esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Description of Experiments  
#### 3.4.1 Baseline: Comparison of Feature Extraction and Classification Methods  
We conducted several baseline experiments to be compared with different methodologies (or potential methodological issues.) In this stage, the feature extraction toolboxes and classification methods described earlier were applied to the three corpora. In the baseline experiments, we employed, as in the rest of the experiments, a k-fold cross-validation in which speakers' audios belonged exclusively to either the testing or training folds for each cross-validation iteration (speaker-wise cross-validation). Additionally, the folds were purposefully created with a balance in class, gender, and mean/standard deviation of age for each corpus. As explained in Section 3, the purpose of performing these baseline tests is _not_ to suggest \"the best\" classification method. Our experiments are designed to evaluate the impact of certain practices which we find to cause inaccurate reporting of results. To this end, the baseline experiments serve as a basis for comparison in each of these specific factors. We trained the classifiers with the training folds, iterating through each combination  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Values** \\ \hline n\_estimators & 20–500, step 20 \\ \hline max\_features & ‘auto’, ‘sqrt’ \\ \hline max\_depth & 10–100, step 10; None \\ \hline min\_samples\_split & 2, 5, 10 \\ \hline min\_samples\_leaf & 1, 2, 4 \\ \hline bootstrap & True, False \\ \hline criterion & ‘gini’, ‘entropy’ \\ \hline \hline \end{tabular}
\end{table}
Table 10: RFC classifier hyperparameters.  
of hyperparameters. The greatest weighted average of test fold accuracy was used to select the best result in the grid search. Only training and testing folds were considered.
# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Data Collection
#### (2)  
Tourism Review Text Data  
Introduction information, review texts, review dates, and ratings for attractions are gathered from the Ctrip travel platform. Reviews posted between January 2020 and October 2024 underwent preprocessing steps, including removal of HTML tags and emojis, conversion from traditional to simplified Chinese, and elimination of non-relevant content.  
Figure 4: Study region and objects.  
This resulted in a final dataset of 58,051 cleaned reviews. The number of reviews per attraction varied significantly based on popularity, ranging from 15 to 5000, with an average of 357 reviews per attraction (see Figure 5a). Review lengths ranged from 4 to 300 Chinese characters, averaging at 29 characters (see Figure 5b).
# Data-Driven Analysis of Outdoor-to-Indoor Propagation for 5G Mid-Band Operational Networks
## 1 Introduction
Measurement setup and data processing are described in Section 3. Section 4 provides a comprehensive analysis of time and power-related parameters that are used to characterize propagation features. The analysis includes a systematic statistical analysis aiming at highlighting the correlation between the propagation parameters. Finally, Section 5 draws conclusions and discusses future work.
## 2 Methodology
Assume a set of initial electron density values \(\mathbf{n_{e}^{(0)}}\) and bring it into the iterative formula to obtain the first set of iterative electron densities \(\mathbf{n_{e}^{(1)}}\);
2. Set \(\mathbf{n_{e}^{(1)}}\) as the input of the second iteration and bring it into the iterative formula to obtain \(\mathbf{n_{e}^{(2)}}\);  
