esa-sceva/satcom-chunk-collection huggingface dataset:
# Shake Table Testing of Standard Cold-Formed Steel Storage Rack
## 2 Experimental Program  
### Rack Geometries  
To evaluate the seismic performance of the storage rack systems in the down-aisle direction, fifteen full-scale shake table tests were conducted at different stages. The configuration and geometry of the specimens were defined based on New Zealand standard storage rack pallets to simulate the behaviour of actual pallets used in seismic areas.  
The racks were of conventional two-bay and two-level rack frames, as shown in Figure 2. The width of each bay in the down-aisle and cross-aisle was 1440 mm and 900 mm, respectively. The height of each level was 1275 mm. All rack specimens were built from typical cold-formed steel (CFS) sections with G350 grade steel. Tracing elements were of C channel profiles with 25 mm depth, 30 mm flange and 1.8 mm thickness and were installed in the cross-aisle direction with 600 mm pitches. The upright profiles of 90 mm depth and 2.5 mm thickness were connected in the down-aisle direction by 1.6 mm thick 105 \(\times\) 50 mm box beams. The geometry of the section as well as the perforation details are indicated in Figure 3. Due to commercial confidentiality reasons, all geometries are presented in non-dimensional form--the same as other studies in this area [38]. Table 1 tabulates the cross-sectional properties of the structural elements used in the rack specimens. The beam to upright connections were of bottless type (hooked connectors), which are commonly utilized in most rack systems due to the easier and faster installation of frames. The frames were anchored to a 30 mm thick steel plate, which was placed on the shake table using bolted connections. The base-plate used in the tests is shown in Figure 4. To simulate the actual condition of palletized merchandise, each pallet was accommodated with two standard precast concrete blocks. Each concrete block weighed approximately 500 kg with dimensions of 755 mm \(\times\) 565 mm \(\times\) 510 mm, which is a normal value for the merchandise used in practice. Therefore, each specimen could contain eight concrete blocks over its two beam levels, which provided a 4000 kg pallet load in total and simulated a fully-loaded, service condition. The rack specimen and the concrete blocks are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Element** & **Section** & **Length (mm)** & **Area (mm2)** & **Ix (mm4)** & **Iy(mm4)** \\ \hline Beam & Box105 \(\times\) 50-1.6 mm & 1350 & 597 & 1,023,099 & 237,210 \\ \hline Upright & 90 Upright 2.35 mm & 2800 & 578.6 & 700,217 & 325,825 \\ \hline \multirow{2}{*}{Brace} & 25 \(\times\) 30 \(\times\) 1.8 (C Channel) & 1062 & 217 & 19,956 & 11,388 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Section details.  
Figure 3: Section and perforation details.  
Figure 2: Schematic views and dimensions of the frame.
# Exploratory Study on Kali NetHunter Lite: A Digital Forensics Approach
## 4 Analysis and Findings
### NetHunter Installation Analysis  
The comparison of the OnePlus 7T's first and second physical image show the locations of the previously installed application. The main folders where traces of NetHunter Lite and its dependencies (NetHunter Store and NetHunter Terminal) can be found were at the following paths:  
\data\app  
\data\data  
\data\local\
hsystem  
\data\media\0\
_files  
\data\misc\profiles\curv\0  
\data\misc\profiles\ref  
\data\System\grhicsstats\163909440000  
\data\system\package\cache\a415 bf8295647e702d578a81b6b3801 ee6f0 ecad  
\data\system\_ce\0\l Launch\params  
\data\user\de\0  
Note that the above items do not include files that have been overwritten and compared with the previous image. Additionally, paths such as \data\system\_ce\0\lrecent\tasks, \data\system\_ce\0\lshapshots, and \data\backup contained newly created content; however, it was excluded due to the irrelevance of this analysis. Moreover, all folders were examined in detail, and only readable files were analyzed.  
Within the \data\data folder, there were three newly created folders com.offset.NetHunter.store, and com.offset.nhterm. The com.offset.nhterm had two empty subfolders. Among other files, the com.offset.NetHunter.store folder contained a database file 'fdroid' showing installed applications and repositories used by the application. In the com.offset.NetHunter folder, there were two databases, 'NetHunterFragment' and 'KalisServicesFragment'. The first database shows different system information (e.g., Kernel Version, Busybox Version, Root Status) provided on the home screen of the NetHunter Application. The second database, 'KalisServicesFragment' shows if the services such as SSH, APACHE2, POSTGRESQL, and DNSMASQ are running. Inside the parent folder (\data\data\com.offset.NetHunter), the folder 'files' contained scripts and files needed to start, stop, modify, and run the application. The scripts were divided per service, containing the paths and commands for the execution.  
