esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Problem Statement
### Collaborative Target Detection Problem
#### 4.2.1 k-Partition Problem  
For a fixed \(k\), the partitioning of a large environment \(E\) consists in generating a k-partition \(P_{E}^{k}\) such that \(\forall i,j\in\llbracket 1..k\rrbracket\), and we obtain \(E_{i}\cap E_{j}=\emptyset\) while minimizing the number \(|T_{i}\cap T_{j}|\) of shared targets between \(E_{i}\) and \(E_{j}\). A path is optimal if it minimizes the sum of the costs between two points. With the addition of obstacles and some constraints, this problem can change from a polynomial complexity to an NP-Complete problem [58]. This problem can be formulated as a linear optimization problem.  
The first challenge is the minimization of shared targets between \(E_{i}\) and \(E_{j}\), as it reduces the communication costs between drones UAV\({}_{i}\) and UAV\({}_{j}\). The second challenge is balancing the weights of the \(k\) sub-environments. For \(k=2\), it is a simple bi-partition of the environment, whereas for \(k>2\), the problem is NP-Complete [58] because there is no optimal solution algorithm in polynomial time.  
Partitioning can be done in three ways, namely shape-based partition, density-based partition, and target-based partition, as illustrated in Figure 6. Shape-based partitioning consists of partitioning the environment \(E\) into \(k\) sub-environments with identical dimensions, such as \(\forall i,j\in\llbracket 1..k\rrbracket\), and we have \(s(E_{i})\approx s(E_{j})\). On the other hand, the density-based partition consists in having a k-partition such as \(\forall i,j\in\llbracket 1..k\rrbracket\), and we have \(|E_{i}|\approx|E_{j}|\). Finally, the one based on targets allows us to obtain a partition of the environment by considering the uniform distribution of targets on each sub-environment, such as \(\forall i,j\in\llbracket 1..k\rrbracket\), where we have \(|T_{i}|\approx|E_{j}|\).  
We conclude that the mathematical model associated with the partitioning of the environment \(E\) within the constraints is described as follows:  
\[\left\{\begin{array}{ll}\underset{i\
eq j\in\llbracket 1..k\rrbracket}{ \text{minimize}}&|T_{i}\cap T_{j}|\\ \text{subject to:}&B(P_{E}^{k})\leq 1+\epsilon\\ &E_{i}\cap E_{j}=\emptyset\\ &E_{i}\
eq\dot{E}_{j}\\ &\cup_{i=1}^{k}E_{i}=E\\ &\cup_{i=1}^{k}T_{i}=T\end{array}\right. \tag{3}\]
## 4 Results and Discussion  
### Low-Frequency Oscillatory Flow Condition  
When the oscillatory flow frequency is sufficiently low, inductive pressure losses become ignorable and the assumption of a quasi-steady state becomes valid. By performing Fourier transform on the periodic functions of the pressure difference and mass flow rate of the communication port under low-frequency oscillatory flow conditions (in this research, 1 Hz has been demonstrated through simulation to be sufficiently low), we can determine its resistance, \(R_{\text{A}}\), under various oscillatory flow velocity amplitudes, \(M_{\text{v}}\), as shown in Figure 7.  
It is evident that the quasi-steady assumption-based linear resistance calculation method provides relatively accurate results, with a deviation of less than 3.2% from the simulation results. Moreover, achieving an oscillatory flow velocity amplitude of 6 m/s requires an oscillatory flow pressure amplitude greater than 0.05 MPa. Considering that the pressure in the LO2 supply pipeline usually drops below 1 MPa, this corresponds to a pressure fluctuation of over 5%. Therefore, it can be concluded that the quasi-steady assumption-based linear resistance calculation method is capable of reflecting the communication port flow characteristics under low oscillatory flow frequencies. It essentially reflects the primary and secondary relationship between the local loss caused by the communication port flow and the injection caused by the supply pipeline.  
This also explains why previous research has shown that the experimental results demonstrate more resistance than predicted. This is because the loss of injection was not considered. By comparing Equations (3) and (5), it is evident that there are two additional terms which should represent the resistance increment caused by the injection and flow velocity of the supply pipeline. Therefore, the common term represents the linear resistance of the orifice, the first term of Equation (5) reflects the impact of supply pipeline e flow velocity, and the third term indicates the influence of injection.  
