esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 4 Results  
### Data Acquisition and Preprocessing Results  
Figure 6 presents the acquired TLS point clouds (Figure 6a) and the generated reference planes (Figure 6b,c). Additionally, the compared MLS point clouds were captured using three different MLS devices, with each MLS device scanning the entire experimental room four times. However, during the data extraction process, the first dataset from MLS-01 was missing due to an unexpected issue. The cause is presumed to be either a malfunction of the internal SLAM algorithm or an error during data transmission. Nevertheless, the remaining three datasets from MLS-01 were successfully extracted without issue.  
In our experiments, a total of 11 MLS point clouds were collected: three from MLS-01 and four each from MLS-02 and MLS-03. The acquired MLS point clouds were processed according to the procedures described in Section 3.3. As a result, data for 22 Columns were generated, producing a total of 88 reference planes. Table 2 presents the number of points for the 22 Columns used in the analysis. In this study, random down-sampling was applied to the four planes of each Column. Therefore, the \"Individual plane\" values in  
Figure 6: Captured TLS point clouds of experimental rooms and the reference planes. (**a**) TLS point clouds acquired in the experiment, (**b**) reference planes and detected vertices of Column A, (**c**) reference planes and detected vertices of Column B.  
Table 2 represent the down-sampling parameters, while the \"Total\" values are four times the \"Individual plane\" values.  
The smallest plane segment contains approximately 9000 points. A dataset with more than 9000 data points is sufficient to represent one plane of the column used in this experiment. Additionally, the number of points is sufficient for applying a Gaussian distribution for detailed analysis. Meanwhile, as shown in Table 2, despite constraints of the MLS scanning speed and distance, the number of points varies across planes. This result can be attributed to the constraint in maintaining equal exposure times across the surfaces, even under constrained MLS scanning speed and distance. This variation may introduce sensitivity issues when applying the proposed analysis method, particularly during random downsampling. Therefore, we evaluated the sensitivity of the analysis with respect to random downsampling. Figure 7 illustrates examples of Columns acquired from the three MLS devices, viewed in the xy-plane. C2M distance measurements were performed for each segment against its corresponding reference plane.
# Implementation Studies of Bacteriophage \(\lambda\) O
## 1 Introduction
Genetic mapping of RK\({}^{-}\) mutations within \(O\) or \(P\) requires that both \(O\) and \(P\) initiator gene products can complement and function _in trans_. When a cell is infected with two phages, one defective in \(O\) and the other in \(P\), complementation is observed suggesting that the products of these genes are diffusible [32]. However, [PERSON] and [PERSON] [33] were unable to demonstrate that cells carrying a ColE1 plasmid expressing \(O^{+}\) could complement, i.e., support the efficient plating of an infecting \(\lambda imm21\) Oam29 phage, even though the plasmid copy number varied between 50 copies at 32 \({}^{\circ}\)C and 260 copies at 42 \({}^{\circ}\)C. However, they could demonstrate _trans_ complementation for phages with amber mutations in \(N\) or \(P\) by plasmids that can express these genes. [PERSON] [34] suggested that \(O\) might act _in cis_ or be poorly complemented under \(N\) defective conditions, and other experiments suggesting that O functions in _cis_ were reported in [22]. These findings throw into question whether it is possible to designate using a phage complementation assay whether an induced RK\({}^{-}\) mutant has an \(O^{+}\) or \(O^{-}\) phenotype. Alternatively, these divergent observations suggest that the ability of \(O\) to complement is more complex than initially assumed. A complicating problem in addressing this historical issue is that the mutant \(\lambda\) phages used in these early complementation assays were never subjected to DNA sequence analysis, so that in many cases their designations depend only upon unreported phage mapping studies, without accompanying proof of mutational site determination. In this report, we examine the sequences of some early \(O\) mutations provided by [PERSON] from his original collection [35], and phages we have acquired over the years from laboratories that have participated in studies on \(O\). We have cloned out \(O\) alleles from the chromosomes of RK\({}^{-}\) mutants and inserted them into a plasmid where the expression of the allele is regulated by CI[Ts], and is repressed in cells growing at 30 \({}^{\circ}\)C, or can be slightly to fully induced at growth temperatures between 37 \({}^{\circ}\)C and 42 \({}^{\circ}\)C. Each of these alleles was examined for their ability to complement the growth of a \(\lambda\)\(O\) amber mutant(s) _in trans_. We have explored the influence of O:P interactions on cell growth and toxicity,since the expression of \(P\) by itself, using the same system, is highly toxic [25; 36]. These studies reveal that some alleles of \(O\) with internal deletions can complement as well or better than \(O^{+}\) and that the co-expression of \(O\)-\(P\), or of \(O\) with portions of the N-terminal end of \(P\), prevents an ability of \(O\) to complement _in trans_.
