esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectroscopic and Imaging Analyses on Easel Paintings by [PERSON]
## 3 Results
### Pigments and Dyes
#### 3.3.4 Grey and Black Hues  
As it is known, ED-XRF is not useful for identifying organic black pigments due to the low Z number of their characteristic chemical elements. However, trace amounts of some inorganic elements such as Ca, S and K can be related to carbon black [23; 24]. These elements were found in most of the grey and black areas, such as in _Annunciation_ (GSA 36), _Muses_ (MC 35) and _Sacred Conversation_ of Montefiorentino, but only future micro-invasive analyses should confirm this. Conversely, bone/ivory black can be hypothesised for the P occurrence, such as in many of the grey and black hues of the Montefiorentino altarpiece.  
As expected, vis-RS was not able to distinguish between carbon black and phosphorous-based black, but it allowed us to detect some blue pigments added, such as natural ultramarine and azurite. The first precious blue pigment was added to obtain the grey hues of the armours in the _Sacred Conversation_ of Montefiorentino (GSMF 44-46, 50). Azurite was identified in the grey hue of the capital of the _Annunciation_ (GSA 32, Table S5). Equally, Cu-based pigment was revealed by ED-XRF in the black shadows of the armour of Count [PERSON] (GSMFF 78), in the black hues of [PERSON]'s armour and chain mail (GSMFF 73-76; Figure S5a-c), in [PERSON]'s mantle (GSMFF 77), in the praying man's black dress (GSPB 22) of the _[PERSON]_ altarpiece, in the black angel's collar of the _Annunciation_ panel (GSA 36), in the black dresses and architecture of the _[PERSON]_ altarpiece, in [PERSON]'s black horn (MC 35), in the dark grey of the architecture of the [PERSON] panel (GSST 16) and in some black areas of _Sacred Conversation_ of Fano (GSMF 34-35). Azurite was observed by [PERSON] in the small-sized devotional panels of _Dead Christ in the Sarcophagus with the Symbols of the Passion_ (GSCP 17) and _[PERSON] and the Virgin_ (GSCM 32-33; Figure S5d,e) (Table S5). In the architectural wooden frame of the Montefiorentino altarpiece, As-based pigments and bone black (Ca\({}_{5}\)(OH)(PO\({}_{4}\))\({}_{3}\))) were detected in the greyish false marble decoration (GSMFF 52-55). Lead white and Fe-based pigments were used to modify the hues as well as a small amount of vermilion (HgS) found in [PERSON]'s black horn (MC 35; Table S5).
## 3 The Conceptual Framework Proposed
### The Five-Step Process of Verification Using Living Labs
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Steps**} & \multicolumn{2}{c}{**Contents**} \\ \hline \multirow{4}{*}{Recognition of the need for verification verification verification} & \multirow{4}{*}{Step 1} & **Selecting the product item and its test methods and certification criteria that need verification** \\ \cline{3-3}  & & **criteria that need verification** \\ \cline{3-3}  & & - Analyzing the features of the product in terms of the quality and safety concerns \\ \cline{3-3}  & & - Analyzing the appropriateness of specific test methods and criteria \\ \cline{3-3}  & & - and the need for verification in the real-use environments \\ \cline{3-3}  & & - Analyzing the feasibility of the verification using living labs \\ \hline \multirow{4}{*}{Development of verification using living labs} & \multirow{4}{*}{Step 2} & **Analyzing the real-use environment of the product** \\ \cline{3-3}  & & - Analyzing user context, technological context, and physical context \\ \cline{3-3}  & & - of the product use \\ \cline{3-3}  & & - Eliciting the key components of living labs in terms of the three real-use environments of the product \\ \cline{3-3}  & & - Prioritizing the key components of living labs \\ \cline{3-3}  & & **Implementing living labs for verification purposes** \\ \cline{3-3}  & & - Analyzing the implications elicited in step 2 and designing living labs accordingly \\ \hline \multirow{4}{*}{Verification and analysis} & \multirow{4}{*}{Step 5} & **Conducting tests or experiments for verification** \\ \cline{3-3}  & & **Analyzing the results and concluding the verification process** \\ \cline{3-3}  & & - Analyzing the results of the tests or experiments to see whether the test methods and criteria are valid or need to be improved \\ \cline{3-3}  & & - Discussing how to utilize the results to improve the current test methods and criteria, if necessary \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The Five-Step Process of Verification Using Living Labs. results. Finally, it can be determined whether the test methods and certification criteria are valid enough to assure the expected performance and safety of the product after sales. Further, directions for the revision or improvement of current test methods and criteria can be discussed if necessary.
