esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Sensors  
Positioning sensors provide real-time location data, allowing the vehicle to navigate accurately within its environment. These sensors, such as GNSS, compass, and inertial measurement units (IMUs), ensure precise movement and alignment, which are crucial for tasks like mapping and path planning. Additionally, optical sensors play a key role in reconstructing the spatial context in which the UGV operates, enhancing navigation efficiency by avoiding obstacles and enabling more effective interaction with the environment. In terms of interacting with the environment, optical sensors, such as RGB or infrared cameras, provide detailed visual information about objects or a terrain. This allows the UGV to identify and analyze crops, soil conditions, or obstacles, making informed decisions about actions like weeding, harvesting, or applying treatments with precision. In particular, the primary purpose of RGB sensors is to generate a stream of plant images that is accurate enough to detect potential crop issues through image classification using deep learning models. Finally, LiDARs can perform tasks ranging from navigation and mapping (2D models) to more sophisticated analyses of the surrounding environment and the generation of accurate geometric models of objects (3D models).  
To better understand the prevalence of technology and the diffusion of various sensors, we used Google Scholar to conduct a simple statistical analysis of scientific works on the use of UGVs for agriculture published in the last decade. We employed the following queries: UGV AND agriculture AND \"RGB-D\"; UGV AND agriculture AND RGB -RGB-D; UGV AND agriculture AND (multispectral OR \"multi-spectral\" OR hyperspectral OR \"hyper-spectral\"); UGV AND agriculture AND LIDAR; UGV AND agriculture AND (GNSS OR GPS); UGV AND agriculture AND IMU. The results, reported in Figure 4 (left), indicate a general exponential growth in interest over the years and, as expected, highlight the importance of GNSS devices for precise localization within the crop, along with the valuable ability to generate prescription maps for deferred precision treatments. LiDARs are gaining popularity, partly due to the recent availability of affordable, high-performance 3D models. Optical sensors are also widely used, as demonstrated by the aggregation of occurrences of RGB, RGB-D, and infrared sensors, although some overlap is present in the publications.  
Figure 3: Dataflow architecture of UGVs for agricultural monitoring. The boxes on the right (‘Field Operations Controllers’ and ‘Actuators’) represent the potential integration of field treatment functionalities, an aspect that is beyond the scope of this review.
## 3 Beamformer Design
### Multi-Dimensional Search Design with CRI Zero Forcing
In each of the loop implementations, the mid points of the two 2-D areas are tested with the problems (7) and (8), where the magnitude squares of the resulting vectors \(\mathbf{w}_{T,i},i=1,2\) are compared to the relay transmit power \(P_{r}\) to determine the next round of 2-D areas with each dimension shrink. The parameter \(\varrho\) (\(0\leq\varrho\leq 1\)) controls the amount that the 2-D areas shrink by after each iteration, where the shrinkage rate decelerates as the parameter approaches one. Note that two 2-D tiles are not obliged to converge to the same area, though the twin algorithms work in the same fashion. As the twin algorithms progress, the two relay transmit vectors \(\|\mathbf{w}_{T,i}\|^{2},i=1,2\) approach \(P_{r}\) since the pair \(\delta_{i}\) and \(\gamma_{i}^{*}\) are tightened in every while loop. Finally, note that the computational complexity of the twin iterative algorithm relies heavily on the SDR and the associated 2D search, which leads us to consider the one-shot algorithm in the following Section 3.2. ```
1.for\(i=1,2\)do
