esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Results
### Reconstruction Evaluation
#### 5.1.3 Three-Dimensional Reconstruction Model--Time of Flight  
The 3D reconstruction model for images obtained by the DepthSense camera--a time-of-flight (ToF) camera--was an alternative 3D reconstruction solution to models using plenoptic cameras. In addition to presenting itself as an alternative solution, with this model, we aimed to verify the quality of 3D reconstruction using a low-cost camera. For this camera type, we opted to use the software from the manufacturer.  
The image acquisition obtained by the DepthSense camera of a given scene is described in two files: a file that stores a 2D image of the scene and a file that describes a point cloud according to the captured 3D scene. These two files were used in the initial phase (input) of our proposed method: a file that stored the 3D information \((x,y,z)\) for each point in the cloud and a file that stored the color of each point of the point cloud.  
Figure 14 illustrates the data initially captured by the DepthSense camera.  
In the next step, outliers that corresponded to noisy points that had been improperly captured by the camera were removed. Based on the remaining points, a regular mesh was created with the aim of constituting the mesh of the 3D reconstruction. As previously stated, filtering was the following phase, as described in the next subsection.
## 4 Experimental Results
### Individual Evaluation
#### 4.2.2 Vidid2014  
To evaluate our method on VDID2014, the dataset was split into 4-fold (i.e., 25% of the reference image and its degraded versions for the test set and the rest for the training-validation set). As can be seen in Table 5, the performances were higher than those obtained on CID:IQ and the best results were obtained for the distance 4*H. Compared to CNN-VD, the improvements in terms of PCC are 5.43% for the distance 4*H and 2.84% for 6*H.  
Table 6 presents the result of each degradation type, where each degradation has five levels. The correlations were generally high for all distortions. Contrary to the results of CID:IQ, all the correlations of our method were higher for the distance 6*H. The highest values were obtained for JPEG and JP2K for both 4*H and 6*H. Compared to the MSSIM and CNN Quality metrics, the proposed method has good performance, giving a higher correlation value for JP2K, JPEG and GB for 4*H and for JP2K, JPEG and WN for 6*H. MSSIM obtained the best results for WN for 4*H, while CNN Quality achieved the best results for GB for 6*H.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**4*H**} \\ \hline
**Distortion Type** & **Our Method** & **MSSIM** & **CNN Quality** \\ \hline
**JP2K** & **0.925** & 0.827 & 0.874 \\ \hline
**JPEG** & **0.969** & 0.876 & 0.874 \\ \hline
**WN** & 0.903 & **0.912** & 0.871 \\ \hline
**GB** & **0.913** & 0.773 & 0.901 \\ \hline  & \multicolumn{3}{c}{**6*H**} \\ \hline
**Distortion Type** & **Our Method** & **MSSIM** & **CNN Quality** \\ \hline
**JP2K** & **0.951** & 0.8461 & 0.896 \\ \hline
**JPEG** & **0.973** & 0.846 & 0.886 \\ \hline
**WN** & **0.930** & 0.895 & 0.895 \\ \hline
**GB** & 0.921 & 0.796 & **0.933** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pearson ( PCC) correlation coefficients between the predicted quality scores and the subjective ones for each distortion of the VDID2014 database.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**4*H**} & \multicolumn{3}{c}{**6*H**} \\ \hline
**PCC** & **SROCC** & **PCC** & **SROCC** \\ \hline
**CNN-VD (NR)** & 0.884 & 0.871 & 0.914 & 0.898 \\ \hline
**Our method (NR)** & **0.932** & **0.907** & **0.940** & **0.922** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pearson ( PCC) and Spearman (SROCC) correlation coefficients between the predicted quality scores and the subjective ones for 4*H and 6*H viewing distances of the VDID2014 database. Highest correlation coefficients are in bold.
