esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling and Analysis of Electric Actuator Control System
### Dead-Zone Non-Linearity Influence Analysis
#### 2.3.3 Command Angle Affects  
Set the dead-zone voltage to 3 V, and the load to 15 N-m, when the step setting angle is 2\({}^{\circ}\), 6\({}^{\circ}\), 15\({}^{\circ}\), the responses simulated under PID control and ADRC at different setting angles. The curves are shown in Figures 12-15.  
Figure 11: Estimation of equivalent disturbance by ADRC under different load disturbances.  
Figure 12: Responses when the set value is 2\({}^{\circ}\).  
Figure 14: Responses when the set value is 15\({}^{\circ}\).  
Figure 13: Responses when the set value is 6\({}^{\circ}\).  
It can be seen from the results that under different angles, the PID controller has a faster response when the set deflection angle is small, but the overshoot is large and the adjustment time is long. As the set angle increases, the system response time becomes longer and the overshoot is gradually reduced. The ADRC controller has an excellent control effect at different command values, and the response times are basically the same. As can be seen from Figure 15, the ESO can estimate the total disturbance of the system in different angle commands. The input command values are the largest source of external disturbance for the system.  
Based on the above analysis, the dead-zone non-linearity has a great influence on the control performance of the actuator. ADRC can compensate the dead-zone non-linearity in a large range, and when the load disturbance and the command value vary within a wide range, it still has better control effects. However, when the PID control is applied, as the dead-zone changes and the external set value changes widely, the control robustness is poor. When the load is small, the limit cycle oscillation will occur, so the existence of non-linear dynamic characteristics of actuators may destroy the stability of the attitude control system. Therefore, in the following missile attitude control, actuator as the inner closed loop, ADRC is adopted to suppress the influence of dead-zone non-linearity on the performance of the actuator.
## 3 Experimental Application  
### Experimental Setup and Initial Testing  
The pre-tuning machine was deployed to validate its functionality and performance using copper test cavities as substitutes for superconducting cavities. The experimental setup, as illustrated in Figure 8, consisted of a network analyzer, a bead-pull system, and a tuning platform. The initial phase of the experiment focused on setting up the system and addressing a list of technical challenges below:  
Figure 8: Experimental setup.  
1. Network analyzer malfunction: during initial testing, the malfunction of the portable network analyzer failed to scan the field distribution.  
2. Mechanical adjustments: Installation of clamps on the copper cavity revealed mechanical inconsistencies that necessitated modification to the clamps. Only two pairs of clamps could be installed initially, while the rest required adjustments to fit the cavity's uneven surface.  
3. Bead-pull system optimization: The initial bead-pull system used a small-diameter bead, which caused issues with smooth movement. For bead selection, the frequency shift must exceed the test system's frequency stability requirements while remaining within the perturbation regime; beads with excessively large or small volumes are unsuitable. Ultimately, a 3 mm diameter aluminum bead was chosen. Furthermore, the line must be thin, non-elastic, and non-metallic; the diameter should also be minimized to avoid perturbing the cavity's RF characteristics. This was resolved by replacing the line with a fishing line, which improved the system's reliability and ensured consistent measurements, as shown in Figure 9.
# Quantitative Assessment of Upper- Limb Volume: Implications for Lymphedema Rehabilitation?
## 4 Methods for Volumetric Assessment
### Centimetric Method  
The centimetric measurement of upper-limb volume is the most common and simple tool in assessing and monitoring lymphedema [7]. The centimetric method typically calculates limb volume using geometric formulas based on the shape of the limb. In particular, the truncated cone formula assumes that the limb shape can be approximated as a series of connected cones of varying diameters along the limb's length [31, 32].  
One significant advantage of the centimetric method is its simplicity and accessibility [41, 42]. Additionally, the centimetric method allows for repeated measurements, enabling the accurate tracking of lymphedema progression and response to treatment interventions over time [10].  
Compared to other tools used to assess upper-limb volume in lymphedema, such as water displacement and geometry, the centimetric method offers ease of use and immediate availability [7]. However, several limitations might characterize the centimetric method. While it provides a quantitative assessment of volume changes, it does not account for changes in tissue composition. In lymphedema, alterations in tissue quality, such as fibrosis or adipose deposition, can contribute to limb enlargement. Therefore, the centimetric method may not capture these specific tissue changes, limiting its ability to provide a comprehensive evaluation of lymphedema [7, 43]. In addition, quantitative data about the limb volume result from a formula approximating limb morphology and might result in potential systematic errors [7, 43].  
