esa-sceva/satcom-chunk-collection huggingface dataset:
# Situational Awareness: Mapping Interference Sources in Real-Time Using a Smartphone App
## 4 In-Field Interference Detection Modules  
According to the theoretical background in Section 2, the best way to detect the interference is to monitor its effects along the receiving chain. For this reason, the detection module of NGeneApp includes so far two monitoring points: the first one on the pre-correlation samples, based on PSD evaluations, the second one on the post-correlation samples, based on the Chi-square GoF statistical test [43]. The two techniques are briefly described hereafter.
## 3 Results and Discussion  
### Radioactivity Analysis  
The specific activity of \({}^{40}\)K in the investigated samples, as calculated by Equation (1), is reported in Table 2. It was found to be: (1.76 \(\pm\) 0.16) Bq/g for Cyprus black, (0.89 \(\pm\) 0.07) Bq/g for Himalayan pink, (0.79 \(\pm\) 0.07) Bq/g for Hawaii red, (1.06 \(\pm\) 0.09) Bq/g for iodized, (12.1 \(\pm\) 1.1) Bq/g for hyposodic iodized, (1.47 \(\pm\) 0.12) Bq/g for Maldon smoked sea, (0.93 \(\pm\) 0.08) Bq/g for common sea, (1.51 \(\pm\) 0.13) Bq/g for Breton sea and (4.55 \(\pm\) 0.32) Bq/g for Persia blue, respectively.  
In order to well understand these experimental results, we must think in terms of the potassium content, as an ion or compound, in the analyzed samples. The highest value of \({}^{40}\)K activity concentration refers to hyposodic iodized salt, which is characterized by a low sodium and a high potassium chloride, potassium citrate and potassium iodate total content (31.3%).  
For Cyprus black, Himalayan pink, Hawaii red, iodized, Maldon smoked sea, common sea and Breton sea food salt samples, \({}^{40}\)K experimental specific activity is of about 1-2 Bq/g. This is due to their similar chemical composition in terms of potassium content (K is present in traces). The Persia blue sample has instead a \({}^{40}\)K activity concentration higher than the previous ones, because in this food salt there is a significant presence of potassium chloride (about 12%).  
Regarding \({}^{137}\)Cs, its activity concentration in all analyzed samples is lower than the minimum detectable activity value, as reported in Table 1, thus excluding an anthropogenic radioactive contamination of the investigated samples.
## Appendix A  
Figure 11 shows the correlation between Average CQI and higher order modulation. Each dot represents a single cell calculated over the duration of one hour during the day. Cells are segmented per operating frequency band; each band is represented as a different color. The impact of frequency can be clearly seen, where higher frequencies, for example, both carriers on 1800 MHz which are represented by red and green, tend to have a higher average CQI, while a lower frequencies, such as 800 MHz represented with blue, have lower average CQI values, which is in line with the theoretical expectations described in Section 4.  
On the Figure A2, given at the top of the next page, the correlation between the Heavy Data Factor and Average CQI is analyzed. As discussed earlier, the main motivation for Heavy Data Factors' introduction was to segment cells with a traffic profile of more network demanding services, like video streaming or FTP download. For these services, the amount of data transmitted during TTI is higher. The only way to detect such behavior is to observe the average data payload per TTI. On the other hand, such an indicator is clearly driven, not just by offered traffic, but with achieved spectral efficiency on top. In line with expectations, cells with higher operating frequencies perform better in terms of average CQI, followed by the Heavy data factor.  
The last two Figures A3 and A4, provide the correlation between Average CQI and PRB utilization in neighboring cells, captured by both metrics discussed earlier. In both cases, the trend of Average CQI decreasing as network utilization increases may be noticed. This is in line with expectations and may be explained by inter system interference increases. Lower frequencies perform worse with the same network load, due to radio propagation characteristics.
## 3 Sliding Mode Path following and Control Allocation  
In this section, we first propose the sliding mode-based attitude and position following control via torque and force in (23). Then, we present the control allocation from the control torque and force to the speed and tilt angle of four rotors. Figure 2 illustrates the TRQ control scheme.  
