esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Additive Fibonacci Generators with Improved Characteristics for Cybersecurity Needs
## 4 Methods of AFG and MAFG Statistical Characteristics Research
### Research of AFG Models  
In AFG models, the logic circuit LC is not involved in the generator structure scheme (Figure 1).  
The AFG hardware model operates in accordance with the following algorithm:  
\[A=2  
Numbers \(X\) and \(X(0)\) are defined by the following expressions:  
\[X=p^{5}x_{i}+p^{4}x_{i-1}+p^{3}x_{i-2}+p^{2}x_{i-3}+px_{i-4}+x_{i-5}, \tag{6}\]  
\[X(0)=p^{5}x_{i}(0)+p^{4}x_{i-1}(0)+p^{3}x_{i-2}(0)+p^{2}x_{i-3}(0)+px_{i-4}(0)+x _{i-5}(0), \tag{7}\]  
where: \(x_{i}(0)\), \(x_{i-1}(0)\), \(x_{i-2}(0)\), \(x_{i-3}(0)\), \(x_{i-4}(0)\), \(x_{i-5}(0)\)--initial values of numbers \(x_{i}\), \(x_{i-1}\), \(x_{i-2}\), \(x_{i-3}\), \(x_{i-4}\), \(x_{i-5}\), respectively.  
Figure 3 on a logarithmic scale shows the dependence of the repetition periods of the AFG pseudorandom sequence numbers from the initial values, \(X(0)\).  
Figure 3: Dependencies of repetition periods from \(X(0)\) (for AFG): (a) hardware model: \(p=5\), \(n=3\), \(A=2^{n}-p=3\), \(X(0)=0\div p^{6}-1\); (b) abstract model: \(p=5\), \(X(0)=0\div p^{6}-1\).  
Figure 2: Current values of pseudorandom numbers \(X\) (for AFG): (a) hardware model: \(p=7\), \(n=3\), \(A=2^{n}-p=1\), \(X(0)=1\); (b) abstract model: \(p=7\), \(X(0)=1\).  
The results (Figures 2 and 3) indicate complete identity of hardware and abstract models for forming a pseudorandom numbers sequence. Similar results were obtained for other \(p\) values, in particular for \(p\) values that are primes.
# Water IoT Monitoring System for Aquaponics Health and Fishery Applications
## 7 Results and Discussion  
The development of this IoT-based aquaponic system has a number of future applications in the fishery industry. One of the first tests conducted is the functional testing, which has allowed us to achieve good performance in a local and a networked environment. This testing has been successful and has shown good performance for the communication and the sensing parts.  
As a proof of functioning and performance, the first test of the WaterMon node is the time measurement for every sensor sending information to the farmhouse. Figure 14 shows that the most time-consuming measurement is DO, which takes an average time of 1.5 s for every measurement and sending cycle; the pH and ORP sensors take an average time of 1.1 s approximately; and the EC and RTD sensors take 0.9 s approximately. This fact is  
Figure 12: Farmhouse system schema.  
Figure 13: Graphical interface on RPi4.  
mostly due to the need of some compensations for the measurements. The analog sensors are more fluent in their measurements, with 0.3 s each.  
In order to evaluate the whole monitoring cycle in the WaterMon node, a test with the node just collecting cycles of parameters has been conducted. Figure 15 shows a plot of the time used for each cycle, which takes, on average, 7430 ms (with a standard deviation of 1.6 ms).  
Figure 16 shows the timing used to submit the information to the farmhouse from the values collected at the node. For a number of monitoring cycle submissions, the average value is 462.7 ms (with a standard deviation of 56.9 ms).  
Finally, an energetic consumption test has been conducted to assess the effectiveness of the energy-aware implementation for this system. Figure 17 shows the power consumption measurement during 100 s of the WaterMon sensor. As it can be seen, the average power reduction for the situation with and without the IC 74 HC237 is 0.76 W in 100 s, when we use the IC. This reduction allows us to extend the lifetime of the power supply for this purpose. For instance, if the power supply is batteries, a commercial power-bank with 20,000 mAh will give the WaterMon sensor without the IC a lifetime of 41.2 h, while if we consider the use of the IC, the lifetime is extended up to 59.8 h, an increase of the power-supply time of 68.9%.  
