esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
1. The results of processing of the recorded ambient seismic noise, as well as the wave recorder data and ERA5 and EUMETSAT reanalysis data, showed a strong dependence of seafloor seismic noise on the presence of sea ice cover, as well as weather conditions, wind speed in particular. Wind-generated gravity waves, as well as infra-gravity waves, are responsible for the increase in the level of ambient seismic noise. The high-frequency range of 5 Hz and above is strongly affected by the coupling effect, which in turn also depends on wind-generated gravity waves and infragravity waves. The described seafloor seismic noise features must be taken into account during HVSR modeling and interpretation.
2. The obtained HVSR curves plotted from the records of one of the OBSs revealed a resonant peak corresponding to 3 Hz, while the curves plotted from the records of another OBS did not show clear resonance peaks in the representative frequency range. Since both OBS were located in the area of sparse distribution of submarine permafrost, the presence of a resonance peak may be an indicator of the presence of a contrasting boundary of the permafrost upper surface under the location of the OBS. The estimation of the depth to permafrost boundary for the St3 site is around 18 m. The absence of a clear resonant peak in the HVSR curve for the Typ2 site may indicate that the permafrost boundary was either absent at this site or its depth was beyond the values corresponding to representative seismic sensor frequency band. Thus, HVSR modeling and inversion techniques can be effective for studying the position of submarine permafrost.  
Conceptualization, A.A.K. and S.A.K.; methodology, A.A.K. and M.E.K.; software, A.A.K. and M.E.K.; validation, A.A.K.; formal analysis, A.A.K.; investigation, A.A.K. and M.E.K.; data curation, A.A.K., S.A.K., I.P.M., L.I.L. and I.P.S.; writing--original draft preparation, A.A.K.; writing--review and editing, all authors; visualization, A.A.K. and M.E.K.; supervision, A.A.K., L.I.L. and I.P.S.; project administration, A.A.K., L.I.L. and I.P.S.; funding acquisition, A.A.K. and I.P.S. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Ministry of Science and High Education of the Russian Federation, project 'Priority-2030', Tomsk State University (HVSR modeling and inversion for studying the submarine permafrost condition); the obtaining and routine processing of raw data of the ocean-bottom seismographs were also partially supported by the Russian Foundation for Basic Research, project no. 20-05-00533.  
Not applicable.  
The authors declare no conflict of interest.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 3 Method
### Experimental Setup  
To set up the experiment, we considered several points. First, we could not use a human subject for the experiment because of ethical considerations. Hence, beef was considered as a substitute for soft tissue. Second, ultrasonic transducers emit ultrasonic waves in a propagation medium such asin air or water environment. Third, the narrow frequency bandwidth of the ultrasonic transducer is a limitation,  
Figure 2: Architecture of ultrasonic communication for body sensor networks.  
especially for FSK modulation. Fourth, the different healthcare communication scenarios, such as in-body to in-body, in-body to on-body, and on-body to off-body, had to be considered. For BSNs, communication could be inside the body. With one sensor on the skin and the other under the skin, the inside sensor could monitor physical parameters, such as blood inside the body, then transmit the information to the receiver on the skin. Next, the sensor on the skin transmits the signal to the hospital via an RF communication method. The distance between the two sensors measures from a few mm up to 1 cm, considered applicable for short-range communications. Therefore, we do not consider long distances for inside body communication. The application is applicable for implantable sensors because it is designed for short-range communications. The experimental setup was operated in air and water environments based on the property of two types of ultrasonic transducers, as shown in Figure 3. The commercial ultrasonic transducer has limited frequencies. We first chose the Murata (40 kHz) transducer for OOK and BPSK modulation. However, it could not receive signals for BPSK modulation because of its narrow bandwidth. Next, we chose the Olympus 500 kHz for BFSK modulation, which is a relatively low frequency commercial ultrasonic transducer. With an increasing operating frequency, the size of the transducer is decreased, resulting in higher signal attenuation [2,18]. It should be considered among frequency and attenuation. Owing to the limited frequencies of the commercial ultrasonic transducer, we did not have many tradeoffs for these factors during the experiments. The tradeoffs among these factors will drive the design for a specific application.  
