esa-sceva/satcom-chunk-collection huggingface dataset:
# Area of Interest Tracking Techniques for Driving Scenarios Focusing on Visual Distraction Detection
## 1 Introduction
### Driver Distraction  
Driver distraction, defined as the shift of attention from essential driving tasks to other activities, forms a part of the broader concept of driver inattention [10]. This encompasses several forms, including Driver Diverted Attention, which is synonymous with distraction, whether the focus is on Driving-Related and Non-Driving-Related Tasks (NDRT). Distractions can impede activities necessary for safe driving [11]. Eye tracking and algorithmic analysis of glance behavior help to quantify driver inattention, despite drivers having visual spare capacity or off-target glances [12]. Distractions are classified by the National Highway Traffic Safety Administration into four types: visual, auditory, biomechanical (manual or physical), and cognitive. Visual distractions involve loss of road awareness due to a blocked field of vision or focus on non-road visual targets [13], while auditory distractions come from sounds or auditory signals diverting attention [14]. Manual distractions involve handling devices or interfaces instead of the steering wheel, reducing reaction time [15]. Cognitive distractions are thoughts that limit focus on driving, often caused by external factors or cognitive overload, leading to a \"Look at but not see\" issue.  
The NDRT necessitate the allocation of visual attention and give rise to visual distraction, a phenomenon that can be quantified through gaze tracking techniques and subsequently manifests as Total Eyes-Off-Road Time (TEORT) [16]. TEORT represents the duration during which the driver's gaze is not directed towards the road but is instead focused on the Area of Interest (AOI) represented by the In-Vehicle Information System (IVIS) interface or any other NDRT including phone usage or eating and drinking.
## 3 Results and Discussion
### Narrative Review
#### 3.1.2 Cost Aspects and Economic Trends  
When discussing the costs of the 3D-printed truss cages for foot arthrodesis, all papers addressing this subject (Table 1) comment on the high cost. A typical 3D-printed truss cage implant typically costs around USD 20,000, although cheaper implants were also reported [18]. [PERSON] et al. [22] mentioned changing the provider for two patients from their six-patient series for cost-related reasons. However, as mentioned in several reviewed studies, the discussion should consider balancing the implant cost with the clinical and long-term financial benefits. Custom 3D-printed truss cages are especially beneficial in complex cases of foot arthrodesis, where conventional implants cannot adequately address large bone defects or challenging deformities. In comparison, alternatives, such as amputation, carry significant long-term financial implications. In studies like [PERSON] et al. [44], it is mentioned that the lifetime cost of amputation can be more than USD 500,000 as it includes prosthetics, rehabilitation, and associated care. On the other hand, limb salvage procedures, including those using a custom 3D-printed implant, have a lifetime cost closer to USD 160,000.  
The economic trends In the manufacturing field suggest that advancements in AM technology (enhanced efficiency and reduce material waste, in situ monitoring to reduce defects, automated post-processing, energy-efficient AM equipment, etc.) are likely to drive cost reductions over time, enhancing the availability of these implants and enabling more equitable access to their benefits. As such, the discussion around the cost of 3D-printed truss cages must consider their long-term value, especially in complex cases where their benefits far compensate for the initial cost. The precise fit of these implants can help to prevent complications such as nonunion, which could lead to costly revision surgeries. For example, revision surgeries using allografts can cost USD 10,000 more than primary TAR, adding to the financial burden on healthcare systems and/or patients. These benefits translate into less time spent in the hospital, fewer revision surgeries, and overall quickerrecovery times, which can reduce the indirect costs, such as lost productivity and extended rehabilitation periods.
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 3 Proposed Methodology
### Solution Blueprint  
The overall framework of the proposed detection solution comprises a listener API, database, conversion, preprocessing, and training modules, as well as a monitoring application. This is primarily located on the application layer of the SDN. A listener application \"Listener API\" is used to connect an SDN controller with this solution. This API is designed to receive a copy of selective incoming requests to the controller. These were all requests with unknown destination MAC addresses. It also fetches the network topology, switch port, MAC, and IP addresses, along with the flows. To the East, this API connects to the solution database. It stores information from the SDN environment in a database. We deployed MongoDB [52] as the database for this solution. MongoDB is updated using the listener API, which operates within the SDN topology. The monitoring module compares the actual traffic with the learned model. It identifies the timing side-channel activity by comparing samples and implementing anomaly detection techniques.  
