esa-sceva/satcom-chunk-collection huggingface dataset:
# Smart Beamforming for Direct LEO Satellite Access of Future IoT
## 3 System Model
### Link Budget  
In order to assess the feasibility of a direct LEO satellite access at Ka-band, the link budget analysis must be conducted. The system parameters are gathered in Table 1. It is noteworthy to mention that the effective isotropic radiated power density (EIRPD) and the antenna gain-to-noise-temperature have been obtained from [35]. Remarkably, based on the link budget we can compute the signal-to-noise-ratio (SNR) in dB, which indicates the reliability of the link, as:  
\[\text{SNR}=EIRPD+G/T-K_{B}-32.45-20\log_{10}(f\epsilon)-10\log_{10}(d_{\text{ MAX}}). \tag{12}\]  
The term 32.45 corresponds to \(20\log_{10}(4\pi\times 10^{9}/3\times 10^{8})\). The model can be further characterized with the atmospheric loss, the scintillation loss, the shadowing margin, and the co-channel interference [35]. It is important to remark that for array antenna systems, the antenna gain-to-noise-temperature becomes:  
\[G/T=G_{R}+10\log_{10}\bigl{(}N_{x}N_{y}\bigr{)}-10\log_{10}(T), \tag{13}\]  
where \(T\) denotes the system temperature. Particularizing for the values of Table 1, we get \(\text{SNR}=16.2\) dB. It must be highlighted that this value cannot be attained with low-cost and low-power terminals at the Ka-Band; thus, this paper focuses on more powerful broadband IoT terminals.  
The link budget analysis could be further complemented by including signal characteristics, such as the data rate, the modulation and coding scheme, and the bit rate. To provide the additional information we need to determine the range of received SNRs, the possible carrier-to-interference ratio (C/I) levels, the block length, and the required energy-bit-to-noise ratio (\(\text{Eb}/\text{N0}\)), to mention a few. To this end, we would need to conduct a more thorough analysis. Otherwise, it is difficult to make a judiciou  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Carrier frequency & \(f_{\text{c}}=29\text{ GHz}\) \\ Minimum elevation angle & \(\psi_{\text{MIN}}=40^{\circ}\) \\ Field of view & \(\theta_{\text{MAX}}=44.44^{\circ}\) \\ Satellite altitude & \(h=600\text{ km}\) \\ Maximum distance & \(d_{\text{MAX}}=882\text{ km}\) \\ EIRPD for terminals & \(EIRPD=-36.82\text{ dBW}/\text{Hz}\) \\ Maximum gain of antenna elements & \(G_{R}=5\text{ dB}\) \\ Satellite antenna gain-to-noise temperature & \(G/T=5\text{ dB}/\text{K}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: System parameters.  
the most suitable signal characteristics. It is worth highlighting that the paper is essentially focused on the design of a blind and open-loop beamforming technique in the context of satellite communications with massive MIMO. Since the physical layer design is not a trivial task and it is not within the main scope of the paper, we defer it to future work.
