esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction of a Numerical Model for Flow Flash Evaporation with Non-Condensable Gas
## 2 Construction of the Numerical Model
### Model Construction and Governing Equations
#### 2.3.3 Species Transport Equations  
The dissolved CO2(aq) and entrained CO2(g) in the water were released and dispersed in the flash chamber. To describe the distribution of CO2 within the flash chamber, component mass fraction equations were added as shown in the following equations [30]:  
\[\frac{\partial}{\partial t}\left(\rho_{\rm I}\alpha_{\rm I}Y_{\rm CO2(aq)} \right)+\
abla\cdot\left(\rho_{\rm I}\alpha_{\rm I}\
u Y_{\rm CO2(aq)}\right) = -\
abla\cdot\left(\alpha_{\rm I}J_{\rm CO2(aq)}\right)-\dot{m}_{ \rm CO2} \tag{11}\]  
\[\frac{\partial}{\partial t}\left(\rho_{\rm v}\alpha_{\rm v}Y_{\rm CO2(g)} \right)+\
abla\cdot\left(\rho_{\rm v}\alpha_{\rm v}\
u Y_{\rm CO2(g)}\right) = -\
abla\cdot\left(\alpha_{\rm v}J_{\rm CO2(g)}\right)+\ \dot{m}_{\rm CO2} \tag{12}\]  
In the above equations, YCO2(aq) and YCO2(g) are the mass fractions of CO2 in the form of solute in water and gas in vapor, respectively. The left side shows the unsteady-state terms and convection terms of the mass fraction, and the right side shows the diffusion terms and interphase transfer source terms, where the source terms are used to characterize the variation in CO2 during the release of dissolved gases. In the turbulent state, the diffusion flux \(\mathbf{J}_{i}\) can be expressed using the following formula:  
\[\mathbf{J}_{i} = -(\rho D_{ij}+\frac{\mu_{\rm t}}{Sc_{\rm t}})\
abla Y_{i}-D_{T,i} \frac{\
abla T}{T} \tag{13}\]  
In this formula, the first term on the right side is the diffusion flux that is obtained based on [PERSON]'s law, which includes the diffusion caused by both the concentration gradient and turbulence. \(\mu_{\rm t}\) is the turbulent viscosity, \(D_{T,i}\) is the thermal mass diffusion coefficient, and \(Sc_{\rm t}\) is the turbulent Schmidt number, which is equal to 0.7. In this numerical model, the diffusion coefficient \(D_{ij}\) of CO2 in water was taken as 4.1 \(\times\) 10\({}^{-9}\) m\({}^{2}\) s\({}^{-1}\)[31], and the diffusion coefficient \(D_{ij}\) of CO2 in vapor was taken as 0.26 \(\times\) 10\({}^{-4}\) m\({}^{2}\) s\({}^{-1}\)[32].
## 2 Background and Related Work
### Semantic Web Services in Production Automation  
With the evolving usage of service-oriented architectures in production systems, the semantic description of services becomes more important. Particularly, within the scope of the SOCRADES project, concepts for the usage of ontologies in the production domain [16] and for the semantic discovery and orchestration of services to production processes [17] have been developed. However, they only show the feasibility of semantic service implementations in simplified setups or simulations. We put emphasis on the implementation of industry-related experimental setups within a real-world research facility, the _SmartFactory_\({}^{\mathrm{KL}}\). In addition, we believe that clearly defined methodologies for the creation of semantics-supported systems as well as the construction of a fundamental structure of reusable ontologies are necessary in order to make semantic technologies applicable to the production domain.  
Concerning the discovery of services, [PERSON] _et al._[18] describe a concept for the discovery and selection of real-world services provided by embedded devices. They build upon the results of the SOCRADES project concerning the DPWS networking discovery mechanism. In addition, they discuss a concept for on-demand provisioning of missing services, which depicts an interesting idea for a future extension of our system. [PERSON] _et al._[19] describe a concept to integrate resource constrained devices into enterprise information systems. To this end, they propose a discovery mechanism based on semantic annotations. The central topic of their work is the adaption of existingweb service standards to work with resource restricted devices. Both works do not focus on the usage of semantic services within the domain of factory automation as the basis for a context-based orchestration.  
