esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 On-Chip Circuit Implementation
### Implant Demodulator  
The downlink data is ASK modulated, and an envelope detector is used as the implant demodulator as shown in Figure 5c. It consists of a peak detector, a band-pass (BP) filter and a Schmitt trigger comparator. The peak detector is a half-wave rectifier driving the capacitor C\({}_{\text{e1}}\) that filters out most of the carrier frequency component. The BP filter in cascade has a center frequency of 400 kHz and is used to capture the clean data to be sent to the Schmitt trigger comparator for quantization. In the steady state, V\({}_{\text{AC2}}\) is a DC voltage of V\({}_{\text{DC}}\)/2 and is connected to the negative input terminal in- of the Schmitt trigger comparator. The data signals are single-ended that drives the positive input terminal in+. The Schmitt trigger comparator has two negative thresholds: \(-\)0.1 V and \(-\)0.3 V. In the idle state, in+ is almost equal to in- and the output is \"High\". When \"1\" is transmitted, it is the same as the idle state. When \"0\" is transmitted, if the negative pulse goes below \(-\)0.3 V, the output will be triggered to \"Low\". Negative pulses due to noise or power regulation with amplitudes lower than 0.3 V will be rejected. The 0.2 V threshold window is used to avoid multiple switching in one negative pulse.  
The Schmitt trigger comparator is shown in Figure 5d. Both the input transistor pair and the current mirror are designed not to be matched to generate different input offset voltages that serve as the negative threshold voltages. When the output is \"low\", M\({}_{\text{N4}}\) and M\({}_{\text{P4}}\) are turned off. The mismatch between M\({}_{\text{N1}}\) and M\({}_{\text{N2}}\), and the mismatch between M\({}_{\text{P1}}\) and M\({}_{\text{P2}}\) together result in a negative triggering threshold of around \(-\)0.1 V. When the output is \"high\", M\({}_{\text{N4}}\) and M\({}_{\text{P4}}\) are turned on. The mismatch  
Figure 5: **(a)** Voltage detector (3-bit flash analog-to-digital converter (ADC)) **(b)** Quantization code **(c)** Internal demodulator **(d)** Schmitt trigger comparator.  
between M\({}_{\rm N1}\) and M\({}_{\rm N2}\) + M\({}_{\rm N3}\) and the mismatch between M\({}_{\rm P2}\) and M\({}_{\rm P1}\) + M\({}_{\rm P3}\) result in another negative triggering threshold of around \(-\)0.3 V.
## Nomenclature  
\begin{tabular}{l l} \multicolumn{1}{c}{} & \\ \(\mathrm{f_{s}}\) & Switching frequency \\ \(\mathrm{V_{in}}\) & Input voltage of PoL power supply \\ \(\mathrm{V_{out}}\) & Output voltage of PoL power supply \\ \(\mathrm{I_{L0}}\), \(\mathrm{I_{Ln}}\) & DC value and nth harmonic amplitude of inductance current \\ \(\mathrm{\Delta i_{L}}\) & Ripple of inductance current \\ \(\mathrm{I_{o}}\) & Load current of PoL power supply \\ \(\mathrm{C_{GD}}\) & Capacitance between gate and drain of integrated Si LDMOSFT \\ \(\mathrm{C_{GS}}\) & Capacitance between gate and source of integrated Si LDMOSFT \\ \(\mathrm{C_{DS}}\) & Capacitance between drain and source of integrated Si LDMOSFT \\ \(\mathrm{C_{RSS}}\) & Input capacitance of integrated Si LDMOSFET \\ \(\mathrm{C_{OSS}}\) & Output capacitance of integrated Si LDMOSFET \\ \(\mathrm{C_{RSS}}\) & Reverse transfer capacitance of integrated Si LDMOSFET \\ \(\mathrm{R_{G}}\) & Gate resistance of integrated Si LDMOSFET \\ \(\mathrm{R_{on}}\) & Drain-source on-resistance of integrated Si LDMOSFET \\ \(\mathrm{V_{DS}}\) & Drain and source voltage of integrated Si LDMOSFT \\ \(\mathrm{V_{GS}}\) & Gate and source voltage of integrated Si LDMOSFT \\ \(\mathrm{I_{DS}}\) & Drain and source current of integrated Si LDMOSFT \\ \(\mathrm{V_{PL}}\) & Miller voltage of integrated Si LDMOSFT \\ \(\mathrm{V_{th}}\) & Gate threshold voltage of integrated Si LDMOSFT \\ \(\mathrm{V_{DD}}\) & Power supply voltage of driver \\ \(\mathrm{m_{n}}\) & Electron mobility \\ \(\mathrm{W_{M}}\) & Channel width of integrated Si LDMOSFET \\ \(\mathrm{L_{M}}\) & Channel length of integrated Si LDMOSFET \\ \(\mathrm{E_{M}}\) & Drain/source width of integrated Si LDMOSFET \\ \(\mathrm{C_{ox}}\) & Gate oxide capacitance per unit area of integrated Si LDMOSFET \\ \(\mathrm{C_{ov}}\) & Gate-to-source/drain overlap capacitance per unit width of integrated Si LDMOSFET \\ \(\mathrm{C_{j}}\) & Source/drain junction capacitance per unit area of integrated Si LDMOSFET \\ \(\mathrm{C_{SW}}\) & Source/drain sidewall junction capacitance per unit length of integrated Si LDMOSFET \\ \(\mathrm{L}\) & Inductance value \\ \(\mathrm{I}\) & Coil-wire length of inductor \\ \(\mathrm{A_{v}}\) & Cross-section area and perimeter of inductance coil-wire \\ \(\mathrm{\mu_{0}}\) & Space permeability \\ \(\mathrm{\rho}\) & Resistivity of inductance coil-wire material \\ \(\mathrm{\delta_{n}}\) & Skin depth of nth harmonic inductance current \\ \(\mathrm{R_{DC}}\), \(\mathrm{R_{AC,n}}\) & DC and nth harmonic AC equivalent resistances of inductor \\ \end{tabular}
