esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Continuous Transmission of Packet Traffic
### Time Difference of Arrival (TDOA)  
Time Difference of Arrival (TDOA) is an ALOHA evolution that consists of a channel-sensing mechanism. This protocol significantly minimizes channel collisions compared to the ALOHA protocol without adding more signaling.  
The propagation delay significantly impacts the performance of the protocol. It is improbable that another station will be prepared to emit and detect the channel once one station starts sending. A collision occurs if the second station senses an open channel and begins sending a frame even when the first station's signal has not yet arrived [66]. These collisions are influenced more by increased propagation times toward transmission times, which results in worse protocol performance.  
There will still be a collision even if there is no propagation delay. A collision occurs when three stations are ready, and two of them wait until the third station has finished transmitting before commencing to transmit simultaneously. Because both stations had the decency to refrain from intervening during the third season, this protocol is still far superior to the Pure ALOHA. As a result, performance will undoubtedly be superior to the Pure ALOHA.  
The uncertainty of the channel is previously in use, and the station does not keep an eye on it to see when the current transmission is over. Instead of immediately repeating the algorithm, it pauses for a predetermined period. This algorithm's use should result in more significant delays and better channel utilization as a natural outcome.
## 2 Theory and Formulation
#### Nonlinear Temperature Changes
Eventually, the Lanczos eigensolver is presented to obtain eigenvalues of the current structure. The vector of displacement for the eight-node element S4R would be formulated as [72,74,76,81,107]:  
\[\{\delta\}=(u,\,v,\,w)^{T}=\sum_{i=1}^{4}[N_{i}]\{\delta_{i}\} \tag{24}\]  
where \(u_{i}\), \(v_{i}\) and \(w_{i}\) are the node displacements \(i\), and the shape function of the node is presented as \([N_{i}]\). Then element strain vector in the system of global coordinate would be formulated as:  
\[\{\epsilon^{\prime}\}=\sum_{i=1}^{4}[B_{i}]\{\delta_{i}\} \tag{25}\]  
where  
\[[B_{i}]=[\Delta][N_{i}] \tag{26}\]  
and  
\[[\Delta]=\left[ \begin{array}{ccc}\partial_{x}&0&0\\ 0&\partial_{y}&0\\ 0&0&\partial_{z}\\ 0&\frac{\partial_{x}}{2}&\frac{\partial_{z}}{2}\\ \frac{\partial_{z}}{2}&0&\frac{\partial_{x}}{2}\\ \frac{\partial_{y}}{2}&\frac{\partial_{x}}{2}&0\end{array} \right] \tag{27}\]  
In addition, the stress and strain vectors of an element would be formulated as:  
\[\{\epsilon^{\prime}\}=[B][\delta^{\epsilon}] \tag{28}\]  
where \([B]=\left[ \begin{array}{ccc}B_{1}&\ldots&B_{8}\end{array} \right]\), \(\{\delta^{\prime}\}=\left\{\{\delta_{1}\}^{T}\right.\), \(\ldots\), \(\left.\{\delta_{8}\}^{T}\right\}^{T}\), and \([A]\) indicates matrix transforming local coordinates into global coordinates and [108] presents the elastic constant. The matrix of stiffness ([Ke]) can be expressed as:  
\[[K^{\epsilon}]=\int\limits_{V_{\epsilon}}[B]^{T}[A][B]^{-1}[B]dV \tag{29}\]  
Then the total structure stiffness matrix can be formulated as:  
\[[K]=\sum_{\epsilon}\left[K^{\epsilon}\right] \tag{30}\]The matrix of mass in the system of global coordinate would be determined as:  
\[[M]=\sum_{e}\left[M^{e}\right] \tag{31}\]  
By using the minimum potential energy principle, the governing equation can be formulated as:  
\[\{\delta\}[K]-\left\{\delta\right\}[M]=[0] \tag{32}\]  
After applying static bending, Equation (33) can be presented as follows:  
\[\{\delta\}[K]=[0] \tag{33}\]  
It is well known that if we want to have an accurate finite element (FE) model, we should pay attention to the mesh convergency [75; 79; 80]. For this matter, the number of elements is increased as long as the natural frequency of the structure does not have any change and the optimum number of elements is selected. According to Figure 2, the convergency condition appears when there are more than 13,000 elements. A validation study between the numerical results and finite element outcomes is presented in Table 5. The maximum relative discrepancies between the numerical and finite element model (FEM) result is less than 4%. In addition, it can be seen in Table 3 that the best GPL pattern in the frequency issue of the structure is pattern 3, and the more rigid structure will be a reason for boosting the frequency of the GPLRC panel. Figure 2: Mesh convergency for the finite element (FE) panel model.
