esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contributions  
This paper investigates energy-efficient UAV trajectory optimization and computing resource allocation in UAV-assisted C-V2X communication and is a significant extension of our previous work in [26]. This paper also extends our previous work in [27], where we investigated some robust and strategic game-theoretic approaches used by the UAV to select different vehicles in each TTI. The main contributions of this paper are as follows:  
1. We propose to examine end-to-end packet latency as well as UAV energy consumption based on FL with varying numbers of vehicles. From the FL iterations, we study the probability of the optimal trajectory prediction of the UAV using different neural network models. This is a significant extension of our previous work in [28] where we analyzed the variation in the UAV transmit power for a varying number of vehicles in a gross data offloading scenario.  
Figure 1: A brief timeline depicting the amalgamation of wireless communication technologies with transportation systems. Also illustrated is the gradual integration of UAVs in vehicular networks in 5G and 6G wireless communication paradigms. A detailed timeline and comprehensive overview of the recent and evolving applications of machine learning techniques in UAV communication frameworks can be found in [14; 15].  
As a function of computation offloading to UAV and the local vehicle model computation time, we plot the average task completion latency for varying numbers of vehicles. Using long short-term memory (LSTM), gated recurrent unit (GRU), recurrent neural network (RNN) and convolutional neural network (CNN)-LSTM models, we compare the average task completion latency for gross data offloading and FL, and the probability of optimal UAV trajectory prediction.
3. We validate the proposed solution by calculating the number of training iterations required to satisfy the service-time constraint for LSTM, GRU, RNN, and CNN-LSTM models. Here, we use the _V2X-Sim_ and _LTE I/Q_ datasets and based on the number of vehicles that exceed a specified time frame to process a task, we conclude the maximum number of vehicles a UAV can support without violating the identified constraints. Furthermore, we utilize the _V2X-Sim_ dataset to verify the FL model convergence characteristics and performance trade-offs [29] for the proposed UAV-assisted C-V2X communications.
4. Unlike existing works where the device-to-device communication largely depends on neighbor discovery [30], in this work, the TTI is selected using the distributed scheduling protocol known as sensing-based semi-persistent scheduling (SPS) [31]. Since the vehicles and the UAV operate at different speeds, SPS is utilized to enable vehicles to independently select and manage the available bandwidth and the UAV communication and computational resources.
## 2 Materials and Methods
### Statistical Analysis  
The first-order model (Equation (1)), modified Gompertz model (Equation (2)), logistic model (Equation (3)), and Cone model (Equation (4)) were utilized to describe and compare the kinetics of biogas production during the anaerobic co-digestion of FW and MA [30,31].  
\[Y(t)=P_{0}\times[1-exp(-kt)], \tag{1}\]  
\[Y(t)=P_{0}\times exp\bigg{\{}-exp\bigg{[}\frac{R_{m}\times e}{P_{0}}(\lambda-t )+1\bigg{]}\bigg{\}}, \tag{2}\]\[Y(t)=\frac{P_{0}}{1+\exp\left[4R_{m}\frac{\lambda-t}{P_{0}}+2\right]}\, \tag{3}\]  
\[Y(t)=\frac{P_{0}}{1+(kt)^{-n}}\, \tag{4}\]  
where \(Y(t)\) is the cumulative biogas yield for a given time (mL/g VS), \(P_{0}\) is the biogas production potential (mL/g VS), \(k\) is first-order decomposition rate constant (1/d), which indicates the rate of degradability of the substrate, \(t\) is the digestion time in days (d), \(R_{m}\) is the maximum biogas production rate (mL/(g VS day)), \(\lambda\) is the lag phase (d), \(e\) is the natural constant (2.72), and \(n\) is the shape factor.  
To evaluate the antagonistic or synergistic effects of biogas production, the calculated biogas yield (CBY) and biogas potential improvement (BPI) were applied according to Equations (5) and (6) [32; 33]:  
\[\text{CBY}=\text{BP}_{\text{FW}}\times\alpha+\text{BP}_{\text{MA}}\times\beta, \tag{5}\]  
\[\text{BPI}=(\text{EBY}-\text{CBY})/\text{CBY}, \tag{6}\]  
where BPFW and BPMA are the biogas potential of FW and MA obtained from 100% FW and 100% MA treatments, and \(\alpha\) and \(\beta\) represent the corresponding percentages of FW and MA in each co-digestion mixture, respectively. EBY represents the experimental biogas yield.  
