esa-sceva/satcom-chunk-collection huggingface dataset:
# RCBi-CenterNet: An Absolute Pose Policy for 3D Object Detection in Autonomous Driving
## 2 Related Work
### Object Detection in Autonomous Driving  
The mainstream 3D object detection techniques in autonomous driving can be divided into three categories [27]: monocular detection [28; 29; 30], Lidar-based detection [31; 32], and camera-Lidar fusion [33; 34; 35; 36]. Each line of work has its pros and cons. Monocular detection works by detecting 2D objects in RGB images that help reason in the 3D space to estimate depth and orientation. However, the lack of explicit 3D information from the input limits the localization accuracy. On the other hand, Lidar-based detection aims to detect 3D objects directly from the point cloud sampled from the real world; however, due to the lack of semantic texture information, DNNs cannot learn rich and meaningful features. Camera-Lidar fusion is a hybrid strategy that combines front view images and point cloud to generate interactive and semantic-rich features to train an end-to-end 3D object detector. The majority of detectors developed under the three categories adopt an anchor-based method to predict bounding boxes, and another line of work utilizes a point-based method [20; 37] to first predict the center point of an object and then regress other pose information. The point-based detector showed superior performance in both AP and inference time on the COCO dataset [20], compared to the anchor-based methods. This study offers a custom point-based solution of object detection in the autonomous driving domain, which falls into the monocular detection category. The same type of work does not exist in the literature. Our work has the potential to be extended to work in a camera-Lidar fusion setting, where the 3D point coordinates can be obtained directly from the point cloud and projected to the 2D image to serve as an accurate position; in addition, a DNN model can extract rich semantic features the RGB images to boost the detection accuracy.
## 3 Experimental Results and Discussion
### Skeleton Curves  
The skeleton curve of each specimen is obtained through the peak points of each hysteresis loop in the hysteretic curve, and Figure 13 shows the skeleton curve of each specimen.  
Figure 13: Skeleton curves of specimens.  
The following conclusions can be obtained by the skeleton curve of the energy dissipation links.  
1. As can be seen from the shape of the skeleton curve, the skeleton curve of all the specimens keeps roughly the same trend. It can be seen that the elastic section, yield section, and strengthening section are easy to observe. With the continuous increase of displacement, the stiffness of specimens shows a gradual downward trend.
2. By comparison of rsl-4, rsl-7, and rsl-8, it can be seen that, when the link length, stiffening rib spacing and yield type are the same, the skeleton curve of the link with relatively large section size has high bearing capacity and high stiffness. It can be seen that the skeleton curve of web connection type link is greatly affected by the size of the section, and the bearing capacity of the link with a larger size of the section is stronger.
3. By comparing rsl-1 with rsl-6, and rsl-4 with rsl-6, respectively, it can be seen that the skeleton curve has the same trend. The load peak of rsl-1 and rsl-6 differs by 15 kN, and the skeleton curve of rsl-4 and rsl-6 are consistent. Therefore, when the cross-section size, number of stiffening ribs, and yield type of the replaceable link are the same, the link length has relatively little impact on the bearing capacity.
4. By comparing rsl-5 with rsl-6, and rsl-4 with rsl-5, respectively, it can be seen that the number of stiffening ribs of rsl-5 increases compared with rsl-6, but the skeleton curve has no change in trend and bearing capacity, and the curve is consistent. Therefore, it can be concluded that the stiffening spacing of the link has little influence on the skeleton curve when the cross-section size, length, and yield type of the link are the same.
5. As the size and length of the section of the link have impact on the changes of Mp and Vp of the link, it is necessary to compare the difference \(\zeta\) between 1.16 and 1.57 as a variable on the basis of the same number of stiffening ribs of the link. By comparing the skeleton curves of rsl-1, rsl-8, rsl-7, with rsl-4, the larger the section size is, the higher the bearing capacity of the skeleton curve. Therefore, the difference of \(\zeta\) does not determine the bearing capacity and deformation of the skeleton curve.  
It can be seen from above that the section size is the parameter with the greatest influence on the skeleton curve.
# Quantum Neural Networks Approach for Water Discharge Forecast
## 3 Results and Discussion
### Results for the Series After the Anomalies Removal  
The same algorithm was applied to the series after removing the values presented in Figure 2 to evaluate the algorithm's behavior in a series with lower variability. The results are represented in Figure 6 for the So series, Figure 7 for S1o, and Figure 8 for S2o.  
