esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Solar-induced chlorophyll fluorescence (SIF) is considered to be a potential indicator of photosynthesis. However, the impact of growth and environmental parameters on SIF at different time-scales remains unclear, which has greatly restricted the application of SIF in detecting photosynthesis variations. Thus, in this study, the impact of growth and environmental parameters on SIF was thoroughly clarified. Here, continuous time series of canopy SIF (760 nm, F760) over wheat and maize was measured based on an automated spectroscopy system. Meanwhile, field measurements of growth and environmental parameters were also collected using commercial-grade devices. Relationships of these parameters with F760, apparent SIF (F760)/solar radiance, AF760), and SIF yield (F760)/canopy radiance of 685 nm, Fy760) were analyzed using principal component analysis (PCA) and Pearson correlation to reveal their impacts on SIF. Results showed that F760 at seasonal and diurnal scales were mainly driven by solar radiation (SWR), leaf area index (LAI), chlorophyll content (Chl), mean leaf inclination angle (MTA), and relative water content (RWC). Other environmental parameters, including air temperature (Ta), relative humidity (Rh), vapor pressure deficit (VPD), and soil moisture (SM), contribute less to the variation of seasonal or diurnal F760. AF760 and Fy760 are likely to be less dependent on Ta, Rh, and VPD due to the removal of the impact from SWR, but an enhanced relationship of AF760 (and Fy760) with SM was observed, particularly under water stress. Compared with F760, wheat AF760 was better correlated to LAI and RWC as expected, while maize AF760 did not show an enhanced relationship with all growth parameters, probably due to its complicated canopy structure. The relationship of wheat Fy760 with canopy structure parameters was further reduced, except for maize measurements. Furthermore, SM-induced water stress and phenological stages should be taken into consideration when we interpret the seasonal and diurnal patterns of SIF since they were closely related to photosynthesis and plant growth (e.g., LAI in our study). To our knowledge, this is the first exploration of the impacts of growth and environmental parameters on SIF based on continuous ground measurements, not only at a seasonal scale but also at a diurnal scale. Our results could provide deep insight into the variation of SIF signals and also promote the further application of SIF in the health assessments of terrestrial ecosystems.  
capory SIF; growth parameters; environmental parameters; wheat; maize 2019; 11, 2002; doi:10.3390/rs11172002
## 4 Experimental Results and Discussions
### Model Interpretability and Explainability  
Electra, the Transformer-based model that achieved the best performance in this study, was used to perform sentiment analysis in Turkish. To interpret the model's decision-making processes, Attention Heatmap [63] and SHapley Additive exPlanations (SHAP) [64] Analysis were applied. These methods were chosen to provide a transparent evaluation of the model's classification process by visualizing which words the model focuses on and how these words contribute to the predictions [1,2]. The tools Matplotlib, Seaborn, and SHAP were utilized for visualization and analysis. Below, positive, negative, and neutral sentences are analyzed in detail using both methods.
## 4 Results  
### Data Acquisition and Control System  
The experimental system shown in Figure 10 allows the execution of robot movements, acquisition of location data and monitoring of thermal data. It consists of 6 stages. Stage 1 is responsible for acquiring the images of the robot through a wireless camera (in a real situation, in deep waters, there is a project developed and tested by the Monterrey Bay Aquarium Research Institute (MBARI) based on a vision system. This control system tracks marine animals with an autonomy of 89 min, and it was tested with the Plychogena medusa) [56], which are sent to the central computer.  
Stage 2 corresponds to the control interface for movement control and establishment of virtual points. Data monitoring is carried out with the temperature sensor that the robot carries on board, and the sensor was placed on the robot's top because according to the CFD, the displacement of fluids in this area is less than in any part of the body, acquiring a more reliable measurement. Then, through the vision system (Stage 3), which applies object segmentation and digital image processing techniques, the robot is located inside the aquatic environment.  
In Stage 4, based on the current position and virtual path points, errors are calculated and provided to the fuzzy controller that through Stage 5 (power stage) supply the current in a controlled manner to contract the SMAs. The potency card shown has been designed and implemented based on the power requirement to handle currents of up to 6 Amps with 36 Volts.  
Finally, Stage 6 corresponds to the robot within the aquatic testing environment and the execution of movements.  
