esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 3 Intelligent Power Management  
This section describes the multimodal dynamic power management strategy. In particular, machine learning (ML) policies are proposed for power management taking into account the available energy stored in the supercapacitor for the IoWT system and the sensors' data statistics.  
A processing chain methodology is presented for analyzing the biosignal statistics and supercapacitor voltage level to efficiently manage the energy of IoWT devices. As illustrated in Figure 5, the variables are intelligently processed by the ML-decision tree to estimate the next sleep period before BLE transmission.
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
###### Abstract  
In recent decades, population growth and economic development have greatly influenced the pattern of land use/land cover (LULC) in Rwanda. Nevertheless, LULC patterns and their underlying change mechanisms under future climate conditions are not well known. Therefore, it is particularly important to explore the direction of LULC transfer in the study area, identify the factors driving the transfer of different types of LULC and their changes, and simulate future LULC patterns under future climate conditions. Based on LULC analyses of Rwanda in 1990, 2000, 2010, and 2015, the LULC pattern of Rwanda in the next 30 years was simulated using an LULC transition matrix, random forest sampling, the Markov chain model, and the PLUS model. The results showed that LULC change in the study area primarily comprised a decrease in forest area and expansion of cropland area, accompanied by a small increase in grassland area and an annual increase in urban land area. Prior to 2000, the LULC in Rwanda was mainly converted from forest and grassland to cropland, with the ratio being 0.72:0.28. After 2010, the LULC was mainly converted from forest to grassland and cropland, with the ratio being 0.83:0.17. Changes in forests, grasslands, and cropland are driven by multiple factors, whereas changes in wetlands, water, urban land, and unused land are more likely to be driven by a single factor. The existing trend of LULC change will continue for the next 30 years, and the future LULC pattern will exhibit a trend in which cropland area will increase in the west and grassland area will decrease, whereas grassland area will increase in the east and cropland area will decrease.  
land use/land cover; driving factors; future LULC +
Footnote †: journal: Applied Sciences
## 5 Experimental Results
### Throughput Analysis  
Despite the GPUs' superior parallel-processing power, the data transfer latency between the CPU and GPU hides the overall performance of the pattern-matching algorithms. For this reason, this study does not cover optimizations to minimize the data transfer costs between the CPU and GPU. The focus of the study is the throughput of the GPU core responsible for executing the pattern-matching process, and this throughput is calculated using Equation (3). Value n represents the total number of bits of the target text of n characters in length (bytes), while Tgpu indicates the execution time of the pattern-matching process on the GPU.  
\[\text{Throughput}=\frac{\text{n}}{\text{Tgpu}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Pattern Count** & **AC (KB)** & **WM (KB)** & **PA (KB)** \\ \hline
100 & 2625.512 & 4331.49 & 8.161 \\
200 & 5181.12 & 8619.935 & 14.097 \\
300 & 7785.044 & 12,908.622 & 19.615 \\
400 & 10,515.412 & 17,197.241 & 25.281 \\
500 & 13,271.48 & 21,486.196 & 31.218 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of the number of states in the automaton of the AC algorithm and PA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pattern Count** & **PA (KB)** \\ \hline
100 & 8.161 \\
200 & 14.097 \\
300 & 19.615 \\
400 & 25.281 \\
500 & 31.218 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Memory space required for the PA tables.  
The previous subsection compares the memory space required during the pattern-matching process for AC, WM, and the PA. The memory space required for the AC and WM algorithms is too large to fit in the 64 KB shared memory of the GPU used in the pattern-matching tests (Table 11). For this reason, the necessary tables are kept in global memory in all pattern-matching tests performed with these two algorithms. However, the memory space required by the tables of the PA is large enough to fit in shared memory. Therefore, there are pattern-matching tests in which the tables required for the PA are kept in global memory, as well as the pattern-matching tests in which the tables are kept in shared memory.  
In this subsection, throughputs obtained from the pattern-matching tests of AC, WM, and the PA are determined using datasets containing different numbers of patterns. Then, factors limiting the pattern-matching performance of these algorithms are discussed using the obtained results. Finally, throughputs obtained for the three algorithms in the pattern-matching tests are compared.
