esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Model Formulation
### Model Setting
Therefore, the HV and AV commuters may experience different levels of walking time costs after parking and self-driving time costs after dropping off, respectively. Let \(v_{j}\) denote the self-driving time from the workplace to parking cluster \(j\) for AV commuters. Let \(w_{j}\) denote the walking time from the parking cluster \(j\) to the workplace for HV commuters. Additionally, the commuters make decisions based on the parking pricing scheme designed and implemented by the public parking manager. As the central cluster is close to the CBD area, the resulting self-driving time from the workplace to the parking location is lower for AV commuters than parking at the peripheral cluster. Similarly, the commuters bear a lower walking time by parking at the central cluster. The slots within each cluster are homogeneous for commuters, so the distance between any two slots is negligible. In this situation, an HV commuter experiences the same walking time by choosing any slot in a cluster. Similarly, the self-driving time from the workplace to any parking slot within a cluster is the same and constant. Since the central cluster is close to the workplace, it allows commuters to save schedule delay costs and provides parking convenience, which may lead to a high parking demand during peak hours. Hence, we also assume that the average parking fee at the central cluster \(p_{1}\) is higher than that at the peripheral cluster \(p_{2}\). Let \(c_{t,j}^{a}\) denote the generalized travel cost of AV commuters who depart from home at time \(t\) and park their vehicles at location \(j\). Similarly, \(c_{t,j}^{h}\) represents the generalized cost of HV commuters departing from home at time \(t\) and parking at location \(j\). Their generalized cost functions are defined as follows. \[c_{t,j}^{a} = \alpha^{a}T(t)+\beta(t^{*}-t-T(t))+p_{j}+\eta^{a}v_{j} \tag{1}\] \[c_{t,j}^{h} = \alpha^{h}T(t)+\beta(t^{*}-t-T(t)-w_{j})+p_{j}+\eta^{h}w_{j} \tag{2}\]  
where \(\eta^{a}\) represents the unit cost of AV self-driving time after parking and \(\eta^{h}\) represents the value of walking time for HV commuters. By making tradeoffs among travel time cost, schedule delay cost, parking fee, and the cost associated with the parking process, commuters make joint decisions on departure time \(t\) and parking location \(j\) to minimize their generalized travel costs. Table 1 summarizes the notations used in this work. The assumptions adopted in this work are listed here. **Assumption 1**. : \(\beta<\alpha^{a}<\alpha^{h}\)_. It is commonly assumed that AV commuters have a lower VOT compared with the HV commuters e.g., (24). Following [PERSON] (25), we assume \(\alpha^{a}>\beta\) to ensure the existence of equilibrium. **Assumption 2**. : _Late arrivals are not allowed, e.g., (26)._
## 3 Results and Discussion
### Resistance to Chloride Penetration
First, the surface treatment improved the quality of the spacer-concrete interface, judging from the penetration depth on the split surface of the composite specimen. Moreover, the effect of the surface treatment of the spacer to the chloride resistance capacity was partially observed in either the diffusion coefficient \(D_{RCM}\) or the column electric flux, as the poor quality at the interface would result in the outlier data that were excluded in the data process. Additionally, for the evaluation of chloride resistance of the composite specimen in engineering practice, this study recommends to note and report the maximum chloride ion penetration depth at the spacer-concrete interface as an additional indicator to the diffusion coefficient  
Figure 7: Chloride ion penetration depths of SS: (**a**) a picture of the split surface of the specimen without surface treatment where a significant defect at the spacer–concrete interface occurs; (**b**) a picture of the split surface of the specimen with surface treatment; (**c**) a schematic plot of the split surface of the specimen without surface treatment where a significant defect at the spacer–concrete interface occurs; and (**d**) a schematic plot of the split surface of the specimen with surface treatment. \(D_{RCM}\). If the maximum penetration depth at the interface is higher than the average penetration depth for a certain critical percentage, the spacer-concrete interface can be identified as a potential penetration path of an aggressive agent and the spacer would require surface treatment to improve the quality of the spacer-concrete interface and ultimately, the durability of the overall structure. Among the international standards of the testing of the resistance of concrete to chloride ion, the precision of testing and the identification of outliers vary. The Chinese standard GB/T 50082-2009 [28] has specified that the individual test determination that is more than \(\pm 15\%\) of the median diffusion coefficients or coulomb electric flux is an outlier. As per NT BUILD 492 [29] and ASTM C1202 [30], the coefficient of variation of the corresponding chloride resistance testing varies from approximately 10% to 30%. As a rule of thumb, the critical percentage was recommended as 30% (twice as the 15% tolerance per China code) taking into account the precision and practicality of this testing method.
