esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Datasets and Methods
### Analysis of LST Variations between and within LCZ  
#### 2.5.1 LST Variations between LCZs  
Visual inspection of the derived LCZ and LST maps was used to determine the qualitative relationship between LCZs and LST. The Zonal Statistics overlay function in ArcGIS was used to obtain the mean LST of each LCZ category. Furthermore, 1000 points were created for each LCZ and used to extract values from the LST layer. The values obtained were further used to generate boxplots which depicted the variations in LST between and within LCZs. Since LCZs are qualitative classes, quantitative analysis of the relationship between surface temperature and LST was performed using Elevation, Aspect, MNDWI, NDBaI, NDBI, NDVI, and UI. The overlay was employed to obtain the global correlation between LST and each of the factors which influence surface temperature distribution. This was conducted in order to understand the extent to which a factor influences LST, as well as to then establish if the same correlations would stand at individual LCZ scale.  
While each LCZ can contribute to either the cooling or warming of the city, understanding how respective sizes affect the overall thermal environment of a city is important. In this study, this was achieved using the Contribution Index (CI), which considers whether a category causes warming or cooling, and its overall significance is determined by its proportion of the entire study area [65]. The Contribution Index for each LCZ category was calculated using Equation (11):  
\[CI=D_{t}\times S \tag{11}\]  
\(D_{t}\) is the difference between the average temperature of an LCZ category and the mean LST of the entire study area, \(S\) is the proportion of the study area occupied by the LCZ (as a decimal). The CI was also useful to gauge how trends in LCZs of high mitigation value could affect the entire thermal environment of a city.
## 2 Materials and Methods
### Research Methods
#### 2.3.4 Comprehensive Assessment Framework  
To comprehensively evaluate the performances among GLASS products, the agreement level of LSP metrics was firstly estimated by calculating their absolute difference (days). In addition, the results of the long-term trend analysis and correlation detection were counted and used to populate the comparisons framework, as framed in Figure 4. This framework was used to assess the matching degree of interannual variation among three products. Physical agreement situations were assumed as the following forms (Figure 4):  
* The pixels with ID 0 indicate that all products are agreed in the trend comparison, i.e., consistently positive, negative, or insignificant changes; while in the correlation comparison, it means that three datasets are synchronously positively, negatively, or insignificantly correlated between each other.
* In the tendency comparison, the pixels with ID 1 represent FVC and GPP agreement in positive, negative, or insignificant changes, while LAI disagrees with the other two products. In the correlation comparison, the pixels with ID 1 indicate FVC has a consistently positive, negative, or insignificant relationship with LAI or GPP, while LAI and GPP are oppositely correlated.
* The pixels with ID 2 indicate that GPP and LAI have the same trend direction in these areas (i.e., positive, negative, or insignificant changes), however, FVC is inconsistent; while in the correlation comparison, it means that GPP has the same positive, negative, or insignificant relationship between FVC or LAI, but the correlation between FVC and LAI is reversed in these regions.
* The pixels with ID 3 intend that LAI and FVC agree (i.e., accordant positive, negative, or insignificant changes), nevertheless, GPP disagrees in the tendency directions; while in correlation comparison, it means that LAI has the same positive, negative, or insignificant correlation between FVC or GPP, while the correlation between GPP and FVC is opposite.  
In summary, pixels with ID 0 represent that the interannual trend and correlation are in total agreement in comparison, and pixels with ID 1, 2, and 3 indicate the total disagreement (i.e., interannual differences). Subsequently, the statistical distributions of comparison results of LSP and interannual variation as a function of the RVP and TNI indicators, respectively, are calculated to reveal the performances of temporal agreements among LAI/FVC/GPP under different characteristics of TC and LCH.
## 3 Procedures of InSAR Image Co-registration
### Strategy for Tie Point Matching  
The second issues in image co-registration should be considered is the tie point matching that includes two factors: the window size, and the strategy for matching. Generally, there are two kinds of methods for tie point matching: one is based on spectral maximization; another is the image cross-correlation maximization [72].  
