esa-sceva/satcom-chunk-collection huggingface dataset:
# Damping Estimation of an Eight-Story Steel Building Equipped with Oil Dampers
## 3 A Framework for Damping Estimation of Passively Controlled Structures
### An Enhanced Strain-Energy Method
#### 3.1.2 Estimation of Equivalent Natural Frequencies  
An energy approach is presented here to quantify damper stiffness by equivalent natural frequencies. It assumes that the added dampers of a structure do not affect its mode shapes, which can be justified by the fact that damper stiffness is usually minor compared with structure stiffness [15; 31]. The basic idea of this approach is based on Equation (2), which establishes a relationship between strain energy and natural frequency, and the critical point in this approach is to determine the distribution of strain energy stored in the entire structure. Specifically, for the \(i\)th mode, the strain energy stored in structure itself may be expressed by  
\[E_{S,i}^{s}=\left(1-\frac{E_{D,i}^{k}}{E_{S,i}}\right)E_{S,i}=(1-\eta_{D,i})E _{S,i} \tag{7}\]  
where \(\eta_{D,md}\) is a strain-energy ratio, and \(E_{D,i}^{k}\) is the maximum strain energy stored in dampers in one cycle of motion. It has a closed-form solution for oil dampers modeled by linear Maxwell models as  
\[E_{D,i}^{k}=\frac{1}{2}\sum_{j}\frac{\tau_{d,j}^{2}\omega_{i}^{2}}{1+\tau_{d, j}^{2}\omega_{i}^{2}}k_{d,j}\Delta_{i}^{2}\phi_{i,dj}^{2}=\frac{1}{2}\Delta_{i}^{2} \Phi_{i}^{T}\widetilde{\mathbf{K}}_{D,i}\Phi_{i} \tag{8}\]where the matrix \(\widetilde{\mathbf{K}}_{D,i}\) is given by  
\[\widetilde{\mathbf{K}}_{D,i}=\mathbf{T}_{D}^{T}diag\Bigg{\{}\frac{\tau_{d,j}^{2} \omega_{i}^{2}}{1+\tau_{d,j}^{2}\omega_{i}^{2}}k_{d,j}\Bigg{\}}\mathbf{T}_{D} \tag{9}\]  
It can be regarded as to the equivalent damping matrix for the \(i\)th mode provided by added dampers. Combining Equations (2) and (8) yields the \(i\)th strain-energy ratio as  
\[\eta_{D,i}=\frac{\Phi_{i}^{T}\widetilde{\mathbf{K}}_{\text{D};i}\Phi_{i}}{ \omega_{i}^{2}} \tag{10}\]  
Then, substitute Equation (2) into Equation (7), we can get  
\[\omega_{s,i}=\sqrt{1-\eta_{D,i}}\omega_{i} \tag{11}\]  
where \(\omega_{s,i}\) is the \(i\)th natural frequency of structure itself.  
Finally, the equivalent natural frequency provided by dampers is given by  
\[\omega_{qq,i}=\omega_{i}-\sqrt{(1-\eta_{D,i})}\omega_{i} \tag{12}\]  
It should be noted that Equations (6) and (12) require modal characteristics of passively controlled structures, including natural frequencies and complete mode shapes. However, in practice, civil engineering structures are often sparsely instrumented. As a result, their measured mode shapes are incomplete and correspond to a few monitored DOFs only. In this study, the mode shape expansion technique [19; 24; 27] is adopted to obtain the complete mode shapes for the investigated building, as detailed in Section 4.
## 3 Description of System Performance Indicators  
We describe the performance metrics discussed in the article in this part, which encompass the performance of both the entire system and a single node.
