esa-sceva/satcom-chunk-collection huggingface dataset:
# Sulfur Development in the Water-Sediment System of the Algae Accumulation Embry Area in Lake Taihu
## 2 Materials and Methods  
### Field Investigation Area and Sampling  
Lake Taihu (30\({}^{\circ}\)55'40\"-31\({}^{\circ}\)32'58\" N and 119\({}^{\circ}\)52'32\"-120\({}^{\circ}\)36'10\" E) is the third largest fresh water lake along the middle and lower reaches of Yangtze River, with an area of 2338 km\({}^{2}\) and a mean depth of 1.9 m [37]. It is a typical large and shallow eutrophic lake with an algal bloom area that can engulf 75% of the lake during blooming seasons [38]. The northwestern area is the primary area for algae accumulation because of the serious pollution from the inflowing rivers and surrounding cities and the influence of the East Asian monsoon [38; 39]. Lake Taihu is a drinking water source for the surrounding cities, including Wuxi and Suzhou. Every year, efforts are made to aid the recovery of the hyper- eutrophic lake. However, serious HABs and associated disasters such as the black water aggregation [25; 40] are observed every year. During blooming seasons, the whole lake is odorous because of the presence of reductive sulfocompounds [41]. To investigate the development of sulfur in the water-sediment system of the algae accumulation area, a year-long field investigation was carried out quarterly in 2014 in Moon Bay, one of the most prominent algae accumulation areas in Lake Taihu.  
Four algae accumulation sites in the littoral area and one site in an open water area (Figure 1) were investigated in January (winter), April (spring), July (summer), and October (autumn) in 2014. The investigation in July (serious blooming season) was carried out after at least 10 days of continuous southeast monsoon conditions to make sure that the typical algae accumulation period was investigated. The accumulation of algae usually followed a descending order from S1 to S5 during the blooming season due to the shape of the bay and the summer monsoon (Figure 1). In each investigation period, three sediment cores were collected from each sampling site using a gravity corer (110 mm diameter \(\times\) 500 mm length; Rigo Co., Ltd., Saitama, Japan). All the sediment cores were transported to the bank immediately for analyses of dissolved oxygen (DO) profiles. A micro-profiling system (Unisense, Aarhus, Denmark) was used for the DO profile analysis. Details of the DO profiles analysis procedure are described further in Section 2.4. Three water samples were collected from each site using 100 mL of polyethylene bottles, without headspace. The water samples were preserved at 4 \({}^{\circ}\)C in a portable refrigerator and transported to the laboratory for the VSC analysis. The sediment cores were also transported to the laboratory without disturbance for further analysis.
## 4 Bromelain Use as an Exfoliant Agent in Cosmetic Applications  
### General Profile and Source of Bromelain  
Bromelain is a complex blend of proteolytic enzymes and isoforms, primarily derived from the _Bromeliaceae_ plant family, with the pineapple (_Ananas comosus_) being its most abundant natural source. This enzyme complex encloses several thiol endopeptidase, phosphatases, glucosidases, peroxididases, cellulases, and glycoproteins [35; 36].  
Although bromelain is present in all pineapple plant parts like the fruit, stem, peels, leaves, core, crown, and root, the concentration varies considerably depending on the specific plant part [23; 35]. Historically, stems were the main origin of bromelain, with high concentrations being found in stems rather than fruit parts [35; 37]. However, researchers have explored other pineapple plant parts, including waste portions, for efficiently extracting bromelain. For instance, pineapple waste like peel (30-42% \(w/w\)), core (9-10% \(w/w\)), stems (2-5% \(w/w\)), crowns (2-4% \(w/w\)), and leaves contain bromelain, though in lesser quantities compared to the fruit (-51%), while other studies have claimed that the crown may own the highest proteolytic activity (173,000 and 322,000 units for _[PERSON]_ and _[PERSON]_, respectively) [23; 38; 39]. Hence, such cost-effective sources reduce environmental waste and align with green chemistry and circular economy principles [40; 41]. The incon  
Figure 2: Classification of proteolytic enzymes. (Parts of this figure were obtained from the free databases: [[https://smart.servier.com/](https://smart.servier.com/)]([https://smart.servier.com/](https://smart.servier.com/)) and [[https://www.freepik.com](https://www.freepik.com)]([https://www.freepik.com](https://www.freepik.com)) (accessed on 28 January 2025)).  
sistency in commercially produced products, influenced by concentration variability in plant parts, geographic origin, and processing techniques, has posed significant challenges in pharmaceutical development [42].
