esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Models for Correlation of Compaction Characteristics of Weakly Cohesive Soils
## 2 Predictive Models for Soil Compaction
This study evaluates a model for predicting soil compaction and uses RMSE as a performance metric for assessing model performance. Some interrelationships of the involved factors in compaction process of the soils used to be implemented in this model are [32; 33; 34; 35; 36]:  
1. dry density--moisture content;
2. dry density--modulus of elasticity;
3. modulus of elasticity--moisture content;
4. modulus of elasticity--layer thickness;
5. modulus of elasticity--number of passes;
6. moisture content--number of passes;
7. settlement--moisture content;
8. settlement--layer thickness;
9. settlement--number of passes;
10. degree of compaction--moisture content;
11. degree of compaction--modulus of elasticity;
12. degree of compaction--drum contact area;
13. degree of compaction--settlement;
14. degree of compaction--number of passes. Understanding these relationships is essential for engineering applications, especially in civil and geotechnical engineering, where soil stability and load-bearing capacity are paramount. Implementing predictive models for soil compaction offers numerous advantages:  
* increased efficiency by optimizing compaction parameters in real time, thus allowing the projects to reduce rework and delays. * improved quality by ensuring that compaction meets the required specifications, leading to better longevity of the pavement and structure. * cost savings, as enhanced efficiency and reduced material wastage can lead to significant cost reductions in construction projects. * risk mitigation, since early identification of potential issues allows teams to address problems proactively, reducing the risk of project failure.
# Automated Processing of fNIRS Data--A Visual Guide to the Pitfalls and Consequences
## Results  
### Channel Exclusion Criteria  
Exclusion criteria for channels based on the three evaluated methods--visual inspection (VI), coefficient of variation (CV) and PHOEBE were markedly different in each subject (Figure 5a). There was no significant difference between the VI and CV methods (\(p_{\text{c}}\) = 0.8) (Figure 5b). We also did not find significant differences in the number of excluded channels in the VI as well as the CV method in comparison to PHOEBE (\(p_{\text{c}}\) = 0.07), which is likely due to the outliers in the CV and VI methods. Motion artifacts, which were especially high in subject 11 and 12, resulted in higher rejection rates by the CV method, which had otherwise a mean of 2.7 \(\pm\) 5 channels excluded. In comparison to the VI method excluded 2.6 \(\pm\) 5 and PHOEBE 5.1 \(\pm\) 5 channels. In the analyses that follow we used the most conservative measure, PHOEBE.  
Figure 5: Channel exclusion criteria. Three channel exclusion methods are compared: visual inspection (VI), coefficient of variation (CV), and PHOEBE. (**A**) For each subject, the number of channels excluded are shown in addition to their overlap with visual inspection (“*”, lighter blue for CV and lighter red for PHOEBE). (**B**) The comparison shows that PHOEBE is the most conservative measurement, excluding more channels than VI or CV.
# A Comparative Evaluation of Nature Inspired Algorithms for Telecommunication Network Design
## 4 Methods
### Evolutionary Algorithm  
The EA, which was employed for the optical node optimization problem, was inspired by evolutionary strategy ((\(\mu+\lambda\)) ES) given in [27; 28], but both the encoding method and genetic operators that were applied here are problem-specific. Its pseudo-code is given in Algorithm 3.  
```
Input: Number of iterations (MaxIters), memory size (MemSize) Output: The best solution found (BestSoFar) \(M\leftarrow\)random initial population [MemSize];  EvaluateMemory(\(M\)); \(t=0\); while\(t<\)MaxItersdo \(NewSolution\gets EmptySolution()\); if\(Rand(0,1)<p_{a}\)then forNodePair in \(N\)do \(realization\gets ChooseRealization(NodePair,M)\); if\(Rand(0,1)<p_{m}\)then \(ChangePath(realization)\);  end if \(AddRealization(NewSolution,NodePair,realization)\);  end if else \(CreateRandom(NewSolution)\);  end if \(Evaluate(NewSolution)\); \(CompareWithMemory(NewSolution,M)\); \(t\gets t+1\)  end if returnTheBestFrom(M);
```  
**Algorithm 2**Harmony search.  
The algorithm maintains a population \(P_{t}\) of individuals. The population is initialized randomly. For each gene, the demand that is associated with it is randomly allocated to a possible path. Then the objective function of each individual is calculated according to (1). The main loop of the algorithm works for the specified number of iterations, which is a parameter of the method. Inside the main loop, an offspring population \(O_{t}\) is generated by reproducing randomly selected elements from \(P_{t}\). Then, chromosomes are randomly mated into pairs, crossed over and mutated. A new population \(P_{t+1}\) is generated by selecting best chromosomes from the sum of the base population and the offspring population.  
