esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Critical Gap for Pedestrian Crossing Using Fuzzy Logic System
## 5 Defuzzification Step  
The final step of the optimization process through the fuzzy control system is the process of the defuzzification. The defuzzification process is carried out based on the membership function of each individual as well as the computation of their decisions. If the performance is not acceptable according to the assessment criteria, then some fuzzy rules will need to be modified then re-run.  
Figure 7 below shows a graphical representation of the defuzzification process. The results of the fuzzy system are shown in Figure 8 and Table 3. The results show that the critical gap values obtained using the proposed fuzzy logic system and using the input data ranges from 0.987 s to 11.00 s with an optimal value of 6.0 s. For the accepted gaps, the fuzzy logic system predicts an optimum value of 1.92 s while the range extends from 0.214 s to 7.5 s. The number of rejected gaps ranges from 0.892-4.0 with an optimum value of 1.93. Finally, the range of length of rejected gap is from 0.144 to 3.4 s with an optimum value of 0.554 s. These values are within acceptable span when compared with the input values. The outcomes look reasonable and are in agreement with findings from previous studies that applied conventional analytical methods. For example, some studies [2; 32; 33; 34] estimated values of the critical gap to be 3-8 s. [39] reported values of critical gap ranges from 5.75 s and 6.25 s using conventional statistical methods. Accordingly, the predicted critical gap values can be accepted as conceivable values.  
If-Then conditions  
Figure 7: Defuzzification process.  
\begin{table}
\begin{tabular}{c} \hline
**Fuzzy Rules (IF-THEN Rules)—Relationship between Inputs and Output** \\ \hline If (Accepted-gap is Low) and (Rejected-gap is Low) and (Length-of-Reject-gap is Low) then (Critical-gap is Low) (1) \\ If (Accepted-gap is Medium) and (Rejected-gap is Low) and (Length -of-Reject-gap is High) then (Critical-gap is High) (1) \\ If (Accepted-gap is Low) and (Rejected-gap is High) and (Length -of-Reject-gap is High) then (Critical-gap is High) (1) \\ \hline \end{tabular}
\end{table}
Table 2: A few examples of IF-THEN rules that have been used as part of the proposed fuzzy decision.
# Real-Time Performance Measurement Application via Bluetooth Signals for Signalized Intersections
## 2 Materials and Methods
### General Principles of Real-Time Performance Measurement Application via Bluetooth Signals
In addition, all sensors and components used in the designed method are connected to a closed-circuit communication system. All data detected from the sensors are transferred to the server belonging to the system owner authority via the closed-circuit communication channel. All calculations, data, and monitoring are carried out through the server. All detected data are backed up on the server. The excess of Bluetooth signals can have a negative effect on data security and interference. In order to prevent this situation from occurring in crowded traffic, some precautions are taken to minimize these effects in the method designed. Data are matched with multiple sensor combinations. The Bluetooth signal detected at any intersection arm is expected to leave the intersection from any of the Bluetooth sensors on the other arms. Unmatched Bluetooth signals are eliminated. In the application made at Refik Cesur Intersection, it is seen that the Bluetooth data in crowded traffic are more; therefore, the average delay calculation per vehicle is more reliable, because the accuracy rates of the delay values increase as the Bluetooth data increase. Refik Cesur Intersection is a crowded intersection with an average daily traffic of 55,000 vehicles. A crowded intersection is selected to observe the situations of the designed method in heavy traffic.
# MD-TransUNet: An Image Segmentation Network for Car Front Face Design
## 3 Experimental Design
### Evaluation Metrics  
In this paper, three evaluation metrics were used to assess the model performance: mFscore, mIOU, and OA. The mFscore (Mean F-score) [33] is the average F-score across all categories, where the F-score [34] calculated as the harmonic mean of Precision and Recall serves as a robust metric to comprehensively evaluate the classification performance of the model. IoU (Intersection over Union) [35] evaluates the extent of overlap between the predicted segmentation results and the ground truth annotations, with mIOU being the average IoU across all categories. OA (Overall Accuracy) [36] indicates the overall classification accuracy, indicating the ratio of correctly classified pixels to the total number of pixels, thereby reflecting classification accuracy.  
