esa-sceva/satcom-chunk-collection huggingface dataset:
# Key Technologies of Photonic Artificial Intelligence Chip Structure and Algorithm
## 2 Hardware Structures for Linear Operation
### Photonic Feedforward Neural Network (PFNN)  
The feedforward neural network (FNN) is the simplest one-way neural network, including an input layer, hidden layer and output layer. The signal transmits unidirectionally from the input layer to the output layer. The essence of realizing the matrix multiplication  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network Scheme** & **MZI Array** & **SOA** & **MRR** & **3D Waveguide** \\ \hline PFNN & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ PCNN & \(\surd\) & & \(\surd\) & \\ PRNN & \(\surd\) & \(\surd\) & \(\surd\) & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The scheme of main neural network.  
function is to complete the multiplication and accumulation (MAC). PFNN can be realized by the MZI array, SOA, MRR and 3D waveguide.  
In the case of PFNN, the MZI is cascaded without reflection. The light stays in forward propagation. In PAICs, linear computing is implemented in the MZI array as shown in Figure 1[20]. The input optical signal with multiple channels propagates in the paralleled MZI waveguide. The phase changes when passing through the MZI array. In this way, the output matrix can be obtained by completing the linear operation. In 2017, Shen proposed and demonstrated the first photonic interference computing unit chip based on the MZI array[21]. The whole network uses an array of 56 MZIs and 213 phase shifters to complete the matrix operation through abundant phase changes.  
Semiconductor optical amplifier (SOA).  
In the basic idea of SOA, the MAC operation is realized by a single amplifier. Once the amplifier is cascaded, it enables a large-scale MAC operation. Generally, weighting is expressed by the attenuation or gain. At the output, the light of the different channels with multiple weighting is summed through the wavelength division multiplexer. Thus, SOA can complete the N \(\times\) M matrix multiplication. The FNN based on SOA is illustrated in Figure 2. By adjusting the drive current of the SOA, the gain coefficient changes. It can be considered that the variation of the gain coefficient corresponds to the change in the transmission matrix coefficient. Then, through the arrayed waveguide grating (AWG) or multimode interference optical coupler (MMI), the weighted results of multiple SOAs are summed. In this way, the weighted summation of each row in the matrix is obtained. This realizes the matrix's multiplication operation[22].
## 5 Discussion  
In this section, we critically analyze our research findings, delve into the limitations and future work regarding the joint optimization method in NeRF, explore the stage transitions during the training process and discuss the potential impact of a research finding based on experimental conclusions and observations.  
1. Although our method has achieved preliminary results in reconstructing inaccurate or pose-less, low-textured and reflective scenes, it still faces several challenges that are worthy of further research and improvement: * When the pose perturbation is too large, the joint pose optimization method still faces reconstruction failures. * In the complete absence of pose data, the spatial relationship between cameras cannot be too distant; otherwise, reconstruction is unachievable. * The method proposed in this paper improves both speed and accuracy; however, there is a slight increase in the occurrence of floaters. Minimizing the generation of floaters while maintaining efficient training is also a direction for further optimization. * Whether it is the BiResNeRF or the baseline method BAA-NGP, during evaluation, the convergence of poses exhibited varying degrees of instability, and an alternative learning rate was adopted for training in specific scenes. Therefore, to more conveniently apply joint pose optimization methods, further exploration is needed on the relationship between different parameters (such as the learning rate) and scene reconstruction. * The research has only been conducted on datasets generated from virtual scenes in Blender. Further research is needed on data acquisition methods, reconstruction methods and limitations in real-world scenarios.  
Figure 11: Projection of aligned poses onto the XY plane.  
Figure 12: Rendering and depth map synthesized from the new perspective.  
2. The speed of error reduction is related to factors such as the learning rate of the neural radiative field and pose parameters and the number of sampling points on the ray, making it a highly complex issue. Although the current method can automatically perform stage transitions and ensure timely transitions after sufficient training in the first stage, the exact position of these transitions could be more precise. Therefore, determining the transition position more accurately remains a question worth further investigation.
3. We also discovered a research finding with potential in the experiments of this paper. Based on Table 3, during the first stage of training, when only utilizing the low-resolution pose estimation module, the average reconstruction time for scenes does not exceed 3 min. This means that, by using a three-layer low-resolution hash coding network, we can complete a preliminary reconstruction of the scene in about 3 min or even less and observe the results in real time. In contrast, BARF requires several hours to display the reconstruction results. This significant improvement in efficiency has brought tremendous momentum to related research in theory and application.
## 2 Light Color Spaces
### Color Space CIE L*u*v* (CIELUV)  
A detailed description of the CIELUV color space is included in a past paper [19]. The key advantage of this color space is that the distance between two points is approximately proportional to the perceived color difference. The color resulting from the addition of two different colors will fall on a connecting line. CIELUV has better uniformity for perceived colors than does CIE1931. Therefore, our proposed modulation schemes [13; 14] are based on the CIELUV color space in order to have perceptual uniformity that is close to that of the human eye in the transmitter (LEDs) and receiver (PDs).
