esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of the User Requirements for the Canadian WildFireSat Satellite Mission
## 2 Wildfire Management Needs Assessment
### Fire Management Functionalities:  
The needs identified above were translated into a set of key Fire Management Functionalities (FMF) necessary to define the User Requirements:  
1. The Area of Interest (AoI) is defined as the whole vegetated Canadian landmass (Figure 2);
2. Daily (or better) coverage of the AoI at a specific and consistent time of day including peak burn (1600-2000 local time), with data delivery (to end-users) before the start of the operational response period (\(\sim\)0700 local) for overnight observations and before the end of day planning period (\(\sim\)1900 local) for peak burn observations;* Detection and mapping of wildfires and their plumes, specifically: 1. The ability to detect wildfires with comparable or improved sensitivity to existing satellite systems, and to serve as an early-detection system for remote access wildfires; 2. There must be sufficient spatial resolution and geolocation accuracy for locating and mapping wildfires in relation to their previous position and other landscape features;
* Estimation of wildfire behavior, specifically: 1. The ability to collect FRP measurements; 2. The ability to characterize sub-pixel wildfire features (e.g., temperature and area);
* Compatibility with other available EO data sources and formats;
* Near-real-time data, with tactical products to be delivered within 30 min, and a 2-h latency for all end-user products as threshold for utility. The interconnectivity of the FMFs with the requirements laid out in the User Requirements Document [49] and the Mission Requirements Document [50] is summarized in Table 3. The development and rationale for these requirements is described in the following sections. Figure 2: Spatial extent of the WildFireSat Area of Interest (AoI), which includes the entire continuously vegetated extent of Canada (gray). For context, the historic burned area on public, primarily forested lands (1980–2019) is overlaid in red [17]. Although the full AoI is not prone to frequent wildfire activity, the vegetated areas shown here are burnable. The spatial distribution of wildfire within the AoI is expected to change under climate change. The distribution of agricultural burning (primarily confined to the southern half of the AoI) is not shown.
# Boosted Transformer for Image Captioning
## 4 Experiments
### Ablation Studies
#### 4.3.1 Influence of CGA  
Single source input: Given an image, we can obtain the projected visual features V and the aligned semantic concepts Y as Section 3.3. A naive way to verify the mutual effectiveness between them is solely using the single representations as the input with the comparison of both. As shown in Table 1, the baseline only used the visual features V as the input of the model. CGA-c represents that the model only uses the aligned concept representations (c) computed by Equation (7) as the input. CGA-y means only regarding the embedding vectors of concepts as the input without associating with visual features. We can see that individual visual features, aligned concept representations, and concept vectors were not enough to express the semantic information of the images compared with the integrated model CGA_1 st-GS, which considers the visual features and the concept representations. In order to exclude the effect of increasing the parameter, we conducted a comparison experiment CGA_1 st-GS(vv), whose inputs in two branches were identical visual features. Comparing CGA_1 st-GS(vv) and the baseline, the result showed that the architecture of the CGA module could also further develop the image information, similar to multi-head attention.  
Layer choice: Concept representations can enhance the semantic expression of visual features effectively, but this does not mean that the more times concept information is utilized, the better our model. We explore which encoder layers should use CGA in Table 1(c) and find that equipping all the encoder layers with CGA, i.e., CGA_4-GS, resulted in the degradation of performance because of overfitting. Moreover, equipping the first layer with CGA, i.e., CGA_1 st-GS, can achieve the best performance in the evaluation metrics. CGA_xth means that we only apply the CGA module to the xth layer of the image encoder, and the other layers are the same as the baseline, as shown in Figure 3b.  
Integration strategy: We show the performance comparison between two integration strategies in Table 1, (c) Gated Sum (GS) and (d) Channel Connect (CC). We can see that the gated sum strategy achieved better performance with all metrics since the image representations and concept representations were already aligned.
