esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Truck-UAV Collaborative Delivery Planning Algorithm Based on Improved Evolutionary Algorithm
### Algorithm Design
#### 4.2.4 Pareto Front Maintenance
Truck route-Generate multi-center routes based on the Clarke-Wright algorithm Current solution. Drone route \(\leftarrow\) Greedily select failed nodes (Priority = Demand/Distance) Current solution \(\leftarrow\) Capacity constraint repair (Current solution, G) Current solution \(\leftarrow\) Time window constraint repair (Current solution, G) P_0.add(Current-solution) end_for Parental population \(\leftarrow\) P_0 Evolutionary optimization cycle: //Offspring generation fort = 1 to T Offspring population \(\leftarrow\)\(\oslash\) while The number of offspring population \(<\) N Parent 1, Parent 2 + Tournament selection (Parental population) Offspring \(\leftarrow\) Crossover (Parent 1, Parent 2, G): Truck path (IX Crossover + Dynamic insertion heuristic nodes Done mission: Uniform crossover + Conflict resolution Take the random mutation value r if r < Mutation critical probability: Offspring \(\leftarrow\) Random operation (offspring, G) Randomly select a mutation operation: Truck path reverse order/Drone mission replacement Add the offspring to the population. End while //Constraint judgment and solution repair for solution in the offspring population: Range constraint satisfaction = Range constraint discrimination (current solution)//Delivery constraint, time constraint, capacity constraint if not Range constraint satisfaction: Current solution \(\leftarrow\) Drone range repair (current solution, G)  f1 = 1/Demand coverage calculation (current solution, G)  F2 = 1 / Delivery cost calculation (current solution, G)  F3 \(\leftarrow\) 1 / Delivery time calculation (current solution, G) end for //Environment selection and frontier update Q_t \(\leftarrow\) P_t.u Offspring population Pareto frontier \(\leftarrow\) Update the Pareto frontier (Q_t, Pareto frontier): Non-dominated sorting (Q_t) Dynamic reference point adjustment Crowded entropy pruning //Update the population P_\(\{t+1\}\)\(\leftarrow\)Environmental selection (Q_t, reference point association strategy) //Termination condition if the change of HV (PF) for 10 consecutive generations < threshold then break end for Function definition: Calculation of road section vulnerability (edge): Importance = edge betweenness \(\times\) road section flow Failure impact = (original network efficiency-efficiency after removing the edge)/original efficiency Return the weighted normalized value of importance and failure impact Demand coverage based on vulnerability: Effective coverage = \(\Sigma\)(node demand\(\times\) (1- path vulnerability) Total demand = \(\Sigma\)(all node demands) Return effective coverage/total demand Capacity constraint repair (solution, G) for truck path in the solution: Find the node with the lowest demand density v Allocate v to the drone or remove it Re-plan the path using [PERSON]'s algorithm end for ```  
**Algorithm 3**Air-ground collaborative distribution algorithm
# Real-Time Simulation and Sensor Performance Evaluation of Space-Based Infrared Point Target Group
## 4 Simulation Experiments and Validation
### Parameter Design
#### 4.1.2 Sensor Design  
The sensor parameters were modeled after SPIRIT III. The main parameters of this sensor are divided into four aspects: spatial, spectral, optical, and radiometric. The spatial aspect sets the FOV and the number of pixels. The spectral aspect sets the number of intervals. The optical aspect sets the focal length and pupil diameter. The radiation aspect sets the NEI and SEI. To accurately simulate the target observation scenario and calculate the chain access durations, we imposed constraints on the distance and elevation angle. The specific parameters are detailed in Table 4. Figure 7a shows the overall situation of this simulation scenario and Figure 7b shows the scenario where three satellites in the Walker constellation observe the target group at the same time.
# A Novel Routing Scheme for Creating Opportunistic Context-Virtual Networks in IoT Scenarios +
## 3 Situational Context
The smart HIFI system has its own virtual profile detailing historical contextual information (e.g., the music it has played), the goals it has (e.g., to save energy), and its skills (e.g., to play music). The situational context would be the composition of the four virtual profiles. From this composed virtual profile, the strategy to coordinate the different entities and identify what functionalities/skills should be executed are identified at run-time. Concretely, the music style and, even, the concrete songs that the HIFI system should play would be detected. The different subsets of information constituting the virtual profiles also allow us to address some problems related to the devices' heterogeneity, since devices communicate, exchange information, and interact on the basis of two fixed concepts: goals and skills. This allows us to transmit information and coordinate the actions that must be done considering their specific capabilities. Some initial work in this direction has already been published in [33]. However, this concept has a key open issue: How are the users' goals distributed to the entities with skills? As detailed below, the intermittent connectivity among devices, the mobility pattern of some devices, and the huge amount of information that has to be distributed make this computational model especially challenging for current IoT networks. Therefore, in this paper, we propose the use of opportunistic IoT networks and novel routing algorithms to allow users to share such information among opportunistic communities formed by IoT devices based on their movement. The next section defines a novel concept for managing opportunistic IoT networks in which situational context is applied.
