esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Process Modelling for Optimization of Molecular Tumor Boards
## 3 Results
In the optimized process, a first interpretation of the Next-Generation Sequencing data, including the generation of a diagnostic report, will be performed by the MIRACUM Pipeline [16]. After the input of the raw sequencing files in FASTQ format and the patient's gender from the MTB Software, the pipeline automatically generates an interactive PDF report containing sequencing quality assessments, the identified and annotated variants, and highlighted hotspot mutations. The recording of Pre-Diagnosis and Pre-Therapy as well as Current Diagnosis into a structured documentation form in the MTB software is completed by a designated documentation specialist. The data from the MIRACUM report is then annotated with further external genetic and drug databases using a variant interpretation software developed in house, which presents relevant clinical data together with the sequencing results and matches them with additional information from public knowledge databases. With help from this interpretation software, the physician can then create the sections Scientific Background and Procedure, the second part of the MTB report. After completion, the second part is transferred into the MTB software, where it is merged with the first part containing the Pre-Diagnosis and Pre-Therapy section, resulting in the final MTB report. After review by a senior physician, the MTB report is ready for presentation at the MTB meeting, where the findings and therapy recommendations are discussed by an interdisciplinary team of physicians. After all have agreed on one or more personalized therapy recommendations, this is noted in the Procedure section and digitally signed by all participants. In the current process, the signed report will now be sent to the referring physician or, in the case of internal patients, can be looked up in Soarian. In the optimized process, the signed MTB report is transferred to Soarian by the MTB Software and, in the case of external patients, sent automatically to the treating physician. Finally, the data from the MTB report is transferred to the Giessener Tumor Documentation Software (GTDS) [17] at the Clinical Cancer Registry (KKR). In the current process, the MTB physician manually enters Pre-Therapy and Pre-Diagnosis, Current Diagnosis, and Procedure into the GTDS. In the optimized process, this can be performed automatically since the structured form of documentation in the MTB software enables an automated transfer into the GTDS.
# Segmentation of Liver Tumor in CT Scan Using ResU-Net
## 4 Result and Discussion
### Comparative Analysis
#### 4.4.1 Segmentation of Liver  
Table 4 highlight the overall comparison of our network with the state-of-art techniques. [PERSON] et al. [31] utilized spiralled convolutionary neural networks and proposed a method for autonomously segmenting liver and tumors in CT and MRI abdomen scans (CFCNs). During training, the spiralled FCN will divide the hepatic as an ROI, then tumors inside the expected liver ROIs. For the hepatic and liver tumor, the spiralled FCN produced DSC values of 0.943 and 0.56, respectively. [PERSON] at al. [36] developed a unique 3D to 2D fully convolutional network (3D-2D-FCN) to improve liver segment. The dice score for the 3D-2D-FCN system was 0.935. Moreover, [PERSON] et al. [14] applied CNN to liver segmentation and obtained a DSC score of 0.80. [PERSON] et al. [24] implemented 3D hybrid residual attention-aware (RA-UNet) and obtained a DSC score of 0.971 for liver segmentation. [PERSON] et al. [26] utilized the SegNet network for segmentation of Liver CT scan and obtained an accuracy of 0.988. [PERSON] et al. [25] introduced a modified U-Net to segment the liver CT scan and obtained DSC scores of 0.961 for liver segmentation.  
\begin{table}
\begin{tabular}{|c c c c c c c|} \hline
**Approach** & **DSC** & **Accuracy** & **Precision** & **Specificity** & **VOE** & **RVD** \\ \hline [PERSON] et al. [31] & 0.94 & - & - & - & 10.7 & \(-\)1.4 \\ \hline [PERSON] et al. [36] & 0.935 & - & - & - & 56.47 & - \\ \hline [PERSON] et al. [14] & 0.80 & - & 0.826 & - & 29.04 & - \\ \hline [PERSON] et al. [24] & 0.971 & - & - & - & 0.074 & 0.002 \\ \hline [PERSON] et al. [26] & - & 0.988 & - & - & - & - \\ \hline [PERSON] et al. [25] & 0.961 & 0.993 & - & - & - & \\ \hline
**Proposed ResU-Net** & **0.9781** & **0.991** & **0.982** & **0.9618** & **0.13** & **0.018** \\ \hline \end{tabular}
\end{table}
Table 4: Result comparison of ResU-Net based liver segmentation with state-of-art techniques.  
