esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Final Considerations
#### 4.2.2 Limitations  
The model applied in this study, as any model, assumed a considerable level of simplification of the socioecological system under consideration. Several assumptions were made for operational reasons, the first of which is the no-conflict rule in the four scenarios selected. This means that the model did not address possible conflicts between forest industry and other sectors/groups of the society with interests over forestland, assuming that thinning/felling or no thinning/felling is simply the result of supply and demand functions based on preferences of the forest industry. It did not involve, therefore, other relevant stakeholders such as conservation institutions, environmental NGOs, tourism organizations, and forest, bekeeping, or hunting and owners' associations, that have potentially conflicting interests with industry. Similarly, the model and the study did not consider the use of forests for the supply of ecosystem services that may or may not be compatible with wood-driven management. There is a growing interest in forests in the region as suppliers of highly valued ecosystem services such as carbon sequestration, mushroom production, or water regulation [65] which requires further development of the model in order to incorporate trade-offs among ecosystems services in supply-demand assessments [66], an important line for future research in the region and elsewhere. That also brings additional challenges in terms of simplification of the process considering the computational demand of such a large problem.  
In terms of economic assessment, there are factors which are not addressed in this model, including competition from potential sources of demand outside the geographic boundaries of the study area and the effects of demand on the establishment of new markets and on changes in land use and land cover in the region [67]. These factors are even more complex to model due to the difficulty of establishing relationships among factors and decisions.  
Lastly, this model did not attempt to find industries' best location, as in [68; 69]. The objective of our study was to evaluate individual industries once their location was defined, although best location can by approximated by trial and error with the model in its present form. Future development of the model should therefore allow this possibility based on supply, demand, and supply-demand interactions in the future.
## 3 Results and Discussion
### Refractive Index  
Figure 2 illustrates the refractive indices of the glycol contamination--four levels--in the glycol engine oil. From 0.25 to 2.5 THz, the mean refractive index range started at 1.467 (1) (0.25 THz for 500 ppm) and ended at 1.464 (1) (2.5 THz for 0 ppm). The refractive index decreased with an increase in terahertz frequency, which was similar to previous research with other oils [20; 22; 25]. While refractive index was measured at all 615 frequencies, Table 1 detailed data for refractive indices at 0.25 THz intervals. We note that these errors (standard deviations) do not include any systematic errors caused by the THz system, but represent relative errors between successive measurements. The direction of change (increase in refractive index with increase in glycol contamination in the engine oil) was similar to the effect of increased oxidation [28; 29] or water contamination [26], but the opposite of increased gasoline fuel contamination [27]. There was a relatively consistent spacing among the four curves of the refractive index along the 0.25 to 2.5 THz range.  
The highest refractive index was obtained at the level of 500 ppm glycol contamination; the lowest refractive index was obtained at the level of 0 ppm glycol contamination. Figure 2 demonstrated the overlap in 95% confidence intervals between two pairs of glycol contamination levels, creating one grouping of 0 ppm and 150 ppm and a second grouping of 300 ppm and 500 ppm. In general, this confidence interval was consistent across the frequency range. The curve shapes showed a difference among the four levels of glycol contamination. From the frequency 0.25-2.5 THz, the one-way ANOVA presented highly significant differences (\(p<0.01\)) in the refractive index among the levels of glycol contamination. Based on LSD, the glycol contamination concentrations of 300 ppm and 500 ppm were significantly different from the concentrations of 0 ppm and 150 ppm glycol contamination for frequencies from 0.25 to 2.5 THz. The uncontaminated sample (0 ppm) and the 500 ppm contamination were clearly discernible. Although [PERSON] [8] found that 500 ppm was not detrimental, this finding clearly shows that THz-TDS is capable of determining significant contamination of 300 ppm and beyond, at least under laboratory conditions.
## 3 System Model
### System Overview  
We consider the HFL framework in the cloud-edge-end coordination system consisting of a cloud server, a cluster of edge servers \(\mathcal{K}=\{k:k=1,...,K\}\), and a cluster of mobile devices \(\mathcal{N}=\{n:n=1,...,M\}\). Let \(H_{n}=\{x_{n},y_{n}\}\) denote the dataset of device \(n\), where \(x_{n}\) and \(y_{n}\) denote the features and labels in this dataset, respectively, and \(|H_{n}|\) denote the size of dataset. The edge layer serves as an intermediate aggregation layer, where the edge server receives the local model of the mobile device and uses semi-asynchronous updating to obtain the aggregated edge model. All edge servers upload edge models to the cloud at fixed intervals for synchronous global aggregation.  
