esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions and Future Perspectives
Leveraging these advancements could lead to a robust, low-cost, and miniaturized biosensing platform capable of continuous monitoring of Alzheimer's biomarkers, thereby significantly advancing preclinical testing. Despite its promise, this study recognizes some limitations that warrant future investigation. These include the need to evaluate the probe's performance in complex biological matrices within the multiplex of the brain fluids, leading to the optimization of the microfluidic system for enhanced sensitivity and the optical and physical characterization of the probe and its interactions with evolving compounds. Besides biosensor optimization, the transition of microfluidic-sensing platforms from laboratory research to clinical applications must also address other critical factors, including scalability and regulatory approval. For scalability, this involves producing biosensors using efficient and cost-effective procedures and materials that allow for large-scale production, while maintaining sensitivity and specificity. Our optical microfluidic-sensing strategy fits well in this endeavor by miniaturizing biomarker analysis with microfluidic devices, which, in addition to reducing reagent and volume requirements, also utilizes low-cost fabrication strategies and materials for production. Moreover, our biosensor, based on fluorescent optical sensing, has the advantage of enabling continuous analysis over long periods, without losing efficiency and without requiring intermediate cleaning or functionalization steps--a competitive advantage over electrochemical-sensing devices. On the other hand, regulatory approval remains the most challenging factor for clinical implementation. Compliance with international standards (such as FDA, CE, and ISO 13485) requires extensive preclinical testing, clinical validation, and adherence to Good Manufacturing Practices (GMPs). Demonstrating biosensor reliability through analytical validation and real-world patient trials is crucial for approval, although it often involves collaboration with regulatory experts and healthcare professionals, a process that can take several years. Addressing these challenges will be crucial for advancing towards better preclinical microfluidic platforms, ensuring its reliability in real-time and continuous monitoring scenarios, particularly in BoCs for AD studies. In conclusion, this work bridges the gap between macroscale setups and microscale systems, providing a foundation for future advancements in biomarker detection technologies. This research establishes a link between concept and application, contributing to the ongoing efforts to enhance treatments and diagnostic precision for neurodegenerative diseases. Overall, this study exemplifies the potential of fluorescence-based technologies for in situ biomarker detection in advanced microfluidic devices, which, besides the exploration of their clinical translation in biomedical research, can have applications in other research fields, such as environmental, food quality, and pharmaceutics, among others.
# Old Landslide Detection Using Optical Remote Sensing Images Based on Improved YOLOv8
## 3 Method
### WloU Loss Function
#### 3.3.1 Wise-IoUv1  
When training an old landslide dataset, encountering numerous low-quality samples, commonly referred to as difficult samples, is inevitable. Geometric measures, such as aspect ratio and distance, can exacerbate the penalty for these challenging samples, resulting in a reduction in the model's generalization performance. An effective loss function should alleviate the geometric penalty when the predicted bounding box aligns well with the ground truth bounding box, while also emphasizing less intervention training to enhance the model's overall generalization ability. Building upon this foundation, distance attention is crafted based on distance measurements, yielding a WIoUv1 loss function with a two-layer attention mechanism.  
\[\mathcal{L}_{WIoUv1}=\mathcal{R}_{WIoU}\mathcal{L}_{IoU} \tag{3}\]  
Following Equation (3), \(\mathcal{R}_{WIoU}\) can be calculated by Equation (4).  
\[\mathcal{R}_{WIoU}=exp\left(\frac{\left(x-x_{gt}\right)^{2}+\left(y-y_{gt} \right)^{2}}{\left({W_{g}}^{2}+H_{g}^{2}\right)^{s}}\right),\ \mathcal{R}_{WIoU}\in[1,\,e) \tag{4}\]  
In Equation (4), the parameters are shown in Figure 8. Since the \(\mathcal{R}_{WIoU}\) is always greater than 1, this will effectively enhance the \(\mathcal{L}_{IoU}\) of the ordinary quality anchor frame. On the contrary, because the \(\mathcal{L}_{IoU}\) is less than 1, it will notably diminish the \(\mathcal{R}_{WIoU}\) for high-quality anchor frames and reduce the emphasis on the center point distance when the anchor frame and the target frame are well aligned. If the anchor frame and the target frame are well aligned, to prevent \(\mathcal{R}_{WIoU}\) from creating gradients that hinder convergence, \(W_{g}\) and \(H_{g}\) are separated from the calculation step (* indicates this operation). Since it effectively removes the factors that impede convergence, it does not introduce any new metrics, such as aspect ratio, normalized length, or other geometric measurements.
