esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Image Acquisition  
Histopathological images obtained from the Experimental and Clinical Research Center of Erciyes University were analyzed using a k-nearest neighbor (k-NN) model. The model was employed to define regions of interest (ROIs), focusing specifically on follicles containing oocytes. Ground-truth images manually annotated by experts were used to guide the k-NN model in segmenting the images. The model identified four reference points to delineate the boundaries of specific regions within the images, ensuring accurate segmentation of follicular regions containing oocytes. Regions identified by the model were cropped from the original images to create new ones for detailed analysis. The images were acquired with an initial resolution of 432 dpi. Following preprocessing, the resolution was preserved to retain the quality of the critical features for analysis.  
In the newly generated images, we have 36 samples from the Cisplatin (Cis) class, 23 samples from the Exosome (Exo) class, 8 samples from the Cisplatin+Exo (CisExo) class, 10 samples from the Exosome+Cisplatin (ExoCis) class, and 14 samples from the Control class. The distribution of images across these groups was imbalanced. For the classification model to produce accurate and reliable results, each class (group) must be adequately represented. To address this imbalance and prevent biased learning--where the model might prioritize majority classes while underrepresenting minority classes--the ADASYN (Adaptive Synthetic Sampling) method was employed [31].  
ADASYN is specifically designed to address this issue by improving classification performance when minority classes are underrepresented. It increases the number of minority-class samples by generating new synthetic examples that are similar but not identical to the existing data. The process involves the following:  
* Weighting of samples.
* The neighbors of each minority class sample are identified.
* More synthetic data are generated for minority-class samples in less-represented regions.  
* Synthetic data generation.
* For each minority-class sample, synthetic data points are created through linear interpolation between the sample and its k-nearest neighbors.
* Depending on the density of minority class samples, different numbers of synthetic data points are generated. Less-dense areas receive more synthetic data.  
This method generates synthetic samples to strengthen the representation of minority classes, such as CisExo (8) and ExoCis (10), ensuring balanced learning across all groups. As a result, the classification model is expected to produce more balanced and accurate results.
# Image-Guided Surgical and Pharmacotherapeutic Routines as Part of Diligent Medical Treatment
## 3 MRI Field Components
#### Safety in MRI  
In modern MRI schemes, one looks for briefer cycles with superior chronological and spatial resolutions. Such features seek mainly optimized performance of the **scanner** and comfort of the patient. As mentioned previously, the final image resulting from an MRI is produced by an interaction between its different fields and the biological tissues subjected to the imaging process. This interaction is at the origin of the signals converted into an image. Such an interaction may produce other undesirable effects regarding patient health or image quality. Due to these effects, understanding their behaviors is necessary for patient safety and the technical characteristics of scanner signals and images. Thus, the design of the scanner components and the realization concerns of different patient treatments could be adapted.  
There are possible biological effects due to the interaction of the different magnetic fields with living tissues. We will discuss these fields successively. Theoretically, the harmless field is the static one B\({}_{0}\), as only body-embedded ferromagnetic matters could produce patient security troubles depending on the field strength. In general, the medical staff ponder such a problem prior to the imaging procedure. The RF field B\({}_{1}\) could be the most menacing for living tissues as the specific absorption rate (SAR) activated is relatively important depending on field strength and frequency. A high SAR with a long exposure duration can produce a temperature rise non-tolerated by the body [97; 98]. In the case of MRI, the RF sequences are short enough and respect the limits fixed by the international safety standards [99; 100]. The coils in pulsed low-frequency field gradients in MRI are constructed in such a way that the 3D gradient is uniform and can be regulated [95]. A difficult question concerns the output of the gradient involving the field strength and the rate of change of course. Shorter cycles with less imaging time can be achieved with higher gradient output. However, higher outputs may lead to disagreeable peripheral nerve stimulation (PNS) [101]. In general, PNS in this context appears not to worry the patient;  
Figure 7: MRI components and fields: (**a**) electromagnet B\({}_{0}\), (**b**) gradient coils (one couple for one axis), (**c**) RF coil B\({}_{1}\).  
however, excessive gradient outputs can steer to life-worrying cardiac stimulation [101]. Again, the medical staff can consider such a problematic prior to the imaging procedure.  