## 3 Results
### General Characteristics of Studies
#### 3.2.1 Correlations Between Gaze Behavior and Shooting Performance  
Table 5 presents the results of the 14 studies in terms of variables regarding correlations between gaze behavior and shooting performance, with a particular focus on the relationships between key metrics such as QE duration, FFD and FD; and shot scoring and SA. The studies showed that longer QE duration [6; 7; 20], FD [3; 45], and FFD [9; 44] were positively correlated with improved shooting accuracy. Three of the articles emphasized the importance of QE duration [6; 39] and FFD [44] in terms of shooting performance in defense. A better FD throughout the shooting process can mitigate the effects of differences in shooting distance and physical condition [42; 46]. Figures 2 and 3 illustrate positive correlations between QE duration and SA, as well as between FFD and SA. The correlation coefficient of 0.67 (\(p\) = 0.04 < 0.05; see Table 4) indicates a statistically significant positive relationship between QE duration and SA. Similarly, the correlation coefficient of 0.55 (\(p\) = 0.03 < 0.05; Table 6) confirms that FFD is also positively associated with SA. However, Figure 4 demonstrates a non-significant correlation between FD and SA. The plot shows no clear trend, with the fitted line remaining nearly flat, indicating that changes in FD do not substantially impact shooting accuracy. The correlation coefficient of 0.67 (\(p\) = 0.82 > 0.05; Table 6) further supports the absence of a statistically significant relationship between FD and SA. Figure 2: Scatterplot of correlation between QE duration and shooting accuracy (SA). Note: The yellow regression line represents their linear relationship, with blue dots as data points and the gray shaded area as the confidence interval (CI).
# Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach
## 3 Methodology
### Proposed Model Design
#### 3.1.2 LSTM Neural Network  
As depicted by Figure 1, a set of repeating block-chain facilitates effective learning of the time series information by the LSTM NN. The horizontal line (from \(C_{t-1}\) to \(C_{t}\)) which runs from one block to another above the graph is called the cell state. Cell state runs across the blocks with slight linear interactions to ensure that constant state information is maintained [31]. Additionally, LSTM NN uses three different gates (see,, and in Figure 1). The gates use sigmoid and tanh layer, with a pointwise multiplication to allow for selective passing of information [32]. With the aid of the gates which act as a multi-level feature selector, LSTM NN can memorize or forget information. Other artificial NNs have a common challenge of long-term dependency problem.  
Contrarily, LSTM NN is designed to overcome this problem by using gates which control the processes of memorizing necessary information and forgetting the unnecessary information. This helps LSTM NN to forecast the output of the next time series data, based on the feature of the previous time series data. The equation below illustrates the calculation processes involved.  
1. Forget gate equation: \[F_{t}=\sigma\big{(}W_{f}\times[h_{t-1},X_{t}]+b_{f}\big{)}\] (7) where \(F_{t}\) is a vector with values from 0 to 1, with \(\sigma\), \(W_{f}\), and \(b_{f}\) representing the logistic sigmoid function, weight matrices and bias of the forget gate, respectively. From (3), the sigmoid layer determines if the new information is necessary to be used for update or unnecessary and ignored. Then, tanh function adds weight to each value that passed and decides their level of importance ranging from \(-1\) to 1. Similar operations are repeated in input and output gates shown in (8) through (11). 2. Input gate equations: \[I_{t}=\sigma(W_{i}\times[h_{t-1},X_{t}]+b_{i})\] (8) \[I_{t}=\tanh(W_{i}\times[h_{t-1},X_{t}]+b_{i})\] (9)  
Figure 1: The structure of long short-term memory (LSTM) neural networks (NN) blocks with their corresponding symbols and meanings.  
3. Output gate equations: \[O_{t}=\sigma(W_{o}\times[h_{t-1},X_{t}]+b_{o})\] (10) \[h_{t}=O_{t}\times\tanh(C_{t})\] (11)
4. Cell state equation: \[C_{t}=\left\{(F_{t}\times C_{t-1})+\left(I_{t}\times I_{t}\right)\right\}\] (12) where \(W_{i}\) and \(W_{o}\) denote the weight matrixes, \(b_{i}\) and \(b_{o}\) represent the network's bias vectors, of the input and output gates. Tanh represents the hyperbolic tangent function.
