esa-sceva/satcom-chunk-collection huggingface dataset:
# Quiescent Optical Solitons with Quadratic-Cubic and Generalized Quadratic-Cubic Nonlinearities
## 4 Generalized Quadratic-Cubic Nonlinearity
### Generalized Temporal Evolution  
In this case, Equation (34) reads as  
\[i\Big{(}q^{l}\Big{)}_{t}+a\Big{(}|q|^{n}q^{l}\Big{)}_{xx}+\Big{(}b_{1}|q|^{m }+b_{2}|q|^{2m}\Big{)}q^{l}=0. \tag{48}\]  
Figure 5: Profiles of stationary dark and singular solitons, respectively. Figure 6: Profiles of stationary dark and singular solitons, respectively. Again, (48) reduces to (34) for \(l=1\), which represents the case with linear temporal evolution. Plugging (10) into (48) paves the way for the fundamental equation  
\[ak^{2}\Big{(}l^{2}+l(2n-1)+(n-1)n\Big{)}U^{n}{U^{\prime}}^{2}+ak^{2}(l+n)U^{n+1}U ^{\prime\prime}+b_{1}U^{m+2}+b_{2}U^{2m+2}-l\omega U^{2}=0. \tag{49}\]  
Assume \(n=m\). Thus, Equation (49) changes to  
\[ak^{2}\Big{(}l^{2}+l(2m-1)+(m-1)m\Big{)}U^{m}{U^{\prime}}^{2}+ak^{2}(l+m)U^{m+1} U^{\prime\prime}+b_{1}U^{m+2}+b_{2}U^{2m+2}-l\omega U^{2}=0. \tag{50}\]  
The restriction  
\[U=V^{\frac{1}{m}} \tag{51}\]  
collapses Equation (50) to  
\[ak^{2}m(l+m)VV^{\prime\prime}+ak^{2}l(l+m){V^{\prime}}^{2}+m^{2}V\Big{(}b_{2}V ^{2}+b_{1}V-l\omega\Big{)}=0. \tag{52}\]  
By the implementation of the balancing procedure in (52), one translates (4) to  
\[V(\xi)=\lambda_{0}+\lambda_{01}R(\xi)+\lambda_{10}Q(\xi)+\lambda_{11}R(\xi)Q (\xi)+\lambda_{02}R(\xi)^{2}+\lambda_{20}Q(\xi)^{2}. \tag{53}\]  
Inserting (53) along with (5) and (6) into (52) retrieves the results:  
Result-1:  
\[\lambda_{0}=-\frac{b_{1}(2l+3m)}{4b_{2}(l+m)},\;\lambda_{01}= \lambda_{20}=\lambda_{11}=\lambda_{10}=0,\;\lambda_{02}=-\chi\lambda_{0},\] \[k=\pm\frac{m}{2(l+m)}\sqrt{\frac{b_{1}}{2a}},\;\omega=-\frac{b_ {1}^{2}(2l+m)(2l+3m)}{16b_{2}l(l+m)^{2}}. \tag{54}\]  
Putting (54) along with (7) into (53) leaves us with the straddled soliton  
Figure 5:0 Figure 5:1  
Taking \(ab_{1}>0\) and \(\chi=\pm 4c^{2}\) turns (55) into dark and singular solitons  
Figure 5:2  
and  
Figure 5:3  
Figure 7 depicts the plots of dark and singular solitons (56) and (57), respectively. The parameter values chosen are: \(b_{1}=1\), \(b_{2}=-1\) and \(a=1\). Result-2:  
Figure 5:4 Figure 5:5  
Substituting (58) along with (8) into (53) provides us with the straddled soliton  
Figure 5:6  
Setting \(ab_{1}>0\) and \(\eta=\pm b\) condenses (59) to dark and singular solitons  
Figure 5:7  
and  
Figure 5:8  
Figure 8 depicts the plots of dark and singular solitons (60) and (61), respectively. The parameter values chosen are: \(b_{1}=1\), \(b_{2}=-1\) and \(a=1\).
