esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis and Comparison of the Model Performance
### Comparative Analysis of Multi-Types Model
The \(R^{2}\) values for all models under different inputs are shown in Figure 13. All models exhibit consistent performance in both training and testing processes across DS1-DS7 databases. For FDNN, DNN, and XGBoost models, the impact of different data types on model performance improvement varies. The training results of the database consisting of three types of data did not significantly outperform the database consisting of two types of data. Increasing the data types rather reduces the model performance, such as in DS4 and DS1. Performance across all models is superior in the DS7 dataset compared to other datasets, indicating that the fusion of multiple datasets synergistically enhances model predictive performance. That is because incomplete information can create conflicts that reduce the effectiveness of the model. Richer information will neutralize the conflicts and convert them into model performance boosts. In engineering scenarios, various data types often contain overlapping information due to the influence of human factors. Therefore, the improvement in model training effectiveness through the combination of multiple data sources into a single database is not a simple additive effect. Figure 12: \(MAE\) of all models under different datasets. Figure 13: \(R^{2}\) of all models under different datasets.
###### Abstract  
In recent decades, frequent compound dry and hot events have posed a great threat to humans and the ecological environment, especially in Inner Mongolia, which has arid and semi-arid characteristics. In this study, monthly temperature and precipitation data from 115 meteorological stations in Inner Mongolia from 1982 to 2020 were used to establish a standardized dry and hot index (SDHI). Theil-Sen median trend analysis, Mann-Kendall test, partial correlation analysis, and stepwise multiple regression models were used to characterize the changes in compound dry and hot events and the normalized difference vegetation index (NDVI) from 1982 to 2020, and the relationship between the SDHI and NDVI was quantitatively evaluated. The results showed that the overall SDHI values in Inner Mongolia showed a significant decrease at a rate of 0.03/year from 1982 to 2020, indicating an increase in the severity of compound dry and hot events. NDVI values showed a significant increasing trend and NDVI showed mutated 2001. Among the grassland vegetation types, SDHI and NDVI trends were more significant in forests, and meadow steppe, desert steppe, and desert were more susceptible to compound dry and hot events, and forests had the greatest severity of compound dry and hot events. The results of the partial correlation analysis showed that the average value of the partial correlation coefficient between the SDHI and NDVI was 0.68, and the area of positive correlation was 84.13%. Spatially, it showed strong response characteristics in the middle and gradual weakening towards the east and west sides. The correlation between NDVI and climatic conditions varied greatly in different vegetation areas. The forest area is most sensitive to the influence of temperature, and the desert steppe area is most affected by compound dry and hot events. The overall vegetation growth in Inner Mongolia was most affected by temperature conditions, followed by compound dry and hot conditions, and the influence of drought conditions was the least significant. The results of the relative importance analysis confirmed this. The research results provide a more detailed understanding of compound dry and hot events in arid and semi-arid regions and useful insights and support for ecological protection.  
compound dry and hot event; standardized dry and hot index; normalized difference vegetation index; inner Mongolia +
Footnote †: journal: Remote Sensing  
202214397771463977
## 3 Results
A possible trigger was identified in 25 (37.9%) of the flares, including upper respiratory tract infections (15.2%), drugs, generally antibiotics (10.6%), stressful life events (7.6%), corticosteroids withdrawal (3.0%), hypocalcaemia (1.5%), and pregnancy (1.5%). The flare needed hospitalization when complicated by infection (13.6%), hepatitis (4.5%). In particular, a case of COVID-19 pneumonia was fatal in one patient aged 84 with ischemic cardiopathy. No differences were found in severity, localization triggers, complications, and flare patterns between patients with history of plaque psoriasis versus those without. A mean of 2.4 \(\pm\) 1.1 different systemic treatments (range 1-5) were prescribed to manage the flares. In this series, the most common prescribed treatments were systemic \begin{table}
