esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Theoretical Basis of the Algorithms Used in This Research
### Dnn
#### 2.2.1 Number of Hidden Layers  
Choosing the appropriate size of the hidden layer in a DNN is very important to achieve good performance. If the hidden layer is too small, the neural network may not be able to learn complex patterns in the data, leading to underfitting [35]. On the other hand, if the hidden layer is too large, the neural network may become too complex and start to overfit the training data [36]. Therefore, it is necessary to select an optimal number of hidden layers that balances the complexity and performance of the model [37]. Another approach is to use more sophisticated methods (e.g., Bayesian optimization or GAs) to search for optimal hyperparameters of the neural network, including the number of hidden layers.
## 3 R-SIN Architecture Model Based on DaaC
### Realization Methods  
According to the guiding principles of applicability, simplicity, relevance, organization, and reusability of architecture design [25], we make the reconfigurable architecture design and implementation algorithm, and process it as follows.  
As shown in Algorithm 1, the variables that are used in the algorithm include \(RD\), \(RE\), \(RG\), \(RS\), \(RE\), and \(RN\), and their meaning is consistent with definitions 3-8 in the previous text. We are not to explain this process further in this part. Based on the pseudo-code shown in Algorithm 1, we further analyze the implementation process of the algorithm as follows:  
1. For the superior instructions received, we can analyze its specific content, and determine information indicators to provide information support including position, orientation, period, and other indicators. Then, we can collate and sort out the reorganization need, and further formulate and prepare the \(RG\).
2. Combined with the characteristics of the \(RG\), and according to the indicators above, we can find the \(RD\) that meets the needs in the DC. Then, we can match the \(RE\) in the ERP with the selected \(RD\).
3. Combining the \(RG\) and the characteristics of entity resources that have been identified, we can make the \(RS\). Under the guidance of the \(RS\), we can establish the connection between the selected \(RE\), and ensure that the normal transmission of data information can be carried out.
4. Once all three parts of the work have been completed, the first version of the \(RN\) can be output. By testing the function and performance of the first version of the \(RN\), we can judge its shortcomings and provide the feedback on the reorganization effect. It can provide the basis for optimization.
5. According to the feedback effect, we can optimize the \(RN\), and determine whether the output network meets the requirements. If it is not satisfied, we can adjust the \(RS\), and repeat the above steps. Meanwhile, we can realize the further optimization of the \(RN\). When the requirements are met, the algorithm ends.  
It is important to note that since this paper is the description and analysis of a task-oriented, reconfigurable architecture, therefore, every time we build it into practice, it results in a momentary state. Therefore, it is not possible to achieve the reconfigurable goals through one iteration. That is, according to the reorganization and feedback, we need to repeat the steps listed above many times. Finally, we can achieve the R-SIN.  
In addition, to highlight the iterative nature of the process in the above algorithm, we use \(RN\) to represent the reconfigurable network in the initial moment. When \(RN\) is the same as the R-SIN, the algorithm ends.
# Link Load Correlation-Based Blocking Performance Analysis for Tree-Type Data Center Networks
## 3 Blocking Performance Analysis for Tree-Type Data Center Networks
### Link Load Correlation-Based Blocking Probability Performance Analysis  
As observed in [14], the EFPA method is not applicable to ring networks, as the loads of adjacent links are highly correlated. In the tree DCN shown in Figure 1, one \(L_{1}\) layer link and \(K\)\(L_{0}\) layer links, connected by the same edge switch, form a claw network. Under uniform traffic, each \(L_{0}\) layer link carries \(N_{s}-1\) types of connection. Among them, \(K-1\) types of connections occur locally and are distributed by the edge switch to the other \(K-1\)\(L_{0}\) layer links, while the other \(N_{s}-K\) types of connections will be transferred to the core switch and then distributed to the other edge switches. Therefore, in the local claw topology, most traffic in each \(L_{0}\) layer link continues to pass through the following \(L_{1}\) layer link, resulting in a relatively strong correlation between these two layer links. Taking this into consideration, we modified the EFPA method and revised the blocking probability calculation for CC connections, making it more applicable to tree DCNs. In the following subsection, the blocking rate calculation methods for EC connections and CC connections are provided, based on the link load correlation.