Figure 2: The TRQ control scheme.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 1 Introduction
Furthermore, quantization errors, watermark errors, and rounding errors to represent the differences between the cover image and the watermarked image can reduce the compensation information required to restore the cover image. [PERSON] et al. [12] proposed a new lossless watermarking method for cover images that effectively embeds the watermark into low-order Zernike moments and embeds the distortion caused by the robust watermarking as compensation information to be reversibly hidden in the watermarked image. Traditional robust lossless watermarking algorithms possess certain robustness and visual quality. However, most of them are designed to address specific types of image attacks and lack scalability. They become inadequate when the watermarked image is subjected to new noise attacks. In contrast, deep learning-based robust watermarking algorithms achieve robustness against such attacks by training on existing models. This paper proposes a two-stage robust lossless watermarking algorithm for DWI images based on deep learning to address the issues above. In the first stage, a robust watermarking framework combining wavelet transform and Transformer is employed to accurately extract the watermark information for copyright authentication, achieving robustness against various types of image attacks. Additionally, the reconstructed watermarked image exhibits excellent visual quality. In the second stage, reversible watermarking techniques embed the compensation information and image hash value into the watermarked image. The image hash value is utilized to detect whether the protected image has been attacked, and under the condition of no attack, the compensation information allows for lossless recovery of the cover image, ensuring the integrity of medical image data. The major contributions of this study are as follows. 1. We propose a two-stage robust lossless DWI watermarking framework based on deep learning in the wavelet domain. Separate branching networks are designed for different frequency bands, which can effectively reduce inter-frequency conflicts and improve the image reconstruction quality and robustness of watermarking. 2. In the watermark embedding network, we design a frequency-enhanced attention module using both low- and high-frequency information, and this approach effectively integrates multi-frequency features to improve the reconstruction performance. 3. Compared with other algorithms our proposed algorithm achieves better image reconstruction quality and watermark robustness. The remainder of this paper is structured as follows. Section 2 introduces the related work. In Section 3, the proposed method is described. In Section 4, the experiments are described, and the experimental results are discussed. Finally, the conclusions of this paper are drawn in Section 5.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 2 Critical Research Agenda
### Inverters as VRE Interfaces with Grids  
The technological differences between power electronics converters and synchronous generators mean that future power grids with high proportions of VRE require fundamental changes in their modeling, analysis, control, and protection schemes [38; 39; 40]. Modeling tools for inertia-less grids require more detail and a higher time resolution than those available today. The real-time stability and controllability of different types of VER inverters (e.g., grid-forming, -supporting, and -following inverters) need to be analyzed more accurately for grids with high shares of VRE. New approaches toward frequency and voltage regulation; black-start capability; and distributed control of generation, storage, and load assets are required. Traditional protection schemes for power grids with high proportions of synchronous generators are subject to significant modification given the different characteristics of the short-circuit currents in VRE-dominated power grids.
## 2 Mathematical and Physical Models
### Vibrational-Electronic Energy Equation Source Term  
The vibrational-electronic energy source term \(\omega_{ve}\), which appears in Equation (6), is obtained by adding the vibrational energy source term and the electronic energy source term  
\[\omega_{ve}=\omega_{v}+\omega_{e} \tag{13}\]  
The vibrational energy source term \(\omega_{v}\) considers the following three terms: (i) the energy exchange between translational and vibrational modes, (ii) the energy exchange between electronic and vibrational modes and (iii) the vibration energy gained and lost due to molecular dissociation and atomic recombination in the cell [19]. Thus,  
\[\omega_{ve}=\sum_{s=\text{ mol.}}\frac{\rho_{s}}{M_{s}}\frac{e_{ss}^{*}-e_{ss}}{ \tau_{bs}}+\sum_{s=\text{ mol.}}\frac{\rho_{s}}{M_{s}}\frac{e_{ss}^{**}-e_{ws}}{\tau_{cs}}+ \sum_{s=\text{ mol.}}\omega_{s}D_{s} \tag{14}\]  
where \(e_{ss},e_{ps}^{*},e_{ps}^{**}\) are vibrational energy perunit mass of species \(s\), the vibrational energy perunit mass of species \(s\) evaluated at temperature \(T\) and electronic temperature \(T_{e}\) separately; \(\tau_{cs}\), \(\tau_{cs}\) are electronic-vibrational and translational-vibrational energy relaxation time for molecular species \(s\). The average vibrational energy perunit mass of molecule species \(s\)\(D_{s}\) is created or destroyed at rate \(\omega_{s}\).  
