esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 2 Materials and Method
### Transfer Learning Method  
Since deep CNNs have achieved unprecedented results in many complicated computer vision classification tasks, it is proper to employ a deep CNN to deal with sperm cells classification problems. Here, the original AlexNet initially proposed by [PERSON] in [23], as shown in Figure 4a, was modified to classify the sperm into corresponding classes morphologically. The architecture of the AlexNet-based transfer learning model for sperm image classification is shown in Figure 4b.  
Figure 4: The architecture of the deep learning network. (**a**) The original AlexNet. (**b**) The optimized AlexNet networks for sperm classification.  
Figure 3: The visualization of image preprocessing. (**a**) The original image. (**b**) The gradient image. (**c**) The binary image after being morphologically eroded and dilated. (**d**) Ellipse fitting to the sperm head. (**e**) The cropped image. (**f**) The aligned image. (**g**) The aligned and cropped image.  
AlexNet can be divided into two parts, the feature extraction and the classifier. The first five convolutional layers extract the features of the input image, and the remaining three fully-connected layers classify the acquired features. Three Max-pooling layers, which are used to better summarize the outputs of neighboring layers, follow the first two convolutional layers as well as the fifth convolutional layer respectively. The size of convolution kernels and the number of channels are depicted in the Figure 4a. Dropout layers are employed after the first two fully-connected layers to reduce overfitting in the classifier. In the proposed approach, the intact feature extraction part of AlexNet with pre-trained parameters on the ImageNet was adopted to be the feature extractor for capturing the spatial information of the sperm heads. Compared with the original AlexNet model, the classifier was rewritten to better suitable for the task. The nodes of the first two fully connected layers in the prediction part were redesigned. Besides, following each ReLU activation function with the fully-connected layer, a Batch Normalization was added to improve the performance of the algorithm. Although Batch Normalization was introduced primarily for accelerating the training speed of deep CNNs by reducing internal covariate shift, it also increased the accuracy of specific classification algorithms [28]. The number of neurons in the last fully-connected layer is equal to the number of sperm categories, and a softmax layer was implemented in the final fully-connected layer. The information of the hyperparameters in classifier of the proposed network is summarized in Table 1.
###### Abstract  
Arid and semi-arid regions are highly sensitive and vulnerable to climate change and human activities. Clarifying their spatial distribution is of great significance for understanding regional drought dynamics. This research examines the Shendong mining region, employing time series data of vegetation growth anomalies derived from total primary productivity data to delineate ecological drought. The SPI dataset, representing meteorological drought, is utilized to identify drought frequency, duration, and intensity for both types of droughs based on the run theory. The drought characteristics of different land use patterns are analyzed, and the center of gravity of meteorological and ecological droughs in the study area are calculated. The results show the following: (1) The frequency, duration, and intensity of meteorological drought in the Shendong mining area are 0.74 times per year, 9.2 months, and 0.91, respectively. The frequency, duration, and intensity of ecological drought are 0.33 times per year, 18.2 months, and 0.09, respectively. (2) The intensity of meteorological and ecological droughs is generally consistent across different land use types. The frequency of meteorological drought is minimal for croplands and high-coverage grasslands. The duration of meteorological drought is shortest for high-coverage grasslands. High- and medium-to-high-coverage grasslands and cultivated lands have lower ecological drought frequencies. Low- and medium-to-low-coverage grasslands have relatively shorter ecological drought durations. (3) In regions where land use alterations are evident, the frequency and duration of meteorological drought in areas where cropland has been converted to grassland are relatively low. The frequency, duration, and intensity of ecological drought for croplands converted to grasslands and grasslands converted to croplands are similar. (4) The average incidence of meteorological drought transitioning into ecological drought in the study area is roughly 55%, with areas of stable land use demonstrating a more robust correlation between meteorological and ecological drought in croplands. In the converted areas, the correlation between meteorological drought and ecological drought is higher for croplands converted to grasslands. (5) The transition frequency from meteorological drought to ecological drought exceeds 60% in mining areas. Compared to other mining areas, the meteorological drought intensity near Jitu well and Daliuta well is notably higher. The research findings reveal the spatial distribution attributes and transition dynamics of meteorological and ecological droughs in the Shendong coal mining region, providing reference for the implementation of ecological restoration projects and protection measures in the area.  
