esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
As demonstrated in Figure 3b, the acquisition of two spectra in-parallel merits about one order of magnitude of sensitivity compared to consecutive acquisition. Spectral changes can be monitored within the entire FEL bandwidth at the same time. The sensitivity in the middle of the FEL spectrum reaches a \(10^{-3}\) relative reflectivity change within about 10 min of acquisition for a 245 meV window. The reflectivity spectra acquired in this way reproduce the features of conventional X-ray absorption spectra, so that equivalent information may be gained by pump-probe experiments in reflectivity. As the spectrometer grating analyzes the full reflected beams after sample interaction, the temporal resolution is not diminished by monochromatization. This makes the presented setup ideally suited for time resolved XAS and reflectivity studies in FELs.  
Conceptualization, P.S.M., C.S.-L., H.A.D. and M.B.; Data curation, R.Y.E., D.T. and I.V.; Formal analysis, R.Y.E.; Funding acquisition, H.A.D. and M.B.; Investigation, R.Y.E., P.S.M., D.T., I.V., G.B., S.D., M.K., H.A.D. and M.B.; Methodology, R.Y.E., F.D., C.S., H.A.D. and M.B.; Project administration, M.B.; Resources, F.D., [PERSON], C.D., C.S.-L., H.A.D. and M.B.; Software, R.Y.E. and A.S.; Supervision, M.B.; Validation, M.B.; Visualization, R.Y.E.; Writing--original draft, R.Y.E.; Writing--review and editing, R.Y.E., J.O.S., C.S.-L., H.A.D. and M.B. All authors have read and agreed to the published version of the manuscript.  
M.B., P.S.M., J.O.S. and R.Y.E. received funding from the Helmholtz Association through grant VH-NG-1105. IV acknowledges support by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under the X-Ray Scattering Program Award Number DE-SC0017643. Work at UU was supported by the Swedish Research Council (VR) grants 2017-06711 and 2018-04918. F.D. received funding from the EU-H2020 Research and Innovation Programme under the Marie Sklodowska-Curie grant agreement No. 701647.  
We acknowledge DESY (Hamburg, Germany) and the Helmholtz-Zentrum Berlin, both members of the Helmholtz Association HGF, for the provision of experimental facilities. FEL measurements were carried out at the FL24 beam line of FLASH. Synchrotron measurements were carried out at the PM3 beam line of BESY II, operated by HZB. The samples were prepared at the MPI-Halle by A.S. and S.S.P.P.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Real-Time Tension Distribution Design for Cable-Driven Parallel Robot
## 5 Tracking Control of Cable-Driven Robot Considering Model Uncertainties in Jacobian Matrix  
### Analysis of Model Uncertainties' Impact on the Jacobian Matrix  
Considering the model uncertainties, Equation (15) becomes  
\[\dot{L}^{\prime}=-\alpha\frac{\partial E}{\partial\dot{L}^{\prime}}=-\alpha( \boldsymbol{U_{m\times m}}-\texttt{Tanh2}(\dot{L}^{\prime}))^{T}\tilde{A}e^{\prime} \tag{42}\]  
where \(\tilde{A}^{T}\) represents the estimation of \(A^{T}\), \(\dot{L}^{\prime},e^{\prime}\) represent the new estimation of \(L\) and the error \(e\) using \(\dot{A}^{T}\), respectively, \(e^{\prime}\) can be calculated with \(e^{\prime}=\hat{A}^{T}\texttt{tanh}(\dot{L}^{\prime})-\hat{\tau}\), and \(\hat{\tau}=\frac{\tau_{d}-(f_{\texttt{min}}+\lambda)\dot{A}^{T}\dot{1}}{\lambda}\).  
