esa-sceva/satcom-chunk-collection huggingface dataset:
# Closed-Form Method for Atmospheric Correction (CMAC) of SmallSat Data Using Scene Statistics
## 3 Results
In CMAC, the lower limb of reflectance is comparatively precise. NIR 8A curves are virtually identical between the two methods. Rochester experienced the highest range of Atm-I levels among the SoCal QIAs. As previously mentioned, each of the 28 images analyzed for the SoCal QIAs appeared visually clear across the entire range of median Atm-I recorded for the images. Of the 168 unique image-QIA combinations, only four images exceeded an Atm-I of 1050 DN, and those by less than 17 DN. Summary tables for the statistics are presented in for the six QIAs available through a link in Supplementary Materials. The precision of the curves in the Figure 11 example QIA is indicated by how tightly the curves plot and is comparatively better for the suite of SoCal QIAs due to the more expected dynamic range of Atm-I in contrast to the Reno QIA analysis, which included three extremely hazy images. From the analysis of hundreds of images, most atmospheric correction is performed within an approximate Atm-I range of 800 to 1050 DN. We have observed alarming exceptions that occurred during recent summers in the United States from the west coast to the Great Lakes region due to wildfires in Canada and the western United States that drove Atm-I imagery above 1050 DN over millions of hectares of farmed land for extended periods, adversely impacting image uses for precision agriculture and other applications. CMAC's high accuracy in the lower limb of visible-band surface reflectance, generally less than about 2000 DN, is crucial for precision agriculture and AI feature extraction because these darker features define the analytics of interest in vegetation indices and in prominent observable features [34; 35; 36]. The suite of 12 curves as shown in Figure 11 is provided in Excel spreadsheets for each of the six SoCal QIAs are available through the link in Supplementary Materials.
## 2 Methodology  
### The Mathematical Model of EIT  
The image-reconstruction problem in EIT involves solving the inverse problem of determining the conductivity distribution inside an object under test by solving for the boundary current and boundary voltage of a low-frequency current field [25]. The EIT system is depicted in Figure 1.  
In EIT, the current field is often treated as a quasistatic field, where the potential distribution function and conductivity distribution function within this electric field region satisfy the Laplace equation [26]. The forward operation of the positive EIT imaging problem is used to model boundary voltages:  
\[V=F(\sigma) \tag{1}\]  
where \(\sigma\) is the conductivity vector and \(F\) is the forward operator. The reconstruction process is usually stabilized using regularization with the following equation:  
\[\hat{\sigma}_{rec}=\arg\min_{\sigma}\frac{1}{2}\|F(\sigma)-V_{\text{meas}}\, \|^{2}+\lambda G(\sigma) \tag{2}\]  
where \(V_{\text{meas}}\,\) is the forward model prediction of the measured voltage \(F(\sigma)\), \(G(\sigma)\) is the regularization function, \(\lambda\) is the hyperparameter controlling the level of regularization applied, and \(\|\cdot\|\) is the L2-norm. The function \(G(\sigma)\) is usually defined in the following form:  
\[G(\sigma)=\|L(\sigma-\sigma^{*})\|^{2} \tag{3}\]  
The regularization matrix \(L\) and the a priori estimate of the conductivity distribution \(\sigma^{*}\) are important components in this context. Various options for the matrix \(L\) include unitary matrices, positive diagonal matrices, approximations of the first-order and second-order differential operators, and the inverses of Gaussian matrices. From (2) and (3), we obtain:  
\[\hat{\sigma}_{rec}=\arg\min_{\sigma}\frac{1}{2}\|F(\sigma)-V_{\text{meas}}\, \|^{2}+\lambda\|L(\sigma-\sigma^{*})\|^{2} \tag{4}\]  
The framework represented by this formula is called quadratic regularization because of its use of the L2-norm. The optimization problem for the above framework can be solved by replacing \(F(\sigma)\) with a linear approximation when making small changes to the initial conductivity distribution \(\sigma_{0}\).  
\[F(\sigma)\approx F(\sigma_{0})-J(\sigma-\sigma_{0}) \tag{5}\]  
Figure 1: The framework of EIT image-reconstruction system.  