# DCP: Prediction of Dental Caries Using Machine Learning in Personalized Medicine
## 3 DCP Model
### Prediction Models
#### 3.3.7 Long Short-Term Memory (LSTM)  
RNN is an ANN that learns the time-dependent relationship between input data by combining the past information stored in the hidden layer with the current input value. However, RNN may have gradient loss problems or explosions for long-length sequences [52]. Out of the proposed variant of RNN, LSTM has become a state-of-the-art model for some problems in recent years. The LSTM unit structure comprises cells composed of a forget gate, an input gate, and an output gate. This LSTM design has overcome the RNN vanishing gradient problem. The forget gate is a form of taking the sigmoid function to the weighted sum of the information of the previous cell \((h_{t-1})\), and the current input data \((x_{t})\), is biased to determine how much the previous cell state is to be forgotten and converts a value between 0 and 1, indicating the degree to which information from the past is remembered.  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},\ x_{t}]+b_{f}) \tag{7}\]  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1}\,x_{t}]+b_{i}) \tag{8}\]  
\[\widetilde{C}_{t}=tanh(W_{C}\cdot[h_{t-1},\ x_{t}]+b_{C}) \tag{9}\]  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{10}\]  
\[o_{t}=\sigma(W_{0}[h_{t-1}\,\ x_{t}]+b_{0}) \tag{11}\]\[h_{t}=o_{t}*tanh(C_{t}) \tag{12}\]  
Equation (7) means that the sigmoid layer decides the retention and removal of information. If it has a value of 1, it is \"kept\", and if it has a value of 0, it is \"removed\".  
Equations (8) and (9) are the steps to decide the storage of new information. In Equation (8), the value to be updated is determined through sigmoid. Equation (9) creates a new candidate value \(\widetilde{C}_{t}\) to be added to the cell state.  
Equation (10) is the update step, and the past cell state, that is, \((C_{t-1})\), is updated to a new state \((C_{t})\).  
Equations (11) and (12) are the processes of deciding the output value to the output and how much to extract the finally obtained cell State value.
# End-to-End Deep Learning CT Image Reconstruction for Metal Artifact Reduction
###### Abstract  
Metal artifacts are common in CT-guided interventions due to the presence of metallic instruments. These artifacts often obscure clinically relevant structures, which can complicate the intervention. In this work, we present a deep learning CT reconstruction called iCTU-Net for the reduction of metal artifacts. The network emulates the filtering and back projection steps of the classical filtered back projection (FBP). A U-Net is used as post-processing to refine the back projected image. The reconstruction is trained end-to-end, i.e., the inputs of the iCTU-Net are sinograms and the outputs are reconstructed images. The network does not require a predefined back projection operator or the exact X-ray beam geometry. Supervised training is performed on simulated interventional data of the abdomen. For projection data exhibiting severe artifacts, the iCTU-Net achieved reconstructions with SSIM = \(0.970\pm 0.009\) and PSNR = \(40.7\pm 1.6\). The best reference method, an image based post-processing network, only achieved SSIM = \(0.944\pm 0.024\) and PSNR = \(39.8\pm 1.9\). Since the whole reconstruction process is learned, the network was able to fully utilize the raw data, which benefited from the removal of metal artifacts. The proposed method was the only studied method that could eliminate the metal streak artifacts.  
