esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Optical Fits  
Size and size distribution information was extracted from a fit to the measured extinction spectra by means of an algorithm based on Mie theory [45]. The theoretical calculation of the optical density is based on the implementation [46] of [PERSON]'s algorithm [47] for a multi-layered sphere, which reduces to Mie theory if only one layer is considered. Then, a solid sphere can be characterized by its size parameter \(x=2\pi n_{m}r/\lambda=kr\) and its relative refractive index \(m=n/n_{m}\), where \(r\) represents its radius, \(\lambda\) is the wavelength of the incident wave in vacuum, and \(n\) and \(n_{m}\) are, respectively, the refractive index of the sphere and the medium surrounding the particle. Then, the scattering coefficients can be calculated as [47]:  
\[a_{n}=\frac{\psi_{n}(x)}{\xi_{n}(x)}\frac{D_{n}^{(1)}(x)-D_{n}^{(1)}(mx)/m}{D_ {n}^{(3)}(x)-D_{n}^{(1)}(mx)/m}, \tag{1}\]  
\[b_{n}=\frac{\psi_{n}(x)}{\xi_{n}(x)}\frac{D_{n}^{(1)}(x)-m\;D_{n}^{(1)}(mx)}{D _{n}^{(3)}(x)-m\;D_{n}^{(1)}(mx)}. \tag{2}\]  
The functions \(\psi_{n}(z)=zj_{n}(z)\) and \(\xi_{n}(z)=zl_{n}(z)\) are the Riccati-Bessel functions, defined using the spherical Bessel, \(j_{n}\), and Hankel, \(h_{n}\), functions [48]. Likewise, \(D_{n}^{(1)}\) and \(D_{n}^{(3)}\) are the logarithmic derivatives of the Riccati-Bessel functions:  
\[D_{n}^{(1)}(z)=\frac{\psi\prime_{n}(z)}{\psi_{n}(z)},D_{n}^{(3)}(z)=\frac{\xi \prime_{n}(z)}{\xi_{n}(z)}. \tag{3}\]  
Once the scattering coefficients are known, the extinction efficiency factor is calculated as in [49]:  
\[Q_{ext}=\frac{2}{x_{L}^{2}}\sum_{n=1}^{\infty}{(2n+1)\text{Re}\{a_{n}+b_{n}\}}. \tag{4}\]  
Finally, the fit of the optical density was performed using the chi-square distribution as the objective function, as implemented in MieLab software [50]:  
\[\chi^{2}(r,\sigma,n_{m},c)=\frac{1}{N}\sum_{i=1}^{N}\frac{\left(\text{OD}^{i} (\lambda_{i},r,\sigma,n_{m},c)-\text{OD}_{\text{exp}}^{i}\right)^{2}}{\text{ OD}_{\text{exp}}^{i}}, \tag{5}\]where \(N\) is the number of points used for the fit, \(r\) is the sphere radius, \(\sigma\) is the standard deviation of the radii distribution, and \(c\) is the atomic concentration. Finally, OD\({}^{\text{i}}\) and OD\({}^{\text{i}}_{\text{exp}}\) are, respectively, the values of the calculated and experimental optical density.
# Sequence-Information Recognition Method Based on Integrated mDTW
## 3 Methodology
### Joint Modeling with Sequence Processing Networks
The gradient descent method or its variants are commonly used optimization algorithms that can adjust the parameters of the model based on the gradient information of the loss function, therefore gradually reducing the training error. In this process, we need to choose appropriate learning rates and other hyperparameters to ensure the stability and efficiency of the optimization process. Through continuous iteration and optimization, we will eventually obtain a joint model that cannot only utilize the flexibility of mDTW to process time-series data but also leverage the powerful representation capabilities of deep-learning models.
## 3 Discussion
### Relationship between SIF and GPP  
Under natural light, the chlorophyll fluorescence emitted by vegetation is significantly correlated with the photosynthetic rate; therefore, SIF is assumed to has the potential to characterize GPP [5; 6]. Previous studies showed that there is a significant correlation between SIF and GPP at different temporal and spatial scales [8; 9; 21; 22], but with the decrease in temporal scales, the linear correlation between SIF and GPP gradually weakens [14; 15; 16].  