## 5 Conclusions  
The comparative performance on the energy consumption, throughput, accuracy, latency, and computational complexity of the proposed ACRLPC framework with a few other relevant frameworks has led to some interesting insights worth sharing. As illustrated in the results, ACRLPC was found to be superior to the others under most metrics. It was the most energy efficient, achieving the fastest throughput+accuracy+latency at the lowest energy cost to make it. These results highlight the needs of energy efficiency and performance optimization at the level of network framework components. The TCH-MAC framework managed to produce competitive throughput and accuracy compared to the other frameworks, and, even with relatively more energy consumed, it is still a strong candidate for situations when throughput is the dominant concern. Although FDU-MAC and UW-ALOHA-QM give reasonable delays in some cases, due to their higher energy consumption and latency, they are only capable of tackling less performance-sensitive cases.  
Figure 8: Computational complexity comparison of the ACRLPC framework and benchmark methods.  
An in-depth analysis of how various factors impact the implementation of these frameworks is crucial to achieving a balanced trade-off between energy consumption, throughput, accuracy, latency, and computational complexity. This understanding is essential for identifying the strengths and limitations of each framework. However, future research should focus on further optimizing these frameworks, exploring hybrid models that integrate the distinct advantages of different approaches. For example, incorporating techniques, such as edge learning, and leveraging emerging smart distributed infrastructures could offer significant improvements.  
Conceptualization, W.U.R. and Q.G.; methodology, W.U.R. and F.Z.; software, W.U.R. and M.A.; validation, Q.G., F.Z. and S.Z.X.; formal analysis, W.A. and M.T.; investigation, W.U.R. and M.I.K.; resources, Q.G. and F.Z.; data curation, M.A. and S.Z.X.; writing--original draft preparation, W.U.R.; writing--review and editing, F.Z. and M.T.; visualization, W.A.; supervision, Q.G.; project administration, F.Z.; funding acquisition, Q.G. All authors have read and agreed to the published version of the manuscript.  
This research is funded and supported by the Fundamental Research Funds for the Central Universities No. 3072025Z0502 and Heilongjiang Provincial Natural Science Foundation JQ2023A004.  
Dataset available on request from the authors.  
The authors declare no conflicts of interest.
## 1 Introduction  
### Background and Motivation  
The contribution of hybrid energy systems (HES) is increasing in the world at industrial, commercial, and domestic levels to address environmental and economic concerns [1]. A HES consists of two or more energy resources operated together to obtain an improved efficiency and a better energy balance. Due to advanced technology, compact and high-efficiency micro-combined heat and power systems (micro-CHP) are gaining popularity at domestic level [2; 3; 4; 5; 6; 7]. According to the American Council for an Energy-Efficient Economy's report, the modern CHP systems can work at an efficiency as high as 80% in contrast to the commonly prevailing methods of generating electricity and thermal power separately, which have a lower combined efficiency of 45% [8]. Similar to HES, electric vehicles (EVs) are another important and eminent element of modern life. Despite being eco-friendly and less reliant on scarce petroleum products, EVs are challenging to the power grid's stability and economy. They require ample power to charge their batteries resulting in an increased demand on the network and potentially worsening peak power demand events. This situation has called for various charging techniques and other strategies to enable increased penetration [9; 10; 11].  
In addition to HES and EVs, the modern power system has an increased share of inexhaustible renewable energy systems (RES) which are human- and eco-friendly. Due to technological advancements, their integration at household level is increasing and results in improved economy and reliability especially when the power systems allow a bidirectional power flow. Moreover, the role of RES is also attractive for the power system operator as it reduces the requirement of network expansion, especially when many EVs are integrated.  
Keeping in view the higher penetration of RES and EV at a smart house (SH) level which is equipped with a micro-CHP system, this work fulfills the need for a comprehensive study that explores their optimal operation in a bidirectional utility grid. Moreover, the possibility of combined operation of HES and EVs will increase if their economic operation is analyzed and optimized carefully. As such, this work presents an intelligent hybrid energy management system (IHEMS) for an SH which has an EV and a RES that are connected to a bidirectional utility grid.
