esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review on Anaerobic Digestion of Lignocellulosic Wastes: Pretreatments and Operational Conditions
## 4 AD Stability or Inhibition
### Effect of Ammonia
#### 4.1.2 Mitigating Ammonia Inhibitory Effects
Importantly, other different approaches have been studied to control ammonia inhibition during the AD process, for example anammox, which is fundamentally based on a reaction driven by a specialized group of planctomycete-like bacteria that convert (oxidize) ammonium to nitrogen gas, where nitrite is used as the electron acceptor under anoxic conditions [122]. Other approaches are struvie precipitation [123] and the use of zeolite as support media for the immobilization of microorganisms in different high-rate reactor configurations. Furthermore, zeolite has also been used as an ion exchanger for the removal of ammonium in anaerobic digestion due to the presence of Na\({}^{+}\), Ca\({}^{2}\)\({}^{+}\), and Mg\({}^{2}\)\({}^{+}\) cations in its crystalline structure. Also, carbon fiber textiles (CFT) were used as supporting materials for better retention of microorganisms. Nevertheless, these approaches were reported to be expensive at large scale [124; 125].
# Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique
## 4 Results and Analysis  
### The Influence of Polarization Characterization  
At the same energy point, four probes were used to measure the inconsistency of the four probes. The maximum deviation of the four measurements was divided by two times the average of the four measurements to obtain the error value; the experimental data of the four probes are shown in Table 1. The table is plotted as shown in Figure 7. The energy obtained by the four probes is the same under the same environmental conditions, and the error is controlled at about 2%.
# An Ultra-Wideband Frequency Selective Rasorber with Low Infrared Emissivity
## 2 Analysis, Design and Experimental Verification  
Firstly, it is essential to clarify the technical approach adopted by this paper, as illustrated in Figure 1.  
Figure 1: Framework of this paper.  
This paper establishes an analysis method suitable for FSR in Section 2.1 in order to gain a more intuitive understanding of its working principle. Subsequently, based on conclusions obtained from this analysis, RS and FSS are designed in Section 2.2. Subsequently, the two components are linked in a cascade fashion, and the design of an ultra-wideband FSR is achieved through adjustments to the interlayer spacing and resistance.  
Building upon this foundation while considering wavelength differences between radar bands and infrared bands, an IRSL with a band-pass in the microwave band and band-stop in the infrared band was designed in Section 2.3, and then loaded onto the FSR. Through simulation and theoretical formulas, the absorption/transmission of IRSL-FSR within radar bands as well as emissivity within infrared bands are calculated.  
Finally, in Section 2.4, the aforementioned FSR along with IRSL-FSR were processed and tested to verify the accuracy and effectiveness of the designs.
# Brazing Ti-48 Al-2 Nb-2 Cr Alloys with Cu-Based Amorphous Alloy Filler
## 2 Materials and Methods
### Brazing  
The size of the TiAl sample for brazing was 4 mm \(\times\) 4 mm \(\times\) 4 mm and 10 mm \(\times\) 10 mm \(\times\) 4 mm. Before joining, the surfaces of the samples were ground by 1000 grit silicon carbide paper and cleaned ultrasonically for 600 s. The Cu-based filler was placed between the Ti-48 Al-2 Nb-2 Cr base materials to form a sandwich type, as shown in Figure 1a. The brazing experiments were done by JVLF211 vacuum brazing equipment. The accuracy of temperature controlling was \(\pm\)1 K. The brazing temperature was from 1183 K to 1273 K, and the brazing time was set as 600 s. The brazing process was conducted in a vacuum of 1 \(\times\) 10\({}^{-3}\) Pa. At the beginning, the brazing sample was heated up to 573 K at a rate of 0.167 K/s and held steady for 1200 s to make the organic glue volatilize and keep the brazing surfaces clean. Then, the temperature was increased to the target brazing temperature at a heating rate of 0.167 K/s, and kept constant for 600 s. Finally, the brazed samples were cooled down at a rate of 0.083 K/s to 573 K, and then cooled in the furnace.  
After joining, the microstructure and composition of the joints were examined by scanning electron microscopy (SEM, SU8010, Hitachi, Tokyo, Japan) with an energy-dispersive X-ray spectrometer (EDS) and X-ray diffraction. The structural analysis of the fractured surface of the joint was tested by XRD. Shear testing was performed by a universal testing machine Instron 5500 to evaluate the strength of the brazed joint. A schematic diagram of the shear test is shown in Figure 1b. The average shear strength of each brazing condition was determined from three brazed samples.
