esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Furthermore, the effectiveness of timbre features has been explored for audio classification [19] and music mood classification [20], but it has not been analyzed for speech emotion recognition. In this paper, we analyzed timbre features to improve the recognition rate of emotions in a speech in the valence dimension. Furthermore, sequential forward selection (SFS) was applied to find the best feature subset among the timbre acoustic features. The effectiveness of the timbre features for speech emotion recognition was evaluated by compared to well-known MFCC and energy features of the speech signal. Through the experiments, significant improvement was achieved using the selected best feature subset. Timbre features proved to be effective with the classification of discrete emotions and in the classification of emotions in the valence dimension. Average classification accuracy improvements of 24.06% and 18.77% were achieved with the binary valence classification and the classification of discrete emotions using the combination of baseline and timbre acoustic features on the Berlin Emotional Speech Database. The rest of this paper is structured as follows: Section 2 gives information about related works that investigated acoustic features of a speech signal for emotion recognition. Section 3 describes general speech emotion recognition systems, acoustic feature extraction methods, and classification models. Emotion databases, the experiments, and the results are given in Section 4. Analysis of the results and discussion is presented in Section 5. Finally, Section 6 presents the conclusion of this work and future research directions.
# Micro-Satellite Systems Design, Integration, and Flight
## 3 ChipSat RTOS and RF Development
### Packet Formulation
#### 3.4.4 Pre- and Post-Amble  
To help with demodulating the signal, the data can have identifiers added to the packet after FEC. The packet usually has an identifier at the start and the end to distinguish where the packet begins and ends. A preamble is an identifier that comes before the packet, and a post-amble is one that comes after the packet. For the Alpha mission, only a preamble was used. This fulfilled requirement 5.0 (see Table 4). The preamble helps the demodulator identify the signal and find where each packet starts. This way, the signal can be captured and demodulated. For Alpha, the preamble consisted of four 8-bit Barker codes (following requirement 5.1 in Table 4). After the Barker codes, the FEC-encoded data came. See Figure 22 below.  
If the transmitter and receiver agree on the same Barker codes, they can use any value they want. These four 8-bit Barker codes make sure that the data sequence is distinct and recognizable. In other words, a preamble of this size prevents the signal from being confused with a random noise. The packet length became 40 8-bit transmissions by adding 4 8-bit identifiers.
## 4 Experimental Results
### CpB Analysis  
Figure 5 shows the number of processor cycles required per byte for the encryption of the real-time video feed by the selected LWSCs. Grain-v1 exhibits the highest number of CpB, indicating relatively higher computational demands. Trivium and Mickey follow with notable differences in processor cycle consumption compared to Grain-v1. In contrast, Sosemanuk has the lowest CpB, whereas Salsa and Grain-128a demonstrate slightly higher cycle counts compared to Sosemanuk, without any significant differences.
## 3 The Study Area and Spatial Datasets
### Computing Landslide Susceptibility Index  
When the final BE-LMTree model is satisfied in the performance assessment check, the model is used to compute susceptibility index for all the pixels of the study area. These susceptibility indices are then converted to the ASCII raster format in ArcGIS using a Python application that was developed by the authors. Finally, the landslide susceptibility map is classified by five susceptibility classes: very high, high, high, moderate, low, and very low [87].
## 2 Materials and Methods  
### Dataset  
This study uses two image datasets: the RSSCN7 [10] and WHU-RS19 image datasets [11]. Table 1 summarizes the characteristics of these datasets.  
The RSSCN7 image dataset is a public dataset released by Wuhan University in 2015. There are seven different scene categories: includes grassland, fields, industrial areas, rivers/lakes, forests, residential areas, and parking lots. The overall dataset contains a total of 2800 images. Each of these categories contains 400 images with four different telemetry sampling rates, and the sizes of all images are 400 \(\times\) 400 pixels, as shown in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & **Images per Class** & **Classes** & **Total Images** & **Image Sizes** & **Year** \\ \hline RSSCN7 & 400 & 7 & 2800 & 400 \(\times\) 400 & 2015 \\ WHU-RS19 & \(\sim\)50 & 19 & 1005 & 600 \(\times\) 600 & 2012 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main characteristics of the applied databases.  
