esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis and Results
### Path Model Assessment
#### 5.2.2 Assessment of Structural Model  
The structural model is evaluated following the validation of the measurement model or outer model as a valid and reliable model. The method would include assessing the model's likelihood for prediction as well as the correlations between the variables [113]. To examine the model's hypothesized relationships, the structural model is essentially assessed. The coefficient of determination (R\({}^{2}\)) of endogenous constructs, effect size (f\({}^{2}\)), and path coefficients are the three parameters that determine how well the model can predict the future. The R2 score shows how much variance in dependent variables the related model can explain. The R\({}^{2}\) ranges from 0 to 1, with values of 0.75, 0.50, and 0.25 denoting considerable, moderate, and weak predictive accuracy, respectively, in accordance with [PERSON], [PERSON] [113]. Table 11 presents the results of the PLS algorithm analysis. It could be observed that 64.7% of the variance in Cybersecurity Leadership is explained by SE, GF, OFT, CR, PV, SR, and RE. Moreover, 53.9% of the variance in cyberattack prevention is explained by CL, SE, GF, OFT, CR, PV, SR, and RE. In addition to determining the endogenous construct's R\({}^{2}\) values, the latent predictor construct's effect size was calculated. Table 11 below presents the R\({}^{2}\) value for each endogenous construct.  
To evaluate whether the omitted construct significantly affects the endogenous constructions, the effect size (2) is used. [PERSON] [65; 115] stated that values of 0.02-0.14, 0.15-0.34, and larger than 0.35, respectively, imply modest, moderate, and substantial impacts. The f\({}^{2}\) value for each path is displayed in Table 12 below. The effect size varies from a low of 0.005 (Perceived seventy \(\rightarrow\) Cybersecurity prevention) to a maximum value of 0.089 (Self-Efficacy \(\rightarrow\) Cybersecurity prevention).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **(CP)** & **(GF)** & **(OFT)** & **(PS)** & **(PV)** & **(RC)** & **(RE)** & **(CL)** & **(SE)** \\ \hline (CP) & 0.830 & & & & & & & \\ (GF) & 0.522 & 0.732 & & & & & & \\ (DFT) & 0.483 & 0.482 & 0.873 & & & & & \\ (PS) & 0.372 & 0.423 & 0.340 & 0.710 & & & & \\ (PV) & 0.301 & 0.315 & 0.466 & 0.228 & 0.779 & & & \\ (RC) & 0.488 & 0.485 & 0.712 & 0.256 & 0.437 & 0.839 & & \\ (RE) & 0.554 & 0.505 & 0.451 & 0.598 & 0.337 & 0.432 & 0.717 & \\ (CL) & 0.710 & 0.601 & 0.636 & 0.443 & 0.481 & 0.622 & 0.624 & 0.828 & \\ (SE) & 0.448 & 0.408 & 0.531 & 0.427 & 0.522 & 0.488 & 0.523 & 0.616 & 0.820 \\ \hline \hline \end{tabular} Cybersecurity Prevention (CP), Government Frequent Alerting (GF), Organization Training (OFT), Perceived Severity (PS), Perceived Vulnerability (PV), Response Cost (RC), Response Efficacy (RE), Cybersecurity Leadership (CL), Self-Efficacy (SE).  
\end{table}
Table 10: Cornell–Larcker criterion.
