esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Silicon-Fluoride-Acceptors (SiFAs): An introduction
Under regular basic labeling conditions, the active esters could initially not be labeled with \({}^{18}\)F\({}^{-}\) as a result of their tendency to hydrolyze quickly. Very recently, [PERSON] and co-workers found a way to circumvent this problem by adding oxalic acid to the labeling cocktail [32]. The N-succinimidyl-3-(_diert_-butyl[\({}^{18}\)F]fluorosilyl_) benzoate ([\({}^{18}\)F]SiFB), an active ester in the style of the most frequently-used \({}^{18}\)F-active ester [\({}^{18}\)F]SFB, was obtained in high radiochemical yields and used for protein labeling (_cf._ Section 9). To shed some light on the mechanism and unanticipated efficiency of the isotopic exchange, Density Functional Theory (DFT) calculations were performed on three model fluorosilanes in the gas phase as well as in acetonitrile as a solvent. The Gibbs free energies calculated in the gas phase and acetonitrile were \(-\)50 to \(-\)40 kcal/mol and \(+\)5 to 10 kcal/mol in acetonitrile, respectively. The fluoride ion in acetonitrile exhibits a drastically reduced basicity which accounts for the large difference in Gibbs energies in the gas and solution phases. The formation of the pentacoordinate silicone is therefore less favored and results in a very fast isotopic exchange, as observed experimentally. In a follow-up study from 2011, it was shown that the activation energy (AE) of the isotopic exchange in acetonitrile is 15.7 kcal/mol [32]. This data was acquired from an Arrhenius plot of ln k against 1/T within a temperature range of \(-\)20 \({}^{\circ}\)C to 0 \({}^{\circ}\)C. Comparing these 15.7 kcal/mol to the AE of the simple carbon-\({}^{18}\)F formation (e.g., the synthesis of 2-[\({}^{18}\)F]fluoroethyl tosylate), which was just 1.3 kcal/mol higher, it became apparent that the 4 times larger pre-exponential factor of the isotopic exchange is responsible for the faster rate constant. Even at very low temperatures, the isotopic exchange was very fast and yielded the radioactive SiFA in high yields. Figure 3: Structures of small molecule SiFA building blocks.
## 3 Results
### GPS Antenna Calibration Results  
In this section, the antenna calibration results for GPS are illustrated. The elevation mask angle was set to \(0^{\circ}\) so that all available observations were utilized to construct the PCC measurements and then fit the phase pattern. Figure 9 shows the GPS antenna calibration results, i.e., the fitted phase patterns. Compared with the reference phase pattern shown in Figure 8, we can see that the shapes of the fitted phase pattern were consistent with the reference. And the most obvious difference was the larger magnitude of the calibration results.  
To better demonstrate the accuracy of the calibration results, we depict the distributions of the phase pattern fitting deviations in Figure 10. We can see that in the regions where the elevation angles were higher than \(10^{\circ}\), the deviations were at the millimeter level. However, in the regions where the elevation angles were lower than \(10^{\circ}\), the phase pattern fitting deviations could reach up to several centimeters. Table 2 summarizes the statistics of the calibration deviations. It shows that the root mean square errors (RMSEs) of all eight calibration results were at the mm level, but the maximum deviation was relatively large. Even the best calibration result, (a), held a maximum deviation of 19 mm. A reasonable explanation is that there were more frequent cycle slips at low elevation angles, resulting in only a few available observations. This can be validated by Figure 6a, from which we can see that at elevation angles lower than \(10^{\circ}\) (indicated by the gray shadow), the distribution of available satellites was extremely sparse.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Statistics** & **a** & **b** & **c** & **d** & **e** & **f** & **g** & **h** \\ \hline RMSE (mm) & 4.191 & 7.218 & 7.500 & 7.402 & 7.365 & 7.226 & 7.956 & 9.211 \\ Max. (mm) & 19.061 & 15.132 & 20.036 & 12.119 & 11.838 & 15.104 & 12.563 & 20.471 \\ Min. (mm) & \(-15.842\) & \(-36.827\) & \(-38.949\) & \(-38.266\) & \(-38.896\) & \(-36.868\) & \(-41.133\) & \(-48.909\) \\ \hline \end{tabular}
\end{table}
Table 2: Statistics (root mean square error (RMSE), maximum deviation (Max.), and minimum deviation (Min.)) of GPS antenna calibration deviations.  
Figure 9: GPS antenna calibration results (**a**–**h**) different examples.
