esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Analysis of Orbit Determination Performance
### Analysis with Measurement Errors--Global Network  
When adding a fourth ground station, observation geometry improves. Orbit determination results based on this global network are shown in Table 4. It can be observed that the RMSE and the SiSRE\({}_{\text{orb}}\) significantly improve by 30-45% with respect to the scenario with three ground stations, especially for shorter orbital arcs. Again, it can be seen that increasing the measurement interval reduces the negative impact of noise and biases.  
The behavior of the OD error inside the estimated 2 h orbital arc is provided in Figure 4a. Similarly to the three ground station scenarios, OD errors tend to grow when approaching the extremes of the estimation interval. The average extrapolation error obtained with the parameters estimated over the 2 h fitting interval (Figure 4a) is shown in Figure 4b, where the three radial, across-track and along-track components are also provided. It can be seen that the prediction error at 1 h, approx. 25 m, is smaller than in the three regional ground station scenarios (40 m). It should be noted that the radial error is not significantly affected and remains at a 1.5 m level. Both OD and prediction performance show the benefits of including an additional ground station, located such as to significantly improve observation geometry.  
Figure 3: Average absolute post fit error and extrapolation error for the 24 MEO satellites obtained for a regional network of three ground stations, with ISR and GSR observations affected by noise and biases. The orbit parameters are estimated over a 2 h long arc. (**a**) Post fit error. (**b**) Extrapolation error.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Orbital arc** \\ **Length** \\ \end{tabular} & \begin{tabular}{c} \(\sigma_{\text{CSR}}\) \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} \(\sigma_{\text{ISR}}\) \\ **(cm)** \\ \end{tabular} & \begin{tabular}{c} \(b_{ij}\) \\ **(cm)** \\ \end{tabular} & \begin{tabular}{c} **Radial RMSE** \\ **(cm)** \\ \end{tabular} & \begin{tabular}{c} **Along-tr. RMSE** \\ **(cm)** \\ \end{tabular} & \
\begin{tabular}{c} **Cross-tr. RMSE** \\ **(cm)** \\ \end{tabular} &
\begin{tabular}{c} **SiSRE\({}_{\text{orb}}\) \\ **(cm)** \\ \end{tabular} \\ \hline
1 h & 50 & 1 & None & 62.83 & 441.24 & 335.83 & 93.98 \\ \hline
2 h & 50 & 1 & None & 7.56 & 44.73 & 45.65 & 11.04 \\ \hline
3 h & 50 & 1 & None & 6.32 & 36.46 & 36.23 & 9.04 \\ \hline
1 h & 50 & 1 & [1, 10] & 82.13 & 488.99 & 630.62 & 130.07 \\ \hline
2 h & 50 & 1 & [1, 10] & 12.80 & 90.01 & 75.75 & 19.60 \\ \hline
3 h & 50 & 1 & [1, 10] & 10.71 & 57.86 & 58.25 & 14.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average OD RMSE for a MEO constellation linked to a regionl network of three ground stations. ISR and GSR measurements are affected by noise and biases.
###### Abstract  
The frequency band in the millimeter-wave (MMW) and sub-terahertz (sub-THz) range has shown great potential in mobile communication technology due to the advantages of ultra-large bandwidth and ultra-high data rates. Based on the increasing research activities on MMW/sub-THz waves, biological safety at relevant frequencies must be explored, especially when high-power illumination occurs. Here, its non-ionizing nature plays a vital role, which makes it safe for humans at low illumination powers. However, under high power, the biothermal heating on the skin surface is still a main concern, and lots of research has been conducted in a laboratory. In this article, we analyze the thermal heating effect of human skin in outdoor environments, where atmospheric conditions can significantly impact the propagation of MMW/sub-THz waves. Our analysis is based on rat skin, which has a similar structure to human skin. A theoretical model combining Pennes' bioheat transfer equation (BHTE), the ITU model, and the Mie scattering theory is developed. Good agreement between calculation results and measured data confirms the efficiency of this model. The influence of rainfall rate, humidity, operating frequency, illumination time, power density, and propagation distance is presented and discussed.  
