esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Description
### Unmanned Aerial Vehicle
#### 3.1.2 Software Components for the UAV  
From a software perspective, there was another collection of tools that was used to integrate all of the components that the drone was made of. The most prominent were as follows:  
1. Raspbian: this is the operating system run by the Raspberry Pi 3B+ mounted on the drone [32]. It is used for typical operating system duties: organizing memory accesses, providing a mechanism to manage the underlying hardware or, more importantly in the case of this proposal, providing a software ground from where to execute other programs that are more user oriented. Raspbian can make use of both a Graphical User Interface of its own or just a Command Line Interface, but its capability for running the AutoPilot planner is the most important functionality that it offers to the whole system presented in this paper.  
Figure 4: UAV built from scratch to meet the formulated requirements. Note the MDAS subsystem added to the bottom part.  
Figure 3: Hardware components used to build the drone.  
2. ArduPilot: this is the flight planner used to manage all aspects of the taking off, in-air and landing navigation of the drone. It displays essential information on arming (turning on) or disarming the UAV and provides dashboards to recalibrate essential flight parameters of the drone, such as propeller regimes or commands to be carried out during flight. Although it has been conceived to be used in drones based on Arduino (hence the name ArduPilot), it is compatible with drones that make use of Raspbian and Raspberry Pi devices as the hardware backbone of the vehicle. ArduPilot works in the following manner: once it has been installed as a program, it will run as any other common piece of software on top of the operating system (in this case, Raspbian). Once it is executed, the vehicle operator will be given the option to configure the hardware, taking into account the kind of UAV (copter, rover, plane or even submarine) depending on what has been mounted and the controller set of the hardware (in our case, Navio2). Afterwards, as shown in Figure 5, further configuration details are completed.
3. Secure Shell (SSH) is used as the protocol and program used to communicate with the Raspberry Pi installed at the UAV from a terminal. For configuration and parameter changes, it is required to set the UAV to its desired parameters, so this protocol and its Command-Line-Interface-based tool are most useful.  
The last step that was carried out for the UAV to fully serve the purposes conceived for Galileo was attaching the corresponding prototype of the designed MDAS system. Its main features and performance are described in the next subsection.
## 2 Results
### DEGs and DEPs Involved in the Astaxanthin Metabolic Pathway  
In _H. pluvialis_, isopentenyl diphosphate (IPP) is synthesized in the methyl-d-erythriot 4-phosphate (MEP) pathway [41] with a total of seven genes involved, including _dxr_, _dss_, _ispD_, _ispE_, _ispF_, _ispG_, and _ispH_ (Figure 2). In the early stage of exposure to blue light, the expression levels of _ispD_ gene encoding 2-C-methyl-D-erythriot 4-phosphate cytidyltransferase and _ispE_ gene encoding 4-diphosphocytidyl-2-C-methyl-D-erythriitol kinase were significantly lower than those treated with white light. Results showed that blue light showed little effect on the expression levels of _dx_s encoding 1-deoxy-D-xylulose-5-phosphate synthase, _dxr_ encoding 1-deoxy-D-xylulose-5-phosphate reductoisomerase, _ispF_ encoding 2-C-methyl-D-erythriitol 2,4-cyclodiphosphate synthase, _ispG_ encoding 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate synthase, and _ispH_ encoding 4-hydroxy-3-methyl-2-enyl diphosphate reductase (Figure 2).  
The astaxanthin synthesis is initiated with the precursor of astaxanthin, i.e., \(\beta\)-carotene. The IPP produced in the MEP pathway is catalyzed by IPP isomerase (IPI) to generate DMAPP, which joins three IPP molecules to produce GGPP, catalyzed by farnesyl diphosphate synthase (FPPS) and GGPP synthase (GGPS). Subsequently, two GGPP molecules are catalyzed by phytoene synthase (PSY) to produce phytoene, which is used to generate \(\xi\)-carotene catalyzed by phytoene desaturase I (PDS). Then, \(\xi\)-carotene is used to generate lycopene, catalyzed by \(\xi\)-carotene desaturase II (ZDS). Finally, lycopene cyclase (LCYB) catalyzes the transformation of lycopene into \(\beta\)-carotene, which is catalyzed by both beta-carotene hydroxylase (CRTR-B) and beta-carotene ketolase (BKT) to generate astaxanthin (Figure 3). Results of the transcriptome analysis showed that blue light exposure significantly up-regulated the expression of _ipi_, with its expression level escalated as the exposure time increased. However, on day 7 of the treatment with SA, the gene expression level of _ipi_ was significantly decreased. On day 2 after exposure to blue light, compared with white light, the expression levels of _fpps_, _pds_, _psy_, and _crtR-B_ (encoding \(\beta\)-carotene hydroxylase) were significantly decreased. On day 7 after the exposure to blue light, the expression level of _psy_ was significantly increased, while the expression of _crtR-B_ was significantly decreased. The addition of SA resulted in a significant decrease in the expression of _psy_on day 7 (Figure 3). On day 2 after the blue light exposure, the expression level of _crtR-B_ was significantly down-regulated compared with that of _H. pluvialis_, treated with white light. However, the gene expression of _crtR-B_ was increased as the exposure time increased. Blue light exposure showed no significant effect on the expression of gene _bkt_ encoding \(\beta\)-carotene ketolase. However, with the increase in exposure time, the expression of _bkt_ was significantly decreased.
