esa-sceva/satcom-chunk-collection huggingface dataset:
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 3 Preliminary
### Background of Evidential Deep Learning  
Existing deep learning-based models typically use a softmax layer at the top of deep neural networks (DNNs) for classification, using maximum likelihood estimation (MLE) to find the best network parameters. However, these softmax-based DNNs are unable to estimate predictions for classes outside of the closed set, because the softmax scores are inherently point estimates of the predictive distribution. For out-of-distribution categories, the outputs of the softmax layer tend to be overconfident in false predictions. Out-of-distribution anomaly detection can be implemented in a variety of ways, with EDL [17, 18] networks being a natural approach that allows the network to effectively say \"no\".  
EDL overcomes the limitations of softmax-based DNNs by introducing [PERSON]'s evidential framework, Shafer theory (DST) [22], and subjective logic (SL). EDL combines multi-class classification and uncertainty modeling to provide interpretable methods. Specifically, EDL interprets the standard output of the classification network as a distribution over any possible belief state categories. Assuming that category probabilities follow a prior Dirichlet distribution, the set of distribution parameters is replaced with the parameters of the Dirichlet density for novelty detection.
## 4 SCODE - Primarily Proposed Secure Routing Protocol
### Overview
attempts to modify it before forwarding to \(C\), the common inspectors of \(A\) and \(B\) will detect the change because they also receive the same messages sent out from \(A\) and \(B\), so that they know the message originated from B is not the same as the original message sent out from \(A\). Analysis and simulation results in [12] have shown that SCODE is much more energy-efficient than existing approaches such as TTDD [17] and DD [18] while it always delivers 90% of packets to the mobile sinks successfully. More importantly, SCODE is secure against common attacks in sensor network routing mentioned in Section 2: spoofed, altered, or replayed routing information, selective forwarding, sinkhole, _Sybil_, wormhole, and _HELLO_ flood (unidirectional link) attacks. Though SCODE is dominant over existing routing protocols, it still has a number of limitations, as pointed out in Section 1.
# A Novel Love Wave Mode Sensor Waveguide Layer with Microphononic Crystals
## 4 Conclusions  
Love wave mode sensors show great potential in MEMS for physical, chemical and biological monitoring, with their unique small sizes and wireless features. To extend the application of Love wave mode sensors, this paper proposed a novel structure of a waveguide layer in the delay line with microphononic crystals. Through the analysis of the band structures of the piezoelectric phononic crystals, the band pass was found. Therefore, the specific PnCs were introduced in the Love wave mode delay line waveguide layer. To effectively analyze the propagation of the Love wave mode sensor in the novel structure, two delay lines and quasi-three-dimensional models were introduced to calculate the responses of the Love wave. From the analysis of the results, it can be seen that, with the specific microphononic crystals, the propagation attenuation of the Love wave can barely be increased. Furthermore, experiments verify the results that the introduction of micro PnCs does not cause a serious disturbance in attenuation. Additionally, liquid environmental experiments were conducted to verify its viability.  
As this microstructure can be extended to microreaction tanks for microcontrol, this novel Love wave mode sensor with a specific design has a promising application for combining acoustic sensors and microfluidics. Therefore, in future work, the application of this new structure can be further studied.  
Conceptualization, Y.T. and H.L.; methodology, Y.T.; software, L.W.; validation, Y.T.; formal analysis, W.L.; investigation, Y.T.; resources, H.L.; data curation, W.C.; writing--original draft preparation, Y.T.; writing--review and editing, Z.L. and X.M.; visualization, L.W.; supervision, H.L.; project administration, X.M.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (12004413). Additional support was provided by the Key Research Project of Guangdong Province (2020B0101040002), the Technology Innovation 2025 Program of the Municipality of Ningbo (2019B10122), the Research and Development Program in Key Disciplines of Hunan Province (2019 GK2111), the Technology Nova Program of Beijing (Z201100006820012) and the Tianjin Enterprise Science and Technology Commissioner Project (19)CTPJC56200).  
Not applicable.  
The authors declare no conflict of interest.
