esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 2 System Model and Problem Formulation
### Computing and Communication Model
#### 2.2.2 Communication Model  
After the user \(n\) completes the local training, the trained model is uploaded to the UAV by FDMA. According to [PERSON]'s formula, the transmission rate of user \(n\) is  
\[R_{n}=b_{n}\log_{2}\biggl{(}1+\frac{g_{n}p_{n}}{N_{0}b_{n}}\biggr{)},\ \forall n\in\mathcal{N}_{m} \tag{7}\]  
where \(b_{n}\) is the bandwidth allocated to user \(n\) and \(p_{n}\) is the transmission power of user \(n\).  
We set the neural network model \(\mathbf{w}\) size as \(s\), so the communication energy consumption and delay of user \(n\) in each global round are  
\[E_{n}^{comm}=p_{n}t_{n}=p_{n}\frac{s}{R_{n}},\ \forall n\in\mathcal{N}_{m} \tag{8}\]  
\[t_{n}=\frac{s}{R_{n}},\ \forall n\in\mathcal{N}_{m} \tag{9}\]  
According to [10], when the required global accuracy reaches \(\epsilon_{0}\), the global round needs to satisfy the condition: \(I_{g}\geq\frac{2t^{2}}{\gamma^{2}_{\epsilon}}\log\frac{1}{\epsilon_{0}}\frac{ 1}{1-\eta}\), so \(I_{g}=\frac{2t^{2}}{\gamma^{2}_{\epsilon}}\log\frac{1}{\epsilon_{0}}\frac{ 1}{1-\eta}\). So, the total energy consumption of users in the entire FL process is  
\[E^{total}=I_{g}\sum_{n\in\mathcal{N}_{m}}^{|\mathcal{N}_{m}|}\Big{(}E_{n}^{comm }+E_{n}^{comp}\Big{)} \tag{10}\]  
The total latency of user \(n\) is  
\[T_{n}=I_{g}\bigg{(}\frac{I_{l}C_{n}D_{n}}{f_{n}}+t_{n}\bigg{)},\forall n\in \mathcal{N}_{m} \tag{11}\]  
We set \(T\) as denoting the completion time of FL, for the sake of ensuring the FL learning performance, and we defined that users who participate in FL must meet the requirements: \(T_{n}\leq T\), \(\forall n\in\mathcal{N}_{m}\).
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Satellite Links
#### 3.5.3 Satellite Characteristics  
In this step, the user is required to input the satellite characteristics required for link budget calculations. The values requested are as follows:  
* Satellite power density for transponder saturation;
* Receptor merit factor;
* Saturated effective isotropic radiated power;
* Input back-off with clear sky;
* Output back-off with clear sky.
## 3 Materials and Methods
### Data Augmentation and Sample Balancing  
After dividing the clippings, the classes are unbalanced, with the majority negative class. The imbalance of classes can hinder the learning of deep learning models. A strategy to overcome this problem is using data augmentation, which consists of creating synthetic images, or modified copies, based on real samples [40].  
A challenge in adopting data augmentation is the definition of which techniques to employ among the variety of existing possibilities. A strategy to follow is to define which techniques best fit the problem and which hyper-parameter ranges are acceptable to avoid unwanted distortions in the images [40].  
An alternative for the problem in question is randomly reducing the number of negative class images (undersample). However, in order to avoid losing samples of the negative class and to balance the data, an increase was applied to the data of the positive class (oversampling), using random operations of mirroring and rotations in addition to variations in brightness and contrast in the images.  
In this work, data augmentation was performed on the positive class of training data, and validation samples were balanced. No technique was applied to the test set to obtain a  
\begin{table}
\begin{tabular}{l l l l} \hline \hline  & **Total** & **Positive** & **Negative** \\ \hline Training (70\%) & 43,897 & 5802 & 38,095 \\ Validation (10\%) & 3700 & 335 & 3365 \\ Test (20\%) & 6884 & 711 & 6173 \\ \hline Total & 54,481 & 6848 & 47,633 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Division of clippings in the training, validation and testing databases.  
