esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiment and Results
### Results
Moreover, the recognition rate, precision, and F1-measure were lower in three-level EDESC than those in two-level EDESC; this is attributed to recognition errors that occurred between the two classes, wherein nonstressed and mildly stressed states were not easily distinguished. Detailed experiments were conducted to investigate the effect of intra-frequency self-attention and inter-frequency band mapping on emotional stress recognition in the 3 DCGSA method. Intra-frequency self-attention is a method of applying 3 DCGSA to only one frequency band instead of a combination of frequency bands. Equations (7) and (8) were applied, and the experimental results are presented in Table 5.  
The main characteristics of the simulation can be described as follows:  
* In the case of intra-frequency self-attention, the \(\gamma\) band had superior accuracy than other frequency bands. This experimental result is consistent with psychological studies [36] that reported that \(\gamma\) band activity is closely related to memory, learning, reminiscence, selective concentration, and high-level cognitive processing. * When comparing the results of the inter-frequency band mapping model with those of the intra-frequency self-attention method, the advantages of this framework could be clearly seen. Combining EEG features of all frequency bands, (\(\theta\), \(\alpha\), \(\beta\), \(\gamma\)), resulted in much better recognition performance compared to using only one frequency band. This means that combinations of frequency bands help to achieve the best results by making the most of complementary information. * When applying the RA method, using GRU instead of additive connection showed improved results in both intra-frequency self-attention and inter-frequency band mapping. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **F-Measure** \\ \hline RF-FS & 71.47 & 73.15 & 72.77 \\ EF-SVM & 73.21 & 73.30 & 72.92 \\
2D-AlexNet & 75.39 & 77.07 & 76.69 \\ HBRGUA & 77.24 & 77.33 & 76.95 \\ CRNN & 79.53 & 79.46 & 78.85 \\ SF3 DCNN & 80.62 & 80.72 & 80.60 \\ PLNTF & 81.24 & 82.92 & 82.54 \\ GTR & 83.32 & 83.25 & 82.64 \\
3 DRADNN & 85.04 & 84.85 & 84.43 \\ SFSCAN & 86.67 & 86.53 & 85.29 \\ SF3 DCRA & 88.15 & 88.25 & 87.13 \\
3 DCGSA & 90.12 & 90.21 & 89.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Stress recognition results using the three-level EDESC dataset. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & \multicolumn{2}{c}{**Recognition Accuracy (\%)**} \\ \hline  & **RA** & **GRU-RA** \\ \hline \(\theta\) & 84.95 & 87.06 \\ \(\alpha\) & 86.53 & 88.55 \\ \(\beta\) & 88.54 & 90.72 \\ \(\gamma\) & 90.07 & 92.33 \\ (\(\theta\), \(\alpha\), \(\beta\), \(\gamma\)) & 93.99 & 96.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison between intra-frequency self-attention and inter-frequency band mapping based on the DEAP dataset.
# Robotics in Gastrointestinal Endoscopy
## 2 Review of Robot Assisted Endoscopic Devices (Robot Assisted ESD System)
### Robotic-Driven Endoscope Locomotion  
Colonoscopy is a technically challenging procedure, as it is performed by manually pushing a semi-rigid endoscope through the tortuous colon lumen. Colonoscopy robots are being actively developed to reduce pain in patients, reduce the risk of perforation, and promote cecal intubation, regardless of the endoscopist's ability. Flexible self-propelled devices have been developed to reduce pain and discomfort during colonoscopy. They follow the loop smoothly without painful stretching the bowel, potentially avoiding the need for sedation. To reduce the risk of cross-infection, these endoscopes are being attempted to be single-use.  
The currently developed autonomous colonoscopy robot can be classified as follows, based on the actuation principle (Table 2): (1) using electro-pneumatic mechanisms, that is, the Aer-O-ScopeTM System, Endotics System, ColonoSight; (2) electromechanically actuated robotic colonoscopes, that is, the NeoGuide Endoscopy System, the Endoculus; (3) magnetic-actuated tethered robotic colonoscopes, that is, magnetic flexible endoscope, the Endo EU project capsule. Products that have been commercialized are the Aer-O-ScopeTM System, Endotics System, ColonoSight, and the NeoGuide Endoscopy System. Endoculus, magnetic flexible endoscope and the Endo EU project capsule are currently in the development stage. All commercialized products showed cecum intubation rate of over 90%, however, none of these robotic endoscopes has entered clinical routine yet [25; 26; 27; 28].  
