esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Parameter Setup
### High-throughput Sequencing Technology
#### 3.2.2 Polymerase Chain Reaction (PCR) Amplification, Sequencing, Quantification  
Methyl coenzyme M reductase (mcrA) gene was chosen to be the target gene to study methanogens [43; 44; 45; 46]. Amplicon sequencing was performed on a MiSeq platform to collect raw sequencing data (Illumina, San Diego, CA, USA). Primers for amplifying had the following gene sequences: MLf, GGTGGTGTMGGATTCACACARTAYGCWNAAGC, and MLr, TTCATTGCRTAGTTWGGRTAGTT [43]. The procedure of polymerase chain reaction (PCR) was as follows: Each sample was run in triplicates for PCR amplification using a Bori LineGene9600 plus real-time PCR instrument (Hangzhou Langji Scientific Instrument Co., Ltd., China) with Taq DNA Enzyme, template DNA, primers, 10 xPCR buffer and dNTP mixture. The PCR cycling program was as follows: an initial denaturation at 95 \({}^{\circ}\)C for 3 mins and 35 cycles consisting of denaturizing at 95 \({}^{\circ}\)C for 30 secs, annealing at 56 \({}^{\circ}\)C for 30 secs and extension at 72 \({}^{\circ}\)C for 40 secs. Subsequently, the PCR products were confirmed by agarose gel electrophoresis, purified with the Gel Extraction Kit, and quantified through an ultra-trace visible ultra-violet (UV) spectrophotometer (ND5000, BioTeke Corporation). The PCR products generated contained barcodes and primers.
## 4 Experimental Results and Discussion  
This section presents experimental results to demonstrate the performance of the proposed scheme in terms of various watermarking characteristics like imperceptibility, robustness, security, embedding rate and computational time using MATLAB 2014b with an Intel i5 processor, 2.00 GHz, 4 GB RAM. Test cover images have been taken from USC-SIPI [36] and the UCID [37] dataset as shown in Figure 10. For convenience of the representation, 12 images have been taken for visualization. The size of cover image (gray-scale and color) is \(512\times 512\) and watermark (binary) is \(64\times 64\).  
Figure 9: ISF generation from the cover image.
## 3 Emerging and State-of-the-Art Technologies
### Techniques for Reconfigurable RF Front-End Systems and Antennas
By combining SPDT elements into more complex topologies, researchers have demonstrated advanced PCM-enabled systems, including crossbar switching matrices [206], tunable filters [207], tunable attenuators [185], and reconfigurable antennas [208]. Despite their significant potential, PCM-based devices face challenges that have limited their widespread commercialization, including moderate power handling, limited switching speed, and restricted cycling endurance [209]. Continued research is needed to address these issues and fully realize PCM-based RF switching technologies. In recent years, liquid metals have emerged as a compelling solution for reconfigurable and flexible RF circuits, particularly in wearable and conformal applications [210]. These materials, which remain liquid at or near room temperature, offer several advantages, including controllable flow within microfluidic cavities and superior mechanical elasticity compared to conventional conductors such as copper. Liquid metals can be patterned and integrated using modern fabrication techniques such as 3D printing [211], injection molding [212], or spraying [213] onto rigid or flexible substrates. Movement or reconfiguration of liquid metal within hollow channels can be achieved via micropumps or electrochemically controlled capillary forces [210], enabling enhanced tunability and high power-handling capabilities in RF devices. Applications include tunable filters [214], reconfigurable antennas [211], and frequency-selective surfaces [198], as shown in Figure 10e. Among available options, eutectic gallium-indium (EGaIn) and galinstan are the most commonly used liquid metals due to their low melting points, high thermal conductivities, and low viscosities [210]. Despite these advantages, integrating liquid metals into RF systems presents several challenges. These include the need for improved manufacturing precision and more accurate flow control, as discrepancies between simulated and measured performance often arise due to nonuniform distribution of metal within the channels [169]. Furthermore, many liquid metals are toxic or reactive, necessitating robust packaging to prevent leakage and ensure long-term device reliability [215]. Advancements in precision manufacturing and encapsulation technologies will be crucial to unlocking the full potential of liquid metal-based RF components. Figure 11: Examples of tunable phase shifters with (**a**) ferroelectric material, (**b**) ferrite [179], (**c**) multiferroic, and (**d**) graphene [180].