# Lithium Battery SoC Estimation Based on Improved Iterated Extended Kalman Filter
## 4 Experimental Results and Simulation Analysis  
In order to verify the accuracy of the SoC estimation by the LM-IEKF algorithm, MATLAB software is used in this paper. A constant temperature of 25 \({}^{\circ}\)C is maintained, as well as a 1 A constant electric discharge condition. The Federal Urban Driving Schedule condition and Beijing dynamic stress test (BJDST) condition are used, respectively. In the experiment, the EKF algorithm, IEKF algorithm, and LM-IEKF algorithm are used to estimate the SoC of the battery. The maximum number of iterations of IEKF is set to 20, \(SoC_{est}\) is defined as the estimated value of SoC, \(SoC_{real}\) is the actual value of SoC, and SoC error = \((SoC_{est}-SoC_{real})\times 100\%\).  
According to the empirical values, the covariance matrices obeying the process noise \(w_{k}\) and observation noise \(v_{k}\) of the system are set as follows.  
\[Q_{k}=\left[\begin{array}{ccc}0.01&0&0\\ 0&0.01&0\\ 0&0&0.01\end{array}\right],\] \[R_{k}=0.16\]  
The error covariance matrix \(P_{0}\) is set as follows.  
\[P_{0}=\left[\begin{array}{ccc}0.01&0&0\\ 0&0.01&0\\ 0&0&0.01\end{array}\right]\]
# Tensor-Based Approaches for Nonlinear and Multilinear Systems Modeling and Identification
## 5 Tensor-Based Approaches for Nonlinear Systems
### Block-Oriented Models
#### 5.5.3 Wiener-Hammerstein Model  
The Wiener-Hammerstein model, whose structure is illustrated in Figure 9, is a combination of the Wiener and Hammerstein models described previously. Its output \(y(t)\) is given by  
Figure 8: Block diagram of a Wiener model.  
\[y(t)= \sum_{i=1}^{M_{g}}g_{i}\vec{v}(t-i)=\sum_{i=1}^{M_{g}}g_{i}\vec{c}(w(t -i)) \tag{59}\] \[= \sum_{i=1}^{M_{g}}g_{i}\sum_{p=0}^{P}c_{p}w^{p}(t-i)\] (60) \[= \sum_{p=0}^{P}c_{p}\sum_{i=1}^{M_{g}}g_{i}\prod_{j=1}^{p}\sum_{m_{ j}=1}^{M_{g}}l_{m_{j}}u(t-i-m_{j}). \tag{61}\]  
Defining the changes of variables \(i_{j}=i+m_{j}\), for \(j\in\langle p\rangle\), and reordering the sums lead to the following I/O relationship:  
\[y(t)=\sum_{p=0}^{P}c_{p}\sum_{i_{1}=2}^{M_{g}}\cdots\sum_{i_{p}=2}^{M_{g}}\sum _{i=1}^{M_{g}}g_{i}\prod_{j=1}^{p}l_{i_{j}-i}u(t-i_{j}), \tag{62}\]  
where \(M_{g}=M_{g}+M_{h}\) stands for the memory of the overall system. The Wiener-Hammerstein model is associated with a Volterra model whose \(p\)th-order kernel is given by [49]  
\[h_{i_{1},\cdots,i_{p}}^{(p)}=c_{p}\sum_{i=1}^{M_{g}}g_{i}\prod_{j=1}^{P}l_{i_ {j}-i}\,\ i_{j}=2,\cdots,M_{g}\,\ j\in\langle p\rangle. \tag{63}\]  
The corresponding tensor \(\mathcal{H}^{(p)}\in\mathbb{R}^{M_{g}-1\times\cdots\times M_{g}-1}\) is a rank \(M_{g}\) tensor admitting a PARAFAC decomposition written as  
\[\mathcal{H}^{(p)}=c_{p}\sum_{i=1}^{M_{g}}g_{i}\overset{p}{\underset{j=1}{ \overset{p}{\sim}}}\mathbf{a}_{i}=c_{p}\mathcal{I}_{p,M_{0}-1}\overset{p}{ \underset{j=1}{\overset{p}{\sim}}}\mathbf{A}^{(j)}, \tag{64}\]  
where  
\[\mathbf{a}_{i}=\left[\begin{array}{c}\mathbf{0}_{i-1}\\ \mathbf{1}\\ \mathbf{0}_{M_{g}-i}\end{array}\right]\in\mathbb{R}^{M_{g}-1}\,\ i\in\langle M_{g}\rangle, \tag{65}\]  
\[\mathbf{A}^{(j)}= \Big{[}\begin{array}{ccc}\mathbf{a}_{1}&\cdots&\mathbf{a}_{M_{ g}}\end{array}\Big{]}=\mathcal{T}_{M_{g}-1,\,M_{g}}(\mathbf{1})=\left[ \begin{array}{cccc}l_{1}&0&\cdots&0\\ \vdots&l_{1}&&\vdots\\ l_{M_{h}}&\vdots&\ddots&0\\ 0&l_{M_{h}}&&l_{1}\\ \vdots&&\ddots&\vdots\\ 0&0&\cdots&l_{M_{h}}\end{array}\right],\ j\in\langle p-1\rangle, \tag{66}\] \[\mathbf{A}^{(p)}= \mathcal{T}_{M_{g}-1,\,M_{g}}(\mathbf{1})diag(\mathbf{g})\in \mathbb{R}^{M_{g}-1\times M_{g}}. \tag{67}\]  
Figure 9: Block diagram of a Wiener–Hammerstein model.