Figure 5: The influence of different environmental variables on SIF–GPP correlation.  
The nonlinear relationship between daily SIF and GPP can be probably explained in the following reasons. First, there is the basic difference between the light response of SIF and GPP [14]. Photosynthetic effective radiation absorbed by leaves can be used to drive photochemical reactions, which represent the key driving factor of SIF and GPP [4]. When the light intensity is too high and the light energy received by a plant's leaves exceeds its utilization capacity, photoinhibition may occur, making GPP saturated or even decreased. While SIF will maintain an increasing trend, meaning that SIF-GPP presents a nonlinear relationship on the daily scale [14; 15; 16]. In this study, we also found that the linear correlation between SIF-GPP gradually decreased until it was not significant when PAR increased continuously. With the increase in temporal scale, the saturation effect of GPP with the increase in PAR decreases (Figure 6), which results in a more linear correlation between SIF and GPP [16; 22; 23].  
Second, the vegetation canopy structure has a potential impact on the SIF-GPP relationship. The positive correlation between the fluorescence yield (SIF\({}_{\text{yield}}\)) and the light energy utilization efficiency (LUE) affected by the vegetation canopy structure has been verified in different ecosystems, partially explaining the internal reason for the linear relationship between SIF and GPP [24; 25; 26].  
At last, tower-based spectrometers mainly receive SIF signals emitted from the top canopy leaves and from the internal canopy, which is different from the GPP from flux observation. As a result, the relationship between SIF and GPP might be influenced by the observation approaches, and results in the low correlation at shorter temporal scales. As the temporal scale increases, this influence becomes weaker, thereby the linear correlation between the GPP and SIF is also improved [27].
## 3 Results
### Life Cycle Assessment
#### 3.4.4 Total Emissions  
The LCA results showed a general decrease in GHG emissions associated with the production of ornamental plants in containers after completely or partially replacing the peat-pumice substrate with a co-composed material based on dredged sediments and green waste (Table 5). The impact of the various inputs of cultivation was considered together with the impact of the different substrates tested in this study. Cultivation in the control substrate (CGM) was the most impactful, with 1.47 kg CO\({}_{2}\)eq for each plant produced. The three substrates where CGM was mixed with the various composted matrices (GM1, GM2, GM3) showed a reduced emission level, ranging from 1.30 kg CO\({}_{2}\)eq for plants grown in 3 S:1 GW to 1.32 kg CO\({}_{2}\)eq for the ones with 1 S:3 GW. GM4, GM5, and GM6 were the less impactful substrates with an emission per single plant equal to 1.12, 1.13, and 1.15 kg CO\({}_{2}\)eq/plant, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Input** & **CGM** & **GM1** & **GM2** & **GM3** & **GM4** & **GM5** & **GM6** \\ \hline
**Sediments** & & & & & & & \\ Excavation & 0.005 & 0.003 & 0.002 & 0.01 & 0.005 & 0.003 \\ Transport & 0.025 & 0.018 & 0.009 & 0.05 & 0.035 & 0.017 \\ \hline
**Green Waste** & & & & & & \\ Gathering and & & 0.013 & 0.026 & 0.043 & 0.026 & 0.051 & 0.086 \\ processing & & & & & & \\ Transport & 0.002 & 0.005 & 0.007 & 0.004 & 0.009 & 0.014 \\ \hline
**Composing** & & & & & & & \\ Composting site & & 0.020 & 0.020 & 0.020 & 0.04 & 0.04 & 0.04 \\ Heaps handling & & 0.040 & 0.040 & 0.040 & 0.08 & 0.08 & 0.08 \\ Urea & & 0.005 & 0.005 & 0.005 & 0.01 & 0.01 & 0.01 \\ \hline
**Control substrate** & & & & & & & \\ Peat extraction & 0.39 & 0.20 & 0.20 & 0.20 & & & \\ Peat transport & 0.14 & 0.07 & 0.07 & 0.07 & & & \\ Pumice extraction & 0.01 & 0.005 & 0.005 & 0.005 & & & \\ Pumice transport & 0.02 & 0.01 & 0.01 & 0.01 & & & \\ Mixing & 0.01 & 0.005 & 0.005 & 0.005 & & & \\ \hline
**Cultivation** & & & & & & & \\ Plastic pot & 0.56 & 0.56 & 0.56 & 0.56 & 0.56 & 0.56 & 0.56 \\ Plastic pipings & 0.16 & 0.16 & 0.16 & 0.16 & 0.16 & 0.16 & 0.16 \\ Structures & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 & 0.10 \\ Fertilization & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 & 0.03 \\ Mulching & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 \\ Irrigation & 0.04 & 0.04 & 0.04 & 0.04 & 0.04 & 0.04 & 0.04 \\ \hline
**Total emissions** & 1.47 & 1.30 & 1.31 & 1.32 & 1.12 & 1.13 & 1.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: GHG emissions related to the different inputs involved in the production of the growing mix tested (kg CO\({}_{2}\)eq).  
