esa-sceva/satcom-chunk-collection huggingface dataset:
# Incremental Formula-Based Fix Localization
## 5 Results of the Experiments
### Result of RQ2: Effectiveness in Fault Localization  
To evaluate the fault localization effectiveness, we report and compare (1) the number of successful fault localization runs, the number of successful fix localization runs, and the average _EXAM score_ of each technique for each buggy program. For recall, a run of a technique was said _successful fault localization_ if it found an MFC that fixes at only faulty statements (i.e., all fix locations in the MFC are actually faulty statements). A fault localization run was considered _successful fix localization_ if it outputs an MFC that is both a feasible fix candidate and a correct fault location. EXAM score is the percent of program code lines that the developer needs to examine before identifying the fault. EXAM score is computed as the ratio of the number of lines of code that the developer examined before reaching an actual faulty line to the total number of code lines in the program.  
Table 2 shows the experimental results for the TCAS programs in terms of fault localization effectiveness. The first section of the table shows the version name (_Ver_) and the number of fault localization runs (_Ftc_) of each technique for each buggy version.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**\#Succ. Fault Localization**} & \multicolumn{8}{c}{**\#Succ. Fix Localization**} & \multicolumn{8}{c}{**EXAM Score**} \\ \hline
**Ver** & **Ftc** & **Agx** & **PF** & **FS** & **FI** & **BA** & **LF** & **  
The columns in the first section, _\"#Succ. fault localization\"_, of Table 2 show the number of runs that each technique succeed in reporting correct fault location. We obtained the result of Bug-Assist (BA) and LocFaults (LF) from Reference [19; 22]. In a total of 2156 runs of each technique, the number of runs that successfully output the correct fault locations of the AgxFaults, PF, and FS techniques is 2156 (100%) runs, while those of Bug-Assist (BA), LocFaults (LF), and FI is 2087 (96%), 1345 (62%), and 121 (5.6%) runs, respectively. Specifically, FI reported the correct fault location only for version v36, in which the faulty statement is data-dependent on the program output.  
The columns in the second section, _\"#Succ. fix localization\"_, of Table 2 show the number of runs that each technique can output an MFC that is both correct fault location and feasible angelic fix candidate. As the result showed, all 2156 in a total of 2156 runs of the AgxFaults and PF are successful fix localization runs, while FS and FI techniques succeed in 2027 and 1345 runs, respectively.  
The columns in the last sections of the table show the EXAM score of each technique. On average, the EXAM score for Agx was 6.8%, PF was 6.9%, FS was 11.5%, FI was 17.7%, and execution slice was 17.6%.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 3 Materials and Methods
### Interpolator  
After performing the aforementioned operation, we can obtain the position of optimal sampling instant \(P_{n}\) for each symbol. However, as we are dealing with a passband carrier signal, we need to determine the position of each sampling point and resample them in order to recover the passband signal without Doppler effects. This necessitates the use of interpolation algorithms.
# Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms
## 3 Materials and Methods
### Feature Selection Using Optimization Methods
#### 3.2.1 Particle Swarm Optimization (PSO)  
Particle Swarm Optimization (PSO) was proposed by [PERSON] and [PERSON] [35] in 1995; it is a population-based stochastic optimization algorithm based on swarm behaviors found in nature such as in schools of fish and flocks of birds. In PSO, a population of candidate solutions, called particles, moves in search of the optimal solution to a given problem. Feature selection using PSO involves selecting a subset of features that can best represent the data for a particular task, such as classification or regression. The objective function used in PSO for feature selection is often based on a performance metric of the classification or regression model, such as accuracy or mean squared error.  
The PSO algorithm starts by initializing a population of particles, each of which represents a candidate solution to the feature selection problem. Each particle is associated with a velocity and a position in the search space. The velocity and position of each particle  
Figure 1: Flowchart of the methodology.  
