esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Tuning Algorithm for Tuneable Clamping Table for Chatter Suppression in Blade Recontouring
###### Abstract  
The production and repair of blades for aerospace engines and energy turbines is a complex process due their inherently low stiffness and damping properties. The final recontouring operation is usually performed by milling operations where regenerative chatter is one of the main productivity limiting factors. With the objective of avoiding specific stiffening fixtures for each blade geometry, this paper proposes a semi-active tuneable clamping table (TCT) based on mode tuning for blade machining. The active mode of the device can be externally controlled by means of a rotary spring and eddy current damping modules. Its in-series architecture allows damping to be introduced to the critical mode of the thin-walled part without any direct contact in the machining area and enables a more universal clamping. Its chatter suppression capabilities are maximized by means of a novel self-tuning algorithm that iteratively optimizes the tuning for the measured chatter frequency. The benefits of the iterative algorithm are validated through semidiscretization and initial value time-domain simulations, showing a clear improvement in blade recontouring stability compared to regular broad-bandwidth tuning methods.  
blades; milling; chatter; damping; fixture +
Footnote †: journal: Applied Sciences
# Crop Land Change Detection with MC&N-PSPNet
## 2 Materials and Methods
### Attention Mechanism
#### 2.2.2 Nam  
The NAM [22] (Normalization-Based Attention Module) embedded in feature fusion in this paper is a normalization-based attention mechanism designed to reduce the weights of less significant features. This method applies sparse weight penalties to attention modules, making these weights computationally more efficient while maintaining the same level of performance. It helps the model to capture richer and more distinctive feature information at different levels, facilitating the detection of agricultural areas affected by factors such as planting patterns and crop types. Adding contribution factors for weights to the attention mechanism further suppresses insignificant features. The NAM used in this study incorporates the scaling factor of Batch Normalization to represent the importance of weights, avoiding the need for additional fully connected layers and convolutional layers as seen in SE [23], BAM [24], and CBAM.  
The integration method of the NAM adopts the CBAM module, redesigning the channel attention (see Figure 4a) and spatial attention sub-modules (see Figure 4b). Thus, the NAM can be embedded at the end of each network block. For the channel attention sub-module, the scaling factor from BN (Batch Normalization) is used (i.e., the variance in BN, as shown in Equation (4)), reflecting the magnitude of changes in each channel and the importance of that channel. Equation (5) represents the final output feature obtained, where \(\gamma\) is the scaling factor for each channel, enabling the derivation of weights for each channel. If the same normalization method is applied to each pixel in space, spatial attention weights can be obtained, as shown in Equation (6), referred to as pixel normalization. To suppress unimportant features, a regularization term is added to the loss function, as shown in Equation (7).  
\[\mathrm{B_{out}}=\mathrm{BN(B_{in})}=\gamma\frac{\mathrm{B_{in}-\mu_{\beta}}}{ \sqrt{\sigma_{\beta}^{2}+\epsilon}}+\beta, \tag{4}\]  
\[\mathrm{M_{c}}=\mathrm{sigmoid(W_{\gamma}(BN(F_{1})))}, \tag{5}\]  
\[\mathrm{M_{s}}=\mathrm{sigmoid(W_{\lambda}(BN_{s}(F_{2})))}, \tag{6}\]  
\[\mathrm{Loss}=\sum_{(x,y)}1(f(x,W),y)+\mathrm{p}\sum\mathrm{g(\gamma)}+\mathrm{ p}\sum\mathrm{g(\lambda)}, \tag{7}\]
## 4 Discussion  
### GEE and other LULC Analysis Platforms  
The recent accessibility of PlanetScope data in GEE within the NICFI program offers an unprecedented high-resolution broad time series that represents a substantial advance for detecting and monitoring LULC dynamics in tropical areas. The availability of PlanetScope data in GEE, together with other various satellite imagery datasets (MODIS, Landsat, Sentinel), may offer even less-experienced GEE users an excellent opportunity to improve the detail of the land cover analysis in tropical areas. In this regard, this research contributes to assessing the potentiality of PlanetScope data for land cover classification in GEE. The initiative by NICFI runs alongside many other land cover mapping and monitoring satellite-based programs for the Amazon area, such as MAPBIOMAS [83], TerraBrasilis [84], PRODES [85], and Brazil Data Cube (BDC) [44]. At the global level, other recent global Sentinel-based mapping initiatives, such as those by ESA [86] and ESRI [87], aim to provide a homogeneous land cover map and detect and assess land cover dynamics. The approaches used in some of the programs for land cover classification of the Amazon area are often very advanced, based on object-oriented approaches and ML algorithms (such as TerraBrasilis and BDC). They provide efficient web platforms for land cover analysis and various application program interfaces (APIs) to access spatial data and algorithms.  