Binary bits were generated by a pattern generator. A waveform generator was used to generate ASK, FSK, and PSK signals. The output ultrasonic signals were measured by an oscilloscope. Figure 3b shows the ultrasonic transducer (Olympus) that was used in water for FSK modulation [19]. The transducer shown in Figure 3c (Murata) was used in air for ASK and PSK modulation [20].
# Hybrid Oversampling and Undersampling Method (HOUM) via Safe-Level SMOTE and Support Vector Machine
## 2 Literature Review
The support vector machine (SVM) was developed by [PERSON] et al. [25] to solve pattern recognition and classification problems, and some studies that utilize the SVM as a machine learning tool are as follows. Hairiness, which affects the quality of yarns, was predicted using an SVM and artificial neural network (ANN) in the study of [PERSON] et al. [26]. An SVM and ANN were used for image processing in the work of [PERSON] and [PERSON] [27]. They focused on classifying fabric images as defective and non-defective. [PERSON] and [PERSON] [28] calculated defect classification accuracy for different types of yarn-dyed fabrics via neural networks and SVMs. The SVM classification scheme was more robust and effective in the study. A proximal SVM was utilized as a classifier to recognize power-loom and handloom fabrics in the study of [PERSON] et al. [29]. Studies addressing imbalanced data in textile datasets are rare, but researchers have considered this challenge in their studies in the last few years. [PERSON] et al. [30] focused on fabric defect classification and proposed a method that provides uniformly distributed samples in each class of training data of an imbalanced dataset. In another study, [PERSON] et al. [31] proposed an online testing system for yarn quality. In this study, the proposed method, validated using well-known imbalanced datasets, was applied to a real textile problem. In the problem, after being accepted by the raw material quality department, yarns meeting the required quality values undergo some processes in the production area before weaving. Despite having an accepted quality level and convenient processing parameters, some of these yarns cause quality defects (suchas yarn break-off) in the fabric weaving process. The primary goal of this study was to unearth the possible relationship between yarn acceptance quality parameters, processing parameters, and yarn break-off defects. The DSS (HOUM) demonstrates that detecting and blocking these yarns before they enter the weaving process might increase efficiency in many areas, such as employees, equipment, sustainability, and profitability.
# Induction Machine-Based EV Vector Control Model Using Mamdani Fuzzy Logic Controller
###### Abstract  
The substantial rise in the demand for electric vehicles (EVs) has emphasized an environment-friendly and intelligent design for speed control strategies. In this paper, a Mamdani fuzzy logic controller (MFLC) was proposed to vigorously control the speed of EVs at discrete levels. MFLC member functions (MFs) are tuned for EVs operating at three different speed modes (40, 60, and 80 km/h). The proposed speed controller operation for the speed tracking of EVs was designed and tested in MATLAB (Simulink) environment. The proposed speed controller validated a remarkable improvement in dynamic speed control compared with existing P-I, FLC, Fuzzy FOPID (ACO), Fuzzy FOPID (GA), and Fuzzy FOPID (PSO) controllers. Its stability under a user-defined drive pattern is also observed. In this proposed work, the speed controller highlights the better tracking of user-defined speed response compared to the conventional aforementioned controllers. Moreover, the speed tracking of the designed model was tested for robustness against speed transients at predefined time instants, respectively. The comparison suggests that the MFLC model removes overshoot and significantly reduces the steady-state time.  
