esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
## 5 Conclusions  
The recent developments on big data allow carrying out satellite-based continuous land cover monitoring over huge areas using high temporal resolution data. Such monitoring systems can give insights into the study of forest deforestation and degradation, agriculture intensification and vegetation alterations in response to climatic change. Due to the tremendous amount of information, monitoring systems are increasingly based on automatized algorithms and indices computing. Therefore, particular attention has to be played concerning the accuracy of the obtained products and the propagation of error.  
In this study, we show that the availability of intra-annual satellite observations varies significantly over space and that the scarcity of valid observations during a part of the year has an adverse effect on the certainty of the estimate of indices such as phenological metrics. However, the minimum number of valid observations needed to adequately capture the phenological curve is not absolute and depends on the timing (temporal pattern) of the cloud-free observations and the type of vegetation. Therefore, the modelling approach proposed in this paper enables the evaluation of the uncertainty of each availability temporal pattern. Further research has to be done in order to determine the most robust methods and metrics using this modelling approach.  
Additionally, the combination of Landsat data with satellite constellations such as Gaofen, PlanetScope and Sentinel will improve the frequency of the observations and permit to overcome many of these limitations.  
Phenological metrics derived from remotely sensed data must be interpreted with caution, in particular when the number of observations is limited. The use of situ sensors and phenocams and the knowledge of the plant species' phenological behaviour obtained from field and laboratory studies can be critical to carry out this task. The evaluation procedure proposed in this study can provide guidance for further evaluation of the certainty of any metrics using data sets from other sensors.  
Conceptualization, methodology, script writing and processing, J.-F.M.; Results analysis, writing, review and editing, J.-F.M. and F.S.d.A. All authors have read and agreed to the published version of the manuscript.  
The first author thanks the _Programa de Apoyos para la Superacion del Personal Academico_ (Direccion General de Asuntos del Personal Academico, Universidad Nacional Autonoma de Mexico) for financial support for a sabbatical semestre at the Federal University of Ceara. This research was supported by the Project PAPIME PE117519 _Hernimentas para la enselanza de la Geondtica con programas de codigo abierto_ (Direccion General de Asuntos del Personal Academico, Universidad Nacional Autonoma de Mexico).
# Simulation-Oriented Analysis and Modeling of
## 6 Micro-Simulation Implementation
### Impact of Distraction on Traffic Flow  
#### 6.2.1 Impact of Distraction Fractions on Traffic Flow  
Figure 7 compares the differences in the confidence intervals of drivers' speeds under different distraction fractions. When the confidence intervals between two groups of data do not overlap, it indicates that there is a significant difference between these two groups of data. It is observed that on both expressways and surface roads, the average speed significantly decreases when the fraction of distraction increases from 15% to 25%. This decrease can be attributed to the disruption of traffic flow stability as the distraction fraction increases, compelling drivers to adopt slower speeds in response to hazardous scenarios induced by distractions (there are other factors that may also contribute, but it is the effect of distractions highlighted here). Furthermore, it is also evident that the speeds on surface roads are significantly lower than those on expressways, which aligns with real-world driving scenarios.  
As shown in Figure 7(a), on expressways with high traffic volume, the average speed decreases and the COV of speed increases as the fraction of distracted driving rises. This indicates that an increase in the fraction of distracted drivers significantly reduces overall traffic flow efficiency and safety. Similarly, Figure 7(b), demonstrate that distraction significantly lowers traffic flow efficiency and safety. Moreover, by comparing Figure 7(a),b, it can be observed that there is a broader distribution of drivers' average speeds on expressways with lower traffic volume. This is because drivers have more freedom in a less congested environment, leading to a wider range of speed choices. Figure 7(c) reveals that on surface roads, the complexity of the road environment prompts drivers to adopt a more conserva  
Figure 7: Significance analysis of the impact of distraction fraction on traffic flow.  
tive driving style. Furthermore, because surface roads often have sections where vehicles and non-motorized traffic mix together, drivers' speeds are significantly lower than those on expressways.
