esa-sceva/satcom-chunk-collection huggingface dataset:
# Aligning EMR Structure with Treatment Cycles: Enhancing Data Management and CDSS Functionality
## 6 Empirical Evaluation of the Cycle Approach's Utilization in EMRs
### Results  
#### 6.4.1 Success Rate Evaluation  
The results from the statistical analysis highlight the significance of adopting a cyclical approach in EMRs for the evaluation of treatment effectiveness. To illustrate the impact of a cyclical approach in treatment analysis, we visualized the treatment success rates from two different perspectives. This visualization (Figure 4) compares the treatment success rates under cyclical and non-cyclical conditions.  
The left panel in Figure 4 presents a bar plot illustrating the average treatment success rate per cycle, with the error bars indicating standard deviations. This visualization highlights a progressive improvement in the success rate as the treatment cycles advance. The observed trend suggests that treatment responses are not uniform across cycles, reinforcing the importance of capturing cyclical data structures in EMRs. Different colours are used for each bar (Cycle 1, Cycle 2, and Cycle 3) to visually distinguish the treatment cycles, enhancing the clarity of the progressive success rate trend. The third cycle exhibits the highest success rate, indicating that iterative treatments may yield cumulative benefits over time. Conversely, the right panel depicts a histogram with a kernel density estimate (KDE) overlay [64], showing the overall distribution of the treatment success rates across all patients, irrespective of cycle classification. Without the cyclical structure, the distribution appears heterogeneous, failing to differentiate the impacts of successive treatment phases. This limitation underscores the loss of critical insights when treatment cycles are not explicitly incorporated into the analysis.  
These findings demonstrate that incorporating a cyclical approach enables a more nuanced treatment success analysis, distinguishing patterns hidden in traditional, non-cyclical datasets. The Python code used to generate these visualizations is available in the Zenodo repository at [[https://zenodo.org/records/15066593](https://zenodo.org/records/15066593)]([https://zenodo.org/records/15066593](https://zenodo.org/records/15066593)) (accessed on 21 March 2025), allowing further exploration of this approach.  
Figure 4: Treatment success rate evaluation using different approaches.
# DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
## 6 Experiment and Analysis  
### Comparison  
This section compares this scheme's features and computational cost with some schemes [23; 24; 25]. These schemes are recent schemes in VANET and are all based on CP-ABE. Table 2 compares the features between our ORCP-ABE scheme and relevant schemes in recent years. The access policy is the implementation of the access policy in each scheme. \"Outsourcing Calculation\" refers to whether the scheme supports outsourcing part of the computation in the encryption or decryption process to the RSU for completion. \"Attribute Assignment\" refers to whether the scheme implements the classification and assigns weights to attributes in the access structure based on the attribute relationships. \"Attribute Revocation\" refers to whether the scheme can provide specific attribute revocation for malicious users. As shown in Table 2, our scheme implements attribute assignment and supports attribute revocation at the user level. Our scheme is flexible in dealing with malicious users by revoking only some of their attributes.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Scheme** & **Access Policy** & **Outsourcing Calculation** & **Attribute Assignment** & **Attribute Revocation** \\ \hline
[23] & TREE & Yes & No & No \\
[24] & TREE & Yes & No & No \\
[25] & LSS & Yes & No & No \\ Ours & TREE & Yes & Yes & Yes \\ \hline \end{tabular}
\end{table}
Table 2: Comparison of features.  
Table 3 illustrates the comparison of computational cost. The values in Table 3 are calculated by mathematical formulas and codes after reproduction. We use \(|S|\), and \(|T|\) to represent the number of attributes of the user attribute set \(S\) and access structure \(T\), respectively. \(|I|\) represents the number of attributes satisfying the access structure \(T\) and \(|L|\) represents the number of rows of the access matrix. \(|R|\) is the number of ciphertexts involved in the attribute revocation.\(E\) and \(E_{T}\) represent exponential operations on \(\mathbb{G}\) and \(\mathbb{G}_{T}\), respectively, and \(P\) represents bilinear pairing operations. As shown in Table 3, our scheme is more efficient than the other three schemes in the encryption phase since we simplify the access structure by attribute assignment.