## 1 Introduction
Sensitive sensors require weakly guided waveguides with low core confinement, which can be achieved with waveguides of smaller core diameters, and conversely, optical interconnects require a waveguide with large core confinement, which can be achieved by increasing the core diameter. By using a TPP DLW technique for the fabrication of waveguides [24], any core diameter can be prescribed per application specification, and even combined in the same structure with adiabatic transitions. Figure 1d shows a cross-section image of our DLW fabricated waveguide after slicing through with a focused ion beam (FIB), having a core diameter of \(\sim\)2 \(\upmu\)m, width of supporting narrow fin of \(\sim\)0.26 \(\upmu\)m, and fin height of \(\sim\)1.7 \(\upmu\)m (with a supporting pedestal block for better substrate adhesion). The design optimization of the circular core waveguide supported by the nano-fin is presented in Section 2. Section 3 discusses its fabrication and nano-characterization. The device characterization is presented in Section 4, followed by the conclusions in Section 4.1.
# Analysis of Controllability in Cyber-Physical Power Systems under a Novel Load-Capacity Model
## 3 Fault Propagation of CPPS under Cascading Failure
### The Process of Cascading Failure  
In CPPS, cascading failures can occur for a variety of reasons, including physical failures, information failures, operational failures, malicious attacks, and more. When one node or component fails, it can potentially affect the surrounding nodes or components, resulting in additional failures. This chain reaction can spread quickly and result in the entire system being unable to operate normally.  
In the domain of power systems, the conditions of a system are commonly categorized into three classifications according to the magnitude of the load ([PERSON] et al., 2016): light load, heavy load, and overload. Consequently, subsequent to a redistribution of the load, the condition of the node can be categorized into three classes based on this criterion:  
* Underloading Node: The node's load is within its rated range.
* Heavy-Loading Node: The load on the node exceeds the rated range but does not exceed the capacity of the node. Therefore, the node is still in a normal operating state, but it cannot remain in this state for a long time or it will cause the node to fail.
* Overloading Node: The node load exceeds the capacity of the node and the node fails.  
For example, in Figure 4a, the different colors of the six nodes represent their respective states. Due to a certain condition, node \(N_{1}\) becomes overloading. At this point, \(N_{1}\) fails and its load is transferred to its neighboring nodes. Subsequently, in Figure 4b, due to capacity constraints, the originally lightly loaded five nodes become burdened with increased load. As a result, \(N_{4}\) becomes heavy loading, and \(N_{2}\) and \(N_{5}\) become overloading. Subsequently, \(N_{2}\) and \(N_{5}\) will repeat the process of load redistribution until the network stabilizes or collapses.  
When node \(i\) fails due to overloading, the load of the failed node is distributed to the connected nodes in proportion to \(Q_{ij}\), where \(Q_{ij}\) is defined as  
\[Q_{ij}=\frac{C(j)-L(j)}{\sum\limits_{n\in f(n)}C(n)-L(n)} \tag{23}\]  
After load redistribution, the new load of neighboring node \(j\) can be calculated as  
\[L(j)^{\prime}=L(j)+Q_{ij}L(i) \tag{24}\]  
As mentioned above, redistributing the load on node \(j\) can cause it to become overloading, potentially increasing the area of failure.
# Optimal Configurations of Mueller Polarimeter for Gaussian-Poisson Mixed Noise
## 2 Materials and Methods
### Effect of Linear Retarders on Polarization States  
The Poincare sphere is a way to pictorially describe polarization states. Neglecting the first Stokes parameter \(S_{0}\), the three other Stokes parameters can be plotted directly in three-dimensional Cartesian coordinates. When we characterize the polarization states through the Poincare sphere, any polarization state can be represented by a point on the surface of the Poincare sphere. Besides, the instrument matrix is determined by multiple specific polarization states, so each instrument matrix can also be represented by a polyhedron composed of points on the surface of the Poincare sphere. Then, the problem of how to optimize the instrument matrix of the PSG can be transformed into a geometric optimization problem of how to select \(N\) points on the Poincare sphere.  
