esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Definition of the Problem
### Equations of Strains and Displacements
#### 2.6.2 In a Plastic Region  
The radial and tangential strains are composed of the plastic strain components (\(\varepsilon_{r}^{p}\) and \(\varepsilon_{\theta}^{p}\)) and the elastic strain components (\(\varepsilon_{r}^{e}\) and \(\varepsilon_{\theta}^{e}\)), as follows:  
\[\varepsilon_{r}\ =\ \varepsilon_{r}^{e}+\varepsilon_{r}^{p}, \tag{12}\]  
\[\varepsilon_{\theta}\ =\ \varepsilon_{\theta}^{e}+\varepsilon_{\theta}^{p}. \tag{13}\]  
Based on the plastic potential equation Equation (6), the plastic tangential strain and radial strain are formulated using the plastic multiplier \(\lambda\):  
\[\varepsilon_{r}^{p}\ =\ \lambda\frac{\partial g(\sigma_{r},\sigma_{\theta})}{ \partial\sigma_{r}}, \tag{14}\]  
\[\varepsilon_{\theta}^{p}\ =\ \lambda\frac{\partial g(\sigma_{r},\sigma_{\theta})}{ \partial\sigma_{\theta}}. \tag{15}\]According to Equations (6), (14), and (15), the relation between the plastic tangential strain and the radial strain is:  
\[\varepsilon_{r}^{p}\ =\ -K_{\psi}\varepsilon_{\theta}^{p}. \tag{16}\]  
By incorporating Equations (7)-(10) and Equations (12)-(16), the differential equation for the radial displacement \(u_{r}\) is:  
\[\frac{du_{r}}{dr}+K_{\psi}\frac{u_{r}}{r}\ =\ \frac{1}{2G}(C_{1}\sigma_{r}+C_{2} \sigma_{\theta}-C_{3}p_{0}), \tag{17}\]  
where:  
\[C_{1}=1-\
u-K_{\psi}\
u,\ C_{2}\ =\ K_{\psi}-K_{\psi}\
u-\
u,\ C_{3}\ =\ C_{1}+C_{2}.\]
## 2 Problem Definition  
### Energy Commitment  
Researchers have provided valuable studies on energy and energy management [25; 26; 27; 28; 29]. A significant issue in energy studies is the operation of energy carriers. The primary energy carriers are those that are directly extracted from natural sources such as crude oil, hard coal, and natural gas. All carriers of energy, not primary but produced from primary energy, are called secondary energy carriers [30]. Table 1 shows an overview of renewable versus non-renewable energy and primary versus secondary energy.  
Researchers over time have conducted wide research into the operation of power systems and power supply to consumers. In the unit commitment study, the on or off status of the units and the optimal production of each unit are determined for a specific period of study based on the predicted electric demand. Although resolving the UC provides valuable information, dependencies between energy carriers are not considered.  
Therefore, the following can be mentioned:  
* The UC focuses solely on the study of the electrical grid and the supply of electrical demand.
* In the UC, only the forecast of electricity demand is available.
* Independent optimization of energy networks does not necessarily lead to the optimization of the entire energy network.
* In the UC, possible substitutions between energy carriers are not considered.  
The study of EC is introduced with the attitude of operation of energy carriers in an integrated energy network, considering the possibility of possible substitutions between energy carriers. In the proposed EC study, instead of forecasting electricity consumption, forecasting energy demand for other energy carriers is also available, and based on this, the optimal operation of energy carriers is presented simultaneously.  
EC determines the most appropriate pattern of using energy carriers wherein first technical problems and then economic issues are adequately addressed. If the energy carriers are used according to the peak demand, it will cost a lot. In fact, energy carriers should be optimally utilized, as managing energy resources properly can save a considerable amount of money. In the EC issue, first, the energy demand (actually the energy demand curve) must be determined. This energy demand curve, similar to the UC problem, can be a 24-h curve. In the UC problem, for every hour of the study, there is an electricity demand that must be met by the appropriate combination of units. However, in the EC, for every hour of the study, there is an energy demand of different types, including electricity, gas, car fuel, aircraft fuel, etc., which must be met using the appropriate combination of energy carriers.
