esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Channel Shear Connector  
Channel shear connectors are a popular type of connectors that have been increasingly used in different countries due to three main reasons. First of all, this type of connector is more affordable than other types of connectors such as perfobond connectors, because it does not require any peculiar manufacturing process [9]. Secondly, a channel shear connector can be easily installed by even the simplest construction equipment, and it does not need any specific tool, such as a high-power generator in the case of headed stud connectors, for installation [10]. Thirdly, a semi-skilled operator is even capable of installing this connector properly, thus reducing extra costs.  
In spite of the considerable advantageous of channel shear connectors, few studies have been carried out to evaluate the performance of these connectors. The load-slip behavior of channel connectors was firstly investigated by [PERSON], [PERSON], and [PERSON] et al. [11; 12]. Based on these researches, provisions such as the American Institute of Steel Construction (AISC) [13] also proposed relations to calculate the shear capacity of channel connectors. The behavior of channel connectors in engineered cementitious composite (ECC), reinforced concrete (RC), and fiber-reinforced concrete (FRC) were investigated by [PERSON] et al. [14; 15; 16]. [PERSON] et al. evaluated the performance of channel connectors in lightweight aggregate concretes [17; 18] and observed reductions in the shear capacity of channel connectors due to the lower strength of the concrete. [PERSON] and [PERSON] [19; 20] reported the bearing capacity of channel connectors in solid slabs and metal deck slabs and proposed two mathematical relations to estimate the shear capacity of these connectors.
# Improvement and Performance Evaluation When Implementing a Distance-Based Registration
## 2 System Environments and Distance-Based Registration
### Mobility Model  
To analyze the performance of location registration, the mobility model of subscribers is very important. In this study, we analyzed the performance of a distance-based location registration based on a 2-D random walk model used in many studies [9; 11; 15; 16].  
The 2-D random walk mobility model assumes that the probability of choosing one of six surrounding cells is the same for all surrounding cells. For example, if a UE in ring 0 in  
Figure 1: Location area of distance-based registration (D = 3).  
Figure 1 moves to a neighboring cell, it will move to one of the six cells in ring 1. In this case, the probability of the UE to move to one cell out of six surrounding cells is equally 1 out of 6 for all six cells.
## 3 Results
### Differential Multiplicities  
It is well known that the NLD \"a\" parameter affects the shape of the energy spectra and is often extracted by fitting the simulated spectra onto the experimental data measured at different energies and by separately considering the different particle types [35; 36]. However, it is worth noting that this leading parameter, in connection with the moment of inertia (calculated by taking into account the size and shape of emitting nuclei), strongly affects decay cascades by influencing particle emission competition. In other words, the final evaporation multiplicities depend on it; see for instance [23]. Thus, in order to further investigate the validity of the assumed NLD \"a\" parameter and the deformation contributions in our calculations, the differential multiplicity ratios between \(\alpha\) particles and protons at different polar angles were considered. The energy spectra present in the paper do not allow us to extract the differential multiplicities at different angles because of the limited resolution of the figures and the variable binning sizes. Therefore, we considered the double differential multiplicities corresponding to the maximum of the spectra at different laboratory angles. In Table 2 the double differential multiplicity ratios among \(\alpha\) particles and protons measured at the most forward and most backward directions are reported.  
Figure 2: Angular distributions of \(\alpha\) particles and protons in the center of mass system. The experimental data (dots) from [12] are compared with LILITA_N21 calculations using the SS (solid red line) and NS (solid blue line) and the GANES calculations (dashed green line) assuming emission from spherical nuclei, data from [12]. The calculated curves have been normalized to the data at around 90\({}^{\circ}\). For more details, see the text.  
The experimental data were compared only with the LILITA_N21 simulations, as the maxima are quite well reproduced. The emission probability in the SM depends on the available energy, and therefore such a comparison makes sense only if ratios of differential multiplicities at the same energies are considered. Consequently, we did not include in the comparison the GANES calculations, as these show maxima at energies about 10 MeV larger than the experimental ones. The differences between the calculations and the experimental data range from 4-20% and 24-60% in the case of the NS and SS prescriptions, respectively. The large improvement observed by adopting the NS model is very interesting, as better reproduction of the experimental data was obtained for the spectra measured at \(\theta_{lab}=10^{\circ}\), whereas the NS parameters were obtained by fitting the nuclear shapes at \(\theta_{lab}=45^{\circ}\). Furthermore, these results should mainly be attributed to the general reliability of the code more than to the introduction of two free parameters fitted on the experimental data. In fact, none of the fitting procedures for the particle multiplicities were taken into account.
