esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Exploring the Volatile Compounds Associated with Baking
#### 2.2.4. Pyridines and Pyrimidines  
Pyridines and pyrimidines were detected in _proas_ for the first time. Although they were not present in high amounts, they might still be relevant to the overall aroma due to their low OTs. Pyridines give roasted and popcorn odors to foods [14,43].  
2-Acetyltetrahydropyridines (or 6-acetyletrahydropyridines) are possibly the most common pyridines in similar foodstuffs. They are potent Maillard flavor compounds that contribute substantially to caramel, roasty, and bready aromas [51,71] in several bakery products [14,16,32,71], and are formed by the degradation of proline and hydroxyproline [20]. However, these compounds were not detected in _proas_. Instead, 1-acetyl-1,2,3,4-tetrahydropyridine (Pd8) was identified as the main pyridine. Although it exhibits a similar mass spectrum to 2-acetyletrahydropyridine, Pd8 showed a very intense peak at _m_/_z_ 68 (Figure S3), which is not expected in 2-acetyletrahydropyridine [72]. This compound has also been identified in the crusts of wheat bread [42] and lupin protein isolate-enriched wheat bread as an important contributor to its aroma profile [42]. Thus, it might also be an important _broa_ volatile, contributing to a nutty odor [42].  
2-Pentylpyridine (Pd9) may also be relevant for _proas_ aroma, contributing to roasted and nutty odors [51]. Although it was present in lower amounts than other pyridines, it shows a very low OT [48]. This compound has not been described in similar foods, probably due to its low concentration, but it has been described in several fried foods [73].  
4-Methylpyrimidine (Pd2) was the only pyrimidine detected in _proas_. This compound has not been described either in bread or in other maize-based foods, and it was not possible to obtain any information regarding its odor characteristics.
## 3 The Proposed Key Generation Protocol  
### System Model  
In this article, we examine a scenario involving two AIS transceivers, [PERSON] and [PERSON], who seek to establish a secure communication channel using AIS technology to exchange confidential information. The scenario involves two ships unexpectedly converging in the same maritime region without any prior shared information. Given their close proximity, an AIS is considered the most practical communication method in terms of both cost and functionality. Our study focuses exclusively on AIS unicast communications and does not consider the use of relay nodes. Therefore, the focus of this study is Class A AISs, as the ITU-R M.1371-5 document explicitly states that Class B AISs cannot transmit Addressed Binary Messages (Message 6) or Addressed Safety-Related Messages (Message 12). These two message types are the primary means for unicast communication in Class A AISs. In other words, Class B AISs only support broadcast communication.  
Additionally, we assume the presence of a malicious node, Eve, with all ships involved being within each other's AIS communication range. This scenario is illustrated in Figure 1. The CIR measurement values of the legitimate channel and the eavesdropping channel are expressed as \(h_{XY}(t_{i})\), where \(X,Y\in A,B,E\). These measurements are derived based on the hardware's sampling rate, with one value obtained per sample. In this article, we focus on the amplitude of the CIR, which is calculated for each sample using its real and imaginary components, as follows:  
After the probing signals pass through the Rayleigh fading channel and are influenced by Additive White Gaussian Noise (AWGN), the signals \(y_{A}(t_{i})\) and \(y_{B}(t_{i})\) received by [PERSON] and [PERSON] at time \(t_{i}\) can be expressed as  
\[y_{A}(t_{i})=h_{BA}(t_{i})s(t_{i})+n_{BA}(t_{i}) \tag{1}\]  
\[y_{B}(t_{i})=h_{AB}(t_{i})s(t_{i})+n_{AB}(t_{i}) \tag{2}\]  
The signals received by [PERSON] from [PERSON] and [PERSON] at time \(t_{i}\) can be expressed as  
\[y_{AE}(t_{i})=h_{AE}(t_{i})s(t_{i})+n_{AE}(t_{i}) \tag{3}\]  
\[y_{BE}(t_{i})=h_{BE}(t_{i})s(t_{i})+n_{BE}(t_{i}) \tag{4}\]  
where \(s(t_{i})\) represents the transmitted signal at time \(t_{i}\), and \(n_{XY}(t_{i})\), with \(X,Y\in A,B,E\), denotes the instantaneous value of the AWGN for the legitimate channel and the wiretap channel at time \(t_{i}\).  
