esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To accommodate lots of training data and complex training models, \"distributed\" deep learning training has become employed more and more frequently. However, communication bottlenecks between distributed systems lead to poor performance of distributed deep learning training. In this study, we proposed a new collective communication method in a Python environment by utilizing Multi-Channel Dynamic Random Access Memory (MCDRAM) in Intel Xeon Phi Knights Landing processors. Major deep learning software platforms, such as TensorFlow and PyTorch, offer Python as a main development language, so we developed an efficient communication library by adapting Memkind library, which is a C-based library to utilize high-performance memory MCDRAM. For performance evaluation, we tested the popular collective communication methods in distributed deep learning, such as Broadcast, Gather, and AllReduce. We conducted experiments to analyze the effect of high-performance memory and processor location on communication performance. In addition, we analyze performance in a Docker environment for further relevance given the recent major trend of Cloud computing. By extensive experiments in our testbed, we confirmed that the communication in our proposed method showed performance improvement by up to 487%.  
Intel knights landing; distributed deep learning; python; collective communication; high bandwidth memory; docker; MPI +
Footnote †: journal: Applied Soft. 2020, 10, 6717
# Ground Control System Based Routing for Reliable and Efficient Multi-Drone Control System
## 3 GCS-Routing Design
### Routing Table Management
#### 3.2.1 GCS-Routing Operations in GCS  
Algorithm 1 shows GCS-routing operations in GCS, which were classified into initialization, distribution, and maintenance phases. In the initialization phase (Lines 1-3), i.e., drones are not deployed yet or a route between drones is not constructed yet, GCS estimates link costs among drones based on drone geographic information. The GCS then generates a routing table using [PERSON]'s algorithm and applies the routing table to itself.  
In the distribution phase (Lines 4-10), GCS distributes routing tables to each corresponding drone while preventing loop construction. The GCS routing table is already constructed, hence GCS has a tree where GCS is a root node. After generating drone routing tables, GCS creates an aggregated route message, \(M_{t}\), for each subtree, \(t\), where the one-hop distance child node of GCS is a root node. \(M_{t}\) comprises routing tables for drones in subtree \(t\). Subsequently, GCS distributes \(M_{t}\) to its child nodes that then perform the remaining distribution process (see Algorithm 2).  
```
1:Aggregated route message \(M\)Route update:
2:whiledo
3:if\(M\) received then
4:\(m\leftarrow\)findRouteInfo (M)
5:\(M\gets M-m\)
6:itself\(\leftarrow\)applyRouteInfo (\(m\))
7:endif
8:if is not a leaf then
9: classify \(M\) into \(M_{1},M_{2},\cdots,M_{n}\) for each subtree
10:for each subtree, \(t\)do
11: a root of subtree \(t\leftarrow\)sendRouteInfo (\(M_{t}\))
12:endfor
13:else
14: delete \(M\)
15:endif
16:endwhile
17:Link monitoring:
18:whiledo
19:rc = monitorLinkFailure0
20:if rc == 1 then
21: report link failure to GCS
22:endif
23:endwhile
```  
**Algorithm 2** GCS-routing operations in the drone.  
In the maintenance phase (Lines 11-19), when network topology changes due to drone movements or link failure is reported, GCS updates the drone routing tables. As discussed above, GCS manages and schedules drone movements, so it knows drone final destinations. Thus, before the actual topology change, GCS can predict network topological changes and generate routes for future topology. When drone movements are scheduled, GCS generates new route tables and issues them to the corresponding drones using the same distribution strategy of Algorithm 1. The topology also changes when a link failure occurs due to obstacles between drones. When GCS receives a link failure report from a drone, it carries out the cost update process. GCS updates the failure link's cost and regenerates drone routing tables. However, in the maintenance phase, GCS distributes routing tables only to drones that need to be updated, avoiding unnecessary communications and operations.
## 2 Materials and Methods
### Sample Preparation and Characterization  
PFOS with 95% purity was purchased from Sigma-Aldrich (St. Louis, MO, USA). Its molecular formula is C\({}_{8}\)HF\({}_{17}\)O\({}_{3}\)S, and its molecular weight is 500.13 g/mole. After CP test sampling, the samples were stored in a refrigerator at 4 \({}^{\circ}\)C. PFOS was identified and quantified by UPLC/MS using the Waters Acquity UPLC system in multiple-reaction-monitoring (MRM) mode according to the standard curve (R\({}^{2}\) > 0.98). Its quantification was based on a linear four-point calibration curve (0.3-961 ug/L), and each CP-treated sample was diluted due to the high concentration of PFOS they contained. Standard PFOS samples were diluted in MeOH to prepare stock solutions of 250 ug/L. PFOS was mixed and diluted with MeOH to make a 7.5 ug/L of internal standard (IS) working solution. PFOS separation was achieved using the Acquity UPLC BEH C18 column (2.1 \(\times\) 50 mm, Waters, Milford, MA, USA), with mobile phase A consisting of 2 mM ammonium acetate in water/MeOH (95:5) and mobile phase B consisting of 2 mM ammonium acetate in MeOH.  