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 2 Overview of Pioglitazone  
Pioglitazone, also known as 5-[4-[2-(5-ethyl-2-pyridinyl)ethoxy]benzyl]-1,3-thiazol-2,4-dione based on the International Union of Pure and Applied Chemistry (IUPAC), is a TZD compound, as shown in Figure 1, which is derived from a pyridine derivative through a nucleophilic aromatic substitution reaction with a 72% yield [13]. Pioglitazone appears as needles from dimethylformamide and water, while pioglitazone hydrochloride (HCI) appears as colorless prisms from ethanol, with a melting point of 183-184 \({}^{\circ}\)C and 193-194 \({}^{\circ}\)C, respectively [14]. The solubility of pioglitazone hydrochloride (HCI) increases with increasing temperature [15]. Low aqueous solubility and oral bioavailability can be overcome through temperature increase, cosolvents, and formulation strategies such as cyclodext inclusion complexes or nanostructured lipid carriers (NLCs). More specifically, polyethylene glycols (PEGs) are safe cosolvents for oral or parenteral pioglitazone HCl formulations. The additions of PEGs 200, 400, or 600, and a temperature increase significantly enhanced the solubility profile of pioglitazone [15]. Positive results were achieved through the addition of cyclodext in ethanol solution or encapsulation of pioglitazone HCl. Cyclodext caused a 6-fold increase in dissolution rate of pioglitazone HCl-\(\beta\)-cyclodextrintrin when compared to the pure drug, while lipid carriers achieved controlled release up to 24 h [16,17]. The latter was also observed during the administration of a newly discovered racemic compound that serves as a more thermodynamically stable and less soluble form of pioglitazone HCl [18].  
The molecular structure and properties of pioglitazone have been well-characterized, providing a solid foundation for its pharmacological profile. The pharmacological activity of pioglitazone has been attributed to its hydroxy and ketone derivatives during in vivo studies on rats and dogs [13]. Similarly to other thiazolitiones, pioglitazone activates the PPAR-\(\gamma\) receptor, increasing insulin sensitivity, without inducing insulin release from pancreatic cells. It is typically administered once a day with or without food, metabolized by the cytochrome P450 hepatic enzymes with a half-life of approximately 9 h, and excreted in the urine. The pharmacokinetics of pioglitazone are dose and time-dependent, leading to non-linear changes in plasma concentration and potential toxicity. According to clinical evidence, pioglitazone follows the same pharmacokinetic profile in healthy participants, diabetic patients, and patients with renal failure, irrespective of age, gender, and race. However, dose adjustment is required for patients with hepatic failure due to decreased metabolic function and by extension decreased plasma concentration [19].
## 6 Experimental Results
### Evaluation of Structural Information  
Figure 6 shows some sample results of different edge segmentation methods at the same level of recall. For the original edge picture corresponding to each test sample, the truth value of the edge segmentation result can be given by manual annotation, and then, the difference between the segmentation result of each method and the true value can be compared, including the part that was correctly retained, the part that was mistakenly rejected, and the part that was incorrectly retained. As we can see, the splitting method based on ray accumulation retains too many wrong spot edges, usually because the same long edge is incorrectly divided into different sector areas, and the length of each segment does not reach the set threshold, so it cannot be correctly rejected. The segmentation method based on line segment detection can detect most of the spot edges, but for the less straight edges in the distance, especially the boundaries of the passable area in the distance, the set distance threshold cannot be reached, so it will be incorrectly retained. The segmentation method based on polyline approximation can successfully remove most of the distorted edges, while only a small number of effective edges are erroneously rejected because they are exactly in the direction of the camera's field of view rays.  
More specifically, as shown in Table 1, when the recall of all methods is controlled at about 0.73, the segmentation method based on polyline approximation can achieve the best segmentation effect, which is 24.3 percent higher than the segmentation method based on ray accumulation and 11.9 percent higher than the segmentation method based on line segment detection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Precision** & **Recall** \\ \hline Ray-accumulation-based segmentation & 0.621 & 0.731 \\ Line-segment-detection-based segmentation & 0.745 & 0.729 \\ Polyline-approximation-based segmentation & 0.864 & 0.730 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of the precision and recall rates among different edge segmentation methods.  
Figure 5: **(a)** The experimental platform with four fisheye cameras. **(b)** The outdoor and **(c)** indoor parking scenes.