\begin{tabular}{c c} \hline \hline
**Disease Severity** & \\ \hline GPPASI, mean \(\pm\) SD & 22.9 \(\pm\) 13.5 \\ GPPGA, mean \(\pm\) SD & 3.3 \(\pm\) 0.8 \\ BSA, mean \(\pm\) SD & 47.9 \(\pm\) 29.1 \\ VAS itch, mean \(\pm\) SD & 6.2 \(\pm\) 3.3 \\ VAS pain, mean \(\pm\) SD & 6.2 \(\pm\) 3.0 \\ DLQI, mean \(\pm\) SD & 21.0 \(\pm\) 4.9 \\ Fever (>38 \({}^{\circ}\)C) & 26 (39.4) \\ Leucocytosis (WBC \textgreater{} 12 \(\times\) 10\({}^{9}\)/L) & 39 (59.1) \\ \hline \hline
**Disease Localization** & \\ \hline Trunk & 63 (95.5) \\ Superior limbs & 61 (92.4) \\ Inferior limbs & 56 (84.8) \\ Folds & 40 (60.6) \\ Head-neck & 38 (57.6) \\ Genital area & 32 (48.5) \\ Nails & 22 (33.3) \\ Oral mucosa & 7 (10.6) \\ \hline
**Triggers** & \\ \hline Infections & 10 (15.2) \\ Drugs & 7 (10.6) \\ Stressful events & 5 (7.6) \\ Corticosteroids withdrawal & 2 (3.0) \\ Hypocalcaemia & 1 (1.5) \\ Pregnancy & 1 (1.5) \\ \hline \hline
**Complications** & \\ \hline Infections & 9 (13.6) \\ Hepatitis & 3 (4.5) \\ Death & 1 (1.5) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Severity, localization, triggers, and complications of GPP flare. corticosteroids alone in 46 (69.7%) or in combination with systemic retinoids in 24 (36.4%) cases. The last common treatment lines were: izekizumab (22.7%), risankizumab (16.6%), secukinumab (19.7%), adalimumab (9.1%), guselkumab (3%), tildrakizumab (3%), and brodolumab (3%), followed by few cases treated with non-biologic treatments (cyclosporine and methotrexate), NB-UVB and PUVA phototherapy. Such treatments were generally in combination with systemic corticosteroids and retinoids and associated with short hospitalization stay and complete resolution of the pustules. In 44 out of 66 (66.7%) included patients, the average treatment time course was 4.5 \(\pm\) 1.2 weeks (mean \(\pm\) SD).
# A Novel Implicit Neural Representation for Volume Data
## 3 Methodology
### Peak Signal-to-Noise Ratio  
To compare the quality of the reconstructed image with the original one, we use the Peak Signal-to-Noise Ratio (PSNR) measurement technique. It is given for grey-scale images by the equation:  
\[PSNR=10.log_{10}\Big{[}\frac{I^{2}}{MSE}\Big{]} \tag{2}\]  
where MSE is the mean square error which is given by the equation below:  
\[MSE=\frac{1}{N.M}\sum_{i=1}^{N}\sum_{j=1}^{M}(y_{ij}-\overline{y}_{ij}) \tag{3}\]  
\(I\) is the maximum intensity value of the image, which is 255 in this work. \(N\) is the number of rows and \(M\) is the number of columns. \(y\) is the original image and \(\overline{y}\) is the reconstructed image.
## 3 Cognitive Mobile BTS
### Idle Mode Control  
If there are no ongoing communications between the MSs and the BTS, the procedure will rely exclusively on information available at the cognitive BTS. Therefore, cell reselection will be based on the channel occupancy reports from the SSM.  
In this case, if an interfering source is affecting the downlink, MSs communications may end with a radio link failure. A downlink signaling failure takes place after the expiration of the signaling failure counter \(DSC\)[33]. When the MS camps on a cell, it is initialized to \(\lfloor 90/N\ +\ 0.5\rfloor\), where \(N\) is the value of the BS_PA_MFRMS cell parameter [35]. Later, if a paging message is successfully decoded, \(DSC\gets min(DSC+1,\lfloor 90/N\ +0.5\rfloor)\), otherwise \(DSC\gets DSC-4\). A downlink failure occurs when \(DSC\leq 0\). As we discuss in [21] the failure time is 5.296 s if all the messages are blocked by the interference source. Indeed, this time actually does not depend on \(N\) since BS_PA_MFRMS also controls the frequency of the paging messages.  
When a radio link failure occurs, the MSs will invoke their basic cell search procedure until the interference stops and they are reconnected to the cognitive BTS. Let us remark that no transmission will be emitted by the MSs during reconnection. However, if the interference does not disappear, the MSs will not be able to reconnect, and the BTS will be unaware since messages are not exchanged during reconnection.  
