esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurements
### Criteria of Fair Weather  
For the study of fair-weather AEFs, having a clear definition of fair weather that incorporates clear quantitative criteria to uniquely determine and classify fair days is necessary. However, fair weather criteria are highly subjective from place to place [45] and are determined based on atmospheric conditions; thus, fair days were defined in this reference with the following detailed definitions: (1) the visual range should be 2 to 5 km or greater, and the relative humidity should be less than 95%; (2) there should be no low stratus clouds and negligible cumuliform clouds; and (3) the wind speed at 10 m should be less than 8 m/s. In contrast, [PERSON] [46] defined fair days as having a cloud volume of less than 3/8, a wind speed of less than 4 m/s, and no precipitation. Similar definitions have been put forward by other scholars. [PERSON] and [PERSON] [43] considered fair weather to exist when snow and low clouds are absent, high clouds are characterized by fewer than three oktas, and the wind speed is less than 10 m/s. Among the most recent studies mentioned in this paper, the researchers define fair days as follows: (1) maximum relative humidity \(<\) 95%; (2) a visual range \(\geq\) 5 km; (3) an average wind speed at 10-12 m less than 9 m/s; and (4) hourly average AEF values (HAEFs) \(\geq\) 0 V/m.  
Figure 3: Photo of the observation scene at the Antarctic Chinese Zhongshan Station.
## 3 Analysis
### Convergence Analysis
#### 3.1.1 Convergence in One-Dimensional Setup
We here illustrate multiple stopping points using a two-beacon setup. Let \(d\) be the distance between the two beacons where the two beaconsare located at \(x_{1}=-\frac{d}{2}\) and \(x_{2}=\frac{d}{2}\) respectively. The net force receives by a node at location \(x\) is \(F(x)=F_{x_{1}}(x)+F_{x_{2}}(x)\). By setting \(F(x)=0\), we can solve for locations where the net force is zero. One immediate solution to \(F(x)=0\) is \(x=0\). Besides, when \(d<2\), two other solutions can be found, which are \(x=\pm 1\). The condition where \(d<2\) suggests that the two beacons are placed too close to each other. In practical design, beacons should be spread out in location to avoid generating multiple solutions which cause ambiguity in localization.
## 4 Flight Test Results and Discussion
### Random Forest Analysis
#### 4.4.1 Latency Time Analysis  
Since the regressor of the random forest algorithm is suitable for continuous parameters, this analysis was used to define the important parameters affected by the latency time. The number of estimators or trees was 100, as suggested by the default value in the random forest module. The result of the analysis is plotted in Figure 15 with the query time as the most important factor, with up to 97.9% importance.  
The other two factors in the top three important factors for latency time were broadcast time (0.87%) and process time (0.79%). Meanwhile, the other parameters had a less than 0.2% effect. To evaluate the validity of the random forest regressor model, the validation metrics of the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and R-squared score of the model were calculated. MSE represents the average of the squared differences between the predicted and actual values. RMSE takes the square root of MSE to obtain a metric in the original unit of the target vari  
Figure 15: Important parameters for latency time.  
average of the absolute differences between the predicted and actual values. R-squared is the proportion of the variance in the target variable that can be explained by the model.  
The calculated MSE was 0.0173, RMSE was 0.1315, and MAE was 0.0328. Smaller MSE, RMSE, and MAE values represent a better model for predicting the result. In contrast, the R-squared score was 0.9913, which indicates that the model had a good fit to the data and the default of 100 estimators was good enough for the model.  
The accuracy is not commonly used as a metric for regression problems since it is typically used for classification tasks. Another evaluation parameter for the regression model is uncertainty, as shown in Figure 16. It was plotted in a scatter format over the instances. The mean of this uncertainty was 0.031, with a standard deviation of 0.393. This narrow spread of data suggested more consistent or certain predictions. However, there were some outlier values at instances around 200, 600, and 750. These outliers were representative of certain outlier values in the query time data that had very high values, as shown in Figures 12c and 13c.  
