esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 5 Joint Channel Estimation Algorithm Based on DFT and DWT
### Steps of the Joint Channel Estimation Algorithm  
The specific steps used in the joint channel estimation algorithm based on DFT and DWT are as follows:  
Step 1: Based on the received signal and training sequence, estimate \(\hat{H}_{\text{LS}}\) using the LS channel estimation algorithm;  
Step 2: Perform wavelet-denoising on \(\hat{H}_{\text{LS}}\) to obtain \(\hat{H}_{\text{DWT}}\) after wavelet-denoising;  
Step 3: Calculate the estimated value \(\vartheta_{s=uf}^{2}\) of the noise variance in the frequency domain according to Equation (35);  
Step 4: Conduct \(N\)-point IFFT on \(\hat{H}_{\text{LS}}\) to get the time-domain channel impulse response \(\hat{h}_{\text{V}}\) with energy dispersion;  
Step 5: Calculate the threshold value \(T\) according to Equation (21) and make a threshold judgment on \(\hat{h}_{\text{V}}\) through Equation (22) to obtain \(\hat{h}_{\text{ST-DFT}}\), and count the number \(K\) of zero-set paths in \(\hat{h}_{\text{V}}\);  
Step 6: Based on Equations (36) and (37), calculate the MSE estimates of the wavelet-denoising algorithm and the ST-DFT algorithm \(\widetilde{\text{MSE}}_{\text{DWT}}\) and \(\widetilde{\text{MSE}}_{\text{ST-DFT}}\);  
Step 7: Compare \(\widetilde{\text{MSE}}_{\text{DWT}}\) and \(\widetilde{\text{MSE}}_{\text{ST-DFT}}\). If \(\widetilde{\text{MSE}}_{\text{DWT}}>\widetilde{\text{MSE}}_{\text{ST-DFT}}\), conduct \(N\)-point IFFT on \(\hat{h}_{\text{ST-DFT}}\) to obtain \(\hat{H}_{\text{ST-DFT}}\) as the channel estimation value; if \(\widetilde{\text{MSE}}_{\text{DWT}}\leq\widehat{\text{MSE}}_{\text{ST-DFT}}\), directly output \(\hat{H}_{\text{DWT}}\) in Step 2 as the channel estimation value.
## 3 The Proposed Protocol
### Authentication and Key Agreement Phase
* \(GWN\) computes \(B_{10}=\big{(}V_{i}^{new}\parallel S_{M}^{U_{i},new}\parallel h(V_{i} \parallel EID_{i})\big{)}\oplus V_{i}\) and \(B_{11}=PID_{i}^{new}\parallel r_{s}^{\prime}\oplus h(V_{i}^{new}\parallel h(r _{u}\parallel T_{1}))\) and further computes \(B_{12}=h\big{(}V_{i}^{new}\parallel h\big{(}r_{s}^{\prime}\parallel SK\big{)} \parallel S_{M}^{U_{i},new}\Big{)}\). * V9: \(GWN\to U_{i}:\big{\{}F_{U_{i}}^{new}(t),B_{10},B_{11},B_{12}\big{\}}\); \(GWN\) sends related information \(F_{U_{i}}^{new}(t)\), \(B_{10},B_{11},B_{12}\) to \(U_{i}\). V10: \(U_{i}\) uses \(V_{i}\) to recover \(V_{i}^{new*}\) and \(S_{M}^{U_{i},new*},h(V_{i}^{*}\parallel EID_{i}^{*})\), i.e., \((V_{i}^{new*}\parallel S_{M}^{U_{i},new}\parallel h\big{(}V_{i}^{*}\parallel EID _{i}^{*}\big{)}=B_{10}\oplus V_{i}\). Then, \(U_{i}\) uses private key \(d_{i}\) and then computes \(PID_{i}^{new*}\parallel r_{s}^{\prime}=B_{11}\oplus h\big{(}V_{i}^{new*} \parallel h(r_{u}\parallel T_{1})),r_{s}^{s}=\big{(}{r_{s}^{\prime}}^{*}\big{)} ^{d_{i}}\),\(SK^{*}=h\big{(}h(r_{u}\parallel T_{1})\parallel r_{s}^{*}\parallel h \big{(}V_{i}^{*}\parallel EID_{i}^{*}\big{)}\big{)}\), and \(B_{12}^{*}=h(V_{i}^{new*}\parallel h\big{(}r_{s}^{\prime}\parallel SK^{*} \big{)}\parallel S_{M}^{U_{i},new*}\big{)}\). If \(B_{12}^{*}\) equals \(B_{12}\), \(U_{i}\) accepts session key \(SK\) and completes the authentication; otherwise, \(U_{i}\) rejects the session key. After accepting the session key, the PDA computes parameters \(A_{1}^{new}=\big{(}V_{i}^{new}\parallel S_{M}^{U_{i},new}\big{)}\oplus h(ID_{i} \oplus PW_{i})\) and \(A_{2}^{new}=h\big{(}V_{i}^{new}\parallel k_{i}\parallel S_{M}^{U_{i},new}\big{)}\)mod \(n_{0}\). Finally, the PDA updates the value from \(\big{\{}PID_{i},A_{1},A_{2},F_{U_{i}}(t)\big{\}}\) to \(\big{\{}PID_{i}^{new},A_{1}^{new},A_{2}^{new},F_{U_{i}}^{new}(t)\big{\}}\). Additionally, Figure 4 summarizes the login, authentication, and key agreement operations of the user and BASN.
