esa-sceva/satcom-chunk-collection huggingface dataset:
# High-Precision Depth Map Estimation from Missing Viewpoints for 360-Degree Digital Holography
## 1 Introduction  
A depth map image represents information related to the distance between the camera's viewpoint and the object's surface. It can be reconstructed based on the original (RGB color) image and generally has a grayscale format. Depth maps are used in three-dimensional computer graphics, such as three-dimensional image generation and computer-generated holograms (CGHs). In particular, phase information, which is an essential element for CGHs, can be acquired from depth maps [1, 2]. The 360-degree RGB images and their corresponding depth map image pairs are required to observe 360-degree digital holographic content. If a specific location does not have a depth map (missing viewpoint), its corresponding holographic 3D (H3D) scene will not be visible. Depth map estimation compensates for missing viewpoints and contributes to the formation of realistic 360-degree digital hologram content. In this study, we propose a novel method for learning depth information from captured multi-view points and estimating depth information from various missing viewpoints. An overall schematic of the proposed multi-view method is shown in Figure 1.
# Joint Resource Allocation and Learning Optimization for UAV-Assisted Federated Learning
## 2 System Model and Problem Formulation
### Network Model  
The UAV's hover altitude is \(h\) and angle is \(\theta\), \(\theta\) is the effective illumination angle of the UAV, so the coverage radius of the UAV is \(r~{}=~{}h\tan(\theta)\). Assume that all users within the coverage radius participate in FL training. The horizontal placement of the UAV \(m\) and user \(n\) are represented as \(\mathbf{q}_{m}=\{x_{m},y_{m}\}\) and \(\mathbf{q}_{n}=\{x_{n},y_{n}\}\), respectively. The distance between the UAV \(m\) and user \(n\) is \(d_{nm}=\sqrt{h^{2}+\|\mathbf{q}_{m}-\mathbf{q}_{n}\|_{2}^{2}}\), therefore, the channel gain of user \(n\) is \(g_{n}=g_{0}\Big{(}\frac{d_{nm}}{d_{0}}\Big{)}^{-\alpha}\), where \(g_{0}\) represents the channel power gain at \(d_{0}\) is 1 meter (m), the value is same as that in [27].
## 4 Experimental Results and Discussion
### Experimental Settings and Metrics  
In this paper, we use the large-scale Chinese word vector data open-sourced by Tencent AI-Lab Lab in 2018 with a dimension of 200, so we set the Embedding-Dim of all models to 200 and the Optimizer to Adam. All experiments were run on a machine equipped with a Tesla P-100 PCIE graphics card and an Intel(R) Xeon(R) CPU E5-2600 v4 @ 2.00 GHz model CPU. Through several experiments, the results achieved by the following parameter settings were found to be better under the available computing resources, as shown in Table 2, where the effects of the values of the negative feedback coefficient \(\lambda\) in Equation (37) and the positive feedback factor \(\alpha\) in Equation (40) on model \(F1\) are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Max Length & 256 \\ Learning Rate & 0.001 \\ batch-size & 32 \\ dropout & 0.1 \\ \(\lambda\) & 1 \\ \(\alpha\) & 0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental parameter setting.  
Figure 6: The number of various types of time in the datasets.  
Named entity identification includes both entity boundaries and entity types, so \"Exact-match Evaluation\" is used to evaluate the validity of the model [43]. This method recognizes an entity as correct only if both entity boundaries and types are true [44]. It involves three metrics: precision (\(P\)), recall (\(R\)), and \(F1\) and is calculated as shown in (41)-(43).  
\[P=TP/(TP+FP) \tag{41}\]  
\[R=TP/(TP+FN) \tag{42}\]  
\[F1=P\times R\times 2/(P+R) \tag{43}\]  
True Positive (\(TP\)): The number of entities that are predicted to be positive samples and are actually positive samples.  
True Negative (\(TN\)): The number of entities that are predicted to be negative samples and are actually negative samples.  
False Positive (\(FP\)): The number of entities that are predicted to be positive samples but are actually negative samples.  
