esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### Radiometric Quantities and Apparent Optical Properties
The data of irradiance quartet were used to derive a comprehensive set of AOPs at each discrete depth, including, \(\overline{\mu}_{d}\), \(\overline{\mu}_{u}\), \(\overline{\mu}\), \(R\) (all dimensionless), and various diffuse attenuation coefficients,  
Figure 3: The instrument package used to measure the irradiance quartet and upwelling radiance. As shown, the five radiometric sensors are aligned to the same depth level. collectively denoted with a symbol \(K\) (units of m\({}^{-1}\)). The \(K\) coefficients for various irradiances were derived from  
\[K_{x}(z_{1}+\Delta z,\lambda)=-\frac{\ln E_{x}(z_{2},\lambda)-\ln E_{x}(z_{1}, \lambda)}{z_{2}-z_{1}}, \tag{1}\]  
where \(x\) represents \(d\), \(u\), \(od\), and \(ou\), \(z_{1}\) is a depth corresponding to the upper boundary of the layer over which \(K_{x}\) is determined, \(z_{2}\) is the lower boundary of this layer, and \(\Delta z=(z_{2}-z_{1})/2\). Similarly, other \(K_{x}\) coefficients were also calculated, specifically the diffuse attenuation coefficient of net irradiance, \(K_{E}\), by replacing \(E_{x}\) with net irradiance, \(E=E_{d}-E_{u}\), and the diffuse attenuation coefficient of upwelling zenith radiance, \(K_{Lu}\), by replacing \(E_{x}\) with \(L_{u}\). PAR quantum scalar irradiance, \(E_{opAR}\) (in units of \(\mu\)mol quanta m\({}^{-2}\) s\({}^{-1}\)), was calculated by the conversion of spectral \(E_{o}\) from energy units to quantum units and integration between 400 and 700 nm. Similarly, PAR quantum downward irradiance, \(E_{dPAR}\), was calculated from spectral \(E_{d}\). The corresponding diffuse attenuation coefficients, \(K_{opAR}\) and \(K_{dPAR}\), were calculated by replacing \(E_{x}\) with \(E_{oPAR}\) and \(E_{dPAR}\), respectively, in Equation (1). The spectra of all AOPs were inspected to ensure the quality of data. Because at each depth multiple radiometric measurements were taken, AOPs were initially determined from each radiometric measurement. The unrealistic AOPs, for example \(R>1\) or average cosines \(>1\), or obvious outliers were then discarded. The abnormal data were often observed near the surface, most likely owing to the effects of wave-induced light fluctuations in \(E_{d}\) and \(E_{od}\)[83; 84]. After this quality control, the accepted spectra of radiometric quantities were averaged to generate final data of irradiances and radiance for each depth of measurement. The average spectra of irradiances and radiance were then used to compute the final AOPs. Such final data from 11 stations (five in 2010 and six in 2011; see Figure 1) that included valid measurements at more than four depths were selected for further analysis in this study.
