esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparing GLIE Fluctuations in Live Jurkat and Primary T Cells
The same set of data of Jurkat and lymphocytes results (described in Figure 12) was used for both training and testing of the classifier. Following these results, it can be seen that GLIE parameters better differentiate between  
Figure 13: SICS fluctuation parameters in primary T cells (n = 219) in comparison to Jurkat cells (n = 161). Parameter \(a\) values (**a**) or parameter \(b\) values (**b**) or parameter _SSE_ values (**c**) or parameter _SA_ values (**d**) in primary T cells vs. Jurkat cells. (**e**) Receiver-Operating-Characteristic (ROC) curves for the differentiation of Jurkat cells from primary T cells utilizing binary logistic regression. These curves are based on the results of SICS or GLIE fluctuation parameters in panels **a–d** and Figure 12 panels **a–e**. Error bars in panels **a–d** are SEM, and _p_-values are presented in the corresponding panels. primary T cells and malignant Jurkat cells. Notably, in a clinical setup for malignancy diagnosis, typically hundreds of cells are examined. Thus, the AUC of 0.92 and percentage of correct classifications of 86% for a single cell examination will produce much higher discriminating results when more cells are examined. For example, moderately increasing the number of cells from 1 to 10 and averaging the results provides an AUC of 0.997 and 95% of correct classifications. It was important to verify if other physiological condition other than malignancy such as activation or proliferation and differentiation could also raise GLIE fluctuations parameter values. To verify this subject primary T cells were stimulated with human anti CD3 or with PMA while incubated on the microscope stage (as further detailed in the Materials and Methods section). The results of GLIE fluctuations parameters in these cells before and after stimulation with anti-CD3 or stimulation with PMA are summarized in Figure 14.  
Figure 14: GLIE fluctuation parameters in primary T cells before and after stimulation with anti-CD3 or with PMA. (**a**) GLIE fluctuation parameters before and after incubation with anti-CD3 (n = 193). (**b**) GLIE fluctuation parameters before (n = 131) and after (n = 102) incubation with PMA. Error bars are SEM; \(p\)-values are presented in the corresponding panels. As can be seen in Figure 14, GLIE parameters (except for \(SSE\)) were significantly lower after both cellular stimulations. For control, GLIE fluctuation parameters were evaluated in fixed Jurkat cell. Their results were very low even in comparison to primary T cells (Supplementary Materials Figure S2). The results so far suggest that GLIE fluctuation parameters could accurately differentiate cellular malignancy from a wide range of cellular physiological conditions that include activation, proliferation and differentiation.
# Application of Hyperspectral Imaging for Assessment of Tomato Leaf Water Status in Plant Factories
## 3 Results and Discussion
### Autocorrelation Coefficient of Wavelength Bands  
Hyperspectral data include extensive image and spectral information. However, not only do these spectra contain a considerable amount of redundant information, but a strong autocorrelation exists among different wavelength bands. To reasonably increase the amount of useful information  
Figure 7: PLS regression for wavelength and water status. (**a**) RAW and WC; (**b**) RAW and MC; (**c**) LOG and WC; (**d**) LOG and MC; (**e**) DIFF and WC; (**f**) DIFF and MC. RMSE: root mean squared error. R-Square: coefficient of determination (blue denotes the calibration and red represents the validation).  
and optimum index factors for modeling, a spectral autocorrelation calculation was employed to assist in selecting the characteristic spectra (Figure 8). For RAW and LOG, two strong autocorrelation zones are observed in the wavelength bands of 950-1350 nm and 1400-1700 nm. In contrast with the autocorrelation coefficient of RAW, the inverse-logarithm processing enhances the correlation between the wavelength bands. Nevertheless, first-order differential processing decreases the correlation between wavelengths in most wavelength bands.  
Figure 8: _Cont._
# Robust Additive Value-Based Efficiency Analysis
## 5 Case Study concerning Evaluation of Healthcare System in Poland
### Discussion  
Three provinces, namely POM, WLKP, and LBU, prove to be the best in the comprehensive analysis of the healthcare system in Poland. However, the results for the three subcategories differ. This section discusses the conclusions that can be derived from the cross-category analysis, indicating various provinces' strong and weak points. For illustrative purposes, we refer to three example vivodyedships representing the top (WLKP), middle (PKR), and bottom (OPO) performers in the hierarchy's root.  
