esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Discussion and Conclusions  
In accordance with the requirements of the survey vessel Polaris, we proposed a novel optical design with prime and zoom lenses and a high resolving power in the full light spectrum in shallow water and the blue-green light spectrum in deep water. We specifically applied a genetic algorithm and Zemax software to optimize a 3x zoom lens for use in deep water with a minimized chromatic aberration value. This camera can prevent sensitive chromatic aberration and maintain its imaging quality during underwater surveying when spectra change.  
Both the prime and zoom lenses achieved the following experimental results:  
First, the developed lenses enabled independent operation without an airtight box. In the past, cameras were placed in waterproof airtight boxes for underwater photography, which caused problems such as chromatic aberration and focus-point displacement and made deep-water photography difficult. Therefore, we proposed an optical design for both prime and zoom lenses. The prime lens had a focal length of 75 mm and an aperture of f/2, and the zoom lens had an effective focal length of 5-15 mm and an aperture of f/2.8-f/4.5. Conventional waterproof boxes were not used this optical system. We adopted a waterproof dome window on the outer surface as the basic structure and optimized it in accordance with different water depths and light spectra to obtain a distortion within \(\pm\)2% and high-resolution underwater imaging quality.  
Second, both the prime and zoom lenses could reach at least a 20% increase in resolving power at a cutoff frequency of 70 lp/mm compared with conventional cameras with waterproof doom.  
The zoom lens was optimized using the genetic algorithm and designed to be applicable at all water depths. Compared with conventional underwater cameras, its volume and weight were reduced by more than 50% and 60%, respectively, and its resolving power was increased by 30-40%.  
[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON], carried out the experiment and wrote the manuscript with support from [PERSON] ([PERSON]) and [PERSON]. [PERSON] fabricated the sample [PERSON]. ([PERSON]) helped supervise the project. [PERSON] and [PERSON] conceived the original idea, supervised the project. [PERSON] is the head of Polaris vessel in this research. His contribution is related to funds and supervision. All authors have read and agreed to the published version of the manuscript.  
This study was part of the Ministry of Science and Technology project number MOST108-2221-E-992-088, MOST 109-2221-E-005-074, MOST 109-2218-E-005-012, MOST 110-2221-E-005-054-MY2, MOST 109-2221-E-005-074 and the 2019 Ocean Characteristic Cross-Campus Research Project (No. 108D12) and  
Data Availability Statement: Due to the nature of this research, participants of this study did not agree for their data to be shared publicly, so supporting data is not available.  
The authors declare that they have no conflict of interest.
# Entropy-Based Dynamic Rescoring with Language Model in E2E ASR Systems
## 3 Related Works
### Rescoring with Pretrained LM  
The objective of ASR is producing the most likely sequence of tokens based on the input feature sequence; in most of the cases, ASR systems produce output tokens (words) one by one. In a neural-network-based E2E system, after a history of recognized words, it would produce the probability distribution over the candidate tokens. To realize efficient search, beam search is often applied to find the token sequence with the highest probability.  
In practice, however, beam search receives a score which is a likelihood of the E2E model's output tokens. Thus, a LM can be integrated into the beam-search process by simply adding the likelihood of LM output tokens to the original score.  
\[\begin{split} s(y|x)=&\log(P_{E2E}(y|x))\\ &+\alpha\log(P_{LM}(y))+\beta\text{penalty}(|y|_{c})\end{split} \tag{5}\]  
In Equation (5), \(s(y|x)\) is the final score of output sequence \(y\) based on input features \(x\), and \(\beta\text{penalty}(|y|_{c})\) is a penalty which is a function of the \(y\) length, aiming at suppressing longer candidates since longer sequences tend to include meaningless words, such as ah. \(\alpha\) and \(\beta\) are hyper-parameters to control the importance of each term in this equation.
