esa-sceva/satcom-chunk-collection huggingface dataset:
# A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks
## 3 Adversarial Attack Taxonomy
### Attacker's Goal  
The attacker's objective is to influence the outcomes of the ML system either by misleading the system or by introducing perturbations to the input. The attacker's goal can be then outlined as follows:  
* Security Infraction: Refers to security violations and can be classified into three main dimensions.
* Availability Attack: The attacker intends to minimize the model's performance at testing or deployment phases, thereby making it unreliable and useless. Availability attacks can be executed through data poisoning when the attacker gains control over a portion of the training dataset, or through model extraction when the attacker predicts some relevant parameters of the target model.
* Integrity Attack: Focuses on undermining the integrity of an ML model's output, leading to erroneous predictions made by the model. The attacker can induce an integrity breach by executing an evasion attack during the testing or deployment phases or a poisoning attack during the training phase.
* Privacy Attack: The attacker's objective could involve gaining information about the system data, leading to data privacy attacks, or about the ML model, resulting in model privacy attacks.
* Attack Specificity: Based on their impact on the model output integrity, the attack specificity can be divided into three distinct categories:
* Confidence Reduction: The adversary intends to decrease the prediction certainty of the target model.
* Untargeted Misclassification: The adversary endeavors to change the predicted classification of an input instance to any class other than the original one.
* Targeted Misclassification: The adversary seeks to generate inputs that compel the classification model's output to become a particular desired target class or endeavors to make the classification output for a specific input correspond to a specific target class.
# Ground-Borne Vibration Model in the near Field of Tunnel Blasting
## 2 Dimensional Analysis
### Limitations of Classical Blasting Vibration Sadovsky Formula  
In the classic Sadovsky formula \(V=K(\sqrt[3]{Q}/R)^{\vartheta}\), only the \(Q\) and \(R\) among the above factors are considered in the study of blasting vibration velocity. The influence of other factors (including \(\rho\) and \(c_{r}\)) on the variation in the blasting vibration velocity is all reflected in the coefficients \(K\) and \(\partial\), and these two coefficients are mainly obtained by regression fitting according to the field monitoring data. Therefore, when using this formula to predict blasting vibration, the construction must be carried out first to obtain clear monitoring data, rather than the prediction of blasting vibration before construction. This will inevitably lead to the weak timeliness of vibration prediction and cannot give timely and accurate guidance to construction.  
Meanwhile, since \(K\) and \(\partial\) comprehensively reflect the impact of many factors on blasting vibration, including the geological conditions of the blasting site, blasting attenuation conditions, and blasting mode, the specific performance of these factors cannot be defined. They will change under the influence of different blasting designs and geological conditions, so the values of \(K\) and \(\partial\) will also change accordingly and unpredictably. This is the reason why the Sadovsky formula is not universal in general [17].  
On the other hand, even if the specific values of each influencing factor are determined, the specific \(K\) and \(\partial\) values cannot be determined through theoretical analysis but can only be obtained by fitting the vibration monitoring data of the project. Therefore, the Sadovsky formula based on the regression fitting of the field monitoring data can only be effectively applied to the project in which the monitoring data is obtained but has little guiding significance for other projects. This is reflected in many engineering practices [18; 19; 20; 21], and the reason is closely related to the factors considered in the formula and its derivation method.
# Porous Organic Polymers-Supported Metallocene Catalysts for Ethylene/1-Hexene Copolymerization
###### Abstract  
Porous organic polymers (POPs) have received much attention in adsorption, separation, and catalysis. In this paper, porous organic polymers with different pore structure were used as metallocene catalyst supports, and ethylene/1-hexene copolymerizations were conducted using the POPs-supported metallocene catalyst. The pore structure of the prepared POPs and the supported metallocene catalyst were characterized by nitrogen sorption porosimetry and non-local density functional theory simulation, and the molecular chain structure of the produced ethylene/1-hexene copolymers were investigated through gel permeation chromatography (GPC), IR analysis, differential scanning calorimetry (DSC), and temperature rising elution fractionation (TREF). The results show that the loading amount of active sites varied with different pore structures of the POP supports, and the active species scattered in different pore sizes had a moderate impact on the molecular chain growth and the molecular weight distribution. The IR, DSC, and TREF analysis revealed that different branching degree, double bond content, and chemical composition distributions were detected from the molecular chain structure of the ethylene/\(\alpha\)-olefin copolymers from different POPs and silica-supported metallocene catalysts, despite their similar IR, DSC, and TREF curves due to the same active species. Scanning electron microscopy (SEM) showed that porous ethylene/\(\alpha\)-olefin copolymers with varied surface morphology were obtained from the POPs-supported metallocene catalysts with different pore structure.  
