esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results  
### Experiment Setup  
The developed positioning system was tested in an experimental work area with a floor space area of 2.5 m \(\times\) 4.5 m, and the distance from LED luminaires and camera images sensor was at a height of 2 m from the ground to the ceiling. The experimental setup of the proposed system comprised six LEDs mounted on the ceiling. The LEDs were spaced 2 m from each other and 0.25 m from the edge of the experimental region. Each of the LED luminaires transmitted their individual coordinate information to the smartphone camera image sensor, and the image sensor continuously received this coordinate information from the ceiling LED luminaires. Figure 9 shows the experimental area, where Figure 9a,b represents the measurement area and the real-time experimental environment, respectively.
# Effects of Drying Process on the Volatile and Non-Volatile Flavor Compounds of _Lentinual edodes_
## 2 Materials and Methods
### HS-SPME-GC-MS Analysis  
The volatile compounds were analyzed following [PERSON] et al.'s methods with a minor modification [17]. An SPME (solid-phase micro-extraction) manual device equipped with a 50 \(\upmu\)m/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Bellfonte, PA, USA) was used to extract volatile compounds from samples. Mushroom homogenate (1.8 g mushroom powder in 20 mL NaCl saturated solution) was added into 40 mL-vial containing a magnetic stirring bar. After being spiked with 25 \(\upmu\)L of cyclohexanone (0.95 mg/mL of ethyl alcohol) as internal standard, the vials were immediately seal with a PTFE septa (Supelco, Bellfonte, PA, USA). Samples were balanced at 50 \({}^{\circ}\)C for 15 min, then the fiber was inserted into the vial to extract volatile compounds for 1 h. Finally, the fiber was inserted into the injection port of GC and desorbed for 5 min under the splitless mode.  
An Agilent 7890A GC coupled with an Agilent 5975 MS was used to analyze volatile compounds. A HP-5 MS fused silica capillary column (30 m \(\times\) 0.25 mm I.D., 0.25 mm film thickness, Agilent technologies, Santa Clara, CA, USA) was installed in GC. GC conditions were set as follows: the carrier gas of helium with a flow rate of 1 mL/min; the injector temperature was 250 \({}^{\circ}\)C; the oven temperature programming was set at 40 \({}^{\circ}\)C for 3 min initially, then 3 \({}^{\circ}\)C/min to 150 \({}^{\circ}\)C, held for 1 min, finally 5 \({}^{\circ}\)C/min to 220 \({}^{\circ}\)C, maintained for 2 min. The ionization source temperature was set at 230 \({}^{\circ}\)C. The MS was obtained by electron impact mode at 70 eV in a range from 30 amu to 395 amu.  
Volatile compounds were identified by Kovats retention index (RI) and the database (Wiley7.0 and NIST05) [15]. The Kovats retention index (RI) of unknown compounds was calculated using n-alkanes (C7-C30) injected under the same conditions. The quantity of volatiles was calculated with the internal standard (cyclohexanone) [18].
## 5 Discussion
### Ablation Study on Structural Attention Modules  
To validate the effectiveness of the proposed Spatial Sparse Structural Attention (SSSA) and Temporal Sparse Structural Attention (TSSA) modules, we conducted comprehensive ablation experiments on the ModelNet dataset. The experiments evaluated three configurations: (1) an SSSA-only model with spatial attention only, (2) a TSSA-only model with temporal attention only, (3) and the full model with both the SSSA and TSSA modules. All experiments used the surround-view capturing scheme with identical training protocols.  
As shown in Table 6, we conducted ablation experiments using the MSSAViT-SV model as the benchmark model. On ModelNet10, MSSAViT-SV with only the SSSA module achieves 94.88% average class accuracy and 95.04% instance accuracy, while MSSAViT-SV with only the TSSA module demonstrates superior performance with 96.79% and 97.25%. Notably, their synergistic integration in the full MSSAViT architecture yields significant enhancements, reaching the best accuracies of 98.40% in average class accuracy and 98.46% in average instance accuracy, confirming the complementary nature of spatial- and temporal-scale modeling. We also find similar trends on the larger-scale ModelNet40 benchmark. The combined model outperforms the SSSA-only model with 3.26% average class accuracy and 1.94% average instance accuracy, and the TSSA-only model with 1.86% average class accuracy and 1.42% average instance accuracy. This progressive improvement across both  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Output Size of the FEM** & **Total Parameters** & **Trainable Parameters** \\ \hline \(7\times 7\times 160\) & 6.1 M & 1.5 M \\ \hline \(14\times 14\times 112\) & 7.7 M & 3.1 M \\ \hline \(28\times 28\times 40\) & 19.3 M & 14.7 M \\ \hline \(56\times 56\times 24\) & 152.4 M & 147.9 M \\ \hline \hline \end{tabular}
\end{table}
Table 5: Impact of FEM output sizes on model parameters.  
datasets demonstrates the scalability of handling fine-grained inter-class variations. The experiments conclusively establish that the TSSA module contributes more dominantly to semantic feature discrimination than the SSSA module, and the joint optimization enables global temporal and local structural context aggregation beyond the single linear combination effects.  