# Robustness of Cyber-Physical Supply Networks in Cascading Failures
## 4 Modeling Cascading Failures in Cyber-Physical Supply Networks  
### Cascading Failure Model  
In the cyber-physical supply networks, random failures are high impact, low probability events, whereas targeted failures are low impact, high probability events [34]. Targeted failures can be reduced by controlling associated risks such as forecast inaccuracy, quality, and production system breakdown. However, random failures are very hard to control because they are triggered by uncontrollable factors [35]. Therefore, this study concentrated on the random failures caused by nature, the political system, and available capacity. If a node \(v_{i}^{p}\) in the physical supply network fails, it will influence its neighboring nodes and the dependent nodes in the cyber supply network, which may cause further failure to the physical supply network. The dependency links between the interdependent network play the role of connection and do not receive the redistributed load of the failed node, so the redistribution of load flows only on the intralayer network.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
## 2 Materials and Methods  
The AquaCrop model was utilized to evaluate and forecast crop yields for winter wheat, maize, winter rapeseed, and sugar beets in the central-west region of Poland (Figure 1). Files necessary for each crop type were parameterized, calibrated, and validated using ERA-5 climate data, in-situ soil, and plant phenology data [13]. Following the calibration and validation phase, in-situ Leaf Area Index (LAI) measurements obtained using the LAI-2200C Plant Canopy Analyzer and satellite Sentinel-2 Normalized Difference Vegetation Index (NDVI)-based simulations were conducted for the four different crop types between 2018 and 2023. As part of this extensive study, we rigorously tested the AquaCrop model over six years, from 2018 to 2023, monitoring agrometeorological conditions from sowing to the end of the vegetative season. This finely tuned model, based on six years of data, will be employed to forecast yields in the forthcoming years and will undergo further validation by end-users. Various statistical analyses, including on metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE), were utilized to evaluate the accuracy of the obtained yield values and simulation results.
## 3 Results
### Model Verification
In scenario C, with a 45\({}^{\circ}\) tilt, the numerical model averages 6.14 \(\pm\) 1.07 Nm, while the comparative model provides an average of 6.81 \(\pm\) 0.78 Nm, indicating a 9.70% difference. The overall average difference across the three scenarios reveals that the numerical model consistently estimatesneck moments about 9.53% lower than the comparative model. This difference between scenarios does not follow a linear pattern and does not consistently increase, indicating an average variation lower than 10%. It is important to acknowledge that differences in anthropometric data among study samples can lead to variations beyond those caused by calculation precision or methodological choices. After a thorough evaluation of all primary calculations with published experimental data, the model is considered to have up to a 10% error margin.
# Blockchain-Empowered Fair Computational Resource Sharing System in the D2D Network +
## 5 Problem Formulation
### Selfishness Avoidance  
As derived in Section 4.5, the balance of each user is updated as represented in (7). When computing cooperative task assignment for the D2D network at each task period, the supernode needs to make sure that each requester has enough balance for the task period according to the task assignment matrix \(M\) in that period \(\Psi\). Since the exact amount of \(R_{k}^{\Psi}\) and \(H_{k}^{\Psi}\) for user \(u_{k}\) is not known at the beginning of task period \(\Psi\), the supernode ideally needs to make sure that:  
\[\Pr(B_{k}^{\Psi}-\alpha R_{k}^{\Psi}+\beta H_{k}^{\Psi}+\eta_{k}^{\Psi}\geq 0) \geq\xi,\forall k\in\{1,2,...,u\},\Psi\geq 1. \tag{12}\]  
where \(\xi\to 1\). However, though the exact amount of \(R_{k}^{\Psi}\) and \(H_{k}^{\Psi}\) for user \(u_{k}\) is not known at the beginning of task period \(\Psi\), their expected value, namely \(R_{k}^{\Psi,exp}\) and \(H_{k}^{\Psi,exp}\), can easily be obtained in advance from \(M^{exp}\) in that period \(\Psi\):  
\[R_{k}^{\Psi,exp}=\sum_{l=1}^{\phi_{k}/t}\sum_{j=1}^{h}M_{k,j,t}^{\Psi,exp}, \tag{13}\]  
and:  
\[H_{k}^{\Psi,exp}=\sum_{i=1}^{p}\sum_{t=1}^{\phi_{i}/t}M_{i,k,t}^{\Psi,exp}. \tag{14}\]  
For simplicity, we will relax the constraint (12) to the following:  
\[B_{k}^{\Psi}-\alpha R_{k}^{\Psi,exp}+\beta H_{k}^{\Psi,exp}+\eta_{k}^{\Psi}\geq 0,\ \forall k\in\{1,2,...,u\},\Psi\geq 1. \tag{15}\]  
This way, user connectivity has been taken into account, and more cooperation is expected to be done, while leaving the possibility that a user's balance becomes lower than zero after task period \(\Psi\) such that the user will need to earn back enough credit before asking for more help.