## 3 Integration of Visual Servo Control
### Stability Analysis  
The closed-loop visual servo system based on the proposed method can be written as:  
\[\dot{\mathbf{\epsilon}}=-k_{p}\mathbf{\epsilon}+\bar{k}_{f}\Delta\mathbf{q}_{b}+\bar{k}_{ h}\Delta q_{b} \tag{19}\]  
Figure 6: The control block of the macro-micro manipulator system.  
which:  
\[\tilde{k}_{f} =k_{f}\,^{.Tool}\,T_{BOE}\,^{.BOE}\,J_{CMM\_BOE}\cdot G^{-1} \tag{20}\] \[\tilde{k}_{bl} =k_{b}\,^{.Tool}\,T_{BOE}\,^{.BOE}\,J_{CMM\_BOE} \tag{21}\]  
Assuming that the configuration of the manipulator is non-singular and the inverse kinetic solution of the redundant manipulator is closed-form without self-motion, which means \(\mathbf{e}=0\) is the only solution of \(\mathbf{q}_{m}\to\mathbf{q}_{d}\). When the EEF reaches the desired berth position, the base deflection should be damped to zero. The steady state convergence value of the visual error is \(e_{\infty}=e_{0}\exp\left(-k_{p}t\right)+(\tilde{k}_{f}\Delta\mathbf{q}_{b}+\tilde {k}_{bl}\Delta\mathbf{q}_{b})/k_{p}\), in which it can be seen that the errors are exponential decay and the steady-states are affected by the base deflections. The final states of \(\Delta\mathbf{q}_{b}\) are influenced by motion of the manipulator under the Assumptions of 1 and 2. The isolated equilibrium of the closed-loop system is \(\left[\Delta\mathbf{q}_{b}^{T}\quad\Delta\mathbf{q}_{b}^{T}\quad\mathbf{e}^{T}\right]^{T} =\left[0\quad 0\quad 0\right]^{T}\). The configuration of the manipulators does not vary quickly compared with the elastic vibration, it is reasonable to treat the matrices as constant. Then, the closed-loop system can be described by the flowing state-space form:  
\[\left[\begin{array}{c}\Delta\mathbf{q}_{b}\\ \Delta\mathbf{\tilde{q}}_{b}\\ \mathbf{\dot{e}}\end{array}\right]=\left[\begin{array}{ccc}0&I&0\\ -\mathbf{H}_{b}^{-1}K_{b}&-\mathbf{H}_{b}^{-1}\big{(}\mathbf{D}_{b}+G\big{)}&0\\ \tilde{k}_{bl}&\tilde{k}_{f}&-k_{p}\end{array}\right]\left[\begin{array}{c} \Delta\mathbf{q}_{b}\\ \Delta\mathbf{q}_{b}\\ \mathbf{e}\end{array}\right] \tag{22}\]  
The stability of the overall system can be analyzed by investigating the poles of the closed-loop system, the eigenvalues are determined by the roots of the characteristic polynomial:  
\[p(\lambda)=\big{(}\lambda+k_{p}\big{)}\Big{(}\lambda^{2}+H_{b}^{-1}(D_{b}+G) \lambda+H_{b}^{-1}K_{b}\Big{)} \tag{23}\]  
It is sufficient to validate that all roots are contained in the left open complex half-plane when \(G\) is positive definite, it is globally asymptotically stable in the equilibrium.
