esa-sceva/satcom-chunk-collection huggingface dataset:
# Will Zero Vulnerability Computing (ZVC) Ever Be Possible? Testing the Hypothesis
## 5 ZVC Hypotheses: New World, New Rules
### The ZVC Hypotheses  
**Hypothesis 1:**_Can **Supra OS (SOS)** completely obliterate the primary attack surface by denying third-party permissions to all non-native applications?_  
**Hypothesis 2:**_Can a hardware module isolate critical data that require sporadic access to offer **in-computer offline storage (ICOS)** within the connected device itself without compromising its functionality?_  
As stated earlier, it is impossible to build a computer without third-party permissions. Permissions create vulnerabilities, allowing attack surfaces that attack vectors use to breach computers. An \"attack surface\" is essentially the entire externally facing area of a computing environment. It contains all the vulnerabilities or attack vectors that a hacker can use to gain access to a computing system. The attack surface is simply the total digital resource exposed to threats across the enterprise. There is no legacy hardware or software that does not come without third-party permissions by default. As illustrated in Figure 4a-d, third-party permissions may be exploited at the hardware, firmware, or OS level (primary attack surface) or even via the application layer (secondary attack surface). The magnitude of the attack surface is directly proportional to the density of vulnerabilities [42]. The attack surface (represented in Figure 4 by dents or pits and permissions by represented by bumps) is mandatorily created in legacy computers because of the assumption that third-party applications must run locally; therefore, permissions are mandatory. As such, banning all permissions and completely obliterating the attack surface of a computing device are de facto impossibilities in cybersecurity research. However, in the era of 5G communication and ultrafast data transmission speeds, the assumption that the application must be executed locally is outdated and misplaced. Currently, there is no conceivable applications that cannot run on a remote server.  
Figure 4: \(Cont.\)While banning all third-party permissions to reduce the attack surface to zero may be quite a stretch to conceive of in the pre-5G era, it is quite surprising to note that the state-of-the-art at no point in the evolution of computers made it impossible to integrate a switchable in-computer offline storage (ICOS) memory to complement the computer's standard hard drive. Yet, our legacy computers lack such a mechanism to keep sensitive data offline at users' behest, while the users' computers remain online.
## 1 Introduction
The low handling time at the warehouse is achieved since the parts that are picked for the preparation of the kits, that belong to the same station sequence, are of the same typology, which are usually stored close to each other. The proximity of the picked parts has a strong positive effect on the picking time because it significantly reduces the distance traveled by the picker. In fact, according to [8], traveling activity accounts for roughly 50% of the total manual picking time. However, the station-sequence feeding policy also suffers from three main drawbacks, which are all related to low levels of the station-sequence stock:  
* A lack of flexibility to the change of sequence and mix of the assembled models. If the stock of the station sequence is low, the probability to stop the assembly as a consequence of even one missing part increases when the sequence of assembled models changes. Figure 1: (**a**) A scheme of how the station-sequence policy works; (**b**) An industrial example of station-sequence application. * A lack of flexibility toward variations in the assembly line cycle time. If the stock of the station sequence is low, the probability to stop the assembly as a consequence of even one missing part increases when the assembly line cycle time has significant variations. * A lack of flexibility with respect to the variation of the parts feeding period. If the stock of the station sequence is low, the probability to stop the assembly as a consequence of even one missing part increases when the parts feeding period has significant variations. In this paper we are going to study the station-sequence parts feeding policy considering its dynamic time-dependence and we are going to analyze how its performance is affected by perturbations of both the assembly line cycle time and the parts feeding period, as well as by changes in the model mix. This study was carried out through a set of simulations, as suggested in other contributions such as [9], implemented on Siemens' Plant Simulation. The main results are the quantitative definition of the main influencing factors and their impact on system performance. Finally, we provide a brief discussion on how Industry 4.0 technologies can improve the operations of a company that adopts a station-sequence parts feeding policy. The rest of the paper is structured in this way: Section 2 reports the literature review, Section 3 describes the simulation model, Section 4 discusses the results of the simulation, Section 5 introduces the main possibilities according to I4.0 implementation and Section 6 presents the conclusions.