The concentration of total organic carbon (TOC) was analyzed using a TOC analyzer (TOC-L, Shimadzu, Kyoto, Japan). Fluoride (F\({}^{-}\)) and SO\({}_{4}\)\({}^{2-}\) concentrations were determined using an ion chromatograph (Dionex Aquion IC system, Thermo Fisher Scientific Inc., Seoul, Korea) equipped with an IonPac AS14 (4 mm i.d.) column and a conductivity detector. The eluent solution contained sodium carbonate (Na\({}_{2}\)CO\({}_{3}\), 3.5 mM) and sodium bicarbonate (NaHCO\({}_{3}\), 1 mM), with a constant flow rate of 1.2 mL/min.
## 3 Results and Discussion
### Effect of Inflation Pressures and Rim Width
#### 3.3.3 Static Lateral Stiffness  
Section 3.2.4 described modelling the tire static lateral stiffness as multiple individual knobs acting as springs in parallel that constituted the tread stiffness. The tread stiffness then combined with the bald carcass stiffness as springs in a series to yield the total lateral stiffness of the treaded tire. Now, in Figure 18, that simplified model is applied across a range of inflation pressures, yielding results comparable to the measured value of the \(29\times 2.3\)\" knobby tire. The figure shows the individual knob stiffness as derived from the tread shear measurement multiplied by the number of knobs in contact. On the basis of the footprints shown in Figure 15, \"n\", the number of knobs in contact, plotted here on the secondary axis, decreased with inflation pressure. Hence, the product of those terms yielded the theoretical tread stiffness, which also decreased with inflation pressure. It is interesting to note that the individual knobs did not change in stiffness, but the fact that fewer of them were in contact reduced the effective tread stiffness. Meanwhile, the as-measured bald carcass stiffness was shown to increase with inflation pressure. Combining these two as springs in series resulted in a theoretical total tire lateral stiffness within 15% of the measured values across the range of inflation pressures.  
As before, possible sources of the difference between calculated and measured total stiffness included difficulty in determining exactly how many knobs, or fractions thereof, were included in the contact patch and, for low inflation pressures, the participation of shoulder knobs, whose shear stiffness was not measured.
## 3 Results
### Simulation Verification with ADAMS and MATLAB/Simulink
#### 3.1.1 Obstacle Avoidance Simulation  
In order to verify the autonomous obstacle avoidance ability of a wheel-footed robot in unstructured terrain, a dynamic obstacle avoidance simulation experiment based on Trot gait modulation was designed. When the robot encounters an uncrossable obstacle, the system triggers a steering command according to a preset node to realize path switching. The simulation results are displayed in Figure 10.  
To evaluate the motion stability of the robot, we conducted an analysis of the center-of-mass displacement data. First, we calculated the sample mean to reflect the central tendency of the data:  
\[\overline{x}=\frac{1}{n}\sum_{i=1}^{n}x_{i} \tag{16}\]  
Based on n = 100 data points, the mean value of the center-of-mass displacement was obtained as \(\overline{x}=-0.00180\) m. Next, we calculated the sample standard deviation to quantify the degree of data dispersion:  
\[s=\sqrt{\frac{1}{n-1}\sum_{i=1}^{n}\left(x_{i}-\overline{x}\right)^{2}} \tag{17}\]  
The calculated s = 0. 00206. To assess the reliability of the mean, we derived the 95% confidence interval using the t-distribution:  
\[CI=\overline{x}\pm\left(t_{\alpha/2,n-1}\cdot\frac{s}{\sqrt{n}}\right) \tag{18}\]  
For n \(-\) 1 = 99 degrees of freedom, the critical t-value (\(t_{0.025,99}\)) at the 95% confidence level was approximately 1.984. After calculation, the standard error was 0.00035, and then the 95% confidence interval was obtained as [\(-0.00318\), \(-0.00178\)].  
It is evident from the results that despite minor fluctuations in the center-of-mass displacement during the robot's movement, the overall trend remained stable.
