esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Organic Culture Medium for Autotrophic Production of _Chlorella vulgaris_ Biomass
## 3 Results and Discussion
### Evaluation of Organic C. vulgaris Biomass

Table 3: Growth performance parameters of _Chlorella vulgaris_ grown with the optimized organic medium (Bioscape and EcoMix4, 1:1.2, m/m) in 10 m\({}^{3}\) tubular photobioreactors for 13 days. The results are shown for each experimental replicate (photobioreactor) as well as the mean and standard deviation. Regarding carbohydrates, organically grown _C. vulgaris_ displayed a higher content, 4.4 g 100 g\({}^{-1}\), as compared to that detected in the biomass produced using synthetic medium, 1.5 g 100 g\({}^{-1}\). This difference can be a sign of stress in the cultures grown with the organic medium, which can be related to the unfavourable N:P ratio. Similarly, a slightly higher content of dietary fibres was detected in the biomass grown using the organic (16.9 g 100 g\({}^{-1}\)) than in the synthetic (15.5 g 100 g\({}^{-1}\)) culture media. Dietary fibres are present in plant cells and can help increase the feeling of satiety, regulate blood glucose levels, and solve obstipation problems [27; 28]. Organically grown biomass and non-organically grown biomass had similar contents of ash and moisture, namely 9.2-10.8 and 2.8-3.0 g 100 g\({}^{-1}\), respectively. Low moisture values are important to increase the shelf life of the product once it is kept as a microalgal powder [29]. The pigment content of organically and non-organically grown biomass revealed to be very similar (10.7-12.6 mg g\({}^{-1}\)). The biomass of _C. vulgaris_ is a rich source of chlorophyll, and the values here reported are in range of the values previously reported by other authors, 8.58-15.4 mg g\({}^{-1}\)[30; 31]. Photosynthetic pigments have well described antioxidant properties and can be used as natural colourants [32]. For questions of food safety, it is important to ensure that there are no harmful concentrations of heavy metals such as lead, cadmium, tin, and arsenic present in food. The analysis for these contaminants is regulated by the legislative Commission Regulation (EC) No 629/2008 of 2 July 2008, setting maximum levels for certain contaminants in foodstuffs. Algae are also included since they tend to accumulate cadmium naturally, so for dried products derived from seaweeds the limit established is 3 mg kg\({}^{-1}\)[33]. Organically grown biomass displayed residual mean values of tin (2.5 mg kg\({}^{-1}\)) and lead (0.09 mg kg\({}^{-1}\)), both below the values allowed by the European legislation. The non-organically grown biomass presented residual contents of lead (0.12 mg kg\({}^{-1}\)) and arsenic (0.21 mg kg\({}^{-1}\)), which are also in accordance with the European legislation, and so there was no need to eliminate these compounds from the original medium. All remaining heavy metals were below the limits of quantification of the methods used.
## 3 Test Results and Theoretical Analysis
### Analysis of the Deformation Properties during Post-Peak Loading and Unloading
After the third unloading confining pressure, the radial strain increases to \(3.26\times 10^{-2}\), which increases by 11.04%; the radial strain of the sample decreases to \(3.26\times 10^{-2}\) after the fourth loading confining pressure, which decreases by 4.82%. After the fourth unloading confining pressure, the radial strain increases to \(3.52\times 10^{-2}\), which increases by 8.01%. For the coal sample with a water content of 0.65%, the radial strain of the sample decreases from \(0.95\times 10^{-2}\) to \(0.63\times 10^{-2}\) after the first loading confining pressure, which decreases by 33.93%. After the first unloading confining pressure, the radial strain increases to \(1.20\times 10^{-2}\), which increases by 26.43%; the radial strain of the sample decreases to \(0.83\times 10^{-2}\) after the second loading confining pressure, which decreases by 30.35%. After the second unloading confining pressure, the radial strain increases to \(1.46\times 10^{-2}\), which increases by 21.74%. The radial strain of the sample decreases to \(1.07\times 10^{-2}\) after the third loading confining pressure, which decreases by 26.24%. After the third unloading confining pressure, the radial strain increases to \(1.70\times 10^{-2}\), which increases by 17.03%; The radial strain of the sample decreases to \(1.32\times 10^{-2}\) after the fourth loading confining pressure, which decreases by 22.65%. After the fourth unloading confining pressure, the radial strain increases to \(2.09\times 10^{-2}\), which increases by 22.83%. In summary, with the increase cyclic loading and unloading confining pressure times, the radial strain decrement and increment of raw coal sample after loading and unloading confining pressure gradually decrease.