Both PSA and PSG are composed of polarizers and retarders. In order to generate a special point on the surface of the Poincare sphere, it is first necessary to figure out the effect of both polarizers and retarders on the polarization state on the Poincare sphere. The Stokes parameters of a beam of light through a polarizer are easy to know, but retarders are unintuitive. The Mueller matrix of a retarder is  
\[M_{\theta,\delta}=\begin{pmatrix}1&0&0&0\\ 0&\cos^{2}2\theta+\sin^{2}2\theta\cos\delta&\sin 2\theta\cos 2\theta(1-\cos \delta)&-\sin 2\theta\sin\delta\\ 0&\sin 2\theta\cos 2\theta(1-\cos\delta)&\sin^{2}2\theta+\cos^{2}2\theta\cos \delta&\cos 2\theta\sin\delta\\ 0&\sin 2\theta\sin\delta&-\cos 2\theta\sin\delta&\cos\delta\end{pmatrix} \tag{7}\]  
where \(\theta\) is the angle of fast axis and \(\delta\) is the retardance.  
We can accurately calculate the polarization state of polarized light after passing through a retarder by the Mueller Matrix in Equation (7). However, if we need to generate a special polarization state, it will be complex to choose the incident polarization state or retarder's angle and retardance. In addition, in order to make the polarization states satisfy certain geometric relationships, for example, the two states should be origin symmetry to each other on the Poincare sphere when the illumination/analysis polarization states number is bigger than four, the first thing to figure out is how a retarder makes the polarization state on the Poincare sphere change. Then, it will be easy to use the retarder to generate polarization states which we need to make them meet certain conditions.
## 2 Materials and Methods  
The protocol was carried out at the Upper Limb Day Hospital (DH), Department of Neurorehabilitation of the Bambino Gesu Children's Hospital, Rome, Italy, EU. The clinical protocol was conducted, recorded, and reported in accordance with Good Clinical Practice guidelines and the Declaration of Helsinki. As the children were underage, all participants' caregivers were asked to sign an informed consent form before taking part in the study; all children were, however, asked to agree to participate in the study [25]. Ethical review and approval were waived for this study due to the explorative nature of the study and the non-intervention. All children with agenesis or upper limb amputation, referred to the DH, were evaluated for this study by a specialist in Physical and Rehabilitation Medicine (PRM) together with an orthopedic technician. In case the child did not already have a prosthesis, the prosthesis was chosen from two options: aesthetic passive prosthesis and myoelectric prosthesis [26; 27]. Following prosthesis delivery, the first clinical evaluation by PRM, a hand surgeon, together with both the physiotherapist (PT) and occupational therapist (OT), was conducted. All children already had an ongoing IRP at other centers, mainly in the form of motor re-education. These interventions were continued following the recommendations of the referring physicians, with the addition of a prosthesis-based re-education program established at the DH. Children were then re-evaluated after six months of motor re-education with and without the prosthesis. Only one child received a second follow-up evaluation at 12 months. During the multidisciplinary examinations conducted by PRM, a hand surgeon, and a PT or OT, the children underwent the following assessments:  
UBET (The Unilateral Below Test) [28]. The UBET was developed to assess the function of bimanual tasks for both prosthesis wearers and non-wearers. UBET consists of nine tasks for each of the four age-specific categories defined by the developmental stages of hand function (2-4 years, 5-7 years, 8-10 years, and 11-21 years) (Table 1). UBET is divided into two subscales: Task completion and Method of use. Task completion assesses the level of function on a 5-point interval scale designed to distinguish the ease of completing the task and the quality of movement displayed by a child. Task completion is the main score to be used to assess function, as the essential assessment is whether the child is able to perform a specific task. The scale for Task completion ranges from 0 to 4, with 4 representing completion of the task without difficulty and 0 representing inability to complete the task.
