esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Discussion
### Experiment 3: Versus Conventional Control Method
The resulting intersection performance improved by over 14% compared to when these components were removed. Furthermore, the agent trained by MD4-AQF achieved significantly better performance compared to a conventional signal control method in a statistical sense. The quantitative advantage in the median of the average vehicle delay was nearly 20%. Such an improvement stands out when compared to the typical performance gains of 10% to 21% seen in analogous MFDRL studies, relative to conventional actuated control methods, under similar conditions of high-resolution simulation software and peak-period traffic demand. The motivation for this study lies in unlocking the potential of MFDRL for DRBPS, a topic that has been scarcely addressed in previous studies. Working with DRBPS aims to align with key requirements in traffic engineering, making the research more likely to be implemented in practice. As a consequence, DRBPS brings certain limitations in signal control in real-world intersections and increases the difficulty of agent training. DRBPS presents challenges due to the concurrent yet partially independent decision-making required for the two rings. In contrast to previous studies and our prior work [25], the novelty of this study lies in understanding and addressing these emerging challenges. Overall, we believe that this study achieves its original intent. The proposed MD4-AQF method successfully implements phase control under DRBPS with signal timing constraints in traffic engineering. The trained agent successfully learns how to make good choices for the phases from a set of fine-grained green time options at one-second intervals. The prior work, D3 ynQN, serves as both a representative method from previous studies and the algorithmic foundation for MD4-AQF. The comparative experiments between D3 ynQN and MD4-AQF in Section 8.2 show that directly applying an existing method to DRBPS can yield unsatisfactory results. The extensions proposed in MD4-AQF to adapt D3 ynQN for DRBPS have proven effective. They are critical to MD4-AQF significantly outperforming conventional methods. MD4-AQF achieved a median average vehicle delay of 49.62 s in the tested traffic demand scenarios. In contrast, the medians for D3 ynQN and FASC baselines were 56.78 s and 61.24 s, respectively. Here, we explored the practical significance of the performance improvement achieved by MD4-AQF. We consulted the level-of-service (LOS) criteria established in the \"Highway Capacity Manual\" for motorized vehicles at signalized intersections [51]. MD4-AQF improved vehicle delay from LOS grade E (55-80 s) to grade D (35-55 s). This implies a significant reduction in vehicle stoppage and queuing. The benefits are particularly promising in terms of improving driver experience, alleviating traffic congestion, and reducing exhaust emissions.
## 4 Conclusions  
This paper presented a novel, wideband, S-shape MIMO antenna with enhance isolation characteristics. The four-element MIMO antenna system showed isolation of \(-15\) dB, which with the help of an isolator, was improved to \(-26\) dB. The antenna is designed on 0.254 mm ultra-thin RO5880 substrate with relative permittivity of 2.3. The proposed antenna had compact dimensions of 24 \(\times\) 24 mm and the peak gain achieved was 7 dB, with bandwidth ranging between 25 and 40 GHz. The ECC value among any two radiating elements is less than 0.05, and efficiency is above 85% throughout the whole operating bandwidth. The fabricated prototype showed excellent agreement with simulations. Through valid measurement results, the proposed antenna is a suitable candidate for future mobile phones covering mmwave band and 5G high-data-rate devices.  
Conceptualization, A.G.A.H., M.A.K. and S.H.K.; methodology, M.A.K., A.G.A.H., E.M.A., M.A. and M.D.; software, M.E.M. and S.H.K.; validation, S.I.S., M.A.K. and A.N.N.; formal analysis, E.M.A., M.A. and M.D.; investigation, E.M.A., M.A. and M.D.; resources, A.N.N. and S.I.S.; data curation, S.H.K. and M.E.M.; writing--original draft preparation, M.A.K. and S.H.K.; writing--review and editing, S.H.K., J.I., M.A. and M.D.; visualization, J.I., M.A. and M.D.; supervision, A.N.N. and J.I.; project administration, S.H.K., M.A. and M.D.; funding acquisition, S.I.S., M.A. and M.D. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from Universidad Carlos III de Madrid and the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant 801538.  
Not applicable.
