esa-sceva/satcom-chunk-collection huggingface dataset:
# A System for a Real-Time Electronic Component Detection and Classification on a Conveyor Belt
## 5 Conclusions  
In this study, a real-time integrated SMD component detection and classification system with a camera and a CNN deep learning model was proposed. In addition, in this work, we provide a performance comparison of several CNN deep learning models deployed in the system. The performance of the algorithms investigated was compared using mean average precision and inference time. The proposed hardware setup shows how to build a low-cost integrated system for a conveyor in order to detect packed and unpacked moving SMD components. The system meets the requirements for real-time detection in an industrial environment with all limitations. Experimental results were obtained using the dataset containing 7061 images of 8198 capacitors, 10,467 resistors, 8543 diodes, 1328 transistors. Based on the results of this study, we can conclude that:  
1. Proposed system includes a hardware setup with a camera and an implemented CNN deep learning model, and meets the requirements of real-time detection and counting of SMD components on a conveyor belt;
2. When detecting SMD-type capacitors on a conveyor, the highest mAP of 97.3% was achieved using YOLOv4-tiny algorithm implementation on the Tesla P100-16 GB and Jetson Nano 4 GB microcomputers;
3. When detecting SMD-type resistors and transistors, the highest mAPs of 97.7% and 98.6% (for resistors) and 95.4% and 97.2% (for transistors) were achieved using YOLOv4 algorithm implementation on Tesla P100-16 GB and Jetson Nano 4 GB microcomputers, respectively;
4. When detecting SMD-type diodes, the highest mAP of 88.6% and 88.7% was achieved using YOLOv3-Keras algorithm implementation;
5. When detecting four types of SMD components on a conveyor, the highest mAP using an Nvidia Jetson Nano 4 GB microcomputer was achieved with the YOLOv4 algorithm, which is 94.08% with 1000 ms IT; using the YOLOv4-tiny algorithm on a Jetson Nano 4 GB microcomputer, IT is 56.4 ms, with an mAP score of 88.03%;
6. When detecting four types of SMD components on a conveyor, the highest mAP using Google Colab's Tesla P100-16 GB was achieved with the YOLOv4 algorithm, which is 88.43% with 20.8 ms IT.  
Conceptualization, D.V.; methodology, D.V. and V.A.; software, D.V.; validation, D.V.; formal analysis, D.V.; investigation, D.V.; resources, D.V.; data curation, D.V.; writing--original draft preparation, D.V. and V.A.; writing--review and editing, D.V. and V.A.; visualization, D.V.; supervision, V.A.; project administration, V.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author. The data are not publicly available due to privacy issues.  
The authors declare no conflict of interest.
## 4 UAV Navigation and Control
### Path Planning and Trajectory Design
#### 4.2.3 No-Fly Zone  
The 2D path planning and the 2D path-plus-speed optimization techniques are suitable for stationary, quasi-stationary UEs in wide and non-dense areas. However, these methods are not effective for UAV navigation in dense urban areas and, particularly, for mobile UEs and IoVs. Notably, in the RISeUAV or ground RIS-assisted UAV-BS communication, the channel gain of the indirect CLoS link significantly increases when the UAV altitude is high. Therefore, the UAV may be required to fly at lower altitudes among the buildings to provide LoS links for IoVs. It makes the crash avoidance navigation critical which should be considered in the trajectory optimization function [126].  
In some works, the concept of no-Fly zones has been considered [130; 133] that should not be violated by the UAV path. [PERSON], et al. [89] have considered 3D path-plus-speed optimization for RIS-assisted UAV communication for ground UEs. No-fly zones have been modeled as Min and Max boundaries of 3D Cartesian coordinates. However, when it comes to real-time trajectory optimization for autonomous navigation, the formulation is problematic because modeling obstacles is a mixed-integer programming [134] in the context of convex programming, which imposes computational burdens. Moreover, kinodynamic motion constraints have not been considered. To this end, the third group of works considered 3D trajectory optimization using the UAV kinematic equation of motion.