## 5 Conclusions  
Based on the above investigations, conclusions can be drawn as follows:  
* In the test of the top slab, cracking was formed at Sections A-A and I-I, and the tooth plate. The cracking was observed at the triangular anchorage block and Section C-C when testing the bottom slab. The other sections of the top and bottom slabs showed good mechanical performance and elastic condition during the eight loading cases. Higher local concrete stress level of the anchorage zone was only formed at a small area. The specimens were not destroyed under the equivalent force based on the design of the real bridge, which results in safety and reliability of the two slabs.
* For the eight loading cases, the linear relationship between the stress and load are observed at concrete and steel bars, respectively. It can be concluded that there was a good bond between the concrete and steel bars, which exhibits the entire structure is in a good condition. Consequently, the specimens were safe and reliable.
* The tensile stress of the concrete surrounding the post-tensioned prestressing anchorage zones surpassed the tensile strength of concrete when the deformation of the concrete could not be restrained.  
Figure 14: Distribution of the cracking: (**a**) The top slab; (**b**) The bottom slab.  
* To avoid the concrete at the anchorage zone continuing to crack, some crack-control recommendations are summarized as follows: Firstly, the connection between tensile anchorage block and the bottom slab should arrange more reinforcing bars with hooks to improve the tensile capacity of this part. Secondly, the stirrups should be designed with closed-loop to increase the restraint of the stirrups. Therefore, they can effectively restrain the central concrete of the anchorage zone.  
Conceptualization, W.Z.M. and H.Y.G.; Methodology, H.Y.G; Software, W.Z.M. and Y.N.H.; Validation, Y.N.H.; Formal Analysis, W.Z.M. and H.Y.G.; Investigation, W.Z.M. and Y.N.H. and Q.H.P.; Writing-Original Draft Preparation, W.Z.M. and H.Y.G. and Y.N.H.; Writing-Review & Editing, W.Z.M. and Y.N.H.; Supervision, Q.H.P.  
This study was funded by the National Natural Science Foundation of China (Grant Nos. 51108382 and 51508474), the Fundamental Research Funds for the Central Universities (Grant No. 2682015 CX07), and the Science and Technology Research and Development Plan of China Railway Construction (Grant No. 2014-C34).  
The authors gratefully acknowledge National Natural Science Foundation of China, for Financial support.  
The authors declare no conflict of interest.
## 3 Sounding System
### Data Frame Design
The resampling of the PN sequence was about eight and had a total duration of 5.12 ms, as shown in Figure 5. The modulated data blocks were located just after the PN sequence. As shown in Figure 6, a second data frame was designed for the study of the correlation coefficient between the ordinary and extraordinary channels and their respective delay spread. This second data frame consists of a group of equispaced PN sequences. The spaces between the known sequences do not present any kind of signal, and present a theoretical value of zero amplitude. The purpose of this design is the correct correlation of the PN sequences, as it is fundamental for the correct computation of the multipath values and the correlation coefficients between channels. A key factor for our study was the separation between PN sequences, as it indicates the separation between the packets. This separation is the direct measurement cadence of our system. For the first data frame (Figure 5), a measurement was performed every 165 ms (6.06 Hz). For the second data frame used (Figure 6), a measurement was performed every 60 ms (16.17 Hz).
