esa-sceva/satcom-chunk-collection huggingface dataset:
# ISAC towards 6G Satellite-Terrestrial Communications: Principles, Status, and Prospects
## 2 The Development Status and Key Technology Of ISAC
### Prototypes Studies
#### 2.3.4 Prototype Based on the Optical Wireless Frequency Band  
As the next generation of wireless communication technology, 6G will integrate communication and sensing capabilities. It is gradually extending into the optical spectrum in some home and industrial applications. Due to the significant gap between the optical spectrum and the traditional electromagnetic spectrum, it does not cause electromagnetic interference to traditional radio frequency bands. It is particularly suitable for sensitive environments such as smart healthcare and industrial manufacturing.  
In [39], to meet the high communication rate and high-precision sensing requirements in scenarios such as healthcare and industry automation, the Huawei 6G research team has proposed ISAC with optical wireless. Through optical wireless links such as visible light and infrared spectra, precise sensing and localization of robots can be achieved, and remote commands can be given to the robots. The optical link in the prototype also enables high-speed wireless transmission of real-time video between the robot and the controller. By detecting subtle changes in facial color or abdominal movement, the prototype can monitor a person's heartbeat and respiratory status in a real-time and non-contact manner.
# Process Parameters Optimization of Thin-Wall Machining for Wire Arc Additive Manufactured Parts
## 2 Additive Production of Thin Walled Component  
The selected test case is an National Advisory Committee for Aeronautics 9403 airfoil, (NACA, USA) as shown in Figure 1. The stock to be machined was initially created using WAAM (Figure 1a) and a subsequent 5-axis milling step was used to obtain the final shape (in Figure 1b). In WAAM, subsequent layers of metal are selectively deposed using a gas metal arc-welding source, in which raw material in the form of wire is molten through an electric arc. This enables one to achieve high productivity and to manufacture very large components if compared with alternative AM processes [19].  
The airfoil was created deposing 41 layers of ER70S-6 (i.e., a standard filler metal for carbon steels) onto a brick-shaped substrate made of AISI 1040. The deposition was carried out using the process parameters presented in Table 1.  
Such parameters resulted in an average layer height of 1.8 mm and in a layer width of about 6.8 mm and have been selected based on previous studies [20]. The filler material was deposed following the airfoil camber line, creating a constant cross-sectional profile. The final shape of airfoil was achieved by the subsequent 5-axis milling process. The WAAM machine prototype developed by the Manufacturing technologies research laboratory (MTRL) of the University of Firenze was used to carry out the deposition [21]. The deposition process is shown in Figure 2a, while Figure 2b shows the airfoil after the WAAM step. Figure 2b highlights that a significant surface waviness issue affects the WAAM component, requiring a milling step, performed on Mori Seiki (Nagoya, Japan) NMV 1500 DCG milling machine, to meet the surface finishing requirement. As stated in the introduction, two specimens of the selected test case were manufactured. After the deposition step, the geometry of the WAAM parts was acquired through a Euro Apex C776 coordinates-measurement-machine (CMM), (Mitutoyo, Japan). This step allowed us to check the required machining allowances for the milling process. The CMM measurement operation is shown in Figure 3a, while Figure 3b shows the CMM measurements results. CMM measurements highlighted that the airfoil surfaces presented an average deviation from the reference surfaces of about 0.24 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Current (A)** & **Voltage (V)** & **Deposition Speed (mm/min)** & **Wire Feed Speed (m/min)** \\ \hline
80 & 20 & 200 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Welding parameters used in the deposition.  
Figure 1: The selected test case after the deposition (**a**) and the milling process (**b**).
## 1 Introduction  
Ruminant performance is known to be dependent on forage nutritive value and intake. The determination of nutritive value is based on digestibility and degradability measurements, which, in combination with intake measurements, are considered to be the reference methods for determining the feed value of forage [1]. These in vivo feed evaluation methods are considered the gold standard and are generally used in research programs [2]. However, these methods are expensive, involve intensive animal studies, and, therefore, are not routinely used to evaluate the nutritive or feed values of forages for ruminants. Generally, in vivo feed evaluations are used as a reference to compare and estimate the nutritive value of forages using less expensive chemical/laboratory-based methods [3] or in vitro methods, including an estimation of in vitro digestibility [4; 5; 6] or visible (VIS)/near-infrared (NIR) spectroscopy [7; 8]. Research studies using NIR spectroscopy models to predict the nutritive value and/or intake of forages require a large number of samples [7; 9]. The nutritive value of these samples obtained from previous in vivo trials are needed [7; 9]. However, this is a time-consuming process, and, in some cases, these values were obtained for in vivo trials carried out before the development of the model.  
