esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight CoAP-Based Bootstrapping Service for the Internet of Things
## 4 Experimental Results
### Performance Evaluation
#### 4.2.3 Message Processing Time  
We have measured the processing time of the messages in the Smart Object (the most constrained device). This time includes the processing of a request and the time that the EAP state machine takes to process the EAP message. The response time measures the time it takes to create the response and send it to the Controller. The results are shown in Figure 11 for each message exchange.  
A small advantage can be seen in the last exchange, where the AUTH check and key generation are done in both CoAP-EAP and PANATIKI. This is motivated by two reasons. First, the PANA message is longer than CoAP message and the cryptographic operations are performed over a longer message and, therefore, the cryptographic operations are performed over a longer message. Second, the processing time also includes the PANA_AUTH_KEY generation in PANATIKI and the COAP_PSK generation in the case of COAP-EAP. The COAP_PSK generation is simpler since it involves fewer parameters. For example, PANA includes the first two messages (PAR/PAN) in the key derivation (Section 5.3 in [55]). The reason is that a cryptographic algorithm negotiation process is performed in these two messages and confirmed in the key derivation process. In CoAP-EAP, we do not include this, since our assumption is that a particular KDF will be used and selected by the controller, so no cryptographic negotiation is performed (see Section 3.6.5). In short, the COAP_PSK derivation is simpler than PANA_AUTH_KEY.  
With a total average processing time of \(\approx\)104 ms for PANATIKI and 93 ms for CoAP-EAP, a \(\approx\)11% of reduction in the processing time can be appreciated in CoAP-EAP over PANATIKI in total.  
However, if we observe only the EAP lower-layer (the EAP-PSK processing time is common in both alternatives), with a total average processing time of \(\approx\)69 ms for PANATIKI and \(\approx\)59 ms for CoAP-EAP we can confirm an improvement of \(\approx\)15% in CoAP-EAP to process the messages. Although  
Figure 11: Contiki message processing time.  
we can observe that CoAP-EAP takes less time to process the messages, this improvement is limited by the EAP method, which has an important weight in the message processing time. In other words, the EAP method implementation has a key impact because it limits the level of improvement. This leads us to conclude that it is important to design very lightweight EAP methods especially adapted for IoT networks.  
In any case, if we contrast these values with the effect of fragmentation and message size in the bootstrapping time, we can see that this message processing time is practically negligible.
# Collision Study on New Aluminum Alloy W-Beam Guardrail
## 3 Finite Element Model Construction
### Establishment of the Novel W-Beam Guardrail Model  
The W-beam guardrail is a common road safety facility composed of cylindrical posts, W-beam rails, and blockouts. According to relevant standards, all parts of the traditional W-beam guardrail are made of Q235 [33] material. The W-beam rail of the novel aluminum alloy guardrail is made of Al 6061-T6 [34] material, while the posts and blockouts are made of Q235 material. In practical situations, the components of the guardrail are fixed together using bolts or welding. When establishing the finite element model, the connections between the three components of the guardrail are simplified as rigid connections. Additionally, the posts of this type of guardrail are buried 1.25 m into concrete with a spacing of 4 m. When creating the finite element model, the contact between  
Figure 8: Finite element model of the vehicle.  
Figure 7: NCAC vehicle rigid wall collision test.  
the posts and the ground is constrained to six degrees of freedom to simulate real-world conditions. In the collision model, the yield stress of Al 6061-T6, as referenced in [34], is set to 290 MPa. The stress-strain curve is depicted in Figure 9. The dimensional information of the posts and blockouts, as well as the construction details of the guardrail, are shown in Figure 10a.  
In the finite element collision model, finite element simulation models of traditional steel guardrails, novel steel guardrails, and novel aluminum alloy guardrails are established. To compare experimental results, only the guardrail W-beam is replaced in the finite element model, while the remaining collision conditions remain unchanged. The local finite element models of the guardrail are depicted in Figure 10b.
