esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Due to the growing air quality concern in urban areas and rising fuel prices, urban bus fleets are progressively turning to hybrid electric vehicles (HEVs) which show higher efficiency and lower emissions in comparison with conventional vehicles. HEVs can reduce fuel consumption and emissions by combining different energy sources (i.e., fuel and batteries). In this sense, the performance of HEVs is strongly dependent on the energy management strategy (EMS) which coordinates the energy sources available to exploit their potential. While most EMSs are calibrated for general driving conditions, this paper proposes to adapt the EMS to the specific driving conditions on a particular bus route. The proposed algorithm relies on the fact that partial information on the driving cycle can be assumed since, in the case of a urban bus, the considered route is periodically covered. According to this hypothesis, the strategy presented in this paper is based on estimating the driving cycle from a previous trip of the bus in the considered route. This initial driving cycle is used to compute the theoretical optimal solution by dynamic programming. The obtained control policy (particularly the cost-to-go matrix) is stored and used in the subsequent driving cycles by applying one-step look-ahead roll out, then, adapting the EMS to the actual driving conditions but exploiting the similarities with previous cycles in the same route. To justify the proposed strategy, the paper discusses the common patterns in different driving cycles of the same bus route, pointing out several metrics that show how a single cycle captures most of the key parameters for EMS optimization. Then, the proposed algorithm (off-line dynamic programming optimization and one-step look-ahead rollout) is described. Results obtained by simulation show that the proposed method is able to keep the battery charge within the required range and achieve near-optimal performance, with only a 1.9% increase in fuel consumption with regards to the theoretical optimum. As a reference for comparison, the equivalent consumption minimization strategy (ECMS), which is the most widespread algorithm for HEV energy management, produces an increase in fuel consumption with respect to the optimal solution of 11%.  
hybrid electric vehicle; energy management strategy; dynamic programming +
Footnote †: journal: Applied Sciences
# Depth Segmentation Approach for Egocentric 3D Human Pose Estimation with a Fisheye Camera
## 4 Experiments
### Implementation Details
#### 4.2.2 Joint Position Module  
The joint position module takes the following three types of features as input: (i) two embedding features after encoding in the SegDepth and Heatmap modules, and (ii) the conv features of the depth map in the segmentation depth module. The joint position module first concatenates two embedding features and then concatenates them again with the depth map conv features. The total concatenated features are the inputs for the joint position module and it predicts the 3D joint positions (\(\hat{P}\in R^{16\times 3}\)) as output. The loss function of the 3D joint position module is as follows:  
\[L_{P}=\sum_{i}^{N}||P_{i}-\hat{P}_{i}||^{2}, \tag{5}\]  
\(P\) denotes the GT of the joint positions, \(N\) denotes the total number of joints, and \(\hat{P}\) denotes the predicted joint positions.  
The entire framework is trained on the sum of the losses of the modules as follows: (1), (2) and (5).  
\[L_{total}=L_{D}+\lambda_{H}L_{H}+\lambda_{P}L_{P}, \tag{6}\]  
The weights of each loss are as follows:  
\[\lambda_{d}=1e+2,\lambda_{s}=1e+1,\lambda_{H}=2e+2,\lambda_{P}=2e+2, \tag{7}\]  
These weights were chosen to balance the contributions of each module's loss term by aligning their scales to the range of 10-100. This alignment allows all modules to contribute proportionally to the total loss, which is what achieves balanced multitask learning.  
The framework was trained with the training set for 10 epochs and the batch size was 20. The training optimizer was [PERSON] [42]. The initial running rate was \(1\times 10^{-3}\) and it was halved after every 2 epochs to avoid overfitting. To match the size between the outputs of the decoders (depth map, segmentation, and heatmap) and the GT, bilinear interpolation was used. The deconv layer in the segmentation depth decoder had a kernel size = 4, stride = 2, and padding = 1 to double the size of the input image. The modified ResNet-50 was pre-trained with the ImageNet dataset [43]. All training was done on two GTX3090 GPUs and took about 10 h to complete.
## 2 Results
### Differences in Sugar Contents between High and Common Resin Yield  
As shown in Figure 2, a total of 10 sugars were detected in the needles, including D-arabinose (Ara), L-rhamnose (Rha), D-fructose (Fru), L-fucose (Fuc), D-galactose (Gal), glucose (Glu), inositol (Ino), sucrose (Suc), maltose (Mal), and trehalose (Tre). The results showed distinct differences in Ara, Rha, Fru, Fuc, Gal, Glu, Ino, and Tre between the high and common resin yields. Compared with the common resin yield, Ara, Fuc, Gal, Glu, and Ino accumulated more, whereas Rha, Fru, and Tre dramatically decreased in the high one. Sucrose content demonstrated an increasing tendency in the high resin yield, despite no statistically notable difference being investigated between them.  
