esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, its capacity for distinguishing between structures is limited, so differentiation maneuvers including ankle dorsiflexion or hip adduction/rotation can help differentiate between the symptoms caused by neural and non-neural tissue irritation [12; 13; 14; 15]. The goal of these maneuvers is to selectively increase or decrease the load on the nervous system without altering the mechanical stress on non-neural structures that might contribute to SLR-related symptoms. A change in SLR-provoked symptoms following one or more structural differentiation maneuvers suggests that the symptoms are at least partially related to neural tissue irritation [15]. Although clinical guidelines recommend the use of the SLR test, evidence indicates that it demonstrates limited effectiveness in diagnosing lumbar radicular pain [13]. One potential factor contributing to its suboptimal diagnostic performance is limited reliability [16]. A specific aspect that warrants investigation is whether the reliability of angular hip measurements during the SLR could be a contributing factor to this limitation. The accurate and consistent measurement of the hip angle is crucial for standardizing the test and ensuring reproducible results, especially considering the usefulness of the test for the evaluation of subjects with flexibility deficits [17]. In addition, collecting information on the range of motion achieved in hip flexion during the SLR test may provide relevant information on complementary factors. A relationship has been demonstrated between adaptive neural movement, whose limitation may reduce the range of motion values achieved during the test, and the presence of lumbar and radicular symptoms [18]. Furthermore, range of motion is a widely used variable which is collected in combination with the SLR test in other musculoskeletal conditions, as in the case of hamstring injuries, where the restriction of movement persists up to 30 days after injury [19] and has been postulated as a valid predictor for the time to return to play [20]. The use of motion analysis software offers a potential solution for assessing these biomechanical aspects, but its reliability for such purposes must be thoroughly evaluated. This highlights the need for research into intra- and inter-examiner reliability when using Kinovea to measure hip angles during the SLR test. Establishing the validity and reliability of such software could improve the biomechanical assessment of the SLR, ultimately enhancing its diagnostic utility and addressing the current limitations in its application. Since conflicting results have been reported regarding Kinovea and SLR reliability, with outcomes showing wide variations depending on the setup and perspective used [21], this study aimed to analyze the intra- and inter-reliability of Kinovea in measuring hip flexion mobility at the positive response moment during a passive Straight Leg Raise test from an orthogonal perspective.
## 3 Our Contribution
### Systematic RLNC Based UWSN
#### 3.3.3 Packet Delivery Probability (PDP)  
Initially, the individual sink transmits its information with transmission power \(\mathcal{P}\) to all neighbouring UWSN nodes, and each node estimates its distance to the destination based on the received signal strength (RSSI) or signal to noise ratio (SNR). If the distance in between the transmitter and receiver is \(x\), then, with the signal propagation topology characterized with the spreading factor \(k\) (\(k=1.5\)), the attenuation is derived as (72)  
\[A(x)=x^{k}a^{x} \tag{72}\]  
where \(a=10^{a(\mathcal{P})/10}\) and \(a(\mathcal{P})=\frac{0.11P}{1+P^{2}}+\frac{44P^{2}}{4100+P^{2}}+2.75\times 10^ {-4}\mathcal{P}^{2}+0.003\).  
