esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Differences in Cell Morphology and Cell Membrane Fatty Acid Composition
the ratio of saturated fatty acids to unsaturated fatty acids (S/U) in the cell membrane from liquid state fermentation was 0.95, indicating the content of unsaturated fatty acids and saturated fatty acids in cell membrane was similar. Under acid stress, the content of saturated fatty acids in the cell membrane of the two types of cells increased, and the S/U ratio in solid-state fermented cells increased to 2.64, while the S/U ratio in liquid-state fermented cells increased to 3.92, thus the content of saturated fatty acids in liquid-state fermented cells increased more significantly than that in solid-state fermented cells. Before acid stress treatment, palmitic acid, stearic acid, oleic acid, and linoleic acid exhibited high content in the cell membranes of the two types of cells (Figure 3). After acid stress treatment, in solid-state fermented cells, the content of linoleic acid in the cell membrane increased significantly from 1530.59 \(\upmu\)g/g to 2972.23 \(\upmu\)g/g, and the content of oleic acid also increased significantly from 682.13 \(\upmu\)g/g to 1746.20 \(\upmu\)g/g, However, under acid stress, in liquid-state fermented cells, the content of linoleic acid of the cells decreased from 5241.26 \(\upmu\)g/g to 4685.70 \(\upmu\)g/g; the content of oleic acid increased slightly from 1320.58 \(\upmu\)g/g to 1351.64 \(\upmu\)g/g; the content of palmitic acid in the cell membrane increased significantly from 5036.58 \(\upmu\)g/g to 22,115.55 \(\upmu\)g/g; while that of stearic acid increased significantly from 2025.53 \(\upmu\)g/g to 13,065.27 \(\upmu\)g/g. This might explain the reason for the significant changes in the S/U ratio of cell membrane fatty acids. The S/U ratio in the cells of _Lactobacillus plantarum_ ZDY2013 (recently reassigned as _Lactiplantiobacillus plantarum_ ZDY2013) under acid stress was significantly higher than that without acid stress [26], which was consistent with our results that saturated fatty acid saturation in cell membranes of _Lacticaseibacillus paracei_ Zhang increased under acid stress, thus improving the cell membrane rigidity in response to acid stress stimulation. The content of palmitic acid in _Lactobacillus casei_ ATCC 334 (recently reassigned as _Lacticaseibacillus paracasei_ ATCC334) increased under acid stress [54]. In contrast to the findings reported by [PERSON] et al. that unsaturated fatty acid under acid stress increased [55], our results indicated that the S/U ratio in cell membranes increased after acid stress, and the increased content of two saturated fatty acids (palmitic acid and stearic acid) enhanced the rigidity of cell membrane, which was beneficial to tolerance of _Lacticaseibacillus paracei_ Zhang to acid stress.
# Spectrogram Classification Using Dissimilarity Space
## 4 Clustering
### Hierarchical  
Hierarchical clustering is a clustering technique that groups data by building a hierarchy of clusters. The hierarchy tree that is obtained is divided into \(n\) levels chosen for the application at hand. There are two main categories of hierarchical clustering:  
* _Agglomerative_: each pattern starts in its own cluster; then, moving up the hierarchy, each cluster in one level is obtained by merging two clusters in the previous level.
* _Divisive_: all patterns start in one cluster; then, by moving down the hierarchy, each pair of clusters is obtained by splitting a single cluster in the previous level.  
In this work, the default MATLAB implementation of hierarchical clustering is used, which is the agglomerative type. The MATLAB algorithm loops through the following three steps:  
1. Find the similarity or dissimilarity between every pair of objects in the dataset using a distance metric.
2. Group the objects into a binary hierarchical cluster tree by linking objects in pairs based on their distance. As objects are paired into binary clusters, the newly formed clusters are grouped into larger clusters until a hierarchical tree is formed.
3. Determine where to cut the hierarchical tree into clusters. Here, MATLAB's _cluster_ function is used to prune branches off the bottom of the hierarchical tree and to assign all the objects below each cut to a single cluster. In this way, \(k\) clusters are obtained.  
After applying this algorithm, centroids, as the mean vectors of each cluster, are computed.