are updated based on its own best-known solution and the best-known solution of its neighbors in the search space. During each iteration of the PSO algorithm, the particles move through the search space, and the fitness of each particle is evaluated based on the performance metric of the classification or regression model. The fitness of each particle is then used to update its velocity and position in order to converge toward the optimal solution. A maximum number of iterations or a criterion of convergence is used to control the termination point. The PSO algorithm works as follows:  
Initialization: particles identified by their position and velocity are initialized;  
Evaluation: each particle is evaluated by its fitness function;  
Update of the particle's velocity and position: Each particle updates its velocity and position based on its own best-known position (i.e., the position that resulted in the best fitness so far) and the best-known position of the swarm. The new velocity and position are computed using Equations (1) and (2):  
\[v_{i}^{t+1}=v_{i}^{t}+c_{1}r_{1}\Big{(}pbest_{i}^{t}-x_{i}^{t}\Big{)}+c_{2}r_{2 }\big{(}g_{best}-x_{i}^{t}\big{)} \tag{1}\]  
\[x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1} \tag{2}\]  
where v\({}_{i}\) is the velocity; \(c_{1}\) and \(c_{2}\) are the acceleration coefficients; \(r_{1}\) and \(r_{2}\) are random numbers between 0 and 1; \(pbest_{i}^{t}\) is the best-known position of the particle; \(g_{best}\) is the best-known position of the swarm; and \(x_{i}^{t}\) is the current position of the particle;  
Termination: the algorithm continues to iterate until a termination criterion is met, such as a maximum number of iterations or the convergence of the solution.
# Space-Borne System-in-Package Based on High Reliability Microwave Interconnections
## 3 A Novel High Reliability Interconnection Structure
Based on the parameters in the previous example, which are shown in Table 4, the initial values of the diameter and the length of the air coaxial line are calculated to be 0.7 mm and 0.5 mm, respectively. The results of the measured microwave matching performance are shown as Figure 13. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline \(\epsilon_{r1}\) & 4.2 \\ \(\epsilon_{r2}\) & 9.9 \\ \(D\) & 1.60 mm \\ \(d\) & 0.30 mm \\ \(L_{1}\) & 2.20 mm \\ \(del\) & 0.20 mm \\ \(w\) & 0.37 mm \\ \(h\) & 0.38 mm \\ \(W_{b}\) & 0.35 mm \\ \(h_{b}\) & 0.10 mm \\ \(dd\) & 0.70 mm \\ \(L_{2}\) & 0.50 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of the novel horizontal interconnection structure. Figure 13: Actual photo and reflection loss of the novel horizontal interconnection. (**a**) Photo of the novel interconnection; (**b**) S-parameter of the novel interconnection. It can be seen that the reflection loss at 40 GHz of the port can be better than \(-21\) dB with this interconnection structure. The length \(L_{2}\) of the coaxial part in the gradual air can be adjusted during processing, and the gap _del_ between the inner core of the coaxial seal and the microstrip substrate can be adjusted during assembly. Other parameters are the same as those in Table 4. Its RF performance is shown in Figures 14 and 15. The S21 parameters do not differ much, so detailed analysis is no longer necessary. The stress of the structure is also analyzed, and the result is about 26.7 MPa, which fully meets the reliability requirements, as shown in the Figures 16 and 17. The glass seal can also be used to achieve the vertical layout of the microwave circuits [21], and the bonding gold ribbon can be used to achieve the high reliability of the vertical transmission of microwave signals. Because the ribbon is used for overlapping and interconnection, the stress is similar to the horizontal interconnection. Only the impedance mismatch caused by the vertical geometric position needs to be solved. High and low characteristic impedance lines can be designed on both top and bottom microstrip lines to match them [22; 23]. The design is shown in Figure 18. The main parameters of the vertical transition design are shown in Table 5.  
Figure 14: Return loss corresponding to the length of the gradually opening air coaxial. Figure 15: Return loss corresponding to the horizontal gap. Figure 16: Analysis model of the novel horizontal interconnection. (**a**) The stress analysis model; (**b**) Meshes of the model. Figure 17: Analysis model of the novel horizontal interconnection. Figure 18: Models of the vertical interconnection. For the actual process and test, as shown in the Figure 22, it has achieved excellent RF performance with microwave high-frequency active circuits.
