esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Discussion
### Parametric Study
#### 6.3.1 Effect of the Current Density  
The current density is a key metric for the performance of the fuel cell and the entire system. The influence of the current density on the energy efficiency of the essential components of the system is depicted in Figure 6. Current densities ranging from 930 to 1830 A/m\({}^{2}\) are shown in this graph. The efficiency of the SOFC decreases as the current density increases because the SOFC cell voltage drops as the current density increases. In addition, the net power output of the SOFC increased with the current density. At 930 A/m\({}^{2}\), the maximum net electrical cycle efficiency was 66.4%, while the lowest was 46.4% at 1830 A/m\({}^{2}\). The energy efficiency of the SOFC-GT system also diminished from 71.55% to 50% with increasing current density. It is mainly because of an increase in the mass flow rate from 0.37 to 0.53 kg s\({}^{-1}\), and an increase in current density from 930 to 1830 A/m\({}^{2}\), to maintain the power output of the system at predetermined levels. Conversely, at a current  
Figure 5: Exergy destruction rates of the main components of the present system.  
density of 930 A/m\({}^{2}\), the highest heating cogeneration (integration of the SOFC with heating load) efficiency was 70.72%, and at a current density of 1830 A/m\({}^{2}\), the minimum was 47.52%. The lowest gain in efficiency of the trigeneration cycle when compared to the net electrical efficiency is likewise significant at 47.52%. It is also noteworthy that the energy efficiency of the subsystem appears to be unaffected by changes in the current density.  
Figure 7 shows the influence of the current density on the exergy efficiency. The net electrical exergy efficiency was observed to decline with increasing current density owing to a decrease in cell voltage, decreasing the power produced by the SOFC. For the range of current densities investigated, the net electrical exergy efficiency decreased from 52.3% to 36.4%. The cooling cogeneration exergy efficiency is approximately 0.5% greater than the net electrical exergy efficiency, owing to the small cooling energy flows in the system relative to the electrical energy flows.  
Figure 6: Effect of the current density on the energy efficiency of system.  
Figure 7: Effect of the current density on the exergy efficiency of system.
# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 4 Fuzzy Logic in User Experience Assessment
### Applicability of Fuzzy Logic  
Fuzzy logic is a mathematical technique for dealing with uncertainty and imprecision systematically. It is based on the concept of fuzzy sets [46], which are sets that have degrees of membership rather than crisp boundaries. Fuzzy logic can be used to model complex phenomena that are difficult to capture with conventional methods, such as human perception, reasoning, and decision-making. One such phenomenon is the user QoE, since it is not a binary concept, but rather a continuum that can vary from bad to excellent.  
When employing fuzzy logic, one can benefit from its advantages compared to other methods like machine learning and neural networks. One key advantage lies in its ability to handle and represent uncertainty and vagueness, which are inherent characteristics of human perception. Unlike traditional binary logic or crisp systems, fuzzy logic allows for gradual transitions between true and false states, mimicking human decision-making processes that involve shades of gray rather than strict yes-or-no choices. Moreover, fuzzy logic provides an intuitive and interpretable framework for capturing complex relationships between input variables (e.g., video characteristics, network conditions, user preferences, social context, etc.) and output assessments (quality rating, in our case). In contrast, machine learning and neural networks may achieve high predictive accuracy, but they often lack interpretability, making it challenging to understand why specific predictions are made. This \"black box\" nature can be problematic when modeling human perception, as the underlying factors driving the predictions may remain unclear.  
Additionally, fuzzy logic-based models require less data for training compared to complex neural networks, making them more suitable for scenarios with limited datasets, such as subjective human ratings. The fuzzy-based models are computationally efficient and can be implemented with relatively simpler algorithms, making them practical for real-time applications, such as real-time QoS management and provisioning, where quick decision-making is essential. This ability to handle uncertainty, interpretability, and efficiency made it our favorable choice when modeling human perceptions of video quality.
# A Brief Review of Mueller Matrix Calculations Associated with Oceanic Particles
## 1 Introduction  
It is well known that the scattering of light by a particle is determined by the detailed characteristics of the scattering particle, particularly its size, chemical composition (thus, the index of refraction), the overall shape, and detailed surface texture (e.g., surface roughness). Oceanic particles vary greatly in size and morphology. While the Lorenz-Mie theory has been used frequently to simulate the optical properties of oceanic particles (e.g., [1; 2; 3; 4; 5]), these particles are predominately nonspherical. Significant differences exist in the optical properties simulated by using \"equivalent\" spheres and non-spherical shapes, such as spheroids (e.g., [6]). In addition, even the simplest biological cell has a membrane and plasma contained within the membrane. Previous studies have shown that accounting for the cell structure can better simulate the optical properties of various phytoplankton species, particularly the scattering at large scattering angles [7; 8; 9; 10; 5; 11]. Advanced light scattering methods have been developed to deal with complex shape and structure. Here, we briefly summarize light-scatteringcomputational methods for oceanic particles. Light scattering in an absorbing medium has been extensively discussed [12; 13; 14]. For generality, however, only a nonabsorbing medium is discussed here. Beginning with [PERSON]'s equations, in Section 2, we will show exact volume-/surface-integral equations for mapping the near field to the far field. Furthermore, we introduce both the amplitude scattering matrix and the scattering phase matrix. In Section 3, several scattering methods will be introduced. In Section 4, discussions are given that are based on oriented particles and particles in random orientation.
