esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Performance Evaluation and Analysis
#### 5.3.5 Impact of the Proposed Encoding Framework on Different Decoders and CPU Architectures  
The proposed encoding algorithm presented in this manuscript is based on the HM 16.0 reference encoder and decoder implementations on an Intel x86 CPU architecture. For instance, the bpp and MSE parameters defined in Section 3 are based on the corresponding values generated by the HM 16.0 encoder. Furthermore, cpp values utilized throughout the modeling phase in Section 3 correspond to the decoding-complexity levels profiled for HM 16.0 decoder implementation. The decoding-complexity level is tightly coupled with the implementation details, CPU architecture, and hardware level optimization. Therefore, it is important that complete decoder profiling is carried out for each decoder implementation on each CPU architecture to achieve an optimal decoding-complexity/energy reduction. However, the focus of this work is to present a framework which can be used to achieve decoding-complexity/energy reduction by generating joint decoding-complexity and rate-controlled bit streams. Therefore, decoder profiling for individual implementation and architecture is considered outside the scope of this work. However, the experimental results presented in Tables 3-5 correspond to the decoding-complexity and associated energy reductions when decoding bit streams use openHEVC decoder implementation on an Intel x86 CPU. These results correspond to the decoding-complexity/energy reductions achieved when bit streams are encoded with 40% less decoding-complexity to that of HM 16.0 decoder. Similarly, the experimental results in Table 6 present the decoding energy reduction achieved when decoding the bit streams using MXplayer [62] running on a Samsung Galaxy Tab A device that consists of an Exynos 8890 processor with ARMv8 Instruction Set Architecture [63]. It can be observed that the percentage energy reduction level is different to that in the Intel x86 results. However, overall, the bit streams generated by the proposed algorithm outperform the energy reduction per 1 dB PSNR quality loss compared to the state-of-the-art methods. Thus, the resultant energy reductions with the proposed encoding framework with different CPU architectures and decoder implementations (even though they are sub-optimal) are still significant, despite being optimized for a different encoding and decoding architecture.
# Reinforcement Learning with Dynamic Movement Primitives for Obstacle Avoidance
## 4 Simulations and Experiments
### Experiments  
The goal of this task is for the real 7-DOF robot to track the trajectory learned from the demonstration, avoiding collision with an obstacle in the meantime. In addition, the RL method is used to optimize the performance in the task.  
In the demonstration process, we pulled the end-effector of the robot according to the planned trajectory and the poses of the end-effector will be recorded over time. Here, we focus on trajectory and obstacle avoidance of the robot end-effector, and joint angles are solved automatically using inverse kinematics of the robot. The demonstrated trajectory in end-effector space is shown in Figure 7. We use a DMP with ten basis functions to train each pose of the end-effector. It should be noted that we are mainly concerned with the change in the translational pose position of the end-effector, so the rotational pose is fixed [3], which means only three working DMPs are learning the Cartesian positions of end-effector of the 7-DOF robot. The initial potential strength is 0.1, and the cost is designed as (22); only \(G\) is changed to \(G=1\). The variance of the exploration noise of each pose is respectively 0.01, 0.001, and 0.01. The acquired point cloud of obstacle is processed by applying standard filtering techniques [14]. In addition, for the sake of simplicity, we are only going to consider the case where the obstacle is a sphere.  
Figure 8 depicts the trajectories of a real experiment for the 7-DOF robot, the blue line is the desired trajectory (learned from demonstration), the red dotted line is the obstacle avoidance trajectory with constant value of potential \(\lambda\) and trajectory shape \(\theta\), and the green dotted line is the learned trajectory with PI2. The training and learning process is the same as the simulations, so this will not be described in more detail again in this paper. Compared with the training trajectories before and after the learning, it is obvious that the potential field strength and trajectory shape obtained by reinforcement learning method can make the real robot perform better in obstacle avoidance. This is consistent with the simulation results. Although the robot only performs the trajectory learned offline, the training information is projected into the training space using obstacles and trajectory information in the real space. If trajectory tracking error is not taken into account, the proposed reinforcement learning framework is available in most cases; furthermore, the RL framework can be extended to more practical applications.  
