esa-sceva/satcom-chunk-collection huggingface dataset:
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 6 Discussion, Limitations, and Future Work
### Technical Limitations
#### 6.3.3 Emerging Threats and Adversarial Attacks  
The effectiveness of the proposed framework relies heavily on the accuracy and reliability of input data. However, changes in the network infrastructure or new attack vectors, including intentional adversarial attacks, can negatively impact the framework's performance by resulting in false negatives. To address this limitation, it is important to fine-tune the NIDS detection rules and use machine learning-based NIDS with anomaly detection capabilities to ensure that emerging threats and adversarial attacks are successfully detected. Additionally, advanced machine learning techniques and data cleaning methods can be employed to further improve the framework's effectiveness. Moreover, continuously monitoring for and adapting to new threats is also crucial to ensure that the framework remains effective in the long term.
# Sustainability-Based Analysis of Conventional to High-Speed Machining of Al 6061-T6 Alloy
###### Abstract  
High-speed machining is considered to be a promising machining technique due to its advantages, such as high productivity and better product quality. With a paradigm shift towards sustainable machining practices, the energy consumption analysis of high-speed machining is also gaining ever-increasing importance. The current article addresses this issue and presents a detailed analysis of specific cutting energy (SCE) consumption and product surface finish (Ra) during conventional to high-speed machining of Al 6061-T6. A Taguchi-based L16 orthogonal array experimental design was developed for the conventional to high-speed machining range of an Al 6061-T6 alloy. The analysis of the results revealed that SCE consumption and Ra improve when the cutting speed is increased from conventional to high-speed machining. In particular, SCE was observed to reduce linearly in conventional and transitional speed machining, whereas it followed a parabolic trend in high-speed machining. This parabolic trend indicates the existence of an optimal cutting speed that may lead to minimum SCE consumption. Chip morphology was performed to further investigate the parabolic trend of SCE in high-speed machining. Chip morphology revealed that the serration of chips initiates when the cutting speed is increased beyond 1750 m/min at a feed rate of 0.4 mm/rev.  
high-speed machining; specific cutting energy; surface finish; sustainable manufacturing; Taguchi methods +
Footnote †: journal: Applied Sciences  
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# A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation
## 3 Channel Estimation Algorithm Based on Space-Time Block-Code
### LS Channel Estimation Algorithm Based on STBC  
#### 3.2.1 Channel Estimation Algorithm for the Single-Symbol Scheme  
Basically, a pilot occupies one symbol in OFDM's data block of each antenna in the transmitter. The received signals will be the overlap of all of the signals induced from each antenna on the receiver side. The received signal at the antenna on the receiver side is \(Y=\mathbf{X}_{1}\mathbf{H}_{1}+\mathbf{X}_{2}\mathbf{H}_{2}+\mathbf{W}\), from which it can be seen that all signals are interfered by each other. Since traditional LS is unable to identify the fading coefficient in the case of multiple channels, some measures are adopted for the transmitter so as to make the receiver able to identify signals that impinge different channels [17; 18]. Through designing the special pilot, each sub-carrier on the receiver side will contain just one channel parameter, which will make it available to separate signals on each antenna in the transmitter. Based on such idea, all sub-carriers are separated into two parts \(K1\) and \(K2\) where:  
\[K1=\{0,2,\ldots N-2\}\quad K2=\{1,3,\ldots N-1\}\]  
Now the pilot at the \(K1\) frequency is specified to be sent by antenna 1 in the transmitter, while that at the \(K2\) frequency is sent by antenna 2 in the transmitter. The received sub-carriers labeled with even number only contain channel information existing in the antenna pair consisting of antenna 1 on the transmitter side and the other antenna on the receiver side. In the same, the received sub-carriers labeled with odd numbers only contain channel information existing in the antenna pair consisting of antenna 2 on the transmitter side and the other antenna on the receiver side. Consequently, the channel characteristics can be split out from each individual pilot sub-carrier. Then the characteristics of a channel can be retrieved through interpolating with the opposite sub-carriers in the channel in frequency domain. The detailed implementation can be explained as follow.  
