esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions and Future Work  
In this paper, we presented a novel generative AI framework designed to enhance the security posture against SQLi attacks by leveraging the advanced capabilities of Large Language Models (LLMs). Our framework achieves two pivotal objectives:  
1. Generation of Novel SQLi Attacks: Utilizing in-context learning with carefully curated examples, we harnessed generative AI to produce a diverse array of SQLi payloads. This approach significantly reduces the occurrence of hallucinations--a common issue in AI-generated content--by ensuring that generated attacks are both syntactically and semantically valid. The high validation rate of 92.5% against a MySQL database attests to the effectiveness of this method.
2. Automated Defense Mechanisms: By systematically testing the generated payloads against a vulnerable web application fortified with a WAF, we identified attacks that successfully bypassed existing security measures. These bypassing attacks were then subjected to classification using machine-learning algorithms. Leveraging natural language processing techniques, we automatically generated new WAF rules tailored to counter these specific attack patterns. The iterative refinement of WAF defenses culminated in the successful blocking of all previously bypassing attacks.  
Our experimental evaluation demonstrated the framework's efficacy and generality. With the GPT-4o LLM, we generated 514 new SQLi payloads, of which 89% bypassed the ModSecurity WAF equipped with the latest OWASP Core Rule Set. Remarkably, the introduction of just 23 new WAF rules was sufficient to block 475 unique SQLi attacks, highlighting the broad applicability and effectiveness of the generated defenses.  
By planning to open-source our framework, we aim to democratize access to advanced cybersecurity tools, enabling a wider range of practitioners to proactively defend against evolving threats. This approach lowers the technical barriers associated with traditional methods, such as manual rule crafting, and fosters a collaborative environment where defense strategies can be rapidly shared and improved upon.  
Our work also brings to light important considerations regarding the dual-use nature of generative AI in cybersecurity. While these technologies offer powerful tools for defenders, they can equally be exploited by malicious actors. The reluctance of some publicly available models to generate attack vectors reflects a broader debate on responsible AI usage. We acknowledge the complexity of this issue and advocate for ongoing dialogue among policymakers, technologists, and the cybersecurity community to establish guidelines that balance innovation with security.
# Nearshore Benthic Mapping in the Great Lakes:
## 2 Materials and Methods
### Data Classification and Analysis
#### 2.5.2 Field Data Classification  
Drop camera photos and sediment grain size classifications from the July 2018 survey and video track log locations from the summer and fall 2019 surveys were used to classify ground truth points with the appropriate Tier 1 and 2 classification codes (Tables 2 and 3). The CMECS Coding System Approach and corresponding Code Set [48] was used to apply the codes to the points. In general, the videos were linked to the point locations using time-stamp information and then bottom points were attributed based on video reviews and interpretation with consensus among researchers. Points were separated approximately in half between samples used in classification versus validation as well as by CMECS component, substrate versus biotic. The points set aside for classification were used to create regions of interest (ROIs) or training samples required for supervised classification techniques. More specifically, ROIs represent the ground truth points in the form of grid cells corresponding to the pixel size of the 3 m airborne or 10 m satellite classification input imagery and data. In some cases, more than one ground truth point fell within a 3 m or 10 m grid cell area, and thus, more than one point described an ROI. In those cases, an ROI cell was attributed with the class code of the majority of points within that cell unless in special cases where the number and type of points were equally split and best judgment was used to define classes in those cases. In total, the 2018 and 2019 points were combined into a comprehensive and classified ground truth ROI dataset for use in image classification and validation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Short Name (Index \#)**} & **\# of Classification** & **\# of Validation** & **\# of Classification** & **\# of Validation** \\  & **Samples (3 m)** & **Samples (3 m)** & **Samples (10 m)** & **Samples (10 m)** \\ \hline \hline \multicolumn{5}{c}{**Subsirte Component**} \\ \hline Coarse Unconsolidated (1) & 52 & 46 & 19 & 14 \\ Fine Unconsolidated (2) & 540 & 540 & 182 & 178 \\ Anthropogenic (3) & 38 & 34 & 13 & 10 \\ TOTAL & 630 & 620 & 214 & 202 \\ \hline \hline \multicolumn{5}{c}{**Aquatic Vegetation Bed (1)**} & 50 & 50 & 19 & 17 \\ \multicolumn{5}{c}{**Biotic Null (2)**} & 575 & 575 & 190 & 190 \\ \multicolumn{5}{c}{**TOTAL**} & 625 & 625 & 209 & 207 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of ground truth points (samples) for classification and validation used for Tier 1 classification of the 3 m airborne and 10 m satellite data (# = number).
