esa-sceva/satcom-chunk-collection huggingface dataset:
# Blockchain-Based Multimedia Content Protection: Review and Open Challenges
## 4 Limitations, Open Challenges and Future Research Directions
### Limitations and Research Challenges of the Blockchain Technology  
In this section, we discuss the limitations and research challenges of the blockchain technology:  
* The blockchain suffers from a scalability problem due to the limited block size, e.g., Bitcoin can reach 7 transactions per second due to the Bitcoin protocol restricting block sizes to 1 MB. A possible solution to this problem is to increase the block size, but it creates a strain on the security due to the fact that an increase in the probability of orphaned blocks would distinctly affect the bandwidth and validation costs. The higher the block size limit, the larger the transaction load, blockchain congestion, and transaction delays. Consequently, a decrease in the transaction fee would result in less security. Thus, a trade-off between security, scalability and decentralization is a challenge in the development of a blockchain. Permissionless blockchains establish that the recorded data is accessible, and thus, enable its public access to all participants. However, this may compromise data privacy. Furthermore, in the case that a sensitive or confidential data is uploaded mistakenly to the public blockchain, there is no way to rectify the damage.
* The codes of smart contracts are susceptible to the inclusion of bugs due to human error or incomplete information. Moreover, the self-executing nature of smart contracts implies reduced flexibility to give effect to the actual intentions of the parties.
* A programming language to implement smart contracts is an ongoing research field. Currently, the most used object-oriented and high-level language to implement complex smart contracts in Ethereum is Solidity (Turing-complete language), which is still evolving and has a number of limitations, such as lack of general purpose libraries, type checking and multi-threading support, among others. Other popular programming languages (Python, C++, Java) are also used to code smart contracts. However, making programs readable (human readable code and human readable execution) in each form remains a challenge. In the case of Bitcoin, the scripting language to write a simple code is not Turing complete and does not support all possible programming structures, specifically loops.
* Blockchain could suffer from 51% attacks, where some nodes may attain the majority in a network and abuse it, e.g., they may reverse transactions to perform double spending and prevent other miners from confirming blocks.
* User's privacy can be breached within the blockchain, e.g., user's real IP address can be traced, transaction history can be linked to reveal his/her true identity or linkability through his/her connected set of nodes.
* Blockchain lacks interoperability due to the lack of universal standards. Existing blockchain networks have their own parameters, such as consensus models, transaction schemes, cryptocurrency, and smart contract functionality. Moreover, the uncertainty and speculative nature of cryptocurrency still prevent its widespread adoption.
## 2 Multiaxial Fatigue Assessment Method  
### Critical-damage-Plane Function  
Various critical-damage-plane functions have been applied for fatigue evaluations with multiple stresses. Typical methods, and their advantages and disadvantages, are presented in Table 1[21]. A typical \"stress--number of cycles\" formula is introduced and applied in this paper for multiaxial fatigue-life evaluation due to its low dependence on fatigue experiments on specific structures [22]. Using the shear-stress amplitude in the critical-damage plane as the typical fatigue effect, the equation can be described as follows:  
\[t_{xy,\mu}=f(N_{f})=\frac{t_{-1}}{1-\kappa N_{f}^{b1}} \tag{1}\]  
where \(t_{xy,\mu}\) is the shear-stress amplitude; \(N_{f}\) is the fatigue life in the form of the stress cycle number; \(t_{-1}\) is the fatigue limit in torsion; and \(\kappa\) and \(b_{1}\) are fatigue strength-related indexes that can be obtained by fatigue experiments on metal materials.  
Then, the Basquin equation is applied to convert the multiaxial fatigue problem into a pure-torsion fatigue problem, which can be described as follows:  
\[t_{xy,a}=f(N_{f})=\tau_{f}^{\prime}(2N_{f})^{b2} \tag{2}\]  
where \(\tau_{f}^{\prime}\) and \(b_{2}\) are two fatigue-strength indexes under the condition of pure torsion loading.  
