esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Gradient Warming on Carbon and Water Fluxes in Zoige Plateau Peatland
## 3 Results
### Effect of Gradient Warming on Water Fluxes  
From June to September, E, T, and ET fluctuated within the ranges of 0.16-0.46 mmol m\({}^{-2}\)s\({}^{-1}\), 0.20-1.46 mmol m\({}^{-2}\)s\({}^{-1}\), and 0.36-1.73 mmol m\({}^{-2}\)s\({}^{-1}\), respectively, and no consistent trend was observed (Figure 3, Table 2). T and ET were 36.98% and 23.71% higher in the TL treatment, respectively, than in the CK, and these differences were significant (\(p<0.01\)). In contrast, the TH treatment did not significantly alter T and ET (\(p>0.05\), Table 3). The average E was lower in both warming treatments than in the control, but the effect was not significant (\(p>0.05\), Table 3). Significant interactions of time and treatment with E, T, and ET were observed. As  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**Time**} & \multicolumn{3}{c}{**Treatment**} & \multicolumn{3}{c}{**Time \(\times\) Treatment**} \\ \cline{2-10}  & _df_ & \(F\) & \(p\) & _df_ & \(F\) & \(p\) & _df_ & \(F\) & \(p\) \\ \hline GPP & 10 & 79.09 & \(<\)0.001 & 2 & 2.05 & \(<\)0.001 & 20 & 4.53 & \(<\)0.001 \\ RE & 10 & 49.31 & \(<\)0.001 & 2 & 7.23 & \(<\)0.05 & 20 & 4.06 & \(<\)0.001 \\ NEE & 10 & 30.78 & \(<\)0.001 & 2 & 60.16 & \(<\)0.001 & 20 & 2.73 & \(<\)0.001 \\ E & 10 & 12.56 & \(<\)0.001 & 2 & 1.75 & 0.1835 & 20 & 1.85 & \(<\)0.05 \\ T & 10 & 26.52 & \(<\)0.001 & 2 & 32.84 & \(<\)0.001 & 20 & 11.06 & \(<\)0.001 \\ ET & 10 & 23.89 & \(<\)0.001 & 2 & 28.89 & \(<\)0.001 & 20 & 12.47 & \(<\)0.001 \\ WUEc & 10 & 12.24 & \(<\)0.001 & 2 & 28.62 & \(<\)0.001 & 20 & 14.66 & \(<\)0.001 \\ WUEe & 10 & 7.85 & \(<\)0.001 & 2 & 21.50 & \(<\)0.001 & 20 & 5.54 & \(<\)0.001 \\ T/ET & 10 & 20.04 & \(<\)0.001 & 2 & 7.87 & \(<\)0.01 & 20 & 4.56 & \(<\)0.001 \\ \hline \hline \end{tabular} Notes: GPP, gross primary productivity; RE, ecosystem respiration; NEE, net ecosystem carbon exchange; E, soil evaporation; T, plant transpiration; ET, evapotranspiration; WUEc, canopy water use efficiency; WUEe, ecosystem water use efficiency; T/ET, ratio of soil evaporation and evapotranspiration.  
\end{table}
Table 3: Results of RM-ANOVA for the effect of warming on carbon and water fluxes and water use efficiency.  
Figure 2: Dynamics of gross primary productivity (GPP, **a**), ecosystem respiration (RE, **b**), and net ecosystem carbon dioxide exchange (NEE, **c**) under gradient warming treatments.  
shown in Figure 3, E, T, and ET showed larger fluctuations in both the TL and TH treatments than in the CK.
## Materials and Methods
### Validation Parameters  
Method validation was carried out using the recommendations of the association of analytical communities (AOAC) in terms of precision, repeatability, and recovery as described in detail in [PERSON] et al. [21].  