where \(J\) is the Jacobi matrix for the calculation of \(F(\sigma_{0})\) when the initial conductivity estimate is \(\sigma_{0}\). Defining \(\delta\sigma=\sigma-\sigma_{0}\) and \(\delta V=F(\sigma)-V_{\text{meas}}\), the following formula is obtained:  
\[\delta\sigma=\Big{(}J^{T}J+\lambda L^{T}L\Big{)}^{-1}J^{T}\delta V+\lambda L^{T }L\Big{(}\sigma-\sigma_{ref}\Big{)} \tag{6}\]  
This equation is iteratively solved by \(\sigma_{i+1}=\sigma_{i}+\delta\sigma\). The disadvantage is that this technique does not reconstruct step changes, regardless of the choice of \(L\). A smooth solution is preferred.
## 5 Ubunye Service Allocator  
When the service allocator receives classified network services, it directs each service to its appropriate destination. For example, services that require low latency, such as autonomous vehicle control and voice calls, are routed to nodes that respond quickly. In contrast, services that need more bandwidth, such as video streaming, are directed to nodes with higher traffic capacity. This approach prevents one service from negatively impacting another. Additionally, organizing services reduces overload and enhances the network's overall capacity, improving user experience.  
The classic auction algorithm is a practical resource allocation approach widely used in various contexts, including edge computing. Bidders compete for available resources by submitting bids to acquire those resources. Each bidder submits a bid based on their need and ability to pay, and the bidder who submits the highest bid wins. The Ubunye service allocator adapts the classic auction algorithm, in which the auctioned objects are the user requests, and the edge nodes are the bidders.  
In our proposal, the resource is the value object in the auction, which could be bandwidth, CPU capacity, or memory on an edge server (bidders). The auction process involves evaluating the bids submitted by the bidders, where the auctioneer determines the winner based on previously defined criteria, such as the highest bid or the optimal combination of bids that maximizes the system's efficiency. This mechanism allows for a dynamic and efficient allocation of resources, ensuring that they are used optimally and benefiting service providers and consumers. The creation of [PERSON]'s algorithm was an important milestone in auction theory, offering a robust practical and theoretical solution to resource allocation problems [27].  
According to [28], the auction algorithm can solve problems involving the assignment of a number of tasks \(t\) to a number of agents \(a\) (in this case, edge nodes, based on the cost of assigning each node). It is an efficient method for solving resource allocation problems, especially useful in service allocation, converging to an optimal solution in many cases. However, its effectiveness depends on the correct modeling of the problem and the appropriate choice of parameters [29]. The auction algorithm runs an iterative process in which agents (or players) compete with each other to obtain resources. Each agent \(i\) has an initial value \(v_{i}\), representing its utility or preference for the resource being auctioned [29]. The following paragraphs describe the operating steps used on the Ubunye auction algorithms.
## 3 Results and Discussions
### Plasmid Construction and Expression of Luciferase Mutants  
The genes encoding the WT and mutant luciferase were cloned into the pCold II vector and transformed into _Escherichia coli_ BL21 (DE3) competent cells. Expression of the recombinant luciferase was induced with isopropyl-\(\beta\)-D-thiogalactopyranoside (IPTG) at 16 \({}^{\circ}\)C. The expressed proteins were analyzed by sodium dodecyl sulfate-polyacrylamide angle electrophoresis (SDS-PAGE) using a 15% polyacrylamide gel. The results showed that the mutant luciferase exhibited a specific band with a molecular mass of approximately 61 kDa, while no recombinant protein was detected in the control _E. coli_ BL21 (DE3)/pCold II cells (Figure 3a).  
In this study, luminescence intensity was employed as a proxy for luciferase activity. All mutants demonstrated detectable luciferase activity, with luminescence intensity increasing progressively as the concentration of luciferin was raised. Notably, the luminescence intensity of \(Mut2\) reached \(9.6\times 10^{4}\) relative light units (RLU) per mg of protein (Figure 3b). It is important to highlight that, since luminescence intensity was measured rather than the actual concentration of the product, the calcula  
Figure 3: The purification and the activity of LAK and mutants: **(a)** SDS–PAGE (15%) analysis of purified luciferase. The lanes are as follows: M, marker proteins with relative molecular masses. Lane 1–3 _Mut1–Mut3_; and **(b)** Luciferase activity was measured at 25 \({}^{\circ}\)C. Data are given as the means \(\pm\) SD, \(n=3\).  