meteorological drought; ecological drought; drought transition; drought correlation 2025171064
## 5 Experiment and Validation Results  
An HH800 robot is employed to examine the proposed method to clarify the effectiveness and practicability of it. The proposed method is examined in contrast with the other four methods to show its advantages. To demonstrate the effectiveness of the proposed method (IWO-NN-SKCM) in improving the robot position accuracy, four calibration methods are carried and compared in both the calibration and validation process. The conventional kinematic calibration method (KM) [6], as well as the simultaneous identification of joint compliance and kinematic parameters method (SKCM) [5], and the combination of NN compensator and SKCM method (NN-SKCM) [36] are used in this experiment calibration.
## 3 Modeling and Design Optimization
### Optimization Design  
#### 3.2.1 Motor force  
In Equation (18), the motor force is in direct proportional to \(\tau^{2}h\), which reflects the motor volume. In other words, the motor force is decided by the parameters of \(\lambda\) and \(\gamma\) when the motor volume remains constant. As a planar positioning device, the structural stability is important. Therefore, the motor height is always restricted to lower the center of mass. The specific influence of \(\lambda\) and \(\gamma\) on the motor force is analyzed in this part on the condition that the motor height is constant.  
Assuming that the motor height is constant, the sum of the magnet height (\(h_{M}\)) and the distance between the upper and lower magnet (\(h\)) is invariable. The parameter \(h\) decreases with the increase of parameter \(h_{M}\). Although the force increases with the increase of \(h_{M}\), it will gradually decrease due to the reduction of the effective conductor region. Figure 5 shows the side view of an effective coil side and the corresponding magnets. The total height of the motor can be written as  
\[H=2h_{M}+h \tag{19}\]  
Figure 6 shows the force characteristic versus parameter \(\gamma\). In Figure 6a, the pole pitch remains unchanged. As the motor height increases, the force basically shows a linear increase trend. Additionally, the optimal value of \(\gamma\) in which the force reaches a maximum is always around 0.5. That is to say, the planar motor force increases with the increase of the motor height when the parameter \(\gamma\) is constant and the pole pitch \(\tau\) equals 50 mm. Additionally, the planar motor force reaches the maximum value when the parameter \(\gamma\) equals 0.5. To see whether the optimal value of \(\gamma\) is universal or not, it needs to be verified by considering the effect of the pole pitch.  
Figure 6b shows the force characteristic versus parameter \(\gamma\) with different pole pitches when the total height remains unchanged. It can be seen that the force increases with the increase of the pole pitch, and the optimal value of \(\gamma\) is always around 0.5 no matter what value the pole pitch is. Therefore, the pole pitch can only have an effect on the force, but it cannot affect the optimal value of \(\gamma\).
# The Applications of MALDI-TOF MS in the Diagnosis of Microbiological Food Contamination
## 2 The Principles of MALDI-TOF MS
### Compatibility with Emerging Data Analytics  
MALDI-TOF MS is inherently digital, generating mass spectral data that are well suited to advanced computational analyses, including machine learning, biomarker discovery, and predictive modeling. As a result, it integrates naturally with digital microbiology platforms and laboratory information management systems (LIMSs), enabling real-time surveillance, data mining, and integration with broader food safety informatics systems [32; 61; 92].  
The potential to link spectral data with genomic, phenotypic, and metadata (e.g., product type, geography, antimicrobial resistance) will enhance traceability and outbreak-investigation capabilities in the coming years.  
To sum up, MALDI-TOF MS offers a compelling combination of speed, accuracy, affordability, and versatility, which positions it as a superior alternative to many conventional microbial identification methods in food microbiology. While it does not fully replace all traditional techniques--particularly in applications requiring strain-level typing, toxin detection, or culture-independent diagnostics--it significantly enhances the efficiency and diagnostic power of food safety laboratories across the globe.