## 5 Conclusions  
An adverse effect of high salinity stress (\(\sim\)9 dS m\({}^{-1}\)) was evident in non-grafted plants, especially in the Valouro-RZ cultivar. A positive effect of grafting was observed when Maxifort was used as a rootstock. A modified nutrient solution [N2] was found to have a significant impact when used in combination with moderate salinity level (\(\sim\)6.0 dS m\({}^{-1}\)), as it exhibited a high mean yield (24 kg m\({}^{-2}\)), with a reduction of about \(\sim\)20% in yield when compared with the control (i.e., combination of S1 and N1). High saline water significantly affected WUE, whose value was the highest at 34.7 kg m\({}^{-3}\) for the control (S1 and N1), followed by the S2 (28.0 kg m\({}^{-3}\)) and S3 conditions (17.5 kg m\({}^{-3}\)). These results revealed that the studied tomato cultivars could be grown successfully under a hydroponic system using irrigation water with up to 6.0 dS m\({}^{-1}\) salt concentration in combination with the N2 recipe without affecting total fruit yield. The results also indicated that grafting could mitigate some of the negative effects of salinity stress on tomato plants grown under hydroponic conditions.  
Conceptualization, V.C.P., R.M. and K.A.A.-G.; methodology, R.M., E.T. and N.S.; software, R.M.; validation, K.A.A.-G., V.C.P. and N.S.; formal analysis, R.M.; investigation, E.T.; resources, K.A.A.-G.; data curation, R.M.; writing--original draft preparation, R.M. and E.T.; writing--review and editing, E.T., K.A.A.-G., V.C.P. and N.S.; visualization, K.A.-A.-G.; supervision, R.M.; project administration, E.T. and K.A.A.-G.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.  
Figure 3: Principal component analysis (PCA) results: factor (F) and water use efficiency (WUE).  
**Funding:** This research was funded by the National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdelaziz City for Science and Technology, Kingdom of Saudi Arabia, grant number 11-AGR1939-02.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors are grateful to the National Plan for Science, Technology, and Innovation (MAARIFAH), King Abdulaziz City for Science and Technology, Kingdom of Saudi Arabia, for funding. The authors are also acknowledged the Research Chairs Program, Deanship of Scientific Research, King Saud University, Riyadh, for providing facilities for this study.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Coloured Petri Nets-Based Modeling and Validation of Insulin Infusion Pump Systems
## 6 Empirical Evaluation
### Procedure and Measures  
We used CPN/Tools software to conduct the evaluation based on four phases: first training stage, first evaluation stage, second training stage, and second evaluation stage. We prepared the modelers in the training phase to specify CPN models using the CPN/Tools. We conducted the following activities for the first training stage: (i) we asked modelers to answer a questionnaire regarding personal information, experience with formal methods, and experience with reusability; (ii) the trainer presented a short course regarding concepts and examples related to CPN, CPN/Tools, and the reference model. The trainer also presented an overview of the ACCU-CHEK Spirit; and (iii) the modelers applied the learned techniques to build simple examples assisted by the trainer. The trainer used slides and CPN examples to enable audio-visual presentations while using the particular problem-solving method to support hands-on learning activities. During the first evaluation stage, the modelers instantiated the reference model to represent ACCU-CHEK Spirit's commercial insulin infusion pump system. They responded to a questionnaire regarding the models' reusability attributes. We divided the modelers into control (size eight) and treatment (size four). The treatment group comprises modelers who have few experiences using CPN (i.e., less than ten hours of a CPN course), while the control group comprises modelers who finished a six months class about CPN and, consequently, at least, six months of experience using CPN. We asked each subject to instantiate the reference model within two hours. We prepared the evaluation material, and the trainer was available during the evaluation to help to solve misunderstandings on the experiment's guidelines and questionnaire wording. The next step was the second training stage, in which the reference model was explained in more detail. This step was followed by the second evaluation stage, comprising the reference model's usage to extend the specification. The following activities were conducted in the second evaluation stage: (i) each modeler implemented two new requirements within two hours: add a new battery representation and add a feature for recharging the battery; (ii) the modelers were asked to answer a questionnaire about effort and re-use. We defined metrics to evaluate the hypotheses and assess the RQs. Thus, we addressed time to measure productivity [31], computing the time required by each group to finish the problems that we asked modelers to solve during the evaluation phase. Moreover, we measured reusability using two factors [32]: understandability and adaptability. Under
## 2 Materials and Methods
### Measurement System  
A sensor of type 8702B500 from Kistler (Kistler Instrumente GmbH, Munich, Germany) was used for acceleration measurements. With this type of sensor, the signal is generated by a shear ceramic measuring device and converted into a voltage signal by evaluation electronics integrated in the sensor. The sensor is characterized by low lateral sensitivity and low sensitivity to base strain. The sensor has a resonant frequency of 54 kHz and has a measurement range of up to \(\pm\)500 g [16]. The supply voltage as well as the signal processing was provided by a RogaDAQ 2, which is recognized as a standard sound device by a PC. Manufacturer of this portable high performance data acquisition device is ROGA-Instruments (Nentershausen, Germany). The RogaDAQ 2 supports measurements from 0 Hz to 20 kHz with an input limit of \(\pm\)5 volts.  