## 2 Materials and Methods  
### Region and Vegetation Types of Interest  
In this study, we focused on the high-latitude forests (between 50\({}^{\circ}\)N and 60\({}^{\circ}\)N) over the Northern Hemisphere. This area mainly includes evergreen forests and deciduous forests. These forests play an important role in the global carbon cycle [(21)], making a substantial contribution to global carbon sink. Besides, these forests are sensitive to global warming, showing an extending growing season under the influence of increasing winter and the spring temperatures [(22)]. The forests are divided into three sets according to their region and vegetation type, i.e., North American evergreen needleleaf forests (ENF), Europe-Asia (Eurasia) ENF and Eurasia deciduous broadleaf forests (DBF). The landcover information was obtained from MODIS land cover product (MCD12C1, 2011).
###### Abstract  
Grassland ecosystems are an important component of global terrestrial ecosystems and play a crucial role in the global carbon cycle. Therefore, it is important to study the carbon dioxide (CO\({}_{2}\)) process in the Middle Tien Shan grassland ecosystem, which can be regarded as a typical representative of the mountain grasslands in Xinjiang. Eddy covariance (EC) and the global carbon fluxes dataset (GCFD) were utilized to continuously monitor the Middle Tien Shan grassland ecosystem in Xinjiang throughout the 2018 growing season. The findings revealed notable daily and monthly fluctuations in net ecosystem exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (Reco). On a daily basis, there was net absorption of CO\({}_{2}\) during the day and net emission during the night. The grassland acted as a carbon sink from 600 to 18:00 and as a carbon source for the remaining hours of the day. On a monthly scale, June and July served as carbon sinks, whereas the other months acted as carbon sources. The accumulated NEE, GPP, and Reco during the growing season were \(-329.49\) g C m\({}^{-2}\), 779.04 g C m\({}^{-2}\), and 449.55 g C m\({}^{-2}\), respectively. On the half-hourly and daily scales, soil temperature (Ts) was the main contributor to CO\({}_{2}\) fluxes and had the greatest influence on the variations in CO\({}_{2}\) fluxes. Additionally, air temperature (Ta) showed a strong correlation with CO\({}_{2}\) fluxes. The grassland ecosystems exhibited the strongest CO\({}_{2}\) uptake, reaching its peak at soil temperatures of 25 \({}^{\circ}\)C. Moreover, as the air temperatures rose above 15 \({}^{\circ}\)C, there was a gradual decrease in NEE, while CO\({}_{2}\) uptake increased. The applicability of GCFD data is good in the grassland ecosystem of the Middle Tien Shan Mountains, with correlations of 0.59, 0.81, and 0.73 for NEE, GPP, and Reco, respectively, compared to field observations. In terms of remote sensing spatial distribution, the Middle Tien Shan grassland ecosystem exhibits a carbon sink phenomenon.  
CO\({}_{2}\) fluxes; eddy covariance; GCFD; grassland ecosystem; Middle Tien Shan Mountains 2023
## 4 Case Study  
### Description of the Dam Project and Datasets  
A Roller Compacted Concrete (RCC) gravity dam is located in southeast China. The dam crest elevation is at 634.40 m and the maximum height of the dam is 72.40 m. The length and width of the dam crest are 206.00 and 7.50 m, respectively. The upstream face of the dam body is vertical, and the downstream face of the retaining dam section has a dam slope of 1:0.72. The overflow dam section is located in the middle of the dam, and the weir crest elevation is at 621.00 m, with three overflow surface holes and three \(12\times 12\) m curved gates set (Figure 5).  
This paper mainly studies the horizontal displacement of the top of the RCC gravity dam. A gravity dam has the characteristic of ensuring a stable state by the own gravity of the dam and cohesion with foundation. Therefore, dam failures are often the results of overtopping and penetration cracks in a section of the dam, which can be directly identified by the horizontal displacement. The displacement at the top of the dam is the most obvious and reflective of the deformation of the whole dam section, so the horizontal monitoring system of gravity dams is usually arranged at the top of the dam. Therefore, in this study, the horizontal displacement at the top of the dam is modeled and analyzed in order to verify the correctness of the proposed model.  
