esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Attention-Based Approaches  
#### 3.5.1 Spatial Attention Models  
Spatial attention models have become crucial in enhancing video caption generation by highlighting key regions within frames that are essential for understanding content. Their primary goal is to capture the most prominent areas, extracting high-resolution visual features necessary for accurate captioning. By complementing temporal attention frameworks, spatial attention mechanisms delve into the spatial details of each frame, expertly identifying significant objects and activities. This combination of spatial and temporal cues improves the overall comprehension of video content.  
An early notable example is the Attention-Based Multimodal Fusion for Video Description (ABFVD) model by [PERSON] et al. [20], which integrates image and motion attributes through a multimodal attention system. However, ABFVD's reliance on basic feature combinations may limit its ability to adaptively assess feature relevance across different descriptive contexts. In contrast, [PERSON] et al. [43] introduced the spatial-temporal attention (STAT) model, skillfully combining both spatial and temporal dimensions to focus on relevant regions and frames. As illustrated in Figure 12, the STAT model takes global motion features, local features, and model status information as input, producing dynamic visual representations that are fed into each LSTM decoder iteration. Nevertheless, a significant limitation of spatial attention models is their potential over-reliance on predefined object detection methods or region proposal networks, which may not consistently identify contextually relevant areas. Additionally, these models can be computationally intensive, especially with large and complex scenes, posing challenges for real-time implementation and resource management.
Figure 1 shows commonly available natural jute fibers. India produces approximately 55% of the world's total production of jute, followed by Bangladesh and China [17]. In Peru, it is recognized as a priority resource, supporting both economic and environmental goals. Jute absorbs CO\({}_{2}\) and releases O\({}_{2}\), helping combat climate change. It grows well in regions like Loreto and Ucayali, where the climate and soil are ideal for its cultivation [18]. Surface modification of fibers using chemical agents enhances the mechanical properties of composites, particularly when using 15 mm fiber length [10]. To optimize the performance of NFCs, it is essential to utilize treated fibers and assess their erosion wear resistance [7]. Notably, H\({}_{2}\)O\({}_{2}\) treatment has been shown to improve mechanical properties further [20]. Additionally, jute fiber-reinforced composites exhibit mechanical characteristics comparable to those of glass fiber-reinforced composites [21]. Jute fiber demonstrates a promising balance between mechanical performance, cost, and sustainability, making it a suitable candidate for structural applications such as motorcycle fairing design, where strength-to-weight ratio is critical [22]. On the other hand, [PERSON] et al. [23] developed a hybrid composite incorporating 20% jute fiber and varying pomegranate peel-powder content (2-8%). Mechanical tests revealed that strength improved up to 6% filler, but higher concentrations led to agglomeration and property deterioration. The study concluded that this combination not only enhances mechanical performance but also reduces costs, eliminates synthetic fibers, and promotes  
Figure 1: (**a**) Untreated (raw jute) and (**b**) chemically treated jute fibers. Adapted from [PERSON] et al. [19]. more sustainable manufacturing. [PERSON] and [PERSON] [24] investigated biodegradable polymers as eco-friendly alternatives to synthetic fibers, aiming to reduce environmental impact and resource depletion. They focus on natural fibers like jute, which are renewable and offer better mechanical properties, though they face challenges such as poor bonding with the matrix and water absorption. Their research shows that adding TiO\({}_{2}\) to jute composites enhances their strength, flexibility, and resistance to erosion. Fiber-matrix compatibilization systems improve the bond between fibers and matrices, enhancing adhesion, load transfer, and overall mechanical performance [25]. These systems, including coupling agents and matrix modifiers, help overcome challenges like poor fiber-matrix compatibility and defects [26]. Manual laminating, used in motorcycle fairing design, involves layering jute fibers soaked in epoxy resin, allowing precise control over fiber orientation and layer thickness, which affects the fairing's strength and performance [27]. Epoxy resin, known for its strength, adhesion, and stability, is widely used in composites for aerospace, automotive, and construction applications, offering durability and resistance to extreme conditions [28].
