esa-sceva/satcom-chunk-collection huggingface dataset:
# On IoT-Friendly Skewness Monitoring for Skewness-Aware Online Edge Learning
## 3 Three Versions of Running Skewness and Efficient Calculation Methods
### Performance Comparison among the Three CSS Calculation Methods
#### 4.3.2 Experimental Results  
Inspired by the principle of repeated measures design [47] and by the suggestion of the minimum observation amount [48], we ran the programs ten or more times at each experimental setting. At last, we obtained the three programs' average running times with respect to different workloads, as exemplified and illustrated in Figure 1.  
In general, the running time increases along with the size of the first-type workloads (from \(N=10^{4}\) to \(N=10^{6}\)). As for the second-type workloads (from \(N=10^{6},C=2\) to \(N=10^{6},C=10^{6}\)), it is not surprising that the running time keeps decreasing when skipping more and more skewness calculations. Nevertheless, skipping skewness calculations will have little impact on the running time if the update interval is larger than 100 price records. The reason is the diminishing marginal returns from the reduced workloads. Given the predefined sample size \(N=10^{6}\), when the sample skewness is updated after every 100+ price records, the code within the if-else block will be executed less than \(10^{4}\) times. Our extensive experiments have shown that on our testbed, the program execution at such a workload level is so fast that the corresponding running time would be negligible.  
Specifically, when dealing with the first-type workloads, the method from this research is slightly slower than the GSL method (about 10 ms on average in our tests), while it is clearly faster than the GoL method (almost 200 ms at the workload size \(N=10^{6}\)). When dealing with the second type of workloads, this research shows a clear advantage over both the GSL method and the GoL method. By using the metric _speedup_ to measure the performance difference, our method can be more than 10% times faster than the other two methods in this case, as demonstrated in Figure 1.  
It should be noted that in addition to fitting in the chunk-by-chunk scenario [15], the second workload type is also practical and meaningful when practitioners only need to monitor some _samples_ of CSS values. In practice, the three algorithms can conveniently be modified to update CSS only when receiving request signals. As such, in the case of  
Figure 1: CSS algorithm performance comparison among the GSL method, the GoL method, and this research’s method with respect to different workloads.  
data-intensive skewness monitoring, we can obtain significant performance enhancement even by halving the update frequency (i.e., updating CSS after every two data records). For example, by switching the workload from \(N=10^{6}\) to \(N=10^{6},C=2\) (cf. Figure 1), the GSL method and the GoL method receive about 15% and 27% performance improvements, respectively, while our method sees more than 22% performance improvement.
# Evaluation of AI-Assisted Telemedicine Service Using a Mobile Pet Application
## 2 Background
### Advanced Technology Based on AI
However, telemedicine still has some technical limitations. First of all, when a doctor diagnoses a patient remotely, it is difficult to recognize the psychological state. Moreover, the possibility of false diagnosis cannot be ignored depending on the performance of the mobile camera or image recognition algorithm. In addition, there is a limit to detailed diagnosis with only basic mobile devices. The patient's blood pressure and pulse can be diagnosed with a simple device, but it is difficult to know the nerve response or internal state of the body [39, 40].
