esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Deep Learning to Construct Breast Cancer Diagnosis Model
## 2 Materials and Methods
### Image Recognition
#### 2.2.1 Convolutional Neural Network (ConvNet)  
CNNs are types of biologically-inspired feed-forward networks characterized by a sparse connectivity and weight sharing among their neurons [29]. It accepts two-dimensional input data in contrast with other DL algorithms [30]. A CNN can also be referred to as a sequence of convolution and subsampling layers where the entire network will take an image input of size (h,w,c), where H is height, W weight, and C is the number of channels in the image. Those channels are mostly referred to different (RGB) colors [31] and output the conditional probability distribution over the categories p(y | x). This is carried out by a sequence of the nonlinear level image [32]. For each pixel in an image, the kernel multiplies the pixel and its adjacent pixels that the kernel covers by the opposite kernel pixels. The products are then totalled and their results are set as the pixel value in the convolved image at the preliminary pixel location [33].  
The CNN architecture consists of three layers. Two consecutive conventional pooling layers and a final fully-connected layer [34]  
The convolutional layer: is the main layer that forms a ConvNet and is also the more computationally intensive layer. Its main function is to extract features from the input image pixels. The first few layers of ConvNet can extract features at lower levels. As the network progresses to a deeper level, the features that can be extracted from the convolutional layers gradually increases. The calculation is based on the Element-wise multiplication of Input and Filter and then summed up. The so-called Filter (also known as Kernel) can be regarded as a window composed of several weights, by which the window is padded on the image, and each pixel value in the area covered by the window during the padding is multiplied with the weight of the window at its corresponding position, resulting in a convolution, hence, the name convolutional layer.
# Strategies and Challenges in Detecting XSS Vulnerabilities Using an Innovative Cookie Collector
## 3 Cookie Collector Methodology
### Phase 2: Rapid Prototyping Cycles
#### 3.2.2 Cycle 2 (Multi-Browser Expansion)  
**Prototype:** Implementation of a bot for additional browsers (Firefox, Brave, Opera, Edge). Connection of each bot with the cookie database of each browser to extract information. Identify the location of the cookie database of each browser. Automate and generalize the search so it works on anyone's computer.  
**Testing:** Running each bot to look up a URL and collect the cookies. Individual and joint tests (five bots at a time).  
**Findings:** Edge, Chrome, Opera, and Brave are based on Chromium for their operation. Cookie databases have the same storage path; only the folder name changes for each browser. The cookies of the four browsers have the same number of attributes.  
**Improvements:** analyze and select the common attributes of the cookies of each of the browsers.
# A Survey on Recent Advanced Research of CPS Security
## 3 Reviews
### New Attacks
#### 3.21.4 Vehicle Security  
[PERSON] et al. [25] introduced a new stealthy attack model to robotic vehicle (RV) systems. They proposed false data injection, artificial delay, and switch-mode attacks against RVs under the surveillance of IDS as well as the corresponding launching algorithms. These techniques analyze the predefined detection threshold and monitoring window and adjust their input parameters to bypass detection. The authors also launched attacks on two real-world RV platforms and found that the attacks can deviate a drone from its orbit or cause it to crash when landing.
###### Abstract  
This paper introduces a novel joint model architecture for Speech Emotion Recognition (SER) that integrates both discrete and dimensional emotional representations, allowing for the simultaneous training of classification and regression tasks to improve the comprehensiveness and interpretability of emotion recognition. By employing a joint loss function that combines categorical and regression losses, the model ensures balanced optimization across tasks, with experiments exploring various weighting schemes using a tunable parameter to adjust task importance. Two adaptive weight balancing schemes, Dynamic Weighting and Joint Weighting, further enhance performance by dynamically adjusting task weights based on optimization progress and ensuring balanced emotion representation during backpropagation. The architecture employs parallel feature extraction through independent encoders, designed to capture unique features from multiple modalities, including Mel-frequency Cepstral Coefficients (MFCC), Short-term Features (STF), Mel-spectrograms, and raw audio signals. Additionally, pre-trained models such as Wav2 Vec 2.0 and HuBERT are integrated to leverage their robust latent features. The inclusion of self-attention and co-attention mechanisms allows the model to capture relationships between input modalities and interdependencies among features, further improving its interpretability and integration capabilities. Experiments conducted on the IEMOCAP dataset using a leave-one-subject-out approach demonstrate the model's effectiveness, with results showing a 1-2% accuracy improvement over classification-only models. The optimal configuration, incorporating the joint architecture, dynamic weighting, and parallel processing of multimodal features, achieves a weighted accuracy of 72.66%, an unweighted accuracy of 73.22%, and a mean Concordance Correlation Coefficient (CCC) of 0.3717. These results validate the effectiveness of the proposed joint model architecture and adaptive balancing weight schemes in improving SER performance.  
