esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Unmanned Road Widths in Open-Pit Mines Based on Offset Reaction Times
###### Abstract  
In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes. A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for the safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m\({}^{3}\) and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31 m\({}^{3}\)/t. These results demonstrate that unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.  
unmanned driving; truck dynamics; offset reaction time; road width; economic benefits Article Design of Unmanned Road Widths in Open-Pit Mines Based on Offset Reaction Times  
2024 14 2995  
angle can result in significant economic benefits with the implementation of end-slope mining. However, there is also a certain risk to slope safety, and protecting the slope stability is a prerequisite for applying the end-slope mining method [9]. For open-pit mines that have been put into production, the surface boundary is fixed, the upper step of the slope remains unchanged when an optimization process is carried out, and the lower step is pushed outward to optimize the slope angle for end-slope mining. For new intelligent mines, the bottom boundary can be taken as the boundary, through optimizing the width of the transportation road and increasing the slope angle of the end slope, to reduce the area of land acquisition and reduce the damage to the land excavation machinery.  
This paper optimizes open-pit mine transportation road width designs under unmanned conditions. We analyze differences between manned and unmanned roads in open-pit mines to provide a basis for improved transportation efficiency and safety. An in-depth study of unmanned road widths is conducted. Reducing the width of the transportation flat pan can lead to an improvement in the slope angle and can decrease the average stripping ratio. Therefore, optimizing the road width using unmanned technology while ensuring slope safety can result in significant economic benefits. This is a crucial technology for constructing intelligent open-pit mines.
## 8 RAN Decomposition Scheme
### FWA System and Functional Split Implications
#### 8.5.1 High Bit-rate PtP-fiber and PtP-WDM  
The dedicated PtP optical fiber solutions demand a huge amount of fibers for the network elements connection. This can be lessened through fiber sharing, by exploiting the PtP-WDM scheme [48]. As stated earlier, there are concerted efforts on novel TRx that can support different solutions such as PtP optical fiber and PtP-WDM. However, note that different application requirements vary with the associated performance, cost, and form factor. Furthermore, it has been observed that, based on the requirements for cost and performance, a number of modulation formats like electrical duobinary (EDB), optical duobinary (ODB), non-return-to-zero (NRZ), and 4-level pulse amplitude modulation (PAM-4) can offer optimal solutions in the access network [8].  
Figure 30: vRAN with PTNI for different use cases (**a**) mode A, (**b**) mode B, (**c**) mode C, and (**d**) mode D.
## 6 Conclusions  
In the present paper, the use of natural volcanic glass from Cuba as an adsorbent in the removal of Cu\({}^{2+}\) from dyes and its subsequent use to separate a mixture of \(n\)-paraffins by IGC were evaluated. It achieved great results in the copper removal experiments, related to the calcium carbonate content present. Copper replaces calcium from calcite during the adsorption process. Moreover, due to the partial solution of carbonates after acid activation, the sample without acid pretreatment achieved better copper removal results. Moreover, the Cu-containing sample was further studied in IGC to  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**N-Paraffin**} & \multicolumn{2}{c}{**y = ax + b**} & \multirow{2}{*}{**r**} & \multirow{2}{*}{**Q\({}_{\text{diff}}\)**} \\ \cline{3-3} \cline{5-5}  & & **a** & & **b** \\ \hline \multirow{5}{*}{**Volcanic Glass**} & C5 & 5560 & \(-\)11.23 & 0.995 & 46.2 \\ \cline{2-5}  & C6 & 6170 & \(-\)11.23 & 0.997 & 51.3 \\ \cline{2-5}  & C7 & 6670 & \(-\)11.11 & 0.995 & 55.4 \\ \cline{2-5}  & C8 & 7406 & \(-\)13.97 & 0.993 & 61.5 \\ \cline{2-5}  & C9 & 8117 & \(-\)13.10 & 0.995 & 67.4 \\ \hline \multirow{5}{*}{**Cu\({}^{2+}\)-Volcanic Glass**} & C5 & 6932 & \(-\)16.83 & 0.991 & 57.6 \\ \cline{2-5}  & C6 & 7410 & \(-\)16.81 & 0.990 & 61.6 \\ \cline{2-5}  & C7 & 7810 & \(-\)16.98 & 0.998 & 64.9 \\ \cline{2-5}  & C8 & 8669 & \(-\)17.93 & 0.998 & 69.5 \\ \cline{2-5}  & C9 & 8969 & \(-\)17.41 & 0.998 & 74.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Fitting parameters of the adsorption isotherms of C\({}_{5}\)–C\({}_{9}\) on volcanic glass and Cu\({}^{2+}\)-volcanic glass at 273 K.  
