esa-sceva/satcom-chunk-collection huggingface dataset:
# DNA-PRESENT: An Improved Security and Low-Latency, Lightweight Cryptographic Solution for IoT
## 6 Comparison Analysis  
The validation of encryption algorithms typically hinges upon three critical factors: security, cost, and performance. In resource-constrained environments, where lightweight cryptographic solutions are essential, it is imperative that any proposed design not only enhances security but also maintains efficiency with minimal resource overhead. DNA-PRESENT, the proposed variant of the PRESENT block cipher, has undergone rigorous evaluation against six major cryptographic criteria. These evaluations were conducted in direct comparison with the base algorithm, PRESENT, and the results unequivocally demonstrate that DNA-PRESENT achieves superior performance in key security metrics, including confusion and diffusion, with a reduced number of rounds.  
In terms of cryptographic strength, DNA-PRESENT outperforms PRESENT in several critical areas--notably, the avalanche effect, key sensitivity, plaintext sensitivity, bit error rate, and resilience against differential trails. These improvements underscore the robustness of the proposed scheme in enhancing the security of lightweight cryptographic solutions without compromising efficiency.  
While it is acknowledged that the implementation cost of DNA-PRESENT slightly increased due to the integration of additional operations, the design remains well within the lightweight cryptography domain. The gate count was carefully managed to strike an optimal balance between enhanced security and minimal hardware overhead, ensuring that DNA-PRESENT adheres to the stringent requirements of lightweight encryption algorithms. Importantly, the execution time of DNA-PRESENT was also reduced when compared to the original PRESENT cipher. This reduction in execution time, despite the introduction of new operations, can be attributed to the decrease in the number of rounds required, resulting in faster encryption and decryption processes.  
Overall, DNA-PRESENT represents a significant advancement over PRESENT, offering a refined balance of security and efficiency. The trade-offs made in terms of implementation cost are offset by substantial improvements in both cryptographic strength and operational speed, making it a highly suitable candidate for secure communication in resource-limited environments.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 2 Materials and Methods
### Time-Frequency Peak Filtering (TFPF)  
#### 2.2.1. TFPF Principle  
The TFPF algorithm can extract the effective signals from the original data to achieve the purpose of suppressing random noise. The original data containing random noise is modulated by a frequency modulation factor \(\mu\) to obtain analytic signals. By calculating the Pseudo Wigner-Ville Distribution (PWVD) spectrum and its peak value of the analytic signals, the instantaneous frequency estimation of the analytic signal is obtained. In this way, it can achieve the denoising purpose [19]. The specific steps are as follows:  
(1) The original data can be expressed as the sum of effective signals and random noise:  
\[S(t)=x(t)+r(t) \tag{7}\]  
where \(x(t)\) is the effective signals and \(r(t)\) is random noise.  
(2) Modulate the signal with a frequency modulation factor \(\mu\):  
\[z(t)=e^{\beta 2\pi\mu\sum_{i=0}^{t}S_{i}} \tag{8}\]  
(3) Calculate the PWVD spectrum of the analytic signal:  
\[W_{z}(t,f)=\int_{-\infty}^{+\infty}Z_{S}\Big{(}t+\frac{\tau}{2}\Big{)}Z_{S}^{ \ast}\Big{(}t-\frac{\tau}{2}\Big{)}e^{\beta 2\pi f\tau}d\tau \tag{9}\]  
(4) Calculate the peak value of the PWVD spectrum as an instantaneous frequency estimation of the analytic signal:\[\hat{x}=\hat{f}(t)=\frac{arg\max_{x}[W_{z}(t,f)]}{\mu} \tag{10}\]
## 2 Related Work
### Heuristic Clustering Algorithm  
The heuristic clustering algorithm is a combination of a clustering algorithm and an intelligent algorithm based on biological principles. Due to the complexity of the problem and the high computational costs, people pay more attention to adding heuristic optimization algorithms to clustering algorithms to achieve more efficient results. PSO-clustering (PSO-C) is the earliest research that applies intelligent optimization algorithms to cluster routing protocols. It selects CH nodes by minimizing the distance between CHS and other nodes in the cluster [25]. In 2011, the adaptive probabilistic prediction clustering protocol, called LEACH-GA, was introduced, effectively extending the lifetime of the LEACH protocol [26]. In 2012, an artificial bee colony algorithm was used to cluster nodes in WSN, and energy was used as an indicator to design the fitness function [27]. In 2012, an evolutionary algorithm is used in routing clustering protocol (ERP), and a fitness function based on separation degree and cohesion degree is proposed [28]. In 2016, the PSO-HSA was improved based on PSO-C, and CH nodes were selected by combining the harmony search algorithm and PSO algorithm, which balanced the local constraints of the two algorithms and extended the life of the network [29]. In 2016, the biogeography optimization-based energy-efficient clustering protocol (BEECP) used the binary version of the biogeography optimization algorithm for clustering and optimized it with discrete coding, making the routing protocol more efficient to extend the life cycle [30]. In 2018, a protocol merged the GA algorithm with the K-means clustering algorithm (KGA) for cluster head selection in network [31]. In 2022, parallel fish migration optimization with compact technology based on the memory principle (PCMFO-Memory) proposed the idea of memory reduction, saving the best set of CH nodes in each round and applying it to the next round, which improved the efficiency of the clustering algorithm [32].
