esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Performances of Original Models  
After calibrating the TL-LUE model and VPM using the reference data, validation using the data for the remaining years was conducted (Figures 4 and 5). The TL-LUE model performed the best at the US-MMS site (R\({}^{2}\) = 0.80, RMSE = 2.14 gCm\({}^{-2}\) day\({}^{-1}\)) and performed the worst at US-AR1 (R\({}^{2}\) = 0.41, RMSE = 1.45 gCm\({}^{-2}\) day\({}^{-1}\)). In general, the model showed significantly better performances at forest sites (R\({}^{2}\) ranged from 0.56 to 0.80, mean R\({}^{2}\) = 0.68) than at grass-type sites (R\({}^{2}\) ranged from 0.41 to 0.78, mean R\({}^{2}\) = 0.60). VPM performed the best at the forest site of IT-Mbo (R\({}^{2}\) = 0.77, RMSE = 2.13 gCm\({}^{-2}\) day\({}^{-1}\)) while the worst at the grass-type site of US-Goo (R\({}^{2}\) = 0.41, RMSE = 1.69 gCm\({}^{-2}\) day\({}^{-1}\)). Different from the TL-LUE model, for VPM, the overall performance for the forest sites (mean R\({}^{2}\) = 0.58) was similar to that of the grass-type sites (mean R\({}^{2}\) = 0.58).  
Figure 4: Validation of daily GPP estimated by TL-LUE model with tower measurements (GPP\({}_{\text{EC}}\)) at 16 sites (The red line represents the fitting line, and the dashed line represents the 1:1 line).  
From these results, it can be seen that the two models exhibited consistently good or poor performances at some sites due to the similarity in their model structures. For example, the two models performed less satisfactorily at the sites of US-SRG and US-Goo, with R\({}^{2}\) < 0.5; both show consistently good performances at the US-PFa and IT-Mbo sites (R\({}^{2}\) > 0.6). Meanwhile, due to the differences in input data, significant differences existed between the performances of the two models at the same site. VPM performed better (R\({}^{2}\) = 0.66, RMSE = 0.78 gCm\({}^{-2}\) day\({}^{-1}\)) than the TL-LUE model (R\({}^{2}\) = 0.41, RMSE = 1.45 gCm\({}^{-2}\) day\({}^{-1}\)) at the US-AR1 site.
# Research on Automatic Recognition Method of Artificial Ground Target Based on Improved HED
## 2 Research on Edge Detection Method of Deep Learning Based on Improved HED
### Improved HED Network  
The traditional HED algorithm was designed based on the VGG network structure. Because of pooling in each layer, the resolution of the feature map output from the upper layer of the network was relatively low. After deconvolution, the position information loss of the edge detection results from the last two side output outputs was too large, and the internal texture information of the result target after fusion with the lower layer side output was too much. However, the network high-level feature map had rich semantic features, which could better filter out the texture edges of the target, so that the output results could better reflect the appearance details of the target. Therefore, the edge extracted from the high-level side output was more meaningful for image edge extraction. Therefore, improving the resolution of the edge information output from the last two sides was conducive to improving the performance of image edge extraction.  
Therefore, the HED network structure had been improved, the third and fourth pooling layers had been removed and the deconvolution layers in the last two side output layers had been modified accordingly, so as to extract more refined edges while retaining high-level semantic information. The improved HED network structure is shown in Figure 3.  
The performance of the improved HED algorithm was evaluated on the BSDS500 data set. The BSDS500 data set is a visible light image data set provided by Berkeley University. There were 500 pictures in total, 200 training pictures, 200 test pictures and 100 verification pictures. The ground truth was manually marked. The CaffeModel network architecture was adopted, and F-measure was used to measure the contour detection effect.  
\[F-measure=\frac{2\cdot precision\cdot recall}{precision+recall} \tag{1}\]  
where precision is the accuracy rate and recall is the recall rate. The improved HED algorithm is better than HED algorithm. 0.5% higher than HED algorithm.  
In order to verify the effectiveness of the improved HED algorithm, we used multiple groups of images for experimental comparative analysis. From Figure 4, it is not difficult to see that although the abstract edge probability map output by the HED network could clearly display the target, and the contour was fuzzy. In contrast, the improved HED network had a higher image resolution and clearer contour due to the reduction in the pooling layer, so the edge detection performance of the improved HED network was significantly better than that of the HED network.  
Figure 2: HED model output diagram.