## 3 Background
### Stable Marriage Problems  
A _stable marriage problem_ (SM) [1] of size \(n\) is the problem of finding a stable marriage between \(n\) men and \(n\) women. Such men and women each have a strict preference ordering over the members of the other sex. A _marriage_ is a one-to-one correspondence between men and women. Given a marriage, \(M\), a man, \(m\), and a woman, \(w\), the pair \((m,w)\) is a _blocking pair_ for \(M\) if \(m\) prefers \(w\) to his partner in \(M\) and \(w\) prefers \(m\) to her partner in \(M\). A marriage is said to be _stable_ if it does not contain blocking pairs. The sequence of all preference orderings of men and women is usually called a _profile_. In the case of classical stable marriage problem (SM), a profile is a sequence of strict total orders. Given an SM, \(P\), there may be many stable marriages for \(P\). However, it is interesting to know that there is always at least one stable marriage. Given an SM, \(P\), a _feasible partner_ for a man, \(m\) (resp., a woman, \(w\)), is a woman, \(w\) (resp., a man \(m\)), such that there is a stable marriage for \(P\), where \(m\) and \(w\) are married. We will write \(pref(x)\) for the preference ordering of man or woman \(x\). The set of all stable marriages for an SM forms a lattice, where a stable marriage, \(M_{1}\), _dominates_ a stable marriage, \(M_{2}\), iff for every man, \(m\), \(M_{1}(m)\geq M_{2}(m)\) (where \(M_{1}(m)\) is the woman married to \(m\) in \(M_{1}\)) and there is a man \(m^{\prime}\) s.t.\(M_{1}(m^{\prime})>M_{2}(m^{\prime})\). The top of this lattice is the stable marriage, where men are most satisfied, and it is usually called the _male-optimal_ stable marriage. Conversely, the bottom is the stable marriage, where men's preferences are least satisfied (and women are happiest, so it is usually called the _female-optimal_ stable marriage). Thus, a stable marriage is male-optimal, iff every man is paired with his highest ranked feasible partner.  
The Gale-Shapley (GS) algorithm [2] (Algorithm 1) is a well-known algorithm to solve the SM problem. This algorithm consists of a number of rounds in which each un-engaged man proposes to the most preferred woman to whom he has not yet proposed. Each woman receiving a proposal becomes \"engaged\", provisionally accepting the proposal from her most preferred man. In subsequent rounds, an already engaged woman can \"trade up\", becoming engaged to a more preferred man and rejectinga previous proposal, or if she prefers him, she can stick with her current partner. The algorithm takes \(O(n^{2})\) steps and constructs a matching that is stable and _male-optimal_, since every man is paired with his highest ranked feasible partner, and _female-pessimal_, since each woman is paired with her lowest ranked feasible partner.  
```
Set all men and women as free whilethere is a free man \(m\)do \(w\leftarrow\) the first woman in \(pref(m)\) to which he has not yet proposed if\(w\) is freethen  match \(m\) with \(w\) if\(m>_{pref(w)}z\), where \(z\) is \(w\)'s current partnerthen  match \(m\) with \(w\) and set \(z\) free else \(w\) rejects \(m\) and \(m\) remains free
```  
**Algorithm 1**GS
###### Abstract  
In industrial plants, metal strips are quenched using convective heat transfer. This involves accelerating gas through a nozzle system onto the material to be quenched, resulting in a fast and uniform cooling process. The efficiency of the heat transfer is determined by the specific nozzle system. The presented work is focussed on the analysis of the heat transfer by forced convection through impingement jets both theoretically and experimentally. A unique method for determining the forced convective heat transfer coefficient is described and its application to industrial size nozzle arrays of annealing-line cooling sections is presented.  
heat transfer; strip cooling; ultra-fast cooling; impingement jet; measuring method; convective heat transfer; forced convection; heat transfer coefficient +
Footnote †: journal: Applied sciences  
Acticle  
A High-Resolution Method for the Experimental Determination of the Heat Transfer Coefficients of Industrial Nozzle Systems in Heat Treatment Plants  
Gas cooling fulfils both requirements and can achieve cooling rates up to 150 K/(s-mm) as a function of the strip thickness [2]. The convective heat transfer depends not only on the properties of the cooling gas, but also on the velocity and the degree of turbulence of the existing flow. These complex dependencies are summarised by the heat transfer coefficient for forced convection \(h_{force}\).  