The next folder containing information is \data\data\local\
hsystem\kali-arm64. As presented in Figure 1(a), this folder holds data for operations such as boot, etc, media, run, system, temp files, root. The capture folder contains any relevant files regarding Kismet, Mana, Nmap, WiFiite, and other services provided by NetHunter Lite.  
The \data\media\0\
h_files folder has five subfolders, as shown in Figure 1(b). The folders contain configuration files for DNSMASQ, dhscp, dnsspoof, hid-cmd, and a file for the DeAuth service. Additionally, the folder contained duckscripts, Python modules, and one backup file of the database.  
Other folders worth mentioning are \data\system\_ce\0\l Launch\params, showing the launch parameters of the application, and \data\user\_de\0\com.offsec.NetHunter, containing two cache files that are not readable. Folder \data\
isc\profiles contained traces of the NetHunter application but did not present any valuable information.
# A New Data Processing Approach for the SHPB Test Based on PSO-TWER
## 2 Related Work
### Objective Function  
Firstly, for each given set of \(x_{\rm I}\) and \(x_{\rm T}\), \(\sigma_{T}\), \(\sigma_{I}\), and \(\sigma_{R}\) are calculated under a condition, where \(\sigma_{T}\), \(\sigma_{I}\), and \(\sigma_{R}\) represent functions related to \(x_{\rm I}\) and \(x_{\rm T}\), as shown in Equation (6):  
\[F(\sigma_{T},\sigma_{T},\sigma_{R})=(x_{\rm I},x_{T}) \tag{6}\]  
where \(x_{\rm I}\) is the time-window length of the incident wave and \(x_{T}\) is the time-window length of the transmitted wave.  
Based on the above discussion, the stress equilibrium factor evaluates the stress equilibrium state of the specimen by describing the differences between the incident wave, the reflected wave, and the transmitted wave at each instant. When the results of picking up these three waves are accurate, the stress equilibrium factor for all points within the signal time domain will be close to 1. However, if the selected positions are incorrect, the value of the stress equilibrium factor will be greater than 1. When the sum of stress equilibrium factors is minimized, this indicates that the position of the particle has reached its optimal state. Therefore, the objective function can be constructed as shown in Equation (7):  
\[\text{min}(F{=}\text{sum}(\sigma_{\rm eq}^{i}(t))=\sum\sigma_{\rm eq}^{i}(t)= \sum_{t}\frac{\sigma_{T}^{i}(t)}{\sigma_{I}^{i}(t)+\sigma_{R}^{i}(t)}) \tag{7}\]
## 4 Methodology
### Features Extraction
#### 4.2.3 Mel Frequency Cepstral Coefficients (MFCCs)  
Mel frequency cepstral coefficients (MFCCs) are a simulation representation of the auditory system of humans. The methodology of MFCCs makes them a convenient feature that captures the speech signal [27]. The advantage of MFCCs is the feature extraction algorithm's lower complexity of implementation [28]. MFCCs produce number selection coefficients that are the outcomes of the speech signal. These coefficients are calculated in several steps, as described in Figure 6.  
Figure 6: Calculating mel frequency.
## 2 Materials and Methods
The same material, a commonly used Steel AISI 1085, was selected for both parts, shaft and hub. The mechanical properties are the following: \(E_{\text{i}}\) = 200 GPa, \(\sigma_{\text{Y}}\) = 276 MPa and \(\
u_{\text{i}}\) = 0.30. On the other hand, the material selected for the contact ring is a commercial Copper-Beryllium alloy with a lower Young modulus (\(E_{\text{o}}\) = 124 GPa) and a similar Poisson coefficient (\(\
u_{\text{o}}\) = 0.28). The yield strength is notably higher (\(\sigma_{\text{Y}}\) = 965 MPa). A uniform mesh of 4-node quadrilateral elements was applied for the shaft, hub, and contact ring, as shown in Figure 3a. Diverse meshes were tried, increasing the number of elements in terms of the mesh factor \(K\) for each mesh, until the required convergence was reached. The mesh factor \(K\) is defined as the ratio between the number of elements of each mesh and the number of elements of the coarsest mesh considered in the mesh convergence. To validate the FEM results, it was considered that far away from the edge of the hub (hub central section, point B in Figure 2), the local effect of stress concentration (point A in Figure 2) disappears, and the stress distribution must be the one predicted by design equation (1) if the shaft and hub are composed of different materials or equation (2) if the shaft and hub are composed of the same material. This way, these cases, i.e., (i) complete hub composed of Cu-Be alloy (the same as the contact ring) and (ii) the complete hub and shaft composed of the same steel (AISI 1085) were simulated considering diverse meshes increasing the number of elements in terms of the mesh factor \(K\). Lately, the radial stress (equal to the contact pressure but opposite in sign) at the middle of the interface shaft/contact ring (point B in Figure 2) was compared with the value given by theoretical expressions (1) and (2) as shown in Figure 4. This way, the mesh convergence test revealed a good agreement between the FEM results and the analytical values obtained from Equations (1) and (2) with a relative error (\(\varepsilon\)) lower than 5%. As results, a total mesh of 88,000 elements and 88,782 nodes was selected for the FEM simulations. Figure 3b shows a detailed view of the mesh at the contact interface shaft/contact ring where elements of size 0.5 mm were located. Figure 3: Mesh used in FEM simulations: (**a**) general view, (**b**) detail view at the hub edge where stress concentration appears.