## 2 State of the Art
Similarly, in [20], the authors use voice sound with background noise characteristics of conversations represented as spectrograms. Then, a deep neural network of four layers is used to eliminate the noise in the spectrograms. Finally, ref. [21] presents a method for enhancing laser-detected speech signals by optimizing spectrograms to mitigate a low signal-to-noise ratio and non-stationary noise challenges. The approach incorporates GAN with spatial attention and integrates short-time objective intelligibility (STOI) into the loss function, successfully improving speech quality under severe noise interference conditions. This research stands out in the field due to several key innovations. Primarily, it targets evaluating the impact of four distinct real-world background noises on speech audios. To achieve this, we have developed a unique deep learning model designed to effectively eliminate these noises from a series of speech recordings. Our choice of a variational autoencoder for this purpose distinguishes our approach. Moreover, our study is the first of its kind to incorporate a subjective evaluation method. This involves a panel assessing the clarity of speech audios post noise removal. While other studies rely solely on mathematical metrics, our approach adds a crucial human dimension to the assessment. This step is often overlooked in other research due to the technical challenges and potential information loss when converting spectrograms back into raw audio, possibly leading to subpar results that others might choose not to report. Our willingness to undertake this complex transformation and evaluation process further underlines the novelty and depth of our work.
# ATC Human Factors Involved in RPAS Contingency Management in Non-Segregated Airspace
## 4 Results
### Results of the Operational and Workload Questionnaires  
Additional questions introduced in the workload and operation subjective questionnaires (see Tables 7 and 8, respectively) should allow us to better perceive the workload increase experienced by the ATC controllers. Figure 7 provides the results associated with the workload questionaries.  
Q1 provides similar results as those recorded in the TLX questionnaire, with minor increases between scenarios (a) and (b), with more significant ones between (b) and the rest. Questions Q11 and Q12 reproduce similar results for stress and fatigue, with very low levels and minor increases when managing RPAS contingencies. Q10 indicates a minor reduction in situational awareness when managing RPAS contingencies, while the simple presence of the RPAS does not imply a reduction in awareness.  
Questions Q13 and Q14 evaluate the capability of the ATCO to perform some of the required functions: traffic sequencing and separation management. The results show that those functions could be implemented with relative ease, but no correlation is shown between scenarios. The implementation of both functions seems to depend more on the actual flow of traffic than on the scenario.  
Questions Q21 and Q22 evaluate the capacity of the ATCO to organise the functions to be performed, stay ahead of potential conflicts, and correctly plan the actions to be performed. In both cases, the ATCO felt they were well ahead of the tasks to be performed, with significant increases when flight intent was used.  
Finally, questions Q24 and Q27 address the situations in which the ATCO starts to become overloaded and, thus, it is late in its reactions or becomes too focused on a single task. The results clearly show that such a situation does not occur in any scenario. The ATCO never becomes overloaded or too focused.  
Overall, the ATCOs exhibit an increase in workload when managing RPAS contingencies. However, the TLX analysis demonstrates that such an increase is minimum and the workload questionnaire supports that ATCOs can maintain their operational capacities, efficiently implementing the assigned functions with the required performance.  
Figure 7: Subjective rating: ATC workload perception on RPAS contingency scenarios compared with baseline scenarios (see questions in Table 7).
## 1 Introduction
Consequently, our method prioritizes minimizing the gap between semantic information and world/agent data, including maps and six degrees of freedom (6-DOF) poses. In the second stage, for large-scale environments, the map-merging stage is required to construct a global map, i.e., the multiple drivable region maps are integrated into a single map. During the map-merging stage, our proposed merging method is performed based on overlapping regions specified by semantic information. This approach avoids the drift problem that frequently occurs in large-scale drivable region mapping. In these two stages, a large-scale drivable region map is obtained. In summary, this study proposes a new method to create a large-scale map of drivable regions through semantic map generation and map-merging algorithms. Initially, input images captured by RGB-D sensors undergo semantic segmentation to distinguish between roads, vehicles, and non-drivable areas. We improved the accuracy of the semantic location information through loop closure refinement and map-merging. Finally, leveraging the camera pose derived from the SLAM system, the drivable regions are visualized in real time. To the best of our knowledge, this study represents the first instance of using map-merging and semantic visual SLAM to construct a large-scale semantic map. The main contributions of our proposed method are as follows:  
* Semantic visual SLAM and map-merging methods are proposed to identify drivable areas in large-scale environments. Our method allows for the visualization of drivable areas that traditional visual SLAM cannot depict;
* A loop closure refinement for semantic information is developed to build seamless semantic 3D maps. This approach allows for the simultaneous use of semantic segmentation and visual SLAM results to create a unified semantic map, minimizing the gap between semantic information and world/agent data;
* A 3D point cloud registration-based semantic map-merging is performed not only on 3D point descriptors but also on semantic labels to create a large-scale map. Our proposed method uses fewer points in overlapping areas, thereby accelerating the map-merging process;Our proposed system was tested in real-world experiments by attaching a single RGB-D camera to a vehicle. We constructed a large-scale drivable map from multiple segmented datasets, demonstrating higher accuracy than existing methods.
