esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping Atmospheric Exposure of the Intertidal Zone with Sentinel-1 CSAR in Northern Norway
## 2 Materials and Methods
### Data
#### 2.1.3 Tides and Sea-Level Data  
Norway has a network of 24 permanent tidal gauges that provide observations every 10 min. The tidal gauge on Tromsey is at Handsjordnesbukta. Tidal model-based fore- and hindcasts anywhere along the Norwegian coast and gauge observations are provided by the Norwegian Mapping Authority [12].  
The main tidal reference levels are also provided for any location. For the Tromso gauge, the tidal reference levels from the highest to lowest are as follows:  
* HAT--the highest astronomical tide (167 cm): the highest tide which can be predicted to occur (note that meteorological conditions may add extra height to the HAT),
* MHWS--mean high water springs (119 cm): the average of the two high tides on the days of spring tides,
* MHW--mean high water (90 cm): the average of all high water,
* MHWN--mean high water neaps (61 cm): the average of the two high tides on the days of neap tides.
* MSL--Mean sea level (0 cm): the average sea level for the period 1996-2014,
* MLWN--mean low water neaps (\(-60\) cm),
* MLW--mean low water (\(-90\) cm),
* MLWS--mean low water springs (\(-119\) cm),
* LAT--the lowest astronomical tide (\(-174\) cm) [11].  
All tidal levels in this study are given relative to MSL (1994-2014). Figure 3 shows the tidal graph at Handsjordnesbukta with indicated S1 tidal level observations (a) for the whole year of 2019 and (b) for one S1 repeat cycle from 1-12 August 2019 (see Table 1).
## 8 Conclusions  
Mobile M2M communications are currently receiving attention from the academia due to its potentiality in ubiquitous applications, like mobile healthcare, telemetry, or in intelligent transport systems, and also due to the emergence of IoT paradigm. In this paper, we reviewed the standardization efforts and application protocols, and draw considerations on the impact of their use in constrained devices.  
M2M communications in cellular and wireless networks will face several challenges, and we reviewed literature that focus on performance evaluation and improvement, either in terms of delay or resource usage efficiency. Future work should focus in exploring the M2M gateway and mechanisms to efficiently collect and aggregate data while attaining the time requirements of data, to reduce energy and bandwidth consumptions, as resource usage efficiency is a common denominator in the literature due to mass scale envisioned for mobile M2M communications. Techniques such as data aggregation, concatenation, or compression mechanisms might be useful to reduce both non-useful and useful data. Overall, resource usage efficiency in mobile M2M communications is still an open research area, and further studies on the impact of multitude and diversity of devices and traffic in the performance of communications are necessary.  
Figure 6: Battery consumption for different transmission schemes using 3G.  
Finally, we performed a preliminary study on the feasibility of using smartphones as M2M gateways that collect and aggregate information from sensors. We concluded that, in order for their use to the feasible in terms of a normal depletion time of a smartphone's battery, it is a good advice to maximize the collection of data necessary to be forwarded from nearby sensors, and maximize the intervals between transmissions.  
This work was partially supported by the project I-City for Future Health NORTE-07-0124-FEDER-000068, by Fundacao para a Ciencia e a Tecnologia under the project PEst-OE/EEI/LA0008/2013, by the European Project FP7 - Future Cities FP7-REGPOT-2012-2013-1, and by PT Inovacao.  
The authors declare no conflict of interest.