# Embedded Zero-Shot Image Classification Based on Bidirectional Feature Mapping
## 4 Results and Analysis
### Comparision
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Models**} & \multirow{2}{*}{**CUB**} & \multicolumn{4}{c}{**SUN**} & \multicolumn{4}{c}{**AWA2**} \\ \cline{3-10}  & & & _U (\%)_ & _H (\%)_ & _S (\%)_ & _U (\%)_ & _H (\%)_ & _S (\%)_ & _U (\%)_ & _H (\%)_ \\ \hline \multirow{5}{*}{Generative Methods} & f-CLSWGAN & 57.7 & 43.7 & 49.7 & 36.6 & 42.6 & 39.4 & 61.4 & 57.9 & 59.6 \\  & f-VAEGAN-D2 & 60.1 & 48.4 & 53.6 & 38.0 & 45.1 & **41.3** & 70.6 & 57.6 & 63.5 \\  & Composer & 63.8 & 56.4 & 59.9 & 22.0 & **55.1** & 31.4 & 77.3 & **62.1** & **68.8** \\  & cycle-CLSWGAN & 61.0 & 45.7 & 52.3 & 33.6 & 49.4 & 40.0 & 64.0 & 56.9 & 60.2 \\  & LisGAN & 57.9 & 46.5 & 51.6 & 37.8 & 42.9 & 40.2 & & & \\  & TCN & 52.0 & 52.6 & 52.3 & 37.3 & 31.2 & 34.0 & 65.8 & 61.2 & 63.4 \\  & DAZLE & 59.6 & 56.7 & 58.1 & 24.3 & 52.3 & 33.2 & 75.7 & 60.3 & 67.1 \\  & LFGAA & **80.9** & 36.2 & 50.0 & **40.0** & 18.5 & 25.3 & **93.4** & 27.0 & 41.9 \\  & SGMA & 71.3 & 36.7 & 48.5 & – & – & – & 87.1 & 37.6 & 52.5 \\  & DSAN & 56.6 & 46.9 & 51.3 & 41.1 & 33.2 & 36.7 & 78.8 & 58.6 & 67.2 \\  & BFM (ours) & 61.3 & **61.9** & **61.6** & 25.3 & 48.4 & 33.2 & 72.8 & 61.3 & 66.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of image classification models in a generalized zero-shot learning setting. Note that in the table, bold fonts indicate the best performance in this metric, and underlined fonts indicate the second best performance in this metric. ”–” indicates that the method corresponds to the results not given in the literature. Compared with the two generative models that perform better, our model has lower complexity. The number of training parameters of the BFM model proposed in this paper is about \(4.56\times 10^{6}\). The number of training parameters of model f-VAEGAN-D2 is about \(1.48\times 10^{7}\), and the number of training parameters of model com is about \(5.81\times 10^{6}\). The number of training parameters of the BFM model is 69.2% and 21.5% lower than that of the other models, respectively. Under the same training environment, the training time of our BFM model is about half that of model com.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 1 Introduction
### Durham Cathedral  
Durham Cathedral tower displayed movement due to bell ringing and was ideal for the authors to measure for several reasons. First, there were ten bells in a frame 60 m above the foundation level [6], and such a large mass swinging at such a height produced measurable tower displacements on the order of 0.43 mm. The frame was constructed from structural steel fixed into the masonry of the tower, which was the most effective frame arrangement to minimise tower vibrations according to [2], and the MEMS sensors coupled with a low-frequency accelerometer for displacement monitoring could validate the effectiveness of this arrangement in terms of minimising vibrations. The bells are hung in either a N-S or an E-W orientation and the mass of the largest bell is 1425 kg. The maximum horizontal and vertical forces produced by the bells when rung individually using the procedure described in [5] are available in [6]. The velocities in the N-S and E-W directions were measured using high-sensitivity low-frequency geophones (velocity transducers) with a pen trace, and the maximum velocity recorded was 3.5 mm/s at the ringing chamber whilst ringing rounds one to ten. Using this  
Figure 2: Diagram of a bell and bell wheel. The blue box indicates where the sensor was located in all experiments in the Cathedral. The black slider holds the bell in position close to its balance point so that minimum effort is required to ring it.  
velocity and an approximate equation, the maximum N-S and E-W displacements were estimated to be 0.43 mm and 0.31 mm, respectively. The maximum base stress was found to be 0.07 MPa assuming simple cantilever bending [6]. These values were the largest recorded but not necessarily the maximum that occurred during ringing. The investigation only included the ringing of one of the many different patterns that the bells were rung in. Each pattern produced forces that varied differently with time and therefore different tower displacements. The natural frequencies of the tower movement were measured to be 1.28 Hz E-W and 1.31 Hz N-S with a damping ratio of 0.016 [6].  
