esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 4 Numerical Results
### Execution Times and Discussion of Parameter Values  
We present here some characteristics of ITC-ADL based on experimental evidence. The average running times of ITC-ADL, in the configurations described above, are shown in Table 5, together with those of AK-SVD. Over the considered \(n\) and \(s\) values, the ratio between the execution times of ITC-ADL and AK-SVD varies between 3.34 and 4.12. Without reporting any actual times, we note that the ITC and the SNR have almost no influence. In addition, the execution time is roughly proportional with the number of iterations \(K\) and the number of signals \(N\). The other parameters have obvious influences: more frequent dictionary size changes (smaller \(c\)) increase the time; same effect has a larger number of candidates \(n_{\text{cand}}\); the size steps \(\delta_{+}\) and \(\delta_{-}\) only slightly affect the time. We note that our implementation is not fully optimized, but it is based on a very efficient implementation of AK-SVD [30]. We chose the parameter values for the experiments reported in the previous section with the aim to show that a single set of values ensures good results for all considered dictionary sizes and sparsity levels. In fact, ITC-ADL is quite robust to the parameter values. Nevertheless, fine tuning is possible when \(n\) and \(s\) have a more limited range of values. We present below a few results that show the effect of the parameters on the outcome of ITC-ADL. We considered only the cases \(n\in\{128,256\}\) \begin{table}
\begin{tabular}{c c c c c c|c c c c c} \hline \hline
**SNR ( dB)** & \(\mathbf{s=4}\) & **6** & **8** & **10** & **12** & **SNR ( dB)** & \(\mathbf{s=4}\) & **6** & **8** & **10** & **12** \\ \hline \multirow{3}{*}{\(10\)} & 1.0427 & 1.0500 & 1.0617 & 1.0859 & 1.4794 & 10 & 1.0426 & 1.0504 & 1.0905 & 1.1843 & 1.5705 \\  & 0.9430 & 0.9670 & 0.9717 & 0.9835 & 0.9540 & & 0.9429 & 0.9674 & 0.9981 & 1.0723 & 1.0123 \\ \hline \multirow{3}{*}{\(20\)} & 1.0400 & 1.0408 & 1.0448 & 1.0508 & 1.0646 & \multirow{3}{*}{20} & 1.0385 & 1.0414 & 1.0466 & 1.0563 & 1.1320 \\  & 0.7105 & 0.8184 & 0.8342 & 0.8434 & 0.8792 & & 0.7095 & 0.8189 & 0.8356 & 0.8479 & 0.9375 \\ \hline \multirow{3}{*}{\(30\)} & 1.0685 & 1.0482 & 1.0488 & 1.0616 & 1.1677 & \multirow{3}{*}{30} & 1.0535 & 1.0466 & 1.0610 & 1.0882 & 1.2282 \\  & 0.3024 & 0.4160 & 0.4996 & 0.5599 & 0.7156 & & 0.3017 & 0.4152 & 0.5066 & 0.5718 & 0.7482 \\ \hline \multirow{3}{*}{\(40\)} & 1.1130 & 1.0600 & 1.1090 & 1.2461 & 1.3096 & \multirow{3}{*}{40} & 1.1304 & 1.0574 & 1.0790 & 1.2541 & 1.2866 \\  & 0.1147 & 0.1647 & 0.2471 & 0.4346 & 0.5666 & & 0.1164 & 0.1627 & 0.2410 & 0.4363 & 0.5613 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ratios RMSE/RMSEt and RMSE/RMSEn on test data, when \(n_{\text{true}}=256\). Left: ERNML\({}_{1}\). Right: EBIC.
# RMCSat: An F10.7 Solar Flux Index CubeSat Mission
## 4 Satellite Architecture and Subsystems
### Communications and Link Budget  
The communications plan relies on the use of a single ground station at RMC in Kingston, Ontario, Canada, and a store and forward mode of data collection. The ground station consists of a 3.0 m parabolic S-band antenna with a high rate S-band receiver and a Yagi antenna and associated transceiver for UHF and VHF signals. Other locations with similar S-band downlink and UHF/VHF capabilities are suitable if required as the geographical location of the ground station is not critical to the mission.  
