esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Introducing acoustic communication technology as the auxiliary communication mode and using the \"ASK + FSK\" complex modulation, with the anti-noise technology of \"double microphone input\" combined, a time-sharing rental vehicle terminal with high noise immunity and acoustic communication function was developed, and the effective distance was tested as well. The following conclusions were obtained:  
* The \"ASK + FSK\" compound modulation mode was adopted to reduce the influence of noise, jitter, and other false triggering from the modulation/demodulation mode, so as to improve the anti-noise ability.
* The acoustic enhancement technique of \"double microphone input\" was used to filter the correlated weak background noise by using the correlation function to preserve the more coherent signals, so as to achieve the purpose of noise reduction
* The maximum recommended communication distance of the developed vehicle terminal was 1 m, and the effective distance was within the acceptable range of the user, which could assist the GPRS communication channel to solve the problem of the delay and slow the response caused by the weak GPRS intensity, which enhanced the management platform of controlling the electric vehicle and greatly enhanced the user experience.  
[PERSON] and [PERSON] designed the experiments; [PERSON], [PERSON], [PERSON] analyzed the data; All authors have approved the submitted manuscript.  
This work was supported by the National Key Technology Support Program (2015 BAG10B00), the National Social Science Fund of China (19 BY077) and the Humanities and Social Sciences Projects of the Ministry of Education of China (18 YJC790137), the National Natural Science Foundation of China (51907034; 51867003; 61473272), the Natural Science Foundation of Guangxi (2018 JJB160056; 2018 JJB160064; 2018 JJA160176), the Basic Ability Promotion Project for Yong Teachers in Universities of Guangxi (2019 KY0046; 2019 KY0022), the Guangxi Thousand Backbone Teachers Training Program, the Boshike Award Scheme for Young Innovative Talents, the Guangxi Bagui Young Scholars Special Funding.  
The authors declare no conflict of interest.
###### Abstract  
The adoption of forecasting approaches such as the multiplicative Holt-Winters (MHW) model is preferred in business, especially for the prediction of future events having seasonal and other causal variations. However, in the MHW model the initial values of the time-series parameters and smoothing constants are incorporated by a recursion process to estimate and update the level (\(L_{T}\)), growth rate (\(b_{T}\)) and seasonal component (\(SN_{T}\)). The current practice of integrating and/or determining the initial value of \(L_{T}\) is a stationary process, as it restricts the scope of adjustment with the progression of time and, thereby, the forecasting accuracy is compromised, while the periodic updating of \(L_{T}\) is avoided, presumably due to the computational complexity. To overcome this obstacle, a fuzzy logic-based prediction model is developed to evaluate \(L_{T}\) dynamically and to embed its value into the conventional MHW approach. The developed model is implemented in the MATLAB Fuzzy Logic Toolbox along with an optimal smoothing constant-seeking program. The new model, proposed as a collaborative approach, is tested with real-life data gathered from a local manufacturer and also for two industrial cases extracted from literature. In all cases, a significant improvement in forecasting accuracy is achieved.  
collaborative forecasting; fuzzy logic-based model; level-prediction model; multiplicative Holt-Winters method; seasonal demand forecasting
# Assessing Landsat Images Availability and Its Effects on Phenological Metrics
## 4 Discussion
For instance, the enhanced vegetation index (EVI) presents little response to soil exposure and less saturation over dense vegetation than the NDVI. This author suggests using different vegetation indexes for different biomes. Remotely sensed imagery provides information at aggregated spatial resolutions. Therefore, signals may contain information plant species with different phenologies or even from vegetated and non-vegetated covers. The signal can also be affected by changes in multi-canopy layers and by the sun-sensor-target geometry. Furthermore, there are many smoothing and curve-fitting methods, resulting in significantly different estimates of metrics' values [48]. Finally, in the present study, we do not treat a critical dimension of land surface phenology which is the relationship of the metrics with ground-based observations, ecological processes such as carbon uptake, photosynthesis, gross primary production or sap flow density [49; 50; 51] and anthropic actions as fire and land use management [52].