## 2 The Lockhart Wavefolder
### Explicit Formulation  
Equation (21) describes an implicit relationship between the input and output voltages of the circuit; it cannot be solved algebraically. Instead, a closed-form solution for \(V_{\text{out}}\) can be derived with the help of the Lambert-W function. The use of the Lambert-W function \(W()\) has been previously researched within the context of VA modeling. Several authors have used it to solve the implicit I-V relationship of diodes [56; 58; 59]. [PERSON] and [PERSON] used \(W()\) to model the Buchla Lowpass-Gate, a synthesizer circuit that employs a resistive opto-isolator (also known as a vactrol) in its control path [16]. Strictly speaking, \(W()\) is multivalued; however, in this work, we only utilize the upper branch of the function. This branch is known as \(W_{0}()\) in the literature [56; 60].  
The Lambert-W function is used to solve equations of the form  
\[\left(A+Bx\right)\exp\left(Cx\right)=D, \tag{22}\]  
which have the explicit solution  
\[x=\frac{1}{C}W\left(\frac{CD}{B}\exp\left(\frac{AC}{B}\right)\right)-\frac{A} {B}, \tag{23}\]  
where \(A,B,C\) and \(D\in\mathbb{R}\)[58].  
Equation (21) can be arranged in the form described by Equation (22) by first rewriting it as  
\[V_{\text{out}}+\frac{2R_{\text{L}}}{R}V_{\text{in}}=\lambda R_{\text{L}}I_{ \text{s}}\exp\left(\frac{\lambda V_{\text{in}}}{\eta V_{\text{T}}}\right) \exp\left(\frac{-\lambda V_{\text{out}}}{\eta V_{\text{T}}}\right), \tag{24}\]  
and dividing both sides by \(\exp\left(-\lambda V_{\text{out}}/\eta V_{\text{T}}\right)\), which gives us  
\[\left(V_{\text{out}}+\frac{2R_{\text{L}}}{R}V_{\text{in}}\right)\exp\left( \frac{\lambda V_{\text{out}}}{\eta V_{\text{T}}}\right)=\lambda R_{\text{L}} I_{\text{s}}\exp\left(\frac{\lambda V_{\text{in}}}{\eta V_{\text{T}}}\right). \tag{25}\]  
Solving for \(V_{\text{out}}\) as defined in Equation (23) yields an explicit model for the Lockhart wavefolder  
\[V_{\text{out}}=\lambda\eta V_{\text{T}}W\left(\Delta\exp\left(\lambda\beta V_ {\text{in}}\right)\right)-\alpha V_{\text{in}}, \tag{26}\]  
where  
\[\alpha=\frac{2R_{\text{L}}}{R},\quad\beta=\frac{2R_{\text{L}}+R}{\eta V_{ \text{T}}R}\quad\text{and}\quad\Delta=\frac{R_{\text{L}}I_{\text{s}}}{\eta V_ {\text{T}}}.\]Table 2 summarizes all parameter values for the proposed Lockhart wavefolder model.
## 3 Results
### Source Analysis of Heavy Metal(loid)s
#### 3.3.3 Principal Component Analysis of Heavy Metal(loid) Elements  
To further analyze the heavy metal(loid) pollution characteristics of construction and agricultural lands, the variables were subjected to principal component analysis. This analysis is one of the most important means for determining the sources of heavy metal(loid)s [32] (Table 6A). The three principal component factors obtained by rotational solving contributed approximately 92.39%, and the first principal component factor (PC1) contributed 58.60% of the variance. Six elements (As, Hg, Cd, Pb, Cu, and Zn) exhibited high loadings, indicating similar origins. The second principal component factor (PC2) contributed 18.99%, with high loadings of Ni and Cu. The contribution of the third principal component (PC3) was approximately 14.81%, and Cr had a high loading. Figure 5A depicts that the elemental components with higher scores in PC1 and PC2 were identical, indicating similar origins. Pearson's correlation analysis indicates a highly significant correlation between these elements.  