In principle, several different options are available for mounting the accelerometer on the print head. Kistler offers magnetic mounting adapters, adhesive mounting adapters, adhesive axes, or mounting bolts as mounting options. Due to the high frequencies to be expected in the signal, a mounting bolt was selected for fastening the sensor, which represents the stiffest of all connection options [17]. Figure 1 depicts the mounting of the sensor on the print head. Highlighted are the accelerometer (1), the threaded nut for position assurance (2), the threaded nut for clamping (3), the spacer plate (4), and the surrounding steel plate (5).
# Review on the Service Safety Assessment of Main Cable of Long Span Multi-Tower Suspension Bridge
## 4 The Service Safety Assessment
The comparison between deterministic approaches in codes and reliability-based approach indicated that the selection of friction coefficient, traffic load modeling, and assessment approaches are important to results. Besides, lots of researchers conducted tests to obtain the coefficient of friction between main cable and saddle in cases of distinct suspension bridges (Table 1). According to the monitoring technology of service safety status of main cable, [PERSON] [59] employed acoustic detection system to detect fractured steel wires in order to dynamically monitor the service safety of main cable. [PERSON] [60] studied the service safety of main cable under coupled roles of corrosion and fatigue load based on the probability analysis method. [PERSON] [61] developed a strain and temperature monitoring system for steel wires of main cable based on the remote on-line fiber grating technology. [PERSON] et al. [62] and [PERSON] et al. [63] developed a nondestructive corrosion monitoring system which can dynamically monitor the temperature, humidity, and corrosion level of main cable in order to explore the main cable degradation. In conclusion, lots of scholars introduced calculation methods of load-bearing safety factor of main cable based on the inverse reliability method and considering the secondary stress, studied the effects of corrosion induced fractured steel wire, secondary stress, and wrapping angle on the load-bearing capacity of main cable. Meanwhile, the effects of design parameters of suspension bridge on the anti-sliding safety factor based on [PERSON]'s formula were analyzed. The anti-sliding safety factor was calculated using the reliability method and ratio of friction force to static tension difference method. The anti-sliding safety between main cable and main cable saddle was evaluated. The service safety of main cable was monitored dynamically based on acoustic detection and probability analysis. The fiber grating technology and corrosion monitoring system were combined to dynamically monitor the temperature and corrosion degradation of main cable.
# Dynamic Quality Monitoring System to Assess the Quality of Asphal Concrete Payement
## 2 Development of the Dynamic Monitoring System for Asphalt Concrete
### _Composition of Digital Monitoring System_
#### 2.2.3 Quality Control System for Asphalt Mixture Transportation  
The quality control system for asphalt mixture transportation is composed of vehicle positioning, temperature collection, on-board video monitoring and intercom subsystems. Based on the BDS system [27; 28; 29], the positioning module is installed on asphalt concrete transportation vehicles, which can collect transportation vehicle information, driver information, transportation track and transportation speed. Additionally, radio-frequency identification (RFID) technology is used to install labels and readers on the truck, mixing station and paving site to collect the start and end transportation time in order to calculate the transportation cycle parameters [30; 31; 32; 33]. The temperature of the asphalt mixture during asphalt concrete transportation is monitored in real time by the temperature sensor. Figure 3 shows the system architecture for the real-time monitoring system for asphalt mixture transportation.
