esa-sceva/satcom-chunk-collection huggingface dataset:
# Ground Deformation Associated with Deep Excavations in Beijing, China
## 3 Characteristic Analysis of Deformation
### Factors Influencing Maximum Surface Deformation
#### 3.3.3 Embedded Depth Ratio  
The embedded depth ratio (EDR), as defined by [PERSON] et al., concerns the ratio of the embedded depth below the bottom of the excavation site to the excavation depth [1].  
Figure 8: Effect of the excavation depth on maximum ground surface settlement.  
Figure 9: Effect of the length–width ratio on maximum ground surface settlement.  
It is thought that by ensuring sufficient wall stiffness, and appropriately increasing the embedded depth of the pile, it could improve the anti-uplift stability coefficient and reduce the surface settlement [26]. Based on experience, in some cases, when the EDR is greater than 0.9, the prevention of the surface settlement is not prominent.  
Figure 10 illustrates the relationship between the EDR and \(\delta vm\). The EDR of deep foundation pits in Beijing is primarily concentrated in the range of 0.28 to 0.44, averaging at 0.36. The \(\delta vm\) decreases with the increase of EDR, which predominantly lies between 0.33 and 0.38. There are three stages that occur as the rate decreases. During the steep stage, when the EDR varies from 0.28 to 0.34, the \(\delta vm\) decreases rapidly and linearly. During the declining stage, the rate at which \(\delta vm\) decreases becomes slower than that of the previous stage, and during the stable stage, there is an inflection point in the curve, and the effect of EDR on reducing the surface settlement is not notable. It is evident that for an ultra-deep foundation pit, when the EDR reaches a certain value, the effect is not prominent, but the project cost will significantly increase. Therefore, it is not advisable to blindly increase the EDR.
###### Abstract  
Free space optics (FSO)-based optical body area networks (OBANs) are receiving massive attention as an opportunity to address the limitations of their radio frequency (RF)-based counterparts. This boom in research interests is primarily due to multitude of benefits, including high capacity, immunity to electromagnetic interference (EMI), rapid installation, cost efficiency, and license-free use of spectrum. Securing the transmission of patient health data against interception in OBANs using insecure FSO channels is a challenging task. Therefore, we propose a low-cost, flexible, and secure OBAN based on FSO technology and a time-delayed two dimensional (2D) spectral/spatial optical code-division multiple access (OCDMA) system. The proposed architecture consists of eight sensors attached to the bodies of patients. The sensors operate at a rate of 50 kbps. Electrical data generated from each sensor are used to modulate an optical carrier and then encoded using 2D-spectral/spatial double weight-zero cross correlation (DW-ZCC) code. The 2D encoded optical signals are then time delayed to eliminate the multiple parallel FSO channels between the transmitter and medical center. The combined optical signal consists of eight 2D-encoded time-delayed optical signals transmitted towards a remote medical center over an FSO channel with a range of 1 km. The received signal is decoded and the data from each sensor are recovered after photodetection at the medical center for further analysis. The overall performance of the sensors is analyzed using bit-error rate (BER) and quality factor (Q-factor) plots for different weather conditions and lengths of the FSO channel, considering the log-normal channel model. The capital expenditure (CAPEX) of the proposed architecture is analyzed and compared with the conventional 2D-spectral/spatial FSO system to determine the overall impact of introducing time delay units on the cost of implementation.  
