esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Simulation Environment
#### 4.1.3 Protocol Architecture  
The protocol architecture of DGRL is shown in Figure 5. Network logics are dictated by controller and wise-visor. The adaption layer between the wise-visor and nodes is responsible for formatting messages received from nodes in such a way that they can be handled by the wise-visor, and vice versa. On top of the mac layer, the forwarding (FWD) layer handles incoming packets as specified in the flow table. The FWD layer updates the flow table according to the configurations sent by the control plane. The In-Network  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting** \\ \hline \
\begin{tabular}{c} \hline Soft replacement \\ Reward discount \\ Learning rate for the Actor model \\ Learning rate for the critic model \\ Initial random exploration rate \\ Final random exploration rate \\ \end{tabular} &
\begin{tabular}{c} \(TAU=0.1\) \\ \(gamma=0.95\) \\ \(a_lr=0.0001\) \\ \(c_lr=0.001\) \\ \(Initial\_epsilon=1.0\) \\ \(Final\_epsilon=0.01\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters of reinforcement learning.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Layer Name** & \multicolumn{3}{c}{**Parameter Details**} \\ \cline{3-4}  & & **Hidden Units** & **Activation** & **Trainable Weights** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Actor model \\ \end{tabular} } & GCN1 & 256 & ReLU & \\  & GCN2 & 8 & ReLU & \\  & Denses1 & 64 & ReLU & 10,774 \\  & Denses2 & N & Linear & \\  & Output & 1 & Softmax & \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Critic model \\ \end{tabular} } & GCN1 & 256 & ReLU & \\  & GCN2 & 8 & ReLU & \\ \cline{1-1}  & Dense layer & 64 & ReLU & \\ \cline{1-1}  & Output & 1 & Linear & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Details of model struct.  
Figure 4: Hyperparameter evaluation in DGRL. (**a**) TAU. (**b**) a_lr. (**c**) c_lr.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Setting** \\ \hline \
\begin{tabular}{c} Number of nodes \\ Channel rate \\ Average channel delay \\ Average packet length \\ Buffer length \\ \end{tabular} &
\begin{tabular}{c} 14 \\ \(8\) kbps \\ \(58.0014521\) ms \\ \(1026.28\) bit \\ \(24,28,32,36,40,44,48\) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of WSN.  
Packet Processing (INPP) layer runs on top of the forwarding layer and it is responsible for operations like data aggregation or other in-network processing. Topology discovery (TD) layer uses beacon packets to help nodes discover their interconnected nodes. This part of the protocol structure is set according to the literature [4].
## 4 Exploratory Study
### Experimental Settings  
Our experiment aimed to explore the capability of LLMs in performing SQLI black-box detection. Specifically, we designed two methods: LLM4 Sqli-Manual and LLM4 Sqli-Sqlmap. The former investigates the ability of LLMs to perform SQLI black-box detection independently, while the latter examines LLMs' ability to guide automated SQLI detection scanners. The workflow of LLM4 Sqli-Manual is illustrated on the left side of Figure 1. Initially, a crawler retrieves the content of a web application based on a user-provided URL, passing the HTTP request-response pairs to LLMs as initial information. LLMs then generate a payload from this information and pass it to a replayer to test the web application. The results are analyzed by the user, and if no injection point is found, the results are fed back to LLMs for iterative testing.  
The implementation of LLM4 Sqli-Sqlmap is depicted on the right-hand side of Figure 1. This approach incorporates Sqlmap as a scanner, chosen for its support of command-line operations, ease of automation, comprehensive features, and wide recognition. Unlike LLM4 Sqli-Manual, in LLM4 Sqli-Sqlmap, LLMs generate parameters of the Sqlmap command rather than payloads. Sqlmap executes the command, and based on the results returned by Sqlmap, the program determines the success of the injection and decides the subsequent steps.  
This study selected three advanced LLMs--OPENAI's GPT-4, GPT-3.5, and AnthropicAI's Claude-3--to evaluate their performance in SQLI black-box detection. These models were chosen due to their prominence in the research community and consistent usability. To facilitate automated testing, we interacted with these models via API calls. The criterion for determining the success of LLMs on the SqliMicroBenchmark target was its ability to accurately identify and confirm the presence of SQLI vulnerabilities in the target and successfully retrieve the database name. This criterion aims to evaluate the models' ability to recognize SQLI vulnerabilities rather than to encourage or conduct any form of illegal attack. Additionally, we implemented the measures outlined in Appendix A to ensure the validity of the experiments.