# Nero: A Deterministic Leaderless Consensus Algorithm for DAG-Based Cryptocurrencies
## 3 The Nero Algorithm
### Overview and Intuition  
Our goal is to develop a consensus algorithm that achieves consensus for a cryptocurrency with a DAG ledger instead of a blockchain. Typically, in the latter, there is an algorithm for choosing the leader of the round, who proposes a block of transactions to be validated, and the other processes are also validated or not (binary consensus). In a blockchain, a non-malicious double spend attempt can happen in the selection of the leader when the algorithm selects more than one leader for the same round who proposes different blocks of transactions (this can happen in Bitcoin if more than two miners find the solution of the proof of work more or less at the same time, although it is very unlikely due to the high latency of more or less 10 min). However, if only one leader is chosen in a given round, a double spend attempt can only happen intentionally if the leader includes conflicting transactions in the same block.  
With a DAG structure, it is different because there is no leader in each round, so every process can propose transactions and those transactions can be conflicted, i.e., have the same origin block, which is called a double-spend attempt. Since the latency is very low, different correct processes can receive different conflicting transactions in a different order and make a conflicting vote. Because the probability of double spending attempts happening in an unintentional way is higher than in a blockchain, the amount of computational resources needed to resolve them is also higher. Because of that, and assuming that conflicting transactions (not votes) can only happen in a malicious way or due to a programming error, our consensus algorithm discards all of them and votes nil instead of choosing a winner transaction to be validated from the set of conflicting transactions.  
Similarly, if a correct process receives at least two conflicting transactions, it votes nil on that election. If it has only received one transaction but at least \(f+1\) processes vote nil, it means that at least one correct process received a double spend attempt, and so it also votes nil. This way, the amount of computational resources to solve a double spend attempt, or fork, will be lower, and the latency of the consensus algorithm will also be lower.  
Now we need to take into account the processes that vote in a malicious way, so we need a way to validate messages. One way to do that is to append a proof to each message with the messages of the previous round in which it is based; however, this has a very high communication complexity. Another way is to send only the hashes of the messages as proof. If the receiving process does not have the corresponding messages to the hashes yet, it considers the message pending and waits for the previous messages to validate it. This reduces the communication complexity; however, we can still reduce it more and not send any proof at all. A validation algorithm checks if the received message is valid, pending or invalid according to the received messages of the previous round.
## 3 Results and Discussion
### Links with Combustion Noise Indicator  
Figure 11 presents the link between the combustion noise indicator and the second maximum of pressure derivative (Figure 11a) and NOx emissions (Figure 11b). The main combustion was majorly responsible for the combustion noise, as shown by the direct correlation between the two quantities and by the different behaviour of the first maximum of pressure derivative discussed in Reference [1].  
In Sets 1 and 2, CDI generally had a prevailing influence on combustion noise, leading to a reduction of related parameters as a consequence of lower oxygen availability and the slower development of combustion. The same behaviour was observed in References [40; 41; 42], proving that I\({}_{n}\) can describe changes in noise when controlling variables affecting this aspect.  
In Sets 3 and 4, rail pressure was responsible for the observed trends, as a consequence of the different effects related to its increase discussed in Sections 1.2 and 3.1. A similar outcome was shown in Reference [43], where the link between rail pressure control, pressure rise rate and combustion noise was presented referring to an automotive diesel engine with a displacement of 1.7 dm\({}^{3}\), a common rail fuel injection equipment and a volumetric compression ratio very close to the engine tested in this work.  
Taking into account the linear relationship shown in Figure 11a and the minor variations in the combustion of the pilot injections shown in Figures 5 and 6 and discussed in Reference [1], it can be concluded that \((\text{dp}/\text{d}\theta)_{\text{MAX\,2}}\) may be applied to describe combustion noise changes when controlling the main engine subsystems.  
The direct relationship between combustion noise indicator and \(\text{NO}_{\text{X}}\) emissions, presented in Figure 11b for different test sets, confirmed the similar effects on these quantities obtained by increasing EGR rate (Sets 1 and 2) or by reducing rail pressure (Sets 3 and 4).