For HPLC, 6-point calibration lines were obtained by means of diluted solutions of each authentic commercial standard (Merck, Saint-Quentin Fallavier, France). Each sample was injected three times, and arithmetic means were calculated to generate linear regression equations plotting was done by the peak areas (y) against the injected quantities (x) of each standard. Coefficients of determination (\(R^{2}\)) were used for linearity verification. The limits of detection (LOD) and of quantification (LOQ) was calculated using signal-to-noise ratios of 3:1 and 10:1, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Run ID** & **Run Order** & **X\({}_{1}\)** & **X\({}_{2}\)** & **X\({}_{3}\)** & **Experimental TPC (mg/g DW)** & **Predicted TPC (mg/g DW)** \\ \hline Obs1 & 10 & 0 & +1 & \(-1\) & 7.93 \(\pm\) 0.14 & 7.93 \\ Obs2 & 6 & +1 & 0 & \(-1\) & 6.16 \(\pm\) 0.22 & 6.31 \\ Obs3 & 1 & \(-1\) & \(-1\) & 0 & 5.03 \(\pm\) 0.11 & 5.18 \\ Obs4 & 17 & 0 & 0 & 0 & 11.33 \(\pm\) 0.10 & 11.37 \\ Obs5 & 15 & 0 & 0 & 0 & 11.37 \(\pm\) 0.08 & 11.37 \\ Obs6 & 7 & \(-1\) & 0 & +1 & 7.51 \(\pm\) 0.07 & 7.35 \\ Obs7 & 12 & 0 & +1 & +1 & 9.06 \(\pm\) 0.13 & 9.23 \\ Obs8 & 4 & +1 & +1 & 0 & 6.01 \(\pm\) 0.11 & 5.86 \\ Obs9 & 18 & 0 & 0 & 0 & 11.41 \(\pm\) 0.12 & 11.37 \\ Obs10 &
## 5 Discussion
### Limitations and Future Directions  
Although our method considers both time and frequency axes together, it is limited in that the suggestion in this work depends on a clustering method for a transformed signal such as Felzenszwalb [34], which is exploited in this work. Moreover, the clustering method could not take account of the important bands of frequency. For example, according to the AASM manual [26], humans consider the frequency at most beta waves, which is between 14-30 Hz. However, the clustering method does not consider the bands. Therefore, our method captures the over-explained sample from 0-100 Hz as shown in the region filled with light green in Figure 3. In the future, we plan to elaborate our method to consider the frequency more precisely and apply the model given the signal, not only EEG, but also different ones such as ECG or EMG.
## 3 Background  
In this section, we establish the formulation of the relation extraction task and the few-shot RE settings.  
**Sentential relation extraction:** A sentence, denoted by \(T=(t_{1},t_{2},t_{3},\ldots,t_{l})\), is essentially a sequence of words. The entities in a sentence are represented by the set \(M=m_{1},m_{2},\ldots,m_{k}\), where every \(m_{k}=(t_{i},w_{2},\ldots,t_{j})\) is a segment of the sentence \(T\). These mentions are annotated using entities from a knowledge graph (KG), such that \(m_{i}\):\(e_{j}\), where \(e_{j}\in E\). A relation instance is defined as \(x=(T,e1,e2)\), implying the presence of a target relationship that is expected to be predicted by RE models for the given sentence's \(e_{1}\) and \(e_{2}\) entities. In the event that no corresponding relationship is found in the KG, the label NA is applied. The task of sentence-level relation extraction asserts that only the sentence containing a given pair of entities is pertinent for predicting the correct relation, while disregarding all other sentences in the set. This approach is commonly referred to as the sentential RE task.  
**Low-shot relation extraction:** Low-shot relation extraction necessitates models that can identify previously unseen relations with minimal samples. This task involves performing relation extraction using only a limited number of annotated instances for newly introduced relations. Each few-shot relation extraction task consists of a support set, \(R=r_{1},\ldots,r_{N}\), containing annotated instances for \(N\) novel relations. Specifically, for relation \(i\), the support set \(r_{i}=r_{i}^{0},\ldots,r_{i}^{K}\) contains \(K,K\geq 0\) such instances. The aim is for models to acquire the ability to transfer knowledge and accurately extract the target relation, \(y_{t}\), for any given instance, \(x\), through the K-shot support set.
# Automated Chest X-Ray Diagnosis Report Generation with Cross-Attention Mechanism
## 3 Results  
### Comparison of the Effects of CBAM Attention Mechanism  
In chest X-ray images, the middle part usually contains important anatomical structures such as the lungs, heart, and trachea. These are the areas that doctors focus on when making diagnoses because they can reflect pathological changes in a variety of diseases (such as pneumonia, tumors, emphysema, heart problems, etc.).  
The surrounding areas (such as black background, bone edges, and shoulder areas) generally do not contain key information and are less important for diagnosis. Therefore, when using artificial intelligence models for image analysis, we should focus on the feature information in the middle area and reduce the interference of irrelevant surrounding areas.  
In order to verify whether adding CBAM attention to ResNet101 is really effective in alleviating data bias, we need to look at the weight distribution diagram on the image after visual feature extraction. The Figure 2 is the attention heat map extracted by ordinary ResNet-101 and ResNet101-CBAM.
