esa-sceva/satcom-chunk-collection huggingface dataset:
# The Implementation of Precise Point Positioning (PPP): A Comprehensive Review
## 4 Practical Considerations and Experimental Results
### Kinematic Results  
A road test was conducted in Calgary, Canada, spanning approximately 50 min, predominantly on highways. GNSS data for the test were captured using the NovAtel SPAN OEM6 receiver, a dual-frequency GNSS receiver incorporated within the NovAtel SPAN ProPak6 system.  
A reference solution was derived from the SPAN unit by leveraging its tactical-grade IMU-KVH. The data were postprocessed in a tightly coupled DGNSS/INS mode utilizing NovAtel's Waypoint Inertial Explorer Software. The base station data were acquired from the UCAL IGS station with a baseline of fewer than 30 km. Figure 6 provides a visual representation of the test trajectory.  
The visible GPS/GLOAASS satellite fluctuations during the test are visually presented in Figure 7. In the initial 40 min, characterized primarily by open-sky conditions, the count of visible satellites fluctuated between 8 and 18. However, a notable drop to zero satellites was observed as the vehicle transitioned under two sequential overpass bridges.  
Figure 5: An illustration of DF-PPP of static data in a kinematic mode utilizing data from YELL station on 20 July 2023.  
Figure 6: The kinematic test trajectory, conducted in Calgary, Alberta, Canada.  
Figure 8 shows the 3D position errors of the standard DF-PPP solution versus time. The solution converged to a less than 20 cm error level after 5.5 min. The solution remained within the same accuracy level until the first overpass. When the number of satellites dropped to zero, there were no observations, and the navigation filter had to be reset so the ambiguities could be re-estimated. Then, the solution started to reconverge again until the second overpass. These data exemplify the PPP initial convergence, stable open-sky solution, and reconvergence in GNSS-challenging environments. During the stable period after the initial convergence, from the 10 th minute to the 40 th minute, the RMSE measured 17 cm, 4 cm, and 7 cm for the east, north, and up directions, respectively.
# Multi-Session Electrocardiogram-Electromyogram Database for User Recognition
## 3 Measured Method of Multi-Session Biosignal Benchmarking DBs  
The multi-biosignal DB for user recognition research was constructed by simultaneously measuring ECG and EMG in subjects. During the execution of a specific hand gesture by each subject, the EMG signal from the muscle was recorded using two channels, while the ECG signal generated from the heart was recorded using one channel. Given that the constructed multi-biosignal DB contains simultaneously recorded signals, we have the flexibility to use the signals either in the form of multi-biosignal data or individually for each signal. The benchmarking multi-biosignal DB introduced in this study comprises two datasets (CSU_MBDB1 and CSU_MBDB2). CSU_MBDB1 includes ECG and EMG data recorded as 36 subjects performed six hand gestures, while CSU_MBDB2 encompasses ECG and EMG data recorded as 58 subjects performed the same six hand gestures.
# An Efficient Digital Channelized Receiver for Low SNR and Wideband Chirp Signals Detection
## 3 Results and Discussion
### Performance Evaluation of the Proposed IFM Algorithm  
This subsection analyzes the proposed IFM algorithm's performance under different input SNRs. Refs. [17; 32] were used for comparison. Because the frequency of each sampling point changes, the average processing cannot be performed directly for the frequency estimation of wideband chirp signals. In this case, the results of the frequency estimation algorithm in Refs. [17; 32] are the same. System parameters are set according to Table 2. Chirp signal bandwidth and center frequency are set to 100 MHz. The frequency root-mean-square error (RMSE) is calculated by the following formula  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(\hat{f_{i}}-f)^{2}} \tag{27}\]  
As shown in Figure 11, the frequency estimation accuracy is excellent when the input SNR is higher than 0 dB. When the input SNR is below 0 dB, the performance of the proposed algorithm decreases because the noise interference is serious, even though the filter bank has improved the SNR. The improved IFM algorithm cannot thoroughly suppress noise interference under this condition. Compared with Refs. [17; 32], the IFM algorithm proposed in this paper has higher estimation accuracy. The accuracy difference is not apparent when the input SNR is higher than 8 dB. The accuracy difference becomes significant when the SNR is lower than 8 dB. When the input SNR becomes higher, the algorithm based on instantaneous phase measurement is less affected by noise. When the input SNR is lower than 8 dB, the noise interference to the instantaneous phase becomes serious. However, the IFM algorithm proposed in Refs. [17; 32] cannot suppress the noise interference when the input signal is a chirp signal, it can only maintain good estimation accuracy for point frequency signal when the SNR is low. The IFM algorithm proposed in the study can suppress noise interference by the local flat mean algorithm and improve the estimation accuracy.  