###### Abstract  
In this work, a metal-organic framework (MOF), copper benzene dicarboxylate (Cu-MOF), was tested for the adsorptive recovery of organic dyes (Sunset Yellow FCF, Tartrazine, _Orange II_, and Methyl Orange) from aqueous solutions. Studies were also carried out to determine the effects of various parameters, and isothermal and kinetic models were proposed. The adsorption capacity of Cu-MOF was much higher than that of activated carbon. The experimental data are best described by the Langmuir isotherm model (R\({}^{2}\) > 0.997) and show the ability of Cu-MOF to adsorb 435 mg/g of the dye under optimal conditions. The study of the kinetics of the dye adsorption process followed a pseudo-second-order kinetic model indicating the coexistence of physical and chemisorption, with diffusion within the particles being the rate-limiting step. Thermodynamic studies were also carried out, and they led to the conclusion that the adsorption of the dye was a feasible, spontaneous, and exothermic process (\(-\)25.53 kJ mol\({}^{-1}\)). The high organic dye recovery shows that Cu-MOF can be used as an efficient and reusable adsorbent for the extraction of dyes from aqueous solutions. These studies may lead to economic interest in this adsorbent material for environmental purposes.  
metal-organic framework; organic dye; adsorption; solid-phase extraction; separation Article
# Computer Vision Algorithms on a Raspberry Pi 4 for Automated Depalletizing
## 2 Background and Motivation
### Computer Vision Techniques for Object Detection and Localization  
Although the palletizing process aims at providing a regular arrangement of items, not all use cases for unpalletizing systems can rely on strict geometric assumptions. Figure 1 shows some examples of more complex cases.  
Figure 1: Some use cases: (**a**) pellet fuel bags, (**b**) stones, (**c**) leather (image credit: [PERSON] on Flickr, CC BY-NC-SA 2.0), (**d**) coffee bags.  
Computer vision techniques have revolutionized the field of object detection and localization, with numerous algorithms and approaches being developed and applied across various domains [29; 30]. These techniques leverage sophisticated image processing algorithms and machine learning models to identify and locate specific objects within complex visual scenes [31].
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 7 Discussion
### Future Improvements  
To address these challenges and further strengthen the approach, several future improvements can be considered.  
First, introducing automated testing for infrastructure deployments can greatly improve confidence and reliability. Tools such as Azure Resource Manager (ARM) What-If deployments, Bicep Linter, and integration tests can be added to the CI pipeline to validate templates before deployment. This reduces the risk of breaking changes and enables teams to catch errors earlier in the development cycle.  
Second, Policy as Code is a powerful addition that enables the automatic enforcement of compliance and security standards. Azure Policy can be defined and deployed as code to prevent configuration drift and enforce organizational requirements such as allowed locations, SKU restrictions, or security configurations. Integrating policy checks into the CI/CD pipeline ensures that only compliant resources are deployed, reducing manual review effort and increasing security posture.  
Another area for improvement is cost optimization. As cloud usage grows, controlling costs becomes critical. Teams can integrate Azure Cost Management APIs or third-party tools into their workflows to estimate costs before deployment, monitor spending, and enforce budgets. For example, workflows can include steps to validate that requested resources meet cost thresholds or automatically tag resources for cost tracking. By incorporating cost optimization into the deployment process, organizations can avoid unexpected bills and promote responsible cloud usage [35].  
Improved secret management is another avenue for enhancement. While GitHub Secrets provides encrypted storage, integrating with Azure Key Vault can offer greater control, automated rotation, and enhanced auditing capabilities. GitHub Actions supports fetching secrets from the Key Vault at runtime, ensuring that secrets do not remain in the GitHub repository itself. This also simplifies rotation processes and aligns with best practices for managing high-value credentials.  
Furthermore, environmental drift detection can be introduced. Tools such as Azure Resource Graph or manual Azure CLI queries can be used to validate that deployed resources match the defined state. This helps to detect and correct manual changes in production that bypass the pipeline, preserving the benefits of software-defined infrastructure.  
Finally, team collaboration and documentation can be enhanced. While code provides a source of truth, human-readable documentation describing the architecture, modules, and deployment process is vital for onboarding, auditing, and knowledge sharing. Automated documentation generation from Bicep files or GitHub Actions workflows can help keep this information up to date and accessible.
## 5 Case Study  
This section analyses the performance of the DE algorithm solving the problem proposed. The case study uses real data from the high-speed line from Calatayud to Zaragoza in Spain. The length of the journey studied is 85.4 km. The track profile, maximum speed profile and neutral zone distribution of this journey are shown in Figure 2. The train is a Talgo-Bombardier train class 102. It is 200 m long and its empty weight is 324 t. The operational restriction \(vc_{min}\) is set to 150 km/h and the restriction \(sc_{min}\) is set to 50 km to avoid low speed values.  
