esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Branch-and-Price-and-Cut Algorithm
### Enhancement Strategies
#### 4.8.3 Time Complexity Analysis  
The time complexity of the proposed algorithm for solving the studied problem is determined by the interplay between the branching process, column generation iterations, the addition of cutting planes, and finding upper bounds. In the branching process, the worst-case number of nodes in the branching tree is \(O(2^{n})\), where \(n\) is the number of requests. At each node of the branching tree, solving the RLMP via the simplex algorithm installed in CPLEX has a complexity of \(O(m^{3})\), where \(m\) represents the number of generated columns. The addition of cuts, at most one per request, further increases the problem size, but this is bounded by \(O(n)\) cuts in total. The label setting algorithm used to solve pricing subproblem has a worst-case complexity of \(O(2^{n}\cdot n)\). The pricing subproblem is solved at each iteration of the column generation process, and column generation typically requires \(I_{cg}\) iterations to converge at each node. In addition, \(I_{cg}\) is usually polynomial in practice. Finding an upper bound via IP solver has a complexity of \(O(2^{m})\) in the worst case. Consequently, in the worst case, this time complexity of the proposed algorithm can be approximated as \(O(2^{n}\cdot[I_{cg}\cdot(m^{3}+2^{n}\cdot n)+2^{m}])\). While the theoretical worst-case time complexity of the proposed algorithm may appear high, the proposed algorithm benefits from its ability to exploit the problem structure, resulting in tighter bounds and higher-quality solutions for large-scale instances. These advantages are verified by experimental results in Section 5.
# Low-Power Two-Phase Clocking Adiabatic PUF Circuit
## 2 Fundamental Adiabatic PUF
### Proposed Adiabatic PUF  
The proposed adiabatic PUF circuit topology is shown in Figure 3a. It consists of a static CMOS inverter (P1 and N1), which plays a key role in charging and discharging the PUF cell semi-adiabatically using a trapezoidal power clock signal of Vpc and controlled by the Cb. Similar to the QUALPUF circuit, the proposed PUF cell consists of a cross-coupled inverter (P2, P3, N2, and N3) to evaluate the response-bits (output nodes of Rb and Rb-in Figure 3a). As an improvement, we control the current flow from one of the output nodes to slowly flow to the ground through transistor N4 by controlling its operation speed with a ramped Vpc- signal. Furthermore, the peak value of current Id (N3) in Figure 3a is significantly lower than that of current Id (M4) of QUALPUF depicted in Figure 2a.  
Figure 2: (a) QUALPUF circuit and (b) its timing chart.  
Notably, in our proposed adiabatic PUF circuit, we apply two phases of power clock signals Vpc and Vpc-, as depicted in Figure 7b.  
The supply voltage Vpc swings between 0.9 and 1.8 V (peak to peak voltage is 0.9 V), while the Vpc- fluctuates between 0 and 0.9 V. The Vpc and Vpc- signals are set such that all output nodes are discharged back to Vpc and ground simultaneously to avoid floating charges for the next charging operation. Accordingly, the proposed adiabatic PUF circuit will consume constant and extremely low power along the period of challenge-bit logic \"0\".
## 2 Materials and Methods
### Second-Order Multiresponse Surface (SOMRS) Model with Multivariate \(t\)-Distribution  
A first-order response surface model illustrates the relationship between the response variable \(Y\) and \(k\) input variables (levels of factors) \(x_{j}\), \(j=1,2,\ldots,k\). If a series of observations are made as much as \(n(n\geq k+1)\), which produces a response value of \(y_{u},u=1,2,\ldots,n\), then the first-order response surface model will have \(p=k+1\) parameters and can be written as  
\[E(Y_{u})=\beta_{0}+\beta_{1}x_{u1}+\beta_{2}x_{u2}+\ldots+\beta_{k}x_{uk}, \quad u=1,2,\ldots,n \tag{3}\]  
For a second-order response surface model, there are \(m=1+2k+\frac{1}{2}k(k-1)\) parameters and can be written as  
\[E(Y_{u})=\mathbf{g}^{\prime}(\mathbf{x}_{u})\mathbf{\beta},\quad u=1,2,\ldots,n \tag{4}\]  
with \(\mathbf{g}^{\prime}(\mathbf{x}_{u})=\begin{bmatrix}1&x_{u1}&x_{u2}&\cdots&x_ {uk}&x_{u1}^{2}&x_{u2}^{2}&\cdots&x_{uk}^{2}&x_{u1}x_{u2}&x_{u1}x_{u3}&\cdots&x_ {u(k-1)}x_{uk}\end{bmatrix}\) and \(\mathbf{\beta}=\begin{bmatrix}\beta_{0}&\beta_{1}&\beta_{2}&\cdots&\beta_{k}& \beta_{11}&\beta_{22}&\cdots&\beta_{kk}&\beta_{12}&\beta_{13}&\cdots&\beta_{(k -1)k}\end{bmatrix}^{\prime}\).  
