esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 2 Background
### ICMPv6 Overview  
IPv6 is fundamentally reliant on two essential protocols, ICMPv6 and NDP, and each one consists of different classes of messages. The ICMPv6 comprises two message types, error messages code (i.e., 1 to 127) and information messages code (i.e., 128 to 255). Meanwhile, the NDP protocol is an ICMPv6 subset that utilizes five information messages. Additionally, the IPv6 protocol includes ICMPv6 as an obligation protocol. It performs a variety of essential roles, such as allowing the IPv6 node to ensure the uniqueness of the assigned IPv6 address in a link-local network with the help of the duplicate address detection (DAD) procedure [5; 16].  
Meanwhile, the DAD process is an integral part of the autoconfiguration (SLAAC) operation that permits nodes in the IPv6 network to generate unique IPv6 addresses. In addition, ICMPv6 also supports other vital features, such as determining the PMTU (path maximum transmission unit) and address resolution. As mentioned in RFC 2463 [17], the ICMPv6 protocol is the essential protocol of IPv6. It is responsible for several critical operations, including being used for communication between the router and hosts in IPv6 networks.  
Additionally, the ICMPv6 protocol provides feedback on any issues along the destination routing path. For example, if the packet is not found by the router, it cannot be sent to the destination. ICMPv6 message characteristics have primarily been utilized to indicate errors, such as reporting unavailable destination hosts. Unfortunately, these core features have been designed for mitigation rather than security protection. Subsequently, attackers utilize the lack of security protection to carry out DDoS flooding attacks by misusing the new features of ICMPv6 messages [5].
###### Abstract
They have indicated that the simulation results from the two models, as mentioned above, are consistent with the experimental data. They have pointed out that at a transitional Reynolds number, the turbulence intensity of inlet boundary conditions significantly influences the velocity fields. In contrast, the influence of the length scale can be ignored. However, [PERSON] et al. (2018) have ignored the influence of the Coanda effect in the experiment of [PERSON] et al. (2017). Meanwhile, the Coanda effect has enormous implications for the ventilation flow. They have focused too much on the comparison between simulation results and experimental data and lacked the comparison of prediction accuracy between turbulence models. They have only considered the case where the Reynolds number is 1500, resulting in a lack of comparative data at different Reynolds numbers. Therefore, their simulation results cannot be a good comparison with the experimental results of [PERSON] et al. (2017). Over the past years, the low-Reynolds number \(k\)-\(\varepsilon\) models have been extensively used to predict turbulent jet flows. However, the ability of the models to predict a transitional ventilation flow with the Coanda effect has received less attention. To address this gap, the mixed ventilation experimentally investigated by [PERSON] et al. (2017) is numerically simulated using six low-Reynolds number \(k\)-\(\varepsilon\) models. Turbulent models were compared for the prediction of the velocity distribution as well as the influence of the Coanda effect of mixing ventilation in a built environment at low-Reynolds numbers. Due to the Coanda effect and the interaction between the jet region and the recirculation region, the flow is subjected to transition, attachment, separation, and high anisotropy. This paper studies three different Reynolds numbers (Re = 1000, 1750, and 2500, respectively). More details of the six models are given in Section 2.2.
# Patient Safety Culture in Hospital Settings Across Continents: A Systematic Review
## 4 Discussion
### A Global Perspective of PSC  
Mian findings showed that the means of the responses for the different dimensions of PSC across all continents were 55.3%, ranging from 35.3% to 73.4%. This underscores the necessity for focused enhancements in healthcare systems worldwide to bolster patient safety. It is clear that the current state of hospital patient safety systems falls considerably short of meeting the recommendations outlined by the IOM [1]. Research conducted in Europe revealed a mean overall patient safety score of 55.1%. This does not seem consistent with a positive safety culture, which could be the result of national culture or prevailing health policies that permit variation in health system structures [29]. Similarly, in South America, the overall score was found to be 49%, aligning with recent study findings [103]. However, Africa displayed the lowest rate among continents, at 44.5%, underlining the necessity for urgent intervention, potentially stemming from insufficient human resources, budget, and leadership [104]. Conversely, Asia demonstrated the highest overall rate at 58.4%, though there is room for improvement to reach satisfactory levels [105; 106].
