esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 5 Discussion
### Proposed Research Directions for Anomaly Detection in IoT Environments  
According to the presented research, future work should focus on several key areas. It is critical to expand the range of datasets and ML methods used. Diverse data from different IoT domains are essential for creating highly generalizable models. At the same time, the impacts of different preprocessing techniques and hyperparameter selection on the performance of these models should be investigated. Hybrid models combining rule-based systems with ML are worth exploring, which can effectively leverage the advantages of both approaches. The use of ensemble methods can further improve detection accuracy and reduce the number of false alarms. The development of explainable AI (XAI) is crucial for increasing confidence in anomaly detection models. With XAI, it is possible to understand the decisions made by a model, which makes it easier to interpret the results and identify potential errors. In the context of data privacy, federated learning is a promising solution. It allows training models on distributed data without the need for the centralization of information, which is especially important in the IoT. However, the impact of non-IID data on the effectiveness of federated learning should be noted and appropriate strategies should be considered. In addition, communication bandwidth constraints can be a challenge in IoT environments. Therefore, it is necessary to develop methods for reducing throughput that do not negatively affect the quality of the model. Threat-Adversarial ML poses a serious challenge to IoT systems. Models must be developed that are resistant to attacks using, among others, malicious input data. The effective implementation of anomaly detection systems also requires effective mechanisms for responding to detected threats [68, 69, 70, 71].  
Monitoring and analyzing large datasets of inter-cellular signaling traffic provides the ability to understand IoT client characteristics, network performance, network clustering, typical signaling profiles in a specific IoT industry, misconfigured devices, and observed sudden changes in devices that reflect potential problems. This helps make decisions about network management, service improvement, and IoT traffic modeling [72, 73].
## Appendix A Examples of the Logical Rules-Guided in-Context  
Triples Retrieval  
In this appendix, some examples are given to explain the details of the parts: logical rules-guided in-context triples retrieval.  
The aim of this part is to construct training data to pre-train the in-context triple extractor. Assuming that a triple is sampled from the knowledge graph \(\mathcal{G}\):  
([PERSON], nationality, USA)  
The logical rules about the relation \"nationality\" can be learnt from the knowledge graph by existing logical rule learning models:  
* (x, nationality, _y_)\(\leftarrow\)(x, works in, z)\(\land\)(z, located in state, _w_)\(\land\)(_w_, state in country, y_)
* (x, nationality, _y_)\(\leftarrow\)(x, born in, z)\(\land\)(z, located in state, _w_)\(\land\)(_w_, state in country, y_)  
Then, we can extract triples which support each rule from the knowledge graph \(\mathcal{G}\):  
* {([PERSON], works in, Facebook), (Facebook, located in state, California), (California, state in country, USA)}
* {([PERSON], born in, New York City), (New York City, located in state, New York State), (New York State, state in country, USA)}  
Thus, we can obtain the set of in-context triples through combining the extracted triples above:  
{([PERSON], born in, New York City),  
(New York City, located in state, New York State),  
(New York State, state in country, USA)  
([PERSON], works in, Facebook),  
(Facebook, located in state, California) }  
Finally, training data can be constructed which mainly contain the in-context triples with the corresponding query triple.
## 6 Examples
### Example 1: Cyclic Matrix Linear System  
A cyclic matrix, \(\mathbf{A}\), is generated from the first row \((1,\ldots,n)\). The exact solution is assumed to be \(x_{i}=1,\ i=1,\ldots,n\), and \(\mathbf{b}\) can be obtained from Equation (1). First we apply GMRES to this problem with \(n=300\) and \(\varepsilon=10^{-10}\). The initial values are \(x_{i}^{0}=1+1/i,\ i=1,\ldots,n\). For this problem, we encounter the situation that \(\eta_{k}<1\) after 150 steps, not \(\eta_{k}=1\) as shown in Figure 0(a) by a solid line. As shown in Figure 0(b), the residuals blow up for GMRES.  
