esa-sceva/satcom-chunk-collection huggingface dataset:
# Faster MDNet for Visual Object Tracking
## 4 Experiments
### Results on VOT2018  
The VOT2018 [25] dataset evaluates the performance of algorithms for not only short-term tracking but also long-term tracking. Each time that a tracker fails to track an object on this dataset, it will restart tracking. The performance of trackers is evaluated based on the accuracy and number of failures. Accuracy refers to the average overlap rate under a test video sequence and is expressed as follows:  
\[Accuracy=\frac{1}{N_{v}}\sum_{t=1}^{N_{v}}\frac{1}{N_{rep}}\sum_{k=1}^{N_{rep} }\frac{S_{G}\cap S_{P}}{S_{G}\cup S_{P}} \tag{15}\]  
where \(t\) is the present number of video frames, \(k\) is the \(k\)-th test of the current measurement, \(N_{v}\) represents the number of valid tracking frames, and \(N_{rep}\) is the number of repetitions.  
Robustness is the ratio between the number of tracking failures and the number of valid tracking frames. The EAO value reflects the relationship between the length of sequences and the average accuracy.  
Table 3 shows comparisons of other state-of-the-art trackers with our tracker. It can be seen that the proposed tracker has the same accuracy value as PrDiMP [16], one of the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trackers** & **Precision** & **AUC** & **FPS** \\ \hline MDNet [20] & 0.907 & 0.674 & 1 \\ VITAL [21] & 0.902 & 0.674 & \textless{}1 \\ RT-MDNet [22] & 0.868 & 0.657 & 25 \\ Ours-conf & 0.912 & 0.681 & 35 \\ Ours & **0.919** & **0.695** & **36** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of several MDNet-based methods on OTB100 containing precision, AUC score, and FPS.  
Figure 3: Results of current trackers and ours on OTB100 in terms of precision plots and success plots. OTB100 using one-pass evaluation for testing trackers.  
most advanced methods. In terms of robustness and Expect Average Overlap (EAO) value, our method achieves 0.432 and can also be competitive with leading trackers.  
We summarized the EAO value, FLOPs, and parameters of Ocean [13], PrDiMP [16], SiamFC [7], SiamRPN++ [11] and our method on the VOT2018 [25] dataset. To visually compare the performance of trackers, we set the vertical coordinate as the ratio of the EAO value to the number of parameters. The higher value of the vertical coordinate proves the better performance of the method. As shown in Figure 4, only the proposed tracker and SiamRPN++ [11] have a model FLOPs number lower than 10,000 M, and our method has the best cost-effectiveness.
# Landslide Displacement Prediction Based on Variational Mode Decomposition and GA-Elman Model
## 2 Theory and Method
### Displacement Prediction Process  
The landslide displacement prediction process based on VMD and the GA-Elman model used in this paper is shown in Figure 1. The main prediction steps are described below.  
1. Preprocess the monitoring data. The displacement, rainfall, and reservoir level data observed at the monitoring points are preprocessed, and the types of influencing factors are identified.
2. Decompose the data. VMD is used to decompose the displacement data into three subseries of trend, periodic, and random terms, and to decompose the influence factor data into two subseries of periodic and random terms.
3. Consolidate the datasets. The decomposed data are integrated into corresponding datasets according to the decomposition; then, the training set, test set, and validation set are divided up.
4. Train the GA-Elman model and compare the prediction results. The training set is substituted into the GA-Elman model separately to train the model parameters, and then the test set is substituted into the model to determine the optimal training combination.  
Figure 1: Flowchart of displacement prediction.  
5. Determine the optimal prediction model for cumulative displacement. The optimal prediction model for cumulative displacement is obtained by accumulating the optimal training combinations from the trend dataset, the periodic dataset, and the random dataset.
6. Verify the feasibility of the optimal prediction model. Substitute the validation set data into the optimal prediction model and verify the feasibility of the model combined with the operation results of each evaluation index.
