esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method  
Using ICR [28], a new image, \(\mathbf{I}_{new}\), is generated using a weighted combination of neutral images, \(\mathbf{I}_{i}\) and \(\mathbf{I}_{j}\), as  
\[\mathbf{I}_{new}=(1-\lambda)\cdot\mathbf{I}_{i}+\lambda\cdot\mathbf{I}_{j}, \quad\text{where }0\leq\lambda\leq 1 \tag{1}\]  
In Equation (1), the weight, \(\lambda\), decides the ratio of \(\mathbf{I}_{i}\) and \(\mathbf{I}_{j}\) to be reflected in \(\mathbf{I}_{new}\). If \(\lambda\) is 0.5, the two images are assumed to have been reflected equally. In the case of ICR, it is easy to generate images by applying a single parameter for all pixels. However, the changes in some areas within the image will not be reflected accurately, owing to variations. Extrinsic factors, such as occlusions, cause changes in the neutral image. For example, pixels around the eyes change significantly when wearing sunglasses. On the other hand, areas unrelated to these variations generally retain the pixel information of the neutral image. Therefore, it is necessary to obtain the weights for each pixel.  
In this paper, we propose a method to enlarge the training set. A new image, \(\mathbf{I}_{new}\), with variations, is generated via a combination of the neutral image, \(\mathbf{I}\), and the variational image, \(\hat{\mathbf{I}}\), derived from \(\mathbf{I}\) by referring to the ICR. The method of generating a new image is redefined as follows:  
\[I_{new}(u,v)=(1-\mathcal{B}(u,v))I(u,v)+\mathcal{B}(u,v)\hat{I}(u,v). \tag{2}\]  
In Equation (2), \(\mathcal{B}(u,v)\) is the pixel weight at a certain position in both \(\mathbf{I}\) and \(\hat{\mathbf{I}}\). A new image, \(\mathbf{I}_{new}\), is generated, containing the variations only in some areas while maintaining the characteristics of the neutral image as much as possible.  
Figure 1: Overall procedure of the proposed method.
# A Parallel-Phase Demodulation-Based Distance-Measurement Method Using Dual-Frequency Modulation
## 6 Results  
### Results from Single-Frequency Modulation  
In this section, using the parallel phase demodulation method, the standard deviations with respect to the object distance are analyzed. The conditions of the experiment are determined by two independent variables: the modulation frequency and the modulation voltage amplitude. The measured distance ranged from 1 m to 4.5 m at intervals of 0.5 m. For each distance, 10,000 repeated distance measurements were conducted to evaluate the standard deviations of the result. The remaining factors were fixed; i.e., the sampling frequency at 625 MS/s, the number of samples for a single distance measurement at 4, and the integration time at 9.6 \(\upmu\)s.  
Figure 7 shows the results of the experiment for different modulation frequencies. The modulation frequencies were 31.25, 15.625, and 7.8125 MHz. The modulation voltage amplitude was fixed at 12 V. Figure 7 shows that as the modulation frequency increased, the standard deviation decreased, which can be described by Equation (10). For the case of 7.8125 MHz modulation frequency, the maximum value of the standard deviation was about 0.05 m. On the other hand, for 31.25 MHz modulation frequency, the maximum value of the standard deviation was lower than 0.01 m.  
Figure 8 presents the results of the experiments for different modulation voltage amplitudes, 12, 8, and 4 V. The modulation frequency was fixed at 31.25 MHz. It shows that as the modulation voltage amplitude increases, the standard deviation decreases. The maximum value of standard deviation is  
Figure 6: Signal flows of the measurement system.  
Figure 7: Standard deviation per modulation frequency.  
lower than 0.02 m. A monotonically increasing trend of standard deviation is shown for all cases of modulation voltage amplitude in general, which can be described by Equation (10).  
