esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
For the past twenty years, there has been increasing interest and investment in solar photovoltaic (PV) technology. One particular area of interest is the development of concentrating PV (CPV), especially for use in building integration. Many CPV designs have been developed and investigated. This paper aims at producing a mathematical modelling using MATLAB programme to predict the current-voltage (I-V) and power-voltage (P-V) characteristics of a static CPV. The MATLAB programme could also simulate the angular response of the CPV designs-which has never been explored in the previous literature. In this paper, a CPV known as the rotationally asymmetrical dielectric totally internally reflecting concentrator (RADTIRC) was analysed. A specific RADTIRC design that has an acceptance angle of \(\pm 40^{\circ}\) was investigated in this paper. A mathematical modelling was used to simulate the angular characteristics of the RADTIRC from \(-50^{\circ}\) to \(50^{\circ}\) with an increment \(5^{\circ}\). For any CPV, we propose that the value of opto-electronic gain, \(\mathrm{C}_{\mathrm{opt}\mathrm{e}}\) needs to be included in the mathematical model, which were obtained from experiments. The range of incident angle (\(\pm 50^{\circ}\)) was selected to demonstrate that the RADTIRC is capable of capturing the sun rays within its acceptance angle of \(\pm 40^{\circ}\). In each simulation, the I-V and P-V characteristics were produced, and the short circuit current (\(\mathrm{I}_{\mathrm{sc}}\)), the open-circuit voltage (\(\mathrm{V}_{\mathrm{oc}}\)), the maximum power (\(\mathrm{P}_{\mathrm{max}}\)), the fill factor (FF) and the opto-electronic gain (\(\mathrm{C}_{\mathrm{opt}\mathrm{e}}\)) were determined and recorded. The results from the simulations were validated via experiments. It was found that the simulation model is able to predict the I-V and P-V characteristics of the RADTIRC as well as its angular response, with the highest error recorded for the \(\mathrm{I}_{\mathrm{sc}}\), \(\mathrm{V}_{\mathrm{oc}}\), \(\mathrm{P}_{\mathrm{max}}\), FF and \(\mathrm{C}_{\mathrm{opt}\mathrm{e}}\) was \(2.1229\%\), \(5.3913\%\), \(9.9681\%\), \(4.4231\%\) and \(0.0000\%\) respectively when compared with the experiment.  
solar photovoltaic; concentrating photovoltaic; simulation; experiment +
Footnote †: journal: Applied Sciences
## 2 Background and Related Work
### Medical
DPWS is based on the SOA concept [28] and provides three message exchange patterns for device-to-device communication defined in the Basic Integrated Clinical Environment Protocol Specification (BICEPS) [30]: publish-subscribe when a service provider pushes information to a service consumer (also for asynchronous communication), request/response when a service consumer pulls information (also for synchronous communication) from a service provider, and streaming when a service provider consecutively pushes information to 0 or more service consumers. In addition, it also realizes mechanisms for the dynamic exploration of services during runtime: the explicit discovery, where a service consumer actively searches for a specific service, and the implicit discovery, where a service provider publishes its services in the network. According to [28], the safety and security of SDC are not sufficiently examined. **Smart Cyber Operating Theater (SCOT(r)):** The SCOT project is a Japanese initiative to introduce interoperability in the OR to connect medical devices with computing systems. Therefore, OPeLiNK(r) based on the industrial middleware Open Robot/Resource interface for the Network (ORiN) is used [31]. In contrast to SDC and MDPnP, there already are systems commercially available [32], but it concentrates more on the HOR, while the other standards include all medical devices in hospitals [28]. As SCOT does not rely on an SOA, it is only relevant in the context of this paper as integration within SDC as proposed by [PERSON] et al. [33].
