esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Attention Fusion Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution
## 4 Results
### Implementation Details
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Method** & **Optim\_g** & **Betas** & **lr** & **Gamma** & **Loss** & **Batch** & **Patch Size** & **Use\_hflip** & **Use\_rot** \\ \hline LGCNet [21] & ADAM & [0.9,0.999] & \(1\times 10^{-1}\) & 0.1 & \(L_{2}\) & 128 & \(41\times 41\) & false & false \\ IDN [51] & ADAM & [0.9,0.999] & \(1\times 10^{-4}\) & 0.1 & \(L_{1}\) & 64 & \(26\times 26\) & true & true \\ LESRCNN [73] & ADAM & [0.9,0.999] & \(1\times 10^{-4}\) & 0.5 & \(L_{2}\) & 64 & \(64\times 64\) & true & true \\ MADNet [74] & ADAM & [0.9,0.999] & \(1\times 10^{-3}\) & 0.5 & \(L_{F}\) & 16 & \(48\times 48\) & true & true \\ CTN [48] & ADAM & [0.9,0.999] & \(1\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(48\times 48\) & true & true \\ FeNet-baseline [46] & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(192\times 192\) & true & true \\ FeNet [46] & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(192\times 192\) & true & true \\ FDENet [57] & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(192\times 192\) & true & true \\ DARN-S [56] & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 64 & \(64\times 64\) & true & true \\ AMFFN [41] & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(48\times 48\) & true & true \\ IFIN-S [55] & ADAM & [0.9,0.99] & \(1\times 10^{-3}\) & 0.5 & \(L_{1}\) & 16 & \(60\times 60\) & true & true \\ BMFNet [58] & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(192\times 192\) & true & true \\ TARN [49] & ADAM & [0.9,0.999] & \(3\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(256\times 256\) & true & true \\ DAFEN-S(ours) & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(192\times 192\) & true & true \\ DAFEN(ours) & ADAM & [0.9,0.999] & \(5\times 10^{-4}\) & 0.5 & \(L_{1}\) & 16 & \(192\times 192\) & true & true \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Implementation details and hyperparameter settings of our methods and comparative lightweight methods.
## 7 Edge Intelligence and Networking for IoV
### Edge Network Intelligence for IoV
#### 7.3.2 Intelligent Forwarding and Routing for IIoV  
The named-data link state routing (NLSR) protocol [95] is one of the routing protocols for ICN networks that allows the router to return the content if it exists in the cache or to forward the interest packets to the nearest content router without predicting the probable location of the content. The same protocol was evaluated in [96] to test its real-world performance in a test-bed. Multipath forwarding and in-network caching (MUCA), proposed in [97], is an intra-domain name-based routing protocol that utilizes multi-path forwarding, link state database synchronization and in-network caching. The paper claims that MUCA outperforms the existing named-data link-state routing protocol (NLSR) [95]; however, according to [98] it is obsolete. The authors of [99] propose instead a multi-criteria decision-making (MCDM) approach based on interest forwarding and cooperative data caching.  
Recently some research has been undertaken in designing an intelligent forwarding strategy. For example, [100] used a support vector machine (SVM) to forecast the success of interest packets in predicting content cached locations with the aim of minimizing the content search time and to maximize throughput. As an NDN-forwarding plane enables choosing the next forwarding hop independently, without relying on routing, an intelligent forwarding strategy can be designed for adaptive and intelligent data forwarding [101]. Similarly, [101] proposed FS-RL, an intelligent forwarding strategy based on RL in named-data networking. Moreover, the authors of [102] considered the feasibility of using reinforcement learning, specifically Q-learning for efficient and adaptive forwarding in NDN networks. They achieved higher interest satisfaction and shorter delay compared with the existing BestRoute and Flooding strategies. In [103], a random neural-network-based reinforcement learning interest forwarding scheme was proposed. The forwarding scheme takes advantage of the in-network caching capability of NDN architecture.  
