esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Sorption Capacities of Used Sorbents  
The obtained sorption capacities of microalgae and clinoptilolite, and their mixtures, towards N-NH\({}_{4}\)+ during the process carried out in dynamic conditions are shown in Figure 6.  
[PERSON] et al. (2008) showed that the initial concentration is one of the most important factors influencing the obtained values of clinoptilolite adsorption capacity towards ammonium nitrogen [30]. These authors obtained for the initial ammonium nitrogen concentration of 3750 mg N-NH\({}_{4}\)+/L a clinoptilolite adsorption capacity of 20.4 mg/g and an ammonium nitrogen removal efficiency of 58% at pH = 7. At lower initial concentrations of ammonium nitrogen, e.g., 402 and 200 mg N-NH\({}_{4}\)+/L, the adsorption capacity was correspondingly lower and amounted to 5 and 3 mg/g, respectively. In the author's research, similar relationships were obtained during tests conducted in dynamic conditions (Figure 6). The authors showed that the higher the initial concentration of ammonium nitrogen in relation to the concentration of clinoptilolite and _C. vulgaris_, the higher the sorption capacity obtained for these sorbents. The results presented in Figure 6 show that when the concentration of clinoptilolite was lower (50 g/L--sample 5Z), the maximum sorption capacity was the highest at 24.9 mg/g, and at a concentration of 100 g/L (sample 10Z), the sorption capacity was only 12.4 mg/g. For all mixtures of microalgae and zeolites, the maximum sorption capacity was 12.4 mg/g. The maximum sorption capacity for N-NH4\({}^{+}\) by the utilization of immobilized _C. vulgaris_ as a sorbent was higher at 7.5 mg/g and 13.8 mg/g than after using immobilized _S. armatus_--2.6 mg/g and 5.2 mg/g. The same was noted after the utilization of microalgae in the form of a suspension. For suspended _C. vulgaris_, the maximum sorption capacity for N-NH4\({}^{+}\) was at a level of 6.4 mg/g and 12.2 mg/g, and for suspended _S. armatus_ it was 4.4 mg/g and 5.5 mg/g.  
Figure 6: Sorption capacity of sorbents: (**A**)–_C. vulgaris_, (**B**)–_S. armatus_, (**C**)–microalgae and zeolite.  
To sum up, it can be concluded that in order to determine the optimal levels of sorption capacity of the sorbents used, preliminary tests are necessary with the use of different doses of sorbents and different contact times for the sorbent with wastewater.
# Study of the Failure Mechanism of Soft Rock Mining Roadways Based on Limit Analysis Theory
## 2 Analysis of the Deformation and Failure of a Soft Rock Mining Roadway
### Physical Similarity Simulation Experiment Verification  
With the 1506 return airway of the Anyang Coal Mine as the research object, a physical simulation experiment was carried out. Through the loading system, different confining pressures were applied to the model to simulate different mining stress boundary conditions, and the bearing capacity and deformation evolution patterns of the surrounding rock under different mining stresses were analyzed. The model construction size was 1000 mm (length) \(\times\) 200 mm (width) \(\times\) 80 mm (height).
# OFDM Network Optimization Using a QPSK Based on a Wind-Driven Genetic Algorithm
## 4 Performance Evaluation
### Testing of the Algorithms  
Before incorporating the GAWDO into the QPSK, it was first tested on the Rastrigin and Ackley benchmark functions given in Equations (9) and (10), respectively.  
\[\mathrm{g(x)}=20+{\mathrm{X_{1}}}^{2}+{\mathrm{X_{2}}}^{2}-10(\cos 2\pi\mathrm{X _{1}}+\cos 2\pi\mathrm{X_{2}}) \tag{9}\]  
\[\mathrm{g(x,y)}=-20\exp[-0.2\sqrt{0.5(x^{2}+y^{2})}-\exp[0.5(\cos 2\pi\mathrm{x }+\cos 2\pi\mathrm{y})]+\exp(1)+20 \tag{10}\]  
The GAWDO was tested alongside the conventional WDO and GA for comparison. The conventional WDO and GA were derived from [23; 26; 34; 35]. These preliminary tests were done to confirm the robustness and accuracy of the various algorithms. All the algorithms were run five times and their results are presented in Tables 2 and 3.  
