esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Medical Image Segmentation Enhancement:
## 4 Results and Discussion
### Experimental Setup
#### 4.1.1 Preprocessing Parameters  
In the preparation of the datasets, a standardized set of preprocessing parameters was meticulously and consistently applied to all images. These preprocessing procedures encompassed the conversion of images into 8-bit format and grayscaling to ensure uniformity in the dataset. Additionally, histogram equalization (HE) and image adjustment parameters were meticulously calibrated to enhance the quality and suitability of images for subsequent segmentation and optimization processes.  
The initial step of image conversion into 8-bit format was imperative due to the inherent variability within the dataset. This dataset consisted of images with both 8-bit and 16-bit depths. To maintain uniformity in image representation and facilitate consistent processing, all images were converted into 8-bit format. In the 8-bit representation, each pixel is defined within the range of 0-255, spanning 256 levels, ensuring consistent data quality for subsequent processing.  
Moreover, as some datasets featured images with three color channels (despite their appearance being similar to that of grayscale), they were transformed into grayscale representations with a single color channel. This harmonization of image attributes across the dataset facilitated standardized preprocessing procedures, ensuring that all subsequent segmentation and optimization tasks were conducted on images optimized for analysis.  
The effectiveness of these preprocessing measures can be visually appreciated in Figure 6, which demonstrates the results of histogram equalization (HE) applied to lung CT scan and chest X-ray images, along with their corresponding histogram distributions. This rigorous preprocessing lays the foundation for achieving consistent and high-quality results in the subsequent medical image segmentation processes, ultimately enhancing the accuracy and reliability of healthcare applications.  
Figure 6 highlights significant improvements in image clarity after preprocessing, which are particularly evident in the comparison between Figure 5(a),c. Histogram analysis reveals varying distributions in Figure 5(a),b, but preprocessing (Figure 5(c),d) achieves a uniform distribution through histogram equalization, which enhances overall visual quality and preserves critical details. Our model processing, employing histogram equalization, effectively enhances image clarity and visual quality, which contributes to a more precise depiction of features.
_Algorithms_**2015**, \(8\), 177-189; doi:10.3390/a8020177  
_article_  
**An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors**  
**[PERSON] 1,*, [PERSON] 1,2 and [PERSON] 1**  
1 School of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China;  
E-Mails: [EMAIL_ADDRESS] (S.Z.); [EMAIL_ADDRESS] (Y.L.)  
2 School of Electronics and Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +86-574-8813-0019; Fax: +86-574-8822-9505.  
Academic Editor: [PERSON]  
_Received: 19 March 2015 / Accepted: 28 April 2015 / Published: 7 May 2015_  
**Abstract:** The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm's performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets.  
keywords:spectral clustering; similarity measures; Gaussian kernel function; importance of nearest neighbors +
Footnote †: journal: Journal of Laplacian
## 3 Overview of the Inhibitors Targeting the OxPhos Pathway
### Inhibitors of Cyt-bd Oxidase  
Aurachin D is a quinone analogue of the aurachin class that inhibits Cyt-bd oxidase of _E. coli_[136] (Table 3). When tested against membrane vesicles of _M. smeg_, aurachin D demonstrated dose-dependent inhibition of oxygen consumption of up to 50% [159]. Oxygen consumption was further inhibited by 90% in membrane vesicles without QcrCAB, indicating the likelihood of aurachin D targeting Cyt-bd in mycobacteria as well. On its own, aurachin D did not display activity against replicating _M. smeg_ nor _M. tb_[159; 160]. In spite of the lack of observable activity alone, aurachin D in combination with Q203 resulted in a \(\sim\)10-fold decrease of the MIC of Q203 against _M. tb_, as well as enhanced bactericidal killing of _M. tb_ by \(>\)2 log10 CFUs, demonstrating that aurachin D does indeed potentiate the activity of Q203 [160]. However, it remains to be demonstrated that aurachin D is a specific Cyt-bd inhibitor in mycobacteria as given its structural relation with MK, it may interfere with other respiratory complexes. This is particularly important since it was recently demonstrated that small-molecules are able to enhance the bactericidal potency of QcrB inhibitors without targeting the Cyt-bd [161].  