The proposed response solution blueprint for responses against timing side-channel attacks is based on historical data. The solution comprises several essential modules including data collection, data analysis, response, and feedback modules. The integration of the detection and response mechanisms is shown in Figure 3. In the following subsections, we delve into the specifics of each module, shedding light on their individual functionality and collective synergy for the detection and response to timing side-channel attacks.
## 3 Materials and Methods  
### Firewall Data  
The dataset employed in the analysis presented in Table 2 encompasses a variety of attributes pertinent to network traffic. These attributes include the source port, destination port, Network Address Translation (NAT) source port, NAT destination port, and metrics related to data flow, such as the volume of bytes and the number of packets sent and received. Additionally, the dataset captures the duration of network activities, providing a comprehensive overview of network traffic characteristics essential for effective firewall packet classification. Furthermore, the dataset records the action taken in response to the network traffic, with activities such as allowance, denial, dropping, or reset-both [36].  
Here is a brief description of each of the fields in the dataset:  
Source Port: this designates the port number on the originating machine from which the network traffic is initiated.  
Destination Port: this signifies the port number on the recipient machine to which the network traffic is directed.  
NAT Source Port: NAT, an acronym for Network Address Translation, is a methodology firewalls use to correlate an internal (private) IP address to a distinctive public IP address. This field represents the source port number post the application of NAT.  
NAT Destination Port: this constitutes the destination port number after the NAT implementation.  
Bytes: this quantifies the cumulative number of bytes constituting the data in the network packet.  
Bytes Sent: this represents the volume of data bytes dispatched from the source to the destination.  
Bytes Received: this demarcates the quantity of data bytes that the source accrues from the destination.  
Packets: this denotes the overall count of network packets embodied in the network traffic.  
Elapsed Time (s): this signifies the total duration of the network communication.  
pkts_sent: this quantifies the network packets transmitted from the source to the destination.  
pkts_received: this enumerates the network packets the source garners from the destination.  
Action: the \"Action\" field in a firewall denotes the prescribed response to network packets and acts as a categorical variable, including 'allow', 'deny', 'drop', and'reset-both' as potential values. 'Allow' permits passage of the packet through the network; 'deny' blocks it; 'drop' silently discards it without notifying the source; and'reset- both' sends a reset packet to both source and destination, terminating the connection. As demonstrated in Figure 1, the distribution of actions is as follows: 'allow' at 37.640 instances, 'deny' at 14.987, 'drop' at 12.851, and'reset- both' at 54, reflecting the varying frequencies of each response type in the analyzed dataset.
## 5 Conclusions  
This study employed a chute test apparatus to investigate the effects of ice content and initial deposition configuration on the scraping effect of rock-ice avalanches. The following conclusions were drawn:  
The scraping effects (in terms of scraping length and depth) of rock-ice avalanches generally increase with higher ice content. The mixed accumulation form exhibits significantly stronger scraping effects compared to the ice on top of stones and stones on top of ice forms.  
During the shoveling-scraping process, the leading particles of the rock-ice avalanches primarily induce shear failure in the stable substrate. As the flow progresses, subsequent particles gradually push the sheared substrate material forward, resulting in shear displacement and the characteristic scraping features.  
The low friction characteristics of ice particles, along with the presence of meltwater, significantly enhance the mobility of rock-ice avalanches. This, in turn, increases the kinetic energy prior to the scraping event, leading to a gradual amplification of the energy converted into scraping, thereby intensifying the scraping effect.
## 3 Selected MPC Algorithms  
### Conventional Model Predictive Control Algorithm (CMPC)  
The conventional FCS-MPC algorithm is formulated in the discrete-time domain. A prediction horizon \(N=1\) based on the accurate system model is used to predict the future value of the state variable (\(V\)) and feed the measurements back at the \(K\)t instant, as shown in Figure 2(a). In this scenario, a single objective CF, which is described mathematically in (15), defines the control system objective. In fact, the state variable \(V\) moves toward the reference trajectory \(V^{*}\) and is defined as \(V^{P}\). Finally, new measurements, new predictions, and performance optimization of the CF are repeated during each sampling interval [3].  
