esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model and Problem Statements
### Problem Formulation
#### 3.2.4 Harvesting of Energy Unit  
In the DO2 QIEO method, IoT objects that can convert renewable resources to electricity include RF energy harvesters, wind turbines, and photovoltaic modules. It has a battery to help balance the power supply and demand. The stored renewable energy could be used for offloading as well as local computing. The total consumed energy by the IoT object at time slot \(t\) is represented by \(E_{total}(t)\) with \(E_{total}(t)=E_{offload}(t)+E_{blockchain}(t)\) and the amount of harvested energy at time slot \(t\) is represented by \(\beta(t)\). Additionally, \(b(t)\) represents the battery stage at the start of time slot \(t\), and it evolves based on Equation (24) [28]:  
\[b_{t+1}=\max\{0,\;b_{t}-E_{total}(t)+\beta(t)\} \tag{24}\]  
If the IoT object's energy is inadequate, it will drop the computation operation, i.e., \(b_{t+1}=0\). Additionally, the RF-enabled wireless energy transfer technology in [50], in which the IoT system is operated by a designated wireless power transmitter that produces constant and sustainable microwave energy over the air, is viewed as a special case. Let \(W(0,1)\) represent the energy conversion efficiency, \(\mu_{t}\) is the transmitting power, \(d_{distance}\) is the distance between the IoT system and the energy transmitter at time slot \(t\), \(C\) is the cumulative gain of the IoT antenna and the RF energy transmitter antenna at time slot \(t\)[45].  
\[\beta(t)=W*\mu_{t}*(d_{distance})^{-\tau}*C \tag{25}\]  
The quantity of energy collected at time slot \(t\) could be calculated by the IoT system based on the power harvested history and the modeling approach in [51]. The \(\beta^{\prime}\) represents the expected amount of energy produced \((t)\), Additionally, the estimation error for \(\beta(t)\) is denoted by \(\Delta(t)\)[28].  
\[\Delta(t)=\beta(t)-\beta^{\prime}(t) \tag{26}\]
# Shear Properties of Asphalt Mixtures under Triaxial Compression
## 2 Materials and Test
### Specim Preparation  
Current specification of Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering (JTG E20-2011) in China states that the cylindrical specimen with a diameter of 100 mm and a height of 150 mm can be used for triaxial test, the cylindrical specimen can also be tested with other sizes if needed. Meanwhile, the specimen for uniaxial compression test (that is the stress state with confining pressure of 0) of asphalt mixture in the specification is 100 mm in diameter and 100 mm in height. In order to be consistent with the specimen size of the uniaxial compression test, the cylindrical specimen with the diameter of 100 mm and the height of 102 mm was obtained by the gyratory compactor. Then, the upper and lower surfaces of the specimen were polished with a double-sided saw to obtain the specimen with a height of 100 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mixture Type** & **AC-13** & **SMA-13** & **OGFC-13** \\ \cline{2-4}
**Sieve Size (mm)** & \multicolumn{3}{c}{**Passing Rate (\%)**} \\ \hline
16 & 100 & 100 & 100 \\
13.2 & 95 & 95 & 94.4 \\
9.5 & 76.5 & 62.5 & 64.7 \\
4.75 & 53 & 27 & 23.1 \\
2.36 & 37 & 20.5 & 15.1 \\
1.18 & 26.5 & 19 & 10.8 \\
0.6 & 19 & 16 & 7.5 \\
0.3 & 13.5 & 13 & 5.5 \\
0.15 & 10 & 12 & 4.3 \\
0.075 & 6 & 10 & 3.6 \\ Optimum asphalt–aggregates weight ratio & 5.7\% & 6.2\% & 5.4\% \\ Air void & 4.5\% & 4.3\% & 19.5\% \\ \hline \end{tabular}
\end{table}
Table 1: Gradation and mix composition of asphalt mixtures.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test** & **SBS Modified Bitumen** & **Technical Requirements** \\ \hline Penetration (25 \({}^{\circ}\)C) (0.1 mm) & 55.9 & 30–60 \\ Penetration index & 0.53 & \(\geq\)0 \\ Ductility (5 \({}^{\circ}\)C/cm, 5 cm/min) & 34.2 & \(\geq\)20 \\ Softening point (\({}^{\circ}\)C) & 79.4 & \(\geq\)60 \\ Viscosity (135 \({}^{\circ}\)C, Pa-s) & 2.30 & \(\leq\)3 \\ \hline \end{tabular}
\end{table}
Table 2: Basic properties of the asphalt binder.  
