esa-sceva/satcom-chunk-collection huggingface dataset:
# Vertical Handover Algorithm for WBANs in Ubiquitous Healthcare with Quality of Service Guarantees
## 4 MADM Handover Algorithm
### Handover Decision-Making
#### 4.2.3 The Normalized Attributes and Handover Decision-Making  
All the attribute values are normalized by the Euclidean normalization method (\(r_{ij}\)) to construct the normalized decision matrix, which provides the highest-ranking consistency [33]. Each element \(r_{ij}\) is calculated as follows.  
\[r_{ij}=\frac{x_{ij}}{\sqrt{\sum_{i=1}^{n}x^{2}_{ij}}} \tag{12}\]  
where the values \(x_{ij}\) concern the \(i\)th network (\(i\) = 1, 2,..., \(n\)) and \(j\)th attribute (\(j\) = 1, 2,..., \(m\)). The ranking of the networks is executed according to the TOPSIS approach [34], which is based on the attribute weights in Section 4.2.1 and the normalized values above. The network with the highest ranking is the best network that satisfies the QoS application requirements. The proposed handover procedure is summarized in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Attributes** & **Emergency Linguistic** & **Emergency Value** & \(W_{1}\) & **Normal Linguistic** & **Normal Value** & \(W_{2}\) \\ \hline RSSI & High & 7 & 0.174 & Medium & 5 & 0.130 \\ Delay & Very High & 9 & 0.456 & Medium & 5 & 0.130 \\ Utilization & High & 7 & 0.174 & Low & 3 & 0.053 \\ Packet Loss & High & 7 & 0.174 & Medium & 5 & 0.130 \\ Cost/Byte & Low & 1 & 0.031 & Very High & 9 & 0.557 \\ \hline \hline \end{tabular} RSSI: Received Signal Strength Indicator.  
\end{table}
Table 4: The attribute weights for the WBAN traffic categories.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**AHP Scale of Importance for Comparison Pair (\(a_{qp}\))** & **Linguistic Value** \\ \hline Very High & 9 \\ High & 7 \\ Medium & 5 \\ Low & 3 \\ Very Low & 1 \\ Intermediate values between the two adjacent judgments & 2; 4; 6; 8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scale of relative importance.
# Step 12: Compile the grid search model: compile_model(grid_search_model)
## 3 Results and Discussion
### Optimal Route  
A route model for the distribution of relief goods will be included from the route generated in order to determine the time and cost of distributing the relief goods.  
Distribution Centre 1 (DC1) with combination route DC1 \(\rightarrow\) N6 \(\rightarrow\) N20 \(\rightarrow\) N36 \(\rightarrow\) N37 \(\rightarrow\) DC1 with total demand 2.431.  
\[j =6.9 DC1N6+3N\;6N\;20+0.28N\;20N\;36+12.6N\;36N\;37+16.1N\;37 DC1\] \[=38.88\;\text{km}\] \[w =6.9 DC1N6+3N\;6N\;20+0.28N\;20N\;36+12.6N\;36N\;37+16.1N\;37 DC1\] \[=84\;\text{min}\] \[h =2000 DC1+(3.5*(6.9 DC1N6+3N6N20+0.28N20N36+12.6N36N37+16.1N37 DC1)+ 500K1 DC1)\] \[+0\text{route}1 DC1+0\text{route}1 DC1\] \[=2000+136.08+500+0\] \[=2636.08\]  
Time (\(w\)) and cost (\(h\)) are generated based on route 1 (DC1 \(\rightarrow\) 6.0 \(\rightarrow\) 20.0 \(\rightarrow\) 36.0 \(\rightarrow\) 37.0 \(\rightarrow\) DC1) based on the bi-objective model, so the sum weighted method is used to change it to a single objective model by proposing \(\gamma\) weighting parameters. The model generated by \(P_{1}\) using Equation (17) is a single-objective mixed integer linear programming problem that can be solved using existing commercial problem solvers.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Distribution** & **Number of** & **Number of** & **Demand** & **Vehicles** & **Exact** & **DLRP** \\ \cline{3-6}
**Center** & **Distribution** & **Centers** & **Points** & & **Route** & **Route** \\ \cline{3-6}
**DC1** & 1 & 21 & 5.225 & 4 & 5.10909 \(\times\) 10\({}^{19}\) & 387,467,405 & 363,626 \\
**DC2** & 1 & 7 & 7.417 & 2 & 5040 & 3129 & 122 \\
**DC3** & 1 & 10 & 5.088 & 4 & 3.628,800 & 514 & 50 \\  & & Route formation & & & 5.10909 \(\times\) 10\({}^{19}\) & 387,471,048 & 363,798 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the number of routes generated.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Distribution Center** & **Number of** & **Number of** & **Vehicles** & **Route** & **Route** \\
**Dictionary Centers** & **Demand Points** & & **Combinations** & **Route** \\ \hline DC1 & 1 & 21 & 4 & 387,467,405 & 363,626 \\ DC2 & 1 & 7 & 2 & 3.129 & 122 \\ DC3 & 1 & 10 & 4 & 514 & 50 \\ \hline \hline \multicolumn{2}{c}{Total route combinations} & \multicolumn{2}{c}{387,471,048} & \multicolumn{2}{c}{363,798} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of establishing distribution routes with deep learning.  
