esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Learning for General Type-2 TSK Fuzzy Logic Systems
## 2 Background
### Principle of Justifiable Granularity  
The purpose of this principle [25] is to specify the optimal size of an information granule where sufficient coverage for experimental data exists while simultaneously limiting the coverage size in order to not overgeneralize. These differences are shown in Figure 4.  
A dual optimization must exist which can consider both objectives, where (1) the information granule must be as specific as possible; and (2) the information granule must have sufficient numerical evidence.  
As the length of an information granule is perceived by two delimiting sides of an interval, the dual optimization is performed once per each side. As shown in Figure 5, the left side interval from the Median of the data sample \(a\) and the right side interval from the Median of the data sample \(b\) creates two intervals to be optimized, where \(Med(D)\) is the Median of available data \(D\) which initially constructed said information granule.  
Shown in Equations (12) and (13) are the search equations \(V(0)\) for optimizing \(a\) and \(b\) respectively, where \(V(0)\) is an integration of the probability density function from \(Med(D)\) to all prototypes of \(a\), or \(b\), multiplied by the user criterion for specificity \(\alpha\), where \(\alpha\) is a variable which affects the final length of \(a\) or \(b\), such that \(\alpha_{0}\) has the highest experimental data, and \(\alpha_{max}\) represents the most specific possible length and has minimal experimental data.  
\[V(a)=e^{(-\alpha|Med(D)-a|)}\int_{a}^{Med(D)}p(x)dx \tag{12}\]  
\[V(b)=e^{(-\alpha|b-Med(D)|)}\int_{Med(D)}^{b}p(x)dx \tag{13}\]  
Figure 4: Visual representation of both contradicting objectives in data coverage, where (**a**) complete experimental data coverage is obtained; and (**b**) a limited coverage of experimental data is obtained.  
Figure 5: Intervals \(a\) and \(b\) are optimized from available experimental data for the formation of said information granule, where both lengths start at the median of the information granule’s experimental data.
## 3 Materials and Methods
### Exploratory Data Analysis Using Statistical and Visualization Techniques
#### 3.2.4 To Check Correlations between Input Variables and the Output Variable  
The calculation of correlations between the input and output variables allowed us to discover which input variables were highly correlated with the output either positively or negatively. As we can see in Table 3, the freezing temperature showed a strong correlation with the amount of chemicals present.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variables** & **Count** & **Mean** & **Std** & **Min** & **Max** \\ \hline Surface temperature & 3827 & 0.62 & 4.63 & \(-\)14.60 & 14.20 \\ Air temperature & 3827 & 0.84 & 4.96 & \(-\)20.00 & 10.40 \\ Dew point temperature & 3827 & \(-\)2.39 & 4.87 & \(-\)21.90 & 3.70 \\ Level of grip & 3827 & 0.75 & 0.15 & 0.11 & 0.82 \\ Ice layer & 3827 & 0.02 & 0.06 & 0.00 & 0.51 \\ Precipitation & 3827 & 0.44 & 0.72 & 0.00 & 5.30 \\ Concentration & 3827 & 25.49 & 77.48 & 0.00 & 352.70 \\ Conductivity & 3827 & 1.95 & 1.76 & 0.00 & 8.60 \\ Snow height & 3827 & 2.50 & 5.19 & 0.00 & 43.00 \\ Freezing temperature & 3827 & \(-\)0.46 & 1.53 & \(-\)21.10 & 0.00 \\ Maximum wind speed & 3827 & 5.78 & 2.64 & 0.50 & 15.40 \\ Amount of chemical (output) & 3827 & 0.24 & 0.90 & 0.00 & 16.90 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical data description.  
Figure 3: Distribution of the output variable.  
3.2.5. To Explore Highly Correlated Feature with the Output through Scatter Plot  
Figure 4 shows a strong relationship between the freezing temperature and the amount of chemical. In this figure, the different colors of the data points display the different levels of grip on the road surface after spreading the chemical (salt). As clearly shown, by dropping the freezing temperature, the amount of chemical (salt) used on the wheel track increased, in order to enhance the driving quality on the road surface. Additionally, the level of grip was high in most cases, which indicates the effectiveness of WRM to provide drivers with safe transportation. When the freezing temperature was approximately \(-20\)\({}^{\circ}\)C, the appropriate grip could be achieved by using almost 8.5 g of chemicals (salt). However, some points illustrate considerable chemical usage (between almost 9 and 17 g) when the freezing temperature was around \(-20\)\({}^{\circ}\)C. These data points indicate the inefficiency of WRM, which would not only lead to spending the WRM budget on excess material (chemical/salt), but also to damaging the environment, vehicles, and road infrastructure. Therefore, using an excess amount of chemicals is not necessary and must be avoided.