## 2 Results and Discussion
### LC/DAD/ESI-MS\({}^{n}\) Bioactive Compounds Analysis
Caffeic, ferulic, and \(p\)-coumaric acids are the most common acidic moieties of phenylamides, whereas aliphatic polyamines, spermidine, and spermine are their dominant amine components [32]. The total amount of phenylamide compounds in the bee pollen samples varied widely, in the range of 0.3 to 25.6 mg/g (Table 3). Among the samples, the lowest phenylamide levels were observed for the Cistaceae and Boraginaceae families (samples BP3 and BP5), at 0.33 mg/g and 2.01 mg/g, respectively, whereas the maximum levels were found in samples BP4 and BP7, with values above 20 mg/g, which corresponded to _Rubus_ sp. and _Castanea_ sp. \(N^{1}\)-feruloyl-\(N^{5}\), \(N^{10}\)-dicaffeoylspermidin (isomer) (\(m/z\) 644), \(N^{1}\), \(N^{5}\), \(N^{10}\)-tri-\(p\)-coumaroylspermidin (isomer) (\(m/z\) 582), diferuloyl comauoxyl spermidine (isomer), (\(m/z\) 642) and tetracoumaroyl spermine (isomer) (\(m/z\) 785) were detected at high concentrations, and are responsible for the high values of phenylamide observed in the BP4, BP6, and BP7 samples. The contribution of the phenylamides to the total concentration of bioactive compounds varied significantly, from 5% in _Cistaceae_ sp., to 86% in _Castanea_ sp. A previous work on bee pollen samples, collected from the northern region of Portugal, and dominated by _Crepis capillaries_, _Cytisus striatus_, and _Plantago_ sp., reported that phenylamidesconstituted 86% to almost 100 % of the total bioactive compounds; high concentrations of phenylamide compounds such as \(N^{1}\)-acetyl-\(N^{5}\), \(N^{10}\)-di-\(p\)-coumaroylspermidine, \(N^{1}\), \(N^{5}\)-di-\(p\)-coumaroyl-\(N^{10}\)-caffeoylspermidine, \(N^{1}\), \(N^{5}\)-di-\(p\)-coumaroyl-\(N^{10}\)-caffeoylspermidine, tetracoumaroyl spermine, and its isomers were found [10]. More recently, a study on _Castanea_ bee pollen from the north of the Iberian Peninsula described a phenolic profile resembling the one of sample BP7, where \(N^{1}\), \(N^{5}\), \(N^{10}\)-tri-\(p\)-coumaroylspermidine, and \(N^{1}\), \(N^{5}\), \(N^{10}\)-tricaffeoylspermidine were the most abundant compounds, with a concentration up to 9 mg/g and 12 mg/g, respectively [12]; this corresponds with the values obtained from the BP7 sample. The wide variability in phenylamide concentrations between samples can be explained in two possible ways, as follows: first, there are significant differences between the total bioactive compounds of bee pollen samples, as they are dependent on plant origin; second, the phenylamide compounds accumulated in the vacuoles of the pollen, and in the bilayer wall (exine and inline), may have been affected by the extraction method in different ways, since the extraction yield of each compound can be influenced by the wall thicknesses, structure, and apertures of the pollen grain [8].
## 3 Results and Discussion
### Effect of Contact Time  
The adsorption kinetics within 60 min are very important. This corresponds to a sufficient contact time that is able to eliminate the maximum amount of dye before reaching the chemical equilibrium point. For the time \(60\leq t\leq 80\), the elimination speed becomes very low and reaches equilibrium, hence the ideal contact time for this experiment at equilibrium is 90 min (Figure 14). The dye adsorption rate of BS-HVL is much faster than that of BC-HVL. The  
Figure 12: Effect of the amount of adsorbent BS-HVL (**a**) and BC-HVL (**b**) on the adsorption of dyes X-PR and MGC (t = 90 min; T = 21 \(\pm\) 1 \({}^{\circ}\)C; C0 = 50 mg L\({}^{-1}\); stirring speed 150 rpm and pH = 7 \(\pm\) 0.3).  
maximum amounts adsorbed at \(t\leq\) 60 min are 25 mg g\({}^{-1}\) and 28 mg g\({}^{-1}\) for BC-HVL and 42.5 mg g\({}^{-1}\) and 43 mg g\({}^{-1}\) for BS-HVL by X-PR and MGC, respectively.
# MAC/PHY Comprehensive Visible Light Communication Networks Simulation
## 3 Mac Layer in IEEE 802.15.7 Standard  
The IEEE 802.15.7 standard defines both the PHY and MAC layers for short-range optical wireless communications using visible light. The MAC sublayer is responsible for the channel access coordination of the user nodes (devices) in the network. The simulator developed in this work is based on this standard since it is the most extended and has a detailed description. Moreover, it is the most embraced by the scientific community and its information is open access.  
Some of the tasks performed by the MAC layer are beacon management, channel access control, Guaranteed Time Slots (GTS) management, frame validation, acknowledge frame delivery (ACK), and association/disassociation of nodes [3].  