Data processing was performed in Microsoft Excel 2020. The difference in cumulative biogas yield of different mixing ratios was analyzed by one-way ANOVA with the posthoc test at a significance level of \(p<0.05\) and \(p<0.01\). Graphing was conducted using Origin 2021 (Origin Lab, Northampton, MA, USA).
## 3 Finite Element Model
### Validation of Model  
[PERSON] et al. [12] performed centrifuge experiments on medium-loose sand overlying normally consolidated clay with sand thickness \(H_{s}\) of 6, 5, and 3.2 m and \(H_{s}\)/\(D\) ratio between 0.16 and 1.0. [PERSON] et al. [16] performed numerical simulations to model the complete penetration resistance profile of a spudcan in sand overlying clay. Two experimental penetration resistance-depth outputs (L1 SP1 and L3 SP1) measured from centrifuge experiments performed on medium-loose sand overlying normally consolidated clay were used to validate the numerical model. \(H_{s}\)/\(D\) for L1 SP1 and L3 SP1 were 1.0 and 0.53, respectively. In the numerical simulations, the properties of the soil were assigned following those provided in [PERSON] et al. [16] were used. The elastic modulus (\(E\)) and Poisson's ratio (\(\
u\)) of sand were set to 25 MPa and 0.3, respectively. \(\phi^{\prime}\) of sand was set to 31\({}^{\circ}\), as reported in [16]. \(s_{u}\) was set to  
Figure 2: Computational model of spudcan foundation penetrating into sand overlying clay profile.  
12.96 kPa, and the strength gradient \(k\) was set to 1.54 kPa/m. The centrifuge test outputs and numerical simulations results are compared in Figure 4. Overall, good agreement between the experiment recordings and CEL outputs are shown. As for the L3 SP1 test, an overestimation of the penetration resistance in the clay layer is revealed. However, the peak resistances in the sand layer predicted in both tests show favorable correspondences.  
[PERSON] and [PERSON] [21] performed centrifuge experiments on medium dense sand overlying stiff clays with a sand thickness \(H_{\text{s}}\) of 6 and 4 m, and \(H_{\text{s}}\)/D ranging from 0.33 to 1.0. Two experimental penetration resistance profiles (SP3 and SP6) measured from centrifuge experiments performed on  
Figure 4: Comparison of penetration resistance profiles measured from centrifuge tests and calculated from finite element (FE) analyses.  
Figure 3: Sensitivity study outputs: (**a**) mesh density effect; (**b**) penetration velocity effect.  
medium dense sand overlying stiff clay were used to further validate the numerical model. In the numerical simulations, the value of \(E\) and \(\
u\) of sand were set to 50 MPa and 0.3, respectively [16; 26], \(\phi^{\prime}\) of sand was set to 37\({}^{\circ}\), as reported in [21]. The values of \(s_{u}\) were set to 43.9 and 32.6 kPa, for SP3 and SP6, respectively. The strength gradient \(k\) was set to 3.4 kPa/m. The centrifuge test outputs and numerical simulations results are compared in Figure 5. Overall, good agreement between the experiment recordings and CEL outputs are shown. The peak resistances in the sand layer predicted in both tests show favorable correspondences.
## 1 Introduction
Furthermore, we need Multi-Attribute Decision Modeling (MADM) to efficiently utilize our hesitant fuzzy set to generate hesitant fuzzy entropy matrix and determine our entropy weight coefficients [14; 15; 17; 18]. In this paper, we propose a hesitant fuzzy entropy based opportunistic clustering and data fusion scheme in which a hesitant fuzzy set is created by acquiring the multi-attribute values of sensor nodes. Moreover, the hesitant entropy matrix is generated after finding entropy values of each attribute in the hesitant fuzzy set using data standardization process. Subsequently, the entropy weight model [14; 15; 17] is employed to determine the entropy weight coefficients for each sensor node and, finally, the threshold attribute values are determined and then compared with the original attribute values for making a decision about new CH. This entire process is part of the CH election procedure which is initiated by the BS and continued by every CH for all communication rounds. The BS creates a CH election set and adds sensor nodes in this set which have original attribute values greater than threshold attribute values. After that, the BS invokes these added sensor nodes by sending them a CH election set message. Furthermore, the hesitant fuzzy entropy technique is also used for reliable data fusion performed by CHs in such a way that CMs integrate and forward their sensed data to the assigned CH by removing the redundant information from the sensed data. CHs perform data aggregation on integrated data packets by concatenating them into a single larger data packet of a specified length. Later on, CHs exploit hesitant fuzzy entropy and entropy weight coefficients to detect the change in sensed data periodically and then send the aggregated data to the BS upon detecting that change in sensed data [19; 20]. The rest of the paper is organized as follows: Section 2 describes the related research work conducted for heterogeneous clustering in WSNs, entropy-based clustering schemes, hesitant fuzzy entropy, and OR. System modeling is presented in Section 3. Our proposed scheme, HFECS, is presented in Section 4. Section 5 describes the case study of HFECS in neighborhood area networks. The performance evaluation and simulation results are described in Section 6. Finally, Sections 7 and 8 discuss and conclude the paper, respectively, and provide some future research directions.