Simultaneously, the robot's dynamics, as shown in Equation (6), are as follows:  
\[M(q)\ddot{q}+C(q,\dot{q})\dot{q}+G(q)=A^{T}\hat{f} \tag{43}\]  
Equation (43) can be rewritten as follows in order to analyze the effect of the estimation error on the robot's tracking behavior:  
\[M(q)\ddot{q}+C(q,\dot{q})\dot{q}+G(q)=\tau_{d}-\tau_{d}+A^{T}\hat{f}=\tau_{d}+ \tau_{e} \tag{44}\]  
with \(\tau_{e}=A^{T}\hat{f}-\tau_{d}\)  
Equation (41) becomes  
\[\ddot{q_{e}}+K_{d}q_{e}+K_{p}q_{e}+K_{i}\!\!\int_{0}^{\mathtt{t}}\!q_{e} \mathtt{dt}=M^{-1}(A^{T}\hat{f}-\tau_{d}) \tag{45}\]  
Considering that \(q_{e}\) and \(\dot{q_{e}}\) are both measurable, we suggest that an adaptive control law can be utilized to minimize the model errors. Furthermore, the previous equation can be rewritten as follows:  
\[\ddot{q_{e}}+K_{d}q_{e}+K_{p}q_{e}+K_{i}\!\!\int_{0}^{\mathtt{t}}\!q_{e} \mathtt{dt}=M^{-1}(A^{T}\hat{f}-\hat{A}^{T}\hat{f}+\hat{A}^{T}\hat{f}-\tau_{d} )=\tau_{e_{1}}+\tau_{e_{2}} \tag{46}\]  
where the \(\tau_{e_{1}}=M^{-1}(A^{T}\hat{f}-\hat{A}^{T}\hat{f})\) denotes the error exerted by model uncertainties and \(\tau_{e_{2}}=M^{-1}(\hat{A}^{T}\hat{f}-\tau_{d})\) denotes the error between the desired wrench and approximated wrench.  
Here, we can rewrite the term \(M^{-1}A^{T}\hat{f}\) as \(M^{-1}A^{T}\hat{f}=Ya\), where \(Y\) denotes a regression matrix and \(a\) represents the uncertain parameters in the Jacobian matrix. Then, \(M^{-1}\hat{A}^{T}\hat{f}\) can be represented as follows:  
\[M^{-1}\hat{A}^{T}\hat{f}=Y\hat{a} \tag{47}\]  
where \(\hat{a}\) denotes the estimation of \(a\).  
Equation (46) then becomes  
\[\ddot{q_{e}}+K_{d}q_{e}+K_{p}q_{e}+K_{i}\!\!\int_{0}^{\mathtt{t}}\!q_{e} \mathtt{dt}=Y\hat{a}+\lambda M^{-1}e^{\prime} \tag{48}\]  
According to Equation (48), \(q_{e}\) is affected by both \(Y\hat{a}\) and \(M^{-1}\lambda e^{\prime}\). With the existence of \(M^{-1}\lambda e^{\prime}\), it is challenging to find a suitable time-changing law of \(\hat{a}\) to accomplish the adaptive control law based on Equation (48). In this study, we employ a fault detection and isolation technique to build an auxiliary system with a state solely affected by \(Y\hat{a}\). We then propose an adaptive control law based on this novel system.
# The Integration of Earthwork Design Review and Planning Using UAV-Based Point Cloud and BIM
## 2 Literature Review
### Design Review for Earthwork  
Earthwork design includes calculation of the quantities of cut and fills, balancing of these quantities, slopes or grades of site, and planning to move these materials. Earthwork design should be reviewed at the design stage to minimize errors in these required elements and get an accurate estimation. The site for the earthwork is designed at specific elevations which are compared with the terrain model and the volume of cut and fill are calculated. The cost of the earthwork depends on an accurate estimation of these quantities. It seems that the DEM process has high importance in earthwork and its measurement process should be accurate to use as a reference plane. DEM generation with GNSS and UAV was compared by [PERSON] et al. [9] to use it as a reference plane which concludes better accuracy of UAV. This study has focused to increase the use of UAV at the beginning stages in the road earthwork and its comparison with other most common methods (i.e., GNSS) in the construction in terms of accuracy leads to consider UAV a better option for surveying earthwork. Using UAV for volume calculation is an efficient and reliable option [11]. UAVs have become a rapidly developing method for surveying applications and need the attention of industry experts in the design process. An experimental evaluation of the UAV for terrain modeling was performed and the UAV was considered as a cost-effective and time-saving solution [21]. It has been formulated by researchers that UAV performance is better than other techniques in terms of its accurate DEM generation that will help to calculate earth volumes [5; 8; 9]. However, the earthwork process also includes hauling distance to move these volumes and pathways for the movement of the equipment. The UAV scope for these elements needs to enhance by integrating them with other techniques.