The objective function computed during the training process for So levels off at the second iteration at a value higher than for S. On the training set, the QNN better fit So, except for the first three values, where a significant deviation is noticed.  
Figure 6: QNN model for So (**a**) on the training set and (**b**) test set. (**c**) The evolution of the objective function during the training (11 iterations).  
As can be seen in Figure 7c (Figure 8c), the objective function for S1o (S2o) leveled off at higher (much lower) values than for S1 (S2).  
For S1o, the extremes are well captured in both the training and testing sets. At the beginning of 2006 and the middle of 2007, a few significant differences between the measured and simulated series exist.  
In the case of S2o, the predicted values are higher than the recorded ones, with a bigger bias than in the cases of S and S1o. However, the forecast series and the registered one have the same shape, indicating the model's capacity to catch all kinds of values (high or small) well. Comparisons of Figures 6-8 show a lower accuracy of S2o compared to So and S1o. A similar remark can be made by analyzing Figures 3-5.  
Figure 7: The QNN model for S1o on (**a**) the training set and (**b**) test set. (**c**) The evolution of the objective function during the training (8 iterations).
## 3 SEIRS-F Agent-Based Model
### Transition Rules
#### 3.2.3 Susceptible to Failed  
A sensor moves from a susceptible to a failed state when its subsystems (including power subsystem, sensing subsystem, processing subsystem, communication subsystem, and other specific subsystems) fail, or the software fails, or the battery power is exhausted. The probability of a node switch from susceptible to failed depends on four characteristics: the reliability state of the sensor, as a failed node will not fail again; the reliability level of the sensor, which contains the reliability level of hardware and software; battery power, which is the essential condition of sensor operation; and the risk of physical attack, as sensors deployed in hostile environments are highly likely to be physically damaged. This characteristic affects the number of sensors that are initially physically destroyed and fail.  
That is:  
\[P[x_{i}(0)=S(0)\to x_{i}(0)=F(0)]=\delta(X_{9})\;when\;X_{1}=\;normal\; state \tag{5}\]  
\[P[x_{i}(t)=S(t)\to x_{i}(t+1)=F(t+1)]=1\;when\;X_{1}=\;normal\; state\;AND\;X_{5}\leq 0 \tag{6}\]  
\[P[x_{i}(t)=S(t)\to x_{i}(t+1)=F(t+1)]=\lambda(X_{2})\;when\;X_{1}=\;normal\; state\;AND\;X_{5}>0 \tag{7}\]  
where \(X_{1}\) represents the reliability state of the sensor, \(X_{2}\) indicates the reliability level, \(X_{5}\) means the remaining battery power of the sensor, and \(X_{9}\) reflects the risk of physical attack, which affects the number of nodes that fail initially due to physical damage.
# Radar Error Correction Method Based on Improved Sparrow Search Algorithm
## 3 Improved Sparrow Search Algorithm
### Improved Sparrow Search Algorithm
#### 3.2.1 Elite Chaotic Reverse Learning Strategy  
The elite chaotic reverse learning strategy uses chaotic mapping and reverse refraction learning to generate candidate solutions in a larger range, and then selects elite individuals with a better quality as the initial population through sequencing.  
Chaotic sequences have good non-repetitive ergodicity and randomness. Common chaotic systems include logistic mapping [33], tent mapping [34], cubic mapping [35], and Bernoulli mapping [36], etc. Here, Bernoulli mapping with good uniformity is selected to enhance the randomness and diversity of the initial population. The iterative formula for the chaotic Bernoulli mapping is as follows:  
\[z_{k+1}=\begin{cases}&z_{k}/(1-\lambda),z_{k}\in(0,1-\lambda]\\ &(z_{k}-1+\lambda)/\lambda,z_{k}\in(1-\lambda,1)\end{cases} \tag{19}\]  
where \(k\) is the number of chaos iterations, and \(\lambda\) is usually 0.4. Then, map the chaotic sequence into the sparrow search space as follows:  
\[x_{i,j}=x_{L}+(x_{U}-x_{L})\times z_{i,j} \tag{20}\]  
where \(x_{U}\) and \(x_{L}\) are the upper and lower bounds of the search space, respectively.  