Figure 6: EndoMODRA (Harvard University, Cambridge, MA, USA) [24]. Image of a distally-mounted robotic endoscopic module with integrated actuation and sensing. (**Bottom**) shows the view through the endoscope during an ex vivo dissection.
###### Abstract  
The backoff algorithm employed by the medium access control (MAC) protocol of the IEEE 802.15.4 standard has a significant impact on the overall performance of the wireless sensor network (WSN). This algorithm helps the MAC protocol resolve the contention among multiple nodes in accessing the wireless medium. The standard binary exponent backoff (BEB) used by the IEEE 802.15.4 MAC protocol relies on an incremental method that doubles the size of the contention window after the occurrence of a collision. In a previous work, we proposed the adaptive backoff algorithm (ABA), which adapts the contention window's size to the value of the probability of collision, thus relating the contention resolution to the size of the WSN in an indirect manner. ABA was studied and tested using contention window sizes of up to 256. However, the latter limit on the contention window size led to degradation in the network performance as the size of the network exceeded 50 nodes. This paper introduces the Improved ABA (I-ABA), an improved version of ABA. In the design of I-ABA we observe the optimal values of the contention window that maximize performance under varying probabilities of collision. Based on that, we use curve fitting techniques to derive a mathematical expression that better describes the adaptive change in the contention window. This forms the basis of I-ABA, which demonstrates scalability and the ability to enhance performance. As a potential area of application for I-ABA, we target wireless body area networks (WBANs) that are large-scale, that is, composed of hundreds of sensor nodes. WBAN is a major application area for the emerging Internet of Things (IoT) paradigm. We evaluate the performance of I-ABA based on simulations. Our results show that, in a large-scale WBAN, I-ABA can achieve superior performance to both ABA and the standard BEB in terms of various performance metrics.  
internet of things; wireless sensor networks; backoff algorithm; BEB; IEEE 802.15.4; MAC protocol; WBAN 2024
## 5 Discussion
### Discussion of Example 2  
Representing a configuration space with more than two dimensions is not an easy task. In the general case, it has the form of an n-dimensional parallelepiped with side lengths determined by the joint constraints. Example 2 considers a three-dimensional case. As dimensionality increases, the analytical mapping of spaces becomes complex. In such cases, it is believed that the proposed method is useful because the algorithm generates sets of points that form the map.  
For the robot from Figure 14, whose dimensions and joint constraints are given in Figure 15, with the help of the proposed algorithm and the created program, 10 different main regions (Figure 17) are obtained in which the robot has different qualities. For example, if the robot moves only in region 1 or 10, it will reach the target point only with an RR or an LL configuration type, respectively.  
Region 2 can be reached with both RR and RL configurations, and a transition between the two configurations is possible without the robot having to leave region 2--Figure 18. When transitioning from RR to RL, the robot passes through a relative singularity of type \(S_{0,3}\). This is also confirmed by Figure 19 where it is shown that region 2 is a subset of the region with singularities of type \(S_{0,3}\).  
Points in region 3 can be reached with three main configuration types: RR, RL and LR. A transition through singularities of type \(S_{0,2}\) and \(S_{0,3}\) is possible between the different types of solutions, as the robot stays in the same region.  
In the small region 4, solutions RR, RL and LL are possible. The transition between RR and RL is possible in the same area through \(S_{0,3}\) (see also Figure 19). However, the transition between RR and LL or RL and LL cannot take place until the gripper is out of that region. In Figure 19, it can also be seen that region 4 is not part of region \(S_{0,2}\).  
Region 5--RR and LL types of solutions are possible here and it is not possible to switch from one type to the other without leaving the region.  
Region 6--All solution types are possible as well as transitioning between them without leaving the zone. Analogous considerations are made for mirror zones 7,8,9 and 10.  
From Figure 19, conclusions are drawn regarding the possibility of realizing trajectories that pass through several regions. The robot links can move by changing different types of configurations between the areas that are simultaneously covered by \(S_{0,2}\) and \(S_{0,3}\); these are in region 3, and a large part of 6 and 8. A trajectory that goes from 1 to 10, through 5, or vice versa cannot take place. To go from region 10 to 1 or vice versa the robot must pass through a region covered by \(S_{0,2}\) and \(S_{0,3}\).
