esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 4 System Simulations and Performance Evaluations
### Different Numbers of Source-Destination Pairs for the Sparse and Congested Periods
Although this is better than the strategy of constantly relaying packets to the vehicle with the highest DQN delay weight, the resulting forwarding path might not be the shortest, leading to a slightly higher average overhead ratio than that obtained with IDRF_DQN. Enhancing IDRF_DQN_RF, the IDRF approach considers untrained real-time vehicles. Despite using the analysis unit in the real-time phase to gather real-time vehicle information, the average overhead ratio of the IDRF remains higher than that of IDRF_DQN_RF due to the necessity of traversing more hops in unknown real-time environments. It is noteworthy, however, that even though the IDRF exhibits the highest average overhead ratio among the three methods proposed in this paper, it still performs better regarding this aspect compared to other relevant research approaches. By comparing the data for different numbers of pairs for sparse and congested periods, it can be observed that when the number of pairs is set to 32, the average packet delivery ratio of the IDRF surpasses that of VRDRT during both sparse and congested periods. This improvement is remarkable, as the IDRF outperforms all other methods. Furthermore, when the number of pairs is set to 32 for the congested period, the average packet delivery ratio of IDRF_DQN_RF also exceeds that of VRDRT. Additionally, the three methods introduced in this paper, IDRF_DQN, IDRF_DQN_RF, and IDRF, consistently demonstrate lower average end-to-end delays and overhead ratios than other methods. When the performance results of Epidemic represent a score of 100%, those of all other routing protocols can be normalized accordingly. The statistical analysis presented in Table 8 indicates that the IDRF achieves significant performance enhancements over TDRL-RP and VRDRT for both sparse and congested periods. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sparse Period** & \begin{tabular}{c} **IDRF over** \\ **TDRL-RP** \\ \end{tabular} & \begin{tabular}{c} **IDRF over** \\ **VRDRT** \\ \end{tabular} & \
\begin{tabular}{c} **Congested Period** \\ **TDRL-RP** \\ \end{tabular} &
\begin{tabular}{c} **IDRF over** \\ **VRDRT** \\ \end{tabular} \\ \hline Average packet delivery ratio & \(+19.24\%\) & \(+4.97\%\) & Average packet delivery ratio & \(+6.27\%\) & \(+3.56\%\) \\ \hline Average end-to-end delay & \(-32.00\%\) & \(-12.73\%\) & Average end-to-end delay & \(-24.57\%\) & \(-15.86\%\) \\ \hline Average overhead ratio & \(-5.14\%\) & \(-11.68\%\) & Average overhead ratio & \(-6.59\%\) & \(-16.68\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Average performance improvements obtained with the IDRF over TDRL-RP and VRDRT with different numbers of pairs for sparse and congested periods.
# Joint Transmit Power Allocation and Splitting for SWIPT Aided OFDM-IDMA in Wireless Sensor Networks
## 2 System Model and Problem Formulation
### Feasibility of The Proposed Algorithm in Practical Applications  
Finally, we further discuss the feasibility of the proposed system as well as that of the joint power allocation and splitting algorithm in practical applications. As aforementioned, the proposed system is implementable to the energy constrained scenario of 5 G, as it allows the RNs to wireless harvest the energy from RF signal through SWIPT, and also to enjoy the high spectral efficiency provided by OFDM-IDMA. Such benefit is obtained due to the fact that the proposed system is designed by combining OFDM-IDMA and SWIPT together. Hence the practical implementation of the proposed system relies on the feasibility of these two techniques. From the aspect of OFDM-IDMA, we found that the transceiver design is relatively mature nowadays and the field programmable gate array (FPGA) implementation for the iterative MUD with low complexity has already been realized [27]. While on the other hand for SWIPT, there are also emerging research works discussing the related circuitry implementation, where the positive views of realizing SWIPT in practical case has been reported [28; 29]. In general, the feasibility of SWIPT relies mostly on the EH circuits and the energy-information division protocols at the receive nodes. The main concern of EH circuits is the imperfect property of the power conversion from RF signal to direct current electricity. Fortunately, we have already noticed that and properly adopted the non-linear model to approximate such conversion behavior [21]. That is to say, the non-linear property of power conversion has been taken into account when developing the jointly power allocation and splitting algorithm in our work. Hence the proposed algorithm turns out to match with the practical scenario. As for the energy-information division protocols, we employed the PS protocol in our work. It has been proved in [28] that PS is closer to the information theoretical optimum than other protocols. More importantly, with PS the functions of both EH and information decoding can be achieved by sharing single receive antenna. It facilitates the hardware design in the situation that there is strict constraint on the size of receive equipments.