Compared to these environments, the GEE platform appears to be more user-friendly for developing specific analyses due to the easy-to-use programming interface, the intuitive JavaScript code, the availability of advanced algorithms, the constantly growing RS imagery database, and the Map-Reduce model's efficiency [31; 84]. Furthermore, the most recent state-of-art ML algorithms can be used in GEE through the Python Google Colab environment. These features, in many cases, make GEE a preferred choice compared to traditional workstations, which are often equipped with expensive software and hardware. In this regard, GEE offers a valuable opportunity for researchers in developing countries  
Figure 4: Comparison of user’s (**a**) and producer’s (**b**) accuracies, and F-scores (**c**) of the classification approaches (Y axis) obtained for the various LULC classes (X axis). Water bodies are omitted. Codes used in the legend for classification approaches are reported in Table 2.  
to access a robust RS cloud-based image processing environment. However, GEE may present various drawbacks compared to other environments due to its relatively restricted programming framework. In addition, the free GEE version cannot be used for commercial uses and may not be suited for \"production\" workloads due to some processing, storage, and image size limits [88].
## 2 Methodology
### Data Collection and Expert Validation  
The dataset utilized in this study consists of 28 completed projects forming part of the European Road Corridor 10 in the Republic of Serbia. This corridor stretches from the northern border with Hungary and proceeds southward, splitting near the city of Nis into two branches: the southern branch extending toward Athens, Greece, and the eastern branch leading toward Asia Minor via Bulgaria. The selected projects include major highway sections, interchanges, and critical infrastructure elements constructed as part of Corridor 10's development. These projects were chosen based on the availability of detailed risk assessment records, ensuring the reliability of the dataset for this research. Additionally, the dataset includes projects with an accepted contractual value exceeding 3 million euros, a completion deadline longer than one year, and a minimum ICP of 1.5 million euros, indicating a high occurrence of adverse events. Among these, 25 projects were selected for model training, while three were reserved for testing purposes. This dataset captures diverse project characteristics and provides a reliable basis for developing models and integrating empirical probabilities [8; 17; 40].  
To validate the methodology and the derived coefficients, this study engaged a panel of 14 experts from various disciplines relevant to road infrastructure projects, including hydrotechnics, geotechnics, structural engineering, environmental engineering, urban planning, procurement, and project management. Each expert possessed extensive professional experience, having managed an average of 30 projects and accumulated at least 10 years of expertise in road infrastructure. The multidisciplinary composition of the panel ensured comprehensive coverage of technical, environmental, and procedural aspects. Through iterative discussions and validations, the experts reviewed and refined the identified project characteristics and coefficients, leveraging their experience from both Serbian and international projects. Many of them had held key roles in the analyzed projects, such as Employer's Representatives, Engineers, and Implementation Consultants, providing critical insights into project planning and execution. Given that parameter evaluation has already been established in the literature [8], this study builds upon those findings to further refine the applied methodology.  
The data from real projects used to quantify the project complexity coefficients were cross-validated by the expert panel to ensure consistency and accuracy. This process involved comparing the derived coefficients against empirical project outcomes to confirm their relevance and reliability. By integrating expert validation with data from real projects, this study ensured that the methodology is both scientifically grounded and practically applicable.
