esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Railway bridges are constantly exposed to the cyclic loading of trains during their life cycle. Due to such loading, bridges have the potential for structural damage and collapse. Thus, regular observation is needed to secure bridge safety. Conventionally, bridge deformation measurements are conducted by contact type measurement methods such as a strain-gauge, or by non-contact type measurement methods, such as a laser tracker. However, it is complicated to survey because the two types require skilled technicians and cost. Moreover, both conventional methods can generally only measure at limited points on the bridge.  
This study presented a sequential photogrammetry to measure the span 3D displacement of a railway bridge. To automate the photogrammetric process almost, we proposed a relative orientation method that uses coplanarity and scale adjustment using the feature points on the bridge. We measured the 3D displacement of a bridge span using two cameras of the same type with the proposed method. Compared to a precise 3D laser tracker, the difference was about 1 mm in the directions of the three axes and the 3D displacement forms were also similar. There is almost no displacement in either the \(X\)- or \(Z\)-directions in the laser tracker. However, it cannot indicate the entire torsion of the span since this survey is only for one point. Thus, through the proposed method, we remotely confirmed the occurrence of sag and twist due to the cyclic loading of the train simultaneously in this bridge.  
If a high-speed camera is used with the proposed technique, the dynamic deformation of the bridge can be continuously measured at intervals of 0.1 s or more, as well as selectively measuring only the event time during which the loading is applied. The proposed method is advantageous because it does not require a control point survey or the installation of a photogrammetric board to implement photogrammetry, but only requires measurement of the distance between two feature points on the bridge for scale-adjustment of the ROPs. The distance can also be precisely measured using a portable laser rangefinder. Therefore, the whole photogrammetric-process could almost be automated. Through the proposed method, it is possible to find the cause of the risk of a railroad bridges, and prevent safety accidents caused by manpower inspection. This method may also replace other inconvenient and costly tests.  
Conceptualization, H.L.; data curation, H.L.; formal analysis, H.L. and J.O.; methodology, H.L. and J.O.; validation, H.L. and J.O.; writing--original draft, H.L.; writing--review and editing, H.L. and J.O. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2018R1D1A1B06049484).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 3 Multi-Layer Network Architecture  
A multi-layer architecture is utilized here that is comprised of MS terminals, fog nodes in cluster distribution, and the cloud core, as depicted in Figure 1.  
MS Terminals (Layer I): User terminals that demand various services of different delay and capacity specifications. Terminals can be mobile stations, sensors, vehicles, desktops, laptops, etc., which are distributed across the fog nodes in each cluster, i.e., each cluster is comprised of multiple fog nodes.  
Primary Nodes in Fog-RAN (Layer II): This layer consists of distributed, homogenous fog nodes that are collocated with RRHs at the proximity of MS terminals in Layer I and cloud-RAN in Layer III. They are equipped with beamforming architectures to provide high bandwidth links with the MSs. It is the gateway at the edge of the network that receives traffic requests and thus provides services at stringent delay bounds and limited resources.  
Secondary Nodes in Fog-RAN (Layer II): Another set of fog nodes that possess higher resources as compared to the primary nodes, albeit less resources than the cloud core. Every secondary node manages a cluster of primary nodes through direct links. This intermediate structure combines the benefits of Layers II and IV, i.e., higher resources at the expense of a slight increase fronthaul delays.  
Cloud-RAN (Layer IV): This layer is comprised of widely dispersed cloud nodes that possess abundant resources. It acts as the network BBU and contains the NFs that are offloaded to the fog nodes via wireless fronthaul links that operate on microwave sub 6 GHz.
# Decision Framework for Predictive Maintenance Method Selection
## 5 Evaluation of the Proposed Framework
### Case 3: Engine Condition Trend Monitoring for a Military Transport Aircraft  
_Case description:_ This department maintains military transport aircraft. For the engines, a fixed-interval preventive maintenance policy has been applied successfully in the past. Recently the engine condition trend monitoring (ECTM) technique has become available. ECTM is the process of using measured characteristics (i.e., compressor speeds, intertwine temperature and fuel flow) during specified flight conditions (i.e., altitude, airspeed, outside air temperatures) and comparing these to predicted values to provide confirmation of engine gas path efficiency and predict maintenance needs based on these data [39].  