# Hardware-Aware Mobile Building Block Evaluation for Computer Vision
## 4 Hardware-Aware Mobile Building Blocks Evaluation
### Bottleneck versus Inverted Bottleneck  
Bottlenecks were introduced, together with residual connections, in ResNets [8]. The main idea behind this was to lower the computational cost of each building block by performing the convolutions at a lower dimension, such that more building blocks can be stacked and deeper networks can be created without significantly increasing the computational cost.  
A standard bottleneck structure consists of three convolutional layers: a \(1\times 1\) pointwise convolution to reduce the channel size, a standard convolution to improve the features, and a final \(1\times 1\) pointwise convolution to restore the channel dimensions. A parallel residual path connects the input and output of this block. In MobileNetV2 [2], an _inverted_ bottleneck was introduced where the residual connection is moved to connect the bottlenecks. In practice, this means that an inverted bottleneck consists of a pointwise convolution to expand, instead of reduce, the channel dimension, a (depthwise) convolution and a final pointwise convolution to restore the original channel dimension. The motivation for this design was that it is more hardware efficient as only the bottleneck lower dimension tensors need to be fully saved to memory as the intermediate higher channel dimension tensors are only used in depthwise convolutions. This means the tensor can be split into smaller ones.  
A third possibility is to use no bottleneck, since the RegNet paper found that their best models used a bottleneck expansion/reduction factor of 1.0 [20]. When using no bottleneck, we also drop the final pointwise convolution present in bottleneck structures, since it is no longer required to make channel sizes match. All our building blocks in this comparison make use of depthwise convolutions.  
The results, as shown in Figure 7, indicate that using inverted bottlenecks degrades performance and fails to deliver the promised expected inference speedup. In general, across all hardware platforms, the blocks without a bottleneck and with a regular bottleneck show very similar performance. It can be observed that the VPU and server-grade GPU slightly favour the building block without a bottleneck as it executes faster compared to bottleneck blocks. These are the same hardware platforms as those where the standard convolution outperformed the depthwise separable convolution, indicating that pointwise convolutions underperform on these platforms.  
Figure 6: While grouped convolutions should, in theory, be able to combine advantages from depthwise and standard convolutions, current implementation across various hardware accelerators and inference platforms fail to leverage these advantages, making grouped convolutions not an optimal choice.
###### Abstract  
This paper presents a new model based on the Monte Carlo simulation method for considering the uncertainty of electric vehicles' charging station's load in a day-ahead operation optimization of a smart micro-grid. In the proposed model, some uncertain effective factors on the electric vehicles' charging station's load including battery capacity, type of electric vehicles, state of charge, charging power level and response to energy price changes are considered. In addition, other uncertainties of operating parameters such as market price, photovoltaic generation and loads are also considered. Therefore, various stochastic scenarios are generated and involved in a cost minimization problem, which is formulated in the form of mixed-integer linear programming. Finally, the proposed model is simulated on a typical micro-grid with two 60 kW micro-turbines, a 60 kW photovoltaic unit and some loads. The results showed that by applying the proposed model for estimation of charging station load, the total operation cost decreased.  
Monte Carlo simulation; electric vehicles charging station; smart micro-grid; uncertainty; mixed-integer linear programming Article
## 3 The Proposed Conversational System: CORES
### Workflow of CORES
#### 3.5.7 Response Generation Process in CORES  
The response generation process, as outlined in Algorithm 2, involves the use of GPT-4 models. This process mainly consists of the following steps:  
**(1)** **Semantic Analysis of User's Inputs**  
When the conversational recommender agent receives the identified emotions from RoBERTa and the user's query, it leverages a GPT-4 model that includes the conversation prompt template and historical conversations. The goal of this step is to classify the user's intent, extract relevant entities, and derive the underlying meaning of the user's input.  
