esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
Here, the noise disturbance areas, which were circled by red dotted lines in the spectrum of a raw-filtered vibration signal that inputs to ANR-GRS and LADT modules, were mostly removed in the output of the ANR-GRS module and LADT module (the spaces with red narrows in the output spectra of ANR-GRS and LADT modules). However, the output signal of LADT indicated outstanding efficiency in reducing noise relative to the ANR-GRS module; the noise areas of the second and fifth principal frequency segments (the segment contains the second and fifth harmonics of the meshing frequency) in the output sub-band of the LADT module were much lower than those in the output signal of ANR-GRS. This verifies the effectiveness of the localized adaptive optimization process of the LADT scheme. In addition, the fault-related components, which were marked by blue-dotted circles in the input and output of LADT, were exactly the same. In other words, the LADT approach reduces noise in the largest amount possible by obeying the principal rule of a condition-monitoring fault diagnosis system to preserve the original fault-informative elements, such as sideband frequency tones and meshing frequency harmonics inside of the raw vibration signals. The output optimized sub-bands from LADT were then converted to visualized feature spaces, for better expression of defect-related components induced by vibration characteristic of MDTF defect types in the time-frequency domain, using the proposed WVI method. Similarly to the example signal in Figure 4 (Section 3.2), the wavelet-based vibration images carried the defect-correlated factorials and exposed the attributes through color images. Figure 11 demonstrates the scalograms of the seven defect types of a gearbox under four rotational speeds. Through visualization, the scalograms of the same defect type under four rotational speeds showed the proximate parallel zones with the different energy levels. In addition, the energy of the useful components (pixel illuminations) has grown according to the uptrend of rotational speeds. Those discriminate notifications were quantized in the massive process of feature extraction and optimization achieved from DCNA performance.
## 4 Control of the Double-T Converter
### _Intra-Stacks Voltage Balance_  
The balance of the dc-links voltages of each stack is carried out by exchanging power between a quadrature current \(i_{q}\) that circulates in addition to the energy balance currents. This current can be defined as \(i_{q}=I_{q}\sin(\omega_{c}t+\pi/2)\) and is phase-shifted \(90^{\circ}\) with respect to \(i_{u1}\) and \(i_{u2}\). Then, \(i_{u1}+i_{q}\) and \(i_{u2}+i_{q}\) are the currents circulating internally in the converter. Moreover, in each sub-module there is a voltage \(v_{bal}=V_{bal}\sin(\omega_{c}t+\pi/2)\) that is in phase with the current \(i_{q}\). This allows exchanging power and generating the internal balance of the stacks keeping the dc-links of the N sub-modules with the same voltage. This voltage balance controller is based on a proportional (\(P\)) controller that receives the average value of the N dc-links voltages. The controller output is multiplied by \(\sin(\omega_{c}t+\pi/2)\) and \(V_{bal_{N}}\) is then generated. Finally, as \(i_{q}\) is in quadrature with respect to the currents \(i_{u1}\) and \(i_{u2}\), the intra-stack balance acts decoupled with respect to the energy control.
# Accurate Energy Consumption Modeling of IEEE 802.15.4e TSCH Using Dual-Band OpenMote Hardware
###### Abstract  
The Time-Slotted Channel Hopping (TSCH) mode of the IEEE 802.15.4e amendment aims to improve reliability and energy efficiency in industrial and other challenging Internet-of-Things (IoT) environments. This paper presents an accurate and up-to-date energy consumption model for devices using this IEEE 802.15.4e TSCH mode. The model identifies all network-related CPU and radio state changes, thus providing a precise representation of the device behavior and an accurate prediction of its energy consumption. Moreover, energy measurements were performed with a dual-band OpenMote device, running the OpenWSN firmware. This allows the model to be used for devices using 2.4 GHz, as well as 868 MHz. Using these measurements, several network simulations were conducted to observe the TSCH energy consumption effects in end-to-end communication for both frequency bands. Experimental verification of the model shows that it accurately models the consumption for all possible packet sizes and that the calculated consumption on average differs less than 3% from the measured consumption. This deviation includes measurement inaccuracies and the variations of the guard time. As such, the proposed model is very suitable for accurate energy consumption modeling of TSCH networks.  
