esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Secure Routing Mechanisms for the Low-Powered IoT Network: A Literature Review
## Security Issues and Challenges in WSN-IoT
### Security  
Physical, network, and data protection are significant issues in WS-IoT frameworks. The growth in the number of connected devices on communication networks in the IoT [61] leads to increased security risks and new security challenges. Protection risks are acquired by any node that connects to the Internet, whether it is a limited or smart device [62]. On the Internet of Things, you can find almost any security issue. As a result, a few primary security criteria in the IoT, such as acceptance, confirmation, classification, confidence, and information security, should be considered.  
Consequently, things should be safely associated with their assigned networks, flexibly controlled, authenticated, and authorized [63]. Physical security tampering, stealing, and attacks are performed on IoT devices. The attacker can grab and steal a node or exchange it with a malicious node, causing harm to the whole network; moreover, the intruder can break the node or steal valuable or peculiar information that could be used against the system [64]. Maintaining a secure network means stopping intruders from finding their way into the system and causing severe damage by sending malware, sniffing, spoofing, stealing sensitive data, man-in-the-middle eavesdropping, or performing DoS attacks. Outsiders or employees within the organization can be intruders. Data security entails ensuring data integrity and privacy while data is transferred within the framework. Security is a method of protecting information from tyrannical forces or unauthorized access. IoT security depends heavily on information security, also known as computer security. [65].
# Millimeter-Wave Multi-Channel Backscatter Communication and Ranging with an FMCW Radar
## 4 Multi-Tag Scenario
When the signature code correlates with itself,  
\[\sum_{n=0}^{L}d(n)\times d(n)=L,\ \ d(n)=\{-1,1\}. \tag{13}\]  
The power of \(L\) bits will be summed up. When the signature code correlates with other signature codes,  
\[\sum_{n=0}^{L}d_{1}(n)\times d_{2}(n)\approx 0<L,\ \ d_{1}(n),d_{2}(n)=\{-1,1\}. \tag{14}\]  
When the signature code correlates with other radar reflection signals from an object, the correlation result can be represented as  
\[s(t)=\sum_{i=1}^{I}H_{i}\times y_{i}(t-\Delta t_{i})+n(t)+c(t) \tag{9}\]0  
where \(T_{s}\) is the symbol period. If the symbol rate of the signature code \(\frac{1}{T_{s}}\) is much larger than \(\Delta f_{k}\), then \(e^{i(\varphi_{k}-2\pi\Delta f_{k}\Delta t)}\) doesn't change too much within \(LT_{s}\) time. When \(\sum_{n=0}^{L}d(n)=0\) holds, then  
\[s(t)=\sum_{i=1}^{I}H_{i}\times y_{i}(t-\Delta t_{i})+n(t)+c(t) \tag{9}\]1This signal is removed by correlation. In this case, the correlation increases the signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) of the target tag signal. Hence, data from each tag can be distinguished. As mentioned before, the normal radar signal from other objects appears as a single tone on the spectrum at the receiver. To maintain the normal FMCW radar sensing functionality, the single tone needs to be selected by a BPF. This requires its SIR and is high enough within the BPF's bandwidth. From the radar signals perspective, the signals from tags are interferences. To lower the interference power, the tags in use are normally passive. Furthermore, benefiting from the signature codes, the power of the signals from tags can be re-enforced at the receiver side by applying correlation, which means that the tag does not need to provide a large, transmitted power. The idea is similar to the spread spectrum communication. In this case, the normal radar signal can guarantee its SIR to realize sensing while the data from tags can also be recovered. Alternatively, the communication signal can be reconstructed after data recovery and subtracted from the observation (10), prior to applying standard signal processing for sensing. The main limitation of the communication data rate is the bandwidth of the radar module and the tag in use. The received signal \(s(t)\), which is also the transmitted signal from the tag, has a bandwidth of \(B+2B_{s}\), where \(B\) is the FMCW chirp bandwidth and \(B_{s}=\frac{1}{T_{s}}\) is the baseband signal bandwidth. \(B+2B_{s}\) need to be smaller than the RF bandwidth of the radar module and the tag. After mixing with the local FMCW signal, \(r(t)\) has a bandwidth of \(B_{s}+\Delta f_{k}\). It needs to be smaller than the baseband bandwidth of the radar module.
