esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Facilities  
Theexperiment was conducted in two office rooms with dimensions of 6.52 m \(\times\) 3.13 m \(\times\) 2.68 m each (Figure 1), located on the third floor of the San Agustin Building of the School of Engineering at Pontificia Universidad Catolica de Chile (33.4993\({}^{\circ}\) S, 70.6150\({}^{\circ}\) W). The office room with a typical double-clear glazed window filled with air was the reference office, herein called the \"AIR room\". The other office was equipped with a double-clear glazed window filled with PCM RT25 from Rubitherm(r) and was called the \"PCM room\". Thus, both offices have the following characteristics:  
* WWR was equal to 56%.
* The east-oriented wall was the only facade exposed to outdoor conditions. This facade consisted of glazes and opaque envelopes.
* The opaque part of the east-oriented facade was a heavy-concrete wall of 0.20 m without insulation.
* An upper window consisted of double-clear glazing filled with air to provide daylight.
* Interior walls, ceilings, and floors are adiabatic because each office is adjacent to other occupied spaces on the same level and the above and underneath floors.
* The glazed facade of the PCM room was double-glazed with a cavity filled with PCM. This window was 5 + 4 mm laminated glass, 11.5 mm PCM and 4 + 5 mm laminated glass. Table 1 shows the thermophysical properties of the glazed facade (heat transfer coefficient U-value, visible transmittance T\({}_{\text{vis}}\) and solar transmittance T\({}_{\text{sol}}\)) calculated using WINDOW 8.0 [28] and, the experimental data of Goia [29], that used the same paraffin wax and the window properties as our experiment. Figure 2a shows the cross-section of the PCM window, and Figure 2b shows the partial enthalpy of the PCM paraffin according to the manufacturer datasheet [30]. A total of 20.8 kg of paraffin wax was used.
* The glazed facade of the AIR room was double-glazed with a cavity filled with air. This window was 5 + 4 mm laminated glass, 11.5 mm air and 4 + 5 mm laminated glass. Table 1 shows the thermophysical properties of the glazed facade (heat transfer coefficient U-value, visible transmittance T\({}_{\text{vis}}\) and solar transmittance T\({}_{\text{sol}}\)).
* Internal gain sources in both rooms were the HVAC system and the measurement equipment (laptop, datalogger, UPS, and sensors). There were no internal gains related to people, artificial lighting, or typical office equipment.
* A wall-mounted heating and cooling HVAC of 18,000 Btu/h (approximately 5.3 kW) was installed in each office to control the indoor air temperature. The thermostats were set at 25 \({}^{\circ}\)C for heating and cooling. HVAC can work only in heating or cooling mode. The heating and cooling modes were set according to the weather conditions in each season.
# A Multi-Frequency Omnidirectional Antenna Based on a Ring-Shaped Structure
## 2 Theory and Design
The surface currents between the different ring structures affect each other, resulting in a shift of the maximum radiation direction of the antenna to the left. The higher the frequency, the larger the deviation angle, especially at 2.69 GHz where the back-lobe appears in the radiation direction map and directivity is slightly affected. However, in all three frequency bands, the maximum deviation of the radiation direction is small, and the H-plane orientation map is still approximately a positive circle, so the antenna still has omnidirectional radiation properties. Figure 6 shows the gain of the antenna in the operating frequency band, which reaches a maximum gain of 4.63 dBi. Overall, the proposed antenna has excellent omnidirectional radiation properties at all operating frequency points, which will meet the needs of portable multi-band mobile devices. \[f_{0}=\left[\sqrt{\left(C_{SRRs}R_{SRRs}^{2}\right)/L_{SRRs}-1}\right](2\pi C_{SRRs }R_{SRRs}) \tag{1}\]3 Antenna radiation direction diagram at **(a)** 1.10 GHz; **(b)** 1.33 GHz; **(c)** 1.63 GHz; **(d)** 1.97 GHz; **(e)** 2.08 GHz; **(f)** 2.69 GHz.