Figure 1: Coefficient matrix and the 3D reconstruction voxels. Set the result of \(\mathbf{n_{e}}^{(k)}\) as the input of the k + 1 th iteration and repeat step 2 to obtain \(\mathbf{n_{e}}^{(k+1)}\) until the preset conditions are satisfied. The preset conditions here could be a preset iteration time k, the deviation between \(\mathbf{n_{e}}^{(k)}\) and \(\mathbf{n_{e}}^{(k+1)}\) could be smaller than a given value, or other conditions could be used to evaluate the equality of \(\mathbf{n_{e}}^{(k+1)}\). MART is an entropy optimization algorithm, and the iteration formula is in the multiplication form so that the output \(\mathbf{n_{e}}^{(k)}\) could remain positive. \(\mathrm{STEC}_{i}/\left<A_{i}^{T},\mathbf{n_{e}}^{(k)}\right>\) inside the round brackets, which can be denote as \(\Delta\mathbf{STEC}\), is the ratio of measured \(\mathbf{STEC}\) to the iterated \(\mathbf{STEC}\), and \(\mathbf{n_{e}}^{(k)}\bullet\Delta\mathbf{STEC}^{\lambda_{k}A_{ij}/\|A_{i}\|}\) is regarded as the modification of \(\mathbf{n_{e}}^{(k)}\) toward the real \(\mathbf{n_{e}}\). The selection of the initial value can affect the accuracy of CIT, and theoretically, the closer the initial value is to the true value, the more accurate the results are. When no measured data are available, the electron density data in the IRI model can be used as aforementioned. After the initial value is determined, it is brought into the iterative formula in Equation (2), and we repeat step 2 for the iterative calculation of CIT. In this paper, the STECs on the small-satellite constellation and AIS paths are applied to obtain an initial ionospheric electron density (IED) for MART that is closer to the true IED. An adjustment factor \(\delta\), defined as the mean ratio between the measured \(\mathbf{STEC}\) and the \(\mathbf{STEC}\), calculated using the coefficient matrix A and the initial electron density, is written as:  
\[\delta=\mathrm{mean}\Bigg{(}\frac{\mathbf{STEC}}{A\bullet\mathbf{n_{e}}^{(0)}} \Bigg{)} \tag{4}\]  
then, the modified initial IED is adjusted by multiplying by \(\delta\), as follows:  
\[\mathbf{n_{eM}^{(0)}}=\delta\bullet\mathbf{n_{e}}^{(0)} \tag{5}\]  
The adjustment factor \(\delta\) causes an overall change in the initial electron density \(\mathbf{n_{e}}^{(0)}\) to make it close to the true electron density. To make a distinction between the two methods with different initial IEDs, we denote the original method by MART0 and the new method by MART1. In MART1, two more steps containing the calculation of \(\delta\) using Equation (3) and the optimization of \(\mathbf{n_{e}}^{(0)}\) using Equation (4) are added to the MART process after the \(\mathbf{n_{e}}^{(0)}\) is selected.
# An Efficient SC-FDM Modulation Technique for a UAV Communication Link
###### Abstract  
Since the communication link of an unmanned aerial vehicle (UAV) and its reliability evaluation represent an arduous field, we have concentrated our work on this topic. The demand regarding the validity and reliability of the communication and data link of UAV is much higher since the environment of the modern battlefield is becoming more and more complex. Therefore, the communication channel between the vehicle and ground control station (GCS) should be secure and provide an efficient data link. In addition, similar to other types of communications, the data link of a UAV has several requirements such as long-range operation, high efficiency, reliability, and low latency. In order to achieve an efficient data link, we need to adopt a highly efficient modulation technique, which leads to an increase in the flight time of the UAV, data transmission rate, and the reliability of the communication link. For this purpose, we have investigated the single-carrier frequency division multiplexing (SC-FDM) modulation technique for a UAV communication system. The results obtained from the comparative study demonstrate that SC-FDM has better performance than the currently used modulation technique for a UAV communication link. We expect that our proposed approach can be a remarkable framework that will help drone manufacturers to establish an efficient UAV communication link and extend the flight duration of drones, especially those being used for search and rescue operations, military tasks, and delivery services.  