Figure 1: Chart of AC (Asphalt Concrete)-13, SMA (Stone Mastic Asphalt)-13, and OGFC (Open Graded Friction Course)-13 gradations.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Problem Formulation  
The agricultural land monitoring task can be formulated as a machine learning problem, where we learn to predict a label \(\hat{y}\in\mathcal{L}\) for a previously unseen object (field parcel)  
Figure 2: The region of interest (ROI) and raster data extent over southern Finland.  
given a collection of training observations \(\{(X,y)\}\). For notational simplicity, we present the details for classification problems (\(y\) are discrete and mutually exclusive categories) although the representation could also be used for regression (continuous \(y\), such as crop yield) or structured output problems.  
Our focus here is on learning a suitable representation for objects that are pixel subsets of raster images. We denote individual pixels by column vectors \(\mathbf{x}\in\mathbb{R}^{D}\) where the individual elements correspond to different channels (e.g., spectral bands of MSI or polarization channels of SAR). Each object \(o\) is defined by some subset of the pixels of an image \(A_{i}\) captured within a geospatial region of interest \(A\), and hence can be represented by a matrix \(X_{oi}\in\mathbb{R}^{D\times n_{o}}\) storing the \(n_{o}\) pixels for this object as its columns. Note that this formulation can be generalized in various ways; see Section 4.2.  
Even though the focus of this work is on SAR and MSI data for soil tillage applications, we note that the approach is applicable to any task that satisfies the requirements of: (1) multi-banded raster data on a region of interest; (2) objects defined in terms of pixel segments of the images with a (3) class annotation on each object, using a shared coordinate reference system between the segment annotations and the rasters.
## 5 Results
### Image Quality  
An example of the images collected during the survey is provided in Figure 12. The images were collected at approximately 6 fps, and a total of around 700 GB data was collected during the two-day  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Occlusion Type**} & \multicolumn{3}{c}{**Visible**} & \multicolumn{3}{c}{**Not Visible**} \\ \cline{2-7}  & **Counts** & \begin{tabular}{c} **\% of Total** \\ **Samples** \\ \end{tabular} & \begin{tabular}{c} **\% of Total** \\ **Visible** \\ \end{tabular} & \begin{tabular}{c} **Counts** \\ \end{tabular} & \
\begin{tabular}{c} **\% of Total** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **\% of Total** \\ **Non-Visible** \\ \end{tabular} \\ \hline Other buildings & 532 & 53.2 & 67.2 & 14 & 1.4 & 6.7 \\ Vegetation & 170 & 17.0 & 21.5 & 72 & 7.2 & 34.5 \\ Walls, fences & 33 & 3.3 & 4.2 & 33 & 3.3 & 15.7 \\ Other occlusions & 49 & 4.9 & 6.2 & 70 & 7.0 & 33.5 \\ Unspecified & 7 & 0.7 & 0.9 & 20 & 2.0 & 9.6 \\ _total_ & 791 & 79.5 & - & 209 & 20.5 & - \\ \hline \end{tabular}
\end{table}
Table 5: Results of occlusion analysis performed on a random sampling of 996 buildings within the viewshed around the survey path.  
survey. The omnidirectional images are obtained by dynamic stitching of the raw video-streams captured by the six individual cameras. The resulting image has a field of view of 360 \({}^{\circ}\) horizontally and about 170 \({}^{\circ}\)vertically, with a resolution of about 12 mega-pixels. An example is shown in Figure 13. The quality of the images, assessed during the visibility analysis described in the preceding section, has been deemed adequate for the purpose of the system, which is the rapid assessment of medium to extensive damage. The resolution of the images allows for the detection of features described in Figure 12. Moreover, the panoramic format of the images allows for an intuitive and thorough analysis of the built-up environment, particularly suitable in the narrow streets characterizing the selected district.  