# Use of Ferritin-Based Metal-Encapsulated Nanocarriers as Anticancer Agents
## 4 Discussion
Our results show that HFt-cisPt nanocarriers are good potential anti-neuroblastoma molecules: the cytotoxic effect on SH-SY5Y cells is time-dependent and concentration-dependent. As for treatment with transferrin-conjugated cisplatin [51], HFt-cisPt administration has high antiproliferative activity (IC50 = 8 uM after 72 h of treatment) and is likely to result in prolonged plasma half-life with respect to the unconjugated drug. Furthermore, treatment of SH-SY5Y cells with free cisPt elicits slightly more cytotoxic effects than HFt-cisPt at higher concentrations (above 10 uM, Supplementary Figure S1), although other studies showed cytotoxicity of cisPt only at very high concentrations (e.g., Refs. [52; 53]). Silver nanoparticles (AgNPs) have widespread applications in medicine, but there are limited studies on the potential of AgNPs as anticancer agents, such as a report on the anti-proliferative activity of AgNPs against human glioblastoma cells (U251) [54; 55]. We show that HFt-Ag nanocarriers are very toxic for neuroblastoma cells, even at low concentrations, induce oxidative stress-dependent damage and determine cell death: after 48 h treatments, cell viability is \(<\)5% after treatment with 1 uM Ag. Toxicity is therefore very high at lower concentrations than those toxic upon administration of the ion alone [55]: in fact, treatment of SH-SYSY cells with free AgNO3 elicits cytotoxic effects comparable to HFt-Ag only at higher concentrations (Supplementary Figure S1). Due to this high activity, it would be important to pay particular attention to the future in vivo experiments evaluating the overall toxicity in the animal models. In fact, differently than cisplatin-based drugs, silver-based drugs for cancer have been not fully characterized to date. HFt-Au is not cytotoxic at the concentrations used, do not affect cell cycle nor significantly modify the subGo-G1 apoptotic fraction. Even more, upon treatment with HFt-Au, the GSH content of the cells transiently decreases within 4 h of incubation, and increases in the long term, as already observed in the same cells, upon treatment with polyphenic crude extract from _Brassica oleracea_[56]. A possible explanation is the exceptionally positive reduction potential of gold (E gold = +1.83 V, vs. silver: +0.80 V, iron: \(-\)0.44 V): therefore, HFt-Au could be able to induce a transient oxidative stress that perturbs GSH homeostasis and may cause a long-term enhanced synthesis of this peptide. In general, the cytotoxic effect of the HFt-metal complexes is time-dependent and is strictly related to the metal release from the HFt. Although this is a slow process, the exact release mechanism of metal ions from HFt is not yet clear. It is possible that the slightly acidic pH present in the endosome and lysosome vesicles (pH 4.0-6.0) might be a stimulus to the release process. After all, the release of iron from ferritin is pH-dependent and low pH facilitates the release. In addition, the ultimate fate of the HFt is the lysosomal compartment, where the protein degradation can produce a massively metal release.
## 2 Method
### The Four-Dimensional Tracking Model  
To explore the feasibility and accuracy of the four degrees of freedom (DOF), \(x,y,z\) and pitch angle, the 4-D tracking model is divided into three cases for discussion as shown in Figure 3. In the figure, the distance between dual receivers is \(r\). And \(d_{1}\) and \(d_{2}\) are the distances between the transmitter and the dual tracking receivers in \(y\)-axis direction, respectively.
