esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 2 Materials and Methods  
To verify the recommendations for ABAC in BDP, we designed and conducted a test using a large dataset of messages generated at multiple sensitivity levels. The data are processed in parallel through a series of analytic and privacy-preserving programs to represent a data processing lifecycle. This was hosted on a cloud service provider, i.e., Amazon Web Services (AWS), using storage and computer platforms, Elastic Cloud Compute (EC2), Simple Server Storage (S3), and Infrastructure as a Services (IaaS). This configuration represents a hypothetical yet realistic data analysis environment that may be accessed by users with different permission levels.  
Industry and government are investing significant resources into gathering, extracting, transforming, and analyzing large datasets. Without thoroughly tested and trusted BDP security, these types of data lakes are available to only a small group of highly trusted users. The lack of broad use represents a significant potential loss in this investment.  
Figure 3: A standard architecture includes multiple points that contribute to the AC process.  
Cloud service providers are helping to enable access to tools to wrangle these large data sets with services such as the AWS Elastic Map Reduce (EMR). Such services provide an operational configuration of BDP frameworks based on open-source software from the Hadoop ecosystem. The current default security configuration is primarily based on security firewalls, proxy servers, and/or virtual private networks at the cluster boundary. These services have only recently integrated fine-grain security services. Service providers are enhancing cloud BDP security based on open-source systems, such as Apache Ranger and Atlas. More complete, robust, and layered security is the operational goal. Currently, the design and configuration of this type of layered security for applications deployed in the cloud currently remain the responsibility of the cloud users.  
To further understand the challenges associated with the security of BDP applications running in the cloud, we built an Apache Hadoop cluster with Spark parallel processing, allowing us full access to configuration and log files to achieve a deeper understanding of the security and performance implications. The focus of the experiments the ABAC services for data stored in Hadoop and processed in parallel through Spark applications. This prototype is the second key component of our framework: an experiment that realistically represents a dataset and data processing program with complex security challenges.
## 5 Learning and Prediction
### Detection Performance  
In order to evaluate the detection performance according to TCR, 11,000 training data are selected as mentioned above and the test is performed for each TCR. The AUC and probability of detection are observed for each TCR while the ROC curve is measured for the total test data. Figure 10 shows the learning parameter \(\beta\) of the model for the 11,000 training data. The parameters of Figure 10 show a similar shape as in Figure 8.  
For the 11,000 test data, the ROC curve of the model with the parameters in Figure 10 is displayed in Figure 11. AUC of 0.8958 implies that the model operates as an excellent classifier noting that the worst AUC is 0.5 in binary classification [14]. These results are observed in the classification table of Table 2. According to this table, the hit rate is 0.8115.  
In addition, AUC values according to TCR are displayed in Figure 12. The minimum is 0.74 at TCR = \(-\)5 dB while the maximum is 0.94 at TCR = 3 dB. As the target signal power is getting better over the clutter power, AUC improves, which shows excellent discrimination. The probability of detection is plotted with respect to TCR in Figure 13, where the detection probability of 0.62 at TCR of \(-\)5 dB is observed, while, for TCR greater than or equal to 2 dB, the detection probability of about 0.9 is maintained.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-5}  & & **No Target** & **Target** & **Total** \\ \hline \multirow{3}{*}{**True class**} & **No target** & 18,530 & 3640 & 22,170 \\  & **Target** & 3784 & 18,046 & 21,830 \\ \cline{1-1}  & **Total** & 22,314 & 21,686 & 44,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification table accumulated over the ten trials.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-5}  & & **No Target** & **Target** & **Total** \\ \hline \multirow{3}{*}{**True class**} & **No target** & 4576 & 924 & 5500 \\  & **Target** & 1037 & 4463 & 5500 \\ \cline{1-1}  & **Total** & 5613 & 5387 & 11,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Classification table for the model with \(\beta\) of Figure 10.  
Figure 10: Learning parameter \(\beta\).  
Figure 11: ROC curve with the learning parameter \(\mathbf{\beta}\) of Figure 10 for the test data set.  
Figure 12: AUC values with respect to target to clutter ratio (TCR) for the test data set.  
Figure 13: Probability of detection with respect to TCR for the test data set.
