esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Multi-Exposure Fusion via Sparse Representation of Gradient Features
### Local Gradient Sparse Descriptor Extraction
#### 3.1.2 Local Gradient Sparse Descriptor Extraction  
During the process of local gradient sparse descriptor extraction, luminance gradient maps of the multi-exposure source images are obtained by Equation (4), as shown in Figure 5a,b. Then, the luminance gradient map, \(S_{L\
abla}\), is divided into overlapping image patches, as in [PERSON]'s method [24]. However, the patch size is odd, \(n=2r+1\), and as big as the patch size used in the dictionary learning. The next step is to shape each image patch into a vector through column-by-column extraction, as shown in Figure 5c,d. It is noteworthy that the small red piece in this figure represents the center of the image patch. Finally, each vector is now represented by a gradient domain from the over-complete dictionary obtained in the previous section, as shown in Figure 5d, wherein the black piece represents the nonzero sparse coefficient. Thus, the image patches can be expressed as:  
\[V_{L\
abla}=[d_{1}\,d_{2}\,\cdots d_{k}]\left[\begin{array}{cccc}s^{1}(1)&s^ {2}(1)&\cdots&s^{J}(1)\\ s^{1}(2)&s^{2}(2)&\cdots&s^{J}(2)\\ \vdots&\vdots&\ddots&\vdots\\ s^{1}(k)&s^{2}(k)&\cdots&s^{J}(k)\end{array}\right]+\varepsilon, \tag{6}\]  
where \(V_{L\
abla}\in R^{n^{2}\times J}\) denotes the matrix that is lexicographically ordered from the luminance gradient maps, \([d_{1},d_{2},\cdots\cdots d_{k}]\in R^{n^{2}\times k}(n^{2}<k)\) is the over-complete dictionary, \(s^{j}=[s^{j}(1)s^{j}(2)\cdot\cdots\cdot s^{j}(k)]^{T}\) is the sparse coefficient of each image patch, \(J\) is the number of image patches, and \(\varepsilon>0\) is the error tolerance. If \(D=[d_{1},d_{2},\cdots\cdots d_{k}]\), and \(S=[s^{1},s^{2}\cdots,s^{J}]^{T}\), which is a sparse matrix, Equation (6) can be expressed as:  
\[V_{L\
abla}=DS. \tag{7}\]  
Then, the gradient sparse descriptor of the central pixel in the image patch, \(A_{i}(x,y)\), can be calculated as  
\[A_{i}(x,y)=\|s^{j}_{i}\|_{1}, \tag{8}\]  
Figure 4: Dictionary learning in the gradient domain: (**a**) Clear images of indoors and outdoors; (**b**) Luminance gradient map; (**c**) Lexicographical vector; (**d**) Over-complete dictionary.  
where \(\left\|\cdot\right\|_{1}\) denotes the \(l_{1}\)-norm, \(s_{i}^{j}\) is the \(j\)th sparse matrix of the \(i\)th source image.  
Generally, the larger the gradient value of the corresponding patch in multi-exposure source images, the better the exposure result of these regions, and the gradient sparse descriptor \(A_{i}(x,y)\) is also larger. However, in the HDR scenes, the invalid larger amplitude gradient may be found in a particular exposure source image, which does not represent a good exposure result. Fortunately, the gradients in the adjacent region of these invalid gradients are almost zero. So, the local gradient sparse descriptor \(A_{i}(x,y)\) of these regions is much smaller than the corresponding well-exposed image regions in other source images. Therefore, \(A_{i}(x,y)\) can effectively suppress the invalid large-amplitude gradients and remove halo artifacts from the fused image.
## 3 Current Measurement Techniques
### Soil Moisture Measurement Techniques  
#### 3.1.1 Estimation from Fluxes  
Soil moisture can be estimated from the fluxes above the surface, through the linkage of water and energy balances. Relevant variables can be obtained using flux-towers that measure latent and sensible heat fluxes by eddy covariance methods and radiative fluxes. The footprint for the turbulent fluxes is in the order of 10 m, depending on the instrument height and the horizontal wind speed. Because storage is estimated by integrating the fluxes, systematic errors in the fluxes will accumulate over time in the storages.
