esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
###### Abstract  
This study delves into the pivotal role of shipper satisfaction in cultivating efficacious cooperation between dry ports and seaports. The research endeavors herein present a comprehensive indicator system tailored to evaluate the efficacy of the cooperation of the sea ports and dry ports. This system encompasses 14 key indicators, thoughtfully categorized into four dimensions, offering a holistic perspective on the multifaceted factors that underscore the synergy between dry ports and seaports. The establishment of a performance evaluation model for this collaborative nexus draws upon the support vector machine (SVM) technique, a choice substantiated by its suitability for the available dataset and its relevance within the research context. Leveraging the indicator data as a training dataset, the SVM approach culminates in the construction of a cooperative matrix, ultimately facilitating the discernment of performance rankings within dry port and seaport collaboration. This research methodology not only yields valuable insights into the evaluation of this distinct collaboration but also presents a versatile framework with potential application for addressing various challenges encountered within the cooperative milieu of dry ports and seaports.  
dry ports; \"two-port\" collaboration; evaluation model; support vector machine (SVM) +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Optimization Design Based on Radial Basis Interpolation of Surrogate Models
#### 2.2.3 Optimizing the Design Process  
The sum of the root mean square deviations corresponding to the n Mach number patterns is used as the objective function for optimization. And the constraints are m-section flexure plate length limits.  
\[G_{obj}=\sum_{1}^{n}RMSE, \tag{22}\]  
\[L_{max}>X_{m}>L_{min}, \tag{23}\]  
where \(L_{min}\) is the minimum length of the given flexible ledge, and \(L_{max}\) is the maximum length of the given flexible ledge.  
The optimization process of the multi-point flexible wall nozzle structure is illustrated in Figure 3. This process comprises four principal stages: (1) variable determination and sampling point selection, (2) analysis of the corresponding profile overlap of sampling points, (3) establishment of the surrogate model, and (4) identification of the optimal result through the use of the surrogate model.  
(1) Determination of Variables and the Selection of Sampling Points  
To ensure precise control of the flexible pneumatic structure's shape, we must determine each structural parameter and select support point positions as design variables. The value range and sampling space of these design variables must align with the actual structure. Initially, we determined the number of sampling points and used the Latin hypercube sampling method to sample the space. We then employed the random sampling function rand() to select the specified number of test points.  
(2) Sampling Points Corresponding to the Profile Overlap Analysis  
We defined the initial driving quantity as the y-coordinate, where the pneumatic profile's arc length equals the spacing between driving points, and we then determined the solution range for each driving point.  
Using the nozzle flexible plate's large deflection deformation analytical solution method, we obtained an elastic curve solution. By comparing this with the pneumatic theoretical contour, we calculated the root mean square error of the profile--our optimization objective. We performed this optimization process on N theoretical profiles, calculated their root mean square errors, and then aggregated these errors to obtain the final sampling point results.  
(3) Establishment of the Surrogate Model  
We verified the surrogate model's accuracy using the shape function and checkpoints. If specifications were not met, we increased the number of sampling points.  
(4) Optimizing the Surrogate Model  
We used the Python Optimization Toolbox's \"Optimization Tool\" to find the optimal solution for the surrogate model. Specifically, we employed the genetic algorithm (GA), which offers efficient, parallel, and global search capabilities. The GA automatically acquires and accumulates search space knowledge while adaptively controlling the search process to find the optimal solution, effectively avoiding the local optima during optimization.
# Camera-Based Continuous Heart and Respiration Rate Monitoring in the ICU
## 2 Materials and Methods
### Reference Vital Signs  
In addition to the video recordings, contact-based vital signs measurements are also collected. For the purpose of this study, a single-lead ECG was collected from the patient monitor (Intellivue MP70/MX750, Philips Medical, Best, The Netherlands) as the gold standard for heart rate. Furthermore, airway flow, pressure, and ECG thoracic impedance were collected as the golden standard for respiration rate. For the first 24 patients, contact sensor data were collected using a second laptop connected to the patient monitor using the iXTrend software package version 2.1 (ixitos GmbH, Aken, Germany). For the remaining patients, Data Warehouse Connect version 4.0 (Philips Medizin Systeme Boblingen GmbH, Boblingen, Germany) was used to collect reference data from the patient monitor. The collection software was changed during the duration of the study due to the replacement of patient monitors in the ICU, and these new patient monitors were not compatible with the iXTrend software.
