esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Comparison
### Experimental Environment and Parameters  
The PCC\({}^{2}\)LS algorithm is coded in the C++ programming language and compiled with GNU g++ using the -O option. PCC\({}^{2}\)LS and MSLS run on an Intel Core i5-11400H processor clocked at 2.70 GHz. LPNMR'09 runs on a Genuine Intel Core 2 CPU 6600 at 2.40 GHz. ACO and ACO + PCS operate on a Genuine Intel Core 2 CPU E8500 running at 3.16 GHz. GRASP runs on an Intel Xeon CPU E7-4830 with a clock speed of 2.13 GHz. VNDS runs on an Intel i3 CPU clocked at 2.1 GHz. In order to obtain more high-quality candidate solutions for the proposed algorithm, we have conducted several experiments to finalize the parameters involved. The number of loop iterations is set to 50,000, the probability parameter _fp_ is set to 0.5, and the number of perturbed vertices is set to 0.05 \(\times\) 1 \(V\).
## 3 Literature Review
### DarkTrack2021  
[PERSON] et al. presented the DarkTrack2021 [19] dataset to tackle the difficulty of tracking unmanned aerial vehicles in low-light situations. The dataset consists of 110 annotated sequences containing more than 100,000 frames, providing a varied evaluation platform for tracking UAVs during nighttime. The researchers created an effective low-light enhancer called the Spatial-Channel Transformer (SCT), which combines a Spatial-Channel Transformer with a robust non-linear curve projection model to effectively enhance low-light images. The Spatial-Channel Attention Module (SCT) employs a technique that effectively combines global and local information, resulting in enhanced image quality by reducing noise and improving illumination in nighttime scenes. This study utilizes the proposed ADTrack algorithm together with 16 state-of-the-art handmade correlation filter (CF)-based trackers to evaluate their performance on tracking benchmarks UAV123@10 fps, DTB70, and UAVDark135. The aim is to demonstrate the comprehensive robustness of the proposed ADTrack algorithm in all-day UAV tracking. Evaluations conducted on the public UAVDark135 and the new DarkTrack2021 benchmarks demonstrated that SCT exhibited superior performance compared to existing methods in tracking UAVs during nighttime. The practicality of the approach has been confirmed through real-world tests. The DarkTrack2021 dataset and SCT code are openly accessible on GitHub for additional research and experimentation.
# Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication
## 3 Experimental Results and Discussion
### Parameter Settings  
In the experiment, we normalize the traffic data to the interval [0, 1] and the purpose is to speed up model training. In addition, we treat 70% of the dataset as the training set and the remaining 30% as the test set. In this experiment, the learning rate of the model is 0.001, the size of batch is 32 and the size of epoch is 800. In addition, the model uses L2 regularization to prevent over-fitting. Adam optimizer with gradient descent is employed to train the model.  
In the multi-source information fusion of the dataset based on V2X technology, we set the size of the hidden layer to 20, 40, 60, 80, 100, 120, and 140, respectively. Table 4 shows the comparison of prediction performance under different hidden neurons. Figure 10 shows the comparison of prediction accuracy under different hidden neurons in the dataset. It can be seen from the results that the over-fitting phenomenon occurs when the number of neural units in the hidden layer increases from 20 to 140, the prediction accuracy increases at first and then decreases. When the number of neurons in the hidden layer is 100, the _Accuracy_ is 0.9544. Therefore, the number of hidden layer neurons set in the model is set to 80.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**The Number of** & _RMSE_ & _MAE_ & _Accuracy_ \\
**Hidden Neurons** & & & \\ \hline
20 & 2.8942 & 1.9852 & 0.9505 \\
40 & 2.7863 & 1.9723 & 0.9516 \\
60 & 2.7021 & 1.9623 & 0.9528 \\
80 & 2.6967 & 1.9483 & 0.9531 \\
100 & 2.6547 & 1.9372 & 0.9544 \\
120 & 2.7654 & 1.9521 & 0.9529 \\
140 & 2.7735 & 1.9652 & 0.9517 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The predicted errors in different hidden neurons.
# Bushfire Disaster Monitoring System Using Low Power Wide Area Networks (LPWAN)
## 3 Design and Implementation
### Data Collection and Transmission in LPWANs  
Deployed sensors capture data such as temperature, humidity, air condition, and so forth, and transmit them to neighbouring devices which forward/relay them towards the gateway, which is meant to collect data in its own network. There can be multiple gateways, which are aggregated by a customer edge (CE) to communicate with a base station (BS). Whilst the internal communications can be done by LPWAN protocols, the interface from the CE to BS is done by the public network such as 3G or 4G LTE.
