esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Building of Stress-Strain Relationship Model  
The suggested model was evaluated using a dataset including six testing samples (20% of the entire dataset), which was obtained from a secondary source. These facts were not taken into consideration during the optimization procedure. Comparing stress strength capacity from observed data with that predicted by the suggested model in Figure 14 demonstrates this model's accuracy can be relied upon. The proposed model yielded a value that was quite near to one standard deviation above the mean (1.03). Figure 14 shows that the R-value is 0.9997, which implies that the actual and anticipated strength capacities are in good agreement with one another. This data shows that the proposed model accurately predicts concrete's strength capacity when varying fiber proportions are taken into consideration.  
Moreover, [PERSON] and [PERSON] [47] proposed a new criterion for determining the external validity of the PSO model, which they believe is more reliable. It is suggested that at least one slope of regression lines According to this criterion, it is recommended that at least one regression line's slope (\(k\) or \(\dot{k}\)) across the origin be close to one in accordance with this criterion. Since [PERSON] and [PERSON] [48] made their proposal, a new indicator of model predictability has emerged (\(R_{m}\)). The requirement is satisfied in the case of \(R_{m}=40.5\). The squared correlation coefficient (through the origin) between the predicted and experimental values, or the correlation coefficient between experimental and predicted values, \(R_{o}^{2}\). Table 7 lists the validation criteria that were taken into consideration, as well as the relevant results acquired by the model. According to these findings, the generated model is sufficiently and precisely satisfying the conditions that have been established.  
Figure 13: The proposed model’s (ARE) distribution.
# Distributed Information Compression for Target Tracking in Cluster-Based Wireless Sensor Networks
## 5 Simulation
### Number of Sample Points  
Figure 20 depicts that the error distance of TCAT and CTCI decreases as the number of sample points increases. Observe that as the number of sample points is larger than 15, the error distance becomes stable. Figures 21 and 22 depict that the data size and energy consumption of CTCI are directly influenced by the number of the sample points. Therefore, there is a trade-off between the error distance and the energy consumption on the number of the sample points in CTCI.  
Figure 19: Impact of the transmission range on the average energy consumption.  
Figure 20: Impact of the sampling points on the average error distance.
## 4 Discussion  
The results of the simulation experiment made it possible to determine the optimal value of lag \(\lambda\) between the occurrence of a delay and the moment of implementing the duration compression measures.  
For both cases analyzed in the example, this lag was the same (two days). Let us consider process four (roof cladding): its baseline start was scheduled for day 73 (\(s_{4}\) = 73). The expected value of its duration is \(t_{4}\) = 30 (baseline), and, if accelerated by switching to the first option, \(t_{41}\) = 28. Therefore, \(\Delta_{41}\ =\ 2\). If all predecessors of process four are completed by day 70 (earlier than scheduled in the baseline), so \(s_{4}^{r}\ =\ 70\), the process must start on day 73 anyway--because of the _railway policy_ that rules this process. As \(\Delta_{41}+\lambda\ =\ 4\), if the actual start of process four happened between day 75 and 77: \(75\leq s_{4}^{r}\leq 77\), this process should be conducted according to the first option of duration compressing measures. If process four was observed to start later than on day 77 (\(s_{4}^{r}>77\)), switching to the second option of duration compressing measures was advised.  
Oddly enough, a lower total cost \(C(\lambda)\) was obtainable by introducing more expensive duration reduction measures from the second group of options: \(C(\lambda)\) = 828,882.84 EUR in case III, whereas \(C(\lambda)\) = 946,833.96 EUR in case II. However, this fact is attributable to the possibilities of stronger compression offered in case III--stronger compression means a reduction in delay penalties (\(C_{p}\)). The expected values of duration compression measures (\(C_{w}\)) proved similar: (case II--276,897 EUR, case III--282,029 EUR).  
