esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 3 Methods
### Theoretical Analysis of the Improved BPA  
An improved BP algorithm is proposed for GNSS-R BSA, which can optimize the signal processing of heterogeneous architecture platform of CPU and GPU. The GNSS-R SAR system may work for thousands of seconds on a single mission resulting in the acquisition of data that can reach hundreds of gigabytes. A stream processing structure was used to process such a large amount of data.  
The block diagram of the proposed algorithm is shown in Figure 3. The algorithm can be divided into three steps: range compression, back projection and phase compensation, and azimuth compression.
# SpaceSheep: Satellite Communications for Ovine Smart Grazing
## 2 Materials and Methods
### Satellite Link Integration  
The main case from SheepIT considered animal grazing in the Douro region, at Quinta da Ervamioira, an [PERSON] vineyard (Quinta de Ervamioira, Vila Nova de Foz Coa--41.02107689068905, --7.110593416910681), which is a place where cellular communications have very poor coverage [20], owing to the geographical rugged relief [21] that inhibits the communications between the WSN and the cloud [13]; thus, compromising the dispatching of the alarm messages. This is an ideal deployment setting for satellite-based communications [22], whereby the coverage can be provided in areas that are technically challenging or economically unfeasible for other technologies. One important characteristic of satellite-based communications is the cost per bit. Pricing models are associated with the data volume contracted by the customer; however, mobile satellite communication service providers usually place data transmission costs between EUR 4.79 and 9.58 per Mbps.  
As a result, the evolution conducting the gateway of our previous solution [11], in addition to integrating and assessing a satellite communications interface that sends alarm messages, was optimized to reduce the amount of data being sent by monitoring the messages encoding optimization. In its initial form, a RabbitMQ message broker [14] producer API ensures communication between the application running in the cloud and the gateway that encodes the messages using JavaScript Object Notation (JSON) [23]. To evaluate the best high-availability and fault-tolerance solution for processing these messages, we tested Apache AVRO [24], MessagePack [25; 26], and Protocol Buffers [27] for encoding the APIs.  
Our testbed setup, illustrated in Figure 1, used the satellite network belonging to the EchoStar Mobile operator (Milton Keynes, UK), connected via a Hughes 4200 portable data terminal [27]. This terminal acted as a field concentrator interface for our edge computing device with the cloud service. Using a satellite link allowed the SheepIT system to be deployed in the remote unconnected zones, thereby guaranteeing smart monitoring with 24/7 operations.  
Figure 1: Satellite network integration.
# Pairwise Likelihood Estimation of the 2 PL Model with Locally Dependent Item Responses
## 1 Introduction
### Marginal Maximum Likelihood (MML) Estimation for the Normal Copula Model for Item Residuals  
The 2 PL model with a normal copula for correlated item residuals (see Section 2.5) can be estimated with MML by modeling the vector \(\mathbf{X}_{t}\) of item responses \(X_{it}\) within a testlet \(t\) (\(t=1,\ldots,T\); see (8)) as  
\[\mathsf{P}(\mathbf{X}=\mathbf{x};\mathbf{\gamma})=\int\prod_{t=1}^{T}\mathsf{P}(\mathbf{X}_{t }|\theta;\mathbf{\gamma}_{t})f(\theta)\,\mathrm{d}\theta. \tag{18}\]  
The vector \(\mathbf{\gamma}_{t}\) contains all item parameters \(a_{i}\) and \(b_{i}\) of the items within the testlet \(t\) and the entries of the correlation matrix of item residuals \(\mathbf{\varepsilon}\) within the testlet.  
