esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection
## 3 Method
### Improved Multi-Scale Feature Fusion Structure  
Multi-scale feature fusion can be used to increase microscopic item detection accuracy. The feature fusion layer in the standard YOLOv5s model uses PANet [30] to improve the efficiency of information transfer from the bottom to the top layer in FPN, augmenting the whole feature pyramid layer with the precise location information in the lower layer, as well as reducing the information path's length between features at the lower and upper layers. Generally speaking, the perceptual field is the region that is applied to the input image, and as the number of layers deepens, the perceptual field of the network progressively grows. As a result, the shallow feature map with high resolution has a smaller, richer spatial location information-rich perceptual field that is better suited for the detection of small objects, whereas the deeper feature map with low resolution has a larger and more advantageous perceptual field for the detection of large objects. Three feature maps are  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature Map Size** & **Feel the Wild-Size** & **Anchor Frame Size** \\ \hline
38\(\times\) 38 & large & (23,24), (31,31), (44,47) \\
76\(\times\) 76 & middle & (10, 12), (14,15), (17,20) \\
152\(\times\) 152 & small & (4, 4), (5,6), (8,8) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Size of the anchor frame following clustering.  
produced by the feature fusion in the YOLOv5 standard model, and these are utilized for the detection of large, medium, and small objects. Given that traffic sign objects are often small objects, this research improves the multi-scale feature fusion structure.  
In order to enhance the model's sensitivity to small object features without expanding its size, the bottom convolutional module, the C3 module in the backbone network, and the 19 \(\times\) 19 feature fusion module in the feature fusion layer are removed initially. The 32-fold downsampling is then substituted with 16-fold downsampling, and a new feature fusion module measuring 152 \(\times\) 152 is subsequently added to the feature layer. Figure 1 illustrates the improved feature fusion structure. In order to increase the model's capacity for small object recognition, the upgraded FPN and PANet modules work together to augment the semantic and spatial information of traffic signs.
# Optimal Operation of CCHP Smart Distribution Grid with Integration of Renewable Energy
## 2 Mathematical Model of the Components of the Proposed System
### Electric Subsystem
#### 2.1.5 Exchanging Electric Energy with the Utility Grid  
The CCHP system can trade energy with the main system, and this cost is formulated as follows:  
\[C_{U}^{t}=P_{U}^{t}\cdot c_{U}^{t} \tag{9}\]where \(P_{U}^{t}\) is the exchange of power with the main grid. It takes either positive for buying power or negative for selling power. \(c_{U}^{t}\) is the electric energy open market price (EEOMP) (EUR/kWh).
# Energy Trading with Electric Vehicles in Smart Campus Parking Lots
###### Abstract  
Energy trading with electric vehicles provides opportunities to eliminate the high peak demand for electric vehicle charging while providing cost saving and profits for all participants. This work aims to design a framework for local energy trading with electric vehicles in smart parking lots where electric vehicles are able to exchange energy through buying and selling prices. The proposed architecture consists of four layers: the parking energy layer, data acquisition layer, communication network layer, and market layer. Electric vehicles are classified into three different types: seller electric vehicles (SEVs) with an excess of energy in the battery, buyer electric vehicles (BEVs) with lack of energy in the battery, and idle electric vehicles (IEVs). The parking lot control center (PLCC) plays a major role in collecting all available offer/demand information among parked electric vehicles. We propose a market mechanism based on the Knapsack Algorithm (KPA) to maximize the PLCC profit. Two cases are considered: electric vehicles as energy sellers and the PLCC as an energy buyer, and electric vehicles as energy buyers and the PLCC as an energy seller. A realistic parking pattern of a parking lot on a university campus is considered as a case study. Different scenarios are investigated with respect to the number of electric vehicles and amount of energy trading. The proposed market mechanism outperforms the conventional scheme in view of costs and profits.  
electric vehicle; energy trading; smart parking lot; energy market Article
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 2 Background and Related Work
### Related Work  
The state-of-the-art encoding algorithms that focus on fast CU size selection can be broadly categorised into three main categories--statistical rule-based approaches, texture properties-based algorithms, and machine learning-based approaches. The fast encoding algorithms proposed in the literature for intra-prediction typically focus on fast CU size selection or early estimation of optimal prediction mode (out of 35 intra-prediction modes [16]). The following subsections provide a summary of the existing work in each of these categories, primarily focusing on fast CU size selection algorithms available in the literature.