Figure 14: Time Measurements of WaterMon Sensors.  
Figure 15: Monitoring Cycle Time.
## 3 Suggesting Rolling Force on a Single Disc Cutter for the Design of Cutterhead Drive
### Analysis of Relationships between Thrust Force and Torque  
In this study, the analysis of torque induced by thrust force of shield TBM was carried out according to the results obtained from full-scale tests performed with four different UCS cases in artificial rock mass. As mentioned in Table 3, the analysis of four different rotational speeds of cutterhead affecting the relationships between thrust force and torque was carried out as well as the levels of thrust force and UCS of rock mass affecting the torque were also analyzed. According to the results obtained from the 100 MPa UCS case (Test# 4), as shown in Figure 8, a basic analysis method is introduced here for the development of the required relationships. The interrelations between thrust force and torque were analyzed at the rotational speeds (RPM) of 1.5, 4.5, 6.0, and 7.5. The relationships showed similar trends for all rotational speeds except in the case of 1.5 RPM. The reason behind the difference observed at 1.5 RPM was same as discussed previously in Section 3.1. In all the test results, a linear increase in the torque was observed with the increase in the levels of thrust force. The R\({}^{2}\) values were observed in the range of 0.84 and 0.93. Between all the RPM cases, an average torque value of 185 kN\(\cdot\)m was observed to develop at the application of 1000 kN thrust force, whereas, it largely increased up to average 464 kN\(\cdot\)m when the thrust force reached the level of 5000 kN.  
Figure 7: Relationships between penetration depth and torque, (**a**) At 1.5 RPM; (**b**) At 4.5 RPM; (**c**) At 6.0 RPM; (**d**) At 7.5 RPM.  
Figure 8: _Cont._  
Based on the test results, the regression lines obtained at 7.5 RPM level according to UCS of rock mass are shown in Figure 9. On the application of constant thrust force, greater penetration depth was observed at low UCS which resulted in higher torque development. It is thus deduced that the relationships between thrust force and torque are affected by UCS more significantly as compared to the rotational speed of the cutterhead.
## 3 Design and Training Methods for DQN Feature Constrained Control
### Design of the Reward Function  
After defining the action space \(A\) for DQN to control moving features, the environment computes the corresponding reward \(r\) based on the feature's state \(S\) in the offline camera FOV environment. The computation of reward \(r\) is given by the following equation:  
\[r=\left\{\begin{array}{rl}-R,&if\;s_{(x,y)}\in\;\text{outside the field of view}\\ -R/2,&if\;s_{(x,y)}\in\;\text{the forbidden area}\\ 0,&if\;s_{(x,y)}\in\;\text{the dangerous area}\\ R,&if\;s_{(x,y)}\in\;\text{VS mission area}\end{array}\right. \tag{6}\]  
where \(R\) is the maximum reward value, which is a positive constant. As can be seen from Figure 1 and Equation (6), the designed reward function aims to encourage the features to  
Figure 1: Discrete camera field-of-view environment.  
perform the correct actions, thereby ensuring that the features are maintained within the VS mission area (the desired region of the camera's FOV).  
After designing the state set \(S\), action set \(A\), and reward function \(r\), the Q-network needs to be trained to implement the feature constraint control based on DQN. The specific training methods and results will be provided in the following chapter.
# Applications for Microwave Kinetic Induction Detectors in Advanced Instrumentation
## 4 MKIDs in Particle Physics
### MKIDs for Neutrino Physics Experiments  
Particle physics experiments require an ever-improving level of technology to detect exceedingly rare and ever more difficult to find events. In particular neutrino physics is now a trending topic among many research groups. The possibility of using cryogenic detectors for neutrino physics experiments has been proposed and investigated since the 1980s [113]. The first project that investigated the possibility of deploying MKIDs for neutrino experiments was MARE [114], which unfortunately was cancelled ahead of time after a disappointing phase, MARE-1, which used TES detectors and Si thermistors [115]. Since then, MKIDs have significantly improved their performances and are under test for experiments such as CALDER [116], CUORE [117] with its planned upgrade CUPID [118], and HOLMES [119]. The proposed working scheme employs MKIDs as photodetectors in addition to neutron transmutation doped (NTD) Ge thermistors in order to suppress the background signal produced by spurious excitations and possible \(\alpha\)-decay of the radioactive medium.