## Materials and Methods
### Hemodynamics Parameters  
The WSS and its related indices has been extensively used in human hemodynamics for studying the performance of coronary stents [40; 41; 43; 44; 45; 46; 47; 48; 49]. Although they have been used as significant morphologic and hemodynamic parameters for assessing intracranial aneurysm rupture [50; 51; 52], their application for evaluating the performance of FDSs has been rarely performed. In general, the blood flow field and pressure have been used as parameters to assess the performance of the FDS treatment [1; 3; 4; 8; 20; 21; 22; 53]. In this regard, we believe that it is rational to evaluate WSS-related variables to quantify the changed hemodynamics upon the stent implantation. By using the aforementioned models, we evaluated velocity, pressure and WSS-related variables such as time average wall shear stress (TAWSS), oscillatory shear index (OSI) and relative residence time (RRT). As noted previously, all the variables were evaluated during the third computed cardiac cycle for each aneurysm with or without the deployment of a FDS. All hemodynamic factors were registered at every time step. The WSS-related variables were computed starting from the instantaneous WSS vector \(\vec{\tau_{w}}\) registered at each time instant of the cardiac cycle (\(T\)). The TAWSS for a pulsatile flow represents the spatial distribution of the tangential, frictional stress caused by the action of blood flow on the vessel wall temporally averaged on the entire cardiac cycle. It can be calculated by integrating the WSS vector over the cardiac cycle, i.e.,  
\[TAWSS=\frac{1}{T}\int_{0}^{T}|\vec{\tau_{w}}|dt \tag{2}\]  
The OSI is a non-dimensional parameter that measures the directional change of WSS during the cardiac cycle [54]. OSI is often used to describe the disturbance of a flow field. It is defined by:  
\[OSI=0.5\Bigg{(}1-\frac{|\int_{0}^{T}\vec{\tau_{w}}dt|}{\int_{0}^{T}|\vec{\tau_ {w}}|dt}\Bigg{)} \tag{3}\]  
A combination of TAWSS and OSI reflects the residence time of blood near the wall as proposed by [PERSON] et al. [55]. Thus, a new metric termed RRT can be defined to quantify the state of disturbed flow:  
\[RRT=\frac{1}{(1-2\cdot OSI)\cdot TAWSS}=\frac{1}{\frac{1}{\parallel}\int_{0}^{ T}\vec{\tau_{w}}dt|} \tag{4}\]  
RRT is an additional hemodynamic factor to describe the slow fluid motion near the aneurysm wall [54; 56]. It is especially important when considering vascular diseases such as aneurysms because the reduction of blood flow within the aneurysm sac is the principal role played by FDSs. As such, this index can provide the measurement of the flow blockage that leads to the thrombosis post the stent treatment.
## 4 Analysis of Acoustic Emission Technology Characteristics
### Acoustic Emission Characteristic Waveform Analysis  
This section further investigates the acoustic emission (AE) signal characteristics of the beam failure process by analyzing the AE signals of the three test beams using post-processing software for AE signal analysis. The AE signals underwent fast Fourier transform (FFT) processing to examine their waveform and frequency characteristics. Figure 10 illustrates the waveform and frequency characteristics of the AE signals for the P3 beam as an example.  
Figure 10 shows that the AE signals of the P3 beam during the compaction stage are weak, with small amplitudes and a narrow frequency range, primarily concentrated around 165 kHz. During the stage of stable crack expansion, the internal energy of the beam continuously accumulates and releases, resulting in a large number of AE signals. However, their amplitude changes are relatively uniform. After undergoing fast Fourier transform (FFT) processing, it is found that the frequency range widens during this stage, mainly concentrated around 135 kHz and 175 kHz. During the experiment, at approximately 70 kN load (around 80% of the failure load), microcracks within the beam fully develop and extend. This leads to the formation of through-cracks, accompanied by a significant number of AE signals. Spectral analysis reveals a further widening of the frequency range, mainly concentrated around 175 kHz and 295 kHz. Therefore, 70 kN is considered the critical load for the P3 beam. As the load continues to increase and enters the stage of complete failure, the upper part of the test beam undergoes concrete crushing. During this stage, the waveform signals have reduced amplitudes compared to previous stages, and the frequency components are mainly concentrated around 165 kHz.  
