esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study focuses on the development of an effective and reliable model for simulating the impact of shot peening on the tooth root bending strength of gears. The stress state induced by the shot peening process is simulated via explicit dynamic simulations using the open-source solver Code_Aster. The stress state for both the shot-peened and non-peened gear samples taken as reference have been analysed via the Findlay multiaxial fatigue  
Figure 6: Position and early nucleation direction of the cracks for the reference \({}_{NP}\) and the shot-peened \({}_{SP}\) gears. Tooth root bending (TRB) and Fank Fracture (FF) failures according to the literature.  
criterion. As reference configuration, the STBF test, i.e., the standard procedure for the determination of the material properties in the gear sector, was employed. The simulation of the shot-peening process highlighted a compressive stress state with a peak slightly below the surface. This is the typical profile of shot-peened components. The maximum compressive stress was observed to be 660 MPa. The presence of the compressive stress state has a significant impact on the tooth-root resistance of the gear: the loading cycle in the presence of residual stresses results in significantly less severe stress (about 5.9 times). The presence of compression stresses, in fact, mitigates the positive peaks of stress leading to a less critical condition. The nucleation site, which typically lies on the critical section as defined by the ISO 6336 standard, shifted to a bigger diameter. This finding can be explained by the fact that in gears, shot peening is typically limited to the fillet region. This evidence is aligned with the failure of case-hardened and nitrided gears where the crack generates at locations on the surface that are no longer treated. This phenomenon seems to be common for shot-peened and case-hardened gears where fillet regions are protected by the residual stress state. While specific data dealing with the shift of the crack nucleation site to bigger diameters are not available for shot-peened gears, many scholars reported the failure due to another mechanism called Flank Fracture. This mechanism is similar to tooth root bending, with a crack that nucleates on the loaded flank and then propagates throughout the entire width. However, different from tooth root bending, the crack nucleated on the active part of the flank. This evidence supports the results of this study where the residual stress promotes nucleation of the crack outside the fillet region.  
While the actual findings are specific for the gear geometry, material and shot-peening parameters considered in the example shown, the procedure is of general validity and can be applied to any configuration, allowing for a better understanding of the impact of shot peening on the fatigue performance of gears.  
This research received no external funding  
The author declares no conflict of interest.  
The following abbreviations are used in this manuscript:
# Intelligent Compaction Measurement Value in Variability Control of Subgrade Compaction Quality
## 3 Methodology
### Numerical Simulation of Modulus of Vibration Compaction \(E_{vc}\)
#### 3.2.2 Development of 3D FE Models and Database  
A three dimensional (3D) finite-element (FE) model of an elastic double-deck subgrade was established using the ABAQUS program, as shown in Figure 4a. In the horizontal direction, the size of double-deck subgrade structure is 20 m \(\times\) 10 m. In the vertical direction, the depth of the underlying layer is 6 m, and the filling layer is 0.31 m. The aggregates and soil particles are in contact with each other, and then, the friction between layers was larger [19]. Thus, the filling layer and underlying layer were gradually compacted because of their good bonding performance. The model was meshed with eight-node solid brick elements. The size of the filling layer's elements was 10 cm, whereas it was 50 cm for the underlying layer.  
The deflection database was established by numerical simulation of models, and \(E_{vib}\) of the filling layer and underlying layer is chosen to range from 9.5 MPa to 10.8 MPa with a variable step of 0.1 MPa. Considering that both the model and the concentrated load are axisymmetrically distributed, only one fourth of the models were analyzed and simulated. All combinations of \(E_{vib}\) in the variation are simulated to calculate the deflection of the  
Figure 3: Schematic diagram of computing principle: (**a**) Businisk solution; (**b**) load distribution on top of the subgrade under the action of a rigid roller wheel.  
top surface of the subgrade at the load center. A total of 196 modulus combinations are simulated and calculated. The single simulation results are shown in Figure 4b.  