# Optimization of Truck-Drone Parcel Delivery Using Metaheuristics
## 1 Introduction
### The Original Solution to the TSP-D  
[PERSON] et al. [10] have proposed several heuristics that are able to provide fast and acceptable solutions for instances of reasonable sizes. First, they constructed a truck-only tour using either the Concorde TSP solver or [PERSON]'s minimum spanning tree algorithm. Next, two approaches were proposed to classify some nodes as drone nodes. The first approach was a fast greedy heuristic, and the second approach was an exact partitioning algorithm based on dynamic programming. In both approaches, the order in which the nodes are visited remains unchanged. Both the greedy heuristic and the exact partitioning algorithm depend on the initial TSP tour. Therefore, the authors decided to start with a variety of initial tours, in order to determine whether they could obtain a better solution. They applied several heuristics based on the local search (LS) method, which modified the initial tour. Then they iteratively sought improvements. Several neighborhood functions were considered, which yielded several versions. In one of the versions, all neighborhoods were examined and the best neighbor generated in each iteration was selected.  
The authors chose to run their experiments using the most challenging instances, where there were no limitations on the drone range and all locations could be visited by both drone and truck [23]. The greedy approach solved all instances with 100 nodes in a few seconds, but with limited savings in time relative to the original TSP tour. In contrast, the run time of the exact partitioning heuristics method increased tremendously with the number of nodes.
## Case Study and Results
### BESS Configuration Case  
According to the configuration presented previously, the schematic figure of the BESS configuration is shown in Figure 6, and the parameters of the model established for BESS configuration in the case study are listed in Table 10. The reason Equation (17) does not include the distribution network basic capacity cost is that the cost is a constant parameter for the case. The simulation is presented in the next section.  
Figure 5: Optimized charging load in operating time.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Daytime Elec.** & **Night Elec.** & **Electricity** & **Peak Power** & **Elec. Fee** \\
**(kWh)** & **(kWh)** & **Price** & **(kW)** & **(CNY/day)** \\ \hline
5128.8 & 0 & TOU & 1601.7 & 4163.0 \\
2344.6 & 2784.2 & TOU & 912.0 & 2618.3 \\ \hline \end{tabular}
\end{table}
Table 8: The cost of different charging modes.
## 5 Results and Discussion
### Quantitative Results
#### 5.1.3 Object re-ID  
In this study, to evaluate object re-ID, we have applied the algorithm on 10 random episodes from Season 4 of _New Girl_ and 10 random episodes from Season 3 of _Friends_ TV shows. The dataset does not possess ground truth labels. Thus, the approach was manually validated--if the object was re-IDed correctly it was marked _True_; else it was marked _False_. The _True_ and _False_ values were consolidated per object class for all episodes of _New Girl_ and _Friends_ separately, and the object classes that had a minimum of 20 occurrences in all the episodes of SOAP put together were chosen to estimate the accuracy. The accuracy was then calculated for each SOAP separately. Table 3 shows the accuracy results for the object  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON] et al. [20]**} & \multicolumn{4}{c}{**Our Approach**} \\ \hline
**Video** & **Precision** & **Recall** & \(\mathbf{F_{shot}}\) & **Precision** & **Recall** & \(\mathbf{F_{shot}}\) \\ \hline
23353 & 0.95 & 0.99 & **0.96** & 0.877 & 0.99 & 0.945 \\
23357 & 0.91 & 0.97 & 0.939 & 0.874 & 0.99 & **0.940** \\
23358 & 0.92 & 0.99 & **0.954** & 0.775 & 0.99 & 0.873 \\
25008 & 0.94 & 0.94 & **0.94** & 0.849 & 0.99 & 0.918 \\
25009 & 0.97 & 0.96 & **0.965** & 0.726 & 0.98 & 0.841 \\
25010 & 0.93 & 0.94 & 0.935 & 0.955 & 0.99 & **0.977** \\
25011 & 0.62 & 0.9 & 0.734 & 0.863 & 0.99 & **0.927** \\
25012 & 0.66 & 0.89 & 0.758 & 0.890 & 0.890 & **0.89** \\ \hline Average & 0.853 & 0.948 & 0.899 & **0.861** & **0.986** & **0.912** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance comparison for shot detection using boundary-level metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**[PERSON] et al. [27]**} & \multicolumn{4}{c}{**[PERSON] et al. [26]**} & \multicolumn{4}{c}{**Our Approach**} \\ \hline
