esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimode Tree-Coding of Speech with Pre-/Post-Weighting +
## 8 Voice Activity Detection (VAD) and Mode Decision
These test sequences are taken from a standard ITU-T speech test data set [34] for standardized speech codecs. Their bandwidth is 300 to 3400 Hz, and they are sampled at 8000 samples/sec. Each sample is represented as a 16 bit linearly quantized word. The terminology dBov is the dB level relative to the overload of the system. The relative frequency of these classifications according to modes using our algorithm on each sentence are shown in Table 2.  
The thresholds chosen by experiment in the design presented here are:  
* \(f_{VAD}=0.04\)
* \(y_{VAD}=5\)
* \(d_{VAD}=0.6\)  
After the VAD decision, then the voicing decisions use the thresholds:
* \(y_{VOICE}=0.33\) of average \(y_{u}\)
* \(d_{VOICE}=\) average \(d_{ml}\)
* \(f_{VOICE}=0.3\) of average \(f_{e}\)  
where the averages are over a frame, and these are updated on a frame-by-frame basis. \(ZC_{VOICE}\) was not used in our final design, although it can be used to fine tune unvoiced decisions if needed. It is important to notice that the threshold values given here are all normalized values, being normalized to the power level of the input speech levels. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sequences** & **M/F** & **Active Speech Level (dBov)** & **Sentence** \\ \hline lathe & F & \(-18.1\) & A lathe is a big tool. Grab every dish of sugar. \\ we were away & M & \(-16.5\) & We were away a year ago. \\ af1s01 & F & \(-31.9\) & You are the perfect hostess. Are you going to be nice to me? \\ af1s02 & F & \(-32.2\) & You know my outlook on life. I jumped at least two feet. \\ af1s03 & F & \(-33.1\) & He took out his pipe and lit it up. It was the same in the public bar. \\ am1s01 & M & \(-34.9\) & The wind slammed the door. It did not seem like summer. \\ am1s02 & M & \(-33.5\) & There wasn’t a scrap of evidence. The jar was full of water. \\ am1s03 & M & \(-34.5\) & He carried a bag of tennis balls. The scheme was plotted out. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Details of the narrowband speech test sequences. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Sequence** & **S** & **UV** & **V** & **ON** & **H** \\ \hline lathe & 0.3693 & 0.1535 & 0.4707 & 0.0065 & 0 \\ we were away & 0 & 0.0292 & 0.9655 & 0.0053 & 0 \\ af1s01 & 0.6012 & 0.1681 & 0.2244 & 0.0063 & 0 \\ af1s02 & 0.5675 & 0.1838 & 0.2444 & 0.0044 & 0 \\ af1s03 & 0.4506 & 0.2719 & 0.2687 & 0.0088 & 0 \\ am1s01 & 0.6663 & 0.1231 & 0.2050 & 0.0056 & 0 \\ am1s02 & 0.6306 & 0.1713 & 0.1906 & 0.0075 & 0 \\ am1s03 & 0.5894 & 0.2019 & 0.2006 & 0.0081 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mode decision results for narrowband sequences. This is as typical in VAD and mode decision rules used in other codecs such as AMR. Therefore, these values are robust to variations in input speech power and across different utterances. The VAD and Mode not only impact reconstructed speech quality but also the transmitted bitrate.
## 3 Experimental Apparatus  
### PA Measurement  
Figure 2 presents the setup used for PA measurements. A pair of independently controlled Q-switched neodium-doped yttrium aluminum garnet (Nd:YAG) lasers emitting at 532 nm (Rayture Systems Co. Ltd., Tokyo, Japan, GAIA Dual) was employed to generate laser pulses. The time delay between the first and second pulses was pre-selected using a delay generator (Quantum Composers Inc., Bozeman, MT, USA, 9618+). The typical pulse energy was 9.0 mJ, and the pulse duration was 10 ns (full-width at half-maximum). The timings of the laser irradiation \(t_{1}\), \(t_{2}\) were detected by a photodetector (PD) (Thorlabs Inc., Newton, NJ, USA, DET10A2) capable of detecting wavelengths ranging from 200 to 1100 nm. These data from the PD facilitated the calculations of the actual time delay \(t_{p}\) between the first and second pulses. A pair of laser pulses was aligned colinearly in the same region and adjusted to the same height as of an UST (Olympus Medical Inc. (now Evident Corporation), Tokyo, Japan V311-SU). The PRF was controlled using a two-channel function generator (RIGOL Technologies Inc., Suzhou, China, DG1022Z). The phase difference \(\varphi\) between the pulse and LS signals was also adjusted with the generator, enabling the irradiation of laser pulses at arbitrary timing with a sinusoidal period. The generated PA waves were detected by the UST with a center frequency of 10 MHz. The UST was connected to an oscilloscope via an amplifier (FEMTO Messtechnik GmbH, Berlin, Germany, HVA-10M-60-B). The detection angle \(\theta\) of the UST was adjusted to acquire the maximum amplitude of the PA signal. The acquisition of the PA signals was triggered at the first pulse irradiation based on the PD.  
