esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results
### Evaluation of Model Adaptability  
The analysis of the results demonstrates how each model adapted the difficulty when players reached the maximum number of allowed failures. The evaluation criteria encompassed reductions in orders and tasks, increases in allowed failures, and reactivity, quantified through the Average Error Ratio (errors/failures \(\times\) 100) and Average Difference (errors \(-\) failures). Tables A4-A6 present the simulation results, detailing for each model the level and challenge, the adjusted challenge designed by the AI, and the consistency percentage (% of agreement), based on 50 iterations per model/level. ChatGPT implemented substantial reductions in orders (from 2-3 to 1 at the medium and advanced levels) and tasks (from 15 to 5-12), increasing allowed failures to 10-12. However, it exhibited variable consistency (13.04-100%) and delayed reactivity (ratios: 128.43-424.69; differences: \(-\)0.84 to \(-\)7.22), indicating tardy adjustments. DeepSeek reduced tasks (from 12-15 to 8-10) and increased failures (to 5-8, 100% consistency in most cases) but retained orders at the medium level (2 in 80.95% of cases), limiting adaptability. Its reactivity was moderate (ratios: 100-381; differences: 0 to \(-\)2.98). Groke balanced task reductions (from 15-20 to 10-15) and failure increases (to 3-5), with high consistency (100% at advanced). Its reactivity was optimal (ratios: 89.33-200; differences: 0.32 to \(-\)2), particularly at the advanced level (ratio: 101.75; difference: 0). Overall, ChatGPT provided aggressive adjustments but its slow reactivity compromised user experience, DeepSeek was consistent but limited in adaptability, due to minimal order reductions, and Groke integrated balanced adjustments with rapid reactivity, minimizing player frustration. Groke was the most effective model, owing to its precise reactivity and well-calibrated adjustments, optimizing the learning experience.
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions  
### Frequent Topological Changes  
DMNs face challenges in V2V communication due to mobility and frequent topological changes. To maintain dependable connections, complex algorithms must learn from the environment's conditions. Sophisticated cryptography and intrusion detection are necessary to maintain secure connections in such environments.
## 4 Simulation Results  
In a simulation environment, we the fix positions of \(\text{SS}_{1}\), \(\text{SS}_{2}\), PT, and PR at (0,0), (1,0), (0.65,1), and (0.65,0.5), respectively, while that of the secondary relays is \((x_{\text{R}},0)\), and where \(0<x_{\text{R}}\leq 0.5\). With these positions, we can see that \(d_{\text{SS}_{1}\text{SR}}\leq d_{\text{SS}_{2}\text{SR}}\) and \(d_{\text{SS}_{1}\text{PR}}>d_{\text{SS}_{2}\text{PR}}\). In this section, the system parameters are fixed as follows: the path-loss exponent equals 3 (\(\mu=3\)), the outage threshold of the primary network equals 1 (\(\theta_{\text{Ph}}=1\)), the target QoS of the primary network equals 0.001 (\(\varepsilon_{\text{OP}}=0.001\)), and the outage threshold of the secondary network equals 0.001 (\(\theta_{\text{Sth}}=0.001\)). Finally, in the figures presented below, the markers denote the simulated results (Sim), and the solid lines denote the theoretical results (Theory).
# Real-Time Contrail Monitoring and Mitigation Using CubeSat Constellations
## 4 Distributed Systems with CubeSats
### Feasibility Studies and Deployment Scenarios  
Before deploying a full-scale CubeSat constellation for contrail monitoring, it would be necessary to conduct a series of feasibility studies to assess the system's viability. These studies would involve simulations of the CubeSat constellation in various deployment scenarios, evaluating factors such as coverage area, data resolution, and system reliability. One possible deployment scenario could involve a small-scale pilot project focused on a high-priority region, such as the North Atlantic Corridor. This region, with its high volume of air traffic and frequent contrail formation, would serve as an ideal testbed for the CubeSat system. By deploying a small number of CubeSats in this region, researchers could collect valuable data on the system's performance, identify any technical challenges, and refine the design before scaling up to a global deployment.  
