esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive and Active Solar Systems in Eco-Architecture and Eco-Urban Planning
## 3 Research: Passive and Active Solar Systems in Eco-Architecture and Eco-Cities
### Heliostats and Their Application  
One form of the active use of technology in insulating a building are the heliostats, which follow the apparent movement of the sun. They are equipped with flat or low curvature mirrors, and their goal is to reflect sunlight and direct it to a specific point. Heliostats are a basic element in tower-type heliopower plants; radiation is directed to the top of the tower, where a special receiver is placed whose task is to convert solar radiation from the many heliostats into heat, which is then converted into electricity. Despite the relatively simple operation, their construction raises many doubts and problems such as folding, assembly and, above all, synchronization of the work of sometimes hundreds of thousands of mirrors [20; 34]. Heliostats are independent devices, each equipped with its own drive and position control system, and the construction of a heliopower plant has enormous costs, 40% of which are the building and commissioning of a heliostat field [35]. In addition, heliopower plants give rise to the high costs of servicing the heliostat field, e.g., cleaning the mirrors [36]. However, these devices can be selectively used in the urban fabric to illuminate a densely built-up area. Many solutions of this type already exist in cities.
# Time Series Anomaly Detection Using Signal Processing and Deep Learning
## 2 Literature Review
Using basis expansion techniques, such as Fourier, Wavelet, or B-spline bases, FDA transforms a time series into a smooth, continuous representation. This enables the robust modeling of temporal information, reducing noise and highlighting deviations from normal behavior. Functional models have received a lot of success in different time series tasks [38,39]. Anomaly detection can be performed by fitting a functional model to a collection of a normal time series and measuring how new observations deviate from the expected functions. Recent approaches leverage functional Autoencoders [19,40], which combine FDA with deep learning. In these methods, time series data are encoded into a lower-dimensional latent space using functional transformations and are then reconstructed. Functional Autoencoders excel at capturing complex, non-linear dependencies in a time series and enable two-way dimensionality reduction in the feature as well as time domain. This dual nature allows efficient representation learning and remapping. Although functional Autoencoders have not yet been applied explicitly for anomaly detection, their ability to capture intricate patterns and temporal relations make them a promising and logical next step for advancing anomaly detection techniques. Despite their success, Autoencoder models can be sensitive to noise and irrelevant features in the input time series, which can deteriorate their anomaly detection performance. On the other hand, signal processing cannot learn complex relations. This motivates the need to merge these two ideas. Preprocessing techniques, such as filtering, can improve the quality of the input data and increase modeling success. Different applications of time series filters can be found in the literature: smoothing (e.g., moving averages and the Gaussian filter), Wavelet Transform [30], Non-Linear Filtering [41], Bayesian [42], Fourier Transform (e.g., FFT), Kernel methods [43,44], Savitzky-Golay filter [45], or Decompositions (e.g., STL [46,47]). Bandpass filtering [31] is a signal processing technique that allows the retaining of frequency components within a specified range while removing those outside the range such as removing low-frequency trends or high-frequency noise. Bandpass filters are widely used in applications such as communication systems, biomedical signal processing, and vibration analysis to remove noise and isolate relevant frequency components to enhance signal quality [48,49]. While bandpass filtering has been used extensively in signal processing, its integration with machine learning models for time series anomaly detection has not been widely explored. Combining bandpass filtering with Autoencoder-based models provides an opportunity to leverage the strengths of both approaches: the noise reduction capabilities of filtering and the representation learning from Autoencoders.
