esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine Internet for Internetworking in Oceans: A Tutorial
## 6 Challenging Issues for Marine Internet
### Super-Scale WANET with Real-Time Agility  
As mentioned above, marine Internet is a super-scale WANET with opportunistic networking, which needs to accommodate various types of networks to provide cost-effective network services according to application requirements in dynamic networking environments. This requires the WANET to be smart enough to make real-time decisions on selecting proper communication and networks automatically when networking conditions change. Basically, the following issues have to be addressed.  
* Transmission control adaptive to dynamic user distributions: The coastline network is the key to provide cost-effective network services to marine Internet users. As illustrated in Figure 6, the TBS can use broadcast or unicast communication to link vessels according to the user distributions, i.e., broadcast is favorable for dense distributions usually occurring in near water areas, where users usually carried by vessels can directly communicate with the TBS when under its coverage. Unicast is better used to link a TBS to a particular vessel usually located in far water area, and large transmission power is needed for long-range high-speed communication. However, moving vessels change user distributions so that these transmission arrangements have to change too. It is necessary to study when and how to change so that the overall communication quality can be maintained and optimized.
* Real-time network selection with predictable performance: As discussed in Section 5, network nodes in marine Internet needs to select networks on real-time in order to provide the users with cost-effective services. An open issue is how a network node can perceive possible changes to occur in the candidate network so that the selected network can provide satisfactory services for a predictable time; otherwise, the node may frequently change networks, which will affect networking performance. This issue becomes more challenging when candidate networks are NANETs or AANETs due to their dynamic networking performance, which will further affect several end-to-end networking performance indicators, as discussed below.
## 1 Introduction
However, their results indicate that the thickness of the thin layer ring has no significant effect on the ice formation. [PERSON] et al. [16] calculated the influence of geometrical effects on the ice formation in a rectangular water-filled tank. The results showed that the solid fraction for the aligned cylinder case is smaller than for staggered ones. [PERSON] et al. [17] simulated in 2D the solidification of water around a bent tube with a cooling fluid flow inside it. They revealed that an increase of the wall temperature, an increase of the initial liquid PCM temperature, or an increase of the curvature radius would reduce the interface position and consequently reduce the solidified mass and decrease the interface velocity. [PERSON] et al. [18] numerically investigated the thermal effects of the coolant inlet temperature on the charging process in an ice storage system. Their results showed that a lower coolant temperature enhances the thermal efficiency as well as the ice formation rate. [PERSON] et al. [19] numerically studied the melting process in a cold storage system containing a phase change material (PCM). They examined the effects of some parameters like the length and number of fins on the thermal performance of the storage. Their observations indicated that, with larger fins, the area influenced by these fins is enlarged and the discharge process occurred with a higher speed. According to above literature review, although extensive research has been accomplished ice-on-coil storage tanks, little research has been devoted to the shell and coil tube ice storage systems especially the effect of HTF (heat transfer fluid) channel geometry in horizontal ice storage systems [20]. Hence, as there is no specific numerical study on the melting process in a shell and coil tube ice storage system, the present work intends to determine the effects of the main geometrical and operational parameters on the melting process in a shell and coil tube ice storage system by a 3D numerical flow solver. A parametric study covering the influence of the inlet temperature and flowrate of the HTF is performed with a particular attention paid on the effect of natural convection on the melting process. The influences of the coil geometrical parameters including coil pitch, diameter, and height are presented and discussed in details. The novelty of the present study is that, according to the comprehensive literature review performed, few significant studies about the discharge process (melting) in ice-on-coil ice storage system with horizontal coil tube have been published so far. The benefit from using horizontal coil tube instead of straight tube to increase the melting rate was not also considered completely in previous researches. On the other hand, the melting process in a horizontal shell and coil tube ice storage system has not been studied numerically in previous published studies. Also, in this paper, a comprehensive study is presented by considering the influence of both operational and geometrical parameters numerically.