# An Adaptive Security Framework for Internet of Things Networks Leveraging SDN and Machine Learning
## 6 Performance Analysis
### Detection Accuracy

Table 10: Ensemble classifier detection accuracy. misclassified as anomalous. HTTP headers and payloads exhibited wide variability, so distinguishing attacks requires deep packet inspection. The models can be refined by training on more HTTP samples to improve their accuracy. For Voice over IP call traffic, the ensemble reached a 0.92 true-positive rate at finding attacks with a low 0.018 false-positive rate. The real-time nature and audio encoding patterns of VoIP packets differ from HTTP. The ensemble models adapted reasonably well, as evidenced by the high attack detection and minimal false alarms. Further training on VoIP protocols like SIP and RTP could enhance precision. In the FTP file transfer scenario, the ensemble classifier performed very well, correctly identifying 0.96 of the attacks with just 0.012 false positives. The structured nature of the FTP sessions improved the modeling compared to HTTP exchanges. Attacks manifested as deviations from expected sequences of commands, responses, and data connections. More samples of normal FTP behaviors would enable tighter false-positive control. With customized IoT traffic patterns, the ensemble achieved a reliable 0.93 true-positive attack detection but a slightly higher 0.024 false-positive rate. The unfamiliar application-layer protocols and encodings presented a challenge. Providing more IoT protocol training data would improve accuracy by capturing intrinsic IoT behaviors. The highest 0.98 true-positive rate occurred when detecting the DDoS flooding attacks, which exhibit obvious traffic surges. The dramatic volume spikes are clear indicators of DDoS, allowing precise detection with minimal 0.007 false positives. However, lower-volume DDoS attempts could be harder to distinguish. For reconnaissance probes, the irregular scanning patterns facilitated a 0.91 true-positive detection with a reasonable 0.015 for false alarms. Distinguishing reconnaissance from general access attempts remains challenging, though. Expanding the feature set with additional connection and fingerprinting metrics would assist recognition. Man-in-the-middle attacks were detected with 0.95 accuracy and 0.011 false positives by looking for session anomalies. More samples of failed MITM attacks could better train the classifier on such patterns. Specific crypto attack indicators may also improve detection. Finally, malware infections were identified with a 0.97 true-positive rate and 0.008 false alarms by analyzing abnormal sequences. Signature-based anti-virus scans would complement the ensemble classifier by detecting known malware strains. Integrating host data, like suspicious process behaviors and file system changes, could also enhance malware detection.
# Improving Ship Maneuvering Safety with Augmented Virtuality Navigation Information Displays
## 2 Methodology
### Detailed Assumptions of Simulation Study (Simulation Scenarios)
#### 2.3.1 Scenario 1. Container Ship  
The simulation test scenario assumed that experts would perform a series of simulation passages consisting of safe berthing of the container ship Green Breeze, port side alongside Hutnikow Quay in the port of Swinoujicic (Figure 3).  
The maximum contour of the ship presented in the Pilot_max system overlapped with the collision contour presented in the Pilot_precise system. Therefore, simulation passages were conducted for one system, assumed to be more accurate, i.e., Pilot_precise.  
The detailed assumptions of Scenario 1 were as follows:  
* Objective to be achieved: The lowest resultant value of the speed at the moment of contact of the ship's hull with the fender while achieving the shortest possible maneuver time.
* Main ship parameters: \(\circ\): LOA = 121.4 m, breadth = 20.8 m, draught = 6.88 m; \(\circ\): Displacement = 11,250 t; \(\circ\): Block coefficient = 0.709; \(\circ\): Main engine: 1 \(\times\) diesel, total power = 4000 kW; \(\circ\): Max speed = 14.2 kn; \(\circ\): Type of propulsion: 1 \(\times\) fpp, clockwise; \(\circ\): Number of rudders: 1; \(\circ\): Max rudder angle = 35.0 deg.; \(\circ\): Number of thrusters: 1;  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Vessel Type/Name** & \multicolumn{6}{c}{**The Matrix of Vessel—Simulated Maneuver Configurations**} \\ \hline Container ship/ & 1 & 3 & - & - & - & 8 & 3 & 5 \\ m/v “Green Breeze” & 1 & 2 & 3 & 9 & - & 2 & 1 & 2 \\ \hline Sea ferry/ & 1 & 1 & - & - & 7 & 4 & 3 & 4 \\ m/f “Skania”; & 1 & 1 & - & - & - & 6 & 5 & 7 \\ \hline Cruise ship/ & 1 & 1 & - & - & - & 5 & 6 & 6 \\ m/v “Lirica” & 1 & 2 & - & - & - & 5 & 6 & 6 \\ \hline Towed train/ & & & & & & & & \\ “Wagenborg Barge 4”, & 1 & 2 & - & - & - & 5 & 6 & 6 \\ m/t “Java Victor” & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The matrix of vessel—simulated maneuver configurations.  