## 2 SNR Definition and Channel Models
This technique can serve as a link-to-system level interface for simulations [19]. The rationale behind using the EESM method, which employs instantaneous SNR (similar to our short-term SNR), is that it provides a more realistic performance indicator for the receiver. Conceptually, if the fading process is ergodic, the long-term average of SNR converges to the ensemble-average SNR. However, the codeword length for the LTE physical downlink shared channel (PDSCH) is too short to capture the ergodic nature of the channel models (see Section 3 for a description of LTE DL). Consequently, in simulations, we often observe a significant difference between the ensemble-average SNR and the short-term SNR calculated over a codeword, leading to a substantial disparity in the BER performances based on the two different SNR definitions. Therefore, we examine the differences between these two SNR definitions in greater detail in Sections 4 and 5.
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 1 Introduction  
Water quality (WQ) is usually determined by the general composition of water in relation to its physical, biological, and chemical properties [1, 2]. DO (dissolved oxygen) is one of the freshwater properties and undoubtedly one of the most important components for the survival of the aquatic life. DO concentration is an important WQ indicator of water pollution in the aquaculture ecosystem [3]. In the aquaculture farms, the required concentration of dissolved oxygen typically depends on the fish species and the water temperature. However, concentrations below 3 mg/L are related to stress in the aquatic species, increasing mortality and disease in most of the species. If this drop in DO concentration can be accurately forecasted, the aquaculture farmers can take early remediation actions to avert this catastrophe by increasing DO concentration, for example, through activating an aeration system [4, 5].  
Given the crucial role of DO in aquafarming [6], the short-term forecasting of DO concentration is critical in ensuring good WQ management in aquaculture. The short-term forecasting will enable aquaculture farmers to foresee any falling DO concentrations in their farms and give them time to take remedial action to avoid loss/damage to aquatic life. Apparently, it is imperative that in aquaculture,short-term forecasting models are adopted for regular monitoring and control of DO concentration to prevent the potential death of aquatic lives which results from low DO concentration. In other words, the need for efficient DO concentration forecasting models in aquaculture industry cannot be over emphasized.  
Given the above-mentioned importance of water quality monitoring [7; 8; 9], especially DO concentration for improved productivity in aquaculture, this paper proposes a hybrid prediction model to solve the challenge of poor DO concentration forecasting accuracy in aquaculture management. The hybrid model is designed by combining ensemble empirical mode decomposition (EEMD) technique with long short-term memory (LSTM) neural network (NN). The applied EEMD-based LSTM (long short-term memory) method allowed for the decomposition of the original sensor data into multiple intrinsic mode functions (IMFs), which are applied to improve DO concentration forecasting accuracy.  
The rest of the paper is organized thus: Section 2 presents the related literature review. Section 3 discussed the methodology used in this study. Section 4 contains the experiments discussion and results. Section 5 presents the general discussion and Section 6 concludes the paper.
# A Vision-Based Two-Stage Framework for Inferring Physical Properties of the Terrain
## 2 Proposed Method
### First Stage: From RGB Image to the Terrain Type
The larger the filter size, the larger the receptive field, which means that the network tends to perceive the global area and more contextual information. Filters of different sizes make the network extract the information of areas of different sizes. Moreover, the pyramid fuses feature maps from different pooling operations. It aggregates contextual information from different regions, thereby giving the networks a comprehensive perception of local and global information. Figure 8: Pyramid pooling module. Figure 7: The images at the top show that ice and rock have similar appearances in the local image due to the influence of illumination or other factors. The images at the bottom show that a large perception area has more contextual information. As can be seen from the figure, contextual information is very beneficial for distinguishing types correctly. The red lines indicate the position of the small area in the large area. Figure 9 shows the complete network architecture called TerrainNet. We use TerrainNet to realize the inference from RGB image to terrain type. Additionally, we use the [PERSON] [41] optimization algorithm to update the parameters, as shown in Equation (3). \[ \begin{split} m_{t}&=\beta_{1}m_{t-1}+(1-\beta_{1})g_{ t}\\ v_{t}&=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2}\\ \hat{m_{t}}&=\frac{m_{t}}{1-\beta_{1}^{t}}\\ \hat{v_{t}}&=\frac{v_{t}}{1-\beta_{1}^{t}}\\ \theta_{t}:&=\theta_{t-1}-\alpha\frac{\hat{m_{t}}}{ \sqrt{\vartheta_{t}+\epsilon}}\end{split}  \tag{3}\]  
where \(g_{t}\) is the gradient, \(\theta_{t}\) is the parameter to update, \(m_{t}\) is the biased first moment estimate, \(v_{t}\) is the biased second raw moment estimate, \(\hat{m_{t}}\) is the bias-corrected first moment estimate, \(\vartheta_{t}\) is the bias-corrected second raw moment estimate, \(\alpha\) is the learning rate, \(\beta_{1}\), \(\beta_{2}\)\(\in\) [0, 1] are the exponential decay rates for the moment estimates and \(\epsilon\) is used to prevent the denominator from being zero. The details of the network's hyperparameter settings will be presented in section \"Experiment\".