## 2 FANET Architecture and Routing Protocol
### Routing Protocols
#### 2.2.8 DTN-Based  
The delay-tolerant network (DTN) is a network architecture for communication in the absence of stable end-to-end connections, mainly for sparse networks. It is characterized by unstable connections between nodes, frequent link disruptions, high transmission delays, and low communication bandwidth. Existing DTN-based routing protocols can be divided into three main types: deterministic, stochastic, and weighted graph.  
For sparse networks where the behavior of communicating nodes can be easily predicted, [PERSON] et al. [63] propose fountain-code-based greedy queueing and a location-assisted routing protocol to reduce transmission delay. For unpredictable topological network changes, [PERSON] et al. [64] designed a routing protocol that can adapt to stochastic dynamic networks using a fuzzy-inference-system-assisted swarm intelligence algorithm based on the DTN architecture. For scenarios where UAVs conduct urban patrols with similarly defined routes, it is easy to predict the location of each communication node because the movement route is predetermined. In this case, a weighted graph is designed, where the closer the two UAVs are, the greater the weight. The nodes for each hop are then selected using the probabilities estimated in the previous step. TENSR [65] is a typical case.  
Improving message delivery rates and decreasing network cost ratios often come at the expense of increased transmission delays in existing schemes. Therefore, it is crucial to explore ways to enhance the performance of UAVs in DTNs by integrating the deployment, trajectory optimization, energy usage, and storage allocation of UAV nodes.
## 3 Results and Discussion
### Biogas and Innovative Biogas  
Due to the presence of carbon dioxide in the biogas, more energy is required to ignite the combustion. Consequently, the initial temperature of CAI engine must be higher with biogas 1, made of methane and carbon dioxide, than with pure methane, Figure 8, and the temperature range assuring auto-ignition reduces. In these conditions, impe is smaller than that obtained fuelling the engine with pure methane at the same equivalence ratio. However, the engine fuelled with biogas 1 can be adjusted to obtain the same impe as that reached with pure methane with ER = 0.4, by varying the equivalence ratio. It is found that with ER = 0.42, with an initial temperature of 478 K, the CAI engine fuelled with biogas1 delivers the same maximum impe as that with methane, Figure 8, and the associated indicated engine efficiency results of 47.1%.  
As for pure methane, adding hydrogen to biogas makes the air-fuel mixture more reactive, and consequently, auto-ignition starts at lower temperatures. In biogas 2, a mole of hydrogen is produced together with 9 moles of methane, and carbon dioxide will accordingly increase, remembering that in the digestion process, the CO\({}_{2}\)/H\({}_{2}\) ratio is 1, while CO\({}_{2}\)/CH\({}_{4}\) is 2. Thus, in biogas 2, two opposite effects on the auto-ignition face, the decrease of the required initial gas temperature induced by hydrogen and its increase induced by carbon dioxide. We find that CAI engine fuelled with the innovative biogas 2 can be phased to obtain the same maximum impe, as with pure methane, keeping ER equal to 0.4, by simply reducing the initial gas temperature to 457 K, Figure 9. The same can be done by increasing the hydrogen content to obtain a CH\({}_{4}\)/H\({}_{2}\) ratio equal to 8:2 in biogas 3, and the maximum impe value is reached with an initial gas temperature of 453 K and ER = 0.4. Thus, the effect of hydrogen on the auto-ignition prevails on that of carbon dioxide in the two innovative biogas considered; consequently, as already observed for methane-hydrogen blends, hydrogen in the biogas has the potential of shifting the initial temperature to lower values, and the more hydrogen, the lower the initial temperature.  