# Review on Quality Control Methods in Metal Additive Manufacturing
## 1 Introduction  
Metal additive manufacturing (metal AM) produces three-dimensional metal objects by repeatedly adding and solidifying metal materials layer by layer. Metal AM has four standard categories: powder bed fusion (PBF), direct energy deposition (DED), binder jetting, and sheet lamination [1]. PBF and DED produce 3D-shaped metal products directly from metal materials through deposition. During the deposition process, powder or wire-type metal materials are melted by concentrated energy sources, such as plasma arc, laser, or electron beam. The molten metals are positioned on a substrate or an existing material and cooled for solidification.  
PBF and DED often experience common quality issues, such as layer misalignment, dimensional errors, and residual stress generation, owing to repeated melting and cooling. Such quality issues can also be found in welding, one of the conventional metal manufacturing processes that utilize deposition. For example, [PERSON] et al. [2] analyzed the plasma spectrum during welding to quantify the elemental composition of the welded area, and [PERSON] et al. [3] used it to identify the elemental composition of DED products. They showed that using the plasma spectrum, which can be observed during deposition, is feasible for identifying the elemental composition of the product in real time during DED, as well as welding. This example suggests that quality monitoring methods between metal AM and other metal manufacturing, especially for DED and welding, can be interrelated and adopted with each other.  
This paper provides a comprehensive review of quality monitoring methods for metal AM and welding and recommendations for possible directions in developing quality monitoring methods for metal AM. Section 2 introduces metal AM and welding andhighlights their similarities. Sections 3 and 4 present a summary and discussion of quality monitoring studies for metal AM and welding from numerical and experimental points of view, respectively. Section 5 provides the conclusions and recommendations.
## 8 Challenges and Future Directions
### Challenges
#### 8.1.3 Accuracy and Reliability  
While HAR models have achieved an overall high accuracy, there are still issues with differentiating activities with subtle differences. For the real-life deployment of these models, it is important to have highly accurate models, especially with health applications. There is also low accuracy in identifying complex activities in comparison to simple activities. When there are limited training data, there is low recognition accuracy.  
When there is data drift from what the model was trained on, there is reduced accuracy, which means that the model would need to be re-trained on these new data. This makes it difficult to implement reliable HAR systems that have to deal with real-world data. It is also computationally expensive to have to re-train a new model every time there is data drift.
## 3 Results
### Integration of TSNP in Biopolymers for Enhanced Antimicrobial Activity
#### 3.3.2 Antimicrobial Activity of BC Blends  
The antimicrobial evaluation of the prepared BC blends was performed by incubating the samples in inoculated broth, with _E. coli_ and _S. aureus_ as respective representatives of Gram-negative and Gram-positive strains. The results for PCL, PLA, and PHB blends incorporated with TSNPs can be observed in Figure 8. As it can be observed, the PCL and PLA blends showed no significant antimicrobial activity against either of the tested strains, even with the incorporation of TSNPs. Contrary to the results exhibited by the PLA and PCL blends, the blend made with PHB showed significant antimicrobial activity against both, _E. coli_ and _S. aureus_ strains after the incorporation of TSNPs. This is likely due to the highly homogeneous absorption of TSNPs that was observed in the BC/PHB blends, allowing for a visibly higher antimicrobial response compared to the other blends, which absorbed a smaller amount of TSNPs.  
Figure 6: Bare BC blends (white) and BC/TSNP blends (dark blue). (**a**) BC/PHB blends, (**b**) BC/PLA blends, and (**c**) BC/PCL blends.  
Figure 7: SEM micrographs of BC/TSNP films, showing distribution of TSNP clusters (white spots) at (**a**) \(115\times\) magnification and (**b**) \(1.29\)\(\mathrm{k}\mathrm{x}\) magnification.