# Visualization System for Transparency Requirement Analytics
## 2 Literature Review
### Background: Transparency Engineering  
The significance of technology's role in engineering transparency is growing. It is stated that the gradually evolving third generation of transparency policies will be technologically driven and collaborative in nature ([PERSON] [7], [PERSON] and [PERSON] [8]).  
Several models are proposed to define the analysis of transparency requirements. In this section, we examine two models in which we are especially interested.  
The first model, the Transparency Actors Wheel [9; 10], focuses on the flow of information between relevant stakeholders. Identifying stakeholders facilitates the comprehension of where information originates, who provides it, who receives it, and whether specific channels are utilized to transmit information. This model divides the flow of information elements into four components: information provider (IP), information receiver (IR), information entity ((IE), it can be information provider/receiver or another entity, depending on the information flow), and information medium ((IM), it refers to the channel through which information travels).  
The second model, known as Transparency Depth Pyramid, focuses on the different levels of transparency, where the transparency requirements can be categorized as follows [11]:  
* Data transparency: A data transparency question (\"what, when, where, or who?\") is one that has an answer that contains data, content, or information. For example, in a hosting service platform, data transparency reveals to the client the server performance and price for each plan.
* Process transparency: A process transparency question (\"how?\") is one that has an answer that includes procedures, processes, behaviors, and interactions. For example, how data are encrypted in servers and how servers are immune to cyber attacks in a hosting service platform.  
Policy transparency: A policy transparency question (\"why?\") has an answer that includes goals, intentions, and policies. For example, policy transparency in a hosting service platform reveals why encryption is required in servers or why users have limited storage capacity.  
In our work, we focus on TranspLan, which is a prototype system based on transparency models and descriptions proposed by [PERSON] et al. [5]. The views of TranspLan and their limitations are presented in the two next sections.
## 3 Methodology  
### The LSTM Model  
LSTM consists of three layers: input, hidden, and output. In contrast to standard neural networks, LSTM hidden layer neurons are linked to each other. This interconnectednessfosters correlations between data points, enabling the model to grasp intricate patterns and dependencies within the input sequence. To tackle this challenge, LSTMs employ three essential gates: a forgetting gate, an input gate, and an output gate. These gates meticulously manage the flow of information, ensuring that vital long-term information is retained while irrelevant input data are discarded. Visually, the LSTM model resembles a chain-like structure, as illustrated in Figure 1, comprising repeating basic units known as memory cells.  
The input gate \(i_{t}\) controls the flow of input data into the cell state, deciding what information needs updating and what is essential for accurate predictions. Meanwhile, the Forgetting Gate \(f_{t}\) filters out unnecessary information from the previous step and updates the cell state accordingly. Lastly, the output gate \(o_{t}\) determines the output value to be transmitted to the next hidden cell. Here are the equations governing the gates in an LSTM:  
\[\left\{\begin{array}{l}i_{c}=\sigma(W_{ii}x_{t}+b_{ii}+W_{ki}h_{t-1}+b_{hi}) \\ f_{t}=\sigma\Big{(}W_{if}x_{t}+b_{if}+W_{hf}h_{t-1}+b_{hf}\Big{)}\\ g_{t}=\tanh\Big{(}W_{if}x_{t}+b_{ig}+W_{hg}h_{t-1}+b_{hg}\Big{)}\\ o_{t}=\sigma(W_{io}x_{t}+b_{io}+W_{ho}h_{t-1}+b_{ho})\\ c_{t}=f_{t}*c_{t-1}+i_{t}*g_{t}\\ h_{t}=o_{t}*\tanh(c_{t})\end{array}\right. \tag{1}\]  
\(h_{t}\) is the hidden state at time \(t\) and \(c_{t}\) represents the neuron's \"memory\" at that moment. The input at time \(t\) is \(x_{t}\), while \(h_{t-1}\) is either the hidden state at time \(t-1\) or the initial hidden state at time \(0\). The input, forgetting, unit, and output gates are denoted by \(i_{t},f_{t},g_{t}\), and \(o_{t}\), respectively. Here, \(\sigma\) represents the sigmoid function, and * signifies element-wise vector multiplication.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 5 Discussion
### RefineNet versus CEM  
The RefineNet model also extracts high-level semantic features by deepening the hierarchy. It employs a multi-path refinement structure to fuse low-and high-level semantic features, compensating for missing information in down-sampling. The accuracy of its extraction results is therefore significantly better than that of the SegNet model. However, due to the noise introduced by the deep convolution structure, the edges of the crop planting area in the extraction result remain coarse.  
