esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Iterative ARTP Mutagenesis and Screening  
When compared with other breeding methods, ARTP has a higher mutation frequency [9]. Before performing continuous ARTP mutagenesis and screening, the effects of different ARTP treatment times on strain growth were investigated as survival rates and mutation frequencies can be significantly affected by implanted doses [40]. At first, strain lethality was gradually increased when treatment times increased from 0 to 150 s (Figure 5A). Then, lethality slightly declined when the time ranged from 150 to 180 s (Figure 5A). This phenomenon was explained by the repair mechanism inside the cell (SOS) becoming activated under corresponding ion dose ranges [41; 42]. After this, more mutant strains were produced. When treatment times exceeded 180 s, self-repair mechanisms lagged behind cellular damage, and therefore the strain was practically dead [40]. Thus, 180 s, which corresponded to the optimal ion dosage, was chosen for mutation induction.  
After six rounds of ARTP mutagenesis and screening (the top four mutant strains with the highest NM potency in each round were chosen as starting strains for the next), a high-producing NM strain, S\(f\)6-2 was finally screened out from 300 prescreened strains. It displayed a higher NM potency at 45% more than the wild-type strain (_S. fradiae_ GC6010) and generated 7780 \(\pm\) 110 U/mL (Figure 5B). Furthermore, the cumulative effects of multiple rounds of ARTP mutagenesis on NM potency in mutants were investigated. As the number of iterations increased, the proportion of mutant strains gradually increased, with the proportion of positive mutant strains showing an increasing trend (Figure 5B,C). Thus, multiple ARTP mutagenesis rounds still generated good cumulative effects in mutants for NM production. We also assessed the genetic stability of S\(f\)6-2 for industrial production; the mutant displayed remarkably stable NM production levels over six generations (Figure 5D).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Addition Amount (\(\upmu\)g)** & **Measured Amount (\(\upmu\)g)** & **Recovery Rate (\%)** & **Average Recovery Rate (\%)** & \(RSD\)**\%** \\ \hline
3 & 2.8943 \(\pm\) 0.0289 & 96.4770 & & \\
5 & 4.7008 \(\pm\) 0.0564 & 94.0163 & & \\
7 & 6.6878 \(\pm\) 0.0535 & 95.5401 & & \\
9 & 9.0537 \(\pm\) 0.1358 & 100.5962 & 98.4684 & 3.2700 \\
11 & 11.1122 \(\pm\) 0.2224 & 101.0200 & & \\
13 & 13.3626 \(\pm\) 0.2138 & 102.7892 & & \\
15 & 14.8260 \(\pm\) 0.3707 & 98.8401 & & \\ \hline \end{tabular} The experiments were performed in triplicate. \(RSD\): relative standard deviation.  
\end{table}
Table 2: Spike and recovery experiments.
# Nanometer-Resolution Imaging of Living Cells Using Soft X-ray Contact Microscopy
## 2 Soft X-ray Contact Microscopy  
The principle of soft X-ray contact microscopy is well known. It can be considered a variant of X-ray contact microradiography, in which the shadow image of a specimen is obtained by irradiating the sample on the photographic plate with X-rays [21; 22]. Soft X-rays in the \"water window\" spectral range, produced with a specially designed X-ray tube and fine-grained photographic emulsion, have been used for imaging cellular structures with a sub-micrometer resolution. This spatial resolution corresponds to the resolution of the light microscope system utilized in this experiment to inspect the micro radiograms [23; 24]. Resolution in the nanometer range was achieved when a photoresist was used to record the microradiogram instead of a photographic plate and electron microscopy to examine the microradiogram in place of an optical microscope. This SXCM technique, based on X-ray photoresist, was adapted from X-ray lithography techniques and was quickly recognized as a promising microscopy technique for examining biological specimens [25; 26]. The unique properties of photons at soft-X-ray wavelength makes it possible to image cells based on the organic composition of unstained subcellular structures. X-ray techniques, including SXCM, enable the study of the remodeling of cell and extracellular matrix (ECM) architecture during normal and disease processes. The investigation of single focal adhesion (FA) in cancer, a crucial evaluation of metastatic potential, is possible thanks to SXCM.  
The soft X-ray contact microscopy of biological objects with nanometric resolution was demonstrated for the first time using an X-ray tube and synchrotron operating in the \"water window\" range [27; 28].  