## 7 Conclusions  
The automated 3D reconstruction system proposed in this study significantly advances AH practices by shifting from non-automated, scanning-based virtual reconstruction to AI-driven methodologies, a core contribution. A primary strength lies in its novel \"photo-to-BIM\" pipeline, which replaces conventional LiDAR-generated point cloud inputs with more accessible video and photographic data that can be captured by ubiquitous devices like smartphones or drones, thereby mitigating issues of high cost, complex operation, and significant noise inherent in traditional methods. This approach integrates SOTA ML models: GS and its noise-reducing variant Mip-splatting for RFR, followed by SuGaR for efficient textured mesh extraction. The research specifically highlights Mip-splatting's superior capability in reducing excess points or noise compared to the original GS, which is crucial for a cleaner subsequent point cloud-to-mesh process, even though some noise persisted. Furthermore, the combined use of Mip-splatting and SuGaR demonstrates viability for outdoor 3D reconstruction of AH, outperforming certain other methods in specific metrics. Another significant strength is the creation of a valuable, reusable synthetic dataset of 60 Romanesque-Mudejar churches, comprising 32,400 render images, which supports non-invasive research and model training while overcoming challenges associated with real-world data acquisition and direct intervention. By automating geometric information generation and bypassing manual CAD modeling, the system lightens professionals' cognitive load, reduces modeling errors, shortens completion times, lowers costs, and minimizes on-site resource expenditure. This comprehensive approach thus represents a crucial initial step towards a full Scan-to-BIM/HBIM pipeline for AH, bridging the gap between human-driven CAD and AI-automated virtual reconstruction.
# Generalized Paxos Made Byzantine
## 4 Crash Fault Tolerant Protocol
### Agreement Protocol  
The consensus protocol allows learner processes to agree on equivalent sequences of commands (according to our previous definition of equivalence). An important conceptual distinction between the Fast Paxos protocol and our simplified Generalized Paxos is that, in Fast Paxos [12], each instance of consensus is called a ballot and agrees upon a single value, whereas in our protocol, much like the original Generalized Paxos, instead of being separate instances of consensus, ballots correspond to an extension to the sequence of learned commands of a single ongoing consensus instance. In both protocols, ballots can either be _classic_ or _fast_.  
In classic ballots, a leader proposes a single sequence of commands, such that it can be appended to the commands learned by the learners. A classic ballot in Generalized Paxos follows a protocol that is very similar to the one used by classic Paxos [2] (cf. Algorithms 1 and 2). This protocol comprises a first phase where each acceptor conveys to the leader the sequences it has voted for. This allows the protocol to preserve safety and also allows leader to resend unlearned commands. This is followed by a second phase where the leader picks an extension to the sequence of commands relayed in _phase 1b_ messages and broadcasts it to the acceptors. The acceptors send their votes to the learners, who then, after gathering enough support for a given extension to the current sequence, append the new commands to their own sequences of learned commands and discard the already learned ones.  
```
1:\(ballot\_type=\bot,ballot=0\)
2:upon receive(\(BALLOT,bal,type\)) do
3:\(ballot=bal\)
4:\(ballot\_type=type\)
5:upon command_request(\(c\)) do # receive request from application
6:if\(ballot\_type=fast\_ballot\)then
7:send(\(P2A\_FAST,ballot,c\)) to acceptors
8:else
9:send(\(PROPOSE,c\)) to leader
```  
**Algorithm 1** Generalized Paxos--Proposer p  
```
1:functionmerge_sequences(\(old\_seq,new\_seq\))
2:for\(c\)in\(new\_seq\)do
3:if!contains(\(old\_seq,c\)) then
4:\(old\_seq=old\_seq\bullet c\)
5:return\(old\_seq\)
6:endfunction
```  
**Algorithm 2** Generalized Paxos--Process p  
In fast ballots, multiple proposers can concurrently propose either single commands or sequences of commands by sending them directly to the acceptors (we use the term _proposal_ to denote either the command or sequence of commands that was proposed). In this case, concurrency implies that acceptors may receive proposals in a different order. If the resulting sequences are equivalent, then they are successfully learned in two message delays. If not, the protocol must fall back to using a classic ballot.  