# Delay-Sensitive Service Provisioning in Software-Defined Low-Earth-Orbit Satellite Networks
## 6 Experimental Result
### Link Load Comparison  
We calculate the traffic load on each link according to the routing results provided by the algorithms. Figure 7 shows the link loads of all network links. The link loads are sorted in descending order shown in the figure. Here, we only consider the link load in one time slot.  
In this experiment, the bandwidth capacity of link is 100 Mbps. Only the routing solution provided by the TGM algorithm lead's several linksexceeds the bandwidth capacity, which causes flow congestion. Compared to all the comparison algorithms, the LCP algorithm achieves the fewest maximum link loads due to load balancing considered in the algorithm. About 83% of the links (196 out of 236) have resource utilization rates below 40% during the network operation. This allows the link to accommodate more best effort (BE) traffic flows. The load balancing in LCP is slightly higher than that of the ILP model but still has high link-load-balancing performance.  
Figure 8 shows the link load after adding the 10 BE traffic flows with a bandwidth requirement of 50 Mbps. All BE flows are forwarded using the shortest path. It can be observed that after adding the BE flows, the LCP algorithm has the fewest congested links;  
Figure 6: Path handovers for all TS flows.  
Figure 7: Link load with only TS flow.  
70% of the links (166 out of 236) have resource utilization rates below 40% during network operation; and most links having utilization rates between 20% and 40%, representing the best performance in load balancing.  
Then, we test the link load in one network topology cycle. Figure 9 shows the maximum link loads for each algorithm at every time slot. It can be seen that, in most time slots, ILP achieves a good load balancing, with the maximum link load stabilizing at around 70%. The proposed LCP algorithm and LSHADE also achieve load balancing and perform better than the two comparison algorithms.  
In this experiment, we observe the similar result in the other time slot; for simplicity, we only show the result of one slot here.
## 4 Biosensors in the Field of Healthcare
### Wireless Power Transfer to Medical Implants  
One of the greatest challenges facing medical implants is the transmission of power wirelessly. If an efficient and safe solution of wireless power transfer (WPT) is achieved, medical smart implants will enter a new revolutionary era. WPT is defined as a wireless transfer of energy from an external source to power or charge the implantable device without connecting wires. Currently, there have been various attempts to deliver wireless power to implanted devices based on the principle of electromagnetic fields. Yet, difficulties in the miniaturisation of the antenna and its orientation have been an unresolved obstacle. Attempts to address these problems are divided into sections termed near-field, mid-field, and far-field WPT [142].
## 3 Methods
### The Weighted Voronoi Diagram (WVD) Model for Dividing the Service Scope of Emergency Shelters
#### 3.2.1 The Model for Service Scope of an Urban Emergency Shelter
The calculation process is expressed as follows:  
\[R_{j}=R_{1}+R_{0}\sum_{j=1}^{n}\frac{A_{j}w_{j}}{5}(j=1,2,3,\ldots,n) \tag{17}\]  
where \(R_{i}\) is the weighted coverage distance of an emergency shelter. \(R_{0}\) is the coverage radius of emergency shelters in various grades (i.e., 2 km for a fixed emergency shelter; 8 km for a central emergency shelter). \(R_{1}\) is the radius of the emergency shelter itself, \(A_{j}\) is the score of each influence factor, and \(w_{j}\) is the weighted value of each influence factor. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Influence Factors** & **Explanation** & **Weight Values** \\ \hline The field conditions & Whether the terrain in the study area is flat, whether the foundation is strong, and whether there is a geological disaster-prone area & 0.0337 \\ \hline The terrain slope (Unit: degree) & Slope of location in different places & 0.0337 \\ \hline The site types & Available area and facility availability for different site types & 0.0750 \\ \hline The traffic convenience (Unit: m) & Whether the width of the road around the site can meet the needs of evacuation and rescue transportation & 0.0422 \\ \hline The actual effective area (Unit: m\({}^{2}\)) & The actual area of the refuge area in the site after removing the water area, roads, trees, houses, mountains, etc. & 0.5587 \\ \hline The possibility of disaster impact & Mainly refers to the flammability of buildings in the site and the distance from the surrounding buildings & 0.1514 \\ \hline The conditions of infrastructure & Including basic requirements for domestic water, fire, temporary power supply and communication in the premises & 0.1053 \\ \hline \hline \end{tabular} Note: In the process of weight calculation, each indicator is divided into five levels: excellent (5 points), good (4 points), general (3 points), poor (2 points) and very poor (1 point).  