In conclusion, while the centimetric measurement of upper-limb volume offers simplicity, accessibility, and the ability to track changes over time, it does have limitations in capturing precise limb volume and tissue composition changes. Thus, the optimal choice of assessment tool should consider these limitations and should be based on the specific clinical or research requirements and the availability of resources in each setting.
## 1 Introduction  
With the recent advancement in deep learning, a variety of models based on the convolutional neural network (CNN, see Figure 1) have been introduced in face recognition, most notably DeepFace [1], DeepID [2], FaceNet [3], and the VGG model [4]. These models apply convolution and pooling repeatedly to extract local and global features of input image. In order to enhance the classification ability, a set of fully connected layers are applied to feature maps from the final convolutional layer. In a typical CNN, \(n\) feature maps are extracted from the input image using \(n\) trained convolutional filters which are then transferred to the next layer. The size of the extracted feature maps is reduced through a pooling layer, and \(m\) feature maps (derived from \(m\) trained convolutional filters) are generated from the initially-obtained \(n\) feature maps through the current convolutional layer. Typically, the number of feature maps outputted by the shallower-side convolutional layers are limited by available memory and computational power, inevitably using only a subset of useful patterns for face recognition. This can also be interpreted as excluding some potentially useful patterns, which we define as _untrained_ patterns in this paper, thereby yielding suboptimal matching performance. Devising a way to reintroduce these _untrained_ patterns into the model is the main motivation of this work.  
In surveillance applications, face recognition methods can be applied to identify a target person. Typically, images involved in these applications are acquired in an unconstrained environment with potentially large variations in pose, illumination, expression, resolution and image quality. Recent face recognition methods have achieved robustness to variations in pose, illumination, and expression, but issues caused by low image resolution and quality still persist, requiring an expensive data augmentation step to achieve advertised performance.  
In this paper, we propose PSI-CNN, a pyramid-based scale-invariant convolutional neural network model, which utilizes additionally extracted _untrained_ patterns to maintain high matching accuracy in unconstrained environments. Our contributions are as follows:  
1. We present a comprehensive review of the literature which builds foundation to this work.
2. Our proposed PSI-CNN model utilizes additionally extracted feature maps from various image resolutions, achieving robustness to scale changes in input images, potentially enabling its application in unconstrained environments with variable z-distance values.
3. We provide an extensive analysis of the recognition performance achieved by models employing the PSI-CNN architecture. This is achieved by not only utilizing a publicly available database but also creating our own dataset collected under unconstrained environmental conditions with variable facial-region size.  
The structure of this paper is organized as follows. After reviewing relevant literature in Section 2, our proposed PSI-CNN model is illustrated in Section 3. Experimental results and discussions are included in Section 4, followed by conclusions in Section 5.
## 3 Research Methods
### Numerical Simulation Modeling  
#### 3.2.1 Model Assumptions  
To investigate the reinforcement effect of diaphragm walls in shield tunnels under flower tube grouting conditions and to enhance computational efficiency through model simplification, this study adopts the following assumptions:  
1. The shield tunnel lining is idealized as an equivalent homogeneous circular structure, disregarding internal structural details.
2. The seepage effects of pore water are neglected, focusing solely on the mechanical behavior of the soil.  
Figure 4: Schematic diagram of contact network column monitoring.  
Figure 3: Distribution of surface railroads and placement of monitoring points in the simulated interval section.  
3 Geological conditions within the study area are assumed to be homogeneously distributed but stratified according to actual geological data, with the physical and mechanical parameters of each soil layer determined by both field and laboratory tests.
4 The physical-mechanical properties of grouting materials and soil are treated as constant values, without considering their time-dependent characteristics.