Figure 2: Experimental setup of the PA velocity measurement (PD: photo detector, PRF: pulse repetition frequency, LS: loudspeaker, UST: ultrasound transducer). A pair of pulsed lasers was irradiated an object immersed in the water. The object, a GC, was affixed to the LS membrane via an arm. The pre-selected time delay was controlled by the delay generator. The irradiation timings were detected by the PD. The PRF and LS oscillation were regulated via the function generator. The generated PA signals were measured employing the oscilloscope through the UST.
# Applying Neural Networks with Time-Frequency Features for the Detection of Mental Fatigue
## 2 Materials and Methods
### Data Acquisition and Preprocessing  
EEG data recordings were performed using a 64-channel electrode cap (Biosemi, Activetwo System, Amsterdam, Netherlands), according to the standard 10-20 positioning system at a 512 Hz sampling rate. Bipolar electrooculogram signals were recorded from electrodes placed at the outer canthi, as well as above and below the eyes. The raw EEG data were down sampled to 256 Hz, band-pass filtered from 1 to 40 Hz, and re-referenced to the average of the all electrodes. Additional artifact correction was performed by utilizing Independent Component Analysis (ICA) and rejecting the components highly correlated with the electrooculogram signals [15]. Due to significant artifact contamination, data from 2 participants were excluded, resulting in 20 subjects for further analysis. Signals were then segmented into trials and adjusted relative to a 100-ms pre-stimulus baseline. An additional removal of the first 0.5 sec stimulus onset data was employed to alleviate influences due to stimuli effects [16], resulting in 18 (per condition) 3-sec trial-based epochs. Electrode Iz was removed from analysis for symmetry reasons, leaving 63 channels for subsequent analysis. In order to negate the contribution of cognitive processing irrelevant to working memory, only correct answers were included in the following analysis, resulting in 2401 samples: 1234 samples of Fatigued data and 1167 of Rested data. Preprocessing was implemented in Matlab 2022b (Mathworks Inc., Natick, MA, USA) using the EEGLAB tool [17].
## 1 Introduction  
The finite-difference time-domain (FDTD) method [1] is widely recognized as one of the dominant tools in computational electromagnetics, particularly in the study of the broadband spectral response of electromagnetic structures. In such large frequency intervals, most materials are dispersive; namely, they exhibit a frequency-dependent variation of their electrical permittivity and magnetic permeability. From the early stages of its development, the FDTD was demonstrated to be capable of accurately simulating the dispersive properties of most common materials, both dielectric and metallic [2].  
In several cases involving advanced natural or artificial materials, it is not only their dispersive nature that has to be taken into account but also their anisotropy. This problem has been addressed in various FDTD techniques, which have been proposed for the numerical simulation of certain types of both anisotropic and dispersive media [3, 4, 5, 6, 7, 8, 9].  
As concerns the FDTD modeling of dispersive materials, the complex-conjugate pole-residue pairs (CCPR) model, a generalization of the traditional Drude and Lorentz models, has been proven to provide a more accurate description of material dispersion [10, 11] with reduced memory requirements and similar conditions for numerical stability [12]. Recently, we have demonstrated how the CCR can be combined with electric anisotropy for the accurate investigation of nematic liquid crystals (NLC) [13]. The dispersion of the ordinary and extraordinary NLC indices were described by the CCPR model, and the FDTD scheme accounted for both the dispersion and the NLC anisotropy. CCPR modeling has been also combined with implicit FDTD formulations--in particular, the leapfrog alternating-direction implicit FDTD--confirming the robustness of the approach for the accurate description of dispersive materials [14].  