## 2 Results and Discussion
### Comparison of JumpShot-HTS DART-QqQ-MS/MS and SVP-DART-SPMESH-Orbitrap-MS/MS  
Since HRMS detectors are less commonly associated with routine, quantitative analyses, the ruggedness of the optimized SPMESH-DART-MS method was assessed using a DART ion source coupled to a QQQ-MS/MS detector. Switching to a chromatography-free QQQ-MS/MS approach risked unacceptable limits of detection due to isobaric interferences often associated with ambient ionization based mass spectrometry [28]. The MS/MS transitions and collision energies were optimized for the QQQ-MS/MS analysis (_m_/_z_ 167.4 _m_/_z_ 124.6, CE = 20 eV), while all other parameters for the SPMESH extraction and DART ionization remained unchanged. Figures of merit are summarized in Table 1. As expected, the DART ionization coupled to a lower resolution instrument had background noise (\(\sim\)0.5 ng/kg; Figure 6). SPMESH-HRMS/MS demonstrated a higher LOD (\(<\)0.5 ng/kg vs. 1.5 ng/kg) due to absence of isobaric interferences. Method calibration was successfully performed without the use of an internal standard, and the average repeatability was adequate (5% RSD). However, repeatability on the HRMS without internal standardization was poor (17% RSD). We expect repeatability and LOD of the SPMESH-DART-QQQ-MS/MS approach to be enhanced with the addition of the deuterated internal standard [29].  
During QqQ-MS/MS analyses, a JumpShot-HTS DART source was used in place of the SVP-DART ion source. The X-Y positioning stage of the JumpShot includes a second linear rail, which allowed for the automated movement of the ion source from row to row. The JumpShot-HTS also allows users to use variable scan rates or jump from location to location, as opposed to the constant scan speed required by the SVP-DART. Currently, approximately half of a SPMESH-DART-MS/MS analysis is spent collecting data from the inter-well regions of the sheet that contain no extracted volatiles (see Figure 6). We were able to program the JumpShot-HTS to move rapidly between spots of the SPMESH sheet (0.5 mm/s scan rate for spots, 10 mm/s between wells) which decreases total run time for a 24 well plate to \(\sim\)8.5 min (from 17 min).  
Figure 6: DART-QQ-MS/MS selected ion chronograms for IBMP (_m_/_z_ 167.4 _m_/_z_ 124.1) at 1.5 ng/kg in _Vitis vinifera_ (cv. Barbera) grape homogenate with no detectable IBMP as previously determined by HS-SPME-GCMS. Wells 1 and 2 are analytical replicates spiked with IBMP at 1.5 ng/kg, wells 4 and 6 are spiked with IBMP at 4.5 ng/kg, and wells 3 and 5 are blank wells.
# Improving Low-Frequency Panel Absorbers with Two-Dimensional Acoustic Black Hole
###### Abstract  
Many rooms struggle with the absorption of low-frequency sound due to its long wavelengths. The integration of existing solutions into these spaces is often challenging due to their intricate installations and large depths. To address this problem, a new type of resonance absorber has been developed: the Distributed Mode Absorber (DMA). It consists of a thin vibrating front panel and a volume of enclosed air behind it. This straightforward structure can be utilized to create acoustically functional furniture that can be seamlessly incorporated into rooms. This article is devoted to the structural optimization of the DMA front panel, using the Acoustic Black Hole (ABH) effect known within structural dynamics. A numerical model is constructed using a Finite Element Analysis (FEA) and examined numerically. Several geometric parameters of the ABH are studied with regard to their influence on the vibrations of the front panel. Prototypes are developed and manufactured based on these insights. The quality of the numerical model is verified during the subsequent validation. Finally, the sound absorption of the improved DMA is compared with that of the reference DMA.  