IEEE 802.15.4e; TSCH; energy modeling; OpenMote 2018  
18, 437; doi:10.3390/s18020437
# Computational Flow Dynamic Analysis in Left Atrial Appendage Thrombus Formation Risk: A Review
###### Abstract  
Atrial fibrillation (AF) is a common cardiac arrhythmia characterized by irregular and rapid electrical activity in the atria, leading to ineffective contraction and poor blood flow. More than 90% of the left atrial (LA) thrombi that cause thromboembolic events during atrial fibrillation (AF) develop in the left atrial appendage (LAA). AF modifies the hemodynamics of the left atrium, which can result in thrombosis of the LAA, systemic embolism, and stroke. The current options to reduce thromboembolic events are oral anticoagulation, surgical LAA exclusion, or percutaneous LAA occlusion. However, the mechanism underlying thrombus development in the LAA remains poorly understood. Computational fluid dynamics (CFD) analysis can be used to better understand the risk of thrombus formation and subsequent embolic events. CFD enables the simulation and visualization of blood flow patterns within the heart, including complex structures such as the LAA. Using CFD, researchers can analyze the hemodynamics of blood flow, identify areas of stagnation or turbulence, and predict the risk of thrombus formation. The correlation between blood flow dynamics, atrial fibrillation, and the risk of stroke has been highlighted by CFD studies investigating the underlying mechanism of thrombus formation in the LAA. This review study intends to provide a comprehensive overview of the factors involved in thrombus formation and their implications for clinical practice by synthesizing the insights acquired from these CFD studies.  
computational fluid dynamics; left atrial appendage; thrombus formation; hemodynamics; atrial fibrillation
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### MRP
#### 3.5.3 Bee Colony Optimization  
The Bee Colony Optimization (BCO) protocols are inspired from honeybees foraging behaviors. Insects are capable of individual proactive abilities and self-organizing capacity [13]. Honeybees can be grouped into a colony and living within a hive, and show impressive auto-solving problem capabilities. Scout bees explore the surrounding of the hive in order to detect possible sources of food, when a flower (food) is discovered, the scout bee returns back to the hive to recruit the forager bees through a special dance called waggle dance [70]. The BCO are widely used to solve the clustering NP hard problems efficiently.  
ABC-C  
ABC-C is a novel energy efficient clustering mechanism, based on the Artificial Bee Colony (ABC) algorithm [71]. ABC simulates the intelligent foraging behavior of honeybee swarms and has been successfully implemented in clustering techniques. Three bees' groups in ABC are performed: onlookers to choose a food source, scouts which perform randomly search, and employed bees which go to the food source visited by another bee. The clustering mechanism of the ABC-C is based on the clustering technique of LEACH protocol where CHs perform data aggregation processes of their clusters. For intra-cluster communication, CHs perform TDMA with the BS. The election of CHs is performed by ABC algorithm at the BS which is a node with unlimited energy supply. Sensor nodes are assigned to the clusters by using the shortest distances until the CHs. After receiving and calculating cross-distance values, the nodes send these values to the BS in order to be used during the selection process. To evaluate ABC-C, the authors considered a network which contains 100 nodes randomly placed. The performance of ABC-C is compared to that of LEACH and PSO-C about the network lifetime (number of alive nodes), amount of received signals and the energy consumption. The results have shown that ABC-C improves the network lifetime (highest number of alive nodes) than the other algorithms. The proposed algorithm also outperforms LEACH and PSO-C in terms of energy use and of the number of received signals.
# A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems
## 4 UWA Modern Technologies
Overall, these future research directions highlight the need for innovative approaches and optimized solutions for UWAC, as well as the potential for UWC to have a significant impact on a range of important applications in the marine environment.
# A Multichannel, High-Bandwidth Wirelane Receiver for D2D Interconnects
## 3 Receiver Circuits
### Mini-Pll
#### 3.3.3 Charge Pump  
The charge pump circuit adopts differential input and differential output, which can well suppress common mode noise and prevent clock feedthrough. Its circuit structure is shown in Figure 12a. QA_P, QA_N, QB_P, QB_N are two pairs of phase difference information output by the frequency discriminator, M15, M16, M17, M18, M4, M5, M6 form a current mirror structure. M7, M8, M9, and M10 are four switch transistors, all of which use NMOS transistors, thereby eliminating the mismatch caused by the simultaneous use of PMOS transistors and NMOS transistors as switch transistors and reducing current mismatch. M11, M12, M3, and M14 are respectively connected between the switch transistors and the output terminal, which can isolate the clock feedthrough. M1, M2, and M3 are kept on under the control of the common mode voltage \(V_{CM}\) and the common mode points \(VCP\) and \(VCN\) fed back by the filter. As shown in Figure 12b, is the variation of differential signal (\(VDS=VCP-VCN\)) with time, at about 0.42 \(\mu\)s, the PLL is locked and sampled to the best position of the data.
## 4 Targeted Delivery of Antibiotics to Sites of Infection
### Active Targeting  
In order to maximize the potential for infection-specific targeting, nanocarriers are conjugated to targeting agents, namely, ligands, that specifically bind to overexpressed receptors on the target site. These receptors are poorly or not even expressed on normal cells (healthy tissue or microbiome) but homogeneously present on bacterial pathogens or infectious tissues. These bindings allow antibiotic-loaded nanocarriers to be retained longer close to the infection site, overcoming fast clearance issues through the lymphatic drainages associated with small-molecule antibiotic treatment.  
