esa-sceva/satcom-chunk-collection huggingface dataset:
# WLQRP: A Weighting Link-Quality-Based Routing Protocol for Underwater Sensor Networks
## 2 Related Works
The algorithm uses a multi-level hierarchical network structure based on the distance between cluster heads and the sink node and the residual energy of a cluster head to determine the size of the competition radius. In ref. [32], the authors propose a multi-layer cluster-based UWSN energy saving (MLCEE) protocol to solve the problem of hotspots and energy consumption. MLCEE first divides the entire network into layers and then clusters nodes in the same layer. Finally, the cluster head (CH) selects the next hop in the CH based on the larger fitness value, the smaller hopid, and the smaller number of layers. [PERSON] et al. [33] proposed CARP, which utilizes link quality, avoids loops, and bypasses nulls through a simple topology. This network improves packet delivery and saves energy; but again, at the cost of higher latency. In [34], [PERSON] et al. introduced E-CARP, an enhanced iteration of the channel-aware routing protocol (CARP) initially developed by [PERSON] et al. E-CARP allows sensing data to be cached at sink nodes to prevent these packets from being forwarded across the network, reducing data retransmission. Furthermore, by enhancing the technique for selecting relay nodes in CARP, the selected relay nodes are given a higher reuse priority, which reduces the communication cost and improves the network performance to a great extent. Compared with the above-mentioned related works, two prominent advantages of WLQRP are summarized as follows:  
1. WLQRP scrutinizes historical transmission success records among nodes, encompassing not only the past transmission success rate between nodes and their immediate next-hop nodes but also factoring in link quality to subsequent next-hop nodes. This comprehensive channel quality evaluation during data packet transmission culminates in selecting the optimal hop for each data packet. Consequently, WLQRPenhances relay node selection efficiency, elevates data packet transmission success rates, curtails packet retransmissions, and diminishes energy consumption. 2. WLQRP takes the energy of the next-hop node into the calculation of link quality, which can effectively balance the energy consumption between nodes and extend the lifetime of the network. 3. WLQRP can harness topological information to circumvent connectivity gaps and shadow regions during routing, effectively minimizing the likelihood of encountering voids.
## 3 Results
### Impact of Pleiades Band Designations on Water Reflectance and Turbidity Retrievals
#### 3.3.2 In Situ Water Reflectance and Turbidity Algorithms for Pleiades, OLI and MODIS  
A polynomial relationship can provide satisfactory results in the maximum turbidity zone of the GE [19]. This relationship was tested on the SeaSWIR 2013 dataset (turbidity values ranging from 57.9 to 1484.7 NTU) (Figure 10). It provides good results with R\({}^{2}\) of 0.78 (N = 34) and the extra validation dataset (SeaSWIR 2012 turbidity and water reflectance measurements) (N = 16) (Table 4) showed MAPE values of 16.6%, 15.0%, 15.4%, respectively for Pleiades, OLI and MODIS. The three algorithms show similar patterns but still with some scatter. This is due to the NIR band spectral differences among the three sensors (Figure 9). The validation results show that the difference is lower for OLI sensor than MODIS and Pleiades. This probably results from the narrow bandwidth of OLI compared to the wider spectral range of Pleiades which may introduce more uncertainty.
## 3 Results
### Dynamic Measurements  
Long term measurements were performed in order to check the viability of the MOF-FP sensor. For this aim, the MOF-FP sensor was buried in the soil at ~5 cm deep, whereas the capacitive sensor was at ~20 cm, as it has been shown in Figure 4a. This difference must be underlined as it will affect directly on the results and it is produced due to the size difference of the sensors. On the one hand, MOF-FP sensor presents reduced dimensions, allowing for the moisture measurement within a small volume of soil while on the other hand, capacitive sensors need a significant soil volume (as it will be described) to work properly. After the sensor's installation, water was carefully added to the soil until saturation (water dropping at the bottom of the sample--Relative humidity = 100%). All of the sensors  
Figure 8: Capacitive and optical sensor’s response towards soil humidity: (**a**) Soil and (**b**) Soil2.  
Figure 7: Capacitive and optical sensor’s response towards soil humidity: (**a**) Soil and (**b**) Soil2.  