Figure 7: Representation of Q\({}_{\text{diff}}\) vs. carbon number of the paraffin (C\({}_{5}\)–C\({}_{9}\)).  
separate olefins, and great separation results were achieved. The results were also compared with bare mineral. In both cases, separation of the mixture was achieved. Several factors play important roles in the separation process: microporosity, hydroxy groups, and copper species. Its high availability, low cost, capacity to operate at low temperatures, and stability make this material a potential and attractive candidate to be used in both heavy metal removal and paraffin separation for industrial purposes.  
Thanks to project CTQ2015-68951-C3-3R (Ministerio de Economia y Competitividad, Spain, and FEDER Funds). A.I.M. thanks the Ministry of Economy and Competitiveness for a Ramon y Cajal contract (RyC2015-17870).  
[PERSON] and [PERSON] designed the experiments and prepared the materials. [PERSON], [PERSON], and [PERSON] performed the experiments and characterizations and wrote the paper in collaboration with [PERSON].  
The authors declare no conflict of interest.
## 5 Conclusions  
In this research, we proposed a security mechanism with the integration of blockchain-based smart contracts and malware detection using deep learning to enhance the security of smart systems. By integrating Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Graph Attention Networks (GATs), we examined their effectiveness in detecting malware attacks targeting smart contracts. We demonstrated that the ANN model achieved the highest accuracy (97.60%) and was balanced among the key performance metrics, and was therefore the most suitable for efficient security through the smart contract life-cycle. We also showed that the GAN-based feature selection approach was applied to optimize the dataset and improve model performances. Thus, the results obtained in our experiment reveal that a scalable and reliable solution to prevent cyberthreats and malicious attempts in decentralized networks and smart systems is to combine a blockchain-based smart contract with an AI-driven malware detection model using the GAN feature selection algorithm. Future research will focus on the further optimization of deep learning models, advanced research for adaptive threat detection, and scalability improvements for real-world deployments.  
Conceptualization, I.B., L.H. and K.C.; methodology, I.B., L.H. and K.C.; software, I.B., L.H. and K.C.; validation, I.B., L.H. and K.C.; formal analysis, I.B., L.H. and K.C.; investigation, I.B., L.H. and K.C.; resources, I.B., L.H. and K.C.; data curation, I.B., L.H. and K.C.; writing--original draft preparation, I.B., L.H. and K.C.; writing--review and editing, I.B., L.H. and K.C.; visualization, I.B., L.H. and K.C.; supervision, I.B., L.H. and K.C.; project administration, I.B., L.H. and K.C.; funding acquisition, I.B., L.H. and K.C. All authors have read and agreed to the published version of the manuscript.  
The APC were funded by lbn Tofail University.  
The original data presented in the study shall be made available upon request.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Objective** & **Accuracy Score** \\ \hline \multirow{3}{*}{[41]} & Propose a Federated Learning-based approach that employs a random forest model for detecting IoT malware samples. & 95\% \\  & Evaluate the effectiveness of deep learning models for malware detection. & 96.6\% \\ \multirow{3}{*}{Our work} & Combined blockchain and deep learning architecture to improve security in smart systems. & 97.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with other related studies.
