esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems
## 3 Privacy Techniques
### Anonymization  
Anonymization is a privacy-preserving technique in an FRS that is used to protect users' data privacy by concealing their identities [107; 108]. The main idea of this technique is to mask users' identity information, such as user ID, username, and other user-related information, while still maintaining their data utility. This technique can protect users' identity information in the FRS while allowing the system to generate useful and accurate recommendations.  
There have been several studies that have utilized the anonymization technique in FRS, showing the effectiveness of the technique [109; 110; 111]. Anonymization is easy to implement, as it only requires masking the user's identity information. The limitations of using anonymization in FRSs include the potential for data leakage, as well as the risk of data distortion due to the masking of user identity information [112].
## 2 Background of FECG  
Fetal electrocardiogram (FECG) is a biomedical signal, see the example shown in Figure 1, that provides an electrical representation of fetal heart rate (FHR). It is composed of three parts: (i) the P-wave, (ii) the QRS-complex, which is associated with the contraction of the ventricles, and due to the magnitude of the R-wave it is extremely reliable, and (iii) the T-wave, which corresponds to the repolarization phase and follows each heart contraction [31, 32, 33, 34, 35, 36]. The delay associated to the R-R interval leads to the heartbeat's frequency and provides useful information about the heart condition. Morphologies of interest include the shape, size, and duration of individual and groups of FECG waveforms as well as the various ratios relating these quantities to each other [37, 38, 39, 40, 41, 42, 43, 44, 45].  
The FECG extracted from the maternal electrocardiogram (MECG) is exceedingly small, about 5 times less in amplitude compared to the MECG and is sometimes embedded in the noise signals [42, 43, 44, 45, 19, 46].  
As shown in Figure 2, the FECG signals are usually buried in noise and artefacts, such as interference noise, muscle contractions, instrumental noise, etc., which easily corrupt the FECG signal [47, 48, 49]. Therefore, signal processing is essential to remove the noise and artefacts to extract the actual FECG signal to analyze and determine the growth of the fetus [50].  
Figure 1: Key features of FECG [45] (Reprinted with permission from ref [45] [PERSON] (2012)).
###### Abstract  
GNSS-based applications are susceptible to different threats, including radio frequency interference. Ensuring that the new applications can be validated against the latest threats supports the wider adoption and success of GNSS in higher value markets. Therefore, the availability of standardized GNSS receiver testing procedures is central to developing the next generation of receiver technologies. The EU Horizon2020 research project STRIKE3 (Standardization of GNSS Threat reporting and Receiver testing through International Knowledge Exchange, Experimentation and Exploitation) proposed standardized test procedures to validate different categories of receivers against real-world interferences, detected at different monitoring sites. This paper describes the recorded interference signatures, their use in standardized test procedures, and analyzes the result for two categories of receivers, namely mass-market and professional grade. The result analysis in terms of well-defined receiver key performance indicators showed that performance of both receiver categories was degraded by the selected interference threats, although there was considerable difference in degree and nature of their impact.  
Global Navigation Satellite Systems (GNSS); radio frequency interference; receiver test standard +
Footnote †: journal: Sensors
## 4 Literature Review: Other Research on CWSN
### Common Control Channel in CR-WSNs  
In any type of CRN, a common control channel is necessary for spectrum sensing information sharing, transmitter-receiver handshake, neighbor discovery, channel access negotiation, clustering, topology change, routing information updates, emergency message broadcasting _etc._ Moreover, a common channel is used to send control messages to the neighbors to inform the state of the operation, and the destination for facilitating the continuous operation of the SUs without interruption.  
The common control channels can be classified into two types:  
1. Common control channel: A channel temporally and opportunistically used by SUs. This is common between at least two SUs. This is not dedicated only for the SUs' control message exchange. Therefore, PUs can use it anytime.
2. Dedicated common control channel: A dedicated channel used mainly for control packet exchange. This channel is common among all the SUs in the networks. This channel is considered as being free of PU interference and always available for SUs.  
