esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Lattice Structures  
In early industrial structural design, the solid material structure is used primarily due to its adequacy and other material properties. Although the solid material structure satisfies the design requirements to a large extent, there are still numerous defects such as waste materials, an inflexible design, a large structural volume, and manufacturing difficulties. Furthermore, as science and technology advance, the requirements for structural properties have become more stringent. Currently, industrial structural design places a greater emphasis on light weight and excellent performance. For instance, in the aircraft industry, the structural design must not only fulfill the criteria of lightweight materials but also call for exceptional mechanical qualities. On this basis, low-cost and high-performance scientific materials may be created and accomplished. In the biomedical implant and aerospace sectors, the use of cellular lattice structures decreases the weight of the structure and enables the design application of lightweight and high strength.  
[PERSON] and [PERSON] were the first to suggest the notion of a cellular structure. This cellular form featured open-cell and closed-cell foams, as well as honeycombs [55]. But lattice structure, which is also a type of cellular material, is different from foams and honeycombs mostly because of the shape, size, and properties of the unit cell [56].  
In the past decades, Triply Periodic Minimal Surface (TPMS) porous structures have been the subject of study shown in Figure 4. The goal of geometric design is to make TPMS that are similar to some of nature's most interesting porous structures. Porous TPMS structures are perhaps the most potential biomechanical manmade structures for bioengineering. For long-term use in a dynamic bio-skeletal system, the fatigue properties of AM porous materials are very important.  
Figure 4: The geometry of **(a)** rhombic dodecahedron, **(b)** diamond, and **(c)** truncated cuboctahedron structures. Reprinted with permission from ref. [57]. 2015, Elsevier.  
The fundamental cell is composed of trusses. Moreover, thin-walled TPMS and skeletal TPMS classes include TPMS-obtained elementary cells. Control must be exercised over the mechanical and thermal performance. TPMS structures are utilized as trusses, energy absorbers, heat exchangers, etc., to their competitive advantage [58]. To examine the behavior of a hybrid AM lattice structure under multiple loading directions, the strain rates, unit cell stacking order, and loading circumstances shown in Figure 5 were synthesized.  
When hybrid lattice constructions are loaded in the transverse direction rather than the stacking direction, the maximum stress increases by up to 10% for all configurations. The flow stress fall after the peak was minimized when the structure was stressed in the transverse direction, hence minimizing the tensile failure of lattice struts.
# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
## 4 Challenges of Modern LMD
### Frames  
According to [PERSON] and [PERSON] [39], regulatory standard frames for autonomous vehicles have been developed. Political, social, and long-term perspectives were all taken into account. Some countries, such as Estonia, have passed legislation allowing robots to share sidewalks with pedestrians, while others are hesitant to change. Laws from different countries may also cause issues. Even within a single country like the United States, the implementation of land-based autonomous delivery robots is subject to a variety of laws and regulations. Varying national legislation may make it difficult for land-robot delivery firms to negotiate their business models and persuade end users of the competitive benefits of deploying land-based robots [39]. Autonomous vehicle rules in the United States have been discussed by researchers [40]. Regulations exist in seven states and three localities, according to the authors. San Francisco, California, provides 180-day speed, insurance, and demand permits to robots. There is no weight restriction, but robots must emit a warning sound. According to the authors, San Francisco has the strictest laws in the United States. The state of Arizona has speed limits and requires robots to obey pedestrian laws. There are no restrictions on insurance, braking, or license plates, as is explained by [PERSON] and [PERSON] [40].  
Another study also addresses the problem of protecting personal information saved online and locally on the terrestrial robot [39]. This means that robots pose a threat to GDPR because they collect personal information. Since the introduction of privacy and safety laws for the public, this has been an issue [117]. According to [PERSON] and [PERSON] [87], government policies and a lack of standardization may hinder autonomous vehicle implementation. Robot manufacturers will face difficulties customizing many robots due to a lack of standardization. The authors wished to make recommendations to policy makers when developing and standardizing regulations [87]. Autonomous vehicles lack policies and regulations because they are a new and uncertain technology. Due to increased research, as [PERSON], [PERSON], and [PERSON] [107] say, futuristic urban design may include zones where autonomous cars are restricted or authorized to move faster.  
Data security issues can cause psychological distress in some people [117]. Autonomous drones raise ethical concerns. This includes issues with camera integrity and high unemployment. Another issue with drones is security. Drone operations can be disrupted and made dangerous by hacking or GPS interference [101]. Using GPS-Spoofing attacks, drones could be hijacked for illegal purposes, such as terrorist attacks. Such attacks can be detected by comparing GPS and INS (Inertial Navigation System) positions, but INS may be faulty due to accumulated errors [118].
