esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ontology for Spatio-Temporal Media Management and an Interactive Application
## 5 Web3602--Interactive Application Applying SDM Ontology
### Implementation
#### 5.3.6 Moving the Viewing Position  
The function of moving the viewing position is in the experimental stage and is a tentative implementation. Because Web360\({}^{2}\) plays back 360\({}^{\circ}\) videos, the viewing position is limited to the position of the video camera that captured every 360\({}^{\circ}\) video. This limitation was caused by the fact that the filmed objects were not objectified. It is not easy to efficiently convert a moving object into a 3D object and play it back at a quality suitable for viewing. Because audio is converted into object data using location information, it is possible to playback audio from arbitrary locations. As the video data volume is larger than a realistically available communication bandwidth, there are restrictions on switching the viewing position. To quickly change the viewing position, loading at least the first part of all video data in advance is necessary. However, this will increase the communication load before playback starts, and the user will have to wait until playback begins. With the current Web360\({}^{2}\) implementation, the application loads only the video data of the initial viewing position, and then playback starts; therefore, it takes time to change the viewing position. There is a trade-off between the amount of communication before playback starts and the smoothness of changing the viewing position.
## 3 Results  
### Power Characteristics  
The laser powers were determined for both continuous wave (CW) and pulsed operation modes at duty-cycles from 1% up to 30%. Figure 6 shows the emitted laser power values as a function of the set point voltage. For pulsed operation mode, the duty-cycle of 15% is shown here as an example.  
In the case of measurement of the output power in the continuous wave operation mode, a value of 12.7 W was achieved. Therefore, the real power value is 0.8 W below the manufacturer's specifications of 13.5 W. Nevertheless, the discrepancy is within the acceptable limit of 20% and, therefore, in accordance with the guidelines of the International Electrotechnical Commission (IEC) 60601-2-22 [22]. Figure 7 shows the achievable power values (peak power) depending on the different pulse durations.  
For pulse durations of 5 \(\upmu\)s and 10 \(\upmu\)s and duty-cycles of 5 and 10%, maximum peak powers of up to 34.5 W were achieved. Figure 8 illustrates the relationship between the set point voltage (SPV), the achievable average power of these two pulse durations and  
Figure 6: Emitted average output powers for CW and pulse operation modes for the example of the 15% duty-cycle and corresponding pulse durations from Table 1.  
Figure 7: Graph of the pulse peak power P\({}_{\text{peak}}\) as a function of the duty-cycle and the pulse duration (above the CW limit line). Below the CW limit line, the average output power P\({}_{\text{avg}}\) of the single pulses (1 Hz) is shown as a function of the pulse duration. All curves approach the CW limit line with increasing duty-cycle.  
the resulting peak powers. The single pulses in the millisecond range (ms) show a linear increase in the average power with increasing duty-cycle up to the CW limit line. As expected, the resulting peak values according to Equation (2) are above the CW limit line and increase with decreasing pulse duration.
# Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control
## 1 Introduction
[38], the sensor and actuator faults were considered and a robust fault-tolerant model predictive control approach was proposed. By minimizing the upper bound of the objective function, the MPC state feedback law is obtained in terms of linear matrix inequalities (LMIs). Simulation results have illustrated the effectiveness of these methods. Different from the above methods, we focus more on the optimization algorithm than the MPC framework. In order to confirm the robust stability of the closed-loop system, the efficiency and reliability of the optimization algorithm should be guaranteed first. This article proposes a rapid semi-analytical powered descent guidance algorithm for planetary landing. By using this algorithm, the following two aims are achieved: (1) real-time guidance computation and (2) rapid reachability/controllability determination. To achieve real-time guidance computation, a decoupling method is adopted and the fuel-optimal control mode is derived. Six explicit constraint equations are obtained and the solutions of which are in closed form, so that the real-time guidance computation is guaranteed. In order to make full use of the real-time advantages of the analytical method, and improve the performance of the proposed method in the face of practical complex constraints, the model predictive control framework is chosen. Several techniques are adopted to guarantee the recursive feasibility. The article is organized as follows. In Section 2, the planetary landing guidance is first transformed into an optimal control problem, and the optimal solution for the one-dimensional problem is derived. Then, a dynamic decoupling method is introduced, and the decoupled three-dimensional fuel-optimal control modes are derived through [PERSON]'s maximum principle. In Section 3, the motion modes in three directions are analyzed in detail, and the reachability determination method is presented. In Section 4, we apply our semi-analytical algorithm to model the predictive control framework and adopt several techniques to enhance the robustness. The simulation results are presented in Section 5, and conclusions are summarized in Section 6.
