esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Based on the target scattering model and the normal mode theory in a shallow water waveguide, a mathematical model of the acoustic intensity under the coupling condition of the target and the environment was deduced, and the interference striations in the monostatic and bistatic configuration were obtained by simulations. Further, a field experiment was carried out in a lake, and the data were collected for a spherical target in two frequency bands, i.e., 20-40 kHz and 40-  
the glass sphere and the flat plate as the target, and the structural characteristics of the interference striations are obtained. Later, [PERSON] [16] studied the interference structure of active sonar reverberation signals and proposed an interference striation enhancement algorithm based on a variable window STFT transformation, which made the reverberation interference structure clearer. [PERSON] [17] carried out a controlled tank experiment.  
The interference striations features of the active sound field are of great significance and can be applied to many fields, such as target detection [18; 19] and target tracking [20]. In the current research, the \(\delta\) function and 1 are used to approximate the scattering function of a target. This approximation method does not contain the target parameters (shape, structure, material, etc.) and scattering angle information [21]. This leads to the interference striations obtained, which do not reflect the target features. In addition, the impact of a configuration of active sonar and its operating parameter (frequency, wideband, etc.) on the interference striations has not been analyzed theoretically and experimentally in depth. All of the above limit the understanding and application of the active interference striations. Therefore, this paper pays close attention to the following problems: What are the different phenomena of interference striations between the monostatic configuration and the bistatic configuration? What is the effect of the target's scattering function on the interference striations? How do the sonar work parameters and the motion state of the target affect the interference striations?  
The theoretical model of the interference striations was derived based on the normal mode theory and the target scattering theory in shallow water waveguides in Section 2. Simulations were performed for two scenarios, i.e., the monostatic configuration and the bistatic configuration, and the target scattering characteristic and the interference patterns for the two scenarios were obtained in Section 3. Four typical phenomena, i.e., the bending phenomena, the density phenomena, the slope phenomena, and the discontinuity phenomena, were obtained and analyzed theoretically in Section 4, and the above problems were answered in this part. Furthermore, the lake experiment was carried out, and the theory in this paper was verified by the measured data in Section 5.
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 6 Numerical Investigation
### Real-Life Example  
Let us consider 'US Unemployment' data (monthly, 1948-1981, thousands) for male (20 years and over) [18]. This time series has a complex-form trend and was analysed in [9; 14] (Section 2.8.5) using IOSSA and FOSSA. It is interesting if autoEOSSA would be able to automatically extract the trend. The time series has length \(N=408\). As in [14], we take the window length \(L=204\) and the number of signal components \(t=13\).  
The chosen parameters of autoEOSSA are \(T_{1}=0.5\) and \(\omega_{0}=\frac{1}{24}\).  
Let us take a look at the graph of the elementary components before we improve the separability (Figure 11).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **trend\_mse** & **iterations\_num** \\ \hline autoIOSSA + FOSSA & mean: 6.2082, med: 7.1549 & mean: 8, med: 10 \\ autoEOSSA & **mean: 0.0985, med: 0.0818** & mean: 1, med: 1 \\ autoSSA & mean: 7.4691, med: 7.4710 & mean: 1, med: 1 \\ autoFOSSA & mean: 1.1389, med: 0.8167 & mean: 1, med: 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: MSE of trend estimates. Example (9).  
Figure 11: ‘US Unemployment’. Leading 13 elementary time series before separability improvement.  
In this example, the trend is complex and is described by an exponential series and harmonics with periods ranging from 35 to 208. In Figure 11, one can see that the elementary components numbered 4 and 11 have distortions caused by mixing with the seasonal components. The seasonal components \(5,6,13\) are also distorted. This suggests that there is a lack of separability between the trend components and the periodic components.  
Let us now look at the graphs of the elementary components after improving the separability with the autoEOSSA method (Figure 12). One can see that the mixture has been eliminated.  
Figure 13 demonstrates the resultant decompositions. The trend extracted by autoSSA has small fluctuations and the extracted trend is most distorted at the edges. The autoEOSSA method extracts the trend well.  
Figure 12: US Unemployment’. Leading 13 elementary time series after separability improvement by EOSSA.  
Figure 13: US Unemployment’. Decompositions using autoSSA and autoEOSSA.
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 4 Requirements, Suggestions, and Evaluation  
In this section, we present the necessary prerequisites for the successful deployment of the proposed framework, we discuss possible enhancements, and close with future evaluation plans.  