Performances of the proposed method and two commercial cameras utilizing conventional sequential AMCW [30], are compared in Table 1 in terms of distance standard deviation versus distance and integration time. The performance parameters of commercial cameras were taken from a reference paper [30]. The relatively very short integration time of the proposed method was chosen to be 9.6 \(\upmu\)s, which is suitable for raster scanning type distance measurements. From Table 1, it is obvious that the proposed method can measure a single distance in a very short integration time. The proposed method presents relatively low standard deviation contributed by no DC components in the demodulation process, as shown in Equations (10) and (13).
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Storage and Transport
#### 2.2.3 Cooling Large Food Production, Storage, and Distribution Facilities  
As just discussed for supermarkets, connecting a cold storage to a cooler, and supply the produced cold to the space for storing food cold is possible. If the whole space has to be cooled to freezing temperatures, a possible approach becomes integrating the storage into that space. It has several advantages. Generally, it has practically no losses, because all the heat exchange between storage and its environment leads to cooling of the space and the contained food. Also, cooling still continues even in electricity blackouts.  
The company Viking Cold Solutions(tm) Inc., Houston, TX, USA ([[https://www.vikingcold.com](https://www.vikingcold.com)]([https://www.vikingcold.com](https://www.vikingcold.com)), accessed on 18 October 2022) focuses on such a system, for frozen food production, storage, and distribution facilities. For this, macro-encapsulated PCM is placed above the food (Figure 6), and stores cold supplied by the cold production unit via cold air into the space where the food is stored. The cold stored in the PCM cools the food, maintaining stable temperatures, even if cold production outside is turned off. The effect can last for many hours, thus allows to run cold production at maximum design efficiency which saves energy, reduces equipment wear down, and also allows use of electricity during times of low electricity prices, thus saving cost. According to information on their website, the efficiency of cold production is increased by up to 26%, while electricity cost can be reduced even up to 50% by shifting electricity demand to times of lower electricity prices. In addition, this approach can serve as a backup in case of failure of electricity supply of the cold production unit. Besides these advantages for the user, the option to shift electricity demand to off-peak demand hours also allows better flexibility for the utility, meaning the electricity supplier. The technology was tested in a customer installation and validated by an external consulting company in a two-year study [39]; the report is available for everybody at the company's website. Mentioned on their website is also that the approach is useful for solar-PV driven systems. In developing countries, where grid electricity is often not available or not reliable, solar PV is often the first option.  
Figure 6: Approach to cool frozen food production, storage, and distribution facilities (source: Viking Cold Solutions(tm) Inc.).
## 3 Results
### Results of Oddball Counting Error Rate
#### 3.3.2 Oddball Counting Error Rate Under Partial eHMI Penetration Condition  
The mean and SD of oddball counting error rate across different eHMI information content conditions are presented in Table 7. Based on the results of the Shapiro-Wilk normality test, significant deviations from normality were observed for the single-level low (\(p<0.001\)), single-level medium (\(p=0.037\)), single-level high (\(p=0.004\)), two-level low-medium (\(p=0.006\)) conditions, suggesting that these data do not follow a normal distribution. As a result, a Friedman test was conducted to compare the differences between the eHMI information content conditions. The Friedman test indicated that the differences between the eHMI information content conditions were not statistically significant (\(\chi^{2}(5)=8.41\), \(p=0.135\)).
