esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 A Model of Deployment-Independent, Self-Organizing, Cyber-Physical Systems  
In this paper, we present an approach to conceive self-organization in distributed systems that facilitates deployment independence, i.e., the ability of an application to run with no change on various deployments while retaining its original functional semantics. It is rooted in the idea (illustrated in Figure 1) to organize the structure and behavior of a system so that (i) the developer can focus on a logical model that mostly abstracts details concerning deployment, scheduling and communication; and (ii) the logical model can be straightforwardly partitioned into a set of deployable software components, which deployers (e.g., devops team members or automated systems) can \"freely\" distribute on available infrastructure. The application logic will obtain the functional goals independent of the actual deployment, yet the choice of the deployment at hand typically affects non-functional properties, such as performance and cost.  
Figure 1: Deployment independence via pulverization. Designers of the business logic target the logical system (**a**), whose abstractions are identifiable nodes and neighbors. Once the deployment target (composed of hosts and communication channels) is identified (**b**), logical devices are _pulverized_ into sub-components, which are then mapped to hosts (**c**). In the latter figure, green dashed lines represent network interactions between single-device sub-components.  
Let us consider a cyber-physical system consisting of: a _cyber system_ (Section 3.1), which provides a logical model for the system; and a _platform system_, which models networks of hosts for deployment (hence subsuming physical systems as well as simulation platforms) and to which the cyber system can be mapped to by a so-called _deployment_ (Section 3.2). In the following, we describe this model informally at first and then formally by a structural operational semantics [62], e.g., in the style of calculi like \(\pi\)-calculus [63] (the quintessential process algebra for mobility) or the field calculus [64] (the main foundation of aggregate computing). The main goal of the formalization, presented incrementally in the following, is to provide an unambiguous specification of what constitutes pulverization, clarify subtle aspects of the model, and state the deployment-independence property rigorously. The model will then be instantiated in the macro-programming approach of aggregate computing in Section 4.
## 3 System Characterization Using FEM  
The analytical characterization of the multi-electrode setup was carried out using finite element modeling (FEM) in COMSOL Multiphysics. The model of the setup is shown in Figure 3. Skin epidermal cells are modeled as a 20 \(\upmu\)m thick layer at the bottom of the culture well. The epidermal cell layers are modeled as a homogeneous layer at the bottom of the dish to reduce computational complexity. The conductivity of epidermal cells is taken as 0.4 S/m and the relative permittivity as \(1.14\times 10^{3}\)[38]. The epidermal cell layer is topped by DMEM growth media, with a conductivity and relative permittivity of 1.68 S/m and 80, respectively [39]. The electrodes are designed for copper from the materials library in COMSOL Multiphysics (\(\sigma=5.9987\times 10^{7}\) S/m, \(\epsilon_{r}=1\)). Amongst the six surfaces of the electrode, the surface facing the center of the well is left intact, and the rest of the surfaces are defined with FR4 (circuit board) material from the materials library of COMSOL Multiphysics (\(\sigma=0.004\) S/m, \(\epsilon_{r}=1\)) to achieve an insulating PCB layer.  
Current density and electric field distribution at different regions of the culture well were evaluated to determine the efficacy of the design. The setup was simulated as per the design topology mentioned in Section 2. Copper electrodes of 3.5 mm \(\times\) 0.5 mm were used  
Figure 3: Simulation model: (**a**) a 3D view of the setup (**b**); cross-sectional view of the setup.  
Figure 2: Schematic diagram of proposed multi-electrode setup in culture well.  
as the source and sink electrodes. The electrodes were placed near the bottom of the well. The separation between the electrodes was 13 mm.  
The analysis was carried out for different electrode surface areas. The width of the electrodes was kept fixed at 3.5 mm, and the height (\(h_{elc}\)) varied from 0.5 mm to 2.00 mm to denote the changes in surface area. The electrodes were placed perpendicular to the bottom of the culture well. A comparative analysis of current density and electric field distribution was used to depict the effect of surface area and the position of the electrodes in providing electrical stimulation.
## 3 Results  
All surgical extraction sockets healed uneventfully. After eight weeks, in all GA patients, clinical complete re-epithelialization healing sites over the reabsorbable barriers were observed. Four months after surgery, maxillary ridge CBCT measurements of GA patients showed adequate bone width for 3D implant placement. Clinical differences were observed between the two groups in the healing of the coronal parts of the regenerations. In Group A (test), the vertical variation (difference between initial volume and the volume measured at the end of the therapy) was 89%, and in Group B (control) it was 102%. The horizontal variation was 92% in Group A (test) and 100% in FC + Group B (control) (Table 1).  
