esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Application Discussion
### A Capacitive Accelerometer  
The capacitive accelerometer [45] has become very attractive for high-precision applications due to its high sensitivity, low power consumption, wide dynamic range of operation, and simple structure. The capacitive accelerometer is not only the central element of inertial guidance systems, but also has applications in a wide variety of industrial and commercial problems, including crash detection for vehicles, vibration analysis for industrial machinery, and hovering control for unmanned aerial systems. Most of the capacitive accelerometers consist of two main modules: the sensing structure and the signal processing circuit; the former plays a critical role in the overall product performance. The sensing structure in this example, as in Figure 8, mainly includes five parts: a fixed electrode, a movable electrode, a coil, a counter weight, block 1, and block 2. The material they are made of is listed in Table 4. The capacitance between the two electrodes varies with the vertical displacement of the movable plate under the excitation of acceleration, which can be clearly presented through the finite element simulation, as in Figure 9. The nodes in the effective area on the movable electrode have been offset relative to the original position under the excitation of acceleration. The increment of capacitance is expressed as [44]:  
\[ \begin{array}{l}\Delta C=\varepsilon\cdot A\\ A=\frac{n}{i=1}\left(\frac{S_{i}}{h+\delta_{i}}-\frac{S}{h}\right)\end{array}  \tag{25}\]  
where \(\varepsilon\) is the dielectric constant; h represents the original distance between the electrodes; \(S\) denotes the effective area on the movable electrode; \(\delta_{i}\) is the displacement response of the \(i\)-th node, and \(S_{i}\) is the area of corresponding element. Note that the performance function of \(A\) is based on the FEM, which contains 114,517 8-node thermally coupled hexahedron elements in total, and it takes about \(1/3\) h to solve each time, when using a personal computer. The displacement of the movable electrode, in addition to the response to acceleration, may be caused by varying ambient temperature. This can be found from the simulation result in Figure 9, where the load is changed from acceleration to varying temperature. Reducing the effect of thermal deformation on accuracy has become a problem that must be faced in the design process. Therefore, the EBRO model of the capacitive accelerometer is formulated as:  
\[\min v,\;\max\!Bel\left(f=\frac{A(\mathbf{X},\mathbf{\alpha})}{\Delta T}\leq v\right) \tag{26}\]  
where \(f\) represents the sensitivity of error to temperature at 35 \({}^{\circ}\)C; \(v\) denotes the performance threshold; and \(\mathbf{X}\), \(\mathbf{P}\) denote the design vector and parameter vector. The components of \(\mathbf{X}\), \(\mathbf{\alpha}\) are the structural sizes, as shown in Figure 8, where \(\alpha_{1}\), \(\alpha_{2}\), \(\alpha_{3}\) are the mounting angle of the counter weight, block 1 and block 2, respectively. The value ranges are given as \(6.0\,\mathrm{mm}\leq\overline{X}_{1}\), \(\overline{X}_{2}\leq 12.0\,\mathrm{mm}\), and the
# Bridge Crack Detection Based on SSENets
## 2 Materials and Methods
### Skip-Squeeze-and-Excitation Module
#### 2.3.2 Squeeze  
Each pixel obtained from conventional convolution is only related to the context in the local receptive field and cannot take advantage of the context outside the receptive field. To solve this problem, we use the Squeeze operator to aggregate global information into a channel descriptor d. We apply global average pooling to generate a channel-wise vector from the input feature map \(\text{FM}_{i}\). It can shrink the context with the size of \(H_{i}\times W_{i}\) to the size of \(1\times 1\) in spatial dimension. The formula of channel descriptor is shown in Equation (3):  
\[d_{c}=\text{F}_{sq}(\text{FM}_{ci})=\frac{1}{H_{i}\times W_{i}}\sum_{m=1}^{H_{ i}}\sum_{n=1}^{W_{i}}FM_{ic}(m,n), \tag{3}\]  
where \(d_{c}\) is the value of c-th channel in the channel descriptor d and \(FM_{ic}\) refers to the c-th channel of the feature map \(\text{FM}_{i}\). We choose the simplest aggregation strategy [32], which can improve the capability of the module while minimizing the complexity of the module.  
Figure 4: Simplified model of SSE module.