Keywords:MMW/sub-THz waves; biothermal heating effect; human skin; outdoor weather; water layer +
Footnote †: journal: Applied Sciences
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 3 The Proposed Methods
### An Evaluation of the Results of Image Defogging
#### 3.4.1 Subjective Evaluation  
Subjective evaluation is a qualitative method of analysis. It is based on people's subjective feelings and visual experience and judges the advantages and disadvantages of an image by observing its clarity, resolution, and color accuracy and the level of detail of features. This evaluation method requires a lot of time and human resources and is easily affected by subjective feelings, but the combination of objective evaluation indicators can make the evaluation results more reliable.
# Impact of Road Traffic Tendency in Europe on Fatigue Assessment of Bridges
## 3 Fatigue Load Models  
To discuss the impact of the three considered traffics on fatigue assessment of steel details of road bridges, some relevant case studies have been considered, varying the bridge scheme and the span, as described in detail in Section 4.  
Besides real traffics, to measure the differences in terms of results of conventional fatigue assessment, also the notional fatigue load models of EN1991-2, LM2, \"set of standard lorries\", and LM4, \"set of equivalent lorries\", briefly described in the following, have been taken into account. In fact, these fatigue load models, although accurately calibrated on the basis of Auxerre traffic, are generally a little on the safe side.  
EN1991-2 [1] (note 2 of SS4.6.1(2), items c and e, explicitly states that use of road bridges' fatigue load models no. 2 and no. 4 is allowed only when \"the simultaneous presence of several lorries on the bridge can be neglected. If that is not the case, they should be used only if they are supplemented by additional data.\"  
To account for the simultaneous presence of lorries on the same lane, artificial traffic samples have been generated starting from LM2 and LM4 sets of vehicles. These artificial traffics have been generated using a Monte Carlo algorithm, specifically developed [35] and suitably adapted via the Latin hypercube sampling technique [36]. In the algorithm, the analytical probability that two vehicles are simultaneously travelling the lane is explicitly considered, according to [35].  
Since the comparison mainly focuses on the effects of traffic composition, an identical value of the total lorry flow has been adopted for all the considered traffics.
# Seamless Handover Scheme for MEC/SDN-Based Vehicular Networks
## 5 Performance Evaluation  
The developed handover scheme for an MEC/SDN VANET is evaluated in this section. Various simulation scenarios are considered, and the results are presented and discussed.  
Figure 5: Sliding windows to calculate PER.
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 1 Introduction
In this respect, the mobility concept in NDN can be considered to be a disruption of transaction as opposed to a breakdown of links. If transactions could be completed while hiding the collapse of the link, the NDN's mobility procedure could be simplified. Therefore, the proposed scheme introduces dual connectivity to both hide the link breakage to the NDN system and support producer mobility. This strategy that can be expressed as a soft handover. It defines a new NDN link service called the Producer Mobility Link Service (PMLS). The PMLSs in the old NDN faces hide the link breakage to the NDN system. They repair the old link so that the connectivity with the previous NAR can be maintained for a while and re-synchronize the on-going transmissions, if any exist. In addition, the PMLS at the pNAR buffers the Interests arriving during the breakage period and forwards the buffered Interests to the producer following reconnection. The old NDN face is removed after the new location information on the contents of the producer is disseminated over the NDN network by the Named-data Link State Routing Protocol (NLSR) routing protocol at the new NAR. As a result, no Interests miss being delivered due to the producer's movement. In other words, the PLMS extends the lifetime of the current routing information until the new routing information is disseminated over the network. Therefore, there is no need to modify the NDN router architecture for the mobility service, and the NDN router architecture and mobility service can evolve independently of each other.
# Nero: A Deterministic Leaderless Consensus Algorithm for DAG-Based Cryptocurrencies
## 3 The Nero Algorithm
### Data Structures  
We abstract the data structures and refer to only the contents relevant to the operation of the consensus algorithm.  
**Block.** Data containing the value transferred from the sender account to the receiver account.  
**Transaction.** To simplify the presentation, we abstract the contents of a transaction that are not relevant to the consensus algorithm, and we assume that a transaction is composed of the origin block hash and the new block hash.  
**Round.** A simple integer that starts at 0 and ends in the round where the value of an election is decided.  
**Elections.** Each correct process maintains a hashmap of the active elections, where the key is a block hash (corresponding to the origin block of a transaction) and the value is the state of the election. An election can have multiple competing transactions (called forks) if they have the same origin block and the purpose of the consensus is that all correct processes validate one of the transactions or none.  