## 3 Methodology
### Obtain the Reconstructed Phase Vector from a CPPCA Solution  
The gradient of the log-likelihood (Equation (16)) with respect to \(W^{*}\) can be deduced as [35]:  
\[\frac{\partial\mathcal{L}(\Theta)}{\partial\mathbf{W}^{*}}=-M\Big{(}\mathbf{ C}^{-1}\mathbf{S}\mathbf{C}^{-1}\mathbf{W}-\mathbf{C}^{-1}\mathbf{W}\Big{)}=0 \tag{24}\]  
where the superscript \(*\) stands for conjugate operator. There are three possible solutions for Equation (24) when \(\mathbf{C}^{-1}\) exists. The first one is \(\mathbf{W}=\mathbf{0}\). It does not make any sense, as it indicates a zero observation vector. The second is \(\mathbf{W}\
eq\mathbf{0}\), \(\mathbf{C}=\mathbf{S}\). On the ground that there is no chance to obtain an infinite number of samples, it would not take place in practice. The third case is \(\mathbf{W}\
eq\mathbf{0}\), \(\mathbf{C}\
eq\mathbf{S}\), and all potential solutions for \(\mathbf{W}\) may be written as:  
\[\mathbf{W}_{ML}=\mathbf{U}_{q}\Big{(}\Lambda_{q}-\sigma^{2}\mathbf{I}\Big{)}^ {1/2}\mathbf{R} \tag{25}\]  
where \(\mathbf{U}_{q}=\big{[}\mathbf{u}_{1},\mathbf{u}_{2},...,\mathbf{u}_{q}\big{]}\) and \(\Lambda_{q}=\mathrm{diag}\big{(}\lambda_{1},\lambda_{2},...,\lambda_{q}\big{)}\) are composed of the principal eigenvectors and corresponding eigenvalues of \(\mathbf{S}\), respectively, and \(\mathbf{R}\) is an arbitrary orthogonal rotation matrix.  
Figure 1: The pseudo-codes of the proposed method.  
As it only requires the eigenvector with the largest eigenvalue to reconstruct interferometric signals, Equation (25) can be rewritten as:  
\[\mathbf{w}=\mathbf{u}_{1}\Big{(}\lambda_{1}-\sigma^{2}\Big{)}^{1/2}r_{1} \tag{26}\]  
where \(r_{1}\) represents the rotation parameter. Equation (26) and Equation (9) have a similar mathematical form. As a result, \(\mathbf{w}\) can be considered to be reconstructed signals. Note that only the phase component is concerned in the phase reconstruction. The reconstructed phase based on the CPPCA method consists of three parts: the phases of \(\mathbf{u}_{1}\), \(\lambda_{1}\) and \(r_{1}\). The phase of \(\mathbf{u}_{1}\) is the expected interferometric phase, and the subsequent two can be eliminated in the differential process.  
In the traditional EVD approach, the eigenvalue can be directly used to evaluate the reliability of a phase reconstruction result. In the CPPCA case, the eigenvalue can be obtained by separating \(\mathbf{u}_{1}\) from the estimate of \(\mathbf{w}\). Unfortunately, such a separation operation is time-consuming. In [19], a so-called goodness of fitness (GoF) factor is proposed for the reliability assessment purpose. However, it is defined based on the upper triangular matrix of \(\hat{\mathbf{C}}\). In this paper, in order to further reduce the computational cost, a pseudo-GoF (PGoF) factor is designed:  
\[\gamma_{CPPCA}=\frac{1}{N-1}\sum_{n=1}^{N-1}e^{j(\varphi_{n}-\varphi_{n+1})}e^ {j(\theta_{n}-\theta_{n+1})} \tag{27}\]  
where \(\phi_{n}\) stands for the observed phase on the \(n\)-th SAR image.