This feasibility study considers the cost effectiveness of the CubeSat system compared to traditional satellite systems. While CubeSats are generally less expensive to build and launch than larger satellites, the cost of deploying and maintaining a large constellation could be significant. Another important aspect of the feasibility studies would be the assessment of the system's scalability. As air traffic continues to grow, particularly in emerging markets, the demand for contrail monitoring and mitigation is likely to increase. The CubeSat system would need to be designed with scalability in mind, allowing for the addition of new satellites to the constellation as needed. This could involve designing the system with modular components that can be easily upgraded or replaced, as well as ensuring that the data processing and communication infrastructure can handle the increased data volume.
## 2 Mathematical Model
### Determination of Annual Heat Loss and Gain through Building Walls  
To calculate the building's annual energy consumption, it is essential to determine the heat loss through the exterior walls in winter and the heat gain through the exterior walls in summer. This calculation requires knowledge of the Heating Degree Days (\(HDD\)) and Cooling Degree Days (\(CDD\)) values, as well as the thermophysical properties and thickness of the wall components and the heat transfer coefficients of both the internal and external environments. In the base case scenario, the wall structure is assumed to consist of 2-cm thick extruded polystyrene (XPS) as the insulation material, 20-cm thick brick as the wall material, and a total of 5-cm thick plaster, with 2.5 cm on both the inside and outside (Figure 1).  
Heat loss (\(q_{A,H}\)) through the walls in winter can be calculated using Equation (7), where \(R_{i}\) represents the convective heat resistance on the inside, \(R_{o}\) signifies the convective heat resistance on the outside, \(R_{w}\) denotes the thermal resistance of the uninsulated wall, and \(R_{ins}\) stands for the thermal resistance of the insulation material. Convective heat resistance is calculated using the formula \(1/h\), where \(h\) is the heat transfer coefficient. In the present study, interior and exterior heat transfer coefficients are chosen as 8.29 W/m\({}^{2}\)K and 28.35 W/m\({}^{2}\)K, respectively [33; 41].  
\[q_{A,H}=\frac{86,400\ HDD}{(R_{i}+R_{o}+R_{w}+R_{ins})} \tag{7}\]The amount of energy required to heat the building (\(E_{A,H}\)) is determined by dividing the annual heat loss by the efficiency of the heating source (\(\eta\)). The efficiency values for each heating source are presented in Table 1 In the base case scenario, the heating source is assumed to be fuel oil.  
\[E_{A,H} =q_{A,H}/\eta \tag{8}\]  
With a similar approach, during summer months, heat gain (\(q_{A,C}\)) from the walls is calculated based on \(CDD\) with the help of Equation (9). The amount of energy required to cool the building (\(E_{A,C}\)) is determined by dividing the annual heat gain by the coefficient of cooling system performance (\(COP\)). In all scenarios, the cooling source is assumed to be air conditioning.  
\[q_{A,C}=\frac{86,400\;CDD}{(R_{i}+R_{o}+R_{w}+R_{ins})} \tag{9}\]  
\[E_{A,C} =q_{A,C}/COP \tag{10}\]
# GPS Multipath Analysis Using Fresnel Zones
## 4 Multipath Analysis from a Spatially-Limited Reflector Using Fresnel Zones
### Measurement Concept and Data Preparation
It should be noted that prior to the field test, both antennas were individually calibrated for their phase center offset and variations in the anechoic chamber of the University of Bonn [41]. Accounting for the height difference between the antenna reference point and the antenna phase center is an essential step during the simulation described in the next section.
## 2 Material and Method
### Data Preprocessing  
Missing values and measurement errors can lead to unpredictable results. To avoid removing these, anomalous data were filled with imputed data and then normalised. For the former, we replaced missing data with the average of the data from the previous seven days as per [8; 9; 10]. We then normalised the data so that all model inputs had equal weights and the Sigmoid activation function could be applied in the deep-learning models as per [11; 12; 13]. Normalization reduces the data range to zero and one [0, 1]. [PERSON]'s MinMaxScaler function was used to normalise the data in this study, based on Equation (1).  
\[X_{i}^{\prime}=\frac{X_{i}-min(x)}{max(x)-min(x)} \tag{1}\]  
Figure 1: Daily dataset of (**a**) the energy consumption, (**b**) the production flow for the polymerisation stage, and (**c**) the production flow for the solid-state polymerisation stage.  
where \(X_{i}^{\prime}\) is the rescaled value, \(X_{i}\) is the original value, \(min(x)\) is the minimum value in feature, and \(max(x)\) is the maximum value in feature.
