esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Cold Atmospheric Pressure Plasma in Density
## 3 CAPP Application in Periodontology
Authors noted that not only the exposed, but also the peripheral area of the titanium discs were decontaminated by CAPP. The authors also observed an increase in anti-VEGF expression after CAPP treatment using an in vitro gingival epithelium model. Not directly focused on periodontitis, some studies on CAPP therapy let us intrigued on its potential on periodontal therapy. [PERSON] et al. [31, 76] observed an increase in the \(\beta\)-defensins levels in fibroblasts and keratinocytes, and type I collagen induction after 2 min of cell activation in vitro using a microwave plasma torch (MicroPlaSter \(\beta\) plasma torch system) with argon. These findings are extremally important since \(\beta\)-defensins is a component of the innate immune response that avoid the increase in the count of pathogenic bacteria in the gingival sulcus during the first stages of periodontal disease, and expression of human \(\beta\)-defensins mRNA is decreased in patients with chronic periodontitis when compared to healthy individuals [77]. Additionally, induction of type I collagen indicates wound healing stimulation. Meanwhile, [PERSON] et al. [33] demonstrated increase on migration and proliferation of fibroblasts in response to ROS produced by He-CAPP. In a recent study, the same group [78] studied _P. aeruginosa_ and _S. aureus_ biofilms and demonstrated that CAPP damaged the cell membrane in a way that prevents antimicrobial resistance, and has a synergistic effect with other antimicrobial drugs. These antimicrobials, antibofilm, and tissue stimulating evidences of CAPP therapy taken together show the importance of studying this technology as an adjuvant therapy for periodontitis that remains as a chronic disease where current gold standard therapy (scaling and root planning) is based only on damage control, in a sense that pathogenic microbial colonization can be reduced but recolonization commonly occurs in the short-term, and tissue reattachment can be achieved, but still there is no gain of lost tissue.
# Reduction of Compression Artifacts Using a Densely Cascading Image Restoration Network
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Quality Factor** & **JPEG** & **ARCNN [30]** & **DnCNN [33]** & **DCSC [42]** & **RDN [44]** & **Ours** \\ \hline \multirow{3}{*}{Classic5} & 10 & 27.82 & 29.03 & 29.40 & 29.25 & **30.00** & 29.64 \\  & 20 & 30.12 & 31.15 & 31.63 & 31.43 & **32.15** & 31.87 \\  & 30 & 31.48 & 32.51 & 32.91 & 32.68 & **33.43** & 33.15 \\ \hline \multirow{3}{*}{LIVE1} & 10 & 27.77 & 28.96 & 29.19 & 29.17 & **29.67** & 29.34 \\  & 20 & 30.07 & 31.29 & 31.59 & 31.48 & **32.07** & 31.74 \\ \cline{1-1}  & 30 & 31.41 & 32.67 & 32.98 & 32.83 & **33.51** & 33.16 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: PSNR (dB) comparisons on the test datasets. The best results of dataset are shown in bold. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Quality Factor** & **JPEG** & **ARCNN [30]** & **DnCNN [33]** & **DCSC [42]** & **RDN [44]** & **Ours** \\ \hline \multirow{3}{*}{Classic5} & 10 & 0.780 & 0.793 & 0.803 & 0.803 & 0.819 & **0.825** \\  & 20 & 0.854 & 0.852 & 0.861 & 0.860 & 0.867 & **0.880** \\  & 30 & 0.884 & 0.881 & 0.886 & 0.885 & 0.893 & **0.903** \\ \hline \multirow{3}{*}{LIVE1} & 10 & 0.791 & 0.808 & 0.812 & 0.815 & 0.825 & **0.830** \\  & 20 & 0.869 & 0.873 & 0.880 & 0.880 & 0.888 & **0.895** \\ \cline{1-1}  & 30 & 0.900 & 0.904 & 0.909 & 0.909 & 0.915 & **0.922** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: SSIM comparisons on the test datasets. The best results of dataset are shown in bold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Quality Factor** & **JPEG** & **ARCNN [30]** & **DnCNN [33]** & **DCSC [42]** & **Ours** \\ \hline \multirow{3}{*}{Classic5} & 10 & 25.20 & 28.78 & 29.10 & 29.24 & **29.35** \\  & 20 & 27.50 & 30.60 & 31.19 & **31.41** & 31.40 \\  & 30 & 28.93 & 32.00 & 32.36 & **32.66** & 32.52 \\ \hline \multirow{3}{*}{LIVE1} & 10 & 25.33 & 28.77 & 28.91 & 29.17 & 29.03 \\  & 20 & 27.56 & 30.79 & 31.08 & 31.47 & 31.21 \\ \cline{1-1}  & 30 & 28.92 & 32.22 & 32.35 & **32.81** & 32.43 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: PSNR-B (dB) comparisons on the test datasets. The best results of dataset are shown in bold. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Number of Parameters** & **Total Memory Size (MB)** \\ \hline ARCNN [30] & 106 K & 3.16 \\ DnCNN [33] & 667 K & 46.31 \\ DCSC [42] & 93 K & 102.34 \\ IDCN [43] & 11 M & 254.13 \\ RDN [44] & 22 M & 861.97 \\ Ours & 479 K & 42.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Comparisons of the network complexity between the proposed DCRN and the previous methods. Figure 9: Inference speed on Classic5.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
## 4 Experimental Works and Results
### Experimental Setup
#### 4.1.3 Control Message Overhead  
Figure 6 shows the impact of varying the number of malicious nodes on the number of control messages exchanged in the network. As mentioned earlier, the ratio of malicious and benign nodes is 1:100; i.e., we introduced malicious nodes chronologically as 3, 6, 9, and 12 in the network of 30, 60, 90, and 120 nodes. Again, MMTM-RPL is compared with DCTM-RPL [28], RBAM-IoT [29], RPL-MRC [30], and DSH-RPL [25]. The number of nodes is shown on the \(x\)-axis, and the number of messages is shown on the \(y\)-axis for the three types of attack. Note that the message overhead was a minimum for MMTM-RPL. For a rank attack, the average numbers of messages were 8437.5, 12,410, and 10,125 for MMTM-RPL, DCTM-IoT, and RBAM-IoT, respectively. The control message overhead increased as the number of attackers increased because the attackers were scattered equally over the network. As a result, each attacker affects a huge number of valid nodes. For each mechanism, all nodes within the radio range of a malicious node reset their trickle timers whenever they receive a DIS multicast, causing them to transmit numerous DIO signals that are disseminated across the network. However, MMTM-RPL limits the number of trickle timer resets and the transmission of DIO messages to decrease network overhead. Compared to the state-of-the-art, the overhead was reduced by 39%, 48.7%, and 45% for three, six, or nine attackers, respectively. The average number of messages was a minimum for sinkhole and Sybil attacks. Figure 6 shows that the control message overhead grew as the number of data packets transmitted during the simulation increased. However, compared to other mechanisms, MMTM-RPL considerably minimized the control overhead.  
Figure 5: Comparison of average residual energy for different attacks: (**a**) Rank attack, (**b**) Sybil attack, and (**c**) Sinkhole attack.  
Figure 6: \(Cont\).