False Negative (\(FN\)): The number of entities that are predicted to be negative samples but are actually positive samples.  
Precision (\(P\)) represents the accuracy of the prediction in the positive sample results; a larger value indicates that more of the predicted samples are correct. Recall (\(R\)) represents the probability of predicting a positive sample among the actual positive samples; a larger value indicates that more of the actual positive samples are predicted. The \(F1\) metric is the average of precision and recall, and a larger value indicates better model quality.
## 3 Results and Discussion  
### Model Training and Distillation Training Process  
During conventional training (see Figure 6), the YOLOv10l model exhibited a consistent decline in loss values, converging towards a minimum, while mAP and other key performance metrics progressively increased, approaching their peak. This training phase completed after approximately 210 epochs. The training dynamics for other models mirrored this pattern. In contrast, during distillation training, WD-YOLO, distilled from YOLOv10n, completed its training in just 153 epochs. This accelerated completion was due to the effective and reliable performance of the teacher model, enabling the student model to finalize its learning more quickly and converge ahead of schedule. Post-convergence, WD-YOLO achieved an accuracy of 0.604 and an mAP50 of 0.654, surpassing those of the YOLOv10l model. These results validate the integration of novel modules such as DSConv and WTConcat, confirming that the model sustains high recognizability and precision. Furthermore, with its base as an N-type model, WD-YOLO contains significantly fewer parameters than its teacher model YOLOv10l--approximately only one-ninth.
## 2 Challenges, Trends and Constraints in WSAN Hardware Design
### Open-Source-Hardware Architectures versus Open-Architecture Systems
#### 2.3.5 Robustness and Reliability Constraints  
Of course, all the aforementioned constraints can harm, in a lower or higher degree, the robustness and the reliability of the total system, but there are also some additional issues that relate to the real-life applications:  
1. _Lack of compliance to norms and regulations_: Because the majority of the existing OSEP solutions are considered as prototyping development tools, they are not tested and certified in terms of specific norms and regulations for particular application domains.
2. _Poor system's integrity and reliability:_ The absence of a central power management mechanism, the erroneous electromagnetic sources from sketchy handling of the unused BECs' pins (these pins may act as antennae), and the uncontrolled performance in the various shields from different vendors, constitute only some of the issues that are responsible for poor reliability. In addition, certain security issues may arise for some MCUs which are very popular in the OSH platforms [89].
3. _No form factor and encapsulation provision:_ Any WSAN application domain has its own particular requirements for the form factor and the encapsulation of the systems in order to facilitate the deployment and to ensure the longevity of the systems. The existing OSEP solutions do not care about the physical dimensions of the final implementation.
## 2 U-Net and Compressed Sensing  
### Speech Enhancement Based on U-Net  
In recent years, speech enhancement based on deep learning has made great progress, among which U-Net is a very classical model structure [11; 12; 13; 14; 15]. The essential components within this framework are encoder, decoder and skip connections. A brief overview of the model structure is shown in Figure 1. The encoder mainly down-samples the input speech to obtain higher level speech features. The decoder is the reverse process of the encoder, which up-samples speech feature maps and finally outputs the enhanced speech with the same length as the original input; The skip connections combine each encoding layer with its corresponding decoding layer. They help to retain the features of each encoding layer to the decoding layer. The transitional operations in Figure 1 can generally adopt modules such as convolution, LSTM and attention mechanism, etc., which can further pay attention to the characteristic information output by the encoder.  
