esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Scale Temporal Convolutional Networks for Effluent COD Prediction in Industrial Wastewater
###### Abstract  
To identify the complex time patterns in the process data and monitor the effect of wastewater treatment by predicting effluent chemical oxygen demand more accurately, a soft-sensor modeling method based on the multi-scale temporal convolutional network (MSTCN) was proposed in this paper. Data at different time scales are reconstructed according to the main frequencies determined by the Fourier transform approach, and the correlations between variables during that period are calculated and stored in the corresponding adjacency matrix. The specific temporal convolutional network (TCN) is built to learn the temporal dependencies within each sequence at the current scale, while the graph convolutional layer (GCN) captures the relationships among variables. Finally, predictions with less error can be obtained by integrating output features from GCN and TCN layers. The proposed model is validated on an annual dataset collected from a wastewater treatment plant employing biological processes for organic matter removal. The experimental results indicate that the proposed MSTCN reduces RMSE by 35.71% and 22.56% compared with the convolutional neural network and TCN, respectively. Moreover, MSCTN shortens the training period by 6.3 s and improves RMSE by 30.41% when compared to the long short-term memory network, which is outperformed in extracting temporal dynamic characteristics.  
soft sensor; wastewater treatment; modeling; indicator prediction; deep learning Article
## 3 Results and Discussion
### Model Validation
#### 3.2.2 S. aureus Isolates in Co-Cultures  
According to data in Table 3, the fast-growing isolate 2064 of _S. aureus_ compared with the numbers of isolate 14733 showed a large difference in milk and milk with 1% NaCl (\(RMSE\) = 0.66 and 0.91 log CFU/mL, respectively). On the other hand, the differences within the same isolate but between different media (milk without NaCl vs. 1% NaCl) were lower in the case of halotolerant _S. aureus_, 0.39 and 0.34 for the isolates 2064 and 14733, respectively. Unlike _E. coli_, these \(RMSE\) values pointed out isolate variability. The lowest calculated \(RMSE\) values for populations in the background were also calculated for LAB in all combinations and ranged between 0.19 and 0.30 log CFU/mL. For _G. candidum_, the higher \(RMSE\) values of 0.51-0.63 log CFU/mL were calculated between their numbers in milk and milk with the addition of 1% NaCl, which also confirmed its sensitivity to NaCl.  
Within the validation indices (\(B_{\mathrm{f}}\) and \(A_{\mathrm{f}}\)), the fast-growing isolate 2064 model data and the experimental data of isolate 14733 were used. While the calculated \(B_{\mathrm{f}}\) values for the growth of _S. aureus_ isolates were 1.184 and 1.284, the \(A_{\mathrm{f}}\) values were 1.371 and 1.500 in milk and 1% NaCl in milk, respectively.  
LAB growth was accurately predicted in all cases of co-culture with _S. aureus_ since the calculated \(B_{\mathrm{f}}\) values were within 0.980 to 1.016 and the \(A_{\mathrm{f}}\) values ranged from 1.023 to 1.035. Furthermore, the values of the interaction coefficient \(I_{\mathrm{LS}}\) varied between 0.91 and 1.26 (Table 3).  
Referring to the interpretation of the \(B_{\mathrm{f}}\) when used for model performance evaluations involving pathogens, three categories were recommended by [55]. \(B_{\mathrm{f}}\) in the range of 0.90 to 1.05 can be considered good; 0.70 to 0.90 or 1.06 to 1.15 can be considered acceptable and less than 0.70 or greater than 1.15 should be considered unacceptable. In almost all cases with two isolates of each _E. coli_ and _S. aureus_ contaminant in our study, the valuesof the \(B_{\rm f}\) factors were in the range of 0.90-1.05, which means that the H-GD model can be considered as suitable also for growth prediction in co-cultures with three different microbial populations.