# Digitalization of the Ex-Core Neutron Flux Monitoring System for APR1400 Nuclear Power Plant
## 2 Design of the Digital ENFMS for APR1400  
ENFMS for APR1400 NPPs shall satisfy all requirements specified in design specifications for ENFMS [10; 11]. The design specifications include requirements on ENFMS functions and performances which are common to both analog and digital equipment. In addition, digital ENFMS shall also satisfy specific design requirements and guidelines for the digital equipment and software in the APR1400 man-machine interface systems.  
The specific requirements include software requirements, software verification and validation requirements communication requirements, test requirements, human factor engineering guidelines, system security requirements, cyber security requirements, and the secure development and operational environment (SDOE) requirements. These are based on industrial standards and nuclear regulatory guidelines of which some are listed as follows:  
* IEEE Std 7-4.3.2, IEEE Standard Criteria for Programmable Digital Devices in Safety Systems of Nuclear Power Generating Stations
* IEEE Std 730, IEEE Standard for Software Quality Assurance Processes
* IEEE Std 828, IEEE Standard Configuration Management in Systems and Software Engineering
* IEEE Std 829, IEEE Standard for Software and System Test Documentation
* IEEE Std 830, IEEE Recommended Practice for Software Requirements Specifications
* IEEE Std 1008, IEEE Standard for Software Unit Testing
* IEEE Std 1012, IEEE Standard for System, Software, and Hardware Verification and Validation
* IEEE Std 1028, IEEE Standard for Software Reviews and Audits
* IEEE Std 1042, IEEE Guide to Software Configuration Management
* IEEE Std 1074, IEEE Standard for Developing a Software Project Life Cycle Process
* USNRC Regulatory Guide 1.152, Criteria for Use of Computers in Safety Systems of Nuclear Power Plants,
* USNRC Regulatory Guide 1.168, Verification, Validation, Reviews, and Audits for Digital Computer Software Used in Safety Systems of Nuclear Power Plants
* USNRC Regulatory Guide 1.169, Configuration Management Plans for Digital Computer Software Used in Safety Systems of Nuclear Power Plants
* USNRC Regulatory Guide 1.170, Test Documentation for Digital Computer Software Used in Safety Systems of Nuclear Power Plants
* USNRC Regulatory Guide 1.171, Software Unit Testing for Digital Computer Software Used in Safety Systems of Nuclear Power Plants
* USNRC Regulatory Guide 1.172, Software Requirement Specifications for Digital Computer Software and Complex Electronics Used in Safety Systems of Nuclear Power Plants
* USNRC Regulatory Guide 1.173, Developing Software Life-Cycle Processes for Digital Computer Software Used in Safety Systems of Nuclear Power Plants
* KINAC/RS-015, Regulatory Standard on Cyber Security for Nuclear Facilities
* KINAC/RS-019, Regulatory Standard on Identification of Critical Digital Assets for Nuclear Facilities
## 2 Materials and Methods
### Multi-Head Attention Mechanism (ATT)  
The ATT (see Figure 7) first presented by [51] was a new advance in DL architectures, especially in the transformer model. This mechanism has been used in several applications to improve learning capability [52]. This mechanism uses three main matrices: the query matrix \(Q\), which represents the encoded temporal features to which attention is paid; the key matrix \(K\), which represents the encoded features available for comparison; and the value matrix \(V\), which contains the features used to compute the attention result. These matrices simultaneously focus on different parts of the input sequence, thus capturing a wide range of contextual information.  