On the basis of defining the critical fatigue-damage plane where the maximum nominal stress is located, the fatigue-life prediction formulation can be deduced as follows:  
\[t_{eq,a}=f\Big{(}N_{f}\Big{)}=\sqrt{\left(C_{a}^{*}\right)^{2}-C_{a}^{*}N_{max} ^{*}+\left(\frac{t_{-1}^{2}}{f_{-1}^{2}}\right)(f_{-1}+2C_{a}^{*})N_{max}^{*}} \tag{3}\]  
where \(t_{eq,a}\) is the equivalent stress amplitude, \(C_{a}^{*}\) and \(N_{a}^{*}\) are the shear-stress amplitude and maximum nominal stress on the critical plane, respectively; \(t_{-1}\), \(f_{-1}\), \(\kappa\) and \(b_{2}\) are four material-related indexes that can be obtained by existing fatigue experiments. Table 2 presents the comparison results of fatigue experiments and theoretical predictions. As shown in the figure, the difference is within the dispersion zone of three times, showing the accuracy of the method.
# Panoptic Segmentation-Based Attention for Image Captioning
## 6 Experimental Results
### Evaluation
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Method** & **BLEU-1** & **BLEU-2** & **BLEU-3** & **BLEU-4** & **ROUGE-L** & **METEOR** & **CIDEr** \\ \hline Hard-Attention [2] & 71.8 & 50.4 & 35.7 & 25.0 & - & 23.0 & - \\ VIS-SAS [48] & 72.5 & 52.6 & 38.1 & 28.1 & 55.4 & 23.7 & 82.1 \\ [PERSON] et al. [3] & 69.7 & 51.9 & 38.1 & 28.2 & 50.9 & 23.5 & 83.8 \\ ATT-FCN † [13] & 70.9 & 53.7 & 40.2 & 30.4 & - & 24.3 & - \\ [PERSON] et al. [26] & 71.2 & 51.4 & 36.8 & 26.5 & - & 24.7 & 88.2 \\ Areas of Attention [4] & - & - & - & 30.7 & - & 24.5 & 93.8 \\ SCA-CNN [14] & 71.9 & 54.8 & 41.0 & 31.1 & 53.1 & 25.0 & 95.2 \\ [PERSON] et al. [49] & 72.2 & 55.3 & 41.8 & 31.6 & 53.1 & 25.0 & 95.2 \\ [PERSON] et al. † [23] & 72.4 & 55.5 & 41.8 & 31.3 & 53.2 & 24.8 & 95.5 \\ [PERSON] et al. [54] & - & - & - & 33.1 & 53.9 & 25.8 & 99.3 \\ [PERSON] et al. [50] & 74.0 & 57.6 & 44.0 & 33.5 & 54.6 & 26.1 & 103.4 \\ [PERSON] et al. [51] & 74.3 & 57.9 & 44.2 & 33.6 & 54.8 & 26.1 & 103.9 \\ Adaptive [15] & 74.2 & 58.0 & 43.9 & 33.2 & - & 26.6 & 108.5 \\ Att2 all [28] & - & - & - & 34.2 & 55.7 & 26.7 & 114.0 \\ [PERSON] et al. [52] & - & - & - & - & - & 26.9 & 116.1 \\ Stack-Cap (C2F) [53] & **78.6** & **62.5** & **47.9** & **36.1** & **56.9** & **27.4** & **120.4** \\ \hline DetectionAtt & 77.1 & 60.4 & 45.4 & 33.7 & 55.6 & 26.3 & 112.3 \\ InstanceSegAtt & 77.9 & 61.4 & 46.7 & 34.9 & 56.3 & 27.0 & 117.3 \\ PanopticSegAtt (w/o Dual-Attend) & **78.2** & **61.8** & 46.9 & 34.9 & 56.4 & 26.9 & 118.2 \\ PanopticSegAtt & 78.1 & 61.7 & **47.1** & **35.3** & **56.6** & **27.3** & **119.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The results obtained on the MSCOCO Karpathy test split [42]. † indicates ensemble models. Higher is better in all columns. Scores were multiplied by a factor of 100. Figure 5: Histogram of CIDEr scores of DetectionAtt, InstanceSegAtt, and PanopticSegAtt.