# HPT: A High Spatial Resolution Multispectral Sensor for Microsatellite Remote Sensing
## 2 Sensor Characteristics
### High Precision Telescope (HPT)
To maintain the LCTF performance over a wide temperature range, a temperature sensor is attached to the LCTF, and the optimum voltages at the measured temperature are applied automatically using a look-up table stored in the voltage-controlling circuit. The response time for switching from one central wavelength to another depends on the temperature and on the combination of wavelengths. For example, the response time at 25 \({}^{\circ}\)C ranges from 39 ms (for a switch from 850 nm to 840 nm) to 259 ms (for a switch from 710 nm to 970 nm), and it is slower/faster at lower/higher temperatures, respectively. The exposure of the NIR CCD is designed to start after sufficient time has elapsed for the switching of the wavelengths. Figure 4: Photograph of the liquid crystal tunable filter (LCTF) (unit: mm).
## 3 Results and Discussion
### Electrical Characterizations of ZnO Nanowires and Ag Nano-Solder Interconnection  
In order to explore the melting state of the interface between the nanowires and nano-solder, the typical characterization method is the transmission electron microscope (TEM). However, TEM characterization is rather challenging due to the large size of heterojunctions composed of the nano-solder and nanowires. Hence, this paper tested the electrical properties before and after femtosecond laser irradiation to determine whether good soldering was achieved or not. The femtosecond laser fluence of 55.25 mJ/cm\({}^{2}\) and irradiation time of 60 s are more beneficial for nanowires interconnection (according to the experimental results in Section 3.2). Thus, the structure shown in Figure 5a is used to characterize the electrical properties of nanowires interconnection under the above femtosecond laser irradiation conditions. Two Au electrodes are deposited on both ends of the interconnected nanowires. Voltage-current scanning is from \(-\)6 V to 6 V, and the acquisition time of each point is 1s. Figure 5b shows the current conduction of the nanowire interconnection structure before and after femtosecond laser soldering. The experiment is established in a daylight environment. It can be seen that the conduction current of the nanowires is feeble before soldering, and there is almost no conduction under positive pressure. After 60 s of femtosecond laser irradiation, the ZnO nanowires have successfully achieved current conduction. Under positive bias, the interconnection current reached a maximum of 0.7 uA. At the same time, the I-V curve shows the typical I-V response of the dual Schottky barrier, which is due to the contact barrier between the Au electrodes and the ZnO nanowires. There is no electron energy barrier at the interface since the electron affinity of ZnO is 4.5 eV and the work function of Ag is 4.26 eV. Besides, Au has a work function of 5.1 eV. There is an electron energy barrier at the contact since the work function of Au is massively larger than the electron affinity of ZnO [21]. Figure 5b shows that the  
Figure 4: SEM images of the interconnection melting state at different irradiation time before and after soldering: (**a,d**) 45 s; (**b,e**) 60 s; (**c,f**) 75 s.  
negative bias junction current is slightly larger than the positive bias current, which may be caused by the inconsistent contact barrier between the electrode and the nanowire on both sides. In short, the results illustrate the successful soldering interconnection through electrical characterizations of ZnO nanowires and Ag nano-solder interconnection before and after femtosecond laser irradiation.
## 2 Technical Background
### UAV Classification, Configuration, and Advancement in Flight Technology  
UAVs can be broadly classified into two types: multi-copter types, such as drones, and fixed-wing types, such as airplanes [55]. The differences between the two types are shown in Table 1. UAVs are widely used in military and civilian fields due to their high mobility, rapid deployment, and low cost [56; 57]. Examples of use include reconnaissance, transportation, inspection, agricultural irrigation, disaster relief, and many other fields. This paper assumes using a multi-copter type with excellent maneuverability and hovering  
Figure 2: Beamforming procedure.  
performance. The configuration of a typical multi-copter UAV is shown in the Figure 3. The flight controller sends commands to each Electric Speed Controller (ESC) according to the instructions from the transmitter [58]. In addition, during flight, the position information, altitude information, etc., obtained from the air pressure sensor, acceleration sensor, GPS, etc., are transmitted to the transmitter. At the same time, the flight controller continues to send commands to each ESC to maintain stable expectations in the face of environmental changes. Currently, it is possible to set the flight path in advance and have the flight controller operate the planned path by itself, and there are also UAVs with built-in collision avoidance systems. The transmitter is equipped with levers, switches, and other interfaces necessary for control, and it communicates wirelessly with the aircraft. Microwaves are generally used for this communication. A battery powers the aircraft. To lengthen the flight time, the battery capacity must be increased, but there is a tradeoff in that the weight increases, resulting in higher power consumption. In the future, UAVs are expected to have longer flight times by using higher power efficiency, lightweight high-capacity batteries, and even concurrent wireless power transfer technology [43; 59].