## Appendix A Proof of Theorem 3  
Let us consider the Lyapunov function candidate (51); then, its temporal derivative is given by  
\[\dot{V}\big{(}\mathbf{e},\mathbf{\dot{\zeta}},\mathbf{\ddot{W}}(t),\mathbf{\ddot{b}}(t),t\big{)}= 2\mathbf{e}^{\top}\mathbf{P_{1}}\mathbf{\dot{e}}+2\mathbf{\dot{\zeta}}\mathbf{P_{2}}\mathbf{\dot{\zeta}} +\frac{1}{\gamma_{1}}\mathbf{\ddot{W}}(t)^{\top}\mathbf{\dot{W}}(t)+\frac{1}{\gamma_{2} }\mathbf{\ddot{b}}(t)\mathbf{\dot{b}}(t). \tag{101}\]  
Substituting Equations (35) and (41) in (101), we obtain  
\[\dot{V}= 2\mathbf{e}^{\top}\mathbf{P_{1}}\Big{(}\mathbf{A}\mathbf{e}+\mathbf{B}\Big{[}a_{1} \zeta_{1},+a_{2}\zeta_{2}+\mathbf{W}(t)^{\top}\mathbf{\Psi}\big{(}\mathbf{\dot{\zeta}} \big{)}+\mathbf{\dot{b}}(t)u(\mathbf{\zeta})+\mathbf{K_{id}}^{\top}\mathbf{\ddot{\zeta}}-f_{2} \left(\zeta_{1},\zeta_{2},t\right)\Big{]}\Big{)} \tag{102}\] \[+2\mathbf{\dot{\zeta}}^{\top}\mathbf{P_{2}}\Big{(}\mathbf{A}\mathbf{\dot{\zeta}} +\mathbf{B}\mathbf{\ddot{W}}(t)u(\mathbf{\zeta})+\mathbf{B}\mathbf{\ddot{W}}(t)^{\top}\mathbf{\Psi} \Big{(}\mathbf{\dot{\zeta}}\Big{)}+\mathbf{B}\mathbf{W_{0}}\mathbf{\ddot{\Psi}}+\mathbf{B}\mathbf{\dot{ f}}_{\rm e}(\mathbf{\zeta},t)-\mathbf{B}\mathbf{K_{id}}^{\top}\mathbf{\ddot{\zeta}}\Big{)}\] \[+\frac{1}{\gamma_{1}}\mathbf{\ddot{W}}(t)^{\top}\mathbf{\dot{W}}(t)+\frac{ 1}{\gamma_{2}}\mathbf{\ddot{b}}(t)\mathbf{\dot{b}}(t).\]  
The control signal \(u(\mathbf{\zeta})\) is chosen as in (42); then, substituting in the temporal derivative of the Lyapunov function (102), we obtain  
\[\dot{V}= \mathbf{e}^{\top}\Big{(}\mathbf{A_{1}}^{\top}\mathbf{P_{1}}+\mathbf{P_{1}}\mathbf{A_ {1}}\Big{)}\mathbf{e}+\mathbf{\dot{\zeta}}^{\top}\Big{(}\mathbf{A_{2}}^{\top}\mathbf{P_{2}}+ \mathbf{P_{2}}\mathbf{A_{2}}\Big{)}\mathbf{\dot{\zeta}}+2\mathbf{e}^{\top}\mathbf{P_{1}}\mathbf{B}\mathbf{K _{id}}^{\top}\mathbf{\ddot{\zeta}}+2\mathbf{\dot{\zeta}}^{\top}\mathbf{P_{2}}\mathbf{B}\mathbf{ \ddot{W}}(t)^{\top}\mathbf{\Psi}\Big{(}\mathbf{\dot{\zeta}}\Big{)} \tag{103}\] \[+2\mathbf{\dot{\zeta}}^{\top}\mathbf{P_{2}}\mathbf{B}\mathbf{W_{0}}^{\top}\mathbf{ \ddot{\Psi}}+2\mathbf{\dot{\zeta}}^{\top}\mathbf{P_{2}}\mathbf{B}\mathbf{\ddot{f}}_{\rm e}(\mathbf{ \zeta},t)+2\mathbf{\dot{\zeta}}^{\top}\mathbf{P_{2}}\mathbf{B}\mathbf{\ddot{b}}(t)u(\mathbf{\zeta} )+\frac{1}{\gamma_{1}}\mathbf{\ddot{W}}(t)^{\top}\mathbf{\dot{W}}(t)+\frac{1}{\gamma_ {2}}\mathbf{\ddot{b}}(t)\mathbf{\dot{b}}(t),\]  
where matrices \(\mathbf{A_{1}}\) and \(\mathbf{A_{2}}\) are defined in (44).
