esa-sceva/satcom-chunk-collection huggingface dataset:
# A Perfect Decomposition Model for Analyzing Transportation Energy Consumption in China
## 1 Introduction
[20; 21; 22; 23; 24; 25; 26; 27] analyzed many index decomposition approaches and pointed out the advantages of LMDI including eliminating residual term and using time independence. Many researchers analyzed the problems of the LMDI method, which occurred when processing negative numbers and zero values [28; 29; 30; 31]. A new decomposition method called the LMDI was introduced [21]. This approach could completely decompose the remaining items and deal with zeros appearing in the data set in the decomposition process. However, it is lack of the consideration of changes of intermediate demand, and it ignores the influence of the energy consumption or consumption structure changes. Another method introduced a'mean rate-of-change index' (MRC) [28] to give different weights for decomposed terms. This method provides more plausible and reasonable results, because it ensures residual-free decomposition even when data contain negative values, which cannot be handled by the LMDI method. In addition, the Shapley decomposition, which calculates the influence of factors on the energy consumption variation according to the total contribution of various factors [24], makes it possible to present a correct and symmetric decomposition without residual [32]. Thus, the residual can be resolved completely. In summary, every method has its own advantages and disadvantages. The Laspeyres and Divisia index decompositions are the most primitive methods. However, neglecting residual term is their common problem. The complete decomposition model is widely used to solve the residual, the index weight and the change of the positive and negative numbers are neglected. In LMDI decomposition, the residual term can be totally decomposed, while zero and negative values remain as a problem in data processing. Analyzing energy consumption trends and strength is beneficial to solving the problem of energy distribution imbalance and then to improving energy efficiency [33; 34; 35; 36]. The calculation results also have many errors according to different decomposition methods of the residual items. Therefore, it is necessary to seek a more scientific decomposition method to accurately analyze influence factors of energy consumption. The residual term is related to changes in both quantity and direction of influence factors, and is more likely decided by negatively changed factors. To this end, this paper proposes a perfect decomposition method that considers the changes of influence factors and the changing direction. The remainder of this paper is organized as follows: Section 2 proposes the methodology; Sections 3 and 4 describe an empirical case study and discuss the results; Conclusions are made at last.
# Study on the Control of Torque Distribution of 4 WD Corn Harvester Operation Drive
## 4 Conclusions  
The results showed that with both inter-axle and inter-wheel torque control in operation, the adjustment time for harvester drive wheel slippage was reduced by more than 35% compared to without control. When the inter-wheel torque distribution control failed to work in the case of double drive wheels slipping on the same axle, the adjustment time was still shortened by 11.1% than without control. Before the applied torque control, the speed fluctuation was obvious, the average speed of the slipping wheels was overshoot, and the maximum overshoot was 17.86% in the case of the front left wheel slip. After applied the torque control, the overshoot phenomenon was eliminated and the speed fluctuation was significantly reduced.  
The drive torque distribution control could improve the traction performance and field passing ability of the three-row 4 WD corn harvester, enhance its adaptability to the complex field environment, effectively reduce the speed fluctuation during operation, and greatly improve the harvesting quality of the harvester. In addition, real-time torque control could distribute the torque from the slipping wheels to other well-attached driving wheels, which effectively avoids power waste and plays an energy-saving role. This study also provides a new methodology for the design and improvement of drive systems on harvesters and other walking agricultural machinery.  
Conceptualization, D.Z. and B.W.; validation, P.H., Y.X. and X.L.; writing--original draft, P.H.; writing--review and editing, D.Z. and B.W.; supervision, J.Z., H.Y. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Key Projects of Science and Technology Development Plan of Jilin Province, China (Grant no. 20170204015 NY); Provincial School Joint Construction Project of Jilin Province, China (Grant no. SXGJX2017-6); the National Key Research and Development Program of China (Grant no. 2016 YFD070190103).  
The authors declare no conflict of interest.