## 4 A General Model for Analytical Performance Analysis
### Performance Metrics  
Four performance metrics, which are average success probability, resource utilization ratio, the statistics of delay and the number of transmission request, are chosen to evaluate the performance of the HSTMAC protocol. These four performance metrics are defined as follows.  
The average success probability, i.e., \(P_{S}\), is defined as the ratio of the total number of successful devices to the total number of newly arrived devices during \(I_{\max}\) cycles:  
\[\begin{array}{ll}P_{S}^{H2H}&=\frac{\sum\limits_{i=1}^{I_{\max}}Z_{i}^{H2H} }{A_{\max}}\\ &=\frac{\sum\limits_{i=1}^{I_{\max}}\sum\limits_{q=1}^{Q}Z_{i}^{H2H}[q]}{A_{ \max}}\\ \\ P_{S}^{M2M}&=\frac{\sum\limits_{i=1}^{I_{\max}}Z_{i}^{M2M}}{N2M}\\ &=\frac{\sum\limits_{i=1}^{I_{\max}}\sum\limits_{i=1}^{Q}Z_{i}^{M2M}[q]}{N2M} \end{array} \tag{25}\]The resource utilization ratio \(U\) is defined as the ratio of the total number of RBs used for successful packet transmissions to the total number of reserved RBs:  
\[U^{H2H}=\frac{\sum\limits_{i=1}^{I_{\max}}\sum\limits_{q=1}^{Q}\delta_{1}Z_{i}^{ H2H}[q]}{\sum\limits_{i=1}^{I_{\max}}L_{TP,i}} \tag{27}\]  
\[U^{M2M}=\frac{\sum\limits_{i=1}^{I_{\max}}\sum\limits_{q=1}^{Q}\delta_{2}Z_{i}^ {M2M}[q]}{\sum\limits_{i=1}^{I_{\max}}L_{SP,i}} \tag{28}\]  
The delay of a successful device is defined as the time intervals (granularity in cycle) between its first arrival time and the time when it sends its packet successfully. So the statistical average delay \(D\) is computed as:  
\[D=\frac{\sum\limits_{i=1}^{I_{\max}}\sum\limits_{q=1}^{Q}\sum\limits_{j=\max[ i-(q-1)(W_{BO}+1),1]}^{i-(q-1)}(i-j)Z_{i_{j}}[q]}{\sum\limits_{i=1}^{I_{\max}} \sum\limits_{q=1}^{Q}Z_{i}[q]} \tag{29}\]  
The average request number \(B\) is defined as the average number of access attempts that a successful device has conducted:  
\[B=\frac{\sum\limits_{i=1}^{I_{\max}}\sum\limits_{q=1}^{Q}qZ_{i}[q]}{\sum \limits_{i=1}^{I_{\max}}\sum\limits_{q=1}^{Q}Z_{i}[q]} \tag{30}\]
# CarrDet: Attention-Symmetry-Based Microscopic Carrier Detection for Wastewater Treatment
## 3 Framework
### Carrier Feature Block Attention
#### 3.2.1 Channel Attention  
We flatten the feature \(f\) along the channel axis by using average pooling operations in order to aggregate spatial information, thereby efficiently computing the channel attention. The process can be summarized as  
\[F_{c}=AvgPool(f) \tag{2}\]  
where \(F_{c}\in R^{C\times 1\times 1}\) denotes the channel compression feature. Then the similarity measures are performed to evaluate the cosine similarity between any pair of channels, which leads to the derivation of the similarity matrix \(S_{c}\) through cosine similarity calculations:  
\[S_{c}=\frac{F_{c}^{i}\cdot F_{c}^{j}}{\left\|F_{c}^{i}\right\|\times\left\|F_{ c}^{j}\right\|} \tag{3}\]  
where \(F_{c}^{i}\), \(F_{c}^{j}\) are the compression feature of the \(i\)-th and \(j\)-th channel of \(f\), with \(i,j\in[1,C]\) and \(S_{c}\in R^{C\times C}\).  