# Automated Methodology for Dependability Evaluation of Wireless Visual Sensor Networks
## 4 Proposed Methodology
### Fault Tree Model Generation  
Following the methodology flow, the model for the whole system must be generated according to the SHARPE language and syntax. Algorithm 7 looks into each path found in the previous step and, for each network element, the algorithm writes the corresponding SHARPE code to a text document that will be the input of SHARPE. This code describes the structure of the Fault Tree and the CTMC models. According to Figure 9, this structure is an OR-gate per device including battery and hardware events as inputs (Line 8), and an OR-gate per path, including device and link events as inputs (Line 13). Then, the structure is completed by a AND-gate including each path event as input (Line 15). Finally, the generated code is organized in a text document, getting each reference between hardware, battery and link events and generating their CTMC code (Line 17). The availability events of hardware and links, according to Figures 4 and 7, and availability events of battery, according to Figure 6, are associated with their CTMCs in Lines 6, 11 and 7, respectively. Each availability event is called a basic event. If a basic event occurs two or more times in an FT, it is called a repeated event. Notice that, in order to use this methodology to perform a system reliability evaluation, it is only necessary to remove the repair activities from the CTMC models, which means setting the repair rates \(\mu_{hw}\), \(\mu_{bt}\) and \(\mu_{lk}\) equal to zero.
```
Data: FaultTree = FaultTreeGeneration(\(\lambda_{hw}\), \(\mu_{hw}\), \(\lambda_{bti}\), \(\mu_{bt}\), \(\lambda_{lk}\), \(\mu_{lk}\), btStages, Adj[][], NFC); Input: Failure and Repair Rates of Hardware, Battery and Link; Battery Stages; Adjacency Matrix, Network Failure Condition. Output: Fault Tree associated to the Network Failure Condition.
1 paths = PathsDiscovery(Adj, NFC); // Algorithm 5
2 foreachnode n in NFCdo
3 foreachpath p in paths, from n to gateway do
4 foreachelement item in path p do
5 ifisDevice(item)then
6 hw = Hardware_MarkovChain(item, \(\lambda_{hw}\), \(\mu_{hw}\));
7 bt = Battery_MarkovChain(item, \(\lambda_{bti}\), \(\mu_{bt}\), btStages);
8 input = OR(hw, bt);
9
10 end if
11 elseifisLink(item)then
12 input = Link_MarkovChain(item, \(\lambda_{lk}\), \(\mu_{lk}\));
13
14 end if
15 path_Gate = OR(path_Gate, input);
16
17 end if
18 AND_Gate = AND(AND_Gate, path_Gate);
19
20 end if
21 NFC.subs(AND_Gate, n);
22
23 end if
24 returnNFC;
```
**Algorithm 7** Fault Tree Generation
# Implementation of Efficient 5G AKA Protocol for Light-Weight Environment
## 4 Discussion  
Experiments show that existing vulnerabilities are supplemented through encrypted protocols. The measurement protocol process time was used to analyze the speed of the improved protocol.  
Figure 9 shows the time graph of the process time.  
We performed the experiment 500 times and sorted the measurements. In the case of AES encryption, the measured average time was 0.000442 s and using LEA encryption, the measured average time was 0.000024 s. The measured maximum time using the AES encryption was 0.00176 s and using LEA encryption was 0.00065 s. When the time was sufficiently large, the performance of LEA was 1.5-2 times faster than that of the AES encryption.  
Figure 8: Encrypted authentication message and algorithm.  
Figure 9: Measurements time graph.  
A discussion is needed for the degree of increase in speed. In Section 2.3, we expected that \(\text{T}_{\text{LEA\_AKA}}\) would be faster than \(\text{T}_{\text{AKA}}\) only in encryption time. However, in total time, \(\text{T}_{\text{LEA\_AKA}}\) is more than twice as fast as \(\text{T}_{\text{AKA}}\). Therefore, we must redesign experiments more carefully to accurately measure time.  
In the processing time using LEA, because the encryption and decryption functions were added, the time is bigger than the \(\text{T}_{\text{AKA\_LEA}}\) in Figure 8. Therefore, we need to improve encryption and decryption functions.
# The XMUSPEECH System for Accented English Automatic Speech Recognition
## 3 Experimental Results
### Effect of Accent/speaker Embeddings  
To further improve the performance of the ASR system, we explored various embeddings as auxiliary features while sharing the same acoustic model and training strategy. The WERs achieved by the seven systems are reported in Table 3.  
Model M1 is a model trained without any auxiliary embeddings, and its WER was highest in Table 3, so we can conclude that using embeddings as complementary features can significantly improve the performance of an accented English speech recognition system.  