## 2 Optical Interconnection Architecture
### Backplane Transfer Function
The same considerations apply for the reception plane 3, as signals traverse two through ports. The reception plane 2 is reached after traversing a combination of one drop and one through port. In fact, it is half way from a totally flat response (reception plane 3) and a Gaussian shaped transmission and group delay (reception plane 1). Finally, the response at plane 4 has a shape, which is driven by the last drop port signals have to pass through. Note that signals are received after traversing another drop port at the specific RSC, thus, the final transmission and group delay are once more \"drop port-shaped\" before the RX. This is shown in Figure 7 for all the RXs at plane in the architecture reported in Figure with \(S=4\) planes and \(N/S=4\) TX/RX per plane. Figure 6: Transfer function “-” and group delay “- -” after the PSC at different planes. The group delay gives a hint of the pulse spread at the RX. In fact, it is the reciprocal of the _group velocity_ (_i.e._, the propagation velocity of a group of waves whose frequencies are distributed over an infinitesimally small bandwidth centered on frequency \(f_{0}\)) multiplied by the propagation distance. If we suppose \(f_{1}\) and \(f_{2}\) being the leading edge and the tail frequency of a spectrum centered around \(f_{0}\), the pulse spread can be determined by taking the difference between the group delay at frequency \(f_{0}\) and that at either \(f_{1}\) or \(f_{2}\)[11].
## 4 Experiment Setup and Results
### The Relationship between the Recharge Time and Distance  
With the aid of the previously found information, i.e., the current is maximum with 2000 \(\Omega\) resistive load, the time of operation is 1.3 s, and the time of each pulse cycle at each of the distances, we could measure the maximum average power of the pulse [36]. In addition, to make sure that the calculated power is maximum, we calculated and averaged it over different resistance values to yield the maximum average power. In order to calculate the average power, we used the equations given below (Equations (9)-(11)), where \(p\), \(E_{p}\)  
Figure 8: Charging time vs distance.  
Figure 7: The relation between the pulse width, (**a**) load current and (**b**) load.  
\(P_{avg}\), \(T_{p}\), and \(T_{c}\) are the pulse power, pulse energy, average power, pulse width, and cycle time, respectively:  
\[p=\frac{V^{2}}{R} \tag{9}\]  
\[Ep=\frac{p}{T_{p}} \tag{10}\]  
\[Pavg=\frac{Ep}{T_{c}}. \tag{11}\]  
Furthermore, the online calculator provided by Powercast shown in Figure 9 was used to obtain the theoretical values so that they can be compared with the experimental results. We found that the power received by the patch antenna was more than that of the dipole. Also, as the distance increases, the values measured were found to be remarkably close to the theoretical ones. The theoretical power was calculated using the Friis equation and with the help of the Powercast power calculator.  
The transmitter-integrated antenna has a gain of 6 dBi and the receiving antenna could be either of gain 1 dBi or 6 dBi depending on the antenna used. Figure 10 compares the theoretical and experimental results, which shows a close match especially at a large distance.  
Figure 9: Powercast online calculator.
# Spreading-Based Voice Encryption by Means of OVSF Codes
## 4 Experimental Results and Analysis
### Results and Analysis  
To implement and evaluate the proposed method, each voice file of the dataset was encrypted with a different seed value (85 tests) and then recovered back. Here, the encrypted file was compared against the original one, through \(CC\) and \(S_{p}D\). Additionally, the entropy and \(DL\) of both signals were calculated. Finally, the similarity between the original and recovered signals was assessed through \(CC\).
### Broader Technological Perspectives for Maritime Industry  
In addition to Machine Learning, several other advanced technologies could substantially contribute to tackling the complexity and challenges in maritime systems. We briefly discuss some of them below:  
1. Artificial Intelligence (AI): AI, the umbrella under which Machine Learning falls, can play a significant role in the maritime industry. AI can automate complex tasks, improve decision-making processes, and enhance efficiency in operations. For instance, AI algorithms can aid in predictive maintenance, reducing operational downtime by identifying potential mechanical issues before they result in equipment failure [16].
2. Internet of Things (IoT): IoT refers to the network of physical objects ('things') embedded with sensors, software, and other technologies for exchanging data with other devices and systems over the internet. In the maritime context, IoT devices can provide real-time monitoring and data collection, enhancing safety, efficiency, and environmental sustainability. For example, IoT sensors on vessels can monitor fuel consumption and emissions, aiding in the regulation of environmental standards [12].
3. Big Data: The maritime industry produces an enormous amount of data, and big data analytics can help manage and leverage this information. Analyzing the data from various sources such as vessel tracking systems, weather reports, and port state controls can lead to improved decision-making processes, optimized routes, and efficient vessel traffic management [31].