From the foregoing analysis, one can notice that optimizing the effectiveness of the MRI components, even in view of patient well-being, cannot be achieved disregarding patient security. Indeed, each case could be different and inquiring about personal data prior to treatment is essential. Note also that the interaction of the patient tissues with the different fields can cause image artifacts due to, e.g., embedded devices in the body. Considering inquired personal data, such artifacts can be counterweighed.
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
## 2 Related Research
### Low-Illumination Color Image Enhancement Based on Retinex Theory  
Retinex theory is a model used to explain how the human visual system perceives brightness. The fundamental principle of Retinex theory is that an image can be separated into its illumination and reflection components. The original image \(I(x,y)\) acquired by a camera or sensor can be represented using the following formula [13].  
\[I(x,y)=S(x,y)L(x,y) \tag{1}\]  
In the above formula, \(S(x,y)\) and \(L(x,y)\) represent the reflection and illumination components, respectively, and \(x,y\) is the pixel coordinates of the image. In Retinex theory, \(S(x,y)\) has a greater impact on \(I(x,y)\) than \(L(x,y)\) because it determines the unique characteristics of the image [14]. This reduces the effect of the illumination component on the reflection component, thus restoring clear image information [4]. Unlike traditional linear and non-linear methods that can only enhance certain types of image features, Retinex achieves a balance between dynamic range compression, edge enhancement, and color constancy, making it suitable for enhancing various types of images.  
The process of improving low-light images using Retinex is as follows:The low-light image you want to improve is represented by Equation (1).
2. To separate the illumination and reflection components, we take the logarithm as in the following formula [15]. \[logI(x,y)=log(S(x,y)L(x,y))\] (2)
3. The illumination-enhanced image \(\hat{S}(x,y)\) can be obtained by subtracting the illumination estimate from the original image and taking the inverse logarithm, as shown below. \[log\hat{S}(x,y)=logI(x,y)-log\hat{L}(x,y)\] (3)  
The methods based on Retinex theory such as SSR (single scale Retinex) [16] and MSRCR (multi-scale Retinex with color restoration) [17] estimate the illumination and reflectance of an image and adjust the dynamic range of illuminated pixels to improve the image. [PERSON] et al. [18] improve upon existing Retinex-based image enhancement algorithms by incorporating Gabor filters and the Retinex theory. [PERSON] et al.'s technique extracts the luminance component I in the HSI color space and applies the MSRCR technique to enhance the luminance component of low-light images. Additionally, they utilize the SSR algorithm based on Gabor filters in the RGB color space of low-light images to obtain images with improved texture and detail.  
[PERSON] et al. [19] propose an MSRCR image enhancement algorithm based on Gaussian filtering and guided filtering, using multiscale Gaussian filtering and guided filtering to estimate accurate illumination components in low-light images.
# A Review of Degradation and Life Prediction of Polyethylene
## 2 Aging Degradation of Polyethylene
### Types of Degradation  
Premature failure of polyethylene materials is caused by irreversible chemical reactions or physical alterations. Abiotic and biodegradation are the two categories into which polyethylene degrades. Biodegradation is the term used to describe the degradation caused by the action of microorganisms that alter and consume polyethylene and change its properties. Abiotic degradation is defined as deterioration caused by external environmental variables, such as temperature and UV irradiation. Even though each of these two degradation mechanisms can be used to classify the deterioration of polyethylene, the two types work together in nature [43].
# A Novel Digital Signature Scheme for Advanced Asymmetric Encryption Techniques
## 6 Conclusions  
In conclusion, our research presented an innovative digital signature scheme that is both robust and efficient, outperforming other modern techniques in terms of signature size and execution time. The proposed scheme provides a novel digital signature for asymmetric encryption techniques, utilizing a public key in the form of \(k+r\times p\), where both \(k\) and \(p\) are private keys used in both the decryption and signature processes. Through our analysis of the proposed scheme, we demonstrated its robustness against private key recovery attacks and forgery attacks.  
