esa-sceva/satcom-chunk-collection huggingface dataset:
# Targeting KRAS in PDAC: A New Way to Cure It?
## 4 KRAS Inhibitors for PDAC
### \(\text{Kras}^{\text{G12D}}\) Inhibitors
#### 4.2.2 Peptide Nucleic Acids (PNAS)  
Peptide Nucleic Acids (PNAS) are synthetic nucleotide analogs whose molecular structures are very similar to those of DNA and RNA [78]. PNAS have good hybridization properties and can specifically bind to complementary DNA or RNA, distinguishing similar sequences even at the level of single base mismatches [79; 80]. Meanwhile, PNAS can bind specifically to the mRNA of the target gene and inhibit its translation process [81]. Moreover, PNAS have stable chemical structures and are not easily degraded by nucleases or proteases. Based on the above characteristics, treatment using PNAS has great potential to become a new tool in the fight against malignant tumors. In a recent study, several PNAS were designed for the KRAS\({}^{\text{G12D}}\) mutated gene fragment and tested in the human metastatic pancreatic adenocarcinoma cell line AsPC-1 containing the KRAS\({}^{\text{G12D}}\) mutation. The results showed that PNAS significantly inhibited tumor cell activity and reduced the expression of the KRAS\({}^{\text{G12D}}\) mutated gene [82]. The successful inhibition of the KRAS\({}^{\text{G12D}}\) mutant gene by PNAS at the cellular level raises the possibility for subsequent animal experiments.
# Link Investigation of IEEE 802.15.4 Wireless Sensor Networks in Forests
## 2 Experimental Methodologies
### Study Sites  
In this paper, we consider three city forest environments to evaluate the performance of low-power radio links. The first forest is the Bajia Rural Park forest of Beijing; the second one is a forest inside our university campus; the third one is on a mountain of the Jiufeng National Forest Park of Beijing. We will later call the three forests the Bajia forest, the Campus forest and the Jiufeng forest, respectively. The three forest sites are different in terms of the tree density, the vegetation distribution and the understory terrain. Figure 1a,b shows the Campus and the Bajia forests, and their tree densities are about 17/100 m\({}^{2}\) and 20/100 m\({}^{2}\), respectively. The understory of the Campus forest, involving sparse short shrubs, is covered by lush vegetation, which is 40 cm in height on average, while the understory of the Bajia forest is covered by a thin vegetation layer with an average height of 23.6 cm.  
The Jiufeng forest site is shown in Figure 1c. In comparison with the two above in-city forests, the Jiufeng forest, along a hillside, has a very complex and diverse understory, including shrubs, weeds, withered branches and foliage and even bare soil; the tree density is about 25/100 m\({}^{2}\). In fact, the Jiufeng forest site is a typical forest terrain of concern by hydrologists and forestry scientists, and that is the reason that we choose the Jiufeng forest as a study site. Additionally, Figure 1c also shows the placement of wireless sensor nodes marked with circles, and the experiments in the Bajia and the Campus forests use the same wireless sensor nodes, as well. Each sensor node is installed on the top of a steep bracket, around 1.2 m away from the ground.  
In experiments under all three sites, the temperature was 20 \({}^{\circ}\)C on average, and the wind stayed moderate, swinging the forest gently. In particular, neither people nor other moving ground objects passed through the study sites. We did not find any significantly detectable IEEE 802.11 (WiFi) and IEEE 802.15.1 (Bluetooth) signals of 2.4 GHz in the surroundings. We use a Bosch's handheld laser ranging device of DLE40 to determine the distance between the sending and the receiving nodes and the size of the vegetation.
# Learning-Assisted Rain Attenuation Prediction Models
## 2 Preliminaries
### The Factor of LARA Models  
The rationale-cause-based analysis must first be sought in order to develop a good rain attenuation model. In other words, it means finding the factors that affect the rain attenuation. Although the rainfall rate is the most important factor determining attenuation, other factors also possess significance as per several studies. A brief review is presented here regarding more rain attenuation factors.  