The satellite passes over the RMC ground station four to six times each day, with a mean access time of 573 s as determined using the Systems Tool Kit software. The operations plan reserves a 25% error margin given 573 s is the mean access value, thus 430 s is used for rate calculations. A minimum downlink data rate of 62.6 kbps is required if the downlink is completed during one pass. This rate sits well within the S-band system capabilities. The maximum VHF uplink rate of the ground station is 1.2 kbps. Consequently, the estimated 1 Mb of TT&C data is uplinked over two passes resulting in a rate of 1.16 kbps.  
If the UHF system is required due to S-band failure, only TT&C data and solar observations can be downlinked (5.75 Mb per day). To achieve an adequate link margin of 3.0 dB for the UHF system (Table 9), the maximum data rate is limited to 3.5 kbps. Four passes are required to downlink the data using the UHF system at a rate of 3.3 kbps.  
Link margin calculations are presented in Table 9. The link margins of the S-band downlink, VHF uplink, and UHF downlink are calculated as 13.96 dB, 17.37 dB, and 3.06 dB, respectively.  
The storage capacity of the onboard computer is 4 GB, with the option of up to 32 GB. This offers 148 days, or up to 1188 days, of storage before payload data must be overwritten. Additional storage exists within the S-band transmitter, up to 32 GB. If the UHF system is required for downlink, successive passes can be used to downlink more observational data once the daily data are received.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Property** & **2695 MHz** & **2800 MHz** \\ \hline BWFN & 47.2\({}^{\circ}\) & 44.1\({}^{\circ}\) \\ \hline Minimum observation time & 752 s & 703 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Minimum observation times required for 2695 MHz and 2800 MHz based on antenna BWFN.  
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **Solar Observations** & **Full Orbit Observations** \\ \hline Time of observation (seconds) & 1052.8 & 5738.99 \\ Number of lines per second & 6 & 6 \\ Lines of data in one orbit & 6317 & 34,434 \\ Lines of data per day & 95,099 & 518,400 \\ Data generated per orbit (byte) & 315,840 & 1,721,697 \\ Data generated per day (byte) & 4,754,944 & 25,920,000 \\ Data generated per day (Mb) & 4.75 & 25.92 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Data generation for RMCSat payload. An additional 40% margin is added to the minimum observation time of 752 s and 50 bytes is assumed per line of data recorded.
# Fractional Frequency Reuse Optimal SINR Threshold Selection Using NIR and ISODATA
## 2 Literature Review
### [PERSON]'s Index of Fairness  
A more comprehensive FFR scheme performance evaluation is accomplished by considering both fairness and throughput [13]. [PERSON]'s fairness index is important as it grades how throughput is equitably distributed among subscribers. [PERSON]'s fairness index is expressed as:  
\[J(x)=\frac{\Bigl{(}\sum_{i=1}^{N}x_{i}\Bigr{)}^{2}}{N\sum_{i=1}^{N}x_{i}^{2}} \tag{8}\]  
The number of users in each coverage areas is denoted by \(N\), while the throughput that the \(i\)th user experiences is given by area of coverage \(x_{i}\).
# Acoustic, Myoelectric, and Aerodynamic Parameters of
## 3 Results
### Study Methodology  
Table 1 shows the summary of the methodology used in the 19 studies included in the review. In most of them, a sample made up of two groups was observed: an experimental group made up of people with vocal pathology and a control group made up of people without vocal pathology [12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Another three were made up of a group of volunteers with vocal pathology [26; 27; 28] and two more were composed of volunteers without vocal pathology [29; 30]. In all of the observational studies, the groups were established in a non-randomized controlled manner. The sample size of the included studies ranged from 10 [26] to 387 [15] participants, except for one study [20] which included a sample of 1410 participants due to the use of a previous database.  
Regarding the parameters analyzed in the different studies (Table 2), three categories can be established: acoustic measurements, aerodynamic parameters, and parameters of myoelectric activity.  
Within the section on acoustic measurements, a small section has been included referring to the auditory perceptual analysis assessment scales and instruments used in the different articles.  
