esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards a Machine Learning Model for Detection of Dementia Using Lifestyle Parameters
## 2 Materials and Method
### Work Plan
#### 2.3.3 Shortlisting Parameters
\begin{table}
\begin{tabular}{l|r|r|r|r|r|r} \hline  & **cgf/stdrwclck** & **cgftdclkdraw** & **cgfdclkimgcl** & **cg?memcom1 yr** & **cgf/tofmemprob** & **cg?presidnal** \\ \hline Round 1 & 1 & 5 & 1 & 3 & -1 & 1 \\ \hline Round 2 & 1 & 4 & 1 & 3 & 5 & 1 \\ \hline Round 3 & 1 & 5 & 1 & 3 & -1 & 1 \\ \hline Round 4 & 1 & 4 & 1 & 3 & -1 & 1 \\ \hline Round 5 & 1 & 5 & 1 &... & 3 & -1 & 1 \\ \hline Round 6 & 1 & 4 & 1 & 3 & 5 & 1 \\ \hline Round 7 & 1 & 4 & 1 & 3 & -1 & 1 \\ \hline Round 8 & 1 & 5 & 1 & 3 & 5 & 1 \\ \hline Round 9 & 1 & 5 & 1 & 3 & -1 & 1 \\ \hline Round 10 & 2 & 5 & 1 & 3 & -1 & 1 \\ \hline \end{tabular}
\end{table} 
Table 8: Round details with dementia incidence breakdown. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Round Index** & **Not Dementia** & **With Dementia** \\ \hline Round 1 & 2207 & 44 \\ Round 2 & 2193 & 68 \\ Round 3 & 2173 & 78 \\ Round 4 & 2154 & 97 \\ Round 5 & 2128 & 123 \\ Round 6 & 2110 & 141 \\ Round 7 & 2067 & 184 \\ Round 8 & 2041 & 210 \\ Round 9 & 1982 & 269 \\ Round 10 & 1979 & 272 \\ \hline \end{tabular}
\end{table} 
Table 9: List of temporal analysis that have high impact (‘#’ indicates round number). Figure 2: Indicative view of data frame for individual. Columns indicate domain wisdom based and statistically significant parameters. We created 2251 data frames for each subject. The breakdown of the total number of subjects, both with and without dementia, is provided in Table 8.  
List of Final Parameters  
After carrying out domain wisdom, Statistical and Temporal analysis, we finalize the parameters that are presented in Table 10.  
To maintain the integrity of the result, we ensured that all the shortlisted fields did not have more than 15% values missing. We removed parameters that have had than 15% null values. One more precaution we needed to take was to ensure that the shortlisted parameters did not have a strong correlation and dependence amongst each other, which would introduce bias in any machine learning technique we were about to apply. We observed that there was no significant correlation amongst parameters, and we proceeded to the next steps for applying algorithms. To ensure the integrity of data we remove rows that have an invalid response for the shortlisted parameters. This leaves us with a total of 4060 rows and 219 dementia-positive patients. The breakdown of control and dementia positive population is 3841 and 219, respectively. The data reflects real-life distribution. However, we also needed to ensure that our model was not affected by overfitting, so we need to balance the data. We used a selective sampling method to balance data. We undersampled the control population and finally, we obtained 219 control and 219 dementia subjects.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 3 Proposed Method
### Score Prediction Sub-Network
\[MC(F_{i})=Conv_{3\times 3}(F_{i})\bigotimes Conv_{3\times 3}(Conv_{3\times 3}(F_{i})) \bigotimes Conv_{1\times 1}(F_{i}), \tag{1}\]  
where \(MC(\cdot)\) denotes the multi-scale convolution operation, \(F_{i}\) represents the input feature map, \(Conv_{3\times 3}(\cdot)\) represents the 3 \(\times\) 3 convolution layer with stride 1 and padding 1, \(Conv_{1\times 1}(\cdot)\) represents the 1 \(\times\) 1 convolution layer with stride 1, and \(\bigotimes\) indicates the concatenation operation. Subsequently, the spatial attention module is utilized to extract prominent spatial features from two different features. The spatial attention module [42] improves the model's performance by retaining critical information while ignoring unimportant regions. Specifically, we first perform max-pooling and average-pooling operations on the multi-scale convolutional feature maps to generate two 2D feature maps. We concatenate these two feature maps and learn spatial weights through a 5 \(\times\) 5 convolution operation. Subsequently, the final spatial attention feature map is obtained by performing element-wise multipli  