image reconstruction; deep learning; metal artifacts; computed tomography +
Footnote †: journal: Medical Image Computing
# Effects of Fractional Time Delay as a Low-Power True Time Delay Digital Beamforming Architecture
## 7 Conclusions  
This work proposes a fractional time delay DBF architecture using a VFD lowpass FIR filter that has a four gigahertz-wide functional bandwidth, and requires no upsampling or downsampling at the delay unit. Results show that only five fractional time bits are required to achieve acceptable DBF performance. Simulations verify that this new design creates a maximum QSLL of \(-35.3\) dB and produces no beam squinting. Additionally, if a narrower bandwidth is desired, filter lengths can be further reduced to optimize implementation complexity. The fractional time delay FIR filter is drastically improved by the adoption of an LUT-based implementation where filter coefficients do not need to be calculated on demand. Additionally, the LUT-based design is easily implemented on standard FPGAs and could be adapted to standard CMOS processes for ASIC designs.  
Conceptualization, B.B. and C.S.; methodology and design, B.B., Z.L., and C.S.; formal analysis, Z.L.; investigation, Z.L.; writing--original draft preparation, Z.L.; writing--review and editing, Z.L. and C.S.; supervision, B.B. and C.S.; project administration, B.B. and C.S.; funding acquisition, B.B. All authors have read and agreed to the published version of the manuscript. This research was funded by Analog Devices, Inc.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon request.  
[PERSON] and [PERSON] were employed by Analog Device Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Results and Discussions
The results of this investigation might also assist public health agencies in comprehending the views expressed by the global population about current treatment options for Long COVID, irrespective of whether they have previously received those treatments or are considering doing so. Scholars from several disciplines have conducted studies to understand and interpret the paradigms of transmission of news from news sources to social media sites [130; 131]. Therefore, public health agencies could utilize the framework outlined in this study to monitor and examine Twitter conversations regarding Long COVID during days characterized by notable news events, such as the availability of new treatments or medications for Long COVID, or the publication of studies regarding severe adverse reactions to newly suggested treatments or medications for Long COVID. Monitoring and examining the discourse on Twitter about Long COVID during such days is expected to assist with the immediate evaluation of the views of the target populations of those treatments or medications. An investigation of such views on Twitter might also be useful in aiding public health agencies in establishing rapid responses as well as applicable regulations regarding such treatments or medications. This paper also shows that a substantial volume of Tweets comprising views, opinions, concerns, and attitudes pertaining to Long COVID across a diverse range of topics have been published on Twitter within a short period of time. Public health agencies may consider this finding for applicable policymaking to analyze the historical and current discussions surrounding Long COVID on Twitter, and to evaluate whether the ongoing public discourse on Twitter effectively addresses the information disparity between the public's requirements and the information provided by healthcare and medical sectors. Moreover, conducting such an inquiry might also play a crucial role in identifying misinformation or the dissemination of any conspiracy theories linked to Long COVID on Twitter.
## 5 Results and Discussion
### Indoor Experimental Results
The performance of the exposure detection algorithm (according to the \(t_{minExposurePeriod}\) of the DT trained on the similarity features calculated from the fingerprint data of Wi-Fi APs and the beacon data) is summarized in Table 3.  
Through these indicators, it can be observed from Table 3 that 97% of exposures predicted by the algorithm were real exposures, and 79% were correctly predicted as exposed cases among the real exposures. We demonstrated the effectiveness of the proposed space-based indoor Wi-Fi contact tracing method in a real-life environment. To address the challenge of manually labeling the participant's movement in each space, we employed a BLE beacon for automatic labeling during the collection of datasets. However, since the reception rate of the BLE beacon signals was low, there was a limitation in improving the recall performance.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 1 Introduction
We experimentally demonstrate basic radar functionality inside an anechoic chamber with a binary target detection test. 3. We capture the Doppler information from two different moving targets: a car and a drone. 4. We use time-frequency analysis on experimentally captured micro-Doppler data to extract information about rotating propellers. The initial setup and testing of the system are described in our previous work in [30]. The description and numbering of the data collections in this paper are given in Table 1. The remainder of the paper is organized as follows. Section 2 shows the results of the far field data collection. Section 3 shows the results from stationary target testing inside an anechoic chamber. Section 4 shows the results and detections of moving targets in a radar scene, and Section 5 shows the time-frequency analysis of an experimentally collected micro-Doppler generated by a rotating blade.