# On the Stability of Graphene-Based Aqueous Dispersions and Their Performance in Cement Mortar
## 4 FESEM Measurements on Cement Mortar Specimens  
In order to investigate the presence of graphene additives and explore their dispersion in the cement matrix, as a final step, 1 TG and 1 GO fragments were analyzed by FESEM. In Figure 9, representative results are shown: the left-hand panels refer to the 1 TG specimen, and the right-hand ones refer to 1 GO. Figure 9a shows a Gr-TG flake halting the crack's propagation, while in Figure 9b, a flake filling a circular pore created by an air void is visible. To better identify graphene flakes incorporated within the mortar, the back-scattered electron mode was used. In this mode, images are displayed in grayscale, with variations dependent on the atomic weight of the material. Since C is the lightest element compared to others present (such as O, Na, Mg, Al, Si, S, K, Ca, Fe), it appears the darkest. Once we identified the graphene flakes, an EDX analysis was performed to check the chemical composition.  
The extrapolated EDX from the latter flake is shown below (Figure 9c). GO flakes are shown in Figure 9d,e at different length scales. The EDX spectrum shown in Figure 9f is referred to the GO flake of Figure 9d.  
Both EDX spectra (normalised to the carbon peak) show the presence of Al, Si, and Ca, typical components of OPC. In addition, as expected, the spectrum of 1 GO shows that the oxygen peak is double that of 1 TG. When 1.0 wt% of Gr-TG and 1.0 wt% of GO are added to the admixture, they do not disperse uniformly, thus forming isolated flakes and clusters (Figure 10a,c).  
As evidenced in Figure 10b,d, no hydration products of cement are found among the overlapped layers of Gr-TG and GO. In this case, sheets are prone to promptly ungroup or slide over each other when stresses are applied around the cluster, as seen in Figure 10d. This finding can explain why these dosages failed to improve the mechanical properties.  
Figure 8: (**a**) CPV plotted in logarithmic scale in the range [0–12,000] nm. (**b**) Histogram with PV contributions for each range, showing the range in which each specimen demonstrates the greatest contribution to porosity.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 2 Scenario Description and Assumptions  
### In-Network Caching for Streaming Content Delivery Scenarios  
Content transmission in ICN is performed through two types of packets: request and data. The request packet is designed for requesting a target chunk by the chunk name, and the data packet is for delivering the chunk back to the requester. Unique chunk name is used for chunk-by-chunk communications in ICN. Generally, the name structure used in ICN is hierarchical, e.g., /Prefix/FileName/ChunkName, and streaming content such as a video file is typically structured into chunks, which are transferred in sequence.  
The in-networking caching for streaming content delivery is illustrated in Figure 2. Once the ICN router receives a request, the cache space is traversed according to the chunk name carried by the request to locate the target chunk. If the target chunk exists in the cache space, it is forwarded to the client directly, which results in a cache hit. Otherwise, according to the forwarding strategies, the request is forwarded to other routers, which leads to a cache miss in this router. Thus, every ICN router in the network is able to act as a content producer for returning the data chunk directly. For the ICN router receiving a data chunk, whether to cache is determined according to a cache decision algorithm, and if the cache space is exhausted, the cache replacement process will be triggered. The chunk selected by a cache replacement algorithm is evicted to make room for incoming chunks.  
An in-network cache replacement algorithm is designed not only to identify an evictee to make room for new chunks but also to rationally manage the cache space globally to enhance the network performance in terms of the cache hit ratio, chunk delivery distance, retrieval time, etc.
## 7 Challenges and Opportunities  
### Privacy and Security  
Although the major goal of the FDL idea is to protect data privacy, sharing some local models may nevertheless divulge private information. As a result, the authors presented a safe aggregation approach in [193], which allows clients to encrypt their local models while enabling the FL server to aggregate them without decrypting them. Analyzing the global aggregated model, on the other hand, can nevertheless assist in revealing the participation of some learners. As a result, building algorithms to offer privacy at the participant level is critical rather than preserving obtained data.  
Providing privacy at the UAV in the context of UAV networks necessitates high computing workloads and those required for local learning. As a result, efficient FDL algorithms that balance clients' limited resources and privacy protection are critical. Similar to typical ML algorithms, the local models are updated regularly as new data is acquired. By inserting poison data or poison gradient updates, an adversary can affect the outcome of the FL process. In this context, FDL algorithms must resist data-poisoning and model-poisoning adversaries, whereas robust strategies against such adversarial assaults are also essential.  
Another critical security challenge is that of selecting legitimate edge computing UAVs by the IoT devices for task offloading when there are eavesdropper UAVs in the environment [73]. This particularly is challenging in the context of edge computing since security solutions introduce additional latency, whereas the major attraction of using edge computing is to improve the response time of task computing needs by the IoT devices. The UAV MEC systems need to be designed to tackle this challenge. The optimization of UAV transmit power, UAV jamming signals, and UAV locations are useful in this respect. As was stated in [73], there exists a trade-off between security and the latency of UAV MEC systems which is dependent on the UAV energy budget and UAV self-interference efficiency.  