We conducted an in-depth analysis of the impact of the SSSA and TSSA modules on the recognition results using ModelNet10 as an example. Figure 8 shows the confusion matrices of the SSSA-only and TSSA-only models. Comparative analysis revealed that the largest recognition errors were concentrated in the desk, dresser, and nightstand categories. In Figure 9, we present typical samples of these three categories. Their main characteristic is that there is great similarity in appearance and structure, especially from the sides and top view. However, the significant difference that distinguishes a desk from the other two is the hollowed-out area under the desktop. In
# A Novel Cryptography-Based Architecture for Secure Data Asset Sharing and Circulation Systems
## 4 Security Technical Framework
### Cryptography Service Layer  
Through the mechanism of micro-service and containerization based on cryptography functions, six basic cryptography services are provided, such as identity authentication, access control, secure storage, operation management, secure return and key management, so as to ensure the whole life cycle security of data assets in uncontrolled environments. In addition, the function of a cryptography service can be adjusted and continuously expanded according to the changes of security protection requirements in the process of data sharing and circulation. In addition to data encryption, it can also provide support for data authentication, data transmission, data storage, data traceability, sharing and utilization.
# A Tale of Many Networks: Splitting and Merging of Chord-like Overlays in Partitioned Networks
## 3 Our Merging Approach: Ring Reunion Algorithm
### Parallelization  
Inasmuch as we focus on merging algorithms which act locally in a ring without requiring knowledge about the global ring, these mergers can be improved by starting multiple merger instances simultaneously and in parallel. While this drastically improves the merging speed, it is very important that all merger instances terminate eventually and do not hinder each other in their performance.  
To improve the Ring Reunion Algorithm, we present an extension of the algorithm to start multiple merger instances simultaneously and independently (see for the extended Algorithm the Pseudo Code in Figure 7). The method DISTRIBUTE can be started every time a merger instance is started (line 2). As a result, the initiator node informs its further-most finger contacts to initiate a new merger instance, see also Figure 8. Then, those finger contacts inform their second furthermost finger contacts, respectively; this continues, and thereby, the information to start new instances is equally distributed among all nodes in the initiator node's ring (lines 7-10). These nodes are informed about the presence of a second overlay construct and start a merging attempt in this second overlay upon receiving related contact information. Concurrently, a distributed counter is decreased with each message, so that exactly \(2^{\lambda}-1\) additional merger instances are initialized, where \(\lambda\) is a fixed parameter (line 7).  
Hereafter, all nodes that have received a DISTRIBUTE message start the MERGE method (line 11) and begin to merge a given ring (lines 14-24). Figure 8 demonstrates that, with each step, \(i\), \(2^{i}\) nodes are asked to start a merger instance, so that after 3 steps, \(2^{3}=8\) instances are started. The terms _Reunion(i)_, e.g., Reunion2, Reunion3, etc., in our evaluation correlate to the exponent \(i\).  
Figure 6: Node tries to merge own ring. Merger stops immediately when it receives itself as contact.