optical body area network; optical code division multiple access; time-delay; 2D DW-ZCC; free space optics; log-normal channel model; CAPEX 2023  
2023139347  
2023
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 3 Results and Discussion
### Simulation Results
Moreover, the U-factor in the first process was 39% bigger than in the BIM model baseline, which was inconsistent as, in Design Builder, a template was created with all Revit materials imported. Consequently, the material properties should have been the same. In relation to the ground floor, clearly there were issues regarding the recognition of the imported data. First of all, in the simulation of the third case, there was no information about the area of this floor, and its U-value was much lower than in the BIM (by 80%). Moreover, the gbXML also showed differences with Revit, as its surface area was 40.67% smaller and its U-factor was 36.63% lower. Finally, referring to the sloped roof, the U-factor values did not differ much from the baseline model. However, in the first simulation process, any information about the roof area was extracted and, in the IFC, there was a difference of \(-\)13.87%. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Opaque Surface** & **Exterior Wall** & \multicolumn{2}{c}{**Ground Floor**} & \multicolumn{2}{c}{**Sloped Roof**} \\  & **Conduction Addition** & **Area** & **U-Factor** & **Area** & **U-Factor** & **Area** & **U-Factor** \\  & **[kWh]** & **[m\({}^{2}\)]** & **[W/m\({}^{2}\)K]** & **[m\({}^{2}\)]** & **[W/m\({}^{2}\)K]** & **[m\({}^{2}\)]** & **[W/m\({}^{2}\)K]** \\ \hline Revit & - & 309.00 & 0.33 & 113.00 & 3.03 & 98.00 & 0.41 \\ gbXML.in & 0.76 & 132.84 & 0.46 & 67.05 & 1.92 & No & 0.44 \\ Design Builder & & & & & & information & 0.44 \\ IFC in & & & & & No & & information & \\ CYPETHERM & 728.50 & 243.05 & 0.37 &
\begin{tabular}{c} No \\ information \\ \end{tabular} & 0.61 & 84.40 & 0.36 \\ HE & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Opaque Surface Conduction Elements. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Windows Heat** & **Area of** & **Glass** & **Glass** & **Windows** \\  & **Addition** & **Openings [m\({}^{2}\)]** & **U-Factor [W/m\({}^{2}\)K]** & **SHGC** & **Frame [Total m\({}^{2}\)]** \\ \hline Revit & - & 39.24 & 3.69 & 0.78 & 6.70 \\ gbXML in Design & 2501.83 & 38.13 & 2.93 & 0.13 & - \\ IFC in & 15,015.64 & 33.00 & 3.68 & 0.70 & - \\ CYPETHERM HE & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Window properties. These differences between BIM and BEM must be taken into account, as they are relevant to the energy analysis. In addition, in a smaller scale building, it is easier to correct the differences in the thermal properties of the envelope materials. However, when it comes to a bigger building, the work required to correct them would take a long time and would be prone to human error, by missing some property that was transferred incorrectly. Therefore, it is crucial that these values are either the same as in the BIM authoring tool, or present very small differences that can be disregarded.
###### Abstract  
The recently introduced Real Driving Emissions (RDE) light-duty vehicle emissions regulation requires testing with Portable Emissions Measurement Systems (PEMS) during type approval and in-service conformity. The studies on the accuracy of PEMS today are limited. An inter-laboratory correlation exercise with PEMS took place in Italy in 2017. Eight laboratories measured exhaust emissions from a Golden Euro 6 gasoline vehicle with a Golden PEMS installed in it, along with the individual lab's own PEMS, following the regulated laboratory method (bags from the dilution tunnel). The data of the exercise were used to estimate the repeatability and reproducibility of the methodology with PEMS. The statistical analysis estimated reproducibility of 2.9% (bags) to 5.5% (lab PEMS) for CO2, 20-25% for CO (all methods), 23-31% for NOx (all methods), and 29% (tunnel, Golden PEMS) to 39% (lab PEMS) for particle number. The mean differences of the PEMS to the regulated method were \(\pm\)1.5 g/km (or \(\pm\)1%) for CO2, \(<\)16 mg/km (or \(<\)5%) for CO, \(<\)4 mg/km (or \(<\)11%) for NOx and 1 \(\times\) 10\({}^{11}\) particles/km (40%) for particle number. The results of this study confirm the satisfactory performance of PEMS and the permissible tolerances introduced in RDE regulation.  
vehicle emissions; real-driving emissions (RDE); Portable Emissions Measurement Systems (PEMS); validation test; round robin; repeatability; reproducibility
# Active Thermography for the Detection of Sub-Surface Defects on a Curved and Coated GFRP-Structure
## 3 Experimental Set-Up
### Measurement System  
For the execution of the test series a test set-up was developed, which is shown in Figure 3. It allows a flexible arrangement of the test sample, the excitation unit and the camera. A clamping device adapted to the sample geometry can be moved in horizontal and vertical direction via linear axes. A rotation motor enables a rotational adjustment of the sample. The excitation unit can be swivelled as well as placed on the base plate of the test facility. The camera can also be flexibly positioned on the base plate and its height can be adjusted. For the experiments performed in this paper a 1 kW halogen lamp in reflection mode (excitation and camera are on the same side of the sample) is used as excitation unit. The infrared camera is the Image IR 8300 from the company InfraTec with an image format of \((640\,\times\,512)\) px\({}^{2}\), a temperature resolution of 0.025 K and a spectral range of 2 um to 5 um. The chosen objective is a 100 mm telephoto objective in combination with a 500 mm close-up (macro) objective, which has an image field of \((48\,\times\,39)\) mm\({}^{2}\) with a spatial resolution of 75 um. The distance between the camera lens and the test specimen is 500 mm.  