Figure 3: The situational-navigational plan of Scenario 1 for container ship passages. Red—initial position, black—destination.  
\(\circ\)  Bow thruster power = 360 kW.
* Navigational-maneuvering conditions: \(\circ\)  The initial position of the ship: parallel to berth, distance--two ship's breadths from Hutnikow Quay; \(\circ\)  ship's final position: Hutnikow Quay; \(\circ\)  initial SOG: longitudinal vw = 0.0 kn, transverse to port side vp = 0.5 kn; \(\circ\)  heading: HDG = 200\({}^{\circ}\).
* The criteria used for assessment: \(\circ\)  Primary: value of the resultant speed of the ship determined at the moment of contact with a fender; \(\circ\)  Secondary: efficiency of maneuvering--time length of maneuvers.
* The moment of measuring significant parameters: contact of the hull of the ship with a fender. The types of displays used, the name of the series and the number of trials in a given series of the Scenario 1 are presented in the Table 2.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 3 Methods
### Methods of Parameter Estimation
#### 3.2.4 Weibull Distribution (W2)  
For estimation with MOM, parameter \(\alpha\) has the following approximate forms depending on the coefficient of variation \(\mathrm{C}_{\mathrm{v}}\geq 0.1\):  
\[\alpha=\exp\left(\begin{array}{c}0.000006199-1.000228322\cdot\ln(\mathrm{C} _{\mathrm{v}})+0.144564211\cdot\ln(\mathrm{C}_{\mathrm{v}})^{2}+\\ 0.055543308\cdot\ln(\mathrm{C}_{\mathrm{v}})^{3}-0.016265526\cdot\ln(\mathrm{C }_{\mathrm{v}})^{4}-0.013691787\cdot\ln(\mathrm{C}_{\mathrm{v}})^{5}-\\ 0.000586501\cdot\ln(\mathrm{C}_{\mathrm{v}})^{6}+0.001239068\cdot\ln(\mathrm{C }_{\mathrm{v}})^{7}+0.000240162\cdot\ln(\mathrm{C}_{\mathrm{v}})^{8}\end{array}\right) \tag{73}\]  
\[\beta=\frac{\mu}{\Gamma\left(1+\frac{1}{\alpha}\right)} \tag{74}\]  
For estimation with the L-moments, parameters have the following expressions [11; 16; 17]:  
\[\alpha=\frac{-\ln(2)}{\ln\left(1-\frac{\mathrm{L}_{2}}{\mathrm{L}_{1}}\right)} \tag{75}\]  
\[\beta=\frac{\mathrm{L}_{1}}{\Gamma\left(1+\frac{1}{\alpha}\right)} \tag{76}\]
## 4 Discussion
* A reference vessel is an alternative characterized by desirable features, meaning that it does not meet any of the risk criteria. * Including a reference vessel in the analysis facilitates the selection of units based on their potential threat level. * In most real-world cases, the majority of vessels will score lowly, making suspicious units easily distinguishable in comparison. * The MCDA method is a supporting tool; its results should be treated as decision support rather than definitive conclusions. In the third stage, vessels identified as high-risk are subjected to enhanced monitoring. Early detection of anomalies enables a faster response by relevant authorities and more effective protection of subsea infrastructure. The proposed method is based on mathematical models and IT technologies; however, final verification must be performed by a human expert, such as a naval officer or analyst. In the future, artificial intelligence--utilizing machine learning--may play a greater role in supporting decision-making processes. The proposed concept is developed with this direction in mind. Currently, there are no civilian tracking systems that analyze anomalies, so it was not possible to compare our results with those of other systems. Anomaly detection systems are still in the research phase, and their results have been presented, among others, in [27,28]. This is consistent with the authors' approach, as reflected in criteria C1 and C2. However, the authors' method is broader, incorporating additional criteria C3 through C8. As a result, this study is more comprehensive and better adapted to the nature of terrorist threats.