## 3 Analysis of High Ground Temperature Monitoring Results
### Analysis of Temperature Monitoring Results at Different Rock Mass Depths  
The 60-day temperature data of the typical surface of the tunnel during the construction period were taken for analysis, and the monitoring results are shown in Figure 6.  
From Figure 6, it can be seen that after tunnel excavation, the temperature of the jointed rock mass varies with the depth of rock mass. When the depth from the tunnel axis is less than 8.3 m, the rock mass temperature increases radially from the tunnel wall to the deep rock mass. The closer to the tunnel wall, the greater the amplitude of temperature change. For instance, on the 10 th day, the temperatures of probes 1#, 2#, 3#, and 4# were 19.03 \({}^{\circ}\)C, 62.4 \({}^{\circ}\)C, 72.8 \({}^{\circ}\)C, and 79.9 \({}^{\circ}\)C, respectively. In addition, the temperature of the three temperature sensors varies over time, and the temperature of jointed rock mass at  
Figure 4: Profile of tunnel section ranging from 3 + 500 m to 5 + 500 m between the 2# and 3# branch tunnels.  
Figure 5: Temperature monitoring layout.  
different depths shows a simultaneous increase or decrease. At the depth of 11.3 m from the tunnel axis, the temperature of the jointed rock mass stabilizes at around 80 \({}^{\circ}\)C, indicating that at this depth it is in a constant temperature zone of jointed rock mass.
## 3 Description of the Generalized Algorithm of the Extended Simple Equations Method (SEsM)
For a Variant 1 of the Step 3, these special functions are \(V_{l_{0},l_{1},\ldots,l_{j_{1}}}(\xi_{1};k_{1},l_{1},m_{1})\) and \(V_{l_{0},l_{1},\ldots,l_{j_{2}}}(\xi_{2};k_{2},l_{2},m_{2})\), as their explicit forms are determined on the basis of the specific form of the simple equations chosen (For reference, see Equation (34), where \(\mu_{j_{1}}\) and \(v_{j_{2}}\), \(\xi_{1,2}\), \(k_{1,2}\), \(l_{1,2}\) and \(m_{1,2}\) are defined.). For Variant 2 of Step 3, the special functions are \(V_{l_{0},l_{1},\ldots,l_{j_{1}}}(\xi_{1},\phi;m_{1})\) and \(V_{l_{0},l_{1},\ldots,l_{j_{2}}}(\xi_{2},\phi;m_{2})\), whose exact forms are determined by the type of fractional simple equations used (for reference, see Equation (35), where \(\mu_{j_{1}}\) and \(v_{j_{2}}\), \(\xi_{1,2}\), \(\phi\) and \(m_{1,2}\) are defined). The above-described algorithm in its verbal form can be most intuitively illustrated through the following block diagram. The generalized algorithm presented above is designed to support researchers in this field to extract analytical solutions of models of type (19), which describe dynamics of natural processes. Using this algorithm, researchers can select among the variants provided in Steps 1, 2, and 3, making their choice on the basis of the specific characteristics of the wave dynamics which is expected for the natural phenomenon being modeled. \[\Omega(v(x,t),D_{t}^{\phi}v(x,t),D_{x}^{\phi}v(x,t),D_{tt}^{2\phi }v(x,t),D_{xx}^{2\phi}v(x,t),\ldots)=0,\ 0<\phi\leq 1\]6 Block diagram illustrating the generalized SEsM algorithm for systems of FNPDEs. **Remark 5**. : _We briefly outline below how the algorithm can be applied in practice by providing several common heuristic criteria that may assist in selecting the most appropriate variants._  
* _Nature of wave interactions:_ _Use of composite functions with different independent variables when modeling multi-scale or interacting wave phenomena; Use of a common variable for coherent or synchronized structures._
* _Transformations:_ _Fractional traveling wave transformations may be beneficial when standard forms fail to reduce the system effectively or when additional degrees of freedom are required._
* _Fractionality:_ _Choose fractional ODEs if the system exhibits pronounced nonlocality or memory effects; otherwise, integer-order ODEs may provide a better approximation._
* _Analytical tractability:_ _Simpler variants may be preferred when aiming for closed-form expressions or when the PDE system has known similarity reductions._  
In the remainder of this article, we apply two different variants of the generalized SEsM algorithm described above to Equations (1) and (2).