Figure 2: The predicted mutant sites of LAK. The whole structure is shown in the gray cartoon and the mutant sites are shown in rainbow sticks.  
and _kcat_) are considered apparent or relative values. These values are derived from the luminescence measurements and should be interpreted with this caveat in mind.
## 5 Complexity Analysis  
This section of the manuscript focuses on the computational complexity analysis of the developed SAEA algorithm. To determine the computational complexity of the SAEA algorithm, the major algorithmic steps must be analyzed, which are presented in Algorithm 5. ```
1:\(\textit{TourSel}(Offpring_{g},Fit_{g},\Omega,\Phi)\)
2:\(Offpring_{g}\)--offspring chromosomes in generation \(g\); \(Fit_{g}\)--fitness of individuals in generation \(g\);
3:\(\Omega\)--tournament size; \(\Phi\)--number of individuals selected at each tournament
4:\(Pop_{g+1}\)--population chromosomes in generation \(g+1\)
5:\(|Pop_{g+1}|\leftarrow\odot\)\(\vartriangleleft\) Initialization
6:while\(|Pop_{g+1}|\
eq|Offpring_{g}|\)do
7:\([Tour,Fit_{g}^{Tour}]\leftarrow\textit{RandSel}(Offpring_{g},Fit_{g},\Omega)\)\(\vartriangleleft\) Select the individuals for the tournament
8:\(q\gets 1\)
9:while\(q\leq\Phi\)do
10:\(i\gets argmin(Fit_{g}^{Tour})\)\(\vartriangleleft\) Select the fittest individual from the tournament
11:\(Pop_{g+1}\gets Pop_{g+1}\cup\{Tour_{i}\}\)\(\vartriangleleft\) Add the selected individual to the next generation chromosomes
12:\(Tour\gets Tour-\{Tour_{i}\}\)\(\vartriangleleft\) Remove the selected individual from the tournament
13:\(q\gets q+1\)
14:endwhile
15:endwhile
16:return\(Pop_{g+1}\)
```  
**Algorithm 4** Tournament Selection. The data structures, required by the SAEA algorithm, are initialized in step 0. The generation and counter values are set to \"1\" in step 1. The initial population chromosomes are generated in steps 2-6 (where half of the population is created using the FCFS-SR heuristic, while another half is created randomly--see Section 4.3 of the manuscript for more details). The fitness of the initial population chromosomes is assessed in step 7. After that, SAEA starts an iterative procedure (steps 8-18), where the generation count is updated in step 9. A copy of the fittest individual is stored in step 10 before applying any algorithmic operators (i.e., the \"elitism\" strategy). The parent chromosomes are identified using the RWS mechanism is step 11. The crossover operation is conducted in step 12 to produce the offspring chromosomes. The generated offspring chromosomes are mutated in step 13. Any infeasible offspring chromosomes are repaired in step 14. The fitness of the offspring chromosomes is assessed in step 15. The fittest individual is transferred to the next generation in step 16. The remaining offspring chromosomes to be present in the next generation are identified using the Tournament Selection mechanism in step 17. The SAEA algorithm terminates an iterativeprocedure, once the maximum allowable number of generations has been reached. The best solution, representing the berth schedule with the least possible sum of the total weighted vessel turnaround time and the total weighted vessel late departures, is determined in step 19.
# Research Progress in Methods for the Analysis of the Internal Stability of Landslide Dam Soils
## 4 Criteria Considering Stress Conditions
### State of Confined Compression  
Internal erosion under confined compression refers to the phenomenon of internal erosion occurring in soils under vertical load and lateral confinement. Stress levels significantly influence the critical hydraulic gradient of soils; the higher the stress level, the greater the corresponding critical hydraulic gradient. This is because the stress level affects the contact degree between coarse and fine particles. The higher the stress level, the greater the contact area and number of contact points between coarse particles, leading to a more stable skeletal structure. As a result, soil particles are less likely to be lost from the skeleton under the action of seepage.  
