esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
By incorporating this framework, the GD algorithm aims to enhance the default Adaptive Data Rate (ADR) mechanism in the LoRaWAN. The key of the GD algorithm is to identify the optimal distribution format for the intensive SF values by assigning weights or the probabilities of success for SF allocation to each SF category. The workflow begins by grouping nodes based on their RSSI levels, followed by the assignment of a defined SF value to nodes in each group. The most biased SF group is ingested into the GD algorithm. Within this algorithm, the biased SFs are reassigned to higher SF values through finely tuning the weight factor (\(w\)) via an intricately linked geometric distribution factor (\(p\)-value). This iterative process continues until the optimal \(p\)-value and corresponding weight factor (\(w\)) that yields the highest DER of the network are identified. In fact, we also considered developing the algorithm based on binomial and negative binomial distributions. Unlike the GD, both distributions were not directly developed to optimize the probability of success for SF allocation in the biased node environment. Beyond its flexibility in finely tuning the weight factor to attain the optimal DER, the GD algorithm also exhibits algorithmic simplicity that allows ease of development and implementation. Similar to SNR-based and RSSI-based SF allocation schemes, the GD algorithm workflow leverages RSSI data measured by the gateway (see Section 5). This allows for direct integration with LoRaWAN and enables dynamic adaptation to various scenarios. The workflow can be structured to run the algorithm iteratively, to continuously update the SFs. For instance, RSSI levels of nodes can be regularly updated to enable re-grouping before the next iteration. The summary of SF allocation schemes and contributions are summarized in Table 1.
## 7 Future Perspectives and Emerging Trends
### Internet of Things (IoT) Integration for Real-Time Monitoring  
The integration of nanosensors into the Internet of Things (IoT)'s framework is a rapidly growing trend in bridge maintenance. By connecting nanosensors to a network infrastructure, real-time data collection, transmission, and analysis can be achieved [102]. IoT integration enables remote monitoring of bridge structures, centralized data management, and seamless communication between sensors and monitoring systems.  
IoT-enabled nanosensor networks provide enhanced situational awareness [103], enabling bridge maintenance personnel to monitor multiple parameters simultaneously and respond promptly to any abnormalities. The data collected from the nanosensors can be processed in real-time, facilitating predictive maintenance and enabling the implementation of data-driven decision-making strategies.
## 4 Experiments
### Performance Metrics  
In experiments, we exploit four common performance metrics: _recall_, _G-mean_, _AUC_ and Balanced Accuracy Score (_balancedscore_) [56]. The larger value of the four metrics is, the better the performance of the classifier is. All these metrics are based on confusion matrix (Table 2).  
Where the defective modules are regarded as buggy (or positive) samples and non-defective modules as clean (or negative) samples. According to confusion matrix, the definition of _PD_ (the probability of detection, also called _recall_, _TPR_), _PF_ (the probability of false alarm, also called _FPR_) and _precision_ are as follows.  
\[PD=recall=\frac{TP}{TP+FN} \tag{8}\]  
\[PF=\frac{FP}{FP+TN} \tag{9}\]  
\[precision=\frac{TP}{TP+FP} \tag{10}\]  
_recall_ and _G-mean_ are proved more suitable for imbalanced learning [20].  
\[G-mean=\sqrt{recall\times precision} \tag{11}\]  
_AUC_ measures the area under the _ROC_ curve which describes the trade-off between _PD_ and _PF_, it can be calculated as follows: [1]  
\[AUC=\frac{\sum_{buggy_{i}}rank(buggy_{i})-\frac{M(M+1)}{2}}{M*N} \tag{12}\]  
where \(\sum_{buggy_{i}}rank(buggy_{i})\) represents the sum of ranks of all buggy(or positive) samples, \(M\) and \(N\) are the number of buggy samples and clean samples, respectively.  
Balanced Accuracy Score (called _balancedscore_) as another accuracy metric is defined as the average recall obtained from each class, the metric can avoid inflated accuracy resulted from imbalanced class. Assume that \(y_{i}\) is the true value of the \(i\)th sample, and \(\omega_{i}\) is the corresponding  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Actual Buggy** & **Actual Clean** \\ \hline Predict Buggy (Positive) & _TP_ & _FP_ \\ Predict Clean (Negative) & _FN_ & _TN_ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
Figure 3: Data distribution of SDP datasets.  