In the situation described above, the proposed protocol enables the AIS transceivers of two ships to generate a shared symmetric key without relying on any third party, which is then used to secure all bits transmitted via the AIS. The key generation process follows the main framework of the classic PLKG and is divided into four stages: channel probing, quantization, information reconciliation, and privacy amplification [8]. These steps are discussed in detail below. An overview of the protocol is illustrated in Figure 2.  
Figure 1: Communication scenarios for key generation.  
Figure 2: Key generation process.
## 4 Limitations and Discussion  
A new CRITIC-GRA model has been proposed for stope dimension optimization considering open stoping stability, mining capacity and costs, but there are still some limitations of the method.  
The properties of the rockmass used for the Mathews stability graph and FLAC3D simulations in the CRITIC-GRA model were assumed to be homogeneous and isotropic. The nonhomogeneous properties of the rockmass can lead to statistical variations for the calculated safety indexes of open slopes which will further affect the dimension optimization. In the next step, the research will focus on the variations and distributions of rockmass properties considering the variations of rockmass quality in different regions of the mined ore body.  
Many critical factors had been taken into account in the CRITIC-GRA model including rockmass quality, stope dimensions, excavation and backfill process etc., but there are still some practical factors that have not been considered, including the safety indicators of stope blasting and groundwater and the economic indicators of mining equipment efficiency and ventilation.  
In the proposed CRITIC-GRA method, the weight values of different influential factors were calculated based on the preset schemes of different stope dimensions. In practical mines, the weight value of a critical value might be more important in different conditions.  
Figure 7: Grey correlation degree of open stope with different dimensions considering the overall stability and economic indexes.  
The reasonable evaluation method of these weight values requires more considerations under different practical circumstances.
## 3 Analysis of the Lane Control Strategy Based on Truck Platooning Simulation
### Analysis of the Lane Control Strategy
#### 3.2.1 Analysis of Exclusive Lanes for Truck Platooning
When the traffic rate reaches 7000 veh/h, truck platooning accounts for a smaller proportion of the conflict rate.
# Q-Pandora Unboxed: Characterizing Resilience of Quantum Error Correction Codes Under Biased Noise
###### Abstract  
Quantum error correction codes (QECCs) are essential for reliable quantum computing as they protect quantum states against noise and errors. Limited research has explored the resilience of QECCs to biased noise, critical for selecting optimal codes. We examine how different noise types impact QECCs, considering the varying susceptibility of quantum systems to specific errors. Our goal is to identify opportunities to minimize the resources--or overhead--needed for effective error correction. We conduct a detailed study on two QECCs--rotated and unrotated surface codes--under various noise models using simulations. Rotated surface codes generally perform better due to their simplicity and lower qubit overhead. They exceed the noise threshold of current quantum processors, making them more effective at lower error rates. This study highlights a hierarchy in surface code implementation based on resource demand, consistently observed across both code types. Our analysis ranks the code-capacity model as the most pessimistic and the circuit-level model as the most realistic, mapping error thresholds that show surface code advantages. Additionally, higher code distances improve performance without excessively increasing qubit overhead. Tailoring surface codes to align with the target logical error rate and the biased physical error profile is crucial for optimizing reliability and resource use.  