For the investigations, two ROIs A and B are defined on the test specimen; see Figure 2. ROI A lies above the marking hole 4 of the test specimen, the region of interest B lies below the marker. Two test series are performed, one for each ROI. In every test series, various excitation times are investigated, each for different distances \(k\) between the sample and the excitation unit. For example, in test series 1, which deals with the ROI A, the test specimen is heated for \(t_{\text{exc}}=10,15,20,25\) and 30 s. For each excitation time, the distances \(k=22,43,60,89,114,155\) and 300 mm are investigated. The infrared camera starts recording at 3 s after the end of the excitation and is done for at least 30 s with a frequency of 20 Hz and an integration time of 1600 us. The test specimen is investigated from the front perspective as well as from two angular perspectives. The observation from two different angles of view \(\alpha\) and \(\beta\) serves to examine the influence of the surface curvature on the defect visibility; see Figure 4. Table 2 shows an overview of the test series.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **ROI** & **Depth \(h_{j}\) [mm]** & **Diameter \(d_{j}\) [mm]** & **Depth-to-Diameter Ratio** \\ \hline
1 & A & 1.5 & 3 & 0.50 \\
2 & A & 1.4 & 2.5 & 0.56 \\
3 & A & 1.5 & 2 & 0.75 \\
4 & A \& B & 0 & 1.5 & - \\
5 & B & 2.4 & 3.5 & 0.69 \\
6 & B & 2.2 & 3 & 0.73 \\
7 & B & 2.6 & 2.5 & 1.04 \\ \hline \end{tabular}
\end{table}
Table 1: Properties of each defect of the modified test specimen in the two ROIs A and B, compare with Figure 2.
## 5 Analyses and Results
### Results of Measurement Model Assessment
#### 5.1.3 Convergent Validity  
AVE and CR are used to calculate construct convergent validity. AVE is calculated using formula 2. The AVE results were found to range from 0.506 to 0.564 as summarized in Table 3. The computed values of AVE for the configuration (CR = 0.892), composition (CR = 0.806), extension (CR = 0.839), integration (CR = 0.904), modification (CR = 0.929), and SaaS quality (CR = 0.937) constructs were 0.547, 0.512, 0.578, 0.578, 0.724, and 0.555, respectively. The AVE results and the CR of each show that all the AVE values are above the minimum cut-off point (\(>\)0.5). Moreover, all constructs meet the criteria of CR \(>\) AVE, indicating the construct convergent validity.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Construct** & **Item** & **Factor Loading** & **CR** & **AVE** & **Construct** & **Item** & **Factor Loading** & **CR** & **AVE** \\ \hline \multirow{8}{*}{SaaS Quality} & QA 1 & 0.75 & & & & Int 1 & 0.64 & & \\  & QA 2 & 0.77 & & & & Int 2 & 0.90 & & \\  & QA 3 & 0.69 & & & & Int 3 & 0.86 & & \\  & QA 4 & 0.73 & & & Integration & Int 4 & 0.78 & 0.904 & 0.578
# Modular Design and Decentralized Control of the Recupera Exoskeleton for Stroke Rehabilitation
## 2. Mechatronic System Design
### Electronic Design and Processing Architecture
#### 2.2.4 Safety Aspects  
An easy to access emergency stop is provided to the therapist in each arm and the backpack. In addition to this, a pedal-based emergency stop is provided to the patient. Each actuator joint has its own independent hard and soft safety limits on position, velocity and current levels. The controller becomes very stiff near the soft limits and the joints automatically shut down in case of violation of the hard limits. This safety implementation is decoupled from mid and high level controllers. Furthermore, there are electronic fuses in each subsystem which provide additional safety.