adaptive weight balancing scheme; affective computing; dimensional emotion representation; discrete emotion representation; joint model architecture; Speech Emotion Recognition (SER) 202515623  
1
## 4 Discussion
Evidence shows that a high percentage of analyzed studies have addressed security concerns in data communication and network traffic within IoT infrastructures, primarily targeting smart surveillance cameras to prevent cyberattacks on interconnected devices in smart cities. Protecting these systems is crucial to prevent data manipulation and safeguard citizens' privacy. However, regions with weak cybersecurity regulations face greater risks, underscoring the need to integrate advanced cybersecurity strategies and privacy models into the implementation of production-ready intelligent surveillance systems. In various regions of Latin America and Africa, law enforcement agencies face technological and human resource limitations, making it difficult to effectively monitor large urban areas. Automated surveillance systems could serve as a strategic tool to enhance public security by enabling early incident detection and efficient police resource allocation. However, effective implementation requires investment in technological infrastructure and specialized training for security forces to accurately interpret data generated by these systems. Additionally, the growing demand for these solutions could position countries like India and China as global providers of intelligent surveillance technology, developing products tailored to the specific security needs of each region--similar to their expansion in other industries. The findings of this study consolidate knowledge from various research efforts on the design and development of IoT-based crime and violence monitoring systems leveraging deep learning. These initiatives can serve as a reference for other countries seeking to implement or enhance technological solutions adapted to their sociopolitical and regulatory contexts. However, the advancement of these systems also raises ethical and legal challenges, as their implementation without adequate regulations could lead to privacy violations and the misuse of citizen data. Therefore, it is crucial to establish regulatory frameworks that balance security with the protection of individual rights, ensuring that these systems are transparent, accountable, and used exclusively for legitimate purposes. Only through ethical governance and well-defined regulations, which are currently not deeply addressed in the analyzed proposals, can these technologies contribute positively to global security without compromising fundamental freedoms.
## 2 Materials and Methods  
The ErAs QD array in GaAs was obtained by growing a GaAs epitaxial layer heavily doped with erbium on a semi-insulating GaAs substrate by molecular beam epitaxy (MBE). The thickness of the epilayer was L = 2 \(\mu\)m, and the Er doping \(\sim\)8.8 \(\times\) 10\({}^{20}\) cm\({}^{-3}\) such that the erbium incorporated into the GaAs in the form of ErAs quantum dots. This was proven by transmission electron microscope (TEM) imagery (Figure 1a). The most likely diameter is estimated to be d \(\approx\) 2.0 nm, and the density of quantum dots is n\({}_{\textrm{QD}}\)\(\sim\)1 \(\times\) 10\({}^{18}\)/cm\({}^{3}\). These ErAs quantum dots contain several quasi-bound levels near the mid-gap of GaAs, allowing strong bound-to-bound, and possibly bound-to-continuum transitions as well, at around 1550 nm. This is proven by the resonant attenuation coefficient around 1550 nm, derived from the infrared transmission measurements, and plotted in Figure 1b. The peak value is \(\alpha\approx 7.4\times 10^{3}\) cm\({}^{-1}\).  
