esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Predicting Human Actions in Soccer Using Tensor-SOM
## 3 SOM as a Behavior Decision Algorithm  
Self-Organizing Maps (SOMs) are capable of representing the structural features of input vectors on a competitive layer through unsupervised learning, and support fast vector quantization using nearest neighbor learning. However, due to the absence of an explicit output mechanism, SOMs cannot directly model input-output relationships.  
To address this limitation, various SOM-based approaches have been proposed to approximate input-output mappings while maintaining key characteristics such as topological preservation. Representative examples include counter-propagation networks [23], Local Linear Mapping (LLM) [24], and Vector-Quantized Temporal Associative Memory (VQAM) [25], which have been applied in fields such as system modeling [26], filter design [27], control [28], and chemical analysis [29]. These methods, however, rely on supervised learning using teacher signals, and are therefore unsuitable when such signals are unavailable.  
To enable learning without explicit supervision, the Self-Organizing Relationship (SOR) network was proposed. SOR uses input-output vector pairs along with evaluation values obtained through trial-and-error to self-organize input-output mappings through competitive learning. Meanwhile, the Modular Network SOM (mnSOM) extends SOM by replacing each unit with a trainable function module, such as a neural network [30]. This modular design allows mnSOM to represent functional mappings and flexibly adapt to various data types and training objectives.  
The TensorSOM framework integrates the key ideas of both mnSOM and SOR: it inherits the modular, function-oriented architecture of mnSOM, and the trial-and-error-basedself-organizing learning approach of SOR. In the present study, we utilize TensorSOM to develop a behavior decision algorithm that can operate effectively under dynamic environmental conditions. By leveraging TensorSOM's ability to flexibly handle structured input-output relationships and perform unsupervised learning based on evaluative feedback, we aim to construct a decision-making framework that is adaptive, modular, and capable of learning from experience without the need for explicit supervision.  
The proposed method is designed to learn adaptive behavior selection strategies through interactions with a changing environment. The overall framework is described in the following section.  
In behavior decision-making algorithms operating under dynamic environmental conditions, it is essential that decisions be made in real time. Although the learning process of SOM and TensorSOM can require substantial time depending on the number and dimensionality of the input data as well as the number of training iterations, once training is completed, the model provides immediate one-to-one mapping from input to output. This property ensures that response times during deployment are effectively instantaneous. Furthermore, the modular structure of TensorSOM allows for the quick incorporation of new input patterns, enabling rapid adaptation and continual learning even after initial training.
###### Abstract  
Vehicular Ad Hoc Networks (VANETs) are wireless networks that improve traffic efficiency, safety, and comfort for smart vehicle users. However, with the rise of smart and electric vehicles, traditional VANETs struggle with issues like scalability, management, energy efficiency, and dynamic pricing. Software Defined Networking (SDN) can help address these challenges by centralizing network control. The integration of SDN with VANETs, forming Software Defined-based VANETs (SD-VANETs), shows promise for intelligent transportation, particularly with autonomous vehicles. Nevertheless, SD-VANETs are susceptible to cyberattacks, especially Distributed Denial of Service (DDoS) attacks, making cybersecurity a crucial consideration for their future development. This study proposes a security system that incorporates a hybrid artificial intelligence model to detect DDoS attacks targeting the SDN controller in SD-VANET architecture. The proposed system is designed to operate as a module within the SDN controller, enabling the detection of DDoS attacks. The proposed attack detection methodology involves the collection of network traffic data, data processing, and the classification of these data. This methodology is based on a hybrid artificial intelligence model that combines a one-dimensional Convolutional Neural Network (1D-CNN) and Decision Tree models. According to experimental results, the proposed attack detection system identified that approximately 90% of the traffic in the SD-VANET network under DDoS attack consisted of malicious DDoS traffic flows. These results demonstrate that the proposed security system provides a promising solution for detecting DDoS attacks targeting the SD-VANET architecture.  
