esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
In this article, agents use continuous action space, starting from completely random decisions, gradually learn how to make effective maneuver decisions by ACRL, and ultimately are able to cope with targets of different situations. Different from previous research, ACRL uses continuous action spaces in maneuver decision-making and regards miss distance as the criterion for air combat rather than whether the target has entered the missile attack zone. Therefore, this study is more in line with reality.  
Designing reward functions is a time-consuming job, and unreasonable rewards can prevent agents from effective learning. Different from previous research, ACRL does not require any handcrafted reward function and only uses the results of air combat as reward signals. Meanwhile, according to Figure 3, ACRL can increase the number of wins during training, which indicates that ACRL is a concise and efficient method, and automatic curriculum learning proposed in this article is vital.  
At the end of the training, the averages of wins of ACRL, PPO-WC, PPO-RC, and SAC-C are 28.5, 12.0, 21.2, and 15.4, respectively. The averages of draws of ACRL, PPO-WC, PPO-RC, and SAC-C are 63.3, 86.7, 80.0, and 78.9, respectively. These statistics indicate that ACRL is more effective than the other three methods.  
As shown in Figures 5b, 6b and 7d,f, agents can make decisions to attack and then move away, which reflects that they have learned to use missiles after training. This ability is acquired through explorations, and there are no handcrafted reward functions throughout the training process to guide the agent in making such decisions, which indicates the effectiveness of ACRL. Meanwhile, these behaviors are interpretable: If one continues to approach the other after launching its missile, it may be hit by the missile launched by the other. Therefore, during the training process, agents learn to stay away from the others after launching missiles in order to reduce the probability of being defeated.  
There are also some limitations: 1. The proposed method can only be used in one-on-one air combat. However, real air combat usually contains many planes. Therefore, the method is not suitable for multi-agent systems. 2. The aircraft model is a three-degree model; therefore, it is a simplified and unrealistic model. Meanwhile, more complex and realistic models are more expensive. 3. This paper does not include airplane guns. However, both missiles and guns are common weapons in air combat.
# Stochastic Game Analysis of Cooperation and Selfishness in a Random Access Mechanism
## 2 Related Work
### Cooperative Game Models  
Cooperative models study the situation of collaboration and coalition between users. In our previous work [12; 23], we proposed a cooperative model of SA and SAZD. Our results showed that in both mechanisms, cooperation leads to the best results. We also showed that the SAZD mechanism outperforms the standard SA in terms of all performance metrics. However, in heavy-traffic conditions, the cooperation between users leads to unfair resource allocation due to the system specifications that allows newly arrived packets to be transmitted immediately after their arrival. Thus, even if the overall system performance is maximized, it is only used by newly arrived packets, making backlogged packets stay on hold for a very long time. To address this issue, we have proposed in [10] an enhanced pricing mechanism that allows us to guarantee a fairness level between newly arrived and backlogged packets.  
The authors of [15] proposed a cooperative scheme for SA with power diversity transmission and an interference cancellation technique for multi-satellite networks. Using an optimization approach, they derived the optimal transmission power distribution. Their results show that the cooperative SA with optimized transmission power outperforms the SA with uniform power distribution. Even though their analysis and results focus mainly on the throughput, they investigated the access delay and energy consumption in [24].  
The authors of [16] studied a cooperative SA full-duplex wireless network with two users. They found that they should only cooperate when the user has a higher successful transmission probability. However, the cooperation concept proposed in their study is a relay-based approach between a source and relay node, which could be extended to multiple user scenarios. The authors also proposed in [17] an optimal cooperation policy that outperforms, in some cases, the full-cooperation and non-cooperation policies.  
In [18], the authors proposed a beamforming collision resolution scheme that exploits multiple satellites' cooperation to decode the collided packets. The beamforming algorithm is used when a deadlock state is reached. Otherwise, they use the successive interference cancellation.