# Genomic Characterization of a Halovirus Representing a Novel Siphoviral Cluster
###### Abstract  
Salt mines are a special type of hypersaline environment. Current research mainly focuses on prokaryotes, and the understanding of viruses in salt mines remains limited. Understanding viruses in hypersaline environments is of great significance for revealing the formation and maintenance of microbial communities, energy flow and element cycling, and host ecological functions. A phage infecting _Halomonas titanica_ was isolated from Yipinglang Salt Mine in China, designated _Halomonas titanica_ phage vB_HiS_YPHTV-1 (YPHTV-1). Transmission electron microscopy revealed that YPHTV-1 had an icosahedral head with a diameter of 49.12 \(\pm\) 0.15 nm (n = 5) and a long nonconctractile tail with a length of 141.7 \(\pm\) 0.58 nm (n = 5), indicating that it was a siphovirus. The one-step growth curve showed that the burst size of YPHTV-1 was 69 plaque forming units (PFUs) cell\({}^{-1}\). The genome of YPHTV-1 was 37,980 bp with a GC content of 36.2%. The phylogenetic analysis of the six conserved proteins indicated that YPHTV-1 formed a cluster with _Bacillus_ phages and was separated from phages infecting _Halomonas_. The average nucleotide identity (ANI), phylogenetic, and network analyses indicated that the phage YPHTV-1 represented a new genus under Caudoviricetes. In total, 57 open reading frames (ORFs) were predicted in the YPHTV-1 genome, 30 of which could be annotated in the database. Notably, several auxiliary metabolic genes were encoded by YPHTV-1, such as ImmA/ IrrE family metalloendopeptidase, mannose-binding lectin (MBL) folding metallohydrolase, M15 family of metal peptidatases, MazG-like family protein, O antigen ligase, and acyltransferase. These genes potentially enabled the host bacterium to resist ionizing radiation, ultraviolet light (UV), mitomycin C, \(\beta\)-lactam antibiotic, high osmotic pressure, and nutritional deficiencies. These findings highlight the role of haloviruses in the life cycle of halobacteria.  
H. 2023 15 1392 19 19 2023 Article  
G. 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Multi-Source HRRP Target Fusion Recognition Based on Time-Step Correlation
## 2 Proposed Model
### Feature Extraction Network
\(F\) is a linear transformation function that divides the input \(\mathbf{H}\) into \(g^{l}\) non-overlapping groups so that \(\mathbf{H}\) can be written as \(\mathbf{H}=\left[\mathbf{H}_{1},\mathbf{H}_{2},...,\mathbf{H}_{g^{l}}\right]\), and the linear layer transforms each \(\mathbf{H}_{i}\) by the weights \(\mathbf{W}_{i}^{l}\) and \(\mathbf{b}_{i}^{l}\) to obtain an output \(\mathbf{Y}_{i}^{l}=\mathbf{H}_{i}\mathbf{W}_{i}^{l}+\mathbf{b}_{i}^{l}\). The output \(\mathbf{Y}_{i}^{l}\) of each group is then stitched to produce the final output \(\mathbf{Y}^{l}\). Figure 4: Comparison of the standard Transformer model encoder structure (**left**) and the Transformer model encoder structure combined with GLTs (**right**). The GLTs module concatenates the output vector \(\mathbf{Y}^{l-1}\) of layer \(l{-}1\) with input \(\mathbf{H}\) through a mixer and disrupts the order. Input it into the linear layer to extract more stable features of the target. The calculation process of the mixer is as follows:  
\[MIX\left(\mathbf{H},\mathbf{Y}^{l-1}\right)=\textit{shuffle}\left[\mathbf{H},\mathbf{Y}^{l-1}\right] \tag{9}\]  
where \(\textit{shuffle}(\cdot)\) is the random grouping of the input features and the output after stitching. Taking the linear transform group with three layers in the upscaling stage as an example, the workflow of the mixer is shown in Figure 5.  
Because of the superior performance of GLTs, the Self-Attention module is used instead of the Multi-Head attention module:  
\[\mathbf{u}_{t}=\textit{Self}_{attention(\mathbf{Y}^{l})}=\textit{Attention}(\mathbf{Q}, \mathbf{K},\mathbf{V})=\textit{softmax}\left(\frac{\mathbf{Q}\mathbf{K}^{T}}{\sqrt{d_{k}}}\right) \tag{10}\]  
where \(\mathbf{Q},\mathbf{K},\mathbf{V}\) are obtained by linear transformation of vector \(\mathbf{Y}^{l}\). After completing the calculation of attention, the next module is the lightweight feedforward neural network layer. This module contains two linear transformation layers and one nonlinear layer to transform the input feature vectors. In this module, the activation function of the nonlinear layer uses the GELU function, and the dimension transformation of the linear transformation layer is first reduced and then expanse:  
\[\mathbf{v}=\textit{Feedforward}(\mathbf{u})=((\mathbf{u}W_{1}+\mathbf{b}_{1})*\phi(\mathbf{u}W_{1 }+\mathbf{b}_{1}))\mathbf{W}_{2}+\mathbf{b}_{2} \tag{11}\]  
where \(\phi(x)\) is the cumulative distribution function of the normal distribution.