## 3 Materials and Methods
### Determining the ELA
#### 3.2.2 Calculating the ELAs of Paleoglaciers  
Indirect estimates of paleoglacial ELAs are commonly based on the identification of glacial erosion and accumulation landforms. However, the use of this single method can inevitably lead to inaccuracies. In this study, therefore, we employed a variety of paleoglacial ELA calculation methods. Specifically, The cirque-floor (CF) method refers to use the altitude of the bases of the cirques as an approximate proxy of the paleoglacial ELAs [43]. Toe to headwall altitude ratio method (THAR) assumes a stable glacier with a constant ELA calculated as being midway between the highest and lowest points of glacial development [44]. The ratio of the difference in altitude between the ELAs and the lowest point of glacial development to the difference in altitude between the highest and lowest points of glacial development is defined as the THAR value [44]. Toe-to-summit altitude method (TSAM) is defined as meaning that an arithmetic mean of the highest mountain peak's altitude and the glacier terminus's altitude is calculated, which represents the glacier's ELAs [45]. Accumulation area ratio method (AAR) refers to a stable glacier where the ELA is constant, so that the ratio between the area of the paleoglacier above the ELA (the glacier accumulation area) and the area occupied by the entire glacier always maintains a definite value, referred to as the AAR value [46]. The ELAs of paleoglaciers can be calculated by referring to the logarithmic relationship between the AAR value and glacier area [47], thus:  
\[\text{AAR}=0.0648\times\text{lnS}+0.483 \tag{7}\]  
Using Areaaltitude balance ratio method (AABR) [48] calculates the ELAs based on the gradient of the mass balance between the ablation area and the accumulation area of the glacier. At the core of the method is the reasonable determination of the BR value. The BR values of glaciers with mass balances observed globally were calculated at a mean value of 1.75 \(\pm\) 0.71 [49]. This was therefore the value we chose to use in this study. In addition, the AAR method and AABR method can be calculated by using the ArcGIS software toolbox ([[https://github.com/cageo/Pellitero-2015](https://github.com/cageo/Pellitero-2015)]([https://github.com/cageo/Pellitero-2015](https://github.com/cageo/Pellitero-2015))) (accessed on 3 October 2023) [50].
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 2 Literature Review
### Geographic and Cooperative Opportunistic Routing Protocol for Underwater Sensor Networks (GCORP)  
The authors of [19] introduced the GCORP. In GCORP, the data packets are transmitted from the sender node to the sinks with the help of intermediary relay nodes. First, the GCORP protocol sets up a network architecture based on sinks. Then, the source node decides on a relay forwarding set based on the depth factor. With the help of relay nodes, the multi-sink architecture is used by the GCORP protocol to collect data packets from the nodes that made them.  
GCORP picks the best relay to send the packets to the surface sinks at each hop. Every surface sink on the sea has a Global Positioning System (GPS) to send periodic beacons to tell the underwater nodes where it is. First, the source node chooses a group of relay nodes that will speed up the packets and increase the chance that they will be delivered (PDP) in the direction of surface sinks. The authors developed an algorithm based on the depth fitness factor to determine how close a packet is to the sink to the surface.  