The horizontal displacement of the dam is measured by the tension wire alignment system, with one measurement point arranged in each dam section (Figure 5). The measurement point of the monitoring system is shown in Figure 6. For gravity dams, the middle dam section is generally the most important section of the dam and is often studied as a typical dam section, so the monitoring data related to horizontal displacements at the EX5 measurement point on the 4 th dam section are selected for analysis (to the left bank is positive, to the right bank is negative).  
For the horizontal displacement monitoring data at EX5, 739 daily observations from 6 June 2016 to 22 October 2018 were selected as the research object, and the missing values and outliers during this period were pre-processed. The monitoring data were divided into training set F (1 st-709 th measurements) and testing set T (710 th-739 th measurements). The processed data series is shown in Figure 7.
## 1 Introduction  
Carbon nanoparticles (CNPs) have caught the significant attention of researchers due to their outstanding electrical, mechanical and optical properties that make them useful components in various fields, such as lithium ion batteries, transistors, diodes, logic gates, sensors and field emitter manufacturing. On the other hand, CNP composites are well known for their physical, chemical and especially electrical properties [1] but heat generation by these materials is a device-limiting factor. However, as an electro-thermal component, they have scarcely been studied, although their thermal effect in the form of a CNP and high-density polyethylene (HDPE) interconnected network inside a polymer matrix has been reported [2; 3]. It is notable that CNP-based heating elements indicate great potential as industrial equipment because of their light weight, clean energy production without the emission of by-products, controllability and low production cost [4; 5; 6; 7]. For example, the heating capacity of Single wall carbon nanotube (SWCNT) films by coating them in glass, regarding the time and input power, indicates a rapid thermal response and stable reversibility [8]. Additionally, multi wall carbon nanotubes (MWCNTs) can be used as temperature sensors, which has been revealed by electrical resistance investigations of free-standing MWCNT films versus temperature [9]. Moreover,the conductive MWCNT/polymer composite has been used as a stain sensor by characterizing its electrical properties [10]. Additionally, carbon nanotubes (CNTs ) have been reported as conductive filler materials in polymer composites, which are useful for controlling the temperature in these composites as heating elements or sensors [11]. In addition, several studies reported on the application of nanoparticles such as CNTs, graphene and silver nanowires in flexible transparent thin film heaters [12; 13]. Additionally, the effect of electro-thermal heating on CNT/polymer composites has been measured and it was indicated that the thermal degradation of CNT/polymer composites is more than for air convection heating [14]. Furthermore, the CNT ratio in the polymer composites as an important factor has been reported and it was indicated that a higher CNT ratio leads to a higher conductivity [15]. Since the performance of an electro-thermal device can be simulated by its electrical resistivity (slope of the V-I characteristic), that converts electrical energy into thermal energy, therefore, this report is focused on current-voltage (I-V) characteristic analysis. The current flow consequently determines the amount of thermal energy and heat generation, and thermal energy can be controlled by controlling the CNP resistance as well [16; 17; 18; 19]. Accordingly, examining different samples with different electrodes plus changing the distances between the electrodes gives rise to the investigation of temperature-dependent CNP networks [20; 21; 22]. In this research, CNPs are synthesized by the arc discharge method in a carbonic medium (high-density polyethylene), and heat production based on fabricated CNPs as an electro-thermal building block is explored.
