esa-sceva/satcom-chunk-collection huggingface dataset:
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Basic Introduction to Extended Hamiltonian Dynamics in Non-Equilibrium Systems  
Classical mechanics and thermodynamics have provided a robust framework for understanding physical systems in equilibrium and near-equilibrium states. However, the dynamics of systems far from equilibrium, especially in the context of interacting particles and fields, requires a more nuanced theoretical approach. This work introduces an extended formulation of Hamiltonian dynamics that incorporates thermodynamic principles directly into the equations of motion, specifically targeting non-equilibrium processes. The insight that classic Hamiltonian mechanics, although effective, falls short in explaining the intricate relationship between energy distribution and entropy evolution in out-of-equilibrium situations is what motivated us to reformulate the theory.
# Parameter Estimation and Error Calibration for Multi-Channel Beam-Steering SAR Systems
## 1 Introduction
For phase inconsistency errors, many estimation methods [21; 22; 23; 24; 25; 26; 27; 28; 29] were proposed for multi-channel strip-map SAR, mainly based on multiple signal classification (MUSIC) theory [21], but this theory is not suitable for multi-channel BS-SAR system because the noise subspace cannot be extracted from the covariance matrix of the Doppler spectrum. Adaptively Weighted Least Squares (AWLS) [27] and Doppler Spectrum Optimization (DSO) [28], which are based on the spectrum power distribution, can be exploited in multi-channel BS-SAR, but are sensitive to the CDC error. Amplitude inconsistency and timing delay errors in multi-channel BS-SAR can be easily estimated using azimuth cross-correlation [29]. The channel position errors along-track are small enough to be ignored, while the position errors in range can be integrated into the phase inconsistency errors [24]. In this paper, we therefore develop algorithms for estimating and correcting the DRR error, CDC error and phase inconsistency errors for multi-channel BS-SAR. Section 2 reviews multi-channel BS-SAR systems and discusses their Doppler spectrum characteristics. Pre-processing algorithms are also reviewed in this section. Section 3 first derives an expression for the spectrum after azimuth pre-processing with DRR error, and this is used to define quantities that we term the 'Symmetric Shift' and 'Scaling Effect'. A DRR error estimation method is proposed based on the Symmetric Shift, and this is then extended to give an estimation method for the CDC simultaneously. Next, motivated by the principle of spectrum optimization, a novel algorithm to estimate phase inconsistency errors is presented based on the minimum entropy criterion, which is robust against CDC error. An adaptive weighing strategy is also presented to suppress noise and increase the precision of the estimation methods. The performance of these methods proposed are assessed using simulated SAR data in Section 4 and conclusions are drawn in the Section 5.
# Network Allocation Vector (NAV) Optimization for Underwater Handshaking-Based Protocols
## 4 Performance Analysis
### Simulation Conditions
#### 4.1.4 Fairness  
There are two reasons for the unfairness existing between the nodes in CSMA/CA-based protocols. The first reason is that the number of neighbors varies considerably along the network because of the random deployment of the sensor nodes. Accordingly, a node with more neighbors might experience more packet collisions resulting in lower throughput. Such an uneven distribution of nodes causes a fairness issue in terms of throughput. The second reason is that the nodes farther away from the destination have a lower probability of occupying a channel than the ones closer to the destination, because the NAVs in the previous schemes are determined without considering the propagation delays between the nodes; thus, a distant node wakes up later and loses a chance to occupy the channel. The proposed scheme aims to eliminate the unfairness caused by the second reason, by determining the NAVs considering the propagation delays of the nodes, as discussed in Section 3.  