# Potentiality of Rod-Type Chitosan Adsorbent Derived from Sewage Sludge
## 3 Results and Discussion
### Optimization of Rod-Type Biosorbent Modification Conditions
#### 3.2.1 Effect of Biosorbent Shape  
A kinetic study was performed to determine the effect of immobilization and biosorbent shape on adsorption. Experiments using raw sludge (RS), bead-type chitosan-immobilized sludge (BCS), and rod-type chitosan-immobilized sludge (RCS) were performed to evaluate the contact time required to reach equilibrium. Chitosan is a biopolymer with a high nitrogen content that confers an adsorption ability on anionic metal ions. As(V) was used to evaluate the anionic metal removal performance of each biosorbent. Figure 1a shows the removal of As(V) by RS, BCS, and RCS as a function of contact time at pH 5.5. BCS and RCS showed 2-3 times higher As(V) removal efficiency than RS. This result is due to the presence of amine groups on the biosorbent [13].  
Figure 1: Effect of (**a**) biosorbent shape, (**b**) chitosan content, (**c**) sludge content, and (**d**) biosorbent size on the adsorption of As(V) on the sludge–chitosan (biosorbent dosage = 2 g/L, agitation rate = 200 rpm, contact time = 6 h, temperature = 25 \({}^{\circ}\)C, Vw = 200 mL, and pH non shift (pH 5.5)).
## 5 Cache Allocation
### Data Path Weight  
Data paths are the division of content domains from the user's perspective, so the weight of data paths is mainly determined by the forwarding process of requests sent by the user. First, the number of users will undoubtedly have an important impact on the number of requests on the path. Data paths connecting multiple users contain a large number of diverse requests. Such nodes should be assigned more cache sizes, so they weigh higher than paths with fewer connected users. Second, we consider the impact of user request habits. We assign greater weights to data paths whose request preferences are consistent with the content domain to which the current data path belongs. Since the request preference of a data path is inconsistent with the content of the current path, which means that the content the user wants is not in that content domain, we will allocate space to it in the content domain corresponding to the content. Finally, similar to content domain weight calculations, the sensitivity of the type of user requesting content to the size of the cache can also have a significant impact on cache performance in data paths.  
Combining the number of users, request preferences, and the sensitivity of the requested content to the size of the cache space, we express the weight \(DPW_{j}\) of the data path \(j\) as:  
\[DPW_{j}=\alpha_{1}*R_{j}*(\alpha_{2}*PR_{j}+\alpha_{3}*S_{j})\hskip 28.452756 ptj \in DP \tag{3}\]  
In Expression (3), \(R_{j}\) represents the number of users connected by the data path \(j\). \(PR_{j}\) indicates that the request preferences of the users connected by path \(j\) are consistent with the content domain to which the path belongs. And DP is a collection of data paths in a content domain. The sensitivity of the type of content requested by the user to the size of the cache is represented by \(S_{j}\). \(\alpha_{1}\), \(\alpha_{2}\), etc. are the weights of each factor, similar to that of content domains, and can be adjusted based on actual conditions or analysis requirements. The sensitivity of content to cache space can be obtained by predefined table lookup. User's request preference can be regarded as the popularity of content among corresponding users, which is collected and saved by each node, and then analyzed and calculated on additional servers. In many scenarios, it is very difficult to obtain the sensitivity of content to cache space and user request preference, but it is easy to obtain the number of users. In order to make our scheme more general and easy to implement, the weight \(DPW_{j}\) of the data path can be simplified to:  
\[DPW_{j}=R_{j}\hskip 28.452756 ptj\in DP \tag{4}\]  
In this case, the calculation of the weight of the data path ignores the user's request preferences and the sensitivity of the requested content to the cache size, only considering the effect of the number of users. This will increase the evaluation error of node importance and reduce the system performance. At the same time, it can make the algorithm easier to implement.
