esa-sceva/satcom-chunk-collection huggingface dataset:
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 3 Types of LiDARs
### Atmospheric Backscattering LiDAR  
Scattering is a physical phenomenon that refers to the redirection of electromagnetic waves when they encounter particles or irregularities in their path. Backscattering, in particular, occurs when a portion of the scattered light is redirected back toward the source due to interactions with atmospheric molecules or aerosols. The intensity and nature of backscattering depend on a couple of factors, such as the wavelength of the laser, the size and composition of atmospheric particles, and the distance the laser pulse travels through the medium. An illustration of the scattering phenomenon is depicted in Figure 4[66, 70, 71, 72].  
In the context of LiDAR, atmospheric backscattering LiDARs are used to analyze the atmosphere of the Earth, mainly to study aerosols, clouds, and atmospheric gases [16, 73].  
The fundamental working principle of atmospheric backscattering LiDAR involves the following: (1) emission of laser pulses into the atmosphere, (2) backscattering of a fraction of the laser light due to interactions with atmospheric particles or gas molecules, and (3) detection and analysis of the signal returned to derive atmospheric properties [7, 70, 74, 75]. The backscatter coefficient (\(\beta\)) quantifies the signal strength. For spaceborne LiDARs, this coefficient is measured as a function of altitude [7, 76].  
Figure 3: Measurement geometry of a spaceborne LiDAR system. The satellite platform has GNSS, IMU, and star trackers for precise geolocation. The emitted laser pulses generate footprints on the ground, defining the system’s horizontal and vertical resolution. The scanning pattern and swath width determine the spatial coverage of the measurements.
## 5 Controller Packet Loss Compensation
### Integrating Processes  
Integrating processes are sometimes found in process control and servo applications. In order to deal with systems with an open-loop integrator, a slight modification to the above method is needed as the cancellation of the process poles with the controller zeros as described in the steps above may be ill-conditioned and result in controllability issues. When an integrator is present in the process, \(A(z)\) may be factored as \((1-z^{-1})\)\(A^{\ast}(z)\) and the use of the controller given by Equation (14) results in a common factor of \((1-z^{-1})\) in both the controller numerator and denominator in the steady-state (_i.e._, as z tends to 1). Firstly, observe that the following condition can be easily checked to detect the presence of one or more integrators:  
\[\left|A(1)\right|=\left|1+\sum_{i=1}^{n}a_{i}\right|\leq\epsilon \tag{17}\]  
where \(\epsilon\) is a suitably small constant e.g., the machine epsilon of the implementation processor. When this condition holds - and assuming that only one integrator is present in the plant - we wish to instead design a PD-like controller. This can be achieved by cancelling the common factor \((1-z^{-1})\) in the controller (14), which is a straightforward procedure [27].  
The incremental version of this PID-like algorithm implementation as given by Equation (15) seems particularly well suited to the proposed compensation method, since one may first calculate \(\Delta u(k)\) and \(u(k)\) from Equations (15) and (16), and since \(e(k)=r(k)-y(k)\), \(\Delta\dot{e}(k+1|k)=-\Delta\dot{y}(k+1|k)\) the latter of which is obtained from Equation (3). This leads on to the straightforward calculation of \(\Delta\dot{u}(k+1|k)\) and \(\Delta\hat{e}(k+2|k)\) and so on, allowing the buffer to be filled with the required projected controls with relative ease. The structure of the proposed system is as shown in Figure 3. The overhead of implementing the proposed adaptive control law is linear in the size of the estimated plant parameter vector, with overall complexity \(O(n+m)\). A structural overview of the elements of the proposed system is as shown in Figure 3.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 6 Network Parameters Adjustment and Performance Enhancement  
In this section, we discuss the influence of different parameters on the performance of the network. The radius of the moving trajectory of the mobile sink has a significant influence on the topology construction. Then, the method for weight calculation determines the usage ratio of the energy. The number of the clusters and the speed of the sink may also affect the performance. Additionally, multiple mobile sinks are also discussed in this section.