The volume was measured before surgery with CBCT and again after 4 months and the images were superimposed. During the measurement (millimeters) before surgery, the dimensions (vertical and horizontal) were recorded, and they were then compared with the same dimensions (vertical and horizontal) when superimposing the image taken after 4 months (Figure 3). The differences in percentages are shown in Table 1.  
The histomorphometric analysis showed different bone behavior between the two groups (Figures 1H and 2F): in GA, a higher bone volume (BV) (62.78 \(\pm\) 7.97%) mean value was observed compared to Group B (48.04 \(\pm\) 9.32%). A higher vital bone (VB) mean value was detected in GA (57.53 \(\pm\) 11.16%) compared to GB (42.41 \(\pm\) 13.06%). Interestingly, similar results for the demineralized dentin residual graft in GA (5.24 \(\pm\) 5.82%) compared to GB (5.29 \(\pm\) 4.83%) were observed in all specimens analyzed (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Group A** & **Patients** & \begin{tabular}{c} **\% Vertical** \\ **Bone Variation** \\ \end{tabular} & \begin{tabular}{c} **\% Horizontal** \\ **Bone Variation** \\ \end{tabular} & \begin{tabular}{c} **\% New Bone** \\ \end{tabular} & \begin{tabular}{c} **\% Vital Bone** \\ \end{tabular} & \begin{tabular}{c} **\% Residual Graft** \\ \end{tabular} \\ \hline \multirow{6}{*}{Group B} & A & 65 & 85 & 54.14 & 51.76 & 02.38 \\  & B & 97 & 99 & 61.69 & 61.52 & 00.17 \\  & C & 99 & 98 & 57.15 & 42.72 & 14.43 \\  & D & 90 & 86 & 66.67 & 59.06 & 07.60 \\  & E & 92 & 92 & 74.25 & 72.63 & 01.62 \\ \hline \multicolumn{2}{c}{Mean Value} & 89 & 92 & 62.78 & 57.53 & 05.24 \\ \cline{2-6}  & \begin{tabular}{c} \% Vertical Bone \\ Variation \\ \end{tabular} & \begin{tabular}{c} \% Horizontal \\ Bone Variation \\ \end{tabular} & \begin{tabular}{c} \% Bone Volume \\ \end{tabular} & \
\begin{tabular}{c} \% Vital Bone \\ \end{tabular} &
\begin{tabular}{c} \% Residual Graft \\ \end{tabular} \\ \hline \multirow{6}{*}{Group B} & F & 117 & 91 & 46.56 & 42.11 & 04.44 \\  & G & 97 & 85 & 38.42 & 29.97 & 08.45 \\  & H & 110 & 138 & 63.59 & 63.43 & 00.16 \\  & I & 100 & 98 & 44.99 & 33.17 & 11.82 \\  & L & 102 & 82 & 46.64 & 43.37 & 01.59 \\ \hline \multicolumn{2}{c}{Mean Value} & 102 & 100 & 48.08 & 42.41 & 05.29 \\ \hline \end{tabular}
\end{table}
Table 1: Histological and histomorphometrical variations of bone biopsies in Group A (GA) and Group B (GB).
# Gradient Agreement Hinders the Memorization of Noisy Labels
## 4 Experiments
### Compared Methods  
In this section, we compare the performance of the proposed GAL with previous methods on the four popular benchmark datasets. Table 1 compares the performance of the proposed GAL and previous co-training methods on CIFAR-10 and CIFAR-100 with synthetic noisy labels. Table 2 presents the evaluation results on Animal-10N with real-world noisy labels. It can be seen that GAL significantly outperforms the other methods in most experimental settings. In particular, there exists an obvious performance drop between the best and the last test accuracy for most methods, especially under high noise levels (e.g., 24.1 for JoCoR and 13.8 for co-learning under 80% symmetric noise rate on CIFAR-10). This implies that the models are over-fitted to noisy labels during the training.  
In sharp contrast, while achieving better accuracy than other methods, the performances of GAL are quite stable under all types and ratios of noise on CIFAR-10 and CIFAR-100 with the maximum drop between the best and the last test accuracy being merely 0.6. By seeking agreement on gradient directions, GAL can effectively hinder the memorization of noisy labels. While the previous co-training methods either achieve agreement on the output predictions (e.g., Co-teaching+ and JoCoR) or seek to maximize the agreement in latent space (e.g., Co-learning), GAL still allows divergence on the final predictions from the two nets, thus obtaining more benefits in test accuracy from model ensembling. Moreover, the performance is further enhanced with the supervision of pseudo labels.  