Applying the developed channel model with both the LSD as well as small scale deviation over signal propagation, data receiving is achieved and, using binary phase shift keying (BPSK), the average Bit Error Rate (BER) is obtained where SNR of a symbol \(r_{s}\) and bit SNR \(r_{b}\) are equal \((r_{s}=r_{b})\). Defining \(\overline{r_{s}}=10^{rs/10}\), BER is obtained as (73):  
\[P_{b}(r_{b})=\frac{1}{2}\Bigg{(}1-\sqrt{\frac{\overline{r_{s}}}{1+\overline{r_ {s}}}}\Bigg{)} \tag{73}\]Each bit SNR of the underwater signal information at the receiver node is obtained as (74)  
\[r_{b}=A-k\times 10\times logd-a(\mathcal{P})\times d\times 10^{-3}-50+18\times \log\mathcal{P} \tag{74}\]  
where A states the sound intensity level (normally 118 dB). Finally, for any pair of nodes with the distance d, the PDP with m bits size is obtained as:  
\[p(d,m)=(1-p_{b}(r_{b}))^{m} \tag{75}\]
# Weakening Detection of Composite Structure Adhesive Layer Based on Nonlinear Guided Waves
## 4 Simulation of Nonlinear Ultrasonic Guided Waves Using Finite Element Methods
### Detection and Analysis of Localized Damage in Adhesive Layers  
To detect local damage in the adhesive layer and analyze its impact on the nonlinear coefficient, this study also employed a method to simulate local damage by increasing the third-order elastic modulus of the adhesive layer. Specifically, the damaged region is located within the range of 40 mm to 60 mm in the adhesive layer, with a length of 20 mm and a thickness of 0.03 mm. In this region, the third-order elastic modulus is increased to 50 times its original values, while the material parameter in the other region remains unchanged. Within the range from X = 0 mm to X = 100 mm, a point probe is set every 10 mm, and signals are extracted at X = 20 mm, X = 40 mm, X = 60 mm, X = 80 mm, and X = 100 mm, respectively. Waveforms and their corresponding frequency spectrum are shown in Figure 12 when there is local damage in the adhesive layer. From the frequency spectrum, it can be observed that the guided wave also presents significant nonlinear effects. Figure 13 is a comparison of received signals at X = 50 mm between undamaged and damaged models. The signal almost completely overlaps, making it difficult to discern any differences. This indicates that the local damage does not cause reflection or attenuation of the guided wave. However, from the frequency spectrum, the received signal of the damaged model exhibits an increase in the second-harmonic amplitude compared to the undamaged model. This suggests that local damage cannot be detected through linear methods of ultrasonic guided waves but may induce changes in the nonlinear coefficient of guided waves. To investigate whether local damage can cause significant changes in the nonlinear coefficient, the study calculates the nonlinear coefficients at each signal reception location mentioned above and plots their variation curves as a function of propagation distance. Figure 14 demonstrates a comparison of nonlinear coefficients between undamaged and damaged models. This reveals that regardless of whether the model has local damage, the nonlinear coefficient exhibits a cumulative effect with increasing propagation distance. However, the nonlinear coefficient increases more significantly within the local damage region. This indicates that the nonlinear effect of guided waves can be used for qualitative assessment of local damage. According to the sudden change in the slope of the nonlinear coefficient from X = 40 mm to X = 60 mm in the damaged plot, the approximate location of the damage can be determined to be between X = 40 mm and X = 60 mm. The method can be used for locating damaged positions within the adhesive layer of composite structures.  
Figure 11: Comparison of the changes in the Fourier coefficient and nonlinear coefficient over propagation distance between the overall weakening and non-weakening of the adhesive layer: (**a**) Fourier coefficient change vs. propagation distance; (**b**) nonlinear coefficient variation vs. propagation distance.
# Novel Multi-Level Dynamic Traffic Load-Balancing Protocol for Data Center
## 7 Conclusions and Future Work  
This paper investigated different load-balancing mechanisms and algorithms, as well as protocols, and also discussed several of the load-balancing problems. The key consideration was to minimize the congestion mismatch problems in the proposed and designed protocol. A multi-level dynamic traffic load-balancing protocol is proposed to overcome the uncertainties (traffic dynamics, topology asymmetry, failures, and capability) issues. The proposed approach has used the multi-state path conditions and path characterizing parameters. The MDTLB simulation results outperform HERMES in terms of both FCT and throughput. Furthermore, MDTLB has achieved the best throughput compared with other protocols for data mining flows.  
In the future, it will be based and tested on hardware that emulate different scenarios such as symmetry and asymmetry topologies. We can also improve the way of judging paths by implementing the dynamic thresholding method by taking T\({}_{\text{ECN}}\) as the secondary input to decision-making logic.  
**Author Contributions:** [PERSON], [PERSON], [PERSON], and [PERSON] conceived and designed the whole system; [PERSON], [PERSON] designed algorithm and model; [PERSON] and [PERSON] conducted the experiment; [PERSON] wrote the research paper.  
**Funding:** This work is supported by National Natural Science Foundation of China (61872387, 61572530, 61872403) and CERNET Innovation Project (Grant No.NGII20160113, NGII20170107).  
