esa-sceva/satcom-chunk-collection huggingface dataset:
# Piezoelectric Silicon Micropump for Drug Delivery Applications
## 2 Materials and Methods
### Basic Characterization  
The basic characterization of the micropumps includes optical stroke measurement, air flow characterization, and water flow characterization.  
The optical stroke measurement evaluates the deflection of the actuator diaphragm due to a quasi-static voltage signal. The actuator position is detected with a white light profilometer (MicroProf 100--CWL, FRT GmbH) including an optical sensor with a range of 300 \(\upmu\)m and resolution of 100 nm. The quasi-static voltage signal in a range from \(-\)50 to 120 V is applied with a piezo amplifier (SVR 500\(-\)3, piezosystem jena GmbH). This voltage expands beyond the normal actuation voltage to enable the detection of mechanical contact between the actuator diaphragm and the chamber bottom shortly outside of the normal actuation range.  
Part of the characterization with air are the maximal achievable flow rate as well as pressure capability of the actuated micropump. We detect the air mass flow with an EL-Flow prestige (\(\pm\)0.5% accuracy, Bronkhorst) at the inlet of the fluid path. The backpressure is measured with the pressure sensor 26 PCCFA6D (\(\pm\)1 kPa accuracy, Honeywell) at the outlet of the closed fluid path. The alternating voltage signal of \(-\)30 to 90 V is applied with an Agilent 33120A frequency generator and amplified with a piezo amplifier. To determine leakage rates, a controlled pressure (CPC3000, WIKA Mensor) is applied to the turned off pump's outlet and the resulting flow is detected (EL-Flow mass flow meter) at the pump's inlet.  
The flow characterization with water includes the achieved mass flow at different backpressures from 0.2 to 40 kPa. It is performed with a purpose-built fluidic setup: The reservoir, sensors and pump are connected with steel capillaries. The inner diameter of the capillaries is sufficiently large and therefore has a neglectable influence on the detected flow rate. A worst-case estimation based on the Hagen-Poiseuille equation results in a pressure loss of only 0.04%. Pressure is applied as a nitrogen head-pressure over the outlet reservoir with a pressure generator (Mensor CPC3000) and verified in the fluid path 30 cm away from the pump with a Transducer WU-15 (WIKA). The water mass flow is determined with a Liqui-Flow from Bronkhorst (\(\pm\)1% accuracy). Amplification of the 15 Hz rectangular waveform with sinusoidal flanks (approximately 130 Hz) is achieved with an SVR 500/3 amplifier from Piezomechanik GmbH.
## 3 Results and Discussion
### The Comparison of Lp_HOGS with Lp_LOGS
#### 3.1.2 The Box Blur  
We added 30%, 40%, and 50% salt-and-pepper noise to the test images with a \(7\times 7\) box blur. The experimental results are listed in Table 2.  
The three performance indicators obtained by the Lp_HOGS model were higher than those obtained by the Lp_LOGS model. The Lp_HOGS model achieved average PSNR values that were 0.362, 0.805, and 1.356 dB higher than those of the Lp_LOGS model when the salt-and-pepper noise was 30%, 40%, and 50%, respectively. In terms of the difference in the PSNR value, Lp_HOGS had a marginally greater advantage over Lp_LOGS in the \(7\times 7\) box blur than in the \(7\times 7\) Gaussian blur. A station image with different degrees of degradation is shown in Figure 4. The images that were deblurred by Lp_LOGS and Lp_HOGS are compared in Figure 5.  
Figure 4: The station image with different degrees of degradation. (**a**) The original image, (**b**) the \(7\times 7\) box blur, and (**c**) the \(7\times 7\) box blur + 40% salt-and-pepper noise. Figure 3: A comparison of the deblurring effects on a passerby image. (**a**) The original passerby image, (**b**) the image deblurred using Lp_LOGS, and (**c**) the image deblurred using Lp_HOGS. (**d–f**) The local enlarged images from the red boxes in (**a–c**), respectively. A comparison of Figure 4a,c with Figure 5b,c revealed satisfactory overall deblurring effects of Lp_LOGS and Lp_HOGS on images with the box blur. However, the image deblurred by Lp_LOGS contained more speckle noise than the image deblurred by Lp_HOGS. The images in the red boxes in Figure 5b,c are enlarged in Figure 5e,f, respectively. The upper halves of Figure 5e,f were mostly smooth areas. The model used to create Figure 5f removed salt-and-pepper noise more thoroughly than that used on Figure 5e and its visual effect was closer to the original image. The lower half mostly contained the edge area. Thus, the denoising effect was not evident. However, Figure 5f retained more detail from the original image. For example, the car logo is clearer. Overall, the visual effect of Figure 5f was closer to that of the original image, indicating that Lp_HOGS exhibited better deblurring performance than Lp_LOGS under the box blur. It was also reconfirmed that Lp_HOGS preserved more detail while suppressing the staircase effect of the deblurred images.
