esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Further, the antagonistic activity of SMO inhibitors can be bypassed by shifts towards phosphatidylinositol-3 kinase (PI3K) signaling, activation of mitogen activated protein kinase (MAPK) signaling, or by induction of atypical protein kinase C (aPKC) activity, the latter of which enhances transcriptional GLI activity independent of SMO function [28; 30; 31; 32; 33]. Along the same line, [PERSON] et al. demonstrated that serum response factor (SRF) and the coactivator megakaryoblastic leukemia 1 (MLK1) together with GLI1 activation drives non-canonical Hedgehog signaling and induces SMOi resistance in BCC patients [31]. Considering the numerous kinases and signaling pathways, also including EGF-MEK-ERK, FGF, PDGF, TGF, WNT, JAK/STAT, or DYRK [34; 35; 36; 37; 38; 39; 40; 41; 42; 43] that interact with the HH signaling pathway and are often integrated and translated into synergistically regulated output signals via the GLI transcription factors, we focused on members of the casein kinase family with the goal of identifying novel druggable target proteins to inhibit oncogenic GLI function in this study. Members of the CSNK family have been shown to play multiple and diverse roles in HH signaling [44]. For instance, casein kinase 1 alpha (CSNK1A1) negatively regulates insect and vertebrate HH signaling by phosphorylating GLI3, and thereby inducing proteolytic processing of GLI3 full-length protein into C-terminally truncated repressor forms [45; 46]. By contrast, CSNK1A1 has also been shown to phosphorylate SMO and thereby contribute to activation of HH signaling [47]. Similarly, CSNK2 activity has been identified as a driver signal and therapeutic target in HH-dependent medulloblastoma [48]. Further evidence for an activating role of CSNK proteins came from studies in Drosophila showing that fly homologues of CSNK1 can phosphorylate and thereby stabilize the GLI homologue cubitus interruptus (Ci), increasing the transcriptional output of hh signaling in insects [49; 50]. Together, these data suggest that pharmacologic inhibition of selected CSNK family members may be a promising therapeutic approach for HH--GLI-driven cancers, particularly malignancies with SMOi resistance. Here, we identify casein kinase 1 delta (CSNK1D) as positive regulator of oncogenic GLI function and show that pharmacological as well as genetic targeting of CSNK1D is sufficient to abrogate HH--GLI signaling in both SMOi-sensitive and SMOi-resistant cancer cells driven by oncogenic GLI. Furthermore, we demonstrate a requirement of CSNK1D for GLI-dependent TIC properties in vitro and in vivo, and introduce a novel CSNK1D inhibitor with therapeutic activity against GLI-driven cancer cells.
# GPS/BDS RTK Positioning Based on Equivalence Principle Using Multiple Reference Stations
## 2 Methodology
### Data Processing Strategy  
The data processing flow of MBRTK for single epoch is shown in Figure 2, which is divided into two main parts. The first part is data organization and parameter estimation. First, the observations of multiple reference and rover receivers are collected for MBRTK. Second, the SD observations between each reference and rover receivers are used to establish the SD observation equations. Then, the equivalent DD observation equations are obtained through the equivalent transform, and the modified system state equation with parameter constraints are performed to estimate the unknown state parameters. Finally, the float ambiguities and covariance matrices can be derived by the modified Kalman filter.  
The second part of the data processing is AR of MBRTK. A modified PAR method is adopted to determine an ambiguity subset. First, the decorrelated ambiguities rather than raw float ambiguities are chosen, and the first subset is determined by reordering with the ascending estimate precision. Second, the number of valid ambiguities of every single-baseline is calculated. If the number of valid ambiguities is less than 4, only the float solution of MBRTK is available. Third, after the second step, all float ambiguities of MBRTK are used to fix a potential subset by the Agrell, Eriksson, Vardy, Zeger (AEVZ) search algorithm [42]. Fourth, the ratio test and the posterior probability test are calculated. If  
Figure 2: A flowchart of data processing of the multi-baseline real-time kinematic (MBRTK) for single epoch where SD = single-differenced, DD = double-differenced, and AEVZ = Agrell, Eriksson, Vardy, Zeger.  
the subset passes the tests, the current ambiguity subset is accepted with sufficient confidence and the fixed solution of the MBRTK is available. On the contrary, if the last ambiguity with the lowest precision is rejected, the second step is repeated until the ambiguity solution of the MBRTK is determined. The critical criterion of ratio value and posterior probability are set to 2.0 and 0.90, respectively.