# Omnidirectional Jump of a Legged Robot Based on the Behavior Mechanism of a Jumping Spider
## 1 Introduction
Animals in nature predominately have multiple legs. The biped robot, the quadruped robot and the six-legged robot are the main forms of multi-legged robots [23; 24; 25]. [PERSON] et al. [26] have designed a quadruped robot, which can jump even in rugged external environments and can achieve the task of crossing obstacles. Some scholars [27; 28; 29] have proposed a rigid mode of imitating the motion principle of the cricket by studying the robot's ability to jump and kick; the robot can adjust its own dynamic balance while it is jumping. Thus, compared with the single robot, the multi-legged robot has better overall stability. Currently, most research focuses on the single direction jump, especially the vertical and forward jump. A vertical jump analysis based on the hydraulic drive has been proposed [16; 30]. Surmounting ability and jumping efficiency are analyzed, and then the vertical jumping form is optimized. [PERSON] et al. [31] have proposed a new structure of quadruped robot to accomplish the task of vertical jumps or forward jumps. The joints of the robot are driven by hydraulics to meet the requirements of torque, compactness, speed and impact resistance. [PERSON] et al. [32] proposed a quadruped jumping robot, which is based on the servo motor drive. The legs are equipped with gears, springs and other components, and the robot can complete a high jump task through two kinds of movement: spring compression and gear drive mechanism. However, there is no in-depth discussion about the research on omnidirectional jumping. In other words, in the process of jumping, the robot jumps mainly through fixed jumping form, and it only achieves a single direction jump of height and distance. The robot must adjust its posture and jumping direction when it is trying to avoid an obstacle. The initial posture must be adjusted first if the robot is going to change the jumping direction, causing the efficiency to be greatly reduced. Hence, without changing the initial pose of the robot, multi-direction jumps become the key problem to be solved. In this paper, a bionic six-legged robot structure with the ability of omnidirectional jumping is proposed, which is based on the jumping mechanism of jumping spiders. The omnidirectional jumping form has been proposed through observing the jumping form, jumping posture and leg stretch of jumping spiders, which allows it to avoid and cross obstacles in all directions. To verify the rationality of the jumping form proposed in this paper, a series of experiments on a six-legged robot is carried out, and the results show that the proposed multi-direction jumping form has outstanding performance, which provided a good theoretical basis for jumping research.
## 4 Results and Analysis
### Algorithm Convergence  
The convergence of the algorithm can indicate how well the model fits the input data, which can reflect the learning ability of the Pruned-VGG16+Bi-LSTM model on our homemade dataset and is an important metric to judge how good the model is. Improper setting of model parameters will lead to difficulty in the convergence of the model and affect the smooth operation of model identification, so we first analyzed the convergence of the model. In this paper, we recorded the change of the comparison curve between the pruning threshold (20%, 40%, 60%) and the loss function of the original model, and we used the magnitude of the loss function to analyze the convergence of the model, which is shown in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Parameters** & **FLOP** & **Memory (MB)** & **Mem R + W** \\ \hline Baseline & 20.03 & 19.56 & 175.71 & 428.02 \\ Pruned 0.2 & 11.6 & 16.16 & 168.61 & 382.04 \\ Pruned 0.4 & 6.19 & 13.4 & 161.66 & 347.15 \\ Pruned 0.6 & 2.96 & 11.2 & 152.3 & 316.14 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of evaluation indexes of network model complexity.  
As is shown in the chart, as the number of iterations gradually increased, the loss function of the original model decreased faster because of the large number of network parameters, and when the number of iterations reached 20, the loss function changed more slowly and gradually stabilized. This indicates that the model can fit the training data distribution well and the network is optimal at this point. It can be seen that the pruned model gradually converges with the increase of training times, which indicates that the model performs well in lip recognition and there is no unusual problem in the homemade dataset.