[PERSON] et al. (2018) conducted internal erosion tests on gravelly soil under confined conditions with vertical stresses of 0.1, 0.3, 0.6, and 0.9 MPa. By analyzing the mechanical equilibrium of individual particles, a critical hydraulic gradient calculation formula that considers the friction between particles was derived.  
\[i_{c}=\big{(}1+0.5K_{0}\tan\alpha^{\prime}\big{)}\frac{\gamma^{\prime}}{\gamma _{w}}+\big{(}1+K_{0}\tan\alpha^{\prime}\big{)}\frac{\sigma_{s}}{\gamma_{w}} \tag{4}\]  
In the Formula (4), \(K_{0}\) is the coefficient of the lateral earth pressure between soil particles, \(r^{\prime}\) is the self-weight stress of soil particles, \(\alpha^{\prime}\) is the effective internal friction angle of the soil, and \(\sigma_{s}\) is the additional stress acting on the particles. It can be seen from Formula (4) that the change in additional stress acting on the soil directly affects the change in the critical hydraulic gradient. Both experimental and theoretical studies have shown that the critical hydraulic gradient of soil increases linearly with the increase in stress level ([PERSON] and [PERSON], 2018).  
It is evident that a larger vertical stress increases the density of the soil, reduces porosity, and increases the initiation and failure hydraulic gradient of the soil ([PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018; [PERSON] and [PERSON], 2018), thereby enhancing the soil's erosion resistance. For landslide dams, the soil along the dam axis is in a confined compressive state due to gravity and lateral constraints, making it an ideal area for studying internal erosion behavior. Understanding the stress conditionsunder which internal erosion is more likely to be triggered in landslide dam soil helps in comprehending the stability and erosion mechanisms of landslide dam soil [57].
## 4 Results and Discussion
### Advantages of the Proposed Model
#### 4.5.2 Impact of anemia Severity Detection Module  
To overcome the pathologist's challenge regarding the detection of the change in morphological features of RBC elements, we employed a severity detection module in the proposed CNN model. The disease severity detection module helps the medical practitioners to detect anemia severity (i.e., mild or chronic) conditions based on pixel-level analysis of morphological deformities. The proposed severity detection algorithm analyzed the anemic image at pixel level and considered the RBC overlapped structure. The overlapped structure's consideration helps to accurately detect densely placed RBCs' ultimate boundaries to predict morphological disorders.  
Figure 11: Confusion matrix of severity detection measures as Mild and Chronic during testing.  
Various datasets have been released and are publicly available for delineation of RBCs and WBC images. Previously developed blood cell datasets like ALL-IDB-I, ALL-IDB-II [76], extended ALL-IDB [19], BCCD, IUMS-IDB [77], SMC-IDB [78], BS_DB3 [79], Ash bank, BBBC [80] dataset equipped with small no of images with less number of RBCs elements. ALL-ID-I contains only 108 images with just 39000 blood elements. All 108 images have no masks of authentication of the proposed results. Other datasets also lack detailed information regarding manual ground truth for authentication purposes and blood cell elements. Most of the prior approaches perform segmentation and classification only on a single cell at the region level and not the pixel-level. These dataset images show RBCs and WBCs with isolated, clear color variations and separated boundaries among cell constitutes. As a result, analysis is rather easy.  
In contrast, the proposed model experiments on the newly proposed RBC dataset, which is more complicated in the following aspects:  
1. Large number of images, i.e., 11,500.
2. Large number of RBC elements in each image, i.e., ~1500, and overall, ~750,000
3. In creating real-life scenarios, images are captured with heterogeneous lighting intensity.
4. Most of the RBC elements show an overlapped structure.
5. Due to the color differences, segmentation and classification become challenging.  
Due to the reasons mentioned above, the test accuracy of the proposed model is lower than previously developed models.
## 3 Results
### Results of the Analysis of the Micro-Samples  
#### 3.3.1 FTIR Data  
FTIR analysis was carried out on a sample (S1, localized in Figure 3) of the translucent finishing layer of the painting with the aim of characterizing the main constituents and choosing the most effective cleaning strategy.  
The spectrum obtained is shown in Figure 8, together with the reference spectra of Paraloid(r) B44 and Paraloid(r) B72.  
In fact, the research in the databases and in the texts reported in Section 2.3.1 resulted in a match with acrylic resins, specifically Paraloid(r) B72 and Paraloid(r) B44. Paraloid(r) B72 is a copolymer of ethyl methacrylate (EMA) and methyl acrylate (MA) with a ratio of 70/30 and the possible addition of butyl methacrylate (BMA) [38; 39]. Paraloid(r) B44 is a copolymer of methacrylate (MMA) and ethyl acrylate (EA) in unknown proportions [39].  