## 4 Protein Quality Evaluation Methods
### In Vitro Models
#### 4.3.3 Semi-Dynamic Methods  
Static methods have limitations in assessing crucial dynamic processes involved in gastrointestinal digestion, such as pH gradients, gradual enzyme and gastric fluid addition, and continuous gastric emptying [94]. In contrast, dynamic models offer a more physiologically relevant approach by incorporating these factors and others. However, these models are highly complex, requiring significant hardware and software resources, and are generally expensive to implement and not easily accessible [89; 95]. Therefore, semi-dynamic models arise as a more accessible option for protein digestibility essays. Semi-dynamic methods combine elements of both in vitro and in vivo methods to measure protein digestibility. Thus, these methods simulate the in vivo digestive process by using enzymes and conditions that mimic the physiological environment of the stomach and small intestine. Researchers have shown increasing interest in semi-dynamic in vitro models in recent years. These models have gained attention due to their unique capability to enable researchers to strategically select and incorporate specific dynamic factors during different phases of the gastrointestinal tract (GI) [96]. By allowing the deliberate inclusion of chosen dynamic elements, such as pH gradients, enzyme and gastric fluid addition, and gastric emptying, semi-dynamic models offer researchers the ability to create a more targeted and physiologically relevant representation of the digestion process. This approach has emerged as a valuable tool in bridging the gap between static and fully dynamic models, providing a flexible and adaptable platform for investigating GI digestion under controlled laboratory conditions. A recent development in the field involves the creation of a cost-effective semi-dynamic method. This method utilizes parameters derived from in vivo data on dairy product digestion to establish a more realistic and physiologically relevant representation of the process [95]. By incorporating equivalent in vivo data, this approach enables researchers to capture key dynamic factors involved in the digestion of dairy products, while maintaining a more affordable and accessible experimental setup. However, there are considerationsabout semi-dynamic methods that should be considered. For instance, these methods do not account for the impact of microbial metabolism, which may affect protein digestibility. Additionally, semi-dynamic methods are unsuitable for measuring protein digestibility in infants, as they have a different digestive physiology than adults. However, there are several considerations related to the food matrix, composition, and human and animal physiology before deciding which model to use for conducting a protein digestibility assay [33]. Table 2 summarizes the protein digestibility of various plant protein sources obtained by in vitro methods.
## 4 Simulation Verification  
### BBD Response Surface Simulation  
In accordance with the Hertz-Mindlin with Bonding model, it can be ascertained that there is a significant correlation between the failure of the bonding bond between the filled particles and several other factors, including the normal stiffness per unit area, shear stiffness per unit area, critical normal stress, critical shear stress, and so forth. In this section, a mathematical model of multiple regression equations is established based on Design Expert software (13) to investigate the degree of influence of each factor on the compressive strength. The factors considered are normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress. The model is developed using the combined approaches of ANOVA, BBD response surface method, and uniaxial compression test. The material parameters referenced in [15] are presented in Table 2, and the bonding parameters referenced in [14] are presented in Table 3. To determine the optimal bonding parameters, a four-factor, three-level response surface test was conducted and coded as shown in Table 4. Figure 3 illustrates the three-stage process of compression force on the bonding bonds between particles, from low to high compression force under pressure, in the bonding model during the compression process. The blue color represents a lower compressive force, ranging from 35,728 N to 45,000 N. The green color indicates a higher compressive force, spanning from 45,000 N to 55,000 N. In contrast, the red color signifies that the compressive force is approaching its maximum value, specifically within the range of 55,000 N to 67,152 N. This representation effectively captures the transition of compressive forces under varying pressure conditions, providing valuable insights into the mechanical behavior of the bonding model.
# Fast Reroute Mechanism for Satellite Networks Based on Segment Routing and Dual Timers Switching
## 2 Related Work
### Overview of the SRDT-FRR Scheme
#### 3.1.1 Scheme Modeling  
This paper defines a satellite network as a graph G = (V,E), where V represents the set of satellite nodes and E denotes the set of ISLs. A satellite constellation consists of N orbital planes, each containing M satellites. The proposed scheme effectively tackles the challenges posed by link failures through the coordinated operation of multiple functional modules, facilitating efficient traffic management and fault recovery.  
As shown in the Figure 1, the SRDT-FRR scheme utilizes a set of collaboratively functioning modules to effectively address the challenges posed by link failures in satellite networks, enabling efficient traffic management and fault recovery. Firstly, the onboard control plane acts as the core decision-making unit, overseeing the overall management and control of the satellite network. Its key functions include facilitating information exchange between satellite nodes, formulating and updating routing strategies, and calculating and configuring backup paths. Secondly, the hardware monitoring module focuses on the high-precision, real-time monitoring of network links at short intervals, specifically targeting millisecond-level link interruptions caused by laser instability. By employing high-sensitivity hardware, the module quickly detects instantaneous link anomalies, triggering the PLR to swiftly switch to a backup path and ensure uninterrupted traffic flow.  
