esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Digital Image Frame Relay Transmission (DIFRT)
### Image Frame Realization  
In the relay transmission mechanism, digital image signals are converted into a data frame in the transmission channel. The data frame format can be depicted as in Figure 4. It contains header identifier, image data, and cyclic redundancy check (CRC). In order to prevent information conflict and mutual-interference from each node, a source and target address are set as header identifier. The transmitter, first node, second node, and final receiver are set to 0 xE0, 0 xD0, 0 xD1 and 0 xF0, respectively. Among the image data, we divide the information of the quantized frequency coefficient matrix into the sequence number, the positive or negative symbol information, and the real value. In this way, the image information is packed into data frames. The CRC can assist the receiver to identify the correctness of transmitted data and filter out useless information. At the final receiver, the image information will be extracted from the data frame and finally reconstructed.  
In addition, due to the attenuation phenomenon resulted from absorption and scattering effects of the optical beam in the transmission channel, data error and loss of each frame are particularly common and inevitable. This effect can influence the quality of the reconstructed image. In this case, we will discuss in detail in Section 6.4.
## 2 Methodology
### DT and Optimization System
#### 2.2.3 Operational Workflow  
Figure 2 summarizes the execution sequence, highlighting the order and principal interactions. The DT-IAS, DT-PV, and DT-EV are executed initially, relying on the data available in the database. Once they are executed, the EVO is triggered, utilizing outputs from the previous DT. Among other key outputs, such as the EV charging scheduling, this optimizer provides the aggregate power demand of the chargers, which is necessary for the IAO. This last optimizer determines the power setpoint for the SESS, which is then consumed by the DT-SESS to assess the SOC of the battery.  
Regarding the sequencing, the DT-PV, DT-IAS, DT-SESS, EVO, and IAO are executed every 15 min, producing forecasts for the subsequent 24 h period. This temporal resolution ensures continuous and accurate monitoring of plant operations and reduces the risk of significant deviations. The DT-EV module consists of two separate algorithms: one for estimating the SOC, executed at 10 s intervals, and another for assessing the SOH, executed weekly. The SOC requires frequent updates due to its rapid fluctuations and its critical role in representing the fleet's current operational status. In comparison, SOH varies more slowly over time, making weekly evaluations adequate for capturing long-term degradation trends.  
The primary objective of this article is to present this methodology and assess its potential advantages compared to the actual operational practices of the industrial facility under study, which is not optimized. A future publication will further compare the benefits of this approach with those offered by other existing optimization solutions.
## 2 Finite Element Model  
Cyclic vehicle loads result in the deflection of U-ribs in OSDs, which causes the out-of-plane deformation of the diaphragm. Consequential stress concentration occurs at the diaphragm-to-rib weld, and fatigue cracking propagates obliquely upward form the bottom of the weld between the diaphragm and the U-rib on the real steel bridges, as shown in Figure 1.  
The finite element model was established using ABAQUS, based on the dimensions of the real bridge. The thickness of the deck and the diaphragm was 12 mm and 8 mm respectively. The section size of the U-rib was 324 \(\times\) 262 \(\times\) 6 mm. The finite element model is shown in Figure 2.  
The material of the finite element model was Q345 qD steel, with an elastic modulus of 2.06 \(\times\) 10\({}^{5}\) MPa and a Poisson's ratio of 0.3. The model was meshed with hexagonal solid element type C3D8R and tetrahedral solid element type C3D10 with a global mesh size of 20 mm. The meshes of the two sides of the diaphragm-to-rib welds were refined with a mesh size of 1 mm; the transition region was meshed with tetrahedral element type. The deck was fixed constraint. The roof of the model was fixed constraints with full degrees of freedom. The loading area was set on the surface of the diaphragm with an area of  
Figure 1: Fatigue cracks formed from the diaphragm-U-rib weld.  
2 mm \(\times\) 164 mm \(\times\) 45 mm, directly below the arc notch [4]. The reference point was set at 10 mm from the weld toe in the horizontal and vertical directions with a nominal stress of 100 MPa. A rectangular extended finite element method (XFEM) crack was set to the left of the diaphragm-to-rib weld, with a plane size of 90 \(\times\) 8 mm and an angle of 45\({}^{\circ}\) in the horizonal direction.  
Several studies on cold reinforcement were conducted. One type of rectangular steel plate was employed to attach to the diaphragm-to-rib weld toe for reinforcement [17]. One type of polygonal steel plate was used for the U-rib crack retrofitting via the FEM method [18]. Based on existing studies, two shapes of steel plates were designed to repair diaphragm-to-rib fatigue cracking in this study, as seen in Figure 3. As shown, the size of the polygonal steel plate was 120 \(\times\) 190 mm, with triangular cuts in both corners, and the hypotenuse of the chamfer fitted the weld in the model. In the same way, the size of the rectangular steel plate was 120 \(\times\) 190 mm with the long edge of plate fitting the weld too. Considering the coordination between the size of the steel plates and the bridge component, the thickness of these plates was set to 8 mm, the same as that of the diaphragm in the model. Binding constraints were adopted to connect the steel plates with the diaphragm.  