The importance of global energy conservation has become evident, particularly since the release of the Intergovernmental Panel on Climate Change's sixth global climate report [3]. Therefore, when designing and retrofitting cooling lines, it is essential to consider not only the required heat transfer coefficients but also the energy consumption of the line due to the necessary movement of the fluid. A cooling section that meets the specified cooling rates is effective, but not necessarily efficient. In the context of nozzle design, efficiency refers to achieving the specified cooling rates with as little effort and, therefore, energy input as possible. This effort can be measured in terms of energy input or manufacturing and maintenance costs. An efficient nozzle design is described as a system which requires minimal energy to generate high flow velocities and effective manufacture and maintain costs.  
This article presents an experimental method for measuring heat transfer coefficients by forced convection of various industrial-size nozzle systems used for cooling sections of annealing lines. Usually, experimental studies are performed to determine the heat transfer coefficient by forced convection of various nozzle systems, supporting plant manufacturers in designing both new and existing plants. This article explains and compares the experimental method developed at the Department for Industrial Furnaces and Heat Engineering at RWTH Aachen University for slot and round nozzle systems with the studies of other authors.
## 2 Materials and Methods
### Satellite Data  
Many ocean color applications use global layer 3 (L3) satellite data due to their regular Earth grid coordinate system. However, the quality of a satellite ocean color determination is usually assessed based on level 2 (L2) data with the original span resolution and satellite geometry. L3 staking and gridded data represent the products of L2 satellite data by matching satellites to points. L2 and L3 satellite data for chlorophyll-a photosynthetic pigment are compared with a conventional in situ dataset, showing similar L2 and L3 in situ satellite imagery for both MODIS (Aqua) and VIIRS-SNPP. This implies that L2 validation results are generally applicable to L3 data. However, when L3 data are generated from L2 data, uncertainties arise. The comparison of L3 data introduces a wider time window between the in situ measurement time and satellite observation time, which can affect the satellite image quality or algorithm performance [30; 31; 32; 33].  
As a follow-up VIIRS instrument, VIIRS-NOAA-20 is essentially built in the same manner as VIIRS-SNPP. Therefore, the sensor characteristics of the two instruments are very similar. Both SNPP and NOAA-20 operate in the 824-km Sun-synchronous polar orbit, which crosses the equator at about 13:30 local time. There is about a 50-min delay between the paths of NOAA-20 and SNPP, which makes the NOAA-20\({}^{\prime}\)s path run through the middle of 2 adjacent SNPP paths, and vice versa. The overlap of the spatial coverages of the two sensors automatically fills each other's gaps caused by high sensor-zenith angles and high Sun glint contamination, and it significantly reduces the missing pixels in the merged images. In addition, the ocean color products from SNPP and NOAA-20 have the same spatial and temporal resolution and are processed with the same software package, i.e., the Multi-Sensor Level-1 to Level-2 (MSL12) ocean color data processing system [30; 31; 32; 33]. Therefore, the statistics of their ocean color products are very similar, and in fact, the data can be directly merged without adjustment to match with each other's statistical properties. However, much data is still missing in the merged VIIRS SNPP/NOAA-20 products [33].
## 7 Bird Nest Deployment
### System Evaluation
#### 7.3.1 Deployment Experience  
As much of the deployment hardware was already in existence from previous deployments, most of our work in the preparation was spent developing the software using the Tenet system. Our remaining time was spent deploying the hardware with two additional trips to James Reserve for routine system maintenance. We deployed eight nodes during a trial run in early May, then deployed the entire 19-node system on 9 May and brought the system up in the late afternoon. The official end date for our deployment was exactly three months later, 9 August at 5 PM. The server machine at James Reserve is connected to the Internet, and thus, we were able to remotely monitor and reconfigure the deployment. We needed to do this several times when either parameter changes were made to the application or a diagnosis of the network was required. As time went by, there were three distinctive failure events that were observable remotely: (1) unreachable sensor node; (2) sensor node pingable, but not returning images; and (3) rebooted Stargate. The first symptom was usually due to a depleted battery, which, in turn, was usually due to a solar panel being in the shade for an extended amount of time. Broken antenna connectors and malfunctioning power systems were other reasons. The second symptom was either due to low battery (not high enough to activate the Cyclops camera) or a loose connection between the mote and the Cyclops. We believe that one of the Stargates rebooted several times during our deployment, due to power outages(James Reserve power is supplied from generators and solar panels, so there are occasional brownouts and outages). We returned to James Reserve twice, once on 4 June and again on 18 July to fix failed nodes and base stations. All repairs were minor and consisted of replacing improperly functioning 12-V batteries, reconnecting connections that had come loose and adding directional antennas to nodes with poor connectivity. These repairs could, in larger deployments, be taken care of by moderately technically-aware domain experts.  