Figure 1: Satellite node module division.  
Upon the occurrence of a link disruption and the subsequent switch to a backup path, the hardware monitoring module activates a backup path activation timer. This timer regulates the duration for which the backup path remains active, thereby preventing frequent switching between the primary and backup paths caused by transient faults. The link fault detection module is responsible for periodically monitoring the status of links within the satellite network. It sends detection signals at relatively long intervals and observes the link response. When a transient fault transitions into a long-term stable fault, the link fault detection module triggers a backup path deactivation timer to manage the timing of backup path switching. By setting an appropriate duration, this module ensures network stability during the fault recovery process.
# Meta-Analysis of the Performance of Previous Concrete with Cement and Aggregate Replacements
## 5 Results and Discussion
### Aggregate Replacement in Previous Concrete
#### 5.3.2 Performance Evaluation
For each proportion, the mean values were obtained by dividing the values corresponding to a specific property by the frequency of assessing this property. The compressive, tensile, and flexural strengths were generally unaffected, with slight fluctuations, by the replacement of NA at all percentages. Furthermore, the permeability seemed to increase with aggregate replacement up to 50%, while a decrease was recorded beyond 50%, signifying that the connected porosity was greatly affected by the larger pore spaces. Conclusively, replacement ranges of 1-100% and 1-50% are considered to be viable for superior mechanical and hydraulic performance, respectively.
# Label-Setting Algorithm for Multi-Destination \(K\) Simple Shortest Paths Problem and Application
## 1 Introduction
This approach takes advantage of the cost and predecessor information on a path (from origin to intermediate nodes) while calculating the KSSP to downstream multiple destination nodes. Thus, the algorithm is able to explore all destinations concurrently in a single run, resulting in significant computational performance gains. The specific objectives of this study include the following:  
* Propose a label-setting framework for a class of multi-destination KSSP problems and identify optimality conditions to find exact solutions for the identified variants. * Develop an exact label-setting solution algorithm for solving the one-to-all KSSP problem and analyze its computational complexity. * Propose a heuristic for practically efficient implementation using bounded labels and appropriate data structures. * To evaluate the computation performance and solution quality of the proposed approach with state-of-the-art algorithms on various real-world networks. * To illustrate the application of this algorithm for a practical problem and demonstrate its efficacy for the use case. This study contributes to the literature by introducing a novel label-setting algorithm for the multi-destination KSSP problem. The solution procedure implements an efficient memory organization technique by proposing a suitable data structure. A heuristic is proposed that achieves significant computational efficiency gains (one to three orders of magnitude improvement over state-of-the-art algorithms) than the exact solution without compromising much on solution quality. The practical application of the algorithm is illustrated in the context of computing the gravity of an edge [14]. This particular application quantifies the importance of an edge relative to others by determining the number of paths passing through it. For this application, it is shown that the proposed method is computationally more efficient than alternative approaches in the literature. The rest of the paper is organized as follows. The next section provides a review of the related literature. Then, the methodology for the exact label-setting algorithm and the heuristic method is described. The results from the various computational experiments are then presented in Section 4, followed by an application of the proposed algorithm in Section 5. The paper concludes with a summary of the major findings and implications of this study and suggests some promising directions for future research.