For the first method, a small window centered around the tie point in the master image is selected, and it is moved on the slave image. When the peak of the Fourier Transform of the product of the master image multiplied by the complex conjugate of the slave image is maximized, the matching is regarded as the best one. A large amount of calculation is carried out in the method.  
About the second method, the cross-correlation coefficient is used as a parameter for evaluating the matching results. The principle is that given a window of pixels on the master image, there will be many possible matching windows of the same size, but the one matching with the highest cross-correlation coefficient is regarded as the best matching. The central pixels of the two windows are then regarded as the corresponding image points found.  
Let \(z_{1}(m,\,n)\) and \(z_{2}(m,\,n)\) represent the two SAR complex images:  
Figure 8: Cont.  
Sensors_**2009**, \(9\)  
\[z_{{}_{1}}\left(m\,,n\right) = a_{{}_{1}}\left(m\,,n\right)e^{\,j\varphi_{{}_{1}\left(m\,,n\right)}} \tag{4a}\] \[z_{{}_{2}}\left(m\,,n\right) = a_{{}_{2}}\left(m\,,n\right)e^{\,j\varphi_{{}_{2}\left(m\,,n\right)}} \tag{4b}\]  
where \(m\) = 0, 1, 2, \(\Lambda\), \(M\)-1, \(n\) = 0, 1, 2, \(\Lambda\), \(N\)-1, and the size of the image is \(M\)\(\times\)\(N\). The cross-correlation coefficient \(\gamma\) is expressed as:  
\[\gamma\,=\,\frac{\left|E\left(z_{{}_{1}}z_{{}_{2}}^{{}^{*}}\right)\right|^{2}} {\sqrt{E\left(\left|z_{{}_{1}}\right|^{2}\right)E\left(\left|z_{{}_{2}}\right| ^{2}\right)}} \tag{5}\]  
The expanded form of equation (5) can be written as:  
\[\gamma\left(m\,,n\right)\,=\,\frac{\left|\sum\limits_{l=-\left(L-1\right)/2}^ {\left(L-1\right)/2}\sum\limits_{k=-\left(K-1\right)/2}^{\left(K-1\right)/2} \!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!
## 4 Experiments and Results
### The Brain Tumour Segmentation on BRATS 2017  
In this section, we evaluate the proposed method on another challenging task of brain tumour segmentation, using the publicly available dataset of the BRATS 2017 challenge [36]. The training dataset contained 285 multi-sequence MRIs of patients diagnosed with low grade gliomas or high grade gliomas. In each case, four MR sequences were available: T1, T1 + gadolinium, T2, and Fluid Attenuated Inversion Recovery (FLAIR). In this study, we employed these multi-modality MRI scans to segment the gliomas, neglecting the difference between oedema, necrosis and non-enhancing tumour, as well as enhancing tumour (i.e., binary classification). We resized all volumes to \((64\times 64\times 64)\), and four MRI sequences for each sample were combined as a multichannel volume as input. Three competitive methods including 3D U-net [37] (along with its dropout and stride two convolution enhanced versions), V-net [38] and the method proposed in [39] were evaluated and compared.  
The five fold cross-validation strategy was employed in this experiment. In each iteration, one fold with 57 samples was used for testing, and the rest were used for training the model. This process was repeated five times until each of these five folds had been used as the testing set. To illustrate the effectiveness of the proposed 3D-DSC module, we replaced the original 3D U-net block as shown in Figure 4a with our 3D-DSC enhanced version as shown in Figure 4b and compared the performance with/out 3D-DSC. Note that only the second 3D convolution in 3D U-net was substituted by two consecutive 3D-DSC modules, and the overall architecture of the network remained unchanged. The Dice scores obtained by different methods are shown in Table 6. We can see that our method achieved the best performance with a Dice score of 0.8932, which outperformed the others by a large margin.  
Figure 6: The validation loss of the \(A_{3}\), \(B_{3}\), and \(C_{3}\) networks. We do not employ the dense connection or extra activation layer during the separated convolution in the \(B_{3}\) and \(C_{3}\) network. In this experiment, we set the batch size to one to highlight the effect of the 3D-DSC module.  
