esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Approach
### RL-Based Optimization Method  
Swarm-based optimizers can be thought of as a policy for updating the position of agents or particles within a design space. In general, swarm-based optimizers update the position of each swarm member based on the particles' present and historical positions and the objective function values. In the study, we seek to develop an approach for discovering such policies using deep reinforcement learning. We train multiple homogeneous particles to communicate in the optimization environment. The idea used is a form of centralized training, decentralized execution, where the experience from all particles is collected into a replay buffer [31; 32] from which batches are drawn and a policy gradient is computed.  
First, we distinguish between two distinct optimization endeavors carried out in this study. The first involves the training of the reinforcement learning policy, which controls the particles to find the peaks or lowest points of surfaces by several repeated attempts. During policy generation, the particles attempt to optimize a benchmark optimization function repeatedly as though they are playing a game, progressively getting better at finding the optimum. We refer to this hereafter as the policy generation phase, which is obtained using the PPO algorithm as described in Section 2.1. The policy generation phase yields a policy, \(\pi_{\theta}\), that can be used to perform global optimization of practical problems. The policy, \(\pi_{\theta}\), is computed using a deep neural network that takes information about the state of the swarm as input and then outputs an action.  
The second phase involves global optimization or policy deployment, where the policy discovered in the phase is applied to the optimization of an objective function. Here, particles are randomly initialized over the entire design space at the initial design iteration. Afterward, the location of the particles is progressively adjusted using the generated policy until they cooperatively find the design optimum. The position update vector used to adjust the position of the particles is computed based on the policy, \(\pi_{\theta}\). The update function \(\pi_{\theta}\) in this study, like swarm-based optimizers, is a function of the current and prior states of particles within the swarm and is obtained by learning the weights (denoted \(\theta\)) of a neural network. The definitions of elements of the MDP are as follows. The state is composed of the particles' present and previous locations within the design space, as well as that of its neighbors. The action is a vector that specifies the distance between the particle's current location and its new location based on the application of policy, \(\pi_{\theta}\). The reward is a numeric value assigned based on how good the current policy is at performing global optimization.  
These two phases, namely policy generation and deployment, are summarized in Figure 1 and discussed in more detail below.
## 5 Conclusions  
This study evaluated the bone heating in the presence of external cooling applied during an All-on-4 implant bed preparation by using classical, cylindrical sleeve guides as well as by using surgical guides with internal coolant pipe that points the irrigation fluid jet towards the drill's point of entry.  
Our findings demonstrate that internally cooled surgical guides can be an effective solution in daily clinical practice, especially in All-on-4 treatments, to reduce the risk of thermal bone damage during dental implant placement. In this type of treatment, the primary stability of the implants is essential for immediate prosthetic loading, but the secondary stability can be affected by excessive bone heating. By significantly decreasing the temperature rise while drilling, internally cooled guides can help improve the success rate of dental implant procedures and reduce patient recovery time, providing a safer and more predictable approach for clinicians.  
Our results suggest that including a coolant channel in the design of the surgical guide while also providing a stand-off distance between the bottom of the guiding sleeve and the target tissue can significantly reduce the intraosseous temperature rise caused by guided surgical drilling.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14178051/s1](https://www.mdpi.com/article/10.3390/app14178051/s1)]([https://www.mdpi.com/article/10.3390/app14178051/s1](https://www.mdpi.com/article/10.3390/app14178051/s1)), Figure S1: Photograph of the surgical template's bottom taken immediately after the thermodynamic measurements. Bone chips are visible around the classical surgical guides (CLA); they accumulated in the space between the template and the artificial bone specimen. In contrast, the areas around the guides with internal irrigation channels (INT) are free of debris, suggesting that the irrigation fluid, directed at the burr's point of entry into the bone, flushed away the chipped bone pieces; Video S1: Video recording from two perspectives of an osteotomy performed with the guide featuring an internally routed irrigation channel. It can be observed that the fluid delivered by the physiodispener via the incorporated irrigation channel is properly evacuated through the spacing found in-between the surgical template and the artificial bone specimen; Data S1: Microsoft Excel spreadsheet of the data generated during this study.  
