esa-sceva/satcom-chunk-collection huggingface dataset:
# Early Detection of Residual/Recurrent Lung Malignancies on Post-Radiation FDG PET/CT
## 5 Conclusions  
In conclusion, this study demonstrates the potential of combining radiomics and CNN models to enhance the detection of residual or recurrent lung malignancies following radiation therapy. Our fusion model outperforms the individual models, offering improved accuracy in distinguishing between benign and malignant post-radiation PET/CT findings. Despite the relatively modest accuracy, this approach highlights the feasibility of AI-driven solutions in complex clinical scenarios where traditional assessments may fall short. Further research with larger and more varied datasets is essential to fully evaluate the clinical applicability of these AI models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Sensitivity** & **Specificity** & **Accuracy** & **AUC** \\ \hline Radiomics-LR [20] & 0.60 & 0.68 & 0.64 & 0.66 \\ Radiomics-RF [20] & 0.60 & 0.66 & 0.63 & 0.68 \\ CNN & 0.56 & 0.68 & 0.62 & 0.65 \\ LR-CNN-Fusion & 0.60 & 0.64 & 0.62 & 0.68 \\ RF-CNN-Fusion & 0.58 & 0.72 & 0.65 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Benign vs. malignancy prediction performance of the fusion models derived from LR/RF and CNN models.
# Monitoring of Preload Variation of Linear Guide Positioning Stage Using Artificial Neural Network
The learning-based approaches can be incorporated with intelligent feature filter to find the optimum features for feeding into a fault classification model [26; 33; 36]. For example, [PERSON] et al. [33] compared the classification performance of the fault severity of a specific defect using two supervised machine learning techniques, SVM and ANN. The effective features were evaluated by three different attribute filters, which identified the dependence of features with the fault type. [PERSON] et al. [36] proposed a novel hybrid model for detection and classification of the fault type and severity of rolling bearing. In their study, support vector machines were optimized by inter-cluster distance in the feature space and employed to generate the fault feature vectors to identify the fault type. It is apparent that classification models based on a machine learning approach are widely adopted in the fault diagnosis of rotary mechanisms with ball bearings. For linear bearing mechanisms, the rolling components are susceptible to multi-fault situations, as a consequence inducing complicated vibration behaviors because of the inherent nonlinear characteristics that interact with the internal preload, lubrication conditions, and external loading conditions [31; 32; 37]. However, previous studies were focused on the development of a classification model for fault identification of linear bearing. Instead, quantifications of the faults such as defect severity or surface wear are less concerned and investigated. In this study, considering the influence of linear guide preload on the dynamic characteristics of the linear positioning system, quantifications of preload variation and degradation of mechanical characteristics are crucial for maintaining system performance. For this purpose, a machine learning based neural network was regarded as an appropriate approach for developing a monitoring system to quantify the degradation behavior of linear bearings. In the study, the relationship between the contact stiffness and preload level of the linear guide was first investigated through an experimental analysis. Furthermore, the stage with different linear guide preloads was assembled for the motion test to assess the vibration in motion. Vibration features with a change of preload state and feeding rate were examined to ensure the dependence among these variables. The predictive models were established and verified based on a dataset collected from tests using a neural network approach. Finally, some scenarios were presented to demonstrate the application of the proposed model.
## 4 Conclusions  
Bacterial infections comprise an increasing threat to public health, especially with the growing level of multiple antibiotic resistance among pathogens. Thus, a search for novel antibacterial compounds currently attracts attention of scientific institutions and health officials. In our work, we show the antibacterial effects of two plant secondary metabolites, PEITC and SFN, against _S Shigella_ strains. The established MIC indicates a potential for these compounds to be safely used. The mechanism underlying their antibacterial effect involves induction of the global stress response, the stringent response, also decreasing expression of the Shiga toxin genes. Bacterial growth inhibition and a decrease in toxicity, as demonstrated with the Vero and HeLa cell lines, indicate a therapeutical potential of ITCs. Most importantly, inhibition of the _S. dysenteriae_ toxic effect against _Galeria mellonella_ by SFN alone and in combination with azithromycin provides an important step in the prospective use of the plant-derived compounds to treat bacterial infections.  
