esa-sceva/satcom-chunk-collection huggingface dataset:
# Removal of Cesium from Radioactive Waste Liquids Using Geomaterials
## 3 Results and Discussion
### Comparison with Other Adsorbents  
For many adsorption studies, the performance of the adsorbent is generally estimated and expressed by the maximum (or equilibrium) adsorption capacity. However, the maximum adsorption capacity is sensitively affected by the initial loading concentration of target pollutant (or more specifically what is left after sorption reaction).  
(If the sorbent is exposed to a higher concentration of targets, it is apt to exhibit a higher adsorption capacity. On the other hand, if the sorbent is exposed to lower levels of target species, it will show lower capacities.)  
Then, in addition to the maximum adsorption capacity, it is effective to estimate using the concept of partition coefficient (PC) as explained below as the following our references.  
PC = Adsorption capacity/Final concentration  
(or = Adsorption capacity/(Initial concentration \(\times\) removal ratio)).  
From Table 5, it can be seen that the maximal adsorption capacity of the fly ash-based geomaterial in this study is not notably high (89.32 mg\(\cdot\)g\({}^{-1}\)) compared to the other adsorbents. However, it is noteworthy that the PC value of this adsorbent is fairly high (31.02 mg\(\cdot\)g\({}^{-1}\).mM\({}^{-1}\)). It can then be regarded as a potential adsorbent for treating Cs in wastewater for practical use, especially when also considering that the synthesis method of the adsorbent is simple.  
In recent years, a lot of research has focused on the solidification of radioactive waste-based on geomaterials, but the current understanding of the fixing mechanism  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Adsorbents** & **Final Concentration** & **Adsorption Capacity** & **Partition Coefficient** & **Reference** \\  & **(mg\(\cdot\) L\({}^{-1}\))** & **(mg\(\cdot\) g\({}^{-1}\))** & **(mg\(\cdot\) g\({}^{-1}\).mM\({}^{-1}\))** & **Reference** \\ \hline nFe/Cu–Z & 26.28 & 77.51 & 2.949 & [43] \\ Natural clinoptoilite from Serbia & 76.00 & 49.26 & 0.6482 & [44] \\ Nanocrystalline mordentie (pulverized) & 4.000 & 37.30 & 9.325 & [45] \\ MWCNT reinforced zeolite-A beads & 30.00 & 113.0 & 3.767 & [46] \\ Carbon–zeolite & 156.0 & 120.9 & 0.7750 & [47] \\ Natural mordentie & 9.574 & 254.8 & 26.61 & [48] \\ Synthetic mordentie & 9.508 & 220.4 & 23.18 & [48] \\ Phosphoric acid activated & 13.30 & 208 & 15.64 & [49] \\ montmorillonite & & & & \\ Ethyl-VER & 2.161 & 43.96 & 20.34 & [50] \\ Bentonite & 8.700 & 177.4 & 20.39 & [51] \\ Clinoptiolite & 12.50 & 12.50 & 1.000 & [52] \\ Fly ash-based geomaterials & 2.879 & 89.32 & 31.02 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 5: The comparison of adsorption properties of several adsorbents.  
is not necessarily sufficient. This study mentioned the fixation mechanism of Cs in the solidification of geomaterials, especially in terms of chemistry.
## 4 Discussion
### Limitations and Future Research  
This exploratory analysis provides preliminary evidence that Latin dance offers a safe, dynamic form of weight-bearing exercise for older adults that engages lower body musculature in a population vulnerable to age-related decline. Several limitations prompt future work to extend these early insights. Firstly, we quantified impacts using accelerometry. While accelerometers accurately measure dance impacts [36], force plates could validate the intensity magnitudes and distribution profiles thought to indicate injury likelihood [46]. Applying this approach to older adults' dance can further establish safety.  