_Challenge:_ To reduce maintenance costs, the department is investigating whether it is both economically and technically feasible to apply a condition-based maintenance approach using ECTM. Table 5 shows the analysis for this case using the prosed framework.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Step in Framework** & **Result and Explanation** \\ \hline \multirow{2}{*}{Starting point} & **Decision Pull—**Ambition to understand deterioration of reactor core (also required by safety regulations) \\ \hline \multirow{4}{*}{Ambition level} & - & Monitor individual system \\  & - & Include changes in future usage of reactor (e.g., different end products) \\  & \(\rightarrow\)**Ambition level 5** \\ \hline \multirow{4}{*}{Types of data available} & - & Expert knowledge \\  & - & High-quality historical data \\  & - & Loading data and usage data \\  & - & Condition and health data \\ \hline \multirow{2}{*}{Match ambition level—data?} & **Yes—**AL 5 requires data on the usage or loads and data on the condition or health, which are available \\ \hline \multirow{2}{*}{Positive business case?} & **Yes—**Possible impacts on safety create enough urgency for a maintenance approach giving insight into future conditions of the reactor \\ \hline \multirow{2}{*}{MM selection} & **Advise:** Apply a model-based approach by using a physical model \\  & & This approach satisfies the originally set ambition level \\ \hline \hline \end{tabular}
\end{table}
Table 4: Application of the decision framework to case 2.  
Conclusion:For this aviation case, there is a match between the ambition level and the data available through the new technology (ECTM). The framework confirms that this degradation-based maintenance approach is the appropriate approach for this company.
## 2 Related Work  
In recent years, researchers have conducted many studies on DGA attacks. Most of the studies focused on detecting AGDs more effectively, while some studies focused on designing a more covert DGA that makes it harder to detect [40; 41; 42; 43; 44; 45]. Detection methods can be divided into domain name-based detection methods and traffic-based detection methods according to their analysis units. Traffic-based methods divide the traffic into groups and classify the groups by extracting behavior features to detect DGA attack events [12; 13; 14; 15; 16]. The domain name-based methods only analyze the queried domain names in DNS traffic to detect AGDs. Common DGAs include arithmetic-based methods, hash-based methods, permutation-based methods and dictionary-based methods. The domain names generated by arithmetic-based methods, hash-based methods, and permutation-based methods usually exhibit randomness in character sequences, which are rarely meaningful in English. Here, the AGDs generated by the three DGA methods are combined and classified as character-based AGDs. The domain names generated by dictionary-based DGA methods are usually composed of English words, which are referred to as dictionary-based AGDs. The two kinds of AGDs are quite different and the features for detection must be designed accordingly. Due to the differences in character-based and dictionary-based AGDs, domain name-based detection methods are further divided into character-based AGD detection methods and dictionary-based AGD detection methods. Our method is a domain name-based detection method, and we mainly focus on the domain name-based detection methods in this section.