Conceptualization, D.N. and A.S.-P.; methodology, K.K. and D.N.; formal analysis, K.K. and D.N.; investigation, K.K., M.K. and D.N.; writing--original draft preparation, D.N. and A.S.-P.; writing--review and editing, D.N. and A.S.-P.; visualization, D.N.; supervision, D.N. and A.S.-P.; and funding acquisition, A.S.-P. and D.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Center, Poland (grant number 2013/11/B/NZ/02582 and 2016/21/B/NZ/02077) to A.S.P., the Foundation for Polish Science, Poland (START 059.2017 \({}^{\prime\prime}\)E9) to D.N. and the APC was funded by the University of Gdansk.  
**Institutional Review Board Statement:** Not applicable.  
Figure 5: SFN inhibits virulence of _S. dysenteriae_ in _G. mellonella_ surrogate model. (**a**) The survival of larvae was assessed at indicated times after injection of PBS (control) or \(10^{8}\) of bacteria. The azithromycin and SFN alone or their combination were administrated 1 h after infection at a concentration indicated and development of the infection was observed during the next 96 h. (**b**) In vivo phagocytosis ratio of FITC-labelled bacteria. (**c**) Representative fluorescent microscopy images of bacteria phagocytosed by hemocytes (indicated by white arrows) from uninfected (PBS) and infected and SFN-treated larvae.
# Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication
## 4 Discussion  
In this work, the I-Q offset \(\Delta V\) caused by the multi-pixel design of Retro-VLC is quantified with a closed-form expression (Equation (4)) and validated by simulation and experimental results (Section 3.1). The I-Q offset and the chosen modulation scheme have a combined effect on the final BER performance. Therefore, for a specific modulation scheme, an optimal number of pixels could be identified to minimize the BER. The bandwidth of the Retro-VLC tag could be significantly boosted to tens of kHz when considering a digital micro-mirror device (DMD) [27] instead of LCD shutter for the optical modulator. However, DMDs have a very limited field of view and do not inherently provide retro-reflection. In our future work, the BER analysis of multi-pixel design will be extended to OFDM modulation. Based on OFDMA, the concurrent BER performance of multiple Retro-VLC tags will be evaluated using our developed Retro-VLC testbed. The hardware design of the Retro-VLC will be optimized to further improve the uplink data rate.  
Figure 10: Sniffing range.  
Figure 9: Working range.  
**Author Contributions:** Conceptualization, S.S.; methodology, M.S., S.S., A.S. and Z.S.; software, M.S., A.S. and Z.S.; validation, M.S., A.S. and Z.S.; formal analysis, S.S.; writing--original draft preparation, M.S. and S.S.; writing--review and editing, J.Z.; supervision, S.S and J.Z.; project administration, S.S. and J.Z.; funding acquisition, S.S. and J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Science Foundation (NSF) under Grant OIA-1757207 and Grant CNS-2150145.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Materials and Methods
### Model Training and Testing  
We trained the random forest classifier on the cleansed dataset, using \"dropout\" as the dependent variable. The independent variable included a range of student data pointssuch as academic performance, demographic information, and course engagement metrics. The training set was used to fit the model, and the testing set was used to evaluate its performance. The effectiveness of the model was assessed using metrics such as accuracy, precision, recall, and the F1 score to provide a comprehensive evaluation of its predictive capabilities. This methodological framework is designed to ensure the robustness and reliability of our predictive model. Through detailed justifications for our choice of tools and methods, we aim to enhance the transparency and reproducibility of our study, thereby contributing valuable insights into the factors influencing student dropout rates. Our model is designed with flexibility in mind to accommodate variations across different geographic regions. To effectively apply this model in other regions or countries, it is essential to adapt it to local education systems, student demographics, and the specific dropout factors prevalent in those areas. Significant policy changes, such as the rise of online learning and modifications in the curriculum, may also affect dropout rates. Additionally, shifts in student demographics and recent socioeconomic factors, particularly those stemming from the pandemic, could influence student behavior and retention. To enhance the study, in future work, we will promise and agree on the collection of more recent data, or we will conduct longitudinal studies to better understand evolving trends. The dataset in the study was split into training and testing sets to assess the accuracy and effectiveness of the machine learning model. Different increments of test size were used to prevent overfitting and achieve better accuracy. Specifically, test sizes of 0.1, 0.2, 0.3, 0.4, and 0.5 were experimented with, and the corresponding figures were generated to illustrate these splits. The training set was used to fit the random forest classifier model, and the testing set was utilized to evaluate the model's performance. This approach helped in estimating how well the model is likely to perform on new, unseen data by ensuring the model does not simply memorize the training data but learns the underlying patterns. For application in new regions, the following types of data are crucial:  
1. Demographic Information: Understanding the socioeconomic and cultural background of students helps tailor the predictive capabilities.
