esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application  
### Study Area  
The target area for the maritime traffic data visualization simulation is Mokpo Port, located on the southwestern coast of South Korea. This port hosts a variety of ship types, including merchant ships for cargo loading and unloading, fishing vessels for fishing activities, and passenger vessels. It has a complex maritime traffic pattern, with many routes connected to neighboring islands. Mokpo Port, with its diverse ship types and complex maritime traffic patterns, was selected as the simulation area for our visualizing maritime traffic data, which includes various piers: fishing ports, coastal passenger terminals, marine police docks, and government ship piers in the north, and passenger ship terminals, the Daebul pier, and the Yongdang pier in the east, as shown in Figure 6.  
Figure 5: Process of flow for traffic stream visualization. (**a**) Preprocessing AIS data to analyze maritime traffic density. (**b**) Applying the Douglas–Peucker algorithm to simplify vessel travel paths and the DTW algorithm to measure the similarity between vessels’ sailing paths. (**c**) Performing clustering using DBSCAN algorithm. (**d**) Identifying traffic flows based on clustering results and extracting representative routes using the curve fitting method.  
Figure 6: Simulation target areas “A” and “B” at Mokpo Port. Area “A” is located near the Mokpo Bridge, while area “B”, known as Mokpo-gu, is a complex area owing to its narrow channel.
## 2 Materials and Methods
### Experimental Design  
The basis of the research consisted of a three-factorial pot experiment conducted in a split plot design, with four replications. The vegetative pot experiment was conducted in the greenhouse of the University of Warmia and Mazury in Olsztyn, Poland (NE, Poland, 53.759\({}^{\circ}\) N, 20.454\({}^{\circ}\) E). The first-order factor was the type of petroleum product: control, diesel oil, and unleaded gasoline 95; the second-order factor was the dose of the contaminating product, in cm\({}^{3}\) kg\({}^{-1}\) d.m. of soil: 0, 8, and 16; and the third-order factor was the type of sorbent: control, dolomite, and perlite. The first step in setting up the experiment was to mix aqueous solutions of CO(NH\({}_{2}\))\({}_{2}\), KH\({}_{2}\)PO\({}_{4}\), KCl, MgSO\({}_{4}\)\(\times\) 7H\({}_{2}\)O with soil (3.4 kg pot\({}^{-1}\)), followed by the addition of the respective petroleum products and sorbents in the designated pots. Subsequently, the soil material was then placed in pots with dimensions of 14.5 cm (\(\phi\) base diameter) \(\times\) 19.5 cm (\(\phi\) top diameter) \(\times\) 16.5 cm (height) and moistened to 60% of the maximum water holding capacity by watering the plants 3-4 times a day with demineralized water. In the end, there were 72 pots in the experiment. A total of 250 kg of soil was used. Fertilization with N, P, K, and Mg was constant throughout the experiment (uncontaminated and contaminated treatments, with and without sorbent application), with the following amounts per kg of soil: N--225 mg, P--50 mg, K--150 mg, and Mg--20 mg. The aqueous solutions were added to the soil once on the day the experiment was set up to cover the nutrient requirements of the maize. This fertilization was the same throughout the experiment. The next step involved sowing maize into the pots. The duration of the pot experiment was 60 days. On the day of maize harvest, the Chlorophyll Meter (Spectrum Technologies, Inc., KONICA MINOLTA, Inc., Chiyoda, Japan) was used to determine the leaf greenness index (SPAD). Subsequently, the plants were harvested, and aerial parts and roots were carefully separated, rinsed with distilled water, and dried in a Binder D-78532 dryer (Binder GmbH, Tuttlingen, Germany) at 60 \({}^{\circ}\)C. Soil samples were also collected on the day of plant harvest for further laboratory analysis.
# Secure and Efficient Federated Gradient Boosting Decision Trees
## 6 Experiments
### Experimental Results
#### 6.2.3 Performance Impact of Data Volume Size  
In most cases, the performance of a machine learning model is influenced by the amount of training data. Therefore, in this experiment, we fixed the number of participants to be three and varied the amount of training data for each participant to evaluate the federated learning model.  
