esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Evaluation of Ensemble Models for Python Code Smell Detection
## 3 Proposed Research Framework
### Data Selection
#### 3.1.9 Performance Measures  
This paper examines Python code smell detection as a binary classification issue. The proposed ML models play a crucial role in distinguishing between smelly and non-smelly code occurrences. These models were rigorously assessed using six performance parameters: accuracy, precision, recall, F-measure, Cohen_kappa, and MCC. These metrics collectively capture various aspects of model performance, such as correctness, sensitivity to minority classes, and overall agreement between predictions and ground truth. MCC and Cohen's Kappa are particularly useful in imbalanced classification settings, as they provide a more balanced evaluation compared to accuracy alone.  
**Accuracy:** Measures the overall correctness of the classifier.  
\[Accuracy(A)=\frac{TP+TN}{TP+TN+FP+FN} \tag{3}\]  
**Precision:** Reflects the proportion of correctly predicted positive instances among all predicted positives.  
\[Precision=\frac{TP}{TP+FP} \tag{4}\]  
**Recall:** Indicates the proportion of correctly predicted positive instances among all actual positives.  
\[Recall=\frac{TP}{TP+FN} \tag{5}\]  
**F-measure:** The harmonic mean of precision and recall, balancing both.  
\[\text{F}-\text{measure}(\text{F})=2\times\frac{\text{Precision}\times\text{ Recall}}{\text{Precision}+\text{Recall}} \tag{6}\]  
**Cohen_kappa:** Evaluates the agreement between predicted and actual classifications, adjusted for chance.  
\[Cohen Kappa=\frac{P_{0}-P_{e}}{1-P_{e}} \tag{7}\]  
**MCC:** A balanced metric that considers true and false positives and negatives, especially useful for imbalanced datasets.  
\[MCC=\frac{(TP\times TN)-(FP\times\times FN)}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+ FN)}} \tag{8}\]
## 5 Case Studies: Validating the CILS Framework
### Case Study Two: HelloTalk
#### 5.2.4 Integration of the CILS into HelloTalk  
Integrating the CILS into HelloTalk has provided deep insights into the roles of technology-enhanced language learning in cross-cultural communication. This partnership included a rigorous empirical study involving 450 learners over a 6-month period, which examined the impact of AI-enhanced adaptive technologies combined with social networking on language learning outcomes. Learners experienced a series of structured tasks that measured improvements in language proficiency and cultural understanding.  
A major finding from this integration is the significant role of live interaction in language learning. Data from the study show that the instantaneous nature of communication in HelloTalk leads to quicker corrections and more dynamic interactions, contributing to a more engaging and effective learning process. This immediacy is crucial for maintaining learner focus and motivation, essential for long-term language learning success.  
The use of the CILS within HelloTalk has demonstrated how AI can enhance cultural awareness substantially. AI algorithms analyze interaction patterns to provide conversation-specific recommendations and culturally pertinent feedback, effectively bridging the gap between language proficiency and cultural competencies. This study found that students improved their ability to navigate cultural subtleties by 40%, enhancing their overall communication effectiveness.  
Another critical aspect is the usability of such technologies. The HelloTalk platform, empowered by AI, has proven to be highly scalable, serving a diverse and extensive user base of over 10,000 active learners. This scalability is crucial for meeting the varied needs of a global audience, providing personalized learning paths that cater to the specific linguistic and cultural backgrounds of each learner [35].
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 3 Expt. 1: Spectral Reallocation Preferences for Full-Bandwidth Speech
### Results
#### 3.5.2 Intelligibility  
As expected, listeners achieved the highest intelligibility in quiet. In noise, listeners were able to maintain intelligibility at a reasonably high level, even at the most adverse SNR, when presented with the possibility of modifying energy in the high band or by adjusting spectral tilt; modifications to the low and mid bands led to lower scores in the most adverse conditions. The intelligibility scores for all types of modification tested in this experiment are substantially in excess of those reported in [70] for unmodified speech from the same corpus and talker, and using the same type of masking noise, where psychometric functions indicate that listeners would achieve scores of 83%, 72% and 54% at 0, \(-\)3, and \(-\)6 dB SNR, respectively.  