In vivo trials are followed by grinding, analysis, and storage of forage samples. The samples are then exposed to atmospheric oxygen, fluctuating temperatures, and certain enzymes released during storage, causing the destruction of the stored samples' nutrients [10]. It is recommended for dried forage samples to be stored at low temperatures [11], in the absence of direct heat, sunlight [12], and oxygen. However, in practice, samples are usually stored at ambient conditions because this is cheaper and freezers are sometimes not available for this function. This practice may change the chemical composition of the samples stored for a long period of time, making them unsuitable in the development of new predictive methods for their nutritive value. [PERSON] et al. [13] found some changes in the NIR spectra of fecal samples after a 3-year storage period. However, to our knowledge, the potential changes in the chemical composition and nutritive value of samples stored under different conditions for many years have never been extensively addressed.  
The objective of this study is to evaluate (i) changes in the chemical composition and nutritive value of forage samples (whole maize forage) stored at frozen or ambient temperatures for a very long time and (ii) potential changes in the VIS/NIR spectra of forage samples according to the storage method. We hypothesized that the storage environment could influence the chemical composition of the forage samples, their spectra, and, consequently, their nutritive value.
# MM-Wave Phased Array Quasi-Yagi Antenna for the Upcoming 5G Cellular Communications
## 6 Conclusions  
A phased array 5G smartphone antenna with Quasi-Yagi radiators was designed and investigated in this manuscript. The antenna array has well-defined/wide-scan radiation beams with end-fire modes at the 26 GHz 5G band. Fundamental characteristics of the presented smartphone antenna array and its elements (Quasi-Yagi radiators) have been studied. SAR and user-impact on the 5G smartphone were also investigated. In addition, using the Quasi-Yagi antenna elements, \(2\times 2\), \(4\times 4\), and \(8\times 8\) planar phased array designs were designed, and their characteristics have been investigated. Based on the obtained result, the Quasi-Yagi antenna element, its linear and planar phased arrays, is promising for use in 5G cellular communications.  
Writing--original draft preparation, N.O.P., M.A., H.J.B., R.A.A.-A., J.R., and E.L.; writing--review and editing, N.O.P., H.J.B., J.R., and R.A.A.-A.; investigation, N.O.P., M.A., and H.J.B.; resources, N.O.P., J.R., and R.A.A.-A.; For other cases, all authors have participated.  
This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement H2020-MSCA-ITN-2016 SECRET-72242.  
Authors wish to express their thanks for the support provided by the innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-72242.  
The authors declare no conflict of interest.  
Figure 20: (**a**) Radiation beams, (**b**) S\({}_{\mathrm{nn}}\), and (**c**) S\({}_{\mathrm{nm}}\) characteristics of the planar arrays.
## 4 Results and Discussion  
### Training of the ANN Model  
Before training the ANN model, all of the collected structural and material parameters of the 1163 specimens and the corresponding test results on the strength and drift capacities were normalized as the input and output vectors using the above approach. Then, 863 specimens in the database were randomly selected as the training set (\(n_{train}=863\)). While the remaining specimens were used as the test set (\(n_{test}=150\)) and validation set (\(n_{validation}=150\)), respectively.  
The number of input and output nodes in the ANN architecture is obviously 10 and 4, respectively. The following parameters of the BPNN were used in training the model: (1) Error tolerance = \(10^{-3}\); (2) learning parameter = 0.15; (3) maximum number of iterations = \(10^{4}\); (4) momentum parameter = 0.05; (5) noise = 0.01. The mean absolute error (\(E\)) and goodness of fit (\(R^{2}\)) were adopted to evaluate the accuracy of the ANN model, as follows:  
\[E_{k}=\frac{1}{N}\sum\limits_{i=1}^{N}\left|\frac{\overline{y}_{ik}-\frac{ \overline{y}_{ik}}{\overline{y}_{ik}}}{\overline{y}_{ik}}\right|,\ (k=1,2,\ldots,4) \tag{6}\]  
\[R_{k}^{2}=1-\frac{\sum\limits_{i=1}^{N}\left(\overline{y}_{ik}-\frac{ \overline{y}_{ik}}{\overline{y}_{ik}}\right)^{2}}{\sum\limits_{i=1}^{N}\left( \overline{y}_{ik}\right)^{2}},\ (k=1,2,\ldots,4) \tag{7}\]  
where \(N\) is the number of specimens in the training set or test and validation set.  
