esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Fried Potato Products in the Form of French Fries and Potato Chips  
With the progress of civilization, there is a continuous increase in the consumption of processed potatoes, in the form of fried products, which include various types of snacks, including French fries and potato chips, which enjoy constant interest from consumers around the world [2,25].  
French fries (chips) are a semi-finished product and ready product that is sold frozen in a wide variety of assortments, usually in the form of strips of various thicknesses, but also in the form of slices, half-moons, and quarters, with or without skin, or even as whole small tubers. Traditional French fries are the most popular variant, defined as deep-fried potato pieces in the shape of strips with a cross-section of 10 \(\times\) 10 mm and a length exceeding 6-7 cm, which contain about 4% fat when pre-fried, while ready-to-eat fries may contain about 7% fat [2]. In the United Kingdom, this product is referred to as \"chips\" and in Germany and France it is called \"pommes fries\"; however, the most popular term used in most countries is French fries [2,26].  
The technological process of the production of French fries consists of the following stages: cleaning, washing and peeling the potatoes (in factories, steam peeling is used), cutting, blanching potato strips in water, pre-drying, and frying in a frying medium (industrially manufactured French fries are pre-fried--I\({}^{0}\) frying stage), after which they have to undergo thermal processing before consumption [2]. It is also possible to obtain French fries using a two-stage frying method, first in oil at a temperature of 175 \({}^{\circ}\)C for 1 min (I\({}^{0}\) frying stage), and then in oil at a temperature of 175 \({}^{\circ}\)C for 5 min (I\({}^{0}\) frying stage). Potato pieces fried in this way constitute the ready product [15,27].  
Potato chips (crisps) are another extremely popular potato snack that are obtained by frying thin slices of potato in fat or oil. The thickness of the slices may range from 1.27 to 2 mm [3,28,29]. After frying, the moisture content of chips varies between 1.7-1.8% (less than 2%), and the fat content may be approximately 35% (33-38%) in total [2,3,29,30]. The fat content of a traditional ready product can be about 1/3 of its weight [29,31]. There are also \"light\" chips, with reduced fat content, which contain approximately 25% of this ingredient [3,32]. The process of obtaining potato chips consists of several technological stages, which include cleaning, washing and peeling the raw material (industrial potato chip production uses mechanical peeling with carborundum), slicing tubers, optional blanching, drying, and frying in oil, and, while in the production plant, the potato chips are additionally seasoned with a blend of spices [2].  
The technological process of the production of French fries and potato chips (including the selection of various types of parameters) has an impact on both the organoleptic properties of ready potato snacks and the content of the formed acrylamide (AA).
# Analysis and Prospects of an Antarctic Krill Detection Experiment Using Drifting Sonar [PERSON]
###### Abstract  
To reduce costs associated with the detection and population assessment of Antarctic Krill and diversity the single detection approach, our team designed and deployed a drifting sonar buoy for krill detection in the waters surrounding Antarctica. The experimental results indicate that the drifting sonar buoy fulfills its primary functions and meets the requirements for krill detection in designated marine areas. The initial experiment lasted seven days, during which the buoy collected 157 records of speed and location data as well as 82 records of sea surface temperature and acoustic data, demonstrating its potential for krill detection. The experiment also revealed shortcomings in the initial design of the drifting sonar buoy, leading to proposed improvements. The paper further compares the advantages and disadvantages of the drifting sonar buoy and traditional fishing vessels in krill detection with the buoy offering unique benefits in low-cost deployment, labor savings, broad monitoring range, and continuous real-time data monitoring. The drifting sonar buoy serves as an excellent complement to fishing vessels in krill detection.  
Antarctic krill; drifting sonar buoy; low-cost deployment; krill detection +
Footnote †: journal: Applied sciences
# Recent Advances in Optical Injection Locking for Visible Light Communication Applications
## 1 Introduction  
Visible light communication (VLC) is a high-speed communication technique utilizing an unlicensed frequency range of 400-800 THz [1]. Since the development of semiconductor lasers and optical fibers, optical communication systems have been widely used in our daily life [2]. With rapid growing wireless data demands, there is currently a push for environmentally friendly, high-speed and sustainable wireless communication methods in both indoor and outdoor environments. VLC can offer both lighting and communication at the same time, which could be a highly promising complement to conventional radio frequency (RF) wireless communication for high-speed local area networks in future 6G systems [3].  
