esa-sceva/satcom-chunk-collection huggingface dataset:
# An Abstraction Technique for Verifying Shared-Memory Concurrency +
## 1 Introduction  
Modern software is typically composed of multiple concurrent components that communicate via shared or distributed interfaces, for example via shared-memory or via message passing. The concurrent nature of the interactions between (sub)components makes such software highly complex as well as notoriously difficult to develop correctly. To ensure the reliability of modern software, verification techniques are much-needed to aid software developers to comprehend all possible concurrent system behaviours. To be able to reason about realistic programs these techniques must be modular and compositional, as well as be supported by automated verification tools.  
Even though verification of concurrent and distributed software is a very active research field [1; 2; 3; 4; 5; 6], most work in this line of research is essentially theoretical, and tends to focus primarily on contributing expressive program logics specialised in reasoning about advanced concurrency features like relaxed or weak memory, fine-grained concurrency, message passing interaction, etc. Even though expressive, it is very challenging for these logics to be integrated into SMT-based automated verifiers like for example VeriFast [7], VerCors [8] and Viper [9; 10]. Instead, most of these works have to be applied inpen-and-paper style, or at best semi-automatically in the context of an interactive theorem prover like Coq [11; 12] or Isabelle/HOL [13].  
This article contributes a concurrency verification technique that applies directly on the level of program code and is supported by automated verifiers. However, rather than doing the verification fully on the level of program code, our approach allows _soundly_ abstracting program behaviour into _abstract models_ which can be reasoned about externally, on a higher level in which irrelevant implementation details are hidden, to (indirectly) prove properties about the program behaviour. The presented verification technique (1) has been implemented in VerCors--an automated SMT-based concurrency verifier; (2) is demonstrated on various (real-world) examples, including a leader election protocol (presented in Section 5); and (3) the metatheory of the technique has been fully formalised and proven sound with the Coq proof assistant. With respect to (2): apart from the examples given in this article, more examples of our approach are given in [14], including the verification of a (reentrant) lock as well as a concurrent parallel GCD algorithm. Our technique has also been used in a real-world industrial case study [15]--on the formal verification of a safety-critical traffic tunnel control system.  
This article extends our earlier VMCAI'20 article [16]. Elaborating on the contributions with respect to this earlier article; we contribute a generalisation of the theory in [16] by combining it with the techniques proposed in [17] and [18] into a single logical framework that is more general than the original. This combined unified framework is proven sound with Coq and is available online at [19].
## 4 Experimental Design
### Experiment Implementation
#### 4.2.1 Application Setup  
The ns-3 allows us to easily set up the behavior of the model. Algorithm 1 shows a general example of an ns-3 application class, commApp, which can be attached to the nodes. This application allows for configuring the nodes' behavior under certain circumstances, such as performing an action after moving a specific distance, what to do after receiving a given message, and so on.  
Please note that commApp is just an outline of the actual class used during our experiments, which includes far more functionality. Since the ns-3 does not support positioning per se, the commApp class is used to emulate a positioning application, represented by the UpdatePositionDecay and UpdatePosition methods. UpdatePositionDecay is in charge of calculating and updating the decay of a positioning measurement, and UpdatePosition is invoked to attempt to perform positioning once the decay has reached the model's positioning error threshold.  
Using this class allows us to determine which sockets will be used for communication, the number of packets that will be transmitted, their size, and the interval at which they are sent. It also allows for setting up triggers when sending or receiving messages, as well as determining the conditions upon which it will begin or stop making requests. Additional functions and variables can be added or modified depending on what we want to achieve with the simulation. Any number of applications can be attached to a node, as required.  