SDN; DDoS attack; VANET; deep learning; real-time intrusion detection; intelligent transportation systems +
Footnote †: journal: Applied sciences  
Article  
Hybrid AI-Powered Real-Time Distributed Denial of Service Detection and Traffic Monitoring for Software-Defined-Based Vehicular Ad Hoc Networks: A New Paradigm for Securing Intelligent Transportation Networks
## 5 Conclusions  
The main purpose of this paper is to design, construct, and evaluate a prediction model of parking space availability, and estimate the parking occupancy rate of the parking lot (from about 658,900 parking records). Four machine learning and deep learning methods, i.e., improved LSTM, simple LSTM, MLR, and SVR, were compared and evaluated, and the short-term prediction model was obtained to achieve more accurate parking occupancy predictions with large-scale parking data. In addition, data processing and evaluation methods are presented, based on data from different parking lots. Considering the factors of real-time parking, the short-term prediction model for intervals of 10 min, 20 min, and 30 min is raised through improved LSTM, which reveals the spatial-temporal features of the available parking spaces around the business center in the big city. Then, using RSME and MAE as evaluation indexes, the accuracy of four models of parking occupancy rate for the intervals of 10 min, 20 min, and 30 min were evaluated. The MAE and RMSE of the improved LSTM with weather influence feature model at 10 min, 20 min, and 30 min were lower than that of the simple LSTM model, SVR algorithm, and MLR. Therefore, LSTM with an added weather influence feature model is obviously the most accurate, and has the best performance.  
Short-term accurate prediction of parking demand is an important part of refined parking management. Especially in the process of parking guidance, accurate prediction of parking demand can greatly improve the effectiveness of information release and solve the dilemma that the information released by parking guidance is inconsistent with the actual information after the vehicle arrives at the parking lot. In future research, in addition to weather factors, the parking environment in different areas should also be further considered. There will be different characteristics around hospitals, schools, and residential communities. At the same time, other prediction methods in depth learning can be considered for cross-comparison analysis, and a combination prediction model can be built to improve the prediction accuracy.  
Writing--original draft, B.J.; methodology, formal analysis, Y.Z.; writing--review, formal analysis, methodology, J.N.; writing--review and editing, investigation, resources, formal analysis, methodology. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Beijing Natural Science Foundation (Project No.: 9123026) and Beijing Social Science Foundation, grant number (Project No.: 19 XCB006).  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
Using the tree-augmented naive Bayes (TANB) approach, [PERSON] [29] studied the impacts of parameters such as pore-pressure ratio, internal friction angle, cohesion, slope angle, and unit weight on the stability coefficient. Using an artificial neural network, [PERSON] [30] investigated the effects of variables such as internal friction angle, cohesiveness, slope height, and unit weight on the stability factor. [PERSON] [31] used a support vector machine (SVM) [32] to evaluate slope parameters. [PERSON] [33] assessed the sensitivity of unit weight, cohesiveness, and internal friction angle to the slope stability factor of safety using partial Spearman rank (PSS) correlation coefficients. [PERSON] et al. [17] used reliability coefficients with cohesion and internal friction angle as components, following other studies of sensitivity analysis for embankment slope parameters [34; 35; 36; 37]. However, fluctuating water levels coupled with the static train loads found a sensitivity analysis of railway embankment slopes complicated. To understand their interactions and the influence of input factors on the response variable, it is critical to build a trustworthy and obvious relationship between output and input variables [38]. Existing studies have concentrated on individual hydromechanical parameters of the soil or external factors (rainfall, water level fluctuation, etc. ), with little focus on interactions and sensitivity to railway embankment slopes. Ignoring factor interactions may not accurately depict the nonlinear relationships between components and the slope's response factor, which is a key drawback in past research. Hence, this study aimed to evaluate the connection between external and internal factors and their impact on the stability of the railway embankments while considering the coupled impacts of the static load stress under fluctuating water levels. To integrate this concept, a method for performing thorough and reliable coupled static stress and seepage sensitivity analyses on railway slopes was developed. Emerging artificial intelligence approaches, such as fuzzy logic (FL), ANN [39; 40] and gene expression programming (GENXPRO) are regarded as robust and reliable for developing slope stability models, even without prior knowledge of the seepage and stress fields within the railway embankment slope environment. Several strategies for improving the accuracy of ANN model predictions have been proposed [29], with the back-propagation algorithm (BP) standing out as particularly effective [41]. To achieve these objectives and establish the integration of both internal and external embankment stability controlling factors, both artificial neural network (ANN) and Box-Behnken design (BBD-RSM) models were selected due to their good precision in the field of civil engineering [41; 42].