A dedicated common control channel is essential for reliable communication in CRNs. Several methods can be used to obtain a common control channel: (a) Acquire or rent a dedicated band; (b) select a channel from the incumbent licensed band using a hopping sequence; (c) use an underlay approach, such as the UWB radio technology; and (d) use the ISM band. A common control channel can be saturated [93] under some network conditions. Therefore, non-dedicated common control channel based MAC protocols [46] are suggested.
## 5 Results
(**a**) CDFs over the lake at 8 m. (**b**) CDFs over the lake at 80 m.  
are presented in Figure 12b. In these environments, there is no significant difference in the estimated Doppler frequency for different speeds. The aircraft suffered from wind action throughout the course so that there was no control over the average drone speed. All measurement campaigns were affected by gusts of crosswinds. For instance, in the Mixed environment and over the Lake, the wind is in the opposite direction of the aircraft's flight. We had the inverse situation in the Caatinga region. As the theoretical Doppler frequency depends on the angle between the aircraft and the BS and the drone speed, its value varies even if we consider a unique drone speed. Table 9 shows the range (minimum and maximum values) of the theoretical Doppler frequency. Comparing these theoretical values to the estimated ones from the measurements (Figure 12), the results are within expectations. Figure 11: Estimation of CDFs for the Caatinga and Mixed environments. (**a**) CDFs for the Caatinga region. (**b**) CDFs for mixed region. Figure 10: Estimation of CDFs over the lake at the same speed. (**a**) CDFs over the lake at \(1\) km\(/\)h. (**b**) CDFs over the lake at \(3\) km\(/\)h.  
To estimate the drone's speed, the level crossing rate was calculated in relation to its power level and then divided by the theoretical level crossing rate, therefore obtaining \(f_{d}\) as a function of the signal strength. As the frequency used in communication is 915 MHz, its wavelength approximates 0.32 m. Using the equation \(\hat{\sigma}=\lambda\cdot\hat{f}_{d}/\cos(\theta)\) (Section 3.3.2), the speed is estimated. Figure 13a shows the estimated flight speed for the lake environment as a function of the angle \(\theta=0\)\({}^{\circ}\) (lowest speed). Figure 13b shows the speed estimation for the Caatinga and mixed environments. The same qualitative comments regarding the \(f_{d}\) estimation are valid for the velocity estimation. \begin{table}
\begin{tabular}{c c} \hline \hline
**UAV Speed (km/h)** &
\begin{tabular}{c} **Doppler Frequency** \\ **(Minimum–Maximum)** \\ \end{tabular} \\ \hline
1 & 0–0.86 Hz \\
3 & 0–2.6031 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Maximum and minimum variation of theoretical Doppler scattering. Figure 12: Doppler frequency estimation. (a) Doppler frequency for Lake region. (b) Doppler frequency for Caatinga and Mixed regions.
# A Method for 5G-ICN Seamless Mobility Support Based on Router Buffered Data
## 6 Conclusions  
This paper proposes a seamless mobility support method based on router buffered data (BDMM) to address the challenges of real-time business mobility support under the 5G-ICN converged network architecture. This method uses ICN identity-based routing to solve business continuity problems caused by network address changes. In addition, BDMM also uses the ICN router to temporarily buffer data during handover, reducing the loss of data packets and achieving seamless handover. In order to improve mobile users' QoE, we design a dynamic buffer resource allocation strategy (DBRAS). This strategy comprehensively considers factors such as the status of the mobile buffer, the transmission rate of the mobile data flow, and the business category of the mobile data flow, and instantly adjusts buffer resource allocation by sensing changes in different types of business traffic to optimize the loss in performance of mobile data flows in the overall network. Experimental results show that, under the guarantee of a low-latency deterministic Name Resolution System, the proposed method outperforms other seamless mobility support methods in multiple performance indicators, including average packet delay, weighted average packet loss rate, network overhead, etc. At the same time, our method also shows satisfactory performance in terms of average handover delay.  
