esa-sceva/satcom-chunk-collection huggingface dataset:
# Exposure to NO\({}_{2}\) and PM\({}_{2.5}\) While Commuting: Utility of Low-Cost Sensor
## 2 Materials and Methods
### Inhaled Dose  
The inhaled dose of NO\({}_{2}\) and PM\({}_{2.5}\) was estimated separately for each pollutant and each mode of transport. The dose of inhaled pollutants per trip was calculated using Equation (1), while per kilometer traveled using Equation (2). The calculations were performed separately for each participant during both seasons. In the review by [PERSON] et al. [21], different formulas have been presented for dose estimation depending on the aims and designs of the studies. In our study, inhaled dose (_ID_) is the mass (\(\upmu\)g) of pollutant estimated on the concentration of pollutant measured during one trip (\(\upmu\)g/m\({}^{3}\)), time (minutes) spent while commuting by bicycle, vehicle or walking, and pulmonary ventilation rate (m\({}^{3}\)/min) according to the intensity of physical activity of the commuter (in accordance to the metabolic equivalent of task MET) in each mode of transport.  
\[ID(\upmu\mathrm{g})=C_{i}\times t\times VE \tag{1}\]  
where \(C_{i}\) is the average concentration of the pollutant measured in one trip, \(t\) is the time of one trip, \(VE\) is the minute ventilation, and km is the distance of the trip (Equation (2)).  
\[Dose\left(\upmu\mathrm{g/km}\right)=\frac{ID}{km} \tag{2}\]The values of the ventilation rates for walking and cycling were obtained from WHO-developed guidance and a practical tool to facilitate the promotion of cycling and walking for everyday physical activity [34]. The report assumes an average intensity of 6.8 MET for cycling and 4.0 MET for walking. An average intensity of 6.8 kcal/kg/h amounts to approximately 100 min of weekly cycling or 170 min of weekly walking at an average intensity of 4.0 MET. For vehicle light-intensity activity, which includes driving, standing up, or seating--MET (1.5 < MET \(\leq\) 3.0)--we followed [35]. The study included a male and a female; hence, the \(VE\) was selected based on the gender and age of the studied participants [36]. For the male, the \(VE\) was 0.0573, 0.0144, and 0.0316 m\({}^{3}\)/min for bicycle, vehicle, and walking, while for the female, 0.0457, 0.0106, and 0.0229 m\({}^{3}\)/min, respectively. In addition, inhalation doses were calculated without gender division. The following simplification was used: \(VE\) was assumed as 0.6 m\({}^{3}\)/h (0.028 m\({}^{3}\)/min) for passive modes of transport (vehicle) and 1.7 m\({}^{3}\)/h (1 m\({}^{3}\)/min) for active modes of transport (bicycle, walking).
## 2 Materials and Methods
### Method II with Varying Frequency Modulation (VF)  
The second modulation strategy would adopt switching frequency as the sole variable to manipulate power, and the converter would be operated as a regular series resonant converter (SRC). Therefore, the secondary bridge will act as a diode rectifier or a synchronous rectifier for further low conduction loss. The typical steady-state waveforms can be seen in Figure 5. According to FHA, the secondary side can be regarded as an equivalent resistance  
\[R_{ac}=\frac{8n_{t}^{2}R_{L}}{\pi^{2}} \tag{16}\]  
and the converter gain is  
\[G=\left|\frac{R_{ac}}{R_{ac}+jX_{t}}\right|=\frac{8}{\sqrt{64+\pi^{4}Q^{2}(F-1/F )^{2}}} \tag{17}\]  
where the quality factor \(Q\) and the normalized switching frequency \(F\) are defined as  
\[Q=\frac{\omega_{r}L_{s}}{n_{t}^{2}R_{L}};\;\;F=\omega_{s}/\omega_{r} \tag{18}\]  
\(\omega_{r}=\frac{1}{\sqrt{L_{s}C_{s}}}\) is the resonant angular frequency.  
Figure 5: The typical steady-state waveforms of Method II.  
According to (17), the converter voltage gain \(G\) is unity and is independent of the load at the resonant frequency, i.e., \(F=1\). In other words, the converter acts as an independent voltage source. Doubtlessly, this feature perfectly meets the requirement of CV operation. Therefore, the whole CV stage can be realized by letting the converter work at resonance state.  