We observe that both spk-ivectors model (M2) and accent-ivectors model (M3) can achieve similar WER reduction, and the spk-ivectors + accent-ivectors model (M4) obtained a further improvement on the development set, which means that both speaker-relevant information and accent-relevant information are helpful for accent adaption in accented ASR. These results can also be observed from M5 to M7, which were the models that applied the x-vector embeddings as auxiliary features.  
In Table 3, we observe that the x-vector embeddings (M5, M6, and M7) outperformed i-vector embeddings (M2, M3, and M4). Compared with Model M4, which utilized the combination of spk-ivectors and accent-ivectors, Model M6, which just augmented input with accent-xvectors obtained the same 7.02% WER on the development set. This is because the x-vector has a more powerful capability to characterize accent-relevant information.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**System**} & \multirow{2}{*}{**Features**} & **WERs (\%)** & **WERs (\%)** \\  & & **on Dev** & **on Eval** \\ \hline TDNN-F (baseline) & MFCC & 9.12 & 9.31 \\ TDNN-F & MFCC + Pitch & 8.97 & 9.18 \\ CNN-TDNNF-Attention & MFCC + Pitch & 8.92 & 9.12 \\ Multistream CNN & MFCC + Pitch & **8.86** & **9.08** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of different acoustic models. (The best result are highlighted in bold.)When combining the accent-xvectors and spk-xvectors in Model M7, we achieved a relative WER reduction of 21.6% on the development set, and 14.8% on the evaluation set, compared with the baseline model M1.
# Efficient Expiration Date Recognition in Food Packages for Mobile Applications
## 4 Results
### Recognizer Results  
After fine-tuning the model, the accuracy can reach 97.06%. To build statistical confidence, k-fold cross validation is performed. Due to the small amount of real data, k is selected as 5. The validation accuracy interval is [94.35%, 99.76%]. The fine-tuning accuracy, 97.06%, falls in the interval. The accuracy is calculated by comparing each output character and the ground truth character, which consists of letters and symbols. The recognition inference in this work is 10 ms per image, while, in the work of [2], it is 92 ms per image; both are tested in the same environment on a single NVIDIA GTX 1080 GPU.  
Table 3 shows that the Torch Script, FP32, and FP16 models all have a similar high accuracy of 97.06%, indicating that they perform almost identically in terms of prediction accuracy, and the INT8 model has a significantly lower accuracy of 44.12%, which suggests a substantial loss in performance. As for the model size, the FP32 and Torch Script models have similar sizes, both around 31.8 MB, and the FP16 model, using half-precision floating points, has a significantly smaller size of 15.9 MB while maintaining the same accuracy, which is beneficial for scenarios where storage or memory efficiency is crucial without compromising on performance. While the INT8 model is the smallest, at 8.54 MB, this shows that it has been heavily quantized. However, this quantization comes at the cost of accuracy.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Rec Acc** & **Det Size** & **Rec Size** \\ \hline Torch Script & 0.9850 & 0.8814 & 0.9303 & 97.06\% & 23.63 MB & 31.96 MB \\ FP32 & 0.9809 & 0.8756 & 0.9253 & 97.06\% & 2.60 MB & 31.80 MB \\ FP16 & 0.9809 & 0.8756 & 0.9253 & 97.06\% & 1.32 MB & 15.90 MB \\ INT8 & 0.7281 & 0.9780 & 0.8347 & 44.12\% & 0.75 MB & 8.54 MB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Combined performance metrics of detection and recognition models.
# A Preprocessing Method for Coronary Artery Stenosis Detection Based on Deep Learning
## 2 Materials and Methods
### Image Fusion  
Medical image fusion represents a critical process involving the integration of multiple images derived from either a single imaging modality or multiple distinct modalities within the medical field. The overarching objective of medical image fusion is to enhance the overall image quality while retaining and accentuating specific features pertinent to the clinical context. This enhancement serves to bolster the clinical applicability of these fused images in the realm of medical diagnosis and evaluation. The domain of medical image fusion spans across a diverse spectrum of scientific disciplines, encompassing image processing, computer vision, pattern recognition, machine learning, and artificial intelligence. The versatile applications of medical image fusion extend to various clinical scenarios, where it equips physicians with the ability to gain a comprehensive understanding of pathological lesions through the amalgamation of medical images obtained from disparate modalities. This transformative approach empowers healthcare professionals with a richer and more informative visualization of medical data, ultimately contributing to more accurate diagnoses and informed treatment decisions.  
