esa-sceva/satcom-chunk-collection huggingface dataset:
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 2 Fragility Analysis Framework
### Proposed Fragility Analysis Framework  
Through the wind-induced fragility analysis of transmission towers under downburst, the possibility of reaching the limit state can be obtained under different wind speeds in all directions; the relationship between the wind load strength and the damage degree of the tower is determined, and the wind resistance of the tower is quantitatively calculated in the way of probability. In this paper, the horizontal tower top displacement is selected as the structural performance index, the structural collapse limit state is divided, and the wind-induced fragility is studied. The procedure of fragility analysis of the transmission towers under downburst is described in Figure 4.  
The basic steps and flow chart are as follows:  
1. Considering the uncertainty of the transmission tower, the Latin hypercube sampling method is used to simulate 20 random structure samples [33], denoted by \(S_{i}\), \(i=1,2,\cdot\cdot\cdot,20\);  
Figure 3: Failure criteria.  
2. Considering the randomness of wind load, the downburst wind field with different direction angles and radial distances is simulated based on the hybrid random theory and converted into the wind load according to the specifications. 3. The nonlinear buckling analysis is carried out for each structure sample \(S_{i}\) to determine the threshold displacement values of the collapse of the structure \(\Delta_{ci}\), \(i=1,2,\cdot\cdot\cdot,20\); 4. The wind-induced response of each uncertain structural sample \(S_{i}\) under different radial distances and direction angles is analyzed. Tower top displacement at different radial distances of the transmission tower is calculated. As the wind-induced response of the transmission tower increases first and then decreases with the increase in the radial distance, the radial distance (\(R_{a_{l}}\), \(R_{b_{l}}\)\(i=1,2,\cdot\cdot\cdot,20\)) between the maximum tower top displacement exceeding \(\Delta_{ci}\) for the first time and the last time can be obtained. At the same time, the average wind speed (\(V_{ai}\), \(V_{bi}\), \(i=1,2,\cdot\cdot\cdot,20\)) is recorded at the 10 m height of the tower at the corresponding position. Thus, the curve of radial distance vs. failure probability of the collapse state of the tower can be drawn. 5. By comparison, the average wind speed (\(V_{i}=Min(V_{a_{l}},V_{b_{l}})\)\(i=1,2,\cdot\cdot\cdot,20\)) at 10 m height when the transmission tower collapses can be obtained, and then the fragility curve of the collapsed state of the tower for a wind attack angle can be drawn. 6. Using the above method, the fragility curves of slight damage and severe damage of the transmission tower can also be obtained.
## 3 Multipath Multimedia Dynamic Source Routing (MMDSR)
### MMDSR Self-Configuration  
Due to the highly variable network topology of MANETs, any proposed routing protocol should be dynamic. Having this in mind, MMDSR is able to self-configure. Here, we will just point out the basics of the self-configuration operation.  
Our framework monitors the current state of the network and in case of changes, the algorithm modifies the routing period of the algorithm and the thresholds to classify paths. We adjust those parameters dynamically depending on a parameter called _NSstate_, which brings information about the global network state and is updated by the algorithm iteration by iteration. _NSstate_ is computed as follows:  
\[NSstate^{j} = w_{RM}\cdot\overline{RM^{j}}+w_{MM}\cdot\overline{MM^{j}}+w_{BW} \cdot\overline{BW^{j}}+ \tag{3}\] \[w_{L}\cdot\overline{L^{j}}+w_{D}\cdot\overline{D^{j}}+w_{J}\cdot \overline{J^{j}}+w_{H}\cdot\overline{H^{j}}\]  
In Equation (3), the upper bars denote averages and the _w_s are the weights assigned to each metric. When the source receives the feedback from the network by means of PMR packets, it calculates the _NSstate_ using Equation (3) from the QoS parameters of all the \(k\) paths (see Equation (2)) on that iteration \(j\).  
The routing period (\(T_{routing}\)) to refresh the multipath scheme also varies dynamically and is calculated according to Equation (4), so that when the network is behaving well (_i.e._, \(NSstate\) is high), \(T_{routing}\) is also high and the paths that form the multipath scheme will be used longer. Conversely, if the network is behaving bad (_i.e._, \(NSstate\) is low) then \(T_{routing}\) is low and the paths are refreshed sooner.  
\[T_{routing}^{j+1}=\gamma\cdot NSstate^{j}+\theta \tag{4}\]  
To obtain the previous equation, a high number of simulations were conducted under a wide range of network conditions where the network performance was good, normal and bad. For the scenario under consideration (also used in the present paper), the obtained values were \(\gamma=10\) and \(\theta=3\).  