Compared to camera images, remote sensing images reveal fewer details, but with a higher number of channels. Therefore, the structure of the CEM induces advantages including the division of the convolutional layer into five coding units and decoding them separately. The CEM model obtains sufficient information for classification processing while reducing the depth of the convolutional network, by using the convolution kernels of \(1\times 1\) and \(3\times 3\) together. In addition to extracting different level semantic features of a pixel, the CEM model also extracts spectral features. These techniques ensure that the model produces high-precision results.  
Figure 10 shows a comparison between the results of the two models. The images in Figure 9 are dated 16 March 2018. As can be seen from Figure 10, only a few pixels are misclassified at the corners of the CEM results, while the RefineNet results show misclassified pixels at the corners and edges of planting areas. Although the number of misclassified pixels in the case of RefineNet is below that for SegNet, it is still significantly higher than that for CEM. In Regions 1, 2, and 4, the RefineNet model classification has many errors, resulting in rough edges, and in the CEM, results show areas in more accurate detail. In Regions 3, 5, and 6, the RefineNet results contain more recognition errors and a considerable amount of winter wheat is not recognized. For the CEM results, the shape obtained is consistent with the actual shapes of areas. Considering the difference in the fusion of high-and low-level semantic features, the strategy adopted in this study is more reasonable than that of RefineNet.  
Figure 9: Segmentation error comparison: (**a**) original images, (**b**) ground truth, (**c**) errors of SegNet results, and (**d**) errors of CEM results.
## 3 Data Acquisition and Processing
### Pronostia Experiment Platform  
This paper used rolling bearing data from the PRONOSTIA experimental platform, which is related to bearings' degradation tests [35]. Figure 5 shows the platform dedicated to testing and validating bearings fault detection, as well as diagnostic and prognostic approaches. The platform was designed and realized at the AS2M 1 department of the FEMTO-ST 2 institute. The main objective of PRONOSTIA is to provide real experimental data that characterize the degradation of ball bearings along their whole operational life (until their total failure).  
The degradation may be different for the same bearing if load and speed are distinct. The data are collected via the sensors located around the bearings, and data-driven techniques has to be applied to predict the degradation trend (balls, inner or outer races, cage, etc.), Figure 5 depicts an example of what one can observe on the ball bearing components before and after an experiment. According to the bearing and to the way the degradation evolves, the degradation \"patterns\" may have particular characteristics, as illustrated in the following. In this paper, we considered several experiments for which different features (health index) were extracted. The choice of features is typical of bearings diagnostics and prognostics [35; 36].  
Experimental dataset datasets are selected from different working conditions. The motor speed is 1800 rpm and the load is 4000 \(N\) in the first working condition; the motor speed is 1650 rpm and the load is 4200 \(N\) in the second working condition; the motor speed is 1500 rpm and the load is 5000 \(N\) in the third working condition. The specific information is shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Working** & **Speed (Rpm)** & **Load (N)** & **Sample Data** \\ \hline
1 & 1800 & 4000 & bearing1\_k, k = 1,2, \(\dots\),7 \\
2 & 1650 & 4200 & bearing2\_k, k = 1,2, \(\dots\),7 \\
3 & 1500 & 5000 & bearing3\_k, k = 1,2,3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specific information of the sample data.  