\end{table} 
Table 1: Influence factors and their weight values of service scope of emergency shelters.
## 2 Related Work
[18] considered incomplete information and proposed an incentive mechanism that integrates contract and reputation to select high-quality clients. However, they did not consider clients' personal decision-making and only incentivized clients with reputations above the threshold to maximize the economic utilities of the server, which may result in a significant loss of data and ultimately lead to poor model performance. Considering the joint optimization objectives of expenditure costs and model accuracy loss, the study of [30] saw the design of a multi-dimensional contract to achieve efficient FL. Although taking the personal decision-making of clients into account, they assumed that all heterogeneous clients have identical local update frequency, which leads to useless waiting times and leaves room for further optimization in resource efficiency. In contrast to the existing works mentioned above, we aim to solve the joint optimization problem of efficiency and incentive from a game-theoretic perspective. Additionally, we consider the edge servers' personal decision-making, multi-dimensional private attributes, and incomplete information, which makes our incentive mechanism more general and practical.
## 3 Results
### Relationship with Yield  
The relationship between the thermal and hyperspectral derived indices with yield was assessed for all 50 cultivars. The three replicates per cultivar were averaged to obtain a mean value per cultivar. The two species displayed the same tendency, so although they were individually identified, the regression analyses took both into account (Figure 8). The analysis was performed for the two dates, although on DOY 76 none of the indices produced good results and the differences were not significant (_data not shown_). On DOY 126, the best relationship with yield (R\({}^{2}\) = 0.53, p \(<\) 0.001) was obtained for the CWSI (Figure 8a). The results for the regression with yield for the whole set of indices can be found in Table 2. Other indices that produced good results were FLD, which obtained an R\({}^{2}\) = 0.47 (\(p\)\(<\) 0.001; Figure 8b), PRI (R\({}^{2}\) = 0.49, \(p\)\(<\) 0.001; Figure 8c) and WDI (R\({}^{2}\) = 0.45, \(p\)\(<\) 0.001; Table 2). Among the structural-related indices, MSAVI obtained the best relationship with yield, with R\({}^{2}\) = 0.31 (\(p\)\(<\) 0.001; Table 2). It can be observed that, for each category presented in Table 2, there was a wide variability in the results, from highly significant (\(p\)\(<\) 0.001) to non-significant (\(p\)\(>\) 0.05). Note that NDVI, the most widely used index for the assessment of structural effects, was not significantly correlated with yield (Figure 8d and Table 2) (R\({}^{2}\)= 0.10; \(p\)\(>\) 0.05). Concerning the chlorophyll content, the best indices were TCARI and TCARI/OSAVI (Table 2) yielding R\({}^{2}\) = 0.35 and 0.41, respectively.