It was shown that genetically inactivating both terminal oxidases completely abolished the in vitro growth of _M. tb_[35]. This also dramatically impacted bacterial fitness in vivo in mice, with a strain having a knockdown of Cyt-bcc-aa3 and a knockout of Cyt-bd being unable to establish an initial infection [35]. Even if an initial infection could be first achieved by regulating the expression of one of the terminal oxidases, the absence of both terminal oxidases led to severe persistence defects, with a decrease of five orders of magnitude in bacterial load in 35 days [35]. These findings are consistent with previous reports, whereby the joint inactivation, either pharmacologically or genetically, of both cytochrome bcc and bd oxidases resulted in bactericidal effects on _M. tb_ in vitro and in vivo [65; 76; 77; 160]. Exploiting the synthetic lethal interaction between these two terminal oxidases is an extremely attractive approach to eradicate _M. tb_. Inhibitors of this alternate terminal oxidase may have been missed in previous screens, which may be due to the fact that many screens have been performed under conditions in which the Cyt-bd is non-essential. In light of this, screening under stressed conditions may lead to the identification of Cyt-bd inhibitors amenable to chemical optimisation.
# Parallel Fin Ray Soft Gripper with Embedded Mechano-Optical Force Sensor
## 4 Characterization and Experimental Results
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sensor** & **Type of Sensing** & **Fabrication Steps** & **Sensitivity (R\({}^{2}\))** & **Durability (Cycles)** & **Operation Range** \\ \hline \multirow{3}{*}{Mechano-optic sensor} & Mechanical deformation-induced optical intensity modulation & \multirow{3}{*}{1} & \multirow{3}{*}{0.16 N} & \multirow{3}{*}{0.96 – 0.96} & \multirow{3}{*}{10 000} & \multirow{3}{*}{0–150 N} \\ \cline{1-1} \cline{5-6}  & Stretchable optical waveguide & & & & & \\ \hline \multirow{3}{*}{Polymer hydrogel sensor [53]} & Variation in resistance of the hydrogel under strain & \multirow{3}{*}{\textgreater{}10} & Deformation of 12 μm & \multirow{3}{*}{0.98} & \multirow{3}{*}{2000} & \multirow{3}{*}{300\% strain} \\ \cline{1-1} \cline{5-6}  & Sensitive sensor & & & & & \\ \hline Shielded liquid metal silicone sensor [55] & Capacitive & \textgreater{}15 & 0.003 N & - & 1000 & \textgreater{}20 N \\ \hline Multi-direction flex sensor [56] & Resistive & \textgreater{}5 & - & - & \textless{}100 & 5 N \\ \hline
3D printed haptic sensor [57] & Resistive & \textgreater{}3 & - & 0.9728 & - & 0–13 N \\ \hline Triaxial force sensor [58] & Capacitive & \textgreater{}8 & 0.0074 N & 0.93–0.98 & - & 0–0.25 N \\ \hline \multirow{3}{*}{Triboelectric nanogenerator pressure sensor [59]} & Triboelectric nanogenerator nanogenerator & \multirow{3}{*}{\textgreater{}8} & \multirow{3}{*}{12.61 pF: kPa\({}^{-1}\)} & \multirow{3}{*}{0.99213} & \multirow{3}{*}{-} & \multirow{3}{*}{5 kPa} \\ \cline{1-1} \cline{5-6}  & Ultrastrechtable hydrogel sensor [60] & Resistive sensor using liquid metal & \textgreater{}10 & \multirow{3}{*}{0.25 kPa} & \multirow{3}{*}{-} & \multirow{3}{*}{1000} & \multirow{3}{*}{150 kPa} \\ \cline{1-1} \cline{5-6}  & Ionic liquid-based force sensor [61] & Resistance change in ionic liquid & \textgreater{}10 & - & - & 12 N \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key performance metrics of the proposed mechano-optic sensor in comparison with previously reported force and pressure sensors.