The SXCM technique is schematically presented in Figure 1. It uses a three-step procedure. In the first step, called the exposure (Figure 1a), soft X-ray (SXR) radiation emitted from the source is transmitted through the sample to expose a high-resolution recording medium that is \"in contact\" with the sample. A thin layer of X-ray resist spun on a silicon wafer is usually used as the recording medium. It allows the recording of the local intensity modulation of radiation transmitted through the sample in its structure. The absorbed radiation dose is then converted into the change in the recording-medium morphology, creating an imprint (relief) structure in the recording medium. This conversion is achieved during the development process, a standard chemical procedure (Figure 1b). Finally, the radiation-induced changes in the recording medium can be digitized, providing a digital image (Figure 1c).  
To provide superior resolution compared to optical methods, the final step (Figure 1c) is performed using atomic force microscopy (AFM), scanning electron microscopy (SEM), or transmission electron microscopy (TEM); however, fluorescence microscopes (FM) are also used when lithium fluoride crystals are used as the recording medium.
# Airborne Radar Staggered PRF Coherent Processing Method for Down-Looking Target Detection
###### Abstract  
Airborne radar plays a crucial role in detecting aerial targets, but its detection capability can be significantly affected by strong ground clutter when operating in down-looking mode. The range-Doppler (RD) results of ground clutter in airborne radar may overlap the range-Doppler results of the targets, causing target occlusion and rendering them undetectable. In addition to target detection, accurate extraction of target parameters is also of great importance. Due to the high-speed movement of the airborne platform, targets may exhibit range-Doppler ambiguity, necessitating the use of appropriate methods to resolve the range-Doppler ambiguity. Aiming at the problem of target occlusion by clutter with range-Doppler two-dimensional ambiguity during airborne radar down-looking target detection, this paper conducts a study on the detection of airborne radar down-looking targets based on orthogonal signals combined with pulse repetition frequency (PRF) staggered transmission. An optimization method is employed to design multiple sets of PRF values, aiming to reduce the radar blind zone. The range-Doppler two-dimensional ambiguity of targets is resolved using multiple sets of PRF, and coherent processing is performed on the corresponding echoes using the transmission intervals, maximizing the utilization of radar transmission energy. This approach enhances the signal-to-noise ratio (SNR) and improves the detection performance and parameter estimation accuracy, particularly for low SNR targets. The superiority of the proposed approach is demonstrated through simulation experiments.  
airborne radar; target detection; ground clutter; coherent processing; range-doppler de-ambiguous 2023  
3
## 3 Effect of the Cross-Sectional Types on Natural Frequencies  
In this section, the top 20 orders of natural frequencies of the Bernoulli-Euler beam, the Rayleigh beam, the shear beam, the Timoshenko beam, and the modified Timoshenko beam are calculated and compared. From the comparisons, the natural frequencies of three section types change with regularity under different length and boundary conditions. The key point of the modified Timoshenko beam is its solution to the overestimation of natural frequencies, and the phenomenon is more obvious when the beam tends to be non-slender.  
To establish the simulation model, the parameters of the beam are set as follows:  
Elastic modulus, \(E=210\times 10^{9}Pa\); Poisson's ratio, \(\
u=0.3\); shear modulus, \(G=\frac{E}{2(1+\
u)}\); mass density, \(\rho=7850\ kg\cdot m^{-3}\); and shear coefficient, \(\mu=\frac{10(1+\
u)}{12+11\
u}\). Three lengths of the beam, i.e., \(L=8\ m,\ L=4\ m,\ and\ L=2\ m\), are used to study the frequency effect for different slenderness ratios. Beams with three cross sections are considered and studied, with their section parameters as follows:  
1. Rectangular section beam: Width: \(b=0.25\ m\), height: \(h=0.5\ m\).
2. Rectangular hollow section beam: Width: \(b=0.25\ m\), height: \(h=0.5\ m\), web thickness: \(t_{1}=0.04\ m\), and flange plate thickness: \(t_{2}=0.02\ m\).
3. Circular section beam: Radius: \(R=0.4\ m\).  
The above section outlines are shown as Figure 3.