## 1 Introduction
However, the coefficients obtained from the inverse method are often not in a complete amplitude ratio and non-dimensional frequency parameter space. Methods to derive the hydrodynamic coefficient database for the usage in the empirical tools are still needed. [PERSON] et al. [22] applied an optimization scheme to obtain an optimal hydrodynamic coefficient database based on model tests with a bare cylinder. Semi-empirical tools, such as VIVANA [23], SHEAR7 [24] and VIVA [25] are widely used in the offshore industry to predict VIV responses. These tools rely on empirical parameters, including hydrodynamic force coefficient database. The hydrodynamic force coefficient database was derived based on model test data. In the present study, a numerical algorithm is developed to obtain hydrodynamic force coefficient database based on the response measurements of elastic pipe VIV tests. The method has been applied to Shell VIV model test ([14; 16]) with staggered buoyancy elements and the optimal set of force coefficient databases for each buoyancy element and the bare riser section is obtained. Significant improvement in prediction is achieved by VIVANA with the optimal force coefficient databases. The obtained hydrodynamic force coefficient database can be applied to other empirical VIV prediction software as well. The sensitivity of using the same database to predict the VIV of different configurations has also been investigated.
# A Parallelized Database Damage Assessment Approach after Cyberattack for Healthcare Systems
## 3 Background
### Classical Methods  
Traditional methods in database recovery scan the log file starting from the malicious transaction until the last recorded transaction. In such methods, all changes are rolled back and affected transactions are re-executed [18].  
A fusion technique is presented in [19] that follows transaction dependency. The main advantage of this approach is having reduced access to the log file by fusing malicious and affected transactions, undoing any newly fused transaction and redoing it if it was affected and not malicious. Transactions are fused into groups. A set of transactions are in the same group if no malicious or affected transactions occur between them.  
The approach presented in [16] is based on utilizing multiple versions of the data to allow executing new read-only transactions while the recovery procedure is running. The proposed solution, called TRACE, proves that there is no downtime while not blocking further read-only operations. In addition, there is a decrease in delays for write transactions. It also has two modes of operation: standby and cleansing.  
Although classical methods of database recovery have their advantages, the main disadvantage is the need to scan the whole log file starting from the malicious transaction until the last committed one. Such approaches are time-consuming for large databases that have extremely large log files. Traditional methods do not have a fast way to detect affected transactions. They always must go through affected or benign transactions to roll them back.
## 3 Singular Perturbation Decomposition and Control Law Design  
The free-floating base and the flexible-joints give the space robot strong nonlinearity and rigid-flexible coupling characteristics. The interaction between rigid and flexible motion, and the rotation angle error and system vibration caused by the flexible-joints, will affect the control accuracy and stability of the system. The control methods used for ground-based robot systems cannot be directly applied to the control of a space robot system. Also, the actuator failure is bound to affect the control quality of the system, and even lead to system failure. Therefore, it is more difficult to realize the asymptotic tracking of the system trajectory for a free-floating space robot with actuator failure and flexible-joints than a system without flexible-joints and actuator failure. In order to achieve asymptotic tracking and vibration suppression of nonlinear and strongly coupled systems, we use the singular perturbation method, which is considered to be a mature and effective control method for the flexible system, to decouple the system's rigid and flexible motion. Then the system is divided into two subsystems with independent time scales. The slow subsystem characterizes the rigid motion of the system, the fast subsystem characterizes the flexible motion of the system. Then, the appropriate control method is designed for the slow subsystem to realize the asymptotic tracking of the system motion trajectory, and the appropriate method is designed for the fast subsystem to realize the active suppression of the system vibration.  
Let  
\[\mathbf{K}_{\theta}=\frac{\mathbf{K}_{1}}{\varepsilon^{2}} \tag{8}\]  
where, \(\varepsilon\) is a positive factor, \(\mathbf{K}_{1}\) is a positive definite diagonal matrix.  
Then the system's dynamic Equations (4) and (7) can be rewritten as follows:  
\[\varepsilon^{2}\ddot{z}_{\theta}=J_{\theta}^{-1}\mathbf{K}_{1}[\mathbf{\tau}+J_{\theta }\ddot{q}_{\theta}-\mathbf{z}_{\theta}] \tag{9}\]  
\[\ddot{\mathbf{q}}_{\theta}=\mathbf{D}_{\theta}^{-1}(\mathbf{q})[\mathbf{z}_{\theta}-\mathbf{C}_{ \theta}(\mathbf{q},\dot{\mathbf{q}}_{\theta},\dot{\mathbf{\theta}})+\tau_{\text{d}}] \tag{10}\]
## 4 Research Opportunities in O-RAN
### Mobility Management  
In V2X communication, the meticulous design of an efficient handover strategy holds paramount importance in effectively managing challenges such as short stays, ping-pong effects, and remote cell scenarios. Furthermore, with the proliferation of UAVs across various applications, such as agricultural plant protection, police enforcement, and environmental monitoring, the reliability of connection, inherently influenced by mobility factors, emerges as a critical area of investigation.  
