esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Atmospheric Turbulence on Optical Wireless Communication in NEOM Smart City
## 3 Atmospheric Turbulence  
Clear air turbulence can significantly affect the transmitted optical beam. Wind and solar heat can lead to inhomogeneities in the temperature and pressure of air. These variations cause random refractive index fluctuations in the atmosphere, leading to the formation of air cells (eddies) of varying sizes and refractive indexes. Variations in the refractive index and propagation path of the optical beam in air can lead to random fluctuations in both the amplitudes and received signal phase. The block diagram of an FSO communications system is shown in Figure 1. The information signal (analog or digital) is delivered through the atmosphere using an optical transmitter. At the receiver end, the optical beam concentrates towards the photodetector, whose output is electrically processed to receive the information signal. The atmospheric turbulence effect on the optical signal depends on the size of the turbulence cell, which can be defined as follows [39]:  
1. When the turbulence cells' diameters are smaller than the laser beam diameter, the laser beam bends and becomes distorted. Small differences in the arrival times of various components of the beam wavefront cause constructive and destructive interference, resulting in temporal variations in the laser beam intensity at the receiver. This effect is known as scintillation, Figure 1a.
2. If the size of the air turbulence cell is larger than the beam diameter, it can bend the optical path. Figure 1b shows how the beams (solid rays) leaving the laser source are deflected as they go through the large air cell, arriving off-axis rather than on-axis as expected in the absence of turbulence.
## 2 Materials and Method Methodology
### Green Visibility Assessment with Green View Index (GVI)  
The DeepLabv3+ model was used to identify images of street environments. This model's effectiveness has been validated on PASCAL VOC 2012 and Cityscapes datasets, achieving performance levels of 89.0% and 82.1% on the test sets without any post-processing [47]. We employed a pre-trained model on the ADE20K dataset, which features over 20,000 images and 150 category annotations, recognized for its diverse scenes and high accuracy in image recognition [48]. Each image contains an average of 19.5 object instances and 10.5 object classes, making ADE20K superior to other datasets in diversity and annotation complexity. Moreover, ADE20K supports benchmarks such as SceneParse150, where leading models like UPerNet and PSPNet achieve state-of-the-art performance, with UPerNet-101 attaining a pixel accuracy of 81.01% and a mean IoU of 0.4266 [49]. Through image semantic segmentation, different object types were assigned distinct labels (Figure 4). The green elements visible to residents, including trees, grass, mountains, plants, vegetation clusters, flowers, and palm trees, were identified. By calculating the proportion of pixels occupied by these green elements relative to the total pixels in the image, we obtained the green view index (GVI). This entire process was completed using Python 3.8 code running on a PC (Windows 11, R5 5600, RTX 2080 Ti, 32 Gb RAM).  
Figure 3: Hexagonal grids with 250 m edge lengths (**a**) and aggregated residential points (**b**) for Wuhan’s central urban area in 2014.  
In 2018, Wuhan released the _15-Minute Community Life Circle Action Guide_, which defined the daily basic living space for residents within an 800-1000 m radius. Accordingly, we used a 1000 m buffer radius in this study [50; 51]. The average GVI of all sampling points within the buffer boundary of the residential aggregation points was used as the visibility indicator. Although recognizing that some streets lacked images in certain years due to the limited coverage when Baidu Street View was first launched in Wuhan in 2014, two factors mitigate the impact of missing images on the research results: First, the GVI for 2014 is largely consistent with the distribution of residential areas. Second, each buffer zone of residential aggregation points contained more than 20 GVI samples, and the average of all the sample points was sufficient to accurately reflect the visibility level of each residential unit [52].  
With reference to previous studies [33; 53], the calculated visibility results were classified into five categories based on \(\leq\)5%, 5-15%, 15-25%, 25-35%, and \(\geq\)35%, where the intervals denote low, below average, average, above average, and high, respectively.
# A Method for Enhancing the Accuracy of Pet Breeds Identification Model in Complex Environments
## 4 Experiment and Analysis
### Comparison of Two Datasets  
To confirm the enhanced performance of the model following fine-tuning on the CPI dataset, we conduct comparative experiments across two datasets. In our experiment, we employ three distinct SOTA deep learning image classification models: Swin Transformer [16], DesneNet [18], and ResNet [18]. Specifically, we use ResNet34, DenseNet121, and the tiny version of Swin Transformer. The selection of these famous architectures is derived from the comparative research in [11; 35; 36], with each model falling under three distinct architectures (residual convolution architecture, dense connection network architecture, and transformer architecture). These three models are pre-trained on the Imagenet dataset, and fine-tuned on two pet breeds classification datasets as follows:  
(1) Fine-tune the network using the images collected by the Oxford-IIIT pet dataset.  