## 3 Results and Discussion
### Thermal Analysis of Hydration  
Figure 5 illustrates the hydration heat curve of the multicomponent solid waste cementitious material over 72 h. Figure 5a displays the hydration heat release rate, with the first heat release peak rapidly forming within 0-1 h. This initial peak corresponded to the exothermic dissolution of aluminate- and sulfate-phase substances in the cementitious material. In the following 2-3 h, the cementitious material continued to hydrate into the induction stage, leading to the generation of some hydration products, such as C-S-H gel and Ca(OH)2. Compared with the induction stage of the EGC0 group, the induction stage of the EGC7-12 group was shortened after EMR and CS additions. In addition, during the acceleration stage (4-13 h), the EGC0 group reached a peak of 2.75 \(\times\) 10\({}^{-3}\) W/g at 10.2 h. With the decrease in the CS content, the second exothermic peak of the EGC7-12 group was delayed mainly because CS incorporation increased the pH value and the Ca2+ concentration and reduced the time to reach Ca2+ saturation, thereby promoting hydration [31]. Between 14 and 20 h, the third exothermic peak, attributed to the generation of AFt [32], appeared with an increase in the EMR content. The time at which the third exothermic peak occurred was also delayed.  
The cumulative heat release of hydration of the cementitious material is shown in Figure 5b. After the addition of EMR and CS, the cumulative heat release of hydration increased with the increase in the EMR content. The cumulative heat release of hydration in the EGC11 group over 72 h was 264.81 J/g, representing 109.65% of that of the EGC0  
Figure 5: Hydration heat curve of multicomponent solid waste cementitious material: (**a**) hydration heat release rate; (**b**) cumulative heat release from hydration.  
group (241.50 J/g). This indicates that the EMR and CS additions effectively enhanced the hydration rate and degree of GBFS.
# Before and After: A Multiscale Remote Sensing Assessment of the Sinop Dam, Mato Grosso, Brazil
## 2 Materials and Methods
### UAS Photograph Acquisition  
Aerial photographs were acquired for the case study sites (Figure 2) by UAS. For Corredeira do Sulpicio, a DJI Inspire 2 quadcopter with an X5S camera (M4/3 sensor, 20.8 MP sensor resolution, 5280 \(\times\) 3956 px image size, 3.3 \(\upmu\)m pixel size) and a DJI MFT 15 mm f/1.7 ASPH lens was used to acquire 549 photographs in jpeg format on 4 September 2018 from an altitude of 60 m AGL. For the flight plan, a double grid pattern was used with 85% front and side overlaps. The flight path and camera triggering were controlled by the DJI Ground Station Pro iOS software. For Ilha do Lair, a DJI Mavic 2 Pro quadcopter with an integrated Hasselblad L1D-2C camera (1\" sensor, 20 MP sensor resolution, 5472 \(\times\) 3648 px image size, 2.35 \(\upmu\)m pixel size) was used on 5 September 2018 to acquire 348 photographs in jpeg format (double grid pattern with 85% front and side overlaps) from 26 m AGL. The flight path and camera triggering were also controlled by the DJI Ground Station Pro iOS software. All photographs included the geolocation and altitude in the EXIF data.  
Landscape models from UAS without real-time kinetic (RTK) or post processing kinetic (PPK) geotags of the photographs result in larger absolute positional errors of the models in real-world projected coordinates [24]. The within model accuracy of distances and volumes nevertheless remains high. Similar to [29], ground control points were not possible to acquire on site. The expected absolute positional accuracy in real-world coordinates of SfM-MVS models generated without ground control points of \(\sim\)1 m error for the Inspire 2 and 2-3 m error for the Mavic 2 Pro [24] were considered adequate for the goals of this study.  
Figure 6: Satellite image mosaics from 2014 (RapidEye constellation) and 2020 (PlanetScope constellation) before and after the operationalization of HPPS. Both sets of images are shown as false color composites (Near infrared/Red/Green) to differentiate the river more clearly from the landscape and minimize the impact of the smoke from fires in the south-east sector of the 2014 imagery. Both mosaics represent low water conditions in July.
