esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
This study proposed a multiple regression model for the indirect estimation of the tidal volume during an exercise stress test, using heart rate and breathing rate directly acquired from a commercially available wearable device, in addition to basic individual characteristics of the subject such as the age and the body mass index. The proposed approach appeared suitable for predicting tidal volume using wearable-device data, including when evaluated on new subjects. The reliability of the model formulation with respect to the selected predictors was also evaluated by considering predictors measured with reference instrumentation; the model appeared unaffected by the way in which heart rate and breathing rate were measured. On the basis of these considerations, the formulated model can be considered not subject-specific and not instrumentation-specific. To the best of our knowledge, only the pilot study by [PERSON] et al. [17] estimated the tidal volume using an approach similar to the one proposed by us. Although both approaches used wearable devices, our approach differs from that of [PERSON] et al. [17] in two major respects. Firstly, the predictors used by [PERSON] et al. [17] were based on electrocardiogram-derived respiration, whose extraction requires extensive processing of an acquired electrocardiogram; by contrast, in our approach, heart rate and breathing rate directly measured from the wearable device were used, without the need of additional extensive pre-processing. Secondly, in the study by [PERSON] et al. [17], only tidal volume measured during a lab-controlled experiment was considered instead of dynamic conditions as the one characterizing exercise stress test. Thus, the very limited pre-processing required and the ability to deal with dynamic conditions represent the two main advantages of the proposed approach. The multiple regression model formulated in our approach includes not only linear terms but also interactions, which is different from what was found in other studies not necessarily dealing with wearable devices [17; 18]. Of note, previous studies did not consider the individual characteristics of the subjects (such as age and body mass index) as predictors. Their addition does not represent a particular issue, since these parameters can be easily provided. At the same time, the inclusion of such information may be important for tailoring the methodology on the basis of the subject's characteristics. Indeed, both age and body mass index showed interaction effects with heart rate and breathing rate (see Table 3). The importance of the selected predictors can be confirmed by the literature. Age and body composition were demonstrated as essential in the clinical assessment of tidal
# Review on System Identification and Mathematical Modeling of Flapping Wing Micro-Aerial Vehicles
## 3 System Identification and Mathematical Modeling of Flapping Wing Micro Air Vehicles (FWMAVs)
### Tailless Flapping Wing Micro Air Vehicles  
The tailless class of FWMAVs is very appealing compared to the tailed class of FWMAVs because the former provides high agility although it is very sensitive to perturbation and environmental disturbances. Modeling and control of such MAVs is particularly challenging. Therefore, the tailless class has been focused on by a wide number of researchers, and different practical models have appeared [5; 10; 11; 12; 14] in the current decade. In the following subsections, we will direct our efforts to highlighting the significant works related to the system identification of tailless FWMAVs.
## 1 Introduction
(2018) [16] investigated injury risk prediction and discovered that injured runners experience higher vertical reaction forces and cumulative loads. In recreational runners, the number of strides per training session may correlate with the likelihood of injury. Male runners who are prone to injury have similarities with female runners and experience a greater impact on the tibia and sacrum, as well as a higher rate of vertical ground reaction force, potentially leading to a higher propensity for injury due to reduced impact absorption ([PERSON] et al., 2022 [17]). Additionally, [PERSON] et al. (2022) [18] focused on bone stress injuries in women, highlighting increased vertical load rate, vertical stiffness, and tibial impingement as contributing factors, while [PERSON] et al. (2022) [19] analyzed plantar pressures in female runners to predict bone stress injuries, linking increased pressure on the first metatarsal with forefoot valgus, increased power, and a shift in load to the forefoot. No studies have been located in the literature that relate the influence of the menstrual cycle on the kinematic variables of running, but there is literature that describes cutting and landing kinematics with an ACL injury risk perspective according to the different phases of the menstrual cycle, placing the menstruation and ovulation phases at the highest risk of injury due to greater vertical ground reaction forces, tibial internal rotation at initial contact and hip internal rotation, accompanied by increased anterior knee laxity ([PERSON] et al., 2014 [20]), and place the mid-luteal phase at the lowest risk of injury by locating a lower maximum valgus angle ([PERSON] et al., 2023 [21]). Moreover, other running technique variables, such as swing excursion, maximum pronation velocity, maximum stance velocity from foot strike to the point of maximum pronation, maximum stance velocity from the point of maximum pronation to toe-off, time from the point of maximum pronation to toe-off, time from toe-off to the minimum swing point, and time from the maximum swing point to foot strike, have received limited analysis but may influence performance. Studying the biomechanics of running, specifically through kinematic variables, provides coaches with insights into athletes' strengths and weaknesses ([PERSON] et al., 2020 [22]). This research aims to identify the running technique variables related to performance and influenced by the menstrual cycle and running intensity.