# Improvement of Arsenic Phytoextraction Using Indigenous Bacteria and Mobilizing Agents
## 3 Results and Discussion
### Microcosm Tests with Oxalate + Ascorbic Acid Mixtures  
The biomass values obtained for the plants cultivated in the controls and in the soils treated with the different mixtures of potassium acid oxalate and ascorbic acid (and in the presence of PGPR) are shown in Figure 3. The related weight concentrations of arsenic absorbed by the roots and shoots of _Z. mays_ and _C. sativa_ are collected in Table 2.  
As evidenced by the results of the [PERSON]'s test (letters associated with the data in Figure 3/Table 2), the biomass values obtained for the treated plants do not appear to have been significantly influenced by the treatments carried out (this applies in particular to the roots). This confirms the non-toxicity of the treatments and highlights that the quantity of arsenic absorbed by the plants does not create problems for their growth and development.  
For both plant species investigated, the best combination of the two additives turns out to be 0.2 M oxalate + 0.01 M ascorbic acid, as this is the treatment that allows maximization of the concentrations of As in plants, both in the aerial parts and in the roots. It is interesting to note the increase in As content compared with the untreated soil and also compared with the microcosms investigated in the first part of this work [50], in which ascorbic acid  
Figure 3: Dry weights (mg pot\({}^{-1}\)) of (**a**) roots and (**b**) shoots of _Z. mays_ and _C. sativa_ grown on soil control (CT) and As-contaminated soils treated with the different mixtures of oxalate and ascorbic acid (microcosm tests). Values are the means of three replicates and the error bars show the standard deviations. Values with different letters are significantly different at the 5% probability level (Tukey’s test).  
Figure 2: (**a**) Germination index (_GI_%) and (**b**) root elongation inhibition (_Init%_) for _Z. mays_ and _C. sativa_ grown on soil control (CT), As-contaminated soil and As-contaminated soil treated with a 0.2 M KHC2O4 + 0.01 M C6H8O6 solution (O). Values are the means of three replicates and the error bars show the standard deviations. Values with different letters are significantly different at the 5% probability level (Tukey’s test).  
was not used. Although it is difficult to predict whether this effect will be replicable in the field, it must be said that this is a consequence of the dissolution of a certain amount of iron oxides/hydroxides, since the extractability of arsenic with these mobilizing agents was accompanied by a greater solubilization of iron, as shown in Table 1.  
The effects of the treatments on the concentration of arsenic in plants were also highlighted by the values for total removal (obtained by multiplying the concentration of arsenic by the biomass produced); the results obtained are shown in Figure 4.  
The arsenic adsorption data confirm what has already been said regarding the data in Table 2. Furthermore, at the microcosm level, the corn plants seemed to possess a slightly higher As-extraction capacity than the hemp plants.
## 2 Materials and Methods  
### Study Area  
Pakistan spans over 803,940 km\({}^{2}\) in area and is located in South Asia between 23-37\({}^{\circ}\)N latitude and 60-78\({}^{\circ}\)E longitude (Figure 1) [22]. It contains a diverse range of geographical features, from glacier-rich mountains in the north to arid plains in the middle and deserts in the southern region [23]. The climatic conditions also vary significantly across Pakistan. Northern areas like Gilgit-Baltistan (GB) experience severe winters with significant snowfall, while the southern plains, particularly in Sindh, endure hot and dry conditions most of the year [24]. There are four climatic seasons in the country, namely, spring, autumn, summer, and winter. The temperature is very hot in summer, winter is cold, and autumn and spring are moderate [25]. Based on rainfall variation throughout the year, [PERSON] et al. [17] and [PERSON] and [PERSON] [26] defined April to June as pre-monsoon season, July to September as monsoon season, October to November as post-monsoon season, and December to March as winter season. Monsoon season contributes 45% of the total annual precipitation in summer [27], whereas nearly 30% of rainfall happens in the winter season due to westerly disturbances in the Mediterranean Sea [28; 29; 30]. Along with the seasonal diversity, the annual rainfall varies significantly with topographic variations [25].  