This problem shows that the original GMRES method cannot be applied to solve this linear system, since \(\eta_{k}<1\) happened after 150, and the iteration of GMRES blows up after around 157 steps. In this situation, by Equation (35),  
\[\|\mathbf{r}_{k+1}\|>\|\mathbf{r}_{k}\|,\]the residual obtained by GMRES grows step-by step; hence, the GMRES method is no longer stable after 157 steps.  
To overcome this drawback of GMRES, we can either relax the convergence criterion, or employ the version of GMRES(\(\eta\)) before it diverges.  
Table 1 compares the maximum error (ME) and number of steps (NS) for different methods under \(\varepsilon=3\times 10^{-10}\). \(m_{0}=10\) is used in GMRES and ROGMRES; \(m_{0}=1\) is used in GMRES(\(\eta\)); \(m_{0}=30\) and \(m_{1}=40\) are used in GMRES(m); \(m_{0}=35\) and \(m_{1}=40\) are used in ROGMRES(m) and GMRES(m,\(\eta\)); \(m_{0}=50\) and \(m_{1}=50\) are used in MPA(m) and A-GMRES(m). As shown in Figure 1a by a dashed line and a dashed-dotted line, both ROGMRES and ROGMRES(m) can keep the value of \(\eta_{k}=1\) up to the termination; as shown in Figure 1b, they converge faster than GMRES. ROGMRES(m) converges faster than ROGMRES. This example shows that ROGMRES can stabilize GMRES when it is unstable, as shown in Figure 1b.  
It is interesting that the MPA(m), with a fixed value \(m=m_{0}=m_{1}=50\), can attain a highly accurate solution with ten steps, as shown in Figure 1b; A-GMRES(m) obtains \(\text{ME}=5.88\times 10^{-14}\) with 15 steps by using \(m=m_{0}=m_{1}=50\). If the orthogonality is not adopted in A-GMRES(m), \(\text{ME}=7.51\times 10^{-14}\) is obtained with 14 steps; it means that \(\eta_{k}=1\) can be kept, even though the orthogonalization technique was not employed. Within ten steps, A-GMRES with \(m_{0}=50\) can obtain \(\text{ME}=7.06\times 10^{-14}\). Within nine steps, A-GMRES(m) with \(m_{0}=50\) and \(m_{0}=51\) can obtain \(\text{ME}=8.02\times 10^{-14}\).  
Figure 1: For example 1: (**a**) \(\eta\) of GMRES, ROGMRES, ROGMRES(m), and MPA(m), and (**b**) the residuals blowing up obtained by GMRES, ROGMRES, ROGMRES(m), and MPA(m) do not blow up.
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms
### Biofilm Matrix
#### 2.1.2 e-DNA  
The extracellular DNA found in the EPS matrix of most bacterial biofilms may be of either endogenous or exogenous origin. Endogenous e-DNA may be produced in a quorum sensing-mediated process (see Section 2.4), or it may derive from cell lysis within some biofilm subpopulations [21]. On the other hand, exogenous e-DNA is host-derived originating from the lysis of polymorphonuclear leukocytes at infection sites [22].  
