esa-sceva/satcom-chunk-collection huggingface dataset:
# Power System State Estimation Approach Considering Transmission Line Temperature
## 4 Improved Two-Step Etc-Se Approach  
### Framework of Improved Two-Step Etc-Se  
The framework of the proposed ITS-ETC-SE approach can be illustrated in Figure 3. The SE and temperature estimation are decoupled and solved via alternate iteration. At each iteration, the SE is first conducted. The nodal voltage and line current are calculated, which serve as the input for calculating the HBE. Then, line temperature is calculated according to the outputs of SE and meteorological data, and utilized to modify line parameters and the admittance matrix for SE. The process is carried out repeatedly until both state variables and line temperatures converge.  
Figure 2: Flowchart of the proposed AJ-ETC-ES approach.
# Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security
###### Abstract
However, most trust-based mechanisms consume power, communication bandwidth, and memory [10; 11]. As well as the high overheads (i.e., power, message, and memory), most mechanisms do not consider the mobility of the nodes and networks. Furthermore, most IoT networks are dynamic by nature [1]. In a trust-based security framework, trust parameters are exchanged among nodes to compute the trust value, contributing to unwanted traffic and congestion. This approach increases energy consumption and message loss and deteriorates network lifetime and performance in terms of availability. Therefore, it is necessary to minimize network traffic when mitigating attacks. First, there is a need to design a mechanism with minimal energy consumption at the node level to increase the network lifetime of LLNs. The energy levels of nodes must be conserved. For example, the Tmote Sky wireless sensor module can monitor temperature, light levels, and humidity. It has 48 kB of flash storage, 10 kB of RAM, and a 16-bit CPU with a battery of 30 kJ that is rarely recharged [13]. With RPL, such devices consume 0.0920 W per minute. Therefore, trust-related complex computations deplete these resources, and the sensor node will become exhausted. Second, as well as control messages and sensed data, in a trust-based mechanism, trust-related data are also exchanged between nodes or sent to a central entity in a multi-hop fashion, causing unwanted traffic that leads to congestion. In both cases, message overhead is generated, which must be minimized to maintain network performance. A high message overhead will deteriorate the performance of the network and may also increase delays and the packet loss ratio. In an LLN, nodes are always dynamic and mobile (i.e., they can join or leave the network), which is ignored in most state-of-the-art approaches. Most have a trust mechanism designed for a static environment. Therefore, it is necessary to design a mechanism that can cope with the dynamicity and mobility of the nodes and the environment. Third, since the value of the trust (of a node) is highly dependent on the trust parameters, their integrity is extremely important. If the trust-related data (parameters) are compromised, the trust value will not depict the true situation. As a result, a malicious node may be identified as benign, leading to devastating consequences. Message floods, complex calculations, and memory overhead plague the majority of current experiments. As a result, IoT networks, which have limited resources, can become ineffective. Moreover, because of the changing dynamics of the network, certain trust parameters cannot be adapted or scaled, which might have detrimental implications in battery-based RPL-based systems. Consequently, more node power is consumed, degrading the overall network lifetime. Therefore, it is necessary to develop trust parameters and strategies for low-power, resource-constrained, and lossy networks. Furthermore, ensuring the security of the data gathered is also crucial for the integrity of trust values.
## 3 Experiments and Discussion  
### Experimental Setup  
To measure the performance of the sensor and the influence of different structures on the sensor, a set of experimental systems was prepared, which is shown in Figure 4. According to the piezoelectricity of the sensor, when excited by a periodic force, there will be a periodic voltage of the same frequency on the two ends of the sensor. In order to produce a controllable periodic pressure, a high-power bass loudspeaker (YD176-8 XPH, Nanjing Electro-acoustic Co., Ltd., Nanjing, Jiangsu province, China) is used as the source of periodic force. The sensor is fixed at the bottom of the screw and adjusted to a right position by moving the screw up and down. All of the beams in this experimental system were rigid beams to reduce the influence of other vibrations on the measurement.  
In the experiment, the exciting signals of different frequencies and amplitudes from the signal generator (FY2200S, Zhengzhou Feeltech Technology Co., Ltd., Zhengzhou, Henan province, China) were put into the loudspeaker, so that the loudspeaker generated periodic force signals that were applied to the sensor. The output signals of the sensor were acquired by the digital oscilloscope (DSO7034B, Agilent Technologies Inc., Palo Alto, CA, USA).  
