esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionospheric Polarization Techniques for Robust NVIS Remote Sensing Platforms
## 4 Tests Performed
### Frame Design  
To achieve the expected results in the tests, one of the most important parts of the study is the design of the frame to be transmitted. Every test consists of the transmission of the frame design in Figure 5. The signal frame is composed of a total amount of 50 packets, and every packet is principally formed by three different modulations (PSK, FSK, and QAM). To synchronize the system with every modulation and to correct all the channel effects, every modulation is preceded by a 600 Hz tone and a PN sequence. In Figure 5, we can see that every data block contains 250 symbols with a resample of 42, resulting in a 2.3 kHz bandwidth. Another important detail about the frame design is the time of each packet, which was carefully chosen to fulfill the coherence time requirements of the ionosphere [28]. Taking into account the most restrictive coherence time as 1.46 s [11], we set the time of the frame at 510.36 ms. To compare the effects of the ordinary and extraordinary waves to the modulation transmitted, it is important to transmit every packet in the coherence time. The total duration of the 50 packets is about 25 s.  
Once the system identifies the start of the frame, the first block is the 600 Hz tone. This tone of a duration of 60 ms is a key issue for correcting the Doppler shift, which affects the rest of the block. The Red Pitaya platform clocks have a low stability, which creates a Doppler shift effect higher than the  
Figure 4: Near vertical incidence skywave (NVIS) test link.  
Figure 3: Receiver node in Cambrils (Spain).  
ionospheric. Our measurements of the platform show us that the maximum value of the Doppler shift received due to the low stability clocks is about \(\pm\)20 Hz. The 600 Hz tone lets us identify the Doppler shift inserted by its variations between 580 and 620 Hz. In the worst case, if we consider a received tone frequency of 580 Hz, the measurement could be done with 34 cycles (60 ms). If we make use of a DC constant instead of a 600 Hz tone, the measurement will be done with the 16 th part of a cycle (60 ms), which is not enough to take a real measurement.  
The second block present on the frame is a 6 th order PN sequence. The PN sequence is used for synchronizing with the modulations. The resampling of the PN sequence is about 8 and has a total duration of 5.12 ms. The order and duration of the PN sequence were carefully designed to avoid the channel effects of a delay spread, Doppler spread, and Doppler shift.  
Finally, the data blocks allow us to study the robustness of each modulation with different polarization techniques. The study performed was done with three different modulations (PSK, FSK, and QAM) with variating orders between 2 and 32 for power transmissions between 3 and 100 W.
# Overview of Active Ingredients Used in Deodorants and Antiperspirants Available on EU Market
## 5 Conclusions  
The antiperspirant market is dominated by aluminium-based compounds. The most common in roll-on antiperspirants are Aluminium Chlorohydrate (prevalence 67.5%), Aluminium Sesquichlorohydrate (25.0%), and Aluminium Chloride (12.5%). Aluminium-zirconium complexes, such as Aluminium Zirconium Tetra-, Penta-, and Octachloroydrex Gly, are very rarely used in roll-on deodorants, with each being noted in only single products. However, such complexes (Aluminium Zirconium Tri-, Tetra-, and Octachloroydrex Gly) are much more common in stick antiperspirants (60% of products), as are aluminium-based compounds such as Aluminium Chlorohydrate and Aluminium Sesquichlorohydrate (60% of products).  
Regardless of the cosmetic form of a deodorant (i.e., spray, stick, or roll-on), the most commonly used ingredients are Triethyl Citrate, present in 51 of 120 (42.5%) formulations, followed by Alcohol in 31 (25.8%), Ethylhexylglycerin in 30 (25.0%), Cappylyl Glycol in 15 (12.5%), Potassium Alum in 12 (10.0%), and Zinc Ricinoleate in 11 (9.2%). Most of these are odour-masking and odour-absorbing agents. Antibacterial ingredients, with the exception of Alcohol in spray deodorants (56%), are rarely used by deodorant manufacturers (in less than 6% of the studied products). These included Zinc Gluconate, Zinc Sulfate, Sodium Usnate, Cetrimonium Chloride, Chlorphenesin, Silver Citrate, and C12-15 Alkyl Benzoate; the latter also has an odour-masking function.  