## 4 Discussion
### Impact of Cropland Protection Policies on Agricultural Production  
Since the 1980s, China has implemented cropland protection policies to ensure national food security. The basic national policy of \"cherishing and rationally utilizing every inch of land, and effectively protecting cropland\" was first proposed in 1986 [47]. After 1990, social phenomena such as 'development fever' and'real estate fever' emerged, causing a surge in construction land demand and accelerated the loss of cropland in Guangdong Province.  
Figure 11: Comparison of two cropland use transitions causing GPP losses.  
Between 1992 and 1994, 8420 km\({}^{2}\) of cropland was lost, primarily due to non-agricultural conversion. To curb this phenomenon, the government adopted the \"Regulations on the Protection of Basic Farmland\" in 1994, introducing protection red lines to regulate agricultural practices [48]. However, as urbanization accelerated at the beginning of the 21 st century, the pressure on cropland protection intensified, prompting the government to introduce a policy of balancing cropland with compensation through the \"dynamic balance of the total amount of cropland\". Despite these efforts, studies indicate that much of the compensated cropland is located in mountainous areas and is of lower quality. Furthermore, cropland protection policies appear to have a limited impact on curbing cropland abandonment, especially in areas with poor agricultural conditions [49; 50].  
To improve the quality of cropland, a comprehensive cropland protection system focusing on land use control and special protection of permanent basic farmland was established in 2012 [51]. After this period, the amount of cropland in Guangdong Province remained relatively stable, and the GPP capacity of newly added cropland has improved. Additionally, the government recognizes the severity of abandoned farmland and has sought to address this issue by encouraging reclamation, improving agricultural production conditions, and promoting cropland transfers. Overall, the state has gradually established a three-in-one cropland protection framework encompassing quantity, quality, and ecology. This has been achieved through measures such as delineating permanent basic farmland, constructing high-standard basic farmland, establishing a mechanism for the compensation of cropland occupation in different areas, and strengthening the supervision of the quality of cropland occupied and compensation. The cropland protection policy in Guangdong Province has played a role in mitigating cropland loss and supporting the sustainable development of agriculture. However, challenges such as declining cropland quality and widespread abandonment persist. It is essential to adopt scientifically informed measures tailored to local conditions for effective cropland protection.
# An Intrusion Detection System for RPL-Based IoT Networks
## 2 Background
### IoT Network Characteristics  
Based on the required characteristics of IoT devices, the network management needed to enable appropriate communication among a group of smart devices is not trivial. Indeed, to establish and maintain a network link, the following aspects must be taken into account:  
* _Wireless_: In order to provide a good link between devices, wired connections are usually the best option. However, in some scenarios where there are a lot of devices to be connected, the wired option is not the most appropriate. In these cases, wireless connections are the most popular and the ones that are used by the majority of IoT devices. Although a wireless connection has some advantages, it may have problems with interference, distance, and weather conditions [30].
* _Limited power_: As stated before, IoT devices are resource-constrained machines with small batteries. Because of this, the transmission range is typically not very large, and it becomes even worse when the battery is running low. Hence, the network must be configured correctly in order to reduce to the minimum the possible problems caused by the limited power.
* _Lossy network_: Taking into account the two previous characteristics, it is clear that, depending on the situation, there will be packet loss and the network may become unstable [31].  
As said, IoT networks have relevant characteristics that must be taken into account when designing or using a protocol for a specific purpose. Fortunately, nowadays, there is a protocol standard that can help to cope with the main problems of a 6 LoWPAN network: the RPL routing protocol. In addition, since some decades ago, IP global addressing has been a concern because of IPv4 address exhaustion [32], and with the increasing number of devices connected to the Internet, there is a need for the transition to IPv6 in order to have a sufficient number of addresses that can be used to respond to the growing demand each year. IoT devices are not exempt from this problem. Hence, the standard protocols to be used by IoT devices would typically rely on IPv6.  
The 6 LoWPAN was developed by IETF in order to fulfil the demand of the network usage by small sensors that need to be interconnected with each other [33]. 6 LoWPAN networks offer encapsulation and header compression mechanisms in order to transmit IPv6 packets over the network standard IEEE 802.15.4, where the channel conditions are far from perfect.