Next, we present the protocol for each type of ballot in detail.
## 3 Results
### Statistical Analysis
#### 3.2.3 Evaluation of the Models Used
In particular, for this last set of articles, the mode was to use around 70% of the data to train the model, considered in six of them. The remaining articles [37,58,59] did not specify the methodology used. To evaluate the results obtained by the selected articles, the accuracy metrics were considered and used in 90.70% of the articles, followed by sensitivity, used in 69.77% of the studies, and specificity, used in 46.51% of the articles considered in Table 3. For the 31 articles that covered the diagnostic problem of PD, it was found that the models with at least two metrics greater than 90% were: refs. [51,64,66,70] using ANN with values over 97% for sensitivity and precision, refs. [38,39,49] considering CNN with values over 99% for accuracy, precision and sensitivity ([38,39] were based on the same study), refs. [29,30] using CNN + RNN with values over 93% in accuracy, sensitivity, and precision, refs. [67,71] utilizing SVM with accuracy and sensitivity values over 93%, and [44] using DFA with accuracy, specificity and sensitivity values over 94%. For these articles, the validation method carried out was evaluated to obtain information about the ability of the model to generalize the result in blind tests. It was found that the predominant method was cross-validation for 9 of the 12 articles, whereas [29,49,64] split the data into training, validation, and test. The models with the best results in the problem of classifying patients with PD and controls were CNN, and the group of ANN models, found in articles [38,39,51,59,64,66,70]. Figure 3: (**A**) models and the number of times they are considered in the articles, indicating their belonging to either the symbolic or subsymbolic processing group. (**B**) Pie chart of the types used, specifying the non-neural ML techniques and those belonging to the ANN subgroup.
## 4 Conclusions, Limitations, Future Directions  
### Conclusions  
This study proposed a hybrid AI-based framework for accurate thermal load prediction in Low-Energy Buildings (LEBs) across three distinct Moroccan climates. Thirteen machine learning models were initially evaluated using a comprehensive set of input features, revealing that while XGBoost demonstrated superior performance during training, SVM emerged as the most robust model during the testing phase--especially for cold and semi-arid climates. In the Mediterranean climate, the ELM model slightly outperformed SVM in heating load prediction.  
Building upon these results, an Integral Feature Selection (IFS) approach was employed in conjunction with the top-performing models to identify the most influential predictor combinations. This not only enhanced the accuracy of thermal load prediction, but also contributed to reducing model complexity.  
The findings demonstrate the potential of integrating advanced ML models with feature selection techniques to support energy-efficient building design and operation. The proposed framework outperforms existing methods and offers a scalable, climate-adaptive solution for optimizing heating and cooling load predictions in LEBs. Future work will focus on incorporating additional parameters--such as window thermal performance--and extending the approach to real-time prediction scenarios.  
The practical value of this research lies in its ability to provide architects, building engineers, and energy consultants with a robust decision-support framework for optimizing thermal load predictions in LEBs. By identifying the most relevant input features through the IFS method and selecting the best-performing machine learning models tailored to specific climates, our approach supports the early design phase of buildings, where decisions on envelope parameters influence energy performance.  
Additionally, the proposed framework is applicable across diverse climatic zones, as demonstrated in the three representative Moroccan cities. This adaptability enhances its transferability to other regions with similar climate profiles, supporting national and regional energy efficiency goals.