# Mechanical Properties and Freeze-thaw Durability of Basalt Fiber Reactive Powder Concrete
## 1 Introduction
Due to its excellent acid and alkali resistance, great mechanical properties and high temperature stability, the application prospect of basalt fiber as fiber-reinforced material is broad [25]. Basalt fiber can not only effectively increase the strength and durability of concrete but also has a positive effect on the toughness and crack resistance [26; 27]. The excellent reinforcement effect of basalt fiber promotes its application in RPC. [PERSON] et al. [28] researched the effect of basalt fiber on the mechanical properties of high-performance concrete. The results showed that the strength of high-performance concrete increased with the increase in basalt fiber content, and the compressive improved slightly, while flexural strength increased significantly. [PERSON] et al. [29] evaluated the effect of basalt fiber content on the mechanical properties of RPC; their results showed that the flexural strength of RPC increased with the increase in basalt fiber content. [PERSON] et al. [30] reported that the basalt fiber could increase the toughness and bending strength of basalt fiber RPC beams, and improve the resistance to steel-concrete interface damage. Most research is about the mechanical properties of basalt fiber-reinforced reactive powder concrete. There is not enough research about the freeze-thaw durability of fiber-reinforced RPC, and the most common fiber in the freeze-thaw durability research of fiber-reinforced RPC is steel fiber [2; 31]. Therefore, there are few studies were reported on the effect of basalt fiber on freeze-thaw durability of RPC. Moreover, in cold regions, where de-icing salt has been adopted, the coupling effect of chloride corrosion and freeze-thaw poses a challenge to the application of RPC and, because of the excellent corrosion resistance of basalt fiber, the improvement of basalt fiber on the freeze-thaw durability of RPC is worth exploring. Therefore, it is necessary to investigate the enhancement effect of basalt fiber on the performance of RPC. In this paper, the mechanical properties and freeze-thaw durability of basalt fiber reactive powder concrete (BFRPC) with different basalt fiber contents (4, 8 and 12 kg/m\({}^{3}\)) were tested and compared with plain reactive powder concrete (PRPC) and steel fiber reactive powder concrete (SFRPC) to investigate the impacts of basalt fiber contents and fiber type on the mechanical properties and freeze-thaw durability of RPC. Moreover, the freeze-thaw resistance of RPC under the fresh-water freeze-thaw cycle and the chloride-salt freeze-thaw cycle was tested to investigate the effects of freeze-thaw conditions on the freeze-thaw durability of RPC. The purpose of this paper is to provide a reference for the practical application of BFRPC in cold regions.
## 2 Materials and Methods  
### Materials  
Table 2 lists the chemical compositions of converter slag and EAF slag used for this study. Converter slag (dried at 105 \({}^{\circ}\)C for 12 h, particle size 106-212 \(\upmu\)m) from one steel plant in Tangshan (Hebei, China) was used, and the chemical composition included calcium oxide (46.60 wt%), ferric oxide (31.52 wt%), silicon dioxide (8.25 wt%), phosphorus pentoxide (2.29 wt%), manganese oxide (1.47 wt%), magnesium oxide (4.10 wt%), alumina (1.34 wt%), chromium trioxide (0.94 wt%), vanadium pentoxide (1.75 wt%), and titanium dioxide (0.51 wt%). EAF slag (dried at 105 \({}^{\circ}\)C for 12 h, particle size 106-212 \(\upmu\)m) from one steel plant in Lishui (Zhejiang, China) was used, and its chemical composition included calcium oxide (27.89 wt%), ferric oxide (2.09 wt%), silicon dioxide (34.57 wt%), phosphorus pentoxide (0.01 wt%), manganese oxide (5.96 wt%), magnesium oxide (4.81 wt%), alumina (3.81 wt%), chromium trioxide (16.95 wt%), vanadium pentoxide (0.26 wt%), and titanium dioxide (2.59 wt%). _Nannochloropsis_ sp. (GY-H1 _Nannochloropsis_ sp. bacterial strain) from Guangyu Biological Technology Co., Ltd. (Shanghai, China) was used. Table 3 lists the chemical reagents used for this study. Chemical reagents cupric sulfate (\(\geq\)99%), sodium nitrate (\(\geq\)99%), calcium hydroxide (\(\geq\)95%), sodium fluoride (\(\geq\)98%), potassium bromide (\(\geq\)99%), anhydrous sodium sulfate (\(\geq\)99%), anhydrous sodium carbonate (\(\geq\)99.8%), potassium phosphate dibasic (\(\geq\)99%), magnesium sulfate (\(\geq\)99%), ethylenediamine tetra acetic acid disodium salt (\(\geq\)99%), sodium hydroxide (\(\geq\)96%), and anhydrous calcium chloride (\(\geq\)96%) from SHENTAI Chemical Industry Co., Ltd. (Tianjin, China); magnesium chloride hexahydrate (99%) and sodium bicarbonate (99%) from Aladdin Biochemical Technology Co., Ltd. (Shanghai, China); iron chloride hexahydrate (99%), manganese chloride tetrahydrate (99%), cobalt nitrate hexahydrate (99%), calcium chloride dihydrate (\(\geq\)99.9%), cobalt \begin{table}