In future work, we plan to further optimize the buffer resource allocation strategy, which can be combined with machine learning and artificial intelligence technologies to design more intelligent algorithms to predict future network traffic and business needs, and adjust buffer resource allocation accordingly. Additionally, we plan to conduct a field-test measurement that uses a Software-Defined Radio (SDR) device to simulate a serving Base Station (BS)/Base Transceiver Station (BTS)/Access Point (AP) and uses a smartphone, tablet, or other SDR device as the UE. We will simulate different types of mobility conditions in real-life scenarios and evaluate the performance of our method under these conditions. For example, we will thus seek to understand how network performance behaves under high-speed movement and changes in communication quality in NLOS environments with severe occlusion to further optimize the design of our method.  
Conceptualization, M.X. and R.H.; methodology, M.X. and R.H.; software, M.X.; writing--original draft preparation, M.X.; writing--review and editing, R.H. and H.D.; supervision, R.H.; project administration, R.H.; funding acquisition, H.D. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Academy Youth Promotion Association 2021 (Project No. E129180101).  
All of the necessary data are included in the article.  
The authors declare no conflict of interest.
# Semi-Autogenous Mill Power Consumption Prediction Based on CACN-LSTM
## 3 Power Consumption Prediction Model of SAG Mill Based on CACH-LSTM  
In order to accurately and reliably predict the power consumption of SAG mills, a hybrid prediction model, CACN-LSTM, which integrates CACN and LSTM, is proposed to predict the power of SAG mills. It consists of three main parts: local feature extraction based on CNN, adaptive feature weighting based on SE, and power consumption prediction based on LSTM. The framework of this prediction network is shown in Figure 5. The specific steps are as follows:  
Step 1. The relevant variables \(X_{\text{n}}\) affecting the power consumption of the grinding process of the SAG mill are inputted into the CNN. First, a one-dimensional convolution operation is performed, resulting in the convolved operation outcome \(c_{n}\). Then, a convolution window of size \(3\times 1\) with a stride of 1 is used to slide down, and after convolution through two convolutional layers, the local feature vector \(C=(c_{1},c_{2},\dots,c_{n})\) is obtained.  
Step 2. The local feature vector \(C=(c_{1},c_{2},\dots,c_{n})\) is input into the SE module. Firstly, the squeeze module performs global average pooling on the input features of each channel, resulting in \(Z_{c}=\frac{1}{H\times W}\sum\limits_{i=0}^{H-1}\sum\limits_{j=1}^{N}x_{m,n,c},c=1,2,\dots,n\). Since the input data in power prediction are all one-dimensional time series, the input data for each channel is 1 column; that is, \(\text{w}=1\). At this time, \(Z_{c}=\frac{1}{H}\sum\limits_{i=0}^{H-1}X_{m,n,c},c=1,2,\dots,n\), \(x_{m,n,c}\) is the element in the m-th row and \(n\)-th column of the c-th channel. Secondly, the excitation module assigns weights to the input features of different channels to obtain \(S=\sigma(W_{2}\delta(W_{1}Z_{c})\). Finally, the weights are assigned to the original features through the Scale module to obtain \(\widetilde{C}=C\otimes S\).  
Step 3. The features extracted through CNN and SE are input into the LSTM, and the final predicted power consumption value of \(Y_{pred}=LSTM(\widetilde{C})\) the SAG mill can be obtained through the LSTM.  
Figure 5: The framework of CACN-LSTM.
## 3 Results and Discussion
### Stability and Bending Tests  
The reproducibility and long-term stability of the Pt/rGOP were determined by evaluating the amperometric current response towards 0.2 mmol/L H\({}_{2}\)O\({}_{2}\) in 0.1 mol/L PBS. The amperometric current responses of 5 sensors, fabricated under the same conditions, were tested for 10 days. This revealed that Pt/rGOP electrodes exhibit a relative standard deviation (RSD) of 3.8% for 10 days, signifying that the Pt/rGOP possesses high reproducibility and stability (Figure 8A). The flexibility is an important factor for flexible Pt/rGOP electrodes, and five Pt/rGOP electrodes were bent 180\({}^{\circ}\) up to 200 times, as shown in Figure 8B. The results show that it retains nearly 92% of original response after being bent 200 times. This proves that the flexible Pt/rGOP electrodes have excellent mechanical stability for electrochemical devices to detect H\({}_{2}\)O\({}_{2}\).