###### Abstract  
In order to improve the performance of the diagonal loading-based minimum variance distortionless response (MVDR) beamformer, a full loading-based MVDR beamforming method is proposed in this paper. Different from the conventional diagonal loading methods, the proposed method combines the backward correction of the steering vector of the target source and the reconstruction of the covariance matrix. Firstly, based on the linear combination, an appropriate full loading matrix was constructed to correct the steering vector of the target source backward. Secondly, based on the spatial sparsity of the sound sources, an appropriate loading matrix was constructed to further suppress interferences. Thirdly, the spatial response power was utilized to derive a more accurate direction of arrival (DOA) of the target source, which is helpful for obtaining a more accurate steering vector of the target source and a more effective covariance matrix iteratively. The simulation results show that the proposed method can effectively suppress interferences and noise.  
speech enhancement; beamforming; minimum variance distortionless response (MVDR); backward calibration; interference suppression Article  
[PERSON], [PERSON] A Full Loading-Based MVDR Beamforming Method by Backward Correction of the Steering Vector and Backward Correction of the Covariance Matrix  
1
# Settlement Prediction of Footings Using \(V_{s}\)
## 5 Conclusions  
A new settlement prediction method was developed and discussed in this study. Complete load-settlement behavior of footing could be predicted using \(V_{S}\)-based [PERSON]'s framework considering soil non-linearity. The main findings from this study are summarized as follows:  
1. For the reliable stiffness estimation of soil layers, the modulus variation by confinement stress and strain level increment was reflected in the developed method based on the \(V_{S}\). The vertical stress increment with depth can be determined from the \(I_{z}\) calculated from the elasticity theory. The settlements of each sublayer are calculated based on the modulus and stress determined at each depth, and then added to give total settlements.
2. The prediction using the \(I_{z}\) based on elasticity theory showed reasonable agreement with the centrifuge test results for 2 nd loading stages due to an enlarged elastic limit. The modulus profile mobilized in the predictions showed that the modulus variation by confining pressure and strain level is properly modeled in the developed settlement prediction method considering the shape of \(I_{z}\).
3. The centrifuge results for the 1 st loading (NC condition) showed very soft deformable behavior compared with the two predictions regardless of the \(L/B\) ratio. It could be concluded that the particle rearrangement such as interparticle slip and rotation governs the deformation behavior rather than the compression of the soil mass related to the soil modulus. To take into account the normally-consolidated (NC) stress state governed by relative particle movements, the empirical coefficient needs to be adopted in the developed method.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (grant number 2014R1A6A3A04056405)[PERSON] Also[PERSON] support from Basic Research Project of Korea Institute of Geoscience and Mineral Resources (KIGAM) is acknowledged.  
[PERSON] organized the paperwork, performed the centrifuge tests and completed the development of the new settlement prediction method; [PERSON] initially suggested the idea, and helped with the centrifuge tests; [PERSON] processed the data collected from the centrifuge tests and interpreted the data; [PERSON] supervised the research activities and provided guidance and suggestions for the proposed method; all authors contributed to the writing of each part.  
The authors declare no conflict of interest.
## 3 Results
### Ecosystem Service Function and Trade-Offs  
Carbon storage, GPP, water conservation, and crop yield have increased generally (Figures 7 and S4). The average annual carbon storage was 1669 kg C ha year\({}^{-1}\) with an annual growth rate of 32 kg C ha year\({}^{-1}\) from 1988 to 2019. The inter-annual variation of total carbon stocks (TCS) of different land covers was significantly different (Figure 7a). Forests showed the highest TCS, increasing from 1.27 million tons (1988) to nearly 3 million tons (2019). The wetland carbon stocks increased from about 0.64 million tons (1990) to nearly 0.68 million tons (2010), and then rapidly declined to lower than 0.64 million tons (2019). The carbon stocks of cropland declined from about 0.0337 million tons (1990) to 0.032 million tons (2019).  