Figure 7: Obstacle avoidance behavior with different values of \(\lambda\).  
In summary, simultaneous learning potential and trajectory shape are available by using the prosed RL framework whether in simulations or real experiments. In addition, it enables the robot to obtain better performance in obstacle avoidance, tracking the desired trajectory and performing other subtasks.
# Learning and Compressing: Low-Rank Matrix Factorization for Deep Neural Network Compression
## 3 Algorithm
### Optimization Algorithm
The latter step compresses the matrix in each iteration via CUR decomposition, of the following form:  
\[\min_{\mathbf{\Theta},r}\lambda\text{C}_{k}(r_{k})+\frac{\mu}{2}\left\| \mathbf{W}_{k}-\mathbf{\Theta}_{k}-\frac{1}{\mu}\mathbf{M}_{k}\right\|_{F}^{2 },\text{s.t.rank}(\mathbf{\Theta}_{k})= r_{k}\leq R_{k},k=1,...,K \tag{9}\]  
In this step, as described in Algorithm 1, we first decompose and approximate the weight matrix, where \(\Sigma=\text{diag}(\mathbf{\mathbf{e}}_{1},\mathbf{\mathbf{e}}_{2},..., \mathbf{\mathbf{e}}_{r})\) and \(\mathbf{\mathbf{e}}_{1}>\mathbf{\mathbf{e}}_{2}>...>\mathbf{\mathbf{e}}_{r}\). To better obtain the optimal rank, we use the Eckhart-Young theorem [39] to convert Equation (9) into the closed iterative form so that this equation can be equivalent to:  
\[\min_{r_{k}}\lambda\text{C}_{k}(r_{k})+\frac{\mu}{2}\sum_{i=r_{k+1}}^{R_{k}} \mathbf{\mathbf{\mathbf{e}}}_{i}^{2}, \tag{10}\]  
Therefore, according to Equation (6), Equation (10) can find the optimal rank by enumerating all values of \(r_{k+1}\) up to \(R_{k}\). Once we obtain the optimal rank \(r_{k}\), \(\mathbf{\Theta}_{k}\) is computed using the low-rank CUR decomposition with \(r_{k}\) and \(c_{k}=\text{O}(\frac{r_{k}\log r_{k}}{c^{2}})\) in \(k\)-th layer. In general, our algorithm requires multiple iterations, and each iteration consists of the learning step and the compression step. The former step reduces the gap between the weight matrix \(\mathbf{W}\) and the low-rank matrix \(\mathbf{\Theta}\) by using the SGD method, and the latter step finds the optimal rank \(r\) by enumeration, and then the optimal rank \(r\) obtained before is used as the input for low-rank CUR decomposition (Algorithm 1) to compress the weight matrix \(\mathbf{W}\). Repeat above operations until \(\mathbf{W}\) and \(\mathbf{\Theta}\) finally coincide.
# Multi-Dimensional Fusion Attention Mechanism with Vim-like Structure for Mobile Network Design
## 3 Materials and Methods
### MDVA Method  
As mentioned above, the goal of this research is to investigate how to model channel attention and spatial attention efficiently and cost-effectively, co-opted Vision Mamba [22], without increasing the number of parameters. In this section, we introduce a novel attention mechanism that differs from CBAM [8] and SENet [6], which rely on a significant number of learnable parameters to establish interdependencies between channels. Drawing inspiration from the structure of Vision Mamba, we propose an attention mechanism that integrates channel and spatial attention with minimal parameter usage, named the multidimensional Vim-like attention.  
