esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods and Results
### Relationship between Fissure Length Distribution and Number of Fissures  
Satellite images, aerial photographs, and GPS mapping (GPS kinematics in an area of approximately 1 km\({}^{2}\)) were combined to analyze the density, length, number, and position of fissures associated with the North Birjand fault.  
The GPS data had an average error of 3 cm. All fissure structures that are mostly distributed as arrays along the fault have been mapped. Their strikes are in the azimuthal range of NS, N30E, and N90E (Figure 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section** & **Strain on Planes XY,YZ (or D)** & **Strain on Plane XZ** & **3D Strain** \\ \hline
1 & 0.32 & 0.321 & 10.27 \\
2 & 0.45 & 0.229 & 10.3 \\
3 & 0.43 & 0.249 & 10.7 \\
4 & 0.3 & 0.269 & 8.07 \\
5 & 0.68 & 0.37 & 25.16 \\
6 & 0.84 & 0.388 & 32.59 \\
7 & 0.39 & 0.271 & 10.56 \\
8 & 0.3 & 0.3292 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of the three-dimensional volume strain (3D strain) along Sections 1 to 8. D = Eigenvalues of the matrix.  
Figure 8: Diagram of the volume strain changes (e\({}_{\text{v}}\)) along the topographic cross-sections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fault Name** & **Fault along Section** & **Fault Attitude** & **Ns—Meter** & **\%V** \\ \hline
1 & 5 & N80W, 60 NE & 11.32 & 25.16 \\
2 & 6 & N80W, 70 NE & 15.12 & 32.59 \\
3 & 7 & N80W, 50 NE & 9 & 10.56 \\
4 & 8 & N55W, 25 SW & 3.231 & 9.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of the net slip (Ns), and volume strain (V) along the desired section in the study area.  
Figure 11: Schematic representation of the relationship between the net slip (Ns) and the volume strain (\(V\)).  
Figure 10: (**a–d**) Faults in the fissure structures canals; (**e**,**f**) folds formed on the hanging-wall of the youngest splay of the North Birjand fault. These folds are visible within the fissure canals.
# Adaptive Aggregated Attention Network for Pulmonary
## 1 Introduction
Moreover, we designed a multi-scale attention module to automatically obtain multi-scale nodule features, including sparse features and dense features, and the multi-scale attention network (MSAN) module can concatenate the spatial features and contextual features effectively. The attention mechanism can automatically concentrate on essential regions and can extract key features for pulmonary nodule classification. Finally, the gradient boosting machine (GBM) algorithm [7] was utilized to differentiate benign and malignant nodules. To verify our model, we conducted experiments on the Lung Image Database Consortium image collection (LIDC-IDRI) dataset. The experimental results prove that our attention mechanisms are effective for improving nodule classification. At last, our proposed framework significantly outperformed previous state-of-the-art methods. Our main contributions are as follows: (1) We applied residual attention and squeeze-and-excitation module to automatically concentrate on regions which are essential for nodule classification and the residual structure ensures the integrity of the features, which can prevent overfitting to some extent and can improve the classification performance. (2) We propose a Multi-scale attention mechanism to automatically obtain multi-scale salient features of nodules which can better extract the features of different scales of nodules and can fuse sparse features and dense features effectively. The rest of the paper is organized as follows. Section 2 discusses previous related works in medical image analysis and pulmonary nodules classification. Section 3 describes the data preprocessing and the proposed framework in detail. The experiments results and discussions are described in Section 4. Finally, we conclude our work in Section 5.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 3 Verification Methods for Quantum Money in Quantum Wallets
### Classical Tokens Paired with Quantum States  
Another approach is to use classical tokens that correspond to each quantum state in the wallet. Each token effectively represents one unit of quantum currency, serving as a count of the stored quantum states. These tokens can be securely stored alongside the quantum money and updated with each transaction, allowing the owner or verifying institution to track the amount of money without accessing the actual quantum states. This classical-quantum pairing enables a non-invasive verification mechanism that avoids direct measurement while preserving the quantum properties of the wallet.