## 5 Conclusions  
In this paper, the MIMO-FMT TR acoustic communication using DA-based turbo equalization has been proposed to jointly suppress noise, residual CCI and ISI after the TR process. In the proposed method, the adopted DA-based turbo equalization is constructed via interference cancellation and soft information-based ADFE adjusted according to the RELS algorithm and decoding. Soft information exchange and interference suppression are proceeded successively and iteratively until the communication performance is stable. The principle of the proposed method is analyzed, and based on the acoustic channel responses measured in a real experiment, the performance of the proposed method is compared with that of two methods. The results show that compared with the MIMO-FMT TR underwater acoustic communication using interference suppression without error control coding (ECC), the proposed method performs better, benefiting from the ECC included in turbo equalization; and compared with the MIMO-FMT TR underwater acoustic communication using interference suppression based on hard-decision equalization and decoding, the proposed method can achieve superior performance due to the exploitation of soft information.  
Figure 11: Output BERs of decoding of the proposed method and the method based on hard decision at 10 dB input SNR: (**a**) BERs of the 1 st subband; (**b**) BERs of the 2 nd subband; (**c**) BERs of the 3 rd subband; (**d**) BERs of the 4 th subband.  
**Author Contributions:** Conceptualization, L.S. and H.L.; methodology, L.S. and H.L.; software, L.S.; validation, L.S.; formal analysis, L.S.; investigation, L.S.; resources, H.L.; data curation, L.S.; writing--original draft preparation, L.S.; writing--review and editing, L.S.; visualization, L.S.; supervision, H.L.; project administration, L.S.; funding acquisition, L.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Shandong Provincial Natural Science Foundation, China, Grant No. ZR2021 MD115.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this paper are available after contacting the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methods for Monitoring Phenology
In recent years, satellite-based monitoring techniques have been complemented by near-surface remote sensing methods, such as time-lapse digital cameras (PhenoCam) [28], in situ measurements taken with radiometric instruments [29], monitoring based on eddy covariance (EC) data [30], and images captured by unmanned aerial vehicles (UAV--temporal resolution depends on the frequency of the flight) [31]. These near-surface remote sensing techniques offer high temporal resolution and better spatial coverage compared to direct ground observations. They provide valuable data that can be used to develop, calibrate, and evaluate satellite-based phenological models and products. Among these four near-surface phenology monitoring techniques, PhenoCam networks have garnered significant attention in the global research community due to their simplicity in data processing and the automated continuous data acquisition capacity at high spatial and temporal (timings pre-programmed) resolutions.
## 2 Methods  
### Target Area and Data Collection  
The Gul-po stream, which is the target stream of this study, flows through five local governments in Korea--Bucheon, Gimpo, Bupyeong, Gyeyang, and Gangseo, comprising an area with the highest density of urban household and industrial pollution sources. It has tributaries such as the Yeo-wol, Gye-san, Sam-jeong, Mog-su, Gyul-hyeon, and Cheong-cheon streams. National industrial complexes are located upstream of the Gul-po stream, and apartment complexes exist in the middle and upstream areas. The downstream area has been used as farmland. The Gul-po sewage treatment plant is also located downstream and processes large amounts of pollutants from nearby factories and farmland [16]. Sincethe Gul-po stream is a basin that has a direct impact on the Han River system, it requires sufficient management to improve the water quality in the Han River estuary and along the coast of Incheon. In addition, efficient water quality management by identifying pollution sources that flow into the stream is essential, because the stagnation of flow occurs in the stream when it joins the end of the main stream of the Han River. The water quality of the stream is significantly worse than that of other streams in the Han River system due to the influence of effluent from a large sewage treatment plant (Gul-po sewage treatment plant) located at the end of its basin [17]. Figure 1 shows the Gul-po basin and location of major facilities, and the sampling point of the Gul-po Basin [18].  
As for the flow and water quality data for this study, the data of six water quality items--BOD (biochemical oxygen demand), COD (chemical oxygen demand), TOC (total organic carbon), SS (suspended solid), T-N (total nitrogen), and T-P (total phosphorus)--and flow discharge data at GulpoA1, GulpoA2, and GulpoA, (water quality measured points) from 2016 to 2020 were collected from the water environment information system (WEIS, 2021) of ME and analyzed. More detailed water quality and flow measured points are shown in Table 1. The 571 time series data were collected from observation points (GP A1-193, GP A2-187, and GP A3-191) and the measured results of water quality and discharge are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Watershed Area** & **Measure Point** & **Basin** & **Location** \\ \hline Upper & Gulpocheon-1 & GulpoA04 & Sangdong, Wonmi-gu, Bucheon City, \\  & (GP A1) & (37.51522, 126.74529) \\ Middle & Gulpocheon1-1 & Gulpocheon3 & Parkchon-dong, Gyeyang-gu, Incheon \\  & (GP A2) & (37.54025, 126.76138) \\ Lower & Gulpocheon-2 & Gulpocheon402 & Gyeyang-dong, Gyeyang-gu, Incheon \\  & (GP A) & (37.50870, 126.72585) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Target areas for the characteristic analysis [18].  
