esa-sceva/satcom-chunk-collection huggingface dataset:
# Policies as Drivers for Circular Economy in the Construction Sector in the Nordics
### The European Policy Framework  
The basic principles and general ambitions for CE in the construction sector are defined in the European CE policy framework [22; 23; 24]. EU legislation and policies are transposed into national legislation and policies, thus forming the basis for the CE policy framework in EU Member States. EU policies set the framework both for recycling targets for waste [22] and for market demand for secondary materials [23].  
The first Circular Economy Action Plan (CEAP) of the EU defines construction and demolition as a priority area in the EU for closing the material loops and promoting a CE [24]. The second CEAP of the EU calls for improved recyclability, but also for the use of secondary materials in products [23]. The ambition for increased recycling is transposed into the revised Waste Framework Directive (WFD 2008/98/EC, amended 2018/851) by setting a mandatory target for the recovery of C&DW of 70 per cent as of 2020 [22].  
The European Green Deal lays down the key framework for the policies of the construction sector [25], and the Renovation Wave states key initiatives to, e.g., increase reuse and recycling in the construction sector [26]. Furthermore, the second CEAP presents initiatives to propose minimum mandatory green public procurement (GPP) criteria as a means to enhance circularity in the EU, as well as key principles for the construction sector to increase material efficiency and promote CE [23].  
Aiming for sustainability in the building sector may increase recycling, as materials with recycled content are often given credit in environmental rating protocols. Examples of well-established protocols are: Level(s) from the European Commission [27], the Building Research Establishment Environmental Assessment Method (BREEAM) from the UK's BRE [28], and the US Green Building Council's Leadership in Energy and Environmental Design (LEED) [29]. The protocols can be used by investors, designers, general contractors and real estate operators for proving the sustainability of a building.  
Achieving CE targets in waste management is often measured based on the recycling rate, which is calculated as the ratio of recycled waste to the generated waste [30; 31]. There is no distinction between preparation for reuse, high-grade recycling, and downcycling (including backfilling) [30]. The Waste Framework Directive sets a target of recycling 70% (by weight) of non-hazardous construction and demolition waste [22]. By 2024, the Commission shall consider material-specific targets for key streams of C&DW [22]. A recycling target for an entire industry creates incentives to recycle materials that have a large impact on the recycling rate and risks reducing the incentives to recycle materials that have a small impact but may be more valuable [21].
# ChaMTeC: CHAnnel Mixing and Temporal Convolution Network for Time-Series Anomaly Detection
## 2 Literature Review
DGHL [25] (Deep Generative model with Hierarchical Latent) introduces a hierarchical latent space representation using convolutional networks, enhancing the model's ability to capture complex patterns in time-series data. This hierarchical approach allows for the modeling of both global and local patterns, which is essential for effective anomaly detection in multi-dimensional time series. BEATGAN [26] is another GAN-based reconstruction method. MTAD-GAT [10] combines forecasting and reconstruction-based networks, leveraging both outputs for anomaly detection. This dual approach not only enhances the robustness of the detection mechanism but also provides a comprehensive view of the data's temporal dynamics. Recent advancements have also introduced attention mechanisms into anomaly detection frameworks. AnomalyTransformer [5] introduces an Anomaly-Attention mechanism to compute association discrepancies, focusing on differences between normal and anomalous patterns. This attention mechanism allows the model to dynamically focus on the most relevant features of the data, improving the detection of subtle anomalies that may be overlooked by traditional methods. DCDetector [7] employs contrastive learning with a multi-scale dual attention model to enhance anomaly detection capabilities, demonstrating the effectiveness of attention-based approaches in this context. By leveraging contrastive learning, DCDetector can better differentiate between normal and anomalous patterns, leading to improved detection performance. AE-FAR [27] (Autoencoder with Feedback Attention Reconstruction) integrates the capabilities of autoencoders and Recurrent Neural Networks (RNNs) with a feedback mechanism driven by reconstruction error. The method leverages autoencoders to learn compact representations of normal data and employs RNNs to capture temporal dependencies within time-series data. A key innovation of AE-FAR is its feedback mechanism, which utilizes the Mean Squared Error (MSE) to iteratively refine the reconstruction process. This iterative refinement process allows AE-FAR to adaptively improve its anomaly detection capabilities over time, making it particularly effective in dynamic environments where data characteristics may change. As a result, advances in time-series anomaly detection reflect the rich interplay between classical methods and modern deep learning techniques. The integration of various methodologies, including clustering, density estimation, and advanced neural network architectures, has led to more robust and effective systems for identifying anomalies. As the field continues to evolve, ongoing research will likely focus on improving these methodologies and exploring new approaches to improve anomaly detection in complex time-series data. Future directions may include exploring hybrid models that combine the strengthsof various techniques and applying transfer learning to improve performance in low-data scenarios.
