esa-sceva/satcom-chunk-collection huggingface dataset:
# Measures and Methods for the Evaluation of ATO Algorithms
## 3 Approach for ATO Testing
### Scenario Evaluation and Comparison  
It is either possible to determine performance changes between multiple scenario variations or analyze the performance of multiple ATO algorithms. Figure 3 visualizes these two objectives.  
If the performance of one algorithm is tested for several (challenging) situations, then the evaluation of scenario variations is necessary. Scenario variations are achieved by extracting a set of concrete scenarios from one logical scenario as described. In other words, the concrete values of one or several properties are varied. All other values stay as defined in the base scenario. By comparing the KPIs for those scenario variations it shall be possible to diagnose how extensive the influence of one (or multiple) changing parameters is on the performance of an ATO algorithm.  
If the performance analysis is supposed to compare different ATO algorithms, then consequentially it is not useful to conduct such a comparison across different scenarios. To investigate the differences in the performance of two or more algorithms, the same  
Figure 3: Two different scopes and the according ways to evaluate the results of concrete scenario simulations.  
scenario must be taken to ensure a level playing field. When testing the algorithms against the same scenario, it is possible to discover deviations in the handling of challenging situations. For this purpose, either the performance for the base scenario or the performance for the same scenario variation is comparable as illustrated in Figure 3.
## 1 Introduction  
The importance of drones for private and industrial purposes has grown rapidly in recent years. In Europe alone, it is estimated that a total of 400,000 commercial drones and around 7 million drones for hobby purposes will be registered in the next 30 years. For reasons of economic efficiency, commercial use in particular is focusing on highly automated operation of drones--with minimal use of pilots and even completely autonomous in the long term. Such unmanned aerial vehicles (UAVs) can be used in the context of traffic/logistics, forestry and agriculture, alarm and warning systems, pipeline monitoring, shipping, etc. A heavy lift drone is considered in this work as a platform carrying the whole structural health monitoring (SHM) system.  
In the literature, several monitoring approaches have been proposed to improve the flight safety of UAVs. One example is presented in [1], where structural vibrations are measured with accelerometers for undamaged and damaged drones. The computation and analysis of a damage index (DI) is performed onboard and in real time. A related study that also processed vibration signals was presented by [PERSON] and [PERSON] in [2] employing artificial intelligence methods. As an alternative, it is also possible to process audio signals for the analysis of structural drone conditions, as demonstrated in [3, 4].  
Recently, guided electromagnetic waves in K-band have shown excellent damage detection properties thanks to their sensitivity to waveguide properties [5]. The performance thereof is improved by the inherent absence of parasitic effects from secondary reflections of the structure and independence upon any surface contaminants. The most common non-destructive testing approach relies on the dielectric properties of the monitored structure. However, during the last decade, different approaches have been introduced, including a jointed electromagnetic waveguide [6] and microwave leakage monitoring [7]. The former one exploits a specific supplementary waveguide confining the electromagnetic wave across the structure. The second approach exploits the dielectric properties of a cavity to establish a proper waveguide when electromagnetic wave propagation is shielded by the boundaries. This last approach has been adopted for pipe inspection to detect wall thinning [8], cracks [9; 10; 11], and slits [12], achieving a detection range of 19-26.5 m for multiple pipe diameters [13].  
The methods' applicability to side incident inspection [14], bent pipe inspection [15], and multi-flaw detection [16] has also been verified. Also, the probability of detection has been established to assess reliability [17]. However, all these examples have been tested in laboratory facilities, employing high-fidelity devices and equipment. In this paper, the authors show for the first time an in situ application attempt, where the system is integrated onboard an autonomous heavy lift drone to manage the data acquisition pipeline in flight.