A generalised linear mixed-effects model using the glmer function from the lme4 library [75] in R [76] was constructed to predict the proportion of keywords identified correctly in each trial. This model had fixed effects of SNR and feature, by-subject random intercepts and per-feature slopes. The model indicated significant effects of SNR (\(\chi^{2}(3)=1232\), \(p<0.001\)) and feature (\(\chi^{2}(3)=78.1,p<0.001\)) together with a significant interaction of the two (\(\chi^{2}(9)=25.6,p<0.01\)). Post hoc pairwise comparisons using function emmeans [77] with Tukey corrections showed that all SNR pairs differed for all features (all \(p<0.001\)) except for the \(0/-\)3 dB SNR pair for the high band (\(p=0.13\)). Intelligibility was not statistically different for all features in the quiet (min \(p=0.50\)) and SNR = 0 dB (min \(p=0.08\)) conditions, indicating that in the least challenging conditions, listeners may have been able to optimise intelligibility to an equivalent level for all types of adjustment. Adjustments to spectral tilt and to the high band were not statistically different at all SNRs, apart from \(-\)3 dB, where they tended to differ (\(p=0.054\)).  
Figure 3: [Colour online] Distribution of preferences (blue bars) and percentage of keywords identified correctly at each preference (points with error bars; points in red correspond to preferences chosen only once). The units on the y-axis represent both percentage correct values (for intelligibility) and preference counts (i.e., height of blue bars). In the latter case, distributions sum to 175 (35 listeners \(\times\) 5 trials).
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## Appendix B Case Observation  
Figure 14 shows an example of detecting precipitation through backscatter data using random forest. In the case of the left side, the observed weather phenomenon at 15:09:36 (hh:mm:ss) on  
21 January 2015 is precipitation and the predicted weather phenomenon is also precipitation. In the case of the right side, the observed weather phenomenon at 23:16:48 on 21 January 2015 is non-precipitation, and the predicted weather phenomenon is precipitation. The blue line indicates the mean value of backscatter data according to the observation value, and the colored part is a section where the standard deviation was added or subtracted from the mean. The distributions of backscatter data are generally similar regardless of precipitation. Since the input of our machine learning model is only backscatter data, it seems natural for the model to output the same prediction result for both cases with similar distribution. Hence, this supports the hardness of predicting precipitation.  
Figure A5 shows an example of detecting non-precipitation from the backscatter data using random forest. In the case of the left side, the observed weather phenomenon at 23:55:12 on is non-precipitation and the predicted weather phenomenon is also non-prediction. In the case of the right side, the observed weather phenomenon at 15:55:48 on 21 January 2015 is precipitation and the predicted weather phenomenon is non-precipitation. Likewise, the distributions of backscatter data are clearly similar regardless of precipitation. As mentioned above, naturally the machine learning model seems to output the same prediction result for both cases. This also supports that it is difficult to predict non-precipitation.  
Figure A6 shows an example of detection through the backscatter data using random forest when the visibility sensor value is equal to or less than 1000 m. In the case of the left side, the observed and  
predicted visibility sensor values at 01:01:46 are both equal to or lower than 1000 m. In the case of the right side, the observed visibility sensor value at 00:46:46 on 22 January 2015 is greater than 1000 m and lower than 20,000 m, and the predicted value is equal to or lower than 1000 m. It is not easy to find clear difference between both cases.  
Figure 11 shows an example of detection from backscatter data using random forest when the visibility sensor value is greater than 1000 m and lower than 20,000 m. In the case of the left side, the observed and predicted visibility sensor values at 00:35:23 on 22 January 2015 are both greater than 1000 m and lower than 20,000 m. In the case of the right side, the observed visibility sensor value at 00:00:00 on 22 January 2015 is 1000 m or lower and the predicted value is greater than 1000 m and lower than 20,000 m. Analogously to the above, we cannot see distinct difference between both cases. It hints that it is very hard to differentiate fog phenomenon by using only backscatter data.
## 2 Internet of Vehicles and Smart Cities
### Main Challenges
#### 2.4.6 Data Dissemination, Routing, and Management  
The IoV also opens doors to the integration of different services and technologies [32]. With the heterogeneity of entities, services, and networks in IoV environments, disseminating and routing data from the source to the target destination while guaranteeing a high quality of service becomes difficult. These heterogeneous environments also limit the coordination and collaboration of different networks and subnetworks. There is also the problem of data management, given that the entities in IoV generate massive amounts of data, making it difficult to aggregate, storage, process, analyze and provide decision making over this data. The challenge here is to design new protocols and schemes that can ensure that not only the data reaches the target under certain QoS limits but also enable cooperation between networks and ease big data management.