## Appendix B  
\[d_{1}=\frac{2\left[R_{1}+\eta\left(z_{Q}-z_{1}\right)\right]}{\eta^{2}R}\]  
\[d_{2}=\frac{\eta R^{2}+\eta\left(z_{Q}-z_{1}\right)^{2}+R_{1}\left(z_{Q}-z_{1 }\right)}{\eta R_{1}}\]  
\[d_{3}=\frac{\left[R_{1}+\eta\left(z_{Q}-z_{1}\right)\right]^{2}+\left(\eta^{ 2}+1\right)R^{2}}{\eta^{2}R^{2}}\]  
\[A_{1}=(d_{2}+d_{1})d_{3}-d_{2}\,\ B_{1}=d_{3}^{2}-d_{3}+d_{1}d_{2}\]  
\[p_{11,22}=\frac{d_{1}\pm\sqrt{d_{1}^{2}+4d_{3}}}{2},\ \ \ h_{11}=\frac{2}{1-p_{11}}\,\ \ h_{22}= \frac{2}{1-p_{22}}\]  
\[V_{11}=-\frac{A_{1}p_{11}+B_{1}}{p_{22}-p_{11}}\,\ \ V_{22}=-\frac{A_{1}p_{2}+B_{1}}{p_{ 22}-p_{11}}\]  
\[d_{11}=\frac{2\left[r_{1}+\eta\left(z_{Q}-z_{2}\right)\right]}{\eta^{2}R}\]  
\[d_{22}=\frac{\eta R^{2}+\eta\left(z_{Q}-z_{2}\right)^{2}+r_{1}\left(z_{Q}-z_{2 }\right)}{\eta Rr_{1}}\]  
\[d_{33}=\frac{\left[r_{1}+\eta\left(z_{Q}-z_{2}\right)\right]^{2}+\left(\eta^{ 2}+1\right)R^{2}}{\eta^{2}R^{2}}\]  
\[C_{1}=(d_{22}+d_{11})d_{33}-d_{22}\,\ \ D_{1}=d_{33}^{2}-d_{33}+d_{11}d_{22}\]\[p_{33,44}=\frac{d_{11}\pm\sqrt{d_{11}^{2}+4d_{33}}}{2},\ \ \ h_{33}=\frac{2}{1-p_{33}}\,\ \ h_{44}= \frac{2}{1-p_{44}}\]  
\[V_{33}=-\frac{C_{1}p_{33}+D_{1}}{p_{44}-p_{33}}\,\ \ V_{44}=-\frac{C_{1}p_{33}+D_{1} }{p_{44}-p_{34}}\]  
\[\begin{array}{c}J_{00}=\int\limits_{0}^{\frac{\pi}{2}}\operatorname{arsinh} \frac{\eta R\cos(2\beta)+\eta R_{1}+z_{1}-z_{Q}}{\sqrt{D_{20}}}d\beta-\\ \int\limits_{0}^{\frac{\pi}{2}}\operatorname{arsinh}\frac{\eta R\cos(2\beta)+ \eta r_{1}+z_{2}-z_{Q}}{\sqrt{D_{20}}}d\beta\end{array} \tag{10}\]  
Integral \(J_{00}\) does not have an analytical solution and will be solved numerically using Mathematica code by default.
## 3 Proposed Method
### Implementation Details  
In our experiments, ResNet-50 was used as the backbone network for feature extraction. The output of class block, \(x\in\mathbb{R}^{d}\), was used as the acquired image representation, and \(d=512\) in our experiment. We measured the feature distances of two images by calculating the cosine distances. The stochastic gradient descent [37] with hyper-parameters (\(weight\_decay=5\times 10^{-4},momentum=0.9,nesterov=True\) ) was adopted for model optimization. We set the learning rate of the fully connected layer to 0.005 and the other layers to 0.001 with a gradual decrease. All the images were scaled to \(256\times 256\) pixels.  