###### Abstract  
C-SAR/01, the successor of China's Gaofen-3 Satellite, which launched on 23 November 2021, is the first synthetic aperture radar (SAR) satellite to be launched in China's civil space infrastructure plan and has served as an invaluable data resource. Radiometric calibration and validation are prerequisites for the quantitative application of SAR data. In this study, the radiometric calibration experiments of C-SAR/01 data of the ultra-fine strip (UFS) and fine strip I (FSI) modes were conducted applying flexible automatic triangular trihedral corner reflectors deployed in Xilinhot SAR satellite calibration and validation site. Accordingly, the image quality and radiometric calibration accuracy were evaluated. The results show that the spatial resolution, peak sidelobe ratio, and integrated sidelobe ratio of UFS and FSI mode data of C-SAR/01 are better than those of the design indexes, and the calibration results from the integral method are more stable than those from the peak method. Furthermore, the standard deviation of the calibration constant for UFS mode data is 0.234 dB, with the relative and absolute calibration accuracies obtained as 0.233 and 0.532 dB, respectively, whereas the standard deviation calibration constant for FSI mode data is 0.198 dB, with its relative and absolute calibration accuracies evaluated as 0.199 and 0.333 dB, respectively.  
C-SAR/01; flexible automatic corner reflector; radiometric calibration; validation Article  
Quick Quality Assessment and Radiometric Calibration of C-SAR/01 Satellite Using Flexible Automatic Corner Reflector  
[PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Q. Quality Assessment and Radiometric Calibration of C-SAR/01 Satellite Using Flexible Automatic Corner Reflector. _Remote Sens._**2023**, _15_, 104. [[https://doi.org/10.3390/rs15010104](https://doi.org/10.3390/rs15010104)]([https://doi.org/10.3390/rs15010104](https://doi.org/10.3390/rs15010104))
# Numerical Simulation of an InP Photonic Integrated Cross-Connect for Deep Neural Networks on [PERSON] Shi
###### Abstract  
We propose a novel photonic accelerator architecture based on a broadcast-and-weight approach for a deep neural network through a photonic integrated cross-connect. The single neuron and the complete neural network operation are numerically simulated. The weight calibration and weighted addition are reproduced and demonstrated to behave as in the experimental measurements. A dynamic range higher than 25 dB is predicted, in line with the measurements. The weighted addition operation is also simulated and analyzed as a function of the optical crosstalk and the number of input colors involved. In particular, while an increase in optical crosstalk negatively influences the simulated error, a greater number of channels results in better performance. The iris flower classification problem is solved by implementing the weight matrix of a trained three-layer deep neural network. The performance of the corresponding photonic implementation is numerically investigated by tuning the optical crosstalk and waveguide loss, in order to anticipate energy consumption per operation. The analysis of the prediction error as a function of the optical crosstalk per layer suggests that the first layer is essential to the final accuracy. The ultimate accuracy shows a quasi-linear dependence between the prediction accuracy and the errors per layer for a normalized root mean square error lower than 0.09, suggesting that there is a maximum level of error permitted at the first layer for guaranteeing a final accuracy higher than 89%. However, it is still possible to find good local minima even for an error higher than 0.09, due to the stochastic nature of the network we are analyzing. Lower levels of path losses allow for half the power consumption at the matrix multiplication unit, for the same error level, offering opportunities for further improved performance. The good agreement between the simulations and the experiments offers a solid base for studying the scalability of this kind of network.  
artificial neural networks; deep neural network; image classification; photonic integrated circuits; semiconductor optical amplifiers; photonic neural network +
Footnote †: journal: Applied Physics Letters B
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 5 Application Analysis of Passenger Flow Prediction
### Results and Analysis of Short-Term Passenger Flow Prediction  
Using the TCN-LSTM combination model to analyze and predict the above passenger flow data, the loss value curves of the training set and test set are shown in Figure 11. It can be seen that both curves drop to a stable value, indicating that the trained network behaves normally. The total passenger flow prediction results are shown in Figure 12. It can be seen that the TCN-LSTM model can approximately capture the changing characteristics of the real URT passenger flow to a certain extent and can reflect the law of short-term traffic passenger flow changing with time. At the same time, to compare and verify the accuracy of the TCN-LSTM model, this paper also established a single LSTM model to predict the passenger flow, as shown in Figure 13.  