It is important to acknowledge that, while accelerometers enable the objective quantification of the external workload via impacts, the measurements may not directly reflect the true internal joint loads experienced by the participants. The direct in vivo measurement of joint forces and moments is challenging, especially in natural dance settings. However, previous research has established relationships between accelerometer-derived impacts and joint loading rates [47; 48]. The strong distal-to-proximal gradient observed, with the highest impacts on the ankles/knees versus the lower back/scapulae, aligns with the expected force attenuation up the kinetic chain during weight-bearing activities, like basketball [35] or running [25]. While future work is warranted, the findings provide initial insights into lower extremity musculoskeletal demands using a validated, non-invasive approach ideal for quantifying workload in older adult populations.  
Secondly, we recorded acute Latin dance responses in a modest sample of experienced dancers (\(n=32\)) unaccustomed to wearing sensors during normal practice. Monitoring training adaptations over cycles in larger cohorts could clarify impact trends while determining sensor interference. Comparing older adults versus young dancers would also reveal normative loading patterns by age, informing prescriptions accordingly. Finally, reproducing the findings across styles, like ballet or ballroom dance, can elucidate genre-specific dose responses.  
Overall, while preliminary, the results demonstrate that Latin dance challenges older adults' lower body musculature while unlikely overloading the dancers. Set to spirited Latin music, the dance could boost older people's physical function and prevent fall risks while protecting against sarcopenia and osteoporosis. Our impact quantification and early multi-site data help to inform safe, sustainable dance programing parameters to empower healthy aging.
# Big Data-Driven Deep Learning Ensembler for DDoS Attack Detection
## 3 Proposed System Model
### Feature Engineering and Selection  
The feature engineering and selection process, which generates and chooses the most relevant features, improves a model's performance.  
**Features of polynomials:** Polynomial features are the method of creating new features using polynomial combinations of the existing characteristics. This helps find the non-linear connections in the data. The procedure described below generates polynomial features from a feature collection \(\{a_{1},a_{2},\dots,a_{k}\}\) up to degree \(b\)[34]:  
\[\text{Poly}(a_{1},a_{2},\dots,a_{k},b)=\{a_{1},a_{2},\dots,a_{k},a_{1}^{2},a_ {1}a_{2},\dots,a_{k}^{b}\} \tag{10}\]  
**Feature selection using the Boruta algorithm:** This research used the Boruta algorithm, an advanced RandomForest classifier wrapper, to identify features. Boruta repeatedly  
Figure 2: Flowchart data balancing algorithm (SMOTE).  
compares real characteristics to randomly mixed shadow features to determine significance. This resilient method retains just statistically relevant characteristics, enhancing model performance and interpretability while lowering computing complexity. By testing model performance, the algorithm determines feature relevance. The following formula calculates the significance score of a feature \(a_{i}\) based on its influence on decision tree impurity [35]:  
\[\text{Importance}(a_{i})=\frac{U}{u=1}\frac{\Delta\mathcal{J}_{u}(a_{i})}{\sum_{ \
u=1}^{k}\Delta\mathcal{J}_{u}(a_{\
u})}, \tag{11}\]  
where \(\Delta\mathcal{J}_{u}(a_{i})\) represents feature significance. \(a_{i}\) in tree \(u\), \(U\) is the forest's tree count, and \(k\) is its feature count.  
Iterative comparison by Boruta selects only statistically important traits and eliminates irrelevant ones. This technique emphasizes relevant features, captures complicated interdependencies, and reduces duplication to improve dataset representation. By using Boruta-based feature selection, we improved model predictive performance while reducing processing complexity. This strategy helped the model concentrate on DDoS attack patterns, improving its accuracy and resilience in recognizing such threats.
# Deep Learning Optimisation of Static Malware Detection with Grid Search and Covering Arrays
## 6 Results and Discussion
### Tuning the Number of Layers  
The process of determining the optimal number of layers in our model was a journey of experimentation and discovery. Utilizing cAgent and grid search, we explored the potential of our search space, starting with a simple foundation of one dense layer and linear activation function. At first, our primary focus was not on the accuracy, but rather on ensuring that the model produced a plausible result. Through the process of iteration, we experimented with different configurations, observing varying accuracy results from 35% to 45%. Finally, through trial and error, we arrived at a good architecture--one dense layer with a vector size of 2381, a second dense layer of 1200, a third dense layer of 1200, and a final dense layer that provided the binary output.  