Blockchain services are deployed at the edge server layer, where edge nodes act as full nodes of the blockchain, while mobile devices and cloud servers participate in blockchain services as clients. In this study, each edge server acts as a miner node, and each device determines the size of its own task-relevant dataset. The device acts as a blockchain client to upload terminal resource information and data information to the blockchain. On the one hand, the blockchain stores model parameters, device scores, and reputation values, and on the other hand, node selection and model verification are implemented.
# Multi-Modal 3D Shape Clustering with Dual Contrastive Learning
## 4 Experimental Results
### Evaluation of Key Components
#### 4.3.2 Evaluation of the Cross-View and Cross-Modal Contrastive Learning  
By adequately exploiting the characteristics of 3D shapes, the proposed method simultaneously performed cross-view contrastive learning within the multi-view modality and cross-modal contrastive learning between the point cloud and the multi-view modalities, so as to better explore the consistent semantic information of 3D shapes. To evaluate the effectiveness of the cross-view and cross-modal contrastive learning, validation experiments were conducted on the ModelNet10 and ModelNet40 datasets. The experimental results are reported in Table 3, in which _\"w/o_ cross-modal contrastive learning\" denotes removing the point cloud branch and only combining the cross-view contrastive losses for the network constraints, and _\"w/o_ cross-view contrastive learning\" denotes removing one of the view branches and constraining the network via the cross-modal contrastive losses of the remaining view branch with the point cloud branch. As shown in the table, the _\"w/o_ cross-modal contrastive learning\" and _\"w/o_ cross-view contrastive learning\" methods only achieved limited clustering performances on the two datasets compared to the DCL-Net. The main reason was that removing either the cross-view contrastive learning or the cross-modal contrastive learning prevented the network from exploring the consistent semantics from more comprehensive 3D shape information. Specifically, when the cross-modal contrastive learning was removed, the network was incapable of perceiving the spatial geometry of 3D shapes, which made it challenging to explore the discriminative 3D shape descriptions from harder contrastive positives. Similarly, when the cross-view contrastive learning was removed, the network could not observe the richer visual information about the 3D shape from different angles, thus failing to ensure the compactness of view features of the same 3D shape and misleading the extraction of consistent information. Therefore, both the cross-view and cross-modal contrastive learning adopted in the proposed DCL-Net were crucial for the 3D shape clustering.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**ModelNet10**} & \multicolumn{4}{c}{**ModelNet40**} \\ \cline{2-9}  & **ACC** & **NMI** & **ARI** & **F-score** & **ACC** & **NMI** & **ARI** & **F-score** \\ \hline _w/o_ ADCL & 0.6901 & 0.7437 & 0.6538 & 0.6958 & 0.5572 & 0.7241 & 0.4573 & 0.4779 \\ \hline _w/o_ RDCL & 0.7921 & 0.7872 & 0.7476 & 0.7782 & 0.4966 & 0.6581 & 0.4321 & 0.4641 \\ \hline DCL-Net & **0.7932** & **0.8218** & **0.7646** & **0.7934** & **0.6120** & **0.7246** & **0.5761** & **0.6022** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The evaluation of the proposed RDCL module and ADCL module.
## 5 Results and Discussion
### Quantitative Analysis of the ConvGRU Models Efficacy
This trend underscores the challenges of modeling under intense ionospheric fluctuations and highlights the superior adaptability of the ConvGRU-L\({}_{\text{C}}\) method during such periods. The extended analysis emphasizes that the choice of modeling technique significantly impacts the accuracy of ionospheric predictions, especially under varying solar activities. The superior performance of the ConvGRU-L\({}_{\text{C}}\) method across both peaks and troughs of solar activity underscores its potential as a reliable tool for ionospheric forecasting.