U  
A _et electronics_
# A Review of Coordinated Control Technology for Chassis of Distributed Drive Electric Vehicles
## 2 Architectures for Coordinated Control of DDEV Chassis
### Hierarchical Architecture
Ultimately, this led to an optimization of the overall vehicle performance. To more clearly elucidate the characteristics of different control architectures, Table 1 provides a comparative analysis of their key features. Decentralized control architectures offer a certain flexibility and economic benefits in terms of their development model. However, their operational mode, in which each subsystem functions independently, significantly constrains their coordinated control capabilities. This makes it difficult to achieve vehicle-level global optimization and, consequently, creates inherent limitations in fully exploiting the intrinsic over-actuated potential of DDEVs. Theoretically, centralized control architectures possess the potential to achieve a globally optimal control and can effectively avoid local control conflicts. However, they face several significant practical challenges. Their demands on the computational power of the central processing unit are extremely stringent, the system development complexity is high, and the risk of a single point of failure is prominent. This risk of the entire system failing from a single point of failure constitutes a significant technical bottleneck, especially for automotive chassis control systems that require an extremely high reliability. In comparison, hierarchical control architectures exhibit more balanced characteristics and prominent overall advantages in performance, flexibility, and reliability. In summary, the hierarchical control architecture is widely considered the mainstream technical direction for the future of coordinated chassis control in DDEVs, offering the greatest potential for development and application. This consensus is based on its outstanding performance in several key areas: handling complex system coupling, achieving multi-objective coordinated optimization, ensuring system reliability and scalability, and adapting to the over-actuated characteristics of DDEVs. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Architecture** & **Decentralized** & **Centralized** & **Hierarchical** \\ \hline \multirow{4}{*}{Features [39]} & Each subsystem controlled independently & Single central controller coordinates all subsystems & Multi-layer structure: high-level decision-making, mid-level coordination/distribution, low-level execution \\ \cline{1-1} \cline{2-4}  & Simple development, aligns with existing supplier models, easy to debug & Easy to achieve global optimization, effectively avoids conflicts & Balances optimization and decoupling, good, modularity, high flexibility, good scalability \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of coordinated control architectures for DDEVs.
# Profile of Accelerations and Decelerations in Young Basketball Players
###### Abstract  
Accelerometry is a crucial tool in basketball for quantifying the external load borne by players in response to the demands of intermittent high-intensity sports. To advance scientific knowledge in this field, it is imperative to study accelerometry across various populations, sexes, and competitive levels. The primary objective of this research was to characterize the acceleration (ACC) and deceleration (DEC) profiles of male under-18 basketball players during official games, identifying differences in acceleration and deceleration thresholds during playing periods and specific positions. Additionally, the interaction between specific positions and playing periods in acceleration thresholds was examined. Acceleration and deceleration were characterized using four variables: maximum ACC and DEC, distance covered, initial velocity and final velocity. These parameters were analyzed to understand the response of players participating in the Euroleague Basketball based on playing position and game period. A one-way ANOVA, along with effect size, was employed for statistical analysis. Demands exceeding ACC and DEC > 4 m\(\cdot\)s\({}^{-2}\) were found to differentiate player interventions. High-intensity ACC was greater in the first period compared to the third and fourth periods. Distinctions were observed between Guards and Forwards concerning high-intensity ACC being superior to the Centers, and Guards differed from Centers in moderate DEC (2-4 m\(\cdot\)s\({}^{-2}\)), presenting higher values. For all playing positions, high-intensity accelerations were greater during the first period compared to subsequent periods. The four variables used for characterizing ACC and DEC were found to be interrelated. Distance covered depended on maximum ACC and DEC, initial velocity on covered distance, and final velocity on maximum ACC and DEC, distance, and initial velocity.  
accelerometry; inertial devices; playing positions; game period; men
# Symmetric Keys for Lightweight Encryption Algorithms Using a Pre-Trained VGG16 Model
## 8 Conclusions  
This paper introduced a new method for generating lengthy symmetric keys for lightweight cryptography, utilizing image features extracted with a pre-trained VGG16 model. This technique effectively merges the robustness of deep learning with the efficiency required for lightweight cryptographic applications. Our method uses VGG16's rich feature representations to make sure that key generation is highly random and unpredictable. This meets important security needs in environments with limited resources. The experimental results show that by creating exceptionally long keys, the proposed method reduces the time required for key generation and improves defenses against brute force attacks. As the first study of its kind, we suggest that subsequent research incorporate this key generation method into lightweight block cipher algorithms.  