Part of U-Net-based speech enhancement models rely on ordinary convolution operations in the codec [4; 5; 6; 8; 15], the contextual information of the speech may be ignored and detailed features may be lost. To tackle these problems, a self-attention mechanism was added to the U-Net based generator to pay attention to the non-local detail features of speech [7]. Although the enhancement performance of this model was improved, the structure of attention was more complicated, which increased the complexities of the model. [PERSON] et al. [13] proposed Wave-U-Net for speech enhancement based on U-Net structure of audio source separation, which used linear interpolation [12]. Their experiments demonstrate the effectiveness of Wave-U-Net. However, high-level features of speech were extracted, while the detailed features were also ignored, during down and up-sampling. In [14], local self-attention was added to skip connections of U-Net to focus on the detailed features of each encoding layer, namely Attention-Wave-U-Net. It consisted of 12 encoding layers and 12 decoding layers. It achieved a high segmental SNR score, but the overall enhancement quality requires further study. In [15], the codec layer was reduced and a variety of data augmentation methods were used to achieve a good denoising effect, which also brings inspiration to this paper. However, the codec in [15] relies on ordinary convolution. In this paper, we aim to further improve the speech enhancement performance of U-Net under the premise of reducing parameters as much as possible. Therefore, we use 5 encoding layers and 5 decoding layers as in [15]. The time-domain dilated residual blocks are constructed to focus on the detailed features and speech context for down and up-sampling. A lightweight Shuffle Attention [25] is applied to the final output of the encoder. It focuses on speech feature information and suppresses the residual  
Figure 1: The architecture of U-Net.  
noise after down-sampling. The specific structure of our model will be introduced in detail in Sections 3.1 and 3.2.
# Biosynthesis of Edible Terpenoids: Hosts and Applications
## 3 Application of Terpenoid Microbial Cell Factories in the Food Industry
### Acting as Food Fortifiers and Dietary Supplements
#### 3.3.1 Lycopene  
Lycopene is a red C\({}_{40}\) tetraterpene, consisting of eight isoprene units (C\({}_{5}\)) linked head-to-tail. It is capable of quenching singlet oxygen and scavenging free radicals to prevent some malignancies and support cardiovascular health [119]. It was reported that lycopene exhibits the highest antioxidant activity among the carotenoid group [120]. Thus, it is widely used as a colorant and functional food. However, the chemical synthesis of tetraterpenes faces difficulties in stereoselectivity and separation [120]. Therefore, many attempts have been made to construct sustainable production cell factories for lycopene.  
In nature, some microorganisms, such as _R. toruloides_ and _Blakeslat trispora_, possess carotenoid biosynthesis pathways and can synthesize mixtures of carotenoids, including lycopene, \(\beta\)-carotene, and astaxanthin. Traditionally, these microorganisms can be directly used for the fermentative production of lycopene. However, it is very difficult to improve the yield and purity of a particular kind of carotenoid. With the advancement in enzyme function mining and metabolic mechanism analysis, researchers focused on integrating heterologous lycopene pathways into non-carotenoid-producing hosts to enhance the titer and yield. _E. coli_ and _S. cerevisiae_, as classic model microorganisms, have been used for large-scale synthesis of lycopene successfully [121].  
[PERSON] et al. developed a high-throughput screening method dependent on the color of lycopene. By using color as an indicator, they performed random mutations and targeted mutagenesis on the rate-limiting enzyme isopentenyl diphosphate isomerase (IDI) and obtained a triple mutant IDI\({}^{\text{L141H}}\)/Y195F/W256C with 2.53-fold higher catalytic activity. Replacing the wild-type IDI with IDI\({}^{\text{L141H}}\)/Y195F/W256C, _E. coli_ containing the complete lycopene synthetic pathway produced 1.8-fold more lycopene, exceeding 1.2 g/L [95]. In order to break the yield bottleneck, [PERSON] et al. performed atmospheric and room-temperature plasma (ARTP) mutagenesis and H\({}_{2}\)O\({}_{2}\)-induced adaptive laboratory evolution (ALE) to improve the supply of FPP. Inspired by the transcriptome analysis of the high-yield mutant strain, they integrated another copy of the \(TmCrtE\) gene to drive GGPP synthesis. In a 7 L fed-batch fermentation, the lycopene achieved the highest level, at a titer of 8.15 g/L [96].