## Effects of RNAi-Mediated Knockdown of Histone Methyltransferases on the Sex-Specific mRNA  
**Expression of _Imp_ in the Silkworm _Bombyx mori_**  
**[PERSON] *, [PERSON] and [PERSON]**  
Division of Biological Sciences, Department of Integrated Biosciences, Graduate School of  
Frontier Sciences, the University of Tokyo, 302 Bioscience-Bldg, 5-1-5 Kashiwanoha, Kashiwa,  
Chiba 277-8562, Japan; E-Mails: [EMAIL_ADDRESS] (H.I.); [EMAIL_ADDRESS] (F.A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +81-4-7136-3694 (ext. 63694); Fax: +81-4-7136-3698.  
_Received: 19 February 2014; in revised form: 25 March 2014 / Accepted: 10 April 2014 / Published: 22 April 2014_  
**Abstract:** Sexual differentiation in _Bombyx mori_ is controlled by sex-specific splicing of _Bmdsx_, which results in the omission of exons 3 and 4 in a male-specific manner. In _B. mori_, insulin-like growth factor II mRNA-binding protein (Imp) is a male-specific factor involved in male-specific splicing of _Bmdsx_. Male-specific _Imp_ mRNA results from the male-specific inclusion of exon 8. To verify the link between histone methylation and alternative RNA processing in _Imp_, we examined the effects of RNAi-mediated knockdown of several histone methyltransferases on the sex-specific mRNA expression of _Imp_. As a result, male-specific expression of _Imp_ mRNA was completely abolished when expression of the H3K79 methyltransferase DOT1L was repressed to \(<\)10% of that in control males. Chromatin immunoprecipitation-quantitative PCR analysis revealed a higher distribution of H3K79 me2 in normal males than in normal females across _Imp_. RNA polymerase II (RNAP II) processivity assays indicated that RNAi knockdown of _DOT1L_ in males caused a twofold decrease in RNAP II processivity compared to that in control males, with almost equivalent levels to those observed in normal females. Inhibition of RNAP II-mediated elongation in male cells repressed the male-specific splicing of _Imp_. Our data suggest the possibility that H3K79 me2 accumulation along _Imp_ is associated with the male-specific alternative processing of _Imp_ mRNA that results from increased RNAP II processivity.
## 5 Conclusions  
This work presents SENIOR, a web ecosystem able to perform predictions of blood pressure values and composed of three main elements, which represents the first contribution of this work. The first element of the ecosystem is a wearable that acquires physical data from the user, such as blood pressure or heart rate. A smartphone application represents the second element, automatically acquiring data from the wearable, asking the user to complete surveys with relevant health information. In addition to the user's location, these data are sent to a web platform integrated into SENIOR, corresponding to the third element, which processes and stores them, acquiring environmental data from external services. These environmental data represent the second contribution of the manuscript, where we present novel features based on weather and pollution data. The last contribution is a proof of concept to evaluate SENIOR, where these data are used to generate machine learning models that can predict blood pressure values and, consequently, infer if a user will suffer a hypertensive crisis. These predictions can be performed in real-time or considering a future temporal window of 8, 16, or 24 h. Finally, this platform has been tested with seven users, presenting promising results aligned with the current literature.  
Nevertheless, more work in the future is needed, requiring a higher volume of users and longer acquisition temporal periods to improve the intelligent models.For that, we aim to evaluate SENIOR in a larger trial once the risk of the COVID-19 pandemic reduces, having users from different countries with different cultures, allowing us to study scenarios with different weather and pollution. Moreover, it would be interesting to personalize the predictions, only returning results from the algorithms that have better results for each user. Additionally, we consider it relevant to make the smartphone application compatible with more wearables in the future, as well as improving the web service to accept a higher volume of users since Flask is just recommended for experimentation and not production services.  
Conceptualization, S.L.B. and J.M.V.; methodology, S.L.B.; software, J.M.V.; validation, S.L.B., J.M.V., and A.H.C.; formal analysis, J.M.V.; investigation, S.L.B. and J.M.V.; resources, A.H.C. and G.M.P.; data curation, J.M.V.; writing--original draft preparation, S.L.B.; writing--review and editing, J.M.V., A.H.C. and G.M.P.; visualization, S.L.B. and J.M.V.; supervision, A.H.C. and G.M.P.; project administration, G.M.P.; funding acquisition, A.H.C. and G.M.P. All authors have read and agreed to the published version of the manuscript.  