###### Abstract  
Discrete event system specification (DEVS) has been widely used in event-driven simulations for sensor-driven Internet of things (IoT) applications, such as monitoring the spread of fire disaster. Event-driven models for IoT sensor nodes and their communication is described in DEVS and they have to be integrated with continuous models of fire-spreading dynamics so that the hybrid system modeling and simulation approach have to be considered for both continuous behavior of fire-spreading and event-driven communications by large-scale IoT sensor devices. The hybrid-integrated modelling and simulation for fire-spreading in wide area and large-scale IoT devices result in more complex model evaluation, including simulation time synchronization, so that simulation acceleration is important by considering scalability in large-scale IoT-driven applications that sense fire-spreading. In this study, we proposed a scalable simulation acceleration of a DEVS-based hybrid system using heterogeneous architecture based on multi-cores and graphic processing units (GPUs). We evaluated the power consumption comparison of the proposed accelerated-simulation approach in terms of the composition of the event-driven IoT models and continuous fire-spreading models, which are tightly described in differential equations across a large number of cellular models. The demonstrated result shows that the full utilization of CPU-GPU integrated computing resources, on which event-driven models and continuous models are efficiently deployed and optimally distributed, could enable an advantage for high-performance simulation speedup in terms of execution time, although more power consumption is required, but the total energy consumption could be reduced due to fast simulation time.  
Keywords:discrete event simulation; hybrid simulation; GPU-based accelerated simulation; simulation kernel distribution on multi-core architecture +
Footnote †: journal: Applied Sciences
## 1 Introduction
The metrics are compared with thresholds values (these can be adjusted in order to increase or decrease the level of similarity pursued) and based on the global results, it can be concluded if a variable affects the MFD or not. If the latter applies, it means that (approximately) a unique MFD exists (density vs. speed, speed vs. flow, and density vs. flow) with the aggregated data from simulations (each performed with a different configuration of the variable) and that it can be used to describe the traffic. Research QuestionsAs the study of the MFD is still underway, it has yet to be completely determined which variables affect the MFD shape of a network. Therefore, it becomes important to establish a method which allows us to analyze if a variable modifies the MFD shape. Thus, our research questions are as follows:  
1. What method can be used to analyze if a variable modifies the MFD shape of a street network? 2. Are the precision of driving, the vehicles' top speeds distribution, the procedure for selecting routes, and the procedure for selecting destinations variables that modify the MFD shape?
# Neighbor-Aware Non-Orthogonal Multiple Access Scheme for Energy Harvesting Internet of Things
## 4 Constrained Stochastic Game
### Action Space  
Because IoT device \(i\) should decide whether to transmit data and its transmission power, the local action space of IoT device \(i\), \(\mathbf{A_{i}}\), can be represented as  
\[\mathbf{A_{i}}=\{0,1,...,A_{\max}\} \tag{3}\]  
where \(A_{\max}\) denotes the maximum transmission power of IoT device \(i\). \(A_{i}\) (\(\
eq\) 0) represents the transmission power of IoT device \(i\). In addition, \(A_{i}=0\) describes the situation where IoT device \(i\) does not conduct a transmission.  
The global action space, \(\mathbf{A}\), can be described as  
\[\mathbf{A}=\prod_{i}\mathbf{A_{i}} \tag{4}\]  
Similar to \(\mathbf{S_{-i}}\), the action space of all IoT devices except IoT device \(i\) is denoted as \(\mathbf{A_{-i}}\).
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 2 Strategies
### Data Conversion  
The competition data provided by Google included both raw GNSSLogger and RINEX formats. The RINEX format is compatible with RTKLIB, but the method and parameters used to convert the raw phone data to RINEX was not fully provided to the challenge participants and did not appear to fully align with methodologies suggested by Google in the competition discussion pages. As a result, it was beneficial to convert the raw phone data to RINEX using available open-source tools, as opposed to utilizing the pre-processed files, to provide more user control over the pre-processing data conversion process. To accomplish this conversion, the Rokubun Android GnssLogger to RINEX converter python code was chosen as a starting point [20]. This code is split into two sections, gnsslogger.py, which pulls out and organizes phone data, and rinex3.py that takes in the organized data and writes it out in the RINEX version 3 format. Within both sets of code, adjustments were made to improve the resulting RINEX files. The details of the adjustments and improvements are documented below.  