## 2 Methodology
### Satellite-Based Augmentation System (SBAS) Orbit and Clock Correction Evaluation Method
#### 2.1.3 Signal-in-Space Range Error (SISRE) Calculation  
The impact of orbit errors on the Signal-in-Space Range Errors (SISREs) relates to the orientation of the line-of-sight. Except for orbit errors, satellite clock offset errors (\(\Delta\)d\(T\)) also account for a dominant portion of the signal-in-space errors. The global average SISRE is usually introduced to represent the root mean square (RMS) of SISREs across the portion of the globe view of the satellite. If there is no special explanation, SISRE refers to global average SISRE in this contribution. The combined orbit and clock SISRE can be computed as [30; 31; 32; 33]:  
\[SISRE=\sqrt{\text{rms}(w_{\text{radial}}\Delta r_{\text{radial}}-\text{c} \Delta\text{d}T)^{2}+w_{along}^{2}\cdot A^{2}+w_{cross}^{2}\cdot\text{C}^{2}} \tag{13}\]  
where \(\text{rms}(*)\) denotes an RMS operation, \(A,~{}C\) are the RMSs of the orbit errors in the along-track (\(\Delta r_{along}\)) and cross-track (\(\Delta r_{cross}\)) directions, \(w_{radial}=0.98\) and \(w_{along}=w_{cross}=~{}0.141\) represent the corresponding weight factors, which have been discussed in the literature [31; 32; 33]. It should be noted that, for the observer on the surface of the earth, the radial orbit error results in a positive range error, while the clock offset error leads to a negative range error. That is to say, only the difference of the radial orbit error and the clock offset error contributes to the SISRE.
# Signal Source Localization of Multiple Robots Using an Event-Triggered Communication Scheme
## 2 Preliminaries
### Communication Topologies  
Communication is very important for the coordination of multiple robots. The robots can receive and send information by communication links. In order to describe the communication links at the mathematical level, one can usually employ graph theory to model communication topologies where the vertices denote the robot and the edges refer to communication links. The undirected and connected graph \(G_{n}(X,E,A)\) is used to present the communication topology for mobile robots in this paper. An undirected graph is a set of vertices and a collection of edges that each connect a pair of vertices.  
We suppose that \(G_{n}(X,E,A)\) is an undirected graph, which includes a set of nodes \(X=x_{1},x_{2},...,x_{n}\), a set of edges \(E\subseteq X\times X\) and an adjacency matrix \(A=[a_{ij}]\). It should be pointed out that, if there exists an edge between the \(i-\)th node and the \(j-\)th node, then \(a_{ij}=1\); otherwise, \(a_{ij}=0\). In addition, \(G_{n+1}\)=\(G_{n}\cup x_{0}\) is an extension of graph \(G_{n}(X,E,A)\), where \(x_{0}\) is a fictitious node, which can represent a virtual leader. When the virtual leader's information can be provided to the robot, there exists an edge between the virtual leader and the robot, i.e., \(a_{i0}=1(i=1,\cdots,n)\); otherwise, \(a_{i0}=0\). The Laplacian matrix of the graph \(G_{n}(X,E,A)\) is \(L_{G_{n}}=[l_{ij}]\in\mathbb{R}^{n\times n}\), where \(l_{ij}\) is:  
\[l_{ij}=\left\{\begin{array}{ll}\sum_{j=1,j\
eq i}^{n}a_{ij},&i=j\\ -a_{ij},&i\
eq j\end{array}\right. \tag{9}\]  
**3. Decision-Control Approach with an Event-Triggered Communication Scheme**  
In this section, a particle filter is used to estimate the position of a signal source. Then, a cooperative control approach with an event-triggered communication scheme is proposed to control robots to locate the signal source. Finally, convergence analysis and velocity design of the virtual leader are given.
# Optimal Power Dispatch of an Offshore Wind Farm under Generator Fault
## 2 Wind Farm Model
### Wake Model  
The wake effect is the aerodynamic interaction among WTs in the WF. When the wind from upstream blows through the WT, and the wind energy in the downstream becomes smaller because the WT extracts a part of the energy from the wind. The result of this process produces a wake. The wake has two main effects. One is the wind velocity deficit, which decreases the available wind energy to the downstream WT. The other is the increase in turbulence, which aggravates the fatigue loads of the downstream WTs. Because this paper mainly studies the active power of the WF, only wind velocity  
Figure 4: Operation state of WT in the maximum \(\omega\) down-regulation strategy: (**a**) Pitch angle surface. (**b**) Rotor speed surface. (**c**) \(C_{\text{p}}\) surface. (**d**) \(C_{\text{t}}\) surface.  
deficit is considered here. To describe the wake effects in the WF model, the Jensen model was adopted for the single wake and the quadratic summation method for multiple wakes [21]. Although the Jensen model is not as accurate as the computational fluid dynamics model, the computational cost of the Jensen model is low. Therefore, it is very suitable for control. The Jensen model can be expressed as follows:  
\[\frac{v}{u}=\frac{1-\sqrt{1-C_{\mathrm{t}}}}{(1-2\alpha X/D)^{2}} \tag{2}\]  
where \(v\) is the downwind wind speed at position \(X\), \(u\) is the ambient free wind speed, \(X\) is the distance between upstream and downstream WT, \(D\) is the diameter of the rotor, and \(\alpha\) is the decay constant, choosing 0.05 for offshore WFs [22].  
