esa-sceva/satcom-chunk-collection huggingface dataset:
# Erythermal Solar Irradiance, UVER, and UV Index from Ground-Based Data in Central Spain
## 5 Conclusions  
Measurements of UV erytmal solar irradiance recorded for over five years at a meteorological station located at an altitude of 702 m a.s.l. in Valladolid, Spain, were processed and analyzed. From the measurements, the UVI index and the cumulative doses were obtained and studied.  
Yearly UVER evolution at the station shows minimum values in January and maximum values in June. Average hourly monthly UVER values were analyzed at the location. The difference between the Q1 quartile and the absolute minimum is (80%), and the difference between the absolute maximum and the Q3 quartile is small (10.6%). As a result, maximum values are typical at solar noon, whereas minimum values are extremely unusual.  
The UVI index shows a slight disagreement between the maximum daily values and the solar noon value. Whereas there are no differences in 62.1% of cases, in 20.3% of cases, the difference was one unit, and in 5.3% of cases were two UVI units. UVI reached extreme, very high, or high values in 46.4% of cases.  
Finally, the cumulative yearly doses for each skin prototype and the standard erytmal dose over an average year were studied at the station. During summer and spring, the doses received were two-thirds of the yearly dose, while in winter and autumn the dose received was one-third of the yearly dose.  
Recommendations concerning the use of sun cream protection at low UVI levels and sunscreen protection at mid-latitudes should be revised. This could prove to be extremely important in the Mediterranean or similar areas with latitudes about 40\({}^{\circ}\) North and less. The conclusion is that the risk of developing skin disease might increase if an SPF 30 sunscreen is not used on days with UVI \(<\) 3 because the cumulative dose could be higher than that corresponding to days with UVI \(>\) 3.  
**Author Contributions:** Conceptualization, A.d.M. and J.B.; methodology, J.B.; software, A.d.M.; validation, J.B., A.M.; formal analysis, J.B. and A.d.M.; investigation, A.d.M.; resources, J.B.; data curation, A.d.M.; writing--original draft preparation, J.B.; writing--review and editing, A.d.M.; visualization, J.B.; supervision, A.d.M.; project administration, J.B.; funding acquisition, J.B. and A.d.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The Spanish Ministry of Science and Innovation funded the research through the (CGL2011-25363) project.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods
### Experimental Devices and Operation  
The structure of the vertical-flow wetland device is shown in Figure 1. The main body of the device is a column structure with a diameter of 15 cm, a net height of 60 cm, and a water outlet at the bottom. From bottom to top, the wetland was divided into a 5 cm gravel layer, a 40 cm substrate layer and a 15 cm aquifer layer, and substrate sampling ports were established at the top, middle and bottom of the substrate layer. According to the different wetland substrate fillers, a total of 3 groups of treatment systems are set up with 2 parallel groups in each group: (1) The substrate consisted of evenly mixed reed biochar + river sand (mass ratio 1:9), the mass of the mixture was 9.0 kg, 16.7 g/kg of reed carbon source was added to the substrate, and the reed carbon source and substrate filler were mixed evenly in the middle layer of the wetland and then reeds were planted, recorded as a reed carbon source wetland (RW); (2) reed carbon source was added to the wetland matrix, recorded as the control wetland (CW); (3) The substrate composition was exactly the same as that of RW, but no reeds were planted, which was recorded as no-plant wetland (N-RW). Five reeds at the same growth stage and the same fresh weight were transplanted in both RW and CW, and the plant roots were placed 10 cm above the substrate. The average pore volume of the filler in the wetland device was measured to be 2.00 L.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Parameters** & **COD** & **TN** & **NH\({}_{4}\)\({}^{*}\)-N** & **NO\({}_{3}\)\({}^{-}\)-N** & **TP** & **pH** \\ \hline concentration & & & & & & \\ mg/L & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental water quality.  
Figure 1: Schematic diagram of vertical-flow constructed wetland devices. Note: g/kg—the mass of reed carbon source per kilogram of substrate filler.  