## 5 Proposed Active Power Control Technique
\(minRSRP_{j}\) is the Reference signal received power measured by the \(FUE_{j}\). Using the above-described statement, the femtocell then effectively tunes its downlink power as:  
\[P_{APC}=max(P_{min},min(\xi\ P_{t},P_{max})) \tag{17}\]  
In (17), \(P_{max}\) and \(P_{min}\) are the maximum and minimum transmit power respectively. Based on the aforementioned information, the received downlink SINR of the macro user equipment on PRB \(k\ (k=1,\ \ldots,K)\) can be calculated as:  
\[SINR_{MUE}^{k}=\frac{P_{M}^{k}PL_{M}}{\sum_{i=1}^{F}x_{i}I_{i}+\sum_{\overline {F}}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\! \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\  
is the pathloss from the interfering HeNB to UE. Similarly, \(P_{M^{\prime}}^{k}\) and \(PL_{M^{\prime}}\) are the transmits power and path loss of the interfering eNB to UE respectively. \(P_{n}\) is the thermal noise density.  
The received SINR of the Femto user equipment using the same analysis can be expressed as  
\[SINR_{FUE}^{k}=\frac{P_{F_{APC}}^{k}DL_{F}}{\sum_{i=1}^{F}x_{i}I_{i}+\sum_{M} ^{P}\!P_{M}^{k}PL_{M}+\sum_{F^{\prime}}\!P_{F^{\prime}}^{k}PL_{F^{\prime}}+P_{n}} \tag{19}\]  
In (19), \(P_{F_{APC}}^{k}\) and \(P_{F^{\prime}}^{k}\) are the transmission power of the serving and interfering femtocell respectively. \(PL_{F^{\prime}}\) is the pathloss from the interfereing HeNB to the UE. Comparing with the existing techniques the proposed technique has the following advantages. 1. The Active Power Control Technique effectively reduces the inter-cell interference and optimize the throughput performance of the MUE. 2. The proposed technique not only reduces ICI to MUE but also maintains the QoS of FUE by considering the RSRP (Reference Signal Received Power) feedback from the Femto user to adjust its downlink transmission power. 3. The femtocell actively tunes its downlink power by using the power levels (\(P_{x}\), \(P_{y}\) and \(P_{z}\)) and time levels (\(TL_{1}\) and \(TL_{2}\)), Hence, the proposed APC approach reduce the unnecessary power consumption to achieve green femtocell network. 4. Compared with existing power control approaches, the proposed approach offers significantly better performance in terms of downlink throughput CDF of the macro user and the femto user, the average throughput, FBS Power consumption and the green impact and CO\({}_{2}\) emission. The outlines of the proposed method are summarized in the Table 1.
## 1 Introduction
In clusters with high error rates, the number of parities is added to increase data protection strength, and in clusters with low error rates, the number of parities is reduced to set differentiated data protection strength. * Hot/cold-aware data placement: It uses the characteristics of hot data, where data update and access attempts are active. Place hot data in the part vulnerable to retention error, and place cold data in the part less vulnerable to retention error. * Analysis of Reliability of SSDs: Error rates of the ECC, RAID, and our proposed policies are compared and evaluated quantitatively by an analytical model. The remainder of this paper is organized as follows. Section 2 discusses background and related work, especially 3D-NAND flash memory. Section 3 demonstrates our two proposed policies and describes the relationship between the two methods. Section 4 discusses analytic formulas used to analyze error rates. Section 5 shows a quantitative comparison of error rates between the proposed and existing methods. Section 6 concludes this work with a summary.
## 4 Reliability, Availability and Fault Tree Analysis
### Fault Tree Analysis
The function \(\phi(\mathbf{x})\) can also be expressed as the union of _minimal cut sets_. \[\phi(\mathbf{x})=K_{1}\cup K_{2}\cup\cdots\cup K_{n} \tag{7}\]  
A cut set \(K_{i}\) is a subset of events whose simultaneous occurrence leads to the occurrence of the top event. A cut set is said to be _minimal_ if does not contain other cut set. There are several algorithms available to automate the process of obtaining the minimal cut sets from a fault tree [43]. After obtaining the cut sets, \(\phi(\mathbf{x})\) can be transformed in a sum of disjoint products, as follows  
\[\phi(\mathbf{x})=K_{1}\cup K_{2}\cup\cdots\cup K_{n}=K_{1}\cup\overline{K}_{ 1}K_{2}\cup\cdots\cup\overline{K}_{1}\ldots\overline{K}_{n-1}K_{n} \tag{8}\]  
where \(K_{i}\) the \(i\)-cutset and \(\overline{K}_{i}\) its complement. Since the terms are pairwise disjoint, the probability of the top event can be obtained as the sum of the probabilities of the individual terms. It is possible to compute several dependability measures from a fault tree. In the context of this work we will focus on reliability and availability. Assume that the top event represents the failure of the system. Thus, the probability of this event occurring during a period of time \(t\) is the complement of the reliability \(R(t)\). If the top event is expressed by its _minimal cut sets_, then to compute the reliability is only necessary to replace each event \(i\), in the respective cut set, by its reliability function \(R_{i}(t)\). After that, the reliability of the system \(R(t)\) can be easily computed using simple probability laws (_i.e._, probability of union and intersection of events). Availability can be obtained in a similar way, by replacing each event by the availability function of each component \(A_{i}(t)\). However, this computation in only valid if the repair processes are all independent and if the number of repairman (_i.e._, number of repair actions) is not limited. Further details can be found in [43].