## 5 Future Directions and Summary
In particular, several realistic IoT scenarios modeled by the BSC feature memory to some extent and the effect of memory may not be made negligible in a straightforward manner. Therefore, the application of memoryless channels as conducted in this paper to these realistic instances may in general yields different capacity results. In addition, it may be possible to exploit the memory effect in terms of gaining more optimum inner and outer bounds on the DKI capacity, as well as the specification of the encoding and decoding modules; cf. [67; 68; 69; 61] for detailed studies on the BSC models with memory. * Multi-user and multi-antenna systems: The results in this paper study a point-to-point single user system, and might be extended to advanced scenarios proper for the future communication network settings including multiple-input multiple-output channels or multi-user channels, which are deemed to be more relevant in the complex IoT systems. * Finite codeword length coding: The obtained bounds on the K-identification capacity region studied in this paper determine the performance limits of BSC with/without Hamming weight constraint when the codeword length can grow arbitrarily. However, in practical applications, the codeword length is finite, where there is no way to afford significant encoding/decoding delays. As a result, studying the non-asymptotic DKI capacity of the BSC is an interesting direction for future research. **Author Contributions:** Conceptualization, M.J.S. and O.D. ; methodology, M.J.S. ; validation, C.D. and H.B. ; formal analysis, M.J.S. and O.D. ; resources, C.D. and H.B. ; writing--original draft preparation, M.J.S. ; writing--review and editing, M.J.S., O.D., C.D. and H.B. ; visualization, M.J.S. and O.D. ; supervision, H.B. ; project administration, H.B. ; funding acquisition, C.D. and H.B. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the German Federal Ministry of Education and Research (BMBF) within the 6G-life project grant number 16 KISK002 (M.J.S. ), the German Research Foundation (DFG) within the Gottfried Wilhelm Leibniz Prize grant number BO 1734/20-1 (H.B. ), the BMBF within the national initiative for \"Post-Shannon Communication (NewCom)\" with the project \"Basics, Simulation and Demonstration For New Communication Models\" grant number 16 KIS1003K (H.B. ), the BMBF within the national initiative for \"Post-Shannon Communication (NewCom)\" with the project \"Coding Theory and Coding Methods For New Communication Models\" grant number 16 KIS1005 (C.D. ), the DFG within Germany's Excellence Strategy grant number EXC-2111--390814868 and EXC-2092 CASA--390781972 (H.B. ), the BMBF grant number 16 KIS1005 (C.D.) and the DFGProject grant number DE1915/2-1 (C.D. ), the BMBF in the program of \"Souveran. Digital. Vernetzt.\", joint project 6G-life, project identification grant number 16 KISK002. **Data Availability Statement:** The data presented in this study are available in this article. **Conflicts of Interest:** The authors declare no conflicts of interest.
# Three-Band Spectral Camera Structure Design Based on the Topology Optimization Method
## 5 Three-Band Multispectral Camera Testing  
The stable imaging results for visible light, mid-wave infrared, and long-wave infrared are shown in Figure 15. As observed in Figure 15a, in the visible light band, the camera can clearly detect the outline of a warship at a distance of 18.5 km. Figure 15b,c demonstrate that, under nighttime and foggy conditions at a distance of 18.5 km, the mid-wave and long-wave infrared images effectively distinguish the warship's outline from the background. Through practical testing and verification, the common-aperture three-band system successfully achieved the required detection range of over 18 km for all three bands.  
Figure 15: _Cont_.
## 5 LA Bidding Model
### The Process of Optimal Bidding Decision-Making  
Based on the LA bidding model, the LA selects residential groups, determines the incentive cost, the bidding quantity, and formulates the optimal bidding strategy. The specific process is as follows:  
1. Divide residents into different groups according to their different awareness level.
2. Use the fuzzy inference system to evaluate the participation willingness of each group of residents, and rank the residents from high to low according to their participation willingness.  
Select the residents with high willingness to participate, and calculate the minimum incentive level that meets the constraint of response reliability.
4 Add new residents with low willingness to participate, increase the response resident size, and recalculate the minimum incentive level that meets the constraint of response reliability.
5 Obtain the lowest incentive levels under different response groups to form a set of bidding strategies.
6 In the strategy set, choose the strategy with the highest net income and participate in the final bidding.
# Resistance of \(L\). _monocytogenes_ and \(S\). Typhimurium
## 4 Conclusions  
The goal of this research was to investigate the influence of the biofilm age on (i) the total biofilm mass of the untreated biofilms, (ii) the cell density of the biofilms prior to CAP treatment, (iii) the CAP efficacy for biofilm inactivation following 10 min of treatment, and (iv) the CAP inactivation kinetics. For \(L\). _monocytogenes_, the total biofilm mass of the untreated biofilms increased following seven and 10 days of incubation (without medium refreshment), indicating an increased production of EPS matrix. For \(S\). Typhimurium, on the other hand, the biofilm mass was independent of the biofilm age. For both biofilm forming species, the cell density prior to CAP treatment decreased following seven and 10 days of incubation, which is most likely due to starvation and/or waste accumulation. In addition, for both species, the biofilms became more resistant towards CAP treatment as the biofilm age increased up to seven days. For \(L\). _monocytogenes_, this was the result of the increased amount of EPS matrix, while for S. Typhimurium, it was hypothesized that this might be the result of collapsing of the biofilm structure. Finally, the biofilm age had an influence on the CAP inactivation kinetics for \(L\). _monocytogenes_, i.e., the increased biofilm age (seven days) resulted in a slower inactivation rate when compared to the one-day old reference biofilm. Nevertheless, following a treatment of 30 min, similar residual cell densities and log-reductions were obtained for both tested \(L\). _monocytogenes_ biofilm ages. For the \(S\). Typhimurium biofilms, the biofilm age had only an influence on the final log-reduction following 30 min of CAP treatment, which was probably the result of the collapsed biofilm structure.  