In the present work, the CCPR dispersion model is incorporated into an explicit FDTD, which accounts for a generalized form of the permittivity or permeability tensor. All elements of the electric and magnetic tensors are allowed to obtain non-zero values. Moreover, they are allowed to independently exhibit frequency dispersion following the CCPR model with an arbitrary number of terms and are capable of capturing the behavior of dispersive material over broad spectral ranges. The FDTD implementation algorithm is outlined, and the associated generalized update expressions are obtained. The efficiency of the technique is corroborated in benchmark examples studying electromagnetic wave propagation through three types of dispersive and anisotropic materials: (i) magnetized plasma, (ii) NLC and (iii) ferrites. The proposed FDTD scheme provides extensive versatility in the modeling of the arbitrary dispersive characteristics of the tensor elements of anisotropic materials and a robust framework for the study of electromagnetic systems that involve dispersive anisotropic media.
## 4 Results
### Experimental Results and Analysis
#### 4.3.1 Horizontal Comparative Experiment  
We conducted a comparative experiment using our proposed OrthoDETR model as the primary approach. In order to evaluate its performance, we compared it against several state-of-the-art convolutional neural network-based methods, including the following:  
Faster R-CNN: This is a classical two-stage object detection method that utilizes a region proposal network to generate candidate regions, followed by a region classification network to predict the class labels and bounding boxes.  
YOLOv8: An advanced one-stage object detection technique that partitions the input image into a grid system and forecasts numerous anchor boxes along with associated class probabilities within each grid cell.  
SSD: A simple yet effective one-stage object detection method that employs multiple feature maps of different scales to predict anchor boxes and class probabilities for objects of varying sizes.  
RetinaNet: An improved one-stage object detection method that introduces a focal loss-based classification branch, which effectively handles the challenge of class imbalance.  
Table 1 below presents a comprehensive comparison of the experimental results obtained from these models, providing a clear visual representation of the effectiveness of OrthoDETR in relation to these established methods.  
Due to the relatively large targets in the dataset, we employed AP50, AP50:95, and AR50:95 as performance evaluation metrics for the model, which measure precision and recall at different IoU thresholds. We also compared FPS to assess the real-time performance of different models. It can be observed that OrthoDETR outperforms other models in AP50, AP50:95, and AR50:95. At the same time, our method demonstrates good performance in terms of inference speed, particularly when compared to two-stage methods like Faster R-CNN. Although it is slightly slower than single-stage methods such as YOLOv8 and SSD in inference speed, the improved DETR's advantages in accuracy and robustness compensate for this shortcoming.  
In conclusion, OrthoDETR's performance metrics (AP50, AP50:95, and AR50:95) are superior to the other models, indicating its high precision and recall rates. In terms of speed, OrthoDETR performs competitively, providing a good trade-off between speed and accuracy. Importantly, OrthoDETR exhibits a lower number of parameters and FLOPs compared to some models like Faster R-CNN, indicating its lower complexity and higher efficiency. This underlines our model's suitability for real-time applications in the industrial and medical sectors, where both accuracy and efficiency are crucial.
# Inter-Blockchain Communication Message Relay Time Measurement and Analysis in Cosmos +
## 3 Data Collection and Methodology
### Message Collection Structure  
#### 3.2.1 Control Process for Transaction Creation  
Some components installed on the Measurement Node to manage transaction formation in the study included the Cosmos Hub Client [17; 18], Osmosis Client [19], and an IBC Tx Generation Controller. After receiving the request, the Cosmos Hub Client creates an IBC Transfer transaction and displays the transaction information. The IBC Tx Generation Controller uses the Osmosis wallet, which stores the key inside the Cosmos Hub client to request the transfer of fungible tokens from the Cosmos Hub wallet. Every transaction transfer 1000 _UATOM_ (1 ATOM = 1,000,000 _UATOM_) from the Cosmos Hub to Osmosis. The transaction data is saved as IBC Tx Generation data, and the IBC Tx Generation Controller reviews it before beginning a transaction propagation instruction; this mechanism was repeated 1000 times in 24 h, with breaks of 86.4 s. Figure 6 shows the transaction generation control mechanism.  
