esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 1 Introduction  
A global navigation satellite system (GNSS) provides convenient position navigation and timing services for its application terminals [1]. It has played a fundamental role in transportation, marine fisheries, geo-hazards monitoring, emergency rescue, etc. However, the power of received GNSS signals on the ground is weak [2], which is 30 dB lower than the thermal noise of receivers [3]. The intentional interference (indicating jamming in this paper) usually leads to the decrease of the carrier-to-noise ratio (_CNR_), which further causes the failure of GNSS signal acquisition and tracking [4] and disables the receiver.  
In navigation countermeasures, distributed wideband interference is often used [5], especially when the number of jammers is equal to or larger than the number of array elements. This is denoted as \"sup-freedom interference\" on the premise that the interference steering vectors are not linearly correlated to each other [6].  
Sup-freedom interference is fatal to GNSS array antenna receivers. On the one hand, it is hard to suppress it completely by utilizing the orthogonality between spatial filter coefficients and interference steering vectors [7, 8]. On the other hand, most GNSS array antennas only have 4-7 elements [9, 10]. It is easier to increase the amount of interference than to increase the number of array elements due to limited space for navigation facilities, the half-wavelength of the L-band GNSS signal, and the lower cost of jamming equipment [11].  
There is some literature on the wideband sup-freedom interference suppression technology of array antennas. In some fields, some research on antenna freedom is increasing,including studies of sparse array [12], virtual antenna array [13], polarizing array antenna [14], and synthetic aperture antennas [15]. They might be helpful against sup-freedom jamming, but they also have limitations, such as receiver dynamic, real-time computing burden, and hardware complexity [16; 17]. Instead of increasing antenna freedom, the current researchers tried to improve the anti-jamming performance from a different aspect: by utilizing the direction sensitivity of spatial anti-jamming performance.  
The contents of the paper are as follows: Section 2 establishes the array signal reception and anti-jamming model. Section 3 analyzes the direction-sensitivity of sup-freedom anti-jamming performance and proposes an antenna rotation method against sup-freedom interference. Section 4 proposes a variable-step iteration algorithm based on gradient descent in order to efficiently obtain the optimal rotation angle. Section 5 presents simulations and analysis. Section 6 discusses the future work. The major contributions are included in Sections 3 and 4. A block diagram of the article structure is shown in Figure 1.  
In order to make it easier for reading, frequently used symbols are listed in Table 1.
## 4 Discussion
### Disadvantages to Be Overcome  
Remote sensing provides ongoing detection without issues of low battery or disengagement due to forgetful acts and is minimally intrusive to uphold privacy. However, there remain disadvantages that require further exploration.  
Overall, improvements in specificity and sensitivity are critical, as residential layouts might have blind spots beyond the coverage of remote sensors. As a general observation from the identified articles, experiment-based research did not consider different application scenarios sufficiently. The granular difference between environmental noise and its impact on acoustic technologies, and different floor materials and the associated changes in vibration technologies, should be further studied to improve specificity and sensitivity. The same observation applies to the combined use of acoustic and vibration technologies, where re-calibration for a range of hard and soft interior surfaces should be examined.
## 3 Results and Discussion
### MOT Effect on the Spectral Characteristics of Global IGS Stations
To have a more clear view of the dominant power spectra, Figure 12 gives an expanded view of the filtered spectra shown in Figure 11 (top panels) from the 1 st to the 7 th harmonics of 1.0 _cpy_ and 1.04 _cpy_. From Figures 11 and 12, we observe that no matter MOT correction is applied or not, the annual signal and the anomalous harmonics of 1.04 _cpy_\(\pm\) 0.008 _cpy_ until the 7 th draconitic signals are the dominant periodic signals in both the vertical and horizontal components of global IGS stations, and the magnitude in the vertical component is about 10-times larger than the horizontal components. Similar as in Figure 10, the annual amplitude of the E component dramatically decreased after considering the MOT effect, but that of the U and N components exhibit a significant increase. We also find that the amplitude of most abnormal harmonics of 1.04 _cpy_\(\pm\) 0.008 _cpy_ until the 7 th draconitic signals decreased to some extent, in particular for the 3 rd draconitic harmonic of the U component. This result confirms our previous finding from Figure 10 that MOT may be a possible source that caused the anomalous harmonics of 1.04 _cpy_, although the contribution may be slightly small. Another interesting result from Figure 11 is that the short-term periodic signals with frequency larger than 25 _cpy_, that is the near fortnightly periodic signal, in the U component show a significant decrease after correcting the MOT effect. This may give some useful clue for us to reduce some of the high frequency periodic signal in the GPS height time series when reprocessing the global GPS data. Furthermore, most long-term periodic signal with frequency equal to or smaller than 0.5 _cpy_ in the U and N components also decreased to some degree. This phenomenon confirms the recent finding that the unmodeled diurnal and sub-diurnal periodic signal could indeed propagate and produce spurious long-term signals in the GPS coordinate time series [25]. Except for these mentioned periodic signals, however, the PSD differences that caused by MOT show a random pattern, which is also similar as that shown in Figure 10.