# A Method to Determine BeiDou GEO/IGSO Orbital Maneuver Time Periods
## 2 Theory of Orbital Maneuver Period Detection  
The orbits of satellites change significantly before and after maneuvering. Considering that pseudo-range observations and the satellite clock are not influenced by maneuvering, the orbital error will increase gradually after the satellite orbit is switched. Based on this, the time period of a satellite orbit maneuver can be determined by using pseudo-range observations and the positions of the station and the satellite. In our previous research, we provided detecting methods for BDS satellite orbital maneuvers [17; 18]. However, the orbit after maneuvering is lost from broadcast ephemeris. The precise products of orbit maneuvering satellites are also lost from the International GNSS Service (IGS) or International GNSS Monitoring and Assessment System (iGMAS), which causes difficulty in determining the time period of orbit maneuvering. Based on this, the time period of satellite orbit maneuvering is determined using predicted orbits by precise products on the day after orbital maneuvers. The orbit before maneuvering is predicted by precise products on the day before orbital maneuvers, and it also can be replaced by broadcast ephemeris or ultra-rapid orbit products at 00:00.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 5 Satellite Experiment Testbed  
In this section, we will introduce a satellite experiment testbed that will be used for the performance evaluation of the algorithms in this paper. To build a satellite experiment testbed, the satellite tool kit (STK) [28] software will be used to export information such as link connectivity and propagation delay among satellites during a orbital cycle period.  
In the testbed, each satellite has packet forwarding capability, and this forwarding capability is provided by OVS running within a Docker container shown in Figure 5. Then, we can install as many Dockers as satellites. For the links between satellites, we design that a link between two satellites is provided by a pair of virtual network interfaces (veth pair). Then, by adding ends of the veth pair to two OVSs that are in two separate Dockers, an inter-satellite link is established. Removing the end from OVS means the link is disconnected. The delay and bandwidth of the inter-satellite links are configured using the traffic control tool provided by the kernel. We note that to realize the variability of the satellite topology and the delay and bandwidth of the links, a testbed manager is proposed and works as the background process of the testbed (not shown in the figure), which dynamically configures the veth pairs in the testbed according to the link connectivity and propagation delay information among satellites input from STK.  
In this testbed, the experiment system is the Iridium satellite system, which is partially shown in Figure 5, with six orbital planes numbered from 11 to 16. Each orbital plane has 11 satellite nodes numbered from 01 to 11. For example, satellite 1101 represents satellite 01 on orbital plane 11. The distance between satellites with the same number but on adjacent orbital planes varies as the satellites move, resulting in changes in the propagation delay and link disconnections or reconnections. Satellites with adjacent numbers on the same orbital plane can always communicate, and the propagation delay remains stable.  
Meanwhile, in the testbed, there is an ONOS controller that is an openflow controller. This controller calculates routing paths for traffic flows and gives routing information (flow tables) to OVSs that tells the OVSs how to forward the packets. Specifically, the ONOS receives routing requests for TS flows and runs the traffic routing algorithm proposed to allocate a routing path for the TS flows and distribute the flow tables to each satellite node (i.e., OVS). If the routing path of the traffic flow changes (path handover), the flow  
Figure 5: Satellite experiment testbed.  
tables must be resent from ONOS to the OVSs along the new path. Table 2 shows the basic configurations used in this experiment testbed.
# Analysis of Multi-Antenna GNSS Receiver Performance under Jamming Attacks
## 2 Methodology
### Multi-Antenna Signal Simulations and Processing
#### 2.2.2 Minimum Power Distortionless Response (MPDR)  
MPDR was performed at the post-correlation stage with the MPDR receiver architecture shown in Figure 3. IF signals from all the antennas are fed to each tracking channel. In each tracking channel, the code and carrier replica signals from the beamformed data are used to de-spread other antenna signals in order to maintain relative phase differences between the antenna elements. The optimum weight vector for the MPDR beamformer for the \(k\)th satellite is given by [24]  
\[\mathbf{w}_{\text{PM}}=\frac{\mathbf{R}_{\text{xx}}^{-1}\mathbf{a}_{k}}{ \boldsymbol{a}_{k}^{H}\mathbf{R}_{\text{xx}}^{-1}\mathbf{a}_{k}} \tag{16}\]  
The covariance matrix \(\mathbf{R}_{\text{xx}}\) was computed at the pre-correlation stage because at this stage, interference power might be reduced due to the correlation process affecting beamformer performance. The combined correlator outputs will be used to generate code and carrier phase measurements.