## 4 Analysis of Results
### Spatial Persistence Analysis of Vegetation NDVI  
The Hurst value of 80.94% of the area in Ordos City is in a range of 0.3-0.5 (Figure 3), and the mean value of the index is 0.43, showing an opposite development trend from the past, the area in which the index is greater than 0.5 are mainly distributed in the northern part of Ordos City as well as the eastern and southeastern areas. Combined with trend analysis, the Hurst index is classified in Table 1. The results show that only 0.03% of the vegetation in Ordos City shows random changes in the future and is spatially scattered; 1.65% of the vegetation in the area shows anti-continuous degradation, mainly distributed in the north along the Yellow River, Dongsheng District, Kangbashi District, Etoke Front Banner, and parts of the southern part of Hangjin Banner and northern part of Etoke Banner; the area with continuous degradation only accounts for 0.45%, mainly distributed in Dalat Banner and parts of northern Hangjin, and scattered in Ejin Horo Banner; in the future, it is worth noting that the area with anti-continuous improvement accounts for the larger partition, about 82.88%, and it is almost distributed in all Ordos City; the area with continuous improvement accounts for 14.99%, scattered in the whole city, mainly in the eastern region.  
In order to further analyze the future vegetation NDVI in Ordos City, three seasons of spring, summer and autumn were analyzed, respectively, as shown in Figure 4. The average Hurst index value is 0.41 in spring, 0.42 in summer, and 0.43 in autumn. The anti-continuous improvement phenomenon accounts for 88.94% in spring, 79.14% in summer, and 82.84% in autumn, the future change of each season is dominated by the anti-continuous improvement phenomenon. Among the three seasons of spring, summer, and autumn, the relative proportion of stochastic changes that occurred in spring is greater than that in summer and autumn; the relative proportion of anti-continuous degradation phenomenon occurred in summer is greater than that in spring and autumn, the relative proportion of continuous degradation phenomenon occurred in summer is also greater than that in other two seasons; the relative proportion of continuous improvement phenomenon occurred in spring is lower than that in other two seasons. Compared with the interannual data,  
Figure 3: Spatial distribution and future evolution trend of vegetation NDVI Hurst index in Ordos City.  
the proportion of areas where continuous improvement occurred in spring and autumn is larger.
# Advancing Crop Yield Predictions: AQUACROP Model Application in Poland's JECAM Fields
###### Abstract  
This study, employing the AquaCrop model, demonstrated notable efficacy in assessing and predicting crop yields for winter wheat, maize, winter rapeseed, and sugar beets in the Joint Experiment for Crop Assessment and Monitoring (JECAM) test area of Poland from 2018 to 2023. In-situ measurements, conducted through field campaigns, included parameters such as electromagnetic radiation reflectance, Leaf Area Index (LAI), soil moisture, accumulated photosynthetically active radiation, chlorophyll content, and plant development phase. The model was calibrated with input data covering daily climatic parameters from the ERA5-land Daily Aggregated repository, crop details, and soil characteristics. Specifically, for winter wheat, the Root Mean Square Error (RMSE) values ranged from 1.92% to 14.26% of the mean yield per hectare. Maize cultivation showed RMSE values ranging from 0.21% to 1.41% of the mean yield per hectare. Winter rapeseed exhibited RMSE values ranging from 0.58% to 17.15% of the mean yield per hectare. In the case of sugar beets, the RMSE values ranged from 0.40% to 1.65% of the mean yield per hectare. Normalized Difference Vegetation Index (NDVI)-based predictions showed higher accuracy for winter wheat, similar accuracy for maize and sugar beets, but lower accuracy for winter rapeseed compared to Leaf Area Index (LAI). The study contributes valuable insights into agricultural management practices and facilitates decision-making processes for farmers in the region.  