The P3 beam exhibits different central frequencies in each stage. The initial compaction stage and the stage of complete failure have only one central frequency. However, during the process of crack development and the formation of through-cracks, two central frequencies gradually change from low to high. Likewise, the central frequencies of each stage in the P1 and P2 beams can be obtained and are shown in Table 6.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Beam Number** & **Loading Phase** & **Center Frequency (kHz)** \\ \hline \multirow{3}{*}{P1} & Steady crack growth & 165 \\  & Through-crack propagation & 150–300 \\ \cline{1-1}  & Completely destroyed & 165 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Waveform analysis results of each test beam.  
Figure 9: Relationship between energy, ringing count, and load of P2 beam. (**a**) P2 beam energy–load changes with time; (**b**) P2 beam ringing count-load changes with time.
## 3 Potential Security Implications of IoT in 6G Environment
### 6G Usage Scenario-Related Vulnerabilities
#### 3.2.6 Integrated Sensing and Communication  
Integrated sensing and communication (ISAC) in 6G poses additional security challenges for IoT devices. These devices, which collect high-precision data for applications like navigation and health monitoring, are vulnerable to privacy breaches and data manipulation [45]. Malicious actors may attempt to falsify sensor readings, such as those related to movement or environmental conditions, resulting in incorrect system responses, particularly in high-stakes applications like fall detection or autonomous navigation [47]. Furthermore, adversarial attacks targeting the AI systems responsible for processing sensor data could significantly undermine decision-making capabilities [5]. Additionally, the integration of AI introduces new attack surfaces, enabling malicious actors to generate deceptive sensor outputs that closely mimic authentic data, thereby making detection increasingly challenging [8].
# A Comparison of Pooling Methods for Convolutional Neural Networks
## 1 Introduction
### Pooling  
There are two pooling methods commonly used in CNNs. Local pooling is the first method to display feature maps by pooling data from small local regions (e.g., 3 \(\times\) 3). The second is global pooling, which creates a scalar value representing the image from the feature vector for each feature across the feature map. This representation is obtained and categorized through fully connected layers. For example, a popular Dense Net network contains four local pooling layers in addition to one global pooling layer [12]. Pooling is a nonlinear process that enables outputs at a particular location to be aggregated within one value. This unique value is computed from the statistics of subsequent outputs, improving the accuracy and sensitivity of feature translation for smaller input data [13; 14]. The pooling layer decreases gradually the input's dimensions, minimizing the memory consumption to preserve the variables and optimize statistical performance [15; 16; 17]. Overfitting is also addressed in neural architectures with the use of pooling layers [18]. In convolution-based systems, pooling is an important step in reducing the dimensions of extracted features. Reducing the number of variables in the value sequence minimizes the dimensions of the feature map. It transforms general feature descriptions into actionable information by keeping the information relevant and removing unnecessary details. Pool operations offer a variety of services. By excluding the partial connections between the convolution layers, the pooling operation provides some spatially transformed representation and reduces the computational cost of the upper layers. Features extracted from the previous layer are sampled on this layer to generate multiple feature maps with limited resolution. The two key goals of the pooling layer are to reduce the number of parameters or weights, thereby reducing computational overhead and avoiding overfitting [19]. Only important information should be extracted and irrelevant information should be removed using the ideal pooling process. In fact, the pooling paradigm employed is essential in solving the computer vision challenge since the prime objective is to transform the combined visual features (mapped by convolution) into a meaningful representation. As a consequence, the pooling process is very important throughout many computer vision architectures. Pooling minimizes computation costs by reducing the resolution of the feature map while retaining the key aspects required to complete the task.
