esa-sceva/satcom-chunk-collection huggingface dataset:
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 6 Biofilm Formation Enhances Antibiotic Resistance
### Lipid Modification  
The human immune system generates antimicrobial compounds, such as calprotectin, to counteract infections. _H. pylori_ can alter lipids to avoid being detected by the immune system and interact with calprotectin when cultured together, resulting in changes to enzymes such as LpxF, LpxL, and LpxR. These alterations lead to a higher amount of biofilm biomass, which assists in enhancing bacterial defense systems [100]. In addition, _H. pylori_ can alter cholesterol to increase its ability to attach to stomach epithelial cells, contributing to its ability to cause disease [101]. The practical attachment to the epithelium is necessary for biofilm formation.
# DRRU-Net: DCT-Coefficient-Learning RRU-Net for Detecting an Image-Splicing Forgery
## 2 Related Works
DCT coefficients are collected from the Y channel representing the luminance of the YCbCr color space and applied as non-overlapping 8 \(\times\) 8 blocks. According to that paper, the reason for considering the Y channel is that it is more useful for finding forensic clues in image forensics. The DCT stream is used as the input to the JPEG artifact learning module. In the JPEG artifact learning module, 1-channel DCT coefficients are converted into 21-channel volume representations to learn the distribution. Then, after the 8 \(\times\) 8 dilated convolution operation and the 1 \(\times\) 1 convolution operation, the quantization table, which is repeatedly arranged to have the same resolution as the output, is multiplied element by element. This output is combined with the output before quantization. The RGB stream of CAT-Net maintains a high-resolution representation and is trained by HRNet. The JPEG artifact learning module method based on the architecture of HRNet maintains the same resolution as the RGB stream learning method to output a feature map. Down sampling is performed with stride-2 convolution instead of pooling. CAT-Net requires datasets for pre-training, and has the disadvantage of separately building a network for pre-training JALM. In addition, since the performance variation is wide depending on whether or not the pre-training is performed, the pre-training is needed. This may reduce the flexibility of the forgery detection networks. Therefore, by applying leaky ReLU in DRRU-Net, valid clues for detecting a forgery which are commonly used in the DCT domain can be found, allowing us to show a good generalization performance on other datasets even though training only a few datasets.
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 3 Convolutional Neural Network
### Full Connected Layer  
The full connected layer refers to a general neural network. After the convolutional layer extracts features and the pooling layer reduces image parameters, the feature information is put into the full connected layer for classification. Each neuron is connected to a pixel of the previous filter, and each connected weight is identical and shared in the same layer. However, as each connection has an independent and dissimilar weight, the full connected layer consumes considerable computing resources [14].
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
## 4 Experiments
### Datasets
#### 4.1.1 MIRFlickr  
It contains 25,000 instances collected from open website, which are annotated by at least one of 24 tags. Similar to the work in [38], we ignored the instances that textual tags appear less than 20 times and finally selected 20,015 instances. We randomly selected \(2k\) instances as the query set and the rest as the retrieval set. Each image is represented as a 512-D GIST feature and each text is represented as a 1386-D bag-of-word (BOW) vector.
# A Survey on Search Strategy of Evolutionary Multi-Objective Optimization Algorithms
## 4 Evaluation Metrics of MOEA
### Multi-Objective Optimization Benchmark Problems  
Multi-objective algorithms require a test function to test the capability of the algorithm when it is improved. The test function needs to be capable of reflecting or containing the basic features of the MOP, such as continuous or discontinuous, derivable or non-derivable, convex or concave, single-peaked or multi-peaked, deception problem or non-deception problem, etc. The widely used benchmark multi-objective test functions are MOP1-MOP7, MOP-C1-MOP-C5 with bias constraints, ZDT1-ZDT6, and DTLZ1-DTLZ7, etc. [103]. This paper focuses on the ZDT test functions that are extensively employed in the evaluation of multi-objective algorithms. To assess the optimization capabilities of MOEA, we consistently employ five multi-objective benchmark problems known as ZDT functions, as demonstrated in Table 2, to evaluate its performance.  