The WHU-RS19 image dataset is extracted from satellite images derived from Google Earth. The spatial resolution of these satellite images is up to 0.5 m, and the spectral bands are red, green, and blue (RGB). The image dataset contains 19 scene categories, which includes airports, beaches, bridges, commercial areas, deserts, farmland, football fields, forests, industrial areas, grasslands, mountains, parks, parking lots, ponds, ports, train stations, residential areas, rivers, and viaducts; there are about 50 images corresponding to each category. The entire data set has a total of 1,005 images, and the original image sizes are 600 \(\times\) 600 pixels, as shown in Figure 2.
## 1 Introduction
[36] reported a new pathway for liquid-solid TENG, using streaming flow by a novel direct charge transfer, where the DC output is produced as a result of the unidirectional charge transfer from water flow into the electrode. Accordingly, electrons can directly transfer from one side to the other at the contact interface when the water contacts a metal surface [37, 38]. This implies a promising technology for harvesting hydrokinetic energy by developing a DC-TENG based on contact electrification and direct charge transfer at the water-metal interfaces. In this paper, we report a direct-current fluid-flow-based triboelectric nanogenerator, the so-called DC-FluTENG, which offers a simple but effective mechanical-to-electrical energy conversion technology for harvesting hydrokinetic energy. DC-FluTENG is based on an extremely simple structure with a metal electrode located near the outlet of a pipe and working in a single-electrode mode. This is different from a conventional single-electrode-based TENG, where the electrode is placed below the charge-generating layer. Consequently, the moving of a water droplet (about 1.06 mL) can produce a peak DC voltage and current of about 35 V and 3.7 \(\mu\)A, respectively, by passing through an elastomeric pipe under a single-electrode mode of operation. Experimental results prove that the proposed TENG can produce a maximum instantaneous power of 57.6 \(\mu\)W under an optimal external load of 10 M\(\Omega\) at a droplet flow rate of 676 mL/min. This device can generate a transferred charge density of about 14.95 mC/m\({}^{2}\). More importantly, DC-FluTENG can act as a DC power source to light up directly 50 light-emitting diodes (LEDs) without requiring a rectifier, and, also, the produced electric energy was demonstrated that can be stored directly in a capacitor to power commercial temperature and humidity IoT sensors. Moreover, the device showed a greatly varied output voltage based on different water-flow rates with a linearity R\({}^{2}\) = 0.998. This work highlights the great potential of DC-FluTENG for practical applications as an energy harvester and a self-powered flow-rate sensor.
# A Review of Small-Scale Vapor Compression Refrigeration Technologies
## 2 Small-Scale Refrigeration Technologies
### Passive Technologies  
As previously mentioned, passive cooling systems do not require power input to perform their function. The following paragraphs describe passive technologies with applicability to small-scale cooling. While application to small-scale systems often leads to miniaturization, it is noted that many of these passive cooling systems also have applicability for larger systems with higher cooling power.  
Natural convection [17] is the simplest, easiest, and least expensive passive cooling technology for thermal management; however, sometimes it is not enough to reach the needed rates of heat removal, so other methods are used. Heat pipes [18] are another passive technology consisting of a closed tube containing a refrigerant that can evaporate and condense in different zones of the device as it absorbs and releases heat, thus enhancing heat transfer. Vapor chambers [19] work on the same principle and are therefore also known as planar heat pipes. Thermosyphons [20] operate by circulating a fluid through a loop, from hotter areas to colder areas, driven by gravity and differences in density between hot and cold regions.  
Another option is the use of heat sinks [21]. These are heat exchangers machined in various geometries from different materials used to transfer heat from a device to a fluid medium, usually air. Immersion cooling [22] is an alternative technique where a single component or a complete system is immersed in a dielectric liquid used as the coolant. Finally, phase change materials [23] are substances capable of absorbing/releasing large amounts of (latent) heat during their melting or freezing.  
Although the cooling demand of small-scale refrigeration is low, the heat dissipation rate of passive technologies is often insufficient, so combinations of passive and active technologies are often used to reach cooling targets. Active technologies are often necessary due to their potential for higher cooling rates.