Based on the rule of LS, the received signals, \(Y(k1)\) and \(Y(k2)\) that correspond to \(K1\) and \(K2\), are written as:  
\[Y(k1) = X_{1}(k1)H_{1}(k1)+W(k1)\quad k1\in K1\] \[Y(k2) = X_{2}(k2)H_{2}(k2)+W(k2)\quad k2\in K2\] \[\Rightarrow\hat{H}_{1}(k1)=\frac{Y(k1)}{X_{1}(k1)}\quad\hat{H}_{2 }(k2)=\frac{Y(k2)}{X_{2}(k2)}\]  
where \(k1\) and \(k2\) represent the sub-carrier in the collection of frequency points in \(K1\) and \(K2\), respectively. \(X_{1}(k1)\) and \(X_{2}(k2)\) denote the pilot symbol carried by sub-carrier \(k1\) and \(k2\), respectively. \(H_{1}(k1)\) and \(H_{2}(k2)\) are the channel impulse responses on sub-carrier \(k1\) and \(k2\), respectively. \(W(k1)\) and \(W(k2)\) are Gaussian white noises with a mean value of 0 and the variance value of \(\delta^{2}\). \(\hat{H}_{1}(k1)\) and \(\hat{H}_{2}(k2)\), which can both be sorted out through interpolation, are the estimated value of \(H_{1}(k1)\) and \(H_{2}(k2)\), respectively.
## 1 Introduction
This paper considers monitoring applications for an outdoor environment such as meteorological and hydrological data or the wetland ecology environment, and aims to reduce network energy consumption. It mainly focuses on the optimal selection of cluster heads, improves the traditional LEACH routing algorithm, and introduces and designs a routing algorithm based on the differential evolution algorithm (DE), the DE_LEACH routing algorithm in short. The DE_LEACH algorithm uses the easy and fast search features of the DE in multi-objective optimization applications, takes energy and distance distribution of neighbor nodes inside clusters into account, and then optimizes the selection of cluster heads. The algorithm has advantages in terms of effectively preventing premature blind nodes and reducing network energy consumption, and is more suitable than others to meet the needs of WSN applications in monitoring applications for outdoor environment such as those mentioned before. The rest of this paper is organized as follows: Section 2 reviews related work on WSN protocols; Section 3 describes the idea of differential evolution algorithms; Section 4 presents the proposed method; Section 5 proves the DE_LEACH algorithm through experiments and analyzes the experiment results, and finally, Section 6 concludes and indicates several issues for future work.
## 3 Method
### LPCNet Synthesizer  
To generate the converted speech, a waveform synthesizer based on a variant of WaveRNN vocoder [30] is used. We chose LPCNet vocoder [31], an efficient neural vocoder, which combines linear prediction with RNN to considerably improve the audio quality of the resynthesized speech. LPCNet generates speech from Bark-Frequency CepstralCoefficients (BFCCs) [46] and two pitch (period, correlation) parameters. This presents a high-level control of the spectral shape outputs as it directly depends on the shape of the linear predictive coding filter.  
To implement the model, we were inspired by open-source code made available by the Mozilla and Google LLC teams [31]. To achieve better control over high-frequency features, we increased the dimension of the input features from 18-D Bark-Frequency Cepstral Coefficients (BFCCs) to 30-D BFCCs. This resulted in the extraction of 32-D acoustic features comprising 30-D BFCCs, 1-D pitch period, and 1-D pitch correlation.