Referring to the equivalent circuit in Figure 5, the input impedance \(Z_{ac}=R_{ac}+jX_{t}\) should be constant in order to keep a constant output current in the CC stage. Thus, the reactance \(X_{t}\) should be adjusted to compensate for the variation of \(R_{ac}\). Apparently, switching frequency or \(F\) has to be reduced when the operation point moves from A to C.
## 3 Numerical Example
### Granular Flume Flow Strike Mast-Like Obstacle
Figure 2: Simulated flow process of a uniform granular stream hitting a square obstacle with a side length of 0.04 m: (**a**) \(t\) = 3.0 s, (**b**) \(t\) = 3.5 s, (**c**) \(t\) = 4.0 s, and (**d**) \(t\) = 4.5 s (unit m). Figure 4: Configurations of an avalanche hitting an obstacle with different side lengths at time \(t=4.5\) s.  
Figure 3: Configurational comparison of an avalanche hitting a square obstacle with different side lengths at time \(t=3.5\) s.  
Successive surges (free surface instabilities or rolling waves) have been observed in free surface granular flow on rough inclined planes with glass beads and sands [6; 31; 32; 33] and on smooth inclined planes [34; 35]. They correspond to an instability threshold value of roughly \(2/3\), as defined by the Froude number \(Fr=u/\sqrt{gh\cos\theta}\) (\(\theta\) the angle of inclination) reported by [PERSON] [36]. In simulations of avalanche-obstacle interaction with obstacles of different side lengths, we were amazed to find successive waves appearing in the tail of the avalanche between the two streams after \(t=5.5\) s (Figure 6, area framed by red dashed rectangle). The calculated Froude number at the tail of the flow \(Fr\approx 10\) in the simulation was larger than the threshold, which supported the simulation results. Even though (1) and (2) are first-order Saint-Venant-type equations, it was found that second-order effects of successive surges could be reproduced well because of the second-order artificial diffusion term (i.e., total variation diminishing term) added to the simulation. Figure 5: Comparison of laboratory data and simulation results (the continuous regression curve is obtained from experimental data in [PERSON] et al. (2007), and the orange square dots are the simulation data). Figure 6: Successive surges in the tail of the fan captured during simulations obtained using the proposed numerical scheme. The computation results of the example in this section indicate that the depth-averaged avalanche dynamics model solved by the TVD-MacCormack scheme captured some basic features of granular flume flow revealed in the laboratory well, such as shock waves and successive surges.
# Transmission Efficiency of Cycloid-Pinion System Considering the Assembly Dimensional Chain
## 4 Transmission Efficiency Model
### Cycloid-Pinion System Kinematics Analysis  
Figure 6 shows the system of vectorial coordinates established by the principle of the transient center of velocity. \(I_{pb}\), \(I_{cb}\), and \(I_{cp}\) represent the instantaneous center of velocity. The lower corner markers \(p\), \(b\), and \(c\) represent the pinion, input crankshaft, and cycloid, respectively.  
When the pin tooth is fixed, the instantaneous velocity center \(I_{cp}\) of the cycloid relative to the pin tooth may be determined according to [PERSON]'s theorem. In turn, the velocity of the cycloid gear relative to the pin tooth at the position of point \(k\) is obtained:  
\[\left\{\begin{array}{l}v_{k}=w_{c}|I_{cp}k|\\ |I_{cp}k|=\sqrt{R_{b}^{2}+d^{2}-2R_{b}d\cos\theta_{i}}-r_{p}\end{array}\right. \tag{13}\]  
where \(\omega_{c}\) is the rotational angular velocity of the cycloid, and \(\left|I_{cp}k\right|\) is the distance between the velocity transient center \(I_{cp}\) and the contact point \(k\). The expression of the contact point velocity component on the coordinate axis is obtained.  
\[\theta_{i}=\left[\frac{2\pi}{z_{b}}(i-1)+w_{in}t\right] \tag{14}\]  
Figure 5: Tooth contact force considering clearance.  
Figure 6: Cycloid and pin tooth contact point (point k) velocity model.  
where \(w_{in}=(e-d)w_{c}/e\), \(d\) is the distance between the velocity instantaneous center \(I_{cp}\) and the origin \(O_{b}\) of the coordinate system \(d=z_{b}e\).
