esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Search Algorithms
### Specific search algorithms  
LexBFS, MCS and MNS are instances of MLS and LEX M, MCS-M and MNSM (defined in [12] from MNS) are instances of MLSM, with the following respective labeling structures:  
* **LexBFS and LEX M**: \(\mathcal{L}\) is the set of lists of elements of \(\{2,3,...,\}\), \(\preceq\) is lexicographical order (a total order), \(l_{0}\) is the empty list, \(Inc(l,i)\) is obtained from \(l\) by adding \(i\) to the end of the list.
* **MCS and MCS-M**: \(\mathcal{L}=\{0,1,2,...\}\), \(\preceq\) is \(\leq\) (a total order), \(l_{0}=0\), \(Inc(l,i)=l+1\).  
* **MNS and MNSM**: \(\mathcal{L}\) is the power set of \(\{2,3,...\}\), \(\preceq\) is \(\subseteq\) (not a total order), \(l_{0}=\emptyset\), \(Inc(l,i)=l\cup\{i\}\).  
**Algorithm LexBFS** (Lexicographic Breadth-First Search)[4]  
```
input : A graph \(G=(V,E)\). output : An ordering \(\alpha\) of \(V\).  Initialize all labels as the empty string; for\(i\gets n\)to\(1\)do  Pick an unnumbered vertex \(v\) whose label is maximal under lexicographic order; \(\alpha(i)\gets v\) (assigns to \(v\) the number \(i\)); foreachunnumbered vertex \(w\) adjacent to \(v\)do  append \(i\) to \(l(w)\);
```  
**Algorithm MCS** (Maximal Cardinality Search)[5]  
```
input : A graph \(G=(V,E)\). output : An ordering \(\alpha\) of \(V\).  Initialize all labels as \(0\); for\(i\gets n\)to\(1\)do  Pick an unnumbered vertex \(v\) with maximum label; \(\alpha(i)\gets v\) (assigns to \(v\) the number \(i\)); foreachunnumbered vertex \(w\) adjacent to \(v\)do \(l(w)\gets l(w)+1\);
```  
**Algorithm MNS** (Maximal Neighborhood Search)[11]  
```
input : A graph \(G=(V,E)\). output : An ordering \(\alpha\) of \(V\).  Initialize all labels as the empty set; for\(i\leftarrow\)to\(1\)do  Pick an unnumbered vertex \(v\) whose label is maximal under inclusion; \(\alpha(i)\gets v\) (assigns number \(i\) to \(v\)); foreachunnumbered vertex \(w\) adjacent to \(v\)do \(l(w)\gets l(w)\cup\{i\}\);
```  
LEX M and MCS-M can be directly extended from LexBFS and MCS using MLSM.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 3 Methods
### Algorithm
#### 3.2.2 Structure of Solution  
To utilize the Hybrid Genetic Algorithm (HGA) for solving the task scheduling problem presented in this paper, it is first necessary to elucidate the process of mapping the solution space to the coding space.  
Figure 5: The structure and workflow of a hybrid genetic algorithm.  
Considering both the client and server, it is possible to determine whether the server's deployment location falls within the service range of the corresponding central laboratory. Accordingly, client-side encoding and server-side encoding based on device allocation can be obtained. The client-side coding sequence is represented as \(X^{C}=\left[X_{1}^{C_{m}},X_{2}^{C_{u^{\prime}}},\ldots,X_{|N|X|}^{C_{m^{ \prime}}}\right]\), where \(C_{m}\in\mathcal{N}_{C}\). The server-side coding sequence is represented as \(X^{S}=\left[X_{1}^{S_{n}},X_{2}^{S_{n^{\prime}}},\ldots,X_{|N|X|}^{S_{n^{ \prime}}}\right]\), where \(S_{n}\in\mathcal{N}_{S}\).  