We propose the following mechanism to minimize the impact of this problem: (i) force periodic communications from the MSs to the cognitive BTS and (ii) perform channel reselection if a given ratio of previously registered MSs do not transmit periodic messages. In GSM, periodic communications can be forced through the \"Location Update\" Procedure.  
MS signaling in Mobility Management (MM) idle state is described in [33]. An MS in MM IDLE state initiates signaling (i) at incoming or outgoing call initiation; (ii) when timers T3211, T3213, or T3212 expire; or (iii) when a new location area is entered. The BTS does not control cases (i) or (iii). Case (i) includes timers that trigger a \"Location Update\" Procedure. Among these timers, T3212 is used after termination of MM service or MM signaling, that is, to perform periodic updating. This timer is configured with values from 1 to 255 /10 h [33]. Therefore, by setting the minimum value, the updating procedure will be performed each 6 minutes. If a given ratio (20% in our experiments) of MSs do not perform the \"Location Update\" Procedure, channel reselection is triggered as described above.  
This clearly mitigates the problems related to persistent noise sources in the downlink channel. Note also that the signaling load associated with selecting such a short period is not significant, since the cognitive BTS will usually serve few MSs and the \"Location Update\" procedures of the MSs are asynchronous. Finally, the timeout value for T3212 is broadcast in the System Information Type 3 message on the BCCH, in the control channel description information element.
# Fully Open-Source Meeting Minutes Generation Tool
## 2 Related Work in Literature
### Optical Character Recognition (OCR)  
Extracting text data enables a machine to understand the information when subjected to visual data. _OCR_ utilizes the pattern recognition to interpret the characters in an image, enabling the digitization and extraction of texts for various applications.  
Recent OCR models leverage machine learning, resulting in varying performance based on datasets and architectures. _EasyOCR_ combines _LSTM_ and _CNN_ for text recognition, offering a simple and fast architecture, but sacrificing accuracy, especially in complex extraction scenarios with varying font sizes [26]. _Tesseract_, developed by Google, provides broad language support and performs well on high-quality printed or written documents. However, its accuracy diminishes when handling low-resolution or compressed images, or text with small fonts [27]. In contrast, _PaddleOCR_ excels at extracting text from images with complex orientation, compression, or composition due to its multi-stage process and training on diverse image styles. However, this complexity results in higher computational costs, making it less suitable for real-time applications or systems with limited computational power [28].  
Given _Tesseract_'s struggles with small fonts and _EasyOCR_'s sensitivity to background variations, _PaddleOCR_ offers a more robust solution for extracting text from challenging images, including those that are slightly blurred or low-resolution. While its more complex architecture increases execution time, leveraging cloud computing can mitigate this drawback by enhancing computational power, making _PaddleOCR_ a suitable choice even in environments where performance is critical.
###### Abstract  
_Haematococcus pluvialis_ accumulates a large amount of astaxanthin under various stresses, e.g., blue light and salicylic acid (SA). However, the metabolic response of \(H\). _pluvialis_ to blue light and SA is still unclear. We investigate the effects of blue light and SA on the metabolic response in _H. pluvialis_ using both transcriptomic and proteomic sequencing analyses. The largest numbers of differentially expressed proteins (DEPs; 324) and differentially expressed genes (DEGs; 13,555) were identified on day 2 and day 7 of the treatment with blue light irradiation (150 umol photons m\({}^{-2}\)s\({}^{-1}\)), respectively. With the addition of SA (2.5 mg/L), a total of 63 DEPs and 11,638 DEGs were revealed on day 2 and day 7, respectively. We further analyzed the molecular response in five metabolic pathways related to astaxanthin synthesis, including the astaxanthin synthesis pathway, the fatty acid synthesis pathway, the heme synthesis pathway, the reactive oxygen species (ROS) clearance pathway, and the cell wall biosynthesis pathway. Results show that blue light causes a significant down-regulation of the expression of key genes involved in astaxanthin synthesis and significantly increases the expression of heme oxygenase, which shows decreased expression by the treatment with SA. Our study provides novel insights into the production of astaxanthin by \(H\). _pluvialis_ treated with blue light and SA.  