The shutter of the system is automatically managed by the camera system's software, and is continuously updated in order to obtain a balanced appearance of the image in the presence of strong brightness differences. This allows the system to promptly compensate for abrupt changes in the ambient illumination. This is exemplified by comparing the images in Figure 13A and Figure 13B. The two images have been taken from almost the same vantage point during two surveys, with the illumination conditions being dramatically different in the two images. Nonetheless, even the picture taken during dark and rainy conditions was suitable for a building assessment.  
Figure 12: Example of an omnidirectional image showing a (non-structural) damage of a building. The distance between building and camera is of about 20 m. The damage is visible in the **lower** part of the image, circled in yellow. The **upper right** inset shows the geographical location of the captured omnidirectional image.
# Occlusion Vehicle Target Recognition Method Based on Component Model
## 2 Occlusion Vehicle Target Segmentation and Recognition Scheme Design
### Design of Vehicle Component Feature Mask Module Based on Swin Transformer
#### 2.2.3 Efficient Attention Unit  
In the process of feature extraction of component regions, there may be a problem of component over-segmentation, which will affect the classification effect of target vehicle components, resulting in misclassification and missed classification. In order to reduce the interference of over-segmentation of vehicle components and enhance the component classification ability of the module, an efficient attention (EA) unit is designed, as shown in Figure 6.  
The efficient attention mechanism uses 1D horizontal global average pooling (X Avg Pool) and 1D vertical global pooling (Y Avg Pool) to extract feature information. Then, the features are concatenated into a feature map using \(Concat\), and \(1\times 1\) convolution is  
Figure 5: Component perception enhancement unit.  
used to obtain the dependency. After that, the split operation is performed along the X and Y directions, and the number of channels is restored by convolution. The Sigmoid nonlinear activation is used to realize the re-weighting of the regional features of the vehicle components and enhance the module's attention to the real components of the vehicle.
## 6 Performance Evaluation  
In this section, we evaluate and compare the performance of GQRT with that of SR [6], MQM [2], QRT [1], and BQRT [1] in terms of the server workload and communication cost. The server workload was measured in terms of the CPU-time that the server takes for evaluation of CM range monitoring queries. On the other hand, the communication cost was measured by the total number of messages transmitted between the server and moving objects. The simulations were conducted on Intel Xeon E5-2620 6-core Processor with 8 GB RAM running on the Linux system.
# A Comprehensive Survey on Knowledge-Defined Networking
## 5 Control Plane
### Control Models
#### 5.3.4 Fully Consistent vs. Eventually Consistent Control  
Data consistency is an essential factor in distributed control KDN architectures, specifically in the logically centralized and physically distributed flat control architecture. A controller's consistency has three aspects namely: state consistency, rule-update consistency, and version update consistency [371]. Version update consistency guarantees that version updates between the controllers are consistent. State consistency ensures that all controllers have the same global view. Rule update consistency ensures that switches under a controller have the same forwarding policies.  
Full/Strong consistency ensures that all the controllers have the same view of the data at all times, and any update made on the network is immediately visible to all controllers, which guarantees that the data will remain consistent at all times. However, full consistency can lead to high latency and reduced scalability.  
The eventual/weak consistency allows a delay between the updates in the network and the time at which those updates are reflected in all the controllers. Thus, for a short period of time, there can be different views by different controllers. Eventual consistency has higher scalability compared to full consistency.  
In dynamic control, when a set of switching devices is handed over from one controller to another during load balancing or in the event of a controller failure, the forwarding rules in the switches may need to be updated [372]. Distributed consensus-based strong consistency algorithms such as Raft have shown poor performance under controller overload conditions and poor scalability and responsiveness despite their strong consistency [373]. On the other hand, eventual consistency models, even though they have high scalability, availability, and communication overhead, do not have tolerable consistency, as there is a time period during which the updates are not consistent [374]. As both methods have pros and cons, some have adapted state consistency using a hybrid approach, having both full consistency for critical operations that affect a large portion of the network and eventual consistency for less critical changes [375].  
Onix and ONOS are examples of controllers that have both full and weak consistency levels. On the other hand, the SMaRtLight and Ravana controllers have strong consistency, while the Hyperflow controller has weak consistency [376].