The multidimensional Vim-like attention module is depicted in Figure 2. As the name implies, an input tensor \(x\) is processed by the multidimensional fused Vim-like attention module, resulting in \(x_{\text{left}}\) through the left branch. The input \(x\) then passes through a structure comprising three parallel branches. Each branch is responsible for capturing one of the dimensions--channels (\(C\)), height (\(H\)), and width (\(W\))--in the \(C\times H\times W\) tensor. The two branches on the right capture and fuse channel attention, while the left branch builds spatial attention, forming a \"\(1+3\)\" configuration. The outputs of all three branches are then aggregated using a simple average after multiplying by the result of the left branch.  
This design aims to build interdimensional interactions to compensate for the independence of channels in traditional attention mechanisms. By integrating these interactions with minimal additional parameters, the Vim-like attention module effectively enhances feature representation without the computational overhead typically associated with other attention mechanisms.
# Study on Quantitative Expression of Cycling Workload
## 2 Cycling Safety and Comfort-Formation Mechanism and Measurement Methods
### Measurement for Cycling Workload
In addition, some researchers have classified workload using physiological data, mainly in the driving and aviation domains, where ECG [34; 35; 36; 37] and EEG [38; 39; 40; 41] are the most commonly adopted physiological techniques. Although most of the research studies clearly presented the workloads of driver and pilot as mental workload (MWL), ECG has been used to evaluate static cycling workload and promising results have been derived, particularly for HRV [42; 43]. Based on the literature analysis for the pros and cons of the available physiologic metrics, we inferred that HRV can objectively express cycling workload and effectively accounts for both the mental and physical aspects. According to theory surrounding HRV [44], there are two methods for the analysis of HRV; i.e., time-domain methods and frequency-domain methods (sometimes called time analysis and spectral analysis, respectively). Frequency-domain methods have two types of records--short-term records (2-5 min) and long-term records (24 h period). Due to time considerations, HRV data in studies usually choose the fluctuations between consecutive instantaneous heart rates for short-term records. There are three main components in the HRV power spectrum--very low frequency (VLF), low frequency (LF), and high frequency (HF). Researchers have shown that an increase in LF is associated with sympathetic activity and a reduction in HF is linked to parasympathetic activity, so an increase in the LF to HF ratio (LF/HF) reflects increases in sympathetic nerve excitability. Among the various frequency-domain HRV measures, LF/HF is the most widely used measure [26]. Additionally, LF/HF is a simple and effective detection index; an increase in the LF/HF is an indicator of an increment in mental workload [45]. There is also evidence [46] that the LF/HF value has the largest correlation coefficient factor with HRV; this finding is significant in the evaluation of physical workload [47; 48]. On the basis of theoretical analysis and the applicability to cycling workload, this study selected LF/HF for HRV as the physiological measurement indicator and combined the proposed subjective scale to explore the cycling workload quantification method. In order to obtain the limit value of the physiological signal (LF/HF) under various cycling workload statuses, a field cycling experiment--using different bikeway characteristics impacting cycling workload--was designed and executed.
## 1 Introduction
Given the above limitations, we have worked to develop an original solution for traffic surveillance, by taking into special account low penetration rate of our proposal (considering both the practical aspects of limited fraction of vehicles with wireless connectivity and limited fraction of communication-enabled vehicles willing to participate to our collaborative surveillance service) and the goal of collecting a large array of different monitoring data about vehicles in targeted areas of particular interest. In particular, our proposal exploits V2I communications between vehicles and Road Side Units (RSUs), deployed at selected traffic lights at the occurrence of relevant intersections to collect various information about local traffic status. To enable a precise and robust view of traffic flow performance and indicators, necessary to enable effective and dynamic traffic management, our solution also discriminates the different roads converging to the intersection and in which direction the vehicles are travelling on these roads. The solution originally presented here is also a follow-up of and exploits the research experience made thanks to the research activities accomplished within the framework of the EU COLOMBO project [7], which aimed at optimizing road network usage and at alleviating traffic congestions through VANET-based optimization of decentralized traffic light control and management. To validate and assess the performance results achievable by our proposed solution, the paper also contributes to the literature in the field with a thorough reporting, analysis, and comparison of several performance indicators of our original technique with regards to the existing literature: the reported performance results have been collected over realistic simulation scenarios deriving from the usage of the state-of-the-art iTETRIS [8] framework, fed with real traces of vehicular traffic measured in the city of Bologna (a medium-size urban environments of around 450,000 inhabitants). Our contribution to the community in the field also includes the availability of the code for the proposed solution and of our original extension for improved usability of the iTETRIS framework, as described in the following sections; the interested readers may refer to [[http://lia.disi.unibo.it/Research/iTetrisUniboExt/](http://lia.disi.unibo.it/Research/iTetrisUniboExt/)]([http://lia.disi.unibo.it/Research/iTetrisUniboExt/](http://lia.disi.unibo.it/Research/iTetrisUniboExt/)) for additional details and downloads.