The mechanism of refracted opposition-based learning (ROBL) [37] is used to further expand the range of alternative solutions. The current reverse solution \(x_{i,j}^{*}\) of the sparrow individual position \(x_{i,j}\) is as follows:  
\[x_{i,j}^{*}=\frac{a_{j}+b_{j}}{2}+\frac{a_{j}+b_{j}}{2m}-\frac{x_{i,j}}{m} \tag{21}\]  
where \(a_{j}\) and \(b_{j}\) represent the minimum and maximum values of the \(j\)-th dimension of the current population, respectively, and \(m\) is the lens scaling factor.  
The specific process of population initialization via the use of the elite chaotic reverse learning strategy is as follows: Firstly, population \(\mathbf{X}\) is randomly initialized as shown in Equation(14). Then, \(\mathbf{X}\) is substituted into Equations (20) and(21), respectively, to generate the chaotic population \(\mathbf{Y}\) and reverse learning population \(\mathbf{Z}\). Individuals in population \(\mathbf{X}\), \(\mathbf{Y}\), and \(\mathbf{Z}\) were sorted according to fitness values, and the first \(n\) individuals were selected to form a new initial population, denoted as follows:  
\[\mathbf{X}^{\prime}=\begin{bmatrix}\mathbf{X_{1}}^{\prime}\\ \mathbf{X_{2}}^{\prime}\\ \vdots\\ \mathbf{X_{n}}^{\prime}\end{bmatrix}=\begin{bmatrix}x_{1,1}^{\prime}&x_{1,2}^{ \prime}&\cdots&x_{1,D}^{\prime}\\ x_{2,1}^{\prime}&x_{2,2}^{\prime}&\cdots&x_{2,D}^{\prime}\\ \vdots&\vdots&\ddots&\vdots\\ x_{n,1}^{\prime}&x_{n,2}^{\prime}&\cdots&x_{n,D}^{\prime}\end{bmatrix} \tag{22}\]
###### Abstract  
Integrating third-party and legacy systems has become a critical necessity for companies, driven by the need to exchange information with various entities such as banks, suppliers, customers, and partners. Ensuring data integrity, keeping integrations up-to-date, reducing transaction risks, and preventing data loss are all vital aspects of this complex task. Achieving success in this endeavor, which involves both technological and business challenges, necessitates the implementation of a well-suited architecture. This article introduces an architecture known as the Dynamic Canonical Data Model through Agnostic Messages. The proposal addresses the integration of loosely coupled software units, mainly when dealing with internal and external data integration. To illustrate the architecture's components, a case study from the Mexican Logistics Company Paquetexpress is presented. This organization manages integrations across several platforms, including SalesForce and Oracle ERP, with clients like Amazon, Mercado Libre, Grainger, and Afull. Each of these incurs costs ranging from USD 30,000 to USD 36,000, with consultants from firms such as Quanam, K&F, TSQL, and TeKSi playing a crucial role in their execution. This consumes much time, making maintenance costs considerably high when clients request data transmission or type changes, particularly when utilizing tools like Oracle Integration Cloud (OIC) or Oracle Service Bus (OSB). The article provides insights into the architecture's design and implementation in a real-world scenario within the delivery company. The proposed architecture significantly reduces integration and maintenance times and costs while maximizing scalability and encouraging the reuse of components. The source code for this implementation has been registered in the National Registry of Coyprights in Mexico.  
enterprise application integration; EAI; loose coupling; external data coupling; software architecture; system integration; legacy systems integration; software units +
Footnote †: journal: Applied
# Improvement of Speech/Music Classification for 3 GPP EVS Based on LSTM
## 2 Conventional 3 GPP Enhanced Voice Services
### Feature Selection  
The speech/music classification method applied to the EVS codec reuses the 68 parameters calculated in the early stages of codec preprocessing to minimize complexity. The EVS codec uses the technique proposed by [PERSON] [14], which is a method of analyzing the correlation matrix of all the features, for the initial selection of the features to be used in the Gaussian mixture model. In this way, we analyze the candidate feature sets with minimal cross-correlation and select the feature by calculating the discrimination probability as follows:  
\[U_{h}=\frac{1}{2}\sum_{j=0}^{l}\left|m_{h}^{(sp)}(j)-m_{h}^{(mus)}(j)\right| \tag{1}\]  
where \(m_{h}^{(mus)}\) and \(m_{h}^{(sp)}\) are the feature histograms that \(h\) generated on the music and the speech training database, respectively, and \(J\) is the whole number of frames in the database. The following 12 feature vectors are selected from the initial 68 feature vectors through the discriminatory probabilities \(U_{h}\); five LSF parameters, normalized correlation, open-loop pitch, spectral stationarity, non-stationarity, tonality, spectral difference, and residual LP error energy [3].
