esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Prediction and parameter optimization are effective methods for mine personnel to control blast-induced ground vibration. However, the challenge of effective prediction and optimization lies in the multi-factor and multi-effect nature of open-pit blasting. This study proposes a hybrid intelligent model to predict ground vibrations using a least-squares support vector machine (LSSVM) optimized by a particle swarm algorithm (PSO). Meanwhile, multi-objective particle swarm optimization (MOPSO) was used to optimize the blast design parameters by considering the vibration of particular areas and the bulk rate of blast fragmentation. To compare the prediction performance of PSO-LSSVM, a genetic-algorithm-optimized BP neural network (GA-BP), unoptimized LSSVM, and BP were used, by applying the same database. In addition, the root-mean-squared error (RMSE), the mean absolute error (MAE), and the correlation coefficient (\(r\)) were regarded as the evaluation indicators. Furthermore, the optimization results of the blasting parameters were obtained by quoting the established vibration prediction model and bulk rate proxy model in MOPSO and verified by field tests. The results indicated that the PSO-LSSVM model provided the highest efficiency in predicting vibrations with an RMSE of 1.954, MAE of 1.717, and \(r\) of 0.965. Furthermore, the blasting vibration can be controlled by using the two-objective optimization model to obtain the best blasting parameters. Consequently, this study can provide more specific recommendations for vibration hazard control.  
particle swarm optimization; LSSVM; blasting; ground vibration; multi-objective; parameter optimization
## 4 Results  
### Seasonal Variations of the MODIS-Derived Indices on Each Continent  
MODIS-derived indices have different variations on different continents (Figure 2). In Eurasia and North America, all indices (LST, NPP, NDVI and LAI) change seasonally, with maximum values in summer (July or August) and minimum values in winter (January or February). The LST in Australia has the opposite trend from that found in Eurasia and North America, but the seasonal change is not as obvious. The fluctuation of NPP, NDVI and LAI are quite small in Africa, Australia and South America. South America always has the largest value of NPP, NDVI and LAI.  
Figure 2: MODIS-derived indices trend of LST, NPP, NDVI and LAI from June 2009 to November 2011 in Africa, Australia, Eurasia, North America and South America.  
Eurasia and North America are located in Northern Hemisphere and the seasonal sensitivity of plant green-up and snow events are the main reasons of the changes in the indices. Australia is located in Southern Hemisphere, with a large percentage of bare areas leads to the small fluctuation of NPP, NDVI and LAI. The Amazon River Basin in South America has the highest vegetation cover in the world; thus, the NPP, NDVI and LAI are all higher in South America. The LST is closely associated with land-cover type, especially in bare areas [39], which is the main reason for higher value in of this parameter in Africa and Australia.
# APT Attack Detection Scheme Based on CK Sketch and DNS Traffic
## 4 Experiments
### Model Training and Results
#### 4.4.4 Throughput Comparison  
In this experiment, the objective was to compare the processing efficiency of network traffic among different sketch structures. The experiment evaluated the throughput of CK sketch, CM sketch, and Count sketch when compressing and counting network traffic. The results of the experiment are depicted in Figure 12, where the horizontal axis represents the number of hash functions present in each sketch structure and the vertical axis depicts the number of packets processed per second. The findings reveal that as the number of hash functions increases, the processing efficiency of all methods for network traffic data decreases. However, CK sketch exhibits a considerable advantage over the other methods in processing network traffic, owing to the fact that, unlike CM sketch and Count sketch, CK sketch only requires multiple hash calculations in case of hash conflict and thus avoids excessive hash operations and saves a considerable amount of time.  
Figure 10: Comparison of the training time of two models before and after sketch processing.  
Figure 11: Comparison of model training accuracy before and after sketch processing: **(a)** Adaboost accuracy; **(b)** Bagging accuracy, **(c)** DecisionTree accuracy; **(d)** ExtraTrees accuracy; **(e)** Definitions accuracy; **(f)** RandomForest accuracy.
# Typhoon Intensity Forecasting Based on LSTM Using the Rolling Forecast Method
## 4 LSTM Models  
Considering the influence of different prediction factors to forecast typhoon intensity, models based on LSTM using different prediction factors are proposed. Based on the above processed data (Section 2) and the model development (Section 3), the models based on LSTM are proposed. Their rolling forecasting processes are shown as the function expression in Table 5. The value of \(i\) in Table 5 is from 1 to 20. The models based on LSTM can obtain the latitude and longitude of the typhoon's center and the moving speed of the typhoon, but only the minimum pressure and the maximum wind speed near the typhoon's center were used to analyze the prediction results and the performance of the prediction models. In addition, the models based on LSTM were developed based on the Tensorflow framework using Keras API (keras.io) and Python 3.6.3, while the model based on FNN was developed with MATLAB 2017b. Therefore, the structure of this experiments and the models based on LSTM are shown in Figure 4.  