Figure 4: Configuration diagram of the wrist PVDF sensor test system.  
Figure 3: Fabrication process of the sensor (**a**) backing and PVDF film; (**b**) the signal preprocess circuit board; (**c**) protective layer; and (**d**) physical map of wrist PVDF sensor.
# An Effective Sensor Architecture for Full-Attitude Determination in the HERMES Nano-Satellites
## 3 Sensor Selection
### Gyroscopes
The selection of the gyroscope data to use, either the primary or the secondary, depends on a quality index of the raw measurements, which can be computed as in [7].
# Research on Obstacle Avoidance Replanning and Trajectory Tracking Control Driverless Ferry Vehicles
## 4 Obstacle Avoidance Algorithm and Transverse Controller Design
### Design of MPC Controller with Planning Module
#### 4.2.2 Tracking Control Layer Design  
Because the controlled object is a driverless ferry car, which is a complex nonlinear system. Firstly, the nonlinear model is linearly discretized [24], then the constraint conditions and objective function are designed, and finally, the angle of the front wheel is resolved, as shown in Figure 17, which is the trajectory-tracking flowchart.  
Figure 16: Reference trajectories and planning trajectory.  
Figure 17: Trajectory-tracking flowchart.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 4 Comparison with Other Methods  
Experiments were conducted using a personal computer with the following basic parameters: the CPU was Intel(r) Core(tm) i3, M380 @ 2.53 GHz. RAM: 4 GB. The operating system was Windows 7, 32-bit, and the software compiler was Visual C++ 6.0.  
A comparison of SIFT, A-SIFT, and SURF algorithms is shown in Table 6. Dataset 2, with a size of 12,000 \(\times\) 12,000 pixels, and Dataset 3 with a size of 15,321 \(\times\) 14,921 pixels, were used for image matching. When an affine relationship in images exists in SIFT and SURF, in situations where rotation, scale, and translation exist simultaneously in images to be matched, the stability and accuracy of these methods decrease. A-SIFT performs better under this condition; however, it has the longest processing time. Our method showed the best performance in the experiment. The mismatched points were partly caused by the variation in ground objects and by wrong matching. The algorithm for matching gross error elimination proposed in this paper detected and eliminated mismatched point pairs effectively.
# A Review of Deep Learning Based Speech Synthesis
## 4 Deep Learning Based Speech Synthesis
### Speech Synthesis Using Deep Mixture Density Networks  
Although the DNN-based speech synthesis model can synthesize speech with high naturalness, it still has some limitations to model acoustic feature parameters, such as the single modality of the objective function and the inability to predict the variance. To address these problems, the authors in [44] proposed the parameter prediction method based on a deep mixture density network, which uses a mixture density output layer to predict the probability distribution of output features under given input features.
## 6 Discussion
This enhanced capability is vital for detecting natural phenomena or environmental alterations. By augmenting remote sensing images with detailed information, pansharpening highlights subtle variations in factors such as land use, vegetation dynamics, water contamination, and air quality. These detailed observations are crucial for understanding environmental changes and their impact on ecosystems. For example, researchers can employ high-resolution remote sensing data to monitor the dispersion of pollutants through water, air, and soil, thereby gaining insights into their behavior in natural settings. Such precise observations assist in evaluating the potential risks of environmental pollutants to ecosystems, flora, fauna, and their persistence and toxic effects. Advancements in remote sensing technology, when paired with pansharpening, enable the integration of various spectral bands, thereby improving both spatial and spectral resolution. This allows researchers to analyze environmental changes from both broad and fine-scale perspectives. In studying pollutant diffusion in the atmosphere, high-resolution remote sensing data can capture the spatial distribution, concentration shifts, and interactions with other pollutants. This provides a solid scientific foundation for environmental pollution control strategies. In the context of ecological and environmental monitoring, pansharpening technology provides enriched geographical background data, aiding in the optimization of environmental management strategies. By examining regional environmental characteristics via remote sensing images, researchers can more accurately predict environmental trends under specific conditions. Additionally, the utilization of pansharpening technology, especially with high-resolution remote sensing data, promotes interdisciplinary collaboration. It bridges fields such as environmental science, geography, and ecology, enabling scientists to gather comprehensive environmental information through remote sensing. When combined with in-depth environmental research, this integration explores the effects of environmental changes on Earth's ecosystems and their practical applications. This not only fosters innovation in environmental monitoring but also offers valuable scientific data and decision-making support for global environmental protection, public health, and resource management.