Our results suggest that the optimal number of layers for our deep learning model is four, with three intermediate dense layers. This finding is consistent with previous studies on deep learning models for static malware detection, which have typically used three or four dense layers [41; 42; 43]. However, the exact architecture and number of neurons in each layer can vary significantly depending on the specific dataset and model configuration. By carefully tuning the number of layers and neurons, we were able to improve the accuracy of our model, demonstrating the importance of diligent hyperparameter tuning.
# Structure Design, Kinematics Analysis, and Effect Evaluation of a Novel Ankle Rehabilitation Robot
## 4 Kinematics Simulation and Analysis
### Inversion and Eversion Movements  
According to the step driver function of the inversion and eversion recovery movement, the kinematic simulation is conducted. The change trend of the platform angle and the displacement of each driving limb is shown in Figure 9. The variation trend of the platform angular velocity and the velocity of each driving limb are shown in Figure 10.  
Figure 8: Variations of angular velocity and limb linear velocity.  
Figure 7: Characteristics of moving platform and driving limb.  
Figure 9: Characteristics of the moving platform and the driving limbs.  
Figure 9 shows that when the ankle inversion is at \(-20^{\circ}\), the CRS1 limb is \(L_{1}=31.14\), and CRS2 limb is \(L_{2}=-31.78\), and PU limb is \(L_{3}=0\); when at \(25^{\circ}\), the CRS1 limb is \(L_{1}=-40.53\), CRS2 limb is \(L_{2}=39.16\), and PU limb is \(L_{3}=0\). When the movement range of the ankle inversion and evasion joint is \([-20^{\circ},~{}25^{\circ}]\), the displacement change range corresponding to each driving limb of the mechanism is  
\[\begin{cases}-40.53\leq L_{1}\leq 31.14\\ -31.78\leq L_{2}\leq 39.16\\ L_{3}=0\end{cases} \tag{16}\]  
Figure 10 shows that when the mechanism does inversion and evasion movement, the maximum velocity of the driving limb is 12 mm/s, and the maximum angular velocity of the moving platform is \(15^{\circ}\)/s. The velocity is gentle and without any impact, which can ensure the safety of the rehabilitation movement.
# A Frequency-Domain Estimation Scheme for Frequency Offset with Large Range in OFDM Systems
## 3 Proposed Frequency Offset Estimation Scheme  
In this section, a frequency offset estimation scheme in the frequency domain, and its low-complexity implementation, are proposed in Sections 3.1 and 3.2, respectively.
## 6 Open Challenges, Future Prospects and Research Directions
### FSO Scalability and Reliability for 5G and Beyond Networks  
The scalability and reliability of FSO systems are critical factors for their adoption in next-generation networks. As the world transitions to 5G and beyond, the demand for high-capacity, low-latency, and resilient communication networks is skyrocketing. This section assesses the potential of FSO to scale with the increasing demands of 5G and beyond, addressing key issues such as network architecture, deployment strategies, resource management, and system integration.