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 4 Conclusions and Outlook  
For the complete utilization of the potential of solar photovoltaic power for a sustainable energy transition, along with additional adaptation measures in the power sector, diversified integration approaches are necessary, due to the variable nature of solar photovoltaic electricity generation. Intelligent permitting processes with local authorities having a good understanding of the risks and benefits of PV installation in a multifaceted land use manner calls for the EU-level training of administrators, similar to the EU Battery and PV academy. Different regions of the world will require different shares of the different renewable energy sources, combined with a range of different storage options, demand management, and sector coupling. However, this paper focuses on presenting different PV applications that have the potential to install PV systems on areas already used for other human activities.  
Energy transition scenarios that foresee extreme high solar photovoltaic power share predict an installed PV capacity of up to 8 kWp per capita globally and 12.3 kWp per capita in Europe. This is feasible, assuming that the conversion efficiency of solar modules will increase to 25% in the coming years. This corresponds to an area requirement of 32 m\({}^{2}\) per capita globally for the solar modules. Depending on the mounting structures, the area for the global capacity per capita can reach up to 50 m\({}^{2}\) when using free standing systems. Due to the higher capacity per capita, these numbers would increase by over 50% in Europe. Thus, it is essential to identify applications that can offer multiple benefits and uses when installing PV, thereby creating energy-land synergies without requiring additional land.  
All authors have contributed equally to the conceptualization, writing, and review of the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest. The views expressed are based on the current information available to the authors and may not in any circumstances be regarded as stating an official or policy position of the European Commission.
## 4 Analysis
### Spatiotemporal Dependence of SBTS  
In order to identify the spatiotemporal dependencies of the variables, the PCA was carried out with the Varimax rotation using the training data set that has the value of imputation target. The variables were clustered by the PCs that had the most loading of a variable. The PC was independent from other  
Figure 4: \(Ang_{t}\) and average speed plots on 12 March 2016.  
Figure 5: Flow-speed and occupancy-flow plots at VDS 4 on March 12 2016. (**a**) Classified by \(Ang_{t}\). (**b**) Classified by average speed.  
PCs and had several highly loaded variables. The variable dependence on the imputation target was identified by the accumulated loadings of the target variable on the PCs. The threshold of 0.95 was used in this study.  
The results of the spatiotemporal dependence between the target detector and surround detectors are shown in Figure 6. The numbers in Figure 6 are the numbers of the detector as in Figure 2 and the notation of Tk is the time slice of the k-minute. The imputation target in this study was chosen to be and referred to as the detector number 6 because it was located roughly in the middle of the queue. As described earlier, the head of the queue was at number 3 and the tail was at number 9. As seen in Figure 6, almost all of the independent variables of the SBTS I were included in the loading of less than 0.95 and were not found with any specific patterns. On the other hand, the SBTS of main interests for congestion management, SBTS II and III, showed unique patterns. The SBTS II's spatiotemporal dependence was constructed by the nearest up- and down-stream detectors, the detectors near the bottleneck activation point, and the most upstream point of the study area. The SBTS III's spatiotemporal dependence was constructed by only the nearest up- and down-stream detectors. As a result, the spatiotemporal dependencies on the imputation target are different for each SBTS and can represent the characteristics of each SBTS.  
Note that, in this study, the suggested method has been applied for the imputation of existing historical data and tested. However, theoretically, it can be directly used for estimating the missing values in a real-time environment by changing the input variables only. In the regression model, instead of the time window [T\({}_{-15}\)\(\sim\)T\({}_{+15}\)] shown in Figure 6, the dependent variables can have a time window of [T\({}_{-30}\)\(\sim\)T\({}_{0}\)].  
Figure 6: Spatiotemporal dependence among variables (**a**) Section-based traffic state (SBTS) I (**b**) SBTS II (**c**) SBTS III.
## 4 Results and Discussion
### Packet Loss  
Whilst the proposed framework achieved 100% successful data transmission from connected vehicles to the gNB, supported by VXLAN virtualisation of the Radio Access Network, packet loss was observed during the final offloading to edge application servers. An average packet loss of approximately 0.38% for Camera, 2.7% for LiDAR, and 7% for RADAR was recorded despite the successful retrieval of all pre-assigned Slice IDs at the gNB. This performance is attributed to the non-configuration of VXLAN transport network between the gNB and edge application servers, highlighting an implementation gap.  
