esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 2 Methods and Data
### Attenuation Research Dataset  
After screening the data based on the abovementioned steps, the dataset used to construct prediction equations for ground motion parameters of moderate and minor magnitudes in loess sites includes 1149 horizontal records. This dataset is suitable for fitting the horizontal attenuation relationships for magnitudes ranging from \(M_{\rm S}\) 3.0 to \(M_{\rm S}\) 6.5 and epicentral distances from 0 to 100 km. It is used to establish various prediction equations for ground motion parameters at the surface of loess soil layers.  
It should be noted that the surface wave magnitude (\(M_{\rm S}\), unitless) is primarily used as the magnitude scale, owing to its wide adoption in China. Thus, most of the magnitude information in the original record headers is surface wave magnitude, with a minor portion being local magnitude (\(M_{\rm L}\), unitless). Local magnitude is converted based on the conversion formula provided in the \"Earthquake Work Handbook\" (China Earthquake Disaster Prevention Center, 1990) [16]:  
\[M_{S}=1.13M_{L}-1.08 \tag{4}\]  
In the attenuation relationship research, selecting the distance parameter is critical. Commonly used distance parameters include epicentral distance, hypocentral distance, fault distance, and fault projection distance, among which epicentral distance and fault distance are more widely applied. This study primarily examines the attenuation relationships of earthquakes up to a magnitude of 6.5. Since most records in the dataset do not contain information about the seismic fault, this article uses the epicentral distance (R) as the distance parameter. Epicentral distance is the horizontal distance between the station and the epicenter, which can be calculated using the geographic coordinates of the station and the epicenter.
## 3 Attitude Controller Design
### Experiment Results and Discussion
#### 4.2.2 PI Control Experiment  
We compare experiment results generated by the I&I controller with the ones generated by the typical cascaded proportional integral (PI) controller. The parameters of PI controller are selected carefully to get good performance. The parameters of PI approach are as follows: angular velocity loop controller \(k_{p}^{p}=0.2\), \(k_{i}^{p}=1\), angular position loop controller \(k_{p}^{p}=0.08\), \(k_{i}^{p}=0.001\). The experiment results of PI control are shown in Figures 6 and 7. It shows that it takes more than 1.3 s to converge to desired attitude, and the yaw control takes even more than 3 s.  
Figure 4: Attitude of NTAUV under I&I control with disturbance on each axis.  
Figure 5: I&I control torque.
## 4 Numerical Simulation of the Model  
### Geometry Meshing  
Since the geometry in this compact model is very simple compared to the detailed model employed in [PERSON] et al. [17; 16], the time required to generate the geometry and mesh it for each of the 81 cases from Table 2 did not exceed 5 min.  
Before running any simulations, a mesh sensitivity study was carried out in order to set a correct mesh size for each of the model sizes: 40, 70 and 100 mm (see values for \(D\) in Table 1). Therefore, cases 1 and 2 from Table 2 were simulated with random values for the pressure loss coefficients and different interval sizes (IS) for the mesh (0.2, 0.3, 0.4, 0.5 and 0.6 mm). It must be highlighted that the thickness parameter (\(t\)) for cases 1 and 2 are at different levels, and therefore, it was also necessary to check whether screen thickness might have any influence on the mesh size requirements. The difference between the model using an IS of 0.2 and 0.6 was less than 6% in the peak for axial or tangential velocities at the centerline (\(x=0\)) of a cross-section located 20 mm downstream from the screen. Additionally, it was necessary to verify that a low thickness did not demand a higher density for the mesh. Therefore, as the main objective of the compact model is to reduce the time required, an IS of 0.6 mm was chosen as the mesh resolution for all the models with a 40 mm value for parameter \(D\).  
Once this IS was established for the small size in all cases, the same study was carried out for case 6 from Table 2, where the model size was fixed to its higher value (100 mm). The mesh sizes simulated for this case were 0.4, 0.8, 1.0 and 1.5 mm. Similar to cases 1 and 2, because the variation was not greater than 6.7% for the peak values (in \(x=0\) for the 20 mm downstream cross-section), an IS of 1.5 mm was selected as the correct choice.  