## 3 Model Construction and Solving Algorithm
### Solving Algorithm
#### 3.2.2 Application of NSGA-II-TOPSIS-GCA Algorithm for MOODPR Problem
_Yangtze River_**2017**, _48_, 101-104. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Extraction of joint scheduling rules for wind-wind-water power generation system. _Water Power_**2019**, _46_, 114-117+126. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Extraction method of scheduling rules for staggered generation of hydropower station. _J. Hydroelectr. Eng._**2019**, _38_, 20-31. * _[PERSON] et al. (2018)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Comprehensive evaluation of load adjustment plans for cascade hydropower stations based on moment estimation method and grey target model. _Syst. Eng.-Theory Pract._**2018**, _38_, 1609-1617. * _[PERSON] et al. (2019)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Interval number grey target decision-making model based on multi-dimensional association sampling and its application. _Syst. Eng.-Theory Pract._**2019**, _39_, 1610-1622. * _[PERSON] et al. (2015)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Research on reservoir multi-objective flood control operation decision-making method based on improved entropy weight and set pair analysis. _Water Resour. Power._**2015**, _33_, 43-46. * _[PERSON] (1999)_ [PERSON]; [PERSON] _An Index of Stream Condition: Reference Manual_, 2 nd ed. ; Department of Natural Resources and Environment: East Melbourne, Victoria, 1999; pp. 15-27. * _[PERSON] et al. (2018)_ [PERSON] Risk Analysis and Multi-Attribute Risk Decision of Combined Flood Control Operation of Reservoirs. Ph.D. Thesis, Huazhong University of Science & Technology, Wuhan, China, 2018. * _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Multi-Stakeholder Coordinated Operation of Reservoir Considering Irrigation and Ecology. _Water_**2022**, _14_, 1970. [CrossRef]

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## 7 Conclusions  
IBFD is a SE technology that can meet the target of the high data demand in the 6G era, where a relay or an existing user assists a far user in an IBFD mode. The exploration of proposed R-IBFD and RA-IBFD has shown the enhancement of the secrecy capacity, along with the SE and reduced interference.  
The system fulfils the requirement of high data rate using proposed R-IBFD and RA-IBFD. The high data rate or high capacity will lead towards the management of big data and high secrecy capacity. The ML is a part of the modern systems of wireless communication, it can be used for multiple purposes where it improved the system's performance without altering and disturbing its surrounding. This paper is an extension of a conference paper [36]. In this paper, an SOP equation is derived for a selection of relay in R-IBFD, MLGA is used for the optimization of power coefficients subjected to secrecy capacity of the given system, this will be particularly helpful for dealing with larger number of users and data. Overall, R-IBFD is an efficient system that provides a better transmission data rate and enhances security. Other ML and statistical algorithms can also be used to improve the performance of the proposed algorithm [39], which will be further explored as a part of future research of the proposed algorithm. Alongside the simulation results, use cases of the proposed algorithm are also given in this paper. Further research is highly required for the flexibility of systems with several real-life 6G communication environment that needs to be explored for other security reasons including active attacks during public holidays.  
Conceptualization, R.K.; methodology, R.K.; software, R.K. and N.T.; validation, R.K. and R.A.; formal analysis, R.K.; investigation, R.K.; resources, R.K. and N.T.; writing--original draft preparation, R.K.; writing--review and editing, R.K., N.T. and R.A.; visualization, R.K. All authors have read and agreed to the published version of the manuscript.  
The author acknowledge the internal research start-up funding, reference: 1012606 FA1, from University of East Anglia (UEA), Norwich.  
This article used randomly generated data for MLGA. Python is used as a tool for the simulation of MLGA, the basic command for randomly generated sequence of Python is used to generate data.  
The authors declare no conflict of interest.
###### Abstract  
Measuring the forces produced at the ankle joint is critical to diagnose musculoskeletal pathologies. In standard clinical practice, ankle force is often assessed through manual joint manipulation and visual observation. This study introduces a simple apparatus, the Ankle Force Transducer (AFT), based on a uniaxial load cell capable of measuring ankle forces in conditions consistent with clinical evaluations. The AFT can be placed at the extremity of any examination couch to measure ankle forces in plantar flexion and dorsiflexion. The repeatability of the AFT was assessed in 30 healthy subjects across three sessions and in two knee postures. One patient with foot-drop condition was evaluated using the same apparatus. The intra-session coefficient of variation for plantarflexion and dorsiflexion forces was around 5% and 8%, respectively. The dominant leg exhibited greater forces than the non-dominant one, and the fully extended knee resulted in significantly larger forces with respect to the flexed knee (\(p<0.001\)). The foot-drop patient showed a 90% reduction in dorsiflexion force in the affected limb. The AFT appears to be a user-friendly tool used to measure ankle forces, which has the potential to provide more repeatable and objective measurements of ankle forces with respect to operator-dependent evaluations.  