When it comes to WLKP, it proves to be the best at the inhabitants' health improvement level; it is necessarily preferred by POM and LBU for the financial management level, and its ranks are between fifth and twelfth, given the consumer satisfaction perspective. This suggests that despite the decent quality of medical services, there is room for improvement in patient satisfaction and financial management. In particular, managers can conduct some training in soft skills for medical staff to improve consumer assessment of the healthcare system.  
PKR is the best province regarding consumer satisfaction, as confirmed by its favorable expected distance and rank. However, when considering the comprehensive results and conclusions drawn for the remaining two categories, it performs poorly. Its expected rank is greater than 13 for inhabitants' health improvement and financial management categories, while in the hierarchy's root, its average rank is 12.024. Hence, the healthcare system managers in this province should focus on improvements in medical decisions and financial management. In turn, other provinces can consider the healthcare system in PKR as the benchmark of proper communication with the consumers.  
Such a cross-category analysis can serve as the basis for designing an improvement plan for provinces that proved to be relatively bad in the comprehensive analysis. In particular, the analysis of OPO's poor performance points to the inhabitants' health improvement category and financial management. It is the worst province for the former perspective for 99% of weight vectors, and its expected rank is only 12.549 for the latter category. Hence, managers should first focus on improving inhabitants' health, due to the high importance of this category in the analysis. Then, they should design a plan for advancing financial management.
# Low-Resolution Target Detection with High-Frequency Information Preservation
###### Abstract  
In the absence of high-frequency visual observation, low-resolution (LR) targets (e.g., objects, human body keypoints) are intrinsically difficult to detect in unconstrained images. This challenge can be further exasperated by typical downsampling operations (e.g., pooling, stride) of existing deep networks (e.g., CNNs). To tackle this challenge, in this work, we introduce a generic, _High-Frequency Information Preservation_ (**HFIP**) block as a replacement for existing downsampling operations. It is composed of two key components: (1) the decoupled high-frequency learning component, which extracts the high-frequency information along the vertical and horizontal directions separately, and (2) the dilated frequency-aware channel correlation component, which decomposes the input feature map into multiple smaller ones in a dilated manner, concatenates them by channel, and then correlates the combined channels in the frequency space. Our module can generally be integrated into existing network architectures for target detection (e.g., YOLO, HRNet). Extensive experiments on low-resolution human pose estimation and object detection tasks show that our HFIP technique can generally boost the performance of state-of-the-art detection models significantly, e.g., improving the object detection accuracy of YOLOv5s by an absolute margin of 3.30% in mAP under a resolution of 640 \(\times\) 640 compared to the COCO benchmark.  
low-resolution object; downsampling operation; high-frequency; target detection +
Footnote †: journal: Applied sciences
## 5 Performance with Hardware Impairments
### Performance with Combiner Losses
However, for the \(P=16\) case, the gain in average cell throughput is reduced from 10% with \(L=1\) dB to more than 45% with \(L=3\) dB. Furthermore, in the latter case, the degradation for the users in the cell edge is of around a factor of 4, which reinforces the conclusion that no more than eight parallel RF chains are recommended in the ABF scheme when \(L=3\) dB. All the results show the great effect that the combiner losses have on the performance of the algorithms, especially when the number of RF chains gets higher. Finally, it is worth noting that the optimum number of RF chains is different for the users in LoS and NLoS conditions. In fact, for \(L=1\) dB, better results are obtained for both types of users when increasing the number of RF chains, as the user multiplexing offered by the massive MIMO setup compensates the degradation due to combining losses. On the other hand, for \(L=3\) dB, LoS users achieve the best performance with eight RF chains, while NLoS users get higher throughputs when using only four RF chains. In the end, the percentage of LoS/NLoS users in the system together with the combiner losses will determine the optimum number of active RF chains for ABF.