# Data Interpolation by Near-Optimal Splines with Free Knots Using Linear Programming
## 4 Numerical Testing
### Scalability Results  
To test scalability of our approach, formulation (1) and (2) is used to test problems with varying number of subintervals. The computation times are summarized in Table 3.  
**Example 5**.: _Scalability and Accuracy Testing. Consider five data points (1, 4); (2, 15); (3, 39.75); (4, 78.25); and (5, 124.75) chosen in such a way that the optimal spline has the known value of a third derivative equal to 6._  
As one can see in Table 3, the algorithm scales well since increase in the computation time is almost linear for the number of subintervals. Moreover, as the number of subintervals doubles, the accuracy increases (the gap, the relative difference between \(\hat{k}\) and \(k^{*}\), decreases) by roughly four times.
# Heterogeneous Wireless Networks for Smart Grid Distribution Systems: Advantages and Limitations
## 3 Smart Grid Distribution System
In self-healing applications, the proposed MCUs, via appropriate sensors, should process current/voltage signals, generate time tag synchrophasor measurement, and report in real time over the hybrid communication network via the MPTCP protocol to a central location at the primary substation. The synchrophasor (time tags) reporting rates are at sub-multiples of the nominal 50 Hz system frequency in the range of 10, 25 or 50 frames per second [25]. Typical systems have overall delay of synchrophasor reporting in the 20 ms to 50 ms range [26].
# Boosting of Denoising Effect with Fusion Strategy
## 2 Related Work
### Residual Learning of Deep CNN-Based Image Denoising Method (DnCNN)  
The DnCNN method has been successfully used in image denoising mainly because of the following three reasons [27]. First, it has a very deep architecture that can increase its own capacity and flexibility. Second, some advances in training CNN-based models have been achieved; these include the rectified linear unit (ReLU) [32], the tradeoff between depth and width [33,34], gradient-based optimization algorithms [35, 36, 37] parameter initialization [38], batch normalization [28], and residual learning [29]. Third, the DnCNN can efficiently perform parallel calculations on modern powerful GPUs; thus, it has the potential to exhibit an improved run-time performance.  
The input of the DnCNN is the mentioned noisy observation in Equation (1). Three types of network layers are introduced in the DnCNN denoiser; the architecture is illustrated in Figure 2, where \"Conv\" stands for convolution, \"BN\" stands for batch normalization, and \"ReLU\" stands for the rectified linear unit. After removing all pooling layers, the size of the convolution filters is \(3\times 3\). For a certain noise level in Gaussian denoising, it is more appropriate to set the size of the receptive field of the DnCNN denoiser to \(35\times 35\) with a corresponding depth of 17. Some explanations of the architecture of the DnCNN denoiser are given below:  
1. Conv+ReLU: In the first layer, 64 feature maps are generated by 64 filters with the size of \(3\times 3\times c\), subsequently, rectified linear units (ReLU, max(0, \(\cdot\))) are utilized for nonlinearity. \(c\) denotes the number of image channels; for a gray image, \(c=1\), and for a color image, \(c=3\).  
Conv+BN+ReLU: 64 filters of size \(3\times 3\times 64\) are used, and batch normalization is added for layers \(2\sim(D-1)\) between the convolution and ReLU. Here, \(D\) represents the depth of the DnCNN.
3. Conv: In the last layer, there are \(c\) filters with the size of \(3\times 3\times 64\) that are used to reconstruct the final residual image.  
