esa-sceva/satcom-chunk-collection huggingface dataset:
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 4 Experimental Evaluation
### Results and Analysis
#### 4.4.3 Results on MSR-VTT Dataset  
The performance of our method is compared with recent models on MSR-VTT dataset, such as RecNet [24], TDDF [47], RUC-UVA [50], Alto [51], PickNet [46], TDConvED [48] and GRU-EVE [49]. The results are summarized in Table 5. It shows our method is competitive to the state-of-the-art method on MSR-VTT on these four metrics. These results confirm the effectiveness of our proposed channel soft attention and semantic reconstructor for video captioning.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **METEOR** & **BLEU-4** & **CIDEr** & **ROUGE-L** \\ \hline S2 VT [20] & 29.2 & - & - & - \\ SA [21] & 29.6 & 41.9 & 51.6 & - \\ h-RNN [44] & 32.6 & 49.9 & 65.8 & - \\ HrRNE [45] & 33.1 & 43.8 & - & - \\ SCN-LSTM [11] & 33.5 & 51.1 & 77.7 & - \\ PickNet [46] & 33.1 & 46.1 & 76.0 & 69.2 \\ TDDF [47] & 33.3 & 45.8 & 73.0 & 69.7 \\ RecNet [24] & 34.1 & 52.3 & 80.3 & 69.8 \\ TDConvED [48] & 33.8 & **53.3** & 76.4 & - \\ GRU-EVE [49] & 35.0 & 47.9 & 78.1 & 71.5 \\ \hline SeqVLAD [6] & 33.8 & 45.9 & 77.5 & 68.9 \\ SR & 34.8 & 51.3 & 80.4 & 70.5 \\ CSA & 35.1 & 49.9 & **83.7** & 71.5 \\ \hline CSA-SR & **35.6** & 52.2 & 83.4 & **72.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison of our CSA-SR and other state-of-the-art methods on MSVD dataset with BLEU-4, METEOR, CIDEr and ROUGE-L metrics.  
Figure 5: Examples of correct, relevant and irrelevant video captions. CSA-SR denotes the sentence generated by our proposed model based on channel soft attention and semantic reconstructor. GT1, GT2 and GT3 denote the ground-truth sentences.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **METEOR** & **BLEU-4** & **CIDEr** & **ROUGE-L** \\ \hline Alto [51] & 26.9 & 39.8 & 45.7 & 59.8 \\ RUC-UVA [50] & 26.9 & 38.7 & 45.9 & 58.7 \\ TDDF [47] & 27.8 & 37.3 & 43.8 & 59.2 \\ PickNet [46] & 27.2 & 38.9 & 42.1 & 59.5 \\ RecNet [24] & 26.6 & 39.1 & 42.7 & 59.3 \\ TDConvED [48] & 27.5 & 39.5 & 42.8 & - \\ GRU-EVE [49] & **28.4** & 38.3 & 48.1 & 60.7 \\ \hline CSA-SR(ours) & 28.2 & **41.3** & **48.6** & **61.9** \\ \hline \end{tabular}
\end{table}
Table 5: Comparison to related approaches on MSR-VTT dataset with BLEU-4, METEOR, CIDEr and ROUGE-L metrics.