## 3 Results
### Effect of Battery Pre-Heating on the Residual Driving Range of a BEV
This is due to the initial estimation of the battery capacity loss which is directly linked to the amount of energy that has to be devoted to heat the battery temperature up to the optimal operating temperature. Specifically, for an initial temperature of 0 \({}^{\circ}\)C, the gap of temperature was 23 \({}^{\circ}\)C whereas an increase of 33 \({}^{\circ}\)C was needed for an initial temperature of \(-10\) \({}^{\circ}\)C. The time step at which the vehicle velocity profiles assumed the first positive value differed for the BTSM-on and BTMS-off conditions since BTMS-on assumes the vehicle remains in the same location (e.g., parking lot) while heating up the battery. Once the battery heating up process has completed, the vehicle was assumed to run the same driving mission and hence a shift ahead of the blue curves with respect to the red curves appears in the charts. The main result highlighted by Figures 34 and 35 is that, due to the increase in the value of \(\lambda_{t_{bp}}\) in terms of battery pre-heating, and considering a fixed value of \(\lambda_{SoH}\) (i.e., fixed amount of life-cycles completed by the battery), the available capacity grows through time allowing for higher residual driving ranges. To clarify, the value of \(\lambda_{t_{bp}}\) could be increased to (0.95, 1) for pre-heating mode due to BTMS thermal management. A non-utilisation of the thermal management therefore led to a considerable loss of BEV electric range when the vehicle was turned on in a very cold driving environment. Moreover, the higher the difference between the initial operating temperature of the battery and its optimal operating temperature, the greater the amelioration that can be applied to the system through a proper exploitation of the BTMS control. Indeed, such an effect could naturally be mitigated only when warmer ambient temperatures are encountered, as a lower difference between the operating battery temperature and its optimal operating temperature would be experienced (i.e., \(\lambda_{t_{bp}}\) tends to 1). Thanks to this set of analyses, the positive influence of pre-heating the battery pack when the vehicle is parked in a cold environment has been demonstrated. Figure 34: Comparison of residual driving range estimated with (blue curve) and without (red curve) thermal management of the BTMS for DM09 for an initial battery pack temperature of 0 \({}^{\circ}\)C.
## 2 Literature Review
### Optimal Inventory Level  
Inventory management under uncertain demand has been a focal point of extensive research, with particular emphasis on determining optimal service levels for periodic review policies. [PERSON] and [PERSON] [14] provided a comprehensive analysis of \((nQ,r)\) policies, demonstrating their advantages over traditional \((s,S)\) policies and establishing the convexity of average cost with respect to the reorder level. Building on this, [PERSON] [15] explored the relationships between periodic \((R,T)\) and continuous \((Q,r)\) review policies, highlighting the resilience of optimal costs in \((R,T)\) models to variations in replenishment intervals.  
In the context of bike-sharing systems, inventory management takes on unique challenges due to the dynamic nature of demand and the spatial distribution of stations. [PERSON] and [PERSON] [16] introduced a User Dissatisfaction Function for single-station optimization, while [PERSON] et al. [17] extended this concept by combining \((s,S)\) policies with network flow matching for a more holistic approach. [PERSON] et al. developed [18] a robust optimization model for bike allocation in new markets. Dynamic inventory management in bike-sharing systems was addressed by [PERSON] et al. [19], who applied Receding Horizon Control, and [PERSON] et al. [20], who used guided local search to set optimal inventory levels while considering station interactions. These approaches represent significant steps towards more adaptive and system-wide inventory management strategies in the complex environment of bike-sharing networks. Our study advances bike-sharing inventory management by using DeepAR forecasts to optimize inventory levels across multiple stations and time periods, integrating demand prediction with rebalancing decisions.
# Understanding the Fracture Behaviors of Metallic Glasses--An Overview
## 2 Fracture Behaviors of MGs
### Typical Mode I Fracture of MGs  
The fracture properties are very important for the engineering applications of structural materials. The mode I fracture properties of metallic glasses is widely studied by 3-point/4-point bending and compact tension tests [21; 22]. The measured fracture toughness of most MGs falls in the range of a few MPa\(\cdot\)m\({}^{1/2}\) to more than one hundred MPa\(\cdot\)m\({}^{1/2}\)[11; 12]. This value is much lower than that of steel and other commonly used engineering alloys [23]. The fracture brittleness is considered as one of the main problems for the engineering application of MGs.  