## 6 Discussion and Conclusions
Finally, a lossy compression evaluation showed that the proposed scheme had adequate performance for current practical applications, as the BER median was 0.0312 after high-quality lossy compression. In [13], the authors analytically showed the performance of their approach under noise and empirically showed the performance under lossy compression using digital images as host signals. The performance of the proposed scheme in this paper confirms the results in [13] for lossy compression; thus, key-based security is guaranteed. This work was funded by PRODEP-SEP and CONACY under grant PN-2017-01-5814. There was no additional external funding received for this study. The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 6 Test and Analysis  
In order to verify the validity of the algorithm, a laboratory test method is used, and the test equipment is a three-axis turntable, as shown in Figure 1. Here, the HMR2300R three-axis strapdown magnetometer and BN-250 positioning + antenna integrated module of the Honeywell company are selected as the test equipment. The equipment is shown in Figures 2 and 3.  
The experimental steps are as follows:  
Step 1: Connect the direction of the sensor to the turntable and make it consistent;  
Step 2: Fix the pointing angle of the turntable;  
Step 3: Adjust the pitch angle of the turntable to be fixed, so that the turntable rolls and records the data;  
Step 4: Adjust the pitch of the turntable to another angle and fix it to make the turntable roll and record data; repeat this step to collect data at different pitch angles;  
Step 5: Fix the pitch angle of the turntable;  
Step 6: Adjust the pointing angle of the turntable to be fixed, so that the turntable rolls and records the data;  
Step 7: Adjust the pointing of the turntable to another angle and fix it, so that the turntable rolls and records data; repeat this step to collect data at different pointing angles;  
The test location and local geomagnetic information are shown in Table 1.  
To verify the compensation algorithm, five 10 mm neodymium iron boron high-strength magnets were placed 0.3 m in the horizontal direction on the left side of the turntable.
## 2 Main Subject
### Proposed Position Estimation System for Active Driving Assistance
AI-based sensor fusion methods can be effective, but when the amount of data is small, the relationships among the data are distinct, such as the UWB/IMU/encoder sensor used in this paper, and the modeling is relatively easy to implement, sensor fusion techniques using complementary filters and Kalman filters do not have a large error range compared to AI-based sensor fusion methods. Considering the resources required for the design and computation of both sensor systems, this paper proposes a sensor fusion system using a complementary filter for direction information estimation and a Kalman filter for position information estimation. Therefore, the fusion in this study was based on the Kalman filter. Since the sensor system to be fused currently has nonlinearity, it should be carried out based on algorithms such as EKF, UKF (unscented Kalman filter), and CKF (cubature Kalman filter), which are expanded Kalman filter algorithms. In this study, sensor fusion was performed through EKF, which is suitable for intelligent wheelchairs with lower computation than CKF and UKF, by evaluating new measurement data to suppress the noise included in the data and correct the result by estimating new results [39].
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 5 Discussions
### Future Directions: Audio Feature Extraction and Model Integration  
The difficulty in detecting QP events from audio lies in extracting audio features and accurately detecting complete QP events, as QP events consist of multiple QP elements, leading to discontinuous audio features. Current research on the CSES-01 satellite's LW recognition proposes using satellite waveform data as audio segments, extracting the Mel-Frequency Cepstral Coefficient (MFCC) features of LWs using speech intelligence techniques, constructing a long short-term memory (LSTM) neural network, and inputting the MFCC features of the waveform data to train a classification model [36]. The frequency of QP emission is about a few hundred to 4 kHz, within the human ear's hearing range, allowing the use of the same MFCC features to construct an intelligent speech model. The LSTM neural network, which introduces time-dimensional information, is suitable for dealing with sound events in auditory features and events with long intervals and delays in the time series. The computational cost of detecting waveform data directly as audio is much lower than the cost of converting them into image data, further improving the model's detection speed and enabling near-real-time detection. However, the feature expression ability of the MFCC and the long- and short-term memory feature ability of the LSTM in the recognition of QP events need to be verified by systematic experiments. Furthermore, for offline satellite data, we can consider ideas such as integrated learning, which integrates different models trained on waveform and image data to improve the accuracy of QP event detection [37].