# Remote Sensing Data for Digital Soil Mapping in French Research--A Review
## 7 Main Progresses, Perspectives, and Prospects
### The Increasing Importance of RS Data in DSM  
The trends we have observed and the future of RS in DSM can be schematically divided into the following two categories:  
1. The use of RS products as surrogates for in situ measurements.
2. The incorporation of RS products as covariates for DSM.  
In this section, we summarize the main conclusions that can be drawn from this review.
# Investigation of the Behaviors of Methanol Spray Impingement and Wall Wetting
## 2 Experimental Setup and Methodology  
### Experimental Apparatus  
The schematic diagram of the experimental setup was given in Figure 1, which consisted of a fuel injection system, a temperature measurement and control system, and an image acquisition system. In this study, a 14-hole low-pressure methanol injector with hole diameters of 0.26 mm was used, of which flow characteristics were given in Figure 2. The injection pulse width and injection time were set in the control software. The injection pressure was controlled by a methanol pump and measured by a pressure gauge (0-1000 kPa, \(\pm\)1 kPa). A piece of frosted quartz glass with an average roughness of 10.31 \(\upmu\)m was placed under the injector as the impact wall. The temperature control system was used to realize different wall temperature conditions, including a box resistance heating furnace, which was used to heat quartz glass, and a temperature sensor (\(-\)50-1000 \({}^{\circ}\)C, \(\pm\)0.1 \({}^{\circ}\)C) to monitor the glass surface temperature. After the glass was heated to the preset temperature, it was placed on the central tray as soon as possible, and methanol spraying was started. The measurement showed that the temperature difference before and after this process was no more than 2 \({}^{\circ}\)C. In the experiment, the DC LED lamp was used as the light source, of which uniformity was further improved by using the acrylic matte diffuser. The side view of the spray was photographed by a diffuse back-illumination (DBI) method [26], and the process of the wall wetting was captured by the camera (Phantom Miro EX4@4000 fps) through a 45\({}^{\circ}\) mirror.  
Figure 1: Schematic diagram of the experimental setup.
## 2 WFT-Net Model
### The Firing Rule of WFT-Nets
To enable a transition \(t\) at a configuration \(c=(m\), \(\theta\), \(\omega\), \(\delta)\), there are two conditions that need to be satisfied: one is that \(t\) is _control-enabled_, i.e., \(t\) must be enabled at \(m\) in the underlying WF-net; the other is that \(t\) is _data-enabled_, i.e., at the process data level, any data element read/deleted by \(t\) must be defined; at the persistent data level, any data element/table related to persistent data operations on \(t\) must be defined, and any tuple to be selected/updated/deleted by \(t\) must exist in tables; at the data constraints level, any data element/table related to the guard \(G(t)\) must be _defined_, any tuple used by \(G(t)\) must exist in each related table, and the value of \(G(t)\) must be _true_. Firing \(t\) will lead to a new marking and can change the states of data elements, tables, and predicates. For each data element \(d\) with respect to \(t\), the _read_ operation never changes its state; the _delete_ operation will change its state from defined to undefined; the _write_ operation can make it become _defined_ if it is undefined; otherwise, it keeps _defined_ and its value is overwritten with a fresh one. For each data table with respect to \(t\), the firing \(t\) can not change its _defined_ state, but persistent data operations except the _select_ will change its original tuples. The result of a predicate \(\pi\) may be changed with the data elements and