trancriptomics; proteomics; blue light; astaxanthin; fatty acid; heme; reactive oxygen species; cell wall; salicylic acid Article
# O-Band Multimode Interference Coupler Power Combiner Using Slot-Waveguide Structures
## 2 The 2 \(\times\) 1 MMI Power Combiner Design and Theoretical Aspects  
Figure 1a shows the refractive index profile of the slot waveguide for the two input tapers at z = 0 \(\upmu\)m for a 1310 nm operating wavelength. In this figure, the red color areas are SiN with an index of 1.994 over a thickness of 348 nm, pink areas are SiO\({}_{2}\) with an index of 1.444 over the cover and the slot regions. The slot thickness is 98 nm and as can be seen, it is located between the two slab layers (red color) shown in Figure 1a. Figure 1b shows a sketch of the MMI coupler for combining two laser sources and the taper input waveguides  
and the output at the x-z plane. In this figure, \(\rm W_{MM}\) is the width of the MMI coupler, \(\rm L_{MM}\) is the length of the MMI coupler and gap is the distance between the two input tapers.  
The beat length of the MMI combiner coupler is given by [15]:  
\[\rm L_{\pi}\approx\frac{4n_{eff}W_{e}^{2}}{3\lambda}, \tag{1}\]  
The beat length is a key parameter that sets the MMI coupler length as shown in Equation (3). In this equation, \(\lambda\) is the wavelength, \(\rm n_{eff}\) is the mode effective index, and We is the effective width of the MMI coupler, which depends on the polarization mode as can be seen in Equation (2), which is given by [15]:  
\[\rm W_{e}=W_{MMI}+\left(\frac{\lambda}{\pi}\right)\left(\frac{n_{cLad}}{n_{eff }}\right)^{2\sigma}\frac{1}{\sqrt{n_{eff}^{2}-n_{cLad}}^{2}}, \tag{2}\]  
where \(\sigma=0\) in case of TE mode and \(\sigma=1\) for TM mode polarization. Thus, for obtaining a more compact device it is better to select TE mode polarization. The MMI coupler length is given by [15]:  
\[\rm L_{MMI}\approx\frac{3 pL_{\pi}}{4N}, \tag{3}\]  
where p is a positive number and N is the number of laser sources. In our study, N = 2 (two laser sources) and p = 1, which were selected for obtaining a shorter MMI coupler length. The insertion loss of the combiner device is described by the next expression:  
\[\rm Losses[dB]=-10 Log_{10}\left(\frac{P_{out}}{NP_{in}}\right), \tag{4}\]  
where \(\rm P_{out}\) is the output power of the combiner and \(\rm P_{in}\) is the input laser power.  
The adiabatic condition rule for designing the inputs and output waveguide taper is given by [38; 39]:  
\[\rm\theta<\frac{\lambda}{2 Wn_{eff}}, \tag{5}\]  
where \(\rm\theta\) is the half-angle of the taper at the \(z\)-axis, and W is the local full width of the taper. The rule requires that the spreading of the waveguide sidewalls needs to be slower than the diffraction spreading of the fundamental mode, and this can happen by selecting an  
Figure 1: \(2\times 1\) power combiner structure design: (**a**) slot waveguide refractive index profile at the x-y plane; (**b**) full schematic sketch of the MMI coupler with the taper waveguides at the x-z plane.  
optimal waveguide taper length that satisfies this rule. Thus, the fundamental mode is well confined inside the tapered waveguide and no mode conversion to other higher-order modes can happen.
## Methods
### Land Use/Land Cover Classification Using Random Forest Classifier
#### 3.3.1 Sample pool scenarios  
Although RFC is not very sensitive to mislabeled pixels in the sample dataset [40; 41], it was still critical to improve land cover accuracy in our sample data as they contained considerable error. First, the overall accuracy of the agriculture class for the 2016 and 2017 CDL are only 89.3% and 81.7%, respectively, and it is likely worse for non-agricultural classes. In addition, CDL pixels are not perfectly co-aligned with ARD and HLS pixels due to differences in their original data and projections and, thus, may lead to incorrect land cover information when selecting the sample dataset. To improve the accuracy of the land cover information, we used sample selection by selecting only core pixels from the CDL, i.e., pixels surrounded by pixels of the same type, to avoid misclassification, which can occur more frequently at the edge, and off-sets between CDL and ARD/HLS pixels. Another way to increase accuracy of the sample data is to compare land cover types of the same pixel between different years (here 2016 and 2017); a pixel presenting the same cover type for two or more years is more likely to be classified correctly. Improvement in land cover accuracy of the sample dataset may reduce the predictive power of RFC models (despite their good accuracy metrics), since complex spatial characteristics of particular cover types may be excluded through this selection process. In addition, selecting only core pixels may lead to a higher degree of spatial autocorrelation in the sample dataset, thereby inflating accuracy metrics [42]. To find a good balance between accuracy and representativeness of the sample dataset, we examined land cover classifications arising from four sample pool scenarios as described in Table 3.