## 2 Reliability Assessment Method Based on Fusion of Simulation and Test Data
### Life Reliability Assessment Method for Zero-Failure Data
#### 2.1.1 Bayes Estimation Method for Failure Probability
Therefore, the likelihood of \(p_{i}\) corresponding to the data \(\big{(}t_{j},n_{j}\big{)}\) is,  
\[L_{j}(p_{i}\big{|}\big{(}t_{j},n_{j}\big{)})=p_{i}^{n_{j}p_{j}}\cdot(1-p_{i})^{ n_{j}(1-p_{j})} \tag{5}\]  
Considering the entire previous test data of \(t_{i}\), i.e., \(\big{(}t_{j},n_{j}\big{)},1\leq j\leq i-1\), the corresponding likelihood is,  
\[L_{B}(p_{i})=\prod_{j=1}^{i-1}p_{i}^{n_{i}p_{j}}\cdot(1-p_{i})^{n_{j}(1-p_{j}) }=p_{i}^{\sum_{j=1}^{i-1}n_{j}p_{j}}\cdot(1-p_{i})^{\sum_{j=1}^{i-1}n_{j}(1-p_ {j})} \tag{6}\]The likelihood function corresponding to the full test data at this point is,  
\[L(p_{i})=L_{A}(p_{i}|s_{i})\cdot L_{B}(p_{i}) \tag{7}\]  
when \(i=1\), \(L_{B}(p_{1})=1\). Step 3: Predict the estimated failure probability \(p_{i}\). After \(p_{1},\cdots,p_{i-1}\) in Equation (7) is replaced with its estimator \(\hat{p}_{1},\cdots,\hat{p}_{i-1}\), the approximate likelihood function \(\tilde{L}(p_{i})=\tilde{L}_{A}(p_{i}\mid s_{i})\cdot\tilde{L}_{B}(p_{i})\) of \(p_{i}\) can be obtained, and the Bayes estimation formula for \(p_{i}(i=2,\cdots,m)\) under the squared loss can be obtained by combining the prior distribution \(\pi(p_{i})\) of \(p_{i}\) and applying the Bayes formula. \[\hat{p}_{i}=\frac{\int_{\hat{p}_{i-1}}^{\hat{\frac{t_{i}}{t_{m}}}}p_{i}\cdot L (p_{i})dp_{i}}{\int_{\hat{p}_{i-1}}^{\hat{\frac{t_{i}}{t_{m}}}}L(p_{i})dp_{i}} \quad,i=2,\ \...,\ \ m \tag{8}\]
## 1 Introduction
These first results already highlighted the power of Raman spectroscopy as an analytical tool that can deliver _in situ_ information about the growth and breakdown of distinct phases, including the detection of metastable or intermediate phases, without the necessity to quench the sample to room temperature (RT). Raman images can be taken during heating and cooling with a high-temperature, time, and spatial resolution, enabling the kinetics of individual reaction steps to be quantified. A single experiment, therefore, contains the information of a multitude of conventional sintering experiments, involving heating and quenching of different samples. In this work, we describe our novel approach to _in situ_ study the sintering process of silicate ceramics by hyperspectral Raman imaging in little more detail. The article mainly focusses on the experimental setup, the analytical methodology, and data analysis and visualization. In addition, the power of _in situ_ Raman imaging for studying high-temperature sintering reactions is briefly demonstrated by three application examples.
## 1 Introduction
### Motivation and Objectives
The association of the equipment with the base station, LOS UAV, NLOS UAV, and V-N is derived in Section 4. Interference of the V-Ns, base stations, LOS UAVs, NLOS UAVs, and clustered jammers is evaluated in Section 5, whereas coverage and SE analysis are presented in Section 6. Mitigating techniques to overcome the effect of jamming interference are presented in Section 7, and the main outcomes of the paper, as well as the simulation setup and limitations, are discussed in Section 8. Finally, the conclusion of the paper is presented in Section 9. A list of recent works addressing their system model, primary contributions, as well as outcomes and limitations, is given in Table 1.