acoustic black hole; finite element modeling; sound absorption; resonance absorber; modal analysis; meta structure; parametric study +
Footnote †: journal: Applied sciences
###### Abstract
The authors in [23] used smooth particle hydrodynamics to investigate pressure distribution on steps in a non-aerated flow region of a stepped spillway subjected to different discharges in a skimming flow condition. The authors in [24] adopted the Neuro fuzzy approach to simulate the stepped spillways with different slopes and steps. They found that the neuro fuzzy approach was best for calculation of energy dissipation as compared to regression analysis. The authors in [25] used FLOW3D to evaluate the step geometry, in which steps contained \(\Lambda\)-shaped steps at angles of 25 degrees in terms of calculating the energy dissipation in the steps spillways. The above mentioned experimental and numerical studies have investigated different parameters, such as energy dissipation, air entrainment, and pressure profiles for different steps geometries. The present study estimates the impact of curved risers on the stepped spillway model having skimming flows on the three stepped spillway parameters, i.e., pressure, total energy dissipation, and energy dissipation distribution. The study was undertaken by numerical solver FLOW 3D using a realizable RNG k-\(\epsilon\) turbulence model, which is capable of accounting the smaller scales of turbulence motion. The main aims of the study are given below. 1. To estimate the total energy dissipation, energy dissipation distribution, and pressure profiles along the curved surface of the steps of the stepped spillway models (with curved risers) and along the vertical surface of steps for simple stepped spillways models under the skimming flow regime of flow. For validation experimental results consisting of pressure profiles along vertical and horizontal surfaces of the steps of a stepped spillway model of slope 1; 0.75 and 13 steps from a previously published paper [14] were compared with the results from numerical solver FLOW 3D. A good agreement was obtained. Furthermore, a scale down spillway model of Khanpur Dam's (Islamabad, Pakistan) spillway was selected using [PERSON]'s number dimensional analysis whose risers were made curved based on the three different angles of suspension, i.e., 30, 60, and 90 degrees. Table 1 shows stepped spillway models having curved risers that have never been studied before. The three parameters are: pressure profiles, total energy dissipation and energy dissipation distribution for both simple and curved riser models, were made for the flow rate of 0.020-0.068 m\({}^{3}\)/s.
## 3 Adaptive Resampling Method
### Complexity Analysis  
Compared with the conventional TEQ, the DAM-TEQ using the adaptive resampling method increases the complexity in two aspects: the adaptive process of the first-stage structure and the multiple parallel inputs of the second-stage structure. The rest of the structure is consistent with the conventional TEQ. Therefore, for ease of calculation and generality, the analysis is mainly based on the equilibrium complexity of one frame of data (concentrated on the RLS), which does not include the decoding process. The number of computations required for a single information symbol is used to characterize the complexity of the algorithm. In the calculation, the FFF length \(N_{a}=N_{1}+N_{2}+1\) and the FBF length \(N_{b}=N_{3}\).  
In the conventional TEQ, \(3(N_{a}+N_{b})^{2}+3(N_{a}+N_{b})\) times multiplication and \(3(N_{a}+N_{b})^{2}+N_{a}+N_{b}\) times addition need to be calculated, and the overall complexity is \(o[(N_{a}+N_{b})^{2}]\). It is assumed that the resampling rate is updated \(N\) times in the adaptive process, and there are \(M\) branches of parallel input in the DAM-TEQ. Compared with the conventional TEQ, the proposed equalizer needs to calculate the number of equalizations additionally as follows: \(N\) adaptive processes in the first-stage structure, including the calculation of \(M\) FFF branches each time, and \(M-1\) FFF branches in the second-stage structure. The added complexity of the proposed equalizer is shown in Table 1. The overall complexity of the equalizer is \(o[(MN_{a}+N_{b})^{2}]\), and the sacrifice of this part of the complexity relative to the performance improvement can be accepted. At the same time, due to the relatively low number of symbols transmitted in long-range UAC, the overall complexity will not be a limiting factor for the equalizer.
# Parameter Combination Framework for the Differential Evolution Algorithm
## 6 Experiments and Discussion
For f1, f5, f6, f12, f14, f17, f18, f20, and f21, L-PVCDE has obvious advantages on mean and Wilcoxon rank-sum test result, while for f4, f8, f24, f25, f27, f28, and f30, L-PVCDE is better than L-SHADE according to Wilcoxon rank-sum test result, but according to the mean the advantage of L-PVCDE is not very obvious. However, it is evident that the parameter-value combination strategy does indeed work for many problems. In the third experiment, we tested PSCDE with multi-operation algorithms (EPSDE [22], MPEDE [15], and CoDE [27]). Because the algorithm parameter strategies originated from jDE and SHADE, jDE and SHADE were also tested to see whether PSCDE led to improvements. The selected algorithms were set to their initial settings in the paper. For PSCDE, the _NP_ was set to 100, which was the case for most algorithms in the comparison, and the basic proportions of all search objects were set to \([1/2,\,1/2]\) in all phases. Every 10 generations, the groups were reassigned. The grouping method was random or index-based, with a 0.5 probability. The switching condition for stage is every 500 generations. We performed the test on \(D=50\) dimensions with 25 runs. The test results are shown in Table 6. The mean and standard deviation of each algorithm are listed in the table, with the smallest mean in bold. PSCDE wins for 11 functions in terms of the mean, and in second place is SHADE, which wins for 7 functions. Because evaluation by the mean is sometimes biased, a rank-sum test was performed for pairwise comparison. The symbols \(-\), \(+\), and \(\approx\) represent each algorithm is significantly worse than, better than, or similar to PSCDE, respectively. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fun**} & \multicolumn{2}{c}{**L-SHADE**} & \multicolumn{2}{c}{**L-PVCDE**} \\ \cline{2-5}  & **Mean** & **Std** & **Mean** & **Std** & \\ \hline
1 & 1.03E+03 & 9.21E+02 & 5.47E-05 & 8.55E-05 & \(-\) \\  
03 & 3.43E+02 & \(-\) \\ \hline \hline \multicolumn{5}{l}{Rank sum test} & \multicolumn{1}{c}{\(+7-16\approx\)7} \\ \hline \hline \end{tabular}
\end{table} 
Table 5: CEC 2014 function for 50D on L-SHADE and L-PVCDE. In this test, SHADE, jDE, and PSCDE have the same population size, _NP_ = 100. In comparison to SHADE and jDE, PSCDE displays good performance.