## 3 Results and Discussion
### Flexural and Compressive Strengths of Mortar Specimens  
Figure 3(a),b show the flexural and compressive strength developments for mortar specimens with various amounts of lithium mica slag. The results show that as the content of lithium mica slag increases, the flexural and compressive strengths of mortar specimens decrease before the 28-day point. However, after 60 days, as the content of lithium mica slag increases, the flexural and compressive strengths of mortar specimens gradually increase. After 150 days, the strength of the mortar specimens with lithium mica slag is equivalent to, or even slightly higher than, the strength of the mortar specimens without lithium mica slag. The microscopic particles in the lithium mica slag may have gradually filled the microscopic voids in the mortar specimens, increasing their compactness and solidity, which could facilitate the hardening process and enhance their strength over time. Therefore, while lithium mica slag may cause a reduction in strength during the initial stages, its ability to fill and harden over time may be the primary factor contributing to the increase in mortar strength.  
Figure 4: The flexural and compressive strengths of mortar specimens with different lithium mica slag contents at different ages.  
Figure 3: The mass changes of mortar specimens with different lithium mica slag contents at different ages.
## 3 Materials and Methods
### Technological and Research Areas
#### 3.2.3 Unmanned Aerial Vehicles  
The use of UAVs in photography reception for photogrammetric processing is a technique in modern photogrammetry. The UAVs carry the cameras and allow for placement at heights and angles appropriate for modeling objects which may extend over several thousand acres. Therefore, they are the key ally for fast data collection and access to points difficult to access or even inaccessible.  
The UAVs are divided into 2 basic families: fixed wing systems (airplanes) and rotary wing systems (polycopters). The former behaves like a typical airplane; that is, they require space for take-off and landing, are constantly moving and are highly efficient in terms of autonomy and coverage. The latter are vertical take-off and landing systems and are particularly energetic, with their main advantage being the capability to swing.  
The following are some of the representative UAVs for the capturing needs of \"Mergin' Mode\" demonstration:  
1. senseFly eBee Plus (20 Mpixel, 1\" sensor).
2. DJI Phantom 4 Pro (20 Mpixel, 1\" sensor).
3. Parrot Anafi (21 Mpixel, 1/2.4\" sensor).
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Compound** & \multicolumn{2}{c}{**S**} & \multicolumn{2}{c}{**Uncooked P-S**} & \multicolumn{2}{c}{**Cooked P-S**} \\ \hline  & **FP** & **BP** & **FP** & **BP** & **FP** & **BP** \\ \hline _Phenelic acids_ & & & & & & \\ \hline Vanillic acid & 0.6 \(\pm\) 0.0 a & 1.0 \(\pm\) 0.1 B & 0.8 \(\pm\) 0.0 a & 1.8 \(\pm\) 0.4 A & n.d. & 1.2 \(\pm\) 0.1 AB \\ \(p\)-Hydroygenozoic acid & 7.7 \(\pm\) 0.9 a & 0.5 \(\pm\) 0.0 B & 7.8 \(\pm\) 0.3 a & 0.2 \(\pm\) 0.0 C & 7.5 \(\pm\) 0.2 a & 0.9 \(\pm\) 0.0 A \\