# Energy-Efficient Controller Placement in Software-Defined Satellite-Terrestrial Integrated Network
## 4 Heuristic Placement Algorithm
So, the computational time complexity of MSAP is \(O\Big{(}t\cdot k\cdot\left|V\right|^{2}\Big{)}\), where \(t\) is the iteration number. ```
0:\(G=(V,E),k\)
0:\(C_{opt}\) - the controller placement, \(P_{\text{min}}\) - the minimum energy consumption
1:Initialize:\(T=T_{0}\), \(T_{final}\), \(\alpha\)
2: Calculate the initial controller placement \(C_{opt}\) in Algorithm 1
3: Compute Equation (9) to calculate the energy consumption \(P_{\text{min}}\) with \(C_{opt}\)
4:while\(T>T_{final}\)do
5:\(C_{new}=C_{opt}\)
6: Select \(\delta\) from 1 to \(k\) randomly
7: Generate all the new neighbor solutions by replacing \(C_{new}(\delta)\)
8: Computing Equation (9) to calculate the energy consumption of every new placement
9: Record the minimum new energy consumption \(P_{new}\) and \(C_{new}\)
10:\(\Delta=P_{new}-P_{\text{min}}\)
11: Generate a random number \(\xi\in(0,1)\)
12:if\(\Delta\leq 0\) or \(e^{-\frac{\Delta}{T}}>\xi\)then
13:\(P_{\text{min}}=P_{new}\)
14:\(C_{opt}=C_{new}\)
15:endif
16:\(T=T\cdot\alpha\)
17:endwhile
18:return\(C_{opt},P_{\text{min}}\)
```  
**Algorithm 2** Modified Simulated Annealing Placement Algorithm (MSAP).
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results
### Multi-Temporal Scale Analysis
The consistent outperformance of weighted fusion across all temporal scales and seasonal conditions provides compelling evidence for the robustness and adaptability of our methodology. The fact that performance advantages persist from ultra-short-term to medium-term forecasting horizons confirms that the approach is not merely addressing a specific temporal niche but rather fundamentally improving the integration of multi-regional forecast information across diverse operational scenarios.
## 3 Simulation Results and Analyses  
A Simulink model is developed to simulate the IIREVs with a step time of 0.01 s, which contains five chargers with three charging modes in real-time operation and balances the power of DC bus. \(SOC_{EV\_art_{P}}\), \(SOC_{EV\_des}\), \(t_{arr_{P}}\), and \(M_{v}\) are randomly generated. \(SOC_{EV\_art_{P}}\) and \(SOC_{EV\_des_{v}}\) are generated in the interval (20%, 50%) and (70%, 100%), respectively. Regarding the EV batteries, lithium-ion batteries were considered, and their capacities are assumed to be capable of handling up to fast charge. Sunpower SPR X21-345 with 21% efficiency under STC is considered as PV panels, and the system loss was estimated at 14%.  
Table 1 provides the parameters used for optimization and power balancing control, and Table 2 provides the options assumed by the EV users, randomly generated in MATLAB, where five EVs are expected to come for charging. The grid peak hours are arbitrarily assumed to be 12:00-13:00 and 15:00-16:00. The energy tariffs are chosen arbitrarily in a way to prioritize the sources used for the EV charging as given by (43).  
\[c_{S}\leq c_{G}\leq c_{PVS}\leq c_{EV\_penalty}. \tag{43}\]  
At each event, like EV arrival, the optimization is executed. Then, the corresponding \(k_{D}\) is calculated as in (33) from the optimized power flow for the corresponding EV arrival event. The obtained \(k_{D}\) is then inserted into the Simulink model, which runs in real-time conditions. At each EV arrival, the desired parameters, \(soc_{S}\) and \(soc_{EV\_}\) currently in charge, are actualized and inserted; then, the supervisory control of the IIREVs executes the optimization, and the EV starts charging.  
The following subsections present different case studies to prove the feasibility of the optimization problem formulated as MILP under different meteorological conditions.
# Glucose Data Classification for Diabetic Patient Monitoring
## 4 Patient Monitoring Elements
### GSM Modem  
The GSM module is a compatible Arduino interface card equipped with a SIM card, which connects to the telephone network as a mobile phone that has its own phone number and operates anywhere in the world where there is a GSM cellular network. The GSM module enables the Arduino board connected to a mobile network to send and receive SMS either in the form of text or in the PDU format. The process of sending and receiving an SMS on a Wavecom modem uses an AT command. Figure 3 shows the GSM modem with External Antenna.
