esa-sceva/satcom-chunk-collection huggingface dataset:
# AVO-Friendly Velocity Analysis Based on the High-Resolution PCA-Weighted Semblance
## 2 Method
### High-Resolution AB Semblance with a PCA-Based Weighting Function
Then, the new semblance can be defined as  
\[S_{PCA}(it,v_{k})=\frac{w(it,v_{k})S_{AB}(it,v_{k})}{w_{\max}(it)}=\frac{w(it,v_{ k})\sum\limits_{i=it-w/2}^{it+w/2}\left(\sum\limits_{j=1}^{N}a_{k}(i,j)b_{k}(i,j) \right)^{2}}{w_{\max}(it)N\sum\limits_{i=it-w/2}^{it+w/2}\sum\limits_{j=1}^{N}a _{k}^{2}(i,j)\sum\limits_{j=1}^{N}b_{k}^{2}(i,j)} \tag{10}\]  
Figure 1: A seismic event at time 0.24 s with a velocity \(v=1950\) m/s. (**a**) CMP gather and (**b**) The corresponding principal components (eigenvalues) of the NMO-corrected CMP gathers with different scanning velocities. Figure 2: The weighting factors \(\beta_{1}\) and \(\beta_{2}\) varying with the scanning velocity. (**a**) Represents the weighting factor \(\beta_{1}\) varying with the scanning velocity and (**b**) represents the weighting factor \(\beta_{2}\). Here, we divide weights \(w(it,v_{k})\) by their maximum value \(w_{\max}(it)\) to normalize the new semblance from 0 to 1, which is convenient for the automatic velocity picking algorithm. The new semblance is a combination of the standard AB semblance \(S_{AB}(it,v_{k})\) and the weighting function. The AB semblance \(S_{AB}(it,v_{k})\) can handle with AVO anomalies, and the weighting function improves the resolution of AB semblance spectrum significantly. For example, Figure 3 shows the semblance of different methods at time 0.24 s in the above case. It can be seen that the proposed PCA-weighted semblance had a narrow pulse at scanning velocity \(v=1950\) m/s, indicating that it had the highest resolution compared with other methods.
# Hybrid Deep Learning Approach for Automatic DoS/DDoS Attacks Detection in Software-Defined Networks
## 5 Conclusions  
In summary, our research has demonstrated the effectiveness of employing deep learning methods to detect and protect against DDoS attacks in software-defined networking (SDN) environments. Our proposed hybrid deep learning model, which combines a 1D convolutional neural network (CNN), a gated recurrent unit (GRU), and a dense neural network (DNN), has shown superior performance compared to traditional machine learning algorithms, accurately detecting DDoS attacks and ensuring efficient operation of SDN networks. Notably, our model is particularly adept at identifying low-rate DDoS attacks and can detect both short-term and long-term patterns in the input data.  
While our research has produced promising results, it is important to consider the limitations of the study. For example, our proposed model was evaluated on a specific dataset, and further testing on different datasets and network topologies is required to verify its generalizability. Additionally, future research should focus on the implementation of effective mitigation strategies once an attack has been detected.  
In conclusion, our research highlights the importance of utilizing deep learning techniques in the detection and defense against DDoS attacks in SDN networks. Furthermore, our findings suggest that our hybrid model can serve as a useful tool in detecting DDoS attacks, ultimately contributing to the overall security and stability of SDN networks. Future research should aim to build on our work by exploring additional strategies for enhancing the detection and response to DDoS attacks in SDN networks.  
Figure 15: Comparison with other methods.  
**Author Contributions:** Conceptualization, H.E.; Methodology, H.E.; Software, H.E.; Supervision, D.Y.-K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Acknowledgments:** This work is a part of the Ph.D. thesis titled \"Designing a Smart Security Framework for Software Defined Networks\" at the Institute of Graduate Studies, Istanbul University-Cerrahpasa, Istanbul, Turkey.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Application of Enhanced YOLOX for Debris Flow Detection in Remote Sensing Images
## 5 Discussion
### Overall Performance Analysis of RS-YOLOX-Nano
#### 5.1.2 Introducing the \(EIoU\) Loss Function  
Different bounding box regression loss functions have varying impacts on detection accuracy. The experiment compared the performance of the original model's \(IoU\) loss function with the \(EIoU\) loss function employed in this study to determine the most suitable regression loss function. The final experiment revealed that the YOLOX-Nano using the \(IoU\) loss function achieved an \(mAP\) of 82.51%, whereas the YOLOX-Nano with the \(EIoU\) loss function reached an \(mAP\) of 82.99%. The combination of the \(EIoU\) loss function with the YOLOX-Nano network, as adopted in this study, resulted in better control over the bounding box boundaries on the dataset, significantly enhancing the detection accuracy of debris flow geological disasters.