Even if the features could be effectively clustered, if our query lies at the edge of the space in its category, we inevitably obtain a considerable amount of true negatives, as shown in Figure 8. One of the solutions to retrieve more true-positives is to enlarge the distance between different clusters. For this purpose, we set the arcFace loss [38] to measure the distance between the images; it uses angular distance to represent the distances between features. Furthermore, the scaling factor \(s\) was set to 10 in our experiments. Algorithm 1 depicts the whole pseudo code algorithm employed to train the proposed neural network architecture.  
\[L=\frac{1}{N}\sum_{i}-\log\frac{e^{s\left(\cos\left(\theta_{y_{i}i}+m\right) \right)}}{e^{s\left(\cos\left(\theta_{y_{i}i}+m\right)\right)}+\sum_{j\
eq y_{ i}}e^{s\cos\left(\theta_{j},i\right)}} \tag{8}\]  
```
1:Two labeled images \(\{p,y_{p}\}\) and \(\{q,y_{q}\}\); Local Net \(\theta\left(\cdot,\mathbf{w}_{l}\right)\); Feature extractor \(\varphi\left(\cdot,\mathbf{w}_{f}\right)\)
2:Feature embedding \(f_{emb}\)
3:while maximum iterations not reached do
4: Extract features: \(f_{i}\)=\(\varphi\left(i,\mathbf{w}_{f}\right)\) for all i=p,q;
5: Extract attention map: \(a_{i}\)=\(\theta\left(i,\mathbf{w}_{l}\right)\) for all i=p,q
6: Deep feature map:\(\varphi(i;\theta)=concat(a_{i}\times f_{i},a_{i})\)for all i=p,q
7:\(\mathbf{y}\leftarrow\frac{1}{C(p)}\sum_{\forall j}s\left(p_{i},q_{j}\right)g \left(p_{j}\right)\) // \(i\) and \(j\) are the positions of \(p\) and \(q\) in the feature map,
8:\(f_{emb}\leftarrow\phi(q;\theta)\)
9: Fine tuning:min(\(L(f_{emb},y_{q})+L(y,if\ y_{q}=y_{p})\))
10:endwhile
```  
**Algorithm 1** Framework of LFASM.  
Figure 7: Diagram of a ResBlock in the Feature Extractor. To compute attention map \(\mathbf{M}\), we applied a convolutional layer and a transposed convolutional layer (both equipped with batch normalization and ReLU) on the convolution outputs in each block.
## 2 Materials and Methods
### Extraction of the Burned Area  
The area of forest burned by the East Coast fire was extracted using satellite-based NBR and NDVI. The process of extracting the burned area was as follows. First, Terra/MODIS data from 1999 for the pre-fire forest were unavailable. Through careful study of our preliminary vegetation index analysis output, we assumed that, according to previous studies [16], the moisture condition in the burned area would have mostly recovered after 10 years from the forest fire event and that the vegetative conditions would significantly differ from those immediately after the East Coast fire of 2000. Additionally, we considered the meteorological conditions affecting that area. Mean air temperature was 15.5 (16.3) \({}^{\circ}\)C and cumulative precipitation was 63.8 (38.9) mm in May 2000 (2012). Both air temperature and precipitation in 2012 were similar to those in 2000. Thus, the NBR\({}_{2.1}\) of 2012 was selected as the pre-fire condition for the estimation of dNBR, and MODIS dNBR\({}_{2.1}\) was calculated in the coastline over Donghae-si, Samcheok-si, and UJin-gun. The burned area was extracted for a dNBR value greater than 0.10 and excluded for a dNDVI value less than 0.0. The extracted burned area was confirmed using previous studies, and it was consistent with reports in related literature [1; 15; 45]. Supplementary material shows the availability of this MODIS-derived burned area through comparison with some burned area from Landsat dNBR of more higher-resolution (Figure S1). Second, the burned area identified using MODIS dNBR\({}_{2.1}\) was classified into three severity levels based on MODIS dNBR\({}_{2.1}\) values: low, moderate, and high. The ranges of dNBR values for fire severity were flexible and changed according to surface conditions, season, and the interval between data used for calculating the dNBR [33; 46; 47; 48]. We defined the MODIS dNBR ranges of 0.10-0.15, 0.15-0.20, and 0.20+ as low, moderate, and high levels of severity, respectively. In this study, high level means most of the trees killed in a pixel. Partially or seriously damaged areas in a pixel are defined as moderate level and low level, which might include burned and unburned areas in a pixel. The burned area from Terra/MODIS was expressed by serious damage to the coastline larger rather than in inland, similar to previous studies [45]. Finally, the land types in the burned areas were classified as forest and non-forest using IGBP MODIS land cover data. The non-forest areas were excluded in order to solely monitor changes in forest recovery.
