esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on the Recent Numerical Studies of Liquid Atomization
## 6 Multi-Fluid and Multi-Scale Eulerian Method
### Theoretical Bases
The eddy size \(\lambda\) is modeled by turbulent kinetic energy \(k\), viscosity \(\
u\) and dissipation rate \(\varepsilon\) as follow [72]:  
\[\lambda=\sqrt{10\
u\frac{k}{\varepsilon}} \tag{39}\]  
If the criteria in Equation (38) are satisfied, the disintegrating mass rate from the continuous liquid to dispersed liquid reads [73]:  
\[S_{dl}=-S_{cl}=\rho_{cl}v_{fl}\frac{\pi\lambda}{6\Delta x} \tag{40}\]  
where \(S_{dl}\) is the mass gain for the dispersed phase and \(S_{cl}\) is the mass loss for the continuous phase. The disintegrated liquid droplets or gas bubbles are referred to as the dispersed interface. The variation of the sizes is referred to as change of interface scales. In the multi-scale method, the change of the interface scales can be realized by discrete population balance model. Using the dispersed droplets as an example as shown in Figure 5, a discrete number of size groups \(G\) is set covering the possible minimum and maximum range of the droplet size. Increasing the number of size groups improves the accuracy but would require more computer resources, and normally 6-9 groups was considered enough. Also, if the many size groups in a dispersed phase show great discrepancy in flow features such as a too-large difference in velocities between different scales, the dispersed phase can be further divided into several velocity groups, each of which contains several size groups. As matter of course, several more groups of N-S equations would be required, thus increasing the computer burden. A total number of \(G\) size groups for the dispersed liquid phase, whose index can be \(i,j\) or \(k\) in order to distinguish different groups. Increasing index means increasing droplet size  
Figure 5: Illustration of the discrete population balance model. Mathematically, the net birth rate of a particular size group can be expressed by:  
\[S_{i}=(B_{B}+B_{C}-D_{B}-D_{C})_{i} \tag{41}\]  
where \(B_{B}\) is the droplet birth rate due to breakup of droplets large than the \(i\)-th size group, \(B_{C}\) is the droplet birth rate due to the coalescence of droplets smaller than the \(i\)-th size group, \(D_{B}\) is the death rate due to breakup of the \(i\)-th size group into smaller droplets and \(D_{C}\) is the death rate due to coalescence of \(i\)-th size group with other size group. Currently, the breakup rate and coalescence rate can be calculated from the [PERSON] and Svendsen model [74] and [PERSON] and Blanche model [75], respectively. Since these two models are derived from bubbles breakup or coalescence, future efforts need to be dedicated to building models regarding droplet breakup or coalescence.
# Estimating Double Cropping Plantations in the Brazilian Cerrado through PlanetScope Monthly Mosaics
## 3 Results
### Textural Features
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **ASM** & **CON** & **COR** & **DIS** & **ENE** & **ENT** & **HOM** & **MAX** & **MEAN** & **VAR** \\ \hline ASM & 1 & & & & & & & & & \\ CON & \(-\)0.343 & 1 & & & & & & & & \\ COR & 0.067 & \(-\)0.381 & 1 & & & & & & & \\ DIS & \(-\)0.561 & 0.924 & \(-\)0.358 & 1 & & & & & & \\ ENE & 0.957 & \(-\)0.477 & 0.178 & \(-\)0.719 & 1 & & & & & \\ ENT & \(-\)0.772 & 0.641 & \(-\)0.234 & 0.862 & \(-\)0.912 & 1 & & & & \\ HOM & 0.822 & \(-\)0.636 & 0.180 & \(-\)0.869 & 0.937 & \(-\)0.966 & 1 & & \\ MAX & 0.969 & \(-\)0.400 & 0.084 & \(-\)0.628 & 0.975 & \(-\)0.834 & 0.880 & 1 & \\ MEAN & 0.025 & 0.049 & 0.423 & 0.059 & 0.149 & \(-\)0.086 & \(-\)0.038 & 0.006 & 1 & \\ VAR & 0.149 & 0.006 & 0.359 & \(-\)0.012 & 0.266 & \(-\)0.189 & 0.061 & 0.123 & 0.973 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coefficients of correlation of 10 GLCM textural features derived from band 4 of PlanetScope mosaic obtained in October 2021. All positive and negative correlations higher than 0.80 are highlighted in red color. ASM = angular second moment; CON = contrast; COR = correlation; DIS = dissimilarity; ENE = energy; ENT = entropy; HOM = homogeneity; MAX = maximum value; MEAN = mean value; VAR = variance.