## 3 Results and Discussion
### Development of Seismic Microzonation Map
#### 3.1.2 Plotting Seismic Microzonation Map  
In this section, five seismic microzonation maps were plotted covering 100-year, 200-year, 500-year, 1000-year, and 2500-year return periods for PMO. The values of the average PSa were used to plot the map. PSa here is the prediction of the amount of the vibration at the seabed in terms of acceleration. Please note that seismic microzonation in this study refers to the inclusion of the local soil effect in the macrozonation study as discussed in the introduction section. Figures 8 and 9 show seismic microzonation maps plotted for the Malay Basin. A detailed map is shown in Figure 8 while Figure 9 presents a seismic microzonation map for the square area in Figure 8 under five return periods. Referring to Figure 9, higher values of acceleration can be seen compared to the macrozonation map provided in the standard [26], where the influence of soil layers during vertical motion was ignored. The amount of vibration was found to be higher with the wave traveling in a vertical motion, especially when there was a thick-soft soil layer. Overall, only 100-year and 200-year return periods shared the same pattern. This was due to PGa values for 100-year and 200-year return periods being mostly the same value. Since PSa was predicted from the PGA value plus the inclusion of soil layers up to the seabed, the value provided in PGa will definitely affect the PSa value. The PSa values in the maps showed incremental trends as the return period increased. This is supported by [PERSON] et al. (2022) [39], where a comparison of PGA values based on return periods showed a linear correlation (where a higher value was obtained for a higher return period).  
[PERSON] (2013) [40] suggested that maps and figures representing characteristics of the complex phenomenon should be used as communication tools for the hazard. Hence, in the current study, the seismic microzonation map for the Malay Basin was proposed to 'ease' non-experts, the general public, and policymakers--to help them understand the potential hazards due to vibration before seismic events. This map can be used as one of the guidelines in the design process and risk assessment for maintenance purposes.  
Figure 9: Seismic microzonation map for five return periods.
## Materials and Methods
### Printability Evaluation  
In this study, different evaluations were performed to show methods of how to measure printability. For this, firstly, a standard diameter of strands (\(D_{s}\)) was calculated and compared with the experimental strand diameter. For this, the following equations were used:  
\[\rho\ =\ \frac{\textit{Mass}}{\textit{Volume}} \tag{3}\]  
\[Q=\ \frac{\textit{Volume}}{\textit{Time}} \tag{4}\]  
\[\textit{Nozzle speed}\ =\ \frac{4Q}{\pi{{(D_{s})}^{2}}} \tag{5}\]  
where \(\rho\), \(Q\), and \(D_{s}\) are density, flow rate, and standard strand diameter, respectively. Here, different solutions for groups 1 to 4 were purged for a limited time, and then purged materials were weighted using the Sartorius Scale (model 225d). From Equation (1), the volume can be calculated and then using Equations (2) and (3), the flow rate and standard strand diameter can be calculated, respectively, for different nozzle speeds. For this evaluation, the pressure was maintained between 0.2 to 0.4 bar and the temperature was the same as the one reported in Section 2.7.2. Based on Equation (3), strand printability was defined as:  
\[\textit{Strand printability}=1-\frac{D_{s}-D_{exp.}}{D_{s}} \tag{6}\]  
where \(D_{exp.}\) is experimental strand diameter. In another evaluation, pore printability was checked as described in Reference [8]. The following equation was used for this purpose.  
\[\textit{Pore printability}=\frac{p^{2}}{16\beta} \tag{7}\]where \(\beta\) and \(p\) are the area and perimeter of a pore of a scaffold. In the last evaluation, pore irregularity was defined as:  
\[I_{x,y}=\ \left|\frac{\left(x,y\right)_{th}-\left(x,y\right)_{exp.}}{\left(x,y \right)_{th}}\right| \tag{8}\]  
where \(I_{x,y}\) is the irregularity of the geometry of scaffolds in different directions of X and Y. \(\left(x,y\right)_{th}\) shows the ideal length of a scaffold in the X and Y directions as per CAD design, while \(\left(x,y\right)_{exp.}\) represents the experimental lengths in these directions.