It is worth noting that we did not adopt any other technique to refine the network, such as dropout, replacing pooling with stride two convolution (s2-conv), and so on, although these techniques could slightly improve the performance of 3D U-net as reported in Table 6. The qualitative segmentation results of different methods are presented in Figure 7, and we can see that fine details could be better recovered by our method. Even though the performance reported here did not represent the state-of-the-art performance on BRATS 2017 [40], it demonstrated that replacing the 3D convolution kernels by the proposed 3D-DSC was able to reduce the risk of overfitting and hence improve the performance via a deeper network.
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 7 Discussion
### Implementation Challenges in Real-World EMRs  
The proposed cyclical framework is currently undergoing pilot implementation in two infertility centers. This domain is particularly well suited to a cycle-based structure, given the complex and multi-staged nature of infertility treatments, which involve different levels of intervention (from prescribing medication to suggesting surgical procedures). At the end of each treatment cycle, clinical outcomes are carefully evaluated based on all practical medical elements and the patient's exclusive medical history, highlighting the critical importance of sequential data tracking.  
The initial implementation phase leverages existing medical forms to mitigate workflow disruption and prevent system overload. Retaining their established structure, these forms are strategically grouped into treatment cycles through sequential sorting. Data form and field revisions will be introduced systematically only after stabilization at this stage. This gradual, structured approach facilitates a seamless transition, enabling the integration of the cyclical method into EMRs without overwhelming users.  
The cyclical approach may be incompatible with existing EMR processes, which primarily support independent, event-based data entry rather than iterative, cycle-oriented documentation. To overcome these challenges, physicians and system designers must collaboratively define standardized treatment cycles, specifying structured data forms, mandatory fields, and sequence requirements to ensure alignment with clinical decision-making needs. Furthermore, flexibility in modifying treatment cycles is crucial, as protocols may evolve due to advancements in medical knowledge, patient-specific considerations, or administrative regulations. Adapting to these dynamic requirements will require continuous evaluation and refinement, ensuring that structured cycle-based data entry enhances workflow efficiency rather than impeding it.  
Implementing this model requires economic, temporal, and human resource investments during the transition phase. However, by providing structured data for the evaluation of outcomes per cycle, this approach ultimately supports cost-saving through better decision-making, more efficient resource allocation, and the precise calculation of treatment cycle expenses, which is not achievable with non-cyclical models.
# Pretreatment Method for DNA Barcoding to Analyze Gut Contents of Rotifers
## Materials and Methods
### Application and Effectiveness Verification of Set Treatment Concentration and Time  
We collected water samples from a eutrophic reservoir (Shin-gal reservoir, Korea; N 37.241536, E 127.0929190) in fall (4 th November 2018). Rotifers dominated the zooplankton community of the reservoir during this season. We repeatedly filtered 10 L of surface water (n = 20) into a 60 \(\upmu\)m mesh sized zooplankton net and obtained a 1 L filtered water sample. From the collected sample, all organisms were removed by hand using a microscope (OLYMPUS CKX41) and 0.5 mL of subsample was extracted to micro-tubes. For every sample taken, we made a negative control to prevent cross contamination.  
For the application and effective verification of a set treatment concentration and time on the DNA fragments of rotifer food sources (Chlorophyceae, Diatomea, Cyanobacteria, Bacteria, Ciliophora, and Heterotrophic nanoflagellates) [9], we compared both treated and non-treated samples with 0.5 mL filtered water samples. In the case of treated sample, after set treatment time, we poured and filtered the sample immediately by washing with distilled water to prevent further effects. Non-treated sample was also filtered in order to proceed with the same DNA extraction process as the treated sample.  
Figure 1: Diagram of necessity of proper pretreatment process in rotifers gut contents analysis; (**A**) Without treatment for removing contaminants and other detectable DNA including extracellular DNA, there is possibility to be confused that detected DNA is from the rotifers gut contents or not, (**B**) Through proper treatment, contaminants and other detectable DNA can be removed without damaging the rotifer individuals, (**C**) and (**D**) Rotifer individuals can be damaged and their gut contents can be overflowed by excessive treatment according to their lorica characteristics.  
