esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effect of Data Collection Methods and Results Accuracy  
#### 4.2.1 Effect of Data Collection Methods  
The route planning takes on a particularly critical significance, affecting the integrity of the data and the accuracy of the results. The zigzag route planning method has been used as one of the main methods, and is more applicable to data collection by rotary-wing UAVs [71]. However, the image resolution and the overlap between images will be significantly different for areas with significant vertical gradient differences. For vertically distributed landforms, route planning methods including nap-of-the object photogrammetry [72], or layered layout [73] can be used, or even manual flight [22] for data acquisition. The above methods are subject to the problem of redundancy. In this study, the ground-based flight route planning method was applied. The measurement accuracy analysis of LiDAR point  
Figure 17: (**a**) A 3D model of the pit rim structure, (**b**) DOM of the gully inside the pit rim structure, (**c**) SVF visualization result of the gully, (**d**) SIM visualization result of the outcrop, (**e**) a 3D model of the outcrop’s geomorphological measurement, (**f**) SVF visualization results of the ridge end, (**g**) ridge slope measurement based on the LiDAR point-cloud, (**h**) SIM 3D bedding visualization results, and (**i**) bedding strike, dip and dip direction measurements based on the LiDAR point-cloud (the green arrow represents perpendicular to the strike line).  
cloud and 3D model results indicated that this method ensured the integrity of data in areas with large topographic fluctuations, and DEM accurately indicated the topography.  
However, when collecting field data, a single route type is not necessarily suitable for all landforms. Furthermore, the optimization of route planning parameters (e.g., flight height, lens angle [74], and overlap) should be fully considered in accordance with the actual topographic characteristics. Combined with multi-source data collection methods [75], ensuring the acquisition of high-quality and high spatial resolution results has significant advantages for multi-type and multi-scale identification and analysis.
## 3 Methods for Closed Path Determination
### Watershed Transformation Method  
Watershed transformation (WT) is a technique to extract ridges from an elevation map. This idea originated from the field of topography, and was firstly introduced as an image processing method by [PERSON] and [PERSON] [25]. Since then, many modifications and improvements to the method have been proposed [26; 27; 28; 29]. The kernel of watershed transformation is to treat the two-dimensional grayscale input image as a topographic map, as shown in Figure 2, with the grayscale value of each individual point representing the elevation. After this transformation, the image is intuitively divided into several catchment basins, each of which corresponds to a regional minimum in the elevation dimension. The boundaries between diverse catchment basins are considered as the ridges, which are commonly termed watershed lines.  
To perform watershed transformation on an image, flooding is the most commonly used strategy, and [PERSON]'s flooding algorithm is the corresponding extensively used algorithm [27]. Its core idea is to flood the entire topographic map, which is equivalent to raising the zero-elevation plane with time. With the flooding, the catchment basin corresponding to the global minimum is first filled with water, following which the other catchment basins start to fill with water one by one. After a certain amount of time, water from different catchment basins meet, and we build barriers to prohibit this phenomenon. The resulting barriers comprise the watershed lines, which correspondingly segment the input image into different regions.  
Specifically, to apply watershed transformation on an intensity sum map to determine a closed path, we first perform preprocessing on the intensity sum map, such as thresholding and filtering [26], and then we perform the watershed transformation using the built-in function 'watershed' of MATLAB [30]. Moreover, considering that we aim to determine the net TC value in an OV beam, we should obtain only one closed path out of the multiple watershed lines. This can be realized by merging small regions into their neighboring large regions until there is only one region.
# Virtual Source Array-Based Multiple Time-Reversal Focusing
## 3 Adaptive VSA-Based Multiple TR Focusing  
In this section, VSA-based single TR focusing is extended to simultaneous multiple focusing at arbitrarily selected locations beyond the VSA (hereafter referred to as the adaptive VSA). Generally, the weight vector, \(\mathbf{w}\), can be derived from the following objective function with multiple constraints:  
\[\mathbf{\underset{w}{\min}}\mathbf{w}^{\dagger}\mathbf{R}\mathbf{w}\quad\text{ subject to}\quad\mathbf{M}^{\dagger}\mathbf{w}=\mathbf{f}. \tag{3}\]  
The solution to this optimization problem is well known and can be found using the Lagrange multiplication method as  
\[\mathbf{w}=\mathbf{R}^{-1}\mathbf{M}\left(\mathbf{M}^{\dagger}\mathbf{R}^{-1} \mathbf{M}\right)^{-1}\mathbf{f}, \tag{4}\]  
where \(\mathbf{R}\) is the cross spectral density matrix, which contains the information of the synchronized transfer functions corresponding to the selected multiple locations.  