Now, assume that there are \(n=1,\ldots,N\) independent and identically distributed realizations of \(\mathbf{X}\) from (18), leading to item responses \(\mathbf{x}_{1},\ldots,\mathbf{x}_{N}\in\{0,1\}^{1}\), where \(\mathbf{x}_{n}=(\mathbf{x}_{n1},\ldots,\mathbf{x}_{nT})\) and \(\mathbf{x}_{nt}\in\{0,1\}^{t_{t}}\). The model parameter \(\mathbf{\gamma}=(\mathbf{\gamma}_{1},\ldots,\mathbf{\gamma}_{T})\) can be obtained by maximizing the negative log-likelihood function  
\[l(\mathbf{\gamma})=\sum_{n=1}^{N}\log[\mathsf{P}(\mathbf{X}=\mathbf{x}_{n};\mathbf{\gamma})]= \sum_{n=1}^{N}\log\!\left[\int\prod_{t=1}^{T}\mathsf{P}(\mathbf{X}_{t}=\mathbf{x}_{nt }|\theta;\mathbf{\gamma}_{t})f(\theta)\,\mathrm{d}\theta\right], \tag{19}\]  
Note that the probabilities \(\mathsf{P}(\mathbf{X}_{t}=\mathbf{x}_{nt}|\theta;\mathbf{\gamma}_{t})\) require the evaluation of the multivariate normal distribution of dimension \(l_{t}\). The variance matrix of the parameter estimate \(\widehat{\gamma}\) can be obtained by taking the inverse of the negative observed information matrix that contains the second-order partial derivatives (i.e., the Hessian matrix)  
\[\left.\frac{\partial^{2}l}{\partial\gamma_{h}\gamma_{k}}\right|_{\mathbf{\gamma}= \widehat{\gamma}}, \tag{20}\]  
where \(\gamma_{h}\) and \(\gamma_{k}\) are entries of the parameter vector \(\mathbf{\gamma}\).  
Consistent parameter estimates of \(\mathbf{\gamma}\) can be expected if the normal distribution assumption of \(\theta\) and the multivariate normal distribution of item residuals within a testlet (i.e., the normal copula model) are correct.
# Frequency Comb-Based WDM Transmission Systems Enabling Joint Signal Processing
## 1 Introduction
### Optical Frequency Combs: A Background
#### 1.1.2 Integration Potential of Frequency Combs
Recent demonstrations indicate that these combs can be precisely engineered to cover the light wave communications C + L bands [52]. The performance in terms of optical linewidth and OSNR is sufficiently high to encode high-baud rate complex modulation formats [52; 53]. A fundamental challenge with temporal cavity solitons is that the conversion efficiency from the CW laser to total comb power is inversely proportional to the number of generated comb lines [54]. This means that the power of the individual lines is inversely proportional to the square of the number of comb lines, and a high-power CW laser is needed to achieve the target OSNR. Alternatively, mode-locked dark-pulse combs, as recently demonstrated in silicon nitride microresonators [55], display much higher conversion efficiency [56]. These comb sources have enabled complex modulation formats while keeping a CW pump power of 100 mW [57]. One drawback of this comb is that the spectral envelope is not as smooth as with temporal cavity solitons. It is still unclear which of these two sources provides a better solution as the WDM source. Other open research questions relevant for coherent communications using microresonator frequency comb technology are the line spacing stability [58; 59; 60] and hybrid integration. A main motivation for using frequency combs as WDM sources is the potential of reducing hardware complexity and power consumption by replacing a large amount of individual laser sources, each of which needs individual thermal control and other control circuits. In the Supplementary Material of [52], the power consumption of microresonator combs is compared to that of discrete lasers and found to be lower if a sufficient number of lines is generated (the exact number depends on the specific comb design). Apart from this, the power consumption of comb generation has not been well studied.
# Effect of Fibers on Mixture Design of Stone Matrix Asphalt
## 3 Results and Discussion
### Effect of Fiber on Percent Voids in Mineral Aggregate in Bituminous Mixtures (VMA)  
Figure 5 displayed the percent voids in mineral aggregate in SMA mixtures. The VMA increased with fiber content increasing. The measured VMA values were between 15% and 18%. At the same fiber content, the VMA value had the minimum gap between lignin fiber and blended fiber. It was only 16.4% even when the mineral fiber content was up to 0.5%. The values were more than 17.3% for the other fibers. This indicated that the mineral fiber had the minimum impact on VMA. The same trend was found for Marshall Stability. Compared with the previous literature, the mix designer was able to judge the proper VMA requirement for each kind of fiber.  
Figure 4: VFA of SMA specimens with four different fiber types.  
