esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
Over the past 60 years, remarkable progress has been achieved in the field of remote sensing and its applications for weather and climate forecasting, primarily driven by advancements in satellite technologies. Despite these accomplishments, the future of this domain holds even greater potential--constrained only by our imagination--as technological breakthroughs continue to redefine the limits of what is possible.  
The deployment of near-real-time weather forecasting models is expected to have a transformative impact across numerous sectors. In the defense sector, where weather conditions critically influence operational success, such capabilities are of strategic importance for mission planning and execution across air, land, and maritime domains [45]. Similarly, in civilian sectors, including emergency response and disaster management, accurate and timely weather forecasts have proven essential, enabling earlier warnings and more effective evacuation procedures that can save lives [37].  
These advancements have been made possible by the collective efforts of international institutions and private entities, including the World Meteorological Organization (WMO), national meteorological administrations, NOAA [46], Tomorrow.io, aerospace corporations [47], IBM, Booz Allen Hamilton, and key contributors from China and Europe. Their innovations--ranging from improved satellite constellations to AI-powered forecasting models--represent critical milestones in our ongoing pursuit of enhanced environmental awareness and disaster resilience. Ultimately, the driving force behind these technological achievements remains clear: to safeguard human life by improving our ability to predict, monitor, and respond to increasingly complex weather and climate phenomena.  
Conceptualization, E.N., O.C., Y.P. and J.G.; methodology, E.N. and O.C.; validation, Y.P., M.H. and J.G.; formal analysis, E.N. and O.C.; investigation, E.N. and O.C.; resources, E.N., O.C., Y.P., M.H. and J.G.; writing--original draft preparation, E.N. and O.C.; writing--review and editing, Y.P., M.H. and J.G.; visualization, E.N.; supervision, Y.P, M.H. and J.G.; project administration, Y.P.; funding acquisition, Y.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study.  
The authors declare no conflict of interest.
## 3 Geometrical Model of Reader Antenna Deployment
\[\left|\frac{e^{-j\frac{2\pi}{\lambda}d1}}{d1}+\Gamma\frac{e^{-j\frac{2\pi}{ \lambda}d2}}{d2}\right|^{2}=\left[\frac{Cos\frac{2\pi}{\lambda}d_{1}-jSin \frac{2\pi}{\lambda}d_{2}}{d_{1}}+\Gamma\ \frac{Cos\frac{2\pi}{\lambda}d_{1}-jSin\frac{2\pi}{ \lambda}d_{2}}{d_{2}}\right] \tag{7}\]  
\[\left|\frac{e^{-j\frac{2\pi}{\lambda}d1}}{d1}+\Gamma\frac{e^{-j\frac{2\pi}{ \lambda}d2}}{d2}\right  
For RFID reader-to-tag communication, the choice of antenna type plays an important role after determining the appropriate height of the reader antenna (15 m).  
This proposed system considers the reader antenna as having a directional radiation pattern and the tag antenna as having an omnidirectional radiation pattern. Mechanical downtilt angle is used to estimate coverage area.  
The optimum downtilt angle is needed to maximize the area and capacity. It depends on two key features including geometrical factor (\(\theta_{geo}\)) and antenna vertical beamwidth (\(\theta_{var}^{3 dB}\)). \(\theta_{geo}\) is given as follows [29].  