After the 3 groups of constructed wetlands were erected, the test water in Table 1 and the activated sludge filtered through two layers of gauze were fully mixed in a volume ratio of 7:3, and the mixed solution was added to each wetland and immersed substrate to make a hanging of the film on the substrate and the hydraulic retention time was 24 h. After 21 days of domestication, the NO3-N concentration in the wetland effluent tended to be stable, and the wetland systems were commissioned as successful. The constructed wetland entered the continuous operation phase, and the simulated micro-polluted water was pumped into each wetland; the hydraulic residence time was 24 h, and the continuous operation was carried out at room temperature (15-25 \({}^{\circ}\)C) for 3 months.
# A Reversible Data-Hiding Method with Prediction-Error Expansion in Compressible Encrypted Images
## 3 Proposed Method
### Advantages of Proposed Method  
The proposed method has three main advantages that are summarized in Table 1 and precisely described as follows.  
* _High hiding capacity_ The proposed method encrypts a target image in units of blocks, so interpixel correlation in each block can be stable before or after encryption. Since we could obtain prediction values with high accuracy in the encrypted domain, the hiding capacity of our method was around 1 bpp using PEE-HS. The proposed method has high hiding capacity, so we could embed not only copyright information and time stamps, but also information on image content, e.g., categorical and annotation data. With such information, we could determine the type of target images in the encrypted domain.
* _Effective compression performance_ The proposed method preserves the interpixel correlation in each block after encryption and data hiding. Thus, international standards for lossless image compression, such as JPEG-LS and JPEG 2000, can be effectively utilized for the marked encrypted images produced by our method. Consequently, the proposed method alleviates the constraint on transmission and storage.  
Figure 7: Options for restoration in proposed method. (**a**) Data extraction and decryption(**b**) decryption without data extraction.  
* _Flexible restoration_ While the conventional method [29] has to extract the payload before decryption, our method could omit the data extraction process and decrypt only marked encrypted images in common with the conventional method [30]. The flexible restoration process provides a user with any one of the following three types of privilege: data extraction and decryption, data extraction only, and decryption only. This advantage allows for us to expand the range of applications.  
Conventional methods [29; 30] are deficient in at least one of the above three advantages. In contrast, the proposed method has all three in the RDH-EI field. These advantages are necessary to more widely apply RDH-EI techniques. As a practical application, for example, the proposed method could provide privacy preservation with flexible access control for Internet services such as image storage or sharing.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
###### Abstract  
All sensing systems have some inherent error. Often, these errors are systematic, and observations taken within a similar region of space and time can have correlated error structure. However, the data from these systems are frequently assumed to have completely independent and uncorrelated error. This work introduces a correlated error model for GNSS reflectometry (GNSS-R) using observations from NASA's Cyclone Global Navigation Satellite System (CYGNSS). We validate our model against near-simultaneous observations between two CYGNSS satellites and double-difference our results with modeled observables to extract the correlated error structure due to the observing system itself. Our results are useful to catalog for future GNSS-R missions and can be applied to construct an error covariance matrix for weather data assimilation.  
GNSS; reflectometry; observation error; correlated error 1