Mathematically, the ATT is defined as Equations (20) and (21).  
\[\text{MultiHead}(Q,K,V) =\text{Concat}(\text{head},\text{head}_{2},\dots,\text{head}_{h})W ^{O} \tag{20}\] \[\text{head}_{i} =\text{Attention}(QW^{Q}_{i},KW^{K}_{i},VW^{V}_{i}) \tag{21}\]  
where, \(Q\), \(K\), and \(V\) represent the query, key, and value matrices, respectively. The projection matrices \(W^{Q}_{i}\), \(W^{K}_{i}\), and \(W^{V}_{i}\) correspond to each attention head \(i\), and \(W^{O}\) is the output projection matrix. The function Attention is defined as the scaled scalar product attention (see Equation (22)).  
\[\text{Attention}(Q,K,V)=\text{softmax}\bigg{(}\frac{QK^{T}}{\sqrt{d_{k}}} \bigg{)}V \tag{22}\]  
Figure 7: Illustration of the multi-head attention mechanism, showing how multiple attention heads operate in parallel and are concatenated to form the final output.  
where \(d_{k}\) denotes the dimensionality of the keys. The softmax function ensures that the attention weights are positive and their sum equals one, thus facilitating the effective weighting of values according to their relevance to queries.  
We use this mechanism to improve the ability of our hybrid model to capture long-term dependencies and complex relationships in climate data. To this end, the ATT can identify hidden patterns and time-varying correlations. This results in more accurate and robust forecasts, especially for dynamic and seasonal features.
# Finite Difference Algorithm on Non-Uniform Meshes for Modeling 2D Magnetotelluric Responses
## 1 Introduction  
The magnetotelluric method is a passive electromagnetic exploration technique that measures orthogonal components of the electric and magnetic fields on the Earth's surface [1]. The field source is naturally generated by variations in Earth's magnetic field, which provide a wide and continuous spectrum of electromagnetic field waves. These fields induce currents into the Earth, which are measured at the surface and contain information about subsurface resistivity structures. With rapid advances in numerical modeling and ill-posed regularized inversion, the magnetotelluric method has become one of the most important tools for geophysical exploring [2; 3].  
The magnetotelluric numerical modeling aims to solve the boundary value problem derived from frequency-domain Maxwell's equations and calculate the spatial distribution of electric and magnetic fields in the subsurface for a given conductivity distribution and a range of frequencies. Numerical modeling approaches such as finite difference (FD), finite element (FE), have been developed and applied as the process of forward modeling for 2D magnetotelluric regularized inversion [4; 5; 6; 7; 8]. The FD method based upon the differential form of the partial differential equations (PDEs) is to be solved. Each derivative can be replaced with an approximate difference formula and the computational domain is usually divided into rectangular cells. The efficiency and accuracy of the FD method for modeling magnetotelluric responses were proven for the 2D geo-electric model [9; 10]. The FE method is another numerical approach that is often used for 2D and 3D magnetotelluric modeling, which involves assumed functional forms for the model and fields in small regions of specified geometry [11; 12; 13; 14].  
The FE method is more accurate for modeling a complicated medium, especially for topography, because of the greater flexibility of mesh discretization. Meanwhile, there are some different approaches to the numerical approximation of the 2D magnetotelluric forward problem [15; 16; 17; 18]. There are some comprehensive reviews of these numerical methods and their advantages and disadvantages [19; 20]. The references stated here represent a general view on magnetotelluric modeling.  
In this paper, a non-uniform mesh approach in the FD numerical method was presented for a general 2D magnetotelluric modeling. The main challenge in applying the method for solving the magnetotelluric boundary value problem is to calculate spatial derivatives. To verify the accuracy of the FD forward algorithm, the resulting numerical was compared to both an analytical solutions and the FE numerical solutions.
# Efficient User-Serving Scheme in the User-Centric Cell-Free Massive MIMO System
## 4 Proposed Scheme and Power Control
### Analysis of the Previous Scheme  
In this Section 4.1, according to the notation of the existing scheme [20], an unserved user is expressed as \(k^{*}\), and an AP newly connected for the service of \(k^{*}\) is expressed as \(m^{*}\). The limitations of the previous scheme [20] are as follows:  
(a) There is no consideration for the occurrence of another unserved user.  