\[\mathbf{R}=\mathbf{g}(\mathbf{r}_{\text{TRM}}|\vec{r}_{s1})\mathbf{g}^{ \dagger}(\mathbf{r}_{\text{TRM}}|\vec{r}_{s1})+\mathbf{g}(\mathbf{r}_{\text{ TRM}}|\vec{r}_{s2})\mathbf{g}^{\dagger}(\mathbf{r}_{\text{TRM}}|\vec{r}_{s2})+ \sigma^{2}\mathbf{I}. \tag{5}\]  
For simplicity, we consider only two focal locations, \(\vec{r}_{s1}\) and \(\vec{r}_{s2}\), which can be easily generalized to more locations. \(\sigma^{2}\) and \(\mathbf{I}\) denote a small diagonal loading factor for inverse matrix calculation and the identity matrix, respectively. The constraint response, \(\mathbf{f}\), which is frequently referred to as the directional constraint [13], is a \((P\times 1)\) matrix that forces the response of multiple locations to be unity. \(\mathbf{M}\) represents an \((M\times P)\) constraint matrix, which is composed of synchronized transfer functions for the selected locations.  
In conventional multiple TR focusing [6], this approach might be impractical because the transfer functions corresponding to the different PS locations are required to form the constraint matrix, \(\mathbf{M}\); this requires additional measurements. However, our approach simply applies the time delay corresponding to the selected multiple locations to a-priori sampled transfer functions (i.e., synchronized transfer functions). Thus, the constraint matrix, \(\mathbf{M}\), can be easily formed when the sampled transfer functions are provided. By backpropagating the weight vector, \(\mathbf{w}\), with multiple constraints, simultaneous multiple focusing with distortionless response, \(\mathbf{f}=[1\;\;1]^{\text{T}}\), can be achieved at the selected locations beyond the VSA,  
\[p(\vec{r})=\sum_{m}w_{m}^{*}g(\vec{r}|\vec{r}_{m})=\mathbf{w}^{\dagger} \mathbf{g}(\vec{r}|\mathbf{r}_{\text{TRM}}), \tag{6}\]  
where the weight vector, \(\mathbf{w}\), reduces to \(\mathbf{g}(\mathbf{r}_{\text{TRM}}|\vec{r}_{s})\) in VSA-based single TR focusing.
## 2 Methodology
### Fuzzy C-means Clustering  
To extract useful patterns/structures from large datasets, different clustering algorithms have been developed and classified into two categories: distinct clustering and fuzzy clustering. In distinct clustering, such as K-mean clustering, every data element is assigned to exactly one cluster [45]. However, the data elements on the boundary of multiple clusters may not belong to any of the multiple clusters. To overcome the classification uncertainty, fuzzy clustering assigns every data element to multiple clusters by combining every data element with a set of membership levels. With the cluster information, a fuzzy inference system can be produced to model data behaviors with the least number of rules.  
Fuzzy C-means (FCM) clustering is a robust fuzzy clustering algorithm, appropriate for clustering overlapped datasets. The degree of a data point in FCM is specified by a membership. The membership assigns a large value for the data element close to the cluster center and a small value for the data element away from the cluster center. FCM partitions a choice of \(n\) vector \(xi\), (\(i\) = 1, 2,..., \(n\)) into fuzzy sets and estimates the cluster center in each set by minimizing the objective function.  
