esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic
### Proposed Data Packet Format  
In our push-based data forwarding scheme, we modified the existing data packet format and inserted some extra information to disseminate the critical content. The following extra fields can be inserted to modify/update the data packet in order to control the data broadcasting in the VNDN environment. The important fields of the proposed data packet are presented in Table 1.
When dealing with antennas in mobility, the terminal is usually subjected to high scan angles with performance degradation beyond \(\pm 60^{\circ}\)[1]. Phased array antennas are a suitable candidate for beam-shaping and beam-steering capabilities that avoid mechanical components. However, planar phased arrays have a FoV that is limited by scan losses, which are mainly dictated by the cosine radiation of conventional patch radiators.  
Regarding State of the Art (SoA), different solutions for enhancing the scan range of the phased array are intensively investigated. The different solutions are based on improving the Half-Power BeamWidth (HPBW) of the array element [2; 3; 4; 5] aiming to mitigate the decadence in the scan range due to the element pattern. These solutions can extend the FoV both in elevation and azimuth but they require ad hoc and complex design. In addition to that, multi-mode/reconfigurable antennas [6; 7; 8; 9] make use of elements that can be reconfigured to generate different radiation beams. The phenomenon is exploited, changing the radiation beam depending on the steering angle. The FoV is then subdivided into multiple subspaces, each one assigned to a specific beam. However, these solutions result in complicated ad hoc solutions for which the feeding network has to deal with multiple input/output for selecting the right mode. In addition, planar steering is not always supported, depending on the number of radiation beams the antenna is able to generate and their distribution in space.  
A further category of solutions includes metasurfaces such as the High-Impedance Periodic Structure (HIPS) [10; 11], Wide Angle Impedance Matching (WAIM) [12; 13; 14] solutions and dielectric domes, such as radome [15; 16; 17]. These solutions require the design of additional layers which often adds complexity to in-package solutions.  
On the other hand, conformal arrays are an efficient solution for coverage extension [18; 19; 20; 21; 22; 23; 24], but they cannot be manufactured through planar PCBs, resulting in complicated and expensive dedicated manufacturing. By faceting the conformal array [25; 26; 27; 28], the array can be easily manufactured through planar PCBs faces. As result, complexity in design and manufacturing can be easily reduced by keeping the performance acceptable.  
In this paper, the authors present an analysis of the advantages and disadvantages of different non-planar faceted architectures in the framework of satcom Communications-On-The-Move (COTM) applications. A 3D phased array architecture is presented for mitigating FoV limitation in planar phased arrays for COTM terminals. The paper extends the work conducted in [29] by presenting an analysis of the cost and efficiency of different linear faceted arrays. Minimizing the number of facets with the minimization of performance degradation, the best linear candidate is extended to the 3D case.  
The paper is organized as follows: the scenario is addressed in Section 2, the mathematical formulation in Section 3 and the numerical results in Section 4. Finally, conclusions are drawn in Section 5.
## 3 Results and Discussion
### Weld Zone Microstructure
The HAZ average grain size values are 37 \(\upmu\)m, 32 \(\upmu\)m, and 29 \(\upmu\)m for 3 mm/s, 4 mm/s, and 5 mm/s welding speeds, respectively, while the average base metal grain size is 25 \(\upmu\)m. The grains are coarsened in the HAZ compared with grain sizes in base metal. According to the Al-Mg binary diagram, the temperature in the HAZ is easily raised to a temperature just below the melting temperature of the alloy. As a result, grain coarsening is frequently observed in the HAZ, which seriously impairs the mechanical properties of welded joints [37]. Less \(\beta\)-phase is observed in the HAZ as the welding speed increased. The width of the HAZ is also affected by the welding speed, and the width of the HAZ gradually narrows as the welding speed increases. This is due to the fact that faster welding speeds result in more rapid cooling rates and less heat input. Figure 8 compares the microstructure of the base metal, weld metal, and the HAZ of the transverse AZ31 weld joint cross-section prepared with 4 mm/s speed. In the HAZ, grain coarsening is observed compared to the base metal. Second phase particles are present in both the fusion zone and the HAZ within the grains. The un-melted base metal grains act as a substrate for nucleation at the fusion boundary; subsequent epitaxial growth occurs near the fusion boundary, due to the relatively high thermal gradient. Figure 5: SEM images of the weld fusion zone with 4 mm/s welding speed. (**a**) second phase particles, (**b**) EDS spectrum of point 1, and (**c**) EDS spectrum of point 2. Figure 6: XRD pattern of the weld fusion zone and base metal corresponding to 3 mm/s welding speed. Figure 7: HAZ optical microstructure of AZ31 welded joint prepared with different welding speeds. (**a**) 3 mm/s. (**b**) 4 mm/s. (**c**) 5 mm/s.