The average annual GPP was 2.81 g C m\({}^{-2}\) day\({}^{-1}\), and the annual growth rate was 0.0366 g C m\({}^{-2}\) year\({}^{-1}\) (Figure 7b). Cropland contained the highest total GPP, and its total GPP increased from 27,000 tons of carbon per day (2000) to 32,000 tons of carbon per day (2008). In general, the total GPP of the forest increased while that of shrubland and cropland decreased over time.  
From 2000 to 2015, the average annual water conservation capacity was 9400 mm, with an annual change rate of 3.27 mm year\({}^{-1}\). The water conservation capacity of different land covers was different with an overall growth trend (Figure 7c). Cropland showed the highest capacity of 100 billion tons/year, followed by forest (50 billion tons/year), waterbody (40 billion tons/year), wetland (30 billion tons/year), impervious surface (15 billion tons/year), shrubland (3 billion tons/year), and bare land (3 billion tons/year). For all land covers except grassland and bare land, the annual capacity increased from 2000 to 2003, decreased in 2005, and then continued to rise until 2015.  
The crop yield changed dramatically in the study period, with an annual mean crop yield of about 7.8 million tons (Figure 7d). There were three obvious time points of the sharp decline in crop yield: 2001-2003, 2008-2009, and 2018-2019.  
The trade-offs and synergies (correlations) among different ES varied considerably (Figure 8). Overall, water conservation and crop yield showed weak synergies with carbon storage, and there was a weak synergistic relationship between water conservation and crop yield. The relationships between GPP and water conservation varied geographically,  
Figure 6: The spatial distribution of annual change COHESION of different landcovers in the study area from 1988 to 2019.  
synergistic in the central and trade-off in the surrounding regions (Figure 8f). GPP was significantly synergistic with carbon storage and crop yield in the most regions (Figure 8b,c).
## 4 Conclusions  
Recently, a new stabilization technology developed to treat MSW incineration FA has been proposed. It is based on the use of bottom ash, produced at the same incinerator plant. This new process is considered a zero-waste approach, because it involves the use of waste materials produced on the same plant. As a consequence, the landfilling of these wastes can be neglected, following the recommended material use close loop by utilizing 'waste' for suitable and sustainable applications.  
This work demonstrates the sustainability of the proposed technology by using a new simplified approach and allowing to compare the obtained inert material with natural resources.  
In addition, the effects of the new produced filler on the structure and properties of obtained composites based on the use of a resin are assessed by means of structural and mechanical analyses.  
Figure 9: Embodied energy, primary productions (MJ/kg) vs. price (EUR/kg) of various materials and composites.  
For comparison purposes, also calcite (CaCO\({}_{3}\)) was used as a standard filler, to formulate composites that were produced and analyzed under the same conditions.  
Regarding mechanical properties of the new stabilized sample- composite, the comparison with CaCO\({}_{3}\) based formulation shows comparable values in terms of flexural modulus and flexural strength that raise by increasing the filler content around 30% wt, independently of filler type, stabilized sample or calcium carbonate, respectively. The amount of CaCO\({}_{3}\) or stabilized sample in the composites is the main factor causing a reduction in the mechanical properties mainly due to the crowding effect. EDX maps highlighted a more homogeneous distribution of Ca in CaCO\({}_{3}\)-composites with respect to stabilized sample-composites, due to the lower particle dimension in calcite powder.  
In summary, it is possible to conclude that the new proposed filler, realized by using stabilized MSWI fly ash, can be used to produce sustainable composites, allowing to preserve natural resources, as for example of calcite, demonstrating similar mechanical and structural performance and with great environmental and economic benefits.  
If Europe-wide implementation of waste recovery is to be part of the circular economy strategy, it is expected that one of the political priorities should be the implementation of a legal framework that can support new technologies and materials availability for the recycling sectors.