# Quad-Beam 4 \(\times\) 2 Array Antenna for Millimeter-Wave 5G Applications
## 3 Array Antenna Design
### The 4 \(\times\) 2 Array Antenna  
This section proposes a final 4 \(\times\) 2 array antenna (Ant4). It is to be noted that the interelement spacing between rows is not equal, and as a result, the pair of antennas generates a quad-beam. The proposed Ant4 is shown in Figure 13. The array factor (AF) for this particular case is as follows:  
\[AF(\theta,\ \varphi)=AF_{x}(\theta,\ \varphi)\cdot\ AF_{y}(\theta), \tag{17}\]  
where  
\[AF_{x}(\theta,\ \varphi)=\Big{(}1+e^{jkEs1\cos{(\theta)}\sin(\varphi)}+e^{j(k( E1+Es3)\cos{(\theta)}\sin(\varphi)}+e^{j(k(2\cdot Es1+Es3)\cos{(\theta)}\sin( \varphi)}\Big{)}\ \text{and} \tag{18}\]  
\[AF_{y}(\theta)=\Big{(}1+e^{jkEs2\sin(\theta)}\Big{)}. \tag{19}\]  
From the above equation, it is clear that the pair (A1, A2) and (A3, A4) causes the generation of the beam at \(\theta_{1}\) and \(\varphi_{1}=0^{\circ}\). Likewise, (A3, A4) and (A7, A8) generate a beam at \(\theta_{2}\) and \(\varphi_{2}=90^{\circ}\), while (A7, A8) and (A5, A6) lead to a beam at \(\theta_{3}\) and \(\varphi_{3}=180^{\circ}\). The fourth beam is due to (A1, A2) and (A5, A6) at \(\theta_{4}\) and \(\varphi_{4}=270^{\circ}\). At \(\theta=0^{\circ}\), the intensity of all quad beams is low, thus canceling the radiation pattern and generating a null with minor lobes. The estimated radiation pattern from the above equation is compared with the simulated pattern shown in Figure 14. Thus, Ant4 results in the dual-beam in the xz-plane at an angle of \(\theta=\pm 54^{\circ}\), with a half-power beamwidth (HPBW) of \(31^{\circ}\). In the yz-plane, the dual-beam is achieved at an angle of \(\theta=\pm 58^{\circ}\), with an HPBW of \(34^{\circ}\). Ant4 results in a high X-polarization of \(-24\) dB in the xz-plane and \(-18\) dB in the yz-plane, as shown in Figure 15. Ant4 achieves bandwidths ranging from 29.81-30.5 GHz, as illustrated in Figure 16a, with a peak gain of 10.3 dBi and an average total efficiency of 77%, as depicted  
Figure 12: A demonstration of the 3D radiation pattern formation of Ant3 (**a**) and its respective 2D pattern (**b**).  
in Figure 16b. It is worth noting that as the array antenna aperture increases, the bandwidth increases towards the lower frequency.  
Figure 14: Comparison of the 3D radiation obtained from (**a**) the equation, and (**b**) the simulated radiation pattern from Ant4 at 30.2 GHz.  
Figure 13: The proposed \(4\times 2\) array antenna (Ant4), with Es3 = 4 mm, SL4 = 20 mm, and SW4 = 18 mm.  
Figure 15: The simulated radiation pattern of Ant4 representing the co- and X-polarizations in (**a**) xz-plane and (**b**) yz-plane.