To be able to use the EA, the solution of the problem must be encoded to form a genotype of an individual. The structure of the chromosome has already been depicted in Figure 1 and an example of the chromosome based on Polish network is depicted in Figure 3. Demand 1 is realized by means of four transponders (first, traversing path 1 and using slice 2; second, 40 G traversing the same path, using slice 7; third, 100 G transponder-path 2 and slice 13; and fourth, 200 G transponder-path 1 and slice 17). Demand 2 has two transponders and the last demand 66 has just one transponder, traversing path 1, using slice 5. The crossover is performed using _crossover operator_ as described earlier. The mutation is performed for each chromosome. Each demand is mutated separately with probability \(p_{m}\) using _change path operator_.
# Evaluating the Impact of Mobility on Differentially Private Federated Learning +
## 3 Internet of Vehicles  
This section begins with a brief introduction to the terminologies related to the Internet of Vehicles. _Connected vehicles_ refer to vehicles that can communicate with their external environments via wireless communication technologies [42]. With the connectivity property, they are also represented as vehicle-to-everything (V2X); here, \"everything\" often includes neighboring vehicles, road infrastructure, pedestrians, satellites, and so on. _Vehicular networking_, as evolved from vehicular ad hoc networking, represents networking perspectives among connected objects on top of the connectivity. Connected vehicles and vehicular networking can be considered as building blocks of the _Internet of Vehicles (IoV)_, which represents a mobile and distributed computing environment. It is characterized by collecting, sharing, and processing information in a collaborative manner between con  
Figure 2: An inference attack on federated learning.  
nected vehicles [1]. These technologies together are expected to enable the evolution to the next-generation road infrastructure: _cooperative intelligent transportation systems (C-ITSs)_. While the terms are somewhat different, this work uses them interchangeably as it is more concerned with how to apply federated learning and differential privacy to them.  
Two of the most distinguishing features of IoV are the mobility and time-space validity of data. First, vehicles move at a very high speed along roads. In addition, the speed can change in a wide range, for example, when a vehicle traveling at 60 km/h stops in front of a crosswalk. The high speed and varying mobility make the connectivity unstable, which eventually adversely affects the operations of federated learning. Next, data generated in IoV tend to have locality in terms of space and time. Vehicle-generated content has its own spatial scope of utility and temporal scope of validity. For instance, road safety messages are only interesting to vehicles in the vicinity and may only be valid for 30 min, even in the same location. The time-space validity of the data implies the scalability of data processing applications like federated learning, since old data are obsolete.
## 4 Methods
### RST Approach  
The RST approach was developed to study and monitor several natural/environmental hazards, such as earthquakes, floods, oil spills, forest fires, and volcanic activity (e.g., [61; 62; 63; 64; 65; 66; 67]).  
In more detail, RST considers every anomaly in the space-time domain as a deviation from a normal state that may be determined by processing multiannual time series of homogeneous (i.e., same calendar month and overpass time) cloud-free satellite records [54]. The Absolutely Local Index of Change of Environment (ALICE) index was used to identify anomalous variations of the signal related to possible perturbing events:  
\[\otimes_{V}(x,y,t)=\frac{V(x,y,t)-\mu_{V}(x,y)}{\sigma(x,y)} \tag{1}\]  
where \(V(x,y,t)\) is the satellite signal measured at time \(t\) in a specific spectral band or band combination at the same location \((x,y)\), and \(\mu_{V}(x,y)\) and \(\sigma_{V}(x,y)\) stand for the temporal mean and standard deviation of \(\mathrm{V}(x,y,t)\).  
The RST\({}_{\mathrm{VOLC}}\) and RST\({}_{\mathrm{ASH}}\) algorithms are two RST configurations specifically developed for volcanological applications. RST\({}_{\mathrm{VOLC}}\) jointly uses the \(\otimes_{\mathrm{MIR}}(x,y,t)\) and \(\otimes_{\mathrm{MIR-TIR}}(x,y,t)\) indices to detect volcanic hot spots [42]. The first one analyzes the MIR signal (i.e., \(\mathrm{V}(x,y,t)=BT_{\mathrm{MIR}}(x,y,t)\)), the second one the difference of MIR and TIR (around 11 \(\upmu\)m) brightness temperatures (i.e., \(\mathrm{V}(x,y,t)=BT_{MIR}(x,y,t)-BT_{TIR}(x,y,t)\)).  
