esa-sceva/satcom-chunk-collection huggingface dataset:
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 4 Attack Types
### Denial of Service Attacks (DoS)  
**Denial of service**. DoS attacks are attacks that intend to shut down a machine or network, making it inaccessible for its intended users [44]. They are accomplished by flooding a target or sending it information, or by programs which cause it to use up more resources (CPU, memory, energy) than it should and eventually crash. Most of the attacks in the routing section can also be classed as DoS attacks, as they deny the service of communications to the intended users.  
**Distributed denial of service**. A DDoS attack is a DoS attack carried out by multiple entities. The intentions of a DDoS attack are the same as those of a DoS attack, but distributing the attack creates more attack power (due to more machines), making it harder for the target to combat. It also creates more than one attack point, making it more difficult to detect the attack location and block the malicious entities.  
**Energy exhaustion**. This is a type of DoS attack that can be especially concerning for low-power or wireless networks. In some networks, the nodes may operate on battery power, and if an energy exhaustion attack is successfully carried out over a short period of time, the nodes can run out of power. Some of these nodes are in hard-to-reach areas, costing the organization that owns the network money and time by creating a need to replace the batteries early. A good example of this is provided in [23, 28], where the nodes are in underwater networks, making node access difficult and energy exhaustion attacks detrimental to the networks' operations, which could cause the nodes to be down for a prolonged period of time due to a dead battery.
# Rapid Prototyping of Multi-Functional Battery Energy Storage System Applications
## 3 Related Work and Background
### Ontologies for Smart Grid Applications  
Ontologies search for a formal representation and categorization of information abstracted from real systems. This is achieved by referencing the components: classes, individuals, relations, attributes, rules, etc. [26]. The main advantage of ontologies is the inference of new knowledge from explicit knowledge by the execution of reasoning mechanisms. The most well known ontology language is Ontology Web Language (OWL), a key benefit offered is the expression of complex knowledge in a machine-readable form. Moreover, due to OWL being based on description logics (DL), an expressive formal knowledge representation is obtained.  
DL give mechanisms to describe accurately ontologies by introducing the terminological (\(TBox\)) and assertional (\(ABox\)) component [27]. The \(TBox\) contains sentences describing concepts and relation between concepts. Instantiation of those concepts are carried out by the \(ABox\). For instance, the statement: _an EMS embeds more than one service_, belongs to the \(TBox\), and the concepts identified are EMS and service. In turn, the sentence: _frequency-watt mode is a service_ belongs to the \(ABox\), where the individual frequency-watt is an instance of the concept service.  
A mechanism to enhance the reasoning process embedded within ontologies is by stating rules, which are statements in the form of \"if X then Y\" sentence. The W3C consortium recommends Semantic Web Rules Languages (SWRL) to denote them.  
The tasks typically solved with ontologies corresponds mainly to three categories. The first one is the ontology alignment, the study [28] proposes an ontology matching process to align data models of two broadly accepted smart grid standards the CIM and IEC 61850. Additionally, in the study [29] an ontology-driven approach transforms control systems into IEC 61499 control applications by using eSWRL an extension of the SWRL. Another goal of the various ontology applications is the integration of knowledge (second category). An ontology for smart grid interactions in Building Energy Management Systems (BEMS) to optimize end-user consumption is built in [30]. In that light, models for communication technologies, interaction between stakeholders and grid structure were addressed. Another example is an smart grid information model that combines weather, spatial and time ontology applied to dynamic demand response applications [31]. The third category is related to identification of critical errors and faults at the design phase of the engineering process [32; 33]. It is worth noting that none of the exposed approaches is multi-functional BESS oriented.
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 3 Proposed Method
### Patch Feature Extraction  
For industrial anomaly-detection tasks, a good feature extractor should distinguish the difference between normal and anomaly samples. When the dataset contains anomaly samples without any labels, it is hard to train a good feature extractor. Some recent work uses networks that are pretrained on ImageNet for anomaly detection [15; 16; 18; 34], and these works illustrate the ability of pre-trained networks to extract discriminative features for anomaly detection. We use a network pretrained on ImageNet to generate the patch-embedding vector.  