Figure 3: Bar graph of the total sum rate for 20 users and the sum of above the median.  
Figure 2: Distribution of per-user throughput for four users.  
It simply selects an AP that is close to the unserved user.  
\[R_{d,k}^{cf}=\log_{2}\left(1+\frac{N^{2}\rho_{d}\left(\sum\limits_{l=1}^{L}\eta_{ lk}^{1/2}\gamma_{lk}\right)^{2}}{N^{2}\rho_{d}\sum\limits_{k^{\prime}\
eq k}^{K} \left(\sum\limits_{l=1}^{L}\eta_{lk^{\prime}}^{1/2}\gamma_{lk}\frac{\beta_{lk}} {\beta_{lk^{\prime}}}\right)^{2}|\mathbf{\varphi}_{k^{\prime}}^{H}\mathbf{\varphi}_{k}|^ {2}+N\rho_{d}\sum\limits_{k^{\prime}=1}^{K}\sum\limits_{l=1}^{L}\eta_{lk^{ \prime}}\gamma_{lk^{\prime}}\beta_{lk}+1}\right) \tag{12}\]  
\[R_{d,k}^{uc}=\log_{2}\left(1+\frac{N^{2}\rho_{d}\left(\sum\limits_{l=1}^{L} \eta_{lk}^{1/2}\gamma_{lk}\alpha_{lk}\right)^{2}}{N^{2}\rho_{d}\sum\limits_{k^ {\prime}\
eq k}^{K}\left(\sum\limits_{l=1}^{L}\eta_{lk^{\prime}}^{1/2}\gamma_{ lk}\alpha_{lk}\frac{\beta_{lk}}{\beta_{lk^{\prime}}}\right)^{2}|\mathbf{\varphi}_{k ^{\prime}}^{H}\mathbf{\varphi}_{k}|^{2}+N\rho_{d}\sum\limits_{k^{\prime}=1}^{K} \sum\limits_{l=1}^{L}\eta_{lk^{\prime}}\gamma_{lk^{\prime}}\alpha_{lk}\beta_{lk }+1}\right) \tag{13}\]  
The phenomenon that may occur due to a) is as follows. If the user \(k^{*}\) of the existing scheme [20] is served only by the AP \(m^{*}\), a new unserved user occurs. This causes a problem whereby, in a situation where the number of antennas of the AP is not large, the probability of generating new unserved users increases as the number of users increases. In other words, it is judged that this is an unreasonable service provision method, because the service to existing users is not guaranteed when a new connection is created.  
In case (b), even if there are many APs serving user \(k^{*}\), there is a problem in that AP \(m^{*}\) may occupy a fairly high percentage of the sum throughput of user \(k^{*}\). This can be inferred from the distribution in Figure 2 of Section 3. If a connection with a new user is created without considering this non-uniformity of throughput, the throughput of other users that are already receiving the service will be greatly reduced. Consequently, it causes a situation in which the average throughput of the entire network is lowered, which leads to the deterioration of the system performance.
# Adaptive Data Synchronization Algorithm for IoT-Oriented Low-Power Wide-Area Networks
## 3 Remote File Synchronization in IoT LPWANs
### Reference Scenario
For instance, information about temperature and humidity in environmental monitoring systems or blood pressure measure in medical applications are represented by only a few bytes (at most kilobytes when considering aggregate measures). In general, regarding the mechanism described in Figure 2, when the chunk dimension approaches the entire file size, finding matchings becomes ever more sporadic and the number of literal bytes to be transmitted grows. Consequently, the size of the file \(\Delta\) grows, also increasing the data traffic. On the other hand, if the chunk size is reduced the compression ratio offered by the checksums decreases, therefore the advantage of using signatures is less significant.