RST\({}_{\mathrm{ASH}}\) combines the \(\otimes_{TIR}(x,y,t)\) and \(\otimes_{MIR-TIR}(x,y,t)\) indices to identify volcanic ash clouds [68]. The \(\otimes_{\mathrm{MIR}}(x,y,t)\) index, which analyzes the difference of brightness temperatures measured in the TIR band at around 11- and 12-\(\upmu\)m wavelengths (\(\mathrm{V}(x,y,t)=BT_{11}(x,y,t)-BT_{12}(x,y,t)\)), is also sensitive to airborne dust [52]. Hence, it can be used for investigating Saharan dust outbreaks even in combination with other RST indices, as performed in [68].
## 3 Group Decision-Making with Consensus Based on Subjectivity and Sentiment Analysis
### Group Decision-Making with Consensus Measures
The preference values calculated for each of the experts are aggregated to calculate the collective preference value specific to each alternative. In this way, we can determine the overall expert opinion. Formally, we aggregate all the \(p_{ij}\) values using Expression (7). As the values of \(p_{ij}\) lie between [0, 1], the resulting collective preference values also lie in the same interval. With the \(CA_{i}\) values it is possible to score each of the alternatives, order them and generate the ranking that allows us to determine which solution are the best. It is important to remember that for our study, all experts are considered to be of equal importance in the analysis process. A complete description of the presented method is shown in Algorithm 1. ```
0: Alternatives \(A\), Posts \(T\), Sentiment Dictionaries \(D\), Threshold \(\gamma\)
0: Final ranking, Consensus level, Expert feedback
1: Initialise decision-making process (hashtags, experts, post collection)
2: Clean and preprocess posts (remove hashtags, links, stopwords)
3:for each post \(t\in T\)do
4: Identify associated alternative \(a_{t}\) and expert \(e_{t}\)
5: Compute sentiment score: \(S_{t}=\frac{N_{\text{pss}}(t)-N_{\text{neg}}(t)}{N_{\text{pss}}(t)+N_{\text{ neg}}(t)}\)
6: Normalise polarity: \(P_{t}=\frac{S_{t}+1}{2}\)
7: Compute reliability: \(Z_{t}=\left|\frac{N_{\text{pss}}(t)-N_{\text{neg}}(t)}{N_{total}(t)}\right|\)
8:endfor
9: Aggregate \(P_{t}\) values to get \(P_{e,a}\)
10: Build preference matrix \(P\) and reliability matrix \(Z\)
11: Compute consensus \(C_{A}=\frac{1}{n}\sum_{j=1}^{n}\left(\frac{1}{m}\sum_{i=1}^{m}p_{ij}\right)\)
12: Compute subjectivity per expert: \(Subj_{e}=\frac{N_{\text{pss}}(e)+N_{\text{neg}}(e)}{N_{total}(e)}\)
13:if\(C_{A}\geq\gamma\)then
14: Compute collective preference \(C_{i}=\frac{1}{m}\sum_{e=1}^{m}p_{ei}\)
15: Rank alternatives by descending \(C_{i}\)
16:else
17: Provide expert feedback and restart from preprocessing
18:endif
19:return Ranking, \(C_{A}\), subjectivity and reliability feedback
```  
**Algorithm 1** Sentiment-based GDM with Consensus and Reliability Measures (SGDM-CR)
# Rank and Wormhole Attack Detection Model for RPL-Based Internet of Things Using Machine Learning
## 4 Results and Discussion
### Results and Findings
* The advanced metrics used for evaluating the multiclass classification show promising results where the model achieves low cross-entropy value (0.116), which indicates high accuracy. The high values of Cohn's Kappa and MCC indicate that the model performs comparatively better. * The above metrics also confirm the unbiased accuracy, which might have been present if only overall accuracy was used for evaluation. * The proposed model outperforms benchmark research and classifiers in terms of accuracy, precision, and recall for two different types of attacks, that is, RPL-specific RA and SN-inherited WHA. * The model achieves high performance during the learning phase, which is presented through the assessment of the model through the training set after fine tuning. The final evaluation conducted on the testing set shows enhanced performance in terms of attack detection through multiclass classification.