Figure 3: Various MPC schemes, where (**a**) depicts that no modulator was used; and (**b**) depicts that a modulator was used.  
\[g=(V^{*}_{c/\alpha}-V^{p}_{c/\alpha}(k+1))^{2}+(V^{*}_{c/\beta}-V^{p}_{c/\beta}(k+ 1))^{2} \tag{15}\]
## 4 Results and Discussion
### Validation Using SSH of DTU18 Verification Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **MHL** & **CMDL** & **RMDL** & **MRMDL** \\ \hline MAD (m) & 5.09 & 4.82 & 4.27 & 3.92 \\ RMSE (m) & 8.12 & 7.95 & 7.35 & 6.19 \\ PCC & 0.94 & 0.96 & 0.97 & 0.98 \\ Running Time (s) & 30.34 & 33.92 & 530.31 & 54.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance metrics of different model retrieval results. Figure 5: Scatter density plots of different models; (a) Scatter density plot of MRMDL model; (b) Scatter density plot of CMDL model; (c) Scatter density plot of RMDL model; (d) Scatter density plot of MHL model. Figure 6b depicts the SSH probability density function (PDF) for the MRMDL and DTU18 validation models. It has been demonstrated that the SSH distribution retrieved by MRMDL is generally consistent with the DTU18 validation model. However, in comparison with the DTU18 validation model, the probability of occurrence of SSHs between \(-60\)-\(35\) m, \(38\)-\(54\) m, and \(60\)-\(80\) m for MRMDL retrieval is much lower. The probability of occurrence between \(-25\)-\(-15\) m and \(-5\)-\(30\) m is significantly higher than that of the DTU18 validation model. This is primarily due to the unbalanced distribution of data with various SSHs in the training dataset. Figure 7a,b presents the global SSH distributions of the DTU18 model and the new MRMDL model, respectively. Figure 7c presents the SSH bias of the new MRMDL method with the DTU18 validation model. Figure 7 demonstrates the overall agreement between the SSH produced by the new MRMDL model and the SSH from the DTU18 validation model. However, the primary use of CYGNSS satellites is not ocean altimetry. The retrieval error is still significant due to the limited DDM time delay resolution, the narrow receiver bandwidth, and the weak peak antenna gain. Figure 7c shows that the SSH bias shows inconsistency in different geographical regions; this is primarily due to the uneven data distribution. Figure 6: Error distribution histograms and probability density functions of the retrieval results of the four models; (**a**) Error distribution histogram for retrieval results from the four models; (**b**) MResNet and DTU18 validation model SSH probability density function. Figure 7: Global SSH and bias of DTU18 model and the new MRMDL model; (**a**) SSH of DTU18 model; (**b**) SSH of the new MRMDL model; (**c**) SSH bias of the new MRMDL model and the DTU18 model.
## 2 Materials and Methods
### B. Channel Model
\begin{array}{ll}f_{h_{i}}(h_{i})&=\frac{\alpha_{i}\beta_{i} \varepsilon_{\text{mod},i}^{2}\varepsilon_{i}^{k_{i}}h_{i}^{z_{i}-1}}{A_{\text {mod},i}\Gamma(\alpha_{i})\Gamma(\beta_{i})\Gamma(k_{i})}\\ &\times\frac{1}{2\pi i}\int_{l}\frac{\Gamma\left(-s+\varepsilon_{\text{mod},i }^{2}-1\right)\Gamma\left(-s+\alpha_{i}-1\right)\Gamma\left(-s+\beta_{i}-1 \right)}{\Gamma\left(-s+\varepsilon_{\text{mod},i}^{2}\right)}\Big{(}\frac{ \alpha_{i}\beta_{i}}{A_{\text{mod},i}}\Big{)}^{s}ds\\ &\times\int_{h_{i}}^{\infty}\Big{[}\ln\Big{(}\frac{h_{\text{ap},i}}{h_{i}} \Big{)}\Big{]}^{k_{i}-1}h_{\text{ap},i}^{-z_{i}+s}dh_{\text{ap},i}\end{array}  \tag{14}\]  
where \(j=\sqrt{-1}\), \(l\) represents the appropriate integral contours in the s-plane. Setting \(t=\ln\big{(}h_{\text{ap},i}/h_{i}\big{)}\) and use [34] (Equation (3.381.4)). The internal integral of the above formula with respect to \(h_{\text{ap},i}\) can be expressed as:  
\[ \begin{array}{ll}I_{1}&=\int_{h_{i}}^{\infty}\Big{[}\ln\Big{(}\frac{h_{\text {ap},i}}{h_{i}}\Big{)}\Big{]}^{k_{i}-1}h_{\text{ap},i}^{-z_{i}+s}dh_{\text{ap},i }=h_{i}^{-z_{i}+s+1}\int_{0}^{\infty}t^{k_{i}-1}e^{-t(z_{i}-s-1)}dt\\ &=h_{i}^{-z_{i}+1+s}\frac{\Gamma(k_{i})}{(z_{i}-s-1)^{z_{i}}}=h_{i}^{-z_{i}+1+s} \Gamma(k_{i})\frac{\Gamma^{k_{i}}\left(-s+z_{i}-1\right)}{\Gamma^{k_{i}}\left(- s+z_{i}\right)}\end{array}  \tag{15}\]  