# IPN HandS: Efficient Annotation Tool and Dataset for Skeleton-Based Hand Gesture Recognition
## 4 IPN HandS Dataset
### Dataset Statistics  
The refined IPN HandS dataset comprises a total of 7770 instances across approximately 800,000 video frames. Each gesture instance is labeled with precise start and end frames and is accompanied by dense hand skeleton annotations. All frames from gesture instances are annotated with at least one hand skeleton, while several contain two. For non-gesture segments, the number of visible hands varies, including frames with zero, one, or two hands, due to natural hand movement and occlusion. Overall, the dataset includes over 700 thousand individual hand skeletons, averaging approximately 0.9 annotated hands per frame.  
Table 4 provides a detailed breakdown of the 14 gesture classes, showing the number of instances, total annotated frames, and average gesture duration per class. The no-gesture category accounts for over 2500 segments, representing roughly one-third of the dataset. This highlights the importance of modeling background activity and distinguishing intentional gestures from spontaneous hand motion. Among the gestures, pointing actions (P1F, P2F) exhibit the longest durations, while classes such as clicking and grabbing are shorter but frequent in interactive settings. Note that all videos were recorded at 30 frames per second (fps), meaning the shortest gestures span approximately 630 milliseconds (19 frames). Excluding pointing actions, the average duration across gesture classes is around 1.1 s (33 frames), making these fast and compact motions especially challenging for recognition systems that must detect and classify subtle temporal patterns within a limited window. At the same time, this also underscores the importance of precise temporal annotation, since any misalignment in gesture boundaries may result in the omission or distortion of the entire gesture sequence, ultimately degrading model performance.  
Figure 5: Temporal refinement of the zoom-in gesture. The original annotation (**top**) fails to delimit the core gesture motion, while the refined version (**bottom**) more accurately captures the start and end boundaries.  
Figure 6 illustrates the gesture classes of IPN HandS using overlaid hand skeletons. Each column shows sequential frames spanning the start, motion, and end of a gesture instance.
# Stock Price Prediction Using a Frequency Decomposition Based GRU Transformer Neural Network
## 3 Methods
### Hybrid GRU-Transformer  
#### 3.4.1 RNN  
Recurrent neural network(RNN) is a class of neural network where its hidden states are connected to the hidden states of the previous outputs, so its output is affected by the previous states [49]. By retaining a state that can represent information from an arbitrarily long context window, RNN exhibits temporal dynamic behavior. As shown in Figure 3, a typical RNN consists of three parts: input units X, hidden units h and output units Y. Mathematically, the RNN model can be formulated as:  
\[h_{t}=f(UX_{t}+Wh_{t-1}) \tag{2}\]  
\[Y_{t}=f(VH_{t}) \tag{3}\]  
* \(h_{t}\): the hidden state at time t
* \(Y_{t}\): the output state at time t
* \(X_{t}\): the input state at time t
* U: the weight matrix from input layer to hidden layer
* W: the value of the previous hidden layer is used as the weight matrix for the input of the next layer
* V: the weight matrix from hidden layer to output layer
* \(f\): the activation function, usually nonlinear
## 4 Identification of Biochemical Pathways in Glandular Trichomes
Genes from primary metabolism pathways were found to be highly expressed in mint trichomes, with, for example, genes of the glycolytic pathway, the pentose phosphate and the oxidative phosphorylation pathway accounting for more than 35% of all ESTs as well as lipid transfer protein (LTP) homologs (32%), which probably play a role in metabolite transport [16, 153] since, in tobacco, the trichome-specific _LTP1_ gene was shown to play a role in the secretion of terpenoids [160]. In contrast, in basil, more than 25% of glandular trichome ESTs were related to the phenylpropanol pathway or involved in phenylpropene biosynthesis [39]. In basil, tobacco and cultivated tomato, proteomics studies on glandular trichomes have been carried out as well, allowing for a detailed comparison between their transcriptomes and proteomes [e.g., 157, 161] and this led to new insights in the posttranscriptional regulation of trichome metabolism [161]. Included among the many proteins (1552 in total) identified in tomato trichomes were: enzymes involved in the MEP pathway; enzymes involved in synthesis of the flavonoid compound rutin; enzymes that take part in synthesis of volatile aldehydes (e.g., lipoxygenase C and hydroperoxide lyase; HPL); and, defense-related proteins, such as PPOs [157]. Moreover, a sesquiterpene synthase was identified that produces \(\beta\)-caryophyllene and \(\alpha\)-humulene from _E,E_-farnesyl diphosphate in glandular trichomes of leaves, but not in glandular trichomes of the stem [157], while other sesquiterpene synthases are preferentially expressed in other organs, for instance, in glandular trichomes of the stem [76, 158]. This indicates that, depending on the plant organ, there can be differences between if and when genes are expressed in glandular trichomes and thus which metabolites they accumulate.