Individual contributions of each of the authors to the research are the following: conceptualization, M.G., C.S., M.B. and J.V.I.; methodology, M.G.; software, M.G.; validation, M.G.; formal analysis, M.G.; investigation, M.G. and B.E.; resources, J.L.W. and J.V.I; data curation, M.G.; writing--original draft preparation, M.G.; writing--review and editing, M.G., C.S., M.B., J.L.W. and J.V.I; visualization, M.G.; supervision, M.G., C.S., M.B. and J.V.I; project administration, M.G.; funding acquisition, J.V.I.  
This work was supported by project C24/18/046 of the KU Leuven Research Council and by the Fund for Scientific Research-flanders, project G.0863.18.  
The authors declare no conflict of interest.
## 5 Results and Discussion  
A complete set of data concerning the mass fractions of all 38 elements in the investigated samples are provided in the Mendeley repository [36], while Table 1 reproduces only the mass fractions' average values together with corresponding values of the UCC [17], NASC [18], PAAS [8], SMWR [19], as well as AWSS [20].  
In interpreting compositional data of river sediments and adjacent soils, the geochemistry of local formations usually plays a determinant role. Additionally, due to a variable mobility, different mineral fractions are mixed and deposited along river beds depending on water speed, grain sizes and specific gravity. In this regard, Figure 2 illustrates the mass fractions of investigated elements in sediments normalized to UCC (Figure 2) [16], and in soils normalized to SMWR (Figure 2) [19]. The UCC [17] was chosen as an appropriate descriptor of the continental crust, while SMWR [19] drew on the lithogenesis theory [39], according to which, the mobilization of suspended and dissolved materials in drainage basins represent the initial stage of pedogenesis.  
Here, a significant fraction of chemical elements including highly mobile alkaline and calc-alkaline ones were transported as suspended sediments and deposited along river beds and banks. Therefore, other descriptors such as NASC [17], AWSS [19] or PAAS [8] showed to be almost identical.  
According to Table 1 and [36], the elemental composition of sediments and soils appears, excepting chalcophile Zn, As, Sb ad Hg, closer to UCC [17] and SMWR [19]. After a careful analysis of the experimental data presented in [36], mass fractions of potentially contaminating elements As, Sb and Hg showed increased values only for samples collected in the vicinity of mines and adults located on the Djidjikrut, Kanchoch, Chore and Mogiyon tributaries (Figures 1a, 2 and 11).  
This finding is well evidenced in Figure 3a, where the As, Sb and Hg standard deviations appear disproportionately high due to the fact that in a very few places, such as mines or mine adits, their mass fractions exceeded the UCC [17] one by two to three orders of magnitude. Excepting these, the average mass fractions of investigated elements were shown to be relatively closer in both sediments and soils, as suggested by ANOVA analysis (Table 2) and by both biplots reproduced in Figure 3. In addition, a Spearman's correlation coefficient of 0.83 sustains this observation (Figure 3b).  
Figure 2: Box-plots illustrating the distribution of investigated elements in Zarafshon sediments (**lower**); and soils (**upper**). All mass fractions of soils normalized to UCC [17] and sediments to SMWR [19]. The values corresponding to As and Hg are divided by 10 and by 100 in the case of Sb.  
Figure 3a evidenced also that the ratio between the same elements in sediments and soil slightly varies around one, excepting the above mentioned PCE As, Sb and Hg and, to a lesser extent, Na of which the average mass fraction was slightly higher in sediments than in soils, as will be discussed in the next section.