## 4 Results and Discussion
### Linear Frequency Correction Method  
Based on the mechanism of the influence of oscillatory flow frequency on the linear resistance of the communication port, a method for calculating the linear resistance can be established. As analyzed earlier, we can use function \(F\) to represent the effect of oscillatory flow frequency on linear resistance linearity and use function \(G\) to represent the decrease in flow coefficient. As shown in Figure 12a, we can then define the linear resistance as follows:  
\[R^{\prime}{}_{QS}(\widetilde{M}_{A},f)=F(f)R_{QS}(\widetilde{M}_{A})+G(f)(1- F(f))R_{LA2}(\widetilde{M}_{A}) \tag{9}\]  
where \(F\) and \(G\) are monotonic functions of oscillation frequency that satisfy \(F(0)=G(0)=1\), and \(R^{\prime}{}_{QS}\) is the frequency-corrected result of \(R_{QS}\).  
The simulation results are well matched as depicted in Figure 12b. The fitting results of functions \(F\) and \(G\) obtained from the simulation results are the following:  
\[F(f)=-0.0164f+1 \tag{10}\]  
Figure 11: Sketch map of oscillatory flow frequency influence. (**a**) Asymmetric condition, (**b**) Symmetric condition.  
Figure 12: Linear resistance curve of accumulator inlet. (**a**) Schematics plot of frequency correction method, (**b**) Fitting of Frequency correction functions.  
\[G(f)=-0.0047f+1 \tag{11}\]  
Before frequency modifications, the deviation of communication port resistance between the quasi-steady assumption-based result and simulation result is 27%. This value is less than 8.5% under 10 Hz and less than 26.5% within 5-50 Hz after modifications. The results also indicate that the oscillation flow frequency within the communication port should be lower than 14.5 Hz, in order to achieve an attenuation of less than 30% in its linear resistance. In such scenarios, the quasi-steady assumption-based method is suitable for determining the linear resistance of the communication port.
# Personalised High Tibal Osteotomy Surgery Is Accurate: An Assessment Using 3D Distance Mapping
## 2 Materials and Methods
### Patient Cohort  
The study inclusion/exclusion criteria and relevant power-analyses of the determination of the patient population, as well as the demographic and clinical data of the clinical trial, have previously been published in detail by these authors [34; 37] and reported briefly as follows.  
Relevant inclusion/exclusion study criteria are summarised here below:  
* Inclusion: age 40-65 years; BMI < 40; varus deformity <20\({}^{\circ}\); diagnosis of non-inflammatory degenerative joint disease; mono-compartmental medial knee OA.
* Exclusion: BMI \(\geq\) 40; patients' inability to provide informed consent; patients' non-compliance with postoperative rehabilitation and evaluation schedules; patients with comorbidities or with very poor bone quality; other lower limb joint surgeries.  
A cohort of 25 patients was selected and treated for personalised HTO: females/males: 6/19; age (mean \(\pm\) standard deviation): 54.4 \(\pm\) 7.4 years (min to max: 39 to 65 years); Body Mass Index: 26.8 \(\pm\) 4.0 (20.9 to 37.5); Kellgren-Lawrence grade: 2.6 \(\pm\) 0.5 (2 to 3); PRE hip-knee-ankle (HKA) angle: 189.3\({}^{\circ}\pm\) 3.2\({}^{\circ}\) (184.0\({}^{\circ}\) to 195.7\({}^{\circ}\); HKA > 180 indicates varus knee alignment).  
As this was an initial single-arm trial in humans using the novel personalised HTO procedure, a power study was not necessary. Among the various evaluations within the clinical trial [31; 34; 42], all patients underwent comprehensive medical imaging inspections (see next section) and clinical examination PRE and six months POST operation. Clinical outcomes were based on patient-reported outcome measures, including the Knee Osteoarthritis Outcome Score (KOOS) [43], here considered as a total score averaged across all domains and also as individual domains (0 = worst condition, 100 = best condition), and the visual analogue score (VAS) for pain during activity [44] (VAS pain score, 0 = no pain, 10 = worst pain).