The results of the sensitivity analysis of the optimal lag to changes in penalty rate (Figure 5) indicate that the lower the penalty rates, the less stable the optimal solutions. Thus, even a small change in the contract regarding the penalty rate, may call for repeating the optimization procedure. A smaller penalty rate gives the construction manager more time to implement the schedule compression measures.  
The results can be compared with the effects of actions taken intuitively by construction managers. In practice, to avoid contractual penalties, these actions are undertaken each time a delay occurs, and implemented immediately. Such actions are consistent with the proposed approach with \(\lambda\) = 0. The total cost corresponding to such a rule is 1,056,214.98 EUR for case II and 1,045,415.87 EUR for case III. Therefore, the application of the proposed method enables a reduction in the cost and financial penalties on average by 109,381.02 EUR (case II) and 216,533.03 EUR (case III).  
Figure 5: Effect of penalty rate on the optimal lag (\(\lambda\)) and the total cost of delays and duration compression measures for case III.
## 2 Materials and Methods
### Methods  
Figure 1 depicts BDHE-Net, the proposed framework for classifying building damage. The framework incorporates a data augmentation module, a building damage attention module, and a multilevel feature adaptive fusion module, which is employed to suppress the model weights likely to favor the intact category, enhance the extraction of building damage features from the model, and strengthen the extraction of housing integrity information at different scales, respectively.  
.2.1 Data Augmentation Module Based on Oversampling Techniques and Label Polygon Dilation Techniques  
In earthquake disasters, it is common for the number of intact houses to be much higher than the number of damaged houses, with a serious imbalance in the sample size of categories. When traditional convolutional neural networks are used directly for building damage assessment, the model predictions may be biased towards a large number of categories, thus resulting in a decline in the classification accuracy of damaged houses. Therefore, we designed a data augmentation module to enhance its ability to perceive the damaged house category. The data augmentation module consists of the following two components: oversampling and label polygon dilation techniques.  
Figure 1: The structure of the proposed BDHE-Net framework.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image Category** & **Image** & **GT** \\ \hline UAV & & \\ Remote sensing & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Examples of UAV and satellite remote sensing images of the same size.  
Considering that the number of severely damaged and collapsed types is significantly less than that of the slightly damaged categories, we judge the input image and copy it once if there is a slightly damaged type, and twice if there is a severely damaged category or collapse category. Therefore, the model can learn multiple times from these images to better extract features of damaged houses.  
Secondly, when a building suffers severe damage or collapse, there will be debris around the house, which is significantly different from intact or slightly damaged buildings. This characteristic is critical for building damage classification. Therefore, we used labeled polygon inflation techniques based on an inflation factor k. For the severely damaged category, k was set to 5-10% of the labeled area, while for the collapsed category, k was to 10-15% of the labeled area because of more debris and gravel around collapsed buildings than in the severely damaged category. In this way, the characteristics of the severely damaged and collapsed categories can be amplified. Figure 2 shows a comparison between before and after dilation.
# Influence of Cargo Luggage on the Vertical Drop Crashworthiness of Aircraft Mid-Fuselage Section
## 5 Influence of Cargo Luggage on Fuselage Section Crash Response
### Acceleration Response  
The vertical acceleration curves of the fuselage section without and with cargo luggage are shown in Figure 16. At approximately 13 ms, the first peak acceleration occurred after the two models touched the ground. The acceleration of the model without cargo luggage was slightly greater than that of the model with cargo luggage. However, at approximately 60 ms, the acceleration response curve with the cargo luggage model had a secondary peak value, which was greater than the acceleration value of the model without cargo luggage at that time. This is because cargo luggage, as a relatively soft structure, plays an energy-absorbing and buffering role in the crash process but it limits the subsequent deformation of the lower structure of the cargo hold floor, rendering it impossible for the lower structure to absorb the impact through further deformation. At approximately 103 ms, a small peak was observed in the acceleration response curve in the model without cargo luggage, and this was caused by the cargo floor tilting and coming into contact with the cabin floor. At approximately 148 ms, the pillars on both sides of the cargo hold of the model without cargo luggage touched the ground, resulting in the final peak of the curve; then, the acceleration gradually stabilized at about 0. As the cargo luggage had the function of energy absorption and buffering, the deformation of the fuselage frame was small; thus, the last two peaks relative to the curve without cargo luggage were not present here. After 75 ms, the acceleration gradually decreased and finally stabilized at approximately 0 g.  