It should be noted that there is no specific rule or heuristics [37] to determine the number of hidden layers and the corresponding number of nodes. In general, an ANN model with too many neurons tends to fail due to an overfitting of the training data, whereas those with few neurons may not be able to capture the complex underlying relationship. Therefore, several trials were conducted for ANN models with different nodes and a single hidden layer to obtain an optimal ANN architecture for the strength and deformation capacity prediction of RC columns. For an optimal ANN architecture, the mean absolute error and goodness of fit of the ANN model should satisfy the following criteria: (1) Well-distributed around 0 (smaller \(E_{k}\)) and (2) as close as possible (larger \(R_{k}^{2}\)). The mean value (\(E=\overline{E}_{k}\), \(R^{2}=\overline{R}_{k}^{2}\)) of the four properties was used.  
Figure 5 shows the test results of the trained ANN models with 10-50 hidden neurons. It can be observed that the mean absolute error and goodness of fit of the three properties are within the range of [0.126, 0.142] and [0.870, 0.882], respectively. As can be seen, the N-10-21-4 ANN architecture has the smallest \(E\) and largest \(R^{2}\), and thus was determined as the optimal ANN architecture.  
Figure 5: Comparison of artificial neural network (ANN) architectures.
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 2 Preliminaries
### Error Estimation  
Error estimation is one of the most important methods to ensure security in quantum cryptographic protocols. However, so far, in the two-party quantum encryption protocol, no method to improve the security of the protocol by error estimation has been seen. This is due to the asymmetry of the information in the two-party encryption protocols and the coupling between the measurement results and final key. We find that joint measurement reduces this coupling and try to introduce the error estimation method into the two-party encryption protocol. In this paper, because of the asymmetry of the information, we use the random sampling method for error estimation.  
In QKD, the operation process of the random sampling method can be described as follows: Among the raw key \((k_{0},\ldots,k_{l-1})_{A}\) and \((k_{0},\ldots,k_{l-1})_{B}\) owned by [PERSON] and [PERSON], randomly extract a certain percentage \(p\) of the key at the corresponding positions and publishing these bits through the classical channel with trusted authentication. The inconsistency rate of the sampling key can be regarded as the code error rate of the raw key (since the extracted key has been published, it cannot be used in subsequent processing steps and needs to be discarded). In the two-party quantum cryptographic protocol, due to the asymmetry of information (for example, in the ROT protocol, after performing base matching, [PERSON] does not discard the key that failed to match, but performs key separation according to his chosen \(c\)), [PERSON] will perform random sampling from all keys, and require that the preparation base and key of the sampling part be made public, and then calculate the code error rate.  
Assume that the error rate of the raw key owned by [PERSON] and [PERSON] is \(e\) and the key length is \(l\), compared with the [PERSON]'s key, [PERSON]'s raw key has \(el\) errors. The amount of randomly extracted key bits is \(pl\) and satisfies \(el<pl\), that is, \(e<p\). Assume that there are \(m\) bit errors in the extracted \(pl\) keys, then consider that the error rate of the raw key is:  
\[e^{\prime}=\frac{m}{pl}.\]  
In this paper, in order to ensure the security of the two-party encryption protocol, we put the error estimation process before the base matching. Thus, we can get:  
\[e^{\prime}=\frac{\text{num}(x_{i}\
eq y_{i}|\theta_{B_{i}}=\theta_{A_{i}})}{ \text{num}(\theta_{B_{i}}=\theta_{A_{i}})}.\]
## 6 Results  
### RSSI Experimental Results  
Following the experimental protocol described in Section 5.1, we obtained the results shown in Figure 11.  
Inaccuracies in the measured results were expected due to the RSSI's high sensitivity to interference and multipath and non-line-of-sight propagation [71]. From that, we obtained \(A=-80\) dBm and \(\eta=3\). According to the manufacturer, the WuR sensitivity was between \(-28\) and \(-40\) dBm, which provided a wake-up range of, at most, \(1.5\) m in the current conditions.