###### Abstract  
In this paper, a set of cooperative non-orthogonal multiple access (NOMA) transmission schemes is proposed, in which two half-duplex relaying user terminals (UTs) successively deliver the AP signals toward two NOMA UTs. The successive relaying (SR) of two HD relays emulates the full-duplex (FD) relaying transmission such that the whole transmission of one data block is finished in one time slot. Different from FD relaying, where the removal of self-interference (SI) at the relay is challenging, the SR is suffering from cross-relay interference (CRI) between the two HD relays and the handling of this CRI is the key technical challenge. This CRI suppression is implemented by beamforming with multiple antennas at the relaying UTs, while the beamforming also does the role of maximizing the NOMA transmission efficiency. Two beamforming schemes are proposed, where the first method employs semi-definite relaxation (SDR) with four dimensional (4-D) search. Here, the four dimensions come from the four parameters reflecting the CRI levels at the relaying UTs and signal strengths at the NOMA UTs. This scheme alternates until the 4-D search range is reduced and the sum throughput rate saturates. The second method is computationally simpler than the first one since it avoids the SDR and alternation, and it finds the beamformer set in one shot calculation. Both schemes rely on zero forcing of the CRI. These two schemes along with the HDR relay-based cooperative NOMA scheme are compared in the numerical experiments, and the results exhibit their pros and cons in several aspects.  
cooperative non-orthogonal multiple access; successive relaying; beamformer; semi-definite relaxation +
Footnote †: journal: Applied  
0000-0001-9999-4083-246
## 2 Description of the Study Region
### Hydrogeological Context  
The Jurassic dolomites constitute an important hydrogeological unit underlying nearly 800 km\({}^{2}\) (Figure 1: 4). This aquifer behaves as unconfined in the northwestern portion of the study area (Figure 2). Towards the south, it is confined by Miocene marls, while it is limited by dipping faults of the Prebetic units to the east, by the olistostromic units of the Guadalquivir River depression to the south and west it pinches out [8; 18]. In the western sector, the general groundwater flow path is NNW to SSE. Fracture systems compartmentalize the aquifer, changing piezometry levels and groundwater flow paths from place to place [18].  
Diffuse recharge occurs by direct infiltration from precipitation whereas preferential recharge from the Guadalimar River streamflow infiltration occurs in those northern sectors where riverbed intersects the carbonate formations (Figure 1). It is also worth considering the existence of lateral transferences from eastern Prebetic units (Figure 1: 5).  
The exploitation of the confined sector of the aquifer began in the early 1990s. The mechanical drilling methods before that data did not exceed 100 m depth from shallow Miocene strata only. The Jurassic dolomite aquifer has been exploited with pumping rates between 15 and 40 L/s only since 10 years ago thanks to deeper boreholes with depths in the 200-600-m range. Today, the pumping rate is much lower given the overexploitation that the aquifer unit suffers.  
Different groundwater chemical facies can be differentiated. Shallow boreholes (dolomites pumped at 200-300 m) generally yield slightly mineralized waters. Salinity increases considerably with depth, and deeper chemical facies pass from magnesium-calcium sulfate, sodium bicarbonate-sulfate, sodium bicarbonate, to sodium chloride [18]. Chemical reduction processes in the deeper confined parts [18] and mixing of groundwater from Triassic, Jurassic, and Miocene aquifers have been described [9; 19].
## 4 Proposed Method
### Construction of a Traceability Matrix  
In this study, the artifacts to be connected are the functional requirements and the components and interfaces that comprise the component architecture. We use the traceability matrix method to demonstrate the traceability between these two elements. The columns of the traceability matrix used to visualize the traceability between the two elements are structured as shown in Table 1.  
Additional columns related to the designer's interests can be added or removed. In this study, the traceability matrix shown in Table 1 focuses on the traceability between two elements: the functional requirements, represented by the 'Req. ID' column, and the components and interfaces that make up the component architecture, represented by the 'Component' and 'Interface' columns.  
To generate the traceability matrix \(TM_{i}\) corresponding to the \(i\)-th design result, a structured feature list \(L\) and mapping information \(M_{i}\) are required. We generate a traceability matrix using \(T\) and \(L\) as inputs with the following algorithm shown below.  
In lines 2-4 of the algorithm in Algorithm 3, the attributes RID and \(SF_{id}\) of \(SF_{id}\) are entered into the 'Req. ID' and 'SF. ID' rows of \(TM_{i}\), respectively, whereas the mapping information pairs in \(M_{i}\) are added to the component and interface columns corresponding to each structured feature. The difference is that line 2 extracts information from \(L\), whereas lines 3-4 extract information from \(M_{i}\).  