# AI-Enabled Interference Mitigation for Autonomous Aerial Vehicles in Urban 5G Networks +
## 5 Reinforcement Learning
### Q-Learning
\[Q_{\pi}^{\star}(s_{t},a_{t}):=E_{s^{\prime}t}\bigg{[}w_{s,s^{\prime},a}+\gamma max _{a^{\prime}}Q_{\pi}^{\star}(s^{\prime},a^{\prime})\bigg{|}s_{t},a_{t}\bigg{]} \tag{1}\]  
Here, \(\gamma:0<\gamma<1\). \(s^{\prime}\) is the next state. \(a^{\prime}\) is the next action. These steps are repeated until the optimal solution is converged. This is also represented in Figure 8.  
In this paper, Q-learning for interference mitigation is considered the baseline method against which the proposed algorithm is compared. \begin{table}
\begin{tabular}{l l} \hline \hline
**State–Action** & **Q-Value** \\ \hline (S1, A0) & 4 \\ (S3, A4) & 2 \\ (S5, A1) & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Example of Q-table with state–action pairs mapped to Q-values.
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 2 Related Works
### Fusion of Appearance and Gait-Based Person Re-ID  
The fusion of gait and appearance features in person Re-ID has shown promise, yet only a limited number of studies have explored this approach in depth.  
A pioneering work was proposed by [PERSON] et al. in [33], where the authors used Gait Energy Images (GEI) to capture key characteristics from silhouettes, integrating those with color features to enhance long-term person Re-ID. Similarly, [PERSON] et al. merged GEI with color and texture features in [34], while [PERSON] et al. [35] developed a bi-modal Re-ID method that encodes visual appearance characteristics based on color, texture, and semantic attributes, as well as gait information, using GEI and local color and texture characteristics. They employed a score-level fusion technique to combine these features for enhanced Re-ID accuracy.  
Recent works relying on deep learning architectures have combined silhouette-derived gait attributes with sophisticated appearance feature extraction methods. For example, in [36], [PERSON] et al. proposed a model that merges appearance features extracted using ResNet with gait features obtained from an Improved Sobel-Masking Active Energy Image (ISMAEI) model, providing a thorough representation of gait.  
Addressing challenges in scenarios with long-term clothing changes, [PERSON] et al. introduced GI-REID [37], a dual-stream framework that integrates a gait recognition stream based on silhouettes with an image-based Re-ID stream. This approach demonstrates the efficacy of combining different biometric features to enhance person Re-ID.  
Building on this, the work by [PERSON] et al. reported in [38] focused on combining gait recognition with RGB modalities, particularly in scenarios involving changes in clothing. This study leveraged silhouettes for gait recognition, reflecting the constant evolution of gait-based identification methods. The authors employed a unique fusion technique that combines dynamic gait information extracted from silhouettes with static appearance features from RGB data, enabling effective identification despite clothing variations.  
Furthermore, [PERSON] et al. [39] integrated visual appearance and gait features using a novel approach that utilizes silhouettes for gait analysis. This method demonstrated its ability to handle complex indoor scenarios. This paper innovatively applied graph convolutional networks to integrate spatiotemporal gait features from silhouettes with high-level appearance characteristics, offering a more robust and context-aware approach to person Re-ID in diverse indoor environments.  
In light of these advancements, we propose a novel approach that combines visual appearance features with skeleton-based gait features, moving beyond traditional silhouette-based methods to address the challenge of person Re-ID.