**(2)** **Determining the Answer Based on the Extracted Intents**  
The recommendation engine works with the response generator to assess whether the user's input has an existing response or requires additional information. If the intent corresponds to a final result, the system stores this result in the conversation history and provides a recommendation explanation to the user's VR. If the intent is new or requires an updated response, the process moves to the next step.  
**(3)** **Response Generation based on Matching Results**In this step, the system generates responses by combining three components: the response generator, the recommendation explanation, and the recommendation's VR visualization and insights. GPT-4 processes the user's request using identified entities (such as tokens and emotions from Step 1), and applying the appropriate response format. CORES provides detailed explanations for recommendations, clarifying aspects such as item features (e.g., colors, sizes), emotions, and context (e.g., store location, delivery time). The generated responses can be visualized in 3D VR. Additionally, CORES utilizes GPT-4's capabilities with chart generation tools to provide new insights that better meet the user's needs. The process is driven by the input prompt _P_ompt_1, and further details can be found in Appendix A.
```
Q: User Query. Pr_C: Conversational Recommender Template. Pr_E: Exploration Generation Template. Pr_V: Visualization Generation Template. Pr_I: Insights Generation Template. GPT_f: GPT_4 Generation function. H1: conversations history; Outputs: R: Response Results; Begin (1): R \(\leftarrow\) 2 (2): Foreach Q\({}^{i}\)\(\in\) user queries Do (3): I, E \(\leftarrow\) GPT_4 (Q\({}^{i}\),Pr_C,H1,R\)) (4): IF (I is Final Answer) Then (5): H1 \(\leftarrow\) Prompt_4 (Q\({}^{i}\),Pr_C,E) (6): return E; (7): END IF (I is Explication Generation) Then (9): R1 \(\leftarrow\) Prompt_1 (Q\({}^{i}\),Pr_C,Pr_E,E) (10): R \(\leftarrow\) R \(\cup\) R1 (11): END IF (I is VR Visualization Generation) Then (13): R2 \(\leftarrow\) Prompt_4 (Q\({}^{i}\),Pr_C,Pr_V,E\)) (14): R \(\leftarrow\) R \(\cup\) R2 (15): END IF (I is Insight Generation) Then (17): R3 \(\leftarrow\) Prompt_1 (Q\({}^{i}\),Pr_C,Pr_I,E\)) (18): R \(\leftarrow\) R \(\cup\) R3 (19): END IF (20): End FOR (21): Return R  End
```  
**Algorithm 2**Response Generation
# Faster MDNet for Visual Object Tracking
## 4 Experiments
### Visualization Result  
Additionally, we visualize the tracking results compared with C-COT [5], DaSiamRPN [10], DiMP [15], MDNet [20], SiamFC [7], and SiamRPN++ [11] on several video sequences from OTB100 [24]. Some challenging examples are shown in Figure 5. The proposed method can still effectively track objects under the influence of occlusion, motion blur, illumination, scale change, etc.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Trackers** & **AUC** & **Precision** & **P\({}_{norm}\)** \\ \hline ATOM [14] & 0.703 & 0.648 & 0.771 \\ DaSiamRPN [10] & 0.638 & 0.591 & 0.733 \\ DiMP [15] & 0.723 & 0.633 & 0.785 \\ ECO [6] & 0.554 & 0.492 & 0.618 \\ MDNet [20] & 0.606 & 0.565 & 0.705 \\ PrDiMP [16] & 0.750 & 0.691 & 0.803 \\ RT-MDNet [22] & 0.584 & 0.533 & 0.694 \\ SiamFC [7] & 0.571 & 0.533 & 0.663 \\ SiamRPN++ [11] & 0.733 & 0.694 & 0.800 \\ TransT [18] & **0.814** & **0.803** & **0.867** \\ Ours-conf & 0.705 & 0.672 & 0.788 \\ Ours & 0.738 & 0.696 & 0.802 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons with several trackers in terms of AUC score, precision, and normalized precision on TrackingNet. Among them, P\({}_{norm}\) normalizes the precision and avoids the influence of image scale and the boundary size of the target on the measurement.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Trackers** & **ATOM** & **C-COT** & **DiMP** & **ECO** & **MDNet** & **PrDiMP** & **RT-MDNet** & **SiamFC** & **SiamRPN++** & **TransT** & **Ours** \\  & [14] & [5] & [15] & [6] & [20] & [16] & [22] & [7] & [11] & [18] & **Ours** \\ \hline UAV123 & 0.642 & 0.513 & 0.653 & 0.522 & 0.528 & 0.680 & 0.528 & 0.498 & 0.613 & **0.691** & 0.648 \\ NfS & 0.584 & 0.488 & 0.620 & 0.466 & 0.429 & 0.635 & - & - & 0.5
## 4 Results
### IFPC Method  
Figure 12 shows the result of searching for anomalies by the Isolation Forest method on the full dataset for each sensor channel. The complex anomalies plot in Figure 12 shows the IFPC (Isolation Forest--predicates' conjunction) method results.  
