esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Discussion
### Skeletal Malformations Classification  
For the ISBI Grand Challenge, the class labels of skeletal malformations based on ANB, SNB, SNA, ODI, APDI, FHI, FMA, and MW were also annotated for each subject. The skeletal malformation classifications for ANB, SNB, SNA, ODI, APDI, FHI, FMA, and MW are defined in Table 6 and the definitions (calculations) of ANB, SNB, SNA, ODI, APDI, FHI, FMA, and MW are summarized as follows:  
1. ANB: The angle between Landmark 5, Landmark 2 and Landmark 6
2. SNB: The angle between Landmark 1, Landmark 2 and Landmark 6
3. SNA: The angle between Landmark 1, Landmark 2 and Landmark 5
4. ODI (Overbite Depth Indicator): Sum of the angle between the lines from Landmark 5 to Landmark 6 and from Landmark 8 to Landmark 10 and the angle between the lines from Landmark 3 to Landmark 4 and from Landmark 17 to Landmark 18
5. APDI (AnteroPosterior Dysplasia Indicator): Sum of the angle between the lines from Landmark 3 to Landmark 4 and from Landmark 2 to Landmark 7, the angle between the lines from Landmark 2 to Landmark 7 and from Landmark 5 to Landmark 6 and the angle between the lines from Landmark 3 to Landmark 4 and from Landmark 17 to Landmark 18
6. FHI: Obtained by the ratio of the Posterior Face Height (PFH = the distance from Landmark 1 to Landmark 10) to the Anterior Face Height (AFH = the distance from Landmark 2 to Landmark 8). FHI = PFH/AFH.
7. FMA: The angle between the line from Landmark 1 to Landmark 2 and the line from Landmark 10 to Landmark 9.
8. Modify-Wits (MW): The distance between Landmark 12 and Landmark 11.  
\[MW=\sqrt{(x_{L12}-x_{L11})^{2}+(y_{L12}-y_{L11})^{2}}.\]  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{2}{c}{**Our own data from Shandong University**} \\ \hline
**Anatomical Landmarks** & **Mean Radial Error (mm)** \\ \hline
1. sella turcica & 2.4 \\
2. nasion & 1.4 \\
3. orbitale & 2.5 \\
4. porion & 3.0 \\
5. subspinale & 3.6 \\
6. supramentale & 3.3 \\
7. pogonion & 1.7 \\
8. menton & 1.2 \\
9. gnathion & 1.2 \\
10.goni & 2.4 \\
11.lower incisal incision & 1.8 \\
12.upper incisal incision & 1.3 \\
18.anterior nasal spine & 2.0 \\ \hline Average: & 2.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of Mean Radial Error on our own dataset.  
The Successful Classification Rate (SCR) for ANB, SNB, SNA, ODI, APDI, FHI, FMA, and MW with the detected landmarks are presented in Table 7 (We run the official python program offered by ISBI Grand Challenge [6] to automatically get the SCR results). To make a comparison, we also show the SCR by the state-of-the-art methods in Table 7. As presented in Table 7, some of the SCRs of the proposed method are much higher than those of the existing methods (eg: ANB, SNA, ODI, FHI). For SNB measure, Lindner [7] is 0.1% percent higher than our result.
# Positioning Methods and the Use of Location and Activity Data in Forests
## 2 Types of Positioning Technologies
### GNSS with Two-Way Satellite Communication
However, for operational forestry and wildland fire applications, devices like inReach or SPOT receivers can be thought of as providing an open circuit rather than one-way location transmission.
# MinHash-Based Fuzzy Keyword Search of Encrypted Data across Multiple Cloud Servers
###### Abstract  
To enhance the efficiency of data searching, most data owners store their data files in different cloud servers in the form of cipher-text. Thus, efficient search using fuzzy keywords becomes a critical issue in such a cloud computing environment. This paper proposes a method that aims at improving the efficiency of cipher-text retrieval and lowering storage overhead for fuzzy keyword search. In contrast to traditional approaches, the proposed method can reduce the complexity of Min-Hash-based fuzzy keyword search by using Min-Hash fingerprints to avoid the need to construct the fuzzy keyword set. The method will utilize Jaccard similarity to rank the results of retrieval, thus reducing the amount of calculation for similarity and saving a lot of time and space overhead. The method will also take consideration of multiple user queries through re-encryption technology and update user permissions dynamically. Security analysis demonstrates that the method can provide better privacy preservation and experimental results show that efficiency of cipher-text using the proposed method can improve the retrieval time and lower storage overhead as well.  