## 4 Discussion
### Satellite-Based Inundation Applied in Data-Driven Flood Risk Management  
Evidence indicates that satellite-based inundation observations are valuable in data-driven flood risk assessment. In our study, there was a spatial mismatch between the historical heavy rainfall distribution (Figure 6a) and the actual high-frequency inundation area distribution (Figure 6b). We speculate that the discrepancy arises from a complex surface runoff process, from rainfall falling to inundation formation, during which many natural and unnatural factors, such as topography and spillways, play important roles. This indicates that risk estimation is potentially biased when performed without remote sensing-based inundation, as we inferred in the introduction. Therefore, we believe that the addition of historical inundation can help data-driven models overcome the drawbacks of subjectivity, uncertainty and lack of physical modeling by providing a physical basis [29; 84; 85]. The inclusion of recent extreme inundations in this study produced an assessment outcome closer to the actual scenario under the current climatic conditions.  
Additionally, as the other case study reported in the Supplementary Materials shows, the freely available datasets of remote sensing- and satellite-based precipitation and inundation data we used can be replaced according to the available data condition of the interested area. The framework can be dynamically updated with the inclusion of more flood events to remain adaptable to the changing climate over time.
## 1 Introduction
Using a controller and a real-time acoustic positioning system, the AUV was guided to move to the underwater node where the acoustic and optical communication modems were installed, and the optical communication link was automatically established. The performance of the scheme was verified by simulation in terms of positioning time, communication link establishment time, and communication link recovery time. However, their work is still in the stage of simulation and analysis, and the actual acoustic-optic cooperative communication system has not been verified. In this paper, an underwater optical-acoustic cooperative communication scheme that combines UWVLC with UAC is proposed from the perspective of enhancing the stability and reliability of underwater communication. This solution utilizes the advantages of long-distance, high-stability, and no strict line-of-sight transmission of UAC and provides auxiliary alignment and Automatic Repeat reQuest (ARQ) for optical communication. Moreover, we introduce channel coding into the optical-acoustic cooperative communication platform to improve the Bit Error Rate (BER) performance. Inspired by the traditional ARQ protocol, an error-free data transmission protocol for underwater optical-acoustic cooperative communication is proposed. The large-scale data are transmitted through optical communication, while the packet loss feedback function is realized by UAC. Hence, the system can achieve strong error control performance without establishing a bidirectional optical communication link. Based on our previous work [19], this paper describes the design and implementation of the underwater optical-acoustic cooperative communication platform in detail, the improvement the transmitting circuit, the experiment conducted in comparison with the previous circuit, and the addition of a BER monitor to monitor the BER performance during transmission. In addition, we measured the communication distance under different water conditions and the BER at different transmitter offset angles. Based on the measurement results, we estimate that the achievable connection distance in clear water is about 11 m.  
The rest of this paper is organized as follows. Section 2 describes the overall design of the system platform and clearly explains each structural unit. Section 3 describes the simulation of the proposed transmission protocol. The series of experiments carried out to examine the energy efficiency, BER performance, and transmission throughput of the system are described in Section 4. Finally, Section 5 provides concise conclusions on effective methods for designing underwater optical-acoustic cooperative communication platforms and improvements.
## 4 Performance Comparison of Observability Indices
### The Process of Numerical Simulation of Kinematic Calibration  
The error model Equation (27) is accurate only if the kinematic parameter error d\(\mathbf{C}\) is minimal. Therefore, if the d\(\mathbf{C}\) is large, it is necessary to gradually modify the theoretical kinematic parameters iteratively to make them gradually approach the actual kinematic parameters, and ultimately complete the calibration. The simulation of kinematic calibration includes three parts: measurement pose selection, generating measurement value of the primary mirror poses, and identification and compensation of kinematic parameter errors. The calibration process is shown in Figure 4 and explained as follows:  