2. Set \(\gamma_{i,min}=0\), \(\gamma_{i,max}=\min_{j=1,2}P_{r}\|\mathbf{g}_{i,j}\|^{2}\) and \(\delta_{i,min}=0,\delta_{i,max}=P_{r}\sigma_{0}^{2}(\mathbf{G}_{\tilde{i}})\). 3. Set \(\mathbf{w}_{R,i}=\mathbf{h}_{i}^{*}/\sqrt{\|\mathbf{h}_{i}\|}\). 4.endfor
5.while (\(\gamma_{i,max}-\gamma_{i,min}\) and \(\delta_{i,max}-\delta_{i,min}\) for \(i=1,2\) do not reduce more than a set of marginal values) do
6.for\(i=1,2\)do
7. Set \(\gamma_{i}^{*}=(\gamma_{i,min}+\gamma_{i,max})/2\) and \(\delta_{i}=(\delta_{i,min}+\delta_{i,max})/2\). 8.endfor
9. Perform the optimizations in (7) and (8). 10.for\(i=1,2\)do
11.if\((\|\mathbf{w}_{T,i}\|^{2}\leq P_{r})\)then
12. Set \(\gamma_{i,min}=(\varrho\gamma_{i,min}+(1-\varrho)\gamma_{i}^{*})\) and \(\delta_{i,max}=((1-\varrho)\delta_{i,max}+\varrho\delta_{i})\). 13.else
14. Set \(\gamma_{i,max}=((1-\varrho)\gamma_{i,max}+\varrho\gamma_{i}^{*})\) and \(\delta_{i,min}=(\varrho\delta_{i,min}+(1-\varrho)\delta_{i})\). 15.endif
16.endfor
17.for\(i=1,2\)do
18. Set \(\mathbf{w}_{R,i}=(\frac{\mathbf{P}_{\mathbf{G}_{i}\mathbf{w}_{T,i}^{2}}( \mathbf{h}_{i})}{\|\mathbf{P}_{\mathbf{G}_{i}\mathbf{w}_{T,i}^{2}}(\mathbf{h }_{i})\|})^{H}\). 19.endfor
20.endwhile
21.for\(i=1,2\)do
22. Set \(\gamma_{i}^{*}=\gamma_{i,min}\) and \(\delta_{i}=\delta_{i,max}\). 23.endfor
24. Perform the optimizations in (7) and (8). 25.for\(i=1,2\)do
26. Set \(\mathbf{w}_{R,i}=(\frac{\mathbf{P}_{\mathbf{G}_{i}\mathbf{w}_{T,i}^{2}}( \mathbf{h}_{i})}{\|\mathbf{P}_{\mathbf{G}_{i}\mathbf{w}_{T,i}^{2}}(\mathbf{h }_{i})\|})^{H}\). 27. Scale \(\mathbf{w}_{T,i}\) such that \(\|\mathbf{w}_{T,i}\|^{2}=P_{r}\). 28. Calculate \(\gamma_{1}^{1,i},\gamma_{1}^{2,i},\gamma_{2}^{1,i},\gamma_{2}^{2,i},\gamma_{1} ^{R,i}\) and \(\gamma_{2}^{R,i}\) using (3) and (6). 29.endfor
30. Calculate \(R=\frac{\sum_{i=1,2}^{2}\mathbf{h}_{i}^{2}}{2}\) using (5). ```  
**Algorithm 1**The twin iterative 2-D search algorithms for the SR-CNOMA
# Dynamic Responses of Asphalt Concrete Waterproofing Layer in Ballastless Track
## 7 Conclusions  
According to the laboratory test results, construction of test section and numerical simulation for the ACWL in the ballastless track, the main conclusions of this research are summarized as follows:  
* The main technical requirements of asphalt mixture for the ACWL were proposed based on the laboratory tests, including rutting test, fatigue test, low temperature bending test, immersion Marshall test and freeze-thaw split test. A test section of ACWL for the ballastless railway was constructed in north China and the field test results prove that the proposed requirements are reasonable and reliable.
* The viscoelastic properties of asphalt concrete obtained from the ACWL in the test section were investigated based on the dynamic modulus test. The Prony series for the generalized Maxwell is an effective tool to convert dynamic modulus to relaxation modulus in the linear viscoelastic domain, which contributes to the implementation of viscoelastic constitutive relationship in the finite element model.
* The reliability of the developed 3D finite element model was proved by the field measured data. The results indicate that it is unfavorable for the dynamic responses of ACWL when the wheelset is located at the expansion joint of base plates. The tensile strain at the bottom of ACWL is generally at a relatively low level, which means the long service life of ACWL is guaranteed. Additionally, the vertical displacement and vertical stress of each structural layer are obviously reduced due to the application of ACWL in the ballastless track structure.
* The validated 3D finite element model could be the basis for the future studies, such as the parameter sensitivity analysis and the long-term performance of the ACWL in the test section will be continuously monitored.  
**Author Contributions:** Formal analysis, J.Z.; Funding acquisition, X.C.; Investigation, Q.F. and G.X.; Methodology, J.Z.; Resources, D.C.; Supervision, X.C.; Writing--original draft, J.Z.; Writing--review and editing, X.C.  