In addition, the frequency RMSE of the bandwidth is greater than the center frequency. The reason is that the sampling points located at the rising and falling edges of the signal are more affected by noise. Under this condition, the maximum frequency measured by the algorithm becomes small, and the minimum frequency becomes large. This result significantly impacts the bandwidth measurement's accuracy more than the center frequency measurement. For that  
\[\begin{cases}f_{BW}=f_{max}-f_{min}\\ f_{CF}=\frac{f_{max}+f_{min}}{2}\end{cases} \tag{28}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter Type** & **Signal 1** & **Signal 2** & **Signal 3** \\ \hline Bandwidth & 19.95 MHz & 19.68 MHz & 99.86 MHz \\ Center frequency & 40.31 MHz & 69.92 MHz & 183.33 MHz \\ Pulse width & 5 \(\upmu\)s & 5 \(\upmu\)s & 20 \(\upmu\)s \\ \hline \end{tabular}
\end{table}
Table 4: The estimated results.
###### Abstract  
This work focuses on the three-dimensional integrated guidance and control (IGC) problem for a flight vehicle with a body-aligned strapdown seeker. The strapdown seeker cannot provide the line-of-sight (LOS) angular rate information and causes difficulties in the controller design. Additionally, external disturbance and gain-loss actuator faults also lead to the loss of control performance. To solve these problems, an extended state observer (ESO) is firstly developed to estimate the LOS angular rate by applying the observed body-line-of-sight angles provided by the body-aligned strapdown seeker. Based on backstepping and dynamic surface control techniques, the fault-tolerant IGC is then designed to deal with the gain-loss actuator fault, and adaptive approaches are applied to improve the robustness of the system. Finally, the uniformly ultimately bounded stability of the flight control system is guaranteed via Lyapunov synthesis, and numerical simulations are conducted to verify the effectiveness of the system.  
flight vehicle; integrated guidance and control; fault-tolerant control; strapdown seeker
# Exploring the Impact of Ambient and Character Sounds on Player Experience in Video Games
## 2 Experiment 1
### Results
#### 2.2.2 Flow  
The analysis on flow also showed no significant main effect of ambient sounds (F(1,31) = 0.45, \(p\) = 0.51, and \({\eta_{p}}^{2}\) = 0.01), character sounds (F(1,31) = 2.35, \(p\) = 0.14, and \({\eta_{p}}^{2}\) = 0.07), or the interaction between the two (F(1,31) = 0.27, \(p\) = 0.61, and \({\eta_{p}}^{2}\) = 0.01).
## 2 Materials and Methods
### Intervention Program
#### 2.4.3 Intervention Protocol  
Based on previously well-tolerated intervention protocols using VR in different health conditions, it was decided to conduct 12 intervention sessions, with two sessions per week on non-consecutive days over a period of 6 weeks [26; 27]. A physiotherapist, specifically trained to administer the protocol, monitored patients throughout the iVRE intervention. During the sessions, the patient interacted with an avatar, and the game was simultaneously displayed on a laptop screen, allowing the physiotherapist to observe the patient in real-time. The physiotherapist provided guidance and ensured safety based only on the patient's feedback but did not interact with the patient regarding the execution of the exercises, an action performed exclusively by the avatar and the iVRE interface. This physiotherapist only administered the protocol and did not participate in the evaluations.  
At the beginning of each session, patients performed a standardized 10-min warm-up on a cycle ergometer at a moderate intensity, rated 4 to 5 on the CR-10 subjective perception of exertion scale [47]. Subsequently, the iVRE intervention was started, lasting 15 min, in line with recommendations to prevent adverse effects in people with chronic pain and FM, and because previous studies using various immersive VR devices have reported high user satisfaction with protocols lasting between 10 and 20 min [27; 31; 44; 48]. The iVRE intervention was designed to provide a gradual and consistent increase in physical activity, considering each patient's tolerance to avoid adverse effects. The iVRE session was subdivided into three phases:  
* Phase 1 (3 min): General joint mobility exercises for the spine and limbs, guided by the avatar in the FitXR app. This phase aimed to prepare the body for subsequent physical demands, promoting flexibility and blood circulation.