Figure 9: Capacitive and optical sensor’s regression slopes: (**a**) Soil1 and (**b**) Soil2.  
were monitored every 10 min during \(\sim\)14 days uninterruptedly showing the results shown in Figure 7. After \(\sim\)12 days for Soill and \(\sim\)9 days for Soil2, humidity was raised again until saturation to check the sensor's reversibility and adaptability to changing environments.  
As can be seen in Figure 7, there is a discrepancy in terms of kinetics between both sensors. The optical sensing head measures the humidity directly in contact with it while capacitive sensors integrate the moisture in a volume of soil. As previously commented, due to the size, the principle of operation of each sensor, and their burial depth, the optical sensor is able to measure localized humidity variations, and therefore it measures moisture variations before the capacitive sensor.
## 1 Introduction
In our previous work [50], the energy efficiency of the pump drive is assessed taking into account the losses in the cable without taking into account the losses in the transformer. Various load points of the motor were considered, corresponding to a typical pump cycle without speed control. The paper compares annual and lifecycle energy consumption for IM, DOL PMSM, and DOL SynRM (Figure 1). All three types of motors under consideration have a similar stator design, but different rotor design. All motors have a short-circuited aluminum winding on the rotor and an asynchronous start. However, DOL PMSM (Figure 1b) when the speed is close to the nominal switches to the synchronous operation mode due to the synchronizing torque of the permanent magnets. DOL SynRM (Figure 1c) also enters synchronous mode after starting due to the synchronizing torque provided by the magnetic anisotropy of the rotor. Due to the absence of basic (fundamental) electrical losses in the rotor, when operating in synchronous mode, DOL PMSM and DOL SynRM usually have a higher efficiency than IM. In [50], it is shown that the power factor of the motor has a significant effect on the losses in the cable and on the energy consumption of the system as a whole. Therefore, when choosing different types of motors, it is important to consider not only their efficiency and energy efficiency class, but also their power factor. However, in [50] the losses in the transformer are not taken into account. Transformer losses can significantly depend on the reactive component (power factor) on the load. Low-power pump units usually operate in groups as a part of a pumping station powered through a common cable and connected to a transformer [51]. The losses in this transformer should be considered while calculating the energy efficiency of the pumping system. The aim of this paper is to perform further analysis of the various motor types described in [50] taking into account not only the losses in motors and cables but also losses in the transformer. The main criteria for comparing motors are: (1) energy and cost savings during the pump life cycle and (2) payback period. While assessing the payback period, replacing an IE2-motors with a motor of a higher efficiency class is considered. Both the use of a more energy-efficient motor in a new pump unit being put into operation and the replacement of the motor in a pump unit in operation are considered.
## 2 Related Works  
The information obtained from customers can be varied, dispersed, and with high volumes of data. In this research, we refer to the analysis of information in three aspects within the eCRM: customer satisfaction, the study of customer behavior and the forecast of tourist demand.  
Automatic sentiment analysis of the reviews the clients post online is a hot topic [9; 10; 11]. Usually, such reviews are labelled as positive, negative, or neutral. Several countries implement such eCRM, by automatic classification of the reviews: China [12], the Sultanate of Oman [9], Serbia [13], Spain [14; 15] and Japan [16], among others. Reviews are also used for several purposes, such as better understand of tourist attractions [17] and facilitation of service improvement.  
Another use of computational intelligent algorithms in eCRM is to forecast the tourism demand [18; 19; 20; 21], while other researchers use it for the classification of tourist scenarios [22; 23].  
On the other hand, information from social networks has also been used to identify tourist attractions [24] and user behavior [25]. Based on this knowledge, the study of the behavior of a user acquires, in contemporary research, a privileged position to propose appropriate marketing strategies to promote a site [24; 26].  
With a different goal, [PERSON] and [PERSON] aim to understand the extent to which a large luxury department store affects the attractiveness of the place, using Harrods (London, UK) as a case study [27]. The results showed that the Harrods building is central in most of the analyzed images, representing the main point photographed in a radius of 1 km, and the exterior of the building is added as an attribute of department stores and demonstrates the role of large warehouses in the attractiveness of a place. The study also offers helpful suggestions for marketing managers to develop more personalized retail strategies [27].  
Currently, the real-life impact of areas such as artificial intelligence, soft computing, computational intelligence, and neural networks, with special emphasis on deep learning, is undeniable [28; 29; 30; 31; 32; 33]. Recently, computational intelligence techniques are being used to determine the tourists' attitudes towards technology [34], to develop intelligent facilities [35; 36; 37], to search for hot destinations [38; 39], to analyze the impact of smart destinations in tourism [40; 41; 42], and for customer segmentation [43].  