## 6 Conclusions and Future Work  
The proposed strategies for resource allocation in NDNs are known to be effective in optimizing the overall network performance; however, these strategies may suffer in a limited network environment. That is because a fundamental trade-off problem between resource efficiency and user satisfaction might arise. To this end, a resource allocation scheme based on data classification has been proposed with a weighting factor that is used to decide at which level we want to control the aforementioned trade-off. Simulation results show that our strategy outperforms the sharing strategies, since it proves its ability to control any desired trade-off point of their respective efficiency and satisfaction levels.  
As future work, further experiments should be performed to evaluate the robustness of our approach against other state-of-the-art approaches. We also want to add other factors to the objective function of the resource allocation which may help at enhancing and improving network performance.  
Figure 8: Caching performance vs. tree depth in terms of hop reduction ratio when varying \(k\).  
**Author Contributions:** Data curation, C.A.K.; Investigation, A.T.H. and N.L.; Methodology, C.A.K., B.Z., C.T.C. and N.L.; Software, A.T.H.; Supervision, C.A.K., B.Z. and A.e.K.T.; Validation, C.T.C.; Writing--original draft, A.T.H.; Writing--review & editing, C.A.K., B.Z., C.T.C., N.L. and A.e.K.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is derived from R&D project RTI2018-096384-B-I00, funded by MCIN/AEI/10.13039/501100011033 and \"ERDF A way of making Europe\".  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 VIs as Proxies for Other Canopy Attributes
### VIs and \(f_{c}\)
Although there was a clear linear relationship between NDVI and \(\mathrm{f_{c}}\) (r\({}^{2}\) = 0.81) for the combined images, each species produced a separate regression line corresponding to different NDVI values for full cover shown in Figure 2. Arrowweed, an eercophile, was clearly different from saltcedar and cottonwood. Hence, in mixed scenes, attempts to derive \(\mathrm{f_{c}}\) by NDVI would produce bias according to the dominant species present in a given image or pixel. A single satellite pixel often encompasses mixtures of plant types and bare soil. Errors in estimating \(\mathrm{f_{c}}\) or LAI by NDVI do not necessarily lead to proportionately large errors in estimating energy fluxes by two-source models. If a pixel is fully vegetated but has low NDVI, it is treated as a partially vegetated pixel in two-source models, with resulting lower estimated latent heat flux than a pixel with high NDVI, a situation close to reality. In a sensitivity analysis of two versions of the two-source Atmosphere-Land Exchange Inverse (ALEXI) ET model, forced variations in LAI of 20% produced variances in estimated sensible heat flux of 10-15% for the so-called \"parallel model\" compared to 3-7% for the improved \"series model\", which corrected for clumping of vegetation and its  
Figure 4: Relationship between NDVI and fractional vegetation cover for high-resolution aerial DyCam images obtained on the Lower Colorado River. Scenes dominated by arrowweed, cottonwood or saltcedar were selected from a larger collection of images. The regression line of best fit for the combined data are given, as well as lines of best fit for individual species. From data in [33]. effect on heat flux from partially vegetated surfaces [43]. Model performance can also be improved by including ground data on LAI and f\({}_{\rm c}\) into the model, when such information is available. However, given the complexities of actual mixed landscapes, estimates of LAI and f\({}_{\rm c}\) from VIs and their transformations must be regarded as only approximations for many applications. Furthermore, they are difficult to validate with ground data. Fractional cover can be estimated through aerial photography or on the ground by transect methods [35] but LAI differs among species and changes continuously over a growing season, and actual LAI at validation sites is often poorly known.