# Source Anonymity against Global Adversary in WSNs Using Dummy Packet Injections: A Survey
## 4 Comparison of Fake Packet Techniques
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Technique** & **Intervals** & **Timers** & **Random Numbers** & **Probability** & **Clusters** & **Proxies** & **Distribution Type** \\ \hline _PeC_[39] & \(\bullet\) & \(\bullet\) & - & - & - & - & - \\ _ConstRate_[32] & \(\bullet\) & - & - & - & - & - & Uniform \\ _ProbRate_[32] & \(\bullet\) & - & - & - & - & - & Exponential \\ _FitProbRate_[32] & \(\bullet\) & - & \(\bullet\) & \(\bullet\) & - & - & - \\ _Baseline_[33] & \(\bullet\) & - & - & \(\bullet\) & - & - & - \\ _PFS_[33] & \(\bullet\) & - & - & - & - & \(\bullet\) & \(\bullet\) & Exponential \\ _TFS_[33] & \(\bullet\) & - & - & - & \(\bullet\) & \(\bullet\) & Exponential \\ _OSAP_[40] & - & - & - & - & \(\bullet\) & - & Exponential \\ RCM [41] & - & \(\bullet\) & - & - & \(\bullet\) & - & - \\ _GFS_[42] & \(\bullet\) & \(\bullet\) & - & - & \(\bullet\) & - & - \\ _NAA_[43] & \(\bullet\) & - & - & - & - & - & - \\ _GOA_[43] & \(\bullet\) & \(\bullet\) & \(\bullet\) & - & - & - & - \\ _HGA_[43] & \(\bullet\) & \(\bullet\) & \(\bullet\) & - & - & - & - \\ _PBA_[43] & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & - & - & - \\ _DRA_[44] & \(\bullet\) & - & - & - & \(\bullet\) & - & Uniform \\ _OFS_[45] & \(\bullet\) & - & - & - & - & \(\bullet\) & - \\ _ASLP_[46] & \(\bullet\) & - & \(\bullet\) & - & \(\bullet\) & \(\bullet\) & Exponential \\ _TCH-WSN_[47] & - & - & - & - & \(\bullet\) & - & - \\ _TESP_[28] & - & - & - & - & \(\bullet\) & \(\bullet\) & - \\ _CSPSLP_[49] & - & - & - & - & - & \(\bullet\) & - \\ _DWLS_[50] & \(\bullet\) & - & - & - & \(\bullet\) & - & Uniform \\  & & & & & & Uniform \\ _GAFG_[51] & \(\bullet\) & - & - & \(\bullet\) & \(\bullet\) & - & Exponential \\  & & & & & & & Poisson \\ _SoSi_[52] & \(\bullet\) & - & - & - & - & - & - \\ _SECLOUD_[53] & - & - & - & - & \(\bullet\) & - & - \\ _UHT_[54] & \(\bullet\) & - & - & - & - & - & Poisson \\ _PA-SLP_[30] & \(\bullet\) & - & - & \(\bullet\) & - & \(\bullet\) & Uniform \\ _FACP_[34] & \(\bullet\) & \(\bullet\) & - & \(\bullet\) & - & - & - \\ _DUD_[55;56] & \(\bullet\) & - & \(\bullet\) & \(\bullet\) & - & - & Uniform \\ _DAD_[55;56] & \(\bullet\) & - & \(\bullet\) & \(\bullet\) & - & - & Uniform \\ _CAD_[57;56] & \(\bullet\) & - & \(\bullet\) & \(\bullet\) & - & - & Uniform \\ _EDAD_[57] & \(\bullet\) & - & \(\bullet\) & \(\bullet\) & - & - & Exponential \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of fake packet techniques.
## 3 Case Studies of Pilot and Full-Scale STRBs
### Overall Evaluation
During the operation of an STRB system, the heavy metal content may increase in the bottom sludge layers that contain sludge from older applications, due to the mineralization of organic solids [45; 50]. In some cases, the heavy metal content may exceed the respective legal standards for land application [49; 52]. However, it has also been reported that the residual sludge quality in STRBs, even after 20 years of operation, can comply with the Danish and EU regulations, considering the heavy metal content in relation to phosphorous content [45]. Moreover, no iron was detected in the gravel layers of STRBs treating sludge from WW, while the iron content in the drained water was low over a 5-year trial. This implies that iron is retained in the residual sludge layer and does not clog the drainage pipes. A high iron content in the feed sludge means that the establishment of aerobic conditions in the residual sludge is crucial for preventing iron precipitation as ochre that might create clogging issues.
# Malicious Cognitive User Identification Algorithm in Centralized Spectrum Sensing System
## 4 Algorithm Simulation and Performance Analysis
### Algorithm Performance Comparison  
We created a simulation in order to compare the difference of the reputation convergence value of honest users and malicious users between the \(\beta\)-MIAMC algorithm proposed in this paper and the traditional \(\beta\)-SCCSA algorithm with the same attack probability. This used the situation of the probability of the malicious user attack being \(p\) = 0-50%, with the results shown in Figure 6:  
Figure 5: Reputation value of cognitive users under different attack probabilities.  