Figure 5: Example of a clipping with the presence of amastigote. Clipping of the image on the (**left**); clipping of the mask in the (**center**); intersection of the images on the (**right**).  
realistic evaluation of the model. Data augmentation was implemented with the help of the Albumentations library, proposed by [PERSON] et al. (2020) [41].  
In order not to interfere with the synthetic samples of the positive class, a probability of 10% was used to perform variations in brightness and/or contrast in the images. Thus, in only 10% of data enhancement operations, there is an increase or decrease in brightness and/or contrast, with variations of up to 20%. In addition, other data enhancement techniques that modify the values of the pixels in the image, such as saturation, noise, and equalization, among others, were not applied due to the creation of synthetic images of the positive class of the disease. Table 3 presents the new division of clippings in the balanced datasets.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 5 Conclusions  
In this study, pristine hemp seeds (PHSs) were successfully carbonized at 500 and 700 \({}^{\circ}\)C to the adsorbents CHS-500 and CHS-700, respectively. Batch adsorption studies were carried out using different parameters, including pH value (2, 4, 6, 8 and 10), contact time (5-720 min), initial concentration (20, 40, 50, 80 and 100 mg/L) and temperature (288-308 K). FTIR and SEM analyses showed that PHSs, CHS-500 and CHS-700 were porous with -CH, -OH, -NH2 and -C=O groups on their surfaces. All adsorbents demonstrated a gradual increase in concentration between 20 and 100 mg/L. The time effect showed a fast adsorption rate at the beginning of the adsorption process, with a slower increment at a later stage after 100 min, where CHS-500 and CHS-700 showed higher sorption capacities than PHSs. The temperature effect showed that the sorption capacities increased from 288 to 308 K for all the sorbents. All adsorbents displayed their highest cadmium and methylene blue sorption capacities at pH 8. The Langmuir isotherm model was the best fit for the experimental data, with correlation coefficients close to 1. PSO better described the adsorption process of cadmium and methylene blue by PHSs, CHS-500 and CHS-700 than the PFO and IPD models. \(\Delta\)G\({}^{\circ}\) indicated that cadmium and methylene blue sorption by PHSs, CHS-500 and CHS-700 was feasible. The proposed uptake mechanism was due to electrostatic attraction as well as \(\pi\) and hydrogen bonding. The maximum adsorption capacities of CHS-500 were 31.12 and 49.89 mg/g for cadmium and methylene blue, respectively. On the other hand, CHS-700 had capacity values of 35.08 and 53.98 mg/g for cadmium and methylene blue, respectively. Both carbon adsorbents adsorbed more methylene blue than cadmium, with the highest being obtained for CHS-700.  
Conceptualization, methodology, formal analysis and supervision by N.D.S. Writing of original draft preparation, review, editing and supervision by P.M.T. Investigation by B.R.M. Administration by J.S.M. and T.X. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Vaal University of Technology, Vanderbijlpark, South Africa.  
Not applicable.  
The findings of this research are obtainable from authors on request.  
The authors declare no conflict of interest.
## 2 Experimental
### Validation  
Linearity for the proposed method was assessed by linear regression analysis. Calibration curves for standard solutions and for aqueous solutions after the d-SPE procedure were constructed by plotting the intensity of the target analytes to their nominal concentration. Experiments were conducted at triplicates. Least square linear regression analysis was employed for the calculation of the slope, the intercept, and the coefficient of determination for each metal. The detection limits (LODs) and quantification limits (LOQs) were estimated as the concentration equal to 3 and 10 times the standard deviation of the measurement divided by the slope of calibration line of the aqueous standard or of standard addition for each analyte, respectively [22; 35].  
Enrichment factor (EF) was determined as the ratio of metal ions' concentration in the solution after elution to the initial nominal concentration in the aqueous sample. Percent extraction recoveries were calculated by comparing the found concentration and the theoretical concentration followed by multiplying by 100 [35; 36].  
The accuracy of the d-SPE-ICP-AES method was assessed in terms of bias between the real and experimentally determined concentrations in spiked samples, since there was no available certified reference material (CRM) on hand. Precision was estimated by analyzing spiked meat samples. For this purpose, intra-day repeatability was assessed by analyzing in five replicates spiked samples, while inter-day precision was determined by analyzing in triplicate-spiked samples of the same concentration levels in 5 days [35].