```
1 class commApp:public Application{
2 public:
3 commApp ();
4 virtual \(\sim\)commApp();
5 voidSetup (Ptr\(<\)Socket\(>\) socket, uint32_t packetSize, uint32_t nPackets, Time pkInterval);
6 private:
7 virtualvoidStart (void);
8 virtualvoidStop (void);
9 voidScheduleTx (void);
10 voidSendPacket (void);
11 voidUpdatePositionDecay(void);
12 VoidUpdatePosition (void);
13 Ptr\(<\)Socket\(>\) m_socket;
14 uint32_t m_packetSize;
15 uint32_t m_nPackets;
16 Time m_pkltInterval;
17 EventId m_sendEvent;
18 bool m_running;
19 uint32_t m_packetsSent;
20 uint32_t m_decayRate;
21 uint32 t m decayThreshold;};
```  
**Algorithm 1** Application Class Example
## 4 Performance Evaluation
### Experimental Settings and Results  
In this section, two experiments were set up to complete the performance evaluation described in the previous section. One is the effect of the number of drones on the public key request time and identity authentication time (Exp.1). Another is the influence of the number of blockchain nodes on the public key request time and identity authentication time (Exp.2). The number of drones in this experiment refers to the number of drones that simultaneously initiate public key requests or identity authentication requests. In the following, they are introduced in detail.  
In Exp.1, we conducted experiments respectively in the environment where there were no malicious nodes and where there were less than \(1/3\) malicious nodes in the blockchain system. In an environment without malicious nodes, the number of normal nodes is equal to 20. In another environment, the number of normal nodes is equal to 15. The variation trends of the public key request processing time and the identity authentication processing time with the increase in the number of drones in these two environments are shown in Figures 5 and 6, respectively.  
It can be seen from Figures 5 and 6 that the processing time of the public key request and identity authentication increases linearly with the increase in the number of UAVs. Moreover, in an environment with less than \(1/3\) of malicious nodes, the two processing times are not affected, which also shows that blockchain can tolerate the existence of a certain number of malicious nodes and the normal operation of the system is not affected by these malicious nodes.  
In Exp.2, to study the influence of the number of blockchain nodes on the processing time of the public key request and identity authentication, we conducted experiments under different numbers of UAVs (i.e., 50, 100, and 150). The variation trends of the public key request processing time and the identity authentication processing time with the increase in the number of blockchain nodes are shown in Figures 7 and 8, respectively.  
It can be seen from Figures 7 and 8 that the processing time of the public key request and identity authentication does not increase with the increase in the number of blockchain nodes. When the number of drones is constant, the two processing times tend to a certain value. For instance, When the number of drones requesting the public key at the same time is 50, the processing time of the public key request is about 1.4 s, and the processing time of identity authentication is about 1.5 s.  
Figure 5: Public key request time according to the number of drones.  
Figure 6: Identity authentication time according to the number of drones.
## 3 Results
### Synthesis and Resynthesis: The Statements and Their Dimensions, Constructs and Factors
#### 3.3.1 IoT Technology
Sensing technologies include radio-frequency identification (RFID), wireless sensor networks (WSNs), near-field communication (NFC) and Bluetooth technology (BT). In addition, there are difficulties in collecting data for the identification of \"smart objects\", which are embedded in computing and communication resources, and problems in the management of \"smart objects\" because they are numerous, heterogeneous and dynamic [17]. Data transfer over network technology is considered a problem for company decision makers. The full operation of the transfer of data by the network technology requires addressing, routing, end-to-end transmission, gateways and traffic characterization. Network technologies depend on wired technologies, wireless technologies, cellular networks and satellite communication technology [17]. * IoT Barriers  
There are organizational, technological, governmental and ecosystem maturity barriers [20]. Organizational barriers include lack of a senior management support system, resistance from the top management system, low perceptions regarding the digital revolution, risky investment in technologies, unavailability of a digital strategy, unavailability of a data-based service system and fluctuations in production size. Technological barriers include the high cost of technology, the unavailability of I4.0 standards, the unavailability of a data security system, low IT levels, the unavailability of IT infrastructure and service centers and the quantity of parts to be produced. The governmental and ecosystem maturity barriers include the unavailability of government policies, lack of support from government, the low maturity of the manufacturing industry, the unavailability of a technology ecosystem and the lack of consultants and trainers in the area.
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
## 1 Introduction
The energy absorption of the core is investigated through a compartmentalized foam configuration. Experimental evaluation involves studying the reduction in maximum deflection of a fixed aluminum plate (representing the protected structure) under blast loading conditions. Initially, the dynamic response of a bare aluminum plate subjected to blast loading is examined, followed by studies with the addition of three different configurations of mineral foam layers. The study maintains a constant explosive charge mass and distance from the structure to the detonation center, reducing the standoff distance upon adding the SC, thereby increasing the energy transfer to the cladding structure system. Using a validated numerical model provided in [37], the influence of factors such as explosive charge mass and mineral foam core configuration is evaluated. This paper is structured into four primary sections. First, a detailed description of the air-blast experiments, along with the corresponding experimental findings, is provided. In the Section 3, the numerical modeling approach is presented. This is followed by a comparative analysis between the experimental and numerical results, complemented by a parametric study. Finally, the concluding remarks are presented.