## 2 Modeling of PFSSP  
### Encoding Method and Initial Solution Creation  
In this study, the optimization objective was to find the sequence of operations on each machine that would lead to a minimization of the makespan. An important task in any optimization problem is to determine the encoding method. Over the years, many different methods were used to encode the solutions of scheduling problems, such as the direct method [29], the binary method [30], the circular method [31], and the permutation with repetition [32] method. As the jobs must be processed in the same order on each machine in PFSSP, a solution is created using permutation encoding for a single machine. The length of the chromosome (solution) can be determined by the size of the problem. For example, for a 9 \(\times\) 3 problem (9 jobs with 3 operations each) the chromosome length would be 9. An example of permutation for the 9 \(\times\) 3 problem is shown in Figure 1. According to Figure 1, the order of operations on any machine will be job 3, 1, 2, 9, 5, 4, 6, 8 and 7. By utilizing permutation encoding, only feasible initial solutions would be created, which would reduce the computational cost of the algorithm.
# Blockchain Solution for Buildings' Multi-Energy Flexibility Trading Using Multi-Token Standards
###### Abstract
If there is no heat energy storage in the community, the building's heat generation can be traded with other community members. Because the amount of heat can be adjusted based on the comfort preferences of its residents, there is some flexibility in the heat generation levels. This flexibility can be used to increase or decrease the amount of heat produced by a building and injected into the community heat grid. The amount of heat generation from each building is tokenized and made available in the heat market setup. This means that the building owners can sell their excess heat to others who need it or purchase additional heat when they require it. We propose the use of blockchain, smart contracts, and multi-token standards to allow buildings to digitize their flexibility from electricity and heat energy carriers and to trade it in multi-energy markets (see Figure 1). This will not only enable them to play a more active role in the energy system owing to better integration and to obtain new revenue streams thanks to higher levels of flexibility, but will also bring advantages from a transaction perspective. Blockchain provides ideal support for an immutable, tamper-proof public ledger and the trading of digital assets, while ERC-1155 can be adapted for digitizing energy regardless of the type. Thus, one building prosumer that produces both electricity and heat will have transactions with others in different markets trading both electricity and heat. Each matched pair of prosumers will only require a single transaction to transfer both types of energy. The adoption of ERC-1155 for energy trading allows a smart contract to hold tokens of different types, including fungible (money-like), non-fungible (individual), and semi-fungible (one of a specific bunch with the same/similar scope). In this way, we will use fungible tokens that can act as currency in electricity and heat markets to perform financial transactions, while the non-fungible tokens are used for digitizing the electricity and heat flexibility produced by a building. Finally, the solution paves the way for processing batches of energy transactions in the ERC-1155 protocol, supporting the transfer of multiple independent tokens between the addresses of the sender and receiver. The rest of the paper is organized as follows: Section 2 presents the related work on buildings' energy interoperability and blockchain tokenization, Section 3 describes our solution for building flexibility digitization and trading on heat and electricity markets, Sections 4 and 5 present the evaluation results and discuss the advantages related to the reduction in the number of transactions and decrease in gas consumption, while Section 6 concludes the paper.
# Research Trends and Future Direction for Utilization of Woody Biomass in Japan
## 4 Production Costs of Wood Biomass
### Woody Biomass Production Costs
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & \multicolumn{4}{c}{**Cost Structure**} & \multicolumn{4}{c}{**Production Cost**} \\ \cline{3-10}
**Paper** & **Region and Year** & & & & & & & & **Forest** & & **Reference** \\ \cline{3-10}
**No.** & **Main-** & **Sub-** & **Dip** & **Indirect** & **Trans.** & **Converson-** & **Non-** & **Converson-** & **Non-** & **Product** & **Product** \\ \hline
1 & N.A., 2006 & ✓ & & & & & & & N.A. & 4092-98572 & [31] \\