## Appendix 3 Derivation of Equation 21  
We first need to consider Figure 6 in order to obtain \(a\) and \(b\) as a function of \(d_{I}\) and \(d_{2}\). We immediately see that:  
\[\begin{split} a&=d_{2}\cos\theta_{1}\\ b&=d_{2}\sin\theta_{1}+\frac{d_{3}}{2\sin(\beta\,/\,2) }\end{split} \tag{10}\]  
Replacing these expressions in Equation 9, one obtains:  
\[\begin{split}&\phi(\omega)=\omega/c\Bigg{(}d_{2}\cos\theta_{1} \cos\theta_{4}+\Bigg{(}d_{2}\sin\theta_{1}+\frac{d_{3}}{2\sin(\beta\,/\,2)} \Bigg{)}\sin\theta_{4}\Bigg{)}\\ &=\omega/c\Bigg{(}d_{2}\cos\bigl{(}\theta_{1}-\theta_{4}\bigr{)} +\frac{d_{3}}{2\sin(\beta\,/\,2)}\sin\theta_{4}\Bigg{)}\end{split} \tag{11}\]  
This last equation is equivalent to Equation 21
# A Sentiment-Aware Contextual Model for Real-Time Disaster Prediction Using Twitter Data
## 4 Experiments
### Training Setting  
The dataset was divided into training and validation sets in the ratio of 7:3, generating 7613 training and 3263 validation samples. For the SentiBERT, the embedding dimension was 768, max sequence length was 128, and layer number was 12; for the BiLSTM module, the layer number was 1, and the feature number was 768; for the CNN module, the sizes of the four filters were set to 2, 3, 4 and 5. For the overall architecture, we used a learning rate of \(1\times 10^{-4}\), the Adam optimizer, and experimented with different batch sizes (16 and 32) and training epochs (6, 8, 10, 12, and 14). All experiments were implemented using Python 3.9.4 and PyTorch 1.8.0 on Google Colab with an NVIDIA Tesla K80.
## 4 Results
### Spatial Distribution Map of Negative Sample Credibility  
#### 4.2.1 Calculation of the Membership Function  
Based on the absence of discrete factors in the preprocessed evaluation factors, this study calculated the membership functions for the continuous evaluation factors using 2681 historical landslides in the study area. The workflow for deriving membership functions for each continuous factor was as follows. (1) Discretization and frequency calculation: Continuous evaluation factors were automatically discretized into graded intervals using Origin software 2022 landslide occurrence frequency (frequency count/total samples) within each interval was then statistically analyzed (blue histogram in Figure 7). (2) Nonlinear regression modeling: To precisely characterize the nonlinear relationship between the evaluation factors (\(x\)) and the landslide frequency (\(y\)), regression fitting via Equation (10) was applied. This established a mapping function \(f(x)\) between the median value of the graded intervals and the landslide frequency (red curve and equation in Figure 7). (3) Normalization and closed-form derivation: The normalized membership degree of \(f(x)\) was computed according to Equation (11). Piecewise closed-form expressions were then derived based on the fitting results and frequency distribution characteristics (Equations (20)-(22)).  
As shown in Figure 7, the landslide-prone areas in the Liangshan Yi Autonomous Prefecture exhibit the following environmental characteristics: SEI below 850, elevation ranging between 1750 and 2250 m, and SMC between 0.18 and 0.22. Further investigation reveals that topographic constraints confine human engineering activities, resulting in SEI, elevation, and SMC exerting greater influence on landslide occurrence. In contrast, factors such as DD, fracture density, and engineering geological rock groups have relatively less impact on landslide initiation.  
\[S_{x}=\begin{cases}0,&x<0.14\\ \exp(-2\cdot(\frac{x-0.206}{0.042})^{2}),&0.14\leq x\leq 0.26\\ 0,&x>0.26\end{cases} \tag{20}\]  
\[S_{x}=\exp\left(-2\times\left(\frac{x-1832}{1017.6}\right)^{2}\right), \tag{21}\]  
\[S_{x}=\begin{cases}1,&x<851.6\\ \exp(-2\times(\frac{x-851.6}{18477})^{2}),&x\geq 851.6^{\prime}\end{cases} \tag{22}\]  
Figure 7: Relationships and fitting curves between evaluation factors and landslide frequency: (**a**) SEI, (**b**) elevation; and (**c**) SMC.