Optical injection locking (OIL) is an optical frequency and phase synchronization technique that is based on photon-photon interactions. Different from multiple section laser diodes, in which lights from different sections affect each other, there is one laser affecting the other in OIL. The latter may occur when external lights are shone into laser cavities [4]. The schematic diagrams of typical OIL schemes are illustrated in Figure 1. A typical external OIL configuration consists of master lasers and slave lasers. The masterlight can be injected into the slave laser and the output light can be blocked from the master laser via a circulator. A self-injection-locking configuration consists of a reflector through which light can be partially fed back. It is typically well acknowledged that the OIL technique is able to improve the device performance of semiconductor lasers, and there has been a mass of studies with regard to using the technique to build high-quality systems for telecommunication applications [5].  
In VLC systems, OIL lasers are recently utilized as high-quality light sources for high-bitrate data links. Studies have demonstrated the enhancement effects of the OIL technique on the characteristics of different devices in the visible color regime. Additionally, the application of OIL is now demonstrated in various VLC systems, such as free-space VLC, visible light-based optical fiber communication and underwater wireless optical communication (UWOC).  
In this paper, we review the recent advances in OIL and its applications in VLC. Firstly, the enhancement effects of OIL on the characteristics of different semiconductor optoelectronic devices are discussed, which could play vital roles in the future development of VLC systems and LiFi networks. Then, the most recent applications of OIL in VLC systems, including the implementation of OIL in fiber communication and UWOC systems, are analyzed. Finally, we elucidate the expected future trends in high-quality devices, such as ultranarrow-linewidth lasers for coherent optical communication.
# Variance-Triggered Two-Step GPS Acquisition
## 6 Related Works
The RD technique excludes the adjacent samples to the highest peaks. Other time-domain acquisition methods approach the problem of recovering signals from highly incomplete measurements [7; 21]. Due to the necessity of using at least two large matrices, the memory complexity of those methods increase, which can be a limitation for some applications. On the other hand, the proposed VTTS technique uses two small matrices of size \(1023\times N_{\mathrm{b}}^{\mathrm{CS}}\) and \(2P\times N_{\mathrm{b}}^{\mathrm{FS}}\). As shown in Section 4, the VTTS has lower memory requirements. On the phase search domain, a few other techniques also exploit variations in the number and size of frequency bins [11; 22]. Differently from this work, in [13], an adaptive strategy for refining the width of the Doppler bins focusing on hardware implementations was investigated, but the VTTS approach proved to work better with the use of large fixed bin sizes at the CS step rather than with an adaptive approach. Strategies that use two steps like in the proposed VTTS have been proposed in the frequency domain. In [5; 6], the authors apply a folding technique on the frequency domain and the coherent integration time is extended to enhance detection performance and, indirectly, reduce mean acquisition time. In the Multi-Peak Double-Dwell [3] approach, a first step selects multiple peaks as candidates; in the second step, the candidate whose signal-to-noise ratio is enhanced by the longer time integration is selected. In the time domain, following the two-step strategy, a Binarized Convolution Neural Network (BCNN) [23] was used to reduce the search space in a first step, dealing with a long integration time in a reduced search space in a second step [24]. As frequency-domain techniques require supplementary hardware and the BCNN-based approach is quite unconventional and experimental, we consider that comparing these methods to the proposed VTTS acquisition is unsuitable for this work but may be targets for future research.
# Robust Template-Based Watermarking for DIBR 3D Images
## 2 Background
### Analysis of DIBR Attack  
In the DIBR process, pixels are only translated horizontally, where the translation magnitude is determined by the depth. Similar to the cover model being considered as random, the depth image is also close to a random signal [37]; hence, the pixel's moving distance can also be assumed to be random. Consequently, the pixels move irregularly, unlike in common translation. Thus, the 2D transformed domain coefficients are distorted by DIBR.  