Conceptualization, A.T.K.; methodology, A.T.K.; software, A.T.K.; validation, A.T.K., A.T.M. and E.K.G.; formal analysis, A.T.K.; investigation, A.T.K., A.T.M. and E.K.G.; resources, A.T.K.; data curation, A.T.K.; writing--original draft, A.T.K.; writing--review and editing, A.T.K., A.T.M. and E.K.G.; visualization, A.T.K., A.T.M. and E.K.G.; supervision, A.T.M. and E.K.G.; project administration, A.T.K., A.T.M. and E.K.G.; funding acquisition, A.T.K. and A.T.M. All authors have read and agreed to the published version of the manuscript.  
The authors received no specific funding for this study.  
Data Availability StatementData is contained within the article.  
The authors declare no conflicts of interest.
## 3 Physical Layer
### Spectrum Sensing Techniques
#### 3.1.2 Cyclostationary Feature Detection  
Another sensing method is cyclostationary feature detection [24]. Similar to the matched filter, _a priori_ information about the target signal is required. Since modulated communication signals can be interpreted as multiplexed sinusoidal waves with periodicity, they are classified as cyclostationary [14]. Features of different signals can therefore be identified via spectral correlation analysis [15, 17]. An important advantage of this approach over matched filter is that various target waves can be distinguished based on their spectral correlation functions, eliminating the need for multiple antennas where more than one signal is to be monitored [14, 20]. Another favorable outcome of this attribute is that regular data transmissions can remain active as signal detections take place since they do not affect one another [20]. However, this method has its limitations. Apart from the reliance on prior signal knowledge, the computational complexity, for example, may be an issue depending on the processing power of the device. Furthermore, identifying signal features could take a relatively long time, consequently adding to the power consumption.
## 3 Results
### Testing Results for a Pelletized Adsor
#### 3.3.1 \"Deep Charge\" Tests  
After the first characterization of the small storage prototype, another series of tests were realized, with the aim of finding out whether the charge of the system at a lower temperature could hold some benefit on the performance and the amount of energy stored. In this case, the operation of the system is still realized at the following three temperature levels:  
* Heat of desorption is provided at the temperature of the heat source, HT;
* Heat of condensation is released at a low temperature, LT;
* Heat of adsorption is released at the temperature of adsorption, MT;
* Heat of evaporation is provided at a temperature level suitable for the user, LT.  
The condensation and evaporation temperatures of the tests then are equal. This allows simulating, for example, the case of short-term storage realized with condensation taking place at a very low external temperature.  
The results of the analysis are shown in Figure 10a,b for discharge power and stored energy, respectively. The performance indicators are represented as a function of adsorption temperature. In particular, three different evaporation/condensation temperatures have been tested (7, 10, and 15 \({}^{\circ}\)C), and for each of them, three different adsorption temperatures (30, 35, and 40 \({}^{\circ}\)C). The tendency reported in the graphs is clear: there is a benefit in realizing the charge of the system at a lower temperature with respect to adsorption temperature.  
Figure 10: (**a**) The effect of “deep charging” on discharge power, composite adsorber; (**b**) the effect of “deep charging” on stored energy, composite adsorber.  
Figure 9: (**a**) The effect of adsorber flow rate, composite adsorber; (**b**) the effect of the flow rate of the phase changer, composite adsorber.  
Indeed, the lower the difference between adsorption and condensation temperature, the higher the discharge power and energy recovered, especially when the useful effect has to be provided at 7 or 10 \({}^{\circ}\)C. The findings described here are further highlighted in Figure 11, where the case of a 10 \({}^{\circ}\)C evaporation temperature has been considered. The green points represent the tests with \"deep charge,\" while the purple line represents the tests in which the condensation and adsorption temperatures are equal (\"normal charge\"). It is possible to see that, for the three adsorption temperatures tested (3T), an increase of about 20%-25% in the stored energy is achieved by means of deep charge.  
Such results have led to the realization of another run of tests, at four temperature levels (4T), as described in the next section.
## 2 Materials and Methods
### Wireless Sensor Network
#### 2.1.4 Implementation  
The system underwent an initial laboratory testing phase lasting three weeks. This rigorous testing confirmed the system's stability, ensuring a consistent connection and the continuous monitoring of soil parameters. Additionally, the microcontroller programsimplemented a watchdog feature, which automatically resets the nodes in the event of unexpected errors. This proactive approach effectively resolves a significant portion of potential issues, demonstrating the system's readiness for field implementation.  