Putting Equation (15) into Equation (14). After simplification, we can obtain:  
\[ \begin{array}{ll}f_{h_{i}}(h_{i})&=\frac{\alpha_{i}\beta_{i} \varepsilon_{\text{mod},i}^{2}\varepsilon_{i}^{k_{i}}}{A_{\text{mod},i}\Gamma( \alpha_{i})\Gamma(\beta_{i})}\\ &\frac{1}{2\pi i}\int_{l}\frac{\Gamma\left(-s+\varepsilon_{\text{mod},i}^{2}-1 \right)\Gamma\left(-s+\alpha_{i}-1\right)\Gamma\left(-s+\beta_{i}-1\right) \Gamma^{k_{i}}\left(-s+z_{i}-1\right)}{\Gamma\left(-s+\varepsilon_{\text{mod},i }^{2}\right)\Gamma^{k_{i}}\left(-s+z_{i}\right)}\Big{(}\frac{\alpha_{i}\beta_{i }}{A_{\text{mod},i}}h_{i}\Big{)}^{s}ds\end{array}  \tag{16}\]The above formula can be simplified by \(I\)-function [28]. Therefore, the PDF of the composite channel containing random fog, Gamma-Gamma distribution atmospheric turbulence and Beckmann distribution generalized pointing error, \(f_{h_{i}}(h_{i})\), is as follows:  
\[f_{h_{i}}(h_{i})=\frac{\alpha_{i}\beta_{i}\varepsilon_{\text{mod},i}^{2}z_{i}^{k_ {i}}}{A_{\text{mod},i}\Gamma(\alpha_{i})\Gamma(\beta_{i})}L_{2,4}^{4,0}\left[ \begin{array}{c}\alpha_{i}\beta_{i}\\ A_{\text{mod},i}\end{array} h_{i}\right| \begin{array}{c}\left(\varepsilon_{ \text{mod},i}^{2},1,1\right),(z_{i},1,k_{i})\\ \left(\varepsilon_{\text{mod},i}^{2}-1,1,1\right),(\alpha_{i}-1,1,1),(\beta_{ i}-1,1,1),(z_{i}-1,1,k_{i})\end{array} \right] \tag{17}\]  
where \(l_{p,q}^{m,n}[\bullet]\) is the \(I\)-function.
## 5 Conclusions  
In this study, we explored the effects of backpack loads on leg muscle activations during slope walking. It was concluded that the hip, knee, and ankle extensor muscle activations increased during slope walking, and the hip muscle increased the most among the hip, knee, and ankle muscles. Moreover, muscle activations increased pronouncedly with loads during slope walking, and the knee extensor muscle activations increased more than the hip and ankle muscles. The results in our study imply that the hip and knee muscles play an important role during slope walking with loads. Our results are important for the design of assistant devices, such as exoskeleton robots, to enhance people's walking ability, especially for hikers and soldiers. The hip and knee extension movements during slope walking should be considerably assisted to lower the muscle activations. Future studies could explore the effects of loads and grades on more muscles and involve more participants, including female subjects, to expand the insights into the muscle strategy for providing more suggestions for the design of assistant devices.  
Full postal address: Room A1043, Lee Shao Kee S&T Building, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/10/14/4890/s1](http://www.mdpi.com/2076-3417/10/14/4890/s1)]([http://www.mdpi.com/2076-3417/10/14/4890/s1](http://www.mdpi.com/2076-3417/10/14/4890/s1)): Table S1: Normalized mean (mean \(\pm\) SD) EMG activities during inclined walking with a range of backpack loads, Table S2: The ratio of the EMG between inclined walking and level walking across all backpack loads, Table S3: The ratio of the EMG between walking with backpack loads and without backpack loads across all grades.  
**Author Contributions:** Conceptualization, Y.L. and Q.S.; methodology, X.G.; software, Y.L. and M.Z.; validation, Y.L. and M.Z.; formal analysis, X.G.; investigation, Y.L. and L.Q.; resources, M.Z.; data curation, Y.L., L.Q., and M.Z.; writing--original draft preparation, Y.L.; writing--review and editing, Q.S. and X.G.; visualization, Y.L., L.Q., Q.S., and X.G.; supervision, X.G.; project administration, Y.L.; and funding acquisition, Q.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The study was funded by grants from the National Natural Science Foundation of China (Grant No. 51905035 and 51905291), the China Postdoctoral Science Foundation-funded project (Grant No. 2019M660478), and the Ministry of Science and Technology national key R&D program (Grant Number: 2017 YFB1300500).  