tables related to it. Formally, let \(R^{sel}_{t}\) be the set of tuples that are retrieved in \(R\), \(R^{ins}_{t}\) be the set of tuples that are inserted into \(R\), \(R^{upd}_{t}\) be the set of tuples in \(R\) that are updated, \(\overline{R}^{upd}_{t}\) be the set of tuples that have already been updated, \(R^{del}_{t}\) be the set of tuples that are removed from \(R\), and \(R^{G}_{t}\) be the set of tuples that are used by \(G(t)\) in \(R\). Therefore, the enabling and firing rules of transitions can be defined as below. **Definition 3** (Enabling and firing rules). : _Let \(N=(P\), \(T\), \(F\), \(\mathcal{D}\), \(\mathcal{R}\), \(\mathcal{O}_{\mathcal{D}}\), \(\mathcal{O}_{\mathcal{R}})\) be a WFT-net, \(t\) be a transition, and \(c=(m\), \(\theta\), \(\omega\), \(\delta)\) be a configuration. \(t\) is enabled at \(c\), denoted as \(c[t)\), if it holds the following two conditions:_  
1. \(t\) _is control-enabled at_ \(c\)_, denoted as_ \(c[_{c}t)\)_, i.e.,_ \(m[t)\)_;_
2. \(t\) _is data-enabled at_ \(c\)_, denoted as_ \(c[_{d}t)\)_, i.e.,_ 1. \(\forall d\in\mathcal{D}^{dt}_{t}\cup\mathcal{D}^{dt}_{t}\cup\mathcal{D}^{ts}_{t} \cup\mathcal{D}^{upd}_{t}\cup\mathcal{D}^{del}_{t}\cup\mathcal{D}^{G}_{t} \colon\theta(d)\)__ \(=\top\)_;_
3. \(\forall R\in\mathcal{R}^{sel}_{t}\cup\mathcal{R}^{ins}_{t}\cup\mathcal{R}^{upd}_{t }\cup\mathcal{R}^{del}_{t}\cup\mathcal{R}^{G}_{t}\)_; (_\(\omega(R)=\top\)_\(\wedge\)__\(R_{t}^{upd}\cup R_{t}^{del}\subseteq R\)); (iii) \(\eta\big{(}G(t)\big{)}=true\).
## 5 Case Studies
### Comparison of Topology Processing Time by the Number of Nodes  
Table 5 depicts the distribution network layout of the 'area A' of KEPCO, used for case studies. As shown in Table 5, there are 441,284 Terminals, 164,222 ConnectivityNodes, and 269,268 ConductingEquipments. The class inheriting from ConductingEquipment includes EnergySource, EnergyConsumer, Switch, Junction, LineSegment, PowerTransformer, and BusBarSection. EnergySource class is used to model distributed power generation, while EnergyConsumer class is chosen to represent loads. The Switch class is used to indicate all switches used by KEPCO, including automatic and manual switches, automatic load transfer switches, and composite switches. Line section information is represented by using the ACLineSegment class, while bus information within a substation is modeled by using BusBarSection class. Additionally, the PowerTransformer class is used to model the power transformer within a substation, and the step voltage regulator is utilized on distribution lines.  
Figures 19 and 20 depict the graphs illustrating the TP time for each database as the number of target system nodes in the DMS increases. Figure 19 compares the TP time of the RDB and the ODB, while Figure 20 compares the TP time of the RDF triple store and the GDB within the ODB. As shown in Figures 19 and 20, the TP time of the RDB escalates rapidly compared to the ODB using both the RDF triple store and GDB as the number of nodes increases. Table 6 shows the exploration time of the topology based on the number of nodes. When there are 10,000 terminals, the exploration speed of the RDF triple store is approximately 186 times faster compared to the RDB, while the GDB is approximately 557 times faster. However, with 400,000 terminals, the exploration speed of the RDF triple store is approximately 19,745 times faster than that of the RDB, while the GDB is approximately 73,340 times faster. As the number of nodes increases, the impact on the speed of TP becomes more pronounced. Therefore, through a case study, it is demonstrated that applying an ODB is more advantageous for large-scale systems, although several CIM-based commercial products utilizing RDBs with highly sophisticated SQL queries can significantly enhance TP performance.