Conceptualization, A.N., R.-A.T. and S.A.; methodology, M.N. and S.A.; formal analysis, M.N.; investigation, R.-A.T., V.P. and S.A.; resources, A.N.; visualization, R.-A.T. and M.N.; supervision, A.N.; writing--original draft preparation, M.N. and S.A.; writing--review and editing, A.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data acquired during this research are included in the article and in the Supplementary Materials.  
The authors declare no conflicts of interest.
## 4 Influence of MICP on Properties of Unsaturated Soil
### Water Holding Capacity of MICP Unsaturated Soil  
MICP technology mainly changes the water holding capacity of unsaturated soil by generating CaCO\({}_{3}\) precipitation to bio-cement soil particle pores [109]. [PERSON] et al. [110] compared the water absorption of soil samples before and after treatment with MICP technology and found that the water absorption of soil samples after treatment is far greater than that of untreated soil. MICP treatment significantly improved the soil water holding capacity and anti-cracking capacity. Figure 8 shows the SWCCs of plain soil, and improved expansive soil are compared under the conditions of compactness of 90% and 95%, respectively. Results show that the pore will become smaller with the increase in compaction degree, thus changing the water holding capacity of soil samples. The SWCC of soil improved by MICP technology changes greatly compared with that of expansive soil before treatment, and the saturated water content of soil improved by MICP technology is higher than that of plain soil. It indicated that the volume of water content of the improved expansive soil changes less under the same matrix suction change, the water holding capacity was stronger, and the water stability was better.
## 2 Layout Issues of Low-Power LC-Based DCO
### Layout Method for High-Linear-Frequency Control  
The high-frequency DCO is one sensitive circuits where oscillation frequency can be changed by parasitic components generated in the layout. In particular, the parasitic components of routing metal are some of the important design parameters to be considered in the DCO because it is difficult to design an LC resonance circuit that can be linearly adjusted according to the parasitic line inductance (\(L_{p}\)).  
\[\omega_{DCO}^{2}=\frac{1}{\big{(}L_{s}+L_{p}\big{)}\big{(}C_{\text{var}}+C_{p} \big{)}} \tag{3}\]  
where \(L_{s}\) is the main inductor and \(C_{\text{var}}\) and \(C_{p}\) are the capacitor and parasitic capacitance of the switching capacitor in the capacitor bank, respectively. Here, the ratio between the maximum frequency and the minimum frequency can be expressed as:  
\[\frac{\omega_{DCO,minfreq.}^{2}}{\omega_{DCO,maxfreq.}^{2}}=\frac{C_{p}\big{(} L_{s}+L_{p}\big{)}}{C_{\text{var}}\big{(}L_{s}+L_{p}\big{)}+L_{s}} \tag{4}\]  
In Equation (4), the oscillation frequency of the DCO shifts according to the parasitic inductance. In addition, it has a nonlinear oscillation frequency due to the different parasitic inductances according to the position of the switches. The capacitor bank, including a linear capacitor unit, has a nonlinear oscillation frequency due to unexpected parasitic line inductance. As a method for minimizing the parasitic component of the routing metal, an ultra-thick metal (UTM) layer or Al metal layer commonly used metal options due to their low resistivity. In addition, a previous study [9] showed that using aluminum (Al) is a suitable method to reduce the parasitic inductance in LC resonance circuits.  
For example, as shown in Figure 1(a), the TDCO layout effectively reduced the parasitic inductance component using an Al metal layer. The capacitor bank circuit was covered by the Al metal layer to reduce the parasitic line inductance between the main inductor and  
Figure 2: Equivalent circuit of a DCO including parasitic line inductance (\(L_{p}\)): (a) \(g_{m}\) cell is located in tail of the capacitor bank; (b) \(g_{m}\) cell is located in the neck of the inductor.  
the capacitor bank. It shortened the parasitic inductance, as shown in Figure 3. Capacitor banks, including short-circuit effects, have the desired oscillation frequency range due to parasitic inductance reduction. To verify this effect, we compared and verified the effect on reducing parasitic inductance by adding Al metal to the TDCO layout. For accurate parasitic extraction, an electromagnetic (EM) simulator can be used. However, this process requires a long simulation run-time and a complex setup. For accurate prediction of the results, implementation and simulation must be synchronized. Therefore, we compared the performance of the Al metal layer using actual fabrication and measurement without using EM simulation.