An Energy-Efficient Data Gathering Scheme in Underwater Wireless Sensor Networks Using a Mobile Sink (EEDG)  
The authors in [20] suggested EEDG routing protocols. The mentioned routing protocol aims to fix the low bandwidth, packet loss, and energy consumption issues in UWSNS in three steps. In the first step, the energy consumption load is achieved by grouping the sensor nodes into smaller groups to select as temporary forwarder nodes. In the second step, the proposed MAC protocol reduces collision rates and packet loss by requiring ordinary nodes to deliver data to their forwarder nodes only during specified time frames. Finally, the suggested graph structure reduces the delay in data collection over the entire network by allowing the mobile sink to connect forwarder nodes to the degree set for them in the graph.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 3 The Scheme of Network Architecture and Purification
### Purification Circuit for Different States  
The accurate realization of teleportation needs high-fidelity entangled states. Purification is a common method to improve the fidelity.
# Optimal Route Planning for Truck-Drone Delivery Using Variable Neighborhood Tabu Search Algorithm
## 4 Model Solution  
The optimal route problem for truck-drone delivery is NP-hard [24], and the tabu search algorithm is a commonly used metaheuristic algorithm to solve NP-hard problems [25]. The traditional tabu search algorithm generates a fixed neighborhood by searching, which affects the search range and reduces the convergence speed [26]. Considering that the real delivery process covers many customer points, the traditional tabu search algorithm has limitations in solving this large-scale combinatorial optimization problem. Therefore, we use a variable neighborhood tabu search algorithm to solve the proposed model, which expands the range of tabu search by changing the neighborhood structure [27; 28].
# IEC-61108-7 SBAS Standard for Shipborne Receivers: Preliminary Testing Validation Activities +
## 3 Sample Results  
### TC-001: Static Accuracy Test  
This test evaluates the receiver accuracy in static conditions over a 24 h period. Figure 3 shows sample results using live GPS+EGNOS signals, including the SBAS correction age, the number of satellites, and horizontal/vertical errors. The standard requires a horizontal error lower than 10 m (95%), which is fulfilled in this test case. The number of satellites varies between 5 and 11, as shown in the upper left box. The geometry indicator (HDOP--horizontal dilution of precision) and the error time evolution are shown in the  
Figure 2: Timeline of the SBAS activation for TC-016.  
upper right box; the DOP values are always below 2, and the error is very stable, showing sub-meter performance.
## 4 Results and Analysis  
### Results  
The maximum development height of water-flowing fracture in coal seam overburden of 24 numerical models is obtained by numerical simulation test method and rock fragmentation theory calculation method, respectively, and the results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Numbering** & \(a\) & \(\eta\) & \(b\) & **Numbering** & \(a\) & \(\eta\) & \(b\) \\ \hline M1 & 1.071–1.109 & 0.016–0.024 & 3.692–11.836 & M13 & 1.057–1.073 & 0.011–0.015 & 1.788–4.705 \\ M2 & 1.071–1.111 & 0.016–0.024 & 3.772–12.208 & M14 & 1.054–1.067 & 0.010–0.013 & 1.420–3.616 \\ M3 & 1.064–1.090 & 0.014–0.019 & 2.800–8.132 & M15 & 1.056–1.071 & 0.011–0.014 & 1.659–4.315 \\ M4 & 1.057–1.074 & 0.012–0.015 & 1.886–5.006 & M16 & 1.053–1.065 & 0.010–0.012 & 1.312–3.309 \\ M5 & 1.056–1.071 & 0.011–0.014 & 1.700–4.438 & M17 & 1.052–1.063 & 0.010–0.012 & 1.190–2.973 \\ M6 & 1.055–1.070 & 0.011–0.014 & 1.620–4.199 & M18 & 1.051–1.062 & 0.010–0.012 & 1.137–2.829 \\ M7 & 1.088–1.097 & 0.021–0.021 & 5.989–8.984 & M19 & 1.063–1.091 & 0.014–0.020 & 2.611–8.016 \\ M8 & 1.066–1.098 & 0.015–0.022 & 2.906–9.251 & M20 & 1.055–1.071 & 0.011–0.014 & 1.561–4.266 \\ M9 & 1.060–1.082 & 0.013–0.017 & 2.147–6.252 & M21 & 1.052–1.066 & 0.010–0.013 & 1.250–3.312 \\ M10 & 1.054–1.069 & 0.011–0.014 & 1.428–3.852 & M22 & 1.050–1.061 & 0.009–0.012 & 0.977–2.521 \\ M11 & 1.053–1.066 & 0.010–0.013 & 1.282–3.408 & M23 & 1.048–1.059 & 0.009–0.011 & 0.882–2.256 \\ M12 & 1.052–1.065 & 0.010–0.013 & 1.219–3.221 & M24 & 1.048–1.058 & 0.009–0.011 & 0.841–2.143 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Position of bearing layer and correlation coefficient table. Figure 4: Relationship between bearing stratum and water-flowing fracture zone. Note: The range of the red line is the “stress arch” in the stress arch theory.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 1 Introduction