To measure the network's fairness performance, the Jain's fairness index [37] is used. For \(n\) nodes, the Jain's fairness index is defined by  
\[J(x_{1},x_{2},\ldots,x_{n})=\frac{\left(\sum\limits_{i=1}^{n}x_{i}\right)^{2}}{ n\times\sum\limits_{i=1}^{n}x_{i}^{2}} \tag{16}\]  
where \(x_{i}\) is the throughput of node \(i\) (\(i=1\), \(2\),..., \(n\)). The index value ranges from \(\frac{1}{n}\) (worst case, which occurs when only one node is able to deliver data while others deliver nothing) to 1 (best case, which occurs when all nodes deliver the same amount of data to the destination node). In Figure 11, it can be observed that the fairness decreases as the traffic increases, owing to the increase in the throughput differences among the nodes. As expected, the proposed scheme shows higher fairness values compared to the schemes in [24; 25]. This is largely due to the fact that in the proposed scheme, the neighboring nodes have almost equal probability of channel occupation regardless of the node position--a consequence of considering the propagation delays of the nodes. Meanwhile, the fairness performance of [24] is the worst when compared to others, as [24] sets the NAV whenever it overhears a packet and a node that has many neighbors will have a relatively longer silent period, reducing its channel-occupation probability.  
Figure 11: Fairness.
# Three Rapid Methods for Averaging GPS Segments
## 3 Three Rapid Methods  
The expected input for the algorithm is a set of trajectories; each consisting of an ordered sequence of locations (x, y). We assume that the trajectories provide point-to-point paths roughly from the same target and to the same destination locations. The direction of the travelling is not defined, i.e., some of the trajectories may have reverse direction; GPS errors are expected.  
We next present three new methods for solving the problem:  
* RapSeg-A: Median for curvy segments
* RapSeg-B: Medoid for curvy segments
* RapSeg-C: Divide-and-conquer approach  
All methods use the same overall structure, as shown in Figure 4. Three descriptors are first calculated to describe the set of trajectories. Based on these descriptors, we estimate whether a simple linear model can describe the set, or a more complex model is necessary. We design two alternative models for these: the linear model and the curvy model.
## 3 Results and Discussion
### Evaluation of Microbiological Status Based on Identified Bacteria in Pepper Samples Tested
Ensuring the right conditions, i.e., the intensity of the thermal process, cooling rate, final temperature, temperature monitoring, and refrigerated storage time, as agreed by the Sous Vide Advisory Committee [54], allows food safety to be ensured. The appearance of coagulase-negative Staphylococcus such as _S. haemolyticus_ in the ATM and MAP samples may reflect inadequate hygiene and cross-contamination, as this bacterium is a normal skin commensal, but high contamination with enterotoxigenic staphylococci may be a potential indicator of the quality and evaluation of food poisoning risk [55]. Figure 3: Krona chart: bacteria strains isolated from peppers after heat treatment 15 min/70 \({}^{\circ}\)C (n = 70). Studies have shown that the use of sous vide heat treatment combined with appropriate packaging methods (VAC and MAP) significantly extends the shelf life of red peppers. This means that producers and distributors can plan for longer storage periods, reducing losses due to food spoilage. The choice of the appropriate food packaging method can be tailored to the specific needs of distribution and storage [56; 57]. Sous vide processing effectively reduces the number of TVC, Enterobacteriaceae, and the number of microscopic filamentous fungi, which ensures a higher microbiological quality of the product. This makes peppers safer for consumers and significantly reduces the risk of bacterial contamination. This can influence marketing strategies, emphasising the safety and quality of the product. However, further research is needed on other vegetables to better understand the impact of packaging methods and gas atmosphere composition. Each vegetable has unique structural properties, nutritional value, and a profile of bioactive substances that can act as biopreservatives. In the future, different gas combinations for MAP should be investigated to determine the most effective mixtures for extending shelf life and maintaining the quality of different vegetables, testing CO2, N2, and O2 in various proportions. With the possibilities offered by sous vide technology, manufacturers can introduce new products to the market, such as ready-to-eat meals from peppers and other vegetables. This allows them to diversify their offer and attract new customer segments. Companies implementing modern processing and packaging technologies can gain a competitive advantage in the market. Innovative approaches to food production can attract customers looking for modern and safe products [58]. Figure 4: Krona chart: bacteria strains isolated from peppers after heat treatment 8 min/80 \({}^{\circ}\)C (n = 49).