## 3 Numerical Results
### Deformation Characteristics under Different Compression Rates
#### 3.3.2 Deformation Characteristics of Node \(\rm B_{e}\)  
This section mainly displays the \(\rm B_{e}\) node's radial displacement and equivalent stress under different compression rates in the experimental results. Figure 7 shows the equivalent stress and radial displacement changes of node \(\rm B_{e}\) under different compression ratios (10-15%) at a linear speed of 320 mm/s.  
It can be seen that the radial displacement changes curve of node \(\rm B_{e}\) was roughly symmetrical with that of the \(\rm A_{e}\) node, except that the maximum radial displacement of node \(\rm B_{e}\) was after 180\({}^{\circ}\). As the \(\rm B_{e}\) node approached the loading plate in cell column I, its radial position slowly approached the roller axis, opposite to the \(\rm A_{e}\) node moving away from the roller axis. The reason for this is that the \(\rm A_{e}\) node also exerted pressure on cell column I while moving away from the axis. Node \(\rm A_{e}\) shifted outward from its original radius, causing a component of the pressure on column I to node toward the axis, leading to the behavior of the \(\rm B_{e}\) node approaching the axis. Macroscopically, it can also be seen as a manifestation of negative Poisson's ratio effect. After the \(\rm B_{e}\) node left the loading plate, the rebound did not stop. The radial displacement of the \(\rm B_{e}\) node exceeded the original outer contour radius, and within the experimental range, the smaller the compression rate, the greater the radial displacement exceedance. The exceedance amplitude at a 10% compression rate was 1.84%, and the exceedance amplitude at a 15% compression rate was 0.68%. Subsequently, the radial displacement of the \(\rm B_{e}\) node began to exhibit an oscillation  
Figure 7: Equivalent stress and radial displacement of node \(\rm B_{e}\) under different compression ratios (10–15%) at a line speed of 320 mm/s: (**a**) equivalent stress and (**b**) radial displacement.  
with gradually decreasing amplitude, and its value gradually approached the original outer contour radius. From the locally enlarged image, it can be seen that there are slight oscillations throughout the curve, and within the experimental range, the amplitude of these oscillations is negatively correlated with the compression rate.  
Making a comparison with the equivalent stress curve of the \(\mathrm{A_{e}}\) node, both of them in the \(\alpha\)-C to \(\beta\)-R interval can be divided into three curve peaks, and the rotation angle of the roller at the time of the peak stress at the \(\mathrm{B_{e}}\) node was about 24\({}^{\circ}\) later. The effect of compression rate on the equivalent stress of the \(\mathrm{B_{e}}\) node can be divided into three situations. Before the first wave peak (160\({}^{\circ}\)), the higher the compression rate, the greater the stress value. There is no strict correlation between compression rate and stress value from the end of the first wave peak to \(\beta\)-R. After \(\beta\)-R, the higher the compression rate, the smaller the stress value, until the curve gradually approaches zero.
## 4 Simulation Verification
### Validation of Results  
In order to guarantee the feasibility and accuracy of the bonding parameters in the ore bonding model, it is necessary to verify them through simulation using the discrete element software EDEM. This process should be conducted using the characterized bonding parameters as a benchmark. The simulation process is shown in Figure 7. The blue color indicates a relatively small compressive force, ranging from 35,000 N to 45,000 N. The green color represents a larger compressive force, spanning from 45,000 N to 55,000 N. The red color implies that the compressive force is approaching its maximum value, specifically within the range of 55,000 N to 65,000 N. This clear delineation effectively presents the variations in compressive forces, offering valuable insights for further analysis and understanding of the mechanical behavior under study. The results demonstrate that the compressive strength of the simulation model is 199.9 MPa, with a simulation error of less than 5%. This indicates that the established simulation model and characterized bonding parameters can realistically model the crushing of ore under uniaxial compression testing.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & \multirow{2}{*}{**df**} & **Mean** & \multirow{2}{*}{**F-Value**} & \multirow{2}{*}{**\(p\)-Value**} & **Significance** \\  & **Squares** & & **Square** & & \\ \hline Model & 14,575.81 & 14 & 1041.13 & 69.82 & \textless{}0.0001 & Significant \\ \(x_{1}\) & 13,044.25 & 1 & 13,044.25 & 815.33 & \textless{}0.0001 & Significant \\ \(x_{2}\) & 742.46 & 1 & 742.46 & 46.41 & \textless{}0.0001 & Significant \\ \(x_{3}\) & 107.94 & 1 & 107.94 & 6.75 & 0.0194 & Significant \\ \(x_{4}\) & 6.02 & 1 & 6.02 & 0.3763 & 0.5482 & \\ \(x_{1}x_{2}\) & 214.77 & 1 & 214.77 & 13.42 & 0.0021 & Significant \\ \(x_{2}x_{4}\) & 279.06 & 1 & 279.06 & 17.44 & 0.0007 & Significant \\ \(x_{1}^{2}\) & 24.01 & 1 & 24.01 & 1.50 & 0.2383 & \\ \(x_{2}^{2}\) & 24.64 & 1 & 24.64 & 1.54 & 0.2325 & \\ \(x_{2}^{2}\) & 2.67 & 1 & 2.67 & 0.1671 & 0.6882 & \\ \(x_{3}^{2}\) & 25.50 & 1 & 25.50 & 1.59 & 0.2249 & \\ \hline Residual & 255.98 & 16 & 16.00 & & & \\ Lack of Fit & 206.39 & 14 & 14.47 & 0.5945 & 0.7785 & Not significant \\ Pure error & 49.59 & 2 & 24.80 & & & \\ Cor total & 14,754.76 & 26 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Variance analysis of response surface test after optimization.