## 2 Materials and Methods
### Data Processing and Analysis  
#### 2.4.1 Processing and Analysis of Observational and Environmental Data  
The observational data collected over 48 h were merged based on the time they were recorded, creating a consolidated \"2-in-1\" dataset. This approach involved combining observation results from the same time points (e.g., 07:00) on both days. Consequently, two observation scores were obtained for each behavior within each pen during the 5 min observations spanning a 24 h period. The highest score recorded for each behavior in the nine-pen treatment (either heat stress or cooling) at a specific time of observation (e.g., at 07:00) was then doubled, resulting in a maximum possible score of 18. This method of combining data increased the number of available samples during a 24 h period and enhanced the robustness of circadian rhythm analysis. The two days of temperature and relative humidity data were merged in the same way as the visual observation data. All subsequent data processing and statistical analyses were conducted using this \"2-in-1\" dataset.  
For data analysis, seven new behaviors, which were combinations of selected sow and piglet individual behaviors within each pen, were defined and used. These combined behaviors included sow on-feet that aggregated the sow activities of eating, standing, drinking, and sitting [Equation (1)]. Sow on-body encompassed the lying, nursing, and sleeping behaviors of sows [Equation (2)]. Piglet moving combined the behaviors of nursing and walking of piglets [Equation (3)]. Pig moving represented a combination of sow on-feet and piglet walking [Equation (4)].  
\[\text{Sow on-feet}=\text{eating}+\text{standing}+\text{drinking}+\text{ sitting} \tag{1}\]  
\[\text{Sow on-body}=\text{lying}+\text{nursing}+\text{sleeping} \tag{2}\]  
\[\text{Piglet moving}=\text{lying}+\text{nursing}+\text{sleeping} \tag{3}\]  
\[\text{Pig moving}=\text{sow on-feet}+\text{piglet walking} \tag{4}\]  
Three other combined behaviors, i.e., \"pig moving and lying\", \"sow on-feet and lying\", and \"sow and piglet sleeping\", were also defined and quantified as their descriptions suggest. The score for any combined behavior during a 5 min observation period is the sum of the scores for the individual behaviors it comprises. This allows for a score greater than 1 for each pen.  
The 10 visually observed behaviors of sows and piglets (Table 1) were analyzed to determine their circadian rhythms and temporal distributions. Individual behaviors were also examined for the differences between the two treatment groups. Furthermore, analyses were conducted to investigate correlations between the combined behaviors and sensor detection data to enrich our understanding of behavioral patterns and their implications.
# Point Cloud Repair Method via Convex Set Theory
## 3 Results and Discussion  
In this section, we experimentally analyze the method proposed in this paper. We conducted the experiments on an Intel Xeon (R) 2.50 GHz vCPU computer with 8.00 GB memory. To verify the performance of this method, we conducted quantitative and qualitative comparisons with the latest technologies of SnowflakeNet [30], SpareNet [31], PF-Net [11] and SCCR [32]. We evaluated our method on widely used public datasets PCN [33] and Stanford. The PCN and Stanford datasets are public point-cloud datasets that have greatly contributed to the research of 3D vision. In addition, we experimentally analyzed the point-cloud data that we had collected to fully prove the effectiveness of our method. Similar to [34], we used GPSNR and NSHD as quantitative evaluation indicators of the experiment. GPSNR [35] measures the error between Point Clouds A and B on the basis of the optimized peak signal-to-noise ratio (PSNR). The higher the GPSNR is, the smaller the difference between A and B. Normalized symmetric Hausdorff distance (NSHD) [36] is a normalized metric based on the unilateral Hausdorff distance. The lower the NHSD is, the smaller the difference between A and B.
## 4 Simulation and Results  
In this section, the simulation results of an RFID system applied to a wireless sensor network are presented. The system consists of randomly distributed mobile readers and RFID tags. The objective is to investigate the collision environment in the RFID system. The key simulation parameters are provided in Table 4.  