## 4 Results and Analysis  
### Performance of LSTM Hyperparameter Combination Output Based on Bayesian Optimization  
This study aims to optimise the hyperparameters of the LSTM neural network model based on a Bayesian optimisation algorithm, evaluating both the time efficiency, \"time\", and the accuracy, RMSE, of the model after tuning the parameters. The first three optimal parameter combinations were selected, along with the corresponding accuracy and time consumption (Table 2). The results demonstrate that the minimum RMSE of the Bayesian optimised LSTM model on the training set is 149.51 kg/ha, and the time efficiency of finding the optimal hyperparameter combination is 14 min. The Bayesian optimisation algorithm makes full use of historical information when selecting the optimal hyperparameter combination, allowing the optimal hyperparameter combination to be found within a short time and number of iterations.
## 5 The Experimental Case Study (Frame Behaving Nonlinearly)  
For the experimental validation, a laboratory three-story frame, which behaves nonlinearly due to damage occurrence, has been investigated; this is the same case studied by [21], again to ensure the comparison of the results, and proposed by [3]. Please note that this is quite different from the two numerical cases, where the structural damage was not a source of nonlinearity. This is due to the goal \begin{table}
\begin{tabular}{c c c} \hline \hline
**Density \(\rho\)** & **2850** & **kg/m\({}^{3}\)** \\ \hline Young’s Modulus \(E\) & \(7.31\times 10^{10}\) & Pa \\ Poisson Ratio \(v\) & 0.33 & - \\ Free length (clamp to tip) \(l_{\text{tip}}\) & 706 & mm \\ Thickness \(t\) & 2 & mm \\ Max width at clamped section \(b_{\text{max}}\) & 180.00 & mm \\ Mid-length width at the section of changing tampering (\(l\) = 258 mm) \(b_{l=258}\) & 56.10 & mm \\ Min width at the tip section \(b_{\text{min}}\) & 17.04 & mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Geometrical and mechanical properties of the wing spar. Figure 7: (a) Areas subject to decreased Young’s modulus (b) Damage scenarios. of this experimental setup being to mimic breathing crack behaviour [63]. A bumper-column bilinear mechanism achieves this: the column, hanging from the third floor, meets the bumper at the second story. This mechanism generates a directional nonlinear response, with stiffness increasing when the two are touching and pushing against each other. A scheme of the test structure is reported in Figure 8; the shaker and the four sensors' locations are highlighted. A detailed description of the experimental setup and procedure is provided in [59]. The severity of the damage is modelled by moving closer or farther the column's tip--when they are closer, the two elements come in touch for lower amplitudes of the driving force, thus causing major nonlinearities in the recorded response. The experimental THs have a total duration of 25.6 s, with \(f_{s,exp}=320\) Hz. The dynamic input is a band-limited excitation (ranging 20-150 Hz), as defined in [3]. The 17 states (nine undamaged and eight damaged, with the first nine configurations being intended to emulate different operational and environmental conditions) were used for this investigation; they are summarized in Table 6. All the output channels were used for the sensor setup considered. The test set was made up of 10 realisations for each scenario, totalling 180 simulated THs. As for the second numerical case study, the definitions of ECO- and MO-MMFCCs have been changed to fit the structure of interest. For ECO-MMFCCs, the 90% bound of \(f_{\infty}\) is approximately 78 Hz (averaging over the channels 3, 4, and 5 for the 50 realisations of Case 1); the \(f_{s,exp}/2\) limit of LO-MMFCCs becomes 160 Hz. The array of the centre frequencies for MO-MMFCCs underwent some
# MIDOM--A DICOM-Based Medical Image Communication System
## 4 Evaluation
### Benchmarks and Methodology
Although ZIP is not adapted and used for image compression, we used it as it was simple to implement in our solution, it is widely available, popular, and most importantly, lossless. We thus satisfied the need to add at least one more compression method. Benchmarking sets, or studies, consist of multiple monochromatic images, namely:  
1. **Angio**--One image, DICOM modality: XA, \(1252\times 1200\) pixels in size, 1.43 MB large; \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Profile** & \multicolumn{2}{c}{**Bandwidth**} & \multicolumn{2}{c}{**Delay**} \\ \hline  & **Download** & **Upload** & **Download** & **Upload** \\ \hline
3G & 780 Kbps & 330 Kbps & 100 ms & 100 ms \\ Wi-Fi & 40 Mbps & 30 Mbps & 1 ms & 1 ms \\ LTE & 50 Mbps & 10 Mbps & 50 ms & 50 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Network link conditioner parameters. 2. **X-ray**--Two images, DICOM modality: PX, \(2372\times 2372\) and \(2880\times 2372\) pixels in size, total of 11.88 MB large;
3. **MR-1**--383 images, DICOM modality: MR, \(512\times 512\) pixels in size, total of 95.82 MB large;
4. **CT**--307 images, DICOM modality: CT, \(512\times 512\) pixels in size, total of 76.81 MB large;
5. **MR-2**--2534 images, DICOM modality: MR, \(320\times 320\) pixels in size, total of 486.83 MB large.