induction machine; Mamdani fuzzy logic controller; proportional-integral; integrated gate bipolar transistor; inductance; electromagnetic torque; flux; membership function Article
# Adaptive Truncation Threshold Determination for Multimode Fiber Single-Pixel Imaging
## 2 Method  
### Background: Measurement and Recovery  
#### 2.1.1 Measurement of Multimode Fiber Single-Pixel Imaging (MMF-SPI)  
Figure 1 shows the measurement procedure of MMF-SPI. With random input modulations, MMF consecutively shines speckles on a target (\(\mathbf{x}\)). Transmitted light is collected with a focus lens and registered as a light intensity sequence (\(\mathbf{y}\)) via a bucket detector (BD), which has no spatial resolution. Ideally, the noise-free BD signal reads \(\mathbf{y}=\mathbf{A}\mathbf{x}\), where each speckle is previously recorded using an array detector and shaped as a row of a measurement matrix (\(\mathbf{A}\)). The target (\(\mathbf{x}\)) is reshaped into a column vector. Generally, \(\mathbf{A}\) is fixed for recovery after previous captures. Therefore, disturbances such as MMF bending cause illumination deviations (\(\triangle\mathbf{A}\)), resulting in a mismatch between \(\mathbf{A}\) and \(\mathbf{y}_{\text{disturbed}}=(\mathbf{A}+\triangle\mathbf{A})\mathbf{x}\). With noise, the BD signal is \(\mathbf{y}+\mathbf{e}\) instead, where the term \(\mathbf{e}\) includes the following two parts: (1) mismatch noise (\(\triangle\mathbf{A}\mathbf{x}\)) and (2) initially electric BD noise. The noise level of  
e changes dynamically via unavoidable disturbance. Here, we assume that both types of noises can be modeled via Gaussian noise. The rationale for the impact of Gaussian equivalence on bending is discussed in Section 4.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 3 Methods  
Because the sensor installed in an underground mine cannot be directly connected to an external network, we had to use a separate storage device or transmit data to the external network through routers to transfer the data acquired inside the mine. This study used an accelerometer to measure vibration, and a dust sensor was used to measure dust density. The WSN was built such that the data acquired by these sensors could be transmitted to the destination through the ZigBee network. If the coordinator module where the data are collected is located outside the mine, the data can be transmitted to a separate server through an external network, and when located inside the mine, the data can be stored on laptop storage or memory card.
## 3 Results and Discussion
### Lipid Oxidation
#### 3.2.4 DSC Analysis  
The results of DSC analysis were presented on Figure 3. In this study isothermal mode was used to measure IP in minutes. DSC technique makes it possible to study the stability of fats under conditions of elevated temperature, in this case 140 \({}^{\circ}\)C, i.e., conditions similar to the thermal treatment of meat in practice. The lower the IP value, the less stable the fat and the lower the antioxidant activity of the extracts. Except allspice treatment, all investigated extracts slowed down lipid oxidation in raw chicken compared to control sample. As shown in Figure 3, the highest efficiency of delaying the oxidation process was demonstrated by extracts of cloves (IP = 23 min), cardamom (IP = 22), and nutmeg (IP = 19 min). It was reported previously by [13] that cloves and nutmeg delayed oxidation of lipid fraction in pork meat. However, in contrast to the results of this study, lipid oxidation in pork meat was accelerated by the extracts of allspice and bay leaf, whereas the addition of black seed, caraway and cardamom to pork meat had no significant effects on the rate of that process [13]. As measured by DSC, lipids from chicken meat with higher PUFA [4] content tend to oxidized faster, than from pork meat (IP = 8 min for chicken control sample, whereas for pork control sample IP = 44 min [13]).
# Elderly Fall Detection in Complex Environment Based on Improved YOLOv5s and LSTM
## 3 Fall Detection Based on the Improved YOLOv5s
### Improved YOLOv5s Model
#### 3.2.2 Attention Mechanism
The global minimum with a derivative of zero acts as a \"soft floor\" on the weights and implicitly regularizes and inhibits the learning of a large number of weights. Therefore, here, we add the SiLU function to BiFPN. As shown in Figure 3, the SE attention is integrated into the C3 module, the CA attention module is added before the Spatial Pyramid Pooling (SPP) module, and the concat module is replaced by the BiFPN module. With these improvements, the YOLOv5s model's detection accuracy and other performance parameters can be greatly improved. Figure 3: Improved YOLOv5s network structure.