## 3 Methodology
### Threat Model
```
1:procedureFSP(\(S_{Train}\),\(P_{S}\),\(S_{P}\))
2:T = random select (\(S_{Train}\), \(P_{S}\));
3:m = size(T)
4:for\(i\gets 1,m\)do
5:\(xv_{i}=S_{Train}(T_{i})\)
6:d = size(xv)
7:for\(j\gets 1,d\)do
8:\(xp_{i,j}\)=(1-\(S_{P}\)) \(\times xv_{i,j}\)+(\(S_{P}\times R_{ND}\)(0,1)));
9:endfor
10:endfor
11:return\(S_{Pois}\)
12:endprocedure
```  
**Algorithm 2** Feature Scrambling Poisoning (FSP) algorithm The third attack is a variation of the second attack where the attacker has also knowledge of the most likely value of the feature for each sample. This is a more sophisticated attack than the second attack and it requires that the attacker have knowledge (or the acquisition) of a representative (for each class) number of samples for each sample. In this study, this attack is emulated by choosing the 100 samples returned by the SDO algorithm for each label with the lowest score. These templates are called \(LOST\) for the label = 0 and \(NLOST\) for the label = 1. Then, a similar attack to the second attack is implemented but the values of the features of the generic sample are replaced with a weighted sum based on the values of \(LOST\) and \(NLOST\) as described in Algorithm 3. ```
1:procedureTSP(\(S_{Train},P_{S},S_{P}\))
2:T = random select (\(S_{Train},P_{S}\));
3:m = size(T)
4:for\(i\gets 1,m\)do
5:y = label(\(T_{i}\))
6:if\(y==0\)then
7:\(x_{i}=(1-S_{P})\times xv_{i,j}+(S_{P}\times NLOST)\);
8:else
9:\(x_{i}=(1-S_{P})\times xv_{i,j}+(S_{P}\times LOST)\);
10:endif
11:endfor
12:return\(S_{Pois}\)
13:endprocedure
```  
**Algorithm 3** Targeted Feature Scrambling Poisoning (TSP) algorithm
# Modeling and Experimental Study of the Dual Cylinder Fluid Inerter
## 4 Comparative Study with Single-Cylinder Fluid Inerter  
According to the literature [16], the mechanical model of a single-cylinder fluid inerter is constructed. The diameter of the cylinder, the diameter of the piston rod, and the length and diameter of the pipeline are the same as those of the dual-cylinder fluid inerter. The specific parameters are shown in Table 3.  
Figure 4: The curve of inertia force and acceleration.  
Figure 5: The comparison curve between theoretical model and pipe module in Simulink.  
Inertance of the single-cylinder fluid inerter is 828 kg, and inertance of the dual-cylinder fluid inerter is 1529 kg. When \(L_{4}+L_{11}=10\), that is to say, the total length of the pipeline is 10 m, inertance is in a linear relationship with \(L_{4}\), as shown in Figure 6. When \(L_{4}=0\), inertance is the same as in a single-cylinder fluid inerter, and inertance increases linearly with an increase in \(L_{4}\). Under the condition that the total pipe length is the same, different inertance values can be obtained by changing the lengths of \(L_{4}\) and \(L_{11}\).  
In the single-cylinder fluid inerter, as the secondary flow on the section of the pipe increases the damping force when the fluid flows in the spiral pipe, the inertia force is relatively small [16]. When sinusoidal speed signals shown in Table 2 are used, the change curves of inertia and damping forces over time are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Structure Size** \\ \hline
**Diameter of cylinder (mm)** & **80** \\
**Diameter of piston rod (mm)** & **50** \\
**Diameter of pipe (mm)** & **12** \\
**Length of pipeline (mm)** & **10** \\ \hline \hline  & **Parameters of the Fluid Medium** \\ \hline
**Fluid type** & **Water** \\
**Density \(\rho\) (kg/m\({}^{3}\))** & **1000** \\
**Dynamic viscosity \(\mu\) (cst)** & **1** \\ \hline  & **Result** \\ \hline
**Inertance (kg)** & **828** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Table of structure and fluid parameters of the dual cylinder fluid inerter.  