## 4 Why and How to Measure Volatile Organic Compound Emissions from a Material?
### How to Measure VOC Emission Rates from Wood-Based Materials?
The surface emission rates (SER) in \(\mu\)g m\({}^{-2}\) h\({}^{-1}\) can be calculated for every compound or for the TVOCs using the following equation:  
\[\text{SER}=\frac{\text{m}\ -\ \text{m}_{0}}{\text{V}}\times\frac{\text{q}}{\text{S}} \tag{1}\]  
where m is the mass of the compound(s) studied (\(\mu\)g), m\({}_{0}\) is the mass of compound(s) already present in the environment (\(\mu\)g), V is the the sampled volume (m\({}^{3}\)), q is the the sampling rate (m\({}^{3}\) h\({}^{-1}\)), and S is the sample surface area (m\({}^{2}\)). VOC emissions from materials depend on three parameters: the initial emittable concentration (C\({}_{0}\)), the diffusion coefficient (D\({}_{\text{m}}\)), and the partition coefficient (K) [106; 107]. Emissions from materials have been modeled and shown to be influenced by factors such as temperature, humidity, and air flow, through the three parameters mentioned above.
# Snow Depth Variations in Svalbard Derived from GNSS Interferometric Reflectometry
## 2 Data and Methods
### Method
#### 2.2.2 Improved Approach Based on Wavelet Analysis
To further determine the depth-related frequency in the original signal, LSP spectral analysis was performed on the coefficients  
Figure 3: Wavelet decomposition results for the dSNR sequence for GPS PRN 19 on February 28, 2018. The wavelet decomposition of the signal has the following structure: [cD1, cD2, cD3, cD4, cD5, cD6, cA6], where cD is the detailed coefficient of the sequence, and cA is the approximation coefficient of the sequence. at each resolution scale; the results corresponding to the different resolution scales are shown in Figure 4.  
The main frequencies obtained by LSP spectral analysis at cD4, cD5, cD4+cD5 and cD5+cD6 were 14.18, 19.41, 14.25 and 16.35 Hz, and the corresponding snow depths were 35.07, \(-\)14.18, 34.41 and 14.43 cm, respectively. According to the measurements provided by the Ny-Alesund weather station, the snow depth measured at 6:00 UTC on 28 February 2018, was 27 cm. Therefore, cD4+cD5 is more effective for denoising than the other coefficients. For cD4, although also close to the true values, the results do not show good denoising effects. After multi-resolution analysis, the reconstructed spectral-analysis results are in good agreement with the measured snow depth. After estimating the snow depth from individual SNR sequences, a filtering procedure was applied to obtain the daily average snow depth by using observations of all the satellites over the whole azimuth in one day. Results with random errors over three standard deviations were eliminated, as were negative estimations of daily snow depth.
# Commonly Used External TAM Variables in e-Learning, Agriculture and Virtual Reality Applications
## 3 Method
### Phase 1--Definition of the \"What's\"
#### 4.1.7 Technology Acceptance Analyzing the Effect of Certain Moderating Variables  
In addition to the original TAM determinants, PU and PEOU, papers under this category give special attention to the moderating role of some variables being age and gender the most used. [PERSON] et al. [102] applied an extension of the TAM in a public university in Malaysia to understand how age and gender affected the acceptance of computer-based technologies by faculty members. The results evidenced that the causal relationships among exogenous variables and the TAM were invariant for female and male staff members. However, age seemed to have moderating effects over the use of technology. In line with the previous study, [PERSON] et al. [103] analyzed the influence of gender in student teachers' intention to use computers. Once more, the findings showed that gender had no moderating effects within the context of the study. Unlike the precedent researches, [PERSON] et al. [104] found that females had a higher perception of playfulness and attitude towards the use of blended learning environments than males, and males, on the other hand, seemed to have higher intention to use this type of technology. [PERSON] et al. [105] explored the moderating effect of age and gender in the students' acceptance of a Web-based learning system at Brunel University in England. The study outcomes reflected that younger students had a higher perception of usefulness, while older users perceived the system more comfortable to use. Gender was also found to affect some of the relationships within the extended TAM. In addition to gender influence on TAM factors, [PERSON] and [PERSON] [106] considered the effect of students' learning styles defined by [PERSON] and [PERSON] [107], namely, active, sensing, visual and sequential learners, on user satisfaction towards an online learning system. No gender nor learning styles reported significant effects. Likewise, [PERSON] et al. [108] assessed the impact of learning preferences in a blended e-learning system (BELS). They found no mediating relationships among those psychological traits and learner satisfaction or e-learning adoption. From a cultural approach, [PERSON] et al. [109] investigated the effects of four cultural dimensions of individuals on the adoption of e-learning tools by students in Lebanon. The research included the factors masculinity/femininity, individualism/collectivism, power distance, and uncertainty avoidance within the TAM. The validation process evidenced the existing relationship between culture and social norm, perceived usefulness, and perceived ease of use.