## 4 Methodology and Prototype Implementation
### Integration of All Components  
A detailed overview of the implemented prototype is given by Figure 5. To conclude, we chose a service-oriented architecture in order to build a modular and extensible framework. The individual components such as the user interface exchange information via the application protocol \"MQTT\". With the help of the two ComHandlers, the interaction between the EMS and the aEMP is also implemented on basis of an MQTT bridge between both message brokers.  
For the HKS3-based communication from the aEMP to the EMS, the MQTT protocol is encrypted via TLS between the aEMP and the SMGW, and via SSL for SOCKSv5 between the SMGW and the EMS. In the prototype, we integrated a charging station as exemplary DER. The crucial function we focus on is the transparent data tunnel. This function is used to enable a secured bidirectional communication between the aEMP and the decentralized automated EMS. The aEMP adds restrictions to the local optimization conducted by the EMS, which controls the charging according to the consumer's needs and provides flexibility (time of departure, energy to be charged till departure). When replacing the heuristic with a suitable optimization model, further DERs could be integrated easily due to the use of MQTT as the application protocol that supports the efficient exchange of information between many entities. The necessary steps are, firstly, the definition of a shared data format for exchanging states and schedules; secondly, the integration into the optimization problem; and lastly, the actual implementation. This needs to be repeated for each new type of DER. Regarding the constraints dictated by the aEMP, the individual (contractual) restrictions may have to be updated. Aside from this change of parameters, i.e., the maximum power during the yellow or red phase, the employment of this restriction based demand response scheme makes it possible that no further steps are required by the aEMP. Moreover, adding further DERs does not require updates of any SMGW-specific configurations.
# Spoofing Traction Strategy Based on the Generation of Traction Code
###### Abstract  
Traction spoofing is an important component of Global Navigation Satellite System (GNSS) intermediate attacks, and the traction scheme directly determines the concealment of spoofing. However, spoofing via conventional traction strategies can be easily detected using Time of Arrival (TOA) and power detection. Based on a BPSK-modulated signal, a novel traction strategy using traction code is proposed to suppress part of the authentication signal and form an ideal correlation peak. This strategy was modeled and simulated to verify its theoretical feasibility. Effective spoofing data were generated based on the signal generation software to verify the spoofing effect with the reception of the software receiver. It can be inferred that no significant distortion occurred throughout the traction process, and the value range of the traction speed was expanded. The received results in different scenarios demonstrated that the observations' Root-Mean-Square Error (RMSE) percentage change in the proposed strategy is significantly better than those of conventional strategies. A Ratio Test was also performed, verifying that the strategy can bypass Signal Quality Monitoring (SQM) detection. Meanwhile, the proposed strategy remained effective when the C/N\({}_{0}\) increased to 60 dBHz. In summary, the proposed strategy exhibits destructiveness, concealment, and adaptability on the battlefield.  
covert spoofing attack; GNSS; signal suppression; traction code; traction spoofing +
Footnote †: journal:  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-00000-0000-0000-00000-00000-00000-0000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-00000-000000-00000-000000-000000-000000-000 by the monitoring signal power changes. As shown in Figure 1, TOA detection includes change detection of observations [7] and distortion detection of correlation values [8,9]. The observation objects can include the code frequency, carrier frequency, and other information.  