# A Simple and Efficient Method for RSS-AOA-Based Localization with Heterogeneous Anchor Nodes
## 2 Problem Formulation and Cramer-Rao Lower Bound
### System Model and Problem Formulation
\begin{split}\text{P}(\mathbf{\theta}|\mathbf{x})=\prod_{i=1}^{N}\prod_{t =1}^{T}&\left(\frac{1}{\sqrt{2\pi\sigma_{m_{i}}}}\exp\left\{- \frac{\left(\phi_{it}-\arctan\left(\frac{\chi_{2}-\mathbf{a}_{i}}{\chi_{1}-\mathbf{a}_{ i}}\right)\right)^{2}}{2\sigma_{m_{i}}^{2}}\right\}\right.\\ &\left.\cdot\frac{1}{\sqrt{2\pi\sigma_{m_{i}}}}\exp\left\{-\frac {\left(\alpha_{it}-\arccos\left(\frac{\chi_{2}-\mathbf{a}_{i}}{\|\mathbf{x}-\mathbf{a}_{ i}\|}\right)\right)^{2}}{2\sigma_{m_{i}}^{2}}\right\}\right\}\\ &\left.\cdot\frac{1}{\sqrt{2\pi\sigma_{m_{i}}}}\exp\left\{-\frac {\left(p_{it}-P_{0}+10\gamma\log_{10}\frac{\|\mathbf{x}-\mathbf{a}_{i}\|}{d_{0}}\right) ^{2}}{2\sigma_{m_{i}}^{2}}\right\}\right),\end{split}  \tag{7}\]  
where \(\mathbf{\theta}=[\phi_{11},\phi_{12},\cdots,\phi_{1T},\phi_{N1}\cdots,\phi_{NT}, \alpha_{11},\alpha_{12},\cdots,\alpha_{1T},\alpha_{N1}\cdots,\alpha_{NT},P_{11},P_{12},\cdots,P_{1T},\)\(P_{N1},\cdots,P_{NT}]^{T}\). It is worth noting that modeling the standard deviations of different anchor nodes in real-world localization scenarios is challenging, and Gaussian models are a possible and reasonable approach. However, in this paper, exponential distributions are considered to account for the possibility of extremely imprecise anchor nodes, allowing for greater variation in the precision of different nodes. Then, the maximum likelihood (ML) estimator of the hybrid RSS-AOA measurement is obtained as  
\[\hat{\mathbf{x}}=\arg\max_{\mathbf{x}}\mathrm{P}(\theta|\mathbf{x}). \tag{8}\]  
After derivation, the ML estimation can be expressed as  
\[\hat{\mathbf{x}}=\arg\min_{\mathbf{x}}\sum_{i=1}^{N}\sum_{t=1}^{T} \Bigg{(} \frac{\left(\phi_{it}-\arctan(\frac{x_{2}-a_{2}}{x_{1}-a_{1}}) \right)^{2}}{\sigma_{m_{i}}^{2}} \tag{9}\] \[+\frac{\left(\alpha_{it}-\arccos(\frac{x_{3}-a_{3}}{\left\|\mathbf{ x}-\mathbf{a}_{i}\right\|})\right)^{2}}{\sigma_{v_{i}}^{2}}\] \[+\frac{\left(P_{it}-P_{0}-10\gamma\log_{10}\frac{\left\|\mathbf{ x}-\mathbf{a}_{i}\right\|}{d_{0}}\right)^{2}}{\sigma_{n_{i}}^{2}}\Bigg{)}.\]
# Transforming Group Codes in Mealy Finite State Machines with Composite State Codes
## 4 The Main Idea of the Proposed Method
Let the following condition apply:  
\[NL^{k}_{i}\leq S_{L}-1. \tag{22}\]  
In this case, including the variable \(g_{k}\in\tau_{0}\) in any SOP does not lead to the development of a multi-level circuit. Obviously, if condition (22) is violated, then the function \(f^{k}_{i}\in D^{k}\cup\gamma^{k}\) is represented by a multi-LUT circuit. To diminish the number of CLBs required for implementing partial functions, \(f^{k}_{i}\in D^{k}\cup\gamma^{k}\), it makes sense to encode some states, \(a_{m}\in G^{k}\), using the adjacent partial codes \(CS(a_{m})\). This may be performed using, for example, a method similar to that of the algorithm JEDI [30]. For a better understanding of the novelty of the proposed method, it is necessary to show the main difference between the proposed method of state assignment and one-hot state assignment. One-hot codes include the maximum possible number of bits. This leads to a simplification of input memory functions due to a significant increase in the number of flip-flops used (compared to this number for FSMs using MBCs). An increase in the number of memory elements leads to an increase in the number of interconnections, which can significantly increase the FSM cycle time [16]. The method we propose allows for obtaining state codes where the number of bits is practically the same as for maximum binary codes. This allows for an increase in the FSM performance compared to the performance of equivalent OHC-based FSMs. In this paper, we propose a synthesis method for the LUT-based Mealy FSM \(U_{3}\). The synthesis process starts with an STG. The proposed method includes the following steps:  