Figure 4: The architecture of the score prediction sub-network. cation with the input. Finally, the two spatial attention feature maps are concatenated, as shown in Equation (2):  
\[\left\{ \begin{array}{l}F_{MR}=MC(F_{R})\\ F_{MC}=MC(F_{C})\\ F_{WR}=Conv_{5\times 5}(MaxPool(F_{MR})\bigotimes ArgPool(F_{MR}))\\ F_{WC}=Conv_{5\times 5}(MaxPool(F_{MR})\bigotimes ArgPool(F_{MR}))\\ F_{S}=(F_{R}\bigodot F_{WR})\bigotimes(F_{C}\bigodot F_{WC})\end{array} \right., \tag{2}\]  
where \(F_{R}\) denotes the quality restoration feature maps, and \(F_{C}\) denotes the content feature maps. \(Conv_{5\times 5}(\cdot)\) represents the \(5\times 5\) convolution layer, and \(MaxPool(\cdot)\) and \(AvgPool(\cdot)\) represent the max pooling and average pooling operation, respectively. \(\bigotimes\) indicates the concatenation operation, and \(\bigodot\) indicates the multiple operation. To further fuse the spatial attention feature maps, a channel attention module [43] is utilized to learn the importance of the concatenated feature maps across different channels to better capture the relationships between different channels. The concatenated feature maps are processed using global average pooling to obtain a one-dimensional vector, and then a fully connected layer is applied to learn the weights of each channel, generating a weight vector. The weight vector is multiplied with the concatenated feature maps to obtain a fully fused output feature, as shown in Equation (3):  
\[\left\{ \begin{array}{l}F_{WS}=GAP(F_{S})\\ W=FC(F_{WS})\\ F_{M}=F_{S}\bigodot W\end{array} \right., \tag{3}\]  
where \(GAP(\cdot)\) denotes the global average pooling, \(FC(\cdot)\) denotes the fully connected layer, and \(\bigodot\) denotes the multiple operation.
## 4 Conclusions  
This study showed that vermicomposting significantly enhanced macro and micronutrient status. VCM can be regarded as a better treatment than composting during ammonia volatilization, but VPM provided maximum beneficial nutrient retrieved. VCM showed better results in increasing nitrogen and decreasing both carbon and C:N ratio. High nitrogen and low C:N ratio ensured the good quality of the compost. VPM provided increased nutrient contents from its initial to final values, but the greenhouse gases emissions were significantly higher than other treatments. VCM and VPM are recommended to increase the plant growth for an increasing nutrient profile.  
Conceptualization, writing, data curation, and original draft preparation, S.T.R.; supervision, reviewing, and editing, B.Z.; data curation, reviewing, and editing, Z.A.; visualization and investigation, J.L.T.; review and editing, R.A. ([PERSON]); review and editing, H.B.; visualization and investigation, H.L; reviewing and editing, X.R.; data curation, review, and editing R.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R & D Program, Grant Number 2016 YFD0200309-7 and CAS-TWAS Presidential Scholarship for International student.  
We are thankful to the University of Chinese Academy of Sciences (IC-UCAS) and The World Academy of Sciences (TWAS), and the National Key R & D Program (Grant No. 2016 YFD0200309-7).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Treatments**} & \multicolumn{4}{c}{**GHGs Emissions Equivalent (g CO\({}_{2}\)-eq/kg)**} \\ \cline{2-5}  & **CO\({}_{2}\)** & **CH\({}_{4}\)** & **N\({}_{2}\)O** & **GWP** \\ \hline COM & 92.3 & 0.01 & 0.01 & 3.32 \\ VCM & 118.1 & 0.01 & 0.02 & 6.3 \\ VPM & 85.7 & 0.14 & 0.04 & 16.8 \\ VBC & 82.7 & 0.01 & 0.01 & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Total greenhouse gases (GHG) emissions and global warming potential (GWP) in all treatments.