If there are \(q\) response variables \(\mathbf{Y}_{u}=\begin{bmatrix}Y_{u1}&Y_{u2}&\cdots&Y_{uq}\end{bmatrix}^{\prime}\), then a second-order multiresponse surface model can be written as  
\[E(\mathbf{Y}_{u})=\mathbf{B}^{\prime}\mathbf{g}(\mathbf{x}_{u}),\quad u=1,2, \ldots,n \tag{5}\]  
with \(\mathbf{B}=\begin{bmatrix}\beta_{1}&\beta_{2}&\cdots&\beta_{q}\end{bmatrix}^{\prime}\),  
\[\begin{array}{Estimation and Hypothesis Testing for Parameters of the SOMRS Model with a Multivariate t-Distribution  
Parameters estimation of the SOMRS Model with a Multivariate t-Distribution using the Maximum Likelihood Estimation (MLE) method is shown in Figure 1.  
The Likelihood Ratio Test (LRT) method to test the SOMRS model parameters simultaneously is shown in Figure 2.  
Figure 1: Parameters Estimation Flowchart.  
Figure 2: Model Parameter Test Flowchart.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 8 Challenges of IoT, Blockchain and the Integration
### Network and Communication Security  
As explained in Section 6.1, it is vital to address the security issues such as privacy, data integrity, reliability (error handling), and availability, among others. These issues are the most critical requirements for an IoT system. For example, IoT devices installed without adequate security concerns are highly prone to attacks. Such attacks can cause a high loss of farm yield in the smart farming infrastructure not built with adequate security concerns, and end-users neglect to perform the security hardening settings [22].  
Concerning blockchain, relying on blockchain to protect data privacy is far from efficient. It is because the data in a public blockchain are not generally encrypted to permit public validations [14]. Incorporating private data in a public chain makes it harder to securely share data between IoT nodes and relevant users' private data. However, if the user's anonymity is only the security goal of an organization employing an IoT, the anonymized user's data (for example, transactional data from a smart electric meter) can be provided to the public, blockchain network nodes for transaction validation. In that case, the user's unique account identity is sufficient for executing transactions [77].
# Travel Route Planning with Optimal Coverage in Difficult Wireless Sensor Network Environment
## 5 Performance Evaluation  
### Parameters for PSO and ACO  
We firstly execute the PSO algorithm for SPs selection, and different values of the PSO parameters are tried to explore the optimal performance of the algorithm. Then the ACO algorithm is carried out many times with different ACO parameters to find the shortest loop. All the optimal parameters for PSO and ACO are listed in Table 2.  
Figure 9: Path planning using ACO.
## 3 Proposed System Model
### Mission-Planning Toolbox  
The second vital module of the proposed system is the DIY-based mission-planning toolbox. The architecture of the mission-planning toolbox is represented in Figure 3.  
The mission-planning toolbox is also a layered architecture. The top layer is the service layer, which provides mission-critical services to end users. The mission-critical services' configuration is stored in the cloud-based web server. In the cloud-based web server, the tasks are generated based on specific requirements such as device profile and service description. Beneath the cloud layer is the IoT gateways layer, which is responsible for managing the gateways. The gateways are important because tasks are mapped onto the virtual objects using the nearest gateway. Every gateway has specific physical resources associated with it. A physical resource can be managed by a gateway, but the same resource can communicate with a different gateway. Finally, the task allocation layer is the layer which performs task allocation to the physical resources. For every layer, semantic vocabularies are maintained which serve a repository for the metadata of that layer. This helps in task allocation, for example finding the overall information such as which resource is connected to which gateway.