However, none of the above-mentioned research deals with the non-verbal aspects of the communication with the clients. There are numerous theoretical and empirical publications on this topic in the field of Psychology and also in the field of Marketing, for example [44; 45]. However, the literature on the analysis of non-verbal communication of clients is rare. An interesting example is the work of [PERSON] et al. [46] where the authors experimented with groups of students.
# Indoor Positioning Integrating PDR/Geomagnetic Positioning Based on the Genetic-Particle Filter
## 2 Materials and Methods
### Pedestrian Dead-reckoning (PDR) Module
#### 2.1.2 Step Detection  
There are many step detection methods in PDR technology, such as peak detection [36], zero velocity update [37], etc. As shown in Figure 2a, the acceleration changes regularly during the walking process and the acceleration cure has peaks and valleys. The peak detection method realizes the step detection by detecting the peaks and valleys. This method is easy and has a low calculation burden. However, the measurement error will affect it when recognizing steps. In our research, we employ the finite-state machine (FSM) algorithm to the step detection process because this algorithm is easy to be implemented and more resistant to error interference than the peak detection method.  
In fact, FSM is a special peak detection algorithm. It divides the walking process into five states, which can be recorded as \(S_{0}\)\(\sim\)\(S_{4}\). \(S_{0}\) represents the steady state and the acceleration at this time is approximately equal to 9.8 m/s\({}^{2}\); \(S_{1}\) is the taking step state and the acceleration gradually increases; \(S_{2}\) is the finding peaks state after entering the state \(S_{1}\); \(S_{3}\) is the peak threshold judgment state and filter out the false peaks; \(S_{4}\) represents the falling state and the acceleration gradually decreases. As shown in Figure 3, the current walking state is transferred in the above five states and one step will be recognized if the five states all satisfy the threshold conditions. When a  
Figure 2: (a) Acceleration curve of the walking process; (b) acceleration curve of one step.  
appears during the algorithm execution, it will return to the \(S_{0}\) state and re-detect the walking state. Therefore, FSM can effectively eliminate the interference of false peaks and the measurement errors in the step detection process.  
Table 1 gives all the variable names of FSM. It should be noted that \(P_{up}\) and \(P_{down}\) will be reset as zero at \(S_{0}\) state or there exist large measurement errors. They can be calculated as follows:  
\[\left\{\begin{array}{c}P_{up}=P_{up}+1,a_{i}-a_{i-1}>acc\_diff\\ P_{down}=P_{down}+1,a_{i}-a_{i-1}<acc\_diff\end{array}\right. \tag{8}\]  
where \(a_{i}\) and \(a_{i-1}\) are the acceleration values at time \(i\) and \(i-1\), respectively.
## 5 Conclusions  
The following conclusions have been derived from the investigation of cementitious materials and the impact of flexural loading coupled with sulfate exposure on their properties:  
1. The compressive strength of cement-based materials increases first and then decreases with the increase in sulfate corrosion age, reaching its maximum value at 60 d of corrosion; the flexural strength of cement-based materials decreases with the increase in bending stress, and increases first and then decreases with the increase in corrosion age. Bending loads, which accelerate erosion, are to be avoided as much as possible in engineering design;
2. The main reason for sulfate attack on cement-based materials is that sulfate ions enter the cement-based material through diffusion or other means and react chemically with hydrated calcium silicate and other substances to produce expansive products, such as gypsum and ettringite. When engineering inspections are carried out in areas such as salt lakes containing sulfates, an inspection depth of about 40 mm is sufficient;  
Figure 15: Graph of sulfate ion concentration of M3 specimens at different depths under different bending stresses for different erosion ages: (**a**) 0% bending stress for 30 d; (**b**) 0% bending stress for 60 d; (**c**) 0% bending stress for 90 d; (**d**) 0% bending stress for 120 d; (**e**) 0% bending stress for 150 d; (**f**) 20% bending stress for 30 d; (**g**) 20% bending stress for 60 d; (**h**) 20% bending stress for 90 d; (**i**) 20% bending stress for 120 d; (**j**) 20% bending stress for 150 d; (**k**) 40% bending stress for 30 d; (**l**) 40% bending stress for 60 d; (**m**) 40% bending stress for 90 d; (**n**) 40% bending stress for 120 d; (**o**) 40% bending stress for 150 d.  