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.10 pCoCoA  
[PERSON] et al. highlight the issues of CoCoA+ in [8] and propose pCoCoA--a precise congestion control algorithm to solve these issues. The proposed mechanism is based on two major elements:  
1. A method for linking requests to responses precisely even in case of retransmissions
2. Several modifications to the estimation algorithm of RTO  
For linking the requests to responses precisely, the CoAP option of transmission counter (TC) is used. This links the ACK message of each transmission to its corresponding CON message. The TC value is updated even for retransmissions; hence it also detects spurious retransmissions. For the case of spurious retransmission, pCoCoA sets up a flag to consider it for future RTO computations.  
To avoid vanishing of RTTVAR variable due to similar RTT sampling in a short period of time, max mean deviation of RTO is estimated. Therefore, issues because of sudden RTT variation are avoided by using max mean deviation which takes instead more time to decrease, thus decrease of final RTO value is limited. In addition, in the case of spurious transmissions, SRTO estimator grows faster due to increased weight of RTTVAR, which helps in limiting the successive spurious transmissions.
## 4 Case Study of Punjab OSM Data
### Results
#### 4.2.2 Attribute Completeness  
The model (Figure 3) processed two datasets to identify the developments and current status of attribute completeness. Two tag attributes \"name\" and \"maxspeed\" were assessed. The old and recent data of OSM have been compared, and the results are presented in Table 2. This table depicts statistical information about the length of the feature type and the percentage of features with the \"name\" attribute. This revealed some interesting facts that the community in India is growing and focused on contributing to OSM. In spite of the increase in the total length of the OSM network by 4,333,843.6582 m, the length of certain network classes such as living_street, primary, secondary, has decreased. In addition, roads and unclassified classes have vanished from the recent network, whereas new classes have emerged from it, e.g., motorways, track grades and an unknown class type. The thorough analysis suggested that the motorway class was wrongly allocated to the network.  
Figure 6: Class-wise network statistics of OpenStreetMap (OSM) data till January 2016 and February 2017.  
The second parameter analyzed was the \"maxspeed\" tag, and it has been found that only 4 to 5% of features have \"maxspeed\" tag information, which makes it unsuitable for navigation without pre-processing.
# Analysis of Vibration Characteristics of the Grading Belt in Wolfberry Sorting Machines
## 2 Materials and Methods
### Transmission Modeling and Principles
#### 2.2.2 Working Principle and Existing Issues  
The working principle of this screening device is to sort the fresh wolfberries in terms of their transverse diameter size by controlling the size of the gap of the grading belt. Meanwhile, the overall dimensions of the entire machine need to be determined in accordance with the number of belts on the screening surface. The greater the number of belts, the wider the frame, and the higher the productivity of the classifier, as depicted in Figure 2. From the middle to the two sides, the parallelism deviation of the axes of the front and rear belt pulleys gradually increases and moves along the tracks at different angles, as depicted in Figure 3. Owing to the presence of the skew angle, the belt pulleys of (n - 1) groups are all misaligned, resulting in intense lateral vibration in the transmission system, and this is the primary reason for generating noise and affecting the service life of the flexible belt [14; 15]. Lateral vibration exerts a considerable influence on transmission and directly affects the smooth operation of the classifier. In order to eliminate the impact of lateral vibration, the structure of the belt needs to be optimized.  
Figure 1: Overall Structure of the Fresh Goji Berry Grading Machine: 1. Orientation Device; 2. Grading Belt; 3. Main Frame; 4. Pulley; 5. Large Fruit Discharge Unit; 6. Small Fruit Discharge Unit.  
The skew angle of the grading belt is expressed by the following formula:  
\[\theta_{n}=\tan^{-1}\frac{n(d_{\max}-d_{\min})}{l} \tag{1}\]  
In the formula:  
\(\theta_{n}\) (n = 1, 2, 3...)--The deviation angle of the nth belt from the middle to either side of the belt;  
\(d_{max}\)--Maximum belt gap size, 20 mm;  
\(d_{min}\)--Minimum clearance dimension of the belt, 5 mm;  
--The center distance of the pulley, 2200 mm.  