## 4 Active Systems
### From Short- to Long-Range Communications
It occupies a frequency band width of 200 kHz, which corresponds to one resource block in GSM and LTE transmission. Regarding its data rate, it is limited to 200 kbit s\({}^{-1}\) for downlink and 20 kbit s\({}^{-1}\) for uplink, and each message can carry 1600 bytes. As it operates in the licensed spectrum, the network is not subject to unwanted interference. Different from LoRa or Sigfox, NB IoT also handles low bidirectional latency, at the expense of increased energy consumption. When transmitting, its power consumption can vary from 70 to 220 mA according to the transmitted power [119; 137]. Table 1 presents an overview of the differences and unique characteristics of some active communication systems. From this table, it is possible to understand which systems are more suitable for long-range, high data rates, or low power communications. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Frequency Band** & **Range** & **Data Rate** & **Consumption** \\ \hline NFC & 13.56 MHz & 10 cm & 400 kbit/s & 60 mA \\ BLE & 2.4 GHz & 1 Km & 2 Mbit/s & 7.5 mA \\ ZigBee & 2.4 GHz & 100 m & 250 kbit/s & 10 mA \\ Wi-Fi & 2.4, 3.6, 5 GHz & 100 m & 1.3 Gbit/s & 240 mA \\ Mobile Networks (\(\alpha\)/\(\alpha\)) & 900 MHz, 18 GHz, 2.1 GHz & - & 100 kbit/s (2C), 8 Mbit/s (3G) & 330 mA \\ Sigfox & 868 MHz & 10 Km & 100/600 bit/s & 49 mA \\ LoRa & 868 MHz & 10–15 Km & 50 kbit/s & 39 mA \\ NB-IoT & 700, 800, 900 MHz & - & 200/50 kbit/s & 220 mA \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between communication interfaces
## 4 Experiment Results
### CMU-KO8 Dataset Experiment  
For evaluation the robustness of OCM-DRW with respect to occlusion, the next experiment involves the extremely cluttered and occluded CMU Kitchen Occlusion dataset (CMU-KO8) assembled by [PERSON] and [PERSON] [36]. This dataset consists of common household objects in a more natural setting, and cluttered environments under various levels of occlusion, making this dataset very challenging. It contains 8 texture-less kitchen-wares models, together with 100 scene images for each model in both single view and multiple view situations. In addition, each single-view scene image holds only one instance of a model object and contains ground truth labels of the occlusions. According to the dataset, [PERSON] and [PERSON] propose three different variants of LINE2D (rLINE2D, rLINE2D+OPP and rLINE2D+OCLP) [36]. For OCM-DRW evaluation, each model does not have significant scale and  
Figure 11: Recognition results from D-Textureless dataset based on OCM-DRW, BORDER and BOLD.  
(**a**) OCM-DRW successfully detects; (**b**) BORDER fails to detect; (**c**) BOLD also fails to detect.  
Figure 10: Recognition results using different approaches on texture-less datasets.  
rotation changes over the test images in the single-view dataset. Therefore, only 9 templates are trained by the scale and rotation transformation, which creates templates by rotating the template in the range of \([-5^{\circ},5^{\circ}]\) with \(5^{\circ}\) increment and scaling the template in the range of [95%, 105%] with 5% increment. We attain the results of OCM-DRW, BORDER, BOLD, LINE2D and rLINE2D+OCLP in terms of recall versus FPPI (False Positives PerImage) scheme [18] as portrayed to assess the detection rate in the single-view scene. In addition, rLINE2D+OCLP is used to compare with other detectors due to the best recognition result of it among three different variants of LINE2D. SIFT is not included because of the lack of keypoint information for most of the models in this dataset. The similarity measure used by OCM-DRW make this method have good robustness of clutter, which explained in expression (6). Figure 10b shows the performance of OCM-DRW over the others in the single-view database, also revealing OCM-DRW's robustness in heavy occlusion and clutter. OCM-DRW performs better than BORDER and BOLD, it can detect the target object partially covered in the cluttered background as shown in Figure 12.
# A Framework for Communicating and Building a Digital Twin Model of the Electric Car
###### Abstract  
The Fourth Industrial Revolution has had a huge impact on manufacturing processes and products. With rapidly growing technology, new solutions are being implemented in the field of digital representations of a physical product. This approach can provide benefits in terms of cost and testing time savings. In order to test and reflect the operation of an electric car, a digital twin model was designed. The paper collects all the information and standards necessary to transform the idea into a real and virtual model of an electric car. The significance and impact of the study on the improvement of the project are described. The research stand, correlations of components (DC and AC motors, shaft, and wheel of the electric car), and development prospects are presented in the paper. The communication method with the research stand is also presented. The digital twin should communicate in real time, which means obtaining the correct output when the input changes; the input is the AC motor current, and the output is the rotational speed of the DC motor. The relation between inputs and outputs are tested. The kinematics of the electric car are modelled in LabVIEW. The results obtained are compared with historic racing data. The track is also modeled based on satellite data, taking into account changes in terrain height, using the SG Telemetry Viewer application. The parameters of the electric car engine model are tuned based on actual data on the car's speed and current in the electric motor. The achieved results are presented and then discussed.  