## 3 Results
In comparison, the intracellular AuNC and Fe\({}_{3}\)O\({}_{4}\)@Au pNPs mediated an increase of the O\({}_{2}\)\({}^{\bullet}\)- level by 70% and 20%, respectively. This is due to X-ray-induced emission of photoelectrons and Auger electrons from the AuNC and Au nanoparticles, which generated O\({}_{2}\)\({}^{\bullet}\)- due to the reduction of nearby oxygen. The HO\({}^{\bullet}\) formation in MCF-7 cells under X-ray exposure was enhanced by intracellular AuNC by 25%, only, whereas X-ray interactions with intracellular Fe\({}_{3}\)O\({}_{4}\) NP and Fe\({}_{3}\)O\({}_{4}\)@Au pNPs raised the HO\({}^{\bullet}\) concentration for ca. 80% (Figure 9c). The latter is explained by X-ray-induced activation of the Fe\({}_{3}\)O\({}_{4}\) surfaces, which catalyze the HO\({}^{\bullet}\) production through the Fenton mechanism (Figure 11a). The X-ray-provoked impact on the ROS formation in different kinds of cells--MCF-7, A549 and MCF-10A cells--when loaded with Fe\({}_{3}\)O\({}_{4}\)@Au pNPs is illustrated in Figure 10b. Apparently, the ROS formation in A549 cells achieved  
The clonogenic cell survival assay was employed to survey the impact of the Fe\({}_{3}\)O\({}_{4}\) NPs, AuNC or Fe\({}_{3}\)O\({}_{4}\)@Au pNPs on the survival and proliferation of the MCF-7, A549 and MCF-10A cells exposed to X-radiation at single doses of 1, 2, and 3 Gy (Figure 11b,c,d). As expected, the survival curves of irradiated MCF-7 and A549 cells loaded with the diverse kinds of NPs exhibited significantly faster decays than those of the control cells (medium). Amazingly, the AuNC and Fe\({}_{3}\)O\({}_{4}\) NPs coincided in their influence on the survival behavior of both tumor cell lines, although they enhanced the X-ray-induced generation of two different kinds of ROS. The survival curves obtained for the Fe\({}_{3}\)O\({}_{4}\)@Au pNP loaded MCF-7 and A549 cells (blue) showed the steepest decays, which are consistent with the associated dose-modifying factor (DMF) values of 0.448 and 0.422 (Table 1). These DMF values are considerably lower than those obtained for the AuNC and Fe\({}_{3}\)O\({}_{4}\) NP-loaded MCF-7 and A549 cells, which were 0.611 and 0.616, respectively. In contrast, the survival curves of the healthy cells (MCF-10A), when incorporating either Fe\({}_{3}\)O\({}_{4}\)@Au pNPs or Fe\({}_{3}\)O\({}_{4}\) NPs, display similar weak decreases and relatively large DMF values of 0.804 and 0.731, whereas intracellular AuNC caused a more quickly decaying survival curve and a rather small DMF value of 0.344.
# Zero Defect Manufacturing in the Food Industry: Virgin Olive Oil Production
## 3 Proposal for ZDM in VOO Production
### ZDM Definitions
#### 3.1.1 Defects in VOO  
The main causes of the generation of defects in the produced VOO are: the state of incoming fruits; post-harvest and productive process management; and how the final product is stored. The most common defects and their root causes are detailed in [33]:  
* Musty--Characteristic flavour of oils obtained from fruits which have been stored under humid conditions or have been collected with earth or mud on them and have not been properly washed.
* Fusty--This occurs when harvested olives are incorrectly stored and for a longer period than advisable.
* Acid-sour--This defect appears when the olives gathered in the harvest are stored for too long before crushing them to extract the oil.
* Metallic--This is due to oil mills and their machinery whenever some component is not cleaned properly.
* Rancid--flavour of oils which have undergone an intense process of oxidation due to improper storage.  
