esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Heavy Metal Removal Performance
Figure 3: Rapid removal dynamics of heavy metals (Cd, Pb, Cr, Cu, and Zn) monitored over a 24 h period across five concentration gradients (Note: Panels represent, Cd concentrations (**A–E**); Cr concentrations (**F–J**); Cu concentrations (**K–O**); Pb concentrations (**P–T**); and Zn concentrations (**U–Y**) across treatments C1, C2, C3, C4, and C5, respectively; C1 (Cd: 50, Pb: 100, Cr: 500, Cu: 1000, Zn: 10,000 mg L\({}^{-1}\)), C2 (Cd: 5, Pb: 10, Cr: 50, Cu: 100, Zn: 1000 mg L\({}^{-1}\)), C3 (Cd: 2.5, Pb: 500, Cr: 25, Cu: 50, Zn: 500 mg/L), C4 (Cd: 0.25, Pb: 0.5, Cr: 2.5, Cu: 5, Zn: 50 mg L\({}^{-1}\)), and C5 (Cd: 0.125, Pb: 0.25, Cr: 1.25, Cu: 2.5, Zn: 25 mg L\({}^{-1}\)); data points represent the dynamic changes in metal concentrations measured at 2, 4, 8, 12, and 24 h after treatment initiation). For Cr, concentrations were tested from 2.50 to 125 mg L\({}^{-1}\) (Figure 2I-L). The parallel nature of the removal curves showed consistent efficiency across concentration gradients, with final removal efficiencies ranging between 78.5% and 84.2% across all treatments. After 24 h, steady daily removal rates of 2.1-2.8% were observed across all treatments, as evidenced by the consistent slope of the removal curves. In the short-term study (Figure 3I-L), Cr showed characteristic removal patterns over the 24 h period. Cu concentrations ranged from 5.0 to 250 mg/L (Figure 2M-P), showing overall removal efficiencies of 82.4-89.7%. The inflection points in the curves indicate that the system removed 50% of total Cu\({}^{2+}\) ions within the first 36 h across all treatments. In the short-term study (Figure 3M-P), Cu\({}^{2+}\) showed moderate initial removal rates compared to Cd\({}^{2+}\) and Pb\({}^{2+}\) ions. Zn\({}^{2+}\) ions were tested at the highest concentration range (50 to 2500 mg L\({}^{-1}\)), with removal efficiencies between 73.6% and 84.8% (Figure 2Q-T). The gradual slope of the curves indicates that the time required to reach 50% removal was 48-60 h, longer than other metals. Figure 3Q-T demonstrates that removal efficiencies in T1 and T2 were 12-15% lower than those observed for Cu\({}^{2+}\) ions under similar conditions. The 24 h kinetic patterns in Figure 3 reveal distinct removal phases for all metals. The steep initial slopes during the first 4 h displayed the highest removal rates (45-65% of total removal), followed by a 60% decrease in removal rates during the 4-8 h period, shown by the moderating curve gradients. The final 8-24 h period showed further decreased removal rates of 80-90% compared to initial rates, as evidenced by the flattening of the removal curves.
# Therapeutic Potential and Challenges of Pioglitazone in Cancer Treatment
## 3 Therapeutic Use of Pioglitazone
### Immunotherapy  
PPAR-\(\gamma\) immunomodulatory effects have long been known, and there have been multiple studies seeking to assess the significance of said effects in cancer. A study conducted on mice hosting solid Ehrlich carcinoma cells by [PERSON] et al. (2014) concluded that pioglitazone administration led to a statistically significant reduction in tumor size and TNF-\(\alpha\) amount, increase in peripheral neutrophil and splenic T- lymphocyte populations, and increase in splenic T-lymphocyte activity, CD4+/CD8+ ratio and immunoglobulin G (IgGG) levels, indicating elevated immune response. Similar results, although to a varying extent and except peripheral neutrophil counts, were also obtained from co-administration with doxorubicin [126]. Immunomodulation is a property that can be exploited in the context  
Figure 4: Pioglitazone combination therapies for the treatment of breast cancer, lung cancer, renal cancer, hepatocellular carcinoma, colorectal cancer, thyroid cancer, glioma, pancreatic cancer, and hematological malignancies (created using [[https://biorender.com](https://biorender.com)]([https://biorender.com](https://biorender.com)), URL accessed on 25 January 2025).  
of immunotherapy. An example of it is the use of PPAR-\(\gamma\) agonists to reprogram T cells; however, there were no specific reports on pioglitazone [127; 128].