1. Transforming the initial STG into a state transition table. 2. Creating the partition \(\Pi_{AC}\) of the set of states with the minimum number of groups. 3. The encoding of groups \(G^{k}\in\Pi_{AC}\) using maximum binary codes, \(CG(G^{k})\). 4. The encoding of groups \(G^{k}\in\Pi_{AC}\) using one-hot codes, \(OCG(G^{k})\). 5. The encoding of states \(a_{m}\in G^{k}\) using partial state codes, \(SC(a_{m})\). 6. Creating composite state codes, \(CC(a_{m})\). 7. Creating tables of blocks \(LUTer1\)-\(LUTerKC\). 8. Deriving SBFs (17)-(18), representing the circuits of \(LUTer1\)-\(LUTerKC\). 9. Creating a table for block \(LUTerY\tau\). 10. Deriving SBFs (19)-(20). 11. Creating a table for block \(LUTerOH\) and an SBF (21). 12. Implementing the FSM circuit using the resources of a particular FPGA chip.
# Substring Position Search over Encrypted Cloud Data Supporting Efficient Multi-User Setup +
## 3 Background and Building Blocks  
### System and Threat Models  
Consider a cloud data hosting service shown in Figure 1 that involves three entities: the cloud provider, the data owner and the cloud user. The data owner has a collection of documents \(D\) that he wants to outsource to the cloud provider in a form \(C\), encrypted with a secret key \(K\). To enable search capability over \(C\), the data owner constructs a searchable index \(I\) from \(D\) and then uploads both the index \(I\) and the encrypted document collection \(C\) to the cloud provider. When an authorized cloud user wants to perform a search on remote data, she first connects to the data owner to acquire the secret key \(K\) and the trapdoor information. The trapdoor serves to output secure search query \(Q\) without revealing its original input. Moreover, the trapdoor learning process is a one-time operation, and thus, the cloud user does not need to contact the data owner anymore. Finally, the cloud user submits the search query \(Q\) to the cloud provider. Upon receiving \(Q\), the cloud provider is responsible for searching the index \(I\) and returning the matching set of encrypted documents \(L\subseteq C\). Later, the cloud user uses the secret key \(K\) to decrypt \(L\) to its original view.  
As in previous works [6; 11; 12], the cloud provider is assumed to be an honest-but-curious entity: \"Honest\" means that the cloud provider can provide reliable data storage: it is always available to the users; it correctly follows the designated protocol specification; and it provides all services as expected. \"Curious\" means that the cloud provider may execute some background analysis to breach the confidentiality of the stored data. In the rest of the paper, we assume that the cloud provider and the adversary are the same entity. We do not consider the cloud provider being able to link the search query to a specific user.
# An FPGA-Based Performance Analysis of Hardware Caching Techniques for Blockchain Key-Value Database
###### Abstract
In this paper, we implement different caching techniques such as direct mapping, 2-way set-associative mapping, and 4-way set-associative mapping for key-value store-based Blockchain applications on an FPGA. The work presents a detail of cache implementation including the flow diagram and Finite State Machine (FSM). The performance of the mapping techniques is analyzed for different key sizes. Results highlight that the cache hit and cache miss ratio of the 2-way and 4-way associative technique is much better than the direct-mapping technique.
Sensors_**2015**, _15_, 16162-16195; doi:10.3390/s150716162  
_Sensors_  
ISSN 1424-8220  
www.mdpi.com/journal/sensors  
_Article_  
Modular Energy-Efficient and Robust Paradigms for a Disaster-Recovery Process over Wireless Sensor Networks  
**[PERSON] * and [PERSON]**  
Department of Computer Science, University of Bridgeport, 126 Park Avenue, Bridgeport,  
CT 06604, USA; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +1-347-484-8598; Fax: +1-203-576-4766.  