# An Improved CNN Model for Within-Project Software Defect Prediction
## 4 Experimental Setup
### Evaluation Metrics  
To measure the defect prediction results, we used three metrics: F-measure, G-measure, and MCC under different experiment settings. These metrics have been widely used in various software defect prediction studies [5; 23; 24; 31; 43; 44].  
F-measure is the harmonic mean of precision and recall. It ranges from 0 to 1. A higher F-measure means a better prediction performance. We calculated the F-measure as follows:  
\[\text{{precision}}=\frac{\text{{TP}}}{\text{{TP}}+\text{{FP}}}. \tag{2}\]  
\[\text{{recall}}=\text{{TP}}=\frac{\text{{TP}}}{\text{{TP}}+\text{{FN}}}. \tag{3}\]  
\[\text{F-measure}=\frac{2*\text{{precision}}*\text{{recall}}}{\text{{precision}}+ \text{{recall}}} \tag{4}\]  
G-measure is the harmonic mean of true positive rate (TPR, also recall) and true negative rate (TNR). It aims to measure the bad influences of false alarms on software defect prediction. G-measure ranges from 0 to 1. A higher G-measure means a better prediction performance. We calculated the G-measure as follows:  
\[\text{{TNR}}=\frac{\text{{TN}}}{\text{{TN}}+\text{{FP}}} \tag{5}\]  
\[\text{G-measure}=\frac{2*\text{{TP}}*\text{{TNR}}}{\text{{TP}}+\text{{TNR}}}. \tag{6}\]  
MCC describes the correlation between true values and predicted values. It ranges from \(-1\) to 1. An MCC of 1 means perfect prediction, 0 means random prediction, and \(-1\) means that all predictions failed. We calculated the MCC as follows:  
\[\text{{MCC}}=\frac{\text{{TP}}*\text{{TN}}-\text{{FP}}*\text{{FN}}}{\sqrt{( \text{{TP}}+\text{{FP}})(\text{{TP}}+\text{{FN}})(\text{{TN}}+\text{{FP}})( \text{{TN}}+\text{{FN}})}}}. \tag{7}\]
## 3 Results and Discussion
### 2D U-Turn Duct Flow
The SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model underpredicted the velocity near the concave outer wall and overpredicted the velocity near the convex inner wall. Moreover, in this section, the overall velocity profile predicted by the proposed SAS model was better than that predicted by the SST-SAS model. At \(s/H=\pi+2\), the velocity profile predicted by the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model differed dramatically from the experimental results. The velocity near the outer wall predicted by the proposed SAS model matched the experimental results very well, but that near the inner wall differed significantly. It appeared that the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-SAS model overpredicted the separation and slowed down the flow recovery. In contrast, the velocity near the inner wall predicted by the SST-SAS model matched the experimental results very well, but that near the outer wall was underpredicted. Figure 10: Schematic view of the geometry and the boundary conditions for the 2D U-turn flow. Figure 12 shows the distribution of the \(C_{f}\) along the inner and outer walls near the curved part. The improvement in the results predicted by the two hybrid RANS-LES models was clear. At the inner wall (Figure 12a), the results predicted by the two hybrid RANS-LES models were in good agreement with the experimental results. For the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model, the \(C_{f}\) in the upstream region of the reattachment was well predicted, but the recovery of \(C_{f}\) after separation was significantly delayed. At the outer wall (Figure 12b), the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) model failed to predict the peak value of \(C_{f}\) and delayed the recovery of the \(C_{f}\) after separation. The two hybrid RANS-LES models significantly improved the prediction of \(C_{f}\). Especially for the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-SAS model, the \(C_{f}\) after separation was quite consistent with the experimental results. Figure 11: Comparison of the normalized mean velocity profiles at different transverse sections. (**a**) \(s/H=0\); (**b**) \(s/H=\pi/2\); (**c**) \(s/H=\pi_{c}\) (**d**) \(s/H=\pi+2\). It can be seen that, although the baseline model was linear eddy viscosity-based RANS, the hybrid RANS-LES models were indeed able to capture the effect of the strong streamline curvature without any additional correction. This finding is encouraging and expands the application range of hybrid RANS-LES models.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Exploitable Protocols
#### 4.1.3 Radio Link Control  
The header elements of the RLC [9] provide a good basis for transmitting covert information. However, a distinction must be made between the data PDUs in acknowledged mode (AM) and unacknowledged mode (UM). The biggest difference between these RLC modes is the acknowledgement of the packets by the receiving entity. In contrast to the UM, the so-called ACK or NACK messages are sent in AM. Since the RLC AM naturally generates significantly more data traffic and processing overhead than the UM, the use of the modes depends on the performance requirements of the application scenario. However, more bits are available for encoding covert information in the PDU header for AM than for UM PDUs due to the longer RLC sequence number. However, only a fraction of the PDUs can be used for the transmission of covert data when exploiting the sequence number and the segment offset; otherwise, the covert channel would be easily detected.