cloud storage; multi-server; multi-user; fuzzy keyword search +
Footnote †: journal:  
_Future Internet_**2018**, _10_, 38; doi:10.3390/610050038
## 3 Results
### Control Strategies
The category marked as \"Actuators\" in Figure 11 indicates the thermal-comfort-based determination of settings and working parameters for valves [131,132], dampers [133,134], and other HVAC equipment such as compressors [135] and heat pumps [136]. In some works, humidity was one of the controlled parameters, mostly in combination with PMV-based thermal comfort models [117,137]. Other studies included direct control of comfort parameters, especially at a simulation stage [138,139], energy or power supply [140,141,142], or the choice of the ventilation mode [33,95]. A distinct category, labeled as \"Design\" in Figure 11, gathers all the studies in which thermal comfort analysis was not finalized to the real-time control of HVAC equipment, but to comfort-oriented decisions such as material selection, as in [30,112,143], design of building elements and layouts (see, for example, [91,144,145] and [68]), or HVAC installation recommendations, such as in [36,97,146]. In the same category, studies are also included that aimed to provide operating schedules [99,119] or suggestions on most comfortable areas in relation to the occupant's preferences [65,147]. Lastly, it is worth noting that only rarely is thermal comfort satisfaction the sole objective of the control strategy. As discussed in Section 3.2, energy saving is often the main goal of the study or a constraint, but other indoor quality parameters may be considered, too, such as CO\({}_{2}\) concentration [148,149,150,151,152] and visual comfort [109,129,130,153]. Figure 11: Variables and settings for thermal-comfort-based control in the analyzed papers. Figure 10: Use of methodology combinations in the analyzed papers. Focusing on HVAC systems, Figure 12 shows that in almost 60% of the cases the control strategy has been applied to air conditioning (AC) systems. The reason is arguably that advanced control methodologies nowadays are employed, especially in office or commercial buildings, usually heated and cooled through mechanical ventilation. Hydronic systems are an interesting subgroup due to their diffusion in the residential sector, especially in Europe. In Table 9, it can be observed that this category covers mostly heating applications (with the obvious exception of chilled beams) with no building type restriction. Control variables in this case include temperature set-points, control valve position, and other water circuit parameters such as pump speed and heating curve.
## 3 Dataset Overview
### Feature Description  
The complete dataset consists of approximately 210,240 daily records covering the period from 2000 to 2023, with an average of 8760 entries per year. These records represent both wildfire and non-wildfire conditions across selected Chilean regions. The dataset encompasses various environmental attributes crucial for wildfire modeling: (i) Temperature (\({}^{\circ}\)C): Atmospheric heat measurements influencing fuel dryness and fire risk, with daily mean values ranging from 18.3 \({}^{\circ}\)C to 35.7 \({}^{\circ}\)C. (ii) Relative Humidity (%): Air moisture levels, where lower values (below 40%) strongly correlate with higher fire susceptibility. (iii) Wind Speed (m/s): A critical factor governing fire spread rate and direction, with values ranging from 0.5 to 12.8 m/s. (iv) Elevation (m): Altitude of the affected region (100-1500 m), impacting fire behavior through atmospheric conditions and vegetation characteristics. (v) Vegetation Type: Categorical feature with three primary classes (forests, shrulbands, grasslands) encoded using one-hot encoding for model integration. (vi) Urban-Rural Interface (URI): Binary indicator (Yes/No) of proximity to human settlements, crucial for the assessment of fire hazards and prioritization of management.  
The dataset was compiled from multiple sources, including wildfire records from CONAF (Chilean National Forestry Corporation), meteorological variables from national weather stations, and satellite-derived vegetation indices (e.g., NDVI from MODIS). To ensure consistency, all data were standardized to a daily resolution and aligned geospatially. Biases due to data scale or measurement procedure were minimized through feature normalization and quality filtering.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Year** & **Temperature (\({}^{\circ}\)C)** & **Humidity (\%)** & **Wind (m/s)** & **Elevation (m)** & **Vegetation Type** & **URI** & **Probability** \\ \hline
2000 & 28.5 & 40 & 3.2 & 500 & Forest & Yes & 0.8 \\
2001 & 32.1 & 35 & 4.1 & 450 & Shrubland & No & 0.7 \\
2002 & 30.0 & 45 & 2.8 & 600 & Grassland & Yes & 0.85 \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\
2023 & 33.5 & 38 & 5.0 & 700 & Forest & No & 0.9 \\ \hline \end{tabular}
\end{table}
Table 3: Representative wildfire event records from the dataset (2000–2023). Data sourced from CONAF incident reports [36], meteorological data from the Direccion Meteorologica de Chile [37], and vegetation indices from MODIS NDVI products [38].  