## 4 Performance Analysis of an FSO RIS-ACs Link over F-S \(\mathcal{F}\) TI fading Channels
### Outage Probability (\(P_{\gamma}\))  
The OP \((P_{\gamma})\) for a given SNR threshold \(\gamma_{th}\) of an FSO RIS-ACs over F-S \(\mathcal{F}\) TI fading propagation channels for IM-DD with the OOK technique was calculated as:  
\[P_{\gamma}(\gamma_{th})=P_{\gamma}(\gamma\leq\gamma_{th})=F_{\gamma}(\gamma_{ th}) \tag{26}\]  
where \(F_{\gamma}\) was already derived in (23). It is important to note that \(F_{\gamma}\) was valid only for the integer values of \(a_{1}\), since \(F_{\gamma}\) was derived as a finite series expression. The numerical analysis of the OP for an FSO RIS-ACs system over Fisher-Snedecor \(\mathcal{F}\) TI fading channels was provided in the Numerical Results.
## 3 Graph Structure Abstraction for Malicious Attracts
### Interactive Mode for Malicious Attacks  
To illustrate that the graph representation is distinguishable for different attacks, we first select three common attacks among the five attacks studied in this paper and use the graph to describe the interaction pattern between the attacking entity and the attacked entity. The results are shown in Figure 2.  
Each node in the graph represents an independent IP in the real world, and each directed edge represents an interaction between two hosts. The monitoring points of the attacks are labeled in Figure 2. The graphs constructed from the network traffic obtained at the monitoring points can distinguish attacks based on features such as IP address, data direction, and interaction pattern.  
For example, (1) The port scanning attack detects the availability of the target port by performing a handshake. From the monitoring points, the interaction patterns of this attack are as follows. The data comes from a relatively fixed IP, and only the port number changes. Anyway, the data interaction is limited to the handshake process, and the data have a particular temporal order. (2) The DDoS attack launches large-scale requests to the target host by controlling many distributed hosts. From the monitoring points, the interaction patterns of this attack are as follows. The data are from different domains and network segments of distributed IPs, and the ports are common. Anyway, many data are transmitted from attackers to attacked parties, and the data tend to arrive at the target in a tiny time fragment. (3) The representative Smurf attack in the Doc makes all hosts in the attacked network hit by many packets. This attack sends ICMP packets to the broadcast IP of the controlled network and sets the reply address to the broadcast IP of the attacked network. From the monitoring points, the interaction patterns of such attacks are as follows. The IPs of the attacker and the victims are often located in the same network segment, and  
Figure 2: Interaction patterns of three common attacks. (**a**) Description of the interaction pattern of Port Scans Attack. (**b**) Description of the interaction pattern of DDoS. (**c**) Description of the interaction pattern of Smurf Attack.  
the data interaction is unidirectional, with a large amount of data arriving at the target in a tiny time interval.  
Based on the above description of the three attack interaction patterns, graphs have a reasonable contribution to characterizing, classifying, and identifying different attacks in the real world.
## 5 Data and Results  
A test environment has been selected, and a bimodal sensor setup has been put into practice. Five different video sequences have been recorded under very different environmental conditions. The two segmentation algorithms described previously have been performed on the five video sequences in the visible-light and the thermal infrared spectra. In accordance with the evaluation criteria used during the experimentation, the confidence intervals have been tuned to select the best sensor (visible-light _vs._ thermal infrared) segmentation output.  
The complete process is explained next in full detail.
# Thermal Behavior of Alveoli with Phase Change Materials in Fruit Packaging
###### Abstract  
Fruits are perishable fresh products with a short shelf life after harvesting. Perishable foods and their shelf lives are directly related to the temperature at which they are stored. Refrigeration is therefore essential in the conservation of fruits, as it allows the temperature to be lowered, helping to delay microbial, physiological, and chemical changes. This work aimed to compare the thermal behaviors of alveoli with different phase change materials (PCMs) placed inside a modular packaging developed for the transport and storage of fruits. The cooling tests were carried out inside a cold storage chamber with the set-point programmed to 2 \({}^{\circ}\)C. To simulate the placement in packages exposed to the store environment, heating tests were carried out while the chamber door was opened and the packaging was exposed to external environmental conditions. The phase change materials tested were RT2 HC, RT5 HC, and RT8 HC. The temperature variation in the tests during cooling and heating proved that the new type of alveoli with PCM inside the fruit transport packaging is extremely important, as it can extend the useful life of the fruits after they are removed from the cold chamber, managing to maintain adequate conservation conditions for longer in contact with room temperature. The phase change material RT8 HC was the one that showed the best results overall, managing to maintain the temperature of the fruit inside the packaging at a temperature below 10 \({}^{\circ}\)C for up to eight hours after being exposed to ambient conditions of 20 \({}^{\circ}\)C.  