## 5 Discussion
### Visualization and Analyses  
#### 5.3.1 Detection Examples  
For visualization purpose, six example regions for the above experiments are illustrated in Figures 4-9. The green rectangle represents the correctly detected crown (TP) and the red rectangle represents the backgrounds identified as crowns by mistake (FP). From top left to bottom right, the corresponding methods are the manually annotated ground truth, Strong Weak Faster R-CNN, CAS-DA, and CAS-DA with the filtering strategy, respectively.  
The results demonstrate that CAS-DA outperformed Strong Weak Faster R-CNN in all six transfer tasks. In the detection results, the number of true positives (green rectangles) increased while the number of false positives (red rectangle) decreased. In addition, as shown in subimages (c) and (d), the wrongly detected trees (marked by red rectangles) were effectively removed by the proposed filtering strategy, which contributed to further improving the detection precision.
# A Hybrid Model of Conformer and LSTM for Ocean Wave Height Prediction
###### Abstract
In recent years, the development of deep-learning technology has provided new ideas for solving the problem of wave height prediction [11]. [PERSON], [PERSON], and [PERSON]. [12] proposed a model based on nearshore simulated waves and Long Short-Term Memory networks (SWAN-LSTM) for single-point prediction, which performed better than traditional statistical methods. A hybrid model combining Variational Mode Decomposition (VMD) [13] and one-dimensional Convolutional Neural Networks (CNNs) was proposed for non-stationary wave forecasting, outperforming the one-dimensional CNN. A CNN+LSTM network model [14] was used for prediction, outperforming traditional LSTM and random forest algorithms. Modal decomposition [15] was used to decompose wave height data, followed by LSTM model prediction to improve wave height prediction accuracy. Conformer, introduced by [PERSON] et al. in 2020 [16], with its unique structure, can effectively capture long-distance dependencies and local features in time series data by combining self-attention mechanisms and convolutional neural networks, understanding non-linear characteristics of the data. The embedded convolutional layer can extract local features, enhancing the model's adaptability to non-stationary time series, while LSTM [17] is adept at handling time dynamics in sequence data, remembering long-term information through its gating mechanism while forgetting unimportant information, thus maintaining robustness in the face of sudden events. This study combines the self-attention mechanism of Conformer and the Long Short-Term Memory network (LSTM) into a hybrid model, showing great potential in processing complex time series forecasting tasks. The adaptive feature fusion weight network, by dynamically adjusting the contribution of different features in the model, helps reduce unnecessary feature dimensions and focuses the learning ability of the model on the most informative features. This combination takes advantage of both models to overcome the limitations of existing methods in dealing with nonlinear and non-stationary data, providing a more accurate and stable solution for wave height prediction. This paper is structured as follows. Section 2 reviews the related work in the field of wave height prediction and highlights the limitations of existing models. Section 3 details the methodology, including data collection, preprocessing steps, and the Conformer-LSTM model architecture. Section 4 presents the experimental results, comparing the performance of the proposed model with traditional methods, and discusses the implications of the findings, potential applications, and limitations of this study.
## 2 Materials and Methods
### Satellite Data  
The temperature product delivered by the MODIS satellite was applied in this study, namely Terra MODIS Land Surface Temperature/Emissivity 8 Day L3 Global 1 km (i.e., the MOD11A2 product); it was used for generating TCIs.  
The MOD11A2 product offers land surface temperature (LST)/emissivity at a 1000 m resolution in an eight-day gridded Level 3. Band no. 1, named LST_Day_1 km, was extracted for further work, which contains the mean daily temperature calculated as the average value from 2-8 non-cloudy days.  
On the basis of the MOD11A2 product LST_Day_1 km, the Temperature Condition Index (TCI) was calculated according to Equation (2):  
\[TCI\ =\ \ \frac{LST_{max}-LST_{i}}{LST_{max}-LST_{min}} \tag{2}\]  
where  
\(LST_{max}\)**--maximum LST from the multiannual period;  
\(LST_{min}\)**--minimum LST from the multiannual period;  
\(LST_{i}\)**--LST for the particular eight-day period.  