## 4 Discussion
### Benefits from Multi-GNSS Observations  
Multi-GNSS observations help to increase the spatial observations of the ionosphere and to better understand the local morphology of the ionosphere variations. This was also supported by Reference [21]. [PERSON] and [PERSON] used \(\sim\)5800 ground GPS/GLONASS observations to generate latitudinal ROTI maps during the storm phase. In this work, the regional ROTI perturbations were generated by GPS/GLONASS/BDS observations. The benefits of the BD GEO VTEC measurements were partially due to the geostationary orbit dynamics, indicated by Reference [4], to clearly represent both the positive and negative TEC responses in March 2015's storm. In this work, the ROTI calculated from the Multi-GNSS observations provided abundant information of ionosphere irregularities under the geomagnetic storm, to help understand the patterns and changes in the ionosphere irregularities, as Figures 8 and 12 show. One of the challenges for multi-GNSS observations is the computation efficiency, especially for some real-time applications. [PERSON] et al. [47] has proposed a rapid solution for fixed ambiguity calculation with the help of ionosphere delay corrections. According to the algorithm in Reference [47], similarly, precision positioning for the Multi-GNSS case is expected in real-time operation by improving the computation efficiency of the current TEC calibration method. Another extension of the TEC method used in this work was to achieve a better TEC calibration with the help of Multi-GNSS measurements, as [PERSON] and [PERSON] [48] stated, according to the relative study of GLONASS. An improved TEC calibration based on [PERSON]'s method will consider more influential factors, such as the bias produced by inhomogeneous receivers, the bias among the satellite constellations, and even the multipath impacts on the low elevation satellites. A probable high resolution regional TEC map with less than 1 TECU error is to be generated with the combination of improved TEC calibration based on [PERSON]'s methodology and study of Reference [48]. The promising investigations are useful for the fine precise positioning and related PPP-RTK (Precision Point Positioning-Real Time Kinematic).
# Privacy-Preserving Byzantine-Resilient Swarm Learning for E-Healthcare
## 2 Related Work
### Byzantine-Robust Federated Learning
#### 2.2.2 Byzantine-Robust Federated Learning  
Typical FLs, such as FedSGD [38] and FedAvg, are not resistant to poisoning attacks, resulting in untrustworthy and inaccurate global models. By comparing local gradients received from different parties, Byzantine-robust federated learning (BRFL) [16; 39; 40; 41] excludes abnormal gradients to protect the training model from poisoning attacks. Based on the Euclidean distance between any two gradients, Krum [16] excludes the outlier gradients. Specifically, in each training round, a candidate model would be selected. Similarly, Trim-mean [39] first sorts the received local gradients, then removes the larger and smaller gradients among them, and finally takes the mean of the remaining gradients as the global gradient. GeoMed [42] updates the FL model by selecting a gradient based on the geographic median to realize gradient aggregation. [PERSON] [43] first implements the Krum-based aggregation and then updates the global model by averaging the gradient closest to the median value. Meanwhile, based on the cosine similarity between the parties' historical gradients, FoolsGold [35] sets the weights of parties to ward off Sybil attacks in FL.  
However, these schemes do not consider privacy threats, which directly aggregate the global model directly in plaintext. In order to solve this problem, the FL scheme [40] using DP and SMC can not only prevent inference attack during training, but also improve model accuracy. However, because it is difficult to calculate the similarity between the encrypted gradients, the FL scheme [40] is vulnerable to malicious gradients. To this end, privacy-enhanced federated learning (PEFL), which employs HE as the underlying technology, punishes malicious parties via the logarithmic function of gradient data. However, the PEFL scheme still does not avoid malicious behaviors of the servers and inference attacks onto clients.  