In comparison with the latency time analysis in Figures 12 and 13, this result confirmed that the query time was the most important parameter to the latency time. There should be an effort to reduce the query time, such as by updating the normal HTTPS API in the current UTM monitoring application into a more advanced process that could produce a lower query time.
# MFFNet: A Building Extraction Network for Multi-Source High-Resolution Remote Sensing Data
## 4 Results
### Experimental Results
#### 4.2.2 Massachusetts Building Dataset  
We observed that some conventional networks, when applied to the Massachusetts building dataset, tend to inaccurately segment boundaries, as illustrated in groups 1, 2, and 5 (Figure 9). Traditional segmentation models often merge small buildings, whereas MFFNet mitigates this issue. For buildings located in shadowed areas, MFFNet also achieves effective segmentation, as shown in groups 3 and 7. The fourth and sixth groups of images collectively attest to MFFNet's proficiency in delineating the edges of buildings, even for smaller targets. The eighth group demonstrates that UNet and UNet++ are prone to misclassifying terrain with similar characteristics to buildings, while PSPNet, DeepLabV3+, and MFFNet can avoid this phenomenon. These sets of images indicate that MFFNet is capable of independently segmenting each small building in densely populated areas, and MFFNet can alleviate the impact of complex environments, such as shadows, on building segmentation.  
As shown in Table 2, our model achieved the best results and far outperformed other models. MFFNet's MIoU and accuracy reached 81.76% and 94.00% on the Massachusetts building dataset, respectively. It was 1.12% and 0.4% higher than UNet++'s MIoU and accuracy, 1.4% and 0.52% higher than UNet's MIoU and accuracy, 5.57% and 1.95% higher than PSPNet's MIoU and accuracy, and 1.95% higher than DeepLabV3+. The MIoU and accuracy were 3.68% and 1.26% higher. In addition, MFFNet's precision, recall rate, F1  
Figure 9: Comparative segmentation results on the Massachusetts dataset, with each row representing a set of images segmented by different models. The green rectangles highlight areas of particular interest.  
score, and kappa indicators were also higher than those of the other models, which were 94.66%, 94.25%, 94.82%, and 92.63%, respectively.
## 3 Results and Discussion
### Analysis of Volatile Flavor Substances in Fermentation Processes
The phenylethyl alcohol concentration in SY was 65.74 mg/L at the end of fermentation, while it was only 13.26 mg/L in KB. Diethyl succinate (DES) was detected only in SY, at a concentration of 0.4062 mg/L. DES may enhance the wine's flavor profile by imparting fruity and sweet undertones [43; 47]. Studies have suggested that DES also exhibits antiinflammatory properties in cellular models of chronic neuroinflammation, indicating its potential as a novel treatment for this condition [48]. The \(\gamma\)-nonanolactone content in SY was higher than in KB, suggesting that the combined fermentation of CFB can produce more complex and enduring aromas, thereby improving the aroma profile of baijiu. The mixed fermentation of CFB clearly resulted in the production of DES, which further enhanced the richness and uniqueness of SY compared to KB. The PCA plot showed that SY and KB were primarily located on opposite sides, with no overlap, indicating substantial differences in characteristic volatile aromatic compounds like phenylethyl alcohol and ethyl acetate (Figure S3a). The OPLS-DA model revealed significant discrepancies in the levels of 13 volatile aromatic compounds between both groups. These compounds included 2-methyl-1-propanol (A2), 3-methyl-1-butanol (A4), benzyl alcohol (A13), phenylethyl alcohol (A14), ethyl acetate (B1), phenacryl thiocyanate (B5), ethyl lactate (B6), 2-ethoxyphenyl isothiyanate (B7), diethyl succinate (B14), palmitic acid ethyl ester (B25), ethyl oleate (B27), benzaldehyde (C6), and 2,2-dichlorobenzene (G1) (Figure S3c). Thus, the addition of mixed strains significantly increased the levels of characteristic volatile flavor compounds, including phenylethyl alcohol, ethyl acetate, ethyl lactate, and diethyl succinate, thereby enhancing the flavor profiles of CFB.