## 3 Materials and Methods
### Application and Implementation Details
#### 3.7.2 Steel Plates Faults Dataset  
The Steel Plates Faults dataset [76; 77; 78] consists of a total of 1941 instances meant for the classification of surface defects in stainless steel plates. The dataset instances are grouped into 7 distinct typologies of faults: Pastry, Z Scratch, K Scatch, Stains, Dirtiness, Bumps and Other Faults. Each recorded instance consists of 27 attributes representing the geometric shape of the fault and its contour. The target class distribution reveals an imbalanced dataset (see Figure 4a).  
For experiments on the Steel Plates Faults dataset [76; 77; 78], we utilize a deep feed forward neural network with an architecture similar to the one used for the previous dataset but a comparably smaller size of the fully connected layers (81, 54 and 13 output features) for each layer. We train for 200 epochs using the Adam optimizer [74] and a base learning rate of 0.1, which is through a learning rate scheduler adaptively changed to 0.01 at epoch 150 and 0.001 at epoch 180 during training. For the optimizer tuning, we ultimately settle on Adam with \(\epsilon\) values of \(10^{-4}\).  
We maintain a similar objective of a safety-related FD system for this experiment, minimizing the number of false negatives, achieved through prioritizing recall and weighting strategies for the logit and the loss layer. From the target class distribution of the Steel Plates Faults dataset, we observe that two classes, 'Stains' and 'Dirtiness', represent the minority. Applying the effective number of samples formulation [23] to this target class distribution yields \(\mathbf{\alpha}\propto 1/E_{\mathbf{n}}=\) (0.1262, 0.1051, 0.0516, 0.2758, 0.3607, 0.0502, 0.0304). \(\mathbf{\alpha}\) is the vector composed of per class inverse effective number and is used as the class weight quantities for in the ENPr and DENPr schemes during training. Analyzing the composition of \(\mathbf{\alpha}\), we observe that it provides us with an initial vector that acts as a guideline on which we employ the RLPr strategy to obtain a class weight vector. In particular, based on the techniques highlighted in Section 3.4, we perform an alteration on the original \(\mathbf{\alpha}\) vector to obtain (0.1262, 0.1051, 0.0516, 0.2, 0.4365, 0.0502, 0.0304) as our RLPr class weight vector.  
Finally, for both the two datasets, we compare our class rebalancing loss implementation against other SOTA methods, LDAM [47], Focal loss [46] and Class Balanced loss [23], as baselines, including a combination of different weighting strategies recommended in Section 3.6. The results are discussed in Section 4. We implement all the algorithms and experiments in PyTorch (ver. 1.4.0) [79].
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 6 Online Identification Method
### Preprocessing on the Basis of Coding  
Anomalies are embedded within 6G NTN protocol sequences instead of traffic features in attacks such as hijacking UE capability and sequence number desynchronization. Thus, the protocol sequences, not the traffic features, must serve as the inputs to detect and identify anomalies. However, the original protocol sequences contain unstructured descriptions, which are unsuitable for direct input into DL models.  