Figure 6: The relationship between inertance and \(L_{4}\).  
Figure 7: The curve of damping and inertia force over time of a single-cylinder fluid inerter.  
As can be seen from Figures 8 and 9, the maximum damping force of a dual-cylinder fluid inerter is 40.32% (5451/13,519 = 0.4032) of that in a single-cylinder fluid inerter, and the inertia force is 180.96% (2823/1560 = 1.8096).
# Temperature Impacts on Endurance and Read Disturbs in Charge-Trap 3D NAND Flash Memories
## 3 Evaluation Setup  
Raw NAND chips were characterized using an FPGA-based raw NAND chip tester with eight parallel sockets and high-speed PCIE interfaces with a maximum data transfer speed up to 200 MHz. The tester was specially designed with a capability to withstand wide temperature operations from \(-40\) to \(100\)\({}^{\circ}\)C, and a customized software was used as the interface to carry out data program/erase/read scripts and detailed data analysis. Moreover, a high-low temperature test chamber with precise controllability to the temperature and humidity was used to perform cross-temperature tests. In our experiments, we chose the 3D CT TLC NAND flash memory chip with 64 stacking storage layers, 5912 valid blocks, with block containing 768 logical pages with 18,336 bytes per page [19]. As shown in Algorithm 1, experiment processes were divided into the following:  
* P/E cycling: With combined \"Block Program\" and \"Block Erase\" scripts, random data were programmed to the NAND chip and then erased alternately. Here, the generated random data were different in each P/E cycle to ensure the randomness of the characterizations for fair analysis;
* Read Disturb: After data programming, repeated data reading operations were transferred to the tester controller to perform block data reading. The data were not dumped to the controller until we performed a \"Block Read Dump\" script;
* Data Analysis: The programmed data was read out to the customized software using the \"Block Read Dump\" operation, and data in the TLC NAND chips were downloaded to a text file. By comparing the programmed data and the read-out data, error bit information could be analyzed.
# Indoor Carrier Phase Positioning Technology Based on OFDM System
## 5 Conclusions  
The main research direction of this paper is to apply carrier phase technology in OFDM systems to improve ranging and positioning accuracy. Compared with single-point positioning using only TOA measurement, carrier phase information is more accurate than TOA measurement, and it is a possible choice for indoor high-precision positioning.  
Figure 10: The CDF of horizontal localization error with differfent number of BSs.  
Figure 9: Layout of Indoor - Mixed office scenario.  
This paper intends to solve two problems of indoor carrier phase positioning: 1. Phase measurements in a multipath environment. 2. Fast and precise integer ambiguity resolution in real-time positioning scenarios. First, this paper analyzed the effect of multipath propagation on phase measurement in detail, and proposed a correlation profile-based carrier phase measurement method. Second, this paper presents an EKF algorithm to estimate the integer ambiguity by the SD carrier and TDOA measurements. In addition to the integer ambiguity estimation, the algorithm also considers the effect brought by NLOS error. Experiments show that the algorithm proposed in this paper can quickly find the integer ambiguity and virtually eliminate the NLOS error, thus improving the positioning accuracy.  
Methodology, Z.Z. and X.Z.; software, Z.Z. and X.Z.; validation, Z.Z. and X.Z.; investigation, Z.Z. and S.K.; resources, Z.Z.; data curation, Z.Z. and X.Z.; writing--original draft preparation, Z.Z. and X.Z.; writing--review and editing, S.K.; visualization, Z.Z.; supervision, S.K.; project administration, S.K.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
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 reasons.  
We appreciate the guidance of [PERSON] and [PERSON], State Key Laboratory of Wireless Mobile Communications, China Academy of Telecommunications Technology. The authors appreciate the support from Datang Mobile Communications Equipment Co., Ltd. for providing the channel modeling for one of the experiments.  
The authors declare no conflict of interest.