## 4 Simulation Setup  
To evaluate the effectiveness of the proposed CNN-based power allocation method, we conducted simulations in a downlink cell-free massive MIMO network under realistic deployment conditions. UEs were densely clustered in localized high-traffic regions, creating spatial imbalance and heterogeneous interference. The goal of the simulation was to verify how well the proposed method handles per-user performance in terms of SE and EE while minimizing power wastage and improving fairness. To emulate realistic network dynamics, each simulation snapshot assumed fixed user positions within a coherence time, with UE locations randomly redrawn across samples based on clustered spatial distributions. This structure reflects pseudo-mobility and variable load conditions, as is commonly observed in urban deployments, while also preserving quasi-static channel assumptions at the per-snapshot level.  
Performance was evaluated using per-user metrics and visualized through cumulative distribution functions (CDF). These plots highlight not only the average performance but also how power allocation affects users at different percentiles. The per-user average SE and EE serve as key performance indicators, as the proposed model aims to improve not  
Figure 4: Training loss curve of the CNN model over 50 epochs.  
only total system throughput but also user-level quality of service under nonuniform and interference-limited conditions [4]. These metrics provide more meaningful insights into spatially adaptive power allocation behavior across the entire network.
# Two-Color Operation of a Soft X-ray FEL with Alternation of Undulator Tunes
## 3 Experimental Studies at FLASH  
FLASH (Free-electron LASer in Hamburg) [15] started user operation in summer 2005 as the first free-electron laser for XUV and soft X-ray radiation. It is operated in the \"self-amplified spontaneous emission\" (SASE) mode [22] and currently covers a wavelength range from 4 nm to about 90 nm in the first harmonic with GW peak power and pulse durations between a few fs and 200 fs. The electron bunches with maximum energy of 1.25 GeV are distributed between the two branches, FLASH1 and FLASH2 [16], see Figure 2. The facility operates with long pulse trains (several hundred pulses) with a 10 Hz repetition rate. Presently, the facility is being upgraded towards a high repetition rate seeding in FLASH1 branch [23].  
The segmented variable-gap undulators of the FLASH2 branch have a period of 3.14 cm and can provide a maximum undulator parameter K about 2.7. FLASH2 hosts twelve 2.5 m long segments with quadrupoles in the intersections. After successful experiments with two-color lasing, the control system of the undulator was upgraded to make the operation in this regime easier. In particular, it allows user control of two-color operation. In addition, special photon diagnostics methods were developed as described below, and there is online intensity diagnostics suitable for user operation.  
Below in this paper, we present exemplary results obtained during two-color operation of FLASH2 undulator in 2018-2021. Electron energy in these experiments was between 430 MeV and 1240 MeV, and bunch charge was typically in the range of 70-300 pC. We generated FEL wavelengths between 6 nm and 50 nm, and pulse energies in both colors were up to several tens of microjoules. The spectral separation between two colors can be set from \(\sim\)1% to \(\sim\)100%. It is possible to do rough changes of the pulse energy ratio by changing, for example, between 6 + 6 configuration of the undulator segments (as shown in Figure 1) and 5 + 7 configuration, as well as fine-tuning by slightly changing the K values of a few segments.  
Figure 1: Conceptual scheme of two-color lasing with the alternation of undulator tunes. The figure was published in [17].  
Figure 2: Layout of FLASH facility.
###### Abstract  
Double emulsions show great potential for encapsulating active substances and protecting them against external influences. However, they tend to become unstable during storage. Research on double emulsions, therefore, focuses on maintaining their microstructure during their shelf life. Optical measurement methods, such as Raman spectroscopy, have hardly been used to date to analyze the microstructure of double emulsions, mainly due to multiple scattering effects. This study investigates the influence of refractive index matching of double emulsion phases by measuring the Raman signal strength of the inner water phase for different refractive index combinations. Ammonium nitrate and glycerol are added to the inner and outer water phase, respectively, to change the refractive indices of both phases. Additionally, polyvinyl alcohol serves as an emulsifier in the outer water phase. The oil phase consists of silicone oil and Dowsil Resin XR 0497 as the emulsifier. The refractive index of the oil phase is kept constant. For individual phase boundaries of single droplets, the refractive index matching plays a minor role. However, if there are many droplets with correspondingly numerous phase boundaries, which leads to multiple scattering during the measurement, the matching has a significant influence on the signal strength of the inner phase. When measuring double emulsions, the phases should always be matched, as this results in higher signals and improves the sensitivity of the measurement.  
double emulsion; Raman spectroscopy; refractive index matching; multiple scattering; glass capillary device; microfluidic; Fresnel's formula; multiple linear regression Article
## 4 Function Design of the Back-End Service Layer
### User and Monitoring Terminal Binding  
(1) After the user successfully logs in, they enter their assigned term _id. The back-end service layer completes the binding process through the onBind() callback in the Bind script. This process involves querying the \"monitor_terminal\" table in the database to check whether the term_id is already bound to another user.  
