esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Go-Around: Analysis
#### 4.5.3 Go-Around: General Analysis  
This was used to assess whether pilots were able to confidently differentiate among different go-arounds. The positive coefficient (0.0010) associated with the \"hours\" insinuates that the more hours flown, the more likely they are to define each procedure. However, the \(p\)-value of 0.457 indicates this is not statistically significant, hence there is insufficient evidence to prove the above. In response to which phrase best describes a go-around (Table 22), most pilots suggested that a go-around is when a landing approach is discontinued, and the aircraft is placed in a climb. This is consistent with current versions of the definition in Transport \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ordinal 21** & **Coef.** & **Std Err.** & \(p\)**-Value** \\ \hline Hours & \(-0.0034\) & 0.001 & 0.021 \\ \(1/2\) Threshold & \(-1.8601\) & 1.132 & 0.100 \\ \(2/3\) Threshold & 0.4916 & 0.418 & 0.239 \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Ordinal regression (Question 21) in regard to negative attitudes towards go-arounds. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Answer** & **Proportion** \\ \hline I would conduct this procedure if I think my approach is unstable & 59\% \\ I would conduct a go-around if my approach isn’t stable by short final & 36\% \\ I would conduct a go-around if my approach isn’t stable just before touch down & 5\% \\ I would try as much as possible to conduct a landing & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 19: When pilots would decide to go around. Canada research. However, the second most common answer was \"a go-around is a missed landing, and is the same as a missed approach or a rejected landing\". This concept is reflected institutionally, such as AOPA [7] describing it as \"a go-around, a wave-off, an aborted landing, a missed approach, a rejected landing or bummer\". The lack of consistency with the name of the manoeuvre was highlighted in the roundtable discussions, reflecting the lack of clarity around the manoeuvre. Many pilots at the roundtable discussions used \"overshoot\" interchangeably with \"go-around\"; this was noted by an instructor at Rockcliffe, who mentioned they prefer \"go-around\" as it is a clearer, \"punchier\" word which should be standardised. Furthermore, pilots answered which phrases they believed best described a go-around from different stages in Table 23, below. The most popular answer for the go-around from 200 ft mirrored the terminology used in the SLWG definition. However, the most favoured answer for the go-around during the landing phase did not include key points highlighted in the SLWG definition. The SLWG definition mentions that this go-around is a medium-risk manoeuvre, and that the landing gear may contact the runway. Nevertheless, only two pilots selected this option,
# Analyzing Zone-Based Registration under 2-Step Paging in Mobile Communication Network
## 2 Zone-Based Registration and 2-Step Paging
### 2.2 2-Step Paging  
When a call to the UE occurs, this incoming call is connected through the paging procedure. Most mobile communication networks adopt a simultaneous paging method in which a paging message is issued to all of the cells within a corresponding zone. Although this method has the advantage of immediately receiving the acknowledgment of the paging, there is a problem in terms of efficient use of the radio channels because the paging message is transmitted to all the cells in the zone simultaneously. To improve this strategy, 2-step paging [9; 11; 12], in which the zone is divided into two small areas and paging is performed sequentially over two times, can be considered. In this case, if the UE responds in the first paging on the first small area, it is not necessary to perform the second paging on the remaining small area, which reduces the paging cost compared to that of conventional simultaneous paging. In the 2 ZR studies, the paging cost was calculated by assuming that the paging was performed by 2-step paging. By contrast, in 1 ZR, the performance  
Figure 1: Location registration in 1 ZR.  
Figure 2: Location registration in 2 ZR.  
was compared with that of 2 ZR by still applying the method of paging the entire zone simultaneously. As a result, performance comparisons were not performed properly under similar conditions. Therefore, even in the case of 1 ZR, it is necessary to accurately compare performance by considering 2-step paging in a manner similar to 2 ZR [9; 11; 12].  
In this study, we assume that the zone of 1 ZR is composed of \(n\) cells, and it is divided into two small areas to obtain the optimal paging cost when 2-step paging is performed.