With regard to model training, DnCNN adopts the residual learning strategy and trains a residual mapping \(R(y)\approx v\) to predict the residual image; furthermore, it uses batch normalization [28] to accelerate training and reduce the internal covariate shift [28]. Then, the output is obtained using \(x=y-R(y)\). It has been pointed out in [27] that integrating residual learning and batch normalization is particularly helpful for fast and stable training as well as better denoising performance.
# Smart Warehouse Management System: Architecture, Real-Time Implementation and Prototype Design
## 5 Case Study
### Implementation of the Architecture Models for the Case Study
#### 5.1.3 Operational View for the Case Study  
For the case study, the operational view is the same as shown in Figure 4. Each node is equipped with scanners and connected to the other nodes by the communication model. The operations of the inventory start after receiving finished products from the production unit, which are then packed in the cartons and tagged with RFIDs, which are stored in the database under the S/O number. The cartons are kept in the warehouse until they are delivered and then moved to the transporting nodes, which are also equipped with the scanners. The records in the database are updated in real time, which makes it easier to track any carton using the S/O or RFID.  
Figure 7: Context diagram (for the case study).
## 2 Materials and Methods
### Intensity Calibration with Suwannee River Standard  
Since 1982, representatives of the International Humic Substance Society (IHSS) have isolated reference fulvic acids, humic acids, and natural organic matter (NOM) from the Suwannee River in southeastern Georgia, USA. Suwannee River has a remarkable low variance in its elemental composition and is therefore a commonly accepted standard. It has a long history as reference material for multidisciplinary research areas, including the marine community [41]. For the fluorescence measurement of the 'Kallemeter', a sulfuric acid dilution of Suwannee River (sample charge number 2S101H) was used with concentrations of 2.5 ppm and 25 ppm. The EEM spectra of the two concentrations of Suwannee River standard are shown in Figure 5. Receiving reasonable signal intensity in both samples indicated sufficient signal intensity for nominal concentrations at marine study sites.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Specification** \\ \hline Light source & Pulsed Xenon flash, 9.9 Watts \\ Excitation range & 220–750 nm \\ Excitation bandpass & 10 nm (FWHM) \\ Excitation monochromator & Czerny–Turner design, 110 mm focal length \\ Excitation wavelength precision / accuracy & 0.2 nm/\(\pm\)0.6 nm \\ Emission range & 200–950 nm \\ Emission bandpass & None (full set of wavelengths) \\ Emission detection spectrograph & (QE-Pro), Fixed, 101 mm focal length \\ Emission detector & TE-cooled CCD fiber optic spectrometer \\ Emission integration time & 8 ms to 60 min \\ Excitation reference measurement & (USB2000+), CCD fiber optic spectrometer \\ Sensitivity & Water-Raman S/N 34 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the ‘Kallemeter’ EEMS sensor system, fluorescence hardware specifications.  
Figure 5: EEM spectra for two concentrations of Suwannee River standard. Excitation wavelengths were used for the x-axis, while emission wavelengths were applied for the y-axis [42]. Color bar (z-axis) denotes relative fluorescence intensity.
# Quantum Computing for Healthcare: A Review
## 7 Open Issues and Future Research Directions
### Quantum Game Theory  
Quantum computing is likely to impact future game theory applications. The complementary aspect of quantum computing overlaps game theory applications. In game theory, every player is maximizing individual payoffs. A prime example is the Prisoner's Dilemma [201], where each player faces criminal charges. [PERSON] [202] calls for players to cooperate, whereas the Nash equilibrium [203] implies that both players must defeat each other. Thus, there are apparent contradictions among different game theory applications. Quantum game theory is a novel extension of the traditional game theory involving quantum information resources. Quantum computing resources have already been providing better solutions for the Prisoner's Dilemma. Furthermore, players can achieve a Pareto optimal solution, provided the circumstances that they are allowed to share a mutually entangled state.  