Therefore, to address possible security threats, designing a traction strategy to bypass the spoofing detection means of the receiver and stably invading the target tracking loop is an important issue in traction spoofing.
## 2 LEO Satellite Networks Model  
At the beginning of this section, we first explain the definition of the symbols used later in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbol** & **Definition** \\ \hline \(G\) & The direct graph of the LEO system \\ \(V\) & The set of satellites \\ \(E\) & The set of inter-satellite links \\ \(t_{c}\) & The queue check interval \\ \(I_{agg}(t)\) & The average input packet rate \\ \(O_{avg}(t)\) & The average output packet rate \\ \(I_{agg}\) & The prediction average input packet rate \\ \(O_{avg}\) & The prediction average output packet rate \\ \(\lambda_{I}\) & The weight of the past average input rate \\ \(\lambda_{O}\) & The weight of the past average output rate \\ \(\alpha_{0},\alpha_{1},\alpha_{2}\) & Parameters to calculate \(\lambda_{I}\) and \(\lambda_{O}\) \\ \(L_{\max}\) & The max length of the buffer queue \\ \(L(t)\) & The current length of the buffer queue \\ \(q\) & The queue occupancy rate \\ \(p\) & The predicted queue occupancy rate \\ \(T_{1}\) & The threshold between free state and busy state \\ \(T_{2}\) & The threshold between busy state and congested state \\ \(X\) & The traffic reduction ratio \\ \(t_{s}\) & The desired time for a satellite to reside in free state \\ \(N\) & The index of satellite node \\ \(n\) & The direction of inter-satellite link, \(n\in[1,2,3,4]\) \\ \(t\) & The timestamp of link state \\ \(t_{h}\) & The period of HELLO packet \\ \(t_{d}\) & The maximum acknowledgment time of HELLO packet \\ \(LST(N,n)\) & Link state table of satellite \(N\) at direction \(n\) \\ \(NLST(N,n)\) & Link state table of neighbor satellite \(N\) at direction \(n\) \\ \(S\) & The set of states, represented by \([N_{s},N_{d},LST]\) \\ \(A\) & The set of actions, represented by \(N_{next}\) \\ \(P\) & The state transition probability, represented by \(P_{next}\) \\ \(R\) & The reward, represented by \(r\) \\ \(N_{s}\) & The source node \\ \(N_{d}\) & The destination node \\ \(m\) & The number of neighbors for current satellite node \\ \(s_{i}\) & The number of link states of neighbor satellite \(i\) \\ \(RAAN\) & The right ascension of ascending node \\ \(\omega\) & The mean anomaly \\ \(\alpha\) & The weight of inter-plane ISL \\ \(\beta\) & The weight of intra-plane ISL \\ \(r_{d}\) & The reward for success \\ \(r_{s}\) & The punishment for mistake \\ \(\theta^{online}\) & The weight of online DNN \\ \(\theta^{target}\) & The weight of target DNN \\ \(N^{target}\) & The number of iterations to reset \(\theta^{target}\) \\ \(\varepsilon\) & Greedy value for DDQN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of the symbols.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 3 Scheduling Algorithms and Their Performance
### Classical Optimization Algorithms
For single-node multi-GPU placement with memory constraints, column-generation generates only profitable assignment patterns, closing much of the optimality gap while keeping per-decision latency under one second for up to 200 jobs [150]. In summary, ILP/MILP provides a rigorous benchmark and a foundation for principled approximation schemes (LP-rounding, list scheduling), though direct deployment at cluster scale is limited by computational cost. Heuristic and meta-heuristic approaches: Beyond simple greedy rules, meta-heuristics such as genetic algorithms (GA), simulated annealing (SA), Tabu search, and particle-swarm optimization (PSO) have been explored for GPU scheduling, especially in heterogeneous environments. These methods encode a complete schedule (or resource allocation) as a candidate solution and iteratively refine it. In a GA, for instance, each chromosome may represent an assignment of jobs to time slots or GPUs. An initial population of random schedules undergoes crossover and mutation, guided by a fitness function aligned with objectives like minimizing makespan or maximizing throughput. GAs have been applied to CPU-GPU cluster scheduling to optimize multiple criteria simultaneously--such as makespan, energy efficiency, and fairness [107]. SA, by contrast, perturbs a current schedule with local random moves and accepts changes according to a temperature-driven probability: uphill moves are occasionally allowed to escape local optima, with the acceptance probability decreasing over time. Similar strategies underlie Tabu search and PSO. Meta-heuristics excel at exploring large, complex search spaces and can produce high-quality schedules given sufficient runtime. However, like MILP solvers, they incur significant computational overhead--often seconds to minutes per schedule--making them unsuitable for online scheduling, where decisions must occur within milliseconds. As a result, their use is generally confined to offline or semi-offline settings (e.g., precomputing schedules for known workloads or tuning parameters for lighter-weight heuristics). Furthermore, they lack optimality guarantees, and their performance hinges on careful parameter tuning, which itself can be costly [151]. Empirical studies show that GA and SA may slightly outperform greedy heuristics in heterogeneous clusters, but at the expense of minutes of computation time per run. Despite these drawbacks, meta-heuristics offer valuable insights for policy design. For example, one could run a GA offline on historical traces to optimize scheduling-policy parameters, then deploy the tuned heuristic for real-time decisions. In practice, many production-grade managers--such as [PERSON] and [PERSON]--rely on simple heuristics augmented with tunable weights or priorities, striking a balance between responsiveness and solution quality [152].