## 3 Material & Methods
### Risk Analysis
#### 3.2.3 Impact Rating  
The next step implies the value estimation of a potential damage scenario, performed through qualitative conversion tables provided by the TARA. It permits the assignment of a label to each scenario ranging between \"Negligible\" and \"Severe\" based on the scenario's impact on Safety (**S**), Financial (**F**), Operational (**O**), and users' Privacy (**P**). These criteria are then aggregated to obtain the \"Impact Level\" (**IL**), also ranging between \"Negligible\" and \"Severe\". To that end, such rankings allow us to prioritise both the economical and human repercussions to consider in order to adequately mitigate the risks based on the severity of the scenarios. An example of such a rating is provided in Table 2 in which severe and a major damage scenarios are depicted.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Aspect ID** & \multicolumn{2}{c}{**Damage Scenario ID**} & \multicolumn{2}{c}{**Description**} \\ \hline  & D.1 & \multicolumn{2}{c}{Erroneous data are received and provoke full stop of the vehicle} \\  & D.2 & \multicolumn{2}{c}{The data cannot be received and provoke full stop of the vehicle} \\  & D.3 & \multicolumn{2}{c}{An external attacker modifies transmitted data or an update} \\  & D.4 & \multicolumn{2}{c}{An external attacker captures the data transmitted between vehicle} \\  & D.5 & \multicolumn{2}{c}{An external attacker modifies the data transmitted between vehicle} \\  & D.6 & \multicolumn{2}{c}{An external attacker stops the communication between vehicle and the backend} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sample threat scenarios for 3G/4G antenna.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Damage Scenario ID** & \multicolumn{2}{c}{**Impact Category**} & \multicolumn{2}{c}{**Impact Level**} & \multicolumn{2}{c}{**Justification**} \\ \hline D.3 & Severe & Severe & Severe & Severe & Severe & Severe & Severe \\ D.5 & Severe & Severe & Severe & Negligible & Major & & If the vehicle’s software stack is modified, all data can become accessible with a risk of compromising secure driving functions such as braking, maximum speed limit, and respect of signal panels. Serious financial consequences are forecasted, as well as the loss of end-users’ trust. Active modification of ongoing communications can cause an unexpected behaviour of the vehicle or generate erroneous data for the operator.  
\end{table}
Table 2: Impact rating example for damage scenarios applicable to 3G/4G antenna.
## 4 Conclusions  
In this paper, a TG analysis experimental system and a static combustion experimental platform were used to investigate the influence of the sludge blending ratio, atmosphere, O\({}_{2}\) concentration, heating rate, and combustion temperature on the combustion characteristics of blended fuel. This provides experimental data and a practical reference for the later mixing of sludge in power station boilers and a technical discussion for future O\({}_{2}\)/CO\({}_{2}\) combustion. Several conclusions are drawn as follows:  
1. Blending sludge can accelerate the combustion characteristics of single coal. With an increase in the blending ratio, the TG curve shifts to the low-temperature area and the ignition and burnout temperatures of the fuel decrease. Within the 0-50% range of the sludge blending ratio, the burnout temperature only decreases slightly. The ignition temperature drops significantly when the sludge blending ratio is 15%; thereafter, the decline is significantly slower and the CCI reaches the maximum at 15%. It is concluded that the optimal sludge blending ratio is 15% under these experimental conditions.
2. Under O\({}_{2}\)/N\({}_{2}\) with 21%/79% and O\({}_{2}\)/CO\({}_{2}\) with 21%/79% conditions, the change trends of the TG curve of the experimental samples are similar. Compared with the 21% O\({}_{2}\)/79% N\({}_{2}\) conditions, under the 21% O\({}_{2}\)/79% CO\({}_{2}\) atmosphere, ignition is delayed, the burnout temperature is higher, and the CCI is low. In the O\({}_{2}\)/CO\({}_{2}\) conditions, as the O\({}_{2}\) concentration increases, the ignition temperature of the mixed fuel decreases slightly, whereas the burnout temperature decreases significantly. In general, enhancing the oxygen concentration makes the combustion reaction more drastic and improves the CCI.
3. In the O\({}_{2}\)/N\({}_{2}\) atmosphere, with an increment of the heating rate, the TG curve moves to the high-temperature zone, the burnout temperature increases significantly, the combustion rate increases, the combustion reaction becomes more intense, and the CCI is improved.  
In the future, the combustion and operation mode of the boiler can be optimized and adjusted based on the normal operating experience of the boiler. This is of great significance for the reform of China's energy structure, allowing full use of the original thermal power units to be made and, at the same time, realizing the utilization of sludge waste resources, which is a good means of saving environmental costs and creating economic benefits.  
Investigation, Y.M.; project administration, Z.L.; resources, Q.C.; writing--original draft, D.X.; writing--review and editing, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the National Natural Science Foundation of China (No. 51776078) and National key R&D program of China (2019 YFC1904003).  
The authors would like to show particular thanks to China Power Fuxi Power Dev. Co. Ltd. for the foundation support and the experimental study.  
The authors declare no conflict of interest.
## 2 Definition of the Problem
### Interaction between the Rock Mass and the Passive Bolts  
The pull-out tests show that the load-displacement curve is non-linear because of the decoupling at the bolt-rock interface and the confining pressure influences the shear strength of the interface greatly [32; 33]. The spring-slider model [25] was employed here to characterize the relationship between the relative displacement \(\Delta u_{s}\) and shear stress \(\tau_{s}\) at the bolt-rock interface, as shown in Figure 3.  
