esa-sceva/satcom-chunk-collection huggingface dataset:
# Large Language Model-Informed X-ray Photoelectron Spectroscopy Data Analysis
## 3 X-ray Photoelectron Spectroscopy  
XPS, also referred to as electron spectroscopy for chemical analysis (ESCA), is a powerful and versatile technique widely utilized for surface analysis [27, 28]. Its principle lies in the photoelectric effect, where a material absorbs X-ray photons and consequently emits photoelectrons. The kinetic energy of these emitted photoelectrons and their relative number is meticulously measured to provide critical information about the elemental composition, chemical state, and electronic state of the material under investigation.  
XPS functions on the basis that the kinetic energy of an electron emitted from a material is a function of the binding energy of the electron within the atom and the energy of the incident X-ray. This relationship can be formulated as follows:  
\[KE=h\
u-BE-\phi, \tag{1}\]  
where \(KE\) denotes the kinetic energy of the photoelectron, \(h\
u\) is the energy of the incident X-ray, \(BE\) is the binding energy of the electron, and \(\phi\) is the work function of the electron energy analyzer that accounts for energy losses related to the spectrometer itself.  
A typical XPS spectrum is a plot of the number of electrons detected (usually represented as counts/s) versus the binding energy of the electrons, measured in electron volts (eV). Each chemical element originates a set of specific XPS peaks in the spectrum, and their binding energies are also modulated by the atomic local bonding environment. The area under each peak is directly proportional to the amount of the corresponding element present in the surface region of the material.  
XPS analysis is particularly beneficial due to its capability of detecting all elements (with special conditions required for detection of hydrogen and helium), its sensitivity (can detect elements present at the 0.1 atomic % level), its ability to provide information about the chemical state of the elements present, and its surface sensitivity, down to a few atomic layers.  
While the method of curve fitting in XPS data analysis has been widely applied, the process of interpreting the curve-fitted peaks--representing different chemical states--often requires substantial domain expertise. This paper presents an approach to leverage the abilities of LLMs to assist in the interpretation of curve-fitted XPS data, providing a valuable tool to enhance the efficiency and accessibility of XPS analysis.
## 4 Experiment Results and Discussion
### Experiments on the MNIST and Fashion-MNIST Dataset
#### 4.1.1 The Used CNN Architecture and Its Training Method  
To evaluate the proposed CNN-SNN conversion method on these datasets, we used the CNN architecture shown in Figure 5, which is similar to that used in [24]. Specifically, the network is organized into a 28 \(\times\) 28-12c5-2 ap-64c5-2 ap-10o architecture, where the input is given in a 28 \(\times\) 28 grey-scale image, the first convolutional layer consists of 12 kernels of size 5 \(\times\) 5 followed by 2 \(\times\) 2 average pooling, the second convolutional layer consists of 64 kernels of size 5 \(\times\) 5 followed by 2 \(\times\) 2 average pooling, and the last layer is a fully connected layer with 10 nodes.  
For the MNIST dataset, the CNN was trained with the Adam optimizer for 100 epochs, with a fixed learning rate of 0.001 and a batch size of 50. In addition, to overcome the overfitting problem, spatial dropout was applied to the convolutional layers with a probability of 0.5. For the data augmentation, the cut-out method [35] was used, which replaces a few randomly selected rectangular regions with randomly selected values to make the trained CNN models robust to occlusions on the input images. The trained CNN model achieved an accuracy of 99.79% and 99.31% for the training and testing datasets, respectively.  
For the Fashion-MNIST dataset, we trained the CNN model with the Adam optimizer for 250 epochs with a batch size of 50, where the learning rate first was initialized at 0.001, then were scaled it by multiplying 0.1 at epoch 180, and a spatial dropout of a probability of 0.50 was applied for the convolutional layers. The trained CNN model achieved an accuracy of 92.70% on the testing set. Table 1 provides the configuration parameters of the CNN model trainings for both datasets.  
Figure 4: Samples of the MNIST and F-MNIST datasets.  
Figure 5: Our CNN-SNN conversion scheme.