# Agricultural Evolution: Process, Pattern and Water Resource Effect
## 3 Results
### Land Use Changes Related to Cultivated Land
#### 3.2.2 Cumulative Transition Probability  
Cumulative transition probability of different land-use types was calculated by formula 4. Results (Table 7) indicated that Pij between paddy and other land-use categories was 0.89% (from paddy to other categories) and 4.65% (from other categories to paddy) of the total area, respectively, while that of dry farmland was 4.73% (from dry farmland to other types) and 9.86% (from other types to dry farmland), respectively. The dry farmland gain accounted for the largest percentage (9.86%), following by the transformation from grassland to other land-use categories and from dry farmland to other land-use categories. Table 7 indicated that paddy expansion mainly contributed by dry farmland (about 52.66% of the transformation from other land-use categories to paddy), followed by marsh and grassland. Dry farmland had the greatest contribution to paddy decrease (about 84.45%). The mutual transformation between two cultivated land types was the dominant land-use categories of cultivated land conversions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Paddy** & **Dry** & **Forest** & **Grassland** & **Water** & **Settlement** & **Marsh** & **Unused** & **Total** \\ \hline Paddy & - & 0.75 & 0.00 & 0.05 & 0.01 & 0.02 & 0.04 & 0.00 & 0.89 \\ Dry & 2.45 & - & 1.35 & 0.34 & 0.06 & 0.21 & 0.25 & 0.07 & 4.73 \\ farmland & 0.01 & 0.79 & - & 0.09 & 0.00 & 0.01 & 0.03 & 0.01 & 0.94 \\ Grassland & 0.68 & 6.04 & 0.85 & - & 0.08 & 0.06 & 0.86 & 1.15 & 9.73 \\ Water & 0.05 & 0.24 & 0.01 & 0.27 & - & 0.00 & 1.17 & 0.74 & 2.49 \\ Settlement & 0.00 & 0.00 & 0.00 & 0.00 & 0.00 & - & 0.00 & 0.00 & 0.01 \\ Marsh & 1.31 & 0.87 & 0.01 & 0.88 & 0.33 & 0.01 & - & 0.44 & 3.86 \\ Unused & 0.15 & 1.16 & 0.19 & 0.95 & 0.17 & 0.06 & 0.82 & - & 3.51 \\ Total & 4.65 & 9.86 & 2.42 & 2.58 & 0.66 & 0.38 & 3.18 & 2.42 & \\ \hline \hline \end{tabular} Note: unused mean other unused land.  
\end{table}
Table 7: Cumulative transition probability of different land-use change types (unit: %).
## 3 Results
### Stage 2
#### 3.2.4 Transfer Matrix  
Therefore, to simulate the behavior of the system, the differential equation describing the time evolution of the state vector x and the equation relating the output vector y to the state vector x can be used. The transfer matrix \(G(s)\) is calculated as the ratio between the output Laplace transform (\(x3\)) and the input Laplace transform (\(u\)), with the initial conditions equal to zero; see Equation (4).  
Where \(C\) is the output matrix, which in this case is [0 0 1];  
\(A\) is the matrix of system coefficients, previously calculated;  
\(B\) is the input matrix, which in this case is [0; 0; 1].  
\(sI\) is the identity matrix multiplied by s, which is used to do the subtraction operation \(sI-A\). The result of this operation is a transfer function \(G(s)\) in terms of the complex variable s, which represents the relationship between the input and output of the system in the frequency domain. We proceed to solve the matrix operation that is inside the parentheses; see Equation (24).  
\[(sI-A)=s.\begin{pmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{pmatrix}-\begin{pmatrix}-0.008541&0&0\\ 0.008541&-0.037147&0\\ 0&0.0037147&-0.02786\end{pmatrix} \tag{24}\]  
The following matrix is obtained; see Equation (25).  
\[(sI-A)=\begin{pmatrix}s+0.00851&0&0\\ -0.008541&s+0.037147&0\\ 0&-0.037147&s+0.02786\end{pmatrix} \tag{25}\]  
To obtain the transfer matrix, the equation must be inverted as follows; see Equation (26).  
\[(sI-A)^{-1}=\frac{adj(sI-A)}{det(sI-A)} \tag{26}\]  
The matrices obtained from the system are replaced in the transfer matrix \(G(s)\), the respective operations are carried out and the output transfer matrix is calculated; see Equation (27).  