###### Abstract  
In this work, we design an intelligent reflecting surface (IRS)-assisted Internet of Things (IoT) by enabling non-orthogonal multiple access (NOMA) and unmanned aerial vehicles (UAV) approaches. We pay attention to studying the achievable rates for the ground users. A practical system model takes into account the presence of hardware impairment when Rayleigh and Rician channels are adopted for the IRS-NOMA-UAV system. Our main findings are presented to showcase the exact expressions for achievable rates, and then we derive their simple approximations for a more insightful performance evaluation. The validity of these approximations is demonstrated using extensive Monte Carlo simulations. We confirm the achievable rate improvement decided by main parameters such as the average signal to noise ratio at source, the position of IRS with respect to the source and destination and the number of IRS elements. As a suggestion for the deployment of a low-cost IoT system, the double-IRS model is a reliable approach to realizing the system as long as the hardware impairment level is controlled. The results show that the proposed scheme can greatly improve achievable rates, obtain optimal performance at one of two devices and exhibit a small performance gap compared with the other benchmark scheme.  
intelligent reflecting surface; non-orthogonal multiple access; achievable rate; hardware impairment +
Footnote †: journal:  
0000-0001-9993-3227-2244][PERSON]  
0000-0001-9993-3227-2244][PERSON]  
0000-0002-4307-3227-2244][PERSON]  
3 Faculty of Automotive Engineering, School of Engineering and Technology, Van Lang University,  
Ho Chi Minh City 70000, Vietnam
# Computed Tomography-Driven Design and Fused Filament Fabrication of Graded Density Bone Scaffolds
## 2 Materials and Methods
### Cad-CAM Integration
#### 2.3.2 Conversion from HU Values to Relative Density Scalar Field  
Depending on the anatomical site and factors such as age, sex, and pathologies, the scientific literature suggests that a healthy bone has a cortical porosity range of 5-15% and a physical density range of approximately 1.9-2.0 g/cm\({}^{3}\)[46; 47; 48; 49]. trabecular bone has a porosity range of 50% to 95%, with the same physical density range, while bone marrow has a physical density range of 0.98-1.1 g/cm\({}^{3}\)[46; 48; 49; 50; 51]. The typical HU values range from approximately 150 to 400 for trabecular bone, while the literature values for typical cortical bone suggest a range of 500 to 1800 HU [40; 49].  
By performing a Boolean intersection operation between the HU scalar field and the surface model, the voxels contained inside the surface model are selected. The HU minimum \((HU_{min})\) and maximum \((HU_{max})\) values of this subset found in the scientific literature, along with the porosity value \(P=1-\rho\) for the specific bone tissues (trabecular bone \(P_{trab}\) and cortical bone \(P_{cor}\)), were adopted to derive a linear equation that maps HU values to \(P\) and then to relative density \(\rho\) values, as illustrated in Figure 6. Higher value of porosity can lead to part instability in the Z direction. To avoid this, \(P\) should be limited to \(P_{E_{w,min}}\), which depends on the \(E_{w,min}\), \(L_{cell}\), and infill type model coefficients. By means of this correlation, it possible to calculate \(\rho\) associated with the HU values in the ROI.  
Other useful conversion methods of HU values are feasible, such as converting HU values to physical densities and elemental compositions, although they are not helpful in this context [52].