To develop a Byzantine-resilient, and at the same time, privacy-preserving federated learning framework, we use TFHE to protect user privacy, cosine similarity to remove malicious gradients, and blockchain to avoid malicious behavior of servers.
# Nutrition in Pediatric Intensive Care: A Narrative Review
## 7 Micronutrients  
Trace elements and vitamins are essential for many biochemical processes and metabolic cycles. In patients on an oral diet or enteral nutrition, complex enteral feeding formulas, or even mixed hospital nutrition (ideally prescribed in cooperation with the nutrition support professionals), contain adequate amounts of trace elements (e.g., Cu, Fe, Zn, Se and others) and vitamins (water- and fat-soluble). Although vitamin C supplementation in critical illness in particular has been recently broadly investigated with slightly promising results [41; 42], routine supplementation is currently not recommended outside of clinical trials. On the other hand, in patients on parenteral nutrition, the provision of essential trace elements and vitamins is vital for proper metabolism function. Complex multivitamins are available on the market, however, with different regional availability. The dosing and administration should be based on the manufacturer's recommendation, preferably by a prolonged infusion (up to 24 h). Recommended dosing of vitamins and trace elements by ESPEN (level of expert opinion) is listed in Table 4[43; 44].  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Fat-Soluble Vitamins (Vitamin A, D, E, K)**} \\ \hline Vitamin A & 150–300 ug/kg/d \\ Vitamin D & 40-150 IU/kg/d up to 400–600 IU/d \\ Vitamin E & 2.8–3.5 mg/kg/d or 2.8–3.5 IU/kg/d 11 mg/d or 11 \\ IU/d \\ Vitamin K & 10 \(\mu\)g/kg/d (or 200 ug/d \\ \hline \multicolumn{1}{c}{**Water-soluble vitamins (Vitamin C, B vitamins)**} \\ \hline Vitamin C & 15–25 mg/kg/d up to 80 mg/d \\ Vitamin B1 (Thiamine) & 0.35–0.50 mg/kg/d up to 1.2 mg/d \\ Vitamin B2 (Riboflavin) & 0.15–0.2 mg/kg/d up to 1.4 mg/d \\ Vitamin B3 (Niacin) & 4–6.8 mg/kg/d up to 17 mg/d \\ Vitamin B5 (Pantothenic acid) & 2.5 mg/kg/d up to 5 mg/d \\ Vitamin B6 (Pyridoxine) & 0.15–0.2 mg/kg/d up to 1.0 mg/kg/d \\ Vitamin B7 (Biotin) & 5–8 mg/kg/d up to 20 mg/d \\ Vitamin B9 (Folic acid) & 56 mg/kg/d up to 140 mg/d \\ Vitamin B12 (Cyanocobalamin) & 0.3 ug/kg/d up to 1 ug/d \\ \hline \hline \end{tabular}
\end{table}
Table 4: Recommended dosing of vitamins and trace elements by the European Society of Clinical Nutrition and Metabolism (ESPEN).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Weight** & **mL/kg/d** & **mL/kg/h** \\ \hline A: the first 10 kg & 100 & 4 \\ B: between 10 and 20 kg & +50 mL/kg/d & +2 mL/kg/h \\ C: any kg above 20 kg & +25 mL/kg/d & +1 mL/kg/h \\ Daily calculation & A + B + C & A + B + C \\ \hline Example: 22 kg patient = (first 10 kg \(\times\) 100 mL/kg/d) + (second 10 kg \(\times\) 50 mL/kg/d) + (remaining 2 kg \(\times\) 25 mL/kg) = 1000 mL + 500 mL + 500 mL + 1550 mL/day. Should be used only for guidance and individualized by patients’ clinical condition and diagnosis: Abbreviations mL/kg/d—milliters per kilogram of patient weight per day, mL/kg/h—milliters per kilogram of patient weight per hour. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Holiday and Segar formula for maintenance fluid calculation.
## 2 Related Work
### Q-Learning-Based Routing Protocols  
The emergence of deep reinforcement learning (DRL) has led to increasing interest in intelligent routing protocols for FANETs [33; 34; 35; 36; 37; 38]. Q-learning, as a model-free RL method, can iteratively refine routing decisions through interactions with the environment, making it particularly suitable for dynamic and rapidly evolving networks.  