Figure 4: The MAPE value of different models.  
In order to test the effectiveness of different feature spaces, one-dimensional time-domain vibration signals are transformed into three different feature spaces by the time-frequency domain algorithm, the EEMD algorithm and the wavelet packet algorithm, then performance degradation indexes are selected from these features for construct training matrix and the testing matrix, which are the input of the performance degradation prediction model.
# A Cascaded Ensemble of Sparse-and-Dense Dictionaries for Vehicle Detection
###### Abstract
Thus, an ensemble of such SDDs (ESDD) for all subcategories of vehicle and background are capable of well representing both of them even though their appearances are various due to the aforementioned challenges, and each constituting SDD's subcategory-specific reconstruction ability enables the ESDD to classify vehicle from background. We further organize the ESDD as a two-level cascade of ESDD (CESDD) to perform two-stage coarse-to-fine classifications which is more robust and reduces computations; input samples are firstly classified by the first level of ESDD corresponding to easy-to-distinguish subcategories and then, based on the first-level classification results, selectively passed to SDDs within the second level (corresponding to hard-to-distinguish subcategories) for re-classification. To attain further accuracy enhancement, the CESDD is further coupled with a downstream AdaBoost process whose weak classifiers take corresponding SDDs' reconstruction residues as input features and are aggregated to produce final classifications. The CESDD model is used as a window classifier in a simple-to-implement sliding-window scanning process over input image pyramids to generate multi-scale detections. These detections usually contain duplicate ones, so we adopt non-maximum suppression (NMS) process then; specifically, instead of the often-used greedy NMS [2] which brutally trade-off between accuracy and recall rate through inter-detection IoU thresholding, we adopt the mean-shift-like NMS [7] which could effectively remove duplicate detections while properly handling the detections of very near vehicles, and some adaptations are introduced for bounding box refinement and false positive reduction. To summarize, the main contributions of this work are:  
* an SDD-ensemble-based vehicle/background classification approach (ESDD);
* an effective organization of SDD ensemble into a two-level cascade for sliding-window classification robustness and computation saving (CESDD);
* a cooperation mechanism of CESDD with a downstream AdaBoost process for accurate sliding-window classification;
* an adapted mean-shift-like NMS method for duplicate detection removal. The remainder of the paper is organized as follows: Section 2 reviews related works; Section 3 introduces the training of CESDD; Section 4 introduces vehicle detection with CESDD; Section 5 presents experiments on CESDD; Section 6 gives a conclusion of this work and further improvement directions of CESDD.
# Detection of Pediatric Femur Configuration on X-ray Images
### Problem Statement  
Bone configuration on each image frame can be understood as its orientation and position, i.e.,  
\[g=\left[\theta\;x\;y\right]^{\top}, \tag{1}\]  
where \(\theta\) denote the orientation of the bone and \(x,\;y\) stand for its position. To define the configuration of the bone on each image, we assume keypoints \(\mathbf{k}_{j},j=1,\ldots,f\). With each image, we correlate the keypoint set:  
\[K\triangleq\left[\mathbf{k}_{1}\;\mathbf{k}_{2}\;\ldots\;\mathbf{k}_{f}\right]=\begin{bmatrix} x_{1}\;x_{2}\;\ldots\;x_{f}\\ y_{1}\;y_{2}\;\ldots\;y_{f}\end{bmatrix},\;K\in\mathbb{R}^{2\times f}, \tag{2}\]  
where \(x_{j}\) and \(y_{j}\) are the coordinates of \(j\)-th keypoint \(\mathbf{k}_{j}\). For each image frame, we assume that it is possible to obtain valid femur configuration \(g\) from the selected keypoint set. Therefore, we can state that  
\[g=\phi_{g}(K),\quad\phi_{g}:\mathbb{R}^{2\times f}\rightarrow\mathbb{R}^{3}, \tag{3}\]  
where \(\phi_{g}\) is a transformation from keypoint matrix (2) to the bone configuration (1). To avoid the ambiguity of bone configuration, we assume that the transformation \(\phi_{g}\) should be equal for all image frames.  