sample weight, then we adjust the sample weight to \(\widehat{\omega_{i}}=\frac{\omega_{i}}{\sum^{I}(y=y_{i})\omega_{i}}\), where \(l(x)\) is the indicator function. Given predicted \(\widehat{y_{i}}\), \(balancedscore\) is defined as  
\[balancedscore(y,\widehat{y},\widehat{\omega})=\frac{1}{\sum\widehat{\omega_{i} }}\sum l(\widehat{y_{i}}=y_{i})\widehat{\omega_{i}} \tag{13}\]
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 4 Basic Idea and Implementation
### System Framework  
The SR-IOV-based virtual network resource allocation framework proposed in this paper is based on the workload perception of the VM to schedule and allocate the VF after the bandwidth level is divided. As shown in Figure 4, we divide the system framework into four main parts, namely the _I/O Status Monitor module_, the _Network Status Monitor module_, _VF Schedule module_, and the _Transmit Rate Scheduler module_. Among them, I/O Status Monitor module and Network Status Monitor module are called status monitoring module.  
Figure 3: Design according to basic ideas.  
In the host, there is a _Network Status Monitor module_ running as a daemon to collect network data running by the VM. In addition to the network data of the VM, in order to better distinguish between I/O-intensive and CPU-intensive VMs, we also need to obtain data about the running state of the VM, which is in the _I/O Status Monitor module_, including CPU use, VM running time and VM waiting time and so on. The _Status Monitor module_ continuously sends data to the _VF Schedule module_ according to the set period, and the _VF Schedule module_ uses the collected data to perform VF scheduling allocation. In addition, owing to the workload of VMs is different, the network resources required for each VM are different. Therefore, the _Transmit Rate Scheduler module_ is used to set a maximum bandwidth for each VF in a hierarchical method, so that _VF Schedule module_ can allocate VFs with different bandwidth sizes for different loads of VMs.  
For better management, we put all the VMs into two queues, _Priority Queue_ (PQ) and _General Queue_ (GQ), where PQ is a _red-black tree_ and hosts network-intensive VMs, while GQ hosts other types of VMs, including disk I/O-intensive and CPU-intensive VMs. The dynamic allocation of VMs is based on temporal locality, that is, network-intensive VMs are also likely to be network-intensive VMs in the next sampling period. Using the data information collected by the _Status Monitor module_, the _VF Schedule module_ can always allocate VF resources to network-intensive VMs. The _Transmit Rate Scheduler module_ limits the bandwidth of the VF and hierarchically divides it. The VM can always obtain the desired bandwidth resources as needed, thereby maximizing the use of hardware resources and improving the overall performance of the system.
## Nomenclature  
ALOS & adaptive line-of-sight  
GUAS & global uniformly asymptotically stable  
HGESO-ASMPFC & high-gain extended state observer based adaptive sliding mode path following control  
ILOS & integral line-of-sight  
LOS & line-of-sight  
SMC & sliding mode control  
\(a_{v},a_{r},a_{y}\) & linear model coefficients in Nomoto equations  
\(b\) & control coefficient in Nomoto equations  
\(D\) & the initial distance of the vessel away from the bank  
\(l_{z}\) & vessel's inertial moment about the vertical axis  
\(m\) & vessel's mass  
\(m_{ij}\) & the added mass  
\(o_{b}\)-\(x_{b}y_{b}z_{b}\) & the body-fixed reference frame  
\(o_{b}\)-\(x_{b}y_{b}z_{b}\) & the inertial reference frame  
\(u,v,r\) & surge and sway velocities and yaw rate of the vessel in the body-fixed reference frame  
\(x,y,\psi\) & reference  
\(x_{G}\) & the longitudinal position of the vessel's center of gravity  
\(y_{d}\) & the desired path  
\(y_{e}\) & the cross-track error  
\(a_{1},a_{2},\varepsilon\) & the designed positive constants in high-gain extended state observer  
\(\beta\) & the sideslip angle  
\(\delta\) & rudder angle  
\(\psi_{d}\) & the guidance angle  
\(\Delta\) & the designed look ahead distance  
- & the error between the real value and the estimation value of the corresponding variable