For multiple wakes, the velocity deficit of the \(j\)th WT can be calculated as:  
\[(1-\frac{v_{j}}{u})^{2}=\sum_{i=1}^{n}(1-\frac{v_{ij}}{u})^{2} \tag{3}\]  
where \(v_{j}\) is the wind speed of the \(j\)th WT, \(n\) is the number of wakes that the \(j\)th WT is in, and \(v_{\mathrm{ij}}\) is the wind speed of the \(j\)th WT under the influence of the wake of the \(i\)th WT.
## 5 Evaluation and Results
### Sample SQLi Generation
#### 5.2.4 Final Backend SQL Query  
The vulnerable application generates the following backend query:  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System** & **Details** \\ \hline
**LLM** & OpenAI GPT-4o, Google Gemini Pro \\ \hline
**Operating System** & Ubuntu 22.04 LTS \\ \hline
**Database** & MySQL v8.0.40, PostgreSQL v14.15 \\ \hline
**Webserver** & Apache v2.4.52 \\ \hline
**Web Application Firewall** & ModSecurity v2.9.5 default configuration, AWS WAF \\ \hline
**Attack Detection Rules** & OWASP CRS v4.9.0 \\ \hline
**Vulnerable Application** & Custom PHP app \\ \hline
**Clustering algorithms** & Scikit-learn v1.6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: System components used in the evaluation setup. All components used their default configurations. GPT-4o was configured with a temperature setting of 0.7, and Gemini Pro with a temperature setting of 1.  
SELECT * FROM users WHERE id = -7552 OR (SELECT CASE WHEN (6872 = 6872 AND ASCII('a') = 97 COLLATE utf8 mb4_bin) THEN 1 ELSE 0 END) = 1; The query operates as follows: * **id = -7552**: Checks for a user with an id of -7552. Typically, this returns no results unless such an ID exists. * _OR (SELECT CASE WHEN...) =_ 1: * The OR operator ensures that if the first condition fails, the injected condition is evaluated. * The CASE WHEN statement evaluates the following conditions: * _6872 = 6872_: A tautology, always TRUE. * _ASCII('a') =_ 97 COLLATE utf8 mb4_bin_: Verifies that the ASCII value of 'a' matches 97, also TRUE. * Since both conditions are TRUE, the CASE WHEN statement returns 1, making the overall expression evaluate to TRUE.
###### Abstract  
A generalized carrier index differential chaos shift keying with simultaneous wireless information and power transfer (GCI-DCSK SWIPT) scheme, is proposed, which is an improved scheme for CI-DCSK SWIPT. Compared to CI-DCSK SWIPT, GCI-DCSK SWIPT is not only more flexible in selecting both index bit number and index carrier number, but also is more practical for considering both path loss and the conversion noise generated by radio frequency (RF) band to baseband. The proposed scheme applied a time-switching manner to harvest the energy carried by the inactive carriers. Theoretical bit error rate (BER) expressions of the scheme over AWGN and multipath Rayleigh fading channels are derived, and the ratio of harvested energy to transmitted energy is derived to describe the probability of self-sufficiency on power supply. In addition, the frame-derived factor and the energy carried by inactive carriers are optimized to obtain better BER performance. Simulation results show that taking both path loss and conversion noise into consideration, the scheme is still self-sufficient with good BER performance. Furthermore, by adjusting the number of active carriers of CCI-DCSK SWIPT, some cases of GCI-DCSK SWIPT outperform conversion noise-aware CI-DCSK SWIPT in BER.  