Till now, we have summarized the basics of the previous QoS-aware adaptive multipath routing protocol [3]. Next, we introduce our novel game-theoretical routing scheme to further improve the transmission of video over MANETs.
# Recent Advancements in Anaerobic Digestion and Gasification Technology
## 6 Feedback for Anaerobic Digestion
### Industrial Waste (IW)  
Industrial organic waste for anaerobic digestion consists of by-products, residues, and waste from numerous activities in the industries. Some of these industries include the food and beverages industry, pulp and paper industry, petrochemical industry, and textile industry. Selected wastes from industries include food and beverage waste, sugar, starch, and fruit processing wastes produced in large quantities. According to [PERSON] et al. [50], industrial organic wastes are characterized by a high content of hydrocarbons, fats, and protein. Industrial wastes are usually not used as feedstock for anaerobic digestion, especially for biogas production, because of their low degradability (30-50%) and recalcitrant chemical properties [39]. This particular property does not affect food waste, which is an example of industrial waste. Hence, they are the most widely used substrate among industrial waste for anaerobic digestion. The pulp and paper industry generates waste of high biological oxygen demand (BOD), high dye content of 300-2800 m/g, and 1200-6500 colour units [20]. To improve the biodegradability of industrial waste for anaerobic digestion, it is recommended that they are co-digested with other substrates because of their low total solid content of \(<\)1%. However, industrial waste for anaerobic digestion employed various technologies which are specific for biogas production via anaerobic digestion. Some of these technologies are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Animal Waste** & **Total Solids (TS)** & **Volatile Solids (VS)** & **pH** & **Carbon/Nitrogen Ratio** & **References** \\ \hline Pig manure & 6.4–7.5 & 6.2–82.8 & 6.4–7.5 & 5.7–13.5 & [40; 41; 42; 43] \\ \hline Goat manure & 33.7–55.5 & 27.7–89.4 & 7.9 & 18.0 & [44; 45] \\ \hline Sheep manure & 22.3–40.0 & 18.7–72.7 & 7.16–8.1 & 11.3–14.7 & [26; 44] \\ \hline Cattle manure & 14.5–22.7 & 11.9–72.0 & 7.1–8.6 & 14.59–18.9 & [26; 40; 44] \\ \hline Poultry manure & 20.0–92.6 & 18.3–84.1 & 6.9–7.4 & 7.5–9.75 & [22; 40; 46; 47] \\ \hline \end{tabular}
\end{table}
Table 3: Optimum ranges of substrate parameters for enhanced biogas production.
## 2 Results  
### Composition of the Sweet Wines Made from FSS  
The fermentation kinetics of the FSS extracts (density of the fermenting liquid versus time) of the three different sweet wine methods are presented in Figure 2. In Methods 1 and 2, the fermentation was complete, and all sugar was utilized at about 144 h, yielding alcohol contents (av.; % vol.) of 15.3% for SW-F wine and 15.0% for SW-S wine (Table 1). The average alcohol level in the fortified SW-A wine was 16.0% (Method 3; Table 1).  
Figure 1: Methods applied for the production of sweet wines from Corinthian currants finishing side-stream (FSS) (©Be: Baumé scale hydrometer density).  
Figure 2: Course of fermentation of Corinthian currants finishing side-stream (FSS) extracts for sweet wine production. SW-F: Sweet wine with added FSS raisins to adjust sweetness. SW-S: Sweet wine with added FSS syrup to adjust sweetness. SW-A: Sweet wine with alcohol added to cease fermentation at the desired sweetness level.  
Methanol was not detected in any of the wines, as expected since it was not present in the raw material [2].  
The sugar levels in the wines differed (\(p<0.05\)), and were in the average ranges of 44-68 g/L glucose, 63-71 g/L fructose, 111-139 g/L total sugar, and traces of sucrose in all cases (Table 1).  
The total titratable acidity (TTA) (av. 7.4 g/L, as tartaric acid) and pH values (av. 3.9) of SW-F and SW-S wines were similar (\(p<0.05\)) (Table 1), while SW-A wine had a slightly lower TTA (6.4 g/L) and pH (3.83).  
The volatile acidity (VA) of the wines was statistically different (\(p<005\)) and in the average range of 0.3-0.7 g/L (as acetic acid), with the highest value being that of SW-A (Table 1).  
Regarding the analysis of individual organic acids by HPLC, the main organic acids were tartaric (2.0-2.6 g/L), malic (3.3-3.6 g/L), and succinic acid (2.0-2.8 g/L), while all wines contained citric and acetic acid at lower concentrations (\(<\)1.0 g/L) (Table 1).  