Photoconductive (PC) switch devices were fabricated by patterning a self-complementary square spiral on the top of the epilayer with standard image-reversal photolithographic processing (Figure 2). The antenna has an active area at the center of 9 \(\times\) 9 \(\upmu\)m, which is compatible with the spot size of a focused 1550 nm fiber laser. The erbium-doped fiber amplifier (EDFA) laser emits 90 fs pulses at 100 MHz repetition frequency. Then we measured the waveform of the THz pulses from the PC switch with a THz time-domain spectrometer. The switch was used as transmitter, pumped separately by 1550 and 780 nm sub-picosecond pulses. Commercial InGaAs (for 1550 nm) and LT:GaAs PC switches (for 780 nm) were used as the receivers. The 780 nm result is a benchmark since it is known to produce electron-hole pairs across the GaAs bandgap, utilizing the quantum dots primarily as recombination centers.  
We also conducted photomixing experiments on the gap device as well as a device with interdigital electrodes fabricated in the gap on the same photoconductive material. The fixed-wavelength (1550 nm) source was a distributed-feedback (DFB) laser diode. The tunable wavelength laser was an external cavity diode laser (ECDL) capable of sweeping from 1520 to 1570 nm. A free-space-coupled half-wave plate was inserted to rotate the polarization direction to be parallel to that of the DFB laser after  
Figure 1: (a) A TEM image of ErAs quantum dots. The image was taken by an EAG Laboratory in Raleigh, NC, USA. (b) Attenuation spectrum of GaAs: ErAs sample.  
Figure 2: (a) Square spiral-antenna design, (b) fabricated square spiral antenna with photoconductive gap at the center.  
combining in their outputs in a 50/50 fiber coupler. Both were then boosted by an erbium-doped fiber amplifier (EDFA) to a combined level of \(\sim\)295 mW.
## 3 Coherent Feature Extraction and Evaluation
### Coherent Feature Evaluation
The rules for consistency testing are as follows: when \(CR<0.1\), it is considered that the inconsistency scale of judgment matrix \(A\) is in the allowable range and can be accepted. If \(CR\geq 0.1\)go back to Step 2 to adjust judgment matrix \(A\) again, then calculate and check the consistency until it meets the consistency condition (\(CR<0.1\)). According to the Formulas (19) and (20) and judgment matrix \(A\), the maximum eigenvalue and the normalized eigenvector of judgment matrix \(A\) can be computed as below. \[\lambda_{\max}=3.0735,\ \omega_{A}=[a_{1},a_{2},a_{3}]=[0.6527\ 0.2851\ 0.0623],\ CI_{A}=0.0367,\ CR_{A}=0.0633 \tag{21}\]  
where \(CR_{A}<0.1\), it denotes that matrix \(A\) meets the requirement of consistency testing. The maximum eigenvalues \(\lambda_{B_{1}}\), \(\lambda_{B_{2}}\), \(\lambda_{B_{3}}\) and the normalized eigenvectors \(\omega_{B_{1}}=[b_{11},b_{12},b_{13},b_{14},b_{15},b_{16}],\ \omega_{B_{1}}=[b_{21},b_{22},b_{23},b_{24},b_{25},b_{26}],\ \omega_{B_{3}}=[b_{31},b_{32},b_{33},b_{34},b_{35},b_{36}]\) of matrices \(B_{1}\), \(B_{2}\), \(B_{3}\) can be computed in the same way. The computation results are shown in Table 2.  
As shown in Table 2, it can be seen that judgment matrices \(B_{1}\), \(B_{2}\), \(B_{3}\) all meet the consistency testing requirement (\(CR_{k}<0.1\)). _Step 4:_ Calculate the total weight of each parameter in the scheme layer and check the consistency of the evaluation system. The calculation results of each parameter weight can be shown in Table 3.  