Since ground truth is not available for a considered problem, the reference configuration of the femur will be based on manually marked keypoints. Therefore, we distinguish the configuration of the femur obtained by manually denoted keypoints (with subscript \(m\)) and the configuration obtained by estimated positions of keypoints (with subscript \(e\)), i.e.,  
\[g_{m}=\left[\theta_{m}\;x_{m}\;y_{m}\right]^{\top},\quad g_{e}=\left[\theta_{ e}\;x_{e}\;y_{e}\right]^{\top}. \tag{4}\]  
The accuracy of the proposed method will be evaluated as a difference between manual and estimated femur configuration. The proposed solution, depicted in Figure 2, consists of the following steps. First, in the initialization phase, the set of image features that correspond to the femur configuration is chosen. Second, femur features are manually annotated on each image frame by a medical expert. The keypoints are treated as a reference and the goal of the proposed estimation algorithm is to reflect those positions as closely as possible. Two different estimation techniques are chosen in this specific scenario, i.e., tailored adaptive threshold estimator and CNN-based estimator. The overall performance of the estimator is evaluated in the test set, consisting of previously unused medical image frames.  
The configuration of the femur is used as a registration coordinate system, to remove the influence of femur configuration changes on the overall kinematics of the joint. If succeeding, the estimation algorithm could be used as an initialization phase of knee joint kinematic model evaluation.
# Scalable and Reliable Data Center Networks by Combining Source Routing and Automatic Labelling
## 4 Implementation  
In order to validate eTorii, we designed and developed an SDN application using the Ryu controller [29], which uses the OpenFlow protocol [30] to control the network devices. Although eTorii is a distributed protocol by definition, as explained in Section 3, the reason to implement it by using SDN (which is logically centralized) is that OpenFlow serves also to easily develop and test protocol proof-of-concepts in environments close to reality, as it can be deployed in hardware using real network traffic traces [30]. The implementation of eTorii has validated the four features described in previous sections, namely: auto-configuration, scalability (no routing table entries are needed apart from the node labelling), resilience (fast recovery after link failure) and flexibility (the protocol works with diverse hierarchical topologies).  
As it can be seen in Figure 5, the developed application is composed of two main  
Figure 4: Example of on-the-fly path repair after two link failures.  
logical parts. The first one, Figure 5a, on the left, is executed just right after starting the network, when the system has to discover and map the network topology that will be handled afterwards. In this initial step, the network controller will generate the HLMAC addresses, in a centralized way, to be assigned to each of the network switches. Additionally, the application also generates a traffic distribution tree for broadcast traffic. Finally, it enters an state in which it will be paying attention to additional events to be handled.  
The second part, Figure 5b, on the right, is basically in charge of handling two types of events and its execution starts right after finalising Figure 5a. The events to be managed are basically of two types: (1) topological, or (2) PACKET_IN events. A topological events occurs as a consequence of a change in the network topology (for example, a new node is added or some link fails), while the PACKET_IN is an OpenFlow message triggered by a switch when no action is defined for a new packet arriving at it. In this latter case, the switch sends this message to the controller and awaits for the routing decision to be made by it. This event is usually generated with the first packets in any communication, which are usually ARP packets in IPv4-based networks. The actions to be applied for each event are as follows: in the case of the topological event, the controller checks whether the network topology has actually been modified and, if so, deletes obsolete forwarding rules and updates the traffic distribution tree; while if an PACKET_IN event, the controller proceeds to calculate and install the required rules in the switches to forward the traffic associated to that PACKET_IN message.  