Since the sugar-phosphate backbone of DNA is deprotonated at physiological pH, the negative charge of the resulted polyanion is balanced by the binding of positively charged Mg\({}^{2+}\) counterions [23]. This causes a local drop in Mg\({}^{2+}\) concentration being the environmental signal that triggers activation of the PhoPQ and PmrAB two-component systems (see Section 2.4) in _P. aeruginosa_ and _S. enterica_ serovar Typhimurium biofilms [24; 25; 26]. At the same time, the slightly acidic microenvironment created by e-DNA accumulation is another signal synergistically contributing towards activation of the same PhoPQ and PmrAB two-component signal transduction pathways. These two-components systems upregulate the PA3552-3559 operon, which is responsible for the expression of genes-encoding enzymes able to modify the lipopolysaccharide-acharide (LPS) in the outer membrane of Gram-negative bacteria by adding amino arabinose to lipid A moiety [27]. This positively charged amino sugar could reduce the affinity of cationic antimicrobial peptides (AMP) to the bacteria resulting in increased bacterial resistance towards AMPs such as polymycin B and colistin. According to [PERSON], [PERSON], and [PERSON], the resistance of wild-type _P. aeruginosa_ biofilms towards polymycin B and colistin was increased by 4-fold and 8-fold respectively by adding e-DNA [27; 28]. On the other hand, e-DNA addition has no effect concerning resistance increase for either antibiotic in a \(\Delta\)PA3553 deletion mutant. Controversial results were obtained by different research groups investigating the role of the PA3552-3559 operonin promoting biofilm resistance to AMPs and aminoglycososide antibiotics [29], suggesting that other mechanisms besides protection of the outer bacterial membrane from the detrimental effect of AMPs might be involved.  
Another potential mechanism underlying the role of e-DNA in the development of antibiotics resistance in biofilms is the horizontal transfer of antibiotic resistance genes (HGT) between bacterial cells (see also Section 2.5). [PERSON], [PERSON], and [PERSON] provided convincing evidence for an efficient HGT between single-resistant bacteria within early \(N\). _gonorhoeae_ biofilms [30].
## 2 Related Work  
### Event Detection  
Deep learning-based event detection leverages neural networks to extract semantic features. The advent of pre-trained models like BERT [6] has introduced the \"pre-training + fine-tuning\" paradigm, enhancing model performance. Methods are generally categorized into span-based classification, sequence labeling, and pointer networks.
# Zeroize: A New Method to Improve the Utilization of 5G Networks When Running VoIP over IPv6
## 3 Zeroize Method Design
### Carrying the Voice Data  
The Zeroize method uses the source IPv6 address of the packet to carry the voice data. The sender client extracts the voice data from the packet. The voice data are then placed in the source IPv6 address field. The remaining bits of the IPv6 address are padded because the voice data are small. This process changes the packet payload length and affects the payload length field in the IPv6 header and the length field in the UDP header [19]. Therefore, the value of these fields should be modified appropriately on the basis of the new size. The pseudocode in Figure 2 clarifies the algorithm of the VoIP client at the sender side. Lines 1-6 are used to identify the variables of the pseudocode. Lines 8-12 are used to place the voice data in the source IPv6 address field and set the remaining bits (of the IPv6 address field) to zeros. Lines 13-17 are used to modify the value of the Payload Length field in the IPv6 header and the value of the Length field in the UDP header. The VoIP client extracts the voice data from the source IPv6 address field and places it in the playout buffer at the receiver. Only the voice data must be extracted without the padding. For this process, the VoIP client should check the used codec to specify the length of the voice data. The used codec is specified in the signaling stage of the VoIP call. Figures 3 and 4 clarify the process performed at the sender and receiver sides by the VoIP client, respectively.  
Figure 2: Zeroize method algorithm: sender side.
###### Abstract  
The main objective of this study is to design titanium alloy femoral stems with cubic porous structures that will be able to reduce stress shielding and promote stem stability. These porous structure designs were introduced into titanium alloy femoral stems as homogeneous and functionally graded porous structures. First, the cubic cellular structures were simulated under compressive loading to measure the yield and modulus of elasticity for various porosity ranges. Based on the selected porosity range, fifteen different arrangements of radial geometrical functionally graded (FG) designs were developed with average porosities of 30, 50, and 70% respectively. Finite element models were developed with physiological loads presenting three different walking speeds (1, 3, and 5 km/h), where the average human body weight was assumed. Stresses at the bone Gruen zones were measured to check the percentage of stress transfer to the bone for each porous stem design and were compared with the bulk stem. Several FG stem designs were shortlisted for further investigation as candidates for hip implants.  