## 4 Conclusions  
In this study, an ultra-wideband graphene-based circular patch-shaped Yagi-like MIMO antenna was numerically investigated for use in the applications of THz wireless communication. The reflectance coefficient, directivity, gain, and efficiency parameters have been analyzed for the proposed antenna. The feasibility of the MIMO antenna has also been investigated in terms of various parameters such as TARC, ECC, CCL, MEG, and DG. The proposed MIMO structure offers better diversity performance parameters. All these values under the 1-10 THz and 15-30 THz bands show acceptable standard values, which helps overcome the challenges associated with short-distance communication such as signal fading, multipath propagation, and increased interference. The suggested THz MIMO antenna provides the operating bands of 1-10 THz and 15-30 THz with good isolation values. This antenna offers an ultra-wideband of 10.96 THz of the bandwidth. All the MIMO antenna values are satisfied for the effective radiation operation in this range. Therefore, this MIMO antenna can work for high-speed short-distance indoor applications under the THz band.  
[PERSON] conceived the project, gathered all the supporting information, and supervised the overall project. [PERSON] computed the overall structure and generated the results. Both authors contributed equally to writing the manuscript. All authors have read and agreed to the published version of the manuscript.  
Figure 11: Calculated real and imaginary impedance values of the proposed MIMO antenna.  
Figure 12: Calculated reflection coefficient of the proposed four-port structure. S parameters are calculated by exciting all four ports.
## 4 Simulation and Results
### Search Algorithm for Optimal Reference Point  
In order to improve the computational efficiency, the dichotomy was applied in the optimal reference point search process. The search process is divided into two layers, the first layer uses a large step search to obtain the approximate position, then a small step search is used to obtain the precise reference point position, as shown in Figure 7.  
**Search Algorithm:**  
**Step 1**: The reference point \(P(t)\) of \(t\) previous moment \(t\) is the initial point \(P_{\text{D}}(1)\) of current moment \((t+1)\), calculating the distance \(D(1)\) between the vehicle mass center \(O(t+1)\) and \(P_{\text{D}}(1)\).  
**Step 2:** Obtain the point \(P_{\text{D}}(n)\) on the path with the large step \(\text{s}_{\text{D}}\), calculating the distance \(D(n)\) between \(O(t)\) and \(P_{\text{D}}(n)\), if \(D(n)>D(n-1)\), then stop.  
**Step 3**: The reference point \(P_{\text{D}}(n-1)\) is the initial point \(P_{\text{d}}(n)\), obtain the point \(P_{\text{d}}(h)\) on the path with small step \(\text{s}_{\text{d}}=\text{s}_{\text{D}}/2\), if \(d(h)>d(h-1)\), then stop.  
**Step 4**: Return to step 3 until \(\text{s}_{\text{d}}<\varepsilon\) (\(\varepsilon\) is the precision value).  
**Step 5**: The point \(P(t+1)\) is the obtained optimal reference point, and expressed as:  
\[\left\{\begin{array}{l}e(t+1)=\left|O(t+1)\text{P}(t+1)\right|\cdot\text{ sign}(\text{P}(t),\text{P}(t+1))\\ \Delta\varphi(t+1)=\theta-\arctan(\frac{y_{h}-y_{h-1}}{x_{h}-x_{h-1}})+\beta(t+ 1)\end{array}\right. \tag{40}\]  
where sign indicates direction of the tracking error.
# Flight Level Assignment Using Graph Coloring
## 2 Flight-Level Assignment as Spectrum Coloring Problem with Hard Constraints
### Factorized Optimization Model  
The two utility models defined in the previous section have inherent differences. Not only do they represent the utilities of different actors (individual utility vs. social utility), but they also differ in the dimensions of the problem they depend on. Fuel consumption is a per-flight utility value which depends solely on an autonomous decision performed for each individual flight, the FL chosen for the cruise phase. The social utility yielded by the interference matrix \(W\), instead, depends on the interplay between these individual autonomous decisions.  