The Prisoner's Dilemma is useful in healthcare to understand the doctor-patient relationship. For example, there are two players: a doctor and an obese patient with high blood pressure and cholesterol. The doctor can prescribe either medicine or advise lifestyle changes such as diet and exercise. Prescribing medicine takes only a few minutes, while the latter, although it is more effective a treatment, requires more time and effort for the doctor. The patient can either comply or be noncompliant and get a second opinion. When the patient and the doctor cooperate, it leads to the best outcome, however, the dominant patient strategy is noncompliance, no matter what the doctor does. If the doctor prescribes ineffective drugs, the patient simply ignores it, and if the doctor actually gives sound lifestyle advice, then the patient goes for a second opinion. So, eventually, the doctor is better off quickly prescribing drugs, since the patient is going to be noncompliant with whatever strategy the doctor employs anyways. The Prisoner's Dilemma happens because the current healthcare system relies on fee-for-service, rather than the health outcomes of the patients; the two parties do not have any incentive to cooperate. Quantum-based simulations can help explain this and other noncooperative behaviors in the healthcare industry, such as medical spending and pricing to encourage healthcare providers and insurance companies to cooperate.
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 5 PUF Stability Evaluation and Automatic Detection of Unstable Bits
### Further Temperature Compensation
#### 5.5.1 Offset Re-Calibration at Different Temperatures  
A temperature variation causes the mean oscillation frequencies of the two employed ROs in the EoA PUF to shift with slightly different magnitudes [9; 36]. This can be seen in Figure 16 for one exemplary RO pair. Thus, the employed mean frequency offset compensation at a single reference temperature (see Section 4) becomes less effective for large temperature changes. For better visibility, the frequency difference of both ROs is plotted in Figure 16b. The overall drift dominates the influence of the challenge, causing some extracted bits to flip.  
Since all PDLs used for offset compensation are affected by temperature in slightly different ways, no general rate of change for the offset tuning can be found. Nonetheless, this shift in mean frequency offset and thus its tuning could occur at more than one calibration point. For illustration, the nine evaluated temperatures were divided into three parts, each covering three temperatures. Next, the offset compensation, as explained throughout Section 4, was re-calculated at the midpoint of each part, i.e., at \(-10\)\({}^{\circ}\)C, \(20\)\({}^{\circ}\)C, and \(50\)\({}^{\circ}\)C. The resulting error rate for the whole temperature range from \(-20\)\({}^{\circ}\)C to \(60\)  
Figure 16: Temperature-dependent mean frequency change of one exemplary RO pair. (**a**) Absolute frequency change over varying environmental temperatures. (**b**) Frequency difference of both ROs over varying environmental temperatures.  
is plotted as the red line in Figure 17. The three calibration regions, in which the 3 sets of calibration data were used, are indicated by the vertical dashed lines.  
By calibrating the offset at the exemplary three temperatures (plotted in red in Figure 17), this flattens the overall temperature dependency when compared to the original error rate (re-plotted in green from Figure 15), but still errors occur.
###### Abstract  
A security-enhanced, spectral-efficient, and power-efficient multi-beam wireless communication scheme based on random frequency diverse array (RFDA) is proposed in this paper. (AN)-aided directional modulation (DM) schemes. Furthermore, with the aid of spatial modulation (SM) technology and cooperative legitimate users (LUs), we can transmit more information bits by the use of LU number informations than the single modulation symbols. Unlike conventional zero-forcing (ZF) beamforming for multi-beam DM, we design a FDA beamforming vector for each LU based on the minimum transmit power method. Numerical simulations show that (1) the proposed scheme is power-efficient compared to the conventional schemes, (2) the proposed scheme can transmit more information bits than the conventional schemes, and (3) the proposed scheme can ensure communication security when eavesdroppers (Eves) are proximal to LUs or even in same locations.  
physical layer security; spatial modulation; MP-WFRFT; cooperative LUs; power efficient
## 3 Results and Discussion
### Thermodynamic Parameters
#### 3.4.3 Thermal Efficiency  
Figure 6 depicts the effect of different treatments on thermal efficiency. It shows that the highest amount of thermal efficiency (10.58%) was achieved at 70 \({}^{\circ}\)C, ultrasonic radiation time of 30 min, and thickness of 4 mm, while the lowest value of this efficiency (1.54%) was obtained at 50 \({}^{\circ}\)C, control sample, and thickness of 8 mm. The results show that, with increasing temperatures, the changes in thermal efficiency have a significantly (\(p<0.05\)) upward trend. The increase in thermal efficiency is due to the fact that, with increasing temperatures, the difference between the temperature of the drying product and the temperature of the drying air increases and leads to the accelerated removal of moisture from the product and thus reduces the time and amount of energy required.  