The watermark damage caused by DIBR can be confirmed by the average energy change of the middle frequency at which the watermark is inserted, since the average energy is the basis of the watermark embedding energy.  
Table 1 shows the average energy change between center and synthesized DIBR image coefficients. DIBR parameters used in this test were as recommended by [3], and average energy change was defined as:  
\[\frac{MSE(O,S)}{\text{mean}(O^{2})} \tag{2}\]  
where \(O\) and \(S\) are the transformed domain's coefficients for the original and synthesized images, respectively; and MSE is mean squared error. The various transform domain coefficients' energy is severely impaired by DIBR. Therefore, the watermark energy, inserted into the transform domain's coefficients, is also damaged, and watermarks damaged by more than 40% are difficult to detect.  
Figure 2: Overall process of the DIBR system.  
However, the average energy change of the Haar discrete wavelet transform (DWT) is reduced to 4% if the distorted coefficients are matched with the undistorted coefficients using the depth image. This is because the wavelet series represent the frequency information of the small spatial block, such as \(2\times 2\), \(4\times 4\), \(\cdots\).  
If the image is divided into small blocks, some of these will be undistorted, since the depth is similar between adjacent pixels within an object. If all depth values are the same in the block, pixel moving distances are all the same. DIBR is treated like a common translation for this case. A smaller block size implies a greater percentage of uncorrupted blocks, as shown in Table 2 for the example of 1800 synthesized DIBR images. Similar to the previous average energy change test, this test also used the recommended DIBR parameters from [3].  
Since DCT and discrete Fourier transform (DFT) express a global frequency that does not include spatial information, the amount of average energy change after matching is still high. In other words, it can be seen that the magnitude of the coefficients is damaged in DCT and DFT, but the magnitude of the coefficients is maintained in DWT.  
In summary, the following two properties of DIBR can be identified: (1) the pixel is moved only in the horizontal direction, and the moving distance is determined by the depth; (2) the percentage of undistorted blocks is high with a small block size. Due to the second property, the wavelet series are robust to DIBR.
## 2 Background and Methods
### Performance Metrics  
In this study, two performance metrics--the mean absolute percentage error (MAPE) and mean square error (MSE)--were used to evaluate the model's performance [35]. The MSE is widely used in the ML model to measure the performance as the loss function. It is the measurement of the Bayesian search to find an optimal combination of hyperparameters from the XGBoost and RF. The formula of the MSE is as follows:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y_{i}})^{2} \tag{4}\]  
Some features have comparatively large values to other body feature measurements (e.g., the body height is significantly larger than other measurements) in the dataset. The MAPE shows the error proportional to its actual value (in %), which complements the MSE to provide a better understanding of the performance of the ML models. The formula of MAPE is defined as follows:  
\[MAPE=\frac{100\%}{n}\sum_{i=1}^{n}\mid\frac{y_{i}-\hat{y_{i}}}{y_{i}}\mid \tag{5}\]
## 6 Baseline Statement and AAMO Approach Results
### Aamo Assessment
#### 6.4.3 _Aamo_ vs. Baseline Comparison
The _Yamauchi_ and _MinPos_ approach results (coloured in purple and green, respectively) obtained under ideal communication conditions are placed together to make the comparison easier; (**b**) The degradation is expressed in terms of the difference between the PL achieved by each of the _AAMO_ instances and the one achieved by the _MinPos_ approach, for each fleet size. the smaller \(|T^{\rm HO}|\) and \(|R^{u}|\) the faster the algorithm will run. Please recall that \(|T^{\rm HO}|\) is upper bounded by the amount of unassigned tasks \(|T^{u}|\). Firstly, from Figures 20 and 21, it is possible to examine how \(|R^{u}|\) and \(|T^{u}|\) change along explorations depending on the fleet size. In all cases, both values show well defined patterns that are easily identifiable. Concerning \(|R^{u}|\) (see Figure 20) it is possible to state that in all _AAMO_ instances--working in a fully asynchronous modality--the probability of two or more robots simultaneously running a decision making process is negligible. Thus the majority of time either none robot is making a decision or at most one robot is evaluating the available tasks. Results obtained during simulations are summarised in Table 6 and show a behaviour that is consistent with this last statement independently of the fleet size. The low ratio of robot coincidences is remarkable (e.g., for 3-sized fleets, about 96% of the decision making moments have only one robot participating on them). In conclusion, in practice, the