Attempts by other authors were made to build finite element models of the tower but the results included inaccuracies between 10% and 50% [6]. The large difference between the measured and the computed values was due to the simplifications used in the model. For example, while the walls were made of sandstone with a rubble infill, they were modelled as solid and therefore their Young's modulus had to be approximated. The finite element models did not capture the dynamics of the tower due to the underlying assumptions about the materials. The assumption that the angle of the bells as a function of time exactly followed the theoretical curves in [6] led to further inaccuracies that would have not been present if these angles were measured experimentally. This highlighted the need for experimental work to fully characterise the dynamics of towers.
## 4 Results
### Sampling Frequency and Response Time  
The sampling frequency of this system can approach 1 MHz, while response time is less than 4 ms. The embedded real-time operation system can run multiple tasks independently, which shortens the response time of the system greatly.
## 3 System Architecture, Software Services and Deployment
### Cloud-Based Services
#### 3.4.2 Historic Events  
Efficient and rapid access to historical datasets is considered of paramount importance for smart water management applications, e.g., when comparing historical data from different time spans trying to identify consumption patterns or detecting anomalies related to malfunctions. The specific cloud service ensures that all datasets obtained from many different smart water measuring devices are aggregated and then stored on a common repository, following pre-defined tagging patterns, and are available in near-real-time regardless of the time window dictated by the request.
# Secured Real-Time Machine Communication Protocol
## 3 Methodology-SRMCP Protocol Design
### Primary Data Structure-Node  
The primary data structure of SRMCP is defined as the node, which contains the following mandatory fields:  
1. **ID**: A unique identifier per node.
2. **Name**: A human-friendly name that is also used to identify the node. The name should be short and abstract but can be identical for multiple nodes.
3. **Type**: A field that indicates the type of data stored in this node.
4. **Parents**: A list of other nodes to which this node is linked.
5. **Children**: A list of other nodes that are linked with this node.
6. **Value**: The value of the data stored in this node.  
Figure 3: SRMCP packet structure  
7. **Description**: A short description of the node, which is optional.
8. **Read**: A callable serves as the interface to update the node's value from the hardware interface or client for other communication protocols.
9. **Write**: A callable serves as the interface to push the node's value to the hardware interface or through the client for other communication protocols.  
The ID of the node is used as the index for storing the node on both the client side and the server side. The relationships between nodes are recorded via the parent and child fields, but the nodes will be stored as a one-dimensional array. The value field can be a simple native type such as integer or float numbers, or a complex data type that has its own fields. This is possible because JSON encoding is used for the message body when transmitting the value of nodes. The \"Read\" and \"Write\" fields are fired to allow the node data structure to be compatible with hardware interfaces or other communication protocols.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l}
3 GPP & The Third-Generation Partnership Project \\
5G & The Fifth Generation \\ ADC & Analog-to-Digital Converter \\ BER & Bit Error Rate \\ DAC & Digital-to-Analog Converter \\ DSIC & Digital Self-interference Cancellation \\ FD & Full-Duplex \\ IoT & Internet of Things \\ ITU & International Telecommunication Union \\ LLR & Log Likelihood Ratio \\ LoS & Line-of-Sight \\ MSE & Mean Square Error \\ NLoS & Non-Line-of-Sight \\ PLS & Physical Layer Security \\ QC-LDPC & Quasi-Cyclic Low-Density Parity Check \\ QPSK & Quadature Phase Shift Keying \\ RF & Radio Frequency \\ RLS & Recursive Least Square \\ SDR & Software-Defined Radio \\ SI & Self-Interference \\ SPA & Sum Product Algorithm \\ uRLLC & Ultra-reliable Low-Latency Communication \\ \end{tabular}
## 4 Results and Discussion
### Model Fitting
#### 4.3.1 Fluorite Metallurgical Recovery (FMR)  
Figure 4 shows how each of the reagents influenced FMR inside the tested range. As can be seen, the variable that most influenced the FMR was the dosage of OA, achieving the maximum fluorite recovery at the top point of the reagent dosage. Accordingly, if the objective is to carry out a roughing of the starting mineral, the flotation with the largest value of OA must be carried out in order to achieve a sterile with the minimum fluorite grade. On the other hand, an excess of QT and SS inhibit FMR while PS and WD have a minimum at their center point being more economically viable to use a lower dosage of the two reagents.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coefficient** & \(\mathbf{R^{2}}\) & **Durbin-Watson Statistic** & **Average Absolute Error (\(\varepsilon\))** \\ \hline Y\({}_{1}\) & 91.58 & 3.02 (\(p=0.94\)) & 3.60 \\ Y\({}_{2}\) & 98.51 & 1.17 (\(p=0.01\)) & 0.46 \\ Y\({}_{3}\) & 93.23 & 1.91 (\(p=0.22\)) & 0.50 \\ Y\({}_{4}\) & 96.64 & 1.90 (\(p=0.22\)) & 0.99 \\ \hline \end{tabular}
\end{table}
Table 7: ANOVA correlation coefficients.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Coefficient** & \(\mathbf{Y_{1}}\) & \(\mathbf{Y_{2}}\) & \(\mathbf{Y_{3}}\) & \(\mathbf{Y_{4}}\) \\ \hline a\({}_{0}\) & 44.83 & 66.95 & 9.28 & 25.95 \\ a  
Comparing the graphs with the results of the \(p\)-values in Table 6, it can be seen how the factors with lower \(p\)-values have a greater effect on FMR. In this case only factor \(X_{4}\) (oleic acid) has a significant effect on FMR because the \(p\)-values of the rest factors and second order effect are above 0.05. This fact can be seen in the graphs of Figure 4 where only AO has a marked slope in the studied range.