## 2 Methodology
### Robustness Computation Method
#### 2.4.2 Computation of the Auxiliary Function  
From the definition of auxiliary function \(P\), it is obvious that function \(P\) cannot be calculated directly according to the accumulated counts when ruined watermark bits have increased from 1 to \(L\). This is because the circumstances have intersections. To count the permutations \(P(L,N,D)\) without repetition and omission, the inclusion-exclusion principle of combinatorial mathematics is adopted. The inclusion-exclusion principle is expressed as follows. If there are sets \(A_{1},A_{2},\ldots,A_{n}\), the count of whole elements is calculated as:  
\[\begin{split}\left|\overset{n}{\cup}A_{i}\right|& =\sum\limits_{i=1}^{n}|A_{i}|-\sum\limits_{1\leq i\leq i\leq n} \left|A_{i}\cap A_{j}\right|+\sum\limits_{1\leq i<j<k\leq n}\left|A_{i}\cap A_ {j}\cap A_{k}\right|-\ldots\\ &+(-1)^{n-1}|A_{1}\cap A_{2}\cap\ldots\cap A_{n}|,\end{split} \tag{5}\]  
where the symbol \(|\) represents the counts or cardinalities of the sets. By using the principle in auxiliary function \(P\), the sets containing \(i\)-th watermark bit which has been ruined are denoted as \(P_{i}(1\leq i\leq L)\), then the function \(P\) is:  
\[P(L,N,D)=C_{L\times N}^{D}-\left|\overset{n}{\cup}P_{i}\right|, \tag{6}\]  
\(C_{L\times N}^{D}\) represents the count of all the possible permutations when deleting \(D\) watermark bits from \(L\times N\) watermark bits. So \(\left|\overset{n}{\cup}P_{i}\right|\) is calculated according to Equation (5). Specifically, the function \(P\) (\(L,N,D\)) is calculated as:  
\[P(L,N,D)=C_{L\times N}^{D}-\sum\limits_{i=1}^{M}(-1)^{i+1}C_{L}^{i}\times C_{ (L-i)\times N}^{D-i\times N}, \tag{7}\]  
Figure 8: An example does not belong to enumerations of function \(P\).  
with \(M=\left\lfloor\frac{p}{N}\right\rfloor\), the operation \(\lfloor\ \rfloor\) rounds the value down to an integer.
# Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
## 3 Methodology
### Range of Motion Response for Exercise Modes  
In this section, we discuss the different exercises from the results of the IMU sensor, which serves as a method to create an algorithm to detect the mode of use. We could look at the data resulting from the IMU sensor of the six-mode exercise when humans perform it. From the properties of the accelerometer and gyroscope, we focus on movement from different modes with regard to acceleration and gyroscope behavior in different axes and values. Further, we created criteria to evaluate the ability of each mode to be correct. These are maximum (max), minimum (min), and in the range of nine variables as well as six from two signals from the IMU, which are acceleration and gyroscope (\(\omega\)-X, \(\omega\)-Y, \(\omega\)-Z) of the X, Y, and Z-axes. The rest is the angle of the X, Y, and Z-axes, which are called sequentially roll (\(\phi\)), pitch (\(\theta\)), and yaw (\(\psi\)).  
Figure 6: Signal from the IMU sensor working on two cycles.  
Figure 7 presents the distribution values of the gyroscope in each region corresponding to the criteria of the exercise modes. For example, if we calculate the maximum gyroscope of X axes (GX) in one cycle of the biceps' mode, it will be located in the region of \(-1.35\) to \(1.34\) degrees/s. Similarly, in the other modes, the location of the maximum gyroscope of each mode is different.  
In Figure 8, we can clearly see the difference of each mode through the distribution of acceleration that can help identify the method of exercise.
## 4 Result Analysis and Discussion  
In this section, we perform simulations to investigate the performance of the proposed satellite selection algorithm. The satellite data were obtained from 4448 Starlink satellites that were observable in orbit on 19 July 2023. The simulated receiver was placed in Houston (29.76\({}^{\circ}\)N, 95.36\({}^{\circ}\)W). During the simulation, the receiver remained stationary while continuously acquiring and tracking the visible satellite signals. The simulation lasted 24 h, and we recorded the number of observable satellites every 2 min.  
Figure 2 shows a plot of the number of satellites that can be observed versus the time of day, using 2 min intervals for observation.  
The simulation results show that the number of observable satellites varied from 171 to 270 satellites, and the average number of observable satellites in a day was 195.8. Considering that Starlink plans to launch more than 40,000 satellites, its future constellation size is much larger than what can be observed in the current simulation. Therefore, in this study, we selected the moment with the highest number of observable satellites in each hour and obtained a total of 24 time points for satellite selection.  
Figure 1: Flowchart of the proposed algorithm.