###### Abstract  
In this study, the hardware and software design and implementation of an autonomous electric vehicle are addressed. We aimed to develop an autonomous electric vehicle for path tracking. Control and navigation algorithms are developed and implemented. The vehicle is able to perform path-tracking maneuvers under environments in which the positioning signals from the Global Navigation Satellite System (GNSS) are not accessible. The proposed control approach uses a modified constrained input-output nonlinear model predictive controller (NMPC) for path-tracking control. The proposed localization algorithm used in this study guarantees almost accurate position estimation under GNSS-denied environments. We discuss the procedure for designing the vehicle hardware, electronic drivers, communication architecture, localization algorithm, and controller architecture. The system's full state is estimated by fusing visual inertial odometry (VIO) measurements with wheel odometry data using an extended Kalman filter (EKF). Simulation and real-time experiments are performed. The obtained results demonstrate that our designed autonomous vehicle is capable of performing path-tracking maneuvers without using Global Navigation Satellite System positioning data. The designed vehicle can perform challenging path tracking maneuvers with a speed of up to 1 m per second.  
vehicle path following; NMPC; nonlinear model predictive control; longitudinal control; direct multiple shooting method; vehicle MPC; visual inertial odometry  
1  
[PERSON]; [PERSON]; [PERSON] [PERSON] Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments.  
1  
A. B. J. O. D.  
However, recently, researchers around the world have made significant efforts to deal with this challenge.  
The third essential element of an autonomous electric vehicle is its proper design and performance of the hardware. The process includes designing the electronic control units (ECUs), data processors, internal communications, vehicle bus, mechanical actuators and electromechanical actuators.  
In this study, we designed and implemented the vehicle hardware, electronic drivers, communication architecture, localization algorithm, and controller architecture. The system's full state is estimated by fusing visual inertial odometry (VIO) measurements with wheel odometry using an extended Kalman filter (see Section 4). For the high-level control part, we proposed a nonlinear model predictive control (see Section 5).
## 2 Materials and Methods
The custom-built impact device (Figure 3) was integrated with a high-sensitivity force sensor operating in the millivolt range to capture force signals during the impact event. These signals were amplified, filtered, and transmitted to a data acquisition (DAQ) card (National Instruments), and subsequently recorded using NI SignalExpress software 2015 at a sampling frequency of 25 kHz. Figure 2: (**a**) Epoxy set; (**b**) vacuum bag method. As outlined in the ASTM D7136 standard, [PERSON]'s second law of motion was applied to derive the kinematic response of the impactor over time. Velocity was obtained by integrating the acceleration with respect to time, and displacement was subsequently derived through a second integration of the velocity data. To ensure that only a single impact was recorded per test, an anti-rebound mechanism was also incorporated into the setup to prevent multiple impacts. Energy losses due to internal damping within the sensor and frictional resistance in the system components were considered negligible. Therefore, the total impact energy was approximated as the sum of the energy absorbed by the specimen and the rebound energy of the impactor. The absorbed energy was primarily attributed to plastic deformation and fracture mechanisms occurring within the laminate structure. All twenty-four specimens were subjected to an initial low-velocity impact test, conducted in accordance with ASTM D7136. Subsequently, half of the specimens, three from each of the four groups, were thermally conditioned in a circulating-air oven at 150 \({}^{\circ}\)C for 30 min. After cooling to ambient temperature, each specimen underwent a second low-velocity impact using the same setup as in the initial test. This procedure allowed for a comparative assessment between two test conditions: (i) specimens subjected to two consecutive impacts without any thermal treatment, and (ii) specimens exposed to a second impact following post-impact heat treatment after the initial impact, enabling the evaluation of the self-healing effect induced by thermal activation. Figure 3: Low-velocity impact device with a specimen.
# Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
## 2 Supply and Demand Flexibility of DGC for Cluster Division
### Frequency Regulation Flexibility Response  
When the net load of the system fluctuates in a short time scale of less than 15 min, it is necessary for every DG that can be used for scheduling to respond quickly. In order to explore the speed at which the cluster will respond to the frequency regulation of the system, the response characteristics of the components within the cluster need to be analyzed, most notably the available DG flexibility. The responsive capacity \(P_{i,left}\) is the remaining capacity of each DG after satisfying the supply-demand balance of upward/downward ramping flexibility of the system, and the expression is as follows:  
\[P_{i,left}(t)=P_{\max,i}-\Big{(}P_{i}(t)+\Delta P_{i}^{\tau,M}(t)\Big{)}, \tag{4}\]  
where \(P_{\max,i}\) is the maximum capacity of the \(i\)th DG, \(P_{i}(t)\) is the output of the \(i\)th DG at time \(t\), and \(\Delta P_{i}^{\tau,M}(t)\) is the change in output of the \(i\)th DG at time \(t\) to satisfy the supply and demand balance.  
In addition to the response capacity, the cluster response characteristics are related to internal DG ramp rate R. The main factor is the DG response time, which is \(t_{left}=P_{left}/R\). Assuming that there are three DGs in the cluster with climbing speeds \(R_{1}\), \(R_{2}\), and \(R_{3}\), and \(R_{1}>R_{2}>R_{3}\), the total responsive capacity \(P_{\varphi}\) is shown in the following expression:  
\[P_{\varphi}=\sum\limits_{i=1}^{3}P_{i,left}(t). \tag{5}\]  
According to the analysis above, if a faster climbing device can respond to more capacity, then the cluster response time will be shorter. The resulting equivalent response speed at time \(t\) is shown in Equation (6):  
\[k_{c}(t)=\frac{\sum\limits_{i=1}^{3}P_{i,left}(t)}{\max(t_{1,left},t_{2,left },t_{3,left})}. \tag{6}\]  
Due to the characteristic fast regulation of energy storage, when a system frequency fluctuation occurs, more energy storage will increase the output in response to the adjustment of the system's frequency. In order to meet the cluster flexibility based on the balance of supply and demand, the cluster response speed should be maximized: if the cluster has a demand for uphill climbing at time \(t\), the slower DG will be given priority to increase the output and the storage output will be increased; if the cluster has a demand for downhill climbing at time \(t\), reducing the storage output will be given priority, and the DG with a faster climbing speed will be given reduced priority.
# Biochar for Wastewater Treatment--Conversion Technologies and Applications
## 4 Biochar as an Efficient Adsorbent for Organic and Inorganic Pollutants
### Heavy Metals
With Fe coating, the maximum AsIII sorption capacity increased from 19 mg/g to 31 mg/g, while AsV sorption increased from 5.5-7.1 mg/g to 15-16 mg/g. The reported sorption mechanism is through the complexation of AsV and AsIII with Fe on the biochar. The relatively high affinity of AsIII as compared to AsV could be due to the net charge of the biochar surface (negatively charged in the pH range of 2-9) and to the speciation of As in aqueous solution (AsIII mainly exists as H\({}_{3}\)AsO\({}_{3}\) and AsV exists as H\({}_{2}\)AsO\({}_{4}\)- and HAsO\({}_{4}\)\({}^{2-}\)), which strongly depend on the pH value used during the adsorption experiment (8 for AsIII and 6 for AsV) [114]. Researchers have also focused their attention to understand the competitive adsorption of heavy metals on biochars and the mobility of single versus multiple heavy metal species [105; 115]. In the study performed by [PERSON] et al. [105], sesame-straw-based biochar has been reported as more efficient in adsorbing metal in single metal systems than in a multi-metal system. [PERSON] et al. [104] showed that the adsorption ofPb\({}^{2+}\) was more promoted than that of Cd\({}^{2+}\) in a coexisting system, while in a single system Pb\({}^{2+}\) ions are adsorbed on the same adsorption sites as Cd\({}^{2+}\) ions. The hydrated ionic radius of the metal ion relative to the pore size plays an important role in competitive adsorption; as the hydrated ionic radius is small as the species are preferentially adsorbed compared to species with larger ionic radius [116]. Then, in addition to the biochar properties, the adsorption of heavy metals also depends on the nature of metals and their competitive behavior for biochar sorption sites.