Both benchmark functions given above have a global minimum of [0;0]. From Table 2, it can be seen that the GA and the WDO often struggle with retaining the global minima. They tend to converge in local minima. In optimization cases, an algorithm that converges poorly and settles to local minima is regarded as inaccurate and unreliable. That particular  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Settings** \\ \hline Creation function & Uniform \\ Crossover function & Single point, 0.8 \\ Population type & Double vector \\ Population size & 1000 \\ Pareto fraction & 0.4 \\ Selection function & WDO \\ Penalty factor & 100 \\ Migration fraction & 0.2 \\ Migration interval & 20 \\ Fitness scaling function & Top \\ Migration direction & Both \\ Mutation function & Uniform \\ Stall test & Geometric weighted \\ Stall time limit & 60 \\ Non-linear solver & Augmented Lagrangian \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the genetic algorithm.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Title 1** & **Title 2** & **Title 3** & **Title 4** & **Title 5** \\ \hline GA & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) \\ WDO & (0; 0) & (0; 0) & (0; 0) & (0; 0) \\ GAWDO & (0; 0) & (0; 0.4) & (0; 0) & (0; 0) & (0; 0) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Algorithm results on the Rastrigin function.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Title 1** & **Title 2** & **Title 3** & **Title 4** & **Title 5** \\ \hline \multirow{4}{*}{GA} & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) & (X\({}_{1}\); X\({}_{2}\)) \\  & (0; 0.5) & (0; 0) & (\(-1\); 3) & (0; 0) & (0; 0) \\ WDO & (0; 0) & (0; 1.5) & (0; 0) & (0; 0) & (0; 0) \\ GAWDO & (0; 0) & (0; 0.4) & (0; 0) & (0; 0) & (0; 0) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Algorithm results on the Ackley function.  
algorithm must not be given much priority with regards to optimizing much more sophisticated problems [11]. Henceforth, the GAWDO was the only algorithm that was further used on the QPSK.
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 3 System Model
### Atomic Deal  
The cross-chain system heavily relies on atomic swap transactions for secure cross-chain data interactions on the master chain. This process simplifies the asset transactions between different blockchains. It involves an atomic data interaction process to cater to various subchain needs, requiring participants to meet specific cross-chain requirements for successful interactions.  
Figure 2: Main-sub 1 + N cross-chain structure.  
Unlike traditional hash time lock technology, we employed a two-phase transaction approach for the atomic swap protocol. This method guarantees that all resource operations are either successfully committed or rolled back, preventing system-disrupting transaction failures. The two phases are as follows.  
1. Preparation Phase: Participants initiate transactions and await coordinator responses.
2. Commit/Rollback Phase: Coordinators request commit or rollback from participants and await their responses. Successful responses result in final commits, while failures trigger rollbacks.  
In our system, we implement part of the atomic swap protocol on the master chain, breaking down the process into four phases. This follows the two-phase transaction model, ensuring reliable and secure cross-chain transactions.  
1. Negotiation phase: The parties find each other through the relay platform and draft an exchange agreement. The agreement confirms the transaction content. Each party decides whether to join and finish the transaction by its own will.
2. Transaction phase: The parties entrust their transaction content to the relay platform according to the agreement. The platform verifies and records the transaction content on the master chain.
3. Verification phase: The parties check whether the entrusted transaction content matches the agreement. They decide whether to continue or stop the transaction by their own will.
4. Commitment phase: The parties confirm that the transaction is completed and send confirmation to the relay platform. The platform records this cross-chain transaction as a security endorsement.  
These four phases are the basic atomic swap process designed by this cross-chain system. But, under application scenario limitations, the transaction phase and verification phase may need some changes. Since the application scenario here is DNS domain name data cross-chain sharing, the parties in the consensus roots system are assumed to be mutually trusted. As such, the atomic swap process here needs to consider situations where parties may have sensitive domain name information and domain names that cannot be exchanged. To meet these limitations, the transaction phase and verification phase need some adjustments. For example, in the negotiation phase, cross-chain content can be prepared in advance, and parties can see the content and then decide whether to join the cross-chain interaction or not. In general, to fit different application scenarios, the atomic swap protocol can be customized to some extent to make sure it can better adapt to specific needs.