The objective function will be produced by the time parameter (set weighting \(\gamma=1\)) and the cost parameter (parameter weighting \(\gamma=0\)):  
\[\begin{array}{ll}\text{Minimum }\updownarrow_{1}(\text{route1})&=1\times 54 \text{ route1 DC1}+(1-0)\,2645.00\text{ route1 DC1}\\ &=54+264500\\ &=2.69900\end{array}\]  
Table 5 shows a single objective function (\(P_{1}\)) based on the route recommendations generated, with a total of 38 demand points and 3 distribution centers based on the above calculations.
# Isogeometric Schwarz Preconditioners with Generalized B-Splines for the Biharmonic Problem +
## 1 Introduction
Previous works on overlapping domain decomposition methods (DDM) for IGA have concentrated on NURBS-based IGA discretizations such as overlapping additive Schwarz preconditioners (see, for instance, [17] for Galerkin IGA approximation of scalar elliptic problems, [18] for Galerkin IGA discretizations of linear elasticity systems, [19] for collocation IGA approximation of scalar elliptic equations and [20] for collocation IGA discretization of linear elasticity). Further works on NURBS-based IGA preconditioners are extended to BDDC [21; 22; 23; 24], FETI-DP [25; 26], BPX [27; 28], IGA multigrid [29; 30; 31]. Fast solvers for NURBS-based IGA have also been studied in [32; 33; 34]. For the biharmonic problems, several NURBS-based IGA techniques are presented in [35; 36] and NURBS-based IGA overlapping Schwarz preconditioners are analyzed in [37] (see also the references therein for DDM for finite element discretizations of biharmonic problems). The present paper is organized as follows. In Section 2, we give a brief review of GB-splines and their fundamental properties. Section 3 introduces the proposed overlapping domain decomposition preconditioners. Several numerical results in two- and three-spatial dimensions are reported in Section 5. Finally Section 6 contains some conclusions and future directions of research. We will adopt the following concise notation throughout the paper. Given two numbers \(a,b\), we write \(a\lesssim b\) when \(a\leq Cb\) for a generic constant \(C\), which is independent of the knot vector \(\Sigma\) and the mesh size \(h\) but depending on the spline degree \(p\) and the geometric map \(\mathbf{F}\) (defined below), and we also write \(a\approx b\) when \(a\lesssim b\) and \(b\lesssim a\).
# MAC Protocols for mmWave Communication: A Comparative Survey +
## 6 Challenges & Future Research Directions
### Machine Learning Approaches  
ML techniques such as deep RL (DRL) and federated learning (FL) can be implemented into the PNC to comprehend the network better when there are multiple PNCs present in a network because they highly influence the centralized WPAN. Intelligent control strategies for distributed management are challenging to establish. The novel technique of FL stands at the top of the list. FL's purpose is to train an AI model in a distributed fashion across numerous devices, here in our case in the PNCs, utilizing local data without having to share them with other PNCs. After that, it uses central learning to decrease space and temporal complexity. However, FL is yet to be used for mmWave MAC protocols and scheduling.  
Moreover, RL performs interestingly well for unknown environments, such as implementing RL-based protocol for outdoor 5\(G\) networks or indoor WLANs by estimating multiple channels or network parameters with the help of an agent. However, in dense networks, which are the primarily envisioned network architectures for mmWave enabled 5G, DRL tends to work significantly better than RL. DRL is different from the traditional supervised DL. When the state space or network matrices become complicated, the DRL is good at estimating the previous experience, actions, and states from its experience replay memory, in our case, stored in the PNC. Concurrently, our survey found that very limited resources are available for utilizing the potential of RL, and there is no study of DRL for mmWave MAC protocols, thus necessitating further investigation.