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 4 Discussion
### Limitations  
In our experiments, we only collected data on six different terrains, namely carpet, concrete floor, grass, asphalt, soil, tiles. However, there exist many practical terrains such as pebbles, sand, gravels, exercise mats, footpaths, etc., which should be considered in data collection. This would help in analyzing the behavior of the proposed approach under broader spectrum. Similarly, our database consists of only 40 subjects with a male to female ratio of 30:10, which is unbalanced as only 25% of the population is female. Extending the database and including more female participants to balance the population and testing the proposed approach with the extended dataset is another important direction of the future work. Another limitation of the proposed approach is placement of the sensors i.e., chest and lower back. There exist many other practical sensor placement locations such as wrists, side and back pockets, chest pockets, etc. that should also be considered. This would help in terrain classification in a ubiquitous manner. Another important aspect is that, although the data from both the sensors were collected simultaneously, there was no stringent requirement for synchronization, as both time series were independent from each other. Human intervention was only needed in the pre-processing step for training data preparation to retain the time series data when the subject is walking. Since we were interested in terrain classification using single strides, these strides were extracted automatically using local minima from the time series data. For practical applications, automatic segmentation of strides and decision making (for terrain classification) using sequential analysis is indeed a direction of future work.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Classification Accuracy (\%)**} \\ \cline{2-9}  & \multicolumn{3}{c}{**Binary**} & \multicolumn{3}{c}{**Ternary**} & \multicolumn{3}{c}{**Quaternary**} & \multicolumn{3}{c}{**Quinary**} & \multicolumn{3}{c}{**Senary**} \\ \cline{2-9}  & \multicolumn{3}{c}{**Indoor-Outdoor**} & \multicolumn{1}{c}{**Hard-Soft**} & \multicolumn{1}{c}{**Pair-Wise**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \multicolumn{1}{c}{**} & \\ \hline [PERSON] et al. [49] & Robot & - & 90.00 & - & - & \multicolumn{3}{c}{63.75, 62-34} \\  & & \multicolumn{3}{c}{(Indoor)} & - & - & (Indoor), (Outdoor) & - & - \\ \hline [PERSON] et al. (2018) [33] & IMU & 96.3 (Indoor only) & - & - & - & - & - & - \\ \hline [PERSON] et al. (2015) [11] & Vision & - & - & - & 82.0 & - & - & - \\ \hline [PERSON] et al. (2018) [34] & Vision, IMU & - & - & - & - & - & - & 86.0 \\ \hline [PERSON] et al. [48] & Acoustic & - & - & - & - & - & - & 92.00 \\ \hline
**Proposed Approach** & **IMU** & **97.48** & **92.08** & **96.53** & **83.87** & **91.97** & **90.42** & **89.10** \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of classification rate with existing approaches in literature.
## 4 Results and Discussion
### Discussion
#### 4.2.2 Number of Training Samples  
Deep learning models typically require more labeled samples for training. Therefore, it is difficult to implement their applications in remote sensing fields when only a few samples are available. The proposed method (XGB-FR) is comprised two major steps. The first utilizes contrastive learning for the inherent representation of time-series spectral features. Although labeled crop types were used in the proposed type-wise contrastive learning, we could generate many augmented samples for training through type-wise random selection, random channel masking, and random timestamp masking. This step is not dependent on the number of labeled samples and does not need a massive quantity of samples. The second step is classification using XGBoost. It is a traditional machine  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **4-Band** & **VI** & **10-Band** & **4 + VI** & **10 + VI** \\ \hline OA & 0.7318 & 0.7586 & 0.8449 & 0.8413 & 0.8521 \\ F1 & 0.5212 & 0.5903 & 0.7553 & 0.7460 & 0.7654 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy comparison for different combinations of features.  
Figure 8: Accuracy comparison along the dimension of feature representation.  
learning technique that only requires a small number of samples. However, LSTM-based classifiers contain a greater number of parameters, which requires more samples to train LSTM networks. Sample augmentation was, therefore, utilized in our experiments. Thus, the proposed XGB-FR method could learn and exploit the inherent time-series features of crop classification with a small number of training samples.