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 6 System Implementation
### Security Multi-Party Sharing Module  
In the security multi-party sharing function module, the security situation of data assets in the multi-party sharing process is visually presented and analyzed through components such as Amap, log distribution maps, scattered circle maps, multi-dimensional radar maps, and statistical maps, so as to facilitate timely discovery of security risks and adjustment or modification of security protection strategies, as shown in Figure 10.  
The node information that is undergoing data sharing is displayed in Amap. The connection between nodes intuitively reflects the network connection relationship of data sharing participants, and can intuitively perceive the whole process of data sharing. In the data sharing log search interface, managers can quickly filter target logs by entering keywords, selecting component types, setting security levels or adjusting time ranges and other multi-dimensional conditions. At the same time, real time synchronously presents the time distribution diagram of matching logs on the right side of the interface, and clearly shows the distribution density of logs in different time periods based on the timeline. In the process of data sharing, it records according to the preset log template, and through statistical analysis of the selected logs, the change trend of key indicators such as the number of logs and risk level in time series is intuitively presented by a line chart and bar chart, and the analysis results are given in three aspects: continuous trust evaluation, access control evaluation and data sharing status evaluation. In the continuous trust evaluation view, the scatter circle diagram is used to display the overall information of all data sharing events recorded in the log. Each scatter represents a data sharing event. Through the attributes such as the position, color and size of the scatter, the type, trust level and influence scope of the event are visually presented. In the access control evaluation view, the access control policy of each data sharing participant is evaluated using a multi-dimensional radar chart, which helps managers discover risks in real time and adjust access control policies in time. In the data sharing status evaluation view, combining the continuous trust evaluation results and the access control evaluation results, the comprehensive trust results of each data sharing participant are given, which helps managers quickly lock high-risk data sharing participants and data sharing things.  
Figure 10: Security multi-party sharing module interface in secure data asset sharing and circulation systems.
## 4 Experimental Results and Analysis
### Geometric Correspondence
#### 4.3.4 Object Identification  
The Sobel operator is used to extract edge information from the color-segmented image for matching purposes. Next, the dot product of the gradient vectors of the multi-scale template image and the image to be matched is computed using the similarity measure function.  
The similarity between two points is determined by the inner product of their gradient vectors. If the inner product is 0, the similarity is also 0. Conversely, if the corresponding points match precisely, the inner product is 1. The magnitude of the inner product directly correlates with the level of similarity. Prior to the matching process, we set a predetermined threshold value. Once the inner product surpasses this threshold, we consider the matching to be successful. As seen in Figure 11, the spots that are correctly matched in the original image are shown by red rectangle boxes [37].
# A Study of Eye-Tracking Gaze Point Classification and Application Based on Conditional Random Field
## 2 Proposed Method
### Conditional Random Field Model  
The Conditional Random Field (CRF) [15,16] is a discriminative undirected graph model which models the conditional probability of multiple variables after a given observation, in order to solve the problem of sequence labeling. The CRF model often assumes that the observation sequence is \(x=\{x_{1},x_{2},\cdots,x_{n}\}\), and the corresponding class label sequence is \(y=\{y_{1},y_{2},\cdots,y_{n}\}\). The CRF can then construct a conditional probability model \(P(y|x)\), expressed as Equation (1),  
\[P(y|x)=\frac{1}{Z}\exp\left(\sum\sum P_{V}(y_{i-1},y_{i},x,i)+\sum\sum P_{U}(y _{i},x,i)\right), \tag{1}\]  
where \(P_{V}(y_{i-1},y_{i},x,i)\) is the Transition Feature Function of the neighboring class marker positions on the whole observation sequence, \(P_{U}(y_{i},x,i)\) is the State Feature Function of the marker positions \(i\) on the observation sequence, and \(Z\) is the normalization constant, also known as the normalization factor.  