To determine how much attention should be given to each channel during further processing, combine the compression features of the channels \(F_{c}\) and the similarity matrix \(S_{c}\) in a matrix multiplication operation, resulting in a channel attention vector \(A_{c}\in R^{C\times 1\times 1}\). \(A_{c}\) represents the importance or relevance of each channel in the feature maps. Finally, feature \(f\) along the channel axis is multiplied with the channel attention vector \(A_{c}\), generating image feature \(F_{channel}\in R^{H\times W\times C}\).  
This operation enhances the contrast between carrier edge channel features and background channel features, emphasizing the former while suppressing the influence of the latter.
# Design of a Power Converter for Solar Energy Storage System
## 2 Circuit Structure and Operation Model
### R2B Mode: Where Renewable Energy Is Stored in a Battery  
Figure 6 shows the circuit diagram of a converter operating in a mode in which a renewable energy source stores energy to a battery. In this mode, two sets of synchronous boost-type rectifiers are connected in parallel, thus constituting an interleaved circuit topology. An asymmetric PWM scheme is used to control the duty cycles of the lower arm switches \(S_{2}\) and \(S_{4}\) to regulate the amount of energy stored by the inductors \(L_{1}\) and \(L_{2}\), thereby achieving output power regulation. Because the voltage of the renewable energy port fluctuates depending on the ambient conditions, this mode involves the use of an interleaved synchronous boost-type rectifier to transfer energy, and this interleaved boost-type design aids in lowering the current stress of the inductor. When the entire gating signals sent to the power switches reach 50%, the voltage of the battery port \(V_{bat}\) becomes approximately twice that of the renewable energy port \(V_{reg}\). Hence, if the output voltage of the renewable energy port is more than twice that of the battery port, then the duty cycle of the gating signals sent to the principal power switches \(v_{\text{CS2}}\) and \(v_{\text{CS4}}\) falls below 50%. By contrast, if the output voltage of the renewable energy port is less than twice that of the battery port, then the duty cycle of the gating signals exceeds 50%.  
Figure 4: Circuit diagram for D2B mode.  
Figure 7 depicts a scenario in which the voltage supplied by the renewable energy port is less than twice the voltage of the battery port. The theoretical waveforms demonstrate that the gating signals \(v_{\text{GSI}}\) and \(v_{\text{GSI2}}\) complement each other, as do the gating signals \(v_{\text{GSI3}}\) and \(v_{\text{GSI4}}\). Dead time zones are incorporated into the gating signals to prevent shooting through, and the duty cycle exceeds 50%. All voltage polarities and current flow directions are defined in Figure 6. The energy from the renewable energy port is converted and transmitted to the battery unit through power switches \(S_{1}\)-\(S_{4}\) and the voltage is increased by \(L_{1}\) and \(L_{2}\).  
Figure 5: Theoretical waveforms for D2B mode.  
Figure 6: Circuit diagram for R2B mode.  
For the mode in which renewable energy is stored in the battery, only the interleaved synchronous rectifier is active. Although a small amount of energy is induced on the DC grid side, this amount is negligible. As expressed in Equation (8), the equation form for the renewable energy port voltage \(V_{reg}\) and battery port voltage \(V_{bat}\) with respect to the duty cycle \(D\) of switches \(S_{2}\) and \(S_{4}\) is similar to that of a boost converter. Thus, adjusting the duty cycle \(D\) may affect the energy storage time of \(L_{1}\) and \(L_{2}\) and the battery port voltage.  
\[V_{bat}=\frac{V_{reg}}{(1-D)} \tag{8}\]
## 3 Preliminaries
### ECDH Key Establishment Scheme  
KDC considers the elliptic curve of form \(y^{2}=x^{3}+ax+b\) over field \(F_{p}\) and elliptic curve domain parameters: \((p,a,b,G,n,h)\), where \(p\) is the prime number defining the order of the finite field. The values of \(a\) and \(b\in p\), such that \(4a^{3}+27b^{2}\
eq 0\). A point \(G(X_{G},\,Y_{G})\) on the elliptic curve is considered as the generator point. \(n\) is the smallest positive integer such that \(n\cdot G=O\) (point at infinity). Cofactor \(h=\#E(Fp)n\), where \(\#E(Fp)\) is the number of points on the elliptic curve.  
Figure 3: Matrix method operation.  