## 3 Results
### Personal Opinions (Open-Ended and Multiple-Choice Questions)
#### 3.4.2 Open-Ended Responses (Q19 and Q20)  
To complement the quantitative findings, participants' responses to the two open-ended questions were thematically analysed. Selected verbatim comments have been translated from Spanish into English and are included below to illustrate key themes and provide deeper insight into the pre-service teachers' perceptions. For context, the group (A, B, or C) of each participant is indicated alongside their respective quote. These comments were chosen to reflect common patterns, highlight particularly insightful views, and preserve the tone and intent of the original responses.  
Regarding the first open question (Q19), which asked about the positive aspects of the activities with digital news articles (see Table 6), pre-service teachers across Groups A, B, and C highlighted several key strengths of the digital news-based activities: (1) students frequently noted the value of learning to assess the credibility of information, (2) many appreciated how the content linked science to real-life contexts, (3) several participants found the approach refreshing and engaging, (4) many noted the activities' potential classroom use and relevance to future teaching, and (5) several participants reported learning things they did not previously know.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Options** & **Group A** & **Group B** & **Group C** & **Total** \\  & **(_n = 28_)** & **(_n = 28_)** & **(_n = 21_)** & **(_N = 77_)** \\ \hline Digital news activity & 10.7 & 10.7 & 0.0 & 7.8 \\ TikTok activity & 57.1 & 64.3 & 12.0 & 59.7 \\ Both activities & 7.0 & 25.0 & 42.9 & 29.9 \\ None of them & 7.1 & 0.0 & 0.0 & 2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Percentages of pre-service teachers’ responses to multiple-choice question (Q21).  
Regarding the second open question (Q20) (see Table 7), aspects for improvement in the digital news activities, although feedback was mostly positive, participants identified some areas needing refinement: (1) the most frequent concern was the lack of internet access in classrooms, particularly for Groups A and B, (2) some students suggested expanding the range of news topics, (3) a few respondents pointed to the need for more time or shorter texts, (4) one student felt that the activity could have benefited from deeper classroom discussion, and (5) one participant expressed discomfort with the subject matter.
# Investigation of the Pressure Coefficients Correlation Field for Low-Rise Building Roofs
###### Abstract  
The use of hyperbolic paraboloid roofing (HPRs) is increasingly common in contemporary architecture, especially for buildings with large spans, making use of tensile structures or light shells. These structures are very sensitive to wind loads because of their light weight. In particular, they tend to oscillate under wind action, generating complex pressure distributions and cable instability. Therefore, for this shape structure, the investigation of wind-pressure coefficients correlations plays a relevant role in the design of structural elements. Therefore, based on wind tunnel tests, this paper investigates the behavior of four rectangular low-rise building models with HPR when immersed in a turbulent boundary layer flow. The test results were synthesized in correlation maps of the pressure coefficients. The results were evaluated as functions of different model heights and curvatures, and considering three different angles of wind incidence (\(0^{\circ},45^{\circ}\), and \(90^{\circ}\)).  
pressure coefficients; correlation coefficients; pressure field; low rise building; correlation field 20222020 October 202220 October 202220 October 2  
flow patterns of a high-rise building with a 3:1 side ratio. Spatial-temporal correlations of aerodynamic forces and flow fields under a conventional wind profile and a twisted wind profile were comprehensively compared.  