## 2 Materials and Methods
### Transportation Network Energy Demand  
Jordan employs a net-metering scheme for connecting PV generation to the grid. This allows one total renewable energy self-consumption, without the need to install any storage systems. All excess energy generated during the day, or during high generation summer months is injected to the grid and credited in kWh. At the end of every billing year, the net generation and consumption are calculated, and only excess energy drawn is billed. In essence, the electric grid is used as a free storage sink, but any excess energy at the end of the billing cycle is forfeit to the grid operator. Therefore, it is very important to correctly size the designed PV system, such that the annual generated energy matches actual annual energy demand. The first step in designing the PV system for AMMAN BRT is to determine the annual energy needs. Generated electricity shall be used to cover required energy for charging the electric buses, for charging LED streets lights, and for the service buildings' electricity.  
To properly characterize the load demand, the electric buses that will be deployed must be selected, and transportation network routes, distance, and operation must be well defined. For proper comparison between business as usual and the proposed upgrade, buses of the same size and passenger capacity are selected. The 18-m BYD K11 electric bus was selected to replace the diesel buses. It is worth mentioning that smaller electric buses (12 m) are widely used in many public transportation projects, but the larger size 18-m electric buses are new to the market. The bus has a 652 kWh battery that requires three and half hours to charge, which is enough to run the bus throughout the day for the distance required by operation plans [14].  
Although the capital expenditure (CAPEX) of the electrical buses is higher than that for diesel buses, the total cost of ownership (TCO) is very different for the two options, since the operational cost for electric buses is much lower due to less energy consumption, less moving parts, no oil change, and longer life span [15; 16]. The total demand of the proposed BRT project along with all services was projected to be 32 GWh, as shown in Table 1.
# Effective 5G Wireless Downlink Scheduling and Resource Allocation in Cyber-Physical Systems +
## 5 Performance Evaluation
### Fairness Measurements
#### 5.2.2 Delay Fairness  
Delay fairness measures how long an arbitrary user needs to wait for resource allocation. Figure 6 plots the delay fairness for LTE, URLLC and eMTC with the EPA, EVA and ETU channel models. For all these cases, the proposed SRA and baseline approaches show full fairness (100%) for up to three users. In the case of the URLLC and eMTC use-cases, dynamic and greedy show similar performance for OFDM and FBMC waveforms, as shown in Figure 6d-i. Dynamic-OFDM outperforms dynamic-FBMC by up to 0.01% (\(\approx\)1.5%), as shown in Figure 6a-c for LTE and Figure 6d-f for URLLC. In the case of eMTC, dynamic-OFDM outperforms dynamic-FBMC by up to 0.01% (\(\approx\)1%), as shown in Figure 6g-i. In the other cases, its fairness is slightly higher than or similar to the fairness of greedy.
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 6 Conclusions  
In this study, a multi-channel cluster tree protocol was proposed for interference in time-critical smart-grid applications. To increase the network scalability in the typical IEEE 802.15.4 standard for WSNs, a network configuration method of a cluster tree topology was used. Moreover, a beacon enabled a mode with a superframe structure in which the active and inactive sections were repeated to reduce the power consumption. The proposed scheme can improve performance and reliability by proposing a channel allocation method for multi-channel operations and an intercluster scheduling method that prevents beacon collisions. The simulation results show how the network performance improves, compared with existing protocols, when the proposed scheme is used. To achieve better network load balancing, we plan to extend the proposed scheme using mechanisms such as data fusion or aggregation. This will enable us to further increase the speed of certain parts of the network in stable situations. Additionally, we intend to implement the proposed scheme in a real-world scenario testbed.  