_TCI_ is the normalized index of LST, which relates the current LST value to the range of multiannual values, placing it in the interval 0-1. The maximum LST value in a pixel corresponds to TCI = 0, while the minimum corresponds to TCI = 1. Figure 8 presents the conditions when the TCI is homogeneously equal to zero across Poland. In parallel, the maximum LST for day of year (DOY) 241 presented in Figure 9 is spatially diversified within Poland.  
The spatial diversification of the maximum LST is justified to a large extent by the climatic feature expressed by the HTC median (Figure 6) coupled with topography (Figure 7); in upland and mountainous areas, LST does not exceed 30-40 \({}^{\circ}\)C, while in the lowlands, it can even achieve 50 \({}^{\circ}\)C and incidentally higher.  
The TCI describes the effect of vegetation stress in response to temperature [11]. The vegetation condition is estimated in relation to the maximum/minimum temperatures. A low TCI, significantly lower than 0.5, implies that the vegetation is in a bad condition. However, spatial comparisons of TCI can be ambiguous, as the same values can present different levels of vegetation stress. In our approach, HTC is the factor that enables us to incorporate the variable climatic conditions into a final method of drought assessment. TCI was considered as the main input for constructing the drought index.  
Figure 7: Digital Terrain Model (DTM) for Poland.
# Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag
## 5 Prototype Realization and Measurements
### Measurements of the Realized Antenna  
Despite the handmade realization of the antenna prototype, the measurement of the reflection coefficient of the antenna reveals minor discrepancies compared to the simulations, as shown in Figure 15. It is well-matched at both frequencies of 1.7 GHz and 3.4 GHz but simulation results appear a little bit better than measurements.
# Power-Efficient Random Access Design for Machine Type Communication
## 1 Introduction
At the same time, we analyze the effective bandwidth for the proposed random access signals for different coverage classes. After considering the collision probability, the optimal bandwidth of the proposed random access signal is mismatching with the effective bandwidth. Then we propose a time division multiplexing (TDM) solution to solve the mismatching between the optimal bandwidth and the effective bandwidth for the goal of power-efficient. Additionally, we propose the resource configurations for random access channels and analyze the random access capacity. In Section 2, we briefly describe the random access procedure in the tradition LTE system. In Section 3, we provide a power consumption analysis of an MTC device, address the limitations of LTE random access when applied to MTC, and then propose a new random access design that is power-optimized for MTC, henceforth referred to as the narrowband LTE MTC or simply LTE-nMTC. Section 4 provides an MTC link budget analysis and presents simulation results for verification of our design. Section 5 draws the conclusion.
## 3 Methodology
### System Architecture
```
1:Input: Query \(Q\)
2:Output: Corrected response \(R_{\text{final}}\)
3:Initialize models \(LLM_{1},LLM_{2},\ldots,LLM_{N}\) // Multiple LLMs
4:Initialize summarizing GPT model \(GPT_{\text{summary}}\) // Logs the prompts each LLM used
5:Initialize error logs for each \(LLM_{i}\)
6:Set threshold \(T_{\text{error}}\) for self-reflection
7:Set threshold \(T_{\text{variance}}\) for hallucination detection
8:// Repeat until stopping criteria are met
9:while stopping criteria are not met do
10:// Multi-Agent Query Processing
11:for each \(LLM_{i}\)do
12: Generate initial response \(R_{i}^{(0)}\gets LLM_{i}(Q)\)
13: Track error for \(R_{i}^{(0)}\) in error log ErrorLog\({}_{i}\)
14:// Repetitive Inquiries within \(LLM_{i}\)
15:for r = 1 to N (dialogue rounds) do
16: Use the error log ErrorLog\({}_{i}\) to generate the corresponding prompt
17: Generate new response \(R_{i}^{(r)}\gets LLM_{i}(Q)\)
18: Calculate variance \(\sigma_{i}^{2}\) between \(R_{i}^{(r)}\) and previous responses
19:if\(\sigma_{i}^{2}>T_{\text{variance}}\)then
20: Flag \(R_{i}^{(r)}\) as potential hallucination
21:endif
22:endfor
23:if error count in ErrorLog\({}_{i}>T_{\text{error}}\)then
24: Trigger self-reflection for \(LLM_{i}\)
25: Update internal parameters of \(LLM_{i}\) to avoid future errors
26:endif
27:endfor
28:// Multi-agent voting
29: Aggregate responses from all \(LLM_{i}\) and generate chain-of-thought (CoT)
30:for each \(LLM_{i}\) in all agents do
31: Perform voting based on \(R_{i}\) and chain-of-thought
32:if conflicting responses between agents then
33: Initiate voting process
34: Adjust weights of models based on their reliability using softmax
35:endif
36:endfor
37:// Summarizing response
38: Aggregate all responses from \(LLM_{1},LLM_{2},\ldots,LLM_{N}\)
39: Perform final verification of aggregated responses with \(GPT_{\text{summary}}\)
40: If inconsistencies are found, correct or refer for further evaluation
41:// Final response check
42:if the aggregated response passes all validation checks (low variance, consistent reasoning, number of rounds) then
43: Exit loop and finalize the response
44:endif
45:endwhile
46:Output:\(R_{\text{final}}\)