Figure 4: The structure of typhoon intensity forecasting experiments.
## 5 Conclusions  
In this paper, we have proposed a coupled thermoelasticity theory of Lord-Shulman and Maxwell's equation, to examine thermoelastic wave propagation for FG cylindrical panels with inner and outer surfaces bonded by piezoelectric layers, and subjected the panels to a thermal shock loading. The coupled governing equations of the problem have been solved using a computationally efficient and accurate numerical hybrid method built upon a discrete LW-DQM method and a NURBS-based multi-step time integration scheme. The geometric and physical compatibility conditions have been enforced exactly at the interfacial layers. The model is able to accurately simulate the variations of the field variables throughout the thickness, as demonstrated by the fast rate of convergence of the numerical results. A wide parametric study has considered the effects of the material graded index, piezoelectric layer thickness, external electrical voltage, and opening angle on the thermoelastic behavior of FG cylindrical panels, while providing the following promising conclusions for practical applications:  
* The model can simulate thermoelastic wave propagation in multilayer panels.
* An increased material gradient index \(n\) decreases the heat wave speed.
* The classical thermoelasticity theory based on a null value of relaxation time is unable to accurately predict the thermoelastic behavior of laminated panels under a thermal shock loading.
* An increased core thickness-to-piezoelectric thickness ratio \(\rm h_{c}/h_{p}\) yields an increased value of all the thermoelastic field variables.
* An increased opening angle of the panel \(\theta_{0}\) increases the radial displacement and decreases the hoop stress component.
* The location of the maximum values for the hoop stress is almost independent of the geometrical opening angle \(\theta_{0}\).
* The opening angle \(\theta_{0}\) does not allow any sensitive variation in the distribution and time histories of the temperature.
* An increased external electrical voltage decreases the radial displacement and increases the hoop stress component without affecting the temperature variation.  
Conceptualization, Y.H., P.M., R.D. and F.T.; Formal analysis, Y.H., P.M., R.D. and F.T.; Investigation, Y.H., P.M. and F.T.; Validation, P.M., R.D. and F.T.; Writing--Original Draft, Y.H., P.M., R.D. and F.T.; Writing--Review & Editing, R.D. and F.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## 3 Conventional and Proposed Methods
### Proposed Method
#### 3.2.2 Adaptive Selection Method for \(\beta\)  
The parameter \(\beta\) in the nonlinear allocation method is not a fixed value. Instead, it is more effective when dynamically adjusted according to the channel environment. In this context, the adaptive adjustment of \(\beta\) is explained using the CSI standard deviation method, which adjusts \(\beta\) based on the variation in channel gains.  
\[\tilde{g}=\frac{1}{MN}\sum_{m=1}^{M}\sum_{n=1}^{N}g_{m,n}. \tag{27}\]  
\[\sigma=\sqrt{\frac{1}{MN}\sum_{m=1}^{M}\sum_{n=1}^{N}(g_{m,n}-g)^{2}}. \tag{28}\]  
In this context, \(\tilde{g}\) represents the average gain, and \(\sigma\) denotes the standard deviation of the gains.  
The parameter \(\beta\) is dynamically determined by the following equation.  
\[\beta=\beta_{\text{min}}+k\cdot\frac{\sigma}{\tilde{g}}. \tag{29}\]  
In this context, \(\beta_{\text{min}}\) and \(k\) represent the minimum value of \(\beta\) and the sensitivity adjustment coefficient for the standard deviation, respectively. The term \(\frac{\sigma}{\tilde{g}}\) is the normalized value of the CSI gain standard deviation by its mean, which indicates the variation in channel quality.  
When the channel quality is uniform (\(\frac{\sigma}{\tilde{g}}\) is small), \(\beta\) is set to a lower value to achieve a more uniform power distribution. Conversely, when the variation in channel quality is large (\(\frac{\sigma}{\tilde{g}}\) is large), \(\beta\) is set to a higher value to concentrate power on better channels. The adjustment coefficient \(k\) is expressed using linear regression in the following form:  
\[k=w_{1}\cdot\tilde{g}+w_{2}\cdot\sigma+w_{0}. \tag{30}\]  
In this context, \(w_{1}\), \(w_{2}\), and \(w_{0}\) are coefficients determined through regression analysis [18]. Linear regression utilizes the Least Squares Method (LSM) to learn the optimal coefficients by minimizing the following loss function:  
\[L(w_{1},w_{2},w_{0})=\frac{1}{N}\sum_{i=0}^{N}(k_{i}-(w_{1}\cdot\tilde{g}_{i}+ w_{2}\cdot\sigma_{i}+w_{0}))^{2}. \tag{31}\]  
In this context, \(N\) represents the number of samples, while \(\tilde{g}_{i}\), \(\sigma_{i}\), and \(k_{i}\) denote the average gain, standard deviation, and target \(k\) value for each sample, respectively.  