\(\theta_{n}\) is related to the quantity of belts and the center distance. On the basis of comprehensive consideration to ensure the smooth transmission of belts and the volume of the equipment, the value range of the grading belt is determined as 4\({}^{\circ}\) to 6\({}^{\circ}\). Through optimization and experiments, the optimal angle is selected therefrom.  
Figure 3: Schematic diagram of the belt deflection angle calculation.  
Figure 2: Schematic diagram of the transmission mechanism.
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 5 Results
Although every individual score only partly contributes to the final set-similarity score, one low score alone affects the overall set-related score. For the final decision, we applied a conservative threshold that corresponds to a false acceptance rate of 3% (in every configuration). The focus on false acceptance avoidance leads to a relatively low accuracy ratio (67.7%). The overall score is obviously dependent on the quality of individual data analysis streams. Let us observe the average accuracy (GAR at 3% FAR) of some individual streams and the image segmentation step obtained in the experiments:  
* accuracy of the single-image car type classification stream was 75.1%,
* single-image car model classification accuracy was 66.3%, and
* accuracy of the semantic segmentation (a subjective evaluation of the proper detection of the car mask region) was 89.7%. It is clearly visible that a proper detection of the object's ROI in the image allows for the improvement of the classification accuracy of the entire approach. Let us illustrate this fact with an example. In Figure 9, two different cars of the same type and model are compared two times--by matching entire images (the left pair of images) and by matching the previously detected object ROIs (the right pair of images). We expect that the proper similarity score for the same view of the same car model should be rather high (>0.5). For the set of images on the left, we got a low similarity score of 0.190 (the same car model under a slightly different view but with a very different background), while for the ROI image pair on the right side the score was 0.492 (where the background was eliminated in ROI masked images).
# Precision Diagnosis of Glaucoma with VLLM Ensemble Deep Learning
## 5 Results  
### Refuge Dataset  
We conducted experiments using a test dataset comprising 40 glaucoma images and 360 normal images. The results can be seen in Table 2. Using only the original fundus images, we achieved an accuracy of 0.9725, a sensitivity of 0.825, and a specificity of 0.9889. Compared to the glaucoma classification results based solely on fundus images, significant results were obtained with VROI data (sensitivity 0.8500) and JET (sensitivity 0.8750). The final ensemble results showed an accuracy of 0.9875, a sensitivity of 0.9000, a specificity of 0.9972, and an AUC of 0.9754. This represents an 8% increase in sensitivity and a 1% improvement in overall accuracy compared to retinal fundus images.  
In the case of MAKO, although the classification accuracy for normal data increased (specificity 0.9889 to 1.0000), the results were unique compared to other data as the diagnosis of glaucoma decreased. Among the 40 glaucoma images used in the test, four images were not correctly classified by any of the eight models. Consequently, even the ensemble model did not improve the classification of these images.
## 2 Wireless Communication for Maritime Applications
### Communication between Sensors and Relays
#### 2.1.2 Bluetooth Low Energy  
According to Bluetooth Special Interest Group (SIG), around 40 billion Bluetooth devices are expected to be operating in 2021. The main driver of the latest growth in Bluetooth adoption is the IoT [12]. It operates in the ISM band of 2.4 GHz (therefore, it is shared with other applications such as WLANs, microwave ovens, and medical devices) [12].  
To improve upon Bluetooth's power consumption figure, Bluetooth Low Energy was created. This is achieved by having a device inactive most of the time (over 99% of it). With this, battery lifespans on the order of magnitude of the year are possible, albeit with low data rates being achieved [12]. The range of BLE is around 100 m [10] and the achievable data rate at that range is 125 kbps [10].