4. Blockchain: Blockchain technology can potentially revolutionize various aspects of the maritime industry, including supply chain management and document verification. It can offer secure, transparent, and tamper-proof platforms for transactions, documentation, and communications, enhancing the efficiency and security of maritime operations [20].  
While these technologies hold substantial potential for the maritime industry, their successful implementation requires overcoming several hurdles, including regulatory issues, technological limitations, and concerns about cybersecurity. The industry needs to work collaboratively to address these challenges, and leveraging these technologies could revolutionize maritime transport, making it more efficient, safe, and sustainable.
## 4 Conclusions  
The conclusions on the law and mechanism of the effect of red mud admixture on the mechanical properties of AASR cementitious materials are as follows:  
1. The addition of the proper amount of red mud makes the alkalinity of the AASR system increase, which is beneficial to the development of AASR strength, while when the amount of red mud is too great, the compressive strength at the same age decreases with the increase of red mud, and the 28-day compressive strength decreases by 78.8% compared with the baseline group.
2. The mechanism of the effect of red mud content on the compressive strength of AASR indicates that it is difficult to leach the silicon and aluminum components in red mud, as only a small part of red mud participates in hydration, the degree of hydration reaction of the system was low, and the formation of hydration products makes the strength of the system develop slowly.
3. Due to the difficulty of red mud dissolution, it slows down the hydration process of the AASR system, which makes the initial and final setting time longer.
4. The red mud content does not change the type of hydration products of the AASR cementitious material, but with the increase of red mud content, the amount of hydration generation decreases, which is one of the reasons for the reduction of the AASR system's compressive strength.
5. The addition of red mud introduces more harmful pores in the specimen, and when the content of red mud is 70%, the 28-day connected porosity reaches 39.05%; at the same time, the filling and refinement effect of the reaction products on the pores is small, resulting in a loose paste structure and reduced strength.  
Conceptualization, J.L.; data curation, J.L., K.Y., P.W. and L.Z.; writing--original draft preparation, J.L. and J.Y.; writing--review and editing, J.L., Q.L. and P.C.; supervision, P.C. and Q.L.; project administration, Q.L.; funding acquisition, P.C. and Y.M. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the following institutions for providing facilities and the financial support: National Natural Science Foundation of China (NO. 52268039); Guangxi Natural Science Foundation, China (NO. 2021 GXNSFBA220047 and AD22035104); Open funds (NO. 22-J-21-21) from Guangxi key laboratory of new energy and building energy saving; and College student innovation project from Guilin University of Technology (NO. 202210596314).  
Not applicable.  
The authors declare that they have no conflict of interest.
# Clone Node Detection Attacks and Mitigation Mechanisms in Static Wireless Sensor Networks
## 3 Cybersecurity Attacks in Wireless Sensor Networks
### Classification of WSNs, Attacks in WSN and Security Issues  
The classification of wireless sensor networks (WSNs) can be achieved based on the application, but its features mainly change based on the type. In general, WSNs are classified into various categories as follows:  
* Deterministic and nondeterministic,
* Static and mobile,
* Static base station and mobile base station,
* Single base station and multi base station,
* Self reconfigurable and non-self configurable,
* Single-hop and multi-hop WSN,
* Homogeneous and heterogeneous.  
Depending on the topic in question, several methods can be used to detect attacks on WSNs. Additionally, the strategies used in one type to establish security measures may differ from another type. However, in this paper, we focus our attention more on the static WSNs.  
General Issues in Wireless Sensor Networks  
There exist various issues that may occur in wireless sensor networks. Generally, topology issues and design issues are frequently met. Based on the topology matter of wireless sensor networks, it largely includes:  
* Coverage topology,
* Sensor holes,
* Geographic routing.  
Based on the design matter of wireless sensor networks, it largely includes:  
* Coverage problems,
* Low latency,
* Transmission media,
* Fault,
* Scalability.  
Major Issues of Wireless Sensor Networks  
The major issues that can occur in a wireless sensor network environment that can impact the performance and the design are mostly:  
* Operating system and hardware used for WSN,
* Properties of wireless radio communication,
* Deployment,
* Synchronization,
* Middleware,
* Architecture,
* Schemes for medium access,
* Calibration,
* Position or localization,
* Sensor networks programming models,
* Data dissemination and data aggregation,
* Database centric and querying,
* Network layer,
* Transport layer.