[PERSON] et al. [3] listed 10 factors that could affect rain attenuation in the radio link at the frequency component above 10 GHz. The factors include temperature, path length, frequency, precipitation rate, wetness, wind speed, direction, and visibility of the wind. [PERSON] et al. [4] considered the polarization angle, frequency, station height, height precipitation rate (time percentage), elevation angle, and latitude can affect the attenuation of rainfall.  
[PERSON] et al. [5] considered the precipitation intensity, frequency, latitude, the angle of elevation, and azimuth as attenuation factors for the earth-space link. [PERSON] et al. [6] consider the precipitation rate, azimuth, elevation angle, precipitation height, the percentage of time exceedance, and frequency as a slant link factor as parameters that impact attenuation. [PERSON] et al. [7] claimed that the rainfall rate and several parameters can affect the rain attenuation, such as moisture and air pressure, wind direction, and wind speed. [PERSON] et al. [8] developed a supervised machine learning model to take the frequency, rain rate, polarization, and path length as features from the International Telecommunication Union Radiocommunication Sector (ITU-R) database.  
Apart from the link length and precipitation rate, most factors may have less influence on rain attenuation, but in order to achieve the highest level of predictive accuracy, it may be necessary to consider such loosely affecting factors in the model. Figure 2 presents a general illustration of the LARA model's input and output parameters. From now and onwards, we will use parameters or features or factors as of equivalent meaning.  
Figure 1: The visual orientation of the paper.
## 3 The AgroTRACE Infrastructure
### IoT Communication  
Concerning the communication between the devices, a LoRaWAN network is used, which covers a wide range (up to 20 km signal transmission) and demonstrates very low energy consumption. An additional advantage of LoRaWAN network is the zero operating cost of information transmission, since it does not demand that mobile, fixed or satellite telephone networks are used. Regarding AgroTRACE, the network of IoT devices interconnected in a LoRa environment, depicted in Figure 4, consists of the following elements:  
* IoT information system supporting data management (cloud), data record and information display.
* Web application and mobile application informing users about the measurements.
* LoRa nodes for recording information and transmit it at the LoRa gate at a distance of up to 20 km.
* Autonomous node systems with solar panels (where required).
* LoRa nodes with sensors in fields and other points of interest, e.g., warehouses and packaging plants.
* LoRa nodes at tractors.
* LoRa nodes for the transported containers.
* LoRa gateway for receiving signals from LoRa nodes and sending them to the information system.
* An IoT platform, through which data are collected and sent to other servers and apps.
## 3 Methodology
### Discussion of Literature Review Results  
In this section, the results of the articles reviewed in the previous section are synthesized, highlighting the main contributions, common themes, and potential gaps in the literature. The themes--human-computer interaction (HCI), Robotic Process Automation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Articles** & **HCI** & **RPA** & **DevOps** & **RE** \\ \hline
[29] & X & & & X \\ \hline
[30] & & X & & \\ \hline
[31] & & X & & \\ \hline
[32] & X & & & \\ \hline
[33] & & X & & \\ \hline
[34] & & & X & X \\ \hline
[35] & & X & & \\ \hline
[36] & & X & & X \\ \hline
[37] & & & X & \\ \hline
[38] & & & X & \\ \hline
[39] & & & X & \\ \hline
[40] & & X & & \\ \hline
[41] & X & & & X \\ \hline
[42] & & X & & \\ \hline
[43] & X & & X & \\ \hline \% Themes p/articles & 27\% & 47\% & 33\% & 27\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identified articles and the respective themes of the articles found.  
(RPA), DevOps, and Requirements Engineering (RE)--are analyzed for their prevalence in the selected articles, and the relationships between them are discussed.  
As shown in Table 3, each article discusses one or more of the four key themes. The distribution of these themes across the articles is as follows:  
* Human-computer interaction (HCI): featured in 27% of the article, this theme is primarily explored in the context of improving user experience and optimizing human interaction with automated systems.
* Robotic process automation (RPA): this most prevalent theme, appearing in 47% of the articles.
* DevOps: discussed in 33% of the articles, this theme focuses on the integration of automation into the software development lifecycle.
* Requirements Engineering (RE): identified in 27% of the articles, this theme explores the need for detailed Requirements Engineering in the development of automated systems.