# Radiometric Identification of Signals by Matched Whitening Transform
## 2 Framework for Radiometric Identification
### The Whitening Transform  
Let \(\mathbf{X}\in\mathbb{R}^{p\times n}\) be the data matrix consisting of \(n\) measurements of \(p\) variables with the covariance matrix \(\mathbf{\Sigma}\). Statistical whitening is a linear transformation that transforms the data such that the covariance matrix of \(\mathbf{Y}=\mathbf{W}\mathbf{X}\) is the identity matrix. The whitening transform matrix is not unique. In fact, [46] mentions five different projection matrices that whiten the data, with the most prominent ones being the PCA and ZCA whitening [47]. Specifically,  
\[\mathbf{W}^{PCA}=\mathbf{\Lambda}^{-\frac{1}{2}}\mathbf{U}^{T} \tag{1}\]  
where \(\mathbf{U}\) and \(\mathbf{\Lambda}\) are the matrices of eigenvectors and eigenvalues in the decomposition of the covariance matrix \(\mathbf{\Sigma}=\mathbf{U}\mathbf{A}\mathbf{U}^{T}\). The whitening transformations produce decorrelated data but to what end? More importantly, what role does whitening play in radiometric identification? This is where the matched whitening transform deviates from the existing use of PCA in radiometric identifications. PCA is best known for data compression by guiding the removal of the components of \(\mathbf{Y}\) with insignificant energy. The features that remain are not necessarily the best for classification. Yet, almost all PCA-based radiometric classification techniques use the features that survive compression in a subsequent discriminant function to classify the data. ZCA has the added property of zero-phase by undoing the rotation caused by the PCA. Neither of the two are applicable here. Producing uncorrelated data is a preprocessing step from which lower dimensionality feature vectors are extracted. Dimensionality reduction does not apply to IQ samples as there are only two dimensions to begin with and are largely decorrelated already. PCA has been used in deep learning as well by accelerating the convergence in convolutional neural networks [48].
# Blind Spoofing GNSS Constellation Detection Using a Multi-Antenna Snapshot Receiver +
## 6 Results and Analysis
### Modularity Comparisons  
In this section, the metrics that are based upon the Louvain algorithm applied to the normalized correlation matrix \(\mathbf{C}_{\mathrm{norm}}\) are presented. First, the number of clusters is evaluated. The CDFs of the number of clusters after clustering is shown in Figure 12. In a spoofed case it is assumed that fewer clusters would be generated, as is seen in the figure; however, the difference is insufficient to produce adequate detection results.  
The next approach is to investigate the difference in community sizes. The CDFs for the community are shown in Figure 13. In a spoofed scenario, it is assumed that fewer, larger communities would exist. This was observed in the data, but as with the previous case, insufficient isolation between the real and spoofed data was obtained.  
Figure 12: CDF comparison for the maximum-to-mean of the Eigenvalues for a six-element array  
Figure 13: CDF comparison for the maximum-to-mean of the Eigenvalues for a six-element array.  
Lastly, the modularity of the clustering was observed. The CDFs for the modularity are shown in Figure 14. In a spoofed case, the communities would be clustered more densely, resulting in lower modularity. This is consistent with the results.  
The results of using modularity also achieved perfect detection, similar to what was observed with the fraction of detections and the Eigenvalue maximum-to-mean ratio approaches. This approach is an iterative method; hence, it potentially requires more processing than Eigenvalue methods. However, the Louvain algorithm is optimized for efficient processing [48] on large data sets. Therefore, this would not present an issue.
# Evaluation of Glottal Inverse Filtering Techniques on
## 3 Experiments
### OPENGLOT Dataset  
OPENGLOT is composed of several speech datasets and was conceived to become a coherent and common environment for the evaluation of GIF methods. It was developed and released in 2019 by [PERSON] et al. [32]. In this work, the performance of the GIF methodswas evaluated using the first repository of OPENGLOT as a common reference. This dataset consists of a collection of synthetic vowels obtained by filtering glottal flow signals generated with an LF model [10]. The audio files, sampled at 8 kHz, include both the synthetic vowels and the glottal flow signals that constitute the ground truth.  