## 3 Result and Discussion
### Impedance Characterization of Porcine Tissue Using the Needle
The impedance measurement with monopolar injection needle could not analyze the impedance characteristics of each tissue layer from the total measured impedance data. Despite the limitation of the monopolar needle that the measured impedance was varied with relative distance between the two electrodes and the depth of the needle electrode, it was able to distinguish the impedance of the dermis, hypodermis, or muscle.
# A Comprehensive Review of Acoustic Methods for Locating Underground Pipelines
## 4 Commercially Available Acoustic Locators  
### Pipe Locators Based on the Traditional Acoustic Transmission Method  
As mentioned in preceding section, the traditional acoustic transmission method lays the foundation for most commercially available acoustic locators for pipe. In this section, three pipe locators are introduced, including non-metallic pipe locator NPL-100 produced by FUJI TECOM INC (Tokyo, Japan) [81], plastic water pipe locator RD500 produced by RADIDDETECTION (Bristol, UK) [82] and System Combiphon produced by SEWERIN (Gutersloh, Germany) [83].  
The traditional acoustic transmission method involves acoustic generator and receiver parts. Usually the acoustic source systems consist of a transmitter or a vibrator with oscillators, where more than one transmitters or oscillators may be included in kits to suit different applicational conditions. Response signals can be picked up by the high sensitive geo-sensor and headphones. The region with most intensity of signals corresponds to the location of buried pipes. Figure 18 shows the operation procedure of buried pipelines localization using the System Combiphon [116]. All the locators based on the traditional acoustic transmission method require access to pipes like fire hydrant, tap and so on. The effective scope and the main features according to the brochure are summarized in Table 2.  
Figure 18: Buried pipelines localization using the System Combiphon [116].
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 4 Receiver Design
### Hybrid Analog-Digital Equalizer  
In this section the two-step equalizer is proposed, where, in the first step, we compute the analog equalizer, while in the second one, we compute the digital part. The analog equalizer is computed to minimize the error between hybrid and full digital schemes. Owing to hardware constraints, the analog equalizer is constant over the subcarriers. Finally, for the digital part, a ZF approach is explored, to remove the interference that was neglected in the analog part. This means the inversion of only low-dimensional matrices, and the digital equalizer is computed on a per subcarrier basis.
# A Minimum Mean-Square-Error (MMSE) Decoder for Quasi-Orthogonal Space-Time Block Code
## 5 Conclusions  
In this paper, a decoding method for QO-STBC was proposed to achieve a lower BER result than the conventional algorithms, and to overcome the rank-deficient problem. The proposed algorithm contains the estimation algorithm of the noise variance to overcome the implementation problem from the MMSE. Moreover, it has low computational complexity without matrix inversion. Therefore, the proposed algorithm achieves a low BER and small computational complexity. Simulation results verified that the proposed algorithm had a small BER compared to the conventional algorithms in a Rayleigh fading channel.
# A Survey of the Recent Trends in Deep Learning Based Malware Detection
## 4 Direction for Future Work
### Moderate Sized and Updated Dataset  
As highlighted in the previous section, most of the survey papers have taken a small dataset which is not enough for research to produce reliable results. This problem is mainly due to the constraints of handling big data or due to the unavailability of the labelled datasets. Small datasets that have been used in research produce biased results that can't be reproduced in a real environment. Problems of unavailability of labeled data, imbalanced data, or unavailability of enough samples for a particular class of malware can be coped with through Few Shot Learning (FSL) and its variants. So that improved or state-of-the-art results can be achieved without jumping into the problems of handling and processing large datasets. Secondly, some of the datasets used for research purposes were quite old. Since malware is being produced daily with the latest and new characteristics, therefore, research carried out on outdated data might not be helpful in real-time. It is recommended that up-to-date data should be collected which should consist of all the latest variants of malware. Another issue that needs to be taken care of is the reflection of real data distribution in the datasets for training and validation of proposed frameworks.
## 2 Materials and Methods
### Data and Preprocessing
#### 2.2.2 Remote Sensing Data  
In this study, the time series (2012-2015) of the GLASS LAI/_f_PAR product [35] was obtained to optimizeMOD_17 model. GLASS LAI is a high-quality remote sensing data product with a long time series, no missing data, and global coverage. During the production procedure of this product, cloud removal, snow removal, missing value filling, filtering and other processes had been carried out to reduce some errors and increase the practical applications and the possibility of obtaining data, and they were provided free of charge to users.  