The low fracture toughness of MGs can be understood by their special fracture mechanism. According to the experiments and literature, a typical mode I fracture process of MGs can roughly be divided into three stages:
## 5 Result and Discussion
### Simulation Results
#### 5.2.1 IEEE Test System
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & & & & & \\ \cline{3-8}
**Time** & **Contract** & \multicolumn{6}{c}{**(Energy Band)**} \\ \cline{3-8}
**(h)** & **(kW)** & \multicolumn{6}{c}{**30\%**} & **40\%** & **50\%** & **60\%** & **70\%** & **80\%** \\ \hline
1 & 55 & 40.04 & 43.12 & 46.2 & 49.28 & 52.36 & 55.44 \\
2 & 30 & 21.06 & 22.68  
Table 3 compares the operational costs of the DN for all of the cases. In the base case, that is, when no power was purchased from the DN, the operational cost was $519,482/h, while the operational costs for Cases 1 and 2 were $516,841/h and $517,212/h, respectively. Although the operational cost in the case of the proposed bi-level optimization model (Case 2) was 0.2% ($1033/h) higher than that for Case 1, it was 0.3% ($1608/h) lower than that for the base case. These results are relevant to the problems relating the MG operation; that is preventing shortages in the energy supply to the DN from worsening, even though the operational costs for both Case 2 and the base case were higher than that for Case 1.  
Figure 9: Scheduled power scheme for Case 2 for energy band of 80%: (**a**) Results for energy band, (**b**) Scheduled power volumes for distributed units.  
To demonstrate the effectiveness of the bi-level optimization model for cooperation between the DSO and MGO, the following two scenarios of Case 2 were considered: Scenario A: no bi-level optimization model with Case 2, and Scenario B: bi-level optimization model with Case 2. Table 4 presents a comparison of the operational costs for each scenario in Case 2. In Scenario A, the MG purchased power from the wholesale market at the RTP, because the feedback information including the operational costs of MG and energy band, was not considered. According to the comparison results, the operational cost of the MG in Scenario A was $512,638/h, which was lower than that in Scenario B, namely $517,874/h, but the operational cost of the DN in Scenario A was higher than that in Scenario B. The optimal results of the proposed approach for each case are summarized in Figure 5:0 Comparing the operation cost of the DSO, Case 2 was lower than Case 1, while the MGO profit in Case 2 was lower than Case 1. It can be observed that the DSO operational cost was the lowest, even though the MGO profit was little reduced in Case 2. Therefore, the results indicated in our study confirm that the proposed approach provides the DSO and MGO with the optimal solution for reliable and economical operation.
## 3 The Diurnal Reference Check (DRC)  
### Methodology  
We formulate the DRC and adjust its thresholds using matchups with \(T_{SAT}\) from 2012-2018 data. Table 2 lists eight MDSs available for this period, along with the number of accumulated matchups in each MDS and LEXTs for each satellite. The orbits of the satellites listed in Table 2 are different. The Metop-A/B and Terra fly mid-morning 'am' orbits, whereas the S-NPP, NOAA-20, and Aqua are in the afternoon 'pm' orbits and observe the ocean close to the peak of the SST diurnal cycle, usually occurring around 3 p.m. LST [12; 17]. These five satellites are maintained in stable orbits. The orbits of the three NOAA satellites (15/18/19) drift. In 2012-2018, the NOAA-19 was close to the peak of the DC, whereas the NOAA-15/18 overpassed at times when the SST was already decreasing.  
The goal of this study is to revisit the iQuam QC and improve its performance in the presence of significant DS in \(T_{IS}\)'s. This requires quantitative characterization of the DS in the \(T_{IS}\) records from individual buoys. We introduce the corresponding metrics as follows. The \(T_{IS}\) record from each buoy is subdivided into 24-h segments in terms of LST. Each segment is further subdivided into nighttime subsegment, which includes \(T_{IS}\)'s sampled between 11 p.m. and 7 a.m. LST, and daytime subsegment, with \(T_{IS}\)'s sampled between 8 a.m. and 8 p.m. LST. The segments in which either nighttime or daytime subsegment includes less than three \(T_{IS}\) samples are excluded from the analysis. In order to minimize the potential distortion of the metrics by outliers, each subset is filtered by excluding \(T_{IS}\)'s satisfying the condition \(\mid\)\(T_{IS}-M(T_{IS})\)\(\mid\)\(>\) 4 \(\times\)\(S(T_{IS})\), where \(M(T_{IS})\) is median, and \(S(T_{IS})\) = 1.4826 \(\times\)\(M(\)\(T_{IS}-M(T_{IS})\)\(\mid\)) is the corresponding RSD, both calculated over a given subset. After that, the following metrics are calculated:  
* Maximum SST in the daytime subsegment, \(T_{MAX}\).