**Conflicts of Interest:** I declare that none of the authors have a conflict of interest.
## 3 Results and Discussion
### Comparison of Simulation Performance Across Climate Zones  
To investigate the influence of background climate on model fidelity, a climate-stratified evaluation was performed. The 105 FLUXNET sites were categorized based on the globally recognized Koppen-Geiger climate classification system, which delineates climate zones according to the distinct seasonal patterns of temperature and precipitation ([PERSON] et al., 2017). For this analysis, sites were aggregated into five principal climate regions: Tropical, Arid, Temperate, Continental, and Polar. The model's performance in simulating annual ET and GPP within each zone is quantitatively presented in Tables 4 and 5, with the distribution of simulation biases visualized in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Climate**} & \multirow{2}{*}{**Site-Year *} & **Observed** & **Modelled** & **Absolute** & **Relative** & **Correlation** & **RMSE/** & **MAE/** & \multirow{2}{*}{_p-Value_} \\  & & **ET** & **ET** & **Error/** & **Coefficient** & **mm\_yr–1** & **mm\_yr–1** & **mm\_yr–1** & **\(p\)-Value** \\ \hline Tropical & 42 & 1127.30 & 822.82 & 304.48 & 27.01 & 0.62 & 343.51 & 304.48 & \(p<0.001\) \\ Arid & 77 & 343.73 & 317.30 & 26.42 & 7.69 & 0.64 & 108.11 & 60.52 & \(p<0.001\) \\ Temperate & 142 & 525.08 & 468.05 & 57.03 & 10.86 & 0.15 & 222.48 & 160.44 & 0.07 \\ Continental & 328 & 433.80 & 399.56 & 35.34 & 8.14 & 0.01 & 156.46 & 121.82 & 0.84 \\ Polar & 108 & 388.01 & 245.20 & 140.44 & 36.52 & 0.64 & 191.98 & 149.04 & \(p<0.001\) \\ \hline \hline \end{tabular}  
* * Site-Year: Total number of observation years summed across all sites within each climate zone.  
\end{table}
Table 4: Quantitative evaluation of annual ET simulations against FLUXNET observations across climate zone.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Climate**} & \multirow{2}{*}{**Site-Year *} & **Observed** & **Modelled** & **Absolute** & **Relative** & **Correlation** & **RMSE/** & **MAE/** & \multirow{2}{*}{_p-Value_} \\  & & **GP/** & **GP/** & **Error/** & **Coefficient** & **g\(\mathbf{C}\)\(\cdot\)m\({}^{-2}\)\(\cdot\)yr–1** & **g\(\mathbf{C}\)\(\cdot\)m\({}^{-2}\)\(\cdot\)yr–1** & **g\(\mathbf{C}\)\(\cdot\)m\({}^{-2}\)\(\cdot\)yr–1** & **g\(\mathbf{C}\)\(\cdot\)m\({}^{-2}\)\(\cdot\)yr–1** & **\(p\)-Value** \\ \hline Tropical & 42 & 1978.
# An Intersection Signal Control Mechanism Assisted by Vehicular Ad Hoc Networks
## 3 Intersection Signal Control Mechanism  
### System Model and Environment Settings  
Figure 3 illustrates a typical urban intersection. Without the right turning, which generally has its exclusive lanes out of control of the signal lights at most current urban intersections, the number of traffic flows (\(m\)) is eight. \(D_{i}(i\in[1,\ m])\) represents the \(i\)th flow, and the value of \(p_{ij}\) indicates whether the \(i\)th and the \(j\)th traffic flows can pass the intersection at the same time (\(p_{ij}=1\ (i\
eq j)\) means no conflict, and vice versa). In a traditional signal control mechanism, according to the eight traffic flows as shown in Figure 3a, there are four signal phases, consisting of north-south bi-directional straight paths, east-west bi-directional straight paths, north-east and south-west left turnings, and west-north and east-south left turnings. However, in the condition of a dynamic combination with no conflict, 12 phases can be realized in Figure 3b:  
\[\sum_{i,j\in[1,\ m]}p_{ij}=12,\ (i\
eq j). \tag{3}\]The green light duration (\(T_{g}\)) can be determined dynamically within the range of \([T_{g},\overline{T_{g}}]\) via the signal control system, and the yellow light duration (\(T_{y}\)) is set as a fixed value. Due to the different width and different number of lanes, and other conditions of each traffic flow, the maximum number of vehicles (\(\overline{k_{i}}\)) that can pass in a unit time is different and the actual value can be acquired according to the statistical data of the intersection. The moving direction (i.e., driving path) of far vehicles after they reach the intersection can be gathered in advance by also using a VANET. However, in consideration of the personal privacy, instead, far vehicles can be divided according to the proportion of each flow in the recently collected statistical data.  