# Quantum Key Distribution in Kubernetes Clusters
## 4 Integration in a Kubernetes Cluster
### Quantum Software Stack Operator  
As mentioned in Section 2.2, an operator is a software extension to Kubernetes, which leverages CRs and controllers following the control loop pattern. Therefore, we could create additional CRs to map out SAEs and key requests in Kubernetes. Then, using custom controllers, we could add a logic behind events of the life-cycle related to those resources (e.g., creation, removal, update). We defined two CRs, Sae and KeyRequest, and developed the _QSS Operator_ to operate them. All the components of the classical QSS, such as QKS and QKDM, are available as deployments applied to the Kubernetes cluster.  
In Figure 3, a PTP exchange between two clusters is depicted and described in Section 4.2. Sae, when applied, creates a new logical SAE in the scope of the cluster, registers this SAE to Keycloak, and makes sure that this is available at the application level of the QKD network. KeyRequest instead starts the KRP at the QKS level depending on the number of keys requested and their length.  
From a cluster perspective, the final goal is to have a QKD secret available for an application as a result of the declaration of the KeyRequest resource. In Figures 4 and 5, we reported examples of Sae and KeyRequest descriptors for creating the those resources.  
apiVersion: \"qks.controller/v1\" kind: KeyRequest metadata: name: request1 spec: number: 1 size: 128 master_SAE_ID: sae_1 slave_SAE_ID: sae_2
# Real-Time, Deep Learning Based Wrong Direction Detection
## 4 Experiments and Results
### YOLOv3 Model Training  
For the detection experiments, we trained the model by annotating real-world data. We had video files from which we extracted frames and created our own CCTV dataset. We annotated extracted images with a specific program [18] and trained our YOLOv3 network using 1750 images on a PC with an NVidia RTX 2080 Ti GPU, an Intel Core i5-7600 CPU, and 16 GB RAM. We chose YOLOv3-416, in which images are resized to \(416\times 416\) pixels, with learning rate \(\alpha=10^{-3}\) batch size = 64 on frozen layers, and batch size = 8 for unfrozen ones, over 100 epochs. We used the Adam optimizer with \(\alpha=10^{-3}\) for frozen layers and \(\alpha=10^{-4}\) after unfreezing the layers. The YOLOv3 network convergence time depends on the quantity of data used in training; so, in our case, it took four to five hours to converge. Since our model is trained with images from the camera used in one fixed position our model applies only to the particular camera.
## 4 Conclusions  
The localized meteorological data used for this study have revealed the seasonal and geospatial variability of rain heights over South Africa. This is contrary to the ITU recommendations, which suggest a fixed rain height across the seasons in a year. The ITU curve grossly underestimates rain height, with a percentage difference of up to 33% in the coastal region during the summer season. The four kernels of KDE produce very good estimations of rain heights at all stations, though the triangular kernel provides the smallest ISE. However, the Gaussian kernel remains the most preferred due to its optimum bandwidth, which gives the best smoothing. The estimation results yield percentage errors less than 2% across all stations, except for the coastal stations, where a maximum percentage error of 5.3% was obtained at Durban during the winter season. This implies that KDE is suitable for the prediction of rain heights across South Africa. The station results were used to generate a rain height contour map for the entire of South Africa. The comparison of rain-induced attenuations computed using kernel-estimated seasonal rain heights and ITU-recommended rain heights shows that rain height variability has a significant impact on attenuation. The ITU rain heights underestimate attenuation across all nine stations, with a difference of up 7 dB at 0.001% exceedance at the coastal stations. Negligible differences were only noticed at Bloemfontein and Upington, which are arid regions dominated by a dry climate. The analysis of the research results emphasized the need for spatial and seasonal approaches to satellite communication link design to adequately compensate for propagation impairment due to rain. The results are expected to serve as a robust reference database for local radio engineers when performing link budget calculations.  
Conceptualization, Y.B.L. and P.A.O.; methodology, Y.B.L.; software, Y.B.L., C.T. and P.A.O.; validation, Y.B.L. and P.A.O.; formal analysis, Y.B.L., C.T. and J.S.O.; investigation, Y.B.L.; resources, C.T. and E.V.W.; data curation, Y.B.L.; writing--original draft preparation, Y.B.L.; writing--review and editing, Y.B.L., P.A.O. and J.S.O.; visualization, Y.B.L. and J.S.O.; supervision, P.A.O., C.T. and J.S.O.; project administration, C.T. and E.V.W.; funding acquisition, Y.B.L. and E.V.W. All authors have read and agreed to the published version of the manuscript.  
The research received no external funding.  
Not applicable.  
The data used for this study is openly available in Copernicus CDS at [[https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=download](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=download)]([https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=download](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=download)) (accessed on 5 September 2024).  
The authors declare no conflict of interest.