## 3 Results and Discussion
### Characterization of Microcapsules
#### 3.2.2 Sem  
The morphology of the microcapsules was observed by SEM (Figure 2). The microcapsules had complete surfaces with approximately spherical shapes. Some wrinkles were observed on the surfaces of the microcapsules, and a small number of microcapsules had dents on the surface. The spray-dried microcapsules did not show a perfect spherical shape. Although they were spherical in an aqueous solution, embedded oil microcapsules [16; 27] were more likely to remain spherical after drying, while microcapsules with irregular core materials, such as probiotics [15] or PRJSR, tended to shrink. The reason for this phenomenon is that PRJSR is an irregular solid crystal, but this feature also ensured that the microcapsules would not be excessively wrinkled and shrunken. However, there were hardly any holes on the microcapsule surfaces. This was due to the proper ratio of PRJSR:GE:AG. If the concentration of the outer shell material was too high, the microcapsules might have become irregular or adhesive, resulting in damage after spray drying [27]. The unbroken surfaces indicate that these wall materials could offer protection against leakage [28].  
These observations confirmed that the microcapsules were successfully prepared through the complex coacervation and spray drying process.
## 3 System Model and Problem Modeling
### Problem Modeling  
Let the set of association factors between GPFs and LEO satellites be denoted as \(\alpha_{\rm s}=(a_{n,s}(t):\forall n\in\mathcal{N},t\in\mathcal{T})\). The set of association factors between GPFs and APs is denoted as \(\alpha_{\rm m}=(a_{n,m}(t):\forall n\in\mathcal{N},m\in\mathcal{M},t\in\mathcal{ T})\). The set of horizontal positions of APs in each time slot is denoted as \(\mathcal{Q}=(q_{m}(t):\forall m\in\mathcal{M},t\in\mathcal{T})\), and the set of transmission powers of GPFs in each time slot is denoted as \(\mathcal{P}=(p_{n}(t):\forall n\in\mathcal{N},t\in\mathcal{T})\). \(\mathcal{N}\) represents the GPF set, \(\mathcal{M}\) represents the AP set, and \(\mathcal{T}\) represents the time slot set.  
In this paper, we conduct joint optimization of the device association factors \(\alpha_{\rm s}\) and \(\alpha_{\rm m}\), AP flight trajectories \(\mathcal{Q}\), and GPF transmission powers \(\mathcal{P}\) to maximize the long-term system total energy efficiency \(\eta\). The optimization problem is expressed as follows:  
\[(\mathbf{P1})\max_{a_{\rm s},a_{\rm m},\mathcal{Q},\mathcal{P}} \eta \tag{12}\] \[\text{C1: }a_{n,s}(t)\in\{0,1\},\forall n\in\mathcal{N},t\in \mathcal{T};\] \[\text{C2: }a_{n,m}(t)\in\{0,1\},\forall n\in\mathcal{N},m\in \mathcal{M},t\in\mathcal{T};\] \[\text{C3: }\sum_{m=1}^{M}a_{n,m}(t)+a_{n,s}(t)\leq 1,\forall n\in \mathcal{N},t\in\mathcal{T};\] \[\text{C4: }\sum_{m=1}^{N}a_{n,m}(t)\leq L_{M},\forall m\in \mathcal{M},t\in\mathcal{T};\] \[\text{C5: }\sum_{m=1}^{M}\sum_{n=1}^{N}a_{n,m}(t)+\sum_{n=1}^{N}a_{n,s }(t)\leq L_{\rm S},\forall t\in\mathcal{T};\] \[\text{C6: }0\leq p_{n}(t)\leq p_{max},\forall n\in\mathcal{N},t\in \mathcal{T};\] \[\text{C7: }\sum_{t=1}^{T}(R_{n,\rm immediate}(t)\cdot\Delta t)\geq \mu_{n,\rm immediate},\forall n\in\mathcal{N};\] \[\text{C8: }\sum_{t=1}^{T}(R_{n,\rm non-immediate}(t)\cdot\Delta t) \geq\mu_{n,\rm non-immediate},\forall n\in\mathcal{N};\] \[\text{C9: }\|q_{m}(t+1)-q_{m}(t)\|^{2}\leq(v_{max}\cdot\Delta t)^{2}, \forall m\in\mathcal{M},t\in\mathcal{T}.\]  
C1 and C2 represent binary variables, indicating the association factors between GPFs and LEO satellites and APs, respectively. C3 indicates that any GPF can only choose a single AP or LEO satellite for data transmission in any time slot. C4 and C5 denote upper limits on the number of GPFs that APs and LEO satellites can associate with in any time slot. C6 represents an upper limit on the total transmission power of GPFs in any time slot. C7 indicates that each GPF needs to meet its respective minimum immediate communication workload requirement. C8 signifies that each GPF needs to satisfy its respective minimum non-immediate communication workload requirement. C9 denotes that the position variation of APs in each time slot is constrained by their maximum flight speed.