The mathematical expression for calculating the mFscore is presented below:  
\[\text{Fscore}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}}, \tag{17}\]  
\[mFscore=\frac{\sum_{i=1}^{N}\text{Fscore}_{i}}{N}, \tag{18}\]  
In the equation, the formulas used to calculate Precision and Recall are given below:  
\[\text{Precision}=\frac{TP}{TP+FP}, \tag{19}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{20}\]  
In the equation, _TP_ represents the count of true positives, _FP_ represents the count of false positives, and _FN_ represents the count of false negatives.  
The formula for calculating mIOU is presented below:  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}}, \tag{21}\]  
\[\text{mIoU}=\frac{\sum_{i=1}^{N}\text{IoU}_{i}}{N}, \tag{22}\]  
The formula to calculate OA is provided below:  
\[\text{OA}=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{23}\]  
In the equation, TN denotes the count of true negatives.
## 3 Experiments and Analysis
### Experiment Indicators  
In order to accurately evaluate the experimental results, the following important indicators were used in this paper, including Sensitive (SE), Specificity (SP) and Accuracy (Acc), and the calculation formulas are shown as follows:  
\[\text{SE}=\frac{|\text{TP}|}{|\text{TP}+\text{FN}|} \tag{3}\]  
\[\text{SP}=\frac{|\text{TN}|}{|\text{TN}+\text{FP}|} \tag{4}\]  
\[\text{Acc}=\frac{|\text{TP+TN}|}{|\text{TP+TN}+\text{FP}+\text{FN}|} \tag{5}\]  
TP, FP, TN and FN represent true positive, false positive, true negative and false negative, respectively. SE represents true positive rate and SP represents true negative rate. In this paper, TP represents the vascular pixels predicted by the model as blood vessels, and FP represents the vascular pixels predicted by the model as background. TN represents the background pixel predicted by the model as the background, and FP represents the background pixel predicted by the model as the blood vessel [28].  
SE represents the ratio of the number of correctly predicted vascular pixels to the number of total vascular pixels, which is the probability of correctly predicting vascular pixels.  
SP represents the ratio between the number of correctly predicted background pixels and the total number of background pixels, which is the probability of correctly predicting background pixels.
## 5 SDN-based VANETs Security Challenges
### Black Hole and Gray Hole Attacks  
Another risky attack that particularly influences security applications is the black hole attack [55] (shown in Figure 16), where vehicle assailants connect with different vehicles by guaranteeing that they have the best course to the goal or have the best position to advance the bundle. After different vehicles send their bundle to aggressors, the pernicious vehicle disposes of all parcels from the system which causes it to lose colossal bundles including basic data and security messages. In addition, pernicious vehicles go about as dark hubs and misinforming bundles, sifting them per their advantages. A solitary malevolent hub or many malignant hubs chose a few bundles to advance and drops others [55].  
More or less, a name-based sending plan is used to advance solicitations and convey information back to buyers. Illuminating the black hole attacks can be accomplished either by verifying the sending plane itself or by utilizing secure name spaces to advance name-prefixes that do not exist in the forwarding information based (FIB) table. Furthermore, as the based sending plane advances Interest/Data packets without realizing who is mentioned or who will serve, these types of attacks may not influence SDN-based VANETs even by reporting that they have the best course. In any case, as the based sending plane uses various leveled names to distinguish substances and administrations, a malevolent hub can, without much of a stretch, screen the sending framework and channel dependent on content names the permitted and denied parcels, which make these types of attacks hard to tackle in such cases particularly when a gathering of pernicious vehicles dispatches the attacks [92; 93].  
Figure 15: Impersonation attack in SDN-based VANETs.
###### Abstract  
Stress shielding remains a major concern in cementless total hip arthroplasty (THA) due to the stiffness mismatch between femoral stems and surrounding bone. This study investigated the biomechanical and clinical performance of a novel Ti-33.6 Nb-4 Sn (Ti-Nb-Sn) alloy stem with a graded Young's modulus achieved through localized heat treatment. A finite element model (FEM) of the Ti-Nb-Sn stem, incorporating experimentally validated Young's modulus gradients, was constructed and implanted into a patient-specific femoral model. Stress distribution and micromotion were assessed under physiological loading conditions. Clinical validation was performed by evaluating radiographic outcomes at 1 and 3 years postoperatively in 40 patients who underwent THA using the Ti-Nb-Sn stem. FEM analysis showed low micromotion at the proximal press-fit region (4.89 \(\upmu\)m rotational and 11.74 \(\upmu\)m longitudinal), well below the threshold for osseointegration and loosening. Stress distribution was concentrated in the proximal region, effectively reducing stress shielding distally. Clinical results demonstrated minimal stress shielding, with no cases exceeding Grade 3 according to [PERSON]'s classification. The Ti-Nb-Sn stem with a gradient Young's modulus provided biomechanical behavior closely resembling in vivo conditions and showed promising clinical results in minimizing stress shielding. These findings support the clinical potential of modulus-graded Ti-Nb-Sn stems for improving implant stability in THA.  
