esa-sceva/satcom-chunk-collection huggingface dataset:
# Segmenting Individual Tree from TLS Point Clouds Using Improved DBSCAN
## 2 Methods and Materials
### Evaluation Methods  
We compared the segmented trees with the reference trees from overall segmentation, trunk detection and small tree segmentation. There are three types in the segmentation results. If a tree is correctly segmented from the TLS data, it is called true positive (TP). If a tree is not split from the TLS data but is assigned to nearby trees, it is called false negative (FN). If a tree does not actually exist but is separated from the point clouds, it is called false positive (FP). _TP_, _FN_, and _FP_ represent correct segmentation, under-segmentation, and over-segmentation, respectively. The higher _TP_ value, the lower _FN_ and _FP_ value, the higher accuracy. To evaluate the segmentation method, _recall_, _precision_, and F1-score were calculated using the following Equation [28].  
\[recall=\frac{TP}{TP+FN}\times 100\%, \tag{10}\]  
\[precision=\frac{TP}{TP+FP}\times 100\%, \tag{11}\]  
\[\text{F1-score}=\frac{2\ *\ recall\ *\ precision}{recall+precision}, \tag{12}\]  
_Recall_ represents the percentage of correct segmentation in the actual number. _Precision_ represents the percentage of correct segmentation in the detected number. F1-score comprehensively considers the overall accuracy of under-segmentation and over-segmentation errors. _Recall_ and _precision_ range from 0 to 100%. F1-score ranges from 0 to 1. The higher _recall_ and _precision_ are, the higher F1-score is. If all trees are correctly divided, then _recall_ and _precision_ are equal to 100% and F1-score is equal to 1.
## 3 Proposed Approach
### Optimal Number of Clusters  
We determined the optimal number of clusters using the elbow method [60]. It can fit the model with a number of clusters \(k\) ranging from 2 to 14. As a quality measure, \"distortion\" is used, which measures the sum of squared distances from each point to its center. Figure 3 shows the distortion score for several values of \(k\). We also plot the training runtime (in seconds) to see the trade-off between the distortion score and the runtime. We use the knee point detection algorithm (KPDA) [60] to determine the optimal number of clusters. Note that, based on results shown in Figure 3, the perfect number of clusters is 4. However, we choose \(k=5\) for all hard clustering approaches because we have five different variants in our data (see Table 1). The KPDA chose 4 as the best initial number of clusters due to the Beta variant not being well-represented in the data (see Table 1). However, to give a fair chance to the Beta variant to form its own cluster, we choose 5 as the number of clusters.
## 3 Results and Discussion
### Comparison with Previous Plasma Catalytic Studies and Other Conversion Technologies
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Catalyst** & **Parameters** & **CH4 Conv. (\%)** & **CO2 Conv. (\%)** & **Remarks** & **Ref.** \\ \hline SiO2 & 6 g, 9.25 kV & 17.0 & 10.9 & The plasma-assisted catalytic biogas conversion was studied for value-added liquid chemicals; 0\% liquid yield was achieved. & [14] \\ Ag/CZSM5 & 6 g, 9.25 kV & 18.7 & 13.6 & The plasma-assisted catalytic biogas conversion was studied for value-added liquid chemicals; 33.1\% liquid yield was achieved. & [14] \\ Ir/CZSM5 & 6 g, 9.25 kV & 21.2 & 12.8 & was achieved. & [14] \\ Ni/Al2O3 & 0.25 g, 50 W & 18 & 13 & The plasma-assisted catalytic biogas conversion was studied for value-added liquid chemicals; 25.0\% liquid yield was achieved. & [14] \\ BaTiO3 & 0.2 g, 86 W & 28 & 17 & The plasma-assisted catalytic DRM was investigated for the discharge behavior characteristics. The Ni-dispersed catalyst contributes to the expansion of discharges and enhancement of DRM activity with enhanced product distribution. & While BaTiO3 led to a higher hydrogen (H2) yield, Ni/SiO2 and Ni-Fe/SiO2 catalysts demonstrated greater selectivity for carbon monoxide (CO), indicating the enhancement of the reverse water-gas shift (RWGS) reaction. The catalyst was packed within the 100 cm3 discharge volume of the reactor used in this investigation. & This work was achieved. & \\
5 Ni-5 Fe/Al2O3 & 0.5 g, 23 W & 34.8 & 19.7 & The plasma-assisted catalytic biogas conversion was studied for value-added liquid chemicals; 38.6\% liquid yield was achieved. & This work was achieved. \\ \hline \end{tabular}
\end{table} 
Table 5: Comparative study for different catalysts in plasma-assisted biogas conversion. consumption but also allows for rapid startup, operational flexibility, and easier reactor design. When combined with a catalyst, NTP creates a synergistic environment where plasma-generated species enhance reaction rates, while the catalyst promotes selectivity and stability. This hybrid system offers improved performance over stand-alone thermal or photocatalytic processes and presents a promising pathway for efficient, low-temperature biogas conversion. Nonetheless, further work is needed to optimize catalyst design and scale-up strategies to fully harness the potential of NTP technologies.