In addition, 81.7% of deodorants and 100% of antiperspirants contain Parfum. However, other popular fragrance agents include Limonene, Linalool, Citronello, Citral, Geraniol, and Coumarin.  
It is likely that future trends in deodorants, in line with current trends in the entire cosmetics market, will go towards natural products, i.e., essential oils and substances isolated from them, as well as plant extracts. In addition to their ability to inhibit the growth of bacteria, they can impart a pleasant smell, which predisposes them to be effective deodorants. Probably, the deodorant market will completely reject synthetic antibacterial substances in favour of those of natural origin.  
It is time for an alternative to aluminium compounds to finally appear on the antiperspirant market. The first aluminium antiperspirant, aluminium chloride, was introduced to the market over 100 years ago, and subsequent modifications also based on aluminium compounds.  
This research was supported by the Medical University of Lodz, grant No. 503/3-066-02/503-31-001.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The author declares no conflict of interest.
# Position Sensing with a Compact 0.1 THz Amplitude Modulated Source
## 2 Materials and Methods  
In this section, the measurement setup of the triangulation method is explained in more detail. The measurement setup consists of an AM THz source with a custom-made horn, focal lenses and the broadband antenna-coupled titanium-based microbolometer, designed and fabricated in the Laboratory for Microelectronics (LMFE, Ljubljana, Slovenia), as the sensing element [15]. A linear stepper motor from Thorlabs is used for sensor positioning [16]. On a custom-made printed circuit board (PCB), a Texas Instruments ADS8568 analog-to-digital converter (ADC) [17] and an Atmel SAMD21E18A microcontroller [18] are used to feed the sensor data to the PC for further processing and display. Each system part is explained in detail in the next subsection.
# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Algorithms  
#### 3.2.1 Optimization  
Optimization algorithms comprise many examples that can be used for several applications. Figure 9 shows the usage of such methods. Among them, the optimization of hyper parameters in machine learning [46] and deep learning models is frequently found. Nevertheless, [PERSON] et al. in [47] use Ant Colony Optimization (ACO) to improve UAV path planning for collecting images in vineyards: the optimization is applied to different variables, such as the viewing angles when capturing crop images. [PERSON] et al. in [48] show that SGD can be successfully used to optimize the parameters of different existing neural network architectures to improve accuracy, recall, and F1 score. A comparison with the Adam optimizer is reported, showing that SGD within the Swim Transformer (ST) scores best than others. Furthermore, [PERSON]. [49; 50] report the usage of both SGD and Gravitational Search Algorithm (GSA) to optimize neural networks also in the context of transfer learning methods, while [PERSON] et al. in [51] report the result of different algorithms optimizing a CNN. One of the main concerns when searching optimal solutions remains the presence of local minima. To avoid falling in this condition, cosine annealing is used in [52]. This method consists in tuning the learning rate, according to a cosine function, moving continuously from lower to higher values. Furthermore, in this study, the authors enhance the cosine function by changing its period during the learning phase, according to different conditions and criteria.  
Optimization algorithms could be used also in Extreme Learning Machine (ELM), which does not rely on the back-propagation method during the training. For instance, in [53], Particle Swarm Optimization (PSO) is applied, instead of randomly initializing the weights of the hidden layers as defined by the original algorithm. Also GA, one of the most well-known meta-heuristics, is used for the same purpose [54]. Moreover [PERSON] et al. in [55] report a comparison between PSO and GA. An interesting application of GA is reported in [56], which uses it for feature selection in a dataset composed by hyper-spectral and textural data. In general, meta-heuristics inspired by nature are very commonly used. For example, Ref. [57] uses Salp Swarm Algorithm (SSA) to perform feature selection in image-based disease detection. In [58], an enhanced version of Shuffled Shepherd Optimization (SSO) is used to improve convergence during the training of a CNN. In [59],  
Figure 9: Histogram showing the recurrence of optimization algorithms among those used in the reviewed studies.  
Sun Flower Optimization (SFO) is used to optimize image segmentation methods. ACO is used in [60] to optimize the characteristic wavelength variables of spectral data.  