A Note: [[https://www.mdpi.com/journal/agronomy/1404854](https://www.mdpi.com/journal/agronomy/1404854)]([https://www.mdpi.com/journal/agronomy/1404854](https://www.mdpi.com/journal/agronomy/1404854))  
Acticle Advancing Crop Yield Application in Poland's JECAM Fields
## 4 Discussion
Despite being relatively small, the dataset employed in this work is one of the largest public collections providing both spatial, temporal, and kinematic data of patients affected by different neurodegenerative diseases, and it also comprises a dataset of healthy controls. This constrains the possibility of conducting a fully stratified analysis; nevertheless, this study can serve as a preliminary step toward achieving such an objective. In future work, a more granular examination of subgroups may be possible with larger and more diverse datasets. Although we acknowledge that, from a purely clinical point of view, distinguishing among pathological gait patterns may not always be necessary once a diagnosis is established, our work is not intended to replace standard diagnostic procedures. Rather, it seeks to complement them by leveraging ML models and XAI techniques to uncover objective, discriminative gait features across different neurodegenerative diseases. In this regard, the classification of pathologies based on gait patterns holds promising potential for practical applications. For instance, this approach could be used as an auxiliary tool in screening programs aimed at identifying individuals at risk for specific neurodegenerative conditions, aiding early clinical decision making. Moreover, the use of standardized, data-driven gait analysis opens possibilities for remote patient monitoring and integration into telemedicine platforms, supporting longitudinal assessments and follow-up in a home setting, which are particularly relevant for patients with limited mobility or living in remote areas. This framework may offer valuable support in complex (e.g., patients showing low engagement) or ambiguous cases where the clinical presentation may not point clearly to a specific condition, and it can also assist in tracking disease progression or evaluating treatment efficacy over time.
## 1 Introduction
Mass production, by nature, requires frequent design iteration and continuous testing during the beginning phase, when a new batch of satellites is produced due to the required tests and verifications to verify the design [1]. These necessitate the frequent assembly and disassembly of satellite subsystems. Here, easy reconfigurability plays a key factor in determining the speed of the integration. The arrangement of components and the nature of interconnectedness inside the satellite affects the speed of the development when it comes to easy access. However, in the subsequent development, as the learning curve increases, the ease and small number of assembly steps become significant. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Organization** & **Number Launched** & **Target Number** & **First Launch** & **Form Factor** \\ \hline Planet Labs & 519 & \(>\)150 & 2013 & 3U \\ Spire & 160 & \(>\)150 & 2013 & 3U \\ GeoOptics & 9 & 50 & 2017 & 6U, 12U \\ Helios Wire & 5 & 30 & 2017 & 16U \\ Swarm Technologies & 177 & 150 & 2018 & 1U/4U \\ Kepler Communications & 19 & 140 & 2018 & 3U, 6U \\ Fleet Space & 7 & 140 & 2018 & 1.5U, 3U, 12U \\ Astrocst & 12 & 80 & 2018 & 3U \\ Aistech & 4 & 20 & 2018 & 6U, 2U \\ Guudian & 15 & 38 & 2018 & 6U \\ KuvaSpace & 3 & 100 & 2023 & 6U, 2U \\ Lacuna Space & 6 & 240 & 2019 & 3U, 6U \\ Saffevolution & 7 & 1024 & 2019 & 6U, 2U \\ UnseenLabs & 7 & 50 & 2019 & 6U \\ Kleos Space & 12 & 40 & 2020 & CUBESAT \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Commercial CubeSat constellations with more than two functional spacecraft in orbit as of July 2022 [8]. On the other hand, an increase in the number of structural parts increases the inter-connectivity of parts, which also increases interdependency. Any problem with one of the components may affect the adjacent connected parts. Hence, the implemented mechanical interface between the internal subsystems and structure plays a crucial role in speeding up the integration and delivery of the final products. In the present paper, a unique interface design is introduced to tackle the issues associated with the most common mechanical interface.
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 3 Diagnosability Evaluation Based on Structural Analysis
### Diagnosability Evaluation Analysis  
Both qualitative and quantitative evaluations of diagnosability have their scope of application. From the point of view of the amount of diagnosability information, quantitative evaluation provides more information with which to determine whether a fault can be diagnosed and to quantitatively characterize the difficulty of fault diagnosis. In contrast, qualitative evaluation can only determine whether a fault has been detected or isolated. From the designer's point of view, quantitative evaluation is more valuable. However, qualitative evaluation is more efficient from the testing point of view. In terms of evaluation difficulty, qualitative evaluation favors a structural and functional approach to the system. Therefore, qualitative evaluation requires a higher level of a priori knowledge, such as the structure and function of the system. In contrast, quantitative evaluation favors a data-processing perspective and requires more data analysis and processing ability.  