Figure 9: Comparison of the volume ejected, using CFD and the analytical model.
## 5 Discussion
### Challenges and Limitation of the Proposed Work  
Deep learning models, such as Complex-Valued Convolutional Neural Networks (CV-CNNs), Temporal Convolutional Networks (TCNs), and their hybrids, have demonstrated great promise in this field. The hybrid approach combining CV-CNNs and TCNs effectively leverages the strengths of each: CV-CNNs excel at feature extraction from complex-valued IQ data, while TCNs are adept at capturing temporal dependencies. However, deploying such advanced architectures introduces several challenges and considerations.  
Here are some of the challenges for AMC with deep learning models:  
* Complexity of wireless signals: Real-world wireless signals often exhibit overlapping characteristics between modulation schemes, particularly for closely spaced modulations like BPSK and QPSK. Extracting discriminative features in such scenarios is non-trivial.
* Computational demands: Hybrid architectures, including CV-CNN-TCN models, require significant computational power for training and inference. Devices with limited resources may struggle to handle the complexity, particularly in real-time applications.
* Overfitting: Due to the high capacity of deep learning models, overfitting is a significant concern. Ensuring robustness to unseen data requires techniques like dropout, data augmentation, and cross-validation.
* Latency constraints: Real-time AMC applications, such as those in cognitive radio or adaptive communication systems, demand low-latency processing. Sequential data processing models, like RNNs, are often unsuitable, while TCNs must be carefully optimized to meet latency requirements.
* Adaptability to new modulation schemes: The introduction of new or non-standard modulation schemes necessitates retraining or fine-tuning the model. Ensuring adaptability while minimizing retraining efforts remains an open problem.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Obtaining Pure Oxides and Metals
#### 3.7.3 Electrolysis  
Electolysis of gallium solutions usually involves two steps. The first stage is called initial concentration. It is usually carried out in electrolysers with a rotating drum that is partially immersed in mercury [77]. The cathode is the rotating drum made of steel and covered with sodium amalgam. Mercury also contains some sodium metal [96]. The cathode surface is constantly renewed as a result of the drum movement, and its overvoltage prevents the creation of hydrogen. Gallium dissolves in mercury with some iron and sodium, together with some impurities such as lead, copper, and zinc, if present in the solution. The saturated amalgam of gallium and sodium is directed to a decomposition vessel, where mercury is purified from gallium and the accompanying metals using hot water or NaOH solution. Mercury is returned to the electrolyser and the obtained concentrate is sent to the second stage [96; 98]. Again, the electrolysis process is carried out. It is usually carried out at approximately 70 \({}^{\circ}\)C on platinum or stainless-steel electrodes. This time, hydrogen is released in the process, as a result of which the current efficiency of the process usually does not exceed 10%. An amalgam of gallium and sodium is obtained on the cathode, and after its decomposition, gallium with a purity of up to 99.9% can be obtained [96].  
Indium is obtained via electrolysis of acidic solutions (when the pH is approximately 2-2.5) of indium chloride or sulfate. Electrolysis of the InCl\({}_{3}\) solution at a pH of 2.2 and a sodium chloride concentration of 100 g/dm\({}^{3}\) allows for the obtainment of metal with a purity of up to 99.999%. In the process, the anode is raw indium and the cathode is refined indium. Electrolysis of indium in a formic acid solution allows indium to be obtained in the form of a sponge. However, during the process indium may be contaminated with metals such as cadmium, copper, zinc, tin, and thallium [98].  
Research is underway to obtain indium on a molybdenum electrode in a molten NaCl-KCl eutectic system at 750 \({}^{\circ}\)C. The method is currently at the stage of collecting the theoretical basis for the development of an efficient and cost-effective process for indium secondary resources recycling based on molten salt electrochemistry [99].  
The method of recovery and reuse of spent ITO waste through electrochemical techniques has also been described in the publication [100]. An indium-tin alloy can be prepared via electrolytic reduction of the s-ITO target in a molten CaCl\({}_{2}\) at 850 \({}^{\circ}\)C. Then, hydroxides can be synthesized via electrolysis of an In-Sn alloy in an aqueous ammonium chloride solution. Finally, oxide nanocomposites, which are used to prepare ITO, can be produced through calcination. Importantly, the process can be extended to a large-scale production.