The effective use of the provisioned architecture requires the deployment of a Universal UAV CC (UUCC) that would act as a single point of access for identifying mobile flying objects while they are operating in real environments. The existence of a UUCC would be the first step to establish a well-defined global process for registering and identifying all UAV flights. Additionally, the proposed framework provisions interdrone communication, a fact that requires certain mechanisms to control autonomous flights' interoperability and co-operation. Interoperability mechanisms set the grounds for defining the types of communications between drones (exchange of data, fleet formulation). For example, the exchange of data between two independent UAVs operating at the same time in two neighbor areas would help in maximizing the area covered, while achieving energy conservation.  
The proposed framework, presented in Section 3, supports the management and control of UAV and multi-UAV systems, while preserving the security and privacy of the different stakeholders participating in mobile IoT missions without being differentiated across different application domains. Enhancement of the proposed framework would include variations of the suggested security and privacy mechanisms based on the context of the UAV application and the hardware/mechanical characteristics of the participating UAVs. A classification of the UAVs based on their hardware attributes and their application domain would be particularly useful in filtering security and privacy measures, both at device and network level, ensuring the smooth operation of the mission without overloading the UAV device and the aerial network with procedures that, in some cases, could be out of scope.  
The suggested framework, along with the corresponding architecture, is formulated but not yet validated. Therefore, the next logical step would be to build a simulation model as proof of concept of the proposed UAV secure-connectivity framework. Metrics related to security, privacy, and connectivity would be employed in order to evaluate framework and communication effectiveness. Based on the findings, the involved security and privacy techniques would be tuned to adjust to the needs of real-world applications.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 4 PADP Algorithm Description
### Problem Formulation
#### 4.4.1 Definition  
The current and future positions of sensor node \(i\) are denoted \(p_{i}=[p_{i}^{\ 0},p_{i}^{\ 1},p_{i}^{\ 2}\ldots,p_{i}^{\ n}]\), where \(p_{i}^{\ 0}\) is the current position of node \(i\), as determined by the localization service. The future positions  
Figure 3: Naive deployment (**top**) predictive deployment (**bottom**).  
\(p_{i}{}^{1},p_{i}{}^{2}\ldots,p_{i}{}^{n}\) are predicted by the IMM described in Section 3. We consider a deployment of \(m\) sensor nodes in Layer 1, where the position of each sensor node at Layer 1 is predicted for \(n\) time slots into the future. Furthermore,  
\[Y_{i\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!  
\[\sum_{i=0}^{m}\max_{q}Y_{iqk}\geq\gamma,\forall k \tag{6}\]
* Connectivity constraint: sensor nodes in Layer 1 are clustered during surface gateway deployment to guarantee the connectivity of these sensor nodes. A cluster consists of a group of connected sensor nodes, but members of a cluster are not connected to the sensor nodes of any other cluster. Connectivity requires that there must be at least one sensor node in each cluster covered by the surface gateways. Letting \(\phi(v)\) represent the cluster \(v\), this constraint may be expressed as: \[\sum_{i\in\phi(v)}\sum_{q}Y_{iqk}\geq 1,\forall k,v\] (7)
# A Systematic Literature Review on Distributed Machine Learning in Edge Computing
## 4 Answering the RQs
### RQ2--Techniques and Strategies
#### 4.2.2 Model Partitioning  
DNN partitioning is the paradigm of distributed machine learning which segments a DNN model into some successive parts and deploys each part on different sites. Figure 3 depicts the elements of Model Partitioning.  
Complying with this paradigm, the authors [PERSON], [PERSON], and [PERSON] [35] use DL in edge computing to process multimedia information. The lower layers of a DNN--the ones closest to the input layer--are offloaded onto edge servers and the others onto the cloud. In DL, the intermediate data are smaller than the initial data. In this way, this technique reduces traffic in the network. In addition, as the initial data undergo some modifications to an intermediate state before being sent to the cloud, the technique can also contribute to the privacy problem.  
[PERSON] and [PERSON] [80] design a new Convolutional Neural Network (CNN) splitting algorithm to efficiently distribute CNN between edge and cloud to reduce bandwidth consumption. Since random layer partitioning may increase bandwidth consumption, several parameters are taken into account to select the optimal splitting layer, such as input image dimensions, bandwidth constraints and task load at the border of the network. The authors show that the best splitting occurs at layers with lower output dimensions than the input images. Because the pre-trained CNN architecture is not modified, there is no loss of accuracy.  
[PERSON] et al. [81] run multiple Kubernetes pods for each edge node. Each pod hosts several docker-containerized Tensorflow jobs that could be categorized as model computation or parameter update jobs for the NN. Model splitting and parallelization occur between nodes. The cloud is responsible for Kubernetes management, with dynamic scaling and maintaining consistency when a node is no longer up but the job is not finished yet.
# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 2 Related Work  
### Cache Pollution Attacks  
Fundamentally, cache pollution attacks come in two different forms: locality disruption and false locality. The term locality refers to a phenomenon in which certain content objects from a given requesting region tend to be more popular in that region than other content objects. Thus, a specific arrangement of content objects based on their popularity is representative of a locale or region.  
Therefore, locality disruption refers to an attempt by an attacker to simply churn the cache of a given locale in hopes of knocking some of the popular content objects out of the cache. Thus, the attacker will request many different content objects from across the universe of content (the content space). A false locality attack, on the other hand, is an arguably more aggressive attack in which an attacker wishes to push even the most popular content objects out of the cache in favor of unpopular items. Thus, the attacker wants the cache full of content objects that are not at all representative of the desires of users utilizing the cache. Hence, the locality expressed by the cache is false relative to the actual users' desires. It is important to note that most security approaches to cache pollution attacks tend to seek to prevent one (or sometimes both) of these types of attacks [10]. We will discuss these briefly.
# Location-Based Deferred Broadcast for Ad-Hoc Named Data Networking
## 5 Simulation Evaluation
### Compared Protocols  
To study and evaluate our proposed LBDB, we have chosen other four broadcast schemes from the current literature that use different approaches to address broadcast storm issue. We have run the simulation 50 times with different random seeds to obtain the average value of these metrics.  
1. **No FP Timer**: in this broadcast scheme, no forwarding priority timer is used. Each potential forwarder only sets up CA timer to avoid collision.
2. **Rapid Forwarding**[5]: the protocol assigns forwarding priority based on the distance to the previous hop. A node with farther distance from the previous hop will wait for shorter time, which could spread interest rapidly.
3. **DASB**[29]: this is an enhanced version of Rapid Forwarding. Both of them use the distance to the previous hop to set up deferred timer. Difference is that Rapid Forwarding cancels the pending transmission if a node overhears an interest that has the same name as its pending interests, while DASB considers the node's forwarding eligibility first based on the locations before it determines whether to cancel the pending transmission or not. If the node is in a proper location that may help to spread the interest, it will not cancel its own pending transmission.
4. **NAIF**[22]: this is a probabilistic broadcast scheme, which adjusts a forwarding rate dynamically based on forwarding statistic information it overhears locally, and the estimates a missed rate. If the missed rate is 0, this relay node will continue to decrease the forwarding rate. Otherwise, it will increase the forwarding rate. Each node decides whether to forward the interest according to its forwarding rate.
## 3 Results
### Analysis of Crop Row Fitting Results
When examining local segments in detail, polynomial fitting often fails to fully capture all data points, and in regions with pronounced curvature, the polynomial fitting results tend to exhibit a linear trend, making it difficult to represent the actual curvature variation. In contrast, spline fitting can better capture local nonlinear characteristics and more accurately reflect the curved shape of the crop rows. In the context of crop row recognition, recognition accuracy is defined as the ratio of successfully identified crop rows to the total number of actual crop rows [33; 34]. A crop row is considered successfully recognized if the majority of its constituent plants are accurately included within the row line generated by the algorithm. Conversely, if a substantial number of plants deviate from the generated row line or if significant emissions are observed within a given row, the recognition is deemed to have failed. To comprehensively and systematically evaluate the performance of the proposed method under varying planting densities, images were categorized based on the number of actual crop rows: those containing fewer than 19 rows and those with 19 or more. From each category, 20 images were randomly selected for accuracy evaluation. The recognition results are summarized in Table 6. The proposed algorithm achieves an overall recognition accuracy of 96.6%, demonstrating strong robustness and promising practical applicability in the task of sugarcane crop row recognition.
# Experimental Evaluation of Display Field Communication Based on Machine Learning and Modern Design
## 4 Laboratory Experimentation of the Proposed DFC Scheme
### Image Extraction  
To extract the image from the distortion-corrected display image, we need to get the four corner points' coordinates of the Lena image. For that, we use some correction algorithms from the OpenCV library. As shown in Figure 5, when we capture the image using a high-resolution camera, the display pixels of the white background are also clearly captured in addition to the data-embedded Lena image. So, at first, we remove the noise mixed in the image using the Gaussian Blur [40]. Then, the keypoints detection method [41] is used to find the four corner points' coordinates of the image. Finally, the perspective transform function [42] is used to obtain the distortion-corrected image as shown in Figure 5b.  
Figure 4: Display area detection process. (**a**) An image received by the camera. (**b**) Image with bounding box drawn. (**c**) Cropped image.  
Figure 5: Image extraction process. (**a**) Cropped image. (**b**) Distortion-corrected image.