```
0: Structured Feature List \(L\), \(i\)-th mapping information \(M_{i}\)
0:\(i\)-th Traceability Matrix \(TM_{i}\)
1:for all id-th \(SF_{id}\) in \(L\)do
2:\((Req.ID,SF.ID)\leftarrow\langle SF_{id,ID},SF_{id}\rangle\)
3:\((SF.ID,Component)\leftarrow\langle SF_{id},c_{m}\rangle\)
4:\((SF.ID,Interface)\leftarrow\langle SF_{id},I_{n}\rangle\)
5:endfor
6:return\(TM_{i}\)
```  
**Algorithm 3**Generating \(TM_{i}\)  
The structured features serve as intermediaries that connect the requirements and component elements. By adding a column for the identifiers of the structured features in the traceability matrix, designers can visually confirm the connections between two elements. More detailed relationships can be identified by referencing the structured feature identifiers in the structured feature list, enabling a link between the traceability matrix and structured feature list. The list of structured features also includes attributes related to actions and hardware elements, allowing designers to verify the actions or hardware elements of the components in the component architecture. By linking the traceability matrix with the structured feature list, designers can effectively trace the relationships between requirements and component architecture elements.
## 3 Results and Discussion
### Mass Balance of Anticancer Drugs  
The mass balance for each target anticancer drug in the Kanzaki-Ai River basin could further be estimated by calculation of percent contribution of total efflux load in the influent load through the farthest downstream boundary site (R5). In the present article, the total efflux load for each anticancer drug was obtained by summation of the mass fluxes from two data sets from Kanzaki River (R3 and T4) and Ai River (R1, S1, T1, T2, and S2). The influx load was estimated as the load passing through the boundary site (R5). The resulting mass balance data are shown in Figure 5[30].  
The load of CP was reduced by 70% as it was flowed downstream indicating occurrence of attenuation, but the attenuation rates for the other anticancer drugs were smaller, roughly in the range of 15-50%. BLT has a high recalcitrant property and detected at high concentrations in the river waters [75; 76]. Therefore, some of the anticancer drugs are attenuated while being flowed to the lower reaches, but the rate of the attenuation is slow and such anticancer drugs become the matter of environmental pollution.  
Today, STPs play an important role for maintaining the quality of river water environment. In Japan, the contributions of pharmaceutical components, including endocrine-disrupting chemicals (EDCs) such as estrogen, to the pollutant loads in the river waters range from 50% to nearly 100% [73; 77], because of the high coverage of segregating systems by STPs (more than 90% of urban areas) [35]. As a result, STPs become indispensable facilities responsible for reducing the levels of pharmaceuticals included in sewage effluent [16]. The conventional activated sludge (CAS) process, which is often used as the convenient physicochemical and biological treatment at STPs, covers 35% of biological water treatment in Japan [35]. However, its insufficient ability to eliminate anticancer drugs at STPs, as described above, necessitates the introduction of additional post-treatment technologies. Importance of such an advanced water treatment systems at STPs is becoming widely recognized [78; 79; 80], because of high power for removing not only PPCPs but also other chemicals such as EDCs [81], persistent organic pollutants (POPs) [82], bacterial pathogens, and viruses [83].  
Figure 5: Mass balances of anticancer drugs in the Kanzaki-Ai River basin. N.A.: not available. The abbreviation of each anticancer drug is shown in Table 1 (reproduced from [30]).