## 4 The Coalition Formulation Game Model
### Performance Analysis  
**Propriety 1**.: _The coalition formation process based on the join-and-leave rule converges to a final stable partition in a finite number of steps._  
Proof.: When the join-and-leave rule operates, it may transform the current coalition partition into another partition. Consequently, there exists a sequence of switch operations, i.e., \(\pi^{(0)}\rightarrow\pi^{(1)}\rightarrow\pi^{(2)}\rightarrow\cdots\). As the number of players, i.e., UAVs, is finite in our proposed game, the coalitions they can form are also finite, and the number of coalition structures is also finite. Therefore, from any \(\pi^{(0)}\), the sequence always terminates to a stable partition \(\pi^{*}\) after finite iterations. Furthermore, the joining and leaving of coalitions can happen only when it leads to utility improvement in the new coalition and coalition structure; no loop appears during the process, meaning that the number of steps is bounded. Since the coalition structure changes only when the utility is increased and there must be a maximum utility for the limited number of coalition structures, the coalition formation process must converge to a final stable partition in a finite number of steps. If \(\pi^{*}\) is not stable, there exists a UAV that can perform the join-and-leave operation. According to the Pareto order, an arbitrary UAV switches from the current coalition to another only when its utility increases without hurting the utilities of other coalition members in both the original and new coalition. Then, the arbitrary UAV does switch its coalition to pursue higher utility. Hence, \(\pi^{*}\) is not the final stable partition, which contradicts the previous argument. Consequently, the final state must be stable. Therefore, Propriety 1 is proven.  
**Propriety 2**.: _The complexity order of the proposed Algorithm 1 is \(O(N*D_{N})\)._  
Proof.: The complexity of Algorithm 1 lies in the complexity of the join-and-leave operations. For a given multi-UAV network, there are \(D_{N}\) different coalition structures for \(N\) UAVs. In this algorithm, during each time step \(t\), every UAV compares its current individual payoff with the corresponding payoff after it joins to a new coalition. Then, the total number of comparisons required to converge to a final stable partition is \(O(N*D_{N})\), as the maximum comparison number for every UAV is \(D_{N}-1\). However, the complexity of the algorithm can be significantly reduced by leveraging the historical information set, preventing nodes from joining or leaving the same coalition more than once.
# Highway Microgrid Project Evaluation under Energy Transportation Integration
## 4 Case Study
### Evaluation Result Analysis
#### 4.3.2 Comparison of Microgrid Planning Schemes  
The results are calculated by using the grey correlation degree, as shown in Figure 3. According to the analysis of Figure 3, the comprehensive evaluation result of Scheme 1 is 0.9127, Scheme 2 is 0.7891, and Scheme 3 is 0.8264. Among the three schemes, the evaluation result of Scheme 1 is the best, indicating that the benefit of Scheme 1 is at a relatively high level, and the integrated operation of \"Source-Network-Load-Storage\" of the highway microgrid is beneficial to the benefit evaluation of microgrid projects. The evaluation result of Scheme 3 is second only to Scheme 1, indicating that although the charging system will increase the project investment cost, due to its role in promoting the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **NPV** & **IRR** & **Energy Use** & **Power Supply** & **Carbon Emission** & **Emission Reduction** \\  & & & **Efficiency** & **Reliability** & **Reduction** & **Benefit** \\ \hline AHP & 0.2814 & 0.1533 & 0.0911 & 0.25 & 0.1143 & 0.1099 \\ Entropy weight & 0.1929 & 0.1465 & 0.1643 & 0.1561 & 0.2061 & 0.1641 \\ AHP-Entropy weight & 0.2562 & 0.1638 & 0.1023 & 0.2077 & 0.1215 & 0.1485 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weights determined by three methods.  
local consumption of new energy, it can achieve friendly interaction with the microgrid under the further improvement of the market mechanism, which is beneficial to the overall benefit evaluation of the highway microgrid project. Scheme 2 ranked last, illustrating the importance of distributed PV for highway microgrid projects, because distributed PV uses solar power generation, lower operating costs than other schemes, and less primary energy consumption, and the PV system produces fewer pollutants, with good environmental benefits. It can be concluded that the highway microgrid project combined with \"Source-Network-Load-Storage\" considers the cost and investment income of the microgrid project scheme comprehensively. And in terms of energy supply, comprehensive utilization of energy and maximum environmental benefits are achieved within the expected parameters.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 1 Introduction
### Limitations of Classical Assumptions  
Classical queueing abstractions--most notably the \(M/G/k\) model--underpin much of the early literature on GPU and cluster scheduling. However, these models typically assume light-tailed service time distributions, often exponential. In contrast, empirical traces from modern deep learning (DL) clusters reveal markedly heavier tails:  
* Alibaba GPU Trace (2021): the 90 th percentile job duration is 3.5\(\times\) the mean;
* Google Borg Trace (2019): the 90 th percentile job duration is approximately 3\(\times\) the mean.  