_Appl. Sci._**2015**, 5, 275-293; doi:10.3390/app5030275  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci.  
_Review_  
**A Review on the Role of Color and Light in Affective Computing**  
**[PERSON] \({}^{1,2}\) and [PERSON] \({}^{2,*}\)**  
\({}^{1}\) Department of Safety of Vital Activity and Environment, South-West State University,  
50 let Oktyabrya, 94, 305040-Kursk, Russia; E-Mail: [PERSON]\({}^{2}\) Instituto de Investigacion en Informatica de Albacete, Universidad de Castilla-La Mancha,  
02071-Albacete, Spain  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]SS];  
Tel.: +34-967599200; Fax: +34-967599224.  
Academic Editor: [PERSON]  
_Received: 13 May 2015 / Accepted: 30 July 2015 / Published: 5 August 2015_  
**Abstract:** Light and color are ubiquitous environmental factors which have an influence on the human being. Hence, light and color issues have to be considered especially significant in human-computer interaction (HCI) and fundamental in affective computing. Affective computing is an interdisciplinary research field which aims to integrate issues dealing with emotions and computers. As a consequence, it seems important to provide an updated review on the significance of light and color in affective computing. With this purpose, the relationship between HCI/affective computing and the emotions affected by light and color are introduced in first place. So far, there is a considerable number of studies and experiments that offer empirical results on the topic. In addition, the color models generally used in affective computing are briefly described. The review on the usage of color and light in affective computing includes a detailed study of the characteristics of methods and the most recent research trends. The paper is complemented with the study of the importance of light and color from demographic, gender and cultural perspectives.  
**Keywords:** color; light; affective computing; human-computer interaction; review
## 5 Data Analyses and Results
### DIC Analyses  
A sensitivity study was performed on the application of the DIC technique to different datasets to test the potentialities and limitations, and the displacement of the Montescalioso landslide area was calculated using all the available datasets.  
Alternately, both DIC software programs described in Section 4.2 were used for each dataset to ensure the mutual control of the displacement measurements, define the movement directions and assess the background noise. The latter represents a fundamental parameter because it allows for a precise quantification of the environmental and instrumental/sensor errors.  
Because the available processed datasets are characterized by regular and repetitive temporal resolutions, an in-depth study of the background noise with a high degree of reliability was undertaken for the entire imagery dataset. Furthermore, specific background/instrumental errors were assigned based on the collecting sensor (CSK, LANDSAT 8 OLI-TIRS, airborne LiDAR and aerial camera).
# Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems +
## 4 Computational Tests
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Problem** & \(m\) & \(\mathcal{MM}\)**MAS**+Lk & \(\mathcal{MM}\)**MAS**+US & **HULK** \\ \hline KROA100.tsp & 0.05 & 20,383.9 (0.7) & 20,254.8 (0.1) & **20,235.5** \\  & 0.1 & 20,457.7 (0.9) & 20,334.4 (0.3) & **20,280.3** \\  & 0.2 & 20,482.0 (2.0) & 20,178.2 (0.5) & **20,073.3** \\  & 0.4 & 20,373.5 (1.8) & 20,188.4 (0.8) & **20,022.9** \\ \hline KROA150.tsp & 0.05 & 25,477.0 (0.8) & 25,347.2 (0.3) & **25,274.2** \\  & 0.1 & 25,635.1 (2.4) & 25,189.4 (0.6) & **25,043.1** \\  & 0.2 & 25,758.1 (2.5) & 25,419.6 (1.2) & **25,128.6** \\  & 0.4 & 25,800.4 (3.3) & 25,275.1 (1.2) & **24,969.3** \\ \hline KROA200.tsp & 0.05 & 28,118.8 (2.2) & 27,524.4 (0.0) & **27,521.0** \\  & 0.1 & 28,250.7 (2.2) & **27,645.6** & 27,649.7 (0.0) \\  & 0.2 & 28,814.1 (4.4) & **27,595.1** & 27,842.3 (0.9) \\  & 0.4 & 29,038.8 (4.8) & 28,067.7 (1.3) & **27,705.5** \\ \hline d198.tsp & 0.05 & **13,280.9** & 14,157.0 (6.6) & 13,403.0 (0.9) \\  & 0.1 & **13,078.4** & 13,837.3 (5.8) & 13,393.0 (2.4) \\  & 0.2 & **13,147.6** & 14,014.3 (6.6) & 13,513.9 (2.8) \\  & 0.4 & **12,943.2** & 13,887.8 (7.3) & 38,842.3 (3.1) \\ \hline lin318.tsp & 0.05 & 39,520.4 (0.4) & 40,624.7 (3.2) & **39,355.2** \\  & 0.1 & 39,371.9 (0.3) & 40,615.7 (3.5) & **39,252.9** \\  & 0.2 & 39,057.7 (0.1) & 40,312.8 (3.3) & **39,032.5** \\  & 0.4 & 38,795.8 (0.0) & 40,091.8 (3.3) & **38,793.8** \\ \hline pcb442.tsp & 0.05 & **48,811.3** & 49,709.1 (1.8) & 48,944.7 (0.3) \\  & 0.1 & **48,776.4** & 49,683.1 (1.9) & 49,044.5 (0.5) \\  & 0.2 & **48,692.5** & 49,770.8 (2.2) & 49,359.8 (1.4) \\  & 0.4 & **48,501.4** & 49,615.1 (2.3) & 49,416.6 (1.9) \\ \hline u574.tsp & 0.05 & 37,456.7 (8.5) & 35,920.9 (4.1) & **34,521.9** \\  & 0.1 & 37,260.4 (8.1) & 35,698.2 (3.6) & **34,473.1** \\  & 0.2 & 36,997.4 (5.5) & 35,564.3 (1.5) & **35,055.9** \\  & 0.4 & 36,964.7 (5.9) & 35,451.3 (1.6) & **34,907.4** \\ \hline rat783.tsp & 0.05 & 8173.1 (0.5) & 8364.1 (2.9) & **8129.6** \\  & 0.1 & 8064.4 (0.6) & 8267.5 (3.1) & **8019.9** \\  & 0.2 & 8034.6 (0.1) & 8239.4 (2.7) & **8024.4** \\  & 0.4 & 7995.4 (0.2) & 8202.9 (2.8) & **7980.0** \\ \hline pcb1173.tsp & 0.05 & **54,478.8** & 55,930.7 (2.7) & 54,639.2 (0.3) \\  & 0.1 & **54,321.4** & 55,904.8 (2.9) & 54,476.0 (0.3) \\  & 0.2 & 54,419.8 (0.2) & 55,991.1 (3.1) & **54,287.6** \\  & 0.4 & 59,943.1 (10.8) & 55,814.4 (3.2) & **54,105.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results regarding the average offline performance of the presented methods on symmetric DTSPs. The number between brackets means the distance in percentage from the current value to the best value, in boldface.
## Data
### Sea Surface Wind  
Wind speed is one of most essential parameters producing SST differences related to the diurnal variability of the SSTs [37; 38], skin-bulk SST difference [39], and so on. In order to evaluate the errors of satellite-derived SSTs and to understand dependency on the wind field, we used the wind speed data observed by the Special Sensor Microwave Imager Sounder (SSMIS). The SSMIS wind speed data are processed and provided by Remote Sensing System (RSS) [40].
# Energy Efficient Multicast Communication in Cognitive Radio Wireless Mesh Network
###### Abstract  
Multicasting is a basic networking primitive used in a wide variety of applications that is also true for cognitive radio-based networks. Although cognitive radio technology is considered to be the most promising technology to deal with spectrum scarcity, it relates to completely different aspects of networking and presents new challenges. For cognitive radio-based multicast sessions, it is important to use the spectrum efficiently by reducing the number of channels used as well as engaging fewer nodes in data relaying. This will benefit the network in three ways. First, it will decrease the number of transmissions. Second, it will help to reduce energy usage. Third, it will spare more channels and relay nodes for simultaneous multicast sessions. To achieve these advantages, efficient channel selection and relay nodes are required based on hop-to-hop communication. In this paper an algorithm has been developed that attempts to minimize energy consumption by selecting the minimum possible number of relay nodes and channels for a multicast session, taking into account the sporadic availability of the spectrum. The proposed method performs effectively compared to the flooding method in terms of energy consumption for the provided examples in multicasting.  