## 3 Results and Discussion
### Effect of Agar Concentration  
Figure 5 shows the results of callus induction in meristem and stipe using PESI solid medium with different agar concentrations (1.2% and 1.5%). The highest callus induction rate (39.85 \(\pm\) 8.27%) was observed in stipe at 1.5% agar concentration, followed by meristem (33.44 \(\pm\) 6.50%) at 1.5% agar and 18 \({}^{\circ}\)C. Subsequent experiments utilized 1.5% agar concentration.  
Figure 4: Callus induction in meristem (**a**) and stipe (**b**,**c**) on PESI solid medium supplemented with 1.5% agar.  
Figure 3: Effect of media type on callus induction. Values are means \(\pm\) standard errors (S.E.) of 10 individual replicates per experiment (\(n\) = 10). PESI: Provasoli ’s enriched seawater medium; ESAW: Enriched artificial seawater medium; ASP2: Artificial enriched seawater medium; VS: Von Stosch’s enriched seawater medium. *: Indicates significant differences using [PERSON]’s HSD test using a one-way ANOVA test at \(p\) < 0.05.
## 3 Methods
### Supervised Feature Representation  
Supervised feature representation focuses on extracting discriminative features and learning class-specific representations by mining the labeled information in the data. Supervised classification learning (SCL) aims to fully use labeled data to achieve effective feature representation. As shown in Figure 3, the supervised classification branch employs a ResNet18 as a backbone network to encode input data into high-dimensional feature representations. ResNet18 has an 18-layer architecture that balances computational efficiency and representational power. Its residual connections improve gradient flow for stable training. Unlike deeper variants such as ResNet50 or ResNet101, it reduces both computational cost and overfitting risk. Its proven performance across diverse classification tasks further underscores its suitability as an encoder.  
In the classification head, we designed a linear residual structure, as shown in Figure 3. The formula is shown in (3), where \(z_{k}\) is the final output feature, \(\bar{x}_{z}\) is the supervised encoder output feature, and \(n\) is the number of repetitions of the intermediate linear layer.  
\[z_{k}=\bar{x}_{z}+Linear(\bar{x}_{z},n) \tag{3}\]  
The linear residual structure increases the number of linear layers of the classification head through skip connections, and deepens the nonlinear transformation enables network to capture more complex feature patterns.  
The spectrogram data in this study show class imbalance that causes the trained classifier to favor the majority class and ignore the minority class samples, which can lead to distortion of probability output of a few classes, thus affecting the reliability of classification results. To address this issue, a balanced cross-entropy loss function is proposed. First, the weights of different categories are calculated based on their proportion in the dataset. Then, these weights are used to initialize the cross-entropy loss function.  
The number of samples in each category in the dataset is defined as \(c_{i}\), and the weight of category \(w_{i}\) can be calculated by Formula (4).  
\[\hat{w}_{i}=\frac{c_{i}}{\sum_{j=1}^{K}c_{i}} \tag{4}\]  
The weighted cross-entropy loss function can be defined as  
\[L_{supervised}=-\sum_{i=1}^{K}\hat{w}_{ij}y_{i}\log(\hat{y}_{i}) \tag{5}\]
## 4 Results and Discussion
### ORC Performances
#### 4.2.3 Cycle High Pressure  
The high pressure of the cycle, an important parameter of an ORC installation, is also impacted when using a zeotropic mixture compared to a pure fluid. For the experimental tests conducted in the mixture of NovecTM649 and HFE7000 with respective mass proportions of 80% and 20% and compared to the pure fluid NovecTM649, there is a reduction of about 7.5% in the high-pressure value for equivalent turbine power generation, as shown in Figure 11.  
Figure 10: The ORCs’ distribution according to their gross second law efficiency.  
Figure 9: The ORCs’ distribution according to their gross thermal efficiency.  
This pressure drop between the pure fluid and the mixture can also be a selection criterion in the choice of a mixture to be used in an ORC with an equivalent power generation range. Indeed, in an ORC, such criteria as the type of materials used for the components, the thickness of the pipes, or the safety devices are directly related to the pressure levels of the system and generate additional costs in the case of higher pressures.  
As observed in Figure 11, the high pressures for the mixture do not reach the same values as those obtained with the pure fluid. This pressure level decrease could be explained by different reasons. One a them is a lower mixture heat transfer coefficient, which is linked to the operating parameters, and the heat transfer area at the heat exchangers involves a high-pressure adaptation in the thermodynamic organic Rankine cycle. This hypothesis needs to be validated, due to possible interactions with other parameters, as the turbine's adaptation with the variation in the working fluid could also impact the high-pressure value. Further investigations are currently ongoing.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 3 Design of the HECRA
### Cluster Head Selection Phase  
In the initial state, after the node deployment is completed, each node has no information about the surrounding environment; at this time, each node broadcasts a \"HELLO\" message or the message content node ID information. When the node successfully receives the \"HELLO\" message, the sender's ID is input to the cache, and its node degree is increased by \(1\). The node's waiting time is set to the node with the maximum distance or transmission propagation delay.  