## 2 Initial Cell Search Procedure  
Cell search is the procedure where the mobile devices; i.e., user equipment (UE), search for a BS to establish an initial connection or initiate potential handover. The cell search procedure enables the UEs to obtain the identity of the BS and estimate the frame timing of the identified cell. Generally, the number of physical layer cell identities is sufficiently large and varies between different generations of cellular networks. For example, 3 GPP LTE cellular networks support 510 different cell identities, which are divided into three groups with 170 identities per group [20]. For the cell search procedure, each base station periodically broadcasts Primary Synchronization Signal (PSS) and Secondary Synchronization Signal (SSS). The UE first searches for the presence of the PSS by scanning various frequency bands over the interested region. Detection of PSS signals at the UEs is implemented by performing the matched filtering between the received signal and the referenced PSS signal. If the matched filter output is maximized, the UEs will know the timing of the BS with a high probability. The frequency offset of the BS is also obtained by the UEs based on a frequency locked mechanism. Once the UE detects the PSS signal, the UE can search for the SSS signal. After the PSS and SSS are detected, the UE will be able to obtain the cell identity and proceed to decode the BS broadcast channels for further information about the BS.
## 1 Introduction
With the above advantages and the technological advances made in eye-tracking software development, the eye-tracking method has become increasingly popular in the fields of consumer research and marketing in recent years as a means of exploring the ways in which consumers view visual stimuli in real-world conditions [16]. In this context, many studies have utilized this method to examine customers' attention to marketing stimuli, such as products or product packages [17; 18; 19; 20; 21; 22; 23], print advertisements [24; 25; 26; 27; 28; 29], and online advertisements [30; 31; 32; 33; 34; 35], evidencing that it is a valid and efficient method. Therefore, by using the eye-tracking technique, the present study mainly aims to investigate how the content elements of online banner ads influence customers' attention. Additionally, the paper examines the effects of gender, discount rate and brand familiarity on the visual attention to banner elements. In order to achieve these objectives, we selected banner ads from the apparel category, because the goods in this category were the most popular ones purchased online in the EU in 2019 according to a recent report of individuals' e-commerce statistics [36]. Online banner ads in this field contain a variety of different contents, which are tailored to retain effectiveness and draw customers' visual attention. However, [PERSON] and [PERSON] [37] found that there are three main elements of banner ads, namely brand, pictorial and text elements, which have remarkable influences on visual attention to advertisements. In this respect, the content elements of banner ads--which are image (as a pictorial element), discount information (as a text element), and brand--were considered, and eight distinct online banner ads comprised of these three components were used in this study. Experiments were conducted in order to measure the participants' attention using eye-tracking technology. The eye-tracking data gathered from the banner ads' exposure to the participants were analyzed through statistical tests, and the results were interpreted. This research makes the following contributions. First, the present study further extends earlier work by examining the impact of the content elements of online banner ads on visual attention. Second, this paper explores the effects of gender, discount rate and brand familiarity on visual attention to banner elements. Third, it serves a valuable reference for researchers in designing eye-tracking experiments and data analysis for the investigation of online banner ads. Finally, this study provides marketers and advertisers with guidelines on the optimization of the content elements of banner ads in order to design effective online banner ads, which lead to more visual attention.