The shear stress \(\tau_{s}\) and tension force \(F_{n}\) of the bolt can be expressed as:  
\[\tau_{s}\ =\ \begin{cases}\frac{K_{s}\Delta u_{s}}{\pi d_{s}},&\frac{K_{s} \Delta u_{s}}{\pi d_{s}}\leq c_{s}+\sigma_{\theta}\tan\varphi_{s},\\ \sigma_{\theta}\tan\varphi_{s},&\frac{K_{s}\Delta u_{s}}{\pi d_{s}}>c_{s}+ \sigma_{\theta}\tan\varphi_{s},\end{cases} \tag{18}\]  
\[F_{n}\ =\ \begin{cases}0&r\ =\ R+l_{b},\\ \int_{R+l_{b}}^{r}\pi nd_{s}\tau_{s}dr&r<R+l_{b},\end{cases} \tag{19}\]  
where:  
\[\Delta u_{s}\ =\ u_{r}-u_{r}^{ini}-u_{b}^{rig}-u_{b}^{elo},\ u_{b}^{elo}\ =\ \int_{R+l_{b}}^{r}\frac{F_{n}}{E_{b}A_{b}}dr.\]  
\(K_{s}\), \(c_{s}\), and \(\varphi_{s}\) are the shear stiffness, cohesion, and friction angle at the rock-bolt interface, respectively; \(A_{b}\) is the cross-sectional area of the bolt; \(u_{b}^{rig}\) and \(u_{b}^{elo}\) are the rigid displacement and the elongation of the bolt, respectively; \(d_{s}\) is the effective diameter of the bolt, as shown in Figure 4, which may equal the diameter of the bolt, grout hole, or inside the grout because these interfaces are relatively weak in pull-out tests; and \(r_{b}=R+l_{b}\) is the radial distance from the far end of the bolt to the tunnel center. Note that \(K_{s}\), \(d_{s}\), \(c_{s}\), and \(\varphi_{s}\) can be easily obtained from conventional pull-out tests [25].  
Figure 3: The spring–slider model of the bolt–rock interface.  
As shown in Figure 5, the compression between the end plate and rock mass produces a tension force \(F_{n(R)}\) (Equation (20)) at the near end of the bolt and support pressure \(p_{cp}\) (Equation (21)) on the tunnel periphery. These two equations were used as boundary conditions in the calculation procedure (Appendix B):  
\[F_{n(R)}\ =\ K_{cp}\Delta u_{s(R)}, \tag{20}\]  
\[p_{cp}\ =\ \frac{F_{n(R)}}{l_{z}R\omega}, \tag{21}\]  
where \(K_{cp}\) is the stiffness of the end plate, where \(K_{cp}\) = 0 in the absence of the end plate.  
Note that, compared with the original spring-slider model [25], the effects of the bolt elongation and the end plate were considered in this work.
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 2 Related Work
We believe that the success rate of any security scheme largely depends upon the mode of operations of the adversaries, but it is to be noted that most of these schemes do not precisely describe the mode of operation for adversary models during route discovery phase and data transmission phase, to identify patterns followed by malicious nodes, while selecting trusted route for data transmission. We address this issue by introducing an efficient trust-based scheme which integrates attack pattern discovery to the trust mechanism by observing the packet forwarding behavior of nodes continuously. The scheme attempts to find attack patterns before a node launches packet dropping attack. The scheme identifies a distrusted neighbor during the trust update procedure and discovers an alternate route after discarding the untrustworthy route from the routing table which contains that malevolent node as next hop in a hostile setting.
# Numerical Analysis on Self-Propulsion of a Waterjet-Propelled Ship with Different Propulsion Models
## 3 Results and Discussions
### Computations of Self-Propulsion
#### 3.2.1 Convergence History and Computational Cost  
Figure 9 presents the convergence history of the horizontal force with respect to the impeller rotational speed under different \(Fr\) number. When the computation with the MRF model converges, the net force \(F_{net}\) of waterjet-propelled ship (including hull, impeller blades, guide vanes, ducting channel, shaft, hub, etc.) is close to zero. Figure 10 shows the convergence history of the net force \(F_{net}\) of waterjet-propelled ship with the body-force model.  