Through this approach, an adaptive estimation method for the adjustment coefficient \while maintaining a manageable BER in moderate SNR conditions. The carrier frequency of 2.4 GHz was selected due to its common use in wireless communication, offering a good balance between propagation characteristics and available bandwidth. Forty-eight subcarriers were used in the OFDM system to ensure sufficient frequency diversity while maintaining computational efficiency. The Doppler frequency of 5 Hz reflects the relative motion between the tethered balloon and maritime users, capturing realistic channel variations caused by ship movement. These choices ensure that the simulation accurately represents practical nearshore communication conditions while effectively evaluating the proposed method.
## 3 Graph Neural Networks for Next-Generation Networks
### GNN in Core Networks
#### 3.3.2 Routing
Accordingly, the paper proposes to encode the link load in place of link capacity and to aggregate the link-wise effective queue occupancy along a path to infer the delay/jitter. On the other hand, [PERSON] et al. [111] aimed to perform routing optimization for a network whose traffic could change every second. The authors propose a GNN-based framework that takes as input the network states (e.g., link bandwidths and traffic demands) and the network topology to generate the routing table. The generated routing tables are then post-processed to guarantee the reachability of the routing path. The model is optimized using the supervised training scheme. The authors apply the Genetic Algorithm onto the given networks to produce the sub-optimal routing tables, which are later used as the ground-truth labels for the training phase. [PERSON] et al. [112] propose a MPNN-based framework for optimizing routing, a so-called AutoGNN. Given the network information as the input, this model combines the graph structural information with the local link information to generate a weight for each link as the output. A weighted shortest path algorithm is then performed on the weighted graph to calculate the shortest path, thus providing automatic routing optimization. The AutoGNN is trained to reduce the overall transmission delay using a Reinforcement learning (RL) mechanism, so-called REINFORCE [120], coupled with an OMNet++ simulator for providing network traffic data. The above methods are centralized in nature, aiming to optimize the routing decisions from a global perspective. In contrast, [PERSON] et al. [115] propose an MPNN-based model to perform the routing optimization locally. The model, as the core module of a controller, provides the next node for each routing request received on individual nodes. The authors propose the generation of a network state matrix, which is the input for the MPNN-based model. The entire model is trained using the DRL framework which was aimed to maximize the rewards composed of (i) Packet Delivery reward, ensuring that the routing algorithm selects nodes in the neighbor of the current node, and (ii) Delay reward again composing of Queueing delay and Link delay. The training environment is set up using a Mininet Simulator to simulate a SDN and Ryu Software to simulate controller behavior. Similarly, [PERSON] et al. [117] propose a distributed traffic routing control algorithm based on a deep graph reinforcement learning framework that combines the GCN model with a DRL training scheme called Actor-Critic [121]. This framework leverages a GCN model to extract the structural information of the network topology and then generates the next hop policy for each routing request received on individual nodes. The experiments are conducted within an environment interacting with an OMNet++ simulator. It is shown that the framework is capable of reducing packet transmission delay, increasing packet delivery ratio, and reducing the probability of network congestion.
# Electolytically Ionized Abrasive-Free CMP (EAF-CMP) for Copper
## 2 Experimental Setup
### Experimental Conditions  
The abrasive-free solution prepared for the experiments contained H\({}_{2}\)O\({}_{2}\), citric acid, and BTA as oxidizers, complexing agents, and corrosion inhibitors, respectively. The prepared abrasive-free CMP solution was fixed at a pH of 3. Cu wafers 100 mm in diameter (Cu film thickness: 1.5 \(\upmu\)m) were used. The rotational speeds of the platen and head were  
Figure 1: Schematic of an electrolytically ionized CMP system.  
Figure 2: Electrolytically ionized CMP system (POLI-400, G&P Technology Inc.) for the experiment.  
80 rpm, and the applied pressure on the wafer and retaining ring were 29.4 and 39.2 kPa, respectively. The slurry flow rate was 150 mL/min. The voltage applied to the electrodes was fixed at 30 V. The CMP experiment was performed for 1 min per wafer with in situ conditioning. Table 1 lists the detailed conditions of the CMP experiments.  