## 3 SDN Performance Challenges
### SDN Security Threat and Vulnerabilities
However, solutions to address these issues were not provided [45]. Towards this goal, [PERSON] et al. [51] implemented FlowRanger as a controller application to mitigate DoS attacks. FlowRange consists of 3 components: (1) trust management (TM), (2) queuing management (QM), (3) message schedule (MS). TM is solely responsible for calculating trust value for each packet-in event based on its source, QM places a message in the priority queue corresponding to the queue value and MS processes messages according to weighted Round Robin strategy. To some extent, FlowRanger improved network performance and mitigated the impact of a DoS attack. This is achieved by prioritizing the serving of legitimate flow first in the controller. However, reference [45] noticed that FlowRanger does not prevent aggressive flooding the switch flowtable TCAM and controller. In contrast, [PERSON] et al. [45] leverage the OpenFlow features to the proposed SDN-Guard to protect SDN against DoS attack on controller processing capacity. SDN-Guard consists of three (3) component modules: (1) Monitoring module which monitors the traffic and decides where to redirect malicious traffic through the path with least utilized link in terms of bandwidth and switches TCAM. (2) Timeout management to assign timeout value to each rule according to probability. (3) Malicious flow rule aggregation, which assigns large hard timeout. SDN-Guard had optimized the network performance in terms of the parameters mentioned in [49; 50]. However, generating a path using a monitoring module may not always be the shortest path, an increase in the number of traffic flow may augment the number of rules to be placed on the flow table, which in turn may increase the chance of flow table overflow. This issue was not peculiar to their initial work and is included in their follow-up work in [47]. It may be effective to conclude as the traffic flows increase, the scheme may introduce another loophole for DoS attack, which may force switches to refuse writing rules from a legitimate source. To mitigate such a problem, [PERSON] et al. [46] proposed LRU flow entry rule eviction aimed to improve the SDN performance. The scheme can be effective with acceptable performance in fixed network with small flow table size, but in large network scale with dense flow arrival it may lead to significant communication overhead because of its architectural design of being reactive event-driven. Therefore, since DoS attacks are considered to be the biggest security challenges in SDN. Control and data planes always being the primary choice for attackers, more intelligent are required to reduce the interaction between the switch to the controller, furthermore switch flow table resources need to be efficiently managed, especially in large scale dynamic network environments to mitigate such concerns.
# Real-Time Method and Implementation of Head-Wave Extraction for Ultrasonic Imaging While Drilling
## 5 Test and Analysis
### Field Experiment  
To further verify the head-wave extraction method, we used an assembled UILWD tool to conduct field tests in an experimental drilling well. The result of the downhole real-time processing was compared with that of surface processing to verify the reliability of the proposed downhole method.  
Figure 12 demonstrates the head-wave arrival time extraction from the waveforms of the three channels. Among them, the black curves represent the original data of ultrasonic logging, while the red curve reflects the arrival time of the head wave extracted in a real-time downhole. It can be seen that the extracted arrival time of the head wave is consistent with the actual situation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Depth Point**} & **Amplitude** & **The Arrival** & **Amplitude** & **The Arrival** \\  & **Extracted by** & **Time Extracted** & **Extracted by** & **Time Extracted** \\  & **FPGA (mV)** & **by FPGA (us)** & **DSP (mV)** & **by DSP (\(\mu\)s)** \\ \hline
1 & 1930.10 & 54.6 & 1930.00 & 54.5 \\
2 & 2029.69 & 59.7 & 2028.58 & 59.5 \\
3 & 2061.80 & 65.2 & 2061.79 & 65.0 \\
4 & 1784.68 & 60.0 & 1784.68 & 60.0 \\
5 & 809.98 & 64.5 & 808.98 & 64.0 \\
6 & 808.53 & 65.0 & 807.52 & 64.5 \\
7 & 1114.67 & 36.5 & 1114.66 & 36.5 \\
8 & 878.30 & 51.4 & 876.29 & 51.0 \\
9 & 876.30 & 51.2 & 876.29 & 51.0 \\
10 & 2825.25 & 75.5 & 2823.24 & 75.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of extracted data between DSP and FPGA chips.  