* Synergies between the themes: The intersection of RPA with HCI and DevOps stands out as a key area of focus. RPA systems benefit significantly from HCI principles, which help in designing more user-friendly interfaces, thereby improving system adoption and efficiency. Furthermore, the integration of DevOps practices in RPA development enables continuous integration and deployment, ensuring that automation solutions are scalable and adaptable.
* Challenges in RE: Requirements Engineering (RE) is frequently discussed in relation to the early stages of system development, especially in RPA projects. Despite the progress in each theme, several gaps merit further investigation:
* Lack of unified frameworks: While the individual themes are well explored, there is a lack of a comprehensive approach that integrates HCI, RPA, DevOps, and RE in a cohesive manner. The development of such frameworks could provide more practical guidance for the implementation of automation systems.
* Industry-specific applications: many of the studies focus on general applications of RPA, with limited exploration of sector-specific adaptations.
### Background
However, it might be challenging to obtain a near-perfect or flawless CSI. Deep learning (DL) technology may be applied to address all these constraints. The performance of different wireless communication systems is primarily improved by the employment of DL methods. Current communication systems heavily utilize DL-assisted NOMA technologies for a variety of uses. The authors in [19] examine how such systems are used in the literature to assess their contribution to system performance improvement and the various difficulties that arise in such systems when trying to change their design or create technologies optimized for 5G network performance. In recent studies, DL methods have been applied in the NOMA system to enhance the system's functionality. Figure 2 shows a NOMA-VLC system with a DL-based signal demodulator to successfully attenuate nonlinear and linear distortions.
# Active Clamp Boost Converter with Blanking Time Tuning Considered
## 4 Digital Control Flow Chart
### Auto Tuning of the Last Blanking Time of \(S_{2}\)  
From Section 2, we can see that as the auxiliary switch \(S_{2}\) is cut off, the resonant inductor \(L_{r}\) begins to be demagnetized, and the demagnetization path will pass through \(D_{o}\), leading to the power dissipation in \(D_{o}\). Accordingly, cutting off \(S_{2}\) early reduces the required time flowing through \(D_{o}\), hence the power dissipation in \(D_{o}\) will be decreased, increasing the overall efficiency. Therefore, a lookup table is built up with the relationship between the cut-off time point of \(S_{2}\) and load current \(I_{o}\). However, it is noted that, as for the auto tuning of the cut-off time point for \(S_{2}\), the resonant current \(i_{Lr}\) should be larger than the input current \(I_{in}\) before the main switch \(S_{1}\) is switched on so as to make sure that \(S_{1}\) is switched on with ZVS. In the following, how to construct this lookup table is mentioned below.  
Step 1  
Under an open-loop test with ten points from light to rated load, the maximum efficiency for each sensed input current is obtained by manually tuning the cut-off time point of the auxiliary switch \(S_{2}\). This sensed input current is digitalized and then used to generate one interval value in a look-up table.  
Step 2  
For each point, the corresponding cut-off time point of \(S_{2}\) under the maximum efficiency is recorded.  
Figure 13: (a) System operation flow chart; (b) look-up table operation flow chart. PWM, pulse width modulation.  
Step 3  
A lookup table with cut-off time point of \(S_{2}\) versus interval value created from the input current is established as shown in Figure 13b. After this, as an actual input current is sensed and digitalized; this value will be stored in the register REG and then compared with interval values in the lookup-up table to obtain the required cut-off time point of \(S_{2}\), called L_T. The corresponding gate driving signal for \(S_{2}\) is obtained as illustrated in Figure 14. Accordingly, the proposed converter will do a good performance on efficiency under different input current levels. It is noted that the hysteresis band is applied to the exchange of two cut-off time points to avoid oscillation.  
It is noted that the proposed auto-tuning technique is used in the steady state. First, the input current \(I_{in}\) is sampled at a time point within the turn-on time \(t_{on}\) of \(v_{gs1}\). After this, based on the lookup table, the corresponding cut-off time point of \(v_{gs2}\) for the auxiliary switch \(S_{2}\) will be determined, which will be used in the next cycle of \(v_{gs2}\). As to \(v_{gs1}\) for the main switch \(S_{1}\), it is almost not changed because the duty cycle of \(v_{gs1}\) is generated from the controller, hence the system stability is almost not affected.
