esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Small Size Mass Loading Using Transversely Coupled SAW Resonator
## 3 Result and Discussion  
In order to study the effect of small-size mass loading on the response of TC-SAWR device, we designed a SiO\({}_{2}\) cylinder with a diameter of 100 \(\upmu\)m and a height of 300 nm attached to the device surface, as shown in Figure 7.  
The attachment site was located at the central point of IDT1. The simulation model and the photo of fabricated TC-SAWR device are shown in Figure 8.  
Figures 9 and 10 showed the simulated and measured results of frequency response with and without SiO\({}_{2}\) load, respectively. They consistently showed that the frequency response curve of TC-SAWR had no distortion under small size load.  
More specifically, under the effect of mass loading, the insertion loss increased around 2 dB, and the center frequency shifted around 14 kHz in the simulation result. On the other hand, the insertion loss increased by 2 dB, and the center frequency shifted around 10 kHz in the measured result. The differences between simulation and experimental results were caused by multiple factors, such as the thickness error of SiO\({}_{2}\) load, the fabrication error of SAW chip and the solution accuracy of the 2-D COM theory.  
Figure 8: (**a**) Geometric model of TC-SAWR with SiO\({}_{2}\) load constructed in COMSOL; (**b**) fabricated TC-SAWR device with SiO\({}_{2}\) load.  
Figure 7: TC-SAWR device with a SiO\({}_{2}\) cylinder attached to its surface.  
Furthermore, there was a larger passband ripple in the measured frequency response. According to the analysis in previous sections, the main peak of TC- SAWR's frequency response was composed of the 0-order symmetric resonant mode (S\({}_{0}\)) and the 1-order antisymmetric resonant mode (A\({}_{1}\)), which means that the passband ripple was caused by the large frequency difference between two resonant modes. The deviation of resonant modes' frequencies mainly depends on the width of the central busbar and the aperture size of the TC-SAWR. In order to reduce the frequency difference between the two resonant modes and increase the Q-value of TC-SAWR, optimizations of the structure will be deployed in the future.  
Figure 10: Measured result of the frequency response before and after loading.  
Figure 9: Simulation result of the frequency response before and after loading.
# Experimental Study on Mechanical Properties of Anisotropic Slate under Different Water Contents
## 4 Analysis of Anisotropic Properties of Slate Parameters
### Poisson's Ratio Anisotropy  
Figure 15 displays the variation in Poisson's ratio with bedding angle. It shows that the Poisson's ratio for slate alters with increasing bedding angle and confining pressure, although no distinct pattern emerges. Comparing different water contents reveals that the Poisson's ratio in natural and dry states is relatively low, approximately 0.1, whereas in the saturated state, it is significantly higher, about 0.28. This difference indicates that water exposure deteriorates slate, diminishing its deformation resistance. Macroscopically, this is evidenced by increased hoop strain and a corresponding rise in Poisson's ratio.
# Fourier Domain Adaptation for the Identification of Grape Leaf Diseases
## 2 Materials and Methods
### Methods
#### 2.2.4 Farthest Point Sampling  
Farthest point sampling (FPS) is a widely utilized sampling algorithm recognized for its capability to ensure uniform sampling of samples. This methodology operates by iteratively selecting the furthest point from the existing set of sampled points. The algorithm follows the following detailed steps:  
1. Select a point, which is denoted by \(p_{0}\), from the data point as the initial point to form the initial sampling point set: \(s=\{p_{0}\}\);
2. By calculating the distance from all points to \(p_{0}\), an N-dimensional array called \(l\) is generated. Select the point corresponding to the maximum value in \(l\) as \(p_{1}\). Update the sampling point set: \(s=\{p_{0},p_{1}\}\);
3. Calculate the distances from all points to \(p_{1}\). For each point \(p_{i}\), the distance from it to \(p_{1}\) is compared with the value in \(l[i]\). If the distance from \(p_{1}\) to \(p_{i}\) is less than the value in \(l[i]\), the distance is used to update \(l[i]\). Therefore, the array \(l\) always stores the nearest distance from each point to the sampling point set \(s\);
4. Proceed by selecting the point consistent with the maximum value in the array \(l\) as \(p_{2}\). Renew the sampling point set: \(s=\{p_{0},p_{1},p_{2}\}\). Repeat Step 2 to Step 4 iteratively until the quantity required of \(n\) target sampling points is achieved.  
This process ensures a gradual selection of points that maximizes the spatial spread, effectively achieving uniform and diverse sampling.