Given this coexistence of individual contributions to utility and also interactions between actions, it makes sense to model the whole problem as a _factorized optimization problem_, that is, a problem \(P\) of the form  
\[\text{minimize}\sum_{i\in V}\Phi_{i}(x_{i})+\sum_{s\in S}\Psi_{s}(x_{s}), \tag{9}\]  
where \(V\) is a finite set of _variables_ and \(S\) is a finite collection of subsets of \(V\) representing constraints. \(\Phi_{i}\) and \(\Psi_{s}\) are real-valued functions called, respectively, _variable functions_ and _factor functions_, representing the impact on the objective function of the value of each independent variable, and of combinations of variable values (i.e., interdependences between variables).  
In our setting, \(V\) would be the set of routes in our scenario and, for each \(i\in V\), \(x_{i}\) represents the FL chosen for that route. Hence, the \(\Phi_{i}\) functions would represent fuel consumption and would be defined as \(\Phi_{i}(x_{i})=F(d_{i},x_{i})\), where \(d_{i}\) is constant for each route \(i\) and represents the distance for that route.  
In the same way, \(S\) represents the pairs of routes in which the trajectories intersect, and for each \(s\in S\), defined as \(s=(i,j)\) the function \(\Psi_{s}\) is defined as \(\Psi_{s}(s)=W_{ij}=W(x_{i},x_{j})\), taking values directly from the interference matrix.  
This mapping allows to generalize techniques that we have applied successfully to very different domains, such as Wi-Fi channel assignment [29]. These techniques assume an augmented graph coloring problem, which we describe in the following section.
## 4 Machine Learning Algorithms and Performance Evaluation Indices
### Performance Evaluation Indices  
Four commonly used evaluation metrics in regression analysis were employed to assess the seismic response prediction performance of NPP structures for each machine learning algorithm developed in this study. These metrics are presented in Equations (2)-(5). Here, RMSE represents root mean squared error, MSE stands for mean squared error, MAE denotes mean absolute error, and R\({}^{2}\) represents the coefficient of determination.  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}}\right) ^{2}}} \tag{2}\]  
\[MSE=\frac{1}{n}\sum\limits_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}}\right)^{2}} \tag{3}\]  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}{\left|{y_{i}-\hat{y}_{i}}\right|} \tag{4}\]  
\[R^{2}=1-\frac{\frac{1}{n}\sum\limits_{i=1}^{n}{\left({y_{i}-\hat{y}_{i}} \right)^{2}}}{\frac{1}{n}\sum\limits_{i=1}^{n}{\left({y_{i}-\overline{y}_{i}} \right)^{2}}}=1-\frac{MSE}{Var(y)} \tag{5}\]  
In these equations, n refers to the number of observations, \(\hat{y}_{i}\) is the predicted value, \(y_{i}\) is the observed value, \(\overline{y}_{i}\) represents the average of the observed values, and \(Var\) the variance for the target value. The first three error metrics are used to compare the relative accuracy between ML models, as their absolute values are not directly significant. Conversely, the coefficient of determination (R\({}^{2}\)) quantifies prediction accuracy, with values approaching 1 denoting higher accuracy and those closer to 0 indicating reduced predictive performance.
# A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output
###### Abstract  
We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator's estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and experimental results. We derive an augmented GPS phase-lock loop (PLL) linear model, which includes the effect of coherent averaging, to be used in conjunction with this proposed metric. The augmented linear model allows more accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) as compared to traditional linear models. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, so that time-consuming direct simulations and costly experimental testing can be reserved for PLL designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered.  
navigation; GNSS; GPS; PLL; carrier tracking; tracking error; interference; wideband interference; oscillator; phase-noise +
Footnote †: journal: Sensors