# Current Applications of Ultrasound in Fruit and Vegetables Osmotic Dehydration Processes
###### Abstract  
Ultrasound (US) is a promising technology, which can be used to improve the efficacy of the processes in food technology and the quality of final product. US technique is used, e.g., to support mass and heat transfer processes, such as osmotic dehydration, drying and freezing, as well as extraction, crystallization, emulsification, filtration, etc. Osmotic dehydration (OD) is a well-known process applied in food processing; however, improvements are required due to the long duration of the process. Therefore, many recent studies focus on the development of OD combined with sonication as a pretreatment method and support during the OD process. The article describes the mechanism of the OD process as well as those of US and changes in microstructure caused by sonication. Furthermore, it focuses on current applications of US in fruits and vegetables OD processes, comparison of ultrasound-assisted osmotic dehydration to sonication treatment and synergic effect of US and other innovative technics/treatments in OD (such as innovative osmotic solutions, blanching, pulsed electric field, reduced pressure and edible coatings). Additionally, the physical and functional properties of tissue subjected to ultrasound pretreatment before OD as well as ultrasound-assisted osmotic dehydration are described.  
ultrasound mechanism; ultrasound assisted osmotic dehydration; physical properties; color; chemical properties; bioactive compounds; microbial growth; structure +
Footnote †: journal:  
is well known that the seasonality of fruits and vegetables results in the formation of food surpluses. All crops collected in the same period cannot be consumed in fresh form. Thus, to avoid or reduce food losses, the use of different pretreatments and processes is required [3].  
The technologies most frequently used to preserve food are freezing [6] and dewatering processes such as drying [7; 8; 9; 10] or osmotic dehydration. The latter can be applied as a single process [11; 12; 13] or before drying process [14; 15; 16; 17]. However, in recent years, there is a growing interest in new, non-thermal technologies which can be used as a pretreatment before mentioned processes [18; 19; 20; 21] or in assistance to them [22; 23; 24].  
US is one of the most promising treatment methods and supports for food production processes as proved by scientific and industrial research conducted currently on a large scale [25; 26]. The research undertaken indicates that, depending on the type of raw material, its structure and anatomy, as well as properties of applied sound waves (length, frequency, intensity and, attenuation coefficient) and duration of application, one can obtain results of both shortening time [27] and extending mass exchange [28] in drying processes. It also impacts the heterogeneous physical and chemical properties [29; 30] and the structure of final products [31; 32]. Therefore, this review focuses on the latest applications of US in fruits and vegetables OD processes.