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 2 Preliminaries
### Security Model
#### 2.3.3 IND-CPA  
Indistinguishability against chosen plaintext attack (IND-CPA) is an important criterion for the secrecy of a public key cryptosystem. We let \(\Pi=(\texttt{Gen},\texttt{Enc},\texttt{Dec})\) denote a public key cryptosystem consisting of key generation, encryption, and decryption algorithms. To describe IND-CPA, we introduce the _IND-CPA game_ played between an adversary \(\mathcal{A}\) and an oracle \(\mathcal{O}\): \(\mathcal{A}\) is a PPT Turing machine, and \(k\) is the security parameter.  
1. \(\mathcal{O}\) generates a public key pair \((sk,pk)\leftarrow\texttt{Gen}(1^{k})\).
2. \(\mathcal{A}\) generates two messages \((m_{0},m_{1})\) of the same length arbitrarily and throws a query \((m_{0},m_{1})\) to \(\mathcal{O}\).
3. On receipt of \((m_{0},m_{1})\), \(\mathcal{O}\) selects \(b\in\{0,1\}\) uniformly at random, computes \(c=\texttt{Enc}(pk,m_{b})\), and replies to \(\mathcal{A}\) with \(c\).  
\(\mathcal{A}\) guesses on \(b\) by examining \(c\) and outputs the guess bit \(b^{\prime}\).  
We view \(b\) and \(b^{\prime}\) as random variables whose underlying probability space is defined to represent the choices of the public key pair, \(b\) and \(b^{\prime}\). The _advantage_ of the adversary \(\mathcal{A}\) is defined as follows to represent the advantage of \(\mathcal{A}\) over tossing a fair coin to guess \(\mathcal{O}\)'s secret \(b\):  
\[\mathsf{Adv}_{\mathcal{A}}=2\cdot\Pr[b^{\prime}=b]-1.\]  
when we let  
\[\Pr[b^{\prime}=0|b=0]=\frac{1}{2}+\alpha_{0}\text{ and}\Pr[b^{\prime}=1|b=1] =\frac{1}{2}+\alpha_{1},\]  
We have  
\[\mathsf{Adv}_{\mathcal{A}}=\alpha_{0}+\alpha_{1}\]  
This definition of the advantage is consistent with the common definition found in many textbooks:  
\[\mathsf{Adv}_{\mathcal{A}}=\Pr[b^{\prime}=0|b=0]-\Pr[b^{\prime}=1|b=0]\]  
Definition 2: A public key cryptosystem \(\Pi\) is secure in the sense of IND-CPA, or simply IND-CPA secure, if \(\mathsf{Adv}_{\mathcal{A}}\) as a function in \(k\) is a negligible function.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 2 Related Work
### Static Analysis  
Static analysis examines software without execution by inspecting Windows API calls, string signatures, control flow graphs, opcodes, and byte sequences. These elements reveal program behavior and malicious intent, particularly through execution flow patterns in control flow graphs and CPU instruction codes. While cost-effective for PE file analysis, the method requires preprocessing steps like unpacking to extract meaningful features. The studies on static analysis in the literature are summarized in Table 1.  
Signature-based detection relies on unique byte sequences or hash values to identify known malware samples. However, these methods exhibit zero detection capability against unknown variants and can be easily evaded through simple code modifications such as instruction reordering or NOP insertion [4]. The exponential growth of malware variants also creates scalability challenges requiring continuous signature database updates.
# Framework for Fast Experimental Testing of Autonomous Navigation Algorithms
## 4 Initial Framework Implementation
### Execution Phase
#### 4.2.3 Control  
At the level below the planning, we find the local control level of the vehicle. In this case, we also developed a basic and functional controller designed for low-speed vehicles--in the case of our robot roughly 1.3 m/s of maximum speed. The developed controller is designed for Ackermann vehicles, but given the versatility of the framework, it would be easy to integrate a new controller for vehicles with different kinematic constraints.  