The unconfirmed transactions are stored in the Cosmos Hub's mempool of full nodes. Concurrently with IBC Transfer transactions, IBC RecvPacket and IBC Acknowledgement transactions are established and approved. To prevent spam on the mainnet, set a minimum gas price and accept only transactions within that range [20]. This experiment employs a basic transaction fee of 260 _UATOM_ to allow transactions to proceed without being filtered or blocked. The RPC (Remote Procedure Call) [21] command' gaiaid tx ibc-transfer transfer [src-port] [src-channel] [receiver] [amount] [flags] is used to build inter-blockchain communication transactions, specifically IBC Tx Generation Controller transactions. The transaction creation command of the Cosmos Hub is written in Go [22]. The transaction can be broadcast immediately by entering the critical password and selecting '-yes'in the flag field. The IBC Tx Generation Controller saves the transaction data output from the Cosmos Hub client as IBC Tx Generation data, along with the time after entering the critical password.  
This controlled transaction creation technique enables the systematic generation and tracking of inter-blockchain communication transactions for extra analysis and evaluation.
## 4 Experimental Verification  
To verify the correctness and effectiveness of the proposed dynamic harmonic compensation sliding mode control algorithm for harmonic current suppression, an experimental platform was established. The experimental setup includes a surface-mounted PMSM and its drive control system, as shown in Figure 8. The equipment used in the experiment includes a PMSM with a rated stator voltage of 24 V, a rated current of 10.0 A, four pole pairs, a rated speed of 3000 r/min, a stator resistance of 1.6 \(\Omega\), d-axis and q-axis inductances of  
Figure 6: A system control block diagram.  
Figure 7: A structural block diagram of the current error-proportional resonant-sliding mode control surface.  
3.5 mH each, a magnetic flux linkage of 0.0593 Wb, and a rotor inertia of 2.45 \(\times\) 10\({}^{-4}\) kg\(\cdot\)m\({}^{2}\). The switching frequency is set to 10 kHz. The control system platform consists of two main parts: the power circuit and the control circuit. IGBTs are used as switching devices in the power circuit, and the core control chip is the STM32F103 RCT6.  
In the experiment, the total harmonic distortion (THD) of the 5 th and 7 th harmonic currents and the content of the 6 th-order torque ripple were analyzed after applying the torque ripple suppression algorithm. These results were compared with the THD of the 5 th and 7 th harmonic currents and the content of the 6 th-order torque ripple without the torque ripple suppression algorithm. The parameters of the quasi-proportional-resonant controller were set as follows: \(k_{pr}=0.1\), \(k_{r}=80\), and \(w_{cr}=0.2\) rad/s.
## 3 Analysis  
### Analysis of Dry Density Variation with Depth of Soil between Piles  
At the three pile centroid locations, laboratory tests were conducted by excavating and sampling at 1 m intervals along the depth of the artificial exploration well (Table 3) to analyze the treatment effect of the bored piles. The changes in soil density are displayed in Table 3, where T1 is the soil dry density before treatment.  
As displayed in Table 3, the dry density of natural ground ranges from 1.24 to 1.35 g/cm\({}^{3}\), with an average of 1.30 g/cm\({}^{3}\). When the center distance of the pile is 1.0 m, the soil dry density ranges from 1.51 to 1.75 g/cm\({}^{3}\), and the average density is 1.61 g/cm\({}^{3}\). When the center distance of the pile was 1.25 m, the soil dry density was 1.4 to 1.69 g/cm\({}^{3}\), and the average density was 1.50 g/cm\({}^{3}\). When the center distance of the pile was 1.5 m, the soil dry density ranged from 1.29 to 1.41 g/cm\({}^{3}\), with an average of 1.34 g/cm\({}^{3}\). The investigation demonstrated that the soil dry density increased dramatically as pile separation decreased. When the center distance of the pile was 1.0, 1.25, or 1.5 m, the average density in the depth range improved by 23.8%, 18.5%, and 3.1%, respectively. Compared to the natural foundation, when the center distance of the pile was less than or equal to 1.25 m, the average density increased significantly after the bored pile treatment. However, when the center distance of the pile was greater than 1.5 m, the average dry density of the soil did not increase substantially within the depth range, which was close to the natural foundation.
# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 4 HSV Color-Space-Based Automated Object Feature Extraction
### HSV Color-Space-Based Automated Object Localization Algorithm  
The HSV color-space-based automated object localization algorithm consists of four steps, as shown in Figure 5:  
Figure 4: HSV color-space-based robot grasping in previous work in FaaS platform: (**a**) robot handler; (**b**) robot grasping; (**c**) user’s HSV color configuration and monitoring GUI.  
Figure 3: Sequence scenario of automated object feature extraction for robot grasping.  
* HSV color-space-based auto HSV color picking;
* Region of interest (ROI) random sampling;
* HSV similarity comparison;
* ROI merge operation.  