# A DEM Study on Bearing Behavior of Floating Geosynthetic-Encased Stone Column in Deep Soft Clays
## 1 Introduction
[PERSON] and [PERSON] [42] established a finite element model of the FGESC. Both the improvement factor and stress concentration ratio of the FGESC are analyzed, and the optimum depth of the encasement of the FGESC is three times the diameter of the column. [PERSON] et al. [43] used a centrifuge model to study the deformation characteristics of the FGESC under an embankment. They found that the FGESCs at the edge of the embankment are prone to bending failure. The existing studies mainly focus on the macroscopic level of the FGESC and do not consider the possible large deformation of the FGESC under load. The discrete element method (DEM) can effectively solve the large deformation problem. The DEM is widely used in geotechnical engineering [44; 45; 46; 47], which has advantages in simulating the gravels of the column. [PERSON] [48] and [PERSON] [49] used the discrete element method to simulate the stone columns in soft clay. The aggregates that make up the column are simulated by balls with different characteristics and sizes. However, according to the authors' knowledge, there is no discrete element method to study the FGESC in the deep soft clay. This study built a three-dimensional DEM model for an FGESC in soft clay. The aggregates, geogrid encasement, and surrounding soil were simulated by the DEM. The deformation and bearing capacity of the FGESC in deep soft clay are studied. Moreover, the influence of different lengths of geogrid encasement is analyzed. Meanwhile, a small-scale laboratory test is conducted to verify the reliability of the numerical model.
## 4 Evaluation
### Routing without a Replication Technique  
Most routing protocols do not utilize a replication technique in the sense that custodians are allowed to forward messages whenever relay nodes present better forwarding utilities. Replicating messages in this manner spontaneously increases delivery guarantees. Although simple to implement, the performance of this approach may vary under different network conditions. There is the tendency to generate excess copies of messages originating from nodes with relatively low forwarding utilities. Such cases are more likely in higher node population (since there may be more number of relay nodes with higher forwarding utilities) and degrees of spatial locality (as sources and destinations--i.e., sensors and their gateways--may be located in different local districts). Furthermore, there is no way to reduce or terminate replication of messages that already have enough copies in the network to guarantee delivery. Therefore, the issue when routing without a replication technique is the high risk of excessive transmission overhead and resource expenditure. In Figures 15 and 16 for instance, routing without a replication technique records the highest transmission overhead and average energy consumption. The results show that transmission overhead and average energy consumption is reduced when the number of copies is controlled with a replication technique. Hence, allowing the forwarding utility and encounter opportunities alone to determine the number of message copies that are generated does not ensure acceptable resource consumption in OppNets for sensed data collection.
## 4 A Survey on FPGA-Based Sensor Systems
### Multimedia Applications
The core consists of a camera interface, an image compressor for color filter array (CFA) data and an error corrector encoder. The implementation has been done in a Silicon Blue iCE65 FPGA. The core, clocked at low frequency (24 MHz), consumes 9.6 mW at 1.2 V. The authors, in order to avoid complex image compressor calculations and maintain the power consumption low, have implemented an image compressor that operates directly on the data from the image sensor (CFA) based on an algorithm developed by [PERSON] _et al_. [78]. However, FPGAs are not only used in order to accelerate fixed tasks. Given that partial reconfiguration is supported in several FPGAs, some authors have proposed algorithms in order to optimize image processing in systems based on partial reconfiguration. In this respect, [73] presented a batch processing methodology for images to be used on sensor nodes with partial reconfiguration capabilities. The system is based on a strategy called ECfEE (Energy Efficient and Computation Efficient with Frequency Adaptation). The goal of the strategy is to find an efficient partition strategy to execute the maximum of tasks at a frequency that minimizes the overall power consumption. However, the authors did not provide the energy consumption of the platform application (edge detection and DES encryption). In [74], a video sensor node equipped with energy harvesting is used to implement a task scheduler based on the available energy and past experiences in order to reconfigure the FPGA accordingly. The sensor node is based on an Atmel FPSLIC platform, which consists of an FPGA (AT40K) and 8-bit AVR microcontroller. However, given that the FPGA does not support partial reconfiguration, it must be completely reconfigured, consuming enough energy to deplete the battery rapidly. On the other hand, acceleration of tasks in hardware consumes 4.48 mJ (image thresholding) whereas it required 8.93 mJ in the AVR microcontroller. In this case, the FPGA reconfiguration requires 25 mJ. Moreover, performing edge detection, the AVR microcontroller consumes 28.08 mJ whereas the FPGA only consumes 6.60 mJ. Nevertheless, reconfiguring the FPGA with the edge detection algorithm requires 37.4 mJ. To summarize, FPGAs, either low-power (e.g., IGLOO) or high-performance (Virtex-5), behave better in power consumption than processing on commercial nodes (MICA2) and 8-bit microcontrollers (AVR) [66, 71]. In other cases, power consumption never exceeded 10 mW when performing image compression and processing on IGLOO platforms [72].