Firstly, there are n data points (\(x_{1},x_{2},x_{3},\)..., \(x_{n}\)), with the cluster center \(c_{i}\), \(i\) = 1, 2,..., \(C\) randomly selected. The membership matrix (U) is estimated using the following equation:  
\[\mu_{ij}=\frac{1}{\sum\limits_{k=1}^{C}\left(\frac{d_{ij}}{d_{ij}}\right)^{ \frac{2}{n-1}}} \tag{8}\]  
where, \(d_{ij}\) = 1 \(|\ c_{i}-x_{j}|\) 1 is the Euclidean distance between the \(i^{\text{th}}\) cluster center and the \(j^{\text{th}}\) data point; \(\mu_{ij}\) is the coefficients in the membership matrix; \(m\) is the index of fuzziness; \(c\) is the total number of clusters.  
Secondly, the objective function can be calculated according to the following equation:  
\[J(U,\ c_{1},\dots,c_{2})=\sum\limits_{i=1}^{c}J_{i}=\sum\limits_{i=1}^{c}\cdot \sum\limits_{j=1}^{n}\mu_{ij}^{m}d_{ij}^{2} \tag{9}\]  
Finally, a new \(c\) fuzzy cluster center \(C_{i}\) (i = 1, 2,..., \(C\)) can be estimated as follows:  
\[C_{i}=\frac{\sum\limits_{j=1}^{n}\mu_{ij}^{m}x_{j}}{\sum\limits_{j=1}^{n}\mu_ {ij}^{m}} \tag{10}\]  
In ANFIS, the fuzzy inference system with initial structure has an obvious effect on the modeling accuracy. Therefore, ANFIS has two limitations: slow computational convergence and potential of being trapped in local minima. To overcome these limitations, the fuzzy inference system needs to be optimized with heuristic optimization techniques, such as Genetic algorithm (GA).
## 3 Experimental Results  
### Experimental Setup  
To verify the feasibility of this method, a verification experiment was carried out. The infrared detector operates in the 3-5 \(\upmu\)m waveband, and it is composed of 640 \(\times\) 512 pixels with a 14-bit digital output. The system has a diameter of 600 mm and a focal length of 1200 mm. The calibration source's emissivity is 0.97, and the temperature range is 0-125 \({}^{\circ}\)C.
## 6 Palladium
### Catalysts Applicable Towards Several Cross-Coupling Reaction Types
The reactions could also be carried out in capillary microreactors filled with monolith, with good results, albeit somewhat lower yields when using an aryl bromide as the substrate. [PERSON], [PERSON] and co-workers have attached palladium to a polyionic Amberlite resin to provide a ligand free catalyst that can be applied in Heck, Suzuki and Sonogashira reactions [69]. Formate was used as counterion to effect in situ reduction of Pd(II) to Pd(0), followed by deposition of the active palladium species on the resin surface. Heck reactions of aryl iodides with ethyl acrylate afforded the desired coupling products in up to 90% yield after 5-10 hours at 90-100 \({}^{\text{o}}\)C. Sonogashira coupling of aryl iodides was also effected in high yields, up to 96% when using a 3-methyl substituted aryl iodide. Suzuki coupling of aryl bromides using this type of catalyst gave yields in the range of 80-90%, and diarylation of 1,2-bromobenzene could also be effected in 55% yield. Investigation of the recyclability of the catalyst in the Suzuki reaction showed that diarylation of 1,4-dibromobenzene could be performed up to five times without any great loss of activity. The palladium content was followed by x-ray photoelectron spectroscopy (XPS), measuring the Pd/N ratio, and showed that although some leaching occurred in the first cycle, the level then remained essentially the same throughout the following cycles. A brief mention will also be made of some additional methods, without going into much detail. [PERSON] and [PERSON] functionalized Wang polystyrene with different dendrimers, using pendant phosphines as ligands for palladium, and applied these supported catalysts in Heck and Suzuki coupling reactions [70]. A positive dendritic effect was seen in all cases, _i.e._ third generation dendrimer supports performed markedly better than the non-dendronized polymer or the first generation dendronized support. Finally, [PERSON] and co-workers investigated the use of pH-responsive core shell polymeric microspheres as supports for palladium(II) chloride [71]. The quasi-homogeneousmicrospheres consisted of an inner polystyrene core and an outer poly(methacrylic acid) shell, employing a pendant iminodiacetic acid unit as the ligand to palladium. Both Heck and Suzuki reactions could be carried out in water using this catalyst, recycling up to four times was possible without loss of catalytic activity.