porous organic polymer (POP); ethylene; 1-hexene; metallocene catalyst; copolymerization +
Footnote †: journal: Physics Letters B
## 3 Results  
Correlations between the absolute torque values were high ([PERSON]'s correlation coeffi: IPSI Flex-CON Flex: r = 0.905; IPSI Ex-CON Ex: r = 0.871), and multicollinearity was thus present. Therefore, only the ratios IPSI Ex/Flex, CON Ex/Flex, and CTR were included in the following MANOVA. These variables were normally distributed in both groups, as assessed by the Shapiro-Wilk test (IPSI Ex/Flex: \(p\) = 0.224; CON Ex/Flex: \(p\) = 0.321; CTR: \(p\) = 0.876). There were no outliers in the data. No multivariate outliers were found, as assessed by the Mahalanobis distance MD (cut-off (3) = 16.27, MDmax = 7.78, \(p\) > 0.001). Linearity between the dependent variables was verified by visual inspection of the scatter plots. The homogeneity of the error variances was checked with [PERSON]'s test and showed no significance for the dependent variables (IPSI Ex/Flex: \(p\) = 0.517; CON Ex/Flex: \(p\) = 0.881; CTR: \(p\) = 0.779). [PERSON]'s test confirmed the homogeneity of covariances (\(p\) > 0.001). The one-way MANOVA showed a statistically significant difference between the groups on the combined dependent variables, with F(3, 22) = 6.640, \(p\) = 0.002, partial \(\eta^{2}\) = 0.475, [PERSON]'s \(\Lambda\) = 0.525. Post-hoc univariate ANOVAs showed no statistically significant difference between the groups for CON Ex/Flex, with F(1, 24) = 0.006, \(p\) = 0.941, and partial \(\eta^{2}\) < 0.001. However, a statistically significant difference between the two groups was found for IPSI Ex/Flex (F(1, 24) = 5.864, \(p\) = 0.023, partial \(\eta^{2}\) = 0.196) and for CTR (F(1, 24) = 19.885, \(p\) < 0.001, partial \(\eta^{2}\) = 0.453). The group with the higher pelvic torsion angle exhibited a higher (but statistically not significant) torque in the extensors on the side of the anteverted hemi-pelvis (IPSI Ex), and consequently a statistically significant higher torque ratio between extensors and flexors (Table 2). A contralateral difference between the flexor and extensor torque in both subgroups was not found. The torque ratio on the contralateral side (CON Ex/Flex) was also not significantly different. However, the crossed torque ratio at \(p\) < 0.001 was significantly higher in the group with higher pelvic torsion (Table 2). The effect size was partial \(\eta^{2}\) = 0.453, which represents a large effect. In the linear regression models, the crossed torque ratio (CTR) showed the strongest correlation with the pelvic torsion angle with a correlation coefficient of 0.668 (Table 3). At the same time, the subgroup with higher pelvic torsion reported statistically significant higher values for tenderness to palpation (Table 2, partial \(\eta^{2}\) = 0.309, large effect). The linear regression models showed that 39% of the variance of tenderness to palpation can be explained by the pelvic torsion angle and 44% of the variance of the pelvic torsion angle can be explained by the crossed torque ratio (Figure 4).
## 2 Methods and Data
### Long-Term Field Data Collection and Preprocessing  
The data in this research were collected from 1 January 2018 to 31 December 2018. A sensor module applied to the mechanical ventilation system was used to monitor indoor environmental parameters including temperature, CO\({}_{2}\) and PM\({}_{2.5}\) concentration. In addition, the system's status (i.e., power on/off) and air flow were collected in every 5 min. Outdoor environmental parameters including temperature, relative humidity and PM\({}_{2.5}\) concentrations were obtained from the weather stations near the monitored households at a time interval of 2 h. The timestamp interval of outdoor environmental data was pre-processed as 5 min by forward filling, which is consistent with indoor environmental data. Missing values, outliers and inconsistencies were removed and forward filled.