T +
Footnote †: journal: Applied sciences  
_Article_  
Biomechanical and Clinical Validation of a Modulus-Graded Ti-Nb-Sn Femoral Stem for Suppressing Stress Shielding in Total Hip Arthroplasty
## 8 Evaluation of the MRLG Protocol
### Evaluating the Impact of Varying the Data Rate  
In this section, we assess the performance differences between the CTP and the MRLG protocols when increasing the traffic injected in the WSN. Notice that larger data rates are associated with more frequent report messages generated by active sensors. This means that applications such as intruder tracking can obtain more precise location estimations due to more frequent updating. Thus, the purpose of this set of tests is to determine whether routing tasks are negatively affected by data traffic, and also to determine the threshold over which loss and delay become prohibitive for real-time tracking purposes.  
Figure 6: Varying the number of source nodes: (**a**) number of routing/injected packets, (**b**)normalized routing load.  
Concerning the simulation parameters, we maintain the values defined in Table 2, except for the packet injection rate per-source, which we now vary from 0.05 to 1 pkt/s.  
Figure 7 shows the performance of both protocols in terms of packet loss ratio. We can see that the percentage of loss for the CTP protocol was about 300% larger than for the MRLG protocol. As expected, both protocols showed an increasing trend for larger traffic loads, which was due to congestion. Again, the lower routing overhead introduced by MRLG leaves more room, in terms of bandwidth, for data packets, which explains the performance differences observed. Notice that the IEEE 802.15.4 technology supports only very low data rates (up to 250 kbit/s), meaning that bandwidth constraints impose very strong performance limitations.  
Figure 8 shows the results obtained in terms of average end-to-end delay for both routing protocols. We can see that the delay differences were small for low load values. However, these differences increase for larger load values, reaching up to 80 ms for a traffic load of 1 packet per second (per source). In general, low delay values must be sought to favor a timely reaction of the sink to the reports received.  
Figure 9(a) shows the number of routing and injected packets. In both cases, we find that the routing load remained mostly stable as the injected traffic increased, although CTP generated three times more  
Figure 8: Average end-to-end delay when varying load.  
Figure 7: Packet loss ratio when varying load.  
packets than MRLG. This result manifests the effectiveness of the MRLG algorithm at minimizing the control overhead, while maintaining full effectiveness.  
Figure 9(b) shows the normalized routing load. Again, we find substantial differences between both algorithms, especially for low traffic loads, reinforcing the aforementioned conclusions.
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 3 Traffic Adaptive Data Dissemination
### Broadcast Suppression Technique  
To mitigate the broadcast storm in well-connected networks, first, a sender uses a classification mechanism to classify its neighbors into different directional clusters. Then, a sorting technique considers both road traffic and network traffic status to allocate different rebroadcast orders to the neighbors in directional clusters. After that, the transmitter inserts the rebroadcast order list, _i.e._, priority list, into the data message and sends it. When the neighbors of the sender receive the data, a _delay-contention scheme_ is used to convert the rebroadcast order to the respective delay and perform a rebroadcast schedule. Thus, the neighbors start to contest the opportunity to rebroadcast. A neighbor having higher order will rebroadcast earlier. If a vehicle receives redundant data (an echo) from a member of the same directional cluster, it will cancel its rebroadcast schedule and switch to an idle state. When the delay timer has expired, if a vehicle does not receive any data or just receives the data from the vehicles of other directional cluster, it will rebroadcast first. Therefore, the suppression technique not only can suppress redundant broadcasts, but also let the data messages be disseminated in all potential directions. Notice that the protocol makes a local centralized decision in the sender to decide the rebroadcast order of neighbors. In fact, the local centralized decision can solve the hidden terminal problem in the multi-directional dissemination [10; 29].