# Study About the Influence of Diatoms on the Durability of Monumental Limestone
## 3 Results and Discussions
### Results of Water Absorption Test by the Pipette Method  
Figure 22 shows the volume of water absorbed (in milliliters) as a function of time. Analysis of the data reveals that the diatom-treated samples required more time to absorb each increment of 0.1, 0.2, 0.3, 0.4, and 0.5 mL of water compared with the control samples. This suggests that water penetration into the diatom samples was slower and less efficient, likely due to increased sealing of the rock pores.  
Furthermore, as the volume of water absorbed increased, the trend line for the diatom samples diverged progressively from that of the control samples, with its slope becoming steeper. This indicates that, as the test progressed, the diatom samples required significantly more time to absorb each additional 0.1 mL increment compared with the controls.  
By the end of the test, the time needed to absorb 0.5 mL of water was 78% longer in the diatom samples than that in the control samples, demonstrating the waterproofing effect generated by the biotereatment.  
In addition, volume of water absorbed after 24 h of testing was examined, as illustrated in Figure 23. It can be seen that the samples with diatoms exhibit a 40% lower water absorption compared with that in the control samples.  
Figure 22: Average time in minutes elapsed on the Y-axis until absorbing the different volumes of water on the X-axis.
# A Lightweight Detection Method for Meretirix Based on an Improved YOLOv8 Algorithm
###### Abstract  
Clam farms are typically located in remote areas with limited computational resources, making it challenging to deploy traditional deep learning-based object detection methods due to their large model size and high computational demands. To address this issue, this paper proposes a lightweight detection method, YOLOv8-RFD, based on an improved YOLOv8 algorithm, tailored for clam sorting applications. The proposed enhancements include the following: replacing the original backbone network of YOLOv8 with a Reversible Columnar Network (RevColNet) to reduce feature redundancy and computational load; upgrading the C2f modules in both the backbone and neck networks to C2f-Faster to optimize feature fusion strategies and improve fusion efficiency; and incorporating a Dynamic Head (DyHead) to enhance feature extraction and detection accuracy by adaptively adjusting the detection head structure. Experimental results on a custom clam dataset demonstrate that, compared to the original YOLOv8 model, the proposed method reduces the number of parameters by 22.75% and computational demand by 18.52%, while slightly improving detection accuracy. These improvements not only maintain but also enhance detection performance, significantly reducing computational cost, and confirming the method's suitability for deployment in resource-constrained environments. This provides a reliable technical foundation for the sorting of clams.  
clam; YOLOv8; lightweight detection; feature fusion; sorting Article
# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
## 4 Experiments  
### Experimental Setup  
#### 4.1.1 Datasets  
To evaluate the effectiveness and generalization performance of our method, we chose to use the most widely used large benchmark databases: FaceForensics++ (FF++) [26] and Celeb-DF [27].  
FF++ comprises a real dataset and four forged datasets, including Deepfakes (FF-DF) [28], Face2 Face (FF-F2F) [29], FaceSwap (FF-FS) [2], and NeuralTextures (FF-NT) [30], with each dataset comprising 1000 videos, where 720 videos are used for training, 140 videos are reserved for testing and the rest for validation. The FF++ dataset primarily focuses on manipulated facial expressions and identities within videos, with consistent backgrounds, to provide a benchmark for face forgery detection. FF++ offers three levels of compression versions: RAW, High Quality (HQ), and Low Quality (LQ). Due to the wider usage of the HQ version, we opt for the HQ version in our experiments.  
Celeb-DF contains 5639 high-quality forged videos and 590 real videos. This dataset challenges detection algorithms with its subtle manipulations and broad distribution across varied backgrounds, natural expressions, and lighting conditions. Its comprehensive scope, combined with the presence of compression artifacts, demands sophisticated methods for robust generalization. Hence, we use Celeb-DF for cross-dataset evaluation.
## 4 Simulation and Analysis
### Evaluation Index  
The performance evaluation encompasses the following three selected metrics: RMSE, MAE, and R\({}^{2}\), as presented in Table 3.  