# Numerical Study on Tree Belt Impact on Wind Shear on Agricultural Land
## 1 Introduction
This study examines the relationship between tree belts and the wind airflow characteristics, focusing on the influence of the tree belts on the boundary layer profile and their impact on small-scale wind turbines, located in their vicinity. Particular attention is given to the determination of the range and magnitude of the wind speed deviations and turbulence, and the practical applications of these data for sustainable agricultural management and wind energy optimization. Although tree belts are known to affect local airflow and turbulence, few studies have quantitatively assessed their influence on wind velocity and shear at heights relevant for small-scale wind energy systems. This study aims to investigate how different vegetation porosities affect wind profiles and turbulence intensity downstream of forest belts, using validated CFD numerical simulations. The outcomes are intended to support the optimal vertical placement of wind turbines in rural and agroforestry contexts, where such vegetation structures are common.
# Observation of an Electromagnetically Induced Grating in Cold \({}^{85}\)Rb Atoms
## 2 Experiment Setup  
The cold \({}^{85}\)Rb atoms are obtained by a traditional magneto-optical trap (MOT) method. The cooling and repumping lasers are corresponding to the hyperfine transitions of \(5S_{1/2}(F=3)-5P_{3/2}(F^{\prime}=4)\) and \(5S_{1/2}(F=2)-5P_{3/2}(F^{\prime}=3)\), respectively. The atoms with a Gaussian distribution, temperature of T \(\sim\) 100 uK, radius of \(\omega_{z}\sim\) 0.6 mm and peak atomic density of N \(\sim\) 10\({}^{10}\) cm\({}^{-3}\), are populated in \(5S_{1/2}(F=3)\) state. A V-type energy level configuration is employed to achieve the EIG as shown in Figure 1a. The probe laser operating around the D2 line of rubidium (780 nm) excites the atoms from \(5S_{1/2}(F=3)|1\rangle\) ground state to \(5P_{3/2}(F^{\prime}=4)|2\rangle\) excited state. While the coupling laser operating around the D1 line of rubidium (795 nm) pumps the atoms to a \(5P_{1/2}(F^{\prime}=3)|3\rangle\) excited state.  
The scheme to construct EIG depends on the periodic modulation of the refractive index in the cold atom ensemble. The beams spatial configuration of this experiment is shown in Figure 1b. The cooling and repumping lasers for the MOT are omitted for a more intuitive illustration. Three laser beams are incident into the MOT with \({}^{85}\)Rb cold atoms. The coupling and probe laser beams corresponding to 795 nm and 780 nm are provided by two external cavity diode lasers (DL pro, Toptica, Munich, Germany). The saturation absorption spectroscopy (SAS) method is used to lock the frequency of these two lasers. In addition, the laser frequencies are monitored by a wavelength meter (WS-7, HighFinesse, Tubingen, Germany). We firstly use an anamorphic prism pair for shaping the coupling laser into an elliptical profile to expand the effective area of the standing-wave field. This shaped laser is then split by a beam splitter into two lasers. Then, the two lasers with a small angle \(\theta\) about 0.13 degree are recombined together in the center of the cold atoms ensemble. Figure 1c is a standing-wave field perpendicular to the direction of laser beams, generated by the two interference crossed coupling lasers. The spatial period of the stanging-wave along the \(x\)-direction perpendicular to the propagation direction \(z\) is \(d=\lambda_{c}/(2 sin\theta)\approx 175\) um, where \(\lambda_{c}\) is the wavelength of coupling laser. The probe laser, whose frequency can be precisely detuned by a double-pass configuration based on an acousto-optics modulator (AOM), is counter-propagating with the standing-wave coupling lasers in the medium and will be diffracted into high-order patterns. By using a charge-coupled device (CCD), the intensity and spatial distributions of the probe laser propagated through the EIG will be observed in real time.
# A C-Based Framework for Low-Cost Real-Time Embedded Systems
## 8 Performance Evaluation and Analysis
### Memory Footprint  
Table 4 contains information about the RtFw memory footprint, broken down into categories as follows:  
* The base modules of the framework, comprising its real-time tasks (Section 4.1) and configuration data structures (Section 6).
* The Modbus and local real-time I/O modules.
* The Modbus RTU, CAN, and TCP proxies (Section 4.5).
* The shared memory management modules, including mirroring.
* Additional utilities, mainly consisting of functions to dump framework data structures in a human-readable format as a high-level debugging aid.