## 4 Conclusions  
The presented research results and their analysis led to the following conclusions:  
* The tested nozzles sufficiently meet the generalized criteria regarding macroparameters (unit flow rate, CV coefficient) and microparameters, including qualification in the field of optimal fraction for produced droplets at the medium level (236-340 \(\upmu\)m). This is also confirmed by the results of numerous studies carried out in other scientific centers and research institutions.
* Pronounced changes in the nature of the volume distribution (volume increase) observed in specific bands of the sprayed stream during the nozzle tests caused a significant change in droplet size, ranging from the medium level for the assessment of the entire stream to coarse or even very coarse droplets. The assembly of such streams emitted by nozzles located on the spraying boom can lead to the synergistic effect of the described disturbances in the considered areas and can contribute to a significant deterioration in the quality and effectiveness of spraying. The effects of poor spraying quality include reduced treatment effectiveness (reduced pest or disease control) or crop damage (overdose of a plant protection product). These effects can also lead to environmental hazards and economic losses.
* In cases where the volume distribution shows a clear and local change in character despite a positive verification result, the authors recommend carrying out additional tests, consisting of spot measurement of droplet characteristics in individual bands of the liquid stream corresponding to the width of a single groove of a grooved table.
* The Doppler laser anemometry method can be used to assess the lateral distribution of liquids on sprayed surfaces and to determine the corresponding value of the CV coefficient as an alternative to analogous measurements on a grooved table. This is confirmed by the results presented in Figure 10.  
Conceptualization, D.L. and J.P.; methodology, D.L. and J.P.; data curation, D.L. and J.P.; writing--review and editing, D.L. and J.P.; visualization, W.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 3 Models and Goals
### System Model  
Our system model consists of three entities: vehicle; Location Based Services (LBS); and Trusted Authority (TA). These are shown in Figure 2. TA is the registration authority that registers and provides certificates to the vehicles. It contains details of vehicle registration data and has a pseudonyms pool to assign to vehicles during registration. TA is considered trusted in our system model, does not compromise the privacy of vehicles. LBS is a location service provider that makes available various locations of interest to vehicles. For example, if a vehicle requires the nearest hospital or restaurant location, it can a request to LBS. However, LBS is not a trusted entity in the system model and can assist an adversary in compromising vehicles' location. The third entity in the system model is the vehicle that is moving on the road, and our prime concern in this research is to provide location privacy to it. The vehicle is equipped with an OBU, which keeps a record of all events during a vehicle's journey on the road network. The data in the OBU is given security with the help of a tamper-proof device. For the precise location data, the vehicle utilizes GPS.
## 3 Architecture
### IPCRSA Overview
#### 3.1.2 Gateway Node  
In IPCRSA, the GNs serve the functions of User and CRN access and management. On the CRN side, the GNs perceive and monitor the real-time resource status information of each CRN. They identify the types of CRNs, generate computing resource service identifiers, and finally report the identifiers and the real-time status information of corresponding CRNs to the connected SNs. On the User side, when computing resource scheduling is successful, the GNs return a scheduling success response packet to the Clients. Based on the information in the response packet, the Clients establish a connection with the GNs. Users operate the CRNs managed by these GNs to complete relevant service deployment operations.
# Repellency of Veratraldehyde (3,4-Dimethoxy Benzaldehyde) against Mosquito Females and Tick Nymphs
## 3 Results  
### Repellency of Veratraldehyde  
The repellency of veratraldehyde was bioassay against _A. albopictus_ females using the arm-in-cage test (Table 1). At doses of 0.25 and 0.20 mg/cm\({}^{2}\), the chemical exhibited more than 95 and 83% repellency for 2.5 h, respectively. However, depending on the dose, it showed 83% repellency 1.5 h after treatment and 75% activity 0.5 h after treatment at 0.10 and 0.03 mg/cm\({}^{2}\), respectively. DEET was used as a positive control and showed more than 95% repellency at both 0.25 and 0.20 mg/cm\({}^{2}\) for 2.5 h. However, DEET also gave decreased repellent activity at lower doses, 0.10 and 0.03 mg/cm\({}^{2}\), similar to the pattern observed for veratraldehyde.  