## 4 Materials and Methods
### Plasmid Construction, Transformation, and Screening of Mutants
[46] reported, for the YFP, mVnVenus, the excitation was at 514 nm and the emission was between 520 and 550 nm; for the CFP, mCerulean3, the excitation was at 458 nm and the emission was between 460 and 490 nm; and for the RFP, mRuby2, the excitation was at 561 nm and the emission was between 590 and 620 nm. A microplate reader (Varioskan Flash, Thermo Scientific, USA) was used to detect the YFP, CFP and RFP fluorescence signals to verify the expression of the target constructs. The original fluorescence abundances were corrected with the cell density to make the data comparable. The fluorescence imaging was carried out using a confocal laser microscope with respective filter sets and the excitation and emission wavelengths previously described for each reporter (LEICA-TCS SP8(A7-345), Leica, Germany). The transformants were further confirmed by the genomic PCR and qRT-PCR methods (Supplementary Figure S4). The expression of the ScCK, AtIPK, IDI, and MsLS reporter fusions to full length was confirmed via SDS PAGE and western blotting using \(\alpha\)-StrepII tag-HRP linked antibody (IBA Lifesciences). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Construct Name** & **Vector** &
\begin{tabular}{l} **Antibiotic Resistance** \\ **in** _C. reinhardtii_ \\ \end{tabular} & **Source** \\ \hline pOpt\_CTP\_mVenus\_Hyg & V1 & hygromycin B & This study \\ pOpt\_CTP\_mCerulean3\_Paro & V2 & paroamycin & This study \\ YFP\_ScCK & & hygromycin B & This study \\ CFP\_AtIPK & & paroomycin & This study \\ ScCK\_YFP & & hygromycin B & This study \\ AtIPK\_CFP & V4 & paroomycin & This study \\ ScCK\_AtIPK\_YFP & V5 & hygromycin B & This study \\ AtIPK\_ScCK\_YFP & V6 & hygromycin B & This study \\ ScCK\_YFP\_AtIPK & V7 & hygromycin B & This study \\ AtIPK\_CFP\_ScCK & V8 & paroomycin & This study \\ pOpt\_CTP\_mRuby2\_Ble & V9 & zecoin & This study \\ pOpt\_CTP\_aadA & V10 & spectinomycin & This study \\ MsLS\_aadA & & spectinomycin & This study \\ IDI\_RFP & V12 & zecoin & This study \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Genetic constructs used in this study.
## 1 Introduction
[24] used the Kano model to evaluate customer satisfaction factors and classified them into different categories based on customer feedback. Although this evaluation method helps companies understand customer needs and improve their products and services to increase customer satisfaction, it lacks quantitative indicators to reflect the magnitude of the impact of each indicator on customer needs. [PERSON] et al. [25] used a text mining approach to analyze online customer reviews (OCR). This approach helps companies understand customer opinions and sentiments, but it faces challenges and limitations in terms of data quality, semantic understanding, and technical support. Therefore, in practical applications, it is crucial to decompose reviews into representative factors to ensure the accuracy and validity of the analysis results. In summary, improving customer satisfaction through core factors is crucial for communication operators. Through continuous improvement of core factors and innovative services, operators can win the favor of subscribers, enhance customer loyalty and gain an advantage in the fierce market competition to achieve sustainable and steady development and promote the development of the communication industry [26]. There are fewer related research studies and a lack of high-level quantitative models and reliable evaluation systems. In order to establish a reliable core evaluation system, as well as to achieve high-precision prediction of satisfaction, a customer satisfaction scoring prediction based on the combination of secondary dimensionality reduction and machine learning is established. Firstly, based on data cleaning and systematic clustering, the preliminary dimensionality reduction of user-satisfaction-influencing factors is carried out to establish a preliminary user satisfaction evaluation index system. Then, based on exploratory factor analysis, the preliminary evaluation indexes are downscaled twice, and the core evaluation index system of user satisfaction is established. Finally, the data are re-cleaned by removing the unreasonable scores of the core influencing factors and then predicted using the gradient-boosted decision tree (GBDT) algorithm. This study can provide communication operators with core influencing factors with less information redundancy, and operators can improve the core influencing factors and carry out high-precision prediction after improvement to test the improvement effect. This paper is organized as follows. Section 2 briefly describes the schematic framework of systematic clustering, exploratory factor analysis and the GBDT algorithm. Section 3 describes the establishment of the core indicator system through the combination of systematic clustering and exploratory factor analysis. Section 4 describes the implementation of the GBDT algorithm prediction under the established core indicator system. Section 5 summarizes the work.
# Constructing Condition Monitoring Model of Harmonic Drive
## 2 Related Work  
### Log Mel Spectrogram  
The Log Mel spectrogram, commonly used for speech emotion detection and acoustic scene analysis [2,3] and machinery anomaly detection tasks [4], is a spectrogram based on the human ear's perception of sound, rather than linear frequencies.  