## 5 Simulation Characterization
### Simulation Metrics  
#### 5.2.1 Privacy Metrics  
We define the privacy awareness ratio (PAR) metric that rates the number of privacy-aware nodes in the forwarding path for any message between source A and destination B. By privacy-aware node, we mean a node that uses one of the three proposed routing selection modes previously defined. This contrasts the privacy-unaware node, which proceeds in its default routing selection setting (Epidemic, Prophet, and Spray and Wait) without involving the three proposed selection modes. We stated that in the privacy-awarerouting, we enforce privacy policies by prioritizing nodes satisfying the node policy to be selected. When the node cannot find relay nodes satisfying the selection mode criteria, it proceeds with its common routing strategy to balance message privacy preservation and delivery time. Let \(n\) be the total number of selected privacy-aware nodes in the path between \(A\) and \(B\) and \(N\) be the total number of nodes constituting that path; then, \(PAR\) is calculated by:  
\[PAR_{policy}(A,B)=\frac{n}{N} \tag{5}\]  
_PAR_ equals 1 when all intermediary hops successfully enforce the privacy-aware selection policy on all the path nodes. When we use routing algorithms without privacy policy enforcement, we can determine that the \(PAR\) can be 0 or greater than 0 since it can unintentionally select trust nodes when constructing the path between \(A\) and \(B\). Therefore, we compute the privacy enhancement ratio (\(PER\)):  
\[PER=\frac{PAR_{with-privacy}-PAR_{without-privacy}}{PAR_{without-privacy}} \tag{6}\]
## Appendix A
### Transcript Preprocessing  
Since all the transcripts were in Chinese, we applied Jieba, a Python Chinese word segmentation module, to conduct text segmentation. Jieba is a widely used tool in natural language processing, and it supports three types of text segmentation modes, including accurate mode, full mode, and search engine mode. Here, we selected the accurate mode since it cuts a sentence into the most accurate segmentations and is thus most suitable for text analysis in our research.  
Furthermore, we also conducted basic data cleaning by deleting meaningless punctuation such as \"...\", spaces between strings, and strings without Chinese characters. However, we did not set the stop words for all the transcripts, such that the original oral style of the influencers and the example transcript collection could be retained for subsequent analysis.
# An End-to-End Video Steganography Network Based on a Coding Unit Mask
## 2 Proposed End-to-End Video Steganography System
### Architecture
#### 2.1.1 Coding Unit Mask and Convolutional Block Attention Mechanism  
Most video coding standards are based on CU partition mode to carry out transforming, quantization, entropy coding, etc. Figure 2 shows the CU division of one video frame in which the content in the red box is magnified in the lower left corner. We can see that the CU division fits the content of the video frame very well, and the CU blocks in flat areas are large, while those in areas with complex details and textures are small. The CU division of the VVC compressed video frame represents the diversity and complexity of its content. Therefore, we extracted the CU partition during VVC encoding and made it into a binary mask, named the CU mask, which assisted CNN to better extract features to hide messages in areas of the video that are not easy to detect, such as areas with intricate textures. Finally, it improved the imperceptibility of stego video frames.  
Meanwhile, as shown in Figure 3, the convolutional block attention module (CBAM) [29] was used as our attention mechanism. It successively includes two parts--channel attention (CA) module and spatial attention (SA) module--which assist in focusing on \"what\" and \"where\" is meaningful regarding the input features, respectively. It is a plug-and-play module that requires few parameters to boost the representation of CNNs.  
Figure 1: Architecture of the proposed PyraGAN.  
Each channel of the features can be regarded as a feature detector. The CA assigns a weight to the input \(F\)-dimensional features by learning, and obtains a tensor of size \(F\times 1\times 1\), in which the larger the weight coefficient is, the more important the channel it represents for feature learning of the model. Then, the result is copied and expanded into the tensor of \(F\times H\times W\) (\(H\) and \(W\) represent the height and width of the features, respectively) along the spatial dimension. Afterwards, this along with the input features are multiplied pixel by pixel. So far, CA makes the network learn to focus on the channel features that contribute greatly to the result. Sequentially, SA assigns weights to the intermediate \(F\)-dimensional features, and outputs a tensor of size \(1\times H\times W\) in which the larger the weight coefficient is, the more important the feature is at this location for model learning. Then, the result is copied and expanded into the tensor of \(F\times H\times W\) along the feature channel dimension and is multiplied by the features obtained from the previous layer. So far, SA makes the network learn to focus on the positions of features that contribute greatly to the results. Finally, the output feature size of the attention mechanism is the same as the input feature size, which is \(F\times H\times W\).