esa-sceva/satcom-chunk-collection huggingface dataset:
# Waste Reduction Methods Used in Construction Companies with Regards to Selected Building Products
## 2 Literature Review
### Construction Waste Classification  
Many factors can be used to classify construction waste. One of them is legislation applicable in a country or a group of countries. The European Union has developed the European Waste Catalog for its Member States. This catalog lists construction waste, which includes the following products: (1) concrete, bricks, ceramic tiles, ceramics, and gypsum-based products; (2) wood; (3) glass; (4) plastics; (5) asphalt, tar, and tarred products; (6) metals (including metal alloys); (7) cut and fill soil; (8) insulation products; (9) mixed construction waste; and (10) hazardous construction waste [14; 16].  
In Poland, in the National Waste Management Plan for 2022, which is in line with the guidelines of Directives 2018/851 and 2008/98/WE, waste was also divided. The fourth category is \"other\" waste, which includes \"waste from the construction, renovation, and disassembly of buildings and road infrastructure\".  
In the UAE, categories of construction waste vary in each individual emirate. There are three types of construction waste in Sharjah: mineral construction, mixed construction, and asphalt [17]. Abu Dhabi also lists three categories of construction waste: (1) non-toxic solid waste (biodegradable solid waste, non-recyclable and non-biodegradable solid waste, recyclable and biodegradable solid waste); (2) non-toxic liquid waste; and (3) toxic waste (liquid and solid). In contrast, construction waste in Dubai is classified into four main categories: municipal, green, construction, and hazardous [13].  
Research carried out by the Deloitte group at the request of the European Union Commission in 2015 which aimed to examine the current situation in the field of construction and demolition waste management in EU Member States showed that in most Member States the main types of waste are concrete, bricks, tiles, and ceramics [18]. According to studies carried out in Canada, the most common construction waste includes: concrete, steel, brick, insulation products, glass, ceramics, aluminum, plastic, paints, wood, gypsum board, cardboard, and asbestos-containing products [5].  
Construction waste can be also classified as per the mass of generated waste [3], composition [5], properties of the products [19], or the size of a company that generates it [20].
## 2 Diversity Measures Utilised in Population Based Algorithms
### Swarm Diversity
#### 2.1.3 Average Distance around the Swarm Centre  
The average distance around the swarm centre (ADSC) computes the swarm diversity by calculating the average distance of particles \(\mathbf{x}_{i}\) to a central point \(\bar{\mathbf{x}}\) in each dimension [10; 11]. The central point, \(\bar{\mathbf{x}}\), can be either the average over all particles or the best particle in the swarm. This diversity measure is calculated as  
\[D_{\text{ADSC}}=\frac{1}{n}\sum_{\begin{subarray}{c}i=1\\ \mathbf{x}_{i}\in\mathbb{S}\end{subarray}}^{n}\|\mathbf{x}_{i}-\bar{\mathbf{x }}\|_{2} \tag{5}\]  
where \(n\) is the number of particles in the swarm \(S\).  
A small value of the ADSC indicates convergence around the centre of the particles, while a larger value means that the solutions are scattered around the centre.  
If the ADSC was to be used to compute the swarm diversity for niching algorithms using a central position or a best particle position, an incorrect niche diversity trend will be illustrated. This is because of the large distances that are likely to exist between a central point and a candidate niche or between the global best position and the candidate niches. As such, the ADSC measure has to be adapted in order to be applied to niching algorithms.  
The ADSC measure is adapted for niching algorithms by using the neighbourhood best in a candidate niche as the reference point for all particles in that candidate niche or using an average point over all particles in a candidate niche. For the reported work, the neighbourhood best was used. That is, in Equation (5), the term \(\bar{\mathbf{x}}\) refers to the neighbourhood best position in the candidate niche to which particle \(\mathbf{x}_{i}\) is a member. Using this adaptation, ADSC is a suitable diversity measure to quantify diversity for niching algorithms because it now computes the average distances of each particle to its neighbourhood best.  