Nevertheless, this protocol does not consider the network's transmission efficiency. [PERSON] et al. [14] proposed an improved routing protocol that uses the AUV for cluster head selection, cluster formation, data transmission, and sleep-wake scheduling, which greatly saves the sensor node energy; nevertheless, cluster head nodes must wait for the AUV to collect the data, which increases the network latency. [PERSON] et al. [15] presented a routing protocol called EERBLC that focuses on enhancing energy efficiency in routing by utilizing layers and unequal clusters, which proposes a new cluster head selection method based on energy balance and node degree and a forwarding node calculation method based on the forwarding rate and residual energy. This approach can prolong the network's operational lifetime and enhance data transmission capabilities, but the protocol lacks consideration of node mobility. [PERSON] et al. [16] proposed the EECMR, which divides the network into layers and selects the cluster heads based on the residual energy and node depth, which can effectively prolong the network lifetime; Nevertheless, the communication before the layers is too dependent on relay nodes, and the communication latency is high. To summarize, some scholars have also failed to fully consider the residual energy of nodes, connectivity of nodes, and data transmission efficiency in their studies, and the application of AUV is more limited in previous studies. Based on this, HECRA is proposed in this paper. The innovations and contributions made in this paper are as follows:  
1. The protocol introduces node residual energy and node degree as influencing factors in the cluster head selection phase, which makes the energy of nodes more balanced. 2. The protocol formulates a communication strategy based on the depth and residual energy of the cluster head nodes in the data transmission phase to optimize the message forwarding path. In this paper, we innovatively introduce AUV as a dynamic relay node for transmitting messages between cluster-head nodes that are unable to directly communicate with each other, which improves the transmission efficiency and stability of the network. The rest of the paper is structured as follows. The proposed system model is covered in Section 2. The detailed design process for HECRA is discussed in Section 3. Section 4 evaluates the performance of the proposed HECRA and compares it with existing cluster routing algorithms. Finally, Section 5 concludes the paper.
## 4 CFD Modelling
### Modelling of the Wind Turbines  
The rotor disks are approximated as momentum sinks to represent the axial force \(T\) across the disk, associated with a constant thrust coefficient \(C_{T}\) over the rotor area:  
\[T=0.5\rho U_{ref}^{2}C_{T}A \tag{16}\]  
where \(A\) is the surface area of the rotor disk and \(\rho\) is the air density.  
For the estimation of the thrust coefficient and the thrust force, a reference freestream wind speed \(U_{ref}\) must be estimated. Once \(U_{ref}\) is known, the thrust coefficient \(C_{T}\) is estimated using the manufacturer-given thrust curve of the turbine, considered as uniform over the rotor disk. However, in real flow cases the flow and the thrust coefficient are not uniform over the rotor disk because of the atmospheric boundary layer shear and the finite number of rotating blades with varying span-wise properties. A non-uniform force over the rotor disk area \(C_{T}\) would perhaps require coupling of the CFD solver with a Blade Element Momentum (BEM) theory solver. However, this option requires detailed data for the blade geometry and the \(C_{L}\), \(C_{D}\) curves of the airfoils along the span of the blades that are usually not available. Local effects produced by the radial variation of \(C_{T}\) would result in a non-uniform near-wake profile expected to diminish at a distance of about one rotor diameter downstream. In all cases, a reference wind speed must be estimated.  