This work has been partially supported by Armasuisse S+T with project CYD-C-2020003.  
Not applicable.  
Figure 16: Comparison between real-time and future predictions using Random Forest.
###### Abstract  
In opportunistic mobile networks (OpNets), nodes should be in listening state to discover the neighbors for opportunistic message forwarding. While in OppNets, contacts between nodes are sparse, most of the node's energy is consumed in idle listening state, which highlights the need for energy saving in contact probing. Duty cycle operation can be applied to address this problem. However, it may cause the degradation of network connectivity when the state of node is turned to be sleeping. In this paper, we propose an adaptive scheduling mechanism based on self-similarity, in which _LMMSE_ predictor is used to predict the future contact information. The state of a node will be set as listening or sleeping adaptively according to the predicted result of future contacts with other nodes. Finally, we validate the effectiveness of the proposed mechanism by conducting a large amount of trace-driven simulations, which show that the proposed mechanism outperforms the random working mechanism and periodical working mechanism in terms of the number of effective contacts, delivery ratio, transmission delay and cost.  
Keywords:opportunistic mobile networks; self-similarity; duty cycle; scheduling mechanism +
Footnote †: journal: Information 2017 8, 87
# Earthquake Shake Detecting by Data Mining from Social Network Platforms
## 2 Methodology
\[Entropy(S)=-\frac{m}{s}\log_{2}\frac{m}{s}-\frac{n}{s}\log_{2}\frac{n}{s} \tag{1}\]  
In this formula, \(s\) corresponds to the number of total regions in Taiwan, \(m\) corresponds to the data with positive classification, and \(n\) corresponds to the data with negative classification. Information gain measures the reduction in entropy that results from partitioning the data on attribute \(A\), which is another way of saying that it represents how effective an attribute is for classifying the data. \[Gain(S,A_{i})=Entropy(S)-Entropy(A_{i}) \tag{2}\]  
The first term denotes the entropy of the data before the split, whereas the second term corresponds to the entropy afterwards. It is desired that the information gain is as high as possible and the entropies of the partitioned data as low as possible, which splits the training data into relatively heterogeneous groups. Figure 2 shows the pseudo code for constructing the decision tree model. In terms of the method, it can calculate the information gain of each attribute and the result is shown in Table 2. \begin{table}
\begin{tabular}{c l l} \hline \hline
**Level** & **Feelings for Earthquake** & **Inside of House for Earthquake** \\ \hline
0 & No feelings. & \\
1 & A slight tremor can be felt. & \\
2 & The most people can feel quake. & The pendants tremor slightly. \\
3 & Someone has feeling of fear. & The house and pendants are shaking. \\
4 & Startled and woke up at midnight. & The house is shaking violently and the heavy \\
5 & Most people feel scared. & furniture moved. \\
6 & Shaking violently and standing with difficulty. & The wall has a crack and heavy furniture may \\
7 & Shaking violently cannot move. & Some buildings have damage, the door and \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Seismic scale level table from the Central Weather Bureau. The result shows that shaking feeling and sleep-affecting have the same value of 0.1339 for the information gain and are greater than other attributes. Furthermore, the attribute of shaking feeling is the most mentioned one in posts by the semantic analysis. The decision tree model is developed in this investigation as shown in Figure 3. Then, we perform the semantic analysis from PTT messages according to the keywords of the seismic intensity scale table from the CWB. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Attributes** & **Entropy(S)** & **\(\mathbf{Gain(S_{A})}\)** \\ \hline Body-feeling & 0.7401 & 0.1339 \\ Sleep-affecting & 0.7401 & 0.1339 \\ Furniture-toppling & 0.7971 & 0.0769 \\ Damage situation of house & 0.8433 & 0.0307 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The value of entropy and information gain of every attributes. Figure 3: The decision tree model structure. Figure 2: The pseudo code of the classification.