## 5 Simulation Analysis
### Simulation Results of S21 Transmission Coefficient Without Block  
The graph compares the S21 coupling coefficients (in dB) measured and computed throughout a frequency spectrum from 1 GHz to 15 GHz. The Y-axis displays the coupling coefficient from 0 dB to \(-\)80 dB, reflecting energy transmission efficiency between the system's two ports, whilst the X-axis denotes frequency in GHz. Two separate curves are illustrated: the green dashed line represents the measured S21 values acquired via empirical testing, while the red dashed line denotes the simulated S21 values generated by computational modelling. Both graphs demonstrate analogous broad patterns, with several peaks and troughs that indicate variations in signal transmission efficiency over the frequency spectrum. The curves show a significant level of agreement in the lower frequency range, namely between 2 GHz and 7 GHz, where the measured and simulated values nearly correspond (Figure 11), indicating dependable performance forecasts in this domain. However, significant disparities emerge once the frequency surpasses 7 GHz, causing the two curves to diverge in their behavior. This discrepancy indicates possible enhancements for the simulation model, emphasizing inconsistencies that may affect its precision in forecasting real-world performance, particularly at elevated frequencies. The graph underscores the significance of juxtaposing actual observed data with predicted outcomes, providing a basis for improving the modelling methodology to improve accuracy over the whole frequency range.  
Figure 11: S21 transmission coefficient without block.
## 4 Crowdsourcing Applications: From a General Framework to Spatial Planning Applications
Shortest path algorithms are adopted in combination with information derived from the public, so that web applications relating, for example, to transportation issues are created. The public supports such a process by either providing content or by even participating in the designing phase of the application. At this point, the essential role of maps as a means of communication among users and planners should be mentioned. Users are offered the opportunity to interact with web maps by depicting several problems detected in their neighborhood or in any other public area onto a map background, while on the other hand, planners can collect the information gathered from the maps in order to further exploit it during the planning process [15]. In many cases, users may also represent a possible solution of a specific problem on the map. Another approach, concerning citizens' participation in the decision making process, is that of spatial planning games. From such a perspective, citizens are involved in a creative and interesting process, which helps them to be more familiarized with the whole planning process. There are some web applications performed under such an approach, which are very popular with the public, as prior knowledge of \"how to use\" each specific application is not necessary [16]. Hitherto, it has been clear that crowdsourcing can be adopted in several cases in order for effective solutions to be reached by exploiting the knowledge and experience of the public. Such an approach reinforces democratic processes and transparency in the planning process.
###### Abstract  
In this paper, a novel embedded helix dielectric rod antenna is presented for high gain radiation with circular polarization (CP) and low side lobe levels for IoT Applications. Different from the conventional dielectric rod antennas, this proposed antenna is an integrated structure that combines the advantages of the helix and dielectric rod antennas. The presented antenna mainly consists of three parts: a tapered helix as primary feeding for CP, a dielectric rod with printed loops embedded for higher directivity, and a dielectric rod end for improving the gain further. After studying and analyzing the working principles of each part, an optimum design operating at 8-9.7 GHz is carried out as an example. A prototype is also fabricated and tested. The measured results show that the prototype can provide 18.41 dB maximum gain within the length of 7.7 \(\lambda\). The side lobe level is below \(-20\) dB, and the axial ratio is better than 1.14 dB in the whole frequency band. Compared with the traditional helix antenna and dielectric rod antenna with the same electric length, the presented antenna has a higher gain with a lower side lobe level and with good polarization purity.  
Internet of Things (IoT); dielectric rod antenna; aperture; tapered helix; radiation efficiency; side lobes level (SLL); directivity; relative gain; metal reflector; axial ratio (AR); (FBR) front to back ratio +
Footnote †: journal: Sensors
# Changes of Gait Patterns after Correction of Refractive Error in the Elderly with Cataracts
## 3 Results  
### The Effect of Correction Glasses for Distance on Gait Patterns in Elderly Subjects  
The changes in each factor that had an effect on gait patterns before and after wearing correction glasses for the elderly participants in this study are shown in Table 1. In the participants, the mean step length increased from 45.57 \(\pm\) 5.37 cm to 47.78 \(\pm\) 5.81 cm (\(p\) < 0.05), and the mean stride length increased from 89.09 \(\pm\) 10.41 cm before wearing correction glasses to 94.09 \(\pm\) 12.57 cm after wearing correction glasses (\(p\) < 0.05). The mean cadence decreased from 108.16 \(\pm\) 12.59 before the participants wore correction glasses to 102.67 \(\pm\) 19.74 (\(p\) < 0.05) after, and the step width changed from 10.21 \(\pm\) 2.07 cm before the participants wore correction glasses to 10.93 \(\pm\) 1.68 cm after they wore correction glasses (\(p\) < 0.05). However, no significant difference in the average walking velocity was observed before and after the participants wore glasses for the correction of distance (\(p\) > 0.05).