Moreover, this also confirms the existence of a minimum linear resistance corresponding to oscillatory flow velocity amplitude. According to Equations (5) and (6), the minimum value of \(R_{\text{QS}}\) can be written as follows:  
\[R_{\text{QS}}\geq\frac{\Big{(}b+\frac{4\sqrt{6\pi^{2}k^{2}+6b^{2}}}{3\pi} \Big{)}k}{C_{\text{D,0}}\sqrt{K_{T}\frac{2}{3\pi}}}R_{\text{LA,1}}\approx 0.96R_ {\text{LA,1}} \tag{8}\]  
Equation (8) indicates that using \(R_{\text{LA,1}}\) to represent \(R_{\text{LA}}\) during communication port design is reliable for small oscillation conditions, with a deviation within 4%. Furthermore, if experimental or simulation results of communication port linear resistance are obtained, this method can also be used for reverse-engineering of the coefficients, providing a method for measuring communication port flow characteristics.
# Improving Text Recognition Accuracy for Serbian Legal Documents Using BERT
## 2 Related Work  
### Large Language Models--State of the Art and Position in Text Recognition Task  
Representation learning techniques offer a chance to bridge the gap between text and vectors. Because character and word embeddings may embed discrete texts into continuous vector space, they are important for a variety of NLP tasks. Many representation learning approaches based on embedding techniques have proven their quality in different downstream tasks [11; 12; 13; 14].  
The development of a transformer architecture [15] with self-attention mechanisms--BERT [16]--marked the beginning of significant advancements in artificial intelligence. [PERSON] et al. [8] proposed a method to detect OCR errors, using the BERT language model and FastText sub-word embeddings, and managed to achieve 70.9% precision when generating correction candidates. Numerous efforts were influenced by BERT's effectiveness in pre-training tasks on large-scale unlabeled corpora. New architectures and models inspired by transformers, including GPT-2 [17] and BART [18], began to appear and demonstrate their effectiveness when using their general-purpose semantic features for a range of natural language processing applications. Various pre-trained language models (PLMs) found their way into many different fields in NLP [17; 19]. The scientific community persisted in experimenting with bigger models by increasing the volume of data and the size of the model. When the number of parameters increased to more than 10 billion, the models began to behave and perform differently from the smaller models. In contrast to the 175-billion-parameter GPT-3 and the 540-billion-parameter PaLM, the 330-million-parameter BERT and the 1.5-billion-parameter GPT-2 were shown to be less capable of in-context learning and exhibited surprisingly strong conversational skills. The research community began addressing language models as large language models (LLMs) as a result of the emergence of these models.  
Dedicated PLMs have also been trained for the legal domain. PLMs developed for the legal sector offer a more capable foundational system for legal operations. As an example, [PERSON] et al. [20] suggest a language model pre-trained on Chinese court papers, such as those from civil and criminal cases, to address this problem. [PERSON] et al. presented SrBERTa [21]--a language model designed to understand the formal language of Serbian legal documents, trained using Cyrillic legal texts contained within a dataset created specifically for this purpose.