digital twin; simulation; vehicle-in-the-loop; Modbus TCP/IP; frequency inverter +
Footnote †: journal: Applied Sciences
_applied sciences_  
_Article_  
**Improving a Fuel Cell System's Thermal Management by Optimizing Thermal Control with the Particle Swarm Optimization Algorithm and an Artificial Neural Network**  
[PERSON] 1,2, [PERSON] 1, [PERSON] 1,3\({}^{\copyright}\), [PERSON] 1 and [PERSON] 1,3,*  
1 School of Automation Engineering, University of Electronic Science and Technology of China,  
Chengdu 611731, China; [EMAIL_ADDRESS] (B.D.); [EMAIL_ADDRESS] (X.Z.);  
[EMAIL_ADDRESS] (C.Y.); [EMAIL_ADDRESS] (Y.L.)  
2 New Energy Technology of CAERI Co., Ltd., Chongqing 401122, China  
3 Hydrogen and Fuel Cell Institute, University of Electronic Science and Technology of China,  
Chengdu 611731, China  
* Correspondence: [EMAIL_ADDRESS]  
**Abstract:** The thermal management of proton exchange membrane fuel cell systems plays a significant role in a stack's lifetime, performance, and reliability. However, it is challenging to manage the thermal system precisely due to the multiple coupling relationships between the stack's components, its operating environment, and its thermal management system. In addition, temperature hysteresis (temporal inconsistency of temperature with electrochemical reactions and fluid mechanics) imposes more difficulties on thermal control. We aim to develop an effective thermal control model for the fuel cell system to improve the temperature regulation accuracy and response speed and thus achieve highly stable temperature control. A dynamic mechanistic model is first developed based on the physical processes of the stack and its thermal management system. The model is then validated through experiments. Based on this dynamic mechanistic model, a control model is proposed for stack thermal management with the particle swarm optimization algorithm and an artificial neural network. It is applied and compared with the traditional PID algorithm. The simulation results indicate that the regulation time of the coolant inlet temperature as the current changes is reduced by more than 74%, and the overshoot is reduced by more than 50%. Therefore, the control model can enhance the dynamic response capability and temperature control precision under complex operating conditions with constantly changing load current and preset stack temperature, ensuring the temperature's stability and thus improving the fuel cell system's reliability and durability.  
**Keywords:** proton exchange membrane fuel cell; dynamic thermal management; particle swarm optimization; artificial neural network; control model
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 4 Analysis of Link Connectivity and Probability of Coverage
### Link Connectivity  
We denote the probability of connection of an SU pair by \(p_{con}\). According to the above analysis of the maximum communication distance \(r_{\max}\) in Section 4.1 and the probability that a pair of SUs have the spectrum \(p_{spe}\) in Section 4.2, we have the \(p_{con}\) as follows:  
\[p_{con}=\left\{\begin{array}{ll}p_{spe}&r\leq r_{\max}\\ 0&r>r_{\max}\end{array}.\right. \tag{11}\]  
Combining Equation (11) with Equation (10), we can obtain \(p_{con}\) if \(2r_{d}<r_{\max}\) as follows:  
\[p_{con}=p_{top}\cdot p_{spe}=\left\{\begin{array}{ll}e^{\left(\pi r_{d}^{2}+ (\pi-\theta_{0})r_{d}^{2}+r_{d}\sin(\frac{\theta_{0}}{2})r\right)\lambda_{p}}&r \leq 2r_{d}\\ e^{2\pi r_{d}^{2}\lambda_{p}}&2r_{d}<r\leq r_{\max}\\ 0&r>r_{\max}\end{array}.\right. \tag{12}\]  
If \(2r_{d}>r_{\max}\), \(p_{con}\) can be expressed as:  
\[p_{con}=\left\{\begin{array}{ll}e^{\left(\pi r_{d}^{2}+(\pi-\theta_{0})r_{d} ^{2}+r_{d}\sin(\frac{\theta_{0}}{2})r\right)\lambda_{p}}&r\leq r_{\max}\\ 0&r>r_{\max}\end{array}.\right. \tag{13}\]
## 3 Results
### Kinematic Behavior of Coupling System with LBB and without LBB
#### 3.2.1 Head Behavior Using the Coupling System  
The accelerometer located in the center of gravity of the head's dummy recorded accelerations every 1 ms during the 120 ms in which the frontal impact analysis was performed.  