## 4 Discussion
Moreover, because the lordotic angle and augmented vertebral body height of the AwC model were the same as the intact model in this current study, this may also explain the relatively minor difference in stress on the adjacent endplate. The variation in stress on each endplate showed a similar trend for the TSF, AwC-TSF and AwC-TSF-S models under all motions. The presence of posterior pedicle screws (AwC-TSF, AwC-TSF-S and TSF) acted to decrease the maximum von Mises stress on the inferior endplate of L2 and the inferior/superior endplate of L3, while the stress increased on the inferior endplate of L1, superior endplate of L2, inferior endplate of L4 and superior endplate of L5. The use of cement-augmented screws (AwC-TSF-S) did not increase the stress on adjacent vertebrae. A biomechanical study by [PERSON] et al. [31] showed that using clinically-relevant amounts of cement-augmentation did not lead to stress peaks under the endplate. It is therefore unlikely that bone cement alone would cause detrimental stresses on the adjacent vertebrae that could lead to vertebral fracture [32]. The results of this current study support the hypothesis that the use of cement-augmented screws (AwC-TSF-S) does not put the adjacent vertebrae at increased risk of fracture. There are a number of limitations to this study that should be identified. Previous studies reported that the pull-out strength of screws is increased when augmented with cement [10; 33], but the pull-out strength was not evaluated directly in this current study. Given the complexity of the physiological spine and variations in fracture types, the models simulated in this study were greatly simplified. Only a specific single-level burst fracture (L3) was simulated, and kyphotic spinal deformity or anterior column collapse were not considered in the unloaded condition; each group had the same angle of lumbar lordosis in the initial unloaded condition. The structure of the vertebral body was assumed to be isotropic and homogenous. The model also did not consider the mechanical effects of muscle contraction, so true physiological loading was not adopted in this analysis. The complexity of the human spine and variations in material properties and boundary conditions makes it a suitable candidate for finite element modeling. Moreover, the finite element method often provides advantages where there are such individual variations, allowing cause-effect relationships to be isolated and fully explored.
For instance, some have suggested preparing double-network (DN) hydrogels, and found that PAMPS/PAAm DN gels sustained more than 20 times the stress that PAMPS/PAAm SN (single-network) gels can withstand [8]. Some researchers blended PVA with chitosan and gelatin and achieved better protein adsorption and higher cell proliferation on PVA composite gels [9; 10]. Others, on the other hand, have combined Al\({}_{2}\)O\({}_{3}\) or Ti with PVA-H and succeeded in implanting the composite material in a dog' femur [11]. Graphene oxide (GO) is single-layer graphene with oxygen functionalities and is known as an excellent nanofiller. GO not only exhibits the physical properties of graphene but also shows dispersibility in water and other organic solvents because of the presence of oxygen functionalities. This important property enables mixing of GO with PVA on a molecular level. Some studies [12; 13; 14; 15; 16] on PVA/GO composite materials reported good dispersion of GO in a PVA matrix. The addition of GO enhanced the mechanical strength [12; 13; 14; 15] because GO integrated with PVA with strong hydrogen bonds and the external force could be transferred to GO, which has high mechanical strength. Furthermore, GO is reported to present no toxicity issue and can adhere to cells [12; 15; 16]; therefore, it is expected to be an excellent biomaterial in bone tissue engineering, drug delivery, and other biological fields. In this study, GO was used as a composite material to prepare a PVA/GO composite hydrogel (PVA-GO-H). To improve the fixation of the articular cartilage into the defect, good adhesion on the direct contact surface between PVA-H and underlying bone is necessary [11; 17]. Therefore, we expected that GO composition could enhance the adhesion to the bone. Several methods are used to prepare PVA-H, such as the cast-drying method (CD) [18], the freeze-thawing method (FT) [19], and low-temperature crystallization (LTC) [20]. In general, CD is used to prepare hydrogels with high strength and transparency. However, it is unlikely to obtain PVA-GO-H with good dispersion by the CD method because of the precipitation of GO in solution. Consequently, we used the LTC method to prepare PVA-GO-H. PVA-H and PVA-GO-H with different GO concentrations prepared using different oxidation times for GO were prepared and then their contact angles, mechanical properties, and biocompatibility were investigated. As a result of our work, high-water-content hydrogels suitable as artificial cartilage materials with excellent mechanical properties and biocompatibility could be anticipated.