# ISAR Imaging Analysis of Complex Aerial Targets Based on Deep Learning
## 4 Analysis of Simulation Results and Experimental Results
### Measurement Results
Finally, the imaging result is placed in the prediction set of CapsNet for recognition, and the final recognition result is shown in Figure 11.  
As illustrated in Figure 11, it can be seen that the radar ISAR imaging system based on CapsNet proposed in this paper can accurately recognize aircraft simulation assembly as a two-dimensional scattering image of the aircraft. The experimental results corroborate the efficacy of the radar ISAR imaging system utilizing CapsNet.
###### Abstract  
Low-cost Global Navigation Satellite System (GNSS) receivers are currently used in various engineering applications. These low-cost devices are regarded as suitable sensors for applications in areas with a high risk of instrument damage. The main objectives of this research were to identify the size of displacements that can be detected in relative and absolute positioning modes by low-cost GNSS instruments and to compare the results of selected antennas. Additionally, geodetic and low-cost GNSS instruments were compared in the level of observations. For this study, low-cost SimpleRTX2B V1 boards, which house ZED-F9P GNSS chips, and three low-cost antennas, namely, Survey, Tallysman TW3882, and Survey Calibrated, were selected. While antenna calibration parameters are known for the last antenna, this is not the case for the first two. For testing purposes, a geodetic network consisting of four points was established; horizontal and vertical movements were imposed by a special mechanism with high accuracy. In relative positioning mode, the results indicate that the Survey Calibrated antenna can detect horizontal and vertical displacements with sizes of 4 mm, and 6 mm, respectively. In the detection of horizontal displacements, the performance of the Survey antenna was not as good as that of Tallysman, and the sizes of detected displacements were 6 mm and 4 mm for the first, and second antennas, respectively. Vertical displacements of 9 mm were detected using both Survey and Tallysman antennas. In absolute positioning mode, Survey Calibrated also had better performance than the Tallysman antenna, and spatial displacements of 20 mm or greater were detected by low-cost GNSS instruments. The observations made with low-cost and geodetic GNSS instruments were compared, and the latter showed better performance. However, the differences in cycle slips and the noise of phase observations were inferior. Considering their cost and proven performance, it can be concluded that such sensors can be considered for setting up a highly accurate but low-cost geodetic monitoring system.  
GNSS; displacements; low-cost receivers; low-cost antennas; sessions Article  
[PERSON], [PERSON], [PERSON], [PERSON], O. Performance Evaluation of Low-Cost Multi-Frequency GNSS Receivers and Antennas for Displacement Detection  
1
# Action Recognition Based on the Fusion of Graph Convolutional Networks with High Order Features
## 4 Experiments  
### Datasets  
**NTU-RGBD**[11] contains 56,880 video clips of 60 actions. The samples were taken from 40 different people by using a Kinect v2 camera. The ages of subjects are between 10 and 35. They used three cameras simultaneously to capture three different horizontal views from the same action. For the camera position setting: the three cameras were at the same height but three different horizontal angles: \(-45^{\circ}\), \(0^{\circ}\), \(+45^{\circ}\)[11]. The dataset provides two methods to evaluate the performance of action classification: cross-subject and cross-view. The training set of cross-subject includes 40,320 samples, which consists of actions performed by 20 subjects. The testing set contains 16,560 samples, which consists of samples taken by another 20 subjects [11]. The cross-subject training set includes 37,920 samples taken by Cameras 2 and 3, and testing set contains 18,960 samples taken by Camera 1.  