The results of the consistency testing of hierarchical total sorting are as below. \[CR=\frac{a_{1}CI_{1}+a_{2}CI_{2}+a_{3}CI_{3}}{a_{1}RI_{1}+a_{2}RI_{2}+a_{3}RI_{ 3}}=0.0597<0.1 \tag{22}\]  
Where, when \(CR<0.1\), it denotes that the weights of each parameter in the scheme layer are reasonable and effective. As shown in Table 3, it can be seen that the order of weight from large to small is \(\mathrm{SMR}>\mathrm{PRI}>\mathrm{CF}>\mathrm{PW}>\mathrm{PA}>\mathrm{AOA}\), and the SMR feature has the largest weight among these six characteristic parameters. According to the comprehensive evaluation results above, a sketch map of using SMR in REID is shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(k\) & \(\lambda_{k}\) & \(b_{k1}\) & \(b_{k2}\) & \(b_{k3}\) & \(b_{k4}\) & \(b_{k5}\) & \(b_{k6}\) & \(CI_{k}\) & \(RI_{k}\) & \(CR_{k}\) \\ \hline
1 & 6.4084 & 0.0371 & 0.1068 & 0.2027 & 0.426
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 4 STDM Watermarking Using Visual Saliency-Based JND Model
### Watermark Embedding Procedure  
As shown in Figure 2, a new method is adopted in this section. Luminance STDM watermarking using visual attention-based JND model is composed of embedding and detection procedures.  
Figure 2: The proposed watermarking scheme.  
The watermark embedding process as follows:  
1. Calculate the saliency value of each pixel in the carrier image, according to Equations (13)-(17), and binarize the gray scale image according to the threshold to obtain a region of \"saliency\" area and \"non-saliency\" area of the image.
2. Divide the carrier image into eight by eight blocks and perform a DCT transform to determine the DCT coefficients. The coefficients are scanned by zig-zag arrangement. Then select the four to ten DCT coefficients (in zig-zag-scanned order after the eight by eight block DCT on each image) to form a single vector which denoted as the host vector \(x\).
3. The JND coefficients are calculated according to Equation (4). According to step (1), the JND coefficients belonging to the \"saliency\" or the \"non-saliency\" areas are multiplied by the different modulation factors to obtain the coefficients and form the visual redundancy vector \(s\).
4. Following Equation (18), we can get the perceptual slack vector \(s\). Then the host vector \(x\) and the perceptual slack vector \(s\) are projected onto the given projection vector \(u\), which is set as the \(KEY\), to generate the projections \(x_{u}\) and \(s_{u}\). Then we can obtain the quantization step size \(\Delta\) via \(s_{u}\), which can be multiplied by the embedding strength in practice.
5. One bit of the watermark \"m\" is embedded in the host projection \(x_{u}\).
6. Finally, the modified coefficients are transformed to obtain the watermarked image.
## 1 Introduction
Notably, the normalized burn ratio (NBR), computed as the difference between NIR and SWIR reflectance divided by their sum has been used widely for burned area mapping [33; 34; 35; 36]. The purpose of this research is to investigate which Sentinel-2A MSI bands provide greater burned area discrimination. Pairs of largely cloud-free Sentinel-2A images, which were sensed 10 or 20 days apart were selected before and after visually identified fire events that produced evident burned areas at sites in tropical, sub-tropical, and boreal regions are examined. Both top of atmosphere and surface reflectance MSI data are examined. Parametric and non-parametric statistical techniques are used to quantify the spectral burned to unburned separability for different MSI bands and a select number of spectral indices.
## 3 Results and Discussion
### Freeze-Thaw Effect  
A freeze-thaw test was conducted to evaluate the durability of the geopolymer specimens. The compressive strength of all the geopolymer specimens decreased after the freeze-thaw test (Figure 9A). The residual compressive strength of the GP0 specimen was found to be the lowest, as compared with the other specimens after 25 freeze-thaw cycles. It was observed that increasing fiber contents in the geopolymer matrix increased the freeze-thaw resistance. The strength values of GP3, GP5, and GP7 geopolymer specimens dropped to about 8%, 4.18%, and 5.1%, respectively, after the same number of freeze-thaw cycles. The GP5 specimen exhibited better freeze-thaw resistance among other specimens, due to randomly distributed fibers which prevented the mortar layer from splitting away. A previous study [44] revealed that the durability of composite material was enhanced by increasing the fiber volume fraction up to 0.08%. However, it was noticed that the fiber volume fraction above 0.08% decreased the freeze-thaw resistance.  
Figure 8: (**A**) Color changes at different temperatures in all the geopolymer specimens: GP0 to GP7 specimens (left to right, respectively); (**B**) fracture pattern.  