## 3 Results and Discussion
### Recoveries and Enrichment Factors  
To evaluate the absolute recovery of the LLME-SPME method, another calibration data set using standard liquid injection was collected across the range of 0.5-150 ppm. The recovery was calculated using the calculated TCE mass on-column of the LLME-SPME-extracted sample relative to the absolute TCE mass contained within the vial before extraction. The enrichment factor of LLME-SPME method was defined as the ratio of the calculated TCE masses on-column from the samples extracted by LLME-SPME with 20 \(\upmu\)L hexane and without hexane (SPME). The recovery and enrichment factor  
Figure 5: Effects of fiber coatings (**A**), stirring (**B**) and salting out (**C**) with 95% confidence intervals (_n_ of 3).  
results are listed in Table 2. The absolute recoveries are in the range of 29%-41% for the samples extracted by LLME-SPME and 11%-17% for the samples extracted by SPME. The enrichment factors with the addition of hexane are \(2.6\pm 0.2\), \(2.4\pm 0.4\), and \(2.2\pm 0.3\) for the samples at low, medium, and high concentrations.
## 3 Multi-Exposure Fusion via Sparse Representation of Gradient Features
### Exposure Quality Extraction  
In the multi-exposure source images, there are more local details in the well-exposed regions. In the over-exposed or under-exposed regions, the luminance values are too extreme to present valid information. Therefore, the pixel luminance value is used to measure the local exposure quality. First, the luminance value of the images is normalized. Then, considering that there are more noises in the region under low illumination conditions, the exposure quality of pixels is calculated to obtain the exposure quality map:  
\[B_{i}(x,y)=\left\{\begin{array}{cc}1,&\eta+\alpha<\widetilde{l}_{i}(x,y)<1- \alpha,\\ &0,&otherwise\end{array}\right., \tag{9}\]  
where \(\eta(0\leq\eta\leq 0.45)\) and \(\alpha(0\leq\alpha\leq 0.5-\eta)\) are the thresholds, \(\widetilde{l}_{i}(x,y)\) is the luminance map of the source image, and \(B_{i}(x,y)=1\) denotes that the related pixel is in the well-exposed region. Thus, the influence of over-exposed or under-exposed regions to the fused image can be effectively removed.
# On the Influence of Data Imbalance on Supervised Gaussian Mixture Models
## 4 Addressing Imbalanced Data
### Probability Threshold Tuning  
In the two-class case, MAP rule is equivalent to the use of a probability threshold \(\zeta=0.5\) for assigning an observation to the minority class. Adjusting the threshold \(\zeta\in(0,1)\) may serve as a simple solution in scenarios with different misclassification costs or in problems with imbalanced data. The classification rule entails assigning an observation \(\mathbf{x}_{i}\) to class \(C_{1}\) if \(\Pr(C_{1}|\mathbf{x}_{i})>\zeta\). However, the widely used default value \(\zeta=0.5\) may not always yield the best results [17]. Optimal threshold selection can be accomplished,  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{3}{l}{(a) Generic case} \\ \hline  & \multicolumn{2}{c}{Actual/Observed} \\ \cline{2-3}  & Negative & Positive \\ \hline Predicted & & \\ \hline Negative & \(c(0|0)\) & \(c(0|1)\) \\ Positive & \(c(1|0)\) & \(c(1|1)\) \\ \hline \hline \multicolumn{3}{l}{(b) Imbalance ratio (IR) case} \\ \hline  & \multicolumn{2}{c}{Actual/Observed} \\ \cline{2-3}  & Negative & Positive \\ \hline Predicted & & \\ \hline Negative & 0 & 1 \\ Positive & IR & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cost matrix for binary-class data.  
for instance, through cross-validation, hence choosing \(\zeta_{\text{CV}}\) as the value that maximizes a cross-validated suitable evaluation metric, such as F2 or balanced accuracy.  