Figure 1: Water quality monitoring site in Gul-po: (**a**) The Gul-po basin; (**b**) mimetic diagram of Gul-po [18].
### Structure and Objectives of This Review  
As the above information shows, food security in West Africa is increasingly threatened by rapid population growth, low agricultural productivity, and the impacts of climate change. Smallholder farmers, in particular, face substantial challenges due to limited resources and their reliance on rainfall. RS technologies offer a promising solution to address these challenges, providing valuable data for improving agricultural monitoring, enhancing productivity, and supporting sustainable practices in the region. This review aims to provide a comprehensive overview of RS products and studies focused on smallholder farming systems, assessing the potential of Earth Observation (EO) for mappingsmall-scale agriculture and cropping systems in West Africa. We analyzed 163 scientific papers published between 1 January 2000 and 30 April 2024, with the overall structure of the review outlined below:  
* The introduction in Section 1 presents the relevance of the potential of RS to monitor and map agricultural and cropping systems in West Africa amid increasing and multidimensional challenges of global change.
* Section 2 first provides a geographical overview of the study area and secondly explains the literature selection process by providing an overview of the literature databases used and the filters applied.
* Section 3 presents the results of the review process. It aims to identify the potential of EO for mapping small-scale agriculture and cropping systems in West Africa. First, the evolution of the research field over time is described. This is followed by a detailed spatial breakdown based on the affiliation of the first authors, the origin of the study funding and the location of the study area. The sensors used and the temporal and spatial scales are presented in the next subsection. Section 3 concludes with an in-depth analysis of the research foci. The classified studies are analyzed on their main findings, RS potential and their challenges and limitations in order to identify relevant research gaps.
* The discussion of the results, the limitations of the review, the need for integrating high-resolution RS data and future research directions are presented in Section 4.
* Section 5 highlights the main findings, and concludes with the potential of RS to detect the impacts of global change in West Africa and how RS can support sustainable intensification.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 3 Congestion Detection and Identification
### Observations and Insights
When multiple flows pass over the link, Switch 3 is more prone to congestion, and the degree of congestion is more severe, with Switch 1 having the next highest degree of congestion and Switch 2 having the lowest degree of congestion. Various problems arise in network nodes when traffic from different senders jointly compete for bandwidth. The prerequisite for solving network congestion is to detect congested nodes. Combining congestion signals such as queue depth and delay is more effective than relying on a single signal for a congestion description. Therefore, it is necessary to use the findings of the test experiments as a basis for further research. Figure 4: CDF of the queue delay in Switches 1–3. Figure 3: The buffer situation of the switches. (**a**) Switch 1; (**b**) Switch 3. Figure 2: In congestion scenario, queue lengths and packet marking in the switch. (**a**) Queue length in switch; (**b**) Packet marking in switch.
# Hierarchical Self-Supervised Learning for Knowledge-Aware Recommendation
## 3 Preliminaries  
This section introduces key notations used throughout the paper and formalizes our studied task.  
User-Item interaction graph. For the recommendation scenario, we define the user-item interaction graph as \(\mathcal{G}_{u}=\{(u,y_{ui},i)|u\in\mathcal{U},i\in\mathcal{I}\}\). Herein, \(\mathcal{U}\) represents the set of users, \(\mathcal{I}\) represents the set of items, and \(\mathcal{V}=\mathcal{U}\cup\mathcal{I}\) is the node set. The user-item interaction matrix is denoted as \(\mathcal{V}\), where \(y_{ui}=1\) indicates an interaction (e.g., view and purchase) between user \(u\) and item \(i\) while \(y_{ui}=0\) signifies no interaction. Graph \(\mathcal{G}_{u}\) contains information about the connections between users and items, forming the basis for recommendation models.  
Knowledge graph. Knowledge graphs serving as graphical representations of information depict entities as nodes and their relations as edges. The structured format facilitates the organization and retrieval of information. A knowledge graph is formally represented as triplets \(\mathcal{G}_{k}=\{(h,r,t)|h,t\in\mathcal{E},r\in\mathcal{R}\}\), where \(\mathcal{E}\) denotes the set of entities, \(\mathcal{R}\) denotes the set of relations, and each triple \((h,r,t)\) signifies relation \(r\) connecting the head entity \(h\) to the tail entity \(t\).  