To more intuitively demonstrate the ability of the TCN-LSTM model to capture the passenger flow characteristics, the prediction of inbound/outbound passenger flow in different time segments of rail transit was analyzed for specific high, medium, and low passenger flow stations. The prediction results of the single LSTM model were compared as shown in Figures 14-16. It can be seen that the prediction performance of the LSTM single model is poor, and the prediction error of the peak passenger flow is large. The prediction results of the two models show that the TCN-LSTM model is superior to the single LSTM model both in the short-term passenger flow prediction and data generalization of URT.  
\begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Variables** & **Illustrations** & **Variables** & **Illustrations** \\ \hline \(Y_{1}\) & Average daily passenger flow/person time on working days & \(Y_{4}\) & Temperature/\({}^{\circ}\)C \\ \(Y_{2}\) & Weekend holiday attribute (0 means working day, and 1 means weekend) & \(Y_{5}\) & Weather conditions (1 means sunny; 2 means cloudy; 3 means cloudy, and 4 means rainy) \\ \(Y_{3}\) & Passenger flow per hour/person time & \(Y_{6}\) & Air quality index/dimensionless relative value \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influencing parameters of passenger flow analysis and prediction.  
Figure 11: Structural diagram of TCN-LSTM model.  
Figure 12: Prediction results of short-term passenger flow based on TCN-LSTM model.  
Figure 13: Prediction results of short-term passenger flow based on LSTM model.
## 2 Literature Review
### Maintenance as Part of the Modern Manufacturing System  
As stated in the previous paragraphs, it is clear that maintenance besides being a crucial part of the production line, as it ensures the continuous and flawless operation of the physical assets, is also a very complex process. Therefore, there is a considerable amount of research work trying to cover all of its aspects. However, maintenance activities can be further categorized regarding the status of the physical assets. Concretely, the most common types of technical maintenance found are: (i) Corrective Maintenance, (ii) Preventive Maintenance, (iii) Risked-based Maintenance, and (iv) Condition-based Maintenance [15]. Throughout recent decades, many types of technical maintenance policies have been developed in an aim to cover the majority of the industrial needs. However, with the big diversity in the general policies that firms adopt, there is no optimal solution regarding the equipment maintenance plan. With that said, the maintenance policy adopted a company, and is highly dependent on the general business model and, by extension, to the general goals to be achieved. As a result, all of the above-mentioned policies are equally important for the research community and the industrial domain as well. Regardless, the scope of this research work is to present the design and development of a framework that supports field technicians mainly in corrective scenarios, where a malfunction has already occurred and, in many cases, standard procedures cannot be applied. Therefore, the research work of this paper could be classified as an enhanced corrective maintenance policy, which is based on the provision of service for on-demand guidance.  
The current industrial era is characterized by the immense decentralization of the manufacturing networks. Thus, the actual manufacturing plant may be in a different region than the firm's headquarters, which makes communication of the two departments more difficult [3; 16]. However, the ongoing technological advances in the ICT have enabled the remote monitoring, remote operation, and the remote maintenance of the modern manufacturing systems [17; 18]. Modernized maintenance under the term of Smart Maintenance have occurred over the last decade.
## 2 Background and Theory  
This section introduces the fundamental concepts necessary to understand the methodology and objectives of this study. It explores essential aspects such as machine learning classifiers, feature extraction techniques, and preprocessing methods. Furthermore, it provides an overview of the key principles, theoretical foundations, and critical definitions to ensure a thorough understanding of the research framework.
## 4 Application Example
### Influence of the Location of the Air Intake Wells  
The ventilation characteristics are studied with the change in the relative position between the air intake well and spillway tunnel. With the locations of the no. 1 and no. 3 air intake wells unchanged, the location of no. 2 moves up along the axis of the spillway tunnel by 0, 100, 200 and 300 m. The air velocity and air pressure in the different cases are shown in Figure 11. The corresponding total ventilation rates of the air supply system are 4537, 4504, 4467 and 4424 m\({}^{3}\)/s, respectively.  