Figure 6: The difference between the honest user and the malicious user convergence results under different attack probabilities.  
As seen from the Figure 6:  
1. As the attack probability increases, the difference between the reputation value of honest users and malicious users gradually increases, essentially, a greater attack probability means that it is easier to accurately distinguish honest users and malicious users;
2. Under the same attack probability, the reputation value difference calculated by the \(\beta\)-MIAMC algorithm is obviously larger than the reputation value difference calculated by the \(\beta\)-SCCSA algorithm. It can be seen that the algorithm proposed in this paper can better and faster distinguish between honest users and malicious users.  
Correctly distinguishing malicious users is the key for improving the performance of spectrum sensing. After performing 10,000 Monte Carlo experiments on the basis of the above experiment set, the analysis of the changes in the false alarm probability of malicious users under different attack probability are shown in this paper. As shown in Figure 7,  
Analysis of the above figure shows that with an increase in the attack probability of malicious users, the false alarm probability of malicious users decreases. This is due to the greater attack probability of malicious users, which makes it more difficult for them to hide. We compared the false alarm probability of the malicious users of the proposed \(\beta\)-MIAMC algorithm and the traditional \(\beta\)-SCCSA algorithm with the same attack probability and the trend of the false alarm probability of malicious users calculated under the change in attack probability. From this, we can see that with the same attack probability, the false alarm probability of the malicious user obtained by the \(\beta\)-MIAMC algorithm is reduced by at least 20% compared with the \(\beta\)-SCCSA algorithm. With an increase in the attack probability, the false alarm probability of the malicious user under the \(\beta\)-MIAMC algorithm is gradually reduced. When the attack probability is 6%, the false alarm probability of the malicious user is reduced to 3%. When the attack probability is 7%, the false alarm probability of malicious user converges to 0. It can be seen that the \(\beta\)-MIAMC algorithm proposed in this paper can distinguish malicious users better and faster.
## 3 Consistent Path Congestion Model
### Consistent Path Congestion Model and Its Related Algebra
#### 3.1.1 Differential Path Congestion Indicator  
The situation that _\"the congestion of the current path is worse than the backup path to a certain degree\"_ in continuous time is denoted by the Differential Path Congestion Indicator (DPCI) \(\omega_{ij}(\theta,t)\):  
\[\omega_{ij}(\theta,t)=\begin{cases}1,&\text{if }L_{i}(t)>L_{j}(t)+\theta\quad \text{ at time }t;\\ 0,&\text{otherwise}.\end{cases} \tag{1}\]  
Whenever \(\omega_{ij}(\theta,t)\) is 1, it means that at time \(t\) the latency of path \(i\)\(L_{i}(t)\) exceeds path \(j\)\(L_{j}(t)\) by more than \(\theta\) seconds, as shown in Equation (2). Here the congestion is measured by latencies; thus \(\theta\)which is the Latency Differential Sensitivity, denotes a certain amount of congestion with a certain amount of latency.  