# A Distributed RF Threat Sensing Architecture
## 2 Related Work
### Jamming Attacks
Based on this technique, the forward ants unicast or broadcast depending on the availability of the channel information for the end of the channel. If the channel information is available, the ants randomly choose the next hop. The source on receiving the channel information verifies the prevalence of the attacker and avoids the particular channel for transmission. * Spot Jamming: the attacker focuses all their power on a single frequency or a narrow band of frequencies. In [22], the authors investigate the effects of interference in mmWave radar by utilizing another identical device to emulate spot jamming. Spot jamming effects, among other jamming attacks on GPS, are investigated in [23; 24]. This can be very effective if the targeted frequency is critical for communication. * Sweep Jamming: in sweep jamming, the attacker rapidly changes the frequency of the jamming signal across a range of frequencies. Different waveforms can be used for the sweeping signal, such as sinusoidal, square, triangle, saw-tooth, and chirp. This can disrupt communication over a broad spectrum and is harder to mitigate. In [25], researchers propose an anti-jamming algorithm based on distributed multi-agent reinforcement learning, for sweep and smart jamming strategies. Each terminal take the spectrum state of the environment as an input. Then the proposed algorithm employs Q-learning, along with the primary and backup channel allocation rules, to select the optimum communication channel. * Barrage Jamming: the attacker jams multiple frequencies simultaneously. This type of jamming is very effective. It requires significant power and is typically used to disrupt communication across a wide band of frequencies and systems. Barrage jamming detection and classification for Synthetic Aperture Radar (SAR), based on a convolutional neural network (CNN), is presented in [26]. * Intelligent Jamming: the attacker uses advanced techniques, such as machine learning algorithms, to adapt their jamming strategy based on the network's current state and responses. The evolution of AI/ML has been heavily implemented in jamming strategies as well, which makes such jamming techniques much more effective and harder to counter. Reference [27] presents intelligent jamming in LTE networks, while intelligent anti-jamming is discussed in [28].
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 5 Comparison of the Algorithms
### Quantitative Comparison
#### 5.1.2 Scenarios with the Probability Distribution  
In the second scenario type, a probability distribution of the possible locations of the targets is considered; see Figure 2b. Note that as the search mission in this case ends when all the targets having been seen, it is likely that not every discretization cell is finally observed. Therefore, since the task may end prematurely, the efficiency and the fitness measured as per Equations (13) and (14) are not absolute metrics anymore. For the comparison, 50 scenarios are generated; for each scenario, five different probability distributions are analyzed, considering 100 different initial conditions for each one.  
In Figure 16a, the distributions of the efficiency have been represented, considering Model 3. Again, A1 turns out to be the best, followed by A3, A7 and A4. However, in this scenario type, A1 is clearly above the others, reaching a mean efficiency of 0.54. The second best algorithm, A3, reaches a mean efficiency of 0.44. A similar situation occurs when the fitness is compared; see Figure 16b. Note that also for this scenario type, A7 presents a high variability in both efficiency and fitness among the scenarios.