5 The model boundary is sufficiently distant from the tunnel to minimize boundary effects on the calculation results.
6 The initial stress field is generated exclusively by the self-weight of the soil, excluding other external forces.
## 4 Discussion
### Possible Uncertainty Sources and Future Work  
It should be acknowledged that this paper may entail some uncertainty. This study has utilized long-time series of NDVI, GPP, T\({}_{\rm min}\), and T\({}_{\rm max}\) data, and it has employed three attribution analysis methods to explore the impact of air temperature factors on vegetation growth. However, variations exist among statistical methods regarding the exploration of this relationship. Moreover, statistical analyses can have uncertainties, as correlation does not necessarily imply causation [66]. Vegetation growth may also display nonlinear responses to environmental factors, and interactions among various factors could also exist. Moreover, statistical methods would presume a temperature-dominated influence on plant growth while potentially neglecting plant feedback on temperature [67]. Therefore, future research should focus on investigating the non-linear effects of various climatic factors on vegetation growth. Utilizing other approaches, such as physical process models, machine learning models, or hybrid models, will enable the estimation of the non-linear contributions of different driving factors and further validation of the results of statistical analysis. This would provide a more comprehensive and accurate understanding of the complex relationships among air temperature factors that impact vegetation growth. The dominant function of T\({}_{\rm min}\) warming implies that the effect of T\({}_{\rm min}\) on the TP may persist and, thus, may be more significant than that of T\({}_{\rm max}\) in the future. Consequently, exploring the mechanisms behind the asymmetric effects of minimum and maximum temperature on vegetation growth should be a focus of future research. Other factors, such as changes in cloud cover, solar radiation, and vegetation growth activity, may also contribute to this decrease in diurnal temperature range, and these factors also warrant further investigation in the future. Moreover, in addition to temperature, precipitation, wind speed, humidity, and snowmelt also have an impact on vegetation dynamics [21; 68], although the mechanisms underlying the joint influence of air temperature and these other factors on vegetation growth are not yet fully understood. Given the spatial heterogeneity of climatic conditions, it is crucial to conduct comprehensive attribution analyses that link spatiotemporal changes in vegetation growth on the TP with multiple climatic variables. Such analyses are essential in order to fully capture the complexity of plant growth in the region and to facilitate better predictions for the future.
# _electronics_
###### Abstract  
To solve the transmission correlation issue arising from traditional named data networking (NDN) caching during segmenting contents, where users must obtain data blocks related to the requested file blocks to recover the source data, in this paper, we propose an NDN-based coded caching strategy for low Earth orbit (LEO) satellite networks, using coding operations to remove the transmission correlation of data. To achieve efficient content distribution, the satellite node's coded package is strategically placed with the objectives of minimizing the backhaul link load and content acquisition latency. The optimization problem is solved by using a multi-colomy ant colony algorithm, enabling fast content retrieval. We designed a cluster cooperative distribution mechanism to simplify the satellite network's management and reduce the load of satellite links for content distribution when covered by multiple satellite nodes. Finally, we compare the multi-objective coded caching (CCMO) introduced in this paper with the most popular (MP) strategy and the random-based coded caching (CCR) strategy; the average cache hit ratio, backhaul link load, and content acquisition delay are compared and analyzed. The results show that CCMO performs better.  
named data networking; low Earth orbit satellite networks; coded caching strategy; content distribution; multi-colony ant colony algorithm +
Footnote †: journal: Electronics Electronics  
1
Footnote 1: These authors contributed equally to this work.
# LiDAR as a Geometric Prior: Enhancing Camera Pose Tracking Through High-Fidelity View Synthesis
## 3 Online Rendering and Tracking
### Formal Description  
#### 3.2.1 Point-Based RGBD Synthetic Rendering  
This process, which we refer to as point-based rendering, generates a synthetic image by directly projecting points from the 3D cloud. Let the camera pose at time \(t\) be represented by a rigid body transformation \(\mathbf{T}_{t}=[\mathbf{R}_{i}|\mathbf{t}_{t}]\in SE(3)\), where \(\mathbf{R}_{t}\in SO(3)\) is the rotation matrix and \(\mathbf{t}_{t}\in\mathbb{R}^{3}\) is the translation vector that transforms points from the world frame \(\mathcal{F}_{W}\) to the camera frame \(\mathcal{F}_{C}\).  
The projection of a 3D point \(\mathbf{P}_{i}=(\mathbf{X}_{i},\mathbf{C}_{i})\) from the point cloud \(\mathcal{P}\) onto the image plane is given by the pinhole camera model:  
\[\mathbf{p}_{i}^{s}=\mathbf{K}[\mathbf{R}_{t}|\mathbf{t}_{t}]\mathbf{X}_{i} \tag{1}\]  
where \(\mathbf{p}_{i}^{s}=[u_{i}^{s},v_{i}^{s},1]^{T}\) are the homogeneous coordinates of the projected point in the synthetic image \(I^{s}\), and \(\mathbf{K}\) is the camera intrinsic matrix:  
\[\mathbf{K}=\begin{pmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{pmatrix} \tag{2}\]  
Here, \((f_{x},f_{y})\) are the focal lengths and \((c_{x},c_{y})\) are the principal point coordinates.  