## 4 Discussion
Cinnamic acid, another metabolite isolated in our study, has also been reported with anticancer properties. An increase in toxicity and anti-proliferative effect against different breast cancer cells was reported after combining different drugs, including cisplatin, paclitaxel, doxorubicin, veliparib, tamoxifen, and dactolisib, with cinnamic acid derivatives [25; 26]. In addition, cinnamic acid and pregenolone hybrids were shown to inhibit human A549, MCF-7, HepG2, and HL-60 cancer cell lines [27]. The methyl dihydro-jasmonate, found in this study, also has antitumor effects [26]. Production of vitamins has been reported among _Chorella_ species, whereas riboflavin production, found in this study, has been reported by cyanobacteria [26]. Another metabolite found in this study is cryptophycin, which is produced by the cyanobacteria _Nostoc_. This metabolite acts as a microtubule inhibitor and shows an action mechanism similar to that of Vinca alkaloids, showing an antitumor effect against L1210 murine leukemia cells. Its mode of action involves binding to tubulin and altering its assembly. It is also cytotoxic to LoVo and KB cells by promoting apoptosis and has anti-proliferative properties on ovarian SKOV3 and breast MDA-MB-435 carcinoma cell lines. In addition, this metabolite was toxic against colorectal and nasopharyngeal cancer cells, showing 100 to 1000 times higher potency compared with currently available drugs [26]. The costunolide found in our methanol fraction is a metabolite that has been isolated from different medicinal plants, which has stimulated the interest of researchers since there are studies demonstrating its potential anticancer activities against breast cancer, leukemia, ovarian cancer, liver cancer, and bladder cancer [28]. Erythrocyte hemolysis assays are important in the characterization of extracts before their use in in vivo biological systems. It is a simple and inexpensive test for the evaluation of cytotoxicity [10]. According to [PERSON] et al. [16], extracts that show IC\({}_{50}>90\)\(\mu\)g/mL are not considered toxic. Therefore, the crude extract and collective fractions of _Ch. sorokiniana_ may be considered to have low toxicity towards human erythrocytes, as they presented IC\({}_{50}>800\)\(\mu\)g/mL. The relationship between antitumor, antioxidant, and hemolytic activity assays is used to evaluate the biocompatibility or potential damage that an extract may cause to healthy cells [10]. Our results demonstrate the antitumor, antioxidant, and non-hemolytic potential of _Ch. sorokiniana_ and its involvement in metabolic mechanisms leading to such activities. In this study, we also identified nine metabolites in the CFs that showed the highest antioxidant and cytotoxic activities. In addition, the antitumor properties of the strain tested (UTEX 1239), obtained from an industrialized process, were not previously evaluated. It is then necessary to continue investigating mechanisms in which bioactive compounds may be involved, as well as their potential clinical use.
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 6 Implementation and Tests with Real Hardware
### Discussion about the Implementation  
Different lessons have been learned with the implementation, which will be discussed in this subsection. Regarding the steps that are common to the three methods, it is important to remark that the size of the firmware must be kept low, for two reasons: first, considering the tight constraints imposed by airtime limitations, the time required for updating a project grows with the number of chunks; and second, the hardware of the IoT device also poses some space constraints: the new firmware must fit in the internal flash of the chip and also in the external flash non-volatile memory. These elements must be well-dimensioned from the beginning, considering that a hardware modification is not an option in many of these projects.  
There is another related consideration: if the nodes have different connected elements (e.g., different sensors), several versions of the firmware will be required. In these cases, two options can be considered: first, compiling all the functionalities and using them selectively; and second, using preprocessor directives and performing conditional compilation. In principle, the best option for systems with resource constraints is the second one, as the resulting binary is smaller. This is clearly the best option when the firmware is updated using unicast frames. However, if a broadcast method is used, it could make sense to compile all the functionalities and generate a single version of the firmware, as this would increase the scalability.  
Figure 24: Unicast OTA procedure, with a distance of 2 m and no obstacles: RSSI of each frame. The red line corresponds to the sensitivity of the receiver.  
Figure 25: Unicast OTA procedure with a significant distance and obstacles: RSSI of each frame. The red line corresponds to the sensitivity of the receiver.  
The most critical moment of the firmware update is the flashing of the new version, the copy of the binary between the two flash memories, and the reboot with the new code. This must be performed very carefully with several mechanisms to ensure that no problem arises, as this would render the node inoperative.  
Finally, one comment regarding the _only unicast_ method: it has been shown that although it is not as scalable as the other two, it performs well with a quite simple control mechanism (\"stop and wait\"). This can be an advantage in noisy environments or if the number of firmware versions is significant.