4-Feruloylquinic acid & 1.1 \(\pm\) 0.1 b & n.d. & 1.4 \(\pm\) 0.1 a & n.d. & 1.5 \(\pm\) 0.0 a & 0.8 \(\pm\) 0.0 \\ Vanillin & 0.1 \(\pm\) 0.0 & 2.0 \(\pm\) 0.2 & n.d. & n.d. & n.d. & n.d. \\ \(p\)-cumamic acid & 0.2 \(\pm\) 0.0 b & 1.4 \(\pm\) 0.2 B & n.d. & 1.4 \(\pm\) 0.1 B & 3.9 \(\pm\) 0.1 a & 2.5 \(\pm\) 0.1 A \\ Ferulic acid & 2.2 \(\pm\) 0.5 a & 71.0 \(\pm\) 2.2 A & 1.3 \(\pm\) 0.1 c & 7.14 \(\pm\) 1.7 A & 1.6 \(\pm\) 0.1 b & 50.2 \(\pm\) 1.1 B \\ Ferulic acid isomer 1 & n.d. & 22.4 \(\pm\) 0.4 A & n.d. & 18.2 \(\pm\) 0.5 B & n.d. & n.d. \\ Diferutic acid & 0.3 \(\pm\) 0.0 b & 0.8 \(\pm\) 0.0 A & 0.8 \(\pm\) 0.0 a & 0.1 \(\pm\) 0.0 C & 0.3 \(\pm\) 0.0 b & 0.3 \(\pm\) 0.0 B \\ Diferutic acid isomer 1 & n.d. & 0.4 \(\pm\) 0.0 b & n.d. & 0.9 \(\pm\) 0.0 A & n.d. & \\ Diferutic acid isomer 2 & n.d. & 0.1 \(\pm\) 0.0 & n.d. & n.d. & n.d. & n.d. \\ \hline _Fluence-Cy glycoside_ & & & & & & \\ \hline Apigenin-6-C-arabinoside-8-C-hexoside isomer 1 & 0.7 \(\pm\) 0.0 a & n.d. & 0.5 \(\pm\) 0.0 b & n.d. & 0.5 \(\pm\) 0.0 b & n.d. \\ Apigenin-6-Carabinoside-8-C-hexoside isomer 2 & 0.7 \(\pm\) 0.0 a & n.d. & 0.6 \(\pm\) 0.0 b & n.d. & 0.1 \(\pm\) 0.0 c & n.d. \\ \hline Total (single factor) & & & & & & \\ \hline Total (FP + BP) & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Free and bound phenolic compounds determined in semolina (S) and uncovoked and cooked pasta made with 100% semolina (P-S), expressed in mg/kg dry weight (dw).
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 9 Introduction to Mobile Dog Robots
### Operational Capabilities
#### 9.1.1 Payload and Tracking  
The operational payload of the SPOT robot is high (up to 14 kg) compared with that of the Ghost Robotics robot (9 kg) and ANYmal Robotics robot (10 kg). The location tracking capabilities of both SPOT and Ghost robots are almost the same. The SPOT robot uses high-resolution GPS, an Orion RTLS tag, and robot odometry to track, the Ghost robot uses an integrated GPS waypoint and playback route automation from the OCU, and the ANYmal Robotics robot uses the points for autonomous navigation. Overall, all robots know their absolute location although they use different technologies.
## 2 Material and Method
### Instrumentation and Techniques  
The surface morphology of the modified MWCNTs was obtained by a scanning electron microscope (SEM, FEI Quanta 200 Environmental Scanning Electron Microscope, Hillsboro, OR, USA) and a transmission electron microscope (TEM, JEM-1400 Transmission Electron Microscope, Peabody, MA, USA). The zeta potential of the MWCNTs during modification at different pHs (1-11) was measured by a zeta analyzer (Brookhaven BI-90 Plus Particle Size Analyzer, Champaign, IL, USA). Using the BET surface area analyzer (Beckman Coulter SA3100 Surface Area Analyzer, Indianapolis, IN, USA), the surface area and pore size of the modified MWCNTs were analyzed. Nonyl phenol in water samples was extracted by solid phase extraction (SPE) and subsequently quantified by high-performance liquid chromatography-mass spectrometry (HPLC-MS) [40]. The SPE was carried out according to the method proposed by [PERSON] and [PERSON] [41]. Briefly, the Onsis HLB SPE cartridge (500 mg, 6 mL, Waters, Milford, MA, USA) which was first pretreated with methanol and deionized water of 6 mL each. Then, a water sample of 400 mL with \({}^{13}\)C\({}_{6}\)-sulfamethazine (use as a surrogate) added were loaded to the cartridge at 3-6 mL/min. After a sample loaded, the cartridge was rinsed with 6 mL DI water and dried with N\({}_{2}\) gas. Then, analytes were eluted with 4 mL methanol and 4 mL methanol-diethyl ether (1:1 ratio, 50:50, \(v/v\)). The eluates were collected to pass for drying with N\({}_{2}\) gas and reconstituted in 0.4 mL of 25% aqueous methanol. Finally, the obtained liquid was filtered through a 0.45 \(\upmu\)m PVDF membrane before the HPLC-MS/MS analysis.