## 2 Research Methodology
### Feature Extraction
#### 2.2.1 Demodulation and Resampling  
The sensor signal was multiplied by its carrier frequency (for both channels respectively) and low-pass filtered (2 kHz) to demodulate the signal and extract the base band. The signal was further resampled from 215.52 kHz to 35.92 kHz.
## 2 Materials and Methods
### Genotyping  
DNA was extracted with the Genomic DNA Mini Kit (Central European Biosystems). Briefly, genomic DNA was isolated from peripheral blood lymphocytes. This procedure consisted of five steps: red blood cell lysis, cell lysis, DNA binding, washing, and elution.  
First, we centrifuged the samples for 10 min at 800\(\times\)\(g\). Then, the buff-colored layer was pipetted, being careful not to disturb the other blood components, and up to 200 \(\upmu\)L was transferred into a microcentrifuge tube. We discarded supernatant after 10 min incubation with RBC Lysis Buffer and centrifugation. We repeated the previous step and resuspended the pellet by adding 200 \(\upmu\)L RBC Lysis Buffer. For cell lysis step, we added 250 \(\upmu\)L of GB Buffer to the tube and incubated it at 70 \({}^{\circ}\)C for 30 min. For DNA binding step, we added 250 \(\upmu\)L absolute ethanol to the sample and transferred all of the mixture to the GD Column. GD column was then placed in a new Collection Tube after centrifugation. We performed the washing sequentially by adding W1 Buffer, centrifuging, adding Wash Buffer, and centrifuging again. Finally, we added 100 \(\upmu\)L preheated Elution Buffer to GD Column and incubated it at 37 \({}^{\circ}\)C for 10 min. Samples containing DNA were collected by centrifugation at 16,000\(\times\)\(g\) for one minute.  
All oligonucleotide primers and probes were obtained from Roche (Mannheim, Germany). We selected three functional SNPs: DAB2 (A/T) rs11959928 (minor allele frequency (MAF): A = 0.33), DACH1 (C/A) rs626277 (MAF: A = 0.354), and PRKAG2 (A/G) rs7805747 (MAF: A = 0.182).  
PCR was performed on a LightCycler 480 (Roche) in 20 \(\upmu\)L of reaction volume. PCR reactions contained 10 \(\upmu\)L 2\(\times\) master mix including ResolLight high-resolution melting dye (Roche), 175 nM primers (100 nM in case of PRKAG2) (Table 1), 4 mM MgCl2, and 1 \(\upmu\)L (\(\approx\)20 ng/\(\upmu\)L) of genomic DNA. All amplifications were initiated with a 5 min hold at 95 \({}^{\circ}\)C, followed by 40 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 60 \({}^{\circ}\)C for 30 s, and an extension at 72 \({}^{\circ}\)C for 30 s. The melting program included denaturing at 95 \({}^{\circ}\)C for 1 min, annealing at 40 \({}^{\circ}\)C for 1 min, and subsequent melting that included a continuous fluorescent reading of fluorescence from 60 \({}^{\circ}\)C to 90 \({}^{\circ}\)C, and fluorescence values were acquired at 0.025 \({}^{\circ}\)C intervals. Melting curves were evaluated using Light Cycler 480 software release 1.5.0 SP4 (Roche). Different normalization regions of 71.8-79.5, 79.2-84.6, and 84.1-88.4 were used for analysis of amplicons of DACH1, PRKAG2, and DAB2, respectively. In the case of DAB2, to differentiate between homozygous variants, a known genotype was added to the unknown sample before PCR amplification [16]. Distilled water was used as a blank in each experiment.