# Tele-Assessment of Executive Functions in Young Adults with ADHD: A Pilot Study
## 8 Study Limitations  
This research is a pilot study with some peculiarities that will be outlined below. The estimated power with the available sample size, calculated through power analysis, was approximately 55%, which reflects the exploratory nature of the present investigation but raises some methodological limitations of the research that need to be mentioned. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Measures** & \
\begin{tabular}{c} **Mean (SD)** \\ **ADHD Group** \\ \end{tabular} &
\begin{tabular}{c} **Mean (SD)** \\ **TD Group** \\ \end{tabular} & \(\chi^{2}\) & **gdl** & \(p\) & \(\epsilon^{2}\) \\ \hline Fb1 mean RT CR congruent & 483.3030 (153.436) & 497.1765 (133.552) & 0.03083 & 1 & 0.861 & \(4.67\times 10^{-4}\) \\ Fb1 mean RT CR & 495.4848 (174.124) & 509.9412 (150.192) & 0.00393 & 1 & 0.950 & \(5.96\times 10^{-5}\) \\ Fb1 accuracy CR congruent & 96.8182 (17.402) & 100.0000 (0.000) & 2.092 & 1 & 0.148 & 0.03169 \\ Fb1 accuracy CR congruent & 92.1212 (24.013) & 95.5882 (17.090) & 0.140 & 1 & 0.709 & 0.00212 \\ \hline Fb2 mean RT CR congruent & 98.1818 (3.0150) & 99.7059 (1.715) & \(1.57\times 10^{-4}\) & 1 & 0.990 & \(2.38\times 10^{-6}\) \\ Fb2 mean RT CR & 96.8182 (4.812) & 98.6765 (3.328) & 0.187 & 1 & 0.665 & 0.00284 \\ Fb2 accuracy CR congruent & 96.8182 (17.402) & 100.0000 (0.000) & 8.48 & 1 & 0.004 * & 0.1285 \\ Fb2 accuracy CR congruent & 92.1212 (24.013) & 95.5882 (17.090) & 3.89 & 1 & 0.049 * & 0.0590 \\ \hline Fb3 mean RT CR congruent & 872.8824 (203.359) & 851.2059 (155.161) & 0.0632 & 1 & 0.801 & \(9.44\times 10^{-4}\) \\ Fb3 mean RT CR & 1006.0000 & 982.7059 (171.574) & 0.3539 & 1 & 0.552 & 0.00528 \\ Fb3 accuracy CR congruent & 94.5000 (5.572) & 96.7647 (8.648) & 8.25 & 1 & 0.004 * & 0.1232 \\ Fb3 accuracy CR congruent & 87.2059 (10.907) & 91.2647 (10.103) & 3.06 & 1 & 0.080 & 0.0456 \\ \hline \multicolumn{7}{l}{* Significant.} \\ \end{tabular}
\end{table} 
Table 8: Mean reaction time (RT) of correct responses (CR) in the congruent and incongruent trials of the three blocks of the Flanker task (Block 1 central condition, Block 2 peripheral condition, and Block 3 mixed-rule condition).
# A Survey on Machine-Learning Techniques for UAV-Based Communications
## 2 Physical Layer Issues  
A wide range of communication aspects exists that can be enhanced through ML solutions, including (i) the development of accurate channel models in various environments and the mitigation of path-loss (PL) through prediction of the topology; (ii) the tackling of severe interference from other UAVs and from ground nodes with timely training, using mobility and user association data; and (iii) the configuration of transmission parameters towards achieving specific performance targets.
## 4 Experimental Evaluation of the GAGAN Model
### Numerical Analysis
The superior performance of GAGAN models in discriminator training, reflected by significantly lower d_loss values, demonstrates the successful integration of Genetic Algorithms to optimize the discriminator's weights. This evolutionary approach allows GAGAN to fine-tune the discriminator more effectively than traditional gradient-based optimization alone, thereby providing more accurate and consistent feedback to the generator. Consequently, even when some GAGAN models exhibit higher g_loss values, the improved discriminator performance helps maintain the adversarial balance, driving the generator to refine its outputs continuously. In contrast, DCGAN models, despite showing stable g_loss values, tend to suffer from higher and less-stable discriminator losses. This suggests a limitation in their ability to train an effective discriminator, which may hinder the generation of high-quality, realistic images. The consistent gap in discriminator performance between the two approaches highlights the advantage of the GAGAN framework in achieving more robust training. By effectively combining the global search capabilities of Genetic Algorithms with the feature extraction power of DCGANs, GAGAN proves to be a more efficient and balanced method for generating high-quality synthetic data. This improved training dynamic ensures that GAGAN can better handle challenges like mode collapse and maintain a stable adversarial process throughout training, ultimately leading to higher fidelity and diversity in the generated images.
## 4 Discussion
### Principle Component Analysis and Heatmap  
The chosen PCs between the score plots indicated that the property loadings of Chitiraikar, Iluppi Poo Samba, Karukka, Karunguruvai, Kuruvaikalanchium, Kuzhiyadichan Samba, Mappilai Samba, Milagu Samba, Mattaikkar, Nootripathu, Norungan, Panangattu Kudavazhai, and Poongar were on the positive side, and the others remain on the negative side.  