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**ID** & **Conflict Driver** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline D-01 & Communication issue & 79.7\% & 93.2\% & 85.9\% & 59 \\ D-02 & Compensation issue & 53.7\% & 100.0\% & 69.9\% & 36 \\ D-03 & Ecological issue & 89.0\% & 99.7\% & 94.0\% & 291 \\ D-04 & Economic issue & 71.2\% & 98.8\% & 82.8\% & 85 \\ D-05 & Facility charge issue & 80.0\% & 100.0\% & 88.9\% & 4 \\ D-06 & Facility operation and utilization issue & 74.7\% & 98.2\% & 84.8\% & 57 \\ D-07 & Opposite movement and response & 92.7\% & 91.9\% & 92.3\% & 582 \\ D-08 & Perception and emotional issue & 93.7\% & 91.4\% & 92.5\% & 81 \\ D-09 & Professional investigation issue & 98.2\% & 91.8\% & 94.9\% & 61 \\ D-10 & Project objective issue & 72.4\% & 82.6\% & 77.2\% & 92 \\ D-11 & Project organization issue & 88.1\% & 97.4\% & 92.5\% & 38 \\ D-12 & Public-private partnership issue & 100.0\% & 100.0\% & 100.0\% & 4 \\ D-13 & Regional benefit and loss, added value issue & 80.0\% & 93.3\% & 86.2\% & 30 \\ D-14 & Relevant laws, institution, guideline issue & 86.8\% & 82.5\% & 84.6\% & 40 \\ D-15 & Residential right and environment issue & 81.9\% & 92.5\% & 86.9\% & 93 \\ D-16 & Resource procurement issue & 75.0\% & 100.0\% & 85.7\% & 3 \\ D-17 & Technical issue & 48.8\% & 100.0\% & 65.6\% & 21 \\ D-18 & Transparency and legitimacy issue & 87.2\% & 87.2\% & 87.2\% & 47 \\ O-01 & Stakeholder & 100.0\% & 81.8\% & 90.0\% & 44 \\ O-02 & Project attribute & 93.6\% & 60.1\% & 73.2\% & 218 \\ O-03 & Undefined & 100.0\% & 24.6\% & 39.4\% & 114 \\  & Micro average & 85.7\% & 85.7\% & 85.7\% & 2000 \\  & Macro average & 83.2\% & 88.9\% & 83.6\% & 2000 \\  & Weighted average & 88.1\% & 85.7\% & 84.7\% & 2000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: KPC performance. NLP model is constrained due to the required computing power and available dataset. In this circumstance, it is necessary to consider the trade-off between the cost needed for the model improvement and performance from the practical useability perspective.
## 3 Therapeutics in HCC
### Combination Therapies with ICIs  
In the IMbrave150 clinical trial [109], patients were randomly assigned to receive the standard dose of atezolimamb (an ICI anti-PD-L1) plus a high dose of the VEGF inhibitor bevacizumab, or sorafenib. The trial showed improved overall survival (OS), confirmed progression-free survival (PFS), and significantly preserved health-related quality of life in the combination treatment arm.  
In 2020, the FDA approved the atezolizumab plus bevacizumabab combination as first-line therapy for patients with metastatic or unresectable HCC who had not received systemic treatment. In clinical practice, this combination has shown great efficacy, whether or not the patients had previous systemic treatment, significantly improving OS [110].  
The interaction between VEGF/VEGFR blockade and PD-1/PD-L1 inhibitors works at multiple levels. The reduction and normalization of tumor vasculature are likely only part of the effect, and various lines of research suggest that functional control of innate immune populations, including myeloid-derived suppressor cells (MDSCs), plays a key role [111]. In this context, bevacizumab has shown the ability to reduce MDSCs in patients with other cancer types, such as lung or colorectal cancer [112; 113]. Additionally, preclinical mouse models of HCC have demonstrated that the superior therapeutic activity of a combination of anti-PD-1 and anti-VEGFR2 antibodies is associated with enhanced M1 and decreased M2 TAM levels, as well as an increased level of infiltrating CD8+ T cells.  
A key question remains: which agent would be most beneficial in enhancing the effects of the ICIs-anti-angiogenic combination? Bevacizumab nebularizes VEGF-A, while VEGFR inhibitors block proximal signal transduction from VEGF receptors.