# The Loop-Mediated Isothermal Amplification for the Rapid Detection of Porphromonas Gingivalis
## 2 Materials and Methods
### Isolation of Bacterial DNA in Saline  
The isolation of genomic DNA from Gram (+) and Gram (-) bacteria, stored in saline solution, was performed using the Novabeads Bacterial DNA Preparation KIT (Novaym, Poznan, Poland). This method utilised synthetic composite magnetic beds that enabled selective detection of DNA and RNA. In the isolation process, specific buffers were also used to obtain a high-quality product. The buffer set included the STEL Buffer (a buffer for suspending bacterial cells), Lysis Buffer (lysis buffer), Binding Buffer (nucleic acid binding buffer), Pre-wash Buffer (pre-rinse buffer), Wash Buffer (proper rinse buffer), and TE Buffer (DNA elution buffer).  
The DNA Isolation procedure was performed according to the protocol attached by the manufacturer to the kit. In the preliminary stage, before starting the isolation process, the magnetic bead particles were thoroughly mixed and left on the laboratory bench until it reached room temperature (RT). After thawing, test samples obtained from gingival crevices and gingival pockets containing bacteria (500 uL of saline solution) were added to 300 uL of lysis buffer, vigorously mixed for about 10 s at 10,000-12,000 rpm at RT, and incubated for 5 min at RT. After this time, 200 uL of Binding Buffer was added to the samples, and the mixture was vigorously mixed. Then, 25 uL of buffer containing magnetic bed particles was added, thoroughly mixed, and incubated for 5 min at RT. The mixtures prepared in this way, contained in 1.5 mL Eppendorf tubes, were placed in a magnetic stand. After separating the magnetic bed with the bound DNA from the mixture of particles by means of a magnet, the liquid fraction was discarded. DNA bound to the magnetic bed particles was purified with Pre-wash Buffer, Wash Buffer, and 80% ethanol, sequentially.  
Finally, magnetic bed particles with bound DNA were resuspended in 50 uL of elution buffer (TE buffer) and incubated twice at 65 \({}^{\circ}\)C for 5 min with agitation at 800 rpm. The second incubation was in an open tube. After incubation was completed, the tubes were placed in a magnetic stand to separate the magnetic bed particles, and the bacterial DNA present in the liquid fraction was transferred to clean tubes. After the concentration was determined using a NanoDrop NP1000 spectrophotometer, DNA samples were stored at \(-20\) \({}^{\circ}\)C for further analyses.
# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## 4 Experiments and Analysis
### Results and Discussions  
In this paper, EMG bio-signals while walking were collected and the prediction accuracy of stroke diseases of 90.38% by Random Forest of machine learning and 98.958% by LSTM algorithm of deep learning were obtained. The system proposed in this study utilized real-time EMG data in four locations, left and right biceps femoris and gastrocnemius muscle, at 1500 Hz from EMG healthcare devices. The results of the analysis and prediction of stroke diseases based on these real-time bio-signals is a good approach as it is significant for the medical staff and hospitals to take a preemptive measure for early detection and prediction diagnosis of stroke diseases in patients in the danger group.  
Since machine learning and deep learning are AI (artificial intelligence) methodologies, model generated should be comprehensively interpreted together by clinical diagnosis experience and knowledge of medical staff, electronic medical records, clinical data, emergency blood test and MRI (magnetic resonance imaging) information, etc. to ensure accurate stroke disease analysis and predictions. Therefore, it is desirable to combine stroke diseases prediction studies, which are analyzed by using medical knowledge and clinical experimental results of the medical team, rather than just using the AI-based model develop in this paper by itself. In addition, since the service proposed in this paper is the result of experimenting with EMG bio-signals only during walking, the prediction of multi-modal should also be considered by collecting multimodal bio-signals such as EEG (electroencephalogram) and ECG (electrocardiogram) as well. Finally, we hoped that research for early detection and prediction of strokes and chronic diseases based on bio-signals will be actively carried out by expanding into daily life activities such as sleep and driving. From this paper, EMG-based experimental results in this study are aimed at helping medical staff accurately predict and diagnose stroke diseases, and initial results showed that it is significant that AI methodologies and bio-signals collected can be used to help diagnose diseases only in everyday life.