## 5 Conclusions  
In this paper, an aerodynamic model if non-planar quadrotor considering the mutual interference is established. The numerical simulation of an isolated single rotor and non-planar quadrotor along with the experiments demonstrate that the proposed model is able to better explain the unique aerodynamic interference of non-planar quadrotor. The conclusions of this paper are as follows:  
1. The non-planar quadrotor rotor thrust will increase by 5-6% compared to the isolated rotor without interference. The tilt angle increased the actual induced velocity and outflow to increase the thrust of rotor;
2. The thrust becomes stable when the spacing is larger 1.4 \(d\) and the power is also significantly affected by the high-intensity vortex attached to the surface of the rotor and the tip vortex;
3. The flow field of the non-planar quadrotor is extremely complex, mainly based on unsteady flow caused by the intense outflow. There is a large pressure gradient in the vortex due to the periodic unsteady airflow pulsation generated by the rotor itself and the wake diffusion. Each vortex is a distorted sub-zone that causes the pressure distribution pulsations;
4. Power consumption of the non-planar quadrotor increases with the thrust which will cause a substantially balanced power loading. Moreover, the power loading of the non-planar quadrotor is less than the isolated rotors showed a better performance compared with the traditional planar rotors;
5. The unique aerodynamic performance of the non-planar quadrotor is significantly improved at a larger tilt angle with smaller spacing where the tip vortex is relatively strong, which is beneficial to optimize the multi-rotor system for the further studies.  
[PERSON] carried out experiments and analyzed experimental results; [PERSON] wrote the manuscript with assistance of [PERSON].  
This research was supported by the National Natural Science Foundation of China, grant No. 51505087 and 51775114, the State Scholarship Fund from China Scholarship Council, grant number 201806655008, and Fujian Provincial Industrial Robot Basic Components Technology Research and Development Center, grant number 2014H21010011.  
The authors thank the Key Laboratory of Fluid Power and Intelligent Electro-Hydraulic Control (Fuzhou University), Fujian Province University for applying the experimental field.  
The authors declare no conflict of interest.
# Spectrum Demand Forecasting for IoT Services
## 4 Spectrum Forecasting Model
### Required Capacity  
To find the required capacity for a given offered traffic, two situations are differentiated:  
* High number of devices per gateway: Networks designed for either mMTC or eMBB, i.e., cellular networks or LPWANs, will naturally have an important number of connected devices creating congestion and reducing the network's performance with delayed or lost packets. Following an ITU recommendation [29], a gateway with these traffic characteristics, should be modeled with an \(M/G/1\) queue with non-peremptory priorities. This model has been known in the literature as the Cobham model [45]. For a short description of the model, the reader is referred to [29], for an applied version the reader is referred to [46]. For the Cobham model, the capacity for each scenario is obtained as follows: \[C_{i}=\max\left\{C^{1},\dots,C^{n}\right\}\] (5) \[C^{n}=fcn(D_{n},\tilde{\
u}_{n},m_{2,n})\] (6)  
Each scenario should have several services organized by priority from 1 to \(n\). \(C^{n}\) is the required capacity for the \(n\)-th priority service, described as a function of the mean required delay \(D_{n}\), the packet size variance \(\tilde{\
u}_{n}\) and the second moment of the packet size \(m_{2,n}=\tilde{\
u}_{n}^{2}+\mu_{n}^{2}\). Hence, the maximum capacity among all service priorities \(C_{i}\) is the required capacity (in bits/s) for the \(i\)-th scenario. The details of the function \(fcn()\) in (6) can be found in [29] Section 4.2.
* Low number of devices per gateway: A small number of devices should be one that has a very low probability of creating traffic congestion or a significant delay on the gateway. Short-range communications and private networks may be typical examples. To simplify the model, in this case, the required capacity is assumed to be equal to the offered traffic: \(C_{i}=A_{i}\).
# Securing IoT: Mitigating Sybil Flood Attacks with Bloom Filters and Hash Chains
## 5 Performance Evaluation
### Informal Security Analysis
#### 5.3.4 Routing Table Falsification Attack  
A common attack vector against RPL-based IoT networks involves routing table falsification. In this type of attack, a malicious node intentionally misleads other nodes by advertising routes to nonexistent or unreachable destinations. By disseminating fake DAO control messages to the parent node, the attacker can induce the parent to store incorrect routing information, potentially disrupting network communication and leading to security vulnerabilities.  
To enhance the resilience of RPL-based networks against routing table falsification attacks, nodes can implement a verification mechanism that requires child nodes to provide the root hash of their sub-DODAGs. This root hash serves as a cryptographic fingerprint of the sub-DODAG's topology and membership. By comparing the received root hash against existing Bloom filter entries, parent nodes can validate the legitimacy of the advertised route before storing it in their routing tables. This additional layer of security can significantly reduce the effectiveness of falsification attacks, as malicious nodes would need to compromise multiple nodes and manipulate their sub-DODAGs in order to create a convincing fake route.