## 3 Feature Extraction Using Wavelet Packet Transform (WPT)
So Battle-Lemarie is adopted as the basis wavelet package function in this paper to decompose the signals to be analyzed into different frequency bands and make each frequency band energy independent and irredundant [33]. Several optional measuring nodes are chosen, and vibration signals from these nodes are analyzed by using the WPT. Apart from the effect of selecting the mother wavelet function, the decomposition level of analysis, at which the wavelet analysis should be performed, has immense effect on the performance of wavelet-based methods. Reasonable level requirements are not known in advance and depend on a broad range of parameters, including structural characteristics, signal characteristics and type, location and severity of the damage, etc. Different decomposition levels have been suggested by several researchers [34]. In this paper based on our previous work [17; 35] The level of decomposition of the wavelet packet is determined using both healthy and damaged structural models and set on 7 levels [17]. The frequency band energy is then calculated and normalized. The wavelet package relative energy of the signals from sensor \(s\) is:  
\[E_{p}^{s}=\{E_{m},\ m=1,\ldots,M\}, \tag{7}\]  
where, \(s=1,2,\ldots S\), \(p\) is the acquiring number, \(p=1,2,3,\ldots,P\). The wavelet package relative energy (WPRE) \(E_{p}^{s}\) of the signals from sensor s is combined to obtain the fused feature vector [17]:  
\[E_{p}=\Big{\{}E_{p^{\prime}}^{1},E_{p^{\prime}}^{2},\ldots,E_{p^{\prime}}^{s} \Big{\}}. \tag{8}\]  
This fused feature vector will be used as the input of the surrogate model after implementing interval analysis method.
## 2 Materials and Methods
### Ionospheric Data and Methodologies
#### 2.3.3 Swarm Magnetic Data Processing  
Magnetic data of ESA _Suxarm_ mission were investigated using the MAgnetic _Suxarm_ anomaly detection by Spline analysis (MASS) algorithm defined and used in several papers searching for electromagnetic pre-earthquakes signals [4; 5; 66; 67; 68]. Basically, MASS computes a first-order approximation of the derivative of the magnetic signal (X-North, Y-East, Z-Centre, or F-Scalar) and subtracts a cubic spline fitted on the same derivative data [66]. What remains is called the residual signal or simply the residual, and it is analysed by a moving window (of 3\({}^{\circ}\) latitude in this work) that moves along the track with a step of 1/5 of window length. If at least one root mean square rms is greater than \"kt\" times, the Root Mean Square RMS of the whole track and geomagnetic conditions are quiet (\(\mid\) Dist \(\mid\)\(\leq\) 20 nT and ap \(\leq\) 10 nT), the centre of the window is inside the research area, and the track is defined as anomalous. \"kt\" is the threshold to define an anomaly, i.e., how many time the residual in the window must be greater than the one in the whole track. For a normal distribution of residual, it acts on the number of standard deviations.  
The cumulative number of the anomalous tracks in the function of the time is represented to search for possible variation with respect to the mean trend that is expected to be almost linear with a positive but not null slope, as other phenomena apart from the one under investigation can also generate magnetic anomalies. On the other hand, an increase with respect to such a trend could be, in principle, due to the phenomenon under investigation (in this case, La Palma volcano eruption).
# Zero-TCE: Zero Reference Tri-Curve Enhancement for Low-Light Images
## 4 Experiments and Results
### Implementation Details  
The framework is implemented using PyTorch and runs on an NVIDIA 4090 GPU equipped with 24 GB of memory. During training, we employ a batch size of 8. The initialization of the weights for each layer follows a standard Gaussian distribution with a mean of zero and a standard deviation of 0.02, while the biases are set to a constant value. To achieve better network optimization, we employ the Adam optimizer. To balance the impact of various loss functions on image quality, after referencing the Zero-DCE algorithm and conducting experimental validation, we set the values of \(W_{col}\) and \(W_{tr_{A}}\) to 0.5 and 20, respectively. Furthermore, addressing the potential for significant distortion in images processed by the Zero-DCE algorithm, we introduce the SSIM loss and prioritize it as a core component, making its effect comparable to the combined action of other loss functions. Consequently, we assign a value of 0.5 to \(W_{total}\) to ensure the comprehensive enhancement of image quality.  