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 2 Related Work
For the edge nodes within this scenario, they serve the dual roles of being both client nodes for federated learning training and nodes on the blockchain network. Leveraging the capabilities of blockchain technology, a distributed ledger is designed using a directed acyclic graph for local ledger storage of transactions in each edge node. Furthermore, ISFL-Sys integrates a novel incentive mechanism through blockchain's smart contracts, specifically tailored for the federated learning context. This mechanism is designed to motivate device nodes to actively participate in the training process, resulting in increased training efficiency. During the model aggregation process, an innovative trust evaluation model is introduced. This model employs trust scores to effectively identify malicious clients, preventing intentional upload of erroneous model parameters by these malicious entities. This countermeasure safeguards the accuracy of the global model against potential degradation caused by malicious contributions. Within the trust evaluation model, a novel and optimized Euclidean distance evaluation  
Figure 1: Schematic diagram of blockchain structure. method is proposed. This method reduces computational resource consumption while still providing relatively accurate trust assessment results. This optimized approach enhances the overall efficiency of the trust evaluation process. In conclusion, the ISFL-Sys system addresses the challenges posed by existing federated learning solutions by leveraging blockchain technology. It establishes a distributed learning environment, introduces an incentive mechanism, and employs a trust evaluation model to enhance security, efficiency, and accuracy in federated learning scenarios. The detailed specifics of the ISFL-Sys system solution will be presented in the following sections.
## 3 Results and Discussion
### Evaluation of the Pockels Coefficient of the Interfacial Water
#### 3.4.1 Thickness of the Electric Double Layer of Water  
The electrolyte concentration could not be increased because it would react with the electrode. Then, as the Debye Huckel length \(d\) (\(d=0.304M^{-\frac{1}{2}}\) where \(M\) is the molar concentration of electrolyte) increases with decreasing concentration, thickness \(d\) of the EDL (diffuse layer) is increased, resulting in the smaller interfacial \(E\) field \(V/d\) for the same applied voltage \(V\). Therefore, for the same Pockels coefficient, the \(\Delta n\) at the interface in direct contact with the electrode is small. In other words, the thicker the interfacial layer (=EDL) is, the smaller the \(\Delta n\) within the layer becomes. However, it should be noted that this (i.e., \(d=0.304M^{-\frac{1}{2}}\)) is a steady-state value with a direct current (DC) \(E\) field applied, while in this experiment with the AC \(E\) field applied, the EDL is transiently formed and must be considered thicker. The formation dynamics of the EDL have only recently been addressed in papers [27; 28], and it is currently difficult to estimate the effective thickness in this experiment. Therefore, one can safely consider \(d_{w}\) to be between 30 nm and 300 nm. In this case, for the same Pockels coefficient, the \(\Delta n\) of water at the interface directly in contact with the metal depends on the assumed \(d_{w}\). In contrast, when the penetration length \(\delta_{w}\) of SPP into water is larger than \(d_{w}\), the energy shift of SPP resonance does not sensitively depend on \(d_{w}\) as shown below, so that the Pockels coefficient can be determined in a reliable manner.
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 4 Forward Speed of the Train  
### Forward Speed  
The forward speed of the train has a direct influence on the energy of the signal. For example, if the train passes over a defect with low speed, it might not create enough energy for that defect to be detected. As a result, there may be some passes that show high energy levels at a specific location, but some others with low amplitudes. Figure 7 shows the forward speeds for the first five passes from the dataset. It can be seen that there is considerable speed variation, which is the result of several factors, such as driver behavior, temporary speed restrictions, earlier delays, or weather. The second pass has the lowest speed through most of the sample zone.  