Figure 3: Finite element models.  
Figure 2: Model size (Unit: mm).
# Energy-Efficient Clustering Scheme for Flying Ad-Hoc Networks Using an Optimized LEACH Protocol
## 3 Related Work
As a result, it may either transfer data from CHs from a single hop or use multi-hop to the sink. In [15], the authors proposed a Time-based Cluster-Head Selection Algorithm for LEACH (TB-LEACH), which alters the CH selection process to maximize the network's life cycle. Set-up and steady stages are the two primary phases of TB-LEACH. Instead of the random selection of CHs used in LEACH, the TB-LEACH algorithm picks CHs based on a random timer in the first phase. The number of CHs in each round is fixed, unlike LEACH, and is denoted by a counter. The TB-LEACH (Time-based LEACH) was introduced by [15] to modify the process of CH selection for enhancing a network's life cycle. The two main phases of TB-LEACH are establishment and steady steps. The TB-LEACH algorithm selects CH based on a random timer in the first phase instead of the random selection of CHs used in LEACH. As opposed to LEACH, in each round, the number of CHs is fixed and indicated by a counter. If the random duration expires and the counter fails to reach the required number of CHs, the node broadcasts a CH-ADV advertisement message to all nodes in the current round, announcing that it has become a CH via CSMA, even if the node is not a CH node (normal node). Following the determination of CHs, the remaining tasks are identical to the LEACH algorithm. In [16; 17; 18; 19], the authors the Energy-Aware Distance-based Cluster Head selection and Routing (EADCR) to extend the network lifespan of WSNs using the Fuzzy C-Means (FCM), residual energy of nodes, Euclidean distance from the BS, and cluster centroid. Clusters are formed in the EADCR by employing the FCM method at the BS. Then, using a fitness function, each cluster chooses its CH, and the initial energy and residual energy of all nodes influence the fitness function. Table 1 shows a comparison of LEACH descendants, Table 2 describes advantages and disadvantages, and in Table 3, a comparison is conducted on the basis of various parameters.
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 3 The Proposed DCT-Based DFC System
### Data Embedding
Hence, if we embed data in the low-frequency region, there is an advantage that data restoration and decoding at the receiver will be good. When the peak signal-to-noise ratio (PSNR) in an image is 30 dB or above, it is generally agreed that the distortion between the original and received image is not significant [32]. However, if data are embedded at the low-frequency components, the PSNR may fall below 30 dB and the image quality will be distorted. For instance, in a 256 \(\times\) 256 pixel image, if we embed a total of 500 bits in rows 5 to 15 (lower sub-band), with \(\alpha=0.35\) (see Section 3.1.1), the PSNR of the image becomes 30 dB. Therefore, data must be embedded to ensure both data recovery and image quality. The data embedding is carried out using the addition allocator on the pixel values of a DCT-converted image. As the data embedding is performed in the frequency domain, the data-embedded image, \(\mathbf{D}_{F}\), is expressed as  
\[\mathbf{D}_{F}=\mathbf{I}_{F}+\mathbf{X}. \tag{5}\]  
To display the image frame on the electronic screen, the above frequency-domain data-embedded image must be converted back to the spatial domain. This is done by IDCT (cf., Figure 1), and the spatial-domain image to be displayed on the screen is expressed as  
\[\mathbf{D}_{t}=\mathbf{C}^{H}\cdot\mathbf{D}_{F}. \tag{6}\]
## 3 Results and Discussion
### Physicochemical Properties
#### 3.1.6 Determination of Total Sulfhydryl Content in MFHS  
The total sulfhydryl content of surimi significantly decreased after Monascus fermentation (\(p<0.05\)) (Figure 4c). During the gelation of surimi, myofibrillar proteins unfolded to expose free sulfhydryl groups, which were oxidized to disulfide bonds to promote the formation of a gelation network. According to the above analysis results, Monascus fermentation can promote the formation of the gel network structure of surimi. Additionally, the formation of disulfide bonds decreases the free sulfhydryl content and increases the gel network structure; however, the total sulfhydryl content does not significantly change. Furthermore, the total sulfhydryl content in the surimi decreased, which may be related to the protease produced during the Monascus fermentation. A significant analysis showed that there was no distinct difference in the content of total sulfhydryl groups between the unfermented group and the 1 h group. The main reason was that the protease produced by Monascus fermentation in the 1 h group promoted the unfolding of the myofibrillar protein structure, and the exposed sulfhydryl groups were oxidized to disulfide bonds to form the gel network structure of hairtail surimi. Furthermore, the hairtail surimi network structure was hydrolyzed and the disulfide bond was destroyed under the continuous action of proteases, which resulted in a significant reduction in the total sulfhydryl groups in surimi after 3 h and 5 h of fermentation. This phenomenon was also consistent with the gel quality decrease after 3 h of fermentation; it is also one of the reasons for the better gel quality of the surimi in the 1 h Monascus fermentation group that we mentioned above.  