\begin{tabular}{c c c c} \hline
**Types of Microalgae** & **Types of Growth Condition** & **Best Growth Condition** & **Reference** \\ \hline _Thalassiosira alleini_ & Leaching Solution of Slag & 40–80\% & [22] \\ \hline _Isochrysis galluna_ & Leaching Solution of Slag & 40–80\% & [22] \\ \hline _Thalassiosira angulate_ & Leaching Solution of Slag & 100\% & [22] \\ \hline _Amphidinium cartene_ & Leaching Solution of Slag & 100\% & [22] \\ \hline _Dunaliella tertiolecta_ & Slag or Leaching Solution of Slag & Arbitrary & [22] \\ \hline _Tetraselmis tetrathlele_ & Slag or Leaching Solution of Slag & Arbitrary & [22] \\ \hline _Synechococcus_ sp. & Slag or Leaching Solution of Slag & Arbitrary & [22] \\ \hline _Desmodesmus subspicatus_ & Slag or Leaching Solution of Slag & Arbitrary & [26] \\ \hline _Chlorella vulgaris_ & Slag or Leaching Solution of Slag & Arbitrary & [26] \\ \hline _Alexandrium tamarense_ & Slag or Leaching Solution of Slag & Arbitrary & [25] \\ \hline _Nitzschia laevis_ & Slag or Leaching Solution of Slag & Arbitrary & [27] \\ \hline \end{tabular}
\end{table}
# Detail-Preserving Shape Unfolding
## 4 Implementation Details  
In general, the proposed algorithm can be solved with the following process: given an initial guess \(\mathbf{P}_{0}^{\prime}\), the local rotations \(\{\mathbf{R}_{i}\}\) are estimated by Equation (2), then \(\{\mathbf{R}_{i}\}\) are fixed and \(\mathbf{P}_{1}^{\prime}\) is obtained by solving Equation (7). The above progress is iteratively performed until an satisfactory solution is generated. This strategy is widely used in computer graphics [46; 19]. Our technique can be applied into both triangular mesh and tetrahedral mesh as the data type used in our algorithm is graph structure. However, the representation of tetrahedral mesh obtains the better detail-preserving ability because of the implicit volume constraints. Hence, all experiments are performed on the tetrahedral meshes in our paper. In the following, we will discuss several important parameters used in the optimization.
## 3 Experimental Results
### Measured E/O Response of the 10G-Class DML  
We start by showing the measured back-to-back (B2B) response of 10G-class 1550 nm DML. The results are shown in Figure 2 to characterize the DML used in this experiment. The measured frequency response was normalized to \(\mid\!H_{DML}(f=0)\!\mid\!1\). The frequency responses were measured by using an optical network analyzer (ONA) connected directly to the DML. As shown in the figure, the measured 3 dB bandwidth of the 10G-class DML was about 4.5 GHz, 8 GHz, 10 GHz, 12 GHz, and 12.5 GHz for the values of DML input bias current \(i_{b}\) = 20 mA, 30 mA, 40 mA, 50 mA, and 60 mA, respectively. In the datasheet of our DML [32], an S21 frequency response characterization of the device is shown. However, it is not specified at which bias current \(i_{b}\) this measurement was performed. From our measurements, a similar frequency response as that reported in the datasheet was obtained at 60 mA. When observing Figure 2, please note the DML BW strong dependency versus the DML bias current \(i_{b}\). The BW is positively correlated to \(i_{b}\) when \(i_{b}\) is below the suggested maximum \(i_{b}\) (100 mA, which is indicated in the datasheet [32]). We experimentally demonstrated that \(i_{b}\) = 60 mA keeps the operation of the DML on the safe side as well as guaranteeing optimum performance [10]. Therefore, we will only use \(i_{b}\) = 60 mA in the rest of the paper.
## 4 Discussion
Thermography enables the measurement of temperature distribution on the engine's surface, facilitating the identification of faults directly related to the heat displayed. Thermographic images provide intuitive visual analysis, facilitating the interpretation of problems without the need for advanced knowledge of spectral analysis. Vibration and sound analysis, on the other hand, only indicate the presence of a problem without identifying its exact location. It is also particularly useful and safe in hazardous or challenging environments where other techniques, such as vibration, may require sensors mounted directly onto the engine. Infrared thermography is less susceptible to noise, which means it can be used directly without the need for pre-processing, unlike ultrasonic signals. The suitability of thermography as a monitoring tool depends on the type of problems you are trying to detect. For induction motors, it is particularly effective in analyzing thermal problems, and its use is valuable in developing and proposing new methodologies for detecting and classifying faults in the induction motor and gearbox. In addition, the use of thermography contributes to and complements all the techniques used for the diagnosis and continuous identification of incipient failures in industrial machinery.