As for the case of pure methane, the indicated efficiency of the CAI engine fuelled with biogas is almost constant when the optimal operational conditions are found, and for the sake of brevity, we do not report its graph.  
Figure 8: Effect of initial gas temperature on impe (methane and Biogas 1). The best operating conditions are circled.  
Figure 10 shows the pressure traces for all biogases at optimal operating conditions. It can be observed that the maximum pressure values and peak pressure positions are similar for the considered fuels. The higher the hydrogen content, the lower the initial gas temperature required for auto-ignition. A lower equivalence ratio is required for the target imp when the engine is fuelled with innovative biogas that are mixtures of methane, hydrogen, and carbon dioxide, with hydrogen produced together with methane in the digestion process.
# Intelligent Transaction Scheduling to Enhance Concurrency in High-Contention Workloads
## 3 Preliminaries
### Reinforcement Learning  
Reinforcement learning is a practical approach for adaptive decision making in dynamic environments. In this framework, an agent iteratively refines its actions based on feedback from the system state. This learning paradigm is typically modeled as a Markov Decision Process, represented by the tuple \((\mathcal{S},\mathcal{A},\mathcal{P},\mathcal{R},\mathcal{S}^{\prime})\).  
The agent interacts with the environment at discrete time steps. At each step \(t\), the agent observes a state \(s^{t}\) from the state space \(\mathcal{S}\) and selects an action \(a^{t}\) from the action space \(\mathcal{A}\). After executing the action, the environment provides a reward \(r^{t}\), and the agent transitions to a new state \(s^{t+1}\) according to the transition probability distribution \(\mathcal{P}\).  
The process of selecting actions is guided by a policy \(\pi(a|s)\), which specifies the probability of choosing an action \(a\) in a given state \(s\). The objective of the learning-based optimization process is to derive an optimal policy \(\pi\) that maximizes the expected cumulative reward:  
\[R(\pi)=\mathbb{E}_{\pi}\left[\sum_{t}\gamma^{t}r_{t}\right] \tag{3}\]  
where \(\gamma\in[0,1]\) is the discount factor that controls the importance of future rewards.
## 4 Discussion
### Snow Drought and Snowmelt Flood
#### 4.3.2 Snowmelt Flooding  
Climate change and increasing economic losses have led to growing global concerns regarding flooding [72; 73; 74; 75; 76; 77]. The shift from snowwall to rainfall and changes in the timing of snow accumulation and melt under a warmer climate have caused snowmelt floods to possess new characteristics [78; 79; 15; 53], such as fewer ROS floods at lower elevations and more ROS floods at the higher elevations across western North America [10]. Both the frequency and intensity of snowmelt floods across the SAM tended to increase between the 1950s and 2000s [69; 70]. However, the impacts of increased snow cover, shrinking river ice, and increased reservoirs on extreme hydrological processes in the SAM require further assessment. Therefore, the National Key Research and Development Program of China approved in 2019 'Research on key technologies for monitoring, forecasting and prevention of snowmelt flooding in arid areas', to develop a comprehensive strategy to mitigate and prevent snowmelt flooding in the SAM.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 2 TCS Design and Requirements
### Relevant Thermal Parameters of TCS  
The relevant thermal parameters of TCS, including type of orbit, satellite attitude during orbit, type of coatings and thermo-optical properties, element and thermal conductivity materials, thermal contact conductance coefficient between elements, equivalent thermal conductivity of multilayer heat insulation assembly (MLI), heat conductivity grease and heat conductivity membrane, etc. are listed in Tables 1-5.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Subjects** & **Parameters** \\ \hline Orbit height & 645 km \\ Orbit eccentricity & 0 \\ Orbit inclination & 98.04\({}^{\circ}\) \\ Orbit period & 5855.15 sec \\ Local time of descending node & 10:30 am \\ \hline \hline \end{tabular}
\end{table}
Table 1: Orbit parameters of satellite in general.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Flight Mode** & **Attitude** & **Working Mode** \\ \hline Sun pointing & -OZ axis points to the Sun, -OY axis points to the & \\  & orbit normal, OX axis is defined by the & Long-term \\  & right-hand rule & \\  & -OZ axis points to the Earth, -OY axis points to & \\ Nadir pointing & the orbit normal, OX axis is defined by the & Short-term \\  & right-hand rule & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Flight mode and attitude of the satellite orbit.  