Figure 9: Convergence history of the net force \(F_{net}\) of waterjet-propelled ship with the MRF model.  
The CPUs used in the present CFD simulations are two 32-core AMD processors at 2.50 GHz. The MRF model and body-force model require about 2.35 hours and 1.68 hours of computational time per 1000 iterations, respectively. Compared with the MRF model, the body-force model can reduce the number of grids by about 30%, and save about 28% in computational time.
## 3 Result and Discussion  
### Optimization of Mass Spectrometry and Chromatographic Conditions  
During the method development process, mass spectrometry condition and its compound specific parameters were carefully optimized to achieve best possible response. The sample tuning of 5-AIQ was performed by direct infusion of standard solution (500 ng/mL) in mass spectrometry using IntelliStart software. The compound 5-AIQ was given single peak of precursor ion [M + H]\({}^{+}\) at \(m/z\) of 145.0 with strong intensity in ESI positive mode. After the mass fragmentation by MRM mode, the precursor ion produced four abundant product ions [M + H]\({}^{+}\) at \(m/z\) of 91.0, 117.4, 101.3 and 128.3 (Figure 1). Finally, MRM precursor to product ion transition of 145.0 \(>\) 91.0 and 145.0 \(>\) 117.4 were used for identification and quantification of 5-AIQ. Similarly, the IS (pantoprazole) ionization was also optimized to achieve best possible transitions at \(m/z\) of 384 \(>\) 138.1. Finally, the compound dependent parameters: cone voltage and collision energy were optimized to achieve high intensity response.  
An Acquity HILIC column was initially tried for separation of 5-AIQ due to its water solubility properties, but the result was not satisfactory and thus was switched to a C\({}_{18}\) column. Compared to normal Acquity BEH C18 column, Acquity C18 column showed better peak shape and high sensitivity and was selected for stationary phase separation. A mixture of aqueous and organic mobile phase composition: 10 mM ammonium acetate, 0.1% formic acid and acetic acid in waters and methanol, ACN were tried in different ratio and flow rate to achieve good peak shape with satisfactory peak resolution. The presence of ammonium ions improved the response of protonated ions and finally, the composition of mobile phase containing 10 mM ammonium acetate and acetonitrile in ratio of 35:65 (\(v/v\)) at flow rate of 0.3 mL/min was used for chromatographic elution of 5-AIQ and IS, and their retention time were 0.93 and 0.96 min, respectively.  
Figure 1: Representative precursor to product ion (\(m/z\)) transition of 5-AIQ in ESI positive mode.
# A Process Decision-Making Method for Planar Machining of Box-Type Components
## 3 Constructing a Hierarchical Information Model for Process Decision-Making  
### Planar Machining Process Decision-Making  
The process decision-making for planar machining of box-type parts is a crucial component of the overall process design. Its primary role is to determine the machiningscheme and process parameters for planar surfaces, while ensuring precise interpretation of process information. This facilitates efficient utilization of machining data throughout the entire lifecycle of box-type part manufacturing [31]. The decision-making process for planar machining of box-type parts involves multiple stages, including structural analysis, design feature analysis, inference of machining schemes, and selection of process parameters, as illustrated in Figure 2.  
(1) Structural Analysis of the Part: This stage aims to analyze the structure of the box-type part and identify the planar features that require machining.  
(2) Design Feature Analysis: In this phase, a detailed analysis of the design features of both the box-type part and the target machining planes is conducted. The results of this analysis provide essential prerequisites for subsequent machining scheme inference and process parameter reasoning.  
(3) Machining program Reasoning: Based on the design feature information of the part and its machining planes, machining schemes for various machining features are derived. These inferred machining schemes serve as the foundation for process parameter reasoning in the next step.  
(4) Process Parameter Reasoning: Following the design feature information of the target machining planes and the inferred machining schemes, specific process parameters for the machining planes are determined in accordance with relevant process design rules. Additionally, the process parameters for the raw part blank are generated directly based on the part's feature information and process design rules.
## 3 Loss Function  
Our proposed network is trained by minimizing a joint loss function defined as below:  
\[L=L_{WCE}+L_{Dice} \tag{2}\]  
where \(L_{WCE}\) denotes the weighted multi-class cross entropy and \(L_{Dice}\) denotes the dice loss.