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of the Attacks on CC
#### 5.1.4 Permanent Denial-of-Service Attacks  
Permanent denial-of-service (PDoS) or Plashing is a fast-moving attack designed to disable the victim's and prohibit it from providing services. This type of cyber attack, a strain of DDoS attacks with more emphasis on the victim's hardware, started to increase in frequency in 2017 as more occurrences involving this hardware-damaging attack were found [46; 47]. Furthermore, PDoS aims to cause perpetual harm to network equipment via programming, especially configurable network hardware, such as routers. Although PDoS attacks are rare, successful attacks are highly damaging to CC resources to the point of requiring the replacement or re-installation of equipment. Unlike DDoS attacks that disable a service temporarily, PDoS causes permanent hardware damage. It exploits the CC's security flaws or misconfigurations in the remote administration function on the hardware management interface to alter the device's firmware with a faulty version, damaging the device to the extent that requires fixing or even destroying essential system functions. All CC resources, such as LB, firewalls, VM, physical servers, storage units, and processors, are vulnerable to PDoS attacks [48]. Moreover, since a PDoS attack focuses on the hardware, it requires far fewer resources than a DDoS attack. PDoS is more destructive and has been gaining popularity among adversaries. CSP encountering a PDoS attack will incur business loss since services will be affected, and it could take a very long-time fixing fault. For instance, in 2009, the federal bureau of investigation (FBI) raided DCs in Texas because of fraud against several organizations that worked from out of the DCs [49]. In another case, a significant information loss occurred to a CSP providing storage services in Magnolia after experiencing Omni drive failure, leading to its shutting down without notice in 2008.  
A Help Net Security site ran a report [50] on a universal serial bus (USB) device that disables a machine just by putting it into the USB port. According to the report, the most recent PDoS USB attack works by injecting some electrical power through the machine with the help of a voltage transformer to release a flood of negative electricity into the USB port. An example of a PDoS USB device is PhlashDance, built by [PERSON] in his security lab in 2008 to exhibit the inner working of a PDoS attack [51].
## 4 Experimental Results and Analysis  
### Finite Element Simulation Results and Analysis  
The finite element analysis software used in this paper is Ansys Maxwell. An MFL detection model is built as shown in Figure 6. The yoke is made of ferromagnetic material. The excitation coil adopts 4000 turns of copper wire. The DC excitation voltage is 60 V, and the lift-off between the rail and the yoke is 2.0 mm. Two detection points with a lift-off of 2.0 mm are set up above the rail.  
The width and depth of the defect are 2.0 mm and 1.0 mm, respectively and the lift-off is 2.0 mm, which means that a = 1.0 mm, b = 1.0 mm, and z = 2.0 mm. According to Formula (4), when the distance between a pair of sensors is \(\sqrt{a^{2}+z^{2}}\), which is 2.2 mm, the absolute value of the difference between the outputs of the two sensors is the maximum. The distance between two sensors is set to 1.8 mm, 2.2 mm, and 2.6 mm, respectively. The simulation results are shown in Figure 7a-c.  
Figure 6: Simulation model.  
Figure 7: _Cont._  
According to the simulation results in Figure 7a-c, the phase difference of the pair of sensors becomes increasingly significant with the increase in the distance between them. When the distance is 2.2 mm, one sensor detects the maximum in the x direction and the other sensor detects the minimum in the z direction, which is indicated by the red line in Figure 7. The absolute value of the difference between them is the maximum which is in accordance with the above conclusion.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 6 Conclusions  
This paper presents 3D-LUNA, a novel approach for 3D localization of underwater nodes using airborne VLC beams. By leveraging the intensity of light received by submerged nodes, 3D-LUNA eliminates the need for surface-based reference points, making it a viable solution for stealth-sensitive applications such as military operations, environmental monitoring, and underwater exploration. Simulation results confirm that 3D-LUNA achieves superior accuracy compared to omni-directional VLC methods, particularly when depth information is unavailable. The method remains effective across a range of transmission powers and demonstrates adaptability to both stationary and mobile underwater nodes. Additionally, the proposed error mitigation strategies improve localization robustness under uncertain measurement conditions. The results presented in this work demonstrate the potential of 3D-LUNA as a reliable and infrastructure-free localization framework for underwater environments. Compared to traditional acoustic-based localization methods, which often report localization errors ranging from 1.5 to 5 m under similar simulation conditions, 3D-LUNA consistently achieves sub-meter accuracy, often within 0.3 to 0.7 m, depending on beam configuration.  