# De Novo Transcriptome Identifies Olfactory Genes in _Diachassimomorpha Ionigicaudata_ (Ashmead)
## 3 Results
### Phylogenetic Analysis  
We next compared the sequences of olfactory proteins identified in Ashmead and some other homology organisms and analyzed their phylogenetic relationship. First, we constructed the phylogenetic tree for the top 5 OBPs (Table 3) and 28 other OBP sequences obtained from NCBI. Detailed information, including accession numbers and species, can be accessed in Table S3. The phylogenetic tree of OBPs (Figure 3A) showed high similarity (68.9% to 96.5%) between Ashmead OBPs and other species, such as _Aethina tumida_ (Atumi), _Aphidus gifiensis_ (Agifu), _Cephus cinctus_ (Ccinc), _Cotesia chilonis_ (Cchil), _Diachasma alloeum_ (Dallo), _Fopius arisanus_ (Faris), _Megadile rotundata_ (Mrotu), _Meteorus pultricornis_ (Mpulc), _Microlitis demolitor_ (Memo), and _Microlitis mediator_ (Mmedi). We next performed the phylogenetic analysis for CSPs. The top 5 highly expressed CSPs (Table 3) were compared with 22 CSPs from other species, like _Vespa velutina_ (Vvelu), Ccinc, _Sclerodemus_, Mpulc, and _Yenuma signatus_ (Ysign). Detailed accession numbers of these CSPs can be accessed in Table S3. It is clear that the Ashmead CSPs can be clustered with other known CSPs. The phylogenetic analysis supported the identification and characterization of OBP and CSP transcripts in this study.
## 6 Supervised and Unsupervised Machine Learning Model
### Supervised Machine Learning
#### 6.1.2 Hidden Markov Model  
The hidden Markov model (HMM) uses a Markov Chain in which a certain set of states could be partially observable (hidden) or observable. An example of the Markov Chain with five hidden states (HS), denoted by \(s_{1}\), \(s_{2}\), \(s_{3}\), \(s_{4}\) and \(s_{5}\), is shown in Figure 17 [58]. The transition information is quantified in terms of livelihood or the transition probability value, denoted as \(a_{ij}\), where \(i\) is the original state and \(j\) the subsequent state. The state information could be deduced within the state, i.e., \(a_{ii}\) or between two different states, i.e., \(a_{ij}\). The transition probability may be arranged in a Stochastic Matrix known as the Transition Matrix (TM). Similarly, the probability of the Markov Chain for Observable States (OS) could also be obtained, where its Stochastic Matrix is known as the Emission Matrix (EM). The HMM is applicable for a time-series dataset where the HMM utilizes Markov Chain to create a probabilistic correlation between states. By using the HMM, the ML can predict the fuel consumption based on the environmental condition given. A comparison of the prediction data (PR) for fuel consumption of a tugboat with the ground truth (FR) is shown in Figure 18. Note that vs. represents the vessel speed. Figure 18 indicates the correlation between the vessel speed and the fuel consumption, thereby could  
Figure 16: Flow chart for machine learning process [61].  
be used for assisted decision making on the optimal vs. to achieve fuel efficiency. The fuel consumption profile for a tugboat differs from other harbour craft vessels such as the ferries or patrol vessels, where the latter have their fuel consumption influenced significantly by the vessel speed, i.e., an increase in fuel consumption increases with an increase in vessel speed. On the other hand, the tugboat working operations involve tugging and cruising in which both operations could lead to high fuel consumption. During the tugging operation, there is a significant drop in the vessel speed as shown in Figure 18, but the fuel consumption (FR) remains high. One way to deduce the correlation between the FR and vs. is by taking into consideration the vessel shaft RPM. However, if the vessel shaft RPM is not available, the classification model based on the K-mean clustering method [61] could be used to classify the different operational activities of the tugboat based on their FR and VS. This classification model can then be utilized to train the machine learning model, as described in Section 6.2.2.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 5 Security Analysis
### Formal Analysis  
#### 5.1.1 BAN Logic  
This section presents the Burrows-Abadi-Nikoogadam (BAN) logic [33] of the proposed protocol. The BAN logic method is a formal approach used for analyzing and verifying the correctness of authentication protocols. Table 2 presents the notation and definitions, and the BAN logic rules are provided below.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(\theta_{1},\theta_{2}\) & Principals \\ \(\sigma_{1},\sigma_{2}\) & Statements \\ \(\theta_{1}\equiv\sigma_{1}\) & \(\theta_{1}\)**believes**\(\sigma_{1}\) \\ \(\theta_{1}\sim\sigma_{1}\) & \(\theta_{1}\) once **said**\(\sigma_{1}\) \\ \(\theta_{1}\Rightarrow\sigma_{1}\) & \(\theta_{1}\)**controls**\(\sigma_{1}\) \\ \(\theta_{1}\lhd\sigma_{1}\) & \(\theta_{1}\)**receives**\(\sigma_{1}\) \\ \#\(\sigma_{1}\) & \(\sigma_{1}\)**is fresh** \\ \((\sigma_{1})_{K}\) & \(\sigma_{1}\)**is encrypted** by \(K\) \\ \(\theta_{1}\xleftarrow{\
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 5 Results and Discussion
### Throughput  
The amount of data successfully received at the destination in a given period of time is called throughput. This data includes the retransmitted packets.  