# A Review of Lithium-Ion Battery Fault Diagnostic Algorithms: Current Progress and Future Challenges
## 2 Types of Fault in the Li-Ion Battery System
### Internal Battery Faults
#### 2.1.6 Accelerated Degradation  
Cell degradation is a common characteristic in most batteries and occurs due to a variety of reasons, such as aging and self-discharging mechanisms. However, accelerated degradation is abnormal and can cause severe problems in Li-ion battery applications. The degradation process is accelerated during storage at elevated temperatures [27]. External degradation is also accelerated due to factors, such as impedance increase, higher frequency of cycle, change in SOC, and voltage rates [28; 29]. Some mechanisms of accelerated degradation are corrosion of current collectors, changes in the electrode material, and reactions between electrodes and electrolyte. Accelerated degradation can cause the battery to have a shorter lifespan, which can be a major issue in applications such as EVs. It can also cause surface layer formation and contact deterioration, which results in electrode disintegration, material disintegration, and loss of lithium. These phenomena can lead to transport barriers, which result in penetration of the separator and cause an internal short circuit and, ultimately, a thermal runaway [30].
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 4 Proposed Heuristic Coding-Based On-Demand Broadcast
### Scheduling Algorithms with Heuristic Coding Implementation  
We select several representative on-demand scheduling algorithms from each group and present the implementation of their coding versions. In particular, we analyze how the coding versions of different algorithms can fulfil their original scheduling objectives.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{c l l l} ADP & Adaptive dynamic programming \\ CC-VSI & Current-controlled voltage source inverter \\ DSP & Digital signal processors \\ ESS & Energy storage system \\ FCS-MPC & Finite control set model predictive control \\ FPGA & Field programmable gate array \\ MLP & Multi-layer perceptron \\ MPC & Model predictive control \\ \end{tabular}  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controller** & **THD** & **Overshoot** & **Response Time (\(\mu\)s)** & **Switching/Cycle** \\ \hline PI & 2.30\% & 40.5\% & 492 & 333 \\ PR & 1.02\% & 8.2\% & 462 & 333 \\ Hysteresis & 1.60\% & 1.0\% & 447 & 669 \\ MPC & 1.35\% & 0.9\% & 238 & 935 \\ ADP & 1.68\% & 26.1\% & 465 & 333 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison of current controllers.
## 4 Discussion
### Limitations of CGANs  
Although CGANs are effective for predicting S2 from L8 imagery, there are certain limitations that should be addressed. For example, CGANs have a more complex training process that requires significantly more time to train, around four-fold for each CNN. The performance of CGANs greatly depends on the quality of the dataset and the complexity of the task. CGANs require considerably more manual supervision during training since there is no specific objective function to minimize, unlike convolution networks that only look to reduce the loss function. Therefore, training CGANs typically requires running multiple experiments, which increases overall training time substantially. However, the NoGAN training methodology proposed by [PERSON] [49] reduces some of the training uncertainty and helps stabilize this process. Despite these difficulties, CGANs are effective in image translation [30] and other image-processing tasks [56]. Although CGANs and other deep learning algorithms require large datasets and computational resources for training, access to publicly available satellite datasets [57] and computational resources reduces such issues.
## 5 Machine Learning, Dependability, and 6G
### Dependability of Federated Learning
#### 5.2.1 Reliability  
ML techniques rely heavily on data. Data quality is fundamental for achieving high accuracy during the learning task. Client selection is a critical issue in FL, as clients are the ones updating the local models previous to the global aggregation, it is fundamental to properly select the clients that train the models using the highest quality of data. Most of the FL systems select their clients in a random manner, or based on resource conditions. Such selection of course might affect the global performance, as non-trustable nodes can also be selected. Moreover, the complexity of conceiving client selection in a communications network due to its dynamic nature also hinders their reliability. Even further, as it is difficult for the centralized entity that performs the selection to actually monitor a large-scale behavior, the selected untrustable clients are unlikely to be removed. Moreover, since the FL process consists of several rounds, previously selected untrusted clients might also be selected for future rounds. This can further damage the learning accuracy. Similarly, security vulnerabilities and lapses can also affect reliability.