# A Blockchain Network Communication Architecture Based on Information-Centric Networking
## 5 Method Analysis
### Blockchain Partitioning Method Based on Enhanced Resolution System  
To cluster based on geographical location and latency, existing research methods use community partitioning algorithms such as the K-Means algorithm [35; 36], but these algorithms require a large amount of computational complexity and bandwidth consumption for clustering partitioning. The BCBPT protocol [37] recommends the closest available nodes to each other based on the physical geographical proximity of the DNS service nodes. However, partitioning based on the round-trip latency of _ping_ values between nodes also leads to significant bandwidth consumption and a high time complexity of \(O(N^{2})\). Compared to existing community partitioning algorithms, the partitioning method based on the distributed enhanced resolution system in this paper has the following advantages:  
* Time Complexity: By using the latency information of the existing infrastructure enhanced resolution system as prior conditions, the additional probing and calculation of _ping_ value latency between nodes is reduced. Nodes only need to query the enhanced resolution system to obtain the addresses of other intra-domain ICN nodes, resulting in a time complexity of \(O(1)\), thus reducing time complexity and bandwidth consumption.
* Security: The enhanced resolution system in the network acts as an independent third-party trusted infrastructure, similarly to the \(DNS\) service in the \(IP\) network. It only provides deterministic latency resolution services and does not participate in data transmission in the network. Therefore, a certain level of security in the network partitioning process can be ensured.
* Feasibility: When a new node joins or leaves the resolution domain, it only needs to send registration or deregistration message information to the enhanced resolution system nodes. The enhanced resolution system nodes update the registration information, without the need for a large-scale recalculation of the entire network. This reduces the computational complexity and improves scalability.
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 4 Results and Discussion
### Model Evaluation  
Evaluation is a significant stage that uses the test data to predict the results and evaluate the models. After that, the test data are passed to the model. The model then performs some actions and utilizes the acquired knowledge to predict iris landmarks on the images during the learning phase. Graphs and tables are used to represent data. We can quickly evaluate the performance of any model using graphs, as each model's performance is represented in a single graph. Additionally, the table is essential for expressing the numerical and textual analysis and comparing the proposed model and existing literature studies.  
Performance metrics are used to measure the performance of the model. Accuracy is the main statistical performance parameter primarily used for the evaluation of a model. If iris landmarks are detected, there are two possibilities: whether a landmark is true or not, a true positive \((TP)\)and a false positive \((FP)\) can be found. Similarly, if iris landmarks are not detected, there are also two possibilities: i.e., \((TN)\) as true negative and \((FN)\) as a false negative. The formula to measure the accuracy of a model is shown in Equation (8).  
\[Accuracy\ (ACC)=\ \frac{TP+TN}{TP+FP+TN+FN}\times 100 \tag{8}\]  
The Mean Absolute Error (MAE) is a statistical measure of errors between the measured value and actual value. The formula for measuring MAE is shown in Equation (9). The model size is also a parameter used to compare the size of the model. Response Time \((RT)\) is also an evaluation parameter. The formula for calculating RT is given in Equation (10).  
\[MSE=\frac{1}{N}\sum_{i=0}^{N}\mid y-\hat{y_{i}}\mid \tag{9}\]  
\[RT=sp_{t}-ep_{t} \tag{10}\]  
where \(sp\) is starting prediction, \(ep\) is ending prediction, and \(t\) is the time for RT calculation.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 6 Discussion  
### Performance Analysis of Cosmos Inter-Blockchain Communication  
This research thoroughly examines the performance of Cosmos' inter-blockchain communication protocol, particularly emphasizing message delivery times within the Cosmos ecosystem [28; 29]. We have obtained valuable insights into the behavior and characteristics of message delivery times through data collection and statistical inference.  
Our findings show that the delivery time of inter-blockchain messages in Cosmos follows a lognormal distribution, ranging from 28.176 to 138.662 s. It is worth noting that the entire delivery time on Cosmos is roughly 2.8 times that of the Cosmos Hub blockchain; this suggests that elements within the ecosystem contribute to message delivery delays.  
