esa-sceva/satcom-chunk-collection huggingface dataset:
# Nanomaterials in Photocatalysis: An In-Depth Analysis of Their Role in Enhancing Indoor Air Quality
## 4 Photocatalytic Oxidation
### Important Factors Affecting Photo Oxidation  
The efficiency of photocatalytic oxidation is influenced by the properties of the photocatalyst and target pollutant, as well as various external parameters, including humidity, light intensity, pollutant concentration, residence time, and airflow rate. These factors significantly impact the photocatalytic process, affecting pollutant adsorption, charge carrier recombination rates, and the radiation interacting with the photocatalyst.
# Evaluation and Testing System for Automotive LiDAR Sensors
## 4 System Implementation
### Implementation of the FoV Test  
The test to determine the sensor's FoV consists of using a target with a well-known size and reflectivity placed at a known distance on top of the rail system's target holder. Because the rail system can only provide variable ranges, it is possible to take advantage of the RotGon to move the sensor in both the horizontal and vertical directions while checking when the target moves outside of the sensor's FoV. Using the position data from the RotGon, it is possible to find the sensor's FoV. The procedure is illustrated in Figure 11.  
The test starts with a routine that uses the services provided by the _Target Detection_ package described above to find a target inside the sensor's point cloud data. If the target is detected, the algorithm starts measuring the FoV. First, it starts by finding the maximum and minimum angles in the vertical axis to achieve the vertical FoV (the blue square in the image). Next, the same concept is applied to the horizontal axis to find the horizontal FoV (the red square in the image). Finally, the values retrieved in the previous tasks are used as  
Figure 11: Field of view flowchart overview.  
the starting conditions to test the consistency of both the horizontal and vertical FoVs in the limits of all quadrants (the green square in the image).  
\[FoV_{1} =P1-P3\] \[FoV_{2} =P2-P4\] \[FoV =\frac{FoV_{1}+FoV_{2}}{2} \tag{5}\] \[FoV_{min} =\frac{P1+P2}{2}\] \[FoV_{max} =\frac{P3+P4}{2}\]  
In all procedures, in order to obtain the maximum and minimum detection angles, the system increments/decrements the RotGon angles until the target reaches the four different positions illustrated in Figure 12:  
* _P1_--Last position where the target is completely outside of the FoV;
* _P2_--First position where the target is completely inside of the FoV;
* _P3_--Last position where the target is completely inside of the FoV;
* _P4_--First position where the target is completely outside of the FoV after _P3_;  
Then, based on Equation (5), the minimum detection angle (\(FoV_{min}\)), maximum detection angle (\(FoV_{max}\)), and FoV are calculated for each axis. Because this method uses the mean values of two known positions to achieve the minimum and maximum values, the target size is automatically removed from the calculations. Moreover, and in order to calculate an FoV as close as possible to the real value, at the limits of the FoV (where the target starts to disappear) we use the lowest angular step provided by the RotGon, which is 0.01\({}^{\circ}\).
# Mechanically Reconfigurable, Beam-Scanning Reflectarray and Transmitarray Antennas: A Review
## 3 Array Movement  
In Sections 2.1 and 2.2, various mechanical RA designs deploying micro-motors or actuators to move or rotate each unit cell have been demonstrated to independently control the phase responses of individual unit cells. However, implementing individual control for the array elements results in increased cost and complexity, especially for arrays with many elements. This section reviews some works on mechanically reconfigurable TAs and RAs using array movement approaches, which only need a few actuators to implement the mechanical reconfiguration.  
Array movement approaches that shift or rotate the whole array to scan the beam have been shown to provide a good compromise between practicality and performance. Most mechanical beam-steering systems using array movement methods only need one or two actuators. Therefore, mechanical beam-steerable phased arrays using array movement methods provide less complex and less expensive beam-steerable antenna options for applications that do not require high speed beam scanning and arbitrary beam-shaping capabilities. Mechanical beam-steering systems using array movement methods can be categorized into three main groups: (i) Risley-prism-based antennas, (ii) translatable or rotatable TA or RA antennas and (iii) tiltable ground plane RA antennas.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 3 System Model and Problem Formulation
### Channel Modeling for UAV-Assisted Non-Terrestrial Networks  
The channel model incorporates both the direct path between the NTN node and the user, and the reflected path through the RIS. These paths are subject to various wireless propagation phenomena, including path loss, shadowing, and small-scale fading.  