The aim of local control is to reach a local goal. Once it is reached, this module warns the upper level (planning) requesting a new local goal. The controller tracks the straight line that joins two consecutive goals (Figure 5, left). These lines can be generated with only one point since the orientation is specified in the ROS pose message used to communicate the goals. The controller is proportional to the error signals in distance and angle with respect to the line. Error signal in distance \(e_{d}\) is obtained as the distance between the vehicle base link and the nearest point in the goal straight line (green in Figure 5, right). Error signal in angle \(e_{\alpha}\) is obtained as the angle between the goal line (green in Figure 5, right) and the orientation vehicle line (red in Figure 5, right). The control action consists of two variables: steering angle (\(u\)), and speed (\(v\)). On the one hand, we compute the desired steering angle \(u\) as (Figure 6):  
\[u=e_{d}k_{d}+e_{\alpha}k_{\alpha}, \tag{1}\]  
where \(e_{d}\) and \(e_{\alpha}\) are the errors in distance and orientation respectively, and where \(k_{d}\) and \(k_{\alpha}\) are constants adjusted experimentally for each vehicle in which the framework is implemented. On the other hand, the \(e_{d}\) error is saturated so that \(|u|\) does not exceed the maximum possible value \(u_{max}\). This saturation is required because the linear speed applied as a part of a control action is calculated as a function of the steering as:  
\[v=v_{max}-|u|k_{v}, \tag{2}\]  
where \(k_{v}=v_{max}-v_{min}/u_{max}\), \(v_{max}\), and \(v_{min}\), are the maximum and minimum velocities configurable as parameters. Note that if \(|u|>u_{max}\), the resulting speed would be less than \(v_{min}\). For this reason we saturated the \(e_{d}\) error signal shown in (1). Figure 6 shows the block diagram of the described controller for steering variable.  
Figure 5: (left) Representation of local goals on the road. Each local goal is expressed as a point \((x,y,\theta)\). With this information, we can generate a line (in green) with an orientation \(\theta\) that crosses the point \((x,y)\) (in red). (right) Error signal in distance \(d\) is obtained as the distance between the vehicle base link and the nearest point in the goal straight line (green). The error signal in angle \(a\) is obtained as the angle between the goal line (green) and the orientation vehicle line (red).
# Impact Analysis of Security Attacks on Mobile Ad Hoc Networks (MANETs)
## 4 Common Attacks within MANETs
### Rushing Attack  
Rushing attacks target route discovery mechanisms in on-demand routing protocols like the AODV and DSR. When a node receives a route request packet, a rushing attacker prematurely forwards it to the destination or intermediate nodes. This aims to suppress the original RREQ, tricking the destination or intermediate nodes into dropping it as a duplicate. The attacker can also employ rushing attacks on route reply messages [28].  
By forwarding a fabricated RREP before the legitimate one arrives, the attacker increases their chances of being chosen as the preferred route for data forwarding. This allows them to inject themselves into the data path and potentially disrupt communication [39].
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 2 Previous Work  
One of the early development in collision avoidance is of velocity obstacle (VO) [18]. VO is a cone in the velocity space of agent, which represents the set of velocities that will lead an active agent towards a collision with a passive agent. To avoid the collision, active agent has to select its velocity outside VO. The early approach was developed for simple agent dynamics to avoid collision with a passive agent which is moving along a straight path with constant velocity. In [27], authors proposed NLVO algorithm, which expands the concept of VO to allow an agent with linear equation of motion to avoid collision with a passive agent with known, possibly nonlinear trajectories. The generalized velocity obstacle (GVO) algorithm proposed in [26] does principally the opposite of NLVO. It considers a problem of car-like agent avoiding passive agents moving along linear paths.  
In contrast of avoiding passive agents in [19], authors examined the issue of collision avoidance among active agents and proposed the concept of reciprocal collision avoidance. The approach was based on dividing the responsibility for collision avoidance among agents involved in a collision. Their approach generates collision free piecewise paths for active agents. Efforts have been made to extend the applicability of this approach to consider the kinodynamic constraints of agents. In [23], authors proposed the concept of Acceleration-Velocity Obstacle, (AVO), that takes into account the acceleration constraints of the robot. In [24], authors proposed the idea of continuous control obstacle, to generate a continuous collision free path rather than piecewise linear path. All these reciprocal collision based approaches require that all dynamic objects should employ the same algorithm to avoid a collision. Thus, these approaches cannot easily be extended for avoiding a collision with passive agents moving along an arbitrary path, possibly nonlinear and only partially predictable.