cold preservation; refrigeration; phase change material; fruit simulator; shelf life extension +
Footnote †: journal: Applied sciences
# The Isothermal Oxidation of High-Purity Aluminum at High Temperature
## 1 Introduction
During stage III (approximately 650 \({}^{\circ}\)C-1050 \({}^{\circ}\)C) the growth of \(\gamma\)-alumina continues at a rate which is limited by the inward diffusion of oxygen anions along grain boundaries [9; 14]. Growth of the \(\gamma\)-alumina layer can be accompanied by phase transformations into other transition polymorphs, such as \(\delta\)-alumina and \(\delta\)-alumina. Due to the similarity in density of these polymorphs with \(\gamma\)-alumina [12], such transitions are not expected to affect the oxidation rate significantly. At sufficiently high temperature the transition alumina polymorphs become destabilized, and begin to convert to \(\alpha\)-alumina, indicating the end of Stage III. When the first \(\alpha\)-alumina crystallites begin to form at the end of stage III, the thickness of the \(\gamma\)-alumina layer decreases due to the greater density of the \(\alpha\)-alumina phase (a volume contraction of 13.8% is expected on converting \(\gamma\)-alumina to \(\alpha\)-alumina), and the oxidation rate increases momentarily [15]. Stage IV is considered to start when the oxide scale is completely transformed to \(\alpha\)-alumina, at about 1050 \({}^{\circ}\)C. Once most of the oxide layer is transformed to coarse and dense \(\alpha\)-alumina crystallites resulting in continuous polycrystalline coverage, grain boundary diffusion slows down, and the oxidation rate decreases rapidly. One significant difference between the present and most of the earlier work is that in the present work, the temperature range for stage I (growth of amorphous oxide layer) was traversed without exposure of the sample to an oxidizing environment; air was introduced in one of the later stages. The oxidation of molten aluminum was studied by [PERSON] et al. [16]. In addition, there are a number of investigations in literature in relation to oxidation kinetics and behavior of aluminum alloys [17; 18; 19; 20; 21]. The present work is not intended to contradict, or expand on previous studies of aluminum oxidation, but is intended be more of a phenomenological objective, and selecting the isothermal test conditions provides useful information. More specifically, the ignition of aluminum particles is known to be directly influenced by the strength of the oxide barrier which forms on the particle exterior. Additionally, the thickness and mechanical integrity of the barrier is directly influenced by the conditions under which it forms. Hence, the isothermal testing protocol provides known initial conditions and the start of the experiment will be the beginning of air exposure after the sample has been brought to the stated initial temperature. Hence, in summary, how the barrier influences particle ignition is the ultimate application of this study but extending the current study to particle ignition is a separate topic. Insight into how the barrier influences particle ignition will contribute to the development of a model that quantifies the risk associated with a launch pad abort or accident involving solid fueled rocket motors and high hazard payloads.
## 5 Discussion and Limitations  
The results of this study offer a detailed comparison of traditional pallet exchange systems and those managed by Nolpal. The Nolpal's system addresses one of the key contributors to carbon emissions in logistics: minimizing empty trips and reducing transportation distances. Furthermore, the Nolpal system has significantly enhanced the economic performance of the trademark industry under consideration; by opting for a rental model rather than maintaining an owned pallet fleet, the trademark industry has achieved a reduction in the costs associated with pallet management; the approach proposed by Nolpal offers improved flexibility, allowing the trademark industry to adjust pallet quantities based on fluctuating demand, thereby avoiding overstocking or shortages.  
Nonetheless, this study has limitations, such as focusing only on the transportation stage of the pallet lifecycle, excluding production and end-of-life phases. Future studies could expand this scope by incorporating these additional stages to provide a more comprehensive lifecycle analysis of the systems under investigation.  
Moreover, the findings suggest that continuous innovation in supply chain management is critical. Logistics stakeholders and policymakers should consider adopting models similar to Nolpal's to achieve ambitious sustainability targets.
