esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantification of SPECT Concentric Ring Artifacts by Radiomics and Radial Features
## 2 Materials and Methods  
### Phantom Data Acquisitions  
All the test data were acquired as part of routine quarterly QA using the Jaszczak phantom [3]. The phantom included a cylindrical water bath, with acrylic inserts of six rod sizes, solid spheres of six sizes, and a uniform volume devoid of rods or spheres [12; 13]. Phantoms were loaded with a water solution containing 666-740 MBq \({}^{99\text{m}}\)Tc. Phantoms were positioned on the patient imaging table, not in a specialized phantom holder. Each 360\({}^{\circ}\) set of projection data was comprised of 128 projections acquired as 128 \(\times\) 128 matrices with 32-38 \(\times\) 10\({}^{6}\) total counts. Magnification factors of 1.00-185 were used (pixel size = 3.3 \(\pm\) 0.7 mm; range = 2.1-5.2 mm).  
All data sets were reconstructed by filtered back projection (FBP) with a Hanning post-filter (cutoff = 1.0 cm\({}^{-1}\))and corrected by a simple Chang attenuation correction with attenuation coefficient of \(\mu\) = 0.11 cm\({}^{-1}\), or adjusted as necessary in order to obtain the most uniform appearance of transaxial reconstructions of the volume of the phantom containing no rods or spheres.  
From more than 200 acquisitions performed for 12 different SPECT systems between 2016-2020, a medical physicist (KJN) with over twenty years' experience in assessing SPECT system phantom testsidentified40 studies (Table 1) at the time of data acquisition as either having evidence of concentric ring artifacts; others were acquired to determine whether concentric ring artifacts were resolved following camera-tuning performed within 1 week after obtaining an unacceptably non-uniform-appearing result. A 41 st phantom study, for which observers agreed that no artifacts were visible, was used as a reference standard. At the time of the QA phantom data acquisitions, camera ages ranged from 1 month to 21 years old, with a mean age of 13.2 \(\pm\) 6.2 years (median 15.0 years). Ten systems were dual-detector systems and twowwere single-detector systems. Considering the mean age of the SPECT systems that generated the data, most of the studies were generated by old scanners, which tend to drift electronically more frequently than newer machinery.
# Accumulation of Spherical Microplastics in Earthworms Tissues-Mapping Using Raman Microscopy
## 1 Introduction
On a global scale, it has been observed that wind currents from sub-Saharan Africa carrying heated air over Europe contribute to the intercontinental drift of particles, which results in plastic reaching ice caps and areas uninhabited by humans [18]. The accumulation is related to the permanent presence of plastics in the tissues of living organisms, documented based on microscopic analysis [19] or molecular staining [20]. So far, accumulation has been described in many species of saltwater fish, birds, and humans. In the case of fish, as many as 47.8% of the tested individuals showed the presence of plastics at a concentration of 4.11-2.85 pcs/individual, and the particle size ranged from 0.054 um to 0.765 um. The presence of plastics has many negative effects, such as a sense of false satiety, internal injuries to the intestines and digestive tract, physiological stress, reduced fertility and survival of offspring, growth inhibition, and the dominance of organisms better adapted to the plastic-laden environment. Scientists indicate that, in addition, particles are often vectors of toxins that lead to the inflammation and death of organisms [12]. The problem of accumulation seems to be extremely difficult to combat because the multitude of colors, shapes, and particles increases the chances of mistaken consumption of plastics as natural food. Additionally, microplastics float at different depths in the water, which also favors their accidental ingestion by fish or crustaceans. The aim of this study was to determine the molecular accumulation of low-density spherical polyethylene in a selected species of earthworms (_Lumbricus terrestris_). Taking into account the individual anatomy, previous publications, and the nature of this research, the precise determination of the route of accumulation, the impact on biochemical processes, and the amount of the material turn out to be extremely important. Earthworms play a key role in the soil ecosystem, regardless of whether we are discussing cosmopolitan species adapted to urban soils or bioindicators of clean, rural soil [18]. Earthworms, often called soil engineers, contribute to the better circulation of organic matter, increase the absorption of mineral compounds by plants, produce soil aggregates, and increase carbon circulation in the soil. There are many reports documenting the ingestion of microplastic particles by earthworms and their ability to forage and recognize contaminants selectively, but there are few publications describing the subsequent fate of the particles in the digestive tract. The purpose of this study was to determine the potential for permanent microplastic accumulation in _L. terrestris_ muscle tissues exposed to 38-63 um low-density polyethylene (38-45 um 1.00 g/cm\({}^{3}\) and 53-63 um 1.00 g/cm\({}^{3}\)) contained in the soil at a concentration of 0.1% dry weight for an incubation period of 3 months. An additional goal was to use Raman microscopy as an innovative method for measuring plastics of very small sizes.