(2) If the query result is negative, the binding is successful and a prompt message saying \"Binding succeeded\" is displayed. If the query result is positive, the binding is canceled, and a prompt message saying \"This monitoring terminal has been bound to another user\" is displayed.  
(3) After binding is completed, the monitoring terminal transmits monitoring data. When the cloud server detects data sent by the bound terminal on port 7201 through the WebSocket or HTTP protocol, the back-end service layer parses the data and stores them in the corresponding table in the database. Figure 13 illustrates the process of binding between the user and the monitoring terminal.  
Figure 12: Flowchart for access verification.  
Figure 13: Flowchart of binding a user and a monitoring terminal.
## 2 Materials and Methods
### Risk-Based Maintenance
[PERSON] and [PERSON] [54] reviewed six groups of methods: (1) hazard analysis such as event tree analysis (ETA) to identify the failure scenario, as hazard refers to the source of loss or damage, (2) likelihood assessment, which includes cause modeling and likelihood estimation to calculate occurrence of the undesired failure, (3) consequence assessment such as the impact intensity model which includes effects modeling and impact estimation to quantify the potential consequences of the credible failure scenario in terms of production loss, environmental, health and safety loss, and operating expenses (asset loss and maintenance expenses), (4) risk estimation, mainly the risk matrix to assign a risk class or level, (5) risk acceptance such as \"As low as reasonably possible (ALARP)\" to check if the estimated risk exceeds a specific level, and (6) maintenance planning such as the analytical hierarchy process (AHP) to select the most effective maintenance concept. It is clear that fuzzy logic and expert opinion methods have influenced the likelihood and consequence estimations [54]. It should be mentioned that NORSOK Z-008 has recommended several generic maintenance planning methods to be used such as functional and technical hierarchy, consequence classification, failure mode and effect analysis, the Reliability Block Diagram, Maintenance Task Selection, manning study, spare parts evaluation. The drawback of risk-based maintenance is that most of the risk analysis approaches are deficient in uncertainty and sensitivity analysis, which can mislead and generate non-essential maintenance efforts if it is misinterpreted [54]. In addition, the RBM considers historical data as part of the risk assessment process to determine maintenance intervals for preventive maintenance work. These data can include information on past failures, maintenance records, equipment performance, and operational history that helps identify recurring failure patterns, determine failure rates, and understand the consequences of failures. However, it is not a one-time task and requires regular review and re-assessment to adapt to changing equipment conditions, operational requirements, and new insights gained from ongoing risk assessments and maintenance performance evaluations.
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
## 3 A Vertex-Cover-Based Approach
### Index Construction  
Our index is constructed in two steps. First, we compute the vertex cover, \(S\). Second, we process each vertex of \(S\) to construct two-hop labels. Below, we discuss the details.
# Portable Optical Fiber Bragg Grating Sensor for Monitoring Traffic Density
## 3 Experimental Measurement of Transport Vehicles  
Figure 4 shows the measuring scheme that was used to monitor traffic density.  
The measuring station comprised an FBG sensor, which was connected to an FBG interrogator unit entitled FBG Guard Fast 1550 (Safibra, Prague, Czech Republic) through a fiber optic cable. This unit contained a wide-spectrum radiation source with an output power of 1 mW and an output spectral characteristic of 1510-1580 nm. The reflected light from the FBG sensor was guided through a diffraction grating to a CCD field that converted the light spectrum into an electric signal. The unit featured a spectral domain resolution of 1 pm and a maximum sampling rate of 10 kS/s, making it possible to measure very small and fast processes. Part of the device was also a computing unit that sent measured data from the FBG sensor in real-time over ethernet to a user's computer. At this point, the collected data were processed in the Matlab environment.  