Conceptualization, J.-W.K.; methodology, J.-W.K. and J.L.; software, J.-W.K. and J.L.; validation, J.-W.K. and J.L.; formal analysis, J.-W.K., J.K. and J.L.; investigation, J.-W.K. and J.L.; resources, J.-W.K.; data curation, J.-W.K., J.K. and J.L.; writing--original draft preparation, J.-W.K. and J.K.; writing--review and editing, J.L.; visualization, J.-W.K., J.K. and J.L.; supervision, J.K. and J.L.; project administration, J.K. and J.L.; funding acquisition, J.K. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the MSIT, grant numbers 2021R1 AA4A5028907 and 2022R1A2C1009951.  
Not applicable.  
The data used in this paper can be obtained by contacting the first author.  
Figure 27: PER as a function of the distance between the IEEE 802.15.4 and WLAN devices.  
**Acknowledgments:** This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (grant number: 2021R1A4A5028907 and 2022R1A2C1009951).
## 4 Experiment
### Implementation Details and Metrics
#### 4.2.2 Evaluation Metrics  
Accuracy, precision, recall, specificity, and F1-score are commonly used as evaluation metrics in classification tasks.  
True Positive (TP) denotes an optimistic prediction and a positive label. False Positive (FP) indicates both a negative label and an optimistic prediction. False Negative (FN) indicates both a positive label and a pessimistic prediction. Moreover, True Negative (TN) shows a pessimistic prediction and a negative label. Accuracy indicates the number of correctly predicted samples as a percentage of the total, which is calculated as:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{7}\]  
Precision indicates the percentage of positive samples that are genuinely predicted to be positive by the model and is calculated as:  
\[Precision=\frac{TP}{TP+FP} \tag{8}\]  
Recall indicates the percentage of samples that are actually positive that are predicted to be positive and is calculated as:  
\[Recall=\frac{TP}{TP+FN} \tag{9}\]  
Specificity indicates the percentage of samples that are predicted to be negative out of those that are actually negative and is calculated as:  
\[Specificity=\frac{TN}{FP+TN} \tag{10}\]  
The F1-score, which is the weighted summed average of precision and recall, is calculated as follows:  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{11}\]The above metrics are all for the binary classification task. For the multiclassification task, precision, recall, specificity, and F1 are calculated separately for each category and then averaged to obtain macro-precision, macro-recall, macro-specificity, and macro-F1.
# Study on New Bolted T-Stub Connection with Inserted Plates under Axial and Cyclic Loads
## 4 Test Observations, Results and Analysis
### Cyclic Test
#### 4.3.3 Equivalent Viscous Damping Ratio  
In order to evaluate the energy dissipation capacity of specimens more scientifically and comprehensively, different coefficients for measurement have been put forward, such as the energy ductility coefficient [35], the hysteresis energy coefficient [36] and the equivalent viscous damping (EVD) ratio [37]. Here, the EVD coefficient is adopted, since it is the analysis method advocated in JGJ/T 101-2015 [34], and has gained widespread use so far [38]. Physically it represents the ratio of energy dissipated by the specimen to the energy required to generate the same displacement by an equivalent elastomer [39], depicting the degree of fullness of the hysteresis curve. The larger EVD coefficient reflects the better energy dissipation capacity of specimens. Figure 12 shows the relationship between the EVD ratio and the displacement of specimens.  
Figure 12: _Cont._  
Figure 12a suggests the ratios of specimens in group TC are alike at the initial stage. With the rise of displacement, the existence of inserted plates began to exert positive impacts, which led to a growth of approximately 30% in the damping coefficient relative to specimen TC-N-50-0-H. When the plate width increased from 38 to 40 mm, the damping ratio remained a small climb. Similarly, the increment of flange length also showed a slight increase of 3% in the ratio.  
In comparison with Figure 12b, specimen WA-P-50-40 shared a similar changing feature of damping coefficients to specimen TC-P-50-40 during the first half of loading. After the displacement exceeded 4 mm, the ratio of specimen WA-P-50-40 was capable continuing to rise until the specimen failed, while specimen TC-P-50-40 stayed nearly unchanged. The coefficients of the former were about 25% more than the latter for second half stage alone, and increased by about 14% as a whole. This again proves that the welded specimen had better ductility and energy dissipation capacity compared with the rolled specimen.  
