esa-sceva/satcom-chunk-collection huggingface dataset:
# Predicting Runoff from the Weigan River under Climate Change
## 2 Data Sources and Methods
### Data Sources  
The basic data used in this study included hydrological and meteorological data, digital elevation (DEM) data, glacier cataloguing data, soil data, land-use data and CMIP6 climate model data. The hydrological and meteorological data (temperature, precipitation and runoff data) were sourced from the CN05 grid observation dataset, the measured runoff data from the Heizi Reservoir (II) hydrological station, and the inflow runoff data from the Kizil Reservoir. The DEM data use SRTMDEM raw elevation data with a precision of 90 m, sourced from the Geospatial Data Cloud Platform. The glacier cataloguing data included the first and second glacier cataloguing data sourced from the National Glacier Frozen Soil Desert Science Data Center. The soil data were drawn from the 1:100,000 soil data provided by the Nanjing Soil Institute of the Second National Land Survey. The land-use data were adopted from the 1980 and 2000 national land-use data, with a resolution of 30 m, which were sourced from the Geospatial Data Cloud Platform. A global climate model is an important tool for predicting future climate change [23], and the model used in this study was sourced from the latest 6 th International Coupled Model Comparison Program (CMIP6). This study selected 7 models from different countries, climate centres and climate scenario simulations (Table 1), and selected 4 greenhouse gas emission scenarios, including SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 6 Discussion
### Rock Surface Manufacturing  
A final discussion point is focused on the differences arising from the rock surface condition in case of ornamental stones, such as the granites and marble tested herein in the polished and smooth versions. Observed results show that polished varieties are affected by lower emissivity than the corresponding smooth ones. This is due to the different reflection power gained by polished surfaces, which act more as reflectors (mirrors), thus are characterized by a lower emissivity [20]. Therefore, the choice of a proper emissivity value prior to the thermal image processing is needed, especially if quantitative considerations have to be carried out. This represents a key point considering the growing application of IRT to cultural heritage and ornamental stones.
## 3 Modeling the GNSS Observations with the Proposed HFM  
In common GNSS tomographic models, only GNSS signals passed from the top boundary are considered as effective observations for the tomographic model [10]. In this section, an improved GNSS tomography algorithm based on HFM is introduced, as shown in Figure 6. The process of GNSS tomography equations using integral signals and inside signals is discussed, respectively. The horizontal and vertical constraints are also described in detail.  
Figure 5: The anisotropic height factor changes with height.  
Additionally, in most tomography models, to obtain as many effective signals as possible, the influence of the elevation angle is neglected. However, signals which enter the neutral atmosphere at low elevation angles (e \(<\) 15) are significantly affected by atmospheric bending, according to [PERSON] and [PERSON] [38]. As the bending effect is not modeled by the tomographic algorithms, rays with an elevation angle below 15\({}^{\circ}\) are first eliminated.
## 3 High-Quality ToF Camera Data Acquisition Strategies
### ToF Depth Data AI Training  
In Section 3.2, the depth data from the ToF (time-of-flight) sensor and the corresponding labeled values for the exact distance of each pixel were obtained. To develop an AI algorithm to improve the accuracy of ToF sensor values, a deep learning training process is required. The deep learning network model should take 400 \(\times\) 300 pixel data as input and provide an output of the same size, 400 \(\times\) 300 pixels, using a CNN. In this study, we conducted tests using U-Net [17], which is widely used for segmentation and has a symmetrical structure that allows input data to be transformed directly into output data. MATLAB (2023b)was utilized, and the U-Net library provided by MATLAB was used. Specifically, the training set included 40 images, and the remaining 10 images were used as the test set. The detailed structure of the network model is as follows: the input layer receives 400 \(\times\) 300 pixel depth images, and the output layer provides images of the original size, 400 \(\times\) 300 pixels. The loss function used was the mean squared error (MSE) loss function. Training was performed over a total of 100 epochs, with a learning rate set to 0.001. During training, the loss function value started at approximately 0.02 in the initial epoch and decreased to about 0.005 in the final epoch.  
As shown in Figure 6, the training was conducted using depth data obtained from real-world environments and depth data obtained from 3D simulations as the labeled data. Using the inference executable file created through this process, we were able to input depth data obtained from ToF without preprocessing and obtain depth data with noise preprocessed and performance improved through the AI algorithm.