# Molecular Imaging with \({}^{68}\)Ga Radio-Nanomaterials: Shedding Light on Nanoparticles
## 3 Radiolabeling Nanomaterials
### Surface Radiolabeling
#### 3.1.2. Chelator-Free Approach  
In this radiolabeling strategy, the radioisotope is directly incorporated into the surface of the nanoparticle taking advantage of its affinity towards the nanoparticle surface. On the one hand, the major advantage is that the radioisotope can be incorporated to the nanoparticle in a single step. On the other hand, radioisotope desorption from nanoparticle surface is still the main drawback. Some examples in the literature have exploited this property to produce \({}^{68}\)Ge/\({}^{68}\)Ga generators, to radiolabel nanoparticles with \({}^{69}\)Ge as well as to radiolabel iron oxide nanoparticles with several radioisotopes, including \({}^{68}\)Ga [8, 66, 67, 68, 69, 70].  
Figure 2: Most common \({}^{68}\)Ga chelators: (**a**) R-DOTA; (**b**) R-NOTA; and (**c**) R-NODAGA.
has been made in the theories of rock anisotropy for various media, including triaxial anisotropy, uniaxial anisotropy, and orthotropic anisotropy [2; 3; 4].  
Current research in mining engineering primarily focuses on the development of deep mining technologies to address resource depletion and improve extraction efficiency. For thousands of years, mining engineering has largely relied on empirical methods for design. However, as mining depths continue to increase, these methods are no longer sufficient to meet the challenges of the 21 st century. In addition to studying the complex mechanical properties of rocks, advancements are also being made in environmental impact assessments, the application of intelligent and automated technologies, and research on hydraulic fracturing techniques [5]. The establishment of seismic monitoring and early warning systems aims to reduce the risks associated with seismic events, while the development of multidisciplinary coupling models helps us to understand the comprehensive impact of underground mining on the environment [6]. These advancements collectively enhance the safety, economic viability, and sustainability of mining engineering [7].  
Underground engineering excavation refers to the process of excavating and developing tunnels, underground chambers, pipelines, and other structures below the Earth's surface [8]. This area of research has consistently been a critical topic in underground engineering, encompassing geotechnical engineering, groundwater flow, and the stability of underground structures. As human activities extend deeper into the Earth, the rock mechanics challenges encountered are becoming increasingly complex and varied.  
As issues arising from complex rock mechanics continue to emerge, there is increasing demand for sophisticated tools. The advent of advanced numerical simulation techniques and artificial intelligence technologies facilitates the study of these complex rock mechanics problems. By applying knowledge from interdisciplinary fields such as mathematics, computer science, physics, and chemistry, these technologies provide valuable insights into the nature of complex rock mechanics challenges.
###### Abstract  
The carbon cycle of the terrestrial biosphere plays a vital role in controlling the global carbon balance and, consequently, climate change. Reliably modeled CO\({}_{2}\) fluxes between the terrestrial biosphere and the atmosphere are necessary in projections of policy strategies aiming at constraining carbon emissions and of future climate change. In this study, SMOS (Soil Moisture and Ocean Salinity) L3 soil moisture and JRC-TIP FAPAR (Joint Research Centre--Two-stream Inversion Package Fraction of Absorbed Photosynthetically Active Radiation) data with respective original resolutions at 10 sites were used to constrain the process-based terrestrial biosphere model, BETHY (Biosphere, Energy Transfer and Hydrology), using the carbon cycle data assimilation system (CCDAS). We find that simultaneous assimilation of these two datasets jointly at all 10 sites yields a set of model parameters that achieve the best model performance in terms of independent observations of carbon fluxes as well as soil moisture. Assimilation in a single-site mode or using only a single dataset tends to over-adjust related parameters and deteriorates the model performance of a number of processes. The optimized parameter set derived from multi-site assimilation with soil moisture and FAPAR also improves, when applied at global scale simulations, the model-data fit against atmospheric CO\({}_{2}\). This study demonstrates the potential of satellite-derived soil moisture and FAPAR when assimilated simultaneously in a model of the terrestrial carbon cycle to constrain terrestrial carbon fluxes. It furthermore shows that assimilation of soil moisture data helps to identity structural problems in the underlying model, i.e., missing management processes at sites covered by crops and grasslands.  
carbon cycle; SMOS soil moisture; JRC-TIP FAPAR; multi-site assimilation; uncertainty evaluation 2019 11 27; doi:10.3390/rs11010027
## 3 Research Methodology  
All stages of the research conducted to obtain reliable results--upon which the final conclusions are based--are presented in this section. The entire research process is illustrated in Figure 2.  