## 5 Achieved Results and Performance Comparison
### Performance Comparison
#### 5.3.3 Virtex-6 Platform Comparison  
The proposed design achieves 1.14 times faster processing speed while using 1.4 times less hardware resources than [20]. The throughput/area ratio is 1.61. It reflects the effectiveness of the system's architecture. Additionally, while our architecture uses slightly more hardware resources than [19], its processing time is significantly faster. Furthermore, our architecture effectively controls resources compared to [21], using only 1.07 times more hardware resources while operating 1.42 times quicker. Increasing the throughput/area ratio noticeably by 1.32-fold demonstrates its flexibility and effectiveness. Based on the achieved results, it can be concluded that for applications needing improved throughput, quick processing, and efficient resource utilization, our architectural approach for Virtex-6 offers a viable alternative.
## 2 Materials and Methods  
An IR camera (VarioCAM HDx head S 625, Jenoptik, InfraTec GmbH, Dresden, Germany) was mounted vertically, with the lens facing downward looking through a round Ge-window placed in the vacuum chamber wall of a freeze-dryer (Epsilon 2-100D, [PERSON], Osterode, Germany) [5; 6]. The IR camera was equipped with a 40 mm lens providing a field of view of approximately 140 \(\times\) 180 mm. The VarioCAM HDx camera has a 640 \(\times\) 480 pixel microbolometer measuring in the 7.5 to 14 \(\upmu\)m LWIR interval and has a thermal resolution better than 40 mK in a range from \(-\)40 \({}^{\circ}\)C to 600 \({}^{\circ}\)C. The temperature range employed in this work ranged from \(-\)40 to 40 \({}^{\circ}\)C. Calibration (temperature accuracy checks) at the factory revealed a deviation of 0.0 K at \(-\)7.7 \({}^{\circ}\)C and 0.1 K at 23.5 \({}^{\circ}\)C compared with black-body radiators and a calibrated pyrometer. The stainless steel top shelf in the freeze-dryer that was partially monitored by the IR camera was coated with masking tape (3M type 101E) using 50 mm wide strips that had been neatly placed next to each other. The shelf temperature could therefore be directly monitored using the IR camera because the tape layer is drastically increasing the emissivity. A value of 0.93 for emissivity was applied throughout data treatment in the dedicated software (IRBIS 3 Professional, Jenoptik). The IR camera recorded images of the material in the Ge cuvettes as well as the shelf of the freeze-dryer with a high spatial resolution (0.28 \(\times\) 0.28 mm). A prism (IR-mirror) with a 45\({}^{\circ}\) angle (Umicore Electro-Optic Materials, Olen, Belgium) was placed next to five cuvettes to monitor the side of the cuvettes with the IR camera looking from above. Furthermore, D-mannitol (>98% pure) and sucrose (>99.5% pure) were obtained from Sigma Aldrich (Steinheim, Germany) and dissolved in Type-1 water (18.2 M\(\Omega\) cm, 0.053 \(\upmu\)S cm\({}^{-1}\), Merck Millipore, Billerica, MA, USA) to obtain a mannitol/sucrose solution, 4:1%, (m/m). Additional information and detailed figures about this setup can be found in previous works [5; 6].  
After freeze-drying, the water content of the mannitol/sucrose formulation was measured in each of the five cuvettes using volumetric Karl Fischer titration (Metrohm, Herisau, Switzerland).  
The freeze-drying cycle for the formulation was controlled and monitored using machine readings of shelf temperatures, product temperatures and pressure. The freeze-drying program can be deduced from those readings and is therefore not reported separately. Product temperature and resistivity readouts (Lyo RX) were obtained from a Pt100 probe or LyoRX probe immersed in the product and connected by a wire to the freeze-dryer as discussed before.
## 3 Design Challenges of MAC Protocol for IBSN
### Types of Access Mechanisms Recommended for IBSN
#### 3.2.3 Hybrid Access  
The hybrid access mechanism uses CSMA for non-realtime data communication and TDMA for real-time data communication. The data is sent in time-slots based on the priority of the nodes generating data. The time slots can be set depending on the application. The main disadvantages of the hybrid access protocols are resource exhaustion of the client nodes, high energy consumption, and longer beacon-periods for the dynamic allocation of time-slots.  