## 2 Methodology
### Validation  
To assess the numerical models, Figure 7a compares the average heat transfer coefficient of the minichannel heat sink predicted by the single-phase approach using water as the working fluid against experimental results recorded by [PERSON] and [PERSON] [25]. The single-phase approach predictions are shown to follow reasonably well the experimental results, with a 16.7% maximum discrepancy at the Reynolds number of 92. In addition, Figure 7b compares the friction factor from plain water flowing through the minichannel single-phase simulation against the friction factor correlation for laminar square duct flows, the Darcy-Weisbach friction factor \(f=62.2\)\(Re^{-1}\)[55]. As this friction factor correlation applies to a fully developed laminar duct flow, \(f\) was estimated from the pressure drop between the end of the hydrodynamic entrance region and the minichannel outlet. The hydrodynamic entrance region length \(L_{h}\cong 0.05 ReD_{h}\)[55]. There is good agreement in trend and values between the numerical predictions and the empirical correlation, which both follow the \((1/Re)\) proportionality.  
The two-phase mixture model was run using water for both phases and produced the same predictions for the average heat transfer coefficient and for the friction factor as the single-phase approach, over the range \(92\leq Re\leq 455\).  
Figure 6: Residuals history obtained using the two-phase mixture model at \(Re=455\) with \(\alpha_{nf}=4\%\) and \(\Phi=25\%\).
## 5 Results and Discussion
### Ablation Analysis
#### 5.1.1 Ablation Analysis of Temporal Encoding Backbone  
Rows \(6\)\(\sim\)\(8\) investigate the impact of different temporal backbones (Section 3) separately. The average differences between BLSTM, the fully convolutional network, and Transformer under original CTC across the four tested benchmarks were:  
\[\Delta_{Base}(6)=(\overline{\Delta_{F.Acc}}=-11.54\pm 2.53, \overline{\Delta_{mAP}}=-10.05\pm 2.69,\overline{\Delta_{Edit}}=-9.07\pm 2.69)\] \[\Delta_{Base}(7)=(\overline{\Delta_{F.Acc}}=-6.46\pm 1.42, \overline{\Delta_{mAP}}=-5.41\pm 1.45,\overline{\Delta_{Edit}}=0.26\pm 0.07)\] \[\Delta_{Base}(8)=(\overline{\Delta_{F.Acc}}=3.58\pm 0.78, \overline{\Delta_{mAP}}=3.20\pm 0.85,\overline{\Delta_{Edit}}=8.83\pm 2.62)\]LSTM performed worse than the fully convolutional network, even though the recurrent model has full context of every decoding time step compared to the convolutional network that only looks at a limited time window of the input. This is in accordance with current trends shifting from recurrent networks towards purely convolutional/self-attentional models in other related domains, such as translation [46; 47] and speech [48; 49]. Dedicated explorations [50; 51] blame this inferiority to the inherent limitations of recurrent structures. Although the gating mechanism alleviates the difficulty of gradient propagation, the maximum memory is still restricted to a limited distance, usually not exceeding \(\Theta(10^{2})\) time steps. The fully convolutional model has a smaller number of parameters and trains faster than BLSTM and bidirectional Transformer; the best-performing backbone was the Transformer.  
Training Time  
Transformer and the fully convolutional network took approximately the same amount of time to compute a batch of 32 samples (Table 3). This is in accordance with the theoretical complexity (\(\Theta(Td^{2})\)) for each layer of both models, where \(d\) is the dimension of channels. Transformer has fewer channels (512) for every self-attention block, but it is in effect a deeper model, with \(3\times(6+6)=24\) layers in total. However, the fully convolutional network took fewer iterations to converge, completing the full curriculum in 3.5 days, compared to the 14 days for Transformer. This may be due to the more complex contraction among the self-attention queries, keys, and values during the gradient propagation and weight updating. In contrast, the fully convolutional model has no reverse connections among learnable modules given a fixed context. The BLSTM naturally took more time to run one batch, since the computations within its layers have to be executed sequentially; consequently it converged in almost double the clock time of the fully convolutional network.
## 4 Conclusions  
The most recent findings in our ongoing research continue to support the effectiveness of the innovative deaeration method for minimizing the dissolved oxygen concentration, thereby enhancing the stability of the bioactive compounds during storage.  