In the context of medical image fusion methodologies based on the intensity-hue-saturation (IHS) model, a fundamental transformation known as the IHS transform plays a pivotal role in the process. Initially, the input medical image, typically represented in the red-green-blue (RGB) color channels, undergoes a conversion known as the RGB-IHS transform. This transformation translates the image into the IHS color space, comprising intensity, hue, and saturation components, effectively creating a matrix representation of the image in this alternative color space (IHS-RGB). Subsequently, the fused medical image is reconstructed by employing the inverse IHS transform (IHS-RGB). In this fusion approach, two medical images derived from different imaging modalities are initially converted into the IHS color space through the RGB-IHS transformation. Following this, the final fused image is synthesized by reversing this process using the IHS-RGB transformation:  
\[I=\frac{R+B+G}{3} \tag{4}\]  
\[\begin{cases}H=\frac{G-B}{3T-3B},S=\frac{I-B}{I}&\text{if }B<R,G,\\ H=\frac{B-R}{3T-3R},S=\frac{I-R}{I}&\text{if }R<B,G,\\ H=\frac{R-G}{3T-3G},S=\frac{I-G}{I}&\text{if }G<R,B\end{cases} \tag{5}\]\[\begin{cases}R=I(1+2S-3S\times H),\\ G=I(1-S+3S\times H),\\ B=I(1-S)&\text{if }B<R,G,\\ R=I(1-S),\\ G=I(1+5S-3S\times H),\\ B=I(1-4S+3S\times H)&\text{if }R<B,G,\\ R=I(1-7S+3S\times H),\\ G=I(1-5),\\ B=I(1+8S-3S\times H)&\text{if }G<R,B\end{cases} \tag{6}\]  
This comprehensive technique harnesses the benefits of the IHS color space, enabling the integration of distinct modalities into a singular representation that preserves essential features for improved clinical interpretation and analysis.
## 3 Proposed MISO Converter
### Basic Operating Principles
#### 3.2.2. State 2 [\(t_{1}\leq t\leq t_{2}\)]  
As shown in Figure 8, the switch \(S_{1a}\) is on, the switches \(S_{1b}\), \(S_{2a}\), and \(S_{2b}\) are off, the diodes \(D_{2}\) and \(D_{4}\) are on, and the diodes \(D_{1}\) and \(D_{3}\) are off. During this state, the input voltage \(V_{d1}\) continues to excite the magnetizing inductance \(L_{m1}\) and the leakage inductance \(L_{lkp1}\), whose voltages on them are \(k_{1}V_{d1}\) and \((1-k_{1})V_{d1}\), respectively. At the same time, the magnetizing inductance \(L_{m2}\) and the leakage inductance \(L_{lkp2}\) are demagnetized and the voltages on them are \(k_{2}(V_{d2}-V_{C5})\) and \((1-k_{2})(V_{d2}-V_{C5})\), respectively, forcing diodes \(D_{2}\) and \(D_{4}\) to conduct. There are three key paths in this state. Path 1 is that the current \(i_{lkp2}\) flows through the body diode of \(S_{2b}\) and hence the leakage inductance \(L_{lkp2}\) releases energy to the clamping capacitor \(C_{5}\); path 2 is that the energy-transferring capacitor \(C_{2}\) charges the energy-transferring capacitor \(C_{1}\) and at the same time the leakage inductance \(L_{lks1}\) is magnetized in the opposite direction with a voltage of \(-nk_{1}V_{d1}+V_{C1}+V_{C3}-V_{o}\); path 3 is that the energy-transferring capacitors \(C_{2}\) and \(C_{3}\) are connected in series and release energy to the load and at the same time the leakage inductance \(L_{lks2}\) is magnetized with a voltage of \(V_{C5}-nk_{2}(V_{d1}-V_{C5})+V_{C2}+V_{C3}-V_{o}\). It is worth mentioning that the two coupling inductors have transformer behavior. In addition, the diode \(D_{1}\) is connected in parallel with the energy-transferring capacitor \(C_{1}\), which has a reverse-biased voltage of \(V_{C1}\), and the diode \(D_{3}\) is connected in parallel with the energy-transferring capacitor \(C_{3}\) and has a reverse-biased voltage of \(V_{C3}\), such that both the diodes \(D_{1}\) and \(D_{3}\) are cut off in this state. As the switch \(S_{2b}\) is turned on, state 2 ends.  