Specifically, 56 glottal flow signals were generated as excitation to cover four different phonation types (from lax to tense: whispery, breathy, normal, and creaky) and an F0 range between 100 and 360 Hz with steps of 20 Hz. On the other hand, the VTs were modelled as digital 8 th-order all-pole filters defined with 4 formants. Six different vowels were considered (\(a\), \(e\), \(i\), \(o\), \(u\), \(\ae\)) using the male formant frequencies extracted from [PERSON] and [PERSON] [36], which, in turn, refer to the previous study conducted by [PERSON] and [PERSON] [19]. For a more comprehensive assessment, the corresponding female vowels have been also generated considering the female formant frequencies also reported in [19]. As a result, the experiments have been conducted on a total of 672 vowels. The code used for the generation of the OPENGLOT Repository I vowels is available at the main OPENGLOT website ([[http://research.spa.aalto.fi/projects/openglot](http://research.spa.aalto.fi/projects/openglot)]([http://research.spa.aalto.fi/projects/openglot](http://research.spa.aalto.fi/projects/openglot)), accessed on 5 May 2023).
## 2 Acoustic Emission Method  
AE signals refer to elastic waves whose energy attenuates from high order to low order, which are generated due to the internal damage of materials and obtained by sensors attached to the surface of materials [20]. The number of AE signals obtained in the monitoring process is related to material properties, damage process and intensity [16]. Meanwhile, the value of AE characteristic parameters depends on the intensity of local damage. Therefore, AE technology can reveal the failure process and fracture mode of materials so that it can be used for health monitoring of concrete structures.  
Typical AE signals and characteristic parameters collected by AE measurement system are shown in Figure 1. The threshold level set by users is a key parameter of AE testing. A reasonable threshold can eliminate the influence of environmental noise on the accuracy of data and reflect the actual cracking of materials [21; 22]. Amplitude (A) is the maximum voltage corresponding to the peak of signal waveform and measured in decibels (dB). Rise time (RT) is the time interval from when the signal first crosses the threshold until it reaches the maximum value. AE energy is the elastic energy released by AE events, which is considered as quantification of the intensity of damage. AE count is the number of times the signal amplitude crosses the threshold value. Duration is the time interval between the first and last crossing of the signal and threshold, which is measured in microseconds (us).  
To classify the cracks of concrete, the RA value and average frequency (AF) are proposed based on the JCMS-III B5706 code, and defined by Equations (1) and (2).  
\[RA\ value=\frac{\text{{Rise Time}}}{Amplitude},\ ms/V \tag{1}\]  
\[Average\ frequency\ (AF)=\frac{\text{{Counts}}}{\text{{Duration}}},\ kHz \tag{2}\]  
It has been found that there are differences in signal waveforms under different fracture modes [23]. Tensile events are characterized by higher AF and lower RA values than shear events. This is mainly because the energy released by tensile crack appears in the form of longitudinal wave, which propagates faster than shear wave, with high amplitude and short rise time. Shear events release energy mainly in the form of shear waves, the slower wave velocity leads to longer rise time and duration [24]. Therefore, the fracture mode of materials can be determined through the analysis of RA-AF value. Typical signal waveforms under different fracture modes are shown in Figure 2.
## 3 Background
### IPFS (Interplanetary File System)  
The interplanetary file system (IPFS) is a decentralized, peer-to-peer file system that enhances the security and efficiency of data storage and sharing. Unlike traditional web protocols that rely on location-based addressing, such as URLs, IPFS employs content-based addressing. This approach ensures data authenticity and availability, as files are identified by a unique cryptographic hash derived from their content.  
**How IPFS Works**  
IPFS organizes data using a Merkle Directed Acyclic Graph (DAG), as shown in Figure 3, a sophisticated data structure where each block of data is identified and linked through a unique cryptographic hash. In this system, every data block references other blocks by their hash values, creating an interconnected web of content-addressed nodes [31; 32]. This design inherently enhances data integrity--any modification to a data block will result in a completely different hash, thereby immediately signaling a change or potential tampering.  
Figure 3: Merkle DAG.  
Beyond ensuring data integrity, the Merkle DAG enables efficient versioning and data deduplication. Much like how Git manages version control, IPFS can represent changes to files over time by linking different versions within the DAG. This not only allows users to retrieve historical versions of files but also minimizes storage redundancy when multiple files share common data blocks.