## 4 Experiments and Evaluation  
### IoT Knowledge Management: CHARIOT  
Our PM-based adaptive control mechanism running a conveyor system have been developed and integrated as part of the CHARIOT project. The CHARIOT framework allows real-time data-driven control through its four-layer structured, namely, _physical layer_, _middleware layer_, _agent layer_, and _knowledge layer_, as shown in Figure 7. The physical layer is part of the CHARIOT runtime environment integrating many different runtime environments, such as, Robotic Operating System (ROS) ([[https://www.ros.org/](https://www.ros.org/)]([https://www.ros.org/](https://www.ros.org/)), accessed on 30 April 2021) and KURA ([[http://www.eclipse.org/kura/](http://www.eclipse.org/kura/)]([http://www.eclipse.org/kura/](http://www.eclipse.org/kura/)), accessed on 30 April 2021). CHARIOT runtime environment is able to integrate simple and resourceful devices, where the former runs on, for example, a raspberry PI and the latter already has its own computational resources. The physical layer directly runs on the device nodes and it installs libraries and interfaces for the devices. The device drivers of our conveyor system scenario run on ROS. The middleware layer serves as a bridge between the physical layer and the CHARIOT framework, where the device agents interface the ROS agents in the physical layer using a websocket protocol (i.e., a fast enough protocol for real-time control).  
The device agents physically run on the distributed devices; however, they are a part of the CHARIOT middleware, that is, the agent framework. They act as an abstraction of the real devices and as an interface to the agent layer. The low-level planning and motor control still run on the physical layer (i.e., on ROS drivers) whereas the application agents on the agent layer make high-level decisions and send command signals for the devices, for example, a speed request, through their device agents. Additionally, as a part of the runtime environment, each device runs a data gateway. To provide a real-time access to device data and satisfy a continuous data analysis, we use publish/subscribe type communication from the device to the CHARIOT database through the data gateway components. The knowledge layer provides machine learning functions as services for generic use. An application agent requests for a certain machine learning training and/or prediction on a certain data. For example, the _PM Application Agent_ requests for an anomaly detection on the conveyor motor data, receives the result from the cloud and notifies the _Adaptive Control Application Agent_. The services are made available through Chariot Cloud API, and once a machine learning request is created, the results (we call knowledge) are available to any other agents running on the CHARIOT framework. Thanks to this framework, we are able to run our distributed conveyor system with heterogeneous devices and PM-based adaptive control mechanism in real-time.
## 2 Materials and Methods
### Description of the XMed-Dry Network and the Multiplatform Observations
#### 2.1.3 AERONET AOD  
AERONET [33] is a global network of nearly 1000 sun-photometers whose aerosol data are processed according to the same retrieval algorithm and made available online at the following web-portal [12]. For this study, we have utilized Version 3 AOD measurements at level-2 (cloud screened and quality checked) at the stations of Athens (Greece; 23.7\({}^{\circ}\) E, 38.0\({}^{\circ}\) N, 130 m elevation), Rome Tor Vergata (Italy; 12.6\({}^{\circ}\) E, 41.8\({}^{\circ}\) N; 130 m elevation) and Gozo (Malta; 14.3\({}^{\circ}\) E, 36.0\({}^{\circ}\) N, 111 m elevation). The station of Lecce (University) does not have valid level-2 data for the period considered and has been replaced by the above-mentioned Rome Tor Vergata site.
# Computational Analysis of Parallel Techniques for Nonlinear Biomedical Engineering Problems
## 3 Computational Efficiency and Numerical Results
### Biomedical Engineering Application 2: Blood Rheology Model
The computational time and memory used to solve biomedical engineering applications using the MatLab programming software are displayed in Table 20. Our recently developed approach finds all solutions parallel to the nonlinear Equation (50) and has comparatively better performance in terms of memory use and computational CPU time. Table 20 depicts the overall behavior of the proposed technique. The stability analysis is shown by the number of iterations used and the basic arithmetic operations, and the functions and their derivative evaluations in each iteration step are presented in Tables 20-22. The consistency analysis of the parallel scheme is presented in Table 22 by displaying the overall error of the parallel schemes. In terms of stability, our recently created approach BF\({}^{[s]}\) is more stable than existing literary methods PM\({}^{[s]}\), NN\({}^{[s]}\), NF\({}^{[s]}\) since it uses fewer iterations and fewer fundamental mathematical operations and their functions. Assessments for concurrently approximating all of the roots of (50) of the output of Table 22 are also summarized in Figure 12a-d. In terms of memory and CPU time, our method BF\({}^{[s]}\) outperforms other known parallel schemes PM\({}^{[s]}\), NN\({}^{[s]}\), NF\({}^{[s]}\) that choose initial starting values randomly, as shown in Figures 11a-d and 12a-d.  