Figure 2: Deep Reinforcement Learning framework for wildfire prediction.
## 2 Dataset Used for the Model Development
### Video-Sequence Preparation and Subjective Quality Assessment  
The source material of the video database originates from two video collections. Eleven uncompressed source videos were obtained from the Bristol Vision Institute HFR collection [8], originally recorded at 3840 \(\times\) 2160 pixels and 120 frames per second (fps). For public release, these videos were spatially downsampled to 1920 \(\times\) 1080 pixels in YUV 4:2:0 8-bit format and have a duration of 10 s each. In addition, the dataset incorporates five high-motion sports videos captured by Fox Media Group at the same high resolution in YUV 4:2:0 10-bit format, with durations ranging between 6 and 8 s. To ensure a diverse set of visual content, measures such as Spatial Information (SI) and Temporal Information (TI) were computed using the MSU Quality Measurement Tool (version 14.1), compensating for metrics not provided in the public release of the database. In [3], the LIVE-YT-HFR database authors report that the test sequences were generated by applying a frame-dropping technique, which avoids motion blur and produces sequences at varied frame rates. Specifically, 30 test sequences per source were derived at six frame rates (24, 30, 60, 82, 98, and 120 fps) and then encoded with VP9 compression at five Constant Rate Factor (CRF) levels, from low, i.e., lossless (CRF = 0) to the very high compression (CRF = 63), with intermediate settings chosen to ensure a uniform bitrate difference.  
Alongside test sequences, the database holds the results of the subjective experiments. The subjective study employed the Single-Stimulus Continuous Quality Evaluation (SSCQE) method [9]. A total of 85 undergraduate students (14 females, 71 males, aged 20-30) participated under standardized conditions on a 27-inch display (3840 \(\times\) 2160 resolution) with a viewing distance of approximately 75 cm. To minimize bias, the sequences were divided into four subsets (120 videos each), and every participant rated 240 videos across two sessions using uniquely randomized playlists. A continuous five-point quality scale was used to collect ratings, and analyses were performed to verify both inter- and intra-subject consistency. The raw data of these experiments are available on the author's GitHub page [7].
## 5 ICOREs for Sequence Analysis
### Approximate Motif Matching and HMMs
#### 5.3.2 From Approximate Matching to Profile HMMs  
Proceeding from an approximate matcher to an HMM is mainly a change of view. Indeed, the grammar MatchAffi_S already has the architecture of a profile HMM. This is shown in Figure 3. Here are some differences and adaptations:  
* _HMMs are normally described as Moore machines, which emit symbols on states, independent of transitions._ In our grammar, \"emissions\" are associated with the transitions: symbol \(s_{i}\) is generated on transition into from \(S_{i}\) to \(S_{i+1}\). In this sense, they are closer to Mealy machines.
* _In a profile HMM, the target model is built from a multiple sequence alignment, rather than from a single sequence_. In the target \(S\), the \"characters\" now are columns from that alignment. Each matching state of the HMM (square state) is a position in the target sequence.  
* _Scoring parameters in HMMs are position-specific_. This implies that the scoring algebra of the ICORE should take into account the position in the core term: In \(\texttt{rep}(s_{i},b,x)\), column \(s_{i}\) is represented simply by \(i\), which is used to look up the score parameter for this position.  
Looking at HMMs as special ICOREs sheds a new light on profile HMMs. For example, the textbook model allows for state sequences such as \(D_{1}I_{1}I_{1}I_{1}D_{2}D_{3}I_{3}I_{3}I_{3}...\), which is but one of many different transition paths for the same situation: an adjacent group of (say) 6 insertions and 3 deletions has \(\binom{6+3}{3}\) state paths, and their probabilities must be added to obtain the correct probability of this situation. Returning a most likely state path is meaningless in this situation. Our MatchSeq_S, derived from EditDistance, shares this undesirable property.  
But starting from an ICORE using grammar AffiTrace instead, we obtain alternative profile HMMs with better properties. Consider Figure 3d: a matcher derived from AffiTrace inherits the avoidance of such ambiguity, by enforcing the convention that deletions must precede insertions whenever they are not separated by a replacement. With this architecture, we have a state transition sequence \(A_{1}D_{1}D_{2}D_{3}I_{3}I_{3}I_{3}I_{3}I_{3}\) as the only representation of the critical situation, and it collects the probability of this situation in a single state path.