## 3 Case Study  
A case study was conducted on a concrete dam located in China with a height of 63 m, a crest length of 196.62 m, a left bank width of 8 m, a right bank width of 6 m, and a bottom width of 46.5 m. The main structures include a barrage, a water diversion system, a powerhouse, and a switching station. The dam was put into storage in 1993 and passed completion acceptance in 1995. To better ensure the safe operation of the dam, in August 2000, renovation of the automated system was conducted for automatic monitoring of the deformation. A lead line was placed at the top of the dam to monitor the horizontal displacement of the dam, with a total of seven points, numbered E01-E07. In addition, there is an inverted plumb line at each end of the lead wire to monitor the horizontal displacement, numbered IP1 and IP2. The data were acquired once a day, and the layout scheme is shown in Figure 3.  
In this study, the radial displacement at the monitoring point E04 in the overflow section of the dam was selected as the test case (negative toward upstream and positive toward downstream). The E04 dataset was created between August 1998 and January 2021, and the first 60% of the data was selected for model training, and the rest of the data was used as the test set to verify the prediction performance of the model. Mean absolute error (MAE) and root mean square error (RMSE) are used to evaluate the predictive accuracy. See the following formula for details:  
\[MAE=\frac{1}{a}{\sum\limits_{i=1}^{a}}{\left|{{y_{i}}-{\widetilde{{y_{i}}}}} \right|} \tag{30}\]  
\[MSE=\frac{1}{a}{\sum\limits_{i=1}^{a}}{\left({{y_{i}}-{\widetilde{{y_{i}}}}} \right)^{2}} \tag{31}\]  
where \(a\) is the number of samples; \(y_{i}\) is the measured value of the dam's displacement; and \({\widetilde{{y_{i}}}}\) is the predicted value of the dam's displacement.
## 4 Conclusions and Future Work  
In the present work, the influence of the process parameters in the laser cutting process was analyzed. For this purpose, four scenarios with variations in the emissivity of the material, thickness and assist gas were considered and each experiment was monitored simultaneously in the VIS and IR spectrum. The main conclusions reached after the analysis of the obtained results were:  
* The measured intensity of the VIS signal was associated with the vapor plume formation during the cutting process, whereas the IR signal was related to the temperatures reached.
* For the VIS and the IR monitoring signals, it was found that they were independent of the process parameters as long as the cutting quality was good. In contrast, the mean level increased as the cutting quality became worse. Therefore, their values can be useful for monitoring defective cuts online.
* This investigation revealed failures in defect detection for the thinner aluminum plate in the VIS spectrum, attributed to the low signal value generated by the characteristics of the case. In contrast, stainless steel displayed signal saturation with good cut quality, usually typical of cut losses.
* Gain system adjustment was revealed as the key factor for success in the detection of cutting defects. Too low a gain prevent detection of defects, whereas gains that were too high saturated the monitoring signal, generating false results.  
To sum up, this paper shows the potential of photodiode monitoring for industrial applications as an easy solution to work with adaptive control systems. However, it evidences the lack of the actual commercial systems. The presented results are considered to be the key to possible development of an innovative monitoring system that will address the problems of the current systems. At present our research group is working on a new monitoring system that includes monitoring with photodiodes, an infrared camera, and a visible camera. The three systems will be installed coaxially to the laser cutting head and the information will be complementary, allowing the monitoring of all the laser cutting process scenarios that a service metal cutting center deals with. This development will constitute a scientific milestone for cutting monitoring systems, being the first coaxial system to integrate the three techniques in an industrial environment.  
Conceptualization, S.M.G. and J.F.; methodology, S.M.G.; investigation, J.R. and S.M.G.; validation, J.R. and S.M.G.; formal analysis, S.M.G., J.A., and J.F.; resources, J.F.; writing--original draft preparation, S.M.G.; writing--review and editing, S.M.G., J.I.A., and J.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basque Government in the Elkartek project \"Digicut: Estudio fundamental de procesos de corte y definicion de las bases para su digitalizacion\" (KK-2019/00071).  