## 2 Materials and Methods
### Procedures
#### 2.3.2 Training Protocol  
The training program for the AEL and CMJ groups had a frequency of three times per week for five weeks. For the CMJ group, the training consisted of performing 3-4 sets of 5 repetitions of the CMJ without additional load (BM only) with 2 min of recovery between sets. For the AEL group, training consisted of performing 3-4 sets of 5 repetitions of CMJ with extra weight via dumbbells representing 10-20% of their BM with 2 min of recovery between sets. As mentioned above, the execution of the CMJ with accentuated eccentric load consisted of performing the descent (eccentric phase) in <2 s [2], holding dumbbells equivalent to 10-20% of their BM, which were released onto mats located on the sides of each leg at the end of the eccentric phase, initiating the concentric phase of the jump without the dumbbells. The reason athletes executed the AEL CMJ in <2 s is because in previous research [2; 22], completing eccentric times of < two seconds increased maximal performance, velocity, and power of a subsequent concentric repetition compared to tempos of > four seconds. Specifically, rapid eccentric actions (e.g., eccentric overload or AEL) with velocities or durations less than < two seconds can improve maximal power output, horizontal deceleration, and stretch-shortening cycle skills that are important for performance in many sports [22]. Tempos > four seconds in duration have been shown to increase time under tension (TUT) by accumulating higher concentrations of blood lactate, growth hormone, and testosterone, while reduced tempos allow for a greater training volume to be completed, reducing these effects [2]. Given the few studies with chronic AEL interventions, the program was based on current recommendations and on progressive overload on a weekly basis. In the first and second week, both groups performed three sets with five repetitions in each set; in the third and fourth week, they performed four sets with five repetitions in each set; and in the fifth week, they performed three sets with five  
Figure 2: Training program for AEL and CMJ groups. AEL: accentuated eccentric loading. CMJ: countermovement jump. BW: body weight.  
repetitions in each set. Attendance to all training sessions was monitored and recorded by the research team. All participants demonstrated full compliance with the intervention protocols, achieving 100% attendance across the five-week training period.
# Evaluating the FLUX.1 Synthetic Data on YOLOv9 for AI-Powered Poultry Farming
## 3 Materials and Methods  
This section describes the key tools and methods used for data collection and analysis, which were later utilized in the development of AI models. The new component, which is an addition to the previous version of the system [4], is related to generative AI. The IoT-based platform for smart poultry farms integrates real and synthetic data, edge computing, and cloud-based processing for automated monitoring. The methodology follows these key steps (Figure 2):  
**Step 1**--Dataset Collection: Real images are captured by an AI-enabled camera (Edge 1) inside the closed poultry farm Radinovic located in Montenegro, during daylight conditions. Synthetic data are generated using a fine-tuned generative AI model (FLUX.1) [19, 41] to reduce manual data collection efforts.  
**Step 2**--Preprocessing: The dataset undergoes annotation, optional augmentation (rotation, scaling, and noise addition), and structuring to enhance generalization;  
Figure 2: A high-level system architecture of the IoT-based platform for smart poultry farms.  
**Step 3--**Model Training: The YOLOv8 and YOLOv9 architectures are evaluated. After initial experiments, YOLOv9 is selected for fine-tuning due to its higher predictive accuracy;  
**Step 4--**Edge Deployment: The trained model is ready to be deployed on Edge 2, which processes real-time camera input and provides local (on device) inference for object detection.  