# A Simple Method to Convert Cellular Polymers into Auxetic Metamaterials
## 3 Results and Discussion
### Auxetic Foams
#### 3.2.2 Poisson's Ratio
The tensile Poisson ratio of the original foams (D16, D21, D30 and D16) were measured with average values of 0.38, 0.34, 0.37 and 0.38, respectively. The Poisson ratio of the treated samples is shown in Figure 5. As expected, the relation between the Poisson ratio and tensile engineering strain were similar; the Poisson ratio increased with the engineering strain, as observed in the literature and in our previous work [6; 11; 17; 18; 19]. Figure 5 also shows that an NPR was observed up to large strains (above 1.0, the maximum strain being 1.4 in this case). However, most of the literature reported auxetic foams (NPR) only at very small strains of 0.3 [16] and 0.6 [18]. Even our previous work showed that the maximum strain to obtain an NPR was 0.8 [6]. Therefore, the advantages of the current method, combining vacuum with mechanical compression (VMC), were that it is not only environmentally friendly as no solvent is used but also that NPR (Figure 5A-D) can be achieved up to much higher engineering strains, i.e., above 100%. Finally, the effect of the treatment temperature was studied. In this case, two temperatures were selected: 100 \({}^{\circ}\)C and 105 \({}^{\circ}\)C. The temperature value was appended to the sample codes with their value, i.e., T100 and T105 (PE-T100-D21-P1.6 and PE-T105-D21-P1.6). Figure 5B shows that the curves were very similar for both samples. This was expected, because a difference of only 5 \({}^{\circ}\)C when the sample was already above the softening temperature would not significantly affect the main driving force of the applied vacuum and/or mechanical pressure. Figure 6 presents the Poisson ratio as a function of the compressive engineering strain for the auxetic and original samples. Compared to the tensile NPR, the compressive NPR was less negative in value. Figure 5: Poisson’s ratio as a function of tensile engineering strain for auxetic foams produced under different conditions (temperature and mechanical compression) with their initial foams based on (**A**) D16, (**B**) D21, (**C**) D30 and (**D**) D36. Figure 6: Poisson’s ratio as a function of compressive engineering strain for auxetic foams produced under different conditions (temperature and mechanical compression) with their initial foams based on (**A**) D16, (**B**) D21, (**C**) D30 and (**D**) D36. The values of the compressive NPR at each pressure had a smaller variance than those of the tensile NPR (see Figures 5 and 6). Auxetic behavior could be reached at larger tensile engineering strains (strain above 100%) than compressive strains (only at 50-60%). When the compressive engineering strain was above 50%, most of the compressive Poisson's ratios were positive (Figure 6B,D). However, this was the same trend as for the original foams, for which the NPR increased with increasing the compressive strain.
## 3 Experiments and Results  
As previously mentioned, co-seismic displacement reflects the ground displacement during an earthquake and is a key parameter for estimating the earthquake's magnitude. Co-seismic displacement provides a measure of fault slip, which is directly related to the magnitude. Therefore, accurately obtaining the co-seismic displacement caused by an earthquake can more directly help infer the magnitude. High-frequency GNSS provides absolute three-dimensional displacement information that is not affected by tilt or baseline drift. This makes it a reliable reference for accelerometer data, especially during and after an earthquake, as GNSS displacement data can maintain high accuracy and stability. SM (strong-motion) accelerometers, with their high temporal resolution, can capture high-frequency components of the shaking, including rapid acceleration changes. Since the time resolution of GNSS is relatively low, it may lack sufficient high-frequency information, making SM a good complement to GNSS in capturing high-frequency components. Therefore, this paper integrates GNSS displacement monitoring data with SM instrument acceleration monitoring data to obtain a wideband co-seismic displacement with both high-precision surface displacement information and high sampling rate. To evaluate the performance of the presented VC-AKF in the loose integration system of displacements and accelerations, the simulation experiment and practical seismic application are conducted as follows.