This structured, multi-stage approach ensures methodological rigor and relevance. The initial broad testing (Stage I) enables the identification of promising configurations across diverse conditions. By narrowing the focus in Stage II, we validate these selections under more realistic and demanding scenarios, allowing us to observe nuanced system behavior. Finally, the evaluation phase synthesizes these insights to draw reliable, application-driven conclusions. Each step builds upon the previous, ensuring that the final results are both robust and practically meaningful.
# Geo-DMP: A DTN-Based Mobile Prototype for Geospatial Data Retrieval
## 3 Geo-DMP Mobile System
### System Framework
At fixed time intervals, DTN requests region information by issuing a query to the interface. The interface reads the current GPS coordinates from the trace reader or GPS module, inquires the database with the coordinates, and returns the regions in which the node is located now. Then, DTN saves this regional information to the \"Movement History Maintenance\" module for future uses of making routing decisions locally and exchanging movement knowledge upon contacting other nodes. The creation process of the map segmentation database will be given in Section 3.3.
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### Audio-Based Modality
#### 3.2.4 Location-Based  
[PERSON] [134] proposed a technique that exploits the _location-aware audio superimposition_ method to enhance the social aspects of museum visits. An infrared transmitter detects the visitor's location, which signals a computer to play or stop playing a pre-recorded description audio message. [PERSON] et al. [135] stressed that immersion, besides interaction, involves the \"creation of presence\", which is the feeling of being in a particular place. They have created a location-aware augmented reality game where the user navigates a field looking for ghosts. Based on the user location, sound effects were triggered indicating the paranormal activity level, thus exploiting the _location-aware sound effects_ method. [PERSON] et al. [136] presented an RF-based location system to play digital sounds corresponding to the user's location. They have created a fantasy AR game where the user enters a game space (convention hall, gallery, etc.) with RF beacons set up. Through superimposed environmental sounds, sound effects, and narrator's guidance, the story of the game evolves. In another research paper [137], the authors described the technical aspects of a smartphone application that helps blind people experience their surrounding environment. They utilized _location-aware sound effects_ and non-spatialized sound details, such as the full name of their location. It is worth mentioning that navigation accuracy posed an important challenge in both previous papers.
# A Simple and Compact Laser Scattering Setup for Characterization of a Pulsed Low-Current Discharge
## 1 Introduction
Furthermore, the laser blocking mask has to be removed to calibrate the setup or to measure the temperature of heavy particles if Rayleigh scattering is used for this purpose. A detailed description of setups using a blocking mask can be found in [21; 27]. To improve the stray light rejection, the spectral resolution of experimental setups is often increased by coupling multiple spectrographs in series. Double grating [32] or even triple grating [21; 28; 33] spectrographs are most commonly used. The latter are proven to be highly effective for electron densities below \(10^{20}\) m\({}^{-3}\) and enable, due to the high spectral resolution, in most cases, a setup calibration or estimation of \(T_{h}\) without major modifications (both based on Raman scattering) [21; 26; 27; 34]. Nonetheless, non-equilibrium plasmas can be also characterized with a simpler laser scattering setup. The wide dynamic range of modern detection devices should allow to detect both the Rayleigh and Thomson scattering signals with one and the same setting. Hence, the determination of plasma parameters should be possible, even without a laser blocking mask with an appropriate choice of components and under certain experimental conditions (discussed in the following). This would have the advantage that no physical change is made to the setup or the acquisition settings, thus reducing potential sources of error. In addition, a simple layout can be much more compact and mobile. Such a simple laser scattering setup working without a blocking mask is proposed in this paper. In the next section, the theoretical considerations for a simplified scattering setup are discussed, followed by the description of the setup in Section 3. In Section 4, the proposed layout is applied to estimate the plasma parameters of a pulsed low-current high-voltage discharge operated at atmospheric pressure. A discussion of the results is presented in Section 5, followed by a conclusion.