The VMA with polyester fiber, flocculent lignin fiber and blended fiber increased with fiber content increasing from 0.1% to 0.4%, and then the increment gradually reduced with fiber content from 0.4% to 0.5%. However, the content change of mineral fiber had a negligible impact on VMA when the content was less than 0.2%. The mixture with mineral fiber had a similar variety with the only difference being the relatively lower VMA values with fiber contents from 0.2% to 0.6%. When the content of flocculent lignin fiber, blend fiber, and polyester fiber was 0.3%, and mineral fiber content was 0.4%, the VMA values of SMA mixtures were 16.7%, 16.7%, 16.5%, and 16.1%, respectively, which explained much more mineral fiber content was needed to achieve a target VMA value in the SMA mixture design.  
Furthermore, mineral fiber had a similar density with aggregate and smaller oil absorption and smaller specific surface area; it cannot absorb much asphalt binder to fill the outside space of the mineral fiber. Therefore, effective asphalt content was not sensitive to the content change. Therefore, the SMA mixture with mineral fiber can obtain a lower VMA under the same compaction effort. These test findings can be a reference and used to promote the use of fibers in SMA mixtures.
## 4 Pressure Loss Correction Model for Bend Section
### Correction Model
Additionally, as the horizontal inclination angle increases, the corresponding pressure loss value also increases accordingly. Figure 10: The curve of the distribution of pressure loss in concrete pumping with bend radius. Figure 9: The curve of the distribution of pressure loss values with the horizontal inclination angle of bend. Therefore, by considering the combined effects of horizontal inclination, radius of curvature, and pumping flow rate on the pumping pressure loss, along with the interaction between these factors, the expression for the pumping bend correction factor is:  
\[\lambda=c_{1}r^{2}\theta+c_{2}r\theta+c_{3}r\theta+c_{4}rv+c_{5}r+c_{6}\theta+c _{7}v+c_{8} \tag{18}\]  
The results of the pressure loss conversion in Table 4 are fitted using the least squares method. The fitted results are shown below:  
\[ \begin{split}&\lambda_{1}=-2.0663\times 10^{-8}r^{2}\theta+1.274 9\times 10^{-5}r\theta-0.00157v\theta+0.0021 rv\\ &+0.0025r+0.00807\theta+0.7545v+0.3001\end{split}  \tag{19}\]  
The fitting results reflect the combined effect of pumping velocity, horizontal inclination, and the radius of curvature of the bend on the pumping pressure loss in the elbow section of the pipeline.
# Wavelet-Based Optimum Identification of Vehicle Axles Using Bridge Measurements
## 6 Field Test of Fifth Wushui Bridge, China
### Wavelet Analysis of Test Results with Multiple Presence  
Axle detection is particularly challenging when there are multiple vehicles present, and this is especially the case when vehicles are present simultaneously in adjacent lanes. In one such run, the two  
Figure 11: Wavelet coefficients for different mother wavelets and scales: (**a**) \(db2\), scale = 8 (optimal); (**b**) \(db2\), scale = 4 (less than optimal); (**c**) \(db2\), scale = 16 (greater than optimal); (**d**) \(db1\), scale = 7 (optimal); (**e**) \(db3\), scale = 9 (optimal); (**f**) \(db6\), scale = 10 (optimal).  
Figure 12: Wavelet coefficient functions for Lane 3 FAD signals (both with db2 at scale of 8).  
trucks crossed the bridge simultaneously with Truck B in Lane 3 and Truck A in Lane 4. Like Lane 3, there are two FAD sensors in Lane 4, sensor numbers 14 (labeled L4-FAD-1) and 18 (labeled L4-FAD-2). All four raw FAD signals are illustrated in Figure 13. The signal for L3-FAD-2 is contaminated with significant white noise for reasons that are unclear.  