## 4 Network and Strategy for Scheduling
### Encoder  
[PERSON] et al. [27] proposed the pointer network, which utilizes RNN or its variants as the fundamental network architecture. However, as mentioned by [PERSON] et al. [30], the sequential information of the input sequence does not significantly impact our specific optimization problem, unlike in machine translation tasks. In fact, employing RNN solely as an encoder increases both training and computation time. Thus, [PERSON] directly employ a one-dimensional CNN to process the nodes, enabling them to be passed to the Decoder  
Figure 3: Overview of the Orchestrator. Demonstrates the process within the Orchestrator.  
for decoding. Nevertheless, this method poses challenges for the network to effectively learn additional information.  
The proposed Encoder is constructed based on the Encoder structure from Transformer [35], but without the inclusion of positional encoding. As shown in Figure 4, a similar network architecture is adopted as described in [32]. First, the features of each sub-task are mapped into a higher-dimensional space using a liner projection (\(d_{h}=128\) in this paper). Then, the Attention layer is constructed by combining Multi-head Attention sublayers, Feed-Forward sublayers, skip-connection [36], and Normalization [37], similar to the Transformer architecture. Specifically, \(L\) Attention layers are utilized within the Encoder module.  
\[Q_{l}=h^{(l)}W_{l}^{Q},K_{l}=h^{(l)}W_{l}^{K},V_{l}=h^{(l)}W_{l}^{V} \tag{4}\]  
\[head_{m}^{(l)}=softmax(\frac{Q_{l}K_{l}^{T}}{\sqrt{d_{k}}})V_{l} \tag{5}\]  
Figure 4: Illustration of the Encoder. First, the features of all tasks are grouped together, and then the grouped features are passed to the Encoder for encoding.  
\[\begin{split} MultiHead(h^{(l)})&=MHA(Q_{l},K_{l},V_{l})\\ &=Concat(head^{(l)}_{1},\ldots,head^{(l)}_{m})W^{O}_{l}\end{split} \tag{6}\]  
where \(h^{(l)}\) is the embedding for all sub-tasks and \(W^{O}_{l}\), \(W^{K}_{l}\in R^{M\times d_{h}\times d_{k}}\), and \(W^{V}_{l}\in R^{M\times d_{p}\times d_{h}}\) are learnable parameters in layer \(l\). \(d_{k}=d_{v}=\frac{d_{h}}{M}\), and \(M=8\) is the number of heads in the attention.  
Within each Attention layer, there is also a Feed-Forward sublayer. This sublayer consists of two linear hidden layers with a dimension of \(d_{ff}=512\), with a ReLU activation function applied between the layers.  
\[FFN(h^{(l)})=ReLU(h^{(l)}W_{1}+b_{1})W_{2}+b_{2} \tag{7}\]  
Each sublayer adds a Batch Normalization layer and a skip-connection.  
\[h^{(l)^{\prime}}=BN(h^{(l)}+MHA(h^{l}) \tag{8}\]  
\[h^{(l+1)}=BN(h^{(l)^{\prime}}+FFN^{l}(h^{(l)^{\prime}})) \tag{9}\]  
After obtaining the output \(h^{(L)}\) from the final Attention layer, the sequence embedding, denoted as \(\hat{h}^{(L)}\), is calculated by taking the average of \(h^{(L)}\). This sequence embedding serves a similar role to the final state obtained by an RNN after processing all sub-tasks. The \(h^{(L)}\) is used for decoding in the Actor network and \(h^{(L)}\) is used for decoding in both the Actor and Critic network.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 4 Experimental Setup
### Evaluation Metrics  
The evaluation of the proposed SLIT in this study was conducted using three standard metrics often utilized in time series forecasting. These metrics are as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Area** & **ID** & **Highway** & **Name** \\ \hline \multirow{4}{*}{Central} & nfb0061 & No. 1 & Hsinchu System Interchange to Toufen Interchange \\  & nfb0063 & No. 1 & Toufen Interchange to Touwu Interchange \\  & nfb0064 & No. 1 & Touwu Interchange to Toufen Interchange \\  & nfb0247 & No. 3 & Tongxiao Interchange to Yuanli Interchange \\  & nfb0248 & No. 3 & Yuanli Interchange to Tongxiao Interchange \\ \hline \multirow{4}{*}{South} & nfb0117 & No. 1 & Chiayi System Interchange to Xinying Service Area \\  & nfb0123 & No. 1 & Xinying Interchange to Xaying System Interchange \\  & nfb0124 & No. 1 & Xiaying System Interchange to Xinying Interchange \\  & nfb0327 & No. 3 & Tianliao Interchange to Yanchao System Interchange \\  & nfb0328 & No. 3 & Yanchao System Interchange to Tianliao Interchange \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._Mean Absolute Error (MAE): Measures the average magnitude of errors in a set of predictions, without considering their direction.