In addition, the synchronous scheduling problem should satisfy some time constraints, including that the server or client can only process one calibration task at the same time, and the tasks in the later order must be executed after the completion of the tasks in the previous order. These constraints can be expressed in the form of arranging the order of execution of the tasks, which is described in more detail above. Accordingly, the sequential node can be obtained. The sequential node is \([a,b,\ldots,d]\) based on the order of task execution \(P=\{a,b,\ldots,d\}\), where \(X_{a}^{st}\leq X_{b}^{st}\leq\cdots\leq X_{d}^{st}\), \(\forall X_{a},X_{b},\ldots,X_{d}\in\mathcal{N}_{X}\).  
In summary, in order to map the solution space to the encoding space, we use the following chromosome to represent a decision scheme:  
\[chromosome\ =\ \Big{\{}\ node\Big{(}X^{C}\Big{)}\ \ \&\ node\Big{(}X^{S}\Big{)}\ \ \&\ node\Big{(}P\Big{)}\Big{\}} \tag{23}\]
## 5 Delivery
### Transportation  
It is necessary for the developers to understand the regulations enacted by national (and international) authorities regarding export of satellites. Some countries may even put in place special regulations for CubeSats. Within the European Union, CubeSat developers do not need to meet any special requirement. However, most typical launch sites for CubeSats are not located within the European Union, such as Cape Canaveral in the US or Baikonur in Kazakhstan. Consequently, an export license issued by the Customs Office may be required. One may also need to ensure that no international traffic in arms regulations (ITAR) apply to the CubeSat package through its route.  
Standard shipping companies may not be sufficiently reliable for shipping CubeSats (from Europe,) since they do not seem to offer special services tailored to CubeSat packages, and they do not provide reasonable insurance for such parcels. Further, they do not assume any responsibility if the parcel is stopped at customs, and it will be very difficult to claim for any damage if something happens internally.  
A viable option for the CubeSat transportation would be to deliver it to its destination in person. Nevertheless, travelers should make prior arrangements and be prepared during the trip with sufficient documentation, such as manuals, blueprints, export licenses and any other proof of shipment, in order to minimize difficulties with the customs and security checks.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 5 Covert Channel Attacks in 2.5D/3D ICs
### Existing Works  
Although covert channel attacks in 2.5D and 3D systems are largely unexplored, there are several recent works on power covert channels in 2.5D FPGAs and TCCs in 3D ICs, as described below.
## 3 Experimental Details
### Material Characterization  
X-ray diffraction was performed to investigate the phase structures of the samples. Field emission scanning electron microscopy and high-resolution transmission electron microscopy were utilized to clarify the morphologies and crystalline structures. An X-ray photoelectron spectrometer was utilized to analyze the surface compositions and chemical states of samples. Optical properties were investigated using a UV-vis spectrometer. A PL spectrometer was used to characterize the charge recombination.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 2 Vertical Plane Modelling
### EMAT-EM Model
#### 2.1.3 EMAT-Lorentz Force Calculation  
The analytical solution to a straight wire has been described in Section 2.1.2. **A** (vector potential) generated by a meander-line-coil is the addition of **A** generated by every single straight wire. The zone where the meander-line-coil mainly operates on is selected to model EM simulation to increase the modelling effectiveness. The distribution of **A** and **F** at \(z=0\) are shown in Figures 4 and 5. The generated periodic fields have different directions for any neighbouring wires, since their applied ACs are opposite, and therefore, the periodic distribution of **A** and **F** has six positive values and six negative values. The outermost **A** and outermost **F** are largest, because **A** is under the outermost wires, and thus is only determined by the fields on one side.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 2 Disassociation and Utility Awareness for Associations
### The Privacy-Utility Trade-off in Disassociation  
Giving an exact general definition for the utility of the data in the domain of anonymization is irrational. In this work, a utility rule is an association which is important for accurate analysis, especially for aggregate query answering accuracy.  
* Let \(UR=\{ur_{1},...,ur_{u}\}\) be a set of predefined associations, that we refer to as utility rules and are important in future analysis.
* Let \(s(ur,\mathcal{T})\) be the support of the utility rule \(ur\) in the original dataset.