[PERSON] et al. proposed the set of ZDT test functions in 2000, which is currently the most widely used set of multi-objective test functions [103]. It includes DZT1-DZT6, six different forms of test functions. They are characterized by  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Functions** & **Dimension** & **Definition** & & **Search Range** \\ \hline \multirow{3}{*}{ZDT1} & & \(f_{1}(x)\) & \(=\)\(x_{1}\) & & \\  & & \(f_{2}(x)\) & \(=\)\(g(x)[1-\sqrt{\frac{x_{1}}{g(x)}}]\) & \(x_{i}\in[0,1]\) \\  & & \(g(x)\) & \(=\)\(1+9\frac{\sum_{i}\sum_{j}x_{i}}{h-1}\) & & \\ \hline \multirow{3}{*}{ZDT2} & & \(f_{1}(x)\) & \(=\)\(x_{1}\) & & \\  & & \(f_{2}(x)\) & \(=\)\(g(x)[1-(\frac{x_{1}}{g(x)})^{2}]\) & \(x_{i}\in[0,1]\) \\  & & \(g(x)\) & \(=\)\(1+9\frac{\sum_{i}\sum_{j}x_{i}}{h-1}\) & & \\ \hline \multirow{3}{*}{ZDT3} & & \(f_{1}(x)\) & \(=\)\(x_{1}\) & & \\  & & \(f_{2}(x)\) & \(=\)\(g(x)[1-\sqrt{\frac{x_{1}}{g(x)}}-\frac{x_{1}}{g(x)}\sin(10\pi x_{1})]\) & \(x_{i}\in[0,1]\) \\  & & \(g(x)\) & \(=\)\(1+9\frac{\sum_{i}\sum_{j}x_{i}}{h-1}\) & &All of the functions have two objective functions, \(f_{1}(x_{i}),f_{2}(x_{i})\). Additionally, the plotted graph is straightforward to grasp because the form and position of the Pareto optimal frontier are known.
2. The number of decision variables is highly flexible and can be varied as needed.
# An In-Depth Survey of Bypassing Buffer Overflow Mitigation Techniques
## 4 OS-Based Mitigation Techniques
### Protecting the ELF Binaries
#### 4.3.2 Relocation Read-Only (RELRO)  
Several other mechanisms have been introduced to harden the binary executables and one of them is Relocation Read-Only (RELRO). As we have already seen that Global offset table (GOT) is used to resolve dynamically linked function of shared libraries. Procedure linkage table contains a jump stub to GOT and resides in.plt section [49]. The.plt section is used for having the instructions that point to the GOT and resides in.got.plt section. When a shared library function is called for the very first time, GOT points back to the PLT, and a call is made to dynamic linker that finds the actual address of that function. After finding the address of the function, it is written to GOT. When the call is made second time, the  
Figure 21: Payload Stage 2.  
GOT already contains the address. It is known as \"Lazy binding\". The noticeable point is that PLT should be at a fixed location from the.text section, GOT should be at known location because it contains information required for the program, and the GOT should be writable for performing lazy binding.  
Since, GOT is writable and resides at a fixed location it can be exploited to launch buffer overflow attacks. Thus, to prevent this vulnerability from exploitation, it is required that all the addresses are resolved at the beginning and then marked the GOT as read-only. RELRO [49] is a mitigation technique which in general makes Global Offset Table read-only so that GOT overwriting techniques cannot be used during buffer overflow exploitation. It has two levels of protection: Partial RELRO and Full RELRO [50]. Partial RELRO makes the.got section read-only (but not.got.plt section), due to which GOT overwriting can be done. Full RELRO makes the entire.got section read only including.got.plt section. Thus, any GOT overwriting technique is not allowed.
## 1 Introduction  
Glandular trichomes are epidermal outgrowths characterized by the presence of a secretory cavity or \"gland\", which houses secretory gland cells capable of synthesizing secondary metabolites and exporting them into a secretory reservoir [1, 2]. Glandular stalked trichomes (GSTs) on cannabis female flowers emerge from an epidermal cell and develop multicellular stalks which are usually less than 500 \(\upmu\)m long [3, 4, 5, 6]. In cannabis, glandular trichomes contain a rosette of secretory cells that synthesize and export biosynthetic enzymes into the secretory reservoir [7, 8]. The direct precursor of cannabinoids, cannabigerol (CBG), is synthesized in the plastid of the secretory gland cell, and then exported (by an unknown mechanism) into the secretory reservoir, where it is converted to tetrahydroocannabinolic acid (THCA), cannabidiotic acid (CBDA), or cannabichromenic acid (CBCA) by their respective synthases [7].  
Current models indicate that the regulation of secondary metabolite production is associated with the regulation of flowering and trichome development [9, 10]. After an epidermal cell receives a signal to initiate trichome morphogenesis, it develops into a sessile glandular trichome [1]. In concert with the maturation of the flower, sessile trichomes will develop into the bulbous phase and then eventually reach the capitate-stalked glandular (CSG) phase [11]. This is the mature phase at which cannabinoids are predominantly produced [10].  