Time series of sensor's channels data and complex anomalies obtained by the IFPC method for 2020-2022  
Figure 13 shows another result of the IFPC method--a list of dates.  
Figure 11: List of dates obtained by the k-meansPC method.  
Figure 12: Results of testing the IFPC method for 2020-2022  
Thus, using the IFPC method to detect anomalies, we identified 57 dates, including all three dates with actual laboratory-recorded instances of contamination exceeding the MPC--7, 8, and 9 June in 2022.  
Table 1 summarizes the results of the experiments.  
Unlike benchmark approaches, it is important to note that the IFPC method is not sensitive to data gaps, making it suitable for in situ systems designed to detect water contamination exceeding the MPC. This capability opens up new possibilities for implementing autonomous hardware and software systems with automatic sampling functions. Additionally, since the IFPC method does not rely on preliminary time series processing, incorporating additional factors (such as temperature, turbidity, or oxygen concentration) is a straightforward technical task that does not require changes to the analysis methodology.  
The experiments confirmed the hypothesis. The IFPC method for complex anomaly detection in multidimensional time series determines the dates the laboratory-recorded instances of water contamination exceeding the MPC. This proves that indirect measurement sensors data can be used to identify such events.  
At the same time, combining predicates with other anomaly detection or forecasting methods seems promising, but this study could further develop this approach in future work.
# Resource Allocation for Cognitive LEO Satellite Systems: Facilitating IoT Communications
## 4 Joint Resource Allocation in Cognitive LEO SatComm System
### Optimal IoT User Number  
Here, we will illustrate the optimal IoT user number to maximize the sum rate of the IoT transmissions, subject to the receive power constraint and the primary SINR requirement constraint, which can be mathematically formulated as **P5**:  
\[\begin{split}\max_{p,q,K}&\quad\frac{K}{N}\log(1+ \gamma_{s})\\ \text{s.t.}&\quad\quad\begin{cases}\gamma_{p}\geq \beta^{*}\\ p\leq\bar{P}.\end{cases}\end{split} \tag{27}\]  
For every given \(K\), there exists one optimal \(p\) and \(q\), which can ensure the maximization of the sum rate of the IoT system. When given \(p\) and \(q\), the optimization of \(K\) involves the log function and non-closed formula of \(\gamma_{s}\). Thus, it is difficult to gain the closed-form of the optimal \(K\) when given \(p\) and \(q\). In fact, when \(K\) is small, the sum rate of the IoT users increases with the growth of \(K\). When \(K\) is large, the sum rate of the IoT users decreases with the growth of \(K\) since in this case, the SINR of the IoT users will dominate the sum rate of the IoT system. Based on this fact, we can apply the one-dimensional search scheme to find the optimal tuple \((p^{*},q^{*},K^{*})\) to solve the optimization problem **P5**. Specifically, for each \(K\), we calculate the optimal \(p^{*}\) and \(q^{*}\). By searching different \(K\), we can gain multiple tuples \((p^{*},q^{*},K)\). By comparing the sum rate of the IoT system with \((p^{*},q^{*},K)\), we can obtain the optimal tuple \((p^{*},q^{*},K^{*})\). The details of the proposed algorithm for solving problem **P5** are summarized in Algorithm 1. Specifically, we first initialize \(K\) and \(\zeta\), where the initial \(K\) is a small number and \(\zeta\) is greater than one. Then, we iteratively update \(p\), \(q\), and \(K\) until the objective function gradually decreases.  