Figure 4: Effect of Monascus fermentation on TVB-N, WHC, total sulfhydryl content, and myosin secondary structure contention of hairtail surimi (fermentation temperature 25 \({}^{\circ}\)C; different lowercase letters represent significant differences (\(p<0.05\))).
## 4 Implementation of MMAS Based on Spark Platform  
The ant colony algorithm is essentially a parallel algorithm. In every iteration, the ant colony algorithm can be divided into two stages. In the first stage, each ant iterates through all cities independently. Calculate the walking distance and save the current path. In the second stage, compare all the walking distances of ants to get the shortest one and then operate the pheromone. These two stages can be realized respectively by Map and Reduce, so the MapReduce framework is suitable for the ant colony algorithm. The implementation of MMAS based on MapReduce framework and Spark platform is as follows:  
Figure 2: The working principle of Spark.  
1. Data parallelism. At first, initialize the city distance matrix and the pheromone matrix. Let the number of ants be \(m\) and establish an array of size \(m\). Use _parallelize_() of Spark to convert the array to a distributed dataset, then use this dataset as the input of \(map\)() to start \(m\) Map tasks. The array that has no effect on results is only used for starting Map tasks, and the elements in it can be any value.
2. Map Tasks. Because all Map tasks will use the same algorithm, we only need to complete the process that one ant constructs a path. Construct path according to MMAS and use the 2-opt algorithm for optimization. Then output the walking distance and path of each ant. The calculations of the algorithm are mainly concentrated in Map stage. The outputs of \(map\)() are terms of key-value pairs, in which the distance is key and the path is value. Sort the distances using _sortByKey_().
3. Reduce Tasks. Use _take_(1) to find the shortest distance and its corresponding path.
4. Iteration: Update the pheromone matrix according to the results of Reduce tasks, and then save the current minimum distance and optimal path. Iterate Map tasks and Reduce tasks to obtain the final result.
# From Femtoseconds to Hours--Measuring Dynamics over 18 Orders of Magnitude with Coherent X-rays
## 1 Introduction
Besides the FEL application, XPCS will play a major role at the currently designed and already built next generation of storage ring sources [7,8], as they promise to extend the time scale of XPCS towards nanoseconds. In this review we will first introduce the XPCS concepts including higher-order correlations, XSVS and the role of coherence. Afterwards, setup parameters for XPCS are discussed. The main part of this review discusses dynamics studies from the last 10 years, focussing on the new possibilities thanks to accessing faster timescales at storage rings and the  
Figure 1: **(a)** Airy fringes from Fraunhofer diffraction from slits of different sizes as indicated. Data has been measured in the commissioning of the ID10 beamtime at ESRF, the first dedicated beamline for coherence applications [1]. Figure reproduced from [2]. Copyright 1997 Society of Photo-Optical Instrumentation Engineers (SPIE). **(b)**\(g_{2}\) functions from Fe\({}_{3}\)Al at the \((\frac{1}{2}\)\(\frac{1}{2}\)\(\frac{1}{2}\)) superlattice reflection measured at ID10 [21] showing fluctuations above the order-disorder transition temperature of \(T_{c}=823.8\) K.  
performance of XPCS at FEL sources. Recent highlights that will be addressed in this review include the observation of two types of liquid water [29] as well as probing real-time dynamics in liquid water on femto- to picosecond time scales [30; 31]. Furthermore, new routes of understanding stress relaxation in glasses have been revealed recently [32]. The possibility of reaching faster time scales in a real time experiment enables the study of in-operando dynamics, e.g., ion transport in lithium batteries [33], and paves the way to sub-\(\upmu\)s dynamics of soft matter [34; 35] and biological systems [36; 37; 38; 39]. Finally, future opportunities will be addressed with special attention on possibilities at the new facilities.