A routing protocol for vehicular named data networking was proposed in [104] to address the problem of reverse path partitioning (RPP) during consumer mobility. The mechanism, auxiliary forwarding set (AFS), considers mobility factors to identify and select forwarding nodes. The authors of [105] proposed a new method using Bloom filters which combine routing of network connectivity with a building and forwarding engine. A fuzzy interest forwarding approach that exploits semantic similarities between the names carried in interest packets and the names of potentially matching data in CS and entries in FIB is proposed in [106]. These are promising initiatives but they do not evaluate large-scale, highly dynamic, and frequently changing topologies which are a primary characteristic of IoV networks.
## 3 Analysis of Predictive Modeling Methods
### Designed Bridge Deflection Prediction Method  
There are two difficulties with traditional bridge deflection predictions: (1) previous signal smoothing methods are unable to retain the overall trend characteristics and short-term fluctuation trends at the same time and are prone to lose part of the time-series information; (2) the prediction model lacks access to multi-level spatiotemporal information and fails to make full use of feature correlations over long distances, affecting the accuracy of bridge deflection prediction. To solve the above problems, this paper proposes a bridge deflection prediction method based on cascaded residual equilibrium and the multiscale spatiotemporal attention network, which is used to achieve high-precision deflection prediction in the case of complex and drastically varying deflection characteristics and the specific process is shown in Figure 9.  
The bridge deflection prediction method proposed in this paper consists of the following important parts: Firstly, the raw bridge deflection dataset is initially smoothed and filtered of high-frequency noise by Gaussian smoothing in cascade residual smoothing to capture the overall trend change of bridge deflection. Secondly, the Gaussian smoothed data were compared to the raw data, and the residuals were calculated. The residuals represent subtle features and short-term fluctuations that Gaussian smoothing fails to capture, preserving short-term variations and localized features in the deflection data. To further extract the trend information in the residuals, quadratic exponential smoothing is applied  
Figure 8: Multiscale spatiotemporal attention network.  
Figure 9: The method designed in this paper.  
to the residuals so that the new data generated ultimately retains both the long-time trend of the bridge deflection and the short-time fluctuation characteristics. Then, the multiscale spatiotemporal attention network captures the multi-level spatiotemporal information of bridge deflection, filters the important features to ignore the unnecessary information, and outputs the prediction results through linear mapping. Finally, the partitioned training set is used for model training to verify the effectiveness of the test set on the trained model.
## 4 Results
### Feature Vector Selection  
Based on the previous analyses of the formation process and spatial-temporal characteristics of bus bunching, twelve features were initially selected as the input vectors of the model:  
1. \(x_{1}\): the number of the bus stop j.
2. \(x_{2}\): the dwell time of bus i \(-\)1 at stop j.
3. \(x_{3}\): the dwell time of bus i at stop j.
4. \(x_{4}\): the time of bus i \(-\)1 travel from stop j \(-\)1 to stop j.
5. \(x_{5}\): the time of bus i travel from stop j \(-\)1 to stop j.
6. \(x_{6}\): the average travel time between stop j \(-\)1 to stop j in the previous 15 min of the same type of period last week.
7. \(x_{7}\): the headway of bus i at stop j \(-\)1.
8. \(x_{8}\): the average headway at stop j in the previous 15 min of the same type of period last week.
9. \(x_{9}\): the departure interval of bus i at the first stop.
10. \(x_{10}\): the number of 15-min period groups.
11. \(x_{11}\): the type of the day (morning peak time: 1, evening peak time: 2, off-peak time on weekdays: 3, and rest day: 4).
12. \(x_{12}\): the day of the week.  
Factors were selected based on the process of bus operation and by different dimensions so that we can measure the importance of different factors to gain a deeper understanding of which aspects have a greater impact on bus bunching. The feature \(x_{1}\) represents the location of the bus stop, reflecting the spatial characteristics of bus bunching. As discussed above, the process of bus bunching could be divided into two stages, namely the bus traveling stage and the bus dwelling stage. \(x_{2}\) and \(x_{3}\) represent the time the bus dwells at stops, indicating the influence of passenger flow and bus queuing at stops. \(x_{4}\), \(x_{5}\), and \(x_{6}\) represent the real-time and historical condition of the bus traveling between bus stops, reflecting the traffic condition. \(x_{7}\) and \(x_{8}\) represent the bus headway of two adjacent  
Figure 9: The _Cov_ at different bus stops on a work day and non-work day: (**a**) On a work day; (**b**) On a non-work day.  
buses. \(x_{9}\), \(x_{10}\), \(x_{11}\), and \(x_{12}\) reflect the temporal characteristics of bus bunching. The data samples of feature vectors are shown in Table 3.