The performance of five distributed collision avoidance protocols from the literature--FTSMAC [24], Pulse [19], MCMAC [20], CORA [22] and NFRA [27]--is compared based on the obtained results.  
* Pulse: Utilizes a control channel for notifications between readers and a single data channel for tag interrogation.
* MCMAC: Allows RFID readers to manage multiple data channels, announcing occupied frequencies via a control channel for more efficient resource utilization.
* CORA: Designed for mobile, dense deployments in RFID networks; CORA relies on local learning within each reader's neighborhood to activate and identify colliding neighbors.
* FTSMAC: Utilizes a distributed RFID anti-collision protocol with a notification system for distributing frequency resources, enabling readers to query tags simultaneously in assigned time slots within the same data channel group.
* NFRA: Based TDMA protocol for dense RFID networks, coordinating reader communications via a polling server and ensuring collision-free operation through centrally deployed query servers. These protocols are evaluated in terms of their ability to mitigate collisions effectively. The simulation is implemented using MATLAB 2019a and comprises various modules, including Wireless Sensor Network Mobility, the Reader RFID Module, the Tag RFID Module, Collision RRI, Collision RTI, Reader-Tag Communication, Reader-Reader Communication, and the Anti-Collision Protocol. By analyzing the simulation results, insights are gained into the collision behavior of RFID systems. The performance of the different protocols is assessed, providing a better understanding of their effectiveness in collision avoidance and optimizing communication between readers and tags in the wireless sensor network.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of readers (sparse network) & 10, 20, \(\ldots\), 50 \\ Number of readers (dense network) & 50, 100, \(\ldots\), 250 \\ Number of tags & 100 \\ Reader and Tag position & Random \\ Type of antenna & Omni-directional \\ Read range of data channel (rr) & 3, 5 m \\ Read range of control channel (err) & 2 \(\times\) rr \\ Number of Data Channel & 7 \\ Number of Time Slot (case 1) & 7 \\ Number of Time Slot (case 2) & 1, 2, 3, 4, 5, 6, 7 \\ Number of control channel & 1 \\ protocols compared & FTSMAC, Pulse, MCMAC, CORA \\ Backoff & (ReaderID \(-1\)) \(\times\) CW \\ CW & Convergence time of all readers \\ Tmin & 5 ms (Standard EPC) \\ T & Neighboring readers’ response time \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation parameters.
# Analysis of the Sugarane Biomass Use to Produce Green Hydrogen: Brazilian Case Study
## 5 Environmental Impacts and Sustainable Solutions
### Second-Generation Ethanol as a Sustainable Pathway for Green Hydrogen
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Method** & **Technique Used** & **Results** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{Fermentation (2G)} & Biological fermentation of lignocellulosic biomass & Biohydrogen yield depends on feedstock and enzymes used & Environmentally friendly, scalable & Requires pretreatment, complex optimization \\ \hline \multirow{3}{*}{Electocatalytic reform} & Reforming with Ni-Cu hollow microtubes bilayer & High conversion efficiency & Insensitive to cooking, efficient process & High initial setup cost \\ \hline \multirow{3}{*}{Plasma reactor} & Plasma discharge-based hydrogen production & Enhanced hydrogen output, robust performance & Resistant to fouling & Requires advanced technology \\ \hline \multirow{3}{*}{Fermentation with iron nanoparticles} & Anaerobic bacterial consortium with iron nanoparticles & Increased microbial activity and hydrogen yield & Improves fermentation efficiency & The stability of nanoparticles can be a challenge \\ \hline \multirow{3}{*}{Nanomaterial-assisted processes} & Nanomaterials and consolidated processing approaches & Improved catalytic and conversion efficiency & Innovative, enhance existing methods & High cost of nanomaterials \\ \hline \multirow{3}{*}{Pretreatment and delignification} & Acid hydrolysis, hydrothermal processes & Releases fermentable sugars & Essential for lignocellulosic biomass processing & Energy-intensive, possible inhibitor formation \\ \hline \multirow{3}{*}{Integration with energy systems} & Coupling fermentation with generation technologies & Maximized energy recovery and hydrogen output & Efficient resource utilization & Complex system design \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of seven methods for biohydrogen production. Figure 13: Pathways for green hydrogen production using 2G ethanol and integrated processes.