The three principal component factors of the rotation solving soil samples from the agricultural land (Table 6B) contributed approximately 78.43%, whereas PC1 contributed approximately 35.34%, with As, Hg, Cd, and Pb having the highest scores. Ni and Cr had the highest PC2 scores (with approximately 24.75%). PC3 had the highest scores for Cu and Zn, explaining approximately 18.34% of the overall variation. The results of the Pearson correlation analysis are shown in Figure 5B. Therefore, heavy metal(loid)s have similar sources in construction and agricultural soils.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Item** & & **As** & **Hg** & **Cd** & **Pb** & **Ni** & **Cu** & **Cr** & **Zn** \\ \hline \multirow{8}{*}{Construction land (A)} & As & 1 & & & & & & & \\  & Hg & 0.950 ** & 1 & & & & & & \\  & Cd & 0.954 ** & 0.906 ** & 1 & & & & & \\  & Pb & 0.925 ** & 0.816 ** & 0.918 ** & 1 & & & & \\  & Ni & 0.541 * & 0.456 * & 0.640 ** & 0.616 ** & 1 & & & \\  & Cu & 0.795 ** & 0.769 ** & 0.764 ** & 0.777 ** & 0.631 ** & 1 & & \\  & Cr & 0.266 & 0.157 & 0.380 & 0.281 & 0.278 & 0.154 & 1 & \\  & Zn & 0.874 ** & 0.756 ** & 0.920 ** & 0.898 ** & 0.608 ** & 0.701 ** & 0.408 & 1 \\ \hline \multirow{8}{*}{Agricultural land (B)} & As & 1 & & & & & & & \\  & Hg & 0.583 ** & 1 & & & & & & \\  & Cd & 0.680 ** & 0.817 ** & 1 & & & & & \\  & Pb & 0.515 ** & 0.620 ** & 0.517 ** & 1 & & & & \\  & Ni & 0.199 & 0.294 & 0.346 * & 0.256 & 1 & & & \\  & Cu & \(-\)0.017 & 0.361 * & 0.411 ** & 0.061 & 0.586 ** & 1 & & \\  & Cr & 0.250 & 0.168 & 0.197 & 0.163 & 0.721 ** & 0.286 & 1 & \\  & Zn & 0.203 & 0.397 * & 0.325 * & 0.404 ** & 0.093 & 0.293 & \(-\)0.012 & 1 \\ \hline \hline \end{tabular} Note: * indicates that the correlation is significant at the \(p<0.05\) level (two-sided); ** indicates that the correlation is significant at the \(p<0.01\) level (two-sided).  
\end{table}
Table 5: Pearson correlation coefficients of different elements in construction land and agricultural land.
## 2 Materials and Methods
### Software Tool Effectiveness Assessment
#### 3.2.3 Student Satisfaction Assessment Through Survey  
Student satisfaction surveys play a crucial role in evaluating the effectiveness and acceptability of interactive simulation software as educational tools. The surveys provide broad insights into students' personal perceptions of the usefulness, ease of use, and impacts of the tool on their learning experiences. Such information from the surveys can also present both the strengths and weaknesses of the software, along with possible areas for improvement. Moreover, satisfaction levels correlate directly with students' motivation and engagement, which in turn reflects in their learning efficiency.  
A survey was conducted to assess students' satisfaction with the use of an interactive software model for learning Hamming codes by the general method. The total number of students who participated in the survey was 54. The students were given the opportunity to rate their satisfaction with the software model on various criteria on a five-point Likert scale, as follows: \"Strongly Dissatisfied\", \"Dissatisfied\", \"Neutral\", \"Very Satisfied\", and \"Satisfied\". The students rated the software tool on the following criteria: \"User Interface Design\", \"Easy orientation\", \"Website Speed Performance\", \"Task-solving Guide\", \"Description of the Mathematical Algorithm\", \"Practicing and Reinforcing Problem-Solving Skills\", \"Error Statistics\", and \"XP Reward Points\". The results of the survey are given in Figure 21.  
Figure 21: Results of the survey on the software model for learning Hamming codes by the general algorithm.  
The results in Figure 21 show that the software is positively evaluated on most criteria, especially practical skills and navigation abilities. However, there are also ways to improve it, mostly regarding the parts dealing with error analysis and the reward system. This data can be used as a base for further improvements to improve both users' satisfaction and the overall effectiveness of the training process.
## 4 Transmission Through the Switch
### SWIPT-Based Transmission  
If the switch selects SWIPT, we consider that the system adapts BPSK modulation, with AWGN noise of 0 mean and variance \(\sigma^{2}\).  
\[y_{SR}(t)=\sqrt{\frac{P_{SR}^{t}}{d_{SR}^{m}}}h.x(t)+n_{SR}(t).\]  
The system adapts a PS ratio of \(\rho\) and the signal components allocated for EH and ID are given as \(y_{SR}^{EH}=(1-\rho)y_{SR}(t)\) and \(y_{SR}^{ID}=\rho y_{SR}(t)\), respectively.  
