follows: \[\Delta Pi=-\frac{wi}{wi+wj}(\parallel Pi-Pj\parallel-d)\frac{Pi-Pj}{\parallel Pi -Pj\parallel}\] (3) \[\Delta Pi=-\frac{wj}{wi+wj}(\parallel Pi-Pj\parallel-d)\frac{Pi-Pj}{\parallel Pi -Pj\parallel}\] (4) where \(wi\) and \(wj\) are the weights of each vertex.
# High-Speed Surface Property Recognition with a 140 GHz Frequency
## 4 Dataset Acquisition and Composition  
The data acquisition was conducted by employing the platform and surfaces as in Figure 2. In low-speed scenarios, scatter sampling is typically performed at one angle per pause (of the rotational stage), ensuring precision and stability. However, high-speed scenarios require continuous sampling, which introduces a trailing effect, as we mentioned in Section 1. To emulate real-world high-speed conditions, we continuously sampled while the Rx rotated without pausing. We set the sampling speed to match the rotational stage's safety speed limit while maintaining a minimum angular distance of 15\({}^{\circ}\) between the Rx and Tx. We also included acceleration and deceleration zones (20\({}^{\circ}\)) at the beginning and end of the motion. The sampling strategy was meticulously designed to ensure comprehensive data coverage across all surface types. Each rotation cycle was programmed to capture data systematically around the entire circumference of the sample, ensuring no angular sector was underrepresented. This systematic collection was essential to construct a dataset that accurately represents the variability inherent in real-world surface textures.  
In order to improve the robustness and generalization ability of our model, we need a diverse dataset from a limited number of rough surface samples. We achieved this by methodically moving an incident beam across the surface to gather sufficient data for model training. Considering the illumination area (by the channel beam) with a diameter of 3 cm on the surface, we moved the beam in both horizontal and vertical dimensions in 5 mm increments. With each movement, we collected a set of scattering data, referred to as a sampling sequence. Additionally, to mitigate the peak displacement resulting from the tailing effect, we rotated our receiver (RX) in both clockwise and counterclockwise directions at each sampling location. By doing so, we acquired 72 sampling sequences for each surface. In total, this process yielded 504 unique sampling sequences across seven different surfaces.  
To demonstrate the effect of the rotation speed of the stage and sampling parameters (average number and query interval), we conducted a comprehensive analysis using a range of speed and parameter settings, as summarized in Table 1. Rotation speed refers to the speed at which the motorized rotation stage moves during the uniform motion phase. The average number represents the numerical configurations for the built-in averaging feature of the sampling apparatus. It has been confirmed that its variation within a numerical range has a negligible impact on the final classification F1 score. Query frequency indicates how often the Python main control program retrieves data from the sampling device. It can be seen that rotation speed and query interval (frequency) can affect the cumulative length of the acquired sequences, including data from the acceleration and uniform motion zones near the Tx side.
## 3 Experimental Results
### Safe Distance  
The third feature is increased distance to potential obstacle during the turn, i.e., the moment of the minimal stability of a car (Figure 12).  
Due to prediction, the vehicle starts the turn earlier. This not only makes the trajectory smoother and increases turn radius but also allows avoiding potential causes of turn--road twist or unexpected obstacle. According to data from Table 7, the average distance to an obstacle increased by 35%.  
These numbers, as well as the values of the target quality function, indicate the remoteness of the results occurred by each method from the desired ones (if they were equal to zero, the car would move along the chosen trajectory without deviations caused by instability). In other words, these numbers could be interpreted as the error amount of methods. Figure 13 demonstrates distance and angle errors for all compared methods. In these terms, PPD performs better than PD and has similar changes of amplitudes with the GP-RMEP controller.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**\#Road**} & \multirow{2}{*}{**Overall**} & \multicolumn{3}{c}{**0.85 of Critical**} & \multicolumn{3}{c}{**0.9 of Critical**} & \multicolumn{3}{c}{**0.95 of Critical**} \\ \cline{3-10}  & & **Friction**\(\mu\) & **PD** & **PPD** & **GP-RMEP** & **PD** & **PPD** & **GP-RMEP** & **PD** & **PPD** & **GP-RMEP** \\ \hline
1 & 0.5 & 8.88 & 12.14 & 12.33 & 8.73 & 11.81 & 12.07 & 8.34 & 11.4 & 11.84 \\
2 & 0.3 & 8.09 & 10.81 & 11.6 & 7.93 & 10.79 & 11.38 & 7.91 & 10.74 & 11.14 \\
3 & 0.1 & 8.03 & 10.87 & 11.23 & 7.89 & 10.8 & 11.16 & 7.86 & 10.07 & 10.9 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Distance to the obstacle for each parameter combination, meter.  