## 3 Methodology
### Transmission
#### 3.1.3 Pilot Insertion and Pulse Shaping  
In this phase, the quantum bits were generated from \((Z)\), and the pilot insertion was carried out in quantum bits, for effective detection of noises during transmission through the channel. The pilots, which are known reference signals, were inserted at regular intervals in the transmission stream to help the estimation of the channel behavior for the receiver. Thus, the pilot-inserted signal is denoted as \((\rho)\). Following this, the pilot-inserted signal \((\rho)\) underwent pulse shaping using a Gaussian Filter \((GF)\) for optimization of the temporal and spectral properties of the signal. The \(GF\) smoothed the signal's transitions,shaping its pulse to reduce high-frequency components and provide better control over its spectrum. For \((\rho)\), a \(GF\) was applied for pulse shaping and is given as follows:  
\[\bar{\rho}=\rho*G^{\prime} \tag{14}\]  
\[G^{\prime}=\frac{1}{\sqrt{2\pi\omega^{2}}}exp\left(\frac{t^{2}}{2\varpi^{2}}\right) \tag{15}\]  
where \(\bar{\rho}\) represents the pulse-shaped signal, \(G^{\prime}\) represents the Gaussian function, represents the time variable, \(exp\) and \(\pi\) are an exponential function and \(\pi\) value (constant), respectively, and \(\varpi\) represents the width of the pulse.
# Biodiesel and Biolubricant Production from Waste Cooking Oil: Transesterification Reactor Modeling
## 2 Materials and Methods
### Kinetic Study: Foundations  
A kinetic study for the second transesterification reaction was carried out in duplicate. Equation (2) shows the overall reaction considered, where A = FAMEs, B = TMP, C = biolubricant, and D = methanol that was recovered.  
\[A+\frac{1}{3}B\leftrightarrow\frac{1}{3}C+D \tag{2}\]  
The tests were carried out in an isothermal batch reactor. Samples were withdrawn at intervals for the analysis of the concentration of FAMEs over the reaction time. As methanol (D) was continuously removed, the kinetic law of an irreversible reaction can be considered, as shown by Equation (3) as follows:  
\[-r_{A}=\frac{-dC_{A}}{dt}=k\times C_{A}^{\alpha}\times C_{B}^{\beta} \tag{3}\]  
where \(k\) is an apparent rate constant that takes into account the effects of the catalyst concentration, temperature, and pressure; C\({}_{A}\) is the concentration of FAMEs; C\({}_{B}\) is the concentration of TMP; and \(\alpha\) and \(\beta\) are the reaction orders with respect to A and B,respectively. If \(C_{A}\) and \(C_{B}\) are related through stoichiometry, and considering the concept of conversion (\(X_{A}\)), Equations (4)-(6) are obtained as follows:  
\[C_{A}=C_{A_{0}}\times(1-X_{A}) \tag{4}\]  
\[C_{B}=C_{B_{0}}\times\left(\theta_{B}-\frac{1}{3}X_{A}\right) \tag{5}\]  
\[\theta_{B}=\frac{C_{B_{0}}}{C_{A_{0}}} \tag{6}\]  
Thus, by replacing the conversion in Equation (3), Equation (7) is obtained as follows:  
\[\frac{dX_{A}}{dt}=k\times C_{A_{0}}^{\alpha+\beta-1}\times(1-X_{A})^{\alpha} \times\left(\theta_{B}-\frac{1}{3}\times X_{A}\right)^{\beta} \tag{7}\]  
By separating the variables and integrating this equation between the limits (t = 0, \(X_{A}\) = 0) and (t = t, \(X_{A}\) = \(X_{A}\)), Equation (8) is obtained as follows:  
\[\int_{0}^{X_{A}}\frac{dX_{A}}{(1-X_{A})^{\alpha}\times\left(\theta_{\beta}- \frac{1}{3}X_{A}\right)^{\beta}}=\int_{0}^{t}k\times C_{A_{0}}^{\alpha+\beta-1 }\times dt \tag{8}\]  
This equation depends on \(\alpha\) and \(\beta\), but the transesterification reaction is usually described as a first-order reaction with regard to each reagent, which implies that \(\alpha=\beta=1\) and, therefore, a second-order reaction [48]. Furthermore, if the ratio of the initial concentrations of A and B fulfils the stoichiometric relationship shown in Equation (2), the following conditions can be applied (Equations (9) and (10)):  
\[C_{B_{0}}=C_{A_{0}}/3 \tag{9}\]  
\[\theta_{B}=1/3 \tag{10}\]  
Thus, Equation (11) is obtained after substituting Equation (10) into Equation (8) as follows:  
\[\int_{0}^{X_{A}}\frac{3\times dX_{A}}{(1-X_{A})^{2}}=\int_{
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
###### Abstract  
Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDCs), especially the second goal of \"zero hunger\", potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5 th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.  