## 4 Case Study--Numerical Experiments  
To demonstrate buffer allocation problems, this section presents a case study that utilizes numerical experiments to investigate and evaluate the proposed methodology in inter-facility material transfer operations and outbound logistics processes. These experiments aim to validate the effectiveness and efficiency of the methodology in optimising the buffer allocation problem within the intra-organisational material flows and the corresponding outbound logistics activities. We conducted the case study using a manufacturing facility with multiple workstations as the testbed. Applying the analytical method based on GEM, we formulated a finite queueing network model that considered various parameters, such as arrival rates, service times, and buffer capacities. By solving the GEM equations, we obtained analytical expressions for performance measures. Simultaneously, we developed a simulation model to replicate the dynamics of the facility and conducted multiple numerical experiments to collect data on system performance metrics. The results from the analytical method and simulation were compared to assess the effectiveness of the proposed methodology in optimising buffer allocation. This empirical illustration demonstrates the practical application of the proposed methodology. It showcases its efficacy in addressing the buffer allocation problem in the context of intra-organisational material flows and outbound logistics processes.
## 3 Routing Protocol: RFAODV
### Overview of RFAODV
#### 3.2.2 Route Maintenance  
Because the moving speed and range of underwater nodes are smaller than those of terrestrial ad hoc nodes, and the energy of node, communication bandwidth is more limited, RFAODV cancels the mechanism of sending hello packets regularly, and adopts the following two mechanisms for route maintenance.  
Data link layer feedback. In underwater acoustic networks, MAC protocols with ACK are usually used for communication quality, such as Macaw, Aloha and so on. Therefore, we can determine whether the link is normal from the data link layer.  
Energy judgment. In order to reduce the energy consumption of one or several nodes in an ideal position, we can set a principle. If the energy of the node decreases by 50%, 70%, 90%, the RRER packet is sent to inform the neighbor nodes that the route is invalid.  
Up on receiving notification of link interruption, source nodes can restart the discovery process if they still require a route to the destination.
## 2 Literature Review
### Vision-Based Earthmoving Activity Detection
An upgraded Faster R-CNN-LSTM framework improved the mean Average Precision (mAP) by 15% and achieved a detection accuracy of 99.99%, effectively identifying unsafe behaviors and clarifying ambiguous actions among construction workers [70]. CNN-LSTM models effectively handle occlusion by learning long-term dependencies and motion dynamics, which improves recognition accuracy in construction environments that often encounter visual obstructions [71]. The single-source external video data collected through surveillance/RGB external cameras limits continuous monitoring due to obstructions in the camera's line of sight or adverse visual conditions. The performance of the excavator's 3D pose model has been evaluated by collecting data from 12 equal regions of 360 degrees, while the excavator constantly rotated its cabin to assess the effect of visual obstructions in the case of a single RGB camera source [58]. In case of single-source RGB data input, visual obstructions can significantly disrupt visual tracking,making it challenging to monitor a target and often leading to tracking failures [72; 73]. Most of these studies rely on a single-source input data stream collected through surveillance or external cameras. However, accurate monitoring and recognition of construction equipment activities require additional data sources. A multi-stream approach has classified the behavior of bus drivers by processing the spatiotemporal features of multiple cameras attached to the bus [74]. Recently, the medical field has implemented multi-stream input data for electrocardiogram (ECG) signal classification, blood glucose estimation, and spatiotemporal-based behavioral multiparametric pain assessment system [27; 28; 29]. However, the multi-stream input data for activity recognition in construction equipment has yet to be explored in the earthwork construction industry. An overview of the recent literature is listed in Table 1, which includes different approaches used for object detection and activity recognition at construction sites. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
# Experimental Test and Prediction Model of Soil Thermal Conductivity in Pernafrost Regions
## 3 Testing Scheme
### Distribution of Dry Density and Water Content, and Effect on Thermal Conductivity  
#### 4.3.1 Dry Density and Water Content Distribution Characteristics  
The dry density (\(\rho_{d}\)) and water content (\(w\)) of three soil clusters are statistically calculated, and the cumulative proportion is shown in Figure 8. It shows that the HWHCS has relatively lower dry density and higher water content among all three soil clusters. The main dry density distribution interval of the three clusters (probability within 20%-80%) is 1.34-1.78, 1.44-1.86, and 1.57-2.03 g/cm\({}^{3}\), and the mean values is 1.648, 1.813, and 1.936 g/cm\({}^{3}\) respectively. Likewise, the main water content distribution interval for every cluster is 15.32%-35.88%, 12.2%-30.6% and 7.7%-25.7%, and the average values are 28.73%, 24 % and 17.46%, respectively.