In addition, for the state of the traffic flow at a future moment, the calculation in our proposed mechanism adopts the method of forward recurrence based on the information at the current time. If the traffic volume is large, the amount of calculations required is great as well. Considering that the hardware performance of each decision-making device at different intersections is not same, we can appropriately adjust the decision time point (\(t_{e},t_{e}\geq t_{c}\)) forward from the end time of the current signal according to the actual condition. In other words, the decision-making needs to be started in advance to ensure that the calculation is completed on time.
## 3 Simulation Results
### Case 2: SST as a Variable Frequency Drive  
When the operation of SST as VFD was evaluated for the case when SST delivered power to an induction motor with 1.0 p.u mechanical loading, the obtained results showed satisfactory results. SST maintained the unity power factor. The grid side current harmonic contents were within IEEE defined THD limits. With the induction motor at the rated loading, the settling time of SST-VSC at the DC -link increased by 0.15 s, as shown in Figure 18. SST-VSC took a little bit more (0.32 s) time to settle for the inductive load.  
For the case when SST generated the voltage signal of 50 Hz for the inductive load, it could be observed from Figure 19 that the SST input current and the voltage were in phase, thus ensuring the unity power factor. For this case, filtered SST output voltage is shown in Figure 20.  
When SST generated 60 Hz voltage signal for the load, the same parameters were evaluated, as in the previous case, and are displayed in Figure 21. Similarly, a voltage signal of other frequencies can be generated using the proposed SST for the resistive as well as the inductive load.  
Figure 16: (**a**) SST input current at the grid side. (**b**) THD analysis of phase A input current.  
Figure 17: SST-VSC voltage and current at the DC -link.  
Figure 19: Result displaying that phase A voltage and current are in phase.  
Figure 20: SST 50 Hz filtered output voltage.  
Figure 18: SST-VSC response with induction motor loading.
###### Abstract  
As the usage growth rate of Internet of Things (IoT) devices is increasing, various issues related to these devices need attention. One of them is the distribution of the IoT firmware update. The IoT devices' software development does not end when the manufacturer sells the devices to the market. It still needs to be kept updated to prevent cyber-attacks. The commonly used firmware update process, over-the-air (OTA), mostly happens in a centralized way, in which the IoT devices directly download the firmware update from the manufacturer's server. This central architecture makes the manufacturer's server vulnerable to single-point-of-failure and latency issues that can delay critical patches from being applied to vulnerable devices. The Open Connectivity Foundation (OCF) is one organization contributing to providing interoperability services for IoT devices. In one of their subject areas, they provide a firmware update protocol for IoT devices. However, their firmware update process does not ensure the integrity and security of the patches. In this paper, we propose a blockchain-based OCF firmware update for IoT devices. Specifically, we introduce two types of firmware update protocol, direct and peer-to-peer updates, integrated into OCF firmware update specifications. In the direct scenario, the device, through the IoT gateway, can download the new firmware update from the manufacturer's server. Meanwhile, in the peer-to-peer scheme, the device can query the update from the nearby gateways. We implemented our protocol using Raspberry Pi hardware and Ethereum-based blockchain with the smart contracts to record metadata of the manufacturer's firmware updates. We evaluated the proposed system's performance by measuring the average throughput, the latency, and the firmware update distribution's duration. The analysis results indicate that our proposal can deliver firmware updates in a reasonable duration, with the peer-to-peer version having a faster completion time than the direct one.  
blockchain; firmware update; IoT; IoTvity; OCF 20202010, 10, 6744; doi:10.3390/app10196744
# A Review-Unguided Optical Communications: Developments, Technology Evolution, and Challenges
## 3 Hybrid RF-FSO Systems Techniques and Their Advanced Achievements
### FSOC for WiMAX
are via WiMAX microwave signals, while the main tower-to-tower access is via FSO beams. Directional microwave antennas can also be used at base stations to provide sectorial coverage, such as connectivity to rural stations, bus-stop hotspots, hotels, etc. The optical beam (visible or invisible) would also be broadcasted to the open field to provide high-speed data-access optical hotspots. The incorporation of FSO communication technology with a WiMAX network provides several advantages in terms of a reduction in physical connectivity, operations with reduced RF power, providing high-speed data access everywhere, a reduction  
Figure 25: WiMAX top-level architecture. in tower-top system volume, the creation the optical access hotspots, and less backbone routing. Thus, nowadays, FSO-WiMAX is an emerging technology and it attracts several researchers and commercial interest.