As part of future work, we intend to extend the 3D-LUNA framework in several key directions. First, we plan to incorporate learning-based techniques to enhance localization robustness under highly dynamic and optically heterogeneous underwater conditions. We also aim to refine the physical modeling by accounting for complex aquatic phenomena such as surface-wave-induced refraction, turbidity variations, and thermocline effects. In addition, we seek to validate the proposed approach through experimental testing in real or controlled underwater environments.  
Conceptualization, J.B.S. and M.Y.; methodology, J.B.S. and M.Y.; validation, J.B.S., M.Y. and F.-S.C.; formal analysis, J.B.S. and M.Y.; investigation, J.B.S., M.Y. and F.-S.C.; writing--original draft preparation, J.B.S. and M.Y.; writing--review and editing, J.B.S., M.Y. and F.-S.C.; visualization, J.B.S. and M.Y.; supervision, M.Y. and F.-S.C.; project administration, M.Y. and F.-S.C.; funding acquisition, M.Y. and F.-S.C. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Science Foundation, USA, Contract #0000010465.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 11: Localization performance comparison for varying numbers of transmissions.
## 4 Experiments and Discussion
### Comparison with Other SLAR Methods for Oil-Slick Detection  
In this section, we compare the proposed method with other published results for oil-slick detection from a SLAR image (Table 3). In particular, we have compared our method with other methods for the detection of oil spills in SLAR images presented in [33], which are based on region segmentation algorithms presented in [40; 41]. Unlike the studies shown in [33], which work with sub-images, our method works well with radar full images. On the one hand, our method incorporates the denoising steps to improve the detection process. This additional process improves the success of detection and the results do not, therefore, contain False Positives (FP), nor is their appearance reduced, as occurs with the Graph and JSEG methods.  
On the other hand, the proposed method reduces over-segmentation in the detection process. That is, the oil slick is detected as a unique dark spot region and the detected oil spill does not, therefore, appear segmented in several regions as if it were composed of several dark spot regions. All these disadvantages are dealt with by our method, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ground-Truth** & **Graph Method**[33; 40] & **JSEG Method**[33; 41] & **Proposed Method** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Our proposal versus other state-of-the-art methods.  
Table 3 shows two test sub-images containing oil spills and zones which can be look-alikes due to noise caused by aircraft. The first test achieved accuracies of 0.76 versus 0.21 and 0.19, while the second achieved accuracies of 0.73 for this proposed method versus 0.44 and 0.14 for Graph and JSEG method, respectively. The comparison was done using the same evaluation metric again. As can be seen in the table, our approach can be used to discriminate oil spills from image, with a strong background noise, improving the Jaccard index result obtained by other methods.
## 1 Introduction
A bandpass filter was loaded on top of the waveguide to achieve a triple-notch band, and a decoupling structure was used to decrease the mutual coupling. In a UWB-MIMO antenna, achieving low mutual coupling along with multiple notches in a compact size is challenging task. All these properties along with high isolation is achieved in [24; 25; 26]. In [27], a MIMO antenna with four slot antenna elements with a rhombic shape in the ground plane produced triple notches for WiMax, WLAN, and X-band communication. With a compact size of 34 \(\times\) 34 mm\({}^{2}\), the L-shaped and C-shaped are used for WiMax and WLAN notches, and the electromagnetic gap structure is used for the X-band communication notch. Parasitic strips are used for high isolation between the adjacent antenna elements. In [28], a compact millimeter-wave MIMO antenna for 5G applications is designed with high isolation of more than 25 dB was attained using the cross-polarization technique. Similarly, in [29], isolation improvement for the UWB-MIMO antenna system using a slotted stub was reported with the implementation of extended ground plane along with the decoupling elements of cross shape. In [30], a sub-6 GHz dual-band \(8\times 8\) MIMO antenna for 5G smartphones was designed with low isolation using short neutral line. Self-decoupled structures are used in [31] to improve the isolation between antenna elements. All the literature has been focused on creating a separate notching element for each notch in the frequency domain. Later, mutual coupling between the elements of antenna is reduced by using the techniques found in the literature. However, notching with mutually exclusive elements are compromised in either the size of the overall design or with high mutual coupling between the notching elements. On the contrary, this paper introduces only two notching elements to create three band notches using the coupling between the notching elements. This way, the size of the overall design is not compromised and coupling between the notching elements are used as an additional advantage.