The standard supports three network topologies. Peer-to-peer configuration is defined for establishing communication between two endpoints, broadcasting mode describes a one-to-all communication mode, and finally, star topology specifies a flexible bidirectional communication mode between user nodes and a central node. This work focuses on star-topology scenarios, where all the mechanisms from VLC standard's MAC layer are needed. As it was briefly commented, this topology comprises of a coordinator that manages communication and multiple devices connected to it. The communication between a user and coordinator is carried out using a half-duplex VLC link. Since star-topology defines a centralized network, users cannot communicate directly between them. If they need to send a message to another user inside the network, they have to ask the coordinator to act as an intermediary. The following subsection shortly describe some key MAC procedures of IEEE 802.15.7.
Moreover, the ratio between SD and BI (percentage of network activity) can be modified at the beginning of the network operation by setting both Super Order (SO) and Beacon Order (BO) parameters. The maximum value of BO and SO is 14, and SO cannot be higher than BO. SO increases the number of optical clocks per slot, taking as the initial value the base time _aBaseSlotDuration_. Since SD has a fixed number of slots (_aNumSuperframeSlots_), the difference between BO and SO will determine the aforementioned ratio. Equations (1) and (2) define the amount of optical clocks assigned to each of these periods:  
\[SD=aBaseSlotDuration\times aNumSuperframeSlots\times 2^{SO} \tag{1}\]  
\[BI=aBaseSlotDuration\times aNumSuperframeSlots\times 2^{BO} \tag{2}\]  
It is worth mentioning that all the processes in the MAC layer are based on the number of optical clocks. The relation between optical clocks and bit depends on the PHY configuration (modulation and encoding). Finally, if a specific ratio \(\eta_{MAC}\) of channel utilization were intended, it is straightforward to demonstrate from the previous equations that the difference between BO and SO should follow Equation (3):  
\[BO-SO=-\log_{2}\eta_{MAC} \tag{3}\]
# Cyber4 Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones
## 4 Threat Models and Attack Scenarios
### RF Jamming  
RF connects a drone to the ground transmitter or remote control. Radio frequencies in ranges lie between 2.4 GHz to 5.8 GHz, and 2.4 GHz and 5.8 GHz are the most common frequency used to control a drone remotely. The attacker tries to identify the operating channel frequency and tune it to that frequency. On the same frequency as the target device, RF jammers broadcast strong signals compared to the target device's signals. The combination of broadcasted signals overwhelms the receiver, preventing it from decoding any target signals. Figure 3 shows a generalized RF jamming attack scenario. These attacks violate drone availability. The usage of frequency hopping and multiple narrow bandwidth signals with short bursts of transmission make jamming difficult.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Drone Attacks** & **Tools/Mechanisms** & **Impact** & **Security Requirements** & **Attack Surfaces** & **Key Papers** \\ \hline \multirow{4}{*}{Eavesdropping} & Sniffing tools, & & & & \\  & Password Cracking, & Confidentiality & Use encryption technique & Z2 & [72, 73, 74, 12, 12, 75, 76, 77, 12] \\  & Scrambling/Distortion & Confidentiality & Use encryption technique & Z2 & [72, 73, 74, 12, 12, 75, 76, 12] \\ \hline \multirow{4}{*}{Physical Attacks} & Drone & & & & \\  & Countermeasures & & & & \\  & Equipment, High & & & & \\  & Power & & & & \\  & Microwave (HPM) & Availability & Use physical security & & & \\  & Devices, & & & & \\  & Nets \& Net Guns, & & & \\  & High-Energy Lasers & & & & \\ \hline \multirow{4}{*}{GPS Spoofing} & Use logic attacks & & & & \\  & and resource attacks, & & Use freshness and & & \\  & Ping of Death, SYN & Availability & Countermeasure scheme & & \\  & Flood, & against the resource & & & \\  & SYN Flood, & & exhaustion. & & \\  & x & & & & \\ \hline \multirow{4}{*}{GPS Spoofing} & Mock GPS with & & & & \\  & Joystick. Mock & & & & \\  & Locations, Fake GPS & Availability & Use anti-spoofing & & & \\  & ByteRev, Fake GPS & & techniques & & \\  & Go Location Spoofer. & & & & \\ \hline \multirow{4}{*}{Channel Jamming} & HPM Devices, & & & & \\  & RF Jammers, & & & & \\  & Radio Frequency (RF) & Availability & & & \\  & Analyzers, HackRF & & & & \\  & and BladeR & & & & \\ \hline \multirow{4}{*}{Routing Attacks} & Waypoint alterations, & & & & \\  & black & & & & \\  & or grey hole attacks & & & & \\ \hline \multirow{4}{*}{Use of Fake Drones} & Key loggers, & & & & \\  & Third-party Violations, & & & & \\  & Firmware replacement & & & & \\  & attacks & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._