## 4 Results and Discussion  
### Reagents, Laboratory Equipment and Measurement Procedure  
In this work, the colorimetric reaction used to determine ascorbic acid (AA) was based on the reduction of Fe(III) to Fe(II) by AA, which forms a red color complex with orthophenanthroline. In this experiment, L(+)-ascorbic acid pure p.a., CAS: 50-81-7 (POCH, Gliwice, Poland), 1,10 phenanthroline p.a., CAS: 66-71-7 (POCH, Poland), and ammonium iron(III) sulfate dodecahydrate pure p.a., CAS: 7783-83-7 (POCH, Poland) were used. The 1 M acetate buffer was prepared in our laboratory by mixing 1.0 mol\(\cdot\)L\({}^{-1}\) sodium acetate (CH\({}_{3}\)COOH) with 1.0 mol\(\cdot\)L\({}^{-1}\) acetic acid (CH\({}_{3}\)COONa) (both reagents purchased from Avantor Performance Materials, Gliwice, Poland) and adjusting to the desired pH using 10N HCl. All solutions, including AA at a concentration of 2.5 gL\({}^{-1}\), phenanthroline at a concentration of 4.0 gL\({}^{-1}\), ammonium iron(III) sulfate at a concentration of 2.5 gL\({}^{-1}\), and 1 M acetate buffer (pH 4.6), were prepared with double-distilled water. Small laboratory equipment was used in the experiments, including a precise analytical balance (RADWAG, model AS 60/220.XS, Radom, Poland), a magnetic stirrer (WIGO, Toszek, Poland), automatic pipettes of various volumes (Eppendorf, Germany), and glassware, i.e., beakers and Petri dishes. To adjust the pH values of the buffer solution, we used the SevenCompact S210 laboratory pH meter (Mettler Toledo, Greifensee, Switzerland). All experiments were performed at room temperature.  
Solutions with different concentrations of vitamin C for direct color assessment were prepared by first adding 9 mL of distilled water to the beakers, then 1 mL of acetate buffer, then the appropriate volume of ascorbic acid, and 0.5 mL of ammonium iron(III) sulfate. After waiting for 3 min, 0.5 mL of phenanthroline was added. The additions of AA were 0, 20, 50, 80, 100, 150 200, 300, and 400 \(\upmu\)L. Figure 3 shows the photo of the samples prepared for further testing. The parameters studied for optimization were reagent concentration, volume, and the time between the individual stages (the influence of chemical kinetics). After waiting about 20 min, 10 mL of each colored solution was poured into Petri dishes with flat bottoms and photos were taken with a smartphone camera.
# Robust Burst Detection Algorithm for Distributed Unique Word TDMA Signal
## 3 Algorithm Description
### DUIWDC Detection Algorithm  
As shown in Figure 2, DUW is uniformly dispersed in a burst signal, and we allocated the sliding detection windows with the same structures as DUW. As shown in Figure 5, sliding detection window has a total of \(P\) groups, and there are \(L_{0}\) symbols between the adjacent two groups, with \(N\) symbols for each group length. Sequence \(r_{0}(k)\) is composed of the received data selected by the sliding detection window.  
As shown in Figure 5, sequence \(r_{0}(k)\) is composed of \(P\) discrete receiving data groups, its waveform and frequency offset are discontinuous. As shown in Figure 6, the symbols within each group are continuous, while actual distance between the two symbols at the adjacent group connection part is \(L_{0}+1-N\) symbol.  
Figure 5: DUW burst signal detection principle.  
Figure 6: The discontinuity of \(r_{0}(k)\).  