## 3 Feature Extraction with GraphSAGE
### Neighborhood Node Sampling  
For the target node \(v_{i}\), the full sampling method is used to obtain a neighbor node set \(N_{i}\) containing \(q\) neighbor nodes, so the sampling number is \(q\). \(q\) neighbor nodes are taken out at a certain time as the nodes for information aggregation. If the number of neighbors of a particular node is less than \(q\), a resampling method with a put-back action is used. If the number of neighbors of a particular node is greater than \(q\), a negative sampling method without a put-back action is used. This sampling method learns the feature based on neighbor relationships and generates self-observed hidden state features.  
Figure 2: GraphSAGE node feature extraction process.
## 1 Introduction
### Efficiency  
Recent studies [12,24,25] applied the gateway to collect data from sensors and subsequently forward the data to an AP using Wi-Fi wireless technology. However, using both of a gateway and an AP, the wireless resource must to be allocated to the additional data transmissions between them, reducing the throughputs and raising the contention problem. Applying the proposed IoT AP, the data collected from sensors are forwarded to the data server or data center via the Internet in a more efficient way because the gateway and AP has been integrated as an IoT AP.
# Multi-Level Joint Feature Learning for Person Re-Identification
## 4 Results
### Influence of the Number of Parts  
In this paper, we use the method of dividing a pedestrian image into several parts to extract local features. The visualization of the delicate parts is shown in Figure 9. Intuitively, the granularity of the part feature affects the results. When the number of parts is one, the learned feature is a global feature. As the number of divided parts increases, the retrieval accuracy increases. However, accuracy does not always increase with the number parts, as shown in Figure 10. Rank-1 accuracy of three datasets shows that when the number of parts increases to eight, the performance drops dramatically. The over-increased parts actually compromise the extraction of local features. Therefore, we use six parts in our experiments.  
Figure 8: Evaluation of GLB and MFF are shown above. Rank-1 to Rank-10 accuracy is compared on three datasets. From (**a-d**) is the evaluation performance on Market-1501, DukeMTMC-reID, CUHK03-detected and CUHK03-labeled.  
Figure 9: Visualization of the parts under six values.  
Discussion: we divide the pedestrian image into six parts to get the best results. We consider different proportions and attributes of body parts. We divide the image into six parts according to the position of the elbow joint, croth, knee joint, etc., as shown in Figure 2. Due to the limitation of joints, the grate range of human motion is limited to these six parts. The image is divided into six parts to ensure that the local features of each part have a high degree of recognition when a pedestrian is engaged in a wide range of activities. In addition, we also consider the effect of attributes on the results. The relevant attributes in pedestrian images include clothing categories (dresses, shorts etc.), clothing color, hat, hair, etc. The recognition of the attribute features of each part is also strengthened after dividing the image into six parts.
## 3 Three-Dimensional Finite Element Analysis Model
### Boundary Conditions  
The boundary conditions of the model were composed of geometric boundary conditions and hydraulic boundary conditions. For the geometric boundary condition, the top surface in this model was a free boundary, and both the bottom and side surfaces were set with normal constraints. In addition, in order to simulate soil consolidation, reasonable hydraulic boundary conditions need to be set. In this model, the top surface of the soil was set as a free drainage boundary, and both the side and bottom surfaces were set as a constant head boundary. Moreover, the model was given an initial pore pressure field, which can be calculated by the water pressure. While the water level was set on the ground surface, and the water pressure at any depth \(h\) is \(\gamma_{w}h\).
## 4 Experimental Results and Analysis
GMSD, primarily assessing image gradient magnitude similarity as an objective metric, does not fully encompass subjective image quality perception, which is influenced by factors like color, contrast, and noise. Hence, different GMSD scores may occur for identical MOS values. Similar issues are observed with the IFC metric. In contrast, GSM, MAD, MSSIM, SSIM, VIF, and VSI metrics exhibit relatively better performance, showing more pronounced growth trends in scatter plots and fitted curves. Among them, SSIM performs notably well despite its high data dispersion. Conversely, PSNR performs the least favorably, analyzing images solely from a pixel perspective and neglecting their multidimensional attributes, diverging from real-world conditions. Clearly, individual evaluation methods yield notably dispersed results in MOS scatter plots, lacking definitive trends. Further curve fitting analysis underscores the disparity between subjective and objective assessments, distinctly highlighting substantial differences between objective quality assessment results and subjective ratings. Figure 17: A scatter plot of subjective scores versus predicted scores for objective evaluation metrics in the TID2013 database: (**a**) GMSD, (**b**) GSM, (**c**) IFC, (**d**) MAD, (**e**) MSSIM, (**f**) PSNR, (**g**) SSIM, (**h**) VIF, and (**i**) VSI.