## 1 Introduction
Additionally, we observe that existing SQLi attack generation approaches in the literature (see Section 2) address attack generation and mitigation techniques separately. In contrast, GenSQLi leverages generative AI to automatically generate SQLi attacks and the corresponding WAF rules for their mitigation. Our experimental results demonstrate the efficacy of this approach. Using GPT-4o LLM, we successfully generated 514 new SQLi payloads, 92.5% of which were validated against a MySQL database. Of these attacks, 89% bypassed state-of-the-art WAFs such as ModSecurity [11] equipped with the latest version of the OWASP CoreRuleSet [12]. By applying our machine-learning classification and GPT-4o LLM rule-generation methodology, we were able to develop and validate security rules that effectively blocked 99% of all previously successful attacks with only 23 new rules. To facilitate the replication of our work, we have provided detailed appendices that include the prompts used. Wealso present results with the Google Gemini-Pro LLM, where a lower percentage of attacks (56.5%) were successful. Our experiments were carried out with the MySQL database. We have also validated GenSQLi with the PostgreSQL database and AWS WAF. We plan to open-source the GenSQLi framework so that the capabilities of generative AI can be harnessed by cybersecurity practitioners to safeguard against evolving SQLi attacks. The paper makes the following contributions--  
1. Novel Framework for SQLi Mitigation: Development of a generative AI-based framework that generates novel SQL injection attacks and automatically evolves WAF defenses. 2. In-Context Learning for Attack Generation: Utilization of generative AI models with curated examples to produce diverse and validated SQLi payloads, minimizing hallucinations. 3. Automated WAF Rule Optimization: Integration of machine-learning and generative AI techniques to classify bypassing attacks and generate effective WAF rules. 4. Demonstrated Efficacy: Experimental validation showing improved WAF security by blocking 99% of previously bypassing SQLi attacks with only a limited number of new rules. The remainder of the paper is structured as follows: Section 2 reviews the existing literature on the application of generative AI in cybersecurity. Section 3 provides a concise background on SQL injection (SQLi) attacks and generative AI. Section 4 outlines the proposed framework in detail. Section 5 presents the evaluation results. Section 6 discusses the broader implications of the proposed approach. Finally, Section 7 concludes the paper by summarizing the findings and suggesting directions for future research.
# Multi-Agent Reinforcement Learning: A Review of Challenges and Applications
## 3 The Limits of Multi-Agent Reinforcement Learning  
MARL algorithms can be coarsely divided into three groups depending on the kind of reward given by the environment: fully cooperative, fully competitive, and mixed cooperative-competitive. In the cooperative setting, all the agents collaborate to maximize a common long-term return. An example of this setting is a smart energy grid, in which multiple buildings (agents) with different energy production capabilities have to share energy in order to minimize the outside-grid energy demands. Another is an autonomous driving setting in which the vehicles have to collaborate in order to avoid collisions while trying to maximize traffic flow and possibly fuel efficiency. In competitive settings, the return of all the agents sums to zero. A variety of board and card games, including chess, Go, and poker, belong to this setting, and are of great interest in the reinforcement learning community as benchmarks for algorithms. Mixed settings combine the aforementioned characteristics and present a general-sum reward; a typical example of this is constituted by team games in which the agents have to cooperate with their own team-mates while competing with the opposing teams. The transition from single-agent to multi-agent settings introduces new challenges that require a different design approach for the algorithms.