Several works dealing with the orchestration of services in factory automation propose a pure syntactical orchestration of services based on BPEL [20, 21]. Indeed, these approaches are rather suited for static workflows than for a dynamic orchestration of services [22]. In particular, [PERSON] _et al._[23] propose a concept closely-related to our vision of a context-based orchestration of services to adapt production processes. They describe an orchestration system, which uses ontologies to transform abstract process models to concrete BPEL processes tailored to the capabilities of the respective industrial plant, which are represented as services in a UDDI repository. This work depicts an important basis for the future research on semantic orchestration of services in the production domain. In this article, we build on the results of this approach, but focus on the construction of a fundamental structure of reusable ontologies, the creation of a methodology for the semantic discovery and context-based orchestration of services in factory automation and the implementation of industry-related experimental setups within the _SmartFactory_\({}^{\mathrm{KL}}\).
# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
## 2 Materials and Methods
### Conformer  
The Conformer structure is a deep-learning architecture that combines self-attention mechanisms and convolutional neural networks, mainly used for processing time series tasks. It was proposed by [PERSON] and other researchers in 2020 [16]. The Conformer structure typically includes several key parts: self-attention layers, convolutional neural networks, and feedforward networks. These components interact with each other in a specific way within the module to improve processing efficiency and model performance. The self-attention mechanism helps the model capture long-term dependencies in time series data, while the convolutional layers capture local features, and the feedforward network further transforms features, adding nonlinear processing capabilities to enhance the overall expressiveness of the model. This combination makes Conformer very suitable for processing time series data. Figure 1 shows the structure diagram of Conformer.  
In the self-attention layer, the model no longer relies on traditional sequence processing methods, such as Recurrent Neural Networks (RNN), which process information step by step, but directly captures the interactions between all elements in the sequence by calculating the weight distribution of all element pairs [18]. This mechanism is achieved by generating three vectors, query, key, and value, and calculating the compatibility score between the query and all keys. The values corresponding to the keys with high scores will have a greater influence. This processing method allows for each sequence element to \"pay attention\" to the other elements directly, thereby dynamically focusing on the information most important for the current task.  
The design of the feedforward network is to increase the model's nonlinear processing capability and transform high-dimensional features, thereby enhancing the learning model's expressive power and generalizability. In terms of computation, the feedforward network is usually composed of two densely connected linear layers, with a nonlinear activation function, such as ReLU (Rectified Linear Unit) or GELU (Gaussian Error Linear Unit), embedded in the middle. The purpose of this configuration is to introduce the necessary nonlinearity, allowing for the network to learn and approximate complex functional relationships.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 4 Classification of ML Models Applied in the Optimization of Wastewater Treatment Processes
### ML Models Applied in the Optimization of Wastewater Treatment Processes
#### 4.2.2 Recurrent Neural Networks (RNNs)  
RNNs such as LSTM and GRU models are essential in time-series modeling because of their ability in processing sequential data in an efficient way, and in capturing long-term dependencies.  
Designed to overcome RNNs' limitations (vanishing gradient problem), LSTM architecture, with its gating mechanisms (special cell structure with forget, input, and output gates), allows them to retain information over long data sequences and to learn and remember long-term dependencies, abilities that are essential in modeling the dynamic and high nonlinear behavior of wastewater treatment processes, and in efficient time-series modelling for wastewater management [9]. Additionally, the use of LSTM networks with CNNs (CNN-LSTM) has demonstrated their utility in handling the intricate temporal dynamics of wastewater treatment systems [33; 80].  
Overall, LSTM networks outperform models like Deep Belief Networks (DBNs) and CNN-LSTM hybrids in terms of COD trend prediction and in handling time-series data essential for wastewater quality prediction [9].  
A simpler variant of LSTM networks is GRUs, which ensure faster training times and reduced risk of overfitting [24]. Because of their robustness in handling time-series data that presents complex temporal patterns and their ability to recognize patterns over long data sequences (as in the LSTM case), both GRUs and LSTM networks have been successfully applied in equipment failure forecasting and in water quality prediction [24; 34; 81]. According to [PERSON], [PERSON] et al. and [PERSON] and [PERSON] [33; 80], LSTM application in predicting essential wastewater parameters (such as nitrous oxide (N\({}_{2}\)O) and COD) in wastewater treatment optimization, and in ensuring normative compliance, showed superior performance (significantly better prediction accuracy and stability) than classical RNNs. The integration of LSTM networks (due to their ability to model complex temporal dependencies and nonlinearities) with IoT has been successfully applied in enhancing the WWTPs' operational efficacy, in improving real-time monitoring systems, and in better predictive maintenance [9]. Overall, due to its advantages, LTSM networks are a useful tool in the wastewater treatment domain, where accurate predictions can improve process optimization and environmental protection [9; 33].
# AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio
## 2 Background
### Opus 1.2 Codec with Channel Mapping  
The Opus codec is an open-source audio codec intended for real-time VoIP and videoconferencing applications and for streaming audio over the Internet [9]. Opus can handle very low bitrate narrowband speech as well as very high-quality multi-channel music. It has been adopted by applications such as Skype, Google Hangouts and YouTube. It is included in the WebRTC API as \"mandatory to implement\" [10].  
Figure 1: Ambisonics spherical harmonics for orders up to three. FOA includes the top two lines of basis functions (4 channels) and 3 OA includes all four lines (16 channels). The ACN channels 2, 6 and 12 contain only vertical components.  
Ambisonic spatial audio can be encapsulated in Ogg format by encoding it with the Opus codec and setting the channel mapping family value to 2 or 3. For FOA, it is recommended to use channel mapping 2, which encodes each Ambisonic channel independently. For HOA, channel mapping 3 provides a more efficient encoding. The channel mapping family 2 allows for so-called mixed-order Ambisonic representations where only a subset of the full Ambisonic order number of channels is required [11].  
This work investigates, via MUSHRA subjective listening tests, the effect of Opus codec compression using channel mapping family 2, on the listening quality and localization accuracy of spatial audio as perceived by listeners. The results of MUSHRA subjective listening tests have been used to validate and optimize a full reference objective spatial audio quality metric described in Section 4 of this paper.
## 3 Wireless Access-Metro Networks
### Enabling Wireless Technologies  
Long Term Evolution (LTE)  
LTE has been defined by the third generation partnership project (3 GPP). The first amendment of LTE (release 8) provides a transmission rate of 300 Mbit/s and operates in both time division duplex (TDD) and frequency division duplex (FDD) modes. LTE aims at providing a smooth evolution from earlier 3 GPP and 3 GPP2 cellular networks such as wide-band code division multiple access/high-speed packet access (WCDMA/HSPA) and code division multiple access (CDMA2000) [17]. In 2008, LTE-Advanced (also known as requirements for further advancements for evolved-universal terrestrial radio access) was initiated to enhance LTE radio access in terms of system performance and capabilities [18]. To support the requirements of international mobile telephony (IMT)-Advanced [19], the following characteristics are considered for LTE [17]:  
1. **Carrier aggregation**, where multiple fixed bandwidth carriers are aggregated to support huge transmission bandwidth with very high transmission rates, e.g., the 20 MHz carriers are aggregated to a bandwidth of up to 100 MHz.
2. **Relaying support**, to increase coverage and decrease deployment cost.
3. **Extended multi-antenna transmission**, to increase the number of downlink/uplink transmissions which enlarges the total transmission rate.
4. **Coordinated multipoint (CoMP) transmission/reception**, where multiple cells perform transmission/reception simultaneously to improve the performance of cell-edge nodes.
5. **Backward compatibility**, where both first LTE released end-users and LTE-Advanced end-users are able to access the network, thereby providing a cost-efficient pay-as-you-grow migration.  
WiMAX  
The initial IEEE 802.16 WiMAX standard was established in the frequency band of 10-66 GHz, providing up to 75 Mbit/s line-of-sight (LOS) connections in terms of both PMP and mesh modes. IEEE 802.16a provides non-LOS connections in the frequency band of 2-11 GHz (licensed and unlicensed). IEEE 802.16 is a connection-oriented standard, _i.e._, prior to transmitting data between subscriber stations (SSs) and base station (BS), connections must be established. Each connection is identified by a 16-bit connection identifier (CID). The WiMAX MAC layer is responsible for assigning CIDs as well as allocating bandwidth to SSs. It consists of the following three sublayers: \((i)\) convergence sub-layer (CS), whereby different higher-layer protocols are implemented in different CSs, e.g., ATM CS and packet CS are used for ATM and Ethernet networks, respectively; \((ii)\) common part sub-layer (CPS),which is responsible for bandwidth allocation and generating MAC protocol data units (MPDUs); and \((iii)\) security sub-layer [20]. The IEEE 802.16e (known as Mobile WiMAX) standard offers scalability in both radio access technology and network architecture. While the spectrum allocation is applied as a radio access technology of Mobile WiMAX, its flexibility in network deployment provides various services [21].
# Helping the Visually Impaired See via Image Multi-labeling Based on SqueezeNet CNN
## 3 Experimental Results
### BlindSys: A Smart IT Solution to Assist the Visually Impaired  
Figure 6 shows an overview of the BlindSys system for guiding VI people and helping them recognize objects and text in indoor environments. The BlindSys hardware is composed of a high resolution camera that is mounted at the chest of the VI person, a wireless headphone for voice communication with the VI user, and a mobile device that houses the BlindSys software and enables interface with the system.  