## 4 Results and Discussion
### Further Discussion on the Cavity Profile Optimization
#### 4.3.1 SEP-Type Cavity Profile  
Figure 18 shows the impact of the exponent Za on the diaphragm stress (\(\delta\)) and swept volume (Vmo). As shown in Figure 18a,c, with constant cavity depth and radius parameters for both first-stage and second-stage diaphragm compressor heads, increasing the exponent Za initially reduced diaphragm stress before inducing a subsequent stress increase, while simultaneously causing a gradual increase in diaphragm swept volume. Figure 18b,d shows the optimized stress distributions for first-stage and second-stage diaphragm compressors under minimum stress design conditions. The results indicated that within the Za range of [2,10], the first-stage diaphragm achieved a minimum surface stress of 179.96 MPa with a corresponding swept volume of 0.001116 m\({}^{3}\), while the second-stage compressor diaphragm reached a minimum stress of 175.72 MPa with a swept volume of 0.000362 m\({}^{3}\)  
Figure 16: Diaphragm deflection variation with the crank angle under different experimental conditions. (**a**) First-stage diaphragm center deflection. (**b**) Second-stage diaphragm center deflection. (**c**) Radial deflection of the first-stage diaphragm under the case #3 condition. (**d**) Radial deflection of the second-stage diaphragm under case #3 condition.
## 3 VN Allocation and Enforcement Methodology
### Fair Allocation and Enforcement Methodology
#### 3.5.2 Fair Allocation Enforcement Methodology  
After the VN is mapped to the underlying physical network and the redundant bandwidth is fairly allocated among tenants, we enforce these bandwidth guarantees. Non-blocking VN only requires the bandwidth guarantee enforcement at the senders, so congestion will not take place anywhere. For blocking VN, congestion will occur at the endpoint link or the network fabric--in this case, we take measures to control the flow rate of each sender according to the feedback of the receiver. At the receiver, we monitor and detect the congestion, then calculate the reasonable traffic sending rate for senders to avoid congestion, afterwards we send feedback to the senders, and senders limit the sending rate based on the received feedback.
# Results and Achievements of the ALLIANCE Project: New Network Solutions for 5G and Beyond
## 2 Network Architecture and Project's Main Achievements
### Achievements  
We summarise the main results and achievements of the project below. More details are then further provided in the following sections dedicated to each of the main components of the ALLIANCE network architecture.
## 2 Results and Discussion
### Sorption Properties
#### 2.2.5. Sorption in Fixed-Bed Columns  
Non-optimal operating conditions have been used for testing the sorption of Sr(II) in fixed-bed columns. Indeed, the ratio of column radius to bead radius was not large enough to effectively minimize the wall effects. However, these preliminary results give the first indication on the possibility of using AO-APEI in a dynamic system. Figure 6 compares the breakthrough curves obtained at different flow rates (for C\({}_{0}\): 54 mg Sr L\({}^{-1}\)).  
The modeling of experimental breakthrough curves (points) with the Thomas model is represented by the solid curves. The fit of experimental data is improved by high flow rates. Table S8 reports the parameters of the model for the three curves and compares calculated and experimental values for the sorption capacities: they are remarkably close. As the flow rate increases, the sorption capacity tends to decrease: the lower residence time and the wall effects may explain that the sorbent is not fully saturated. The sorption capacity, even at low flow rate, is significantly lower than the value deduced from the Langmuir equation: for C\({}_{\rm eq}\) = 54 mg Sr L\({}^{-1}\), the sorption capacity in the batch reactor is found close to 114 mg Sr g\({}^{-1}\) instead of 80.8 mg Sr g\({}^{-1}\) in the fixed-bed column. The calculated values for saturation capacity (i.e., q\({}_{0}\), mg Sr g\({}^{-1}\)) and rate parameter (i.e., k\({}_{\rm Th}\), L mg\({}^{-1}\) h\({}^{-1}\)) are correlated with the flow rate (i.e., Q, L h\({}^{-1}\)) according to Equations (1) and (2):  
\[\mbox{q}_{0}=52.0\ \mbox{Q}^{-0.271}\ (\mbox{R}^{2}\  
Figure 5: Effect of the pH on the selectivity coefficient for the separation of Sr(II) from Mg(II), Al(III), Ca(II), and Na(I) (equimolar solutions: C\({}_{0}\) = 1.8 mmol L\({}^{-1}\); SD: 125 mg L\({}^{-1}\); contact time: 48 h; agitation speed: 140 rpm: room temperature: \(25\pm 1\ ^{\circ}\)C).