## 3 Methodology
### Examination of Analysis Methods  
Between 2004 and 2006, the Taiwan Chelungpu Fault Drilling Project (TCDP) drilled a 2-km-deep hole (TCDP-A, Figure 2) 2.4 km east of the surface rupture of the Chi-Chi earthquake (near the town of Dakeng in Taichung municipality). [PERSON] et al. [23] adopted geophysical well logs at depths between 500 and 1900 m to determine the physical properties of the fault zone and its neighboring damage areas. The well-logging data provided [PERSON] and [PERSON] [11] a suitable reference for inspecting the validity of the inverted velocity structures by using microtremor array measurements. Therefore, they conducted microtremor array measurements at two sites (DAK and TCD) close to the TCDP-A sites (Figure 2). In this study, the analysis methods and techniques used closely resembled those in [11]; thus we include Figure 4 (adopted from [11]) to certify that our inversion results (in Section 4) are reliable. In the figure, the S-wave velocity estimated from the differential inversion technique [22] gradually increases from 1.52 km/s at the depth of 585 m to 2.22 km/s at the depth of 1710 m. This velocity structure is similar to that from the well-logging data,which ranged from 1.4 km/s at the depth of 597 m to 2.98 km/s at the depth of 1705 m. This figure also displays the depth function (blue line) of the S-wave velocity, which is the regression result of the TCDP well-logging data at depths between 494 m and 1707 m in [24]. These results reveal that the S-wave velocity structures (black lines) estimated from microtremor array measurements were consistent with the depth function results. Therefore, the inversion velocity structures are reasonable and reliable. This also indicates that microtremor array measurement is a suitable approach for estimating deep or shallow S-wave velocity structures.
## 2 Preliminaries  
### Data Anonymization  
When providing personal data to a third party, it is necessary to modify data to preserve user privacy. Here, modifying the user's data (i.e., a particular record) such that an adversary cannot re-identify a specific individual is referred to as _data anonymization_. As a basic technique and to prevent re-identification, an identifier, e.g., a person's name or employee number, is deleted or replaced with a pseudonym ID by the data holder. This process is referred to as _pseudonymization_. However, simply modifying identifiers does not ensure the preservation of privacy. In some cases, individuals can be re-identified by a combination of features (i.e., a quasi-identifier); thus, it is necessary to modify both the identifier and the quasi-identifier to reduce the risk of re-identification. In most cases, the identifiers themselves are not used for data analysis; thus, removing identifiers does not significantly sacrifice the quality of the dataset. However, if we modify quasi-identifiers in the same manner, although the data may become anonymous, they will also become useless. A typical anonymization technique for quasi-identifiers is \"roughening\" the numerical values.
## 5 Network Setup and Results: A Case Example
### Network Setup  
The first step in the simulation of the network is its setup, intended as the deployment of the nodes composing the network itself. This step can be done either graphically, via drag&drop from a palette, or textually, using the internal language of OMNeT++ to describe network connections and parameters (called NED). In the same way, the connections between the nodes are created. As already mentioned, the network uses the Hardware-in-the-Loop capability. The SpaceWire PXI Analyser is used to simulate a Unit Under Test. It is connected to SpaceART through a SpaceWire cable, and SpaceART acts as a bridge towards the simulator. The PXI analyser can be configured to generate and/or consume data at a specific rate.
# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
## 3 Numerical Validation and Evaluation
### Results and Analysis  
Figure 7 shows the ERV of the eight types of damage scenarios under the damage degree of 15%. It can be seen from the figure that the structural damage will have a certain impact on the ERV value. Taking the second unit damage in a single damage scenario as an example, the wavelet packet frequency band ERV varies greatly at the positions of first and second units, which can preliminarily judge the approximate location of the damage. Compared with other units, the energy released is also more. Similarly, when the damage occurs at other element locations, the damage index of the nearby element changes significantly, but when the damage occurs at the first and tenth unit positions at the support, the change of ERV of the mid-span unit is also severe and unstable.  
Figure 7: Two-dimensional ERV diagrams of different working scenarios. (**a**) ERV value of working scenario S1. (**b**) ERV value of working scenario S2. (**c**) ERV value of working scenario S3. (**d**) ERV value of working scenario S4. (**e**) ERV value of working scenario S5. (**f**) ERV value of working scenario S6. (**g**) ERV value of working scenario S7. (**h**) ERV value of working scenario S8.  
Therefore, taking the acceleration signal as the original data, the ERV constructed by wavelet packet frequency band energy method can only preliminarily identify the approximate location of the structural damage. In order to obtain more accurate identification results, it is necessary to combine the neural network for further judgment.