## 4 Results
### Configuration of the Simulation Environment
#### 4.2.4 Configuration of LTE  
Cost-Hata (Cost 231) was utilized as the route loss model of LTE, which has been widely adopted in American and European countries, for estimating transmission power loss in cellular communication [82]. To reduce interference and increase the strength of the intended received signal, LTE's uplink power control was activated [83]. The maximum transmit power followed the 3 GPP standard and was set at 23 dBm [84]. The \"PiroEW2010\" Adaptive Modulation and Coding (AMC) scheme had the Radio Resource Control (RRC) and error models activated, and the Bit Error Rate (BER) was set to 0.00005. Additionally, based on the quantity of user equipment, the Sounding Reference Signal (SRS) periodicity was dynamically determined.  
The \"Trace Fading Loss Model\" was utilized to simulate multipath propagation effects in LTE for both uplink and downlink. It contained pre-calculated fading traces for the simulation, with the fading trace for 60 km/h utilized for high-mobility user equipment set to a trace length of 10 s, 10,000 samples, and a trace window size of 0.5 s. The Uplink E-UTRA Absolute Radio Frequency Channel Number (EARFCN) was set to 18,100, while the Downlink EARFCN was set to 100. A point-to-point Evolved Packet Core (EPC) with a data rate of 1000 Mbps and a latency of 10 \(\upmu\)s was used to install a Packet Data Network (PDN) Gateway (PGW) and Serving Gateway (SGW), which are both required for LTE operation. PGW and SGW nodes were placed close to eNodeBs at fixed positions, as they would be situated in an actual scenario, to ensure equality in simulation runs.
## 4 Conclusions  
This work reports the electrochemical detection of Rhodamine B by the MIP modified SPCE, assisted via MSPE using MMWCNTs-PS-DVB as an adsorbent. The developed electrode (MIP-SPCE) presented higher sensitivity in comparison with the bare SPCE, and the TiO\({}_{2}\)-MWCNTs-SPCE. The developed method shows high sensitivity and selectivity towards Rhodamine B with a detection limit of 1.44 mg L\({}^{-1}\). The use of the developed electrode (MIP-SPCE) allows for the implementation of a simple, effective, and rapid method for electrochemical detection of Rhodamine B in food samples. This modification also introduces higher stability and appropriated selectivity, and the simplicity of the method allows it to be used as a rapid screening test for Rhodamine B. On the other hand, some limitations of the use of MIP-SPCE include a short electrode lifetime, poor inter-electrode reproducibility, and not being user-friendly.  
The following are available online. The file of supplementary information is available online and contains the following information: Section S1.1: Synthesis of MMWCNT-PS-DVB composite, Figure S1: Characterization of the MMWCNT-PS-DVB composite by means of (a) FTIR spectra, (b) Raman spectra, and (c) TEM image.  
**Author Contributions:** Formal analysis, Y.B.; Funding acquisition, A.R.; Investigation, M.Z.; Methodology, Y.B. and K.M.; Supervision, A.R.; Validation, K.M.; Writing--original draft, K.M.; Writing--  
Figure 6: Schematic illustration of the construction process of MIP.  
review and editing, R.S., M.Z., and A.R. All authors have read and agreed to the published version of the manuscript.  
The Spanish Ministry of Science and Innovation, JJCC Castilla-La Mancha and University of Castilla-La Mancha are gratefully acknowledged for funding this work with Grants PID2019-104381 GB-I00, JCCM SBFLY/17/180501/000262 and 2020-GRIN-28882, respectively.  
The data presented in this study are available in this article.  
The authors declare no conflict of interest.  
Samples of the compounds are not available from the authors.