The authors declare no conflict of interest.
## 2 Application of Biosensors in Biogas Monitoring
### Determination of Different Products in AD
They can enhance the transformation of biomass to chemical intermediates, specialty chemicals, fuels and products, and can be utilized as an important instrument for increasing the efficiency of bioenergy. Nanomaterials can interact with some species of algal biomass and active sludge. For satisfying the energy demands of the world, algal biomass has been widely predicted to be the future energy source. Macroalgae, such as seaweed and water hyacinth, interact substantially with nano-molecules. The concentration of CH\({}_{4}\) and biogas production is enhanced by directing modified nano-particles to the optimization of anaerobic digestion processes. Thus, the optimization of the process of energy production to a larger or commercial scale is limited only by the gain of a low dosage of nano-molecules [46]. The synthesis of nano-structured nanoparticles is carried out mostly by wet chemical methods, which include surfactant mediation, electro-deposition, emulsion, precipitation, etc., in this study. The rate of methane production is raised by using nanoparticles in an anaerobic digester. Due to their toxicity, it may not be possible to add particles directly. Nanoparticles are added to overcome this problem. Conventional biogas production can convert only 30 to 40% of the organic matter, which is  
Figure 3: Various applications of nanotechnology. not very effective in comparison with energy sources. Thus, for the improvement of biogas production, there is a desperate need for modern approaches. Iron strongly enhances anaerobic digestion, but its function in a standard configuration is debatable in an anaerobic closed reactor. However, improved nano-molecules are able to slowly dissolve biodegradable nano-particles and avoid various problems such as bacteriostaticity by facilitating the production of ions in the reaction medium. [47]  
This work is grounded on the evaluation of physical parameters for finding a path to increase the yield rate of biogas. Better outcomes are shown by nanoparticles in comparison with their bulk form. There are few metal oxide particles in the literature that enhance the quality and yield of biogas [48]. On the nano scale, great potential is exhibited by nanoparticles as catalysts, absorbents for wastewater treatment, coatings, ion exchangers, gas sensors, flocculants, pigments, magnetic recording devices, toners, magnetic information storage devices, xerography inks, magnetic resonance imaging (MRI), medicine and bio-separation. The latest studies of nanoparticles suggest an increase of 40 to 200% in biogas production by using nano-molecules in the digestive [49]. The 100 ppm of 7 nm nanoparticle was used, resulting in an increase of 180% in biogas production. The deductive reasoning and applications of nano-structured oxides/hydroxides have been reported by [PERSON] and [PERSON] (2010) [50].
## 3 Measurement Setup  
In order to model the future 5G wireless propagation channel, the co- and cross-polarization antennas setup was employed in this work. The 38 GHz radio signal was transmitted and measured at various Tx-Rx separation distances to determine the parameters for large-scale path loss. Data were gathered at the park beside the Registrar office located in UTM Kuala Lumpur campus. This environment emulated the older environment of the hostel that contained the old-fashion building with only two floors, while the park was situated in the middle, as depicted in the captured image illustrated in Figure 1a. In this scenario, the Tx and Rx were fixed at 18\({}^{\circ}\) half-power beam width (HPBW) in the elevation and azimuth planes via a narrowband continuous wave (CW) signal generator to generate 30 GHz mmWave frequency signal. Two varied sets of data, up to 50 m, for the LOS and NLOS cases were captured, respectively, as illustrated in Figure 1a,b. The Tx was set on a 5-m antenna mast on the ground floor (identical location for the access point of the outdoor environment), while the Rx was strapped on a column at a height of approximately 1.7 m. Measurements of varied combinations for a total of 30 Tx-Rx pairs were examined in both cases of the NLOS and LOS scenarios. The calculation complexity and simulation time of shooting and bouncing rays (SBR) method was proportional to the number of devices and objects presented in an environment. Because of the higher reflection and diffraction of mmWave signals in an urban environment, the use of highly directional horn antenna was preferred to achieve an acceptable output at the user end [64]. Therefore, these measurements were performed using the directional narrow-beam Rx antenna with a 3D transmitter-receiver separation distance varying between 20 and 50 m. In order to create a LOS link between the transmitter and the receiver for the LOS scenario, both antennas were boresight aligned with high directivity to each other and no obstacle between them. Moreover, for the NLOS, both the transmitter and the receiver were separated by buildings, such that no LOS link was established and both Tx and Rx antennas were directed towards each other at the corner of the building (yellow and red arrows show the direction of antennas, as shown in Figure 1c, to point out the complete measurement scenario from the top view).