**Funding:** This research was funded by National Natural Science Foundation of China (grant number 51778136).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experiment and Discuss
### Comparative Analysis and Experimental Results
#### 4.3.2 Sensitivity Analysis for the Hidden Layer Scale  
By referring to some representative literature, we used 300 as the upper limit of the hidden layer scale, with an increment of 20. With the increase in the hidden layer scale, the optimal classification accuracies of three contrastive classification models are illustrated in Figure 8.  
With the enlargement of the hidden layer scale, the average accuracy of classification models based on ELM and KELM changes obviously. The accuracy is relatively low when the hidden layer scale is small. It shows that ELM and KELM are sensitive to the hidden layer scale. To achieve high accuracy, the model must contain a largely hidden layer. Since the size of the ELM-based model is high related to the hidden layer scale, a larger model will cause a relatively long classification time.  
Figure 8: Sensitivity analysis.  
Figure 7: Number of non-zero-weight hidden nodes of the SBELM-based model.  
The performance of the SBELM-based classification model basically maintains a relatively fixed region, and it is always superior to the ELM-based and KELM-based by nearly 10%. When the number of the hidden layer nodes is in the range of 200-300, the classification accuracy of the SBELM-based model is relatively stable, remaining between 97% and 98%. It indicates that the SBELM-based model is relatively insensitive to the initially hidden layer scale.  
The SBELM-based model can also achieve high classification accuracy even with fewer hidden nodes. A small hidden layer scale can greatly decrease the computational cost of training. Based on the above comparison results, the maximum hidden layer size is chosen as 200.
## 3 Integrity Monitoring Method of Space-Ground Integration
### Integration of Integrity Monitoring for Medium, High, and Low Orbits  
The medium- and high-orbit GNSS receives integrity information and integrates it with the results of autonomous integrity monitoring to derive conclusions on integrity monitoring. This information is then broadcast to users via medium- and high-orbit GNSS satellites.
## 3 Materials and Dataset  
The proposed model with hyperparameter investigation was developed and trained using a publicly available dataset of hematoxylin and eosin (HE)-stained histological images of digital WSI of human colorectal cancer (CRC) and normal tissue. These images were manually extracted from 86 H&E-stained human cancer tissue slides. It is a set of 100,000 training and 7180 test images that do not overlap and are 224 \(\times\) 224 pixels (px) at 0.5 micrometer per pixel (MPP). There are nine different tissue classes: Adipose tissue (ADI), background (BACK), debris (DEB), lymphocytes (LYM), mucus (MUC), smooth muscle (MUS), normal intestinal mucosa (NORM), cancer-associated stroma (STR), and colorectal adenocarcinoma epithelium (TUM). The tissue samples included CRC slides from primary tumors and tumor tissue from CRC liver metastases; the normal tissue classes were supplemented with non-tumorous regions from gastrectomy specimens to increase variability. The dataset is available and was constructed by [PERSON] et al. [15]. For training the deep learning model, code was generated to balance all classes (mainly based on data augmentation of the class with the lower number of patches, more detail in Section 5, Experiments and Results), because as shown in Figure 4, the number of patches is different for each tissue.  
Figure 3: Block diagram for pathology WSI classification with VGG19 deep learning model, in a multi-class problem.  
Automatic recognition of different tissue types in histologic images is an essential component of digital pathology diagnosis. Although histological images often contain multiple tissue types, few studies have addressed the problem of different classes. As shown in our study, homogenization of the different tissues is crucial in classifying them into multiple classes. In this dataset, the images with and without color normalization are used. Figure 5 shows an example of the dataset used.  
Neural network preprocessing, training and deployment was performed in Python (version 3.9.0) on one workstation with CPU 11 th Gen Intel(R) i7-11700 K, 3.6 GHz, with 128 GB RAM and Nvidia RTX30-60 GPU.
## 1 Introduction  
Air conditioner (AC) remanufacturing, like any other remanufacturing business, is motivated by saving resources, reducing environmental impacts, and generating financial profit. This study investigated one aspect of the remanufacturing process, which is the environmental inventory of the processes involved in the remanufacturing practice. The study considered remanufacturing ACs as an example of products characterized by high energy consumption during their use phase.  
Resource consumption and environmental impacts occur during all phases of a product's life cycle. This study followed a life cycle approach to perform an investigation that analyzed the resource saving and environmental impacts throughout all phases in a product's life cycle. The analysis was conducted on an AC that operates in a hot region (Saudi Arabia) that also has a long summer of temperatures that can reach 55 \({}^{\circ}\)C. Operation in such a climate requires long operating hours, which makes the contribution of the use phase in the product life cycle significant. The case also is sensitive to the source of energy that operates the product. The AC is operated by electricity that is generated mainly (in this case study) through natural gas and crude oil. The environmental impact associated with electricity generation can be linked directly to the use phase of the AC.  
