esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review  
Various models have been developed to determine the relation between PM\({}_{2.5}\) and AOD [15,16]. AOD-based estimation is hindered by clouds, snow cover or high concentration of air pollution [17], as air pollutants, themselves, are indicators of AQI [18,19]. Regression analysis showed a positive correlation between PM\({}_{2.5}\) and CO, and NO\({}_{2}\), and SO\({}_{2}\) concentration, and a negative correlation with O\({}_{3}\), for the period of 2007 to 2011. The study also showed that PM\({}_{2.5}\) was 4% higher on weekends compared with weekdays [20]. The positive correlation studied in [20] by regression analysis of five-year data from 2007 to 2011 was verified in this work using remote sensing data and the spatial concentration of these pollutants from June 2018 to April 2021.  
Emissions from fossil fuel-based thermal power plants, industries, and tremendously growing traffic, has deteriorated the environment of Pakistan, especially the metropolitan cities [17; 21; 22]. The health and economic effects of indoor and outdoor ambient air conditions have already been studied [3; 4; 5; 6]. Vehicular emission plays a vital role in smog in Pakistan mainly due to the lack of use of emission control devices. The number of vehicles in Pakistan increased from 2 million to 10.6 million within 20 years, i.e., an 80% increase, which reduced to 66% from 2006 to 2015, due to saturation [23; 24].  
Many independent studies have been performed globally, to determine the maximum pollution contributing site, or primary cause of pollution [25]. Similar studies have highlighted the relationship between the maximum pollution-contributing site in the mega-cities of Pakistan, including Karachi, Lahore and Islamabad [26; 27; 28; 29]. Independent studies on the impact of traffic on CO emissions have been studied, and their relevancy to mass transit vehicles and mass transit systems have been explored [30]. Similarly, the emission levels of various vehicles and their correlation with other factors such as the number of vehicles, speed, and type of fuel, have also been studied [24]. Previous investigations have shown that air quality is directly related to weather conditions, and the corresponding activities of sites [31; 32]. Only one author has discussed the emission source as the prime contributor of PM\({}_{2.5}\) for two sites [13].
# Shape and Weighting Optimization of a Subarray for an mm-Wave Phased Array Antenna
## 4 Conclusions  
In this study, QP is used to perform weighting optimization for individual subarrays in an aperiodic array environment. First, the AF was transformed into a quadratic function composed of real numbers to apply QP to the array environment, and QP parameters were derived so that the AF could conform to the QP objective function. Through these procedures, the optimized weighting of each subarray was derived to achieve a low SLL. Secondly, the beam-scanning characteristics of an array antenna with TRMs in all 576 radiating elements and an array antenna with TRMs in only 96 subarrays were compared. It was confirmed that the beam-scanning performance metrics of the SLL and HPBW when applying optimized weighting to the array antenna composed of 96 TRM subarrays and the beam-scanning performance of the array antenna composed of 576 TRMs in a narrow beam-scanning system are identical. Finally, AP calculations with optimized weighting applied to each subarray were solved while taking into account mutual coupling. In order to verify the beam characteristics from the AP calculations, a cavity-backed antenna operating in the mm-wave was designed and configured in a 24 \(\times\) 24 array. Applying optimized weighting to the array antenna, the beam characteristics from the simulation and from the AP calculations were compared. It was confirmed that these two results were almost identical. Hence, the proposed optimization method is expected to be applicable to radar systems and communication systems based on a phased array antenna using a subarray.  
Writing, T.J.; Optimization of subarray, T.J., J.Y., and K.C.H.; Antenna design and simulation, T.J. and J.Y.; Formula expansion, T.J.; Application of active element pattern, T.J., J.Y., and K.C.H.; Review of optimization results, T.J., J.Y., K.O., J.K., D.W.W., and K.C.H.; Conceptualization, T.J., J.Y., K.O., J.K., D.W.W., and K.C.H.; Writing review and editing, J.Y., K.O., J.K., D.W.W., and K.C.H.; All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant-in-aid of HANWHA SYSTEMS.  
Data Availability StatementData sharing is not applicable.  