Computational Efficiency: Parallel techniques save significant calculation time, as demonstrated by their use in four separate biological applications. * Better Convergence Rate: Compared to conventional schemes, the suggested scheme assures fast convergence by determining solutions more effectively in fewer iterations. * Memory Efficiency: A comparison of memory utilization demonstrates the scheme's applicability in resource-constrained environments. * Error Analysis: Including error graphs shows how accurate the solutions are and confirms that the approach is reliable. * The Percentage Computational Efficiency: The quantitative comparison highlights a method's competitive advantage, especially in large-scale problems. Although the suggested parallel approaches improve computational efficiency [49; 50], convergence rate, and memory optimization in three biomedical applications, there are significant drawbacks to the method. Its applicability to extremely high-dimensional issues and reliance on parallel hardware may limit its utility in resource-constrained settings. It is necessary to investigate the error behavior at the edge cases, like ill-conditioned systems. The method will be optimized for various computer architectures, its scalability will be improved, and experiments with applications to stochastic and multi-scale models will be conducted in the future.
## 5 The Algorithm
### (d;\(\theta\),W,T)-mp  
For a truck driving on the highway (\(\mathrm{Rd_{h}}\)), the (\(d\);\(\theta\),\(W\),\(T\))-MP algorithm, which is limited by the turning angle (\(\theta\)), vehicle loading capacity (\(W\)), and transportation lead time (\(T\)), can be found by extension of the d-MP algorithm and is shown in Theorem 3 [19].  
**Theorem 3**.: _Any state vector of a system considering the constraints of the turning angle, vehicle loading capacity, and transportation lead time \(X_{\angle\theta,W,T}=(x_{1},\,x_{2},\,\ldots,\,x_{n})\) is a (d;\(\theta\),\(W\),\(T\))-MP candidate, i.e., the maximum flow of the system \(M(X_{\angle\theta,W,T})=d\), if and only if the flow vector (\(f_{1},f_{2},\,\ldots,f_{p}\)) satisfies the following terms, as shown in Equations (5) and (7)-(11)._  
\[w_{i}\leq W\quad\text{for}\ i\ =\ 1,\ 2,\ \ldots,\ n \tag{10}\] \[\sum_{i=1}\left\{t(e_{i})\big{|}e_{i}\in P_{j}\right\}\leq T \tag{11}\]  
where \(w_{i}\) is the weight limit of the edge \(e_{i}\), which was used to present the maximum vehicle loading capacity; \(t(e_{i})\) is time spent driving on the edge \(e_{i}\); \(W\) and \(T\) are the upper limitations of the maximum vehicle loading capacity and the total transportation lead time, respectively.  
In Theorem 3, Equation (5) is the total flow of each MP, and Equations (7)-(11) are the constraints of the flow of each MP with the angle \(\theta\), each edge, each angle, the vehicle loading capacity, and the total transportation lead time.  
Next, we check whether the state vector candidates are real state vectors in the MFN according to Theorem 4 [19].  