2. Root Mean Square Error (RMSE): Provides a measure of the magnitude of errors by squaring the average difference between predicted and actual values, emphasizing larger errors.
3. Symmetric Mean Absolute Percentage Error (SMAPE): Offers a normalized measure of prediction error in percentage terms, symmetrically penalizing both over-predictions and under-predictions, thus ensuring fairness regardless of the direction of errors. These metrics are calculated as follows: \[\text{MAE} =\frac{1}{N\times l}\sum_{i=1}^{N}\sum_{j=1}^{l}|y_{i,j}-\hat{y}_{i,j}|\] (2) \[\text{RMSE} =\sqrt{\frac{1}{N\times l}\sum_{i=1}^{N}\sum_{j=1}^{l}(y_{i,j}- \hat{y}_{i,j})^{2}}\] (3) \[\text{SMAPE} =\frac{100}{N\times l}\sum_{i=1}^{N}\sum_{j=1}^{l}\frac{|y_{i,j}- \hat{y}_{i,j}|}{(|y_{i,j}|+|\hat{y}_{i,j}|)/2}\] (4) where \[\bullet\quad y_{i,j}\] is the actual travel time for the \[i\] -th sample at the \[j\] -th time point; \[\bullet\quad\hat{y}_{i,j}\] is the predicted travel time for the \[i\] -th sample at the \[j\] -th time point; \[\bullet\quad N\] represents the total number of samples in the dataset; and \[\bullet\quad l\] is the number of time points in the prediction horizon.
## 3 Proposed Work  
### SDN-Based Group Management Framework  
The architecture for group management service is depicted in Figure 1. The devices that communicate frequently and form a communication pattern are united as a group. The Group Management Server (GMS) is built-in with an authentication service function, a key management service function, and an SDN controller. Service functions are virtualized functions that can be provided as a service. The GMS stores the attributes of the group, including the group ID, group member ID, application ID, IP address, MAC address, and location. The key management function stores the key materials of the group. The authentication function authenticates the devices by collaborating with the GMS.  
The authenticated devices will then be allowed to form a group. The key management service function will then distribute the key materials. Using SDN, the communication link is set up in such a way that only group members can communicate. This ensures security against the spread of node-capture attacks and phishing-based attacks by cutting off unnecessary communications.  
Figure 1: Group management server (GMS).
# FPN-SE-ResNet Model for Accurate Diagnosis of Kidney Tumors Using CT Images
## 2 Materials and Methods
### Loss Function  
The medical image predominantly comprises a minor fraction dedicated to lesion regions, exacerbating the pronounced disparity in positive and negative sample distribution. This imbalance notably impacts the accuracy of lesion region segmentation. In 2017, [PERSON] introduced focal loss as a potential remedy for this concern [82]. In object detection, focal loss was introduced to tackle the imbalanced positive and negative sample distribution challenge. In the context of medical image segmentation, where labels are influenced by clinical expertise, the potential for erroneous annotations exists. The focal loss conducts pixel-level classification, rendering the impact of these inaccuracies substantial within the network. While focal loss has its constraints, its effectiveness in mitigating imbalanced samples makes it a promising candidate for addressing similar issues in medical image segmentation. [PERSON] initially introduced the Dice loss function [83]. The Dice loss,tailored for object segmentation utilizing the Dice similarity coefficient (DSC), emerges as a dedicated choice, particularly prevalent in medical segmentation endeavors [84]. Medical imagery often has a small region of interest, leading to a learning process skewed toward background predictions. Because the region of interest only makes up a small portion of the scan, this can affect the accurate recognition of foreground regions, frequently leading to incomplete detection or potential omissions [85].