Figure 1: Photosynthetic pigment contents of masson pine needles in the high and common resin yield. FW means fresh weight. “Common yield” means the common resin-yielding clone, and “High yield” means the high resin-yielding clone. Error bars refer to the standard deviation (SD) of three biological replicates. ** indicates a significant difference between the high and common ones at \(p\) < 0.01. Chl a, chlorophyll a; Chl b, chlorophyll b; Chl C, total chlorophyll; Car, carotenoids.
## 4 Discussion
A key strength of this study lies in the introduction and evaluation of novel 18F-FDG-PET parameters related to CSF glucose dynamics, which, to the best of our knowledge, have not been previously described in the literature. However, there are notable limitations to consider. CSF regions were defined using the WFU PickAtlas toolbox, which allows the identification of intraventricular spaces (lateral, third, and fourth ventricles), but does not include subarachnoid spaces. As such, our findings specifically reflect intraventricular FDG dynamics and cannot be extrapolated to extracerebral CSF compartments. Future studies employing dedicated segmentation pipelines or dynamic imaging may provide a more comprehensive assessment of global CSF glucose metabolism. Additionally, the selection of the control group, consisting of individuals with functional neurological disorder or tension-type headache, represent a potential limitation. Nonetheless, these subjects were carefully screened to exclude any abnormal CSF findings or structural/metabolic brain alterations. Additionally, the cross-sectional nature of this study limits our ability to assess causal relationships or temporal dynamics. In particular, the lack of longitudinal follow-up prevented us from determining whether the 18F-FDG-PET alterations observed were associated with a more severe trajectory of disease progression. Future longitudinal studies involving larger and more diverse populations, such as those with preclinical AD, as well as consideration of other physiological variables (e.g., diet, physical activity) that may influence brain bioenergetics and glucose transport, will be crucial to validate and expand upon these findings.
## 1 Introduction  
Recognition of the combustion state of the flame in the cement rotary kilns refers to the process of analyzing and identifying real-time images of the flame during the clinker calcination process, ensuring the smooth progress of cement production through the recognition, detection, and control of the combustion state. A flame combustion state recognition method based on a rotary kiln can provide many advantages in the cement production process. Such a method is conducive to real-time monitoring of the firing process, ensuring that workers detect abnormal conditions and take necessary measures to ensure the safety and stability of the production process. Analyzing the color, shape, and other characteristics of the flame is beneficial for detecting and evaluating the quality of cement and taking measures to improve the quality of cement production. Flame combustion images help understand the combustion efficiency, and adjusting the supply of oxygen and raw materials based on image analysis can improve production efficiency, reduce production costs, and decrease carbon emissions. However, due to complex factors such as high temperature, high pressure, and high levels of smoke inside the rotary kiln, workers' inaccurate recognition of the combustion state can affect the effective control of the calcination process. Existing machine learning-based flame combustion state recognition methods have low accuracy and poor generalization ability due to the influence of the complex environment inside the furnace. Furthermore, deep learning-based flame combustion state recognition methods face difficulties in modeling due to low data volume and severely uneven sample distributions. Therefore, this paper proposes a new method for recognizing the combustion state of cement rotary kilns that can accurately and quickly identify the flame combustion state, even with a small and unevenly distributed data volume.
# Domain Adaptation Speech-to-Text for Low-Resource European Portuguese Using Deep Learning
###### Abstract  
Automatic speech recognition (ASR), commonly known as speech-to-text, is the process of transcribing audio recordings into text, i.e., transforming speech into the respective sequence of words. This paper presents a deep learning ASR system optimization and evaluation for the European Portuguese language. We present a pipeline composed of several stages for data acquisition, analysis, pre-processing, model creation, and evaluation. A transfer learning approach is proposed considering an English language-optimized model as starting point; a target composed of European Portuguese; and the contribution to the transfer process by a source from a different domain consisting of a multiple-variant Portuguese language dataset, essentially composed of Brazilian Portuguese. A domain adaptation was investigated between European Portuguese and mixed (mostly Brazilian) Portuguese. The proposed optimization evaluation used the NVIDIA NeMo framework implementing the QuartzNet15\(\times\)5 architecture based on 1D time-channel separable convolutions. Following this transfer learning data-centric approach, the model was optimized, achieving a state-of-the-art word error rate (WER) of 0.0503.  
machine learning; deep learning; deep neural networks; speech-to-text; automatic speech recognition; NVIDIA NeMo; GPUs; data-centric; Portuguese language +
Footnote †: journal:  
0023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Intelligent Content Creation  
#### 3.3.1 Introduction to Intelligent Content Creation  
Intelligent content creation refers to the utilization of artificial intelligence, particularly multimodal large language models (MLLMs), to automate and enhance the development of educational materials. This includes generating textbooks, lesson plans, assessments, interactive simulations, and multimedia resources tailored to specific learning objectives and individual learner needs [55]. By leveraging MLLMs, educators and content developers can produce high-quality, engaging, and personalized educational content more efficiently and effectively.  