Different blocks produce feature maps containing different hierarchical semantic information in the ResNet-like architecture. Since anomalies may occur at different scales, and to avoid features being too biased towards ImageNet classification, we choose the feature map of blocks 1,2,3 outputs of WideResnet-50 to extract the patched-level features as shown in Figure 3. We use \(\phi_{q},q\in\{1,2,3\}\) to denote the output of block \(q\).  
To fuse the information from different hierarchies and align feature maps spatially, we use bilinear interpolation to resize \(\phi_{2}\) and \(\phi_{3}\) to the same resolution on \(h\) and \(w\) as \(\phi_{1}\), as shown in Equation (1). Here, \(\phi_{q}\in\mathbb{R}^{c\times h\times w}\)is a three-dimensional tensor of channel \(c\), height \(h\), width \(w\).  
\[\hat{\phi}_{q}=resize(\phi_{q}),q\in\{2,3\} \tag{1}\]  
Then we concatenate \(\phi_{1},\phi_{2},\phi_{3}\) on the channel, as shown in Equation (2).  
\[\hat{\phi}_{\{1:3\}}=cat(\phi_{1},\hat{\phi}_{2},\hat{\phi}_{3}) \tag{2}\]  
We fuse the feature vectors with the neighborhoods to reduce the resolution while retaining information. \(avg\) is the average pooling with kernel size \(k\) and striding \(s\), as shown in Equation (3).  
\[\hat{\phi}=avg(\hat{\phi}_{\{1:3\}},k,s) \tag{3}\]  
Each \(c\)-dimensional feature slice at positions \(i\in\{1,...,h^{\prime}\}\) and \(j\in\{1,...,w^{\prime}\}\) is the patch feature vector of the corresponding image-patch at the spatial position. \(c\), \(h^{\prime}\), \(w^{\prime}\) are the channel, height, width of \(\hat{\phi}\).  
Figure 3: Extracting feature map from WideResnet-50 pretrained on ImageNet.  
Figure 2: The structure of our proposed method PalRE.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 2 Overview of mmWave HetNets
### Characteristics of 5G Networks and Devices
#### 2.1.2 Spectra  
The proposed mmWave HetNet consists of a wide range of available spectrum bands, from low bands (licensed bands) and middle bands (sub-6 GHz unlicensed bands) to higher bands (mmWave bands). Due to various transmission power levels and channel characteristics, different spectra may have different network coverages, which brings stiff challenges to mobility management and network association.
## 1 Introduction
The Yagi geometry helps make the design attain a significantly higher gain that is demanded, belonging to the mmW band along with considerably better impedance matching and helps it accomplish the additional characteristic to resonate within the satellite X-band at the same time. In [23], a planar and dual-band Multi-Input Multi-Output (MIMO) mmW structure intended for 5 th Generation communication applications was introduced. The dual-band design operating in 27 GHz and 39 GHz consists of monopole elements. The model is undoubtedly fabricated on Rogers 4003C substrate. The design, apart from acquiring a decent radiation efficiency of 98 to 99%, offers a decent mutual coupling among the ports and a maximum gain spread of 5-5.7 dBi. The suggested model has become successful in acquiring a low-profile structure with excellent compactness and a good gain; however, the usage of monopole elements leads to a significantly less directive radiation pattern. Designing a single wideband antenna with good directivity and gain for the V-Band [24], or 50-75 GHz mmW band, is the primary goal of this research. The proposed design has the benefits of being lightweight, high-gain, somewhat efficient, and broadband. The objective is to significantly reduce the training cost and convergence speed while creating a universal antenna technique with specifications and changeable design characteristics. Therefore, a method for antenna optimization termed Self-Adaptive Bayesian Neural Network surrogate model-assisted differential evolution is proposed. The dimensions of the suggested design are modest.