* Let \(s(ur,\mathcal{T}^{*})\) be the support of the utility rule in the disassociated dataset.  
To evaluate the probabilistic preservation of a utility rule in a disassociated dataset, the confidence of a utility rule is analyzed.  
**Definition 1** (\(\alpha\)-confidence).: _The \(\alpha\)-confidence of a utility rule \(ur\) is evaluated as follows:_  
\[\alpha=\frac{s(ur,\mathcal{T}^{*})}{s(ur,\mathcal{T})}. \tag{1}\]  
The term confidence is used to determine the strength of the association between the items of a utility rule \(ur\) after disassociation. Statistical queries are based on the support of the associations in question. The \(\alpha\)-confidence represents a ratio of the preservation of a utility rule's support of a utility rule, reflected in the final output of the disassociation.  
In [1] the utility of an association in disassociated datasets is evaluated theoretically under the \(k^{m}\)-anonymity privacy model, and is proven to be:  
\[\frac{1}{\delta^{|ur|}}\leq\alpha\leq 1. \tag{2}\]  
From this perspective of privacy-utility trade-off, we are motivated to contribute with a more insightful horizontal partitioning process, tolerant to the predefined utility rules for future data analysis accuracy.  
The next section provides a general description of the clustering problem and then the role of swarm intelligence algorithms for the improvement of data clustering.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 3 Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme
### Simple Adaptive Weight (SAW) Technique  
In the literature, several multicriteria decision-making algorithms have been proposed. However, most of the proposed techniques suffer from computational complexity when being applied. In this study, the SAW technique is adopted, which was found to be simple to use and more efficient in making decisions that involved multicriteria parameters [40,41]. When the IV receives the GWrep, it uses the SAW technique to rank each potential MGW and choose the best MGW based on the mentioned criteria. The SAW technique involves two stages. The first stage is the scaling stage, whereby each metric needs to be scaled into non-dimensional values. The second stage is the weighting stage; here, every scaled metric is given its weight or priority in deciding the best MGW.
# UAV Dynamic Non-Terrestrial Transmission Channel Analysis Based on SSCM-RT Model
## 2 Theoretical Background
### Channel Fading Model Parameters
When the frequency is below 50 GHz, the relationship between the path loss and frequency is linear; however, as the frequency grows, the path loss increases quickly. To prevent severe and quick channel fading, the high-altitude UAV's frequency choice should ideally not exceed 50 GHz. The path loss exponent exhibits a similar trend, as illustrated in Figure 3. These are mainly related to atmospheric attenuation. Due to the absorption characteristics of the atmosphere, oxygen, and water vapor in the air absorb electromagnetic waves more severely, with an absorption peak near the frequency of 60 GHz. Figure 2: The frequency dependence of path loss by using the statistical channel model.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 3 Proposed Speech Separation Algorithm
### Speech Separation According to Different Mixing Condition
#### 3.2.1 Case I: \(M^{\prime}<\text{N}\)  
The number of components at frequency bin is less than the number of existing sound sources because of the sparsity of speech signal in the time-frequency domain.  
First, when \(M^{\prime}\) is equal to 1, the dimension reduction is not conducted. To minimize the computation cost and prove the effectiveness of the proposed framework, only the simplest DSB is used in this situation [27], where the attribution of signal components should be judged because the number of speech components is less than the number of existing sound sources. In Section 3.1, beamforming has been performed in each frequency bin, and the source angle in each frequency band is determined by searching the maximum beam power over the possible directions. Here, we use a similar method to determine the attribution of components by pointing the beam towards the estimated positions of sound sources. Then, the first \(M^{\prime}\) beams with the highest energy are the activated sound sources in current frequency bin. Then, the matrix of DSB is expressed as  
\[B(f)=\frac{1}{M}\cdot H_{1}(f)^{\text{H}} \tag{19}\]  
\[Beam(f,t)=B(f)\cdot X(f,t) \tag{20}\]  
where \(H_{1}(f)\) is the column of Equation (7) corresponding to the source with the highest beam energy, \(Beam\) is the signal generated by the beamformer, and \(B\) is the beamformer coefficient.  