```  
**Algorithm 1** Hallucination detection, reduction, and final response generation.
## 5 Unsupervised Learning for Power Allocation
### Power Allocation Formulation  
In this paper, the constrained optimization problem P1 is decomposed into the following power allocation problem P3, as shown below:  
\[(\text{P3}): \underset{p}{\text{max}}E_{H}[E_{\text{sum}}] \tag{14}\] \[s.t.\ \ C_{1}:p_{m}^{n}\geq 0,p_{m}^{\text{max}}\geq\sum\limits_{n =1}^{N}p_{m}^{n}=p_{tot,m}\geq 0\] \[\ C_{2}:p_{m,u}^{n}\geq 0,p_{m}^{n}=p_{m,u_{0}}^{n}+p_{m,u_{1}}^{n}\]  
where the constraint \(C_{1}\) is formed by combining the constraints \(C_{1}\) and \(C_{2}\) in Problem P1, indicating that the sum of the channel powers \(\sum\limits_{n=1}^{N}p_{m}^{n}\) in cell \(m\) does not exceed the maximum allowable power of the base station \(p_{m}^{\text{max}}\). The goal of the problem is to find a power allocation scheme that maximizes the energy efficiency of the NOMA system. Considering the unique advantages of unsupervised learning for continuous optimization problems, this paper proposes an attention-based ULNN for power allocation in the downlink multi-cell NOMA systems.
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 5 Lectin-ADA Conjugation
### Lectin Biomedical Applications  
Lectins play many roles in biomedicine and diagnostics, such as being a mediated therapeutic against cancer, an antiviral drug, antibacterial agent, insecticide, and antifungal agent [5]. Mushroom lectins (MLs) have been studied intensively, and the glycan-binding protein extracted from various medical/edible mushrooms has shown antioxidant, antidiabetic, antimicrobial, and antiviral activities [101]. Because they are known for differentiating a sugar residue and cell agglutination, they have become a tool to investigate carbohydrates on the cell surface and isolate and characterize glycoproteins [102].  
Previous literature has studied the application of lectins for molecule recognition. WGA has been used to study the protein interactions with peptidoglycan, and it has been studied intensively using nuclear magnetic resonance spectrometry. These studies have identified aromatic residues that act as the key players for sugar binding at positions 21, 23, and 30 in hevein domains [103; 104]. Furthermore, the signal assignment from \({}^{1}\)H NMR, when comparing spectra between the absence and presence of the lectin, showed a broadening of the GMDP ligand signals in the presence of the lectin [105].  
Lectins are used as diagnostic tools, such as _Lens culinaris_ agglutinin (LCA), and bind specifically to \(\alpha\)1-6 fucose and have been used to detect hepatocellular carcinoma (HCC) [106]. Lectins have also been investigated for ovarian cancer diagnosis, such as WGA, LCA, _Ulex europaeus_ agglutinin (UEA), _Vicia villosa_ lectin (VVL), and _Glycine max_ agglutinin (GMA) [107]. Six different lectins: Peanut agglutinin (PNA), _Dolichos bifforus_ agglutinin (DBA), _Solanum tuberosum_ lectin (STL), LCA, UEA I, and WGA have been investigated for their binding affinity towards the 5637, HT-1376, and SV-HUC cells (urothelial cell lines). This study finds that WGA has shown the highest bioadhesive potential, while PNA can be a potent tissue discriminator for cancer diagnosis. Furthermore, _Pinellia rentata_ lectin (PTL), _Aleuria anunthia_ lectin (AAL), and _Sambucus nigra_ agglutinin (SNA) could potentially be used in pancreatic cancer [108].
## 2 Data and Methods
### MODIS Land Products Subsets  
Collection 5 MODIS Land Products Subsets for FLUXNET sites are readily available from the Distributed Active Archive Center (DAAC) at Oak Ridge National Laboratory [28]. These subsets represent MODIS product cutouts of 7 km by 7 km around each FLUXNET tower, greatly simplifying the task of MODIS data retrieval for the FLUXNET sites.  