## 5 Simulation and Experiment
### Comparison Experiment
#### 5.2.2 Comparison with Other Improved Algorithms  
To further prove the advantages of DABAA, this paper compares DABAA with SLGA and ABC-Q algorithms. SLGA algorithm [36] is an improved GA algorithm, [PERSON] et al. combined GA algorithm with SARSA and Q-learning to reduce the number of iterations of GA and improve the performance of GA algorithm. ABC-Q algorithm [26] is an improved ABC algorithm, [PERSON] et al. combined ABC algorithm with Q-learning to dynamically adjust the update dimension of ABC algorithm and improve its convergence accuracy. Table 7 shows the data statistics obtained after the four algorithms for running 9 tasks for 20 times, including the optimal QoS fitness value and average time consumption. Figure 6 shows the distribution of 20 QoS fitness values obtained by the four algorithms for solving the task T(400,20). It can be seen that the convergence accuracy of DABAA algorithm is better than the other three improved algorithms.  
The average time consumption for DABAA, MMAS, SLGA and ABC-Q is shown in Figure 7. It can be seen that the average consumption time of DABAA is less than the other three algorithms, this is because DABAA accelerates the convergence speed through the optimal fusion evaluation strategy, avoids the invalid iterations. According to Figures 6 and 7, it can be seen that the performance of DABAA is better than other algorithms, with better convergence speed and accuracy.  
Figure 5: Results of accuracy with different task sizes.
## 4 Bayesian Learning-Based Clustered-Sparse Channel Estimation  
In this section, the parametric form of Bayesian channel estimation is given first, and followed by the cluster distribution learning. Then, the partition-based clustered-sparse Bayesian learning algorithm is presented to obtain the clustered-sparse distribution and Bayesian channel estimate. At last, the complexity and performance analysis of this algorithm is given.
# Social Media Analytics for Disaster Response: Classification and Geospatial Visualization Framework
###### Abstract  
Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system's dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model's effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters.  
deep learning; disaster response; interactive map; social media; tweet +
Footnote †: 2020: _Article_
# Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data
## 3 Considerations on Data Mashup
### Mashups of Vertically Partitioned Datasets  
We consider three actors in a privacy-preserving data mashup protocol: the data consumer, the data provider, and the data mashup coordinator. The _data consumer_ is the party that acquires individuals' data for a specific purpose. A data consumer could be, for example, an institution that seeks to acquire LA datasets for sociological studies. The _data provider_ is the party that supplies the individuals' data, e.g., an educational center that provides the students' data. Finally, the _data mashup coordinator_ represents a point of connection between data consumers and providers. Its function is to coordinate the datacollection, integration, and anonymization. The mashup coordinator may be a third party or the data consumer itself.  