# Short-Term Power Load Forecasting Based on an EPT-VMD-TCN-TPA Model
## 6 Conclusions  
Accurate and effective power load forecasting is of great significance for the safe, economical, and stable operation of power grids. However, the complexity and non-smoothness of power load data bring difficulties to power load forecasting. Therefore, this paper proposes a combined decomposition (EPT-VMD) combined with TCN-TPA network forecasting for short-term power load forecasting, i.e., the EPT-VMD-TCN-TPA forecasting model. The method first extracts the trend component and residual fluctuation component of the original load series using EPT, and the trend component has obvious periodicity, which can improve the prediction precision reading. Secondly, the residual fluctuation components are decomposed into intrinsic mode function components with different center frequencies using VMD, and the ratio of the residual energy after decomposition is used to determine the number of decompositions k of VMD, which effectively avoids over-decomposition and under-decomposition. Using a network model with TCN-TPA for training and prediction of components, the TCN-TPA network can be better applied to multivariate time series prediction. Finally, the prediction results of each component are accumulated to obtain the predicted values of the final load. We also discuss the performance of the EPT-VMD-FE-TPA-TCN prediction model using FE.  
To test the performance of the proposed EPT-VMD-TCN-TPA model, experiments were conducted on real datasets from two Areas provided by the 9 th National Electrician Mathematical Modeling Competition in China. The predictive precision of the models was evaluated by MAE, MAPE, RMSE, and R\({}^{2}\), and the significance of the differences in model prediction performance was assessed using the DM test. The results show that our proposed method has better prediction accuracy than other methods. The MAPE of the EPT-VMD-TCN-TPA method proposed in this paper was 1.25% and 1.58% for the test results on two regions, respectively. Although the method proposed in this paper achieved good results, only meteorological data and load data were considered in this paper. Therefore, we suggest that more influencing factors, such as the date factor, electricity price, and social development level, should be considered in future studies. In addition, suitable optimization algorithms can be used to optimally select the model hyperparameters to produce more accurate prediction results.  
Conceptualization, S.Z. and Q.Z.; methodology, S.Z.; software, S.Z.; validation, S.Z. and Q.Z.; formal analysis, S.Z.; data curation, S.Z. and Q.Z.; writing--original draft preparation, S.Z.; writing--review and editing, S.Z. and Q.Z.; supervision, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Algorithm-Based Real-Time Analysis of Heart Rate Measures in HIIT Training: An Automated Approach
## 2 Materials and Methods
### Participants  
The initial recruitment process yielded 33 interested individuals, of whom 28 met the eligibility criteria and were enrolled in the study. Three participants subsequently withdrew (two due to scheduling conflicts and one due to an unrelated injury), yielding a final sample of twenty-five healthy adults (18 males and 7 females; age: 20.7 \(\pm\) 2.3 years; height: 172.4 \(\pm\) 8.1 cm; weight: 69.8 \(\pm\) 10.2 kg; BMI: 23.5 \(\pm\) 2.4 kg/m\({}^{2}\); resting heart rate: 65.3 \(\pm\) 7.8 ppm; maximum heart rate: 187.2 \(\pm\) 6.5 ppm; training experience: 3.4 \(\pm\) 1.5 years) who completed the study. The recruitment protocol employed a non-probabilistic purposive-sampling methodology within the university population. Subject acquisition was facilitated through institutional channels of communication, including departmental notice boards and electronic correspondence systems.  
Figure 1: Flowchart of the experimental design.  
Initial screening was conducted via a comprehensive health and physical activity habits questionnaire to establish preliminary eligibility.  
For consideration in the study, the participants were required to meet the following inclusion criteria: (a) university students, (b) regular endurance/aerobic exercise training at least six months previous to the study, (c) no orthopedic, respiratory or cardiovascular diseases that would interfere with exercising, (d) no medications that interfere with heart rate response, and (e) familiarity with HIIT protocols. Specifically, the endurance/aerobic exercise training consisted of 3-4 weekly sessions of moderate-intensity continuous training, each lasting 30-45 min at 60-75% of maximum heart rate, and included running, cycling, and swimming predominantly. The exclusion criteria included (a) pregnancy, (b) uncontrolled hypertension (blood pressure \(>\) 140/90 mmHg), (c) recent musculoskeletal injury (\(<\)3 months), and (d) inability to complete the familiarization session.  