Figure 14: Deformation diagrams of fuselage frame without cargo luggage. (**a**) t = 0 ms. (**b**) t = 100 ms. (**c**) t = 180 ms.  
Figure 15: Deformation diagrams of fuselage frame with cargo luggage. (**a**) t = 0 ms. (**b**) t = 100 ms. (**c**) t = 180 ms.  
From the perspective of the total vertical rigid body acceleration, although the presence of cargo luggage increased the initial crash kinetic energy and also inhibited the deformation and energy absorption of the substructure of the cargo floor, cargo luggage had a certain energy-absorbing and buffering effect.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
###### Abstract
Meanwhile, visible light communication systems exploit the existing lighting infrastructure to provide high-speed and secure data communication [11; 12; 13] and are expected to become commonplace in homes and office following the release of the IEEE 802.11 bb [14] Standardization currently scheduled for 2022. VLC leverages the huge bandwidth available in the nonionizing visible electromagnetic spectrum [15]. Light is a suitable communication medium in medical environments [16; 17; 18] where there are strict electromagnetic compatibility conformance standards. VLC-based health monitoring [19] and notification systems were developed for the blind [20]. VLC systems can be built with very low-cost [21] using standard light emitting diodes (LEDs) and photodiodes (PDs), such as those commonly used in DVD players. High-speed VLC systems direct the transmission of focused light between the transmitter (Tx) LED and receiver (Rx) PD. On the other hand, currently, most GR systems for visible light operate on reflected light captured by multiple PDs. A non-ML-based motion detection system using VL comprising multiple PDs was proposed in [22]. The work focused on communications performance, and there were no gesture classification accuracy results. Gesture patterns are statistically repeatable and can be learned by repeated sampling using ML. A summary of recent hand GR research using ML is tabulated in Table 1. Infrared (IR) systems are less affected by ambient light and can generally achieve higher classification accuracies. However, most IR systems do not achieve the high visible light (VL) data-rates and at the same price-point. Using the decision-trees algorithm, authors reported a 98% classification accuracy using IR proximity sensors [23]. Feature extraction using SVM achieved 95% accuracy on data collected from an accelerometer [24]. Back-propagation was used to track hand trajectories using an inertial sensor with 89% accuracy [25]. A smart electronic-skin comprising an array of detectors and LSTM processing was proposed [26]. By tracking the shape of shadows cast through hand-blocking using a 32-sensor array, researchers achieved 96% accuracy [27]. Although the system achieved good performance, the large \(6\times 6\) ft array is rather impractical, and additionally, not aimed at communications. Classification performance is generally improved by deploying multiple PDs on the ceiling and floor. As the cost and computational complexity generally scale with the number of detection chains, these should be kept to a minimum. K-nearest neighbors (KNN) is a low-complexity, nonparametric algorithm that can distinguish gesture classes based on the Euclidean distances between samples. An accuracy of 48% was achieved using KNN with a single PD and increased to 83% by employing two PDs [28]. Classification of reflected IR waves was achieved using a hybrid KNN and SVM [29].
# Seismic Random Noise Attenuation via Low-Rank Tensor Network
## 4 Discussion  
Despite the extraordinary denoising performance of LRTNet, there are still several limitations and areas for improvement that warrant further discussion:  
* As presented and analyzed in Section 3, LRTNet exhibits more severe signal leakage compared to Pyseistr and TNN-SSTV. While the severity is not significant in either qualitative or quantitative comparisons, this remains a notable limitation. Additionally, the local similarity metric, which is a tensor of the same size as the input signal, provides only a coarse quantitative comparison through its mean and variance. Future work could explore more refined metrics or visualization techniques to better capture the spatial and temporal characteristics of signal leakage, enabling a more nuanced evaluation of denoising performance.