## 3 Results and Discussion
### Adsorption Kinetics  
The pseudo-first- and pseudo-second-order kinetic models have been commonly used to predict adsorption kinetics. The relationship between adsorption and adsorption time was used to determine if the adsorption was a pseudo-first- or pseudo-second order kinetic model. A linear form of the pseudo-first-order model was described by [PERSON] (1993), as follows:  
\[\ln(q_{\mathrm{e}}-q_{\mathrm{t}})=\ln q_{\mathrm{e}}-k_{1}t \tag{3}\]  
Linear data from for the pseudo-second-order model could be determined using the following equation suggested by [PERSON] and [PERSON]'s [PERSON] and [PERSON] (1993):  
\[\frac{t}{q_{\mathrm{t}}}=\frac{1}{k_{2}q_{\mathrm{e}}^{2}}+\frac{t}{q_{\mathrm{ e}}} \tag{4}\]  
where \(q_{\mathrm{e}}\) and \(q_{\mathrm{t}}\) (mg/g) are the amounts of EDTAA adsorbed on the AERs at equilibrium and at various times \(t\) (min), and \(k_{1}\) (min\({}^{-1}\)), and \(k_{2}\) (g mmol\({}^{-1}\) min\({}^{-1}\)) are the rate constants for the pseudo-first-, and pseudo-second-order equation, respectively. The term \(h\) (mmol g\({}^{-1}\) min\({}^{-1}\)) means the initial adsorption rate calculated by \(k_{2}q_{\mathrm{e}}\)\({}^{2}\) from Equation (4) for the pseudo-second-order model.  
The linear fittings of the pseudo-first- and pseudo-second-order kinetic models are shown in Figure 12. The values of \(k_{1}\) and \(k_{2}\) for the adsorption of EDTA on AERs decreased as the initial concentration increased from 0.25 to 1.0 mM at pH 7. The adsorption of EDTA on AERs was investigated to determine the correlation coefficients (R\({}^{2}\)) of the two models. An R\({}^{2}\) value higher than 0.97 was found from the pseudo-second-order kinetic model. From the pseudo-first-order kinetic model, the R\({}^{2}\) values ranged between 0.94 and 0.97, which were lower than the values from the pseudo-second-order kinetic model. For the case of the pseudo-second-order kinetic model, the \(q_{\mathrm{e}}\) obtained from the experiment (\(q_{\mathrm{e,exp}}\)) were similar to the \(q_{\mathrm{e}}\) calculated from the pseudo-second-order kinetic model (\(q_{\mathrm{e,cal}}\)) (Table 1). Based on the higher R\({}^{2}\) and the more similar \(q_{\mathrm{e}}\), the pseudo-second-order kinetic model was more suitable than the pseudo-first-order kinetic model. Therefore, we believe that EDTA was adsorbed onto AERs through a chemical interaction rather than a physical one [PERSON] (1993). The pseudo-second-order kinetic models were applied to the experimental data described in Figure 6b, which shows the effect of adsorption time on the adsorption capacity of EDTA. The calculated values for the pseudo-second-order kinetic model (the solid lines in Figure 6b) matched well with the experimental values. The kinetics data for the adsorption of EDTA onto AER are listed in Table 1.  
Figure 11: Changes in removal efficiency of EDTA (\(\blacksquare\)) and adsorption capacity (\(\square\)) of CERs in EDTAA/Cs (1:1) containing different EDTA concentrations (0.001–0.5 mM). [CERs] = 2 g/L; pH = 7; adsorption time = 5 h.