# 6G Technology for Indoor Localization by Deep Learning with Attention Mechanism
## 4 Numerical Results
(**b**) Layout of the 289 (17 \(\times\) 17) receiving antennas within a 10 \(\times\) 10 m environment. Each red dot represents a receiving antenna. From Tables 2 and 3, it is evident that the CNN with the SA mechanism performs better than the CNN with the CA mechanism. From the above experiments, we observe that when both the transmitting and receiving antennas are in LOS waves, the positioning accuracy is significantly high for CNN. Moreover, the CNN with the SA mechanism achieves the best RMSE values regardless of the scenario. The numerical results also indicate that the CNN with the CA mechanism improves upon the baseline CNN in most of the tests conducted; however, its performance varies more depending on the scenario. This research underscores the effectiveness of integrating attention mechanisms into CNN-based localization systems, with the CNN using the SA mechanism showing significant improvements in RMSE values. This suggests that modeling spatial relationships in feature maps is critical for enhancing localization accuracy. Our study confirms the potential of attention-enhanced CNNs for improving 6G localization precision in indoor environments and provides a foundation for further exploration of hybrid attention mechanisms. Based on the tables provided, here is the result analysis comparing the average RMSE (Root Mean Square Error) values for CNN with SA (self-attention) and CNN with CA (channel attention) across different noise levels (10% and 15%). The following values are calculated based on the scenarios provided in each table. CNN with SA demonstrates a significant reduction in RMSE compared to CNN, with an average improvement of approximately 46.8% in the 10% noise case, 55.1% in the 15% noise case, and 54.9% in the 20% noise case. This shows that the addition of self-attention mechanisms enhances the network's ability to accurately estimate positions even in noisy environments, likely by capturing spatial dependencies more effectively. CNN with CA, while not as effective as CNN with SA, also provides notable improvements, with an average reduction in RMSE of 19.4% under 10% noise, 22.7% under 15% noise, and 25.1% in the 20% noise case. Channel attention likely enhances the feature extraction along specific channels, though it appears less effective than SA in spatially complex environments. In summary, incorporating SA yields the highest improvement in RMSE, suggesting that SA's ability to model spatial relationships offers a substantial advantage for positioning accuracy. CA also contributes to error reduction, though to a lesser extent, indicating that hybrid models leveraging both attention types could be a promising future direction for further reducing RMSE.
# La-CTP: Loop-Aware Routing for Energy-Harvesting Wireless Sensor Networks
## 4 Dissection of CTP in EH-WSN
### Experiment T-II
With such an assumption made by CTP, \(S\) considers it unnecessary to frequently broadcast beacons into its neighborhood, and then passively continue to prolong its beacon timer exponentially; Figure 4b shows that the \"S.broadcast\" events are very sparse after time point T-II: 260 s so that \(Y\) does  
Figure 4: Snapshots and beaconing of the CTP in experiment T-II, during which node \(X\) is shut down at the 260 th second. The qualities of link \(l_{XS}\) and \(l_{YS}\) in T-II are the same as that of link \(l_{XS}\) in T-I in terms of dynamics. In the beaconing plots, for the clarity, we only show randomly-sampled 20% of the points after time 260 s for two kinds of beaconing events, “A.broadcast” and “Y.receive”. (**a**) critical snapshots; (**b**) route beaconing. not have ample opportunities to timely learn about the routing status of \(S\), especially when link \(l_{YS}\) is time-varying. **Remark 2**: _Only relieving one local loop is not necessarily helpful to the entire CTP network. The loop in EH-WSN acts like a tornado, not only moving around but also possibly attracting the \"unstably-installed\" surroundings--the close-by nodes without stable routes. Without knowing each other's real situation, however, both the node attracted by loop and the passive potential forwarding node continually shorten their beacon times and then create a sham normality of network operation, which in turn significantly reduces the chances of relieving loops._
# An Improved Routing Approach for Enhancing QoS Performance for D2D Communication in B5G Networks
## 4 Results and Discussion
### Packet Drop  
The packet drop variation of the MBLCR and its counterpart routing schemes during data transmission is shown in Figure 8. It can be seen that the MBLCR has better performance than the counterpart routing schemes. The MP-OLSRv2, MEQSA-OLSRv2 and EMBLR schemes do not utilize the awareness of channel condition with back-off time exponent factor for data packet transmission. Therefore, these routing schemes have a higher collision probability for data packets. In comparison, the MBLCR exploits the back-off time exponent factor value prior to data packets being transmitted to the link, which mitigates data packet collision among the devices and the number of dropped packets. Moreover, the MBLCR limits the flooding of RREQ packets and data packet retransmission when link failure occurs among the devices, minimizing the number of packet drops in the network. As the device's speed increases, data packet drops in the network also increase. The MP-OLSRv2's packet drop (29.62%), the MEQSA-OLSRv2's packet drop (23.14%), and EMBLR packets dropped (10.45%), surpass that of the MBLCR scheme at the 60 m/s device speed.