**Acknowledgments:** We thank [PERSON] and [PERSON] from Beijing Sports University for providing the lab experiments and giving suggestions for the analysis. We thank all the participants in this study.  
**Conflicts of Interest:** The authors have no conflict of interest concerning this manuscript.
## 2 Materials and Methods
### Methods  
All lateral cranial radiographs were analyzed by a single author of this study. The cephalometric radiographs were saved electronically, and cephalometric analysis of each radiograph performed using a Webceph software. All cephalometric points were applied manually. During the measurements, 33 measurement points were marked. The results of 10 angles, 3 reference lines, and 2 segments were included in the study. Example measurements are shown in Figure 1.  
The analysis considered the following parameters: SNA describes the anterior-posterior position of point A (the point lying deepest on the anterior outline of the alveolar process) in relation to the anterior cranial fossa, SNB describes the anterior-posterior position of point B (the point lying deepest on the anterior outline of the alveolar portion of the mandible) in relation to the anterior cranial fossa, ANB describes the position of mandible in relation to maxilla, FMA included between lines FH (corresponds to Frankfurt horizontal plane--passes through points orbitale and tragion) and GoGn (line of mandibular body defined by points Gonion and Gnathion), Gonial angle, ODI stands for overbite depth indicator, APDI stands for anteroposterior dysplasia indicator, IMPA located between long axis of lower incisor tooth and GoGn line, U1 to NA (mm), and U1 to NA (deg) determines anteroposterior  
Figure 1: Example measurements included in the study. 1. SNA; 2. SNB; 3. ANB; 4. FMA; 5. Gonial Angle; 6. APDI; 7. ODI; 8. IMPA; 9. L1 to LOP; 10. U1 to NA (mm); 11. U1 to Na (deg); 12. L1 to NB (mm); 13. L1 to NB (deg); 14. Upper lip to E-plane,15. Nosolabial angle.  
position of upper incisor teeth in relation to maxillary base, L1 to NB (mm) and L1 to NB (deg) are the anteroposterior positions of the lower incisors in relation to the base of the mandible, and L1 to LOP is the anteroposterior positions of the lower incisors in relation to the lower occlusal plane, the upper lip to E-plane is the location of the upper lip in relation to the aesthetic line that passes through the tip of the nose and chin (upper lip should be 3 mm apart), and the Nasolabial angle.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 5 Discussion
### Drawbacks and Improvements of Simulation Environment  
Because of the complex tunnel environment, the verification test environment needs to be discussed for such a complicated engineering mechanics problem.  
1. For the principle verification test, image acquisition module calibration experiment and wireless antenna attitude test, a relative stable environment was needed; thus, these tests were conducted in the laboratory reasonably;
2. For the data processing test and temperature change test, a real tunnel environment was needed. However, because of the busy, dusty and vibrational construction sites, it was not only hard to study effects of single factor, but also inconvenient to conduct the tests in such a complex tunnel environment. Therefore, the tests were conducted in laboratory and studied without dust, and it would be better to simulate a dusty environment during the lab test;
3. For the system accuracy test, the deformation was simulated by a micro motion platform with a 1 \(\upmu\)m accuracy. The micro motion platform was placed on a table and moved horizontally. However, for tunnel settlement deformation, it settled vertically. Given the isotropy of the image sensor for vertical and horizontal direction, the test was reliable, and the field test verified this adequately. In addition, it would be better to use a vertical micro motion platform for the deformation simulation in a vertical direction;
4. For the laser transmitter feasibility test, this test was conducted in a foggy night; thus, the simulation environment was similar to a tunnel, a dusty and dark environment in low visibility.
5. For the field test, the environment is dusty and even wet. In order to prevent the interference of tunnel dust and water mist, dirt shrouds and waterproof devices were used in laser transmitter module, image acquisition module and some electric parts. To improve the permeability of the laser beam, an automatic device needs to be added to clear the mud consisting of dust and water mist on the laser receiving screen and laser transmitter module glass window.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Time**} & **Deformation from** & **Deformation from** \\  & & **Total Station (mm)** & **Monitoring System (mm)** \\ \hline
1 & 16:00 & 0 & 0 \\
2 & 17:00 & 2.51 & 2.46 \\
3 & 18:00 & 4.90 & 4.95 \\
4 & 19:00 & 6.21 & 6.22 \\
5 & 20:00 & 7.10 & 7.15 \\
6 & 21:00 & 8.01 & 7.99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Partial field test data.