**Theorem 4**.: _The state vector candidate is a real state vector if and only if the cyclic direction does not exist in the system of the network._
### Qualitative vs. Quantitative Thermal Cameras  
Thermal cameras work by capturing the amount of infrared radiation emitted by objects within a scene. Though they all aim to capture the same type of information (namely heat), there are two types of thermal cameras. First is qualitative thermography (sometimes referred to as \"relative thermal imaging\"), where the goal is to show the relative differences of infrared radiation throughout the camera's field of view. These are often used for inspection and security purposes as they often provide distinct contrast between colder and hotter elements in the field of view, regardless of absolute temperature. Second is quantitative thermography (sometimes referred to as \"absolute thermal imaging\"), where each sampling point in the field of view is mapped to an absolute temperature measurement. This enables accurate capture of absolute thermal differences between elements in the scene, as well as a consistent visual response for any thermal signature. In recent years, advances in thermal imaging technology have made the use of thermal cameras increasingly popular, either in isolation or in conjunction with traditional Closed Circuit TeleVision (CCTV)-cameras. Quantitative thermal cameras could be seen as the ideal solution as they provide essentially the same functionality as qualitative thermal cameras but with the added benefit of accurate thermal readings. The technology required to construct an absolute thermograph is significantly more complicated than that of relative thermography, and--as such--is much more costly to produce and purchase. For the purpose of many tasks, the absolute temperature readings are redundant for the purpose of the thermal camera, and as such do not justify the cost, thus making qualitative thermal cameras much more common in deployed vision systems.  
In this paper, a methodology is detailed that employs predicting continuous weather-related meta-variables for auxiliary guidance. In addition, an overview of the largest thermal object detection dataset in a stationary surveillance context is provided. The Long-term Thermal Drift (LTD) [1] dataset will be used to evaluate and train models as it contains both object-centric annotations, as well as fine-grained weather information for each sample. Further methods describe how fine-grained weather prediction can be leveraged to condition the network during training to guide the network to become weather-aware. Specifically, this will be divided into direct and indirect conditioning methods. Finally, this is followed by a discussion of extensive experiments, which evaluated the impact of the aforementioned methodology (conditioned on temperature, humidity, and time-of-day) and the impact on performance metrics with respect to the respective weather conditions. While the analysis does not show a direct improvement in accuracy metrics, it does show that auxiliary conditioning in this way allows the networks to extract and somewhat model the underlying weather signal.
## 2 Materials and Methods
### Experimental Protocol  
The DD group participated in the experiment at the Clinic for Neurology and Psychiatry for Children and Youth in Belgrade, Serbia. The control group was recruited and tested at the high school \"Gimnazija Smederevo\" in Smederevo, Serbia. Prior to the experiment, all participants were informed about the experimental protocol. Written informed consent and permission were obtained from all participants and the parents or guardians of minors. The Ethical Committee of the Clinic for Neurology and Psychiatry for Children and Youth, Belgrade, approved the study (Approval No. 21-79/02, dated 16 July 2018).  
The handwriting experimental protocol consisted of the following three tasks [24; 41]:  
* First task--Tracing Single Semicircles: Participants were instructed to trace four semicircles, each completed in a single stroke. The semicircles were presented as dashed outlines within separate squares and rotated clockwise by 90\({}^{\circ}\) increments.
* Second task--Tracing Composite Figures: Participants traced three composite figures, each consisting of four semicircles oriented differently. The dimensions and orientations matched those in Task 1, and the figures were also presented as dashed outlines within individual squares
* Third task--Repetitive Handwriting of a Cursive Letter: Participants continuously wrote the lowercase cursive Latin letter \"1\" within two rectangles: a larger rectangle measuring 40 \(\times\) 160 mm (LR subtask) and a smaller rectangle measuring 9 \(\times\) 160 mm (SR subtask). The number of letters was not predefined; participants stopped writing at their own discretion.  
A visual representation of the A4 templates used for the handwriting tasks is provided in Appendix A. Each task was performed on a separate A4 sheet affixed with adhesive tape to the writing surface of a digital graphic tablet, positioned consistently before each task. Prior to every task, participants received brief verbal instructions, and a short explanatory text was displayed above each task on the template. For the research presented in this paper, we focused specifically on analyzing handwriting generated during the SR subtask of the third task, which involved the continuous writing of the lowercase cursive letter \"1.\".  
The handwriting was acquired with the digital graphic tablet Wacom(r) Intuos 4 XL (Wacom Europe GmbH, Krefeld, Germany) with a cordless pen (which resembles a standard ballpoint pen in shape and weight) that enables on-surface and in-air handwriting (sampling frequency = 200 Hz, pen accuracy \(\pm\) 0.25 mm, pressure levels = 2048). Custom-made acquisition software LabHand 0.7 [42] was used for the measurement recording. The recorded measurement files contain three signals: pen position (\(X\)(\(t\)) and \(Y\)(\(t\)) signals) and pen pressure (\(p\)(\(t\)) signal).