# Recurrent Neural Network-Based Hybrid Localization for Worker Tracking in an Offshore Environment
## 5 Conclusions  
This paper proposed RHLS, an indoor positioning system that estimates locations by fusing various embedded sensors of smartphones using an RNN. The performance was verified through comparison by conducting experiments on a ship at anchor. To the best of our knowledge, there was never before an actual indoor location system test in an offshore environment.  
In experiments, it was confirmed that RHLS had improved accuracy and computation time over the existing methods. The main feature of this system is that it organically combines data with different cycles. The existing probability-based sensor fusion algorithms must obtain the probability distribution of wireless signals or sensors through extensive data collection or assumptions. In contrast, the proposed method collects data and trains using those data; thus, it has a location service provision environment to reduce the required effort. The tracking effect is generated from the RNN structure; therefore, it can be confirmed that the accuracy improves over time. Finally, the RHLS was able to significantly increase the speed compared to existing sensor fusion algorithms. The proposed RHLS was able to dramatically reduce the prediction process while using an ANN. This can solve the problem of information loss according to the calculation time, which was a problem raised earlier.  
In the proposed system, the user location is calculated by combining sensor values based on the Wi-Fi signal vector. If a new Wi-Fi or Bluetooth signal is added for positioning, it is necessary to train using the corresponding newly added data because of the feature of the proposed method. However, this situation does not happen very often, because the layout of the infrastructure on a ship is completely specified when the ship is first designed and, once set up, subsequent changes are  
Figure 13: Comparison of tracking accuracy as the sequence progresses: (**a**) main deck; (**b**) A deck; (**c**) first deck; (**d**) second deck.  
very rare. Nevertheless, if the AP is broken or removed and another Wi-Fi vector is input at the same location, it is likely to generate an error. In addition, the proposed method assumes that the user has a similar step length in consideration of a slow walking situation, not a running or fast walking situation. This is because it is not common to move quickly in a place like a ship. However, if the step length changes, the network should be retrained.  
In this regard, improvements are planned for the proposed method such as applying more efficient and accurate RNN techniques for general land buildings with more in-depth experiments, including analysis of the completed weight matrix to investigate the RNN parameters and the positioning errors. In addition, it is necessary to conduct training or inspection as future work using data with different steps or a dynamically changing environment to confirm that the proposed system is robust.  
This research received no external funding.  
The author declares no conflicts of interest.
## 3 Results
### Hydrometallurgical Extraction of Cr  
The recovery of chromium from tannery sludge ash was examined in the sample that presented the highest percentage in the metal's hexavalent form content, namely thermally treated at 700 \({}^{\circ}\)C. As approximately 90% of total chromium content (Cr\({}_{\text{total}}\)) existed in the Cr(VI) form, a high extraction rate was also expected. Indeed, according to Figure 4, over 90% of the Cr\({}_{\text{total}}\) was leached, when 1 N of the examined acids was applied. The further increase in acid concentration presented no significant improvement in the leaching efficiency. Moreover, the L/S ratio equal to 25 proved to be suitable for extracting the majority of the initial Cr\({}_{\text{total}}\) under the aforementioned experimental conditions, a value that directly correlated with the metal's content in the raw tannery sludge [7].  
In addition, regarding the mechanism of extraction, the application of various acids causes rather insignificant differences regarding efficiency and, also, considering the standard deviation bars. This is attributed to the extremely soluble Cr(VI) species, which is the dominant form of chromium in the resulting ash. Accordingly, due to the presence of the almost insoluble Cr(III) form, a relative stability in the leachable Cr\({}_{\text{total}}\) was observed for the highly acid normality values. Furthermore, the limited extraction at acids' concentration below 0.25 N was observed, due to its partial consumption of acid for the preliminary neutralization of alkaline ions [6]. The presence of such ions was indicated by the pH and electrical conductivity measurements.  
By contrast, the leaching by acids presented notable variations, regarding Ca extraction (Figure 4). Considering the selectivity of this process, its leachability of Ca was also investigated, since it was proved from the chemical characterization of the samples that the tannery sludge ashes contained high levels of Ca, equal to those of Cr. When H\({}_{2}\)SO\({}_{4}\) was employed, Ca formed the insoluble in acidic media CaSO\({}_{4}\) compound and therefore, less Ca was transferred to the leaching solution [33]. Moreover, the CaSO\({}_{4}\) formation was proportional to acid normality and, thus, for concentrations above 1 N Ca extraction was minimized. Instead, both HNO\({}_{3}\) and HCl proved capable to extract most of the Ca content, while the difference between them was attributed to the slightly higher solubility of the respective metal nitrate salt [34].  