Figure 11: Car trajectory on the track tuned with prediction SAF of standard PD controller for friction coefficient \(\mu\) = 0.1 with target speed equal to \(1.05V_{CR}\). The blue curve corresponds to the trajectory controlled by PPD controller, and red to the original PD controller.  
Figure 12: Distance between the car and obstacle (road turn) that caused the turn friction coefficient \(\mu\) = 0.3 with target speed equal to \(~{}0.95V_{CR}\).
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 6 Conclusions  
In this paper, we propose utilizing the data of vehicles collected from a real steel logistics park by the IIoT to optimize the pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS). The PVSP-MSS is firstly formulated as a multi-objective mixed-integer linear programming model. Then, an enhanced algorithm based on SPEA2 is proposed to solve the optimization problem with a high efficiency. In the ESPEA, a cooperative initialization strategy is proposed to provide an initial solution for scheduling optimization. Moreover, an insertion decoding method is proposed to effectively utilize the idle time of a yard. Additionally, local search technology based on critical paths is proposed to improve the solution quality. The experimental results show that compared with other algorithms, the ESPEA can achieve better scheduling results.  
This study effectively addresses the problem of cross-yard pickups in logistics scheduling in the steel industry and significantly improves the vehicle scheduling efficiency. However, this study does not consider the vehicle rescheduling problem in cases of vehicle delays and other exceptional situations. In future research, we will expand the model and develop more advanced algorithms to tackle more complex engineering scheduling problems.  
Conceptualization, J.W. and Z.X.; Formal analysis, J.W., L.X. and H.X.; Investigation, M.H. and L.X.; Methodology, J.W. and H.X.; Project administration, Z.X.; Supervision,  
Figure 11: Pickup vehicle scheduling scheme.  
Figure 10: The Pareto frontier of all algorithms for instance 5.  
Z.X.; Validation, Z.X. and H.X.; Writing--original draft, J.W. and H.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fujian Industry-University Cooperation Project under Grant 2022H6005; the National Natural Science Foundation of China under Grants 61973085, 62071071, and 62101174; and the Natural Science Foundation of Hebei Province under Grants F2021402009, F2021402005, A2020402013, and F2023402011.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The researchers received generous support from [PERSON] and [PERSON] of Santseel Minguang Co. Ltd. We sincerely appreciate their invaluable contribution in providing the necessary data for the experiments conducted in this study.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} PVSP-MS & Pickup vehicle scheduling problem with mixed steel storage \\ ESPEA & Enhanced algorithm based on SPEA2 \\ IIot & Industrial Internet of Things \\ LD & Linear dichroism \\ MILP & Mixed-integer linear programming \\ OSC & Operation sequence chain \\ YAC & Yard allocation chain \\ YWB & Yard workload balancing mechanism \\ MPT & Minimum pickup time mechanism \\ RVS & Random vehicle sequential mechanism \\ HV & Hypervolume \\ \end{tabular}
## 3 Processing and Analysis of Measurements
### Signal Separation and Spectrum Analysis
 \tag{3}\]  
Therefore, the time-frequency-amplitude distributions of the non-stationary COP and acceleration signals can be expressed as:  
\[H(\omega,\ t)=\sum A_{k}(t)\cos(\int\omega_{k}(t)dt) \tag{4}\]  
In order to clearly observe the energy distributions of measurements in the frequency domain, the marginal spectrum of the COP and acceleration signals can be formulated as:  
\[S(\omega)=\sum_{k}\int_{0}^{T}H_{k}(\omega,\ t)\ dt \tag{5}\]  
where, \(H_{k}(\omega,t)\) represents the time-frequency distribution of the \(k\)-th IMF. The signal compositions and characteristics can then be observed and analyzed within the different frequency scales.