## 4 Results and Discussion
### Optimal Number of Clusters  
Based on the twelve trip chain datasets, the silhouette coefficient method is applied using the mathematical programming software Python 3.5 on a Lenovo laptop with an Intel(R) Core (TM) i7-10750H 2.60 GHz CPU and 32.0 GB of RAM to identify the optimal number of clusters. This study calculated the silhouette scores corresponding to cluster numbers K ranging from two to ten. The results are as shown in Figure 3.  
Figure 3 shows the results of silhouette coefficients corresponding to different cluster numbers K ranging from two to ten for twelve datasets. The numerical value of the silhouette coefficient is related to the clustering effect and the K value corresponding to the maximum silhouette coefficient is the optimal number of clusters. Based on the silhouette coefficients presented in Figure 3, the optimal number of clusters (optimal value of K) for intercity trip chains with six directions in two years is as shown in Table 4. The dataset with the highest number of clusters is Beijing-Shijiazhuang while the dataset with the least number of clusters is Tianjin-Shijiazhuang.
# Decorrelation-Based Deep Learning for Bias Mitigation
## 5 Discussion  
One of the crucial aspects of our proposed method is to mitigate bias without compromising the performance of the model by optimizing the decorrelation loss along with loss related to the task and tuning hyperparameter (\(\lambda\)). The choice of \(\lambda\) depends on the complexity of the task and network architecture. The results from all five datasets outperformed the traditional approaches in mitigating different types of biases. This higher performance indicates that the DcCNN and DcANN models significantly mitigate the bias and present relevant feature information to the network compared to other methods. Further, this also demonstrates the generalization ability of the proposed approach across different domains for bias mitigation.  
In Figure 5, we observe the oscillations in distance correlation values. From our experiments, we verify that these oscillations are due to the size of mini-batches. Increasing the batch size not only reduces the oscillations but also leads to an unbiased estimate of distance correlation. We also notice that regularization such as dropout and weight decay helps the baseline model to improve its performance. As for our proposed approach, the input bias variable also plays a vital role and it should clearly define the bias present in the data or task. For example, a possible concern is our proposed method might not show significant improvement for the domain adaptation tasks due to the lack of enough quantitative information about different domains and a limited number of domains. We apply our method for digit domain adaptation tasks [36] where we use MNIST, USPS, SVHN, and synthetic numbers datasets as training and validation datasets. We evaluated the results on the MNIST-M dataset as a test dataset. However, we observed that the improvement using our approach is not that significant. For such cases, we simply recommend collecting and using more domain-relevant information as a bias variable or using domain distributions as a bias variable.
# IoT-Enabled Smart Cities: A Review of Concepts, Frameworks and Key Technologies
## 2 IoT Technologies and Architecture
### Middleware/Processing Layer  
The middleware or processing layer can serve many different functionalities. For instance, it can act as a data aggregator module, since data may be collected from heterogeneous devices with different protocols that may not have been originally designed to communicate and interact with each other. Therefore, the middleware layer has to enable interoperability among connected devices, performing the necessary programming and/or model abstractions. Interoperability should be ensured at different levels:  
* Technical level [30], in order to efficiently achieve and ensure end-to-end connectivity among devices, gateways, brokers, servers, etc.;
* Syntactical level, for managing the variety of protocols and formats;
* Semantic level, for exploiting Semantic Web technologies, such as XML, RDF, OWL Ontology and linked data (LD), to achieve unambiguous data representation and data semantic enrichment, thus improving the expressiveness level of the system [31].  