Finally, for the middle value of the diameter (70 mm), the growth rate was respected. For the low value of \(D\) (40 mm) an IS of 0.6 mm was used, and for the high value (100 mm), an IS of 1.5 mm was used, and thus the growth rate was 2.5 (100 mm/40 mm = 1.5/0.6 mm). Similarly, for the middle value of \(D\) (70 mm), an IS of 1.05 mm was employed (70/40 mm = 1.05/0.6 mm). A sample of the mesh used is presented in Figure 3.
## 6 Hardware and Software for Data Protection
### Types of IDS
#### 6.3.1 Network IDS and Host IDS  
Accordingly, to the Open Systems Interchange (OSI) seven-layer model [34], understanding where the network topology lies is important. In a research article presented by [PERSON], there exist two types of IDS. They are Network IDS and Host IDS. The Network IDS is able to monitor incoming network traffic and the Host IDS is able to monitor the operating system machines and is able to take snapshots of machine files and tell the difference if anything changes [35].  
Network IDS has an edge of importance as it is able to give coverage in detection across the entire network whereas the Host IDS is specific to the machine, its host. According to Cisco documentation [32], the definition of a Network Intrusion Detection System (NIDS) is as follows:  
\"The goal of intrusion detection is to identify unauthorized use, misuse, and abuse of computer systems by both system insiders and external penetrators.\" [32]  
Cisco reiterates that the intrusion detection problem is becoming a challenging task due to the heterogeneous computer networks since the increased connectivity of computer systems. This gives greater access to outsiders and makes it easier for intruders  
Figure 4: A basic firewall topology (Author’s interpretation of SME business network).  
to avoid identification. Intrusion detection systems (IDS) are based on the beliefs that an intruder's behaviour will be noticeably different from that of a legitimate user and that many unauthorized actions are detectable as stated in the paper by [PERSON], (1994) [36].  
The paper of [PERSON], (2005) [35] explains that the concept of IDS works on the theory of kernels. The kernel is responsible for classifying IDS into two groups namely normal and anomaly.  
Normal is often referred to as Signature-Based Detection whereby IDS is able to recognise bad patterns of malware and anomaly is Anomaly-Based Detection where the IDS is able to identify any deviations from good traffic via Machine Learning.  
Complementing the IDS is of course the Intrusion Prevention Systems (IPS) and how it acts as a management centre for the detection. IPS is able to describe the suspect intrusions, sound alarms, watches for attacks that originate from within the system, studies signature patterns and is able then to terminate connections and offer access controls. In the next sub-section, a closer look at the normal group detection called Signature-Based Detection is examined.
## 4 Experimental Result Analysis
### Influence of Material Properties on Group Stud Effect  
In this experiment, high-strength steel fiber concrete was used for both the prefabricated slabs and the cast-in-place concrete. The material property tests indicated that the compressive strength of the prefabricated concrete reached 66.4 MPa, while the cast-in-place concrete in the slots reached 112.2 MPa. The use of high-strength concrete enhanced the shear capacity of the studs, but also exacerbated the group stud effect. Specifically, the high strength of the concrete allowed the outer studs to bear significantly more load, which led to premature shearing in some of the outer studs during the later stages of loading.  
Additionally, the high compressive strength of the concrete contributed to the overall deformation capacity of the specimens, especially in the scaled specimen S-13. The synergy between the high-strength concrete and the studs allowed the specimen to maintain high load-bearing capacity even at larger displacements. This suggests that in applications where high-strength concrete is used, special attention must be given to optimizing the arrangement and density of studs to minimize the negative impacts of the group stud effect on load-bearing capacity.