Figure 5: Cultivation phase emissions (kg CO\({}_{2}\)eq) per single plant grown in a 10 L pot.
# Autonomous Navigation of Robots: Optimization with DQN
## 6 Discussion of Results
### DQN Scenarios Validation
Figure 23: Navigating obstacles in the third scenario: evaluating robot’s adaptability. Figure 22: DQN algorithm reward evolution for second validation scenario. In the fourth and final scenario, a combination of static and mobile obstacles was introduced, making the path determination task much more challenging for the robot. Despite this added difficulty, the DQN algorithm was able to learn and adapt to the changing environment, resulting in a high reward score of almost 6000 points. This indicates that the DQN algorithm is capable of handling complex and dynamic environments in autonomous navigation tasks. See Figure 25.  
Based on the results of all four scenarios, it can be concluded that the DQN algorithm is a promising approach for autonomous path determination in robotics. The algorithm demonstrated the ability to learn from past experiences and adapt to changing environments, while successfully avoiding obstacles and reaching the goal. However, it is worth noting that the algorithm's performance may vary depending on the complexity of the environment and the number and type of obstacles present. Therefore, further experimentation and optimization are necessary to fully assess the capabilities of the DQN algorithm in autonomous navigation tasks. The main challenge of scenario 4 is that it involves a combination of both static and dynamic obstacles. This makes the path to the goal much more complex for the robot and requires it to navigate through a more cluttered environment. As a result, the robot frequently collides with obstacles at the beginning of the training, leading to negative rewards. However, as the robot continues to interact with the environment, it learns to navigate more efficiently towards the goal, resulting in a significant increase in reward points. See Figure 26.  
Figure 20.0 DQN algorithm reward evolution for third validation scenario. Figure 20.1 Dynamic and static obstacles: evaluating robot’s adaptability in Scenario 4. In the fourth scenario previously described, the environment posed a greater challenge for the robot due to a combination of static and dynamic obstacles. To overcome this challenge, the use of the Lidar sensor was implemented to obtain information about the surroundings of the robot. As shown in the Figure 27, the Lidar sensor emitted a beam of light that was reflected on the walls, allowing the robot to map the entire environment. This map was stored and used for later visualization in Rviz, which is a tool used for graphing two sources of information: odometry readings of the encoders connected to the wheels and information from sensors such as Lidar or Kinect. By using this environment recognition method, the control algorithm based on autonomous learning was able to locate itself in the environment and identify the obstacles present in order to avoid collisions.
## 7 Conclusions  
In this paper, for the difficult problem of elevator fault monitoring, an NB-IoT-based elevator online monitoring system is designed, which is highly integrated with the sensor and microcontroller, to realize a low-cost, easy-to-install, and highly reliable elevator monitoring solution, and at the same time, the system uses six-axis sensors for posture determination and a high degree of error compensation for the zero-point drift in barometric pressure sensors, thus realizing the accurate transmission of data, which reduces the occurrence of false alarms for faults. The findings of this study mainly include the following:  
1. The related hardware design, including the MCU peripheral circuit design, sensor peripheral circuit design, NB-IoT module peripheral circuit design, power supply, and power switching circuit design, ensures that the edge monitoring equipment operates stably for a long period of time, as well as continues to operate normally in the event of a power failure.