Statistically significant differences were also observed among the three types of wine regarding the TPC (Folin-Ciocalte reaction), AC (radical scavenging), and polyphenol content (PPC) (ferric ammonium citrate reaction). Specifically, the TPC was in the (av.) range 244-325 g GA/L, AC was 18-35 mg AA/L, and PPC was 459-576 mg/L; the highest values were observed in the case of SW-F wine.  
Finally, free and total sulfate, which was analyzed in order to regulate its residual content in the final products, was found at very low levels (\(<\)40 mg/L). Sulfite was added (as potassium metabosulfite) during the production of the wines, as described in the Section 4, and is common practice in winemaking to avoid spoilage, avoid spontaneous fermentation, and act as an antioxidant, as well as for its ability to bleach pigments and suppress oxidized odors [13].
## 3 Train Dynamics Model  
Considering the external resistance in the process of automatic train operation, the nonlinear dynamic model of train operation can be described as  
\[\left\{\begin{array}{l}\dot{p}(t)=v(t)\\ m\dot{v}(t)=F(t)-g(v(t))-f(p(t),v(t),t)\end{array}\right. \tag{1}\]  
where \(m\) is the total mass of the whole train, \(p(t)\), \(v(t)\) and \(\dot{v}(t)\) are train position, train speed and train acceleration respectively, \(F(t)\) is the train traction or brake force, \(g(v(t))\)is the basic resistance, and \(f(p(t),v(t),t)\) is additional resistance.  
The basic resistance \(g(v(t))\) refers to the resistance of the train operating along the straight rail under the ideal route conditions, which exists in any case of the train operating. The basic resistance is usually described by the Davis equation [28] as follows:  
\[g(v(t))=a_{0}+b_{0}v(t)+c_{0}v^{2}(t) \tag{2}\]  
where \(a_{0}b_{0}\) and\(c_{0}\) are the basic resistance coefficient obtained by fitting historical data. Generally, the model can model the basic resistance. However, for different trains, routes, and even different weather, the coefficient will change, which leads to the uncertainty of system model parameters. Therefore, an adaptive control strategy is designed to identify the unknown parameters of the basic resistance and ensure the good convergence of the system.  
Additional resistance \(f(p(t),v(t),t)\) refers to the extra external resistance in the non-ideal route environment, which mainly includes the route resistance formed by route curve, ramp, and tunnel. The type of resistance mainly depends on the route where the train is operating.  
\[f(p(t),v(t),t)=\omega_{r}+\omega_{i}+\omega_{s} \tag{3}\]  
where \(\omega_{r}\), \(\omega_{i}\) and \(\omega_{s}\) are route curve, ramp, and tunnel additional resistance.  
Considering the delay of speed sensor, based on the train operating dynamics (1), the following train dynamic model is given  
\[\left\{\begin{array}{l}\dot{p}(t)=v(t)\\ \dot{v}(t)=u-\frac{a_{0}+b_{0}v(t-\tau)+c_{0}v^{2}(t-\tau)}{m}-\frac{f(p(t),v( t),t)}{m}\end{array}\right. \tag{4}\]  
where \(\tau\) represents the delay of speed sensor transmission when the urban rail train is operating at high speed, and \(u=\frac{F(t)}{m}\) is the control input of the ATO controller.  
Figure 1: Composition diagram of ATO system.  
Define \(x_{1}=p(t)\), \(x_{2}=v(t)\), thus, Equation (4) is rewritten as follows:  
\[\left\{\begin{array}{l}\dot{x}_{1}=x_{2}\\ \dot{x}_{2}=u+g+f(x)\end{array}\right. \tag{5}\]  
where \(x_{1}=p(t)\) and \(x_{2}=v(t)\) are the train position and train speed, \(f(x)=-\frac{f(x_{1},x_{2},t)}{m}\) is the additional resistance, and \(g=-\frac{g(x_{2}(t-\tau))}{m}=-\frac{a_{0}+b_{0}x_{2}(t-\tau)+c_{0}x_{2}^{2}(t -\tau)}{m}\) is the basic resistance considering speed delay.  