Prior to participation, all volunteers completed the Physical Activity Readiness Questionnaire (PAR-Q+) to ensure their safety for high-intensity exercise. Participants were instructed to maintain their regular diet, avoid alcohol consumption for 24 h before testing sessions, avoid caffeine intake for at least 3 h prior to testing, and refrain from intense physical activity for 48 h before each experimental session. The study protocol was approved by the Bioethics Committee of the University of Murcia (reference number: 3495/2021) and conducted in accordance with the Declaration of Helsinki. All participants provided written informed consent prior to participation.
# Dynamic Correction of the Influence of Long Measuring Path Irregularity in Antenna Tests
## 5 Measurement Results
### 2P Calibration
That is, for any other positions of the stand, the error terms of the VNA model are assumed to be the same. The curve 2 (teal line) corresponds to the case of dynamic calibration, when calibration process is carried out in each position of the measuring stand. Curve 3 (blue line) shows the AUT RP difference when applying normal and dynamic calibrations. Figure 8: Far field antenna measurement diagram. Figure 10: Reflection coefficient of the AUT: (**a**) magnitude and (**b**) phase. Measurement of the radiation pattern was carried out in the usual way for the main and cross-polarization. The obtained error in the magnitude of the transmission coefficient is on average 0.26 dB over studied frequency band and reaches a peak value of 0.58 dB.
## 5 IETF ROLL Working Group
### Implementation and Evaluation
#### 5.2.3 Sensor-to-Sensor Traffic  
The routing for sensor-to-sensor communication is based on the communication paths between the root and the sensors. These paths are initiated by the communication of DAO-messages to their preferred parent. According to [61] this path setup can lead to congestion on the nodes around the root. In addition, the limitations on packet sizes for constrained devices for non-storing mode introduce the risk of fragmentation for paths with multiple hops. In contrast, for operation in storing mode, the limited memory influences the number of paths a node close to the root can store.  
The authors of [62] state that the importance of point-to-point traffic flows in low-power and lossy networks is underestimated. In the paper it is demonstrated for a network consisting of about 1,000 nodes that the shortest cost _peer-to-peer_ (P2P) routes performs significantly better than the current RPL standard (using up and down P2P routes). This illustrates the need of additional point-to-point routing mechanisms. This conclusion is also confirmed by [63]. In this paper a solution is provided, called P2P-RPL, which extents RPL and performs better in a network of 27 fixed nodes running Contiki that has an average node degree of 4.39. While data packets in standard RPL traverse approximately 5 links on average, the links that are traversed when using P2P-RPL are halved for the same network. For even deeper nested DODAG trees, even higher gains are expected. P2P-RPL also decrease the traffic load around the sink: in storing mode with standard RPL 74,53% of the routes traverse the root, while for P2P-RPL this is only 16,03%.
## 5 Result Analysis  
### Result Analysis of Questionnaire Survey  
Based on the questionnaire survey data gathered and the comprehensive analysis of the data, twenty-two indicators were identified as the important components for the RCI development level. After calculating and analyzing the questionnaire survey data, it was found that the total effects of the dimension layers on RCI development level ranged from 3.742 to 4.383. Given that six out of ten top indicators were located in the technical dimension (Figure 4), it is believed that the core dimension for RCI development level was the technical dimension. The ratio of new buildings assemblage (A16, 4.685), application of standardized products (A12, 4.628), number of the national industrial bases (A42, 4.587), application of modular design (A11, 4.540) and support rate of the masses (A53, 4.469) were identified as the most important indicators of RCI development level, which were the top five indicators, indicating that these indicators played vital roles in the index system. In addition, it could be distinctly observed that the economic dimension ranges from 3.157 to 4.376, ranking this dimension fourth out of the five dimensions. The results indicated that the level of economic development was not a key factor that could have a significant influence on the performance of construction industrialization, which was not quite the same as in other industries.