Traditional content creation in education is often time consuming and requires significant expertise. Intelligent content creation addresses these challenges by automating parts of the content development process and enabling the creation of materials that can adapt to diverse learning styles and preferences [1]. The integration of multiple modalities--text, images, audio, and video--enhances the accessibility and inclusivity of educational resources, catering to a wider range of learners.
## 3 6G State-of-the-Art
### Evolution of 6G
#### 3.2.3 HCSs
A basic goal of 6G communication is to operate battery-free whenever and wherever possible, with a target efficiency of 1 pico-joule per bit [386,387]. Apart from the benefits of high-power THz waves, 6G communication enables directed beam communication via MIMO antenna arrays, allowing devices to deliver power beams in a specific direction. This technology has the ability to provide sufficient energy to devices covered by the network. 6G trends, technologies, and applications state that the services will redefine those provided by the 5G by morphing the classic URLLC, eMBB, and mMTC and providing new services (see Figure 5). The following services will be introduced:Figure 5: Comparison between 5G and 6G. Service Classes, Their Performance Indicators, And Example Applications.
## 4 Hardware Description
### Description of Hardware Components
#### 4.2.1 Arduino Mega 2560  
The Mega 2560 is an ATmega2560 based microcontroller board [36]. It consists of 54 digital input/output pins (of which 15 can be used as PWM outputs), 16 analog inputs, 4 UARTs (hardware serial ports), 16 MHz crystal oscillator USB connection, a power jack, ICSP header, and reset button.
# A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges
## 5 Different VANET Technologies Based on Transmission Range
### Short-Range Communication Technologies
#### 5.1.4 Applications  
The short-range communicating technologies can assist various vehicular applications. Some of the application are listed below.  
* Vehicle Localisation: The GPS used in the vehicles can easily determine the position of the vehicle but in the dense urban environment its localisation becomes more challenging. However, these short-range technologies, especially UWB, can assist localisation in dense environments as they do not require the Line of Sight (LoS) and can easily penetrate in the obstacles [155]. UWB can be regarded as the technology of choice for range-based localisation especially for the vehicles moving in dense clustered environments [157].
* Real Time Driving Assistance: These short-range communication technologies can be utilised for communication between RSUs and the vehicle's OBU for gathering of real-time driving data which may help in lane assist and avoidance of road accidents [155].
* Vehicle Identification: As already discussed, these technologies consumes less energy; therefore, they can be useful to implement at toll plazas where the vehicle identification and classification plays a crucial role for tax collection [36].
* Forward Collision Warning Under Low Speed Circumstances: These short-range technologies can be used for short-range V2V communication in low speeds, which can be used to prevent collisions between the self-driving vehicles or for lane and parking assist. This technology offers the best solution to avoid forward collisions between self-driving cars at low speeds [41].
* Others: The above-mentioned technology can provide additional advantages such as identifying the car owner, detecting the position of passengers in the vehicle and deactivating the air bags on emergency cases [140; 147; 155].
## 3 Coupling Performance and Integrated Optical Modulator
Figure 3 reports integrated optical modulation performance of the proposed structure consisting of the Si\({}_{3}\)N\({}_{4}\) input waveguide, the amorphous Ge\({}_{0.83}\)Si\({}_{0.17}\) taper, the Ge-based on Si optical modulator, the amorphous Ge\({}_{0.83}\)Si\({}_{0.17}\) taper, and the Si\({}_{3}\)N\({}_{4}\) output waveguide using 3D-FDTD simulation (Lumerical Inc. Vancouver, BC, Canada) for the fundamental transverse-electric (TE) mode at the optical wavelength of 1550 nm. As shown in Figure 3a, the optical mode propagated from the input Si\({}_{3}\)N\({}_{4}\) waveguide to the output Si\({}_{3}\)N\({}_{4}\) waveguide effectively. Figure 3b shows that both the extinction ratio (ER), 10 log10((\(\mathrm{{I}}_{\mathrm{Out,On}}\)/\(\mathrm{{I}}_{\mathrm{Out,Off}}\)), and the insertion loss (IL), 10 log10(\(\mathrm{{I}}_{\mathrm{In}}\)/\(\mathrm{{I}}_{\mathrm{Out}}\)), increased as the length of the Ge-based device increased. Interestingly, the structure obtained an integrated performance of \(\sim\)5 dB ER simultaneously with \(\sim\)5 dB on-state IL with the modulator length of 15 \(\upmu\)m. This reported IL included the coupling loss between the Si\({}_{3}\)N\({}_{4}\) input (respectively output) waveguide and the Ge-based on Si device, the optical absorption due to amorphous taper, and the optical absorption due to the