# Effect of Ajwa Date and Germinated Barley on Lipid Profile in Rats Fed High-Fat Diet
## 3 Results
### Liver Function
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Items \({}^{*}\)**} & \multicolumn{4}{c}{**Treatments \({}^{**}\)**} \\ \cline{2-5}  & **NC** & **PC** & **ABM20** & **ABM30** & **ABM40** \\ \hline
**TC (mg/dL)** & 67.70 \(\pm\) 3.90 \({}^{\rm b}\) & 106.10 \(\pm\) 6.0 \({}^{\rm a}\) & 83.53 \(\pm\) 17.10 \({}^{\rm b}\) & 70.12 \(\pm\) 20.86 \({}^{\rm b}\) & 73.55 \(\pm\) 12.80 \({}^{\rm b}\) \\
**TG (mg/dL)** & 32.83 \(\pm\) 3.02 \({}^{\rm b}\) & 57.18 \(\pm\) 15.95 \({}^{\rm a}\) & 39.34 \(\pm\) 7.0 \({}^{\rm b}\) & 29.57 \(\pm\) 4.12 \({}^{\rm b}\) & 28.27 \(\pm\) 6.79 \({}^{\rm b}\) \\
**LDL (mg/dL)** & 52.65 \(\pm\) 2.19 \({}^{\rm a}\) & 70.84 \(\pm\) 3.18 \({}^{\rm b}\) & 66.73 \(\pm\) 6.23 \({}^{\rm b}\) & 46.82 \(\pm\) 3.28 \({}^{\rm a}\) & 49.37 \(\pm\) 5.62 \({}^{\rm a}\) \\
**HDL (mg/dL)** & 19.77 \(\pm\) 8.69 \({}^{\rm a}\) & 18.37 \(\pm\) 2.16 \({}^{\rm a}\) & 18.45 \(\pm\) 6.40 \({}^{\rm a}\) & 25.55 \(\pm\) 5.72 \({}^{\rm a}\) & 21.04 \(\pm\) 4.17 \({}^{\rm a}\) \\
**VLDL(mg/dL)** & 7.30 \(\pm\) 0.88 \({}^{\rm c}\) & 11.80 \(\pm\) 1.16 \({}^{\rm a}\) & 8.30 \(\pm\) 1.37 \({}^{\rm b}\) & 6.57 \(\pm\) 1.09 \({}^{\rm c}\) & 8.02 \(\pm\) 0.67 \({}^{\rm b}\) \\ \hline \end{tabular}  
* TC, total cholesterol; TG, total glycerides; LDL, low-density lipoprotein; HDL, high-density lipoproteins; VLDL, very-low-density lipoproteins. \({}^{**}\) NC = Negative control, PC = Positive control, ABM20 = Ajwa barley mix 20%, ABM30 = Ajwa barley mix 30%, ABM40 = Ajwa barley mix 40%. \({}^{\rm a-c}\) Values in a row without the same superscript letter are different (\(p\leq\) 0.05); values are expressed as mean \(\pm\) SE.  
\end{table} 
Table 5: Effect of Ajwa date mixture and germinated barley on blood lipid profile in rats fed a high-fat diet.
## 2 Materials and Methods  
### Study Area  
The coastal areas studied in this research comprise 12 provincial administrative regions of China: Jiangsu (JS), Hainan (HN), Guangxi (GX), Liaoning (LN), Fujian (FJ), Guangdong (GD), Shandong (SD), Tianjin (TJ), Taiwan (TW), Zhejiang (ZJ), Shanghai (SH), and Hebei(HB) (excluding some islands and the Hong Kong and Macau Special Administrative Regions due to lack of data). Among these regions, the metropolitan regions in China, such as the Pearl River Delta (PRD), Bohai Sea, and Yangtze River Delta (YRD), are among the most dynamic and fast-growing regions in the country [32]. The research area spans a total land area of 496,342.5 km\({}^{2}\), extending from northeast to southwest for approximately 2967 km and reaching a maximum width of approximately 550 km from east to west. About 43% of the population lives in China's coastal areas, and the GDP of coastal provinces accounts for about 60% of the country's total [33]. The predominant landform types in Coastal China are hills and plains, with a significant concentration of plains in the north and hills in the south. The LULC types in the study area are predominantly composed of forests and croplands, exhibiting a distribution with croplands in the north and forests in the south, divided by Jiaxing City in Zhejiang Province (Figure 1). In recent decades, due to urbanization, swift economic expansion, and overexploitation of land, China's coastal areas have experienced severe ecological destruction and environmental deterioration [34; 35; 36].
## 4 Numerical Examples  
In this section, two examples are used to verify the effectiveness and accuracy of the AIFEM in transient heat transfer problems. The first example is a two-dimensional thin plate, and the second example is a three-dimensional TPS. In addition, the MCM results with a sample size of \(10^{5}\) are used as exact solutions for the calculation examples for comparison. In this section, Matlab is used to call Abaqus for calculation; Abaqus 6.14 is used to calculate the determined values in the calculation example, and then Matlab R2023a is used to post-process the interval results of the temperature response.