The expression of \(r_{0}(k)\) is:  
\[\begin{array}{l}r_{0}(k)=r_{0}(iN+j)\\ =r(iL_{0}+j),\ \ \ i=0,1,\cdots,P-1\\ j=0,1,\cdots,N-1,\ \ \ k=iN+j\end{array}, \tag{6}\]  
As described in Section 3.1, \(r^{*}(k)r(k-1)\) can convert the frequency offset to one fixed phase offset, so it can be eliminated by one modulo operation. However, as shown in Figure 7, \(r_{0}{{}^{*}}(k)r_{0}(k-1)\) contains two different kinds of time delays, namely one symbol and \(L_{0}+1-N\) symbols. In addition, it will convert the frequency offset to two different fixed phase offsets, namely \(e^{-|x_{0}T}\) and \(e^{-|(L_{0}+1-N)x_{0}T}\). So, they cannot be eliminated by one modulo operation.  
Therefore, based on the delay within the group and the delay between the two groups, we divide the dual correlation formula into two parts to calculate the modulus separately, that is:  
\[\begin{array}{l}corr^{\prime}=\left|\sum\limits_{i=0}^{P-1}\sum\limits_{k= iN+1}^{(i+1)N-1}{r_{0}{{}^{*}}(k)r_{0}(k-1)\{{a^{*}}(k)a(k-1)\}^{*}}\right|\\ +\left|\sum\limits_{i=1}^{P-1}{r_{0}{{}^{*}}(iN)r_{0}(iN-1)\{{a^{*}}(iN)a(iN-1) \}^{*}}\right|\end{array} \tag{7}\]  
Herein, the first part is the dual correlation between symbols within each group, and the second part is the dual correlation between symbols in the adjacent group connection part.  
Bring Equations (1) and (6) into Equation (7), consider the non-correlation between noise and UW, and ignore influence of noise, Equation (7) can be rewritten as follows:  
\[\begin{array}{l}corr^{\prime}=\left|\sum\limits_{i=0}^{P-1}\sum\limits_{k= iN+1}^{(i+1)N-1}{{s^{*}}(k)s(k-1)\{{a^{*}}(k)a(k-1)\}^{*}}{e^{-{\rm{i}}{ \omega_{0}T}}}\right|\\ +\left|\sum\limits_{i=1}^{P-1}{{s^{*}}(iN)s(iN-1)\{{a^{*}}(iN)a(iN-1)\}^{*}} {e^{-{\rm{i}}(L_{0}+1-N)\omega_{0}T}}\right|\end{array} \tag{8}\]  
As shown in Equation (8), the frequency offset is converted to two different fixed phase, and fixed phase is eliminated by two modulo operation. Then the sliding detection window is aligned with the UW of received signal, and \(corr^{\prime}\) will be maximum.
## 4 Verification of Repair Effect
### Compressive Strength  
The compressive strengths of the specimens before and after repair were tested by a YAW-300C compression testing machine (Liling Testing Machine Co., Ltd., Jinan, China) in accordance with GB/T 50081-2019 [33], three specimens in each group, the loading rate was set as 0.5 MPa/s, the results are shown in Table 10.  
It can be seen from Table 10 that at 300, 400, 500, and 600 \({}^{\circ}\)C, the compressive strength of the repaired specimens increased by 0.23%, 2.7%, 16.16%, and 202.68% compared with the damaged specimens and reached 84.69%, 79.42%, 76.41% and 72.3% of that of the undamaged specimens, respectively. When the damage temperature was low, the compressive strength of the repaired specimens was not significantly improved. On the other hand, when the damage temperature was high, the compressive strength of the repaired specimens showed significant improvement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Damage Temperature/\({}^{\circ}\)C** & **Undamaged/MPa** & **Damaged/MPa** & **Repaired/MPa** \\ \hline
300 & 67.15 & 56.74 & 56.87 \\
400 & 67.15 & 51.93 & 53.33 \\
500 & 67.15 & 44.17 & 51.31 \\
600 & 67.15 & 16.04 & 48.55 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Compressive strength of concrete specimens under different conditions.  
Figure 13: Process of immersion method. Note: The water-based repair fluid may cause rehydration in damaged concrete, but it was not considered in this study.