The main signatures of such polymers are 1725 cm\({}^{-1}\) (C=O stretching); 2982, 2954 and 2929 cm\({}^{-1}\) (stretching of C-H bonds); the 140-1300 cm\({}^{-1}\) region (bending of the C-H bonds); and the 1300-900 cm\({}^{-1}\) region (stretching of the C-O bonds). The presence of a synthetic resin of acrylic typology is thus confirmed, as mentioned by the restorer [PERSON] who worked on the painting during the restoration campaign directed by prof. [PERSON] in 2004.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Point** & **Color** & **Detected Elements** & **Hypothesized Pigments** \\ \hline X1 & Blue (clear) & As, Sr, Fe, Ca, Rb, Co, Bi, Zr, Pb & Smail blue + lead white \\ X2 & Blue (clear) & As, Fe, Sr, Co, Ca, Bi, Zr, Ni, Rb, Pb & Smail blue + lead white \\ X3 & Blue (clear) & Fe, As, Sr, Ca, Co, Rb, Bi, Ni, Pb, Zr & Smail blue + earth pigments + lead white \\ X4 & White & Ca, Sr, Fe, As, Rb, Zr, Pb & Calcium carbonate + lead white \\ X5 & White & Ca, Sr, Rb, Zr, Fe, Pb & Calcium carbonate + lead white \\ X6 & Yellow & Fe, Ca, Sr, Rb, Zr, Pb & Calcium carbonate + yellow ochre \\ X7 & Yellow & Fe, Ca, Sr, Rb, Zr, Pb & Calcium carbonate + yellow ochre \\ X8 & Yellow & Fe, Ca, Sr, Rb, Pb & Calcium carbonate + yellow ochre \\ X9 & Yellow & Fe, Sr, Ca, Rb, Pb, As & Yellow ochre \\ X10 & Red & Fe, Ca, Sr, Hg, Rb, Pb & Red earth + cinnabar \\ X11 & Red & Fe, Hg, Ca, Sr, Rb, As, Pb & Red earth + cinnabar \\ X12 & White & Ca, As, Sr, Pb, Bi, Fe, Zr, Co, Rb, Ni & Smail blue + lead white + earth pigments \\ X13 & Green & Fe, Sr, Ca, As, Pb, Rb, Zr, Co, Bi, Ni & Earth pigment + lead white + small blue \\ X14 & Green & Fe, Sr, Ca, Rb, Zr, Pb & Earth pigment + lead white \\ X15 & Pink (clear) & Ca, Fe, Sr, Rb, Pb & Calcium carbonate + earth pigments + lead white \\ X16 & Pink & Ca, Fe, Sr, Hg, Rb, Pb & Earth pigments + cinnabar + lead white \\ X17 & Brown & Fe, Ca, Sr, Rb, Hg, Mn, Pb & Red earth + cinnabar + under \\ X18 & Red & Ca, Sr, Fe, Rb, Zr, Pb, Mn & Red earth + cinnabar + under \\ X19 & Red & Fe, Sr, Ca, Zr, Rb, Pb & Red earth + lead white or minimum \\ X20 & Blue (clear) & Sr, Fe, Ca, Zr, Rb, As, Co, Pb, Ni, Bi & Smail blue + lead white \\ \hline \end{tabular}
\end{table}
Table 2: Chemical elements detected through XRF, reported in descending order of X-ray counts.
## 5 Obstacles and Challenges in Evolving V2X Communication
### Use Case Specific Challenges
#### 5.4.2 Vulnerable Road Users  
Vulnerable road user (pedestrians, cyclists, etc.) use cases are active research areas in the V2X domain. Due to the specific channel access technology depicted in Table 2, the power consumption of V2X radios is usually high. Vulnerable road users usually carry user equipment with a limited battery capacity, which limits the usability of V2X radio chips. There are multiple approaches to overcome this issue. One of them is to connect to RSUs via a different channel access technology, like Bluetooth, WiFi, or regular cellular communication, and ask the RSU to communicate the information from the VRU. Another, also promising, technique is to use the V2X radios in transmission-only mode. This means that the radios can save battery life but reception is not possible. The need for a GNSS signal in the case of C-V2X communication might also add further battery consumption issues.