## 3 Results and Discussion
### Aroma Volatile Compounds  
Fourteen aroma volatile compounds were identified and quantified in the green tea samples brewed five times (Table 3). It must be remembered that these teas were extracted only with hot water and headspace was analyzed, much as tea might be smelled before tasting. Thus, because no chemical extraction was used and no intentional concentration of volatiles was done, the number of compounds is much lower than that reported in most previous studies. There were two aliphatic alcohols ((\(z\))-4-hexen-1-ol and 2-ethyl-1-hexanol), two aromatic alcohols (benzenemethanol and benzeneethanol), two terpene alcohol (linalool and geraniol), one aliphatic aldehyde (nonanal), two aromatic aldehydes (benzaldehyde and phenylacetaldehyde), three ketones (4-methyl-3-penten-2-one,  
Figure 1: Principal component analysis biplot of descriptive sensory analysis of green tea \({}^{\text{a, b}}\). jasmone, and \(\beta\)-ionone), one furan (linal oxide), and two other compounds (1H-indole and 1-ethyl-1H-pyrrole-2-carboxaldehyde). The concentrations of these compounds are shown in Table 4. The concentrations of the volatile compounds in the current study are generally lower than the threshold reported in previous literature. We hypothesize that the reason for this is that green tea generally is consumed at a higher temperature whereas the typical threshold evaluation by previous researchers is conducted at room temperature. Also, the volatile compounds in tea are present in a complex matrix of volatiles whereas reported thresholds are for the simple compounds. The Kovats RI's calculated based on retention time and previous reports by other researchers are shown in Table 3.  
The compounds also do not automatically decrease or increase over time. Although some of this may be sampling error, it also is quite likely that changes in the leaves as the teas are repeatedly dosed with hot water in subsequent brews are likely to affect the release of compounds. For example, nonanal appears to generally increase in most teas suggesting that the leaves are releasing more of the compound as the cells in the leaves are disrupted during subsequent brewing. However, in some teas the nonanal decreases in some later samples suggesting that for those teas most of the nonanal has been released in prior brews. \begin{table}
\begin{tabular}{c c c} \hline
**Compounds** & **Kovats RI Calculated** & **Kovats RI Reported** \\ \hline
4-Methyl-3-penten-2-one & 815.8 & 800 [28] \\ (\(z\))-4-Hexen-1-ol & 873.3 & 868 a  \\ Benzaldehyde & 985.3 & 962 [29] \\
2-Ethyl-1-hexanol & 1047.6 & 1028 [30] \\ Benzenemethanol & 1058.3 & 1033 [31] \\ Benzeneacetaldehyde & 1058.3 & 1045 [30] \\ Linalool oxide & 1094.8 & 1070 [32] \\ Linalool & 1126.5 & 1100 [33] \\ Nonanal & 1122.5 & 1103 [28] \\ Benzeneethanol & 1140.5 & 1113 [34] \\ Geraniol & 1234.0 & 1259 [35] \\
1H-Indole & 1328.3 & 1286 [36] \\ Jasmone & 1421.6 & 1394 [36] \\ \(\beta\)-Ionone & 1405.4 & 1482 [30] \\ \hline \end{tabular}
\end{table} 
Table 3: Fourteen volatile compounds found in six Korean green teas and Kovats retention indices calculation for the compounds.
# Estimating Significant Wave Height from SAR with Long Integration Times
## 2 Estimating Significant Wave Height from SAR with Long Integration Times
### Azimuth Cutoff Calculation  
Azimuth cutoff \(\lambda_{c}\) is the minimum detectable azimuth wavelength by SAR system [17; 18]. In order to obtain the azimuth cutoff \(\lambda_{c}\), it is necessary to calculate the azimuthal autocorrelation function \(ACF(x)\). Then, Gaussian function \(C(x)\) is used to fit \(ACF(x)\).  