## 2 Literature Review
### The Attributes of Technological Factors
#### 2.3.3 Perceived Ubiquity  
Perceived ubiquity incorporates the concepts of spatial, temporal, and mobility [43]. It can be defined as ready access to products or services at any time and in any place [44]. With more people worldwide owning mobile phones and the rapid development of mobile and wireless technology, perceived ubiquity is becoming increasingly popular. Studies have reported that one significant advantage of an online channel is its facilitation of product purchases from any location [45]. Consumers can benefit from using mobile apps, through which they can access a service whenever and from wherever they wish. Thus, the Internet has become a vital alternative to completing tasks with reduced time, money, and effort [46]. In this study, perceived ubiquity is considered a key factor of virtual SST because consumers can operate its functions through smartphones or websites without time and space constraints.
## 3 Results and Discussion
### Method Validation
#### 3.2.6 Method Selectivity, Specificity, Accuracy/Trueness and Recovery from Market Table  
The proposed analytical method showed good selectivity and specificity to detect and quantify metformin, glipizide and gliclazide among the unknown excipients used in the marketed tablets of these agents. The method also showed good accuracy/trueness in quantifying and recovering these analytes from crushed tablet excipients. Method trueness/recovery was verified by the close experimental data detected for these agents to the claimed theoretical values at three different concentration levels (1 ug/mL, 2 ug/mL and 5 ug/mL) determined in triplicates. Across these concentration levels, metformin recovery % ranged between 99.01% \(\pm\) 1.01 and 100.01% \(\pm\) 0.82, glipizide had a recovery % that ranged between 99.61% \(\pm\) 0.52 and 100.31% \(\pm\) 0.96 while gliclazide was recovered at values ranged between 97.62% \(\pm\) 2.04 and 98.52% \(\pm\) 0.92. Recovery % results are presented in Figure 4.
# A Study on the Dynamic Behavior of a Sieve in an Industrial Sifter
## 2 Multi-Frequency Vibrating Screens  
Free vibrating screens are characterized by spatial, non-linear vibrating motion of the sieves. The riddles of these machines have six degrees of freedom and can vibrate longitudinally in all three directions in space, as well as torsionally around three mutually perpendicular axes. This is due to both the drive system, constructed to provide the stimulating forces in selected directions, and the suspension system, having different susceptibility to elastic deformation for all six degrees of freedom. Hence, the other name of these machines: screens with spatial sieve movement. Such machines are designed to achieve the three-dimensional movement of the screens to ensure the most favourable screening process. They are driven by at least two independent driving vibrators, not mechanically synchronized using a gear, but operating on the principle of dynamic self-synchronization. This creates an opportunity to apply different frequencies of stimulating forces in individual drive units. Therefore, we can talk about two-, three-, or multi-frequency screens.  
The mentioned vibrators can be in the form of the inertial ones, widely used in vibration machines, such as screens, vibration mills, granulators, or crushers [1; 29; 30; 31; 32; 33; 34; 35].  
For screening of fine wet particles or near-aperture particles, [PERSON] et al. [36] elaborated a cantilevered vibrating sieve (CVS) with open screen holes, composed of cantilevered sieve rods. Such a sieve exhibited a higher screen-penetrating probability than traditional sieves. The authors reported that additional vibrations emerged during the screen body motion, and a lower difference between the natural circular frequency and the vibration frequency can cause a higher vibration amplitude of the cantilevered sieve rod.  
The vibrating sieves often comprised various springs affecting their motions. [PERSON] et al. [37] reported that spring failures cause structural damage and low screening efficiency of mining vibrating screens. Therefore, spring failures should be effectively diagnosed to prompt maintenance for the safety and reliability of such screens.