Figure 7: Circuit operation for state 1.
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 3 Treatment Approaches for \(\beta\)-Thalassemia
### Gene Therapy  
#### 3.3.1 Regulation of \(\beta\)-Globin Expression  
During human development, two transitions of \(\beta\)-globin occur: embryonic hemoglobin transforms into fetal hemoglobin around 6 weeks of gestation, followed by a gradual transition to adult hemoglobin after birth [32].  
DNase hypersensitive sites (HSs) are located in the locus control region (LCR) 40-60 kb upstream of the \(\beta\)-globin gene, which contain chromatin accessibility and DNA-enhancer elements. The tandem incorporation of \(\beta\)-globin with the LCR enhances the robust expression of \(\beta\)-globin. Although the mechanism underlying the conversion from \(\gamma\)-globin to \(\beta\)-globin remains unclear, extensive research has identified several transcription factors involved in this process, including BCL11A, GATA-1, SOX6, KLF1, NF-E4, COUP-TF, DRED/TR2/TR4, and MBD2 [33].  
Among these transcription factors, BCL11A, KLF1, and HRI play critical roles in regulating gene expression, including that of \(\gamma\)-globin. BCL11A inhibits the expression of \(\gamma\)-globin through interactions with factors such as GATA1, SOX6, and FOG-1 [33], whereas LRF/ZBTB7A independently silences \(\gamma\)-globin expression. KLF1 is a transcriptional regulator that plays a crucial role in erythroid development, facilitating the transition from fetal to adult globin expression. Additionally, studies have identified a positive feedback loop between TAL1 and KLF1, which is essential for the terminal differentiation of erythrocytes [34]. Furthermore, [PERSON] et al. [35] discovered that miR-326 enhances the expression of \(\gamma\)-globin by inhibiting KLF1. These regulatory mechanisms are crucial for elucidating the transition from \(\gamma\)-globin to \(\beta\)-globin.
# EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training
## 3 Methodology
### Entity Attributes Encoder  
Entity attributes can provide rich background knowledge for an entity, which can help improve the performance of RE. We extract entity descriptions, entity aliases, and entity types as shown in Figure 1 from the public database of Freebase [6] and Wikidata [8]. We first query the word embeddings of these attributes and input them to an attention-based BiLSTM encoder to obtain a vector representation \(\mathbf{s_{a}}\) of each attribute, as \(\mathbf{s}\) in Equation (2).  
Furthermore, we note that not all attribute information is beneficial for RE, which may contain irrelevant information and introduce some additional noisy information. Therefore, we propose to use knowledge graph embedding TransE [27] to discriminate useful attributes. TransE treats relations as a translation from head entity \(e_{h}\) to tail entity \(e_{t}\), i.e., \(e_{h}+r\approx e_{t}\). Therefore, the relation can be approximated in the following way: \(\mathbf{r}=\mathbf{e_{t}}-\mathbf{e_{h}}\). We calculate the weight of each attribute by multiplying \(\mathbf{s_{a}}\) with \(\mathbf{r}\). The final representation of the entity attributes information is denoted as follows:  
\[\alpha_{i} =\frac{\exp(\mathbf{s_{a}}\cdot\mathbf{r})}{\sum_{i=1}^{t}\exp( \mathbf{s_{a}}\cdot\mathbf{r})}, \tag{3}\] \[\mathbf{d_{c}} =\sum_{i=1}^{t}\alpha_{i}\mathbf{s_{a}},\]  
where \(\alpha_{i}\) is the attention weight of attribute \(\mathbf{s_{a}}\). The entity attribute embedding \(\mathbf{d_{c}}\) is calculated by the weighted average of all attributes.  
Finally, we concatenate the sentence encoder output with the entity attribute encoder output to obtain the new sentence representation. It can be expressed as:  
\[\mathbf{\overline{s}}=[\mathbf{s}:\mathbf{d_{h}}:\mathbf{d_{t}}]. \tag{4}\]  
To calculate the confidence level for each relation, we feed the sentence representation into a softmax classifier after a linear transformation, as follows:  
\[\text{p}(r|s,\theta)=\text{Softmax}(\mathbf{M_{s}}\mathbf{\overline{s}}+ \mathbf{b_{s}}), \tag{5}\]  
where \(\mathbf{M_{s}}\) is the transformation matrix and \(\mathbf{b_{s}}\) is the bias.