\[L_{i}(t)>L_{j}(t)+\theta \tag{2}\]  
Then \(\omega_{ij}(\theta,t)\) is regarded as the output of a two-states continuous time random process. We name this underline process the _Continuous-Time Differential Path Congestion Model (CDPCM) for Path_i _and_j. It is worth mentioning that the \(\omega_{ij}(\theta,t)\) does not preserve commutativity, that is:  
\[\omega_{ij}(\theta,t)\
eq\omega_{ji}(\theta,t) \tag{3}\]  
For the convenience of analyzing, we need to transfer the continuous random process into a discrete one by sampling. When sampling \(\omega_{ij}(\theta,t)\) at a sampling interval \(\Delta t\), we get a discrete random series \(\omega_{ij}(\theta,\Delta t_{n})\); \(n\) is integer and \(n\geq 1\), which can be deemed as the output of a two-states discrete time random process. We name this process the _Discrete Time Differential Path Congestion Model (DDPCM) for Path \(i\) and_j. Similarly, \(\omega_{ij}(\theta,\Delta t)\) does not preserve commutativity either, in other words,  
\[\omega_{ij}(\theta,\Delta t_{n})\
eq\omega_{ji}(\theta,\Delta t_{n}). \tag{4}\]
## 5 Discussions of Simulation Analyses
### Thermal Analyses of Six Cross-Sections  
In order to obtain more details of heat transfer characteristics in the fan groups, temperature distributions of six cross-sections are added into the simulation analyses, as shown in Figure 8. Cross-sections \(Z-236.5\), \(Z-651.5\), and \(Z-991.5\) are parallel to the ceiling of the engine compartment and through the central axes of upper, middle and lower fans, named as Planes 1-3, respectively. Cross-sections \(Y-273.26\), \(Y+126.74\) are parallel to the side plane of the engine compartment and also through the central axes, named as Planes 4-5, respectively. Cross-section \(X+506.5\) is parallel to the front plane of the engine compartment and through the front outlet grids, named as Plane 6. The six cross-sections are used to study the local temperature distributions of different positions and the changing trends of the temperature field in different directions.  
Figure 9 shows the temperature field of Plane 1 (Cross-section \(Z-236.5\)). The red region represents the high temperature area in the torque converter radiator, corresponding to the fan hub areas without flowing air and the places not covered by the fans. However, the temperatures immediately become lower after air leaves the radiator, as is clearly seen in the figure. From Figure 9 it can also be seen that, with an isolation board, hot air is prevented from flowing into the engine region and the temperature in this region is almost kept at about 30 \({}^{\circ}\)C.  
Figure 6: Temperature distributions of the outlet (**left** one) and inlet (**right** one) in the air cooling region.  
Figure 7: The entire flow field of air cooling region.  
Figure 10 shows the temperature field of Plane 2 (Cross-section \(Z-651.5\)). The average temperature of this region is higher than that of Plane 1 because cooling air arrives here last and there are two heat sources, the intercooler and the engine radiator. In the fan hub areas, heat is stacked here, whereas in the other areas heat is blown away by the strong wind. Meanwhile, the two fans are interacting and coupling in their edges so that temperature distribution is like a wave. An interesting phenomenon of energy convolution appears in each edge of the fan group because of the air backflow explained above in Figure 6. The temperature field of Plane 3 (Cross-section \(Z-991.5\)), shown in Figure 11, is almost the same as that of Plane 2, but the average temperature of Plane 3 is lower than that of Plane 2. This is because the air inlet grids are opened at the bottom of the engine compartment and air is sucked into the lower fan groups in priority, so that the bottom fans enjoy more and cooler intake air used in heat dissipation.  
Figure 8: Six cross-sections set in the cooling region.  
Figure 10: Temperature distribution (**left**) and velocity distribution (**right**) of Plane 2.  
Figure 9: Temperature distribution (**left**) and velocity distribution (**right**) of Plane 1.
# Formation Control of Nonlinear Multi-Agent Systems with Nested Input Saturation
## 1 Introduction
### State of the Art and Contributions
However, such approaches reveal certain limitations. For instance, while neuro/fuzzy approximating structures have been utilized to compensate for model uncertainties, they escalate complexity by necessitating additional adaptive parameters and computational overhead. Furthermore, the shaping of the transient and steady-state response in systems under model uncertainties and input saturation lacks systematic methods that: (i) guarantee the best achievable performance specifications, (ii) facilitate the control parameter selection and (iii) predict the steady-state behavior when input saturation is inactive. Thus, while existing research demonstrates progress in confronting input limitations and uncertainties in MAS formation control, the challenges of ensuring robust performance and stability under actuation constraints still remain open. This highlights the need for further exploration into methodologies that offer stronger performance guarantees against input constraints, emphasizing on a more comprehensive approach to address these nonlinearities in practical scenarios. Driven by this, we build on our previous work [30] aiming at designing a novel robust scheme for controlling nonlinear MAS, considering input constraints while ensuring adaptive performance attributes. The main contributions of our work can be outlined as follows:  
* In contrast to existing literature regarding MAS framework [15; 17; 18; 19; 20; 23; 24; 25; 26; 27; 28; 29], the proposed methodology considers the conflict between output performance and input constraints both on the amplitude and rate of the control signal. * The controller does not exploit either knowledge of the system dynamics or any disturbance observer leading to a lower complexity control scheme compared to [15; 18; 19; 20; 26; 27; 28; 29]. * The control protocol is decentralized with easy gain tuning, which facilitates its integration on a real robotic swarm, as illustrated in Figure 1.  