The target running time, which is taken from a real commercial timetable in this high-speed line, is 26 min while the minimum running time is 22 min. The four minute difference allows the train to drive slower than the maximum speed to save traction energy.  
The proposed method is applied to obtain the set of driving commands of the energy optimal speed profile that fulfils the arrival hour as well as the intermediate target points. Two cases are analysed: a journey with one intermediate timing point and a journey with two intermediate timing points. For the shake of simplicity, all the tolerances \(\varepsilon_{l}\) have equal value.  
The performance of the DE algorithm proposed is compared with the performance of GA using the same constraint handling process. GA algorithm has been selected for this comparison because it is one of the most used Nature Inspired Techniques solving the classical mono-objective eco-driving problem [50; 53; 54; 56; 82; 87; 88; 89]. As DE algorithm, GA evolves a population of solutions, called individuals, through iterations. Only the best individuals of the population at each iteration survive while the rest of the population is eliminated. These surviving individuals are called the elite group. The population eliminated is replaced in the next iteration by the offspring individuals generated by means of mutation and crossover of the elite individuals.  
Figure 2: Track profile, maximum speed limits and neutral zones distribution.  
Tables 1 and 2 show the tuned parameters used in the DE algorithm and the GA applied to the case study. The parameters have been tuned by a trial-and-error process looking for the best performance in both cases as usual in this kind of algorithms. Besides, the population size is set in both cases so that the algorithms perform the same number of simulations. This is an important issue to make a fair comparison given that the number of simulations determines the number of solutions explored at each iteration and it is the most expensive time consuming process of the algorithms in this study. DE and GA handle the constraints in the problem using the fitness function presented in Equation (5) with \(\alpha=1\) MWh/s.  
Different solutions can be obtained in different executions of the same algorithm because of the random search nature of evolutionary algorithms; thus, the performance of the algorithms has been evaluated executing 20 tests for the same journey.
## 3 Analytical Model Calculation and Validation
### Analytical Calculation
#### 3.1.2 Boundary Conditions  
The tangential component of the magnetic field strength and the normal component of the magnetic flux density must be continuous at the interface \(y=0\) and \(y=-h_{2}\); therefore, the boundary conditions are:  
\[\begin{array}{l}B_{\mathrm{x}}^{\mathrm{L}s}+B_{\mathrm{x}}^{\mathrm{L}r}=B_{ \mathrm{x}}^{\mathrm{II}},y=0\\ B_{\mathrm{y}}^{\mathrm{L}s}+B_{\mathrm{y}}^{\mathrm{L}r}=B_{\mathrm{y}}^{ \mathrm{II}},y=0\\ B_{\mathrm{z}}^{\mathrm{L}s}+B_{\mathrm{z}}^{\mathrm{L}r}=B_{\mathrm{z}}^{ \mathrm{II}},y=0\\ B_{\mathrm{x}}^{\mathrm{II}}=B_{\mathrm{x}}^{\mathrm{III}},y=-h_{2}\\ B_{\mathrm{y}}^{\mathrm{II}}=B_{\mathrm{y}}^{\mathrm{III},t},y=-h_{2}\\ B_{\mathrm{z}}^{\mathrm{II}}=B_{\mathrm{z}}^{\mathrm{III},t},y=-h_{2}\\ \end{array} \tag{9}\]  
It is found that if Equation (9) holds, the TE potential and its normal derivative are continuous on the interface:  
\[\begin{array}{l}W_{\mathrm{a}}^{\mathrm{L}s}+W_{\mathrm{a}}^{\mathrm{L}x}=W _{\mathrm{I}}^{\mathrm{II}},y=0\\ \frac{\partial W_{\mathrm{a}}^{\mathrm{L}s}}{\partial y}+\frac{\partial W_{ \mathrm{a}}^{\mathrm{L}x}}{\partial y}=\frac{\partial W_{\mathrm{a}}^{\mathrm{ II}}}{\partial y},y=0\\ W_{\mathrm{I}}^{\mathrm{II}}=W_{\mathrm{a}}^{\mathrm{II}},y=-h_{2}\\ \frac{\partial W_{\mathrm{a}}^{\mathrm{II}}}{\partial y}=\frac{\partial W_{ \mathrm{a}}^{\mathrm{III}}}{\partial y},y=-h_{2}\\ \end{array} \tag{10}\]  
Therefore, only the four boundary condition equations in Equation (10) need to be solved.