# Mechanical Faults Analysis in Switched Reluctance Motor
## 2 Materials and Methods  
### Switched Reluctance Motor Construction  
The SRM is a brushless electric motor with salient poles on the rotor and stator and is made of iron (Figure 1a,b).  
The magnetic circuit in this motor is asymmetrical--there are different numbers of rotor and stator poles (usually more of them on the stator). With the rotation of the rotor, the power supply is delivered accordingly to the subsequent stator phases to produce a reluctance torque. The magnetic flux also changes dynamically, and thus its inverse--reluctance--hence the name of the motor.  
Figure 1: SRM cross-section—generated by 2D FEMM® 4.2 software (**a**) and real motor installed on the test bench (**b**).  
The electric phase equation of the motor can be represented as follows--the induced voltage in the motor is given by the following equation:  
\[U_{k}=\frac{d\varphi_{k}}{dt}+Ri_{k} \tag{1}\]  
where \(\varphi\) is the phase flux, \(i\) is the current running through the \(k\) phase, and \(R\) is the resistance on the semiconductor elements. The flux (\(\varphi\)) is nonlinear, depending on the phase current (\(i\)) and rotor rotation angle value. Consequently, phase voltage \(U_{k}\) is also nonlinear.  
Although permanent magnet or induction motors are more common due to their higher energy density, the SRM is preferred for its more robust durability and reliability to external (e.g., environmental) factors such as high temperature, humidity, or electromagnetic interaction. It can also reach a very high rotational speed, even exceeding 10,000 rpm. Due to the lack of magnets, there is no risk of demagnetization of the material by extreme temperatures or electromagnetic pulses. Examples of the employment of reluctance motors are fiber spinners, conveyor belts, group travel drives, electro spindles, actuators, wrapping and folding machines, dosing pumps, equipment for industrial processes, etc.
## 5 Conclusions and Future Perspectives
### Effects of AAI on Mitochondrial Function  
AAI has been shown to reduce cell viability and induce apoptosis in a manner that is both dose- and time-dependent. In studies using NRK-52E cells, a model system for kidney epithelial cells, AAI treatment has been associated with several markers of mitochondrial dysfunction. These include elevated levels of reactive oxygen species (ROS), a reduction in mitochondrial membrane potential (MMP), a decrease in mtDNA copy number, and diminished ATP production. Importantly, these findings underscore the role of mitochondrial impairment in AAI-induced nephrotoxicity. The potential for therapeutic intervention has been explored with mitochondria-targeted antioxidants. For instance, the peptide Szeto-Schiller 31 (SS-31) has been demonstrated to mitigate AAI-induced mitochondrial dysfunction and reduce apoptosis, suggesting a promising avenue for treatment.
## 2 Materials and Methods
### Data  
#### 2.2.1 Flux and Meteorological Data  
The flux and meteorological data used in this study were collected from the China Terrestrial Ecosystem Flux Observation and Research Network (ChinaFLUX) [14; 23; 26], the Coordinated Observations and Integrated Research over Arid and Semi-arid China (COIRAS) [27], and the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) [28], which were observed by nine flux stations distributed in the Tibetan Plateau region. The data spans from 2003 to 2014. These flux sites exemplify the broadest grassland ecosystem types, encompassing an extensive range of spatial, ecological, and weather-related circumstances [23].  
Carbon flux data were processed using various methods, including triple coordinate rotation, Webb-Pearman-Leuning (WPL) correction, and outlier removal. The temporal resolution of the MODIS data was eight days, while that of temperature and photosynthesis active radiation was half an hour. The eddy covariance system was used to concurrently record these meteorological data, and any missing values were supplemented using the technique proposed by [PERSON] et al. [29]. The data were then averaged and summed over eight days [14; 23]. Finally, a total of 1421 site observation data points with a temporal resolution of eight days were obtained. The primary attributes of the nine flux sites in northern China's grasslands were shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Site** & **Grassland Type** & **Latitude** & **Longitude** & **Elevation (m)** & **Operation Period** \\ \hline AR & & 38.04\({}^{\circ}\) N & 100.46\({}^{\circ}\) E & 3033 & 2014 \\ GL & Alpine Kobresia Meadows & 34.35\({}^{\circ}\) N & 100.56\({}^{\circ}\) E & 3980 & 2007, 2010–2011, and 2013 \\ HBKO & & 37.61\({}^{\circ}\) N & 101.31\({}^{\circ}\) E & 3148 & 2003–2004 \\ \hline HBSH & Alpine Shrub Meadows & 37.67\({}^{\circ}\) N & 101.33\({}^{\circ}\) E & 3293 & 2003–2012 \\ \hline DXSW & Alpine Swamp Meadows & 30.47\({}^{\circ}\) N & 91.06\({}^{\circ}\) E & 4286 & 2009–2010 \\ HBSW & Alpine Swamp Meadows & 37.61\({}^{\circ}\) N & 101.33\({}^{\circ}\) E & 3160 & 2004–2008 and 2010–2012 \\ \hline DXST & & 30.5\({}^{\circ}\) N & 91.06\({}^{\circ}\) E & 4333 & 2004–2005, 2007, and 2009–2010 \\ NMC & Alpine Meadow Steppes & 30.77\({}^{\circ}\) N & 90.96\({}^{\circ}\) E & 4730 & 2009 \\ ZF & & 28.36\({}^{\circ}\) N & 86.95\({}^{\circ}\) E & 4293 & 2009 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Primary attributes of the nine flux sites in northern China’s grasslands [14; 23].  