# Visible Light Communication: An Investigation of LED Non-Linearity Effects on VLC Utilising C-OFDM
###### Abstract
As a result, the DC and AC currents must be regulated appropriately to prevent deterioration in output illumination or complete failure in the worst-case scenario. Thus, different OFDM schemes were tested, namely, conventional DC-biased OFDM, F-OFDM and C-OFDM. To compare these systems, we varied the DC bias and peak-to-peak AC signal amplitude in order to understand the impact of the LED electro-optic non-linearity on the BER performance of the optical OFDM architectures, whilst considering the PAPR. We find that the C-OFDM system outperforms. The proposed system has the advantages of being resistant to LED non-linearity dispersion and having a lower PAPR than other systems. The remainder of the paper is organised as follows: In Section 2, the test setup is described. The results are discussed in Section 3, and the conclusion is presented in Section 4.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 3 Results and Discussion
### Classification with Model 1
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Class** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **AUC** & **F-Score** \\ \hline \multirow{4}{*}{SVM Linear} & Healthy & 0.851 & 0.653 & 0.923 & 0.758 & 0.927 & 0.701 \\  & 1 & 0.843 & 0.797 & 0.861 & 0.686 & 0.923 & 0.738 \\  & 2 & 0.937 & 0.833 & 0.959 & 0.816 & 0.939 & 0.825 \\  & 3 & 0.981 & 0.946 & 0.995 & 0.986 & 0.984 & 0.966 \\  & **Average** & 0.903 & 0.807 & 0.935 & 0.812 & 0.944 & 0.807 \\ \hline \multirow{4}{*}{SVM Quadratic} & Healthy & 0.854 & 0.778 & 0.883 & 0.709 & 0.915 & 0.742 \\  & 1 & 0.877 & 0.730 & 0.933 & 0.806 & 0.930 & 0.766 \\  & 2 & 0.937 & 0.833 & 0.959 & 0.816 & 0.973 & 0.825 \\  & 3 & 0.981 & 0.959 & 0.990 & 0.973 & 0.983 & 0.966 \\  & **Average** & 0.912 & 0.825 & 0.941 & 0.826 & 0.950 & 0.825 \\ \hline \multirow{4}{*}{3-NN} & Healthy & 0.817 & 0.708 & 0.857 & 0.646 & 0.891 & 0.675 \\  & 1 & 0.832 & 0.649 & 0.902 & 0.716 & 0.866 & 0.681 \\  & 2 & 0.937 & 0.813 & 0.964 & 0.830 & 0.952 & 0.821 \\  & 3 & 0.974 & 0.959 & 0.979 & 0.947 & 0.989 & 0.953 \\  & **Average** & 0.890 & 0.782 & 0.926 & 0.785 & 0.924 & 0.783 \\ \hline \multirow{4}{*}{Weighted k-NN} & Healthy & 0.817 & 0.708 & 0.857 & 0.646 & 0.907 & 0.675 \\  & 1 & 0.832 & 0.662 & 0.897 & 0.710 & 0.906 & 0.685 \\  & 2 & 0.937 & 0.792 & 0.968 & 0.844 & 0.968 & 0.817 \\  & 3 & 0.981 & 0.973 & 0.985 & 0.960 & 0.989 & 0.966 \\  & **Average** & 0.892 & 0.784 & 0.927 & 0.790 & 0.943 & 0.786 \\ \hline \multirow{4}{*}{LR} & Healthy & 0.866 & 0.764 & 0.903 & 0.743 & 0.924 & 0.753 \\  & 1 & 0.877 & 0.757 & 0.923 & 0.789 & 0.939 & 0.772 \\ \cline{1-1}  & 2 & 0.918 & 0.813 & 0.941 & 0.750 & 0.964 & 0.780 \\ \cline{1-1}  & 3 & 0.959 & 0.905 & 0.979 & 0.944 & 0.994 & 0.924 \\ \cline{1-1}  & **Average** & 0.905 & 0.810 & 0.937 & 0.806 & 0.955 & 0.807 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metrics scores for Model 1, with 4 classes. similar metric results, obtaining a difference lower than 1% between each metric, and the same happens with the 3-NN and Weighted k-NN algorithms; however, the first group performs slightly better than the second group, mainly in the AUC metric, which is almost 2% higher. The SVM Quadratic is the algorithm that presents the best results, obtaining in some metrics a difference of 4% when compared to the algorithm that presents worse metric results. In Figure 5, the ROC curves obtained with the Quadratic algorithm, for each class, are illustrated. From Figure 5, it is perceptible that, although all the AUC results are higher than 91%, the classes with the best classification are Classes 2 and 3, with the two highest AUC values.
## 3 UWOC System Model
### The Receiver Unit  
An optical detection system or a receiver is one of the main components of UWOC. The optical signal will go through an optical filter and focusing lens on the receiver side. The photon detector will then capture it. Since a photodiode can only transform light intensity variations from an LD or laser into corresponding current changes [78; 79], a trans-impedance amplifier is cascaded in the following stage to convert current into voltage. The transformed voltage signals will then go through a low-pass filter responsible for shaping the voltage pulse to reduce the thermal and ambient noise levels without causing significant inter-symbol interference (ISI) [80; 81].  