# Compact Fuzzy Systems Based on Boolean Relations
## 2 Framework
### Formula Simplification  
For expressions obtained in normal forms applying the distributive property together with the absorption property in \(\mathcal{K}\), the number of literals and operators can be reduced. As an example, Table 2 shows a possible codification for the \(Y\) function.  
The respective equation for \(Y\) from Table 2 is:  
\[Y_{1}=(A_{1}\wedge A_{2})\vee(A_{1}\wedge\bar{A}_{1}\wedge A_{2}\wedge\bar{A} _{2})\vee(A_{1}\wedge\bar{A}_{1}\wedge A_{2})\vee(A_{1}\wedge A_{2}\wedge \bar{A}_{2}) \tag{15}\]  
Using the distributive and absorption properties:  
\[Y_{1}=(A_{1}\wedge A_{2})\wedge[1\vee(\bar{A}_{1}\wedge\bar{A}_{2})\vee\bar{A} _{1}\vee\bar{A}_{2}] \tag{16}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \(A_{1}\) & \(A_{2}\) & \(f\) & **DNF** \\ \hline
0 & 0 & 0 & - \\
0 & \(u\) & \(u\) & \(A_{1}\wedge(A_{2}\wedge\bar{A}_{2})\) \\
0 & 1 & 1 & \(\bar{A}_{1}\wedge A_{2}\) \\ \(u\) & 0 & \(u\) & \((A_{1}\wedge\bar{A}_{1})\wedge\bar{A}_{2}\) \\ \(u\) & \(u\) & \(u\) & \((A_{1}\wedge\bar{A}_{1})\wedge(A_{2}\wedge\bar{A}_{2})\) \\ \(u\) & 1 & 1 & \(1\wedge A_{2}\) \\
1 & 0 & 1 & \(A_{1}\wedge\bar{A}_{2}\) \\
1 & \(u\) & 1 & \(A_{1}\wedge\bar{A}_{2}\) \\
1 & 1 & 1 & \(A_{1}\wedge A_{2}\) \\ \hline \end{tabular}
\end{table}
Table 1: Example of terms use for elaborate DNF in \(\mathcal{K}\).  
\begin{table}
\begin{tabular}{c c c} \hline \(A_{1}\) & \(A_{2}\) & \(\gamma\) \\ \hline
1 & 1 & 1 \\ \(u\) & \(u\) & \(u\) \\
1 & \(u\) & \(u\) \\ \(u\) & 0 & 0 \\ \hline \end{tabular}
\end{table}
Table 2: Codification example for a Kleene table.  
Then the simplified equation for \(Y\) is:  
\[Y_{1}=A_{1}\wedge A_{2} \tag{17}\]
# The Evolution of Volatile Memory Forensics
## 1 Introduction  
The acquisition and analysis of volatile memory to identify cyber threats is currently an active area of research in cybersecurity. The importance that computer systems play in modern life continues to grow, and with it, the creativity and capabilities of those who wish to gain access to them unlawfully. Businesses are currently experiencing 50% more cyberattacks per week compared to 2020 [1]. In particular, fileless malware, which only utilizes legitimate programs to infect a computer and leaves no footprint in the file system, continues to increase in prevalence, and is often capable of evading antivirus software products. In fact, fileless malware is believed to be 10 times more successful than other malware types at evading detection [2]. Because of this, we remain in the dark about the full extent of the damage fileless malware causes. However, one security company saw fileless malware attacks increase by 900% during 2020 [3]. As the use of fileless malware continues to skyrocket, memory forensics will remain a central pillar of forensic methods moving forwards. Even in the absence of fileless malware, the data stored in a system's random access memory (RAM) is of high forensic value. Volatile memory contains fragments of encrypted files' contents, lists of running processes, and lists of network connections. Due to the rising use of full disk encryption and other protective measures, it is much more difficult, and often impossible, to extract such information from the file system. When passing through RAM, however, this obscured information can be extracted in an unencrypted, readily available format. Because of this usefulness, a lot of research hasbeen conducted on the best ways to extract volatile memory, including ways to address the challenges of page-smearing, slow performance, storage costs, and subversion from malware itself.