It can be seen from Equation (1) that the representation of the model mainly consists of feature function expressions. Therefore, the relevant feature functions of the gaze point should be established.
## 2 Materials and Methods
### Calibration of RHT Model Parameters
#### 2.2.3 Strain Rate Parameter Calibration  
In the RHT model, the hydrostatic pressure is represented through Hugoniot polynomial coefficients and the \(p-\alpha\) compression relationship. Under quasi-static conditions, the failure strength of the rock can be obtained by triaxial compression tests. The dynamic failure strength is obtained from the static breaking strength, and the failure surface stress is described as the yield surface through the compressive strength, regularized yield function Willam-Warnke function [27]:  
\[\sigma_{y}\Big{(}p_{0}^{*},\dot{e}_{p},\dot{e}_{p}^{*}\Big{)}=f_{c}\sigma_{y} ^{*}\Big{(}P_{0}^{*},F_{r}\big{(}\dot{e}_{p}\big{)},\dot{e}_{p}^{*}\Big{)}R_{3} \Big{(}\theta_{l},P_{0}^{*}\Big{)}, \tag{6}\]  
where \(f_{c}\) is the unconfined uniaxial compressive strength; \(\sigma_{y}^{*}\) is the normalized yield function; R\({}_{3}\) is the Lode angle function, which accounts for the reduced strength of shear and tensile meridians [13]; \(\theta_{l}\) is the Lode angle; Fr is the dynamic strain rate increase factor; \(p_{0}^{*}\) is the normalized pressure (the parameters with a superscript * in the paper are the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Item Name** & \(\rho\) **(kg/m3)** & **P (MPa)** & **P\({}_{\text{cut}}\) (MPa)** & **E (GPa)** & \(\mu\) & \(v_{0}\) **(m/s)** & \(\alpha_{0}\) **\%** \\ \hline Mineral rock & 3121 & 116.3 & 6.06 & 54.4 & 0.25 & 4591 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Basic physical parameters of the mineral rock model.  
result of the normalization process); \(\dot{\epsilon}_{p}\) is the strain rate; \(\dot{\epsilon}_{p}^{*}\) is the effective plastic strain; and \(p_{0}^{*}=p_{0}/p_{c}\), \(p_{0}\) is the hydrostatic pressure.  
\[F_{r}\big{(}\dot{\epsilon}_{p}\big{)}\begin{cases}\Big{(}\dot{ \epsilon}_{p}/\dot{\epsilon}_{0}^{\dot{\epsilon}_{0}}\Big{)}^{\beta_{c}}&P\geq f _{c}/3\\ \frac{P+f_{c}/3}{f_{c}/3+f_{r}/3}\Big{(}\dot{\epsilon}_{p}/\dot{\epsilon}_{0}^{ \dot{\epsilon}_{0}}\Big{)}^{\beta_{c}}-\frac{P-f_{t}/3}{f_{c}/3+f_{t}/3}\Big{(} \dot{\epsilon}_{p}/\dot{\epsilon}_{0}^{\dot{\epsilon}_{0}}\Big{)}^{\beta_{t}}&- f_{t}/3<P<f_{c}/3\\ \Big{(}\dot{\epsilon}_{p}/\dot{\epsilon}_{0}^{\dot{\epsilon}_{0}}\Big{)}^{ \beta_{t}}&P\leq-f_{t}/3\end{cases}, \tag{7}\] \[\beta_{c}=\frac{4}{20+3f_{c}},\] (8) \[\beta_{t}=\frac{2}{20+f_{c}}, \tag{9}\]  
where \(\dot{\epsilon}_{0}^{\dot{\epsilon}}\) is the reference strain rate under compression, \(\dot{\epsilon}_{0}^{\dot{\epsilon}}\) is the reference strain rate under tension, \(\dot{\epsilon}_{t}\) is the tensile strength, \(\beta_{c}\) and \(\beta_{t}\) are the material constants for compression and tension, \(\dot{\epsilon}_{0}^{\dot{\epsilon}}=3.0\times 10^{-5}\) s\({}^{-1}\), \(\dot{\epsilon}_{0}^{\dot{\epsilon}}=3.0\times 10^{-6}\) s\({}^{-1}\), \(\dot{\epsilon}^{\dot{\epsilon}}=3.0\times 10^{25}\) s\({}^{-1}\), respectively [28; 29].