The process is described in Sections 4.2.1 and 4.2.3.
# Rapid In-Field Volatile Sampling for Detection of _Botrytis cinerea_ Infection in Wine Grapes
## 2 Results and Discussion  
### Controlled Environmental Condition for Gas Sampling  
Key volatile compounds related to _B. cinerea_ infection in grapes, including 3-octanone, 3-octanol, 1-octen-3-ol, 1,5-diemthyltetralin, benzyl alcohol, phenylethyl alcohol, and 1,5-dimethylnaphthalene, were detected from a controlled environment, using ZIF-8 and Tenax(r)-TA (Figure 1). In this experiment, 2-methyl-4-pentanol was applied on a filter paper as an internal standard (IS) to enable consistent volatilisation and avoid interference from grape contamination and potential absorption into the waxy berry layer, thereby confounding the analysis. The controlled environment ensured relatively stable conditions of temperature and humidity and limited air movement, so the partitioning of volatile from solution to the air within the chamber was consistent throughout the experiment. The relative response rate for most compounds using ZIF-8 was compatible with Tenax(r)-TA.  
The whole bunches of _B. cinerea_ control (LFD signal intensity 12-14) or infected (LFD signal intensity 37-97) grapes were placed into the same glass container system for the headspace volatile analysis, and the results were shown in Figure 1 (right). The variance for some compounds, such as phenylethyl alcohol, may be caused by the biological diversity of grapes with _B. cinerea_ infection of different severities.
# Clustering Optimization of IPMSM for Electric Vehicles: Considering Inverter Control Strategy
## 2 Clustering Optimization Design Procedure
### Optimization Algorithm and Fitness Function  
The multi-objective PSO algorithm is utilized to obtain the optimal design, which includes adaptive mesh generation based on the roulette algorithm and global best updating as presented in [34].  
In order to improve the vehicle range and driving comfort, two objectives are considered: maximize the driving cycle efficiency and minimize the torque ripple. Efficiency is the most important target for EV motors, which has a great influence on vehicle range and comfort, especially considering the current constraints of battery technology faced by vehicles. Maximum driving cycle efficiency can ensure the maximum efficiency of the drive system in the driving cycle. Likewise, the torque ripple is another key factor influencing the performance of the EV motor, which can lead to electromagnetic noise and vibration, affect vehicle comfort, and shorten the service life of the power train. Accordingly, the design objectives could be written as  
\[\min f_{1}(\mathbf{x})=1-\frac{\sum\limits_{j=1}^{m}\left(w_{j}T_{j}\Omega_{j} \right)}{\sum\limits_{j=1}^{m}\left(w_{j}(T_{j}\Omega_{j}+P_{\text{loss},j}) \right)}\times 100\%, \tag{3}\]  
\[\min f_{2}(\mathbf{x})=\sum\limits_{j=1}^{m}\left(\frac{T_{nj}w_{j}}{T_{j}}\right) \times 100\%, \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RPs** & **Speed (rpm)** & **Torque (N\(\cdot\)m)** & _w (\%)_ \\ \hline \multirow{4}{*}{UDDS} & 1 & 738 & 27 & 7.8 \\  & 2 & 4934 & 21.66 & 9.9 \\  & 3 & 2546 & 14 & 46.26 \\  & 4 & 1126 & 91 & 18.63 \\  & 5 & 2157 & 50 & 17.37 \\ \hline \multirow{2}{*}{HWFET} & 1 & 5308 & 21 & 47 \\  & 2 & 2051 & 68 & 5 \\ \cline{1-1}  & 3 & 4269 & 20 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of RPs in two driving cycles.  
Figure 2: Driving cycle clustering results. (**a**) Speed curve of UDDS. (**b**) Torque-speed curve of UDDS. (**c**) Speed curve of HWFET. (**d**) Torque-speed curve of HWFET.  
where \(x\) is the optimization variable, the subscript \(j\) represents the RP \(j\), and \(m\) is the total RP number.  
Furthermore, the fitness function can be defined as  
\[\begin{array}{c}\min F(x)=\min(f_{1}(x),f_{2}(x))\\ s.t.\ g_{1}(x)=30000-\ P_{\text{out}}|_{n_{\text{max}}}\leq 0\\ g_{2}(x)=2-\ ^{n_{\text{max}}/n_{\text{N}}}\leq 0\\ g_{3}(x)=k_{\text{s}}-0.8\leq 0\end{array}. \tag{5}\]