# A Survey on Energy Drainage Attacks and Countermeasures in Wireless Sensor Networks
## 4 Recent Review Based on State-of-the-Art Works for Energy Drain Attack
### Network Layer
#### 4.3.6 Sybil and Vampire Attacks  
A Sybil attack is accomplished by a malicious device illegitimately taking on multiple identities. In a vampire attack, two attack models are known. In the first case, the malicious node composes a fake packet repeatedly, and the path between the source node and the destination node is stretched to include all the nodes in the network. This is carried out during the forward process by a malicious node. The vampire attack is one example of an energy-draining attack.  
In 2021, [PERSON] et al. [78] published a survey of the most up-to-date methods to detect Sybil attacks and countermeasures, including encryption, trust, received signal indicator (RSSI), and artificial intelligence. [PERSON] and [PERSON] [79] proposed a dual trust-based multi-level Sybil (DTMS) to detect Sybil attacks using a multi-level detection system to verify the identity and location of each node under a clustering architecture. A trust value based on the node's behavior and dynamic reward and penalty was performed in DTMS. To reduce communication, data aggregation was introduced.  
We reviewed two research works for vampire attacks in WSNs. First, [PERSON] et al. [80] proposed resource-conserving protection against the energy-draining protocol to detect vampire attacks by selecting routes with the maximum priority. It was represented as the highest value with energy efficiency and security performance through an analytic hierarchy process. In 2024, [PERSON] and [PERSON] [81] proposed a novel deep learning-based vampire attack detection model involving the following steps: data collection, attack detection, mitigation, and optimal path selection. After isolating vampire nodes, the routing path was established with the rest of the nodes.
# Small Infrared Target Detection via a Mexican-Hat Distribution
###### Abstract  
Although infrared small target detection has been broadly used in airborne early warning, infrared guidance, surveillance and tracking, it is still an open issue due to the low signal-to-noise ratio, less texture information, background clutters, and so on. Aiming to detect a small target in an infrared image with complex background clutters, this paper carefully studies the characteristics of a target in an IR image filtered by the difference of Gaussian filter, concluding that the intensity of the adjacent region around a small infrared target roughly has a Mexican-hat distribution. Based on such a conclusion, a raw infrared image is sequentially processed with the modified top-hat transformation and the difference of Gaussian filter. Then, the adjacent region around each pixel in the processed image is radially divided into three sub-regions. Next, the pixels that distribute as the Mexican-hat are determined as the candidates of targets. Finally, a real small target is segmented out by locating the pixel with the maximum intensity. Our experimental results on both real-world and synthetic infrared images show that the proposed method is so effective in enhancing small targets that target detection gets very easy. Our method achieves true detection rates of 0.9900 and 0.9688 for sequence 1 and sequence 2, respectively, and the false detection rates of 0.0100 and 0 for those two sequences, which are superior over both conventional detectors and state-of-the-art detectors. Moreover, our method runs at 1.8527 and 0.8690 s per frame for sequence 1 and sequence 2, respectively, which is faster than RLCM, LIG, Max-Median, Max-Mean.  
small infrared target; target enhancement; target detection; mexican-hat distribution; difference of Gaussian filter +
Footnote †: journal: Applied Soft. 2019, 9, 5570; doi:10.3390/app9245570
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 2 Proposed Approach
### 2-Layer Sparse Graph Construction  
After abstracting the image, the superpixels are mapped into a graph \(G=(V,E)\) with \(N\) nodes \(V=\{v_{i}|1\leq i\leq N\}\), and edges \(E=\left\{e_{ij}|1\leq i,j\leq N\right\}\). Node \(v_{i}\) corresponds to the image superpixels and edge \(e_{ij}\) links nodes \(v_{i}\) and \(v_{j}\) to each other with an affinity matrix \(W=\left[w_{ij}\right]_{N\times N}\). In this paper, as proposed in [16], a 2-layer sparse graph is adopted. As shown in Figure 4, the graph is generated by connecting each node to its neighboring nodes and the most similar node sharing a common boundary  
Figure 3: Main steps of the proposed approach.  