2.1.4. Average of the Mean Distance around All Candidate Solutions  
The average of the mean distance around all candidate solutions (ADAA) is used to measure the average distance between each pair of particles [11]. This measure is calculated as  
\[D_{\text{ADAA}}=\frac{1}{n}\sum_{i=1}^{n}\left(\frac{1}{n}\sum_{ \begin{subarray}{c}\mathbf{x}_{i}\\ \mathbf{x}_{i}\in S\\ j\
eq i\end{subarray}}^{n}\|\mathbf{x}_{i}-\mathbf{x}_{j}\|_{2}\right) \tag{6}\]  
This measure shares similar characteristics with the SD measure. The only difference is that an average distance of a candidate solution from all other solutions is calculated and an overall average distance is computed. As such, when utilised for calculating diversity for niching solutions, ADAA will encounter similar drawbacks as the SD measure.  
To adapt the ADAA measure for niching algorithms, an ADAA diversity value is computed for each candidate niche. The values are then summed for all candidate niches and an average calculated. This ensures that the result implies the average of the mean distance around all candidate solutions in each candidate niche.
# Research on the Face Forgery Detection Model Based on Adversarial Training and Disentanglement
## 4 Experiments
### General Comparisons
#### 4.3.3 Cross-Dataset Evaluation  
To evaluate our method across distinct datasets, we train our model on FF++ and evaluate its performance on Celeb-DF. The design of this experiment aims to test the model's generalization ability when encountering different forgery data distributions.  
The findings, presented in Table 5, indicate that our method achieves commendable performance. Our method obtains not only the highest score in the AUC metric but also achieves the second-highest score in ACC. With an AUC of 64.66% and an accuracy of 62.05%, these results may not represent the leading edge in forgery detection but still underscore the effectiveness of our method in cross-dataset detection. Despite the distribution differences between FF++ and Celeb-DF, our outcomes fully showcase the advantages of our approach. Through the application of adversarial training and feature disentanglement, our method notably improves the model's ability to discern forgeries in unseen datasets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Train Set**} & \multirow{2}{*}{**Method**} & \multicolumn{5}{c}{**Test Set (AUC (\%))**} & \multirow{2}{*}{**AVG**} \\ \cline{3-3} \cline{5-6}  & & **FF-DF** & & & **FF-FS** & **FF-F2F** & **FF-NT** \\ \hline \multirow{2}{*}{FF-DF} & Xception [26] & **99.47** & 46.67 & 71.32 & **75.75** & 73.30 \\ \cline{2-6}  & Ours & 99.38 & **69.54** & **74.84** & 75.71 & **79.87** \\ \hline \multirow{2}{*}{FF-FS} & Xception [26] & 57.57 & 98.73 & 67.35 & **53.02** & 69.17 \\ \cline{2-6}  & Ours & **62.76** & **99.02** & **78.87** & 51.54 & **73.05** \\ \hline \multirow{2}{*}{FF-F2F} & Xception [26] & **82.03** & 44.72 & **99.35** & 63.69 & 72.45 \\ \cline{2-6}  & Ours & 79.50 & **84.88** & 98.70 & **64.29** & **81.84** \\ \hline \multirow{2}{*}{FF-NT} & Xception [26] & 82.07 & 50.38 & 64.28 & **95.64** & 73.09 \\ \cline{2-6}  & Ours & **93.49** & **77.87** & **79.59** & 95.10 & **86.51** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cross-method evaluation on FF++. We compare our work with Xception [26]. Specifically, we use the sub-datasets for testing. The bold number represents the highest AUC value.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **Backbone** & **Train Set** & **Test Set** & **AUC (\%)** & **ACC (\%)** \\ \hline Xception [26] & & & & 49.88 & 51.53 \\ SLADD [23] & & & & 58.10 & **69.40** \\ CORE [16] & XceptionNet [33] & FF++ & Celeb-DF & 55.24 & 55.86 \\ Capsule [7] & & & & 50.25 & 55.86 \\ RECCE [36] & & & & 49.65 & 51.65 \\ \hline Ours & & & & **64.66** & 62.05 \\ \cline{2-6} \cline{5-6}  & & & & & \\ \end{tabular}
\end{table}
Table 5: Cross-dataset evaluation results on Celeb-DF. Our method secures the highest AUC and and ranks second in ACC performance. The bold number represents the highest AUC value, and the underlined number denotes the second highest AUC value.