Figure 8: Figure shows that the samples of Liver Tumor segmentation in (**A**,**B**).
## 2 Hourglass Buoy Model
### Radiation Force  
Although not entirely consistent with the large motion assumption mentioned earlier, a linear radiation force model is used here to keep the focus on the effect of the FK forces. The radiation forces are split into two components, one related to velocity and the other to acceleration, shown in Equation (9) [30],  
\[F_{r}=\int_{0}^{\infty}h_{r}(\tau)\zeta(t-\tau)d\tau-m_{a}\zeta^{\varphi} \tag{9}\]  
where \(h_{r}\) is the radiation impulse response function and \(m_{a}\) is the added mass. However, in our analysis, we consider regular harmonic waves of Equation (2) where the integral of Equation (9) becomes Equation (10).  
\[F_{r}=-b\zeta-m_{a}\zeta \tag{10}\]  
where \(b\) is the linear damping constant specific to the regular wave frequency. Substituting Equation (10) and Equation (8) into Equation (1), and neglecting the diffraction force yields the nonlinear model we will use for the rest of the analysis.  
\[M\zeta+b\zeta-\frac{1}{3}\pi\rho g\overline{\alpha}^{2}(\eta-\zeta)^{3}-\frac {2\pi}{\chi^{2}}\rho g\overline{\alpha}^{2}\eta\left[2-e^{-\chi h}-e^{-\chi( \eta-\zeta)}-\chi(h+\eta-\zeta)\right]=F_{c} \tag{11}\]  
where \(M=~{}m+m_{a}\).  
A point absorber is often modeled using the Cummins equation shown in Equation (12) [8; 38; 39].  
\[M\zeta+\int_{0}^{\infty}h_{r}(\tau)\zeta(t-\tau)d\tau+k\zeta=f_{f} \tag{12}\]  
where \(f_{f}\) contains all external forces applied to the buoy, including diffraction, and those supplied by the power take-off. It is instructive to compare Equation (12) to the nonlinear model of Equation (11). The first two terms were introduced in Section 2.2. The third term of Equation (12), \(k\zeta\), models the linear hydrostatic force and is valid for small motions about an equilibrium. This is replaced with a cubic term in Equation (11) appropriate for potentially large vertical displacement. The fourth term on the right of Equation (11) is the dynamic Froude-Krylov force and could be included in the \(f_{f}\) of Equation (12) as it can be classified as an external force. Typically, the \(f_{f}\) includes the linear diffraction force, which we have neglected as mentioned previously, and the PTO control force, \(F_{c}\) of Equation (11).
# Faudistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability
## 2 Related Work, Contribution, and Outline
### Contribution  
In contrast to existing work [24], we do not only validate the applicability of individual communication technologies for platooning, nor do we only consider hybrid networks for the vehicle layer. The main contributions of our work in this paper are listed as follows:  
1. We present _Feudalistic Platooning_, an extensive platooning concept including a vehicle and a platoon layer. It uses hybrid networking targeting both safety- and efficiency-related bottlenecks caused by communication deficiencies and surrounding traffic. Within a platoon, the message flow is steered by diversified role-based communication which unites either two only or alternatively many more communication networks and technologies.
2. We employ IEEE 802.11p and cellular V2X (C-V2X) and discuss how to improve operability, coexistence with surrounding traffic, and modularity, to relieve networks, to compensate network outages, and to increase efficiency. For C-V2X, we do not consider 5G but LTE. However, these are interchangeable.
3. We simulate _Feudalistic Platooning_, analyze the data flow and load using a comprehensive, open source suite for inter-vehicle communication simulation, and discuss its impact.
# A Systematic Review on Reinforcement Learning for Industrial Combinatorial Optimization Problems
### Research Questions  
A systematic literature review is conducted in this paper. The focus is on combinatorial optimization problems that represent real-world industrial problems using RL. This paper analyzes and compares different approaches to three important design decisions of any RL framework, the agent-environment interactions. The objectives of this paper can be described by the following research questions (RQ):  
* _RQ1_: how to sufficiently encode the problem (state representation);
* _RQ2_: what control is necessary over the problem (action space);
* _RQ3_: how to guide the agent towards the desired behavior (reward design);
* _RQ4_: what the main limitations are for practical implementation;
* _RQ5_: which future developments are needed.  