agriculture 5.0; drones; remotely piloted aircrafts (RPAs); precision agriculture; remote sensing; Internet of Things (IoT); digital agriculture; sustainable development goals; sensors; agricultural robots +
Footnote †: journal: Applied Sciences
## 1 Motivation  
The electroencephalographic (EEG) signal is an exceptionally valuable tool for studying human brain activity [1]. Thanks to its non-invasive nature and high temporal resolution, it has broad applications in scientific research, medical diagnostics, and brain-computer interface (BCI) technology. EEG analysis allows researchers to identify cognitive processes, diagnose neurological disorders, and develop technologies that enable device control through thought. However, accurate EEG analysis can be significantly hindered by artifacts--interfering signals that do not originate from neuronal activity. Among the most common artifacts are those caused by eye movements (EOG), muscle activity (EMG), and cardiac activity (ECG) [2]. Their presence not only reduces signal quality but can also lead to incorrect data interpretation. This issue is particularly critical in clinical research and in applications such as BCI, where precision is paramount. Muscle artifacts are especially challenging, particularly in studies that require participant motor activity, such as human-machine interaction or the analysis of steady-state visually evoked potentials (SSVEP). Facial muscle movements--like jaw clenching--generate high-amplitude interference that can overshadow genuine brain signals. These disturbances are difficult to remove because of their irregular nature and because their frequencies often overlap with those of interest in EEG data.  
Effective artifact removal is therefore essential to extract the relevant information from the EEG signal, such as SSVEP responses. These potentials are widely used in BCI and in diagnostics--for instance, to monitor visual functions--and are among the most sensitive indicators of the brain's response to visual stimuli. Consequently, developing and evaluating advanced artifact removal methods is vital to enable more precise use of EEG data in both research and clinical settings. Furthermore, the challenge of eliminating muscle artifacts while preserving key EEG components requires sophisticated algorithms that offer both accuracy and real-time data processing capabilities.
# Antijamming Improvement for Frequency Hopping Using Noise-Jammer Power Estimator
###### Abstract  
In frequency-hopping spread-spectrum (FHSS) systems, jammer detection and mitigation are important but difficult. Each slot of the FHSS experiences frequency-selective fading and unequal transceiver-frequency gains that hinder the detection of jammed slots and result in a poor bit-error rate (BER). To increase BER performance, we first propose a noise-jammer power estimator (NJPE) that estimates noise and jammer powers regardless of different channel gains, and derived its normalized Cramer-Rao bound (NCRB). Second, we developed a jammer detector based on gamma distribution, and designed a restoration method combining all nonjammed slots. Computer simulations verified the derived NCRB of the proposed NJPE by normalized mean squared error (NMSE), and showed that the jammer-detection probability of the proposed jammer detector was better than that of conventional detectors. The BER performance of the proposed method was also shown to be better than that of conventional methods.  
jammer; military communication; jammer detection; jammer mitigation; frequency-hopping spread spectrum; Cramer-Rao bound Article