\[\text{Throughput}=(\sum\text{Packets received}\times\text{Packet Size})/\text{Total Simulation Time} \tag{4}\]  
The decrease in the convergence time after the fast-recovery phase affects the end-to-end throughput. EMFast TCP achieves higher throughput as compared to Fast TCP, MFast TCP, TCP New Reno, and SCTP. Table 2 shows the throughput of EMFast TCP, Fast TCP, TCP New Reno, and SCTP with and without additional flows. Results show that EMFast TCP provides better throughput by using CCaFR as compared to Fast TCP, MFast TCP, TCP New Reno, and SCTP.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{2}{c}{**Convergence Time (s)**} \\ \cline{2-3}  & **Without Any Competing Flow** & **With Four Competing Flows** \\ \hline SCTP & 8.22 & 8.50 \\ TCP New Reno & 8.46 & 8.92 \\ Fast TCP & 5.43 & 5.18 \\ MFast TCP & 5.43 & 5.18 \\ EMFast TCP & 3.87 & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Convergence time of the protocols after fast-recovery phase (average for five tests) [Table S1].
## 3 Results
### Spatial Variation of Atmospheric Oxygen Consumption and Balance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multirow{2}{*}{**Oxygen**} & \multirow{2}{*}{**Density of Oxygen**} & \multirow{2}{*}{**Proportion of Green Area**} & \multirow{2}{*}{**Oxygen**} & \multirow{2}{*}{**Density of Oxygen**} & \multirow{2}{*}{**Oxygen**} \\  & & **Doxygen** & & & & & **Oxygen** \\  & & **Emission** & & **(\%)** & & **Consumption Rate** & **Consumption (10\({}^{8}\) yuan)** & **Unit of GDP** \\  & & **(10\({}^{4}\) t km\({}^{-2}\))** & & & & & **(10\({}^{4}\) t km\({}^{-2}\))** & & **(10\({}^{4}\) t 10\({}^{8}\) yuan\({}^{-1}\))** \\ \hline Guangzhou & 25.17 & 0.08 & 0.72 & 0.11 & 2.01 & 10,748.28 & 1.41 \\ Shenzhen & 43.04 & 0.15 & 0.47 & 0.32 & 6.46 & 9581.51 & 1.29 \\ Zhuhai & 44.10 & 0.02 & 0.57 & 0.04 & 0.88 & 1208.60 & 1.35 \\ Foshan & 46.68 & 0.05 & 0.51 & 0.10 & 2.33 & 5651.52 & 1.47 \\ Jiangmen & 2.80 & 0.10 & 0.88 & 0.11 & 0.28 & 1570.42 & 1.77 \\ Dongguan & 85.91 & 0.03 & 0.30 & 0.10 & 2.58 & 4246.45 & 1.57 \\ Zhongshan & 43.89 & 0.03 & 0.43 & 0.07 & 1.32 & 1850.65 & 1.46 \\ Huizhou & 2.37 & 0.15 & 0.94 & 0.16 & 0.36 & 1729.95 & 2.31 \\ Zhaoqing & 1.10 & 0.14 & 0.97 & 0.14 & 0.15 & 1085.87 & 2.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Atmospheric oxygen balance for each city in the PRD in 2011.  