## 3 Proposed Recommendation Approach (MSMPPA)
### Recommending Top-N Item to the Target User  
The final section of the conceptual framework offers top-n best items to the targeted user. Considering the expected rating of items determined by the preceding component of the framework, the system creates a list of the top-n best items and recommends these items to the target-ted user. Due to the expected rating, the RS has solely relied on this created list to provide recommendations. Algorithm 1 defines the algorithmic design of the proposed approach.  
```
1:Input: User-Item rating dataset (UI), a list of users (U), a list of items (I), a list of items' attributes k, and the equilibrium factor \(\omega\).
2:Output: A list of Top-N items that are recommended to target user u.
3: For \(\forall i\in\) I, \(\forall j\in\) k, compute a matrix of items' categorical attribute
4: For \(\forall u\in\) U, \(\forall v\in\) U, u \(\
eq\) v, calculate the similarity of users' u, vs. (\(Cor_{1}\)) by one of the traditional SeMs.
5: For \(\forall u\in\) U, \(\forall v\in\) U, u \(\
eq\) v, calculate correlated similarity within users u and vs. (\(Cor_{2}\)) by user's interest of items' categorical attributes.
6: Combine \(Cor_{1}\) and \(Cor_{2}\) on a specific value of balance factor \(\omega\).
7: Apply the proposed prediction approach and calculate the predicted rating of each target item.
8: Making a list of Top-N best items based on the rating predicted for each targeted user.
```  
**Algorithm 1** Recommending Top-N Items to the Target User.
## 3 Results and Discussion
Figure 11: FAPA-MS spectrum of all analytical signals recorded at 160 \({}^{\circ}\)C.  
Figure 10: FAPA-MS spectrum recorded in the second heating phase from 120 \({}^{\circ}\)C to 155 \({}^{\circ}\)C.  
To validate the method described in the experimental section, the FAPA-MS analytical technique, the molecularly imprinted polymer from the AFFINILUTETM MIP-NSAIDs columns, and drug solutions with concentrations of 0.5-1.50 ug (Table 4) were used. [PERSON] et al. [34] reported that NSAIDs in wastewater reached 0.005-1.59 ugL\({}^{-1}\) for diclofenac, 0.05-3.35 ugL\({}^{-1}\) for ibuprofen, and 0.03-0.7 ugL\({}^{-1}\) for naproxen. [PERSON] et al. [35] reported that the diclofenac content in river water was 0.009-0.282 ugL\({}^{-1}\) and the naproxen content was 0.021-0.313 ugL\({}^{-1}\). The presented analytical method allows for the determination of the concentration of individual NSAIDs at these levels. The sample was concentrated by adding the appropriate volume of river/lake or sewage water (minimum amount of NSAIDs: 0.2 g in the whole volume) to the column, but not exceeding the total volume of 100 mL. Using a larger volume of water sample applied to the column can reduce the method detection limit by 20 times.
## 3 Results and Discussion
### Material Properties
#### 3.4.5 Adaptability (Margin Fit) Properties  
Figure 8 displays the percentage distribution of included studies investigating the milled restorations' adaptability.  
The effect of tooth preparation design on the marginal adaptation of ceramic-reinforced CAD-CAM RCB onlays was investigated by [PERSON] et al. [50]. The study revealed that the traditional tooth preparation design resulted in better marginal adaptation than the other designs. This outcome indicates that the conventional approach is the recommended method for clinical practice. Furthermore, the findings of this study highlight the importance of proper tooth preparation in ensuring the success of CAD-CAM restorations [53].  