Figure 2: Flow chart of counting and localization of sound sources.  
The sound sources at other angles are set to zero at this frequency bin, then the processing matrix can be expressed as  
\[W_{[1]}(f)=\left[\begin{array}{c}0\\ \vdots\\ B(f)\\ \vdots\\ 0\end{array}\right] \tag{21}\]  
where \(B(f)\) is arranged in the row corresponding to the source with the highest beam energy.  
Second, if \(M^{\prime}>1\), the fixed beamforming is not effective enough for separation because of interferences, therefore ICA can be used. Then, the processing matrix can be expressed as  
\[W_{[2]}(f)=\left[\begin{array}{c}0\\ \vdots\\ W_{\text{ICA}}(f)\cdot V(f)\\ \vdots\\ 0\end{array}\right] \tag{22}\]  
where \(W_{\text{ICA}}(f)\) can be calculated by Equations (11) and (14), which contains \(M^{\prime}\) rows, and what should be noted is that \(W_{\text{ICA}}(f)\cdot V(f)\) is not continuously arranged in \(W_{[2]}(f)\), similar to Equation (21), but each row corresponds to the \(M^{\prime}\) sound source with the highest beam energy.
# GNSS Applications to Assess Performance in Olympic Sailors: Laser Class
## 2 Materials and Methods
### Maneuvers and Courses  
The maneuvers are actions with which the sailors change the direction of the boat. When the maneuver is carried out against the wind, it is called \"tack\", and \"jibe\" when  
Figure 1: Regatta race courses.  
Figure 2: Velocity Made Good. Note: VMG = Velocity Made Good.  
navigating downwind or broad reach. During the tack, the boat changes direction as the bow passes through the wind, while the gybing is the stern. In both actions, the sails shift from the starboard or port side to the opposite side. Sailing courses are determined by the angle between the bow of the boat and the wind, and it is 45\({}^{\circ}\), 120\({}^{\circ}\), and 180\({}^{\circ}\) in upwind, broad reach, and downwind, respectively.
## 3 Materials and Methods  
### Causal Loop Diagrams (CLDs)  
Causal loop diagrams (CLDs) allow visualization of interrelations in the system, ensuring a quality system analysis. Economic enterprises use them for evaluating system processes, covering all system concepts.  
Causal loop diagrams help better understand the complex system, allowing scientists to measure its interrelationships and validate their hypotheses on system dynamics models.  
CLDs comprise a set of words (nodes) and edges (arrows) modeling causal relationships between the variables. A change in the causal variable alters the effect variable, be it positive or negative. A link marked + is positive, showing a causal relationship when an increase (or decrease) of the causal variable brings an increase (decrease) in the effect variable. A link marked - illustrates a negative correlation when an increase (or decrease) in the causal variable leads to a decline (or increase) in the effect quantity. All links between the two variables are testable hypotheses [23].  
Polarity of causal links and graphic is illustrated in the following Table 1.  
The polarity of causal links refers to the system structure, revealing potential consequences if a change occurs. It does not describe the behavior of variables, i.e., the actual effect.  
The causal loop involves a chain of causal links where a variable disturbs one or more quantities, retroactively affecting the causal variable. We call them feedback loops or closed loops, as it describes a closed situation. If the cycle is not sealed, we call it an open loop. As with the causal links, loops have either positive or negative polarity:  
Reinforcing Loops-labeled with R or '+'  
Balancing Loops-labeled with B or '-'  
Reinforcing loops are when the sum of negative causal links equals zero or an even number, and the variables in the loop grow exponentially. Balancing loops occur when  
\begin{table}
\begin{tabular}{c c c} \hline
**Causal Link** & **Illustration** & **Formula** \\ \hline Positive (+) & X & Y & \(\partial Y/\partial X>0\) in the event of propagation \\  & & \(Y=\int_{t_{0}}^{t}\left(X+\ldots\right)\!ds+Y_{t_{0}}\) \\ \hline Negative (\(-\)) & X & Y & \(\partial Y/\partial X<0\) in the event of propagation \\  & & \(Y=\int_{t_{0}}^{t}\left(-X+\ldots\right)\!ds+Y_{t_{0}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Polarity of causal links and graphic illustration [23].  
the total negative relations equals an odd number and variables strike a balance. If the quantity is in a positive and negative loop at a time, the exponential growth moves toward equilibrium.  