After the sound sample is processed by Short Time Fourier Transform (STFT), the spectrum is obtained; the spectrum energy is then passed through the Mel filter to obtain the Mel Spectrum. The frequency \(f\) is the Mel Frequency, as shown in Equation (1), and finally, the logarithmic operation of the result is obtained to give the Log Mel spectrogram.  
\[F_{mel}(f)=2595\times log_{10}\Big{(}1+\frac{f}{700}\Big{)} \tag{1}\]
## 3 Methodology
### Model Execution  
#### 3.2.1 Basic and Advanced CA Modeling Tools  
The _Model Execution_ tool group contains the _Basic CA_ and _Advanced CA_ tools (Figure 6), with key differences outlined in Table 2. The _Basic CA_ tool supports rapid implementation of binary models by including nested _Model Parameterization_ functions within a single ArcGIS DockPane (Figure 6a). The _Advanced CA_ tool supports binary or multi-class models, relying on a _Transition Rule_ file to parameterize simple to complex model behaviors (Figure 6b). Each time a _Basic_ or _Advanced CA_ modeling tool is run, a _Model Parameter Report_ is generated and saved to the output directory. The _Model Parameter Report_ supports interpretation and communication of model behavior. The report includes information about model execution time, datasets used, output directory information, the number of timesteps, and a table of linguistic transition rule explanations that can be copied or modified for scientific communications.
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 2 Overall Design
### Genetic Algorithm Design
#### 2.3.1 Data Sources  
The solar altitude angle (\(\alpha\)) and azimuth angle (\(\gamma\)) data used in this study were derived from the day shuttle almanac written by Professor [PERSON]. To ensure sample representativeness, an office model with blinds was using DIAlux Evo 10.0 software, and the angles of slats were obtained following the blinds' control rules. In this paper, \(\alpha\) and \(\gamma\) data at 11 time points between sunrise and sunset during the 24 solar terms from the years 2021, 2022, and 2023 in Dalian, Liaoning Province were shown. A total of 651 data sets of data were used as training samples for the GA\(-\)BP prediction model to improve the model's prediction speed and accuracy. An additional 24 data sets, representing randomly selected values for \(\alpha\), \(\gamma\), and the angle of slats at different times throughout the year, were used as test samples for network training. The subset data are shown in Table 3.
# Robust Optimal Tracking Control of a Full-Bridge DC-AC Converter
###### Abstract  
This paper develops a full-bridge DC-AC converter, which uses a robust optimal tracking control strategy to procure a high-quality sine output waveshape even in the presence of unpredictable intermissions. The proposed strategy brings out the advantages of non-singular fast convergent terminal attractor (NFCTA) and chaos particle swarm optimization (CPSO). Compared with a typical TA, the NFCTA affords fast convergence within a limited time to the steady-state situation, and keeps away from the possibility of singularity through its sliding surface design. It is worth noting that once the NFCTA-controlled DC-AC converter encounters drastic changes in internal parameters or the influence of external non-linear loads, the trembling with low-control precision will occur and the aggravation of transient and steady-state performance yields. Although the traditional PSO algorithm has the characteristics of simple implementation and fast convergence, the search process lacks diversity and converges prematurely. So, it is impossible to deviate from the local extreme value, resulting in poor solution quality or search stagnation. Thereby, an improved version of traditional PSO called CPSO is used to discover global optimal NFCTA parameters, which can preclude precocious convergence to local solutions, mitigating the tremor as well as enhancing DC-AC converter performance. By using the proposed stable closed-loop full-bridge DC-AC converter with a hybrid strategy integrating NFCTA and CPSO, low total harmonic distortion (THD) output-voltage and fast dynamic load response are generated under nonlinear rectifier-type load situations and during sudden load changes, respectively. Simulation results are done by the Matlab/Simulink environment, and experimental results of a digital signal processor (DSP) controlled full-bridge DC-AC converter prototype confirm the usefulness of the proposed strategy.  