## 3 Results and Discussion
### Stage III
#### 3.3.7 Process Optimization  
Parameters that were taken into consideration for optimization were \(R_{p}\), \(a_{w}\) and MC. The optimal spray drying parameters (IAT, IAH, FC) were selected using RSM considering selected criteria. \(R_{p}\) was maximized while MC and \(a_{w}\) were minimized, as these parameters are crucial for the economic aspect of production and powders' stability. As for the remaining powder properties (\(D_{50}\), \(d_{L}\), \(d_{T}\), HR), they were not a crucial factor in optimization as their minimization or maximization is dependent on the character of the producer and its expectations for the final product. Determined by the field of industry, not necessarily the food or biotechnological sector, producers can prioritize different powder properties, such as texture, chemical reactivity, antioxidant activity, bioactive compounds retention over flowability, density, or particle size. All of the terms (IAT, IAH, FC) and powders' parameters (\(R_{p}\), MC, \(a_{w}\)) had the same importance. The Stat-Ease 23.1.8 program that was used to generate the optimal variants found 87 combinations, and the best solution was selected, taking into consideration the highest value of desirability. The IAT of 120 \({}^{\circ}\)C, IAH  
Figure 13: Median particle diameter (\(D_{50}\)) response surface plots for paired factors: (**A**) inlet air temperature (IAT) and inlet air humidity (IAH); (**B**) inlet air temperature and feed solution concentration (FC); (**C**) feed solution concentration (FC) and inlet air humidity (IAH) when the third factor was adjusted to its midpoint.  
of 0.1 g/m\({}^{3}\) and FC of 29.571% were found to be optimal parameters with a desirability of 0.89. According to the predicted model, this variant would have \(R_{p}\) of 78.98%, MC of 5.226%, and \(a_{w}\) of 0.105.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
## 2 Materials and Methods
### Solar Radiation within Jordan and the Cooling Demand  
The potential for successfully applying solar cooling technologies within the country of Jordan is particularly high. As a result of its geographic position, as well as the average number of 650 cooling degree days, solar radiation is provided for most of the year. Furthermore, the peak demand for cooling coincides with elevated solar radiation in the country. Even a small fraction of indirect radiation produces solar radiation that exceeds 2 MWh/m\({}^{2}\) per year, which exceeds the average recorded in the Middle East and North Africa (MENA) region [45].  
As a country in which the average ambient temperatures are exceptionally high, it is anticipated that the demand for cooling and refrigeration technologies will double by 2030 compared to 2015 levels. The systems that are currently used generally do not have good energy efficiency, experience more leaks, and release substances that damage the climate and ozone into the atmosphere. As a result of its elevated levels of solar radiation, it is expected that Jordan will be able to benefit from solar thermal cooling systems that depend on renewable energy and do not directly produce emissions since they use natural refrigerants. The charts shown below reveal the daytime and night-time temperatures (monthly averages).  
There will be increased demand for air conditioning going forward. In addition to economic expansion and rising standards of living, elevated temperatures and heat waves could also be contributory factors. Even today, one out of every two commercial buildings utilises a chiller system. It is expected that commercial chillers will have a lifetime of around 20 years. Therefore, approximately 5% of all installed chillers must be replaced on an annual basis.  
The expanding demand for compression chillers in Jordan provides an opportunity to discontinue the utilisation of older chiller systems and move to more sustainable and ecological options. The installation of alternative air conditioning systems with increased energy efficiency will facilitate the achievement of a considerable reduction in emissions, which will enable the country to comply with the climate agreements it has ratified. Based on the assumption that that solar cooling systems will have a market penetration rate of 25%, it is predicted that a 3.74 Mt CO\({}_{2}\)-e/year reduction in emissions can be accomplished [46].