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 4 Our Proposed Mechanism
### The Dynamic Change Method of Smart Devices and Information
#### 4.3.1 Device Joining  
Devices in the smart grid need to rely on a specific domain before they can be accessed across domains, so devices need to join a specific domain. The domain manager is responsible for managing the joining of devices. Let us take device \(D_{i}\) as an example; if device \(D_{i}\) wants to join domain A, as shown in Figure 2, the join process is as follows:  
First, the device \(D_{i}\) generates a public-private key pair. Device \(D_{i}\) picks a random number \(r_{i}\longleftarrow Z_{p}\) in \(Z_{p}\) as its private key \(sk_{i}\) and then calculates its public key as \(pk_{i}=sk_{i}.G\). Then, \(D_{i}\) sends its public key \(pk_{i}\), identity \(ID_{i}\), and timestamp \(t_{i}\) to \(M_{A}\) to apply to join domain A, where the identity is unique and is different for each device. After \(M_{A}\) receives a request from a device, it first checks the legitimacy of \(ID_{i}\) and, if it is not legal, it exits directly and the device fails to join. If it is legal, \(M_{A}\) will generate a certificate for device \(D_{i}\) with the following structure:\[\begin{split}& Cert_{i}=Sig(ID_{i}||pk_{i}||t_{i})_{sk_{A}}\end{split} \tag{1}\]  
This means that \(M_{A}\) uses its private key to sign the identity \(ID_{i}\), public key \(pk_{i}\), and timestamp \(t_{i}\) sent by \(D_{i}\), to obtain the certificate \(\mathit{Cert}_{i}\) of device \(D_{i}\). Then, \(M_{A}\) sets an expiration time \(T_{i}\) for the certificate, usually in days, to prevent the device certificate from expiring frequently. \(M_{A}\) saves the certificates \(\mathit{Cert}_{i}\) and \(T_{i}\) to the local database, and calculates the hash value of the certificates to obtain \(h_{0}(\mathit{Cert}_{i})\). Next, \(M_{A}\) sends \(\{T_{i},h_{0}(\mathit{Cert}_{i}),\mathit{state}\}\) to the blockchain proxy node \(P_{A}\). Here, \(\mathit{state}\) indicates the status of the certificate, with \(\mathit{state}=1\) meaning the certificate is valid. After that, the blockchain proxy node \(P_{A}\) synchronizes this information to the federated chain through the consensus algorithm and reaches consensus. Finally, \(P_{A}\) feeds the synchronization result to \(M_{A}\), which then sends the certificate \(\mathit{Cert}_{i}\) to the device \(D_{i}\). With this, the device is successfully joined to domain A.
## 3 Results and Discussion
### Bio-Particle Distribution by Coulter Counter and Astaxanthin Recovery
The statistical \(\mathbf{r}^{2}\) values for relationships between the astaxanthin recovery and cell disruption degree were estimated to be 0.83 and 0.86 for the microscopic and Coulter counter methods, respectively. This result suggests that the Coulter counter method can be used effectively for the rapid assessment of _H. pluvialis_ cell disruption for astaxanthin recovery. Microscopy phenotyping generally requires time-consuming and laborious experimentation by skilled personnel. However, it should be noted that this approach is very useful in understanding the actual morphological changes of the rigid _H. pluvialis_ cysts and the spatial distribution of the target biomolecules during mechanical treatment (Figure 1). Therefore, a synergistic combination of these methods is recommended for the development of practical _H. pluvialis_ biorefinery processes that co-produce various bio-products, including lipids, proteins, carbohydrates, chlorophyll, and astaxanthin. Several analytical techniques have been reported in order to evaluate the disintegration efficiency of algal biomass by mechanical treatments: particle-size analyzer (homogenization for _Chlamydomonas reinhardtii_ and _Pseudokirchneriella subcapitata_[28]; bead beating for _C. reinhardtii_[29]), dynamic light scattering (ultrasonication for _Parachlorella kessleri_[30]), and flow cytometer (bead milling for _Chlorella_ species [31; 32]). However, after cell disruption, the morphological characteristics of the algal cell and/or the internal target substance(s) have not been studied in detail.