Frequency Division Multiple Access Frequency Division Multiple Access (FDMA) is a complex hybrid access mechanism, in which a full frame of frequency band is made available for communication. This results in the continuous flow of data between the sensor nodes, which improves efficiency of transmission. In FDMA, each channel is assigned to only one node at a time. Each node shares a channel simultaneously, though transmits at single frequency. FDMA is used with both analog and digital signals [29]. It requires high-performing filters in radio hardware, in contrast to TDMA and CSMA. As each node is separated by its frequency, minimization of interference between nodes is done by sharp filters.  
ALOHA is a random access technique in which, whenever a packet is generated, it is transmitted immediately without any further delay. Successful reception of a packet depends only on whether it is collided or not with other packets. At the end of packet transmission, each user knows whether its transmission has been successful or not. If collision occurs, the user schedules its re-transmission to a random time. The randomness is to ensure that the same packets do not collide repeatedly. In the ALOHA technique, the node checks for the availability of data packets to be transmitted. If they are available, then the node transmits them, otherwise the process ends.  
Slotted ALOHA is a variant of Pure ALOHA with a channel that is divided into slots. Restriction is imposed on users to start transmission on slot boundaries only. Whenever packets collide, they overlap completely instead of partially. So only a fraction of slots in which the packet is collided is scheduled for re-transmission. It almost doubles the efficiency of slotted ALOHA as compared to pure ALOHA. Successful transmission depends on the condition that only one packet is transmitted in each frame. If no packet is transmitted in a slot, then the slot is idle. Slotted Aloha requires synchronization between nodes which lead to its disadvantage.  
The hybrid access is not optimized in terms of energy efficiency but it is optimized in terms of high throughput and reliable packet delivery.
## 2 Materials and Methods
### Satellite Data  
The Sentinel-3/OLCI sensor is used in this study for the detection and quantification of _Sargassum_ over the study area. OLCI sensor acquires data for 21 bands spectral resolution at a spatial resolution of 300 m and a temporal revisit of two days with the two-satellite constellations [22]. Its Signal-to-Noise Ratio (SNR) ranges from 2188 to 152 from 400 nm to 1020 nm bands, respectively, thus allowing the acquisition of high radiometric quality data over dark waters such as those encountered in the open ocean. The dataset used in this study consists of seven images that were acquired on the following dates: 8 July 2017, 13:55 (UTC); 27 May 2018, 14:21; 14 June 2020 14:02; 14 September 2020 14:17; 28 December 2020 13:55; and finally, 2 May 2021, 14:15. Those scenes were selected because they exhibit significant occurrences of _Sargassum_ and also because they were acquired at various seasons and for different years. The Level-1 data product, namely the top-of-atmosphere radiance (TOA), were downloaded from the European Copernicus platform [23]. As an example, a false color composite (NIR-Green-Blue) image acquired on 8 July 2017, 13:55, is shown Figure 2. The regional study area that is analyzed in this paper, is west of Dominics Island (see the white square in Figure 2). Such an area covers 22,500 km\({}^{2}\).
## 8 Conclusions  
In this paper, an overview of the methods of conversion from the residue number system to the positional number system is given. Reverse conversion methods for general form moduli, such as the Chinese residue theorem, mixed-radix conversion, the Chinese residue theorem with fractional numbers, new CRT I, and new CRT II, were considered. The application of RNS moduli of the special form \(2^{n}\pm 1\), \(2^{n}\) was also considered. Based on the modified mixed-radix conversion presented by Algorithm 3 and the method of reference points (Section 5), a method of reverse conversion of a number from a residue number system to a positional number system is proposed (Section 6). The results of physical synthesis have shown that the use of moduli of a special type makes it possible to significantly reduce the computational time and the claimed area, however, the task of working with moduli of a general type often arises.  
The reference points method proposed by the authors in [18] makes it possible to obtain computationally efficient implementations, but the used area can differ up to 30 times within one dimension (for the sets {7, 17, 31, 65, 127, 256} and {1023, 1025, 8192}), which leads to the need to check a large number of sets of a given dimension in the search for the optimal set.  