Figure 11: Test waveforms at 10 different depth points.  
Furthermore, Figure 13 shows the extraction of the head-wave amplitude. The blue dotted curve represents the actual head-wave amplitude obtained by surface data processing, while the red solid curve reflects the head-wave amplitude extracted by downhole hardware. It can be seen that both results basically coincide.  
Figure 12: Verification of the downhole arrival time extraction of the head wave. (**a**) Channel 1; (**b**) Channel 2; (**c**) Channel 3.  
To quantitatively verify the accuracy of downhole head-wave extraction, a comparative statistical analysis of the downhole extraction result and the surface processing result was performed. Table 3 presents the error analysis of the downhole real-time processing result. Notably, the maximum relative error of the head-wave arrival is \(<\)5%, and the proportion of the error that is \(<\)3% is 95.55%. This means that the maximum true error of the arrival time of the head wave does not exceed 2 us (five sampling points) in the actual acquired data. The maximum relative error of the head-wave amplitude is \(<\)15%, and the proportion of errors less than 5% is 92.23%.  
Figure 13: Verification of downhole head-wave amplitude extraction. (**a**) Channel 1; (**b**) Channel 2; (**c**) Channel 3.
# Research on Automated Fiber Placement Surface Defect Detection Based on Improved YOLOv7
## 4 Experimental Setup and Result Analysis
### Ablation Study
#### 4.4.4 Test Results and Analysis
fusion optimization, and accuracy improvement in bounding box regression brought about by the integration of multiple improvement strategies. Table 5 presents the performance of each model on the test set. It is observed that compared to the original YOLOv7 (85.3% AP, 48 FPS), the comprehensive performance metrics (including mAP and FPS) of YOLOv7 + BiFormer, YOLOv7 + AFPN, YOLOv7+WIoU, and Improved YOLOv7 have all been improved. This indicates that the improvement strategies have enhanced the ability of the original YOLOv7 to handle AFP defect detection tasks in composite materials to a certain extent. The table shows that the YOLOv7 + BiFormer model significantly outperforms the original YOLOv7, achieving a mAP of 93.8% and 55 FPS. In particular, in the detection of complex small object defects, the BiFormer attention mechanism enables the accurate capture of fine-detail features and global contextual information. Combined with sparse attention mechanisms, it enhances detection accuracy and recall. Similarly, we observe that YOLOv7 + AFPN also achieves a higher mAP value (90.5%) and faster frame rate (52 FPS) compared to the original YOLOv7. The innovation of the AFPN lies in its introduction of an adaptive feature fusion scheme based on an FPN, which balances global semantic information and local detail information better, thereby improving the accuracy of target localization and classification. For YOLOv7 + WIoU that incorporates the WIoU loss function, it achieves an mAP of 92.3% and an FPS of 55. The usage of the WIoU loss function optimizes target detection performance in two ways: first, its design takes into account the diversity of target scales, shapes, and positions in the detection task, enabling more accurate allocation of optimization resources for each anchor point; second, to address the issue of low-quality samples in the training data, such as defects under different lighting angles with varying recognition difficulty, the WIoU loss function dynamically allocates gradient gains based on the quality of defects in the preselected boxes. This not only reduces the corresponding loss but also prevents large harmful gradients from affecting model learning and optimization. The Improved YOLOv7 model, which incorporates all of these improvement strategies, achieves optimal performance in both mAP and FPS, reaching 95.8% and 62 FPS, respectively. This result fully confirms the effectiveness of the improvement strategies in this study and the performance improvement brought about by their combined use in target detection tasks.
## 1 Introduction
[14] obtained initial results for the precise orbits of BeiDou-3e satellites by the real-valued ambiguity solution based on 11 BeiDou-3e stations and 34 BeiDou-2 stations, and the precision achieved the levels of about 20-60 cm in terms of three-dimensional root mean square (RMS) values. With the addition of the five BeiDou-3e satellites, the number of operational BeiDou satellites in orbit has reached 19, which has further improved the spatial geometry in the service area. Thus, it is interesting to analyze the combined solution for POD and PPP of all available BeiDou satellites in service, including BeiDou-2 and BeiDou-3e satellites. The availability and collection of data are described in Section 2. In Section 3, the signal performance is analyzed in details. Section 4 describes the POD strategy, and then the results are analyzed and validated. The PPP results are presented in Section 5. Conclusions and further work are discussed in the final section.