###### Abstract  
In this paper, we present the design of a millimeter-wave 1 \(\times\) 4 linear MIMO array antenna that operates across multiple resonance frequency bands: 26.28-27.36 GHz, 27.94-28.62 GHz, 32.33-33.08 GHz, and 37.59-39.47 GHz, for mm-wave wearable biomedical telemetry application. The antenna is printed on a flexible substrate with dimensions of 11.0 \(\times\) 44.0 mm\({}^{2}\). Each MIMO antenna element features a modified slot-loaded triangular patch, incorporating 'cross-shaped slots in the ground plane to improve impedance matching. The MIMO antenna demonstrates peak gains of 6.12, 8.06, 5.58, and 8.58 dBi at the four resonance frequencies, along with a total radiation efficiency exceeding 75%. The proposed antenna demonstrates excellent diversity metrics, with an ECC \(<\) 0.02, DG \(>\) 9.97 dB, and CCL below 0.31 bits/sec/Hz, indicating high performance for mm-wave applications. To verify its properties under flexible conditions, a bending analysis was conducted, showing stable S-parameter results with deformation radii of 40 mm (Rx) and 25 mm (Ry). SAR values for the MIMO antenna are calculated at 28.0/38.0 GHz. The average SAR values for 1 \(\times\) 4 linear MIMO antenna for Millimeter-Wave, Wearable and Biomedical Telemetry Applications.  
Sensors 2024244427. [[https://doi.org/10.3390/s24144427](https://doi.org/10.3390/s24144427)]([https://doi.org/10.3390/s24144427](https://doi.org/10.3390/s24144427))  
L
# Damage Diagnosis of Frame Structure Based on Convolutional Neural Network with SE-Res2 Net Module
## 5 Conclusions  
In order to improve the damage diagnosis accuracy of frame structures in strong noise environments, a new neural network model was proposed based on the convolution neural network with training interference which has excellent performance in bearing fault recognition. Meanwhile, the proposed new model used a squeeze-and-excitation neural network and Res2 Net module which are excellent in image recognition. Taking the frame structure model from the University of British Columbia as the experimental object, the proposed new model was used in a damage type diagnosis experiment. In order to verify the superiority of the proposed new model, convolution neural network with training interference, LeNet-5, one-dimensional convolutional neural network and support vector machine were used under the same conditions to compare their accuracy and anti-noise ability. The following conclusions can be drawn from the experimental study.  
1. The training process of the proposed new model has a fast convergence speed and high multi-classification accuracy; the accuracy of the training dataset is 99.78% and the accuracy of the test dataset is 100%.
2. Compared with other similar models, the proposed new model has the highest accuracy of 99.6% when the training epoch reaches 30, and the average value of accuracy can reach 77.3%, which is higher than other models. Therefore, the proposed new model can achieve higher accuracy in the early stage of training and has better performance than other models.
3. In the comparative experiment of anti-noise ability, the anti-noise ability of the proposed new model is about 4-5% stronger than that of convolution neural network with training interference. Compared with other models, the new model performs better and has stronger anti-noise performance. It can accurately diagnose the damage of a structural frame in a strong noise environment.  
W.F., formal analysis, software, investigation, validation and writing--original draft; Z.L., methodology, formal analysis, software, supervision and writing--original draft; C.C., funding acquisition, methodology, project administration, formal analysis and writing--review and editing; Y.X., methodology, formal analysis, investigation and writing--review and editing; J.R., formal analysis, investigation and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Comparison results of anti-noise capability.  
**Funding:** This work was supported by the Nature Science Foundation of Hebei Province, grant no. E2020402060; Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province (Hebei University of Engineering), grant no. 202206; and Key Research and Development Project in Hebei Province, grant no. 19211906D.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Results
### Sperm Morphology and Sperm Parameters  
Both sperm morphology and sperm parameters were observed under the microscope. It was found that the sperm of the HFD group showed abnormal morphology of both heads and the sperm count was also reduced (Figure 9). The total sperm count, motility, and abnormal sperm count in each group are further compared in Table 5. The results showed that the total number of spermatozoa and motility in the HFD group decreased significantly. It was found that EPE extract improved the motility of sperm and reduced the number of abnormal sperms. The results showed that EPE can improve the total number of sperm and reduce the abnormal sperm caused by obesity.  