Task Description. We formally describe the recommendation task addressed in this paper:  
* Input: a user-item interaction graph \(\mathcal{G}_{u}\) and a knowledge graph \(\mathcal{G}_{k}\).
* Output: a predictive function \(y_{ui}\) that estimates how likely a user would adopt an item.
## 2 Related Work
### Expanding the Number of Interferograms with Data Augmentation  
To overcome challenges posed by limited dataset sizes and imbalanced data distribution--which often result in model overfitting and poor generalization--researchers have introduced image data augmentation techniques. The fundamental principle of data augmentation lies in applying various transformation operations to original samples to generate numerous new instances that closely resemble--but are not identical to--the originals (e.g., [PERSON] et al. [32]). Data augmentation has proven particularly effective in constructing InSAR image datasets, especially when deformation data are scarce, as it significantly increases the number of available samples. In a study by [PERSON] et al. [21] that focused on detectingoseismic deformation in InSAR imagery using deep learning, data augmentation techniques--including horizontal and vertical flipping, random 30\({}^{\circ}\) rotations, and lateral shifts--were employed to alleviate the scarcity of real deformation interferograms. As a result, 32 interferograms were expanded to 3168 samples, enabling the final model to achieve a detection accuracy of 85.22% through transfer learning.  
The effectiveness of data augmentation methods is largely determined by the size and quality of the original dataset. Although data augmentation can increase dataset size through various transformations, the generated data remain constrained by the inherent characteristics and distribution of the original dataset, making it difficult to fundamentally resolve the problem of data scarcity. In other words, even with data augmentation, class imbalance remains a significant issue [38], and the limited number of deformation samples may be inadequate to fully capture the diversity of global seismic deformation patterns. Compared to data synthesized using physical models, augmented data generally lack the diversity and complexity required to represent real-world conditions.
## 1 Introduction
In this way, network resources are expected to scale more easily according to changing needs and infrastructure costs are expected to decrease. Figure 2: End-to-End SDN- and NFV-based 5G Network Architecture. In this study, we evaluated cloud-based dynamic network scaling and network slicing approaches for next-generation wireless networks. SDN-NFV-based network scaling methods and RAN network slicing performance regarding physical resource utilisation are investigated. An SDN application has been developed that allows network resources to be dynamically scaled according to changing user needs to ensure continuous quality of service. This application detects congestion on the network slice via the OpenDaylight SDN Controller [12] and enables dynamic scaling of network resources via NFV-MANO. Section 4 describes the SDN-NFV-based QoS management. This study also discusses the applicability of RAN network slicing in the case of shared physical resource blocks for different network slices. The ability of network slices created to meet different application needs to dynamically share the physical resource block is investigated. Simulation tools are used to show the effect of the number of users, traffic type and numerology changes on physical resource utilisation. Section 2 describes end-to-end network slicing and the physical resource usage of 5G RAN network slicing. Section 3 analyses the physical resource block usage according to different network slices of QoS type, bandwidth and numerology in the simulation environment.
# Research on the Outburst-Rockburst Coupling Disaster Law Based on True Triaxial Unloading Tests
## 3 Analysis of Mechanical and Acoustic Characteristics of Outburst-Rockburst Coupling Disasters
### Meso Failure Characteristics  
The internal meso structure of the No.2 coal specimen with an initial gas pressure of 1.8 MPa was scanned by industrial CT, as shown in Figure 5. Figure 5a shows a physical diagram of the failure mode on the unloading surface of the specimen. As can be seen from the figure, there was an obvious \"triangular\" crushing opening with a large cross-section in the upper right corner of the unloading surface, and an \"oval\" crushing opening was also observed in the middle of the specimen. Additionally, small coal fragments and many powdery coal particles were distributed in a disorderly manner in front of the crushing opening. Combined with the gas pressure changes during the sudden stress drops in the test process, the \"small structure\" instability failure (coal and gas outburst) was identified during the first structural instability failure. In Figure 5c, there is a V-shaped main crack in the specimen, which was formed by the penetration of many cracks. Meanwhile, there were many small cracks around the crack extension direction, which indicated that before the rockburst, the main crack development area had been damaged under crustal stress and gas deterioration. The coal and gas outburst resulted in the rapid development of cracks in the coal body, which reduced the bearing capacity of the specimens. Under the action of crustal stress, the further extension and penetration of cracks led to rockburst. Therefore, the essential mechanism of the outburst-rockburst coupling disasters can be explained that under the synergistic action of crustal stress and gas, the initial gas in the coal seam exerted a deterioration effect on the coal body, which contributed to the further development of cracks in the coal body, thus reducing its bearing capacity. Finally, the coal body system became completely unstable under the disturbance of mining, resulting in outburst-rockburst coupling disasters.  
Figure 4: The stress–strain curve in the unloading direction under different gas pressures.