The VI user can use the system via voice commands or through a graphic user interface (GUI) to select one of four functions: 1) indoor navigation, 2) text detection and recognition, 3) scene description, or 4) face detection and recognition. Indoor navigation operates on the video stream in real time, while the other functions operate on a single image captured upon request from the video stream. To ease operation of the system, the mobile device has the simple GUI shown in Figure 7b, where the whole screen is divided into four large buttons. The VI user can press the buttons simply by touching an area close to the corners of the device. In order to avoid accidental touching, the VI user should tap the button twice consecutively. BlindSys has another main GUI, shown in Figure 7a, that can be used for testing and operator support. All processing results are communicated to the VI user via voice using text-to-speech synthesis.  
The BlindSys software is based on three main parts. 1) The main application contains the application GUI and the business logic under it that manipulates all the gathered information to give navigation details to the user (video extracted information, IMU (inertial measurement unit) information, and so on). 2) Auxiliary applications that are independent from the main application are responsible for performing other functions such as OCR, face recognition, scene description, and so on. 3) Libraries are used to implement the different parts of the main application or the auxiliary applications. They are linked to the application statically or dynamically.  
Figure 6: Overview of the BlindSys system for the visually impaired (VI). Diagram shows the camera on the chest, tablet, and headphones for voice communication.  
The scene description module can detect the presence of a set of objects in a scene image without giving exact locations. The module can detect a set of 15 objects that are commonly found in an indoor environment.
## 6 Sensitivity Analysis
### Sensitivity Analysis under Centralised Decision-Making
#### 6.1.1 Impact of Demand Sensitivity Factor for Logistics Services  
To analyse the impact of changes in the logistics demand elasticity coefficient \(k_{T}\) on optimal profit, optimal demand, and optimal decision-making in agricultural supply chains, the value of \(k_{T}\) is taken between 1 and 40. Only the value of \(k_{T}\) was changed, leaving the values of the other parameters at the base setting, and the graphs describing the relationship between optimal profit, optimal demand, optimal decision variables and the sensitivity coefficients of logistics services are plotted in supply chains under centralised decision-making.  
From Figures 3 and 4, it can be seen that the profit of the supply chain, the profit of the producer, the profit of the retailer, the market demand of the offline channel, and the demand of the online channel, all increase with the increase in the coefficient of elasticity of demand for logistics services. As the elasticity coefficient of demand for logistics services increases, the optimal profit, optimal demand, and optimal decision variables grow faster, indicating that as consumers pay more attention to the level of logistics services, the overall profit of the supply chain under centralised decision-making will increase at a higher level of logistics services.
## 4 Simulation Model  
To implement the mathematical model, a simulation model of QN is proposed. It is based on the following principles:  
* The arriving flow consists of sequentially incoming groups of requests. The size of the group and the duration between each arrival to the system are random variables whose distribution law is set by the user.
* The system consists of \(S\) nodes. The requests transit from one node to another in accordance with the routing matrix \(P\).
* Each node is a queuing system, which has \(m_{i}\) servicing channels and \(n_{i}\) places in the queue (\(i=1,2,\ldots,S\)).
* Channels of each node work independently, and each channel has its own distribution of service time and the size of the serviced group.
* As soon as the channel completed the service, it receives a request from the queue (if there is one). If the line is empty, the channel goes into idle mode.
* Channels can be blocked to prevent loss of requests between nodes in case of system overflow.  
The simulation model is implemented as the authors' software module using Object Pascal programming language [40; 41]. The main functions of the software module are generating the number of requests in arriving groups and the durations between arrivals; generating the service time and the number of simultaneously served requests in the channel; checking the system status for overflow; displaying the service process in graphical and tabular forms; saving the simulation results in MS Excel format.  
The accuracy of the simulation model is evaluated on a series of three types of problem with known characteristics. The first one is \(m\)-channel QS with losses (Erlang problem). The second and the third ones are two- and three-phase QS with blockings and _BMAP_, respectively [44]. The performance indicators of the QS obtained by the designed simulation model are compared with those found by analytical formulas for each task. Based on the results of the comparison, it was found that for all types of problem, the maximum relative error does not exceed 4%.  
The following sections describe model experiments in which the operation of real railway stations is studied. Each experiment includes a description of the station, the construction of its mathematical model in the form of QS, and the numerical simulation of the QS. The quality of operating of the selected station is evaluated based on the simulation results.