# A Novel Dual-Component Radar-Signal Modulation Recognition Method Based on CNN-ST
## 3 Signal Model and Time-Frequency Transformation
### Dual-Component Radar Signals under Different SNRs  
For recognizing intra-pulse modulation radar signals, even quadratic frequency modulation (EQFM), linear frequency modulation (LFM), normal signal (NS), binary phase-shift keying (BPSK), binary frequency-shift keying (2 FSK), sinusoidal frequency modulation (SFM), FRANK, and four frequency-shift keying (4 FSK) are adopted in this paper. The Gaussian white noise (GWN) is utilized to disturb those signals, for simulating the realistic application environment in the actual battlefield. The received dual-component radar signal is written as [31]  
\[\mathrm{y}(t)=\sum_{i=1}^{k}A_{i}rect(t/T_{i})e^{i(2\pi f_{c_{i}}t+\varnothing _{i}(t)+\varnothing_{0_{i}})}+n(t)\ \ (k=1,\ 2) \tag{1}\]  
where \(n(t)\) is \(i\)-th noise component; \(k\) represents signal number; and \(A_{i}\), \(f_{c_{i}}\), \(\varnothing_{0_{i}}\), and \(T_{i}\), denote amplitude, carrier frequency, initial phase, and pulse width of the signals, respectively.  
This paper is mainly intended to classify dual-component signals under different SNRs, wherein dual-component signals are obtained by randomly overlapping two single-component signals, and thus \(k\) is set as 2. Given the received overlapping-signal sample dataset \(D=\{(x_{i},t_{i})|1\leq i\leq N\}\) including \(N\) samples with eight types of signals, the \(i\)-th sample is represented as \(x_{i}\), and \(t_{ij}=[t_{i1},t_{i2},\cdots,t_{8}]\) represents the true label vector \(x_{i}\), such that sample \(x\) with a label vector \(t=[0,1,0,0,0,0,1,0]\) indicates the presence of radar signals at the second and seventh positions, which are overlapped in \(x\).
###### Abstract
[PERSON] et al. [22] demonstrated that using the output of a WRF two-way nested mesoscale model (0.5 km finest resolution) as the initial and boundary conditions of a steady CFD simulation considerably enhanced the CFD predictions of pollutant transport. Similarly, [PERSON] et al. [7] coupled the WRF model (five nested domains with 0.5 km finest resolution) with an LES CFD code to investigate the airflow and pollutant transport patterns in a dense urban area of Beijing. The results of the [PERSON] et al. [12] study also demonstrated that the coupled WRF/CFD approach is an essential tool in understanding and predicting urban flow and dispersion patterns. The NWP/CFD coupling techniques have also been employed in the wind energy field for various applications, such as wind energy estimation [8; 23; 24] and studies of the effects of strong wind events on wind turbines [25; 26]. These works demonstrate that the NWP/CFD coupling approach can be reliable and NWP-driven CFD simulations can add significant value to various engineering and environment airflow studies. On 18 September 2020, gusts associated with a short-lived (10 min) period of strong wasterly winds in Auckland caused two trucks to tip over while heading south on the Auckland Harbour Bridge (yellow arrow in Figure 1). The bridge's super structure was damaged in this incident [27] and this resulted in severe disruption to traffic flows while the bridge was closed for damage assessment and repairs. Following this event, we were motivated to investigate two research questions:  
1. How accurately does a very high-resolution (i.e., sub-km) NWP model(s) predict such a short-lasting extreme wind event? 2. What are the effects, i.e., wind speed-ups, of the Auckland Harbour Bridge structure itself on the local airflow and is it feasible to couple detailed CFD models with the Auckland Model to produce bridge-specific gust forecasts? To address these questions, we use sub-km-resolution NWP forecasts of wind speed as the initial and boundary conditions of a CFD simulation using a RANS approach to investigate the airflow around the Auckland Harbour Bridge and to estimate the highly localised wind speed up effects that may have contributed to the vehicle overturning. In addition, a detailed analysis of the sub-km NWP model against local observations during this event is described. The rest of the paper is structured as follows. Section 2 presents a description of the NWP and CFD models and setups, along with the measurement stations used for the validation. The results of the study, including the NWP performance and CFD simulations of the localised winds, are summarised in Section 3. A summary description of how the findings of this study can be used to mitigate wind hazards in urban areas is provided in Section 4. Lastly, conclusions are presented in Section 5.
# Object-Independent Grasping in Heavy Clutter
## 4 Discussion
### Causes of Failures  
Though the DBP algorithm demonstrated the highest success rate, it showed 16.7% failures in grasping cosmetic containers. These failures are most likely caused by the environmental changes that occur after estimating the grasp pose. When a gripper approaches the object, its fingers often contact the surrounding objects. Such contact is likely to change the surrounding environment and the pose of the target object. Therefore, unless the grasp pose is corrected accordingly, the chance of successful grasping is reduced.  