Additionally, the comparative analysis encompasses LSTM, Bi-LSTM, CNN-Bi-LSTM models, in addition to the proposed MHSA-1D CNN-Bi-LSTM hybrid model. The specific outcomes are presented in Table 4.  
The superiority of the proposed MHSA-1D CNN-Bi-LSTM hybrid model over the LSTM, Bi-LSTM, CNN-Bi-LSTM models is evident, as demonstrated by the significantly improved results presented in Table 4.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 4 Results
### Results Obtained from the CIC-IoT2023 Dataset Using a Supervised Machine Learning Approach
#### 4.3.1 Research Analysis According to [49]  
Research [49] has investigated anomaly detection in IoT networks, utilizing the CIC-IoT 2023 dataset. The methodology encompasses several key steps.  
Step 1: Data Preparation and Processing  
The CIC-IoT 2023 dataset was preprocessed. The attacks were grouped and the data normalized and scaled. The labels of the attack categories were converted into a numerical format. Then, the dataset was split into a training set and a test set in a ratio of 70:30. Different feature selection techniques were applied to improve the learning process and model performance. Different selection schemes were compared, selecting the optimal number of features.  
Step 2: Model utilization  
The RF model was chosen as the classification model. This model was configured with the appropriate hyperparameters to achieve the best possible results.  
Step 3: Model evaluation  
The performance of the model was assessed using metrics such as the accuracy, precision, sensitivity, and F1-score.  
Step 4: Result verification  
The RF model with Recursive Feature Elimination (RFE) feature selection achieved the highest accuracy, indicating its effectiveness in classifying IoT attacks. In addition, the selection of fewer features allowed for a significant reduction in data size, which is beneficial for IoT devices with limited resources.
## 4 Key Agreement, Distribution, and Security Bootstrapping
### Shared Group-Key Distribution  
The above mechanisms apply to scenarios where communication occurs between two parties (unicast and point-to-point communications). In different communication scenarios, such as point-to-multipoint (multicast) or multipoint-to-point communications, other mechanisms must be investigated and adopted. In such scenarios, the adoption of a shared group-key is appealing.  
Secure group communications provide confidentiality, authenticity, and integrity of messages exchanged within the group, through the use of suitable cryptographic services and without interfering with the communication data path. In order to achieve secure group communications, nodes must share some cryptographic material that must be handled properly so that group membership changes, both predictable and unpredictable, can be managed. In fact, any membership change should trigger a _rekeying_ operation, which is intended to update and redistribute the cryptographic material to the group members, so that: (i) a former member cannot access current communication (_forward secrecy_[63]); (ii) a new member cannot access previous communication (_backward secrecy_[64]). The authors in [65] define a DTLS record based approach to secure multicast communication in lossy low-power networks.  
Assuming that the used cryptographic primitives cannot be broken by an attacker with limited computational power (_i.e._, for whom it is infeasible to carry out a brute force attack in order to solve the problems behind cryptographic schemes, such as discrete logarithm, or inverting MD5/SHA-1), the main challenge is the distribution of the group keys and their updates: this problem is referred to as _Group Key Distribution_ (GKD). The GKD problem can be tackled according to the two different approaches:  
* current communications can be deciphered independently of previous communications (stateless receivers): this approach is denoted as _broadcast encryption_[66, 67];
* users maintain state of the past cryptographic material (stateful receivers): this approach is termed _multicast key distribution_[68].  
In the case of a multicast key distribution, centralized [69] or distributed [70] approaches can be adopted. In a distributed approach, the group key is computed and maintained by the group members themselves: an example of a distributed approach is the Tree-based Group Diffie-Hellman (TGDH) protocol [71]. In a centralized approach, the task of key distribution is assigned to a single entity, denoted as Key Distribution Center (KDC). This approach allows to have a simple mechanism with a minimal number of exchanged messages. Logical Key Hierarchy (LKH) [63] and MARKS [72] are key distribution protocols that try to optimize the number of exchanged messages between a KDC and the group members. LKH is based on key graphs, where keys are arranged into a hierarchy and the KDC maintains all the keys. MARKS is a scalable approach and does not need any update message when members join or leave the group predictably. However, MARKS does not address the issue of member eviction with a subsequent key revocation.