## 2 Material and Methods
### Literature Review
#### 2.1.3 Cilacap as a Study Area  
The district is located in an area that is vulnerable to severe earthquakes that could potentially trigger a tsunami. Based on data from paleo-tsunami deposits, tsunamis hit Cilacap district in 1883, 1982, and 2006 [51; 52]. The last tsunami generated waves as high as 5-8 m in the southwest to the center of the island of Java. The waves were triggered by a 7.8-Richter-Scale earthquake off the coast of Pangandaran in the south. Waves moved from Pangandaran beach to Widarapayung beach in Cilacap and reached a height of 21 m on Nusakambangan Island, located in the northern city of Cilacap [53]. In the future, Cilacap district has the potential to be hit by a tsunami with a wave height of 20 m due to a 8.7-9.2 SR megathrust earthquake in southern Java [51; 54; 55].  
The Indian Ocean borders Cilacap district in the south and west, as well as rivers in the south and east. The geographical location gives this district the potential to be hit by tsunami waves from three directions, especially in the southern part of the district [54; 56; 57].  
Cilacap is the largest district in Central Java province, Indonesia, having an area of around 6.2% of the total area of Central Java province. Several areas in Cilacap have become marine industries and port centers and are connected to the rail network. This district has a special economic role in the oil industry and agriculture sector. The district of Cilacap is one of the most densely populated urban-coastal areas in Central Java Province, Indonesia [58]. In addition, the district is developing dynamically, with several buildings being located just a few meters from the shoreline [59]. The Indonesia National Disaster Management Authority (BNPB) estimates that tsunami in Cilacap will impact 10 sub-districts with a total of 86,149 people exposed.
## 4 Proposed Methodology
### Optimization Model  
(1) Objective Function  
Considering the wind power forecasting error and smart appliances' DRP, this paper focuses on both the supply side and demand-side strategies to maximize the wind accommodation. The formula is as follows:  
\[\text{max}\ F=\sum_{t=1}^{T}\sum_{i=1}^{Nw}P_{wi}^{t}\Delta T \tag{21}\]  
where \(T\) is the number of time intervals; \(N_{w}\) is the number of wind farms; \(P_{wi}^{t}\) is the wind output in the time interval \(t\); and \(\Delta T\) is the time interval.  
(2) Active Power Balance Constraint  
Once the smart appliances participate in the DR program and response, the total load energy varies. Meanwhile, the forecasting error on load prediction can also lead to the variation of total load. Therefore, there is load fluctuation as a result of DR actions and load forecasting error, which is as follows:  
\[\Delta P_{ij}=P_{ij}-P_{ij0}+P_{aj}-P_{jj} \tag{22}\]  
where \(\Delta P_{ij}\) is the load fluctuation of the \(j\)th household; \(P_{ij}\) is the total power after DR of smart appliances in the \(j\)th household; \(P_{j0}\) is the prediction load power of smart appliances in the \(j\)th household; \(P_{aj}\) is the actual power of non-smart appliances in the \(j\)th household; and \(P_{\beta j}\) is the forecasting power of non-smart appliances in the \(j\)th household.  
The active power balance constraint is shown in Equation (23).  
\[\sum_{i=1}^{N_{g}}P_{gi}+P_{wind}+\Delta P_{w}=\sum_{j=1}^{N_{l}}P_{lj} \tag{23}\]  
where \(N_{g}\) is the number of generators; \(N_{L}\) is the number of households; \(P_{gi}\) is the output power of the \(i\)th generator; \(P_{wind}\) is the wind forecasting power; \(\Delta P_{w}\) is the fluctuation of the wind power; and \(P_{lj}\) is the output power of the \(j\)th household's total loads.  
(3) Generator Capacity Constraint  
\[P_{gi,\text{min}}\leq P_{gi}\leq P_{gi,\text{max}} \tag{24}\]  
where \(P_{gi,\text{min}}\) and \(P_{gi,\text{max}}\) are the minimum and maximum capacities of the \(i\)th generator, respectively.  
Figure 2: Schematic diagram of coordinated real-time control of DR and automatic generation control (AGC) units. DO: dispatching order; RTCO: real-time control order; WPFE: wind power forecasting error.