In gnsslogger.py, changes were made to expand compatibility to all phone types used in the competition and to more closely follow filtering rules described by Google in a competition discussion post describing their methodology for generating the included baselinesolutions [12]. These changes were primarily integrated by including an observation filter function that served to reject invalid or very low-quality observations as defined by:  
* \(\mathrm{C/N_{0}}\) was less than 20 dB-Hz;
* Received space vehicle time uncertainty was greater than 500 nanoseconds;
* Maximum pseudorange uncertainty was greater than 150 m;
* Multipath indicator was set greater than 0;
* Maximum carrier phase uncertainty was greater than 0.1 m;
* Status of the code lock was invalid;
* TOW or TOD values were not decoded and set for all constellations;
* Constellation identifier was invalid.  
Finally, the script was modified to provide an option to ignore the cycle slip and half-cycle ambiguity flags that were present in the provided raw data files, as investigating these components showed that their inclusion resulted in usable data often being thrown out. This option was enabled for the processing of the raw data for this solution.  
The changes to the rinex.py file were much less involved. The core change made to this script was to replace the single character, unused legacy signal to noise ratio (SNR) field of the RINEX file with the pseudorange and carrier phase uncertainty estimates from the receiver. These receiver estimates can be included in RTKLIB's determination of measurement weights, but for this solution, only elevation-based weighting was utilized.
## 4 Optimization Methods of Base Placement
### Generation of Occupied Grid Map and Inverse Reachability Map
#### 4.2.2 Inverse Reachability Map (IRM)
\[T_{global}^{TCP}=\{x,y,z\} \tag{1}\]  
The inverse reachability map is generated from the description of the reachability map, and each point indicates that the WMRA has some inverse kinematics solutions at that position to reach the tool center point. Different colors represent different numbers of inverse kinematics solutions. With more inverse solutions of kinematics, the robot can reach the target position in more positions, and the WMRA has higher reachability to the tool center point. The IRM includes all of the base placements that can cover the tool center point (TCP) and can visually present the coverage ability of the TCP by different base placements. Thus, the IRM can be utilized to find suitable base placements for the WMRA. After constructing the occupancy grid map and the IRM, we analyzed and compared the data to remove unreachable base placements caused by home environment factors, selected the optimal point from the remaining base placements, and completed the base optimization.
# Gold Nanofilm-Coated Porous Silicon as Surface-Enhanced Raman Scattering Substrate
###### Abstract  
Metallic film-coated porous silicon (PSi) has been reported as a lucrative surface-enhanced Raman scattering (SERS) substrate. The solution-based fabrication process is facile and easy; however, it requires additional reducing agent and extra chemical treatment, as well as hinders the suitability as a reproducible SERS substrate due to irregular hot spot generation via irregular deposition of metallic nanocrystallites. To address this issue, we report a unique one-step electronic beam (e-beam) physical vapor deposition (PVD) method to fabricate a consistent layer of gold (Au) nanofilm on PSi. Moreover, to achieve the best output as a SERS substrate, PSi prepared by electrochemical etching was used as template to generate an Au layer of irregular surface, offering the surface roughness feature of the PSi-Au thin film. Furthermore, to investigate the etching role and Au film thickness, Au-nanocrystallites of varying thickness (5, 7, and 10 nm) showing discrete surface morphology were characterized and evaluated for SERS effect using Rhodamine 6G (R6G). The SERS signal of R6G adsorbed on PSi-Au thin film showed a marked enhancement, around three-fold enhancement factor (EF), than the Si-Au thin film. The optimal SERS output was obtained for PSi-Au substrate of 7 nm Au film thickness. This study thus indicates that the SERS enhancement relies on the Au film thickness and the roughness feature of the PSi-Au substrate.  