Another option is available by noting that, if the IR value is used for adjusting class-prior probabilities, as discussed in Section 4.1, the corresponding threshold value is simply obtained as  
\[\zeta_{\text{IR}}=\frac{1}{1+\text{IR}}=\widehat{\tau}_{\text{I}},\]  
where \(\widehat{\tau}_{\text{I}}\) is the sample proportion of cases in the minority class. Therefore, setting the threshold to match the proportion of the minority class in the training set is equivalent to a cost-sensitive approach using the cost matrix in Table 2b. Figure 1 depicts the relationship between IR and the corresponding probability threshold.  
In accordance with this rationale, a threshold value can also be derived by employing the adjusted estimates of prior probabilities \(\widetilde{\tau}_{k}\), for \(k=\{0,1\}\), proposed by [PERSON] et al. (2018). This approach essentially employs an EM algorithm to iteratively update the posterior probabilities, enabling the computation of the adjusted prior probabilities and then setting \(\zeta_{\text{AD}}=\widetilde{\tau}_{\text{I}}\) (for additional insights, refer to [PERSON] et al. (2012) (Section 4.6)).
# Electromagnetic Field Distribution and Data Characteristics of SUTEM of Multilayer Aquifers
## 3 Forward Modeling Mathematical Method of SUTEM
### TEM Field Excited by Rectangular Transmitting Loop  
The fundamental approach for solving the transient electromagnetic field as excited by the rectangular transmitting loop is divided into three main steps: first, the frequency domain electromagnetic field excited by the vertical magnetic dipole source is solved using the above scalar potential; then, the Gauss integral is used to integrate the magnetic dipole moment according to the rectangular transmitting loop, such that the frequency domain electromagnetic field under the large fixed-source transmitting loop is obtained; finally, the frequency domain electromagnetic field is converted to the time domain by a cosine transformation.
## 3 Materials and Methods
### Methodology for User Authentication  
Figure 3 shows a representative example of a PHP code that defines a function named \"getUserId\", designed for user authentication in the SQL database. The PHP code was generated by ChatGPT based on the following prompt:  
* _\"Generate a PHP function to authenticate users using login credentials. The function should connect to a MySQL database and verify if the username and password match entries in the 'user_table' table. If successful, the function should return the corresponding user ID. Otherwise, return \(-1\)\"._  
The process begins with establishing a connection to the database using \"mysqli_connect\", where the placeholders 'host', 'username', 'password', and 'database' should be replaced with the actual configuration values. The connection success is verified and, if any errors occur, the program terminates with an error message. Subsequently, an SQL query is crafted to search for a user in the 'user_table', whose provided username and password match the parameters passed to the function. The query is executed using \"mysqli_query\". The function then checks if any user was found and, if not, it returns \(-1\). If a user is found, it retrieves the user ID from the first result and returns it. In essence, the \"getUserId\" function serves to authenticate a user in the database, providing the user ID upon successful authentication, or \(-1\) if no matching user is found. The vulnerability in the PHP code provided by ChatGPT lies in its susceptibility to SQL injection attacks.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 1 Introduction  
Accurate disease diagnosis remains the crucial first step before initiating any treatment. In this context, medical imaging plays a pivotal role in clinical analysis. In recent years, however, the percentage of utilization of imaging examinations, such as MRI and CT scans, has continued to rise [1]. This has contributed to the increased need for faster and more accurate image analysis tools.  
Medical imaging is primarily regarded as a technique for visualizing functional and structural features of the human body through modalities such as x-rays and magnetic resonance imaging. Moreover, the integration of imaging technologies within healthcare networks has significantly enhanced the quality and efficiency of medical services, driven by advances in computer science and imaging engineering.  
The interpretation of medical images remains a time-consuming and expertise-demanding task, prone to variability and potential errors, especially in resource-constrained environments [2]. To address these challenges, the automatic generation of captions for radiology images has emerged as a promising solution. In this work, caption generation refers to the automatic assignment of multiple medical concept labels (UMLS CUIs) toradiology images, rather than the generation of full natural language sentences. The task is addressed as a multilabel classification problem. Automatically generating descriptive labels can assist radiologists by speeding up analysis and reducing diagnostic errors.  