# An Efficient Algorithm for Mapping Deep Learning Applications on the NoC Architecture
## 4 Evaluation
### Analytical Model  
Inspired from [34], in the following section, the energy and latency model is described. As per the bit energy model, the total communication energy consumption \(CC\) is given by the following formula:  
\[CC=\sum_{\begin{subarray}{c}a,b\\ a,b\end{subarray}}^{n}[B_{ta,tb}\times(N_{m}\times E_{Switch}+N_{m-1}\times E _{Link})] \tag{2}\]  
Figure 4: AI task mapping on processor cores.  
In this equation, the \(B_{lat.tb}\) parameter indicates the bandwidth between two routers \(t_{a}\) and \(t_{b}\), \(E_{Switch}\) is the switch energy consumption, and \(E_{Link}\) is the energy consumption by the link of the NoC element. \(n\) gives us the count of neurons or nodes in the considered AI application which need to be mapped, and \(N_{m}\) represents the Manhattan distance between source tile \((x_{a},y_{a})\) and the destination tile \((x_{b},y_{b})\).  
Its formula is as follows:  
\[N_{m}=|x_{a}-y_{a}|+|x_{b}-y_{b}| \tag{3}\]  
In order to compute the communication cost per bit, we divide \(CC\) by total number of bits communicated during the simulation.  
To obtain an efficient solution, \(CC\) has to be optimized using the techniques proposed in this article. The average value of latency can be calculated from the following equation:  
\[Lat_{avg}=\frac{1}{N}\sum_{x=1}^{N}\frac{1}{N_{x}}\sum_{y=1}^{N}Lt_{b} \tag{4}\]  
Here, latency of packet \(b\) is given by \(Lt_{b}\), \(N_{x}\) gives the packets received by the core \(x\), and number of processor cores in the platform are given by \(N\). Furthermore, the average throughput of the network is given by  
\[Throughput_{avg}=\frac{1}{N\times T_{sim}}\sum_{x=1}^{N}N_{x} \tag{5}\]  
where \(T_{sim}\) is the simulation time. Finally, the average hop distance is defined as follows:  
\[HopDistance_{avg}=\frac{n_{fills}}{n_{links}} \tag{6}\]  
where \(n_{fills}\) indicates the number of flits flowing in the NoC architecture, and \(n_{links}\) is the total number of NoC links.  
The simulation parameters for this analysis are given in Table 3. The simulation is conducted using OCTAVE [40], which is a freely available scientific programming language. Using the equations mentioned above, we have generated the results for a 2D mesh with varying sizes, from 4 till 16 cores. The considered application is a neural network graph with four layers, comprising input, output, and two hidden layers. For the sake of comparison, we have used the direct mapping that is the state-of-the-art mapping technique for most of the articles mentioned in the related work.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline NoC type & 2D Mesh \\ NoC sizes & \(2\times 2\), \(3\times 3\), \(4\times 4\) \\ Embedded applications & Artificial intelligence (neural network) \\ Packet length & 128 bits (1 flit) \\ Mapping algorithm & Multilevel and direct mapping \\ Simulation time & 1000 s \\ Clock frequency & 2000 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of parameters.
# Incorporating Aleatoric Uncertainties in Lake Ice Mapping Using RADARSAT-2 SAR Images and CNNs
## 7 Conclusions  
We have presented a framework that incorporates aleatoric uncertainty in the CNN. Doing so allows the model to learn another label for a given patch, which can be considered an additional degree of freedom in the solution for that patch. As expected, and confirmed in the results, the impact on the solution was greatest where the uncertainty values were large. These regions are typically found in areas that show strong changes in texture and tone, usually near the ice edge, and in regions marked by abrupt changes of lake ice concentration. Adding aleatoric loss and bathymetry information improved the accuracy of mapping water and ice (specifically black ice). Notwithstanding, model improvements in water mapping were highly visible in deeper zones whereas for ice detection, only the metrics for Zone 2 in Lake Erie improved.  