## 2 Results and Discussion
### Method Validation
#### 2.3.3 Accuracy and Precision  
Within- and between-run accuracy for both analytes were mostly within the accepted ranges (85-115% of nominal concentration), comprising 93% of samples for impenem and 87% of samples for piperacillin. The precision met the acceptance criteria (RSD \(\leq\)15%) for all calibrators. The results for accuracy and precision are listed in Tables 3 and 4, respectively.  
Figure 2: Optimization of impenem recovery [%] for different adsorbents (**A**) and desorption solvents (**B**).  
Figure 3: Recovery of piperacillin and impenem for different durations of desorption (**a**) and extraction (**b**).
# Mdpj _sciences_
## 2 Related Work
### Combined Data-Based Vehicle Maintenance Demand Prediction  
This category covers methods that integrate multiple types of data, such as sensor data, maintenance records, and environmental data, to enhance the accuracy and reliability of maintenance demand prediction. [PERSON] et al. [1] utilized GIS data to predict the remaining useful life (RUL) of automotive components, emphasizing environmental impacts on automotive health and combining AI with a health index and Cox-PHM for a robust prognostic model. [PERSON] et al. [13] introduced a parallel stack autoencoder proficient in feature extraction for large dataset fault correlation analysis. [PERSON] et al. [14] integrated operational and archival data to rapidly identify vehicle quality issues, enhancing predictions. [PERSON] et al. [15] evaluated machine learning methods such as random forest, SVM, ANN,and Gaussian process for engine parts maintenance, while [PERSON] and [PERSON] [16] employed an SVM-based approach for sensor system multi-fault diagnostics in autonomous vehicles, a crucial aspect for their reliability. This reflects machine learning's transformative role in automotive maintenance, prognostics, and diagnostics, propelling the industry towards greater automation. [PERSON] et al. [17] explored the application of deep learning in vehicle fault diagnosis, with their Deep-SBM showing excellent performance and enhancing real-time fault prediction in IoT frameworks. [PERSON] et al. [18] combined sparse autoencoder error fusions with LSTMs, developing a time-series prediction approach for mechanical failure tracking. [PERSON] et al. [19] examined fault detection and prediction in autonomous vehicle sensors, demonstrating how integrating data-driven methods with deep learning enhances security and reliability. [PERSON] et al. [20] employed uncertainty mathematics and deep confidence networks for accurate sensor system health prognostics, highlighting the promise of deep learning in these technologies.  
Although existing research has demonstrated proficiency in predicting maintenance demands for specific vehicle components, a substantial proportion of these studies focuses on fault diagnosis and the prediction of individual systems or components, thereby revealing a gap in providing a holistic prediction of overall vehicle maintenance demands. In contrast, the methodology presented herein aims to predict the comprehensive maintenance demands of a vehicle, thereby improving the safety and usability of vehicles, and ensuring increased operational efficiency and prolonged service life.
## 3 Performance Analysis of System
### EE and SE Analysis of GCI-DCSK System  
In our system, \(p\) bits will be transmitted per frame, so the number of information bits transmitted per unit of energy is formulated as \(EE=\dfrac{p_{1}+p_{2}}{P_{s}[p_{2}+1+\mu^{2}(n-p_{2})]E_{c}^{p}}\), which is used to measure the energy efficiency of the system [22]. The number of bits transmitted on a single carrier in a frame is formulated as \(SE=\dfrac{p_{1}+p_{2}}{n+1}\), which is used to measure the spectral efficiency of the system. Obviously, the EE and SE of our proposed system are more flexible than CI-DCSK1/2 SWIPT system, which is shown in the Table 1. Compared with GCI-DCSK, EE becomes smaller at the same SE due to the use of inactive carriers to transmit energy for harvest.