The authors declare no conflict of interest.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 2 Related Works
### IoT Platforms for Smart Cities
Thus, this experimental setup provides insights about the performance and evaluation methods of LoRa networks. With the monitoring and managing of urban air pollution in mind, [PERSON] et al. [18] deployed a network of air quality sensors through static (fixed locations) and mobile installations (on top of trans) within the city of Zurich in Switzerland. The data collection is performed using the Global System for Mobile Communication (GSM). With the aim of handling the solid waste management in a city, [PERSON] et al. [19] proposed a complete IoT-based system to process the tracking, collecting, and monitoring of the solid waste in an automated and efficient manner. This system implies that each garbage bin is equipped with two infrared (IR) sensors at the middle and top of the bins to detect the level of garbage collected, a weight sensor at the bottom of the bins, a gas sensor to detect harmful gases, and a microcontroller equipped with LoRa communication capabilities in order to collect sensory data and forward it to a LoRa gateway. The system also considers the possibility of introducing further environmental sensors. Similar to the previous IoT system, [PERSON] et al. [20] presented a similar approach for water grid management. This system was implemented in Mori village situated in the south-eastern delta of Andhra Pradesh, India. Recurring to water quality sensors and LoRa capable nodes, this system employs an alert triggering mechanism in which various alerts can be triggered to alarm the authorities in case of any changes in water quality or flow. Table 1 summarizes the core characteristics of the aforementioned IoT platforms and correlates them with our proposed platform. Therefore, this work proposes a multi-technology platform (with short- and long-range communications) for data gathering over smart cities, where the implementation of a proprietary multi-gateway MAC protocol for LoRa, along with different forwarding mechanisms over an opportunistic and delay-tolerant network, are evaluated.
## 3 Results  
### Study on the Adsorption Characteristics of Biochar  
#### 3.1.1 Experimental Exploration of Adsorption Kinetics  
The solution with the initial concentration of As(V) was configured in the laboratory in advance, and the nano-MnO\({}_{2}\)-modified biochar was added to the solution at a solid/liquid ratio of 3 g/L for adsorption kinetic evaluation, and the adsorption curve of the adsorption material to As(V) in solution was obtained by fitting the pseudo-first-order model and pseudo-second-order kinetic model (Figure 1).  
The adsorption capacity of Nano MnO\({}_{2}\)-modified biochar for As(V) increased rapidly 50 min before the adsorption started, and reached adsorption equilibrium after 180 min. The analysis found that in the initial stage of adsorption, there were a large number of active sites (hydroxyl bonds) on the surface of the nano-MnO\({}_{2}\)-modified biochar, which could react with As(V) to form a chelate with stability and affinity and be adsorbed on the surface of biochar. In addition, manganese oxide can combine with As to form ligands or co-precipitate, enhancing its fixation ability to As [19]. However, with the progress of the adsorption process, the active site of the biochar surface was gradually occupied, the adsorption rate was gradually reduced, and the adsorption process gradually tended to a dynamic equilibrium. Therefore, after 180 min of adsorption, the adsorption capacity of As(V) hardly changes; that is, the adsorption process reaches dynamic equilibrium.  
The R\({}^{2}\) values of the pseudo-first-order model and the pseudo-second-order model were 0.81 and 0.97, respectively (Table 4). The pseudo-second-order fitting correlation coefficient R\({}^{2}\) was higher than that of the pseudo-first-order fitting coefficient, indicating that the pseudo-second-order kinetics better describe the adsorption behavior of As(V) on the material.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 3 Traveling Wave Head Detection Algorithm Based on CEEMDAN-TEO
### Algorithm Adaptation Analysis
#### 3.4.1 Different Transition Resistance  
A phase A grounded short circuit simulation test was performed on the transmission line located 50 km from the M terminal for various transition resistances. The proposed method was used to calculate the fault location, and the results are shown in Table 1.  
As demonstrated in Table 1, the proposed method exhibits smaller error and higher positioning accuracy compared to [PERSON] et al. [19] under different transition resistances. Furthermore, when a high-resistance ground fault occurs, the ranging error is 251 m, meeting the requirements for fault location.
## 5 Experiment and Result Analysis
### Experiment Purpose  
The experimental design is based on the key processes involved in the proposed BTPP scheme, including specific implementation details such as the construction of the transaction output note, the generation of the Pedersen commitments and range proofs, and the legitimacy verification of the transaction so as to verify and evaluate the efficiency and feasibility of the BTPP scheme, and the experimental purposes can be divided into the following points:  
* To verify the correctness of the BTPP scheme. One of the purposes is to verify the feasibility and correctness of the key steps of the BTPP scheme and thus verify that the BTPP scheme is correct.
* To test the efficiency improvements brought by the relevant designs in the BTPP scheme and verify its efficiency and feasibility. The BTPP scheme is able to achieve efficient note ownership transfer and verification operations by improving the generation of stealth addresses and the note mechanism. One of the purposes is to verify the efficiency of the relevant operations, thus verifying the efficiency and feasibility of the BTPP scheme.
## 4 Experimental Validation and Analysis
### Selection of Key Parameters  
This paper conducted experiments using monitoring samples from four datasets, FD001 to FD004 [26; 27]. Figure 6 illustrates the degradation process of the monitoring parameters for engine 32 in the FD001 dataset as the operating cycles increase, with the final lifecycle being the actual failure time of the engine. From the graph, it can be observed that some monitoring parameters, such as W31 and W32, showed a clear degradation or upward trend when increasing operating cycles, whereas others, such as T2 and P2, did not show a significant trend when increasing operating cycles.  