# Multi-User Linear Equalizer and Precoder Scheme for Hybrid Sub-Connected Wideband Systems
## 4 Multi-User Equalizer Design
### Problem Formulation  
It can be shown that the digital part of the equalizer that minimizes the BER is given by  
\[\mathbf{W}_{d,k}=\left(\mathbf{W}_{a}^{H}\mathbf{H}_{\alpha q,k}\mathbf{H}_{ \alpha q,k}^{H}\mathbf{W}_{a}+\sigma_{n}^{2}\mathbf{W}_{a}^{H}\mathbf{W}_{a} \right)^{-1}\!\!\mathbf{W}_{a}^{H}\mathbf{H}_{\alpha q,k} \tag{13}\]  
since it maximizes the overall signal-to-interference-plus-noise-ratio (SINR) of the \(u\)th user at time \(t\), \(\mathrm{SINR}_{u,t}\), i.e., the SINR relatively to data symbol \(s_{u,t}\)[23].  
Let us now describe the method to compute the analog part of the considered sub-connected architecture. By using the matrix inversion lemma [34], the overall analog-digital equalizer matrix simplifies to  
\[\mathbf{W}_{a}\mathbf{W}_{d,k}=\mathbf{W}_{a}\!\left(\mathbf{W}_{a}^{H} \mathbf{W}_{a}\right)^{-1}\!\!\mathbf{W}_{a}^{H}\mathbf{H}_{\alpha q,k}\!\left( \mathbf{H}_{\alpha q,k}^{H}\mathbf{W}_{a}\!\left(\mathbf{W}_{a}^{H}\mathbf{W} _{a}\right)^{-1}\!\!\mathbf{W}_{a}^{H}\mathbf{H}_{\alpha q,k}+\sigma_{n}^{2} \mathbf{I}\right)^{-1}\!\!. \tag{14}\]  
Assuming quadrature phase shift keying (QPSK) constellations for simplicity and without loss of generality, the average BER can be written as  
\[\mathrm{BER}=\frac{1}{N_{c}U}\sum_{t=0}^{N_{c}-1}\sum_{u=1}^{U}\mathcal{Q} \!\left(\sqrt{2\mathrm{SINR}_{u,t}}\right) \tag{15}\]  
where \(\mathcal{Q}\) represents the well-known Q-function and with the \(\mathrm{SINR}_{u,t}\) given by  
\[\mathrm{SINR}_{u,t}\!\left[\mathbf{W}_{a}\right]=\left(\frac{\sigma_{n}^{2}}{ N_{c}}\sum_{k=0}^{N_{c}-1}\!\!\left[\left(\mathbf{H}_{\alpha q,k}^{H} \mathbf{W}_{a}\!\left(\mathbf{W}_{a}^{H}\mathbf{W}_{a}\right)^{-1}\!\!\mathbf{ W}_{a}^{H}\mathbf{H}_{\alpha q,k}+\sigma_{n}^{2}\mathbf{I}\right)^{-1}\!\! \right]_{u,u}\right)^{-1}-1, \tag{16}\]  
where \(\left[\mathbf{A}\right]_{u,u}\) represents the entry of the \(u\)th row and column of the matrix \(\mathbf{A}\). From (16) we see that \(\mathrm{SINR}_{u,t}\) is independent of the time index. So, we can simplify (15) as  
\[\mathrm{BER}=\frac{1}{U}\!\!\sum_{u=1}^{U}\mathcal{Q}\!\left(\sqrt{2\mathrm{ SINR}_{u}\!\left[\mathbf{W}_{a}\right]}\right)\!, \tag{17}\]  
with \(\mathrm{SINR}_{u}\!=\mathrm{SINR}_{u,1}=\mathrm{SINR}_{u,2}=\ldots=\mathrm{ SINR}_{u,N_{c}}\).  
The optimization problem to compute the analog part of the equalizer may be mathematically formulated as  
\[\left(\mathbf{W}_{a}\right)_{\mathrm{opt}}=\arg\min_{\mathbf{W}_{a}}\mathrm{ BER}[\mathbf{W}_{a}]\text{, s.t. }\mathbf{W}_{a}\in\mathcal{W}_{a}, \tag{18}\]  
where \(\mathcal{W}_{a}=\left\{\mathbf{W}_{a}:\mathbf{W}_{a}=\mathrm{diag}[\mathbf{w} _{a,1}\!,\!\ldots,\mathbf{w}_{a,r}\!,\ldots,\mathbf{w}_{a,N_{R^{c}}}]^{\prime} \right|\mathbf{w}_{a,r}(n)\!\left|^{2}=1/N_{rx}\right\}\) denotes the feasible set for the analog equalizer.