By projecting every point \(\mathbf{P}_{i}\in\mathcal{P}\) onto the image plane, we generate a synthetic RGB image \(I^{s}\) and a corresponding depth map \(D^{s}\). The color of a pixel \((u,v)\) in \(I^{s}\) is determined by the color \(\mathbf{C}_{i}\) of the projected point \(\mathbf{P}_{i}\), and its depth value in \(D^{\mathrm{s}}\) is the z-component of the point in the camera coordinate system. A z-buffer is employed to handle occlusions.
## 2 Materials and Methods
### Microbiological Analyses  
Frozen SHGC samples (10-25 g) were aseptically prepared and homogenized in 90 mL of 4-strength Ringer's solution (Biokar Diagnostics, Pantin, France) using a stomacher apparatus (BagMixer(r) 400 P, Interscience, Saint-Nom-la-Breteche, France) for 90 s at high speed. Serial decimal dilutions were prepared, and microbial populations were enumerated using either the \"spread plate method\" (100 \(\upmu\)L) or the \"pour plate method\" (1.0 mL) in 90 mm Petri dishes (Frailbo, Maia, Portugal). The following microbial groups were analyzed:  
* Total Aerobic Mesophilic Bacteria (AMB): Plate Count Agar (PCA; Biokar Diagnostics, Pantin, France) (30 \({}^{\circ}\)C, 72 h).
* Lactic Acid Bacteria (LAB): De Man, Rogosa & Sharpe (MRS) agar, acidified to pH 5.4 \(\pm\) 0.1 with acetic acid (Sigma-Aldrich(r), Darmstadt, Germany) (30 \({}^{\circ}\)C, 72 h).
* Marine Bacteria: Marine agar (Condalab, Madrid, Spain) (20-25 \({}^{\circ}\)C, 72 h).
* _Enterococcus_ spp.: Compass(r) _Enterococcus_ agar (Biokar Diagnostics, Pantin, France) (44 \({}^{\circ}\)C, 24 h).
* Yeasts and Molds: Chloramphenicol Glucose Agar (CGA; Biokar Diagnostics, Pantin, France) (25 \({}^{\circ}\)C, 5 days).
* Coagulase-Positive Staphylococcus (_Staphylococcus aureus_): Baird-Parker RPF agar (BP; Biokar Diagnostics, Pantin, France) (37 \({}^{\circ}\)C, 48 h).
* Total Coliforms (TC) and _Escherichia coli_: Compass(r) ECC agar (Biokar Diagnostics, Pantin, France) (37 \({}^{\circ}\)C and 44 \({}^{\circ}\)C, 24 h, respectively).
* _Listeria monocytogenes_: Half-Fraser broth, Fraser broth, and Palcam agar (Biokar Diagnostics, Pantin, France) (37 \({}^{\circ}\)C, 5 days).
* _Salmonella_ spp.: Buffered Peptone Water (BPW), Rappaport-Vassiliadis Soja (RVS) broth, Muller-Kauffmann Tetrathionate-Novobiocin (MKTTN) broth, Xylose Lysine Desoxycholate (XLD) agar, and Brilliant Green Agar (BGA) (Biokar Diagnostics, Pantin, France) (37 \({}^{\circ}\)C, 5 days).  
Microbiological results were expressed as Log CFU-g\({}^{-1}\) and interpreted according to the United Kingdom's Health Protection Agency (HPA) guidelines [48], the Portuguese National Health Institute (INSA) guidelines [49], and European Commission Regulation (EC) No. 2073/2005 [50] for assessing the microbiological safety of ready-to-eat foods. Additional test methods are detailed in Table S1 (Supplementary Materials).