In this study, the performance of global precipitation products was evaluated at the provincial scale initially. Later, to understand the influence of elevation on the datasets' precipitation estimation, the study area was categorized into four regions based on the elevation. The northern high mountainous area was considered region I. This region is also called a glacial region, as it contains more than 7000 glaciers, and most of the region is covered by snow in the winter season. The elevation of this area ranges between 3000 and 8000 m [17]. Under this region, the GB province and the extreme northern part of KPK province fall. Due to the significant variation in the amount of rainfall, the area under 500-3000 m elevation was divided into two regions. The sub-northern area, also called the sub-mountainous area, was considered region II, while the western part of Pakistan with the same elevation range (500-3000 m) was considered region III. The main difference between the two regions is that region II is under a monsoon area, where it receives significantly higher precipitation than the western high-elevation region (region III) [31]. The area with 0-500 m elevation was considered region IV. The precipitation products were validated against ground stations located in the Punjab and Sindh provincesand the coastal area of Balochistan to elucidate their performance in the low-elevation region of Pakistan. The number of available ground observation stations for each defined region can be observed in Figure 1. [PERSON] et al. [32] also used a similar elevation zone to validate GPPs' performance; however, the western mountains and sub-northern mountains were merged to make a single zone in their study.
## 3 Network Loss Function
### Reflectance Denoising Module Loss Function  
Low-illumination coal-rock images captured in underground environments typically contain significant noise. As a result, the reflectance maps decomposed from these images must undergo denoising to align with the cleaner reflectance maps derived from high-illumination images. Therefore, the high-illumination reflectance maps are treated as ground truth for the design of the loss function. After denoising, the output reflectance map should preserve the overall structure and remain as consistent as possible with the high-illumination reference in terms of both visual and structural characteristics. Accordingly, the loss function for the reflectance denoising network is composed of two terms: one to enforce structural consistency and another to ensure image-level similarity. The corresponding loss function is defined as follows:  
\[l_{RR}=\left\|\hat{R}-R_{h}\right\|_{2}^{2}+\left\|\
abla\hat{R}-\
abla R_{h} \right\|_{2}^{2}-SSIM\big{(}\hat{R},R_{h}\big{)} \tag{11}\]
## 1 Introduction
Through laboratory tests performed on damaged concrete samples, the benefits of adopting this retrofitting approach were assessed. Specifically, it was proven that the post-tensioning features of these elements can play a decisive role in increasing the bending and shear capacity of concrete beams and columns. Additionally, experimental outcomes reveal that this approach allows the satisfaction of the confinement requirements provided by the Eurocode 8 [25]. In 2010, [PERSON] et al. [22] examined some specimens under compressive load where PTMSs were installed in both active and passive ways. By comparing the results obtained from the experiments and strain-stress diagrams, the active installation exhibited a higher capacity than the passive one. In 2012, [PERSON] et al. [23] investigated a two-story concrete building under different seismic accelerations (i.e., from 0.05 g to 0.35 g) by simulating the effect of the earthquake by means of a shaking table. Strain-gauges were adopted to measure the strain of specific target points until failure. In this study, PTMSs are selected as a strengthening system for concrete joints and their effect and performance are assessed by varying geometric and mechanical parameters. Despite the fact that the well-known structural efficiency of these retrofitting approaches is clearly acknowledged in the scientific community, few authors focused on the identification of the optimal parameter that controls the final performance of both approaches. Additionally, the real novelty of the current research resides in the characterization of a retrofitting system in which both approaches are considered simultaneously. In this article, the behavior of the composite joints has been deeply investigated through the creation of a model in ABAQUS (rel. 2022.0) and validated by comparison with experimental tests. A first preliminary sensitivity analysis has been conducted, aiming to verify the correct sizing of the concrete joint and the dependency of the global behavior with respect to concrete class. In the second stage, post-tensioned metal straps have been added to evaluate eventual benefits and identify changes in the global behavior of the joint. The reliability of the results has been validated towards comparison with well-known refined models pointed out by other authors. The paper's structure unfolds as follows. It starts with a brief introduction outlining the specific joint typology addressed in this study. Additionally, details of the case study are introduced. Subsequently, a comprehensive state-of-the-art composite bridge, in which concrete joints are adopted as retrofitting solutions, is described. Then, the methodology adopted for the modelling phase and the results obtained by the sensitivity analysis are presented. Finally, an overview of the results obtained is pointed out and final outcomes and future developments of the research are discussed.