The diagnosability evaluation is a characterization of the ability to diagnose faults. Suppose the diagnosability index of a fault is more significant than its threshold and that the difference between the two is enormous. In that case, the difficulty of fault diagnosis is minor, and more straightforward methods can be used. Suppose the diagnosability index of the fault is more significant than its threshold value, but the difference between the two is insignificant. In that case, the difficulty of fault diagnosis is enormous, and it is necessary to design precise and complex methods. Suppose the fault diagnosability index value takes less than its threshold value and is close to zero. In that case, it indicates that the system provides less fault information. The structure and function of the system need to be optimized and designed to increase the fault information [46]. It can be seen that the diagnosability evaluation is the basis of diagnosability design, and diagnosability design is the basis of diagnosability evaluation. Diagnosability evaluation and design promote each other and work together to improve fault diagnosis performance.
# Bridge Crack Detection Based on SSENets
## 2 Materials and Methods
### Skip-Squeeze-and-Excitation Module
#### 2.3.1 Skip-Connection  
The appearance of VGGNets [33] proves that the performance of network increases with the increase of network depth. However, with the increase of network depth, vanishing gradient would appear. The essence that CNNs can iterate continuously is the back propagation of parameters. The chain rule of back propagation will make the gradient less than 1 close to 0 after iteration, so that the parameters far from the output layer cannot be undated. Therefore, it is impossible to increase the number of network layers without limitation in order to improve the network performance.  
Figure 3: Structure of Skip-Squeeze-Excitation (SSE) Module.  
Figure 2: Illustration of the crack detection network, SSENets.  
To alleviate the vanishing gradient caused by the depth increase of the network, this paper designs the SSE module using the skip-connection strategy. SSE module selects the feature map of different depths as input, and uses the channel weight \(\text{d}^{\prime}\) generated by the shallow layer to recalibrate the feature map \(\text{FM}_{j}\) generated by the deeper layers. This strategy can increase the gradient correlation of the model, and alleviate the vanishing gradient of CNNs with the increase of the depth of the model. Therefore, it makes the model easier to optimize, and improves the detection accuracy. The simplified model of SSE module is shown in Figure 4. We assume that the input of the model is \(x_{n}\), the output is \(x_{n+2}\) after two hidden layers. The formula of \(x_{n+2}\) is shown in Equation (1):  
\[x_{n+2}=x_{n+1}\odot\mathcal{F}(x_{n+1},W_{n+1}), \tag{1}\]  
where \(W_{n}\) represents the parameters of the hidden layer. Operator \(\odot\) represents Hadamard product of the matrix. From the chain rule, the partial derivative of loss function _Loss_ to parameter \(W_{n}\) is shown in Equation (2):  
\[\frac{\partial\textit{Loss}}{\partial W_{n}}=\frac{\partial\textit{Loss}}{ \partial x_{n+2}}\cdot\frac{\partial x_{n+2}}{\partial x_{n+1}}\cdot\frac{ \partial x_{n+1}}{\partial W_{n}}=\frac{\partial\textit{Loss}}{\partial x_{n+ 2}}\bigg{[}\mathcal{F}(x_{n+1},W_{n+1})+x_{n+1}\cdot\frac{\partial}{\partial x _{n+1}}\mathcal{F}(x_{n+1},W_{n+1})\bigg{]}\frac{\partial x_{n+1}}{\partial W_ {n}}, \tag{2}\]  
It can be seen from the formula that the square brackets contain two items, even if the partial derivative \(\frac{\partial}{\partial x_{n+1}}\mathcal{F}(x_{n+1},W_{n+1})\) approaches 0 with the increase of iteration times and the depth of the model, \(\frac{\partial\textit{Loss}}{\partial W_{n}}\) won't be 0. Therefore, our model can alleviate vanishing gradient of the network.