# Interoperability Analyses of BIM Platforms for Construction Management
## 3 Results  
For the assessment of IFC interoperability, a subset of data present in the reference model was chosen, namely, \"Name\", \"Geometrical Properties\", \"Material Type\", and \"Phase Number\". To emphasize the significance of the chosen data, they are the most exploited ones in 4D BIM models and construction management tasks. \"Name\" is obviously crucial because after completing the data exchange, the defined name for a specific element must be properly shown in the receiving software package; otherwise, inconsistencies will occur. \"Geometrical Properties\" is also fundamental when assessing the geometry and shape of the 3D element. Inaccuracies appearing at this stage may lead to wrong or inadequate interpretation of the 3D model. \"Material Type\" carries information about the construction duration of the specific element, depending on the construction material. It is necessary to obtain the correct information of \"Material Type\" in a 4D BIM after completing the IFC exchange. \"Phase Number\" identifies the construction sequence phase of the specific element. It contains \"time\" data, which is fundamental information for 4D BIM in scheduling, planning, and managing construction activities.  
Once the IFC model was imported, for each phased element, an assessment was performed based on the following scale:  
1. Good interoperability: the exported parameter is successfully transferred and correctly received by the BIM software importing it.
2. Medium interoperability: the exported parameter is transferred but not correctly received by the BIM software importing it; some details may have been lost; however, the imported information can still be used and is meaningful.
3. Poor interoperability: the exported parameter is transferred but not in the form it was in in the original BIM software; the parameter has changed and could be misleading.
4. No data found: the exported parameter is not found in the BIM software importing it.  
Figure 5: The Gantt chart exported by Tekla Structures and used as a benchmark for analyses involving construction project management software (CPM) software packages.  
The same scale was also used in the interoperability analysis based on the exchange of Gantt charts, evaluating the reliability of the fields \"Task Names\", \"Start Date\", \"End Date\", and \"Task Duration\".
## 2 Materials and Methods
### Benchmark Dataset  
The dataset utilized in our study was collected from the study by [PERSON] et al. [3]. This dataset was also used in the progression of iEnhancer-2L [3], iEnhancer-EL [15], and EnhancerPred [27]. The enhancers in this dataset were extracted from nine diverse cell lines where they were withdrawn as DNA groupings out of short 200 bp clips of the same length. The nine different cell lines were HUVEC, K562, HepG2, H1 ES, HSMM, NHLF, GM12878, NHEK, and HMEC. The CD-HIT software program [28] was utilized to prohibit pairwise sequences with more than 20% of characteristics in common. The benchmark dataset encompassed 1484 enhancers, of which 742 were strong enhancers, while the remaining 742 were weak enhancers. Furthermore, 1484 were non-enhancer specimens. Hence, the benchmark dataset can be defined as follows:  
\[E=E^{+}\cup E^{-} \tag{1}\]  
\[E^{+}=E^{+}_{strong}\cup E^{+}_{weak} \tag{2}\]  
where \(E\) represents the overall number of positive and negative sequences. Union in the set theory is shown by the symbol \(\cup\). The \(E^{+}\) subset contained 1484 enhancer sequences, whereas \(E^{-}\) contained 1484 non-enhancer sequences. The first 1484 enhancers were divided into two portions: \(E^{+}_{strong}\) comprised 742 strong enhancers, and \(E^{-}_{weak}\) comprised 742 weak enhancers. \(E^{+}_{strong}\) (strong enhancers) and \(E^{-}\) (non-enhancers) were identified in all nine tissues emphasized above; nevertheless, \(E^{-}_{weak}\) (weak enhancers) substantially vary within diverse tissues. To account for this, weak enhancers were based on human embryonic stem cells. Similar to other studies, we used the training set to build two different models for two different problems: identification of enhancers in stage 1 and classification of enhancers in stage 2. We started by randomly dividing the training set into five folds, utilizing stratified sampling for both layers. Each fold was utilized as the validation set, whereas the remaining four folds were employed as the training set for the preparation of the CNN model.