# Augmentation of Deep Learning Models for Multistep Traffic Speed Prediction
## 3 Methodology
### Dataset Description  
The dataset used in this study is publicly available [41]. The dataset covers four connected freeways, I-405, I-5, SR-520, and I-90. There are 323 sensor stations located on these connected freeways to collect the data every 5 min from the Seattle area. The dataset covers the whole year of 2015.
# An Intelligent TCP Congestion Control Method Based on Deep Q Network
## 5 Results and Discussion
### Throughput Comparison  
We define the throughput as the size of the bytes acknowledged by the sender per second. As shown in Figure 4, the network throughput of TCP-DQN is about twice than that of HighSpeed and cubic methods, and is about three times than that of NewReno method. In addition, the throughput of TCP-DQN is quite stable compared with the other three methods. Figure 5 shows the cumulative probability density function curve  
Figure 3: DQN architecture.  
of throughput to compare the four congestion control methods. The TCP-DQN performs much better than the other three methods. About 80% of the sampling points of TCP-DQN have a throughput of more than 6 MB/s. The result suggests that the traditional congestion control algorithms, such as NewReno, cannot adapt to the WAN environment of virtual data space, and is not suitable for data migration in virtual data space. Cubic and HighSpeed have significant performance improvements over NewReno, but they still cannot make full use of the available bandwidth to realize high-speed transmission. While TCP-DQN has the best performance; after a certain amount of learning, it can make full use of network bandwidth to realize efficient data migration in virtual data space.
# Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks
## 4 Algorithm Details
### Neighborhood Evaluation
#### 4.3.2 Fast Neighborhood Evaluation  
In the main framework of the local search described in Section 3, we begin each iteration by evaluating all incorrectly labeled arguments within the mislabeled set _mislabel_. However, after executing the chosen argument label flip, only the correctness of the labels of the neighboring arguments to the argument is affected. This means that, in the next round of evaluation, we only need to assess the neighboring arguments to the flipped argument and then insert the results into the heap according to the magnitude of the evaluation  
Figure 6: Example of abstract argumentation frameworks.  
values. In subsequent iterations, this operation alone is required, without the need to traverse all incorrectly labeled arguments, significantly enhancing our search efficiency.  
In the DNTS algorithm, we also use the fast neighborhood evaluation. Algorithm 4 replaces lines 10 to 12 of Algorithm 1.  
```
1:\(v,L,\mathit{mislabel}\)
2:\(EvaluateHeap\)
3:procedureFastNeigBoreEvaluate
4:if\(definition(v,L)\) is \(\mathit{false}\) and \(l(v)\
eq\mathit{Grounded}(v)\)then
5:\(AddToMislabel(v)\)
6:\(NEIGHOREVALUATE(v,L)\)
7:else
8:\(MislabelRemove(v)\)
9:endif
10:for\(u\in Neighbors(v)\)do
11:if\(definition(u,L)\) is \(\mathit{false}\) and \(l(u)\
eq\mathit{Grounded}(u)\)then
12:\(AddToMislabel(u)\)
13:\(NEIGHOREVALUATE(u,L)\)
14:else
15:\(MislabelRemove(u)\)
16:\(HeapRemove(u)\)
17:endif
18:endfor
19:return\(EvaluateHeap\)
20:endprocedure
```  
**Algorithm 4** Fast neighborhood evaluation  
Algorithm 4 illustrates the process of fast neighborhood move evaluation. First, we individually check the correctness of the argument label \(L(v)\) after performing the optimal neighborhood move according to \(definition\). If the label is incorrect and the argument is not a part of the grounded extension, it is added to the set of incorrectly labeled arguments _mislabel_ via the function \(AddToMislabel\) and evaluated. If the labeling is correct, it is removed from _mislabel_ via the function \(MislabelRemove\). The algorithm checks the correctness of the label for each argument in the neighboring argument set \(u\) of argument \(v\). If any label is incorrect, it is added to the set of incorrectly labeled arguments _mislabel_ and evaluated. If it is correct, it is removed from _mislabel_ and from the evaluation heap \(EvaluateHeap\). Eventually, the evaluation heap \(EvaluateHeap\) is returned. The time complexity of this module is \(O(|u|^{2}+|u|logN)\), and its space complexity is \(O(N)\).