## 2 Materials and Methods
### Remote Sensing Data  
In this study, images from four medium-spatial-resolution satellites were used: Landsat 5, 7, 8, and Sentinel 2 A/B. Our study area is covered by paths 17/18 and row 30 of Landsat and tiles T17 TNJ, T17 TNH, T17 TPH, and T17 TPJ of Sentinel-2, encompassing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & \multicolumn{2}{c}{**Location**} \\ \cline{5-6}  & & & & **WLO + HH** & **WLO** & **HH** \\ \hline
**Source** & **Organization** & **Published Data** & **Chl-_a Extraction** & **Fraction of** & **Fraction within** & **Fraction within** & **Study Site (\%)** \\ \hline Hamilton Harbour Water Quality Data & ECCC & 1987–2019 & [42] & 68\% & 5\% & 98\% \\ \hline Great Lakes Nearshore-Water Chemistry & MECP & 2000–2017 & [43; 44] & 15\% & 43\% & 2\% \\ \hline Great Lakes Water Quality Monitoring & ECCC & 2000–Present & [45] & 17\% & 52\% & 0\% \\ and Surveillance Data & & & & 100\% & 100\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of in situ data sources used in this study. ECCC = Environment and Climate Change Canada; MECP = Ministry of Environment, Conservation and Parks (Province of Ontario).  
Figure 2: Boxplots of in situ data, categorized by location, seasonality, and Carlson’s Trophic State Index (TSI). The green background represents oligotrophic/mesotrophic (light green) and eutrophic/hypereutrophic (dark green) classes based on [PERSON]’s TSI. The plus markers indicate outliers.  
two Landsat and four Sentinel-2 scenes. Each of these satellites possesses unique features and characteristics that are summarized in Table 3. The differing spatial, spectral, radiometric, and temporal resolutions of these satellites impact their capacity to estimate Chl-_a_ concentrations in water bodies [11]. The literature suggests that the inclusion of an ultra-blue band at approximately 440 nm and the improved signal-to-noise ratio (SNR) of Landsat 8 and Sentinel-2 yield more accurate Chl-_a_ concentration estimates compared to the previous generation satellites [48; 49].  
In total, 236 matchup scenes were selected after quality control for cloud, snow, shadow, and overlapping land pixel artefacts. The scenes were distributed as follows among the different satellites: Landsat 5: 79 scenes; Landsat 7: 89 scenes; Landsat 8: 49 scenes; and Sentinel-2: 19 scenes. Each scene contained at least one high-quality pixel coinciding with an in situ measurement location. Most of the downloaded RS scenes were acquired within \(\pm 1\) day of the in situ sampling dates and at most within \(\pm 4\) days. Such close temporal matching is important given the rapidly changing algal patterns often observed in lakes [4].
## 3 Results and Discussion
### Multivariate Data Analysis of Volatiles Dataset Acquired from SPME-GC/MS  
Although notable differences in volatile constituents were observed visually between both fruits, multivariate data analyses were employed to classify both fruits in an untargeted manner using PCA and OPLS modelling (Figure S14). Samples segregation was observed along PC1 and PC2 to account for more than 80% of the total variance (Figure S14A,B). Supervised OPLS was further applied for its superiority in class separation [47] to obtain a model with good variance and prediction power (R\({}^{2}\) = 0.88, Q\({}^{2}\) = 0.82) (Figure S14C,D). The loading plot and S-loading plots identified metabolites mediating for samples separation as denoted by Mol. ion/Rt and labelled with their peak numbers in accordance with Table S1 (Figure S14B,D).  