(2) Fine-tune the model using the CPI dataset, which is the method proposed in this paper (see Figure 2).  
The categories in the CPI dataset are divided in an 8 : 2 ratio, with 80% allocated for training and 20% for testing. To ensure the fairness of this comparative experiment, we consistently employ the test set separated from the CPI dataset for model testing. Therefore, we remove the pictures selected for the test set from the Oxford-IIIT Pet dataset beforehand to ensure that only data unseen by the model appear in the test set. In this comparative experiment, the size of all images in the Oxford-IIIT Pet and CPI datasets are adjusted to 224 \(\times\) 224, ensuring consistency in image size between fine-tuning and pre-training stages for each model.  
In the two aforementioned scenarios, all parameters of the pretrained networks are fine-tuned using backpropagation algorithms and the SGD optimizer. At the same time, we use the Top-1 accuracy as the primary evaluation metric in the test, with the Top-5 accuracy, mP, mR, and m\(F_{1}\) score as auxiliary evaluation metrics. Comparing the CPI dataset withthe original Oxford-IIIT Pet dataset helps us to analyze the impact of complex images on classification accuracy in practical applications.  
The training results are shown in Table 2. It is evident that models exhibit superior performance after fine-tuning the CPI dataset, when compared to those fine-tuned on the Oxford-IIIT Pet dataset. For Top-1 accuracy, ResNet34 is improved by 0.36 percentage points, DenseNet121 is improved by 0.09 percentage points, and Swin Transformer is improved by 1.26 percentage points. We conduct comparative experiments on two datasets using three different architectures. The experimental results demonstrate that our CPI dataset is more suitable for fine-tuning pet breed classification models.
# LPWAN-Based Vehicular Monitoring Platform with a Generic IP Network Interface
## 3 Technological Background
### Schc  
A key technological enabler to send IPv6 datagrams over an LPWAN network is the SCHC scheme [28], recently drafted by the IETF lpwan work-group. The base proposal enables the compression and fragmentation of IPv6/UDP packets over any LPWAN network. To this end, communication peers share a pre-provided context that indicates how to compress and decompress the packets received. This context remains static during the life-time of the device, and contains a number of compression and fragmentation rules to be applied to different kind of packets. Thus, a short RuleID that is known beforehand is included in the packet header, instead of sending longer header fields.  
In the scheme considered in this work, both the vehicle on-board unit and an application gateway have to agree on the SCHC compression rule applied. Hence, this approach is mainly applicable to the LPWAN communication and packet management segment, with the great advantage of interconnecting vehicles to the Internet in the uplink and downlink directions seamlessly for both ends of the communication. This is an interesting feature, aimed at integrating LoRaWAN end-devices within the IPv6 ecosystem, increasing their communication capabilities.  
The potential of SCHC was initially evaluated in our previous work in Ref. [29], providing promising results by transmitting Internet Control Message Protocol version 6 (ICMPv6) packets in a real environment. In this paper, this work is extended by addition of a data gathering platform (that uses OBD-II for the case of regular cars), a monitoring web-based interface and, above all, a remarkable development of a generic UNIX network interface to transmit IPv6/UDP packets over LPWAN.
# Arsenic: A Review on a Great Health Issue Worldwide
## 2 Chemical Forms and Properties of Arsenic  
Arsenic (As, atomic number 33) is a metalloid found either in many minerals in combination with sulfur and other metals, or as a pure element [30]. Arsenic exists in three allotropes: gray, yellow, and black arsenic; the gray form is the most common and important to industry. Gray arsenic is brittle and has a relatively low Mohs hardness of 3.5; its density is 5.73 g/cm\({}^{3}\) (Table 1). The most volatile, least dense, and the most toxic form is represented by yellow arsenic, which is soft and waxy, with a density of 1.97 g/cm\({}^{3}\); it is rapidly transformed into gray arsenic by light. Black arsenic, glassy and brittle, can be formed by cooling vapor at about 150-220 \({}^{\circ}\)C and by crystallizing arsenic in the presence of mercury vapors. The most common oxidation numbers of As are \(-3\), \(+3\), and \(+5\), in which the element is able to form inorganic and organic compounds, both in the environment and within the human body. Arsenic does not melt at atmospheric pressure, but sublimes from solid to vapor at 615 \({}^{\circ}\)C.  