# The Application of Augmented Reality in the Automotive Industry: A Systematic Literature Review
## Results
### General Characteristics of 'in-car' AR-Based Systems  
Table 2 shows the characteristics of 'in-car' AR systems, which are generally focused on improving the driving experience. The features being discussed are the visualization device, the displayed information, the environment type where the system was developed and the type of asset used to augment the driver experience.  
As we can see, 6 studies from the total of 19 (31.58%) were performed in laboratory conditions, using different types of simulators, 12 of them (63.16%) were performed in real environments and 1 of them [59] cannot be categorized according to one of these types. A percentage of 26.32% of the reviewed papers have presented works with more than one type of asset. The rest of the studies have used 2D graphics (68.24%) and 3D graphics (31.58), and other works provided text, video, audio or animated graphics as displayed information. Regarding the content of the displayed information, a percentage of 21.05% of works provided more than one information type. Most of them presented the information on HUD-like systems regarding the following elements: pedestrian tracking (31.58%), intersection guidance (21.05%) or vehicle detection (15.79%). Other studies provided information about speeding or traffic sign detection.
## 2 Device Structure and Simulation Methodology  
Figure 1 shows the bird's eye and cross-sectional views of gate-normal NWTFETs. It features a gate-source overlapped thin intrinsic epitaxial layer to fix the tunnel width. The device parameters are summarized in Table 1. The channel length (\(L_{\text{ch}}\)) is 20 nm. The gate-source overlap length (\(L_{\text{ov}}\)) is 40 nm. Both the inner width (\(W_{\text{inner}}\)) and inner height (\(H_{\text{in\,ner}}\)) are 20 nm, respectively. The gate insulator thickness (\(T_{\text{ins}}\)) is 2 nm. The epi-layer thickness (\(T_{\text{epi}}\)) is 2 nm. The source (\(N_{\text{S}}\)) and drain doping concentrations (\(N_{\text{D}}\)) are \(10^{20}\) cm\({}^{-3}\), respectively.  
3D TCAD device simulation has been performed by using a commercial simulator [26]. For the accurate calculation of the BTBT generation rate, a dynamic nonlocal BTBT model is used after calibration [27]. Additionally, the Shockley-Read-Hall recombination, Philips unified mobility model and Fermi distribution are used in our simulation. On the contrary, quantization effects and the gate leakage current have not been considered. The threshold voltage (\(V_{\text{T}}\)) is defined as the gate voltage (\(V_{\text{G}}\)) when the drain current (\(I_{\text{D}}\)) is equal to 0.1 nA/\(\mu\)m, while the turn-on voltage (\(V_{\text{turn-on}}\)) is defined as \(V_{\text{G}}\) when \(I_{\text{D}}\) is equal to 0.01 fA/\(\mu\)m. The \(I_{\text{on}}\) and \(I_{\text{off}}\) are defined as \(I_{\text{D}}\) when the overdrive  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline \(I_{\text{ch}}\) & 20 nm \\ \(I_{\text{ov}}\) & 40 nm \\ \(W_{\text{inner}}\), \(H_{\text{inner}}\) & 20 nm \\ \(T_{\text{ins}}\) & 2 nm \\ \(T_{\text{epi}}\) & 2 nm \\ \(N_{\text{S}}\) & \(1\times 10^{20}\) cm\({}^{-3}\) (p-type) \\ \(N_{\text{D}}\) & \(1\times 10^{20}\) cm\({}^{-3}\) (n-type) \\ \(N_{\text{ch}}\), \(N_{\text{epi}}\) & Intrinsic \\ \hline \end{tabular}
\end{table}
Table 1: Device parameters for simulation.  
Figure 1: (**a**) Bird’s eye and (**b**) cross-sectional views of simulated gate-normal nanowire tunnel field-effect transistors (NWTFETs) with the normalized radius of curvature (\(R_{\text{norm}}\)) of 0.  
voltages (\(V_{\text{G}}-V_{\text{turn-on}}\)) are 0.5 V and \(-\)0.2 V, respectively. The average \(SS\) (\(SS_{\text{avg}}\)) is calculated from \(V_{\text{G}}=V_{\text{turn-on}}\) to \(V_{\text{G}}=V_{\text{turn-on}}+0.5\) V. Drain-induced barrier thinning (\(DIBT\)) is calculated as the \(V_{\text{T}}\) difference between \(V_{\text{D}}=0.05\) V and 0.5 V.