Figure 2: Visual comparisons of different graph-based methods. (**a**) Original image; (**b**) Ground truth; (**c**) Saliency maps produced by 2-layer neighborhood graph; (**d**) Saliency maps produced by 2-layer neighborhood graph with nonlocal connections; (**e**) Saliency maps produced by 2-layer sparse graph; (**f**) Saliency maps produced by 2-layer sparse graph with the nonlocal connections.  
with its neighboring nodes. As is illustrated in Figure 2, contrast to the 2-layer neighborhood graph, the 2-layer sparse graph can effectively avoid the disturbances of the dissimilar redundant nodes. In addition, the nodes on four sides are connected and any pair of boundary nodes are considered to be adjacent as well as well as [23; 24; 27].  
In this paper, we define the weight \(w_{ij}\) of edge \(e_{ij}\) in 2-layer sparse graph as:  
\[w_{ij}=\left\{\begin{array}{ll}e^{-\frac{||l_{i}-l_{j}||}{\sigma^{2}}}& \text{if node $v_{i}$ is connected with $v_{j}$}\\ 0&\text{others}\end{array}\right. \tag{1}\]  
where \(||l_{i}-l_{j}||\) is the Euclidean Distance between the node \(i\) and \(j\) in CIELAB color space. \(\sigma\) is a parameter controlling strength of the weight. We define the affinity matrix of the graph as \(W~{}=~{}[w_{ij}]_{N\times N}\), and a degree matrix \(D~{}=~{}\text{diag}\{d_{11},d_{22},\ldots,d_{NN}\}\) is generated, where \(d_{ii}=\sum_{j}w_{ij}\).
# Tissue Engineering Strategies for Retina Regeneration
## 6 Current Clinical Trials Using Biomaterial Scaffolds  
In a Phase1/2a clinical study, [PERSON] et al. [13] implanted clinical-grade hESC-RPE grown on 3.5 mm \(\times\) 6.25 mm parylene membrane substrate (CPCB-RPE1) in five patients suffering from geographic atrophy (GA) associated with advanced non-neovascular AMD. Postoperative findings demonstrated that there is no progression of vision loss. In one eye, the improvement was seen by 17 letters, and improved fixation was seen in two eyes. The appearance, size, position of the implant also did not change, and no adverse events were noted [13]. [PERSON] et al. and his team engineered a 6 mm \(\times\) 3 mm RPE patch which constitutes of differentiated hESC derived RPE monolayer placed on a human-vitronectin-coated polyester membrane (polyethylene terephthalate, PET). The patch delivered to the subretinal space of the retina in patients with AMD using a microsurgical tool survived and integrated with the host retina. There was a focal improvement in photoreceptor anatomy over the transplant in both patients with a visual acuity improvement of 15 letters or more [124].  
In another clinical trial study from RIKEN Center for Developmental Biology (Japan), iPSC-RPE cells were prepared as a sheet by growing them on collagen support. After confluence, the cells were treated with collagenase to obtain a cell sheet on its own ECM. The cell sheet was transplanted along with immunosuppression in a patient suffering from neovascular AMD. When it was assessed at one year, the sheet remained intact, but the best-corrected visual acuity had neither improved nor worsened. However, the trial was forced to stop later because of mutations noticed in the second patient's iPSCs and due to changes in the regulatory rules in Japan [53]. In all the above clinical trials, specialized surgical tools and devices were used for implant delivery. These devices minimized the extent of the retinotomy and allowed precise positioning [125; 126]. The main endpoints of these studies were safety and some efficacy. In future studies, large multicentral clinical trials with more patients are needed to measure the efficacy and statistical significance of advanced phase clinical trials.