* The main program of the application firmware, which initializes RtFw and implements a minimal control function.  
\begin{table}
\begin{tabular}{l r r r} \hline \hline
**Category** & **Text + RO Data (B)** & **RW Data (B)** & **BSS (B)** \\ \hline RtFw base & 5257 & 0 & 136 \\ Event management & 1536 & 0 & 0 \\ I/O modules & 5384 & 0 & 0 \\ Proxies & 3112 & 0 & 40 \\ Shared memory management & 868 & 0 & 56 \\ Utilities & 6879 & 0 & 0 \\ Main program & 1481 & 0 & 2272 \\ \hline Total & 24,517 & 0 & 2504 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Memory footprint divided by category.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of I/O Points**} & \multicolumn{3}{c}{**Event to Input \(t_{E,I}\) (us)**} & \multicolumn{3}{c}{**Event to Output \(t_{E,O}\) (us)**} \\  & \(\mu\) & \(\sigma\) & **Min** & **Max** & \(\mu\) & \(\sigma\) & **Min** & **Max** \\ \hline
1 & 315.19 & 0.67 & 313.75 & 316.77 & 709.32 & 1.15 & 706.86 & 713.53 \\
2 & 314.12 & 0.72 & 312.72 & 315.71 & 811.35 & 2.24 & 807.33 & 818.12 \\
4 & 316.20 & 0.65 & 314.69 & 317.74 & 953.94 & 2.73 & 948.61 & 961.69 \\
8 & 318.46 & 1.36 & 316.69 & 322.67 & 1241.20 & 3.93 & 1232.23 & 1255.63 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Event-driven reactivity, \(t_{E,I}\) and \(t_{E,O}\) of Figure 13, as a function of the number of 16-bit I/O points, 490 sample points.  
The footprint measurement confirmed that RtFw can profitably be used even on extremely low-cost microcontrollers with limited memory resources. More specifically, its memory requirement amounts to less than 5% of the total Flash memory available on the LPC1768 (512 KB) and less than of 4% of its RAM (64 KB).  
Besides RtFw, the application firmware must typically be linked against several additional libraries, for instance, the Modbus master and slave protocol libraries, the lwIP library, the FreeRTOS library, and the C runtime libraries. Their memory footprint has not been reported because they would still be needed even if RtFw were not used at all. Moreover, quantifying the exact memory footprint of these libraries would have been hard, because it highly depends on application-level code requirements and how the libraries themselves have been optimized. For instance, the C library heap can be reduced in size or eliminated completely if the application does not require dynamic memory allocation. Similarly, the C library newlib[56] offers specific configuration options for systems with limited RAM memory[57].
## 4 Discussion
[24] reported that platelet integrin \(\alpha_{\text{IIb}}\beta_{3}\) colocalized with integrin \(\beta_{2}\) and cooperated in NF-\(\kappa\)B activation, our results accord with their findings. MAPKs are serine/threonine protein kinases consist of of three major subgroups, p38 MAPK, ERK1/2, and JNK1/2, which occurred in platelets and activated by various agonists [25]. Our findings demonstrated that BAY11-7082 attenuates collagen induced JNK1/2 and ERK1/2 phosphorylation in human platelets. Similarly, [PERSON] et al. [26] found the involvement of JNK1 in ADP-dependent collagen-induced platelet aggregation. They also demonstrated that glycoprotein (GP)Ib-von Willebrand factor (vWF) interaction triggers integrin \(\alpha_{\text{IIb}}\beta_{3}\) activation in a JNK1-dependent manner during rolling and adhesion of platelets to vWF. Several studies with different experimental setups have indicated that ERK2 is also involved in platelet aggregation. [PERSON] et al. [27] and [PERSON] et al. [28] have independently reported the involvement of ERK2 in platelet aggregation. They demonstrated that collagen- or thrombin-induced ERK2 activation depends on TxA\({}_{2}\) formation and ADP/ATP release reaction. Furthermore, [PERSON] et al. [29] have indicated that ERK2 affects platelet secretion. A partial amount of hydrogen peroxide formed by platelets is converted into HO\({}^{\bullet}\), which are involved in the initial phase of platelet activation [15]. The ESR study revealed that BAY11-7082 did not scavenge HO\({}^{\bullet}\) in activated platelets, indicating that platelet HO\({}^{\bullet}\) formation does not seem to be regulated by NF-\(\kappa\)B signal, and which may be played an upstream regulator for NF-\(\kappa\)B signal. However, we do not rule out the possibility that other unknown signals are involved in the NF-\(\kappa\)B-mediated inhibition of platelet activation by CBN.