## 3 Results  
### Changes in Body and Organ Weights  
To investigate the immunomodulatory effects of HM on CPA-induced immunosuppressive mice, mice were injected with CPA and given HM extract four times. CPA injection greatly reduced representative immunity markers, such as body weight, spleen, and LN weight, whereas HM extracts considerably reversed these reductions in a dose-dependent manner.  
During the 5-day experiment, all CPA-treated mice underwent a significant initial weight loss compared with the intact controls (\(p<0.05\)). However, on receiving the experimental treatments (\(\beta\)-glucan and 4, 2, and 1 mL/kg HM), the treated mice showed significant body-weight gains of 114.3%, 132.3%, 118.45%, and 89.92%, respectively, compared with CPA control mice (\(p<0.05\)) (Table 1).  
Compared with the intact control mice, all CPA-treated mice were found to have significantly lower initial spleen weights (\(p<0.05\)), particularly the CPA controls, which showed a \(-58.17\%\) reduction. However, compared with these CPA control mice, all animals receiving each of the four subsequent experimental treatments showed significant increases in relative spleen weights of 48.34%, 61.82%, 49.82%, and 34.73% for the \(\beta\)-glucan and 4, 2, and 1 mL/kg HM treatments, respectively (\(p<0.05\)) (Table 1).  
Compared with the intact control mice, all CPA-treated animals had significantly lower submandibular LN relative weights at the end of the treatment period (\(p<0.05\)), particularly the CPA controls, which showed a change of \(-74.6\%\). However, compared with the CPA control mice, those receiving the \(\beta\)-glucan and 4 and 2 mL/kg HM treatments showed significant increases in the submandibular LN relative weights of 70.03%, 155.79%, and 90.20%, respectively (\(p<0.05\)), although no significant difference was detected at the lowest dose of 1 mL/kg HM (Table 1).
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 2 The Proposed Hybrid Evaluating Method
### Error Measures  
Indicators used for evaluating the performances of APT prediction methods usually include mean error, mean square error (MSE), mean absolute error, mean percentage error and mean percentage absolute error [20; 31; 32]. For indicators such as mean percentage error and mean absolute percentage error (MAPE), when the value of the indicator reaches a threshold, the predicted results are believed as effective results. For the indicators such as MSE and mean absolute error, the smaller the absolute values of the indicators are, the better the predicted results are believed [33]. Particularly, for APT prediction, the predicted result of the APT in the target year draws great attention, which directly determines the next expansion time of the airport. In this paper, the MAPE is employed as the indicator to verify the adaption of different models for the target year prediction. The MAPE can be obtained by:  
\[e_{it}=\left|\frac{Y_{it}-y_{it}}{y_{it}}\right|*100\%, \tag{28}\]  
where \(e_{it}\) is the APT predicting error of the \(i\)th airport in \(t\)th year, \(Y_{it}\) is the APT predicting the result of the \(i\)th airport in \(t\)th year and \(y_{it}\) is the APT observation of the \(i\)th airport in \(t\)th year.  
The acceptable error of the predicted result can be defined as:  
\[e_{it}\leq\varepsilon, \tag{29}\]  
where \(\varepsilon\) is the maximum MAPE that can be accepted. Usually, when \(e_{it}\leq 10\%\), the models are regarded as good models. When \(e_{it}>20\%\), the models are believed to be failure models.  
The Civil Aviation Administration of China (CAAC) divides airports into three levels according to the APT of each airport. The distribution of 203 studied airports is shown in Table 1. Since the APT fluctuations of small airports and new airports are usually larger than that of the large airports and the airports under stable operation, \(\varepsilon\) is set to be 10%, 15% and 20% to study the available models for different types of airports. Four evaluation criteria are set in this paper to see the change of the number of predictable airports after changing the maximum allowable prediction error percentage.