\[G(s)=\frac{-0.009827s^{2}+0.0006386s+8.834*10^{-6}}{s^{3}+0.07354s^{2}+0.00159s+ 8.834*10^{-6}} \tag{27}\]
## 3 Experimental Results and Discussion
### Crack Pattern and Failure Mode
#### 3.3.1 Early Stage and Intermediate Stage of FP and HP Series Specimens  
Both the FP series and HP series specimens showed similar crack patterns at the early and intermediate stages of loading but exhibited different failure modes towards the late stage of loading. At the early stage of loading, the first flexural crack in both series was observed at L1, L2, and/or L3 locations when loads ranging from 50 kN to 65 kN were applied. As the loads were increased incrementally from 100 kN to 165 kN, the first diagonal tension cracks were observed at the center of the compressive strut within the shear span. These flexural cracks were observed to widen rapidly when both series were loaded beyond 200 kN. As the specimens moved towards failure, differences in the progression of the diagonal tension crack development were observed in the FP and HP series. The diagonal tension cracks in the FP series specimens developed rapidly whilst the diagonal tension cracks in the HP series specimens developed gradually.
# Energy Consumption Model for Sensor Nodes Based on LoRa and LoRaWAN
## 1 Introduction  
Wireless Sensor nodes enable a wide range of applications, such as infrastructure security, environment monitoring, event detection, etc. [1]. Such applications have the purpose of collecting information about a given phenomena or event. These sensor nodes are generally deployed in harsh and inaccessible environments. For these reasons, sensor nodes are supposed to operate over long time periods without human intervention [1, 2].  
Modeling of energy consumption is an important consideration in the design of communicating sensors for monitoring a specific target application. Communicating sensors should perform the following tasks for most applications [1, 3]: sense events, local information processing of sensed events and packet transmission to the access point [3, 4]. Each task consumes a power consumption amount during a given time duration. Therefore, an accurate energy consumption model of the sensor node is essential to estimate the sensor lifetime [5]. This energy model allows optimizing the power consumption of the sensor node.  
The proposed energy model is based on LoRa and LoRaWAN technologies. The LoRa system operates at the _ISM_ frequency bands, which makes it an attractive solution for the Internet of Things and Machine to Machine (\(M2M\)) systems [6, 7]. Its low power associated to the long range communication pushes LoRa to the top of the LPWAN technologies. Due to its unique modulation, LoRa is a very versatile technology that can be adapted to different environment types and application classes [8].  
In this paper, we focus on the LoRaWAN protocol which was developed by LoRa Alliance to serve IoT applications. The LoRaWAN characteristics have a great influence in determining the battery lifetime of the sensor node [9; 10]. In fact, using LoRaWAN technology can optimize the energy consumption by adapting its principal parameters.  
The goal of this work is to propose an energy consumption model for sensor nodes using LoRa modulation and LoRaWAN protocol. This model is evaluated using different LoRaWAN modes. Besides, we study the impact of LoRaWAN parameters such as acknowledged transmission, spreading factor, coding rate, payload size and communication range on the sensor node consumption.  
The rest of the paper is organized as follows. Section 2 presents related works. The wireless sensor design is described in Section 3. Section 4 depicts our energy consumption model. The LoRa modulation technique and the LoRaWAN characteristics are exposed in Section 5. Numerical results are explained in Section 6. Finally, conclusion and future works are given in Section 7.
## 4 Performance Evaluation
### Simulation Results
As there are more resource blocks, there is more communication bandwidth available, which lowers the possibility of packet collisions. As a result, packets encounter less interference and are reliably received by UEs, which enhances the PRR. Compared to conventional fixed or semi-persistent resource allocation techniques, which may not dynamically adapt to varying network conditions, our approach demonstrates a greater efficiency in managing interference and improving overall network performance. Notably, this performance hierarchy emphasizes the value of a strategic resource allocation method, with four resource blocks outperforming three, three outperforming two, and two outperforming one. This method is essential for balancing the competing demands of effective resource use and communication reliability. Traditional resource allocation strategies often follow a static or contention-based approach, which can lead to suboptimal performance in high-density vehicular environments. In contrast, our methodology, which considers distance and dynamic adjustments, significantly enhances packet reception reliability while minimizing resource contention. Figure 5 highlight the benefits of tailoring resource allocation plans to the particular needs and performance criteria of 5G NR sidelink communication situations. The results  
Figure 5: Packet Reception Rate (%) based on the distances between two UEs. (**a**) 1 RB; (**b**) 2 RBs. (**c**) 3 RBs; (**d**) 4 RBs. show that by carefully balancing the trade-off between connection reliability and resource consumption efficiency, it is possible to improve communication effectiveness by allocating resource blocks optimally based on distance and resource limits. By comparing our approach with conventional allocation schemes, we further demonstrate its ability to enhance network dependability and efficiency, making it a promising solution for next-generation vehicular communication systems.