# Agile Stage-Gate Approach for Design, Integration, and Testing of a 1U CubeSat
## 3 Results
### Challenges in Implementing Agile Stage-Gate Methodology  
By introducing the Agile methodology in a Stage-Gate approach, there is a considerable increase in interactions between stakeholders, which consequently leads to risks.  
During execution of the project, NASA and AEM provided technical advice and closely supervised the UPAEP teams.  
Despite the benefits of partnering, such as access to knowledge and people who are experts on a topic, this approach also has disadvantages due to loss of autonomy as well as possible negative impacts on reputation if a problem is not resolved.  
Other problems identified were related to the difficulty in dedicating resources to the tasks obtained from innovative iterations. In the case of a university, and since AztechSat-1 is an academic project, there was no pre-existing Agile culture, leading to resistance in applying this methodology among members of the team. Holding sprint meetings was also a challenge.  
Because a nanosatellite is a physical product, the development process is more complex as it requires more specialized infrastructure and material requirements than a software project.  
On the other hand, subsystem leaders must provide information and explain what is planned to be accomplished. In addition, leaders must motivate staff and maintain harmony. Employing undergraduate students close to graduation as team members also carries risks, and measures must be taken to mitigate such problems.  
Figure 17: Overview of themes.  
Finally, there are critical behaviors that must be considered to improve development time and innovation. For example, the exchange of information and coordination between teams are key factors for project success.  
One of the main challenges is teams being dedicated to one project. However, when the team is reunited and face-to-face meetings are held, then improvements can be achieved through communication.
## 1 Introduction
Attempts to improve the mitigating properties of V-structures included the proposed use of compound V-angles [23; 24], and others [25; 26] studied the utilization of a series of multiple shallow V-structures. These compound and multi-V structures have yielded mixed results, and no clear improvement to the traditional V-structure has been obtained. [PERSON] [27] showed that use of a sandwich V-structure with a relatively weak and compressible honeycomb core may offer some additional protection. The sandwich V-structures required relatively large bend radii in their construction. A recent numerical study examined the influence of the bend radius (of the \"V-tip\" at the apex of the V-structure) and internal angle on the impulse transfer from explosions at different stand-off distances [4]. The model was validated using experimental results on rigid V-structures with sharp tips (effectively a bend radius of zero). The results indicated that impulse transfer was sensitive to the bend radius when the stand-off distance was kept constant, such that more impulse was transferred when the bend radius was increased (but since the ground clearance was reduced, this is viewed as a potential benefit). When the ground clearance was kept constant, the stand-off distance increased with increasing bend radius, and no measurable difference in global impulse transfer could be distinguished. As the computational simulations in [4] employed a rigid model for the V-structure, the spatial distribution of the impulse across the structure and its influence on ensuing damage were not considered. This also means the prospect of material rupture in the V-structure (and potential ingress of shock, ejecta, combustion products and high-velocity fragmentation) was not considered. Material rupture can occur at low levels of permanent deformation in materials with high strength and low plastic capacity [28], so the transient response of structures (including rupture) should be measured to provide a more thorough assessment of the V-structure's protective capacity. Although V-structures have been researched for many years [4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26], the influence of bend radius may be a significant but overlooked factor in the blast-mitigation efficacy of V structures. We sought to address this issue through an experimental study on the influences of bend radius and internal angle on the transient displacement, permanent damage and impulse transfer characteristics of flexible, steel, 1:8-scale V-structures subjected to blast loading. This will show, for the first time, the relative importance of bend radius as an influencing factor (compared to internal V-angle).