\[\theta_{geo}=\tan^{-1}\frac{(H_{t}-H_{r})}{d} \tag{15}\]  
Thereafter, the downtilt angle (\(\theta\)) is determined from,  
\[\theta=\theta_{geo}+\frac{\theta_{par}^{3 dB}}{2} \tag{16}\]  
Using (15) and (16), the variation of downtilt angle with distance (\(d\)) is graphically presented in \[d1 =\sqrt{d^{2}+\left(H_{t}-H_{r}\right)^{2}} \tag{2}\]0 The figure exhibits that the downtilt angle should be decreased with increase in horizontal distance from reader to tag (\(d\)). Taking the example of Figure 3, if the distance d is desired to be within 35-38 m, the preferred downtilt angle should be 45.4\({}^{\circ}\). When Reader antennas are tilted downward, the Half-Power beamwidth of the horizontal radiation pattern is reduced in the central direction, but is increased in the sideways direction [5; 6]. Figure 5 shows a simulated radiation pattern for a helical reader antenna at various tilt angles. As downtilt angle increases, the gain of the reader antenna decreases and half-power beamwidth (HPBW) increases. \[d1 =\sqrt{d^{2}+\left(H_{t}-H_{r}\right)^{2}} \tag{2}\]1 Maximum strength of two ray transmission at two different heights. From Figure 5, we can observe that for the example taken, as the downtilt angle is 45.4\({}^{\circ}\), the corresponding HPBW is considerably larger than that for zero downtilt angle. Accordingly, the deployment of adjacent reader antennas after accurately determining the antenna height from ground and downtilt angle will create an interference zone in the reader-tag communication channel as illustrated in \[d1 =\sqrt{d^{2}+\left(H_{t}-H_{r}\right)^{2}} \tag{2}\]2  
\[d1 =\sqrt{d^{2}+\left(H_{t}-H_{r}\right)^{2}} \tag{2}\]3 Downtilt angle determination with preferred height of reader antenna. \[d1 =\sqrt{d^{2}+\left(H_{t}-H_{r}\right)^{2}} \tag{2}\]4 Radiation pattern for three different tilt angles (0\({}^{\circ}\), 45.4\({}^{\circ}\), 90\({}^{\circ}\)).
## 2 System Model and Problem Formulation
### Path Loss Model  
The links from ground users and UAV are LOS or no LOS (NLOS) channels due to the presence of buildings, vegetation etc., which can obstruct the signals propagation. In detail, the channel gain between the \(k\)-th UAV and the BS is, according to [20; 21; 22],  
\[\rho_{kBS}=\zeta_{LOS}(d_{kBS})^{-\alpha}, \tag{1}\]and if the \(UAV_{k}\) is in LOS with the \(n-th\) user, the channel gain is defined as  
\[\rho_{n,k}=\zeta_{LOS}(d_{n,k})^{-\alpha}, \tag{2}\]  
where \(d_{k,BS}\) and \(d_{n,k}\) represent the distances between UAV\({}_{k}\) and BS and between UAV\({}_{k}\) and UE\({}_{n}\), respectively. \(\zeta_{LOS}\) denotes the additional attenuation factor of the LOS channel at the reference distance \(d_{ref}=1\) m, and \(\alpha\) is the path loss exponent at the air channel. UAVs are in \(LOS\) with probability \(p_{LOS}(d)=\frac{1}{1+Cexp(-(B\varphi_{n,k}-C))}\), where \(B\) and \(C\) are constants related to the environment, whereas UAVs are in NLOS according to the complementary probability \(p_{NLOS}(d)=1-p_{LOS}(d)\).
## 3 Results  
### Model Performance  
As shown in Figure 3, OOB.Error decreased rapidly with increased Ntrees. When Ntrees increased to 63, the OOB.Error started to be stable. Thus, Ntrees was set to 63, and values of Minleaf and Mtry were located at 1\(\sim\)3 and 6\(\sim\)23, respectively. According to the following grid search (Figure 4), when Minleaf = 1 and Mtry = 10, the average R2 obtained from ten-fold cross-validation was the largest (0.901) and the average RMSE was the smallest (279.456) (Figure 4).  
Figure 3: Relationship between Ntrees and OOB.Error.  
Models were built based on \"optimal\" parameters and training samples, and model training and validation were also performed (Figure 5). In the model training process, R\({}^{2}\), RMSE, and bias for the model were 0.986, 109.039, ions cm\({}^{-3}\), and 0.815 ions cm\({}^{-3}\), respectively. In the validation process, R\({}^{2}\) = 0.878, RMSE = 258.617 ions cm\({}^{-3}\), and bias = 14.973 ions cm\({}^{-3}\), respectively. The efficiency of the model was evaluated for NAI \(>\) 4000 ions cm\({}^{-3}\), and the R\({}^{2}\) and bias reached 0.68 and 321.90 respectively. These results proved an excellent model performance to be applied regionally.