Keywords:quantum error correction codes (QECCs); noise resilience; thresholds; surface codes; bit and phase flip errors; fault tolerance +
Footnote †: journal: Applied Sciences
## 2 Laser Control of Ring Currents in a Molecule Having a Single Aromatic Ring
### Basic Ide of Laser-Driven Ring Currents: Molecular Symmetry and Angular Momentum Eigenstates
Even though there exists no excited state that is an exact eigenstate of \(\hat{l}_{z}\) in such a system, sufficiently short laser pulses can prepare a coherent superposition of the optically allowed quasi-degenerate states as mentioned in Section 1. The approximate angular momentum eigenstates can be defined in analogy to Equation (1) as follows:  
\[\left|\pm\right\rangle\equiv 2^{-1/2}(\left|X\right\rangle\pm i\left|Y\right\rangle), \tag{2}\]  
where the matrix elements \(\left\langle\pm\left|\hat{l}_{z}\right|\pm\right\rangle\) are close to \(\pm\hbar\). After a laser pulse prepares either \(\left|+\right\rangle\) or \(\left|-\right\rangle\), it subsequently evolves in time as a coherent nonstationary state because of the nonzero energy gap between \(\left|X\right\rangle\) and \(\left|Y\right\rangle\):  
\[e^{-i\hat{H}_{0}t/\hbar}\left|\pm\right\rangle=2^{-1/2}\Big{(}e^{-i\omega_{X} t}\left|X\right\rangle\pm ie^{-i\omega_{Y}t}\left|Y\right\rangle\Big{)}=e^{-i \omega_{X}t}2^{-1/2}\Big{(}\left|X\right\rangle\pm ie^{-i2\Delta\omega t} \left|Y\right\rangle\Big{)}, \tag{3}\]  
where \(\hat{H}_{0}\) is the field-free electronic Hamiltonian, \(\omega_{X}\) (\(\omega_{Y}\)) is the angular frequency of \(\left|X\right\rangle\) (\(\left|Y\right\rangle\)), and \(2\Delta\omega\equiv\omega_{Y}-\omega_{X}\). Note that \(\Delta\omega\) is positive (negative) when \(\left|X\right\rangle\) is lower (higher) in energy than \(\left|Y\right\rangle\). The approximate angular momentum eigenstates can be transiently created within the period of the electronic state change \(T\equiv\pi/\left|\Delta\omega\right|.\) This means that transient ring currents can be achieved by selective generation of an approximate angular momentum eigenstate. In Section 2.2, we will propose the strategy for generating predominantly either \(\left|+\right\rangle\) or \(\left|-\right\rangle\) by a polarized laser field.
## 6 Case-Study: Parallel Workflows
### Experiment 1: Evaluating Performance of Parallel Workflows
#### 6.1.1 Enhanced Performance Stability with Capping  
In the absence of resource capping, clients consume resources on a physical node arbitrarily, resulting in undesirable consequences. Firstly, the chaotic CPU usage (see Figure 13) leads to frequent context switching and cache invalidation at the operating system level, resulting in increased overheads and longer training times. Secondly, without explicit placement or resource requirement hints, the Kubernetes scheduler tries to maximize CPU utilization by fitting as many containers as possible onto a single physical machine.  
Figure 12: CPU utilization for different FL clients’ (c0–c19) placement strategies. The default strategy is non-deterministic (different CPU per execution) and with significant variation among clients.  