## Appendix A Proof of Theorem 1  
In the case of silencing \(k\) RRHs, interference reduces to \(I_{j}=\sum\limits_{j=k+1}^{N}h_{j}d^{-\alpha}\). Then, we proceed as follows from (2)  
\[\begin{array}{ll}F_{\text{SIR}_{5}}(\theta|\text{q})&=\mathbb{P}\Big{(}\text {SIR}_{5}<\theta\Big{)}=\mathbb{P}\Big{(}h_{0}<\theta\delta^{\alpha}\text{q} \Big{)}\\ &=1-\exp\Big{(}-\theta\delta^{\alpha}\text{q}\Big{)},\end{array} \tag{1}\]where \(\text{q}=\sum\limits_{j=k+1}^{N}h_{j}\). The last step comes from using the CDF of \(h_{0}\). Next, we decondition (A1) of q using the fact that q follows a gamma distribution [49] with PDF \(\text{f}_{\text{Q}}(\text{q})=\frac{\text{q}^{(N-k-1)}\text{e}^{-\text{q}}}{(N-1 )!}\). Thus, we proceed to calculate the CDF as  
\[\begin{array}{rl}F_{\text{SIR}_{\text{S}}}(\theta)&=\int\limits_{0}^{\infty}F_ {\text{SIR}_{\text{S}}}(\theta|\text{q})\text{f}_{\text{Q}}(\text{q})\text{d} \text{q}\\ &\stackrel{{(a)}}{{=}}1-\int\limits_{0}^{\infty}\exp\bigg{(}- \text{q}\beta^{a}\theta\bigg{)}\frac{\text{q}^{N-k-1}}{(N-k-1)!}\text{d}\text {q}\\ &\stackrel{{(b)}}{{=}}1-\bigg{(}\frac{1}{1+\beta^{a}\theta}\bigg{)} ^{N-k}\frac{1}{(N-1)!}\int\limits_{0}^{\infty}\text{e}^{-x}x^{N-1}\text{d}x \\ &\stackrel{{(c)}}{{=}}1-\bigg{(}1+\beta^{a}\theta\bigg{)}^{k-N}, \end{array}\] (A2)  
where \((a)\) comes from substituting the respective CDFs. Also, we substitute the exponent of the exponential in \((a)\) with \(x\) follows for integral limit as \(\text{q}\to\infty\) then \(x\to\infty\). Similarly, when \(\text{q}\to 0\) then \(x\to 0\) where \(\text{d}\text{q}=\frac{dx}{1+\beta\theta}\) and the following equation \((a)\) reduced to form \((b)\). The integral in \((b)\) is the definition of gamma function and reduces \(\Gamma(N)=(N-1)!\) since \(N\in\mathbb{N}^{*}\), which simplifies into \((c)\), that is the closed form solution for silencing scheme concluding the proof.
## 5 Evaluation
### Performance Benchmark  
A dedicated multi-threaded client application was developed for performance benchmarking. The application acts as an information provider that constantly publishes information to the IIP with a predefined time interval for a certain period. Another client application, which subscribes to an information channel that belongs to the information provider application, was developed to receive notifications from the IIP, playing the role of information consumer. The main aim of this evaluation was to compare the performance of the standalone IIP prototype with the clustered version in terms of one complete flow of publication and notification average time. In addition, comparisons between the inclusion of the security and privacy scheme and also without it were conducted.  