## 3 Multi-Domain Informative Path Planning  
The framework of the whole algorithm is shown in Figure 6. The input the presented framework is scattered informative spots. Through the heuristic GLNS and k-means solver, the spots are clustered, and the path planning is described in Section 3.1. Then in Section 3.2, whether unknown obstacles exist is solved, and replanning is explained for path segments that pass obstacles. The trajectory regeneration based on that path found in the former section is explained as well.  
To simulate the target motion space, we can randomly create \(m\) spots. Both the number of spots and the positions must conform to a Gaussian distribution. When passing through the target space, the HAUV has to sample or move to some spot of scientific relevance. Therefore, a target spot should be selected from all the information spots. Since all the terrain map is fixed, KD tree _treTree_ stores the environmental information to enable quick reactions to the NNS problem. An example of this information mapping is in Figure 7.  
Figure 6: The framework of the whole algorithm. The inputs of this system are randomly created information spots, obstacle sets, and current fields. The output is a smooth, safe, low-cost trajectory conforming to all constraints.  
To solve this problem, informative spots are divided into \(K\) clusters. \(K\) is the number of groups that the HAUV is supposed to visit. We found the solution to our example by using the KMEANS solver in MATLAB. The result returned contains the group number of each spot and the geometric center spots \(c_{1},c_{2},\dots,c_{n}\) of each group. Therefore, the problem can be formulated by finding a target spot in each cluster, a GTSP problem.  
The GLNS algorithm was presented by [PERSON] and [PERSON], based on adaptive large neighborhood searching [50]. They proved that this algorithm is an efficient solver for the GTSP problem [51]. It was confirmed to be efficient at finding solutions to GTSP problems in [16].  
In this work, we present a method to calculate the energy costs that the HAUV incurs in different environments based on the 3D Euclidean distance between each pair of target spots, and set a weighted edge for each distance according to the environment via heuristics. Based on the KD tree, we regard the replanning of the path segment as a fixed-radius near neighbors problem. The inputs for our solver are informative spots clusters, and output is the optimal path.
# A Novel Application Based on a Heuristic Approach for Planning Itineraries of One-Day Tourist
## 4 Complexity and Performance Analysis  
In this section, the analysis of the complexity and performance of the proposed algorithms are provided. The following results describe the algorithms complexity:  
* the Algorithm 1 requires the implementation of the ILP 1 problem including two TSP and 4 decision variables. Hence, considering a branch-bound approach, the complexity of Algorithm 1 is \(O(2^{4})+2*O(K)=O(K)\), with \(O(K)\) TSP complexity;
* the Algorithms 1.1, 1.2 and 1.3 show complexity \(O(N*K)\) since the ILP 1 problem is included in a N-dimensional \"while\" loop, where N is the number of Pol of graph \(G_{i}\)  
Figure 5: The UML diagram for the node addition procedure.  
We can conclude that the complexity of the heuristic approach is \(O(N*K)\). In order to be compliant with application time constraints, the TSP has been implemented using the Lin-Kernighan algorithm that often keeps its tours within 2% of the Held-Karp lower bound [48], then \(K=O(N^{2.2})\). Therefore, our heuristic approach shows complexity \(O(N^{3.2})\). Let us underline that the proposed application interacts with users to find the final best solution. Therefore, the time to complete the heuristic approach application and provide the final solution strongly depends on the velocity of the user given the necessary inputs to the application.  