Academic Editor: [PERSON]  
_Received: 10 January 2015 / Accepted: 25 June 2015 / Published: 6 July 2015_  
**Abstract:** Robust paradigms are a necessity, particularly for emerging wireless sensor network (WSN) applications. The lack of robust and efficient paradigms causes a reduction in the provision of quality of service (QoS) and additional energy consumption. In this paper, we introduce modular energy-efficient and robust paradigms that involve two archetypes: (1) the operational medium access control (O-MAC) hybrid protocol and (2) the pheromone termite (PT) model. The O-MAC protocol controls overhearing and congestion and increases the throughput, reduces the latency and extends the network lifetime. O-MAC uses an optimized data frame format that reduces the channel access time and provides faster data delivery over the medium. Furthermore, O-MAC uses a novel randomization function that avoids channel collisions. The PT model provides robust routing for single and multiple links and includes two new significant features: (1) determining the packet generation rate to avoid congestion and (2) pheromone sensitivity to determine the link capacity prior to sending the packets on each link. The state-of-the-art research in this work is based on improving both the QoS and energy efficiency. To determine the strength of O-MAC with the PT model; we have generated and simulated a disaster recovery scenario using a network simulator (ns-3.10) that monitors the activities of disaster recovery staff; hospital staff and disaster victims brought into the hospital. Moreover; the proposed paradigm can be used for general purpose applications. Finally; the QoS metrics of the O-MAC and PT paradigms are evaluated and compared with other known hybrid protocols involving the MAC and routing features. The simulation results indicate that O-MAC with PT produced better outcomes.  
keywords: wireless sensor networks; operational medium access control protocol; QoS; energy-efficiency; pheromone terme; disaster recovery +
Footnote †: journal: Journal of Power and Energy
## 5 Performance Evaluation
### Experimental Results
#### 5.2.1 Scenario 1: Task Scheduling in Fog Environment
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{4}{c}{**Makespan (s)**} & \multicolumn{4}{c}{**Total Cost (G5)**} \\ \cline{2-10}  & **TCaS** & **BLA** & **MPSO** & **RR** & **TCaS** & **BLA** & **MPSO** & **RR** \\ \hline
40 & **191.44\({}^{*}\)** & 207.57 & 215.27 & 456.11 & 733.85 & **730.88** & 740.38 & 755.98 \\
80 & **394.69** & 416.09 & 445.35 & 963.08 & **1540.23** & 1540.85 & 1553.43 & 1581.82 \\
120 & **607.71** & 654.92 & 686.20 & 1495.66 & **2363.37** & 2367.59 & 2381.16 & 2418.90 \\
160 & **819.97** & 917.67 & 932.33 & 1912.08 & **3176.17** & 3183.80 & 3197.01 & 3246.69 \\
200 & **940.87** & 1067.49 & 1065.94 & 1905.70 & **3755.21** & 3767.37 & 3778.27 & 3835.40 \\
250 & **1,270.96** & 1490.65 & 1479.84 & 3054.80 & **4988.94** & 5017.17 & 5007.59 & 5079.06 \\
300 & **1,473.79** & 1765.49 & 1712.76 & 3309.14 & **5832.69** & 5877.41 & 5862.52 & 5935.94 \\
350 & **1,649.04** & 2010.74 & 1911.27 & 3683.18 & **6607.52** & 6653.37 & 6632.38 & 6738.19 \\
400 & **1,944.58** & 2421.98 & 2300.51 & 4347.64 & 7738.56 & 7816.04 & 7759.95 & 7875.39 \\
450 & **2,235.16** & 2840.79 & 2751.29 & 5350.35 & **8845.90** & 8926.57 & 8876.30 & 9016.59 \\
500 & **2,503.09** & 3174.39 & 3067.26 & 6023.74 & **9902.64** & 9995.97 & 9921.76 & 10,097.75 \\ \hline \hline \end{tabular}  
* The bold indicates the superiority of the corresponding algorithm throughout four algorithms.  
\end{table} 
Table 7: Makespan and total cost comparison of the four algorithms. Figure 5: Time–cost trade-off comparison of the four algorithms.
## 3 Experimental Procedure and Data Analysis
### Dynamic Stiffness Calculation with Forced Vibration Data  
According to the four planned excitation frequencies, the spindle is regulated to each excitation frequency. The vibration data under the condition of a 14 Hz excitation frequency are taken as an illustration of the data processing. The distribution of the forces and displacements are illustrated in  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Setting** & **Values** \\ \hline Hammer impact tip & Hard \\ Impact force & 200–300 N \\ Frequency span & 100 Hz \\ Frequency resolution & 0.125 Hz \\ Measuring time & 8 s \\ Average times & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameter settings in the modal analysis system.  