To solve this problem, we need to convert 6G protocol sequences into images, which need to fulfill two demands: one is that a DL model can process it, and the other is to retain enough information to reverse back to protocol data. A single 6G protocol message consists of many sequences that include semantic English words, phrases, and special symbols as non-numeric components, along with numeric components. To preserve all this information, we utilize the one-hot encoding or ASCII encoding method to transform each message into a matrix. Each row of the matrix represents a sequence, and each column corresponds to a character in that sequence. For the non-numeric components, we first apply a regular expression tokenization algorithm to separate them, then use either one-hot encoding or ASCII encoding methods; for the numeric parts, we directly apply one-hot encoding or ASCII encoding methods. This encoding process converts both non-numeric  
Figure 8: Self-evolving classification method.  
and numeric components into unique binary vectors. Next, we combine the numeric and non-numeric vectors, group them in 8-bit segments, and pad the final group with zeros if it contains fewer than 8 bits. We then convert the grouped sub-vectors from binary to decimal, ensuring that the values fall within the range of [0, 255]. These can be represented via a grayscale image and then input into the DL model for anomaly detection. The grayscale image mapping serves as an intuitive example (e.g., mapping temporal signaling features to pixel intensity), but it can be replaced with graph embedding or other forms without affecting the core contribution.  
The preprocessing method preserves the integrity of the protocol sequence information and ensures the reversibility of the mapping process. In addition, the images can be processed by the detection model. Therefore, the essential condition for identifying anomalous protocol sequences via XAI mechanisms is fulfilled.
## 2 Materials and Methods  
### Data  
The study area, as shown in Figure 1, covers inland and coastal regions of South Korea, where the evaluation of the GK2A_FDA is feasible using ground-based visibility meters. The domain also includes several islands. The study period spans three years, from 2021 to 2023. A quantitative evaluation was conducted using GK2A fog detection products at 10 min intervals with visibility meter data. Subsequently, case studies were performed using supplementary datasets, including cloud top height from GK2A, low-level cloud base height from ceilometers, and meteorological variables such as temperature, relative humidity (RH), and wind speed from ASOS (Automated Synoptic Observing System) and AWS (Automated Weather System) stations (Table 1).
## 4 Simulation Results and Analysis
### Static Localization Performance  
Figure 10 provides a comprehensive overview of our localization system's performance across the entire test area. Key observations are as follows.  
The linear arrangement of beacons along the x-axis significantly influences the system's performance, creating a unique error pattern. Estimated positions (red dots) cluster around actual positions (thin red crosses), visually indicating the system's precision. Error circles (blue dashed for maximum, green dotted for average) vary in size across the test area, suggesting non-uniform accuracy. Accuracy appears to be higher near the center of the beacon arrangement and decreases towards the edges of the covered area, particularly in  
Figure 10: Localization results for robot height 1.0 m. The red crosses represent actual positions; red dots are estimated positions. Blue dashed circles show maximum error and green dotted circles show average error. Blue circles indicate beacon positions at (0, 0), (2, 0), and (1, 0).  
the y-direction. There is a slight elongation of error circles in the y-direction, particularly for points further from the x-axis, likely due to the linear beacon arrangement.
## 2 Two-Stage Time-Domain Equalization Method  
As shown in Figure 1, the DL-based two-stage time-domain equalization structure proposed in this study consists of three main components: the THz wireless communication system, the DL-based two-stage time-domain equalization module, and the equalization model validation module.
## 6 Performance Evaluation  
This paper has discussed the distributed management scheme ability to serve applications requiring a real-time and reliable communication as well as a high throughput. This section illustrates how these requirements are fulfilled. To this end, we first assess the end-to-end data delivery delay of D-MSR and WirelessHART. Next, we evaluate the communication schedules and network throughput. Following this, we assess the packet delivery ratio in case of internal and external interference. Furthermore, the power consumption in the D-MSR network and WirelessHART is being evaluated.  
Finally, we evaluate the management efficiency of D-MSR in terms of delay and overhead during node joining, management resource reservation, end-to-end connection establishment, and coping with changes and disturbances in the network.
## 4 Experimentation and Results
### Result of Product Detection
#### 4.3.1 Product Detection with YOLOv5  
Figure 7 presents a concrete example of product detection achieved by our YOLOv5 model. Each detected product is framed by a bounding box highlighting its location in the image. This visual representation demonstrates the model's ability to identify and precisely **delineate** the different products present in a scene. Each box **enclosing** a product illustrates the effectiveness of our model in detecting and localizing objects of interest in **real-world** images.  