In this subsection, the amplitude data needs to be calibrated in the first instance, which includes slant-to-ground conversion, multi-look processing, and range energy normalization [19]. After the above calibrations, the SAR image is transformed into the wavenumber domain by two-dimensional FFT (2D-FFT). \(\lambda_{c}\) is computed by fitting a Gaussian function \(C(x)\) to the \(ACF(x)\). The Gaussian function is written as,  
\[C(x)\sim exp\left(-\left(\frac{\pi x}{\lambda_{c}}\right)^{2}\right) \tag{1}\]  
where \(x\) is the azimuth distance.  
Figure 1: Flow chart of the proposed method.
# Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation) +
## 4 An Alternative Approach: The Hybrid SPSA (Stochastic Perturbation Stochastic Approximation)  
In addition to the SPSA approach, a new one was developed and computationally tested by expanding and adapting a suggestion that was made by [11]. The optimization problem was the same as in Section 3 but the objective function was redefined as follows:  
\[Z(\mathbf{X})=w_{1}\sum_{lt}\left(\left(\sum_{ijr}a_{ijr}^{lt}x_{ijr}\right)-\hat{ y}_{lt}\right)^{2}+w_{2}\sum_{ijr}\Big{(}x_{ijr}-x_{ijr}^{H}\Big{)}^{2}+w_{3} \sum_{k}\left(tt_{k}-\hat{t}_{k}\right)^{2} \tag{8}\]where \(a_{ijr}^{lt}\) is the assignment matrix that is estimated from the DTA [5; 9], which is assumed to be constant on a neighborhood of the origin-to-destination (OD) matrix \(\mathbf{X}\).  
The idea behind this approach is to find the best approximation of the gradient by considering the analytical components of the objective function for which the analytical derivation is computable, where the non-analytical terms are:  
\[\frac{\partial Z}{\partial x_{ijr}}=2w_{2}\sum_{lt}\left(a_{ijr}^{lt}x_{ijr}- \hat{y}_{lt}\right)a_{ijr}^{lt}+2w_{1}\left(x_{ijr}-x_{ijr}^{H}\right)+w_{3} \hat{g}_{SPSA}^{TT}(\mathbf{X})_{ijr} \tag{9}\]  
where \(\hat{g}_{SPSA}^{TT}(\mathbf{X})\) is calculated by using the SPSA (stochastic perturbation stochastic approximation) approach but the other terms are the formal partial derivatives of their analytical forms. This is the gradient used in this hybrid SPSA:  
\[\mathbf{X}^{k+1}=\mathbf{X}^{k}-a_{k}\cdot\
abla Z\left(\mathbf{X}^{k}\right) \tag{10}\]
## 3 Micromanipulation by Vibration-Induced Acoustic Wave  
### Manipulation by SAW  
In-plane micromanipulation is the most prominent feature of SAW-based manipulation. Since surface acoustic waves always propagate along the surface, the manipulation of particles also occurs in the plane. The in-plane micromanipulation environment is usually a liquid-filled channel or chamber that can be integrated on a tiny silicon chip named lab on a chip. Figure 1a,b show the concept of in-planar manipulation. More examples are shown in Figure 2. According to the previous description, we classify these devices into TSAW and SSAW devices. We introduce the features and their relationships in the next two subsections. In-planar manipulating devices are generally operated in a manner that is dominated by ARFs, primarily to achieve transport, sorting, and patterning of large numbers of particles. Table 1 provides some research schemes of in-planar manipulating devices in recent years, where their information is introduced. The particle sizes are mostly on the micron and submicron scales, the driving frequency is larger than 1 MHz, and the power is usually low.  