\(E_{vc}\) is output by the corresponding deflection obtained with \(E_{vib}\) of the top surface and \(E_{vib}\) of the underlying layer. The database was established with simulation results, which are interpolated linearly with a step size of 0.01 MPa of \(E_{vib}\). Finally, the number of deflection values for combinations of the underlying layer and filling layer with \(E_{vib}\) varying from 9.5 MPa to 10.8 MPa is 17,424.
# Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions
## 2 Security Issues in Multimedia Sensor Networks
### Cryptography Algorithms  
There are many cryptography algorithms available for different purposes, and some of them are in fact being used on the Internet to provide safe communications. For wireless sensor networks, some of those algorithms are being directly used or adapted, as sensor nodes get more powerful and security threats become more common in WSN scenarios.  
Some of the most used cryptography algorithms for wireless sensor networks are presented in Table 1. Unless some restriction applies, those algorithms can also be employed to protect WMSNs.  
Some works have compared the performance of those and others cryptography algorithms, regarding the constraints of wireless sensor networks [39; 40]. The performed analysis may be useful when designing secure wireless multimedia sensor networks.
# Energy-Efficient Device Discovery Mechanism for Device-to-Device Communications in 5G Networks +
## 3 Proposed Hybrid D2D Discovery Mechanism
UE A sends a beacon to discover UE A\({}^{\prime}\). If UE A\({}^{\prime}\) receives the beacon correctly, it responds a message to UE A. 2. When UE A receives the response correctly, it transmits an acknowledgment (ACK) to UE A\({}^{\prime}\) and then D2D communications start; otherwise, switch to step (2c). 3. Whenever the number of retransmissions reaches a pre-defined number \(R_{T}\), named the retry limit, the discovery process stops and these two UEs utilize the cellular  
Figure 3: Some cases of locations of user equipment (UE) pairs [24]. service to communicate; otherwise, switch to step (2a) after a random time which is chosen using the binary exponential backoff algorithm without frozen backoff time. To summarize, the proposed hybrid D2D discovery mechanism consists of two phases. Phase 1 operates over the underlay cellular network to sift D2D candidates from all UEs, thus avoiding unnecessary contentions and collisions of D2D discovery and communication messages. Phase 2 operates over unlicensed bands to discover D2D devices using the Wi-Fi Direct protocol and binary exponential backoff algorithm, thus without consuming any spectrum resources of the cellular network. Notably, the binary exponential backoff algorithm without frozen backoff time is used in our design in order to further reduce the discovery delay. Additionally, after a successful discovery, the source UE A acts as a group owner (GO) and the target UE A\({}^{\prime}\) acts as a client.
## 4 Numerical Simulation
### Formation Keep with Bearings Only  
Based on the second simulation, the worse condition is considered, agent \(i\) could not obtain distance and velocity referred information, which means agent \(i\) must realize formation keeping with only bearings information. In this part, we consider that agent 8 lost all communication from step 500 to 800, and it can detect the relative bearing of agent 2 and agent 6, the maneuvering starts at step 300, and finishes at step 400. The turning rate is changing based on \(\omega=\left(0.01-\frac{t-1000}{5000}\right)\)rad/s.  
The duration of the communication interruption is longer compared with test two, and no distance and velocity information can be sensed. Therefore, the positional error of agent 8 is larger, which is shown with the bold line in Figure 12. However, fortunately, the positional error of agent 8 is decreasing slowly, although there exists only bearings information, the formation is kept to some extent, which is shown in Figure 13. Due to communication breaks, control of velocity and angular rate are not so smooth, which can be found in Figures 14 and 15, but the boundaries are satisfied in general. As can be seen from Figure 14, the turning maneuvering impacts the formation a lot, the time-varying angular rate will cause shock, and agents behind will suffer more effects. In this paper, no devices damage is considered, but the formation control results of persistent communication interruption show that agents without communication can be capable of roughly tracking refereed agents, although the tracking is not ideal.  
Figure 11: Track with communication breaks (test 2).  
Figure 12: Position errors (test 3).