In total, our system collected 102,173 images from 19 nestboxes, providing biologists with information about nestbox occupancy and the breeding behavior of three species of birds. Figure 12 is one subset of those images captured by a camera node during the post-hatching phase. One highlight of the observations made from the images was that of a snake that was able to enter the nestbox and consume nestlings on 19 May. This unusual event has been captured in a series of images in Figure 13. Withoutthese images, using traditional methods of a biologist manually checking nestbox contents daily or even less frequently, the reason for the disappearance of nestlings from the nest would have been unknown.
## 2 Related Survey  
There have been few surveys on AI-enabled applications and AI-6G in smart cities. We classify these surveys into two categories based on the survey's approach: (1) a top-down approach highlighting the requirements of AI applications in terms of networks' capabilities [16; 17] and (2) a down-top perspective focusing on AI-enabled 6G networks for agile, flexible, and efficient systems [18; 19; 20; 21].  
Concerning the top-down approach, [PERSON] et al. [16] presented the projected 6G architecture and its characteristics along with potential technologies enabling the envisioned network generation systems. The authors focused on quantum communication and machine learning, blockchain, tactile internet, and free duplexing and spectrum sharing technologies. Furthermore, the authors discussed e-heath and bio-sensing, HC, and IoT applications that will be underlined by 6G networks. However, the authors did not analyze the requirements of the technology-enabled applications in 6G. Using a similar approach, [PERSON] et al. [17] explained the 6G networks architecture, characteristics, and deployment scenarios. In addition, the authors analyzed the requirements for applications in 6G systems enabled by HC, tactile and haptic internet, edge-cloud computing, and IoT technologies. However, these works [16; 17] do not focus on employing self-learning models on the underlined 6G networks layer for security, agility, flexibility, and energy efficiency.  
Regarding the down-top approach, [PERSON] et al. [18] proposed an AI-enabled 6G architecture for radio network resource management and service provisioning. Similarly, [PERSON] et al. [19; 20] analyzed the potential of AI for 6G networks design and optimization. [PERSON] and [PERSON] [21] presented a survey on AI-enabled 6G networks for radio interface, intelligent traffic control, resource management, performance and energy optimization, and security. However, these works do not analyze the requirements of AI applications in 6G networks.  
Table 1 summarizes the related survey and their comparison with our work. In contrast to these surveys based on top-down or down-top approaches, in this paper, the  
6G technology is prospected from a holistic perspective, where self-learning models are, respectively, inserted into the main technical layers of 6G to achieve the requirements of building an integrated smart city digital ecosystem as a whole, not just looking at application needs or network requirements. In this approach, in addition to AI-enabled 6G network systems and employed self-learning models, middleware and technology-enabled applications and their requirements for an intelligent and connected contextual computing and communication smart cities ecosystem are analyzed.
## 4 Multihoming and Interworking
### GLI-Split with Classic IPv6 Nodes
#### 4.3.4 Local Traffic with Global GLI-Addresses  
When a classic IPv6 node in a GLI-domain wants to communicate with another node in the same domain, it should receive a local GLI-address from the DNS. If it accidentally obtains a global  
Figure 8: IPv6 host 2001:db8::11 in a non-GLI-domain communicates with the non-upgraded GLI-node 4 using NAT-based GAP.  
GLI-address with a set GAP-bit for such a node from an external DNS, the GLI-gateways have to follow special rules to handle this correctly. We illustrate this using Figure 9. Nodes 3 and 4 are in the same GLI-domain. Node 4 wants to send a packet to node 3 and has obtained a global address of node 3. Unlike an upgraded host, the classic host 4 cannot contact the local mapping system to find out the correct local address. It just sends a packet with the global GLI-destination-address \"_N(g).3_\" and the packet is forwarded to the gateway N.  
Gateway N recognizes that both sender and receiver of the packet are inside its own GLI-domain. It substitutes the global GLI-address of the destination with an appropriate local GLI-address. The local source address is replaced by a global source address \"_N(g).4_\", reflecting N as gateway, node 4 as source, and setting the GAP-bit. The packet reaches node 3 and when node 3 responds to that packet, the previously set GAP-bit ensures that source and destination address are simply swapped. Thus, also the response message is returned to gateway N. Gateway N handles this packet like the one before so that node 4 receives response messages with the global address of node 3 in the source field. This way, bidirectional communication is possible. The described operation of the gateway is stateless. There is no need to build or store any mapping table. The gateway uses only the information in each packet to translate source and destination addresses.