## 4 Statistical Analysis and Results
All other journals have fewer than 20 articles. Figure 8 presents a Choropleth map showing the number of published articles by country from the selected literature. The top five results show that China has the highest contribution, with 48 articles. The second through fifth positions are held by the United States, Turkey, Italy, and Iran, with 27, 25, 23, and 22 articles, respectively. Since workplace risk analysis primarily concerns the workforce, it was deemed appropriate to create a map reflecting the number of published articles per country's workforce (Figure 9). The top five results show that Taiwan has the highest number of published articles relative to its workforce. Italy, Slovenia, Finland, and Switzerland rank second to fifth, respectively. \begin{table}
\begin{tabular}{c c c} \hline
**Name** & **Frequency** & **Percent** \\ \hline JLPPI & 39 & 12.1\% \\ RESS & 36 & 11.1\% \\ SS & 28 & 8.7\% \\ AAP & 27 & 8.4\% \\ RA & 19 & 5.9\% \\ HM & 16 & 5.0\% \\ FSS & 15 & 4.6\% \\ SR & 12 & 3.7\% \\ NH & 6 & 1.9\% \\ EAAI & 5 & 1.5\% \\ Remaining 53 & 120 & 37.1\% \\ \hline \end{tabular}
\end{table} 
Table 5: Absolute numbers and percentages of journals in the study. Figure 7: Distribution of selected articles among the top 10 journal sources. Figure 10 shows the number of articles by publisher. The literature review revealed that 65.6% of the articles were published by the Elsevier publishing house, with a significant variation existing between it and other publishers. Wiley follows with 7.3%, IEEE and Springer with 6.25%, and Taylor and Francis at 4.86%. The remaining publishers share the remaining 8%. Figure 8: Number of publications by country. Figure 9: Number of publications by country based on workforce. The distribution of contributing authors in this field was extracted by processing the citation database. Figure 11 illustrates all authors from the 289 articles in a word cloud, where authors with the most publications have their names displayed in larger font sizes. The research identified that the authors with the most papers on the subject are [PERSON]. (eleven articles), [PERSON] (nine), and [PERSON] (nine). Out of 634 authors, only 10 have published more than five papers each. The next 127 authors have published two to five papers, while the rest only have one paper. The top 10 authors in terms of absolute number of articles, along with their percentage of the total database, are shown in Table 6. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Author** & **Number of Articles** & **Percentage** \\ \hline
1 & [PERSON]. & 11 & 3.82\% \\
2 & [PERSON] & 9 & 3.13\% \\
3 & [PERSON] & 9 & 3.31\% \\
4 & [PERSON] & 6 & 2.08\% \\
5 & [PERSON] & 6 & 2.08\% \\
6 & [PERSON] & 6 & 2.08\% \\
7 & [PERSON] & 5 & 1.74\% \\
8 & [PERSON]. & 5 & 1.74\% \\
9 & [PERSON] & 5 & 1.74\% \\
10 & [PERSON]. & 5 & 1.74\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Top 10 researchers applying techniques for risk assessment. Figure 11: Word cloud of authors in risk assessment. Figure 10: Publishers of the articles under review.
# Empowering LLMs with Toolkits: An Open-Source Intelligence Acquisition Method
## 4 Experiments
### Evaluation Metrics  
To comprehensively analyze the impact of our method on the LLMs' performance in OSINT acquisition, we designed four evaluation metrics: the response accuracy, tool utilization rate, tool choice accuracy, and valid parameter rate. When evaluating different aspects, we employ distinct metrics. In the analysis of the experimental results, the following statistics are required: the total number of questions \(N_{total}\), the number of correctly answered questions \(N_{correct}\), the number of questions answered using tools \(N_{t\_used}\), the number of questions answered using dedicated tools \(N_{d\_used}\), and the number of questions where valid parameters were transmitted \(N_{v\_param}\).  