**Election State.** The state of an election is represented by a hashmap, where the key is the election hash and the value a set of round states.  
**Round State.** The state of a round is represented by a hashmap, where the key is the round number and the value the tally of the messages of that round. It also contains the validated and pending messages.  
**Tally.** Each correct process computes a tally of the messages received in each round, with the total number of votes or commits in each value (block hash or nil).  
**Messages.** A message is composed of a:  
* Type: VOTE or COMMIT,
* Value: a block hash or a nil value,
* Round.  
A message \(m\) can be valid, pending or invalid. A message \(m\) of round \(r\) is considered valid by a correct process \(p\) if there is a set of at least \(2f+1\) messages received by \(p\) in the previous round \(r-1\) that are compatible with the value and type of message \(m\). A message is considered pending if there is no set compatible, but it is still possible to have that set by receiving the remaining messages of round \(r-1\). It is considered invalid if it is not possible to have a compatible set with the message.  
**Timer.** At the start of each round, a correct process starts a timer \(t_{r}\), where \(r\) is the round.  
**New round.** A correct process starts a new round \(r+1\) when it has received at least \(2f+1\) messages in round \(r\) and the timer \(t_{r}\) has expired. During GST, all messages from correct processes will be received before the timer expires; however, a correct process has no way of knowing that it is in GST, i.e., if it receives \(2f+1\) messages and the timer expires, it is not guaranteed that those messages are all from correct processes.
## 2 Materials and Methods
### Cytotoxicity Evaluation of p(HEMA) Hydrogel Formulations  
An MTT (3-(4.5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide) tetrazolium assay was carried to detect the metabolic action of living cells using an MTT Cell Proliferation Assay (ATCC\({}^{\oplus}\)30-1010K) [29]. HEK 293 cells were seeded at a density of 1 \(\times\) 10\({}^{4}\) cells per well containing 100 \(\mu\)L medium in 96-well plates for 24 h. The dried films (discs, number of replicates, \(n=5\) in each case, with an average diameter of 3.735 \(\pm\) 0.46 mm and average thickness of 0.79 \(\pm\) 0.04 mm) were soaked in RPMI 1640 media for softening before transferring to the wells and then they were incubated for 24 h. After, 10 \(\mu\)L of MTT reagent was added to each well, followed by incubation at 37 \({}^{\circ}\)C in 5% CO\({}_{2}\) and 95% humidity for 4 h. Subsequently, 100 \(\mu\)L of detergent reagent (stop solution) was added to each well and placed in an incubator for 2 h. Then, absorbance was recorded at 570 nm using a Synergy\({}^{\text{TM}}\) HTX Multi-Mode Microplate Reader (BioTek, Winooski, VT, USA). Cell viability was calculated as shown in Equation (1).  
Cell viability (%) = (mean absorbance of treated cells/mean absorbance of control cells) \(\times\) 100
# Automatic Docking Trajectory Design-Based Time-Varying-Radius Dubins for Unmanned Surface Vessel
## 4 Simulations and Experiments  
### Simulation Setup  
First of all, the automatic docking trajectory is verified with Matlab simulation tools. There are two kinds of dockyard movement trajectories, one of them is the translational motion following the mother ship, and the other is Eulerian-model-based motion with the movement of water. The proposed ADTA-ACO and ADTA-TRD are compared in terms of docking voyage time. The main parameters used in this simulation are listed as Table 2.