In this regard, identifying target molecules and designing nanocarriers are the key factors in applying active targeting drug delivery systems. Several ligands that target bacterial cells and infectious tissues are described in the following section and summarized in Table 3.  
It is worth noting that nanocarriers must first reach the target area via the EPR effect (passive targeting) before ligand-receptor interaction with target cells (active targeting) occurs. Therefore, the design of actively targeting nanocarriers should always be accompanied by optimizing the factors associated with passive targeting.  
Figure 8: Schematic illustration of a charge-adaptive nano-system for prolonged and enhanced in vivo antibiotic delivery. These nanocarriers are designed to shield unspecific interactions during circulation at pH 7.4 but bind avidly to bacteria in acidity, enhancing antibiotic delivery (reproduced with permission from [62], copyright 2016 Royal Society of Chemistry).
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 3 Problem Modeling
### Satellite Network Security Routing Algorithm  
For clearer expression, in the table below (Algorithm 1), the symbol \(e\) refers to the iteration count of the ant colony, \(\epsilon_{\max}\) represents the maximum value of the ant colony iteration count, \(N\) represents the number of ants, and \(M\) represents the set of nodes that the current ants have not yet traversed.  
Input: Set the source node \(S\) and destination node \(D\)  
Output: a satellite network security routing  
1. The satellite network is initialized and the nodes collect communication data  
2. Generating a Comprehensive Trust Vector \(SR\) Using D-S Evidence Theory  
3. Use \(SR\) to generate \(T_{c}=\{l_{1},l_{2},l_{3}\cdots l_{n}\}\) and form a dataset for training VAE  
4. The satellite node uses the VAE network to collect the security evaluation factor \(m\) of the theoretical node.  
5. While \(\epsilon<\epsilon_{\max}\)  
6. Set the tabu list to empty  
7. For \(i=1\) to N do  
8. While Ant \(i\) has not reached \(D\)  
9. If \(M\) is not null  
10. Select the next hop \(j\) based on the Formula (17)  
11. Add \(j\) to the tabu list  
12. End if  
13. Terminate this search  
14. update pheromones according to Formula (19)  
15. End while  
16. \(i=i+1\)  
17. End for  
18. \(\epsilon=\epsilon+1\)  
19. End while  
20. Generate a secure route  
**Algorithm 1** Satellite network security routing algorithm
# On Smartphone Power Consumption in Acoustic Environment Monitoring Applications
## 3 Results and Discussion
### Impact of Audio Sampling Rate  
The results presented in the previous subsection were obtained when the audio signal was sampled at 44.1 kHz rate. To evaluate how the audio sampling rate affects the power consumption, we performed a series of experiments at other sampling rates, namely 8 kHz, 22.05 kHz, and 48 kHz, using a subset of our test devices. For fair comparison, we only tested the audio recording mode. Based on the results of these experiments, we found no statistically significant evidence that smartphone power consumption depends on audio sampling rate.
## 4 Proposed Framework
### Interoperability and Scalability
#### 4.4.2 Scalability  
Due of the framework's ability for horizontal scaling, additional IoT devices and edge servers could be introduced with no interference with current operations.  
**Edge Server Scalability:**  
By embracing the common ontology \(O\) and taking part in the federated learning procedure, newer edge servers could be smoothly incorporated. The following procedures are followed by every novel edge server \(E_{k}\):  
1. Obtains via the central unit the common ontology \(O\).
2. The ontology \(O\) is aligned alongside its local IoT device data \(A_{k}\).
3. Trains the global model locally and transmits adjustments into the central unit, which helps in the federated learning process.  
**IoT Device Scalability:**  
The framework aligns the data structures of new IoT devices with the common ontology \(O\), making it simple to add them. Each new device \(D_{new}\) aligns its data \(f_{new}\) with the ontology as follows:  
\[M:f_{\text{new}}\to o_{\text{new}},\quad o_{\text{new}}\in O \tag{23}\]  
This design ensures the framework remains adaptable and scalable, capable of supporting the continuous evolution of IoT networks.
## 5 Discussion
### Hallucination Problems
#### 5.2.1 Native Models  
1. SQLCoder Model The SQLCoder model is specifically designed for converting natural language into SQL queries. Due to its specialized architecture, it is optimized for handling structured query generation, which results in relatively fewer hallucination issues. SQLCoder has demonstrated an ability to accurately translate straightforward queries into SQL, typically reflecting the intent of the input query with high precision. This makes SQLCoder less prone to hallucinations compared to more general-purpose models. However, it is not entirely free from limitations. In cases where the input query is highly ambiguous or involves complex attributes not commonly seen in the training data, SQLCoder may still exhibit minor hallucination problems, where the generated query does not fully align with the original intent.