**NTU-RGBD-120**[12] is an extension of NTU-RGBD, which is much larger and provides much more variation of environmental conditions, subjects, camera views, etc. It contains 114,480 video clips of 120 actions. The ages of subjects are between 10 and 57, and heights are between 1.3 m and 1.9 m. The dataset provides two criteria to evaluate the performance of action classification: cross-subject and cross-setup. The training set of cross-subject includes 63,026 samples, which consists of actions performed by 53 subjects. The testing set contains 50,919 samples taken by another 53 subjects [12]. The cross-setup training set includes 54,468 samples consisting of the samples with even collection setup IDs. Testing set contains 59,477 samples, which consists of samples with odd setup IDs. Different setup IDs correspond to changeable vertical heights of the cameras and their distances to the subjects.
## 2 Methods
### Loss Function  
This experiment aimed to reduce the mode collapse problem and provide a more stable training process by using the mean squared error loss instead of the traditional cross-entropy loss, with the objective functions of the discriminator \(D\) and generator \(G\) as follows:  
\[L_{D}=\frac{1}{2}\Big{(}E_{y\sim Pdata(y)}\Big{[}(D(y)-1)^{2}\Big{]}+E_{x\sim P x (x)}\Big{[}(D(G(x)))^{2}\Big{]}\Big{)} \tag{5}\]  
where \(E_{y\sim Pdata(y)}[(D(y)-1)^{2}]\) denotes the mean square error between the discriminator's output of the real image and 1. \(E_{x\sim Px(x)}[(D(G(x)))^{2}]\) denotes the mean square error between the discriminator's output of the generated image and 0.  
\[L_{G}=\frac{1}{2}E_{x\sim Px(x)}\Big{[}(D(G(x))-1)^{2}\Big{]} \tag{6}\]  
To achieve OCT high-quality image reconstruction, we added different loss functions to co-train the MFFA-GAN generator network: the \(L1\) loss constrains the pixel-by-pixel absolute error between the denoised image and the original image, guaranteeing high-precision alignment of the global structure; the edge consistency loss [38] strengthens the similarity measure of the image's edge features by quantifying the absolute value of the differences between neighboring pixels; the perceptual feature loss [39], based on the pre-trained VGG-16 network, improves the visual effect of the reconstructed image through contrast optimization in the high-level semantic feature space. The total loss of the generator can be attributed to the following components:  
\[L=\alpha L_{G}+\beta L_{1}+\gamma L_{edge}+\lambda L_{vgg} \tag{7}\]  
Figure 5: Structure of the efficient local attention.  
where \(\alpha\), \(\beta\), \(\gamma\), and \(\lambda\) are the coefficients of each loss term.
# Delving Deep into Reverse Engineering of UEFI Firmwares via Human Interface Infrastructure
## 2 Background  
### Visual Forms' Representation  
Another innovation of UEFI over Legacy BIOS is in the specification of building blocks for user interfaces [8]. There are various scenarios where a platform component might want to interact in some fashion with the user. Examples of this are when presenting a user with several choices of information, sending information to the display, or offering a user menus for configuring the system, such as the BIOS Setup program shown in Figure 1. The potentially arduous task of managing user interface elements is simplified for UEFI modules requiring such functionality by UEFI providing some basic graphical elements like forms, strings, images, and fonts.  
Figure 1: UEFI Management System User Interface.  
UEFI calls this user interface support Human Interface Infrastructure (HII). HII is a set of protocols that allows for a UEFI module to register user interface and configuration content with the BIOS.  
The implementation of the UEFI user interaction interface involves various types of files, such as VFR (Visual Forms Representation) and UNI. The strings of the UEFI interactive interface come from the UNI files, while the framework of the entire form is derived from the VFR files. In UEFI, there are roughly four components that constitute such a form: Strings, Forms, Fonts, and Images, as shown in Figure 2.  
Strings are provided by UNI files, while Forms are supplied by VFR files. Forms describe the organization of the window, offer methods for user interaction, and dictate how the interactive content is stored. It is provided in a binary format, which, within the EDK framework, is referred to as IFR (Internal Forms Representation). IFR is generated by compiling VFR. Therefore, we define the organizational structure, interaction methods, and related content of the UEFI interactive interface by writing VFR files.