The paper is structured as follows: Section 2 introduces the problem addressed in this work. Subsequently, Section 2.1 provides essential preliminary information about the PPC methodology. In Section 3.1, we formulate the neighborhood error, incorporating the system's formation characteristics. Section 3.2 presents the design of the proposed control scheme and provides theoretical analysis for the SISO case, while Section 3.3 elaborates on the extension of the proposed controller to MIMO MAS. Section 4 presents comparative simulation results, demonstrating the superior performance of our approach compared to a well-established proportional control scheme. Moving to Section 5, we showcase experimental results highlighting the effectiveness of the proposed scenario within a real MAS setup. Finally, in Section 6, we draw conclusions from our findings.
## 3 Results
### Toxicity of the PAM and its Possible Degradation under Anaerobic Conditions
#### 3.1.1 Toxicity of the PAM
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{\begin{tabular}{c} **Day of** \\ **Incubation** \\ \end{tabular} } & \multicolumn{5}{c}{**VFA Concentration**} & \multicolumn{2}{c}{**Ammonium**} & \multirow{2}{*}{\begin{tabular}{c} **Methane** \\ **mmol/L** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Methane** \\ **mmol/L** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Methane** \\ **mmol/L** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Methane** \\ **g/L** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Methane** \\ **Content, \%** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Yield, mL** \\ \end{tabular} } \\ \hline Control & 0 & 44.871 & 6.965 & 22.783 & 5.20 & 35.2 & 0 & 0 \\ Control & 6 & 18.617 & 3.289 & 0.159 & 1.35 & 36.5 & 77.29 & 85.98 \\ T-0 & 22 & 1.705 & 0.015 & 0.025 & 0.10 & – & 79.22 & 144.63 \\  & 42 & 0.512 & 0.003 & 0.007 & 0.03 & 76.5 & 23.27 & 146.47 \\ \hline T-5 & 0 & 44.871 & 6.965 & 22.783 & 5.20 & 35.2 & 0 & 0 \\  & 6 & 24.986 & 4.186 & 0.696 & 1.84 & 58.4 & 76.48 & 76.4 \\  & 22 & 3.343 & 0.020 & 0.051 & 0.20 & – & 77.59 & 131.37 \\  & 42 & 0.011 & 0.003 & 0.004 & 0 & 89.6 & 25.07 & 138.44 \\ \hline T-40 & 0 & 44.871 & 6.965 & 22.783 & 5.20 & 35.2 & 0 & 0 \\  & 6 & 35.412 & 6.566 & 4.472 & 2.96 & 44.2 & 76.73 & 60.93 \\  & 22 & 0.429 & 6.712 & 0.053 & 0.52 & – & 80.01 & 136.41 \\  & 42 & 6.170 & 0.026 & 0.007 & 0.37 & 100.3 & 41.39 & 147.56 \\ \hline T-80 & 0 & 44.871 & 6.965 & 22.783 & 5.20 & 35.2 & 0 & 0 \\  & 6 & 33.418 & 6.386 & 8.896 & 3.21 & 68.2 & 75.46 & 42.13 \\  & 22 & 0.524 & 6.610 & 0.030 & 0.52 & – & 78.51 & 129.48 \\  & 42 & 4.822 & 2.884 & 0.005 & 0.49 & 103.2 & 48.70 & 136.49 \\ \hline T-200 & 0 & 44.871 & 6.965 & 22.783 & 5.20 & 35.2 & 0 & 0 \\  & 6 & 42.764 & 9.203 & 24.309 & 5.31 & 61.8 & 73.23 & 20.24 \\  & 22 & 0.362 & 7.214 & 0.070 & 0.55 & – & 78.16 & 106.60 \\  & 42 & 3.211 & 4.444 & 0.012 & 0.51 & 104.8 & 33.59 & 111.46 \\ \hline  & 0 & 44.871 & 6.965 & 22.783 & 5.20 & 35.2 & 0 & 0 \\  & 6 & 44.671 & 8.234 & 26.129 & 5.51 & 101.4 & 70.94 & 11.12 \\  & 22 & 12.164 & 8.250 & 0.192 & 1.34 & – & 79.07 & 104.09 \\  & 42 & 0.475 & 8.307 & 0.004 & 0.63 & 131.7 & 31.33 & 117.