## 3 Results and Discussions
### Parameter Analysis of Solar Irradiance (DNI)  
Effects of the IR-reflector on the performance of HCEs in the PTC system mainly depend on the operating temperature and solar irradiance. As explained above, higher operating temperatures contribute to a more effective role of the IR-reflector. In this part, the roles of different IR-reflectors in the NHCEs at the inlet temperature of 500 \({}^{\circ}\)C on the overall performance are investigated under different values of DNI.  
The energetic efficiency of the PTC system with different HCEs versus DNI is shown in Figure 11. It can be seen that the gap between the energetic efficiency of NHCE and that of the CHCE is gradually reduced with the increasing DNI from 400 W/m\({}^{2}\) to 800 W/m\({}^{2}\), which is also proved in Figure 12. The percentages of energetic efficiency enhancement of NHCE-IRR-I, NHCE-IRR-II, and NHCE-IRR-III at the DNI of 800 W/m\({}^{2}\) are 4.3%, 6.7%, and 11.4%, respectively.  
Figure 11: Energetic efficiency of PTC system with different HCEs versus DNI.  
Figure 10: Percentage of exergetic efficiency enhancement of NHCEs versus inlet temperature.  
The energetic efficiency of the PTC system with different HCEs versus DNI is shown in Figure 13. Similar to the energetic efficiency of the NHCE, the gap between the energetic efficiency of NHCE and that of CHCE is gradually reduced with the increasing DNI, which demonstrates that role of IR-reflectors in the NHCEs in enhancing overall performance of PTC system is reduced at higher solar irradiance. As exhibited in Figure 14, the percentages of exergetic efficiency enhancement of NHCE-IRR-I, NHCE-IRR-II, and NHCE-IRR-III at the DNI of 800 W/m\({}^{2}\) are 4.8%, 7.4%, and 13.0%, respectively.  
Figure 12: Percentage of energetic efficiency enhancement of NHCEs versus DNI.  
Figure 13: Exergetic efficiency of PTC system with different HCEs versus DNI.
# A Survey of LoRaWAN for IoT: From Technology to Application
## 6 MAC Layer Performance Evaluation  
Section 5 reviews studies related to physical layer performance, while this section reviews studies that are related to LoRaWAN MAC layer performance. First, it makes a detailed review of the LoRaWAN MAC models that are used to assess network performance. Then, power usage evaluation and security mechanisms and security vulnerabilities are covered.
# Sound Localization Framework for Construction Site Monitoring
###### Abstract  
This study developed a construction sound localization framework (CSLF) using the time delay of the arrival technique and the generalized cross-correlation phase transform. Based on the spatial characteristics of construction sites, this study identified sound-source locations at construction sites to overcome the limitations of existing sensor-based localization methods. Verification tests were performed on the CSLF in both interior and exterior environments. The interior tests showed that the average error was less than 0.5 m for each axis; which is 0.26, 0.27, and 0.36 m for the x-, y-, and z-axes, respectively. The exterior tests conducted at an actual construction site showed average x-, y-, and z-axis errors of 0.19, 0.21, and 0.24 m, respectively. These results show that the CSLF can effectively identify sound locations at construction sites within an arm's length. Furthermore, CSLF is applicable on the sound data categorization-related research. The system can categorize an accidental or abnormal sound and locate the sound source for safety management at the construction site. It can also be utilized to monitor construction productivity by categorizing and locating the machinery-related sound sources.  
sound-based localization; generalized cross-correlation phase transform; construction monitoring method +
Footnote †: journal: 2022, 12, 10783. [[https://doi.org/10.3390/app122110783](https://doi.org/10.3390/app122110783)]([https://doi.org/10.3390/app122110783](https://doi.org/10.3390/app122110783))
## 3 Engine Instrumentation  
To assure a total monitoring of engine operation in maximum safety conditions, instrumentation lines and commands lines for measuring certain parameters of the engine were executed for controlling the engine, measure the parameters of the auxiliary installations and control the commands of the auxiliary installations.  