DCSK; index modulation; SWIPT; path loss; conversion noise Article
## 4 Discussion
### The Decoupled Effects of RSM and PRE on GPP
RSM mediates the trade-off between carbon assimilation and water loss (transpiration); sufficient RSM enhances photosynthesis, whereas low RSM reduces transpiration to conserve water at the expense of carbon uptake [22]. Conversely, Figure 7b indicates relatively low \(\Delta SA_{GPP}(SA_{PRE}\mid SA_{RSM})\) values in forests and wetlands, suggesting a comparatively weaker influence of PRE on GPP in these ecosystems. Vegetation in forests and wetlands typically possesses extensive root systems capable of accessing RSM, rendering them less sensitive to short-term precipitation variability [61]. Although humid regions are more responsive to VPD, they still depend on RSM during dry spells [24]. The analysis showed that grasslands exhibited the highest \(\Delta SA_{GPP}(SA_{PRE}\mid SA_{RSM})\) values, followed by croplands, indicating that PRE exerted the most pronounced influence on GPP in these two vegetation types. Grasslands, with their relatively shallow root systems, exhibit greater sensitivity to precipitation compared to other vegetation types [61]. Additionally, grasslands experience stronger feedbacks between RSM and PRE, whereby PRE has a more immediate impact on GPP than in forests [62]. Negative correlations between precipitation and RSM were observed in northern hemisphere grasslands, sparse vegetation areas, and xerophytic shrublands [62]. As depicted in Figure 7f, \(\Delta SA_{GPP}(SA_{RSM}\mid SA_{PRE})-\Delta SA_{GPP}(SA_{PRE}\mid SA_{RSM})\) exhibited relatively low values in grassland areas, demonstrating that both RSM and PRE significantly influenced grassland GPP in the study area. This is likely attributable to vegetation utilization of RSM, which results in negative correlations between PRE and RSM.
# Model-Robust Estimation of Multiple-Group Structural Equation Models
## 6 Empirical Example: ESS 2005 Data
### Method  
In this empirical example, we use a dataset that was also analyzed in [32; 63; 64]. The data came from the European social survey (ESS) conducted in the year 2005 (ESS 2005) including subjects from 26 countries. The latent factor variable of tradition and conformity was assessed by four items presented in portrait format, where the scale of the items is such that a high value represents a low level of tradition conformity. The wording of the four items was (see [32]): \"It is important for him to be humble and modest. He tries not to draw attention to himself.\" (item TR9), \"Tradition is important to him. He tries to follow the customs handed down by his religion or family.\" (item TR20), \"He believes that people should do what they're told. He thinks people should follow rules at all times, even when no one is watching.\" (item C07), and \"It is important for him to always behave properly. He wants to avoid doing anything people would say is wrong.\" (item C016). The full dataset used in [32] was downloaded from [[https://www.statmodel.com/Alignment.shtml](https://www.statmodel.com/Alignment.shtml)]([https://www.statmodel.com/Alignment.shtml](https://www.statmodel.com/Alignment.shtml)) (accessed on 27 March 2023). For this empirical example, a subsample of 33,060 persons from 17 selected countries was included to restrict the range of variability of country factor means. The sample sizes per country ranged between 1358 and 2963, with an average of 1997.1 (SD = 464.5). We only included participants in the sample that had no missing values on all four items.  
We specified a one-dimensional factor model with 17 groups (i.e., 17 countries) assuming invariant item parameters (i.e., invariant intercepts, loadings, and residual variances) in the estimation approaches ML, ULS, and RME, with powers \(p=0.25\), 0.5, and 1. In RegML, the SCAD penalty was imposed on group-specific item intercepts.  
The obtained country means and country standard deviations of the factor variable were linearly transformed for all different estimators such that the total population comprising all persons from all 17 countries had a mean of 0 and a standard deviation of 1.  
The analysis was conducted using the mgsem() function from the R [60] package sirt [61]. The processed dataset and replication syntax can be found at [[https://osf.io/v8f45](https://osf.io/v8f45)]([https://osf.io/v8f45](https://osf.io/v8f45)) (accessed on 27 March 2023).
# Real-Time Text-to-Cypher Query Generation with Large Language Models for Graph Databases
## 2 Theoretical Foundations
### Large Language Models  
Large Language Models (LLMs) are artificial intelligences that are trained with enormous amounts of words. In this process, they utilize neural networks to deduce complex relationships between words in test-based training data. Most times, LLMs use the transformer architecture as a foundation for language processing tasks. The transformer architecture is based on the concept of self-attention, in which several parts of the input sequence are put into relation to enable a differentiated view of the sequence [7]. Through training on the data, LLMs learn the connection between words and how they can be used in a language. This offers the possibility to perform certain processing tasks with the human language [8]. Generally, LLMs can be divided into their four core principals: pre-training, adaptation, utilization, and evaluation. All of these are essential for the success of an LLM [9].  