# Type Hierarchy Enhanced Event Detection without Triggers
## 3 Methodology
### Type Hierarchy Concept Module
#### 3.4.1 Hierarchy Concept Graphs  
As several studies have shown, a simple way to represent an event type is to use its event type name directly [10; 38]. However, this approach means limited semantics information, and the names often contain ambiguities, which may introduce errors in the later event detection task. Inspired by previous methods [9; 39], we use type hierarchy concept graphs to enrich the semantics of each event type.  
Figure 3 illustrates that we exploit the external knowledge graph Probase to build hierarchy concept graphs. Probase is a large-scale concept knowledge graph proposed by Microsoft, containing 5,401,933 concepts, 12,551,613 instances and 87,603,947 IgA triples [40]. Figure 4 shows partial examples in the Probase knowledge graph. By using this KG, concepts can be mapped to different semantic concepts. Each event type, such as Arrest-Jail, is represented with a text, including a shortlist of superordinate concepts and a shortlist of subordinate concepts, such as \"legal problem\" and \"rape suspect\", which select from the Probase through IsA relation. The reason why we use superordinate concepts and subordinate concepts is that they can extend the concept of event types or instantiate the concept of event types. Finally, we use \(sup_{t}=\{sup_{t1},sup_{t2}\cdots sup_{tn}\}\) (In our experiments, we set \(n=20\)) to denote the set of superordinate concepts of event type \(t\), and similarly, use \(sub_{t}=\{sub_{t1},sub_{t2}\cdots sub_{tn}\}\) to indicate the set of subordinate concepts.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 3 Preliminaries
### FFT Frequency Estimation Algorithm  
The received signal with noise after the timing synchronization can be described as  
\[r(n)=Ae^{j2\pi fnT_{s}}+w(n), \tag{6}\]  
where \(T_{s}\) is the sampling period, \(f\) is the frequency offset, and \(w(n)\) is the sample of AWGN.  
The FFT frequency estimation algorithm is used to estimate the carrier frequency \(f\) by using the FFT operation on the received signal \(r(n)\). We perform the \(N_{\text{F}}\)-point FFT operation on the received signal \(r(n)\) to obtain \(Y(n)=FFT(r(n))\) and then perform an \(N_{\text{F}}/2\)-point spectrum shift on \(Y(n)\) to obtain \(Y_{shift}(n)=FFTshift[Y(n)]\). Then we can obtain the corresponding frequency point of the maximum value of \(Y_{shift}(n)\) as  
\[k_{\text{max}}=\left\{k\middle|\text{max}\middle[Y_{shift}(k)\right],0\leq k \leq N_{\text{F}}-1\right\}, \tag{7}\]  
which is the relative offset position of the maximum due to the frequency offset.  
Then the absolute offset position of the maximum due to frequency offset is  
\[k_{\text{CFO}}=k_{\text{max}}-N_{\text{F}}/2 \tag{8}\]  
and the estimated value of the frequency offset can be obtained as  
\[\hat{f}_{\text{FFT}}=\frac{k_{\text{CFO}}}{N_{\text{F}}}\cdot f_{s}, \tag{9}\]  
where \(f_{s}\) is the sampling frequency and \(f_{s}=1/T_{s}\).