In the dataset, there are many original features, and the high correlation between many features can lead to the problem of a high dimensionality. To find the features with the highest contribution to the prediction algorithm from the massive feature data, feature selection is required. By using the RF [28] algorithm to evaluate the importance of 21 features in the original sample data, a ranking of feature variable importance was obtained. As shown in Figure 7, the importance ranking of the 21 features in the FD001 dataset is presented. These features are defined in Table 2. From Figure 7, it can be seen that the importance scores of some feature variables, such as T2, P2, Epr, FarB, Nf_dmd, PCNfR_dmd, and P15, are close to zero, indicating that these features have a weak correlation with the engine's lifecycle. Therefore, these features were removed, and the remaining 14 feature variables were considered as inputs to the prediction model.  
Figure 6: The trend of the original monitoring parameters for engine 32 in the training set FD001.  
Figure 7: Feature variable importance ranking chart.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Features Selection Scheme  
In a classification scheme, continuously adding features generates the well-known pattern recognition problem known as the \"curse of dimensionality\", which means that the classificationperformance will not always improve with the increase of added features, especially when the number of training data samples is limited. Sometimes, \"bad\" features may even largely lower the classification accuracy. Moreover, the increase of the number of features makes the classification algorithms time consuming. In this paper, a forward feature selection scheme is considered, to choose the optimum feature sets for each classifier: starting from the best 2 features, the classification chooses to add the feature that provides the largest improvement on classification accuracy at each time. Then, in the comprehensive analysis, feature sets that achieved the best classification performance are employed.
# Use of Binary Classification in Non-Invasive Load Monitoring
## 5 Classifiers Used for Identification  
Two classifiers using artificial intelligence were developed for identification purposes, as follows: one based on artificial neural networks and the other on decision trees. Data processing in both classifiers is a two-stage process. First, the data are sent to the preclassification module, which identifies potential workloads. The answers provided are then evaluated by the decision module, which provides the final answer.
## 5 Conclusions  
We successfully developed a control interface using SCM and Masseter EMG and tested its performance as a game input methodology. We modeled the control interface using an equation and machine learning approaches. The equation model featured more intuitive tracking and speed control, while the machine learning model provided more stable directional control with reduced speed control. The performance of the two schemes was comparable to the different computational requirements in a 1D environment.  
In a 2D environment, the machine learning model was robust and generalizable to all subjects belonging to different demographics as indicated by age variations (27.95 years std = 13.24). The performance of all the subjects confirmed the applicability of the interface with little or no calibration needed.  
The setup and results confirm the usability of the interface system with varying difficulty settings. All participants were able to navigate the scenes with minimal instructions. None of the participants reported tension or discomfort after the experiment.  
Moreover, the system can be applied in control, rehabilitation, and/or game interfaces. Further research improvement will be conducted to implement robot control with the developed system.  
Conceptualization, M.S. ([PERSON]), P.W.L. and J.K.M.; methodology, J.K.M., P.W.L., K.M. and W.N.; software, Y.S. and K.M.; validation, M.S.A.b.S., W.R., W.C. and M.S. ([PERSON]); formal analysis, P.W.L., J.K.M. and M.S.A.b.S.; investigation, J.K.M. and P.W.L.; resources, K.M., J.K.M. and P.W.L.; data curation, P.W.L., J.K.M., Y.S. and M.S.A.b.S.; writing--original draft preparation, J.K.M. and P.W.L.; writing--review and editing, W.R., W.N., M.S. ([PERSON]) and W.C.; visualization, W.R., W.C., M.S. ([PERSON]) and K.M.; supervision, M.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Gifu University ethics (approval procedures number 27-226, Year 2020) issued by Gifu University ethics committee).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
## 3 Technology
### The Path towards First TG-DO Measurements  
A first solution for avoiding detrimental effects from voltage spikes, proposed in 2000, was to exploit reflection at the end of transmission lines (coaxial cables) to obtain their cancellation when two consecutive gate pulses are applied [70; 71; 72]. The need for fine tuning the cable length and the presence of two consecutive gates makes this technique quite complex. In 2002 it was proposed to delete the spikes by combining balanced outputs of two SPADs using a 180\({}^{\circ}\) hybrid junction to generate the differential signal between the two detectors [73]. The technique is efficient in quantum key distribution as two detectors are useful, but can be too expensive when a single detection channel is needed. In 2004 it was proposed to exploit differences in the falling edge spikes to indirectly detect avalanche [74]. Indeed, when and avalanche