**Assumption 1**.: _The nonlinear train dynamics model given in Equation (5) is an input-state stable system._
###### Abstract  
In this paper, the effect of nano-silica on the autogenous shrinkage, hydration heat, compressive strength hydration products of Ultra-High Strength Concrete (UHSC) is studied. The water/binder ratio (w/b) of UHSC is 0.2. The nano-silica replaces 2% and 4% of the mass fraction of the cement in UHSCs, respectively. A new instrument was developed to simultaneously measure the autogenous shrinkage, internal relative humidity, and internal temperature of UHSC. The following results were obtained from the analysis of the experimental data: (1) The trends in the autogenous shrinking of UHSC can be divided into two stages, which are the variable temperature stage and the room temperature stage. The dividing point between the two stages occurs at the age of approximately 2 days. During the room temperature stage, the internal relative humidity and autogenous shrinkage showed a good linear relationship. (2) The compressive strength of UHSC increased significantly with the increase of nano-silica content at 3 days, 7 days, and 28 days. (3) The total accumulated heat of UHSC increased during the 72 h, with the increasing of nano-silica content. (4) The XRD data at the age of 28 days showed that the Ca(OH)\({}_{2}\) peaks of nS2 and nS4 have a tendency to weaken due to the pozzolanic reaction, compared with the peak of nS0.  
nano-silica; autogenous shrinkage; strength; hydration heat; ultra-high strength concrete 2020 10 5202 2010 2020 10.3390/app10155202
# Underwater-Acoustic-OFDM Channel Estimation Based on Deep Learning and Data Augmentation
## 5 Conclusions  
In this paper, we proposed a novel DL-based method for UWA-OFDM channel estimation. First, the CWGAN-GP model was introduced, to generate enhanced channel samples. Then, to reduce the influence of feature-information loss, we built a similar U-Net convolution-network structure to extract channel features in a skip-connected manner, then designed a channel-attention-denoising module, to further optimize the channel information reconstruction. Finally, the channel quality estimated by the proposed method was verified in KAU1 and NOF1 environments for different pilot symbols. The experimental results show that the channel response estimated by the proposed algorithm could fit the real channel samples well, and that the proposed algorithm was superior to the comparison algorithm in MSE and BER. For the low SNR, the proposed algorithm also achieved better performance with less pilot symbols, which could better improve the system-transmission performance. However, mathematical validation of the proposed  
Figure 16: BER vs. SNR for different number of pilot symbols in NOF1 channel.  
Figure 17: BER vs. SNR for different number of pilot symbols in CAD ablation experiments.  
algorithm was difficult, due to the difficulty of deep-learning models. In the future, we will introduce more environmental data, such as number of users and number of channels, into the simulation validation.  
Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON] and [PERSON]; formal analysis, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Wuzhou Science Plan Research Project, grant number 202202037, and by the Key Research Doctoral Fund Project of Wuzhou University, grant number 2022A004.  
Data Availability Statement: Data is contained within the article.  
The authors extend their heartfelt gratitude to the editors and reviewers for their insightful feedback and recommendations. The team is also deeply thankful for the invaluable contributions and advice provided by esteemed colleagues from the School of Computer, Electronics, and Information at Guangxi University and Wuzhou University. This work was supported by Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou, Guangxi, China.  
The authors declare no conflicts of interest.
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 5 Discussion
Also, the authors acknowledge that the Cox model's scalability has to be examined with bigger datasets since the stability of latent classes may vary with larger or more complex datasets. The issue of data collection remains both significant and persistent, particularly in the context of model generalizability. While large-scale surveys are essential for accurate forecasting and effective transport planning, their absence limits the robustness and scalability of modeling approaches. The current study, based on a relatively small sample, faces inherent challenges in generalizing its findings to larger populations. This limitation is especially relevant when using models such as the Cox model with frailty, which relies on identifying latent behavioral groups. Expanding the number of latent classes (e.g., from two to four) and refining model variables would require more comprehensive survey data,ideally incorporating observations from multiple days, household-level characteristics, and broader demographic representation, to improve both accuracy and applicability at scale. Another important aspect of this research is the application of the proposed models within an open-source software environment. Specifically, the PAM library was used for population synthesis and travel pattern generation in the context of the ABM. While the use of open-source tools facilitates the integration of the Cox model with frailty and supports adaptability across different applications, a limitation of the current study is its reliance on a single ABM framework. Future research could benefit from evaluating the proposed methodology across a broader range of ABM platforms to assess its generalizability and performance in varied modeling environments. Nonetheless, the open nature of such software promotes transparency, methodological reuse, and continued innovation in activity-pattern modeling and traffic analysis.
# Smart Contract-Based Right-of-Way Trade with Game Theory in Blockchain-Enabled Internet of Vehicles
## 4 Right-of-Way Trade Strategy
### Optimal Bids Solution 2: Convoy A Bids the Same as Convoy B  
Another solution to the incomplete information game problem is based on A's belief that its optimal bid is \(S_{A}^{*}=S_{B}^{*}\). Four assumptions are made to facilitate the solution. The first two assumptions are the same as Assumptions 1 and 2 in Section 4.4.2. The other two are new.  