A complex of transcription factors (TFs) in the R2R3-MYB and HD-ZIP IV families has been shown to regulate the development of glandular trichomes in other plant species [11; 12; 13]. This complex includes MIXTA-like proteins, which interact with other HD-ZIP IV TFs to coordinate trichome development in concert with flower development [13]. Understanding the genetic regulation of the development of a glandular trichome from sessile to stalked is of particular importance for cannabis biology because it has been shown that cannabinoids are not produced in any considerable amount until the trichome reaches the CSG phase [10].  
In cannabis, the genetic regulation of glandular trichome initiation and development is still unknown. In this study, we identified a novel R2R3-MYB TF in cannabis, _CsMIXTA_, which could positively regulate cannabis CSG development. We showed that _CsMIXTA_ was highly expressed in flowers; _CsMIXTA_ also showed higher expression in trichome cells isolated from female flower tissue. Ectopic expression of _CsMIXTA_ in tobacco not only significantly increased glandular trichome size and density but also promoted trichome branching on tobacco leaves. Our study suggests that _CsMIXTA_ is a TF that could potentially control glandular trichome initiation and development in female cannabis flowers.
## 3 Proposed Framework (Fed-DTB)
### Compliance Score
#### 3.1.3 Algorithm for Trust Management  
The algorithm 1 formalises the trust management process, describing the steps to calculate the trust scores for each vehicle over time.  
```
1:Set of vehicles \(V\), time period \(T\), \(t\) time step, factor weights \(w\), IDS alert threshold \(\tau_{\min}\)
2:Trust scores \(\{\tau_{i}(t)\}\) for all \(v_{i}\in V\), \(t\in T\)
3:for each \(t\in T\)do
4:for each vehicle \(v_{i}\in V\)do
5: Collect data and IDS alerts from vehicle \(v_{i}\)
6: Compute IDS alert score \(\tau_{i_{l}}(t)\) using Equations (11) and (12)
7:if\(\tau_{i_{l}}(t)=0\)then
8: Set \(\tau_{i}(t)=0\)
9: Continue to next vehicle
10:endif
11: Compute network status score \(\tau_{N_{l}}(t)\) using Equation (13)
12: Compute vehicle status score \(\tau_{V_{S}}(t)\) using Equation (14)
13: Compute energy efficiency score \(\tau_{E_{i}}(t)\) using Equation (15)
14: Compute compliance score \(\tau_{C_{i}}(t)\) using Equation (16)
15: Compute federated learning participation score \(\tau_{P_{l}}(t)\) using Equation (17)
16: Compute user feedback score \(\tau_{U_{l}}(t)\) using Equation (18)
17: Compute overall trust score \(\tau_{i}(t)\) using Equation (1)
18:if\(\tau_{i}(t)<\tau_{\min}\)then
19: Vehicle \(v_{i}\) is considered untrusted
20:else
21: Vehicle \(v_{i}\) is considered trusted
22:endif
23:endfor
24:return Trust scores \(\{\tau_{i}(t)\}\)
```  
**Algorithm 1** Trust Management System
## 2 Methodology
### GPR Image Preprocessing
#### 2.3.2 Wavelet Denoising  
A wavelet transform (WT) is a transformation analysis method that was developed based on the Short-Time Fourier Transform (STFT) [35; 36]. The echo signal collected by GPR mainly comprises the target signal and interference noise, which can be expressed as Equation (4):  
\[w(k)=s(k)+u(k) \tag{4}\]  
Equation (5) can be obtained using the above equation's discrete wavelet transform:  
\[w_{j,k}=s_{j,k}+u_{j,k} \tag{5}\]  
where \(w_{j,k}\), \(s_{j,k}\), and \(u_{j,k}\) represent the wavelet coefficients corresponding to the echo signal \(w(k)\), target echo signal \(s(k)\), and noise signal \(u(k)\), respectively.  
Wavelet denoising involves decomposing the measured signal at different scales by using a wavelet transformation. After the denoising is processed in the wavelet domain, the original signal after denoising is obtained by an inverse transform [37; 38]. In this way, the signal interference caused by external conditions can be fully reduced, and then the phase and amplitude transients of the signal can be effectively detected.  