2. We use the complementary fusion of three-axis acceleration and three-axis gyroscope sensors, as three-axis acceleration sensors cannot calculate the heading angles and three-axis gyroscope sensors need to carry out time integration of the angular velocity, to ensure monitoring accuracy; at the same time, the peak-to-peak value of the signal is calculated using the 95% confidence interval algorithm to alleviate the suppression of noise on the high-frequency components of the vibration signal, which has the advantages of strong robustness and insensitivity to noise, ensuring that the signal has a large signal-to-noise ratio, thus obtaining more accurate elevator car posture and vibration operation data.
3. By applying neural network model data training, the height error generated by the zero drift of the barometric pressure sensor is eliminated, thus realizing the accurate judgment of elevator height.  
This system still needs to be perfected in terms of elevator monitoring, and further research will be carried out in the future for the accurate judgment of the elevator switching door status, the image processing of elevator monitoring, and the internal monitoring of the elevator car.  
Conceptualization, Z.L. and J.N.; methodology, J.N. and T.L.; software, Z.L. and J.N.; validation, Z.L., J.N. and T.L.; formal analysis, Z.L.; investigation, Z.L.; resources, Z.L.; data curation, T.L.; writing--original draft preparation, J.N.; writing--review and editing, Z.L.; visualization, T.L.; supervision, T.L.; project administration, Z.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Integration of Production Planning and Scheduling Based on RTN Representation under Uncertainties
## 3 Integrated Model and Solving Strategy  
In this paper, the integrated model of production planning and scheduling considering demand uncertainty and utility uncertainty is solved based on the integrated optimization method and rolling horizon optimization strategy. The solving method of integrated optimization is to construct a bi-level integrated model of planning and scheduling to deal with the demand and utility uncertainties. As the uncertainty of demand and utility are different in time scales, it is necessary to deal with the uncertainty of demand and utility in production planning layer and scheduling layer respectively. The discrete-time model is established in the production planning layer, and chance constrained programming is used to describe demand uncertainty. The continuous-time model is established in the production scheduling layer, and fuzzy theory is used to describe utility uncertainty. When the planning layer completes the Kth optimization, the optimization result \(D(i,k)\) corresponding to the first planning period is equal to \(Def_{r,k}\). Parameter \(Def_{r,k}\) is assigned as a task to the scheduling layer. The double-layer structure of production planning and scheduling is shown in Figure 2.  
Rolling horizon optimization strategy is a general time decomposition method. The planning horizon of the discrete-time model in the planning layer is composed of \(m\) planning periods with equal time durations. The scheduling horizon of the continuous-time model in the scheduling layer is equal to the length of the planning period. The rolling horizon optimization strategy is used to iteratively solve the integrated model of production planning and scheduling. The specific steps of rolling horizon optimization strategy are as follows:  
Figure 1: The triangular membership function of parameter \(U_{u}^{\max}\).  
Figure 2: The double-layer structure of production planning and scheduling.  
a. Solve the production planning model and assign the delivery quantity of the first planning period to the scheduling model.
* Solve the scheduling model of the current period and judge whether the scheduling layer has completed the delivery quantity assigned by the planning layer. If it can be completed, roll the planning horizon and repeat step a; if it cannot be completed, the unfinished tasks will be passed to the next planning period as the backlog order quantity, then roll the planning horizon and repeat the step a.
* The calculation is terminated after obtaining the planning and scheduling schemes for all periods.  
The flow chart of iterative solving strategy of integrated model is shown in Figure 3, and the schematic diagram of rolling horizon optimization strategy is shown in Figure 4.  
Figure 4: Rolling horizon optimization strategy.  
Figure 3: Flow chart of integrated model iterative solving strategy.
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
## 2 Related Work
### Previous Methods for MDD  
There exist a couple of studies that transferred acoustic knowledge from audio modality for MDD. In our previous work [39], we created an MDD model by stacking only a fully connected layer on the top of pretrained wav2 vec2.0. The experimental results showed that the proposed MDD model trained only with one-third of the data achieved the comparable performance to the conventional methods, suggesting the effectiveness of wav2 vec2.0 for learning robust features. In the same period, [PERSON] et al. [13] used a similar way of using wav2 vec2.0 for MDD and also showed its effectiveness on another corpus. Instead of using wav2 vec2.0 for directly extracting MDD features, [PERSON] et al. [14] proposed to use wav2 vec2.0 and a K-means clustering algorithm to convert the original continuous speech into audio vectors and then into discrete acoustic units. By modifying this unit sequence, they augmented the acoustic data and finally improved the MDD performance.  