**Video** & **Coverage** & **Overflow** & \(\mathbf{F_{scene}}\) & **Coverage** & **Overflow** & \(\mathbf{F_{scene}}\) & **Coverage** & **Overflow** & \(\mathbf{F_{scene}}\) \\ \hline
23553 & 0.82 & 0.40 & 0.69 & 0.63 & 0.20 & 0.70 & 0.66 & 0.0083 & **0.79** \\
23557 & 0.77 & 0.24 & **0.76** & 0.73 & 0.47 & 0.61 & 0.65 & 0.2016 & 0.72 \\
23558 & 0.77 & 0.37 & 0.69 & 0.89 & 0.64 & 0.51 & 0.73 & 0.1346 & **0.80** \\
25008 & 0.42 & 0.06 & 0.58 & 0.72 & 0.24 & **0.74** & 0.41 & 0.0100 & 0.58 \\
25009 & 0.95 & 0.76 & 0.39 & 0.69 & 0.53 & 0.56 & 0.67 & 0.124 & **0.76** \\
25010 & 0.66 & 0.40 & 0.63 & 0.89 & 0.92 & 0.15 & 0.66 & 0.012 & **0.79** \\
25011 & 0.70 & 0.14 & **0.77** & 0.94 & 0.92 & 0.15 & 0.61 & 0.048 & 0.74 \\
25012 & 0.53 & 0.15 & 0.65 & 0.93 & 0.94 & 0.11 & 0.63 & 0.0400 & **0.76** \\ \hline
**Average** & 0.70 & 0.30 & 0.66 & **0.8** & 0.63 & 0.43 & 0.63 & **0.074** & **0.74** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison for LSU detection using frame-level metrics.  
re-ID applied on the two SOAP series. These experimental results show that our object re-ID algorithm performs at an average accuracy of 0.87.
# A Routing Protocol for Multisink Wireless Sensor Networks in Underground Coalmine Tunnels
## 4 Design of PCEB-MS Protocol
### UCEBA Algorithm
#### 4.2.2 Calculation of Unequal Radius  
It is necessary to have an unequal competitive radius to control the cluster scale and to determine the final cluster head out of all candidate cluster heads. A certain relation clearly exists between the unequal radius and the residual energy of the node; the radius becomes larger if the residual energy is higher and the scale is larger. In contrast, the radius becomes smaller if the residual energy is lower, which effectively prolongs the network lifetime. The competitive formula proposed in this paper is presented as follows:  
\[R_{ra}=\left[1-\omega_{1}\left(1-\frac{E_{cu}}{E_{0}}\right)-\omega_{2}\left(1 -\frac{d_{to5K}}{d_{\max}}\right)\right]R_{\max} \tag{14}\]  
Accordingly, \(\omega_{1}\) and \(\omega_{2}\) refer to the adjustment coefficient in the 0-1 range. \(E_{cu}\) refers to the residual energy of the existing candidate cluster head node, \(E_{0}\) refers to the initial energy of the candidate cluster node, \(d_{to5K}\) refers to the distance between this candidate cluster head node and the sink, \(d_{\max}\) refers to the maximum distance between all the candidate cluster heads and sink, and \(R_{\max}\) refers to the maximum cluster radius. Clearly, exceeding \(R_{si}\) is impossible for the maximum cluster radius, \(R_{\max}\). Therefore, this study considers that \(R_{\max}\leq R_{si}\).  
An analysis of Equation (14) shows that if the residual energy of the candidate cluster head is less than others and has a smaller competitive radius, then the premature death of the cluster head is avoided. In addition, if the distance between the candidate cluster head and sink is shorter, then the competitive radius is also smaller than others, thereby avoiding the premature death of the cluster  
Figure 8: Unequal clustering mechanism.  
head caused by heavy forwarding of data. Therefore, a competitive radius is used to control the scale of the cluster head and to ultimately achieve an energy balance.
## 2 Main 3-HP Biosynthesis Pathways for the Construction of Microbial Cell Factories
### Glycerol Pathway  
#### 2.1.1 Glycerol Oxidation via the Coenzyme A-Dependent Pathway  
The coenzyme A-dependent pathway was discovered in _Lactobacillus_ spp., which can naturally metabolize glycerol to synthesize 3-HP ([PERSON], 2012). In _Lactobacillus reuteri_, glycerol is converted into 3-hydroxypropionaldehyde (3-HPA) by coenzyme B\({}_{12}\)-dependent glycerol dehydrates (PduCDE), after which propionaldehyde dehydrogenase (PduP) catalyzes the ligation of 3-HPA to coenzyme A, yielding 3-HP-CoA. The final conversion of 3-HP-CoA to 3-HP is catalyzed by phosphotransferase (PduL) and propionic acid kinase (PduW), and the whole production process is accompanied by the generation of one molecule of ATP and one molecule of NADH ([PERSON], 2012; [PERSON], 2012; [PERSON], 2013).