## 5 Performance Comparison of the WSN with Different Spatial Node Distributions
### A Grid Where the Coordinator is Located in One Corner  
Table 5 shows the results for a grid of 49 nodes where the coordinator is located in a corner of the grid and all nodes generate and send traffic to it. The coverage radio is 50 m and the area is 150 m \(\times\) 150 m. Results show that the MPH protocol has a better performance in all metrics with respect to the other three protocols. Regarding the retransmission metric, MPH is 19% less than AODV, 17% less than DSR and 18% less than ZTR. With regard to the CSMA retry metric, MPH shows an average reduction of 30% against AODV, 27% against DSR and 29% against ZTR. Concerning the overhead metric, MPH is 27% less than AODV and 17% compared to DSR. MPH has only 11% more overhead with respect to ZTR. About the discovered routes metric, MPH has a greater average discovered routes, such as 43% against AODV, 28% against DSR and 6% with respect to ZTR. Regarding the recovery time metric, MPH is faster in the recovery of the topology, having 41% over AODV, 39% over DSR and 35% relative to ZTR. Finally, with regard to energy, MPH has an energy savings of 16% compared with AODV, 14% against DSR and 5% in relation to ZTR.
# DRL-Based Dynamic Destroy Approaches for Agile-Satellite Mission Planning
## 4 Experimental Simulation
### Training Performance  
Training Comparison  
The training process of D3 RL model is essentially similar to solving a local optimal solution using the heuristic algorithm [20; 22]. Each iteration is a dynamic destruction of the existing local optimal solution, and the TimelInsert operator is used for a repair.  
Figure 5 illustrates that the agent can continue to achieve an improved task scheduling rate as the number of iterations increases, and with sufficient iterations, the training process converges. In Figure 5a, the scheduled rate gradually decreases as the number of tasks in the intensive _Area_ scenario increases. The convergence of each scenario is influenced by the specific difficulty of task planning, which is the primary reason for the reduction in the scheduled rate. The more challenging the task planning, the more iterations are required for convergence, and it does not simply correlate positively with the number of targets in the region.  
Figure 5b exhibits a similar trend to the _Area_ scenario. Due to the increase in the number of tasks and the expansion of the coverage area, the model for the \"World\" scenario generally approaches convergence after 4000 iterations, which is considerably more than the \"Area\" scenario. Additionally, in the seed sensitivity experiments, it was observed that the \"World\" scenario is much more sensitive to the random seed settings compared with the \"Area\" scenario, particularly for the instance with a task count of 100. This sensitivity is particularly pronounced for the instance with 100 tasks, where the planning rate notably increases for two specific random seed settings. As a result, the average task  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Learning rate & 0.001 \\ Discount factor \(\gamma\) & 0.99 \\ The clip rate \(e_{clip}\) & 0.2 \\ Batch size & 16 \\ Number of iterations in each scene & 3000 \\ Number of training scenes & 20 \\ Number of epoch \(K\) & 2 \\ Destroy rate & 0.2 \\ Initial temperature & 100 \\ Final temperature & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training parameters.  
planning rate for instance 100 is considerably higher than instances with larger numbers of tasks. For subsequent validation experiments, we employed a consistent random seed across scenarios.
## 3 An Efficient Speech CBSS Algorithm in Multipath Channels  
In the traditional frequency-domain convolutional blind source separation (FD-CBSS) algorithm, there will inevitably be problems of permutation ambiguity. There are usually two methods to solve such problems: one is a geometric method based on the direction of arrival (DOA) that has strict requirements on the spatial position of the sensor, and another method is based on the correlation of adjacent frequency bands with mutual parameters. These two methods have their own advantages and disadvantages in solving the ambiguity problem of separating sub-signal permutation. However, both methods require an additional permutation step after the CBSS algorithm, which inevitably increases the time complexity and computational complexity of the FD-CBSS process.  
For this reason, this paper considers the existing CBSS itself and proposes the Fast IVA algorithm using Newtonian learning method to solve the multipath speech CBSS problem. The algorithm is essentially an extension of the one-dimensional random variables in the ICA algorithm to multi-dimensional random variables. It looks at the sampled data of the same source signal at different frequency points as a vector and performs CBSS of the signals in order, thus solving the permutation ambiguity case without designing additional permutation algorithms. The innovation of this paper is that the optimization algorithm process introduces Taylor series polynomials to quickly approximate the contrast function of Fast IVA algorithm. This step efficiently accomplished the CBSS without the need to design additional sequencing algorithms.