To fully harness the potential of wide-dynamic-range adjustment during the training process, we incorporate a diverse set of low-light and overexposed images from the SICE dataset [50] into the training set, comprising a total of 2002 images. This approach ensures that our model is exposed to a wide range of lighting conditions, enhancing its ability to generalize and adapt to various scenarios. Furthermore, to avoid inconsistencies in image size when the network processes them, all training images are resized to 512 \(\times\) 512 pixels, which not only accelerates the network's inference speed but also enhances its accuracy.
# Research and Development Status of Prepared Foods in China: A Review
## 5 Production Process of Prepared Foods
### Sterilization, Preservation, and Transportation of Prepared Food at the \"Ends\"  
Finally, there is the process of sterilization, preservation, and transportation of prepared foods at the \"Ends\". Since prepared foods are rich in nutrients, especially animal prepared foods, this makes them highly susceptible to microbiological contamination during storage and distribution [75], which is not conducive to shelf preservation of prepared foods and can greatly reduce their shelf life and affect sales. Therefore, the sterilization of prepared foods is particularly important. The sterilization, preservation, and transportation of prepared foods are closely linked in the meantime. The sterilization effect of pre-prepared products and the different methods of sterilization will determine the subsequent preservation process and the way that the products are transported. For example, prepared milk products that are only pasteurized need to be kept refrigerated at low temperatures, but those that are instantaneously sterilized at ultra-high temperatures can be stored at room temperature for a longer period. Sterilization technology is mainly divided into thermal sterilization technology and non-thermal sterilization technology. Thermal sterilization technology kills microorganisms and inactivates enzymes through thermal effects such as high temperatures. However, it is also easy to cause damage to the nutrients in the food, and thermal sterilization technology can easily cause the \"overcooked taste\" of meat [5]. Non-conventional food sterilization technologies can avoid these negative effects and include the use of non-thermal effects such as cavitation effect, radiation, and high pressure on the cell wall or cell membrane of microorganisms to destroy them, to achieve the purpose of sterilization [76]. There are a variety of types of new sterilization technologies, including microwave sterilization [77; 78], high-pressure (HPP) sterilization, pulsed electric field (PEF) sterilization, irradiation sterilization, biological sterilization, and so on [79]. However, the main preservation methods are refrigeration, freezing and modified atmosphere, and so on. For example, most of the aquatic prepared foods are preserved by refrigeration and freezing, while the fruit and vegetable prepared foods are mainly preserved in a modified atmosphere.
## 2 Materials and Methods  
_Samples, Isolation, and Serotyping of Salmonella spp._  
The observations reported refer to the testing of 615 tissue samples collected at slaughter from 123 randomly selected healthy pigs of 15 farrow-to-finish herds in Central Greece. Samples were collected after evisceration from the colon, ileum, mesenteric lymph nodes, gall-bladder, and muscle tissue from the pigs' neck.  
Isolation and typing procedures followed the Salmonella ISO 6579-1:2017 for food and animal feeding stuffs, as described elsewhere [31]. Suspect colonies were subcultured on Columbia blood agar (Oxoid, Basingstoke, UK) and examined with Gram stain, and if they were Gram-negative rods they were tested for oxidase production, utilization of Triple Sugar Iron Agar (Merck-Germany, Darmstadt, Germany), and colony appearance on Salmonella Chromogenic Agar (SCA-Biolife-Italy, Milan, Italy). _Salmonella_-suspect colonies showed magenta coloring on the SCA after 18 to 24 h of incubation, while colonies of other members of the family Enterobacteriaceae appeared as blue or uncolored. Suspect colonies were assigned to species using the API 20E (Biomerieux, Craponne, France) and the MicrogenTM GnA+B-ID (Microgen Bioproducts Ltd., Camberley, UK) systems recommended for Gram (\(-\)) bacteria. Isolates identified as _Salmonella_ spp. were tested with a polyvalent slide agglutination test (Remel Europe Ltd., Dartford, UK) detecting O- and H- antigens, and positive samples were sent to the Greek National Reference Laboratory (GNRL) for specific serotyping. All lactose-positive and H\({}_{2}\)S-negative isolates identified as _Salmonella_ spp. were subcultured on Salmonella-Shigella (SS) agar side by side with lactose-negative-H\({}_{2}\)S-positive isolates.  
Additionally, the swab sample and a loopful of BPW culture from the same sample were directly inoculated onto XLD medium, bypassing the pre-enrichment and enrichment stages.