## 3 Results
### Analysis of Vertical Distribution of Air Quality
At Point 6 where vegetation is dense, PM\({}_{2.5}\) levels are significantly lower than at other monitoring points, demonstrating the effective filtering and adsorption of airborne particles by vegetation. Figure 11: Vertical variation of humidity graph. The numbers in the legend correspond to the vertical collection point numbers. Figure 12: Vertical variation of PM\({}_{2.5}\) graph. The numbers in the legend correspond to the vertical collection point numbers. The F-test results, with \(p\)-values below 0.05 for all pairwise comparisons among the seven sampling points, indicate statistically significant differences in PM\({}_{2.5}\) concentration variance between each location pair. This suggests that the geographical layout and structural differences among the sampling points substantially influence PM\({}_{2.5}\) levels, underscoring the impact of spatial configuration on air quality variations. Based on the results of the two-way ANOVA (Table 5), both \"location\" and \"altitude\" exhibit significant effects on PM\({}_{2.5}\) concentrations. The location effect, with a significance level below 0.01, indicates substantial differences in PM\({}_{2.5}\) values across sampling points, suggesting a pronounced spatial variation in PM\({}_{2.5}\) concentrations among different geographic locations, consistent with the F-test findings. Additionally, altitude shows a significance level below 0.01, demonstrating that PM\({}_{2.5}\) concentrations vary significantly with height. The significant interaction effect between location and altitude (\(p<0.01\)) suggests that the influence of altitude on PM\({}_{2.5}\) concentrations varies across different locations. This interaction provides key insights for further investigation into the spatial distribution and vertical variation patterns of PM\({}_{2.5}\) concentrations. Based on the results from [PERSON]'s post hoc test (Table 6), significant differences in PM\({}_{2.5}\) concentrations were observed among most location points at the same altitude, as indicated by the distinct uppercase letter labels across the seven points. This suggests that PM\({}_{2.5}\) levels vary significantly across the seven locations, likely due to differences in geographic layout. However, between Points 1 and 2 at the 10 to 40 m range, no significant differences were detected in PM\({}_{2.5}\) concentrations, which may result from the similar, open spatial layout of these points, leading to comparable impacts on PM\({}_{2.5}\) dispersion. For the same location points at varying altitudes, PM\({}_{2.5}\) concentrations generally showed no significant differences across heights, with a few notable exceptions. Points 3 and 4 displayed significant differences in PM\({}_{2.5}\) concentrations above and below the 35 m mark, while Point 5 exhibited similar variation around the 40 m mark. This pattern may be due to the presence of tall buildings and dense vegetation near these points, where similar geographic layouts likely influence air circulation and particulate matter distribution differently above and below certain heights.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 2 Background  
### RFID Phase Model  
The communication between the passive RFID tag and the reader was established based on a backscattering mechanism. Assuming that the distance between the tag and the antenna is \(d\), the signal total propagation distance is \(2d\). Along with the phase change introduced by distance, the phase is also affected by the hardware characteristics and orientation of the tag. The phase value that the antenna receives can be represented as  
\[\left\{\begin{array}{l}\varphi=\left(\frac{4\pi}{\lambda}d+\varphi_{ini}+e \right)\text{ mod }2\pi\\ \varphi_{ini}=\varphi_{A}+\varphi_{tag}\end{array}\right. \tag{1}\]  
where \(\lambda\) is the wavelength of the signal and \(e\) is the noise-induced measurement error, and \(\varphi_{ini}\) is referred to as the initial phase and contains phase rotation produced by the antenna hardware circuit and the tag reflection, which are denoted as \(\varphi_{A}\) and \(\varphi_{tag}\).
# Conversational AI over Military Scenarios Using Intent Detection and Response Generation
## 4 Experiments
### Performance of the Intent Detection and Slot-Filling Modules  
SVM models are used to perform multi-class classification. The dataset is randomly divided into training and test sets in three different proportions. According to the results shown in Figure 4, the 9:1 ratio outperforms the 7:3 ratio in terms of F1 score and accuracy, and achieves 90% accuracy. The performance for predicting the four intents is then performed based on the model trained with a data ratio of 9:1, as shown in Figure 5. The multi-class classifier had the highest F1 score for \"where\" intent (92%), followed by \"when\" (91.4%), \"who\" (91.1%), and finally \"what\" (88.8%). The average F1-score of the classifier was 90.1%. Due to the limited amount of training data (1307 sentences in four categories), the F1 performance of the trained intent detection model is 88 91%. However, from the perspective of the learning curve (dataset splitting rate), the performance improves as the amount of training data increases. Further comparing the performance of each category, we can see that the \"What\" category has the most errors, followed by \"Who\" and \"When\", and \"Where\" has the best performance. We analyzed the possible reasons and found two: one is language. For example, in Chinese grammar, the query for \"What\" is more complex than the other three categories, which may include \"Why\", \"How\", \"which\", etc. Another possible reason is that when the user's query has multiple intents, the classifier will only predict the class with the highest probability, so the number of false negatives for the \"what\" intent increases.  
Figure 4: Performance of intent classification.  
The performance of the NER model is evaluated by performing five-fold cross-validation on the dataset. Figure 6 shows the performance of NER in recognizing military names, weapons, military locations, military events, and time entities. In terms of F1-score, the five models have the highest accuracy (0.943) for person names, followed by time. Conversely, it performed slightly worse at identifying military event, at 0.848.