## 2 Materials and Methods  
### Tool Wear Monitoring Framework  
To realize tool wear monitoring in corner milling, an unsupervised state framework is constructed in this paper, as shown in Figure 1. First, AE signals are collected during corner milling under different tool wear conditions. Then the intrinsic features of the tool wear condition are extracted from the time-frequency image of the AE signal to form a sample space containing the tool wear information. A clustering algorithm based on adjacent grid search (CAGS) was used to divide the samples into several clusters with different distributions, and the feature density of the clusters was calculated. By analyzing the feature distribution under different tool wear conditions, the evolution law of feature density can be obtained. Finally, tool wear can be identified by the density factor proposed in this paper.  
Figure 1: The unsupervised tool wear monitoring framework for corner milling.
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 7 Manufacturing Processes for CMCs
### Matrix Forming via Chemical Reaction  
Liquid Silicon Infiltration (LSI) process is created by combining processed C/C with melted silicon carbide fibers, allowing for the creation of thin-walled and lightweight CMC components. It is characterized as a three-step process, starting with the manufacturing of carbon fiber-reinforced polymer, followed by carbonization and finishing with liquid silicon infiltration [229]. More specifically, the first step consists of the manufacturing of the CFRP composites with polymer matrices with high carbon yield, which allows for conducting the pyrolysis of the green bodies in one attempt. The second step includes the treatment of the CFRP composites in an inert atmosphere/vacuum, with temperatures ranging from 900 to 1650 \({}^{\circ}\)C [230]. Then, the pyrolysis of the matrix allows for a volumetric construction of 50-60% of the original polymer, and if hindered, this leads to a lower shrinkage of the macroscopical composite, which is almost zero [231]. In addition, bidirectional reinforced composites and in-plane reinforcing fibers, such as woven fabrics, impede the shrinkage without interacting with the plane of the laminate [231; 232]. Furthermore, the lower the fiber volume percentage and the higher the fiber/matrix bonding in the green body, the higher the transverse shrinkage of the composite. This results in providing C/C composites with an interconnecting pore structure that leads to a translaminar crack system, where the cracks showcase the open porosity of the composite material. After this step is completed, the third and final step is the capillary effect of this open porosity in between C/C laminate and low viscosity of melted silicon at 1420 \({}^{\circ}\)C, allowing for a quick composite filling. The continuous exothermic reaction of the C/C and liquid silicon results in silicon carbide encapsulated fiber bundles, offering an increase of 30-50% of the mass for the two-dimensional reinforcements. The final composite consists of three phases which include two-thirds of carbon, one-third of silicon carbide, and a small amount of unreacted silicon [229].  
The LSI process allows for the compact bundles of short carbon fibers to remain intact while the infiltration process with liquid silicon takes place. This happens by creating a green body that includes short fiber bundles and a matrix of phenolic resin. Furthermore, the usage of polymeric precursors with high carbon yields allows for the formation of the characteristic microstructure of the porous C/C preform after the pyrolysis process takes place, leading to discrete translaminar capillary channels where the good penetration of the preform is achieved due to liquid infiltration. This offers the creation of stronger bonds between matrix and fibers and enhances the overall performance of the final composite material [230].
## 6 Comparison
### Security  
Security is paramount in identity systems, and here, both systems present different kinds of advantages. LDAP uses SSL/TLS to encrypt credentials exchanged between client and server, ensuring secure transmission. OAuth relies on HTTPS for secure token exchanges, with tokens issued with a TTL, reducing the risk of reuse after expiration. SAML secures credentials using XML encryption and transmits authentication assertions over the network. These centralised systems use established encryption protocols, providing robust security during transmission and storage. However, the reliance on a central server creates vulnerabilities. If compromised, sensitive data can be exposed, as seen in the 2021 Facebook breach, where over 533 million user records were leaked [62]. Additionally, attacks like brute force or token replay remain risks if not mitigated through strict configurations.  
Decentralised systems employ advanced cryptographic features to address these vulnerabilities. uPort uses selective disclosure, sharing only necessary information, such as confirming age without revealing a birthdate. Sovrin and Hyperledger Indy employ ZKPs to enhance privacy, enabling users to prove identity without exposing unnecessary personal details. These systems follow SSI principles, storing data on distributed ledgers. Blockchain's immutability ensures that data, once written, cannot be tampered with. Furthermore, the absence of a central repository reduces the impact of potential breaches.  
However, decentralised systems also have risks. Vulnerabilities in BFT consensus, like RBFT used in Indy, may allow delays or collusion among malicious nodes/citeref17. Additionally, bugs in smart contracts can lead to unauthorised actions or manipulation of identity data. Key management is another challenge; loss or theft of private keys could result in irreversible loss of access. Scalability-related issues, such as increased latency with high transaction volumes, can also indirectly affect the security and performance of these systems [2; 18].