Furthermore, we presented a real-life signature example and conducted a series of tests with various input sizes to showcase the execution time of both the signature and verification processes. By comparing our proposed scheme with other modern techniques, we established that our approach boasts a smaller signature size of only 320 bits, thanks to the use of a small-sized public key. Additionally, we achieved a faster execution time of only 48.4 ms for the whole signature operation, including signing and verification, due to the fewer operations used by our technique.  
Looking towards future directions, we aim to develop an aggregate signature protocol based on our proposal, allowing for two or more digital signatures to be joined into a single, shorter signature. This would allow different signers to generate signatures using our presented scheme.  
**Author Contributions:** Formal analysis, F.L.; methodology, M.K.; supervision and validation, A.L.; writing--review and editing, M.A.-K. and A.E.; Data Curation and Visualisation, A.E. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No data is available.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Evaluation and Discussion
### Keystream Analysis
#### 4.3.2 Key Space Analysis  
The security level of this steganography method is directly contingent on the number of possible keys. A larger number of keys translates to more potential combinations, rendering it challenging for an attacker to exhaustively test all key combinations through brute force or similar methods. Key space analysis involves evaluating the overall number of potential keys and the difficulty associated with testing each key.  
In this study, the keys are denoted as \(x_{0}\) and \(r\), both serving as inputs to the chaotic logistic map function. The domain for each key is defined as \(x_{0}\in[0,1]\) and \(r\in(3.6,4.0)\). Unlike the float data type, which can be easily determined using the sys.float_info command in Python, \(x_{0}\) and \(r\) are specialized data types defined in the decimal library. This decimal data type permits adjustable precision, with the largest precision being \(4.25\times 10^{8}\) for a 32-bit machine and \(9.99\times 10^{17}\) for a 64-bit machine. However, the feasible precision levels are contingent on the available memory on the machine. For the purposes of analysis, the precision level employed in the key space calculation aligns with the precision used in the embedding and extraction processes, which is 20 digits.  
In the calculation, it is necessary to define a granularity or step for each element in \(x_{0}\) and \(r\). Similar to the key sensitivity analysis, \(x_{0}\) exhibits a significant impact even at a precision level as small as \(10^{-30}\). Consequently, it can be assumed that \(r\) shares the same sensitivity, given that it is of the same data type. Hence, the granularities \(\Delta x\) and \(\Delta r\), for this example, are set at \(10^{-30}\). The total key space is determined by the number of possible values for each parameter, formulated as follows:  
\[\text{Key Space} = \left(x_{0}\text{ count}\times r\text{ count}\right)^{\text{Precision}}\] \[= \left(\frac{(1-0)}{10^{-30}}\times\frac{(4-3.6)}{10^{-30}}\right) ^{20}\] \[= 4\times 10^{1080}\]  
Figure 8: MSE and PSNR values for brute force experiment.  
If an attacker has a machine capable of testing one billion (\(10^{9}\)) combinations per second, the estimated time required to successfully find the key is about \(4\times 10^{1071}\) s, which is more than \(10^{1062}\) centuries. However, this is a theoretical calculation and depends on many factors such as algorithm efficiency, the device used, and the security system of the system. Nevertheless, given the assumptions, this calculation provides a rough estimate of the scale involved.
## 4 OFDM-FTN Applications Based on the UEP Algorithm  
The OFDM method [17; 18] divides data into multiple carrier signals and multiplexes and transmits them by adding orthogonality to minimize the interval between the divided carrier signals. Since the OFDM with a high spectrum efficiency is efficient in a frequency-selective fading channel environment and a multipath channel environment, it can maximize the band efficiency by applying the FTN method and also guarantee the performance in multipath channels. We proposed OFDM-FTN applications based on the UEP algorithm, which shows an excellent performance in a multipath channel environment and has a high spectrum efficiency using multiple subcarriers.  