## 1 Introduction
Online tuning based on model parameters can identify the control system parameters under dynamic working conditions. Combining with the system mathematical model, online tuning formulas can adjust and update the PI controller parameters in real time. Though the online tuning technology based on the intelligent algorithm is complicated and large amount of calculation is needed, its algorithm is relatively simple and highly targeted. Therefore, due to its powerful computing power and strong self-adjustment ability, the online tuning effect is significantly better than the online tuning technology based on model parameters. With the rapid development of computer technology and intelligent algorithms, online tuning technology based on model parameters is gradually being replaced by that based on intelligent algorithms [22]. To provide full play to the superior speed regulation characteristics and flow matching characteristics of CM electric power system, a typical positive flow system in CM is taken as the application object, the motor vector control system based on fuzzy control of the speed PI parameter online tuning is proposed. This paper is organized as follows: In Section 2, mathematical model analysis of PMSM and positive flow system is presented. Design of electric motor control strategy and regulators is introduced in Section 3. Simulation analysis and experiments verification under different conditions are shown in Sections 4 and 5, respectively. Finally, the conclusion is obtained.
## 3 SDR Technology  
SDR technology allows software modules to run on a generic hardware platform used to implement radio functions. By combining an SDR front-end with a software platform, the user can exploit SDR technology to rapidly prototype communication systems, including physical layer design, recording and playback of signals, signal intelligence, algorithm validation, and more. In the following section, we provide a brief introduction to the main aspects of SDR technology. Furthermore, IoT technologies can benefit from SDR technology. Flexible SDR-based IoT gateways can be deployed while reducing overall costs. The reason that SDRs are a good candidate to serve as a platform within the IoT is due to the usage of straightforward modulation techniques, the deployment of low-bandwidth applications and low-data-rate signals.
# Robust Statistics for GNSS Positioning under Harsh Conditions: A Useful Tool?
## 2 Robust Statistics Principles
### Robust Estimates for Regression Problems  
Consider a linear regression problem \(y_{i}=\mathbf{z}_{i}^{\top}\mathbf{x}+\eta_{i}\), with \(i=1,\ldots,n\), and \(\mathbf{x}\) the vector of unknown parameters, or in vector form, \(\mathbf{y}=\mathbf{Z}\mathbf{x}+\boldsymbol{\eta}\) with \(\mathbf{Z}\) defined with the different \(\mathbf{z}_{i}^{\top}\) in its rows. The noise vector \(\boldsymbol{\eta}\) is assumed to be independent and identical along the set of observations. We can define a vector \(\mathbf{r}=\mathbf{y}-\mathbf{Z}\mathbf{x}\) of observation residuals. The regression is generally solved applying a LS estimator (minimization of the \(\ell_{2}\)-norm of the residuals),  
\[\hat{\mathbf{x}}_{LS}=\arg\min_{\mathbf{x}}||\mathbf{y}-\mathbf{Z}\mathbf{x} ||^{2}\Rightarrow\arg\min_{\mathbf{x}}\sum_{i=1}^{n}\left(\frac{r_{i}( \mathbf{x})}{\sigma}\right)^{2}\, \tag{6}\]  
which is optimal when the Gaussian noise assumption for \(\boldsymbol{\eta}\) holds. However, it lacks robustness since a single (arbitrarily large) outlier could completely spoil the estimation. A first approach towards protecting against outlying measurements is the _least-absolute value_ (LAV) or \(\ell_{1}\), consisting on the substitution of the squared residuals as  
\[\hat{\mathbf{x}}_{\ell_{1}}=\arg\min_{\mathbf{x}}\sum_{i=1}^{n}\left|\frac{r_ {i}(\mathbf{x})}{\sigma}\right|. \tag{7}\]  
Nonetheless, the \(\ell_{1}\) method retains a sum of residuals and thus the influence of outliers is still unbounded. This problem can be generalized by considering a general loss function \(\rho(x)\) (referred to as the \(\rho\)-function), and then reformulating the regression as  
\[\hat{\mathbf{x}}=\arg\min_{\mathbf{x}}\sum_{i=1}^{n}\rho\left(\frac{r_{i}( \mathbf{x})}{\sigma}\right). \tag{8}\]  
For instance, considering a scalar variable \(x\) (which is related to the previous definitions as \(x=r_{i}(\mathbf{x})/\sigma\)), \(\rho_{\text{LS}}\left(x\right)=x^{2}\) and \(\rho_{\ell_{1}}\left(x\right)=|x|\) correspond to the aforementioned LS and LAV estimation approaches. The framework of robust statistics proposes loss functions \(\rho\left(\cdot\right)\) such that the estimates are nearly optimal when the noise follows the assumed distribution (e.g., normal) and nearly optimal when the noise departs from it. The score function (referred to as the \(\psi\)-function) is defined as the derivative of an estimator loss function \(\psi(x)=\frac{\partial\rho(x)}{\partial x}\). Several robust estimators of regression have been proposed in the literature, the most popular being: (i) M-estimate; (ii) S-estimate; and (iii) MM-estimate. In the sequel, the loss functions for robust statistics are introduced, as well as some relevant properties, for which Figure 1 provides some pictorial support.