# Effect of Cement Content on the Deformation Properties of Cemented Sand and Gravel Material
## 3 Results and Discussion
### Mechanism Analysis  
To further understand the effect of cement content on the deformation characteristics of CSG material, the internal mechanism of the material under a triaxial load is analyzed here.  
When the cement content is low, there is large porosity in the CSG material and weak cementation between aggregates. In the initial shearing stage of the CSG material, the interaction of the particles is mainly friction and extrusion, which is similar to that of ordinary rockfill material; thus, the initial modulus of the CSG material is small and close to that of the rockfill material, and the shear shrinkage is shown. Then, axial load makes the internal porosity decrease, and the particles are mainly flipped and spanned, which can cause dilatancy of the CSG material. After that, the particles near the shear plane are staggered after overcoming friction, and weak cementation is produced by cement in the CSG material; the CSG material is destructed and its failure strain is approximately 6%-10%. As the axial load continues to increase, the particles near the shear plane continue to overcome the friction, the frictional resistance makes the residual strength of the CSG material slightly less than the failure strength, and the stress-strain curve is similar to that of the ordinary rockfill [28].  
With an increase of cement content, the internal cementation of the CSG material increases and porosity decreases. The initial modulus increases as shown in Figure 5, and the shear shrinkage becomes less obvious as shown in Figures 1b, 2b, 3b and 4b. When some particles of cementation are broken, then flipped and spanned, dilatancy occurs in the CSG material; the axial strain and volumetric  
Figure 17: The verification of the expressions for the deformation characteristics of the cemented poorly graded sand–gravel mixture under different cement contents: (**a**) the relationship between the failure strain \(\varepsilon_{\mathrm{m}}\) and confining pressure \(\sigma_{3}\); (**b**) the relationship between the axial strain of the peak point \(\varepsilon_{\mathrm{n}}\) and confining pressure \(\sigma_{3}\); (**c**) the relationship between the peak volumetric strain \(\varepsilon_{\mathrm{vd}}\) and confining pressure \(\sigma_{3}\).  
strain of the initial dilatancy decreases gradually with increasing cement content, as shown in Figures 8b and 13. When particles near the shear surface continue to be loaded, the cementation is broken, but the smaller dislocation results in the smaller failure strain as shown in Figure 11 when the cement content is higher. In addition, since there are many loose particles in the shear zone after the failure of the specimen, and there is randomness in its crossing and turning, the volumetric-strain-axial-strain curve of the CSG material with different cement content presents an occasional crossover phenomenon, as shown in Figures 1b, 2b, 3b and 4b.
## 4 Systematic Performances Analysis of UWSNs
### Map Relationship Between Systematic Performance Items and Influence Parameters  
From the four measurement formulas above, we can see there is a certain relationship among those four items, and they are determined by basic performance parameters. The coverage and connectivity are impacted by the number of UUVs and sensor nodes, while durability and rapid-reactivity are impacted by the number and coverage scope of sensor nodes, and rapid-reactivity and connectivity are impacted by the communication range diameter and degree of connection. Therefore, a map relationship between the performance items and the parameters is established, as shown in Figure 5.