Other combinatorial methods, such as the Hungarian algorithm (HUNG), are also used with complex transformer-based architectures, such as in [61], where predicted bounding boxes and ground truth are matched together to measure the similarity of the two sets.
## 2 Materials and Methods
### Data Sources  
The sentinel 2 MSI, level 1C Top of Atmosphere (TOA) reflectance collection was used as an input to estimate different water budget components [46]. The collection was filtered based on the boundaries of the study sites and limited to the agricultural year 2017 (from 1 July 2017 to 30 June 2018). It was merged with the cloud probability collection derived from Sentinel 2 images through the s2 cloudless library [47]. From this collection, pixels that  
Figure 1: Case study sites in the Namoi catchment.  
contained a cloud probability greater than 30% were masked out from the Sentinel 2 images. The Sentinel 2 collection was further filtered based on the properties of the images, filtering out images that contained more than 15% cloudy pixels.  
Monthly rasters of the Global Precipitation Measurement (GPM) version 6 were downloaded for precipitation input [48]. These rasters are monthly aggregations of rainfall at 0.1-degree spatial resolution that are processed through a unified algorithm for precipitation combining different sources of passive microwave instruments. We used global data here to demonstrate the application of methods to any location on the globe. Monthly aggregates of temperature, wind speed, and surface pressure from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5) collection at 0.25 degree resolution [49] were combined with the monthly average forcing shortwave incoming radiation band from the Famine Early Warning System Network (FEWS NET) Land Data Assimilation System (FLDAS) collection at 0.1 degree resolution [50] to calculate reference evapotranspiration. Again, the analysis was based on globally available data to highlight the current \"state of play\" in this section.  
Water-use information per field cluster was obtained from the New South Wales Department of Planning, Industry and Environment (DPIE). This information is monthly aggregated. Additionally, modeling of water volumes in reservoirs was based on the Sentinel 2 data and LiDAR data. The LiDAR data was extracted from the ELVIS--Elevation and Depth--Foundation Spatial Data webpage ([[https://elevation.fsdf.org.au/](https://elevation.fsdf.org.au/)]([https://elevation.fsdf.org.au/](https://elevation.fsdf.org.au/)) accessed on 20 February 2022). A series of 1 m resolution DEM tiles was downloaded and mosaicked, covering the study region surface.  
Given the monthly scale of the NSW DPIE water-use data, all other data sources were aggregated to monthly data, including the reflectance information, which was averaged by month. Different spatial and temporal scales can be used for the estimates of the water cycle, but these will depend on the data sources used and the associated temporal resolution. For instance, while the forcing variable of the incoming radiation from the FLDAS data is aggregated by month, it can be replaced by the radiation contained in the Global Land Data Assimilation System (GLDAS) collection, which is based on 3-h estimates. The same applies to the ERA5 climate data, which can also be aggregated at a daily scale.
## 3 Experiment  
### Experimental Setup  
To evaluate the proposed algorithm, we record experimental data by using a Sony DSC-RX100 V camera with a CMOS sensor. The forward-facing camera is mounted on the dashboard inside the host vehicle, as shown in Figure 13. According to the analysis of camera exposure in the study [12], the camera settings can affect to detection results of the proposed algorithm. In our experiment, exposure setting is experimentally chosen to reduce a blinking effect and blooming effect so that the captured images still keep sufficient color and shape information. The camera settings are chosen as follows: exposure time 1/100 s, lens aperture f7.1, ISO 640, resolution 1920 \(\times\) 1080, 30 frames per second, and focal length 35 mm.  
Depending on the road width and traffic volume [21], we choose two scenarios, which are two-lane and four-lane urban roads at night for recording traffic videos. The recorded data used in the experiment contain 9200 test images, which are separated into 18 video segments (with an average of 500 frames per video segment). These video segments are collected in various traffic situations, such as partial vehicle occlusion, turning vehicles, driving on curvy roads, and passing maneuvers.  
Figure 12: Post-processing results: (**a**,**c**) without grouping multiple pairs; (**b**,**d**) with grouping multiple pairs.  