The maximum EVD ratio emerged in specimen WA-P-50-46, demonstrating a superior energy dissipation capacity that involved a rise of around 10% over that of specimen WA-P-50-40. In this case, a width of 46 mm plates refers to a ratio of \(L_{p}\) to \(W\), which is equal to 31% and the ratio of \(n_{1}\) to \(m_{1}\) (equivalent to 4:3, roughly).  
Similarly, EVD ratios of specimen WB-P-75-46 grew by 6% over those of specimen WB-P-75-40 in general (see Figure 12c). However, specimen WB-P-75-52 showed inferior ratios throughout the whole stage. Within the group WB, a 46-mm plate width was best, whereas a 52-mm width was worst in terms of energy dissipation capacity. The ratios in specimens of group WB were not as large as those in group WA on the whole. The reason for this can be attributed to the noticeable pinch action of their hysteresis loops. As a result, discussion of the best plate width is not necessary for group WB, and related practical design is not recommended.
# An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest
## 5 Conclusions  
Aiming at the problems of strong randomness, poor stability, and low anomaly detection accuracy of traditional iForest, a data anomaly detection method, BS-iForest, is proposed in this paper. Compared with the traditional iForest algorithm, this method first judges the data distribution of the subsampling set according to the box plot graph, then uses the subsampling set with more likely abnormal samples to train the isolation tree, reduces the influence of irrelevant samples on the isolation outliers, and improves the accuracy of anomaly detection. For the construction of the spanning isolation tree, some data points with poor performance are screened out according to the accuracy in the training set, which improves the stability. For the samples that intersect the path length interval of the positive and negative examples, the data points with the highest similarity are used for joint judgment, which improves the anomaly detection performance of the algorithm. Many experiments were carried out on the BreastW dataset and Campus-CRS dataset. The results show that the improved Isolation Forest algorithm recommended in this paper has significantly improved the accuracy and enhanced the stability. In future work, the local anomaly detection ability of the isolation forest can be improved because the isolation forest is not sensitive to local outliers.  
Figure 12: Variance of BS-iForest under different numbers of isolation trees.  
**Author Contributions:** Conceptualization, J.C.; data curation, J.C. and J.Y.; methodology, J.C. and J.Z.; software, J.C. and R.Q.; supervision, J.Z. and Y.R.; writing--original draft, J.C. and R.Q.; writing--review & editing, J.C. and Y.R.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under Grant 62072146, in part by the Key Technology Research and Development Program of the Zhejiang Province under Grant 2021C03187, in part by the Key Technology Research and Development Program of the Zhejiang Province under Grant 2022C01125.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** No new data was created or analyzed in this study. Data sharing is not applicable to this article.  
**Acknowledgments:** This paper and its research would not have been possible without the support of the Cloud Computing Center of Hangzhou Dianzi University and the Key Laboratory of Complex Systems Modeling and Simulation.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Communication Standards for Wireless Sensor Networks
### Wireless Fidelity or Wireless local area network  
Wireless Fidelity (Wi-Fi) or wireless local area network technology is established on the basis of IEEE 802.11 standard. Wireless local area networks are prevalent for LAN applications with peak data rates of around 150 Mbps and extreme coverage range of 250 m. Wi-Fi (IEEE 802.11b) operating on 2.4 GHz band achieves maximum data rates of 11 Mbps. Other versions based on IEEE 802.11a standard operates in 5.8 GHz band using Orthogonal Frequency Division Multiplexing (OFDM) and IEEE 802.11g (improved version of Wi-Fi) operating on 2.4 GHz band provides data rate up to 54 Mbps. IEEE 802.11 provides data rates of up to 600 Mbps using Multiple Input-Multiple Output (MIMO) technology. Security concerns for Wireless local area networks are addressed and solved in IEEE 802.11i standard using Wi-Fi Protected Access (WPA-2) encryption. It uses an Advanced Encryption Standard (AES). The main feature of Wi-Fi is existing wide support in most of the electronic devices. It is an upper layer protocol which allows communication over an Internet without using a protocol translator. Restricted number of channels can be used without an overlap in Wi-Fi/WLAN. This means that a restricted number of wireless clients can be connected in a Network. However, advantages of Wi-Fi are high data throughput, wide spread availability, IP support and network scalability. A self-healing network for HAN applications with the combination of WLAN and wireless mesh network can be developed [54; 55; 56; 57; 58].