Arsenic is stable in dry air but forms a golden-bronze tarnish when exposed to humidity, which turns to a black surface layer black upon prolonged exposure. After heating in air, arsenic oxidizes to arsenic trioxide (As\({}_{2}\)O\({}_{3}\)), producing fumes with odor resembling garlic, and arsenic pentoxide (As\({}_{2}\)O\({}_{5}\)). It is possible to prepare arsenic acid (H\({}_{3}\)AsO\({}_{4}\)) with concentrated nitric acid, arsenous acid (H\({}_{3}\)AsO\({}_{3}\)) with dilute nitric acid, and arsenic trioxide with concentrated sulfuric acid; however, arsenic does not react with water, alkalis, or non-oxidizing acids. Arsenic easily forms covalent molecules with several nonmetals and may react with metals to form arsenides. Arsenic comprises about 1.5 ppm (0.00015%) of the earth's crust and is the 53 rd most abundant element. Typical background concentrations of arsenic do not exceed 100 mg/kg in soil [32], and 10 mg/L in freshwater [33,34]. The maximum tolerance limit for arsenic in drinking and public water is 10 mg/L [35]. Concerning soils, the maximum acceptable limit for agricultural soil is 20.0 mg/kg recommended by the European Union [36]. In the crust, arsenic exists together with copper (domeykite Cu\({}_{3}\)As; enargite Cu\({}_{3}\)AsS\({}_{4}\)), iron (arsenopyrite AsFeS), cobalt (cobaltite CoAsS), nickel (nickel NiS), and silver (proustite Ag\({}_{3}\)AsS\({}_{3}\)). In soil and water, in the presence of oxygen, As\({}^{5+}\) is oxidized to As\({}^{5+}\), while in low oxygen environment in deep well water and deep sea and ocean it is present as reduced As\({}^{3+}\)[37].
## 3 Dual-Band Doherty Power Amplifier
### Offset Lines in Dual-Band Doherty Power Amplifier  
The offset lines (Figure 1) are one of the major contributors to the bandwidth limitation in a DPA so that, the offset line should be designed to work in both bands, the offset lines circuit in [25] was implemented using the same circuit that was used for the final impedance transforming network (two-section transformer), where the same phase can be achieved at both frequencies.  
Another approach of designing a dual-band offset line is using either T- or \(\Pi\)-networks. The most notable issue in designing offset lines using T- and \(\Pi\)-networks is that the phase difference between the input and output of these networks at different frequency bands, where it is generally a \(\pm\)90\({}^{\circ}\). Table 1 summarizes the differences at the two operating frequencies.  
As can be seen from Table 1, the T-network is difficult to use in a dual-band DPA due to the different phase response in the two bands. Hence only the \(\Pi\)-network can be used as a dual-band line offset. The primary purpose of the offset line is to absorb the parasitic effect of the main and the auxiliary amplifier, so that as the parasitic of the transistor increase, a longer offset line is required, a solution for reducing the length of the offset line is to reduce the effect of the transistor parasitic as was done by [18], where the use of offset lines was eliminated by absorbing the transistors' output parasitic; at the input side, an unequal power divider was realized using a three-section Wilkinson coupler to compensate for the lower transconductance of the auxiliary amplifier. The harmonic termination of both amplifiers was carefully designed to avoid Smith chart sensitive regions. Since the reactive part of the output impedance of the auxiliary amplifier is not the same for the two band frequencies, there is a need to design an offset line that can produce different phases to increase the impedance so that there will be no power leakage toward the auxiliary amplifier when it is off.