# Application of Multiple-Scales Method for the Dynamic Modelling of a Gear Coupling
## Appendix A Forced Response Computation  
Complete \(p_{th}\) equation of motion in modal coordinates including all the harmonic sets of both the parametric and the excitation force, expressed in Fourier Series:  
Figure 15: (a) Time domain response of G\({}_{1}\) by DTI. (b) Time domain response of G\({}_{2}\) by DTI.  
\[\begin{array}{ll}\ddot{u_{p1}}+\omega_{p}^{2}u_{p1}&=-2\Big{[}i\omega_{p}A_{p}^{ \prime}e^{i\,\omega_{p}\,t}+CC\Big{]}\\ &-\sum_{r=1}^{N}\sum_{s_{1}=1}^{\infty}\biggl{\{}\biggl{[}D_{1 pr}^{\,s_{1}}\,e^ {i\,s_{1}}\,\Omega_{1}\,{}^{t}+\overline{D_{1 pr}^{\,s_{1}}}e^{-i\,s_{1}}\, \Omega_{1}\,{}^{t}\biggr{]}\,\big{[}A_{r}e^{i\omega_{r}t}+CC\big{]}\biggr{\}}\\ &-\sum_{r=1}^{N}\sum_{s_{2}=1}^{\infty}\biggl{\{}\biggl{[}\,D_{2 pr}^{\,s_{2}}\,e ^{i\,s_{2}}\,\Omega_{1}\,{}^{t}+\overline{D_{2 pr}^{\,s_{2}}}e^{-i\,s_{2}}\, \Omega_{1}\,{}^{t}\biggr{]}\,\big{[}A_{r}e^{i\omega_{r}t}+CC\big{]}\biggr{\}}\\ &-\sum_{r=1}^{N}\sum_{s_{3}=1}^{\infty}\biggl{\{}\,D_{3 pr}^{\,s_{3}}\,\overline {\,\Omega_{1}\,{}^{t}}+\overline{D_{3 pr}^{\,s_{3}}}\,e^{-i\,s_{3}}\,\overline {\,\Omega_{1}\,{}^{t}}\biggr{]}\,\big{[}A_{r}e^{i\omega_{r}t}+CC\big{]}\biggr{\}} \\ &-c_{p}\,\Big{[}i\omega_{p}A_{p}e^{i\omega_{r}t}+CC\Big{]}+P_{10,p}+\sum_{k_{1 }=1}^{\infty}[\,P_{1p}^{\,k_{1}}\,e^{i\,k_{1}}\,\Omega_{1}\,{}^{t}+\overline{ P_{1p}^{\,k_{1}}}e^{-i\,k_{1}}\,\Omega_{1}\,{}^{t}]\\ &+P_{20,p}+\sum_{k_{2}=1}^{\infty}[\,P_{2p}^{\,k_{2}}\,e^{i\,k_{2}}\,\Omega_{1 }\,{}^{t}+\overline{P_{2p}^{\,k_{2}}}\,e^{-i\,k_{2}}\,\Omega_{1}\,{}^{t}].\end{array} \tag{10}\]  
Further manipulations of Equation (10) allows to write the right-hand side (RHS) of the equation in a clearer form, by grouping the CC terms (Equation (10)):  
\[\begin{array}{ll}RHS&=-2i\omega_{p}A_{p}^{\prime}e^{i\omega_{p}t}-\sum_{r=1} ^{N}\sum_{s_{1}=1}^{\infty}\Bigl{[}D_{1 pr}^{\,s_{1}}\,A_{r}\,\,e^{i(s_{1}\Omega _{1}+\omega_{r})t}+\,D_{1 pr}^{\,s_{1}}\,\overline{A_{r}}\,\,e^{i(s_{1}\Omega_{ 1}-\omega_{r})t}\Bigr{]}\\ &-\sum_{r=1}^{N}\sum_{s_{2}=1}^{\infty}\Bigl{[}D_{2 pr}^{\,s_{2}}\,A_{r}\,\,e^{ i(s_{2}\,\Omega_{1}+\omega_{r})t}+\,D_{2 pr}^{\,s_{2}}\,\overline{A_{r}}\,\,e^{i(s_{2}\, \Omega_{1}-\omega_{r})t}\Bigr{]}\\ &-\sum_{r=1}^{N}\sum_{s_{3}=1}^{\infty}\Bigl{[}D_{3 pr}^{\,s_{3}}\,A_{r}\,\,e^ {i(s_{3}\,\overline{\,\Omega_{1}\,{}^{t}}+\omega_{r})t}+\,D_{3 pr}^{\,s_{3}}\, \overline{A_{r}}\,\,e^{i(s_{3}\,\overline{\,\Omega_{1}\,{}^{t}}-\omega_{r})t} \Bigr{]}\\ &-ic_{p}\omega_{p}A_{p}e^{i\omega_{p}t}+\,\frac{1}{2}P_{1,0,p}+\sum_{k_{1}=1} ^{\infty}\Bigl{[}P_{1p}^{\,k_{1}}\,e^{i\,k_{1}}\,\Omega_{1}\,{}^{t}\Bigr{]}+ \frac{1}{2}P_{2,0p}+\sum_{k_{2}=1}^{\infty}\Bigl{[}P_{2p}^{\,k_{2}}\,\,e^{i\,k_ {2}}\,\,\Omega_{1}\,{}^{t}\Bigr{]}+CC.\end{array} \tag{11}\]