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Reliable Physical Distance-Based Energy-Efficient Routing Protocol (RPDE2 RP)  
A reliable E2 RP (RPDE2 RP), based on physical distance and remaining energy, was proposed by the author in [147]. RPDE2 RP transmits data packets which are based on the remaining energy of the neighboring nodes and the quality of the link. The two phases of this protocol are (i) data packet transmission and (ii) cost development. Each sink node transmits a hello message during the second phase, where this message will be received by the sensor Rx node and utilized to determine the network cost. The physical distance of the network from every node remaining energy as well as the sink node will be utilized to determine the network cost. Furthermore, in the beginning phase, the Tx anchor node will transmit communicating data packets to network nodes whose cost value is no more than that of the Tx anchor node. The network's nearby nodes are displayed according to the remaining energies of the networks. Any network node with a high level of remaining energy will be given the priority to become a Tx anchor node. Consequently, the priority will be given to a node to become a Tx anchor node if there are two or more nodes in the network that have the same and high remaining energy.
# Enhancing Ability Estimation with Time-Sensitive IRT Models in Computerized Adaptive Testing
## 3 Methods
### Parameter Estimation
#### 3.3.3 Third Approach K-Fold Cross-Validation and Generalization Testing  
To validate the robustness of estimated parameters, a K-fold Cross-Validation (K-fold CV) framework with five folds was implemented. All parameters (\(\theta\), b, \(\lambda\)) were optimized in the training set using the L-BFGS-B algorithm. Only \(\theta\) was re-estimated in the test set, ensuring realistic ability estimation without modifying pre-trained item and time-effect parameters.  
This method allowed for generalizability testing, reducing the risk of overfitting while providing confidence intervals for \(\theta\) estimates across different samples.  
Shortly, the three codes are explained below:  
In the first code (MLE-based approach), certain parameters are kept fixed and optimized, only \(\theta\) and \(\lambda\). However, this approach did not include cross-validation (K-fold CV).  
In the second approach (code), all parameters are optimized together, and regularization is introduced to prevent overfitting. However, statistical tests were not directly applied.  
In the third approach (code) (the version with K-fold CV), cross-validation is utilized to assess model generalizability, the optimized \(\theta\) parameters are evaluated on the test set, and the mean of AIC, BIC, and correlation values are calculated according to all fold.
# Quality Assessment of Organic Kefirs Made with Kefir Grains and Freeze-Dried Starter Cultures
## 2 Materials and Methods  
### Materials  
Pasteurized organic cow milk originated from an organic farm located in Eastern Poland and was purchased at a local market in Szczecin, Poland. The milk was obtained during the summer period. Samples of kefir were manufactured under laboratory conditions. Four variants of organic kefir were produced during the study (Table 1).  
For kefir fermentation, a traditional culture in the form of kefir grains (KG) (Serowar, Szczecin, Poland) and a freeze-dried (lyophilized) culture, Kefir-31 (Biochem s.r.l., Rome, Italy) intended for direct inoculation of processed milk were used. The freeze-dried culture contained the following microorganisms: _Lactococcus lactis_ subsp. _cremoris_, _Lactococcus lactis_ subsp. _lactis_ biovar _duacetyl lactis_, _Leucomostoc mesenteroides_ subsp. _cremoris_, _Lactobacillus delbrueckii_ subsp. _bulgarcius_, and _Saccharomyces cerevisiae_. The culture in the form of KGs was added in the amount of 20 g/1 L of milk [31; 45; 46; 47], while the freeze-dried culture was added at a rate of 0.1 g per 1 L, according to the manufacturer's recommendations. Kefir fermentation was carried out at 30 \({}^{\circ}\)C, which is within the optimum temperature range for the freeze-dried culture and can also be used in the production of kefir using the traditional KGs [33; 47]. The kefirs were produced using the tank method. The procedure of kefir production is shown in Figure 1.  
Figure 1: Scheme of experimental kefir production.  
Each variant of kefir was produced in a quantity of 3 L and bottled in 30 disposable plastic cups with lids of 100 mL each. The kefirs were stored at 4 \(\pm\) 1 \({}^{\circ}\)C for a period of three weeks. The kefirs were analyzed after 1, 7, 14, and 21 days of refrigerated storage (4 \(\pm\) 1 \({}^{\circ}\)C).