Each entity has a key pair, a private key \(d\in\) interval [1, \(n-1\)] and public key \(Q\) (\(Q=d\cdot G\)), where \"\(\cdot\)\" is a scalar multiplication operation. Two devices, e.g., \(X\) and \(Y\), with key pairs \((d_{X},Q_{X})\) and \((d_{Y},Q_{Y})\), can compute the pairwise key (\(PK\)) as follows: Device \(X\) computes the shared secret key \(PK1=d_{X}\cdot Q_{Y}\). Device \(Y\) computes the shared secret key \(PK2=d_{Y}\cdot Q_{X}\).
## 5 Application to Case Study and Discussion
### Result Discussion
In this case, as in the previous application of artificial irrigation, the thermal conductivity in the porous media was better known than the hydraulic conductivity. Therefore, the hydraulic conductivity was also estimated as a function of the thermal parameters. The estimate of river recharge to the aquifer made by [PERSON] et al. [18] was around 0.145 m\({}^{3}\)/s; thus, the difference between the estimates was less than 15%, which was a more than acceptable result considering the associated uncertainties. The type curves presented here were applied to general cases of SW-GW interactions (rivers, lakes) and were not limited to the specific boundary conditions defined in other studies, such as imposed horizontal flow [38] or fine confined layers with horizontal flow [16]. The quantitative contribution of type curves goes further than the qualitative descriptions reported in previous works, in which the estimation of infiltration zonesand the relative vertical-horizontal components in groundwater or the relative vertical flows in small rivers or infiltration areas were reported [15; 17; 36]. The methodology (non-dimensionalization) can be generally applied since the analytical solutions are not based on experimental correlations. However, the case study to be applied must meet some geometrical constraints: (i) the distance between the recharge region and the piezometer where the temperatures are recorded must be short enough to guarantee the absence of a single horizontal flow; (ii) the total length of the domain must be large enough to allow a region of temperature profile development from which the groundwater velocity can be considered horizontal. Additionally, the aquifer is assumed to be saturated; thus, the results can only be applied to a confined aquifer or an unconfined aquifer with a low gradient and full saturation. Finally, if there is a boundary condition downstream of the recharge area (on the right side of the cross-section), the estimated value of \(\rm I_{T}\) must be lower than the position of this condition (it can be a pumping or an impervious edge) to apply the methodology.
## 2 Related Works
### Self-Supervised Learning for Medical Image Analysis  
Self-supervised learning (SSL) learns effective visual features from unlabeled data by creating supervisory signals in upstream pretext tasks. These learned features are then used as initialization in downstream tasks, where further optimization of the task model takes place. Self-supervised learning has become increasingly popular in medical image segmentation, where annotated datasets are limited, but unlabeled data are abundant. Numerous studies have shown the efficacy of self-supervised learning in this field.  
[PERSON] et al. [50] proposed the MVRL model, which utilizes SSL to improve medical image segmentation. This model incorporates multiscale representations, canvas matching, embedding pre-sampling, a centrality branch, and cross-level consistency loss to reduce dependence on expert annotations and enhance performance. When pretrained on unlabeled data, MVRL surpassed existing competitive methods across various datasets, highlighting its effectiveness in non-labeled segmentation tasks and its ability to manage scale variations. [PERSON] et al. [51] employed a multimodal network with SSL for brain tumor segmentation. They introduced a pretext task aimed at filling masked regions, which facilitated SSL and strengthened the network's capability to extract multimodal features and resist noise. Their experimental findings demonstrated that this approach outperformed other methods, validating its efficacy in brain tumor segmentation.  
A core aspect of SSL is the creation of appropriate pretext tasks to help the network learn valuable visual feature representations. The three main types of pretext tasks are prediction, generation, and contrastive learning. Predictive self-supervised learning tasks, such as relative position prediction [52], rotation prediction [53], and jigsaw [54], treat the task as a classification or regression problem. Generative self-supervised learning tasks, including methods like variational autoencoders (VAE) [55] and generative adversarial networks (GAN) [56], aim to uncover latent features through the image synthesis process. On the other hand, contrastive learning involves differentiating between similar (positive) and dissimilar (negative) pairs or enhancing consistency across multiple positive pairs.  
Techniques such as MOCO [24,28], SimCLR [23,27], BYOL [25], and DINO [26] have emerged in this field.