However, as demonstrated in previous experiments, this approach creates a significantly imbalanced load among nodes. As a result, the performance profile becomes highly unstable (see Figure 14), making it challenging to differentiate between different training phases (e.g., local training and client-to-server communication) within the same workflow. On the other hand, _enforcing resource-capping policies brings about several benefits. It leads to more regular CPU utilization and improves the placement of containers on nodes, thereby enhancing the stability and performance of workflows._
## 3 Joint Optimization Algorithm Design
### MAHPPO Algorithm
#### 3.1.1 Action Space  
The action space is denoted as \(\mathcal{A}=\left\{A_{u,k},\forall u\in\mathcal{U}\right\}\). \(A_{u,k}=\left\{\chi_{u,k},\psi_{u,k},V_{u,k},P_{u,k}\right\}\) represents the action space of each agent, where \(\chi_{u,k}\)\(=\left\{0,1\right\}\) is denoted as the mission mode selection of UAV \(u\). If \(\chi_{u,k}\)\(=0\), the agent chooses to make UAV \(u\) collect data at time slot \(k\), meaning that \(\sum_{m=1}^{M}\alpha_{u,m,k}=1\). Similarly, if \(\chi_{u,k}\)\(=1\), UAV \(u\) offloads the data at time slot \(k\), meaning that \(a_{u,0,k}=1\). \(P_{u,k}\) represents the transmit power of the buoy corresponding to UAV \(u\) at time slot \(k\). Note that the value of \(P_{u,k}\) could be obtained by the agent action selection, but we do not know which buoy is associated to UAV \(u\) at time slot \(k\).
# Ultrasound for Meat Processing: Effects of Salt Reduction and Storage on Meat Quality Parameters
## 3 Results and Discussion
### Colour Values
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Storage Day** & **Temp (\({}^{\circ}\)C)** & **Sodium** & **Processing** & \(L^{\star}\) & \(a^{\star}\) & \(b^{\star}\) & **TCD (\(\Delta\)E)** \\ \hline \multirow{4}{*}{0} & & \multirow{4}{*}{-} & \multirow{4}{*}{NS} & Control & 49.97 \(\pm\) 3.65 \({}^{\text{a}}\) & \(-\)0.49 \(\pm\) 1.84 \({}^{\text{a}}\) & 4.45 \(\pm\) 1.08 \({}^{\text{bc}}\) & - \\  & & & US & 47.99 \(\pm\) 2.95 \({}^{\text{a}}\) & \(-\)0.60 \(\pm\) 0.93 \({}^{\text{a}}\) & 3.95 \(\pm\) 1.55 \({}^{\text{c}}\) & 2.04 \\  & & & Control & 49.25 \(\pm\) 2.79 \({}^{\text{a}}\) & \(-\)0.57 \(\pm\) 1.75 \({}^{\text{a}}\) & 4.04 \(\pm\) 0.62 \({}^{\text{c}}\) & 0.83 \\  & & & US & 50.61 \(\pm\) 5.25 \({}^{\text{a}}\) & 0.88 \(\pm\) 1.41 \({}^{\text{a}}\) & 6.09 \(\pm\) 1.39 \({}^{\text{abc}}\) & 2.23 \\  & & & Control & 50.32 \(\pm\) 1.88 \({}^{\text{a}}\) & 0.75 \(\pm\) 0.78 \({}^{\text{a}}\) & 8.34 \(\pm\) 0.56 \({}^{\text{a}}\) & 4.10 \\  & & & US & 55.39 \(\pm\) 1.76 \({}^{\text{a}}\) & \(-\)1.93 \(\pm\) 1.26 \({}^{\text{a}}\) & 7.18 \(\pm\) 0.97 \({}^{\text{abc}}\) & 6.24 \\  & & & Control & 51.56 \(\pm\) 0.56 \({}^{\text{a}}\) & \(-\)1.37 \(\pm\) 1.30 \({}^{\text{a}}\) & 7.14 \(\pm\) 1.73 \({}^{\text{abc}}\) & 3.25 \\
60 & & & US & 53.59 \(\pm\) 2.12 \({}^{\text{a}}\) & \(-\)1.59 \(\pm\) 0.76 \({}^{\text{a}}\) & 7.88 \(\pm\) 0.95 \({}^{\text{ab}}\) & 5.11 \\  & & & Control & 51.53 \(\pm\) 2.49 \({}^{\text{a}}\) & 0.41 \(\pm\) 2.26 \({}^{\text{a}}\) & 8.30 \(\pm\) 1.8 \({}^{\text{a}}\) & 4.25 \\  & & & US & 55.68 \(\pm\) 2.92 \({}^{\text{a}}\) & \(-\)1.77 \(\pm\) 1.34 \({}^{\text{a}}\) & 6.81 \(\pm\) 1.42 \({}^{\text{abc}}\) & 6.31 \\  & & & Control & 52.82 \(\pm\) 2.06 \({}^{\text{a}}\) & \(-\)0.96 \(\pm\) 2.42 \({}^{\text{a}}\) & 7.34 \(\pm\) 0.62 \({}^{\text{abc}}\) & 4.09 \\  & & & US & 55.32 \(\pm\) 5.22 \({}^{\text{a}}\) & \(-\)1.44 \(\pm\) 1.04 \({}^{\text{a}}\) & 7.78 \(\pm\) 1.05 \({}^{\text{ab}}\) & 6.37 \\ \hline \hline \end{tabular}  
* Different letters in the same column are significantly different at \(p<0.05\).  