3. The porosity of cement-based materials decreases initially and then increases with the increase in sulfate attack age, and the porosity of concrete is greater than that of mortar at the same water-to-cement ratio;
4. A multi-physics field coupling model was established. The predicted curve of this model had a high degree of consistency with the experimental curve. However, the model has certain limitations in assuming that there is a certain flux only on the eroded surface of the specimen.  
Methodology, E.H. and G.H.; Validation, K.P.; Formal analysis, Y.C.; Investigation, E.H.; Writing--original draft, Y.W. and Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (52168036) AND the Research and Application of Key Technologies of Green and Low-Carbon Building Materials (2022B03035-2).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# A BDGIM-Based Phase-Smoothed Pseudorange Algorithm for BDS-3 High-Precision Time Transfer
## 4 Results and Discussion
### Performance Evaluation of Phase-Smoothed Pseudorange SPP Time Transfer Based on BDGIM
Going one step further, we analyze the frequency stability of time transfer, which is another indicator for evaluating time transfer performance. We use the modified Allan deviation to indicate the frequency stability of the time link, which can be expressed as [35]  
Figure 5:7  
where \(N\) represents the number of sampling points; \(n\) denotes the smoothing factor, whose maximum value is less than half of \(N\); \(\tau_{0}\) stands for the sampling interval; and \(x_{j}\) denotes the clock difference. Figure 14 shows the MDEV of the five time links. The frequency stability of all time links is greatly improved after smoothing. The average frequency stabilities of scheme 1 and scheme 2 are 2.93 \(\times\) 10\({}^{-12}\) and 1.43 \(\times\) 10\({}^{-12}\) at 960 s, respectively, with an improvement of 51.2%. The average frequency stabilities of scheme 1 and scheme 2 are 4.19 \(\times\) 10\({}^{-13}\) and 1.68 \(\times\) 10\({}^{-13}\) at 61,440 s, respectively, with an improvement of 59.9%. Therefore, the BDGIM-based phase-smoothed pseudorange method also provides a good improvement to the SF SPP time transfer performance in the frequency domain. Figure 5:8 MDEVs of the SF SPP solutions for each time link.
## 3 Mathematical Model
### Operation Mechanism  
As illustrated in Section 3.1, the system is mainly formulated by EVs, RESs, EVBCDIs, and other assistant devices as connection transformers. In order to optimize the system's operation, data of SOC are collected by the intelligent meters installed in the EVBCDIs and then uploaded to the microgrid management system (MMS) and finally to the general management system (GMS) of the whole multi-microgrid system. The GMS optimizes system operation data and then send the dispatchingresults to the EVBCDIs to be performed. A brief description of the operation mechanism for the cooperative optimization is demonstrated as Figure 3.  
Since there are many EVs in each microgrid, it is not impractical to optimize the charging and discharging powers of every EV, nor is it necessary. In this paper, a hierarchical structure of the management system is utilized. EVs are separated into several EV fleets according to the residential areas they belong to, and each fleet is consisted of all the EVs form the same RMG. Charging and discharging powers of EV fleets rather than single EVs are optimized by GMS, and the data are sent to the management systems of EV fleets. The power of single EVs is then optimized according to the data from the GMS. In this paper, only the optimal dispatching of EV fleets is discussed, and the dispatching method of single EVs can refer to [20] and is not comprehensively discussed in this work.
## 2 Main Subject
### Proposed Position Estimation System for Active Driving Assistance
#### 2.2.2 Dead Reckoning  
IMU Dead Reckoning  
The use of the IMU sensor data proposed in this study assumes that the wheelchair moves in the 2D plane and uses only the angular velocity sensor data on the yaw axis instead of the geomagnetic sensor in consideration of the electromagnetic field generated from the motor. Equation (5) shows the calculation of the direction of the wheelchair at time t in the angle change \(\Delta\theta_{Gyro(t)}\) measured between time \(t-1\) and t from the yaw axis angular velocity of the IMU. Therefore, the amount of change in the angle over time can be calculated through the IMU.  
\[\theta_{Gyro(t)}=\theta_{Gyro(t-1)}+\Delta\theta_{Gyro(t)} \tag{5}\]  
The wheelchair has a wheel structure, and mechanical dead reckoning according to the structure must be performed. Both wheels of the electric wheelchair used in this study are equipped with encoder sensors to measure the rotational speed of the wheel. Figure 5 shows the process of mechanical modeling of wheeled mobile robots.  