## 4 Numerical Model
### Simulation Conditions  
To study the influence of grouting reinforcement layer thickness (h) on the grouting reinforcement effect, the surrounding rock of the tunnel vault was divided into five parts with a layer of 0.5 m, as shown in Figure 11. Six working conditions were designed: non-grouting reinforcement, reinforcement of one layer, reinforcement of two layers, reinforcement of three layers, reinforcement of four layers, and reinforcement of five layers, that is h = 0, h = 0.5 m, h = 1.0 m, h = 1.5 m, h = 2.0 m, and h = 2.5 m. The strength parameters of the grouting reinforcement layer were set according to the parameters when the water-cement ratio \(\eta\) = 0.75. To study the influence of grouted surrounding rock strength parameters on grouting reinforcement effect and surrounding rock stability, the following six working conditions were designed, namely water-cement ratio \(\eta\) = 0.75, \(\eta\) = 0.80, \(\eta\) = 0.85, \(\eta\) = 0.90, \(\eta\) = 0.95, and \(\eta\) = 1.00. The thickness of the grouting reinforcement layer was set as three layers, that is, h = 1.5 m. Table 3 shows the parameters of grouted surrounding rock strength. The whole simulation process first balanced the initial ground stress, then excavated the middle heading tunnel, and constructed the middle heading tunnel lining, excavated the  
Figure 10: Numerical model of the double-arch tunnel (unit: m).  
left and right heading tunnels, constructed the left and right heading tunnel linings, and constructed the middle, left, and right partition walls. Then, the upper step of the left heading tunnel was reserved for core soil excavation, the upper step core soil excavation, the middle step excavation, and the lower step excavation.
## Appendix A  
The pseudo code for APSOA is shown as follows:  
```
Input: Targets Set \(T_{i}\), acceleration coefficients \(c_{1}\),\(c_{2}\), inertia weighted parameters, last action \(a_{i}^{t-1}\), last payoff \(U_{i}^{t-1}\), Output: \(U_{i}^{t}\), \(BR_{i}^{t}\)
2.1. Randomly generate current swarm according to \(T_{i}\);
2. Insert last action \(a_{i}^{t-1}\) to current swarm;
3.for each iteration \(g\) = 1,2...do
4.for each particle \(p\) = 1,2...do
5. De-conflict and calculate the profit;
6. Compare and substitute the individual optimal and global optimal;
7. Update particle's location and velocity in (16);
8. Boundary condition processing;
9.endfor
10.endfor
11. Compare the best \(U_{i}^{t}\) of swarm with \(U_{i}^{t-1}\), and select \(a\) with higher \(U_{i}\) as \(BR_{i}^{t}\);
```  
**Algorithm A1 APSOA**  
\[v_{ij}(t+1)=\lambda\cdot v_{ij}(t)+c_{1}r_{1}(t)[p_{ij}(t)-x_{ij}(t)]+c_{2}r_{2 }(t)[p_{gj}(t)-x_{ij}(t)]\]  
\[x_{ij}(t+1)=x_{ij}(t)+v_{ij}(t+1)\] (A1)  
where particle population \(P\) = (\(X_{1}\), \(X_{2}\),..., \(X_{N}\)) is randomly generated, and each particle \(X_{i}\) = (\(x_{i1}\), \(X_{i2}\),..., \(x_{iD}\)) is a solution that will be further iterated. \(D\) is the dimension of the solution (it refers to the number of targets in this paper), and all the components in \(X_{i}\) together determine the position of \(X_{i}\). Each particle has a velocity \(V_{i}\) = (\(v_{i1}\), \(v_{i2}\),..., \(v_{iD}\)); velocity controls the speed and direction of particle evolution. The individual optimal so far is \(p_{best}\) = (\(p_{i1}\), \(p_{i2}\),..., \(p_{iD}\)), and the global optimal is \(g_{best}\) = (\(g_{1}g_{2}\),... \(g_{D}\)).
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 5 Singular Pulses
### Rectangular Pulses  
The fundamental generalized pulse is the rectangular one. Unlike the delta function, the rectangular pulse has a finite energy and therefore can simulate real pulses in many scenarios despite its singular edges (a real rectangular pulse), and therefore it is ubiquitous in the literature.  