# Optimization Techniques for a Distributed In-Memory Computing Platform by Leveraging SSD
## 4 Experimental Results and Analysis
### K-Means Clustering Experiment Analysis  
The normalized job completion time of k-means clustering for the 1.5 GB dataset is shown in Figure 9. The purpose of the k-means clustering is finding the \(\mathbf{k}\) clusters in the dataset based on the distance measurement (e.g., Euclidean distance). In this workload, the algorithm reduces the SSE (sum of squared error) [24] through iterating the distance calculation between the \(\mathbf{k}\) center points and each data point. In this experiment, we iterate this process eight times. The amount of data to be shuffled is very small because the data needed from the previous stage are the information about the center points and SSE in each stage. In our k-means clustering workload, the maximum amount of shuffle read/write data is 1.0 MB, and the minimum is 0.8 MB. Here, the shuffle spill does not occur because the shuffle space is sufficient in all settings. In the experiments with no caching options, there is no difference among options _1, _2, and _3, because these settings do not cache any RDD, and in all three setting, the shuffle space is sufficient.  
Figure 8: The normalized TeraSort job execution time for 10 GB dataset.  
Figure 9: The normalized k-means clustering job execution time for 1.5 GB dataset.  
When caching the RDDs in the main memory or the memory and SSD, the more the storage space for the RDD, the more the performance in the job execution time improves because more RDDs can be cached on the storage space. When comparing the memory_only option and memory_and_SSD option, the memory_and_SSD option showed the better performance improvement. This is because in the memory_only option, the storage space is insufficient even in the M_3 option. In addition, caching the RDDs on the SSD solves such a lack of storage memory. Memory_and_SSD options improved performance by 10% on average as compared with the memory_only option.  
Note that the k-means clustering workload shows an opposite performance tendency from PageRank and transitive closure workloads because of the difference in the amount of shuffle data. We will discuss this in more detail in the next subsection.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 2 Transmission Characteristics  
### Structure of RV Reducer  
The RV reducer integrates planetary gears and cycloidal-pin gear mechanisms, as illustrated in Figure 1. Its primary components include the gear shaft, planet gears, crankshaft, cycloidal gear, pin gear (comprising pins and a pin housing), and flange. The gear shaft transmits input power and engages with the planet gears to induce rotation. The planet gears are fixed to the crankshaft, which acts as the rotational axis for the cycloidal gear and connects to the flange at both ends via bearings. The eccentric structure at the upper end of the crankshaft drives the cycloidal gear's orbiting motion while enabling its self-rotation. Pins are mounted on the pin housing to form the pin gear, which meshes with the cycloidal gear in a small-tooth-difference engagement. The flange supports the crankshaft,is mounted on the frame, and connects to an externally driven machine, delivering output motion and power.  
Typically, an RV reducer consists of two or three circumferentially arranged crankshafts, each driving two or three planet gears and two cycloidal gears. This design serves two primary functions: it divides the input power into multiple paths for transmission to the output mechanism (either the pin gear or flange), and the symmetrical arrangement of the cycloidal gears around the circumference mitigates vibration caused by gear offset and aids in balancing radial forces.  
Under ideal conditions, the reducer maintains a constant reduction ratio. However, in practical applications, discrepancies between the actual output angle and the theoretical output angle arise due to design, manufacturing, and assembly factors. This deviation is referred to as angular TE. The formation of TE is influenced by the inherent properties and motion of the core components, as illustrated in Figure 1.
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 4 Discussion
### The Response of Different Vegetation Indices to Drought and Heatwave  
Our results indicate that SIF is more effective than MODIS vegetation indices in monitoring vegetation photosynthetic responses to drought heatwaves over corresponding timescales. This finding aligns with previous studies, such as that of [PERSON], [PERSON], and [PERSON], who consider SIF a robust indicator for assessing the impacts of droughs and heatwaves on vegetation growth [43; 44; 45]. SIF refers to the phenomenon in which vegetation re-emits absorbed solar energy as fluorescence during photosynthesis [46]. Due to its physiological relevance [47], SIF directly measures photosynthetic processes, providing a more direct and sensitive reflection of vegetation responses to drought heatwaves compared to traditional vegetation indices, which only describe potential photosynthetic activity without direct correlation to actual processes [48; 49; 50].  