Ge-based on Si structure in high transmission mode. The integrated performance of the 5 dB ER and the 5 dB IL were comparable to the state-of-the-art Ge-based FKE optical modulators integrated with an SOI waveguide [7; 8; 9]. It should be noted that a higher ER/IL ratio could have been achieved with longer device. For instance, \(\sim\)6.7 dB ER and \(\sim\)5.8 dB IL could have been obtained with a 20 \(\upmu\)m long device. Nevertheless, higher optical loss due to Ge indirect-gap absorption makes this undesirable for low-power applications. Figure 3c,d report the ER and IL values with respect to the variation in the relative vertical positions between the amorphous taper and the GeSi optical modulator for the 15 \(\upmu\)m long and 20 \(\upmu\)m long Ge-on-Si optical modulator, respectively. While the ER values were relatively robust against fabrication misalignment in both cases, the IL values tolerated fabrication misalignment of \(\pm\)\(\leq\) 20 nm in order to maintain the optimized values of ER and IL simultaneously. Figure 3: **(a)** Optical propagation from Si\({}_{3}\)N\({}_{4}\) input waveguide, amorphous Ge\({}_{0.83}\)Si\({}_{0.17}\) taper, Ge-based FKE optical modulator, and amorphous Ge\({}_{0.83}\)Si\({}_{0.17}\) taper to the Si\({}_{3}\)N\({}_{4}\) output waveguide. **(b)** Extinction ratio (ER) and insertion loss (IL) at different length values of the optical modulator. ER and IL with respect to the variation in the relative vertical positions between the amorphous taper and the Ge-based on Si optical modulator for **(c)** 15 \(\upmu\)m long and **(d)** 20 \(\upmu\)m long optical modulators, respectively.
# Design and Implementation of a Two-Wheeled Self-Balancing Car Using a Fuzzy Kalman Filter
## 3 Fuzzy Kalman Filtering Algorithm Design
### Complementary Filtering Algorithm  
The complementary filtering algorithm, with its characteristics of low computational complexity, high reliability, and low precision requirements for inertial devices, is widely used in the attitude applications of various robots. The complementary filtering algorithm combines a low-pass filter and a high-pass filter, which are then overlaid to enhance the accuracy of attitude angular velocity [20]. The principle of the complementary filter is illustrated in Figure 6.  
From Figure 7, it can be observed that in the low-frequency range below the cutoff frequency of the low-pass filter, the accelerometer primarily influences, while in the high-frequency range above the cutoff frequency of the high-pass filter, the gyroscope dominates. By adjusting the cutoff frequency of the filter, the filtering effects on the gyroscope and accelerometer can be tuned. The time-domain expression of the output angular velocity after the complementary filter is as follows:  
\[\theta(k)=\lambda\theta_{m}(k)-(1-\lambda)[\theta(k-1)+\Delta t\theta^{\prime} (k)] \tag{17}\]  
where the proportional coefficient \(\lambda=\frac{\Delta t}{\Delta t+\tau}\), which represents the degree of trust different data, \(\Delta t\) is the sampling time, \(\tau\) is the time constant of the filter, the angle transformed from the accelerometer is denoted as \(\theta_{m}(k)\), the gyroscope measures angular velocity as \(\theta^{\prime}(k)\), and \(\theta(k)\) represents the angle output after the filtering algorithm is processed.
# A Novel Wireless-Netted UWB Life-Detection Radar System for Quasi-Static Person Sensing
###### Abstract  
In actual life-detection radar applications, a quasi-static person with weak respiration is difficult to find when relying on the echoes from a single fixed observation point. To effectively sense the weak respiration of a quasi-static person in complex through-wall and through-floor conditions, this paper proposes a novel multi-observation point detection system composed of multiple Golay complementary coded radars in which communication and synchronization are carried out wirelessly. The collaboration structure and Golay complementary coded transmitter improve the signal to noise ratio (SNR). Proof-of-principle experiments are carried out with our designed radar prototype and prove that the radar system can detect a respiring target 21 m behind a brick wall or a respiring target behind two levels of reinforced concrete floors, validating the effectiveness of a multi-observation point working mode for the efficient detection of weak human respiration.  
life detection radar; multi-observation point system; through-the-wall radar; Golay complementary code +
Footnote †: journal:  
operation is performed in the slow-time dimension. Meanwhile, the use of a low sidelobe sinusoidal modulation Golay complementary coded signal with a center frequency of about 1 GHz as the transmit signal can further improve the SNR. In addition, the self-positioning technology is deployed in each UWB life-detection radar to make their positions known. Finally, three experiments are carried out involving penetrating walls and reinforced concrete floors, verifying the detection performance of the proposed multi-observation point detection system.