## 3 Results
### Physicochemical Analysis
#### 3.1.2 Instrumental Color Parameters  
The color parameter (\(L^{*}\), \(a^{*}\), and \(b^{*}\)) values are given in Table 1. The analysis of the \(L^{*}\) parameter showed that there were neither significant differences between treatments nor over time within the same sample. According to the luminosity results (parameter \(L^{*}\)), the samples containing the common lambsquarter (MTS2) and oregano (MTS3) powders had \(L^{*}\) values similar to those of the control sample (MTC) from days 1 to 15, while the samples containing the mandarin peel powder (MTS1) showed lower luminosities with a fluctuating trend over time. However, at 30 days, decreases in \(L^{*}\) values were observed in all the experimental samples compared to that of the control sample, which exhibited higher luminosity. Our results align with those of [PERSON] et al. [47], who investigated the addition of jabuticaba (_Mycriaria cauliflora_) extract to fresh sausages at a much higher concentration (4%) compared to that used in our study (0.1%). Similar to our observations, they reported decreases in \(L^{*}\) values after 15 days of storage, from 65.6 at day 0 to 62.5 at day 28. The table below (Table 1) also presents the results for the redness parameter (\(a^{*}\)), revealing differences between the three experimental samples and the control sample throughout the storage period. Specifically, the MTS1 and MTS3 samples exhibited a fluctuating trend, while MTS2 showed a consistent linear increase, similar to that of the control sample. It was also observed that the \(a^{*}\) value of MTS2 (11.04) was significantly (\(p<0.01\)) higher than those of the other two experimental samples MTS1 and MTS3 (9.89 and 8.22, respectively) at 30 days of storage, highlighting that the integration of the common lambsquarter powder appeared to enhance the redness of the mortadella. Regarding the \(b^{*}\) parameter (yellow content), as shown in Table 1, there were notable differences between the treatments. Higher \(b^{*}\) values compared to those of the control sample were recorded in MTS1 on day 1 of storage, followed by a decrease throughout the storage period. In contrast, MTS2 and MTS3 exhibited increasing trends in \(b^{*}\) values over time, similar to that in the \(b^{*}\) values of the control sample (MTC). The results obtained corroborate those obtained in the study by [PERSON] et al. [48], who found that \(b^{*}\) values
# A Systematic Literature Review on Penetration Testing in Networks: Future Research Directions
## 4 Results and Discussion
### Tools to Detect Open Ports  
According to the studies analyzed, the common scanning tools used to detect vulnerable ports are Nmap, Metaspoilt, Wireshark, shadow, Nessus, AirCrack, Brup suit, BeEF, SPARTA, etc. As shown in Figure 4, the most commonly suggested tool is Nmap.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Publication** & **Research Methodology** & **Mitigation Technique** \\ \hline [PERSON] et al. [30] & 2020 & Qualitative & Proposed using Raspberry Pi 3b+ and improved security by using VAPT \\ \hline [PERSON] et al. [31] & 2020 & Qualitative & Proposed comprehensive Nmap detection rules. \\ \hline [PERSON] al. [32] & 2019 & Qualitative & Improved security by conducting three types of attacks to analyze the performance of IDS \\ \hline [PERSON] et al. [9] & 2019 & Qualitative & Proposed system for detecting, fixing and reporting security vulnerabilities in networks to prevent attacks. \\ \hline [PERSON] and [PERSON] [33] & 2020 & Qualitative & Suggested to use sequential neural networks to detect port scans. \\ \hline [PERSON], [PERSON] and [PERSON] [34] & 2019 & Qualitative & Suggested to use intrusion detection and prevention system snort for various Dos and port scan attacks. \\ \hline [PERSON] et al. [35] & 2018 & Qualitative & Proposed using a new port scanning system IPS for SDN to prevent port scanning attacks such as DoS attack. \\ \hline [PERSON] et al. [36] & 2019 & Qualitative & Proposed the first analysis pipeline covering open port detection, diagnosis, and security assessment. \\ \hline [PERSON]. [PERSON] et al. [37] & 2018 & Qualitative & Used IDS and modusfw firewall to defend against DoS attack in SCADA system. \\ \hline [PERSON] et al. [25] & 2021 & Qualitative & Proposed model to investigate different against DDoS attacks to determine the level of protection. They found the proposed model was accurate. \\ \hline [PERSON] al. [39] & 2020 & Qualitative & Proposed automated Post-Breach Penetration Testing through Reinforcement Learning \\ \hline [PERSON], [PERSON] et al. [40] & 2020 & Qualitative & Proposed to use an automated penetration testing framework based on Deep Reinforcement Learning (DQN) technique \\ \hline [PERSON], [PERSON] et al. [41] & 2020 & Qualitative & Comparison of multiple machine learning algorithms to select the best algorithm for penetration testing. \\ \hline [PERSON], [PERSON] al. [42] & 2022 & Qualitative & Proposed (IAPTF) with [PERSON] decision to determine the best efficient option in penetration testing. \\ \hline [PERSON] al. [43] & 2021 & Qualitative &
\begin{tabular}{c} Performed penetration testing by modeling SQL injection. \\ Modeled using [PERSON] decision processes and agents with reinforcement learning. \\ \end{tabular} \\ \hline [PERSON] et al. [44] & 2022 & Qualitative & They proposed a software that includes a toolbox to save time and find the vulnerabilities easily and quickly. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the mitigation techniques for protecting open ports against vulnerabilities.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### Pre-Processing  
**Optical Data Pre-processing**  
We downloaded all Sentinel-2 data from the Copernicus Data Hub ([[https://scihub.copernicus.eu](https://scihub.copernicus.eu)]([https://scihub.copernicus.eu](https://scihub.copernicus.eu))), as Level-1C products. Pre-processing of Sentinel-2 data is performed with the Sentinel-2 Reflectance Data Processing module implemented in the JOANNEUM RESEARCH in-house software IMPACT. First, all of the downloaded Level-1C S-2 images are atmospherically corrected to bottom-of-atmosphere (BoA) values using the integrated Sen2 Cor processor version 2.5.5 [66]. The aerosol type is set to \"rural\", whichbetter fits both test sites then the alternative options \"maritime\" or \"auto\". The atmosphere type and ozone content are set to \"auto\", leading to an automatic determination by the algorithm [66]. Subsequently, we resample the 20 m bands to 10 m spatial resolution and then stack all 10 m and 20 m bands to a 10-band output image. Clouds and cloud shadows must be masked out prior to classification to avoid later misclassifications. The Sen2 Cor processor also generates a scene classification map based on threshold operations while using different single spectral bands, band ratios, and indices [66]. We use this scene classification to derive final cloud and cloud shadow masks. Areas classified as clouds with either \"medium\" or \"high\" probability and areas classified as cloud shadows are extracted from the scene classification and written to a binary mask. This mask is slightly altered by morphological operations (erode, expand), cloud holes are filled, and the masked areas are then removed from the pre-processed S-2 imagery. We also perform a topographic correction that is based  
Figure 2: Test site Gabon.  
on an implementation of the Minnaert correction in IMPACT software [67] using the Shuttle Radar Topography Mission (SRTM) model at 30 m spatial resolution as digital elevation model.  
Landsat 8 data are available as surface reflectance products (Level-2 data) through the USGS EarthExplorer ([[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov))). An accurate cloud mask is also available for each image. It is computed with the CFMask algorithm, which is the C version of the Function of Mask (FMask) algorithm that was developed by [68]. Itperforms an automated object-based cloud and cloud shadow detection in Landsat images [69; 70]. Cloud and cloud shadows are removed from the imagery and the Landsat 8 data is resampled to 10 m cell size to be consistent with S-2 spatial resolution.  
When images from different sensors are used in a combined time series, geometric consistency is a requisite, since geometric errors almost certainly lead to misclassifications [12]. To register the L8 scenes to the S-2 scenes, we use a fully automated multi-modal image matching algorithm that is based on the concept of mutual information maximization [71].