## 3 Research Findings
### Oxidant and Antioxidant Findings  
The statistical analysis applied to the malondialdehydedehyde (MDA), catalase (CAT) and superoxide dismutase (SOD) activities of the research groups are shown in Table 6. Although the MDA averages of the research groups were highest in the electric field group,  
Figure 4: (**a**) Group I (K) Comet Analysis. Yellow arrow, Category 0, cell with no damage. Blue arrow, 1 st category, less damaged cell; (**b**) Group II (EF) Comet Analysis. Blue arrow, Category 1, slightly damaged cell. Green arrow, Category 2, heavily damaged cell; (**c**) Group III (EF + juglone) Comet Analysis. Yellow arrow, Category 0, cell with no damage. Blue arrow, Category 1, slightly damaged cell.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Groups**} & \multicolumn{3}{c}{**Comet Score**} \\ \cline{2-4}  & **Average**\(\pm\)**SS** & **Ortanca (Min-Max)** & \(p\)**Value** \\ \hline Group I (K) & 43.12 \(\pm\) 11.60 & 43 (23–61) & \\ \hline Group II (EF) & 72.25 \(\pm\) 17.95 & 76 (35–91) & \\ \hline Group III (Juglone) & 33.62 \(\pm\) 8.61 & 31 (23–51) & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical evaluation of comet analysis.  
the difference was not statistically significant. In the statistical evaluation applied to the data obtained in terms of CAT values, the difference between the groups was not significant (\(p>0.05\)). Although the CAT activity of the juglone applied group was higher than the electric field applied group, it was determined that the applied dose did not significantly increase the CAT activity. In the statistical evaluation applied to the SOD activities of the research groups, the difference between the groups was found to be significant (\(p<0.001\)). This significant difference is due to the lower mean SOD activity in the juglone group than in the other groups.
# Adsorption of Heavy Metals from Low Concentration Solutions onto Dried _Chlamydomonas reinhardtii_
## 4 Discussion
### Adsorption to Living and Dried Algae: Cd(II) Case  
In this experiment, living microalgae were treated with Cd for 4 days. After 2 and 4 days, the remaining concentration of Cd in the solution was investigated and the Cd adsorption to the microalga was calculated. After the experiment, i.e., after 2 or 4 days, the algae were freeze-dried and further analysed. Up to two days, Cd adsorption appears to be almost the same for both dried microalga and living microalga. However, by continuing the experiment for another two days, a decrease in adsorption is visible for dried algae, while an increase occurs in living microalgae. This difference highlights the reversibility of the adsorption process to dried microorganisms and the stability in living microalgae, probably due to bioaccumulation inside the alga itself. By bioaccumulation, the living microalgae take up the metals' ions and store them inside the cells. In living cells, two mechanisms of uptake of metal ions occur: first biosorption, i.e., the adsorption of metals ions onto the cell surface due to negatively charged functional groups and secondly, bioaccumulation, by which the metals are transported into and stored inside the cells.  
FT-IR spectra show the same trend both in living and dried sorbent, with a slightly higher intensity in two regions: 1244 cm\({}^{-1}\) and 1000 cm\({}^{-1}\). This can be interpreted as a  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metal Ion**} & \multicolumn{2}{c}{**Single-Metal**} & \multicolumn{2}{c}{**Multi-Metals**} \\ \cline{2-5}  & **Species** & **Ionic Strength** & **Species** & **Ionic Strength** \\ \hline \multirow{2}{*}{Pb(II)} & Pb\({}^{2+}\) & \multirow{2}{*}{7.23} & Pb\({}^{2+}\) & \\  & PbOH\({}^{+}\) & & PbOH\({}^{+}\) & \\ \hline \multirow{2}{*}{Cr(III)} & CrOH\({}^{2+}\) & \multirow{2}{*}{20.22} & CrOH\({}^{2+}\) & \multirow{2}{*}{32.75} \\  & Cr\({}_{2}\)O\({}_{3}\) & & Cr\({}_{2}\)O\({}_{3}\) & \\ \hline Cd(II) & Cd\({}^{2+}\) & 5.34 & Cd\({}^{2+}\) & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main ionic species by Hydra-Medusa calculation and corresponding ionic strength (singletions reported for comparison).  
lower interaction on the surface of living microalgae, due to bioaccumulation inside the cell, contrary to what happens to dried microalgal surface.  
SEM images of living adsorbent show smaller, oval-shaped, and less-grouped cells.