The results of the experiment are shown in Table 8 and Figure 6. In Figure 6, the horizontal coordinates in the figure indicate the data ratio of the original dataset, and the amount of data per participant increases as the total number of participants decreases. In this case, the amount of data per participant is 30%, 60% and 90% of the total amount of data. We can observe that the prediction performance of all GBDTs schemes improves as the amount of data increases. Among them, SeFB outperforms Independent, TFL and eFL-Boost.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Schemes**} & \multicolumn{4}{c}{**F1-Score**} & \multicolumn{4}{c}{**ROC-AUC**} \\ \cline{3-10}  & & **60\%** & **70\%** & **80\%** & **90\%** & **60\%** & **70\%** & **80\%** & **90\%** \\ \hline \multirow{4}{*}{Credit 1} & Non & 0.823 & 0.819 & 0.812 & 0.807 & 0.989 & 0.984 & 0.978 & 0.973 \\  & Independent & 0.773 & 0.767 & 0.763 & 0.756 & 0.976 & 0.971 & 0.966 & 0.959 \\  & TFL & 0.797 & 0.795 & 0.792 & 0.782 & 0.979 & 0.977 & 0.975 & 0.967 \\  & eFL-Boost & 0.817 & 0.806 & 0.801 & 0.791 & 0.982 & 0.979 & 0.974 & 0.971 \\  & SeFB & 0.819 & 0.815 & 0.811 & 0.805 & 0.987 & 0.982 & 0.977 & 0.972 \\ \hline \multirow{4}{*}{Credit 2} & Non & 0.914 & 0.908 & 0.901 & 0.885 & 0.937 & 0.935 & 0.930 & 0.926 \\  & Independent & 0.892 & 0.886 & 0.882 & 0.875 & 0.920 & 0.917 & 0.914 & 0.909 \\  & TFL & 0.894 & 0.886 & 0.889 & 0.879 & 0.931 & 0.926 & 0.922 & 0.916 \\  & eFL-Boost & 0.909 & 0.905 & 0.900 & 0.891 & 0.932 & 0.929 & 0.927 & 0.921 \\  & SeFB & 0.911 & 0.905 & 0.901 & 0.885 & 0.936 & 0.933 & 0.930 & 0.924 \\ \hline \multirow{4}{*}{Adult} & Non & 0.735 & 0.729 & 0.722 & 0.713 & 0.925 & 0.911 & 0.906 & 0.902 \\  & Independent & 0.687 & 0.675 & 0.662 & 0.650 & 0.867 & 0.856 & 0.845 & 0.833 \\  & TFL & 0.701 & 0.693 & 0.689 & 0.671 & 0.899 & 0.896 & 0.888 & 0.872 \\  & eFL-Boost & 0.729 & 0.723 & 0.718 & 0.702 & 0.912 & 0.901 & 0.896 & 0.891 \\  & SeFB & 0.731 & 0.728 & 0.721 & 0.711 & 0.918 & 0.909 & 0.902 & 0.901 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance impact of participants’ data imbalance ratio.  
Figure 5: Impact of participants’ data imbalance ratio on Test Errors. (**a**) Credit 1 dataset, (**b**) Credit 2 dataset, (**c**) Adult dataset.
## 6 Conclusions  
This paper demonstrates the successful application of the proposed design algorithm in designing a low-pass filter (LPF) and a power divider. The LPF, designed using the presented algorithm and the proposed artificial neural network (ANN) model, achieves a sharp response within the transmission band, with a sharpness parameter of approximately 185 dB/GHz. It also exhibits excellent performance in the stopband, attaining an attenuation level of more than 20 dB from 2 GHz to 18.2 GHz. The compact size of the LPF (19.3 mm \(\times\) 9.9 mm, 0.15 \(\lambda\)g \(\times\) 0.081 \(\lambda\)g) enables its integration within the divider structure for harmonics suppression. The power divider, consisting of two LPFs in the main branches and an added open stub at port one, effectively operates at 1.5 GHz and successfully suppresses the 2 nd to 13 th harmonics. The proposed power divider demonstrates superior performance, making it a suitable choice for modern communication systems. Through this paper, we endeavor to highlight the potential of machine learning techniques, particularly neural networks, in advancing microwave circuit design. By harnessing the capabilities of these algorithms, engineers can overcome the limitations of traditional analytical methods, and optimize the performance and reliability of complex microwave circuits.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs.** & **Frequency** & **Insertion Loss** & **Input** & **Output** & **Output** & **Harmonics** \\  & & **(IL)** & **Return Loss** & **Return Loss** & **Ports Isolation** & **Suppression** \\ \hline
[9] & 1 GHz & 0.25 dB & 40 dB & 31 dB & 32 dB & 2 nd to 4 th \\
[11] & 2.4 GHz & 0.65 dB & 22 dB & 22 dB & 20 dB & 2 nd and 3 rd \\
[57] & 1.8 GHz & 0.6 dB & 20 dB & 20 dB & 20 dB & 2 nd and 3 rd \\
[58] & 0.9 GHZ & 0.325 dB & 36 dB & N.A. & N.A. & 3 rd \\
[59] & 1 GHz & 0.2 dB & 30 dB & 30 dB & 30 dB & 3 rd to 5 th \\ This work & 1.5 GHz & 0.1 dB & 35 dB & 30 dB & 32 dB & 2 nd to 13 th \\ \hline \hline \end{tabular}
\end{table}
Table 4: The performance results of the proposed WPD compared with similar dividers.  