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
###### Abstract  
Non-orthogonal multiple access (NOMA) and reconfigurable intelligent surfaces (RISs) are recognized as key technologies for beyond 5G and 6G wireless communications. To address the high computational complexity and non-convex optimization challenges, this letter proposes an optimization framework based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. The proposed framework jointly makes use of sensor grouping, power allocation, an RIS computation strategy, and phase shifts to minimize the remote state estimation (RSE) error. Simulation results demonstrate that the MADDPG algorithm, when applied in an RIS-assisted NOMA system, significantly reduces the RSE error.  
wireless sensor networks; uplink NOMA; RIS; MADDPG algorithm; RSE error 2025 25 4878  
26 June 2025  
1
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
## 4 UTM System Architecture  
This section describes our proposed UTM system architecture. Figure 4 depicts an overview of all the software modules involved, as well as their interactions. The modules in green implement specific U-space services. As it was explained in Section 2.1, we focus on those services that are required to address unexpected events during the flight operation of a UAS. In particular, we cover four services with their corresponding modules: _Tracking_, _Monitoring_, _Emergency Management_ (EM), and _Tactical Deconfiction_ (TD). Besides, our system includes additional software modules that provide support to the UTM architecture. First,there is a _Data Base_ (DB) component that is in charge of handling all the relevant information about the state of the airspace, for instance, the current flight plans and tracks for all UAS operations (which are updated by the Tracking module) and the active geofences (which can be activated externally by auxiliary stakeholders like fire brigades or internally by the Emergency Management module). Second, the _U-space Service Manager_ (USM) is a key module that acts as an interface between the UTM system and the rest of the U-space ecosystem. Basically, it receives state information and alerts from both the UAS and the external auxiliary stakeholders, and it communicates back recommended actions to deal with threatening events. These recommendations are generated by means of the interaction between the Tracking, Monitoring, Emergency Management, and Tactical Deconfliction modules.  
Our system is built upon ROS (Section 3.2) and hence, each module consists of a software process implemented as a ROS node. The communication between modules takes place through ROS topics and services. In particular, the system is designed to use services in a preferable manner, as they provide the possibility of acknowledging message reception, which is crucial to reliably manage many of the UTM interactions. In those cases, one of the modules acts as a server while others act as clients, which results in an asynchronous communication between the modules. Upon a client request, the server module will carry out the requested activity and then it will reply, indicating whether the result was successful or not. Nevertheless, there are also a few cases where ROS topics are needed. Topics provide a synchronous communication, and they are used by modules that need to publish information at a constant rate.  
In the following sections, we will provide a more detailed description of the different modules in our UTM system. For each module, we describe its functionality and interactions with other modules, as well as the methodology that we have used to implement them.
# Improved Gradient Descent Iterations for Solving Systems of Nonlinear Equations
## 2 Multiple Step-Size Methods for Solving SNE
### A Class of Accelerated Double Direction (ADDN) Methods  
In [61], an optimization method was defined by the iterative rule  
\[\mathbf{x}_{k+1}=\mathbf{x}_{k}+t_{k}\mathbf{d}_{k}+t_{k}^{2}\mathbf{c}_{k}, \tag{35}\]  
where \(t_{k}\) denotes the value of the steplength parameter, and \(\mathbf{d}_{k}\), \(\mathbf{c}_{k}\) are the search directions vectors. The vector \(\mathbf{d}_{k}\) is defined as in the SM-method from [58], which gives \(\mathbf{d}_{k}=\gamma_{k}{}^{-1}\mathbf{g}_{k}\), and further  
\[\mathbf{x}_{k+1}=\mathbf{x}_{k}-t_{k}\gamma_{k}{}^{-1}\mathbf{g}_{k}+t_{k}^{2 }\mathbf{c}_{k}. \tag{36}\]  
We want to apply this strategy in solving (1). First of all, the vector \(\mathbf{c}_{k}\) can be defined according to [85]. An appropriate definition of \(\mathbf{c}_{k}\) is still open.  