In this study, we adopt the Jaccard coefficient [10; 22] for evaluating the performance of the proposed algorithm. The ground-truth of vehicles in the dataset is labeled manually by human observation. The Jaccard coefficient \(J\) for one video is defined as:  
\[J=\frac{\sum_{t=1}^{n}TP_{t}}{\sum_{t=1}^{n}(TP_{t}+FP_{t}+FN_{t})} \tag{18}\]  
where \(n\) is total number of frames in a video segment, and \(TP_{t}\) (true positive), \(FP_{t}\) (false positive), and \(FN_{t}\) (false negative) are the total number of detected vehicles, falsely detected vehicles, and missed vehicles in frame \(t\), respectively.
## 4 Experiments and Results Discussion
### Comparison of Different Consensus Algorithms  
The top three consensus algorithms most widely used in cryptocurrencies are PoW, PoS and DPoS [52]. PoW is highly secure and scalable for the node network scale, but it has high energy consumption and loses scalability on the transaction scale. PoW-based blockchains take considerable time to confirm transactions by accumulating blocks. Peercoin [20] is the first cryptocurrency (blockchain system) to formalize the notion of PoS, which utilizes coinage and limited search space to reduce energy consumption. So far, many other PoS consensus algorithms have been put forward that have better performance in throughput and delay. DPoS is an extension of conventional PoS, which fixes the rich's problem of becoming richer in PoS through representative democracy. DPoS provides higher scalability, faster confirmation speed and lower consumption at the cost of limiting the number of delegates. According to the principle of Peercoin, we added a PoS consensus module to Ethereum, and comparative experiments of PoW (ethash of Ethereum), PoS and DPoS were carried out.  
The fork means that different BPNs have temporarily accepted different blocks at the same height, and they will reach a consensus after a period of time. Although the fork has little effect on the throughput, it will affect the reliability of the transaction response. Figure 18 shows the average number of temporarily forked blocks for each BPN in an epoch (300 blocks). PoW has more forks than PoS, and DPoS has almost no forks. For each block in PoW and PoS, each BPN will compete for production through computing power or stake, which leads to different BPNs potentially temporarily accepting different valid blocks. Therefore, even if a transaction responds, it will likely take a certain time before it can be considered reliable. In DPoS, each BPN produces blocks in turn according to the agreed order. Regardless of dishonest BPNs and temporary network failures, DPoS can  
Figure 17: OS transaction throughput under four architectures.  
almost maintain zero forks, which means that transactions without irreversible status are considered reliable with 99.9% certainty [21]. The next section will further analyze security.  
Under the same number of BPN/TN and transactions, the average CPU utilization and the average P2P network traffic of each bpi are shown in Figure 19. Compared with PoW, PoS greatly reduces computing power requirements, while DPoS is lower. We can also find that DPoS consumes fewer network resources because, in DPoS, there is only one BPN propagating the produced block in any bpi. On the other hand, as the number of nodes increases, the number of BPNs in DPoS is fixed, while in the other two, all qualified nodes can become BPNs, and the network overhead will be higher.
## 3 Methods
### Cross-Axis Attention  
Establishing long-range interactions between pixels is crucial for capturing spatial structures or shapes in segmentation tasks. However, in the specific context of face parsing, the significant distance variations among different facial components make such interactions particularly challenging. Axial attention has been proposed as an alternative to the standard self-attention mechanism by decomposing attention into two separate branches, each operating along the horizontal or vertical dimension. Nevertheless, conventional axial attention suffers from limited information integration when fusing features from these two directions, which restricts its capacity for global dependency modeling.  