# Combining InSAR and GNSS to Track Magma Transport at Bastic Volcano
## 5 Results  
The static inversion results were evaluated based on the percentage of explained data (%\(Ed\)), which were computed per dataset or as an overall. For the \(i\)th dataset (either a given interferogram or the GNSS data), %\(Ed^{i}\) is evaluated as:  
\[\%Ed^{i}=\left(1-\sqrt{\frac{\left(\mathbf{u_{obs}}^{i}-\mathbf{u_{mod}}^{i} \right)^{T}\left(\mathbf{u_{obs}}^{i}-\mathbf{u_{mod}}^{i}\right)}{\mathbf{u_ {obs}}^{i}{}^{T}\mathbf{u_{obs}}^{i}}}\right)}*100 \tag{9}\]  
where \(\mathbf{u_{obs}^{i}}\) and \(\mathbf{u_{mod}^{i}}\) are the data and model vectors corresponding to all pixels in the masked interferograms or to all GNSS measurements. This value is independent of the covariance matrix,  
Figure 5: Distances between connected nodes in the graph-method. Node numbers are represented in black. (**A**) Distances from node 1 are shown in blue; (**B**) Distance from node 9 are shown in blue and triangles with nodes at a distance of less than two edges from node 9 are colored in cyan.  
the subsampling and the dataset used to run the inversion. Based on this definition, the closer to 100%, the better the data fit.
# Long Time Simulation Analysis of Geometry Dynamics Model under Iteration
## 2 Lie Group and Lie Algebra
Then, the derivation of \(\mathbf{\Gamma}\) is  
\[\dot{\mathbf{\Gamma}}_{1}=S(\mathbf{\Gamma}_{1})\mathbf{\omega};\dot{\mathbf{\Gamma}}_{2}=S( \mathbf{\Gamma}_{2})\mathbf{\omega};\dot{\mathbf{\Gamma}}_{3}=S(\mathbf{\Gamma}_{3})\mathbf{\omega} \tag{19}\]  
For planer rotation, the solution of any part of Equation (19) can obtain the complete rotation matrix according to inverse derivation. For rotation in space, Equation (19) needs to be completely solved to obtain the rotation matrix. The above method avoids complex triangle transformations. The exponential type of rotation matrix is  
\[\mathbf{R}=\mathbf{e}^{S(\mathbf{\eta})} \tag{20}\]  
\(\mathbf{\eta}\) is the Lie algebra type. The exponential matrix can be obtained according to the Rodriguez parameter or Cayley transformation, as in Equations (21) and (22), respectively. The Cayley transformation avoids triangle expression completely. \[\mathbf{R}=\mathbf{I}+\frac{\sin\|\mathbf{\eta}\|}{\|\mathbf{\eta}\|}S(\mathbf{\eta})+\frac{1-\cos \|\mathbf{\eta}\|}{\|\mathbf{\eta}\|^{2}}S^{2}(\mathbf{\eta}) \tag{21}\]  
\[\mathbf{R}=\frac{\mathbf{I}+S(\mathbf{\eta})}{\mathbf{I}-S(\mathbf{\eta})}=\frac{1}{1+\mathbf{\eta}^{T }\mathbf{\eta}}\Big{(}\big{(}1-\mathbf{\eta}^{T}\mathbf{\eta}\big{)}\mathbf{I}+2S(\mathbf{\eta})+2 \mathbf{\eta}\mathbf{\eta}^{T}\Big{)} \tag{22}\]  