2 & Toei-cho, Aichi Pref., 2006 & ✓ & & & & & & & 5492-98585 & N.A. & [32] \\
3 & N.A., 2006 & ✓ & & & ✓ & ✓ & & & 5311 & N.A. & [29] \\
4 & Sano, Tchigi, 2010 & ✓ & ✓ & ✓ & ✓ & & & & 17,988 & 5370 & [33] \\
5 & Nasushibara, Tchigi Pref., 2010 & & ✓ & & ✓ & & & & 8711-8985 & N.A. & [34] \\  & Chichiba era, Saitama Pref., and & & & & & ✓ & ✓ & & & 4410-5141 & N.A. & [27] \\
6 & Simons plateau, Chiba Pref., 2010 & ✓ & & & & & & & 4106–5236 & N.A. & [35] \\
7 & Nasushibara, Tchigi Pref., 2011 & ✓ & & & & ✓ & ✓ & & 4981 & N.A. & [28] \\
8 & N.A., 2011 & ✓ & & & & & & & 11,887–14048 & 5129–7995 & [36] \\
9 & Nasushibara and Kanuma area, & & ✓ & ✓ & ✓ & & & & 28,840–29,940 & 5168–5644 & [37] \\
10 & Nasushibara, Tchigi Pref., 2013 & ✓ & ✓ & ✓ & ✓ & & & 18,537 & 1322 & [38] \\
11 & Nasushibara, Tchigi Pref., 2014 & ✓ & & & & & & & \(\surd\) & 9000–14,000 & N.A. & [39] \\
12 & Tokai region, 2017 & ✓ & & & & & & & & 8,530–10,000 & N.A. & [22] \\
13 & Ibaraki, Fukushima, Tchigi and & & & & & & & & & \(\surd\) & 3500–10,000 & N.A. & [22] \\
14 & Nasushibara, Tchigi Pref., 2021 & ✓ & & & & & & & N.A. & 5206–8040 & [40] \\
15 & Japan, 2021 & ✓ & ✓ & ✓ & ✓ & & & & \(\surd\) & 3500–8500 & N.A. & [23] \\ \hline \hline \end{tabular}  
* 1 USD = 118.07 (JYP (1 January 2006); 1 USD = 92.10 (JYP (1 January 2010)); 1 USD = 81.49 (JYP (1 January 2011); 1 USD = 77.74 (Jeny 2012); 1 USD = 86.58 (JYP (1 January 2013); 1 USD = 105.39 (JYP (1 January 2014); 1 USD = 116.49 (JYP (1 January 2017); 1 USD = 109.56 (JYP (1 January 2020); 1 USD = 103.50 (JYP (1 January 2021); 1 USD = 141.83 (JYP (1 January 2024); N.A.: Not applicable.  
\end{table} 
Table 2: Results of woody biomass production cost. cost items from the main product were often reduced, and by-operation costs were not included. Of note, many studies assumed short transportation distances, resulting in smaller estimated transportation costs. [PERSON] et al. [41] found that the cost of using woody biomass is less than that of heavy oil when the transportation distance is 50 km or less, and [PERSON] et al. [42] estimated that with an increase in transportation distance from 25 km to 100 km, the transportation cost increased by approximately 9000 JPY/t.
# Behavior of Two-Chord Steel-Concrete Composite Columns under Axial Compression
## 4 The Design Strength of TCCFST Members
### Axial Capacity  
#### 4.1.1 Eurocode 4  
Eurocode 4 [23] ascribes the plastic capacity of axially loaded composite members using high-strength concrete to the confining stresses if the relative slenderness is \(\overline{\lambda}\leq 0.5\). The axial strength of a CFST member (\(N_{u,EC4}\)) is defined as follows:  
\[N_{u,EC4}=\chi\left(\eta_{a}A_{s}f_{y}+A_{c}f_{c}\left(1+\eta_{c}\frac{t}{D} \frac{f_{y}}{f_{c}}\right)\right) \tag{8}\]  
where \(D\) is the outer diameter, \(t\) is the wall thickness of the tube, and the correction factors for enhanced strength are \(\eta_{a}=0.25\left(3+2\overline{\lambda}\right)\leq 1.0\) and \(\eta_{c}=4.9-18.5\overline{\lambda}+17\overline{\lambda}^{2}\geq 0\). The relative slenderness \(\overline{\lambda}=\left(N_{pl,Rk}/N_{cr}\right)^{0.5}\) is determined from the characteristic value of a section's plastic capacity, which is defined as \(N_{pl,Rk}=A_{s}f_{y}+A_{c}f_{c}\), and from the critical buckling force \(N_{cr}=\pi^{2}\left(EI\right)_{eff}\left(kL\right)^{2}\), with the effective stiffness of a composite member cross-section being \(\left(EI\right)_{eff}=E_{s}I_{s}+0.6E_{c}I_{c}\), where \(I_{c}\) and \(I_{s}\) are the second moments of area of the filled concrete and the steel tube, respectively. Since Eurocode 4 is limited to concrete with a compressive strength between class C20/25 and class C50/60, in the presented study, the EC-4 method was expanded over the upper limit using the concrete strength reduction coefficient from Reference [37]. The effective compressive strength was determined as \(f_{c,eff}=f_{c}(1-\left(f_{c}-50\right)/200)\). With this approach, the cylinder's compressive strength was decreased from \(f_{c}=95.2\,\mathrm{N/mm^{2}}\) to \(f_{c,eff}=73.6\,\mathrm{N/mm^{2}}\). The influence of the slenderness was included in coefficient \(\chi\) and \(k=0.7\)[24]. The use of Eurocode 4 is also limited for steel with a yield strength between \(235\,\mathrm{N/mm^{2}}\) and \(460\,\mathrm{N/mm^{2}}\). In this research, the yield stress of \(600\,\mathrm{N/mm^{2}}\) was used without any reduction.  