# Enhancement of Text Analysis Using Context-Aware Normalization of Social Media Informal Text
## 3 Proposed Sentiment Analysis Method
### Methods Used for Text Normalization
#### 3.1.1 Out-of-Vocabulary (OOV) Words Detection  
OOV word detection is the process of examining the linguistic validity of the target words in the lexicon of a language. OOV words are detected if linguistically invalid words are found in a document. Generally, a dictionary lookup method is used to detect OOV words.  
The dictionary lookup is a direct method, in which each word in the input document is examined/compared directly against the list of valid words, known as the lexicon/dictionary. If there exist words in the input document which are not found in the lexicon, an OOV word is detected. The errored words are stored as a list with proper index, and the TVH algorithm is revoked, by passing the list of invalid words and dictionary, to suggest a context-aware replacement for the OOV words from the dictionary.  
Figure 3: Block Diagram for spell-checking process.
# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 2 Signal Sampling and Reconstruction Using Compressed Sensing  
Compressed sensing is also known as sparse sampling [19]. This theory overcomes the limitations of the Nyquist sampling theorem; that is, it can directly collect a small number of samples containing those important features of the power quality signal with a lower sampling rate and can realize the synchronization of data acquisition and compression, thereby greatly reducing the cost of sampling, storage, and transmission. Therefore, in the broadband oscillation analysis of massive power data, as long as the signal is sparse after being transformed into a certain domain by the sparse transform basis, an unrelated measurement matrix can be used to reduce the dimensionality of the broadband oscillation signal, greatly reducing the transmission amount of oscillation data. After uploading, the original broadband oscillation signal can be reconstructed for dispatching the subsequent center analysis.
## 2 Hybrid Energy System Model
### Wind Generator Model  
Wind power generation has been modeled using a typical power curve described according to Equations (1)-(4) [33; 34]:  
\[P_{W(t)}=\left\{\begin{array}{c}0;\;0\leq S_{W(t)}\leq S_{W}^{o}\\ P_{W}^{a}+P_{W}^{b}\Big{(}S_{W(t)}\Big{)}+P_{W}^{c}\Big{(}S_{W(t)}\Big{)}^{2};\; S_{W}^{o}\leq S_{W(t)}\leq S_{W}^{r}\\ P_{W}^{max};\;S_{W}^{r}\leq S_{W(t)}\leq S_{W}^{r}\\ 0;\;S_{W(t)}>S_{W}^{f}\end{array}\right.\forall\;t=1,\ldots,T; \tag{1}\]  
\[P_{W}^{a}=\frac{1}{\Big{(}S_{W}^{o}-S_{W}^{r}\Big{)}^{2}}\Bigg{[}S_{W}^{o} \Big{(}S_{W}^{o}+S_{W}^{r}\Big{)}-4S_{W}^{o}S_{W}^{r}\Bigg{(}\frac{S_{W}^{o}+ S_{W}^{r}}{2S_{W}^{r}}\Bigg{)}^{3}\Bigg{]}; \tag{2}\]  
\[P_{W}^{b}=\frac{1}{\Big{(}S_{W}^{o}-S_{W}^{r}\Big{)}^{2}}\Bigg{[}4\Big{(}S_{W }^{o}+S_{W}^{r}\Big{)}\Bigg{(}\frac{S_{W}^{o}+S_{W}^{r}}{2S_{W}^{r}}\Bigg{)}^ {3}-\Big{(}3S_{W}^{o}+S_{W}^{r}\Big{)}\Bigg{]}; \tag{3}\]  
\[P_{W}^{c}=\frac{1}{\Big{(}S_{W}^{o}-S_{W}^{r}\Big{)}^{2}}\Bigg{[}2-4\Bigg{(} \frac{S_{W}^{o}+S_{W}^{r}}{2S_{W}^{r}}\Bigg{)}^{3}\Bigg{]}; \tag{4}\]  
In this way, the relationship between the wind speed (\(S_{W(t)}\)) at a determined time step (\(t\)) and the corresponding wind power production (\(P_{W(t)}\)) is clearly established.  
Figure 1: Hybrid energy system (HES) under study.