And the electronic energy source term \(\omega_{e}\) considers the following three terms: (i) the energy exchange between heavy particles and electrons in translational modes, (ii) the energy exchange between vibration and electronic modes and (iii) the electronic energy gain and loss due to forced ionization reactions. Thus,  
\[\omega_{e}=2\rho_{e}\cdot\frac{3}{2}R(T-T_{\rm{re}})\sum_{s\
eq e}\frac{v_{es}} {M_{s}}-\sum_{s=\rm{dia}}\frac{\rho_{s}}{M_{s}}\frac{e_{vs}^{*}-e_{vs}}{\tau_{ es}}-(R_{\rm{O}}I_{\rm{O}}+R_{\rm{N}}I_{\rm{N}}) \tag{15}\]  
where \(v_{es}\) is effective collision frequency between the electron and the heavy particle of the \(s\) component; \(I_{\rm{O}}\) and \(I_{\rm{N}}\) are molar first-order ionization energy of oxygen (O) and nitrogen (N) atoms, respectively; \(R_{\rm{O}}\) and \(R_{\rm{N}}\) represent the positive reaction rates of the O and N forced ionization reactions, respectively.
## 3 Results and Discussion
### Lithiation
Indeed, this capacity value is even less than the capacity of pristine graphene. It can be explained by the fact that a large amount of silicon \"screens\" lithium, whose ions can be intercalated only on the surface of silicon clusters and on some free areas of pillared graphene (Figure 3c). Comparison with other types of carbon frameworks based on graphene and CNT shows the advantages of using a pillared graphene as an anode material due to mechanical strength and porosity of the atomic mesh. For example, [12] specified the capacity value equal to 1147 mAh:g\({}^{-1}\) for the case of a cell with a wrapped graphene sheet. During intercalation of lithium ions, the graphene sheet opened under the action of silicon that increased its volume. Application of large-diameter CNT as a frame with silicon located inside the tube resulted the capacity at the level of 1374 mAh:g\({}^{-1}\). Figure 3: Lithiation of anode material on the example of model I with: (**a**) with 18 clusters of Si16; (**b**) with 36 clusters of Si16; (**c**) with 59 clusters of Si16. As the Table 1, Table 2 provides detailed information for models II with different mass fraction of silicon. Reasoning similarly to model I, we can conclude that composites with the silicon fraction of \(\
eg\)9-18.4 wt.% would be the most effective. In this range, the simultaneous change in the LiC and Li:Si ratios provides the biggest capture of lithium ions by carbon and silicon atoms. The optimal mass fraction of silicon is \(\sim\)12-18.4 wt.%, since the maximum capacity values were observed in this case. Figure 4 shows the change in the capacity of a battery based on the composite versus the mass fraction of silicon. The optimal mass fraction of silicon for both models of the composite was in the range of 12-18 wt.%. It can be seen from the graphs in Figure 4 that when the mass fraction of silicon reaches 30 wt.% and continues to increase, the capacity begins to decrease rapidly that drops battery efficiency. It is also confirmed by the data in the table for the Li:
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 6 Implementations and Use Cases
### New Mobile Edge Applications
#### 6.2.3 Smart Cities  
In urban settings, 5G mobile edge computing can be used to enhance smart city initiatives globally by providing points for computation and data storage relevant to local events and populations. By harnessing the power of cloud computing and Internet connectivity for large-scale IoT in cities, smart cities can provide urban services, such as electricity grids, transportation systems, and emergency response through deep learning in mobile edge computing. Cities can use mobile edge computing to manage energy consumption in growing urban areas, based on energy profiles for common activities and real-time demand [119]. In addition, placing DL at the mobile edge can also promote public safety and policing efforts in large urban areas through light-weight computer vision systems [120; 121].