item design; implants; hip replacement; femur bone; total hip arthroplasty; computational analysis +
Footnote †: journal: Applied Sciences
## 4 Results
### Part I  
To achieve this, the NITS-302 dataset was used as a basis for evaluating five variants of the two most widely employed CNN architectures (VGG and ResNet). NIST-302 was chosen for this evaluation due to its extensiveness and relatively high image quality, as well as its comprehensive coverage, including images of all 10 fingerprints. We used pre-trained ImageNet networks at a learning rate of 0.0001 and a batch size of 32 with an AdaGrad optimizer to train the selected models. The study utilized a variety of techniques to train our convolutional neural network (CNN) models. For data augmentation, we implemented random horizontal flipping of images, with a probability of 0.5 and a random rotation within a range of \(\pm\)15 degrees. This approach enhanced the diversity of our dataset and aided in reducing overfitting. To address the issue of class imbalance in our dataset, we used a weighted random sampler. The sampler calculates the distribution of the classes in the dataset and assigns weights inversely proportional to the class frequencies. This ensures that each sample's probability of being picked for a batch is relative to its class representation, resulting in a more balanced training process. Our dataset was divided into a training and a testing set, with an 80-20 split (last column of Table 2). This is a commonly used ratio, providing a significant amount of data for training while still leaving an adequate amount for testing.  
Table 3 shows that ResNet18 exhibited overfitting issues and produced suboptimal results. Compared to ResNet, VGG16 and VGG19 achieved a test accuracy of 0.83, which is notably higher than ResNet's 0.75. It appears that VGG19 optimizes the trade-off between model complexity and the generalization of unknown data; this is evident when focusing on the balance between train and test accuracy. Furthermore, the F-score, which represents a consolidated view of model performance based on precision and recall, recommends the VGG models. Specifically, VGG19 achieved the highest test accuracy and F-scores of 0.84 and 0.81, respectively.  
Next, the selected VGG19 model was evaluated across the four databases we used--one internal and three public.  
As can be seen in Table 2, the VGG19 network can be generalized to other datasets, as the classification results range from 70 to 95% with no evidence of overfitting. The results are correlated with the size and quality of the images in the dataset, as indicated in Table 1. IsrPoliceDB yielded the highest classification accuracy rate of 96%, in accordance with the dataset having the largest image size and resolution. A moderately sized image and resolution in the NIST datasets (DB4, 302) achieved an accuracy rate of 80%, while a considerably smaller image achieved an accuracy rate of 68%. These results indicate that the VGG network is capable of capturing positive samples effectively, while minimizing false positives reliably and stably.
## 3 Results and Discussion
### Calculation of Thrust Measurement Uncertainty
An analysis of the data presented in Table 4 indicates that the primary source of uncertainty in the measurement chain is the module of the data acquisition system. This highlights the significant impact of the system's variability on the overall measurement precision. If it is necessary to mitigate this issue, the use of modules with low parameter variation emerges as an effective method to reduce uncertainties in the measurement chain.
# Sem-SLAM: Semantic-Integrated SLAM Approach for 3D Reconstruction
## 4 Experiments
### Quantitative Results  
To comprehensively evaluate the performance of the proposed method, during the quantitative experiment phase, key metrics such as the mean Intersection over Union (mIoU) and the Absolute Trajectory Error (ATE) were selected for comparison with existing SLAM methods based on NeRF.  
Meanwhile, two different environmental scenarios were carefully selected from the Replica dataset [6] and the ScanNet dataset [7], and the experimental results were averaged to ensure the reliability and generalizability of the outcomes.  
As shown in Table 1, in the comparison of the mean Intersection over Union, the proposed method innovatively introduces a multi-scale information extractor. This extractor can efficiently extract information at different scales from the camera pose. Moreover, by employing a full-attention feature fusion method, it seamlessly integrates geometric, shape, and semantic features based on cross-attention and self-attention mechanisms. This unique architecture enables the model to fully exploit the value of features at various levels, significantly enhancing its semantic modeling ability. The experimental results demonstrate that, compared with existing NeRF-based SLAM methods, the proposed method has prominent advantages in terms of reconstruction accuracy and integrity, strongly proving its superior performance in semantic modeling tasks.  