# QuoTa: An Online Quality-Aware Incentive Mechanism for Fast Federated Learning
## 4 Design of QuoTa
### Model Training Time Prediction  
In real scenarios, the CPU frequency may fluctuate around the rated value due to multiple factors, such as device CPU scheduling and multiprocess running. According to the computing capability model in Section 3.3, the average CPU frequency in previous rounds can be considered as an indicator of its computing capability at the next round. The heuristic approach based on the above estimate would select data owner with the highest estimated value, but the above intuitive method possibly falls into a local optimum. When some data owners are barely selected as winners, the average estimate would fluctuate within a large confidence interval, thus failing to reflect their real CPU frequency. Thus, we design a model training time prediction algorithm to estimate each data owner's actual CPU frequency more accurately.  
To obtain a more accurate CPU frequency prediction for model training, we adopt the Upper Confidence Bound (UCB) algorithm [23] to update \(\tilde{f}_{i}^{t}\):  
\[\tilde{f}_{i}^{t}=\tilde{f}_{i}^{t}+\sqrt{\frac{2\ln t}{n_{i}^{t}}}, \tag{14}\]  
where \(\tilde{f}_{i}^{t}\) is the average CPU frequency in previous \(t\) rounds, and \(n_{i}^{t}\) is the total amount of being selected in these \(t\) rounds, as the exploitation part, indicating that data owners with larger average CPU frequencies have higher probability of being selected. However, to avoid choosing the same winning data owners repeatedly who have higher initial frequency values, we also adopt the exploration part \(\sqrt{\frac{2\ln t}{n_{i}^{t}}}\), i.e., we give more chances to select other data owners who are hardly selected in the past. For this reason, their estimated CPU frequency can be obtained by a more reliable confidence interval.  
Before new round begins, the platform will use our prediction approach to calculate each data owner's estimated CPU frequency and then update their computing capability \(\tilde{C}_{i}^{t}\) by Formula (15):  
\[\tilde{C}_{i}^{t}=\frac{\tilde{f}_{i}^{t}}{\tilde{d}_{i}^{t}} \tag{15}\]
## 4 Conclusions  
In this article, the experimental investigation of the effects of different weight percentages of coir fibers (0.5%, 1.0%, 1.5%, and 2.0%) as a reinforcement in the fabrication of sustainable metakaolin-based geopolymer composites for use in the construction industry was presented. The investigation provided a more scientific understanding of the effects of coir fibers on the micro- and macrostructural behavior of coir fiber-reinforced metakaolin-based geopolymer (CFRMG) composites. The main findings of the study are as follows:  
1. Increasing the weight percent of coir fibers in MK-based geopolymers from 0% to 2.0% led to a corresponding decrease in the bulk density of CFRMG composites from 2113 kg/m\({}^{3}\) to 2045 kg/m\({}^{3}\). The decreased bulk density will promote the ease of handling the CFRMG composites.
2. The geopolymer samples had UPV values varying from 2315 m/s to 2717 m/s, with the control sample giving the lowest value of UPV while the 0.5 wt % gave the highest value. The UPV result indicated a more homogeneous pore distribution within the 0.5 wt % CFRMG composite.
3. The addition of 0.5 wt % coir fibers in metakaolin-based geopolymers was most beneficial to the compressive strength development of CFRMG composites as this fiber content gave the highest compressive strength value of 21.25 N/mm\({}^{2}\), which met the minimum compressive strength values of ASTM C216 and ASTM C62 for producing paving bricks and building bricks, respectively.
4. The optimum coir fiber weight percent that favored flexural strength values of CFRMG composites was 1.0% (10.39 N/mm\({}^{2}\)). At higher fiber content (1.5% and 2%), a weak composite interface was created due to increased fiber-to-fiber interaction, causing flexural strength values to decrease.
5. Incorporating coir fibers in the matrices of metakaolin-based geopolymers did not change their crystal structures or result in new chemical bonds in the CFRMG composite structure.