electrochemical etching; surface-enhanced Raman scattering; porous silicon; gold nanofilm; hot spots +
Footnote †: journal: Applied Soft. 2019, 9, 4806; doi:10.3390/app9224806
# A Ku-Band Bi-Directional Transmit and Receive IC in 0.13 \(\upmu\)m CMOS Technology
## 3 Experiment Results  
The Ku-band transmit and receive IC is fabricated in 0.13 \(\upmu\)m CMOS technology and the chip size is 2.5 \(\times\) 1.5 mm\({}^{2}\) including bonding pads. The microphotograph of the Ku-band CMOS transmit and receive IC is shown in Figure 8. The measurement setup of the Ku-band CMOS transmit and receive IC is shown in Figure 9. The S-parameter measurement is performed using a PNA-X network analyzer. The short, open, load, and thru (SOLT) calibration was performed with the on-wafer probe station. The power characteristics are measured with the signal source and the spectrum analyzer. Figure 10 shows the measured gain of the reference state. The gain is 2-5 dB at 12-16 GHz. The input and output return losses are >10 dB at 12-16 GHz as shown in Figure 11. Figure 12 shows the measured  
Figure 6: Block diagram of the Ku-band 6-bit digital step attenuator.  
Figure 7: Schematic of the switchless bi-directional amplifier (operation mode: P\({}_{1}\)\(>\)P\({}_{2}\)).  
Figure 11: Measured return losses of the Ku-band CMOS transmit and receive IC.  
Figure 12: Measured phase characteristics of the Ku-band CMOS transmit and receive.  
Figure 13: Measured attenuation characteristics of the Ku-band CMOS transmit and receive.  
Figure 16: Measured output power of the Ku-band CMOS transmit and receive IC in Tx operation.  
Figure 14: Measured root mean square phase and amplitude errors of the phase shifter.  
Figure 15: Measured RMS phase and amplitude errors of the attenuator.
# Collective Attestation for Manageable IoT Environments
## 6 Evaluation
### Overheads
We assume that \(\ell_{\mathsf{N}}\) = 20 bytes, \(\ell_{\mathsf{B}}\) = \(\ell_{\mathsf{C}}\) = 16 bytes, \(\ell_{\mathsf{mac}}\) = 20 bytes, \(\ell_{|\mathbf{A}|}=\ell_{|\mathbf{S}|}=\ell_{\mathbb{H}}=1\) byte, and \(\ell_{\alpha}=\ell_{\beta}=3\) bytes. To hold the position information, \(\ell_{\mathbb{P}}=2\) bytes (256 \(\times\) 256 scale). For energy parameters, we assume that \(E_{\mathsf{send}}\) = 0.72 mJ/Byte, \(E_{\mathsf{recv}}\) = 0.81 mJ/Byte, \(E_{\mathsf{mac}}\) = 38 mJ, and \(E_{\mathsf{verify}}\) = 65 J (for fair comparison, we use the same verification energy cost as the other schemes). In Figure 4a, we compare the energy cost of CAMIE to SEDA and SANA. As shown in Figure 4a, SEDA and CAMIE have lower energy cost than SANA. It is due to the high cost of signature generation and aggregation in SANA. When the expected number of neighbor nodes is four (4-ary tree), the energy cost of CAMIE is 44% higher compared to SEDA but is reduced to almost one-third (0.29 times) compared to SANA. Figure 4b shows a closer look at the results between SEDA and CAMIE. Since the message size of CAMIE is larger than SEDA, CAMIE has a higher energy cost than SEDA. In CAMIE with the \(|S|=\infty\) setting, a whole swarm network is treated as a region. Thus, CAMIE in the setting is functionally similar to SEDA, but it can still provide a network-wide summary for bad provers or targets. When CAMIE divides a swarm network into regions (\(|S|=4\)), CAMIE can give more detailed region-based information. The gap between the energy costs of CAMIE and SEDA can be reduced by lowering the tag size if we can assume the upper limit of infections or targets. Figure 5 depicts the energy cost of CAMIE when changing the settings of the attestation tree and regions. The energy cost increases as the network is getting bigger since there are more regions in the network. As the expected number of children (\(|C_{i}|\)) rises, the energy consumption also goes higher  
Figure 4: Energy cost comparison with the previous schemes. (**a**) energy cost comparison with SEDA and SANA; (**b**) detailed comparison with SEDA. In Figure 5, we can also see that when the region is smaller (\(|S|=4\)), the increase of energy cost is faster since more regional information (i.e., the region summaries) is generated.