When relevant, model architecture and problem constraints are also discussed. For the targeted industrial problems, most approaches either do not mention transition probabilities or define them as deterministic. The exceptions are some maintenance and resource breakdown problems, which explicitly detail the transition probabilities. For many problems, the traditional MDP representation is not feasible, for example, if there are too many states. When transition probabilities are fixed at one, the MDP representation also becomes irrelevant. Thus, these are considered as out of scope for the work developed here.  
The review methodology is presented in Section 2. Section 3 discusses state representation and format. The action space is described in Section 3 and reward design in Section 5. The research questions are discussed in Section 6. Finally, Section 7 presents future research avenues.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Type** & **Methods** & **Area** & **Objective** \\ \hline
[2] & Survey & Soft computing & Wireless sensor networks & Approach overview \\
[3] & Review & ML & Smart energy and electric & Approach overview \\
[4] & Review & DRL and evolutionary & Job shop scheduling & Overview \\
[5] & Review & ML & 5G wireless communications & Potential solutions for area \\
[6] & Vision article & RL and digital twins & Maintenance & Approach overview \\
[7] & Review & RL & Multiple topics & MDP, RL algorithms and \\
[8] & Survey & RL & Software-defined network & Identifying and analyzing \\  & & & routing & recent studies \\
[9] & Survey & RL and DRL & IoT communication and & Application analysis \\  & & & networking & Application and approach \\
[10] & Survey & DRL & Traffic engineering, routing & Application and approach \\  & & & and congestion & overview \\
[11] & Review & RL & Production planning and & Characteristics, algorithms \\  & & & control & and tools \\
[12] & Review & DRL & Intelligent manufacture & DRL applicability versus \\  & & & alternatives \\
[13] & Review & RL and DRL & Maintenance planning and & Application taxonomy \\  & & & optimization & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected literature reviews and surveys from the query used in this paper.
# Uses of Nanoclays and Adsorbents for Dye Recovery: A Textile Industry Review
## 2 Sewage Treatment
### General Textile Recovery  
In order to reduce water use in the textile industry, wastewater can be totally or partially reused after its deep treatment. This treatment aims to leave a low contents of organic matter, color, suspended matter, and salinity in the water. This generally requires several purification processes, among which a biological process almost always takes place because, otherwise, BOD reduction is insufficient [57].  
Other water recovery processes are known, such as reverse osmosis [58; 59; 60], micro-filtration [61; 62; 63], ultrafiltration [64], and distillation in multiple-effect evaporators and electrochemical processes [65; 66; 67], which allow the economical and profitable recovery of various products. They can also be applied for recycling various process baths, but currently their cost is not economical enough for them to be fully used for waste effluents from the textile industry.  
The objective of all these purification processes is to provide treated water that is of sufficient quality to be reused in industry. In fact, several industries are already supplied with recovered surface water that has undergone a natural dilution and purification process.  
In recent years, different techniques for treating textile effluents have been investigated [68]. Most of the published studies have focused on the elimination of colorants because this is the process that poses the greatest difficulty. Treating textile effluents by electrochemical techniques has been studied by several authors, with satisfactory results in color removal terms. However, this technique usually incurs high operational costs.  
One of the emerging treatments for textile wastewater discoloration uses enzymatic methods [69]. Its main drawback is that the process variables must be well controlled (temperature, pH, salinity, etc.). Furthermore, enzyme separation and purification are a very delicate process. Of the physico-chemical treatments, coagulation-flocculation is the most widespread because it is very effective in removing color. This technique, however, generates a concentrated residue that requires additional treatment. Adsorption discoloration (generally with activated carbon) is influenced by several parameters, such as interactions between the dye and adsorbent, the adsorbent's surface area, and particle size, etc. It is highly efficient for a wide variety of colorants, but is a high-cost technique because the adsorbent material must be regenerated after several treatments [70; 71]. Ion exchange treatments also involve the resin regeneration problem but are not effective for all colorants. The best known physical treatments involve filtration techniques [72; 73].  
Chemical oxidation treatments require the addition of oxidizing compounds such as ozone (O\({}_{3}\)), hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), and permanganate (MnO\({}_{4}\)\({}^{-}\)). Ozone is the most widely used for its high performance in eliminating colorants, but it is not effective when treating insoluble colorants such as fat or disperse dyes.