Table 3 presents the evaluation results on the real-world benchmark Clothing1M of GAL and several baseline methods, i.e., co-training method JoCoR, sample selection methods (TCNet, Fine) and regularization methods (ELR, F-div, Label Smoothing). GAL works well on Clothing1M, demonstrating its effectiveness in learning with noisy labels.
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
## 3 Compression-Assisted Adaptive ECC and RAID Scattering
### RAID Scattering
Using the method of scattering within a stripe group has an effect of reducing the association of pages with each other within a stripe group. In other words, it is possible to increase the size of the stripe group for the same level of association. That is, if a RAID parity in the existing RAID system covered a stripe group with stripe size four, then another RAID parity in the RAID scattering system can cover a stripe group with stripe size eight. This results in reducing the overhead of RAID parity.
###### Abstract  
Due to the dense deployment of base stations (BSs) in heterogeneous cellular networks (HCNs), the energy efficiency (EE) of HCN has attracted the attention of academia and industry. Considering its mathematical tractability, the Poisson point process (PPP) has been employed to model HCNs and analyze their performance widely. The PPP falls short in modeling the effect of interference management techniques, which typically introduces some form of spatial mutual exclusion among BSs. In PPP, all the nodes are independent from each other. As such, PPP may not be suitable to model networks with interference management techniques, where there exists repulsion among the nodes. Considering this, we adopt the Matern hard-core process (MHCP) instead of PPP, in which no two nodes can be closer than a repulsion radius from one another. In this paper, we study the coverage performance and EE of a two-tier HCN modelled by Matern hard-core process (MHCP); we abbreviate this kind of two-tier HCN as MHCP-MHCP. We first derive the approximate expression of coverage probability of MHCP-MHCP by extending the approximate signal to interference ratio analysis based on the PPP (ASAPPP) method to multi-tier HCN. The concrete SIR gain of the MHCP model relative to the PPP model is derived through simulation and data fitting. On the basis of coverage analysis, we derive and formulate the EE of MHCP-MHCP network. Simulation results verify the correctness of our theoretical analysis and show the performance difference between the MHCP-MHCP and PPP modelled network.  
Matern hard-core process; coverage probability; energy efficiency +
Footnote †: journal:  
_Future Internet_**2020**, _12_, 1; doi:10.3390/612010001
## 5 Conclusions  
In this study, an approximated model for the scattering from metallic bars embedded into a dielectric is proposed. The model is \"quasi\" quadratic because it exploits a quadratic approximation of the scattering, which allows the coupling between the bars and the dielectric structure to be taken into account, but it neglects the coupling between the bars. In this way, by exploiting a priori knowledge of the dielectric permittivity and shape, the detection and localization of the bars are reduced to the inversion of a linear operator. The behavior of the singular values of the operator provides indications about the information content of the different measurement configurations. Two examples have been considered. As expected, a wider angle of view (provided in the above examples by bringing the measurement line closer to the dielectric) corresponds to an increased number of singular values above the threshold. The threshold can be fixed according to the expected signal-to-noise ratio of the data, and TSVD guarantees the stability of the inversion algorithm. The proposed model allows for the computation of part of the relevant operator in a closed form so that it reduces the computational burden. The algorithm was validated with numerically simulated data relating to two measurement configurations and different numbers and positionings of the bars inside the dielectric. The results show the feasibility of the approach and encourage a deeper understanding of the model. The application that is considered by the author is reinforced concrete diagnostics. It is worth noting that one of the difficulties in reinforced concrete diagnostics stands in setting up a reliable measurement system, in which the instrumentation's accurate positioning and the minimization of measurement noise play a crucial role. From this point of view, the availability of simple and stable data processing algorithms is helpful. Experimental validation was not within the scope of this paper. However, a laboratory set-up is in progress in order to obtain the data needed to test the method with experimental measurements performed in a controlled environment.  
This research was partially funded by VALERE: VAnviteLli pEr la RicErca research program by Universita degli Studi della Campania [PERSON].  
The author declares no conflict of interest.  