2. ERNIE Model The ERNIE model, with its strong generalization capabilities, excels at processing a wide range of natural language queries, including ambiguous and colloquial expressions. This ability to generalize is beneficial when dealing with natural language queries that are not strictly formal or structured. However, this generalization power can sometimes backfire, leading to the generation of SQL queries that do not fully capture the intent of the original query. This issue is particularly noticeable in cases where the input query includes subtle nuances, domain-specific terminology, or indirect references that the ERNIE model might misinterpret. In such instances, ERNIE may produce a query that appears plausible but deviates from the expected result, leading to hallucination issues.
3. Llama Model The Llama model, while powerful, shows some limitations when handling complex queries involving multiple attributes or intricate conditions. This becomes especially apparent in tasks where the natural language query includes various parameters or requires the model to make inferences based on multiple attributes. As a result, Llama may generate SQL queries that do not align with the user's intent, leading to mismatched outputs. The model's lower accuracy in handling such complex queries suggests that it may struggle to generate queries that accurately reflect the specifics of the natural language input, resulting in hallucination problems. These issues are particularly problematic in scenarios where precision is crucial, such as BIM information retrieval, where small deviations can lead to significant discrepancies in the final result.
## 4 Empirical Analysis
### VARM Analysis Results
#### 4.4.2 Analysis Results of Integrated Logistics Companies  
The VARM analysis results for integrated logistics companies are shown in Table 7. The coefficient for United Parcel Service's GSCPI at Lag 1 was \(-\)0.126483, with a t-value of \(-\)0.04686, indicating that it was not statistically significant. FedEx's GSCPI coefficient at Lag 1 was \(-\)3.030009, with a t-value of \(-\)0.65948, which was also not statistically significant. Similarly, DSV's GSCPI coefficient at Lag 1 was \(-\)4.022230, with a t-value of \(-\)0.22605, showing no statistical significance.
## 5 Conclusions  
In conclusion, this research demonstrated that strategic sensor placement and effective integration of diverse sensor data can significantly enhance the model's accuracy. The key findings are summarized as follows:  
* Model performance: overall, the present LSTM-based model demonstrated high accuracy in classifying the investigated activities;
* Optimal sensor placement: by prioritizing sensor placement on the upper body to effectively capture the examined activities, we found that the chest was the most effective anatomical location for activity classification, achieving the highest F1-score of 0.939.
* Multimodal sensor fusion: fusing data from all sensors, namely accelerometers, gyroscopes, and magnetometers, substantially enhanced classification accuracy.  
Conceptualization, L.B., D.T. and D.B.; methodology, L.B. and D.T.; software, L.B.; validation, D.T. and A.C.T.; writing--original draft preparation, L.B.; writing--review and editing, L.B., D.T., A.C.T., D.K. and D.B.; visualization, L.B. and D.K.; supervision, D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethical Committee under the identification code 1660 on 3 June 2020.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The dataset used in this work is publicly available in [25].  
Author [PERSON] is employed by the company farmB Digital Agriculture S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
###### Abstract  
As the conventional cutting pattern recognition methods for shearer are huge in size, have low recognition reliability and an inconvenient contacting measurement method, a fast and reliable coal-rock cutting pattern recognition system is always a baffling problem worldwide. However, the recognition rate has a direct relation with the outputs of coal mining and the safety quality of staff. In this paper, a novel cutting pattern identification method through the cutting acoustic signal of the shearer is proposed. The signal is clustering by fuzzy C-means (FCM) and a hybrid optimization algorithm, combining the fruit fly and genetic optimization algorithm (FGOA). Firstly, an industrial microphone is installed on the shearer and the acoustic signal is collected as the source signal due to its obvious advantages of compact size, non-contact measurement and ease of remote transmission. The original sound is decomposed by multi-resolution wavelet packet transform (WPT), and the normalized energy of each node is extracted as a feature vector. Then, FGOA, by introducing a genetic proportion coefficient into the basic fruit fly optimization algorithm (FOA), is applied to overcome the disadvantages of being time-consuming and sensitivity to initial centroids of the traditional FCM. A simulation example, with the accuracy of 95%, and some comparisons prove the effectiveness and superiority of the proposed scheme. Finally, an industrial test validates the practical effect.  
cutting pattern recognition; acoustic signal; fuzzy C-means clustering; hybrid optimization; fruit fly optimization algorithm; genetic algorithm; genetic proportion coefficient