## 5 Conclusions and Future Works  
The proposed system proved to be effective in transferring objects between robots and in handling various scenarios of varying complexities without collisions. SCARA robots can effectively coordinate to perform pick-and-place tasks in controlled virtual environments. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of the robotic system, allowing the robots to predict and react in real time to the movements of their companions and changes in the environment, which is crucial for maintaining a safe and efficient work environment.  
This research highlights the importance of effective synchronization and communication between robots to avoid collisions and to optimize trajectories and cycle times. The results demonstrated that the robots could work harmoniously in a dynamic and demanding environment, thereby optimizing industrial production.  
When evaluating the controllers, the NLMPC outperformed the SMC in terms of precision and efficiency for both the robots. The NLMPC exhibited a lower steady-state error and better integral performance indices (ISE, IAE, and ITAE), indicating a faster and more stable response. These results demonstrate that the NLMPC is more suitable for applications requiring high precision and stability, and its implementation is recommended to optimize performance and reliability in advanced robotic systems.  
All the tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots under controlled conditions. The results obtained in this study not only demonstrate the effectiveness of the system in controlled virtual scenarios but also highlight its potential for practical applications in the industry. The ability of SCARA robots to efficiently coordinate and avoid collisions in complex environments suggests that this system could be implemented in various industrial applications with dynamic and complex environments, such as assembly lines, handling of delicate materials, pick-and-place operations, among others, providing significant improvements in efficiency, flexibility, and precision. These potential benefits, supported by the simulation results, establish a solid foundation for future practical implementations and further studies in the field of cooperative robotics.  
A promising area for future work is the implementation of impedance control techniques. This will allow for the analysis of the behavior of the system in avoiding obstacles and performing cooperative tasks, particularly when handling heavier objects. In this study, the primary focus was on handling lightweight objects.
## 3 Results and Discussion  
### Structural Investigation  
The ball-milling's effect on the crystalline structure of SCB-PE/GNPs nanocomposites was studied using X-ray diffraction. The crystallinity degree of a polymer can affect its mechanical properties, i.e., the crystalline phase can lead to increased tensile strength [28; 29]. Figure 1a,b present the XRD patterns of SCB-PE/GNPs no BM and SCB-PE/GNPs w BM nanocomposites, respectively, along with the diffractogram of neat SCB-PE. All the samples present distinct crystalline peaks corresponding to (110) and (200) planes of the orthorhombic (Pnam space group) phase of polyethylene, superimposed on an amorphous halo [24]. The SCB-PE/GNPs nanocomposites of any case present a sharp peak around \(20\approx 26.5^{\circ}\), which corresponds to the diffraction of the (002) crystal plane of GNPs [30]. It can be seen that with increasing filler content, the intensity of the GNPs' diffraction peak increases, in any case of the nanocomposites. The crystallinity degree of all the prepared materials has been calculated using the following equation [31]:  
\[X_{c}\%=\frac{A_{cr}}{A_{cr}+A_{am}}\cdot 100, \tag{1}\]  
where \(X_{c}\) is the degree of crystallinity and \(A_{cr}\) and \(A_{am}\) are the crystalline and amorphous peak areas, respectively.  
The values of the crystallinity degree of all the prepared materials are presented in Table 1. As seen from the resulted \(X_{c}\) values, the incorporation of GNPs in the polymer  
Figure 1: XRD patterns of (**a**) neat SCB-PE and SCB-PE/GNPs no BM, (**b**) neat SCB-PE and SCB-PE/GNPs no BM.  