The rectangular pulse can be written with the aid of the step function or the rectangular function:  
\[A(z=0,t)=A_{0}rect_{1}(t/\theta)=A_{0}[u(-t/\theta+0.5)-u(-t/\theta-0.5)] \tag{25}\]  
where  
\[rect_{L}(\tau)\equiv\begin{cases}1&|\tau|\leq L/2\\ 0&|\tau|>L/2\end{cases} \tag{26}\]  
Equation (25) has two singular points (\(t=\pm 0.5\theta\)) in which the pulse's amplitude is discontinuous. The dynamics of this rectangular pulse can be formulated analytically:  
\[A(t,z)=\frac{1}{2}A_{0}\Biggl{[}erfc\Biggl{(}\frac{t-\theta/2}{\sqrt{-2i\beta _{2}z}}\Biggr{)}-erfc\Biggl{(}\frac{t+\theta/2}{\sqrt{-2i\beta_{2}z}}\Biggr{)} \Biggr{]} \tag{27}\]In Figure 6, the real and imaginary components of (27) are plotted for two different medium's length. The ripples in the pulse's profile appear after a very short distance.  
In Figure 7, a spatiotemporal intensity plot (as a function of \(\tau\) and \(\zeta\)) is presented.  
The intricate structure of this function is most pronounced when the incident signal is a train of rectangular pulses:  
\[A(z=0,t)=A_{0}\sum_{n=-\infty}^{\infty}rect_{1}(t/\theta-2n) \tag{28}\]  
This case is equivalent to the well-known quantum problem of a particle in a 1D box when initially the particle's probability density is uniform over the box [21].  
In this case; the problem's dynamics can be formulated (using Equation (27)) by  
\[A(t,z)=\frac{1}{2}A_{0}\sum_{n=-\infty}^{\infty}\left[erfc\Bigg{(}\frac{t-2n- \theta/2}{\sqrt{-2i\beta_{2}z}}\Bigg{)}-erfc\Bigg{(}\frac{t-2n+\theta/2}{ \sqrt{-2i\beta_{2}z}}\Bigg{)}\right] \tag{29}\]  
However, it should be emphasized that in Ref. [21] a different numerical approach was taken in which no error function analysis was used.  
Sample Application: In 10 Gb/s optical communications channel (over smf28 fiber) with a carrier wavelength of \(\lambda=1.55\) um, the pulses can be simulated by rectangular ones with \(\theta=100\) ps and \(\beta_{2}\cong 20\) ps\({}^{2}\)/Km, in which case \(\zeta=0.3\) in Figure 7 corresponds to \(z=\zeta\theta^{2}/\beta_{2}\cong 150\) Km. However,  
Figure 6: The real (upper panel) and imaginary (lower panel) components of the rectangular pulse (Equation (27)). The dashed curve represents the initial profile (for \(\zeta=\beta_{2}z/\theta^{2}=0\)), while the solid curve represents its final shape (for \(\zeta=\beta_{2}z/\theta^{2}=0.01\) on the right and \(\zeta=\beta_{2}z/\theta^{2}=0.3\) on the left).  
Figure 7: Same as Figure 2, but for the intensity (\(|A(t,z)|^{2}\)) of the pulse presented by Equation (27).  
as Figure 6 illustrates, considerable distortions appear even for \(\zeta=0.01\), which corresponds to a fiber length of \(z\cong 5\) Km.
## 3 Results and Discussion
### Characterisation of Corncob Hydrolysate  
To confirm the solubility of the simple sugars in corncob hydrolysate, the hydrolysate obtained after the organosolv pretreatment was analysed. Table 2 represents the carbohydrates of the hydrolysate corresponding to the available sugars for use by the inoculum. It is important to know that the parameters as proteins constitute a very important substrate in the anaerobic digestion process because, in addition to being a source of carbon and energy, the amino acids derived from their hydrolysis have high nutritional value.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values\% \(\pm\) SD** \\ \hline Cellulose & \(45\pm 2.25\) \\ \hline Hemicellulose & \(34\pm 1.7\) \\ \hline Lignin & \(16\pm 0.8\) \\ \hline Extractives & \(6\pm 0.29\) \\ \hline \multirow{3}{*}{Elemental analysis} & C & \(46.6\pm 2.33\) \\ \cline{2-3}  & H & \(6.2\pm 0.31\) \\ \cline{2-3}  & O & \(46.8\pm 2.34\) \\ \cline{2-3}  & N & \(0.4\pm 0.02\) \\ \hline Ash & \(1.74\pm 0.08\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Corncob characterisation.  