Further signal processing programmes are bypassed through a signal quality analyser for demodulation and decoding [82]. An equalisation is a tool used to reshape the incoming pulse, extract the timing information (sampling), and decide the symbol value. A PC or BER tester will finally collect and analyse the recovered original data to evaluate several important performance parameters, such as BER. In optical receivers, many types of photodetectors can be used; for more details, see ref [62]. The most functional OWC systems use a PIN or an avalanche photodiode (APD) as a receiver [83]. The UWOC receiver system must meet specific requirements to address the effects of noise and attenuation. The receiver's most significant parameters are a large FOV, high gain, fast response time, low cost, small size, high reliability, high sensitivity and responsivity at the operating wavelength, and high SNR [83]. The APD can provide higher sensitivity and gain faster response times. It could also be used in longer UWOC links (tens of metres) and wider bandwidths but at a much higher cost and complex circuits. The noise performance of these two devices is the most significant difference. The main source of noise in PIN photodiodes is thermal noise, while in APD, it is shot noise [79; 84; 85; 86].  
However, the PIN photodiode appears to be a more favourable technology for shorter wavelengths than the APD for the UWOC system [4]. To process and understand the received data, the decision unit in an optical receiver converts the signal into discrete binary values. It compares the sampled voltage to a reference level or threshold (\(D_{th}\)). With the use of the received optical signal, this procedure estimates the underlying BER based on which a decision will be made if the bit is \"0\" or \"1\" for binary \(s(t)\)[87; 88; 89].
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 2 Factors Affecting Microbial spoilage of Meat, Fish and Derived Products  
### Food Structure  
Food that is raw in nature remains protected from the attack of microorganisms due to the specificity of structures such as skin, shell, bran, etc. that are non-decomposable. These structures are attacked by specific species along with suitable enzymes (cellulose, pectinase, protease, etc.). However, meat and fish products are more susceptible to spoilage in their raw form due to suitable conditions for microbial spoilage. Surface contamination usually occurs at earlier phase of raw meat and fish processing. When the protective layer or packaging is removed, food tends to be more vulnerable to spoilage, which also speeds up the spread of microorganisms as soon as the processing phase starts. Meat spoilage is also based on meat moisture, pH, animal age and health, temperature during slaughtering, processing and the packaging method of meat.
## 2 Material and Methods
### Proposed Deep Learning Model for Fault Classification  
In this study, one-dimensional convolutional neural network (1D CNN) architecture, which is suitable for processing sequential data like time series or signals, was employed for fault classification in transmission lines. The 1D CNN based deep learning model architecture proposed in this study is shown in Figure 7, and its description is listed below.  
* Keras, a high-level neural networks application programming interface (API) that runs on top of TensorFlow, is used to define the architecture of the model.
* First Block: Two convolutional layers (Conv1D) are applied successively to the input. Each convolutional layer has 64 filters with a filter size of three and ReLU activation function. The output of the second convolutional layer is passed through a max pooling layer (MaxPooling1D) with a pool size of two and \"same\" padding. This reduces the length of the sequence by half while retaining important features. This block also helps extract features from the input data.
* Second Block: Two convolutional layers are applied to the output of the first block. These layers have the same configuration as the layers in the first block.
* Residual Connection: The output of the second convolutional layer is added element-wise to the output of the first block. This creates a residual connection, allowing the network to learn residual mappings, which can make training deeper networks easier.
* Third Block: Two more convolutional layers are applied to the output of the second block. Again, the layers have  
fed into a dense layer with 128 units and ReLU activation. Finally, the output layer consists of a dense layer for classification tasks, with as many units as the number of classes or outputs in the dataset.
## 4 Results and Discussion
### Cost, Energy, and Throughput Analysis
#### 4.6.2 Reliability  
FDM achieved an 80% success rate, with failures due to nozzle clogs or warping. SLA reached a 90% success rate, but failures were more costly due to resin cleanup. SLA also requires stricter safety protocols for resin handling.
# A Simple and Efficient Local Search Algorithm for the Machine Reassignment Problem
## 4 Our Approach: SMaRT
### General Structure of the Algorithm  
Our approach consists of three main phases (Figure 1). It is important to remark that the algorithm works with feasible solutions in all phases.  