Assuming again \(B_{k}=\gamma_{k}I\), the vector \(\mathbf{d}_{k}\) from (5) becomes \(\mathbf{d}_{k}=-\gamma_{k}^{-1}F_{k}\), which transforms (35) into  
\[\mathbf{x}_{k+1}=\mathbf{x}_{k}-t_{k}\gamma_{k}^{-1}F_{k}+t_{k}^{2}\mathbf{c}_ {k}. \tag{37}\]  
We propose the steplength \(\gamma_{k+1}\) arising from the Taylor expansion (27) and defined as in (29). In addition, it is possible to use an alternative approach. More precisely, in this case, (27) yields to  
\[F_{k+1}=F_{k}-\gamma_{k+1}\Big{(}-t_{k}\gamma_{k}^{-1}F_{k}+t_{k}^{2}\mathbf{ c}_{k}\Big{)}.\]  
As a consequence, \(\gamma_{k+1}\) can be defined utilizing  
\[\gamma_{k+1}=-\frac{\gamma_{k}\mathbf{y}_{k}^{\mathsf{T}}\mathbf{y}_{k}}{ \mathbf{y}_{k}^{\mathsf{T}}(-t_{k}F_{k}+\gamma_{k}t_{k}^{2}\mathbf{c}_{k})}. \tag{38}\]  
The problem \(\gamma_{k+1}<0\) in (38) is solved using \(\gamma_{k+1}=1\).  
We can easily conclude that the next iteration is then generated by  
\[\mathbf{x}_{k+2}=\mathbf{x}_{k+1}-t_{k+1}F_{k+1}+t_{k}^{2}\mathbf{c}_{k+1}.\]  
The \(ADDN\) iterations are defined in Algorithm 2.  
```
0: Functions \(F(\mathbf{x})\), \(\epsilon>0\) and a given initial vector \(\mathbf{x}_{0}\in\mathbb{R}^{n}\).
1: For \(k=0\) chose \(\gamma_{0}=1\) and \(F(\mathbf{x}_{0})\).
2: Check the stop criterion; if \(\|F(\mathbf{x}_{k})\|\leq\epsilon\) is satisfied then stop the algorithm; else, continue with Step 3:.
3: (Line search) Find \(t_{k}\in(0,1]\) using inexact line search procedure.
4: Compute \(\mathbf{x}_{k+1}\) using (37).
5: Determine \(\gamma_{k+1}\) using (38).
6: In case \(\gamma_{k+1}<0\), apply \(\gamma_{k+1}=1\).
7:\(k:=k+1\).
8: Back to Step 2.
9: Outputs: \(\mathbf{x}_{k+1}\), \(F(\mathbf{x}_{k+1})\).
```  
**Algorithm 2** The \(ADDN\) iterations based on (37), (38).
# Improving Waste Management System Efficiency and Mobility with Efficient Path MANET
###### Abstract
However, other reasons, such as communication resources and routing algorithms, also contribute to the proper functioning of the applications. Routing is a methodical identification and decision making about sending data to specific destination nodes in accordance with their addresses. For example, the required action must be done by a certain prospective network member (worker/object device). Another example is a waste transportation system that needs an efficient algorithm to take the right way for waste collection at the right time. Its process consists of two steps: a path should be chosen based on service requirements, and each data packet should be sent along that path [13; 14; 15; 16; 17; 18]. Open Shortest Path First (OSPF) and Routing Information Protocol (RIP) can provide different paths to the same destination by using multiple routing processes. Path selection is used to choose an available path to use. Thus, the router must consider some concepts, such as administrative distance (AD), because they influence the algorithm's path-selection process [19; 20; 21; 22; 23; 24]. Thus, the default AD values (such as 110 for OSPF and 120 for RIP) are assigned to each routing algorithm. These values are set when the router receives the required routing configuration commands. Therefore, the route that minimizes the distance to the destination is selected and designated as the active route/path. The route selection process and algorithm used for each protocol is based on their requirements. For example, the routes are selected based on the lowest hop count criteria in RIP [25; 26]. The present study is focused on path selection optimization, which is dedicated to devising a well-rounded method that implements various constraints with the help of genetic algorithms (GA). At present, our best estimates of multipath routing algorithms indicate that they are all single-path constraint models, and they do not have a bearing on the relations amongst the constraints. MANET has many qualities, and some affect its performance; numerous factors affect its performance, and these elements correlate with the dynamic topology [27; 28]. A large number of constraints must be considered to ensure that every constraint is accounted. An expeditious choice based on the route's effectiveness should be used [29; 30; 31; 32; 33]. Given the advances in recent genetic routing, pathfinding algorithms are currently most often used to identify a feasible path. The rest of the paper is organized as follows: Material and methods are presented in Section 2. Section 3 explains the results and discussion of the experiments. Finally, Section 4 presents the conclusions.