With the no. 1 and no. 3 air intake well locations changed, the ventilation rate is basically the same as before. This result is because the sum of the frictional resistance and local resistance changes slightly in the different cases. However, with the no. 2 air intake well moving up, the airflow in the no. 2 air intake well is sucked into the spillway tunnel earlier than before. Consequently, the air velocity in the no. 1 air intake well decreases greatly, and the air velocity in the no. 2 and no. 3 air intake wells increase. Moreover, the sub-pressure in the spillway tunnel between the no. 1 and no. 2 air intake wells decreases greatly, with a more uniform distribution along the flow direction.  
Figure 11: Influence of the location of air intake wells on ventilation characteristics.  
Figure 10: Influence of the number of air intake wells on the ventilation characteristics.  
The analysis indicates that the air intake well location has a slight effect on the ventilation rate and has a great effect on the air velocity and pressure in the air intake well and spillway tunnel. It is advantageous to have a reasonable air intake well location to obtain a good distribution of the sub-pressure in spillway tunnels.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 1 Introduction
The distributed runtime environment will consist of connected runtime environments, which support the execution of individual service components and manage their communication. The contributions of this paper are threefold. First, we present the syntax and operational semantics of a formal model for representing resource-bounded IoT services. The syntax defines the model configuration, and the semantics represents the meaning of IoT operations, including communication, computations, and resource acquisition operations. The proposed model can be used to understand and define the complex communication and coordination requirements of a broader class of sensor-based systems. We also present the syntax and operational semantics of a proximity model which enables reasoning about the dynamic and evolving relationship between IoT services. Third, the paper presents the design of a runtime environment which implements the actor-based model with artificial-intelligence (AI) capabilities. The rest of the paper is organized as follows: Section 2 presents related work. Section 3 presents the properties and definition of an IoT service. Sections 4 and 5 present the operational semantics of the actor-based model for representing IoT services and the proximity model, respectively. Section 6 presents the design of the runtime environment for supporting IoT services. Finally, Section 7 summarizes the results of this work.
# Applying Knowledge Inference on Event-Conjunction for Automatic Control in Smart Building
## 3 Problem Definition  
Before knowledge inference in the smart building being ubiquitous, there are still several technical challenges to confront:  
* **Integration and interoperability of heterogeneous data set** The most fundamental problem is the compatibility of heterogeneous sensors/devices, providing different networking features, protocols, and interfaces from different vendors. The transition and integration between the heterogeneous sensors are costly and make smart building implementation slow down. High level of manual effort is currently required to integrate the sensor or device nodes, which are often decentralized in both the cyber and physical dimensions, varying with their parameters. Such processes are both time-consuming and error-prone [12]. That leads to, while deploying inference framework for smart building, the developers need to map various data from heterogeneous sensors without a common format or unit.
* **Lack of semantic approach** Relevant description logics (DL) is required to deal with environmental data within smart buildings, such as the type, instance as well as the relevance, relation among the entities for knowledge extraction. However, the complicated indoor environment with various features including general, spatial, temporal, spatio-temporal leads to a standard description logics (DL) for the IoT sensor network in the smart building being lacked. Ontology could be used for constructing the relational graph among sensors and spaces, etc. However, the ontology needs to capture the dependencies accurately, while not being excessively complex to make inference hard.
* **The small and incomplete data features** Although environmental data could be collected over time, the human-motion related sensor data is somehow sparsely triggered in both the spatio and temporal dimensions. Most of the sensor data would be got rid of being labels for machine learning. Thus, it is difficult to collect the label data at a large scale in the real phenomenon. That means while developing knowledge inference on event conjunction by taking into account continuous changes inside the whole environment, the problem of handling small and incomplete data should not be ignored.