* Minimum SST in the nighttime subsegment, \(T_{MIN}\).
* Median SST in the nighttime subsegment, \(T_{NIGHT}\).
* Amplitude of the DS (ADS), \(D\) = \(T_{MAX}-T_{MIN}\) if \(T_{MAX}\)\(>\)\(T_{MIN}\); otherwise, \(D\) = 0.  
The above metrics characterize each segment as a whole rather than individual \(T_{IS}\) counts. This allows for implementing separate checks for filtering the entire low-quality 24-h segments and individual \(T_{IS}\) outliers within the segments.
## 5 Communication Protocol Design
### Evaluation
#### 5.2.1 Experiment Setup  
The three measurements scenarios involved 1, 2 or 4 Marmote SDR nodes and a USRP/GNU Radio base station deployed in an office environment; the four-node setup is shown in Figure 4. The radios were tuned to 2.405 GHz, with the nodes continuously transmitting packets and the base station evaluating the average packet reception ratio based on 1,000 transmitted packets. The packets frames were constructed on the Marmote SDR nodes based on the format described in Section 5.1.1, each carrying a payload of 20 bytes. To control the traffic load, the packet transmission was initiated periodically with a predefined average \(\tau_{\mathrm{avg}}\) interval having 20% jitter. That is, the interval between two consecutive packet transmission was calculated according to:  
\[\Delta\tau=\tau_{\mathrm{avg}}(1+0.2\cdot\mathcal{U}\left[\text{-}1,1\right]) \tag{4}\]  
where \(\mathcal{U}\left[\cdot\right]\) denotes the uniform distribution. The chip rate was fixed at 2 MHz for all cases; therefore, the spreading factor values 8, 16 and 32 reduced the data rate to 250 kbps, 125 kbps and 62.5 kbps, respectively.  
The GNU Radio-implemented receiver, run on an USRP N210 connected to a desktop computer, listened for all sensor nodes and kept track of the packet reception ratio (PRR) for each based on the received _sequence number_ and _CRC 16_ fields. For each measurement, the receiver spreading factor was set manually to match that of the transmitters, while the power control was adjusted based on the perceived PRR.
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs
### Battery Swapping  
To overcome the limitation of battery capacity, some studies have proposed a battery swapping method to recharge drained batteries of UAVs [35]. The battery swapping method is partially autonomous. It is conducted with human assistance or automatically. In the literature, two techniques have been reported for battery swapping: swapping and hot swapping [36]. In hot swapping, drained batteries are replaced with fully charged batteries while the UAV keeps operating. Hot swapping offers the benefit of allowing the UAV to activate and rejoin its operation. While in swapping, UAVs with drained batteries are replaced with another UAV with fully charged batteries. Figure 4 presents an illustration of swapping and hot swapping techniques. Battery swapping techniques consist of three components: (i) a battery swap station; (ii) UAV swarms; and (iii) a control system for managing UAV swarms. Battery swap stations include contact mechanism, landing infrastructure, onboard circuits and ground electronics. In [37], the authors presented an economic evaluation for recharging of UAVs by considering system complexity, coverage and cost. Multi-UAV systems and cooperative algorithms are proposed to offer continuous service by UAVs. Similarly, the authors in [38] proposed an automated system to quickly swap a depleted battery of a UAV along with charging several other batteries. In [39], the authors reported an automated method to swap UAV batteries to support persistent UAV missions. The proposed dual-drum structure enables the efficient swapping of eight batteries. In [38], the authors briefly discussed the design mechanisms of autonomous swapping stations. In [40], the authors demonstrated a swapping system for accurate landing, UAV healthy monitoring and energy management. In a recent study [41], the authors proposed a patrolling algorithm to optimize UAV trajectory and battery swapping. Furthermore, battery swapping techniques also cause some serious concerns, such as GCS feasibility, landing issues, charging/discharging time, limited applications, increased swapping agents, high cost and system complexity.