## 4 Results and Discussion
### Performance Analysis
#### 4.1.4 Throughput  
Throughput is the rate at which data is successfully transmitted or processed over a network within a given period. It is a measure of the efficiency and capacity of a system to handle data transfers. A robust attack detection and mitigation model should demonstrate efficient scalability in throughput to effectively handle varying levels of network attacks. Therefore, it is crucial to analyze the model's performance by assessing throughput in relation to the varying percentage of attacks.  
Figure 6b shows the simulation results for the throughput. The proposed BSHNN approach significantly improves the throughput rate by 21.6%, even though the percentage  
Figure 6: BSHNN method simulation results. (**a**) Delay. (**b**) Throughput.  
of attacks increases. The BSHNN method showcases its superior efficiency in handling elevated network security challenges compared to the ML-IDP [17] system.
## 3 Results and Discussion
### Fatty Acid Composition of Analysed Oils
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Oil Sample**} & \multicolumn{6}{c}{**Induction Time [h]**} \\ \cline{2-6}  & **90\({}^{\circ}\)C** & **100\({}^{\circ}\)C** & **105\({}^{\circ}\)C** & **110\({}^{\circ}\)C** & **120\({}^{\circ}\)C** \\ \hline FO & 16.26\({}^{\text{b}}\)\(\pm\) 0.11 & 8.02\({}^{\text{b}}\)\(\pm\) 0.11 & 5.83\({}^{\text{b}}\)\(\pm\) 0.57 & 4.23\({}^{\text{b}}\)\(\pm\) 0.01 & 2.24 \({}^{\text{a}}\)\(\pm\) 0.01 \\ BO & 8.55\({}^{\text{a}}\)\(\pm\) 0.21 & 4.10\({}^{\text{a}}\)\(\pm\) 0.14 & 2.94\({}^{\text{a}}\)\(\pm\) 0.13 & 2.11 \({}^{\text{a}}\)\(\pm\) 0.01 & 1.07 \({}^{\text{c}}\)\(\pm\) 0.07 \\ PO & 28.59\({}^{\text{d}}\)\(\pm\) 0.13 & 12.77\({}^{\text{d}}\)\(\pm\) 0.13 & 8.63 \({}^{\text{c}}\)\(\pm\) 0.16 & 5.87 \({}^{\text{c}}\)\(\pm\) 0.16 & 2.91 \({}^{\text{d}}\)\(\pm\) 0.04 \\ WCO & 15.59\({}^{\text{b}}\)\(\pm\) 0.00 & 7.70\({}^{\text{b}}\)\(\pm\) 0.38 & 5.42 \({}^{\text{b}}\)\(\pm\) 0.28 & 4.14 \({}^{\text{b}}\)\(\pm\) 0.04 & 2.26 \({}^{\text{b}}\)\(\pm\) 0.01 \\ DO & 33.33\({}^{\text{e}}\)\(\pm\) 0.32 & 14.42 \({}^{\text{e}}\)\(\pm\) 0.30 & 10.06 \({}^{\text{d}}\)\(\pm\) 0.15 & 7.12 \({}^{\text{d}}\)\(\pm\) 0.34 & 3.60 \({}^{\text{e}}\)\(\pm\) 0.01 \\ TO & 20.71 \({}^{\text{c}}\)\(\pm\) 0.24 & 9.27 \({}^{\text{c}}\)\(\pm\) 0.15 & 6.24 \({}^{\text{b}}\)\(\pm\) 0.25 & 4.12 \({}^{\text{b}}\)\(\pm\) 0.06 & 2.10 \({}^{\text{a}}\)\(\pm\) 0.14 \\ \hline \multicolumn{6}{c}{**Induction Time [h]**} \\ \cline{2-6}  & **120\({}^{\circ}\)C** & **130\({}^{\circ}\)C** & **140\({}^{\circ}\)C** & **145\({}^{\circ}\)C** & **150\({}^{\circ}\)C** \\ \hline CO & 28.92\({}^{\text{f}}\)\(\pm\) 0.16 & 13.49\({}^{\text{a}}\)\(\pm\) 0.58 & 6.51 \({}^{\text{a}}\)\(\pm\) 0.06 & 4.52 \({}^{\text{a}}\)\(\pm\) 0.04 & 3.48 \({}^{\text{a}}\)\(\pm\) 0.20 \\ \hline \end{tabular}
\end{table} 
Table 4: Induction time of analysed oils at 90–150 \({}^{\circ}\)C determined using the Rancimat apparatus.