## 4 Results
### Ohiia Brain Dysplasia Scoring within CHD Subgroupings
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Structure** & _Ohiia_ **Average** & **Wild Type Average** & \(p\) **Value \({}^{\ddagger}\)** \\ \hline BDS Total & **4.42** & 0 & **\textless{}0.001** \\ Cerebellar Fissues & **1.5** & 4.4 & **\textless{}0.001** \\  & _Ohiia_ **Incidence** & **Wild Type Incidence** & \(p\) **value \({}^{\star}\)\({}^{\star}\)** \\ BDS Dichotomized & **55/69, (79.71\%)** & 0/10, (0.00\%) & **\textless{}0.001** \\ BDS Hippocamp or Cerebellum & **58/69, (84.06\%)** & 0/10, (0.00\%) & **\textless{}0.001** \\ Applastic Hippocamp & 8/69, (11.59\%) & 0/10, (0.00\%) & 0.384 \\ Hypoplastic Hippocamp & 14/68, (20.59\%) & 0/10, (0.00\%) & 0.223 \\ Dysplastic Hippocamp & **44/68, (64.71\%)** & 0/10, (0.00\%) & **0.002** \\ Combination Hippocamp & 22/69, (31.88\%) & 0/10, (0.00\%) & 0.133 \\ Hypoplastic Cerebrum & 13/68, (19.12\%) & 0/10, (0.00\%) & 0.244 \\ Dysplastic Cerebrum & **43/68, (63.24\%)** & 0/10, (0.00\%) & **0.003** \\ Applastic Cerebellum & 2/69, (20.90\%) & 0/10, (0.00\%) & 0.678 \\ Hypoplastic Cerebellum & 9/67, (13.43\%) & 0/10, (0.00\%) & 0.345 \\ Dysplastic Cerebellum & **49/67, (73.13\%)** & 0/10, (0.00\%) & **\textless{}0.001** \\ Combination Cerebellum & 11/69, (15.94\%) & 0/10, (0.00\%) & 0.325 \\ Applastic Left Olfactory Bulb & **33/69, (47.83\%)** & 0/10, (0.00\%) & **0.024** \\ Applastic Right Olfactory Bulb & **34/69, (49.28\%)** & 0/10, (0.00\%) & **0.02** \\ Hypoplastic Left Olfactory Bulb & 9/67, (13.43\%) & 0/10, (0.00\%) & 0.345 \\ Hypoplastic Right Olfactory Bulb & 7/65, (10.77\%) & 0/10, (0.00\%) & 0.404 \\ Dysplastic Left Olfactory Bulb & 2/67, (2.99\%) & 0/10, (0.00\%) & 0.673 \\ Dysplastic Right Olfactory Bulb & 0/65, (00.00\%) & 0/10, (0.00\%) & — \\ Combination Left Olfactory Bulb & **42/69, (60.87\%)** & 0/10, (0.00\%) & **0.006** \\ Combination Right Olfactory Bulb & **41/69, (59.42\%)** & 0/10, (0.00\%) & **0.007** \\ Hypoplastic Brainstem & 0/66, (00.00\%) & 0/10, (0.00\%) & — \\ Dysplastic Brainstem & 20/67, (29.85\%) & 0/10, (0.00\%) & 0.12 \\ Hypoplastic Midbrain & 0/67, (00.00\%) & 0/10, (0.00\%) & — \\ Dysplastic Midbrain & 18/67, (26.87\%) & 0/10, (0.00\%) & 0.148 \\ \hline \hline \end{tabular} Note: Bolded values indicate statistically significant results. \({}^{\star}\) Student’s \(t\)-test was used to compare Cerebellar Fissues count and BDS Total Score. \({}^{\star}\) Chi-square analysis was used to compare individual structures as well as BDS Dichotomized and BDS Hippocamp or Cerebellum.  