## 3 Results
### Effect of Diet and Age on Systolic, Diastolic, and Mean Arterial Blood Pressure  
Hempeseed enrichment did not alter systolic, diastolic, or mean arterial blood pressure across the 25-week experiment (\(p>0.05\)). However, age exerted a significant main effect on systolic, diastolic, and mean arterial blood pressure (\(p\leq 0.01\) for all three diet groups); see Figure 4A-C.  
The pattern of change across age for systolic, diastolic, and mean arterial pressure for the three diet groups followed a similar trend, starting at roughly the same initial pressure and increasing until the age of 10-15 weeks, where pressure began to stabilize or slowly decrease throughout the remainder of the study; see Figure 4A-C. When the effect of age on blood pressure measurements was analyzed for each diet group separately, the post hoc pairwise comparisons (age, individual weeks 7-29 versus starting age, week 5) showed different patterns of age-related change in blood pressure between diet groups. Specifically, for the control diet group, systolic, diastolic, and mean arterial blood pressure increased after the age of 5 weeks and were significantly greater at weeks 11 and 15 versus week 5. For the 5% HS diet group, the significant rise in blood pressure was delayed: systolic, diastolic, and mean arterial pressure was significantly higher at weeks 15 and 17 versus  
Figure 3: Effect of diet and age on body weight, length, surface area, BMI, LMI, and FMI. (**A–F**) Age exerted a main effect on all parameters (\(p<0.0001\)). (**F**) Diet showed a main effect only on FMI, with 5% HS having higher values than CON and 15% HS (* \(p=0.004\) main effect of group; \(p<0.05\) 5% HS versus CON and 15% HS). \(n=8\) per diet group.  
Blood pressure area under the curve calculations of AUC1 (age 5-15 weeks, adolescence), AUC2 (age 15-29 weeks, adulthood), AUC3 (age 5-29 weeks), and the difference between AUC2 and AUC1 showed no effect of diet on systolic, diastolic, or mean arterial blood pressure (\(p>0.05\)).  
Relationship between Body Mass and Body Composition and Systolic, Diastolic, and Mean Arterial Blood Pressure  
A weak but significant positive relationship between systolic, but not diastolic or mean arterial, blood pressure and BMI was found for both the combined and individual diet groups (all mice combined, \(r^{2}=0.095\), \(p<0.0001\); each diet group, \(p<0.05\)). Lean mass index also showed a significant positive relationship with systolic, but not diastolic, blood pressure: all mice combined (\(r^{2}=0.096\), \(p<0.0001\); each diet group, \(p<0.05\)). Lean mass index was positively related to mean arterial blood pressure for the combined group (\(r^{2}=0.035\), \(p=0.017\)) but for none of the individual diet groups (\(p>0.05\)). Fat mass index was significantly negatively related to systolic blood pressure for the combined group (\(r^{2}=0.037\), \(p=0.015\)) but for none of the individual diet groups (\(p>0.05\)). Fat mass index showed no relationship with diastolic or mean arterial blood pressure.
## 2 Materials and Methods
### Electrochemical Measurements  
All electrochemical tests were performed using the CHI660E electrochemical workstation of Shanghai Chenhua Instrument Co., Ltd. (Shanghai, China). The corrosion properties of Cu in a specified solution were studied. Open potential (OCP), dynamic potential polarization (Tafel diagram), and electrochemical impedance spectroscopy (EIS) data were collected in a three-electrode system in which Cu was the working electrode, standard calomel electrode (SCE) was the reference electrode, and platinum was the reverse electrode. Before each test, the Cu electrode was sealed with electrical tape, exposing an active area of 1 square centimeter. Before each electrochemical measurement, we conducted rough polishing with 1500\(\mu\) silicon carbide sandpaper, then fine polishing with 2500\(\mu\) silicon carbide sandpaper, until the Cu surface was mirrorlike. The purpose was to remove the oxide on the electrode surface and expose the fresh Cu surface. We then rinsed with deionized water and dried with compressed air. OCP stabilized for approximately 600 s to ensure reliable and consistent potential readings. In the OCP \(\pm\) 0.3 V voltage range the scanning rate was 5 mV/s, and the potentiodynamic polarization was measured. EIS was performed under OCP with a frequency range of 0.01 Hz to 1000 kHz and a 5 mV AC signal was applied. The EIS data was analyzed and modeled by Zview 3.1 software.