## 3 System Model
### Deployment  
In this paper, we consider that AR/VR/XR applications are expected to be critical use-cases for NR RedCap UEs. These applications are characterized by constrained battery lifetime and thus require high energy efficiency and also have strict requirements for latency at the air interface. In most cases, a person with an AR/VR/XR device moves in space,which also imposes difficulties in maintaining the required quality of service (QoS). The traffic of these devices can be described as data streaming in a macro-mobility environment.  
We consider a typical city square environment of squared form with side length \(L\) m with a BS located at \((0,0)\); see Figure 1. The system serves a number of AR/VR/XR users that utilize RedCap UEs. We consider a user initially located at the coordinates \((x,y)\) at time \(t=0\) and concentrate on its time dynamics affecting its performance. The height of BS and UE are constant and considered to be \(h_{A}\) and \(h_{\mathrm{UL}}\), respectively.
# Fire Safety Risk Analysis of Conventional Submarines
## 3 Overview of Systems and Risks
### Fire Dynamics on Submarines and Possible Consequences  
Submarines have a number of unique characteristics that contribute to rapid growth and spread of fire and smoke, the effectiveness of fire suppressants, and occupant behaviour in the event of a fire. Some key physical characteristics, highlighted by the US Naval Ships' Technical Manual Chapter 555--Volume 2 Submarine Fireighting [6], and other factors are discussed below.  
* Fire Growth and Intensity--Submerged submarines are pressurised and without ventilation, creating ideal conditions for accelerated fire growth and overall higher intensity.
* Spread of Smoke and Heat--the layout of a submarine places many ignition and fuel sources at the bottom of the submarine whilst access points in many compartments are provided to the decks above which provides ideal conditions and pathways for the spread of rising smoke and heat.
* Occupant Behaviour--in fire scenarios, occupant characteristics can be wide and varied with some contributing significantly to the outcome of a fire incident. Fortunately, in a submarine all occupants are fit, well trained, intimately familiar with the layout of the submarine and there is always at least a subset of the crew that is highly alert and actively monitoring the health of the submarine.  
These characteristics provide both challenges and opportunities to manage fire risks and aspects that are unique to submarines and must be considered carefully in the design and operation of a submarine.
## 4 Experiments
### Comparison with Prior Methods  
To show the effectiveness of our method, we compare the performance of searched architectures with other state-of-the-art works on the COCO test-dev dataset in Table 2. Our discovered models (FastDARTSDet) achieve the competitive and even better performance compared with the peer NAS methods. Specifically, FastDARTSDet with N = 1 achieves 36.4 AP with only 5.8 M parameters, outperforming EfficientDet-D0 by 2.6% AP. FastDARTSDet with N = 2 achieves 40.0% AP with 6.9 M parameters, surpassing EfficientDet-D1 by 0.4% AP with similar parameters. Moreover, compared to prior NAS methods for object detection, our method only requires 4.2 GPU-days, significantly faster than prior NAS methods. Additionally, most of the prior methods independently search either backbone (DetNAS, EfficientDet) or FPN modele (NAS-FPN, NAS-FCOS, Auto-FPN), and only a few methods propose to jointly search both backbone and FPN (SM-NAS, Hit-Detector). On the one hand, compared to independent search method, our search space is much larger; on the other hand, compared to other joint search method, our method is much more efficient and faster. Figure 7: Confusion matrix among 80 classes of the searched model. Figure 8: Ground true (**a**) and predicted (**b**) bounding boxes of images from the validation set of COCO. Different colors in each images denotes different categories. (**a**) Ground Truth; (**b**) Prediction. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Normal Cell**} & \multicolumn{3}{c}{**Architecture**} & \multirow{2}{*}{**mAP**} & \multirow{2}{*}{**Number of Parameters**} & \multirow{2}{*}{**Search Cost GPU-Days**} \\ \cline{2-2} \cline{5-10}  & & **Backbone** & & **FPN** & & & & & \\ \hline \multirow{4}{*}{N = 1} & default & default & 33.9 & 5.81 M & 1.0 \\  & searched & default & 34.1 & 5.93 M & 4.2 \\  & default & searched & 34.9 & 5.87 M & 5.2 \\  & searched & ** searched** & **36.4** & **5.76 M** & 4.2 \\ \hline \multirow{4}{*}{N = 2} & default & default & 38.1 & 6.82 M & 1.0 \\  & searched & default & 38.7 & 7.29 M & 4.2 \\ \cline{1-1}  & default & searched & 39.0 & 7.03 M & 5.2 \\ \cline{1-1}  & searched & ** searched** & **39.9** & **6.94 M** & 4.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance of joint and independent search for backbone and FPN on MS-COCO validation set. The default architecture of backbone is the architecture by DARTS [22]. The default architecture of FPN is the architecture of PANet [60]. Specifically, the ‘default’ backbone utilize the discovered architecture searched on classification task, which cost 1.0 GPU-day. Consequently, for the setting of ‘default’ backbone and ‘default’ FPN, the search cost is 1.0 GPU-day. For the setting of ‘default’ backbone and ‘searched’ FPN, the search cost is 5.2 GPU-day (1.0 GPU-day to search default backbone and 4.2 GPU-days to search FPN). The best setting and the corresponding performance among each block is in bold.