the mode of economic development, and enhance energy usage efficiency, besides improving oxygen emissions in Dongguan, Foshan, Zhuhai, and Zhongshan. Shenzhen had a similar oxygen balance index value compared with Foshan, Zhuhai, and Zhongshan. However, there was a relatively higher density of oxygen emission and consumption in Shenzhen. The large density of oxygen consumption was up to 6.46 \(\times\) 10\({}^{4}\) t km\({}^{-2}\) a\({}^{-1}\), which resulted in a weak oxygen balance despite Shenzhen had the highest oxygen emission rate. Furthermore, Guangzhou had the largest economic size in the PRD. However, the oxygen balance index was only 25.17 in Guangzhou, which could be attributed to its high proportion of green area. Thus the oxygen balance of Guangzhou was relatively better than the other economic centers in the PRD. The oxygen balances of Zhaoqing, Huizhou, and Jiangmen, with oxygen balance indexes of 1-3, were the best in the PRD. This was because on one hand, they had the highest proportion of green areas (97%, 94%, and 88%, respectively) and the higher oxygen emission rate of green land in the PRD. On the other hand, the economic size of these three cities was relatively small, which lead to the lowest density of oxygen consumption. However, the energy utilization efficiency should be paid more attention since the oxygen consumption per unit of GDP in these three cities were the lowest.
## 4 On-line Algorithm
### Strategy for Dealing with Disrupted Road Networks  
After the cutting procedure, the on-line algorithm labels each road edge with its priority to be explored. The sets of edges and priorities define the labeled network. The labeled network is used to guide the UAV to obtain the accessibility information of the victim nodes within the shortest time.  
This orientation criterion depends on the connectivity and the length of an edge. The priority value of each edge is the weighted sum of the edge connectivity (\(edgeC\)) and the edge length (\(edgeL\)). An importance level \(\alpha\) in the co-domain \([0.2,0.8]\) is given to compute the edge length criterion. Equation (1) computes the edge weight criterion (\(edgeW\)) for each edge in the disrupted network [4]. The parameter \(longest\) denotes the length of the longest edge, and the parameter \(highestC\) is the highest edge connectivity value. Initially, the parameters \(edgeL\) and \(highestC\) are determined from the known road network; after the first exploration, these values are calculated using the disrupted network as a reference.  
Thus, only the edges relevant for reaching victim locations are those that have an assigned edge weight criterion.  
\[edgeW:=\alpha\cdot(1-\frac{edgeL}{longest})+(1-\alpha)\cdot\frac{edgeC}{highestC} \tag{1}\]  
To apply the orientation criteria, the on-line algorithm uses a sort insertion procedure to determine an efficient UAV routing.  
The sort insertion procedure selects the best edge to be explored next, i.e., the edge with the highest priority. The weight criterion is sorted in descending order; thus, the edge with the highest value has the highest priority. In each iteration, the on-line algorithm inserts the best edge into the exploration route of the UAV. Once a road edge is explored, the labeled network has to be updated. The movement strategy is used when:  
* A disrupted edge is detected;
* The node that is the current position of the UAV has no adjacent edges to visit.  
Taking the edge weight criterion and the current location of the UAV as a reference, the _next-best edge to explore_ is selected. Initially, the UAV position is the DMC. If there is no adjacent edge to explore, the back and forward strategy is applied. Otherwise, the adjacent edge with the highest edge weight criterion is selected as the _next-best edge to explore_. Once the _next-best edge to explore_ is selected, the UAV explores it by flying over it. Then, the road information of the last exploration is transferred to conduct the cutting procedure and calculate the labeled road network. Thus, the road network information is updated for the following routing decision. This procedure is repeated until the on-line algorithm has the information on the road accessibility of all victim nodes from the DMC.  
The back and forward strategy aims to redirect the UAV based on the edges and nodes, reachable from the DMC, detected up to the moment the disruption takes place. An aerial edge or a road edge, or a combination of both, is used by the UAV to return to the closest reachable node with unexplored adjacent edges.