## 5 Approaches of Optimal State Estimation Utilizing Filtering
### Particle Filter (PF)  
The particle filter (PF) is a technique for estimating the hidden states of non-linear and/or non-Gaussian systems and is very accurate. As a result, a particle filter-based track-before-detect approach is proposed, in which the particle posterior density distribution is directly estimated using beam-former output energy rather than bearing measurements, avoiding measurement-to-track interaction issues [117]. Regarding the estimation of movable systems from partial observations of internal states, particulary when random disturbances are present in the sensors [118], the gap in the filtering design was resolved. The discovery of the particle filters in 1996 by [PERSON] paved the way for estimating the posterior distributions of the states of some Markov processes under noisy  
Figure 23: Performance comparison of 5 th degree CKF non-linear filter with EKF, UKF, and CDKF for UAV tracking [116].  
and partial observations [119]. The geolocation measurement is one of the applications of particle filters.  
[PERSON] et al. proposed a moving target tracking algorithm that used a particle filter based on TDOA/FDOA measurements by stationary sensors. Four base stations were considered to work as sensors that receive emitted signals from the dynamic target. From that signal, the time delay and frequency Doppler were measured, and TDOA/FDOA were calculated, which were used as input parameters to the PF algorithm.The authors established that PF operates with the non-linear properties of the moving target tracking problem successfully. In addition, they concluded that the performance of the proposed algorithm outperforms the one based on the EKF [120].  
Figure 24 shows a performance comparison of EKF and PF for accurate target tracking estimation of the authors' own elaboration. From the figures, it is noted that the Pf algorithm is highly accurate for estimating the target compared to EKF.  
Figure 24: Performance validation of EKF, interpolation filters based on divided differences called: DDF 1 st Order, 2 nd Order and Particle filter PF algorithms applied to AHRS attitude estimation. Heading and pitch angles are estimated in radians based on an INS/MultiGNSS antenna coupling approach. Authors’ own elaboration.
# End-to-End Service Availability in Heterogeneous Multi-Tier Cloud-Fog-Edge Networks
## 3 Materials and Methods
### Traditional Three-Level Cloud-Fog-Edge Architecture
Heavy computing, like scene reconstruction and object recognition, takes place in the cloud. Remote Healthcare. Medical devices for patient monitoring make up the network edge to collect health data. Fog infrastructure aggregates and pre-processes these data, issuing alerts if needed. Centralized electronic health records are maintained in the cloud for analytics and personalized care plans. In different case studies, the layers of network architecture are presented by different components. For example:  
* IoT--Sensors monitor soil moisture, crop growth parameters, weather, and livestock vitals. - Edge--Gateways in fields pre-process sensor data and activate irrigation. - Fog--Local farm servers aggregate data and optimize water/fertil levels. - Smart Retail:
- IoT--RFID tags track inventory. Point-of-sale and shopping apps capture purchase data. - Edge--Store servers filter noise and detect localized trends. - Fog--Regional servers identify buying behaviors and optimize pricing. - Cloud--Central cloud analyzes worldwide sales and shopping habits. * IoT--PLCs, sensors, and actuators control production processes and machinery. - Edge--Local control systems modulate manufacturing lines in real time. - Fog--Plantwide monitoring and control systems enhance coordination. - Cloud--Global cloud optimizes manufacturing operations and schedules. * IoT--Smart meters monitor household electricity usage. Grid sensors track power levels. - Edge--Neighborhood-level servers balance local loads. - Fog--Regional systems regulate distribution across substations. - Cloud--Cloud analyzes usage patterns and controls cross-region transmission.
# Deep Learning-Based Oyster Packaging System
## 2 Oyster Packaging System  
### Systematic Architecture Design  
Intelligent, accurate, and sterile oyster packaging systems are a requirement for automated oyster production lines. To realize the automated grasping and packaging of the oyster production line, the oyster packaging system designed in this study mainly consists of four parts: application layer, function layer, control layer, and hardware layer (Figure 1).  