Additionally, we argue that NDVI responds earlier to drought-heatwave events compared to SIF. This contradicts some previous studies, such as those by [PERSON] et al., examining droughs and heatwaves in Europe in 2018 [51]. This discrepancy may be due to vegetation's physiological adaptations under drought-heatwave conditions [52], prioritizing photosynthesis to maintain necessary energy for growth and survival. Such adaptations may result in reduced NDVI due to drought effects, yet enhanced photosynthetic efficiency is reflected in increased SIF and GPP. For instance, plants under drought conditions may adjust water use efficiency to optimize photosynthesis with limited water resources [53]. Moreover, under high temperatures, plants may exhibit heat stress responses, including adjustments in photosynthesis and the protection of chlorophyll and other physiological processes. These factors explain why NDVI metrics in 2022 could anticipate extreme climate conditions earlier.
# A Hybrid Forecasting Model for Self-Similar Traffic in LEO Mega-Constellation Networks
## 3 Self-Similarity Analysis of Satellite Network Traffic
### EMD-Based Satellite Network Traffic Decomposition
\(h_{1,k}(t)\) will be taken as the first IMF when  
\[\sqrt{\sum_{t=0}^{\mathbb{T}}\frac{[h_{1,k-1}(t)-h_{1,k}(t)]^{2}}{h_{1,k-1}(t )^{2}}}<\xi \tag{5}\]  
where \(\xi\) denotes the threshold value. After \(k\) iterations, the root mean square error (RMSE) between \(h_{1,k}(t)\) and \(h_{1,k-1}(t)\) is less than \(\xi\). Then, \(h_{1,k}(t)\) will be taken as the first IMF that satisfies the condition. **Step 4:** Take \(l(t)=x(t)-h_{1,k}(t)\) as \(x(t)\) and repeat the above steps. When the remaining residual is monotonical and the amplitude is less than \(\xi\), several IMFs, \(C_{i}(t)\) and a final residual \(r(t)\) are obtained, i.e.,  
\[x(t)=\sum_{i=1}^{n}C_{i}(t)+r(t) \tag{6}\]  
**Lemma 1**: _Given the LEO mega-constellation network traffic \(D(t)\) with LRD properties. The IMFs of \(D(t)\) decomposed using EMD have the property of short-range dependence._  
The proof of Lemma 1 is detailed in Appendix A. Commonly, the IMFs decomposed via EMD mainly include three categories: noise-dominated, noise mixed with valid information and valid-information-dominated IMFs. The Pearson correlation coefficient, Shannon information entropy and Hurst index are usually utilized to classify IMFs. However, after identification, noise-dominated IMFs tend to be discarded, resulting in the loss of valid information. To concretely illustrate this issue, the valid information contained in IMF\({}_{i}(i=1,2,\cdots,n)\) is shown in Figure 4.  
Let the valid information ratio of IMF\({}_{i}\) be as \(a(i)\):  
\[a(i)=\frac{\Gamma(\text{IMF}_{i})}{\Gamma(D(t))} \tag{7}\]  
Figure 4: Valid information ratio of IMF\({}_{i}\) (\(i=1,2,\)..., \(n\)). where \(\Gamma(\text{IMF}_{i})\) denotes the valid information of IMF\({}_{i}\) and \(\Gamma(D(t))\) denotes the valid information of the raw satellite network traffic \(D(t)\). With an increase in order, the valid information ratio increases as well. The valid information ratios of all IMFs satisfy  
\[\sum_{i=1}^{n}a(i)=1 \tag{8}\]  
It can be seen that low-order IMFs still contain a small amount of valid information. Thus, discarding low-order IMFs will result in the loss of valid information. Meanwhile, since high-order IMFs contain noise, adopting high-order IMFs directly may inevitably introduce noise. Hence, in contrast to the existing works, the IMFs will not be classified in the EMD proposed in this paper. Instead, all IMFs will be adopted in the ARIMA to obtain residuals and multiple single frequency components.
## 5 Experimental Results and Evaluation
### Security and Privacy Analysis
#### 5.4.1 Sybil Attack  
In a Sybil attack, an adversary can forge multiple fake identities to create malicious nodes. As a result, these malicious nodes can control the DDS network or even the consensus network to some extent. However, in the proposed SAUSA, permissioned network management provides the basic security primitives, such as the PKI and KDC for identity authentication and message encryption. Thus, all nodes with invalid identities are prevented from joining the domain networks. Furthermore, properly defined AC strategies are promising to reduce the impact of Sybil attacks across different application domains.