This current paper focuses on the correlation structure of the pressure field on saddle roofs (i.e., hyperbolic paraboloid roofs) immersed in turbulent boundary layer flows [22]. This type of geometry has been applied to construct cable net membrane roofs to cover large spans. Examples include the J. S. Dorton Arena (1953) in Rayleigh, USA, roofs designed by [PERSON] in Munich (1968-1972), Germany, the Calgary Arena (1983) in Calgary, USA, the St. Christopher's Pavilion (1985) in Sao Cristovoa, Brazil, the Peace and Friendship Stadium (1985) in Athens, Greece, and the Lee Valley velo park (2012) in London, UK.  
Because of their light weight, HPRs are very sensitive to wind loads and, in particular, they tend to oscillate upwards and downwards under wind action. Cable instability may occur under strong upward action because the cable tension in the upward cables decreases until the cable becomes unstable. Several studies have discussed the structural performances of this kind of roof [23; 24; 25; 26; 27; 28], and specific parametric studies [29; 30; 31; 32; 33; 34; 35; 36; 37; 38] given by two of the authors have contributed to the Italian standard [39] that provides pressure coefficients for HPR with square [37], rectangular [37], circular, and elliptical plan shapes [38].  
Wind-tunnel data [29] are utilized in this current paper to study the correlation structure and the correlation fields of several points on roofs under three wind angles, and the results are discussed and compared.
# Reputation-Driven Dynamic Node Consensus and Reliability Sharding Model in IoT Blockchain
## 3 System Model
#### 3.1.1 Reputation Introduction of RE-PBFT
To motivate nodes to complete the consensus more quickly, there will be \(v_{i}\) of different sizes according to the order of nodes to complete the consensus, and \(v_{i}\) can be calculated by the arithmetic sequence general term formula; when the node is abnormal, the normal node exception will only affect the voting situation in three-stage communication, and the influence on the system is small when it does not exceed the maximum limit that PBFT can tolerate. However, when the abnormal node becomes the master node, it may cause the failure of the whole consensus process, a more severe reputation penalty mechanism is needed to halve the reputation of the node that is abnormal in the master node. Whether it is the master node or the ordinary node, the abnormal behavior will not only be punished according to the total number of exceptions, but also severely punished for the continuous number of exceptions, and the reward for completing the consensus will become the corresponding size of punishment. It can be seen that when the node is the first abnormal, the number of consecutive exceptions and the cumulative number of exceptions are both 1, so the penalty formula is equal to \(r_{t}=r_{t-1}-1-v\) or \(r_{t}=r_{t-1}/2-1-v_{i}\) (the reputation equivalent formulas for normal node and master node, respectively), punishment is as strong as incentive; the nodes with continuous abnormal behaviors are more likely to be abnormal nodes, and the punishment is more severe than the cumulative abnormal behaviors. If there is a node with the reputation \(r_{i}\geq 50\), there is a scaling ratio \(\delta=25/r_{i}\), and the reputation of all nodes is multiplied by the scaling ratio \(\delta\) to prevent the reputation from increasing unrestrictedly. Each node in the distributed network maintains a node reputation table locally just as it maintains the blockchain, as shown in Figure 2. After the completion of the consensus round, the node calls the smart contract [39] calculated with a reputation based on the consensus results received from the other nodes. The new reputation values of all nodes are calculated. It is then sent to all the remaining consensus nodes; when the consensus node receives the new reputation sent from the other nodes, it saves the reputation in the local temporary table. When the same result reaches \(f+1\) (\(f\) is the maximum number of malicious nodes that can be tolerated), the consensus node saves the final reputation value locally. In Figure 2, (1) indicates that the node passes the local reputation related parameters into the smart contract to update the reputation; (2) represents the smart contract invocation process; (3) indicates that the calculated reputation is returned to the node; (4) sends the reputation value calculated by nodes to other nodes or receives the result value from other nodes.
## 4 Experimental Evaluation
### Text Prompted Verification
#### 4.3.3 Verification Evaluation  
A common performance measure for verification is the Equal Error Rate (EER), which is defined as the case when the false acceptance rate (FAR) and the false rejection rate (FRR) are of equal value [61]. FAR is the likelihood that the biometric system will incorrectly accept an identity claimed by an unauthorized client; FRR is the likelihood that the system will incorrectly reject an identity claimed by an authorized client. The lower the EER value, the higher the accuracy of the biometric system.