## 2 Theory
### AdaBoost  
A boosting algorithm enhances a weak learning algorithm in the sense of PAC (probably approximately correct) [17] to achieve high accuracy. AdaBoost is a boosting algorithm that creates a new classifier by sequentially training multiple weak classifiers. The goal is to have each subsequent classifier improve the classification accuracy of the previous one. Finally, the algorithm combines the outputs of all the classifiers using a weighted majority vote. In the case of binary classification, the training algorithm for AdaBoost is illustrated in Algorithm 1[18, 19].  
```
1:Given \(D=\{(x_{i},y_{i}):i=1,\dots,m\}\)
2:Initialize weights \(w_{i}=\frac{1}{m}\), \(i=1,2,\dots,m\)
3:for\(t=1\) to \(T\)do
4: Use weak hypothesis \(h_{t}(x)\) providing it with the distribution \(w\) over \(D\)
5: Compute the weighted error \(\epsilon_{t}=\sum_{i=1}^{m}w_{i}^{(t)}1_{\{h_{t}(x_{i})\
eq y_{i}\}}\) of \(h_{t}(x)\)
6: Compute \(\alpha_{t}=\frac{1}{2}\ln\frac{1-\epsilon_{t}}{\epsilon_{t}}\)
7: Update weights \(w_{i}^{(t+1)}=\frac{w_{i}^{(t)}\exp(-\alpha_{t}y_{i}h_{t}(x_{i}))}{Z_{t}}\), where \(Z_{t}=\sum_{i}w_{i}^{(t)}\exp(-\alpha_{t}y_{i}h_{t}(x_{i}))\) is a normalization factor
8:endfor
9:return\(H(x)=\text{sign}\Big{(}\sum_{t=1}^{T}\alpha_{t}h_{t}(x)\Big{)}\)
```  
**Algorithm 1** AdaBoost Algorithm  
In Algorithm 1, \(D\) is the dataset, \(y_{i}\) is the corresponding label vector with \(y_{i}\in\{-1,+1\}\), and \(T\) is the number of weak classifiers or base learners to combine in the ensemble. The weights, \(w_{i}\), are initialized as equal to \(\frac{1}{m}\), where \(m\) is the size of the dataset. At each iteration, \(t\), a weak classifier, \(h_{t}(x)\), is selected that minimizes the weighted error on the dataset. The weighted error, \(\epsilon_{t}\), is then used to compute the weight, \(\alpha_{t}\), of the weak classifier in the final ensemble. The weights of the data points, \(w_{i}\), are updated according to the misclassification of the weak classifier.
# Dentin Bond Strength of Calcium Silicate-Based Materials: A Systematic Review of In Vitro Studies
## 4 Discussion
### Repair Materials  
The introduction of mineral trioxide aggregate (MTA) marked a significant advancement in endodontics, offering attributes like biocompatibility, low solubility, radiopacity, hard tissue inductive and conductive activity, and the ability to set in a wet environment [39]. MTA finds diverse clinical applications, including apexification, vital pulp therapy, the repair of perforations and resorptions, regenerative endodontics, and root canal obturation [44]. However, drawbacks include a prolonged setting time, handling challenges, tooth discoloration, and high cost [39].  
Biodentine, with comparable mechanical properties and clinical applications to MTA, is a fast-setting calcium silicate-based product recommended as a dentin substitute. Notably, it offers superior color stability compared to MTA, contributing to better aesthetic outcomes [39].  
CSCs were introduced to address MTA's limitations in endodontics, serving as a root repair material and canal sealer. EndoSequence Root Repair Material (ERRM), a hydrophilic, insoluble, radiopaque calcium silicate-based nano-bioceramic material, stands out for its bioactive and bioresorbable nature. ERRM actively participates in the dynamic process of bone formation and resorption, promoting bone regeneration and facilitating the repair of periodontal tissue [13].  