## 3 Determination of VSC
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multicolumn{1}{c}{**Sky**} & \multicolumn{1}{c}{**a**} & \multicolumn{1}{c}{**b**} & \multicolumn{1}{c}{**c**} & \multicolumn{1}{c}{**d**} & \multicolumn{1}{c}{**e**} & \multicolumn{1}{c}{**Sky Description**} \\ \hline
1 & 4 & \(-\)0.7 & 0 & 1 & 0 & CIE Standard Overcast Sky, \\ \hline
2 & 4 & \(-\)0.7 & 1 & \(-\)1.5 & 0.5 &
\begin{tabular}{l} Overcast, with steep luminance gradation and slight \\ brightening towards the sun \\ \end{tabular} \\ \hline
3 & 1.1 & \(-\)0.8 & 0 & \(-\)1 & 0 &
\begin{tabular}{l} Overcast, moderately graded with azimuthal \\ uniformity \\ \end{tabular} \\ \hline
4 & 1.1 & \(-\)0.8 & 2 & \(-\)1.5 & 0.15 &
\begin{tabular}{l} Overcast, moderately graded and slight brightening \\ towards the sun \\ \end{tabular} \\ \hline
5 & 0 & \(-\)1 & 0 & \(-\)1 & 0 &
\begin{tabular}{l} Sky of uniform luminance \\ \end{tabular} \\ \hline
6 & 0 & \(-\)1 & 2 & \(-\)1.5 & 0.15 &
\begin{tabular}{l} Partly cloudy sky, no gradation towards zenith, slight \\ brightening towards the sun \\ \end{tabular} \\ \hline
7 & 0 & \(-\)1 & 5 & \(-\)2.5 & 0.3 &
\begin{tabular}{l} Partly cloudy sky, no gradation towards zenith, \\ brighter circumsolar region \\ \end{tabular} \\ \hline
8 & 0 & \(-\)1 & 10 & \(-\)3 & 0.45 &
\begin{tabular}{l} Partly cloudy sky, no gradation towards zenith, distinct \\ solar corona \\ \end{tabular} \\ \hline
9 & \(-\)1 & 0.55 & 2 & \(-\)1.5 & 0.15 &
\begin{tabular}{l} Partly cloudy, with the obscured sun \\ \end{tabular} \\ \hline
10 & \(-\)1 & 0.55 & 5 & \(-\)2.5 & 0.3 &
\begin{tabular}{l} Partly cloudy, with brighter circumsolar region \\ \end{tabular} \\ \hline
11 & \(-\)1 & 0.55 & 10 & \(-\)3 & 0.45 &
\begin{tabular}{l} White-blue sky with distinct solar corona \\ \end{tabular} \\ \hline
12 & \(-\)1 & 0.32 & 10 & \(-\)3 & 0.45 & CIE Standard Clear Sky with low polluted atmosphere \\ \hline
13 & \(-\)1 & 0.32 & 16 & \(-\)3 & 0.3 & CIE Standard Clear Sky, polluted atmosphere \\ \hline
14 & \(-\)1 & 0.15 & 16 & \(-\)3 & 0.3 & Cloudless turbid sky with broad solar corona \\ \hline
15 & \(-\)1 & 0.15 & 24 & \(-\)2.8 & 0.15 & White-blue turbid sky with broad solar corona \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of CIE standard sky types [22]. Figure 4: Vertical Sky Component (VSC) values for the different CIE sky types as a function of the scattering angle, \(\chi:\)**(a)** CIE standard sky types 1–5, **(b)** CIE standard sky types 6–10, **(c)** CIE standard sky types 11–15.
# Transmission Power Adaption for Full-Duplex Relay-Aided Device-to-Device Communication
## 5 Conclusions  
In this paper, we proposed a FD relaying based D2D communication scheme to assist the cellular communication by underlaying its downlink spectral resource. To fulfill the rate requirement of the cellular user by using the least power and to maximize the achievable rates for D2D link at the same time, we derive the optimal transmit power and power split factor for the D2D relay in a closed form. The simulation results show that the scheme we proposed brings significant achievable rate gain for D2D communication. Moreover, the scheme decreases transmit power usage by 80% for the D2D relay node in comparison to the HD mode, even in the presence of \(\gamma_{L}=15\) dB in Figure 4, which extends the lifetime of the battery and improves the energy efficiency of system, as shown in Figure 7.
# A Review on Upper Limb Rehabilitation Robots
## EMG-Driven Exoskeleton Robots
### Exoskeleton Robots Based on the Artificial Neural Network Control Method  
The artificial neural network (ANN) is mainly used to interpret the flexion/extension of the elbow and the supination/pronation of the forearm [55]. EMG signals are amplified after being extracted from seven muscles that are the brachialis, biceps, triceps, posterior deltoid, anterior deltoid, clavicular and pectoralis major [55]. The mean absolute value, number of zero-crossing and waveform length are features used in the ANN controlling algorithm. EMG signals are, firstly, filtered and windowed; then, the features are extracted [73; 74].
# Improving Decodability of Polar Codes by Adding Noise
## 4 Proposed Decoding Algorithms  
In this section, three decoding algorithms for polar codes are proposed by adding noise based on the perturbation decoding principle. The computational complexities are then analyzed.  
Figure 1: Schematic diagram of perturbation decoding principle.