Figure 2: Typical in-plane manipulating devices proposed by **(a)** [PERSON] et al. [16], which represents manipulation in \(\mathrm{R_{P}}\ll\lambda\) conditions; (Republished with permission of the Royal Society of Chemistry from reference [16]. Copyright © The Royal Society of Chemistry 2010; permission conveyed through Copyright Clearance Center, Inc.) **(b)** [PERSON] et al. [45], using acoustic stream in plane, when the particles’ radii are similar to the wavelength size, the acoustic stream plays a vital part in the devices; and; (Republished with permission of the Royal Society of Chemistry from reference [45]. Copyright © The Royal Society of Chemistry 2017; permission conveyed through Copyright Clearance Center, Inc.) **(c)** [PERSON] et al. [13], which is the most typical model of two-dimensional (2D) SSAW manipulation. The frequency and phase can be changed by chirped Interdigital Transducers (IDTs) to change the field, which means the dynamic field devices. (Republished with permission of the National Academy of Sciences from reference [13] Copyright © National Academy of Sciences 2014); **(d)** A new kind of SAW operation method proposed by [PERSON] et al. [50]. The IDTs with different shapes compared with the other systems are placed at the substrate of the operating plane in an array. (Republished with permission of the Royal Society of Chemistry from reference [50]. Copyright © The Royal Society of Chemistry 2019; permission conveyed through Copyright Clearance Center, Inc.)
## 1 Introduction
The predicted results produced by our HMML-BP network are evaluated via comparative absolute relative difference (ARD), Clarke error grid analysis (EGA), Bland-Altman analysis, and so on. These results show that the factor analysis algorithm has the best performance in our HMML-BP network, with all \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Measurement Site** & **Wavelength** & **Model** & **Results** \\ \hline
[26] & Palm & 1550, 1219, 1314, 1409, & PLSR-SAE 1  & Region A: 97.96\%; R\({}_{\text{p}}\): 0.9216 \\
[29] & Wrist & 850, 950, 1300 & IMPS and mNIRS & MARD: 8.3\%; Region A: 90\% \\
[31] & Finger & 1550 & PSO-2 ANN & Regions A and B: 98.28\% \\
[33] & Finger & 935, 950, 1070 & FPGA 3-ANN & Error: 1.02 mg/dL \\
[35] & Finger & 530, 1500 & PPG and PAV & RMSE: 7.46 \(\pm\) 2.43 mg/dL \\
[36] & Finger & 1550 & SA 4 and NARX & RMSE: 0.72 mmol/L \\ This work & Finger & 850, 940, 1300, 1400, & CORR: 0.85 & CORR: 0.85 \\  & 1550, 1650 & HMML-BP & MARD: 9.98\%; Region A: 100\% \\ \hline \end{tabular}
\end{table} 
Table 1: Comparison of the critical parameters of typical NIR glucose sensors. falling in region A with a mean absolute relative difference (MARD) of around 9.97%, thus satisfying the requirements of human daily glucose sensing.
# The Spotting Distribution of Wildfires
## Appendix. Ember Release, Burning, Flying and Fuel Ignition
### Drag, Gravity and Terminal Velocity  
In order to accurately model the falling of firebrands in the atmosphere, it is important to model the drag experienced by the firebrand. Often, the drag is proportional to the object's speed, or speed squared, depending on the Reynolds number of the flow. For firebrands, it is more accurate to model drag as proportional to the speed squared, since we have a relatively high Reynolds number flow [22].  
Let us denote the drag force by \(D\). Then the speed-squared assumption is generally written as:  
\[D=\frac{1}{2}C_{d}\rho_{a}Av^{2}, \tag{109}\]  
where \(\rho_{a}\) refers to the mass density of the ambient fluid, and the object is assumed to have constant cross-sectional area \(A\). The parameter \(C_{d}\) appearing in Equation (109) is called the drag coefficient. This is a dimensionless number, with values typically ranging between 0.001 and 2, assumed to vary with shape. This constant is typically determined by experiment.  