## 3 Dark Current Measurement Based on Focal-Plane Temperature and Dark Current Correction Experiment
### Satellite-Borne LWIR QWIP Detection System
#### 3.1.1 Structure and Parameters of QWIP  
The QWIP fabrication process used in the study is described below. In this study, a 30-cycle quantum well infrared detector structure was grown on a GaAs substrate using a molecular beam epitaxy (MBE) method for the face-front type LWIR QWIP using the GaAs/AlGaAs material system [25]. The grown material was tested in advance with optical parameters and single-component flow tests to confirm that the peak response was basically around 9.7 \(\upmu\)m. Then, the material was put into the focal-plane chip process flow. The grating was plasma-etched; then, the reflective layer was prepared with the ion beam sputtering method. Finally, the etching of interpixel isolation was performed using the inductively coupled plasma (ICP) etching method [26]. After growing an under-bump metallization (UBM) layer [27], the chip cell was prepared as an In column using thermal evaporation. Then, after scribing, the individual chips were interconnected with the readout circuit using mixed inverted soldering; after interconnection, the indium pillar was reinforced with glue; then, the GaAs substrate was thinned. The finished QWIP focal-plane is shown in Figure 4. The QWIP uses a DI-type readout circuit with a response spectral band of 9.01\(\sim\)10.45 \(\upmu\)m [28], encapsulated inside a metal dewar with an integrated pulsed-tubecooler and a target cooling temperature of 41 K. Its main performance indicators are shown in Table 1.
## 3 Design Process
### DAid(r) Smart Socks-Based Biofeedback System Parameter Selection  
The DAid(r) smart socks-based biofeedback system requires precise parameter selection to facilitate accurate and meaningful insights during athletic performance. This system processes sensor data from smart socks, delivering real-time biofeedback through a head-mounted display (HMD) to provide athletes with immediate information about their movement patterns. Key metrics collected by smart socks during exercise include the coordinates of the center of pressure (COP1x, COP1y, COP2x, COP2y) which indicate foot pressure distribution and have demonstrated significant correlations with hip induction and internal rotation, as identified in the study results [24]. Given the strong positive correlation between hip adduction and changes in COP1x (r = 0.785, \(p<0.001\)), COP1x has been identified as highly responsive to deviations in movement, particularly knee valgus positioning during a single-leg squat [24]. This parameter exhibits high sensitivity to both correct and incorrect movement patterns, positioning it as a primary metric for real-time biofeedback. The DAid(r) smart socks system measures several key biomechanical indicators, including plantar pressure distribution, weight shifts, and the center of pressure (COP). COP changes provide insight into postural stability and lower limb mechanics, which are crucial for preventing excessive joint loading and injury. To ensure the real-time feedback in the headset based on COP changes, a custom system is developed using on.NET Framework 4.8 (further in the document mentioned as \"system\"). During system development and collaborative discussions, it was determined that visualizing COP1x changes would offer immediate, intuitive feedback on alignment and stability. Such visualization can effectively highlight shifts in medial-lateral foot pressure, guiding athletes in modifying their movements to optimize form. For example, if a player demonstrates excessive medial-lateral movement in their foot (a sign of poor ankle stability), the system signals an adjustment, allowing for more stable foot positioning and reduced stress on the knee and ankle. Focusing on COP1x as the central parameter for biofeedback visualization  
Figure 3: Aspects of DAid® smart socks-based biofeedback system development.  
enhances the system's capacity to support proper lower limb alignment, potentially reducing injury risk and enhancing performance, especially in the high-demand movements characteristic of football and other dynamic sports. By providing real-time feedback, the system enables athletes to identify and correct improper biomechanics immediately. This targeted parameter selection underscores the DAid(r) smart sock system's value in the real-time assessment and correction of biomechanics in athletic populations, empowering athletes to maintain optimal movement patterns and reduce the risk of injury over time.
# Pruning Adapters with Lottery Ticket
## 3 Results
### Iterative Pruning and Winning Ticket  
Then, we investigate the performance of different pruning strategies during the iterations with the validation set of the GLUE dataset as Figure 3 shows. The baselines of the adapter model with a size of 128 are denoted as straight lines with task names and scores marked on them in the figure. Let \(p\) denote the percentage of weights remaining. We discovered that in most cases, pruning weights and pruning neurons are better than pruning the adapter layers, especially when \(p\leq 40\%\). Meanwhile, pruning weights and neurons are more stable in their performance than pruning the adapter layers; this indicates that pruning adapter layers have a higher risk of removing the essential parameters and thus leads to a performance drop in an early stage of pruning.  
In all datasets, there is a substantial amount of validation accuracy improvements over the standard adapter model when \(p\geq 40\%\), particularly in the CoLA and RTE datasets.  