* Phase 2 (10 min): Interactive game, during which participants executed combinations of punches on virtual targets while also performing squats, lunges, and defensive actions. The patient followed on-screen prompts to determine which punches to execute. During this activity, the patient could skip or modify specific trunk or limb movements according to their preference if fatigue, discomfort, or pain occurred during execution, following the principle of preferred intensity [49].
* Phase 3 (2 min): Cool-down, which included slow breathing exercises and general mobility movements for the spine and limbs, again guided by the avatar. This phase aimed to facilitate recovery and reduce the risk of muscle pain or injury after the activity.  
At the end of each session, patients could see their score and were encouraged to improve their score in the next session to improve adherence and motivation. Finally, the protocol included a 5- to 10-min cool-down period, which comprised slow breathing and free stretching as tolerated.
## 4 Simulation Analysis
### Energy Consumption and Energy Tax
The simulations also proved that the transmission and reception of these data packets have large impact on the overall energy consumption of the network. Figure 12: Energy Tax of the network versus the network sizes. Figure 13: Energy Tax of the network versus the transmission range.
# Spatial and Channel Similarity-Aware Attention-Enhanced Network for Object Counting
## 3 Materials and Methods
### Spatial Similarity-Aware Module
#### 3.3.2 Similarity Feature Fusion  
The similarity plays a pivotal role in comprehensively capturing the intricate relationship between support and query images owing to its informational richness. To fully harness this information, the similarity feature fusion module is introduced to transform the similarity map \(M_{N}\). Initially, \(M_{N}\) undergoes a scaling transformation through convolution, resulting in a modified \(M_{N}^{{}^{\prime}}\in\mathbf{R}^{C\times H_{\text{Q}}\times W_{\text{Q}}}\). Subsequently, a straightforward addition operation is performed between the transformed \(M_{N}\) and the query feature \(f_{\text{Q}}\). This is followed by a series of operations, including normalization, convolution, and dropout. These operations not only enhance local features but also integrate information from different sources, making the features more comprehensive and providing a global perspective. Ultimately, these steps culminate in the derivation of the fused feature \(f_{RQ}\in\mathbf{R}^{C\times H_{\text{Q}}\times W_{\text{Q}}}\). By integrating the query features with the similarity map, the model can more accurately recognize the similarity between the support image and the query image and gain a deeper understanding of these similarities. The resulting fused feature \(f_{RQ}\) more precisely reflects the relationship between the support image and the query image, thereby enhancing the model's expressive power and interpretability. The detailed progression of this process is described below.  
\[f_{RQ}^{{}^{\prime}}=sa(NL(M_{N}^{{}^{\prime}}+f_{Q})) \tag{6}\]  
\[f_{RQ}=sa(NL(F(f_{RQ}^{{}^{\prime}})+f_{RQ}^{{}^{\prime}})) \tag{7}\]  
In the equations mentioned above, \(NL(*)\) signifies layer normalization, while \(F(*)\) collectively represents the convolution and dropout operations previously mentioned. For convenience of presentation, \(f_{RQ}^{{}^{\prime}}\) is used to represent the intermediate variable. Notably, we have incorporated a plug-and-play similarity-aware attention mechanism [31], denoted as \(sa(*)\), into this module. Experimental verification has revealed that the introduction of this mechanism improved the accuracy of the model. Through analysis, it was found that this mechanism integrates similarity and attention mechanisms, effectively complementing the SSA's exploration of the feature map itself. By doing so, the similarity feature fusion module fully leverages the information embedded in the image features, enabling the model to capture the distinguishing boundaries between objects with greater accuracy. This, in turn, enhances the model's counting accuracy.