This discrepancy is consequential: schedulers built on light-tailed assumptions systematically under-provision for queue-length spikes and tail-latency outliers. In heavy-tailed regimes, even a few large jobs can monopolize resources for extended periods, significantly delaying moderate-sized jobs--an effect that exponential models fail to capture. Any queueing-theoretic analysis or simulation framework must therefore incorporate heavy-tail parameter estimates to remain predictive at scale.  
To bridge the gap between theoretical models and real-world DL system behavior, we annotate each optimization paradigm in the following sections with a brief _design takeaway_. These highlight where the paradigm's assumptions diverge from production realities--e.g., in memory locality, co-flow bottlenecks, or preemption latencies--and help identify when classical insights may need revision or augmentation.
# Guidelines towards Information-Driven Mobility Management
## 4 Mobility in ICN
### Consumer Mobility
#### 4.5.1 MN as Consumer  
Figure 2 illustrates a scenario where a MN is attached to its original network, the _home network_. A, B, C and D represent routers. As ICN is receiver-oriented, data transmission for this example starts when the consumer entity expresses interest on a specific content, i.e., MN sends an Interest packet \(I1\) with a specific Name Prefix. The Interest packet is stored in the PIT of ICN devices along the path (routers A, C, and D), until it reaches a node that has the requested content, the producer, or a router that already cached the respective content in its _Content Store_ (_CS_). Meanwhile, and while packet I1 is being transmitted, MN starts to move and reattaches to router B, that serves a _Visited Network_. Based upon ICN principles, once reattachment occurs, MN again sends an Interest packet with the same Name Prefix (I1). When this packet reaches router C, this router already has the content requested stored (D1) and therefore, the forwarding of Interest packet I1 stops. The subsequent data exchange is directly handled between MN and any device that holds content requested by MN.
# Altering the Supply of Shielding Gases to Fabricate Distinct Geometry in GMA Additive Manufacturing
## 2 Results and Discussion
### Oxygen Content  
The oxygen content in the weld was computed using the ASTM E 1019-18 inert gas fusion method. The detection limit of the instrument used was 0.0002% based upon a sample size of 1 g. The precision was 0.0001% based upon the sample size of 1 g. The specimens were extracted from the center of each wall by wire electrical discharge machining. The oxygen content, when using the ternary blend shielding gas, was found to be 0.043 wt. % (430 ppm), while using a binary blend was 0.051 wt. % (510 ppm). There are two potential explanations for this observation. First, the larger amount of CO\({}_{2}\) present in the binary blend could result in a higher oxygen content in the bulk material. Second, the wider bead size produced by the binary mixture correlates to a larger surface area, which may have led to higher oxidation. Oxygen is a surface active element; thus, the presence of oxygen may have some role in increasing the width of the weld-bead in the binary blend. In addition, the ternary blend with hotter gas helium has a larger penetration profile but a narrower bead. To further investigate, a high-fidelity computational fluid dynamics (CFD) model was used to simulate the bead-on-plate model. Although two-bead single layer and multi-layer WAAM models may have different outcomes, in this study, only a single bead, single layer was simulated. The following assumptions were made in the simulation:  
1. The oxygen content remains the same in the molten and solid states.
2. The surface tension changes with the temperature and composition of oxygen, according to Equation (1). Moreover, only the Fe-O system is considered.
3. The physical properties, such as thermal conductivity, specific heat, and viscosity are assumed constant.