The main steps of wavelet denoising are as follows:  
1. Wavelet decomposition: The three-level wavelet decomposition is shown in Figure 3[39]. Each decomposition will produce a low-frequency (LL) sub-image, horizontal high-frequency (HL) sub-image, vertical high-frequency (LH) sub-image, and high-frequency (HH) sub-image.
2. Threshold processing: Since most noise signals are in the high-frequency region, only HL, LH, and HH sub-images are processed. Furthermore, the optimized adaptive median-filtering method [40; 41] is adopted for denoising to obtain the wavelet coefficients \(\hat{w}_{j,k}\) after processing.
3. Wavelet reconstruction: the inverse wavelet transform is used to reconstruct the processed wavelet coefficients \(\hat{w}_{j,k}\), and the target echo signal \(\hat{s}(k)\) after noise removal is attained.  
Figure 4 shows the radar image of the underground cavity model obtained by gprMax 3.0 software, where (a) is the original image and (b) is the preprocessed image. It can be seen that the effective reflected data in the image were almost covered before preprocessing due to the direct wave being too prominent. After preprocessing, the signal-to-noise ratio of the image was significantly improved, and the target signal could be observed.
###### Abstract  
Generalized frequency division multiplexing (GFDM) is a waveform for the next-generation communication systems to succeed in the drawbacks of orthogonal frequency division multiplexing (OFDM). The symbols of users are transmitted with the time- and frequency-shifted versions of a prototype filter. According to filtering operation, the computational complexity and processing load are high for the devices that suffer from energy consumption. The communication systems are required to support the new generation devices that need low energy consumption and low latency issues. Motivated by such demands of the next-generation communication system, we propose a novel GFDM waveform that we call hexagonal GFDM. The contributions of the hexagonal GFDM are that it: (i) supports short transmission time based on its hexagonal time-frequency allocations; and (ii) provides low latency communication with low computational complexity manner. Furthermore, we design a transmitter and receiver structure in a less complicated way with mathematical derivation by using polyphase decomposition and Fourier transform (FT) transformation. The proposed systems are realized analytically and investigated over Rayleigh fading channel model through computer simulations.  
multicarrier modulation; hexagonal time-frequency allocation; generalized frequency division multiplexing (GFDM); polyphase decomposition +
Footnote †: journal: Electronics
## 2 Challenges and Methodology  
In this section, we present the related background, challenges to be addressed and briefly introduce our methodology with several considerations for transient fault detection.  
The term anomaly is commonly used to describe an abnormal state or behavior of a physical system. In this paper, however, we do not use this term only to describe the particular situation to cause an obvious negative consequence for the system. This is because some of the anomalies are often outliers even though anomalies are considered as an early sign of fault occurrence. In particular, anomalies such as outliers and noisy data in sensor data can naturally occur due to environmental uncertainty, even during normal operation. Furthermore, it is difficult to see a repetition of the previous anomaly again under the normal condition. In fault detection, outliers and noisy data derived from the dynamic environment are meaningless measurements. Since this type of anomaly might cause just temporary inconsistencies in the data pattern of the system, it should be excluded from fault detection. The outliner and noisy data are regarded as the normal data with the acceptable uncertainty if we can be sure that such data with uncertainty does not adversely affect the operation of the system. In this paper, we call this type of the anomalies a soft fault and also assume that data defined as the soft fault may be in the normal condition.  
In order to prevent unexpected situations from the cumulative effects of anomaly occurrences, it is necessary to distinguish transient faults from soft faults inherent in the state of a physical system in the safety-critical automotive CPS. Hence, we classify anomalous data into soft and transient faults. The distinguished faults might also be considered suspected attacks injected to sensors by adversaries, but identification between attacks and faults is not within the scope of this paper.
# Reinforcement Learning for Autonomous Underwater Vehicles via Data-Informed Domain Randomization
## 5 Simulation and Experimental Results  
The proposed transfer reinforcement-learning-based data-informed domain randomization was tested in numerical simulations and experiments on a real AUV in a lab tank. In both cases, the source domain was the numerical simulations conducted in Webots, where the mass of the AUV was set as 12 kg. The dynamics model of the simulated AUV is given  
Figure 6: Transfer process between the source controller and target controller.  
in Equation (3), with neutral buoyancy. As shown in Figure 7, the AUV was equipped with six thrusters, each of which can output thrust within \([-50,50]\)\(N\). The dead band was chosen as \([-5,5]\)\(N\). As in Section 2, the AUV is able to move in the \(x\), \(y\), and \(z\) directions of the body frame and rotate along the \(z\) axis. In addition, the control input is mapped to PWM signals of four dimensions. An example of the numerical simulation is depicted in Figure 8, where the origin \(O_{i}\) in the earth-fixed frame is illustrated as a small red ball.  