# Anomaly Detection in Elderly Health Monitoring via IoT for Timely Interventions
## 3 Results
### HADA Integrated into the Developed Model
Notably, anomalies are detected during periods of unusually high or low step counts, highlighting HADA's ability to identify deviations that may signal potential health issues or behavioral changes. By analyzing these trends, the system provides insights into the elderly individual's mobility and overall physical activity, enabling timely interventions if necessary. This underlines the importance of continuous monitoring and anomaly detection in maintaining the health and safety of the elderly. HADA was evaluated based on the specific metrics of AI models. Out of a total of 676 recordings, 9 anomalies were real (TP = 9), 9 anomalies were false (FP = 9), and 658 were classified as usual (TN = 658). Given the fact that only one person was monitored, who did not require emergency assistance, there were no undiagnosed real anomalies (FN = 0). Precision was identified with a value of 50%, recall was 100%, F1-Score was 33%, and accuracy was 98.5%. Although HADA has low precision, this does not mean that the system cannot be used  
Figure 11: Evolution of AR, mR, and MR with detected anomalies. Figure 12: Distribution of DST and SST associated with anomalies. in practice. It does, however, represent a starting point for exploring future research directions in automating the remote patient monitoring process at home. In a network with a response time (ping) of 149 ms, a download speed of 138.31 Mbps, and an upload speed of 48.45 Mbps, the alert signaling occurred in 11 s and 46 milliseconds. This time provides a relative picture of the response speed offered by the Azure infrastructure, influenced by factors such as the patient's internet speed and the speed at which the SMS is sent by the mobile phone operator, among others. This fast response time demonstrates the efficiency of the Microsoft Azure communication infrastructure, which can transmit information in a short interval, ensuring a prompt response for real-time monitoring. The SMS notification includes the patient's phone number and address. The operator calls the patient so that the individual can confirm their health status. In the meantime, if the patient does not respond, a team is dispatched to the patient. The tests presented in this results chapter have shown the efficiency of the Microsoft Azure infrastructure. These evaluations highlight how Azure's capabilities ensure real-time intervention applications for patient monitoring. The low response times and consistent data transmission demonstrate Azure's infrastructure reliability.
## 2 Methods
### Sensor Modeling  
In the case of sensors having a cantilever as the sensing terminal, it is critical that the cantilever has a high natural frequency due to the following reasons:  
1. Systems with low natural frequencies tend to have lower accuracy due to residual vibration after disturbance [36]. A sensor with higher natural frequency will come to its zero state quicker after the disturbance is removed, hence resulting in more accurate sensing.
2. If an external disturbance has a varying force amplitude but with frequency equal to the natural frequency of the sensor's sensing element, the sensor's sensing element will vibrate with larger amplitude. This phenomenon is also known as resonance [37].  
Although not completely established, several studies suggest that since rodent whiskers vary in length, each whisker is tuned to a different resonant frequency. This gradient of varying resonant frequencies is utilised by the rodent's brain to establish the texture and roughness of the sensed surface [38; 39; 40; 41]. A study has shown that the fundamental frequency range in rodents varies from 35 Hz for long whiskers to 110 Hz for short whiskers [9].  
In order to mimic a biological whisker shaft, it is important to have a model from which the frequency response of the whisker can be analysed. In order to establish such a model, three different approaches have been explored i.e., analytical, transfer function based and a finite element model using COMSOL. The benefits of this multi-pronged analytical approach are:  
Figure 3: **(a)** the whisker unit without the elastomer skin. The PVDF (Polyvinylidene fluoride) sensing whisker shaft is soldered to the base plate with a very low temperature soldering iron. The coin vibrator is clamped down with the base plate, with one end just touching the whisker shaft; **(b)** the coin vibrator is covered with elastomer skin which improves the coupling between the vibrator and the whisker shaft; **(c)** fabricated sensor model1 without elastomer skin; and **(d)** fabricated sensor model 2 with an elastomer skin.  
To have the first resonant frequency between 35 Hz and 110 Hz;  
Damping and loading force effects can be analysed;  
The results of models should be in agreement and also match experimental data;  
Effects that whisker shaft length can be determined for any required frequency;  
Effect of whisker material on frequency response can be analysed.
## 4 Field Tests and Analyses
### Field Tests
#### 4.2.2 Full-Section Blasting Test
Figure 9: The full-section blasting effect of the combined long and short holes: (**a**) blasting effect; (**b**) length of hole mark. reduction in the amount of over-excavation is about 64%. Therefore, when the surrounding rock is soft and fragmented, the position of the surrounding holes can be selected to move inward by 5 to 10 cm from the tunnel contour line to reduce the amount of over-excavation. In addition, the degree of blasting over-excavation differs for the three categories of cutting forms. The over-excavation seen in oblique cutting test 1 is 10-30 cm, that of oblique cutting test 2 is 10-28 cm, and that of the combined long and short hole cutting method is 8-20 cm. It can be observed that the over-excavation in the combined long and short hole cutting method is relatively low. This is due to the fact that this cutting method shows stress concentration and an initial free surface during blasting, which is more conducive to rock fragmentation and subsequent blasting. Furthermore, the method of simply reducing the number of cut holes, such as oblique cutting tests 1 and 2, has no significant impact on the amount of over-excavation.