# Ball Tracking Based on Multiscale Feature Enhancement and Cooperative Trajectory Matching
## 2 Related Work
### Kalman Filter  
The Kalman filter [31] is a key processing flow in the detection-based object tracking algorithm. It is essentially a linear minimum variance estimation algorithm, which can be divided into two stages: prediction and update. The core is to use the previous state to predict the current state, and then through observation, the information corrects the prediction results and is more suitable for predicting the state sequence in the dynamic system, so it can be used to estimate the detection state in the object motion trajectory.  
The state transition equation and observation equation in Kalman filtering can be expressed by Equations (1) and (2), where \(A\) represents the state transition matrix, \(H\) is the observation matrix, \(u_{k-1}\) represents the control parameters, and \(W\) and \(V\) represent the process noise and observation noise matrices, respectively.  
\[x_{k}=Ax_{k-1}+Bu_{k-1}+W_{k-1} \tag{1}\] \[y_{k}=Hx_{k}+V_{k-1} \tag{2}\]  
Let \(W\) and \(V\) be Gaussian white noise with a mean value of 0, where \(Q\) and \(R\), respectively, represent the covariance matrices of their respective distributions. In order to simplify the calculation, they are generally taken as constants. In the actual prediction process, since the true noise cannot be known, and there will be errors in the prediction and observation processes, there will also be errors between the final prediction results \(x_{k}\) and the true values \(x_{k}\). Assuming that the error also obeys the normal distribution with mean 0 and covariance \(P_{k}^{-}\), and the resulting predicted covariance is \(P_{k}\), then the complete Kalman filter formula can be obtained as Equations (3)-(7).  
\[\hat{x}_{k}^{-}=A\hat{x}_{k-1} \tag{3}\] \[\hat{x}_{k}=\hat{x}_{k}^{-}+K_{k}(y_{k}-H\hat{x}_{k}^{-})\] (4) \[P_{k}^{-}=AP_{k-1}A^{T}+Q\] (5) \[K_{k}=\frac{P_{k}^{-}H^{T}}{HP_{k}^{-}+R}\] (6) \[P_{k}=(I-K_{k}H)P_{k}^{-} \tag{7}\]  
Equation (3) represents the a priori prediction output, and Equation (4) represents the posterior prediction output \(y_{k}\), modified using the observation results. \(K_{k}\) represents the Kalman gain, \(I\) represents the identity matrix and uses Equations (5) to (7) to complete the state corresponding to the future moment \(k\) through iterative prediction and parameter update.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 2 Effective Sensor Architecture
In particular, the gyroscopes, the magnetometers, and the Sun sensors have one degree of redundancy, each having on-board 2 three-axis gyroscopes, 4 fine Sun sensors, 12 photodiodes, and 2 magnetometers. The GNSS receiver has no redundant components, its failure being counteracted with the support of the three-axis accelerometer together with the SGP4 orbital propagation. The sensor configuration further maximizes the efficiency of the sensor architecture, which is defined as the number of sensors per given attitude determination functionality. The fine Sun sensors are aligned with the four body axes contained in the plane orthogonal to the solar panel's wings, in a way that, during the nominal attitude modes, at least one fine sensor is used. These are not installed on the surfaces not containing solar cells, where the Sun direction is detected with the coarse photodiode sensors. In this way, each of the four primary Sun directions has one main fine sensor and two redundant coarse photodiodes, while the remaining two directions have only the two redundant coarse photodiodes. Globally, the twelve photodiodes cover the full-sphere field of view around the spacecraft and act as redundant coarse Sun sensors (CSSs) in case one of the primary fine sensors fails. Figure 2 shows the spacecraft configuration and highlights the position of the Sun sensors for the three illustrated surfaces. The photodiodes placed on the solar wings' edges are those aligned with the \(z\) axis, while those on the side panels are aligned with the \(-x\) and \(-y\) axes. The configuration is specular for the remaining six photodiodes on the other three faces. The fine Sun sensors (FSSs) are mounted on the side panels to be aligned with the four primary Sun directions. In reality, as shown in Figure 3, they are inclined 6 deg  
Figure 1: Sensor architecture scheme. with respect to these axes in order to have the entire field of view clear from interferences with the solar wings. The other sensors are configured in a way to minimize disturbances and external interferences. Namely, the magnetometers are mounted as far as possible from the magnetic torquers and from the electronic boards, while the gyroscopes and the accelerometers are placed far from the sources of vibrations (e.g., rotating devices).