The fruity sensation, the pungency notes and the bitterness that characterise the particular taste of virgin olive oils are considered their positive attributes. According to the number of defects--or negative attributes--and positive attributes, oils are classified into the following designations [33]:  
\begin{table}
\begin{tabular}{p{56.9 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt} p{85.4 pt}} \hline \hline \multirow{2}{*}{**Industries**} & \multicolumn{2}{c}{**Definitions**} & \multicolumn{1}{p{85.4 pt}}{} & \multicolumn{1}{p{85.4 pt}}{} & \multicolumn{1}{p{85.4 pt}}{} \\ \cline{2-5}  & **Defect** & **Product** & **ZDM Goal** & **New Defect Definition** \\ \hline Manufacturing & Non-conformance to product specifications is detected & PART: is defective or not defective & No defective products leave the production site & – \\ \hline VOO process & Organoleptic and flavour features & VOO: Varying degrees of quality & To produce the best VOO according to predefined objectives & Non-conformance to ZDM multi-objective function \\ \hline \hline \end{tabular}
\end{table}
Table 2: ZDM definitions in the manufacturing and VOO industries.  
Extra virgin olive oil (EVOO)--This is the highest quality rating for an olive oil. It contains zero defects and greater than zero positive attributes.
* Virgin olive oil (VOO)--This oil has a slightly lower quality than extra virgin olive oil. It contains defects from 0 to less than 3.5 and a free acidity of less than 2 percent.
* Lampante virgin olive oil--This type of olive is forbidden for direct sale if it is for consumption purposes. The number of defects is more than 3.5 or it has a free acidity of more than 2 percent.
# Unfolded Algorithms for Deep Phase Retrieval
## 1 Introduction
UPR allows for a unification of the optimization process for system parameters in an end-to-end manner. It should be noted that our proposed methodology is a general framework for improving the performance of phase retrieval systems from both the recovery algorithm design and data-acquisition system viewpoints. We further show that the proposed methodology outperforms two state-of-the-art algorithms, i.e., the SPARTA and IRWF algorithms, in the area of sparse and conventional phase retrieval. Due to the fact that SPARTA and IRWF have been readily shown to outperform existing methodologies in their respective classes of algorithms, we can further assume that the proposed methodology is superior to those algorithms as well. Interestingly, UPR acts as a unified framework for both optimal sensing and recovery within the realm of the first-order methods. The proposed methodology can be viewed as an optimization of both the data-acquisition system and the performance of the underlying signal processing algorithm. As mentioned before, such a framework can be cascaded to the recovery algorithm, which allows for noticeable improvements in recovery accuracy. Finally, we compare our results with said baseline algorithms and different variants of the RWF algorithm that have already shown good performance in the context of phase retrieval. We show that UPR significantly outperforms the baseline algorithms from which it has emerged. It should be noted that some of this work began with out previous work reported in [48]. Here, we expand on the UPR framework by proposing it for a sparse algorithm via SPARTA, expand our results on the IRWF algorithm, and present a general framework for designing task-specific sensing matrices aimed at enhancing the performance of the underlying recovery algorithm. **Organization of the Paper:** Section 2 provides a technical overview of the system model and a discussion of deep unfolding in the context of phase retrieval. Section 3 presents the UPR framework, including its baseline algorithms, initialization method, and our approach to designing the sensing matrix. The performance of UPR is evaluated in Section 4. Section 5 concludes the paper. **Notations:** Throughout this work, we use boldface lowercase letters to denote vectors and boldface capital letters to denote matrices. Superscripts \((\cdot)^{*}\), \((\cdot)^{T}\), and \((\cdot)^{H}\) represent the conjugate, the transpose, and the Hermitian operators, respectively. \(\|\cdot\|_{2}\) represents the Euclidean norm of the vector argument. The operator symbol \(\odot\) represents the Hadamard product of matrices.
