esa-sceva/satcom-chunk-collection huggingface dataset:
# Searching Open-Source Vulnerability Function Based on Software Modularization
## 3 Methodology  
BMVul is a method that detects open-source vulnerability functions based on software modularization. We divide BMVul into the following two stages (Figure 1):  
Software component analysis based on program modularization. In the software component analysis stage, we extract the directed function call information of the binary program and represent the information in the form of a graph. Then, we cluster functions based on the overlapping community detection algorithm OSLOM. Next, we extract string-type features and function complex branch sequences of binary modules and open-source components to match each module of a binary program with open-source components. It identifies the corresponding relationship between binary modules and open-source components and narrows the positioning range of components.  
Open-source vulnerability detection. In the open-source vulnerability detection stage, we create an open-source component vulnerability function set through function signature, which uses function hash and code normalization techniques. Then we search vulnerability functions in the binary module that reuses open-source components.
## 5 Simulation Results and Analysis
### The Impact of Packet Sending Rate  
The target of the simulation experiment conducted in this part was to analyze the effects of the packet-sending rate on the E2E delays and PDRs. The packet-sending rate changed between 1 and 5 Hz. A total of 100 nodes were in the UAV network. The minimum and maximum speeds were 3 m/s and 10 m/s, respectively.  
Figure 11 illustrates the results of the average E2E delays versus the packet-sending rates. As seen from Figure 11, DLGR-2 DQ had the best performance, compared to GPSR, QNGPSR, and TQNGPSR. This was because the total waiting time of the packets in each candidate forwarding node was considered before they were propagated over the channel. As the packet rate increased, the delay of DLGR-2 DQ remained relatively stable, i.e., with a small variance.  
Figure 9: The effects of maximum speed on E2E delays.  
Figure 12 illustrates the results of the PDR versus different packet-sending rates. The PDR of DLGR-2 DQ was the largest, due to the total waiting time of packets in a candidate forwarding node being considered in our proposed routing protocol. DLGR-2 DQ avoided forwarding packets to congested nodes and considered the link quality, so the packet loss was the smallest, compared to that of the other three routing protocols. It was clear that with the increase in the packet-sending rate, the PDR of DLGR-2 DQ decreased.  
Figure 11: The effects of packet-sending rates on E2E delays.  
Figure 10: The effects of maximum speed on PDR.
###### Abstract  
CMOS photodiodes have been widely reported in microsystem applications. This article presents the design and numerical simulation of p-n junction photodiodes, using COMSOL Multiphysics, for three CMOS technologies (0.18 \(\upmu\)m, 0.35 \(\upmu\)m and 0.7 \(\upmu\)m) and three different p-n junction structures: n+/p-substrate, p+/n-well and n-well/p-substrate. For these simulations, the depth junctions and dopant concentrations were set according to the different technologies. Then, each photodiode was spectrophotometrically characterized regarding the current, responsivity and quantum efficiency. The obtained numerical results show that the 0.18 and 0.35 \(\upmu\)m CMOS technologies are those with the highest peak of efficiency when visible spectral ranges are needed, comparative to the 0.7 \(\upmu\)m technology. Furthermore, the three most common p-n vertical junction photodiode structures were compared. The n+/p-substrate junction photodiode appears to be the one with the highest quantum efficiency in the visible range, which is in agreement with the literature. It can be concluded that the photodiodes' characteristic curves and dark current values are consistent with reports in the literature. Therefore, this numerical approach allows to predict the photodiodes' performance, helping to select the best structural design for each required application, before their microfabrication.  
C ARTIC 2022 2022
# Closed-Form Expressions of Upper Bound for Polarization-MDCSK System
## 5 Conclusions  
The closed-form expressions on the tight upper bound for the P-MDCSK system are derived, in which the Q-function is approximated by polynomials. The derived closed-form expressions are expressed in three parts and they are verified by simulations over both additive white Gaussian noise (AWGN) and multipath Rayleigh fading channels. The results show that the theoretical boundaries are roughly 0.4 dB away from the simulations when BER is \(10^{-4}\). Therefore, the closed expression not only reduces the computational complexity but also provides theoretical support for BER performance analysis for chaotic communication. The methodology can be used as a grounded method for other schemes in the same situation. This paper focuses on the mathematical optimization associated with a tight upper bound, and in the future, the method of obtaining a lower bound of P-MDCSK is a worthy focus for research.  