The extracted five PCs explained 95.7% of the total variance, as depicted in the scree plot. PC1 accounted for 36% variance and was primarily attributed to factors such as thickness, seed size, equivalent diameter, volume, surface area, geometric mean diameter, arithmetic mean diameter, and square mean diameter. Similarly, the PC2 (25.6% variance) was mainly associated with length, 1/w ratio, sphericity, and aspect ratio. Likewise, 12.1% of the variance contributing to PC3 was the tapped density, Carr's index, and Hausner ratio. The PC4 with 9.9% variance was attributed to true density and porosity, and PC5 (6.8%) was majorly highlighted by bulk density and Tapped density. The PC analysis showed that the rice varieties differed significantly in terms of both individuals and combinations.  
An agglomerative hierarchical clustering analysis model was built based on the paddy characteristics to assess the similarities in the detection of inter-group variance in physical and engineering features of the grain samples. The differential characteristics were grouped using a complete-linkage method and shown as thermo grams after the Euclidean distance matrix was calculated for the quantitative values of each group of comparisons. A dendrogram was used to display the cluster memberships of the traditional rice samples along with heatmap visualization. Two major clusters were found in the dendrogram. Clusters 1 and 2 are further branched into sub-clusters of two each.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 6 Test Bench and Results
### Data from Sensors
With these data, we can estimate the number of gateways necessary to cover the total area. Figure 26 shows the number of gateways required as a function of the area in ha. As can be seen, we could cover an area of 70,953 ha. using only 82 gateways. Finally, it is defined a method for the Internet connection of the different gateways. Currently, some mobile operators provide services specifically designed for IoT and Machine-to-Machine (M2M) applications at a very low price. Therefore, each gateway will have a 3G dongle to permit the Internet connection and to send the data from the sensors to the server [47]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Status** & **Capacity Required per Cycle** \\ \hline Preheating phase of MQ135 & 1.09 mAh \\ Measurement of sensors & 0.552 mAh \\ LoRa Tx & 0.00139 mAh \\ Sleep Mode & 0.1904 mAh \\ Total transmission & 1.833 mAh \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of consumption calculation per cycle. Figure 25: Diagram operating time of the device in each state.
# Evaluation of 6 LoWPAN Generic Header Compression in the Context of a RPL Network
## 3 Preliminaries
### Header Compression  
Header compression minimises IPv6 header fields using common and contextual information. In RFC 4944 [2], a simple header compression mechanism is defined, IPv6 Header Compression (6 LoWPAN-HC). This compression method is used for compressing the IPv6 header and the headers following the IPv6 headers. The IPv6 headers are compressed with IPv6 Header Compression 1 (6 LoWPAN-HC1), whereas the headers following the IPv6 header are compressed with IPv6 Header Compression 2 (6 LoWPAN-HC2).  
With RFC 6282 [3], 6 LoWPAN-HC1 and 6 LoWPAN-HC2 were completely replaced with IP Header Compression (6 LoWPAN-IPHC) and 6 LoWPAN-NHC, respectively. From now on, when mentioning 6 LoWPAN compression we refer to 6 LoWPAN-IPHC and 6 LoWPAN-NHC.  
The main idea behind 6 LoWPAN-IPHC is to exploit the fact that many fields in the IPv6 header remain constant or exhibit predictable patterns in certain communication scenarios. 6 LoWPAN-IPHC operates by defining rules and contexts that help devices to identify which fields can be elided and which ones must be included in the compressed header.  