Each of these signals should have peaks corresponding to the two axles of the within-lane vehicle, with much lesser (and unwanted) peaks corresponding to the adjacent-lane vehicle. For both Lane 4 signals, there is one dominant peak with a secondary peak that may correspond to an axle or to general bridge vibration. Figure 14 shows the optimum results of the wavelet transform, where two peaks are now clearly visible in each signal. For this multiple-presence example, the inferred axle spacings are 4.71 m and 4.95 m for Truck A (Lane 4), which is 0.2% and 5.4% greater than the statically measured spacing of 4.7 m. It seems likely that the bridge vibration has combined with the second L4-FAD-2 peak, pushing its location to the left. The corresponding results for Truck B (Lane 3) are 4.80 m and 4.72 m (2.1% and 0.4% errors respectively). The noise in the original signal does not appear to have had an adverse influence on the accuracy.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Results
#### 5.4.2 Second Experiment: Query Historical Data  
Figure 15 shows the average response time of a query relating to the historical data for the proposed framework and the traditional cloud architecture. As shown in the Figure, the average response time ranges between 2 and 4 s for the proposed framework, while it ranges from 8 to 12 s for the traditional cloud. Results show that the response time of a query using the historical data is greater than the average response time of a query using the newest data.  
The main reason is the fog server stores the newest data in its storage module; thus, it is responsible for responding to the newest data queries, whereas the cloud stores historical data; thus, responding to historical data queries requires interaction with the cloud. Fog nodes are placed close to clients allowing response time to be noticeably minimized, while the cloud is placed in a distant geographic region. The query tracker of the fog server plays a key role in achieving a promising response time as it caches the newest data by sending requests to the database to retrieve all the newest data every second. Retrieved data will be maintained in the accessing module. Consequently, when the accessing module receives queries from clients, it can return the response immediately without passing the request to the database through the message broker. Therefore, the overhead will be reduced and the response time will be minimized. The device tracker caches the registration  
Figure 15: The response time of a query using the historical data (the proposed architecture vs. traditional cloud).  
information of devices in order to minimize the authentication overhead. Additionally, because of the message broker, all implemented modules can fire the message and continue performing their functions without the need for waiting for the response. A module will be notified to receive messages (if any) in an event-driven manner which in turn increases the performance.  
As shown in the figure, the response time is very high when the traditional cloud architecture is employed. It is the highest in comparison with all previous results. In our proposed architecture, the fog layer plays a key role in reducing the cloud overhead. In the traditional cloud computing architecture, there is no fog server and the cloud is responsible for all functions provided by that server (i.e., in CFC framework, fog server sends only selected data to the cloud for analysis and long-term storage).
## 4 Conclusions  
Based on the experimental results from concrete specimens exposed to freezing and thawing under various relative humidity conditions, it is affirmed that the inclusion of an AEA expansion agent or fibers (specifically steel fibers or HEMPF) leads to an augmentation in both compressive and flexural strength of HSC. Notably, the composite admixture comprising the expansion agent and steel fibers exhibits the highest strength. Different RH influences the strength development of HSC, with lower RH proving more deleterious to this development. Particularly, HSC strength combined with steel fibers displays the least sensitivity to fluctuations in relative humidity.  
HSC, with a strength grade of C80, demonstrates robust freeze-thaw resistance even without air entrainment, boasting a freeze-thaw life of at least 1200 cycles. The AEA expansion agent adversely affects the freeze-thaw resistance of HSC, while high-modulus fiber materials, such as steel fibers and HEMPF, positively contribute to this resistance. The synergistic incorporation of the AEA expansion agent and fibers neutralizes the detrimental impact of the expansion agent, thereby safeguarding the freeze-thaw resistance of HSC.  
The freeze-thaw resistance of HSC is sensitive to the humidity during the curing process. A drier curing environment correlates with a shorter freeze-thaw life for HSC. The addition of the AEA expansion agent substantially diminishes the freeze-thaw life of HSC under dry conditions, reaching only 51% and 62% at 30% RH and 50% RH, respectively. However, the inclusion of steel fibers has no discernible effect on the freeze-thaw life of HSC under dry conditions. The introduction of HEMPF fibers alone elevates the freeze-thaw life of HSC at 50% RH to 95% of the standard-cured freeze-thaw life. The combination of the AEA expansion agent and fibers nullifies the sensitivity of HSC freeze-thaw resistance to curing environment humidity, yielding a freeze-thaw life at 30% RH to 50% RH ranging from 98% to 104% of the standard-cured freeze-thaw life. Similarly, when the AEA expansion admixture and HEMPF fibers are combined in HSC preparation, the freeze-thaw life at 30% RH to 50% RH ranges from 81% to 98%. Based on the experimental results of this study, the concrete with both AEA expanders and steel fibers had the best frost resistance under dry conditions. This provides a reliable concrete mix ratios scheme for concrete construction projects in the cold and arid climate of Northwest China.  