Figure 3: Simulation(sim), upper bound (aub) and closed expression (lb) performance comparisons of P-MDCSK multipath Rayleigh fading channels with \(SF=128\), \(L=3\).  
Figure 2: BER comparison between simulation (sim) and closed expression (lb) in P-MDCSK system over AWGN and multipath Rayleigh fading channels with \(M=8,SF=64,128,256\).  
**Author Contributions:** Conceptualization, M.M. and L.W.; methodology, M.M.; software, M.M.; validation, M.M. and W.X.; formal analysis, M.M.; investigation, M.M.; resources, M.M.; data curation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, M.M., L.W. and W.X.; visualization, M.M.; supervision, L.W., W.X. and M.M.; project administration, M.M.; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The APC was supported by the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications (Grant No. NY223019). And the National Natural Science Foundation of China (Grant No. 62301277).  
**Institutional Review Board Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their gratitude to [PERSON] of Tyndall National Institute, University College Cork for his valuable suggestions on the content and English.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Experimental Procedure and Findings
### Experimental Results  
The shear modulus test conditions according to EN 1337-3 and actual test values are shown in Table 1. The shear force and displacement graphs measured through the performance evaluation (average values of 10 tests under the same conditions) are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Spec. Conditions** & **Actual Test Values** \\ \hline Vertical force (pressure) & 391.8 Ton\({}_{\text{f}}\) * (6 MPa) & 392.5 Ton\({}_{\text{f}}\) (6.01 MPa) \\ Shear strain & 0.7–0.9 & 0.77 \\ Test speed & 150 mm/min & 138 mm/min \\ \hline \hline \end{tabular}  
* Ton stands for Ton-force.  
\end{table}
Table 1: Shear modulus test conditions.  
Figure 2: Test instruments and shear modulus test of elastomeric bearing specimen: (**a**) test instrument (2500-ton press and 300-ton horizontal actuator); (**b**) test specimen; (**c**) shear deformation of 100 mm (shear strain of 0.77).  
Figure 3: Shear force (red line) and displacement (blue line) graph through performance evaluation (10 times raw test data): (**a**) test #1; (**b**) test #2; (**c**) test #3; (**d**) test #4; (**e**) test #5; (**f**) test #6; (**g**) test #7; (**h**) test #8; (**i**) test #9; (**j**) test #10.  
In order to calculate the shear modulus of the elastomeric bearing specimen, the mean values of 10 tests are plotted in Figure 4 as the shear force-displacement graph. In Figure 4, the mean forces at two strain points (0.27 and 0.58) are found, and the factors required for calculating the shear modulus are shown in Table 2. Since the shear test was performed by stacking two bearings, the shear force (F\({}_{\rm x}\)) acting on each bearing is calculated by dividing the measured force by 2.  
Substituting the values in Table 2 into Equation (1), the conventional shear modulus G\({}_{\rm g}\) of the test specimen is calculated as below:  
\[{\rm G_{g}=\frac{(\tau_{s2}-\tau_{s1})}{(\epsilon_{qq2}-\epsilon_{qq1})}= \frac{(0.413-0.195)}{(0.58-0.57)}=0.707\ MPa}\]  
As a result of the test, the shear modulus of the specimen is found to be 0.707 MPa, and according to Table 3, which shows EN 1337-3 Annex D: Shear Modulus Comments, it can be seen that this value corresponds to shore A hardness 50 + 5 grade.