## 2 Materials and Methods
### Model Development
#### 2.2.3 Comparison of ANN and ARIMAX Approach  
The goodness of fit values of ANN and ARIMAX were determined using the coefficient of determination \(R^{2}\), the mean squared error (MSE) and the mean absolute error (MAE). \(R^{2}\) was calculated as follows (4):  
\[R^{2}=1-\frac{\sum\Bigl{(}y_{exp}-y_{pred}\Bigr{)}^{2}}{\sum\bigl{(}y_{exp}- \overline{y}\bigr{)}^{2}} \tag{4}\]The MSE represents the average squared difference between the predicted values estimated from a model and the actual values. The MSE is measured as follows (5):  
\[\text{MSE}=\frac{\sum\Bigl{(}y_{exp}-y_{pred}\Bigr{)}^{2}}{M} \tag{5}\]  
where \(y_{exp}\) represents experimental values, \(y_{pred}\) represents predicted values, and \(M\) is the total numbers of data. When an input is entered into the network, the output (network result) is compared with the target. The MSE is then calculated to show the difference from this comparison. Therefore, the average sum of mean squared errors is expected to be low.  
We also include MAE, since there are studies suggesting that the MSE is not a good indicator of average model performance and that therefore MAE is a better metric for this purpose [66; 67].  
MAE measures the average magnitude of absolute differences between \(N\) predicted vector \(S=\{x_{1},x_{2},\ldots,x_{N}\}\) and \(N\) actual observations \(S^{*}=\{y_{1},y_{2},\ldots,y_{N}\}\). The corresponding loss function is shown as (6):  
\[\text{MAE}=\frac{\sum_{i=1}^{N}\lvert y_{i}-x_{i}\rvert}{N} \tag{6}\]  
where \(y_{i}\) is the prediction and \(x_{i}\) is the true value. This error is expected to be low, like the MSE.  
The lobster export prices for the first three months of 2017 were projected using three prediction methods with data from January 2006 to December 2016: (1) a non-linear autoregressive network (NAR), (2) an autoregressive network with exogenous inputs (NARX for one, two, three, and four selected exogenous variables), and (3) an integrated autoregressive moving average model (ARIMAX) with and without an exogenous input variable. The ARMAX model was used only to compare its prediction accuracy versus the other models since it is the model most commonly used for estimating price forecasts of marine resources [68; 69; 70]. The MSE value of each model was used to calculate the goodness of fit of the forecast.  
The monthly prices database (real, Arimax and NARX best fit exogenous variables) is shown in the Supplementary Materials (Supplementary Material Table S1).
## 3 Results and Discussion
### Mixed Time Partitioning Methods vs. Sub-Cycling and Partitioning Methods
#### 3.1.3 Computational Efficiency of Mixed Partitioning Methods  
We now move to the more complex case in which we test not only various combinations of different time steppers, but also different size of the time step. Intuitively, based on our understanding of the underlying physics, we choose the combination of explicit schemes with the small size of time step in the casting and implicit schemes with coarse-grained time step. From Table 6 we can see that, as in the case of a previous combination of schemes, \(E-I^{m}\) methods are faster than the \(I-E^{m}\) ones. Using explicit schemes in the casting domains give abbreviated run time and a better description of the physical phenomena occurring during casting solidification.  
Here, we also study four different combinations of steppers. We include the run time of the given method in Table 7. For the purpose of these tests, we define the efficiency \(p_{m}\) as a ratio between run time \(t\) for \(m=1\) (the standard approach) and the run time for \(m=15\). From the collected data, we see a computational gain for all studied cases considered.  
This confirms the efficacy of the mixed time partitioning methods when applied to modeling of solidification process. The largest computational gain is possible when an explicit scheme is used in the casting sub-domain and the implicit scheme in the mold sub-domains. It is the most intuitive combination of time integration schemes for the modeled phenomena of interest. In numerical considerations, the observed gain has origins in the way boundary conditions are applied, and the system of equations is solved. Moreover, the results are obtained with an extensive iterative method when we use implicit methods.  