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 5 The Methodology  
The workflow followed in this study was implemented using the Google Earth Engine platform and deploying the JavaScript programming language. The workflow was divided into five stages (Figure 2), described in the following, to ensure a systematic and comprehensive approach to flood detection (Figure 2).
## 6 Practical Application
### Calculation of the Star Connected SVC Compensator  
Considering Table 1 or Table 3, the unbalanced factor \(\delta_{-}\) is given by:  
\[\delta_{-}=\frac{V_{-}}{V_{+}}=0.1762\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phases** & \(\mathbf{V_{zz+}}\) **(V)** & \multicolumn{2}{c}{\(\mathbf{V_{zz-}}\) **(V)**} \\ \hline \(zz\) & **Modulus** & **Angle** & **Modulus** & **Angle** \\ \hline ab & 349.84 & 34.35 & 61.65 & 62.72 \\ bc & 349.84 & \(-\)85.65 & 61.65 & \(-\)177.28 \\ ca & 349.84 & 154.35 & 61.65 & \(-\)57.28 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Positive- sequence line-to-line voltages and negative-sequence line-to-line voltages in the bus.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phase** & \(\mathbf{I_{zL+}}\) **(A)** & \multicolumn{2}{c}{\(\mathbf{I_{zL-}}\) **(A)**} \\ \hline \(z\) & **Modulus** & **Angle** & **Modulus** & **Angle** \\ \hline a & 13.66 & \(-\)24.47 & 23.29 & \(-\)172.38 \\ b & 13.66 & \(-\)149.47 & 23.29 & \(-\)52.38 \\ c & 13.66 & 90.53 & 23.29 & 67.62 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Positive- and negative-sequence line currents in the bus.  
Figure 5: Unbalanced three-wire three-phase linear system connected to an infinite power network with unbalanced voltages.  
The positive-sequence reactive power consumed by the equivalent load is determined by the following expression:  
\[Q_{2L+}=V_{zL+}I_{zL+}sin(\alpha_{zL+}-\beta_{zL+})=Q_{aL+}=Q_{bL+}=Q_{cL+},\]  
where substituting the values in Tables 2 and 3, we obtain:  
\[Q_{aL+}=Q_{bL+}=Q_{cL+}=1536.15\;\mathrm{var}.\]  
On the other hand, the negative-sequence reactive power consumed by the equivalent load is determined by the following expression:  
\[Q_{zL-}=V_{zL-}I_{zL-}sin(\alpha_{zL-}-\beta_{zL-})=Q_{aL-}=Q_{bL-}=Q_{cL-},\]  
where substituting the values in Tables 2 and 3, we obtain:  
\[Q_{aL-}=Q_{bL-}=Q_{cL-}=-826.12\;\mathrm{var}.\]  
The positive-sequence reactive power, consumed by the SVC compensator, is determined by (42), and its value is obtained as follows:  
\[Q_{zP+}=\frac{-826.12-1536.15}{1-0.1742^{2}}=-2437.95\;\mathrm{var}.\]  
Considering \(Q_{zP+}\) in (43), the reactances that the SVC compensator must have are determined by:  
\[X_{P}=X_{aP}=X_{bP}=X_{cP}=\frac{201.98^{2}}{-2437.95}=-16.7333\;\Omega.\]  
As the value is negative, \(X_{P}\) is a capacitor.  