A deep learning-based system capable of automatically generating labels for radiology images is proposed, leveraging transfer learning strategies. Pre-trained models on large datasets such as ImageNet are fine-tuned on the specialized ImageCLEF medical caption detection dataset [3], enabling adaptation to the medical imaging domain.  
Advanced deep learning methodologies have been extensively applied to classification tasks within the medical domain [4]. Convolutional Neural Networks (CNNs), pretrained on large-scale datasets such as ImageNet, are frequently employed as image encoders for medical imaging applications. However, ImageNet primarily comprises images from general visual scenes, which differ significantly from medical images in both content and texture. Consequently, finetuning CNN models is often required to optimize performance when adapting to the medical imaging domain. In [5], a refined version of the Inception V3 CNN architecture was developed to classify skin lesions as benign or malignant, achieving diagnostic accuracy comparable to that of experienced dermatologists. These results demonstrate that CNNs originally trained on non-medical datasets can be effectively repurposed for medical imaging tasks, provided that domain-specific refinements are appropriately implemented.  
These labels explain radiology images in detail, and each image may be associated with multiple labels, making it a multilabel classification problem.
# Microbiome-Based Interventions for Food Safety and Environmental Health
## 4 Current Applications for Microbiome-Based Interventions
### Fecal Microbiota Transplantation  
Gut microbiota imbalances, known as dysbiosis, have been linked to various diseases, driving increased interest in FMT over the past decade [99]. FMT involves transferring screened manipulated donor stool into a patient's gastrointestinal tract to restore microbiota diversity and functionality, aiming to correct the dysbiotic state [99; 100]. FMT has been integral in restoring microbial diversity and structure in the colon, leading to high cure rates for _Clostidium difficile_ infection (CDI) [101]. CDI is primarily caused by the suppression and disruption of colon microbiota, often due to antibiotic use, and is significantly with high morbidity and mortality [102]. FMT restores microbial diversity in the colon by inhibiting _C. difficile_ and preventing reinfection through several mechanisms such as bile acid-mediated inhibition of spore germination and vegetative growth, competition for nutrients, direct suppression by antimicrobial peptides, and activation of immune-mediated colonization resistance [101].  
Several studies have reported the effectiveness of FMT in the treatment of recurrent and refractory CDI, with a recent meta-analysis by [102] concluding that FMT successfully resolved symptoms of CDI in 92% of the cases treated. Similarly, a study by [103] of 111 patients treated with FMT for recurrent or refractory CDI in Israel from 2013 to 2017 reported an overall success rate of 87.4%, with no significant difference in success between different routes of administration (lower gastrointestinal route by colonoscopy (45%), capsules (33%), and upper gastrointestinal route (22%)). Since it has proven effective in treating CDI, FMT is currently being investigated for its potential to treat other GI disorders such as inflammatory bowel disease (IBD). A systematic review of 17 studies by [104] involving 41 patients showed that most IBD patients treated with FMT experienced symptom reduction (19/25), disease remission (15/24), cessation of IBD medications (13/17), and remission of _C. difficile_ infection in every patient receiving such treatment (15/15), suggesting that FMT may be an effective treatment option, particularly when standard therapies have failed.
## 2 Hybrid Multi-Laser Module
effectively modify the optical path length, allowing precise control over the phase of the propagating light. This capability is critical for fine-tuning the device's performance in applications such as wavelength filtering and resonance alignment. Single-mode operation is also achieved through the Vernier effect, with the FSRs of the two micro-ring resonators matching those of the single-gain lasers.