The land-use data were produced by using the fine resolution observation and global land cover monitoring (FROM-GLC-seg) product ([[http://data.ess.tsinghua.edu.cn/index.html](http://data.ess.tsinghua.edu.cn/index.html)]([http://data.ess.tsinghua.edu.cn/index.html](http://data.ess.tsinghua.edu.cn/index.html)) (accessed on 25 June 2021)) from Landsat TM and ETM+ data, using the Random Forest (RF) classifier with high overall accuracy. The CO\({}_{2}\) concentration data are downloaded from the observational dataset from the Mauna Loa Observatory in Hawaii ([[https://gml.noaa.gov/obop/mlo/programs/esrl/co2/co2.html](https://gml.noaa.gov/obop/mlo/programs/esrl/co2/co2.html)]([https://gml.noaa.gov/obop/mlo/programs/esrl/co2/co2.html](https://gml.noaa.gov/obop/mlo/programs/esrl/co2/co2.html)) (accessed on 21 July 2021)). The soil texture dataset is provided by the Resource and Environmental Science Data Center (RESDC) of the Chinese Academy of Sciences ([[https://www.resdc.cn](https://www.resdc.cn)]([https://www.resdc.cn](https://www.resdc.cn)) (accessed on 29 July 2021)); it is a 1:1 million soil-type map which was mapped on the second soil survey in China profile data. The soil types were divided into three categories: sand, silt, and clay; their values were in percentages, and the spatial resolution is 1 km. The DEM data were obtained from ASTER GDEM ([[https://www.gscloud.cn](https://www.gscloud.cn)]([https://www.gscloud.cn](https://www.gscloud.cn)) (accessed on 5 September 2021)) and it was used to calculate the slope and aspect datasets. In order to maintain consistency with the simulated spatial scale of the Biome-BGCMuSo model, we reprojected and resampled the above data into Albers projection, WGS-84 coordinate system with the resolution of 1 km.
## 4 SNR Characterization  
In this section, we present the measured SNRs versus subcarrier frequency offered by the three transmitter schemes. Before presenting the results, we study the performance of the RSOA itself by  
Figure 4: Experimental setups of three RSOA based colorless transmitter schemes in a WDM-PON scenario: (**a**) ASE spectrum-sliced, (**b**) self-seeded RSOA-FCL, and (**c**) externally seeded RSOA. At the optical network units (ONUs), the upstream signal from the arbitrary waveform generator (AWG) is amplified and added to a bias current to drive the RSOA. The ONU and the remote node (RN) are connected by the distribution fiber (DF). At the RN, an optical bandpass filter (OBPF) mimics the WDM filter. In the case of the RSOA-FCL in (**b**), the Faraday rotator mirror (FRM) is placed behind the filter and a coupler to form the Fabry-Perot cavity, shown by the red dashed line. For the externally-seeded RSOA in (**c**), the seeding cw laser is place at the central office (CO). On the receiver side at the CO, pre-amplification and direct detection are performed. A real-time oscilloscope (RTO) records the electrical signal after the photodiode. An optical spectrum analyzer (OSA) is used to monitor the optical signal at the CO. EDFA: Erbium doped fiber amplifier, VOA: variable optical attenuator.  
measuring the electrical modulation bandwidth. In a second step, we then focus on the self-seeded RSOA-FCL. We study the impact of the WDM filter bandwidth in the RN, the fiber cavity length and the bias current on the SNR offered by the RSOA-FCL. Once the optimum operation parameters and limitations of the RSOA-FCL have been understood, we adjust the operation points of the ASE spectrum-sliced and the externally seeded RSOA. So for instance we set the WDM filter bandwidth of the ASE spectrum-sliced source to be identical with the RSOA-FCL. The input power for the externally seeded transmitters is also set for maximum performance. Here we only give the operation points that have been selected while one can find more details on finding the ideal operation points of an ASE spectrum-sliced transmitter in [44, 45] and the externally seeded transmitter [25, 46].