# Experimental and Modeling of Residual Deformation of Soil-Rock Mixture under Freeze-Thaw Cycles
## 4 Model Building and Prediction  
The deep learning method based on an artificial neural network can quickly extract effective data features, according to a large number of existing experimental data, and analyze the evolution law of experimental data. In this experiment, the applicability of the freeze-thaw test data prediction of frost heave and thaw deposition was studied by establishing the LSTM model, and the actual test data and the results of the model output were compared, thus confirming the feasibility of the model prediction.  
The LSTM model is a neural network model proposed by [PERSON] in the 1990s, and its model structure is shown in the Figure 10. LSTM models contain a special unit, a memory block in the recursive hidden layer. The memory block consists of a cell state and three gating mechanisms (input gate, forget gate, and output gate) to control the flow of information. The gating mechanism is implemented by the Sigmoid function. After the data flow in, they first enters the forget gate. After discarding some redundant information to obtain key feature data, they enter the input gate to control the update of data. Finally, they go through the output gate for output.  
Each LSTM unit updates six parameters (see the legend of the Figure 10) in each time step. The LSTM unit extracts the mapping between input sequence \(x_{t}\) and output sequence \(h_{t}\), and the update of the LSTM unit can be represented by the following formula:  
\[i_{t}=\sigma(W_{i}\times[h_{t-1},x_{t}]+b_{i})\]  
\[f_{t}=\sigma\Big{(}W_{f}\times[h_{t-1},x_{t}]+b_{f}\Big{)}\]  
\[o_{t}=\sigma(W_{o}\times[h_{t-1},x_{t}]+b_{o})\]  
\[\widetilde{c}_{t}=tanh\cdot(W_{c}\times[h_{t-1},x_{t}]+b_{c})\]  
\[c_{t}=f_{t}*c_{t-1}+i_{t}*\widetilde{c}_{t}\]  
\[h_{t}=o_{t}*tanh(c_{t})\]  
where \(W\) is the matrix of input weights, \(b\) is a vector of bias, and \(\sigma\) represents a logistic sigmoid function. The four groups of soil-rock mixture samples were all frozen and thawed 20 times, and each group of experiments lasted 10 days. The LSTM model took the data of the first 7 days of each group of experiments as the training set, and the last 3 days of experiments as the test set. The deep learning method of the neural network predicted the effect in the freeze-thaw cycle test of the soil-rock mixture.  
It can be seen from the Figure 11 that the results of the learning and prediction of the LSTM model have achieved good expected results, which are highly consistent with the data obtained from the actual test. Therefore, the long and short-term memory network model in deep learning can effectively and accurately predict the soil-rock mixture. The  
Figure 10: Schematic diagram of LSTM unit structure.  
development law of body freezing and thawing can greatly reduce the time span of the soil-rock mixture freezing and thawing test.  
Figure 11: LSTM model predictions.
## 6 Limitation and Future Work  
The proposed method performed better than other studies in terms of speed and accuracy rate when separating attacks from normal traffic as well as classifying attack types. However, according to the experimental results, the detection rate is slightly lower in classifying the small amount of attack types or the traces are not sharp in the dataset. The proposed method correctly classified the unseen attacks in the testing dataset; however, those attacks are slightly old. We aim to test our method on new unseen attacks in the future. Moreover, we tested our method on the KDD '99 and UNSW-NB15 datasets for this study. We would like to extend our method and test it on different intrusion detection datasets. In future studies, we also aim to enhance the accuracy measure by improving our proposed algorithms as well as developing new algorithms. To increase the level of scalability for the presented method, we aim to test it on bigger hardware resources in future work to handle larger-scale networks.  
Integration into existing systems, real-time monitoring, and response mechanisms are crucial real-world application considerations to evaluate the feasibility of the proposed method. In this context, a new dataset development effort has been undertaken, yielding successful results on a small dataset. However, there are plans for the further enhancement and expansion of this dataset in future studies. This will contribute to better aligning the intrusion detection system with real-world conditions and ensuring more reliable outcomes. These improvements will also facilitate the more efficient implementation of integration processes, real-time monitoring, and rapid response mechanisms. Therefore, our future research endeavors will delve deeper into the practical applicability of IDS and aim to support its successful utilization in real-world scenarios.