In seven studies, subjective and quasi-objective measures were used to assess voices: the Voice Problem Self-Assessment Scale (VPSS) [27], GRBAS scale [14; 17; 19; 27], Visual Analog Scale evaluation (VAS) [19], Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) [21; 25] and Voice-Related Quality of Life (V-RQOL) [25].  
For the recording of objective voice measurements, software programs such as Multi-Dimensional Voice Program (MDVP) [16; 18; 27], FonoView 4.5 [28], Praat acoustic analysis program [21; 23; 24; 25], Dr. Speech [23], Computerized Speech Lab (CSL) [29], among others were used. Phonatory Aerodynamic System 6000 was used by one study for aerodynamic analysis [18]. The Dysphonia Severity Index (DSI) was used by two studies [21; 27].  
In the studies where the aerodynamic parameters were evaluated, the repetition of the syllable [pa] [18; 22; 26; 30] or the syllable [pi] [14; 29] was used.  
Regarding the instruments and materials used in the selected studies, a series of microphones, hardware, and software, both for the vocal register and for the aerodynamic register were used. This is summarized in Table 3.  
Differences were also observed regarding the distance from the microphone to the mouth: 3 cm [19], 5 cm [16; 17; 23], 8 cm [29], 10 cm [14; 21; 24; 25; 28], 12 cm [30], and 15 cm [12; 26].
# Engineering the Future: Evaluation of Virtual Reality Across Project Lifecycle Stages
## 5 Discussion
Expanding the sample to encompass a broader range of industries, geographic regions, and organizational contexts will further strengthen the external validity of the findings. In addition, it would be valuable to explore how specific training programs or organizational interventions may actively shape these evolving perceptions and support more effective adoption. Moreover, researchers may examine how VR can be adapted to support early-stage project tasks and identify strategies that encourage adoption across diverse professional profiles. As VR capabilities continue to evolve, it is also important to assess its potential added value in later lifecycle phases, such as long-term system monitoring or decommissioning, which were not the focus of the current study. Building on these directions, future investigations should delve deeper into the differentiated needs and expectations of specific user groups within systems engineering. While the present study highlights key predictors such as openness to adoption, education level, and prior familiarity, it does not fully capture the task-specific requirements of varied professional roles, including engineers, project managers, designers, and technical operators. Understanding these nuances through subgroup analysis, qualitative interviews, or persona-based modelling could support the development of more tailored and effective implementation strategies. Such customization may ultimately increase adoption rates and amplify the long-term impact of immersive technologies across the engineering project lifecycle. **In addition**, to further support effective implementation, organizations and developers could benefit from applying the Kano model mentioned above as a strategic framework to differentiate between basic expectations, performance needs, and \"delighter\" features of VR across lifecycle stages. By mapping user expectations and satisfaction responses, the model can help identify which phases present the greatest opportunity for value-adding experiences and which require improvements to avoid dissatisfaction. Integrating such models into VR planning and evaluation processes could enhance alignment with stakeholder priorities and maximize user acceptance. This exploratory study sheds light on the adoption of VR across the systems engineering project lifecycle. The findings may be beneficial both when making the principal decision whether to implement the technology (at each stage of the project) and when considering the approach to assimilate the technology.
## 3 Results and Discussion
### The Effect of Temperature
So as to verify this, the experiment was repeated in the choline-based IL at 70 \({}^{\circ}\)C to create a comparison with the imidazolium-based IL experiment and the result are shown in Figure 5. It was anticipated that as the temperature raised, so would the anodic current (approximately 10 fold) because of the lowering of the solution viscosity. It ought to, however, be noticed that the sharp decline in current still occurred at 0.6 V instead of 0.2 V. This is presumably as a result of the super-saturation at elevated temperature; i.e., higher concentration relative to saturated one, and the diffusion of chloride was also higher. One thing that has to be well-known is the oxidation onset potential that shifted to be more negative; i.e., there was less anodic onset potential as the temperature is raised, demonstrating the kinetic accelerating oxidation reaction of the bulk copper. Ultimately, the overall electrochemical behaviour did not alter at that elevated temperature; in the other words, the passivation was still effective. The time for the bulk copper electrode to passivate in the choline chloride-based IL as a function of scan rate is presented in Figure 6. At faster sweep rates, a higher concentration of electrochemically dissolved copper is put more rapidly into the solution and the interfacial region between the electrode's surface and the electrolyte saturates more quickly. It appears as if the potential of passivation increases, but this is just an artefact of the system not being at equilibrium state. \begin{table}
\begin{tabular}{c c} \hline \hline
**Electrolyte** & **Cu/nmole** \\ \hline Choline chloride IL & 90.6 \\ CuCl\({}_{2}\)-2H\({}_{2}\)O Choline chloride IL at 20 \({}^{\circ}\)C & 1.20 \\ CuCl\({}_{2}\)-2H\({}_{2}\)O C\({}_{4}\)mimCl at 70 \({}^{\circ}\)C & 1.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of moles of copper stripped in the first oxidation process, calculated from the overall charge under the peak of the LSV using the GPES software. Figure 5: Cyclic voltammograms of bulk Cu disc in choline chloride-based IL at 20 \({}^{\circ}\)C (black line) and 70 \({}^{\circ}\)C (red line) at 5 mV:s\({}^{-1}\).
# Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer
## 2 Method
### Source-Free Image Generation Module
#### 2.2.2 Channel-Level Relational Consistency Loss  
In unsupervised domain-adaptive tasks, it is usually assumed that there is a fixed classifier, so it can be considered that the global features obtained by the target domain through the target model should be similar to the global features obtained by \"source-like samples\" through the source model. To promote similar channel-level relationships between feature maps \(f^{s}\to F^{s}(\widetilde{x})\) and \(f^{t}\to F^{t}(x_{i}^{t})\), a relation consistency loss is used to constrain the model.  
Previous knowledge distillation work is usually constrained by maintaining batch-level or pixel-level relationships [22; 23]. However, this constrained approach is not suitable for the current task. First of all, the batch-level relationship cannot supervise the generation task of each image well, which will cause damage to the generated effect. Second, the effectiveness of pixel-level relationships will be greatly reduced after global pooling. Compared with the two, the channel-level relationship [24] is computed on a per-image basis and is not affected by global pooling. Therefore, the channel-level relationship is more suitable for computing \(\mathcal{L}_{rc}\).  
Given the feature map \(f^{s}\) of \"source-like samples\" and the feature map \(f^{t}\) of the target domain, we resize them into feature vectors \(\mathcal{F}^{s}\) and \(\mathcal{F}^{t}\), as shown in Equations (9) and (10):  
\[f^{s}\in\mathbb{R}^{D*H*W}\rightarrow\mathcal{F}^{s}\in\mathbb{R}^{D*HW} \tag{9}\]  
\[f^{t}\in\mathbb{R}^{D*H*W}\rightarrow\mathcal{F}^{t}\in\mathbb{R}^{D*HW} \tag{10}\]  
where \(D,H,\) and \(W\), represent the feature map's depth (number of channels), height, and width, respectively. Next,we compute their channel-level self-correlation, the gram matrix, as shown in Equation (11):  
\[\mathcal{G}^{s}=\mathcal{F}^{s}\cdot(\mathcal{F}^{s})^{T},\mathcal{G}^{t}= \mathcal{F}^{t}\cdot(\mathcal{F}^{t})^{T} \tag{11}\]  
Figure 2: Schematic diagram of comparison between source-like samples and target samples. (**a**) target. (**b**) source-like.  
where \(\mathcal{G}^{s},\mathcal{G}^{t}\in\mathbb{R}^{D\star D}\). Like other similarity-preserving losses for knowledge distillation, we apply row-level \(L2-\)norm, as shown in Equation (12):  
\[\widetilde{\mathcal{G}}^{s}_{[i;]}=\frac{\mathcal{G}^{s}_{[i;]}}{||\;\mathcal{ G}^{s}_{[i;]}\;||_{2}},\widetilde{\mathcal{G}}^{t}_{[i;]}=\frac{\mathcal{G}^{t}_{[i ;]}}{||\;\mathcal{G}^{t}_{[i;]}\;||_{2}} \tag{12}\]  
where \([i,:]\) represents row \(i\) in the matrix. Finally, the channel-level relational consistency loss \(\mathcal{L}_{rc}\) is the mean squared error (MSE) between the normalized Gram matrices, as shown in Equation (13):  
\[\mathcal{L}_{rc}=\frac{1}{D}\;||\;\mathcal{G}^{s}-\mathcal{G}^{t}\;||_{F}^{2} \tag{13}\]
## 2 System Principle  
A PBS in an MHV is briefly shown in Figure 1. The system is composed of service brake circuit and parking brake circuit. Usually, double circuits are employed in the service brake circuit, just for avoiding the braking failure in case one of the two circuits has stochastic fault. Each circuit in the system mainly consists of gas reservoirs, control valves, brake chambers and brakes. The gas source of the whole vehicle is provided by an air compressor and stored in a main reservoir with large capacity as well as several normal ones with relatively small capacity. Control valves mainly comprise a hand brake valve and a treadle valve located in the cab, several relay valves, and delay relay valves arrangedin the rear axle of the chassis. Drum brakes, which can offer more powerful braking force compared to disc brakes, are widely used in MHVs [23].  