# Improved YOLOv5-Based Real-Time Road pavement Damage Detection in Road Infrastructure Management
## 3 Methodology
### Improved YOLOv5s
#### 3.2.3 Utilizing Focal Loss Strategy  
Focal Loss was utilized to substitute the binary cross-entropy loss. The Focal Loss is a variant of the conventional cross-entropy loss function, which aims to decrease the impact of accurately classified instances on the overall loss while emphasizing the significance of misclassified cases [42]. In addition, Focal Loss is a technique utilized in object detection to tackle the problem of disproportionate representation of foreground and background classes. YOLOv5 can increase detection performance by employing focus loss, particularly for small objects that are difficult to identify due to their small size and low contrast. Furthermore, focus loss can reduce the probability of incorrect identifications by assigning relatively lower weights to less complicated instances, reducing the influence of unwanted information on the final detection outputs.  
The YOLOv5 model employs the Focal Loss function on the classification branch of the network. This branch forecasts every anchor box's objectness score and class probabilities. The calculation of the Focal Loss is as in Equation (12):  
\[FL(p_{t})=-(1-p_{t})^{\gamma}\log(p_{t}) \tag{12}\]  
The equation involves the predicted probability for the actual class, denoted as \(p_{t}\), and a hyperparameter \(\gamma\), which modulates the degree of weighting assigned to challenging examples. When \(\gamma\) equals zero, the Focal Loss function simplifies to the conventional cross-entropy loss.  
The modified formula for binary cross-entropy with Focal Loss is given in Equation (13):  
\[FL(p_{t})=-\alpha_{t}(1-p_{t})^{\gamma}\log(p_{t})-(1-\alpha_{t})p_{t}^{\gamma }\log(1-p_{t}) \tag{13}\]  
where \(p_{t}\) is the projected likelihood of the actual class, \(alpha_{t}\) is a binary class sign (i.e., \(\alpha_{t}=1\) if the actual class is present and \(\alpha_{t}=0\) otherwise), and \(\gamma\) is the focusing parameter that regulates how much weight is placed on complex examples.
# Quantitative Analysis of Influencing Factors on Changzhou Ship Lock Capacity
## 2 Study Scope and Data  
### Study Scope  
The Changzhou Hub is located in the lower reaches of the Xun River, a main tributary of the Pearl River system, and serves as the final planned cascade in Guangxi. It is a crucial waterway transportation artery connecting Guangxi and Guangdong provinces [25]. The first lane and second lane of the Changzhou ship lock were completed and opened in 2007, while the third lane and fourth lane were completed and opened in 2015. The first lane is designed for 2000-tonnage vessels, with effective dimensions of 200 m in length, 34 m in width, and a minimum water depth at the sill of 4.5 m. The second lane is designed for 1000-tonnage vessels, with effective dimensions of 185 m in length, 23 m in width, and a minimum water depth at the sill of 3.5 m. The third lane and fourth lane are designed for 3000-tonnage vessels, with effective dimensions of 340 m in length, 34 m in width, and a minimum water depth at the sill of 5.8 m [26]. The upstream maximum navigable water level of the Changzhou ship lock is 25.79 m, and the downstream maximum navigable water level is 25.70 m. The maximum lock water head of the first lane and second lane is 16.05 m, with the upstream minimum navigable water level at 18.6 m, and the downstream minimum navigable water level at 4.55 m. The maximum lock water head of the third lane and fourth lane is 17.28 m, with the upstream minimum navigable water level at 18.6 m, and the downstream minimum navigable water level at 3.32 m. The designed annual one-way capacity of the Changzhou ship lock is 136 million tons, which satisfies the passage conditions for most types of vessels. Water level stations are established in the upstream and downstream approach channels to obtain real-time water level changes. The layout of navigation facilities at the Changzhou ship lock is shown in Figure 1.  
Since the construction and operation of the Changzhou ship lock, the cargo volume has increased annually and is exhibiting a rapid growth trend. It surpassed the designed annual one-way capacity in 2019 and operated at near-saturation levels, as shown in Figure 2. Due to factors such as river erosion, channel dredging, and artificial sand mining, the riverbed downstream of the hub has been eroded, leading to a decrease in water levels. The water level difference between the upstream and downstream of the first lane and second lane exceeds the designed maximum operating head, significantly reducing the navigation guarantee rate. During low-water periods, when water volumes are insufficient, the ship lock capacity is severely impacted; large-scale navigation delays and blockages occur frequently, with the repercussions extending to the main lane of the Xijiang River [6].