## 4 The New Computational Procedure
### The DAE Constraints' Parametrization
There is a common way to transform a difficult problem of solving the DAE system into a system of nonlinear algebraic relations. Then, the iterative methods can be used to improve the current solution step-by-step [24]. Opposite the presented procedures, the direct shooting approach (a combination of the multiple shooting method with the DAE solver) can be applied. It is assumed that the solution of the DAE system obtained on a shorter subinterval can be more accurate than a solution calculated for a long domain. However, even in this reduced situation, the DAE solver can fail and result in no solution, and the optimization procedure can be interrupted. Finally, the number of shooting points should be increased, and the whole solution procedure needs to be started again. Therefore, a combination of the multiple shooting method with the modified (parametrized) DAE model solved by an outer numerical procedure is considered. In particular, the modification of the DAE system is not only understood as its linearization:  
\[\left[ \begin{array}{c}\dot{y}(t)\\ 0\end{array} \right]=A\Big{|}_{(y(t_{\lambda}),z(t_{\lambda}),u(t_{\lambda}))} \left[ \begin{array}{c}y(t)\\ z(t)\end{array} \right]+B\Big{|}_{(y(t_{\lambda}),z(t_{\lambda}),u(t_{\lambda}))} u(t), \tag{38}\]  
where \(t_{\lambda}\in[t_{0}\quad t_{f}]\). The linearization of the model constraints should be performed numerically or analytically at the beginning of the computations. Moreover, the obtained linearized model is dependent on the chosen simulation point \((y(t_{\lambda}),z(t_{\lambda}),u(t_{\lambda}))\). One of the main ideas considered in this research is to estimate the unknown model parameters. This approach enables us to apply all forms of mathematical models, which can be effectively treated by the implemented outer DAE solver. The proposed parametrization approach results in the following theorem. Theorem 2: _The parametrization of the DAE system in a semi-explicit form (1) with the \(\mathcal{P}_{m_{f}}\) and \(P_{m_{g}}\) parametrizations for the differential and algebraic model constraints, respectively, introduces:_  
\[n_{y}\times m_{f}+n_{z}\times m_{g} \tag{39}\]  
_decision variables, where \(m_{f},m_{g}\in\mathcal{N}_{+}\)._  
Proof of Theorem 2.: Let us note that:  
\[\left[ \begin{array}{c}\dot{y}(t)\\ 0\end{array} \right] = \left[ \begin{array}{c}\mathcal{P}_{m_{f}}\{f(y(t),z(t),u(t),p,t )\}\\ \mathcal{P}_{m_{g}}\{g(y(t),z(t),u(t),p,t)\}\end{array} \right]\] \[= \left[ \begin{array}{c}\widetilde{f}(P_{f};y(t),z(t),u(t),p,t)\\ \widetilde{g}(P_{g};y(t),z(t),u(t),p,t)\end{array} \right],\]  
where \(m_{f},m_{g}\in\mathcal{N}_{+}\) and \(P_{f}\in\mathcal{R}^{n_{g}\times m_{f}}\), \(P_{g}\in\mathcal{R}^{n_{z}\times m_{g}}\) are the matrices of unknown parameters.
# In Situ and Partial In Situ Synthesis of Cellulose Magnetic/Maghemite Composites
## 2 Materials and Methods
### Equipment and Methods
The ratio of two other infrared bands, I\({}_{3346}\)/I\({}_{1321}\), is known as the hydrogen bond intensity (HBI) closely related to the crystal system and the degree of intermolecular regularity, considering the chain mobility and bond distance, as well as the amount of bound water [7,8]. The magnetic measurements (magnetization curves) were performed on a Lake Shore 8600 vibrating sample magnetometer (3.2 T for applied magnetic field at room temperature). The surface morphologies were visualized by scanning electron microscopy (SEM) on an ESEM Quanta 200 electronic deflection microscope. The settled voltage electron acceleration rate to gather the SEM images was 5 kV. Transmission electron microscopy (TEM) analysis for CM\({}_{0}\) and CM\({}_{15}\) composites was conducted with a Hitachi High-Tech HT7700 instrument, operated in high-resolution mode at 100 kV accelerating voltage. Samples were prepared by drop casting from the diluted dispersions of nanoparticles in ethanol on 300 meshes of holey carbon-coated copper grids (Ted Pella) and vacuum-dried. Thermal degradation was performed using a thermobalance model STA 449F1 Jupiter (Netzsch, Germany) for simultaneous thermal analysis TG-DTA. The thermogravimetric analyzer was calibrated on temperature and sensitivity using the melting points of some standard metals (Hg, In, Sn, Bi, Zn, and Al) from \(-\)38.5 \({}^{\circ}\)C to 600 \({}^{\circ}\)C. Sample masses ranging from 7 to 10 mg were heated from 25 \({}^{\circ}\)C to 600 \({}^{\circ}\)C with a 10 \({}^{\circ}\)C/min heating rate. The helium (99.999% purity) as an inert gas with a flow rate of 50 mL/min and a protective purge for thermobalance of 20 mL/min was used. The samples were heated in open Al2O3 crucible, and Al2O3 was used as reference material. Data collection was conducted with Proteus(r) software, version 8.17.
# MS3D: A Multi-Scale Feature Fusion 3D Object Detection Method for Autonomous Driving Applications
## 3 Proposed Method
### Voxelization  
The voxel-based encoding strategy preprocessed point cloud data as follows:  
Figure 1: MS3D network structure diagram.  