## 3 Case Study
### Analysis of Influence Intensity
#### 3.3.1 Influence Intensity Analysis from the Perspective of the Whole Network  
As mentioned in Section 3.2, the number of stations that were affected by the disruption was the highest at 8:00-8:15 a.m. In order to compare the influence intensity of passenger flow at different stations, the upper bound of the tap-in passenger flow in Equation (2) was replaced with the mean value of the tap-in passenger flow for a certain period at a certain station. Then, the influence intensity of passenger flow at all stations at 8:00-8:15 a.m. was calculated, and the results of the top 15 stations according to the value of influence intensity are shown in Table 2.
## 3 Simulation of Thermal Processes in the Food Industry Using CFD
### Evaporation Processes  
Evaporation plays a vital role in concentrating liquid food products by removing moisture, which in turn improves shelf life and intensifies flavour. CFD simulations of the evaporation process model both the heat and mass transfer mechanisms involved, including convection, conduction, and latent heat transfer. The governing equations typically involve the energy balance equation for latent heat flux and the mass transfer equation that describes the rate of evaporation [97]. The energy balance equation in the context of evaporation can be expressed as (Equation (11)):  
\[\rho C_{p}\bigg{(}\frac{\partial T}{\partial t}+\vv{v}\times\
abla T\bigg{)}= \
abla\times(k\
abla T)+L\frac{\partial Y}{\partial t} \tag{11}\]  
where \(L\) is the latent heat of vaporization, and \(Y\) is the mass fraction of vapor. The mass transfer equation governing the rate of evaporation is given by (Equation (12)):  
\[\frac{\partial C}{\partial t}+\vv{v}\times\
abla C=\
abla\times(D\
abla C) \tag{12}\]  
where \(C\) represents the concentration of the evaporating substance, and \(D\) is the mass diffusion coefficient. Understanding the interactions between the evaporating liquid and the surrounding environment is essential, as variations in temperature, pressure, and airflow significantly influence evaporation rates and, consequently, the concentration of flavours and bioactive compounds in the product. CFD simulations frequently incorporate turbulence models, such as RANS, to predict the effects of air velocity and turbulence on the mass transfer process, which directly impacts the efficiency of the evaporation and the final product characteristics [98]. Despite the complexity of food structures and the nonlinear behavior of fluid systems, the application of CFD in modeling extraction and evaporation processes offers substantial advantages. CFD provides a detailed framework for visualizing flow patterns, temperature distributions, and concentration profiles within the food product. This level of detail enables the identification of optimal processing conditions, resulting in enhanced product quality, reduced energy consumption, and the minimized loss of valuable compounds. Moreover, CFD simulations allow for multi-scale modeling, accounting for the microscale (for example, solvent diffusion in the food matrix) as well as macroscale effects (for example, overall fluid flow and temperature distribution in the extraction or evaporation chamber). The ability to simulate these processes at various scales significantly improves the predictive capabilities of CFD models, providing a comprehensive understanding of the underlying mechanisms. The applications of CFD for the analysis of foods thermal processes have been presented in Table 2.
## 2 Experiment
### Time-Resolved Experiment (Femtosecond Pump-Probe Spectroscopy)  
The optical system is shown in Figure 3. After exciting the surface plasmons of gold at the air-metal interface by the pump light incident from the prism side, the adjustably delayed probe light was applied to the same position from the air side, and the change in the reflectance was detected with the multichannel lock-in amplifier. By sweeping the delay time of the probe light with respect to the pump light, time evolution of the reflection spectral change can be obtained. For the experiment on gold, the output light from the femtosecond laser (Tsunami Spifire Pro, center wavelength of 800 nm, pulse duration of 100 fs and pulse depletion rate of 5 kHz) was divided by a beam splitter into two, one for the pump and the other for the probe. The latter was focused into a sapphire plate to generate white-light continuum ranging from 470-630 nm with temporal duration of 100 fs to be used for broad-band monitoring. The incidence angle of the pump from the prism side was 45 degrees so that the pump energy was resonant with the plasmon in gold [17], and that of the probe from air was also 45 degrees. The wavelength resolution of time-resolved spectra was about 1.5 nm.