Within the framework of the O-RAN architecture, ML emerges as a powerful tool for designing a proactive and data-driven strategy for mobility management. Specifically, leveraging Non-RT RIC, multi-dimensional data can be obtained, including metrics derived from vehicle-related measurements based on UE reports, trajectory information pertaining to vehicle paths, and spatial constraints. Subsequently, using this acquired data, the Non-RT RIC can construct machine learning models by leveraging historical information, enabling informed decisions to facilitate reliable connection support. These ML models, once constructed within the Non-RT RIC, can be effectively deployed and executed by the Near-RT RIC, enabling it to discern optimal radio resource configurations for establishing and maintaining dependable communication links [78].  
As highlighted in [79], the integration of flying UAV BSs with O-RAN introduces notable challenges concerning agility, distributed computation, and dynamic mobility of UAVs. The efficient control of UAV-BSs can be significantly enhanced through the utilization of intelligent O-RAN functionalities, playing a pivotal role in addressing the requirements of unforeseen applications where terrestrial networks may prove inadequate. In this context, it would be imperative to explore innovative approaches rooted in ML for jointly optimizing the trajectory of UAVs acting as flying BSs and the task offloading among the diverse O-RAN elements, including O-RU, O-DU, and O-CU. The comprehensive evaluation of performance metrics, encompassing resource utilization, service acceptance rate, and utility values, alongside a multi-agent learning framework, becomes essential [79].  
In mobility management, location information provides an additional dimension of inputs to improve its decision making. Localization techniques can also be enhanced using ML techniques [80]. With O-RAN, not only can ML techniques can be introduced into the network to enhance the localization, but the output of localization can, in turn, provide useful inputs to improve the mobility management in O-RAN.
# Overcoming Individual Discrepancies, a Learning Model for Non-Invasive Blood Glucose Measurement
## 2 Related Works
### Non-Invasive  
_Google contact lens_: This is a smart contact lens project developed by Google on 16 January 2014. This project aims to assist people with diabetes by constantly measuring the glucose levels in their tears. Tears will flow into the monitor through a small holes in the lens, which containing a sensor that measures the glucose level in the tears. The users obtain their glucose levels by observing the change of the color of the lens. Unfortunately, Google announced the termination of the project on 16 November 2018, due to the large measurement error and difficult sample collection [22].  
_TensorTip\({}^{\text{TM}}\)_ _CoG_: It is a learning non-invasive glucose meter, developed by Cnoga Medical Ltd, that requires more than 100 invasive and more than 50 non-invasive data calibrations before use. The basis for measuring blood glucose is the absorption spectrum of the finger.  
_HG1-c_: MediSensors is San Jose, California, USA based company that developed compact continuous NGM device i.e., _HG1 - c_, which is based on Raman spectroscopy [23].  
_GlucoTrack\({}^{\text{TM}}\)_: It consists of two parts, the handheld monitor and the ear-clip sensor, and integrates three measurement techniques: Ultrasound, electricity, and thermal. Ultrasound technique is used to measure the velocity of sound waves passing through the earlobe, and the velocity is affected by the concentration of blood glucose in the capillaries of the earlobe; electrical technique is used for measuring the change in conductivity of the tissue; and the heat transfer features of the tissue is obtained by the thermal technique.  
_SugarTrac\({}^{\text{TM}}\)_: This device is based on infrared spectrum. It is inserted in the ear canal, and emitting different wavelengths of light to the eardrum, analyzing the reflected signals to calculate glucose levels.