Voxel Grid Construction: The point cloud dataset \(P\) was divided into a spatial grid with dimensions \(X\times Y\times Z\), with voxel height \(H\) defined as the difference between the maximum and minimum values in the vertical coordinate. On the xy-plane, the point cloud was segmented into \(N\times M\) cylindrical voxels using a grid size of \(p\times q\), where \(N=X/p\) and \(M=Y/q\).  
Voxel Representation: Each point \(p_{i}\in P\) was characterized by its 3D coordinates \((x_{i},y_{i},z_{i})\) and reflection intensity \(r_{i}\). The point cloud \(P\) was discretized into voxels \(v\), with each voxel containing a set of points \(\{p_{i}=[x_{i},y_{i},z_{i},r_{i}],i=1,\ldots,N\mid p_{i}\in v\}\). The number of points per voxel was normalized to \(N_{\max}\) through sampling or padding.  
Feature Vector Construction: Each cylindrical voxel \(P_{k}\) in the xy-plane, with a base size of \(dx\times dy\) and fixed height \(H\), aggregated internal points to form a feature vector \(f_{k}\). This vector included global coordinates \((x_{i},y_{i},z_{i})\), reflection intensity \(r_{i}\), and local offsets \((x_{i},y_{i},z_{i})\) relative to the voxel center, represented as \(f_{k}=\big{[}(x_{i},y_{i},z_{i},r_{i},x_{i},y_{i},z_{i})\mid\forall p_{i}\in P _{k}\big{]}\).  
Pseudo-Image Reconstruction: The feature vector \(f_{k}\) was then reconstructed into a pseudo-image \(I\) of size \(H\times W\times C\), where \(C\) is the number of feature channels and \(H\) and \(W\) are the grid dimensions on the xy-plane. This transformation from unstructured point cloud data to a structured feature representation enhanced the input for subsequent models, retaining spatial structure and improving environmental perception accuracy and computational efficiency in autonomous driving systems.
# Inundation Extent Mapping by Synthetic Aperture Radar: A Review
## 2 Techniques to Produce SAR Inundation Maps
### Principles
In addition, more areas can be shadowed by buildings than by natural terrain, resulting in greater uncertainty about flood detection in urban areas.
# Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning
## 4 Results  
### Military Target Detection  
We used the method described in Section 3.3 to detect typical military targets. Four typical military target images were selected from the ImageNet2012 [41]. This dataset was segmented into a training and testing set. The four typical targets are airships, aircraft carriers, tanks, and uniformed soldiers. A total of 2187 images were selected from the dataset, of which 1970 were used for training and 217 for testing. There were no less than 500 pictures in each category. Unless otherwise noted, all the original images in this article about the four typical targets were from Imagenet2012.  
The initial training parameters are shown in Table 3. \(IQU_{threshold}\) is the intersection over union (IOU) threshold. We used multi-scale training methods.  
We used k-means clustering to determine our nine bounding box priors. On the selected dataset, the nine clusters were: (\(55\times 69\)), (\(151\times 91\)), (\(84\times 261\)), (\(200\times 188\)), (\(331\times 137\)), (\(179\times 346\)), (\(358\times 223\)), (\(350\times 303\)), (\(373\times 387\)).  
We used the pre-training weight on the coco data set as the initialization weight. After 17 k steps of training, the model converged. The training loss is shown in Figure 7. As shown in Figure 7a, after 17 k steps of training, the loss was reduced to 0.6, which is basically convergent. The mean average precision (mAP) at IOU = 0.5 (mAP@.5) was 91.55%, as shown in Figure 7b. The results showed that the model we trained had high precision and could meet our application requirements. The total loss was calculated on the training set, and the mAP was calculated on the test set.  