1. The initial theoretical kinematic parameter \(\mathbf{C}\) of the six-hardpoint positioning mechanism is known, and the initial kinematic model K is established based on \(\mathbf{C}\). K includes forward and inverse kinematic models. Then, substitute the initial theoretical pose set \(q_{\mathbf{\mathrm{B}}}\) obtained using the improved DETMAX algorithm into the inverse kinematic model. Then, obtain \(n\) groups of theoretical lengths of hardpoints \(\mathbf{H}_{1}\)-\(\mathbf{H}_{n}\). Let k = 1, \(\Delta_{0}\) = 0. 2. Superimpose the preset kinematic parameter error \(\mathbf{C}_{0}\) onto \(\mathbf{C}\) to obtain the actual kinematic parameter \(\mathbf{C}^{\prime}\). Establish the kinematic model K\({}^{\prime}\) based on \(\mathbf{C}^{\prime}\). Substitute \(\mathbf{H}_{1}\)-\(\mathbf{H}_{n}\) into the forward kinematic model based on K\({}^{\prime}\), then obtain the actual pose \(q_{\mathbf{\mathrm{B}}1}\) of the primary mirror. Superimpose the measurement noise \(\delta q_{\mathbf{\mathrm{B}}}\) onto \(q_{\mathbf{\mathrm{B}}1}\) to obtain the measured value \(q_{\mathbf{\mathrm{B}}}\)\({}^{\prime}\) of the pose of the primary mirror. Finally, the pose error d\(q_{\mathbf{\mathrm{B}}}\) of the primary mirror is calculated. 3. Substitute \(\mathbf{H}_{1}\)-\(\mathbf{H}_{n}\), \(q_{\mathbf{\mathrm{B}}}\), d\(q_{\mathbf{\mathrm{B}}}\), and \(\mathbf{C}\) into the Equation (27) and solve it using the least squares method to obtain the identification result of the kinematic parameter error d\(\mathbf{C}\). 4. \(\mathbf{C}\) is modified with d\(\mathbf{C}\) to obtain the modified theoretical kinematic parameter \(\mathbf{C}^{\prime\prime}\). 5. Using \(\mathbf{C}^{\prime\prime}\) to establish a new theoretical kinematic model K\({}^{\prime\prime}\). Substitute \(\mathbf{H}_{1}\)-\(\mathbf{H}_{n}\) into the forward kinematic model based on K\({}^{\prime\prime}\), and a new primary mirror theoretical pose \(q_{\mathbf{\mathrm{B}}}\)\({}^{\prime}\) is obtained. The deviation between \(q_{\mathbf{\mathrm{B}}}\)\({}^{\prime}\) and \(q_{\mathbf{\mathrm{B}}}\)\({}^{\prime}\) is d\(q_{\mathbf{\mathrm{B}}}\)\({}^{\prime\prime}\). Calculating the sum of squares of elements in d\(q_{\mathbf{\mathrm{B}}}\)\({}^{\prime}\), record as \(\Delta_{\mathrm{k}}\). If \(\mid\Delta_{\mathrm{k}}-\Delta_{k-1}\mid<\varepsilon\), then the iteration ends, otherwise continue with step 6.
# Neuro-Fuzzy Model Evaluation for Enhanced Prediction of Mechanical Properties in AM Specimens
## 2 The ANFIS Architecture for Mechanical Properties Prediction
### Grid Partitioning  
Grid partitioning is a technique used in ANFIS to define fuzzy rules by dividing the input space into a structured grid based on the range of input variables. In this approach, each input variable is divided into several intervals, and a membership function is assigned to each interval, representing different linguistic terms (e.g., \"low\", \"medium\", \"high\"). The combination of these intervals across all input variables forms a grid that represents all possible rules for the fuzzy inference system. Each grid cell corresponds to a unique fuzzy rule that links a specific input variable to an output value. While grid partitioning provides a straightforward way to generate fuzzy rules, it can lead to a rapid increase in the number of rules, especially as the number of input variables grows, creating a \"curse of dimensionality\". Nevertheless, in applications with a manageable number of inputs, grid partitioning enables ANFIS to systematically cover the input space, allowing for the precise rule-based modeling of complex, nonlinear systems. This makes it particularly useful in scenarios where distinct input-output relationships must be captured accurately, such as in predicting mechanical properties in additive manufacturing processes.
## 3 Background
### Literature Review Methodology
#### 3.1.3 Datasets for Training and Evaluation  
The reviewed articles use a wide variety of datasets to train and evaluate their models, and in particular, DDoS attacks. In refs. [1,23] they use the CICD-DDoS2019 dataset, which belongs to CIC and which is frequently used in DDoS attack detection studies using ML and DL techniques, mainly due to its comprehensive coverage of real-time DDoS attacks, including different types of attack and network scenarios. This makes it an attractive and widely used alternative for evaluating ML and DL models in the detection of this type of threat. In addition to the datasets already mentioned, it is important to highlight that the WUSTL-IIoT-2021 dataset has emerged as a standard benchmark for intrusion detection research in Industrial IoT (IIoT) networks [15]. Developed to replicate real-world industrial conditions, this dataset provides a realistic environment for validating detection modelsagainst Denial-of-Service (DoS) attacks and other advanced cyberthreats, offering a robust platform for evaluating model effectiveness.  