## 5 Numerical Results and Performance Analysis
### Numerical Results
#### 5.2.3 Substrate Link Load  
For evaluating the resource utilization outcomes, we compare the average substrate link load among the proposed scheme, NSDP and RFPA schemes in three-layer networks over increased node connectivity. As expected, our model presents a better performance compared to others, trying to balance resource utilization in the whole transport network. In Figure 9a, three models show similar behavior on nearly 100% allocating the access layer network resources at the beginning. However, as the connectivity of nodes increases, the resource utilization of our model drops significantly in the access layer network to avoid network overload. It can be proved that our model considers the network load, instead of over-occupied the access layer network resources for reducing transmission distance. In the aggregation and core layer networks, Figure 9b,c present that the network load in aggregation layer network is high in RFPA scheme, whereas the powerful computation resources in core layer network are disregarded. In our model and NSDP scheme, the trend of resource utilization is similar in the two-layer networks at the beginning. Nevertheless, one interesting observation is that the gap between our model and NSDP widens when we increase the node connectivity, especially in the core layer network. It reveals that our model considers network capacity and link bandwidth jointly, tries to evenly deploy three-layer network resources to balance the network load with a better allocation principle. Compared with over-deploying access layer network resources in NSDP and RFPA schemes, our model tries to reserve more edge DC resources and evenly deploy three-layer network resources.  
Figure 8: Transport network traffic distribution over s-VCC, eRTC and eMBB vs node connectivity in access layer network.
## 3 Experimental Results  
### Analysis of Subjective Scale Scores  
1. Validation of Fatigue Induction via AX-CPT Task: To verify the effectiveness of fatigue induction in this experiment, the Stanford Sleepiness Scale (SSS) was used. As normality testing indicated that the data did not follow a normal distribution, the Wilcoxon signed-rank test, a non-parametric method, was employed to analyze the pre- and post-task SSS scores. Results showed that the post-AX-CPT task scores (mean = 6.13, SD = 1.12) were significantly higher than the pre-task scores (mean = 2.49, SD = 1.23) (\(p\) < 0.001), indicating that the 90 min AX-CPT task successfully induced fatigue in participants.
2. Analysis of NASA-TLX Scale Scores: A two-factor repeated-measures ANOVA was conducted on NASA-TLX scores, with the factors of fatigue state (non-fatigue vs. fatigue) and task type (task interruption vs. pause interruption). The results revealed a significant main effect of fatigue (\(F\)(1, 48) = 17.59, \(p\) < 0.001), with the subjective mental workload significantly higher in the fatigue state than in the non-fatigue state. The main effect of task type was also significant (\(F\)(2, 80) = 32.15, \(p\) < 0.001), showing that the mental workload during task interruptions was higher than during pause interruptions.
# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 4 Discussion
### How Large is the Uncertainty Related to the Long-Period Flux-Gap-Filling?
Compared with the random flux measurement uncertainties (29 g C m\({}^{-2}\) year\({}^{-1}\) in GPP, 40 g C m\({}^{-2}\) year\({}^{-1}\) in RE, 25 g C m\({}^{-2}\) year\({}^{-1}\) in NEE, and 5.2 mm year\({}^{-1}\) in ET, quantified according to [PERSON] and Sims [36] and [PERSON] [35]), the uncertainties in GPP, NEE, and ET due to the long-period flux-gap-filling were much higher than those due to random sampling, whereas the uncertainties related to the selection of the machine learning technique (32 g C m\({}^{-2}\) year\({}^{-1}\) in GPP, 43 g C m\({}^{-2}\) year\({}^{-1}\) in RE, 16 g C m\({}^{-2}\) year\({}^{-1}\) in NEE, and 8.4 mm year\({}^{-1}\) in ET) were comparable to the random flux measurement uncertainties. As expected, the uncertainty in annual NEE due to the long-period flux-gap-filling was almost twice as high as the additional uncertainty of 30 g C m\({}^{-2}\) year\({}^{-1}\) caused by a week-long gap during spring green-up in a deciduous forest (with mean annual GPP of 1339 g C m\({}^{-2}\) year\({}^{-1}\), RE of 1165 g C m\({}^{-2}\) year\({}^{-1}\), and NEE of -174 g C m\({}^{-2}\) year\({}^{-1}\)) when traditional nonlinear models (derived from single-year data) were applied for gap-filling [35].