## 2 Materials and Methods
### Microtomographic Analysis and VOI Determination  
Each bone segment underwent micro-CT scanning using a Skyscan 1174 (Bruker, Kontich, Belgium) under specific parameters: 50 kV voltage, 800 \(\upmu\)A current, 0.5 mm aluminum filter, and 5200 ms exposure time, with a voxel size of 28 \(\upmu\)m and an X-ray energy of 50 kVp. The detailed scanning process spanned approximately 120 min, and image reconstruction was then conducted utilizing NRecon(r) software (version 2.0) with the specified settings.  
The volume of interest (VOI) was demarcated as a cylindrical zone with a diameter of 4.5 mm (50% larger than the osteotomy) and a height of 13 mm, encompassing an approximate volume of 200 mm\({}^{3}\). This dimensioning ensured a comprehensive analysis encompassing the immediate regions surrounding the osteotomy site, as illustrated in Figure 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Group** & **Drill Direction** & **Xenograft Bone** \\ \hline CW & Clockwise & No \\ CW + XB & Clockwise & Yes \\ CCW & Counterclockwise & No \\ CCW + XB & Counterclockwise & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive overview of experimental groups utilized in this study.  
Figure 1: A cylindrical mask with a diameter of 4.5 mm (50% larger than the osteotomy diameter) was used to determine the volume of interest for mathematical analysis. The mask was colored red.
###### Abstract  
Purpose: This research examines the release of mercury from dental amalgam restorations over time, analyzing the relationship between the number of restored surfaces and mercury levels in artificial saliva. Materials and Methods: An in vitro study was performed utilizing 224 plastic model teeth restored with dental amalgam. The teeth were categorized based on the number of surfaces restored, which ranged from Group 1 (1 surface) to Group 6 (48 surfaces). These were then immersed in artificial saliva, and the mercury levels were measured at intervals up to 30 days using atomic absorption spectrophotometry. Results: The release of mercury increased in correlation with both the number of restored surfaces and the passage of time. Significant differences were noted among the groups, with the highest release occurring in the group with the greatest number of surfaces. Mercury levels exhibited a consistent rise from baseline to day 30. Conclusions: The amount of mercury released from amalgam restorations increases in relation to both the number of restored surfaces and the duration of exposure.  
Keywords:dental amalgam; mercury release; artificial saliva; in vitro study; dental restorations +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Cell Surface Marker Expression  
Splenocytes were incubated with Fc-block (BioLegend) to block non-specific binding of antibodies. In some experiments, cells were incubated for 20 min with Fluo-3 (ThermoFisher Scientific). To assess subpopulations and activation marker, the following anti-murine monoclonal and fluorescently tagged antibodies were used: CD3 Alexa Fluor 700, CD11b PE-Dazzle, CD115 Brilliant Violet 421, CD43 APC, CD44 PE-Dazzle 594, CD4 PE-Cy 7, CD8a PerCP/Cy 5.5, CD28 APC, CD45R APC-Cy7, I-A/I-E Alexa Fluor 488, Ly-6C Alexa Fluor 700, CD68 PerCP/Cy 5.5, Ly-6G Brilliant Violet 510, Fc\(\epsilon\)RI\(\alpha\) PE, CD86 PE-Cy7 (all BioLegend). For kinase activation assays, co-cultured splenocytes were fixed in methanol for 1 h at \(-20\)\({}^{\circ}\)C, washed in PBS, and incubated for 20 min with monoclonal antibodies directed against murine phospho-Erk1/2 PerCP/eFluor 710 (eBioscience, Germany) and lineage markers. Multicolor flow cytometry data were analyzed with Kaluza 2.1.1 software (Beckman-Coulter).
# Efficient Multi-Source Anonymity for Aggregated Internet of Vehicles Datasets
## 3 Models
### Proposed Privacy Models
#### 3.3.2 Multi-Source Anonymity  
In this subsection, we describe the new privacy countermeasure. Domains are sets of terms wherein each term is an instance of a concept. Figure 4 showcases the original and generalized values for the age attribute in the taxonomy tree. As shown in the figure, the domain value of the age attribute is [0, 1,..., 78, 79].  