# An Integrated Dictionary-Learning Entropy-Based Medical Image Fusion Framework
## 3 Proposed Framework
### High-Frequency Components Fusion
#### 3.2.2 Dictionary Construction  
Dictionary is an important factor in sparse representation. Training a redundant dictionary with small failure rate is a common solution for sparse representation [62; 63; 64]. However, redundant components of a trained dictionary can cause adverse effects in the accuracy of sparse coding. A certain proportion of redundant elements in the trained dictionary is got from the non-informative blocks in the learning process. As the number of non-informative blocks in medical images is large, using only the informative blocks for learning process can make the trained dictionary productive. To retain the informative blocks and reduce the blocks with limited information, a novel entropy-based informative dictionary construction approach is proposed. The proposed dictionary construction consists of two parts, (1) the informative blocks selection and (2) dictionary learning.  
The block-selection approach is based on information entropy, and can be implemented for both gray-level images and color images. Color images are converted to gray-level images for entropy calculation. Supposing input images have \(w\) gray-levels, and they are split to \(n\) image blocks with \(s\times s\) pixels, the steps of the selection approach can be demonstrated as follows.  
Step 1. Acquiring histogram counts vector \(h_{k},k\in(1,2...n)\) of each image blocks \(\mathbf{I}_{k},k\in(1,2...n)\) by count the pixel numbers of each gray-level. Using \(h_{k},k\in(1,2...n)\) and the image blocks size \(s\times s\), the gray-levels probability of image block \(k\) can be calculated by Equation (3).  
\[P_{k}=h_{k}/(s\times s) \tag{3}\]  
Step 2. Calculating the entropy of each image block use gray-levels probability \(P_{k}\) of each image block \(k\). The entropy calculating process is shown in Algorithm 1.  
```
1: Gray-levels probability \(P_{k},k\in 1,2...w\),
2:
3:Entropy \(E_{S}\) of image block, where \(S=s\times s\)
4:\(E_{0}=0\)
5:for i = 1 to \(S\) do do
6: Compute the entropy of number \(i\)th pixel in image block
7:\(h_{i}=-\sum_{i}P_{i}\log P_{i}\)
8:\(E_{i}\gets E_{i-1}+h_{i}\)
9:endfor
```  
**Algorithm 1** Image Block Entropy Calculation.  
Step 3. Reducing the image blocks with low information entropy. In this step, a threshold \(Th\) is set for the informative image blocks selection. For a image block, if \(E_{S}>Th\), this image block is remained for dictionary learning. On the contrary, if \(E_{S}<Th\) this image block is reduced.  
When the informative image blocks are selected, the K-SVD algorithm can be implemented for dictionary learning. The dictionary learning results with all image blocks and only the informative image blocks are shown in Figure 1.
## 3 Numerical Implementation
### Piezoelectric Pump Finite Element Implementation
Planar/horizontal design was implemented, as shown in Figure 2, and when this design was finished and after defining a starting point, parameter ranges such as diffuser/nozzle angle, nozzle's wide diameter, nozzle length, and length of the chamber were decided accordingly. Between the range of minimum 1 degree and maximum 30 degrees, 10-degree nozzle angle; between the range of minimum 0.1 mm and maximum 1 mm, 1 mm nozzle length; and, finally, between the range of minimum 0.5 mm and maximum 2 mm, 0.5 mm nozzle diameter were used for simulation iterations. The input signal to the piezoelectric pump is defined as follows, where \(V\) is voltage, \(FR\) is the frequency (in Hz) of applied voltage and \(t\) is time:  
\[V(t)=V\,\sin(2\pi\cdot FR\cdot t), \tag{14}\]  
By using COMSOL Multiphysics 5.4 [34], analyses were made in different domains both in a single and a coupled way, and with different time steps, mesh sizes, and discretization values. Different relative tolerance values were tried as well. According to the results of Eigenfrequency study analysis, the piezoelectric actuator's first natural frequency was calculated as 6373 Hz without fluid. Then the transient analysis setup was started. Damping was defined to the linear elastic material, dielectric loss was defined to the piezoelectric disk, a fixed constraint was defined to the outer surface of the brass located under the piezo, and lastly, the ground was defined to the one surface of the piezoelectric material and terminal to the other surface. Force, displacement, and voltage were assigned as 0 for initial conditions. In the piezoelectric actuator's first mode shape, it was seen that it had displacements as shown in Figure 3. According to the piezoelectric actuator data [35], expected displacement could reach up to 50 \(\upmu\)m at 155 Vp-p; hence, arrangements that provide this displacement were determined. The applied voltage was 100 Vp-p, and the frequency was 100 Hz. How voltage and frequency affected the net flow in the optimization part can also be seen in Section 3.2. Figure 2: Geometry and dimensions. Insuin flow is defined as an incompressible flow, at 293.15 K temperature, under 1 atm reference pressure, and without a turbulence model. Cell Reynold's numbers shown in Figure 3f verified that there was no need for a turbulence model. As an initial condition, velocity and pressure were given as 0 (zero). As a boundary condition, after defining the stable wall, the wall condition was given as no-slip, and it was defined with inlet and outlet, as seen in Figure 3. For inlet and outlet, 0 Pa pressure condition was given, and no constraint was defined for flow direction. Absolute tolerance was given as 0.001, and the PARDISO solver was used in a coupled analysis study. The results of displacement of the piezoelectric actuator under insulin pressure, the pressure inside the pump chamber, the current passing through the terminal in the electrical circuit, and velocity values in inlet and outlet were calculated and presented in different plots of this study.