# Three-Dimensional Finite Element Analysis for Pressure on Flexible Wall Silos
## 1 Introduction  
### Background  
Thin-walled metallic containers have been widely used in agricultural, livestock, and industrial production operations, displacing alternative storage and containment methods in countries that handle huge amounts of granular material every year [1]. As the world's top wheat importer, Egypt has begun to build large granular handling and storage facilities, with annual wheat imports reaching 13 million tons [2]. Many large-scale silos have been built around the country in the last 10 years. Moreover, some examples of silo collapse have been reported (Minya governorate 2019 [3] and Alexandria governorate 2018 [4]). Thus, deep investigations and a review and validation of current design approaches, which were developed many years ago and have several limitations, are required to identify the causes. Additionally, caution must be exercised in the design process to avoid overstress or buckling of the silo.  
Industrial silos are typically composed of steel or concrete, and their behavior varies greatly due to the rigidity of the walls and the friction created between the bulk materials and the walls. Rectangular silos (Figure 1) are generally built of thin metal sections, and, if unstiffened, are flexible as opposed to rigid, as deduced from the Janssen equation, which is most commonly used to forecast the lateral filling pressures of a silo wall. As a result, thepurpose of this study is to thoroughly investigate these rectangular silos with thin sections in terms of various silos' dimensions and wall thicknesses in order to determine the wide range of wall-filling pressures that occur on the flexible wall and to provide designers with suggestions to improve the structural efficiency of the silo.  
Straining actions developed in each case are actually different, as measured in many experimental works on both full-scale and pilot-scale silos; however, actual codes do not recognize this large disparity and consider the same actions to be applied in both cases, resulting in an underestimation or overestimation of the real behavior in many cases depending on the silo geometry and bulk material properties. As a result, it is critical to highlight this disparity in behavior by undertaking new research that focuses on the differences in actions taken in each case and their impact on the stability and strength of each type of structure.
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
## 2 Materials and Methods
### MLSE and MAP Equalizers
#### 2.2.2 MLSE Equalization  
The goal of an MLSE equalizer is to determine the sequence that will maximize the likelihood function  
\[\mathbf{\hat{b}}=\underset{\mathbf{b}}{\text{argmax}}~{}Pr\left(\mathbf{y}|\mathbf{b}\right), \tag{14}\]  
which is equivalent to  
\[\mathbf{\hat{b}}=\underset{\mathbf{b}}{\text{argmin}}~{}V(\mathbf{b}), \tag{15}\]  
where  
\[V(\mathbf{b})=\sum_{l=0}^{L}\|y_{l}-(\hat{y}_{l}|\mathbf{b}_{l})\|^{2} \tag{16}\]  
is the path metric of sequence \(\mathbf{b}\).  
The most common and efficient way of obtaining \(V(\mathbf{b})\) is utilizing of the Viterbi algorithm (VA) [19], where the metric is computed a branch by a by branch in the trellis representation of the received symbols.
# Harvesting the Aggregate Computing Power of Commodity Computers for Supercomputing Applications
## 3 Design and Implementation
### High-Performance Data Analytics Cluster
#### 3.2.1 Apache Hadoop Setup  
The Apache Hadoop software is an open-source framework built for reliable, scalable distributed computing tasks with huge data sets over a cluster of multiple computers. The features used for this implementation are (i) the large data set distribution across clusters of computers using a simple programming model (ii) became the de facto standard for storing, processing, and analyzing hundreds of terabytes and petabytes of data and (iii) is cheaper to use in comparison to other traditional proprietary technologies and can handle all type of data.  
Generally, a Hadoop system comprises a computer acting as the master node and multiple computers acting as slave nodes, as shown in Figure 2. Hadoop has two modules, in total, including the HDFS and MapReduce Framework. The HDFS usually only has one NameNode, which manages the directory tree and metadata of related files for the HDFS. It could also own a secondary NameNode that can be employed to backup mirror files, combine logs and mirror files periodically and send them back to NameNode. In general, NameNode and Secondary NameNode are deployed on the master node. In addition, the DataNode of HDFS is responsible for storing data and sending processed data back to NameNode and is usually deployed on the slave node.
## 4 Travel Speed Reliability
### Analysis of Travel Speed Reliability
#### 4.2.1 Travel Speed Reliability at Morning Peak  
Based on different travel speed reliability evaluation thresholds, the 28 road sections of the Beijing 3 rd Ring Road were analyzed during the weekday morning peak, as shown in Figure 11. As the travel speed reliability evaluation threshold increased, the travel speed reliability calculation results became lower.  