Table 3 shows the results of the sugars present in the organosolv hydrolysate and soda hydrolysate, showing that, with the organosolv pretreatment, the total reducing sugars (TRS) were higher (65.84 g/L) than the TRS with the soda pretreatment (4.64 g/L). This indicates that the hydrolysate is suitable for use in the anaerobic reactor. As with the TRS, the concentration of total sugar in the organosolv pretreatment was 157.7 g/L, which is higher than after the soda pretreatment (20.80 g/L), meaning that a greater amount of total sugars was extracted. Organosolv pretreatment improved the formation of sugars or the ability to subsequently form sugars by enzymatic hydrolysis, thus preventing the loss or degradation of carbohydrates and avoiding the formation of inhibitors, the by-products of subsequent hydrolysis and fermentation processes [27]. The non-reducing sugars obtained concentrations that exceeded the totals of TRS, demonstrating an incomplete depolymerisation from the organosolv pretreatment.  
The chemical oxygen demand (COD) of the corncob hydrolysate was 690 g/L. This result is considered high when compared with soda hydrolysate (195 g/L) and lower results of residues commonly present in the paper industry and its effluents without prior treatment, not exceeding a value of 52 g/L, taking into account that thermochemical processes are also carried out in this industry. The COD of the organosolv hydrolysate exceeded the results characteristic of residues from other types of pretreatment, where values ranging from 179 to 193 g/L for soluble COD and total COD, respectively, have been reported [28]. The COD increased with the organosolv process because the organic solvents used for the removal of lignin in the corncob leave the hydrolysate rich in depolymerised hemicelluloses (glucose, xylose, galactose, and mannose), thus increasing the organic matter available for the digestion process.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
This study will help us identify the parameters needed to apply K-means clustering efficiently to an unlabeled MITRE ATT&CK-based Zeek log dataset, creating attack and non-attack clusters. Zeek logs were created using Zeek [6], a network security monitor that captures and analyzes network traffic. Due to the size of this dataset, UWF-ZeekDataFall22 [5], and to address the challenges faced when using Big Data, the Big Data framework, that is, Apache Spark [7] and PySpark [8] using Jupyter Notebooks [9], was used for our development environment. VMware was used to simulate virtualized environments, and the experiments were conducted on personal computers equipped with multi-core processors to handle the computational demands. Hence, leveraging the clustering capabilities of K-means in a Big Data environment, this study aims to enhance the detection of cyber threats and provide a robust, automated approach to cybersecurity monitoring using unlabeled data. An in-depth study is done, using our three different labeling heuristics, to find the ideal number of clusters, seed value, as well as features, needed to group attacks and non-attacks in UWF-ZeekDataFall22 [5]. A visualization of the attack/non-attack data is also performed using PCA. The rest of the paper is organized as follows. The next sub-section, Background, presents the background needed to understand this study; Section 2 presents the related works, that is works related to clustering in the context of network intrusion data; Section 3 describes the UWF-ZeekDataFall22 dataset; Section 4 presents the experimental setup; Section 5 presents the K-means clustering results and discussion; Section 6 presents the limitations of this study; Section 7 presents the conclusions and Section 8 presents the future works.
# Random Walker Coverage Analysis for Information Dissemination in Wireless Sensor Networks
## 4 Simulation Results
On the corresponding illustrations the number of nodes that had received at least one random walker's visit (i.e., \(C(t)\)) is given on the vertical axis, while the number of random walker's steps (\(t\)) lies on the horizontal. Simulation results for 100 networks with \(r_{c}=0.06\) and the mean number of direct neighbors = 12 are presented in Figure 7. On the other hand, in Figure 8, results are depicted for networks with radius \(r_{c}=0.1,0.2,0.5,1.0\) and mean number of direct neighbors = 35, 158, 834, 997, respectively. In Figure 8, it is shown that the coverage of the network is independent of the density of the connections. From the aforementioned results and the associated figures, it seems that the previously stated arguments regarding the network coverage in geometric random graphs through the random walker mechanism are confirmed. Therefore, these simulation results are fully consistent with the analytical expectations presented in this work. Figure 6: Random walker’s spatial displacement from the starting node. The blue line is from the analytical part of this study (i.e., Equation (1)), while the red one is from the simulation results. One can see that the walker is permitted to move in all directions; hence, the distance from the starting node could be either decreased or increased.