\end{table} 
Table 9: Incidence of Brain Dysplasia Score and Subcomponents between _Ohiia_ and Wild type Mouse Mutants. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**CHD vs. No CHD**} & \multicolumn{3}{c}{**Single vs. Biversticolator Morphology**} & \multicolumn{3}{c}{**Conventional vs. Non Conturscal**} & \multicolumn{3}{c}{**Cyanide vs. Avgenatic**} & \multicolumn{3}{c}{**Arch Obstructions vs. No Obstructions**} \\ \cline{2-1
## 3 Numerical Validation Examples
### Six-Story Shear Building
#### 3.1.2 Damage Detection by the Proposed BMUA
Tables 5 and 6 and Figure 6 present updated mass and stiffness parameters and corresponding uncertainties obtained from the proposed and the traditional BMUA in two damage cases. It is seen that the identified reduction in mass and stiffness parameters is almost identical to the actual values in terms of all damage cases by the proposed BMUA, indicating that the proposed BMUA is capable of outstanding performance on damage detection. However, actual and identified values using the traditional BMUA have a significant difference, e.g., the maximum error is 38% and 66% for both damage cases, respectively (see bold italic values in Tables 5 and 6). It can be concluded that the traditional BMUA cannot detect damage induced by both reductions in mass and stiffness. This is attributable to the required assumption in the traditional BMUA that at least one between mass and stiffness has to be known and unchanged due to damage to avoid the coupling effect of mass and stiffness. This example also illustrates that a variation in mass or stiffness reflects that the damage extent cannot be ignored; if not, significant bias on updating results can mislead engineers' judgment. In the proposed BMUA, the coupling effect of mass and stiffness is intrinsically addressed by employing two groups of data acquired from two systems--original and stiffness-modified system. As a result, a successful updating of mass and stiffness is achieved. In summary, the example of a six-story shear building demonstrates the proposed BMUA is superior to the traditional BMUA in simultaneously identifying mass and stiffness.
# Numerical and Experimental Research on the Laser-Water Jet Scribing of Silicon
## 5 Conclusions  
This study put forward theoretical and experimental investigations on the LWJ scribing of monocrystalline silicon. The discoveries can be summarized as follows:  
1. During LWJ machining, the maximum values of generated tensile and compressive stresses were approximately 122 MPa and 253 MPa, respectively, which were far lower than the tensile strength and compressive strength. The maximum temperature exceeded 1730 K, which was higher than melting point of the substrate. Therefore, the dominated substrate ablation mechanism was the laser-induced thermal effect.
2. The ablation volume per pulse decreased with increasing laser pulses for single-spot ablation, which could be attributed to the reflection, refraction, and absorption of laser energy by the water deposited in the ablated structure.
3. Laser pulse energy and effective pulse overlap were the main factors affecting the groove depth for single-row scribing as they decided the incident laser fluence in the unit area. The groove width was not sensitive to the parameter variation as it was mainly dominated by the applied LWJ diameter. The 213 sequence on the same layer and the surface-to-depth sequence on different layers were the most suitable scanning strategies for fabrication of micro-grooves with large DTWR and low taper considering that these strategies provided effective channels for water and slag ejection.  
However, there still exists limitations for this paper. For example, the prediction accuracy of the established model can be improved by introducing ray optics and hydrodynamics theories. The processing crafts for fabricating specific silicon-based micro-electro-mechanical systems needs to be further explored through detailed experiments and performance tests.  
Conceptualization, Y.L.; methodology, Y.L. and Y.D.; software, Y.L. and B.C.; validation, Y.L. and L.Y.; formal analysis, Y.D.; investigation, Y.L. and J.C.; resources, Y.L. and Y.D.; data curation, Y.L. and J.C.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and Y.D.; visualization, Y.L. and L.Y.; supervision, Y.L. and Y.D.; project administration, Y.L. and  
Figure 21: End face morphologies of micro-grooves with large DTWR and low taper. The numbers in this figure refer to the groove number in Table 7.  
Y.D. funding acquisition, L.Y. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This research was supported by National Key R&D Program of China (No.2018 YFB1107600), National Science and Technology Major Project (No. 2019-VII-0009-0149).  
Not applicable.  
The authors would like to thank [PERSON] for the SEM images.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Soil Water Stress Index (SWSI) and the Water Depletion Period  
In order to determine the impact of water depletion on ET and WUE, an eco-hydrological drought indicator called the soil water stress index (SWSI) was calculated [32]. SWSI is a dimensionless number that is calculated as the difference between the daily REW value and 0.4 (a threshold value for REW that induces stomatal closure in most trees), divided by 0.4. Therefore, at field capacity (REW = 1), SWSI equals 1.5; at wilting point (REW = 0), SWSI equals \(-1\); SWSI equals 0 when REW = 0.4.  
To test the effect of soil water limitation on ET and WUE, we selected the period when REW drops from 1 down to its minimum value within the July-December window (Figure 1). This period is hereafter referred to as the water depleted (WD) period. SWSI of each year (SWSIa) was calculated as the algebraic sum of daily SWSI values during the WD period.