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
###### Abstract  
A star type wireless sensor network based on nine-axis micro-electromechanical inertial motion sensors with the potential to include up to 254 sensor nodes is presented, and an investigation into the mechanical and structural effects of bell ringing on bell towers is presented as a possible application. This low-power and low-cost system facilitates the continual monitoring of mechanical forces exerted by swinging bells on their support and thus helps avoid structural degradation and damage. Each sensor measures bell rotation, and a novel method utilising only the instantaneous rotational angle is implemented to calculate the force caused by bell ringing. In addition, a commonly used, however, previously experimentally unconfirmed assumption that allows great simplification of force calculations was also proven to be valid by correlating predicted theoretical values with measurement data. Forces produced by ringing a 1425 kg bell in Durham Cathedral were characterised and found to agree with literature. The sensor network will form the basis of a toolkit that provides a scalable turnkey method to determine the exact mechanisms that cause excessive vibration in mechanical and architectural structures, and has the potential to find further applications in low-frequency distributed structural health monitoring.  
wireless; sensor; network; accelerometer; bell; tower; Durham; Cathedral +
Footnote †: journal:  
Article
# A Novel Reconstruction Method for Irregularly Sampled Observation Sequences for Digital Twin
## 3 Irregularly Sampled Observation Sequence Reconstruction Method Based on PRN-NCDE
### Weighted Loss Function of the PRN-NCDE Model  
Through reparameterization, parameters can be learned by using the gradient descent method. According to [30], the objective loss function in Equation (2) can be simplified as \[\begin{split}&\mathcal{J}\left(W_{cc},W_{dc}\Big{|}\big{\{}\big{(} \mathbf{y}_{n}^{irr},t_{n}\big{)}\big{\}}_{n=0}^{N}\right)\\ &=\sum_{n=0}^{N}-\frac{1}{2}||\mathbf{y}_{n}^{irr}-\hat{\mathbf{y}}_{n}^{ irrr}||^{2}-\frac{1}{2}\lambda\big{(}tr\big{(}\sigma_{0}^{2}\big{)}+\mu_{0}^{T} \mathbf{\mu}_{0}-n-\log\big{(}|\sigma_{0}^{2}|\big{)}\big{)}\end{split} \tag{17}\]  
where \(\mathbf{y}_{n}^{irr}\) and \(\hat{\mathbf{y}}_{n}^{irr}\) are the encoder input and decoder output at the corresponding time, \(\lambda\) is a hyperparameter controlling the variance and \(n\) is the dimension of the latent feature \(\mathbf{x}_{0}\). \(\mathbf{\mu}_{0}\) and \(\sigma_{0}^{2}\) are the mean and covariance of \(\mathbf{x}_{0}\), respectively. \(tr(\cdot)\) denotes the trace operator.  
This objective function measures the reconstruction accuracy of the decoder output \(\hat{\mathbf{y}}_{n}^{irr}\) relative to the encoder input \(\mathbf{y}_{n}^{irr}\). When the actual observation sequence \(\big{\{}\big{(}\mathbf{y}_{n}^{irr},t_{n}\big{)}\big{\}}_{n=0}^{N}\) is not corrupted by noise or the noise level is low, this function enables the model to learn the data distribution effectively. However, if the actual observational data contain high levels of noise, achieving the desired training performance using this objective function is difficult.  
To address this challenge, we propose a weighted loss function for the PRN-NCDE model, which is defined as follows:  
\[\begin{split}&\mathcal{J}\left(\mathbf{W}_{cc},W_{dc}\Big{|}\big{\{} \big{(}\mathbf{y}_{n}^{irr},t_{n}\big{)}\big{\}}_{n=0}^{N}\Big{\{}\Big{(}\mathbf{y}_{n }^{ref},t_{n}\Big{)}\Big{\}}_{n=0}^{N}\right)\\ &=\sum_{n=0}^{N}-\frac{1}{2}||\mathbf{y}_{n}^{irr}-\hat{\mathbf{y}}_{n}^{ irr}||_{(1-0)}^{2}-\frac{1}{2}||\mathbf{y}_{n}^{ref}-\hat{\mathbf{y}}_{n}^{irr}||_{Q}^{2}\\ &\quad\quad\quad-\frac{\lambda}{2}\bigg{(}tr\Big{(}\mathbf{\sigma}_{0 }^{2,fus}\Big{)}+\Big{(}\mathbf{\mu}_{0}^{fus}\Big{)}^{T}\mathbf{\mu}_{0}^{fus}-n-\log \Big{(}|\mathbf{\sigma}_{0}^{2,fus}|\Big{)}\bigg{)}\end{split} \tag{18}\]  
where \(Q\) is the reference latent feature weight calculated in Section 3.3.  
Equation (18) integrates data from the reference observation sequence. It uses the reference feature weight, which is calculated based on the noise level in the actual observation data, to determine the importance of the actual and reference data distributions for parameter training. When noise is low, the loss function will rely more on the actual observation data for training. Conversely, the reference observation data will be used to mitigate the negative impact of noise on model learning.