[PERSON]'s Second Law then provides us with an expression for the terminal velocity. If we focus on the vertical direction, the balancing of the drag force \(D\) with the weight \(W\) implies:  
\[0=\text{Drag}-\text{Weight}=\frac{1}{2}C_{d}\rho_{a}Av^{2}-mg. \tag{110}\]  
Solving for \(v\) in Equation (110), we obtain an expression for the terminal velocity:  
\[v=\sqrt{\frac{2 mg}{C_{D}\rho_{a}A}}. \tag{111}\]  
**Model V1: Constant terminal velocity.** The simplest assumption (V1) is to assume the terminal velocity does not change during transport.  
**Model V2: Experimentally determined model.** Experimental analysis on the Aerospace Corporation's experiments, appearing in [16], revealed that for the cylindrical firebrands in the study the mass \(m(t)\) is related to the terminal velocity \(v(t)\) according to:  
\[v(t)=v(0)\left(\frac{m(t)}{m(0)}\right)^{1/2},\qquad v(0)<0. \tag{112}\]
# Intelligent Resource Orchestration for 5G Edge Infrastructures
## 5 Virtual Resource Load Forecasting
### Forecast Accuracy Measures  
Different error metrics can be used to evaluate the accuracy of forecasting models. Some of the most common error measures are the following:  
* Mean absolute error (MAE): \[MAE=\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|\] (1)
* Mean squared error (MSE): \[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}\] (2)
* Root mean squared error (RMSE): \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}\] (3)  
where \(y_{i}\) are the actual observations (e.g., CPU usage), \(\hat{y}_{i}\) are the predictions made by the forecasting model, and \(n\) is the number of observations.  
In this work, we used the RMSE metric to compare the different forecasting techniques. RMSE represents the standard deviation of the residuals (prediction errors), and it is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data are around the line of best fit.
# Design of Inverted Nano-Cone Arrayed SERS Substrate for Rapid Detection of Pathogens
## 2 Photon Tunneling Inverted Nanocone Design and Numerical Analysis  
The SERS substrate array proposed in this work can address the aforementioned issues. The substrate is made up of transparent cones where the radius decreases along the light path. A high reflective and low loss layer is deposited on the outer surface of the cone. Given the geometry of cones, photons are multi-reflected by the outer layer and thus the number density of photons increases by 4 to 7 times. When this higher concentration of \"photon gas\" reaches the plasmonic metal, the Raman signal enhancement can be as high as \(10^{8}\). The substrate is designed to allow light to come from the bottom of the transparent nanostructures and has no photon blocking problem.  
The incident photons are multi-reflected inside the cavity, known as cavity resonance [22]. This resonance mode can trap the light and concentrate it. In this work, the photon-capturing cavity structure is used to promote the SERS, where the nanorod at the top is going to absorb those concentrated photons. The numerical method used in this paper is Finite-Difference Time-Domain (FDTD) method, done by FDTD Solution (Lumerical Inc., version 1.6, Vancouver, BC, Canada) with 3 nm cubic mesh. In the simulation, 2-D arrays of meshes have been utilized as observing planes, and the SERS refers to the maximum field enhancement in the observing plane. The body of this substrate is made of Polydimethylsiloxane (PDMS), a transparent material that allows light to illuminate from bottom to top. A coating film is made at the surface of the substrate to promote the reflection of photons and will enable \(10^{8}\) EF. This substrate is illustrated as Figure 1. The enhancement effect only happens at the top surface of the cone, whose area is inevitably reduced because of the cavity sloping. Also, the gap between 2 cones should be large enough to locate the magnetic bead and will make the contact area even smaller. To have maximum relative signal output, the pattern and geometry of the substrate is optimized and will be discussed in Section 4 with more details. In the FDTD simulation, the structure is surrounded by water, and materials' optical models are obtained from [PERSON]'s book [23]. The structure is shown in Figure 2. The cone is made of PDMS, and the structure can be obtained by inverse molding of our previous cavity [14]. By pouring liquid PDMS onto the structure and letting it cool down, the microstructure pattern can be transferred into the new structure. The geometric features of the inverse cavity are bottom diameter 500 nm, top diameter 200 nm, height 1000 nm and periodicity 840 nm. This geometry will be optimized in the following section of the designing process.