# A Survey of Detection and Mitigation for Fake Images on Social Media Platforms
## 7 Limitations and Challenges
### Cross-Platform Training  
Our current challenge is that existing datasets for fake image detection are tailored to specific social media platforms. Each platform, such as Facebook, Twitter, Instagram, WhatsApp, TikTok, and Weibo, has a unique style, content, and the way information spreads. Consequently, a deep learning model trained solely on one dataset may not perform accurately when dealing with content from other platforms. This limitation poses a significant hurdle in the field of fake image detection. Adopting a more versatile and adaptable approach to address this limitation is essential. We should consider training deep neural networks using a combination of data from various datasets representing different platforms. This multi-source training can help create a more generic model that recognizes fake images across various sources. Additionally, researchers should explore multi-modal approaches [132; 133] to account for variations in content and style among different platforms, ultimately improving the model's accuracy across diverse social media sources. By embracing a more holistic and comprehensive training approach, we can significantly overcome the limitations associated with cross-platform fake image detection.
# Enhanced Knowledge Graph Embedding by Jointly Learning Soft Rules and Facts
###### Abstract  
Combining first order logic rules with a Knowledge Graph (KG) embedding model has recently gained increasing attention, as rules introduce rich background information. Among such studies, models equipped with soft rules, which are extracted with certain confidences, achieve state-of-the-art performance. However, the existing methods either cannot support the transitivity and composition rules or take soft rules as regularization terms to constrain derived facts, which is incapable of encoding the logical background knowledge about facts contained in soft rules. In addition, previous works performed one time logical inference over rules to generate valid groundings for modeling rules, ignoring forward chaining inference, which can further generate more valid groundings to better model rules. To these ends, this paper proposes Soft Logical rules enhanced Embedding (SoLE), a novel KG embedding model equipped with a joint training algorithm over soft rules and KG facts to inject the logical background knowledge of rules into embeddings, as well as forward chaining inference over rules. Evaluations on Freebase and DBpedia show that SoLE not only achieves improvements of 11.6%/5.9% in Mean Reciprocal Rank (MRR) and 18.4%/15.9% in HITS@1 compared to the model on which SoLE is based, but also significantly and consistently outperforms the state-of-the-art baselines in the link prediction task.  
Keywords:knowledge graph; knowledge graph embedding; logical rule; link prediction +
Footnote †: journal:
# Equivalent Modeling and Verification of a High-Steepness and Lightweight Elliptical Aluminum Mirror
## 3 Equivalent Design Theory of a High-Steepness Mirror
### Flat Plate Equivalent Method for High-Steepness Elliptic Mirror
#### 3.3.1 Radial Equivalent  
Radial equivalence is to transform the elliptic section into a circular section, which is approximated by \"equal area\" method, \(S_{ellipse}=S_{circular}\). The equivalent analytical expression is shown as follows:  
\[\left\{\begin{array}{l}S_{ellipse}=\pi\cdot a\cdot b\\ S_{circular}=\pi\cdot R_{1}^{2}\end{array}\right. \tag{5}\]  
where \(S\) is the area of the equivalent circle, \(a\) is the size of the long half-axis of the oval mirror, \(b\) is the size of the short half-axis of the oval mirror, and \(R_{1}\) is the radius of the equivalent circle.  
The schematic diagram of radial equivalence is shown in Figure 4.
## 3 Network Anomaly Detection
### The Structure of Network Anomaly Detection
#### 3.2.3 Acquisition of Anomaly Score  
To obtain the anomaly score at any given time, we adopt a method based on the reconstruction error. This approach incorporates two key components, namely the reconstruction errors of \(AE_{1}\) and \(AE_{2}\), in the calculation of the anomaly score. Specifically, the score can be formulated as follows:  
\[\mathcal{Y}=\alpha\big{\|}\mathcal{W}-\hat{\mathcal{W}}\big{|}_{2}+(1-\alpha) \big{|}\hat{\mathcal{W}}-\mathcal{W}^{\prime}\big{\|}_{2} \tag{7}\]  
Here, the weight parameter \(\alpha\) determines the contribution of each autoencoder's reconstruction error to the overall anomaly score. These reconstruction errors quantify the discrepancy between the input data and the corresponding reconstructed output of each autoencoder.  
By appropriately combining the reconstruction errors from both \(AE_{1}\) and \(AE_{2}\), the anomaly score provides a comprehensive evaluation of anomalies at each time point. This approach capitalizes on the complementary capabilities of the two autoencoders, leading to improved accuracy and robustness in detecting anomalies within the system.