To calculate the reactances of the NSCC compensator, it is necessary to determine the negative-sequence currents consumed by the SVC compensator. Considering Equation (45), the values of these currents are as follows:  
\[\underline{I_{aP-}}=2.13e^{-j177.28}\;\mathrm{A}\;\;\underline{I_{bP-}}=2,13 e^{-j57.28}\;\mathrm{A}\;\;\underline{I_{cP-}}=2.13e^{j62.72}\;\mathrm{A}.\]
# Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence
## 2 Materials and Methods  
### Overview of the Pilot Area  
This study was conducted in Shihezi University Experimental Field, Shihezi City, Xinjiang Uygur Autonomous Region, China (44\({}^{\circ}\) 27\({}^{\prime}\) N; 85\({}^{\circ}\) 94\({}^{\prime}\) E) from April 2022 to October 2022. The test area is a temperate arid zone climate characterized by abundant light and heat resources. The average annual sunshine hours are 2797.5 h. The annual rainfall is 180-270 mm, and the annual evaporation is 1000-1500 mm. The average annual temperature is 6.6 \({}^{\circ}\)C, the highest temperature occurs in July, the average temperature is 24-26 \({}^{\circ}\)C, the effective cumulative temperature of \(\geq\)10 \({}^{\circ}\)C is 3428.5 \({}^{\circ}\)C, and the average frost-free period is 176 days. The monthly average temperature and irrigation volume are shown in Table 1. The test soil texture in this area was mainly loamy and the basic physicochemical properties were alkaline dissolved nitrogen (ADN) 60.88 mg\(\cdot\)kg\({}^{-1}\), organic matter (OM) 19.90 mg\(\cdot\)kg\({}^{-1}\), effective potassium (EK) 134 mg\(\cdot\)kg\({}^{-1}\), and effective phosphorus (EP) 17.95 mg\(\cdot\)kg\({}^{-1}\), with the previous crop being cotton. The irrigation quota is the local average, 4050 m\({}^{3}\cdot\)ha\({}^{-1}\), divided into ten irrigation times.  
A total of five nitrogen application levels were set up in this study: N0 (0 kg-ha\({}^{-1}\)), N1 (120 kg-ha\({}^{-1}\)), N2 (240 kg-ha\({}^{-1}\)), N3 (360 kg-ha\({}^{-1}\)), and N4 (480 kg-ha\({}^{-1}\)). The cell layout is shown in Figure 1. Nitrogen was applied in the form of urea (46% nitrogen content) with water drops, and phosphorus and potash fertilizers (potassium dihydrogen phosphate) were both applied at a rate of 150 kg-ha\({}^{-1}\). The application timing of fertilizer was synchronized with the drip application timing of local farmers. The cotton planting date was April 25, 2022, and the tip pruning date was July 13. The planting pattern was \"one film, three tubes, and six rows\", and the plant spacing was 10 cm + 66 cm + 10 cm. Each nitrogen treatment plot was replicated three times, with a total of 15 plots and a single plot area of 150 m\({}^{2}\). A randomized block design was used, with protective rows set around the perimeter, and all other field management measures were carried out according to local high-yield cultivation requirements. Sampling from the budding period occurred every ten days. Three cotton plants with the same growth in each plot were collected each time; there were fifteen plots in total, and the number of plants sampled each time was forty-five.