# Weight Feedback-Based Harmonic MDG-Ensemble Model for Prediction of Traffic Accident Severity
## 3 Weight Feedback Based Harmonic MDG-Ensemble Model for Predicting Traffic Accident Severity
### Weight Feedback Based Harmonic MDG-Ensemble Model
Trains Model [Num_Model] with single classification method using train data  Makes Ensemble model _WFEM_ using _Model_, _Weight_ for\(j\) =1 to length of train fold do  Pred_e \(\leftarrow\) Prediction of WFEM for j-th transaction of test data  Real \(\leftarrow\) Real Label of j-th transaction of test fold for\(i\) = 1 to Num_Model do  Pred_s \(\leftarrow\) Prediction of Model[i] for j-th transaction of test data if (Real == Pred_e)//Under accurate prediction if (Pred_e == Pred_s)//Prediction of Model[i] is same as WFEM  Correct[i]++ else if (Real!= Pred_e)//Under inaccurate prediction if (Pred_e == Pred_s)  Incorrect[i]++ endfor endfor  Idx_Correct \(\leftarrow\) index of maximum of correct  Idx_Incorrect \(\leftarrow\) index of maximum of incorrect  Accurate [Idx_Correct] \(\leftarrow\) 1  Inaccurate [Idx_Incorrect] \(\leftarrow\) 1  FWi = Weighti + H_MDG\({}_{i}\)(\(\alpha\times\)Acc\({}_{i}\)-\(\beta\times\)Inacc\({}_{i}\))  Accurate \(\leftarrow\) 0  Inaccurate \(\leftarrow\) 0 endfor return
```  
**Algorithm 1**Weight feedback based harmonic MDG-ensemble algorithm. The weight feedback-based harmonic MDG-ensemble algorithm process of Algorithm 1 is based on the generation of an ensemble model and weight feedback. First, the variable for saving the correct or incorrect value is initialized to '0'. Also, the variable that determines the contribution of accurate or inaccurate predictions of the ensemble model is initializedto '0'. The initial weight of each model is set to '1'. Next, the pre-processed traffic accident data is split into 70% of training data, 20% of test data, and 10% of verification data set. This prevents over-fitting for model learning. The weights were adjusted according to the prediction results of the ensemble models and single models for the test fold. For every single model, if the prediction values of the ensemble and single models are consistent with each other while predicting accurately, the single model's _Correct_ increases; if they are consistent under inaccurate prediction, the single model's _Incorrect_ increases. The _Correct_ and _Incorrect_ tallies of all single models are compared, and thus the _Accurate_ and _Inaccurate_ of the model with the largest value for either is set to '1'. The difference in values multiplied by the weight increase (_Alpha_) and reduction (_Beta_) multiplied by the _Accurate_ and _Inaccurate_, respectively, and the calculated value is multiplied by the harmonic-mean of the MDG. After that, the calculated value is added to an existing weight, and the result is offered as feedback. Finally, _Accurate_ and _Inaccurate_ are set to '0' for the weight feedback to be made for the rest of the fold. These tasks are repeated as often as the number of folds split _TAD_. Thus, the error of the ensemble model is improved.
## 5 Results and Discussion
### Cost Comparison of Different Development Strategies  
Because the low-level intelligent driving system is generally developed based on the less difficult function superposition strategy in the industry, and the high-level intelligent  
Figure 4: Cost comparison of intelligent driving under different technical routes. Costs are expressed in USD.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TCO Composition** & **Low-Level** & **Medium-Level** & **High-Level** \\ \hline Hardware & 70.1–71.7\% & 56.9–63.3\% & 52.3–55.3\% \\ Software & 14.1–16.9\% & 13.6–21.3\% & 12.9–18.0\% \\ Data Traffic & 1.1–1.3\% & 1.2–4.2\% & 5.9–10.5\% \\ Power Consumption & 3.2–4.4\% & 4.9–10.7\% & 7.4–8.3\% \\ Maintenance & 8.6–8.7\% & 11.2–12.2\% & 13.6–14.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: TCO composition share at different levels of intelligent driving.  