## 4 Proposed Scheme (Multi-BSM Approach)
### Attacker's Model
#### 4.1.3 Malicious or Rational Attack  
An attack on the network that is initiated by an individual with the sole aim of personal gains is known as a rational attack, while the aim of the malicious attacker is to harm and degrade the network.  
For the simulation purpose, we focused on an active inside attacker whose intention may be rational or malicious. The types of position falsification attacks that were addressed during the simulations are discussed as follows:  
* **Attack Type 1:** In this type of position falsification attack, the attacker broadcasts a fixed position in the BSMs that is periodically generated.
* **Attack Type 2:** In this, the attacker broadcasts a random position in the BSMs that is periodically generated.
* **Attack Type 3:** In this, the attacker pretends to be a legitimate vehicle for a certain period by broadcasting genuine information in BSMs and initiates a position falsification attack by broadcasting the same position persistently after a while.
## 2 Materials and Methods  
### Sensors  
The device will be developed thinking on the sensors selected and their protocol of communication. In this case, sensors are predetermined by Maxim Integrated Company for its proven operability in cultural heritage purposes [7, 21], as well as its wide range offer of sensors, analogue to digital converters and not expensive prices.  
A practical application for the testing of the system is performed at the end of this paper. Two 1-Wire slaves were assembled in a single double sided printed board of 12 mm \(\times\) 26 mm: a DS18B20 [26], which is a temperature sensor with an accuracy of \(\pm\)0.5 \({}^{\circ}\)C, with a range of measurements from \(-\)10 to \(+\)85 \({}^{\circ}\)C, and an Analog to Digital Converter (ADC) DS2450 (Maxim Integrated Products, Inc., San Jose, CA, USA) [27] which can use one of its four channels to read the analog potential of a HIH 4030-001 RH sensor [28] (Honeywell International, Inc., Minneapolis, MN, USA) with \(\pm\)3.5% RH accuracy (uncalibrated) and a range of measurements from 0% to 100% of relative humidity.  
The Maxim Integrated devices have their own and unique serial number implemented in production and impossible to change. A calibration procedure with aqueous solutions of two salts (lithium chloride and sodium chloride) was applied in laboratory according to the standard ASTM E 104-02 [29] in order to study the measurement errors of RH sensors. All RH sensors were introduced in a small chamber equilibrated with a saturated solution of a salt. Next, calibration equations were obtained for each sensor in order to relate the voltage output and RH.
## 1 Introduction
This is possible thanks to the transformer-based approach, which allows improved area use as they simultaneously act as RF chokes to bias the active devices, and as baluns at the input and output terminals to convert the SE signal into differential mode, and vice versa. To implement the transformers we replicated a state-of-the-art tool into a MATLAB script that generates and equalizes the desired inductances to perform impedance matching for a given frequency and coupling factor value. The content of this paper is organized as follows: the circuit design is discussed in Section 2, and the implementation of the interstage transformers is discussed in Section 3. Then, in Section 4, the simulated S-parameters, _NF_, and non-linearity results are shown, along with a comparison with state-of-the-art LNAs. Finally, in Section 5, the conclusions of this work are drawn.
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 5 Experimental Setup  
### Data  
We have evaluated various speech separation networks using the WHAM! dataset [16] and task. WHAM! is a noisy variant of WSJ0-2 Mix [2], which is used to train models for separating speech in audio mixtures. However, the WSJ0-2 Mix dataset is less practical because it cannot generalize to speeches from a wider range of speakers due to the overlap between the two speakers' audios being 100%. WHAM! improves upon this by blending speeches with background noise captured in cafes, restaurants, and bars. Furthermore, the noise samples utilized in this dataset have undergone processing to exclude any parts that contain intelligible speech. The noise in WHAM! poses a more significant barrier to mixed audios compared to WSJ0-2 Mix, mostly because of its consistently sampled signal-to-noise ratio (SNR), which varies between \(-6\) dB and \(3\) dB.
# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 3 Experimental Setup  
All the experiments were performed on the Google Colab Pro platform that provided the required computational means of the deep learning problem a Tesla T4 GPU (16 GB VRAM), 24 GB of RAM, and dual-core Intel Xeon processor. The time taken to train a fold was between 12 min and 18 min depending with the size of the databases and complexity of the model. The ease of accessibility, including to those arriving at the project without high-end local systems, as well as scalability was guaranteed by this cloud-based infrastructure. We used the following four common measures of performance to assess the performance provided by the proposed framework of breast cancer classification: accuracy, precision, recall, and F1-score. Accuracy shows the percentage of correctly predicted cases (true positive and true negative) out of all cases, whereas precision shows the percentage of true positively predicted cases out of all the predicted cases or cases that were given the status of positive. Recall (or sensitivity) measures how well a model can find the reality with respect to the positives, and F1-score, which acts as the harmonic mean of precision and recall, is a good metric to be employed to an imbalanced dataset [69]. Although the current research widely uses these measurements, their soundness is based on the pioneering works on information retrieval such as the definitions and analysis framework worked out by [70] and the exhaustive principles of evaluations presented in [71], as follows:  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{15}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{16}\]  
\[\text{F1}-\text{score}=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{17}\]  
where  
* _TP_ (True Positives): Number of correctly predicted positive cases;
* _TN_ (True Negatives): Number of correctly predicted negative cases;
* _FP_ (False Positives): Number of negative cases incorrectly predicted as positive;
* _FN_ (False Negatives): Number of positive cases incorrectly predicted as negative.  
These well-established formulas provide a comprehensive view of model performance, particularly in imbalanced medical datasets where relying on accuracy alone may be misleading. They also ensure consistency in evaluating model robustness across various benchmark datasets used in this study.
## 3 R-SIN Architecture Model Based on DaaC
### Definitions and Principles  
#### 3.1.1 Basis Definitions  
**Definition 3 (SIN Reorganization and R-SIN).**_SIN reorganization refers to a process of regrouping the entities of the SIN according to the specific requirements of the task, function, and service. After the reorganization, the corresponding network is called the reorganization SIN, which is abbreviated as the_ **R-SIN**_. The SIN that is discussed is usually an integrated and information-supported national infrastructure. However, R-SIN is a sub-SIN that is built with task- oriented requirements as traction._  
**Definition 4 (Reorganization Goal, RG).**_According to the needs of the task, the RG is a blueprint that is used to guide a new state that is achieved and entered by the SIN through reorganization. Among them, mainly includes that what rather functions the reconfigurable network should have, how to reorganize the various functional entities, and what extent the functional entities should serve, etc._  
**Definition 5 (Reorganization Scheme, RS).**_RS is the guidance for specific networking of R-SIN according to RG. In conjunction with the specific circumstances of space entities, it is also the specific plans for achieving specific tasks, providing specific services, and working with each other between entities._  
**Definition 6 (R-SINs Management Center, RMC).**_RMC is primarily responsible for managing the processes and ways of building R-SIN. It determines the size and composition of the R-SIN tasks based on the task-oriented requirements and superior commands. Meanwhile, RMC is responsible for the delivery of related instructions throughout the build process. It includes sending incentives of building networks, accepting responses to data centers and entity resource pools, and so on._  
**Definition 7 (R-SINs Resource Center, RRC).**_RRC is primarily used to provide the entities and data resources of R-SIN. It includes an entity resource pool (_**ERP**_) and data center (_**DC**_). The ERP contains the entire contents of all the components of the SIN. When building an R-SIN, we only need to select the required entities resources from the pool, and connect them so that it can function as needed._
# An Overview on Push-Based Communication Models for Information-Centric Networking
## 3 Push-Based Approaches in ICN
### Push Communication via Call-Back Approach  
In a call-back push-based approach, producers start the ICN transmission. This requires a special implementation, e.g., an additional protocol so that producers can emit special Interest packets that have as purpose to trigger regular Interest packets by interested consumers. Such particular Interest packet would carry a call-back Name Prefix [13]. The call-back approach may be interesting for one-time events where there is the need to quickly refresh the status of consumers. However, it adds extra overhead (three-step handshake), and results in additional in-network state to allow routers to distinguish call-back Interest packets from regular Interest packets.
# Effect of Different Acoustic Parameters on NOx Emissions of Partially Premixed Flame
## 3 Results and Discussion  
### The Relationship between FINOx with Acoustic Amplitude  
The NOx emissions for partially premixed flame under acoustic excitation are plotted in Figure 4. The linear relationship between acoustic amplitude and EINOx were presented at a fixed frequency. Under three different premixed gas parameters, the lines displayed a negative slope when the frequency was less than 180 Hz, while a positive slope appeared when the frequency was more than 190 Hz. Therefore, a critical frequency (\(f_{c}\)) was defined between 170 Hz and 190 Hz, which was consistent with [PERSON]'s results from a liquid boiler [20].  