# Analytical Selection of Leakage Inductance for Single-Phase Dual Active Bridge Converters
## 5 Results
The findings demonstrated that, across all load conditions, the presented method consistently minimizes the current through the inductor compared to both SPS and DPS modulation methods. Figure 14 illustrates the inductor current waveform during steady-state conditions, comparing the SPS, DPS, and proposed approaches at a 0.6 pu load. The experimental results closely match the simulation outcomes, validating the effectiveness of the power controller-based method. By leveraging the proposed approach, the reactive power due to the first harmonics of voltage and current is effectively eliminated. For instance, at a 0.6 pu load, the reactive power was observed to be \(-482.9\) Vars and \(-325.4\) Vars with the SPS and DPS techniques, respectively, while the presented method eliminates reactive power, indicative of its superior performance. Furthermore, the inductor rms current was significantly reduced with the proposed approach. While the SPS and DPS methods result in an inductor rms current of 5.6 A and 5 A, respectively, the proposed power controller-based approach achieves a mere 4.1 A.  
Figure 12: Experiment test bed showing DAB prototype. Figure 13: Dynamic response of output voltage, active power, and reactive power to load variation. Figure 15 provides a detailed comparison of the root mean square (rms) currents of the inductor under various load conditions for the SPS, DPS, and the proposed method. This bar graph not only showcases the performance variations between the conventional SPS and DPS methods but also emphasizes the efficacy of the proposed method. The considerable reduction in the inductor current achieved by the proposed method not only enhanced efficiency but also played a crucial role in minimizing energy losses and boosting the performance of the converter. This reduction is particularly relevant in applications where power efficiency and minimal energy wastage are critical. Furthermore, the enhanced control over the inductor current under varying loads led to more stable operations and the extended lifespan of the hardware components, including the inductor itself. These advancements are pivotal for optimizing the design and function of power converters in modern electrical engineering applications, contributing to more sustainable and cost-effective energy solutions.
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research
### Crystal Surface Screening/Nucleation Inhibition  
Next, the impact of free macromolecules in solution onto the crystallization process will be described. [PERSON] et al. in their experiments achieved dumbbell-like ZnO hollow architectures by a simple solution-based method using poly(sodium 4-styrenesulfonate) as a crystal growth modifier [28]. In this case, the sheet-like particles were formed due to the presence of polymer. The subsequent formation of crystals was the result of dissolution and the Ostwald ripening process. The ZnO architecture showed unique photoluminescence characteristics at room temperature, suggesting potential usage in optoelectronic applications. [PERSON] et al. presented the generation of zinc oxide nanocrystalline structures in the presence of two water-soluble polymers with totally different chemical structures [29]. The method allowed the obtaining of hierarchical ZnO nanostructures in a controllable manner. The novelty of the paper is in the electrochemical synthesis of inorganic particles in the presence of the water-soluble polymers. In turn, [PERSON] et al. prepared molecularly imprinted polymers in precipitation polymerization, using oleanolic acid as template, acrylamide as functional monomer, ethylene glycol dimethacrylate as cross-linker and azobisisobutyronitrile as initiator in a mixture solvent of chloroform and methanol [30]. The obtained results indicated that molecularly imprinted polymers showed good specific adsorption properties and the ability to induce crystallization of oleanolic acid in supercritical CO\({}_{2}\). [PERSON] et al. synthesized lead sulfide micro- and nanoparticles by a precipitation reaction of lead nitrate with thioacetamide in water solutions of polyacrylamide, polyvinyl alcohol, polyethylene glycol and poly(\(N\)-vinyl pyrrolidone) (PVP), respectively [31]. They obtained polydisperse lead sulfide particles with a cubic shape in case of polyacrylamide and polyethylene glycol, while monodisperse cubic lead sulfide crystallites in the presence of poly(vinyl alcohol) (PVA) and PVP. [PERSON] et al. investigated the maleic acid copolymers role on calcium orthophosphates' crystallization at low temperatures [32], whereas [PERSON] et al. investigated the non-isothermal crystallization behavior of copper/poly(ethylene oxide)/low density polyethylene composite by using differential scanning calorimetry [33]. The results showed that the crystallization of low density polyethylene was retarded by the addition of poly(ethylene oxide) with a higher molecular weight and accelerated by cooling with a larger cooling rate. [PERSON] et al. used solid dispersions of thiamine chloride hydrochloride in the presence of variety of polymers with different physicochemical properties in order to investigate the efficacy of polymers as crystallization inhibitors [34].