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 4 Experiment
### Discussion  
We will discuss the results of the comparison experiments on MVTec, BTAD, and KSDD2 datasets.  
PaDim is a parametric probabilistic model, and it needs to estimate the Gaussian distribution of normal samples. However, PaDim does not consider the noisy samples in the dataset when estimating the parameters of the Gaussian distribution, and the estimated Gaussian distribution deviates severely from the distribution of normal samples, resulting in a degradation of the model performance.  
The self-supervised method CutPaste is sensitive to noisy samples because it considers all samples in the dataset as normal samples, creates artificial anomaly samples by applying special data augmentation to the samples in the dataset, and trains a classifier to discriminate normal samples from artificial anomaly samples. When the dataset contains noise, the noisy samples will generate false labels to mislead the classifier resulting in a degradation of the model performance.  
The reconstruction method DFR uses a pretrained network to extract the feature map and reconstruct it using an autoencoder. DFR uses PCA to determine the size of the latent space of the autoencoder. Benefiting from the noise reduction effect of PCA, DFR has better noise resistance than PaDim and CutPaste.  
Our proposed PaIRE belongs to the probabilistic model, which can estimate the Gaussian distribution of normal samples from the dirty dataset by filtering the noisy samples and increasing the number of clean samples. PaIRE achieves advanced results in all three datasets.
# A Comprehensive Survey on Knowledge-Defined Networking
## 7 Application Plane
### Application Scenarios
#### 7.3.3 Security
SBCIDS receives the latest updates from the controller to securely share the signatures among the Snort nodes using the blockchain. A self-learning network intrusion detection system having an application policy to use Hidden Markov Models (HMMs) in a KDN, which can monitor the whole network and learn from evolving network activity adaptively to react to intrusion detection events, has been presented in [513]. Similarly, there have been numerous recent research attempts at the implementation of self-learning intrusion detection systems in KDN scenarios that have policies to use the prospect of machine learning to adapt and evolve over time [514].
## 4 Discussion
To address the current limitations and improve the system's operational readiness, several enhancements to the SARGAS framework are already underway. These include further optimization of CNN architectures through variations in layer depth, kernel sizes, and regularization strategies; improved filtering of false positives via more robust thresholding and decision logic in the second-stage classifier; and extended integration of motion cues to better handle drone-induced background movement. Additionally, new data will be collected through drone flights under varying environmental and seasonal conditions to increase the diversity of training samples and improve the robustness of both detection branches. These ongoing efforts aim to increase precision, reduce false detections, and ensure greater adaptability of SARGAS across real-world aerial search and rescue contexts.
## 3 Definition of the Industrial Domain and Presentation of the Case Study  
The specific domain of the case study presented in this paper relates to technical systems for harvesting from the ground surface the shell fruits naturally abscised from trees (e.g., chestnuts, hazelnuts, and walnuts, having an average size ranging from 15 to 40 mm and a maximum mass of 20 g per fruit [45; 46; 47]), named \"harvesting machines\" hereinafter.  
Harvesting machines are employed at the end of the cultivation/production process, so they are entrusted with the task of intervening on agricultural products that are earmarked for the final consumer, hence when they have the greatest added value. The primary technical function to be implemented, i.e., _gathering_ fruits, is not trivial [48], as the economic value of the product has to be preserved by minimizing product losses and maintaining, at the same time, organoleptic characteristics (integrity) and the degree of appreciation to the consumer [49]. This includes eliminating or _separating_ many of the foreign and non-edible materials that can be unwillingly gathered (soil, leaves, stems, roots), which is a critical capability in harvesting machines [50]. Still with the aim of keeping a high-quality level of the harvested fruits, another crucial operation is the correct execution of _storing_ operations [51], which are seen as inseparable from the harvesting process as a whole. Contextually, the separated foreign materials have to follow a different flow [52], and some of them need _restoring_ in correct waste management practices.  
To sum up, the execution of the task of \"shell fruit harvesting\" is constituted by three main functions (see Figure 3):  
1. _Gathering_ (the shell fruits from the ground) \(\rightarrow\) system for (1) reaching and (2) removing the shell fruits from the orchard ground; it is worth noting that reaching the shell fruits can imply also an approaching movement of the whole machine;
2. _Separating_ (the shell fruits from other elements) \(\rightarrow\) system for the removal of the shell fruits from the foreign materials; and
3. _Storing/restoring_ (shell fruits/other elements) \(\rightarrow\) system for managing the material flows inside the machine and/or going outside of the machine.  