# Synthesis, Characterization and Biological Investigation of New N-Modified Spinorphin Analogs
## 3 Materials and Methods  
### Synthesis  
All reagents and solvents were analytical or HPLC grade and were bought from Fluka or Merck and used without further purification. The protected amino acids, 1-(9-fluorenylmethyloxycarbonylamino)-cyclopentyl-1-carboxylic acid (Fmoc-Ac5c-OH), 1-(9-fluorenylmethyloxycarbonylamino)-cyclohexyl-1-carboxylic acid (Fmoc-Ac6c-OH), and Fmoc (9-fluorenylmethoxycarbonyl)-Rink Amide MBHA (4-methylbenzhydrylamine) Resin, were purchased from Iris Biotech (Germany). Rhodamine B is from Sigma-Aldrich (Ansbach, Germany). The 3-functional amino acids were embedded as follows: Tyr as N\({}^{\alpha}\)-Fmoc-Tyr(Hu)-OH, Thr as N\({}^{\alpha}\)-Fmoc-Thr(t-Bu)-OH, and Trp as N\({}^{\alpha}\)-Fmoc-Trp(Boc)-OH. The molecular mass and purity of the compounds were confirmed by high-resolution electrospray mass spectrometry (Thermo Fisher Scientific Inc., USA) and as well some spectral analyses were performed: IR (KBr with a Varian 660 FTIR spectrophotometer in the range 4400-600 cm\({}^{-1}\)), UV-Vis (Varian-Cary), SEM (SEM/FIB LYRA I XMU SEM (TESCAN), EDX detector: Quantax 200 by BRUKER), and fluorimetry (Cary Eclipse Spectrophotometry, Agilent, USA) and the detailed characteristics were previously described by us in [28].  
General Procedure for the Peptide Synthesis of Compounds (Rh-S, Rh-S5, Rh-S6)  
All N-modified peptide analogs of spinorphin were synthesized manually by the solid-phase method using Fmoc chemistry and the procedure and used reagents are given in [24]. Peptides were synthesized on Fmoc-Rink-Amide MBHA resin (loading 0.71 mmol/g resin; cross-linking 1% DVB; 100-200 mesh). Peptide chains were elongated in consecutive cycles of deprotection and coupling (see Scheme 1). The coupling reactions were performed using amino acid/TBTU/HOBt/DIPEA/resin with a molar ratio of 3/2.9/3/6/1, in a 1:1 mixture of DMF/DCM [24]. The peptides were obtained as white powders with a purity of \(>\)97% as determined by analytical HPLC. The structures were confirmed by high-resolution electrospray mass spectrometry. Peptide purity was monitored on a reversed-phase high-performance liquid chromatography (RP-HPLC), column: SymmetryShieldTM RP-18, 3.5 u, (50 \(\times\) 4.6 mm), flow: 1 mL/min, H\({}_{2}\)O (0.1% TFA)/CH\({}_{3}\)CN (0.1% TFA), gradient 0\(\rightarrow\)100% (45 min) and 100% (5 min). The crude peptides were purified by semi-preparative HPLC on column XBridgeTM Prep C18 10 um (10 \(\times\) 250 mm), flow: 5 mL/min, H\({}_{2}\)O (0.1% TFA)/CH\({}_{3}\)CN (0.1% TFA), gradient 20\(\rightarrow\)100% (50 min). All analytical data are summarized in Table 1.