Another dataset available and used in several studies is the N-BaIoT, presented in [7], which contains network traffic data from IoT devices compromised by botnets and is used to train and evaluate botnet detection models in IoT environments. This dataset is relevant to this research as it focuses on botnet detection in IoT devices, and its use in conjunction with other datasets can be useful to evaluate the effectiveness of the models proposed in this work.
## 4 Proposed Sensing Results Separation
### Sensing Results Separation without Duplication of Sensing Results
#### 4.2.3 Data Tracking for Sensing Result Separation  
FC can obtain the data set composed of sensing result, which is equal to the frequency number in subcarrier, CSI, and CFO owing to the spectrum detection and the estimation of CFO and CSI. The detected data set is considered to be the particular indicator for specifying the sensor node. FC assigns the label of sensor node to each data set for the sensing result separation into several sensing results of each sensor node. When all the sensors send subcarriers to FC in \(t\)th time slots, the temporal number, \(i(\in 1,2,\ldots,K)\), is assigned to each data set, where the sensing result, the CSI, and the CFO in the \(i\)th data set are \(n_{i},\gamma_{i},\varepsilon_{i}\), respectively. Figure 3 shows the image of label assignment.  
When all the sensors send the subcarriers to FC in the \((t+1)\)th time slot, the temporal number, \(j(\in 1,2,\ldots,K)\), is assigned to each data set, where the sensing result, the CSI, and the CFO in the \(j\)th  
Figure 3: Example of labeling problem for data separation without the duplication of sensing results.  
data set are \(n_{j}\), \(\gamma_{j},\epsilon_{j}\), respectively. Therefore, the cost function required for the transition from the \(i\)th data set to the \(j\)th data set is defined as follows.  
\[f(i,j)=\frac{\left(n_{i}-n_{j}\right)^{2}}{A}+\frac{\left|\gamma_{i}-\gamma_{j} \right|^{2}}{B}+\frac{\left(\epsilon_{i}-\epsilon_{j}\right)^{2}}{C}, \tag{11}\]  
where \(A,B,\) and \(C\) are the normalization coefficients.  
If the sensing result, CSI, and CFO exhibit time continuity, the transition with minimum cost of Equation (11) is the most powerful. We can consider that multiple data sets in the \(t\)th time slot are not connected to a data set in the \((t+1)\)th time slot because the duplication of sensing results does not occur. The number of transition patterns is \(K!\), where the \(s\)th class \((\in 1,2,\ldots,S=K!)\) of valid transition pattern is \(\mathbf{u}_{s}\). As a result, the label assignment of sensor node is considered to be the following optimization problem.  
\[s^{*}=\arg\min_{s}\sum_{\{i,j\}\in\mathbf{u}_{\mathbf{s}}}f(i,j), \tag{12}\]  
where \(s^{*}\) is the class of transition pattern with the minimum cost. For solving this optimization problem, the brute-force protocol is considered. It is that the costs of all the transition patterns are evaluated and then the best transition pattern, which archives the minimum cost, is selected. As \(K\) becomes larger, the number of transition patterns is dramatically increased. For relaxing the solution with low complexity, the sphere decoding is available [26]. In the sphere decoding, if the cost of transition is over a certain value, the transition pattern including it is out of candidates.
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 2 Related Technology and Works
### Licensed Assisted Access (AAC)  
Another mechanism proposed to exploit the unlicensed band is the Licensed-Assisted Access (LAA) [3] drafted in 3 GPP Release 13. The core concept of LAA is Listen-Before-Talk (LBT). To reduce the interference to the Wi-Fi system while accessing the unlicensed band, four mechanisms are specified in LAA, namely carrier selection (CS), listen-before-talk (LBT), discontinuous transmission (DTX), and transmit power control (TPC) [7]. LBT can be regarded as the LTE version of the carrier sense multiple access (CSMA) schemes. Unlike the CSAT scheme, it operates in a distributed manner rather than a centralized one. It provides a flexible and adaptive coexistence solution among heterogeneous networks through quick channel sensing and dynamic spectrum access. The LBT scheme gives all the contending nodes equal spectral access opportunities in the long term. However, it sacrifices spectral utilization in exchange for reducing the probability of collision.