The values obtained by [PERSON] et al. show that the resulting acceleration of the head increases earlier for the LBB, starting at 23 ms, increasing from 2 G to 34 G in 3 ms. At 38 ms, an acceleration of 57 G is reached, and at 62 ms, the maximum acceleration of 62 G is achieved, generating a Head Injury Criterion HIC\({}_{36}\) of 554.3. In the mechanical coupling with LBB, due to the movement that the system allows, the acceleration remains below 10 G during the first 24 ms, and from this point onwards, it begins to increase until it reaches 20 G in 9 ms. The maximum acceleration is achieved at 53 ms, reaching a value of 53 G and obtaining a HIC\({}_{36}\) of 421.5, relative to 9 G. The LBB shows the maximum value and a difference in the HIC\({}_{36}\) of 132.8 (Figure 11).
# A Novel Cascaded Modular Photovoltaic Energy Storage System for Partial Shading Conditions
## 2 Topology, Operation Principle and Optimization Strategy of Proposed PV/ES System
### Control Strategy of PV/ES Module  
The topology and control strategy of an individual PV/ES module is presented in Figure 3. Each PV/ES module contains a PV module, an ES module, and a bidirectional DC/DC converter. As the PV/ES modules are connected in series, as shown in Figure 2, all the currents of the PV/ES modules are equal to the current of the DC bus. However, the currents of the PV modules, which depend on the respective light intensities, are different under partial shading conditions. The current differences between the PV modules and the DC bus can be compensated by the corresponding DC/DC converters as follows:  
\[\left\{\begin{array}{l}i_{\text{bus}}=i_{\text{pv }j}+i_{\text{com }j}\\ j=1,2,\ldots,J\end{array}\right. \tag{1}\]  
where \(i_{\text{bus}}\), \(i_{\text{pv}}\), and \(i_{\text{com}}\) are the currents of DC bus, PV module, and converter, respectively; \(J\) is the number of modules; and the subscript \(j\) represents the \(j\)-th of the PV module.  
Figure 2: The topology of proposed cascaded modular PV/energy storage (ES) system.  
As all of the PV/ES modules are connected in series, the voltage of the DC bus can be obtained as follows:  
\[u_{\text{bus}}=\sum_{j=1}^{\text{J}}u_{\text{PV}\ j} \tag{2}\]  
where \(u_{\text{bus}}\) and \(u_{\text{PV}}\) are the voltages of DC bus and PV module, respectively.  
According to Figure 3, the terminal voltage of the PV module is the same as the voltage of its parallel capacitor controlled by the DC/DC converter. The independent MPPT of each PV module can be achieved by controlling the output voltage of the corresponding DC/DC converter according to the respective light intensity. Therefore, in the proposed cascaded modular PV/ES system, all of the PV modules can operate at their own maximum power point, ensuring that the entire PV/ES system can obtain the maximum solar energy under partial shading conditions.  
To realize the MPPT of an individual PV module, the control strategy of the bidirectional DC/DC converter is also designed, as shown in Figure 3, and the process of the MPPT algorithm based on the incremental conductance method is shown in Figure 4. The MPPT algorithm is adopted to generate the voltage reference value for the PV module according to its voltage and current. A double closed-loop control strategy is designed to control the voltage of the PV module so as to follow its reference value. The PI regulator of the outer loop generates the reference current of the inductance according to the deviation between the reference voltage and the actual voltage of the PV module. Furthermore, the PI regulator of the inner loop generates the duty ratio of the DC/DC converter according to the deviation between the reference current and the actual current of inductance.  
Figure 4: Control process of the maximum power point tracking (MPPT) in the proposed PV/ES system.  
Figure 3: Topology and control strategy of a PV/ES module.