## 2 Testing Apparatus and Methods
### Experiment Program
#### 2.2.2 Experiment Program  
Extracting the controlling factors influencing the stability of filling medium in karst cavity is crucial. The filling medium are mostly soil particles being flushed into karst cavities by surface water, or rock fragments fell off the ceiling of karst caves, deposited and consolidated under the action of self-weight and underground water. [PERSON] and [PERSON] [15] concluded water-sand inrush is likely to occur under high hydraulic pressure through case studies in completely weather granite. [PERSON] [22] and [PERSON] et al. [23] pointed out the hydraulic pressure also has considerable compression on rock and soil mass, and eventually affect its consolidation and liquefaction process; the confining pressure is widely considered as controlling factors for the compaction state of rock and soil mass, further affects its mechanic and permeability characteristics [24]. Based on the literature analysis, the compaction state of filling medium, hydraulic pressure and confining stress are deemed as significant influencing factors on the evolution of water-sand inrush.  
According to the disaster statistics in Pingkan tunnel, water-sand inrush cases occurred more frequently during the period from April to October, which is the rainy season for nearly the whole area of Southwest China. Based on the field investigation and literature research, the hydraulic pressure of underground karst water mostly below 1.0 MPa, unless the karst water is deep-buried or under the action of high tectonic stress. Therefore, in this test, the hydraulic pressure(\(P_{w}\)) of karst water is set to three levels: 0.2, 0.4, 0.6 MPa; the confining pressure(\(P_{c}\)) is set to 0.0, 0.2, 0.4 MPa; the compaction state of the filling medium is controlled by relative density(\(D_{\rm{r}}\)) [25], which is set to Loose, Intermediate and Dense, with the relative density(\(D_{\rm{r}}\)) of being 0.25, 0.50, 0.75, the experiment scheme is shown in Table 2. The experiment assumptions and schemes employed in this study could be well applied into the mountain regions with wide distribution of karst terrains.
## 3 Experimental Setup
### Scenario 1: DRL-Based Scenario at the Signalized Intersection
#### 3.2.2 Action Space  
The purpose of the controller is to find the optimal strategy to reduce the waiting time of all vehicles at the intersection. In this model, the performance of the traffic light system is the action, and we need to define these actions. Collision-free movements are performed in each phase.  
Figure 7: State space of the west arm.  
The traffic light cycle at this intersection includes red, green, and yellow time. The actions of agents are determined based on the green time from all directions. The action space consists of 4 phases, as shown in Figure 5.  
At each time step t, the agent can select one of the possible actions in the action space (a\(\in\)A) to take. If the action implemented at time t is the same as the action performed at time t \(-\) 1, the green time of this phase is extended. Conversely, if the current action is different from the previous action, the signal light will change to yellow time and take another action. At this time, information and data will be collected and processed. The green time can be different for each action because it depends on the state of each action.
## 4 Discussion
### Limitations of This Study  
Despite the relevant findings of this study, several limitations must be acknowledged when interpreting the results. Although the sample comprised 34 elite male football players, yielding a statistical power of 0.66, the confidence intervals associated with the correlation coefficients were relatively wide. The exclusive inclusion of elite male athletes enhanced the internal consistency of the sample; however, it limits the external validity and generalizability of the findings to broader populations, such as female athletes, amateur players, or athletes from other sports. To increase the robustness and generalizability of the findings, future research should involve a larger, more diverse group of players across different categories and experience levels. The absence of a control group of athletes from other sports or non-athletes restricts our ability to determine if the observed associations are specific to elite football. Additionally, we did not control for other variables that might affect postural stability, such as proprioceptive or vestibular disorders and muscle fatigue. Moreover, this study focused on static postural stability, while football involves dynamic control. Future research should incorporate tests that simulate real-game conditions. Lastly, the influence of ocular dominance was not examined, representing another potential source of variability. Addressing these limitations in future studies will enhance the understanding of the link between visual function and postural stability in high-performance athletes. Furthermore, the cross-sectional design of the present study precludes any conclusions about causal relationships between visual function and postural stability. Future research should employ longitudinal designs to clarify the directionality of these associations. Additionally, intervention studies evaluating the effects of optical correction on postural control in athletes with refractive errors are warranted to explore potential benefits for performance and injury prevention.