Studies, such as those by [PERSON] et al. [39], [PERSON] et al. [55], [PERSON] et al. [56], [PERSON] et al. [57], and [PERSON] et al. [35], have consistently shown that ERRM and Biodentine exhibit higher bond strength values compared to MTA. The nanosphere particles of ERRM, contributing to a greater surface area in contact with tissue fluids, and its consistent and favorable handling properties are cited as potential factors for its superior results [39]. Biodentine's enhanced bonding strength is attributed to the addition of calcium chloride to its composition, which acts as a catalyst for tricalcium silicate hydration. This process leads to rapid crystallization and the formation of areas with heterogeneous density, facilitating ion and water diffusion, thereby improving biomineralization and bond strength [39; 57].  
Recent studies have explored the incorporation of additives such as hydroxyapatite, collagen, and calcium phosphate to enhance the bioactive potential of calcium silicate-based materials [58].
###### Abstract  
The simultaneous aggregation of multiple component carriers (CCs) for use by a base station constitutes one of the more promising strategies for providing substantially enhanced bandwidths for packet transmissions in 4 th and 5 th generation cellular systems. To the best of our knowledge, however, few previous studies have undertaken a thorough investigation of various performance aspects of the use of a simple yet effective packet scheduling algorithm in which multiple CCs are aggregated for transmission in such systems. Consequently, the present study presents an efficient packet scheduling algorithm designed on the basis of the proportional fair criterion for use in multiple-CC systems for downlink transmission. The proposed algorithm includes a focus on providing simultaneous transmission support for both real-time (RT) and non-RT traffic. This algorithm can, when applied with sufficiently efficient designs, provide adequate utilization of spectrum resources for the purposes of transmissions, while also improving energy efficiency to some extent. According to simulation results, the performance of the proposed algorithm in terms of system throughput, mean delay, and fairness constitute substantial improvements over those of an algorithm in which the CCs are used independently instead of being aggregated.  
efficiency; multiple component carriers; proportional fair; 4G/5G cellular systems Article
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### Mobile Band-Based Technologies
#### 2.1.4 5G  
5G is the latest innovation in mobile network technologies that is currently being developed. It is expected to be a \"revolutionizing technology\" for the mobile world as well as for IoT communications [15]. However, for now, few to no explanations exist on how to integrate the high-speed technology of 5G with the low power consumption that is needed for IoT applications. Indeed, 5G is meant to enable ultra-high-speed communications, using high frequency (e.g., 60 GHz) and wide bandwidth [16]. It aims to offer a very high data rate (1-10 Gbps). This does not seem applicable out-of-the-box when you consider energy constrained IoT objects. Moreover, this technology is not available right now for use outside of test labs. 5G should begin to be commercially deployed and available around 2020.  
As for now, 5G aims at two things: massive Machine-Type Communications (mMTC), and critical Machine-Type Communications (cMTC) leveraging Ultra Reliable and Low Latency Communications (URLLC). The requirements for cMTC are too strict for IoT, while mMTC was developed for IoT. LTE-M and NB-IoT already fulfill mMTC 5G requirements, so there is no dedicated solution other than eMTC and NB-IoT that is planned to be specified for 5G IoT [17].
# Enhancing Reactive Ad Hoc Routing Protocols with Trust
## 3 The Proposed Method of Trust-Aware Reactive Ad Hoc Routing
### Performance Metrics
#### 3.4.3 Average and Maximum Delivery Delay of RREQ Messages  
As the TARA method delays the RREQ messages through nodes with low PDR and thus penalizes them during route discovery to improve the _PDR_ of the paths in the network, it, on the other hand, prolongs the time of the new route discovery. We call it _route discovery delay (RDD)_. So metrics \(PDR_{avg}\) and \(RDD_{avg}\) are counteractive: improvement of one can deteriorate the other. In order to investigate these dependencies, we define two metrics:  
* average route discovery delay (\(RDD_{avg}\));
* maximum route discovery delay (\(RDD_{max}\)).  
Both metrics are measured in _TU_s. The reason we picked two metrics of \(RDD\) instead of one is that, for some topologies, the maximum reaches really impractical values, thus some routes cannot be found. This could be a problem for network applications when the routes to all nodes need to be found. For these purposes, it is more relevant to consider \(RDD_{max}\). On the other hand, some network applications do not require to communicate with all nodes (for example, it is expected that some sensors are lost or no longer operational), and then the \(RDD_{avg}\) is a more useful metric.