2. Academic Performance Data: Access to comprehensive performance metrics across various subjects is vital to identify at-risk students early.
3. Institutional Data: Information on the educational system's structure, including course offerings and academic policies, is necessary for contextual adaptation.  
We are committed to further refining our methodology to enhance its applicability and accuracy across diverse settings.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## System Model
### Data Combining Scheme  
Demonstration of the cooperative routing process can be found in [6; 7]. In the first phase, the implanted sensor transmits its sensed data to both the relay node \(R\) and the coordinator \(D\), simultaneously. The information sent from \(S\) to \(R\) and from \(S\) to \(D\) in this first phase can be given as  
\[y_{SR}=S_{SR}\cdot\left(PL_{dB}\Big{(}d_{ref}\Big{)}+10n\log_{10}\left(\frac{d _{SR}}{d_{ref}}\right)+s_{dB}\right), \tag{8}\]  
\[y_{SD}=S_{SD}\cdot\left(PL_{dB}\Big{(}d_{ref}\Big{)}+10n\log_{10}\left(\frac{ d_{SR}+d_{RD}}{d_{ref}}\right)+s_{dB}\right), \tag{9}\]  
where \(S_{SR}\) and \(S_{SD}\) represent the slow fading effect that is multiplicative in the intra-body environment. Then, \(R\) forwards the received data to \(D\); similar to Equations (8) and (9), the total information received at \(D\) can be expressed as  
\[y_{RD}=f(y_{SR})\cdot\left(PL_{dB}\Big{(}d_{ref}\Big{)}+10n\log_{10}\left( \frac{d_{RD}}{d_{ref}}\right)+s_{dB}\right) \tag{10}\]  
where \(f(y_{SR})\) represents the processing functions applying on the received signal from \(S\) to \(R\). It should be noted that some information packets are dropped during the transmission process due to the transmission channel fading effect. The coordinator then utilizes a diversity combining scheme to gather the received data from the implanted sensors and the relay nodes.
## 6 Conclusions  
In conclusion, the findings of this study demonstrate that artificial neural networks (ANNs) can serve as a powerful and reliable tool for detecting starch adulteration in milk. The developed ANN model, notable for its portability, cost-effectiveness, and real-time analysis capabilities, provides a practical alternative to more complex spectroscopic and radiographic methods, which, despite their higher accuracy and detail, may not be feasible for on-site testing.  
The ANN-based model addresses significant challenges in the dairy industry by offering rapid and reliable adulteration detection, which is crucial for maintaining food safety standards and protecting consumer health. This is particularly valuable in regions where access to advanced laboratory facilities may be limited.  