## 3 Discussion and Results
### Significance of the Research  
This study evaluated the response of full-scale piles in experimental analysis. The piles were reinforced with three different laminate structures of GFRP tubes. The tests were performed for pure bending, the combination of axial compression load and bending moment, along with the pure axial compression loading conditions. GFRP composite tubes were reinforced in both circumferential and longitudinal directions. The study investigated the use of GFRP reinforced piles as an alternative to the piles reinforced with FRP piles. Compared to the traditional reinforcement methods or use of FRP tubes, the application of the GFRP tubes is a more cost-effective practice in large-scale constructions such as bridge piers and has a considerable environmental impact. A parametric study is presented following the experimental tests for both axial compression and bending load applications.  
Figure 6: Axial load-bending moment interaction curves for GFRP composite tubes with different thickness values and fiber percentages.
# The Viable but Non-Culturable (VBNC) State, a Poorly Explored Aspect of Beneficial Bacteria
## 3 The VBNC State in Beneficial Bacteria  
Beneficial bacteria play a crucial role in maintaining life on our planet. Some realize nitrogen fixation [28], mineral solubilization [29], and greenhouse gas consumption and are thus viewed as gatekeepers preventing excessive methane emissions from escaping the atmosphere [30]. Due to the several processes in which beneficial bacteria participate, they have been used to increase crop production [31]. Some beneficial properties of these bacteria include plant growth promotion [32; 33], the control or inhibition of the activity of plant pathogens [34; 35], improvements in soil structure, bioaccumulation, or the microbial leaching of inorganics [36], the bioremediation of xenobiotic compounds [37; 38], or the production of compounds of industrial interest [39; 40]. Certain bacteria that interact with their hosts establish mutually beneficial relationships. Probiotics, for instance, interact with humans to improve overall health. They can eliminate or remove pathogens [41], reinforce the epithelial barrier, and induce the migration of fibroblasts and epithelial cells [42]. In the immune system, probiotics are related to the modulation and activation of intraepithelial lymphocytes, natural killer cells, and macrophages through the induced production of cytokines [43]. On the other hand, there are beneficial bacteria with industrial applications that favor the production of certain foods, prebiotics, and beverages. In the brewing and wine industry, bacteria play a crucial role in the fermentation process. Lactic acid bacteria (LAB) and yeasts are instrumental in this context. LAB catalyze the conversion of dicarboxylic malic acid into monocarboxylic lactic acid and carbon dioxide (malolactic fermentation MLF) and yeasts convert sugars into alcohol (alcoholic fermentation) [44]. During malolactic fermentation by LAB, no free intermediary products are formed, achieving a more palatable wine by reducing the tart taste of malic acid. Additionally, malolactic fermentation reduces the amount of residual nutrients available to support microbial growth, enhances the wine aroma, improves the microbial stability, and reduces the acidity of wine, making the wine more stable before being bottled [45]. Despite the crucial importance and diverse benefits of bacteria across different levels, our comprehension of beneficial bacteria in the VBNC state remains significantly limited. The progress in this field has primarily concentrated on pathogenic bacteria due to their profound impact on human health. Table 1 shows the beneficial bacteria reported so far to enter the viable but non-culturable state under different conditions. This table displays a variety of conditions that can induce the VBNC state and the respective taxonomic groups to which each described species belongs.
# Design of Adits for People Passing Spacing in High Altitude Highway Tunnels in Cold Regions
## 2 Materials and Methods
### Critical Velocity  
In order to further investigate the characteristics of smoke movement and the spacing of adits for people passing, it is crucial to consider the longitudinal wind velocity. In practical engineering, the longitudinal wind velocity has a significant impact on the fire smoke diffusion. Furthermore, natural wind within tunnels fluctuates frequently due to the constantly changing wind direction and velocity. Furthermore, high-altitude areas experience more severe wind conditions compared to low-altitude regions. Notably, this calculation does not account for mechanical ventilation measures implemented within the tunnel.  
Equations (1) and (2) are chosen to calculate the critical velocity based on previous research [37].  