## 7 Results and Discussion
### Packet Loss  
Packet loss was minimal, which is an indicator of good network performance. The experiment was repeated multiple times, with packet losses observed using the IPerf3 tool. For EIGRP, the packet loss rate was 5%, while OSPF experienced a slightly higher packet loss rate of 5.4%. Though the difference is minor, it suggests that OSPF may face more challenges in maintaining data integrity and reliable communication. This difference in packet loss could lead to retransmissions and increased delay, potentially compromising the reliability of OSPF. Figure 19 presents the packet loss data for both protocols.
# Secure Aviation Control through a Streamlined ADS-B Perception System
## 5 Analysis and Discussion
### Model Analysis  
#### 5.3.1 Binary Classification  
A model that forecasts the class of supplied datasets is called a machine learning classifier model. In supervised learning, test datasets that the machine learning model has never seen before are used to test and evaluate the model after it has been trained with potential attacks using training data. Multiple classifiers were tested while the suggested machine learning model was constructed to attain the best outcomes. The best outcome was found between 20-30% training set size. We have decided to go ahead and continue with 30% to have an optimal training set size and to be more robust to unknown attacks, as the model is intended to be as efficient as possible. It has shown some positive effects on the different binary classification results. The top three used ML classifiers evaluated for this work are listed in Table 7. Random forest was utilized to form an ensemble machine learning classifier from the classifiers described since it produced better overall results.
# Calibration for Sample-And-Hold Mismatches in \(M\)-Channel TIADCs Based on Statistics
## 5 Simulation and Comparison  
A quantizer of 14 bits is utilized in the simulations below. The filters are designed by \"firpm\" function in MATLAB(r), which uses the Parks-McClellan optimal equiripple design algorithm. The \(-3\) dB bandwidth of Channel 0 is \(\Omega_{c}=6\pi f_{s}\). We define a term for sinusoidal signals to indicate the dynamic performance of the TIADC: the Largest-Signal-to-largest-Spurious-component-Ratio (LSSR).  
\[\text{LSSR}\left(\text{dBc}\right)=10\log_{10}\frac{\text{max}\left(P_{signal,i }\right)}{\text{max}\left(P_{spurious,j}\right)}, \tag{31}\]  
where \(P_{signal,i}\) is the power of the \(i\)-th sinusoidal component in the DFT spectrum, and \(P_{spurious,j}\) is the power of the \(j\)-th spurious component including harmonic component. For a single-tone sinusoid, the LSSR is equal to the spurious-free dynamic range (SFDR).
# Recent Developments and Emerging Trends in Paint Industry Wastewater Treatment Methods
## 2 Wastewater from the Paint Industry
### Paint Industrial Wastewater  
The properties of the product are related to the type and ratio of the pain components. On the other hand, they have an impact on the characteristics of the wastewater produced by the paint industry [18]. Typical pollutants found in paint industry wastewater include those exerting high values of suspended solids (SS), COD and BOD, heavy metals, and toxic compounds [19]. Organic pollutants have recently attracted public attention, not only as they significantly degrade water quality, but also present major challenges to existing water treatment systems regarding their removal efficiency [20].  
As mentioned before, around 80% of paint industry effluents come from equipment rinsing [10; 21; 22], so after the treatment, effluent, which is now diluted paint, may be used in manufacturing processes as a component of low-cost paints, or as a coolant or dilatant [23].  
Microbial infection is typical for water-based paints because they contain inorganic and organic materials. The microbial infection occurs during the manufacturing stages of paint production and storage [24]. The changes in various kinds of physical and chemical  
Figure 2: General paint composition [12].  
properties, such as high temperatures and humidity, increase the possibility of microbial contamination in paint [24]. Therefore, wastewater created during manufacturing can be highly contaminated with microorganisms. Wastewater generated during the cleaning of paint manufacturing equipment might be highly contaminated with not only organic compounds and pigments but also microorganisms [25].  
The presence of any component from paint manufacturing in water is unacceptable. Untreated wastewater effluents with a significant number of dyes are considered potentially hazardous to organisms living in freshwater ecosystems. Ecological and environmental problems are caused by the discharging of these effluents directly into water sources. The contaminants included in wastewater can restrict the photosynthesis process of aquatic plants by blocking sunlight entry or poisoning the animal biota [26; 27; 28]. Therefore, sufficient treatment of paint industry effluents is important, and new ecological techniques are needed to further remove color and microorganisms present and reduce the levels of COD and BOD [29].