## 3 Results
### DEAP Model
#### 3.1.2 Feature Extraction  
Discrete wavelet transforms were used to extract different frequency sub-bands from the DEAP database's PPG, GSR, and RESP signals. For the PPG and GSR signals, a Daubechies 4 level 8 wavelet was used. For the RESP signal, a symlet 2 level 6 wavelet was evaluated. From each sub-band, 12 features were extracted: entropy, percentiles (5 th, 25 th, 50 th, 75 th, and 95 th), mean, standard deviation, variance, root mean square value, zero crossing, and mean crossing rate. After the wavelet transform, 108, 108, and 84 features were extracted from the PPG, GSR, and RESP signals, respectively, to create the DEAP ML models.
## 4 Case Study
### Problem Setting
Of particular significance is the incorporation of additional key aspects, such as supply chain visibility, supplier performance monitoring, and reverse logistics optimization, which were absent in the previous iteration. \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**ID** & **Criteria** & **Description** \\ \hline B\({}_{1}\) & Optimization of lead-time & The ability to reduce the duration between placing an order for a product and its delivery to the destination [44]. \\ \hline B\({}_{2}\) & Satisfaction level of Customer & This refers to how satisfied customers are with logistics services, including whether they receive their deliveries on time, the quality of the service, and how well it performs overall [45]. \\ \hline B\({}_{3}\) & Dependability & Consistently delivering logistics services, making sure that deliveries happen on time and keeping the promises made to customers [46]. \\ \hline B\({}_{4}\) & Responsiveness & Being prepared and capable of promptly handling and resolving customer questions or complaints [44]. \\ \hline B\({}_{5}\) & Affordability & The ability to offer logistics services at a fair price compared to the quality of service provided [47]. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Relevant criteria emerged from the literature. Criteria are interdependent with each other, and understanding their varying importance is crucial in the prioritization process of data-driven measures. Each criterion may exert a different level of influence on the effectiveness of the measures being implemented and, ultimately, on the success of the corresponding strategies on the whole. This aspect will be evaluated in the following subsection, where the DEMATEL procedure will be applied to achieve the vector of criteria weights.
# Automated Tree Detection Using Image Processing and Multisource Data
## 4 Proposed Approach  
Vegetation, particularly the location and arrangement of trees, plays a crucial role in spatial planning and sustainable development, especially in the context of green energy. Artificial intelligence and machine learning methods have become increasingly important in fields such as spatial planning. Therefore, the main goal of our proposed system is twofold: first, to automatically estimate the areas covered by high-vegetation plants and compare the results with maps obtained from the administration unit; and second, to assess the quality of classification in identifying trees on the maps using additional data from other map-related sources. High-vegetation areas predominantly consist of trees and fields. However, due to increased complexity, we have chosen to focus specifically on automatically recognizing trees on the maps.  
The available TIFF maps indicating potential areas of high vegetation were not precise enough to allow for automated analysis. Therefore, we used additional elements related to the height above sea level. By incorporating this information, we can assign labels such as \"tree\", \"field\", \"building\", and \"road\" to small map segments (each sized 1 square meter). In this section, we present a flowchart of our proposed system and provide details related to its implementation, along with a description of the algorithm. The proposed approach aims to achieve two main goals:  
* Estimate the percentage of a given area covered by high-vegetation plants and verify the differences between the data held by the administration unit and the data resulting from our proposed algorithm.