Figure 3: Correlation of Cr(VI) content with the percentage of the CaCO\({}_{3}\) content.
## 2 Mathematical Formulation of Object Localization and Tracking Algorithms
### Object Localization and Tracking Algorithm
#### 2.4.2 EKF Algorithm for Object Localization and Tracking
The Kalman update process is as follows:  
\[\tilde{\mathbf{S}}^{(k)} =\Big{(}\mathbf{C}^{(k)}\tilde{\mathbf{p}}^{(k)}\big{(}\mathbf{C}^{(k)}\big{)} ^{\intercal}+\frac{1}{w_{k}}\mathbf{R}^{(k)}\Big{)}^{-1} \tag{37}\] \[\tilde{\mathbf{K}}^{(k)} =\tilde{\mathbf{P}}^{(k)}(\mathbf{C}^{(k)})^{\intercal}\tilde{\mathbf{S}}^{( k)}\] (38) \[\mathbf{\theta}^{(k)} =\tilde{\mathbf{\theta}}^{(k)}+\tilde{\mathbf{K}}^{(k)}(\mathbf{u}^{(k)}- \tilde{\mathbf{u}}^{(k)})\] (39) \[\mathbf{P}^{(k)} =(\mathbf{I}-\tilde{\mathbf{K}}^{(k)}\mathbf{C}^{(k)})\tilde{\mathbf{P}}^{(k)} \tag{40}\]  
where  
\[\mathbf{C}^{(k)}=\Big{(}\sum_{i=1}^{k}w_{i}\tilde{\mathbf{u}}_{i}\tilde{ \mathbf{\theta}}_{i}^{\intercal}\Big{)}\Big{(}\sum_{i=1}^{k}w_{i}\tilde{\mathbf{ \theta}}_{i}\tilde{\mathbf{\theta}}_{i}^{\intercal}\Big{)}^{-1} \tag{41}\]  
and \(\tilde{\mathbf{u}}_{i}\) is the \(i\)th element of \(\tilde{\mathbf{u}}\), \(\tilde{\mathbf{\theta}}_{i}\) is the \(i\)th element of \(\tilde{\mathbf{\theta}}\), and \(w_{k}\) is a weighting such that:  
\[w_{k}=\frac{a+0.5}{b+(\mathbf{u}^{(k)}-\tilde{\mathbf{u}}^{(k)})^{\intercal}\mathbf{R}^{ (k)}(\mathbf{u}^{(k)}-\tilde{\mathbf{u}}^{(k)})} \tag{42}\]  
and the \(m\)th element of diagonal of \(\mathbf{R}^{(k)}\) is \(r_{km}\) such that:  
\[\mathbf{r}_{km}=\frac{1}{k}\sum_{i=1}^{k}w_{i}\Big{(}\tilde{\mathbf{u}}_{ int}-\mathbf{C}^{(k)}(m,:)\tilde{\mathbf{\theta}}_{i}\Big{)}^{2} \tag{43}\]  
where \(\mathbf{C}^{(k)}(m,:)\) is the \(m\)th row of the matrix \(\mathbf{C}^{(k)}\). Here, \(\tilde{\mathbf{K}}^{(k)}\) is the Kalman gain, \(\mathbf{S}^{(k)}\) is the posterior covariance of the residual prediction error, and \(a=b=1\) is suggested in [34]. In the Kalman update process, (42) presents that if the prediction error in \(\mathbf{u}^{(k)}\) increases, the weighting \(w_{k}\) of that data sample will decrease. If the prediction error is too large and dominates the denominator, the weighting \(w_{k}\) will become very small. Furthermore, the posterior covariance of the residual prediction error \(\tilde{\mathbf{S}}^{(k)}\) will become very small to result in a very small Kalman gain \(\tilde{\mathbf{K}}^{(k)}\) and decrease the influence of the data sample \(\mathbf{u}^{(k)}\) when predicting \(\mathbf{\theta}^{(k)}\).