All experiments were carried out in a laboratory environment, where all hosts (machines) were deployed in an isolated network with a switch as the connecting point (there is no connection to an external network, such as the Internet). Experiments in a standalone set-up involve three hosts: one for the information provider application, one for the IIP (all-in-one, single point of failure) and one for the information consumer application. Experiments in a clustered set-up make use of nine hosts: one for the information provider application, seven for the IIP (as described earlier) and one for the information consumer application. All hosts that are used for deploying the IIP (both the standalone version and its clustered counterpart) have similar specifications (_i.e._, Intel Core 2 Duo 2.4 GHz processor, 8 GB RAM running Linux Ubuntu 12.04 LTS). Another two hosts that deploy the information provider and consumer applications also have similar specifications (_i.e._, Intel Core 2 Duo 2.4 GHz processor, 4 GB RAM running Linux Ubuntu 12.04 LTS). All application servers' configurations are kept similar with almost no optimization from their default settings to ensure fairness in the comparisons.
# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 3 Proposed Methods
### Temperature Inversion Classification
The design of our CNN architecture is as follows. The input layer receives data instances of 5 \(\times\) 24 images which is composed of five selected features collected in straight 24 h. The five features are the temperature, relative humidity, wind speed, precipitation, and the previous day PM2.5 concentrations. To accommodate monthly relative range, the features are encoded into numerical codes as 1 (low range), 2 (middle range), and 3 (high  
Figure 6: The learned decision tree from the collected data in 2016.  
range) determined by k-means. Following the input layer, two repetitions of convolution layer and pooling layer are deployed. In the convolution layer, six 3 \(\times\) 3 convolution kernels are applied to generate six feature maps with the ReLU activation function. Each feature map then goes through the max-pooling layer to extract the salient features. The same convolution-pooling architecture is constructed again to extract more generalized feature maps. Next, the feature maps are flattened to form a one-dimensional vector which is fed into the fully-connected network to produce the final output classification. We trained the constructed CNN with the entire 2016 data whose 363 meteorological records were randomly assigned into training set and validation set respecting the size ratio of 8:2. Figure 7 shows the learning curve of the two sets. We observed that the classification accuracy of the training set improved as the number of epochs increased, while the classification accuracy of the validation set stopped improving after 25 epochs. In order to prevent over-fitting to the training set, the training of our CNN model terminated at the 25 th epoch.
# Low-Power 8T SRAM Compute-in-Memory Macro for Edge AI Processors
###### Abstract  
The traditional Von Neumann architecture creates bottlenecks due to data movement. The compute-in-memory (CIM) architecture performs computations within memory bit-cell arrays, enhancing computational performance. Edge devices utilizing artificial intelligence (AI) address real-time problems and have established themselves as groundbreaking technology. The 8T structure proposed in this paper has strengths over other existing structures in that it better withstands environmental changes within the SRAM and consumes lower power during memory operation. This structure minimizes reliance on complex ADCs, instead utilizing a simplified voltage differential approach for multiply-and-accumulate (MAC) operations, which enhances both power efficiency and stability. Based on these strengths, it can achieve higher battery efficiency in AI edge devices and improve system performance. The proposed integrated circuit was simulated in a 90 nm CMOS process and operated on a 1 V supply voltage.  
CMOS; integrated circuit; memory; CIM; static random access memory +
Footnote †: journal: Applied sciences
## 5 Conclusions  
The presented study investigated the influence of different mix designs to determine the optimal parameters for the improvement of BFRPC properties, and the effects of basalt fibers on the damage features of concrete was simultaneously discussed. For this purpose, a series of mechanical tests were carried out and AE technique was used to monitor the failure process of BFRPC under flexural stress. The conclusions are as follows:  
1. The addition of basalt fibers improved significantly the flexural strength without any negative impact on the workability performance. A slightly lower water to binder ratio had to be used to ensure the strength of materials. The appropriate addition of quartz sand formed a stable micro-support framework and improved the distribution of binders within the matrix. The expected values of quartz sand to binder ratio and the silica fume to cement ratio are 0.4 and 1.3, respectively.
2. The activities of AE signals were proportional to the fiber content. The damage fracture process of BFRPC under flexural stress could be accurately divided based on the slope variability of cumulative AE energy. The value of AE amplitude also symbolized the switch of damage stage, which could be used as an early warning of fracture.  