# Remote Sensing-Based Proxies for Urban Disaster Risk Management and Resilience: A Review
## 4 Remote Sensing-Based Proxies for DRM in Urban Areas
### Built-Up RS-Based Proxies
#### Transport Category  
Transportation is also a vital component for DRM, as it provides accessibility to different locations within urban areas. One of the first emergency activities in post-disaster situations is to reopen blocked roads to reach damaged areas for rescue operations. Road network connectivity and condition, i.e., accessibility [92] has been used as a proxy for damage assessment. In addition, monitoring this proxy over a long period of time after a disaster also contributes to measuring recovery of the area [34; 49]. In terms of more detailed proxies, debris on the road (blocked road) [47], and condition of bridges and transport facility [32] have been proposed to evaluate the transportation condition for urban DRM. Presence of vehicles has also been used as a proxy to evaluate the accessibility of the roads after a disaster [34]. However, the presence of vehicles on a road does not necessarily signal the good condition and usability of the roads. On the other hand, reconstruction of bridges and roads is a reliable proxy for road accessibility analysis after a disaster.  
In another study, [PERSON], et al. [39] utilize length of roads as a proxy to evaluate the storm surge vulnerability of urban area, and [PERSON], et al. [48] use it for recovery assessment. However, a non-functioning road does not demonstrate the successful recovery processes. In a different study, [PERSON] [94] estimate width of roads as a proxy to assess the vulnerability of the roads to blockades. The results show that, in a dense urban area, in which high-rise buildings exist and their blow-out debris or in case of collapsing during a disaster event can block the road, the width of roads can be used as a reliable proxy to assess the vulnerability of the transport network. In a similar study, [PERSON], et al. [95] rely on road network density as a proxy for disaster vulnerability assessment. Although denser road networks and wider roads can increase the chance of having access to damaged areas after a disaster, occupying urban areas to reach denser road ratios and wider roads is a controversial. Similar problems exist in using proportion of low-grade highways, such as county roads [96], as a proxy for vulnerability assessment of a city. High proportion of low-grade highways indicates a less developed transportation infrastructure, which leads to higher level of vulnerability of urban areas.
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 1 Introduction  
In recent years, consumption and logistics patterns have changed with the development of economies and the gradual increases in national incomes. Furthermore, distribution channels have changed from direct distribution to the use of distribution centers (DCs). DCs can ship goods both upstream and downstream. The use of intelligent transportation fleet management technology to control the transit of goods is more efficient than traditional methods, and logistics companies receive higher economic benefits. Therefore, commercial vehicle operation (CVO) has been a popular application of intelligent transportation systems (ITS). The components of CVO include fleet administration, freight administration, electronic clearance, commercial vehicle administrative processes, international border crossing clearance, weigh-in-motion, roadside CVO safety, on-board safety monitoring, CVO fleet maintenance, hazardous material planning and incident response, freight in-transit monitoring, and freight terminal management.  
Location determination of a vehicle by an on-board unit (OBU) is important for CVO. Precise location information can be used to support fleet administration, freight administration, freightin-transit monitoring, _etc._ For example, DCs can monitor the locations and movements of OBUs and provide the status of freight to users and receivers. The Global Positioning System (GPS) is the most popular location determination method for OBUs [1]. However, because of interference with GPS signals in cities, location errors may be generated from GPS. Therefore, this paper considers mobile positioning techniques to obtain location information when the signals of GPS satellites are weak. Various techniques have been proposed to analyze the signals from radio frequency identification (RFID) [2], Bluetooth [3], wireless local area networks (WLAN) [4; 5; 6], wireless sensor networks (WSN) [7; 8], and cellular networks [9] for location determination [10]. However, the transmission ranges of RFID, Bluetooth, and WLAN are short, and they may be not suitable for CVO. Therefore, this study proposes a high-efficiency mobile positioning method to analyze the cellular network signals of CVO data. The method can be combined with cloud computing techniques to quickly determine the location of an OBU. Furthermore, a CVO system is proposed and implemented in this study, including OBUs and a CVO server.  