## 5 Technical Tips for Performing Effective Superselective cTACE
### Importance of the Order of Embolization of Each Tumor Feeder  
When multiple feeding branches are present, the order of embolization of each feeder is key to improve the therapeutic effect of cTACE. The main tumor feeder should be embolized last because dense retention of iodized oil and contrast material disturbs the depiction of the residual tumor stain and other small feeders on digital subtraction angiography performed immediately after cTACE. Additionally, embolic materials injected into the main tumor feeder are sometimes pushed back by the reversed flow through the minor tumor feeder when the flow of the main tumor feeder becomes near stasis [74]. In tumors partially fed by an extrahepatic collateral supply, the feeding branch of the extrahepatic artery should also be embolized first, if possible, because arterial blood flow to tumors through the extrahepatic artery immediately increases when the hepatic arterial flow is stopped [75]. Moreover, each tumor feeder should also be embolized from distal to proximal in order to avoid inadvertent occlusion of the distal tumor feeders with overflowing embolic materials during embolization of the proximal tumor feeder [74]. However, feeders that are considered difficult to catheterize, such as the caudate artery or tiny tumor feeders arising from extrahepatic arteries, should be embolized first, because the performance of a microcatheter-microwire system is gradually deteriorated during the procedure [74].
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 7 Discussion  
We have proposed a new method for the construction of the shortest path through a 2D field of obstacles of arbitrary shapes and positions. The novelty of this method resides in part in the definition of a new type of graph that is simpler than previously considered graphs in that its edges are a concatenation of many edges and nodes defined in previously used graphs, such as those of Liu and Arimoto. Thus, this lower cardinality of our graphs evidently reduces the required calculations. Furthermore, our topological analysis of the obstacles revealed that many path segments can be discarded because they can actually never be part of the shortest paths.  
In this work, we present a new theorem, which we have discovered, that allows us to disregard one of the steps that is normally performed in the A* algorithm; this theorem  
Figure 18: Maps of terrains with shortest paths.  
applies for all graphs in which the triangle inequality holds. We subsequently described a new variant of the A* algorithm that generates the shortest path, based on this theorem and our graph definition.  
The efficiency of our method is demonstrated by the construction of the shortest path for some real-world terrains that correspond to mountainous regions, represented by digital elevation maps. The computation times for these examples, on a small laptop, are seen to be less than 0.6 s in all cases.
## 1 Introduction
This method enables the evaluation of facial morphological changes in pediatric patients, the analysis of craniofacial pathologies and asymmetries, and the detailed assessment of soft tissue structures in individuals undergoing oral surgery [11; 13]. The Frankel maneuver (FM) is a diagnostic procedure that simulates Class I occlusal relationship in patients with Class II malocclusion. If a soft tissue profile changes from a convex toward a less convex or straight profile, which is more esthetically pleasing, functional orthodontic appliances can be utilized to encourage forward mandibular growth in peak pubertal growth. Recording the initial soft tissue profile and comparing it with the end result after treatment facilitates a more accurate evaluation of post-treatment outcomes [13]. The effects of the Twin Block appliance are based on numerous studies, which showed that a longer and relatively constant change in the position of the lower jaw, through the use of the appliance and function, triggers a neuromuscular proprioceptive response. This response stimulates bone remodeling to establish and maintain a new balance and a stabilizing position of the teeth [5; 7; 14]. Additionally, it was early proven in experimental animals that the skeletal effect exists exclusively in growing and developing animals, while in adolescent and adult animals, the effect is purely dental due to the resistance and stability of the temporomandibular joint [14]. This is supported by more recent research on functional appliances, which show a significantly greater effect on mandibular growth when applied during the peak of pubertal growth [15; 16; 17]. The hypothesis proposed was that Twin Block appliance therapy will improve skeletal, dentoalveolar, and soft tissue characteristics in patients with Class II malocclusion, leading to reduced skeletal discrepancy between the maxilla and mandible, improved facial profile, and enhanced esthetic outcomes.