## 3 Problem Modeling  
### Problem Description  
The problem under consideration is to design a set of routes operated by a homogenous fleet of EVs. There is a single depot and a set of customers to be served. RSs are dispersed in the city and available for recharging the EVs. Each customer \(i\) has a positive demand and a time window and can be visited by EVs exactly once. When an EV arrives before the earliest service time of customer \(i\), it has to wait until the service time window starts. Arrival after the latest service time is not permitted. EVs will start from the only depot and must return to the depot before a certain time. The SoC of EVs should remain non-negative throughout the planning horizon. An EV must visit an RS to recharge its battery when the energy is not adequate for it to reach the next customer. One crucial concern for this problem is that the unit energy consumption for each EV along each arc is uncertain. When the unit energy consumption increases, it will take the EV more time to recharge, which might result in missing the time windows of the subsequent customers. Considering the complex influence of various external factors on energy consumption, it is hard to estimate uncertainty. Hence, we model and solve the EVRPTW-ECU by employing a robust optimization framework.  
Previous investigations on EVRP with RSs or BSSs assume energy consumption to be a linear function of the transportation distance (for example, [PERSON] et al. [23]), which is over-idealized. In this paper, we will develop a more realistic model based on [PERSON] et al. [43]. The energy consumption is very complex and influenced by a lot of factors, such as the gravitational constant, the drag resistance coefficient, the curb mass, the air drag coefficient, the frontal area, the air density, the constant speed, the motor efficiency of an EV, and the discharging efficiency of the battery.
# Deep Learning-Based Spread-Spectrum FGSM for Underwater Communication
## 5 Simulation Results
### Receiver's Complexity  
In this part, computation complexity (i.e., TNRO) of the decoders used in the proposed SS-FGSM is evaluated and compared to the ML decoders computation complexity used in the SM, GSM, QSM, FGSM, EFGSM, and GCIM. To analyze the computational complexity of SMTs studied in this paper, we assume all schemes are evaluated under the same SE (e.g., 10 bpcu), with antenna configuration of \(4\times 4\) MIMO system for SM, GSM, QSM, FGSM, EFGSM, and the proposed SS-FGSM and SISO antenna configuration for GCIM scheme. We also assume the same spreading code length for GCIM and SS-FGSM schemes. The comparison is evaluated under an \(M\)-QAM modulation order of 256, 256, 64, 128, 16, 64, and 8 for the SM, GSM, QSM, FGSM, EFGSM, GCIM, MLSS-FGSM, and DLSS-FGSM, respectively. The computation complexity is calculated in (33)-(39). The DL-based detector model was obtained based on the TensorFlow library so, for a fair comparison, the number of multiplications and summations will be counted following the DNN used model and the DL layers size. Table 3 shows the receiver's complexity in terms of the TNCO for the proposed scheme in comparison with the conventional SMTs. As shown in Table 3, the proposed DLSS-FGSM significantly reduces the receiver's detector complexity compared to SM, GSM, FGSM, QSM, and EFGSM. While, the GCIM and EFGSM slightly outperform the proposed scheme in the receiver's computation, but they have a poor ABER performance over the underwater acoustic channel. Furthermore, Table 3 shows the high competition complexity of the DLSS-FGSM scheme based on the ML-based detector which restricted its use in underwater communication.  
Figure 9: Performance comparison of energy efficiency.
###### Abstract  
The vibration and noise of permanent magnet synchronous motors (PMSM) are mainly caused by unbalanced magnetic pull (UMP). This paper aims to investigate nonlinear vibration in PMSMs. Firstly, the analytical model of the air-gap magnetic field with an eccentric rotor in PMSM is studied, and the analytical model is verified by the finite element method. Then the dynamic model of an offset rotor-bearing system is established, and the gyroscopic effect, nonlinear bearing force and UMP are taken into consideration. Finally, the dynamic characteristics of different static displacement eccentricities, rotor offsets and radial clearances are investigated in both the time domain and the frequency domain. The results show that the amplitudes of dynamic responses increase with the static displacement eccentricity and rotor offset and high integer multiples of rotating frequency appear with the increase of displacement eccentricity. The coupling effects of bearing force, unbalanced mass force and UMP are observed in the frequency domain, and the frequency components in the dynamic responses indicate that the bearings have an effect on the system.  
PMSM; unbalanced magnetic pull; nonlinear vibration; offset rotor bearing system; gyroscopic effect; coupling effects 1