Another cause is related to the top-down grasping path that is used by most two-finger grippers. In this, the gripper first moves over the target object, then descends vertically to grasp the object. In this way, grasping objects near the wall is very difficult. Because, in grasp pose estimation, avoiding a collision with the wall has priority over grasping the object, the estimation of the correct grasp pose is not easy, especially for small bins.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Method** & **Success Rate (\%)** & **Time (s)** \\ \hline \multirow{4}{*}{2D clutter} & Random & 56.9 & 0.4 \\  & PCA & 81.1 & 0.7 \\  & ANN & 71.1 & 0.6 \\  & DBP & 84.5 & 1.4 \\ \hline \multirow{4}{*}{Bin picking with 45 objects} & Random & 41.2 & 0.4 \\  & PCA & 78.4 & 0.8 \\  & ANN & 76.0 & 0.6 \\  & DBP & **78.6** & 1.4 \\ \hline \multirow{4}{*}{Bin picking with cosmetic containers} & Random & 58.8 & 0.4 \\  & PCA & 42.1 & 0.7 \\ \cline{1-1}  & ANN & 73.3 & 0.6 \\ \cline{1-1}  & DBP & **83.3** & 1.4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grasping success rates in the three environments.
# Deflection Reduction Shaping Commands with Asymmetric First-Order Actuators
## 2 Materials and Methods
### Analytical Deflection Reduction Shaping Command
Because the input shaping technique focuses on zero residual oscillation, the steady-state response of the pendulum system from Equations (2)-(10) can be derived from the superposition principle as follows:  
\[L\ \ddot{\theta}(t)+g\ sin\ \theta(t)=u(t)\ cos\ \theta(t) \tag{2}\]6  
where \(v_{i}(t)\) is the deflection resulting from the \(i\)th term in Equation (10) and is expressed by the following:  
\[L\ \ddot{\theta}(t)+g\ sin\ \theta(t)=u(t)\ cos\ \theta(t) \tag{2}\]7  
where:  
\[L\ \ddot{\theta}(t)+g\ sin\ \theta(t)=u(t)\ cos\ \theta(t) \tag{2}\]8  
\[L\ \ddot{\theta}(t)+g\ sin\ \theta(t)=u(t)\ cos\ \theta(t) \tag{2}\]9  
Figure 1:0  
With the phasor vector notation of the sinusoidal expression of Equation (11), the residual oscillation magnitude can be rewritten considering a geometric approach, to find the second and third impulse times that will yield zero residual oscillation as follows:  
Figure 1:1  
where:  
Figure 1:2  
With the normalization of the phasor vectors with respect to \(\overset{\rightarrow}{H_{1}}\)\(\mid\) and the first impulse time \(t_{1}=0\), the phasor components are expressed as follows:  
Figure 1:3  
Figure 1:4  
Figure 1:5  
When the three phasor vectors sum to zero, they can be placed as a triangle in the vector space as shown in Figure 4 and the magnitude of the residual oscillation \(\text{Amp}(t_{1},\ t_{2},t_{3})\) is zero. By solving for \(\alpha_{2}\) and \(\alpha_{3}\) with the law of cosines and Equation (13), respectively, the impulse times of the UMZV\({}_{\text{DRF}}\) shaper for the start motion yield are as follows:  
Figure 1:6  
Figure 1:7Figure 1:8  
Figure 1:9  
The switching times of the UMZV\({}_{\text{DRF}}\) shaper for stop motion can be obtained from a similar procedure to that of the start motion. However, the signs of the impulse magnitude are asymmetrically changed. The switching times for the stop motion are expressed as follows:  
\[\Theta(s)=-\frac{s\ V_{f}(s)}{L}\frac{1}{s^{2}+\omega_{n}^{2}} \tag{3}\]0  
\[\Theta(s)=-\frac{s\ V_{f}(s)}{L}\frac{1}{s^{2}+\omega_{n}^{2}} \tag{3}\]1  
where \(t_{4}=t_{p}\) is the duration of the switch-on time and \(\beta_{2}=-\beta_{1}\) and \(\gamma_{2}=1/\gamma_{1}\) are defined. From Equations (17)-(20), the entire UMZV\({}_{\text{DRF}}\) shaper for the start and stop commands of flexible systems is presented as follows:  
\[\left[ \begin{array}{c}A_{i}\\ t_{i}\end{array} \right]=\left[ \begin{array}{cccccc}1&-1&A_{3}&-A_{3}&1&-1\\ t_{1}&t_{2}&t_{3}&t_{p}&t_{5}&t_{6}\end{array} \right] \tag{21}\]  
The aforementioned equation is utilized to produce the deflection reduction shaping commands under asymmetric first-order actuators with convolution of arbitrary on-off commands. To maintain the control performance of the proposed input shaper, the rise time of each input velocity should be ensured due to the time delay effect of first-order actuator dynamics.