## 3 Main Results
### The Improved Load and Capacity Metrics of IWSNs  
In the above two methods of expressing node loads, one considers only a single parameter, and the other considers global variables, for which the required computational effort is too considerable. To the practical situation of hierarchical sensor networks, the load of cluster head nodes is not only related to the number of connected lower layer general nodes but also closely related to the neighboring cluster head nodes on the same layer. Therefore, this paper defines the initial load of cluster head nodes as the product of node degree and related adjacent node degree. The initial load can be computed as follows:  
\[L_{i}(0)=(\sum_{j\in\Gamma_{i}}\frac{k_{j}}{D_{a}}k_{i})^{\sigma}, \tag{8}\]  
where \(L_{i}\) is the load of \(i\); \(k_{i}\) represents the degree of the node \(i\); \(k_{j}\) is the degree of the node \(j\); \(\Gamma_{i}\) collects the neighboring nodes of \(i\), and \(D_{a}\) means the average degree of all networks. \(\sigma\) is used to adjust the variability of the initial load, called the weight factor. This method considers the local information of nodes, which can reflect the node load information more realistically in the case of less computation.  
In this model, a linear relationship between the load \(L_{i}\) and the capacity \(C_{i}\) is used. By adding an exponential adjustment parameter to the original capacity-load model, the two parameters for adjusting capacity can be matched to provide more accurate capacity adjustment. It can be expressed as:  
\[C_{i}=(1+\lambda)L_{i}(0)^{\beta}, \tag{9}\]  
where \(\lambda\) is the overload tolerance factor, and \(\beta\) is the exponential adjustment parameter. By adjusting the size of \(\lambda\) and \(\beta\) to expand the capacity of the nodes, in reality, the expansion of capacity will cost more, so infinite growth cannot be achieved. Meanwhile, in order to explore the impact of the capacity of single-hop cluster head nodes on the destructive capability of the network, the nodes directly connected to the sink node are defined as \(\beta_{special}\).
## 3 Results and Discussion
### Cooking Loss and Microbial Counts of Shrimp
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Temperatures** & \begin{tabular}{c} **Heating Time** \\ **(s)** \\ \end{tabular} & \begin{tabular}{c} **Cook Loss** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **TPC** \\ **(log CFU/g)** \\ \end{tabular} & \begin{tabular}{c} **PBC** \\ **(log CFU/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Coliform** \\ **(log CFU/g)** \\ \end{tabular} &
\begin{tabular}{c} **_E. coli_ \\ **(log CFU/g)** \\ \end{tabular} \\ \hline Raw shrimp & 0 & 4.93 \(\pm\) 0.23 a & 3.27 \(\pm\) 0.11 a & 2.21 \(\pm\) 0.14 a & \textless{}1.0 \\
130 \({}^{\circ}\)C & 80 & 1.16 \(\pm\) 0.12 e & 3.45 \(\pm\) 0.52 b & 2.88 \(\pm\) 0.32 b & 1.68 \(\pm\) 0.19 b & \textless{}1.0 \\  & 90 & 2.41 \(\pm\) 0.29 d & 3.26 \(\pm\) 0.39 b & 2.17 \(\pm\) 0.27 c & \textless{}1.0 c & \textless{}1.0 \\  & 100 & 3.45 \(\pm\) 0.11 c & \textless{}2.0 d & \textless{}2.0 d & \textless{}1.0 c & \textless{}1.0 \\  & 110 & 3.50 \(\pm\) 0.15 c & \textless{}2.0 d & \textless{}2.0 d & \textless{}1.0 c & \textless{}1.0 \\  & 120 & 3.66 \(\pm\) 0.18 b & \textless{}2.0 d & \textless{}2.0 d & \textless{}1.0 c & \textless{}1.0 \\
90 \({}^{\circ}\)C & 110 & 1.50 \(\pm\) 0.28 e & 2.81 \(\pm\) 0.38 c & 2.54 \(\pm\) 0.29 b & 1.26 \(\pm\) 0.42 b & \textless{}1.0 \\  & 120 & 2.05 \(\pm\) 0.33 d & 2.45 \(\pm\) 0.05 c & 2.05 \(\pm\) 0.25 c & \textless{}1.0 c & \textless{}1.0 \\  & 130 & 2.73 \(\pm\) 0.19 d & \textless{}2.0 d & \textless{}2.0 d & \textless{}1.0 c & \textless{}1.0 \\  & 140 & 3.46 \(\pm\) 0.24 c & \textless{}2.0 d & \textless{}2.0 d & \textless{}1.0 c & \textless{}1.0 \\  & 150 & 4.39 \(\pm\) 0.11 a & \textless{}2.0 d & \textless{}2.0 d & \textless{}1.0 c & \textless{}1.0 \\ \hline \end{tabular} Each value represents the average \(\pm\) standard deviation of three replicates; different lowercase letters indicate significant differences in the same column (\(p<\) 0.05).  