In the case of a wind farm, a wind turbine may operate in the wake of one or more wind turbines. The accurate prediction of its power requires an accurate prediction of the wake effects from the upstream machines, which, in turn, involves the accurate estimationof the reference wind speed for the reliable estimation of the thrust coefficient. From the numerical modeling point of view, there is no obvious approach to estimate the thrust coefficient since the wind turbine power and thrust curves are usually provided for single-machine operation rather than operation in the wake of another wind turbine generator. Consequently, there is a deficiency in the definition of the appropriate wind speed used for the calculation of the wind turbine's thrust force or power in wake operation [19; 21; 22; 23; 24].  
In the current project a constant reference wind speed of 8 m/s has been used at the hub height at the inlet plane. The reason for simulating this wind speed is to simulate the worst-case scenario. Based on preliminary tests, at a lower speed the total deficit of the velocity downstream was smaller and higher speeds were reducing wake propagation due to higher atmospheric turbulence and thus entrainment. Obtained wake results and the flow behavior downstream are in accordance with other numerical and experimental investigations using Actuator Disk Model (ADM) in different ambient turbulence and inflow conditions [19; 23; 24; 25; 26; 27; 28; 29; 30].
## 3 Optimization Scheme
### The Label-Clip-Slice Strategy and GA to Address Mixed Variables
#### 3.3.1 Labeling of the Topology Variables  
As shown in Figure 3, for the optimal individual in the last iteration of the first-level approximation problem, we save and inspect its code, which is the topology variable of the optimal structure. When the bits of code are \"0\" for continuous \(k\) generations, their bit orders are labeled as \(R_{bit}\). Here, \(k\) is a control parameter in this strategy and needs an appropriate value. If \(k\) is too large, GA labels few variables after many iterations. Moreover, it is very likely to not label any variable for dozens of iterations due to the large search domain and GA operators. If \(k\) is too small, many variables will be quickly labeled within a few iterations, and the number will increase with more iterations. The GA will quickly converge to a local optimum, which is far from the global optimum. Hence, the value of \(k\) for a specific problem needs to be appropriate but not fixed, similar to other parameters in GA. Its appropriate value pertains to the characteristics of the specific problem to address.
## 4 Experimental Results  
### Experimental Setup  
All experiments were performed on a workstation with an Intel Core i7-8700 3.20 GHz six-core processor (Intel Corporation, Santa Clara, CA, USA), 32 GB of RAM, and an NVIDIA GeForce GTX2080 Ti graphics processing unit (GPU) (Nvidia Corporation, Santa Clara, CA, USA). All experiments were pre-processed using PyTorch 1.12.1. Ensemble, CNN model training, and final classification experiments were implemented in the Python 3.10.9 programming language with Python libraries, such as OpenCV-python 4.7.0.72, Scikit-learn 1.2.1, matplotlib 3.7.0, and NumPy 1.23.5.
# Hybrid Window Decoding for Joint Source Channel Anytime Coding System
## 1 Introduction
In order to reduce the decoding complexity while maintaining comparable error correction performance, a hybrid window decoding scheme is proposed. The novelty and contributions of this paper can be summarized as follows. * A joint hybrid window decoding (JHWD) algorithm is proposed, which adaptively implements the local expanding window decoding in the sliding window region with the constraint of syndrome-check based detection. * The density evolution algorithm of the JSCAC system in [18] is improved for the proposed JHWD scheme to analyze its asymptotic decoding complexity and anytime properties. * Some numerical analyses and simulation results are provided to confirm the advantages of the proposed JHWD algorithm over other prior-art decoding schemes for JSCAC systems. The remainder of this paper is organized as follows. The background of the SC-RA-based JSCAC system is briefly introduced in Section 2. The specific descriptions of the proposed JHWD scheme and improved density evolution algorithm are provided in Section 3. The numerical analyses and simulation results are presented in Section 4, followed by conclusions in Section 5.