Mobile Edge Computing using UAVs faces further security challenges due to attacks on the UAVs platforms. These attacks compromise UAV data and hamper their mission or physical safety [194; 195]. A battery depletion attack [196], for example, can drain out the UAV's battery faster, which shortens the mission time, creating unplanned service disruptions. There is a recent trend to use blockchain technology to address the cybersecurityproblems of UAVs [197; 198]. Additional research is needed to devise solutions for making the UAV platforms safe from external attacks so that a reliable UAV MEC infrastructure can be established.
Optical Wave Guiding and Spectral Properties of Micro/Nanofibers Used for Quantum Sensing and Quantum Light Generation  
[PERSON]  
1 Quantum Physics Technology Directorate, Agency for Defense Development, Daejeon 34186, Korea; [EMAIL_ADDRESS] (Y.S.I.); [EMAIL_ADDRESS] (Z.  
an MNF has 1 um\({}^{2}\). A few years ago, the generation of correlated photon-pairs via SFWM was experimentally demonstrated in a micro/nanofiber [42]. By using 15 cm long MNF with the diameter of 900 nm and the laser centered at 1040 nm, correlated photon-pairs were generated at 1310 nm and 850 nm, respectively, that are compatible with the telecom-band fiber system and the high efficiency single photon detector (SPD) for the near IR region. In a very recent study, photon-pairs were generated in a 12 cm long MNF with a 615 nm diameter [43]. For the 725 nm pump beam, the signal and idler photons were created at 632 and 850 nm, respectively, which can be detected by Si-based SPDs with high quantum efficiency.  
In this work, we theoretically investigate optical wave guiding properties and the spectral properties of entangled photon pairs via SFWM process in MNFs. Thanks to the extraordinary properties such as pronounced evanescent field and small mode area, MNFs have become powerful tools for quantum sensing, detection, and photon-pair generation. In particular, by controlling the waveguide dimension, photon-pairs are designed at one's desired wavelengths. Here, based on the exact solutions of the [PERSON]'s equations, we first numerically calculate the propagation constants for guided optical modes through a circular silica-air optical fiber with a subwavelength-diameter, and then evaluate field distributions, fractional power, effective diameters, group velocity, and group-velocity-dispersions according to the changes of the fiber diameter and the wavelength of the light. Finally, optical wave guiding properties based on numerical caculations are applied to estimate spectral properties such as central wavelengths and bandwidths of the created photons via spontaneous four-wave mixing in MNFs.  
This paper is organized as follows: Section 2 is devoted to the analysis of the [PERSON]'s equation in the step-index cylindrical wire model, and from the exact solutions of the Maxwell's equation, Section 3 presents propagation constants and fundamental modes in MNFs. Based on the mode properties, Sections 4 and 5 describe the fractional power, effective diameter, group velocity, and group velocity dispersion that are essential parameters to estimate spectral properties of entangled photon-pair via spontaneous four-wave mixing (SFWM). In Section 6, the SFWM in an MNF is introduced with phase-matching conditions, and finally spectral properties of created photons are shown in Section 7. It is summarized in Section 8.
# Understanding Unsupervised Deep Learning for Text Line Segmentation
## 4 Method
### Pseudo-RGB Image Generation  
Generating a pseudo-RGB image from an input document image is performed in three steps: (1) generating a feature map from the input document image using one branch of the Siamese network, (2) extracting the three principal components of the feature map's vectors using PCA, (3) interpreting the three principal components as a pseudo-RGB image.  
1. Since only the last three fully connected layers in the network receive input from both patches, we can conclude that most of the semantic reasoning for each patch is conducted separately by the convolutional network. Both CNN branches of the Siamese network share the same weights, therefore, a single branch is used to extract the features of each patch. Using the CNN branch, each patch is embedded into a feature vector of 512 dimensions, as shown in Figure 3.  
A sliding window of the size \(p\times p\) is used to obtain the feature map of a complete document image. The patches produced by the sliding window are fed to a branch of the Siamese network. Thus, an image of size \(H_{d}\times W_{d}\) is mapped to a feature map of  
Figure 3: Two-branch CNN architecture that predicts the similarity between two patches. Dotted lines denote shared weights, and fc represents fully connected layer.  
the size \(\frac{H_{d}}{w}\times\frac{W_{d}}{w}\times 512\), where each cell in the feature map is a vector that corresponds to a single patch on the input document image.
2. By applying PCA on the feature vectors, the \(512D\) feature vectors are projected on their first three principal components.
3. To interpret the first three principal components as pseudo-RGB values, they are normalized to values between zero and one, multiplied by 255, and viewed as RGB values, where the first, second, and third components correspond to the red, green, and blue colours, respectively. Subsequently, the features of the document image can be visualized as a pseudo-RGB image in which the central windows of patches with similar patterns are assigned similar colours (Figure 2).