For weight pruning and neuron pruning, a winning ticket can be found when \(p\) is around 21% and 26%. For adapter layer pruning, the winning ticket can not been spotted since the overall structure of the network has been altered too much, but there is no significant performance drop until \(p\) is around 40%. Finally, we test the pruned models in the validation set with the GLUE test set, whose results is shown in Table 2.  
We also see that the average performance for all tasks in GLUE of the winning ticket model is a bit lower than the original adapter model. However, if we only consider the datasets with sizes more than 10 K, as shown in Figure 4, we found that the winning ticket produced by weight pruning and neuron pruning has the same or even better performance than the original adapter model, which implies that the pruning strategy works better on larger datasets, since a dataset of larger size has a larger validation set, and a larger validation set will better guide the pruning to identify the best fit winning ticket.  
Figure 3: Performance of three different pruning schemes on GLUE validation sets at every pruning iteration. Horizontal line represents the performance of adapters before pruning starts. Each type of marker denotes a pruning scheme.  
Figure 2: Comparisons of average performance of different models on GLUE test sets.  
Moreover, we further test an adapter model with size 27, which is approximately the number of parameters in an adapter of size 128 with 21% left. It is surprise to see that the winning ticket by weight pruning outperforms the adapter of size 27, indicating that the pruned adapter model can be better than the one that is only fine-tuned.
# An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
## 3 System Model
### Primary and Secondary Cell Association
\(d_{iu}\) and \(d_{ju}\) are the distances between UE \(u\) and the closest APs utilizing same CC from \(I\) and \(J\), respectively. Then, the cumulative distribution function (CDF) of \(d_{iu}\) is [30]  
\[Pr\{d_{ju}\leq(\frac{\psi_{u}p_{iu}^{t}}{p_{ju}^{t}})^{-\alpha^{-1}}d_{iu}\}=1 -e^{-\phi\pi d_{iu}{}^{2}}, \tag{8}\]  
where \(\phi=(\lambda_{I}+\lambda_{I}(\frac{\psi_{u}p_{iu}^{t}}{p_{ju}^{t}})^{-2\alpha ^{-1}})\). And the probability density function (PDF) of \(d_{iu}\) can be found as  
\[f_{r}(r)=e^{-\phi\pi r^{2}}2\pi\phi r, \tag{9}\]  
with random value \(r\) replacing \(d_{iu}\).
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
## 2 Theory and System Design
### Design of system
When TX is logic level 1, N-MOSFET is on, the voltage-controlled current source generates a constant current, and the LD emits  
Figure 8: Diagram of the operation of the relay station. Figure 10: Pulse width demodulation and receiving circuit. Figure 9: Pulse width modulation and transmitting circuit. light. When TX is logic level 0, N-MOSFET is off, and the LD does not emit light. Therefore, the transmitting system realizes the function of modulating data into a PWM laser signal. The circuit principle of the receiving device is shown in Figure 10, which is mainly composed of a laser receiving circuit and a PWM demodulator (inside the FPGA). The laser receiving circuit is mainly composed of an avalanche photodiode (APD), a trans-impedance amplifier (TIA), and a reverse proportional operational amplifier. The PWM demodulator is mainly composed of an ADC sampler, logic discriminator, pulse width discriminator, shift controller, counter, data discriminator, and data converter. The APD photocurrent I received by the pulse laser receiving circuit is converted into voltage \(V_{1}\), and then the voltage \(V_{1}\) is further amplified by the second amplifier to obtain voltage \(V_{2}\), so the laser signal is converted into the voltage signal RX, where the voltage \(V_{2}\) is:  
\[V_{2}=-\frac{R_{3}}{R_{2}}V_{1}=-\frac{R_{3}}{R_{2}}(-IR_{1})=\frac{R_{1}R_{3}} {R_{2}}I \tag{8}\]  
ADC samples the RX signal, converts the voltage signal into data and sends the sampled data to the PWM demodulator. In the PWM demodulator, the ADC sampler drives ADC to sample the RX signal. The logic discriminator judges the logic level according to the data obtained. When logic level 1 is detected, the counter starts counting. When logic level 0 is detected, the counter stops counting. At the same time, the pulse width detector counts the logic judgment results, the data discriminator judges the data according to the statistical results of the pulse width detector, and finally converts the serial 8-bit data to the parallel 8-bit data output through the shift controller and data converter.