## 3 Methodology  
The approach presented in this paper consists of three components: feature extract module, multi-view interaction module, and late fusion module. The pre-trained models extract the multimodal features in the feature extract module. The multi-view interaction module interacts with multimodal messages with visual commonsense knowledge. Lastly, the late fusion module leverages an attention machine to fuse multimodal representation. The overall architecture is illustrated in Figure 1.  
Figure 1: The overall architecture of multi-view interaction model with knowledge enhancement (MVK). The model consists of three modules: (**a**) feature extract module, (**b**) multi-view interaction module, and (**c**) late fusion module. In module (**a**), the DenseNet and BERT are feature extractors used to extract features at two levels. Module (**b**) has N layers, and each layer performs an inter-modal interaction based on knowledge augmentation.
## 3 Selected Datasets
### Cse-Cic-IDS 2018

Table 3: _Cont._78 features are extracted, as listed in Tables 5 and 6. The dataset encompasses a voluminous 16,233,002 observations, each associated with 78 features. Notably, the last column signifies the categories assigned to each observation [51]. \begin{table}
\begin{tabular}{c l} \hline \hline
**Feature Name** & **Description** \\ \hline dst\_port & Destination Port proto N. of protocol \\ time\_stmp & Timestamp of the event \\ fl\_dur & Flow duration \\ fl\_pkt\_s & Flow packets rate that is the number of packets \\  & transferred per second \\ fl\_iat\_avg & Average time between two flows \\ fl\_iat\_std & Standard deviation time two flows \\ fl\_iat\_max & Maximum time between two flows \\ fl\_iat\_min & Minimum time between two flows \\ f\_wi\_tot & Total time between two packets sent in the forward direction \\ f\_wi\_at\_avg & Mean time between two packets sent in the forward direction \\ f\_wi\_at\_std & Standard deviation time between two packets sent in the forward direction \\ f\_wi\_at\_max & Maximum time between two packets sent in the forward direction \\ f\_wi\_at\_min & Minimum time between two packets sent in the forward direction \\ b\_wi\_tot & Total time between two packets sent in the forward direction \\ b\_wi\_at\_avg & Mean time between two packets sent in the forward direction \\ b\_wi\_at\_max & Maximum time between two packets sent in the forward direction \\ b\_wi\_at\_min & Minimum time between two packets sent in the forward direction \\ b\_wi\_tot & Total time between two packets sent in the backward direction \\ b\_wi\_at\_avg & Mean time between two packets sent in the backward direction \\ b\_wi\_at\_max & Maximum time between two packets sent in the backward direction \\ b\_wi\_at\_min & Minimum time between two packets sent in the backward direction \\ f\_w\_psh\_flag & Number of times the PSH flag was set in packets traveling in the forward direction \\ b\_w\_psh\_flag & Number of times the URG flag was set in packets traveling in the backward direction (0 for UDP) \\ f\_w\_mg\_flag & Number of times the URG flag was set in packets traveling in the forward direction (0 for UDP) \\ f\_w\_mg\_flag & Number of times the URG flag was set in packets traveling in the backward direction (0 for UDP) \\ f\_w\_hdr\_len & Total bytes used for headers in the forward direction \\ b\_w\_hdr\_len & Total bytes used for headers in the forward direction \\ f\_fw\_pkt\_s & Number of forwarding packets per second \\ b\_w\_pkt\_s & Number of backward packets per second \\ pkt\_len\_min & Minimum length of a flow \\ pkt\_len\_max & Maximum length of a flow \\ pkt\_len\_avg & Mean length of a flow \\ pkt\_len\_std & Standard deviation length of a flow \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Features Description CSE-CIC-IDS2018 part 1.