## 2 Materials and Methods
### Development of the Online Platform
#### 2.1.4 Risk Management Module  
Responding to a hazard in a given traffic condition is part of risk management. It mainly teaches the strategies for safely navigating through hazardous situations, emphasizing decision-making skills in high-risk scenarios. Here, the ten questions with multiple-choice answers were displayed along with the images to visualize the situation. The feedback was also given here after every question to make the participants aware of different traffic situations and deal with them accordingly.  
Research has shown that adolescents are primarily involved in accidents near intersections and crossings [60]. Therefore, the platform mainly focused on these situations. The course was organized within Moodle by creating separate sections for each module. Additionally, to develop interactive content, including different traffic scenarios and quizzes, Adobe Captivate (version 11.8) was used. It was then incorporated via SCORM packages into Moodle. In order to access the content of the platform, participants needed to log in first. They received the login information from their school representative, who received it from the platform administrator. Once logged in, the participant could access the content of each module. Each module has ten questions, and the adolescents will receive 10 points per question if they give the correct answer and zero points if they give a wrong answer. Hence, the maximum score that adolescents can receive is 100, and the minimum score is zero. Based on their performances and for future motivation, badges were awarded (gold, silver, bronze, and completion badges).  
The footage was collected by capturing pictures via a rider's view in different traffic situations, as shown in Figure 1. As mentioned above, the images were then used to make the modules using Adobe Captivate. The red box (Figure 1b) shows an example used for risk detection, where respondents are required to choose (from three options) whether the situation depicted poses a risk for riders. While, the yellow arrow (Figure 1a) is used in the knowledge module to assess the respondents' understanding of traffic signs. In total, 40 questions were filled by adolescents at home instead of at school due to COVID-19 restrictions.
## 2 Materials and Methods
### High-Pressure Liquid Chromatography and Quadrupole Time-of-Flight Spectrometry  
The identification of major compounds in 1 mg/mL _A. tegmentosum_ was analyzed using high-pressure liquid chromatography (HPLC) with diode array detection. For the chromatographic separation of _A. tegmentosum_, we used an Infinity 1260 system (Agilent Technologies, Santa Clara, CA, USA) consisting of binary pumps, an on-line degasser, an autosampler, a diode array detector (DAD), and a column compartment equipped with a reverse-phase HPLC column (Acclaim 120 C18; 4.6 \(\times\) 250 mm, 5 \(\upmu\)m; Thermo, MA, USA). Chemstation software (Agilent Technologies) was used for data acquisition and processing. Mobile phases A and B were 2% acetic acid in distilled water and 0.1% formic acid in acetonitrile, respectively. The detailed running conditions of the mobile phases are listed in Table 2. The flow rate, injection volume, and column oven temperature were set to 1 mL/min, 10 \(\upmu\)L, and 35 \({}^{\circ}\)C, respectively. The DAD detector was operated at a wavelength of 320 nm to monitor the major compounds. Quadrupole time-of-flight mass spectrometry (Q-TOF-MS) was performed using an iFunnel Q-TOF LC/MS 6550 (Agilent Technologies), and the detailed analytical conditions for the experiment are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Time** & **Flow Rate** & **MP 1 A [2\% Acetic** & **MP B [0.1\% Formic** & **Comment** \\
**(min)** & **(\(\upmu\)L/min)** & **Acid in DW 2 (\%/v)]** & **Acid in ACN 3]** & **Comment** \\ \hline
0–5 & 1000 & 100\% & 0\% & Isocratic flow \\
5–30 & 1000 & 100–75\% & 0–25\% & Gradient flow \\
30–40 & 1000 & 75–30\% & 25–70\% & Gradient flow \\
40–41 & 1000 & 30–0\% & 70–100\% & Gradient flow \\
41–50 & 1000 & 0\% & 100\% & Isocratic flow \\
50–51 & 1000 & 0–100\% & 100–0\% & Gradient flow \\
51–60 & 1000 & 100\% & 0\% & Isocratic flow \\ \hline \end{tabular}
\end{table}
Table 2: Running conditions of mobile phases.