## 2 Materials and Methods
### Accuracy Assessment Scenarios  
The same TR/ACC pixels were used for all the applied methods to avoid the effects of different partitioning of the dataset on the performance of methods. User accuracy (UA) and producer accuracy (PA), overall accuracy (OA), and Kappa coefficient were determined from the confusion matrix with respect to the ground validation data. The accuracy validation metrics are defined as follows:  
\[\text{Overall accuracy }(\text{OA})=\frac{\sum_{n=1}^{q}\text{n}_{ii}}{n} \times 100 \tag{1}\]  
\[\text{Kappa coefficient}=\frac{\left[n.\sum_{n=1}^{q}\text{n}_{ii}-\sum_{n=1}^{q }\left(\text{n}_{i}\times n_{i}\right)\right]}{\left[n^{2}-\sum_{n=1}^{q} \left(\text{n}_{i}\times n_{i}\right)\right]} \tag{2}\]  
\[\text{User Accuracy }(\text{UA})=\frac{\text{n}_{ii}}{n_{i}}\times 100 \tag{3}\]  
\[\text{Producer Accuracy }(\text{PA})=\frac{\text{n}_{ii}}{n_{i}}\times 100 \tag{4}\]  
where \(n_{ii}\), \(n_{i}\), and \(n_{i}\). are the values of the diagonal entry, the i-th row sum, and the i-th column sum in the confusion matrix, respectively; \(n\) is the total number of validation samples; and \(q\) is the number of rows of the confusion matrix.
## 5 Conclusions
### Limitations and Future Study  
We have classified the review helpfulness information through the CNN-BiLSTM hybrid model. We then conducted an experiment based on the proposed recommendation framework to evaluate the recommendation performance. The limitations of this study are as follows: First, we only used Amazon publicly accessible book datasets. We built a CNN-BiLSTM hybrid model using all the datasets without classifying the book category. However, users may have different preferences depending on the book category. In future studies, it is necessary to classify book categories and measure additional recommendation effects. Additionally, applying the proposed recommendation framework to other domains must be evaluated using datasets from multiple domains. Second, to classify the review helpfulness information, we applied the CNN-BiLSTM hybrid model that showed excellent performance in NLP studies. Recently, BERT, ELECTRA, and GPT-3 models have shown excellent performance in NLP studies. Therefore, future study needs to compare the performance of multiple deep learning models. Third, we proposed a recommendation framework that classifies reviews helpfulness information and then builds users' profiles with helpful reviews to provide recommendation services. In other words, we proposed a framework that only used the review helpfulness information. However, considering item features, purchase history, and other information would further improve recommendation performance. Finally, the early-written review received more helpful votes than the later written review. As this may create a sequential bias problem, future studies should consider the dates of the written review.  
Conceptualization, J.K. and Q.L.; methodology, Q.L. and X.L.; data curation, Q.L. and X.L.; writing--original draft preparation, Q.L. and B.L.; writing--review and editing, Q.L. and J.K.; supervision, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the BK21 FOUR Program (5199990913932) was funded by the Ministry of Education (MOE, Korea) and the National Research Foundation of Korea (NRF).  
Not applicable.  
The data are available on [[http://jmcauley.ucsd.edu/data/amazon/](http://jmcauley.ucsd.edu/data/amazon/)]([http://jmcauley.ucsd.edu/data/amazon/](http://jmcauley.ucsd.edu/data/amazon/)) (accessed on 1 May 2021).  
The authors declare no conflict of interest.
## 4 Algorithm Design  
Building upon practical considerations, our research emphasizes capacity-constrained CI operations as a core assumption while addressing CI-BSI coexistence. Given higher per-swap costs reducing BSI utilization frequency and negligible queuing due to rapid battery swapping, we exclude BSI capacity constraints from our analysis. Since the unit charging cost is significantly lower than the unit battery swapping cost, the focus and challenge of algorithm design under the premise of limited capacity of CI is to avoid potential conflicts in charging tasks and maximize the use of existing charging resources to reduce vehicle energy replenishment costs. To this end, we first propose a charging sequence feasibility theorem to determine whether there is a conflict between two charging tasks. Then, based on this, a dynamic rescheduling mechanism is proposed to eliminate conflicts and ensure the global feasibility of charging schedules. Finally, these ideas are incorporated into the initial solution construction phase of the HACO algorithm. After two-phase construction, this approach generates high-quality feasible solutions that comply with the capacity constraints of CI. Additionally, local search operators designed based on the problem characteristics are used to further optimize and improve the quality of the solutions.
## 5 Simulation Study
### Simulation and Verification of the Effect of Waves on Wind Turbine
#### 5.1.2 Simulation and Verification of the Effect of Random Waves on Wind Turbine  
The influence of random waves on the vibration characteristics of wind turbines is verified under constant wind conditions. In this case, the wind speed at the hub center is also constant at 14 m/s, and the wind and wave are in a consistent direction. In 'Random wave 1', the significant wave height of the random wave is 2.5 m, and the spectrum peak period is 9.5 s. In 'Random wave 2', the significant wave height of the random wave is 2.0 m, and the spectrum peak period is 9.5 s. The time-domain and frequency-domain comparison curves of load and torque are shown in Figures 7-9.  