Figure 4: The relationship between acoustic parameters and NOx emission index (EINOx) for partially premixed flame; (**a**) Case 1; (**b**) Case 2; (**c**) Case 3.  
Figure 3: Acoustic pressure of the experimental system; (**a**) Time domain; (**b**) Frequency domainEINOx and amplitude had linear relationships in each case. EINOx reduced 15% to 30% with the increase of amplitude by every 100 Pa when frequency was less than \(f_{\rm c}\). Furthermore, the slope in this region ranged from \(-0.009\) to \(-0.003\). In contrast, the linear relationships were reversed when the frequency was greater than \(f_{\rm c}\); the positive slopes were also observed to range from 0.001 to 0.006. In a word, EINOx decreased with the increase of acoustic amplitudes while frequency was less than \(f_{\rm c}\), and increased while the frequency was greater than \(f_{\rm c}\).
## 4 Application Example  
The Luozhou Bridge in Fuzhou, China, is a self-anchored suspension bridge with three towers and four spans. The span layout is 80 m + 168 m + 168 m + 80 m, as shown in Figure 5. Main design parameters: the rise-span ratio of main span is 1/6, the height of the main tower is 48.9 m, the elastic modulus of stiffening beam is 2.06 \(\times\) 10\({}^{5}\) MPa, the elastic modulus of main cable is 1.95 \(\times\) 10\({}^{5}\) MPa, the longitudinal stiffness of main tower (single tower) is 50 MN/m, the vertical bending moment of stiffening beam is 1.690 m\({}^{4}\) and the cross-sectional area of stiffening beam is 1.501 m\({}^{2}\).
# The Link between Purine Metabolism and Production of Antibiotics in _Streptomyces_
## 5 Guanosine Metabolism Impinges on Antibiotic Production
### Regulation of Antibiotic Production by c-di-GMP  
Both (p)ppGpp and c-di-GMP affect morphological differentiation and regulation of antibiotic production. As noted above, the master regulator BldD represses genes involved in formation of aerial hyphae (the _bld_ genes) and sporulation (the _vhi_ genes) during vegetative growth, and this occurs when BldD binds c-di-GMP [29; 32]. Accordingly, c-di-GMP is required for repression of the BldD regulon, and overexpression of the diguanylate cyclases CdgA or CdgB blocks formation of aerial hyphae [3; 31]. BldD directly controls expression of _cdgA_ and _cdgB_, a regulatory mechanism that may prevent excessive accumulation of c-di-GMP. A _bldD_ mutant likewise exhibits a bald phenotype as it bypasses formation of aerial hyphae and sporulates prematurely [32]. The production of antibiotics correlates with these morphological phenotypes as both overexpression of diguanylate cyclases and inactivation of _bldD_ leads to a defect in ACT production [73]. BldD controls activity of the _adpA_ gene (_bldH_ in _S. coelicolor_), which encodes an AraC-family activator of _actilI_-ORF4 and _redD_ expression, and it binds directly to _nsdA_, which encodes a repressor of ACT production [3; 74]. Accordingly, either absence of BldD or hyper-activation of its DNA binding by excess c-di-GMP is likely to result in failure to control expression of cluster-situated activators of ACT and RED. Since XdhR also binds _actilI_-ORF4, and since DNA binding by XdhR is attenuated by c-di-GMP ([PERSON] and [PERSON], unpublished), we speculate that XdhR may also participate in fine-tuning of _actilI_-ORF expression in response to cellular levels of c-di-GMP.