## 5 Use Case of 6G-OPENSEC-Security
### Expected Results  
As mentioned at the beginning of this section, the proposed SCL model aims to automate the security processes within deployed services to minimise human intervention in these procedures, having significant outcomes. However, since it belongs to a project that is being developed towards standardising security in upcoming network environments, it is essential to note that not only is it defined with automation in mind (following ZSM's principles), but also flexibility and dynamism. Furthermore, there are several expected advantages to putting the SCL into practice. First, automating security procedures and reducing human error should significantly enhance the overall security posture of network settings. This strengthens resilience against new cyber threats by enabling quicker reaction times to security problems. Because of its service-based security architecture, the SCL's flexibility makes it easy to pivot and respond to a wide range of security breaches and attacks on different kinds of services. Additionally, the policies that are integrated into the service and the overall project have two functions: they provide enforceable standards that services must follow in addition to offering suggestions. As a result, it is intended that the Security Closed-Loop would seamlessly integrate into the wide range of services that the upcoming generation of mobile networks will offer. For these reasons, it's expected a seamless integration of the SCL in different kinds of services that this next generation of mobile networks may require, as well as a streamlined method of securing and continuously maintaining security from instantiation to termination of services.
## 3 3D BEM-DMST Model: Basic Formulation and High-Order Submodels
### Wind Shear  
Realistic atmospheric wind shear profile exhibits wind direction variations throughout the atmospheric boundary layer (ABL) with significant change in turbulence, playing a paramount role in the characterisation of inflow conditions. Furthermore wind shear depends on numerous factors such as stratification, temperature, Coriolis force and many others. Hence, high and medium fidelity (Actuator Line Method) codes are particularly suited to address the problem of the effects on wind turbine performance and wake propagation or breakup [49; 50], particularly when modelling wind shear as in [51]. The engineering codes can deal with simplified wind shear models consisting in power law profiles: in such a way only mean wind speed change is retained. The so-called Hellman's formulation for wind shear profile reported in Paraschivoiu [4] employs the power law defined in Equation (18) where exponent \(\alpha_{w}\) describes the typology of atmospheric boundary layer, \(V_{\infty}\) is deemed the mean flow velocity at equatorial plane which blows at \(z_{EQ}\) level from the ground, \(z_{i}\) is the local vertical coordinate and \(V_{\infty_{0}}\) is the wind speed as function of z-coordinate (see Figure 1a).  
\[\frac{V_{\infty_{i}}}{V_{\infty}}=\left(\frac{z_{EQ}+z_{i}}{z_{EQ}}\right)^{ \alpha_{w}} \tag{18}\]
## 2 The Proposed DLSODC-GWM Technique  
In this study, a new DLSODC-GWM technique has been developed for waste management systems in order to effectually detect and classify small garbage waste objects. The DLSODC-GWM technique involves three distinct subprocesses, namely, IRD object recognition, hyperparameter tuning, and FLNN-based object classification. During the object detection process, the AOA is applied to optimally select the hyperparameter values of the IRD model and thereby improve the detection efficiency. The detailed workings of these three modules are elaborated on in the following.
## 4 Discussion
### Unanswered Questions and Future Research  
Future studies can focus on automated fracture detection with tiny fractures, improving the algorithm to detect, for example, the corners of fractured bones to improve the detection sensitivity of the system. Furthermore, to enhance the network's performance,a stage of preprocessing of the CT images could be introduced to remove the region of no interest for the prediction. Another interesting approach could be the investigation of the combination of deep learning models with radiomics [32]. In fact, radiomics [33] is a method for extracting a large amount of advanced quantitative imaging features from radiographic medical images obtained with computed tomography, using data-characterization algorithms. Radiomic data could be integrated into predictive models to hedge against the risk of overfitting the deep learning approach. Another possibility is to use a local feature detector as the speeded-up robust features (SURF) to improve the system's performance. In their work [34], the authors propose a computer-assisted method for automated classification and detection of calcaneus fracture locations in CT images using a deep learning algorithm. In particular, they compared two types of CNNs, a Residual network (ResNet) and a visual geometry group (VGG). Furthermore, the speeded-up robust features (SURF) method was used to determine the exact location and the type of fracture in calcaneal CT scans.