Figure 11: Cumulative RA and average frequency: (**a**) basalt fiber content of 6 kg/m\({}^{3}\); (**b**) basalt fiber content of 10 kg/m\({}^{3}\); and (**c**) basalt fiber content of 14 kg/m\({}^{3}\).  
3. The fracture mode of BFRPC under flexural test was gradually dominated by shear with the increase of basalt fiber content. This demonstrated the improvement of the fibers against the weak tensile properties and toughness of concrete. It was shown that AE technology can recognize the fracture patterns of specimens by average frequency and RA value, which could assist the damage assessment.  
Further application of AE technology in fiber reinforced UHPC could be achieved by, for instance, the damage source location in the matrix through the use of more AE sensors.  
[PERSON] conceived of and designed the experiments. [PERSON] analyzed the data and wrote the paper. [PERSON] investigated the experiments. [PERSON] audited the content. [PERSON] reviewed the content.  
This research was funded by the Industrial Technology Research & Development Special Project of Jilin Province (2018C042-1); the Transportation Science and Technology Program of Jilin Province (2018-1-9); the Science Technology Development Program of Jilin Province (20180201026 SF); and the National Natural Science Foundation of China (11702108).  
The authors would like to express their appreciation for the anonymous reviewers for their constructive suggestions and comments to improve the quality of the paper.  
The authors declare no conflict of interest.
# Relative Intensity Noise of Hybrid Mode-Locked Bound Soliton Fiber Laser: Theory and Experiment
## 4 Discussion  
In the present work, the RIN noise modelling as well as the RIN spectrum calculation for bound-pulse mode-locked lasers are theoretically investigated by the linearized backpropagation method for the first time. The developed general method can provide an efficient way to calculate the mode-locked laser noises without the need of assuming a particular optical pulse shape, which enable us to deal with more general laser noise problems. As an example, the intensity noise properties of an environmentally-stable hybrid mode-locked Er-doped fiber laser are studied both experimentally and theoretically. The laser can be operated at a 10 GHz repetition rate, with either the single- or bound-soliton output, depending on the lasing power level. With this unique laser setup, we are able to compare the RIN noises of the single- or bound-soliton mode-locking states more precisely. The theoretical calculation predicts that the RIN noises for the bound-soliton states can be few dB lower than the RIN noises for the single-soliton states of the same laser. The experimental measurements confirm this prediction, even though currently, the noise reduction factor is not very large. The results suggest the new possibility of generating high repetition rate optical bound-pulses with lower relative intensity noises, and also demonstrate that the developed theoretical modelling based on the linearized backpropagation approach is a very powerful method for studying general mode-locked laser noise problems.  
Figure 6: The calculated RIN spectra for (**a**) single-soliton state at \(g_{0}=2.5\) and (**b**) bound-soliton state at \(g_{0}=5\) within the span from 1 kHz to 2.5 MHz. (**c**) The integrated intensity noise variance versus \(g_{0}\) within the same span.  
**Author Contributions:** [PERSON] conceived of and designed the study; [PERSON]-[PERSON] carried out the experiment and theoretical simulation; [PERSON]-[PERSON] wrote the draft; [PERSON] reviewed and edited the paper.  
**Funding:** The work was supported by the Ministry of Science and Technology, Taiwan, under the project numbers MOST 106-2221-E-009-102-MY3 and 107-2218-E-009 -056.  