* Verify the efficiency of the proposed approach in classifying map segments containing trees and compare the results with those obtained solely based on the maps derived by the administration unit.  
The methodology proposed in this study uses data from multiple sensors, which collectively enable a comprehensive analysis of urban vegetation. Unlike many existing studies that primarily consist of RGB or LiDAR data [19, 20, 21], our approach integrates  
Figure 4: Image blurring for different kernel sizes: (**A**) 3 \(\times\) 3 kernel ; (**B**) 5 \(\times\) 5 kernel; (**C**) 7 \(\times\) 7 kernel; (**D**) without blurring.  
multi-source data to provide a more detailed understanding of vegetation characteristics. This integration not only improves the detection of individual trees but also identifies vegetation-covered terrain that is not wooded, offering a broader classification of green areas. Such detailed information is particularly valuable for urban decision makers, as it supports a holistic assessment of both wooded and non-wooded vegetation regions. By providing actionable insights, the proposed approach improves the operational efficiency of urban planning and facilitates informed decision-making processes aimed at sustainable city development.
## 3 Path-Planning Approach  
In the last few decades, significant advancements have been made in the field of artificial intelligence, particularly reinforcement learning, a subfield of machine learning, which falls under the machine learning category. Reinforcement learning involves training data by assigning rewards and punishments based on behavior and state. Unlike supervised and semi-supervised learning, reinforcement learning does not rely on pairs of input data or true results and it does not explicitly evaluate near-optimal actions as true or false. As a result, reinforcement learning offers a solution to tackle complex problems, including the control of robots, self-driving cars, and even applications in the aerospace industry. A noteworthy advancement in reinforcement learning is the introduction of a twin delayed deep deterministic policy gradient in 2018. TD3 is an effective model-free policy reinforcement learning method. The TD3 agent is an actor-critic reinforcement learning agent that optimizes the expected long-term reward. Specifically, TD3 builds upon the success of the deep deterministic policy gradient algorithm developed in 2016. DDPG remains highly regarded and successful in the continuous action space, finding extensive applications in fields such as robotics and self-driving systems.  
However, like many algorithms, DDPG has its limitations, including instability and the need for fine-tuning hyperparameters for each task. Estimation errors gradually accumulate during training, leading to suboptimal local states, overestimation, or severe forgetfulness on the part of the agent. To address these issues, TD3 was developed with a focus on reducing the overestimation bias prevalent in previous reinforcement learning algorithms. This is achieved through the incorporation of three key features:  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Starboard Turning** & **Port Turning** \\ \hline Advance (\(L_{pp}\)) & 2.681 & 2.680 \\ Transfer (\(L_{pp}\)) & 1.114 & 1.115 \\ Turning Radius (\(L_{pp}\)) & 2.625 & 0.623 \\ Tactical diameter (\(L_{pp}\)) & 2.935 & 2.932 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Turning maneuverability characteristics.  
Figure 4: Simulation results of turning trajectories at a rudder angle of 35 degrees.  
* The utilization of twin critic networks, which work in pairs.
* Delayed updates of the actor.
* Action noise regularization.  
By implementing these features, TD3 aims to enhance the stability and performance of reinforcement learning algorithms, ultimately improving their applicability in various domains.
## 5 Evaluation and Discussion  
The experiments are conducted to evaluate and compare the performance trends of the proposed TKIFRPM approach with the available approaches. The TKIFRPM method can be compared with all of the available topmost-K identical frequent pattern mining approaches. However, due to limitations in resources and time, the comparison of the TKIFRPM is limited to the MTKPP only. The reason for selecting MTKPP is its operational and structural relevancy to the proposed TKIFRPM to provide better and widely acceptable results. However, the conclusion derived can be theoretically applied to other relevant approaches. The performance of both of the approaches is recorded in terms of runtime (i.e., execution) and memory usage to find/mine the topmost-K identical frequent regular patterns using incremental online datasets.