The repellency of veratraldehyde against _H. longicornis_ nymphs in the filter paper assay also depended on both the test dose and exposure time (Table 2). The repellency of all tested concentrations of veratraldehyde (0.25, 0.20, 0.10 and 0.03 mg/cm\({}^{2}\)) was more  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{
\begin{tabular}{c} **Conc.** \\ **(mg/cm\({}^{2}\))** \\ \end{tabular} } & \multicolumn{5}{c}{**Repellency (\%, Mean \(\pm\) SE)**} \\ \cline{3-8}  & & **0.5 h** & **1 h** & **1.5 h** & **2 h** & **2.5 h** & **3 h** \\ \hline \multirow{4}{*}{Veratraldehyde} & 0.25 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 95 \(\pm\) 3.2 & 58 \(\pm\) 4.4 \\  & 0.20 & 100 \(\pm\) 0 & 99 \(\pm\) 2.3 & 94 \(\pm\) 4.6 & 91 \(\pm\) 1.7 & 83 \(\pm\) 2.6 & 65 \(\pm\) 6.0 \\  & 0.10 & 93 \(\pm\) 4.3 & 90 \(\pm\) 4.1 & 83 \(\pm\) 8.8 & 58 \(\pm\) 5.5 & \\  & 0.03 & 75 \(\pm\) 7.1 & 54 \(\pm\) 4.3 & & & \\ \hline \multirow{4}{*}{DEET} & 0.25 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 84 \(\pm\) 4.3 \\  & 0.20 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 100 \(\pm\) 0 & 95 \(\pm\) 3.3 & 96 \(\pm\) 1.2 & 85 \(\pm\) 3.0 \\ \cline{1-1}  & 0.10 & 96 \(\pm\) 2.6 & 86 \(\pm\) 4.2 & 83 \(\pm\) 4.3 & 82 \(\pm\) 9.5 & 57 \(\pm\) 5.2 & 27 \(\pm\) 3.8 \\ \cline{1-1}  & 0.03 & 67 \(\pm\) 3.3 & 69 \(\pm\) 6.9 & 19 \(\pm\) 5.2 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The repellent activity of veratraldehyde against _Aedes albopictus_ females using the arm-in-cage test.  
than 90% for 3 h. However, the repellant activity decreased faster at lower concentrations, resulting in activity of 85 and 75% at 0.09 mg/cm\({}^{2}\) after 4 and 5 h, respectively, whereas the activities at 0.01 mg/cm\({}^{2}\) were 65 and 48%. On the other hand, although the repellency of DEET at 0.09 mg/cm\({}^{2}\) was 100% for 4 h, at concentrations below this, the activity was found to be lower than that of veratraldehyde, indicating that the repellency of veratraldehyde was better than that of DEET against _H. longicornis_ nymphs.
## 3 Materials and Methods  
The dataset used in the study has been obtained from the Global Footprint Network database [14], encompassing the annual total Ecological Footprint amounts worldwide from 1961 to 2022. This time series is presented in Figure 1.  
The framework of the study for EF forecasting is illustrated in Figure 2. Firstly, total EF (\(10^{4}\) gha) data by year were collected into an Excel file for worldwide Ecological Footprint forecasting. The dataset was divided, allocating 85% for training (1961-2012) and using the remaining 15% to evaluate the model performances. Subsequently, we trained five ARIMA models, Holt-Winters, twelve LSTM models, and the developed hybrid ARIMA-SVR model using the dataset from 1961-2012. EF forecasts were generated for each year until 2022, covering a span of 10 years. The forecasted values of the models were then compared with the test set, and MAPE and RMSE values were measured. In the final stage, we validated the predictive abilities of the models using the Wilcoxon Signed-R  
Figure 1: Time series plot of the total Ecological Footprint (\(10^{10}\) gha) for the world.  
In this study, various time series models with different structures, including classical, deep learning, and hybrid models, were chosen to identify the model that best suits the data and exhibits effective predictive performance in forecasting the Ecological Footprint. The ARIMA model is an effective tool for addressing trend and seasonal components in time series, and it also fits well with linear data [9]. LSTM, an effective deep learning algorithm designed to capture long-term dependencies in time series, also demonstrates a robust predictive effect on nonlinear data [9]. The Holt-Winters model includes a triple exponential smoothing method that can handle irregular changes in time series [14]. In the hybrid ARIMA-SVR model, a more flexible model is developed by combining the predictive capabilities of ARIMA with the SVR's ability to model nonlinear relationships.  