# Traceability and Spectral Analysis of Transmission Error in Rotary Vector Planetary Gear Reducers
## 2 Transmission Characteristics
### Relative Motion Relationships  
The motion transmission in an RV reducer involves multiple components, including both self-rotation and revolution, resulting in complex relative movements. To simplify the analysis, further examination of these relative motions is essential.  
In Condition 1, with the pin gear fixed, the flange, cycloidal gear, crankshaft, and planet gear are closely interconnected. The planet gear and crankshaft are rigidly connected, resulting in the crankshaft adopting the same self-rotation speed as the planet gear. Consequently, the cycloidal gear's revolution is determined by the crankshaft's rotation, leading to \(\omega_{\text{cyc-g}}=\omega_{\text{cra-s}}=\omega_{\text{pla-s}}\). The crankshaft's revolution is directly driven by the cycloidal gear's self-rotation; thus, \(\omega_{\text{cra-g}}=\omega_{\text{cyc-g}}\). Since the crankshaft is mounted on the flange, the flange's self-rotation is driven by the crankshaft's revolution, resulting in \(\omega_{\text{fla}}=\omega_{\text{cra-g}}\). Similarly, the planet gear, due to its fixed connection with the crankshaft, is driven into revolution at the same speed as the crankshaft, \(\omega_{\text{pla-g}}=\omega_{\text{cra-g}}\).  
Condition 2 exhibits similar relationships. When the flange is fixed, applying the same logic as in Condition 1, we deduce that \(\omega_{\text{pla-g}}=\omega_{\text{cra-g}}=\omega_{\text{cyc-s}}=\omega_{\text {fla}}=0\). The cycloidal gear's revolution remains related to the self-rotation of the crankshaft and planet gear, with \(\omega_{\text{cyc-g}}=\omega_{\text{cra-s}}=\omega_{\text{pla-s}}\). The rotational speed relationships among the components are summarized in Table 1.  
Based on the above analysis, all the rotations in the RV reducer can be expressed through four fundamental rotations: the self-rotation of the gear shaft at speed \(\omega_{\text{sun}}\), the self-rotation of the planet gear at speed \(\omega_{\text{pla}}\), the self-rotation of the cycloidal gear at speed \(\omega_{\text{cyc}}\), and the self-rotation of the pin gear at speed \(\omega_{\text{pin}}\). The four fundamental rotations are illustrated in Figure 3, where \(\omega_{\text{sun}}\) and \(\omega_{\text{cyc}}\) rotate in the same direction, while \(\omega_{\text{pla}}\) and \(\omega_{\text{pin}}\) rotate in the same direction but opposite to \(\omega_{\text{sun}}\) and \(\omega_{\text{cyc}}\).  
The calculations for each component in Table 1 can be derived using the following relationships:  
\[\omega_{\text{pla}}=\omega_{\text{pla-s}}=\omega_{\text{cra-s}}=\omega_{\text{ cyc-g}}, \tag{1}\]  
\[\omega_{\text{cyc}}=\omega_{\text{cyc-s}}=\omega_{\text{cra-g}}=\omega_{\text{ pla-g}}=\omega_{\text{fla}} \tag{2}\]