Cross-Axis Attention [25] constructs dual cross-attention mechanisms along two spatial dimensions to better exploit directional information extracted from axial attention. As illustrated in Figure 2, it consists of two parallel branches that compute horizontal and vertical axial attention separately. \(F_{x}\) and \(F_{y}\) represent multi-scale contextual features captured using strip convolutions with different shapes. Taking the upper branch in Figure 2 as an example, \(F_{x}\) is passed to compute attention along the y-axis. To better leverage the multi-scale contextual features from both spatial directions, Cross-Axis Attention computes cross-attention between \(F_{x}\) and \(F_{y}\). Specifically, \(F_{x}\) is used as both the key and value matrices, while \(F_{y}\) serves as the query matrix. The computation is as follows:  
\[F_{A}=\text{CA}_{y}(F_{y},F_{x},F_{x}), \tag{1}\]  
Here, \(\text{CA}_{y}\) denotes the cross-attention operation along the y-axis. Similarly, in the lower branch, the contextual encoding is performed along the x-axis using an analogous approach. The computation is as follows:  
\[F_{B}=\text{CA}_{x}(F_{x},F_{y},F_{y}), \tag{2}\]  
Here, \(\text{CA}_{x}\) denotes the cross-attention operation along the x-axis.  
In this study, Cross-Axis Attention is employed to capture global dependencies, thereby enhancing the network's ability to extract features from irregular facial components. This approach effectively addresses the accuracy issues caused by boundary ambiguity in segmentation tasks, making the network more suitable for complex face parsing scenarios and improving overall segmentation performance.  
It is worth noting that facial images typically exhibit a natural bilateral symmetry. The design of Cross-Axis Attention, which computes attention along both horizontal and vertical directions, inherently supports the modeling of semantic relationships across symmetric facial regions, such as the eyes or the corners of the mouth. This bidirectional structure enhances feature interaction across spatially mirrored parts, thereby improving the consistency of structural understanding and reinforcing boundary delineation in symmetrical areas.
## 5 Cylinder Tube Stacking Sequence Definition
### Design for Pressurized Fluid Radial Retention
Such condition imposes not only that the instantaneous equivalent strain remains below the material elongation at break, but also the avoidance of cyclical hysteretic plastic loops and of the consequent low-cycle fatigue fractures. This latter condition is more stringent. Figure 2(a) represents the hysteretic loop for an elastic perfectly plastic liner steel material, subject to cyclic circumferential strains ranging from 0 to \(\varepsilon_{\text{peak}}\); as usual, \(\sigma_{y}\) is the material yield stress and the \(E\) steel Young modulus rules the slope of the elastic loading/unloading lines. The hatched area represents the density of plastic strain energy locally dissipated each settled loop. As pointed out in the elastic-perfectly plastic stress strain curve in Figure 2(b), a limit peak strain value (\(\varepsilon_{\text{lim}}\)) exists such that, besides a first loading cycle in which yielding occurs, further cycles do not accumulate additional plastic strains. Such \(\varepsilon_{\text{lim}}\) strain value is associated to the limit condition of a null-area (i.e., null plastic strain energy density) hysteretic loop, and can be evaluated as follows. \[\varepsilon_{\text{lim}}=2\cdot\frac{\sigma_{y}}{\text{E}} \tag{8}\]  
Longitudinal fractures induced by the low-cycle fatigue are assumed not to occur in the steel liner if its strain levels are bounded below the \(\varepsilon_{\text{lim}}\) value under operating conditions, i.e., if inner radial displacement of the CFRP tube remains below \(\varepsilon_{\text{lim}}\)\(r_{\text{i}}\), see Equation (6). Table 6 summarizes the three design constraints described above. Figure 4 compares the different UD CFRPs on the radial displacement at the inner cylinder tube. Only the ultrahigh modulus (UHM) carbon fiber reaches the radial stiffness of the reference steel cylinder tube, with a feasible wall thickness of 13 mm. The condition that bounds the minimal thickness of the composite tube (at the point marked by a cross, in Figure 4) is the liner circumferential strain permitted, whereas the thickness of the steel tube is limited by its yielding (\(\sigma_{y}\)). Figure 3: Deformation cycle for liner—composite assembly: (**a**) a hysteretic loop in an elastic- perfectly plastic liner material, and (**b**) the limit case of a zero plastic strain energy density loop. Considering the present component, in the case of UHM fiber the minimum 90\({}^{\circ}\) ply thickness required for bearing the inner pressure is 5.3 mm, whereas the original steel wall thickness is 22.5 mm. The thickness of the steel liner was considered equal to 2 mm in accordance with the minimum technological manufacturing process allowable to ensure both the geometrical and the surface finishing tolerances required.