# Faster MDNet for Visual Object Tracking
## 2 Related Work  
### MDNet-Based Trackers  
Inspired by R-CNN [29] from object detection, MDNet [20] treats object tracking tasks as a task of distinguishing targets and backgrounds. MDNet contains shared layers for generic feature extraction and domain-specific layers with classifiers. Each domain-specific layer corresponds to a video sequence. Such domain-specific layers are updated online during the tracking process. Although MDNet achieves excellent results with generic datasets, it achieves a tracking speed of \(1\) frame per second (FPS), failing to meet the requirements of real-time tracking. VITAL [21] employs the generated adversarial network into MDNet. Through adversarial learning, this generative network allows classifiers to learn more robust features, thereby suppressing masking and preventing the model from overfitting. VITAL effectively improves tracking accuracy but does not even run at \(1\) FPS on GPU. RT-MDNet [22] draws on the idea of Fast R-CNN [30] to extract shared features of the search region and to improve object localization using the ROI Align. The tracking speed of this approach is greatly improved but limits the tracking accuracy. Considering that the idea of these methods has a certain connection with image classification tasks, we make enhancements on MDNet to improve both tracking accuracy and speed.
## 3 Related Works
### IoT-Based Waste Management Strategies in an SC Ecosystem  
This section discusses the application of smart technologies to solid waste management in an SC. Some methods involve the use of sensors, such as IR sensors, ultrasonic sensors, RFID tags, Wi-Fi, and GPRS for real-time data generation and gathering from smart bins that are spatially distributed at different locations. Table 1 presents a summary of previous works on IoT-based techniques for waste management in the smart city domain. A comprehensive survey of IoT-based approaches for waste management in an SC is presented in [2]. Findings from the literature reveal that researchers have focused attention on the use of IoT-based methods for data collection for SC waste management strategies, although none has considered the IoV-based approach for data collection for waste management in an SC. In contrast, our proposed method exploits vehicular networks (IoV) for data collection and transmission for efficient waste management in an SC. In terms of SC waste management, none of the existing approaches from the literature have considered the need to optimize energy consumption for big data collection in an LS-WSN. To this end, we propose the use of multiple data collector vehicles (DCVs) to optimize energy consumption for big data collection in an LS-WSN. However, we note that the use of multiple DCVs also comes with some challenges and therefore propose a swarm intelligence IoV-based method to address some critical issues associated with efficient data collection.  
Figure 3: A working model of the proposed approach in an LS-WSN.
## 1 Introduction
In this scenario, remote sensing emerges as an alternative with operational capacity, since satellite imagery can be combined with meteorological data to generate distributed maps of both eWUE and ET, and ultimately GPP, covering large spatial areas. Important ET monitoring examples are operative at a global or regional scale. On the one hand, while the EEFlux system [52] displays remote-sensing-based ET products through thermal satellite imagery at a global scale, ET estimations at a regional scale are provided by other developments, such as the OPENET application [53] over different states in the U.S., also using thermal satellite imagery, while optical satellite data are used over Spain [54] and over certain regions of Australia [55]. On the other hand, GPP satellite-based estimations have been developed at a global scale [56; 57; 58] as well as at a regional scale [59; 60]. Focusing on almond orchards, our study highlights the significance of incorporating GPP, NEE and eWUE measurements in agriculture for climate mitigation strategies, elucidating its importance in advancing our understanding of Earth's carbon dynamics and fostering effective approaches for a more sustainable future. Data were gathered from a six-season experiment (2017-2022) in two different locations in southeastern Spain, allowing for a comprehensive and novel analysis of NEE, GPP and eWUE response in almond trees. Therefore, this paper aims to (1) quantify and understand NEE and GPP behavior in almond trees using EC data and (2) parametrize eWUE in almond trees using a remote-sensing-based approach. This work will contribute to a better understanding of carbon sequestration and water use efficiency in almond agroecosystems.