## 3 Results  
### Biological Pretreatment Performance  
Figure 4 provides an insight into the performance of the pre-treatment system with respect to COD, TSS and FOG removal. The graphs in the first row illustrate the variation of the concentration of each water quality parameter at the inlet of the system, while the graphs in the second row provide the wastewater quality parameters at the outlet of the treatment unit, and finally the graphs in the last row provide the variation of the removal percentages for each indicated parameter. It was noticed from the COD and FOG concentrations at the inlet of the pre-treatment system that some values were outliers. To evaluate this, the inlet and outlet values of each parameter were box plotted with the aim of identifying potential outliers in each distribution.  
Figure 3: Graphical representation of the study.  
As shown in Figure 5a, the boxplot of the distribution of each parameter, assessed both at the inlet and the outlet of the pre-treatment system, indicated the presence of outliers for FOG and COD, as well as the outlet COD concentrations. For the boxplots, an interquartile rule to identify the outliers was used, with the outliers being replaced by median values which are dependent on the size of the dataset. This culminated in the boxplot in Figure 5b from which the absence of outliers can be noticed. Outliers in this experiment could originate from variations in the activities of the poultry slaughterhouse at the moment of the collection of the sample, or erroneous concentration determination of a given parameter during the experiment. These outliers were corrected for ease of data interpretation and the outcome of the experiment at each PSW treatment stage.  
The performance of the pretreatment system is shown in Figure 6. The fluctuations in the quality of the feed and product of the system and the corresponding removal efficiencies after the data processing indicated FOG removal efficiency oscillating around 80%. Furthermore, Figure 6 also shows removal efficiencies of the TSS and the COD ranging between 45% to 80% for the TSS, and a variation between 25% to about 60% for the COD. These removal efficiencies fluctuated with the quality of the feed to the system. Overall, the pre-treatment system culminated in an average removal efficiency of 80 \(\pm\) 6.3%, 38 \(\pm\) 8.4% and 56 \(\pm\) 7.2% for the FOG, COD and TSS, respectively.  
Figure 4: Performance of the pre-treatment unit before outlier replacement.
## 5 Discussion and Best Practice
### On-board GNSS/IMU (direct georeferencing)  
An on-board GNSS/IMU sensor synchronized with the spectral sensor in combination with a digital surface model (DSM) can provide sufficient data for a full orthorectification process based on on-board data alone, without the need for field work. This is the traditional approach for full-size airborne hyperspectral geolocation, which requires specialist hardware and software integration of the GNSS/IMU unit and the spectral sensor. For a point-measuring spectroradiometer, this is one of very few options for the geolocation of the spectral footprint.  
The advantage of this approach is that it allows for rigorous georeferencing of a 2D, pushbroom, or point-based spectrometer without the need for GCPs. The disadvantages include (i) complex integration of a GNSS/IMU unit on-board the UAV due to the additional weight and cost, power regulation, and time synchronization of all of the sensors, and system calibration of the GNSS/IMU and sensor; (ii) post-processing of on-board GNSS data against a GNSS base station or communication/radio requirements for RTK data link for real-time corrections; and (iii) a dual antenna GNSS or high-grade IMU (e.g., FOG) is required for absolute heading determination, which is essential for multi-rotor UAVs.
## 2 Materials and Methods
### Performance Analysis of the Prediction Model  
The performance of the PLSR model, developed using 80% of the calibration dataset, was evaluated using the coefficient of determination (R\({}^{2}\)), root mean square error of calibration (RMSE), and Residual Predictive Deviation (RPD), as shown in Equations (1)-(3). A 2-fold cross-validation was employed to balance computational efficiency and model robustness, helping to reduce overfitting.  
\[\text{R}^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum_{ i=1}^{n}\left(y_{i}-\overline{y}\right)^{2}} \tag{1}\]  
\[\text{RMSE}=\sqrt{1\frac{\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{n}} \tag{2}\]  
\[\text{RPD}=\frac{\text{SD}}{\text{RMSE}} \tag{3}\]  
where \(y_{i}\) is the measured value, \(\hat{y}_{i}\) is the predicted value, \(\overline{y}\) is the mean of predicted values, \(n\) is the number of samples, and \(\text{SD}_{\gamma}\) is the standard deviation of measured values.