We have found that vital time intervals, such as G to T (Generation to Transmission) and R to A (Relay to Acknowledgement), considerably impact message transmission efficiency. Understanding and optimizing these intervals can enhance inter-blockchain communication effectiveness and reliability.  
Our research also identifies areas for development within the Cosmos Hub ecosystem, which may help to reduce message delivery delays. Future studies will benefit from analyzing the causes of these delays and devising techniques to reduce transfer times in Cosmos.  
The findings of this study have significance for improving inter-blockchain protocols, allowing for smooth communication between blockchains. We can improve the interoperability and performance of blockchain networks by resolving the highlighted concerns and applying the suggested adjustments. As a result, decentralized systems and applications will be more widely adopted and advanced.  
This research gives valuable information about the efficiency of inter-blockchain message delivery inside the Cosmos ecosystem. This paper's statistical analysis and results  
Figure 18: Message transmission time distribution and R to A time interval.  
serve as a platform for future study and optimization efforts in blockchain interoperability. We can improve the effectiveness and dependability of inter-blockchain communication by tackling the stated obstacles and exploiting the information gained, ultimately developing decentralized systems and applications.
# Assessment of Energy Use and Photovoltaic Energy Potential in Saudi Arabian Governmental Schools
## 3 Methodology
### Technical and Economic Analysis of PV Systems  
A PV module (HiKu6 Mono PERC, CS6 W-550 MS) from the Canadian Solar Company was selected for this study based on several factors, including performance, warranty, and local market availability. This paper used the same method of evaluating PV systems technically and financially as Almotairy and Almarshoud [44]. Note that the load increase, the change in the tariff of the electricity produced by the PV system, net zero energy, and net zero bills were considered in this study. This study has limitations regarding climate, building design, and user behavior. As previously explained, the area under study is dominated by hot climate conditions, and in terms of behavior, there is limited interest in rationalizing energy consumption. Also, in terms of building design, there has been no previous interest in thermal insulation of buildings and improving energy efficiency. This makes it essential to study the impact of these factors.  
Figure 2: Qassim region’s average monthly weather data are derived from one-minute interval recordings [43]; GTI is the irradiance incident on a tilted plane at an angle equal to the city latitude.
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 1 Introduction
Image analysis techniques in autonomous vehicles are employed across various facets. For example, computer vision analysis is crucial for vehicular safety applications, including collision avoidance and 3D map building [25; 26]. Real-time image processing methods are designed for road detection, obstacle recognition, traffic light detection, and even number plate recognition [27; 28; 29]. Road detection techniques utilize similarity analysis and structural similarity index analysis to identify road surfaces in grid image areas [30]. Object detection and tracking remain challenging tasks, with image classification algorithms such as CNNs playing a pivotal role in steering vehicles [31; 32; 33]. Figure 1: SAE J3016 Levels of Driving Automation. Environmental image analysis is essential for numerous reasons. Primarily, it ensures safety by enabling vehicles to recognize and respond to dynamic and static objects in their path, thus preventing collisions. It enhances situational awareness, allowing vehicles to make informed decisions regarding speed, direction, and maneuvering based on the current context, such as traffic conditions and road layout. Despite significant progress, several challenges remain. These include the need for more sophisticated object detection and tracking algorithms, especially under adverse conditions such as poor lighting, inclement weather [34; 35], and complex urban landscapes. Moreover, the effectiveness of image analysis techniques in ensuring situational awareness, adaptability, and efficiency must continue to evolve in tandem with technological advancements. Moreover, the proliferation of diverse datasets and evolving benchmarks necessitates a comprehensive review of existing datasets and a comparison of the performances of leading models. This review uniquely aims to fill this gap by systematically comparing available datasets and evaluating key models in terms of their efficacy in diverse real-world scenarios. This includes exploring how these models fare in complex tasks such as 3D map building, real-time road and obstacle detection, and the behavior prediction of other road users. Environmental image analysis ensures the safety, situational awareness, adaptability, and operational efficiency of autonomous vehicles. This review aims to systematically compare and evaluate the performances of current datasets and models, providing a novel perspective that is essential for guiding future advancements in this field.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 2 Detection System in the Peruvian Navy
### Phase 1: Plot Extractor
#### 2.1.2 Preprocessing  
In preprocessing, the image is cropped to a certain dimension. In this stage, different convolutions are applied to the radar images in order to eliminate the interference caused by noise. The Gaussian filter and the arithmetic mode are used to obtain cleaner images. The images are then normalized and sent to the plot detection process.