When \(\dot{Q}/\rho_{a}C_{p}T_{a}\sqrt{g}H^{\frac{5}{2}}\leq 0.15(H/W)^{\frac{-1}{4}}\),  
\[u/\sqrt{gH}=0.81\left(\dot{Q}/\rho_{a}C_{p}T_{a}\sqrt{g}H^{\frac{5}{2}}\right) ^{\frac{1}{3}}(H/W)^{\frac{1}{2}}e^{\frac{-L_{b}}{135H}} \tag{1}\]  
When \(\dot{Q}/\rho_{a}C_{p}T_{a}\sqrt{g}H^{\frac{5}{2}}>0.15(H/W)^{\frac{-1}{4}}\),  
\[u/\sqrt{gH}=0.43e^{-\frac{L_{b}}{135H}} \tag{2}\]  
where \(\rho_{a}\) is the ambient density, \(C_{p}\) is the heat capacity, \(g\) is the gravitational acceleration, \(H\) is the tunnel height, \(L_{b}\) is the back-layering length, where \(L_{b}=0\) defines the critical velocity, \(T_{\alpha}\) is the ambient gas temperature, \(u\) is the longitudinal velocity, and \(W\) is the tunnel width. As shown in Equation (1), ambient density, ambient gas temperature, and heat capacity change as the altitude increases. As a result, the maximum critical velocity of 3.56 m/s is chosen to design simulations.  
According to the data on the tunnel operating period [38; 39], the control wind velocity of tunnel longitudinal ventilation varies from 2.0 m/s (maximum natural longitudinal  
Figure 1: Tunnel Fire Model. (**a**) Longitudinal tunnel model; (**b**) cross-section of the model.  
ventilation velocity) to 8.0 m/s (maximum control longitudinal ventilation velocity). The wind velocity in the tunnel should be accelerated to a figure greater than the critical velocity after the fire was observed. Thus, to study the characteristics of smoke diffusion under natural longitudinal ventilation velocity and control longitudinal ventilation velocity in a tunnel fire, the general velocity group (the longitudinal ventilation velocity is 2 m/s, test 2, 5, 8, 11, 14, 17) and critical velocity group (the longitudinal ventilation velocity is 4 m/s, test 3, 6, 9, 12, 15, 18) are set. Meanwhile, a 0 velocity group (the longitudinal ventilation velocity is 0 m/s, test 1, 4, 7, 10, 13, 16) is set for comparative analysis. The simulation did not take any additional mechanical ventilation measures within the tunnel into account, except for longitudinal ventilation. Table 1 shows the summary of all fire tests.
## 3 Results and Discussion  
### Morphological Characteristics of Erosion Surface  
The moving jet soil-breaking tests were carried out at jet pressures of 5-30 MPa and nozzle speeds of 1-10 cm/s. Some of the erosion groove photographs are shown in Figure 7.  
The arc erosion surface of the solidified gypsum body is depicted in Figure 8, where the \(x\)-axis represents lateral distance, and the negative \(h\)-axis represents depth. With increases in jet pressure and decreases in nozzle speed, the geometric shape of the erosion surface tended to expand; however, changes in jet pressure and nozzle speed had almost no effect on the shape of the erosion surface. The soil-breaking depth near the nozzle was approximately linear with lateral distance, and the growth rate of the soil breaking depth away from the nozzle gradually slowed down to zero with increases in lateral distance. At a given jet pressure, the soil-breaking depth increased greatly with decreases in nozzle speed. For example, at a jet pressure of 5 MPa, the maximum soil-breaking depth was only 5.5 cm at a nozzle speed of 20 cm/s, but it reached 16.5 cm at 1 cm/s. At a given nozzle moving speed, with increases in jet pressure, the soil-breaking depth generally increased,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Nozzle** & & & **Nozzle** \\
**Test** & **Jet Pressure** & **Traversal** & **Test** & **Jet Pressure** & **Traversal** \\
**Conditions** & **MPa** & **Speed** & **Conditions** & **MPa** & **Speed** \\  & & **cm/s** & & & **cm/s** \\ \hline T1-1 & 5 & 1 & T3-1 & 10 & 1 \\ T1-2 & 5 & 2 & T3-2 & 10 & 2 \\ T1-3 & 5 & 3 & T3-3 & 10 & 3 \\ T1-4 & 5 & 4 & T3-4 & 10 & 4 \\ T1-5 & 5 & 10 & T3-5  
but the growth rate was small at low pressures (5-10 MPa) and large at high pressures (10-30 MPa). In practical engineering, increasing the jet pressure and reducing the nozzle speed can increase the range of soil breaking. However, a small increase in jet pressure has little effect, whereas a large increase requires different pumping equipment. Moreover, reducing the nozzle speed reduces the soil-breaking efficiency. Therefore, considering the influences of jet pressure, nozzle speed, mining safety, and cost, the optimal parameters of jet pressure and nozzle speed need to be determined. For this, it is necessary to predict the erosion surface geometry under different jet pressures and nozzle speeds.