To confirm detection of DNA in the rotifers gut without extracellular DNA using the suggested treatment method, we collected a rotifer species, _Asplanchua_ sp., from reservoir water, and applied this method to the treatment. We sorted _Asplanchua_ specimens from the water sample and transferred them to distilled water several times until pure rotifer individuals were collected without other visible particles, particularly phytoplankton cells. We then checked for removal of particles under the microscope and selected clean individuals without attached particle or microorganisms (one individual per a sample, 3 replicates). As with water samples, rotifers samples were filtered after pretreatment for extraction of their DNA. The 47 mm diameter cellulose nitrate filters with a pore size of 0.45\(\upmu\)m pore size (NC 45 ST, Whatman(tm)) were used to filter DNA fragments [37].
# A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations
## 2 Literature Review
Their model, however, gave the same importance weight to individuals' responses neglecting the individuals' level of authority in the organization and their level of involvement in the risk management processes. It also did not consider the uncertainty associated with the responses, neglecting the ambiguity, subjectivity and the imprecision involved in the responses provided by these individuals. In the same year, [PERSON] et al. [10] introduced a new fuzzy enterprise risk management maturity model (ERMMM) enabling construction organizations to incorporate the uncertainty of the responses used in model development. However, no dependency was considered between the sixteen identified attributes which in turn reduced the accuracy of the results. One year later, [PERSON] et al. [11] applied their earlier developed ERMMM model on the three case studies to investigate the influence of the firm size on the enterprise risk management (ERM) implementation. However, considering the dependency between the risk maturity attributes remained lacking. Recently, [PERSON] et al. [12] utilized the Organizational Project Management Maturity Model (OPM3) method introduced by the PMI [13] to measure the risk maturity level of the organization and to consider its impact on the firm size and organizational learning. [PERSON] et al. [8] also developed a new generic risk maturity model (GRMM) to evaluate the performance of the construction organization in the application of the risk management program. However, their models focused on the theoretical aspects of the risk maturity evaluation without considering either the uncertainty resulting from individuals' judgments or the dependency between the identified attributes. In summary, as shown in Table 2, none of the of the above cited models are capable of simultaneously: (1) modeling the interdependencies between the risk maturity attributes, (2) capturing the uncertainty associated with individuals' judgement, and (3) considering the importance weight of individuals' responses in calculating the weights associated with the risk maturity attributes. Unlike the models presented in the literature, this paper presents a novel risk management maturity model which is capable of modeling the interdependencies among the identified risk maturity attributes considered in the model development, capturing the uncertainty associated with the input data provided by individuals who participate in the model development, and considering the relative importance of the responses used in calculating the weights associated with the risk maturity attributes.
## 3 Modeling and Simulation
### Energy Control Strategy  
The energy control strategy directly affects the energy recovery rate, and it includes calculation of the required braking torque based on the driving condition, calculation of the hydraulic braking torque according to the pressure of the accumulator, the target torque for the pump/motor, and other modular. Flow chart of the critical control strategy is shown in Figure 3. Firstly, the braking mode is judged and decided through calculating the required braking torque according to the present driving conditions. If the braking mode is emergency brake, the friction brake is adopted to ensure safety. Otherwise, the target torque is compared with the regenerative braking torque. If the regenerative braking torque supplied by the hydraulic accumulator can meet the requirement, only the hydraulic brake is adopted. Otherwise, the friction brake will be used to compensate the lack of the braking torque. Thirdly, the recycled energy stored in the hydraulic accumulator can be released when the vehicle starts again or accelerates. Thus, due to incompressibility of the used hydraulic oil, possible failure of the regenerative braking system would not result in the crash potentials, and the friction brake could keep safety of the driving cycle. The double-brake system not only can improve the safety factor of the vehicle, but also will increase the energy usage efficiency.