Figure 2: System model. The UAV communicates with the user through both direct and reflected paths.
# Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine
###### Abstract  
The amplifier is a key component of the radio frequency (RF) front-end, and its specifications directly determine the performance of the system in which it is located. Unfortunately, amplifiers' specifications degrade with temperature and even lead to system failure. To study how the system failure is affected by the amplifier specification degradation, it is necessary to couple the amplifier specification degradation into the system optimization design. Furthermore, to couple the amplifier specification degradation into the optimal design of the system, it is necessary to model the characteristics of the amplifier specification change with temperature. In this paper, the temperature characteristics of two amplifiers are modeled using an extreme learning machine (ELM), and the results show that the model agrees well with the measurement results and can effectively reduce measurement time and cost.  
RF amplifier; temperature characteristics; modeling; ELM +
Footnote †: journal:  
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Footnote 0: Copyright** © 2022 by the authors. Liconsee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Use of _Chlorella vulgaris_ and _Ulva lactuca_ as Biostimulant on Lettuce
## 3 Results and Discussion
### Greenhouse Treatment  
Considering the results from the germination assay, the best concentrations of the algal extract were 15% with _C. vulgaris_ and 25% and 75% with _U. lactuca_. Relevant effects of treating lettuce plants with these solutions were observed in roots (Table 2 and Figure 2).  
Figure 1: Germination rate (%) seven days after the beginning of the seed (**a**) lettuce and (**b**) beans treatments in Petri dishes. The results are expressed by mean \(\pm\) standard error. In comparison with the control, statistically significant differences are expressed by the symbol * (_p_-value \(\leq\) 0.05).  
No significant statistical differences were detected between treatments (Figure 1 and Table 2). This indicates that the tested extracts do not affect the lettuce root system. The highest root weight was performed with 15% _C. vulgaris_, while the least was with the non-treated plants (control) with low weight roots (Table 2).  
Concerning the plant aerial part (Table 2), there were no statistically significant differences between the treated and the control plants. It is interesting to notice that the fresh weight (FW)/Dry weight (DW) ratio (Table 2) shows a lower value for the treatments with _U. lactuca_. This result suggests that _U. lactuca_ extracts improve the production of leaf biomass in lettuce compared to non-treated plants and _C. vulgaris_ extract. It leads to a lower water content in leaves of plants treated with _U. lactuca_.  
The root weight results are in accordance with [PERSON] et al. [23], who confirmed that the green algae treatment with _Chlorella vulgaris_ on lettuce positively influenced root growth, especially the weight. Indeed, in our work, the root weight value of the plants treated with _C. vulgaris_ (15%) appeared 36.9% higher than in the control plants (Table 2).
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
## 5 Analysis of the Ultrasonic Channels' Capacity  
The ergodic capacity (in \(\rm bit/s/Hz\), [14]) for a channel modeled with the \(\kappa\)-\(\mu\) shadowed distribution can be calculated using the exact closed-form expression presented in [24]. For this evaluation, the following assumptions apply: The received noise is approximated as Gaussian (although the presence of some non-Gaussian components has been reported [4]), and the noise power spectral density is constant in the bandwidth of interest (this was verified in the measurements), so that the statistics of  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \(\kappa-\mu\) **Model** & & & **Rice Model** \\ \hline
**Channel Code** & \(\kappa\) & \(\mu\) & \(m\) & \(\epsilon\) & \(C(bs/Hz)\) & \(K\) & \(\epsilon\) \\ \hline A6-32 & 2.33 & 0.92 & 1.86 & 0.029 & 3.67 & 0.55 & 0.068 \\ A6-64 & 2.90 & 1.00 & 3.18 & 0.061 & 3.78 & 1.71 & 0.061 \\ A6-128 & 9.56 & 1.27 & 1.67 & 0.114 & 3.81 & 3.44 & 0.23 \\ B6-32 & 3.03 & 0.91 & 2.15 & 0.056 & 3.71 & 0.21 & 0.085 \\ B6-64 & 1.89 & 0.94 & 1.32 & 0.049 & 3.61 & 0.00 & 0.159 \\ B6-128 & 1.99 & 1.01 & 1.86 & 0.022 & 3.69 & 1.01 & 0.028 \\ C3-64 & 7.66 & 0.90 & 18.68 & 0.192 & 4.03 & 5.67 & 0.197 \\ C9-32 & 4.06 & 1.13 & 2.45 & 0.026 & 3.83 & 2.64 & 0.111 \\ C9-64 & 0.03 & 1.02 & 6.32 & 0.053 & 3.61 & 0.03 & 0.104 \\ C9-128 & 1.56 & 1.04 & 2.39 & 0.040 & 3.71 & 1.29 & 0.068 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Fitting parameters of the \(\kappa-\mu\) distribution and the Rice distribution for the measured channels.  