c pacs: 22
# A Systematic Literature Review of Reliable Provisioning for Virtual Network Function Chaining
## 4 Results of the Systematic Literature Review
### Different Algorithms to Provide a Reliable Provisioning
#### 4.2.2 Occurrence of the Failure  
Real-world applications are prone to failure. Failures occur once or multiple times at different points of the algorithm execution or at the same time, depending on the application and its components. However, the algorithms that were proposed can either handle single or multiple failures. The solutions that handle a single failure are the first step in providing a resilient NFV environment. Extending the solution to handle multiple failures is ideal because it is closer to the real-world implementation of VNF-enabled networks.  
In [22], the algorithm accommodates both VNF and link failure. Despite being able to handle both types of failure, the algorithm only protects against single failures. The algorithm in [20] uses multiple disjoint paths but can only handle single link failure. However, using multiple paths will improve network performance in terms of blocking probability and spectrum efficiency. The authors in [19] used replicas to replace a single VNF handling a failure by increasing its resiliency. In [21], the algorithm's main goal is to handle multiple failures. The authors consider failure multiplicity, in other words, multiple simultaneous failures. The drawback is that the proposed solution has high resource consumption compared to other algorithms mentioned in the paper. This is the main point of improvement for future work. In [18], the authors consider the Mean Time Between failures (MTBF). They used a three-year event log of realistic real-world failures. Their approach is to have both backup nodes and links to be able to handle multiple failures. In [39], the authors focus mainly on multiple link failures. Similar to the previous paper, they also used MTBF as part of the algorithm to predict failures. The solution differs from the two previous papers because it is a recovery algorithm. In [10], deep reinforcement learning was used to handle multiple VNF failures. It is a proactive approach that can predict failures and significantly help meet the SLA. In Figure 4, the discussed algorithms are categorized based on the considered number of failure occurrences in their approach.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Algorithm** & **VNF Failure** & **Link Failure** \\ \hline ZT-PFR [10] & ✓ & \\ \hline JPV-ABA [18] & ✓ & ✓ \\ \hline P-E2E [22] & ✓ & ✓ \\ \hline DDQP [25] & ✓ & \\ \hline MP-VPS [20] & & ✓ \\ \hline CS-VA [49] & & ✓ \\ \hline Proposed algorithm [21] & ✓ & \\ \hline BS-PUSH-BS-Pull [42] & ✓ & \\ \hline N+P [19] & ✓ & \\ \hline EVNFO [24] & & ✓ \\ \hline Proposed algorithm [50] & & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Different types of failure in reliable provisioning algorithms.
# SoK: Delegated Security in the Internet of Things
## 6 Discussion
### Computation Delegation
#### 6.4.1 Confidentiality  
The majority of the protocols in this domain provide confidentiality through security techniques such as encryption, key management, and secure communication protocols, e.g., DTLS. These techniques ensure that even if \(\mathcal{A}_{\textit{weak}}\) tries to intercept the communication, the data remain inaccessible without the appropriate keys or permissions [15; 71; 72]. However, we have labelled some protocols as not protected against \(\mathcal{A}_{\textit{weak}}\), particularly those that do not mention the use of cryptographic mechanisms, as they are primarily focused on data processing and analytics [73; 74; 75]. These protocols might not focus on ensuring confidentiality in terms of encryption or secure data transmission and may be vulnerable to data exposure to \(\mathcal{A}_{\textit{weak}}\) because they do not incorporate sufficient protection mechanisms. The same holds true for \(\mathcal{A}_{\textit{mildle}}\). Having accomplices within the system may provide an advantage for \(\mathcal{A}_{\textit{mildle}}\) over \(\mathcal{A}_{\textit{weak}}\) only in the case of a few protocols [15; 76].  
Considering attacks against confidentiality from \(\mathcal{A}_{\textit{strong}}\), it is observed that more than half of the protocols remain resistant [77; 78; 20; 71]. These protocols generally employ architectures that prevent internal adversaries from directly accessing or modifying data, even with full system access. However, several protocols are still vulnerable to \(\mathcal{A}_{\textit{strong}}\), particularly those that focus primarily on data-processing or -management frameworks [73; 79] and do not incorporate sufficient security mechanisms to protect data against internal threats