# Electrospun Nanomaterials for Energy Applications: Recent Advances
## 9 Nanogenerators for Energy Harvesting
Figure 32: Triboelectric nanogenerators (TENGs) and their chief functioning modes: (**a**) vertical contact–separation mode, (**b**) lateral sliding mode (**c**) single-electrode mode, and (**d**) freestanding triboelectric-layer mode (reproduced from reference [251] with permission. Copyright 2018, Elsevier). Polyvinylidene fluoride (PVDF), a semicrystalline polymer with alternating hydrogen and fluorine units attached to the carbon chain, is one of the most efficient piezoelectric materials [251; 255]. It is biocompatible, flexible, chemically resistant, and endowed with high mechanical strength. Its piezoelectricity is due to the electroactive \(\beta\)-phase of the polymer that possesses spontaneous polarization. Electrospun PVDF and PVDF-based composites are extensively utilized in PENGs [251; 255; 260], since, during electrospinningning, the other phases of the polymer can become electroactive (by poling or mechanical stretching) [255]. In addition, with respect to the piezoelectric inorganic semiconductors, PVDF and its copolymers have a greater potential towards flexible and wearable applications because they are lightweight and can be easily processed. Electrospun PVDF membranes are also utilized in the fabrication of TENGs [261; 262; 263]. PVDF membranes coated with polydimethylsiloxane to improve their mechanical robustness and optimize their porosity properties have also been employed to manufacture a wearable TENG capable of converting human biomechanical energy into electricity for next-generation wearables [261]. A humidity-resisting TENG, designed to adapt to the environmental humidity caused by human perspiration during sport, has been constructed by the use of PVDF and surface amino-modified cellulose acetate/polyurethane electrospun membranes [262]. Most recently, EESs and energy-harvesting devices have combined into self-charging power systems for wearable and portable electronics of daily use [264]. [PERSON] et al. [265] have designed an ultra-light and flexible self-charging power system entirely based on electrospun paper-like membranes. The system consists of a TENG as an energy harvester and supercapacitors as EESs. The former is arch-shaped and exploits conductive PAN-derived carbon paper as an electrode and nonconductive stabilized-PAN membrane as a triboelectric layer. In the latter, the nonconductive PAN paper acts as a separator, whereas the conductive one is used as a capacitive electrode material.
# A Study on a Scenario-Based Security Incident Prediction System for Cybersecurity
## 3 Existing Security Incidents and Limitations of Security Products
### Cases of Security Threats in Korea and Abroad
#### 3.1.2 Seoul St. Mary's Hospital Website Hack  
In September 2021, the website of Seoul St. Mary's Hospital, affiliated with the Catholic University of Korea, was hacked, leading to a data breach of members' personal information. The affected users were those who had registered before February 2013, and the exposed data included user IDs, passwords, and resident registration numbers.
## 3 Results and Discussion
### Struvite Decomposition
In particular, a low applicability of sulfuric and nitric acid was found; instead, acetic acid proved to have a high efficiency, comparable with that of HCl. Indeed, the maximum _n_(N):_n_(P) values were obtained with hydrochloric and acetic acid at 50 \({}^{\circ}\)C and pH = 5.5. Anyhow, as HCl is easily available in industrial treatment plants, this reagent was used to perform the struvite decomposition in the subsequent cyclical tests. The precipitate obtained with the defined operating conditions (50 \({}^{\circ}\)C and pH = 5.5) was characterized by a dry solid content of about 20% (humidity of 80%) and the _n_(N):_n_(P):_n_(Mg) ratios were about 1:3.15:3.21. The X-ray diffraction (XRD) analysis showed the appearance of typical peaks of newbergite (MgHPO4\(\cdot\)3H2O) [21] and a substantial intensity reduction of peaks of struvite with respect to those observed on precipitate before the acidosis (Figure 9). These results confirm that, in proper acid conditions, part of struvite is converted in hydrated magnesium phosphates with the release of ammonium in the acid solution, according to the following reaction:  
\[\mathrm{MgNH_{4}PO_{4}\cdot 6H_{2}O\ +H^{+}\to MgHPO_{4}\cdot 3H_{2}O+NH_{4}^{+}+3H_{2}O} \tag{1}\]  
The volume of residual acid solution was only 8% of the leachate treated to obtain the struvite subjected to the dissolution procedure. Therefore, the volume to be managed after the decomposition step is quite low. Furthermore, this solution could be exploited as a fertilizer source as it is characterized by NH4\({}^{+}\) concentrations of up to 18 g(N)/L. In fact, it is mainly composed of ammonium chloride, a compound commonly used as inorganic fertilizer [14]. Obviously, the practical application of residual liquid phase for fertilizers production must be investigated. Figure 8: N:P molar ratio of dissolved ammonium and phosphorus, detected during the struvite decomposition tests.