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 3 Results and Discussion
### Uncertainty Quantification System Performance
#### 3.3.3 System Interpretability and Feature Analysis
This dual visualization approach--combining binned effects with impact direction analysis--provides engineers with a comprehensive understanding of how parameter adjustments will influence predicted outcomes, supporting informed decision-making in mix design and quality control applications. Figure 11: Enhanced SHAP dependence analysis showing binned effects of individual parameters on UCS predictions: (**a**) Curing period. (**b**) Compaction rate. (**c**) Cement content. Figure 12: SHAP feature importance and impact direction analysis for the primary Random Forest model. Each point represents a prediction instance, with colors indicating feature values (red = high; blue = low) and horizontal position showing the impact magnitude and direction on UCS predictions. The vertical ordering reflects overall feature importance. Uncertainty Model Feature Importance  
The uncertainty model reveals fundamentally different patterns compared to the primary model, as shown in Figure 9d. Most notably, the central UCS prediction itself becomes a significant feature for uncertainty estimation, indicating that the magnitude of predicted strength influences the expected prediction error. This relationship suggests that higher predicted UCS values tend to be associated with greater absolute errors, reflecting the increased challenge of precise prediction at higher strength levels where material behavior becomes more complex. The original input parameters show altered importance rankings in the uncertainty model compared to the primary model. While cement content and curing period dominated central predictions, their direct influence on prediction uncertainty is more limited once their effect is captured through the central prediction feature. This behavior reflects the systematic and predictable nature of their influence through established hydration mechanisms--once their effect is captured in the central prediction, their additional contribution to prediction variability becomes secondary. Compaction rate demonstrates a different pattern of influence in the uncertainty model, contributing more significantly to prediction variability than to central predictions. This finding suggests that compaction velocity affects microstructural aspects of the soil-cement matrix that influence result consistency without substantially altering average strength development. Such effects might include subtle variations in particle orientation, pore distribution, or cement homogeneity that manifest as variability rather than systematic strength changes. This differential feature importance between the two models provides valuable practical guidance. For optimizing central strength values, engineers should focus primarily on cement content and curing time. However, for achieving consistent and predictable results, attention to compaction procedures may be more important than their minimal impact on average strength would suggest.
# Improved Remote Sensing Image Classification Based on Multi-Scale Feature Fusion
## 3 Methods
### Structure of the WFCNN Model
#### 3.1.3 Classifier  
The Softmax model is a widely used classifier in FCN, SegNet, DeepLab, RefineNet, UNET, and other models. In WFCNN, Softmax uses the 64-layer feature graph generated by the decoder as input, calculates the probability of the pixel belonging to each category pixel-by-pixel, and organizes it into a category probability vector as the output. The WFCNN uses the category corresponding to the maximum probability value as the pixel category.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Unit** & **Layers before Adjustment** & **Layers after Adjustment** \\ \hline D5 & 512 & 512 \\ D4 & 512 & 512 \\ F4 & 512 & 256 \\ D3 & 256 & 256 \\ F3 & 256 & 128 \\ D2 & 128 & 128 \\ F2 & 128 & 64 \\ D1 & 64 & 64 \\ F1 & 64 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of feature layers per decoding unit.