## 4 Performance Evaluations
### Experimental Environments
#### 4.1.2 Dataset and Data Preprocessing  
We used a dataset provided by KETI (Korea Electronics Technology Institute, Seongnam, Republic of Korea), with some class data publicly available on AIHUB. Table 6 shows the composition of the dataset used in the experiment. Our dataset, used for model training and evaluation, comprises a total of 41,240 video samples, divided into training (28,868), testing (6186), and validation (6186) sets. It covers 510 classes, consisting of 405 individual words and 105 sentences, with each class represented by approximately 80 samples. All videos were captured in full HD (1920 \(\times\) 1080) resolution, and the dataset includes recordings from multiple sign language speakers, ensuring diversity in the sign language utterances.  
Each video sample in the dataset is a recording of a sign language utterance. To prepare the data for training and validation, we employed the following preprocessing steps.  
1. Frame Extraction: Each video is broken down into individual frames.
2. Video Frame Centralization and Resizing: * Each extracted frame, originally at a resolution of 1920 \(\times\) 1080 pixels, is centrally cropped to 1080 \(\times\) 1080 pixels to ensure the sign language gesture is centered. * The centrally cropped frame is then resized to 256 \(\times\) 256 pixels to standardize the input size for the model. * 17 keypoints (0-16) from the Pose model, shown on the left side of Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sets** & **Samples** & **Classes** & **Words** & **Sentences** \\ \hline Training & 28,868 & 510 & 405 & 105 \\ \hline Testing & 6186 & 510 & 405 & 105 \\ \hline Validation & 6186 & 510 & 405 & 105 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dataset composition.  
Figure 2: Overall architecture of sign language recognition model based on Video Swin Transformer.  
21 keypoints (0-20) from each Hand model (left and right), as depicted on the right side of Figure 3.
* A total of 59 keypoints are extracted per frame.
## 3 Results and Discussion
### Wort Fermentation of Sugarcane Samples
#### 3.5.1. Yeasts  
The population dynamics of yeasts, both _Saccharomyces_ and non-_Saccharomyces_, are shown in Figure 4. After inoculation, the plate count of _Saccharomyces_ populations (Figure 4a) ranged from 6.28 to 7.00 log CFU/mL, with significant differences observed between trials. The values refer to the initial viable cell counts of the inoculated _S. cerevisiae_ strains (MN113, SPF21, EC1118), which were introduced at a standardized concentration of approximately 2 \(\times\) 10\({}^{6}\) cells/mL. This observation is indicative of higher values when compared to the indigenous _Saccharomyces_ population detected in untreated juice (4.70 log CFU/mL). The observed counts are consistent with expected plating efficiency and standard inoculation practices in controlled fermentations. Spontaneous fermentation trials (SC1, SC5, SC9, and SC13) had significantly lower values, ranging from 3.23 to 3.81 log CFU/mL, compared to the inoculated trials. In these cases, the untreated trials exhibited higher values than those with chemical treatments. Specifically, trials treated with potassium metabasulfite showed lower values than others. From the next sampling point, the microbial load of _Saccharomyces_ yeasts increased, while the population of non-_Saccharomyces_ yeasts (Figure 4b) began to decrease, with most becoming undetectable. Additionally, a slight decrease was observed in the uninoculated and untreated trials, except for SC10 and SC11 treated with sulfuric acid, which had a value of 3.48 log CFU/mL. The decrease in non-_Saccharomyces_ species populations can be attributed to several factors, including the presence of ethanol and chemical compounds, competition for nutrients, and metabolites produced by _S. cerevisiae_ that inhibit the growth of other yeasts [61].  
Figure 3. Results of microbiological analyses for total mesophilic microorganisms of different samples. T0, T6, T12, and T18 represent the sampling times (in days) during alcoholic fermentation. Lower-case letters indicate statistically significant differences among treatments according to [PERSON]’s test (\(p\leq 0.001\)); treatments sharing the same letter are not significantly different.  
The _Saccharomyces_ spp. population was found to be most responsive to treatments involving sulfuric and lactic acids, as evidenced by the higher cell counts recorded in these trials when compared to other treatments. It is worth noting that on the sixth day of fermentation, _S. cerevisiae_ EC1118 in the SC16 trial attained the highest recorded value of 7.90 log CFU/mL. A decline in yeast count was observed as the process of alcoholic fermentation concluded. In general, treatments involving acidification with sulfuric and lactic acids have been shown to promote increased yeast proliferation. The yeast growth dynamics observed during sugarcane juice fermentation were consistent with those previously reported by [PERSON] et al. (2012).