Figure 2 shows that, in the second pass, there is a low amplitude between 6000 and 7000 m relative to the other passes in this zone. This amplitude level is not useful in this study. Therefore, a cleaning process was employed to remove the low-speed signals from the dataset. In this work, the forward speed of the train at each kilometer point was monitored and data removed for segments of data for which the minimum speed was less than 50 km/h.
## 4 Study Cases and Analysis Results
Figure 16: The process to find the unknown design values of study case 4. Figure 15: Class-E PA waveforms of study case 3. (**a**) \(C_{p}\) surface. (**b**) \(C_{p}\) for \(D=0.45,0.47,0.5,0.53,0.55,\) and 0.57. (**c**) Model and simulated waveforms of the switch current and \(P_{out}\). The optimization goals and maximum ratings were verified in the simulation for each example, the obtained simulated results were summarized and confronted with the predicted values in Table 9. The simulated efficiency in all cases was 0.99. The study cases 1 and 2 show a good performance, ZVS and ZVDS conditions are achieved, and the Absolute Percentage Error (APE) is less than 2.14% in the simulated parameters. In the third design, the predicted and simulated \(C_{p}\) value has an absolute percentage error of 1.06%. The study case 4 accomplish an \(R_{L}\) value 33% greater than the obtained with the typical operation point (the load resistance calculated with \(D=0.5\) and \(q=1.412\) is 8.18 \(\Omega\) for the same specifications). Additionally, the \(I_{RMS_{SW}}\) restriction is achieved and it has an APE of 1.16%. The Mean Absolute Percentage Error (MAPE) of the analyzed parameters is 3.01%, which verifies the adequate design procedure. Figure 17: Class-E PA waveforms of study case 4. (**a**) \(R_{L}\) for \(D\) = 0.25, 0.325, 0.5, 0.625, and 0.75. (**b**) \(I_{RMS_{sw}}\) for \(q\) = 0.3, 0.7, 1.1, 1.5, and 1.9. (**c**) Model and simulated waveforms of the switch current and average \(i_{s}\).
# Practical Use of Starlink Downlink Tones for Positioning
## 2 Signal Detection and Tracking
### Tones Frequency Shift  
Figure 7 shows the frequency offset from Fo measured on all channels. It shows that many tones are tracked simultaneously (between 4 and 30 tones at one time). Tone tracking lasts from a few seconds to two minutes, and is frequently interrupted. This is due to a weak signal strength that oscillates on both sides of the tracking sensitivity (oscillations are highlighted in Figure 5b).  
Figure 8 shows the measured frequencies for two satellites (SV). It can be seen that nine tones are captured in total for SV#1 and only five tones for SV#2. It further shows that tracking is frequently interrupted. This is a result of the weak signal strength of the tones combined with an oscillation in tone magnitude (observed in Figure 5). In other words, tones with a C/No that fluctuates around the tracking sensitivity (~23 dB-Hz) are alternately tracked and lost during the satellite visibility time, which explains the tracking interruptions. Furthermore, we note that the tracking interruptions do not necessarily occur at the same time for tones from the same satellite. This is due to the fact that tones from the same satellite do not have the same magnitude. It follows that high power tones are less likely to be frequently lost than low power tones. We further note that the tracking time of the tones is between a few tens of seconds and a few minutes.  
Figure 8: (**a**) Measured tone frequencies for satellite SV#1; (**b**) Measured tone frequencies for satellite SV#2, showing the difference in the number of tones tracked as well as the intermittent tracking. The latter is due to the low signal strength and the oscillating magnitude of the tones over time. The interruptions do not occur at the same time for tones of the same satellite because they have different amplitudes.  
Figure 7: (**a**) Measured frequency of the tracked tones relative to the frequency Fo; (**b**) number of tracked tones.  
Figure 9 gives the probability density function of the tones' frequency error computed with the measurements of the tracked satellites. This error is the deviation of frequency measurement curves with respect to their polynomial fit. It shows a non-symmetrical distribution of the error with a standard deviation of 10.92 Hz (\(\sim\)0.28 m/s). The frequency tracking error is mainly caused by thermal noise (with an expected value of \(\sim\)4.5 Hz (1\(\sigma\)) in the case of C/No = 31 dB-Hz, T = 10 ms, and \(B_{0}\) = 10 Hz) and by the presence of distortions in the incoming signal that we observed during signal tracking.