The application layer identifies the production application aspects and task requirements of the oyster packaging system. This system realizes the grasping and counting function of oysters by linking with the oyster sterilizing production line, which realizes the automation of the oyster packaging production line. The functional layer predicts the oyster grasping position by a visual perception model, guides the position of the mechanical arm, and plans the trajectory for the oyster grasping to achieve the grasping and transfer of the oyster. The core of the functional layer is to establish the oyster intelligent perception model by deep learning machine vision algorithm, to obtain the position information of oysters being transported on the conveyor belt, and to realize the visual perception, tracking prediction, and counting function of oysters. By combining the mechanical arm control technology, the acquired oyster position information was mapped between the camera coordinate system and the robotic arm coordinate system, which was used to plan the running trajectory of the mechanical arm to grasp the oysters. The control layer consists of the robot operating system (ROS) and the control cabinet, which is mainly used to establish communication, coordinate, and send control commands for packaging tasks. Integrated deep learning algorithms, machine vision technology, and mechanical arm control technology in the ROS upper computer are used for the implementation of software algorithms and the transmission of control data. The control cabinet controls the operation of the mechanical arm and the opening and closing of the mechanical claw and plans the trajectory of the mechanical arm. Trajectory interpolation optimization was performed on the control cabinet, which together enabled control of the hardware layer. The hardware layer is based on the working characteristics of the oyster packaging production line; the relevant hardware of the packaging system is selected and constructed, including the mechanical arm, mechanical claw, and image acquisition equipment. The camera transfers the acquired image information to the ROS upper computer via the universal serial bus (USB). The mechanical arm receives motion commands from the control cabinet via the controller area network (CAN) for position adjustment.  
Figure 1: Intelligent machine vision model for oyster packaging.
## 3 ZigBee and TUTWSN Technologies
### ZigBee  
IEEE 802.15.4 Low Rate Wireless Personal Area Network (LR-WPAN) [25] defines physical and MAC layers for WSNs. LR-WPAN provides a synchronized low duty-cycle operation by optional beaconing mode, cluster-tree network topology, and a superframe structure used during the active period. A superframe consists of a beacon, Contention Access Period (CAP), and Contention Free Period (CFP) as shown in Figure 5. During the CAP, a coordinator receives data from the associated nodes using a slotted variation of CSMA. Normally, a node uses CAP but it can request for a dedicated time slot in CFP e.g. for ensuring low-latency or certain bandwidth. However, CFP can be used only for direct communication with the coordinator.  
During the channel access of the IEEE 802.15.4, nodes avoid collisions by postponing their transmission by a random backoff period. After the backoff period, a node performs a Clear Channel Assessment (CCA) procedure to determine if the medium is free. A drawback in IEEE 802.15.4 is the absence of a mechanism to prevent hidden node problem, such as Request To Send/Clear To Send (RTS/CTS) procedure. While this simplifies the protocol, the performance degrades due to collisions [26].  
ZigBee specifies application framework, device profiles, network layer, and security services for IEEE 802.15.4 LR-WPAN. The routing in ZigBee uses source initiated and routing table driven approaches. A source node generates a route request that is flooded to the target node, which replies with a route response message. The intermediate nodes update their routing tables so that they can forward data between source and target nodes. Only the routing table entry to the next hop neighbor that has the lowest routing cost is maintained. The cost is determined based on link reliability. Unreliable link is assigned with a high cost. Optionally, ZigBee allows reduced functionality devices without routing capacity. These devices route only along the cluster tree topology of IEEE 802.15.4. As beacon indicates the distance to the PAN coordinator that operates as the root of the cluster tree, devices can select a next  
Figure 5: Superframe structure in IEEE 802.15.4 LR-WPAN with beacon enabled mode.  
hop among the neighbors that are nearest to the coordinator. Thus, route messaging is not required.