## 3 Results and Discussion
### Comparison of DRSA with Domain-Specific Methods
The obtained rules, accounting for compounds from expanded chemical space, can be applied to, e.g., virtual screening of databases or used as criticizers of new molecules obtained in the frame of various generative chemistry approaches. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{Measure} & \multicolumn{8}{c}{Run on the same attributes as applied in the Lipinski RO5} \\  & DRSA\({}_{1}\) & DRSA\({}_{2}\) & DRSA\({}_{3}\) & DRSA\({}_{\text{avg}}\) & LiP\({}_{\text{avg}}\) & XGB\({}_{\text{avg}}\) & NN\({}_{\text{avg}}\) & SVM\({}_{\text{avg}}\) & KNN\({}_{\text{avg}}\) & All\({}_{\text{avg}}\) & ChIP\({}_{\text{avg}}\) \\ \hline Sensitivity & 0.80 & 0.80 & 0.80 & 0.80 & 0.78 & **0.87** & 0.85 & 0.80 & 0.82 & **0.87** & 0.91 \\ Specificity & 0.94 & 0.95 & 0.94 & **0.95** & 0.01 & 0.91 & 0.89 & 0.92 & 0.92 & 0.92 & 0.94 \\ Precision & 0.93 & 0.94 & 0.93 & **0.93** & 0.43 & 0.91 & 0.89 & 0.91 & 0.91 & 0.94 \\ Accuracy & 0.87 & 0.88 & 0.87 & 0.87 & 0.39 & **0.89** & 0.87 & 0.86 & 0.87 & **0.89** & 0.93 \\ \(F_{1}\) score & 0.86 & 0.87 & 0.86 & 0.86 & 0.55 & **0.89** & 0.87 & 0.85 & 0.86 & **0.89** & 0.93 \\ \hline \multicolumn{8}{c}{Run on the same attributes as applied in the Ghose filter} \\  & DRSA\({}_{1}\) & DRSA\({}_{2}\) & DRSA\({}_{3}\) & DRSA\({}_{\text{avg}}\) & Ghose\({}_{\text{avg}}\) & XGB\({}_{\text{avg}}\) & NN\({}_{\text{avg}}\) & SVM\({}_{\text{avg}}\) & KNN\({}_{\text{avg}}\) & All\({}_{\text{avg}}\) & ChIP\({}_{\text{avg}}\) \\ \hline Sensitivity & 0.83 & 0.85 & 0.85 & 0.84 & 0.62 & **0.
# TMVDPatch: A Trusted Multi-View Decision System for Security Patch Identification
## 4 Design and Implementation
### Data Collection and Preprocessing  
It is well known that the key to security patch identification work is dataset collection and ground truth labeling. To ensure the authenticity of the dataset, this paper adopts the public dataset called PatchDB [16], the labels in this dataset are finally manually verified by three security experts. The dataset provides patch submission files for security patches and non-security patches, which contain commit hashes, commit message text, and code difference information.  
On this basis, we preprocess the patch submission files. First, we download the modified source files according to the hash value and code difference information. During the process, some code repository are lost and the source files cannot be downloaded. On this basis, we parse the hunk information and locate the function name modified by the patch. It should be noted that, because the source code needs to be parsed later to generate the control dependency graph and data dependency graph, and feature extraction is performed according to the differences in the dependency graphs. Therefore, in order to avoid the problem of function calls between multiple files, this paper only considers patches involving single-file patches, and filters out patches that modify multiple source files. The PatchDB dataset we obtained contains 23 k non-security patch samples and 12 k security patch samples. In our work, we filtered 5 k non-security patch samples, and 2 K security patch samples. Finally, the relative percentage of security and non-security decreased from 1.9 to 1.8. Since the proportion of samples before and after filtering is slightly different, and the experiments in this paper are carried out on the same dataset, we have reason to believe that this limitation will not affect the results.  
Next, we preprocess the submission information text in the patch file, only retains the information text related to the patch description, and filters out irrelevant information such as the subject, date, and reporter email.  
Figure 1: The overall architecture of TMVDPatch.  
Through preprocessing, the source codes before the patch \(Code_{pre}\), the source codes after the patch \(Code_{post}\), and the descriptions of the submission information called \(ComMes\) can be obtained.
## 2 Methodology
### Condition Assessment Equipment Maintenance
#### 2.2.2 Data Acquisition  
Despite modern equipment developed for system monitoring, there is certain information about the system that, due to the complex measurement procedure or the very nature of the information, cannot be collected in this way, for example, the condition of the seals, general condition of the object, corrosion degree, etc. To collect these types of information, the participation of trained and experienced personnel who can perform inspections and measurements or provide an assessment of the condition of the object or system is required. Considering the way of collecting information, we can say that there are two different types of data. The first type of data collected is a dynamic information and as such, is collected with the help of equipment for remote monitoring, data collection, and transmission [7; 17; 19; 29]. Another type of data is static information that is obtained on the spot, that is, by measurement or assessment by an expert on the system itself.  