The signal from the FBG sensor was filtered by a high-pass filter (Butterworth fourth order) with a cut-off frequency of 0.4 Hz in order to remove the slow component corresponding to the fluctuation of ambient temperature. The peaks (individual car axles) were then identified in the signal and individual car passes were detected.
Due to the scientific collaborations that the Hospital Nacional de Paraplejicos has maintained with the Vicomtech technological center [12], Armeo(r) Spring (Hocoma AG, Switzerland) was the robotic device that was available to us to carry out this research. Specifically, the Armeo Spring robotic device has been previously applied in SCI populations [10; 13] and other neurological pathologies such as stroke [14; 15; 16; 17; 18; 19; 20; 21], cerebral palsy (CP) [22; 23; 24; 25; 26; 27], and multiple sclerosis (MS) [28]. Scientific evidence in stroke populations concludes that it is not clear that UE therapy based on robotic devices (UE-RT) shown an improvement in the performance of basic ADL in stroke patients more than usual techniques or no treatment [29]. Previous studies in SCI populations lacked an independent control group as a comparator. Taking this hypothesis as a starting point, the main objective of this study is to analyze the effectiveness of the Armeo Spring device for improving the performance of ADL. For this purpose, a virtual reality application based on the performance of the ADL of drinking from a glass using Armeo Spring has been developed and included within the UE-RT sessions conducted in this study. Therefore, as a novelty, in contrast with a previous study in subacute SCI subjects [13], an independent control group of subjects undergoing no Armeo Spring treatment was included and both the control and intervention groups were dose-matched; and assessments based on kinematic metrics from the performance of a biomechanical study have been included in the methodology. The secondary objectives of this study were to study the clinical feasibility of administering to patients for eight weeks a treatment based on UE-RT to complement the conventional therapy they receive; to analyze the influence of the emotional state of the patients in the study results; and to study the usability of robotic therapy.
## 3 Results and Discussion
### Total Phenolic Content  
The total phenolic contents of the modified and unmodified whey protein are shown in Figure 3. Generally, the control whey protein had a total phenolic content of 778 umol GAE/100 g sample. The total phenolic content observed in unmodified whey protein might be due to the presence of tyrosine, tryptophan, cysteine, histidine, and asparagine, which can reduce the Folin-Ciocalteu reagent [19]. After modification of the whey protein with oxidized phenolic compounds, the total phenolic content of all modified whey proteins was greater than the control (\(p>0.05\)). This result is associated with the incorporation of oxidized phenolic compounds into the whey protein molecule. [PERSON] et al. [9] reported that the modification of gelatin with oxidized phenolic compounds could induce the formation of a gelatin-phenolic complex, in which the obtained gelatin still had the reducing activity of the hydroxyl group, resulting in an increase in the total phenolic content of the modified gelatin [9].  
Change in the total phenolic content of the modified whey protein are associated with the type and concentration of oxidized phenolic compounds. At the same concentration used, the whey protein modified with OTA showed the highest total phenolic content, followed by the proteins modified by OGA and OFA. The total phenolic contents of the whey proteins modified with 5% OTA, OGA, and OFA were 2806, 1256, and 1031 umol GAE/100 g sample, respectively. This might be because OTA could integrate with the whey protein more effectively than OGA and OFA, as evidenced by the lower free amino content of the resulting whey protein, as well as OTA exhibiting a higher reducing power than  
Figure 3: Total phenolic content of whey protein modified with different oxidized phenolic compounds at various concentrations. Different letters in the same concentration of oxidized phenolic compounds indicate significant differences. Different capital letters in the same type of oxidized phenolic compounds and control indicate significant differences.  
OGA and OFA because of a higher abundance of hydroxyl groups. For the whey proteins modified with OGA and OFA, the whey protein modified with OGA showed a higher total phenolic content than the whey protein modified with OFA, although OFA showed a higher interaction with the whey protein, as evidenced by the lower free amino group content of the modified whey protein. This result might be because gallic acid had more hydroxyl groups on the aromatic benzene ring than ferulic acid, resulting in a higher total phenolic content in the whey protein modified with OGA.