* LRTNet leverages DL to tune its inner parameters for LRA. Although DL is not the core of LRTNet, it introduces a common challenge: the gap between synthetic and real field data. Deploying LRTNet, which is trained on synthetic data, directly to real data may result in performance degradation due to domain differences. Training LRTNet on real data could mitigate this issue, but it presents its own challenges. For instance, the lack of ground truth for real data complicates the calibration and validation of the model. Furthermore, geographical variations between training and validation  
Figure 10: The denoising results of TNN-SSTV on a Penobscot volume.  
datasets may still lead to performance gaps. Potential solutions to this include domain adaptation techniques, transfer learning, and the development of semi-supervised approaches that leverage both synthetic and real data.
* Compared to purely DL-based methods, LRTNet has a significant efficiency drawback. DL methods are inherently well-suited for parallelization, enabling the batch processing of multiple data samples. In contrast, LRTNet follows the ADMM framework, which processes data sequentially, one sample at a time. This limitation restricts the scalability of LRTNet, particularly for large-scale datasets. Future improvements could focus on optimizing the ADMM framework for parallel processing to enhance the computational efficiency of LRTNet.
* While LRTNet demonstrates strong performance under controlled noise conditions, its robustness to varying noise levels and types in real-world scenarios remains to be fully explored. Real filed data often contain complex noise patterns that may differ significantly from the synthetic noise used during training. Investigating LRTNet's performance under diverse noise conditions and developing adaptive mechanisms to handle noise variability could further enhance LRTNet's practical applicability.
## 4 Charging Method
### Comparison of Different Charging Methods  
Each charging method has its own set of advantages and limitations. Prior to implementing the charging techniques, it is crucial to familiarize oneself with their features and conduct an analysis of their effectiveness within the system [75]. Features, benefits, and drawbacks of each type of battery charger are summarized in Table 14.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Charging Method** & **Crucial Factors** & **Advantage** & **Disadvantage** \\ \hline Constant Current (CC) & Changing current rate. & Easily adaptable. & Capacity utilization is low. \\ \hline Constant Voltage (CV) & Changing voltage rate. & Easily adaptable. & Responsible for the battery’s lattice collapsing. \\ \hline CC–CV & CC mode current rate modification. & Utilization of capacity is high. & Balancing the charging rate, energy loss, and temperature \\ \hline MCC & The total number of CC stages. & Quick charging and simple implementation. & The challenge lies in achieving a balance between charging speed, capacity utilization, and battery lifetime. \\ \hline Pulse Charging & Sequence of high-current-pulse operating mode. & Minimize energy loss, and reduce the risk of overcharging. & More expensive, and require specialized equipment \\ \hline Trickle Charging & Constant current charging. & Maintaining a healthy battery; convenience. & Can lead to overheating and risk of fire; expensive. \\ \hline \end{tabular}
\end{table}
Table 14: Comparison between different charging methods.  
Figure 17: Trickle charging (TC) method for EV battery with battery current (green) and battery voltage (red).
### Context
#### 1.1.5 Sybil Identities: A New Threat  
Distributed systems theory has provided in-depth analyses of numerous system failure modes and has provided mitigations for many of those. Most of the early work, however, assumed a priori knowledge of system participants (see Section 1.1.1). In addition to this, while the abuse of telecommunications infrastructure is documented as early as the 1960s [29] and attacks on computer systems occurred frequently from the 1980s onwards [30], attacks commonly were carried out by intruders who targeted systems in which they were not members from the outside. Putting that in perspective to the categories outlined before (see Table 2), it becomes apparent that research of the pre-Blockchain era is most applicable to type _iii_ (permissioned/private) systems.  