The performance of the proposed algorithms are validated with benchmark orienteering algorithms presented in [49]. In particular, the data set used in [49] and reported in [50] is used as benchmark (see Figure 6). Since, in the considered benchmark data set the PoI importance is defined by a score, we associate the priorities to the higher score values as reported in Figure 6. Note that in Figure 6, X and Y are the cartesian coordinates of the PoI and the PoIs distance is computed using the Euclidean distance formula [49]. Moreover, for comparison purpose, we assume that (i) the PoI \(V_{d}\) is selected as to minimize the total travel time, (ii) the Euclidean distance includes the stop time for visiting in our approach, (iii) the user preferences are randomly set in the instances simulation. Now, let us report the comparison results between the proposed heuristic and the D-algorithm and S-algorithm proposed by [49]. In particular, Figures 7-9 report respectively the comparison results considering data set of problem 1, 2 and 3, where Tmax represents the total travel time. Comparing the score and time values it can be concluded that the proposed approach performs much better than D-algorithm and little worse than the S-algorithm. However, in case only addition of PoI are needed by Algorithm 1.3, the proposed approach performs better than S-algorithm too. It is also remarked that the performance of Algorithm 1.2 can be further improved when a considerable number of PoIs must be deleted.
## 3 Results and Discussion
### Adsorption Selectivity of the MIPs  
To evaluate the binding specificity of the prepared MIPs and NIPs toward gossypol, an adsorption selectivity experiment was conducted using structurally similar analogues of ellagic acid and quercetin as control templates (Figure 6). Figure 7 presents the selective adsorption capacities of MIPs and NIPs for gossypol and two competitors at concentrations of 200 mg L\({}^{-1}\) for 1 h and 12 h, respectively. Results showed that the adsorption capacity of MIP1 for gossypol was about 1.9 times higher than that of MIP2 and 2.8 times higher than that of MIP3 for the 12 h binding experiment. However, these values of MIP1 and MIP2 were found to be close for the 1 h binding experiment. The result was consistent with the fact that MIP2 had fast binding kinetics due to the advantage of surface layer imprinting, while the MIP1 featured high binding capacity with long equilibrium time. Moreover, the adsorption capacities of MIP1, MIP2, and MIP3 for gossypol were much higher than those of the other two analogues, suggesting that MIP1, MIP2, and MIP3 have higher affinities for the template gossypol. The adsorption capacity data was summarized in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & \(\mathbf{C_{0}}\) **(mg L\({}^{-1}\))** & \begin{tabular}{c} **Regression Equation** \\ **Y = A + BX** \\ \end{tabular} & \
\begin{tabular}{c} **K\({}_{\text{D}}\) (mg L\({}^{-1}\))** \\ **K\({}_{\text{D}}\) = \(-\)1/B** \\ \end{tabular} &
\begin{tabular}{c} **Q\({}_{\text{m}}\) (mg g\({}^{-1}\))** \\ **Q\({}_{\text{m}}\) = \(-\)A/B** \\ \end{tabular} & \(\mathbf{R^{2}}\) \\ \hline MIP1 & 200–500 & \(Q_{\text{e}}/C_{\text{e}}=19.6-0.0776\)Q & 12.9 & 252 & 0.9761 \\  & 600–2000 & \(Q_{\text{e}}/C_{\text{e}}=4.04-0.00617\)Q & 162.1 & 654 & 0.9222 \\  & 200–2000 & \(Q_{\text{e}}/C_{\text{e}}=2.59-0.0042\)Q & 238.1 & 616.7 & 0.9675 \\ MIP2 & 120–260 & \(Q_{\text{e}}/C_{\text{e}}=5.71-0.06880\)Q & 14.5 & 83.0 & 0.9223 \\  & 300–1600 & \(Q_{\text{e}}/C_{\text{e}}=0.95-0.00730\)Q & 137.0 & 130.1 & 0.9099 \\  & 120–1600 & \(Q_{\text{e}}/C_{\text{e}}=0.33-0.0068\)Q & 147.1 & 48.5 & 0.9493 \\ MIP3 & 140–600 & \(Q_{\text{e}}/C_{\text{e}}=1.12-0.0161\)Q & 62.1 & 69.6 & 0.9477 \\  & 140–600 & \(Q_{\text{e}}/C_{\text{e}}=0.89-0.0155\)Q & 64.5 & 57.4 & 0.9355 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Adsorption parameters from the Scatchard analysis of MIPs and NIPs.  
Figure 6: Structures of template gossypol, ellagic acid, and quercetin.  
Figure 7: The selective adsorption capacities of the MIP1, MIP2, MIP3, NIP1, NIP2, and NIP3 toward gossypol, ellagic acid, and quercetin solution, respectively (**A**: 10 mg of adsorbents in 5.0 mL 200 mg L\({}^{-1}\) template solution in methanol for 12 h; **B**: 10 mg of adsorbents in 5.0 mL 200 mg L\({}^{-1}\) template solution in methanol for 1 h).