# Focal Muscle Vibration for Stroke Rehabilitation: A Review of Vibration Parameters and Protocols
## 4 Discussion
The overall lack of follow-up after the FMV interventions made it difficult to determine the long-term effects, even though some studies stated that FMV intervention effects could last as long as two weeks [47] and even four months after the intervention in elder adults [76]. A variety of muscles were targeted for FMV therapy. For upper extremity rehabilitation, triceps brachii and biceps brachii were targeted the most. Shorter fascicle lengths have been reported for the brachialis muscle on the affected side, as shown by ultrasound [77]. The flexor carpi radialis was also frequently targeted because shortening of wrist flexor muscles is associated with poor recovery after stroke [78]. For the lower extremity rehabilitation application of FMV, three out of the five studies targeted the tibialis anterior muscle. This focus could be due to the importance of tibialis anterior in gait and that the affected lower limb exhibited significantly longer delays in initiation and termination of tibialis anterior contraction relative to the unaffected limb in individuals with stroke [79; 80]. Overall, there was a lack of justification for the choice of target muscles and discussions on the clinical rationales and applications of the findings based on the muscles that received vibration. Six studies reported electrophysiology [42; 45; 47; 48; 49; 53]. There was no clear mechanism through which FMV acts on the sensorimotor system. All six studies hypothesized the mechanism of increasing Ia afferent fiber discharges because of the activation of muscle spindles via FMV. The modulation of Ia inputs altered the excitability of the corticospinal pathway as well as the activation of cortical motor regions. However, excitability remained unchanged in other cortical motor representations, indicating that the increased neuronal excitability was specific to the vibrated muscle's movement representation [42; 53]. For FMV applied to low extremity muscles, such as quadriceps during the stance phase of walking, group Ia afferent discharges also contributed to the triggering of locomotor phase transitions [42]. It was also noted that FMV affects not only the contralateral but also the ipsilateral hemisphere, thereby modulating the relationship between the two hemispheres [42]. For FMV applied to upper extremity muscles, it was hypothesized that FMV applied to the forearm improved regulation of reflex excitability and improved cortical control of the movement [48]. In addition, the effects of FMV might depend on the inhibitory/excitatory state within the motor system reflecting the site of lesion for stroke patients [47]. The underlying mechanism of motor and function recovery due to vibration at different muscles could be more complex than simply the activation of Ia afferents. Measurements to detect activation of sensorimotor cortex network, nitric oxide production, and blood flow could be included in the future studies to better understand how vibration at different muscles impact the outcomes.
# Decentralized Robust Control of a Network of Inverter-Based Distributed Generation Systems
## 4 Simulation Results
### Non-Linear Load Analysis  
Due to the drastic growth of power electronic equipment such as uninterrupted power supply, rectifiers, etc., nonlinear loads have become prevalent in the modern power system industry. These loads inject unwanted harmonics into the current and voltage waveforms, resulting in lower quality electrical power [42]. In this article, the nonlinear load is modeled as a diode rectifier, which is a common model in power system studies [43; 44]. Applying this nonlinear load distorts the load current and voltage. Figure 9 demonstrates the harmonic contents of the load voltage. It can be observed that the proposed sequentially-designed decentralized controllers maintain harmonic voltage distortions below the limit  
Figure 6: Time response of the sequentially-designed decentralized controllers for inverter #4 \((Inv_{4})\).  
Figure 7: Time response of the independently-designed decentralized controllers for inverter #4 \((Inv_{4})\).  
specified by the IEEE 1547 standard [45], according to which the maximum harmonic voltage distortion must be 4% for harmonics less than 11.