occurs, the SPAD cannot be electrically modeled as a single capacitance [31], thus changing its response to fast gate transitions and, consequently, the shape of the spurious spikes (see Figure 8 where the second spike, related to the gate OFF transition, is different between the left--no avalanche--and right--avalanche triggered--cases). This technique is not suitable in time-resolved application as it can only detect whenever the avalanche was present during the gating time, not resolving the photon arrival time. In 2006 it was proposed a technique based on capacitive compensation and magic-T circuit (i.e., combination of transformers) to reject common mode signals before amplifying the avalanche pulse [75]. The technique demonstrated optimum performance in associating photon detections to different time gates, but photon-timing performance within the gate was not demonstrated. In 2007, two different techniques were proposed. The former was based on the integration of the avalanche current pulse into a capacitance, that was sampled after the end of the gate pulse to detect whenever an avalanche occurred during the gate pulse [76]. As in the case of Ref. [74], the technique is not suitable for photon-timing applications. The latter is conceptually similar to that proposed in Refs. [70; 71; 72] as voltage spikes are compensated by differentially reading the detector signal during a given gate window and a delayed replica of the same signal during a previous gate [77]. Therefore, its limitations are similar to those of the previous solution. Finally, in 2008, a novel time-gating technique developed at Politecnico di Milano allowed to demonstrate the feasibility of TD-DO measurements at quasi-null \(\rho\) both on phantoms and in-vivo [78]. In this case, as in 2006 the gate signal is applied to both the SPAD and a capacitance with suitable radiofrequency performance, providing a \"dummy\" signal only containing voltage spikes [31]. The two signals are then connected to the two differential inputs of a fast comparator so that the avalanche detection threshold contains the two voltage spikes allowing to avoid their detection (see Figure 8).
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 5 Federated Training of Edge Autoencoders
### Non-IID--Single Digit per Device  
This experimental scenario is designed to evaluate the limits of federated learning under an extreme non-IID data distribution. Each of the ten ESP32-CAM devices is assigned training data corresponding to only a single digit class from the MNIST dataset; for instance, device 0 trains solely on images of digit '0', device  
PSRAM, which aligns with the sample size used in our prior experiments. As a result, no device saw a complete view of the dataset during local training.  
```
0:MNIST dataset \((X,Y)\) with 10,000 samples
1:for each digit label \(d=0\) to 9 do
2: Initialize empty list selected_samples
3:for each sample \((x_{i},y_{i})\) in \((X,Y)\)do
4:if\(y_{i}=d\)then
5: Append \((x_{i},y_{i})\) to selected_samples
6:endif
7:endfor
8: Save selected_samples as a new dataset for the device assigned digit \(d\)
9:endfor
```  
**Algorithm 1** Non-IID Partitioning: Single Digit per Device  
While this setup is not typical in real-world applications, it represents a critical worst-case scenario that tests the collaborative power of federated learning. It investigates whether multiple small isolated learners, each exposed to only a narrow slice of the input space, can collectively produce a global model that generalizes across all classes.  
This non-IID scenario has been explored in previous works [18; 44] using larger more complex AEs. Differently, our approach focuses on edge deployment by implementing smaller resource-efficient models and leveraging a dual-phase early stopping strategy to minimize training epochs and communication rounds. This makes our method well-suited for low-power devices like the ESP32 while preserving effective performance.
## 3 Results and Discussion  
### Secondary Reflection Gratings in Transmission Recording Geometry  
The appearance of secondary gratings is determined by several intrinsic, as well as extrinsic factors or rather by material specific properties as well as recording parameters. Starting point for estimation in general is the visibility \(V\) of the interference pattern, inducing the modulated change of the refractive index,  
\[V=\frac{2\sqrt{I_{S}I_{R}}}{I_{S}+I_{R}} \tag{3}\]  
in which \(\mathrm{I_{R}}\) and \(\mathrm{I_{S}}\) are the intensities of reference and signal wave. A high visibility is accompanied by optimal interference contrast and consequently ensures ideal conditions for grating formation. This is the case if \(V=1\), _i.e._, in the event of equal intensities of reference and signal beam. In the particular case of uni-directional secondary gratings (e.g., RG' in Figure 1) \(\mathrm{I_{R}}\) refers to the intensity of the recording beam and \(\mathrm{I_{S}}\)' is determined by the reflectance \(r\) of the back surface of the sample, which can be calculated by [PERSON]'s formula. In general the reflected wave is much weaker than the incident wave (\(\mathrm{I_{S}}<<\mathrm{I_{R}}\)). This results in low visibility \(V\) and consequently unfavorable conditions for hologram generation. Nevertheless, secondary gratings can be observed and are shown in Figure 3.  
Figure 2: Schematic diagram of imaging in sectional view. Tilted reflection-type grating with period \(\Lambda\) appears in the image plane as sectional grating with periodicity \(\Lambda_{\mathrm{s}}\).