matrix causes an increase in crystallinity. GNPs act as a nucleation site for the macromolecular polyethylene chains, leading to a higher crystalline volume fraction in any case of the nanocomposites with increasing filler content. However, a difference in the crystallinity degree between the ball-milled nanocomposites and their solely melt-mixed counterparts is observed. SCB-PE/GNPs w BM nanocomposites present lower \(X_{c}\) values, indicating that the ball-milling process induced the decrease of the lamellar density [32]. Despite the amorphization effect, the ball-milled nanocomposite samples present higher crystallinity values compared to neat SCB-PE. The lamellae thickness of the two main crystallographic directions (110) and (200) of the neat SCB-PE and its corresponding nanocomposites was calculated using Scherrer's equation:  
\[L_{(hkl)}=\frac{K\lambda}{FWHM_{(2\theta)}\cos\theta}, \tag{2}\]  
where _FWHM_ is the full width at half maximum of the corresponding peak, \(K\) is a geometrical factor (for polyethylene is taken as 0.9 [33]), and \(\lambda\) is the wavelength of CuKa radiation (\(\lambda\)CuKa = 1.5418 A). The results are presented in Table 1. The lamellae thicknesses of the prepared materials do not present significant variations, meaning that the GNPs presence and the ball-milling process did not seem to affect the matrix's crystalline size. This can also be verified by the almost constant and similar values of the ratio L110/L200 (Table 1).
## 3 Evaluation and Results  
### Evaluation Metrics  
The evaluation is carried out in two consecutive steps, i.e., Step 1 and Step 2, with an appropriate variation of the experimental setup. The two setups will, from this point onwards, be referred to as setups Variant 1 and Variant 2.
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### False Data Injection Attacks
#### 3.1.4 Attack 4: Gradual Reduction of SoC Values  
This attack involves gradually reducing the reported SoC values over time, aiming at making the attack stealthy by pretending that the battery is depleting in a subtle way to avoid detection. In other words, instead of making sudden changes that could raise suspicion, the attacker slowly decreases the reported SoC, making it more difficult for the RL agent to detect the manipulation as shown in Equation (12).  
\[f_{4}(SoC_{i}^{(t)})=SoC_{i}^{(t)}\times(1-\alpha_{t}),\quad\alpha_{t}=\frac{t }{T}\times 0.1 \tag{12}\]  
where:  
* \(\alpha_{t}\) is the gradual reduction factor, which increases as time progresses, leading to a slow but steady depletion of the reported SoC.
* \(T\) is the period of the attack which is typically the number of SoC reports in each day, i.e., 24.
## 4 Local Variations of Ne in the Topside Ionosphere during Seismic Activity
### Ionospheric Disturbances over the EQ Area
In panels (**a,b**), each individual day is separated by a vertical dotted line. Figure 9: Stack plot of the differences (\(\Delta\)Ne in \(10^{5}\) cm\({}^{-3}\)) between the Ne values from SwA and SwC along the descending orbits on 14–28 January. the transport is vertical, horizontal, or a combination of both, the most likely candidate for the driving force behind any plasma motion is an E-field generated somehow in the ionosphere above the EQ epicenter. If the E-field varies in the horizontal plane, the plasma moves vertically. Its vertical profile is modified so that _Ne_ changes at a given height. In addition, TIDs may play a role in the plasma motion in the topside ionosphere, but this phenomenon is manifested in regular variations of smaller amplitude detected by both satellites [27; 58]. The observed irregularities are localized in the vicinity of the meridian 81\({}^{\circ}\)E (the EQ epicenter is at 79\({}^{\circ}\)E) and do not extend to adjacent longitudes. Additional validation is supplied through observations from different longitudes. Figure 10 shows a stacked plot of \(\Delta\)Ne along the segments of two consecutive orbits (descending, 05 LT) adjoining the 81\({}^{\circ}\)E orbit from the west and east. These trajectories are separated from each other by approximately 25\({}^{\circ}\) in longitude and 1.5 h in time. The approximate meridians and corresponding UTs are as follows: 128\({}^{\circ}\)E (20:30 UT, 22 January), 104\({}^{\circ}\)E (22:10 UT, 22 January), 81\({}^{\circ}\)E (23:50 UT, 22 January), 57\({}^{\circ}\)E (01:30 UT, 23 January), and 33\({}^{\circ}\)E (03:10 UT, 23 January). Since Figure 10 is not a snapshot and the trajectories are separated in time, the comparison is not entirely correct. However, it is possible to give some qualitative assessment. The largest anomaly in \(\Delta\)Ne is observed along the 81\({}^{\circ}\)E meridian and fades with distance from it. Although it is not possible to separate the temporal and spatial variations well, this picture is in favor of the formation of a localized ionospheric irregularity just near 36\({}^{\circ}\)N, 81\({}^{\circ}\)E. Among the satellites flying side by side at a distance of \(\sim\)200 km, the first one observed a density about 20% larger than the second one.