Figure 6: Example of the \(\kappa-\mu\) model fit and the Rice model fit for one of the measured channels: C9-32.  
the received signal to noise ratio \(\gamma\) are determined by the statistics of the signal received power, \(\alpha^{2}\), _i.e._, the fading statistics. Under these circumstances, the ergodic capacity C is given by [14].  
In Figure 7, the ergodic capacity of several measured channels is depicted as a function of the average signal to noise ratio. The capacity column in Table 3 represents the ergodic capacity of each channel for a received average signal to noise ratio of 12.5 dB by using the expressions in [24]. The capacity obtained from the model agrees with the physical interpretation that the higher the fading, the lower the capacity. Knowing the magnitude order of the capacity of these underwater channels is crucial for the design of a communication link. In the channels under test, it is around 4 b/s/Hz for a 12.5-dB signal to noise ratio, which means that a 1-kHz bandwidth system could theoretically transmit around 4 kbps free of errors.  
\[C=\int_{0}^{\infty}log_{2}(1+\gamma)f_{\gamma}(\gamma)d\gamma \tag{3}\]  
where \(f_{\gamma}(\gamma)\) is the probability density function (PDF) associated with the fading CDF in Equation (2).
## 2 Methodology  
As weather conditions greatly alter the visual appearance of thermal imagery, infusing this knowledge into the models during training could serve as useful guidance toward weather-aware object detectors that outperform naively trained models. To achieve this, a model must be able to accurately estimate the current weather conditions. While more granular prediction schemes have become available for weather estimation, the methods for predicting weather conditions found in the literature are still predominantly performed using a binary scheme. Using a binary scheme as a conditioning method assumes that there is a fixed amount of distribution to the model. In an uncontrolled environment, this is a potentially false assumption as unknown variables could introduce noise to the signal that would make it difficult to distinguish the ground truth close to the bin edges [37; 38]. This is potentially further exacerbated when processing thermal video from cameras with a relative internal thermograph. As detailed in Section 1.4, the prevalence of relative thermal cameras make them a promising modality to investigate, particularly for a real-world context.  
Prior work in auxiliary guidance have primarily leveraged multiple modalities [39; 40; 41; 42; 43], or have focused on guidance in the RGB domain [4; 6; 44; 45; 46]. To our knowledge, the only existing work that performs auxiliary conditioning for thermal-only object detection is the task-conditioned study of YOLO [2]. The authors leveraged a direct conditioning approach (detailed in Section 2.3) on the KAIST Multispectral Pedestrian Detection (KAIST) dataset [47], and managed to detect the objects overlooked by the baseline. However, the KAIST dataset contains thermal images from an absolute thermal camera, thereby resulting in a fairly similar thermal signature to that of pedestrians (as seen in Figure 1). Additionally, it can be observed in Figure 1 that this consistent thermal signature results in very low contrast between objects in certain scenes. Contrast-enhanced versions are available in Figure A1.  
In this section, an overview of the object-centric annotations of the LTD-Dataset [1] and the associated meta-data are detailed. Furthermore, the method proposed in [2] can be adapted for a prediction of a continuous auxiliary variable. Finally, the architecture of a direct conditioning approach (similar to [2]), as well as an indirect conditioning approach that uses a State of the Art (SotA) transformer-based model, is detailed.
# WPFD: Active User-Side Detection of Evil Twins
## 3 Design Principle and Detection Algorithm
### Monitor Mode  
A WNIC in monitor mode will monitor all nearby wireless traffic. Unlike the promiscuous mode, monitor mode allows the WNIC to capture all wireless packets while not connected to an AP. Most WNICs and modern operating systems support monitor mode. However, everyone has their method of transitioning to this model. Microsoft Network Monitor [31] enables users to operate the WNIC in Windows efficiently. In the Unix operating system family, activating monitor mode is more straightforward. There are some networking-related built-in commands for this purpose.  