driving system cannot be developed based on the function superposition, the technical route dispute of the development strategy mainly exists at the medium-level system. The cost comparison of different development strategies is shown by the cerulean arrow in Figure 4. Table 6 shows the TCO composition of related technical routes. For medium-level intelligent driving, the scenario-driven option can reduce the cost by 13% compared with function superposition because it optimizes the integration of functions to reduce the redundancy of sensors and the scale of computational capacity. Considering that the R&D of medium-level systems adopting the function superposition strategy is often based on the already developed low-level system, this means that some functions can be directly inherited. In this study, by subtracting the software R&D cost of L-FSV and L-FSM from M1-FSV and M1-FSM, it is found that M1-SSV and M1-SSM still have about a 10% cost advantage. It should be pointed out that under the scenario-driven development strategy, the medium-level intelligent driving system in simple scenarios can make the ODD of the system cover complex scenarios by upgrading the hardware and software. From M1-SSV to M2-SSV, the TCO needs to be increased by USD 1958, and from M1-SSM to M2-SSM, the TCO needs to be increased by USD 5085.
## 6 Experiment and Analysis
### Baseline Algorithms  
To assess the performance of our method meticulously, we conduct a comparative analysis against several established baseline algorithms:  
* Benefit-first greedy algorithm: This algorithm employs a greedy approach that iteratively selects locally optimal solutions in the pursuit of a globally optimal solution. At each step of the solution process, the baseline algorithm focuses exclusively on maximizing the profit function when determining the charging station sites.
* Demand-first greedy algorithm: In contrast to the aforementioned Benefit-first Greedy Algorithm, this approach prioritizes charging demand when selecting charging station locations, employing a greedy strategy to achieve optimal solutions.
* Genetic algorithm: Widely recognized as a heuristic algorithm, the genetic algorithm emulates the natural evolutionary process to search for optimal solutions [39]. In our study, we enhance the traditional genetic algorithm and devised a Multi-Layer Perceptron (MLP) network for population encoding to address the problem at hand.
## 3 Calibration
### Wavelength and Radiometric Stability  
Both the downwelling irradiance and upwelling imaging spectrometer are monitored for radiometric and wavelength stability. A portable, 10 cm Teflon integrating sphere with a Hg and Ar pen lamp illumination sources produce nine pronounced emission lines (Figure 7) that are used periodically to verify the spectral channel to wavelength relationship over the detector spectral range with sub-nanometer precision. A large aperture, 1 m integrating sphere and an NIST traceable uniform source is used to monitor the radiometric stability and performance of the spectrometers between campaigns.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 2 Related Work
### Statistical Heterogeneity and Drift Metrics
#### Quantifying Divergence  
Divergence can be measured in several complementary ways. _Model-parameter distance_\(\|w_{i}-w\|\) was shown in early empirical studies [1] and confirmed in later work [2,3] to correlate with slower FedAvg convergence when the gap grows. _Gradient dissimilarity_\(\|\
abla F_{i}(w)-\
abla F(w)\|\) features in upper-bound analyses of stochastic optimisation and underpins the convergence proofs of SCAFFOLD and FedNova. More recently, _distributional distances_--for example, the Kullback-Leibler divergence or the 2-Wasserstein metric between empirical label histograms--have guided client clustering and hierarchical aggregation: [PERSON] et al. rely on 2-Wasserstein distance [18], whereas [PERSON] et al. employ KL-regularised objectives to tighten generalisation bounds under covariate shift [19]. Finally, _information-theoretic bounds_ based on entropy have been proposed to upper-bound the expected loss under non-IID sampling, although they require exchanging per-round entropy estimates.  
Mitigation Strategies  
Divergence can be mitigated by three main defence classes. _Proximal regularisers_ (e.g., FedProx, FedDyn) tether each client's local solution to the current global iterate, limiting deviation. _Variance-reduction schemes_ (e.g., SCAFFOLD, FedNova) introduce control variates or normalisation factors that offset client drift. Finally, _distribution-aware scheduling_ clusters clients or re-weights their updates according to measured heterogeneity. All three approaches curb divergence but at the cost of additional hyper-parameters, auxiliary vectors, or communication overhead.  
Gap Addressed by ERFO  
ERFO departs from ad hoc drift proxies by deriving a single-scalar entropy regularizer from a pigeonhole-theoretic bound on KL divergence. Unlike KL or gradient-norm trackers, the entropy term is computed locally with one extra back-propagation and piggybacks on the standard weight update--no additional metadata or parameter tuning--thereby offering a principled, communication-neutral remedy for statistical heterogeneity.