Considering that the engine is equipped with a standard instrumentation [3] consisting of gas generator shaft speed \(N_{GG}\), inlet gas turbine temperature \(T_{3t}\), fuel flow \(F_{f}\), vibrations \(V_{B}\), oil pressure \(P_{UM}\), oil temperature \(T_{UM}\), and, supplementarily, the free turbine shaft speed \(N_{TP}\), to realize the detailed study of the TV2-117A engine working regimes it was necessary to add additional instrumentation [13], with some gasdynamic parameters in different engine stations, including the engine inlet, station 1, output of the compressor, station 2, and the output of the free turbine station 45, as presented in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Regime** & **P [HP]** & **S\({}_{\text{FC}}\) [kg/HP/h]** & **N\({}_{\text{GC}}\) [\%]** & **N\({}_{\text{TP}}\) [\%]** & **T\({}_{\text{3-max}}\) [\({}^{\circ}\)C]** & **F\({}_{\text{C}}\) [kg/h]** \\ \hline Idle (a) & Max. 100 & 1 & 63 + 2/\(-\)1 & 47.2 \(\pm\) 10 & 550 & 100.0 \\ Cruise (b) & 1000 & 0.310 & 94.5 & 99.7 \(\pm\) 2 & 810 & 310.0 \\ Max. Cont. (c) & 1200 & 0.295 & 96 & 99.7 \(\pm\) 2 & 860 & 354.0 \\ Takeoff (d) & 1500 & 0.275 & 98.5 & 97.6 \(\pm\) 1 & 875 & 412.5 \\ \hline \end{tabular}
\end{table}
Table 1: TV2-117A operating regimes.  
Figure 2: TV2-117A free turbine turboshaft.  
On the engine inlet, at station 1, the differential pressure was measured and used to calculate the engine air flow. On station 2 the outlet compressor total pressure and temperature were measured, and on station 45, the outlet free turbine total pressure was measured.  
Regarding the engine control instrumentation, the main commands, through which the engine can be operated, are the starter, ignition IGN, starting fuel valves SFV, start lever SLV, throttle lever TLV, and external commands such as the fuel pump (FP) and a fuel shut-off valve (FSHV). To perform the engine experimentation, the engine was installed on the test bench and connected to all the instrumentation lines, as presented in Figure 4.
# Quality of Tomato Juice as Influenced by Non-Thermal Air Plasma Treatment
## 2 Materials and Methods
### Determination of Color Parameters in Tomatoes Juice  
The color of the tomato juices was measured using a 3 Color spectrophotometer SF80 (Marcq-en-Barcul, France). The results were expressed in accordance with the CIE L*a*b* system, using illuminant D65 and 10\({}^{\circ}\) observer. The color parameters were L* value (lightness, ranging from 0, black, to 100, white), a* value (positive values for reddish colors and negative value for greenish ones), and b* value (positive for yellowish colors and negative for bluish ones). A standard white plate (L* = 92.37, a* = \(-\)0.82 and b* = 1.82) was used to calibrate the instrument. Moreover, the tomato juice was critically evaluated for the total color difference (\(\Delta E\)), which showed a numerical difference in this parameter of the samples after CAP treatment compared to the control sample, according to Equation (1).  
\[\Delta E=\sqrt{\left(L^{*}-L_{0}\ \right)^{2}+\left(a^{*}-a_{0}\ \right)^{2}+ \left(b^{*}-b_{0}\ \right)^{2}} \tag{1}\]  
where, \(L_{0}\), \(a_{0}\), and \(b_{0}\) are the color values of untreated juice samples.  
Figure 1: Experimental set-up.  
The following criterion was used in the analysis of the results: the absolute color differences (\(\Delta E\)) between 0 and 1 were regarded as unrecognizable (indiscernible deviation), values from 1 to 2 denoted a slight deviation recognizable by a person experienced in discriminating color nuances, values in the range of 2-3.5 indicated a deviation recognized even by a non-expert, 3.5-5 represented a clear deviation, and \(\Delta E\) above 5 denoted a substantial color deviation.
# Performance Evaluation of a Long-Span Cable-Slayed Bridge Using Non-Destructive Field Loading Tests
## 2 Non-Destructive Field Loading Test for Pingnan Xiangszhou Bridge
### Non-Destructive Field Loading Test for Pingnan Xiangszhou Bridge
#### 2.3.2 Cable Force Test and Principle in Non-Destructive Field Loading Test  
According to the vibration theory, the relationship between the tension of the cable and its natural frequency for long cables hinged at both ends, as shown in Figure 3, can be written as:  
\[\omega_{nr}^{2}\ =\ \frac{\pi^{2}r^{2}T}{\rho l^{2}}+\frac{EI}{\rho}\Big{(}\frac{ \pi r}{l}\Big{)}^{4}, \tag{1}\]  
where \(\omega_{nr}\) is the _r_-th natural frequency (rad/s), \(T\) is the tension of the cable (N), \(l\) is the length of the cable (m), _\(\rho\)_ is the density of the cable (kg/m), \(E\) is [PERSON]'s modulus (Pa), and \(I\) is the moment of inertia (kg\(\cdot\)m\({}^{2}\)).  