However, while the model shows promising results, its current application is limited to specific starch concentrations and has not been tested across a more comprehensive dataset or for other types of adulteration. Future studies should focus on expanding the model's capability to detect various adulterants and on validating its effectiveness in more diverse environmental conditions. This could involve integrating the ANN model with other detection technologies or exploring the use of larger and more varied datasets to enhance the model's accuracy and generalizability.  
Furthermore, optimization techniques could be employed to refine the model's performance, potentially making it a standard tool in food safety inspections. Future research could also explore the integration of this technology into existing food safety management systems, providing a more robust solution for detecting adulteration across the food supply chain.  
By continuing to develop and refine this technology, we can significantly improve the detection of adulteration, thereby enhancing food safety and consumer trust in dairy products.  
[PERSON] and M.D. took part in the discussion of the work described in this paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Results  
### Turbidity-Suspended Sediment Concentration Relationship  
One of the essential steps of this method is to establish the relationship between the turbidity and the suspended sediment concentration. This relationship was set up based on more than 1000 measurements between 2018 and 2022 that were carried out in the frame of regular monitoring at the LDVWA. The measurements were done for four sections on the alluvial reach of the Danube (Dunaiyivaros, Dombori, Baja, and Mohacs). Based on the results, the relationship between the turbidity and the suspended sediment concentration is very strong for each location (R\({}^{2}\) = 0.92-0.99), and overall, as well (R\({}^{2}\) = 0.95); furthermore, it was determined that the relationship in those four cases was insensitive for the location. Accepting these results, the turbidity measured by the handheld turbidimeter can be converted into suspended sediment concentration in a reliable way [23]. The suspended sediment concentration as the function of the turbidity can be seen in Figure 5.
## 2 Materials and Methods  
### Microfluidic Platform Development  
The prototype device utilizes an epifluorescent optical system. A 4 mW 635 nm diode laser (Edmund Optics) passes through a custom-sized 640 nm, 14 nm bandwidth excitation filter (Semrock, Rochester, NY, USA) and 676 nm, 29 nm bandwidth emission filter (Semrock). The other physical components of the optical hardware (cages, holders) were purchased from Thorlabs (Newton, NJ, USA). A photomultiplier tube (Hamamatsu, Japan) was used for detection of the fluorescence signal. A custom stepper motor (Lin Engineering, Morgan Hill, CA, USA) was used to rotate the disk and provide positional accuracy for channel registration. Control electronics consisted of a Stellaris series motherboard (Texas Instruments, Dallas, TX, USA) paired with custom auxiliary circuit boards (fabricated by ExpressPCB, Santa Barbara, CA, USA). Communication to the device was achieved via Bluetooth connection. Software was created in-house using Code Composer (Texas Instruments). Labview was used to create the graphical user interface (GUI). The device housing was designed in-house and 3D printed by ProtoMold (Maple Plain, MN, USA).  
The device and an overview of principles of the centrifugal sedimentation assay are shown in Figure 1a. Antibody-functionalized microparticles (1 \(\upmu\)m silica beads) and unbound fluorescently-tagged antibodies comprise a detection suspension which is mixed in equal volumes with the sample. The resultant mixture comprising the sample and detection suspension is then loaded into the channel formed by pressure sensitive adhesive in the middle layer of the device. In the channel is pre-loaded the density medium through which the microparticles will differentially sediment from objects that are less dense. The microparticles then form a pellet at the periphery of the disc due to centripetal acceleration and channel geometry, shown in Figure 1b. In the presence of the analyte of interest, the fluorescently-tagged detection antibody will form a complex with the microparticle-bound capture antibody with the antigen serving as a bridge. The fluorescence of the microparticle pellet is measured to quantify concentration of the target analyte in the sample as compared to a standard curve. The entire assay requires less than 30 min (compared to several hours for other in vitro assay approaches). Furthermore, the scale of the device allows for small samples sizes (7 \(\upmu\)L per sample), whereas other assays typically use much larger volumes (e.g., 100 \(\upmu\)L for ELISA).  