## 1 Introduction  
First demonstrated on \(\Lambda\)-type atomic configuration [1], electromagnetically induced transparency (EIT) is a phenomenon observed in multilevel atomic systems [2; 3; 4; 5; 6; 7; 8; 9; 10] where a weak probe field, in the presence of strong coupling fields, experiences suppression of resonant absorption and sees a steep dispersion. Making an initially opaque medium transparent to a probe laser beam, EIT has been explained in terms of the destructive quantum interference effect between different excitation pathways [11], or a dark superposition state [12; 13; 14] in the multistate system. Due to the fact that EIT provides the possibility of wide applications in light velocity control [15; 16; 17], quantum information [18; 19; 20] and nonlinear optics [21], there have been many works carried out on this subject. Traditionally in the study of EIT, the analytical expression of the atomic medium optical susceptibility is calculated based on the specific atomic configuration [2; 3; 4; 5; 6; 7; 8; 9; 10], although the general features of the EIT phenomena are basically the same regardless of the configuration details. On the other hand, EIT-like phenomena can be found in many different systems, such as interacting classical harmonic oscillators [22], coupled microcavities [23; 24; 25], waveguide resonators [26; 27; 28; 29; 30], and so on. Noticing the wide existence of EIT and EIT-like phenomena, we feel this could be a hint for us to find a more general viewpoint for the description of EIT.  
In this paper, we present a perturbative view from master equation for EIT, by treating the applied fields--strong coupling fields as first order and weak probe field as second order perturbation to the atomic system, and by using the perturbative approach of master equation, we find that the analytical expression of the probe response susceptibility could explain the shape of the sharp transmission window of EIT, without involving the details of the atomic energy level configuration. The perturbative approach of density matrix we use here is some kind of standard treatment in many fields, say, nonlinear optics [31], and we adopt it here to bring on the new view of demonstration of EIT, which to the best of our knowledge, has not been done before. We are hoping this work would help deepening our understanding of the wide existence of EIT analogs and EIT-related phenomena.
## 3 System Model
### Implementation  
The proposed system model is divided into two steps. One is a model that predicts the distance and altitude angle between the UE and the center of the cell after propagation delay based on the location information transmitted from the satellite. One is a model that predicts handover triggering occurrence based on the predicted value after propagation delay.
## 4 Experimental Results
### Experimental Criteria and Performance Measures
#### 4.2.2 Hyperparameter Optimization and Performance Measures  
To select hyperparameters for training the model, the authors of this study used a manual search strategy [37]. The authors acquired the optimized training hyperparameters after various tuning iterations through the manual search strategy, as shown in Table 1. They also followed best practices outlined by the research community, for example, selection of an optimization function (Adam) and cross-entropy selection (one-hot encoded). The classification accuracy is considered the most suitable metric to evaluate model performance in a nonstationary environment [38]. This study has used performance measures recognized as primary classification performance indicators by the research community [39,40].
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 1 Introduction
To study the influence of alternating temperature on the effectiveness of EICP in consolidating aeolian sand, aeolian sand in the Taklamakan Desert (Xinjiang Uyghur Autonomous Region, China) was collected for research material, and three experimental groups (T1, T2, and T3) of temperature processes were designed. Based on the testing of the calcium carbonate content, precipitation rate of calcium carbonate, permeability coefficients, and shear velocity wave of the three experimental groups at different maintenance periods, the effectiveness of EICP consolidation of the three temperature groups was compared and analyzed. Meanwhile, SEM was conducted to observe the external cementation states by mineralization.