# Multi-Layer Progressive Face Alignment by Integrating Global Match and Local Refinement
## 1 Introduction
### The Global Methods  
Global methods learn a holistic appearance model to match a new image by optimizing the appearance residual under the current shape parameters. Typical Active Appearance Models (AAM) have been widely used and their extensions mainly focus on fast optimization procedures and texture constraints [1]. [PERSON] et al. [2] enhanced the performance for illumination variations by combining Gabor filter responses.  
[PERSON] et al. [3] constructed three-level cascade CNN networks to align a face. [PERSON] et al. [4] extracted the CNN features to input the Recurrent Neural Network (RNN). [PERSON] et al. [5] combined several cascaded encoder-decoder networks on different resolution images and updated the landmark positions at each stage. [PERSON] et al. [6] regard MLP as a graph transformer network to be embedded into a cascaded regression framework. [PERSON] et al. [7] used a 3-way factorized Restricted Boltzmann Machine (RBM) to build a deep face shape model.  
Several multitask deep convolutional networks have been constructed in the cascade manner for jointly achieving face detection, face alignment, pose estimation, and gender recognition [8; 9; 10]. Head pose is estimated and face variations can be globally restricted by integrating the different deep networks [11]. Three-dimensional information is introduced into the popular CNN networks' frameworks to extract deep features in face alignment [12; 13; 14].  
The global methods can effectively control and constrain the overall face shape. Their advantage is global optimization and good representation for the whole texture. According to the overall shape, initial error, local noise, partial occlusion and local illumination variation can be tolerated and adjusted to a certain extent. However, because the representation range of the global model depends on the training data, it fails to accurately converge when the input image is dissimilar to the trained appearance. Furthermore, landmarks on the chin and cheeks and other external contours lack rich textures and are relatively isolated, which are somewhat restricted by the global shape, and tend to be attracted to external background noise or other intense gradient regions of a face. When the boundaries between the face region and the background are blurred due to other faces or skin-like objects, although the global optimum can be converged and the face shape can be approximately maintained, the accuracy at the face edge is still very limited.
# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 5 Machine Learning Adaptability for WSNs  
Many issues plaguing WSNs can be easily overcome and performance can be greatly improved by utilizing machine learning. Figure 1 shows some ways in which ML is used to extend the lifespan of WSNs include the following:  
* Anticipating the volume of energy that can be collected during a given time slot, which is necessary to increase the longevity of WSNs;
* ML-based localization that actually works;
* Using ML to identify the faulty links;
* Using ML to decrease the dimensionality of data at the level of the sensor or CH.  
Figure 1: Classification of ML techniques.
## 2 System Model
### Channel Modeling  
In the proposed system, \(N\) denotes the number of antennas in BS [1]. \(\mathbf{G}\in\mathbb{C}^{M\times N}\) is the channel between BS and STAR-RIS; \(\mathbf{F}_{r}\in\mathbb{C}^{M\times 1}\) and \(\mathbf{F}_{t}\in\mathbb{C}^{M\times 1}\) represent the channel between STAR-RIS and U1 and the channel between STAR-RIS and U2, respectively. In dense urban scenarios, the direct link between the BS and users is often obstructed by tall buildings and other physical structures. Therefore, in this paper, the channels are modeled as Rician fading channels, where the line-of-sight (LoS) and non-line-of-sight (NLoS) components are jointly considered through the Rician factor. The path loss coefficient of the proposed model is \(\chi=\chi_{1}d_{0}^{-\epsilon}\), where \(\epsilon\) denotes the path loss exponent, \(d_{0}\) denotes the distance between the transmitter and receiver, and \(\chi_{1}\) denotes the path loss coefficient at 1 m. Then, the channels are modeled as  
\[\mathbf{F}_{k}=\chi_{1}d_{k}^{-\epsilon_{k}}\times\left(\sqrt{ \frac{K_{k}}{K_{k}+1}}\mathbf{F}_{k}^{(LoS)}+\sqrt{\frac{1}{K_{k}+1}}\mathbf{ F}_{k}^{(NLoS)}\right), \tag{2a}\] \[\mathbf{G}=\chi_{1}d_{BS}^{-\epsilon_{G}}\times\left(\sqrt{ \frac{K_{G}}{K_{G}+1}}\mathbf{G}^{(LoS)}+\sqrt{\frac{1}{K_{G}+1}}\mathbf{G}^{ (NLoS)}\right),\] (2b) \[k\in\Big{\{}t,r\Big{\}}. \tag{2c}\]  