# Improving Route Selections in ZigBee Wireless Sensor Networks
## 7 Performance Evaluation
### Asymmetric Topologies
#### 7.2.2 Scenario A2: WiFi Interference
As shown in Figure 10(e), all of the procedures struggled to recognize route 3,2,0 as the best route when \(R_{WiFi}<400\) kbps; however, as \(R_{WiFi}\) increased above 450 kbps, all of the procedures started to recognize route 3,2,0 as the best route, with the U-RR showing the best performance: when \(R_{WiFi}=600\) kbps, both the LQI-based and the LS-based procedures were only sending a median of less than 70% of the packets through the route 3,2,0, while the U-RR based procedure sent a median of 90% of the packets through the route 3,2,0. As a result, as shown in Figure 10(f), both the LQI-based and the LS-based procedures had more packet losses and retransmissions: while these procedures showed a median of 62 or more node 3 retransmissions/1000 messages generated, the U-RR procedure had an median of less than 50 retransmissions/1000 messages, representing a reduction of 20%.
## 3 LOKA Scenario
### Communication Protocol  
In terms of wireless operation, over the last few decades standards for low-power wireless communications have been developed to cope with the requirements of different applications. One of them is the IEEE 802.15.4 standard published in 2003 for Wireless Personal Area Networks (WPAN) or the ISO/IEC 1800-7:2009 standard, both for the physical and data-link layer, MAC. Both standards define the lowest layer, meanwhile protocols like ZigBee, RPL (IPv6 routing protocol for low-power and lossy networks), Constrained Application Protocol (CoAP), Time-Slotted Channel Hopping (TSCH) and DASH7 alliance mode (D7 AM) specify the application layer.  
In that way, the IEEE 802.15.4 standard is our choice because it is more suitable for periodic data gathering scenarios as suggest by [PERSON] et al. [34]. Also, in conjunction with the IEEE 802.15.4 standard we have chosen the ZigBee protocol for the application layer. We could have used TSCH instead of ZigBee, but at the use case presented in this paper the TSCH commercial devices were not yet available on the marketplace into commercial devices.  
Thus, ZigBee is the lowest power consumption protocol and the best in terms of reliability commercial devices. ZigBee is a wireless communication standard managed by the ZigBee Alliance based on the IEEE 802.15.14 standard, providing a very low consumption if idle mode. Also, it has flexible network topology, allowing multiple configurations. The scalability is achieved by static and dynamic star and mesh topologies. Our proposal is to include a ZigBee device into each mote type.  
The ZigBee protocol fulfills the high safety requirement since the Advanced Encryption Standard (AES) can be used for the communication. The information packets sent by the sensor motes are encrypted by the emitter and they are only decrypted into the final receiver with a password. Moreover, the ZigBee network is determined by a specific ID that all devices have to know if they want to join it.  
The ZigBee's characteristics are not modified by the algorithm since LOKA only uses the ZigBee protocol without any internal change ensuring that the coordinator configurations allow the end-device configurations. The packet loss rate, the node connectivity errors and the transmission throughput of the ZigBee protocol depends on the protocol, the radio and the environment [35; 36; 37] and LOKA does not modify them.