## 1 Introduction
In addition, the controlled drop-evaporation process allows precise monitoring of the solution concentration, enabling systematic studies and the integration of in situ diagnostic tools, such as X-ray diffraction and thermography. Overall, acoustic levitation offers improved control, accuracy, and detail for the study of crystallization in aqueous solutions in comparison to traditional methods. This work demonstrates measuring the transient thermal effect and spontaneous nucleation within a highly concentrated aqueous urea solution in a contact-free environment. The drop-evaporation method [22; 26] is enabled by using an acoustic levitation apparatus [27; 28] to achieve unprecedented levels of supersaturation. The progression of temperature variations on the droplet surface is monitored by using in situ infrared (IR) thermography. The infrared thermography approach has the potential to offer valuable insights into the thermodynamic aspects of various phenomena while also offering hints about bond formation or chemical reactions. Consequently, it can exhibit a highly effective capability to complement optical microscopy measurements. By comparing the rate of heat release as a function of supersaturation, the nature of the pre-nucleation process within the solution is elucidated. Our result reveals two distinctive stages of heat signatures leading up to final crystallization, which implies two possible stages of crystal nucleation. When the concentration of the solution increases to a level equivalent to supersaturation of S = 0.5, a gradual rise in the droplet temperature is measured. This behavior persists throughout the entire width of the metastable zone [14; 22; 29]. However, as supersaturation reaches a critical limit (approximately S = 2), a brief stagnation in the temperature is observed, which is followed by an instantaneous heat release and rapid growth of polycrystalline urea crystals. The implications of these findings are discussed through comparative analysis with the CNT perspective.
## 3 Results
### Transaction Structure and Maritime Data Ownership Issues for MASSs
#### 3.3.2 Transaction Structures  
Data transactions are often directly contracted between two parties: data providers (sellers) and consumers (purchasers). However, many transactions are sequentially contracted among the original data holders, data providers (seller), data brokers, and data consumers (purchaser) [37]. For example, digital platform-based ship management companies can create new businesses by delegating the data produced through MASS operations from shipowners to data consumers (e.g., shipyards, insurers, or shippers), and providing collected data and application programming interfaces to third parties. In this case, digital platform-based ship management companies should preemptively sign delegation contracts with shipowners at the ship management stage, to reserve their rights to the data.  
Generally, data brokers sign data transaction contracts with suppliers and consumers as license and transfer agreements. License agreements are contracts allowing nonexclusive payments to consumers for exclusive or nonexclusive data use. Such agreements are mainly used for data transactions not related to ship and freight operations. In contrast, transfer agreements convey all or part of the IPR and change the data's ownership; after the transfer agreement, the data provider is no longer entitled to the data [38]. The scope and target of the contract should focus on whether the transfer contract includes the data architecture that forms the core operating assets of the shipping company, such as various drawings, contracts, bills of lading, and ship procedures.
_J. Sens. Actuator Netw._**2013**, \(2\), 46-69; doi:10.3390/jsan2010046  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan  
_Article_  
**Performance of Cooperative Eigenvalue Spectrum Sensing with a Realistic Receiver Model under Impulsive Noise**  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{1,\ast}\) and [PERSON] \({}^{2}\)**  
\({}^{1}\) National Institute of Telecommunications (Inatel), Av[PERSON], 510, Santa Rita do Sapucai, MG, 37540-000, Brazil  
\({}^{2}\) Brasilia University (UNB), Campus Universitario Darcy Ribeiro Asa Norte 70910-900, Brasilia, DF, Brazil; E-Mail: [EMAIL_ADDRESS]  
* Authors to whom correspondence should be addressed; E-Mails: [EMAIL_ADDRESS] (D.A.G.); [EMAIL_ADDRESS] (R.A.A.S.); Tel.: +55-353471-9227; Fax: +55-353471-9314.  
_Received: 25 October 2012; in revised form: 28 December 2012 / Accepted: 31 December 2012 / Published: 22 January 2013_  
**Abstract:** In this paper we present a unified comparison of the performance of four detection techniques for centralized data-fusion cooperative spectrum sensing in cognitive radio networks under impulsive noise, namely, the eigenvalue-based generalized likelihood ratio test (GLRT), the maximum-minimum eigenvalue detection (MMED), the maximum eigenvalue detection (MED), and the energy detection (ED). We consider two system models: an implementation-oriented model that includes the most relevant signal processing tasks realized by a real cognitive radio receiver, and the theoretical model conventionally adopted in the literature. We show that under the implementation-oriented model, GLRT and MMED are quite robust under impulsive noise, whereas the performance of MED and ED is drastically degraded. We also show that performance under the conventional model can be too pessimistic if impulsive noise is present, whereas it can be too optimistic in the absence of this impairment. We also discuss the fact that impulsive noise is not such a severe problem when we take into account the more realistic implementation-oriented model.  