Additionally, our investigations have revealed that flow microwave pasteurization remains a highly promising alternative to traditional thermal pasteurization for the preservation of NFC apple juice. This method ensures the production of a safe and stable product, achieving nearly 100% inactivation of oxidoreductase enzymes. Notably, microwave pasteurization in a continuous flow system consistently leads to NFC apple juice with improved physicochemical and sensorial properties, including higher polyphenol content and antioxidant activity when compared to traditional pasteurization. This enhanced quality of the juice persists throughout the entire storage period under typical conditions, although certain phenolic components may exhibit variations.  
The latest conclusion from current research underscores the significant advantages of the flow microwave pasteurization technique as a valuable alternative to traditional thermal pasteurization. This technology enables the retention of higher levels of bioactive ingredients, contributing to the overall maintenance of the superior sensory quality in preserved NFC apple juice. The developed deaeration technology can also contribute to energy savings in fruit juice production. The lower the dissolved oxygen in the product,  
Figure 3: Sensory quality of fresh apple juice and TP and MFP-preserved apple juice (overall quality assessment in a six-point scale). Mean values denoted with the same letters do not differ significantly statistically, \(p\leq 0.05\).  
the better the thermal conductivity, leading to more efficient attainment of the desired pasteurization temperature.
## Case Study
### Verification of the Proposed Method  
In order to verify the accuracy of the proposed method in Equation (24), the Monte Carlo simulation method is used. The analytical and simulated reliabilities are expected to agree well with each other to demonstrate the applicability of Equation (24). The basic idea of the simulation-based approach is to generate a set of random lateral displacements distributed along the vertical direction of the retaining wall, and compare each of them with the predefined threshold to determine the structural state (failure or survival). For the \(j\)th simulation run (\(j=1,2,\ldots q\), where \(q\) is sufficiently large), the procedure is summarized as follows.  
1. Let \(r_{j}=1\), which becomes 0 if the \(j\)th sampled scenario of lateral deformation leads to structural failure (i.e., maximum lateral displacement exceeds the threshold).
2. Calculate the displacement distribution along the vertical direction of the retaining wall at an interval of 5 mm (excluding the monitoring points) using Equation (14).
3. Generate a set of correlated zero-mean normal random variables with a standard deviation of \(\sigma_{Y}\) using the Nataf transformation method [32], and add them to the sample values from Step 2 respectively. This is to obtain a set of displacement values that are approximately continuous along the depth and have the same statistical characteristics as those used in the analytical approach.
4. Sample a realization of normally distributed \(\epsilon\) with a mean value of 0 and a standard deviation of \(\sigma_{e}\). Use Equation (22) to compute \(k_{i}\) and subsequently utilize Equation (21) to calculate each \(\Delta_{i}\), where \(i=1,2,\ldots n\).
5. The sampled data at depth \(d_{i}\) is calculated as \(y_{i}+\Delta_{i}\) for \(i=1,2,\ldots n\).
6. Combining those sampled data in Steps 3 and 5, for each of them, if the displacement exceeds the predefined limit, then set \(r_{j}=0\).
7. Record \(r_{j}\). With \(q\) simulation runs, the simulated deformation reliability is approximated by \(\frac{1}{q}\sum_{j=1}^{q}r_{j}\).  
The analytical and simulation-based reliabilities with different displacement thresholds are presented in Figure 6 for the purposes of comparison. The number of simulation runs is \(q=10^{6}\) for each case. It can be seen that at the upper tail (e.g., \(\geq\) 73 mm), the results from both approaches agree well with each other, which indicates the accuracy of the proposed method in Equation (24). At the lower tail, the reliability associated with the analytical approach is slightly greater than that of the simulation-based approach due to the impact of [PERSON] assumption in the analytical formulation (c.f. Section 3.1). Nonetheless, as the emphasis is on the behavior at the upper tail for most engineering cases, the proposed reliability assessment method in Equation (24) can be used to reasonably estimate the deformation safety level of retaining structures.  
Figure 5: Dependence of the \(R\)-squared on the fitting order (c.f. \(m\) in Equation (14)).