# MT-CMVAD: A Multi-Modal Transformer Framework for Cross-Modal Video Anomaly Detection
## 3 Supposed Method
### Video Encoder Layer  
The video encoder architecture presented in this work builds upon the classical Transformer encoder framework, incorporating two critical enhancements: dynamic resolution processing and a block-wise attention mechanism, as illustrated in Figure 4. The key technical innovations can be characterized through four principal aspects:  
1. **Adaptive Patch Partitioning**: Implements multi-scale feature extraction through resolution-aware patch merging operations, achieving optimal balance between computational efficiency (14.3% FLOPs reduction) and feature granularity preservation across varying resolutions.
2. **Shifted Window Attention**: Introduces a hierarchical windowing strategy that reduces the computational complexity of self-attention from quadratic \(O(N^{2})\) to linear \(O(NM^{2})\) with respect to token count \(N\) and window size \(M\), while maintaining global contextual interactions through cyclic shift operations [34].
3. **Hybrid Normalization**: Adopts a Pre-LayerNorm configuration for stable gradient propagation during backpropagation, which demonstrates 18.7% faster convergence compared with conventional Post-LayerNorm implementations in ablation studies [35; 36].  
4. **Explicit Spatiotemporal Encoding**: Develops a dual-branch positional encoding scheme combining absolute temporal embeddings (for frame-level localization) with learnable coordinate projections (for spatial position awareness), achieving a 2.4% improvement in temporal alignment accuracy [30; 37].  
Its core implementation can be summarized as the following key technical modules.
# Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells
## 1 Introduction
emphasized that when acoustic density and resistivity logging curves are plotted on the same depth coordinate system, significant deviations in all three curves at the same logging depth confirm the occurrence of undercompaction [7]. Recently, [PERSON] et al. [8] utilized a multi-well logging curve combination method and Bowers' acoustic density crossover diagram to identify the causes of overpressure in the Yinggehai Basin, highlighting the relevance of advanced logging techniques in pressure assessment. Furthermore, [PERSON] et al. [9] combined mud mineral analysis with acoustic time difference-density crossover diagram analysis to determine different compaction stages, yielding ideal predicted pressure results. This showcases the application of innovative methodologies in addressing traditional overpressure assessment challenges. In addition, recent advances in machine learning and artificial intelligence have shown great promise in predicting abnormal pressure conditions. For instance, [PERSON] et al. [10] explored the use of support vector machines and neural networks for predicting pore pressure in complex geological environments, demonstrating improved accuracy and reliability over traditional methods. Similarly, [PERSON] et al. [11] applied deep learning techniques to analyze logging data, revealing patterns in abnormal pressure occurrences that were previously undetectable through conventional approaches. The primary objective of this study is to provide a robust framework for quantifying traditional overpressure mechanisms in the Rio del Rey Basin. We employ a comprehensive approach that integrates theoretical judgment with hierarchical clustering algorithms to calculate the weights associated with different causes of overpressure. By utilizing a powerful LightGBM model enhanced with the SMOTE Bayesian optimization algorithm, we have developed an effective model for identifying the mechanisms responsible for abnormal pressure. This method not only enables the calculation of formation pressure applicable to the combined effects of undercompaction,high-temperature fluid expansion, and structural diapirism but also provides a scientific basis for subsequent oil and gas exploration. By gaining a deeper understanding and quantifying these abnormal pressure mechanisms, this research will offer important support for optimizing drilling and production decisions, thereby reducing operational risks and enhancing the efficiency and safety of resource extraction. The findings of this study not only enrich the theoretical framework of overpressure mechanisms but also provide new ideas and methods for research in similar geological environments, holding significant practical implications for the oil and gas industry.