## Appendix B Sequential Exclusion Algorithm in Baseline-Unknown Case  
In the baseline-unknown case, the rover coordinates are estimated and both the phase and code observations contribute to ambiguity resolution. First determined are the ambiguities delivering different values in the first and the second candidates. The subset of observations, which are from the same satellites and on the same frequencies as these ambiguities, are denoted with \(\beta\). The exclusion is then performed within \(\beta\) in the following sequence:  
* The number of the excluded observations increases from 1 to \(N_{m}\).
* For the same number of excluded observations, code observations have the priority in exclusion.
* For the same observation type (code or phase), the observations with the lowest elevation angle have priority in exclusion.
* For the observations with the same elevation angle, the observation on L2 has the priority in exclusion.
* In case that exclusion of all the combinations in \(\beta\) mentioned above fail the ratio test, the observation with the highest exclusion priority is excluded to re-compute the subset (\(\beta\)). The exclusion restarts then from the beginning.  
In the sequential exclusion of ambiguity resolution, together with the different exclusion possibilities, the ratio test is also attempted without considering the float ambiguities from the previous epoch. In actual RTK processing, a threshold of the tested exclusion cases can be set to limit the computational time. In this contribution, it is set to 1000.
# A Secure Communication in IoT Enabled Underwater and Wireless Sensor Network for Smart Cities
## 5 Simulation and Results
### Performance Analysis of Tbrl with Dbr and R-ERP\({}^{2}\)R
#### 5.4.2 Packet Delivery Ratio  
The delivery ratio of TBRL against DBR and R-ER\(P^{2}\)R is shown in Figure 14. Both protocols hold the data packets till found the best suited next node whereas in TBRL, reliable nodes and paths are already known and sorted through IDFS. Therefore, delivery ratio of TBRL is high.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 6 Discussion
### Wearable Sensor Technology Validation  
It is important to highlight that in gait, balance, and ROM analysis there are gold standard assessments and instruments. These gold standard methodologies are often used to evaluate the performance of parameter quantification when using wearable devices. In balance analysis, a study concluded that there is a strong inverse correlation between the Balance Error Scoring System (BESS) and inertial sensor measurements [29]. However, another study used the BESS and a force platform to validate the use of smart devices and track center of gravity movement of the subject [32; 33]. They found a mean absolute error between 5.87% to 10.42% compared to the force platform measures. On the other hand, a study used the Tinetti Test, BESTest, an optoelectronic system, and a force platform to validate IMUs measurements [47]. They reported correlations between 0.5980 and 0.8658 for static exercises with eyes open or eyes closed when comparing the force platform measurements and the center of mass displacement estimation from the IMUs [47].  
In gait analysis, optoelectronic systems and pressure mats are the most common gold standard instruments used to validate wearable sensor measurements. High correlations between pace, postural, rhythm and phases parameters have been documented in the literature. On the other hand, that is not always the case for variability and symmetry measurements. A study conducted a detailed investigation to explain the poor agreement between parameters extracted from wearable devices and a pressure mat [37]. The study determined that the poor agreement was due to inherent differences between the two systems rather than an inability of the wearable sensor to measure the gait characteristics [37].  
In ROM analysis, a study concluded that although goniometry is a reasonably accurate method to measure joint angles in static situations, it is not precise to measure the angle of catch in individual patients [22]. However, there are studies that have evaluated the validity of IMUs and smart devices to measure joint angles against an optoelectronic system. These studies demonstrate that wearable devices are reliable to measure joint angles, where the error usually ranges between 1\({}^{\circ}\)-6\({}^{\circ}\)[34; 38; 39; 40; 41; 58].  
Studies included in this review that validated their results with gold standard methodologies show discrepancies related to assessment of gait, balance, and ROM. Some studies highly recommend the use of wearable sensors to assess balance; others report variable and not as strong results. However, they introduced the possibilities for future use of wearable devices and suggest potential improvements. Additionally, these results highlight the importance of caution when selecting a reference system for validation studies. Validity is important since it will help ensure that researchers truly measure gait, balance, and ROM in an accurate and objective manner.