# A Lightweight Chaotic Map-Based Key Agreement Scheme for the Advanced Metering Infrastructure
## 5 Security Analysis and Comparison
### Adversary Model  
In the AMI, entities often communicate and transmit data through insecure channels, which are vulnerable to various attacks. In this paper, we choose the well-known and widely accepted CK adversarial model, which defines the adversary \(A\) that can completely control the channel during the entire communication process. Its attack capabilities include listening, modifying, deleting and injecting messages. In addition, the adversary can further obtain the private secret value of the entity. In order to prove the security of this scheme under the CK model, we establish the following definition:  
_Participants_: The scheme includes two entities, \(SM\) and \(DC\). Any other entity is recorded as \(I\).  
_Queries_: Opponent \(A\) can query participants to achieve the interaction between the two. The following queries are used to simulate the attack ability of \(A\).  
\(Execute(SM_{i},DC_{j})\): \(A\) can obtain all communication records between \(SM_{i}\) and \(DC_{j}\) through the query, which simulates passive attacks (such as an eavesdropping attack).  
\(Send(I,m)\): \(A\) carries out an active attack through the query. Adversary A sends a forged message \(m\) to entity \(I\), and \(I\) responds to \(A\), where \(m\) can be obtained by means of interception.  
\(Reveal(I)\): \(A\) can obtain the session key owned by \(I\) through the query.  
\(Corrupt(I)\): Through the query, \(A\) requests \(I\) and obtains its long-term secret value.  
\(Test(I)\): This query measures the semantic security of the session key. The adversary \(A\) sends a single test query to the oracle, and the oracle begins to toss a coin \(b\in\{0,1\}\) after receiving the query. If \(b\) is \(0\), a random key of the same size as \(SK\) is returned to \(A\). If \(b\) is \(1\), the real \(SK\) is passed to \(A\).  
\(h(m)\): After receiving the query from \(A\), \(I\) sends the hash value of the message \(m\) replaced by the random number as a response to \(A\).  
_Partnering_: Entities \(SM_{i}\) and \(DC_{j}\) are partners, when they verify each other and negotiate a consistent key.  
_Freshness_: For a session \(S\) held by participant \(I\), if the session \(S\) has generated a session key, and \(A\) does not issue a \(Reveal(I)\) query, and at most one \(Corrupt(I)\) query is performed, the session \(S\) is fresh.
# Degree-Constrained Minimum Spanning Hierarchies in Graphs
## 5 Algorithm Design
### Exact Solution
#### 5.1.4 Reconstruction of the Optimal Hierarchy  
The variable \(x(n,m)\) indicates how many times the arc \((n,m)\) is used in the directed solution. From the image, a directed DCMSH can be built. A directed, labeled tree can be first reconstructed, and then an undirected version can be obtained. Several directed trees can correspond to the image if the number of occurrences of some arcs is different from one. Let \(i(n)=\sum_{m\in\Gamma^{-(n)}}x(n,m)\) and \(o(n)=\sum_{m\in\Gamma^{+}(n)}x(n,m)\) be the numbers of incoming and outgoing arcs, respectively. Each node occurrence has one and only one predecessor (except the source). A node \(n\) must be duplicated \(i(n)\) times in the labeled tree (i.e., in the DCMSH). For each occurrence of \(n\), there is one and only one incoming arc. For the source, one occurrence has no predecessor and the number of occurrences is \(i(n)+1\).  
The successors can be arbitrarily associated with the occurrences of \(n\), but to respect the degree constraints, each occurrence of \(n\) can have at most \(D(n)-1\) successors. Property 4.3 in [17] indicates that for a node \(n\), an optimal hierarchy exists in which all occurrences of \(n\) except one have the maximal degree \(D(n)\). Following this property, the successors can be distributed between the occurrences of \(n\) such that \(D(n)-1\) successors are connected to each first \(o(n)-1\) occurrences of \(n\). A depth-first search type recursive algorithm can be used for the construction of a directed, labeled tree \(T_{d}\). Figure 5d shows the directed DCMSH built in the given graph. The labels of the nodes in the tree are the labels of the nodes in the image.  
Omitting the direction of arcs from \(T_{d}\), an undirected, labeled tree \(T_{u}\) is obtained.  