# MRI Compatible Planar Material Acoustic Lenses
## 4 Results  
Intensity gain for longitudinal axis cuts and maps have been calculated to compare all the lenses coherently. One parameter, which can be used to evaluate the focusing capacity of a lens is the intensity gain (\(G\)). The intensity gain is related with the intensity with both the intensity with lens (\(I\)) and intensity without lens (\(I_{0}\)), as shown in Equation (7).  
\[G(dB)=10\cdot\log_{10}(I/I_{0}) \tag{7}\]  
Intensity gain values have been calculated from Equation (7). Figure 5, shows the intensity gain for longitudinal cuts on the Z axis for both, numerical and experimental results. It can be seen from Figure 5a, that higher impedance contrast, as in the case of the ideal SZP or brass FZP lens, gives rise to a higher gain levels. As expected, the lower gains are obtained with those materials with impedance contrast values between 1 and 2 and for impedance contrast values lower than 1, the intensity gain increases. ABS, PLA, PLA-Air-PLA, and PMMA polymers, according with the values showed in Table 1, are not able to achieve enough intensity gain as brass FZP. By comparing the experimental results obtained for brass and PLA (see Figure 5b) with numerical ones (see Figure 5a) it can be seen that there is a good agreement. From Figure 5b, it is observed that the air chamber PLA lens has higher intensity gain than full PLA lens. This fact can be explained by the introduction of an air layer. This layer, due to its low acoustic impedance, can block the transmission of the ultrasonic waves approaching its behavior to an ideal SZP. Nevertheless, a focal length displacement of 1.66\(\lambda\) is observed in the FZP lens built with air chamber and PLA. In this case, the displacement is due to the new three-layer configuration (PLA-Air-PLA). The resolution of the 3D-printer and the wall width needed to avoid porosities means that there is an interface between the host medium and the air chamber.  
Figure 4: Implemented lenses, (**a**) PLA and (**b**) brass.  
Figure 6 shows four intensity gain maps, the first three obtained experimentally and the fourth numerically. The experimental ones correspond to PLA, PLA-Air-PLA and brass, while the numerical one has been obtained using an ideal SZP. It has been verified how the results obtained with brass resembles the ideal SZP. This is due to the rigidity of the material. On the other hand, in PLA lens results, a diminishing intensity gain is observed. This intensity gain level can be improved using a PLA-Air-PLA lens. All the lenses are designed with a focal length located at 8.33\(\lambda\) for a working frequency of 250 kHz. When the lens is able to block destructive interference, it is possible to locate the focus in \(F_{L}\). This occurs in brass and the ideal SZP case. Resulting from the lack of blocking capability, the full PLA FZP could not impede the incident pressure wave transferal generating aberrations in the \(F_{L}\).
# Joint Energy Supply and Routing Path Selection for
## 3 A Joint Algorithm of Energy Supply and Routing Selection  
In this section, we introduce the joint algorithm of energy supply and routing selection for rechargeable wireless sensor networks. Firstly, according to the energy supply capacity of the WCD, the residual energy of the nodes and the intensity of energy consumption, how to allocate energy to the sensor nodes is described. Then, we will explain how to perform routing under the condition of charging. Finally, we analyze the theoretical properties and discuss the relevant implementation guidelines.
## 2 Designed NISC-Based Architectures
### Equalizer Architecture
#### 2.1.1 Control Unit  
The control unit has a simple architecture. Its main functionality is to load at run-time the proper control words stored in \(Cmem\) to the different components of the \(EquaNlSC\) module. Moreover, the control unit manages the sequential activity of the design. It specifies the iteration number, as well as the order of symbols in the equalization process. Furthermore, the control unit produces notification signals about the equalizer activity, such as the readiness of output data at the end of the frame. Such signals are used to synchronize the equalizer model with input/output memory blocks and other components in the receiver scheme.
# Power Load Demand Forecasting Model and Method Based on Multi-Energy Coupling
## 3 Model Construction
### Feature Selection Based on Minimal Redundancy Maximal Relevance (mRMR)
#### 3.1.1 MI Calculation  
The MI calculation could be calculated as shown in Equation (2).  
\[I(x_{i},y)=\iint p(x_{i},y)log\frac{p(x_{i},y)}{p(x_{i})p(y)}dx_{i}dy, \tag{2}\]  
where, \(I(x_{i},y)\) is the positive correlation between \(x_{i}\) and \(y\); \(x_{i}\) is the characteristic variable, which presents the \(i\)-th influencing factor; \(y\) is the target variable, which presents the load value; \(p(x_{i},y)\) represent the joint probability density of \(x_{i}\) and \(y\) respectively; \(p(x_{i})\) represent the probability density of \(x_{i}\); and \(p(y)\) represent the probability density of \(y\). Through mutual information calculation, the correlation between each influencing factor and load value can be specifically measured, and the greater the correlation, the greater the value of \(I(x_{i},y)\).  
Figure 5: Load demand calculation with the power grid as the calculation aperture.