When the bending stiffness of the cable can be ignored, the relationship between the tension of the cable and its natural frequency becomes:  
\[\omega_{nr}^{2}\ =\ \frac{\pi^{2}r^{2}T}{\rho l^{2}}, \tag{2}\]  
and then the tension of the cable can be obtained via the following equation:  
\[T\ =\ \frac{\omega_{nr}^{2}\rho l^{2}}{\pi^{2}r^{2}}, \tag{3}\]  
Equation (2) can be further rewritten as:  
\[\omega_{nr}\ =\ \frac{\pi r}{l}\sqrt{\frac{T}{\rho}}, \tag{4}\]  
Thus, the following Equation (5) can be obtained:  
\[\omega_{nr}-\omega_{nr-1}\ =\ \frac{\pi}{l}\sqrt{\frac{T}{\rho}}, \tag{5}\]  
that is, the difference between two adjacent natural frequencies is a constant, and is equal to the first natural frequency. This characteristic is reflected as equally spaced peaks on the spectrum. According to the Equation (3), as long as any natural frequency of the cable is measured, the tension of the cable can be calculated.
# On the Benefits of Listen before Talk Scheme for NB-Fi Networks
## 6 Problem Statement  
Although the performances of Aloha and different CSMA modes have been compared in classic works [12], it is not obvious which mode is better for NB-Fi in typical IoT scenarios such as the one described in this section. First, the classic results do not consider the possibility of NB-Fi using different data rates, the transmissions of which require different subchannel widths and thus have different probabilities to resolve conflicts in the frequency domain. Second, the classic results do not consider the small buffers at the end devices, which are typical for low-memory sensors. Small buffers significantly increase the loss probability at high loads. At the same time, packets lost due to buffer overflow do not contribute to the energy consumption and the average delay. For the described scenario, we state the problem to compare different operation modes: Aloha and the listed CSMA types in terms of device energy consumption, PLR, latency, and throughput, and to provide recommendations on which operation mode to use considering the peculiarities of NB-Fi networks.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 2 Methodology  
This study focuses on a specific aerospace communication module based on the main control module of the satellite, combining digital-analog hybrid characteristics with electro-thermal-mechanical multi-field coupling simulation. It considers the combined impact of various failure mechanisms on module reliability under multi-stress conditions. The study covers both performance degradation and functional failure modes, and a reliability prediction model is developed. The overall research scheme is shown in the figure below. Through this approach, the precise prediction of reliability curves (error < 10%) is achieved, providing theoretical support for the optimization design of aerospace electronic systems.  
The overall research framework is illustrated in Figure 1.  
First, the communication module's circuit topology is analyzed, and key components, along with their primary failure mechanisms, are identified based on environmental adaptability. Corresponding Physics of Failure models are then established.  
Second, a hybrid-precision circuit simulation model is constructed. An IBIS interface model is developed for the digital-to-analog conversion interface using Verilog-AMS, while Verilog and the PostScript Simulation Program with Integrated Circuit Emphasis (PSPICE) are used to simulate digital and analog electrical stress, respectively.  
Subsequently, an electro-thermal-mechanical multi-physics coupling model is developed based on the mission profile power distribution. The finite element method is employed to analyze the thermal accumulation effects induced by electrical stress, and thermal stress simulations are conducted to obtain the temperature field distribution. The temperature distribution is then used as a loading factor for mechanical simulations to evaluate critical regions' stress and strain characteristics. This study uses coupled simulations to transfer thermal and mechanical simulation results to the electrical simulation.  
Finally, a degradation-failure dual-modal reliability-prediction method is established by integrating simulation waveforms of failure modes with multi-physics stress analysis results. This method enables a comprehensive assessment of the impact of various failure  
Figure 1: Overall research framework.  
mechanisms on reliability. The overall reliability of the communication module is calculated using a reliability block diagram, and weak links are identified for further analysis.
## 3 Construction
### Security  
#### 3.2.1 IND-CPA Security  
We can rephrase the description of the \(IND-CPA\) security game process for our proposed scheme, which is equivalent to the one proposed by [PERSON] et al. [21], as follows:  
* \(Setup\): The adversary \(\mathcal{A}\) is given the public parameter \(PK\) after the challenger \(\mathcal{B}\) calls the \(Setup(1^{\circ},U)\) algorithm.