```
Initialize \(K^{(0)},\zeta\) and set \(t=0\); Repeat \(t\gets t+1\)  Calculate \(q^{(t)}\) and \(p^{(t)}\) based on (26) given \(K^{(t-1)}\); \(K^{(t)}=\zeta K^{(t-1)}\); Until the objective function of **P5** gradually decreases.  Obtain optimal tuple \((p^{*},q^{*},K^{*})\).
```  
**Algorithm 1** Solution to **P5**
# Estimation of Time-Series Forest Leaf Area Index (LAI) Based on Sentinel-2 and MODIS
## 3 Results
### MODIS LAI Normalized Growth Curve Extraction  
The comparison of the LAI curves before and after smoothing is shown in Figure 5. It can be seen that the LAI time-series curves showed uneven seasonal variations. There is little difference between the LAI growth curves extracted from MODIS for age group 0 to age group 5 of coniferous forests in the study area.  
From Figure 6, there is a visible difference between the LAI growth curves extracted from MODIS and the measured LAI values. One of the main reasons for this difference was the scale mismatch between the estimated value and the measured value. Despite this difference, the MODIS-extracted LAI curves remained consistent with the growth trend of the actual LAI. In order to better illustrate this problem, this study normalized the MODIS LAI growth curves and the actual LAI values. As can be seen in Figure 6, the normalized MODIS LAI growth curves are in good agreement with the normalized measured LAI values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Principal Component** & **Characteristic Value** & **Variance Contribution/\%** & **Cumulative Contribution/\%** \\ \hline \(C_{1}\) & 3.523 & 88.075 & 88.075 \\ \(C_{2}\) & 0.411 & 10.275 & 98.35 \\ \(C_{3}\) & 0.057 & 1.425 & 99.775 \\ \(C_{4}\) & 0.009 & 0.225 & 100.000 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Contribution rate of PCA component.  
Figure 4: LAI distribution and regression analysis. (**a**) LAI distribution map of study area on 21 August 2019; (**b**) LAI distribution map of study area on 25 August 2020; (**c**) Regression analysis of estimated and measured LAI in study area in 2019; (**d**) Regression analysis of estimated and measured LAI in study area in 2020.  
Figure 5: Comparison for different age groups of coniferous forest LAI curves extracted from MODIS before and after smoothing. (**a**) Age group 0 (0–10a); (**b**) Age group 1 (11a–20a); (**c**) Age group 2 (21a–30a); (**d**) Age group 3 (31a–40a); (**e**) Age group 4 (41a–50a); (**f**) Age group 5 (>50a).
## 4 Discussion
### Analysis of Robustness for Different Frequency Bands and Networks  
The use of hdeEG has recently enabled the investigation of frequency-dependent functional interactions of several cortical regions and networks [10; 18; 49]. The results of our study clearly reveal that variations in EEG connectivity due to head model errors depend on the network and the frequency band considered (Figure 5 and Figures S2-S5). Specifically, the alpha band was the most robust in regard to head model variations, whereas the delta band was the most sensitive one (Figures S3 and S4). It should be noted that we analyzed the electrophysiological activity of participants in the resting state, a condition in which the alpha rhythm dominates the interactions among all brain regions [11; 74]. Therefore, the robustness of the functional connectivity patterns in each band could vary according to the specific experimental conditions. In particular, the strongest brain oscillations are expected to be the least influenced by changes in the head model. We also found that DMN, DAN and SMN were relatively robust, whereas VAN and LN had large connectivity variations in the different test conditions (Figure 8 and Figures S3, S4 and S6). It should be considered that the number and the spatial distribution of network nodes over the cortex may influence the robustness of RSN reconstruction: VAN and LN are strongly lateralized networks with main nodes in the same hemisphere, whereas the other networks have core regions on both hemispheres. The robustness of the EEG-RSN reconstruction may also be influenced by other factors related to the RSN spatial distribution: DMN and DAN encompass deeper brain regions surrounded by uniformly distributed tissue interfaces, whereas key regions of VAN and LN are located closer to air-tissue interfaces, which are more likely to cause distortions in the electrical fields and therefore to affect the accuracy of the source reconstruction.