The eventual dataset provided to the data consumer is built by vertically joining the data partitions held by a set of providers. Each data partition registers different characteristics--or attributes--from the same set of individuals, and all the partitions share a common key field--or a common identifier attribute. For simplicity, we consider that a _vertical data partition_ is a data table. Each row is a data record containing information about a single individual, and each column is an attribute containing information regarding one of the features collected. The attributes of a partition can be classified as identifiers, quasi-identifiers, and confidential. We assume that identifier attributes are not shared with data consumers, except for the common identifier attribute that must be shared with the mashup coordinator in a privacy-preserving manner. We also assume that each data provider decides the amount of masking required for its quasi-identifier attributes.
## 5 Experiment Results and Analysis
### Improvement Based on Forecasting Methods
Similarly, from Figure 10b we can see that accurate forecasting takes more than twice the time compared to achieving 85% accuracy, while to achieve 90% accuracy only takes 4 s more than to achieve 85% accuracy. This implies it could be practical and helpful to spend a little more time to get better forecasting results. Besides, comparing Figure 10a,b, it can be observed the forecasting time cost of the cloudy/rainy/snowy day is much higher than the sunny day. For example, in Figure 10a it only takes about 17 s to achieve 90% accuracy but in Figure 10b it takes nearly 26 s.  
Figure 8: Forecasting results under a 10-min action delay on a sunny day. (**a,c**) show the forecasting results by different iteration time, where the iteration time is 150 s in (**a**) and 125 s in (**c**). (**b,d**) show the average power losses under different settings. Figure 9: Forecasting results under a 10-min action delay. (**a,c**) show the different iteration time forecasting results, where the iteration time is 150 s in (**a**), and 120 s in (**c**). (**b,d**) show the average power losses under different settings.
###### Abstract
First, various anti-loosening washers are inserted between a nut or bolt head and a clamped component [32]. Typical products include plain washers and wedge washers, among others. Second, the shape or size of the internal thread in a nut is modified, and the engagement states between external and internal threads are changed [33; 34]. Typical products include wedge-locking and variable-diameter nuts. Third, two nuts are employed to be fastened to a bolt to achieve excellent anti-loosening performance [35; 36; 37], including normal double nuts, eccentric double nuts, and double-thread bolt connections (a double-thread bolt and two nuts). The shapes of internal threads in eccentric double nuts are unchanged, but there is an eccentricity in eccentric double nuts, and the outer structures of the two nuts differ from the properties of normal double nuts. In a double-thread bolt connection, the bolt is machined to display both left-hand and right-hand threads. Two nuts with opposite thread  
Figure 1: Schematic representation of a bolted joint. directions are assembled on the bolt. Details of eccentric double nuts, and double-thread bolt connections are introduced in the following section. Although some research on the anti-loosening mechanisms of the abovementioned structures has been preliminarily conducted, comparison studies of their anti-loosening performance under vibration are still lacking. In fact, tightening and fatigue resistance performances are also two important characteristics of these anti-loosening bolted connections. For example, both insufficient tightening preload and material fatigue aggravate the vibration-induced loosening behavior of anti-loosening bolted connections. However, to the best of our knowledge, there have been no investigations of the tightening and fatigue resistance performances of the abovementioned anti-loosening bolted connections in previous publications. As such, there is no comprehensive evidence that can be used to provide guidance for the selection of proper anti-loosening bolted connections in engineering. This is the motivation of the present study. In this paper, a comprehensive comparison study on the tightening, anti-loosening, and fatigue resistance performances of several typical anti-loosening bolted connections (shown in Figure 2) is conducted for the first time. In this way, people can understand the performances of these anti-loosening bolted connections; therefore, this study can provide technical assistance and guidance for engineers in selecting appropriate anti-loosening bolted connections in engineering applications. Moreover, we discuss the effects of the different preload distributions of the two nuts on tightening, anti-loosening, and fatigue resistance performances of three bolted joints installed with double nuts. We found that it is necessary to increase the proportion of preload assigned to the upper nut in order to achieve better anti-loosening performance and extend the fatigue life. This work can guide engineers in tightening double nuts with appropriate technologies and processes.