## 5 Results and Discussion
### Comparative Discussion  
Lamb waves exhibit a phenomena known as dispersion where the speed of the wave varies with the excitation frequency. When comparing these results, particularly the 100 kHz and 250 kHz results, the higher frequency waves can be seen to travel further in a shorter space of time.  
Figure 8: 300 kHz three-component displacement magnitude showing Lamb wave interaction with the healthy and disbonded stiffened panel.  
It is due to the dispersion phenomena that the amplitude of the Lamb wave is seen to reduce as it moves across the area of investigation in the higher frequency excitations; therefore demonstrating that the higher frequencies attenuate more.  
Comparing the results from the two different panels, the S\({}_{0}\) mode is seen to have a greater amplitude in the disbonded panel results. This increase in wave amplitude can be attributed to a difference in sensor coupling. Despite using the same method to couple the sensor to each panel, it is very difficult to achieve a consistent and repeatable sensor coupling.  
One significant difference between the three frequencies is the transmitted conical fringe patterns produced by the Lamb wave interaction with the disbonded region. The results from the 100 kHz excitation show a reduction in wave amplitude to the right of the disbonded region. This differs greatly to what is observed in the results from the 250 kHz and 300 kHz excitations where an increased amplitude is seen to the right of the disbonded region. It has been suggested that this may be a function of the difference in the resultant wavelengths of the different excitation frequencies [38]. This is due to the shorter wavelengths of the higher frequencies being more sensitive to interacting with damage. In terms of the design of a damage detection system however there is a trade-off between the minimum damage size detectable and the global coverage required due to the higher attenuation of the higher frequency Lamb waves.  
It may also be noted that the shape of the reflected conical fringe pattern to the left of the stiffener in the 100 kHz excitation results appears to diffract from the entire width of the disbonded region whereas in the other two sets of results the fringe pattern appears to diffract from a much smaller area. It is worth noting that this problem cannot be simply modelled in a similar manner to that of a fluid mechanics problem due to the transmission of the wave through the stiffener and the added complexity of differing wave modes.  
The quality of the results from the 250 kHz and 300 kHz excitations are not as clear as the results from the 100 kHz excitation. This could be attributed to the spatial resolution of the scan points. The number of scan points was kept consistent throughout the study in order obtain results that were comparable and to allow the study to be completed in a timely fashion. By increasing the number of scan points more detailed 3D vibrometry fringe plots could be achieved allowing a better comparison to be made between the experimental results.
## 3 Experimental Results and Discussion
### Evaluation Indicators  
The crack detection model was generated by the proposed network after several training cycles. In the field of object detection, there are a lot of indicators to assess different aspects of the model like accuracy, speed, and size. In this study, what we are most concerned about is the accuracy of the proposed model in crack detection. The average precision (AP) and mean average precision (mAP) are the two most used indicators to evaluate the accuracy of object detection models. AP can be calculated as Equation (1), and it takes the two indicators of precision and recall into consideration which can be calculated as Equations (2) and (3). Thus, AP can effectively evaluate the proposed model's ability level to identify cracks, describe crack shapes with bounding boxes, and determine crack  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Set** & **Transverse** & **Longitudinal** & **Block** & **Alligator** \\ \hline Labeling box number & 135 & 237 & 64 & 156 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The composition of the test set.  
categories. mAP is the average AP of all categories and can be calculated as Equation (4). mAP comprehensively considers the accuracy of all crack categories, providing a more thorough evaluation of the proposed model's performance.  
Therefore, this paper selected AP to quantitatively evaluate the performance of the proposed model on a particular category of cracks and mAP to quantitatively evaluate the performance of the proposed model on the entire dataset.  
\[\text{AP}=\frac{\sum\text{Precision}}{\text{N}(\text{TotalImages})} \tag{1}\]  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
where TP, FP, and FN indicate the true positive, false positive, and false negative, respectively. The P-R curve is drawn based on the precision and recall values, and the integral under the P-R curve is the AP for the particular category.  
\[\text{mAP}=\frac{\sum\text{AP}}{\text{N}(\text{Classes})} \tag{4}\]  
In practical applications, it is more important to successfully detect cracks than to accurately describe cracks with bounding boxes. As part of our evaluation work, the model's detection performance was further assessed using the missed detection rate and false detection rate which are widely adopted in engineering to avoid the error of bounding boxes. Their calculation expressions are shown in Equations (5) and (6).  
\[\text{missed detection rate}=\frac{\text{FN}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{false detection rate}=\frac{\text{FP}}{\text{TN}+\text{FP}} \tag{6}\]  
where TP, FP, TN and FN indicate the true positive, false positive, true negative, and false negative, respectively.