## 9 Effect of PIN Diode Equivalent Resistor on the Beamwidth Control
In [35], the authors used 6 PIN diodes and 12 states but achieved a coverage of only 30\({}^{\circ}\). Finally, in [34], four PIN diodes were utilized to cover 90\({}^{\circ}\). Therefore, the presented design offers optical transparency, making it suitable for integration into various applications such as smart windows and smart glasses without compromising the device's appearance. It boasts a wideband antenna (66.7% bandwidth), more than 10 states for beam control, and allows for a coverage of 300\({}^{\circ}\) with just 3 PIN diodes.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
## 4 Scenarios Simulation Under Adjacent Building Conditions
### Effect of Changing the Load Factor of Adjacent Buildings  
#### 4.2.1 MC Model  
This section uses the MC model to analyze the impact of varying adjacent building loads, comparing 5 F (91 kPa) and 12 F (178 kPa) loads. The comparison was conducted with fixed adjacent building distances and basement depths. According to the simulation results shown in Appendix A, Figure A4, the impact on wall displacement was lower under the 5 F load compared to the 12 F load. This indicates that the magnitude of the load has a direct and significant effect on the wall deformation.
# In-Situ Nanoparticles: A New Strengthening Method for Metallic Structural Material
## 3 Materials Fabrication  
As results of an endogenous method, large amounts of in-situ nanoparticles (5-50 nm) generally form in liquid melt during solidification process and exist in the matrix of as-cast ingots. In-situ nanoparticles generally own several characteristics: uniform distribution inside the alloy matrix, excellent wettability between particles and molten alloy, semi-coherent or coherent interface between  
Figure 1: Strengthening mechanisms of the in-situ nanoparticles being coherent to the matrix [38]. (**a**) when slipping dislocations meet these nano-scale coherent interfaces, they will slip along the coherent interface and enter into the nanoparticles (Some stay inside the nanoparticles, while others pass through the interface and continue slipping); (**b**) schematical diagram of dislocations movement around the coherent interface.  
particles and matrix. According to [PERSON]'s theory [51], in-situ nanoparticle reinforced materials belong to nanostructured materials, and we firstly started the investigation on Cu-3 Sn-8 Zn-6 Pb-1 Fe alloy in 2009 [40].  
There are several key points to obtain myriads of in-situ nanoparticles through casting process. Firstly, it is necessary to add suitable alloying elements with the function of precipitation strengthening. Thermodynamic calculation needs to be conducted to ensure that nanoparticles meet the requirement of precipitation kinetics during solidification. In addition, it should be noted that an in-situ nanoparticle generally owns higher melting point than that of the base metal so as to nucleate prior to the base metal. As a result, theoretical basis for the selection of alloying elements is provided.  
In addition, appropriate casting technique should be carried out to control the nucleation and growth process of nanoparticles in order to obtain superior microstructure. Although the strengthening elements-containing particles can nucleate in base metal liquid, the radius of particles is mainly affected by the critical nucleation radius and growth rate. The critical nucleation radius should be limited at nanoscale which is the premise of ensuring that the particles maintaining at nanometer size during subsequent growth process. In addition, it is essential to keep nanoparticles from growing too fast during solidification and cooling, so nanoscale particles can be finally obtained [40]. In addition, the distribution of the nanoparticles in matrix is another important issue. Some relatively concentrated distribution forms, such as clusters and chains, are supposed to be prohibited. On the contrary, a diffuse and uniform distribution of nanoparticles is preferable. This is because the existence forms of particles in the matrix, such as the shape, size, quantity and distribution, play a decisive role in mechanical properties. In this section, elaborate theoretical calculations and fabrication techniques of in-situ nanoparticle reinforced steel and Cu alloys are presented.
## 3 Data Collection and Methods
### Morphometric Parameters
#### 3.1.2 Stream Indexes  
The stream characteristics are very helpful in order to complete the models for the basin analysis and watersheds behaviors. Stream indexes are completed by calculations of stream order (S\({}_{\text{u}}\)), stream length (L\({}_{\text{u}}\)), stream number, and bifurcation ratio (R\({}_{\text{b}}\)) [20,31].