* **Response Accuracy (\(RA\))**. This metric represents the ratio of the number of questions for which the LLM provides an accurate answer to the total number of test questions. It measures the LLM's capability to solve problems. The formula is \[RA=\frac{N_{correct}}{N_{total}}\] (2)
* **Tool Utilization Rate (\(TUR\))**. This indicates the proportion of questions answered by the agent using tools out of the total number of test questions. It assesses the LLM's ability to utilize tools. The formula is \[TUR=\frac{N_{t\_used}}{N_{total}}\] (3)
* **Tool Choice Accuracy (\(TCA\))**. This metric denotes the ratio of the number of questions where the agent selects the correct tool to answer a specific type of question out of the questions answered using tools. Since our toolkit contains tools designed for the acquisition of OSINT information, the tool choice accuracy reflects the LLM'sjudgment in selecting the appropriate method to acquire the desired OSINT. The formula is \[TCA=\frac{N_{d\_used}}{N_{t\_used}}\] (4)
* **Valid Parameter Rate (\(VPR\))**. This metric represents the ratio of the number of effective parameters transmitted by the agent to the total number of parameters transmitted. It evaluates the agent's understanding of the problem. Effective parameters are those that can be parsed and utilized by the tool's API to resolve questions, whereas ineffective parameters can lead to incorrect responses. The formula is \[VPR=\frac{N_{v\_param}}{N_{t\_used}}\] (5)
## 5 Example Analysis
### Differentiated Service and Communication Technology Adaptation  
Based on the GTOPSIS method, an adaptability analysis of remote and local communication technologies for differentiated services is conducted. Taking the precise load control service as an example, according to Table 2, the initial decision matrix is formed using the performance index data of remote communication technologies, which have been preprocessed for bandwidth and bit error rate. The weighted normalized decision matrix is obtained using Formulas (22) and (23) and the combined weights for precise load control. According to Formulas (24)-(31), the hybrid distance of each remote communication technology to the positive and negative ideal solutions is calculated. A higher resulting relative hybrid closeness indicates better adaptability between the service and remote communication technology.  
During the adaptation of local communication technologies, the original decision matrix is formed from the performance index data of local communication technologies, which has been preprocessed. By executing the same steps above, the local communication adaptation results of the precise load control service can be determined.  
Figure 5 respectively illustrates the relative hybrid closeness results of adapting the four services to remote and local communication technologies, where B1 represents precise load control service, B2 represents electricity information collection service, B3 represents load management terminal monitoring service and B4 represents power market transaction service.  
Figure 5: Remote and local communication adaptation results.
## 3 Results and Discussions
### Effects of Laser Power on Cutting Surface Morphology  
To examine the effect of the laser power on underwater laser cutting, the focal position was fixed at \(-30\) mm, and the laser power was varied from 7 to 13 kW at 2 kW intervals. The front- and rear-surface kerf shapes, the kerf width and taper values, topographic color distribution of the cutting surface, and the measured roughness of the drag line are shown in Figures 4-7. The variation in the front- and rear-surface kerf shapes with laser power is illustrated in Figure 5, which shows that the shape of the front-surface kerf did not change regardless of the laser power. However, upon comparing the rear-surface kerf shapes, the rear-surface kerf width tended to increase with the laser power owing to the increase in the heat input to the material. Furthermore, the quantity of the molten metal increased, and the amount of dorsal that adhered to the backside tended to increase as well.  
Figure 5: Front and rear surface of specimen according to focal position; (**a**,**e**) P = 7 kW, (**b**,**f**) P = 9 kW, (**c**,**g**) P = 11 kW, (**d**,**h**) P = 13 kW (Processing conditions: \(f_{\text{d}}=-30\) mm, \(v=30\) mm/min, \(d_{\text{N}}=3\) mm, \(P_{\text{G}}=10\) bar, Nozzle type = Normal plate).  
Figure 6: Variation in kerf width and taper according to the laser power.  
This can be quantitatively confirmed from Figure 6, which plots the changes in kerf width. As the focal position was not changed, the front-surface kerf width was generally similar regardless of the laser power, while the rear-surface kerf width increased with the laser power. Therefore, the taper value, which represents the difference between the front- and rear-surface kerf widths, was the lowest under the 9 kW power condition.  