Figure 18: The frequency response of the proposed Wilkinson power divider.  
**Author Contributions:** Conceptualization, S.R. ([PERSON]), S.R. ([PERSON]) and M.J.; Methodology, M.J. and M.A.C.; Software, M.J., S.R. ([PERSON]), Y.Y.G. and S.R. ([PERSON]); Investigation, S.I.Y. and M.A.C.; Resources, Y.Y.G.; Writing--original draft, S.R. ([PERSON]), S.R. ([PERSON]), M.J., S.I.Y., M.A.C. and Y.Y.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** All the material and data associated with the study are presented within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 3 Results
### Impact of Pulse Width  
The impact of the finite width of fast output pulses has been investigated by determining the BER at various OOK data rates. In these simulations the non-linearity was taken into account by using Equation (6) to determine the transmitter irradiance which  
Figure 4: (**a**) The BER from a simulation that assumed a fast output pulse width of zero, and when a zero is transmitted there are no detected photons. This means that, to achieve the target BER of \(3.8\times 10^{-3}\), approximately 5.2 photons have to be detected. (**b**) The irradiances used to generate the results in (**a**).  
will generate 5.2 additional photons per bit when a one is transmitted. The results for a pulse width of 1.4 ns were included in these simulations to represent a J30020. The results for this pulse width in Figure 5a show that the pulse width has an impact on the BER at data rates of less than 1 Gbps. In particular, when the pulse width is 1.4 ns the BER is \(10^{-2}\) at approximately 600 Mbps; however, the results in Figure 4a show that the non-linearity does not cause the same BER until approximately 35 Gbps. This confirms that the pulse width has a significant impact on the performance of a J30020.  
The impact of the ISI caused by the width of the output pulses is expected to depend upon the ratio of the pulse width to the bit time. Since the OOK data rate is inversely proportional to the bit time, this is the same as the product of the data rate and the output pulse width. The results in Figure 5b confirm that the impact of ISI depends upon the ratio between pulse width and bit time. Furthermore, the results in Figure 5b show that the BER only starts to increase rapidly when the pulse width equals the bit time. This means that halving the width of the fast output pulses will double the data rate at which ISI creates a significant power penalty.
## 2 Materials and Methods
### Experimental Design  
The operation steps of each procedure were as follows (Figure 2):  
1. [label=.]
2. MB (0.1 mg/mL): The root canals were filled with 5 \(\upmu\)L MB, followed by dark incubation for 5 min at 25 \({}^{\circ}\)C.
3. 3% HP: The root canals were filled with 5 \(\upmu\)L HP, followed by dark incubation for 2 min at 25 \({}^{\circ}\)C [30].
4. MB (0.1 \(\upmu\)g/mL) + 660 nm diode laser: The root canals were treated with MB similar to group A, and the test samples were then exposed to a 660 nm diode laser for 60 s.
5. 3% HP + 980 nm diode laser: The root canals were treated by HP similar to group B and the test samples were then exposed to a 980 nm diode laser for 30 s.
6. MB (100 \(\upmu\)g/mL) + SWEEPS: The root canals were treated by MB similar to group A, and the test samples were then irradiated with Er:YAG laser with 2940 nm wavelength. The device tip (SWEEPS600, Fotona, Slovenia) was placed in the pulp chamber and activated for 90 s (30 s of activation, 30 s of rest, and 30 s of activation again).
7. 3% HP + SWEEPS: The root canals were treated by HP similar to group B and the test samples were then subjected to SWEEPS technique similar to group E.
8. MB (100 \(\upmu\)g/mL) + 660 nm diode laser + SWEEPS: The root canals were treated by MB-mediated SWEEPS similar to group E, and then after 5 min, 660 nm diode laser irradiation was performed similar to group C.
9. 3% HP + 980 nm diode laser + SWEEPS: The root canals were treated by HP-mediated SWEEPS similar to group F, and then after 5 min, 980 nm diode laser irradiation was performed similar to group D.
10. 5.25% NaOCl (positive control): The root canals were filled with 5 \(\upmu\)L NaOCl, followed by incubation for 5 min at 25 \({}^{\circ}\)C.