Air conditioners are energy-use intense products. AC manufacturers are encouraged, sometimes obliged, to develop more energy saving products. The energy efficiency for AC has increased about 47% from 1972 to 1991. The 2015 AC energy efficiency products are 11% more efficient than 1991 models [1]. Technologies that facilitate these improvements are not predictable. However, there is continuing trend to increase AC energy efficiency.  
Remanufactured AC is challenged by the fact that new ACs are always more energy efficient than remanufacted ones. This condition prompts a question, \"Do the savings found through remanufacturing an old product outweigh the energy savings by buying a new product?\" To address this question, there are many aspects need to be investigated one of them, the subject of this paper, is the environmental inventory of remanufactured AC.  
The main objective of this study is to assess the environmental performance of AC remanufacturing. The study aims to identify the environmental gains and losses of a remanufacturing initiative. More specifically, the study aims to quantify the difference in environmental performance between a remanufactured AC and a new AC, this paper identifies life cycle phases of a remanufactured AC, also it identifies the environmental inventory of the significant life cycle phases remanufactured  
In summary, the objective of this study is to evaluate the life cycle inventory of air conditioner remanufacturing, this objective is essential to evaluate the environmental performance of this initiative. This study also aimed to identify the life cycle phases in AC remanufacturing as well as quantifying the environmental inventory of each phase.
## 2 Internet of Vehicles and Smart Cities
### Architecture
#### 2.2.1 Three-Layer Architecture  
The three-layer architecture, presented by [PERSON] [15], is divided based on the interactions with the technologies in the IoV environment, as follows:  
* _Sensor layer_ is responsible for the sensors in the vehicles and surrounding infrastructure.
* _Communication layer_ is responsible for the wireless connections between entities in various vehicle-to-everything (V2X) modes (see Figure 1).
* _Data processing layer_ is responsible for holding statistics tools and storage (acts as the IoV network's intelligence and provides big data processing to vehicles, providing decision making in risk situations).
## 3 Results  
### Study Selection  
A detailed literature search was conducted by two independent investigators (K.-E.K. and E.M.) yielding 1055 studies sourced from PubMed (n = 628), Science Direct (n = 60), and Google Scholar (n = 367). The studies underwent a two-stage exclusion process to ensure relevance and adherence to the study criteria. In the first stage, 355 manuscripts were excluded due to duplication or irrelevance. Of the remaining 700 studies, 628 were excluded after screening titles and abstracts, as they did not meet the predefined eligibility criteria. In the second stage, full-text reviews were conducted for the remaining 72 articles,resulting in the following additional exclusions: 3 studies involved experimental animals, 31 studies lacked data analysis or did not include information on CN or CM, 29 studies did not use ophthalmic imaging, and 1 study was a narrative review. After applying the established exclusion criteria, eight articles were identified that specifically addressed the use of AI in the analysis of CN and CM. The study selection process is visually represented at the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flowchart [24] to enhance transparency in the study selection (Figure 1).