\end{table} 
Table 2: Changes of cooking loss, total plate count (TPC), psychotrophic bacteria count (PBC), coliform, and _E. coli_ in white shrimp using the scaled-up MAIH system set at 130 \({}^{\circ}\)C and 90 \({}^{\circ}\)C for various heating times. coliforms (<1.0 log CFU/g) were found in the other MAIH-heated samples with extended heating time. However, _E. coli_ was not detected in any of the raw or heated shrimp samples (Table 2). This indicates that the scaled-up MAIH at 130 \({}^{\circ}\)C for 90 s or 90 \({}^{\circ}\)C for 120 s was the least effective in reducing coliform count in shrimp. In summary, when the heating condition of the MAIH was set at 130 \({}^{\circ}\)C for 100 s or 90 \({}^{\circ}\)C for 130 s, the scaled-up MAIH method was able to provide effective pasteurization of prepackaged shrimp.
## 4 Discussion
### Relationship between the Stress Combinations and the Failure Modes  
Accordingly, different positions on Mohr circle, different stress states are shown in Figure 11. In the model with a bedding inclination dip of 0\({}^{\circ}\) or 180\({}^{\circ}\), the actual force in the area near the bedding plane is in a pure compressive state at point A (Figure 11). And as a whole the tensile crack mainly occurs in the model, and there are also intervened tensile cracks along the bedding plane (Figure 12a). When the bedding inclination dip is 30\({}^{\circ}\), the actual force in the area near the bedding plane is at point B (Figure 11), which is a compressive shear state. In addition to the slip dislocation along the bedding plane, the compression-shear crack surface also appears in the model (Figure 12b). When the bedding inclination dip is 60\({}^{\circ}\), the actual force near the bedding plane is mainly shear force, which is regarded as state C (Figure 11), and a shear crack surface appears (Figure 12c). It can be seen from the crack distribution of the model (Figure 12d) that there is slip on each bedding plane, and tensile cracks also appear on each slip surface, which indicating that when the bedding inclination dip is 120\({}^{\circ}\), the actual force near the bedding plane is state D (Figure 11). When the bedding inclination dip is 90\({}^{\circ}\), the actual force in the area near the bedding plane is state E, and the tensile cracks parallel to the bedding plane mainly appear in the model (Figure 12e). [PERSON] et al. [38] divided the failure modes of bedding rocks into three categories, i.e., the cross-layer slip failure, the layer-slip failure, and the combination of layer-slip failure and layer-based tensile crack.  
After testing the phyletic under directional loading, [PERSON] et al. [37] divided the failure modes into tensile crack along or across layers, slip failures along layers, and composite shear failures along layers. In this study, combined with the different stress states of the bedding at different inclination dips, the crack genesis and the crack type, the failure modes were summarized into five types: pure compression, compression shear, pure shear, tension shear, and pure tension. Therefore, different positions of the Mohr circle represent different stress combinations, the crack modes of bedding rocks under different stress combinations are different, and the bedding dip of the rocks determines the crack modes. The dips of 30\({}^{\circ}\) and 150\({}^{\circ}\) are exactly the opposite of the horizontal dips, but the Mohr circle clearly shows the difference in the stress states. This reflects the definite anisotropy of the layered rocks.  
Figure 11: Schematic diagram of different stress combinations.
# Evaluation of 5G Positioning Performance Based on UTDoA, AoA and Base-Station Selective Exclusion
## 1 Introduction
Moreover, we want to show that, if the positioning computation also considers the detection and the elimination of ranging observations affected by NLoS, the accuracy improves. The proposed approach is applicable to outdoor use cases with high-accuracy positioning requirements. Therefore, this method will pave the way for the fulfillment of high-accuracy positioning requirements in challenging environments when using cellular networks. The structure of the paper is as follows. Section 2 introduces the signal model, its properties and the observable computation process. Section 3 presents the position algorithm solution and the main integrity-monitoring approaches. Section 4 describes in detail the scenario used in this work and analyses the results. Finally, Section 5 draws conclusions.
## Appendix A Full Questionnaire (Updated Version--60 Questions)
### Configuration Debt--Questions 7 to 9  
Question 7: Is it easy to specify a configuration as a small change from a previous configuration?  