From Figures 7-9, it is found that the fore-aft load on the tower at the MWL and aerodynamic torque of the wind turbine are also significantly affected by random waves. Random waves can be regarded as the superposition of several cosine waves. The frequencies of the wave are mainly distributed in the range of 0-0.4 Hz. The fore-aft load on the tower at the MWL and aerodynamic torque fluctuate greatly with the hydrodynamic frequency, which is caused by the random wave, and the greater the significant wave height of the random wave, the greater the fluctuation of the fore-aft load on the tower at the MWL and aerodynamic torque. However, due to the damp effect of the tower and wind wheel, the vibration caused by waves at the blade root is small. These conclusions are consistent with those in Section 5.1.1.  
Figure 6: Time domain and frequency domain results of the aerodynamic torque without and with regular wave: (**a**) The aerodynamic torque; (**b**) Frequency spectrum density of the aerodynamic torque.
## 5 Key Challenges and Future Directions
### Future Directions  
Any Remote wireless sensor-based transportation management system must be able to tackle the aforementioned issues. WSN innovation presently meets certain standards, making it a popular contender for practical Intelligent Transport Systems (ITS) applications. The vulnerability of WSN and its subsystems should be considered while developing traffic control services depending on WSN. The communication systems, in particular, has to be secured enough to mitigate damage while also being dependable and fault--tolerance  
Figure 9: Access ratio vs. different number of RSUs for the existing and proposed method [8; 25; 26].  
capable of providing services stability in the face of failure. WSN's multisensory aspect is strong since it enables the improved traffic flow multi-objective dilemma to be fed with crucial data in real-time, allowing the optimal transportation planning decisions to be taken at the moment. Blockchain and Artificial Intelligence are emerging technologies and scientists are working closely to integrate these technologies in smart cities since blockchains is distributed technology with enhanced security and cost-effective technology [30; 31] while artificial intelligence-based technology (e.g., machine learning, deep learning [32; 33]) are intelligent technologies. These technologies should be explored in future research to improve the performance of IoV in terms of traffic management systems.
# DC Cabling of Large-Scale Photovoltaic Power Plants
###### Abstract  
The development of Floating Solar Photovoltaic (FPV) systems is a sign of a promising future in the Renewable Energy field. Numerous solar modules and inverters are mounted on large-scale floating platforms. It is important to design the system so that the inverter operates in its optimum range most of the time. In order to achieve this goal on the DC side, serial and parallel connections of solar modules are used. As a result, the cabling of the PV array architecture is an important issue. Modern electrical installation design requires reducing costs in cabling materials, equipment installation, and maintenance. The reduction of losses and the amount of time required to complete the design are also significant. Therefore, the main topic of this paper is DC cabling in large-scale FPV power plants (\(>\)1 MV). The serial-parallel (SP) connection scheme of solar modules and the percentage of power loss in DC cables are considered. Furthermore, a general method for determining cable lengths for FPV power plants is defined. The temperature influence on losses in DC cables is analyzed. A new method for determining the current at the maximum power point (MPP) as a function of temperature is proposed. A case study is conducted using a hypothetical 3 MW FPV power plant, and the obtained results are presented and analyzed.  
cable lengths; DC cabling; floating solar power plants; losses in DC cables; maximum power point +
Footnote †: journal: Applied Sciences
# Evaluation of Orbital Drift Effect on Proba-V Surface Reflectances Time Series
## 4 Results
### Analysis of Simulated Changes in Synthetic Surface Reflectances
Note that the convention used here for VAA and SAA is different from the one reported in the operational products, although the relative position of the sun and viewing azimuth directions are preserved.
# Intersatellite and Downlink Agile Attitude Maneuvers
## 4 Actuation  
This section is focused on describing the dynamics of the attitude actuators and defining their steering law. In this research, attitude control is performed through an array of 4 Single-Gimbal Control Momentum Gyroscopes (SGCMGs). A SGCMG is a momentum exchange device, which can be modeled as a rotor with an additional degree of freedom, as depicted in Figure 1, where index \(j\) refers to the \(j\)-th device. It is composed of a wheel that rotates about an axis which can change its orientation. In fact, the rotor is linked to a gimbal that is able to vary the orientation of the axis of the wheel through a rotation by angle \(\theta_{1}^{(j)}\) about axis \(\hat{g}_{j}\). When a rotation \(\theta_{1}^{(j)}\) occurs the angular momentum of the wheel varies in direction and the reaction torque represents the control torque exerted on the spacecraft.  