We retrieved Collection 5 MOD13Q1 (NDVI and EVI) and MOD15A2 (LAI and FPAR), and Collection 5.1 MOD17A2 (GPP) (hereafter referred to as MOD13, MOD15, and MOD17, respectively) for the period 2001 to 2008 (Table 1). We used MOD17--a process-oriented model driven by FPAR and meteorological data--as a comparison with our simpler, single-metric approaches of estimating GPP. Unfortunately, MOD13 is only archived at 250 m, while the other products have a spatial resolution of 1 km. To make the subsets comparable, we averaged the sixteen MOD13 pixels over the 1 km area surrounding each tower. For each site, we then selected the central pixel from the 7 km by 7 km subset to represent the FLUXNET tower footprint and assembled the 2001-2008 MODIS products time series. The choice of working with the central 1 km pixel is justified by the fact that the geolocation accuracy of MODIS is about 50 m (1 \(\sigma\)) at nadir [29] and because for most FLUXNET sites the representative vegetation cover extends for less than 1 km.  
We only used data from years in which MODIS data were at least 80% complete (_i.e._, years for which more than 37 and 19 valid data per year were available for 8-day and 16-day composite periods, respectively). To compare across MODIS products, we did not use the quality flags (except for the snow flag), because the categories and the procedures defining the MODIS quality assurance fields vary for each MODIS product. Instead of using the quality flags, we applied the maximum value composite (MVC) method [32], and set all the non-processed data values to the corresponding maximum. If all the data in the MVC window were not processed, we labeled the pixel value as missing data. To assess the stability of the results with respect to the composite period, we processed the MODIS products to 8-day, 16-day, and 32-day composites.
# MLoF: Machine Learning Accelerators for the Low-Cost FPGA Platforms
## 3 Machine Learning Algorithms Implementation on Low-Cost FPGAs
### Artificial Neural Networks (ANN)
#### 3.1.2 Activation Function  
The activation function is required within each neuron, which introduces non-linearity into the ANN, resulting in better abstract capability. Three typical activation functions include the Rectified Linear Unit (ReLU), the Hyperbolic Tangent Function (Tanh), and the Sigmoid Function [26]. All of three activation functions listed above are developed in hardware, and details are described as follows.  
Rectified Linear Unit (ReLU)  
The mathematical representation of the ReLU is described as Equation (1):  
\[\text{ReLU}(x)=\left\{\begin{array}{l}x,\ x>0\\ 0,x\leq 0\end{array}\right. \tag{1}\]  
Figure 3: Block diagram of MAC.  
Figure 2: ANN algorithm implementation. (**a**) Block diagram of ANN. (**b**) Scheduling of the ANN processing.  
The hardware implementation is shown in Figure 4 with a comparator and a multiplexer [27]:  
Hyperbolic Tangent Function (Tanh)  
The mathematical representation of the Tanh function is described as Equation (2):  
\[\text{Tanh}(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{2}\]  
This cannot be achieved directly in the hardware using HDL. Therefore, we fit this functionality separately with five sub-intervals [28]. We divide the interval of [0, +\(\infty\)] into five sub-intervals: [0, 1], [1, 2], [2, 3], [3, 4], [4, +\(\infty\)]. Table 2 contains the heuristic functions used to fit the Tanh function for each sub-interval. The performance of each sub-interval is shown in Figure 5 with an error kept within an acceptable range. The sub-intervals enable the Tanh function to be implemented using only adders and multipliers.  
The mathematical representation of the Sigmoid function is described as Equation (3):  
\[\text{Sigmoid}(x)=\frac{1}{1+e^{-x}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Numerical Interval** & **Function** & **Absolute Error** \\ \hline \([0,1]\) & \(y=-0.3275x^{2}+1.0977x-0.0038\) & 0.0038 \\ \((1,2]\) & \(y=-0.1690x^{2}+0.7021x+0.2324\) & 0.0039 \\ \((2,3]\) & \(y=-0.0282x^{2}+0.1703x+0.7370\) & 0.0055 \\ \((3,4]\) & \(y=-0.0039x^{2}+0.0313x+0.9363\) & 0.0101 \\ \((4,\ +\infty)\) & 1 & / \\ \hline \end{tabular}
\end{table}
Table 2: Fitting function at different intervals.  
Figure 4: Block diagram of ReLU.  
Similar to the Tanh function, the Sigmoid function cannot be implemented directly in the hardware using HDL, as well. The Sigmoid function is equivalent to the tanh function [29] when the transformation in Equation (4) is applied:  
\[\text{Sigmoid}(x)=\frac{\text{Tanh}\big{(}\
icefrac{{x}}{{2}}\big{)}+1}{2} \tag{4}\]  
This transformation can be implemented easily on the hardware using a shift operation and an adder based on the tanh function.