# Preddiagnosis of Heart Failure (HF) Using Deep Learning and the Korotkoff Sound
## 5 Discussion
We can see that the accuracy of the proposed algorithm and the HS-based HF algorithm is basically maintained at the same level, which means our efforts have yielded largely positive results. The current results give us more confidence in continuing to work in this area. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Data Set** & **Number of Subjects** & **Method** & **Performance** \\ \hline [PERSON] et al. (2015) & \multirow{2}{*}{Collected by HS acquisition system} & \multirow{2}{*}{88 healthy volunteers and 64} & \multirow{2}{*}{LS-SVM} & Acc 95.39\% \\ \([\)67\(]\) & & & CHF patients & \\ \hline [PERSON] et al. (2016) & \multirow{2}{*}{Physionet databases} & 2575 normal signals and 665 abnormal signals & \multirow{2}{*}{AdaBoost and CNN} & Acc 86.0\% \\ \([\)34\(]\) & & & Sp 94.2\% \\ \hline [PERSON] et al. & \multirow{2}{*}{\begin{tabular}{c} six (A to F) PhysioNet Challenge \\ datasets \& measured HS by \\ digital stethoscope \\ \end{tabular} } & 3153 signals from PhysioNet Challenge and 110 & \multirow{2}{*}{\begin{tabular}{c} Machine-Learning and \\ end-to-end DeepLearning} & Acc 92.9\% \\  & & & \begin{tabular}{c} Se 82.3\% \\ Sp 96.2\% \\ \end{tabular} \\ \hline [PERSON] et al. (2021) & Acquired from the First Affiliated & 41 healthy volunteers and 30 & \multirow{2}{*}{\begin{tabular}{c} VGG-16, VGG-19, \\ ResNet-16, ResNet-50, \\ dysfunction patients \\ \end{tabular} } & Acc 98.7\% \\ \([\)68\(]\) & Hospital of Chongqing Medical & & & \begin{tabular}{c} ResNet-16, ResNet-50, \\ DesNet-121, and \\ AlexNet \\ \end{tabular} & \multirow{2}{*}{\begin{tabular}{c} Sec 98.6\% \\ Sp 98.8\% \\ \end{tabular} } \\ \hline [PERSON] et al. (2022) & \multirow{2}{*}{\begin{tabular}{c} Dataset from First Affiliated \\ Hospital and the University-Town \\ Hospital of Chongqing Medical \\ University \\ \end{tabular} } & \multirow{2}{*}{51 healthy volunteers and 224} & Acc 82\% \\ \([\)69\(]\) & & & & \begin{tabular}{c} Se 82.1\% \\ Sp 95.5\% \\ \end{tabular} \\ \hline Our method & \multirow{2}{*}{\begin{tabular}{c} Dataset of measured KS from the \\ Fourth People’s Hospital of \\ Zhejiang University \\ \end{tabular} } & 116 healthy subjects and 249 & \multirow{2}{*}{\
\begin{tabular}{c} DeepLearning(DL) \\ \end{tabular} } & Acc 96.0\% \\  & & &
\begin{tabular}{c} Se 97.5\% \\ Sp 93.8\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of HF classification algorithms based on acoustics. Figure 9: Comparison of the classification accuracy of four neural networks. However, it should be noted that the work in this paper is an exploratory study, and we also recognize that the KS-based HF prediagnosis algorithm is still in its infancy, with many flaws in the construction of the KS database, the KS generation mechanism, and the association mechanism between KS and HF, which will be the focus of our future research.
# Condition Rating Prediction Using an Interactive Deterioration Model Development Package
## 5 Effect of Using Weighted LASSO
### Effective Number of Explanatory Variables  
The number of bridges varies according to the states, affecting the level of classification scheme used to improve the prediction accuracy. Although LASSO generally suggests a large number of explanatory variables to optimize the penalty regression model, this is unnecessary and impossible since the number of bridges and quantity of inspection data are often insufficient to effectively group.  