Figure 2: Exploded view of the Luojia 1-01 Satellite.
## 3 Methods
### Evaluation Based on Knowledge Distillation
#### 3.3.2 Architecture Search with Distilled Knowledge  
Motivated by [PERSON] et al. (2016), we proposed to introduce knowledge distillation to automatically design a convolutional network that is suitable for Re-ID tasks. More concretely, distillation loss is applied to guide the searching process to allow all candidates to learn the knowledge from a manual design teacher model that is pre-trained and has a good performance in Re-ID tasks. Let \(L_{c}\) denote cross-entropy loss, and \(L_{c}\) is widely used to train convolutional networks for classification tasks:  
\[L_{c}=\sum_{i=1}^{B}-log\frac{exp(l_{i}[c])}{\sum_{c^{\prime}=1}^{C}exp(exp(l_ {i}[c^{\prime}])} \tag{6}\]  
where \(C\) denotes the number of training identities, \(l_{i}[c]\) indicates the c-th element in \(l_{i}\), and \(B\) indicates the number of samples in each batch during training. We introduced distillation loss \(L_{d}\) instead of \(L_{c}\) during the searching phase, \(L_{d}\) aims to compute the distance between teacher's outputs and student's outputs based on cross-entropy loss:  
\[L_{d}=L_{c}(q_{t},q_{s}) \tag{7}\]  
where \(q_{t}\) is the outputs of the teacher model, and \(q_{s}\) is the outputs of the student model. Otherwise, following previous work [PERSON] et al. (2016), we also applied the triplet loss \(L_{t}\) to make the final architecture better suitable to Re-ID tasks:  
\[L_{t}=\sum_{a,p,n}(margin,\max_{p\in B_{p}}||f_{a}-f_{p}||-\min_{n\in B_{n}}||f _{a}-f_{n}||) \tag{8}\]  
where \(B\) is a batch sample, \(B_{p}\) is a set of \(B\) that has the same ID with anchor \(a\), and another set has different IDs with anchor denoted as \(B_{n}\). The margin term indicates the margin of triplet loss. \(f_{a}\) is the feature of sample \(a\), \(f_{p}\) is the feature of a positive sample \(p\), and \(f_{n}\) indicates the feature of a negative sample \(n\). Training data in each batch are sampled by a class-balance data sampler which first samples uniformly some sample identities, and then randomly samples the same number of images for each identity. Considering the above problem mentioned, our search objective \(L_{obj}\) can be summarized as:  
\[L_{obj}=\lambda_{1}L_{d}+\lambda_{2}L_{t} \tag{9}\]  
where \(\lambda_{1}\) and \(\lambda_{2}\) represent the weight ratio of \(L_{d}\) and \(L_{t}\). In the experiment, we refer to Auto-ReID [PERSON] et al. (2016) to set the same weight for these two loss functions.
# Mapping the Most Susceptible Regions to Fire in Portugal
## 2 Materials and Methods  
### Data  
#### 2.1.1 Land Cover and Vegetation Data  
In this study, a range of variables was used to analyse the vegetation state. The assessment of vegetation productivity relied on satellite Gross Primary Productivity (GPP, kgC/m\({}^{2}\)/8 days), namely the MOD17A2 Collection 6 product [34]. Here, we used 8-day composites with a spatial resolution of 500 m, for the period 2001-2021. The annual Net Primary Productivity (NPP) from MOD17A3 Collection 6 was extracted for the same period, and with the same spatial sampling as GPP, being expressed in kgC/m\({}^{2}\)/year.  
The characterization of the country's elevation is based on the Copernicus Global Digital Model (DEM) datasets, namely the Copernicus GLO-90 DEM [35] with a spatial resolution of 3-arcsecond (approximately 90 m). The Copernicus DEM datasets are subjected to terrain corrections and hydrological editing, which allows the correction of systematic random biases [36; 37].  
Land cover information for 2021 was downloaded from the Portuguese General-Directorate of the Territory (DGT 2021). The annual land cover map, developed by [PERSON] et al. [38], is constituted by thirteen land cover classes and includes the most important tree species of the country, with a spatial resolution of approximately 10 m. The accuracy of the map was estimated in 81%, making this product highly suitable for our study.