Figure 8: Bars’ detection (configuration 1 and 2 from **top**) and reconstructed \(\gamma(n)\) (**bottom**). Red circles represent actual bars. Black stars represent detected bars. A threshold of 0.7 has been applied. Note the the two lateral bars are not detected in configuration 2.
## 6 Discussion  
### Computational Performance of Different Optimization Algorithms  
Although many research results have proved the excellent computing performance of QPSO in many optimization problems, there has been little research on the optimization performance of QPSO in the LSSVM model. The computational performance of different meta-heuristic optimization algorithms in different studies is likely to be significantly different [35]. Classical and latest meta-heuristic optimization algorithms (GA [11], PSO [36], GSA [14], WOA [14]) were selected to find the best calculation parameters in Section 5. The calculation parameters and principles of each algorithm refer to the corresponding references. In addition, TE [14], a classical LSSVM parameter determination method, was  
Figure 3: Bland–Altman analysis.  
selected to perform calculations. The results of 100 calculations of different optimization algorithms are shown in Table 9.  
It could be seen from Table 9 that, compared with other meta-heuristic optimization algorithms, the calculation error of QPSO was the smallest, and the best calculation parameters of LSSVM could be found fastest with the QPSO.  
By researchers' manual trial and error, the TE method could find the appropriate LSSVM calculation parameters, which was understood as finding the LSSVM calculation parameters without using an optimization algorithm. The calculation error of the TE was the largest. This result emphasized the necessity of introducing an optimization algorithm into LSSVM research.  
Generally, the algorithm performance is judged by the following three conditions. The best case indicates the fastest optimization speed of the algorithm. Average means the average of the algorithm. In the worst case, it means that the algorithm cannot find the optimal solution. The best case is accidental, so it has no comparative value, and the average case has been discussed in this section. Therefore, this section will continue to discuss the worst case.  
The range of the C was reset to [100, 10,000], and the range of the width parameter \(\sigma\) was reset to [0.001, 10]. The optimal combination of parameters was not in this interval, so the three optimization algorithms could not find the optimal solution within the maximum iteration steps. After calculation, it was found that QPSO completed the calculation fastest, so the complexity of QPSO was the smallest among all algorithms.
# Age- and Sex-Adjusted Reference Intervals in Tear Cytokine Levels in Healthy Subjects
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Reference Intervals Utility  
In order to know the utility of the age- and sex-related RIs estimated with GAMLSS model, we used them to classify subjects from two external independent samples, hereinafter referred to as validation and utility samples, obtained from our own database. Individuals with tear cytokine concentration data along with ocular surface clinical parameters related to the DED diagnosis were selected.  
The validation sample consisted of 40 healthy individuals with a mean age of 31.74 \(\pm\) 15.85 years. There were 19 (47.5%) males and 21 (52.5%) females. Their respective age was 39.31 \(\pm\) 17.92 years for males and 24.90 \(\pm\) 9.88 years for females. This healthy sample had normal results at four DED diagnostic tests: the ocular surface index (OSDI) score \(\leq\) 12 points, fluorescein tear break-up time (T-BUT) \(\geq\) 7 s, corneal fluorescein staining (Oxford scale) = 0, and Schirmer test without topical anesthesia \(\geq\) 5 mm in 5 min [27]. The subjects of this sample should be classified as \"normal\" subjects by the RIs.  
By contrast, the utility sample was used to evaluate the capability of these RIs for the diagnosis of DED. Consequently, tear molecule data from 13 cases of severe DED were selected. There were 4 (30.8%) males and 9 (69.2%) females. Their age was 58 \(\pm\) 7.39 years for males and 58.33 \(\pm\) 9.72 years for females. The clinical selection criteria for these subjects were having an OSDI score \(\geq\) 33 points, T-BUT < 3 s, corneal fluorescein staining \(\geq\) 3, and Schirmer test < 3 mm in 5 min [27]. Tear sample collection and tear cytokine concentration analysis in those subjects had been performed following the same methodology as in this study.  
It should be noted that information about the concentration of some cytokines was missing in some individuals' tear samples. Specifically, levels of IL-7 were missing in all the subjects.  
The percentage of cytokine tear concentration values falling within the lower and upper RIs of each sample was determined.