# A Metal-Insulator-Metal Deep Subwavelength Cavity Based on Cutoff Frequency Modulation
## 4 Conclusions  
In summary, we proposed new plasmonic cavities based on modulating the cutoff frequency of MIM first-order waveguide modes. The first-order waveguide mode has a cutoff frequency that depends on the waveguide width, refractive index, and gap size of the dielectric layer. Light (with a wavelength of 1550 nm) can be confined in a cavity region with a deep subwavelength physical volume (0.00013 \(\lambda_{0}\)\({}^{3}\)-0.00055 \(\lambda_{0}\)\({}^{3}\)) in cavities that are modulated by changing the width, index, and gap size. The second-order cavity mode shows higher \(Q\) factors than the fundamental cavity mode because radiation along the intensity node is suppressed by multipole cancellation.  
The proposed ultrasmall cavity is a good candidate for low-threshold lasers [27], ultrafast optical switches [28], and as the light source for quantum optics [6] due to its extremely small mode size of 1/10,000 \(\lambda_{0}\)\({}^{3}\). In addition, its waveguide-based design allows for efficient light coupling with integrated detectors [29] or other photonic devices [8; 30; 31; 32]. The light coupling can be achieved by using a tapered coupler from a dielectric waveguide [33] or a dielectric air slot waveguide [34]. The cutoff frequency mechanism can be widely applied to build efficient and strong mirror waveguides for various optical components operating at any wavelengths. Since the mirrors of the proposed cavity have adjustable reflectivity, unidirectional emission can be easily achievable. Usually, although the control of directionality is highly demanded for an efficient light source in quantum optics and photonic circuits, it is extremely difficult to control the directionality in the deep subwavelength cavity. The cavity mode can be directly excited by the odd waveguide mode from the dielectric waveguide with an external light source or internal light emitter such as quantum dots. The continuation of this study will involve modulating the nanocavity width, dielectric index, dielectric thickness, and structure to miniaturize key optical devices into deep subwavelength-scaled ones in integrated devices and nano-emitters.  
This research was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. NRF-2016R1C1B2007007) and in part by the Chung-Ang University Research Scholarship Grants in 2016.  
[PERSON] conceived and designed the whole simulation and concept; [PERSON] and [PERSON] performed the simulations and analyzed the data; [PERSON], [PERSON], and [PERSON] drafted and revised the manuscript.  
The authors declare no conflicts of interest.
## 2 Related Work
### Pre-Trained Language Model (PTLM)  
Natural language processing (NLP) research with deep learning is widely studied in various fields [12,13]. Recently, the active undertaking of research using a Pre-Trained Language Model (PTLM) has been carried out. PTLM represents a language model (LM) that has been trained with a large dataset to learn an appropriate way to represent language. In pre-training, the LM performs unsupervised learning using a large corpus without labeling. One of the most commonly used PTLMs, BERT [6] was pre-trained using the Masked Language Model (MLM) and Next Sentence Prediction (NSP) tasks. After pre-training, the LM is fine-tuned with a specific dataset to perform the corresponding task. This method has the advantage of providing good performance even with relatively small amounts of data. In the case of BERT, it achieved state-of-the-art on the GLUE benchmark [14], SQuAD 1.1 [2], SQuAD 2.0 [15], and SWAG [16].  
\begin{table}
\begin{tabular}{l} \hline
**Dialogue** \\ [PERSON]: Fongive my mess. We had a party last night. A lot of people came over and they all brought food and drinks. \\ [PERSON]: Yeah, I can tell. Well, I think it’s pretty obvious what you’ll be doing today. \\ \hline
**Question** \\ What will the woman probably do today? \\ \hline
**Candidate Answer** \\ (a) Get more food and drinks. \\ (b) Make a thorough cleaning. (\(\forall\)) \\ (c) Ask her friends to come over. \\ \hline \end{tabular}
\end{table}
Table 1: A sample question in DREAM dataset. To answer this question, commonsense is required to explain the necessity of thorough cleaning after a party.