Thermal efficiency had an upward trend when using ultrasonic pretreatment. In the case of ultrasonic pretreatment, due to the reduced relative humidity of the drying air, the drying time is significantly (\(p<0.05\)) shorter, and as a result, the share of energy consumption is smaller. The effect of thickness on the changes in thermal efficiency shows that, with decreasing thicknesses, the amount of thermal efficiency increases significantly (\(p<0.05\)). This can be due to the increased drying rate as a result of the decreased thickness, because with decreasing thicknesses, the thermal gradient increases, the removal of moisture from within the product to the surface increases, and the drying time decreases.  
[PERSON] et al. showed that the thermal efficiency of peppermint leaf drying was 38.36%-49.33% for the microwave dryer and 3.69%-5.47% for the hot air dryer [49]. In another study by [PERSON] for drying apple, the thermal efficiency was calculated as ranging  
Figure 6: Thermal efficiency under different conditions for drying kiwifruit slices.  
3.70%-9.44%, and it was shown that, with increasing temperatures, the thermal efficiency increases [46]. [PERSON] et al. calculated the thermal efficiency for drying chamomile in a hot air dryer between 2.12% and 6.87% and stated that the thermal efficiency increases with increasing drying temperatures [44].
## 2 Related Work
### Arabic Text-Based Lexicons  
Two Arabic studies introduced large-scale public lexicons [33; 34] to contribute more resources to Arabic opinion mining. [PERSON] et al. [33] presented a vast lexicon of Arabic tweets annotated for sentiment analysis, known as the AraSenTi lexicon. A sentiment lexicon is a dictionary of words or phrases reflecting positive or negative emotions. One of the popular lexicon usages is to calculate a text's sentiment score [35]. A lexicon is a significant resource for opinion mining to classify the words extracted from datasets as positive, negative, or neutral. There are two ways of constructing lexicons: automatically or manually. Usually, manual lexicons are more accurate than automatically constructed lexicons, but they have smaller sizes [22]. The AraSenTi lexicon reached 131,342 words, each associated with its sentiment score. It contains words of the Modern Standard Arabic and the Saudi dialect. The AraSenTi lexicon is suitable for various research directions, including the review of services and products, as a result of the lexicon's high coverage. There have been different usages for the AraSenTi lexicon in the Arabic literature. For instance, in [36], the authors collected, from the Twitter platform, an Arabic dataset related to COVID-19 to create a model for opinion mining that classifies Arabic tweets regarding the COVID-19 crisis. They labeled the tweets as positive or negative by using the AraSenTi lexicon. In [22], to support the study of Arabic domain-dependent opinion mining, they presented affect lexicons, where each word has a classification, either as positive or negative. The covered domains are social issues, technology, politics, and sports, where a word can be positive in one domain while negative in the other. The total number of positive and negative words in all four domains is 9461, less than those in the AraSenTi lexicon.  
The lexicons for Arabic opinion mining in the literature are scarce, especially for public, large, and non-domain specific lexicons [16]. In [23], the authors built a fanatic lexicon with their methodology that automatically classifies Arabic texts in social media into fanatic and anti-fanatic emotions. The total number of unique phrases in the fanatic lexicon is 1766, distributed in twenty-one contexts. Sports fanaticism is a state of emotion that generates a blind hatred for the competitive teams paired with a blind love for the favorite teams at the same time. Similarly, in [37], the authors produced twelve fanatic domain-specific lexicons that are helpful with Arabic social text for constructing a fanatic classification model to classify the texts into either fanatic or non-fanatic, building tools against fanatic attitude, or identifying and analyzing sports fanaticism.