Figure 15: Column model SC-21 after steel tube cutting (**a**) and cracks in concrete (**b**).
# ECE-TTS: A Zero-Shot Emotion Text-to-Speech Model with Simplified and Precise Control
## 3 Experiments
### Evaluation Metrics  
#### 3.3.1 Objective Metrics  
To evaluate the model's performance, we employ objective metrics that measure both speech intelligibility and the accuracy of emotional expression. These metrics provide a quantitative basis for assessing how well the generated speech aligns with its intended content and emotional attributes:  
Word Error Rate (WER): To assess the intelligibility of the generated speech, we use the Whisper-Large model to transcribe the synthesized audio and compute the Word Error Rate (WER). WER quantifies speech intelligibility by comparing the generated transcription with the target text. The WER score is expressed as a percentage, where lower values indicate higher intelligibility.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Configuration** \\ \hline Processor & AMD EPYC 7F72 24-Core Processor \\ GPU & NVIDIA A100 80 GB \\ Operating System & Ubuntu 20.04 LTS (64-bit) \\ CUDA Version & 12.2 \\ PyTorch Version & 2.5.1 + cu124 \\ Programming Language & Python 3.10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental hardware and software configuration.  
Aro-Val-Domin SIM: To evaluate the consistency of emotional expression between the generated and target speech, we compute the Arousal-Valence-Dominance (Aro-Val-Domin) sequence similarity. Specifically, we apply a sliding window approach with a window size of 0.5 s and a stride of 0.25 s to extract emotion feature sequences from both the reference and synthesized speech. By computing cosine similarity frame by frame and averaging the results, we obtain the Aro-Val-Domin SIM score. A higher score indicates that the generated speech more accurately reflects the intended emotional expression.  
Emo SIM: To assess the similarity of dynamic emotional expression between the generated and reference speech, we compute the Emotion Similarity (Emo SIM) score. Specifically, we apply the emotion2 vec model to extract frame-level emotion embeddings for both the synthesized and reference audio. To ensure alignment, we interpolate the embeddings so that both sequences have the same length. Cosine similarity is then computed frame by frame and averaged across all frames. A higher Emo SIM score indicates that the synthesized speech more faithfully preserves the intended fine-grained emotional trajectory.
## 3 Proposed Voltage-Based Realignment Cell Balance
### Simulation Verification of the Proposed Cell Balance  
In this section, the potential difference realignment passive balancing method proposed above is simulated. The simulation configuration is configured as shown in Figure 10. The basic conditions are the same as in the previous simulations. However, since the conditions for Charts #1, #2, #3, and #4 are modified, the modified conditions for each simulation are set by taking the initial voltage from the series-connected batteries in Tables 1-4, respectively.  
Simulation Results with Proposed Cell Balancing Implementation  
Simulations were performed by applying the proposed cell balance method when the allowable capacity deviation of series-connected batteries was \(\pm\)1%. Figure 10 shows each chart. Taking the voltage result of the battery from Table 1, applying it to the cell balance reference voltage, and, in the case of battery 3 having a relatively small capacity, the lowest voltage was applied as a standard for cell balance. Batteries 1, 2, and 4 applied the highest voltage as a standard for cell balance. Different charge rates resulted in differences in the DCIR. The difference in the DCIR affected the voltage distribution during charging. Therefore, it suppressed the peak voltage of a relatively small-capacity battery and suppressed the aging rate at the same time.  
Figure 11 shows the simulation results. Figure 11a is the simulated voltage result. Figure 11b is the result of extending the voltage of the simulated voltage result from 4.0 V to 4.35 V. Figure 11c is the simulated SOC result. Figure 11d is the simulated SOH result. In Figure 11b, it can be seen that the highest voltage of Bat #3 rises while charging and discharging is repeated. However, after 10 \(\times\) 10\({}^{6}\) s, the highest voltage of Bat #3 is lower than in Table 2 because the cell balance reference voltage was lowered.  
Figure 9: Proposed voltage reference realignment manual cell balance method flowchart.  