## 2 Generation of Precision Timing Information  
The correction of the low-cost oscillators like Ublox is based on the precision timing from the lab. This section will give an overview of the procedure involved in the generation of the precision timing in Singapore.
# Early Age Assessment of a New Course of Irish Fly Ash as a Cement Replacement
## 5 Discussion  
The thermodynamic modelling of the fly ash blended cement appears to be in good agreement with other work in the literature in this area. The depletion of portlandite and precipitation of monocarbonate, co-existing with a stable ettringite phase, all appear to be as expected. These predictions are supported by the experimental data through XRD analysis. The depletion of portlandite and increase in C-A-S-H demonstrate the pozzolanic properties of the fly ash that can add to strength development over time. For all replacement levels at full hydration, portlandite and monocarbonate are shown to be fully exhausted  
Figure 14: Phase assemblage for fully hydrated binder with fly ash replacements ranging from 0 to 35%.  
(Figure 14). Upon reflection, the peak assigned to C\({}_{4}\)AF in Figure 12 may actually represent the re-precipitation of gypsum, as shown in Figure 14.  
In terms of hydrates volume, replacement levels above 20% show a slight reduction. This has been observed in the literature [28; 29; 30], which found reductions in compressive and flexural strength in concrete samples when the fly ash replacement exceeded 20%. Small amounts of hemicarbonate are predicted in the 20% and 35% samples, which correspond with the slow reduction in monocarbonate. Previous works in the literature [23; 31] also show small amounts of hemicarbonate, and [PERSON] et al. [23] postulated that the intermediate formation of hemicarbonate is due to the relatively slow dissolution of limestone and insufficient dissolved carbonate being available. While there is no clear XRD evidence for hemicarbonate in Figure 11, the amounts predicted in the model are very small. Not predicted in the thermodynamic predictions over time or the experimental data, at least within the timeframe here, is the existence of stratlingite. As may be seen in Figure 14, it is predicted to form after completing full hydration for all levels of fly ash replacement, due to the amount of soluble silicate in the ash.  
The ash under investigation has been confirmed as Class F, in accordance with ASTM C618 [6]. In order for it to be classified a fly ash suitable as a cement replacement and for inclusion in concrete, masonry, mortar, etc., it must comply with the requirements set out in EN 450 [32].
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 3 QoS Analysis  
Table 1 shows that 4 QoS parameters must be investigated for possible 5G V2X implementation: data rate, reliability, end-to-end latency and communication range, similarly to [19]. Additionally, our 5G V2X QoS analysis considers two extra parameters, vehicle density and throughput, similarly to [31].