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Location** & **ACU PCBs** & **Motor Type** & **Gear** & **Speed** & **Sensors** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} **C** \\ **C** \\ **C** \\ \end{tabular} } & Shoulder & P, D, C & \multicolumn{2}{c}{TO-Systems Robodrive (RD)} & HarmonicDrive (HD) & \multirow{3}{*}{330\({}^{\circ}\)/s} & \multirow{2}{*}{2 \(\times\) A} \\  & & & ILM 50x 08 & & CPL-14-2A, 100:1 & \\ \cline{3-5}  & & Elbow & P, D, C & & \multicolumn{1}{l}{Maxon EC-flat 45} & HD HFUC-11-2A, 100:1 & 198\({}^{\circ}\)/s & 3 \(\times\) A \\ \cline{3-5}  & & Forearm & D, C & Dynamixel MX28 & 193:1 & 330\({}^{\circ}\)/s & 2 \(\times\) A \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **C** \\ **C** \\ \end{tabular} } & Spine & 2 \(\times\) P, D, C & \multicolumn{2}{c}{Allied Motion} & Ball screw, & \multirow{2}{*}{167 mm/s} & \multirow{2}{*}{2 \(\times\) A, W} \\  & & & HT 01500 M & & 1.5 mm pitch & \\ \cline{3-5}  & & Hip & P, D, C & RD ILM 70x 10 & HD CPL-25-2A, 160:1 & 132\({}^{\circ}\)/s & A, R \\ \cline{3-5}  & & Legs & P, D, C & RD ILM 38x 12 & Ball screw, 2 mm pitch & 266 mm/s & A, R, L \\ \cline{3-5}  & & Ankle & P, D, C & RD ILM 50x 08 & HD CPL-14-2A, 100:1 & 330\({}^{\circ}\)/s & 2 \(\times\) A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configurations of the Actuator Control Units (ACU), motors, gears, speed and sensors. Each ACU consists of zero or more PCBs for Power electronics, Data acquisition and communication, or Computation. To control a motor, it senses the motor position via iC-MU [48] Absolute encoders or Relative encoders (Hall sensors). In addition, Linear potentiometers (WayCon) or draw Wire sensors (WayCon) are used for linear drives to sense the length.
## 5 The Proposed Scheme
### Construction of ABE-RS
#### 5.1.2 ID Key Generation  
\(\mathsf{IdKgen}(GP,uid)\rightarrow(ISK_{uid},IPK_{uid})\). Each DU is assigned a unique identity, denoted as \(uid\), along with an associated attribute set \(S_{uid}\). A random element \(b\in\mathbb{Z}_{p}^{*}\) is selected to compute the identity-based key pair as follows:  
\[ISK_{uid}=b,\quad IPK_{uid}=(g^{\mu})^{H_{1}(uid\|b)}. \tag{5}\]  
To ensure the identity privacy, DUs submit the public identity key \(IPK_{uid}\) for registration, rather than revealing their \(uid\).  
Figure 3: Workflow.
# MSEN: A Multi-Scale Evolutionary Network for Modeling the Evolution of Temporal Knowledge Graphs
## 5 Results
### Training Process Analysis  
This paper studied the evolution of loss and evaluation metrics of the MSEN model during the training process. Figure 4 shows how the experimental results change with increasing epoch on the validation dataset of ICEWS14. The experimental results show that as the number of training epochs increases, the loss first gradually decreases, reaching the lowest point at the 10 th epoch, and then slowly increases again; the values of the evaluation metrics MRR, Hit@1, Hit@3, and Hit@10 gradually increase, reaching a plateau after the 10 th epoch. The results indicate that after 10 epochs of training on the ICEWS14 dataset, the model starts overfitting as evidenced by the increasing loss and stabilizing evaluation metrics. Therefore, 10 epoch is an appropriate number for training the MSEN model on the ICEWS14 dataset to achieve good performance without overfitting.  
Figure 3: **(a,b)** are the effects of the number of layers on GME (TR-GNN) and LME (HT-GNN) in terms of MRR (%), Hit@1 (%), Hit@3 (%), and Hit@10 (%), respectively.