\end{table} 
Table 2: Colour parameters and total colour difference (TCD) of brined pork at day 0 and after 60 days of storage: values represent means \(\pm\) standard deviation (SD, N = 3) of ultrasound-assisted (US) meat containing standard (NS) or reduced sodium content (RS).
## 2 Materials and Methods
### VOC Analysis  
Analysis of VOCs was performed by headspace solid-phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GC-MS). An aliquot of brine (5 mL) was inserted into a 15 mL glass vial, and 50 \(\upmu\)L of internal standard (5-nonanol, 2 mg L\({}^{-1}\)) was added. The vial was closed and placed in a water bath adjusted to 40 \({}^{\circ}\)C. The vial was equilibrated for 15 min at 40 \({}^{\circ}\)C and stirred at 600 rpm using a stirring bar. The volatile compounds were extracted for 30 min on a divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fibre (2 cm, 50/30 \(\upmu\)m; Supelco, Bellefonte, PA, USA). The VOCs adsorbed on the SPME fibre were desorbed at 265 \({}^{\circ}\)C for 15 min in the injector port of a GC interfaced with an MS (internal ionisation source: 70 eV) with a scan range from m/z 30 to 400 (7890A-5975C GC-MS system, Agilent Technologies, Santa Clara, CA, USA). The separation was achieved on a VF-WAX MS capillary column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m film thickness) from Agilent. The initial oven temperature was 40 \({}^{\circ}\)C (5 min), then 40-195 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\), and then 195-240 \({}^{\circ}\)C at 10 \({}^{\circ}\)C min\({}^{-1}\), held for 15 min. The carrier gas was helium at a constant flow of 1 mL min\({}^{-1}\). MassHunter software version B.09.00 (Agilent Technologies, Santa Clara, CA, USA) was used to detect and quantify peaks based on areas as determined by the deconvolution algorithm. A search of the NIST 17 MS library was utilised for the tentative identification of deconvoluted peaks. Chemical names were assigned to peaks with a minimum mass spectral similarity >80 (100 is an exact match). Confirmation was conducted by comparing the retention indices with literature data reported for equivalent columns and real standards. The VOCs were semi-quantified by comparison of peak areas to that of internal standard (5-nonanol).
# Robust Estimation of Vehicle Motion States Utilizing an Extended Set-Membership Filter
## 5 Verification for Robust Estimation Strategy
### Result Analysis  
It can be found from Figures 9 and 11 that the reference values of the states are contained within the hard bounds consisting of the upper bound and lower bound provided by the proposed ESMF-based strategy. When the value of the lateral velocity reaches its local peak or valley, the boundary range also becomes large. Especially, at around time points 8 s and 9.5 s in Figure 9, this phenomenon is obvious.  