Figure 8 presents the confusion matrix that was used to evaluate the performance of our YOLOv5 model in object detection. This matrix illustrates the detailed correspondence between the detections made by our model and the ground truth annotations, highlighting  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criterion** & **Definition** & **Purpose** & **Method** \\ \hline \multirow{3}{*}{Accuracy} & Correct detections & \multirow{3}{*}{Ensures reliability.} & High accuracy in validation confirms \\  & over total instances. & & \\  & & & \\ \hline \multirow{3}{*}{Precision} & True positives over true + false positives. & Reduces irrelevant detections. & Consistently high \\  & & & \\ \hline \multirow{3}{*}{Recall} & True positives over true positives + false negatives. & \multirow{3}{*}{Captures all relevant instances.} & Shows strong positive \\  & & & \\ \hline \multirow{3}{*}{F1-Score} & Balances precision & \multirow{3}{*}{Measures overall effectiveness.} & Consistent across \\  & and recall. & & \\ \hline \multirow{3}{*}{Latency} & Detection time per frame. & Enables real-time tracking. & Low enough for \\  & & & \\ \hline \multirow{3}{*}{Computational Cost} & Resource use per detection. & \multirow{3}{*}{Ensures standard hardware compatibility.} & Efficient memory and \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of model evaluation criteria for YOLOv5 and DeepSORT.  
Figure 7: Product detection.  
the correct detections, false positive detections, and false negative detections for each object category. Each cell in the matrix indicates the number of object occurrences for a specific class, which allows a detailed evaluation of the performance of our model in terms of accurate and consistent detection. This confusion matrix is essential for thoroughly examining our results and helps to understand the strengths and weaknesses of our YOLOv5-based object detection method.
## 4 File Operations in Secure Container Runtimes
### gVisor  
gVisor offers the virtualized environment to sandbox containers and filters system calls by utilizing the user-space kernel. To construct the virtualized environment, gVisor supports ptrace [42] and KVM [43]. Even though ptrace is offered by default, the KVM-based virtualization environments provide better performance than ptrace because of the hardware acceleration [18]. Thus, this paper focuses on gVisor with KVM to eliminate the performance bottlenecks in the system call filtering with ptrace when analyzing the bottlenecks in the file I/O stack itself.  
In gVisor, the filesystem is located in the host OS, and the containers can only access the filesystem through the user-space kernel that consists of Sentry and Gofer. Different from Kata containers, gVisor does not share filesystems so that the containers cannot execute filesystem-related system calls directly. As shown in Figure 3a, 1 Sentry captures the system calls from the containers. When the system call is related to the filesystem, 2 Gofer receives the request and 3 executes the corresponding file I/O function, such as vfs_read. 4 This leads to the actual file I/O processing that works on the storage device of the host.  
Figure 3b shows the call flow of the filesystem-related system calls in gVisor. First, when applications execute pread or pwrite, it involves VMexit from the user space of the non-root mode to the kernel space of the root mode, which is similar to Kata containers. However, the VMexit does not require handling an EPT violation because the application running on the container of gVisor cannot perform file I/O processing directly. Instead of handling the EPT violation, after the VMexit, Sentry receives the file I/O requests and calls the corresponding system call such as pread64, and in turn, Gofer initiates the file I/O system call.
# Quadcopters in Smart Agriculture: Applications and Modelling
## 5 Challenges and Future Directions
### Data Processing
#### 5.2.2 Future Directions  
Future directions could include advanced techniques and software platforms, such as those based on AI and cloud-computing technologies, that can handle and process large datasets effectively and improve decision-making [157; 158]. For instance, the authors in [159] demonstrated that combining cloud-, fog-, and edge computing can enhance data handling in UAV-based applications for smart agriculture. This combination can improve real-time decision-making, increase the accuracy of data analysis, and optimize agricultural tasks such as irrigation management, field mapping, and plant health monitoring.