In [39], the authors extended traditional Q-learning by incorporating node velocity and direction into the state space, allowing decisions to align with mobility patterns. However, Q-FANET lacks mechanisms for predicting link failures or achieving effective load balancing. QTAR [38] (Q-learning-based topology-aware routing) improves upon this by introducing traffic-aware metrics into the reward function, enabling better congestion handling under high load, though it also does not consider trajectory dynamics. QMR [40] (Q-learning based Multi-objective optimization Routing) integrates multi-objective optimization into Q-learning via Pareto-based reward functions to balance latency, energy, and reliability. While theoretically powerful, its high-dimensional state-action space results in longer training times, limiting real-time applicability.  
More recently, PER-D3 QN [41] (Prioritized Experience Replay Dueling Double DQN) applies a prioritized experience replay mechanism and dueling architecture to mitigate Q-value overestimation and improve convergence. The approach builds congestion- and failure-aware state and reward models, showing improved robustness in dynamic FANET conditions. QFRP [37] (Q-Learning and Fuzzy Logic based Routing Protocol) enhances Q-table update granularity by combining HELLO and ACK signals, and integrates fuzzy logic to assess neighbors based on Q-value, link quality, and access delay. IQMR [15] (ImprovedQ-learning-based Multihop Routing) utilizes a \(Q(\lambda)\) learning framework with an elaborate state space encompassing UAV status, connectivity, coverage, and collision risk, along with adaptive learning rates and dynamic HELLO intervals to improve convergence under varying network conditions.  
Overall, Q-learning-based routing protocols are evolving toward multi-dimensional sensing, adaptive parameter tuning, and intelligent decision-making. Nevertheless, existing work primarily responds to current states without integrating future topology evolution or planner-based trajectory information. Our proposed method combines trajectory knowledge with Q-learning to construct a predictive, robust, and high-efficiency routing protocol tailored for dynamic UAV swarm networks.
## 5 Implementation and Evaluation
### Results
#### 5.2.3 Scalability  
Figure 4 illustrates the impact of increasing the number of edge servers (\(m\)) on global accuracy for FedProx and FedAvg. FedProx demonstrates higher accuracy across all values of \(m\), starting at 91.2% for 10 edge servers and gradually decreasing to 87.1% for 30 servers. In contrast, FedAvg starts at a lower accuracy of 85.5% and drops to 80.5% as \(m\) increases. The decline in accuracy for both methods is attributed to increased data heterogeneity with a larger number of edge servers. However, the proximal term in FedProx helps mitigate model divergence, resulting in better scalability compared to FedAvg. These results highlight the robustness of FedProx in maintaining higher accuracy in large-scale decentralized systems.  
In our experimental setup, we designed the system to operate under normal conditions, meaning that the number of edge servers does not exceed 30. This limit was chosen based on the typical scale of IoT-edge deployments in real-world applications, where balancing computational resources, network bandwidth, and model performance is crucial. By maintaining this controlled environment, we ensure that accuracy remains stable (91.2% at 10 servers to 87.1% at 30 servers) and that communication overhead remains manageable. Beyond this threshold, system performance could degrade due to factors such as increased data heterogeneity, synchronization delays, and resource limitations. To scale beyond this, hierarchical federated learning can be used, grouping servers for localized aggregation before global updates. Additionally, adaptive model aggregation and edge-aware clustering will optimize accuracy by reducing model divergence in large-scale deployments.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Optimization** & **Total Cost (MB)** \\ \hline FedProx & Gradient Compression & 420 \\ FedProx & None & 600 \\ FedAvg & None & 580 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Total communication cost for different methods and optimizations.  
Figure 3: Communication cost over training rounds.  
Figure 5 depicts the convergence time required for FedProx and FedAvg vs. the amount of dataset volume processed by every edge server. Indeed, as the volume size of the dataset increases, so does the computational burden on the servers; hence, longer convergence time is required by both methods. However, FedProx often outperforms FedAvg and converges from 2 to 5 s faster across all dataset sizes. A typical case is seen with a dataset size of 10,000 samples, where FedProx converges in 25 s, while FedAvg takes 30 s. Thus, it follows that the optimization approach of FedProx, especially its proximal term, can indeed yield faster convergence, even for bigger datasets. Putting it all together, FedProx is more efficient and scalable and hence ideal for systems with large data handled per server.