Through training, our recognition precision of these four typical targets in different environments reached 91.55% (mAP@.5=91.55%), which highly met our application requirements. Moreover, this recognition process was just a demonstration. In practical applications, specific databases could be added according to the actual needs, and the recognition classes and precision could be increased through retraining (Figure 8). The recognition results of the model after our training are shown in Figure 8. As can be seen from Figure 8, our model could well identify four typical military targets. When the resolution of the input picture was \(416\times 416\), the model detection time was less than 25 ms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Value** & **Variable** & **Value** \\ \hline \(IQU_{threshold}\) & 0.5 & \(\alpha_{initial}\) & \(1\times 10^{-4}\) \\ Batch\_size & 6 & \(\alpha_{end}\) & \(1\times 10^{-6}\) \\ Input\_size & [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] & \(\alpha_{train}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The training parameters.  
Figure 7: The total training loss and mean average precision. (**a**) the total loss; (**b**) the mAP.
## 5 Summary and Outlook  
Limited angle tomography is important for many applications, especially at technical processes but suffers from artifacts and unknown measurement deviations. The hypothesis of the approach of extrapolation of non-scannable angular regions through an neural network with only synthetic data sets used for training can be confirmed. We show that we can reduce the MSE by a factor of 2.24 and the maximum error by a factor of 2.22 on synthetic data and reduce the MSE by a factor of 1.93 and the maximum error by 1.95 when evaluating measurement data. The dense Unet was applied to real measurement data acquired with a high-speed camera-based laser interferometric vibrometer on a bias flow liner model. The measurements validate the approach, demonstrating the big potential for this technology to generate a paradigm shift in limited angle tomography. Using the neural network for extrapolation only, is a simple extension and easier to implement compared to using neural networks for the complete tomographic reconstruction. Established signal processing only needs to be extended with this technique and has not to be changed completely.  
Further investigation is required into the translation of this technique to other specimen and structures especially regarding their sparseness and complexity. In the next steps, the approach can be further improved. We want to use 3D data for extrapolation. By doing so, the measurement artifacts could be minimized, resulting in more appropriate information to feed in the neural network for extrapolation. In addition, more complex structures have to be trained. Finally the approach will be applied at real flow channel measurements.  
Conceptualization, O.R., J.G., R.K. and J.C.; methodology, O.R. and J.G.; software, O.R.; validation, O.R.; formal analysis, O.R.; investigation, O.R. and J.G.; data curation, O.R.; writing--original draft preparation, O.R.; writing--review and editing, J.G., J.C. and R.K.; visualization, O.R. and J.G.; supervision, J.C. and R.K.; project administration, O.R. and R.K.; funding acquisition, J.G., R.K. and J.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the _Deutsche Forschungsgemeinschaft_ (DFG) grant number CZ55/41-1.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to its large amount. It is stored locally and will be made accessible via cloud sharing after request.  
**Acknowledgments:** The authors would like to thank our partners [PERSON], [PERSON] and [PERSON] from _TU Berlin_ and the _Deutsches Zentrum fur Luft- und Raumfahrt_ (DLR), Berlin, Germany.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions and Future Works  
This paper proposed a sensitive motion detection approach called Momentum of Received Signal Strength (MRSS). MRSS can predict an MN's motion without any assistance from positioning systems. It defines three motion states of an MN: approaching state, stationary state, and leaving state. With MRSS, an MN can trigger a handoff at the right time according to its motion. Thus, MRSS can reduce unnecessary power consumption during handoff procedures. Additionally, a Dynamic MRSS (DMRSS) algorithm is presented to improve the sensitivity of MRSS by using different parameter settings at different motion states. Simulation results show that MRSS and DMRSS perform better than other algorithms. Moreover, the MRSS and DMRSS algorithm are simple and can be easily implemented and integrated in many mobile applications, such as intelligent transport systems (ITS), mobile sensor systems, and vehicular communication systems.  
Although DMRSS performs well by using a motion state dependent smooth factor switching method, the suitable smooth factor pairs must be selected manually in advance. In the real world, the network environment (e.g., frequency band, antenna height, transmitter power, antenna gain, fadding model) and the movement of mobile nodes (e.g., velocity, direction) change dynamically. Thus the manual  
Figure 15: The performance analysis of MRSS-based handoff algorithm in the Mobile WiMAX environment.  
configuration can be selected only for some particular communication systems. In the future, we will continue to develop an intelligent learning algorithm that can self-adjust the weight of various EWMA filters and automatically combine the analysis results from these EWMA filters to identify the user motion state in heterogeneous networks.