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Watermark Extraction Module
#### 4.3.3 Similarity Judgment  
Hamming distance is adopted as the standard for convolutional code decoding. Calculating the cosine similarity of watermark sequences \(A\) and \(B\) as the basis of comparison. -  
\[sim=\cos(\theta)=\frac{A\cdot B}{||A||*||B||}=\frac{\sum\limits_{i=1}^{n}{A \times B}}{\sqrt{\sum\limits_{i=1}^{n}{{{A_{i}}^{2}}\times\sqrt{\sum\limits_{i =1}^{n}{{{B_{i}}^{2}}}}}}} \tag{21}\]  
Since the range of independent variable parameters \(A_{i}\) and \(B_{i}\) in Equation (21) is \(x\in\{0,1\}\), according to the function curve of cosine function, the range of \(sim\) value is \([0,1]\). Comparing the cosine similarity with the set confidence interval threshold parameters. If the result of similarity computation is within the set confidence interval, it is determined that the watermark information embedded in the current detected data stream is related to the source data stream, i.e., there is a communication relationship between the two sides of the current detected communication, otherwise there is no communication behavior.
## 4 Conclusions  
The first high-chromium, Laves-phase-strengthened HiperFerAM alloy that benefits from the additive manufacturing process and counteracting process-related defects, such as pores due to thermomechanically triggered [9; 33] Laves phase precipitation, was developed.  
Specific adjustment of the AM process parameters directly allowed for controlling the microstructural parameters, such as the grain size/morphology, as well as the size and number of the Laves phase particles, and thus, the resulting mechanical properties. At ambient temperature, UTS values of \(\sim\)667/\(\sim\)760 MPa (LPD/HPD) were achieved in the as-built condition, with an elongation at fracture of \(\sim\)1/1.6%. Subsequent stress relief due to the annealing of HiperFerAM HPD resulted in a nearly tripled elongation at rupture and a UTS beyond 800 MPa. At 650 C, the UTS of the HiperFerAM HPD variant was 55 MPa higher in comparison to the HiperFer 17 Cr2 sheet material, with almost doubled elongation at rupture (\(\sim\)33.5%). Global sub-grain formation during plastic deformation was argued to be the reason for the increased elongation at rupture. In general, additive manufacturing was found to produce smaller grain sizes than conventional manufacturing (\(\leq\)200/\(\sim\)500 m).  
Furthermore, the minimum creep rates of the HiperFerAM HPD variant ranged from approximately 0.5 to 1 order of magnitude higher than that of conventionally produced HiperFer 17 Cr2. In contrast, the LPD variant exhibited premature power-law breakdown under creep loading, which was caused by instability of the directionally solidified as-built grain structure. Creep properties did not show a dependency on build direction.  
This first mechanically alloyed HiperFerAM custom-made variant for the additive manufacturing process demonstrated high potential for high-temperature application. Furthermore, the substitution of comparatively expensive austenitic 316L steel in a small (special) series of ambient temperature components could be a realistic option. After grain size optimization and stress relief annealing, the resulting HiperFerAM material may offer a UTS of about 1 GPa at a reasonably lower cost.
# Last-Mile Optimization Using Neural Networks
## 3 Proposed Method
### Classification Methods Used
#### 3.5.2 Random Forest  
To address the specific challenges of optimizing delivery in urban areas, the Random Forest (RF) model, chosen for its classification and regression capabilities, employs an ensemble learning approach. During training, this model constructs multiple decision trees. In classification, the model generates the mode of the classes, while in regression, it produces the average prediction of individual trees [4]. The effectiveness of the RF model in our study relies on precise handling of key parameters, such as the number of trees and maximum depth, adjusted using hyperparameter tuning techniques to better adapt to the nuances of product flows in urban environments.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 3 Methodology: Selected Query DETR (SQ-DETR)  
In this study, we propose **selected query DETR (SQ-DETR)**, a novel approach that dynamically eliminates unnecessary object queries in the decoder to enhance computational efficiency. While DETR assigns a fixed number of queries regardless of the actual number of objects in an image, SQ-DETR introduces a query selection mechanism that adaptively prunes redundant queries at each decoder layer based on their importance. This strategy effectively reduces computational overhead while maintaining detection performance.  