## 2 InSAR Coseismic Deformation and Postseismic Time Series
### Postseismic Observations and Time Series
Additionally, postseismic deformation can be approximated by a logarithmic term [13; 31; 43], and the observed time series can be described as follows [13; 40]:  
\[\mathrm{X(t_{i})=at+b\cdot\log_{10}\left(1+\frac{t_{i}}{\tau}\right)+c\cdot\sin \left(2\pi\cdot t_{i}\right)+d\cdot\cos\left(2\pi\cdot t_{i}\right)+e\cdot\sin \left(4\pi\cdot t_{i}\right)+f\cdot\cos(4\pi\cdot t_{i}) \tag{1}\]  
where \(\mathrm{X(t_{i})}\) represents the surface observation signals after a large earthquake at the i-th time epoch (\(t_{i}\)), and a-f are the coefficients that need to be solved. Among them, c and d represent the annual signals, e and f represent the semiannual cycles, and \(\tau\) isthe decay coefficient that denotes the decay pattern of postseismic deformation. The 'pure' postseismic time series can be obtained by estimating the optimum coefficients in Equation (1) using a linear least-squares method. Furthermore, we removed some PS points with low SNR, which are far from the fault traces, and downsampled the dataset to improve inversion efficiency. The evolution of postseismic displacements resulting from this event in both the temporal and spatial domains is shown in Figures 3 and 4. The postseismic displacements from the archived Sentinel-1A/B acquisitions on both sides of the surface traces show opposite movement directions but similar magnitudes. Compared with the coseismic displacements, the cumulative postseismic deformation has a smaller magnitude over 450 days (26 May 2021 to 19 August 2022) after the event (Figures 3 and 4). The maximum cumulative displacement from the descending observations within 450 days after this event is about 0.09 m, whereas the ascending observations show approximately \(-0.07\) m at the same spot along the LOS direction. Therefore, on the fault plane, we reasonably assume that the transient deformation after the 2021 Maduo event may still be due to aseismic strike-slip. There are several separate and obvious near-field postseismic deformation along the rupture traces (Figure S2), implying that aseismic slip heterogeneities could exist on the seismogenic fault, and that stress release may have been segmented after the earthquake. Figure 3: The InSAR time series derived from the descending track within 450 days after the mainshock.
# A Comprehensive Review of Multimodal Analysis in Education
## 3 Methodologies in Multimodal Analysis
### Data Collection Modalities
#### 3.1.4 Interaction Logs  
Interaction logs are critical for tracking learners' engagement with digital platforms. These include mouse clicks, keystrokes, scrolls, touch events, or system events recorded during interaction with educational software [16; 56; 57]. Logs enable the detection of problem-solving strategies, learning paths, or system navigation patterns. Some studies employed custom platforms (e.g., CoTrack or Knowledge Forum) to record and analyse collaborative writing or problem-solving [58; 59; 60]. Logs have also been combined with other modalities, like gaze and voice, to assess learning effectiveness [40].
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 5 Simulation Analysis
### IEEE 69-Node Distribution Network
#### 5.2.2 Computational Efficiency  
From Figure 16b, it can be seen that the computational efficiency of Algorithm 1 was below 50 ms for a 69-node scale distribution network, thanks to its linear time complexity, which indicates that the advantage of the computational efficiency of the algorithm proposed in this paper is more obvious in large distribution networks. For the intelligent optimization algorithm represented by Algorithm 2, the population size of the algorithm is usually no less than the number of sections of the distribution network to ensure the correct rate. Assuming that the distribution network contains \(n\) sections, Algorithm 2 needs to operate on at least \(n\) individuals in the iterative process, and the cost of individual operation is \(\Theta(n)\). Even with the convergence condition of \"no update of global extremum\" [20], the asymptotic lower bound of \(\Omega(n^{2})\) is available in the best case. The test results show that the computational efficiency of Algorithm 2 was around 400 ms, which is more than eight times different from the algorithm in this paper. The fault area determination process of Algorithm 3 still requires iterative operations, and its section location requires exhaustive fault scenarios, which affects location speed. The test results show that the computational efficiency of Algorithm 3 was about 200 ms, which is more than four times different from the algorithm in this paper. The solution of Algorithm 4 involves the computation of its continuous relaxation LP model several times, and the time complexity of the LP problem reached O(\(n^{4}\)) [27]. From Figure 16b, we can see that the algorithm had the lowest computational efficiency, which reached the second level and had a large gap compared with the computational efficiency of the algorithm in this paper.