# Dynamic Response Analysis of Airport Pavement under Impact Loading
## 1 Introduction
In summary, the existing aircraft structure, material, and pavement structure design can no longer meet the development needs with the increasing complexity of aircraft types, heavy loads, and high-speed development. Although the impact test can obtain the bearing capacity of the airport pavement and evaluate its operational safety, there is a delay, and the field testing interrupts the traffic, which is costly in terms of human and material resources. Finite element simulation can solve the problem of timeliness, but most of the existing models still use the application of static loads to carry out relevant studies, which is reasonable under the circumstances of low aircraft loads and speeds. However, under significant dynamic loads, the differences between the static load distribution and the actual forces on the ground during aircraft flight become more and more significant and the dynamics of the pavement structure cannot be described by static characteristics, and the existing theories and methods cannot fundamentally explain and resolve these differences and deficiencies. At the same time, there is a certain effect on the pavement when the wheels pass through the unevenness, which cannot be simulated by static loads [19; 20]. Furthermore, the normal landing of an aircraft has a certain impact on the road surface, the magnitude of which depends on the drift height of the aircraft and the level of the pilot. In the case of a violent landing, the impact loading on the road surface is three times more than the static load [21], and it is not possible to describe the dynamic effect of the impact loading using a static method [22]. Finally, after the construction and operation of the airport pavement, many phenomena have occurred that cannot be explained by statics, such as fatigue cracking and aggregate loosening. If the damage caused by the dynamic effect is still not taken into account, it will lead to considerable blindness when designing, constructing, maintaining, and modifying the airport structure [2]. Therefore, in this paper, a finite element model of the impact resistance of an airport pavement was constructed, dynamic loads were applied to the landing gear to simulate the impact of the aircraft landing on the pavement, and the dynamic response of the pavement under dynamic impact loadings, including tire pressure, landing angle, and settlement speed, was investigated to evaluate the impact resistance of the pavement. It fills the gap in most existing studies, which have not assessed the pavement's load-bearing capacity by means of tests or static loading, and provides data to reveal the impact resistance mechanism of the airport pavement.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 3 Methodology
### Explainable Artificial Intelligence (XAI) Used
#### 3.5.5 Counterfactual Explanations  
Counterfactual explanations are interpretability methods in machine learning that focus on \"what-if\" scenarios, illustrating minimal changes needed for desired output predictions. Originating from the philosophy of causation and decision-making, they provide insights into complex model decision-making, especially in classification tasks. Popularized by [14], they aim to provide explanations for machine learning model decisions that meet legal and non-discrimination requirements.  
Counterfactual explanations in machine learning involve finding an instance \(x^{\prime}\) that is minimally different from the original instance \(x\) but leads to a different prediction outcome. Counterfactual explanation seeks to solve the following optimization problem:  
\[min_{x^{\prime}}d\Big{(}x,x^{\prime}\Big{)}\ subject\ to\ f(x^{\prime})\
eq\ f(x) \tag{7}\]  
where \(f(x)\) is the prediction of the original instance,\(f(x^{\prime})\) is the prediction of the Counterfactual instance, and \(d(x,x^{\prime})\) is a distance metric (such as Euclidean distance) that quantifies the dissimilarity between the original instance and the Counterfactual.  
For example, imagine a machine learning model predicts a 'Low' adaptability level for a student based on features such as 'Class Duration', 'Financial Condition', and 'Institution Type'. A Counterfactual explanation might reveal that a slight increase in 'Class Duration' could change the prediction to 'High' adaptability.  
For instance, if the original feature values are \(x=[1\) (Low Class Duration), 0 (Poor Financial Condition), 0 (Government Institution)] and the model's prediction f(x) is 'Low adaptability', a Counterfactual explanation would search for a new set of feature values \(x^{\prime}\) that lead to a different prediction f(x') = 'High adaptability'. Suppose the Counterfactual feature values are \(x^{\prime}\)= [2 (High Class Duration), 0, 0], indicating that only 'Class Duration' needs to be increased to alter the prediction. The Counterfactual explanation therefore might suggest that improving the 'Class Duration' from Low to High could enhance the student's adaptability level according to the model.  
Counterfactual insights, like increased instructional time positively affecting student adaptability, guide educational resource allocation and intervention strategies. Educators, leveraging Counterfactual explanations, pinpoint influential factors in the learning process for targeted improvements. This forms a feedback loop, enhancing both educational strategies and machine learning models. Beyond education, Counterfactual explanations facilitate understanding and actionable insights in finance, revealing changes for loan approval, and in healthcare, illustrating shifts in patient risk categories based on lifestyle or clinical parameters [29; 30].