# Discrete Integral Optimal Controller for Quadrotor Attitude Stabilization: Experimental Results
## 3 Experimental Platform  
As shown in Figure 3, the experimental platform setup uses an Optitrack Flex 3 vision system to compute the UAV's position and orientation. The UAV has 6 markers, which are looked at by 12 cameras; the information generated by the cameras is sent to Motive software using the USB protocol. This information is sent to Visual C++ software from Motive software using sockets; it allows to compute in C++ language the integral control laws applied to stabilize the UAV at hover. The PC and UAV are communicated via RS-232 wireless protocol through two Xbee Pro S1 modems at 38,400 bits per second. Furthermore, a Futaba RF radio control is used for manual wireless control of the quadcopter, acting as emergency control if a risk situation occurs in the UAV.  
Figures 3 and 4 show how a Parrot's frame was used to build the platform's quadrotor. The UAV has an embedded Rabbit module RCM4300, one inertial measurement unit (IMU)3 DM-GX3 from MicroStrain, one radio receiver and one Xbee Pro S1 modem.  
The vision system obtains the position and orientation of the UAV: \(x(k)\), \(y(k)\), \(z(k)\), \(\theta(k)\), \(\phi(k)\) and \(\psi(k)\). The velocity of each variable is estimated using Visual C++. Integral control laws are computed in the same way: \(u_{PC}(k)\) (height control computed by the PC), \(\tau_{\phi PC}(k)\) (roll control computed by the PC) and \(\tau_{\phi PC}(k)\) (pitch control computed by the PC); these control signals are sent from the PC to the Rabbit microcontroller via RS232 wireless protocol using Xbee modems. The Futaba RF radio generates calibration signals \(u_{c}(k)\), \(\tau_{\phi c}(k)\), \(\tau_{\phi c}(k)\) and \(\tau_{\phi c}(k)\); the first one gives an offset of the \(z(k)\) position to compensate for gravity; the last ones give orientation offsets of the UAV's \(\theta(k)\), \(\phi(k)\) and \(\psi(k)\), respectively. In this way, the UAV's orientation and height position are calibrated. RF radio signals are fixed and are only used to compensate for measurement errors caused by the IMU or the vision system.  
The UAV radio receiver sends the calibration signals to the Rabbit microcontroller using the \(I^{2}C\) protocol. The Rabbit microcontroller reads angular orientation from the IMU (\(\theta_{IMU}(k)\), \(\phi_{IMU}(k)\) and \(\psi_{IMU}(k)\)) by the \(I^{2}C\) protocol. The information generated by the IMU produces computer orientation control signals in a direct way because noise affects the vision system, causing errors in orientation measurement to the PC, so control signals used for orientation are computed directly in the microcontroller. PC control signals, radio calibration signals and the control signals computed in the microcontroller are added to the calculation of \(u(k)\), \(\tau_{\phi}(k)\), \(\tau_{\theta}(k)\) and \(\tau_{\theta}(k)\), which allow generation of the PWM signals applied to the four motors of the UAV (see Figure 5).  
Figure 4: Quadrotor vehicle.  
Figure 3: Experimental platform scheme.
# Research on Passive Ranging Technology of Moving Ship Based on Vertical Hydrophone Array
## 5 Conclusions  
In this paper, we proposed an algorithm to estimate the range of a moving ship. First, the DEMON spectrum analysis method is adopted to extract the low-frequency line spectrum components. Based on the sound intensity of the pressure difference, the extracted single-frequency signal at the given time interval interferes and forms the interference fringes in the plane of time and time interval. Then, the radial velocity of the moving source is derived from the period of interference oscillations. By fitting the time, velocity and range of the CPA point, we estimate the range from the source to the receiving  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Element** & **\#1** & **\#2** & **\#3** & **\#4** & **\#5** & **\#6** & **\#7** & **\#8** & **\#9** \\ \hline RMSE & 0.0431 & 0.0208 & 0.0188 & 0.0212 & 0.0199 & 0.0225 & 0.1070 & 0.0393 & 0.0150 \\ \hline
**Element** & **\#10** & **\#11** & **\#12** & **\#13** & **\#14** & **\#15** & **\#16** & **DF** \\ \hline RMSE & 0.1209 & 0.0012 & \(8.4\times 10^{-4}\) & 1.8572 & 0.0553 & \(1.2\times 10^{-5}\) & \(1.7\times 10^{-4}\) & \(4.9\times 10^{-4}\) \\ \hline \end{tabular}
\end{table}
Table 1: RMSE of each element and the RMSE after fusion.  