# Efficient Top-\(k\) Spatial Dataset Search Processing
## 1 Introduction
* We propose a baseline top-\(k\) spatial dataset search scheme (KSDS) that uses DDSM for similarity calculation to obtain search results (see Section 5). * We propose an optimized top-\(k\) spatial dataset search scheme (KSDS+), which integrates two optimization strategies to filter datasets, thereby improving search efficiency (see Section 6). * We conduct comprehensive experiments to evaluate the proposed KSDS and KSDS+ on two real-world spatial data repositories, and the result shows the good performance of the proposed schemes (see Section 7). This paper is organized as follows. Section 2 reviews related work and discusses existing approaches to top-\(k\) spatial dataset search. Section 3 introduces the paper's notations,  
Figure 1: An example of top-\(k\) spatial dataset search. problem formulation and system model. Section 5 presents a baseline top-\(k\) spatial dataset search scheme, while Section 6 proposes an optimized search scheme to improve search performance. Section 7 presents a comprehensive evaluation of the proposed search schemes. Finally, Section 8 concludes the paper and outlines potential directions for future research.
# Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation
## 6 Conclusions  
A series of laboratory experiments were performed to study the fracture evolution in rock specimens before and after ultrasonic excitation, microcracking in the specimens, and the influence of ultrasonic excitation on the compressive strength of the rock specimens. The following conclusions are made based on the experimental results.  
1. Ultrasonic excitation experiments were designed and performed on different rock types. Microcracks generate and cause the failure of the specimen in relatively short experimental time and under a relatively low axial stress, due to ultrasonic excitation. The fracture length and the fracture propagation speed under ultrasonic excitation are rock-type dependent. The granite specimen and the limestone specimen have the minimum \"average maximum fracture length\" and the maximum \"average maximum fracture length\", respectively. The granite specimen has the shortest failure duration. All four rock types show the characteristics of \"no macroscopic fracture-fracture generation-steady fracture propagation\".
2. Energy continuously accumulates in the rock specimen as the time of ultrasonic excitation increases. This phenomenon not only leads to the generation of macroscopic fractures, but also causes the formation of microcracks that propagate along crystal boundaries and penetrate crystals. The development of these microcracks notably decreases the compressive strength of the rock specimens. The experimental results show that the compressive strength of red sandstone specimens decreases by 45.6 % after 140 s of ultrasonic excitation.
3. Traditional rock breakage technology has the disadvantages of high energy consumption, high costs, and low efficiency. Ultrasonic excitation will become one of the important approaches to the effective breakage of hard rocks. It is highly significant to further investigate the mechanism of ultrasonic excitation and to develop relevant equipment in order to apply the mechanism to high-speed construction involving hard rock breakage.  
Data curation, [PERSON] ([PERSON]); formal analysis, [PERSON] ([PERSON]) and [PERSON]; methodology, [PERSON] ([PERSON]); writing--original draft, [PERSON]; Writing--review and editing, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No.51874282), and the Six Talent Peaks Project in Jiangsu Province (No.GDZB-052). The authors are grateful for these supports.  
The authors declare no conflict of interest.  
The data used to support the findings of this study are available from the corresponding author upon request.
## 3 Research Methodology
### Bivariate Spatial Autocorrelation Analysis  
Spatial autocorrelation is divided into global autocorrelation and local autocorrelation, and the most commonly used statistic is Moran's index (Moran's I). The global Moran's I mainly describes the average degree of global correlation of spatial units with their neighbors, while the local Moran's I reflects the spatial aggregation of correlations [31; 32; 33]. In this paper, the spatial relationship between human activities and EQI in Anhui Provincefrom 2000 to 2018 is investigated using the \"bivariate Moran's I\" function of GeoDa software, in which the bivariate local Moran's I index is formulated as follows.  
\[\mathrm{I_{kl}^{i}}=\mathrm{z_{k}^{i}}\sum_{j=1}^{n}\mathrm{w_{ij}z_{j}^{j}} \tag{4}\]  
\[\mathrm{Z_{k}^{i}}=\frac{\mathrm{x_{k}^{i}}-\overline{x_{k}}}{\sigma_{k}}, \mathrm{Z_{i}^{i}}=\frac{\mathrm{x_{j}^{i}}-\overline{x_{i}}}{\sigma_{1}} \tag{5}\]  
where, \(\mathrm{I_{kl}^{i}}\) is the Moran index of variable k and variable l at i; \(\mathrm{w_{ij}}\) is the spatial weight matrix between i and j; \(\overline{x_{k}}\) is the mean of variable k; \(\mathrm{x_{i}^{k}}\) is the value of variable k at i; and \(\sigma_{k}\) is the variance of variable k [34; 35]. To reduce data magnitude and improve the efficiency of software execution, the raster images were uniformly resampled to 5 km in ArcGIS 10.6 software, and then the raster transfer points were executed to obtain 3574 vector points for evaluation and processing.