A multi-source linkability attack can occur with a higher probability when the data analyzer obtains accurate values of QIs from a source. Therefore, we define a new privacy model: multi-source (\(k\),\(d\))-anonymity for multi-source linkability attacks.  
**Definition 4** (**Multi-Source (_k,d_)-anonymity**).: _A dataset satisfies multi-source (_k,d_)-anonymity if each record is indistinguishable from at least \(k-1\) other records within the dataset and each generalized value contains at least \(d\) domain values for QI attributes. The probability of obtaining the exact values of all QIs is less than 1/d._  
In addition, we consider sensitive attributes. Multi-source (_k,l,d_)-diversity is proposed.  
**Definition 5** (**Multi-Source (_k,l,d_)-diversity**).: _A dataset satisfies multi-source (_l,d_)-anonymity if it satisfies multi-source (_k,d_)-anonymity and each indistinguishable group has at least \(l\) different values for sensitive attributes._  
**Example 1**.: _We continue using the vehicle dataset mentioned earlier for illustration purposes. Figure 5 depicts an example dataset adhering to multi-source (3,2)-anonymity. The upper subfigure illustrates the generalized dataset meeting the (3,2)-anonymity criteria. The lower subfigure displays the domain values encompassed by the generalized values. In Figure 5, if the attacker knows that the age is 27 and the postcode is 1000195, there are at least two potential values for the color and registration date that they could infer: specifically, [2017-2018] and [white, red]. The likelihood of the data analyzer obtaining a precise combination of color and registration date is 1/2, mirroring the probability of accurately inferring from the attribute values. Consequently, attackers cannot obtain accurate values._  
Figure 4: Examples of original and generalized values (* denotes the value is suppressed).  
Figure 5: Example of multi-source (3,2)-anonymity.
# Online Phase Measurement Profilometry for a Fast-Moving Object
## 1 Introduction  
With the development of optics, computer and information technology, 3D measurement technology plays an important role in reverse engineering, industrial 3D testing, medical diagnosis, cultural relic protection and so on [1, 2, 3]. The more commonly used 3D measurement technologies are phase measurement profilometry (PMP) [4, 5] and Fourier transform profilometry (FTP) [6, 7]. Compared with FTP, PMP is a point-to-point phase calculation with multiple frames of deformed patterns, it has higher precision and is more favored by the industry. Common 3D measurement based on PMP requires the fixed position of the object, but the object is moving in online 3D measurement, which will cause the object coordinates in the captured multiple frames of deformed patterns not to correspond, and lead to the error in the PMP phase demodulation. The pixel matching method [8] is used to obtain deformed patterns with consistent object coordinates. Scholars have carried out a lot of research in the field of online 3D measurement. Among them, in order to improve the matching speed, [PERSON] et al. [9] proposed a new pixel matching method using the modulation of shadow areas in online 3D measurement. To improve the measured precision, [PERSON] et al. [10] proposed an online phase measuring profilometry for objects moving with straight-line motion. To avoid the loss of phase information due to frequency filtering, [PERSON] et al. [11] proposed a dual-frequency online PMP method with phase-shifting parallel to the moving direction of the measured object.  
Generally, these methods may have a good effect when the speed of the production line is less than 0.2 m/s. However, when the object moves fast online, the captured deformed patterns will appear as motion blur. In order to adapt to the fast speed, the frame rate of the camera can be increased by adjusting the image acquisition mode, but the resolution of captured deformed patterns is bound to be sacrificed simultaneously. In order to obtain high-resolution deformed patterns and improve the measurement precision, we proposed an online phase measurement profilometry method based on super-resolution imagereconstruction [12; 13], which combines high-resolution image from multiple frames of low-resolution images with sub-pixel [14] shifts among them. In addition, this paper adopts a super-resolution reconstruction method based on the maximum a posteriori (MAP) [15; 16], using [PERSON] and Markov-Gibbs [17; 18] random field models to construct posteriori probability of the high-resolution deformed pattern, the optimal estimation of high-resolution deformed pattern is obtained by minimizing the objective function. In this way, the high-resolution deformed patterns can be obtained. Finally, all the equivalent phase-shifting deformed patterns are demodulated for online 3D reconstruction.