## 3 Experiments and Analysis of Results
### Classification of Different Models
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Classifier** & **Test1** & **Test2** & **Test3** & **Test4** & **Test5** & **Mean Value** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline KNN & 93.37\% & 91.05\% & 96.21\% & 95.73\% & 92.43\% & 93.76\% & 1.96\% \\ SVM & 95.88\% & 95.23\% & 95.50\% & 94.71\% & 96.56\% & 95.58\% & 0.62\% \\ DT & 95.68\% & 96.07\% & 95.11\% & 95.24\% & 91.02\% & 94.62\% & 1.83\% \\ RF & 96.76\% & 95.34\% & 95.28\% & 96.39\% & 92.03\% & 95.16\% & 1.67\% \\ DA & 92.75\% & 94.73\% & 90.69\% & 92.38\% & 92.61\% & 92.63\% & 1.28\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy of electric load identification. Figure 6: Confusion matrix of SVM identification results. time with an average computation time of 0.75 s. For industrial power scenarios, EO-screened features are more efficient regarding model computation time. In conclusion, the approach introduced in this paper can achieve precise electrical load classification and requires minimal computation time. The proposed method is both practical and feasible in real-world industrial power settings.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Feature**} & \multicolumn{2}{c}{**Description**} & \multicolumn{2}{c}{**Robustness**} & \multicolumn{1}{c}{**Frailty**} \\ \hline \multirow{4}{*}{Color-based} & \multirow{2}{*}{Color histogram [5]} & Color histogram for the intensity image in RGB (Red, Green, Blue) color space or in HSV (Hue, Saturation, Value) color space & Signal processing, Flip, Scaling & Color change, Post-production, or edition \\ \cline{2-5}  & & LBP [6] & Local Binary Pattern (LBP), Texture Spectrum model by computing neighborhood pixels & Signal processing & Color change, Post-production, or edition \\ \hline \multirow{4}{*}{Gradient-based} & \multirow{4}{*}{HOG [7]} & Histogram of Oriented Gradients (HOG) counts the occurrences of gradient orientation in localized portions of an image & Signal processing & Geometrical transformations \\ \cline{2-5}  & & Edge [8] & Edge of oriented gradients & Scaling, compression & Color change, Post-production, or edition \\ \hline \multirow{4}{*}{GST [9]} & \multirow{4}{*}{DWT [10]} & A set of spectral dimensions (naturalness, openness, roughness, expansion, ruggedness) that represent the spatial structure of a scene & Scaling, compression & Post-production or edition, cropping \\ \cline{2-5}  & & & Discrete Wavelet Transform & \\ \cline{2-5}  & & DWT [10] & (DWT) coefficients by using a mean value, an STD (Standard deviation) value, and SVD (Singular Value Decomposition) & Compression, Flip & Post-production or edition, blur \\ \cline{2-5}  & & DCT [11] & Discrete Cosine Transform (DCT) coefficients & Scaling & Post-production or edition \\ \hline \multirow{4}{*}{Motion-based [12]} & \multirow{4}{*}{Motion-based [12]} & Object motion and camera motion operated by a block matching consecutive frame blocks algorithm & Signal processing & Desynchronization \\ \cline{2-5}  & & Descriptors can search for abrupt changes in pixel intensity values & Most geometrical transformations & Luminance change \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Examples of common features for image/video processing and their robustness and frailty to image/video transition. 1 Introducing a new deep-learning-based SBD method. The method has three stages: candidate segment selection, places semantic-based segment, and segment verification. The Network is places-centric instead of object-centric. 2 Developing a novel image-query-based video searching/fingerprint detection system. The paper is organized as follows. Section 2 overviews the previous VCD and SBD methods. Section 3 introduces our SBD method and image-query-based video fingerprint method. Section 4 introduces the evaluation method and presents the experimental results on some famous datasets. Section 5 offers our conclusions.
# Optimization Design of the Spaceborne Connecting Structure for a Lightweight Space Camera
###### Abstract  
For a lightweight space camera installed vertically with a satellite platform, due to the different conditions between ground and orbit, the relative deformation between the camera and the satellite platform results in a drift of the camera line of sight (LOS), which affects the imaging quality. This paper proposed an optimization method for the spaceborne connecting structure considering the camera LOS drift. By using a variable density topology optimization method, the configuration of the connecting structure was obtained. Based on the configuration, the sensitivity of its size parameters to the system's performance was analyzed. Analysis data showed that the size parameters have an obvious influence on the camera LOS shift. In order to obtain the optimal combination of size parameters, a multi-objective parametric optimization model was established. Finally, engineering analysis of the optimized structure showed that the system performances meet the design requirements of the satellite, and the lightweight ratio of the connecting structure reaches 54%. This study provides a reference for the design of other similar structures for space cameras.  
remote sensing and sensors; optical devices; optimization design; optical engineering; optomechanics +
Footnote †: journal: Applied Sciences
# Optimal Analysis of 40 Gbps Dispersion Compensated Optical Fiber System +
## 3 Results  
The designed optical communication link is evaluated using OPTISYSTEM software\" (Optiwave Systems Inc., Nepean, ON, Canada). Based on different qualitative parameters used in the simulation, which are shown in the Table 1 below.  