Similarly, [PERSON] et al. [53] investigated the marginal and internal fit of CAD-CAM RCB and ceramic crowns scanned by two intraoral cameras. The study revealed that the composite resin blocks associated with the Bluecam intraoral camera demonstrated the best results for AIS (absolute marginal discrepancy) compared to the other groups. In addition, the marginal adaptation of all groups was within the clinically accepted range, indicating that both materials can be used for CAD-CAM restorations [54].  
Figure 7: The percentage distribution of investigations involving optical properties and a brief description of the only study found.
## 2 Materials and Methods
### Augmented Reality Monitoring and Object Detection  
Microrobots made from low-cost HEXBUG devices mounted with ESP32-CAM boards can broadcast images back to a Raspberry Pi acting as a base station. This paper uses two models: (1) the HEXBUG nano, which moves by vibratory locomotion to access hard-to-reach areas [40], and (2) the HEXBUG Spider, which uses integrated servo motors to rhythmically propel six legs in a coordinated insect gait. An infrared remote link wirelessly directs the movement and rotation of the spider-like exoskeleton.  
A network system, connecting deployed microrobots and a remote operator equipped with a HoloLens 2, can scan infrastructure for specific elements. The HoloLens 2 software is developed using an AR development suite, which consists of the Unity Game Engine, supplemented with the Microsoft Mixed Reality Tool Kit (MRTK) and OpenXR packages [41]. The coding and editing processes are carried out in the Visual Studio code editor. The ESP32-CAM board is programmed using Arduino IDE by modifying the Espressif's CameraWebServer code [42]. In this code, firstly, the camera is initialized; then, using the Wi-Fi configurations, the board connects to the hotspot and starts a camera streaming web server. Figure 5 demonstrates a system in which an ESP32-CAM attached to HEXBUG devices captures raw image data that stream to the AR headset user for either live stream monitoring by a human or broadcasting the data to a Raspberry Pi 4 Model B. The single-board processor can create a flask server to allow a team of AR headset users to view the live stream simultaneously. The board can also use ML-based processing for object detection, such as finding the missing tools required for maintenance tasks using a pre-trained model API on a hammer-screwdriver detection dataset [43] and possibly objects obstructed in narrow spaces, namely, pipes utilizing YOLOv3 [44], which is trained on the COCO dataset. Networked ML detects objects from accumulated sets of pictures and extracts the confidence interval. The processed data are then transmitted over a private network using TCP network sockets, where the Raspberry Pi is a server and several AR headsets may act as clients. The confidence interval and identified objects are then displayed as a correlating string and sprite token in a Heads-Up Display format. A trained ML algorithm can identify specific targets. Viewing in an AR format allows for direct real-time human interactions.  
Figure 5: Human–building interaction for home maintenance with AR interface that presents data collected by networked sensors and surveyor robots with ML interpretation.
## 2 Materials and Methods
### Microstructure Analyses  
The morphologies of the nanocomposites were observed by scanning electron microscopy on a Gemini 500 instrument (Zeiss, Oberkochen, Germany) at a magnification of 20,000-100,000.  
The structural characterization of the nanocomposites was characterized using a X-ray diffractometer from Rigaku, JP, model D/max-2550. The X-ray source was Cu K\(\alpha\) radiation, with a wavelength of 1.54056 A and a power of 18 KW. The scanning angle range was set from 15\({}^{\circ}\) to 85\({}^{\circ}\).  
The structure of the nanocomposites was studied using a Raman spectroscope from Renishaw, UK, model In Via-plus. The Raman spectroscope was equipped with a Leica microscope. The excitation wavelength used was 633 nm.  
The surface elemental composition and chemical state of the nanocomposites were analyzed using an X-ray Photoelectron Spectrometer from Thermo Fisher Scientific, Winsford, UK, model ESCALAB 250 Xi. To control the surface composition of the sample, survey spectra were collected at a step size of 1 eV over the range of 0-1100 eV.  
The nitrogen adsorption-desorption isotherms of the nanocomposites were measured at 77 K using a surface area and pore size distribution analyzer (ASAP2020, Micromeritics Instrument Co., Norcross, GA, USA). Before measurement, the nanocomposite samples were degassed at 105 \({}^{\circ}\)C for over 16 h. The specific surface area of the nanocomposites was calculated using the Brunauer-Emmett-Teller (BET) method, while the pore diameter and volume were determined from the desorption isotherm using the Barrett-Joyner-Halenda (BJH) method.  