Keywords:ankle force measurement; musculoskeletal assessment; ankle strength; instrumental evaluation; foot-drop; clinical scoring systems +
Footnote †: journal:
# A Systematic Analysis of Narrowband IoT Quality of Service
## 1 Introduction
A thorough search of the relevant literature revealed that only [PERSON] et al. [11] performed detailed application layer QoS measurements in a commercial NB-IoT network. The results showed that decreasing signal levels resulted in increased latency and a reduction in throughput, but overall indicated a reliable performance of NB-IoT. However, the work considered a single tone downlink scenario, which did not align with the NB-IoT specification. Furthermore, only limited analysis has been performed on the physical NB-IoT network parameters and their influence on the application layer QoS. This work expands on the previous publications by systematically examining both physical and application layer NB-IoT QoS parameters in a commercial mobile network. The influence of the physical QoS parameters and the underlying mechanisms on the end-user QoS is analyzed and compared against theory, as well as simulations. A long-term measurement allows concluding on how the radio conditions change between day and night. Moreover, this paper presents the suitability of NB-IoT in the following four use cases of smart metering applications in the uplink and downlink direction:  
* Uplink transfer of individual sensor data from a single sensor
* Downlink transfer of individual commands* Uplink transfer of small packets from many sensors
* Transfer of software updates in the downlink and bulk data to the cloud in the uplink  
To the best of the authors' knowledge, this paper contains one of the first systematic analyses of the relevant physical and application layer QoS parameters and contributing factors in a real NB-IoT network and is the first study of NB-IoT QoS in Germany. The remainder of the paper is organized as follows: Section 2 describes the theoretical fundamentals of the NB-IoT architecture, as well as important QoS parameters. Section 3 explains the experimental setup and the measurement methodology. Section 4 presents the results of the QoS analysis. Section 5 discusses the suitability of NB-IoT in four different smart metering use cases. Finally, a conclusion is drawn in Section 6.
# Study on Elastoplastic Damage Coupling of Reservoir Mudstone Considering Permeability Change
## 1 Introduction
With the increase in formation pressure, the strain borne by the reservoir rocks gradually increases, the damage evolution characteristics are more obvious, and the elastoplastic damage coupling effect is more obvious, showing the special mechanical properties of the brittle-ductile transformation that are different from shallow rocks [16]. At present, there is little research on the elastic-plastic damage coupling mechanical properties of rock under high buried depth environments. The only part of the research is mainly focused on granite and salt rock, with underground energy storage and nuclear waste disposal sites as the application target [17; 18; 19; 20]. The research on the elastic-plastic damage coupling mechanical properties of mudstone in oil and gas reservoir engineering is basically blank. However, the coupling and superposition of the long-term high temperature and high pressure in high buried depth environments make the rock deformation and skeleton failure become particularly complex, and the coupling mechanical response of the elastoplastic damage becomes an urgent problem to be studied. In actual petroleum engineering, mudstone is filled with pore fluid. Under the action of the external load, the stress field of mudstone changes, causing plastic deformation and damage of the rock mass. The internal microstructure changes, the skeleton particles are rearranged, and the primary pores and cracks gradually develop and penetrate, resulting in changes in material porosity and permeability. At the same time, the change in pore water pressure inside the deformed rock mass affects the mechanical parameters of mudstone. Therefore, the oil and gas exploitation process of mudstone reservoirs is a complex seepage-stress coupling process. Based on this, this paper takes the mudstone of high buried depth reservoirs in oil and gas reservoir engineering as the research object. On the basis of a conventional triaxial compression test, damage evolution is discussed. Combined with Clausius-Duhan inequality in thermodynamics theory, the elastic-plastic damage coupling mechanical model was built, and then the simulation verification was carried out. Then, based on the relevant research results, the permeability evolution model of mudstone based on the damage was proposed, and the secondary development was carried out based on the finite element software ABAQUS. UMAT and USFLD subroutines were compiled, and the seepage-stress coupling simulation verification was carried out. The relevant results have important guiding significance for deep oil and gas exploitation engineering.