**Keywords:** cognitive radio; direct down-conversion receiver; eigenvalue-based cooperative spectrum sensing; energy detection; generalized likelihood ratio test; impulsive noise; maximum-minimum eigenvalue detection
## 1 Introduction
The GD employment in wireless communications [21,22], radar sensor systems [20,23], and CR networks for spectrum sensing [24] allows us to improve the signal detection performance of these systems in comparison with implementation of widely used conventional detectors. This work differs from the previously published paper [24] by introducing a new advantage of GD employing in CR network systems based on antenna array, which is the _SNR_ wall problem alleviation under the noise power uncertainty. Additionally, the GD optimal detection threshold is defined based on the minimal probability of error criterion under the noise power uncertainly at the low _SNR_ condition. Intuitive approach to reduce the noise power uncertainty at run time by employing the GD in CR network is to define the noise power at the GD AF output, _i.e._, the another narrow band closed to the PU signal frequency band, with the purpose to calibrate the noise power in the PU signal frequency band. Even if we believe that the noise power forming at the GD PF and AF outputs is not the same, the noise calibration error can be much lower than the noise power uncertainty itself. The noise power calibration in real time improves the immunity against the _SNR_ wall phenomenon [3]. In this paper, we investigate the GD noise power calibration effects on the _SNR_ wall problem in coarse spectrum sensing for CR network systems based on antenna array and we define the GD sample complexity under the noise power uncertainty. The complementary receiver operating characteristic (ROC) and sample complexity of the ED, MF, and GD are compared under the same initial conditions for different uncertainty parameters. The real scenario of simulation demonstrates that the GD is able to alleviate the _SNR_ wall problem and achieve the low probability of error in comparison with the conventional ED. The reminder of this paper is organized as follows. Section 2 presents the system model and the GD test statistics. Section 3 delivers the GD signal detection performance under the noise power uncertainty. The real scenario simulation results are discussed in Section 4. The concluding remarks are presented in Section 5.
# A Beam Hopping Scheme Based on Adaptive Beam Radius for LEO Satellites
## 5 Discussion  
According to the simulation results, the proposed algorithm has both advantages and disadvantages. One drawback of adopting the proposed algorithm is that when the number of beams is insufficient or the number of users is too large, the overall system throughput compared to using traditional algorithms may decrease. This is because the proposed algorithm em-ploys wide beams with slow gain attenuation on the one hand, and on the other hand, to reduce computational complexity, it uses more wide beams, resulting in high potential in-terference and low transmission gain. Fortunately, due to thorough research before system deployment and the ease of deployment and technological upgrades of micro satellites in orbit, issues such as an excessive number of users and insufficient satellite beam numbers are generally less likely to occur. Nevertheless, for the algorithm to be more robust, I suggest that in the future, artificial intelligence algorithms such as deep learning can be used on the basis of this algorithm to intelligently utilize various types of beams.  
The advantage of adopting the proposed algorithm is that it significantly reduces the overall system latency and slightly improves the system throughput in many scenarios. This is achieved by effectively utilizing various beams to provide differentiated services for diverse user distributions. Additionally, the low computational complexity makes it easier to implement in practical applications.  
Conceptualization, J.C. and Q.J.; methodology, J.C.; software, J.C.; validation, J.C.; formal analysis, J.C.; investigation, J.C.; resources, J.C.; data curation, J.C.; writing--original draft preparation, J.C. and M.Y.; writing--review and editing, J.C.; visualization, J.C.; supervision, Q.J.; project administration, Q.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Civilian aerospace advanced research program, grant number D030101.  
Not applicable.  
The original contributions presented in the study are included in the article material, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Results and Discussion  
### Simulation Results of the Demand Scenarios  
The selection of the demand pattern scenarios responds to the need of the decision-maker for production scheduling during the typical demand for green coffee that declines in the September-December period and peaks in the March-June period. The planning horizon considered was h = 13 weeks, with a length of each planning period of working hours per week. The following three demand patterns were considered.  
Increasing, with demand skewed towards the end of the planning horizon, 60 customer orders.  
Decreasing, with demand skewed towards the beginning of the planning horizon, 84 customer orders.  
Unimodal, where demand peaks in the middle of the planning horizon and falls under the available capacity on the first and last days of the horizon, 63 customer orders.  