## 3 Results and Discussion
### Photocatalytic Activity Tests
#### 3.1.5 Influence of Visible Light Intensity on Photocatalytic Hydrogen Production  
The influence of visible light intensity on photocatalytic hydrogen production was studied with an initial glycerol concentration equal to 10 wt % at the spontaneous pH of the solution and with the 1.08 Cu_ZnO catalyst dosage equal to 1.5 g/L. In particular, the incident light intensity increased from 8 to 32 mW/cm\({}^{2}\). The obtained results are displayed in Figure 6.  
As expected, at a fixed irradiation time, photocatalytic hydrogen production increased as LED light intensity increased due to the photogeneration of more electrons and holes [58]. These results are in agreement with the literature data that highlight the effect of light intensity on the performances of photocatalytic processes [59; 60].  
Figure 5: Photocatalytic hydrogen production during the irradiation time at different initial pH levels of solution. Glycerol initial concentration: 10 wt %; catalyst dosage: 1.5 g/L; visible light intensity: 32 mW/cm\({}^{2}\); photocatalyst: 1.08 Cu_ZnO.
## 4 Conclusions  
Here, we developed IL10-Stack, a stacking ML approach based on unified deep representation learning for IL-10-induced peptide recognition. We first employed SMOTE to address the imbalanced dataset and then extracted potential IL-10-induced peptide information using AAIndex, UniRep, and a fusion of both feature extraction methods. We then compared the predictive performance of three non-stacking ML algorithms (LR, SVM, and LGBM) and stacking algorithms, with and without feature selection, resulting in two optimal models: IL10-Fuse and IL10-Stack. After testing and optimization, we found that utilizing 1900D UniRep features as the feature set and developing the model based on the stacking algorithm yielded the best performance. Our results demonstrate that IL10-Stack provides more reliable and accurate predictions of IL-10-induced peptides than the most recently reported methods (AUC = 0.920, ACC = 0.910, MCC = 0.820). Ultimately, we established an IL10-Stack web server to make it convenient for scientists to employ this algorithm.  
One advantage of this study is that we used the latest AAIndex and UniRep methods to extract features, which is an improvement over existing methods like ILeukin10 Pred. Additionally, our model construction using unified deep representation learning and the stacking algorithm significantly enhanced the accuracy of IL-10-induced peptide prediction. However, our research also had some limitations, such as a relatively small dataset and the slower running speed of the web server. In the future, we plan to validate the model with more data. We envisage that the use of IL10-Stack to predict IL-10-induced peptides can contribute to the development of immunosuppressive drugs.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13169346/sl](https://www.mdpi.com/article/10.3390/app13169346/sl)]([https://www.mdpi.com/article/10.3390/app13169346/sl](https://www.mdpi.com/article/10.3390/app13169346/sl)), Table S1: Comparison of independent testing results between IL10-Stack and other existing methods.  
Conceptualization, Z.L.; Data curation, J.L. and Z.L.; Formal analysis, J.L. and Z.L.; Funding acquisition, Z.L.; Writing--review and editing, J.L., H.P., J.J. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 62001090), and Fundamental Research Funds for the Central Universities of Sichuan University (No. YJ2021104, No. 20826041G4189).  
Not applicable.  
The data used to support the findings of this study can be made available by the corresponding author upon request.  
The authors declare no conflict of interest.