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the process in the course of batch incubation of digested sludge with different concentrations of the polyacrylamide flocculant supplemented with volatile fatty acids (VFA) mixture as a substrate. trations increased in T-40, T-80, and T-200 between days 22 and 42 of the experiment. It was associated with complete or partial propionate degradation. Partial inhibition of the acetate consumption was revealed in the samples with PAM concentrations above 40 mg/g TS in the late stage of the experiment. H2 concentration in the gas phase did not exceed 0.04%, which indicated the absence of inhibition of hydrogen consumers, i.e., hydrogenotrophic methanogens and/or homoacetogenic bacteria.
# Exploring Carbon-Fiber UAV Structures as Communication Antennas for Adaptive Relay Applications
## 2 Materials and Methods
### Experimental Setup for Antenna Characterization  
#### 2.1.1 Experimental Setup  
The two UAV antennas used for this study are fabricated from carbon fiber, with the key geometric parameter being the antenna's characteristic length--the physical length of the radiating element. As shown in Figure 1, the radiating element consists of a carbon-fiber tube coupled to a small metallic rod, both measured up to the metallic ground plane. Rather than the full 16.9 cm tube length, the effective monopole height has been set to \(L=\lambda/4\), which at 1.2 GHz corresponds to approximately 6.25 cm. The remaining tube length provides structural support and cable routing but does not participate in radiation. The carbon-fiber section itself maintains a consistent 1.5 cm diameter along its length, ensuring uniform current distribution and minimal dielectric loading.  
Figure 1: UAV platform used in experiments. The drone (**a**), carbon-fiber monopole antenna element (**b**), and the antenna mounted on a tripod with feeder cable connection (**c**).  
Each of the two antennas was individually connected to a Vector Network Analyzer (VNA) using a precision-calibrated coaxial cable. This calibration process is essential to eliminate potential sources of error and to ensure that the measurements accurately reflect the true characteristics of the antennas. To establish a suitable testing environment, both antennas were positioned at the same height. This alignment was achieved using a tripod and a laser level, ensuring high placement accuracy. The use of the tripod not only enabled synchronization of the antenna heights but also provided a stable platform that minimized external variables that could affect measurement accuracy. The calibrated coaxial cable connected between each antenna and the VNA ensured that the signals followed a controlled and precise path, as shown in Figure 1c.  
The anechoic chamber provides a precisely controlled RF environment by absorbing stray energy and silencing external interference, effectively recreating an ideal \"free-space\" scenario without ambient noise or multipath reflections. This isolation was critical for our baseline antenna measurements--ensuring that every result reflected the true performance of the carbon-fiber monopole rather than room echoes or outside signals--so that subsequent tests involving deliberate reflections or moving obstacles could be directly compared to these interference-free conditions. The anechoic chamber, shown in Figure 2, provided a reflection-free, noise-isolated enclosure for our antenna tests, ensuring that all measured signals represented direct radiation from the monopole without room echoes or external RF interference.