The method proposed in this work reduces the required area (up to five times for the set {7, 17, 31, 65, 127, and 256}) for RNS with a large number of moduli compared to the reference point method and reduces the area for RNS with three and four moduli compared to the modified MRC. It is possible to combine the reference point method with different configurations of layers and methods based on CRT and MRC to obtain efficient implementations in terms of computational speed and required area.  
Conceptualization, V.K., D.T., M.B., I.M., A.S., N.K., T.E. and M.G.; methodology, V.K., D.T., M.B., I.M., A.S., N.K., T.E. and M.G.; software, V.K. and I.M.; validation, V.K., D.T., M.B., I.M., A.S., N.K., T.E. and M.G.; formal analysis, V.K., D.T., M.B., I.M., A.S., N.K., T.E. and M.G.; investigation, V.K., D.T., M.B., I.M., A.S., N.K., T.E. and M.G.; writing--original draft preparation, V.K., D.T., M.B. and I.M.; writing--review and editing, V.K., D.T., M.B., I.M., A.S., N.K., T.E. and M.G.; supervision, V.K., D.T., M.B. and A.S. All authors have read and agreed to the published version of the manuscript.  
This work was carried out at the North Caucas Center for Mathematical Research within agreement no. 075-02-2022-892 with the Ministry of Science and Higher Education of the Russian Federation. The reported study was funded by Russian Federation President Grant MD-1414.2021.4, MK-1203.2022.1,6 and SP-3186.2022.5.  
Figure 8: Comparison of the area used for methods of translation from RNS to positional number system.
## 1 Introduction  
Motivated by the rising spectrum needs, millimeter-wave (mmWave) communications located in 30-300 GHz (Figure 1) have received great attention in recent years [1, 2, 3, 4], and the channel models have been standardized by groups around the world, such as the 3 rd Generation Partnership Project (3 GPP) (for 0.5-100 GHz) [5]. Furthermore, the sixth-generation (6G) wireless communication systems are desired for high speed, low latency, and high reliability, which the radio frequency (RF) technologies cannot support. To address such issues, visible light communication (VLC) has emerged to be a key technology in 6G [6, 7, 8]. VLC employs the unlicensed frequency spectrum resources at 400-800 THz (Figure 1), which can provide a high data transmission rate and a strong resistance to electromagnetic interferences [9, 10]. However, the complex applications in 6G have different requirements, which may not be satisfied by a single technology, whether RF or VLC technologies. This requires the combination of multiple communication technologies, e.g., hybrid mmWave/VLC systems [11, 12, 13]. These two technologies are expected to complement each other to form a reliable communication system in the future.  
mmWave and VLC channel modeling is the foundation of designing and optimizing hybrid mmWave/VLC communication systems, which can be used to evaluate the performance and determine the performance limit of wireless communication systems [14].  
Moreover, it is also important to understand the differences in the propagation characteristics of VLC and mmWave channels, based on which can exert the respective advantages of mmWave and VLC to design the hybrid mmWave/VLC systems. In the following, we give a review of related works.
###### Abstract  
This study investigated the fusion of spectra and texture data of hyperspectral imaging (HSI, 1000-2500 nm) for predicting the water-holding capacity (WHC) of intact, fresh chicken breast fillets. Three physical and chemical indicators--drip loss, expressible fluid, and salt-induced water gain--were measured to be different WHC references of chicken meat. Different partial least squares regression (PLSR) models were established with corresponding input variables including the full spectra, key wavelengths, and texture variables, as well as the fusion data of key wavelengths and the corresponding texture variables, respectively. The results demonstrated that for drip loss and expressible fluid, texture data was an effective supplement to spectra data, and fusion data as an input variable could effectively improve the predictive ability of the independent prediction set (Rp = 0.80, RMSEp = 0.80; Rp = 0.56, RMSEp = 2.10). While the best model to predict salt-induced water gain was based on key wavelengths (Rp = 0.69, RMSEp = 18.04), this was mainly because salt-induced water gain was measured on mince samples, which lacked the important physical structure to represent the texture information of meat. Our results of this study demonstrated the potential to further improve the evaluation of the WHC of chicken meat by HSI.  
Keywords:p: poultry; hyperspectral imaging; water-holding capacity; partial least squares regression +
Footnote †: journal: Applied Sciences