# Rethinking the Operation Pattern for Anomaly Detection in Industrial Cyber-Physical Systems
## 6 Experiments and Results
### Evaluation Results
#### 6.3.1 Overall Performance.  
As shown in Table 3, the proposed model is efficient in detecting selected forwarding, replay, and unauthorised operation/device attacks because these attacks often break the operation sequence pattern. Specifically, the forwarding and replay attacks drop or add request or response messages, and thus break the sequence pattern. Unauthorised operations and unauthorised slave device attacks will trigger the unknown symbol abnormality at the second defence line. This is because the symbol \(s\) is a combination of the slave device address and operation type \((Add_{slv},FC,Reg_{start},Reg_{ctl})\); the unauthorised slave devicesand unauthorised operations attacks will affect \(Addr_{slv}\), \(FC\), \(Reg_{start}\) or \(Reg_{cnt}\), respectively, and so these attacks produce a new symbol. For unauthorised master device attacks, the model will fail to retrieve the DFA model according to \(IP_{mst}\) and therefore raise anomalies. The model achieves over 99% precision and 100% F1-Score when detecting replay and unauthorised operation/device attacks. For DoS attacks, the proposed method achieves 99% recall because only severe DoS attacks can affect the operation pattern anomalies. The eavesdropping and tampering attacks, which increase the execution time, can be detected with 0.99% precision and 0.99% F1-Score. This evaluation result demonstrates that the proposed model is efficient in detecting ICPS attacks.  
In tampering attack scenarios, attackers are able to tamper with any byte. The tampering position (e.g., function code, data) affects the detection performance. Their impact is summarised as follows.  
* Suppose the tampered packets violate Modbus/TCP protocol. In this case, the first line can detect them as malformed request/response packets at the first defence line.
* If attackers tamper with \(T1\) or \(U1\), packets will fail in the reassembly phase. Consequently, these packets are classified as missing responses or unmatched responses at the first defence line.
* The modifications to \(FC\), \(Reg_{start}\) or \(Reg_{cnt}\) affect the function type \(FN\). They will violate the operation sequence pattern and thus be detected at the second defence line.
* Suppose attackers only tamper with response register data (e.g., temperature value), without affecting other fields. In this case, detecting them using the first two defence lines is difficult. However, tampering attacks, usually using MITM, increase the operation interval or response time. Therefore, the third or fourth defence lines are able to detect this case.
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 4 Results
### Structural Model of the Relationship Between Factors and the Perceived Practicality of VR
Due to the number and complexity of paths, standardized coefficients (\(\beta\)) are not shown in the diagram but are reported in the accompanying text. The diagram in Figure 4 visually illustrates the structural model that captures these complex interrelations across all variables and project stages. The model fit was acceptable: \(\chi^{2}(21)=8.13\), \(p=0.52\); CFI = 0.97; TLI = 0.96; RMSEA = 0.07 (90% CI [0.00, 0.18]); SRMR = 0.09. These results suggest that the structural model fits the data well. Education level was a significant negative predictor during the development stage (b = \(-1.14\), SE = 0.37, z = \(-3.11\), \(p=0.002\); \(\beta=-0.46\)), indicating that **respondents with higher education levels tended to perceive VR as less practical in development tasks**. A marginally significant negative effect **was also found** in the training stage (b = \(-0.49\)  
Figure 4: Structural model of the relationship between independent variables and the perceived practicality of VR in each stage of the project lifecycle. SE = 0.29, z = \(-\)1.70, \(p\) = 0.089; \(\beta\) = \(-\)0.27), suggesting that this trend may extend beyond development and warrants further attention. Familiarity with VR was positively associated with perceived practicality in multiple stages. It was significant in the design and surveys stage (b = 0.63, SE = 0.27, z = 2.35, \(p\) = 0.019; \(\beta\) = 0.42), the development stage (b = 0.46, SE = 0.21, z = 2.21, \(p\) = 0.027; \(\beta\) = 0.27), and the testing stage (b = 0.81, SE = 0.29, z = 2.82, \(p\) = 0.005; \(\beta\) = 0.54), suggesting that **greater familiarity enhances perceptions of VR usefulness in these contexts**. Willingness to adopt VR was another strong positive predictor. It showed significant associations in the design and surveys stage (b = 0.44, SE = 0.16, z = 2.80, \(p\) = 0.005; \(\beta\) = 0.40) and the development stage (b = 0.68, SE = 0.18, z = 3.75, \(p\) < 0.001; \(\beta\) = 0.53). Age (b values ranging from \(-\)0.19 to 0.20) and engineering experience (b values ranging from \(-\)0.14 to 0.11) were not significant in any stage. Similarly, tenure, domain size, and organizational encouragement of innovation showed no statistically significant effects on the perceived practicality of VR across the lifecycle stages. These results highlight that education level, familiarity with VR, and willingness to adopt are the most influential factors shaping perceptions of VR's practicality, with effects varying by project lifecycle stage. Notably, higher education predicted reduced practicality during development (and marginally also during training), while openness and experience with VR predicted increased practicality, particularly during the design, development, and testing stages.