## 2 Materials and Methods
### Study Areas and the Monitoring Stations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station Code** & **Station Type** & **Latitude** & **Longitude** & **Elevation (m)** \\ \hline \multicolumn{5}{c}{Bucharest} \\ \hline B-3 & traffic & 44.441\({}^{\circ}\) N & 26.151\({}^{\circ}\) E & 81 \\ B-6 & traffic & 44.429\({}^{\circ}\) N & 26.120\({}^{\circ}\) E & 80 \\ B-8 & background, regional & 44.617\({}^{\circ}\) N & 26.116\({}^{\circ}\) E & 94 \\ \hline \multicolumn{5}{c}{Brasov} \\ \hline BV-1 & traffic & 45.637\({}^{\circ}\) N & 25.631\({}^{\circ}\) E & 599 \\ BV-3 & traffic & 45.659\({}^{\circ}\) N & 25.616\({}^{\circ}\) E & 599 \\ BV-4 & suburban & 45.718\({}^{\circ}\) N & 25.624\({}^{\circ}\) E & 518 \\ \hline \multicolumn{5}{c}{lasi} \\ \hline IS-1 & traffic & 47.157\({}^{\circ}\) N & 27.574\({}^{\circ}\) E & 50 \\ IS-4 & background, regional & 47.199\({}^{\circ}\) N & 27.545\({}^{\circ}\) E & 165 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Monitoring stations in Bucharest, Brasov, and lasi included in the present study. Figure 1: Map showing Romania and the three urban areas with a focus on Bucharest, Brasov, and lasi areas. Air quality monitoring stations used in the present study are indicated with white arrows. Traffic stations are within the red administrative shape of a specific city, and regional background stations appear out of the red shape (B-8, IS-4, BV-4). Iasi, the capital city of the county with the same name, is located in the Moldova region of Romania and experiences a pronounced continental climate. During winter, air masses from the north and northeast often result in blizzards. With a population of approximately 610,937 [32], Iasi faces significant pollution challenges and is currently under an infringement procedure. The city has repeatedly exceeded the PM\({}_{10}\) limit value of 50 \(\upmu\)g m\({}^{-3}\) due to various factors, including a lack of green spaces, non-compliant construction activities, road traffic, and the long-range transport of pollutants [32]. Air quality monitoring in Iasi is conducted at six fixed locations, categorized as traffic, industrial, urban background, suburban, and regional stations. For this study, data were collected from two monitoring stations: the traffic-type station Pod de Piatra (IS-1), located in the city center, and the regional-type station Aroneanu (IS-4), situated outside the city and not directly influenced by industrial activities (Table 1).
# Optimization of Storage Medium for Hematological Reference Samples in External Quality Assessment
###### Abstract  
Hematological reference samples for external quality assessment (EQA) in hematology are essential for many laboratories in Vietnam, however, the prices are high along with short storing time. This study attempted to establish an optimal formula for the manufacture of hematological reference samples for hematological EQA programs. Human red blood cells (RBCs) were mixed with goose RBCs (pseudo-leucocytes) and goat RBCs (pseudo-platelets) as alternatives for white blood cells (WBCs) and platelets (PLTs), respectively, in different formulas of storage media. The optimal formula was obtained using response surface methodology-central composite design (RSM-CCD). Three main factors affecting the concentrations of RBCs were determined by Plackett-Burmann matrix. The optimal concentrations determined by RSM-CCD were 46% serum, 0.44 g/L neomycin sulfate, and 2.5% glycerol. The model predicted that the maximum number of human RBCs was 4.1 \(\times\) 10\({}^{12}\) cells/L, pseudo-leucocytes was 6.4 \(\times\) 10\({}^{9}\) cells/L, and pseudo-platelets was 188 \(\times\) 10\({}^{9}\) cells/L. The practical concentrations of RBCs, pseudo-leucocytes, and pseudo-platelets were 4.21 \(\times\) 10\({}^{12}\) cells/L, 6.4 \(\times\) 10\({}^{9}\) cells/L, and 187 \(\times\) 10\({}^{9}\) cells/L, respectively, which showed 98.93% similarity with theoretical data. This result could be a premise for further study to improve manufacturing of hematological reference samples for hematology EQA programs.  
hematology EQA samples; pseudo-leucocytes; pseudo-platelets +
Footnote †: journal: Applied Sciences
## 4 Mathematical Calculation of PSD  
We have calculated the PSD for different line code techniques where the long stream of 0 s and 1 s is transmitted towards the receiver in the form of a signal. The various line codes use different energy levels for each bit of transmission. Each line code has a different waveform to represent the digital signal. The following are details of various line codes with the PSD formula.
## 1 Introduction  
In recent decades, remote sensing and geographic information systems (GIS) have become valuable environmental monitoring tools. Figure 1 summarizes the published papers on remote sensing for environmental monitoring and on GIS for environmental monitoring in the last two decades based on data from worldwidescience.org (last access on 26 July 2022) [1]. From Figure 1, we can see that GIS and remote sensing for environmental monitoring grew in population almost equally. Considering the past few years, we can see that both topics are gaining interest according to the increase in published papers. Due to their multiple applications in different fields such as water [2], air [3], soil [4], agriculture [5], marine and terrestrial ecosystems [6, 7], and urban monitoring [8], their use is continuing to grow.