# A Hybrid Scheme for Disaster-Monitoring Applications in Wireless Sensor Networks
## 6 Performance Evaluation
### Data Transmission Performance
In the HT-MAC scheme, superior data frames are much faster than ordinary data frames. With increasing traffic intensity, superior data frame delays change only slightly, far less than the frame delay of ordinary data. The mean queue length of the superior data at the superior node is shown in Figure 15. When \(N=5\), the mean queue length of the superior data for DRX starts to rise rapidly at a traffic intensity of 0.5, and reaches the maximum of the queue buffer at a traffic intensity of 0.65. At \(N=20\), the DRX protocol is similar to that at \(N=5\). When \(N=5\), the mean queue length of the superior data for WirelessHART starts to increase rapidly at a traffic intensity of 0.7. However, when \(N=20\), the mean queue length of the superior data for WirelessHART continues to increase slowly. When \(N=5\) and 20, HT-MAC always has a very short queue length for superior data. Compared with the DRX scheme, HT-MAC has a smaller data-frame delay when the traffic intensity increases. As the traffic intensity increases above 0.4 (\(N=5\)) and 0.5 (\(N=20\)), DRX's superior data frame delay increases rapidly. The results for WirelessHART were similar to those for DRX. When the traffic intensity exceeds 0.4 (\(N=5\)), the superior data frame delay of WirelessHART increases rapidly. HT-MAC's alert data frame delay is clearly better than that of DRX, particularly when the total traffic intensity exceeds 0.4, and the alert data frame delay remains low and is smaller than that of WirelessHART. For \(N=5\), the maximum alert data frame delay of HT-MAC is 9 ms and 15 ms for \(N=20\). The ordinary data frame delay of HT-MAC also remains low when the total traffic intensity increases. However, when the total traffic intensity exceeds 0.4, the ordinary data frame delay of WirelessHART increases. When the total traffic intensity exceeds 0.5, the ordinary data frame delay of DRX increases. The mean upper bound of frame delay is an important performance metric. The simulation results showed that 99.23% of the frame delays satisfied the mean upper bound of the frame delay in an ordinary node, 99.57% satisfied the mean upper bound for superior Node 1, and 99.42% satisfied the mean upper bound for superior Node 2. For HT-MAC, the frame delay performance for the three types of data was better than those of the WirelessHART and DRX schemes. Throughput is also a key indicator in disaster-monitoring applications. For \(N=20\), the mean throughput of the ordinary node is 364 kb/s, and that of the superior node is 732 kb/s. When \(N=5\), the mean throughput of the ordinary node is 973 kb/s, and that of the superior node is 1.9 Mb/s. In the HT-MAC scheme, the delay and throughput specifications for superior, alert, and ordinary data follow the requirements for crucial services such as earthquake surveillance.
###### Abstract  
Background: Breast and prostate cancer represent a significant global public health burden. Among the adverse effects of oncological treatments, fatigue is one of the most prevalent, persistent, and disabling symptoms. Therapeutic exercise has been shown to be effective for its management, with supervision identified as a key factor that may enhance adherence, safety, and intensity control. This systematic review and meta-analysis aimed to compare the effects of supervised exercise programs versus usual care on cancer-related fatigue in patients with breast or prostate cancer. **Methods**: A systematic search (September-December 2024) was conducted in six databases (PubMed, Web of Science, Scopus, Cochrane, PEDro, Scielo), selecting RCTs from the past 10 years in English or Spanish. Studies compared supervised exercise with unsupervised exercise or usual care in stage I-III breast or prostate cancer patients within five years post-treatment. Methodological quality was assessed with the PEDro scale and risk of bias with Cochrane's RoB 2.0. A random-effects model was used to calculate pooled effect sizes (ES, 95% CI), with heterogeneity (I\({}^{2}\)), sensitivity, subgroup, and publication bias analyses. **Results**: A total of 25 interventions from 19 randomized controlled trials involving over 2200 participants were included. Supervised exercise significantly reduced cancer-related fatigue compared to usual care (effect size = 0.34; 95% CI: 0.22-0.47; \(p<0.001\); I\({}^{2}\) = 56%). Sensitivity analyses supported the robustness of the findings. Subgroup analyses revealed greater effects in combined exercise programs, in men, and in patients with prostate cancer. No evidence of publication bias was observed. While 73.7% of studies were rated as having good methodological quality, the risk of bias was often unclear or high. **Conclusions**: Supervised therapeutic exercise programs are effective and safe for reducing fatigue in breast and prostate cancer survivors. These interventions should be incorporated into comprehensive care plans, with individualization based on patients' clinical and demographic characteristics. Further research is needed to identify the most effective and sustainable strategies for different patient subgroups.  
**Keywords:** therapeutic exercise; supervised; fatigue; breast cancer; prostate cancer
## 2 Materials and Methods
### Calibration and Experimental Protocol  
To address our key research questions, using the CARDAMOM framework we performed an initial analysis and evaluated a multi-treatment calibration of DALEC-Crop. This multi-treatment calibration was then used to carry out three successive model experiments. These experiments were designed to determine the accuracy of wheat N retrievals (Experiment 1), validate and identify the key inputs driving yield variability (Experiment 2) and evaluate yield predictions when constraining the model during growth stages when N fertilizers are typically applied (Experiment 3).