## 2 Materials and Methods  
This study is a narrative review. PubMed and Google Scholar were searched for the terms \"Stingers\" and \"Burners\" without using Boolean operators, and the resulting article titles were screened. Exclusion criteria were centered on language, recency, and relevance. Mainly literature published in English within the past five years was reviewed, along with their respective bibliographies. Foundational literature, regardless of the year of publication, was included to provide basic and prominent knowledge and to draw comparisons between older and new developments. Furthermore, we reviewed publications focused primarily on adult athletes. However, the studies had relevant information about adolescents as well. Therefore, reference selection was purely illustrative based on the topic the publication addresses, comprehensiveness, and recency. The types of literature reviewed included case reports, narrative reviews, systematic reviews, retrospective studies, prospective observational studies, expert opinions (including a modified Delphi consensus), and book chapters.  
A narrative interpretation of the literature and the clinical knowledge of the authors who have worked with adolescent, collegiate, and professional athletes was compiled and composed for informative purposes. Since we aimed to consolidate the latest information for the management of stingers, a review of current management and rehabilitation protocols was also similarly constructed using recent publications. Most information was repeated across the literature, except for material about recent advances in management protocols. All literature had level IV and V evidence, mainly due to a lack of system  
Figure 2: Various mechanisms of injury causing stingers: (**A**) stretch injury with depressed shoulder and laterally hyperextended neck, (**B**) direct injury at ErbB’s point, and (**C**) lateral flexion and hyperextension of the neck compressing the brachial plexus. Used with permission from Clinical Tree Publications, DeLee, Drez, & Miller’s Orthopaedic Sports Medicine, Orthopedics, Stingers, Online publication, 27 January 2024. Stingers—Clinical Tree.  
atic reviews and randomized control trials, which are difficult to perform because of the complexity and functional consequences of the injury [10].
## 5 Results
### Topology 1: Results
#### 5.1.1 Scenario 1  
In Scenario 1, a delay of 100 ms was configured on each link h26->h30->h27, and a delay of 50 ms was configured from h26->h29->h27. The static method was configured to select the path h26->h29->h27.  
Average response times obtained from all routing methods were identical except at bundle rate 10 bundles/s, as depicted in Figure 3a. Since the sending rate was within the bandwidth capacity of the links, the average response time looks identical, even though the SNN algorithms selected 10% of the time h30 for exploring globally optimal links. Since there was no packet losses configured, the average packet loss percentage was also negligible and similar for all the routing methods, except at 10 bundles/s, as depicted in Figure 3b. Throughput was also similar for all the routing methods in this scenario, as in Figure 3c.  
Figure 4 depicts the link selection at Node h26 by the SNN algorithms according to their optimization goals in Scenario 1. All SNN algorithms selected h29 more times than h30 since the delay was better on the link connecting from h26 to h29 when compared to h30.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **h26-h29** & **h29-h27** & **h26-h30** & **h30-h27** \\ \hline Scenario 1 & 50 ms & 50 ms & 100 ms & 100 ms \\ Scenario 2 & 50 ms 5\% loss & 50 ms 5\% loss & 100 ms & 100 ms \\ Scenario 3 & 50 ms loss 5\% & 50 ms loss 5\% & 120 ms 512 Kb rate limit & 120 ms 512 Kb rate limit \\ Scenario 4 & 50 ms & 50 ms & 120 ms 640 Kb rate limit & 120 ms 640 Kb rate limit \\ Scenario 5 & 120 ms & 120 ms & 50 ms 640 Kb rate limit & 50 ms 640 Kb rate limit \\ Scenario 6 & 200 ms & 200 ms & 50 ms 640 Kb rate limit & 50 ms 640 Kb rate limit \\  & & & loss 10\% & loss 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Delay and packet-loss configurations tested with Topology 1.  
Figure 3: Routing performance: Scenario 1. (**a**) Average response time; (**b**) Packet loss ratio (%); (**c**) Throughput.
# Introduction to Radar Scattering Application in Remote Sensing and Diagnostics: Review
## 4 Radar and Remote Sensing Applications
### Forests Mapping  
Forests play a vital role in providing natural resources: they absorb carbon dioxide from the atmosphere and release oxygen, they provide natural habitats and food to animals, and they provide wood to humans. To monitor forests, radar and remote sensing techniques are employed, which allows acquiring very valuable data such as forest areas of disturbance [46], reckoning of land surface and budding volume of trees [47], and the measurement of the standard canopy height in the forests [48].