# Ultrawideband Low-Profile and Miniaturized Spoof Plasmonic Vivaldi Antenna for Base Station
## 2 Methods and Principles  
The typical monopole antenna and the plasmonic monopole antenna are illustrated in Figure 1a. Usually, the plasmonic monopole antenna is composed of the corrugated monopole antenna with grooves along the metal wire. Here, we use meander wires to simulate the effects of the grooves for simplicity. The dispersion relations of both antennas are calculated and plotted in Figure 1b. When the groove depth \(b\) is 0 mm, it is the typical monopole antenna. From Figure 1b, it can be seen that when the groove depth is increased (from 0 mm to 3.6 mm), the corresponding asymptotic frequencies are decreased and the dispersion curves are lowered. It can be concluded that when the wave vector \(\beta\) is fixed, the operating frequency is lower for the plasmonic monopole antenna with deeper grooves. Hence, the electrical size of the plasmonic monopole antenna would be smaller, compared with that of the traditional monopole antenna.  
The efficiencies of the typical and plasmonic monopole antennas are calculated and illustrated in Figure 2. Figure 2b shows the efficiencies of the plasmonic monopole antenna with different groove depths from 1 mm to 4 mm. From Figure 2b, it can be seen that the operating frequencies red-shifts (corresponding to longer wavelengths) when the groove depth is increased for the fixed height of the monopole antenna (25 mm). Especially, the efficiency curves corresponding to three typical groove depths are plotted in Figure 2c. It can be clearly observed that the central operating frequency red-shifts when the groove depth \(b\) is 0 mm (typical monopole antenna), 1.4 mm, and 3.6 mm, respectively. Meanwhile, we can observe that there is an optimized efficiency when tuning the groove depth. Hence, it indicates that the plasmonic theory provides a scheme for miniaturization and optimization of the antenna.  
Figure 1: (**a**) The typical and plasmonic monopole antenna. The dimensions of the antennas: \(h=25\) mm, \(a=0.65\) mm, \(b=1.4\) mm, and \(r=0.5\) mm. (**b**) The dispersion relations of both antennas.
# Electrochemical Biosensors Based on Conducting Polymers: A Review
## 3 Electroanalytical Methods
### Impedance  
Electrochemical impedance spectroscopy (EIS) is a sensitive technique for the analysis of biomolecular recognition events of specific binding proteins, nucleic acids, whole cells, antibodies or antibody-related substances, occurring at the modified surface [88; 89; 90]. In particular, many studies on impedometric biosensors are focused on immunosensors since the bonding between antibodies and antigens leads to the formation of an immunocyt resulting in electron transfers and impedance changes (see Section 4.2). Moreover, impedometric biosensors allow direct detection of biomolecular recognition events without using enzyme labels and have the advantages of low cost, ease of use, portability and ability to perform both screening and online monitoring without being destructive. In impedometric immunosensors, conducting polymers are generally used to immobilize the antigens, commonly through covalent attachment, thus allowing the detection of the antibodies due to the high antigen-antibody affinity [91; 92; 93].
# Simulation Study of Two Torque Optimization Methods for Direct Torque-Controlled Induction Motors
## 2 IM Torque Response Optimization  
Both motor torque and stator flux are controlled by a hysteresis controller in traditional DTC to guarantee and secure proper operation under the hysteresis band, as seen in Figure 1. Although it is a simple controller, it has many disadvantages such as variable frequency switching and low efficiency. Another disadvantage is the lack of voltage vector optimization within the hysteresis band. The origin of this problem comes from the hysteresis controller's nature. The hysteresis controller always chooses the previous selection within the hysteresis band [30], as indicated in Figure 1. This performance causes a weak point for the hysteresis controller when operating in discrete systems. To improve hysteresis controller performance, an optimization technique must be employed to elevate the drawback. To get a fast torque response of the IM, the optimization technique must focus on selecting the voltage vector that provides a maximum torque rate. The first step in the optimization procedure is the generation of the torque rate equation as a function of the stator voltage vectors. The second step is the optimization of the stator voltage vectors to produce the maximum rate of torque. However, the optimization process can be performed using the two methods discussed in the following sections.