## 3 System Validation
### System Calibration  
Since the sources of error are constant in nature, we opted to calibrate the algorithm in order to eliminate this drift. To achieve this compensation, a constant, \(K_{compensation}\), was introduced into the algorithm in order to adjust the horizontal movement. This factor is calculated by averaging the ratio between the actual length of every pure horizontal movement and its theoretical length with the following expression:  
\[K_{compensation}=\frac{1}{N}\cdot\sum_{i=1}^{N}\biggl{(}\frac{\textit{real length of the i horizontal movement}}{\textit{theoretical length of the i horizontal movement}}\biggr{)} \tag{13}\]  
where \(N\) represents the number of pure horizontal movements performed in the path. The results of this compensation are shown in the right illustration of Figure 7, where it can be seen that the drift present in the left of Figure 7 is no longer present. This solution will be further refined in the future integration of the algorithm into a mobile robot, which will finally demonstrate its validity in real-world applications. Using a faster camera with a global shutter would also greatly mitigate this issue, but we did not have one at hand, and it would have made the experimental system more costly.  
Figure 6: Employed patterns: Aruco (**left**) and tile pattern (**right**).  
Figure 7: Path estimated using the VO: without (**left**) and with drift compensation (**right**).
# A New Ice Accretion Model for Aircraft Icing Based on Phase-Field Method
## 2 Materials and Methods
### Two-Dimensional Form of Governing Equation
The ice shape provided in Figure 8d is a typical glaze ice. It can be seen that the simulation accuracy of the ice shape from LEWICE is relatively poor, and the shapes of the upper and lower surfaces are very different from the experimental values. Meanwhile,the ice shape predicted by the phase-field model is more similar to the experimental ice shape than the other icing code. Figure 8: Comparison of the ice shapes under different verification cases. Figure 7: Distribution of water film thickness on the surface. As shown in Table 2, the deviation of stagnation thickness provided by the phase-field model and the experiment is smaller than that by LEWICE. The above comparison verifies the effectiveness and accuracy of the phase-field icing model under different temperatures. Then, the accuracy and effectiveness of the phase-field icing model under other icing weather conditions are verified, including different incoming flow velocity, different LWC and different MVD. Figure 9 show the comparison of ice shapes from the experiment, phase-field model and LEWICE under different icing conditions. Table 3 shows that the deviation of stagnation thickness provided by the phase-field model and the experiment is also smaller than LEWICE. This set of data and figures demonstrate that the ice shapes from the phase-field model are closer to the experimental shapes than those from LEWICE. From the simulation of the above eight states, it can be seen that the phase-field icing model proposed in this paper can simulate the icing shape under various conditions, and \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} & \multicolumn{2}{c}{**Case 3**} & \multicolumn{2}{c}{**Case 4**} \\ \hline Experiment & 0.02518 & & 0.0155 & & 0.0223 & & 0.01319 & \\ Phase field & 0.02717 & 7.9\% & 0.01579 & 1.9\% & 0.02493 & 11.8\% & 0.01246 & \(-\)5.5\% \\ LEWICE & 0.03148 & 24.8\% & 0.02092 & 35.0\% & 0.03419 & 53.3\% & 0.01813 & 37.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stagnation thickness of ice shapes provided by different icing codes and experiment. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Case 5**} & \multicolumn{2}{c}{**Case 6**} & \multicolumn{2}{c}{**Case 7**} & \multicolumn{2}{c}{**Case 8**} \\ \hline Experiment & 0.03571 & & 0.255 & & 0.02256 & & 0.01667 & \\ Phase field & 0.02501 & \(-\)30.0\% & 0.02632 & 3.2\% & 0.02679 & 18.8\% & 0.01536 & \(-\)7.9\% \\ LEWICE & 0.02302 & \(-\)35.5\% & 0.02709 & 6.2\% & 0.01402 & \(-\)37.9\% & 0.0125 & \(-\)25.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stagnation thickness of ice shapes provided from Case 5 to Case 8. Figure 9: Comparison of the ice shapes in verification cases from Case 5 to Case 8.  
the simulation results are closer to the experimental values, which better demonstrate the correctness of the model.