# Structural Correlation Based Method for Image Forgery Classification and Localization
## 5 Image Forgery Classification and Localization  
Given the centroid \(\mathbf{T}\) and an image \(\mathbf{U}_{i}\) in the cluster, we can easily estimate the mask of forged regions of \(\mathbf{U}_{i}\) based on \(\mathbf{T}_{Y}-\mathbf{U}_{iY}\). Specifically, \(\mathbf{U}_{i}\cap\mathbf{T}\) denotes the image region including all image pixels that \(\mathbf{U}_{i}\) and \(\mathbf{T}\) jointly have, and \(\mathbf{U}_{i}\setminus\mathbf{T}\) denotes the image region in \(\mathbf{U}_{i}\) but not in \(\mathbf{T}\).  
\[[\mathbf{U}_{i}\cap\mathbf{T}](x,y)=\begin{cases}\mathbf{U}_{i}(x,y),&\text{ if }|\mathbf{U}_{iY}(x,y)-\mathbf{T}_{Y}(x,y)|\leq 3,\\ 0,&\text{otherwise}.\end{cases} \tag{12}\]  
\[[\mathbf{U}_{i}\setminus\mathbf{T}](x,y)=\begin{cases}\mathbf{U}_{i}(x,y),& \text{if }|\mathbf{U}_{iY}(x,y)-\mathbf{T}_{Y}(x,y)|>3,\\ 0,&\text{otherwise}.\end{cases} \tag{13}\]  
Consequently, two image regions \(\mathbf{U}_{i}\cap\mathbf{T}\) and \(\mathbf{U}_{i}\setminus\mathbf{T}\) are extracted as shown in Figure 4. These image regions are refined by using median filter to remove salt and pepper noise.  
We use SIFT to find the matched regions of \(\mathbf{U}_{i}\cap\mathbf{T}\) and \(\mathbf{U}_{i}\setminus\mathbf{T}\). 3 pairs of matched keypoints are utilized to calculate the affine transformation matrix, and subsequently, a warped image is generated for each transformation matrix. To localize the duplicated regions, the zero mean normalized cross-correlation method is adopted [19]. If we can find such regions, the image \(\mathbf{U}_{i}\) is classified as a copy-move image; otherwise, the tampered image is classified as a spliced image. In Figure 4, images \(\mathbf{U}_{1}\) and \(\mathbf{U}_{2}\) are classified as copy-move images and the detected forgery regions are illustrated in the last column. The previously detected regions, \(\mathbf{U}_{i}\cap\mathbf{T}\), are the target regions in white, and the newly found matched regions are the source of the copy-move operation, which are represented in green. In the last two examples of Figure 4, the spliced regions of images \(\mathbf{U}_{3}\) and \(\mathbf{U}_{4}\), are highlighted in white.  
Figure 3: The illustration of image database indexing and one iteration of the proposed image forgery clustering algorithm.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 2 Proposed HEM System
### Appliance Monitoring and Controlling Modules
#### 2.2.1 Smart Plug/Socket  
The smart plug/socket is able to monitor the power consumption of a connected appliance with a rating of up to 13 A, and turn it on/off using an integrated relay. The plug/socket determines and processes the single-phase power line voltage and current of the connected device, and then sends the captured data to a connected master node (when used) or the controller. The plug/socket uses the aforementioned wireless communication module and stores raw data provided by the microcontroller. Figure 3 shows the components (left) that were used to develop the plug/socket prototype (right).  
Figure 3: Smart plug/socket: components and prototype.  
Figure 2: Graphical user interface of the proposed energy management system.  
A printed circuit board (PCB) is used for most of the wired connections, with copper traces being used to achieve compactness and separate the high- and low-voltage connections. A two-sided PCB is used for high-voltage connection at the bottom of the system, and low-voltage connection at the top. The system components are soldered to the PCB to complete the hardware, which is placed inside an isolated box. The female socket side is used for appliance connection, while the male plug side has three pins that are inserted into the wall power socket. Interested readers can find the details of smart socket construction and software used to develop it in [25].  
The following five steps can summarize the operation principle of the smart socket:  
1. The male side of the smart plug is connected to the normal electricity socket to power up the internal components and prepare the smart socket for the connection of an appliance.
2. When an appliance is connected to the smart socket, the sensing modules acquire 100 readings of current and voltage and transmit the data to the microcontroller.
3. Based on the received data, the microcontroller calculates the real power, root-mean-square values of the current and voltage, complex power, and power factor.
4. The microcontroller transmits the calculated data to the main scheduling terminal at a time interval of 2 s.
5. The microcontroller receives and checks feedback data from the scheduling terminal module. If a command is received, the microcontroller sends the appropriate actuating signal to the relay of the smart socket to turn on/off the connected appliance.