Figure 11 shows the structure of the proposed OFDM-FTN applications based on the UEP algorithm.  
The source bits to be transmitted by the bit-stream, \(\mathbf{D}\), and the coded bit stream, \(\mathbf{C}\), passed through the encoder are expressed in Equations (3) and (4), respectively. Through the UEP algorithm, code words are divided into groups by the number of interference ratios. Moreover, each group applies a different interference ratio. The encoded data, applied with the UEP, are divided into the subcarrier number by the S/P and are expressed as \(\hat{\mathbf{C}}_{m}\), as shown in the following Equation (13):  
\[\hat{\mathbf{C}}_{m}=\Big{\{}\hat{c}_{1},\hat{c}_{2},\cdots,\hat{c}_{N/M}\Big{\}}. \tag{13}\]  
In this equation, \(\mathbf{M}\) is the number of sub-carriers, \(\hat{\mathbf{C}}_{m}\) passes through the inverse fast Fourier transform (IFFT), and the transmitted signal is expressed in the following Equation (14):  
\[\mathbf{x}(\mathbf{t})=\sum\
olimits_{m=0}^{M-1}\mathbf{X}_{m}(\mathbf{t})e^{\mathbf{j}2\pi\mathbf{t} m/M}, \tag{14}\]  
Figure 11: The transceiver structure of OFDM-FTN model based on the UEP algorithm: (**a**) Transmitter; (**b**) receiver.  
where \(X_{m}(t)\) are the OFDM symbols of the \(m\)-th subcarriers.  
The transmission signal is passed through the multipath and the AWGN channel. The received signal is given by:  
\[\mathbf{y}(\mathbf{t})=\sum\
olimits_{\mathbf{l}=\mathbf{0}}^{\mathbf{L}-\mathbf{1}}\mathbf{x}(\mathbf{t}-l)\bm {h}_{\mathbf{l}}(\mathbf{t})+\mathbf{\eta}(\mathbf{t}), \tag{15}\]  
where \(L\) denotes the total number of multipaths, means the \(l\)-th path of all multipaths, and \(h_{\mathbf{l}}(t)\) refers to the channel response coefficient on the \(l\)-th path of all multipaths.  
The received signal, \(y(t)\), is divided into \(M\) by S/P and passes through the FFT (fast Fourier transform). After FFT, \(Y_{m}(t)\) is given by:  
\[\mathbf{Y}_{m}(\mathbf{t})=\sum\
olimits_{\mathbf{n}=\mathbf{0}}^{\mathbf{N}/\mathbf{M}-\mathbf{1}}\mathbf{y} (\mathbf{t})e^{-\jmath 2\pi nm/M}. \tag{16}\]  
\(Y_{m}(t)\) are inputted into the BCJR equalizer to remove ISI due to FTN. The output values of the BCJR equalizer are inputted into the LDPC decoder, after passing through the de-interleaver.
# A Hybrid Watermarking Approach for DICOM Images Security
###### Abstract  
A hybrid watermarking approach for DICOM medical image protection is proposed in this paper. The medical image is first separated into two parts: the region used for the diagnosis, called Region Of Interest (ROI), and the remaining, called Region Of Non-Interest (RONI). The patient's name is then extracted from the header of the DICOM image and pertinent features from the ROI. These data are therefore used to construct a watermark based on a Jacobian model. On the one hand, this watermark is used for the zero watermarking of the ROI. On the other hand, it is divided into blocks that are embedded in the RONI using a linear interpolation technique. Experiment results show a good performance of the proposed approach. The average values of SSIM, PSNR, NC, and BER are respectively 0.98, 71 dB, 0.98, and 0.0054, and the watermark embedding and extraction times do not exceed 12 s.  