As for transferring linguistic knowledge from text modality, the researchers usually first aligned the audio with phoneme sequence, and then used the joint representation as input for the MDD module. [PERSON] et al. [20] proposed to concatenate the acoustic representations and the attention results (calculated by Equation (3)) for MDD. Different from the study [19] that relied on manually labeled phoneme boundary, this study used an E2E framework. Moreover, instead of extracting linguistic features as input, [PERSON] et al. [25] proposed an ASR and alignment unified transformer-based MDD framework where the prior target text was used as the condition for the decoder input. Contrastive learning, a kind of technique that maximizes the intra-class similarity and minimizes the inter-class similarity, has been used extensively over the years in various applications [40; 41]. In the filed of SSP, contrastive learning can be used to learn useful representations in a self-supervised manner. [PERSON] et al. [42] proposed a contrastive learning framework that enabled transfer learning clinical time series by exploiting a data augmentation scheme in which new samples were generated by mixing two data samples with a mixing component. For the task of Chinese spell-checking, [PERSON] et al. [43] proposed reverse contrastive learning which explicitly forced the model to minimize the distance in language representation space between similar sample pairs. In the context of MDD, we can anchor the transcription in order to generate the dissimilarity/similarity.  
Figure 1: Left: The structure of the pretrained model wav2 vec2.0 framework and its corresponding criterion in the pretraining stage. Top-right: The details of vector quantization module. Bottom-right: The use of acoustic representations learned from wav2 vec2.0 model for MDD task by adding fully connected (FC) layers.
# Intelligent Computation Offloading Based on Digital Twin-Enabled 6G Industrial IoT
## 1 Introduction
In fact, the above solutions for DT combined with edge computing are still in their infancy and have a lot of room for improvement. First, there is still relatively little research on using DT for the prediction and optimization of offloading decisions. Second, the approach adopted generally assumes that each IIoT device performs a single computing task, regardless of the randomness of task generation. As a new research method to solve time-varying problems, deep reinforcement learning (DRL) can be used to solve dynamic computation offloading and resource allocation problems. DRL can deal with the problem of the continuous action space and learn more complex offloading strategies, which cannot be handled by traditional algorithms. Inspired by the above discussion, this article considers a DT-assisted edge computing network consisting of multiple mobile IIoT devices. Computation offloading consists of three steps, offloading, processing, and feedback. Offloading refers to the use of wireless transmission technology to offload tasks to edge servers (deployed near the base station, not fixed) via mobile access points (APs). Processing requires that the edge servers have sufficient computational resources. Feedback is to return the processing results to the physical device through the AP after processing by the edge server. Generally, the returned data is much smaller than the offloaded data, because the offloaded data is the entire data collected by the physical device, whereas the returned data may only be an instruction. It may not be the same AP that returns the results since the physical devices are movable. The main innovations of this article are summarized as follows:  
1. We propose an integrated digital twin and IIoT architecture to model the network systems and all physical devices in the IIoT. The architecture enables more efficient and optimized network operation. 2. We explore the impact of the heterogeneity of physical devices and the execution time of different tasks on offloading decisions. A solution based on deep deterministic policy gradient (DDPG) is employed to realize effective computation offloading. The solution focuses more on policy exploration than traditional algorithms to improve the adaptability of offloading algorithms and reduce the communication cost. 3. We have conducted extensive experiments on the proposed learning algorithm. The results show that by considering various delay constraints, more tasks can be better executed, and unnecessary contention and the wasted resources phenomenon can be greatly reduced. The rest of this article is organized as follows. The relevant research status is reviewed in Section 2. The problem is then described in Section 3. The formulations used by the MDP to describe the proposed problem are listed in Section 4, and the associated learning algorithm is also given in this section. Subsequently, simulation results are tested and analyzed in Section 5. Finally, we summarize the paper in Section 6.