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
## 3 Baseband Modulator Architecture
### Top-Level Architecture
In real-time scenarios like wireless communications, it is necessary to reduce the reconfiguration latency because if the system takes too long to reconfigure, quality-of-service is degraded. Moreover, there is also an energy consumption overhead during the reconfiguration, and shorter reconfiguration latencies are crucial to reduce it. The DPR latency depends on the size of the partial bitstreams and on the configuration port bandwidth. The configuration interface adopted in this work is the ICAP. This high-bandwidth internal interface permits the FPGA to reconfigure itself. Xilinx sets the maximum ICAP bandwidth at 400 MB/s, for a 100 MHz clock frequency and 32-bit data width [35]. Nevertheless, the ICAP can be overclocked to further enhance the reconfiguration throughput [36]. In the present work, the ICAP is overclocked at 200 MHz, using another clock signal (FCLK1) provided by the PS. Like the input data files, the partial bitstreams for all modulator and demodulator configurations are loaded from an SD card to the DDR memory upon system start-up. To take advantage of ICAP overclocking, a dedicated DMA controller is used to accelerate the partial bitstream transfer to the ICAP.
## 1 Introduction  
Self-organization and self-assembly concepts for the preparation of thin films have been developed in the last decades [1; 2; 3]. They allow the development of self-assembled architectures for organic photovoltaics, electrochemical energy storage in pseudocapacitors, and metal\(-\)organic framework materials for CO\({}_{2}\) capture [4]. Simultaneously during the self-organization process, there is functionalization and sensibilization of substrate surfaces [5; 6]. Along with silicon substrates, carbon substrates have recently been investigated [7; 8; 9; 10]. Carbon substrates are among the most suitable for non-expensive devices [11], including solar cells [12; 13]. They have also been used for fuel cells [14], catalysis [15; 16], microarray fabrication in biosensing [17] (in particular, the authors made an important conclusion that the carbon substrates themselves, as well as chemical compounds based on carbon-carbon bonds, provide significantly increased chemical stability), Si ribbon technology for solar cells [18], and other applications, such as deicing [19]. This is due to: (i) unique properties of both the isolated molecule and self-organized molecular assemblies or aggregations; (ii) the combination of high absorption coefficient of organics and good \(C\) transport properties; (iii) hybrid compatibility with \(C\) planar technology.  
The carbon substrates can usually be functionalized by some modified carbon [20; 21] or other additions [22].  
On the other hand, the chemical bath technological process for the preparation of organic films from solutions, including aqueous solutions, is very simple and not expensive [23; 24; 25]. Self-organization and self-assembly occur during this process. Thanks to self-organization, a clonidine-silicon hybrid with an efficiency of about 8.5% was created [2].  
Copper phthalocyanine is one of the organic substances that has been used in organic solar cells in recent decades [26; 27; 28; 29; 30]. It was interesting to look at other phthalocyanines, such as cobalt phthalocyanine or cyanocobalamin (vitamin B12). In another classification, copper and cobalt phthalocyanines are aromatic heterocyclic amines. Aromatic heterocyclic amines with shorter chains are sulfacyl sodium and clonidine. Previously, these heterocyclic amines were used with silicon substrates to create hybrids for organic solar cells [2; 25].  
This article deals with the morphology of self-organized aromatic heterocyclic organics on the carbon patterned substrate and some of their injection properties. The technology used for the fabrication of organic-silicon hybrids is simple enough and differs by preparation at room temperature, using water solutions of organic components, and a lack of vacuum equipment. To our knowledge, this is the first of such a study in the scientific literature.