Figure 1: Geographical spread of alpine grasslands across China [14; 23]. Black triangles represent the nine flux sites.
## 3 Materials and Methods
### Data Processing
#### 3.2.2 Propagation Loss Calculation  
The expected signal power (\(ESP\)) according to [37] is defined as the signal power at the receiver Equation (2):  
\[ESP_{(dB)}=RSSI_{(dBm)}+SNR_{(dB)}-10\cdot log_{10}^{\left(1+10\frac{SNR_{(dB )}}{10}\right)} \tag{2}\]  
Thus, we can calculate the propagation loss using the link balance Equation (3), as follows:  
\[PL_{(dB)}=P_{Tx(dBm)}+G_{(dBi)}-ESP_{(dB)} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SF** & **Heights** & **Antenna Polarization** \\ \hline  & 6 m & VV \\
12 & 12 m & HH \\  & 18 m & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration corresponding to the measurement methodology.  
\(ESP\) is the expected signal power at a certain point in dB; \(P_{Tx}\) is the power transmitted by the gateway in dBm; \(G\) is the sum of the transmission and reception gains, usually in dBi, and finally, \(PL\) represents the propagation loss of the signal in dB.
# Non-Uniform Clustering Algorithm for UWSNs Based on Energy Equalization
## 4 Algorithm Description  
NUC-EB, a clustering routing algorithm that is improved, is proposed based on the UCUBG clustering algorithm model proposed in [19]. In this algorithm, the selection of cluster heads by considering the number of adjacent nodes and the remaining energy of nodes is combined with UCUBG. Then, the concepts of node density and coverage redundancy are introduced, which can increase the reasonability of cluster head distribution and ensure the coverage of the network operation process. Additionally, this algorithm also combines the actual dynamic environment to ensure the efficiency and effectiveness of data transmission by dividing the forwarding area and designing the network maintenance mechanism.
# Breath Analysis: A Promising Tool for Disease Diagnosis--The Role of Sensors
## 2 The Role of Nanomaterials
### Metallic Nanoparticles (MNPs)  
Noble metals (Au, Ag, Pt, Pd) possess exceptional chemical, physical and electronic properties [50]. They exhibit increased conductivity [13], mechanical robustness [50], oxidation resistance and, thus, chemical stability [13; 50]. In the form of nanoparticles (NPs), additional novel properties--attributed to the increased surface area and the domination of quantum-mechanical properties [51]--are exhibited; optical (localized surface plasmon resonance (LSPR) phenomenon) [36] and electrical [51] are amongst the more interesting. The mechanical, optical, and electrical properties of MNPs are composition, size, periodicity and inter-particle distance dependent [51]. Notably, chemical environment-dependence of those properties renders MNPs promising for gas-sensing [36; 50].  
MNPs are extensively investigated in gas sensors for breath analysis applications and are usually combined with other nanomaterials (e.g., carbon nanotubes (CNTs), graphene, metal oxide semiconductors (MOS)), to form more effective sensing materials, with increased sensitivity and selectivity [13; 36]. In addition, gas-sensor sensitivity is overall enhanced by the presence of NPs on the surface of other sensing nanomaterials, due to defect formation on which gases can preferably adsorb. Smaller NPs lead to greater surface area, increasing the number of defects and ultimately sensor sensitivity [52]. MNPs can be also functionalized with a variety of organic ligands hence forming thin films with tunable chemical selectivity (molecularly-capped NPs, MCNPs), characterized by controllable inter-particle distance and reproducible production [38].