* _Phase_ 1: Adversary \(\mathcal{A}\) can dynamically request the decryption keys \(Sk_{i}\) associated with attribute sets \(S_{1},\cdots,S_{q_{r}}\) from the challenger \(\mathcal{B}\). In response, \(\mathcal{B}\) executes the key generation algorithm to generate \(Sk_{i}\) and sends it to \(\mathcal{A}\).
* _Challenge_: Adversary \(\mathcal{A}\) provides two equal-length messages \(M_{1}\) and \(M_{2}\) and a generator matrix \(A^{*}\) that corresponds to an access structure \(\mathbb{A}^{*}\) that does not satisfy \(S_{1},\cdots,S_{q_{r}}\) to the challenger \(\mathcal{B}\). Then \(\mathcal{B}\) randomly chooses a bit \(\sigma\in\{0,1\}\) and generates the ciphertext \(CT_{A^{*},T}\) by calling the encryption algorithm with \(sk_{Do}\), \(<A^{*},\rho>\), \(PK\), and \(M_{\sigma}\). Finally, \(\mathcal{B}\) sends \(CT_{A^{*},T}\) to adversary \(\mathcal{A}\).  
Figure 2: Membership.  
_Phase 2_: Adversary \(\mathcal{A}\) keeps asking \(\mathcal{B}\) for decryption keys \(Sk_{i}\) corresponding to attribute sets \(S_{q_{i+1}},\cdots,S_{q}\), where each set cannot satisfy the access structure \(\mathbb{A}^{*}\). Upon each request, \(\mathcal{B}\) calls the key generation algorithm and sends \(Sk_{i}\) to adversary \(\mathcal{A}\).
* _Guess_: \(\mathcal{A}\) outputs a guess \(\sigma^{{}^{\prime}}\in\{0,1\}\). The advantage of the adversary in this game is defined as: \[Adv_{(A)}=\mid Pr[\sigma^{{}^{\prime}}=\sigma]-1/2\mid\]  
**Definition 7**.: _If we assume that any adversary with polynomial time has only a negligible advantage in winning the aforementioned game, we can confidently assert that our scheme is secure._
## 1 Introduction  
Monitoring animal health is recognised as a factor of key significance in improving antimicrobial stewardship [1]. To this direction, in-farm surveillance using indicators of the spread of antimicrobial drug resistance (AMR) can prove significant in improving animal health and welfare, and in effect, public health protection. Interestingly, the relevant information available in connection with dairy sheep is limited. Recently, we investigated the spread of AMR bacteria in five dairy sheep farms in Greece to identify indicators for improved field surveillance [2]. This investigation, which was to the best of our knowledge, the first to be conducted on this subject, relied on the detection of two common foodborne pathogens that have proven suitable for monitoring AMR transfer between animals and humans, namely _Staphylococcus aureus_ and _E. coli_[3]. The study farms, referred to as Farms A-E, were selected based on location, accessibility, size (small, medium, large), breeding practice (indoor intensive, semi-intensive, and semi-extensive), and the availability of reliable farm records, which was confirmed onsite through a method of inspection and personal interview. That certain investigation concluded that the spread of AMR isolates in the study population is considerable. This finding was clearly reflected in milk, particularly with regards to the AMR and multidrug-resistant (MDR) _S. aureus_ that was detected in samples collected both from the under and the bulk milk tank. Notably, the latter proved suitable for the cost-effective assessment of AMR, dispensing with the need to collect samples from individual animals. The assessment that was conducted to assess potential sources of the AMR spread within farms was targeted to bedding, faeces, drinking water, and feeds. The latter, particularly the samples collected from the farms' feeders, were found positive to _E. coli_, which was identified in several cases as AMR or MDR, at a percentage that was statistically significantly higher compared to all other types of samples (b = 3.602; \(p\)-value \(<\) 0.01). Notably, the presence of AMR isolates of _S. aureus_ in feeds was found to increase the probability of milk positivity to AMR _S. aureus_ by 3.25 times, which was regarded as indicative that feeds may be a factor of key significance in the spread of AMR inside the farm [2].  
Based on the findings reported above, it was considered advisable to further our previous investigation to the parameters likely to affect AMR positivity of milk and feeds in the study farms to identify potential indicators of their spread and improve field surveillance.