Theorem 4.1: _The labeled tree \(T_{u}\) corresponds to a DCMSH._  
Proof: Since the directed tree \(T_{d}\) (and consequently \(T_{u}\)) refers to the nodes in \(G\), and the arcs (the edges) in \(T_{d}\) (in \(T_{u}\)) correspond to arcs (to edges) in \(G_{d}\) (in \(G\)), neighbor nodes in \(T_{d}\) (in \(T_{u}\)) are neighbors in \(G_{d}\) (in \(G\)). As a result \(H_{d}=(T_{d},h,G_{d})\) and \(H=(T_{u},h,G)\) are hierarchies.  
The computed image covers the whole graph \(G_{d}\). Each node in \(V\) is associated with at least one node of \(T_{d}\) (and with at least one node of \(T_{u}\)). The hierarchy \(H\) spans the node set \(V\). By the construction, the nodes in the tree \(T_{u}\) respect the degree constraints (a node occurrence of the node \(n\in V\) has at most \(D(n)\) neighbors). So, \(H\) is a degree-constrained hierarchy spanning \(V\). It is with minimum cost. Its cost is the sum of the cost of edge occurrences. Because an edge \(\{m,n\}\) is present \(x(m,n)+x(n,m)\) times in \(H\):  
\[c(DCMSH)=\sum_{\{m,n\}\in E}c(m,n)\cdot(x(m,n)+x(n,m)).\]  
This sum is minimized by the ILP.
## 4 Conclusions  
The results of this study indicate that, as the operation time extended, significant changes occurred in the microbial community structure and the relative abundance of denitrifying functional microorganisms within the BSIS reactor dosed with 90 g/L of sponge iron. These changes strongly confirm that the addition of sponge iron can progressively enhance the system's denitrification performance as the operational cycles increase. This finding not only reveals the crucial role of microbial succession in the sponge iron-enhanced denitrification process but also highlights its immense potential for improving system efficiency, constituting an important innovation point of this study.  
The findings of this research provide significant theoretical guidance for optimizing the operating parameters of sponge iron-enhanced SBR systems and maintaining their long-term stable operation. For example, by real-time monitoring of the dynamic changes in key functional microbial communities, operational strategies can be adjusted in a timely manner to ensure the system consistently maintains high-efficiency denitrification performance. Looking ahead, the microbial response mechanisms revealed in this study offer a new perspective for the field. Future research could further explore the patterns of microbial community succession under different sponge iron dosages and operational conditions, as well as how to maximize the enhancement effect of sponge iron through more precise microbial regulation strategies. This will ultimately promote the wider application of this technology.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Biopreservatives
#### 3.2.1 Limonene  
Limonene is a natural monoterpene with antibacterial efficacy and has been widely used in the food industry as a preservative, antioxidant, and flavor. Its derivatives, such as menthol, carveol, and \(\alpha\)-terpineol, which have pleasant fragrances, special biological activities, and physicochemical properties, are also broadly used in the fields of food, pharmaceuticals, and cosmetics [115]. Cell factories offer a new approach to the sustainable production of limonene.  
In _S. cerevisiae_, [PERSON] et al. expressed truncated limonene synthase (tLimS) to de novo synthesize limonene from GPP. They further enhanced the limonene synthesis flux through a \"Push\(-\)Pull\(-\)Restrain\" strategy while simultaneously increasing the supply of acetyl-CoA and NADPH by regulating the glyoxylate cycle and pentose phosphate pathway. In addition, they reconstructed the MVA and limonene synthetic pathways in mitochondria. In order to alleviate the impact of limonene accumulation on cell growth, they also performed a two-phase scale-up fermentation and obtained a limonene titer of 2.63 g/L ultimately [91]. It appears that the titer of limonene in most cell factories has reached the cells' maximum tolerance to limonene. In order to enhance the tolerance of _Y. lipolytica_ to limonene, [PERSON] et al. identified five genes using a transcriptomic analysis. They found that overexpression of an unknown protein YAL10F19492p can increase limonene tolerance and increase the titer up to 8-fold. Then, they carried out short-term laboratory adaptive evolution and obtained strains with higher tolerance. Field emission scanning electron microscopy (FE-SEM) and propidium iodide (PI) analyses revealed that the cell wall and membrane of evolved strains were intact in the presence of limonene, while both the cell wall and membrane of the starting strain were damaged [92]. It would provide new perspectives for improving the sustainable production of limonene.