Before studying the electrolytically ionized abrasive-free solution, a basic experiment on an abrasive-free solution was conducted. A potentiostat with a working electrode (WE), counter electrode (CE), and reference electrode (RE) was used to analyze the electrochemical reaction between the solution and the Cu. The WE was connected to the Cu plate, the CE was a graphite rod, and the RE was a saturated calomel electrode (SCE). The equivalent weight and density of the Cu were 63.54 g/eq and 8.96 g/cm\({}^{3}\), respectively. The circular exposed area between the sample and the solution was 7.79 cm\({}^{2}\). Potentiodynamic polarization curves were obtained in the range of \(-\)1.5 V to 3.0 V with a scanning rate of 5 mV/s.  
The thickness change of the Cu film after CMP was measured using a 4-point probe (CMT-SR200N, Changmin Tech Co., Ltd., Sungnam, Korea). The Cu film thickness was measured by a total of 21 points at 5 mm intervals in the diameter direction of the wafer. The edge exclusion in the thickness measurement was 3 mm. The calculation of MRR and within-wafer non-uniformity (WIWNU) followed the expressions below.  
\[\text{MRR}=\frac{\text{Initial thickness}-\text{Final thickness}}{\text{ CMP time}}\left(\text{nm/min}\right) \tag{1}\]  
\[\text{WIWNU}=\frac{\text{Standard deviation of MRRs}}{\text{Average value of MRRs}}\times 100\ (\%) \tag{2}\]
## 3 Results and Discussion
### Influencing Factors
#### 3.4.4 Stored Gas Composition  
In the \"POWER-to-GAS-to-POWER\" concept for the storage and usage of hydrogen, the hydrogen is stored purely in underground storage systems [13]. As a consequence of pure H\({}_{2(\mathrm{g})}\) injection and storage (no CO\({}_{2(\mathrm{g})}\) is co-injected), the amount of generated CH\({}_{4(\mathrm{g})}\) decreases to an average of 0.32 mol L\({}^{-1}\) of gas after 30 years. H\({}_{2}\)S\({}_{(\mathrm{g})}\) generation is constant with an average of 0.005 mol L\({}^{-1}\) of gas after 30 years (Figure 2h). The loss of stored H\({}_{2(\mathrm{g}/\mathrm{aq})}\) is slightly increased to 77% after 30 years of storage.  
An alternative CO\({}_{2}\) source for methanogenesis is the residual gas that was not removed during natural gas production. Only if the residual gas is consumed or not reactive, does carbonate-bearing mineral dissolution increase and delivers CO\({}_{2}\) for methanogenesis, which is even more pronounced at a longer storage time of 300 years. Finally, the amount of available and reactive carbonate-bearing minerals (here calcite and dolomite) limits the process of methanogenesis if no CO\({}_{2(\mathrm{g})}\) is stored with H\({}_{2(\mathrm{g}/\mathrm{aq})}\). On the other hand, a separate test model shows that a higher amount of co-injected CO\({}_{2(\mathrm{g})}\) (14.8 atm; 10 times higher than in the reference scenario) increases the amount of generated CH\({}_{4(\mathrm{g})}\) to an average of 1.0 mol L\({}^{-1}\) of gas. If the amount of available CO\({}_{2(\mathrm{g})}\) would be infinitely large, the time-dependent kinetic rate constant and the storage time limit the loss of hydrogen by conversion to CH\({}_{4(\mathrm{g})}\).  
Without CO\({}_{2(\mathrm{g})}\) co-injection, the porosity in the reservoir rock decreases from 10% to 9.82-9.98%. This loss in porosity is slightly smaller than in the reference scenario in which 4% CO\({}_{2(\mathrm{g})}\) is co-injected. The changes in mineral dissolution and precipitation are minor in comparison to the reference scenario. The amounts of precipitated kaolinite, K-feldspar, and dolomite are slightly smaller than in the reference scenario and less calcite and illite are dissolved. No changes in the pH are observable. The smaller amount of precipitated K-feldspar and kaolinite leads to a smaller amount of bound water in these minerals. Consequently, the mass of water increases to 1.025 kg in the reservoir rock and is higher than the increase in the reference scenario (1.007 kg). The changes in the mineralogical composition of the cap rock are minor.