# Secure Data Sharing in Federated Learning through Blockchain-Based Aggregation
## 3 EIFFeL Framework and Security Analysis
### Scheme Architecture and Workflow
In fact, the server can maliciously change the aggregate without being detected, as we said before. Although this observation does not show a security flaw in the EIFFeL framework, we regard it as a drawback, and ideally, we should avoid such potential \"attacks\". This is also motivated by some recent findings (e.g., [26]), which show that failure of integrity (e.g., data poisoning) can lead to privacy breaches. Thirdly, there are two inefficient designs in the solution. One is about the parameter generation in Step 1 and the key agreement at the beginning of Step 2. During the FL process, all four steps will be iterated hundreds of times in order to reach convergence. According to the description, the key materials need to be repeated in each iteration. This unnecessarily increases the complexity of the solution. These parameters can be set up in the setup phase so that the key materials can be used in all the training iterations (or, epochs). The other involves the usage of authenticated encryption. In the solution, it is assumed that the public bulletin board does not allow any manipulation of the stored data; therefore, the encrypted data in Step 2 will not be manipulated regardless of whether the encryption is authenticated or not. Therefore, standard symmetric encryption will suffice. Fourthly, the result of the server's operation in Step 2 is confusing. For client \(\mathcal{C}_{i}\), which has been reported but \(|Flag[i]|\leq m\), server \(\mathcal{S}\) intervenes for further verification. \(\mathcal{S}\) requests the shares (in clear) from \(\mathcal{C}_{i}\) for the clients who flagged it (e.g., \(((j,\mathbf{u}_{i,j})||(j,\mathbf{\pi}_{ij}))\) generated by \(\mathcal{C}_{i}\) for \(\mathcal{C}_{j}\)), and verifies the contents with the relevant check string (e.g., \((\mathbf{\Psi}_{\mathbf{u}_{i}},\mathbf{\Psi}_{\mathbf{\pi}_{i}})\)). If the verification fails, \(\mathcal{C}_{i}\) is marked as malicious; otherwise, \(\mathcal{C}_{j}\) is instructed to use the released share for its computations. The server's operation occurs because either \(\mathcal{C}_{i}\) is maliciously flagged \(\mathcal{C}_{j}\) or \(\mathcal{C}_{j}\) is sent the wrong ciphertext to \(\mathcal{C}_{i}\), on purpose (note that \(\mathcal{C}_{j}\) can disclose the right plaintext to make the server's verification pass). However, neither party will be determined as malicious according to the protocol. It is unclear why this is the case.
## 4 Theoretical Analysis
### Packet Delivery Ratio Analysis  
Let the total number of generated packets at each node be \(\lambda\). Then, the throughput of the network is expressed as:  
\[Th=\sum_{i=1}^{n}\left(\prod_{j=1}^{h(i)}p\left(h_{(j)}\right)\  
\[E_{t}=\sum_{i=1}^{n}\left(e_{g}+\prod_{j=1}^{h(i)}p\left(h_{(j)}\right)\sum_{j=1}^ {h(i)}(e_{t}+N_{j}*e_{r})\right)\lambda. \tag{13}\]  
The average energy consumption per successful packet in the entire network is given as follows:  
\[\overline{E}=\frac{\sum_{i=1}^{n}\left(e_{g}+\prod_{j=1}^{h(i)}p\left(h_{(j)} \right)\sum_{j=1}^{h(i)}(e_{t}+N_{j}*e_{r})\right)\lambda}{\sum_{i=1}^{n} \left(\prod_{j=1}^{h(i)}p\left(h_{(j)}\right)\right)\lambda}. \tag{14}\]
# Triaxial Test of Coarse-Grained Soils Reinforced with One Layer of Geogrid
###### Abstract  
Geosynthetics play a pivotal role in modern infrastructure projects, with geogrids serving as a common choice for enhancing bearing capacity and reducing soil settlement in road construction. This study investigates the influence of density and uniformity coefficients on geogrid-reinforced sandy and gravelly soils through a series of consolidated drained triaxial tests. The research covers six distinct soil types from Lithuania, each characterized by particle size distribution analysis and classified using various standards. A polyester biaxial geogrid is employed, and test specimens are prepared with and without geogrid reinforcement. Triaxial compression tests are performed at different cell pressures, mirroring real-world conditions in road construction. The results highlight the critical role of cell pressure in the reinforcement effect, with higher pressures reducing the geogrid's influence. The study also emphasizes the importance of soil type, as gravel soils consistently exhibit higher deviatoric stress than sandy soils. Notably, the geogrid enhances cohesion but reduces the angle of internal friction in most cases. Overall, this research provides valuable insights into the intricate interplay between soil properties, geogrid reinforcement, and cell pressure, shedding light on the mechanical behavior of geosynthetic-reinforced soils in road construction applications.  