## 2 Network Scenarios and Problems
### Packet Scheduling over 802.11 ay from TRA Layer
However, for the case of the multipath algorithm, the utility function does not exist, and the same interpretation of utility maximization seems infeasible (Section III-B [7]). We observe that the traditional fluid-flow approach neither works well nor adapts intelligently over the highly asymmetric, dynamic, and complicated mix of PHY/MAC/TRA layers' advances over 802.11_ay_ networks. Due to its lack of careful consideration of runtime states and suboptimal solutions, it typically performs undesirably over IEEE 802.11_ay_ networks. We introduce a new multi-armed bandit-based scheduling with a packet cloning mechanism and an (upper bound) delay factor that is pledging for dynamic scheduling and congestion avoidance. The proposed approach is to support flexible runtime control and its capability to respond intelligently concerning highly dynamic and time-variant state spaces. To improve further, we develop an idea of a federated learning-based bandit for accounting for the MAC/PHY beamforming/selection mechanism across all available links intelligently, ameliorating the mobility effects. This approach helps to alleviate the adverse impacts due to congestion control approaches at the TRA layer (prevalent with the pioneering MPTCP designs: BALIA [7], OLIA [30] and LIA [23]). A recurrent theme in BALIA [7], OLIA [30] and LIA [23] is presuming loss probabilities and RTTs as given variables (parameter uncertainty) and then use conventional TCP models to determine the (number of packets to be transmitted) congestion windows for each path. However, with the WiFi MAC/PHY advances and impairments' dynamics over 802.11_ay_ networks, the loss probabilities and RTTs are not exogenous quantities. In reality, beamforming and selection mechanisms shall be viewed as functions of the congestion windows for all links because of the coupled interactions in our design. Moreover, as a function of the loss probability and RTTs, the average congestion window cannot be estimated without including uncertainties in the equations of the conventional TCP models.
# Improved First-Order Motion Model of Image Animation with Enhanced Dense Motion and Repair Ability
## 3 Experiments and Results
### Experimental Results and Analysis
#### 3.3.3 Ablation Experiment  
In order to further analyze the benefits with the attention module and the multi-scale occlusion restoration module in our method, we conducted a large quantity of ablation experiments. The results are shown in Table 2.  
From Table 2, we can see that when we used the attention module exclusively, all of our metrics were worse than those by using CBAM together. However, when we included the multi-scale occlusion restoration module again, our method achieved much better performance. Compared with the FOMM and CBAM methods, all the metrics indicate that our method had the better performance. The proposed attention module seeks to identify the most dominant features in a frame by eliminating the average pooling in space and solely relying on maximum pooling. The task of reconstructing the background and addressing occlusions is transferred to the multi-scale occlusion restoration module, which employs a multi-scale occlusion map to perform step-by-step repair. Thus, there is no conflict between the two modules.  
In addition, we also visualized the keypoints, as shown in Figure 13. From the comparison of experimental results, it is obvious that the keypoints detected by our method were more consistent with the human body structure, such as defining a keypoint for the head. However, in FOMM, some keypoints were incorrect. FOMM also had the problem of keypoints positioning error, such as the keypoints on the background, which will lead to the leakage of motion information to the background.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{\mathcal{L}_{1}}\) &
\begin{tabular}{c} **TaiChiHD** \\ **(AKD, MKR)** \\ \end{tabular} & **AED** \\ \hline FOMM & 0.061 & (6.75, 0.031) & 0.167 \\ FOMM + CBAM & 0.059 & (6.16, 0.024) & 0.178 \\ FOMM + our attention module & 0.060 & (6.60, 0.029) & 0.181 \\ FOMM + CBAM + multi-scale occlusion restoration module & 0.059 & (6.54, 0.027) & (0.179) \\ Our method & **0.058** & **(5.84, 0.023)** & 0.170 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study on the TaiChiHD (256) dataset with 10 keypoints (the best result is displayed in bold).  
Figure 13: _Cont._
## 2 Materials and Methods
### Method
#### 2.3.3 CC-PLSR Method  
For comparison, a simpler correlation coefficient (CC) method was also used for band selection. The CC method directly selects bands based on the correlation between spectral data and the target variables. The correlation threshold was set between 0 and 1, in increments of 0.1. The threshold yielding the best prediction accuracy for AAs and TPs was chosen. Both CC+PLSR and CARS+PLSR models were constructed for comparison.