Stakeholder: Organizers  
Score: 3  
Justification: In RL, quick experimentation is crucial. Being able to specify configuration changes easily allows rapid iteration and model improvement.  
Example: A platform allows incremental changes to the learning rate of an RL agent by modifying a single line in a YAML file, facilitating quick experimentation.  
Question 8: Do you have poorly documented or undocumented configuration files?  
Stakeholder: Organizers  
Score: 4  
Justification: Proper documentation facilitates understanding and maintenance of configurations.  
Example: A platform's configuration file lacks comments explaining the purpose of each parameter, leading to confusion among developers.  
Question 9: Have the configuration files been thoroughly reviewed and tested?  
Stakeholder: Organizers  
Score: 4  
Justification: Reviewing configurations is crucial for identifying errors, optimizing performance, and maintaining consistency. Testing ensures that configurations perform as expected under different conditions. Rigorous testing ensures robustness and reliable performance. Review and testing are vital to catch and identify errors early, optimizing performance, maintaining consistency and ensure the system behaves as expected under various configurations.  
Example: A platform undergoes a peer review process for configuration changes, followed by automated tests to validate the new settings.
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 7 Conclusions  
This paper presents the development of a SCADA system testbed to be used in cybersecurity research. The testbed was dedicated to controlling a water storage tank which is one of several stages in the process of water treatment and distribution. The testbed was used to analyze the effects of the attacks on SCADA systems. Using the network traffic, a new dataset was developed for use by researchers to train machine learning algorithms as well as to validate and compare their results with other available datasets.  
Five reconnaissance attacks specific to the ICS environment were conducted against the testbed. During the attacks, the network traffic with information about the devices (valves, pumps, sensors) was captured. Using Argus and Wireshark network tools, features were extracted to build a dataset for training and testing machine learning algorithms.  
Once the dataset was generated, five traditional machine learning algorithms were used to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. These algorithms were evaluated in two phases: during the training and testing of the machine learning models (offline), and during the deployment of these models in the network (online). The performance obtained during the online phase was compared to the performance obtained during the offline phase.  
Three metrics were used to evaluate the performance of the used algorithms: accuracy, FAR, and UND. Regarding the accuracy metric, in the offline phase all ML algorithms showed an excellent performance. In the online phase, almost all the algorithms performed very close to the offline results. The KNN algorithm was the only one which did not perform well. Moreover, considering an unbalanced dataset and analyzing the FAR and UND metrics, we concluded that Random Forest and Decision Tree models performed best in both phases compared to the other models.  
The results show the feasibility of detecting reconnaissance attacks in ICS environments. Our future plans include generating more attacks and checking the models' feasibility and performance in different environments. Moreover, experiments using unsupervised algorithms will be done.  
[PERSON] built the testbed and performed the experiments. [PERSON] and [PERSON] assisted with revisions and improvements. The work was done under the supervision and guidance of [PERSON], [PERSON] and [PERSON], who also formulated the problem.  
**Funding:** This work has been supported under the grant ID NPRP 10-901-2-370 funded by the Qatar National Research Fund (QNRF) and grant #2017/01055-4 Sao Paulo Research Foundation (FAPESP).  
**Acknowledgments:** The statements made herein are solely the responsibility of the authors. The authors would like to thank the Instituto Federal de Educacao, Ciencia e Tecnologia de Sao Paulo (IFSP), Washington University in Saint Louis, and Qatar University.  
**Conflicts of Interest:** The authors declare no conflicts of interest.  
Figure 10: Un-detected rate results.
The ability to wirelessly monitor changes in permeability allows the Magnetoelasticity-Based (MB) sensors to be applied to monitoring stress in bone plates [21] and the tensile force of sutured wound sites [22]. Fortunately, the permeability of MB sensors is sensitive to compressive and tensile stresses. Hence, wireless MB sensors are applied to monitor the degradation behavior of PAB based on the relationship between output power of the MB sensor and external compressive force with the degradation of the PAB. In this study, an MB sensor was used to monitor the degradation behavior of PAB in vitro. PAB was fabricated by coating both sides of an MB sensor with PLA using a 3D printer. PAB samples were subjected to the hydrolytic degradation process in PBS (pH = 7.4) and NaOH (pH = 12) media over 15 days at body temperature (37 \({}^{\circ}\)C). PAB was tested with a self-developed experimental platform every 7 days, and the changes in permeability of the MB sensor were tracked wirelessly. The varying permeability can reflect the degradation behavior of the PAB, and is supported by theoretical analysis. The changes in the weight and volume of PAB were also measured with traditional methods and a scanning electronic microscope (SEM).