A possible area to research is the extension of this present work to many other lakes in Canada monitored on a weekly basis by CIS and for which lake-wide ice fractions are determined. Our current methodology can be leveraged for operational use to provide ice maps with inherent uncertainties. It expands product usability and will make researchers aware of aleatoric uncertainty when incorporating ice fractions in their physical/numerical retrieval models in the form of direct integration of observation error variance or as a quality control flag. Therefore, our proposed product enhances their retrieval accuracy and final-product uncertainty reports.  
Conceptualization, N.S. and K.A.S.; methodology, N.S and K.A.S.; software, N.S. and K.A.S.; validation, N.S.; formal analysis, N.S.; investigation, N.S. and K.A.S.; resources, K.A.S. and C.D.; data curation, N.S.; writing--original draft preparation, N.S.; writing--review and editing, K.A.S., N.S. and C.D.; visualization, N.S.; supervision, C.D.; project administration, C.D.; funding acquisition, C.D. All authors have read and agreed to the published version of the manuscript.  
This research was made possible with funding from the Canada First Research Excellence Fund Global Water Futures Program.  
Not applicable.  
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.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
###### Abstract
Section 2.1 provides a comprehensive overview of the highly adaptive reconfigurable receiver front-end (HARRF) architecture for 5G and satellite applications. Section 3 elaborates on the key design considerations for the switchable receiver front-end, followed by an in-depth discussion of active device technology and S-parameter extraction in Section 4. Section 5 presents the detailed design and analysis of the HARRF LNA, while Section 6 focuses on the SPDT switch design and optimisation. Section 7 discusses the experimental results, including the performance evaluation of the wideband SPDT switch and the reconfigurable front-end. Finally, Section 8 concludes the paper, summarising key contributions and potential future research directions.
## 3 Results and Discussion
On the other hand, we also notice that real data severely deviate from the theoretical predictions when the thickness is smaller than 4 mm. This seems to suggest the following statement as a general conclusion: although fractures can be detected at any thickness in the explored range (from 0.25 mm to 16 mm), it seems that the accuracy of the amplitude measurements with the 3 GHz antenna is not good enough to make a quantitative characterization of the filling material when the thickness is less than 4 mm. Again, the mismatch between the real data and the predictions is underestimating the expected amplitude variations and this would result in underestimating the permittivity of the filling material. This underestimation is consistent with what was observed in the comparison of the air-filled fractures with the  
Figure 9: Amplitude versus thickness for the air-filled fractures (**left**) and the PVC-filled fractures (**right**). Stars are peak-to-peak amplitudes from laboratory tests, after rescaling. Circles are reflection coefficients calculated from the thin bed theory (Equation (1)). resin-filled fractures, and it seems to suggest that the problem persists up to thickness values of about 4 mm. For fractures with an aperture larger than 4 mm and up to the maximum thickness that can be modeled as a thin bed, the amplitude variation is so close to the predicted results that a quantitative characterization of the filling material becomes quite reliable. However, returning to our specific application where the objective of the GPR survey is to detect the change in the filling material, i.e., to perform quality control of the propagation of the injected material within the fracture, we can conclude that the method is successful for any tested thickness, thus validating the methodology on a wide range of fracture apertures (from 0.25 mm to 16 mm). Going back to Figure 9, we can easily explain the deviation between the theoretical predictions and experimental observations when the fracture thickness becomes very small. For very thin fractures, the reflection coefficients are very small and the reflected GPR waves are so weak (Figure 8) that the picking becomes more sensitive to the noise, resulting in less accurate amplitudes. From Figure 9, we can also observe that for very thin fractures, amplitudes are more sensitive to thickness variations, i.e., the derivative of the amplitude versus thickness graph is larger than elsewhere. From this comment, we can derive an important recommendation for a successful use of GPR to perform the quality control of resin injections. If GPR measurements after resin injections are not exactly repeated at the same locations of the measurements performed before injections, the observed amplitude variations might be generated not only by injections but also by thickness differences at the two different locations. Thus, erroneous conclusions might result from the quality control especially where the observed fractures are particularly thin.