## 3 Diffuse Reflectance Spectroscopy: Analyzing Results
### Example Materials
In other words, the requirements in terms of microscopical material absorption are higher for coarse white standards than for small grain materials. In Figure 8, the coarse grained \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) white standard _F1200_ (Imerys Fused Minerals, mean particle size \(d_{50}=3.0\,\mathrm{\SIUnitSymbolMicro m}\)[90; 91]) causes an exaggerated increase of the TiO\({}_{2}\) overall diffuse reflectance compared to the smaller grains of _F2000_ (\(d_{50}=1.2\,\mathrm{\SIUnitSymbolMicro m}\)[90; 91]) or the smallest ones in _CR30F_ (\(d_{50}=0.38\,\mathrm{\SIUnitSymbolMicro m}\)[80]). Surprisingly, a reverse case occurs for the local maximum of diffuse reflectance at \(700\,\mathrm{nm}\). Here, a measurement against the finest white standard _CR30F_ entails the most striking diffuse reflectance peak indicating a local absorption peak, whereas the coarsest _F1200_ shows almost no elevation of the diffuse reflectance around \(700\,\mathrm{nm}\). As uncontrolled impurities of foreign atoms may strongly affect the spectral properties of nanoscaled Al\({}_{2}\)O\({}_{3}\) powders [92; 93], possibly due to an increased treatment process, the inexplicable bump around \(700\,\mathrm{nm}\) may occur from an unintentional, additional absorption of such an impurity. An increase of the white standard's absorption at \(700\,\mathrm{nm}\) therefore results in a local elevation of the diffuse reflectance. However, even if the spurious absorption peak diminishes, choosing a coarse grained white standard does not necessarily benefit quantitative diffuse reflectance studies as it artificially increases the overall measured diffuse reflectance of the material under investigation, thereby easily attaining values exceeding unity. Depending on the situation, a feasible alternative includes utilizing a white standard that, at the cost of absolute diffuse reflectance, possesses a rather flat spectral response throughout the investigated wavelength region. Afterwards, a rescaling of the diffuse reflectance spectrum may be performed by comparing the \"gray\" standard itself with a known reference material [32; 72]. Otherwise, bumps or dips may be falsely attributed to material properties that originally stem from the used white standard. Nevertheless, including the white standard in evaluations is crucial to compare diffuse reflectance between one another. The fact that the observed diffuse reflectance of TiO\({}_{2}\) changes significantly based on the used white standard highlights the need for a common and reliably reproducible white standard to ensure comparability and potential optimization of any material.
## 3 Support Vector Machine  
The SVM [18; 19] was officially introduced by [PERSON], [PERSON], and [PERSON] during the Fifth Annual Association for Computing Machinery Workshop on Computational Learning Theory in 1992. SVMs are probably the most popular machine learning approach for supervised learning.  
Figure 3: Four patterns of orbits: (**a**) Bell curve, (**b**) Oval, (**c**) Sharp-pointed oval, and (**d**) Lemniscate.  
Figure 2: Orbits generated by randomly resizing TJLs.  
The underlying idea of SVMs with kernels is to apply a nonlinear mapping, \(\varphi\), which maps the input into some feature space and then find a hyperplane that best separates the different patterns in the feature space. The problem of finding a hyperplane that separates two patterns with a maximum margin can boil down to an optimization problem, as follows.  
\[\min_{w,b}\quad\tfrac{1}{2}\|w^{2}\| \tag{12}\]  
subject to the constraints:  
\[y_{i}(<w,\varphi(x_{i})>+b)\geq 1\text{ for }\forall i \tag{13}\]  
where \(w\) is a vector normal to the hyperplane; \(b\) is a bias; \(\varphi\) stands for a predefined function; and, \(y_{i}\) is a sign function defined as \(y_{i}=sign(<w,\varphi(x_{i})>+b)\).  
The kernel or Gram matrix on all pairs of data points is defined, as follows.  
\[K(x,u)=\sum_{i}\varphi_{i}(x)\varphi_{i}(u) \tag{14}\]  
The selection of a kernel function is heavily dependent on the data specifics. In general, the Gaussian kernels, also called Radial Basis Functions (RBFs), are mostly applied in the absence of prior knowledge. Afterwards, the solution to the primal problem in (12) can be found efficiently by solving a dual problem, as follows.  
\[\max_{\lambda_{j}}\quad\sum_{j}\lambda_{j}\ \ -0.5\sum_{j}\sum_{k}\lambda_{j} \lambda_{k}y_{j}y_{k}K(x_{j},x_{k}) \tag{15}\]  
subject to the constraints:  
\[\sum_{j}y_{j}\lambda_{j}=0 \tag{16}\]  
\[0\leq\lambda_{j} \tag{17}\]  
where the index j can be restricted to a set of support vectors instead of the set of whole training data. Subsequently, the score function of a SVM used to classify the data is given, as follows.  
\[f(x)=<w^{*},\varphi(x)>+b^{*}=\sum_{j}y_{j}\lambda_{j}^{*}K(x_{j},x)+b^{*} \tag{18}\]  
where \(w^{*},b^{*}\), and \(\lambda_{j}^{*}\) stand for the optimal solutions of the primal problem (12) and dual problem (15).