## 4 Results
### Mental Workload Evaluation
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Tool** & \multicolumn{2}{c}{**Mental**} & \multicolumn{2}{c}{**Physical**} & \multicolumn{2}{c}{**Temporal**} & \multicolumn{2}{c}{**Performance**} & \multicolumn{2}{c}{**Effort**} & \multicolumn{2}{c}{**Frustration**} \\  & \multicolumn{2}{c}{**Demand**} & \multicolumn{2}{c}{**Demand**} & \multicolumn{2}{c}{**Demand**} & \multicolumn{2}{c}{**Performance**} & \multicolumn{2}{c}{**Effort**} & \multicolumn{2}{c}{**Frustration**} \\ \hline \multicolumn{8}{c}{First task} \\ \hline \multirow{4}{*}{Taguette} & Min & 1 & 1 & 1 & 1 & 1 & 1 \\  & Max & 20 & 18 & 20 & 20 & 20 & 20 \\  & Mean & 8.48 & 5.53 & 10.20 & 10.63 & 10.24 & 8.33 \\  & Std. dev. & 5.08 & 5.23 & 4.39 & 5.53 & 4.88 & 5.94 \\ \hline \multirow{4}{*}{ChatCPT} & Min & 1 & 1 & 1 & 1 & 1 & 1 \\  & Max & 18 & 15 & 20 & 20 & 16 & 16 \\  & Mean & 2.94 & 2.19 & 8.93 & 7.75 & 3.20 & 2.99 \\  & Std. dev. & 3.08 & 2.40 & 7.46 & 6.61 & 3.46 & 3.44 \\ \hline \multirow{4}{*}{Gemini} & Min & 1 & 1 & 1 & 1 & 1 & 1 \\  & Max & 20 & 16 & 20 & 20 & 20 & 20 \\  & Mean & 3.07 & 2.40 & 8.49 & 6.50 & 4.58 & 4.20 \\  & Std. dev. & 3.18 & 2.78 & 6.78 & 5.51 & 4.53 & 4.54 \\ \hline \multirow{4}{*}{Kruskal-Wallis} & H & 77.133 & 39.177 & 6.714 & 24.229 & 91.011 & 50.357 \\  & df & 2 & 2 & 2 & 2 & 2 & 2 \\  & sig & **<0.001** & **<0.001** & **0.035** & **<0.001** & **<0.001** & **<0.001** \\ \hline Effect size & \(\eta^{2}\) & 0.2946 & 0.1458 & 0.0185 & 0.0872 & 0.3491 & 0.1896 \\ \hline \multicolumn{8}{c}{Second task} \\ \hline \multirow{4}{*}{Taguette} & Min & 1 & 1 & 1 & 1 & 1 & 1 \\  & Max & 17 & 20 & 20 & 20 & 19 & 20 \\  & Mean & 5.76 & 5.21 & 12.61 & 9.71 & 7.65 & 7.36 \\  & Std. dev. & 4.56 & 5.06 & 5.38 & 6.42 & 4.82 & 5.96 \\ \hline \multirow{4}{*}{ChatCPT} & Min & 1 & 1 & 1 & 1 & 1 & 1 \\  & Max & 16 & 15 & 20 & 20 & 18 & 20 \\  & Mean & 4.07 & 2.61 & 8.64 & 8.36 & 5.45 & 4.99 \\  & Std. dev. & 3.84 & 2.77 & 6.66 & 6.11 & 4.73 & 5.19 \\ \hline \multirow{4}{*}{Gemini} & Min & 1 & 1 & 1 & 1 & 1 & 1 \\  & Max & 15 & 15 & 20 & 20 & 17 & 20 \\  & Mean & 3.52 & 2.62 & 7.51 & 7.25 & 4.79 & 4.99 \\  & Std. dev. & 3.50 & 3.09 & 6.67 & 5.67 & 4.63 & 4.89 \\ \hline \multirow{4}{*}{Kruskal-Wallis} & H & 17.340 & 22.518 & 27.780 & 6.298 & 21.322 & 12.550 \\  & df & 2 & 2 & 2 & 2 & 2 & 2 \\  & sig & **<0.001** & **<0.001** & **<0.001** & **0.043** & **<0.001** & **0.002** \\ \hline Effect size & \(\eta^{2}\) & 0.0616 & 0.0824 & 0.1035 & 0.0173 & 0.0776 & 0.0424 \\ \hline \hline \end{tabular} _Note: Bold values indicate statistical significance at the 0.05 level._  
\end{table} 
Table 9: NASA-TLX values Taguette, ChatGPT, and Gemini, per tasks.
## 2 Methodology
### Experimental Method
#### 2.1.3 Experimental Measurements  
To obtain the distributions of temperature and velocity in the occupied zone, four sampling lines (Lines 1-4) were arranged in the occupied zone, as shown in Figure 1b. Each sampling line contained four measurement points at heights of 0.1 m, 0.6 m, 1.1 m, and 1.7 m above the floor. In addition, the change in the axial velocity of the jet was represented by the value of the air velocities at the measurement points, which were arranged horizontally and vertically on the jet center plane, as shown in Figure 2. The velocity change in the initial section of the jet was more obvious; thus, the measurement points near the center of the middle air inlet needed to be more concentrated; 128 measurement points were distributed in eight rows and 16 columns.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test Series** & **Case** & **V\({}_{0}\) (m/s)** & **L (mm)** \\ \hline \multirow{4}{*}{Series 1} & 1 & 1.21 & \multirow{2}{*}{650} \\  & 2 & 1.62 & \\ \cline{1-1} \cline{2-4}  & 3 & 1.21 & \\ \cline{1-1} \cline{2-4}  & 4 & 1.62 & \\ \hline \multirow{4}{*}{Series 2} & 5 & 1.53 & 650 \\  & 6 & 2.02 & 650 \\ \cline{1-1}  & 7 & 2.02 & 1300 \\ \hline \end{tabular} Note: V\({}_{0}\) = supply air velocity; L = air inlet spacing.  