## 4 Conclusions  
1. The groundwater vulnerability assessment in the Vistula Valley was performed for three variants: base variant A--average depth to groundwater from the period of 1999-2013, hydrogeological drought variant B (lowest long-term depth--2003), and flood risk variant C (greatest long-term depth--2011). In variant A, the moderately high and medium vulnerability classes occupy the greatest area. During hydrogeological drought, the largest area is occupied by the medium vulnerability class. During the potential flood risk in the river valleys, the greatest area is occupied by the moderately high vulnerability class. All these variants reveal a spatial variability in the distribution of the individual classes.
2. Based on sensitivity analysis, depth to water table was the most effective parameter responsible for highest variation in the vulnerability index. The effective weight of parameter D is much higher than the theoretical value. In all variants, the calculated IPZ index indicates (after optimization of the parameter values) the greatest increase in the area of the moderately high and high classes, while the area of the low class decreased. The greatest changes in relations to the theoretical values occurred in variant B for the lowest groundwater level variant.
3. The studies confirm that the assessment of groundwater vulnerability to contamination in areas of shallow depth to groundwater table, which are GDE, should be made for a number of variants, with a particular focus on the highest and lowest observed groundwater levels. Groundwater vulnerability changes are also associated with sensitivity analysis and calculated effective weights.
4. This study suggests that DRASTIC is an effective tool for the groundwater vulnerability assessment studies and can be used for the prioritization of the susceptible areas to avoid contamination as well as a frequent and detailed monitoring of pollution potential in the high and medium vulnerability zones. There is also a need for proper planning in areas of shallow groundwater and considerable changes. Vulnerability maps for various variants of input data can be the basis for plans of land management and flood protection, as well as for prediction of climate change effects.  
Conceptualization, E.K.; methodology, E.K. and S.Z.; software, S.Z. and D.Z.; validation, S.Z.; formal analysis, S.Z.; investigation, E.K., S.Z. and D.Z.; resources, E.K.; data curation, E.K.; writing--original draft preparation, E.K.; writing--review and editing, E.K., S.Z. and D.Z.; visualization, D.Z.; supervision, E.K.; project administration, E.K.; funding acquisition, E.K.  
This research received no external funding.  
The authors declare no conflict of interest.
# Local Government Cybersecurity Landscape: A Systematic Review and Conceptual Framework
## 5 Findings and Discussion
### The Framework: Key Components and Limitations in the Existing Literature
#### 5.2.3 Assessment Frameworks and Standards
The Australian Cyber Security Centre developed the Essential Eight, a comprehensive framework designed to prevent cybersecurity incidents. It offers practical guidelines such as application whitelisting, patching, and configuring Microsoft Office macro settings, making it a valuable tool for both government and private sector organizations. The FedRAMP is a government-wide program in the USA that standardizes the security assessment, authorization, and continuous monitoring of the cloud products and services used by federal agencies. Its goal is to ensure that these services meet stringent security requirements, protecting government data. The FedRAMP certification is a benchmark for cloud service providers, indicating high security assurance. Local governments can take insights from these frameworks in various regional and organizational contexts and adopt best practices in their cybersecurity measures.