## 3 Results and Discussion
### Geographical Patterns of Trend Types  
Following Figure 2, six trend types were considered. A global count of grid cells (Figure 3(b), inset) indicated that greening trends (green hues) prevailed, which is in line with conclusions of previous studies [2; 3; 12; 13], although most of these trends seem to have experienced a setback. At the same time, many of the grid cells with a browning trend contained a burst in which canopies showed anomalously high chlorophyll absorption, possibly because of a short-term relaxation of climatic growth constraints. Another substantial part of the browning trends recently changed into greening (e.g., parts of Australia and Argentina in the late 2000s). As a consequence, monotonic browning represents the smallest class. We assessed the possibility of two stable segments separated by an abrupt change event, but too few examples of this type were found to include them in a separate class.  
Each of the trend types occurred in various geographical regions (Figure 3(b), map). The geographical distribution of the three main trend categories (rows in Figure 2) is discussed in the following sections. These sections point out some of the most conspicuous regions and are not intended to be an exhaustive discussion on candidate drivers. A discussion on potential relationships between NDVI trends and various drivers is, among others, provided by [PERSON] _et al._[40].  
Figure 3: (**a**) Spatial distribution of the timing of the shifts in the global greening and browning regime. The detected change points were binned into 2-yearly classes. The inset depicts the number of 0.083\({}^{\circ}\) grid cells per bin; (**b**) Spatial distribution of types of vegetation activity trends as defined in Figure 2. The inset depicts the number of 0.083\({}^{\circ}\) cells per trend type. In both maps, terrestrial areas that appear white were masked based on the quality flags or median NDVI criterion.
# A Survey on Parameters Affecting MANET Performance
## 2 Routing in MANETs  
In MANETs, each node is responsible for packet forwarding on behalf of the source node, and it also initiates routing discovery mechanisms to discover its neighbors in the network, then find the best route to reach a destination node [3]. When a new node joins the network, it announces itself by broadcasting a hello message to all neighbors and starts learning about the network [4]. In addition, each node holds a routing table database to maintain a record of the current network nodes as well as the number of hops to reach each node inside the network [5]. There are a multitude of routing protocols related to MANETs' discovery and data forwarding. The three main categories for routing protocols in MANETs are as follows:  
* Proactive routing protocols: For example, OLSR, each node maintains its routing table by periodically updating its information [6]; this increases network overhead. On the other hand, routes will always be available with a minimum delay. Proactive protocols provide better performance than reactive protocols as each node continuously updates its awareness of network changes. When a request is received, the packet forwarding procedure is directly handled.
* Reactive routing protocols: For example, AODV and DSR, when a source node tries to perform a packet transmission, it initiates a route discovery mechanism to know how to reach the destination. After the route is determined and updated in the routing table, the packet is forwarded [7]. Reactive protocols have minimal network overhead, but there is a delay time consumed in the route discovery.
* Hybrid routing protocols: For example, ZRP, the close local neighbors to a node are periodically updated, and the global nodes that are not direct neighbors will be updated on demand such as in reactive routing protocols [8].  
This paper describes the AODV, DSR, and OLSR routing protocols and the related routing parameters for each. Figure 2 shows a simple classification of the MANETs' routing protocols.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Meaning** \\ \hline MANETs & Mobile ad hoc networks \\ AODV & Ad hoc on-demand distance vector \\ DSR & Dynamic source routing \\ OLSR & Optimized link state routing protocol \\ RREQ & Route request \\ RREP & Route reply \\ DPC & Delete period constant \\ RERR & Route error \\ TC & Topology control \\ MPR & Multipoint relays \\ mph & Mile per hour \\ DB & Decibel \\ DDoS & Distributed denial of service \\ THPT & Average throughput \\ AETED & Average end-to-end delay time \\ PDR & Packet delivery ratio \\ PLR & Packet loss ratio \\ ROR & Routing overhead ratio \\ NRL & Normalized routing load \\ NL & Network load \\ \hline \hline \end{tabular}
\end{table}
Table 1: The list of abbreviations.