The discussion of the exchangeability of the rotation matrix is as follows. Supposing that \(\mathbf{R}_{a}=\mathbf{e}^{\theta_{a}S(\mathbf{e}_{i})},\mathbf{R}_{b}=\mathbf{e}^{\theta_{b}S( \mathbf{e}_{i})}\), when \(i=j\), \(\mathbf{R}_{a},\mathbf{R}_{b}\) is called isomorphism, which means it has the same structure. If and only if \(i=j\), \(\mathbf{R}_{a},\mathbf{R}_{b}\) satisfies the relation as in Equation (23), which means that the rotation matrix has the same structure satisfying the exchange relation. \[\mathbf{R}_{a}\mathbf{R}_{b}=\mathbf{R}_{b}\mathbf{R}_{a}=\mathbf{e}^{\theta_{a}S(\mathbf{e}_{i})+ \theta_{b}S(\mathbf{e}_{j})} \tag{23}\]Suppose that the rotation matrix in space is \(R=e^{S(f)}\). The definitions of \(R_{x},R_{y},R_{z}\) are in Equation (9). According to Cardan relation, the exponential type of \(R\) is in Equation (24). \[e^{S(f)}=e^{\theta_{1}S_{x_{1}}}e^{\theta_{2}S_{x_{2}}}e^{\theta_{3}S_{x_{3}}} \tag{24}\]  
It is necessary to declare \(f\
eq\left[ \begin{array}{cccc}\theta_{1}&\theta_{2}&\theta_{3}\end{array}  \right]^{T}\), which is important in the following derivation.
## 3 The Proposed Method
### Multi-Way Local Attentive Learning
To our best knowledge, this approach has never been discussed in the literature in the past. ```
0: Training dataset \(D=\left\{x^{(i)},y^{(i)}\right\}\), \(1\leq i\leq m\) Number of partition scheme \(P\) Set of minibatches \(\mathbb{B}=\left\{\mathcal{B}_{1},\;\mathcal{B}_{2},\;\ldots,\;\mathcal{B}_{P}\right\}\) Size of minibatch \(m^{\prime}\) Data attributes \(A=A_{1},\;A_{2},\;\ldots,\;A_{P}\) Trainable weights \(\theta\) Learning rate \(\epsilon\) Dataset Partition:
1. Initialize partition set \(\mathbb{B}\)
2. for\(p\) in \(P\)  Partition the \(D\) into \(\mathcal{B}_{p}\)  Assign \(A_{p}\) to \(\mathcal{B}_{p}\)
3. endfor Training:
1. Randomly initialize trainable weights \(\theta\)
2. for\(epoch\) in\(epochs\)  Assign \(q=epoch\) mod \(P\)  Sample a minibatch of \(m^{\prime}\) from \(\mathcal{B}_{q}\) \(\left\{x^{(1)},x^{(2)},\;\ldots,x^{(m^{\prime})}\right\}\) with corresponding target \(\left\{y^{(1)},y^{(2)},\;\ldots,y^{(m^{\prime})}\right\}\)  Evaluate \(\
abla_{\theta}\sum\limits_{i=1}^{m^{\prime}}L\left(x^{(i)},y^{(i)},\theta\right)\)  Update \(\theta=\theta-\epsilon\frac{1}{m^{\prime}}\
abla_{\theta}\sum\limits_{i=1}^{m ^{\prime}}L\left(x^{(i)},y^{(i)},\theta\right)\)
3. endfor
```  
**Algorithm 2** Multi-Way Local Attentive Learning  
In addition to the method using one model proposed in Algorithm 2, a learning method that uses the same partitioned data and three models with the same amount of partitioned data also can be proposed. Each model learns partitioned data, and the weight values of the trained models are combined into one model to derive a value. We decided that the learning technique using one model will be called multi-way local attentive learning--single model, and the learning technique using multiple models will be called multi-way local attentive learning--multi model. A performance comparison of the two models is discussed in detail in Section 4.