An example of how 6 LoWPAN compresses an ICMPv6 packet is shown in Figure 2. Figure 2a shows the structure of the packet before compression, and Figure 2b shows the structure after compression. In this example, the traffic class and flow label are elided since they were set to zero and not used. The payload length is removed because it is something that can be calculated from the bytes following the 6 LoWPAN-IPHC header. The hop limit has a value of 64, which is a common value and is represented with only 2 bits in the 6 LoWPAN-IPHC header. The IPv6 source address is elided because it can be reconstructed using the source link-layer address of the originator of the message. Finally, the IPv6 destination address is a multicast address, which in this case can be reduced to only 1 byte. 6 LoWPAN-NHC is not defined for ICMPv6 packets; therefore, the next  
Figure 1: Example of Internet Protocol (IP) over IEEE 802.3 (PHY and MAC) [16] (**left**) and IPv6 over Low-power Wireless Personal Area Network (6 LoWPAN) over IEEE 802.15.4 (PHY and MAC) [1] (**right**) protocol stacks.  
header field is kept in the compressed packet. The example shows that the IPv6 header was reduced from 40 to 4 bytes.
## 3 Results
### Intercity Efficiency Analysis  
Table 3 displays the top ten cities with the most efficient intercity transportation across various modes, for each city node. The analysis indicates that the efficiency of intercity transportation in core cities exceeds that of other cities. Within the framework of the comprehensive development of the urban cluster as a whole, the central cities demonstrate rapid progress in transportation infrastructure and make use of the majority of the transportation resources at their disposal.  
Once the transportation efficiency for each city is determined, the overall efficiency of the entire city cluster is computed utilizing Equation (9). In Figure 4, the efficiency of intercity travel by automobile consistently surpasses that of train travel within each urban cluster. In the realm of intercity transportation efficiency for automobiles, most city clusters demonstrate a high performance, with the exception of the Guanzhong Plain (67.70), which shows a subpar efficiency. The Yangtze River Delta (115.21) and Beijing-Tianjin-Heibei (120.13) regions demonstrate higher levels of intercity automobile efficiency in comparison to other city clusters. In the context of intercity train efficiency, the Yangtze River Delta (84.04), Beijing-Tianjin-Heibei (87.44), and Mid-Yangtze River Delta (76.78) demonstrate a superior performance, leveraging their extensive train networks to achieve this objective. However, the Guanzhong Plain (44.87) continues to fall behind, necessitating further development and enhancement of its train transportation infrastructure.  
Figure 4: Comparison of intercity efficiency of city clusters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ranking** & **Train Intercity Travel** & **Automobile Intercity Travel** \\ \hline
1 & Shanghai & Shanghai \\
2 & Beijing & Beijing \\
3 & Hangzhou & Shenzhen \\
4 & Tianjin & Hangzhou \\
5 & Zhengzhou & Tianjin \\
6 & Wuhan & Chongqing \\
7 & Chongqing & Chengdu \\
8 & Shenzhen & Guangzhou \\
9 & Hefei & Zhengzhou \\
10 & Chengdu & Wuhan \\ \hline \hline \end{tabular}
\end{table}
Table 3: Top 10 cities with the best intercity traffic efficiency under different travel modes.