The idea here is first to preprocess the instance data to discard infeasible assignments and then search using fast search approaches to obtain high-quality solutions in reduced execution times. If search approaches become stuck on local optima, the next step is executed. HC1 and HC2 use different movements in order to satisfy the most complex constraints first, then the algorithm used, HC or SA, depends only on the local optima HC finds during its search in the different cases being solved. Eventually, when the large processes shifts move does not become trapped in local optima, final solutions can be obtained only using this step of the algorithm. If during HC1 the algorithm becomes stuck and still has available execution time, it can perform HC2 to continue searching on different neighborhoods. If the algorithm becomes stuck using HC2 and still has available time, it can use the SA strategy that allows escaping from local optima.  
1. Preprocessing of the data: the aim is to pre-compute useful information to reduce the search space.
2. Hill Climbing procedures: Hill Climbing has two steps. The first one, HC1, focuses on overloaded machines. The procedure tries to move the biggest processes from the machines that strongly increase the objective function value. The second step is HC2, which uses as initial solution the feasible solution obtained by HC1. It applies Shift and Swap moves and changes the solution using the first-improvement criteria.
3. Simulated Annealing (SA): This approach uses Shift and Swap moves. This procedure includes a strategy to perform dynamic control of the temperature.
## 3 Results
### Geographically Weighted Regression
The forest land is distributed in the western part of the study area, in which the residuals of some patches in the northeast, southeast corner, and west edge of the forest land are large, whereas the residuals in most other areas are within a normal function fitting interval for four years. The independent variables of the geographic weighted regression of forest land in four years are between \(-\)5 and 0.5, and the NDVI of vegetation coverage index is negatively correlated with groundwater depth. The areas with larger NDVI coefficients are mainly distributed in the smaller patches near the borders in the north and northwest of the woodland. It shows that in these areas, the impact of the tall trees is more obvious in the mountain area on the buried depth of groundwater. Part of the cultivated land in the southern woodland of the mountainous area affects the conservation of groundwater, and part of the groundwater converges to the cultivated land to supplement the water absorbed by the crops. Figure 12 and Table 6 are the geographically weighted regression results of bare ground NDDI and groundwater depth. In 2005, 2009, 2013, and 2017, the bare ground NDDI and groundwater depth R\({}^{2}\) were all above 0.72, and the adjusted R\({}^{2}\) was above 0.68. NDDI and groundwater burial depth have a strong correlation. In accordance with the analysis above, the fitting result of the geographically weighted regression model of the bare ground area is very unstable, and the inter-annual variability is large. The standardized parameters of the middle and eastern bare ground in 2005 and 2017 are relatively large. In 2009 and 2013, the normalized residuals were relatively stable in this area. The fitting of the groundwater depth of the bare ground to NDDI is greatly affected by other factors. The independent variables of bare ground geographic weighted regression in the four years range  
Figure 11: Normalized difference vegetation index (NDVI) geographic weighted regression standardized residuals. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **AICc** & **R\({}^{2}\)** & **R\({}^{2}\) Adjusted** \\ \hline
2005 & \(-\)48.69 & 0.8325 & 0.7889 \\
2009 & \(-\)30.98 & 0.6041 & 0.4918 \\
2013 & \(-\)99.59 & 0.7958 & 0.7387 \\
2017 & \(-\)41.39 & 0.7422 & 0.6698 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Normalized difference vegetation index (NDVI) geographic weighted regression fitting parameters. from \(-2\) to \(6\). The NDDI of the land drought index is positively correlated with the groundwater depth. The fitting coefficient of NDDI is greater than two in most bare land areas, and the impact of land drought on groundwater depth increases with the increase of NDDI.
# A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning
## 4 Discussion
### Class Overlap Challenge  
Network intrusion detection systems face the challenge of providing satisfactory detection results due to class overlap between threat and legitimate data over feature space. This is also one of the causes behind false alarms and false positives observed in ML-based IDS. Another aspect of the class overlap challenge is the temporal shifting of network nodes from being threats to non-threats [41]. Nodes can be malicious or non-malicious at different times due to changes in performance, resource availability, or infections, disinfections, and re-infections. Various authors of the selected reviewed papers proposed different ML optimizations to solve this class overlap challenge. One of the studies introduced a wavelet -based multi-scale Hebbian learning approach to neural networks [42], and the proposed methodology was able to properly differentiate between non-linear and overlapping boundaries.