# A Method for Efficient Quality Control and Enhancement of Mobile Laser Scanning Data
## 7 Conclusions  
Since we started researching mobile laser scanning, numerous new systems become available on the market, and many companies have begun offering 3D capture services. While infrastructure owners, city governments, and others recognize the benefits of efficient data collection, knowledge of MLS data quality is still lacking. Datasheets are of limited value in assessing whether the MLS point clouds meet the application-specific requirements. Ground control points provide independent verification but prove expensive for MLS surveys of long corridors.  
Our contribution addresses an efficient method for quality control of MLS surveys. The idea is to determine point cloud deviations to MLS point cloud data of another pass or another scanner. We evaluated datasets of three challenging scenes and detected systematic errors after a tunnel of almost 20 cm and inhomogeneous point cloud distortions of 8 cm next to high retaining walls. Varying co-registration parameters also indicated poor georeferencing quality.  
In a nutshell, our proposed methodology computes transformation parameters for individual scan profiles to minimize point cloud discrepancies. By eventually transforming profile-by-profile, we account for short-term variations and compensate for them without interpreting the cause of the error. As demonstrated, our approach minimizes deviations between multiple MLS passages and multiple scanners significantly. After removing the systematic components, we propose to average the data, obtaining a quality-enhanced MLS point cloud.  
Conceptualization, S.K.; methodology, S.K.; software, S.K.; measurement campaign, S.K.; data processing, S.K.; writing--original draft preparation, S.K.; writing--review and editing, S.K. and W.L.; supervision, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.  
The investigations presented herein were part of a project on the monitoring of retaining structures supported by the Austrian Highway Agency and accept Open Access Funding by the Graz University of Technology.  
Not applicable.  
The data presented in this study are accessible from the corresponding author upon request.  
Figure 22: Concept of quality-enhanced MLS point clouds.  
**Acknowledgments:** We acknowledge the collaboration with Joanneum Research and Amberg Technologies in data acquisition. The discussions with [PERSON] helped in processing the trajectory data of the Pegasus:Two Ultimate Dual Head system. Many thanks to 3 DProScan for providing the 3D model of the mobile mapping system for the rendering in Figure 1. Last but not least, we thank the staff of ASFiNAG and the State of Styria for providing escort vehicles and controlling traffic during the data collection.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 7 Experiments
### Dataset Splitting at a 5:5 Ratio
#### 7.4.2 Comparison of Loss and Accuracy in the Testing Stage  
According to Table 8, under the condition where all samples were randomly and equally divided, the results were highly favorable. Models with original structures, namely WRN_k4 and WRN_b256, achieved excellent performance in the training set, which meant that the features learned covered the entire training set. However, the difference between the training and testing set results discussed in the previous section was still observed. In addition, the instability of the system caused by the enlargement of some kernels impeded a notable outcome. Consequently, more epochs were required to reduce the loss in training to an ideal level.  