As shown in Figures 10 and 12, the hard bounds provided by the UKF do not contain the reference values of the states at some moments, especially at the top or bottom of the estimated curves. As shown by time intervals \([8.7\;\mathrm{s},9.2\;\mathrm{s}]\) in Figure 10b and \([5.7\;\mathrm{s},6.5\;\mathrm{s}]\) in Figure 10c, the reference values of the lateral velocity and vehicle sideslip angle are out of the range restricted by the upper and lower bounds. Similar situations illustrated by time intervals \([18\;\mathrm{s},20\;\mathrm{s}]\) in Figure 12b and \([14\;\mathrm{s},17\;\mathrm{s}]\) in Figure 12c also illustrate this phenomenon.  
Furthermore, to clearly quantify the performance of superiority, visualized curves and statistical values are utilized, i.e., the absolute error (AE) curves and root mean square errors (RMSEs) between the estimated and reference values of these two strategies. As shown in Figures 13 and 14 and Tables 2 and 3, the AE curves in both two cases based on the ESMF algorithm are under those based on the UKF one in most time, and the RMSEs of the former are also less than the latter.  
Figure 14: AE curves of two strategies under the slalom maneuver. (**a**) Longitudinal velocity; (**b**) Lateral velocity; (**c**) Vehicle sideslip angle.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{v_{x}}\) (km/h) & \(\mathbf{v_{y}}\) (km/h) & \(\mathbf{\beta}\)**(deg)** \\ \hline UKF & 0.03610 & 0.01701 & 0.03248 \\ ESMF & 0.02923 & 0.01203 & 0.02301 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSEs under the slalom maneuver.  
Figure 13: AE curves of two strategies under the DLC maneuver. (**a**) Longitudinal velocity; (**b**) Lateral velocity; (**c**) Vehicle sideslip angle.  
In brief, utilizing the ESMF-based strategy can not only acquire the estimation results with good accuracy but also provide 100% confidence for the estimated vehicle motion states, which is the vital performance of robustness for vehicle state estimation.  
Additionally, the running time of the vehicle state estimation employing these two strategies is listed in Table 4, which shows that under the same maneuver, the ESMF-based strategy needs less time to realize the estimation of vehicle states as compared with the UKF-based one. Therefore, no higher hardware configuration will be required in the practical application of the presented strategy.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 3 Methods
### Object Shape Extraction Module  
Mainstream methods often disregard essential shape information when dealing with object-edge segmentation, resulting in decreased accuracy in segmenting object-edge regions. Inspired by [25], we design an object shape extraction module (OSEM) for video object segmentation. The module processes shape information in the form of object edges, using a gated convolutional layer [25] and a loss function to limit its processing to only boundary-related information. By integrating information read from the backbone network memory with shape information, we generate segmentation results that emphasize the edges of the object. Finally, we utilize shape information to further refine the segmentation results, generating the ultimate segmentation results. Parts (a) in Figure 2 display the schematic structure of our shape extraction module, while Figure 3 shows the shape information outputted by different gated convolutional layers. It can be seen that the shape information becomes clearer and clearer through the shape extraction module.  
Figure 3: This figure shows the shape information during the work of the shape extraction module. The extracted shape information is progressively clearer. Gate represents our gated convolutional layer.  
Figure 2: The main framework of this work. (**a**) The object shape extraction module takes as input the image gradients, the outputs of the first, third, and the last convolutional layers of the backbone network, and the memory readout results from the backbone network. It then generates boundary information for the object as output. Subsequently, we feed this boundary information into a spatio–temporal constraints module to obtain more accurate localized boundary information; Figure 4 illustrates the specific implementation of the spatio–temporal constraints module. This refined boundary information is then used to refine the masks generated by the backbone network. (**b**) The object mask \(\mathbf{m}^{k}\)represents the positional information of the \(k\)-th object. Our training process is divided into two stages. During the training process, we have access to the ground truth labels \(\mathbf{m}^{k}_{t-nt}\) for the object mask, and we only update the memory bank (**M**) in the first step. In the inference stage, we only perform the second step, keeping the memory bank unchanged. We utilize the masks obtained from the network inference for frames \(t-n\) to \(t-1\) to predict the subsequent mask for frame \(t\).