In general, because the evil twin needs to connect to the Internet through the good twin, the evil twin must have two wireless adapters. Users may be tempted to believe that the adaptor is an official AP. Because this adapter has an SSID, the WNIC will recognize it as an AP. The other is used to connect the good twin. As a result, it lacks an SSID and functions similarly to a handheld device or a notebook. Even if an evil twin and the corresponding good twin have the same SSID, their MAC addresses or BSSIDs are different (Basic Service Set IDs). An attacker can impersonate a good twin's BSSID. The assumption, however, is that the two devices share the same BSSID. In that case, they will process all packets sent to that BSSID simultaneously, resulting in the disconnection of a TCP/IP connection between a server and a device with the BSSID. As a result, if the evil twin has the same BSSID as the good twin, it can no longer complete its attack. Figure 2 depicts the WNIC's perspective on an evil twin.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technique** & **Evil Twin Attack Detection Systems** \\ \hline Duplicate RSSI & Client Side & Passive \\ Clock Skew & Client Side & Passive \\ Context-leashing & Client Side & Active \\ Traffic Monitoring & Network & Passive \\ DNS Server & Network & Active \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of different evil twin attack detection techniques.
## 3 Results
### The Development of the Subsets of Similar Accidents, and the Identification of Their Causes  
As a result of the calculations performed with the use of Statistica software, a dendrogram (shown in Figure 5) was obtained. The place of cutting off a dendrogram branch was determined using [PERSON]'s method [41] and marked in Figure 5 with a red line. With such a cut-off, the analyzed data formed three clusters (\(k=3\)).  
In the next step of the analysis, the accidents were classified into three clusters. The \(k\)-means method was used, while at the same time, the previously determined expected number of clusters \(k=3\) was assumed. As a result of the conducted classification of all 213 accidents \(A_{v}\) into groups with a similar set of causes, three clusters of \(Ak\) (\(k=1,\ldots,\) 3) were obtained. The number of accident events \(A_{v}\) in individual clusters is as follows:  
* Cluster \(A1\)--112 accident events \(A_{v}\);
* Cluster \(A2\)--66 accident events \(A_{v}\);
* Cluster \(A3\)--35 accident events \(A_{v}\).  
The detailed characteristics of the obtained clusters are presented in Table 5 and illustrated by the graph in Figure 6. The individual values in Table 5 and Figure 5 show the share of a given cause in the subset of accident events \(Ak\). For example, technical causes \(t_{1}\) occurred in 40% of events in group \(A1\) (\(t_{1}=0.40\)), 24% of events in group \(A2\) (\(t_{1}=0.24\)) and 74% of events in group \(A3\) (\(t_{1}=0.74\)).  
Figure 4: Pareto-Lorenz chart of the identified human causes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Causes**} & \multicolumn{3}{c}{**Cluster**} \\  & \(A1\) & \(A2\) & \(A3\) \\ \hline \multirow{4}{*}{Technical \(T\)} & \(t_{1}\) & 0.40 & 0.24 & 0.74 \\  & \(t_{2}\) & 0.26 & 0.59 & 0.06 \\  & \(t_{3}\) & 0.30 & 0.47 & 0.20 \\  & \(t_{4}\) & 0.26 & 0.29 & 0.23 \\  & \(t_{other}\) & 0.20 & 0.27 & 0.09 \\ \hline \multirow{4}{*}{Organizational \(O\)} & \(o_{1}\) & 0.53 & 0.56 & 0.40 \\  & \(o_{2}\) & 0.22 & 0.76 & 0.23 \\  & \(o_{3}\) & 0.10 & 0.74 & 0.34 \\  & \(o_{4}\) & 0.14 & 0.29 & 0.83 \\  & \(o_{5}\) & 0.17 & 0.38 & 0.31 \\  & \(o_{6}\) & 0.21 & 0.29 & 0.17 \\  & \(o_{7}\) & 0.07 & 0.47 & 0.20 \\  & \(o_{8}\) & 0.13 & 0.21 & 0.43 \\  & \(o_{other}\) & 0.27 & 0.48 & 0.40 \\ \hline \multirow{4}{*}{Human \(L\)} & \(l_{1}\) & 0.30 & 0.29 & 0.60 \\  & \(l_{2}\) & 0.10 & 0.20 & 0.71 \\ \cline{1-1}  & \(l_{3}\) & 0.17 & 0.15 & 0.29 \\ \cline{1-1}  & \(l_{4}\) & 0.24 & 0.11 & 0.06 \\ \cline{1-1}  & \(l_{5}\) & 0.21 & 0.11 & 0.06 \\ \cline{1-1}  & \(l_{6}\) & 0.07 & 0.09 & 0.46 \\ \cline{1-1}  & \(l_{7}\) & 0.13 & 0.03 & 0.11 \\ \cline{1-1}  & \(l_{other}\) & 0.23 & 0.33 & 0.60 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Characteristics of the obtained clusters—the share of a given cause in the group of accident events.  