full-bridge DC-AC converter; non-singular fast convergent terminal attractor (NFCTA); chaos particle swarm optimization (CPSO); trembling; total harmonic distortion (THD)
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 3 The Proposed Method
### Embedding Learning
#### 3.3.2 Joint Modeling KG and Conclusions of Soft Rules  
To model the conclusion label as a 0-1 variable, based on the current KGE model's scoring function, we follow ComplEx and use the function \(F(\cdot)\) defined in Equation (2) or Equation (3) as the scoring function for conclusion triples:  
\[ \begin{split} S_{i}&=\sigma(F(h_{i},r_{i},t_{i})),\\ &(h_{i},r_{i},t_{i})\in\mathcal{C}_{f},\end{split}  \tag{6}\]  
where \(\mathcal{C}_{f}\) is the set of conclusion triples derived from rule \(f\). Aiming to regularize this scoring function so that it approaches 0 or 1 and to distinguish between true and false conclusions, we use a quadratic function with a symmetry axis of 0.5. Therefore, the conclusion score is the smallest when close to 0 or 1. Therefore, we define the deterministic conclusion loss \(L_{dc}\) as follows:  
\[L_{dc}=-\frac{1}{\left|\mathcal{C}_{f}\right|}\sum_{(h_{i},r_{i},t_{i})\in \mathcal{C}_{f}}\|S_{i}-0.5\|^{2}, \tag{7}\]  
According to the definition of rule confidence in [10], the confidence of a rule \(f\) in a KB \(\mathcal{G}\) is the proportion of true conclusions among the true conclusions and false conclusions. Therefore, we can define the confidence loss of a rule as follows:  
\[L_{rc}=\left\|\frac{1}{\left|\mathcal{C}_{f}\right|}\sum_{(h_{i},r_{i},t_{i}) \in\mathcal{C}_{f}}S_{i}-c_{f}\right\|^{2}, \tag{8}\]where \(c_{f}\) is the PCA confidence of rule \(f\). Therefore, the loss of the conclusions of all the rules \(L_{ac}\) can be defined as follows:  
\[L_{ac}=\frac{1}{|\mathcal{F}|}\sum_{f\in\mathcal{F}}(L_{dc}+L_{rc}), \tag{9}\]  
where \(\mathcal{F}\) is the set of all rules. To learn the KGE and rule conclusions at the same time, we minimize the global loss over a soft rule set \(\mathcal{F}\) and a labeled triple set \(\mathcal{L}=(x_{l},y_{l})\) (including negative and positive examples). The overall training objective of Iterlogic-E is  
\[\min_{\theta}\frac{1}{|\mathcal{L}|}\sum_{(x_{l},y_{l})\in \mathcal{L}}L(-f(x_{l})\cdot y_{l})\] \[\qquad+\frac{1}{|\mathcal{F}|}\sum_{f\in\mathcal{F}}(-\frac{1}{| \mathcal{C}_{f}|}\sum_{(y_{l},y_{l},z_{l})\in\mathcal{C}_{f}}\|S_{i}-0.5\|^{2}\] \[\qquad+\left\|\frac{1}{|\mathcal{C}_{f}|}\sum_{(x_{l},y_{l},z_{l} )\in\mathcal{C}_{f}}S_{i}-c_{f}\right\|^{2}). \tag{10}\]  
where the \(f(\cdot)\) function denotes the score function, and \(L(x)\) is the Softplus activation function. In Algorithm 1, we detail the embedding learning procedure of our method. We further impose \(l_{2}\) regularization on embedding \(\Theta\) to avoid overfitting. Following [18; 47], we also imposed nonnegative constraints (NNE) on the entity embedding to learn more effective features. ```
Input: Knowledge graph \(\mathcal{T}=\{(e_{i},r_{k},e_{j})\}\), soft logical rules \(\mathcal{F}=\{(f_{p},c_{p})\}\), the number of iterative learning steps \(M\). Output: Relation and entity embeddings \(\Theta\) (\(\Theta\) consists of two matrices: one containing the vector representation of all entities, and the other containing the vector representation of all relations).
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 4 Proposed Protocol
### Route Discovery  
The proposed protocol (CAEL) initiates the route discovery phase when node A sends an RREQ packet to all nearby nodes to send data packets to node H but does not have routing information for that specific node inside the trilateral zone. The node analyzes its routing table when it receives an RREQ packet to identify the reverse route to the source node. If a route to the source node already exists in the routing table, it is updated; otherwise, a reverse route is created. To find the shortest route to reach the target node, the receiving node first calculates LETs between RREQ sending nodes and its current node, and then selects the shortest one between the current node and its source node. After that, it increases the value of the hop count and transmits the RREQ packet to neighbor nodes. Intermediary nodes may receive several copies of the same RREQ message from different surrounding nodes, but it just drops the request. Intermediary nodes produce RREP messages if any of them have an active route to the target node, and it selects the most reliable route if any of the intermediate nodes are targets themselves.