# A Novel Method Based on Topological Perception Theory for 3D Landmark Building Model Retrieval
## 6 Experiment and Analysis
### Image Matching Results and Analysis
#### 6.1.2 Precision Rate and Recall Rate  
The precision rate and recall rate are important indicators of the efficiency of image retrieval. The precision rate indicates the correctness of the results returned by retrieval, and the recall rate reflects the ability of the system to retrieve correct results. In order to objectively analyze the efficiency of the algorithms, this paper calculated the precision rate and recall rate of each algorithm in each category, as shown in Figure 10.  
From Figure 10, the following results can be drawn: Firstly, the improvement in the voting interval of QDH significantly improved the precision of the non-concave images, but after stripping the non-concave images accurately, many concave unclosed images initially classified as non-concave were included in the category of hole images, which led to a significant reduction in the precision of the class of holes in the presence of the increase in the recall rate of the category of holes. Secondly, the inclusion of the angle distribution feature significantly improved the differentiation between concave unclosed images and hole images, thus achieving a more desirable classification of the four categories of topologies. Lastly, the QDAH algorithm outperformed other algorithms in this dataset.
## 4 User Testing Methodology
The testing approach for Hypothesis 2 was centered around identifying instances of incorrect or misleading responses, with an assessment of their potential impact on end users being conducted. Participants rated the VA's responses based on clarity, accuracy, and any perceived risk of harm. Responses deemed potentially harmful, whether due to inaccuracies or the inclusion of misleading information, were flagged for further analysis. The frequency of erroneous responses was measured in this component of the user testing, along with the severity of these errors, considering the implications of such errors in a real-world academic context. Each participant's interaction with the VA was scored utilizing a structured evaluation framework, incorporating binary ratings (such as correct vs. incorrect) and Likert scale ratings for relevance, coherence, and overall user satisfaction. The application of a mixed-methods approach facilitated the acquisition of quantitative and qualitative insights regarding the performance of the VA through the user testing methodology. The percentage of accurate responses and the frequency of hallucinations were utilized to provide a high-level overview of the reliability of the VA. Qualitative feedback was captured to reflect user perceptions, particularly concerning the VA's ability to handle complex or ambiguous queries and its overall usefulness as an academic support tool. The outcomes of this testing methodology provide a comprehensive understanding of the VA's strengths and limitations. The results offer empirical evidence to support or refute the hypotheses, guiding subsequent phases of refinement and optimization for the VA. The user testing approach is a robust framework for evaluating the VA's readiness for deployment within an educational institution by systematically addressing accuracy and potential harm.
## 2 Wireless Charging
### Technological Developments
#### 2.3.2 WiTricity  
WiTricity, an American high-tech company, has produced a wireless energy transfer system based on alternating magnetic resonance [39]. The system (Figure 3b) has an adjustable output power interval, with a wide range from 3.6 to 11 kW, so that it can satisfy the charging needs of PTEVs equipped with small and short-range battery packs to EVs equipped with large ones [40].