The WSN implementation was performed on a Stevia Morita II crop, owned by Rancho Tajeli. Figure 3 provides a satellite view of the node layout within the stevia crop. The blue circles represent the sensor nodes, while the red square represents the master node. It is worth noting that the master node is positioned near a building with an available internet connection. The layout also demonstrates that the maximum distance between any sensor node and the master node does not exceed 150 m, enabling stable communication without the need for repeaters or signal amplification.  
For deployment in the stevia crop, sensor nodes were strategically installed. The sensor probes were placed following the manufacturer's instructions at a depth of 0.25 m, inserting the steel probes horizontally, close to the roots of individual plants, as depicted in Figure 4. The JXBS-3001 sensor has a T90 of less than 10 s, this represents that it only requires that time to reach 90% of its final stable reading, therefore, any monitoring period should be above this timespan. For this study, a 5 min interval for measuring soil parameters was selected, so a total of 288 measurements per day could be collected [38]. The power banks powering the sensor nodes were replaced with other fully charged power banks every two to three days, ensuring uninterrupted monitoring routines and maintaining the system's reliability.  
Figure 3: Node distribution on Rancho Tajeli.  
Overall, the successful laboratory testing, coupled with the well-planned node layout and the strategic placement of sensor nodes, establishes the system's robustness and suitability for field implementation.
# Lossless WDM PON Photonic Integrated Receivers Including SOAs
## 2 Lossless WDM PON Architectural Model  
The lossless WDM PON architectural model considered for simulation and experimental investigation in this work is shown in Figure 1. The architecture consists of generic PON network elements; OLT, ODN and ONT with an additional proposed photonic integrated receiver including twin parallel SOAs. The architecture can support 32 bidirectional C-band DWDM channels that are transmitted at 25 Gbit/s with 50 GHz-interleaved downstream/upstream channel spacing. The OLT consists of arrays of transmitters and receivers, mux/demux and pre- and booster-EDFAs. The ODN length is 15-20 km and split ratio is 32. To maintain compliance to safety class 1M, the maximum overall output power considered for the OLT booster amplifier is 21 dBm, corresponding to up to 6 dBm per channel. Considering 2 dB for circulator and band split loss, the reference launch power per channel is 4 dBm. The overall expected loss including the ODN, circulator and band splitter (at receiver) is expected to be about 25-30 dB considering the typical loss for fibers and passive components. The ONT has a tunable transmitter for upstream and an integrated receiver. At the receiver, the WDM multiplexed channels are coupled to the SOA and one of the channels is dropped using a relevant micro-ring resonator based tunable filter before detection and conversion into output electrical signal [28]. Receiver sensitivity is considered to be \(-\)16 dBm and target optical signal-to-noise ratio (OSNR) is 19 dB. A polarization insensitive scheme for the integrated receiver can be realized by exploiting twin parallel SOAs and filters: the optical signal is coupled to the photonic integrated chip through dual polarization grating couplers (GC) [29], separating the transverse electric (TE) and transverse magnetic (TM) modes over two different waveguides feeding two twin parallel SOAs. It is assumed that SOAs are placed in the SOI trench using the flip-chip technique. The light between the SOA and other silicon components is coupled through a spot size converter. The SOA is designed to compensate the link, coupling and on-chip losses.
## 5 Future Scope and Applicability of the Proposed Model in Seaports
### Predictive Maintenance and Resource Optimization  
By analyzing video data and sensor information, the model plays a pivotal role in predictive maintenance and resource optimization within seaports. It can detect anomalies, identify potential maintenance needs, and predict equipment failures or malfunctions. This proactive approach allows seaport operators to schedule maintenance activities, optimize resource allocation, and minimize downtime. By preventing unexpected equipment failures, the model ensures uninterrupted operations and boosts the overall productivity of the seaport.