# A Pilot-Pattern Based Algorithm for MIMO-OFDM Channel Estimation
## 3 Channel Estimation Algorithm Based on Space-Time Block-Code  
[PERSON]'s algorithm in space-time block-code (STBC) encodes the identical signal in orthogonality to be transmitted by two antennas, which allows the receiver to easily identify the signal sent from different antennas because of the signal's orthogonality. It can be concluded that the encoding on the pilot signal by using [PERSON]'s algorithm can extensively reduce the complexity of channel estimation [15; 16].  
For a MIMO-OFDMA-OFDM system based on STBC, its algorithm of channel estimation demonstrates the advantage in reducing the complexity of computation. If it is possible to reduce the number of pilot signal, the systematic transmission efficiency could be further improved.
# Clinical Brain Monitoring with Time Domain NIRS: A Review and Future Perspectives
## 3 Clinical Brain Monitoring with TD-NIRS
\begin{table}
\begin{tabular}{c|c c c c c c c c c} \hline \hline
**SYSTEM** & **GROUP** & **SYSTEM** & **NB OF** & **WAVLENGTH** & **WAVLENGTH** & **REPETITION** & **SOURCE TYPE** & **DETECTOR TYPE** & **CHANNEL** & **FREQ** \\
**ID** & **NAME** & **USE** & **USED** & **RATE (MIIZ)** & **S** & **S** & **MED (\%)** \\ \hline
**1** & **Hamamatsu Photonics K.K.** & **TRS-10** & **3** & **761, 795, 835 nm & 5 & Laser diode & PMT & 1 & 0.2 \\
**2** & **Hamamatsu Photonics K.K.** & **TRS-20** & **3** & **759, 797,833** & 5 & Laser diode & PMT & 2 & 0.2 \\
**3** & **Hamamatsu Photonics K.K.** & **TRS-21** & **6** & **782, 883, 896** & 5 & Laser diode & PMT & 2 & 0.2 \\
**4** & **Hamamatsu Photonics K.K.** & **UNIS-1** & **3** & **753, 858, 850 nm & 9 & Laser diode & MPFC & 2 & 0.2 \\
**5** & **Hamamatsu Photonics K.K.** & **NIRS-1** & **3** & **755, 852, 850 nm & 9 & Laser diode & MPFC & 2 & 0.2 \\
**6** & **Sundford University,** & **NA** & **2** & **785, 850 nm** & **NA** & Laser diode & PMT & 34 & \textgreater{}0.05 \\
**7** & **UCL, London, UK** & **MONSTR B** & **2** & **780, 815 nm** & **40** & **T**ssappphire laser & MCP-PMT & 32 & \textless{}0.05 \\
**8** & **UCL, London, UK** & **MONSTR B** & **4** & **690, 750, 800, 850** & 40 & Supercontinuum & PMT & 32 & \textless{}0.01 \\
**9** & **Politecino di Milano, Milan** & & & & & Laser & & & \\
**10** & **RayLux project** & **R**abyLux project** & **R**abyLux project** & **R**abyLux project** & **3** & **685, 760, 822 nm & 20 & Laser diode & hybrid PMT & 1 & 1 \\
**11** & **Physically-Technische** & **NA** & **2** & **803, 807 nm** & **60** & **L**aser diode & PMT & 4 & 20 \\
**12** & **Physically-Technische** & **NA** & **1** & **785 nm** & **50** & **L**aser diode & PMT & 8 & 20 \\
**13** & **P**urbasionali, Berlin, Germany** & & & & & & & & \\
**14** & **Politecino di Milano, Milan** & **NA** & **3** & **690, 785, 830 nm** & **80** & **L**aser diode & hybrid PMT & 1 & 0.02 \\  & **Institute of BioDescriptions and** & & & & & & & & \\
**15** & **Homological Engineering,** & **NA** & **2** & **687, 832 nm** & **80** & **L**aser diode & PMT & 32 & 10 \\  & **W**ransson, Poland** & & & & & & & & \\
**16** & **Philadelphia, Pennsylvania, USA** & & & & & & & & \\
**17** & **Institute of BioDescriptions and** & & & & & & & & \\
**18** & **Homological Engineering,** & **NA** & **1** & **760 nm** & **80** & **L**aser diode & PMT & 8 & 10 \\  & **W**ssenen University, London, Ontario Canada** & & & & & & & & \\ \hline \hline \end{tabular}  
* Systems 10 and 16 also have DCS instrumentation. Here only the TD-NIRS part is reported. Refer to [59, 60] respectively for complete instrumental details. *** Typical values than can vary depending on the instrument settings. See individual references for more details. Abbreviations: PMT: photomultiplier tube; MCP-PMT: microchannel plate PMT, MPFC: multi-pixel photon counters.  
\end{table} 
Table 2: List of the system used in the publication reviewed, with their principle characteristics.