The measurement of magnetic properties was conducted using the Magnetic Property Measurement System (MPMS3) from Quantum Design, San Diego, CA, USA. In this study, measurements were carried out in VSM (Vibrating Sample Magnetometer) mode.
## 4 Related Work
### Mobility Control Security  
The mobility control of a UAV flying base station relies on location and position awareness. The flying base station utilizes its component technology of a UAV/drone to provide mobility, as discussed in Section 1. For a flying base station building on a UAV drone, because of its purpose of serving the users, the mobility control depends on the flying base station's relative location to the user. For example, the flying base station moves to the line-of-sight path and closer to the user equipment to provide better wireless channel quality. Misplacing the flying base stations or the UAVs, e.g., via GPS spoofing [43; 44; 45], can disrupt the UAV flying base station's operations by triggering abnormally frequent battery re-charging [11; 46]. To counter such misplacement and to defend the relative location integrity, previous research has included securing ranging to measure the distance between the nodes against signal-injection threats [47; 48], including advancing distance or time-of-arrival measurements for ranging [49; 50] and the detection of the distance-manipulation/DoS threats [51; 52; 53].
## 4 Discussion
### Bleeding Diathesis from Disorders of Coagulation Factors
#### 4.7.1 Haemophilia  
The most common type of haemophilia is haemophilia A, which is characterized by spontaneous or prolonged bleeding caused by coagulation factor VIII deficiency. The prevalence is estimated to be 1:6000 males [50; 51].  
The severity of the clinical signs depends on the extent of the factor VIII deficiency. If factor VIII biological activity is less than 1%, the haemophilia is severe with frequent spontaneous and abnormal bleeding, originating as a result of minor trauma, secondary to surgery, or an extraction dental [50; 51].  
If factor VIII biological activity is between 1% and 5%, haemophilia is moderately severe with abnormal bleeding secondary to minor trauma, surgery, or tooth extractions, although spontaneous bleeding is rare.  
If the biological activity of factor VIII is between 5% and 40%, haemophilia is mild with abnormal bleeding secondary to minor trauma, surgery, or tooth extractions, with the possibility of spontaneous bleeding [50; 51; 52].  
Haemorrhages are often localized in the periarticular regions (hemarthrosis) and the muscles (haematomas), but any anatomical site can be involved as a result of trauma or injury. Spontaneous haematuria is quite frequent and is a strongly suggestive sign of the disease [51; 52].  
Orally, the most frequent sign of haemophilia is prolonged gingival bleeding episodes, both spontaneous and following trauma, while hemorrhosis of the temporomandibular joint is rare [52]. Dental management must focus on prevention to reduce the need for invasive treatments.  
In patients with mild or moderate haemophilia, non-invasive dental treatments can be performed under antifibrinolytic treatment, while the ablation of calculus and some forms of minor surgery can be performed with the combined administration of antifibrinolytics and desmopressin. In severe haemophilia, replacement of the deficient coagulation factor is necessary, with consideration of hospitalization [52]. Moreover, local anaesthesia is considered one of the riskiest procedures, due to the risk of hematoma formation that can lead to airway obstruction and exits.  
In addition, [PERSON] et al. also suggests administering a combination of tranexamic acid (20 mg/kg) and a single infusion of factor VIII or IX to achieve a peak level of about 30% of factor VIII or IX in vivo prior to dental extraction [53].  
Locoregional anaesthesia or intramuscular injection must not be performed with values lower than 50% of factor VIII compared to the normal value and, in any case, must be preceded by replacement therapy. Intraligamentous and intraosseous anaesthesia should be preferred [52].  
Haemophilia B is a form of haemophilia characterized by spontaneous and prolonged bleeding from coagulation factor IX deficiency. The prevalence is approximately estimated at 1:30,000 males [51] and recommendations are the same as for type A.