## 2 Wireless Body Area Network
### Patient Monitoring & Condition Management  
A WBAN can consist of a number of heterogeneous sensors and devices each having a separate role to play in the overall health management of the patient. The range of sensors that can be utilized as part of a WBAN includes ECG, blood oxidization, Electromyography (EMG), heart rate, BP, movement, and temperature [7, 8].  
Sensors can be combined with actuators to form a closed loop system where sensor data is utilized to manage the levels of medicine administered to the patient. A good example of this is the potential of Continuous Blood Glucose Monitoring (CBGM) and injection in type 1 diabetes patients [9, 10, 11]. An actuator wirelessly connected to the glucose sensor via a WBAN could administer correct doses of insulin based on the sensed glucose levels of the patient at any time. This system has obvious advantages over current manual methods of self test and injection. Figure 2 shows a typical WBAN together with the range of sensors that can be linked directly to a BCU.  
Figure 1: WBAN positioning.  
Figure 2: WBAN consisting of various wireless biosensors.  
In addition to wearable sensors, a WBAN can include implanted sensors such as cochlear implants and various types of nanosensors that can monitor key pre- and post-operative conditions. A WBAN comprising of implantable and wearable sensors can be combined with location management, alarm notification and fall detection for a complete system that could provide the necessary levels of remote monitoring for aged and vulnerable patients who require their independence.
## 3 Designing Control Strategy and Model
### Joint Control Model of Wind and Storage Based on TPA-BiGRU
#### 3.2.2 Temporal Pattern Attention Mechanism  
This paper introduces TPA to strengthen the model's memory of long-term time series information while reinforcing the key features of local short-term information, highlighting the key factors affecting the energy storage output, and improving the model's prediction effect. The designed TPA is calculated as follows [26]:  
\[H_{i,j}^{c}=\sum_{l=1}^{w}H_{i,(l-w-1+l)}\times C_{j,T-w+l} \tag{6}\]  
\[f(H_{i}^{C},h_{t})=(H_{i}^{C})^{T}W_{a}h_{t} \tag{7}\]  
\[\alpha_{i}=sigmoid(f(H_{i}^{C}\cdot h_{t})) \tag{8}\]  
\[c_{t}=\sum_{i=1}^{n}\alpha_{i}H_{i}^{C} \tag{9}\]  
\[h_{t}^{A}=W_{h}[c_{t};h_{t}] \tag{10}\]  
where \(H=[h_{t-w+1},h_{t-w+2,...},h_{t-1}]\) is the hidden state matrix of the original wind storage time series containing multiple moments of information after BiGRU processing, and the length of the time window is \(w\); \(C\) is a convolution kernel of length \(T\), generally taken as the window length; \(H_{i,j}^{c}\) is the convolution value in row \(i\) and column \(j\); \(H^{C}\) denotes the temporal pattern matrix; \(h_{t}\) represents the hidden state information extracted by the neural network from the input wind storage data feature matrix at the current moment; \(W_{a}\) and \(W_{h}\) are the weight parameter matrices; \(\alpha_{i}\) is the attention weight vector, which characterizes the importance of the hidden state information at each moment in the state matrix, and length \(w\); \(c_{t}\) is the feature vector that characterizes the temporal relationship after weighting; and \(h_{t}^{A}\) is the splicing of the feature vector and the current moment state information, and it is also the final output result of TPA.
## 10 Open Challenges and Future Directions
### Communication Infrastructure  
The existence of several communication architectures, the variety of routing protocols, and the variety of UAV types raise the question of the choice of an adapted communication infrastructure. This issue highlights the challenges of interoperability in a distributed manner [95]. Generally, in a decentralized architecture, UAVs are required to exchange data or services based on nested protocols. Unfortunately, it is difficult to do so because the protocols vary from one manufacturer to another. Thus, two UAVs having different protocols will not be able to communicate. It becomes even more constraining when it isa collaborative mission in which UAVs operate synchronously or asynchronously. This challenge opens the following research tracks:  
* **The development of a protocol converter** that allows us to convert a given FANET protocol into a desired protocol in order to ensure interoperability between non-homogeneous UAVs.
* **The standardization of UAV communication rules** in order to evolve towards a reference model used by manufacturers as well as end users.