By calculating its threshold function, each node determines whether to act as a CH during the present round. This paper introduces the residual node energy and node degree influence factor on the basis of traditional LEACH, mainly considering the residual energy \(E\), and node degree \(Ne_{i}\) of node \(i\). This is due to the consideration of node residual energy, which can prioritize the nodes with higher residual energy to act as cluster heads, thus avoiding the low-energy nodes from exhausting their energy prematurely, resulting in a shorter network lifetime. Considering the node degree also ensures that the elected cluster head nodes have better connectivity and can communicate better with the surrounding nodes. The improved threshold function \(T_{i}^{\prime}\) is defined as follows:  
\[\left\{\begin{array}{l}T_{i}^{\prime}=\frac{F}{\max\_F}\\ F=\frac{p}{1-p\left(\text{mod}\frac{1}{p}\right)}+\lambda_{1}\frac{E_{i}}{E_{ init}}+\lambda_{2}\frac{Ne_{i}}{N}\end{array}\right. \tag{8}\]  
where \(r\) represents the number of network operation rounds, \(p\) indicates the percentage of expected CHs to the total number of network nodes in the current round, \(E_{init}\) is the initial node energy, \(N\) is the total number of nodes, \(\lambda_{1}\) and \(\lambda_{2}\) are the weighting coefficients, which are used to regulate the weight of the residual energy and the node degree, and \(\lambda_{1}+\lambda_{2}=1\) and \(\max\_F\) are the maximum values of \(F\).  
Then, node \(i\) generates a random number \(rand\) that is greater than 0 and less than 1 and determines whether it is a CH by comparing \(rand\) with the improved threshold function \(T_{i}^{\prime}\). By this calculation method, nodes with greater residual energy and higher degrees are more prone to being chosen as CHs, which can solve the problem of early death of CHs owing to rapid energy depletion and can also improve network communication efficiency, thus effectively prolonging the lifespan of the network.
# Invasive Prenatal Diagnostics: A Cornerstone of Perinatal Management
## 2 Techniques of Invasive Diagnostics
### Amniocentesis
#### 2.1.1 Genetic Analysis
The highest diagnostic yield was found in foetuses with head and neck abnormalities (40%) and skeletal abnormalities (39.1%). The overall additional diagnostic yield was 23.3%. On the other hand, [PERSON] et al. [95] and [PERSON] et al. [96] analysed the incremental diagnostic yield of WES among foetuses with increased NT and normal karyotyping and CGH without other structural anomalies. Those studies reported that between 4% and 8% of the analysed foetuses showed pathogenic variants detected exclusively by WES. Very little data are available on the incremental diagnostic yield of WGS over WES. A meta-analysis published in 2024 by [PERSON] et al. [97] showed an increased detection rate of pathogenic variants of 1%, which was not statistically significant. However, the turnaround time for WGS was shorter, and it required less DNA. Despite the lack of evidence demonstrating the superiority of WGS over ES, its diagnostic yield will most likely increase with our advancing understanding of pathogenic variants of non-coding regions, and, eventually, it will become the most informative technique. In response to the increasing accessibility and feasibility of exome sequencing (ES), the National Health Service England rolled out a rapid exome sequencing programme called the R21 pathway. It is undertaken for high-risk pregnancies in which genomic diagnosis could guide the management of the foetus. A sample obtained from CVS or anniocentesis is examined towards a nationally agreed panel of genes. Cases are selected within strict inclusion criteria, which provide optimal diagnostic yield for ES, and these are (1) foetuses with anomalies affecting multiple systems, (2) a suspected skeletal dysplasia, (3) large echogenic kidneys and a normal bladder, (4) major central nervous system anomalies in which a neural tube defect has been excluded, (5) multiple contractures (excluding bilateral isolated tailpes), (6) an NT greater than 6.5 millimetres and at least one additional anomaly and in which the CGH is normal, and (7) non-immune foetal hydrogs (fluid/oedema in at least two compartments detected at or after routine second-trimester scan) and a normal CGH. This approach guarantees an optimal diagnostic pathway for genetic disorders.