## 4 Performance Analysis
### Analysis of Measured Magnetic Target Signals
#### 4.2.1 Extraction of the Temporal Frequency Information of the Original Signal  
The short-time Fourier transform, continuous wavelet transform, and synchrosqueezed wavelet transform were adopted respectively to extract the time-frequency spectra of the measured data of the induction magnetometer. The results are shown in Figure 7:  
They produced consistent results for the processing of simulation signals. The synchrosqueezed wavelet transform can better concentrate energy near the real energy axis in the processing of nonlinear and nonstationary signals without energy leakage. The SSWT spectra had a similar phenomenon to the simulation results, that was, the 50 Hz power frequency signal weakened at the point where the target appeared.  
Two different mother wavelets \"Morlet\" and \"Bump\" are compared, as shown in Figure 8.  
Obviously, the image of the Bump wavelet is clearer and better shows the variation of the measured signal.  
The three types of time-frequency diagrams were enlarged to the same scale in the time-frequency domain when the target appeared (near 40-60 s, 30-70 Hz), as shown in Figure 9.  
When the images were enlarged to 40-60 s and 30-70 Hz, it could be observed that the time-frequency resolutions of STFT, CWT, and SSWT differed significantly. Serious energy leakages were observed for STFT and CWT from 35 to 67 Hz and from 43 to 53 Hz, respectively, while only a little energy leakage was observed for SSWT from 49.6 to 50.7 Hz, which made the 50 Hz target magnetic anomaly signal sta  
Figure 8: Comparison of SSWT of mother wavelet “Morlet” and “Bump” where (**a**) is the “Morlet” mother wavelet and (**b**) is the “Bump” mother wavelet.  
Figure 7: Comparison of three different time-frequency maps; (**a–c**) are time-frequency diagrams for STFT, CWT, and SSWT.
## 3 Seismic Behavior of Micropiles
### Factors of Influence
#### 3.2.2 Soil-Related Factors  
Soils can influence the seismic behavior based on the way that they are interacting with MPs or in the way they are influencing the propagation of the seismic waves [17]. This interaction can affect the seismic input motion, as further described below.  
For example, [PERSON] et al. [18] studied the influence of soil homogeneity on the seismic behavior; the results showed a strong deviation between the two studied cases (homogeneous and heterogeneous soil) in terms of damping value and consequently the seismic response; it was observed by the authors that the two types of soil differ considerably in terms of horizontal response and damping value. This concept was referred to by the authors as \"filtration\" of the high frequency component of the ground motion. Taking into consideration the drilling and grouting process of MPs, a certain level of heterogeneity can result, which can further influence the seismic behavior through an \"artificial filtration\" or, more precisely, a \"damping out the high frequency components\" of the seismic ground motion.  
The soil mass, which is modified owing to the existence of MPs, forms a composite MPs-soil block that amplifies the seismic waves [19]; by analyzing the wave propagation in the modified soil due to the existence of MPs, it is possible to use these MPs as a factor of enhancing the seismic behavior by affecting the wave propagation in the soil. This topic is not precisely studied, but some indirect conclusions can be extracted from the existing literature, such as those that have been discussed previously [6; 11; 12; 13; 14], and it is aimed to be further investigated in the future by the author.
## 3 Results
### Efficiency and Optimization
It provided an efficient representation of non-stationary signals, aiding in the identification of patterns and hidden features in the results. Machine learning algorithms such as polynomial regression, logistic regression, and neural networks were applied to create predictive models based on historical data. These models were used to forecast future behaviors and outcomes. Bayesian statistics was employed to estimate probability distributions and make inferences about unknown parameters. This allowed for the evaluation of uncertainty and variability in the results.
# Frequency-Dependent Schroeder Allpass Filters
## 5 Conclusions  
This paper proposed a novel frequency-dependent Schroeder allpass filter, which is both more flexible in design and computationally efficient. The feedforward/-back gain filter is only required to be stable and dampening. The proposed design is about a factor of three more efficient than the previous filter structures by Gerzon. As an application example, we demonstrated the effectiveness of the proposed filter for signal decorrelation. As future work, other allpass filter applications such as artificial reverberation and dispersive systems may be studied. Furthermore, it remains to be shown whether a similarly effective design is possible for allpass feedback delay networks, both for single and multiple input and output (SISO and MIMO) configurations.  
This research received no external funding.  
The author declares no conflict of interest.