## 3 Datasets and Methods
### Dataset Used  
Our previously introduced dataset, AWSTD, was used for our experiments [44]. Only one dataset was selected for the tests since the only comparable dataset to AWSTD is ADFA-IDS, but performing tests on both of them was not practical because of the following reasons:  
* Test on ADFA-IDS with simple ML methods performed by other authors [49] demonstrated poorer results compared to the use of AWSTD [77].
* The data structure of ADFA-IDS is somewhat similar to the AWSTD, but AWSTD wins by the amount of data (malware samples and the number of system calls for every malware sample).
* Since the number of system calls in the ADFA-IDS is typically smaller than in AWSTD, it would be impossible to compare the results for longer system call sequences (>600) that typically produce better intrusion/malware detection results.  
The AWSTD dataset part that contains malware-initiated system calls on Windows OS was used. Malware samples were selected from Virus Share [78] and later appended with the information provided by the Virus Total [79]. System Calls sequences used for our experiment, as well as source code used to train and test ML models, can be found on the GitHub repository [80].  
Our earlier experiments with AWSTD have shown that usable result can be achieved with simple one layer CNN model which consists of the input layer, convolutional layer, global max-pooling layer and output layer [14]. The best results with that configuration were generated for the 1000 first system calls sequences when repetitive calls were reduced, i.e., every sample contains a maximum of two consequent identical application programming interface (API) call instances. The idea of reduction was based on research by [PERSON] [66]. Ninety-nine point three percent accuracy was received with the above-mentioned configuration, which means that the analysis of the first 1000 every program system calls can lead to a successful program-based intrusion or malware detection. Furthermore, up to 90% accuracy was achieved with even less than 40 of the first system calls by the application.  
In this paper, we also apply the reduction of system calls to analyze the efficiency (accuracy, FPR, length of system call sequence needed, training time, and other parameters) of more sophisticated DL models. In order to ensure unbiased comparison with vanilla single-flow CNN models, all extracted data samples from the AWSCTD the results will be provided for 10, 20, 40, 60, 80, 100, 200, 400, 600, 800, and 1000 system calls collections (identical to Reference [14]).
## 3 Results
### Gridding and Interpolation Method  
The points were analyzed with the kriging gridding system. Kriging is an advanced geostatistical procedure that generates an estimated surface from a scattered set of points (x, y) with \(z\)-values. Using these data, we produce 3D maps: the \(x\) and \(y\) coordinates to indicate the point where the measurement is made and the \(z\) coordinate represents either the cross-correlation index or the relative measurement of the impact-time, using a color scale. Since the three-dimensional view is not appropriate for this type of analysis, we display the 3D map in orthographic projection (viewpoint from above) with a light source normal to the surface and placed in its center at a predefined height. The kriging method assumes that the distance or direction between sample points reflects a spatial correlation that can be used to explain variation in the surface. The kriging tool fits a mathematical function to a specified number of points, or all points within a specified radius, to determine the output value for each location.  
To create a grid file, the software used takes randomly the spaced \(xyz\) data and uses it to create a regularly spaced grid file, composed of grid nodes. Each grid node is located at a particular \(xy\) location and has a \(z\) value associated with it. One of the disadvantages of kriging is that it can be slower than other methods. Each grid node value is based on the known data points neighboring the node. Each data point is weighted by its distance away from the node. In this way, points that are further away from the node will have less weight in the estimation of the node. For example, to compute the z value at grid node A, this equation is used:  
\[Z_{A}=\sum_{i=1}^{n}W_{i}\cdot Z_{i} \tag{2}\]  
where \(Z_{A}\) is the estimated value of grid node A, \(n\) is the number of neighboring data values used in the estimation, \(Z_{i}\) is the value at location \(i\) with weight \(W_{i}\). The value of weights will sum to 1 to make sure there is no bias towards clustered data points. We used the default properties of kriging: points with no drifts and a circular search (slope 1 and anisotropy ratio 1, thus a circular region). However, the performed measurement is punctual: only the state of adhesion of the point where the measurement is conducted can be certainly. It is clear that in the phase of graphic \"mapping\" of the object, the used mathematical method will try to connect (following the kriging method) the various areas, but it is understood that the prediction is given by the mathematical approximation used and not by the measure itself. The only certain point is the measured one.