The results of the present study also showed that increasing the PVA fibers up to 5% increased the freeze-thaw resistance. When further increased, the PVA fibers resulted in a reduction of freeze-thaw resistance. This could be due to an increased number of fibers in the unit volume of the matrix, which resulted in less space between fibers and, hence, an overlapping of fiber interface areas occurred. As a result, weak interface areas were increased, which in turn had a bad impact on the freeze-thaw durability [44]. The graphs of weight loss of geopolymer specimens are presented in Figure 9B. The GP0 specimen depicted the most pronounced (10.5%) weight loss, as compared with the others. The weight loss in the GP3, GP5, and GP7 specimens was 8.3%, 5.1%, and 6.3%, respectively, for the same cycles. The weight losses were measured in the range of about 5-11% for all the specimens at the desired number of freeze-thaw cycles. PVA fibers were found to considerably increase the resistance of specimens to surface deterioration. It can be seen from Figure 10 that the physical appearance of the specimens showed no signs of damage, although the compressive strength values were decreased [45]. It was also observed that some white crystals appeared on the surface of the GP0 specimen due to an efflorescence effect, which could be attributed to high alkali concentration and weak Na\({}^{+}\) bonding in the structure of the GP0 specimen [46]. Further, the loss of alkalis into water had a negative influence on the strength development of the GP0 specimen.  
Figure 10: Physical appearance of all the geopolymer specimens.  
Figure 9: Freezethaw resistance in terms of (**A**) % change in compressive strength; (**B**) % weight loss.
## 3 Results
### Model Usability, Performance, and Transferability
#### 3.3.2 Model Performance  
Next, we assess model performance in terms of the speed of operation on a consumer-off-the-shelf (COTS) laptop computer with a dual-core 2.8 GHz Intel Core i7-7600U CPU and 16 GB of DDR4-2400 RAM. We focus on a single performance metric, the timing of simulations. Other aspects of model performance in the form of precision and accuracy are described in previous sections. As shown in Table 9, PPA-SiBGC was between 1200 and 2800% faster than LANDIS-II NECN in our timing tests. This was surprising given that PPA-SiBGC models true cohorts (i.e., individual trees) in an interpreted language while LANDIS-II models theoretical cohorts (i.e., cohorts without a physical basis) in a compiled language. The difference in speed is likely attributable to the parsimony of the PPA-SiBGC model.
## 2 Materials and Methods
### Scanning Methodology
A demonstration of this process is given in Figure 5, showing the original high-resolution signal with the low-resolution signal for some angular offset, their convolution, and the resulting shift calculation on the polar axes. This calculation is repeated at every point in the \((x,y)\) grid, resulting in a direction field \(\theta(x,y)\) representing the fiber direction over the surface of the part, such as that shown in Figure 6.  
\[\Big{(}r_{h}\otimes r_{l,up}\Big{)}[n] =\sum_{m=1}^{N}r_{h}[m]\cdot r_{l,up}[m-n] \tag{9}\] \[\theta_{fiber} =\Delta\theta\cdot\arg\max_{n\in[1,N]}\Big{(}r_{h}\otimes r_{l,up }\Big{)}[n] \tag{10}\]  
Figure 5: Demonstration of circular cross-correlation applied to determining the offset of normalized low-resolution angular data; (**a**) rectangular plot; (**b**) polar plot. Figure 6: Generating direction field from multiple r-scans; (**a**) angular reconstruction at a single point; (**b**) quiver plot generated from x-y-\(\theta\) data. Due to the non-negligible depth penetration of magnetic fields into CFRPs [18], our method detects fiber orientations not just on the surface of the part but into the part itself (as shown in [5]), differentiating our work from other methods that can only assess waviness in the top laminae of a CFRP (see, e.g., [6]). This ability could present the opportunity to look at the waviness of each layer in a lamina where the layup contains more than one nominal orientation. Furthermore, because correlation works independently of whether the signal is real or complex, this method takes advantage of the complex nature of ECT data while still remaining flexible enough to be applied to only real data.