11. Normal saline (negative control): The root canals were filled with 5 \(\upmu\)L normal saline, followed by incubation for 5 min at 25 \({}^{\circ}\)C.  
Figure 2: Schematic representation of study groups. MB: methylene blue, HP: hydrogen peroxide, DL: diode laser, nm: nanometer, SWEEPS: shock wave enhanced emission photoacoustic streaming, NaOCl: 5.25% sodium hypochlorite.
# Real-Time Seismic Data from the Bottom Sea
## 2 Standalone Broadband Ocean Bottom System
### Surface Buoy  
In the design of the surface buoy, multiple factors have been taken into account. Therefore, the buoy has been built to support the weight and tensions of the connection cable that links it to the seafloor unit in different sea and weather conditions. Moreover, the buoy is designed to be large enough to contain electronics for collecting data, communication devices, and energy supply. Finally, the surface buoy built for the moored OBS is portable and easily deployed by scientists unfamiliar with the deployment of marine seismometers and also using boats not fully equipped for research. In the above scenario, the chosen buoy is a 2 m diameter toroidal buoy EMS 2.0 (Figure 5) [11].  
This buoy includes four solar panels (4 \(\times\) 12 V/32 Wp @ 92 Wh/d) of 440 \(\times\) 460 \(\times\) 2 mm, two solar charge controllers and sealed lead-gel batteries 12 V/120 Ah--which supply and store the required energy--an AirLink Raven RV50 modem, and a system controller based on Raspberry Pi with a Sea-Bird UIMM for inductive communication with the seafloor OBS.  
Figure 4: (**a**) Electronic acquisition system and (**b**) block diagram of OBS electronics. The Inductive modem interface connects to the OBS via a serial line.
# Advances in Ionospheric Space Weather by Using FORMOSAT-7/COSMIC-2 GNSS Radio Occultations
## 5 Ionospheric Space Weather Forecast
### Cwb/swoo  
CWB/SWOO has been creating products for ionospheric weather monitoring and forecast based on the F7/C2 occultations that are routinely published on its website \"[[https://swoo.cwb.gov.tw/V2/page/EN/index.html](https://swoo.cwb.gov.tw/V2/page/EN/index.html)]([https://swoo.cwb.gov.tw/V2/page/EN/index.html](https://swoo.cwb.gov.tw/V2/page/EN/index.html))\" (accessed on 1 May 2022). Besides the RO profiles and global scintillation map, global NmF2 and hmF2 of the ionosphere are reconstructed by accumulating F7/C2 observations within an hour period. These products are valuable to monitor the ionospheric condition in near-real-time. Moreover, CWB/SWOO has operated an ionospheric forecast system by additionally assimilating F7/C2 ionospheric RO EDPs since 2020 and produces 6 h ionospheric forecasting hourly \"[[https://swoo.cwb.gov.tw/V2/page/EN/Forecast/IonoForecast.html](https://swoo.cwb.gov.tw/V2/page/EN/Forecast/IonoForecast.html)]([https://swoo.cwb.gov.tw/V2/page/EN/Forecast/IonoForecast.html](https://swoo.cwb.gov.tw/V2/page/EN/Forecast/IonoForecast.html))\" (accessed on 1 May 2022). [PERSON] et al. [65] addressed that the F7/C2 RO profiles could adjust the vertical structure of ionospheric electron density closer to reality mainly in the equatorial and low-latitude regions. For instance, Figure 10 displays a set of products, including the RO scintillation map of the L1 and L2 band, and the forecasts of TEC, NmF2, hmF2, and foF2 at 18:00 UT on 28 October 2021 after an \(\times\)1.0 solar flare occurred. With intense RO measurements, it could provide global ionospheric features and forecast after particular space weather events promptly as a reference for communication, positioning, and other possible usages.