The NN with 3D structure concatenation was not only rapid in terms of convergence during training but also stable. The results of this experiment for the training and testing sets were considerably close. In other words, the 3D concatenation network proposed in  
Figure 13: Terminal convergence results of average loss under the condition where the samples were randomly distributed between training and testing sets at a 5:5 ratio (after epoch 401).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Network** & **Loss** & **Accuracy** \\ \hline Training Set & WRN\_k4 & 0.01442 & 99.6091\% \\  & WRN\_b256 & 0.01703 & 99.7418\% \\  & WRN\_kernel5 & 0.123924 & 96.4154\% \\  & WRN\_ex3D & 0.017713 & 99.4837\% \\  & WRN\_ex3D\_v2 & 0.013119 & 99.6755\% \\  & WRN\_ex3D\_v3 & 0.004656 & 99.8746\% \\ Testing Set & WRN\_k4 & 0.268009 & 93.0769\% \\  & WRN\_b256 & 0.301332 & 91.6729\% \\  & WRN\_kernel5 & 0.589504 & 84.6527\% \\  & WRN\_ex3D & 0.101426 & 97.0276\% \\  & WRN\_ex3D\_v2 & 0.076275 & 97.4981\% \\  & WRN\_ex3D\_v3 & 0.056063 & 98.3794\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Testing results under the condition where the samples were randomly distributed between training and testing sets at a 5:5 Ratio.  
this study was capable of improving the overall NN performance, and stabilizing and accelerating model convergence.  
In the meantime, comparing the results of models WRN_ex3D and WRN_ex3D_v2 revealed that the original 2D convolution layer in the first layer became obsolete after the addition of a 3D structure. Additionally, model WRN_ex3D_v3 was a modification of model WRN_ex3D_v2, indicating that using the compression method with learnable parameters to reduce the depth axis (i.e., the time axis) to one dimension was more effective in bringing out the surplus performance of the NN than simply using the mean method to reduce the number of dimensions.
## 1 Introduction
### Approach and Big Picture
The meta code and further details of the calculations can be found in Section 2.3. In step five, a data complexity reduction is performed. As mentioned, the ratio of features to datapoints can be a crucial influence on model quality. Reducing the number of features can lead to better-performing models despite losing some of the information. There are multiple ways to reduce data complexity to achieve a high performance boost without losing too much information. One way is to keep all features but reduce the dimensionality by transforming the features into a new set of features, for example, using principle component analysis. Another way is to select features based on calculated metrics, for instance, feature importance values of a decision tree algorithm. This work uses feature selection based on the feature importance values of the XGB models. For each dataset, the XGB model with the lowest root mean square error (RMSE) is defined as the best-fitting XGB model. The feature importance values of this model are used to select the features. The percentage of features kept is called filter fraction (ff). A filter fraction of 100% is equal to all features used to train a model. Twelve filter fractions are used, with 10% steps between 100% and 10%. Smaller steps are used between 10% and 1%. The final number of features is rounded up to the next integer. Twelve datasets and twelve filter fractions result in 144 datasets used to train the models. Step six is to train and optimize ANN and XGB models for the reduced datasets in the same way as in step four. It is essential to test whether the differences in the results of the datasets are significant. Other works used a two-sided [PERSON]'s \(t\)-test to compare different models [20] and different datasets [21]. Therefore, this work applies a two-sided [PERSON]'s \(t\)-test with a 0.5 significance level to test whether the results are significant. The test is performed for each dataset. The overall best dataset is used as a comparison set. This work focuses on obtaining insights by  
Figure 4: Big picture of the chosen approach. comparing model results for different datasets, which is why the [PERSON]'s \(t\)-test is performed for the datasets and not explicitly between the ANN and XGBoost models. Finally, the results of the two rounds of computation are visualized and discussed. The corresponding hypothesis is evaluated based on the model quality that can be achieved in each case. Plots of the model scores for varying filter fractions and selected parameters are discussed to assess the robustness of the models and the likelihood that the model score is a result of the feature selection and not of the model parameters.