# The Sensitivity Analysis of Parameters in the 1D-2D Coupled Model for Urban Flooding
## 2 Method
### Parameter Sensitivity Analysis Method  
#### 2.2.1 Parameter Selection  
There are 13 parameters in the SWMM, of which catchment area, imperviousness, slope, width, and pipe length can be directly determined by measurement and analysis. Some data of subcatchments, such as area and slope, are obtained by processing DEM data in ArcGIS. These parameters are measured values rather than empirically assigned ones. The remaining 8 parameters in the SWMM are empirical, and Telemac uses 4 empirical parameters for urban waterlogging simulation. All empirical parameter names and value ranges are determined based on modeling manuals and literature research, as shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Description**} & \multirow{2}{*}{**Unit**} & **Minimum** & **Maximum** \\  & & & & **Value** & **Value** \\ \hline \multirow{8}{*}{SWMM} & N-Imperv & Manning’s Coefficient for Impervious Areas & / & 0.01 & 0.024 \\  & N-Perv & Manning’s Coefficient for Pervious Areas & / & 0.01 & 0.08 \\  & S-Imperv & Depression Storage for Impervious Areas & mm & 1.3 & 3 \\  & S-Perv & Depression Storage for Pervious Areas & mm & 3 & 8 \\  & Maxrate & Maximum Infiltration Rate & mm/h & 50 & 100 \\  & Minrate & Minimum Infiltration Rate & mm/h & 2 & 30 \\  & Dc & Decay Coefficient & / & 2 & 7 \\  & Roughness & Conduit Roughness & / & 0.011 & 0.017 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Names and value ranges of model parameters.
# Prototypical Graph Contrastive Learning for Recommendation
## 4 Experiments
### Experimental Settings  
#### 4.1.1 Datasets and Metrics  
As shown in Table 1, three real-world datasets, Yelp2018, Douban-Book, and Amazon-Book, are utilized to evaluate ProtoRec and other baseline methods. The observed interaction counts for all three datasets are very sparse, and the datasets are divided into training, validation, and test sets in a ratio of 7:1:2. To better demonstrate top-k recommendation performance, recall@k and NDCG@k are employed as evaluation metrics, where \(k\in\{20,40\}\). Recall@k is used to measure the proportion of relevant items that are recalled in the top-k recommendations in the test set. NDCG@k further considers the ranking position of the recommendations, with higher-ranked relevant items contributing more to the metric:  
\[Recall@k=\frac{TP@k}{TP@k+FN@k} \tag{11}\]  
\[NDCG@k=\frac{DCG@k}{IDCG@k} \tag{12}\]  
where \(TP\) and \(FN\) represent true positive and false negative, respectively. DCG and IDCG are defined as follows:  
\[DCG@k=\sum_{i=1}^{k}\frac{rel_{i}}{log_{2}(i+1)} \tag{13}\]  
\[IDCG@k=\sum_{i=1}^{|REL|}\frac{1}{log_{2}(i+1)} \tag{14}\]  
and \(rel_{i}\) is defined as the relevance of the i-th recommended item, typically taking a value of 0 or 1. All baseline methods were tuned using grid search within the value ranges provided in the original papers, and the training was conducted separately on each dataset to find the optimal parameter combinations.
# Improving the Manipulability of a Redundant Arm Using Decoupled Hybrid Visual Servoing
## 3 Simulation and Experimental Setup
### Design of Setup 1  
In the first setup, the DHVS method has been tested in simulation and then validated in the real world. The simulation platform includes two Franka manipulators; one robot is  
Figure 4: The modelled setup 1 in (**a**) the simulation hlenvironment, and (**b**) the real world.  
Figure 3: The control schema of the proposed visual servoing approach (DHVS).  
equipped with a RGB-D camera, and another robot arm holds an object (with a printed tag marker). Four corners of the marker are used as points of interest/visual features in this study. Tracking the object under this condition has been tested via different VS methods. The experimental and simulation setups are identical. Franka robots have 7 degrees of freedom across 7 axes, with 3 kg payload, and positioning accuracy of +/ -0.1 mm in all directions. The proposed method was modelled in simulation using ROS/Gazebo. ROS Melodic on Linux 18 was used for both the simulation and the experiment. The joint state controller was used to publish the joint state (at rate of 1 kHZ) and the joint velocity group controller was used to set the joint velocities computed from the VS approaches. A system with the following CPU specification was used for the visual servoing operation: AMD Ryzen 7 3700x, 8-core with 16 CPU Threads, 3.6 GHz base clock and 36 MB total cache. Figure 3(a) depicts an snapshot of the developed simulation environment in Gazebo, and Figure 3(b) shows the identical experimental setup in the real world.  
By using Setup 1, three different case studies have been designed to compare different behaviours of the DHVS method such as singularity, performance and manipulability with other VS methods.