## 4 Results
* Therefore, the recommended MSBDR is 2 m/s\({}^{2}\) at a speed of 20% over the design speed on horizontal curves. Figure 11: Prediction of truck skidding potential and recommendations of the MSBDRs on horizontal curves with different friction coefficients. Note: The area highlighted in orange indicates a high potential of skidding. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Influence Factor** & **Parameter** & **Symbol** & **Value** & **Unit** \\ \hline \multirow{5}{*}{Vehicle} & Mass & \(m\) & 30,000 & kg \\  & Height of the CG & \(h_{g}\) & 1.8 & m \\  & Synchronous adhesion coefficient & \(\varphi_{0}\) & 0.4 & \\  & Front axle to CG distance & \(a\) & 3.60 & m \\  & Rear axle to CG distance & \(b\) & 4.25 & m \\ \hline \multirow{5}{*}{Road} & Design speed & \(V_{\text{DS}}\) & 80 & km/h \\  & Radius & \(R\) & 250 & m \\ \cline{1-1}  & Superelevation & \(i_{h}\) & 8 & \% \\ \cline{1-1}  & Downgrade & \(i\) & 0 & \% \\ \cline{1-1}  & Friction coefficient (tire-pavement) & \(\mu_{x,max}\) & 0.6 & \\ \hline Environment & Weather & & Dry & \\ \cline{1-1} \cline{2-5} Driver behavior & Speed & \(V\) & \(V_{\text{DS}}/\text{11}V_{\text{DS}}/\text{12}V_{\text{DS}}\) & km/h \\ \cline{1-1}  & Deceleration & \(a_{x}\) & 0–5.5 & m/s\({}^{2}\) \\ \hline \hline \end{tabular} Note: The blue-filled areas indicate that the impact of changes in these variables on truck skidding is being explored.  
\end{table} 
Table 5: Parameter values. Summarizing the above findings, the following general conclusions can be drawn:  
* During braking, there is a higher skidding potential of the rear axle compared to the front axle. * The present study indicates that the effect of roadway alignment on the potential of truck skidding is very limited during braking. * Under any driving condition, there is little concern of truck skidding during car-following braking maneuvers; however, there is a high potential of truck skidding during emergency braking maneuvers. * Under wet road conditions and speeding conditions, there is the potential of truck skidding during sight-distance braking and significant braking. * In summary, the conditions most prone to causing truck skidding are the emergency braking, the combination of braking and wet road conditions, then the combination of braking and speeding. * Therefore, the recommended MSBDRs under different conditions are shown in Table 6.
## 3 Some Statistical Properties
### Reliability Features  
The reliability functions are basic tools in analyzing the ageing and other features of any product lifetime. The survival function is the probability of an item not failing before a time x for the PDD is defined as  
\[\begin{array}{ll}S_{PDD}(x;\alpha,\theta,\beta)&=1-F_{PDD}(x;\alpha,\theta, \beta)\\ &=1-\bigg{(}e^{\frac{\theta\beta}{\alpha}}-\frac{\theta^{4}x^{2}\theta+2 \alpha\theta x^{2}\theta}{2\alpha^{2}(2\alpha^{2}+\theta^{2})}-1\bigg{)}e^{- \frac{\theta\beta}{\alpha}}\end{array} \tag{7}\]  
The hazard function is known as the conditional probability of failure; when it has survived up to time \(x\geq 0\) and for the PDD it is  
\[\begin{array}{ll}H_{PDD}(x;\alpha,\theta,\beta)=&\frac{f_{PDD}(x;\alpha, \theta,\beta)}{1-F_{PDD}(x;\alpha,\theta,\beta)}\\ &=\frac{\theta\theta x^{\beta-1}\big{(}4\alpha^{4}+\theta^{4}x^{2}\theta\big{)} }{\alpha\big{(}4\alpha^{4}+2\alpha^{2}\theta^{2}+\theta^{4}x^{2}\theta+2 \alpha\theta^{3}x^{2}\big{)}}.\end{array} \tag{8}\]  
In Figures 4 and 5, the plots of the survival and hazard functions are presented for some combinations of the parameters.  
Figure 4: The survival function plots of the PDD for some values of parameters.  
Figure 3: Plots of the PDD and DD with various values of parameters.  
The reversed hazard rate function (RHF) function of the PDD is defined as  
\[\begin{array}{ll}RH_{PDD}(x;\alpha,\theta,\beta)&=\frac{f_{PDD}(x_{\alpha}, \theta,\beta)}{f_{PDD}(x_{\alpha},\theta,\beta)}\\ &=\frac{\beta\theta\left(4\alpha^{4}+\theta^{4}x^{2\beta}\right)}{\alpha x \left[2\alpha^{2}(2\alpha^{2}+\theta^{2})x^{-\beta}\left(\epsilon^{\frac{\theta \beta}{2}}-1\right)-2\alpha\theta^{3}-\theta^{4}x^{\beta}\right]}.\end{array} \tag{9}\]  
Figure 6 shows the plots of the RHF for some PDD parameter combinations.