Figure 10: Serial battery aging simulation applying the proposed cell balance. (**a**) Model; (**b**) Stateflow of Chart #1; (**c**) Stateflow of Chart #2; (**d**) Stateflow of Chart #3; (**e**) Stateflow of Chart #4.  
Figure 11: Aging simulation of the proposed cell balance with allowable capacity deviation of \(\pm\)1%. (**a**) Voltage result; (**b**) magnified voltage result; (**c**) SOC result; (**d**) SOH result.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 5 Experiments with Real Data
### Data Processing
#### 5.1.2 Image Distortion Processing  
The original image was distorted using calibrated internal parameters, and Figure 12 shows the partially de-distorted image, where the left side is the original image and the right side is the de-distorted image.  
Figure 11: Image GNSS position trajectory map. (**a**) Trajectory graph from MATLAB. (**b**) Context Capture airborne 3D measurement calculated trajectory graph.
# Robust Damage Estimation of Typhoon Goni on Coconut Crops with Sentinel-2 Imagery
## 2 Related Work
### Vegetation Mapping  
Detection and quantitative analysis of vegetation has been a research topic in remote sensing for decades. [PERSON] [47] proposed a physics-inspired method for estimating canopy densities of natural systems with little or no ground truth data using Landsat data. Others have proposed indices such as the normalised difference vegetation index (NDVI) to retrieve fractional vegetation cover and leaf area index [48]. [PERSON] et al. [49] used a regression method to estimate tree cover from MODIS (500 m GSD) on a planetary scale. More recently, [PERSON] [50] used Sentinel-1 and Sentinel-2 data coupled with crowd-sourced OpenStreetMap data to estimate the vegetation cover in the vicinity of archaeological sites. [PERSON] et al. [51] used random forests to identify four vegetation classes in images obtained using an unmanned aircraft system. Some works were specifically aimed at evaluating vegetation changes. [PERSON] et al. [52] have generated maps that quantify forest loss between the years 2000 and 2012 from satellite data. [PERSON] et al. [53] used deep neural networks to detect various types of forest changes over a period of 29 years.  
Crop mapping has received a large attention in the remote sensing community. It is a frequent use case for multi-spectral images from satellites such as Sentinel-2, Landsat or MODIS. Multi-spectral images allow for species identification, often based on image time series [12; 13], as well as crop yield prediction [54; 55]. In terms of tropical tree crops, oil palms have been mapped in south-east Asia [15; 56] and at global scale [14].  
In our previous work we have developed methods to retrieve the density of oil palms, coconuts and olive trees from Sentinel-2 images [17]. That work has then been extended into an active learning scheme to map oil palm density at large scale with a tractable annotation effort [15]. The present paper builds on that active learning approach and utilises a similar pipeline to map the density of coconut canopies and estimate the effect that typhoon Goni had on them.
## 2 Proposed System  
A schematic overview of the proposed system is shown in Figure 1. In an object-based audio system, the intelligibility module using an OIM may sit prior to the renderer in the pipeline, acting on the metadata. In this case, no changes to the existing rendering algorithms are required. This intelligibility module essentially consists of two main components--the speech intelligibility meter and the mixing level optimiser that sets the SBR. The speech intelligibility meter takes speech and background sound objects and the default mixing level, \(SBR\), as inputs, and it calculates an objective intelligibility score \(OS1\). The objective score is then fed into a decision component, which makes a judgement on whether the intelligibility meets the minimum required (Section 3). If the current default SBR leads to satisfactory intelligibility then nothing is altered. If the speech intelligibility falls below the minimum, then the optimiser will look for a new mixing level \(SBR^{\prime}\) that should push the intelligibility above the minimum required. To further take listener listening effort into account, \(SBR^{\prime}\) may need further adjustment to achieve an optimal mixing level \(SBR^{\prime\prime}\) (See Section 4).
# Feedback Approach for the Relay Channel with Noisy Feedback and Its Security Analysis
## 3 An SK-Type Feedback Scheme for the Gaussian Relay Channel with Noisy Feedback  
In this section, an SK-type feedback scheme for the Gaussian relay channel with noisy feedback is proposed. The key to this scheme involves the introduction of the modulo-lattice function to mitigate the impact of feedback channel noise on the performance of the SK-type scheme. Additionally, an auxiliary signal is generated at the receiver, which is the receiver's estimation of the output of the AF strategy-based relay node, and using a difference between the received signal and this auxiliary signal to do the receiver's MMSE about the transmitted message. The main result and details about this scheme are given below.