Figures 6-8 show the initial aggregate production schedule and the estimated production results along with the cumulative aggregate production and cumulative aggregate demand for each case for the increasing demand pattern, the unimodal demand pattern, and the decreasing demand pattern, respectively. The negative values in these figures indicate the tardy demand.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Replicate** & **Good Green Coffee Beans (Kilograms)** \\ \hline
1 & 27,900.2004 \\
2 & 27,907.4386 \\
3 & 27,905.3017 \\
4 & 27,889.3183 \\
5 & 27,895.6686 \\
6 & 27,853.6233 \\
7 & 27,934.5803 \\
8 & 27,896.9872 \\
9 & 27,863.0423 \\
10 & 27,889.3104 \\ Average & 27,894.7358 \\ Standard deviation & 24.9877 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Simulation results of the pilot replicas.  
Figure 6: Initial production schedule and estimated production results for increasing demand.  
Figure 7: Initial production schedule and estimated production results for unimodal demand.  
For the initial aggregate production schedule, all materials are assumed to be available at the beginning of the planning horizon, while for the estimated production results, delay in the arrival of raw materials occurred.  
The customer service level results for the increasing demand pattern are as follows (Figure 6): no tardy orders for the initial production schedule, i.e., 100% service level, and two tardy orders for the estimated production results, a 96% service level.  
Figure 8: Initial production schedule and estimated production results for decreasing demand.  
Regarding the unimodal demand pattern, the results are five tardy orders for the initial production schedule, a 92% service level, and nine tardy orders from the estimated production results, an 85% service level. Figure 7 shows that the aggregate production is best leveled over time for the unimodal demand pattern. The study of [PERSON] [54] for production scheduling in make-to-order manufacturing systems found the aggregate production is best leveled over time for the increasing demand pattern.
## 3 Dataset and Methods
### Methods
#### 3.2.4 Artificial Neural Network (ANN)  
Radial basis function network (RBFN) technique is implemented using seismic and well data to map porosity of HPHT zone in the Sepat Field.  
The RBFN technique was first introduced by [PERSON] in 1987 [28] and first applied by [PERSON] et al. in 1994 [29]. Several works have shown the effectiveness of the RBFN technique in reservoir characterisation [30; 31].  
RBFN is a feed-forward neural network where the Gaussian bell curve is the basis function. In RBF, each training point has a weight, and the weights are multiplied by Gaussian functions of the attribute distances. A sigma parameter controls these Gaussian functions. The fitting function is \(y=\sum\limits_{i=1}^{n}w_{i}\varphi_{i}\). Note that g is used instead of \(\varphi\) in some publications. The non-linear functions jj are called \"basis\" functions.  
Mathematically, the basis function is:  
\[\varphi_{ijk}=\exp\left[-\frac{d_{jn}^{2}}{\sigma^{2}}\right] \tag{5}\]  
where, \(d_{nj}=\left|x_{n}-s_{j}\right|\) and \(\sigma\) is the sigma parameter, a smoothing parameter. \(\frac{d_{jn}^{2}}{\sigma^{2}}\) is the scaled distance between the point we are estimating, \(x_{n}\) and the training point, \(s_{j}\). The smoothing parameter does the scaling. The computation of the predicted values can be written as:  
\[y(x_{n})\sum\limits_{j=2}^{N}w_{i}\varphi_{nj} \tag{6}\]  
N = 1,2...  
M and \(x_{n}\) are unknown. M is the number of unknown values. RBFN develops a spatial relationship between the seismic attributes and the training data and works in M-dimensional space. The weights are pre-computed using a generalised matrix inversion of the basis functions weighted by the training values.
###### Abstract  
Accurate recognition of agricultural pests is crucial for effective pest management and reducing pesticide usage. In recent research, deep learning models based on residual networks have achieved outstanding performance in pest recognition. However, challenges arise from complex backgrounds and appearance changes throughout the pests' life stages. To address these issues, we develop a multi-task learning framework utilizing the discriminative attention multi-network (DAMNet) for the main task of recognizing intricate fine-grained features. Additionally, our framework employs the residual network-50 (ResNet-50) for the subsidiary task that enriches texture details and global contextual information. This approach enhances the main task with comprehensive features, improving robustness and precision in diverse agricultural scenarios. An adaptive weighted loss mechanism dynamically adjusts task loss weights, further boosting overall accuracy. Our framework achieves accuracies of 99.7% on the D0 dataset and 74.1% on the IP102 dataset, demonstrating its efficacy in training high-performance pest-recognition models.  
deep learning; pest recognition; multi-task learning Article