**Pre-training:** In the pre-training phase, the foundation of the LLMs skills is being created. Through training on vast amounts of data, the LLM learns foundational Natural Language Understanding and Natural Language Generation abilities [9; 10].  
**Adaptation:** After the pre-training phase, LLMs have the capability to perform several tasks concerning human language. Depending on the overall goal of the specific LLM, in the adaption phase, the skills are expanded or the behavior is adjusted to the demands of the developer [9].  
**Utilization:** In the utilization phase, different prompting strategies to tackle the processing of the tasks are designed. For example, the prompting strategy of in-context learning demands the user to frame the task in natural language and input it into a text box. The model then analyses and implements the requested task [9; 11].  
**Evaluation:** To evaluate the effectiveness and the quality of an LLM, different benchmarking tasks must be performed to empirically analyze the abilities of the LLM [9].
## 2 Data Preprocessing
### Noise Removal  
Noise removal is a basic procedure for signal enhancement. Many sensor signals are composed of time series data, so a variety of digital signal processing and time series analysis techniques have been applied for signal enhancement. Different from Fourier analysis which only focuses on frequency domain, wavelet analysis examines data in both time and frequency domains. It has become a powerful noise filtering method, decomposing original signal into low and high frequency domains [7]. The high  
Figure 1: Flowchart of a machine learning process for electric sensor systems.  
frequency domain contains more noise information from the original data, so modifying the wavelet coefficients in the high frequency domain by setting up a threshold is a simple but effective method for sensor signal enhancement [8]. Auto-regressive (AR) analysis is another noise filtering technique, generating AR coefficients to represent and reconstruct original signal. It has been successfully used for chromatographic signal enhancement [9; 10].
## 3 Proposed Method  
### Artificial Potential Field  
The artificial potential field (APF) method is inspired by the electrical charges' concept [18], whereby the objects in the configuration space, where the vehicle is traveling, are presumed to emit potential charges. The goal or target position is assumed to generate an attractive force that pulls the ego vehicle towards it. On the other hand, the obstacle creates a repulsive force the pushes the vehicle away. These forces are calculated in reference to the vehicle's position. These forces will define the behavior of the vehicle as it moves towards the goal, while avoiding any obstacle. The repulsive force will also prevent the vehicle from 'hugging the wall', which is the behavior that some other path planning method suffers [12]. Figure 7 shows the illustration of both force field.  
The total potential is defined as the sum of two potentials, i.e., attractive and repulsive, given as  
\[U\left(q\right)\ =\ U_{att}\left(q\right)\ +\ U_{rep}\left(q\right) \tag{1}\]  
where \(U_{att}\left(q\right)\) is the attractive potential generated at the goal position, with respect to each cell in the configuration space, as follows:  
\[U_{att}\left(q\right)=\frac{1}{2}\delta\cdot d\left(q,q_{g}\right) \tag{2}\]  
where \(\delta>0\) is a positive constant that determines the strength of the attractive field, also known as gravitational constant, and \(d\left(q,q_{g}\right)=\left\|q-q_{g}\right\|\) is the Euclidean distance between each cell \(q:=\left(x,y\right)\) in the configuration space and the goal position, \(q_{g}:=\left(x_{g},y_{g}\right)\).  
Repulsive potential affects every cell in the configuration space as follows:  
\[U_{rep}\left(q\right)=\left\{\begin{matrix}\frac{1}{2}\alpha\cdot\frac{1}{d \left(q,q_{obs}\right)}-\frac{1}{Q^{\prime}}&\text{if}&d\left(q,q_{obs}\right) \leq Q^{*}\\ 0&\text{if}&d\left(q,q_{obs}\right)>Q^{*}\end{matrix}\right. \tag{3}\]  
where \(\alpha>0\) is a positive weighting constant of the repulsive field. Meanwhile, \(d\left(q,q_{obs}\right)=\underset{Q^{\prime}\in Q_{obs}}{\min}\left\|Q-Q^{ \prime}\right\|\) represents the smallest distance between the vehicle's current position, \(Q=\left(x_{start},y_{start}\right)\), and the obstacle position, \(q_{obs}:=Q_{obs}=\left(x_{obs},y_{obs}\right)\). The repulsive potential is defined as zero outside the distance of the influence of the obstacle (\(Q^{*}\)) and positive when it is inside.