## 3 Results
### PlyB221 Displays a Somewhat more Stable Activity than PlyP32  
The optimal pH conditions and NaCl concentrations for the activity of each endolysin were then established using _B. cereus_ ATCC 1087 in a turbidity reduction assay. PlyB221 was active in a wide range of pH (5-9) but the highest activity was obtained at pH 7 and 8 (Figure 7A). PlyP32 optimum was between pH 6 and 7 but, outside this range, its activity dropped to 30% at pH 5 and 8 and no activity was detected at pH 4 or 10 (Figure 7B). For both endolysins, the pH optima determined experimentally are consistent with their predicted p.I, namely 7.67 for PlyB221 and 6.22 for PlyP32. Regarding tolerance to NaCl (evaluated at the endolysin optimal pH, i.e., pH 8 and pH 7 for PlyB221 and PlyP32, respectively), PlyB221 and PlyP32 activities decreased at salt concentrations higher than 300 and 200 mM, respectively (Figure 7C,D). Finally, the endolysin thermal stability was assessed by incubating the proteins at temperatures from 4 to 60 \({}^{\circ}\)C during 30 min followed by a 5 min incubation on ice before assessing the residual activity on _B. cereus_ ATCC 10987. PlyB221 maximal enzymatic activity was preserved up to 45 \({}^{\circ}\)C and the protein retained 75% activity after 50 \({}^{\circ}\)C (Figure 7E). PlyP32 enzyme was stable up to 37 \({}^{\circ}\)C and lost 28% activity at 45 \({}^{\circ}\)C. After 50 \({}^{\circ}\)C, the enzymatic activity dropped to 9% and was completely inhibited after incubation at 60 \({}^{\circ}\)C (Figure 7F).  
Figure 6: PlyB221 activity on purified cell wall extracts. Cell wall extracts from _B. cereus_ ATCC 10,987 were purified and mixed by PlyB221. (**A**) Macroscopic observation of _B. cereus_ cell wall hydrolysis with 100 \(\mathrm{\SIUnitSymbolMicro\text{$\mu$g}}\) of endolysin after 10 min at RT. (**B**) OD\({}_{595}\) monitoring during 30 min (30 \({}^{\circ}\)C) of cell wall hydrolysis using endolysin concentrations ranging from 1 to 100 \(\mathrm{\SIUnitSymbolMicro\text{$\mu$g}}\)/mL.
## 3 Methods for Assessing the Aesthetic Quality of Images
### Deep-Learning-Based Method for Assessing the Aesthetic Quality of Images
#### 3.2.5. Image Aesthetics-Assessment Method Based on Semi-Supervised Adversarial Learning  
Traditional methods for image aesthetic assessment suffer from inefficiency, inherent labeling noise, and incompleteness due to semantically described images. [PERSON] [87] proposed a new semi-supervised deep-active-learning algorithm. The algorithm learns human gaze movement paths hierarchically by sequentially linking semantically significant blocks of objects in each scene, unifying semantically significant region discovery and deep-gaze-movement path-feature learning into a principled framework which employs a small set of labelled images. Finally, a probabilistic model for image aesthetic evaluation is developed using the deeply learnt deep-gaze-movement path features. The aesthetic assessment of an image is related to its style, semantics and content. Therefore, [PERSON] [88] designed a multi-task network in which the aesthetic column was used as a score predictor, the style column as a style classifier, and angular-softmax loss was used to train the main style classifier to maximize the margins between classes in the single-label training data, using a semi-supervised approach to iteratively improve the network's generalization ability. In 2021, [PERSON] [89] proposed a semi-supervised adversarial learning approach in order to alleviate the problem of annotating aesthetic attributes, which is time-consuming, expensive and error-prone, resulting in usable images partially annotated with attributes. The method  
Figure 4: Brain-inspired neural network framework [85].  
is used for image aesthetic evaluation from partially attribute-annotated images, which can effectively reduce the reliance on manual attribute annotations.