Oscillating behavior is common in causal loops (marked with a double strikethrough in diagrams) in variables (mainly in balancing loops) with delayed responses.
## 2 Materials and Methods
### Field Data Collection  
The experiment was replicated in both the LIRF and IIC sites to obtain similar datasets for the evaluation of the airborne, spaceborne, and proximal platforms' derived RS data when used in the prediction of \(\mathrm{ET_{a}}\) using TSEB RS algorithms. A total of three field measurement stations provided the ground-based input data to estimate and evaluate maize \(\mathrm{ET_{a}}\). The following data were measured at each station: \(\mathrm{R_{n}}\), G, nadir-looking surface radiometric temperature (\(\mathrm{T_{s}}\)), incoming shortwave solar radiation (\(\mathrm{R_{s}}\)), shallow soil temperature, and volumetric water content. A flux tower provided measurements of H and LE in each research site (Figures 3 and 4a).
## 4 Experimental Results
### Discussion  
The histological evaluation of Hematoxylin Eosin stained slides from tumor samples, carried out by an experienced pathologist on an optical microscope, is a mandatory step in the diagnostic, prognostic and therapeutic pathway of patients suffering from squamous cell carcinoma of the oral cavity. To date, for the histological diagnosis of OSCCs, the gold standard is the visual analysis of histological preparations, stained with hematoxylin and eosin; tumor recognition basically takes place on the basis of the qualitative assessment of architectural characteristics of the neoplastic tissue, based on the wealth of knowledge pathologist's own. The qualitative assessment is subjective and can suffer from inter-individual variability, especially in borderline situations that are difficult to interpret. The use of a segmentation algorithm could minimize the interpretative variability and speed up the pathologists' work, providing they with a screening tool, particularly useful in those cases in which the histopathological diagnosis must be carried out on the extensive sampling of complex surgical samples that involve the generation of multiple blocks of formalin-fixed and paraffin-embedded tissue samples, from which numerous slides stained with hematoxylin and eosin are obtained.  
Based on the presented results, our contribution is composed of: (i) a novel dataset, the ORCA set, which will allow us to conduct new studies on Oral Squamous Cell Carcinoma. Particularly, the dataset is composed of annotation from the TCGA dataset, a full comprehensive dataset enriched, as well as with diagnostic slide, with clinicopathological information and molecular biology data. This could facilitate the development of molecular characterization deep learning algorithms; (ii) our method relies on 2250 \(\times\) 2250 and 4500 \(\times\) 4500 images, without a tiling processing. Even though an improvement in its accuracy is mandatory for clinical practice, the utilization of so large images can hugely reduce time-demand for a WSI, making our approach easily scalable to clinical routine, when hundreds of slides need to be processed each day. (iii) after demanded improvements and a clinical trial, this kind of algorithm may be part of clinical practice via L.I.S. integration, fastening OSCC diagnosis and helping pathologists to identify OSCC areas on WSI. Indeed, we foresee to extend our method on lymphonodal metastasis, giving the pathologist an easy way to detect small tumor islands, and on distant metastasis, supporting the pathologist with cases of suspect metastasis of OSCC primary tumor.  
We intend to propose this artificial intelligence algorithm as a Computer-Aided Diagnostic, aware that it cannot replace the pathologist in his routine activity, but that it will be able to provide they with valid help, especially for those who find themselves working in generalist diagnostic centres on the territory, not specialized in the diagnosis of an infrequent but extremely lethal disease.