worsening of the TT performance is apparently only related to the incidence of the _HO-Threshold_ on the \(|T^{\rm HO}|\) value. Next, this relation is carefully studied, and some answers are essayed. The parabola described by the TT degradation values in Figure 18 suggests the presence of two factors impacting on this behaviour. One presses the trend upwards and the other in a counter sense. In the following, two particular factors are analysed: the fleet size and the bounded condition of the environment. (i) As the fleet size increase robots make progress faster, causing \(|T^{\rm HO}|\) to increase more quickly as well. When \(|T^{\rm HO}|\) rises, the task selection algorithm becomes slower, and thus the increase in the fleet size could explain the first increasing section of the trend; (ii) In bounded environments, the multi-robot exploration systems typically show two mobility patterns that characterise, in turn, two different exploration stages: (1) One is characterised by the dispersion of the fleet on the terrain. In such a stage, the new available tasks appear closer to each other, and its total amount \(|T^{u}|\) is upward; (2) On the contrary, the second exploration stage is characterised by the convergence of the fleet to the remaining unexplored zones starting when it is no longer possible to disperse the fleet until the end of
# Survivable Deployments of Optical Sensor Networks against Multiple Failures and Disasters: A Survey
## 2 Existing Survivability Technologies in OSNs
### The Fiber Bus Protection Scheme  
The optical sensor network is used to transfer sensing data through fiber links. It is easily damaged with failures and disasters. Multiple survivability techniques designed for optical sensor networks already exist. The fiber bus protection scheme, which can withstand failures at one or more points, is a classic. As presented in Figure 1, the fiber bus protection scheme uses two fiber cablesto achieve sensing path protection. When the branching point (BP) is implemented by a passive I \(\times\) 2, this scheme belongs to dedicated protection. When the BP is implemented by a \(1\times 2\) photonic switch, this scheme belongs to shared protection. The fiber bus protection scheme has been widely researched. In [40], the authors presented the optical fiber bus network for the multiplexing of sensors. The network can survive at least one failure at any location. In [41], a symmetrical dual-fiber bus that uses dedicated-liner or dedicated-path protection switching in response to a failure was developed for optical sensing. In [42], the authors proposed a mathematical model for a WDM self-healing optical fiber bus network to interconnect an array of sensors. The network can determine its own sites of failure from network management information without requiring external resources. In [43], the authors proposed a multi-bus fiber-optic sensor network, which could multiplex different types of fiber sensors, locate the failure position, and recover the sensing services of sensors in a network immediately and automatically to keep them working without being interrupted. In [44], the authors reported a self-healing fiber bus network for the wavelength multiplexing of sensors. The network can recover service after one or more failures by dedicated line or dedicated path protection. In [45], the authors reported a resilient WDM fiber bus network with interconnected sensors. The network self-diagnosed and identified the failed constituent(s) from the patterns of surviving end-to-end connections at its operating wavelengths.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 2 Related Works
### Transformer Tracking  
Transformer [26] is a model structure based on self-attention, known for capturing long-range dependencies and extracting global features. It has found extensive applications in natural language processing. With the continuous integration and development of the fields of natural language processing and computer vision [27], Vision Transformer (ViT) [28] pioneered the introduction of Transformer into computer vision, demonstrating favorable results. In 2021, TrSiam [13] combined Transformers with correlation filters, becoming the first Siamese object tracking method with a Transformer structure that enhanced the tracker's feature representation capability. TransT [14] utilized Transformers to interact with the two branch features extracted by CNNs, effectively fusing features from the target and search regions, thereby improving tracking accuracy. Swin Transformer [29] divided the input region using non-overlapping sliding windows, enabling information interaction and transmission between windows, further enhancing the versatility of the Transformer. In 2022, SwinTrack [30] employed Swin Transformer as the backbone, achieving superior tracking performance compared to CNN or hybrid CNN-Transformer methods. However, Swin Transformer lacks the utilization of scale information at a single hierarchy level and has room for improvement in the representation of local features.
# Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology
## 3 Methods
### Extract Vegetation Spring Phenology  
TIMESAT, a software package, was developed to extract pixel-based vegetation phenological metrics (e.g., start of season, end of season, growth length, and amplitude) from time series of vegetation indices (e.g., NDVI and EVI) [49; 50]. Generally, TIMESAT includes two steps for extracting phenology, that is, data smoothing and extraction of phenological parameters. The former includes three fundamental smoothing approaches, adaptive Savizky-Golay (SG), asymmetric Gaussian and double logistic filtering, while the latter has two typical approaches, absolute threshold based and seasonal amplitude based [51].  
In this study, adaptive SG filtering and seasonal amplitude methods were used. Adaptive SG filtering was used to smooth the NDVI curves because it can obtain smoothed NDVI profiles that match ground measurements satisfactorily [52], and it is an effective smoothing method for preserving key features of the NDVI profile (e.g., relative maximum, minimum, and width of data) [53]. The seasonal amplitude method defines spring phenology as the time for which the NDVI increases to a user defined level (i.e., a certain fraction of the seasonal amplitude) measured from the left minimum level. It is a widely accepted method for extracting vegetation phenology in heterogeneous landscapes where pixels have diverse seasonal amplitudes. Based on previous studies [23; 25], a threshold of 0.3 was used by this study to define vegetation spring phenology. Also, this study only extractsphemology from 1-year NDVI time series in 2016, so following the TIMESAT's instruction, the 1-year NDVI time series was duplicated to make an artificial time series spanning 3 years run the TIMESAT software [51]. The principle of using TIMESAT to extract vegetation spring phenology is described in Figure 5, where the light-green region refers to the 1-year span of the NDVI profile in 2016. Following previous studies in temperate regions of the northern hemisphere [54; 55; 56], to reduce the uncertainties in phenology extraction in a complex urban area, pixels with a start of season earlier than the 60 th or later than 160 th day of the year are excluded in the further analysis.  
Figure 4: False color (Red, NIR, Blue bands as R-G-B) images of a sub-region in Beijing at different spatial resolutions.  
Figure 5: Diagram of extracting vegetation spring phenology from NDVI profile by TIMESAT.
## 3 Simulation Results
### Signal Separation and Reconstruction  
The DNN-processed signals were extracted and removed from the mixed signal simultaneously. The performance of the reconstruction and separation of the original signals is shown in Figure 11. Figure 11(1a) shows the original \(s_{1}\) from the ST-CMDS data testing set; Figure 11(1b) shows the reconstructed \(s_{1}\) using the proposed method; Figure 11(1c) shows the cross-correlation function between them. The same dispositions are adopted for \(s_{2}\) and \(s_{3}\) in Figure 11(2a-c,3a-c), respectively. It can be noticed that the signals \(s_{2}\) and \(s_{3}\) contain rare information in the 0-0.5 s range in Figure 11(2a,3a). However, \(s_{1}\) contains a large amount of information within the same range. Under such conditions, the three mixed signals were well separated and reconstructed. Only a few residual errors could be observed in the 0-0.5 s range in Figure 9(2b,3b). Additionally, the sharp peaks close to 1 in the cross-correlation functions shown in Figure 11(1c,2c,3c) indicate that the two corresponding signals are strongly coherent with each other, while no time shift can be found at the horizontal ordinate.  
Figure 11: Original signals (**a**), reconstructed signals (**b**), and the cross-correlation function (**c**) between them: (**1**) \(s_{1}\), (**2**) \(s_{2}\), and (**3**) \(s_{3}\).  
Figure 10: MSEL results of the three sources (**a**) and the effective SNR ranges (**b**).