geogrid; sand; gravel; road embankment; angle of internal friction; apparent cohesion; density; coefficient of uniformity +
Footnote †: journal: Applied sciences
# DPSIR Model Applied to the Remediation of Contaminated Sites. A Case Study: Mar Piccolo of Taranto
## 7 Impacts
### Contaminants Mobility
Arsenic was found mainly bound to organic matter and residual fractions (50% and 40%, respectively). Lastly, about 70% Mn was present as an oxide. [PERSON] et al. [62] showed a correlation between Pb bound to Fe/Mn oxides and Mn in its reducible form. This highlights that any red-ox variation in the environment could lead to the reduction in manganese oxides, promoting remobilization mechanisms for metals, such as Cu, Pb, and Zn. Mercury was detected by using the Bloom method [63]. Results showed that up to 90% of the total Hg derived from complexes species (Hg elementary and/or bound to amorphous organic compounds with sulfur, amalgam Hg-Ag, or crystalline phases of Fe/Mn oxides), leachable with HNO\({}_{3}\) 12N. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metals**} & **Sediment Station 1** & **Metal Fraction \textless{}63 \(\upmu\)m** & **Sediment Station 2** & **Metal Fraction \textless{}63 \(\upmu\)m** \\ \cline{2-5}  & **(mg/kg d.w.)** & **(\%)** & **(mg/kg d.w.)** & **(\%)** \\ \hline As & 34.36 & 35 & 115.30 & 39 \\ Cd & 4.29 & 30 & 1.69 & 28 \\ Cr & 133.41 & 34 & 77.84 & 38 \\ Cu & 177.00 & 38 & 536.00 & 43 \\ Fe & 49,653.00 & 38 & 28,513.00 & 42 \\ Ni & 75.76 & 39 & 54.73 & 45 \\ Zn & 430.00 & 50 & 815.00 & 57 \\ Pb & 121.00 & 64 & 392.00 & 70 \\ Al & 38,672.00 & 33 & 22,803.00 & 38 \\ Mn & 439.00 & 51 & 386.00 & 57 \\ Sn & 15.31 & 13 & 6.86 & 16 \\ V & 134.37 & 31 & 72.84 & 34 \\ Se & 5.92 & 52 & 2.01 & 47 \\ Hg & 5.37 & 64 & 40.61 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metal concentration in sediments and percentage of metals in the mudstone fraction [31].
## 4 Experiments and Results
### Algorithm Robustness for Data Containing Fewer Coordinate Vertices  
The vector geographic data that were adopted in the above experiments contain large-sized data coordinate vertices, which have a high watermark capacity. This observation indicates that the proposed algorithm exhibits strong adaptability to large volumes of data. To further assess the algorithm's adaptability, particularly concerning vector geographic data with fewer coordinate vertices, 20 vector geographic maps containing a smaller number of coordinate vertices were employed to validate the watermark capacity of the proposed algorithm. All of the experimental maps feature a scale of 1:1,000,000 and unit measurements in meters (m). Table 9 lists the types of experimental vector geographic data and the number of coordinate vertices included in the maps, which are denoted as numbers 1 to 20.  
The experiments comprise two parts: robustness against addition and deletion attacks, and robustness against random noise attacks. This is the same as the experiments conducted for the data containing more coordinate vertices, as described in Sections 4.1 and 4.2. The specific experimental steps also align with those for the data containing a large number of vertices. The robustness against addition and deletion attacks for the experimental maps is presented in Table 10, while the robustness against random noise attacks for the experimental maps is shown in Table 11.  
According to the previous experiments, Table 9 demonstrates that the algorithm effectively enables the embedding of multiple watermarks for datasets with a small number of coordinate vertices. Additionally, it exhibits robustness against deletion attacks. However, with the increase in the number of deletion attacks, the correlation detection value decreases continuously, and the correlation detection value is not equal to 1.0 under the condition of no attacks. A comprehensive analysis shows that this is mainly because some watermark information bits cannot be mapped to coordinate points in the process of mapping between coordinate vertices and watermark information bits, because the experimental data have a small number of coordinate vertices, which leads to a decline in the watermark detection values. The increase in the number of deleted vertices in the deletion attacks results in an increasing mismatch between the original watermark information bits and the extracted watermark information bits, consequently leading to a decline in the relevant detection values.  
The experimental results presented in Table 11 demonstrate that the algorithm exhibits a certain level of resilience against random noise attacks. However, it should be noted that this robustness is comparatively weak when compared to datasets containing a greater number of coordinate vertices. This is mainly because the statistical characteristics of small datasets are not as obvious as those of large datasets, which is also the reason why the robustness of small datasets against random noise attacks is weaker than that of large datasets, as shown in Tables 7 and 8.