As illustrated in Table 2, in the evaluation of the Absolute Trajectory Error, the proposed method, by virtue of the precise capture of camera pose information by the multi-scale information extractor and the comprehensive integration of environmental perception information through cross-modal feature fusion, reduces the trajectory estimation error. The experimental data shows that, on the same dataset, the Absolute Trajectory Error of the proposed method is lower than that of the comparative methods. Moreover, the confidence values in the table indicate that our method achieves a confidence of 0.92, which is higher compared to other methods, further highlighting its superiority in positioning accuracy and emphasizing the crucial role of the method in optimizing trajectory estimation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{3}{c}{**Replica**} & \multicolumn{3}{c}{**ScanNet**} \\ \cline{2-7}  & **Room 0** & **Room 1** & **Avg** & **0000** & **0106** & **Avg** \\ \hline NIDS-SLAM & 60.23 & 30.51 & 54.64 & 70.78 & 63.46 & 10.16 \\ \hline SNI-SLAM & 58.46 & 50.48 & 64.78 & 84.62 & 55.84 & 53.64 \\ \hline Sem-SLAM & 88.56 & 90.26 & 81.45 & 86.21 & 78.96 & 80.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: This table demonstrates the comparison results of the proposed method and existing NeRF-based SLAM methods in terms of the mean Intersection over Union (mIoU) metric on the Replica and ScanNet datasets.
# Hyperspectral Imaging for the Detection of Bitter Almonds in Sweet Almond Batches
## 3 Results and Discussion
### Classification Models of Almonds by Bitterness  
After applying the CENTER algorithm, one sweet sample was identified as a spectral outlier. Then, the training and validation sets for the sweet and bitter categories were selected, which were constituted by 117 samples (72 sweet and 45 bitter) and 40 samples (24 sweet and 16 bitter), respectively.  
Table 1 shows the results of the best PLS-DA models obtained to distinguish between sweet and bitter almonds analysed in bulk using the HSI system. The first derivative--together with normalisation and detrend as scatter correction--was the math treatment that yielded the best results in cross-validation. These results were obtained considering a threshold value of 0.66 provided by the ROC curve, which maximises the specificity and sensitivity, enabling to obtain a higher NER value.  
As shown in Table 1, the 99% (71/72) of the samples of sweet almonds were correctly classified in cross-validation, whereas for the bitter category, the percentage of correctly classified samples was 100% (45/45). As regards the external validation, for both categories, 100% of the samples were correctly classified.  
Once it was demonstrated the viability of using the HSI technology to discriminate almonds according to their bitterness. In order to increase the variability covered by the model, new global models were developed by merging the training and validation sets. The best classification model was obtained using the first derivative math treatment and a threshold value of 0.64 and the classification success obtained was 99.4% (99% in the sweet category and 100% in the bitter category).  
Although there are no published articles based on the use of the HSI technology for the classification of almonds by bitterness, similar works have been carried out using NIR spectroscopy. [PERSON] et al. [5], analysing individual kernels, and [PERSON] et al. [7], analysing samples in bulk, used different NIRS instruments and reported similar results to those obtained in this work with NER of 100% for both categories in external validation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Set**} & \multicolumn{3}{c}{**Category**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**NER**} \\ \cline{2-2} \cline{5-6}  & **Sweet** & & & & & \\ \hline Training & 71/72 & 45/45 & 116/117 & 0.99 & 1.00 & 0.99 \\ Validation & 24/24 & 16/16 & 40/40 & 1.00 & 1.00 & 1.00 \\ Global & 95/96 & 61/61 & 156/157 & 0.99 & 1.00 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Discriminant models for classifying sweet almond batches by bitterness using the mean spectrum.  
Figure 1: Absorbance (**a**) and second derivative spectra (**b**) of the different groups of samples analysed (sweet, bitter and mixtures).