The statistical analyses of this study and the developed LSTM, ARIMA, Holt-Winters, and hybrid ARIMA-SVR models were executed using the Python programming language. Python is a user-friendly programming language with a wide range of applications. The unique design of this high-level programming language allows for easy code reuse.
## 2 Methodology and System Architecture
### Communications with KUKA Robot  
JOpenShowVar is an open-source java cross-platform communication interface to the KUKA robot via its KRC4 controller. Along with its complementary KUKAVARPROXY that runs on the KRC4, they can be used to read/write global variables of the KRC4. The externally controllable and observable global variables can be used in a KRL program whose flow can be altered based on them. This interface is established by connecting the KRC4 via ethernet from the ethernet safety interface (X66) to a PC.  
The reading and writing of variables and data structures of the controlled KuKa manipulators are facilitated through \"JOpenShowVar\", which is a Java open-source cross-platform communication interface with KUKA industrial robots. Figure 7a shows the architecture of the application program interface (API) for \"JOpenShowVar\", which is a client that runs on a remote PC and interacts with user applications. The interface and the interaction with the real-time control process of the manipulator are established through KukavarProxy, which is a TCP/IP server that enables the reading and writing of robot variables over the network. It runs on the KRC4 and allows up to 10 clients to connect. The class responsible for the communications is the \"CrossComClient\", whose API is shown  
Figure 6: System interface using microcontroller.  
in Figure 7b. The functions 'writeVariable' and'readVariable' are the ones responsible for reading from and writing to global variables. This class takes the server (KRC4) IP address as well as the corresponding port.  
KRL and KRLstructs are classes that represent actual variables from different data types in the KRL. This class takes in a string name that represents the exact name of the global variable defined in the KRC4.
## 3 Measuring Course Effectiveness
### A Case Study--The Path towards Integrating Satellite Data into Stock Assessment  
A training course can have wide ranging impacts on operational oceanography, both directly by helping with individual research or management applications, and indirectly by informing planning and infrastructure decisions at organizations charged with operational activities. For example, after attending the inaugural 2006 NOAA Ocean Satellite Course, [PERSON] subsequently used satellite data to identify factors influencing the abundance of Alaska sablefish, an important commercial species [36], and initiated a process for automatic integration of satellite data into a component of the Sablefish Stock Assessment in 2017 [37]. She has continued to be a strong supporter and advocate for the satellite course, and she hosted the WCN 2019 satellite course at the NMFS lab in Juneau, Alaska. Recently, she has collaborated with WCN to develop a specialized webpage that will provide time-series of environmental satellite data for the different fishery management regions in Alaska. Ultimately, data from the webpage will be used to inform annual stock assessment reports. She has also initiated the development of a new ERDDAP, with the help of staff from CoastWatch WCN, to house ecosystem data that will be incorporated into the annual stock assessment and fishery evaluation reports [38]. Stock assessment is one of the primary operational activities of NMFS, and incorporating environmental data (i.e., satellite data) into stock assessments has been identified as a desired objective [3].
distribution [10], Gamma distribution [11] and Lognormal distribution [12], _etc._, were often used to give the best fit of the monitored or simulated stress ranges or stress amplitudes.  
While the uncertainties in material properties and external loads can be depicted through laboratory tests and inspection/monitoring, the life prediction models remain uncertain and are more complex to develop. In general, existing fatigue prediction models can be classified into two groups: (1) S-N curve-based models [9; 10; 11] and (2) the fracture mechanics models. In the former, the Palmgren-Miner rule is often used, and the effectiveness of such methods depends on the classification of concerned details and the fatigue parameters of S-N curves. As to the fatigue fracture models, their early forms adopt the linear elastic or nonlinear fracture approaches, _etc._[13; 14; 15]; the continuum damage model is also a useful tool to analyze the engineering failure problems [16; 17]; unfortunately, damage variables in the continuum damage mechanics lack of explicit physical meaning and are not easily measured through experiments. Furthermore, probabilistic fatigue models [18; 19; 20], such as the combined probabilistic physics-of-failure-based method, the probabilistic time-dependent method, _etc._, have been proposed and applied to take the uncertainties and time-varying features into consideration. However, these methods were mainly developed for engineering problems at macroscale, whereas recent investigations showed that fracture failure may start from the microscopic scale and gradually spread to the macroscopic scale [21]. Therefore, better understanding of the multiscaling fatigue mechanism is crucial for the rational life prediction.  