**Acknowledgments:** The work was supported by the Ministry of Science and Technology, Taiwan.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Collision-Aware Routing Using Multi-Objective Seagull Optimization Algorithm for WSN-Based IoT
## 6 Results and Discussion
### Performance Based on Network Lifetime  
The performance analysis of network lifetime with CAR-MOSOA and existing methods (FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33]) is illustrated in Figure 7. From Figure 7, we can see that CAR-MOSOA achieves better results in relation to the existing methods. In the existing FDEAM [30], EOMR [31], TSGWO [32], and CoCoA [33] methods, once node count rises in the network, additional sensor nodes begin to transfer the data packets indiscriminately. Moreover, the node can expire at a certain time period. In the CAR-MOSOA method, the ideal node is designated to transfer the packets, which causes a rise in battery life and network lifetime. Table 5 shows the comparative analysis of the packet delivery ratio performance. From Table 5, it is seen that the proposed CAR-MOSOA increases the network lifetime at a specified node when compared with existing techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{5}{c}{**Packet Delivery Ratio (\%)**} & \multirow{2}{*}{**Proposed**} \\
**Nodes** & & & & **CAR-MOSOA** \\ \hline
100 & 80 & 70 & 73 & 67 & 83 \\
150 & 86 & 84 & 85 & 80 & 89 \\
200 & 86 & 85 & 87 & 82 & 91 \\
250 & 90 & 87 & 89 & 81 & 93 \\
300 & 92 & 88 & 89 & 84 & 93 \\
350 & 92 & 88 & 90 & 87 & 94 \\
400 & 94 & 89 & 92 & 88 & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of packet delivery ratio.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of**} & \multicolumn{5}{c}{**Network Lifetime (s)**} & \multirow{2}{*}{**Proposed**} \\
**Nodes** & & & & **CAR-MOSOA** \\ \hline
50 & 1245 & 1150 & 1220 & 1103 & 1321 \\
100 & 1170 & 1200 & 1208 & 1093 & 1319 \\
150 & 1138 & 1070 & 1109 & 984 & 1251 \\
200 & 1072 & 990 & 1010 & 935 & 1110 \\
250 & 1007 & 920 & 994 & 847 & 1064 \\
300 & 968 & 860 & 951 & 811 & 981 \\
350 & 927 & 853 & 915 & 803 & 973 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of network lifetime.  
Figure 7: Performance based on network lifetime.  
From all the analyses, the proposed CAR-MOSOA shows that better QoS in WSN is attained with a multi-tier framework. The CAR-MOSOA improves the performance of the network and its QoS by executing a routing procedure to transfer the packets from a base station to the endpoint by choosing the ideal route. The communication cost considered in the clustering and routing process is used to minimize the energy consumption of the network. The packet delivery is improved by generating the collision-aware routing path using the MOSOA. Since, an optimal collision-aware transmission path is discovered by considering the queue length, link quality, communication cost, and residual energy in MOSOA-based routing process. Moreover, the PDR is also improved by avoiding node failure based on the residual energy considered in the CH selection. Therefore, the developed CAR-MOSOA obtains a higher PDR while achieving a greater network lifetime in the WSN-based IoT.
Motor competence (MC) could also act as a mediator in the relationship of PA with cognition. The fact that MC is thought to contribute to goal-directed human movements [41] could offer ground for connecting PA with the cognitive domain, particularly with EFs [42; 43; 44; 45]. [PERSON] et al. [40] concluded that the synergistic physiological integration among all fitness components would enhance adolescents' EFs. Interestingly, according to [PERSON] et al. [46], EFs seem to be a significant mediator (indirect effects) between primary school children's motor abilities and academic achievement. Neuroimaging studies have shown that during a cognitive task the activation in cerebellum and prefrontal cortex is increased in both of them [47; 48]. In line with this research, [PERSON] et al. [49] linked MC and working memory in children 10-12 years. There is large piecemeal evidence [42; 50] or full mediational but cross-sectional evidence [46] supporting the important role of MC for the PA-cognition relationship in children. Nevertheless, to our knowledge, only two interventional studies addressed mediation of PA effects on EFs and non-executive cognition through MC with a true statistical mediation approach [51; 52]. [PERSON] et al. [51] found that gross motor skills (specifically object control skills) mediated the executive function outcomes (specifically: inhibition) of an enriched PA program for primary school children. Similarly, [PERSON] et al. [52] found that the cognitive non-executive outcomes (specifically: intelligence and spatial cognition) of an enriched and multicomponent after-school recreation PA program tailored for primary school children were partially mediated by MC. However, MC (named'motor fitness' by [PERSON] et al. [52]) was assessed only by shuttle run, thus limiting the MC assessment to children's speed and agility skills. To conclude, there is convincing evidence for the positive health effects of PA and physical fitness in obese/overweight children and adolescents [53; 54] and also emerging evidence for positive cognitive effects of PA in this population [55]. However, the relationship between PA and MC in children and adolescents has not been yet well established [56], while research on PA, MC, and weight status and whether they mediate EFs, is scarce. This could be attributed to the observed change in consistency and strength of this relationship over the developmental age. EFs develop at early years (3-6) and continue to grow throughout childhood, adolescence, and young adulthood [15]. In pursuit of other possible mediators of the PA-EFs relationship in childhood, the purpose of the present study is to consider the distinctive mediating role that children's weight status and MC may hold.