Examination of the PCA loading plot revealed that aldehydes were abundant in cucumber, represented by benzaldhyde (peak 14), octanal (peak 15), benzacetaldehyde, (peak 17) and nonadienal (peak 19), in addition to anoth (peak 83), whereas bottle gourd was further enriched with ketones and esters, i.e., fenchone (peak 67) and methyl hexadecanoate (peak 58) (Table S1, Figure S14A,B).  
Figure 3: **(A)** Total ion chromatogram of _C. sativus_ and _L. siccarria_ aroma constituents as analyzed via SPME GC/MS. Assigned compounds names follow those shown in Supplementary Table S1. **(B)** Relative percentile of different volatile categories detected via SPME-GC/MS analysis in _C. sativus_ and _L. siccarria_ fruits.  
Furthermore, OPLS score plot showed better discrimination of samples (Figure S14C). The S-loading plot derived from OPLS model (Figure S14D) confirmed aldehydes' abundance in cucumber as discriminating metabolites, i.e., octanal (peak 15), benzenacetaldehyde (peak 17), and nonadienal (peak 19), versus anethol ether (peak 83) fenchone (peak 67), and methyl hexadecanoate (peak 58) in bottle gourd fruit.  
The overall aroma of bottle gourd and cucumber is manifested by a mixture of characteristic volatiles. Linalool, that is, the major detected constituent in both fruits, is known to impart an orange oil-like aroma, whereas fenchone in bottle gourd possesses a pleasant camphor-like aroma and is incorporated in food as a perfumery flavor [48]. On the other hand, aldehydes in cucumber, i.e., nonadienal, nonenal, and hexenal are known to mediate for the pleasant characteristic aroma of fresh cucumber that is further enhanced upon chewing, while benzaldhyde has an almond-like odor, all of which are shared in bottle gourd as well at variable concentrations. [49; 50; 51]. In addition to their role in taste and odor, such volatiles are known to mediate for antimicrobial activity [52; 51]. The study infers that bottle gourd possesses a consumer-pleasant aroma.
## 3 Optimization Methods and Results
### Comparison with Related Studies  
We compared the proposed design with the state-of-the-art CNN FPGA accelerator designs listed in Table 4. The work in [27], which used the FPGA platform Vertix-7 VX690T (San Jose, CA, USA), realized the AlexNet and occupies more resources. [PERSON] et al. [30] realized the VGG16, which employed Zynq XC7Z020 (San Jose, CA, USA). Moreover, the kind of Zynq FPGA collaborating with an external MCU would consume more power. [PERSON] et al. [31] achieved a low latency compared with [30]; however, the power consumption was still high. [PERSON] et al. [26] achieved the AP2D-Net (San Jose, CA, USA) on Ultra96, and the power was pretty low. In our design, the accelerator with a latency of 0.827 ms costs only 0.714 W under a working frequency of 100 MHz. Although our CNN model is much simpler, compared to the above designs, the latency and the power of our design are far lower, which meets the requirements of real time and low power.
# A Systematic Review of Osteotomies to Correct Hallux Valgus in the First Metastasis
## 3 Results
### Rotational Scarf Osteotomy
#### 3.6.2 Follow-up and Biomechanical Analysis  
[PERSON] et al. [24] analyzed the procedure based on a score, considering a healthy foot without pathology and a foot with hallux valgus as usual. These authors used a [PERSON] pressure plate (Preston Communications Ltd., Dublin, Ireland), a platform-based vertical pressure measurement system installed seamlessly within a 762 mm walkway at the Gait Study Center. This floor-mounted platform has 642 \(\times\) 297 \(\times\) 38 (mm) dimensions, with an active area measuring 194 \(\times\) 394 (mm). It comprises a grid of 2,048 sensors capable of sampling pressure data at a frequency of 55.6 Hz, with each sensor covering an area of 5 mm \(\times\) 5 mm (0.25 cm\({}^{2}\)). The pressure range per unit area spans 0.3 to 15 kg/cm. The collected data are processed using a 400 MHz Pentium II microcomputer (Intel, Santa Clara, CA, USA) running Winfoot software (Preston Communications Ltd., Dublin, Ireland).  
The authors [24] found several limitations in the study. Firstly, they did not have pre-operative clinical and pedobarographic assessment data for comparison. Both osteotomies did not influence the pre-operative pressure pattern. However, the authors aimed to investigate whether the aforementioned corrective procedures for hallux valgus restore plantar pressure distribution to the usual pattern and to discern their differences.  