## 3 Communication in the 60 Ghz Band
### Performance Evaluation  
As part of the deployment and operation of the 5G network, an evaluation of the mmWave link performance was undertaken to better understand the characteristics of 802.11 ad when it is deployed in this manner. The outcomes of this work will then be used to optimize the clustering and link level configuration of the network going forward. Ultimately, our goal is to develop dynamic optimization algorithms that could be used as part of a self-optimizing network (SON) managed by Software-Defined Wireless Networking (SDWN). This algorithm could use information on link characteristics from across the meshed backhaul, along with current monitoring statistics on traffic load, to reconfigure links and optimize routing to perform load balancing and help enforce user QoS.  
To date we have undertaken preliminary work to evaluate the optimal clustering and channel selection based on simple Dijkstra shortest path routing algorithms and calculated the optimal link distance based on the expected 802.11 ad performance using packet error rate in a typical'street canyon' deployment such as an urban environment. This means that we are now in a position to predict link performance for a given deployment and alignment of nodes. Our aim for the work presented in this paper was, therefore, to model a real node deployment as part of the latest phase of the project, which began in September 2021, and then verify our predicted performance using monitoring information once it was operational. The modelled deployment will be explained in detail in the next section.
# A Distributed Energy-Throughput Efficient Cross-Layer Framework Using Hybrid Optimization Algorithm
## 4 Performance Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Sensors** & **10** & **20** & **30** & **40** & **50** \\ \hline GEO & 460 & 455 & 418 & 413 & 385 \\ \hline DECN-MI Comm & 97.72 & 87.48 & 85.6 & 85.1 & 85.56 \\ \hline DECN-MI Waveguide & 102.72 & 89.48 & 88.6 & 88.1 & 88.16 \\ \hline DECEN-MI Comm & 95.92 & 85.68 & 83.6 & 83.1 & 83.56 \\ \hline DECEN-MI Waveguide & 101.32 & 88.08 & 87.8 & 87.2 & 87.1 \\ \hline DECEN-MI WG 3D coils & 103.92 & 90.88 & 89.8 & 89.3 & 89.4 \\ \hline DECMN-MI Comm & 95.84 & 85.61 & 83.47 & 82.96 & 83.08 \\ \hline DECMN-MI Waveguide & 101.19 & 88.03 & 87.63 & 86.88 & 86.84 \\ \hline DECMN-MI WG 3D coils & 103.74 & 90.82 & 89.59 & 89.12 & 88.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of normalized energy consumption (J/pkt) with the varying numbers of sensors.
## 4 Discussion
The shrinkage and distance of the particles depend on the strength of the cohesion between the particles of the material, as well as the shape and size of the particles. The more particles form close to a spherical shape, the more likely they are to bind the particles together leading to high compressive strength [35]. The higher compressive strength in the current study was probably due to more particles forming close to their spherical shape, which further enhanced the bonding, in addition to the densification of the material. The mean Vickers hardness of Ca-SZ(CS) and Ca-SZ(CC) was 977 MPa and 960 MPa, respectively, which are lower in comparison to the Vickers hardness of Ce-TZP, which was around 1200 MPa [35; 36]. [PERSON] et al. [29] developed pure 10 mol% ceria-stabilized tetragonal zirconia polycrystal (10 Ce-TZP) with hardness value of 6.2 GPa. [PERSON] et al. reinforced ceria-calcia stabilized zirconia with alumina, and reported hardness of 10 GPa-13 GPa [22]. Thus, it can be seen in the current study that the zirconia stabilized with calcia produced lower Vickers hardness when compared to the zirconia stabilized using other oxides, which suggests that it is easier to prepare using cutting tools with minimal wear. In general, all mechanical properties show optimum values when the specimens were sintered at 1400 \({}^{\circ}\)C. At 1200 \({}^{\circ}\)C, the heat was not sufficient to fully sinter the zirconia and the presence of pores and voids lead to its lower density, flexural, compressive and hardness values. Whereas, at 1500 \({}^{\circ}\)C, the zirconia was overheated, showing lower mechanical properties. Other properties that are important to dental zirconia should be tested in future research. These results are limited to in-vitro study. Further in-vivo animal studies are needed to test tissue response to the current material, followed by clinical trials. Currently the material is produced in a small quantity. More work is needed for upscaling to prepare for possibility of future commercialization.