## 5 State-of-the-Art and Open Challenges
### Resilience in SFC Environments  
Although our work considered efforts in the literature that try to solve the problem of resilience in NFV and SFC, resilience and fault tolerance are still major concerns in SFC environments, especially where the focus is on vNF reusability [112]. Achieving cost-effective resilience is still an open challenge in SFC implementation frameworks, and there are unanswered questions for future research which include how SFCs respond to failure conditions such as links between SFFs. The failure of the virtual network functions themselves is an aspect that needs to be considered as a long term vision that requires further research, that is, with the consideration of having redundant SFs along the SFP to handle failure scenarios.  
Synthesising network processing graphs or service chains is another approach that is employed to achieve high availability in SFC, as well as the use of a multi-path routing approach [113]. As indicated by [PERSON] et al. [28], simple approaches such as the use of traffic load balancers can help with dynamic re-routing of traffic to alternate processing pipelines in SFC environments. However, an efficient load-balancing algorithm that tracks device states and available resources needs to be implemented, which goes beyond the basic round-robin algorithm that is commonly deployed in today's network environments.  
Approaches such as the work by [PERSON] et al. [88] try to provide SFC resilience and at the same time eliminate the need for NF replication, in order to reduce overhead. This is achieved by collecting and piggybacking NF state changes as packets traverse the service chain; thus, the overhead is reduced because the entire network function is not replicated to a standby node. A key challenge with proposals that employ the use of service replication is the amount of overhead incurred with redundant backup links, nodes, and service chains; thus, the efficient implementation of a high-availability failure mechanism is still a research challenge that requires further attention.
## 5 Energy-Efficient Routing Protocols (E2 RPs) for UWSNs
### Bio-Inspired Energy-Efficient Routing Protocols (Bio-Inspired E2 RPs)
#### 5.2.4. Energy-Efficient Multi-Layer Cluster-Based Routing Protocol (MCE2 RP)  
The author of [139] introduced MCE2 RP, an energy-efficient multi-layer routing protocol based on clusters, for UWSNs. According to the (MLCE2 RP), the seabed is where the sink communicating nodes are located and they have infinite energy. Each communicating node in a UWSN determines the network's overall number of layers by using thecommunicating node's depth. Every communicating node in the network has a network holding time, which is determined using the beginning energy and the remaining energy. A communicating node has a possibility to quickly become a CH in the network if it has a lot of leftover energy and has a short network holding time. Therefore, the communicating node transmits with its adjacent nodes when the network holding time expires. It is not feasible to become a CH if the adjacent node receives the information before the holding period for its own network expires. If two or more than two communicating nodes have the same network holding time, the CH is selected using the Bayesian spam filtering technique. The CH will be selected from among the communicating nodes with the highest probability in the network. The time division multiple access (TDMA) technology is used for the data packet transmission from the cluster member (CM) to the cluster head (CH), in this protocol, communicating nodes that penetrate the first network layer and send data packets directly to the sink node are not considered a part of the cluster. As a consequence of uneven load transfer among sensor Rx nodes that are closer to the network surface sink nodes, this assists in resolving the hotspot issue. However, if the network's CH updates are not considered, the CH's energy will soon run out and the network would finally come to an end.
## 3 Experimental Results
### Comparison of Multi-Focus Image (i.e., Book)  
The evaluation of the third multi-focus image is the book, illustrated in Figure 11. Figure 11 represents the original image. Figure 11b,c illustrate left-focused and right-focused images, respectively. The term \"left-focused image\" refers to the fact that the image's left side is focused, while the right side is not. The right side of the image is focused while the left is not. Figure 11d shows the process of creating the all-in-focus image after the method has been successfully implemented. The following parameters are computed to evaluate the proposed methodology performance: _E(F)_, _AG_, _CC_, _QAB/F_, _SSIM_, _Q\({}_{E}\)_, _QW_, \(L^{AB/F}\), \(N^{AB/F}\), and _SF_. Finally, the performance of the proposed approach is compared to that of other methods previously published in the literature. The results of the comparison are shown in Tables 16-21 of the report. The letter X indicates that metrics are not calculated for the article depicted in the image. According to the literature [21; 24; 25; 26; 27; 30], the proposed method is more successful than those approaches, and the best outcomes of methods are indicated in bold.