## 3 Governing Equations of CNTRC Beams
### Nonlocal Strain Gradient Theory  
The total stress of the beam is defined as follows:  
\[t_{xx}=\sigma_{xx}-\
abla\sigma_{xx}^{(1)} \tag{27}\]  
where \(\
abla=\frac{\partial}{\partial x}\) is the differential operator, and \(\sigma_{xx}\) and \(\sigma_{xx}^{(1)}\) are the nonlocal stress and the higher-order nonlocal stress, respectively, expressed as:  
\[\sigma_{xx}=\int_{0}^{L}E\alpha_{0}\big{(}x,x^{\prime},e_{0}a\big{)}\epsilon _{xx}^{\prime}(x^{\prime})dx^{\prime} \tag{28}\]  
\[\sigma_{xx}^{(1)}=l_{m}^{2}\int_{0}^{L}E\alpha_{1}\big{(}x,x^{\prime},e_{1}a \big{)}\epsilon_{xx,x}^{\prime}\big{(}x^{\prime}\big{)}dx^{\prime} \tag{29}\]  
where \(E\) is [PERSON]'s modulus, \(l_{m}\) is the material length scale parameter or the strain gradient length scale parameter, \(\epsilon_{xx}\) is the strain, \(\epsilon_{xx,x}\) is the strain gradient, \(\alpha_{0}\) and \(\alpha_{1}\) are two nonlocal kernel functions satisfying Eringen conditions, and \(e_{0}a\) and \(e_{1}a\) are two nonlocal parameters. Having in view Equation (27), the one-dimensional problem of NSGT can be written as:  
\[\Big{[}1-(e_{1}a)^{2}\
abla^{2}\Big{]}\Big{[}1-(e_{1}a)^{2}\
abla^{2}\Big{]}t _{xx}=E\Big{[}1-(e_{1}a)^{2}\
abla^{2}\Big{]}\epsilon_{xx}=El_{m}^{2}\Big{[}1 -(e_{0}a)^{2}\
abla^{2}\Big{]}\
abla^{2}\epsilon_{xx} \tag{30}\]  
in which \(\
abla^{2}=\frac{\partial^{2}}{\partial x^{2}}\) is the Laplace operator. For the particular case \(e_{0}=e_{1}=e\), Equation (30) becomes:  
\[\Big{[}1-(ea)^{2}\
abla^{2}\Big{]}t_{xx}=E\Big{[}1-l_{m}^{2}\
abla^{2}\Big{]} \epsilon_{xx} \tag{31}\]  
For \(l_{m}=0\), the last equation can be rewritten in the form:  
\[\Big{[}1-(ea)^{2}\
abla^{2}\Big{]}t_{xx}=E\epsilon_{xx} \tag{32}\]  
which is the nonlocal constitutive equation for the nonlocal elasticity theory.
## 4 Designing an Ontology Framework: A Case Study on Asthma
### Extracting Rules  
In this section, building on the previously selected relevant parameters, we use the chosen algorithms to extract rules for asthma diagnosis based on these parameters. The objective is to create human-readable rules derived from patient data while leveraging decision tree algorithms to enhance ontology reasoning. Decision trees produce rules often expressed in the Semantic Web Rule Language (SWRL), which uses \"if-then\" statements to define conditions and corresponding outcomes [67]. Embedding these rules into ontologies allows systems to draw conclusions, validate relationships and reason more effectively, as highlighted in recent studies on ontology-based rule generation [68]. For example, rules derived from decision trees include:  
IF DietQuality > 3.5  
And BMI > 29.0  
Else If LungFunctionFEV1 \(\leq\) 2.5: Then Class = 0 (Asthma)  
IF DietQuality \(\leq\) 3.5  
And LungFunctionFEV1 > 1.8: Then Class = 0 (Asthma)  
IF Age > 9.50:  
And CoughingID \(\leq\) 0.50: Then Class = 1 (Asthma)  
IF BMI \(\leq\) 35.81:  
And Age \(\leq\) 47.50: Then Class = 1 (Asthma)  
Else If Age > 47.50: Then Class = 1 (Asthma)  
IF BMI \(\leq\) 39.23:  
Then Class = 0 (No Asthma)  
Else If BMI > 39.23: Then Class = 1 (Asthma)  
IF CoughingID > 0.50:  
And BMI \(\leq\) 20.55:  
And Age \(\leq\) 75.00: Then Class = 0 (No Asthma)  
Else If Age > 75.00: Then Class = 1 (Asthma).  
Figure 7: Comparison of decision tree, neural network and logistic regression algorithms based on accuracy, precision, recall and F1-Score values.  
Based on the rules derived from the decision tree algorithm, the next step is to design the extended asthma ontology.