The SNR of the ID signal at \(R\) can be given as:  
\[\gamma_{R_{SWIPT}}=\frac{\rho P_{SR}^{t}|h_{SR}|^{2}}{d_{SR}^{m}\sigma_{SR}^{2 }}. \tag{7}\]  
And the SNR for the signal received at D for SWIPT mode transmission can be given using  
\[\gamma_{D_{SWIPT}}=\frac{\rho P_{SR}^{t}|h_{SR}|^{2}|h_{RD}|^{2}}{d_{SR}^{m}d_{ RD}^{m}\sigma_{SR}^{2}\sigma_{RD}^{2}}. \tag{8}\]  
With linear EH, the amount of energy harvested with the allocated signal component at R can be given as Equation (9),  
\[E_{EH}=\frac{\eta(1-\rho)P_{SR}^{t}|h_{SR}|^{2}}{d_{SR}^{m}}. \tag{9}\]  
The energy harvesting efficiency \(\eta\) significantly impacts the overall power management of the system. When operating in SWIPT mode, the harvested energy must exceed the power required for transmission (\(P_{t}\)) to maintain sustainable operation. The power splitting ratio \(\rho\) creates a direct trade-off between energy harvesting and data transmission capabilities--a higher \(\rho\) improves the data rate but reduces the harvested energy, while a lower \(\rho\) prioritizes energy harvesting at the cost of a reduced data rate.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 9 Conclusions  
In this work, we present a high-throughput, cache-based covert channel leveraging multi-line encoding strategies and the MESI cache coherence protocol. By encoding messages across multiple cache lines per iteration and utilizing both read-only and write-access patterns, our approach significantly improves upon the prior single-line encoding techniques. Notably, our implementation achieves an up to 4940 KB/s throughput with 2 MB huge pages and attains decoding accuracies of 81.23% (read-only) and 83.34% (write-based),outperforming prior single-line Prefetch+Prefetch attacks that are limited to 822 KB/s and binary messages.  
We extended our implementation of the multi-line covert channel to AMD SEV VMs to assess the feasibility in a TEE with hardware support for memory encryption and huge pages. Our results show that write-based encoding yields clear timing variations, enabling accurate decoding even in virtualized environments. Using huge pages provides further latency reductions and improved timing consistency, enhancing both the throughput and stealth.  
Despite the timing imprecision in virtualized environments, overall trends such as a rising latency with a larger bucket size remain observable, confirming our scheme's practicality. Our AMD implementation showed up to an 88.03% accuracy with a bucket size of 40-line writes and demonstrated that write encoding outperforms read encoding due to MOESI-induced state changes.  
We demonstrate that huge pages enhance the channel stability and performance, and our encoding method supports richer message transmissions--up to 9 bits per iteration--while retaining low detectability. Furthermore, we explore the trade-offs between throughput and accuracy using a bucket-based encoding method, and we identify tuning opportunities such as synchronization timing and bucket size adjustment.  
Future directions include applying machine learning models to improving the decoding robustness, experimenting with other cache state transitions (e.g., E to M, I to E), and evaluating more sophisticated cache activities such as atomic operations or flushes. These extensions could increase the stealth, bandwidth, and adaptability of covert communication in shared-memory systems further.  
Conceptualization: X.L.; methodology: X.L.; formal analysis: X.L.; writing--original draft: X.L.; writing--review and editing: A.T.; project administration: A.T.; funding acquisition: A.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s).  
The funders had no role in the design of this study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results
### Parameter Identification
#### 3.2.2 Rebar Diameter
The investigation into the diameter of reinforcing bars confirmed that the magnetic MFL method yields superior results across a broad measurement range. Conversely, the eddy current method performed slightly better than MFL when the transducer operated within its optimal measurement range. This advantage can be attributed to the higher spatial resolution associated with this type of transducer. A significant drawback of the eddy current method is that an increase in rebar diameter produces changes that closely resemble those resulting from an increase in concrete cover thickness. This issue is not present with the MFL method.