## 4 Experiments
### Repeatability Tests  
Five additional experiments were repeated based on the original PID-Net model for repeatability tests. The evaluation indices are given in Table 5. From Table 5, we find that all evaluation indices of repeated PID-Nets are approximate, which shows satisfactory and stable counting performance for the dense tiny object counting task.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **A** & **D** & **J** & **P** & **C** & **H** \\ \hline PID-Net & 97.51 & 95.86 & 92.10 & 96.02 & 96.97 & 4.6272 \\ PID-Net (Re 1) & 97.51 & 95.79 & 91.97 & 95.91 & 95.26 & 4.5865 \\ PID-Net (Re 2) & 97.33 & 95.59 & 91.62 & 95.70 & 96.25 & 4.7290 \\ PID-Net (Re 3) & 97.54 & 95.91 & 92.18 & 96.21 & 96.82 & 4.6023 \\ PID-Net (Re 4) & 97.37 & 95.64 & 91.70 & 95.70 & 95.51 & 4.7471 \\ PID-Net (Re 5) & 97.43 & 95.66 & 91.73 & 92.24 & 96.26 & 4.6395 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The evaluation indices of Repeatability Tests. A, D, J, P, C and H are abbreviations of the Accuracy, Dice, Jaccard, Precision, Counting Accuracy (in %) and Hausdorff Distance (in pixels/per image), respectively.  
Figure 9: An example of segmentation images predicted by different models.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
## 2 Methodology  
### Model and Task  
To conduct the experiments, we used the LLaMA3 model with 8 billion parameters (LLaMA3-8B). It is an auto-regressive language model, a pretrained generative text model. Its context length is 8 K, and the token count is over 15 trillion. It uses grouped-query attention (GQA) to improve the inference scalability. We used a sentiment-analysis-based sequence classification task to evaluate the performances of the base, LoRA, and QLoRA-enabled versions of the LLaMA3-8B model. LoRA and QLoRA allow the training of this enormous 8 B parameter-sized model on a single A100 GPU.  
We implemented the experiments, using the Llama-3-8B checkpoint in the Hugging-Face library. We used the AutoModelForSequenceClassification class to automatically instantiate and load the Llama-3-8B model (LlamaForSequenceClassification) for sequence classification. As a result, it uses a five-class classification head. The Llama-3 model is not primarily meant for sequence classification. Therefore, when loading the Llama for  
Figure 1: Comparison of (**a**) fine tuning, (**b**) LoRA, and (**c**) QLoRA.  
this task, we obtain a warning message that the model should be fine tuned (trained on a downstream task) first before being used for predictions and inferences.  
In QLoRA, the parameters are stored in a 4-bit format. In the forward pass, logits and losses are computed. In the backward pass, the gradients of the loss concerning the trainable parameters are computed. Cross-entropy was used to compute the loss. After each batch, the model parameters are updated, and the gradients are reset. In LoRA and QLoRA, the gradients of only the injected parameters are computed, and, therefore, only the low-rank matrices are updated.
# Learning-Based Traffic Scheduling in Non-Stationary Multipath 5G Non-Terrestrial Networks
###### Abstract  
In non-terrestrial networks, where low Earth orbit satellites and user equipment move relative to each other, line-of-sight tracking and adapting to channel state variations due to endpoint movements are a major challenge. Therefore, continuous line-of-sight estimation and channel impairment compensation are crucial for user equipment to access a satellite and maintain connectivity. In this paper, we propose a framework based on actor-critic reinforcement learning for traffic scheduling in non-terrestrial networks scenario where the channel state is non-stationary due to the variability of the line of sight, which depends on the current satellite elevation. We deploy the framework as an agent in a multipath routing scheme where the user equipment can access more than one satellite simultaneously to improve link reliability and throughput. We investigate how the agent schedules traffic in multiple satellite links by adopting policies that are evaluated by an actor-critic reinforcement learning approach. The agent continuously trains its model based on variations in satellite elevation angles, handovers, and relative line-of-sight probabilities. We compare the agent's retraining time with the satellite visibility intervals to investigate the effectiveness of the agent's learning rate. We carry out performance analysis while considering the dense urban area of Paris, where high-rise buildings significantly affect the line of sight. The simulation results show how the learning agent selects the scheduling policy when it is connected to a pair of satellites. The results also show that the retraining time of the learning agent is up to 0.1 _times_ the satellite visibility time at given elevations, which guarantees efficient use of satellite visibility.  
non-terrestrial networks; satellites; link prediction; reinforcement learning; actor-critic; multipath +
Footnote †: journal: remote sensing  
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