Methodology, H.Y. and H.M.; Validation, H.Y., H.M. and S.S.; Data curation, H.Y. and H.M.; Writing--original draft S.S.; Writing--review & editing H.M. and S.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Chinese National 973 Foundation Grant (Nos. 2009 CB623203 and 2015 CB655102).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflict of interest.
# Design of Constant Speed Controller for Hydraulic Retarder Based on Robust Control
###### Abstract  
Achieving long downhill constant-speed driving of heavy vehicles is of great significance for improving vehicle transport safety. As a kind of auxiliary brake, the hydraulic retarder has the characteristics of large braking torque and compact structure. More importantly, the hydraulic retarder is capable of braking for a long period of time, which enables the vehicle to travel downhill at a constant speed with less or no use of mechanical brakes. However, due to the complexity of hydraulic retarder braking conditions, its output braking torque presents time-varying and non-linear characteristics, and the control of the hydraulic retarder filling rate in order to achieve the vehicle's long downhill constant-speed braking is a challenging problem. This research proposes a constant-speed control strategy utilizing the robust control method to address the issue of prolonged downhill braking at constant speed for heavy-duty vehicles, which achieves constant-speed and stable driving downhill by controlling the filling rate of the hydraulic retarder. Initially, the dynamic model of the downhill process for heavy-duty vehicles and the physical model of the hydraulic retarder are established. Then, based on the concept of sliding mode control, the sliding mode controller with saturation function and the high-frequency robust controller are developed to modulate the filling rate of the hydraulic retarder in response to variations in vehicle speed. In order to verify the effectiveness of the algorithm, three different operating conditions were set according to the vehicle mass and road gradient, and simulation tests were carried out in the MATLAB/Simulink environment. Simulation results indicate that the high-frequency controller exhibits remarkable robustness against dynamic disturbances within the system. Additionally, when variations in vehicle mass and road gradient occur, the root mean square error of the high-frequency controller's speed, in comparison to the fuzzy controller, decreases by 0.1157 km/h, while the maximum absolute error in vehicle speed diminishes by 0.248 km/h. Simultaneously, the high-frequency controller proficiently suppresses chatter, thereby meeting the demand for consistent speed braking in big trucks on prolonged downhill gradients.  
hydraulic retarder; constant-speed control; filling rate; robust control
# Quasi-Passive Resistive Exosuit for Space Activities: Proof of Concept
## 3 Experimental Evaluation  
### Subsystems Validation  
#### 3.1.1 Lab Validation of the Soft Sensor Technology  
The main objective of this validation is to insert the soft sensors in the control loop of the exosuit and, in particular, to use the information provided by them during the swing phase. It can be observed that, compared to previous versions, the signal amplitude is much higher, and the swing phase can also be easily recognized from raw data.  
During these trials, the whole XoSoft Gamma exosuit was worn by a healthy subject who walked on a treadmill for 5 min at constant speed of 3 km/h (0.8 m/s). During the tests, motion capture was assessed using the MTw Awinda 3D human wireless tracker (Xsens, Enschede, the Netherlands), which was used in parallel with the soft sensor to capture the joint movements and act as a reference. Figure 6A shows the soft sensor output and the corresponding bending angle measured by the Xsens system (shown in Figure 6B) in a 30-s time window. These data demonstrate a clear correlation between the two signals of the knee angle estimated by both technologies (shown in Figure 6C). Moreover, the system is able to detect both swing and stance knee angle trends. The root mean square error (RMSE) between the reference signal (Xsens) and the estimated angle (soft sensing) is around \(3.1^{\circ}\pm 1.2^{\circ}\).  
Based on these results, we concluded that the soft sensing system is suitable for use in the control loop of the exosuit, and that it is able to measure the knee bending angle with accuracy that is broadly comparable to commercial tracking systems, but requires low computational effort and is more portable.  
Figure 6: (**A**) Soft sensor output. (**B**) Knee angle measured by the Xsens system. (**C**) Comparison between soft sensors (blue solid line) and Xsens system (red solid line), using a linear combination for fitting the knee angle.