In this paper, a probabilistic fatigue approach is proposed, which is based on a comprehensive and site-specific vehicle load model, finite element (FE) analysis and the fracture based damage model (_i.e._, multiscaling and mesoscopic fracture mechanics), and the cable life is consumed when the fatigue crack grows to a critical level. In the analyses, the influence of the mean stress level on the fatigue life [10] is also considered. A case study on the stay cables of the Runyang Cable-Stayed Bridge (RCB) in China, is made for demonstration.
###### Abstract  
The fermentation process results in the presence of lactic acid bacteria (LAB). Traditional fermented buffalo milk from the Indonesian province of West Sumatra is known as dadih. Bacterioxins are naturally occurring antimicrobial peptides that can be produced by LAB. These bacterioxins have the potential to be used as natural preservatives. This research was conducted with the intention of isolating and partially characterizing a bacteriocin that was generated by _Lactobacillus pentosus_ 124-2 that was isolated from dadih. Using MRS agar media and 16 sRNA, the LAB that was found in dadih was successfully isolated. Bacterioxins were produced using media consisting of MRS broth. After the bacterioxins were obtained, they were subjected to a series of purification processes, one of which included ammonium sulfate (70%) followed by gel filtration chromatography for additional purification. According to the findings, the strain of LAB that was discovered in the dadih was _Lactobacillus pentosus_ 124-2. The specific activity of the bacterioxins rose, allowing for the effective completion of the purification process. The pure bacterioxins had the greatest specific activity values, at 60.59 AU/mg, while the yield values were 0.41% and 3.15-fold. Bacterioxins in their pureest form were able to inhibit the growth of Salmonella species as well as Staphylococcus aureus. The characterization results included partial bacterioxins that were resistant to pH 2, 4, and 6; antibacterial activity that was was stable at a temperature range of 25 \({}^{\circ}\)C to 121 \({}^{\circ}\)C; and resistance to the addition of 2%, 4%, and 6% salt concentrations. Microbial stability against the addition of surfactants EDTA, SDS, and Tween 80 was also obtained. In light of these findings, a bacteriocin derived from _L. pentosus_ possesses the possible ability to be utilized in the food business as a biopreservative.  
bacteroicin; dadih; purification; _Lactobacillus pentosus_; biopreservative
## 4 Discussion
### Limitations of the Current Study
Here, MODIS datasets, such as the Enhanced Vegetation Index (EVI) and PAR, were used to calculate GOSIF data, and then data were resampled to 0.05\({}^{\circ}\), which would have caused errors in the predictions. In addition to the uncertainty caused by additional model inputs, the quality of OCO-2 SIF also represented a source of uncertainty for GOSIF in this study. Currently, there are no satellites dedicated to SIF observation. Hence, it is necessary to obtain more accurate SIF datasets. The greatest limitation of the current research is that the spatial resolution and temporal resolution of SIF data are too low to reflect the greatest advantage of SIF estimation of GPP: real-time estimation [85]. Additionally, the linear relationship between SIF and GPP for different vegetation types is different on different time scales [56; 87]. Further to the above-stated issues, other input variables in the model and the number of training samples will also have an impact on the results of this study. We argue that the uncertainty of GPP can be estimated by remote sensing data across vegetation coverage types. The accuracy of the vegetation coverage type data has a significant impact on the accuracy of the LUE model, which is one of the advantages of using SIF to estimate GPP. But in this study, we also used the land cover types data [17]. Hence, producing accurate estimates of predictions of land cover types and NIRv are further issues that urgently need to be addressed when estimating GPP.