# Inertial Parameter Identification in Robotics: A Survey
## 8 Introducing the BIRDy Matlab Toolbox: A Benchmark for Identification of Robot Dynamics
### Implementation Details
#### 9.3.3 IDIM-IV Implementation  
The IDIM-IV method is only executed on the unfiltered data set. The instrument matrix \(\mathbf{Z}^{i}\) at iteration \(i\) is filled with the noise-free simulated data, obtained by integration of the closed-loop DDM using the current parameter estimate \(\hat{\mathbf{\beta}}_{IV}^{i-1}\) (c.f. Equation (31)). The control structure and gains are the same as those used during the experiment data generation process. Integration is carried out using the Euler (RK1) at the rate \(f_{c}=5 kHz\) of the data generation loop. The data pre-processing step only involves sampling and decimation, to fit the sampled dataset. To stop the sequence of linear LS problems solved by the IDIM-IV method, a set of three stop criteria are implemented:  
* \(\left(\left\|\rho_{IV}^{i+1}\right\|_{2}-\left\|\rho_{IV}^{i}\right\|_{2} \right)/\left\|\rho_{IV}^{i}\right\|_{2}<\text{tol}_{1}\) where \(\rho_{IV}^{i}\) denotes the error at iteration i, defined as \(\rho_{IV}^{i}=\mathbf{y}_{\top}-\mathbf{W}^{(i-1)}\hat{\mathbf{\beta}}_{IV}^{i}\);
* \(\underset{j}{\max}\left|\left(\hat{\mathbf{\beta}}_{IV}^{i+1}(j)-\hat{\mathbf{\beta}} _{IV}^{i}(j)\right)/\hat{\mathbf{\beta}}_{IV}^{i}(j)\right|<\text{tol}_{2}\), \(\forall\hat{\mathbf{\beta}}_{IV}^{i}(j)\
eq 0\) with \(j\) the index and \(i\) the iteration number.
* maximum number of iterations: \(i_{max}=10\).  
where the values of the function tolerance \(\text{tol}_{1}\) and step tolerance \(\text{tol}_{2}\) are set according to the guidelines given in [6; 59]. In our case, we have \(\text{tol}_{1}=2.5\%\) and \(\text{tol}_{2}=2.5\%\).
## 3 System Model
\[\gamma_{t}=\sqrt{E_{s}}\Bigg{(}\frac{\left|H_{R_{1}D}\right|^{2}+\left.\left|H_{R_{2 }D}\right|^{2}}{N_{0}}\right) \tag{7}\]  
Considering the case of a coherent BPSK Modulation scheme, the conditional Bit Error Rate Probability for L-Branch diversity, \(P_{b}\big{(}E\big{|}\{\gamma_{l}\}_{l=1}^{L}\big{)}\) can be given as [28]:  
\[P_{b}\Big{(}E\big{|}\{\gamma_{l}\}_{l=1}^{L}\Big{)}=Q\Big{(}\sqrt{2g\gamma_{t}} \Big{)} \tag{8}\]  
For coherent BPSK modulation, \(g=1\), and \(Q(. )\) represents Gaussian \(Q\) function. The average Bit Error Rate of binary signals using the probability density function (PDF) of the corresponding fading distribution can be given as:  
\[P_{b}=\int\limits_{0}^{\infty}Q\Big{(}\sqrt{2g\gamma_{t}}\Big{)}\,p_{\gamma_{ t}}(\gamma_{t})d\gamma_{t} \tag{9}\]  
where \(p_{\gamma_{t}}\) represents fading distribution of \(\gamma_{t}\). According to the considered case of the three state Markov channel, the \(p_{\gamma_{t}}\) can toggle among Rayleigh, Rician and Nakagami distribution and its corresponding value will be:  
Case-1: For Rayleigh Fading Environment  
\[b_{i}=arg\underset{i\in N}{\max}\{LLR_{N}\}\forall\ i\in(1,2) \tag{2}\]0  
where \(\gamma_{l}\) represents SNR per bit per path and \(\overline{\gamma}\) represents SNR per bit. Case-2: For Rician Fading Environment  
\[b_{i}=arg\underset{i\in N}{\max}\{LLR_{N}\}\forall\ i\in(1,2) \tag{2}\]1  
where \(K\) represents ratio between the power in direct path and indirect path, and \(\Omega\) represents total available power from all paths. Furthermore, \(I_{0}(. )\) represents modified Bessel function of 0 th order. Case-3: For Nakagami Fading Environment  
\[b_{i}=arg\underset{i\in N}{\max}\{LLR_{N}\}\forall\ i\in(1,2) \tag{2}\]2  
where \(m\) is known as shaping parameter and \(\omega\) is controlling spread. This section is further explained to provide a concise and precise description of the experimental results, their interpretations and the experimental conclusions that can be drawn.