## 3 Results  
TLim1% showed a longer time compared to TLim10% (as shown in Table 2), which resulted in different TRIMP values (1186.0 \(\pm\) 367.0 and 767.1 \(\pm\) 234.5, respectively, for 1% and 10%; \(p\) = 0.001). TTotal performed in GT1% was significantly longer compared to GT10% (30.4 \(\pm\) 9.4 min vs. 22.3 \(\pm\) 5.1 min, respectively; \(p\) = 0.035), despite the same number of stimuli received by GT1% and GT10% (4.7 \(\pm\) 1.3 and 4.8 \(\pm\) 1.3 stimuli, respectively; \(p\) = 0.920). However, the TRIMP values observed between GT1% and GT10% showed significant differences (5506.6 \(\pm\) 1684.8 vs. 3986.2 vs. 903.1, respectively). Despite this, significant increases were found after six interval running sessions for VO2 max (\(p\) = 0.002) and VPEak (\(p\) = 0.001) in both the GT1% and GT10% groups, with no significant interaction for the groups. The CON group did not differ after the training period for all dependent variables. Regarding the effect size (ES) calculations between the pre- and post-measurements, the groups that underwent training demonstrated ESs of 0.67 (GT1%) and 0.70 (GT10%), while the CON group demonstrated only 0.03 for VO2 max. Similarly, for VPEak, which demonstrated effect sizes of 0.67 and 0.70 for GT1% and GT10%, respectively, the CON group demonstrated only 0.01. The results are presented in Table 2.  
Significant differences were observed when comparing the metabolic demands of TLim1% vs. TLim10% and the absolute values for both conditions. Although there was significant improvement in VPEak after training, there were no changes in metabolic demand after training for TLim1% and TLim10% as well as for the control group, which was not subjected to any type of intervention. Consequently, there was no influence of specific running tests observed on any of the groups (interaction vs. test vs. time, \(p\) = 0.680). TLim performance with two inclinations showed trivial ESs. HR differed significantly between session 1 (S1) and session 6 (S6) for the group that trained on a 1% slope (\(p\) < 0.001), presenting lower HR values for the same workload. However, there were no significant differences between S1 and S6 for the GT10% group (\(p\) > 0.05). The results are presented in Figure 2A. RPE suffered significant reductions from the third training session (S3) for the GT1% group (\(p<0.001\)) compared to S1, suggesting an adaptive response for effort modulation. A similar pattern was observed for the GT10% group, although of a smaller magnitude. The adaptive RPE responses can be observed in Figure 2B. Figure 3A,B illustrate the intrasession HR and RPE, comparing S1 vs. S6.
## 2 Methodology  
### Overall Framework  
The proposed methodology framework is specifically designed to address the core requirements of traffic signal control optimization, which demands accurate, reliable, and comprehensive traffic parameters, including link flows, travel times, speeds, and OD matrices. Each module serves distinct optimization objectives: data preprocessing ensures data quality essential for reliable signal timing calculations; basic feature processing provides temporal and spatial calibration necessary for coordination control; exploratory analysis identifies traffic patterns crucial for adaptive signal strategies; key feature extraction generates the specific parameters required for signal optimization algorithms; and data visualization supports decision-making in signal control design and evaluation.  
The traffic parameter extraction and analysis method based on vehicle trajectory data proposed in this study adopts a modular design philosophy, constructing a hierarchically structured and functionally comprehensive processing framework. As shown in Figure 1, the entire framework comprises five main modules: data preprocessing module, basic feature processing module, exploratory data analysis module, key feature extraction module, and data visualization module. This design not only ensures the systematicity and completeness of the processing workflow but also provides excellent scalability and reusability.  
The data flow between modules is clear and explicit: raw trajectory data first undergoes cleaning and preliminary organization through the data preprocessing module, removing redundant and anomalous data; it then enters the basic feature processing module for temporal feature transformation, vehicle feature encoding, and spatial coordinate calibration; the processed data is used both for exploratory data analysis to understand basic data characteristics and distribution patterns, and enters the key feature extraction module to extract core traffic parameters such as link flow, speed, travel time, and OD matrices; finally, all results are intuitively presented through the data visualization module to support decision-making analysis.
# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 5 Performance Evaluation and Analysis
### Experiment and Analysis  
In the experiment, the testbed consists of forty remote multi-radio nodes and a multi-radio base station. They are randomly deployed on our laboratory test stand. The multi-radio base station and remote multi-radio node are shown in Figure 2.  
Table 2 lists some key initialization parameters of the experiments in this section.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Value** & **Parameters** & **Value** \\ \hline No. of multi-radio node & 40 & Supply current & 3A DC \\ No. of RF transceivers (\(n\)) & 4 & Supply voltage & 3.3V DC \\ No. of tasks (\(m\)) & 200–1000 & Typical transmit energy consumption & 178 mA (17 dB) \\ No. of packets of each task (\(l\)) & 6 & Typical idle energy consumption & 2.25 mA (0 dB) \\ \hline \end{tabular}
\end{table}
Table 2: Some key initialization parameters.  
Figure 3 shows the experimental test hardware and the PC software user-interface, separately. The experiment environment is deployed based on our designed multi-radio hardware (shown in Figure 3a). The designed multi-radio hardware supports four RF transceivers (\(n=4\)) at most. The current consumption is recorded by the Agilent Digital Multi-Meter supported PC software (shown in Figure 3b).  
The max-min method and the Z-score method are used to normalize the experiment results, aiming at avoiding the confusion that different data units caused. Table 3 lists the normalized value of each algorithm.  
Figure 4 shows the experiment results of the energy consumption performance for FIFO, EMRSA and OETS. In Figure 4, the vertical axis represents the normalized energy consumption. The larger the value, the higher the energy consumption, and the lower the corresponding performance. The horizontal axis represents the number of tasks.  
Figure 4 indicates that OETS outperforms both FIFO and EMRSA. In detail, OETS is at most 36.2% lower than FIFO, and 23.1% than EMRSA, under the same number of tasks. The most important reason for FIFO is that the energy consumption factor is totally without consideration. For EMRSA, it is a greedy algorithm and merely considers whether the energy consumption of the current task is optimal. Hence, it may easily fall into the local optimal. Inspired by the PSO algorithm, the proposed  
Figure 3: Energy consumption test; (**a**) multi-radio node; (**b**) current consumption recorder.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Tasks** & **FIFO** & **EMRSA** & **OETS** \\ \hline
**200** & 0.12 & 0.052 & 0.025 \\
**400** & 0.375 & 0.27 & 0.13 \\
**600** & 0.642 & 0.442 & 0.31 \\
**800** & 0.94 & 0.613 & 0.48 \\
**1000** &  
OETS not only considers the local best solution, but also takes the global particles best solution into consideration. Then, each particle iteratively updates its solution until obtaining the best one. Hence, the energy consumption of the PSO based task scheduling strategy is the lowest.
# A Survey on Hardware Accelerators for Large Language Models
## 3 FPGA-Based Accelerators
### ODE-Based Acceleration  
In 2024, [PERSON] et al. proposed a hybrid approach for the acceleration of the Transformer networks [18]. The proposed scheme uses ResNet as a backbone architecture and replaces a part of its convolution layers with an MHSA (multi-head self-attention) mechanism. Using this approach they managed to significantly reduce the parameter size of such models by using neural ODE (ordinary differential equation) as the backbone architecture instead of ResNet. The proposed hybrid model reduced the parameter size by 94.6% (compared to the CNN-based models) without degrading the accuracy.  
The proposed model was deployed on a modestly sized FPGA device for edge computing applications. To further reduce FPGA resource utilization, the researchers employed a quantization-aware training (QAT) scheme, rather than post-training quantization (PTQ), to minimize accuracy loss. As a result, an extremely lightweight Transformer-based model can be implemented on resource-limited FPGAs, making it ideal for edge applications.  
A performance evaluation on the Xilinx Zynq UltraScale+ MPSoC platform showed that the proposed FPGA implementation achieved 12.8\(\times\) speedup and 9.2\(\times\) energy efficiency compared to an ARM Cortex-A53 CPU implementation. This scheme shows how the parameter size reduction can significantly affect the performance of the hardware accelerators.