## 3 Results and Discussion
### Feature Selection  
The Laplacian score of each feature was calculated and sorted to decide the importance of different features. The absolute minimum number of features that can be selected is one. However, this is not practical and any noise in that feature might cause the algorithm to not  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature Number** & **Feature Level** & **Description** \\ \hline
1 & basic & number of peaks \\
2 & basic & total peak power \\
3 & basic & mean peak power \\
4 & basic & root mean square (RMS) \\
5 & basic & total power \\
6 & combined & total power/RMS \\
7 & combined & sum of power \(>\) RMS/RMS \\
8 & combined & number of data points \(>\) RMS \\
9 & combined & total peak power/RMS \\
10 & combined & mean peak power/RMS \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted features from SAWP.  
function. At the same time, including more features also requires more data points. In this study, the data available are limited so a minimum set of features is preferable. Therefore, two features were decided to be selected. The top two features were feature 1, the number of peaks, and feature 5, the total power. These two features were be utilised as input to the unsupervised clustering algorithms later.
# Scenario-Based Emergency Material Scheduling Using V2X Communications
## SEMS Method
### Evaluate the SEMS Scenario Coefficient  
The SEMS scenario coefficient indicates the urgency of disaster demand, which mainly depends on the damage of disaster depots and the urgency of emergency materials. Here, scenario indicates some kind of emergency material that is demanded by a disaster depot. The urgency of emergency materials is determined by their role in rescue activity and time urgency in a disaster depot. Thus, we get the evaluation indicators from these two aspects. The indicators of damage situation, possibility of secondary disruptions and economy can show the urgency degree of disaster depots, and material effect and timeliness can reflect the urgency of emergency materials. The Analytic Hierarchy Process (AHP) method is used to calculate scenario coefficients. The AHP method is detailed in [28]. Figure 1 presents the architecture for defining the objective and indicators for the AHP. The top layer is the objectives layer, which evaluates the scenario coefficient. The middle layer is the rule layer, which evaluates the urgency degree of disaster depots and materials. The bottom layer is the index layer, which has five evaluation factors. The AHP method first establishes the judgment matrices through expert questionnaires, which consist of the judgment matrix O of the rule layer to the objective layer, and the judgment matrices o1 and o2 of the index layer to the rule layer. The values of the matrices were identified by expert subjective scoring according to [PERSON]'s 1-9 scale. We designed a questionnaire,and then collected the data from the questionnaire. These matrices are shown as \(O=\left[\begin{array}{cc}1&2\\ \frac{1}{2}&1\end{array}\right]\), \(o_{1}=\left[\begin{array}{cc}1&3\\ \frac{1}{3}&1&\frac{1}{4}\\ \frac{1}{5}&4&1\end{array}\right]\), and \(o_{2}=\left[\begin{array}{cc}1&3\\ \frac{1}{3}&1\end{array}\right]\). Table 1 presents the maximum eigenvalues, consistency indexes and consistency ratios of the three judgment matrices.  
As the value of consistency ratio (C. R.) is less than 0.1, the consistency of the judgment matrices is acceptable. Table 2 presents the weight values of the weight vector.
## 5 Conclusions  
Many critical infrastructures are monitored with SCADA systems that process data obtained with a heterogeneous sensor network. SCADA sensor networks are usually composed by many embedded systems with severe resource limitations, joined to the possibility to access physically to the nodes, what make them highly vulnerable to cyberwarfare/cyberterrorism attacks. Cryptography appears as clearly insufficient to maintain data confidentiality and integrity in the network.  
We have proposed a holistic solution that assumes this node vulnerability to address security issues in sensor networks, by exploiting redundancy at different levels.  
The proposed architecture is based on a reputation system that supports decisions at different levels. It is a trust-based framework where trust data only flows from the sensors to the servers and reputation only from the servers to the sensors. This reputation is also affected by independent agents, using unsupervised learning algorithms, with broader view of the global network. We have demonstrated the effectiveness of this approach with the implementation of anomaly detectors based on self-organizing maps and immune systems.  
We have compared the behavior in presence of common attacks with more traditional reputation algorithms, resulting in similar detection time, similar isolation capacity, faster confinement, and highly reduced attack impact even for low redundancy in the sensor network. More importantly, this is done with no previous knowledge about the attacks being performed, and it is more resistant to attack variations, as it is shown in the badmouthing-sybil combined attack.  
Figure 6: Reputation evolution for a sybil attack after a badmouthing attack.  
The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization and scalability, and it is also well-suited in scenarios with low redundancy. Based on these results we claim that our approach provides a practical solution for developing more secure SCADA applications.