M50 CMG, produced by Honeywell, is chosen as the actuator in this research. The characteristics of the device at hand are reported in Table 1, in terms of \(\omega_{R}\) (velocity of the rotor), \(I_{S}\) (axial moment of inertia), \(I_{T}\) (transverse moment of inertia).  
Developing an effective steering law for SGCMGs represents a challenging task due to the existence of singular directions related to specific orientations of each device. The first step is in identifying the actual torque transferred to the spacecraft by an array of 4 SGCMGs.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\omega_{R}\)**[rpm]** & \(I_{S}\)**[kg m\({}^{2}\)]** & \(I_{T}\)**[kg m\({}^{2}\)]** \\ \hline
6500 & 0.1102 & 0.0551 \\ \hline \hline \end{tabular}
\end{table}
Table 1: M50 CMG Honeywell datasheet [28].  
Figure 1: Schematic representation of Single-Gimbal Control Momentum Gyroscope.
## 2 Materials and Methods
### Numerical Simulation Experiment  
#### 2.3.1 Basic Assumptions and Initial Geostress Field  
Based on the actual geological engineering conditions of the limestone mine, the following assumptions are made [34; 35]:  
Rock Mass Assumptions: The rock mass is assumed as an ideal elastoplastic material. Beyond the yield point, with plastic flow, the material's strength and volume remain unchanged, and rock strain hardening or softening is not considered.  
Material Properties: The rock mass is considered homogeneous and isotropic, with plastic flow not affecting the material's isotropy.  
Limitations of Finite Difference Calculations: It is assumed there is no influence from tectonic activity within the site. The initial rock stress is considered as a geostatic field, with inter-layer contacts assumed to be integrative and internal rock layers treated as continuous media.  
Numerical Model: A numerical model is established based on the occurrence of the ore body and rock mass, with limestone being the dominant rock type and the ore body primarily consisting of calcite. The gravity field is taken as the initial rock stress field. The vertical and horizontal movements at the model's base are restricted. The lateral pressure coefficients on the four sides are automatically calculated by the program according to Poisson's ratio, with the overlying rock layer's load being its self-weight.
# A Chest Strap-Based System for Electrocardiogram Monitoring
## 4 Results and Discussion
### Comparison Between the CEMS and Similar ECG Monitoring Products  
To further assess the innovation and practicality of the CEMS, we conducted a comparative analysis of several advanced chest strap ECG monitoring devices currently available on the market. Key aspects such as electrode materials, system portability, and sweat resistance as summarized in Table 1.  
Although commercial ECG monitoring systems now provide reliable signal quality, they still face limitations in portability, wearing comfort, and resistance to sweat.  
Figure 16: Training performance of the exercise intensity classification model. (**a**) Accuracy and loss. (**b**) Confusion matrix.  
Figure 15: Comparison between the CEMS and the PM12B. (**a**) Measurement methods. (**b**) Measurement results.  
As shown in the comparison, the reusable and washable STE used in the CEMS, comprising silver-coated polyamide yarn interwoven with a cotton fiber base, exhibits exceptional sweat resistance. Even during high-intensity physical activity or heavy sweating, the STE retains excellent breathability and adheres securely to the skin, effectively preventing detachment, contact instability, or signal degradation. Its soft, flexible structure also ensures enhanced skin comfort compared to metal electrodes embedded in an elastic band, such as the metal-based designs used in the CHILEAF system, which may exert localized pressure. Moreover, unlike the disposable ECG electrodes used in EkgMove, which require frequent replacement due to reduced adhesion and breathability, the STE offers durable reusability without sacrificing performance. In terms of portability, the thin, flexible, and detachable design of the CEMS electrode allows for easy cleaning and unobtrusive integration into daily wear. In contrast, systems such as Underwear with 12 channels adopt an embedded undergarment structure that, while mechanically stable, increases device bulk and wiring complexity, thereby reducing portability and user convenience.  
In summary, the CEMS demonstrates strong potential for application in portable wearable ECG monitoring due to its comprehensive advantages in sweat resistance, wearing comfort, and portability.