* Assumption 3: Convoy B will submit its optimal bid \(S_{B}^{*}\) to obtain its maximum expected profit \(\mathrm{E}[G_{B}]\).
* Assumption 4: Convoy A has complete information and A can obtain the optimal bid \(S_{B}^{*}\).
* According to Equation (22) in Section 4.4.1, the optimal bid of A should be equal to \(S_{B}^{*}\), \(S_{A}^{*}=S_{B}^{*}\), and B knows the optimal bid strategy of A.  
For convoy B, it does not know the exact value of \(\Delta C_{A}\), but it knows that \(\Delta C_{A}\in[0,\Delta t_{AB}]\), according to Assumption 1. Then, analysis is given under different values of \(\Delta C_{B}\).  
If \(0<\alpha_{B}\Delta t_{BA}<\Delta t_{AB}\), from Equations (12), (15) and (19), \(\mathrm{E}[G_{B}]\) is derived as follows  
\[E[G_{B}]=\frac{1}{\Delta t_{AB}}(-{S_{B}}^{2}+S_{B}\alpha_{B}\Delta t_{BA}). \tag{31}\]  
If \(\Delta t_{AB}<\alpha_{B}\Delta t_{BA}\), \(\mathrm{E}[G_{B}]\) is derived as follows  
\[E[G_{B}]=\left\{\begin{matrix}\frac{1}{\Delta t_{AB}}\Big{(}-{S_{B}}^{2}+S_{B} \alpha_{B}\Delta t_{BA}\Big{)},&\text{if }\ 0\leq S_{B}<\Delta t_{AB}\\ \alpha_{B}\Delta t_{BA}-S_{B},&\text{if }\ \Delta t_{AB}<S_{B}<\Delta C_{B} \end{matrix}\right.. \tag{32}\]  
And B's optimal bid is the point where function \(\mathrm{E}[G_{B}]\) obtains its global maximum value. We calculate the maximum value of each segment and make a comparison. To summarize, in the static game where A has complete information and B has incomplete information, the optimal bids of A and B are as follows.  
\[S_{A}^{*}=S_{B}^{*}=\min\biggl{\{}\frac{1}{2}\alpha_{B}\Delta t_{BA},\Delta t_ {AB}\biggr{\}} \tag{33}\]  
The corresponding maximum profit expectation for B is  
\[\mathrm{E}[G_{B}]_{\max}=\left\{\begin{matrix}\frac{\alpha_{B}\Delta t_{BA}}{ \Delta t_{AB}}^{2},&\text{if }\ 0<\alpha_{B}\Delta t_{BA}\leq 2\Delta t_{AB}\\ \alpha_{B}\Delta t_{BA}-\Delta t_{AB},&\text{if }\ \alpha_{B}\Delta t_{BA}>2 \Delta t_{AB}\end{matrix}\right.. \tag{34}\]
## 6 Experimental Results and Analysis
### Model Training Strategy and Model Validation Parameter Analysis  
To speed up the training and prevent overfitting, we set the weight attenuation coefficient to the empirical value of 0.0005 and used a multi-stepping strategy to set the learning rate. Table 3 presents the learning strategy. In the experiment, the f value was set to 0.60, and the IOU threshold was 0.65. The initial network learning rate was set to 0.003, and the activation function was set to leaky.  
Figure 8: Comparison of experimental results between increased and real data in automatic and manual annotation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Training Steps** & **Learning Rate** \\ \hline
0–1000 & 0.0001 \\
1000–3000 & 0.001 \\
3000–10000 & 0.0001 \\
10000–30000 & 0.00001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multi-stepping strategy for learning rate.  
Different thresholds that affect performance must be considered in evaluating the suitability of the trained model for practical applications. This section discusses the effects of different thresholds on recall rate and accuracy to determine the suitable training thresholds and scale. The trained model was evaluated on the verification set, and different thresholds were used for the verification. Table 4 shows the test results when the candidate frame with prediction confidence higher than the threshold is used as the object detection result.  
Table 4 shows that the average IOU value and accuracy of the model increases for the 500-image verification set when the threshold ranges between 0.00 and 0.55. At the same time, the number of prediction frames gradually decreases. The recall and accuracy rates are 100%, the average IOU is 93.25%, and the number of prediction frames is stable at 800 when the threshold is 0.60. The number of detection frames decreases when the threshold ranges between 0.75 and 0.95, although the detection accuracy value is 100% in the region. This result indicates a missed object. In terms of detection speed, the average detection time per image is approximately 0.0034 s. Real-time requirements for the identification and location of object detection are satisfied.