Figure 5: Dead reckoning in a wheeled mobile robot.  
The speed and the amount of coordinate changes of the mobile robot are calculated by Equations (6) and (7). Equation (6) calculates the speed of the robot through the velocity \(V_{R}\) on the right wheel of the robot and \(V_{L}\) on the left wheel. Equation (7) calculates the X-axis position change, \(\Delta X_{EN}\), and Y-axis position change, \(\Delta Y_{EN}\), through the robot direction, \(\theta_{t}\), and the robot speed, \(V_{robot}\). Based on this, the position change of the robot over time is calculated.  
\[V_{robot}=\frac{V_{R}+V_{L}}{2} \tag{6}\]  
\[\begin{bmatrix}\Delta X_{EN}\\ \Delta Y_{EN}\end{bmatrix}=\begin{bmatrix}cos\theta_{t}&-sin\theta_{t}\\ sin\theta_{t}&cos\theta_{t}\end{bmatrix}\begin{bmatrix}V_{robot}\\ 0\end{bmatrix}dt \tag{7}\]
## 3 System Descriptions and Assumptions
### Problem Formulation  
Based on the above modeling, the optimization objective can be transformed as follows. Under the assistance of the Cache Chord mechanism, take the RCO action \(\text{V}^{d,t}\) in order to minimize the long-term average energy consumption of the user layer in the DESCO model, which can be formulated as the following problem:  
\[\text{P:}\ \min_{\forall t}\ (\lim_{T\rightarrow\infty}\frac{1}{TD} \sum_{t=1}^{T}\sum_{d=1}^{D}\sum_{m\in\text{N}_{d},k\in\text{U}_{\text{N}_{d}} ^{t}}\text{Y}_{n,d,k,t}) \tag{15}\] \[\text{s.t.}\quad nid_{s}\in\text{NID},\forall s\in S,\] (16) \[p_{k}^{d,t}=p_{k}^{d,t-1}+p_{k}^{d,t-1},\forall p_{k}^{d,t}\geq 0,\forall t=T,\forall d\in\Delta,\] (17) \[\sum_{k\in[1,K]}\omega_{k}(p_{k}^{d,t}+\rho_{k}^{d,t})\leq\text{S},\forall t\in T,\forall d\in\Delta,\forall k\in\text{K},\] (18) \[p_{k}^{d,t}\in\{1,0,-1\},\forall k\in\text{U}_{\text{N}_{d}}^{t},\forall t\in T,\forall d\in\Delta,\] (19) \[v_{n}^{d,t}\in\{0,1,2,...,F\},\forall n\in\text{N}_{d},\forall t \in T,\forall d\in\Delta.\] (20) \[\text{sgn}(v_{n}^{d,t})\Big{(}\text{sgn}(\epsilon_{k}^{t})T_{n,k,d,t}^{C}+\text{sgn}(1-\epsilon_{k}^{t})T_{n,k,d,t}^{P}\Big{)}\leq\tau. \tag{21}\]  
Set \(\mathbb{V}^{t}=\{\text{V}^{1,t},\text{V}^{2,t},...,\text{V}^{D,t}\}\) represents the entire server cluster of RCO decisions. (16) mandates that the storage location for any application data must reside within the servers at the edge layer. (17) specifies that the application cache status \(p_{k}^{d,t}\) of MEC server at time \(t\) is determined by its cache status at \((t-1)\) and the application replacement decisions \(\rho_{k}^{d,t-1}\) based on that status. (18) asserts that for any MEC server \(d\), the current tasks and those newly added at the next moment, based on the application replacement decision, must not exceed the server's own cache pool capacity S. (19) illustrates the cache replacement policy of pool \(\mathbb{P}^{d,t}\): addition, deletion, or retention. (20) represents the offloading decision for server \(d\) towards user \(n\) at time \(t\), where \(\{1,2,...,F\}\) denotes data offloading through the server's subchannel, and 0 indicates local computation of the task. The next section will detail how to decouple the complex problem \(P\) and address it through a joint algorithmic approach. (21) represents the latency constraint of real-time computing tasks.