# Flexible Rectenna on an Eco-Friendly Substrate for Application in Next-Generation IoT Devices
## 3 Results and Discussion
### Rectenna: Fabrication and Testing
As expected, the \(\mathrm{P_{out\,DC}}\) average received power (solid line) demonstrates an approximate exponential decay with increasing distance. The graphs also illustrate superimposed periodic fluctuations, primarily resulting from constructive and destructive interference that creates standing wave patterns due to wall reflections. The observed variations are also consistent with the effects of Fresnel zones, which arise due to constructive and destructive interference of electromagnetic waves in the radiative near-field and intermediate-field regions. Moreover,  
Figure 12: Output DC power (\(\mathrm{P_{out\,DC}}\)) of the rectenna as a function of frequency for five electric field intensities (1 V/m, 3 V/m, 10 V/m, 30 V/m, and 100 V/m) at (**a**) vertical orientation of the horn antenna; (**b**) horizontal orientation of the horn antenna; (**c**) horn antenna swept at an angle of 45\({}^{\circ}\). the data suggest that at distances greater than 70 mm, the received power approaches negligible levels. Additionally, we evaluated the rectenna performance in a real use case scenario, harvesting energy from a nearby Wi-Fi router (ASUS ZenWiFi Pro ET12, ASUSTek Computer Inc., Taipei City, Taiwan) at the same distances as with the microwave generator. In this setup, the DC output voltage of the rectenna was measured as a function of distance from the router, while a 4K Netflix movie was being streamed on a television connected to the router via Wi-Fi, as shown in Figure 14a. From Figure 14b, we can see that environmental factors (especially router traffic) heavily influence output. The high standard deviation observed in P\({}_{\text{outDC}}\) measurements is closely associated with fluctuations in Wi-Fi router traffic, such as packet bursts during 4K streaming. When the router transmits high-density packets, for instance, while streaming 4K video, the rectenna can harvest more energy. Conversely, during idle periods, the output diminishes. Therefore, we can conclude that variable data transmission has a substantial effect on rectenna performance. Figure 14: Output DC power (P\({}_{\text{out DC}}\)) of the rectenna placed on the wall as a function of distance from a Wi-Fi router: (**a**) photo of the measurement setup; (**b**) results. Figure 13: Output DC power (P\({}_{\text{out DC}}\)) of the rectenna placed on the wall as a function of distance from a microwave generator: (**a**) photo of the measurement setup; (**b**) results.
# Underwater Excavation Records Using Underwater Acoustic Survey: A Case Study in South Korea
## 4 Application Case: Poor Underwater Visibility (Nakwoldo Site)
### Results and Discussion  
As shown in Figure 7, the seafloor of section A had a predominately flat surface, with a corrugated profile visible only partially in the southern part. The most salient feature within section A was a long wire and waste nets lying flat on the seafloor in the form of a large X. The wire ran southeastward from grid A9, passing through grid A21 and the eastern boundary, and the waste nets were situated northeastward from grid A36 to grid A19. The presence of the wire and waste nets was confirmed during the diving survey. However, the location could only partially be confirmed due to poor visibility.  
During the Nakwoldo site UE, we produced a chart using the method for drawing conventional underwater relic distribution maps. The locations of the excavated relics, wire, and waste nets were marked on this chart (Figure 8). A comparison of the chart drawn using the traditional method and the chart drawn onto the SS mosaic image revealed that the latter was more effective in constraining the conditions of the survey site. However, the rope installed to probe the seafloor was too thin to be identified on the SS image. Nevertheless, no significant changes occurred because no sediment removal survey was conducted. However, it hindered the process of combining the SS images due to the lack of a suitable reference point [7].  
Figure 7: Scanning sonar image of section A of the Nakwoldo underwater excavation site (frequency: 675 kHz; swath: 50 m).  
Conducting an SS survey for a large area at the Dangampo site was challenging because of the time constraints. However, at the Nakwoldo site, it was possible to obtain an image of the entire survey area by operating the SS at every vessel stop during the survey period. Conversely, the lack of a clear reference point at this site complicated the process of combining the image series into a full mosaic image.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 4 Results
### Prototype Testing in an Open Environment
#### 4.2.2 Power System Analysis  
This test was held at 9:00 a.m. outdoors at the Escuela Politecnica Nacional. The weather was cloudy with some wind. This site was selected because of its grass surface, which could cushion a possible fall of the UAV, and the proximity of a power outlet to which the power supply was connected via an extension cord. In the first attempt, the UAV was held at a height of 4 m for 7 min, as shown in Figure 33. The voltage and current plots corresponding to this flight are presented in Figure 34. In these plots, the orange curve represents voltage, and the green curve represents current. The behavior of the graphs indicates that the voltage remained relatively constant while the current experienced a continuous increase. During a brief interval of instability in the system, a current of 15.04 A and a voltage of 16.64 V were recorded.  
In the second test, performed in an open environment, the UAV flew for 3 min at a height of 0.8 m, with a 3 m long cable coiled on the bottom of the UAV, as shown in Figure 35. The corresponding voltage and current plots are presented in Figure 36. In this test, both voltage and current remained more stable compared to the previous test, where the UAV was operating at a higher altitude. The average power consumption recorded was 16.54 V  
Figure 34: Variation of voltage and current consumption in an open environment during the interval from 125 to 260 s.  
Figure 33: UAV equipped with the communication system, operating in a real environment at an altitude of 4 m.  
and 10.50 A. The cable or the voltage regulator was checked for heat generation in both the closed and open environments. However, no increase in temperature was observed.