## 2 Materials and Methods
### Participants  
Although 18 elite boxers and kickboxers aged 19-32 years were initially recruited, only 14 completed all phases of the study due to personal reasons or scheduling conflicts. All participants were free from upper limb injuries within 6 months prior to the study and had fully healed from any other injuries that could affect the results.  
Participants were recruited using purposive sampling from local sports clubs and training centers known for producing high-level athletes. Purposive sampling was chosen to ensure that all participants had the requisite high level of training and performance needed for this study. Invitations were extended to 25 potential participants who met the inclusion criteria, which included having at least a master class ranking and a minimum of 8 years of training experience. This specific number was chosen based on the availability and willingness of eligible athletes to participate in a rigorous and controlled experimental protocol. Out of the 25 invited, 7 declined to participate due to scheduling conflicts or personal reasons, resulting in a final sample size of 18 individuals.  
The sample size was calculated using the G*Power software (version: 3.1.9.7; Dusseldorf University, Dusseldorf, Germany). The parameters used for this calculation were based on an effect size ([PERSON]'s d) estimated from previous similar studies examining the effects of vibration therapy on muscle recovery, an alpha level (\(\alpha\)) of 0.05, and a statistical power (\(1-\beta\)) of 0.80. Specifically, we assumed an effect size of 0.85, which is considered large according to [PERSON]'s conventions. This calculation indicated that a minimum of 18 participants would be sufficient to detect significant differences with the desired power and significance level. The study was conducted in a controlled laboratory setting at the University of Physical Education, ensuring consistency in testing conditions.  
The 18 participants engaged in the study twice, serving as both the experimental and control groups, with group assignment determined by the type of biological regeneration applied (vibration therapy--experimental group, passive rest--control group). Fourteen athletes completed all the required trials. The characteristics of the study group are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Average** & **Min** & **Max** & **SD** \\ \hline Body Mass (BM) [kg] & 76.0 & 52 & 90 & 9.67 \\ \hline Body Height (BH) [cm] & 178.3 & 165 & 189 & 7.77 \\ Body Mass Index (BMI) [kg/m\({}^{2}\)] & 23.9 & 18 & 28 & 2.66 \\ \hline Age [years] & 23.0 & 19 & 32 & 5.81 \\ \hline Fat [\%] & 10.4 & 5 & 21 & 3.73 \\ \hline Total Body Water (TBW) [\%] & 62.9 & 56 & 67 & 3.04 \\ \hline \hline \end{tabular} SD—standard deviation, Min—minimum value, Max—maximum value.  
\end{table}
Table 1: Somatic characteristics of the participants.
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 3 Imaging Method Based on MAF-MDF  
### Moving Average Filter  
Data collected by actual measuring devices for fault events often contain noise and may occasionally suffer from missing data issues [21], which can degrade the accuracy of fault line selection algorithms. Consequently, preprocessing is essential before mapping the zero-sequence currents into a two-dimensional representation. The moving average filter (MAF) possesses a certain degree of noise reduction capability and, in cases of data absence, can endeavor to fill gaps using neighboring data points. Compared to methods such as modal decomposition and wavelet transformation, its implementation is simpler and does not rely on subjective, experience-based parameter tuning. Since there are not enough sampling points to complete the average calculation of a complete window when processing the data at the beginning and end of the sequence by the method of moving mean filtering, a formula suitable for the complete sequence is given in this paper, as shown in Equation (3).  
\[y(n)=\left\{\begin{array}{ll}\frac{1}{2m+1}\sum\limits_{k=n-m}^{n+m}x(k),& \frac{N+1}{2}\leq n\leq T-\frac{N-1}{2}\\ \frac{1}{2n-1}\sum\limits_{k=1}^{2n-1}x(k),&n<\frac{N+1}{2}\\ \frac{1}{2(T-n)+1}\sum\limits_{k=2n-T}^{T}x(k),&n>T-\frac{N-1}{2}\end{array}\right. \tag{3}\]  
where \(y(n)\) denotes the filtered signal value; \(n\) represents the index of the signal sequence; \(2m+1\) signifies the width of the sliding filter window, with the window width set as an odd number; \(x(k)\) is the value of the original signal; and \(T\) represents the length of the time series.