Figure 5: Accelerance frequency response function (FRF) H1 in three directions.  
both the time domain and frequency domain (Figures 6 and 7). According to the force distributions in the time domain (Figure 6a), the three directions have different force amplitudes. The X direction has the lowest amplitude. This is mainly because the X direction is in the axial direction of the spindle. However, the vibration displacement has the highest amplitude among the three directions (Figure 7a). This is in accordance with the experimental results of the impact modal test, which shows the highest magnitude in the X direction at a frequency of 14 Hz (Figure 5). As the pneumatic spindle does not rotate as stably as the electrospinning, high-frequency noise can appear in the excitation vibration. Through fast Fourier transform (FFT), the vibration distribution in the frequency domain shows a high-frequency vibration at approximately 100 Hz (Figure 6b). However, the values can be ignored compared to the dominant vibration amplitude. As the spindle is manually controlled, it is difficult to set the same excitation frequency as the planned value. FFT shows that the excitation frequency is located at a frequency of 13.43 Hz. For this reason, the calculation uses the practical excitation frequency obtained from the FFT of the vibration data. Compared to the force data, the vibration displacements have much less noise. As shown in Figure 7a, noises appearing in the Z direction, which is in the direction of the focal depth axis, mainly come from the measuring system itself. Because of noise in the vibration, the peak value of the dominant excitation frequency is used to calculate the dynamic stiffness \(K_{e}\) (Equation (6)). \(F_{e}\) in the equation is the excitation force, and \(D_{e}\) is the amplitude of vibration displacement.  
\[K_{e}\,=\frac{F_{e}}{D_{e}} \tag{6}\]  
Figure 6: Vibration force in time and frequency domains under 14 Hz excitation frequency.
## 3 Mission Operations
### Flight Predictions  
The measurement data collected during the mission are stored into a micro-SD memory card and cannot be transmitted while flying due to their large size. Recovery of the payload is mandatory for the mission success and accurate prediction of the flight path according to weather conditions and direction and strength of the winds is therefore of paramount importance, so as to allow for a choice of launch parameters (location, time, lift, etc.) that will avoid ending up in areas not accessible, such as deep woods, water or closed property.  
All the existing landing predictors are based on calculation of the vertical and horizontal motion of the HAB and provide the landing site and the flight time as output [20]. A custom landing predictor was developed during this project in order to have a reliable prediction of the flying path. The predictor, written in Python, can be adjusted to fit different flight conditions, like flight in rainy conditions that typically are not accounted for by predictors available online. The code independently models the ascent and the horizontal motion [21; 22; 23; 24] and uses data provided by the US National Oceanic and Atmospheric Administration (NOAA), to be manually imported [25].
# PreNNsem: A Heterogeneous Ensemble Learning Framework for Vulnerability Detection in Software
## 2 Related Work  
### Prior Studies Related to Vulnerability Detection  
From the degree of automation, previous vulnerability detection methods can be divided into three categories: (i) Manual methods: Many static vulnerability tools, such as Flawfinder [2],RATS [16], and Checkmarx [17], are based on vulnerability patterns, which are defined by human experts. Because pattern matching depends on the rule base, the false positives and/or false negatives are often high. (ii) Semi-automatic methods: Features are manually defined (code-churn, complexity, coverage, dependency, and organizational [18]; code complexity, information flow, functions, and invocations [19]; missing checks [20; 21]; and, abstract syntax tree (AST) [22; 23]) for traditional machine learning, such as k-nearest neighbor and random forest. [PERSON] et al. [24] used artificial statistical characteristics to analyze vulnerability trends and dependencies with the Cupra model in multivariate time series. (iii) More automatic methods: Human experts do not need to define features. POSTER [25] presented a method for automatically learning high-level representations of functions. VulDeePecker [13] is a system showing the feasibility of using deep learning to detect vulnerabilities. [PERSON] et al. [26] proposed a hybrid model by employing similarity mining and deep learning architectures for image analysis. [PERSON] et al. [27] analyzed malware images while using a CNN in order to extract features and support vector machine (SVM) for multi-classification.  
PreNNsem is an automated approach and an end-to-end vulnerability detection framework. When compared with the manual and the semi-automatic methods, our method abandons subjectivity. Therefore, the features obtained by our method are more persuasive and comprehensive. POSTER extracts features from the level of the function with a coarser granularity. PreNNsem extracts richer features directly from the word level. Compared with VulDeepecker, we have expanded the corpus in the word embedding layer to increase the precision of semantic expression. We used heterogeneous classifiers to improve the stability and accuracy of classification.