In the following sections, we describe the implementation in detail.
# Vocabulary Retention Under Multimodal Coupling Strength Index (MCSI): Insights from Eye Tracking
## 4 Results  
### Analysis of the Four Groups of Overall Data  
Based on the experimental procedures and subsequent vocabulary assessment, we obtained the following key datasets: (1) lexical retention performance across all four experimental groups (Table 4), and (2) comprehensive eye-tracking metrics for each group (Table 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Accuracy** & **F** & **\(\mathbf{p}\)** \\ \hline A & 59.60\% & & \\ B & 82.07\% & & \\ C & 76.00\% & & \\ D & 92.00\% & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Analysis of accuracy data for four stimulus word tests.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MCSI Scope** & **Synergy** & **Strategy Proposal** &
\begin{tabular}{c} **Cognitive Mechanisms** \\ **Explained** \\ \end{tabular} \\ \hline
0.0–0.3 & weak & Increase in time delay &
\begin{tabular}{c} Competition for corridors \\ leads to fragmentation \\ of resources \\ Independent coding \\ predominates, with limited \\ overlay effects \\ Dynamic inhibition optimizes \\ attention allocation \\ Multi-channel load balancing \\ triggers superplasticity \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 3: MCSI threshold decision matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Group** & **Group Segmentation** & **AFD(s)** & **SD-FD** & **ASC** & **SD-SC** \\ \hline A & T1 & 62.50 & 2.54 & 259.90 & 7.56 \\ A & T2 & 32.57 & 2.46 & 259.90 & 7.56 \\ \hline B & T1 & 57.96 & 2.33 & \\ B & P & 37.24 & 2.18 & 265.04 & 7.27 \\ \hline C & T1 &  
Comparative analysis of Group A/C and B/D revealed both independent and synergistic effects of auditory stimulation. With auditory intervention, Group A/C demonstrated significant improvement in accuracy (59.60% to 76.00%; MD = 16.40%, F = 18.473, \(p\) < 0.001), indicating enhanced lexical memory. Group B/D showed further augmentation when auditory stimuli were combined with visual support (82.10% to 92.00%; MD = 9.90%, F = 29.967, \(p\) < 0.001). Complete statistical results are presented in Table 6.  
Analysis of the saccadic frequency data revealed distinct patterns across experimental conditions. The Group A/C comparison showed no significant difference in saccadic frequency (F = 0.001, \(p\) = 0.974). In contrast, the Group B/D comparison demonstrated significant differences in saccadic frequency (F = 16.066, \(p\) < 0.001), with visual attention becoming more focused on semantically critical regions (Figures 8 and 9). This pattern indicates that voice-guided auditory input enhances visual information extraction efficiency. Complete statistical results are presented in Table 7.
## 3 Closed-Form Approximation of Achievable Spectral Efficiency with Channel Estimation Error
In high power regime, the SINR of RZF is similar to ZF precoding. Based on these facts, we can approximate the SINR of RZF with channel estimation error as follows:  
\[\hat{\gamma}_{k,RZF}\approx\left\{ \begin{array}{ll}\frac{(N_{t}-K)\xi\left(1 -\frac{N_{t}}{\xi}\right)\rho_{r}}{K\left(\left(1-\xi\left(1-\frac{N_{g}}{\xi }\right)\right)\rho_{r}+1\right)},&N_{t}\rho_{r}\gg K,\\ \frac{N_{t}\xi\left(1-\frac{N_{t}}{\xi}\right)\rho_{r}}{K\left(\rho_{r}+1 \right)},&N_{t}\rho_{r}\ll K.\end{array} \right. \tag{27}\]  
Since we assume the system with \(N_{t}>10K\), due to the distinct channel gain, the SINR of RZF precoding is similar to the SINR of ZF precoding. We will show this in Section 5. Readers can refer to [39; 40] for more details of RZF precoding.