hydrophone over time. Finally, range estimation results of multiple elements of the vertical line array are fused to obtain a more accurate estimation result. The simulation and deep-sea experiment analysis all verify that this method can estimate the range effectively. Moreover, the estimation accuracy is significantly improved since it fuses all the estimation results of each element and eliminates the impact of inaccurate results. The relative mean square error of the fusion result is lower than that of most hydrophones. In addition, this method does not require prior knowledge of marine environmental parameters and sound field modelling and thus demonstrates good universality. This method provides a new idea for the passive ranging of moving ships.  
Conceptualization and methodology, Y.L. and Y.C.; validation, Y.L. and Y.Z.; formal analysis, Y.L., Y.Z., and X.Z.; investigation, M.W.; project administration, Z.M.; writing--original draft preparation, Y.L.; writing--review and editing, Y.C. and Z.M.; funding acquisition, Z.M. and Y.C. All authors have read and agreed to the published version of the manuscript. This work is supported by National Key R&D Program of China under Grant No. 2017 YFB0405503, and the National Natural Science Foundation of China under Grant No. 61901488, 61705263, 61705262, 61775238, 61605244. The authors declare no conflict of interest.
# A 3-D Metal-Printed Dual-Polarized Ridged Waveguide Slot Array Antenna for X-Band Applications
## 3 Antenna Design and Analysis Procedure
### Design of Feeding Structure  
For the VP array, central feeding is realized through a coaxial-to-RWG transition connected to a T-function power divider as shown in Figure 8, where Figure 8a is a two-way equal-split divider that offers the symmetrical left-half and right-half of the radiated waveguide and uses a tuned H-shaped coupling slot structure to ensure a signal phase difference of 180\({}^{\circ}\) over broadband. The discontinuous impedance induced at the junction of the power divider is matched to a metal step on the ridge of the feeding RWG.  
The ridge height is properly increased in the proximity of the coupling slot to increase the equivalent capacity of the transmission line and, thus, reduce its impedance. The results of the power divider in Figure 8b show good impedance matching \(|S_{11}|<-22\) dB, and the out-of-phase simulate is 180\({}^{\circ}\) for the two output ports achieved over the full design frequency band of 9.4-10.2 GHz. Then, a coaxial-to-RWG transition is applied to use the SMA connector feed for the VP array as shown in Figure 8c. The RWG is directly excited by the coaxial probe through a transition with a wideband short circuit and an impedance matcher with two irises symmetrically across the center of the coaxial probe above the ridge.  
A wideband short circuit is created when the coaxial probe is attached directly to the ceiling of the RWG, reducing the reflection of incident waves to the input port. By tuning the position of the coaxial probe, combined with the appropriate size and distance of the iris pair, the bandwidth efficiency of the transition is enhanced. Figure 8d shows the results of the transition in the design frequency band of 9.4-10.2 GHz, and matching impedance is achieved with \(|S_{11}|<-25\) dB.  
For the HP arrays, a feed is applied directly from the coaxial-to-RWG transition with an SMA connector as shown in Figure 9. The coaxial probe is attached to the underside of the ridge in the radiated RWG as shown in Figure 9a. The position of the coaxial probe is placed in the center of the radiated waveguide to ensure the equal amplitude and phase for the two sides of the subarray. The V-shaped slot radiation elements in the left-half and right-half of the waveguide are placed in the same direction.  
A pair of symmetric irises on either side of the coaxial probe is added to expand the operating bandwidth of the transition. Figure 9b shows that the feed of the HP array achieved good impedance matching \(|S_{11}|<-25\) dB with equal amplitude and was in phase at the two output ports over the design frequency band of 9.4-10.2 GHz. The simple ridged waveguide slot array feed structure provided good operating performance with high mechanical strength, and low voltage around the feed to avoid electrical breakdown.  
Figure 7: Parameter extraction curves of the resonant slot cutting depth and inclined angle versus the resonant slot conductance for the HP array.