However, the computational gain is not the only criteria. The accuracy of the solution is an equally important criterion which will be discussed in the next subsection.
# Bridge Health Monitoring via Displacement Reconstruction-Based NB-IoT Technology
## 3 Bridge Health Monitoring Experiment
### Small-Scale Bridge with Vibration Induced by Passing Vehicle
#### 3.1.1 Experiment Setup
Table 7 shows the damping ratio for the original and stiffened bridges under free vibration. Similar to frequency measurement, the number of measurements is 5.
# A Watermarking Method for 3D Printing Based on Menger Curvature and K-Mean Clustering
## 3 The Proposed Algorithm  
### Overview  
The proposed method is described in Figure 2. Facets are firstly extracted from the 3D printing model (3D triangle mesh) to compute their Menger curvatures. These facets are then classified into groups by the K-mean clustering algorithm [20] based on the value of the Menger curvatures. The watermark key is the number of groups into which we want to classify facets. This means the watermark key is secret and used to determine the number of groups and the length of watermark bits that are generated to embed into the 3D triangle mesh. The watermark key is defined or chosen by the users. The watermark key is re-used in the watermark extraction process (see Figure 2). With each group of facets, we compute the mean Menger curvature of that group and then embed a watermark bit into that mean Menger curvature by changing the value of mean Menger curvature based on the reference of a special value. Finally, the watermarked 3D triangle mesh is generated according to the watermarked mean Menger curvatures. The watermarked 3D triangle mesh is the input of a 3D printer. After the 3D printing process, the 3D-printed object will be used for the 3D scanning and reconstruction process to obtain the scanned 3D triangle mesh. The facets of the scanned 3D triangle mesh are then extracted to compute their Menger curvatures. Next, the facets of the scanned 3D triangle mesh are also divided into groups based on the value of Menger curvatures. After the facet clustering step, we have to calculate the mean Menger curvature of each group. The watermark data will be extracted from the mean Menger curvature of each group. The detailed watermark embedding and extraction processes are described in Sections 3.2 and 3.3.  
Figure 1: (**a**) Structure of a facet and (**b**) circumscribed circle of a facet.  
Figure 2: The proposed algorithm.
## 1 Introduction
### Industry 4.0 and Industry 5.0  
The development of the manufacturing industry is closely linked to economic and social progress and has therefore undergone considerable evolution in recent decades. Its latest phases are named Industry 4.0 and Industry 5.0 [1]. Their main characteristics include intelligent manufacturing [2], autonomous machines [3; 4], safe machines [5; 6], automation in manufacturing [7], computer support for all phases of the product life cycle (pre-production and production phases) [8], wireless interconnection of individual systems, Internet of Things [9], Internet of Services [10], robotics, self-decision systems, 3D printing [11], sensors, big data [12], digital twin [13], cloud computing [14], artificial intelligence [15], and others. There are several differences between Industry 4.0 and 5.0. The main difference is that Industry 4.0 is technology-driven while Industry 5.0 is value-driven and considers humans as a key part of the production system [16]. Industry 5.0 and industrial progress in general are marked by the emergence of disruptive technologies that bring significant economic and social impacts with them. The benefits of Industry 5.0 can be found where Industry 4.0 fails. This is the area of promoting a more fair and sustainable society in which there is symbiosis between man and machine (robot) [17]. Machine tools together with forming machines [18] are one of the most frequent representatives of production machines [19]. The basic function of a machine tool is to process a workpiece into the desired shape, dimension, and surface quality of functional surfaces by machining. Machine tools are complex technical systems and have seen significant growth in recent years. Just as there are several phases of industrialisation, machine tools have gone through different phases of technological development and are often called Machine Tool 1.0-4.0 [20]. In the recent era of Machine Tool 4.0, the process of convergence of a physical machine tool and its virtual prototype is described as a cyber-physical machine tool (generally called a cyber-physical system) [21; 22].