The remainder of the paper is as follows. Section 2 presents and discusses the various techniques that exploit the cellular network for location determination. A high-efficiency mobile positioning method based on CVO data is proposed and illustrated in Section 3. Section 4 proposes analytical models to analyze the feasibility of the proposed mobile positioning method with various outlier filtering criteria. Section 5 describes a CVO system implementation and analyzes practical records to evaluate the proposed method. Finally, Section 6 discusses our conclusions and proposed future work.
###### Abstract  
Some knee strength measuring devices have an anterior cushioning area but relies on a belt for the posterior pad adjustment, creating an uneven force distribution. This study analyzed whether the distal pad position affects knee strength measurements. Eleven professional basketball players participated in this study, with a total of 22 knees evaluated. Knee flexion and extension dynamometric measurements were performed at different angular velocities: 30\({}^{\circ}\)/s, 120\({}^{\circ}\)/s, and 240\({}^{\circ}\)/s. For each angular velocity, two measurements were performed with varying positions of the measuring tool pad at the anterior and posterior aspects (AA and PA). The hamstring/quadriceps (H/Q) ratio was calculated by dividing the hamstring musculature's maximum peak strength by the quadricep musculature's maximum peak strength at each measurement. The knee work was extracted from the device after finishing the measurements. Significant differences were found between measurement positions in the knee flexion force at 30\({}^{\circ}\)/s (\(p<0.001\)) and 120\({}^{\circ}\)/s (\(p=0.027\)). No differences were found for the extension forces. As for the H/Q ratio, significant differences were found between positions at 30\({}^{\circ}\)/s (\(p<0.001\)). Furthermore, significant differences between positions were found for the knee work at 120\({}^{\circ}\)/s (\(p=0.019\)). These findings suggest that the positioning of the pad on the leg directly influences knee flexor strength measurements, which in turn impacts critical parameters, such as the H/Q ratio and knee work. Given the importance of these variables in injury prevention, particularly for conditions such as anterior cruciate ligament (ACL) injuries, ensuring precise and reliable measurement methods is essential. We recommend using the PA position because it increases knee flexion strength values, potentially leading to more accurate assessments of the muscle function and balance.  
anterior cruciate ligament injury; strength ratio; hamstrings; isokinetic; measurement position +
Footnote †: journal: Applied Sciences
## 2 Related Work  
### Interactions in Remote MR Collaboration  
Based on the target, interactions used in remote collaboration can be broadly classified into the following categories: human-human, human-virtual object (hereafter human-VO), and human-real object (hereafter human-RO). Among these, human-RO interactions, which are interactions between a human and a real object, is difficult to apply to various remote collaborations since they require a physical object in the real world. Therefore, as a result of an investigation into research on remote collaboration based on MR, virtual reality (VR), and augmented reality (AR) conducted over the past three years on the other two interactions, the human-VO interaction was used more frequently than the human-human interaction [14; 15; 16; 17; 18]. These results seem to be due to the purpose of applying MR to remote collaboration. In other words, it can be found that the purpose of realistically delivering a remote user's work is achieved through human-VO interaction.  
The human-VO interaction can be applied to various types of remote collaboration application scenarios [19]. [PERSON] et al. [14], [PERSON] et al. [15] and [PERSON] et al. [16] conducted a remote collaboration application study of the remote expert scenario focusing on collaborations between remote experts and workers. In the remote expert scenario, the human-VO interaction was performed for the remote expert to manipulate the shared virtual object and to give work instructions to field workers. [PERSON] et al. [17] conducted a remote collaboration application study of the co-annotation scenario, focusing on annotating objects or environments of user interest. In the co-annotation scenario, human-VO interaction was performed for users to check annotations that were left or to jointly annotate things or environments. [PERSON] et al. [18] conducted a remote collaboration application study of a shared workspace scenario focusing on performing tasks in a shared virtual space. In the shared workspace scenario, human-VO interaction was performed to manipulate virtual objects shared between users in remote locations.  
In each of the above studies, interaction was mainly used to transform a target virtual object as follows: move, rotate, and scale. When performing move, rotate, and scale, the gestures that were mainly used were pinch and grab [20]. Pinch uses two fingers to transform an object, and grab uses five fingers to transform an object.