## 2 Results
### TLR2/1, NOD2 and IL-1\(\beta\) Strongly Induce CCL20 in PBMCs  
Since mechanisms for CCL20 release from PBMCs have not been thoroughly investigated, we first stimulated PBMC from four healthy individuals in a pilot study with a broad panel of ligands as described in Section 4.7. This screening revealed that TLR2/6, TLR3, TLR4 and TLR7/8 yielded minimal CCL20 release (<200 pg/mL) (Supplementary Figure S1). Consequently, we excluded these ligands in the following stimulation assay in 40 subjects. Cell survival after thawing was generally high (mean 93 \(\pm\) 3.3%). Spontaneous CCL20 release from PBMCs was minor and there were no group differences. In general, P3C (+633%, \(p\) < 0.0001), MDP (+633%, \(p\) < 0.0001) and IL-1\(\beta\) (+359%, \(p\) < 0.0001) yielded the strongest CCL20 responses, while IL-10 reduced release (\(-\)63%, \(p\) < 0.01) compared to unstimulated control cells (Figure 1a). IL-1\(\beta\) release following activation of TLR2/1 and NOD2 is established. We therefore investigated whether CCL20 release after P3C and MDP stimulation was secondary to IL-1\(\beta\) release. IL-1\(\beta\) neutralization prior to IL-1\(\beta\) stimulation reduced both CCL20 (\(-\)54% \(\pm\) 10, \(p\) = 0.006) and IL-6 (\(-\)84% \(\pm\) 10) levels to that seen in unstimulated isotype controls. NOD2 mediated release was mildly affected (\(-\)33% \(\pm\) 12, \(p\) = 0.047) while TLR2/1 mediated release was unaffected (\(p\) = 0.36) (Supplementary Figure S2). To exclude freeze-thaw bias, the experiments were repeated in freshly isolated PBMCs (Supplementary Figure S3) which yielded similar responses. UC subjects released more CCL20 than CD subjects upon stimulation (Figure 1b), relative to spontaneous release from the PBMCs. This was statistically significant after P3C, MDP and IL-1\(\beta\) stimulation and also when CCL20 response was pooled for all stimuli (+242%, \(p\) < 0.01).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Characteristics** & **N** & **UC** & **CD** \\ \hline Number & 8 & 16 & 16 \\ Female sex & 5 & 8 & 7 \\ Age mean (range) & 43 (22–68) & 45 (19–76) & 40 (24–57) \\
5-ASA/S-ASA & 0 & 13 & 4 \\ Corticosteroids & 0 & 1 & 1 \\ Mayo/HBI \textless{} 1 & - & 10 & 9 \\ Mayo/HBI \textgreater{} 1 & - & 6 & 7 \\ \hline \end{tabular} Subjects’ sex, age and use of 5-ASA/S-ASA and corticosteroids are given in numbers. N = Healthy controls, UC = ulcerative colitis, CD = [PERSON]’s disease, 5-ASA/S-ASA = 5-aminosalicylic acid/sulphasalazine. HBI = Harvey-Bradashw Index.  
\end{table}
Table 1: Characteristics of subjects included in peripheral blood mononuclear cells (PBMC) analysis.  
Figure 1: \(Cont.\)
## 6 Results
### Impact of Hidden Units Sclction  
As selection of hidden units can impact on different parameters of system's performance therefore, we performed some experiments by varying the number of hidden units.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameters** & **Value Range** \\ \hline Learning rate & 0.01, 0.001, 0.0001 \\ Number of Epochs & 1000 to 5000 \\ Batch size & 25, 32,45 \\ Hidden units & 8, 16, 32, 64, 100, 128 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model parameters and their values.  
The graph in Figure 7 shows the RMSE estimated for both GCN and optimized GCN by varying number of hidden units. The number of hidden units are important and right selection of hidden units can enhance the prediction effect. As we can see, the optimal number of hidden units in our scenario are 100 as both models show their lowest RMSE value when the hidden units selected are 100. The RMSE of OpGCN is vividly very less as compared with simple GCN.  
The graph in Figure 8 shows the accuracy estimated for both GCN referred as Acc-GCN and optimized GCN referred as Acc_OpGCN by varying number of hidden units. Here also we can observe that the optimal number of hidden units are 100 as the accuracy recorded at this point is highest for both the algorithms. The highest accuracy achieved by GCN is around 0.89 and for optimized GCN is around 0.93.  
In summary, the prediction results are observed to be better if the hidden units are set to 100. We can also observe that when we increase the number of hidden units, the RMSE first starts to drop and then after some point it starts increasing again. Similar pattern can be seen in prediction accuracy. This happens when the hidden unit is bigger than a certain number that increases the model's  
Figure 8: Accuracy (Acc) vs. Number of hidden units.  
Figure 7: RMSE (Root Mean Square Error) vs. Number of hidden units (HU). Comparing RMSE with varying number of hidden units used in the model. GCN: graph convolutional neural network; OpGCN: optimized GCN.  
complexity and imposes additional computational challenges. And as a result, overfitting on training data happens. Therefore, we used optimal value of 100 for our remaining experiments.