## 3 Materials and Methods
### Modeling Long-Term Erosion Trends  
Long-term erosion patterns along the shoreline are accurately modeled using temporal predictive and regression methods applied to historical shoreline positions based on satellites. Such common methods include the EPR, LRR, and WLR, all of which are encapsulated in the DSA approach [80]. [PERSON] et al. [31] applied the WLR, for example, to examine 34 years of shoreline changes in Brazil, while for 83 years of Landsat-derived shoreline data in South Africa, [PERSON] et al. [50] utilized these metrics. [PERSON] et al. [10] showed the tolerance of WLR in evaluating shoreline variability along the Pacific coast of Panama. Where temporal gaps or uncertainty in position are problems, WLR is superior in terms of reliability as it can include confidence intervals across shoreline dates. Dynamic predictive models such as the Kalman Filter have, for their part, been applied for the assimilation of historical and future shoreline data, as well as the prediction of future changes. For example, [PERSON] et al. [2] utilized Kalman-based modeling in projecting shoreline position forward to 2031 and 2041 along India's eastern coast, while [PERSON] et al. [34] combined real-time video monitoring data with sediment transport to calibrate future shoreline trajectories in Senegal. These models show that where historical remote-sensing imagery is combined with either dynamic or statistical modeling methodologies, shoreline erosion patterns may be reliably monitored and predicted on decadal timescales, with these predictions being used in proactive coastal management.
# Optimized DM-RS Configuration for Improved 5G New Radio Network Capacity and Performance.
###### Abstract  
Network load in mobile networks is continuously growing, putting pressure on mobile operators to deliver target network performance and user experience. This paper focuses on network capacity improvement through increased spectral efficiency, which can be achieved with overhead reduction by optimizing the demodulation reference signal (DM-RS) configuration, which is one of the channels/reference signals with the largest contribution to overhead. In a high-speed scenario, the Doppler effect noticeably influences the temporal nature of the channel such that a channel interpolation process is required within a slot. However, increasing the number of symbols used for DM-RS has a negative impact on capacity. The Doppler effect was analyzed for various 5G NR configurations of operating frequency and subcarrier spacing (SCS), and various use cases were considered using user equipment (UE) speed as the main parameter. For suitable use cases, the DM-RS configuration was optimized in networks with live traffic. The impact of the configuration change on 5G/NR spectral efficiency, user experience and link adaptation performance was assessed through a deep-dive analysis of active measurements, available performance Management (PM) counters and key performance indicators. An optimized DM-RS configuration is proposed, and it is demonstrated to achieve gains of 5-15%, depending on the metric used, use case analyzed, network load, traffic mix and other relevant network characteristics such as topology and clutter type.  
5G NR; DM-RS; spectral efficiency; capacity; channel estimation; Doppler shift Article
## 2 Literature Review
### Design of Adaptive Fixtures  
Adaptive fixtures represent a special category of fixturing devices that are designed to adapt to different tool orientations, machining angles, or workpiece shapes. These devices offer increased flexibility compared to traditional fixtures and can be used to machine parts with complex geometries or to perform machining operations that require changing the tool orientation.  
The concept of an adaptive fixture can include various solutions, such as fixtures with adjustable components, indexing fixtures, fixtures with special kinematics, or even robotic fixtures. These solutions allow for adjusting the position and orientation of the workpiece or the tool during the machining process [32; 33; 34].  
Exploring existing solutions for adaptive fixtures reveals a variety of approaches, each with its advantages and disadvantages. However, many of these existing solutions are complex, expensive, or inherently require additional, less accessible equipment, such as 5-axis machine tools or robotic systems. The research presented directly addresses these significant limitations by developing a specialized, cost-effective device for enabling machining with barrel tools on conventional 3-axis CNC machines. This approach allows for performing complex machining operations on simpler and more accessible machine tools, providing a practical and economical solution for many industrial applications that previously lacked such capabilities. The proposed device is specifically designed to compensate for the lack of additional axes of the machine tool by correctly orienting the workpiece, thus ensuring the efficient use of barrel tools where it was previously impractical.