# Deep Reinforcement Learning-Based Video Offloading and Resource Allocation in NOMA-Enabled Networks
## 5 Experimental Results and Discussion
### Parameter Settings  
In this study, we adapt Python 3.7 as the software tool to simulate the framework, and the deep learning framework in JVFRS-CO-RA-DQN is PyTorch 1.4.0. The hardware is a computer with Intel I7-13700 HQ @ 2.5 GHz and 16-GB of memory. To verify the effectiveness of the JVFRS-CO-RA-DQN algorithm proposed in this study, a network consisting of one MEC, four NOMA clusters, and ten UEs is considered for experiments, with the UEs randomly distributed within [0, 200] m from the MEC. The total communication bandwidth \(W\) is 12 MHz. We define \(c_{m}\) in correlation with the data size of the task as \(c_{m}=c_{m}^{hit}l_{m}\), where \(c_{m}^{hit}\) is 100 cycles/bit. The computational capacity of the MEC \(F\) is 12 GHz, the computational capacity of the UEs. \(F_{m}^{loc}\) is [0.4, 2] GHz, the average energy consumption threshold \(E_{m}^{max}\) is 15 J, and the number of bits required to carry information per unit pixel of video \(\tau\) is 24. The minimum transmission power \(p\) of the UEs is 0.5 W, while the maximum tolerance time \(t_{m}^{max}\) of task \(D_{m}\) is 30 ms. Based on the experimental data from the reference [46], we adopt Equation (10) as the analytic accuracy function on both edge servers and the UEs, and the video frame resolution is higher than 40,000 px (200 \(\times\) 200). The parameters are shown in Table 1.
## 4 Experimental Assessment of Damping Coefficients
### Results of the Experimental Campaigns  
The bridges described in Section 4.2 were subjected to two types of experimental tests: ambient vibration tests and tests under railway traffic. The ambient vibration tests were performed to evaluate the modal parameters of each bridge, such as the modal shapes, natural frequencies and damping coefficients, while the tests under railway traffic were used to identify the dynamic response in terms of accelerations of each bridge deck due to the passage of trains. The outcomes of these tests are required to estimate the frequency of each bridge due to the trains passage and to obtain the bridge damping using the Prony method described in Section 2.
## 1 Introduction
India experiences complex agro-climatic situations; hence, the country has been divided into different (homogeneous) agro-climatic zones (ACZs) based on physiography, soil, climate and vegetation by the Indian Planning commission [45]. India may be considered as having 14 mainland ACZs, such as the Western Himalayan Region (WHR), Trans Gangetic Plains (TGFP), Upper Gangetic Plains (UGP), Middle Gangetic Plains (MGP), Lower Gangetic Plains (LGP), Eastern Himalayan Region (EHR), Western Dry Region (WDR), Central Plateau and Hills (CPH), Eastern Plateau and Hills (EPH), Gujarat Plain and Hills (GPH), Western Plateau and Hills (WPH), Southern Plateau and Hills (SPH), Western Coastal Plains and Ghats (WCPG) and Eastern Coastal Plains and Hills (ECPH) to eliminate the heterogeneity in terms of climate and vegetation types. Figure 1: Shows the climatology of SIF (spatial resolution of 0.05 \(\times\) 0.05-degree grid) across India and SST (spatial resolution of 0.25 \(\times\) 0.25-degree grid) in the tropical Indian Ocean during the summer monsoon. Indian landmass is divided into 14 agro-climatic zones, and the tropical Indian Ocean is stratified into (1) Western, (2) Northern and (3) Central regions based on the SST pattern. (Software used: ArcMap version 10.3 (Esri Inc. (Redlands, CA, USA, 2014), [[https://desktop.arcgis.com/en/arcmap/](https://desktop.arcgis.com/en/arcmap/)]([https://desktop.arcgis.com/en/arcmap/](https://desktop.arcgis.com/en/arcmap/)) (accessed on 10 July 2021)).