# A Smart Approach to Electric Vehicle Optimization via IoT-Enabled Recommender Systems
## 4 Numerical Results and Discussion  
Table 1 presents the parameter setup used for simulation. This section details electric vehicle hardware simulation outcomes with two, three, four, and six phases under distinct input voltages, precisely 20 V and 200 V. The focus is on efficiency and power loss computations at various stages, particularly the output voltage during the fall phases of the proposed model at a 20 V input. Table 2 presents the Output parameter value such as voltage, current and power for the input voltage of 20 V) and Table 3 presents the Output parameter value such as voltage, current and power for the input voltage of 20 V). In the case of the two-phase model, the output voltage stabilizes at 38.74 V within 0.2 s, as indicated by waveform data. A comparative analysis is presented for output current and power at a 20 V input, revealing a power output of 46.914 W and an output current of 1.211 A. Moving on to the three-, four-, and six-phase configurations, the respective output voltages settle at 38 V, 39 V, and 40 V within varying time intervals of 0.15, 0.11, and 0.05 s. Corresponding power and output current values for the third, fourth, and sixth phases are 47 W, 48 W, 47.5 W, and 1.215 A,  
In addition to performance metrics, reliability measures are assessed using the anticipated model during experimental outcomes. This includes evaluations of charging efficiency, packet loss, end-to-end delay, and throughput. Notably, the packet loss ratio is employed to assess the effectiveness of the upgraded DVMRP protocol. In the communication process, electric vehicles utilize hop count and direct communication when a base station is involved. Live network performance reliability is verified by tracking the enhanced DVMRP protocol's throughput and end-to-end delay statistics. These parameters are summarized in Table 1.  
The output parameters derived from the MATLAB 2020a simulation at different phases are presented in Tables 2 and 3. The results in Tables 2 and 3 show that as the number of phases grows, so do the output characteristics--voltage, current, and power.  
This observed pattern can be attributed to the diminishing switching stress resulting from the concurrent reduction in induction current. As the number of phases grows, the decline in induction current contributes to a reduction in switching stress. Consequently, the simulation results show a corresponding decrease in power losses and an increase in the efficiency of the models under consideration. This insight underscores the positive impact of a higher number of phases on the overall performance, suggesting improved efficiency and diminished power losses in the simulated models.
# The Impact of 6G-IoT Technologies on the Development of Agriculture 5.0: A Review
## 4 The Future of Farming: 6G-IoT Applications in Agriculture 5.0  
Agriculture 5.0 stands to benefit greatly from the development of 6G wireless networks, which are anticipated to increase network coverage, boost data rate, and decrease latency. 6G networking will serve as a stepping stone for enhancing IoT sensor connectivity, paving the way for revolutionary new smart agricultural solutions [98]. In Figure 5, there are several critical areas in which Agriculture 5.0 can gain significant advantages by implementing 6G-IoT technology. The adoption of 6G networks can considerably facilitate the deployment of smart agricultural applications. These applications include real-time crop monitoring, virtual plant disease assessment, predictive crop maintenance, AI-based learning methods and prediction models, augmented reality applications, and dependable and decentralized farming operations. The following sections will discuss the impact of 6G networks on these smart agricultural applications.
# Adaptive Hybrid Storage Format for Sparse Matrix-Vector Multiplication on Multi-Core SIMD CPUs
## 8 Conclusions  
We presented a new hybrid SpMV storage format, HYB5, which is designed to utilize the hardware parallelism provided by modern multi-core CPUs. HYB5 aims to bring the best of CSR5 and SELL-C-\(\sigma\). It achieves this by storing non-zero matrix elements with irregular patterns in the CSR5 format and the remaining in the SELL-C-\(\sigma\) format. This hybrid scheme allows one to employ an efficient parallel kernel to perform SpMV on regular non-zero elements and use the CSR5 to mitigate the impact of irregular matrix accesses. One of the key challenges of our approach is how to determine the partition between the two storage formats. We achieve this by employing machine learning to automatically construct a predictive model from training samples to predict the right matrix partition on a per matrix basis. The learned model can then be used for any unseen matrix. We evaluate our approach by applying it to over 900 matrices on three distinct multi-core CPU architectures. Experimental results show that our approach outperforms all alternative SpMV storage formats across the evaluation platforms. As the future work, it would be interesting to work with the deep learning method to automatically extract matrix features.  
**Author Contributions:** Conceptualization, J.F., S.C. and C.X.; methodology, J.F. and S.C.; software, S.C.; validation, S.C., J.F. and Z.W.; formal analysis, S.C., J.F.; investigation, S.C., J.F., C.X. and W.Z; resources, J.F., C.X. and W.Z; data curation, S.C. and J.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The NNW project (program no. TC228803J).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.