## 5 Experiments and Results Analysis
### Evaluation Indicators  
This experiment uses common metrics for named entity recognition to measure the performance of the model, which are precision (\(P\)), recall (\(R\)) and \(F1\) value. The specific evaluation formulas are as follows:  
\[P=\frac{TP}{TP+FP}\times 100\% \tag{19}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Learn rate & \(1\times 10^{-5}\) \\ Batch size & 4 \\ Epoch & 50 \\ Lstm\_embedding\_size & 1024 \\ Hidden size & 768 \\ Bert model & RoBERTa-wvm-ext \\ Embedding size & 512 \\ Optimizer & AdamW \\ Dropout & 0.5 \\ Number of layers of IDCNN & 3 \\ Expansion width & 1, 2, 4 \\ Number of IDCNN blocks & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model parameter setting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Configuration Items** & **Configuration Parameters** \\ \hline \multirow{3}{*}{Software} & developing language & Python 3.8 \\  & Software Environment & PyTorch 1.11.0 \\  & Experimental platform & Ubuntu 20.04 \\ \hline \multirow{3}{*}{Hardware} & Graphics card & RTX3090 \\  & Memory & 24 G \\ \hline \hline \end{tabular}
\end{table}
Table 3: Software and hardware configuration table.  
\[R=\frac{TP}{TP+FN}\times 100\% \tag{20}\]  
\[F1=\frac{2\times P\times R}{P+R}\times 100\% \tag{21}\]  
\(P\) represents the number of correctly identified entities as a percentage of the total number of predicted entities. \(R\) represents the number of correctly identified entities as a percentage of the total number of entities labeled in the sample. And \(F1\) value combines the precision and recall rates, which can be evaluated for the model as a whole. Meanwhile, \(TP\) indicates the number of entity labels in the test set correctly identified by the model, \(FP\) indicates the number of entity labels in the test set incorrectly identified by the model, and \(FN\) indicates the number of entity labels in the test set not identified by the model.
## 2 System Design
### Algorithm Design: Structure of CNN  
The pulse signal belongs to the time-series signal so that the pulse waveform classification will deal with the 1-dimension signal. Recurrent neural network (RNN) and long-short term memory (LSTM) are usually used to predict the next moment's output according to a prior time-series signal. However, their performances are not better than the convolution neural network (CNN) when it refers to classification because they concern more about the relationship in the context, not the overall feature. Therefore, we select CNN as the classification method.  
Figure 4a shows a CNN model, which contains two types of layers, the convolution (CONV) layers, and the fully connected (FC) layers. CONV layer refers to 3 operation types: convolution, activation, and pooling separately, as shown in Figure 4b. The input tensor has \(m\) channels of features, and the length of each channel is \(l\). The CONV layer's computational procedures for an output channel are shown in Figure 4c. For each output channel \(j\), each input channel \(i\) is processed by a corresponding sliding window, which is also named convolution kernel. Then the convolution results of \(m\) input channels are accumulated, processed by a rectified linear unit (ReLU), then pooled by a maximum function. After the CONV layers, the feature maps are flattened to a 1-D tensor, which is the input tensor of the FC layer. The FC layer's computational function is  
\[y\ =Wx+b, \tag{1}\]  
here \(y\) is the output tensor, a column vector of length \(n\), \(x\) is the input tensor, a column vector of length \(m\), \(W\) is a matrix of weights, whose size is \(n\times m,b\) is a column vector of bias, whose length is \(n\).  
For the algorithm design of software, the confusion matrix is usually used to evaluate the model, and accuracy is one of the most critical indexes of the CNN model. The definition of a confusion matrix is: true positive (TP) means the number of positive data classified correctly, true negative (TN) means the number of negative data classified correctly, false positive (FP) means the number of negative data classified incorrectly, and false negative (FN) means the number of positive data classified incorrectly. The accuracy, precision, sensitivity, F1 score and macro F1 are calculated by:  
\[Accuracy_{i}=\frac{TP+TN}{TP+TN+FP+FN} \tag{2}\]  
\[Precision_{i}=\frac{TP}{TP+FP} \tag{3}\]  
\[Sensitivity_{i}=\frac{TP}{TP+FN} \tag{4}\]  
\[F1_{i}\ =\frac{2*Precision*Sensitivity}{Precision+Sensitivity} \tag{5}\]  
Figure 3: Data collection and preprocessing. (**a**) roughly select a segment; (**b**) remove high frequency noise; (**c**) remove baseline wander; (**d**) select an appropriate area as a piece of data; (**e**) normalize, label, and store the data.  
\[macro\ F1\ =\frac{1}{n}\sum_{i=1}^{n}F1_{i} \tag{6}\]  
However, in the actual deployment on FPGA, the limited FPGA resource, especially Block RAM (BRAM), will restrict the number of model parameters, especially weights of the CONV and FC layers. Therefore, we optimize the CNN model by adjusting its number of layers and structure to reduce the parameters.