medical images; DICOM; ROI; RONI; watermarking; zero-watermarking; authentication; Jacobian model +
Footnote †: journal: Medical Image and Image Processing
## 4 Methodology
### Crop Yield Parameters
#### 4.1.3 CHIRPS Data  
The rainfall data of the study area were collected from the monthly rainfall data contained in the CHIRPS dataset. The monthly cumulative rainfall data of CHIRPS have been obtained by the integration of global monthly precipitation climatology (CHPLim) data, precipitation estimates based on the TIR band of satellites, and data collected from ground stations. This dataset covers the geographical coordinates ranging from \(-50\) to 50 degrees and time frames ranging from 1981 to the present. Several previous studies have shown the good consistency of this dataset with the data of ground stations [31]. According to [32], the average correlation of this dataset with Iran's rainfall data for spring, summer, autumn, and winter months is 0.61, 0.56, 0.59, and 0.45, respectively. CHIRPS dataset was downloaded from the website of the University of California, Santa Barbara ([[https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_annual/tifs](https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_annual/tifs)]([https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_annual/tifs](https://data.chc.ucsb.edu/products/CHIRPS-2.0/global_annual/tifs)) (accessed on 24 October 2020)). To create the rainfall map shown in Figure 4, we used the 8-year average of daily rainfall data of Kermanshah. This map shows that the annual rainfall is highest in the northern counties such as Paveh and lowest in the western counties such as Qasr-e Shirin. The ArcPy library was used to perform mathematical operations on daily rainfall raster files in millimeters.
## 4 Conclusions
This survey has been carried out within the framework of IEA ES Annex 33/SHC Task 58 \"Material and Component Development for Compact Thermal Energy Storage\", a joint working group of the \"Energy Storage\" (ES) and the \"Solar Heating and Cooling\" (SHC) Technology Collaboration Programmes of the International Energy Agency (IEA). The responsibility for the content of this publication is with the authors. The work of ZAE Bayern is part of the project properPCM and was supported by the German Federal Ministry of Economic Affairs and Energy under the project code 03 ET1342A. The work of CIEMAT is part of two projects: ACES2030 (S2018/EM-4319) which is supported by Comunidad de Madrid and European Structural Funds through and SFERA III (GA No 823802) which is supported by European Union's Horizon H2020 Research and Innovation Programme. The work of DTU was supported by HM Heizkorper GmbH and the Danish Energy Agency through the EUDP program. The work of EHU is part of the project Sweet-TES (RTI2018-099557-B-C22), funded by the Spanish Ministry of Science, Innovation and Universities. The work of GREiA was partially funded by the Ministerio de Ciencia, Innovacion y Universidades de Espana (RTI2018-093849-B-C31--MCIU/AEI/FEDER, UE) and by the Ministerio de Ciencia, Innovacion y Universidades--Agencia Estatal de Investigacion (AEI) (RED2018-102431-T). The authors would like to thank the Catalan Government for the quality accreditation given to their research group (GREiA 2017 SGR 1537). GREiA is certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work is partially supported by ICREA under the ICREA Academia programme. The work of HSLU was partially funded by the Swiss Competence Centre for Energy Research on Heat and Electricity (SCCER HaE). The authors would like to thank the SCCER HaE as well as METTLER TOLEDO AG for the support given. The work of Fraunhofer ISE was part of the projects KOLAN (FKZ 03 ESP357B) and KOKAP (FKZ 03 ET1463A) which were administrated by the Projekttrager Julich (PtJ) and funded by the German Federal Ministry of Economic Affairs and Energy. The work of LAMTE was supported by Intel Corporation, along with the Natural Sciences and Engineering Research Council of Canada (NSERC) and the Canada Foundation for Innovation. The work of LGCgE is partly funded by the French National Research Agency (ANR) under the project \"Hybrid storage and heat exchanger device with PCM--EUROPA\". [PERSON]'s PhD work was funded by the French Alternative Energies and Atomic Energy Commission (CEA) Grenoble. The work of LTTT is part of the project MALATrans and was funded by the German Federal Ministry for Economic Affairs and Energy (BMWi) under the project code 03 ESP227B. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.