Figure 11: Travel speed reliability of the Beijing 3 rd Ring Road based on different travel speed reliability thresholds: (**a**) Threshold-1; (**b**) Threshold-2; (**c**) Threshold-3; (**d**) Threshold-4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Threshold** & **Threshold-1** & **Threshold-2** & **Threshold-3** & **Threshold-4** \\ \hline The values of \(v_{*}\) (km/h) & 20 & 35 & 50 & \(v_{*}=0.71\times\overline{v}_{T,m,A}\) \\ \hline \end{tabular}
\end{table}
Table 3: Travel speed reliability evaluation thresholds.  
The data showed that the travel speed reliability of 96% of the above road sections of the Beijing 3 rd Ring Road was lower than 0.8 based on the Threshold-3 in the morning peak. The travel speed reliability of the Beijing 3 rd Ring Road was larger than 0.7 based on the Threshold-4 in the morning peak in August. However, the road service level reliability for the road sections with smooth traffic status was less than 0.8 if the road sections were in a steady state of congestion for long periods and the travel speed reliability of car commuters for these road sections was larger than 0.7 in the morning peak in August, indicating that these road sections were congested and a reliable type of road section in August.  
After the students back to school, the travel speed reliability of more than 82% of the road sections of the Beijing 3 rd Ring Road significantly decreased. The travel speed reliability of Section 8-9 and Section 4-3 was lower than 0.1 based on the Threshold-1, Threshold-2, and Threshold-3 in the morning peak after students back to school. The maximum travel speed reliability reduction values of the Beijing 3 rd Ring Road sections calculated based on the Threshold-1, Threshold-2, Threshold-3, and Threshold-4 were 0.44 (Section 14-13), 0.57 (Section 1-14), 0.61 (Section 9-8), and 0.59 (Section 1-14) during the weekday morning peak, respectively.
# TDLAS Monitoring of Carbon Dioxide with Temperature Compensation in Power Plant Exhausts
## 3 Experiment Details
The response time of real-time online monitoring can be further reduced by decreasing the sampling frequency. Each measured spectrum was obtained with 20 averaged sweeps, yielding a 4 s measurement time that was fast enough to satisfy the industrial application. Experiments using both direct absorption and wavelength modulation were repeated for three times to evaluate the repeatability of the sensor system.
## 3 Results
### Experimental Orbit Determination from Interferometric Observables
Since the Astra 1M satellite lies above central Africa (19.2\({}^{\circ}\)E longitude), the stable area over the Indian Ocean causes a natural drift force towards the east [30]. This is represented from 12 to 21 April in Figure 7, where the average phase value increases, and in Figure 8, where the Earth-Centered Earth-Fixed (ECEF) coordinate system trajectory of the satellite moves along the \(Y\) axis towards the east. In order to keep the satellite in the nominal position, an orbital maneuver towards the west is periodically performed. Two station-keeping maneuvers are identified during the orbit observation period: on 5 and 21 April. Note in Figure 7 how the average phase value decreases from 5 to 13 April and from 21 to 27 April due to the movement of the satellite towards the west. Figure 8 shows the same behavior representing the retrieved trajectory in the ECEF coordinate system: the ellipse of the spacecraft moves towards the west from 9 to 12 April (Figure 7(a)) and from 21 to 24 April (Figure 7(b)). Thus, the satellite is kept within the assigned nominal station.
# Kinematic Analysis of an Omnidirectional Tracked Vehicle
## 2 Materials and Methods
### Research Plan  
The research was divided into two stages. The first involved the determination of the VR velocity vector for different free roll orientation angles \(\alpha\) and different set speeds of individual tracks \(\dot{\mathrm{q}}\) based on the presented kinematic equations. Then, numerical simulations were carried out for the same angles \(\alpha\) and set speeds \(\dot{q}\). The results obtained were compared. The tests were carried out for three sets of free roll orientation angles: \(\alpha_{1}=135^{\circ}\), \(\alpha_{2}=45^{\circ}\); \(\alpha_{1}=150^{\circ}\), \(\alpha_{2}=30^{\circ}\); \(\alpha_{1}=135^{\circ}\), \(\alpha_{2}=30^{\circ}\). Three sets of set velocities were assumed: \(\dot{q}_{1}=0.1\) m/s, \(\dot{q}_{2}=-0.1\) m/s; \(\dot{q}_{1}=0.1\) m/s, \(\dot{q}_{2}=0\) m/s; \(\dot{q}_{1}=0.1\) m/s, \(\dot{q}_{2}=0.05\) m/s. Simulations lasted t = 1s. Then, additional verification was performed to check the effect of mass on the obtained accuracy. For \(\alpha_{1}=135^{\circ}\), \(\alpha_{2}=45^{\circ}\), \(\dot{q}_{1}=0.1\) m/s, and \(\dot{q}_{2}=-0.1\) m/s  
Figure 14: Visualization of an example simulation. The red crosses are the points of action of the contact forces, white line is a trajectory.  