The key advantage of SQ-DETR lies in its ability to progressively refine object queries throughout the decoding process, ensuring that only the most informative queries remain. Unlike previous methods that either increase query numbers for stability, SQ-DETR performs layer-wise adaptive query selection, achieving a balance between computational efficiency and detection accuracy.  
Figure 1 illustrates the overall architecture of SQ-DETR, which retains the fundamental DETR structure while integrating a query selection module within each decoder layer. The following sections describe the SQ-DETR architecture, query selection mechanism, training strategy, and inference process in detail.
## 3 Finite Element Modeling of the Positive and Negative Stiffness Components
### Estimation of Positive Stiffness  
A preliminary computation is performed to estimate the accuracy of the assumption stated in Section 2.2. First, the mass is suspended by four identical folded beams providing positive stiffness which are considered as four springs having individual stiffness \(k_{\mathrm{s}}\). The equivalent stiffness \(k_{p}\) is evaluated from the dynamic balance as \(k_{p}=4k_{s}\). The bending stiffness of the folded beam is approximated by the equivalent stiffness of the cantilever beam as \(k_{s}=3E_{s}I_{s}/L_{s}^{3}\). Considering \(L_{s}=0.094\), \(b_{s}=0.06\), \(h_{s}=0.004\) mm, the obtained analytical value is \(3.17\times 10^{3}\) N/m. From the finite element side, representing our full geometry with clamped-free boundary conditions, running a stationary analysis and dividing the input force by resulting transverse displacement leads to an apparent stiffness of \(3.17\times 10^{3}\) N/m, which is a relative error of less than 2%. Even if the kinematics expected on the structure restrict the side to moving only vertically due to the attachment to the mass, the cantilever assumption is considered satisfactory.
# Intelligent Transportation System Technologies, Challenges and Security
## 2 Intelligent Transportation Systems
### Intelligent Transportation System Applications
ITS has four groups based on applications: advanced traffic, advanced travelers, advanced public, and emergency management. These groups are shown in Figure 4.  
These real-world examples highlight the diverse applications and benefits of ITS in improving traffic management, enhancing public transit services, and promoting sustainable urban mobility in different cities and regions worldwide. Singapore is renowned for its innovative approach to urban mobility. The city-state has implemented various ITS technologies to manage traffic congestion and enhance transportation efficiency. For example, Singapore's Electronic Road Pricing (ERP) system uses gantry-mounted sensors and smart tolling to adjust road usage fees based on real-time traffic conditions. The Land Transport Authority (LTA) has also deployed traffic monitoring cameras, adaptive traffic signal control systems, and real-time traveler information services to improve traffic flow and reduce roadway congestion. London implemented a congestion pricing scheme in 2003 to alleviate traffic congestion in the city center. The congestion charge applies a fee to vehicles entering designated zones during peak hours, encouraging motorists to use alternative modes of transportation or travel outside peak times. The scheme has successfully reduced traffic congestion, improved air quality, and generated revenue for investment in public transportation infrastructure [17]. Stockholm implemented a congestion pricing system in 2006 to reduce traffic congestion and improve air quality in the city center. The congestion charge uses automatic license plate recognition technology and electronic tolling to charge vehicles entering the congestion zone during peak hours. The scheme has effectively reduced traffic congestion, increased public transit ridership, and promoted sustainable urban mobility [18]. Tokyo has implemented various ITS solutions to manage its complex transportation network and address urban mobility challenges. For example, Tokyo's extensive network of intelligent traffic management systems uses advanced technologies such as adaptive traffic signal control, surveillance cameras, and real-time traffic information to optimize traffic flow and reduce congestion on major roadways. Tokyo's public transportation system, including its extensive rail network and integrated fare payment systems, also provides efficient and reliable mobility options for residents and visitors [19].