## 3 Methodology
### Time Domain Feature Extraction Module  
#### 3.2.1 Time Feature Extraction Based on Bi-LSTM  
Long Short-Term Memory (LSTM) networks improve upon the Recurrent Neural Network (RNN) model by addressing issues such as gradient explosion and vanishing gradients, and they perform exceptionally well in sequence prediction tasks. Bi-LSTM is a bidirectional LSTM model that adds a backward layer to enable bidirectional reading. It consists of two LSTM models: a forward model and a backward model. The forward  
Figure 1: Overall framework of anomaly detection methods. (**a**) Time domain feature extraction module; (**b**) Frequency domain feature extraction module; (**c**) Error analysis and anomaly detection.  
model processes the input sequence in the forward direction, from start to end, utilizing past information, while the backward model processes the sequence in reverse, from end to start, leveraging future information.  
Traffic flow data exhibits typical time series characteristics. Bi-LSTM, at each time step, allows each LSTM unit to access both past and future contextual information, making it highly effective at capturing long-term dependencies in time series data.  
The two LSTMs in Bi-LSTM have the same structure, and in the case of forward LSTM, for example, each time point LSTM in the input \(X=\left\{x^{1},x^{2},x^{3},\ldots x^{4}\ldots,x^{N}\right\}\) combines the current input and the hidden state \(h_{t-1}^{\rightarrow}\) of the previous time point to compute the output \(O_{t}\). At the same time, the hidden state \(\overset{\rightarrow}{h_{t}}\) of the current time point is used again as the input of the next time point, i.e.:  
\[O_{t}=\sigma\big{(}W_{0}\cdot\left[h_{t-1},x^{t}\right]+b_{0}\big{)} \tag{1}\]  
\[\overset{\rightarrow}{h_{t}}=O_{t}\cdot tanh(C_{t}) \tag{2}\]  
where: \(W_{0}\) and \(b_{0}\) are the weights and biases, respectively; \(\sigma\) is the activation function; \(C_{t}\) denotes the state parameters at the current moment; \(tanh\) is the hyperbolic tangent activation function.  
After the input goes through the LSTM, the output \(X^{T}\in R^{h\times T}\) is obtained, where \(h\) is the dimension of the hidden layer of the LSTM. \(X^{T}\) preserves the hidden state information at each time step and is a feature representation containing the temporal correlation of the data. Bi-LSTM, on the other hand, combines the forward and reverse LSTMs, and splices the hidden state \(\overset{\rightarrow}{h_{t}}\) of the forward LSTM with the hidden state \(\overset{\leftarrow}{h_{t}}\) of the reverse LSTM to obtain the Bi-LSTM output. LSTM output \(h_{t}\), i.e.:  
\[h_{t}=\sigma(W\cdot\left[\overset{\rightarrow}{h_{t}};\overset{\leftarrow}{h_ {t}}\right]+c). \tag{3}\]
# Quantum Privacy Query Protocol Based on GHZ-like States
## 3 Quantum Privacy Query Based on the GHZ-like State  
### Creating Indexes  
In this part, the hospital constructs an index of the data information and sends it to the quantum database. The data provider first generalizes the data thoroughly to realize the gradual segmentation of the data. Then, the data provider sends all the segmentation schemes and their corresponding probabilities of selected schemes to the quantum database. As shown in Table 1, we divide each symptom into \(n\) attributes, and for each attribute data, we have values 0 and 1. The first one is the processing of continuous type numerical data. The patient's temperature attribute TEMP is a continuous numerical data type, and its value domain is [36; 41]. We subdivided Temp into the intervals of [35; 37.8] and [37.8; 41], so we can consider that attribute TEMP has two values: Temp takes the value of 0 when Temp is [35; 37.8]; Temp takes the value of 1 when Temp is [37.8; 41]. The second is the processing of discrete numerical data and textual data. The patient's headache attribute Headaches is textual data, and the value of attribute Headaches is [yes, no], where we set the value of yes to 1 and no to 0. Thus, the patient can obtain the value of each bit of the binary according to the form of the questionnaire used by the system, etc. Finally the binary number is converted to the index Index according to Equation (6).  
\[\mathrm{Index}=2^{0}\times\mathrm{Index}_{1}+\cdots+2^{n-1}\times\mathrm{ Index}_{n} \tag{6}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Headaches** & **Temp** & **Sniffle** & \(\cdots\) & **Diarrhea** & **Vomiting** \\ \hline
000\(\ldots\)01 & 0 & 0 & 0 & \(\cdots\) & 0 & 1 \\
000\(\ldots\)10 & 0 & 0 & 0 & \(\cdots\) & 1 & 0 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\
111\(\ldots\)10 & 1 & 1 & 1 & \(\cdots\) & 1 & 0 \\
111\(\ldots\)11 & 1 & 1 &