Based on collected information, dynamic or static, decisions are made about system maintenance [6; 10; 14; 15; 30; 31]. The collected information is appropriately processed and interpreted [3; 18]. The analysis of the objects' condition shows that the condition of a certain observed object most often depends on the condition of several different attributes and that to determine the current condition of the object, it is necessary to use a decision analysis based on multiple attributes, i.e., Multiple Decision Data Analysis (MADA) [20; 32; 33] combined with evidential reasoning [2; 22; 34].
## 3 Results
### Relationship between Biochemical Status and Rehabilitation Outcome  
The univariate analysis showed that only total calcium was correlated with the change from baseline of the BI (Spearman's rho = 0.466, \(p\) = 0.009), whereas ionized calcium, total protein, and albumin were not (Table 2).  
Finally, the multiple linear regression model (Table 3) significantly predicted the change from baseline of the Barthel Index (F5, 24 = 4.074, \(p\) = 0.008, adj. R2 = 0.346). Among the covariates, only total calcium level significantly predicted the outcome.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Baseline Characteristics** & **Mean (\(\pm\)SD) or n (\%)** \\ \hline Age (years) & 66.4 \(\pm\) 12.2 \\ Sex & \\ Men & 18 (60.0\%) \\ Women & 12 (40.0\%) \\ Index stroke type & \\ Ischemic & 21 (70.0\%) \\ Hemorrhagic & 9 (30.0\%) \\ Affected side & \\ Right & 12 (40.0\%) \\ Left & 18 (60.0\%) \\ Comorbidities & \\ Hypertension & 22 (73.3\%) \\ Type 2 Diabetes & 10 (33.3\%) \\ Dislipidemia & 6 (10\%) \\ Heart disease & 7 (23.3\%) \\ Dysphagia & 7 (23.3\%) \\ Language impairment & 7 (23.3\%) \\ Neglect syndrome & 6 (20.0\%) \\ Days from index stroke to enrollment & 90.5 \(\pm\) 41.2 \\ Modified Barthel Index (0–100) & 25.3 \(\pm\) 15.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline characteristics of the sample (n = 30).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Biochemical** & \multicolumn{2}{c}{**\(\Delta\)BI**} \\ \cline{2-3}
**Variables** & **Spearman’s rho** & \(p\)**-Value** \\ \hline Total calcium & **0.466** & **0.009** \\ \hline Ionized calcium & 0.278 & 0.137 \\ \hline Total protein & 0.278 & 0.136 \\ \hline Albumin & -0.095 & 0.623 \\ \hline \hline \end{tabular} Values in bold indicate a \(p\)-value lower than 0.05.  
\end{table}
Table 2: Correlation between biochemical variables and changes in performance in activities of daily living, as measured by the changes of the Barthel Index (\(\Delta\)BI). Values in bold indicate a \(p\)-value lower than 0.05.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Independent Variables** & \multicolumn{2}{c}{**Unstandardized Coefficients**} & **Standardized Coefficients** & **t** & \(p\)**-Value** \\ \cline{2-5}  & **B** & **SE** & **Beta** & \\ \hline Total calcium & 10.989 & 5.298 & 0.366 & 2.074 & **0.049** \\ \hline Time since stroke & \(-\)0.031 & 0.072 & \(-\)0.083 & \(-\)0.435 & 0.667 \\ \hline Age & \(-\)0.412 & 0.225 & \(-\)0.325 & \(-\)1.833 & 0.079 \\ \hline Type of stroke & 7.504 & 6.201 & 0.226 & 1.210 & 0.238 \\ \hline Barthel Index (baseline) & \(-\)0.316 & 0.161 & \(-\)0.314 & \(-\)1.961 & 0.062 \\ \hline Constant & \(-\)41.947 & 57.915 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multiple regression analysis.
## 2 Finite Element Model of Prefabricated Modular RCS Composite Node  
### Overview of Node Design  
#### 2.1.1 Mechanical Properties of Materials  
The material properties of the finite element model in this paper are defined based on the experimental data in relevant literature [16]. The performance tests for concrete and steel are conducted in accordance with the \"Standard Test Methods for Mechanical Properties of Ordinary Concrete\" (GB/T 50081-2002) and the \"Room Temperature Tensile Test Methods for Metallic Materials\" (GB/T 228-2002) [17; 18], respectively. The test results show that the compressive strength of the C30 concrete cube sample used in the prefabricated RCS node RCSJ3 without a floor slab is 30.2 MPa, and the material mechanical properties of steel are shown in Table 1.