**Step 5--**Cloud Integration: Prediction results could be transmitted via an API to a cloud-based platform for farm management. The web application offers insights into farm conditions, enabling automated monitoring and decision-making [42].  
By integrating real and synthetic data, edge AI (AI prediction model on edge device) and cloud processing, the system reduces the importance of real data, automates monitoring and improves decision-making in poultry farming.
## 3 Methods
### Dataset Summary
#### 3.1.2 Signal Properties  
The dataset encompasses physiological signals, specifically electrocardiography (ECG) and galvanic skin response (GSR) obtained with precise configurations to ensure data integrity. ECG recordings were performed using a Lead II setup, in which electrodes were strategically placed on the participant's right arm (Vin\(-\)) at the wrist and on the left calf (Vin+), facilitating comprehensive monitoring of cardiac activity. For GSR measurements, disposable adhesive electrodes were affixed to the index and middle fingers of the participant's right hand, capturing the subtle changes in skin conductance associated with emotional responses. The acquisition of these signals was meticulously performed, with ECG data sampled at a high resolution of 2000 Hz to capture the intricate details of heart activity, while GSR data were recorded at a frequency of 15 Hz, appropriate for tracking slower fluctuations in skin conductance [33].
# Leveraging Industry 4.0 for Sustainable Manufacturing: A Quantitative Analysis Using FI-RST
## 5 Conclusions  
This study demonstrates that the adoption of Industry 4.0 technologies--Manufacturing Execution Systems (MES), the Industrial Internet of Things (IIoT), and Additive Manufacturing (AM)--significantly enhances the sustainability performance of manufacturing enterprises in Fujian Province, China. Utilizing the Fuzzy Integration-Rough Set Theory (FI-RST) model, the impacts of these technologies were quantified across economic, environmental, and social dimensions.  
The key findings include:  
* Economic improvements: Implementing MES and IIoT led to cost reductions and operational efficiency gains, with firms reporting up to 22% productivity improvements and reduced downtime due to IIoT-enabled predictive maintenance.  
Figure 15: Cumulative sustainability performance over three years.  
Environmental benefits: A 25-35% reduction in waste and a 15-20% decrease in energy consumption were observed, particularly in firms utilizing AM and IIoT, contributing to reduced carbon emissions. Social impacts: The integration of AR and IIoT improved worker safety and training efficiency, enhancing job satisfaction and reducing error rates.  
This research bridges gaps in the existing literature by applying the FI-RST model to evaluate the combined effects of these technologies on sustainability. The findings highlight the importance of a holistic approach to Industry 4.0 implementation, as synergies between technologies are critical for maximizing sustainability outcomes.
# Analyzing Data Reduction Techniques: An Experimental Perspective
## 8 Experimental Evaluation
### Lossless Data Reduction Algorithms
#### 8.2.1 Delta Encoding  
The implementation of Delta Encoding faced the challenge of handling the diverse data forms within our dataset. To address this, we categorized the data into numeric and non-numeric types, allowing an adaptive approach to accommodate different structures. When processing string data, the algorithm carefully identified similarities between consecutive strings, recording different characters as delta strings and using a placeholder character (\(\backslash 0\)) for unchanged positions. This analysis provided an understanding of string variations and ensured robust Delta Encoding.  
Regarding numeric values, the algorithm began the process by storing the first numeric value and continued with iterative subtraction between successive values, storing the result as the current subtractor. This systematic approach continued until the last value, providing a seamless Delta Encoding solution for numeric data. The adaptability of our method shines through, highlighting a tailored and comprehensive approach capable of handling the intricacies inherent in the diverse data structures and characteristics of our dataset.  
Table 12 shows the results of Delta Encoding, highlighting the algorithm trade-offs. Despite its slower processing speed, Delta Encoding stands out for its impeccable data persistence, and the ability to faithfully reproduce the original data in its raw form after decoding is an advantage in applications where data integrity is a priority. The algorithm efficiency is most pronounced in datasets characterized by temporal and numeric content. The algorithm reduced more data, \(-46.06\%\) in the Daily Minimum Temperatures in Melbourne dataset, and achieved a better processing time of \(4.1370\) ms per MB in the Electric Production dataset, with both temporal and numeric data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
* The best result for percentage variance. 2 The best result is in bold.  
\end{table}
Table 11: Simple Random Sampling results.