## 2 Mechanisms of Initiating Device Communication
The majority of communication protocols use a schedule to plan their communication and neighbor discovery (e.g., advertising interval in Bluetooth low energy (BLE) [14]). These schedules may vary in response to device conditions and demands [15] but still rely on frequent planned communication, which limits their flexibility. Event-based protocols are currently under development [16], based on the amount of data generated, but solutions using publish/subscribe (PUB/SUB) schemes to transfer data on the occurrence of an event are already well-established (e.g., reading above a threshold). Event-based communication could become more prominent owing to the artificial intelligence IoT (AIoT) that can be used for ultra-low-energy complex event recognition, as demonstrated, for example, by ECG recognition [17]. These complex event detections considerably extend the range of possible use-cases of event-based communication while  
Figure 4: Examples of communication initiation mechanism classifications. Figure 3: Device communication trigger modes reference diagram. limiting the volume of data communicated to the cloud, thus reducing energy consumption. On-demand communication is currently mostly confined to RFID, which is predominantly used for identification and tracking purposes [18]. Passive RFID devices lack innate communication potential, but active RFID devices do not experience this limitation. However, reading can only be conducted using relatively energy-hungry devices [19], limiting the reception task to short-lived or main-powered devices. Tag-to-tag communication is also an option [20], but it requires an external energy source to continuously feed energy to the devices. Therefore, an independent network primarily using on-demand communication is feasible, although RFID cannot be used. Currently, on-demand communication remains undervalued despite its potential for data collection applications and energy-efficient IoT technology. Consequently, we focus on wake-up radio technology, which is an enabler for OD-IoT applications.
## 3 Control Approach
### Formation Problem Formulation  
The trajectory tracking-based formation problem of multiple small spherical robots could be described in the following three mathematics problems.  
1. The tracking problem: \(\mathbf{lim}_{t\rightarrow\infty}\{\mathbf{q}_{i}(t)-\mathbf{q}_{i}^{\prime}(t)\}=0\).
2. The formation problem: \(\mathbf{lim}_{t\rightarrow\infty}\{\mathbf{p}_{i}(t)-\mathbf{p}_{j}(t)\}=\mathbf{d}_{ij}\).  
Figure 5: Definition of the inertial frame and robot body frame.  
Figure 6: Diagram of the formation tracking method.  
Figure 7: Tracking controller based on ESO and MPC.  
The collision avoidance problem: \(\parallel\mathbf{p}_{i}(t)-\mathbf{p}_{j}(t)\parallel\geq 2R\).  
In the above problems, \(\mathbf{p}_{i}\) is the position of the small spherical robot \(i\) in the inertial frame. \(\mathbf{q}_{i}\) and \(\mathbf{q}_{i}^{\prime}\) represent the current- and desired-state information of the small spherical robot \(i\). \(\mathbf{d}_{ij}\) is the relative distance between the robot \(i\) and robot \(j\) in an inertial coordinate system. \(R\) is the safety radius of the robot.
# Sparse Data-Extended Fusion Method for Sea Surface Temperature Prediction on the East China Sea
## 3 Implementation of the Proposed Approach
### Heterogeneous Clustering Method Based on Association  
In order to explore the situation that observation data and remote sensing data have the same relationship in time and space, this section conducts cluster analysis on data that of the same type but from different sources. In this section, different sources of data were clustered based on the relational matrix between data which were mined based on neural network relationship and K-means method. The relational matrix can indicate the internal relationship between data and furthermore the distance between each grid point. Suppose that A comes from relatively accurate but sparse observational data, and B comes from relatively inaccurate but widely covered remote sensing data. According to the characteristics of the two types of data, use the data source of A as the clustering center of the data source of B, and then cluster the data source of B to prepare for the next step of data assimilation.  
Assuming that the source data of A filters out some or all of the data \(M=\{m_{1},m_{2},\ldots,m_{n}\}\) as the original center point, then find the data point closest to these points in the B data source, as the center point \(M^{\prime}=\{m^{\prime}_{1},m^{\prime}_{2},\ldots,m^{\prime}_{n}\}\) of the B set, then cluster all the data in set B according  
Figure 2: The network structure.  
to the K-means clustering method. Among them the distance between the data is the data relationship calculated in the previous step, and cluster sets can be obtained.