It is not always possible to transfer earlier results to majority-based systems where there is no a-priori knowledge of potential leaders (i.e., type _ii_ and _iv_ systems) since those constitute a new phenomenon, popularised with the emergence of blockchain technology. From an attack perspective, such systems are fundamentally different from prior deployments as, in them, malicious entities can operate on the same level as trustworthy ones, for there is no authority to distinguish between those entities. This means that common approaches to mitigating intrusion risks--such as identity management, user authentication, or encryption [31]--are not feasible. Furthermore, common strategies to improve fault tolerance, like increasing redundancy, are futile in a scenario where malicious users can present arbitrarily large numbers of identities. The presentation of arbitrary entities with malice--often referred to as 'Sybil Attack' [32]--threatens large-scale peer-to-peer systems in which trusted authorities are absent. This can be attributed to the fact that, when confronted with conflicting information from multiple peers, decentralised systems need to attempt to resolve those conflicts in a way that maintains a correct system state. Determining which piece of information is to be considered _correct_ is the main challenge in such scenarios.  
Successful Sybil attacks manifest in different ways, depending on the consensus mechanism deployed and the scale of the attack. While successful small-scale attacks might only introduce forks or stall system operation sporadically, others may irrecoverably disable it, forcing users to abandon it or to seek 'hard forks' and the subsequent manual removal of the attacking entities [33], a technique frowned upon due to its economical and ethical ambiguity [34].
## 4 Discussion
Angiogenesis, drug resistance, invasion, and anti-apoptotic response could be improved by STAT3 phosphorylation [13; 27]. Dephosphorylation of the STAT3 pathway has led to an improvement of apoptosis in cancer cells, cell cycle arrest, inhibition of carcinogenesis, and prolonged survival in tumor xenograft models [12; 40]. In our study, BSN significantly reduced STAT3 activation. Junus kinase (JAK) mediated signal pathways by STAT in adjacent cytoplasm. Furthermore, JAK2 and Src, STAT3's upstream signals, were also highly expressed in hepatic cancer cells [41]. BSN could inactivate JAK2 and Src in a dose-dependent manner. By suppressing JAK2 and STAT3 signals, BSN could have an even greater effect on cell death. Therefore, we hypothesized that BSN-induced anti-cancer effects might be related to dephosphorylation of STAT3, JAK2, and Src. We found that BSN-induced suppression of constitutive STAT3 expression was diminished by NAC pre-treatment (Figure 3). ROS generation was one of the early events, but STAT3 activation was a downstream process of ROS generation. BSN induced apoptosis in HCC cells by activating STAT3, which depended on ROS levels. Phosphorylation of STAT3 is crucial to tumor cell proliferation [42]. Additionally, we found that BSN suppressed the expression of a number of STAT3-regulated genes, including proliferative (cyclin D1), anti-apoptotic (Bcl-2, Bcl-xL, survivin and Mcl-l), and angiogenic genes (VEGF). As cyclin D1 is necessary for the transition from G1 to the S phase in the cell cycle, the downregulation of cyclin D1 expression may explain the antiproliferative effects of BSN. [PERSON] et al. (2002) found that the inhibition of STAT3 by an Src inhibitor led to a downregulation of Mcl-l expression in melanoma cells [43]. The activation of STAT3 signaling also induced survivin gene expression and conferred resistance to apoptosis. Several chemotherapeutic agents also blocked Bcl-2 and Bcl-xL, which have been associated with an increase in chemoresistance [44]. This indicated that Bcl-2, Bcl-xL, survivin, and Mcl-l expressions had been downregulated when BSN induced apoptosis in HCC cells. According to these observations, caspase-3-induced PARP cleavage increased, and TUNEL assays detected an increase in apoptotic cells (Figures 1 and 6). STAT3 expression could promote tumor survival and growth whenever expressed, so agents such as BSN could lead to apoptosis by targeting STAT3.