## 2 Literature Review  
The vibration data acquired on an in-service train comprises the acceleration and angular velocity of the axle boxes, bogies, and vehicle bodies [16]. Methods for analyzing these data can be classified as traditional and machine learning (ML)-based methods. Traditional methods can be further classified into two categories: mechanism-based and signal analysis-based methods.
## 2 Materials  
### Reagents for Immunomagnetic Separation and Sandwich Formation  
The SARS-CoV-2 N protein (Catalog# NUN-C5227), the S46 Anti-SARS-CoV-2 N antibody (Catalog# NUN-S46) for conjugation to AuNPs, and the biotinylated anti-SARS-CoV-2 N antibody (Catalog# NUN-S47) for conjugation to M-280 streptavidin beads were obtained from ACROBiosystems (Newark, DE, USA). AuNPs of different nominal diameters (5 nm dia., Catalog# EM.GCC5/4; 10 nm dia., Catalog# EM.GC10/4; 20 nm dia., Catalog# EM.GC20/4; 40 nm dia., Catalog# EM.GC40/4) suspended in DI water were procured from BBI Solutions (Portland, ME, USA). M-280 streptavidin superparamagnetic particles (Catalog# 11205D), PBS buffer (pH 7.4, Catalog# 10010049), and dithiobis(succinimidyl propionate) (DSP) (Catalog# 22585) were obtained from Thermo Fisher Scientific (Waltham, MA, USA). The conjugation of S46 Ab to AuNPs (S46-AuNP) was performed using a previously described thiol chemistry [29]. The stock concentration of the 20 nm AuNPs used in the N protein detection experiments was calculated to be 1.132 \(\times\) 10\({}^{-4}\) g/mL (1.4 \(\times\) 10\({}^{12}\) AuNPs/mL) and does not account for losses incurred during the antibody conjugation process. Unless otherwise mentioned, PBS was used at 1\(\times\) concentration. The conjugation of M-280 particles to S47 antibody (S47-M280) was performed as per Thermo Fisher Scientific's protocol [30]. The concentration of the prepared M-280 particles was calculated to be 5 mg/mL (3.25 \(\times\) 10\({}^{8}\) MBs/mL) and does not account for particle losses incurred during the conjugation process. SARS-CoV-2 N protein was obtained from ACROBiosystems (Catalog# NUN-C5227, 400 ug/mL, 50 \(\mu\)L aliquots). All reagents were of analytical grade and were used as received. Ultra-pure deionized (DI) water prepared with a Purelab System (ELGA Purelab, High Wycombe, UK) was used throughout this work. A custom 3D-printed fixture positioned the SPCE directly above the magnet for magnetic bead capture (Supporting Information, Figure S1). A 2 mm neodymium magnet (Dura Magnetics, Sylvania, OH, USA) was integrated into the 3D-printed fixture. All instances of rotation within the protocol occurred on a Mini Lab Roller (Labnet H5500 Mini Lab Roller (Edison, NJ, USA)). A BioTek ELx800 plate reader (Winooski, VT, USA) was used to analyze an enzyme-linked immunosorbent assay (ELISA) plate. BinaxNOW extraction reagent was procured as part of the BinaxNOW COVID-19 Antigen Self-Test Kit (Catalog# 550147, CVS Pharmacy, Woonsocket, RI, USA).