Figure 5: Classification tree for Wyoming bridges using explanatory variables from eight years (2006-2013) of inspection data for deck elements.  
Indeed, a high level of classification does not guarantee an improvement in the prediction result and occasionally shows negative effects. Figure 7 illustrates the decrease in normalized prediction error for the deck elements from randomly selected states. The classification scheme was applied until the bridges could be grouped using the required number of bridges and inspection data. For example, the sixth explanatory variable for Alaska could not create a bridge group and thus the classification ended at the fifth level. In many cases, the minimum error was observed when the number of explanatory variables was less than the maximum, and thus excessive classification should accordingly be avoided.  
Figure 6: Normalized prediction error comparison for (**a**) deck, (**b**) superstructure, and (**c**) substructure elements when explanatory variables were determined by using single- and eight-year inspection data.  
To suggest a reasonable classification tree level, we compared the prediction error for five years using inspection data from various states. The optimal number of explanatory variables was determined when the improvement in the normalized prediction error was less than 1E-4 with further increase in the level of classification. Figure 8 shows the optimal number of explanatory variables depending on the number of bridges using all inspection data from all states. The trendline was developed using a linear function and is shown in blue, and the 95% confidence interval is plotted in red. In general, the optimal number of explanatory variables increased as the number of considered bridges increased. Regardless of the number of bridges, four explanatory variables should be considered as a minimum, and no state would require more than eight explanatory variables.  
Figure 8: Number of optimal explanatory variables for all U.S. states to minimize the normalized prediction error.  
Figure 7: Normalized prediction error depending on the number of explanatory variables for the deck elements in randomly selected states.
## 5 Prompt Structure Ablation Study
### One-Shot Examples in Prompt  
Adding a single example query (one shot) to the prompt demonstrated a transparent performance boost in most models, as illustrated in Figure 5. The relative cost increase for one-shot examples remained modest, ranging from 5.9% for _Claude-3.5_ to 12.7% for _Mixtral-8x22b_. Thus, including one-shot examples is a cost-effective strategy to improve accuracy.  
The most significant performance gains were observed in high-capacity models such as _Llama3.3-70b_ (75%) and _Qwen-2.5-72b_ (85%), indicating that these models are particularly adept at taking advantage of contextual information provided by the example. Similarly, _GPT-4o-mini_ achieved a notable 69.9% increase in precision with only a 6.7% cost increase, showcasing its efficiency in adapting to single-shot contexts. In contrast, lower-capacity models such as _Mixtral-8x22b_ showed limited performance improvements (12.2%) despite incurring the highest cost increase (12.7%).  
**Recommendation:** The use of one-shot examples is strongly recommended for most tasks, especially when deploying high-capacity models like _Llama3.3-70b_ and _Qwen-2.5-72b_, which demonstrate significant accuracy improvements with relatively low-cost overhead. For cost-sensitive scenarios or using less capable models like _Mixtral-8x22b_, omitting the one-shot example may provide better cost-performance trade-offs.
# Assessment and Review of Heavy Metals Pollution in Sediments of the Mediterranean Sea
## 3 Results and Discussion  
### Mediterranean Sea: A Good Sink for the Sequestration of Pollutants  
The seabed of areas of the Mediterranean Sea studied is mainly composed of sand (<80%) and small amounts of fine sediments, silt, or clay <20%. Gravel-sized sediments there are not abundant and, like clays, are generally deposited in the zones closest to the littoral coasts [2]. These factors, as well as bathymetry, intensify the impact of pollutants compared to more open seas. Wastewater discharge points in heavily populated tourist areas along the coast significantly contribute to the pollution in the Mediterranean Sea. Many discharge points are located between 10 and 25 m deep, extending up to 1 km offshore, leading to a substantial volume of poorly treated or untreated wastewater being discharged due to limited treatment plant capacity (Figure 1). The evaluation and review of marine sediment contamination was based on the regional background level inferred from uncontaminated sands and was compared with global contamination indices [2; 36; 37; 38]. Statistical methods and spatial distribution analysis were applied to each area.