\end{table}
Table 1: Experimental conditions.  
Figure 2: Layout plan of measurement points in center plane of multiple parallel jets.  
To ensure the airtightness of the envelopes, the cracks in doors, windows, and temporarily unused air inlets were sealed. All measurement instruments were calibrated prior to the experiments. The average room air temperature is calculated by the value of three measurement points distributed at a height of 1.1 m in the occupied zone. Detailed information on the measurement instruments is listed in Table 2. In this study, only data recorded in a steady environment were used. The frequency conversion fan and air handling unit were turned on about one hour before the experiments. A steady indoor environment was identified by stationary air temperature monitored using Swema 03+. During the measurement, the sampling lines were measured separately owing to the limitations of the instruments. Collection of air velocities and temperatures along one sampling line lasted 6 min. The instruments were then manually moved to the next sampling line slightly. To minimize the disturbance of movement, the data of the next sampling line were not recorded until Swema 03+ displayed a stable environment again. The interval between two adjacent data collections was at least 10 min.
## 2 Materials and Methods
### Robust Federated Learning Based on FL-QLMS
```
1:Server executes:
2:Initialize global model \(W_{0}\)
3:Initialize validation dataset \(D_{\text{val}}\)
4:for each round \(t=1,2,\ldots\) do
5:\(S_{t}\leftarrow\) random subset of \(k\) clients
6:for each client \(i\in S_{t}\) in parallel do
7:\(W_{i}^{t+1}\leftarrow\text{ClientUpdate}(i,W_{t})\)
8:end for
9:\(W_{t+1}\leftarrow\text{ServerAggregation}(W_{i}^{t+1}i\in S_{t},D\text{val})\)
10:end for
11:
12:functionClientUpdate(\(i,W_{t}\))
13:\(W_{i}\gets W_{t}\)
14:for each local epoch \(e=1,\ldots,E\) do
15:for batch \(b\in D_{i}\) do
16:\(W_{i}\gets W_{i}-\eta\
abla f(W_{i};b)\)
17:end for
18:end for
19:return \(W_{i}\) to server
20:endfunction
21:
22:functionServerAggregation(\(W_{i}^{t+1}i\in S_{t},D\text{val}\))
23:for each \(W_{i}^{t+1},i\in S_{t}\) do
24:\(v_{i}\leftarrow\text{Evaluate}(W_{i}^{t+1},D_{\text{val}})\)
25:end for
26:\(S_{t}^{\prime}\leftarrow\text{TopK}((i,v_{i})i\in S_{t})\)
27:return \(\sum i\in S_{t}^{\prime}\frac{|D_{i}|}{|D_{i}|}\cdot W(i)\)
28:endfunction
29:
30:functionEvaluate(\(W,D_{\text{val}}\))
31:\(//\) W: Local model under evaluation
32:\(//\)\(D_{\text{val}}\): Central validation dataset
33: loss = 0
34:for each \((x,y)\) in \(D_{\text{val}}\) do
35:\(\hat{g}\gets W(x)\) // Predict using model W
36:\(\text{loss}\leftarrow\)\((\hat{y}-y)^{2}\) // RMSE-based loss
37:end for
38:\(\text{RMSE}=\sqrt{\text{loss}/|D_{\text{val}}|}\) // Average RMSE
39:if RMSE > threshold then
40:\(//\) Suspicious of Byzantine node if poor performance
41:\(//\) Add logic for weight adjustment or exclusion
42:endif
43:return RMSE // Model performance metric
44:endfunction
```  
**Algorithm 1** FL-QLMS  
Further considerations include enhancing the local dataset quality assessment model and using it for pre-filtering during client selection. Currently, local models are evaluated based solely on performance against the global validation set, but future implementations could incorporate a meta-model that evaluates aspects such as client data distribution similarity, class ratios, and noise levels, allowing for more proactive responses to outlier and imbalance issues. The inclusion of the Evaluate() function in the FL-QLMS algorithm not only improves its completeness and clarity but also strengthens its ability to handle Byzantine failures in the federated learning process. By providing a clear definition and explanation of this critical component, the reliability and practicality of the proposed collaborative learning model for energy optimization are significantly enhanced. This contributes to the realization of a privacy-preserving and efficient energy management system for electric vehicles.