For optimal FBG structure, the simulation is carried for different input values of CW with laser changes from \(-2\) dBm to \(+2\) dBm. Figure 2 below shows the results using NRZ modulation format, and Tanh Apodized FBG profile gives the better Q factor as compared to Gauss and uniform for 100 km fiber length.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Input power CW laser \(\mathrm{P}_{i}\) & \(-10\) to \(+15\) dBm \\ Laser frequency & 1480 nm, 1550 nm \\ Modulation Format & NRZ \\ Fiber length & 100 km \\ Bessel filter cutoff frequency & 0.75 Bit symbol rate \(\mathrm{Hz}\) \\ Gain of EDFA & 20 dBm \\ Noise & 4 dBm \\ MZ apodization parameter & Tanh, Gauss, Uniform \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter values used for simulation setup.  
Figure 1: Design system layout for simulation.  
The next phase is to define the optimal input power for communication link that gives maximum Q factor. CW laser input power varies within a range of \(-10\) dBm to \(+15\) dBm. Power less then \(-10\) dBm is not chosen because of low OSNR, and the power chosen must not be greater than \(+15\), as this causes nonlinear effects to the system. The following Figure 2(a) shows that simulation result of input power of CW laser. The graph shows that the optimal value of input power is \(+12\) dBm, which gives the high Q factor to the designed optical system. After finding the optimal values of FBG performance and input power of CW laser, the next parameter is an amplifier span length (\(L_{\text{a}}\)). This is the distance at which the optical amplifier would be placed from the transmitter end. EDFA with the gain of \(20\) dBm and noise of \(4\) dBm is used for this simulation.  
Figure 2(b) above shows the optimal value of amplifier span length at which the maximum Q factor occurs. It may be seen that the length of span is \(70\) km where this designed optical link gives the maximum Q factor.  
Figure 3: Optimal analysis for Q factor, (**a**) CW laser input power, (**b**) OF distance.  
Figure 2: Performance of FBG for different input values (**a**) Pi = \(+2\) dBm, (**b**) Pi = \(-2\) dBm.  
All the optimal values of parameters found above are then fed into the system as input in Figure 1. The final simulation values are shown Figure 4b below. The results show a Q factor of 9.58 dB, BER of 4.34 \(\times\) 10\({}^{-10}\), and Eye Height of 1.61.
# LoS Theoretical and Experimental MIMO Study from 1-40 GHz in Indoor Environments
###### Abstract  
In this work, line-of-sight multiple-input multiple-output (MIMO) measurements in the frequency range from 1 GHz to 40 GHz are presented for an indoor environment in the scope of new 5G bands. For the sake of comparison, the measured radio channels are reproduced with great accuracy using ray-tracing techniques by tuning all propagation mechanisms. The relative received power, root mean square of the delay spread (RMS DS) and K-factor provide an insight of how propagation behaves in indoor scenarios within a large and contiguous frequency band. Results show that a decay factor behaves nearly constant with frequency and the RMS DS is quite sensible to frequency. From these results, faithful one-slope 5G models are proposed. Finally, the contribution of the simulated propagation mechanisms to the radio channel is investigated which suggests that the simulation of the low-mmW radio channel can be simplified.  
5G; cm-W; mm-W; channel modeling +
Footnote †: journal: Accepted: 8 October 2020
## 2 Numerical Analysis of Beam-Steering Performance Based on 1-Bit Quantization
The loss of efficiency and beam steering error due to design can be compensated by adjusting the aperture size and the number of unit cell arrangements according to the install environment [11,24].
## 2 Battery Modeling
### Definition of State of Charge  
The state of charge (SOC) is defined as the ratio of the remaining capacity in a battery over the rated battery capacity. Using the CC method, the battery SOC can be calculated as follows:  
\[SOC_{t}=SOC_{t_{0}}-\frac{1}{Q_{rate}}\int_{t_{0}}^{t}\eta_{c}I_{L,t}dt \tag{4}\]  
where \(\eta_{c}\) is the coulomb efficiency, \(I_{L,t}\) is the load current at time \(t\). \(Q_{rate}\) is the rated capacity of the battery.