# Performance of Numerically Optimized Tuned Mass Damper with Inerter (TMDI)
## 2 Modelling
### Primary Structure with TMDI with Inerter in Series with Stiffness and Viscous Damper  
Another topology is described in [25], where the spring \(k_{2}\) is in parallel with the serial arrangement of the spring with stiffness \(k_{b}\), the inerter with inertance \(b\) and the viscous damper with viscous coefficient \(c_{b}\), as depicted in Figure 1d. The idea behind this topology is to produce an additional degree of freedom by setting the inerter between the spring and viscous damper. The relevant equations of motion are given as follows:  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Damper Type** & \begin{tabular}{c} **Mass** \\ **Ratio** \\ \(\mu=m_{2}/m_{1}\) \\ \end{tabular} & \begin{tabular}{c} **Inertance** \\ **Ratio** \\ \(\beta=b/m_{1}\) \\ \end{tabular} & \begin{tabular}{c} **Natural** \\ **Tuning** \\ \end{tabular} & \begin{tabular}{c} **Damping** \\ **Frequency** \\ **(Hz)** \\ \end{tabular} & \begin{tabular}{c} **Bamping** \\ **Ratio** \\ \(\zeta_{2}\) **(Hz)** \\ \end{tabular} & \begin{tabular}{c} **Stiffness** \\ **Coefficient** \\ **\(k_{2}\) **(kh/m)** \\ \end{tabular} & \begin{tabular}{c} **Viscous Damping** \\ **Coefficient** \\ **\(c_{2}\) **(khs/m)** \\ \end{tabular} \\ \hline \hline \begin{tabular}{c} Parallel \\ TMDI \\ \end{tabular} & 1\% & 1\% & [33] & 0.1379 & 7.02\% & 751.17 & 121.66 \\ \hline \hline \begin{tabular}{c} Parallel \\ TMDI \\ \end{tabular} & 1\% & 1\% & \begin{tabular}{c} Numerically \\ optimized \\ \end{tabular} & NA & NA & 760.64 & 76.151 \\ \hline \hline \
\begin{tabular}{c} Parallel \\ TMDI \\ \end{tabular} & 1\% & 0.02\% &
\begin{tabular}{c} Numerically \\ optimized \\ \end{tabular} & NA & NA & 385.77 & 54.919 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of TMDIs with an inerter in parallel to stiffness and viscous damper.  
\[\begin{bmatrix}m_{1}&0&0&0\\ 0&m_{2}&0&0\\ 0&0&-b&b\\ 0&0&0&0\end{bmatrix}\begin{bmatrix}\ddot{x}_{1}\\ \ddot{x}_{2}\\ \ddot{x}_{3}\\ \ddot{x}_{4}\end{bmatrix}+\begin{bmatrix}c_{1}&0&0&0\\ 0&0&0&0\\ 0&0&0&0\\ 0&-c_{b}&0&c_{b}\end{bmatrix}\begin{bmatrix}\ddot{x}_{1}\\ \ddot{x}_{2}\\ \ddot{x}_{3}\\ \ddot{x}_{4}\end{bmatrix}+\begin{bmatrix}(k_{1}+k_{2}+k_{b})&-k_{2}&-k_{b}&0\\ -k_{2}-k_{b}&k_{2}&k_{b}&0\\ k_{b}&0&-k_{b}&0\\ -k_{b}&0&k_{b}&0\end{bmatrix}\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{bmatrix}=\begin{bmatrix}f_{ex}\\ 0\\ 0\end{bmatrix} \tag{12}\]  
As for the first conventional TMDI layout, this TMDI topology was computed for the usually adopted inertance ratio \(\beta=1\%\) and for the very small value of 0.02% as well. For both inertance ratios, the parameters of the TMDI were numerically optimized for minimum structural displacement to ensure the optimality of this mass damper type (Table 4).
# My Smartphone tattles: Considering Popularity of Messages in Opportunistic Data Dissemination
## 5 Keetchi Forwarding Protocol  
Keetchi is the first implementation of the Organic Data Dissemination (ODD) Model [23]. Our forwarding protocol is destination-less and estimates the popularity of messages to use in its forwarding decisions. Additionally, it introduces the concept of _communication neighbourhood change_. This section is dedicated to describing the constituent components and the operation of Keetchi.