# Error Averaging Effect in Parallel Mechanism Coordinate Measuring Machine
## 4 Property of Slider Motion Errors in PCMM  
Linear motion stage is always employed in precision engineering. The stage provides straight line motion reference, which is expected to travel along a straight line. However, in practice, the actual path and orientation of the stage deviate from the straight line of motion because of its own geometric errors and assembling errors. As shown in Figure 3, five unexpected motions--two straightness motion errors and three angular motion errors--occur in five degrees of freedom (5-DOF). The motion straightness errors include two linear errors along \(Y\) and \(Z\) direction, respectively. The angular motion errors around each axis are called pitch, yaw, and roll.  
Over the years, numerous scholars have intensively studied the characteristics of the aforementioned geometric errors on precision machines and made many valuable contributions, especially in analysis and discussion of the relationship between the motion straightness errors and angular motion errors. Their conclusion can be summarized as follows [12; 13; 14; 15; 16; 17]:  
1. The straightness motion error of the linear stage is closely related to the straightness error of the guide rail itself, but these errors are different in that the guide rail straightness error is a \"cause\", whereas the straightness motion error is a \"result\";
2. The angular motion error and motion straightness error are also related, but no definite function relation exists between them;
3. When a different point on the stage is measured, its value of straightness motion error may differ.  
Our study showed that the motion straightness error of the slider is the key error source that influences and even determines the accuracy and performance of the PCMM. This is because since the three sliders are shared with only one linear guider rail, their motion errors will have an impact on the moving platform through six link rods. Figure 4 illustrates one slider working on the guide rail. A detailed discussion about the slider angular motion errors and motion straightness errors will be given, respectively, in the following sections.  
Figure 3: Degrees of freedom (DOFs) of the stage.
## 3 Results and Discussion
### Determination of Phenolic Compounds
Compound 6 was assigned as luteolin-7-\(O\)-rutinoside, following the criteria reported by [PERSON] et al. Phenolic compound 7 at retention time 30.75 presented a pseudomolecular ion [M-H]\({}^{-}\) at \(m\)/z 461, allowing its tentative identification as a luteolin-7-\(O\)-glucuron as previously reported [11]. Figure 3: Phenolic profile characterization by LC-MS of chamomile aqueous extract obtained at 80 \({}^{\circ}\)C.  
Compounds 8 [[M-H]\({}^{-}\) at _m/z_ 555) and 10 ([M-H]\({}^{-}\) at _m/z_ 445) were tentatively assigned as salvianolic acid K and apigenin-7-O-glucuronide, respectively, based on the spectroscopic data described by [PERSON] and colleagues [40]. Compound 11 was identified as hispidulin glucoside by comparison of its UV and mass spectrums with literature data [11; 40]. The major compounds determined in sage aqueous extract were rosmarinic acid, salvianolic acid K, and luteolin-7-O-glucuronide. Apigenin-7-O-glucuronide, salvianolic acid B, hispidulin glucoside, and luteolin-7-O-rutinoside were less abundant phenolic compounds, followed by myricitrin, luteolin-7-O-glucose, and _p_-coumaric acid at lower percentages (Table 2). The presence of both compounds, rosmarinic acid and luteolin-7-O-glucuronide, has been reported in other studies [11; 13; 40] as the main phenolics in infusion of _S.officialis_. Luteolin-7-O-glucose and salvianolic acid were also detected in significant amounts [11]. Our findings are generally in agreement with the study of [PERSON] et al., which reported that rosmarinic acid is the dominant compound in the infusion of _S.officialis_; _p_-coumaric, rutinoside, and glucoside of luteolin were also detected [13]. [PERSON] et al. found rosmarinic acid to be the component with the highest content and variability in all analyzed infusions of _S. officialis_, _S. frucicosa_, and _S. pomifera_. Moreover, salvianolic acid K, luteolin, and apigenin derivatives were the most frequently detected components and occurred in most of the examined _Salvia_ populations [40]. According to many studies, rosmarinic acid is the most abundant phenolic compound in infusions of _Salvia_ species [13; 40]. Figure 4: Phenolic profile characterization by LC-MS of sage aqueous extract obtained at 80 \({}^{\circ}\)C.