Figure 8: Effects of _Echinacea purpurea_ ethanol extract on hypothalamus mRNA protein of (**a**) Kiss1R and testes mRNA protein of (**b**) Steroidogenic Acute Regulatory Protein (StAR) and (**c**) 17\(\beta\)-HSD3 in HFD-induced obese rats after 5 weeks. Data shown as the mean \(\pm\) S.E.M. (\(n\) = 5). The values with different superscript letters (**a**,**b**) are significantly different (\(p\) < 0.05), as determined via one-way ANOVA, and were analyzed by [PERSON]’s multiple range test.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Control** & **HFD** & **EPE1** & **EPE2** & **EPE5** \\ \hline Counts (10\({}^{6}\)) & 30.68 \(\pm\) 1.61 \({}^{\text{ab}}\) & 26.78 \(\pm\) 8.27 \({}^{\text{b}}\) & 31.52 \(\pm\) 1.43 \({}^{\text{a}}\) & 29.50 \(\pm\) 8.95 \({}^{\text{ab}}\) & 30.78 \(\pm\) 3.11 \({}^{\text{ab}}\) \\ Mobility (\%) & 31.89 \(\pm\) 2.38 \({}^{\text{a}}\) & 12.55 \(\pm\) 1.37 \({}^{\text{b}}\) & 17.89 \(\pm\) 1.85 \({}^{\text{b}}\) & 25.31 \(\pm\) 3.03 \({}^{\text{ab}}\) & 24.95 \(\pm\) 3.95 \({}^{\text{ab}}\) \\ Abnormality (\%) & 9.11 \(\pm\) 0.40 \({}^{\text{a}}\) & 18.70 \(\pm\) 2.07 \({}^{\text{c}}\) & 14.31 \(\pm\) 0.80 \({}^{\text{b}}\) & 12.70 \(\pm\) 1.40 \({}^{\text{ab}}\) & 13.09 \(\pm\) 0.71 \({}^{\text{ab}}\) \\ \hline \hline \end{tabular} Data are shown as the mean \(\pm\) S.E.M. (\(n\) = 5). The values with different superscript letters (a–c) are significantly different (\(p\) < 0.05), as determined via one-way ANOVA, and were analyzed by [PERSON]’s multiple range test.  
\end{table}
Table 5: Effects of _Echinacea purpurea_ ethanol extract on sperm parameters in HFD-induced obese rats after 5 weeks.
# An LSM-Tree Index for Spatial Data
## 4 ER-Tree Index
### ER-Tree Index Query
#### 4.4.1 Point Query  
The point query of spatial data is based on one or several query coordinate points to query the data in the database with the exact data coordinates as the query coordinate points. The key-value entry is first obtained by a spatial location query on the ER-tree index and then directly through the SSTable corresponding to the index. The point query algorithm on the ER-tree index is shown in Algorithm 2.  
Algorithm 2 describes the process of the ER-tree index point query. In the first step, the ER-tree is used to judge, layer by layer, whether the QP is located in the tMBR of each SER-tree root node in the ER-tree. If it exists, the QPFindLeaf function is executed to obtain the data <key, value> from the data block on the corresponding SSTable and to store it in the temp_result collection (lines 1-12). The second step is to iterate, through the temp_result collection, to judge whether <key, value> in the temp_result collection equals the QP. If it is equal, it is stored in the result collection (lines 13-17); lines 18-28 refer to the QPFindLeaf function, which uses a recursive way to query the leaf node of the MBR containing the query point QP.
# Monitoring of Structures and Infrastructures by Low-Cost GNSS Receivers
## 3 The Experience  
### Many Examples  
GReD has much experience in monitoring implementations in different situations. For instance, we cite pylons, dams, landslides, penstocks, ancient monuments, retaining walls and bridges.  
It might be worth mentioning the quite successful, 3-year-long Geodetic Integrated Monitoring System (GIMS) project, funded by the European GNSS Agency under the European Union's Horizon 2020 program (grant agreement No. 776335), in which, leading an international consortium, GReD has monitored two landslides in Slovenia by GNSS jointly with SAR and inertial systems [13; 14; 15].  
However, the most mature experience of GReD has been the almost 7-year-long, still ongoing monitoring of a bridge by Autostrade per l'Italia S.p.A. (ASPI), which we shall discuss in depth in this section. We focus the analysis only on the first 4 years of the monitoring activity.  
Let us only remember that here, \"monitoring\" means a set of actions, grouped into the so-called GeoGuard system, including the network project, its implementation in the field, data collection and elaboration, and near real-time representation of the results by a dedicated website, followed by a more-refined post-analysis of these results.