## 2 Amplified Spontaneous Emission
### Perovskites Nanocrystals Thin Films
The DA functionalized NCs showed an ASE threshold reduced of about 2 times with respect to OA NCs, even if much higher than the values found by [PERSON] et al. [5]. Another approach to improve the NCs stability was instead proposed by [PERSON] et al. [37] that synthesized highly stable MAPbBr\({}_{3}\) NCs, improving the ligands passivation thanks to the addition of benzyl alcohol to the growth solution. Good ASE properties were shown by a reasonably low threshold of 14 \(\upmu\)Jcm\({}^{-2}\), under 150 fs pumping at 400 nm, while the stability was demonstrated by the absence of threshold variations during 4 months of storage under ambient condition and by the absence of photodegradation during continuous laser pumping up to 10\({}^{11}\) femtosecond pulses. A very interesting and recent step in the development of perovskite NCs with optimized optical properties was reported by [PERSON] et al. [38], who developed a novel synthetic strategy, called intermediate monomer reservoir synthesis, allowing the growth of highly uniform, low defect and stable CsPbBr\({}_{3}\) nanorods with PLQY of 90% (see Figure 3a). A detailed comparison with CsPbBr\({}_{3}\) nanocubes based on the investigation of the PL temperature dependence allowed to demonstrate a much lower defect trap states in nanorods. Moreover, a much higher stability of the nanorods with respect to the nanocubes was also demonstrated during heating and during immersion in water. Finally, outstanding ASE properties were demonstrated under 1 ns pumping at 355 nm, with an ASE threshold as low as 7.5 \(\upmu\)Jcm\({}^{-2}\) (see Figure 3b,c), comparable with the values typically found for femtosecond pumping. For the sake of comparison we observe that the ASE threshold of CsPbBr\({}_{3}\) nanocubes under nanosecond pumping is typically in the range between 450 \(\upmu\)Jcm\({}^{-2}\) and a few mJcm\({}^{-2}\)[5; 30]. The main ASE (wavelength and threshold) and pump laser properties (wavelength and time length) of all the discussed materials are summarized in Table 1.  
Figure 2: (**a**) Photograph of toluene dispersions of nanocrystals with four different chemical composition under UV light excitation. (**b**) PL spectra of the solutions in (**a**). (**c**) Excitation density dependence of the PL spectra of a CsPbBr\({}_{3}\) NCs thin film in the excitation range 3–25 \(\upmu\)Jcm\({}^{-2}\), showing evidence of the progressive appearance of the ASE band. (**d**) Spectral tunability of ASE by compositional modulation. Adapted with permission from [5]. Nature Publishing group, 2014.
## 1 Introduction  
If customers are discontent with products or services customer complaints occur [1]. Complaint handling services aim to solve customer complaints and improve customer experience. High-quality complaint handling can ensure market share and increase company profits [2]. For researchers and practitioners, analyzing the business process of complaint handling can help the company to manage business processes. As process details of complaint handling have been recorded in various information systems (such as, Customer Relationship Management system or Enterprise Resource Planning system) of the companies, exploiting the recorded data with process-oriented techniques can help gain insight into complaint handling processes. Further, the analysis results of the process data can provide valuable guidance for the improvement of complaint handling service (CHS) quality. Process mining (PM) aims to discover, monitor and improve business processes by extracting knowledge from information systems of companies [3]. PM bridges the gap between process science and data science [3]. In recent years, with the maturity of PM algorithms [3], PM has been applied in various industries, such as in the fields of manufacturing [4], service [5], healthcare [6], finance [7], and education [8,9]. Additionally, it brings new opportunity for process improvement of the complaint handling service (CHS). PM can diagnose CHS processes through different perspectives, such as the control-flow perspective, case perspective, performance perspective, and process comparison perspective. However, how to integrate diagnostic results from different perspectives of PM and analyze the results across the organizational levels is a big challenge. The current methodologies of process analysis based on PM cannot solve the issue well. The demand of for a novel process analysis methodology based on PM is urgent.  
Our previous work proposed an approach to extract case information from natural texts of CHS, and this article is a continuation of our previous work. The structured event log is the data basis for the process analysis based on PM, and our previous work of case information extraction can help us create structured event logs from natural texts for PM. After obtaining structured event logs, the techniques of PM can diagnose CHS processes from different perspectives. The process diagnosis results are analyzed in the light of [PERSON]'s Accimap method [10; 11]. A case study in a big manufacturing company in China is implemented to verify our approach. Our paper is structured as follows. Section 2 describes the background of this study. Section 3 presents the related work. Section 4 details the proposed methodology of this study. Section 5 presents a case study in a manufacturing company in China. Section 6 presents the conclusions and future work.
## 5 Transmission Model of Multipath QKD Network-Based Zero-Trust Relays
### Execution Phase: Probabilistic Model  
Integrating public nodes modifies the security fraction by accounting for their behaviors. The adjusted security fraction for \(n\) paths considering \(b\) public nodes is calculated as follows.  
\[P_{T}(A,B)=\prod_{u=1}^{b}p_{u}(t)\cdot\left[1-\prod_{i=1}^{n}\left(1-\prod_{ j=1}^{m-b}P(S_{i})\right)\right] \tag{7}\]  
where \(p_{u}(t)\) is the security probability of the public nodes over time. In addition, \(\prod_{j=1}^{m_{i}-b}\) is used to exclude the influence of the public nodes. The proposed model adjusts security metrics to account for public node behavior.