Figure 1: **(a)** The microfluidic platform is shown **(left)** performing epifluorescence detection (note: the lid is closed when in actual use). The principles of the sedimentation-based immunoassay (right) illustrate the ability to concentrate a bead-bound analyte while excluding contaminants; **(b)** The 20-channel microfluidic disc is made from lasercut PMMA and PSA layers. The close-up illustrates the channel architecture and the appearance of a used disc with the concentrated pellet of beads shown at the tip of each channel.
# SAR Target Incremental Recognition Based on Hybrid Loss Function and Class-Bias Correction
## 4 Experiments and Discussions
### Comparison Methods  
Some existing incremental learning methods are very similar because they use representative sample memory with a fixed capacity, so the performance of the proposed method is compared with the following typical incremental learning methods.  
* iCaRL [8]: iCaRL is a representative method in incremental learning. It preserves the samples of old target classes, applies distillation loss to preserve weights, and uses the nearest class mean classifier in the feature space. This experiment reviews iCaRL's approach of using network output to classify and the approach of using nearest-mean-of-exemplars classification (which requires samples to compute past class means and thus works only when old samples are retained). They are called iCaRL-CNN and iCaRL-NME, respectively.
* Unified [28]: the Unified approach introduces cosine normalization and fine-tuning methods. It uses model output for classification, and introduces a less forgetting constraint that preserves the geometric structure of the old classes. Unified also uses old sample preservation and knowledge distillation loss. Two versions of this method, called Unified-CNN and Unified-NME, are also experimented in this experiment.
* CBesIL [6]: The CBesIL method is based on the selection of class boundary samples. This method saves the recognition ability of old samples in the form of class boundary samples and proposes a class boundary selection method based on local geometric and statistical information. A class boundary-based data reconstruction method is used to update the sample set during the incremental process continuously.  
The class order of all experimental comparison approaches is the same to ensure comparability of results.
## 3 Impact of EDPs on ROS Production
It has been shown that the VGVAPG peptide in human endothelial cells upregulates the expression of mRNA of membrane-type matrix metalloproteinase-1 (MT1-MMP) and MMP-2 [63]. Similarly, [PERSON] et al. (2004) described that cell culture plates coated with the VGVAPG peptide were characterized by increased expression and activation of MMP-2 and MT1-MMP in two melanoma (M1 Dor and M3 Da) cell lines [66]. Furthermore, the VGVAPG peptide added to the culture medium upregulated the MMP-2, MT1-MMP, and TIMP-2 mRNA expression and activity in the human fibrosarcoma (HT1080) cell line [64; 65]. Furthermore, in human glioblastoma multiforme cell lines CB74, CB109, and CB191 and the rat astrocytoma cell line C6 exposed to the (VGVAPG)3 peptide, mRNA expression of MMP-2 increased with very low stimulation of MMP-9 [49]. The authors correlate the high expression of MMP-2 mRNA with increasing degradation of ECM and production of EDPs. All these mechanisms, i.e., the increase in ROS production, Ca\({}^{2+}\) influx, PPARy signaling pathway activation, and the increase in the expression and/or activity of MMPs lead to accelerated elastin degradation in ECM and production of increased numbers of EDPs (Figure 2).
## 2 Methods and Techniques
### Bland-Altman Plots  
Bland-Altman's analysis and plot are among the most common methods used to assess the relative agreement between two analytical methods. By showing the difference between two estimates _versus_ their mean, three important features of the methods comparison can be determined by visual inspection:  
1. The 1.96 \(\sigma_{\mathrm{diff}}\) boundary for the difference distribution, pointing out how much the two methods spread.
2. The regularity of the distribution along the mean axis, to identify variable-related error patterns.
3. The symmetry of the distribution around the zero, addressing systematic bias of the measurements.  
Bland-Altman plots can be used to know by how much the new method is likely to differ from the old: if this is not enough to cause problems in results interpretation, it is possible to replace the old method by the new or use the two interchangeably [32,42].