Figure 7 shows the shape of the cutting surface according to the laser power, with similar drag line lengths and surface shapes at the upper, middle, and lower areas under all conditions. However, as the laser power increases, the unevenness of the drag line becomes more conspicuous, and the surface roughness of the drag line (Figure 8) also exhibits an increasing trend in general. This can be attributed to the relatively constant front-surface kerf width, even though the amount of melting inside the material increases with laser power. The higher the laser power, the higher the assist gas mass flux required. However, with the same front-surface kerf width, the assist gas inflow remains unchanged; hence, the molten metal discharge is hampered. This ultimately increases the roughness of the drag line.  
The best cutting quality was obtained with a laser power of 9 kW, considering the front- and rear-surface kerf widths, taper value, and cutting surface shape and roughness.
## 2 Data  
The TOMAS cyclone with a maximum central wind speed of 115 km/h swept over the Pacific nation of Fiji from north to south from 10 March to 17 March 2010. We obtained the central wind speed and track information for TOMAS from the Joint Typhoon Warning Center (JTWC).  
The hydrophone array (YL) at 175\({}^{\circ}\) E, 21\({}^{\circ}\) S in the South Pacific is the closest seismic observation array to cyclone TOMAS. We downloaded 24 h continuous observations for the YL array and the three-component seismic stations in Australia (AU) during the active period of TOMAS from the International Research Institutions for Seismology (IRIS) (Figure 1). The YL array contains 89 instruments, from which 21 stations can obtain observation data at depths ranging from 1000 to 3900 m underwater. Since the F-K analysis method targets plane waves, only eight instruments in the depth range of 2000 to 2600 m were selected to avoid excessive time differences caused by large depth differences. The YL array instrument is the LDED Standard Ocean-Bottom Seismometer, which uses a low-noise amplifier with a velocity response of 0.01-100 s. Four AU stations that use a Streckeisen STS-2 broadband seismometer and are located close to the Pacific region are chosen. This seismometer has a low power and a frequency range beyond the traditional short-period and long-period instruments uses three identical oblique mechanical device sensors to ensure that the horizontal and vertical components are tightly integrated and has a velocity response of 0.1 to 120 s. Due to its wider and comparable frequency range compared to the instrument used in AU stations, the YL array can ensure that we can conduct a comparative study on background noise.  
In addition, we use ocean-wave data from the global atmospheric reanalysis data (ERA5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). ERA5 provides hourly estimates of atmospheric, terrestrial, and oceanic climate variables. The data is accurately gridded into 30 \(\times\) 30 km and contains 137 layers of atmospheric data. To investigate the fluctuation in swell height and to contrast it with microseism observations, we acquired hourly swell data for the Pacific region throughout the TOMAS cyclone activity period from the ECMWF [16].
## 4 Experimental Process and Discussion
### Stability Analysis of the System  
To test the stability of the detection system, the C\({}_{2}\)H\({}_{2}\) sample at 50 ppmv was introduced into the gas chamber, and the concentration value was recorded every second. The experiment was carried out for 10 h. The result is shown in Figure 9a. The 2f SA ranged from 1.194 to 1.218 V, and the C\({}_{2}\)H\({}_{2}\) concentration was between 49.6 and 50.6 ppmv. Therefore, the fluctuation was less than \(\pm\)1.2%, and the average value of the data was 50.03 ppmv.  
Figure 8: Detection error of the system on nine C\({}_{2}\)H\({}_{2}\) samples with concentrations of 10, 15, 25, 35, 45, 55, 75, 95, and 100 ppmv.  
Figure 7: (a) 2f SA of 50 ppmv C\({}_{2}\)H\({}_{2}\). (b) 2f SA of 0 ppmv C\({}_{2}\)H\({}_{2}\).  
Allan deviation was adopted to analyze the long-term characteristics of the detection system [23]. The result shown in Figure 9b indicates that the Allan deviation at the initial integration time of 1 s is 0.3 ppmv. If the integration time continues to increase, then Allan deviation is greatly reduced. Moreover, the Allan deviation can be reduced to 0.0018 ppmv when the integration time is increased to 362 s. The results indicate that the detection system has good stability.