## 4 System and Problem Definition for Network Aware Server Consolidation
### The Network-Aware Consolidation Algorithm
```
Input: Set of servers, \(\boldsymbol{S}=\{s_{1},s_{2},\,...,s_{N}\}\); Server utilizations, \(\boldsymbol{u}=[\ u_{1},u_{2},\,...,u_{N}]^{T}\in\mathbb{R}_{+}^{n}\); Flow injection rate \(\boldsymbol{R}=[\boldsymbol{r}_{1},\boldsymbol{r}_{2},...,\boldsymbol{r}_{N }]\in\mathbb{R}_{+}^{n\times N}\); Communication cost \(\boldsymbol{\Psi}=\{\ \psi(i,j)\}\in\mathbb{R}_{+}^{N\times N}\) Output: Utilization profile \(u\) after BCC Consolidation
1:Clustering:
2:\(\{S_{1},S_{2},...,S_{k}\}\in\boldsymbol{S}\leftarrow\text{Graph-partition}( \boldsymbol{S},\boldsymbol{\Psi})\)\(\triangleright\) Kernighan-Lin
3:for\(m=1\) to kdo\(\triangleright\) ascending order of server utilization \(u\)
4:\(S_{m}\leftarrow\)sort \((S_{m})\)\(\triangleright\) loop for migrating server
5:for\(i=1\) to size \((S_{m})-1\)do\(\triangleright\) loop for destination server
6:for\(j=\)size \((S_{m})\) to \(i+1\)do\(\triangleright\) loop for destination server
7:\(\widetilde{u}=u+u_{j}\)
8:\(\widetilde{r}=\boldsymbol{r}_{t}+\boldsymbol{r}_{j}\)
9:if (\(\widetilde{u}<D_{u}\)and \(\forall\ \ x\in\{1,2,...,\theta\}\ \widetilde{r}_{\varepsilon}<B_{\text{W}}\)) then
10: Migrate \((s_{i},s_{j})\)\(\triangleright\) Break from loop in line 6
11:break
12:endif
13:endfor
14:endfor
15:endfor
```  
**Algorithm 2**Algorithm for Bandwidth-Constrained Consolidation (BCC). The necessary steps of the migration function (_Migrate_) used in the pseudo-code of BCC Algorithm 2 are shown in Algorithm 3. The function migrates server \(a\) to server \(b\). At first, all the VMs running in migrating server \(a\) is migrated in the destination server \(b\). So the utilization of the destination server increases which is the summation of the utilization of both the servers. Vector \(\boldsymbol{r_{b}}\) is updated based on all the flows running on server \(b\) post migration. After successful migration,server \(a\) is put into the PowerNap state [45] having zero utilization. In the PowerNap state, most of the components of the server are powered down except the network interface card (NIC), the wireless transceivers, and a small portion of the CPU to get the signal for waking up when required. ```
Input: Source and destination server for migration Output: Updated utilization profile \(\mathbf{u}\) after single migration
1:functionMigrate(server \(a\), server \(b\))
2:\(b\leftarrow\) VMs running on \(a\)
3:\(u_{b}=u_{a}+u_{b}\)
4:\(\mathbf{r_{b}}=\mathbf{r_{a}}+\mathbf{r_{b}}\)
5:\(a\to PowerNap\) state
6:\(u_{a}=0\)
7:\(\mathbf{r_{a}}=\mathbf{0}\)
8:endfunction
```  
**Algorithm 3**Migration Function.
## 5 Conclusions  
By conditioning the solution with the surfactant SLES chemically, the hydrophobicity of HIPS and ABS surfaces was reduced. For SLES concentrations of 0.10 and 0.20 g/L, a difference in the contact angles of approximately 10\({}^{\circ}\) is a satisfactory result that confirms the potential of using SLES as a surfactant to support the flotation of HIPS and ABS mixtures. The contact angle for these two results for HIPS is higher than that of ABS. This indicates that HIPS, as a more hydrophobic material (in the presence of air bubbles), should float on the surface of the flotation solution, and ABS, as a more hydrophilic material, should sink to the bottom. The uneven surface of the plastics, which can be seen in the microscopic images, had an impact on the results of the contact angles and standard deviations. The surfaces are completely wettable by surfactant solutions in accordance with [PERSON]'s model for the wettability of rough surfaces.  
In the future, further investigations are planned using a heavy liquid., i.e., a solution of tap water and NaCl and in the presence of SLES as the surfactant supporting flotation. Another direction of research could be investigating the effect of plastic surface cleaning on their contact angle. Moreover, HIPS and ABS surface wettability tests can be used to test solutions of other surfactants in tap water. The testing of contact angles on other plastics present in the mill obtained from refrigerators (i.e., PP and PA) could be also carried out.  
Conceptualization, K.T. and B.M.; methodology, K.T. and B.M.; investigation, B.M., E.P. and J.L.M.E.; data curation, E.P.; writing--original draft preparation, B.M.; writing--review and editing, K.T.; visualization, B.M.; supervision, K.T.; project administration, K.T.; funding acquisition, B.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Education (Poland), grant number DWD/6/0413/2022.  
Data Availability Statement: Data is contained within the article.  
The materials used for the experiments were from the Polish Recycling Corporation WEE recycling plant (Poland). The tests of moisture content, NIR spectrometer analysis and MFI coefficients were carried out at the Research and Development Centre of the Polish Recycling Corporation.  
Author [PERSON] was employed by the company Polish Recycling Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.