## 3 Results and Discussion
### TSS Concentration Mapping  
The above-developed algorithm was tested over the study for the number of Landsat datasets. The images were carefully selected and processed to produce the TSS map. Figure 5a-d show the Landsat true color images (Red: Band 4, Green: Band 3, Blue: Band 2) acquired. Meanwhile, Figure 5e-f shows the TSS concentration map derived from Landsat dataset utilizing the above-mentioned algorithm.  
Figure 4: Validation graph of the TSS Concentration.  
Figure 5: _Cont_.  
Figure 5: (**a**–**d**) Colored RGB image acquired from Landsat 8 dataset for the date of 23 November 2018, 17 September 2017, 26 June 2016, and 23 July 2020. (**e**–**h**) Corresponding TSS map for given date.  
Figure 5a,e show a Landsat true color image and TSS map constructed over the study area for 23 November 2018. Points A, B, and C represent river estuary, coastal, and open water areas. It can be clearly seen that the highest TSS is observed along the coastal line, with the highest value being 15.99 mg/L, covers an area of 9.43 km\({}^{2}\). The lowest TSS concentration is observed at point C, as represented by purple. The moderate TSS concentration, the yellowish color in Figure 5e, covers an area of about 23.11 km\({}^{2}\).  
Figure 5b shows true color images over the study area for the date of 17 September 2017. It can clearly be seen that the sediment plume at the river mouth appears to be moving north. Meanwhile, Figure 5f shows the TSS concentration produced from the developed algorithm for the abovementioned date. The range of TSS concentrations for this date is 0 to 10.09 mg/L. The sediment plume shown in the true color image was successfully mapped. The concentration of this plume is between 8.06 and 10.09 mg/L, as shown in the legend, and covers an area of 7.00 km\({}^{2}\). The same sediment concentration was observed along the coastal line. The highest TSS concentration is at point A, located at the river mouth. The low TSS observed at points B and C are represented by the bluish and purple color.  
Figure 5c,g are the images obtained over the study area on 26 June 2016. In Figure 5g, the variation of TSS concentration along the coastal line can be clearly seen. The highest TSS concentration is observed at point B and along the coastal line. At this point, the highest TSS concentration is about 15.90 mg/L. At point A, a moderate TSS concentration can be seen, as represented by the yellow and greenish color. Far from the coastal line as at point B, the TSS concentration decreased gradually. For this date, the TSS concentration varies from 2.07 to 15.90 mg/L.  
Figure 5d shows the RGB image for the date of 23 July 2020. The highest TSS concentration is 14.07 and covers an area along the coastal line, particularly over the river estuary, point A. A moderate TSS concentration was observed at points B and C. The TSS value at points C and B is 9.00 and 11.00 mg/L, respectively, as shown in Figure 5h. Visual observation shows that the sediment appeal drifted to the south.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Applications
#### 4.5.2 Mining  
The usage of the IoD in mining applications protects human safety and reduces the cost of mining operations. Moreover, the disruptive technologies help in indoor and outdoor visual inspections, pit planning management, stockpile surveying in the miningindustries. The collected IoD data can be further processed to make efficient decisions in mining industries. The authors propose that the framework of UAV applications in mining areas is divided into three major categories. Firstly, basic data from digital cameras, spectral, lidar, thermal infrared, and gas sensors. Secondly, monitoring objects such as dump, surface subsidence, coal gangue heap, open mining pit, and industrial site. Thirdly, different applications in mining areas [51].
## 4 Experiments and Results
### Pre-Training Effect Evaluation
#### 4.1.3 Results and Analysis  
The results of the experiments on the BraTS Challenge 2021 and BraTS Challenge 2020 are presented in Tables 1 and 2, respectively.  
The following observations and analysis can be gained:  
* For the same amount of fine-tuned data M, the proposed LETCP outperforms the other baselines in segmentation accuracy, and the improvement in Dice can be up to 27%, which indicates that LETCP achieves better pre-training effects and enhances labeled data efficiency in the same data conditions.
* The performance gains of LETCP are particularly high when very small amounts of training samples are used (e.g., 10 and 20). When the number of training samples in-creases, the gains are reduced. The reason is that, as training samples increase, the information gap between the fine-tuning dataset and contrastive pre-training dataset becomes small, and the fine-tuning performance saturates.