Furthermore, the groups were comparable in demographic characteristics, pre-operative deformity, and post-operative regimen, except for follow-up time. The mean follow-up time for the [PERSON] group was shorter than that of the [PERSON] group, which could have affected the result with longer follow-ups.  
The [PERSON] and [PERSON] osteotomies were performed by different surgical teams, which potentially influenced the outcome based on the expertise of each team.  
Figure 11: Rotational scarf osteotomy representation.  
Although the authors aimed to examine post-hallux valgus correction pedobarography and its clinical relevance, correlating this with radiological results would have been advantageous. However, they did not seek final follow-up radiographs.  
Furthermore, additional analysis within study groups, comparing unilateral and bilateral procedures and symptomatic and asymptomatic feet, was limited due to insufficient sample size to achieve statistical significance.  
Patient inclusion criteria were based on subjective assessment.  
Finally, they did not perform null hypothesis testing on the AOFAS scores between the two intervention groups.  
The authors concluded that the osteotomy failed to restore the hallux load-bearing function, determining the procedure's outcome.
## 4 Results  
To demonstrate the effectiveness of our methodology in planning a nanosatellite constellation, this section brings about analyses of the features introduced to the original MILP and the corresponding results. After presenting the orbital parameters used for power harvesting prediction, we first report results regarding a simple example, consisting of a constellation with two satellites, to illustrate the consistency of the scheduling. Then an analysis of objective function values and solution time is carried out for progressively larger constellation sizes to assess the QoS impact of adding more satellites to a constellation. Pareto-optimal results for constellations with two and three satellites are presented to provide even more information to the decision-maker. Finally, an analysis of the impact of adding the redundancy term in the multi-objective function is presented.  
All methodologies were implemented in the Julia programming language using the JuMP library [23] and solved for a GAP \(\leq\) 1% using the Gurobi solver in a server with an Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20 GHz processor (20 cores, 40 threads), 64 GB of RAM, and Ubuntu 20.04.2 LTS 64 bits. Supplementary material is available.
## 4 Results
### Model-Based Verification for ADCS  
Systems engineering for aerospace projects is based on general guidelines and standardized phases that guarantee the expected performance of a system for a defined problem. Therefore, the life cycle of a space project is divided into seven phases according to the specifications detailed in ECSS-M-ST-10-01. Each phase has associated review milestones, of which a \"top-down\" character is observed from the PRR to the PDR to satisfy in a general way the customer's requirements. On the other hand, from the CDR to the RA milestones, a \"bottom-up\" behavior is observed, starting from the lowest-level supplier to the system operator. Consequently, a V-model is often used to describe the life cycle of this type of project.  
The V-Model in this research is complemented with an early model-based V&V process which facilitates the detection and correction of errors from early design stages [51]. Therefore, the model-based approach in system development is fundamental for development time reduction and more rigorous testing [52]. In this context, the different model-based development approaches correspond to characteristic stages of the V-Model and the project phases and documentation specified in ECSS-M-ST-10C [53], as can be seen in Figure 11.  
The initial stages of the cycle, from Phase 0 to Phase B, primarily concentrate on developing functional and technical requirements. This phase encompasses the identification of all activities and resources, and the initial assessment of technical risks leading up to the preliminary design. Within this framework, SIL (Software in the Loop) is proposed as a deliverable for the first development phase to verify the identified systems and initial risks. It is crucial to implement and validate the ideal simulation environment and the MIL (Model-in-the-Loop) environment beforehand to ensure accuracy.  
Figure 10: Hardware-in-the-Loop environment diagram. The space environment considers the same elements as Model-in-the-Loop.  
Phase C advances to detailed system definition, incorporating detailed sensor and actuator models. In this phase, MIL is recommended to address the need for more comprehensive model analysis. SIL tests may also be revisited to ensure system integrity and performance. Phase D progresses further into the system's implementation, where both PIL (Processor-in-the-Loop) and HIL (Hardware-in-the-Loop) methodologies can be employed. These methodologies facilitate a thorough analysis of the performance of not only the physical sensors but also the embedded controller, ensuring robust and reliable system performance.