## 3 Results
### Changes in Vegetation Seasonality Estimations
In most of the plots, the models using a hyperbola to fit the light-response curve (RH, NRH, TRH_exp), except TRH, present the highest cumulative values while the exponential approach shows the lowest (Table 6). Considering the multi-model approach as the nearest to reality based on the statistical performance, all models overestimate the yearly cumulative GPP with two exceptions: the exponential model, which underestimates this value in CL_1 and CL_2, and the TRH model underestimates it in all plots excluding CL_1 (Table 6). Hence, the modelling strategy can affect the distribution of the growing season, its shape, and the cumulative uptake of CO\({}_{2}\) by vegetation throughout the year and the growing season. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **RH** & **TRH** & **TRH\_exp** & **NRH** & **EXP** & **WTD** & **Multi- Model** & **Thermal GS** \\ \hline CL\_C1 & 173 (10.7) & 179 (11.7) & 173 (11.4) & 180 (11.3) & 178 (10.1) & 166 (10.7) & 180 (10.6) & 181 \\ \hline CL\_C2 & 183 (10.4) & 202 (10.7) & 197 (10.8) & 216 (10.4) & 206 (9.6) & 182 (10.6) & 191 (10.7) & 196 \\ \hline CL\_C3 & 166 (8.6) & 172 (9.0) & 164 (8.8) & 175 (8.5) & 171 (8.4) & 154 (9.4) & 169 (8.9) & 165 \\ \hline CR\_C1 & 236 (4.4) & 230 (4.9) & 225 (4.8) & 218 (5.9) & 242 (4.1) & 226 (4.5) & 234 (4.7) & 229 \\ \hline CR\_C2 & 233 (5.3) & 229 (5.5) & 224 (5.7) & 222 (5.2) & 234 (5.3) & 231 (5.3) & 242 (5.6) & 227 \\ \hline CR\_C3 & 220 (4.7) & 229 (5.0) & 221 (4.8) & 220 (4.9) & 228 (4.4) & 212 (4.9) & 225 (4.9) & 214 \\ \hline \end{tabular}
\end{table} 
Table 5: Length of the 2021 growing season (days) and, in brackets, the amplitude of the 2021 GPP (g CO\({}_{2}\)-C m\({}^{-2}\) d\({}^{-1}\)) during the vegetation growing season per plot and model strategy generated by DATimeS [23] and the classical temperature approach. Note that the GPP models abreviations stand for EXP—exponential model [21], NRH—non-rectangular hyperbolic model [20], RH—rectangular hyperbolic model, TRH and TRH_exp—linear and exponential temperature adjusted RH, and WTD_15—water table depth based model [22].
# Implementation Studies of Bacteriophage \(\lambda\) O
## Materials and Methods  
### Complementation Assays and Initial Strategy for Characterizing \(RK^{-}\) Mutants  
Past studies have generated hundreds of RK\({}^{-}\) mutants capable of colony formation at 42 \({}^{\circ}\)C. Since almost all these mutants were selected prior to an ability to combine PCR with rapid DNA sequence analysis of the generated PCR fragment, the characterization of the genetic defect that permitted cell survival and growth at 42 \({}^{\circ}\)C required genetic analysis. This, in principle, involved complementation analysis for expression of genes \(N\), \(cl\), \(cro\), \(clI\), \(O\), and \(P\). Of those mutants that retained an active \(imm\lambda\) region encoding a Ts CI repressor, the cells grown at 30 \({}^{\circ}\)C expressed an immune response to plating by \(imm\lambda\) phage, but not to the heteroimmune phages as \(\lambda imm\)434. Shifting the cells to growth at 42 \({}^{\circ}\)C inactivated the Ts CI repressor and permitted the expression of \(N\), \(cro\), \(clI\), \(O\), and \(P\). RK\({}^{-}\) clones were inoculated into 1.5 mL tryptone broth (TB: 10 g of Bacto Tryptone, 5 g of NaCl per liter) and grown to stationary phase at 30 \({}^{\circ}\)C. One-tenth mL of each culture was mixed with dilutions high titer lysates \(imm\lambda\) or \(imm\)434 phages carrying an amber mutation in genes \(N\), \(O\), or \(P\) (none of which--at the time--were characterized by DNA sequence analysis) plus 2.5 mL of TB top agar (0.65%) agar. The mixture was poured on TB agar (1.1%) plates that were incubated at 42 \({}^{\circ}\)C. In the present study lysates of \(\lambda\) phage with Oam mutants were freshly prepared. A single colony of \(E\). \(coli\) strain 594 or these cells transformed with different versions of O plasmids were grown in LBAmp50 broth (see footnote \"a\" Table 3) at 30 \({}^{\circ}\)C overnight. Then a mixture of cells and soft agar (3 mL of warm top agar, 0.25 mL of cells and 0.25 mL of 0.01 M MgCl\({}_{2}\)) was poured on the top of LB plates. After agar solidification, diluted Oam \(\lambda\) phage lysates were spotted on the agar, allowed to dry, the plates were incubated inverted at 30, 37, 39 and 42 \({}^{\circ}\)C overnight and plaque forming units were counted. The appearance of plaques at elevated plating efficiency indicated complementation for the defective gene carried on the infecting phage by the thermally induced prophage in the RK\({}^{-}\) mutant cells or expressed from the plasmid. 100% plating efficiency was equated to the titer of the amber phage mutant on \(E\). \(coli\) cells with a suppressor tRNA, e.g., on strain TC600 \(supE\). Very low plating efficiency (<10\({}^{-4}\)) suggested phage-prophage marker rescue. The ability of RK\({}^{-}\) cells to complement for the wild type functions expressed from \(N\) or \(P\) has always been very simple to assess. However, the interpretation of whether \(O\) expressed from the induced prophage was able to complement an Oam infecting phage proved problematic.