## 3 Prioritizing Geo-Spatial Data Collection
### Predictive Sampling
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Attribute** & **Description** \\ \hline
1 & _stories_ & Number of stories, including mansard and basement \\
2 & _constr_ & Type of construction (eight different types were listed) \\
3 & _usage_ & Type of usage (residential, mixed residential, non-residential) \\
4 & _apartments_ & Number of apartments \\
5 & _offices_ & Number of offices \\
6 & _carparks_ & Number of parking lots \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Attributes used to constrain the logistic regression. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predict./Observ. ** & **0** & **1–2** & **3–5** & **6–10** & **\textgreater{}10** \\ \hline
0 & 5266 & 1376 & 358 & 53 & 7 \\
1–2 & 150 & 298 & 225 & 33 & 4 \\
3–5 & 177 & 425 & 835 & 381 & 22 \\
6–10 & 20 & 23 & 54 & 127 & 31 \\ \textgreater{}10 & 8 & 2 & 11 & 50 & 64 \\ \hlineThe confusion matrix of the comparison is shown in Table 2. The table shows that the logistic regression is able to capture the general correlation trend between the considered attributes and the number of policies, but cannot precisely infer the particular interval. On the one hand, this can be due to the natural bias of the training and test set, since buildings that are good candidates for having policies have not necessarily already been reached by TCIP, which had a penetration close to 40% in the town when these data were released. A more thorough exploration of different predictive approaches would definitely improve the performance of the inference process. It is also important to note that in order for the predictive sampling to be useful, it has to capture the general correlation trend in order to further optimize the computation of the focus maps and thus the field survey.
## 5 Discussion  
In this section, we investigated various research papers published on the CinC 2020: Program website [52] and IEEE Xplore and utilized the PhysioNet/Cinc Challenge 2020 dataset to effectively classify abnormalities in ECG signal data (Table 3). The objective was to develop a model capable of learning complex patterns within ECG signals and accurately distinguishing between 27 different abnormalities. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Model** & \begin{tabular}{c} **Noise Reduction** \\ **or Augmentation** \\ \end{tabular}  & **Feature** & \ \begin{tabular}{c} **Additional** \\ **Approach** \\ \end{tabular}  & **Test Score** \\ \hline \multirow{4}{*}{[18]} & Residual CNN-GRU & \multirow{4}{*}{ \begin{tabular}{c} Polyphase Filter \\ Resampling \\ \end{tabular}  } & \multirow{4}{*}{NA} & \multirow{4}{*}{24 types, 75 epochs} & \multirow{4}{*}{12.2\%} \\  & with attention & & & & \\  & mechanism & Resampling & & & \\  & (RCNN-GRU-AM) & & & & \\ \cline{1-1}  & Improved residual & & & & \\ \cline{1-1}  & network (iResNet) & & & & \\ \cline{1-1}  & Modified ResNet Type & & & & \\ \cline{1-1}  & Convolutional Neural & & & & \\ \cline{1-1}  & Network (MR-CNN) & & & & \\ \  
Among all the state-of-the-art techniques, the highest test score obtained was 53.3%, achieved by utilizing the Wide and Deep Transformer neural network model [30]. In this work, the authors utilized an extensive feature engineering approach (Handcrafted ECG Features) along with preprocessing techniques (finite impulse response bandpass filter). The second highest official ranking, with a 52.0% test score, was obtained using Modified ResNet with a Squeeze-and-Excitation Layer (MResNet-SE). This approach utilized zero padding to extend the signal sample to 4096 samples and down sampling techniques to sample the signal to 257 Hz [29]. In this study, the authors did not utilize any preprocessing or feature extraction techniques, but they used additional approaches such as a constrained grid-search for addressing data imbalance problems and a bespoke weighted accuracy metric for evaluating model performance. Another study [28] also utilized SE-Resnet and acquired a test score of 51.4% on the undisclosed test dataset. In this work, the authors used squeeze-and-excitation blocks to extract the fine details for 10 s to 30 s segments of the ECG signal and validated model performance using an external open-source dataset. The authors also used wavelet-based denoising techniques to remove the noise from the ECG dataset before training, but no additional feature extraction techniques were utilized. All other methods had lower test