# Analysis and Identification of Nonlinear Acoustic Damping in Miniature Loudspeakers
## 4 Experimental Validation and Discussion
### Measurement Setup  
Before starting the identification process, the equivalent moving mass of the miniature loudspeaker with three different front covers was measured using Klippel R&D System (Klippel GmbH, Germany), as listed in Table 1, along with the nonlinear mechanical resistance. Pink noise signals with rated power were applied to the miniature loudspeaker, ensuring that the miniature loudspeaker worked under large signal condition. The voltage and current data were recorded at the terminals of the miniature loudspeaker and used for parameter identification. The acquisition device was a B&K PULSE (Bruel & Kjaer, Denmark), and the sampling rate was set to 65,536 Hz.
# A Probabilistic VDTN Routing Scheme Based on Hybrid Swarm-Based Approach
## 6 Experimental Tests and Discussed Results
This allows to reduce network resource utilization and minimize the number of dropped copies. This metric constitutes one of the advantages of probabilistic routing in VDTNs whose SCF vehicle selection is independent from geographic calculation of vehicles' position which shows the superiority of probabilistic approaches for this performance indicator. According to Figure 10, the swarm-VDTN does not drop bundle copies similarly to GeoSpray, while ProPHET suffer from increasing number of dropped copies gradually with time. This result is the consequence of the flooding performances and an indicator of the effectiveness of the proposed hybrid bio-inspired probabilistic-geographic approach. Figure 8: Overhead ratio. Figure 9: Average hop count.
# Train Speed Estimation from Track Structure Vibration Measurements
## 3 Procedures for Train Speed Estimation
### Train Speed Estimation from Acceleration Dynamic Response Signals
#### 3.1.2 Signal Feature Extraction  
The track structure undergoes significant deformation when bogies pass through sensors, which produce obvious peaks in vertical acceleration waveform. In ideal status, each peak in the signals reflects one passing moment of one bogie. However, the filtered signals may still oscillate within a small range in field tests. To screen the effective peak points, the threshold condition and minimum time difference condition are proposed in this paper.  
The threshold condition means that the amplitude of the possible effective peak needs to be greater than the critical value. It can effectively remove the peak points generating by the fluctuation while the amplitude of the signals is low. Assuming the maximum amplitude of the waveform is \(A_{\text{max}}\), the amplitude of the possible effective peak needs to be greater than \(\lambda A_{\text{max}}\). It is noted that \(\lambda\) is defined as the threshold condition coefficient and its value range is \([0,1]\).  
Based on the threshold condition, the minimum time difference condition is further presented to ensure that only one peak can be selected when one bogie passes through. Because the time difference of the adjacent bogies passing through the sensor is related to train length and speed, there is one minimum time difference between the adjacent effective peaks. When the maximum train speed at the test site is \(v_{\text{max}}\), and the minimum bogie spacing is \(l_{\text{min}}\), the minimum time difference parameter \(\Delta t\) should be meet the formula of \(\Delta t=l_{\text{min}}/v_{\text{max}}\).  
Figure 6: Diagrams of image treatment process: (**a**) the high-frequency \(D_{1}\) signal recombination waveform diagram; (**b**) the corrosive operated waveform diagram; (**c**) the gray vertical projection and the signal feature extraction results.  
According to the above conditions, the peaks of the pretreated dynamic response waveforms are screened as shown in Figure 6c. According to the results shown in Figure 6c, it can be seen that the information of the bogie passing through is effectively conveyed by the waveforms, and then the passing time of bogies can be identified.