IoT middleware is also typically devoted to providing scalability and reliability, allowing the system to handle a growing number of IoT connections and communication loads at the big data scale as well as supplying stable and fault-tolerant services. Moreover, in order to enable data processing over IoT, database-level data persistence is needed to integrate the data coming with different protocols in a shared model, and this is performed in the middleware layer. Therefore, middleware includes the different data storage modalities and eventually covers other functionalities, such as context identification, information extraction and the reconciliation of collected data. For example, FIWARE provides different generic enablers (Cygnus, QuantumLeap, STH-Comet) to store data coming from the Orion Context Broker.
# Online Uniformly Inserting Points on the Sphere +
## 3 Results
### Analysis of the Upper Bound
#### 3.1.1 Inserting Points in a Pentagon  
For point insertions in each pentagon, there are 5 positions for consideration. Here we compute the gap ratio step by step.  
Consider the pentagon \(AJBFE\) in Figure 3a, assuming that the edge of pentagon \(AJBFE\) is \(l\), and w.l.o.g., point \(A^{\prime}\) is inserted first. According to the definition, the maximal gap is the diameter of the circumcircle of \(\Delta A^{\prime}EF\), as shown in Figure 4a. By the law of sines, we have \(G_{A^{\prime}}=\frac{|A^{\prime}E|}{\sin\Delta A^{\prime}FE}=\frac{l}{\sin \left(\pi-\frac{2\pi}{5}\right)/2}=1.236l\), and the minimal gap is \(g_{A^{\prime}}=|AA^{\prime}|=\sqrt{2l^{2}(1-\cos\frac{\pi}{5})}=0.618l\). Therefore, the gap ratio after the first point insertion on the pentagon is \(r_{A^{\prime}}=1.34\cdot\frac{G_{A^{\prime}}}{SA^{\prime}}=2.68\).  
For the second point \(F^{\prime}\) on the pentagon, as shown in Figure 4b, the maximal gap is the diameter of the circumcircle of \(\Delta A^{\prime}BF^{\prime}\), \(G_{F^{\prime}}=\frac{|A^{\prime}B|}{\sin\Delta A^{\prime}FB}=\frac{l}{\sin\frac {2\pi}{5}}=1.051l\), and the minimum gap is  
Figure 3: Recursive partitions of a pentagon. (**a**) Point insertion in a pentagon and partitions; (**b**) point insertion in an acute isosceles triangle and partitions; (**c**) point insertion in an obtuse isosceles triangle and partitions.  
\(g_{F^{\prime}}=|A^{\prime}F^{\prime}|=|AA^{\prime}|=0.618l\). Thus, the gap ratio after the second point insertion on the pentagon is \(r_{F^{\prime}}=1.34\cdot\frac{G_{F^{\prime}}}{g_{F^{\prime}}}=2.28\).  
After the insertion of the two points \(A^{\prime},F^{\prime}\), the insertion order will not affect the gap ratio. For the third point and the fourth point, see Figure 5, the maximal gap will be the diameter of the circumcircle of quadrangle \(AA^{\prime}F^{\prime}E\) or the circumcircle of \(\Delta A^{\prime}F^{\prime}B\), which is equal to \(1.051l\), and the minimal gap equals to the edge of the internal pentagon \(A^{\prime}J^{\prime}B^{\prime}F^{\prime}E^{\prime}\), which is \(0.382l\). So the gap ratio after the third and forth point insertion on the pentagon is \(r_{3,4}=1.34\cdot\frac{G}{g}=3.69\).  
For the fifth point, the maximal gap will be the diameter of the circumcircle of pentagon \(A^{\prime}J^{\prime}B^{\prime}F^{\prime}E^{\prime}\), which is equal to \(\frac{0.382l}{\sin\frac{g}{8}}=0.650l\), and the minimal gap equals to the edge of pentagon \(A^{\prime}J^{\prime}B^{\prime}F^{\prime}E^{\prime}\), which is \(0.382l\). So the gap ratio after the fifth point insertion on the pentagon is \(r_{5}=1.34\cdot\frac{G}{g}=2.28\).  
Therefore, the gap ratio for points insertion inside the pentagon \(r\leq 3.69\).