* Weighted multi-class cross entropy, commonly used in semantic segmentation [9; 10; 17] to deal with the unbalance classes. Given a pixel \(i\) in the image \(X_{n}\), its formulation can be defined as follow: \[L_{WCE}=-\sum_{i\in X_{n}}\sum_{\varepsilon}^{C}W_{n}^{(i)}Y_{n}^{(i, \varepsilon)}\log(P_{n}^{(i,\varepsilon)})\] (3) where \(W_{n}^{(i)}\) denotes the weight associated with pixel \(i\), and \(C\) is the number of classes. \(P_{n}^{(i,\varepsilon)}\) is the estimated probability of pixel \(i\) belonging to class \(c\), and \(Y_{n}^{(i,\varepsilon)}\) is one for the ground truth of pixel \(i\) belonging to class \(c\) and zero for others. Because most of the images are backgrounds, the classes are unbalance. What's more, pixels near the boundary region are difficult to identify. So we apply larger weight for pixels belonging to foreground as well as pixels near the boundary region. Let a pixel \(i\) in the image \(X_{n}\), the formulation of \(W_{n}^{(i)}\) is defined as follow: \[W_{n}^{(i)}=1+\lambda_{1}\mathbb{I}(abs(\triangledown(Y_{n}^{(i)}))>0)+ \lambda_{2}\mathbb{I}(Y_{n}^{(i)}=L)\] (4) where \(\mathbb{I}(\cdot)\) is an indicator function, with one for \((\cdot)\) is true and zero for others, and \(Y_{n}^{(i)}\) is value of pixel \(i\) in the ground map. \(L\) represents the values for foreground classes. \(\triangledown\) denotes the gradient operator.
* Dice loss, proposed by [34], is commonly used to minimize the overlap error between the predicted probability and the true label. It can deal with class imbalance problems. To make sure all pixel values in the predicted probability are positive and in range 0 to 1 when calculating dice loss, we apply soft-max to the predicted probability. The soft-max is defined as: \[S_{C}(p_{n}^{(i,\varepsilon)})=\frac{exp(p_{n}^{(i,\varepsilon)})}{\sum_{i^{ \prime}\in C}exp(P_{n}^{(i,\varepsilon^{\prime})})}\] where \(p_{n}^{(i,\varepsilon)}\) is the pixel value in feature channel \(c\) at the pixel position \(i\). Given a pixel \(i\) in the image \(X_{n}\), the formulation of dice loss is defined as: \[L_{dice}=\sum_{c\in\mathbb{C}}1-\frac{2\sum_{i\in X_{n}}Y_{n}^{(i,\varepsilon) }S_{C}(P_{n}^{(i,\varepsilon)})+\varepsilon}{\sum_{i\in X_{n}}((Y_{n}^{(i, \varepsilon)})^{2}+S_{C}(P_{n}^{(i,\varepsilon)})^{2})+\varepsilon}\] (5)where \(P_{n}^{(i,c)}\) is the estimated probability of pixel \(i\) in feature channel c. \(Y_{n}^{(i,c)}\) is one for the ground truth of pixel \(i\) belonging to class \(c\) and zero for others.
# Fatigue of Friction Stir Welded Aluminum Alloy Joints: A Review
## 3 Factors Affecting Fatigue Performance
### Residual Stress  
Figure 6 shows the residual stress distribution for aluminum alloy AA5083 joints. The weld zone was in tension in both the longitudinal and transverse directions [67]. The longitudinal residual stress exhibited an M-shape; a similar trend was also observed by [PERSON] [49] for AA2024-T3 FSW joints. The peak longitudinal stresses increased with the welding speed due to steeper thermal gradients during welding and the reduced time for stress relaxation to occur. The NZ region has the highest residual stress in transverse direction for most cases [68]. The welding residual stress affects the fatigue life by affecting the stress ratio, as the stress ratio increases, the crack growth rate increases, but the residual stress influence decreases. For the same thickness of material, the influence of different residual stress fields on the crack growth rate can be judged by comparing the residual stress intensity factors. When the crack is 1 mm, the fatigue life of the joint by FSW was greater than the other two welding methods, TIG, and laser welding; when the crack length reached 5 mm, the fatigue life is similar to the laser welded component, but still greater than TIG method [69]. Residual stress can be reduced or removed by heat treatment aging, shot peening, ultrasonic shock strengthening, vibration aging, etc. [1,70].