In summary, the proposed SDN-Enabled 5G VANET framework demonstrates strong performance across key metrics: 100% classification accuracy, zero packet loss between vehicles and the gNB, a 187% improvement in flow rates over time, and an up to 68%  
Figure 11: Average delay at gNB.  
Figure 10: Average delay at the SDN gateway.  
reduction in delay. Nevertheless, limitations remain, particularly in packet handling during offloading to edge servers and latency at the SDN gateway. Additionally, the current system simulates a 5G infrastructure over a 4G network, which may inherently introduce overhead delays that would not be expected in native 5G deployment.
## 4 Design Architecture
### Performance Considerations  
All components of the architecture were designed for maximum throughput. For validation, the parameters of individual components, which influence the overall system performance, here initialization interval, latency, and throughput, have been determined using simulations on a register transfer level (see Table 3). All components of a DCU allow for initializing one new computation per clock cycle, which is indicated as _Initialization Interval_ in Table 3. Except for the population count, all components have a latency of 1 clock cycle. The population count is calculated in a tree structure and therefore, the latency depends on the signature width.  
Figure 5: Two exemplary design options with three DCUs. **A**: Static signature **B**: Dynamic signature. (**a**) Inputs are streamed from an AXI- or DDR-interface. Results of the DCUs are combined and send back (**b**) Four DDRs, one providing the inputs and three to store results.  
The number of results to be stored depends on the input data and the configured threshold value. Each valid 64-bit result of a PE, which passed the threshold check, is buffered in a FIFO and from there read by the first collector stage. In the used default configuration, each collector stage merges four result streams into one and passes them to the next collector stage until all results are combined into a single FIFO, from where it is written into the DDR. In the worst-case scenario, where all results pass the threshold check and only 8 results can be written to the DDR per clock cycle (512-bit per transfer), the collector is the bottleneck. In the considered use-case scenario, only a few results pass the threshold checker which is why the internal FIFOs do not run full and no pipeline stalls are triggered. In this case, the initialization interval is still one and the latency is determined by the pipeline depth to \(log_{4}(\lceil\#PE\rceil)\). The DDR memory controller of the design offers a 512-bit AXI interface, running at 300 MHz. Hence, the theoretical throughput of read and write transactions is \(512\ Bit*300\ MHz=153.6\ Gbit/s\). The practical achieved performance using Direct Memory Access (DMA) is with about 144 Gbit/s for sequential read and about 136 Gbit/s for sequential write transactions. Applied to the described use-case this means that 281 million signatures per second can be transferred from the DDR to the DCU and 2,125 billion results per second can be written to the DDR.
## 5 Improved SAC (iSAC) Algorithm
### Algorithm Architecture
\[\text{softmax}(z_{i})=\frac{\exp(z_{i})}{\sum_{j=1}^{N}\exp(z_{j})} \tag{19}\]  
(2) Based on the characteristics of the CPN and the purpose of computation offloading in the IoV, the state space and reward function are redefined. The state space mainly includes the remaining resources and the load status of edge servers. Since it is necessary to obtain the time tasks spend on edge servers, rewards may be delayed. Therefore, the reward function is divided into immediate decision rewards and deferred rewards. (3) Introducing PER. The traditional experience replay mechanism uses uniform sampling, thus it may overlook some critical samples and not all samples contribute equally to the update of the value function. The core idea of PER is to prioritize the sampling of experiences with larger Temporal Difference (TD) errors. The TD error is a measure of the deviation between the current value estimate and the target value, defined as in Equation (20):  
\[\delta=r+\gamma\text{Q}(s^{\prime},a^{\prime})-\text{Q}(s,a) \tag{20}\]  
where \(r\) is the immediate reward, \(\gamma\) is the discount factor, \(Q(s,a)\) is the current value estimate, and \(Q(s^{\prime},a^{\prime})\) is the value estimate of the next state. The priority \(P_{i}\) of a sample is related to the TD error, defined as in Equation (21):  
\[P_{i}=|\delta_{i}|+\epsilon \tag{21}\]  
\(\epsilon\) is a positive number that approaches 0, and samples with higher priority are more likely to be sampled.