The structure of the service brake circuit of a PBS on MHV can be simplified as shown in Figure 2. This circuit is made up of three parts: the gas supply part, pneumatic transmission part and mechanical actuator part. In this paper, we focus on the pneumatic transmission part because hysteresis is mainly caused by the long-distance pressure transmission in this part [24]. A complete working procedure of the service brake can be divided into the brake process, brake keeping process and brake release process. Taking the S-cam drum brake as an example, the working procedure can be described as follows. When encountering an emergency braking situation, the driver steps on the brake pedal quickly and the treadle valve is opened soon; this behavior meters out the compressed gas from the supply ports (port 11 and 12) of the treadle valve to its delivery ports (port 21 and 22). Then, the compressed gas travels to port 4 of the relay value through controlling pipeline, making the relay value open. Next, high-pressure gas moves into the brake chamber through braking pipeline and presses the diaphragm, pushing the plunger out. The ejected plunger then drives the brake arm, rotating the S-cam and expanding the brake shoes. As a result, the pads produce a friction torque, compelling the rotating hub to stop quickly. When the brake pedal is released by the driver, the gas is rapidly exhausted from the brake chamber, resulting in the opposite rotating direction of the S-cam. Subsequently, the contact between the pad and the hub is broken and the brake is finally released.  
Figure 1: Schematic diagram of a pneumatic braking system (PBS) on a multi-axle heavy vehicle (MHV) (1—gas reservoir; 2—Treadle valve; 3—Hand brake valve; 4—Relay valve; 5—Delay relay valve; 6—Wheel; 7—Brake chamber; 8—Spring brake chamber).  
Figure 2: Structure of the service brake circuit of a PBS.
## 1 Introduction  
An electroelastic actuator in the form piezo actuator is used for nanotechnology in nanomanipulators, laser systems, nanopumps, scanning microscopy [1]. The piezo actuator is used in photolithography for nano- and microdisplacements when aligning templates, in medical equipment for precise instrument delivery during microsurgical operations, in optical-mechanical devices, in adaptive optics systems, and in adaptive telescopes [2]. It is also used in stabilization systems for optical-mechanical devices, systems for alignment and tuning of lasers, interferometers, adaptive optical systems and fiber-optic systems for transmitting and receiving information [3].  
The application of a multilayer electroelastic engine with a longitudinal piezoelectric is promising for nanomanipulators in nanotechnology [4]. To increase the range of the displacement, a cellular actuator and a multilayer electroelastic engine are used [5]. Mechatronics control systems with a multilayer electroelastic engine with a longitudinal piezoelectric are used in precision engineering [6].  
It is important to solve the problem of positioning a multilayer electroelastic engine with a longitudinal piezoelectric from an arbitrary state with minimal control time [7] with the transition between individual positions of the optimal control system in the shortest possible time. The Pontryagin maximum principle is used to derive minimum time control. The prospects for constructing optimal control systems for nanomechatronics are shown in work [8].  
In this work using the Pontryagin maximum principle for optimal systems, which ensures under optimal control the maximum of the Hamilton function and the minimum of the control time. The expression of the control function is obtained, which has only two values and changes once.  
The switching line is obtained for the optimal control of a multilayer electroelastic engine with a longitudinal piezoelectric in a nanomechatronics optimal system.