A source policy \(\mathbf{u}_{s}\) was trained by RL through interactions with \(\mathcal{F}_{t}\) in the source domain. In each episode, the initial AUV pose and velocities were randomly sampled from a set, where the positions were within a box of \([-10,10]\)\([m]\)\(\times\)\([-10,10]\)\([m]\)\(\times\)\([-10,10]\)\([m]\) and the velocities were within a box of \([-2,2]\)\([m/s]\)\(\times\)\([-2,2]\)\([m/s]\)\(\times\)\([-2,2]\)\([m/s]\). The controller network \(\mathbf{u}_{s}\) output the four-dimensional PWM signal vector, which was converted to a generalized force in the body frame \(X_{b}Y_{b}Z_{b}\).  
In the numerical simulations used to train the source controller, the generalized mass matrix is given as  
\[\mathbf{M}=\begin{bmatrix}m+X_{a}&0&0&0\\ 0&m+Y_{\phi}&0&0\\ 0&0&m+Z_{\oplus}&0\\ 0&0&0&I_{z}+N_{r}\end{bmatrix}, \tag{13}\]  
The coefficient matrix of the drag term is  
\[\mathbf{D}=\begin{bmatrix}-X_{u}-X_{u|u|}|u|&0&0&0\\ 0&-Y_{v}-Y_{v|v|}|v|&0&0\\ 0&0&-Z_{w}-Z_{w|u|}|w|&0\\ 0&0&0&-N_{r}-N_{|r|}|r|\end{bmatrix} \tag{14}\]  
and the Coriolis matrix \(\mathbf{C}\) was assumed to be zero. The parameters can be found in Table 1.  
Figure 8: AUV simulated in the Webots simulator with the ball represents the targeted origin \(O_{s}\).  
Figure 7: A schematic of the AUV used in numerical simulations and experiments, where 6 thrusters are indexed from 1 to 6.  
The target domains differ in the numerical simulation tests and the experimental tests. In the numerical simulation tests, the target domain and target dynamics were again simulated in the Webots simulator, subject to parameters and configurations quite different from the simulated source domain and the source dynamics. The tested scenarios in the case are referred to as sim-to-sim transfer tests. In the experimental tests, the target domain and the target dynamics were of the real AUV in the lab tank, the details of which are introduced later.
## 1 Introduction
### Suggestions
#### 3.2.3 Costs and Benefits  
The costs associated with the provision of inertial response from converter-interfaced DRES is the installation cost of the required FSS and the DC/DC converter for the control of the former. The size of the FSS can be roughly estimated as follows: if we assume that the DRES should be able to provide inertia with \(H=5\) s, i.e., the average inertia time constant of the continental European grid, under ROCOFs as high as 2.5 Hz/s, as anticipated in [19], then the power that should be injected by the FSS is, according to (7), 0.5 pu of the DRES rated power. The FSS will be theoretically discharged until the frequency nadir is reached. In continental Europe the frequency nadir is 49.2 Hz [31] while is other European areas is 49 Hz. Considering the worst case of 49 Hz and ROCOF = 2.5 Hz/s, the nadir will be reached in 0.4 s. Thus, the energy required from the FSS is \(0.5\times 0.4=0.2\) kJ/kW of rated DRES power. Today, the cost of the ultracapacitor bank is of the order of 20-25 k\(\epsilon\)/kWh-5.5-7.0 \(\epsilon\)/kJ, [61; 62]. Therefore, the ultracapacitor cost is 1.1-1.4 \(\epsilon\)/kW of DRES rated power. The cost of a bidirectional DC/DC converter is of the order of 100 \(\epsilon\)/kW [43], therefore the converter cost is 50 \(\epsilon\)/kW of DRES power. Roughly, the total cost is of the order of 51-52 \(\epsilon\)/kW of DRES power, which is very small compared to the cost of converter interfaced DRES; \(\sim\)800 \(\epsilon\)/kW for PV systems and \(\sim\)1200 \(\epsilon\)/kW for wind systems, [63].  
The evaluation of the financial benefits requires simulations at power system level, but some rough estimations are reported in [9], where is stated that in Great Britain the cost for containing the increase of ROCOF increased by 90 M\(\epsilon\) within a year. This happens because the respective TSO engaged numerous synchronous condensers with flywheels in order to keep the decreasing system inertial due to the decommission of conventional power plants.