Figure 5: Dendrogram—a graph showing the clustering of individual \(A^{\prime}\) accident events in the subsequent calculation steps (cut-off place—[PERSON]’s rule [42]).
## 5 Results
### Adnot-Bourges-Campana-Gicquel (ABCG) Results  
From Figure 3, ABCG overestimated the GHI at Marrakech (\(-\)14.5%), Tangier (\(-\)11.9%), Ouarzazate (\(-\)21.4%), Agadir (\(-\)13.5%), and Fes (\(-\)15.9%), whereas it was underestimated at Tantan (3.4%). From Figure 4, which gives the hourly rRMSE, rRMSE was less than 15% in Tantan (14.5%) and Tangier (14.6%), which shows average agreement in these stations between McClear and ABCG, whereas it was more than 15% in the other stations, which indicates poor agreement in these stations between McClear and ABCG. From Figure 5, which indicates rMAE, at four stations, namely Agadir (14.1%), Tantan (11.6%), Tangier (12.2%), and Marrakech (14.7), the rMAEs were less than 15%, which shows good agreement in the stations between the two stations.  
Figure 6: Comparison between McClear and modelled clear sky hourly GHI values in Ouarzazate (**a**), Agadir (**b**), Tantan (**c**), Tangier (**d**), Marrakech (**e**), and Fes (**f**).  
ment. However, at Ouarzazate (21.9%) and Fes (16.0%), the rMAEs were greater than 15%, indicating poor agreement as indicated by high bias.  
From Table 5, which indicates the coefficient of determination (R\({}^{2}\)) at all six stations, R\({}^{2}\) > 0.99 in all stations, indicating excellent performance based on Table 4 with the exception of Ouarzazate with good performance (0.98) and Tantan (<0.98), where the coefficient of (0.97) indicates poor correlation between the ABCG and McClear clear sky models based on Table 4 model benchmarks. From Table 6, ABCG was feasible at one station: Tantan. Here, it had a minimum rMBE and maximum R\({}^{2}\) of (0.99). According to Figure 6, the ABCG clear sky model was not closer to McClear, showing poor agreement but differing at various stations. The performance of ABCG clear sky model with stations is also summarised in the supplementary material Tables S1 to S6 and Figure S2.
## 2 Materials and Methods
### Climate Data  
Climate data was used to avoid anomalies and assess the viability of carrying out dry-season vegetation classification and change trajectory analysis in the months from which the imagery was collected. We used two sources of historical climate data to encompass the entire time period. The Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) from the University of California Santa Barbara is a quasi-global rainfall dataset that spans 50\({}^{\circ}\) S-50\({}^{\circ}\) N across all longitudes. CHIRPS uses 0.05\({}^{\circ}\) resolution satellite imagery and in-situ station data to produce a gridded, monthly dataset the spans from 1981 to near-present [32]. Since the remote sensing analysis contains the earliest usable Landsat imagery of PCNP from 1975, the CHIRPS data does not encompass the earliest part of the study period. WorldClim is another gridded dataset for historical climate data. Specifically, the CRU-TS 4.03 [31] downscaled with WorldClim 2.1 [30] is a monthly climate dataset for global land areas at approximately 21 km spatial resolution. Since this dataset provides climate data that extends back to 1960, it was used to extend our climate data back to 1970. Even though the CHIRPS and WorldClim data are at different spatial resolutions, both were used to assess the climate in PCNP. Based on the 49 years of combined CHIRPS and WorldClim precipitation data, the dry season (January-May) average precipitation in PCNP is approximately 498 mm and 2208 mm in the wet season [30; 31; 32]. Figure 2, which shows the yearly cumulative precipitation Z-scores for the CHIRPS and WorldClim data from 1970 to 2018, was used to assess which years were drier or wetter than average precipitation in PCNP to ensure that anomalous years of imagery were not chosen for analysis.  
Figure 2: Total annual rainfall Z-scores for PCNP from 1981 to 2019. Rainfall data from WorldClim and CHIRPS.