## 1 Introduction
### Titanium Passivation  
Titanium is an extremely reactive metal that undergoes oxidation when exposed to water or air. It forms a tenacious oxide layer (titanium dioxide) that contributes to titanium electrochemical passivity. Titanium dioxide (TiO\({}_{2}\)), the formation of which is referred to as passivation, either in rutile or anatase form, is a dense, highly resistant passive oxide film that protects underlying metal from further oxidation and corrosion. Therefore, titanium exhibits high corrosion resistance because of the presence of this oxide film [31].  
Theoretically, the proposed oxide layer formation starts with adsorbing oxygen on the surface of pure titanium to produce an oxide monolayer. Subsequently, an electron from the titanium will channel through the oxide layer to further adsorb oxygen, thereby producing oxygen ions [32]. An oxygen with a valence of only two electrons is relatively electronegative and will readily bind with lightly held valence electrons of titanium to further thicken the oxide layers until the activation energy for ion transport increases and eventually limits further oxide formation [33]. The TiO\({}_{2}\) layers on the titanium surfaces remained susceptible to corrosive attack despite its high corrosion resistance [31], and is chemically stable only in the dark [34]. At any stage, if either anodization or cathodic potential is applied to titanium, the valance state of the titanium ions within the oxide film is disrupted, thereby leading to thickening and solubility/thinning of the oxide layer through reductive dissolution. The passivation of the implant alters surface composition. These changes can be associated with the changes in surface energy [35; 36]. Likewise, the thickness and stability of the oxide film are relevant to implant performance because corrosion and ion release into the adjacent tissue are undesirable [37].  
In dental implantology, a satisfactory biological response across the entire spectrum of interactions (water-proteins-cells), depending on the chemical and topographic properties of the surface, determines the amount of bone that will come into contact with the biomaterials [13; 38]. Moreover, the hydrophilic status of the material surfaces is a representative marker for surface energy and seems to affect the capacity to adsorb proteins and attract cells for interaction [39]. Osteoblast migration and proliferation occur during the initial stage of healing and critically affect the outcomes of bone-titanium integration. Up to the present time, various modifications for improving the physicochemical and topographic characteristics of dental implant surfaces have been investigated. However, a detailed discussion of each surface modification method is beyond the scope of this review.
## 5 Future Challenges and Scope  
Transmitarray technology has become mature in the last decade. Many research articles are available in the literature to demonstrate the fixed and reconfigurable beam transmitarray discussed in the previous sections. There are still gaps available to explore new characteristics in this area, in particular, for reconfigurable technology. Here, this section discusses different future aspects and challenges related to future technologies. The discussion is focused on the future of terahertz and optical frequencies for this technology, dual-band reconfigurable transmitarrays, and multi-bits transmitarrays.  
Figure 12: (**a**) Simulated transmitarray with ACMP feed source; (**b**) gain pattern with variable source feeds. (reprinted with permission of [112]. Copyright 2019 [PERSON], et al.).