# Optimal Partitioning of Unbalanced Datasets for BGP Anomaly Detection
## 5 Results Analysis
### Observations
The small difference between the balanced and weighted metrics (e.g., 94.13% vs. 94.52% accuracy) further confirms that the OP-ELM method reduces the overfitting of the majority classes while achieving an excellent global performance. Implications for BGP Anomaly Detection: The proposed OP-ELM model represents a significant contribution to BGP anomaly detection by solving two major open issues: class imbalance and dynamic network behavior. It also performs well on both the majority classes and minority classes (e.g., achieving 98.81% accuracy for Class A and 87.75% for Class C), proving it can identify rare but important anomalies (e.g., route hijacks, DDoS attacks) while maintaining the true significance of common events as well.
## 4 The Proposed Hybrid Approach for Intrusion Detection  
Recent literature surveys have shown that several hybrid methods combining feature selection with classifiers can effectively deal with many common intrusion detection problems. However, when faced with high-dimensional, randomized, unbalanced and complex network intrusion data, they often perform poorly. To solve the above problems, we propose an effective intrusion detection framework based on the modified density peak clustering algorithm and deep belief networks, named MDPCA-DBN, as shown in Figure 4.  
The proposed framework is composed of four main phases: (1) data collection, where network traffic packets are collected; (2) data preprocessing, where each symbol feature of the training and testing data is converted into a numerical value and scaled to the range [0, 1]; (3) training classifier, where the proposed hybrid classification model is trained by using the training dataset; and (4) attack recognition, where the trained classifier is used to detect attacks on test data. Each test datum is input  
Figure 4: The framework of the proposed hybrid model.  
to all trained sub-DBN classifiers, and the results of all sub-classifiers are aggregated according to the fuzzy membership degree. The final classification result is output.  
MDPCA can divide the original training dataset into several subsets with similar attributes, thereby reducing the volume of data in each subset. Deep learning based on DBNs can automatically reduce the dimensions of data samples, extract high-level features, and perform classification.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Implementation of Image-Based Devices and Systems
#### 4.2.3 Novel Inspection System Using Inspection Robot  
The effectiveness and security of infrastructure maintenance processes may be increased by using stereo cameras to examine fractures in reinforced concrete structures [61]. On the other hand, current damage techniques for reinforced concrete structures are focused on the classification of two-dimensional surfaces without taking into account the actual level of concrete damage.  
Figure 3: Implementation of image-based devices and systems. (**a**) 2D vision system for digital image correlation; (**b**) 3D vision system for digital image correlation; (**c**) Novel inspection system using inspection robot; (**d**) UAV photogrammetry for displacement monitoring.  
An inspection robot can acquire detailed crack images and point cloud data of 3D representation of the concrete crack [61]. The inspection robot is equipped with a stereo camera (i.e., ZED 2), developed by STEREOLABS to conduct spatial object detection, a monocular camera for object detection, and a Lidar sensor to determine the gap between the structure and the camera, with an edge computing system, and almost-real-time data transmission system. The wireless remote controller is then attached to the inspection robot to provide movement control. The robot moves using four-wheel drive differential steering, which enables visual mapping, 3D convex hull instantiation, and 3D scene reconstruction for quantification of segmental fractures.
## 3 Methodology
### Accuracy Evaluation Metrics  
This study used five statistical metrics, including Overall Accuracy (OA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Sensitivity (SEN), and the ROC curve, to assess the feasibility of this study and select the best-performing model. The descriptions of each metric are shown in Table 4.  
OA and SEN (TPR) have values between 0 and 1, with values closer to 1 indicating better model accuracy; RMSE and MAE values closer to 0 indicate more minor model errors.  
The sensitivity TPR and False Positive Rate (FPR) are the coordinates of the ROC curve, and AUC is the area under the ROC curve used to measure the model's performance. AUC values range from 0.5 to 1.0, with values closer to 1.0 indicating higher model accuracy.