Figure 2: Structure of the information search performed in the present paper to allow for the building of value curves.  
It is worth noting that, although these three functions are necessary to complete the harvesting process, only the functions of \"gathering and storing\" are sufficient to connote a machine as a harvesting machine for shell fruits. Indeed, the functions of separating (the shell fruits) and restoring (the foreign materials) can be performed by different machines in different instances. However, modern solutions tend to integrate all these functions in a single machine [53] and the corresponding capabilities can be thought as critical competing factors, which are then to be included in the value curves diagram.
# Frequency Sensitivity and Mode Characteristics of Body Structure for a Piezoelectric-Based UUV
## 4 Results and Discussion
### Frequency Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Wave Numbers**} & \multicolumn{8}{c}{**Axial Wave Number \(m\)**} \\ \cline{2-7}  & **0** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline \multirow{8}{*}{\(0\)} & 0 & 9162.2 & 18,324.4 & 27,486.6 & 34,656.9 & 35,064.1 & 35,320.7 \\  & 0 & 14,636.8 & 27,663.3 & 33,489.5 & 36,648.8 & 45,811.0 & 54,973.2 \\  & 37,136.5 & 37,479.1 & 39,663.2 & 49,157.8 & 63,381.8 & 78,426.9 & 93,687.6 \\ \hline \multirow{8}{*}{\(1\)} & 0 & 3597.3 & 10,356.8 & 16,986.0 & 22,458.9 & 26,480.8 & 29,244.2 \\  & 21,970.3 & 26,086.0 & 33,923.0 & 41,356.8 & 47,844.2 & 54,419.8 & 61,632.1 \\  & 52,525.5 & 53,591.7 & 57,264.0 & 64,844.3 & 75,049.6 & 87,645.2 & 101,291.9 \\ \hline \multirow{8}{*}{\(2\)} & 1575.7 & 2110.9 & 5015.5 & 9166.2 & 13,478.8 & 17,469.1 & 20,957.9 \\  & 43,940.5 & 45,518.5 & 87,436.5 & 55,000.1 & 60,949.5 & 67,188.1 & 73,721.2 \\  & 83,066.4 & 84,149.3 & 49,598.2 & 92,967.7 & 100,632.0 & 110,116.6 & 121,013.8 \\ \hline \multirow{8}{*}{\(3\)} & 4456.4 & 4588.3 & 5380.2 & 7241.6 & 9904.3 & 12,927.9 & 16,018.4 \\  & 65,910.8 & 66,778.4 & 121,009.3 & 72,946.0 & 77,549.4 & 82,783.3 & 88,492.6 \\  & 117,483.7 & 118,367.7 & 69,236.6 & 125,367.8 & 131,355.1 & 138,827.6 & 147,599.8 \\ \hline \multirow{8}{*}{\(4\)} & 8544.4 & 8645.3 & 9033.2 & 9881.7 & 11,287.0 & 13,196.4 & 15,471.8 \\  & 87,881.1 & 88,465.8 & 156,050.7 & 92,884.7 & 96,442.1 & 100,694.1 & 105,514.9 \\ \cline{1-1}  & 153,185.9 & 153,904.9 & 90,174.0 & 159,588.5 & 164,458.6 & 170,576.5 & 177,837.6 \\ \hline \multirow{8}{*}{\(5\)} & 13,817.8 & 13,915.8 & 14,233.8 & 14,830.2 & 15,763.4 & 17,061.3 & 18,710.3 \\  & 109,851.3 & 110,291.4 & 191,834.1 & 113,702.6 & 116,546.8 & 120,041.5 & 124,105.2 \\ \cline{1-1}  & 189,447.9 & 190,046.5 & 111,592.6 & 194,787.0 & 198,865.5 & 204,015.9 & 210,172.3 \\ \hline \multirow{8}{*}{\(6\)} & 20,270.2 & 20,368.5 & 20,671.9 & 21,202.6 & 21,987.6 & 23,048.8 & 24,395.8 \\ \cline{1-1}  & 131,821.6 & 132,174.9 & 228,035.1 & 134,947.3 & 137,300.2 & 140,236.2 & 143,704.2 \\ \cline{1-1}  & 226,000.5 & 226,510.5 & 133,225.6 & 230,558.1 & 234,052.9 & 238,483.5 & 243,806.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Natural frequencies of unmanned underwater vehicle (UUV) body structure \(\omega\) (rad/s).