Figure 15: Trajectory obtained during an example simulation.  
The results of an additional simulation verifying the effect of mass on the obtained results are summarized in Table 6.  
Figure 16: Error curves \(\Delta\epsilon_{x}\), and \(\Delta\epsilon_{y}\) of the position between the theoretical trajectory of the movement of the point R and the simulation result in relation to the distance travelled on the individual axes during the simulation for \(\alpha_{1}=\)135\({}^{\circ}\), \(\alpha_{2}=\)45\({}^{\circ}\), \(q_{1}=0.1\) m/s, \(q_{2}=0\) m/s, and t = 1 s.
## 2 Experimental Procedure  
The commercially available SS400 steel plates with thickness of 5 mm (Dongkuk Steel Mill Co., Ltd., Seoul, Korea) were taken as base material for this study. Table 1 shows the nominal chemical composition of SS400 steel. The butt-weld joints were made from two steel plates having dimensions 250 mm \(\times\) 125 mm \(\times\) 5 mm connected via square groove configuration with a fixed joint gap of 1.0 mm. The two steel plates were properly aligned using magnetic clampers and 1 mm joint gap was accurately maintained using steel gauge. For connecting the ends of plates, tack welding was carried out. At one side of the plates, a ceramic backing strip was attached. A steel filler wire ER-70S having a 1.2-mm diameter was used in all experiments. Table 2 lists the chemical composition of ER-70S type welding wire (Forney Industries Inc., Fort Collins, CO, USA).  
The welding tests were carried out by an automatic alternating current gas metal arc welding (AC-GMAW) welder (OTC Daihen Inc., Tipp City, OH, USA). A DW-300 power supply manufactured by OTC Daihen Inc. [16] was used to conduct the welding experiments. The welding torch (OTC Daihen Inc., Tipp City, OH, United States) was kept fixed, and the workpiece was moved at a constant speed of 10 mm/s by using a linear stage system. The welding torch was held perpendicular to the base plate. By using a shielding gas blending system (Chiyoda Seiki Co., Ltd., Kobe, Japan), a controlled amount of shielding gases Ar-20% CO: at a flow rate of 18 l/min was provided. The contact tip to workpiece distance (CTWD) was kept at 25 mm with an average electrode extension of 18 mm. Temperature measurements were performed at 20 Hz using K-type thermocouples integrated with the National Instruments (NI) thermocouple card 4353. Figure 2 shows the experimental schematics of the welding process and position of the attached thermocouples in the welding tests.  
Due to a large number of variables in the welding process such as current, EN ratio, welding speed, gap between plates, thickness and geometry of the base plate. In the current study, real welding experiments were performed to investigate the effect of current and EN ratio on the joint properties. Average current (Ia) ranges from 150 A-250 A with an increment of 25 A were used during the welding tests, whereas the three distinct EN ratios such 0%, 30% and 50% were selected. The current  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**C** & **Si** & **P** & **Mn** & **Cr** & **S** & **Mo** & **Ni** & **Cu** & **V** & **Fe** \\ \hline
0.06–0.15 & 0.45–0.75 & 0.025 & 1.40–1.85 & 0.15 & 0.035 & 0.15 & 0.15 & 0.50 & 0.03 & Balance \\ \hline \end{tabular}
\end{table}
Table 2: Chemical composition of filler wire (ER-70S).  
Figure 1: Schematics of the improved current waveform.  
pulse corresponding to three EN ratios at average welding current of 200 A has been presented in Figure 3. A total of 15 experimental runs were carried out with each combination of Ia and EN ratio.