where \(K_{t}\), \(K_{r}\), and \(K_{G}\) denote the Rician factors for \(\mathbf{F}_{t}\), \(\mathbf{F}_{r}\), and \(\mathbf{G}\), respectively. \(\epsilon_{t}\), \(\epsilon_{r}\), and \(\epsilon_{G}\) denote the exponents of path loss for \(\mathbf{F}_{t}\), \(\mathbf{F}_{r}\), and \(\mathbf{G}\), respectively. \(\mathbf{F}_{t}^{(LoS)}\), \(\mathbf{F}_{r}^{(LoS)}\), and \(\mathbf{G}^{(LoS)}\) are the LoS components for \(\mathbf{F}_{t}\), \(\mathbf{F}_{r}\), and \(\mathbf{G}\), respectively. \(\mathbf{F}_{t}^{(NLoS)}\), \(\mathbf{F}_{r}^{(NLoS)}\), and \(\mathbf{G}^{(NLoS)}\) represent the NLoS components for \(\mathbf{F}_{t}\), \(\mathbf{F}_{r}\), and \(\mathbf{G}\), respectively.
# Potential and Limitations of Open Satellite Data for Flood Mapping
Specifically, we combined semi-automatic and manual approaches for flooded area detection and created rapid and accurate maps by integrating satellite images with DEMs and ancillary data. The aim of our research is the definition of a procedure that can also be used by non-remote sensing processing experts to map flooded areas using free satellite data. We developed and tested the presented methodology on flood events that occurred in Spain in 2015 and 2017 and Italy in 2016. We used all available free satellite data services: Sentinel-1, Sentinel-2, Landsat-8, MODIS and Proba-V. We also focused on the factors that limit, or help, the capability of flooded area detection (e.g., the times of satellite passes with respect to the flood and the spatial resolution). The produced flood maps were compared and validated against official maps and Copernicus EMS.
# Relationship between Body Composition and Gross Motor Coordination in Six-Year-Old Boys and Girls
## 1 Introduction
After learning about the findings from previous research, it is clear that dimorphic differences in MC have been established at this age, but the lack of research is reflected in our knowledge on the relationship between body composition and MC in preschool children. This study should provide answers to the questions of which segments of body composition contribute the most to the performance of gross motor coordination, as well as help plan new content to be used in the field of physical education for preschool children. This study aimed to investigate the association between body composition variables (i.e., muscle percent and mass, fat percent and mass, fat-free mass, and percent of body water) and MC in preschool children, and to identify possible sex differences in these relationships. Considering the background of the current literature regarding this topic and the only limited evidence, we hypothesized that fat content (fat percent and mass) will be negatively related to the MC performance in 6-year-old boys and girls.
# Large-Language-Model-Enabled Text Semantic Communication Systems
## 3 System Model and Mechanism
### Performance Metrics
Leveraging LLM-SC, which achieves error-free transmission and facilitates communication compatible across technical and semantic levels, BER serves as a pertinent evaluation metric. (4) Token Error Rate (TER): The token acts as the fundamental unit for transmission and demodulation in LLM-SC. In addition to BER, errors in tokens reflect the challenge of accurately comprehending a sentence. For instance, a low BER combined with errors uniformly distributed across \(t\) tokens results in a high TER, indicating persistent difficulty in sentence comprehension by the receiver. Conversely, errors concentrated within a specific token, despite a potentially high BER, do not impair semantic understanding, thereby maintaining system effectiveness. This metric bears resemblance to the WER discussedin [40]. While each token can correspond to a word, they encompass a broader spectrum of entities and constitute the basic input units for LLMs.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 4 Blockchain System Architecture for Safety Supervision of Special Equipment
### System Function
#### 4.3.4 Accident Responsibility Tracing  
The accident responsibility tracing is the core function of this system, which will only be opened to supervisory agencies after an accident. With this function, supervisory agencies can determine who is responsible for an accident by querying the data in the digital ledger. Hyperledger Fabric provides many query methods (including keyed queries, range queries, and JSON queries) and allows data to be integrated into the preferred database or analytics engine, which ensures the efficient implementation of this function.